[
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\nevaldata/\n# C extensions\n*.so\n# Distribution / packaging\n.Python\nbuild/\nevaldata\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n#   However, in case of collaboration, if having platform-specific dependencies or dependencies\n#   having no cross-platform support, pipenv may install dependencies that don't work, or not\n#   install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# output\ndocs/api\n.code-workspace.code-workspace\n*.pkl\n*.npy\n*.pth\n*.onnx\n*.engine\nevents.out.tfevents*\nYOLOX_outputs\nvisualizations\nresults/\nerror_log.txt\n.idea/\n"
  },
  {
    "path": "Dockerfile",
    "content": "FROM nvcr.io/nvidia/tensorrt:21.09-py3\n\nENV DEBIAN_FRONTEND=noninteractive\nARG USERNAME=user\nARG WORKDIR=/workspace/OC_SORT\n\nRUN apt-get update && apt-get install -y \\\n        automake autoconf libpng-dev nano python3-pip \\\n        curl zip unzip libtool swig zlib1g-dev pkg-config \\\n        python3-mock libpython3-dev libpython3-all-dev \\\n        g++ gcc cmake make pciutils cpio gosu wget \\\n        libgtk-3-dev libxtst-dev sudo apt-transport-https \\\n        build-essential gnupg git xz-utils vim \\\n        libva-drm2 libva-x11-2 vainfo libva-wayland2 libva-glx2 \\\n        libva-dev libdrm-dev xorg xorg-dev protobuf-compiler \\\n        openbox libx11-dev libgl1-mesa-glx libgl1-mesa-dev \\\n        libtbb2 libtbb-dev libopenblas-dev libopenmpi-dev \\\n    && sed -i 's/# set linenumbers/set linenumbers/g' /etc/nanorc \\\n    && apt clean \\\n    && rm -rf /var/lib/apt/lists/*\n\nRUN git clone https://github.com/noahcao/OC_SORT \\\n    && cd OC_SORT \\\n    && mkdir -p YOLOX_outputs/yolox_x_mix_det/track_vis \\\n    && sed -i 's/torch>=1.7/torch==1.9.1+cu111/g' requirements.txt \\\n    && sed -i 's/torchvision==0.10.0/torchvision==0.10.1+cu111/g' requirements.txt \\\n    && sed -i \"s/'cuda'/0/g\" tools/demo_track.py \\\n    && pip3 install pip --upgrade \\\n    && pip3 install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html \\\n    && python3 setup.py develop \\\n    && pip3 install cython \\\n    && pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' \\\n    && pip3 install cython_bbox gdown \\\n    && ldconfig \\\n    && pip cache purge\n\nRUN git clone https://github.com/NVIDIA-AI-IOT/torch2trt \\\n    && cd torch2trt \\\n    && git checkout 0400b38123d01cc845364870bdf0a0044ea2b3b2 \\\n    # https://github.com/NVIDIA-AI-IOT/torch2trt/issues/619\n    && wget https://github.com/NVIDIA-AI-IOT/torch2trt/commit/8b9fb46ddbe99c2ddf3f1ed148c97435cbeb8fd3.patch \\\n    && git apply 8b9fb46ddbe99c2ddf3f1ed148c97435cbeb8fd3.patch \\\n    && python3 setup.py install\n\nRUN echo \"root:root\" | chpasswd \\\n    && adduser --disabled-password --gecos \"\" \"${USERNAME}\" \\\n    && echo \"${USERNAME}:${USERNAME}\" | chpasswd \\\n    && echo \"%${USERNAME}    ALL=(ALL)   NOPASSWD:    ALL\" >> /etc/sudoers.d/${USERNAME} \\\n    && chmod 0440 /etc/sudoers.d/${USERNAME}\nUSER ${USERNAME}\nRUN sudo chown -R ${USERNAME}:${USERNAME} ${WORKDIR}\nWORKDIR ${WORKDIR}\n"
  },
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2021 Yifu Zhang\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "# Hybrid-SORT\n\n [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) ![test](https://img.shields.io/static/v1?label=By&message=Pytorch&color=red)\n\n#### Hybrid-SORT is a simply and strong multi-object tracker.\n\n> [**Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking**](https://arxiv.org/abs/2308.00783)\n> \n\n## Abstract\n\nMulti-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e., spatial and appearance information), which exhibit powerful instance-level discrimination. However, when object occlusion and clustering occur, both spatial and appearance information will become ambiguous simultaneously due to the high overlap between objects. In this paper, we demonstrate that this long-standing challenge in MOT can be efficiently and effectively resolved by incorporating weak cues to compensate for strong cues. Along with velocity direction, we introduce the confidence state and height state as potential weak cues. With superior performance, our method still maintains Simple, Online and Real-Time (SORT) characteristics. Furthermore, our method shows strong generalization for diverse trackers and scenarios in a plug-and-play and training-free manner. Significant and consistent improvements are observed when applying our method to 5 different representative trackers. Further, by leveraging both strong and weak cues, our method Hybrid-SORT achieves superior performance on diverse benchmarks, including MOT17, MOT20, and especially DanceTrack where interaction and occlusion are frequent and severe.\n\n### Highlights\n\n- Hybrid-SORT is a **SOTA** heuristic trackers on DanceTrack and performs excellently on MOT17/MOT20 datasets.\n- Maintains **Simple, Online and Real-Time (SORT)** characteristics.\n- **Training-free** and **plug-and-play** manner.\n- **Strong generalization** for diverse trackers and scenarios\n\n### Pipeline\n\n<center>\n<img src=\"assets/pipeline.png\" width=\"800\"/>\n</center>\n\n## News\n* [12/09/2023]: Hybrid-SORT is accepted by **AAAI2024**!\n* [08/24/2023]: Hybrid-SORT is supported in [yolo_tracking](https://github.com/mikel-brostrom/yolo_tracking). Many thanks to [@mikel-brostrom](https://github.com/mikel-brostrom) for the contribution.\n* [08/01/2023]: The [arxiv preprint](https://arxiv.org/abs/2308.00783) of Hybrid-SORT is released.\n\n## Tracking performance\n\n### Results on DanceTrack test set\n\n| Tracker          | HOTA | MOTA | IDF1 | FPS  |\n| :--------------- | :--: | :--: | :--: | :--: |\n| OC-SORT          | 54.6 | 89.6 | 54.6 | 30.3 |\n| Hybrid-SORT      | 62.2 | 91.6 | 63.0 | 27.8 |\n| Hybrid-SORT-ReID | 65.7 | 91.8 | 67.4 | 15.5 |\n\n### Results on MOT20 challenge test set\n\n| Tracker          | HOTA | MOTA | IDF1 |\n| :--------------- | :--: | :--: | :--: |\n| OC-SORT          | 62.1 | 75.5 | 75.9 |\n| Hybrid-SORT      | 62.5 | 76.4 | 76.2 |\n| Hybrid-SORT-ReID | 63.9 | 76.7 | 78.4 |\n\n### Results on MOT17 challenge test set\n\n| Tracker          | HOTA | MOTA | IDF1 |\n| :--------------- | :--: | :--: | :--: |\n| OC-SORT          | 63.2 | 78.0 | 77.5 |\n| Hybrid-SORT      | 63.6 | 79.3 | 78.4 |\n| Hybrid-SORT-ReID | 64.0 | 79.9 | 78.7 |\n\n## Installation\n\nHybrid-SORT code is based on [OC-SORT](https://github.com/noahcao/OC_SORT) and [FastReID](https://github.com/JDAI-CV/fast-reid). The ReID component is optional and based on [FastReID](https://github.com/JDAI-CV/fast-reid). Tested the code with Python 3.8 + Pytorch 1.10.0 + torchvision 0.11.0.\n\nStep1. Install Hybrid_SORT\n\n```shell\ngit clone https://github.com/ymzis69/HybridSORT.git\ncd HybridSORT\npip3 install -r requirements.txt\npython3 setup.py develop\n```\n\nStep2. Install [pycocotools](https://github.com/cocodataset/cocoapi).\n\n```shell\npip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'\n```\n\nStep3. Others\n\n```shell\npip3 install cython_bbox pandas xmltodict\n```\n\nStep4. [optional] FastReID Installation\n\nYou can refer to [FastReID Installation](https://github.com/JDAI-CV/fast-reid/blob/master/INSTALL.md).\n\n```shell\npip install -r fast_reid/docs/requirements.txt\n```\n\n## Data preparation\n\n**Our data structure is the same as [OC-SORT](https://github.com/noahcao/OC_SORT).** \n\n1. Download [MOT17](https://motchallenge.net/), [MOT20](https://motchallenge.net/), [CrowdHuman](https://www.crowdhuman.org/), [Cityperson](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md), [ETHZ](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md), [DanceTrack](https://github.com/DanceTrack/DanceTrack), [CUHKSYSU](http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html) and put them under <HYBRIDSORT_HOME>/datasets in the following structure (CrowdHuman, Cityperson and ETHZ are not needed if you download YOLOX weights from [ByteTrack](https://github.com/ifzhang/ByteTrack) or [OC-SORT](https://github.com/noahcao/OC_SORT)) :\n\n   ```\n   datasets\n   |——————mot\n   |        └——————train\n   |        └——————test\n   └——————crowdhuman\n   |        └——————Crowdhuman_train\n   |        └——————Crowdhuman_val\n   |        └——————annotation_train.odgt\n   |        └——————annotation_val.odgt\n   └——————MOT20\n   |        └——————train\n   |        └——————test\n   └——————Cityscapes\n   |        └——————images\n   |        └——————labels_with_ids\n   └——————ETHZ\n   |        └——————eth01\n   |        └——————...\n   |        └——————eth07\n   └——————CUHKSYSU\n   |        └——————images\n   |        └——————labels_with_ids\n   └——————dancetrack        \n            └——————train\n               └——————train_seqmap.txt\n            └——————val\n               └——————val_seqmap.txt\n            └——————test\n               └——————test_seqmap.txt\n   ```\n\n2. Prepare DanceTrack dataset:\n\n   ```python\n   # replace \"dance\" with ethz/mot17/mot20/crowdhuman/cityperson/cuhk for others\n   python3 tools/convert_dance_to_coco.py \n   ```\n\n3. Prepare MOT17/MOT20 dataset. \n\n   ```python\n   # build mixed training sets for MOT17 and MOT20 \n   python3 tools/mix_data_{ablation/mot17/mot20}.py\n   ```\n\n4. [optional] Prepare ReID datasets:\n\n   ```\n   cd <HYBRIDSORT_HOME>\n   \n   # For MOT17 \n   python3 fast_reid/datasets/generate_mot_patches.py --data_path <dataets_dir> --mot 17\n   \n   # For MOT20\n   python3 fast_reid/datasets/generate_mot_patches.py --data_path <dataets_dir> --mot 20\n   \n   # For DanceTrack\n   python3 fast_reid/datasets/generate_cuhksysu_dance_patches.py --data_path <dataets_dir> \n   ```\n\n## Model Zoo\n\nDownload and store the trained models in 'pretrained' folder as follow:\n\n```\n<HYBRIDSORT_HOME>/pretrained\n```\n\n### Detection Model\n\nWe provide some pretrained YOLO-X weights for Hybrid-SORT, which are inherited from [ByteTrack](https://github.com/ifzhang/ByteTrack).\n\n| Dataset         | HOTA | IDF1 | MOTA | Model                                                        |\n| --------------- | ---- | ---- | ---- | ------------------------------------------------------------ |\n| DanceTrack-val  | 59.3 | 60.6 | 89.5 | [Google Drive](https://drive.google.com/drive/folders/18IsZGeGiyKDshhYIzbpYXoNEcBhPY8lN?usp=sharing) |\n| DanceTrack-test | 62.2 | 63.0 | 91.6 | [Google Drive](https://drive.google.com/drive/folders/18IsZGeGiyKDshhYIzbpYXoNEcBhPY8lN?usp=sharing) |\n| MOT17-half-val  | 67.1 | 78.0 | 75.8 | [Google Drive](https://drive.google.com/drive/folders/18IsZGeGiyKDshhYIzbpYXoNEcBhPY8lN?usp=sharing) |\n| MOT17-test      | 63.6 | 78.7 | 79.9 | [Google Drive](https://drive.google.com/drive/folders/18IsZGeGiyKDshhYIzbpYXoNEcBhPY8lN?usp=sharing) |\n| MOT20-test      | 62.5 | 78.4 | 76.7 | [Google Drive](https://drive.google.com/drive/folders/18IsZGeGiyKDshhYIzbpYXoNEcBhPY8lN?usp=sharing) |\n\n\n* For more YOLO-X weights, please refer to the model zoo of [ByteTrack](https://github.com/ifzhang/ByteTrack).\n\n### ReID Model\n\nOurs ReID models for **MOT17/MOT20** is the same as [BoT-SORT](https://github.com/NirAharon/BOT-SORT) , you can download from [MOT17-SBS-S50](https://drive.google.com/drive/folders/18IsZGeGiyKDshhYIzbpYXoNEcBhPY8lN?usp=sharing), [MOT20-SBS-S50](https://drive.google.com/drive/folders/18IsZGeGiyKDshhYIzbpYXoNEcBhPY8lN?usp=sharing), ReID models for DanceTrack is trained by ourself, you can download from [DanceTrack](https://drive.google.com/drive/folders/18IsZGeGiyKDshhYIzbpYXoNEcBhPY8lN?usp=sharing).\n\n**Notes**:\n\n\n* [MOT20-SBS-S50](https://drive.google.com/drive/folders/18IsZGeGiyKDshhYIzbpYXoNEcBhPY8lN?usp=sharing) is trained by [Deep-OC-SORT](https://github.com/GerardMaggiolino/Deep-OC-SORT), because the weight from BOT-SORT is corrupted. Refer to [Issue](https://github.com/GerardMaggiolino/Deep-OC-SORT/issues/6).\n* ReID models for DanceTrack is trained by ourself, with both DanceTrack and CUHKSYSU datasets.\n\n## Training\n\n### Train the Detection Model\n\nYou can use Hybrid-SORT without training by adopting existing detectors. But we borrow the training guidelines from ByteTrack in case you want work on your own detector. \n\nDownload the COCO-pretrained YOLOX weight [here](https://github.com/Megvii-BaseDetection/YOLOX/tree/0.1.0) and put it under *\\<HYBRIDSORT_HOME\\>/pretrained*.\n\n* **Train ablation model (MOT17 half train and CrowdHuman)**\n\n  ```shell\n  python3 tools/train.py -f exps/example/mot/yolox_x_ablation.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth\n  ```\n\n* **Train MOT17 test model (MOT17 train, CrowdHuman, Cityperson and ETHZ)**\n\n  ```shell\n  python3 tools/train.py -f exps/example/mot/yolox_x_mix_det.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth\n  ```\n\n* **Train MOT20 test model (MOT20 train, CrowdHuman)**\n\n  For MOT20, you need to uncomment some code lines to add box clipping: [[1]](https://github.com/ifzhang/ByteTrack/blob/72cd6dd24083c337a9177e484b12bb2b5b3069a6/yolox/data/data_augment),[[2]](https://github.com/ifzhang/ByteTrack/blob/72cd6dd24083c337a9177e484b12bb2b5b3069a6/yolox/data/datasets/mosaicdetection.py#L122),[[3]](https://github.com/ifzhang/ByteTrack/blob/72cd6dd24083c337a9177e484b12bb2b5b3069a6/yolox/data/datasets/mosaicdetection.py#L217) and [[4]](https://github.com/ifzhang/ByteTrack/blob/72cd6dd24083c337a9177e484b12bb2b5b3069a6/yolox/utils/boxes.py#L115). Then run the command:\n\n  ```shell\n  python3 tools/train.py -f exps/example/mot/yolox_x_mix_mot20_ch.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth\n  ```\n\n* **Train on DanceTrack train set**\n\n  ```shell\n  python3 tools/train.py -f exps/example/dancetrack/yolox_x.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth\n  ```\n\n* **Train custom dataset**\n\n  First, you need to prepare your dataset in COCO format. You can refer to [MOT-to-COCO](https://github.com/ifzhang/ByteTrack/blob/main/tools/convert_mot17_to_coco.py) or [CrowdHuman-to-COCO](https://github.com/ifzhang/ByteTrack/blob/main/tools/convert_crowdhuman_to_coco.py). Then, you need to create a Exp file for your dataset. You can refer to the [CrowdHuman](https://github.com/ifzhang/ByteTrack/blob/main/exps/example/mot/yolox_x_ch.py) training Exp file. Don't forget to modify get_data_loader() and get_eval_loader in your Exp file. Finally, you can train bytetrack on your dataset by running:\n\n  ```shell\n  python3 tools/train.py -f exps/example/mot/your_exp_file.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth\n  ```\n\n### Train the ReID Model\n\nAfter generating MOT ReID dataset as described in the 'Data Preparation' section.\n\n```shell\ncd <BoT-SORT_dir>\n\n# For training MOT17 \npython3 fast_reid/tools/train_net.py --config-file ./fast_reid/configs/MOT17/sbs_S50.yml MODEL.DEVICE \"cuda:0\"\n\n# For training MOT20\npython3 fast_reid/tools/train_net.py --config-file ./fast_reid/configs/MOT20/sbs_S50.yml MODEL.DEVICE \"cuda:0\"\n\n# For training DanceTrack, we joint the CHUKSUSY to train ReID Model for DanceTrack\npython3 fast_reid/tools/train_net.py --config-file ./fast_reid/configs/CUHKSYSU_DanceTrack/sbs_S50.yml MODEL.DEVICE \"cuda:0\"\n```\n\nRefer to [FastReID](https://github.com/JDAI-CV/fast-reid)  repository for addition explanations and options.\n\n## Tracking\n\n**Notes**:\n\n\n* Some parameters are set in the cfg.py. For example, if you run Hybrid-SORT on the dancetrack-val dataset, you should pay attention to the line 35-45 in ```exps/example/mot/yolox_dancetrack_val_hybrid_sort.py``` .\n* We set  ```fp16==False``` on the MOT datasets becacuse fp16 will lead to significant result fluctuations.\n\n### DanceTrack\n\n**dancetrack-val dataset**\n\n```\n# Hybrid-SORT\npython tools/run_hybrid_sort_dance.py -f exps/example/mot/yolox_dancetrack_val_hybrid_sort.py -b 1 -d 1 --fp16 --fuse --expn $exp_name \n\n# Hybrid-SORT-ReID\npython tools/run_hybrid_sort_dance.py -f exps/example/mot/yolox_dancetrack_val_hybrid_sort_reid.py -b 1 -d 1 --fp16 --fuse --expn $exp_name\n```\n\n**dancetrack-test dataset**\n\n```\n# Hybrid-SORT\npython tools/run_hybrid_sort_dance.py --test -f exps/example/mot/yolox_dancetrack_test_hybrid_sort.py -b 1 -d 1 --fp16 --fuse --expn $exp_name\n\n# Hybrid-SORT-ReID\npython tools/run_hybrid_sort_dance.py --test -f exps/example/mot/yolox_dancetrack_test_hybrid_sort_reid.py -b 1 -d 1 --fp16 --fuse --expn $exp_name\n```\n\n### MOT20\n\n**MOT20-test dataset**\n\n```\n#Hybrid-SORT\npython tools/run_hybrid_sort_dance.py -f exps/example/mot/yolox_x_mix_mot20_ch_hybrid_sort.py -b 1 -d 1 --fuse --mot20 --expn $exp_name \n\n#Hybrid-SORT-ReID\npython tools/run_hybrid_sort_dance.py -f exps/example/mot/yolox_x_mix_mot20_ch_hybrid_sort_reid.py -b 1 -d 1 --fuse --mot20 --expn $exp_name\n```\n\nHybrid-SORT is designed for online tracking, but offline interpolation has been demonstrated efficient for many cases and used by other online trackers. If you want to reproduct out result on  **MOT20-test** dataset, please use the linear interpolation over existing tracking results:\n\n```shell\n# offline post-processing\npython3 tools/interpolation.py $result_path $save_path\n```\n\n### MOT17\n\n**MOT17-val dataset**\n\n```\n# Hybrid-SORT\npython3 tools/run_hybrid_sort_dance.py -f exps/example/mot/yolox_x_ablation_hybrid_sort.py -b 1 -d 1 --fuse --expn $exp_name \n\n# Hybrid-SORT-ReID\npython3 tools/run_hybrid_sort_dance.py -f exps/example/mot/yolox_x_ablation_hybrid_sort_reid.py -b 1 -d 1 --fuse --expn  $exp_name \n```\n\n**MOT17-test dataset**\n\n```\n# Hybrid-SORT\npython3 tools/run_hybrid_sort_dance.py -f exps/example/mot/yolox_x_mix_det_hybrid_sort.py -b 1 -d 1 --fuse --expn $exp_name\n\n# Hybrid-SORT-ReID\npython3 tools/run_hybrid_sort_dance.py -f exps/example/mot/yolox_x_mix_det_hybrid_sort_reid.py -b 1 -d 1 --fuse --expn $exp_name\n```\n\nHybrid-SORT is designed for online tracking, but offline interpolation has been demonstrated efficient for many cases and used by other online trackers. If you want to reproduct out result on  **MOT17-test** dataset, please use the linear interpolation over existing tracking results:\n\n```shell\n# offline post-processing\npython3 tools/interpolation.py $result_path $save_path\n```\n\n### Demo\n\nHybrid-SORT, with the parameter settings of the dancetrack-val dataset\n\n```\npython3 tools/demo_track.py --demo_type image -f exps/example/mot/yolox_dancetrack_val_hybrid_sort.py -c pretrained/ocsort_dance_model.pth.tar --path ./datasets/dancetrack/val/dancetrack0079/img1 --fp16 --fuse --save_result\n```\n\nHybrid-SORT-ReID, with the parameter settings of the dancetrack-val dataset\n\n```\npython3 tools/demo_track.py --demo_type image -f exps/example/mot/yolox_dancetrack_val_hybrid_sort_reid.py -c pretrained/ocsort_dance_model.pth.tar --path ./datasets/dancetrack/val/dancetrack0079/img1 --fp16 --fuse --save_result\n```\n\n<img src=\"assets/demo.gif\" alt=\"demo\" style=\"zoom:34%;\" />\n\n## TCM on other trackers\n\ndownload ReID weight from [googlenet_part8_all_xavier_ckpt_56.h5](https://drive.google.com/drive/folders/18IsZGeGiyKDshhYIzbpYXoNEcBhPY8lN?usp=sharing) for MOTDT and DeepSORT.\n\n**dancetrack-val dataset**\n\n```\n# SORT\npython tools/run_sort_dance.py -f exps/example/mot/yolox_dancetrack_val.py -c pretrained/bytetrack_dance_model.pth.tar -b 1 -d 1 --fp16 --fuse --dataset dancetrack --expn sort_score_kalman_fir_step --TCM_first_step\n\n# MOTDT\npython3 tools/run_motdt_dance.py -f exps/example/mot/yolox_dancetrack_val.py -c pretrained/bytetrack_dance_model.pth.tar -b 1 -d 1 --fp16 --fuse --dataset dancetrack --expn motdt_score_kalman_fir_step --TCM_first_step\n\n# ByteTrack\npython3 tools/run_byte_dance.py -f exps/example/mot/yolox_dancetrack_val.py -c pretrained/bytetrack_dance_model.pth.tar -b 1 -d 1 --fp16 --fuse --dataset dancetrack --expn byte_score_kalman_fir_step --TCM_first_step\n\n# DeepSORT\npython3 tools/run_deepsort_dance.py -f exps/example/mot/yolox_dancetrack_val.py -c pretrained/bytetrack_dance_model.pth.tar -b 1 -d 1 --fp16 --fuse --dataset dancetrack --expn deepsort_score_kalman_fir_step --TCM_first_step\n```\n\n**mot17-val dataset**\n\n```\n# SORT\npython3 tools/run_sort.py -f exps/example/mot/yolox_x_ablation.py -c pretrained/ocsort_mot17_ablation.pth.tar -b 1 -d 1 --fuse --expn mot17_sort_score_test_fp32 --TCM_first_step\n\n# MOTDT\npython3 tools/run_motdt.py -f exps/example/mot/yolox_x_ablation.py -c pretrained/ocsort_mot17_ablation.pth.tar -b 1 -d 1 --fuse --expn mot17_motdt_score_test_fp32 --TCM_first_step\n\n# ByteTrack\npython3 tools/run_byte.py -f exps/example/mot/yolox_x_ablation.py -c pretrained/ocsort_mot17_ablation.pth.tar -b 1 -d 1 --fuse --expn mot17_byte_score_test_fp32 --TCM_first_step --TCM_first_step_weight 0.6\n\n# DeepSORT\npython3 tools/run_deepsort.py -f exps/example/mot/yolox_x_ablation.py -c pretrained/ocsort_mot17_ablation.pth.tar -b 1 -d 1 --fuse --expn mot17_deepsort_score_test_fp32 --TCM_first_step\n```\n\n## Citation\n\nIf you find this work useful, please consider to cite our paper:\n```\n@inproceedings{yang2024hybrid,\n  title={Hybrid-sort: Weak cues matter for online multi-object tracking},\n  author={Yang, Mingzhan and Han, Guangxin and Yan, Bin and Zhang, Wenhua and Qi, Jinqing and Lu, Huchuan and Wang, Dong},\n  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},\n  volume={38},\n  number={7},\n  pages={6504--6512},\n  year={2024}\n}\n```\n\n## Acknowledgement\n\nA large part of the code is borrowed from [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX), [OC-SORT](https://github.com/noahcao/OC_SORT), [ByteTrack](https://github.com/ifzhang/ByteTrack), [BoT-SORT](https://github.com/NirAharon/BOT-SORT) and [FastReID](https://github.com/JDAI-CV/fast-reid). Many thanks for their wonderful works.\n\n"
  },
  {
    "path": "TrackEval/.gitignore",
    "content": "gt_data/*\n!gt_data/Readme.md\ntracker_output/*\n!tracker_output/Readme.md\noutput/*\ndata/*\n!goutput/Readme.md\n**/__pycache__\n.idea\nerror_log.txt"
  },
  {
    "path": "TrackEval/LICENSE",
    "content": "MIT License\n\nCopyright (c) 2020 Jonathon Luiten\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "TrackEval/Readme.md",
    "content": "\n# TrackEval\n*Code for evaluating object tracking.*\n\nThis codebase provides code for a number of different tracking evaluation metrics (including the [HOTA metrics](https://link.springer.com/article/10.1007/s11263-020-01375-2)), as well as supporting running all of these metrics on a number of different tracking benchmarks. Plus plotting of results and other things one may want to do for tracking evaluation.\n\n## **NEW**: RobMOTS Challenge 2021\n\nCall for submission to our [RobMOTS Challenge](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110) (Robust Multi-Object Tracking and Segmentation) held in conjunction with our [RVSU CVPR'21 Workshop](https://eval.vision.rwth-aachen.de/rvsu-workshop21/). Robust tracking evaluation against 8 tracking benchmarks. Challenge submission deadline June 15th. Also check out our workshop [call for papers](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=74).\n\n## Official Evaluation Code\n\nThe following benchmarks use TrackEval as their official evaluation code, check out the links to see TrackEval in action:\n\n - **[RobMOTS](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110)** ([Official Readme](docs/RobMOTS-Official/Readme.md))\n - **[KITTI Tracking](http://www.cvlibs.net/datasets/kitti/eval_tracking.php)**\n - **[KITTI MOTS](http://www.cvlibs.net/datasets/kitti/eval_mots.php)**\n - **[MOTChallenge](https://motchallenge.net/)** ([Official Readme](docs/MOTChallenge-Official/Readme.md))\n - **[Open World Tracking](https://openworldtracking.github.io)** ([Official Readme](docs/OpenWorldTracking-Official))\n <!--- **[MOTS-Challenge](https://motchallenge.net/data/MOTS/)** ([Official Readme](docs/MOTS-Challenge-Official/Readme.md)) --->\n\nIf you run a tracking benchmark and want to use TrackEval as your official evaluation code, please contact Jonathon (contact details below).\n\n## Currently implemented metrics\n\nThe following metrics are currently implemented:\n\nMetric Family | Sub metrics | Paper | Code | Notes |\n|----- | ----------- |----- | ----------- | ----- |\n| | | |  |  |\n|**HOTA metrics**|HOTA, DetA, AssA, LocA, DetPr, DetRe, AssPr, AssRe|[paper](https://link.springer.com/article/10.1007/s11263-020-01375-2)|[code](trackeval/metrics/hota.py)|**Recommended tracking metric**|\n|**CLEARMOT metrics**|MOTA, MOTP, MT, ML, Frag, etc.|[paper](https://link.springer.com/article/10.1155/2008/246309)|[code](trackeval/metrics/clear.py)| |\n|**Identity metrics**|IDF1, IDP, IDR|[paper](https://arxiv.org/abs/1609.01775)|[code](trackeval/metrics/identity.py)| |\n|**VACE metrics**|ATA, SFDA|[paper](https://link.springer.com/chapter/10.1007/11612704_16)|[code](trackeval/metrics/vace.py)| |\n|**Track mAP metrics**|Track mAP|[paper](https://arxiv.org/abs/1905.04804)|[code](trackeval/metrics/track_map.py)|Requires confidence scores|\n|**J & F metrics**|J&F, J, F|[paper](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Perazzi_A_Benchmark_Dataset_CVPR_2016_paper.pdf)|[code](trackeval/metrics/j_and_f.py)|Only for Seg Masks|\n|**ID Euclidean**|ID Euclidean|[paper](https://arxiv.org/pdf/2103.13516.pdf)|[code](trackeval/metrics/ideucl.py)| |\n\n\n## Currently implemented benchmarks\n\nThe following benchmarks are currently implemented:\n\nBenchmark | Sub-benchmarks | Type | Website | Code | Data Format |\n|----- | ----------- |----- | ----------- | ----- | ----- |\n| | | |  |  | |\n|**RobMOTS**|Combination of 8 benchmarks|Seg Masks|[website](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110)|[code](trackeval/datasets/rob_mots.py)|[format](docs/RobMOTS-Official/Readme.md)|\n|**Open World Tracking**|TAO-OW|OpenWorld / Seg Masks|[website](https://openworldtracking.github.io)|[code](trackeval/datasets/tao_ow.py)|[format](docs/OpenWorldTracking-Official/Readme.md)|\n|**MOTChallenge**|MOT15/16/17/20|2D BBox|[website](https://motchallenge.net/)|[code](trackeval/datasets/mot_challenge_2d_box.py)|[format](docs/MOTChallenge-format.txt)|\n|**KITTI Tracking**| |2D BBox|[website](http://www.cvlibs.net/datasets/kitti/eval_tracking.php)|[code](trackeval/datasets/kitti_2d_box.py)|[format](docs/KITTI-format.txt)|\n|**BDD-100k**| |2D BBox|[website](https://bdd-data.berkeley.edu/)|[code](trackeval/datasets/bdd100k.py)|[format](docs/BDD100k-format.txt)|\n|**TAO**| |2D BBox|[website](https://taodataset.org/)|[code](trackeval/datasets/tao.py)|[format](docs/TAO-format.txt)|\n|**MOTS**|KITTI-MOTS, MOTS-Challenge|Seg Mask|[website](https://www.vision.rwth-aachen.de/page/mots)|[code](trackeval/datasets/mots_challenge.py) and [code](trackeval/datasets/kitti_mots.py)|[format](docs/MOTS-format.txt)|\n|**DAVIS**|Unsupervised|Seg Mask|[website](https://davischallenge.org/)|[code](trackeval/datasets/davis.py)|[format](docs/DAVIS-format.txt)|\n|**YouTube-VIS**| |Seg Mask|[website](https://youtube-vos.org/dataset/vis/)|[code](trackeval/datasets/youtube_vis.py)|[format](docs/YouTube-VIS-format.txt)|\n|**Head Tracking Challenge**| |2D BBox|[website](https://arxiv.org/pdf/2103.13516.pdf)|[code](trackeval/datasets/head_tracking_challenge.py)|[format](docs/MOTChallenge-format.txt)|\n\n## HOTA metrics\n\nThis code is also the official reference implementation for the HOTA metrics:\n\n*[HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://link.springer.com/article/10.1007/s11263-020-01375-2). IJCV 2020. Jonathon Luiten, Aljosa Osep, Patrick Dendorfer, Philip Torr, Andreas Geiger, Laura Leal-Taixe and Bastian Leibe.*\n\nHOTA is a novel set of MOT evaluation metrics which enable better understanding of tracking behavior than previous metrics.\n\nFor more information check out the following links:\n - [Short blog post on HOTA](https://jonathonluiten.medium.com/how-to-evaluate-tracking-with-the-hota-metrics-754036d183e1) - **HIGHLY RECOMMENDED READING**\n - [IJCV version of paper](https://link.springer.com/article/10.1007/s11263-020-01375-2) (Open Access)\n - [ArXiv version of paper](https://arxiv.org/abs/2009.07736)\n - [Code](trackeval/metrics/hota.py)\n\n## Properties of this codebase\n\nThe code is written 100% in python with only numpy and scipy as minimum requirements.\n\nThe code is designed to be easily understandable and easily extendable. \n\nThe code is also extremely fast, running at more than 10x the speed of the both [MOTChallengeEvalKit](https://github.com/dendorferpatrick/MOTChallengeEvalKit), and [py-motmetrics](https://github.com/cheind/py-motmetrics) (see detailed speed comparison below).\n\nThe implementation of CLEARMOT and ID metrics aligns perfectly with the [MOTChallengeEvalKit](https://github.com/dendorferpatrick/MOTChallengeEvalKit).\n\nBy default the code prints results to the screen, saves results out as both a summary txt file and a detailed results csv file, and outputs plots of the results. All outputs are by default saved to the 'tracker' folder for each tracker.\n\n## Running the code\n\nThe code can be run in one of two ways:\n\n - From the terminal via one of the scripts [here](scripts/). See each script for instructions and arguments, hopefully this is self-explanatory.\n - Directly by importing this package into your code, see the same scripts above for how. \n\n## Quickly evaluate on supported benchmarks\n\nTo enable you to use TrackEval for evaluation as quickly and easily as possible, we provide ground-truth data, meta-data and example trackers for all currently supported benchmarks.\nYou can download this here: [data.zip](https://omnomnom.vision.rwth-aachen.de/data/TrackEval/data.zip) (~150mb).\n\nThe data for RobMOTS is separate and can be found here: [rob_mots_train_data.zip](https://omnomnom.vision.rwth-aachen.de/data/RobMOTS/train_data.zip) (~750mb).\n\nThe easiest way to begin is to extract this zip into the repository root folder such that the file paths look like: TrackEval/data/gt/...\n\nThis then corresponds to the default paths in the code. You can now run each of the scripts [here](scripts/) without providing any arguments and they will by default evaluate all trackers present in the supplied file structure. To evaluate your own tracking results, simply copy your files as a new tracker folder into the file structure at the same level as the example trackers (MPNTrack, CIWT, track_rcnn, qdtrack, ags, Tracktor++, STEm_Seg), ensuring the same file structure for your trackers as in the example.\n\nOf course, if your ground-truth and tracker files are located somewhere else you can simply use the script arguments to point the code toward your data.\n\nTo ensure your tracker outputs data in the correct format, check out our format guides for each of the supported benchmarks [here](docs), or check out the example trackers provided.\n\n## Evaluate on your own custom benchmark\n\nTo evaluate on your own data, you have two options:\n - Write custom dataset code (more effort, rarely worth it).\n - Convert your current dataset and trackers to the same format of an already implemented benchmark.\n\nTo convert formats, check out the format specifications defined [here](docs).\n\nBy default, we would recommend the MOTChallenge format, although any implemented format should work. Note that for many cases you will want to use the argument ```--DO_PREPROC False``` unless you want to run preprocessing to remove distractor objects.\n\n## Requirements\n Code tested on Python 3.7.\n \n - Minimum requirements: numpy, scipy\n - For plotting: matplotlib\n - For segmentation datasets (KITTI MOTS, MOTS-Challenge, DAVIS, YouTube-VIS): pycocotools\n - For DAVIS dataset: Pillow\n - For J & F metric: opencv_python, scikit_image\n - For simples test-cases for metrics: pytest\n\nuse ```pip3 -r install requirements.txt``` to install all possible requirements.\n\nuse ```pip3 -r install minimum_requirments.txt``` to only install the minimum if you don't need the extra functionality as listed above.\n\n## Timing analysis\n\nEvaluating CLEAR + ID metrics on Lift_T tracker on MOT17-train (seconds) on a i7-9700K CPU with 8 physical cores (median of 3 runs):\t\t\nNum Cores|TrackEval|MOTChallenge|Speedup vs MOTChallenge|py-motmetrics|Speedup vs py-motmetrics\n:---|:---|:---|:---|:---|:---\n1|9.64|66.23|6.87x|99.65|10.34x\n4|3.01|29.42|9.77x| |33.11x*\n8|1.62|29.51|18.22x| |61.51x*\n\n*using a different number of cores as py-motmetrics doesn't allow multiprocessing.\n\t\t\t\t\n```\npython scripts/run_mot_challenge.py --BENCHMARK MOT17 --TRACKERS_TO_EVAL Lif_T --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 1  \n```\n\t\t\t\t\nEvaluating CLEAR + ID metrics on LPC_MOT tracker on MOT20-train (seconds) on a i7-9700K CPU with 8 physical cores (median of 3 runs):\t\nNum Cores|TrackEval|MOTChallenge|Speedup vs MOTChallenge|py-motmetrics|Speedup vs py-motmetrics\n:---|:---|:---|:---|:---|:---\n1|18.63|105.3|5.65x|175.17|9.40x\n\n```\npython scripts/run_mot_challenge.py --BENCHMARK MOT20 --TRACKERS_TO_EVAL LPC_MOT --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 1\n```\n\n## License\n\nTrackEval is released under the [MIT License](LICENSE).\n\n## Contact\n\nIf you encounter any problems with the code, please contact [Jonathon Luiten](https://www.vision.rwth-aachen.de/person/216/) ([luiten@vision.rwth-aachen.de](mailto:luiten@vision.rwth-aachen.de)).\nIf anything is unclear, or hard to use, please leave a comment either via email or as an issue and I would love to help.\n\n## Dedication\n\nThis codebase was built for you, in order to make your life easier! For anyone doing research on tracking or using trackers, please don't hesitate to reach out with any comments or suggestions on how things could be improved.\n\n## Contributing\n\nWe welcome contributions of new metrics and new supported benchmarks. Also any other new features or code improvements. Send a PR, an email, or open an issue detailing what you'd like to add/change to begin a conversation.\n\n## Citing TrackEval\n\nIf you use this code in your research, please use the following BibTeX entry:\n\n```BibTeX\n@misc{luiten2020trackeval,\n  author =       {Jonathon Luiten, Arne Hoffhues},\n  title =        {TrackEval},\n  howpublished = {\\url{https://github.com/JonathonLuiten/TrackEval}},\n  year =         {2020}\n}\n```\n\nFurthermore, if you use the HOTA metrics, please cite the following paper:\n\n```\n@article{luiten2020IJCV,\n  title={HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking},\n  author={Luiten, Jonathon and Osep, Aljosa and Dendorfer, Patrick and Torr, Philip and Geiger, Andreas and Leal-Taix{\\'e}, Laura and Leibe, Bastian},\n  journal={International Journal of Computer Vision},\n  pages={1--31},\n  year={2020},\n  publisher={Springer}\n}\n```\n\nIf you use any other metrics please also cite the relevant papers, and don't forget to cite each of the benchmarks you evaluate on.\n"
  },
  {
    "path": "TrackEval/docs/BDD100k-format.txt",
    "content": "Taken from: https://bdd-data.berkeley.edu/wad-2020.html\n\nBDD100K MOT Dataset\n\nTo advance the study on multiple object tracking, we introduce BDD100K MOT Dataset. We provide 1,400 video sequences for training, 200 video sequences for validation and 400 video sequences for testing. Each video sequence is about 40 seconds long with 5 FPS resulting in approximately 200 frames per video.\n\nBDD100K MOT Dataset is not only diverse in visual scale among and within tracks, but in the temporal range of each track. Objects in the BDD100K MOT dataset also present complicated occlusion and reappearing patterns. An object may be fully occluded or move out of the frame, and then reappear later. BDD100K MOT Dataset shows real challenges of object re-identification for tracking in autonomous driving. Details about the MOT dataset can be found in the BDD100K paper (https://arxiv.org/abs/1805.04687). Access the BDD100K data website (https://bdd-data.berkeley.edu/) to download the data.\n\nFolder Structure\nbdd100k/\n├── images/\n|   ├── track/\n|   |   ├── train/\n|   |   |   ├── $VIDEO_NAME/\n|   |   |   |   ├── $VIDEO_NAME-$FRAME_INDEX.jpg\n|   |   ├── val/\n|   |   ├── test/\n├── labels-20/\n|   ├── box-track/\n|   |   ├── train/\n|   |   |   ├── $VIDEO_NAME.json\n|   |   |   |\n|   |   ├── val/\nThe frames for each video are stored in a folder in the images directory. The labels for each video are stored in a json file with the format detailed below.\n\nLabel Format\nEach json file contains a list of frame objects, and each frame object has the format below. The format follows the schema of BDD100K data format (https://github.com/ucbdrive/bdd100k/blob/master/doc/format.md).\n\n- name: string\n- videoName: string\n- index: int\n- labels: [ ]\n    - id: string\n    - category: string\n    - attributes:\n        - Crowd: boolean\n        - Occluded: boolean\n        - Truncated: boolean\n    - box2d:\n        - x1: float\n        - y1: float\n        - x2: float\n        - y2: float\nThere are 11 object categories in this release:\n\npedestrian\nrider\nother person\ncar\nbus\ntruck\ntrain\ntrailer\nother vehicle\nmotorcycle\nbicycle\n\nNotes:\nThe same instance shares \"id\" across frames.\nThe \"pedestrian\", \"bicycle\", and \"motorcycle\" correspond to the \"person\", \"bike\", and \"motor\" classes in the BDD100K Detection dataset.\nWe consider \"other person\", \"trailer\", and \"other vehicle\" as distractors, which are ignored during evaluation. We only evaluate the multi-object tracking of the other 8 categories.\nWe set three super-categories: \"person\" (with classes \"pedestrian\" and\"rider\"), \"vehicle\" (\"car\", \"bus\", \"truck\", and \"train\"), and \"bike\" (\"motorcycle\" and \"bicycle\") for the purpose of evaluation.\n\nSubmission Format\nThe submission file for each of the two phases is a json file compressed by zip. Each json file is a list of frame objects with the format detailed below. The format also follows the schema of BDD100K data format (https://github.com/ucbdrive/bdd100k/blob/master/doc/format.md).\n\n- name: string\n- labels [ ]:\n    - id: string\n    - category: string\n    - box2d:\n       - x1: float\n       - y1: float\n       - x2: float\n       - y2: float\n\nNote that objects with the same identity share id across frames in a given video, and should be unique across different videos. Our evaluation will match the category string in evaluation, so you can assign your own integer ID for the categories in your model. But we recommend to encode the 8 relevant categories in the following order so that it is easier for the research community to share the models.\n\npedestrian\nrider\ncar\ntruck\nbus\ntrain\nmotorcycle\nbicycle\n\nThe evaluation server will perform evaluation for each category and aggregate the results to compute the overall metrics. Then the server will merge both the ground-truth and predicted labels into super-categories and evaluate for each super- category.\n\nEvaluation\nEvaluation platform: We host our evaluation server on CodaLab (https://competitions.codalab.org/competitions/24492). There are two phases for the challenge: val phase and test phase. The final ranking will be based on the test phase.\nPre-training: It is a fair game to pre-train your network with ImageNet or COCO, but if other datasets are used, please note in the submission description. We will rank the methods without using external datasets except ImageNet and COCO.\nIgnoring distractors: As a preprocessing step, all predicted boxes are matched and the ones matched to distractor ground-truth boxes (\"other person\", \"trailer\", and \"other vehicle\") are ignored.\nCrowd region: After bounding box matching, we ignore all detected false-positive boxes that has >50% overlap with the crowd region (ground-truth boxes with the \"Crowd\" attribute).\nSuper-category: In addition to the evaluation of all 8 classes, we merge ground truth and prediction categories into 3 super-categories specified above, and evaluate the results for each super-category. The super-category evaluation results will be provided only for the purpose of reference."
  },
  {
    "path": "TrackEval/docs/DAVIS-format.txt",
    "content": "Annotation Format:\n\n\nThe annotations in each frame are stored in png format.\nThis png is stored indexed i.e. it has a single channel and each pixel has a value from 0 to 254 that corresponds to a color palette attached to the png file.\nIt is important to take this into account when decoding the png i.e. the output of decoding should be a single channel image and it should not be necessary to do any remap from RGB to indexes. \nThe latter is crucial to preseve the index of each object so it can match to the correct object in evaluation.\n\nEach pixel that belongs to the same object has the same value in this png map through the whole video.\nStart at 1 for the first object, then 2, 3, 4 etc.\nThe background (not an object) has value 0.\nAlso note that invalid/void pixels are stored with a 254 value.\n\n\nThese can be read like this:\n\nimport PIL.Image as Image\nimg = np.array(Image.open(\"000005.png\"))\n\n\nor like this:\n\nann_data = tf.read_file(ann_filename)\nann = tf.image.decode_image(ann_data, dtype=tf.uint8, channels=1)\n\n\nSee the code for loading the davis dataset for more details.\n\n"
  },
  {
    "path": "TrackEval/docs/How_To/Add_a_new_metric.md",
    "content": "# How to add a new or custom family of evaluation metrics to TrackEval\n\n - Create your metrics code in ```trackeval/metrics/<your_metric>.py```.\n - It's probably easiest to start by copying an existing metrics code and editing it, e.g. ```trackeval/metrics/identity.py``` is probably the simplest.\n - Your metric should be class, and it should inherit from the ```trackeval.metrics._base_metric._BaseMetric``` class.\n - Define an ```__init__``` function that defines the different ```fields``` (values) that your metric will calculate. See ```trackeval/metrics/_base_metric.py``` for a list of currently used field types. Feel free to add new types.\n - Define your code to actually calculate your metric for a single sequence and single class in a function called ```eval_sequence```, which takes a data dictionary as input, and returns a results dictionary as output.\n - Define functions for how to combine your metric field values over a) sequences ```combine_sequences```, b) over classes ```combine_classes_class_averaged```, and c) over classes weighted by the number of detections ```combine_classes_det_averaged```.\n - We find using a function such as the ```_compute_final_fields``` function that we use in the current metrics is convienient because it is likely used for metrics calculation and for the different metric combination, however this is not required.\n - Register your new metric by adding it to ```trackeval/metrics/init.py```  \n - Your new metric can be used by passing the metrics class to a list of metrics which is passed to the evaluator (see files in ```scripts/*```).\n"
  },
  {
    "path": "TrackEval/docs/KITTI-format.txt",
    "content": "Taken from download link found at: http://www.cvlibs.net/datasets/kitti/eval_tracking.php\n\n###########################################################################\n#           THE KITTI VISION BENCHMARK SUITE: TRACKING BENCHMARK          #\n#              Andreas Geiger    Philip Lenz    Raquel Urtasun            #\n#                    Karlsruhe Institute of Technology                    #\n#                Toyota Technological Institute at Chicago                #\n#                             www.cvlibs.net                              #\n###########################################################################\n\nFor recent updates see http://www.cvlibs.net/datasets/kitti/eval_tracking.php.\n\nThis file describes the KITTI tracking benchmarks, consisting of 21 \ntraining sequences and 29 test sequences. \n\nDespite the fact that we have labeled 8 different classes, only the classes \n'Car' and 'Pedestrian' are evaluated in our benchmark, as only for those \nclasses enough instances for a comprehensive evaluation have been labeled. \n\nThe labeling process has been performed in two steps: First we hired a set \nof annotators, to label 3D bounding boxes for tracklets in 3D Velodyne \npoint clouds. Since for a pedestrian tracklet, a single 3D bounding box \ntracklet (dimensions have been fixed) often fits badly, we additionally \nlabeled the left/right boundaries of each object by making use of Mechanical\nTurk. We also collected labels of the object's occlusion state, and computed \nthe object's truncation via backprojecting a car/pedestrian model into the\nimage plane.\n\nNOTE: WHEN SUBMITTING RESULTS, PLEASE STORE THEM IN THE SAME DATA FORMAT IN\nWHICH THE GROUND TRUTH DATA IS PROVIDED (SEE BELOW), USING THE FILE NAMES\n0000.txt 0001.txt ... CREATE A ZIP ARCHIVE OF THEM AND STORE YOUR\nRESULTS (ONLY THE RESULTS OF THE TEST SET) IN ITS ROOT FOLDER. FOR A \nRE-SUBMISSION, _ONLY_ THE RE-SUBMITTED RESULTS WILL BE SHOWN IN THE TABLE.\n\nData Format Description\n=======================\n\nThe data for training and testing can be found in the corresponding folders.\nThe sub-folders are structured as follows:\n\n  - image_02/%04d/ contains the left color camera sequence images (png)\n  - image_03/%04d/ contains the right color camera sequence images  (png)\n  - label_02/ contains the left color camera label files (plain text files)\n  - calib/ contains the calibration for all four cameras (plain text files)\n\nThe label files contain the following information. \nAll values (numerical or strings) are separated via spaces, each row \ncorresponds to one object. The 17 columns represent:\n\n#Values    Name      Description\n----------------------------------------------------------------------------\n   1    frame        Frame within the sequence where the object appearers\n   1    track id     Unique tracking id of this object within this sequence\n   1    type         Describes the type of object: 'Car', 'Van', 'Truck',\n                     'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram',\n                     'Misc' or 'DontCare'\n   1    truncated    Integer (0,1,2) indicating the level of truncation.\n                     Note that this is in contrast to the object detection\n                     benchmark where truncation is a float in [0,1].\n   1    occluded     Integer (0,1,2,3) indicating occlusion state:\n                     0 = fully visible, 1 = partly occluded\n                     2 = largely occluded, 3 = unknown\n   1    alpha        Observation angle of object, ranging [-pi..pi]\n   4    bbox         2D bounding box of object in the image (0-based index):\n                     contains left, top, right, bottom pixel coordinates\n   3    dimensions   3D object dimensions: height, width, length (in meters)\n   3    location     3D object location x,y,z in camera coordinates (in meters)\n   1    rotation_y   Rotation ry around Y-axis in camera coordinates [-pi..pi]\n   1    score        Only for results: Float, indicating confidence in\n                     detection, needed for p/r curves, higher is better.\n                     \n\nHere, 'DontCare' labels denote regions in which objects have not been labeled,\nfor example because they have been too far away from the laser scanner. To\nprevent such objects from being counted as false positives our evaluation\nscript will ignore objects tracked in don't care regions of the test set.\nYou can use the don't care labels in the training set to avoid that your object\ndetector/tracking algorithm is harvesting hard negatives from those areas, \nin case you consider non-object regions from the training images as negative \nexamples.\n\nThe reference point for the 3D bounding box for each object is centered on the\nbottom face of the box. The corners of bounding box are computed as follows with\nrespect to the reference point and in the object coordinate system:\nx_corners = [l/2, l/2, -l/2, -l/2,  l/2,  l/2, -l/2, -l/2]^T\ny_corners = [0,   0,    0,    0,   -h,   -h,   -h,   -h  ]^T\nz_corners = [w/2, -w/2, -w/2, w/2, w/2, -w/2, -w/2, w/2  ]^T\nwith l=length, h=height, and w=width.\n\nThe coordinates in the camera coordinate system can be projected in the image\nby using the 3x4 projection matrix in the calib folder, where for the left\ncolor camera for which the images are provided, P2 must be used. The\ndifference between rotation_y and alpha is, that rotation_y is directly\ngiven in camera coordinates, while alpha also considers the vector from the\ncamera center to the object center, to compute the relative orientation of\nthe object with respect to the camera. For example, a car which is facing\nalong the X-axis of the camera coordinate system corresponds to rotation_y=0,\nno matter where it is located in the X/Z plane (bird's eye view), while\nalpha is zero only, when this object is located along the Z-axis of the\ncamera. When moving the car away from the Z-axis, the observation angle\n(\\alpha) will change.\n\nAn overview of the coordinate systems, reference point and geometrical \ndefinitions is given in cs_overview.pdf.\n\nTo project a point from Velodyne coordinates into the left color image,\nyou can use this formula: x = P2 * R0_rect * Tr_velo_to_cam * y\nFor the right color image: x = P3 * R0_rect * Tr_velo_to_cam * y\n\nNote: All matrices are stored row-major, i.e., the first values correspond\nto the first row. R0_rect contains a 3x3 matrix which you need to extend to\na 4x4 matrix by adding a 1 as the bottom-right element and 0's elsewhere.\nTr_xxx is a 3x4 matrix (R|t), which you need to extend to a 4x4 matrix \nin the same way!\n\nThe sensors were not moved between the different days while taking footage.\nHowever, the full camera calibration was performed for every day separately.\nTherefore, only 'Tr_imu_velo' is constant for all sequences.\n\nNote that while all this information is available for the training data,\nonly the data which is actually needed for the particular benchmark must\nbe provided to the evaluation server. However, all 17 values must be provided\nat all times, with the unused ones set to their default values (=invalid).\nAdditionally a 18'th value must be provided\nwith a floating value of the score for a particular tracked detection, where \nhigher indicates higher confidence in the detection. The range of your scores \nwill be automatically determined by our evaluation server, you don't have to\nnormalize it, but it should be roughly linear.\n\nTracking Benchmark\n==================\n\nThe goal in the object tracking task is to estimate object tracklets for the \nclasses 'Car', 'Pedestrian', and (optional) 'Cyclist'. The tracking\nalgorithm must provide as output the 2D 0-based bounding boxes in each image in\nthe sequence using the format specified above, as well as a score, indicating\nthe confidence in the particular frame for this track. All other values must be\nset to their default values (=invalid), see above. One text file per sequence\nmust be provided in a zip archive, where each file can contain many detections,\ndepending on the number of objects per sequence. In our evaluation we only\nevaluate detections/objects larger than 25 pixel (height) in the image and do\nnot count Vans as false positives for cars or Sitting Persons as wrong positives\nfor Pedestrians due to their similarity in appearance. (All ignored objects \nare considered as DontCare areas.) As evaluation criterion we follow the \nHOTA, CLEARMOT and Mostly-Tracked/Partly-Tracked/Mostly-Lost metrics.\n\nRaw Data\n========\n\nRaw data is mapped to the tracking benchmark sequences and available for \ndownload.\n\nThe velodyne and positioning data for training and testing can be found in the \ncorresponding folders. The sub-folders are structured as follows:\n\n  - velodyne/%04d/ contains the raw velodyne point clouds (binary file)\n  - oxts/ contains the raw position (oxts) data (plain text files)\n\n\n\n"
  },
  {
    "path": "TrackEval/docs/MOTChallenge-Official/Readme.md",
    "content": "![Test Image 4](https://motchallenge.net/img/header-bg/mot_bannerthin.png)\n![MOT_PIC](https://motchallenge.net/sequenceVideos/MOT17-04-SDP-gt.jpg)\n# MOTChallenge Official Evaluation Kit - Multi-Object Tracking - MOT15, MOT16, MOT17, MOT20\n\nTrackEval is now the Official Evaluation Kit for MOTChallenge.\n\nThis repository contains the evaluation scripts for the MOT challenges available at www.MOTChallenge.net.\n\nThis codebase replaces the previous version that used to be accessible at https://github.com/dendorferpatrick/MOTChallengeEvalKit and is no longer maintained.\n\nChallenge Name | Data url |\n|----- | ----------- |\n|2D MOT 15| https://motchallenge.net/data/MOT15/ |\n|MOT 16| https://motchallenge.net/data/MOT16/       |\n|MOT 17| https://motchallenge.net/data/MOT17/       |\n|MOT 20| https://motchallenge.net/data/MOT20/       |\n\n## Requirements \n* Python (3.5 or newer)\n* Numpy and Scipy\n\n## Directories and Data\nThe easiest way to get started is to simply download the TrackEval example data from here: [data.zip](https://omnomnom.vision.rwth-aachen.de/data/TrackEval/data.zip) (~150mb).\n\nThis contains all the ground-truth, example trackers and meta-data that you will need.\n\nExtract this zip into the repository root folder such that the file paths look like: TrackEval/data/gt/...\n\n## Evaluation\nTo run the evaluation for your method please run the script at ```TrackEval/scripts/run_mot_challenge.py```.\n\nSome of the basic arguments are described below. For more arguments, please see the script itself.\n\n```BENCHMARK```: Name of the benchmark, e.g. MOT15, MO16, MOT17 or MOT20  (default : MOT17)\n\n```SPLIT_TO_EVAL```: Data split on which to evalute e.g. train, test (default : train)\n\n```TRACKERS_TO_EVAL```: List of tracker names for which you wish to run evaluation. e.g. MPNTrack (default: all trackers in tracker folder)\n\n```METRICS```: List of metric families which you wish to compute. e.g. HOTA CLEAR Identity VACE (default: HOTA CLEAR Identity)\n\n```USE_PARALLEL```: Whether to run evaluation in parallel on multiple cores. (default: False)\n\n```NUM_PARALLEL_CORES```: Number of cores to use when running in parallel. (default: 8)\n\nAn example is below (this will work on the supplied example data above):\n```\npython scripts/run_mot_challenge.py --BENCHMARK MOT17 --SPLIT_TO_EVAL train --TRACKERS_TO_EVAL MPNTrack --METRICS HOTA CLEAR Identity VACE --USE_PARALLEL False --NUM_PARALLEL_CORES 1  \n```\n\n\n## Data Format\n<p>\nThe tracker file format should be the same as the ground truth file, \nwhich is a CSV text-file containing one object instance per line.\nEach line must contain 10 values:\n</p>\n\n</p>\n<code>\n&lt;frame&gt;,\n&lt;id&gt;,\n&lt;bb_left&gt;,\n&lt;bb_top&gt;,\n&lt;bb_width&gt;,\n&lt;bb_height&gt;,\n&lt;conf&gt;,\n&lt;x&gt;,\n&lt;y&gt;,\n&lt;z&gt;\n</code>\n</p>\n\nThe world coordinates <code>x,y,z</code>\nare ignored for the 2D challenge and can be filled with -1.\nSimilarly, the bounding boxes are ignored for the 3D challenge.\nHowever, each line is still required to contain 10 values.\n\nAll frame numbers, target IDs and bounding boxes are 1-based. Here is an example:\n\n<pre>\n1, 3, 794.27, 247.59, 71.245, 174.88, -1, -1, -1, -1\n1, 6, 1648.1, 119.61, 66.504, 163.24, -1, -1, -1, -1\n1, 8, 875.49, 399.98, 95.303, 233.93, -1, -1, -1, -1\n...\n</pre>\n\n \n## Evaluating on your own Data\nThe repository also allows you to include your own datasets and evaluate your method on your own challenge ```<YourChallenge>```.  To do so, follow these two steps:  \n***1. Ground truth data preparation***  \nPrepare your sequences in directory ```TrackEval/data/gt/mot_challenge/<YourChallenge>``` following this structure:\n\n```\n.\n|—— <SeqName01>\n\t|—— gt\n\t\t|—— gt.txt\n\t|—— seqinfo.ini\n|—— <SeqName02>\n\t|—— ……\n|—— <SeqName03>\n\t|—— …...\n```\n\n***2. Sequence file***  \nCreate text files containing the sequence names; ```<YourChallenge>-train.txt```, ```<YourChallenge>-test.txt```,  ```<YourChallenge>-test.txt``` inside the ```seqmaps``` folder, e.g.:\n```<YourChallenge>-all.txt```\n```\nname\n<seqName1> \n<seqName2>\n<seqName3>\n```\n\n```<YourChallenge>-train.txt```\n```\nname\n<seqName1> \n<seqName2>\n```\n\n```<YourChallenge>-test.txt```\n```\nname\n<seqName3>\n```\n\n\nTo run the evaluation for your method adjust the file ```scripts/run_mot_challenge.py``` and set ```BENCHMARK = <YourChallenge>```\n\n\n## Citation\nIf you work with the code and the benchmark, please cite:\n\n***TrackEval***\n```\n@misc{luiten2020trackeval,\n  author =       {Jonathon Luiten, Arne Hoffhues},\n  title =        {TrackEval},\n  howpublished = {\\url{https://github.com/JonathonLuiten/TrackEval}},\n  year =         {2020}\n}\n```\n***MOTChallenge Journal***\n```\n@article{dendorfer2020motchallenge,\n  title={MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking},\n  author={Dendorfer, Patrick and Osep, Aljosa and Milan, Anton and Schindler, Konrad and Cremers, Daniel and Reid, Ian and Roth, Stefan and Leal-Taix{\\'e}, Laura},\n  journal={International Journal of Computer Vision},\n  pages={1--37},\n  year={2020},\n  publisher={Springer}\n}\n```\n***MOT 15***\n```\n@article{MOTChallenge2015,\n\ttitle = {{MOTC}hallenge 2015: {T}owards a Benchmark for Multi-Target Tracking},\n\tshorttitle = {MOTChallenge 2015},\n\turl = {http://arxiv.org/abs/1504.01942},\n\tjournal = {arXiv:1504.01942 [cs]},\n\tauthor = {Leal-Taix\\'{e}, L. and Milan, A. and Reid, I. and Roth, S. and Schindler, K.},\n\tmonth = apr,\n\tyear = {2015},\n\tnote = {arXiv: 1504.01942},\n\tkeywords = {Computer Science - Computer Vision and Pattern Recognition}\n}\n```\n***MOT 16, MOT 17***\n```\n@article{MOT16,\n\ttitle = {{MOT}16: {A} Benchmark for Multi-Object Tracking},\n\tshorttitle = {MOT16},\n\turl = {http://arxiv.org/abs/1603.00831},\n\tjournal = {arXiv:1603.00831 [cs]},\n\tauthor = {Milan, A. and Leal-Taix\\'{e}, L. and Reid, I. and Roth, S. and Schindler, K.},\n\tmonth = mar,\n\tyear = {2016},\n\tnote = {arXiv: 1603.00831},\n\tkeywords = {Computer Science - Computer Vision and Pattern Recognition}\n}\n```\n***MOT 20***\n```\n@article{MOTChallenge20,\n    title={MOT20: A benchmark for multi object tracking in crowded scenes},\n    shorttitle = {MOT20},\n\turl = {http://arxiv.org/abs/1906.04567},\n\tjournal = {arXiv:2003.09003[cs]},\n\tauthor = {Dendorfer, P. and Rezatofighi, H. and Milan, A. and Shi, J. and Cremers, D. and Reid, I. and Roth, S. and Schindler, K. and Leal-Taix\\'{e}, L. },\n\tmonth = mar,\n\tyear = {2020},\n\tnote = {arXiv: 2003.09003},\n\tkeywords = {Computer Science - Computer Vision and Pattern Recognition}\n}\n```\n***HOTA metrics***\n```\n@article{luiten2020IJCV,\n  title={HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking},\n  author={Luiten, Jonathon and Osep, Aljosa and Dendorfer, Patrick and Torr, Philip and Geiger, Andreas and Leal-Taix{\\'e}, Laura and Leibe, Bastian},\n  journal={International Journal of Computer Vision},\n  pages={1--31},\n  year={2020},\n  publisher={Springer}\n}\n```\n\n## Feedback and Contact\nWe are constantly working on improving our benchmark to provide the best performance to the community.\nYou can help us to make the benchmark better by open issues in the repo and reporting bugs.\n\nFor general questions, please contact one of the following:\n\n```\nPatrick Dendorfer - patrick.dendorfer@tum.de\nJonathon Luiten - luiten@vision.rwth-aachen.de\nAljosa Osep - aljosa.osep@tum.de\n```\n\n"
  },
  {
    "path": "TrackEval/docs/MOTChallenge-format.txt",
    "content": "Taken from: https://motchallenge.net/instructions/\n\nFile Format\n\nPlease submit your results as a single .zip file. The results for each sequence must be stored in a separate .txt file in the archive's root folder. The file name must be exactly like the sequence name (case sensitive).\n\nThe file format should be the same as the ground truth file, which is a CSV text-file containing one object instance per line. Each line must contain 10 values:\n\n<frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>\nThe conf value contains the detection confidence in the det.txt files. For the ground truth, it acts as a flag whether the entry is to be considered. A value of 0 means that this particular instance is ignored in the evaluation, while any other value can be used to mark it as active. For submitted results, all lines in the .txt file are considered. The world coordinates x,y,z are ignored for the 2D challenge and can be filled with -1. Similarly, the bounding boxes are ignored for the 3D challenge. However, each line is still required to contain 10 values.\n\nAll frame numbers, target IDs and bounding boxes are 1-based. Here is an example:\n\nTracking with bounding boxes\n(MOT15, MOT16, MOT17, MOT20)\n  1, 3, 794.27, 247.59, 71.245, 174.88, -1, -1, -1, -1\n  1, 6, 1648.1, 119.61, 66.504, 163.24, -1, -1, -1, -1\n  1, 8, 875.49, 399.98, 95.303, 233.93, -1, -1, -1, -1\n  ...\n\nMulti Object Tracking & Segmentation\n(MOTS Challenge)\nEach line of an annotation txt file is structured like this (where rle means run-length encoding from COCO):\n\ntime_frame id class_id img_height img_width rle\nAn example line from a txt file:\n\n52 1005 1 375 1242 WSV:2d;1O10000O10000O1O100O100O1O100O1000000000000000O100O102N5K00O1O1N2O110OO2O001O1NTga3\nMeaning:\ntime frame 52\nobject id 1005 (meaning class id is 1, i.e. car and instance id is 5)\nclass id 1\nimage height 375\nimage width 1242\nrle WSV:2d;1O10000O10000O1O100O100O1O100O1000000000000000O100O...1O1N\n\nimage height, image width, and rle can be used together to decode a mask using cocotools(https://github.com/cocodataset/cocoapi) ."
  },
  {
    "path": "TrackEval/docs/MOTS-format.txt",
    "content": "Taken from: https://www.vision.rwth-aachen.de/page/mots\n\n\nAnnotation Format\nWe provide two alternative and equivalent formats, one encoded as png images, and one encoded as txt files. The txt files are smaller, and faster to be read in, but the cocotools are needed to decode the masks. For code to read the annotations also see mots_tools/blob/master/mots_common/io.py\n\nNote that in both formats an id value of 10,000 denotes an ignore region and 0 is background. The class id can be obtained by floor divison of the object id by 1000 (class_id = obj_id // 1000) and the instance id can be obtained by the object id modulo 1000 (instance_id = obj_id % 1000). The object ids are consistent over time.\n\nThe class ids are the following\n\ncar 1\npedestrian 2\npng format\nThe png format has a single color channel with 16 bits and can for example be read like this:\n\nimport PIL.Image as Image\nimg = np.array(Image.open(\"000005.png\"))\nobj_ids = np.unique(img)\n# to correctly interpret the id of a single object\nobj_id = obj_ids[0]\nclass_id = obj_id // 1000\nobj_instance_id = obj_id % 1000\nWhen using a TensorFlow input pipeline for reading the annotations, you can use\n\nann_data = tf.read_file(ann_filename)\nann = tf.image.decode_image(ann_data, dtype=tf.uint16, channels=1)\n\n\ntxt format\nEach line of an annotation txt file is structured like this (where rle means run-length encoding from COCO):\n\ntime_frame id class_id img_height img_width rle\nAn example line from a txt file:\n\n52 1005 1 375 1242 WSV:2d;1O10000O10000O1O100O100O1O100O1000000000000000O100O102N5K00O1O1N2O110OO2O001O1NTga3\nWhich means\n\ntime frame 52\nobject id 1005 (meaning class id is 1, i.e. car and instance id is 5)\nclass id 1\nimage height 375\nimage width 1242\nrle WSV:2d;1O10000O10000O1O100O100O1O100O1000000000000000O100O...1O1N\n\nimage height, image width, and rle can be used together to decode a mask using cocotools."
  },
  {
    "path": "TrackEval/docs/OpenWorldTracking-Official/Readme.md",
    "content": "![owt](https://user-images.githubusercontent.com/23000532/160293694-6fc0a3da-c177-4776-8472-49ff6ff375a3.jpg)\n# Opening Up Open-World Tracking - Official Evaluation Code\n\nTrackEval now contains the official evalution code for evaluating the task of **Open World Tracking**.\n\nThis is the official code from the following paper:\n\n<pre><b>Opening up Open-World Tracking</b>\nYang Liu*, Idil Esen Zulfikar*, Jonathon Luiten*, Achal Dave*, Deva Ramanan, Bastian Leibe, Aljoša Ošep, Laura Leal-Taixé\n<t><t>*Equal contribution\nCVPR 2022</pre>\n\n[Paper](https://arxiv.org/abs/2104.11221)\n\n[Website](https://openworldtracking.github.io)\n\n## Running and understanding the code\n\nThe code can be run by running the following script (see script for arguments and how to run):\n[TAO-OW run script](https://github.com/JonathonLuiten/TrackEval/blob/master/scripts/run_tao_ow.py)\n\nTo understand the the data is being read and used, see the TAO-OW dataset class:\n[TAO-OW dataset class](https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/datasets/tao_ow.py)\n\nThe 'Open World Tracking Accuracy' (OWTA) metric proposed in the paper is call RHOTA (Recall-based HOTA) within this repository, and the implementation can be found here:\n[OWTA/RHOTA metric](https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/metrics/hota.py)\n\n## Citation\nIf you work with the code and the benchmark, please cite:\n\n***Opening Up Open-World Tracking***\n```\n@inproceedings{liu2022opening,\n  title={Opening up Open-World Tracking},\n  author={Liu, Yang and Zulfikar, Idil Esen and Luiten, Jonathon and Dave, Achal and Ramanan, Deva and Leibe, Bastian and O{\\v{s}}ep, Aljo{\\v{s}}a and Leal-Taix{\\'e}, Laura},\n  journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},\n  year={2022}\n}\n```\n\n***TrackEval***\n```\n@misc{luiten2020trackeval,\n  author =       {Jonathon Luiten, Arne Hoffhues},\n  title =        {TrackEval},\n  howpublished = {\\url{https://github.com/JonathonLuiten/TrackEval}},\n  year =         {2020}\n}\n```\n"
  },
  {
    "path": "TrackEval/docs/RobMOTS-Official/Readme.md",
    "content": "[![image](https://user-images.githubusercontent.com/23000532/118353602-607d1080-b567-11eb-8744-3e346a438583.png)](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110)\n\n# RobMOTS Official Evaluation Code\n\n### NEWS: [RobMOTS Challenge](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110) for the [RVSU CVPR'21 Workshop](https://eval.vision.rwth-aachen.de/rvsu-workshop21/) is now live!!!! Challenge deadline June 15.\n\n### NEWS: [Call for short papers](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=74) (4 pages) on tracking and other video topics for [RVSU CVPR'21 Workshop](https://eval.vision.rwth-aachen.de/rvsu-workshop21/)!!!! Paper deadline June 4.\n\nTrackEval is now the Official Evaluation Kit for the RobMOTS Challenge.\n\nThis repository contains the official evaluation code for the challenges available at the [RobMOTS Website](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110).\n\nThe RobMOTS Challenge tests trackers' ability to work robustly across 8 different benchmarks, while tracking the [80 categories of objects from COCO](https://cocodataset.org/#explore).\n\nThe following benchmarks are included:\n\nBenchmark | Website |\n|----- | ----------- |\n|MOTS Challenge| https://motchallenge.net/results/MOTS/ |\n|KITTI-MOTS| http://www.cvlibs.net/datasets/kitti/eval_mots.php       |\n|DAVIS Challenge Unsupervised| https://davischallenge.org/challenge2020/unsupervised.html       |\n|YouTube-VIS| https://youtube-vos.org/dataset/vis/       |\n|BDD100k MOTS| https://bdd-data.berkeley.edu/ |\n|TAO| https://taodataset.org/       |\n|Waymo Open Dataset| https://waymo.com/open/       |\n|OVIS| http://songbai.site/ovis/       |\n\n## Installing, obtaining the data, and running\n\nSimply follow the code snippet below to install the evaluation code, download the train groundtruth data and an example tracker, and run the evaluation code on the sample tracker.\n\nNote the code requires python 3.5 or higher.\n\n```\n# Download the TrackEval repo\ngit clone https://github.com/JonathonLuiten/TrackEval.git\n\n# Move to repo folder\ncd TrackEval\n\n# Create a virtual env in the repo for evaluation\npython3 -m venv ./venv\n\n# Activate the virtual env\nsource venv/bin/activate\n\n# Update pip to have the latest version of packages\npip install --upgrade pip\n\n# Install the required packages\npip install -r requirements.txt\n\n# Download the train gt data\nwget https://omnomnom.vision.rwth-aachen.de/data/RobMOTS/train_gt.zip\n\n# Unzip the train gt data you just downloaded.\nunzip train_gt.zip\n\n# Download the example tracker \nwget https://omnomnom.vision.rwth-aachen.de/data/RobMOTS/example_tracker.zip\n\n# Unzip the example tracker you just downloaded.\nunzip example_tracker.zip\n\n# Run the evaluation on the provided example tracker on the train split (using 4 cores in parallel)\npython scripts/run_rob_mots.py --ROBMOTS_SPLIT train --TRACKERS_TO_EVAL STP --USE_PARALLEL True --NUM_PARALLEL_CORES 4\n\n```\n\nYou may further download the raw sequence images and supplied detections (as well as train GT data and example tracker) by following the ```Data Download``` link here:\n\n[RobMOTS Challenge Info](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110)\n\n## Accessing tracking evaluation results\n\nYou will find the results of the evaluation (for the supplied tracker STP) in the folder ```TrackEval/data/trackers/rob_mots/train/STP/```.\nThe overall summary of the results is in ```./final_results.csv```, and more detailed results per sequence and per class and results plots can be found under ```./results/*```.\n\nThe ```final_results.csv``` can be most easily read by opening it in Excel or similar. The ```c```, ```d``` and ```f``` prepending the metric names refer respectively to ```class averaged```, ```detection averaged (class agnostic)``` and ```final``` (the geometric mean of class and detection averaged).\n\n## Supplied Detections\n\nTo make creating your own tracker particularly easy, we supply a set of strong supplied detection. \n\nThese detections are from the Detectron 2 Mask R-CNN X152 (very bottom model on this [page](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md) which achieves a COCO detection mAP score of 50.2). \n\nWe then obtain segmentation masks for these detections using the Box2Seg Network (also called Refinement Net), which results in far more accurate masks than the default Mask R-CNN masks. The code for this can be found [here](https://github.com/JonathonLuiten/PReMVOS/tree/master/code/refinement_net). \n\nWe supply two different supplied detections. The first is the ```raw_supplied``` detections, which is taking all 1000 detections output from the Mask R-CNN, and only removing those for which the maximum class score is less than 0.02 (here no non-maximum suppression, NMS, is run). These can be downloaded [here](https://eval.vision.rwth-aachen.de/rvsu-workshop21/?page_id=110).\n\nThe second is ```non_overlap_supplied``` detections. These are the same detections as above, but with further processing steps applied to them. First we perform Non-Maximum Suppression (NMS) with a threshold of 0.5 to remove any masks which have an IoU of 0.5 or more with any other mask that has a higher score. Second we run a Non-Overlap algorithm which forces all of the masks for a single image to be non-overlapping. It does this by putting all the masks 'on top of' each other, ordered by score, such that masks with a lower score will be partially removed if a mask with a higher score partially overlaps them. Note that these detections are still only thresholded at a score of 0.02, in general we recommend further thresholding with a higher value to get a good balance of precision and recall. \n\nCode for this NMS and Non-Overlap algorithm can be found here:\n[Non-Overlap Code](https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/baselines/non_overlap.py).\n\nNote that for RobMOTS evaluation the final tracking results need to be 'non-overlapping' so we recommend using the ```non_overlap_supplied``` detections, however you may use the ```raw_supplied```, or your own or any other detections as you like.\n\nSupplied detections (both raw and non-overlapping) are available for the train, val and test sets.\n\nExample code for reading in these detections and using them can be found here:\n\n[Tracker Example](https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/baselines/stp.py).\n\n## Creating your own tracker\n\nWe provide sample code ([Tracker Example](https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/baselines/stp.py)) for our STP tracker (Simplest Tracker Possible) which walks though how to create tracking results in the required RobMOTS format.\n\nThis includes code for reading in the supplied detections and writing out the tracking results in the desired format, plus many other useful functions (IoU calculation etc).\n\n## Evaluating your own tracker\n\nTo evaluate your tracker, put the results in the folder ```TrackEval/data/trackers/rob_mots/train/```, in a folder alongside the supplied tracker STP with the folder labelled as your tracker name, e.g. YOUR_TRACKER.\n\nYou can then run the evaluation code on your tracker like this:\n\n```\npython scripts/run_rob_mots.py --ROBMOTS_SPLIT train --TRACKERS_TO_EVAL YOUR_TRACKER --USE_PARALLEL True --NUM_PARALLEL_CORES 4\n```\n\n## Data format\n\nFor RobMOTS, trackers must submit their results in the following folder format:\n\n```\n|—— <Benchmark01>\n  |—— <Benchmark01SeqName01>.txt\n  |—— <Benchmark01SeqName02>.txt\n  |—— <Benchmark01SeqName03>.txt\n|—— <Benchmark02>\n  |—— <Benchmark02SeqName01>.txt\n  |—— <Benchmark02SeqName02>.txt\n  |—— <Benchmark02SeqName03>.txt\n```\n\nSee the supplied STP tracker results (in the Train Data linked above) for an example.\n\nThus there is one .txt file for each sequence. This file has one row per detection (object mask in one frame). Each row must have 7 values and has the following format:\n\n</p>\n<code>\n&lt;Timestep&gt;(int),\n&lt;Track ID&gt;(int),\n&lt;Class Number&gt;(int),\n&lt;Detection Confidence&gt;(float),\n&lt;Image Height&gt;(int),\n&lt;Image Width&gt;(int),\n&lt;Compressed RLE Mask&gt;(string),\n</code>\n</p>\n\nTimesteps are the same as the frame names for the supplied images. These start at 0.\n\nTrack IDs must be unique across all classes within a frame. They can be non-unique across different sequences.\n\nThe mapping of class numbers to class names can be found is [this file](https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/datasets/rob_mots_classmap.py). Note that this is the same as used in Detectron 2, and is the default COCO class ordering with the unused numbers removed.\n\nDetection Confidence score should be between 0 and 1. This is not used for HOTA evaluation, but is used for other eval metrics like Track mAP.\n\nImage height and width are needed to decode the compressed RLE mask representation.\n\nThe Compressed RLE Mask is the same format used by coco, pycocotools and mots.\n\nAn example of a tracker result file looks like this:\n\n```\n0 1 3 0.9917707443237305 1200 1920 VaTi0b0lT17F8K3M3N1O1N2O0O2M3N2N101O1O1O01O1O0100O100O01O1O100O10O1000O1000000000000000O1000001O0000000000000000O101O00000000000001O0000010O0110O0O100O1O2N1O2N0O2O2M3M2N2O1O2N5J;DgePZ1\n0 2 3 0.989478349685669 1200 1920 Ql^c05ZU12O2N001O0O10OTkNIaT17^kNKaT15^kNLbT14^kNMaT13^kNOaT11_kN0`T10_kN1`T11_kN0`T11_kN0`T1a0O00001O1O1O3M;E5K3M2N000000000O100000000000000000001O00001O2N1O1O1O000001O001O0O2O0O2M3M3M3N2O1O1O1N2O002N1O2N10O02N10000O1O101M3N2N2M7H^_g_1\n1 2 3 0.964085042476654 1200 1920 o_Uc03\\U12O1O1N102N002N001O1O000O2O1O00002N6J1O001O2N1O3L3N2N4L5K2N1O000000000000001O1O2N01O01O010O01N2O0O2O1M4L3N2N101N2O001O1O100O0100000O1O1O1O2N6I4Mdm^`1\n```\n\nNote that for the evaluation to be valid, the masks must not overlap within one frame.\n\nThe supplied detections have the same format (but with all the Track IDs being set to 0).\n\nThe groundtruth data for most benchmarks is in the exact same format as above (usually Detection Confidence is set to 1.0). The exception is the few benchmarks for which the ground-truth is not segmentation masks but bounding boxes (Waymo and TAO). For these the last three columns are not there (height, width and mask) as these encode a mask, and instead there are 4 columns encoding the bounding box co-ordinates in the format ```x0 y0 x1 y1```, where x0 and y0 are the coordinates of the top left of the box and x1 and y0 are the coordinates for the bottom right.\n\nThe groundtruth can also contain ignore regions. The are marked by being having a class number of 100 or larger. Class number 100 encodes and ignore region for all class, which class numbers higher than 100 encode ignore regions specific to each class. E.g. class number 105 are ignore regions for class 5. \n\nAs well as the per sequence files described above, the groundtruth for each benchmark contains two more files ```clsmap.txt``` and ```seqmap.txt```. \n\n```clsmap.txt``` is a single row, space-separated, containing all of the valid classes that should be evaluated for each benchmark (not all benchmarks evaluate all of the coco classes). \n\n```seqmap.txt``` contains a list of the sequences to be evaluated for that benchmark. Each row has at least 4 values. These are:\n```\n<sequence name> <number of frames in sequence> <sequence image height> <sequence image width>\n```\nMore than 4 values can be present, the remaining values are 'ignore classes for this sequence'. E.g. classes which are evaluated for the particular benchmark as a whole, but should be ignored for this sequence. \n\n## Visualizing GT and Tracker Masks\n\nWe provide code for converting our .txt format with compressed RLE masks into .png format where it is easy to visualize the GT and Predicted masks.\n\nThis code can be found here:\n\n[Vizualize Tracking Results](https://github.com/JonathonLuiten/TrackEval/blob/master/trackeval/baselines/vizualize.py).\n\n\n## Evaluate on the validation and test server\n\nThe val and test GT will NOT be provided. However we provide a live evaluation server to upload your tracking results and evaluate it on the val and test set.\n\nThe val server will allow infinite uploads, while the test will limit trackers to 4 uploads total.\n\nThese evaluation servers can be found here: https://eval.vision.rwth-aachen.de/vision/\n\nEnsure that your files to upload are in the correct format. Examples of the correct way to upload files can be found here: [STP val upload](https://omnomnom.vision.rwth-aachen.de/data/RobMOTS/STP_val_upload.zip),  [STP test upload](https://omnomnom.vision.rwth-aachen.de/data/RobMOTS/STP_test_upload.zip).\n\n## Citation\nIf you work with the code and the benchmark, please cite:\n\n***TrackEval***\n```\n@misc{luiten2020trackeval,\n  author =       {Jonathon Luiten, Arne Hoffhues},\n  title =        {TrackEval},\n  howpublished = {\\url{https://github.com/JonathonLuiten/TrackEval}},\n  year =         {2020}\n}\n```\n***HOTA metrics***\n```\n@article{luiten2020IJCV,\n  title={HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking},\n  author={Luiten, Jonathon and Osep, Aljosa and Dendorfer, Patrick and Torr, Philip and Geiger, Andreas and Leal-Taix{\\'e}, Laura and Leibe, Bastian},\n  journal={International Journal of Computer Vision},\n  pages={1--31},\n  year={2020},\n  publisher={Springer}\n}\n```\n\n## Feedback and Contact\nWe are constantly working on improving RobMOTS, and wish to provide the most useful support to the community.\nYou can help us to make the benchmark better by open issues in the repo and reporting bugs.\n\nFor general questions, please contact the following:\n\n```\nJonathon Luiten - luiten@vision.rwth-aachen.de\n```\n"
  },
  {
    "path": "TrackEval/docs/TAO-format.txt",
    "content": "Taken from: https://github.com/TAO-Dataset/tao/blob/master/tao/toolkit/tao/tao.py\n\nAnnotation file format:\n{\n    \"info\" : info,\n    \"images\" : [image],\n    \"videos\": [video],\n    \"tracks\": [track],\n    \"annotations\" : [annotation],\n    \"categories\": [category],\n    \"licenses\" : [license],\n}\ninfo: As in MS COCO\nimage: {\n    \"id\" : int,\n    \"video_id\": int,\n    \"file_name\" : str,\n    \"license\" : int,\n    # Redundant fields for COCO-compatibility\n    \"width\": int,\n    \"height\": int,\n    \"frame_index\": int\n}\nvideo: {\n    \"id\": int,\n    \"name\": str,\n    \"width\" : int,\n    \"height\" : int,\n    \"neg_category_ids\": [int],\n    \"not_exhaustive_category_ids\": [int],\n    \"metadata\": dict,  # Metadata about the video\n}\ntrack: {\n    \"id\": int,\n    \"category_id\": int,\n    \"video_id\": int\n}\ncategory: {\n    \"id\": int,\n    \"name\": str,\n    \"synset\": str,  # For non-LVIS objects, this is \"unknown\"\n    ... [other fields copied from LVIS v0.5 and unused]\n}\nannotation: {\n    \"image_id\": int,\n    \"track_id\": int,\n    \"bbox\": [x,y,width,height],\n    \"area\": float,\n    # Redundant field for compatibility with COCO scripts\n    \"category_id\": int\n}\nlicense: {\n    \"id\" : int,\n    \"name\" : str,\n    \"url\" : str,\n}\n"
  },
  {
    "path": "TrackEval/docs/YouTube-VIS-format.txt",
    "content": "Taken from: https://competitions.codalab.org/competitions/20128#participate-get-data\n\nThe label file follows MSCOCO's style in json format. We adapt the entry name and label format for video. The definition of json file is:\n\n\n        {\n            \"info\" : info,\n            \"videos\" : [video],\n            \"annotations\" : [annotation],\n            \"categories\" : [category],\n        }\n        video{\n            \"id\" : int,\n            \"width\" : int,\n            \"height\" : int,\n            \"length\" : int,\n            \"file_names\" : [file_name],\n        }\n        annotation{\n            \"id\" : int, \n            \"video_id\" : int, \n            \"category_id\" : int, \n            \"segmentations\" : [RLE or [polygon] or None], \n            \"areas\" : [float or None], \n            \"bboxes\" : [[x,y,width,height] or None], \n            \"iscrowd\" : 0 or 1,\n        }\n        category{\n            \"id\" : int, \n            \"name\" : str, \n            \"supercategory\" : str,\n        }\n    \nThe submission file is also in json format. The file should contain a list of predictions:\n\n\n        prediction{\n            \"video_id\" : int, \n            \"category_id\" : int, \n            \"segmentations\" : [RLE or [polygon] or None], \n            \"score\" : float, \n        }\n    \nThe submission file should be named as \"results.json\", and compressed without any subfolder. There is an example \"valid_submission_sample.zip\" in download links above. The example is generated by our proposed MaskTrack R-CNN algorithm."
  },
  {
    "path": "TrackEval/minimum_requirements.txt",
    "content": "scipy==1.4.1\nnumpy==1.18.1\n"
  },
  {
    "path": "TrackEval/pyproject.toml",
    "content": "[build-system]\nrequires = [\n    \"setuptools>=42\",\n    \"wheel\"\n]\nbuild-backend = \"setuptools.build_meta\"\n"
  },
  {
    "path": "TrackEval/requirements.txt",
    "content": "numpy==1.18.1\nscipy==1.4.1\npycocotools==2.0.2\nmatplotlib==3.2.1\nopencv_python==4.4.0.46\nscikit_image==0.16.2\npytest==6.0.1\nPillow==8.1.2\n"
  },
  {
    "path": "TrackEval/scripts/comparison_plots.py",
    "content": "import sys\nimport os\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nplots_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 'plots'))\ntracker_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 'trackers'))\n\n# dataset = os.path.join('kitti', 'kitti_2d_box_train')\n# classes = ['cars', 'pedestrian']\n\ndataset = os.path.join('mot_challenge', 'MOT17-train')\nclasses = ['pedestrian']\n\ndata_fol = os.path.join(tracker_folder, dataset)\ntrackers = os.listdir(data_fol)\nout_loc = os.path.join(plots_folder, dataset)\nfor cls in classes:\n    trackeval.plotting.plot_compare_trackers(data_fol, trackers, cls, out_loc)\n"
  },
  {
    "path": "TrackEval/scripts/run_bdd.py",
    "content": "\n\"\"\" run_bdd.py\n\nRun example:\nrun_bdd.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL qdtrack\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/bdd100k/bdd100k_val'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/bdd100k/bdd100k_val'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': ['pedestrian', 'rider', 'car', 'bus', 'truck', 'train', 'motorcycle', 'bicycle'],\n            # Valid: ['pedestrian', 'rider', 'car', 'bus', 'truck', 'train', 'motorcycle', 'bicycle']\n            'SPLIT_TO_EVAL': 'val',  # Valid: 'training', 'val',\n            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n    Metric arguments:\n        'METRICS': ['Hota','Clear', 'ID', 'Count']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_dataset_config = trackeval.datasets.BDD100K.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.BDD100K(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)"
  },
  {
    "path": "TrackEval/scripts/run_davis.py",
    "content": "\"\"\" run_davis.py\n\nRun example:\nrun_davis.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL ags\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n    '   'GT_FOLDER': os.path.join(code_path, 'data/gt/davis/'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/davis/davis_val'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'SPLIT_TO_EVAL': 'val',  # Valid: 'val', 'train'\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n        'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/ImageSets/2017)\n        'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/split-to-eval.txt)\n        'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n        'GT_LOC_FORMAT': '{gt_folder}/Annotations_unsupervised/480p/{seq}',\n        # '{gt_folder}/Annotations_unsupervised/480p/{seq}'\n        'MAX_DETECTIONS': 0  # Maximum number of allowed detections per sequence (0 for no threshold)\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'JAndF']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_dataset_config = trackeval.datasets.DAVIS.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity', 'JAndF']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.DAVIS(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.JAndF]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)"
  },
  {
    "path": "TrackEval/scripts/run_headtracking_challenge.py",
    "content": "\n\"\"\" run_mot_challenge.py\n\nRun example:\nrun_mot_challenge.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL Lif_T\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']\n        'BENCHMARK': 'MOT17',  # Valid: 'MOT17', 'MOT16', 'MOT20', 'MOT15'\n        'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'\n        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'DO_PREPROC': True,  # Whether to perform preprocessing (never done for 2D_MOT_2015)\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'IDEucl']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = False\n    default_dataset_config = trackeval.datasets.HeadTrackingChallenge.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity', 'IDEucl'], 'THRESHOLD': 0.4}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            elif setting == 'SEQ_INFO':\n                x = dict(zip(args[setting], [None]*len(args[setting])))\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.HeadTrackingChallenge(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.IDEucl]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric(metrics_config))\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)\n"
  },
  {
    "path": "TrackEval/scripts/run_kitti.py",
    "content": "\n\"\"\" run_kitti.py\n\nRun example:\nrun_kitti.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL CIWT\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/kitti/kitti_2d_box_train'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_2d_box_train/'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': ['car', 'pedestrian'],  # Valid: ['car', 'pedestrian']\n        'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val', 'training_minus_val', 'test'\n        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': ''  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n    Metric arguments:\n        'METRICS': ['Hota','Clear', 'ID', 'Count']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = False\n    default_dataset_config = trackeval.datasets.Kitti2DBox.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.Kitti2DBox(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)\n"
  },
  {
    "path": "TrackEval/scripts/run_kitti_mots.py",
    "content": "\n\"\"\" run_kitti_mots.py\n\nRun example:\nrun_kitti_mots.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL trackrcnn\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/kitti/kitti_mots'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_mots_val'),   # Location of all\n                                                                                            # trackers\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': ['car', 'pedestrian'],  # Valid: ['car', 'pedestrian']\n        'SPLIT_TO_EVAL': 'val',  # Valid: 'training', 'val'\n        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER)\n        'SEQMAP_FILE': None,    # Directly specify seqmap file (if none use seqmap_folder/split_to_eval.seqmap)\n        'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n        'GT_LOC_FORMAT': '{gt_folder}/instances_txt/{seq}.txt',  # format of gt localization\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = False\n    default_dataset_config = trackeval.datasets.KittiMOTS.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.KittiMOTS(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.JAndF]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)\n"
  },
  {
    "path": "TrackEval/scripts/run_mot_challenge.py",
    "content": "\n\"\"\" run_mot_challenge.py\n\nRun example:\nrun_mot_challenge.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL Lif_T\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']\n        'BENCHMARK': 'MOT17',  # Valid: 'MOT17', 'MOT16', 'MOT20', 'MOT15'\n        'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'\n        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'DO_PREPROC': True,  # Whether to perform preprocessing (never done for 2D_MOT_2015)\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'VACE']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\n# python TrackEval/scripts/run_mot_challenge.py --BENCHMARK MOT17 --SPLIT_TO_EVAL train --TRACKERS_TO_EVAL ByteTrack --METRICS HOTA CLEAR Identity VACE --TIME_PROGRESS False --USE_PARALLEL False --NUM_PARALLEL_CORES 1  --GT_FOLDER datasets/mot/ --TRACKERS_FOLDER YOLOX_outputs/yolox_s_mot17_half_repro1/track_results_ByteTrack/track_results --GT_LOC_FORMAT {gt_folder}/{seq}/gt/gt_val_half.txt\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = False\n    default_dataset_config = trackeval.datasets.MotChallenge2DBox.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity'], 'THRESHOLD': 0.5}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            elif setting == 'SEQ_INFO':\n                x = dict(zip(args[setting], [None]*len(args[setting])))\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    if type(dataset_config['SEQMAP_FILE']) is list:         # TODO: [hgx 0409] for dancetrack dataset\n        dataset_config['SEQMAP_FILE'] = dataset_config['SEQMAP_FILE'][0]\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.MotChallenge2DBox(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.VACE]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric(metrics_config))\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)\n"
  },
  {
    "path": "TrackEval/scripts/run_mots_challenge.py",
    "content": "\"\"\" run_mots.py\n\nRun example:\nrun_mots.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL TrackRCNN\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']\n        'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test'\n        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/seqmaps)\n        'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/MOTS-split_to_eval)\n        'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n        'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt',  # '{gt_folder}/{seq}/gt/gt.txt'\n        'SKIP_SPLIT_FOL': False,    # If False, data is in GT_FOLDER/MOTS-SPLIT_TO_EVAL/ and in\n                                    # TRACKERS_FOLDER/MOTS-SPLIT_TO_EVAL/tracker/\n                                    # If True, then the middle 'MOTS-split' folder is skipped for both.\n    Metric arguments:\n        'METRICS': ['HOTA','CLEAR', 'Identity', 'VACE', 'JAndF']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = False\n    default_dataset_config = trackeval.datasets.MOTSChallenge.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            elif setting == 'SEQ_INFO':\n                x = dict(zip(args[setting], [None]*len(args[setting])))\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.MOTSChallenge(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.VACE,\n                   trackeval.metrics.JAndF]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)\n"
  },
  {
    "path": "TrackEval/scripts/run_rob_mots.py",
    "content": "# python3 scripts/run_rob_mots.py --ROBMOTS_SPLIT train --TRACKERS_TO_EVAL STP --USE_PARALLEL True --NUM_PARALLEL_CORES 8\n\nimport sys\nimport os\nimport csv\nimport numpy as np\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\nfrom trackeval import utils\n\ncode_path = utils.get_code_path()\n\nif __name__ == '__main__':\n    freeze_support()\n\n    script_config = {\n        'ROBMOTS_SPLIT': 'train',  # 'train',  # valid: 'train', 'val', 'test', 'test_live', 'test_post', 'test_all'\n        'BENCHMARKS': None,  # If None, use all for each split.\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/rob_mots'),\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/rob_mots'),\n    }\n\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = True\n    default_dataset_config = trackeval.datasets.RobMOTS.get_default_dataset_config()\n    config = {**default_eval_config, **default_dataset_config, **script_config}\n\n    # Command line interface:\n    config = utils.update_config(config)\n\n    if not config['BENCHMARKS']:\n        if config['ROBMOTS_SPLIT'] == 'val':\n            config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis',\n                                    'tao', 'mots_challenge', 'waymo']\n            config['SPLIT_TO_EVAL'] = 'val'\n        elif config['ROBMOTS_SPLIT'] == 'test' or config['SPLIT_TO_EVAL'] == 'test_live':\n            config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'tao']\n            config['SPLIT_TO_EVAL'] = 'test'\n        elif config['ROBMOTS_SPLIT'] == 'test_post':\n            config['BENCHMARKS'] = ['mots_challenge', 'waymo', 'ovis']\n            config['SPLIT_TO_EVAL'] = 'test'\n        elif config['ROBMOTS_SPLIT'] == 'test_all':\n            config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis',\n                                    'tao', 'mots_challenge', 'waymo']\n            config['SPLIT_TO_EVAL'] = 'test'\n        elif config['ROBMOTS_SPLIT'] == 'train':\n            config['BENCHMARKS'] = ['kitti_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'tao', 'bdd_mots']\n            config['SPLIT_TO_EVAL'] = 'train'\n    else:\n        config['SPLIT_TO_EVAL'] = config['ROBMOTS_SPLIT']\n\n    metrics_config = {'METRICS': ['HOTA']}\n    eval_config = {k: v for k, v in config.items() if k in config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in config.keys()}\n\n    # Run code\n    try:\n        dataset_list = []\n        for bench in config['BENCHMARKS']:\n            dataset_config['SUB_BENCHMARK'] = bench\n            dataset_list.append(trackeval.datasets.RobMOTS(dataset_config))\n        evaluator = trackeval.Evaluator(eval_config)\n        metrics_list = []\n        for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity,\n                       trackeval.metrics.VACE, trackeval.metrics.JAndF]:\n            if metric.get_name() in metrics_config['METRICS']:\n                metrics_list.append(metric())\n        if len(metrics_list) == 0:\n            raise Exception('No metrics selected for evaluation')\n        output_res, output_msg = evaluator.evaluate(dataset_list, metrics_list)\n        output = list(list(output_msg.values())[0].values())[0]\n\n    except Exception as err:\n        if type(err) == trackeval.utils.TrackEvalException:\n            output = str(err)\n        else:\n            output = 'Unknown error occurred.'\n\n    success = output == 'Success'\n    if not success:\n        output = 'ERROR, evaluation failed. \\n\\nError message: ' + output\n        print(output)\n\n    if config['TRACKERS_TO_EVAL']:\n        msg = \"Thanks you for participating in the RobMOTS benchmark.\\n\\n\"\n        msg += \"The status of your evaluation is: \\n\" + output + '\\n\\n'\n        msg += \"If your tracking results evaluated successfully on the evaluation server you can see your results here: \\n\"\n        msg += \"https://eval.vision.rwth-aachen.de/vision/\"\n        status_file = os.path.join(config['TRACKERS_FOLDER'], config['ROBMOTS_SPLIT'], config['TRACKERS_TO_EVAL'][0],\n                                   'status.txt')\n        with open(status_file, 'w', newline='') as f:\n            f.write(msg)\n\n    if success:\n        # For each benchmark, combine the 'all' score with the 'cls_averaged' using geometric mean.\n        metrics_to_calc = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA']\n        trackers = list(output_res['RobMOTS.' + config['BENCHMARKS'][0]].keys())\n        for tracker in trackers:\n            # final_results[benchmark][result_type][metric]\n            final_results = {}\n            res = {bench: output_res['RobMOTS.' + bench][tracker]['COMBINED_SEQ'] for bench in config['BENCHMARKS']}\n            for bench in config['BENCHMARKS']:\n                final_results[bench] = {'cls_av': {}, 'det_av': {}, 'final': {}}\n                for metric in metrics_to_calc:\n                    final_results[bench]['cls_av'][metric] = np.mean(res[bench]['cls_comb_cls_av']['HOTA'][metric])\n                    final_results[bench]['det_av'][metric] = np.mean(res[bench]['all']['HOTA'][metric])\n                    final_results[bench]['final'][metric] = \\\n                        np.sqrt(final_results[bench]['cls_av'][metric] * final_results[bench]['det_av'][metric])\n\n            # Take the arithmetic mean over all the benchmarks\n            final_results['overall'] = {'cls_av': {}, 'det_av': {}, 'final': {}}\n            for metric in metrics_to_calc:\n                final_results['overall']['cls_av'][metric] = \\\n                    np.mean([final_results[bench]['cls_av'][metric] for bench in config['BENCHMARKS']])\n                final_results['overall']['det_av'][metric] = \\\n                    np.mean([final_results[bench]['det_av'][metric] for bench in config['BENCHMARKS']])\n                final_results['overall']['final'][metric] = \\\n                    np.mean([final_results[bench]['final'][metric] for bench in config['BENCHMARKS']])\n\n            # Save out result\n            headers = [config['SPLIT_TO_EVAL']] + [x + '___' + metric for x in ['f', 'c', 'd'] for metric in\n                                                   metrics_to_calc]\n\n\n            def rowify(d):\n                return [d[x][metric] for x in ['final', 'cls_av', 'det_av'] for metric in metrics_to_calc]\n\n\n            out_file = os.path.join(config['TRACKERS_FOLDER'], config['ROBMOTS_SPLIT'], tracker,\n                                    'final_results.csv')\n\n            with open(out_file, 'w', newline='') as f:\n                writer = csv.writer(f, delimiter=',')\n                writer.writerow(headers)\n                writer.writerow(['overall'] + rowify(final_results['overall']))\n                for bench in config['BENCHMARKS']:\n                    if bench == 'overall':\n                        continue\n                    writer.writerow([bench] + rowify(final_results[bench]))\n"
  },
  {
    "path": "TrackEval/scripts/run_tao.py",
    "content": "\"\"\" run_tao.py\n\nRun example:\nrun_tao.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL Tracktor++\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/tao/tao_training'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/tao/tao_training'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)\n        'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val'\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n        'MAX_DETECTIONS': 300,  # Number of maximal allowed detections per image (0 for unlimited)\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'TrackMAP']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    # print only combined since TrackMAP is undefined for per sequence breakdowns\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = True\n    default_dataset_config = trackeval.datasets.TAO.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity', 'TrackMAP']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.TAO(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.CLEAR, trackeval.metrics.Identity,\n                   trackeval.metrics.HOTA]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)"
  },
  {
    "path": "TrackEval/scripts/run_tao_ow.py",
    "content": "\"\"\" run_tao.py\n\nRun example:\nrun_tao.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL Tracktor++\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/tao/tao_training'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/tao/tao_training'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)\n        'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val'\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n        'MAX_DETECTIONS': 300,  # Number of maximal allowed detections per image (0 for unlimited)\n        'SUBSET': 'unknown',  # Evaluate on the following subsets ['all', 'known', 'unknown', 'distractor']\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'TrackMAP']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    # print only combined since TrackMAP is undefined for per sequence breakdowns\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = True\n    default_dataset_config = trackeval.datasets.TAO_OW.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity', 'TrackMAP']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.TAO_OW(dataset_config)]\n    metrics_list = []\n    # for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.CLEAR, trackeval.metrics.Identity,\n    #                trackeval.metrics.HOTA]:\n    for metric in [trackeval.metrics.HOTA]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)"
  },
  {
    "path": "TrackEval/scripts/run_youtube_vis.py",
    "content": "\n\"\"\" run_youtube_vis.py\nRun example:\nrun_youtube_vis.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL STEm_Seg\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n            'USE_PARALLEL': False,\n            'NUM_PARALLEL_CORES': 8,\n            'BREAK_ON_ERROR': True,  # Raises exception and exits with error\n            'RETURN_ON_ERROR': False,  # if not BREAK_ON_ERROR, then returns from function on error\n            'LOG_ON_ERROR': os.path.join(code_path, 'error_log.txt'),  # if not None, save any errors into a log file.\n            'PRINT_RESULTS': True,\n            'PRINT_ONLY_COMBINED': False,\n            'PRINT_CONFIG': True,\n            'TIME_PROGRESS': True,\n            'DISPLAY_LESS_PROGRESS': True,\n            'OUTPUT_SUMMARY': True,\n            'OUTPUT_EMPTY_CLASSES': True,  # If False, summary files are not output for classes with no detections\n            'OUTPUT_DETAILED': True,\n            'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/youtube_vis/youtube_vis_training'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/youtube_vis/youtube_vis_training'),\n        # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)\n        'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val'\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n    Metric arguments:\n        'METRICS': ['TrackMAP', 'HOTA', 'CLEAR', 'Identity']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    # print only combined since TrackMAP is undefined for per sequence breakdowns\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_dataset_config = trackeval.datasets.YouTubeVIS.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['TrackMAP', 'HOTA', 'CLEAR', 'Identity']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.YouTubeVIS(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.HOTA, trackeval.metrics.CLEAR,\n                   trackeval.metrics.Identity]:\n        if metric.get_name() in metrics_config['METRICS']:\n            # specify TrackMAP config for YouTubeVIS\n            if metric == trackeval.metrics.TrackMAP:\n                default_track_map_config = metric.get_default_metric_config()\n                default_track_map_config['USE_TIME_RANGES'] = False\n                default_track_map_config['AREA_RANGES'] = [[0 ** 2, 128 ** 2],\n                                                           [ 128 ** 2, 256 ** 2],\n                                                           [256 ** 2, 1e5 ** 2]]\n                metrics_list.append(metric(default_track_map_config))\n            else:\n                metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)"
  },
  {
    "path": "TrackEval/setup.cfg",
    "content": "[metadata]\nname = trackeval\nversion = 1.0.dev1\nauthor = Jonathon Luiten, Arne Hoffhues\nauthor_email = jonoluiten@gmail.com\ndescription = Code for evaluating object tracking\nlong_description = file: Readme.md\nlong_description_content_type = text/markdown\nurl = https://github.com/JonathonLuiten/TrackEval\nproject_urls =\n    Bug Tracker = https://github.com/JonathonLuiten/TrackEval/issues\nclassifiers =\n    Programming Language :: Python :: 3\n    Programming Language :: Python :: 3 :: Only\n    License :: OSI Approved :: MIT License\n    Operating System :: OS Independent\n    Topic :: Scientific/Engineering\nlicense_files = LICENSE\n\n[options]\ninstall_requires =\n    numpy\n    scipy\npackages = find:\n\n[options.packages.find]\ninclude = trackeval*\n"
  },
  {
    "path": "TrackEval/setup.py",
    "content": "from setuptools import setup\n\nsetup()\n"
  },
  {
    "path": "TrackEval/tests/test_all_quick.py",
    "content": "\"\"\" Test to ensure that the code is working correctly.\nShould test ALL metrics across all datasets and splits currently supported.\nOnly tests one tracker per dataset/split to give a quick test result.\n\"\"\"\n\nimport sys\nimport os\nimport numpy as np\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\n# Fixes multiprocessing on windows, does nothing otherwise\nif __name__ == '__main__':\n    freeze_support()\n\neval_config = {'USE_PARALLEL': False,\n               'NUM_PARALLEL_CORES': 8,\n               }\nevaluator = trackeval.Evaluator(eval_config)\nmetrics_list = [trackeval.metrics.HOTA(), trackeval.metrics.CLEAR(), trackeval.metrics.Identity()]\n\ntests = [\n    {'DATASET': 'Kitti2DBox', 'SPLIT_TO_EVAL': 'training', 'TRACKERS_TO_EVAL': ['CIWT']},\n    {'DATASET': 'MotChallenge2DBox', 'BENCHMARK': 'MOT15', 'SPLIT_TO_EVAL': 'train', 'TRACKERS_TO_EVAL': ['MPNTrack']},\n    {'DATASET': 'MotChallenge2DBox', 'BENCHMARK': 'MOT16', 'SPLIT_TO_EVAL': 'train', 'TRACKERS_TO_EVAL': ['MPNTrack']},\n    {'DATASET': 'MotChallenge2DBox', 'BENCHMARK': 'MOT17', 'SPLIT_TO_EVAL': 'train', 'TRACKERS_TO_EVAL': ['MPNTrack']},\n    {'DATASET': 'MotChallenge2DBox', 'BENCHMARK': 'MOT20', 'SPLIT_TO_EVAL': 'train', 'TRACKERS_TO_EVAL': ['MPNTrack']},\n]\n\nfor dataset_config in tests:\n\n    dataset_name = dataset_config.pop('DATASET')\n    if dataset_name == 'MotChallenge2DBox':\n        dataset_list = [trackeval.datasets.MotChallenge2DBox(dataset_config)]\n        file_loc = os.path.join('mot_challenge', dataset_config['BENCHMARK'] + '-' + dataset_config['SPLIT_TO_EVAL'])\n    elif dataset_name == 'Kitti2DBox':\n        dataset_list = [trackeval.datasets.Kitti2DBox(dataset_config)]\n        file_loc = os.path.join('kitti', 'kitti_2d_box_train')\n    else:\n        raise Exception('Dataset %s does not exist.' % dataset_name)\n\n    raw_results, messages = evaluator.evaluate(dataset_list, metrics_list)\n\n    classes = dataset_list[0].config['CLASSES_TO_EVAL']\n    tracker = dataset_config['TRACKERS_TO_EVAL'][0]\n    test_data_loc = os.path.join(os.path.dirname(__file__), '..', 'data', 'tests', file_loc)\n\n    for cls in classes:\n        results = {seq: raw_results[dataset_name][tracker][seq][cls] for seq in raw_results[dataset_name][tracker].keys()}\n        current_metrics_list = metrics_list + [trackeval.metrics.Count()]\n        metric_names = trackeval.utils.validate_metrics_list(current_metrics_list)\n\n        # Load expected results:\n        test_data = trackeval.utils.load_detail(os.path.join(test_data_loc, tracker, cls + '_detailed.csv'))\n\n        # Do checks\n        for seq in test_data.keys():\n            assert len(test_data[seq].keys()) > 250, len(test_data[seq].keys())\n\n            details = []\n            for metric, metric_name in zip(current_metrics_list, metric_names):\n                table_res = {seq_key: seq_value[metric_name] for seq_key, seq_value in results.items()}\n                details.append(metric.detailed_results(table_res))\n            res_fields = sum([list(s['COMBINED_SEQ'].keys()) for s in details], [])\n            res_values = sum([list(s[seq].values()) for s in details], [])\n            res_dict = dict(zip(res_fields, res_values))\n\n            for field in test_data[seq].keys():\n                assert np.isclose(res_dict[field], test_data[seq][field]), seq + ': ' + cls + ': ' + field\n\n    print('Tracker %s tests passed' % tracker)\nprint('All tests passed')\n\n"
  },
  {
    "path": "TrackEval/tests/test_davis.py",
    "content": "import sys\nimport os\nimport numpy as np\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\n# Fixes multiprocessing on windows, does nothing otherwise\nif __name__ == '__main__':\n    freeze_support()\n\n\neval_config = {'USE_PARALLEL': False,\n               'NUM_PARALLEL_CORES': 8,\n               'PRINT_RESULTS': False,\n               'PRINT_CONFIG': True,\n               'TIME_PROGRESS': True,\n               'DISPLAY_LESS_PROGRESS': True,\n               'OUTPUT_SUMMARY': False,\n               'OUTPUT_EMPTY_CLASSES': False,\n               'OUTPUT_DETAILED': False,\n               'PLOT_CURVES': False,\n               }\nevaluator = trackeval.Evaluator(eval_config)\nmetrics_list = [trackeval.metrics.HOTA(), trackeval.metrics.CLEAR(), trackeval.metrics.Identity(),\n                trackeval.metrics.JAndF()]\n\ntests = [\n    {'SPLIT_TO_EVAL': 'val', 'TRACKERS_TO_EVAL': ['ags']},\n]\n\nfor dataset_config in tests:\n\n    dataset_list = [trackeval.datasets.DAVIS(dataset_config)]\n    file_loc = os.path.join('davis', 'davis_unsupervised_' + dataset_config['SPLIT_TO_EVAL'])\n\n    raw_results, messages = evaluator.evaluate(dataset_list, metrics_list)\n\n    classes = dataset_list[0].config['CLASSES_TO_EVAL']\n    tracker = dataset_config['TRACKERS_TO_EVAL'][0]\n    test_data_loc = os.path.join(os.path.dirname(__file__), '..', 'data', 'tests', file_loc)\n\n    for cls in classes:\n        results = {seq: raw_results['DAVIS'][tracker][seq][cls] for seq in raw_results['DAVIS'][tracker].keys()}\n        current_metrics_list = metrics_list + [trackeval.metrics.Count()]\n        metric_names = trackeval.utils.validate_metrics_list(current_metrics_list)\n\n        # Load expected results:\n        test_data = trackeval.utils.load_detail(os.path.join(test_data_loc, tracker, cls + '_detailed.csv'))\n\n        # Do checks\n        for seq in test_data.keys():\n            assert len(test_data[seq].keys()) > 250, len(test_data[seq].keys())\n\n            details = []\n            for metric, metric_name in zip(current_metrics_list, metric_names):\n                table_res = {seq_key: seq_value[metric_name] for seq_key, seq_value in results.items()}\n                details.append(metric.detailed_results(table_res))\n            res_fields = sum([list(s['COMBINED_SEQ'].keys()) for s in details], [])\n            res_values = sum([list(s[seq].values()) for s in details], [])\n            res_dict = dict(zip(res_fields, res_values))\n\n            for field in test_data[seq].keys():\n                assert np.isclose(res_dict[field], test_data[seq][field]), seq + ': ' + cls + ': ' + field\n\n    print('Tracker %s tests passed' % tracker)\nprint('All tests passed')"
  },
  {
    "path": "TrackEval/tests/test_metrics.py",
    "content": "import numpy as np\nimport pytest\n\nimport trackeval\n\n\ndef no_confusion():\n    num_timesteps = 5\n    num_gt_ids = 2\n    num_tracker_ids = 2\n\n    # No overlap between pairs (0, 0) and (1, 1).\n    similarity = np.zeros([num_timesteps, num_gt_ids, num_tracker_ids])\n    similarity[:, 0, 1] = [0, 0, 0, 1, 1]\n    similarity[:, 1, 0] = [1, 1, 0, 0, 0]\n    gt_present = np.zeros([num_timesteps, num_gt_ids])\n    gt_present[:, 0] = [1, 1, 1, 1, 1]\n    gt_present[:, 1] = [1, 1, 1, 0, 0]\n    tracker_present = np.zeros([num_timesteps, num_tracker_ids])\n    tracker_present[:, 0] = [1, 1, 1, 1, 0]\n    tracker_present[:, 1] = [1, 1, 1, 1, 1]\n\n    expected = {\n            'clear': {\n                    'CLR_TP': 4,\n                    'CLR_FN': 4,\n                    'CLR_FP': 5,\n                    'IDSW': 0,\n                    'MOTA': 1 - 9 / 8,\n            },\n            'identity': {\n                    'IDTP': 4,\n                    'IDFN': 4,\n                    'IDFP': 5,\n                    'IDR': 4 / 8,\n                    'IDP': 4 / 9,\n                    'IDF1': 2 * 4 / 17,\n            },\n            'vace': {\n                    'STDA': 2 / 5 + 2 / 4,\n                    'ATA': (2 / 5 + 2 / 4) / 2,\n            },\n    }\n\n    data = _from_dense(\n            num_timesteps=num_timesteps,\n            num_gt_ids=num_gt_ids,\n            num_tracker_ids=num_tracker_ids,\n            gt_present=gt_present,\n            tracker_present=tracker_present,\n            similarity=similarity,\n    )\n    return data, expected\n\n\ndef with_confusion():\n    num_timesteps = 5\n    num_gt_ids = 2\n    num_tracker_ids = 2\n\n    similarity = np.zeros([num_timesteps, num_gt_ids, num_tracker_ids])\n    similarity[:, 0, 1] = [0, 0, 0, 1, 1]\n    similarity[:, 1, 0] = [1, 1, 0, 0, 0]\n    # Add some overlap between (0, 0) and (1, 1).\n    similarity[:, 0, 0] = [0, 0, 1, 0, 0]\n    similarity[:, 1, 1] = [0, 1, 0, 0, 0]\n    gt_present = np.zeros([num_timesteps, num_gt_ids])\n    gt_present[:, 0] = [1, 1, 1, 1, 1]\n    gt_present[:, 1] = [1, 1, 1, 0, 0]\n    tracker_present = np.zeros([num_timesteps, num_tracker_ids])\n    tracker_present[:, 0] = [1, 1, 1, 1, 0]\n    tracker_present[:, 1] = [1, 1, 1, 1, 1]\n\n    expected = {\n            'clear': {\n                    'CLR_TP': 5,\n                    'CLR_FN': 3,  # 8 - 5\n                    'CLR_FP': 4,  # 9 - 5\n                    'IDSW': 1,\n                    'MOTA': 1 - 8 / 8,\n            },\n            'identity': {\n                    'IDTP': 4,\n                    'IDFN': 4,\n                    'IDFP': 5,\n                    'IDR': 4 / 8,\n                    'IDP': 4 / 9,\n                    'IDF1': 2 * 4 / 17,\n            },\n            'vace': {\n                    'STDA': 2 / 5 + 2 / 4,\n                    'ATA': (2 / 5 + 2 / 4) / 2,\n            },\n    }\n\n    data = _from_dense(\n            num_timesteps=num_timesteps,\n            num_gt_ids=num_gt_ids,\n            num_tracker_ids=num_tracker_ids,\n            gt_present=gt_present,\n            tracker_present=tracker_present,\n            similarity=similarity,\n    )\n    return data, expected\n\n\ndef split_tracks():\n    num_timesteps = 5\n    num_gt_ids = 2\n    num_tracker_ids = 5\n\n    similarity = np.zeros([num_timesteps, num_gt_ids, num_tracker_ids])\n    # Split ground-truth 0 between tracks 0, 3.\n    similarity[:, 0, 0] = [1, 1, 0, 0, 0]\n    similarity[:, 0, 3] = [0, 0, 0, 1, 1]\n    # Split ground-truth 1 between tracks 1, 2, 4.\n    similarity[:, 1, 1] = [0, 0, 1, 1, 0]\n    similarity[:, 1, 2] = [0, 0, 0, 0, 1]\n    similarity[:, 1, 4] = [1, 1, 0, 0, 0]\n    gt_present = np.zeros([num_timesteps, num_gt_ids])\n    gt_present[:, 0] = [1, 1, 0, 1, 1]\n    gt_present[:, 1] = [1, 1, 1, 1, 1]\n    tracker_present = np.zeros([num_timesteps, num_tracker_ids])\n    tracker_present[:, 0] = [1, 1, 0, 0, 0]\n    tracker_present[:, 1] = [0, 0, 1, 1, 1]\n    tracker_present[:, 2] = [0, 0, 0, 0, 1]\n    tracker_present[:, 3] = [0, 0, 1, 1, 1]\n    tracker_present[:, 4] = [1, 1, 0, 0, 0]\n\n    expected = {\n            'clear': {\n                    'CLR_TP': 9,\n                    'CLR_FN': 0,  # 9 - 9\n                    'CLR_FP': 2,  # 11 - 9\n                    'IDSW': 3,\n                    'MOTA': 1 - 5 / 9,\n            },\n            'identity': {\n                    'IDTP': 4,\n                    'IDFN': 5,  # 9 - 4\n                    'IDFP': 7,  # 11 - 4\n                    'IDR': 4 / 9,\n                    'IDP': 4 / 11,\n                    'IDF1': 2 * 4 / 20,\n            },\n            'vace': {\n                    'STDA': 2 / 4 + 2 / 5,\n                    'ATA': (2 / 4 + 2 / 5) / (0.5 * (2 + 5)),\n            },\n    }\n\n    data = _from_dense(\n            num_timesteps=num_timesteps,\n            num_gt_ids=num_gt_ids,\n            num_tracker_ids=num_tracker_ids,\n            gt_present=gt_present,\n            tracker_present=tracker_present,\n            similarity=similarity,\n    )\n    return data, expected\n\n\ndef _from_dense(num_timesteps, num_gt_ids, num_tracker_ids, gt_present, tracker_present, similarity):\n    gt_subset = [np.flatnonzero(gt_present[t, :]) for t in range(num_timesteps)]\n    tracker_subset = [np.flatnonzero(tracker_present[t, :]) for t in range(num_timesteps)]\n    similarity_subset = [\n            similarity[t][gt_subset[t], :][:, tracker_subset[t]]\n            for t in range(num_timesteps)\n    ]\n    data = {\n            'num_timesteps': num_timesteps,\n            'num_gt_ids': num_gt_ids,\n            'num_tracker_ids': num_tracker_ids,\n            'num_gt_dets': np.sum(gt_present),\n            'num_tracker_dets': np.sum(tracker_present),\n            'gt_ids': gt_subset,\n            'tracker_ids': tracker_subset,\n            'similarity_scores': similarity_subset,\n    }\n    return data\n\n\nMETRICS_BY_NAME = {\n        'clear': trackeval.metrics.CLEAR(),\n        'identity': trackeval.metrics.Identity(),\n        'vace': trackeval.metrics.VACE(),\n}\n\nSEQUENCE_BY_NAME = {\n        'no_confusion': no_confusion(),\n        'with_confusion': with_confusion(),\n        'split_tracks': split_tracks(),\n}\n\n\n@pytest.mark.parametrize('sequence_name,metric_name', [\n        ('no_confusion', 'clear'),\n        ('no_confusion', 'identity'),\n        ('no_confusion', 'vace'),\n        ('with_confusion', 'clear'),\n        ('with_confusion', 'identity'),\n        ('with_confusion', 'vace'),\n        ('split_tracks', 'clear'),\n        ('split_tracks', 'identity'),\n        ('split_tracks', 'vace'),\n])\ndef test_metric(sequence_name, metric_name):\n    data, expected = SEQUENCE_BY_NAME[sequence_name]\n    metric = METRICS_BY_NAME[metric_name]\n    result = metric.eval_sequence(data)\n    for key, value in expected[metric_name].items():\n        assert result[key] == pytest.approx(value), key\n"
  },
  {
    "path": "TrackEval/tests/test_mot17.py",
    "content": "\"\"\" Test to ensure that the code is working correctly.\nRuns all metrics on 14 trackers for the MOT Challenge MOT17 benchmark.\n\"\"\"\n\n\nimport sys\nimport os\nimport numpy as np\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\n# Fixes multiprocessing on windows, does nothing otherwise\nif __name__ == '__main__':\n    freeze_support()\n\neval_config = {'USE_PARALLEL': False,\n               'NUM_PARALLEL_CORES': 8,\n               }\nevaluator = trackeval.Evaluator(eval_config)\nmetrics_list = [trackeval.metrics.HOTA(), trackeval.metrics.CLEAR(), trackeval.metrics.Identity()]\ntest_data_loc = os.path.join(os.path.dirname(__file__), '..', 'data', 'tests', 'mot_challenge', 'MOT17-train')\ntrackers = [\n    'DPMOT',\n    'GNNMatch',\n    'IA',\n    'ISE_MOT17R',\n    'Lif_T',\n    'Lif_TsimInt',\n    'LPC_MOT',\n    'MAT',\n    'MIFTv2',\n    'MPNTrack',\n    'SSAT',\n    'TracktorCorr',\n    'Tracktorv2',\n    'UnsupTrack',\n]\n\nfor tracker in trackers:\n    # Run code on tracker\n    dataset_config = {'TRACKERS_TO_EVAL': [tracker],\n                      'BENCHMARK': 'MOT17'}\n    dataset_list = [trackeval.datasets.MotChallenge2DBox(dataset_config)]\n    raw_results, messages = evaluator.evaluate(dataset_list, metrics_list)\n\n    results = {seq: raw_results['MotChallenge2DBox'][tracker][seq]['pedestrian'] for seq in\n               raw_results['MotChallenge2DBox'][tracker].keys()}\n    current_metrics_list = metrics_list + [trackeval.metrics.Count()]\n    metric_names = trackeval.utils.validate_metrics_list(current_metrics_list)\n\n    # Load expected results:\n    test_data = trackeval.utils.load_detail(os.path.join(test_data_loc, tracker, 'pedestrian_detailed.csv'))\n    assert len(test_data.keys()) == 22, len(test_data.keys())\n\n    # Do checks\n    for seq in test_data.keys():\n        assert len(test_data[seq].keys()) > 250, len(test_data[seq].keys())\n\n        details = []\n        for metric, metric_name in zip(current_metrics_list, metric_names):\n            table_res = {seq_key: seq_value[metric_name] for seq_key, seq_value in results.items()}\n            details.append(metric.detailed_results(table_res))\n        res_fields = sum([list(s['COMBINED_SEQ'].keys()) for s in details], [])\n        res_values = sum([list(s[seq].values()) for s in details], [])\n        res_dict = dict(zip(res_fields, res_values))\n\n        for field in test_data[seq].keys():\n            if not np.isclose(res_dict[field], test_data[seq][field]):\n                print(tracker, seq, res_dict[field], test_data[seq][field], field)\n                raise AssertionError\n\n    print('Tracker %s tests passed' % tracker)\nprint('All tests passed')\n\n"
  },
  {
    "path": "TrackEval/tests/test_mots.py",
    "content": "import sys\nimport os\nimport numpy as np\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\n# Fixes multiprocessing on windows, does nothing otherwise\nif __name__ == '__main__':\n    freeze_support()\n\neval_config = {'USE_PARALLEL': False,\n               'NUM_PARALLEL_CORES': 8,\n               }\nevaluator = trackeval.Evaluator(eval_config)\nmetrics_list = [trackeval.metrics.HOTA(), trackeval.metrics.CLEAR(), trackeval.metrics.Identity()]\n\ntests = [\n    {'DATASET': 'KittiMOTS', 'SPLIT_TO_EVAL': 'val', 'TRACKERS_TO_EVAL': ['trackrcnn']},\n    {'DATASET': 'MOTSChallenge', 'SPLIT_TO_EVAL': 'train', 'TRACKERS_TO_EVAL': ['TrackRCNN']}\n]\n\nfor dataset_config in tests:\n\n    dataset_name = dataset_config.pop('DATASET')\n    if dataset_name == 'MOTSChallenge':\n        dataset_list = [trackeval.datasets.MOTSChallenge(dataset_config)]\n        file_loc = os.path.join('mot_challenge', 'MOTS-' + dataset_config['SPLIT_TO_EVAL'])\n    elif dataset_name == 'KittiMOTS':\n        dataset_list = [trackeval.datasets.KittiMOTS(dataset_config)]\n        file_loc = os.path.join('kitti', 'kitti_mots_val')\n    else:\n        raise Exception('Dataset %s does not exist.' % dataset_name)\n\n    raw_results, messages = evaluator.evaluate(dataset_list, metrics_list)\n\n    classes = dataset_list[0].config['CLASSES_TO_EVAL']\n    tracker = dataset_config['TRACKERS_TO_EVAL'][0]\n    test_data_loc = os.path.join(os.path.dirname(__file__), '..', 'data', 'tests', file_loc)\n\n    for cls in classes:\n        results = {seq: raw_results[dataset_name][tracker][seq][cls] for seq in raw_results[dataset_name][tracker].keys()}\n        current_metrics_list = metrics_list + [trackeval.metrics.Count()]\n        metric_names = trackeval.utils.validate_metrics_list(current_metrics_list)\n\n        # Load expected results:\n        test_data = trackeval.utils.load_detail(os.path.join(test_data_loc, tracker, cls + '_detailed.csv'))\n\n        # Do checks\n        for seq in test_data.keys():\n            assert len(test_data[seq].keys()) > 250, len(test_data[seq].keys())\n\n            details = []\n            for metric, metric_name in zip(current_metrics_list, metric_names):\n                table_res = {seq_key: seq_value[metric_name] for seq_key, seq_value in results.items()}\n                details.append(metric.detailed_results(table_res))\n            res_fields = sum([list(s['COMBINED_SEQ'].keys()) for s in details], [])\n            res_values = sum([list(s[seq].values()) for s in details], [])\n            res_dict = dict(zip(res_fields, res_values))\n\n            for field in test_data[seq].keys():\n                assert np.isclose(res_dict[field], test_data[seq][field]), seq + ': ' + cls + ': ' + field\n\n    print('Tracker %s tests passed' % tracker)\nprint('All tests passed')"
  },
  {
    "path": "TrackEval/trackeval/__init__.py",
    "content": "from .eval import Evaluator\nfrom . import datasets\nfrom . import metrics\nfrom . import plotting\nfrom . import utils\n"
  },
  {
    "path": "TrackEval/trackeval/_timing.py",
    "content": "from functools import wraps\nfrom time import perf_counter\nimport inspect\n\nDO_TIMING = False\nDISPLAY_LESS_PROGRESS = False\ntimer_dict = {}\ncounter = 0\n\n\ndef time(f):\n    @wraps(f)\n    def wrap(*args, **kw):\n        if DO_TIMING:\n            # Run function with timing\n            ts = perf_counter()\n            result = f(*args, **kw)\n            te = perf_counter()\n            tt = te-ts\n\n            # Get function name\n            arg_names = inspect.getfullargspec(f)[0]\n            if arg_names[0] == 'self' and DISPLAY_LESS_PROGRESS:\n                return result\n            elif arg_names[0] == 'self':\n                method_name = type(args[0]).__name__ + '.' + f.__name__\n            else:\n                method_name = f.__name__\n\n            # Record accumulative time in each function for analysis\n            if method_name in timer_dict.keys():\n                timer_dict[method_name] += tt\n            else:\n                timer_dict[method_name] = tt\n\n            # If code is finished, display timing summary\n            if method_name == \"Evaluator.evaluate\":\n                print(\"\")\n                print(\"Timing analysis:\")\n                for key, value in timer_dict.items():\n                    print('%-70s %2.4f sec' % (key, value))\n            else:\n                # Get function argument values for printing special arguments of interest\n                arg_titles = ['tracker', 'seq', 'cls']\n                arg_vals = []\n                for i, a in enumerate(arg_names):\n                    if a in arg_titles:\n                        arg_vals.append(args[i])\n                arg_text = '(' + ', '.join(arg_vals) + ')'\n\n                # Display methods and functions with different indentation.\n                if arg_names[0] == 'self':\n                    print('%-74s %2.4f sec' % (' '*4 + method_name + arg_text, tt))\n                elif arg_names[0] == 'test':\n                    pass\n                else:\n                    global counter\n                    counter += 1\n                    print('%i %-70s %2.4f sec' % (counter, method_name + arg_text, tt))\n\n            return result\n        else:\n            # If config[\"TIME_PROGRESS\"] is false, or config[\"USE_PARALLEL\"] is true, run functions normally without timing.\n            return f(*args, **kw)\n    return wrap\n"
  },
  {
    "path": "TrackEval/trackeval/baselines/__init__.py",
    "content": "import baseline_utils\nimport stp\nimport non_overlap\nimport pascal_colormap\nimport thresholder\nimport vizualize"
  },
  {
    "path": "TrackEval/trackeval/baselines/baseline_utils.py",
    "content": "\nimport os\nimport csv\nimport numpy as np\nfrom copy import deepcopy\nfrom PIL import Image\nfrom pycocotools import mask as mask_utils\nfrom scipy.optimize import linear_sum_assignment\nfrom trackeval.baselines.pascal_colormap import pascal_colormap\n\n\ndef load_seq(file_to_load):\n    \"\"\" Load input data from file in RobMOTS format (e.g. provided detections).\n    Returns: Data object with the following structure (see STP :\n        data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    \"\"\"\n    fp = open(file_to_load)\n    dialect = csv.Sniffer().sniff(fp.readline(), delimiters=' ')\n    dialect.skipinitialspace = True\n    fp.seek(0)\n    reader = csv.reader(fp, dialect)\n    read_data = {}\n    num_timesteps = 0\n    for i, row in enumerate(reader):\n        if row[-1] in '':\n            row = row[:-1]\n        t = int(row[0])\n        cid = row[1]\n        c = int(row[2])\n        s = row[3]\n        h = row[4]\n        w = row[5]\n        rle = row[6]\n\n        if t >= num_timesteps:\n            num_timesteps = t + 1\n\n        if c in read_data.keys():\n            if t in read_data[c].keys():\n                read_data[c][t]['ids'].append(cid)\n                read_data[c][t]['scores'].append(s)\n                read_data[c][t]['im_hs'].append(h)\n                read_data[c][t]['im_ws'].append(w)\n                read_data[c][t]['mask_rles'].append(rle)\n            else:\n                read_data[c][t] = {}\n                read_data[c][t]['ids'] = [cid]\n                read_data[c][t]['scores'] = [s]\n                read_data[c][t]['im_hs'] = [h]\n                read_data[c][t]['im_ws'] = [w]\n                read_data[c][t]['mask_rles'] = [rle]\n        else:\n            read_data[c] = {t: {}}\n            read_data[c][t]['ids'] = [cid]\n            read_data[c][t]['scores'] = [s]\n            read_data[c][t]['im_hs'] = [h]\n            read_data[c][t]['im_ws'] = [w]\n            read_data[c][t]['mask_rles'] = [rle]\n    fp.close()\n\n    data = {}\n    for c in read_data.keys():\n        data[c] = [{} for _ in range(num_timesteps)]\n        for t in range(num_timesteps):\n            if t in read_data[c].keys():\n                data[c][t]['ids'] = np.atleast_1d(read_data[c][t]['ids']).astype(int)\n                data[c][t]['scores'] = np.atleast_1d(read_data[c][t]['scores']).astype(float)\n                data[c][t]['im_hs'] = np.atleast_1d(read_data[c][t]['im_hs']).astype(int)\n                data[c][t]['im_ws'] = np.atleast_1d(read_data[c][t]['im_ws']).astype(int)\n                data[c][t]['mask_rles'] = np.atleast_1d(read_data[c][t]['mask_rles']).astype(str)\n            else:\n                data[c][t]['ids'] = np.empty(0).astype(int)\n                data[c][t]['scores'] = np.empty(0).astype(float)\n                data[c][t]['im_hs'] = np.empty(0).astype(int)\n                data[c][t]['im_ws'] = np.empty(0).astype(int)\n                data[c][t]['mask_rles'] = np.empty(0).astype(str)\n    return data\n\n\ndef threshold(tdata, thresh):\n    \"\"\" Removes detections below a certian threshold ('thresh') score. \"\"\"\n    new_data = {}\n    to_keep = tdata['scores'] > thresh\n    for field in ['ids', 'scores', 'im_hs', 'im_ws', 'mask_rles']:\n        new_data[field] = tdata[field][to_keep]\n    return new_data\n\n\ndef create_coco_mask(mask_rles, im_hs, im_ws):\n    \"\"\" Converts mask as rle text (+ height and width) to encoded version used by pycocotools. \"\"\"\n    coco_masks = [{'size': [h, w], 'counts': m.encode(encoding='UTF-8')}\n                  for h, w, m in zip(im_hs, im_ws, mask_rles)]\n    return coco_masks\n\n\ndef mask_iou(mask_rles1, mask_rles2, im_hs, im_ws, do_ioa=0):\n    \"\"\" Calculate mask IoU between two masks.\n    Further allows 'intersection over area' instead of IoU (over the area of mask_rle1).\n    Allows either to pass in 1 boolean for do_ioa for all mask_rles2 or also one for each mask_rles2.\n    It is recommended that mask_rles1 is a detection and mask_rles2 is a groundtruth.\n    \"\"\"\n    coco_masks1 = create_coco_mask(mask_rles1, im_hs, im_ws)\n    coco_masks2 = create_coco_mask(mask_rles2, im_hs, im_ws)\n\n    if not hasattr(do_ioa, \"__len__\"):\n        do_ioa = [do_ioa]*len(coco_masks2)\n    assert(len(coco_masks2) == len(do_ioa))\n    if len(coco_masks1) == 0 or len(coco_masks2) == 0:\n        iou = np.zeros(len(coco_masks1), len(coco_masks2))\n    else:\n        iou = mask_utils.iou(coco_masks1, coco_masks2, do_ioa)\n    return iou\n\n\ndef sort_by_score(t_data):\n    \"\"\" Sorts data by score \"\"\"\n    sort_index = np.argsort(t_data['scores'])[::-1]\n    for k in t_data.keys():\n        t_data[k] = t_data[k][sort_index]\n    return t_data\n\n\ndef mask_NMS(t_data, nms_threshold=0.5, already_sorted=False):\n    \"\"\" Remove redundant masks by performing non-maximum suppression (NMS) \"\"\"\n\n    # Sort by score\n    if not already_sorted:\n        t_data = sort_by_score(t_data)\n\n    #  Calculate the mask IoU between all detections in the timestep.\n    mask_ious_all = mask_iou(t_data['mask_rles'], t_data['mask_rles'], t_data['im_hs'], t_data['im_ws'])\n\n    # Determine which masks NMS should remove\n    # (those overlapping greater than nms_threshold with another mask that has a higher score)\n    num_dets = len(t_data['mask_rles'])\n    to_remove = [False for _ in range(num_dets)]\n    for i in range(num_dets):\n        if not to_remove[i]:\n            for j in range(i + 1, num_dets):\n                if mask_ious_all[i, j] > nms_threshold:\n                    to_remove[j] = True\n\n    # Remove detections which should be removed\n    to_keep = np.logical_not(to_remove)\n    for k in t_data.keys():\n        t_data[k] = t_data[k][to_keep]\n\n    return t_data\n\n\ndef non_overlap(t_data, already_sorted=False):\n    \"\"\" Enforces masks to be non-overlapping in an image, does this by putting masks 'on top of one another',\n    such that higher score masks 'occlude' and thus remove parts of lower scoring masks.\n\n    Help wanted: if anyone knows a way to do this WITHOUT converting the RLE to the np.array let me know, because that\n    would be MUCH more efficient. (I have tried, but haven't yet had success).\n    \"\"\"\n\n    # Sort by score\n    if not already_sorted:\n        t_data = sort_by_score(t_data)\n\n    # Get coco masks\n    coco_masks = create_coco_mask(t_data['mask_rles'], t_data['im_hs'], t_data['im_ws'])\n\n    # Create a single np.array to hold all of the non-overlapping mask\n    masks_array = np.zeros((t_data['im_hs'][0], t_data['im_ws'][0]), 'uint8')\n\n    # Decode each mask into a np.array, and place it into the overall array for the whole frame.\n    # Since masks with the lowest score are placed first, they are 'partially overridden' by masks with a higher score\n    # if they overlap.\n    for i, mask in enumerate(coco_masks[::-1]):\n        masks_array[mask_utils.decode(mask).astype('bool')] = i + 1\n\n    # Encode the resulting np.array back into a set of coco_masks which are now non-overlapping.\n    num_dets = len(coco_masks)\n    for i, j in enumerate(range(1, num_dets + 1)[::-1]):\n        coco_masks[i] = mask_utils.encode(np.asfortranarray(masks_array == j, dtype=np.uint8))\n\n    # Convert from coco_mask back into our mask_rle format.\n    t_data['mask_rles'] = [m['counts'].decode(\"utf-8\") for m in coco_masks]\n\n    return t_data\n\n\ndef masks2boxes(mask_rles, im_hs, im_ws):\n    \"\"\" Extracts bounding boxes which surround a set of masks. \"\"\"\n    coco_masks = create_coco_mask(mask_rles, im_hs, im_ws)\n    boxes = np.array([mask_utils.toBbox(x) for x in coco_masks])\n    if len(boxes) == 0:\n        boxes = np.empty((0, 4))\n    return boxes\n\n\ndef box_iou(bboxes1, bboxes2, box_format='xywh', do_ioa=False, do_giou=False):\n    \"\"\" Calculates the IOU (intersection over union) between two arrays of boxes.\n    Allows variable box formats ('xywh' and 'x0y0x1y1').\n    If do_ioa (intersection over area), then calculates the intersection over the area of boxes1 - this is commonly\n    used to determine if detections are within crowd ignore region.\n    If do_giou (generalized intersection over union, then calculates giou.\n    \"\"\"\n    if len(bboxes1) == 0 or len(bboxes2) == 0:\n        ious = np.zeros((len(bboxes1), len(bboxes2)))\n        return ious\n    if box_format in 'xywh':\n        # layout: (x0, y0, w, h)\n        bboxes1 = deepcopy(bboxes1)\n        bboxes2 = deepcopy(bboxes2)\n\n        bboxes1[:, 2] = bboxes1[:, 0] + bboxes1[:, 2]\n        bboxes1[:, 3] = bboxes1[:, 1] + bboxes1[:, 3]\n        bboxes2[:, 2] = bboxes2[:, 0] + bboxes2[:, 2]\n        bboxes2[:, 3] = bboxes2[:, 1] + bboxes2[:, 3]\n    elif box_format not in 'x0y0x1y1':\n        raise (Exception('box_format %s is not implemented' % box_format))\n\n    # layout: (x0, y0, x1, y1)\n    min_ = np.minimum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])\n    max_ = np.maximum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])\n    intersection = np.maximum(min_[..., 2] - max_[..., 0], 0) * np.maximum(min_[..., 3] - max_[..., 1], 0)\n    area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])\n\n    if do_ioa:\n        ioas = np.zeros_like(intersection)\n        valid_mask = area1 > 0 + np.finfo('float').eps\n        ioas[valid_mask, :] = intersection[valid_mask, :] / area1[valid_mask][:, np.newaxis]\n\n        return ioas\n    else:\n        area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1])\n        union = area1[:, np.newaxis] + area2[np.newaxis, :] - intersection\n        intersection[area1 <= 0 + np.finfo('float').eps, :] = 0\n        intersection[:, area2 <= 0 + np.finfo('float').eps] = 0\n        intersection[union <= 0 + np.finfo('float').eps] = 0\n        union[union <= 0 + np.finfo('float').eps] = 1\n        ious = intersection / union\n\n    if do_giou:\n        enclosing_area = np.maximum(max_[..., 2] - min_[..., 0], 0) * np.maximum(max_[..., 3] - min_[..., 1], 0)\n        eps = 1e-7\n        # giou\n        ious = ious - ((enclosing_area - union) / (enclosing_area + eps))\n\n    return ious\n\n\ndef match(match_scores):\n    match_rows, match_cols = linear_sum_assignment(-match_scores)\n    return match_rows, match_cols\n\n\ndef write_seq(output_data, out_file):\n    out_loc = os.path.dirname(out_file)\n    if not os.path.exists(out_loc):\n        os.makedirs(out_loc, exist_ok=True)\n    fp = open(out_file, 'w', newline='')\n    writer = csv.writer(fp, delimiter=' ')\n    for row in output_data:\n        writer.writerow(row)\n    fp.close()\n\n\ndef combine_classes(data):\n    \"\"\" Converts data from a class-separated to a class-combined format.\n    Input format: data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    Output format: data[t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles', 'cls'}\n    \"\"\"\n    output_data = [{} for _ in list(data.values())[0]]\n    for cls, cls_data in data.items():\n        for timestep, t_data in enumerate(cls_data):\n            for k in t_data.keys():\n                if k in output_data[timestep].keys():\n                    output_data[timestep][k] += list(t_data[k])\n                else:\n                    output_data[timestep][k] = list(t_data[k])\n            if 'cls' in output_data[timestep].keys():\n                output_data[timestep]['cls'] += [cls]*len(output_data[timestep]['ids'])\n            else:\n                output_data[timestep]['cls'] = [cls]*len(output_data[timestep]['ids'])\n\n    for timestep, t_data in enumerate(output_data):\n        for k in t_data.keys():\n            output_data[timestep][k] = np.array(output_data[timestep][k])\n\n    return output_data\n\n\ndef save_as_png(t_data, out_file, im_h, im_w):\n    \"\"\" Save a set of segmentation masks into a PNG format, the same as used for the DAVIS dataset.\"\"\"\n\n    if len(t_data['mask_rles']) > 0:\n        coco_masks = create_coco_mask(t_data['mask_rles'], t_data['im_hs'], t_data['im_ws'])\n\n        list_of_np_masks = [mask_utils.decode(mask) for mask in coco_masks]\n\n        png = np.zeros((t_data['im_hs'][0], t_data['im_ws'][0]))\n        for mask, c_id in zip(list_of_np_masks, t_data['ids']):\n            png[mask.astype(\"bool\")] = c_id + 1\n    else:\n        png = np.zeros((im_h, im_w))\n\n    if not os.path.exists(os.path.dirname(out_file)):\n        os.makedirs(os.path.dirname(out_file))\n\n    colmap = (np.array(pascal_colormap) * 255).round().astype(\"uint8\")\n    palimage = Image.new('P', (16, 16))\n    palimage.putpalette(colmap)\n    im = Image.fromarray(np.squeeze(png.astype(\"uint8\")))\n    im2 = im.quantize(palette=palimage)\n    im2.save(out_file)\n\n\ndef get_frame_size(data):\n    \"\"\" Gets frame height and width from data. \"\"\"\n    for cls, cls_data in data.items():\n        for timestep, t_data in enumerate(cls_data):\n            if len(t_data['im_hs'] > 0):\n                im_h = t_data['im_hs'][0]\n                im_w = t_data['im_ws'][0]\n                return im_h, im_w\n    return None\n"
  },
  {
    "path": "TrackEval/trackeval/baselines/non_overlap.py",
    "content": "\"\"\"\nNon-Overlap: Code to take in a set of raw detections and produce a set of non-overlapping detections from it.\n\nAuthor: Jonathon Luiten\n\"\"\"\n\nimport os\nimport sys\nfrom multiprocessing.pool import Pool\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))\nfrom trackeval.baselines import baseline_utils as butils\nfrom trackeval.utils import get_code_path\n\ncode_path = get_code_path()\nconfig = {\n    'INPUT_FOL': os.path.join(code_path, 'data/detections/rob_mots/{split}/raw_supplied/data/'),\n    'OUTPUT_FOL': os.path.join(code_path, 'data/detections/rob_mots/{split}/non_overlap_supplied/data/'),\n    'SPLIT': 'train',  # valid: 'train', 'val', 'test'.\n    'Benchmarks': None,  # If None, all benchmarks in SPLIT.\n\n    'Num_Parallel_Cores': None,  # If None, run without parallel.\n\n    'THRESHOLD_NMS_MASK_IOU': 0.5,\n}\n\n\ndef do_sequence(seq_file):\n\n    # Load input data from file (e.g. provided detections)\n    # data format: data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    data = butils.load_seq(seq_file)\n\n    # Converts data from a class-separated to a class-combined format.\n    # data[t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles', 'cls'}\n    data = butils.combine_classes(data)\n\n    # Where to accumulate output data for writing out\n    output_data = []\n\n    # Run for each timestep.\n    for timestep, t_data in enumerate(data):\n\n        # Remove redundant masks by performing non-maximum suppression (NMS)\n        t_data = butils.mask_NMS(t_data, nms_threshold=config['THRESHOLD_NMS_MASK_IOU'])\n\n        # Perform non-overlap, to get non_overlapping masks.\n        t_data = butils.non_overlap(t_data, already_sorted=True)\n\n        # Save result in output format to write to file later.\n        # Output Format = [timestep ID class score im_h im_w mask_RLE]\n        for i in range(len(t_data['ids'])):\n            row = [timestep, int(t_data['ids'][i]), t_data['cls'][i], t_data['scores'][i], t_data['im_hs'][i],\n                   t_data['im_ws'][i], t_data['mask_rles'][i]]\n            output_data.append(row)\n\n    # Write results to file\n    out_file = seq_file.replace(config['INPUT_FOL'].format(split=config['SPLIT']),\n                                config['OUTPUT_FOL'].format(split=config['SPLIT']))\n    butils.write_seq(output_data, out_file)\n\n    print('DONE:', seq_file)\n\n\nif __name__ == '__main__':\n\n    # Required to fix bug in multiprocessing on windows.\n    freeze_support()\n\n    # Obtain list of sequences to run tracker for.\n    if config['Benchmarks']:\n        benchmarks = config['Benchmarks']\n    else:\n        benchmarks = ['davis_unsupervised', 'kitti_mots', 'youtube_vis', 'ovis', 'bdd_mots', 'tao']\n        if config['SPLIT'] != 'train':\n            benchmarks += ['waymo', 'mots_challenge']\n    seqs_todo = []\n    for bench in benchmarks:\n        bench_fol = os.path.join(config['INPUT_FOL'].format(split=config['SPLIT']), bench)\n        seqs_todo += [os.path.join(bench_fol, seq) for seq in os.listdir(bench_fol)]\n\n    # Run in parallel\n    if config['Num_Parallel_Cores']:\n        with Pool(config['Num_Parallel_Cores']) as pool:\n            results = pool.map(do_sequence, seqs_todo)\n\n    # Run in series\n    else:\n        for seq_todo in seqs_todo:\n            do_sequence(seq_todo)\n\n"
  },
  {
    "path": "TrackEval/trackeval/baselines/pascal_colormap.py",
    "content": "pascal_colormap = [\n    0     ,         0,         0,\n    0.5020,         0,         0,\n         0,    0.5020,         0,\n    0.5020,    0.5020,         0,\n         0,         0,    0.5020,\n    0.5020,         0,    0.5020,\n         0,    0.5020,    0.5020,\n    0.5020,    0.5020,    0.5020,\n    0.2510,         0,         0,\n    0.7529,         0,         0,\n    0.2510,    0.5020,         0,\n    0.7529,    0.5020,         0,\n    0.2510,         0,    0.5020,\n    0.7529,         0,    0.5020,\n    0.2510,    0.5020,    0.5020,\n    0.7529,    0.5020,    0.5020,\n         0,    0.2510,         0,\n    0.5020,    0.2510,         0,\n         0,    0.7529,         0,\n    0.5020,    0.7529,         0,\n         0,    0.2510,    0.5020,\n    0.5020,    0.2510,    0.5020,\n         0,    0.7529,    0.5020,\n    0.5020,    0.7529,    0.5020,\n    0.2510,    0.2510,         0,\n    0.7529,    0.2510,         0,\n    0.2510,    0.7529,         0,\n    0.7529,    0.7529,         0,\n    0.2510,    0.2510,    0.5020,\n    0.7529,    0.2510,    0.5020,\n    0.2510,    0.7529,    0.5020,\n    0.7529,    0.7529,    0.5020,\n         0,         0,    0.2510,\n    0.5020,         0,    0.2510,\n         0,    0.5020,    0.2510,\n    0.5020,    0.5020,    0.2510,\n         0,         0,    0.7529,\n    0.5020,         0,    0.7529,\n         0,    0.5020,    0.7529,\n    0.5020,    0.5020,    0.7529,\n    0.2510,         0,    0.2510,\n    0.7529,         0,    0.2510,\n    0.2510,    0.5020,    0.2510,\n    0.7529,    0.5020,    0.2510,\n    0.2510,         0,    0.7529,\n    0.7529,         0,    0.7529,\n    0.2510,    0.5020,    0.7529,\n    0.7529,    0.5020,    0.7529,\n         0,    0.2510,    0.2510,\n    0.5020,    0.2510,    0.2510,\n         0,    0.7529,    0.2510,\n    0.5020,    0.7529,    0.2510,\n         0,    0.2510,    0.7529,\n    0.5020,    0.2510,    0.7529,\n         0,    0.7529,    0.7529,\n    0.5020,    0.7529,    0.7529,\n    0.2510,    0.2510,    0.2510,\n    0.7529,    0.2510,    0.2510,\n    0.2510,    0.7529,    0.2510,\n    0.7529,    0.7529,    0.2510,\n    0.2510,    0.2510,    0.7529,\n    0.7529,    0.2510,    0.7529,\n    0.2510,    0.7529,    0.7529,\n    0.7529,    0.7529,    0.7529,\n    0.1255,         0,         0,\n    0.6275,         0,         0,\n    0.1255,    0.5020,         0,\n    0.6275,    0.5020,         0,\n    0.1255,         0,    0.5020,\n    0.6275,         0,    0.5020,\n    0.1255,    0.5020,    0.5020,\n    0.6275,    0.5020,    0.5020,\n    0.3765,         0,         0,\n    0.8784,         0,         0,\n    0.3765,    0.5020,         0,\n    0.8784,    0.5020,         0,\n    0.3765,         0,    0.5020,\n    0.8784,         0,    0.5020,\n    0.3765,    0.5020,    0.5020,\n    0.8784,    0.5020,    0.5020,\n    0.1255,    0.2510,         0,\n    0.6275,    0.2510,         0,\n    0.1255,    0.7529,         0,\n    0.6275,    0.7529,         0,\n    0.1255,    0.2510,    0.5020,\n    0.6275,    0.2510,    0.5020,\n    0.1255,    0.7529,    0.5020,\n    0.6275,    0.7529,    0.5020,\n    0.3765,    0.2510,         0,\n    0.8784,    0.2510,         0,\n    0.3765,    0.7529,         0,\n    0.8784,    0.7529,         0,\n    0.3765,    0.2510,    0.5020,\n    0.8784,    0.2510,    0.5020,\n    0.3765,    0.7529,    0.5020,\n    0.8784,    0.7529,    0.5020,\n    0.1255,         0,    0.2510,\n    0.6275,         0,    0.2510,\n    0.1255,    0.5020,    0.2510,\n    0.6275,    0.5020,    0.2510,\n    0.1255,         0,    0.7529,\n    0.6275,         0,    0.7529,\n    0.1255,    0.5020,    0.7529,\n    0.6275,    0.5020,    0.7529,\n    0.3765,         0,    0.2510,\n    0.8784,         0,    0.2510,\n    0.3765,    0.5020,    0.2510,\n    0.8784,    0.5020,    0.2510,\n    0.3765,         0,    0.7529,\n    0.8784,         0,    0.7529,\n    0.3765,    0.5020,    0.7529,\n    0.8784,    0.5020,    0.7529,\n    0.1255,    0.2510,    0.2510,\n    0.6275,    0.2510,    0.2510,\n    0.1255,    0.7529,    0.2510,\n    0.6275,    0.7529,    0.2510,\n    0.1255,    0.2510,    0.7529,\n    0.6275,    0.2510,    0.7529,\n    0.1255,    0.7529,    0.7529,\n    0.6275,    0.7529,    0.7529,\n    0.3765,    0.2510,    0.2510,\n    0.8784,    0.2510,    0.2510,\n    0.3765,    0.7529,    0.2510,\n    0.8784,    0.7529,    0.2510,\n    0.3765,    0.2510,    0.7529,\n    0.8784,    0.2510,    0.7529,\n    0.3765,    0.7529,    0.7529,\n    0.8784,    0.7529,    0.7529,\n         0,    0.1255,         0,\n    0.5020,    0.1255,         0,\n         0,    0.6275,         0,\n    0.5020,    0.6275,         0,\n         0,    0.1255,    0.5020,\n    0.5020,    0.1255,    0.5020,\n         0,    0.6275,    0.5020,\n    0.5020,    0.6275,    0.5020,\n    0.2510,    0.1255,         0,\n    0.7529,    0.1255,         0,\n    0.2510,    0.6275,         0,\n    0.7529,    0.6275,         0,\n    0.2510,    0.1255,    0.5020,\n    0.7529,    0.1255,    0.5020,\n    0.2510,    0.6275,    0.5020,\n    0.7529,    0.6275,    0.5020,\n         0,    0.3765,         0,\n    0.5020,    0.3765,         0,\n         0,    0.8784,         0,\n    0.5020,    0.8784,         0,\n         0,    0.3765,    0.5020,\n    0.5020,    0.3765,    0.5020,\n         0,    0.8784,    0.5020,\n    0.5020,    0.8784,    0.5020,\n    0.2510,    0.3765,         0,\n    0.7529,    0.3765,         0,\n    0.2510,    0.8784,         0,\n    0.7529,    0.8784,         0,\n    0.2510,    0.3765,    0.5020,\n    0.7529,    0.3765,    0.5020,\n    0.2510,    0.8784,    0.5020,\n    0.7529,    0.8784,    0.5020,\n         0,    0.1255,    0.2510,\n    0.5020,    0.1255,    0.2510,\n         0,    0.6275,    0.2510,\n    0.5020,    0.6275,    0.2510,\n         0,    0.1255,    0.7529,\n    0.5020,    0.1255,    0.7529,\n         0,    0.6275,    0.7529,\n    0.5020,    0.6275,    0.7529,\n    0.2510,    0.1255,    0.2510,\n    0.7529,    0.1255,    0.2510,\n    0.2510,    0.6275,    0.2510,\n    0.7529,    0.6275,    0.2510,\n    0.2510,    0.1255,    0.7529,\n    0.7529,    0.1255,    0.7529,\n    0.2510,    0.6275,    0.7529,\n    0.7529,    0.6275,    0.7529,\n         0,    0.3765,    0.2510,\n    0.5020,    0.3765,    0.2510,\n         0,    0.8784,    0.2510,\n    0.5020,    0.8784,    0.2510,\n         0,    0.3765,    0.7529,\n    0.5020,    0.3765,    0.7529,\n         0,    0.8784,    0.7529,\n    0.5020,    0.8784,    0.7529,\n    0.2510,    0.3765,    0.2510,\n    0.7529,    0.3765,    0.2510,\n    0.2510,    0.8784,    0.2510,\n    0.7529,    0.8784,    0.2510,\n    0.2510,    0.3765,    0.7529,\n    0.7529,    0.3765,    0.7529,\n    0.2510,    0.8784,    0.7529,\n    0.7529,    0.8784,    0.7529,\n    0.1255,    0.1255,         0,\n    0.6275,    0.1255,         0,\n    0.1255,    0.6275,         0,\n    0.6275,    0.6275,         0,\n    0.1255,    0.1255,    0.5020,\n    0.6275,    0.1255,    0.5020,\n    0.1255,    0.6275,    0.5020,\n    0.6275,    0.6275,    0.5020,\n    0.3765,    0.1255,         0,\n    0.8784,    0.1255,         0,\n    0.3765,    0.6275,         0,\n    0.8784,    0.6275,         0,\n    0.3765,    0.1255,    0.5020,\n    0.8784,    0.1255,    0.5020,\n    0.3765,    0.6275,    0.5020,\n    0.8784,    0.6275,    0.5020,\n    0.1255,    0.3765,         0,\n    0.6275,    0.3765,         0,\n    0.1255,    0.8784,         0,\n    0.6275,    0.8784,         0,\n    0.1255,    0.3765,    0.5020,\n    0.6275,    0.3765,    0.5020,\n    0.1255,    0.8784,    0.5020,\n    0.6275,    0.8784,    0.5020,\n    0.3765,    0.3765,         0,\n    0.8784,    0.3765,         0,\n    0.3765,    0.8784,         0,\n    0.8784,    0.8784,         0,\n    0.3765,    0.3765,    0.5020,\n    0.8784,    0.3765,    0.5020,\n    0.3765,    0.8784,    0.5020,\n    0.8784,    0.8784,    0.5020,\n    0.1255,    0.1255,    0.2510,\n    0.6275,    0.1255,    0.2510,\n    0.1255,    0.6275,    0.2510,\n    0.6275,    0.6275,    0.2510,\n    0.1255,    0.1255,    0.7529,\n    0.6275,    0.1255,    0.7529,\n    0.1255,    0.6275,    0.7529,\n    0.6275,    0.6275,    0.7529,\n    0.3765,    0.1255,    0.2510,\n    0.8784,    0.1255,    0.2510,\n    0.3765,    0.6275,    0.2510,\n    0.8784,    0.6275,    0.2510,\n    0.3765,    0.1255,    0.7529,\n    0.8784,    0.1255,    0.7529,\n    0.3765,    0.6275,    0.7529,\n    0.8784,    0.6275,    0.7529,\n    0.1255,    0.3765,    0.2510,\n    0.6275,    0.3765,    0.2510,\n    0.1255,    0.8784,    0.2510,\n    0.6275,    0.8784,    0.2510,\n    0.1255,    0.3765,    0.7529,\n    0.6275,    0.3765,    0.7529,\n    0.1255,    0.8784,    0.7529,\n    0.6275,    0.8784,    0.7529,\n    0.3765,    0.3765,    0.2510,\n    0.8784,    0.3765,    0.2510,\n    0.3765,    0.8784,    0.2510,\n    0.8784,    0.8784,    0.2510,\n    0.3765,    0.3765,    0.7529,\n    0.8784,    0.3765,    0.7529,\n    0.3765,    0.8784,    0.7529,\n    0.8784,    0.8784,    0.7529]"
  },
  {
    "path": "TrackEval/trackeval/baselines/stp.py",
    "content": "\"\"\"\nSTP: Simplest Tracker Possible\n\nAuthor: Jonathon Luiten\n\nThis simple tracker, simply assigns track IDs which maximise the 'bounding box IoU' between previous tracks and current\ndetections. It is also able to match detections to tracks at more than one timestep previously.\n\"\"\"\n\nimport os\nimport sys\nimport numpy as np\nfrom multiprocessing.pool import Pool\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))\nfrom trackeval.baselines import baseline_utils as butils\nfrom trackeval.utils import get_code_path\n\ncode_path = get_code_path()\nconfig = {\n    'INPUT_FOL': os.path.join(code_path, 'data/detections/rob_mots/{split}/non_overlap_supplied/data/'),\n    'OUTPUT_FOL': os.path.join(code_path, 'data/trackers/rob_mots/{split}/STP/data/'),\n    'SPLIT': 'train',  # valid: 'train', 'val', 'test'.\n    'Benchmarks': None,  # If None, all benchmarks in SPLIT.\n\n    'Num_Parallel_Cores': None,  # If None, run without parallel.\n\n    'DETECTION_THRESHOLD': 0.5,\n    'ASSOCIATION_THRESHOLD': 1e-10,\n    'MAX_FRAMES_SKIP': 7\n}\n\n\ndef track_sequence(seq_file):\n\n    # Load input data from file (e.g. provided detections)\n    # data format: data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    data = butils.load_seq(seq_file)\n\n    # Where to accumulate output data for writing out\n    output_data = []\n\n    # To ensure IDs are unique per object across all classes.\n    curr_max_id = 0\n\n    # Run tracker for each class.\n    for cls, cls_data in data.items():\n\n        # Initialize container for holding previously tracked objects.\n        prev = {'boxes': np.empty((0, 4)),\n                'ids': np.array([], np.int),\n                'timesteps': np.array([])}\n\n        # Run tracker for each timestep.\n        for timestep, t_data in enumerate(cls_data):\n\n            # Threshold detections.\n            t_data = butils.threshold(t_data, config['DETECTION_THRESHOLD'])\n\n            # Convert mask dets to bounding boxes.\n            boxes = butils.masks2boxes(t_data['mask_rles'], t_data['im_hs'], t_data['im_ws'])\n\n            # Calculate IoU between previous and current frame dets.\n            ious = butils.box_iou(prev['boxes'], boxes)\n\n            # Score which decreases quickly for previous dets depending on how many timesteps before they come from.\n            prev_timestep_scores = np.power(10, -1 * prev['timesteps'])\n\n            # Matching score is such that it first tries to match 'most recent timesteps',\n            # and within each timestep maximised IoU.\n            match_scores = prev_timestep_scores[:, np.newaxis] * ious\n\n            # Find best matching between current dets and previous tracks.\n            match_rows, match_cols = butils.match(match_scores)\n\n            # Remove matches that have an IoU below a certain threshold.\n            actually_matched_mask = ious[match_rows, match_cols] > config['ASSOCIATION_THRESHOLD']\n            match_rows = match_rows[actually_matched_mask]\n            match_cols = match_cols[actually_matched_mask]\n\n            # Assign the prev track ID to the current dets if they were matched.\n            ids = np.nan * np.ones((len(boxes),), np.int)\n            ids[match_cols] = prev['ids'][match_rows]\n\n            # Create new track IDs for dets that were not matched to previous tracks.\n            num_not_matched = len(ids) - len(match_cols)\n            new_ids = np.arange(curr_max_id + 1, curr_max_id + num_not_matched + 1)\n            ids[np.isnan(ids)] = new_ids\n\n            # Update maximum ID to ensure future added tracks have a unique ID value.\n            curr_max_id += num_not_matched\n\n            # Drop tracks from 'previous tracks' if they have not been matched in the last MAX_FRAMES_SKIP frames.\n            unmatched_rows = [i for i in range(len(prev['ids'])) if\n                              i not in match_rows and (prev['timesteps'][i] + 1 <= config['MAX_FRAMES_SKIP'])]\n\n            # Update the set of previous tracking results to include the newly tracked detections.\n            prev['ids'] = np.concatenate((ids, prev['ids'][unmatched_rows]), axis=0)\n            prev['boxes'] = np.concatenate((np.atleast_2d(boxes), np.atleast_2d(prev['boxes'][unmatched_rows])), axis=0)\n            prev['timesteps'] = np.concatenate((np.zeros((len(ids),)), prev['timesteps'][unmatched_rows] + 1), axis=0)\n\n            # Save result in output format to write to file later.\n            # Output Format = [timestep ID class score im_h im_w mask_RLE]\n            for i in range(len(t_data['ids'])):\n                row = [timestep, int(ids[i]), cls, t_data['scores'][i], t_data['im_hs'][i], t_data['im_ws'][i],\n                       t_data['mask_rles'][i]]\n                output_data.append(row)\n\n    # Write results to file\n    out_file = seq_file.replace(config['INPUT_FOL'].format(split=config['SPLIT']),\n                                config['OUTPUT_FOL'].format(split=config['SPLIT']))\n    butils.write_seq(output_data, out_file)\n\n    print('DONE:', seq_file)\n\n\nif __name__ == '__main__':\n\n    # Required to fix bug in multiprocessing on windows.\n    freeze_support()\n\n    # Obtain list of sequences to run tracker for.\n    if config['Benchmarks']:\n        benchmarks = config['Benchmarks']\n    else:\n        benchmarks = ['davis_unsupervised', 'kitti_mots', 'youtube_vis', 'ovis', 'bdd_mots', 'tao']\n        if config['SPLIT'] != 'train':\n            benchmarks += ['waymo', 'mots_challenge']\n    seqs_todo = []\n    for bench in benchmarks:\n        bench_fol = os.path.join(config['INPUT_FOL'].format(split=config['SPLIT']), bench)\n        seqs_todo += [os.path.join(bench_fol, seq) for seq in os.listdir(bench_fol)]\n\n    # Run in parallel\n    if config['Num_Parallel_Cores']:\n        with Pool(config['Num_Parallel_Cores']) as pool:\n            results = pool.map(track_sequence, seqs_todo)\n\n    # Run in series\n    else:\n        for seq_todo in seqs_todo:\n            track_sequence(seq_todo)\n\n"
  },
  {
    "path": "TrackEval/trackeval/baselines/thresholder.py",
    "content": "\"\"\"\nThresholder\n\nAuthor: Jonathon Luiten\n\nSimply reads in a set of detection, thresholds them at a certain score threshold, and writes them out again.\n\"\"\"\n\nimport os\nimport sys\nfrom multiprocessing.pool import Pool\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))\nfrom trackeval.baselines import baseline_utils as butils\nfrom trackeval.utils import get_code_path\n\nTHRESHOLD = 0.2\n\ncode_path = get_code_path()\nconfig = {\n    'INPUT_FOL': os.path.join(code_path, 'data/detections/rob_mots/{split}/non_overlap_supplied/data/'),\n    'OUTPUT_FOL': os.path.join(code_path, 'data/detections/rob_mots/{split}/threshold_' + str(100*THRESHOLD) + '/data/'),\n    'SPLIT': 'train',  # valid: 'train', 'val', 'test'.\n    'Benchmarks': None,  # If None, all benchmarks in SPLIT.\n\n    'Num_Parallel_Cores': None,  # If None, run without parallel.\n\n    'DETECTION_THRESHOLD': THRESHOLD,\n}\n\n\ndef do_sequence(seq_file):\n\n    # Load input data from file (e.g. provided detections)\n    # data format: data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    data = butils.load_seq(seq_file)\n\n    # Where to accumulate output data for writing out\n    output_data = []\n\n    # Run for each class.\n    for cls, cls_data in data.items():\n\n        # Run for each timestep.\n        for timestep, t_data in enumerate(cls_data):\n\n            # Threshold detections.\n            t_data = butils.threshold(t_data, config['DETECTION_THRESHOLD'])\n\n            # Save result in output format to write to file later.\n            # Output Format = [timestep ID class score im_h im_w mask_RLE]\n            for i in range(len(t_data['ids'])):\n                row = [timestep, int(t_data['ids'][i]), cls, t_data['scores'][i], t_data['im_hs'][i],\n                       t_data['im_ws'][i], t_data['mask_rles'][i]]\n                output_data.append(row)\n\n    # Write results to file\n    out_file = seq_file.replace(config['INPUT_FOL'].format(split=config['SPLIT']),\n                                config['OUTPUT_FOL'].format(split=config['SPLIT']))\n    butils.write_seq(output_data, out_file)\n\n    print('DONE:', seq_todo)\n\n\nif __name__ == '__main__':\n\n    # Required to fix bug in multiprocessing on windows.\n    freeze_support()\n\n    # Obtain list of sequences to run tracker for.\n    if config['Benchmarks']:\n        benchmarks = config['Benchmarks']\n    else:\n        benchmarks = ['davis_unsupervised', 'kitti_mots', 'youtube_vis', 'ovis', 'bdd_mots', 'tao']\n        if config['SPLIT'] != 'train':\n            benchmarks += ['waymo', 'mots_challenge']\n    seqs_todo = []\n    for bench in benchmarks:\n        bench_fol = os.path.join(config['INPUT_FOL'].format(split=config['SPLIT']), bench)\n        seqs_todo += [os.path.join(bench_fol, seq) for seq in os.listdir(bench_fol)]\n\n    # Run in parallel\n    if config['Num_Parallel_Cores']:\n        with Pool(config['Num_Parallel_Cores']) as pool:\n            results = pool.map(do_sequence, seqs_todo)\n\n    # Run in series\n    else:\n        for seq_todo in seqs_todo:\n            do_sequence(seq_todo)\n\n"
  },
  {
    "path": "TrackEval/trackeval/baselines/vizualize.py",
    "content": "\"\"\"\nVizualize: Code which converts .txt rle tracking results into a visual .png format.\n\nAuthor: Jonathon Luiten\n\"\"\"\n\nimport os\nimport sys\nfrom multiprocessing.pool import Pool\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))\nfrom trackeval.baselines import baseline_utils as butils\nfrom trackeval.utils import get_code_path\nfrom trackeval.datasets.rob_mots_classmap import cls_id_to_name\n\ncode_path = get_code_path()\nconfig = {\n    # Tracker format:\n    'INPUT_FOL': os.path.join(code_path, 'data/trackers/rob_mots/{split}/STP/data/{bench}'),\n    'OUTPUT_FOL': os.path.join(code_path, 'data/viz/rob_mots/{split}/STP/data/{bench}'),\n    # GT format:\n    # 'INPUT_FOL': os.path.join(code_path, 'data/gt/rob_mots/{split}/{bench}/data/'),\n    # 'OUTPUT_FOL': os.path.join(code_path, 'data/gt_viz/rob_mots/{split}/{bench}/'),\n    'SPLIT': 'train',  # valid: 'train', 'val', 'test'.\n    'Benchmarks': None,  # If None, all benchmarks in SPLIT.\n    'Num_Parallel_Cores': None,  # If None, run without parallel.\n}\n\n\ndef do_sequence(seq_file):\n    # Folder to save resulting visualization in\n    out_fol = seq_file.replace(config['INPUT_FOL'].format(split=config['SPLIT'], bench=bench),\n                               config['OUTPUT_FOL'].format(split=config['SPLIT'], bench=bench)).replace('.txt', '')\n\n    # Load input data from file (e.g. provided detections)\n    # data format: data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    data = butils.load_seq(seq_file)\n\n    # Get frame size for visualizing empty frames\n    im_h, im_w = butils.get_frame_size(data)\n\n    # First run for each class.\n    for cls, cls_data in data.items():\n\n        if cls >= 100:\n            continue\n\n        # Run for each timestep.\n        for timestep, t_data in enumerate(cls_data):\n            # Save out visualization\n            out_file = os.path.join(out_fol, cls_id_to_name[cls], str(timestep).zfill(5) + '.png')\n            butils.save_as_png(t_data, out_file, im_h, im_w)\n\n\n    # Then run for all classes combined\n    # Converts data from a class-separated to a class-combined format.\n    data = butils.combine_classes(data)\n\n    # Run for each timestep.\n    for timestep, t_data in enumerate(data):\n        # Save out visualization\n        out_file = os.path.join(out_fol, 'all_classes', str(timestep).zfill(5) + '.png')\n        butils.save_as_png(t_data, out_file, im_h, im_w)\n\n    print('DONE:', seq_file)\n\n\nif __name__ == '__main__':\n\n    # Required to fix bug in multiprocessing on windows.\n    freeze_support()\n\n    # Obtain list of sequences to run tracker for.\n    if config['Benchmarks']:\n        benchmarks = config['Benchmarks']\n    else:\n        benchmarks = ['davis_unsupervised', 'kitti_mots', 'youtube_vis', 'ovis', 'bdd_mots', 'tao']\n        if config['SPLIT'] != 'train':\n            benchmarks += ['waymo', 'mots_challenge']\n    seqs_todo = []\n    for bench in benchmarks:\n        bench_fol = config['INPUT_FOL'].format(split=config['SPLIT'], bench=bench)\n        seqs_todo += [os.path.join(bench_fol, seq) for seq in os.listdir(bench_fol)]\n\n    # Run in parallel\n    if config['Num_Parallel_Cores']:\n        with Pool(config['Num_Parallel_Cores']) as pool:\n            results = pool.map(do_sequence, seqs_todo)\n\n    # Run in series\n    else:\n        for seq_todo in seqs_todo:\n            do_sequence(seq_todo)\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/__init__.py",
    "content": "from .kitti_2d_box import Kitti2DBox\nfrom .kitti_mots import KittiMOTS\nfrom .mot_challenge_2d_box import MotChallenge2DBox\nfrom .mots_challenge import MOTSChallenge\nfrom .bdd100k import BDD100K\nfrom .davis import DAVIS\nfrom .tao import TAO\nfrom .tao_ow import TAO_OW\nfrom .youtube_vis import YouTubeVIS\nfrom .head_tracking_challenge import HeadTrackingChallenge\nfrom .rob_mots import RobMOTS\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/_base_dataset.py",
    "content": "import csv\nimport io\nimport zipfile\nimport os\nimport traceback\nimport numpy as np\nfrom copy import deepcopy\nfrom abc import ABC, abstractmethod\nfrom .. import _timing\nfrom ..utils import TrackEvalException\n\n\nclass _BaseDataset(ABC):\n    @abstractmethod\n    def __init__(self):\n        self.tracker_list = None\n        self.seq_list = None\n        self.class_list = None\n        self.output_fol = None\n        self.output_sub_fol = None\n        self.should_classes_combine = True\n        self.use_super_categories = False\n\n    # Functions to implement:\n\n    @staticmethod\n    @abstractmethod\n    def get_default_dataset_config():\n        ...\n\n    @abstractmethod\n    def _load_raw_file(self, tracker, seq, is_gt):\n        ...\n\n    @_timing.time\n    @abstractmethod\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        ...\n\n    @abstractmethod\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        ...\n\n    # Helper functions for all datasets:\n\n    @classmethod\n    def get_class_name(cls):\n        return cls.__name__\n\n    def get_name(self):\n        return self.get_class_name()\n\n    def get_output_fol(self, tracker):\n        return os.path.join(self.output_fol, tracker, self.output_sub_fol)\n\n    def get_display_name(self, tracker):\n        \"\"\" Can be overwritten if the trackers name (in files) is different to how it should be displayed.\n        By default this method just returns the trackers name as is.\n        \"\"\"\n        return tracker\n\n    def get_eval_info(self):\n        \"\"\"Return info about the dataset needed for the Evaluator\"\"\"\n        return self.tracker_list, self.seq_list, self.class_list\n\n    @_timing.time\n    def get_raw_seq_data(self, tracker, seq):\n        \"\"\" Loads raw data (tracker and ground-truth) for a single tracker on a single sequence.\n        Raw data includes all of the information needed for both preprocessing and evaluation, for all classes.\n        A later function (get_processed_seq_data) will perform such preprocessing and extract relevant information for\n        the evaluation of each class.\n\n        This returns a dict which contains the fields:\n        [num_timesteps]: integer\n        [gt_ids, tracker_ids, gt_classes, tracker_classes, tracker_confidences]:\n                                                                list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets, tracker_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.\n        [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        [gt_extras]: dict (for each extra) of lists (for each timestep) of 1D NDArrays (for each det).\n\n        gt_extras contains dataset specific information used for preprocessing such as occlusion and truncation levels.\n\n        Note that similarities are extracted as part of the dataset and not the metric, because almost all metrics are\n        independent of the exact method of calculating the similarity. However datasets are not (e.g. segmentation\n        masks vs 2D boxes vs 3D boxes).\n        We calculate the similarity before preprocessing because often both preprocessing and evaluation require it and\n        we don't wish to calculate this twice.\n        We calculate similarity between all gt and tracker classes (not just each class individually) to allow for\n        calculation of metrics such as class confusion matrices. Typically the impact of this on performance is low.\n        \"\"\"\n        # Load raw data.\n        raw_gt_data = self._load_raw_file(tracker, seq, is_gt=True)\n        raw_tracker_data = self._load_raw_file(tracker, seq, is_gt=False)\n        raw_data = {**raw_tracker_data, **raw_gt_data}  # Merges dictionaries\n\n        # Calculate similarities for each timestep.\n        similarity_scores = []\n        for t, (gt_dets_t, tracker_dets_t) in enumerate(zip(raw_data['gt_dets'], raw_data['tracker_dets'])):\n            ious = self._calculate_similarities(gt_dets_t, tracker_dets_t)\n            similarity_scores.append(ious)\n        raw_data['similarity_scores'] = similarity_scores\n        return raw_data\n\n    @staticmethod\n    def _load_simple_text_file(file, time_col=0, id_col=None, remove_negative_ids=False, valid_filter=None,\n                               crowd_ignore_filter=None, convert_filter=None, is_zipped=False, zip_file=None,\n                               force_delimiters=None):\n        \"\"\" Function that loads data which is in a commonly used text file format.\n        Assumes each det is given by one row of a text file.\n        There is no limit to the number or meaning of each column,\n        however one column needs to give the timestep of each det (time_col) which is default col 0.\n\n        The file dialect (deliminator, num cols, etc) is determined automatically.\n        This function automatically separates dets by timestep,\n        and is much faster than alternatives such as np.loadtext or pandas.\n\n        If remove_negative_ids is True and id_col is not None, dets with negative values in id_col are excluded.\n        These are not excluded from ignore data.\n\n        valid_filter can be used to only include certain classes.\n        It is a dict with ints as keys, and lists as values,\n        such that a row is included if \"row[key].lower() is in value\" for all key/value pairs in the dict.\n        If None, all classes are included.\n\n        crowd_ignore_filter can be used to read crowd_ignore regions separately. It has the same format as valid filter.\n\n        convert_filter can be used to convert value read to another format.\n        This is used most commonly to convert classes given as string to a class id.\n        This is a dict such that the key is the column to convert, and the value is another dict giving the mapping.\n\n        Optionally, input files could be a zip of multiple text files for storage efficiency.\n\n        Returns read_data and ignore_data.\n        Each is a dict (with keys as timesteps as strings) of lists (over dets) of lists (over column values).\n        Note that all data is returned as strings, and must be converted to float/int later if needed.\n        Note that timesteps will not be present in the returned dict keys if there are no dets for them\n        \"\"\"\n\n        if remove_negative_ids and id_col is None:\n            raise TrackEvalException('remove_negative_ids is True, but id_col is not given.')\n        if crowd_ignore_filter is None:\n            crowd_ignore_filter = {}\n        if convert_filter is None:\n            convert_filter = {}\n        try:\n            if is_zipped:  # Either open file directly or within a zip.\n                if zip_file is None:\n                    raise TrackEvalException('is_zipped set to True, but no zip_file is given.')\n                archive = zipfile.ZipFile(os.path.join(zip_file), 'r')\n                fp = io.TextIOWrapper(archive.open(file, 'r'))\n            else:\n                fp = open(file)\n            read_data = {}\n            crowd_ignore_data = {}\n            fp.seek(0, os.SEEK_END)\n            # check if file is empty\n            if fp.tell():\n                fp.seek(0)\n                dialect = csv.Sniffer().sniff(fp.readline(), delimiters=force_delimiters)  # Auto determine structure.\n                dialect.skipinitialspace = True  # Deal with extra spaces between columns\n                fp.seek(0)\n                reader = csv.reader(fp, dialect)\n                for row in reader:\n                    try:\n                        # Deal with extra trailing spaces at the end of rows\n                        if row[-1] in '':\n                            row = row[:-1]\n                        timestep = str(int(float(row[time_col])))\n                        # Read ignore regions separately.\n                        is_ignored = False\n                        for ignore_key, ignore_value in crowd_ignore_filter.items():\n                            if row[ignore_key].lower() in ignore_value:\n                                # Convert values in one column (e.g. string to id)\n                                for convert_key, convert_value in convert_filter.items():\n                                    row[convert_key] = convert_value[row[convert_key].lower()]\n                                # Save data separated by timestep.\n                                if timestep in crowd_ignore_data.keys():\n                                    crowd_ignore_data[timestep].append(row)\n                                else:\n                                    crowd_ignore_data[timestep] = [row]\n                                is_ignored = True\n                        if is_ignored:  # if det is an ignore region, it cannot be a normal det.\n                            continue\n                        # Exclude some dets if not valid.\n                        if valid_filter is not None:\n                            for key, value in valid_filter.items():\n                                if row[key].lower() not in value:\n                                    continue\n                        if remove_negative_ids:\n                            if int(float(row[id_col])) < 0:\n                                continue\n                        # Convert values in one column (e.g. string to id)\n                        for convert_key, convert_value in convert_filter.items():\n                            row[convert_key] = convert_value[row[convert_key].lower()]\n                        # Save data separated by timestep.\n                        if timestep in read_data.keys():\n                            read_data[timestep].append(row)\n                        else:\n                            read_data[timestep] = [row]\n                    except Exception:\n                        exc_str_init = 'In file %s the following line cannot be read correctly: \\n' % os.path.basename(\n                            file)\n                        exc_str = ' '.join([exc_str_init]+row)\n                        raise TrackEvalException(exc_str)\n            fp.close()\n        except Exception:\n            print('Error loading file: %s, printing traceback.' % file)\n            traceback.print_exc()\n            raise TrackEvalException(\n                'File %s cannot be read because it is either not present or invalidly formatted' % os.path.basename(\n                    file))\n        return read_data, crowd_ignore_data\n\n    @staticmethod\n    def _calculate_mask_ious(masks1, masks2, is_encoded=False, do_ioa=False):\n        \"\"\" Calculates the IOU (intersection over union) between two arrays of segmentation masks.\n        If is_encoded a run length encoding with pycocotools is assumed as input format, otherwise an input of numpy\n        arrays of the shape (num_masks, height, width) is assumed and the encoding is performed.\n        If do_ioa (intersection over area) , then calculates the intersection over the area of masks1 - this is commonly\n        used to determine if detections are within crowd ignore region.\n        :param masks1:  first set of masks (numpy array of shape (num_masks, height, width) if not encoded,\n                        else pycocotools rle encoded format)\n        :param masks2:  second set of masks (numpy array of shape (num_masks, height, width) if not encoded,\n                        else pycocotools rle encoded format)\n        :param is_encoded: whether the input is in pycocotools rle encoded format\n        :param do_ioa: whether to perform IoA computation\n        :return: the IoU/IoA scores\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        # use pycocotools for run length encoding of masks\n        if not is_encoded:\n            masks1 = mask_utils.encode(np.array(np.transpose(masks1, (1, 2, 0)), order='F'))\n            masks2 = mask_utils.encode(np.array(np.transpose(masks2, (1, 2, 0)), order='F'))\n\n        # use pycocotools for iou computation of rle encoded masks\n        ious = mask_utils.iou(masks1, masks2, [do_ioa]*len(masks2))\n        if len(masks1) == 0 or len(masks2) == 0:\n            ious = np.asarray(ious).reshape(len(masks1), len(masks2))\n        assert (ious >= 0 - np.finfo('float').eps).all()\n        assert (ious <= 1 + np.finfo('float').eps).all()\n\n        return ious\n\n    @staticmethod\n    def _calculate_box_ious(bboxes1, bboxes2, box_format='xywh', do_ioa=False):\n        \"\"\" Calculates the IOU (intersection over union) between two arrays of boxes.\n        Allows variable box formats ('xywh' and 'x0y0x1y1').\n        If do_ioa (intersection over area) , then calculates the intersection over the area of boxes1 - this is commonly\n        used to determine if detections are within crowd ignore region.\n        \"\"\"\n        if box_format in 'xywh':\n            # layout: (x0, y0, w, h)\n            bboxes1 = deepcopy(bboxes1)\n            bboxes2 = deepcopy(bboxes2)\n\n            bboxes1[:, 2] = bboxes1[:, 0] + bboxes1[:, 2]\n            bboxes1[:, 3] = bboxes1[:, 1] + bboxes1[:, 3]\n            bboxes2[:, 2] = bboxes2[:, 0] + bboxes2[:, 2]\n            bboxes2[:, 3] = bboxes2[:, 1] + bboxes2[:, 3]\n        elif box_format not in 'x0y0x1y1':\n            raise (TrackEvalException('box_format %s is not implemented' % box_format))\n\n        # layout: (x0, y0, x1, y1)\n        min_ = np.minimum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])\n        max_ = np.maximum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])\n        intersection = np.maximum(min_[..., 2] - max_[..., 0], 0) * np.maximum(min_[..., 3] - max_[..., 1], 0)\n        area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])\n\n        if do_ioa:\n            ioas = np.zeros_like(intersection)\n            valid_mask = area1 > 0 + np.finfo('float').eps\n            ioas[valid_mask, :] = intersection[valid_mask, :] / area1[valid_mask][:, np.newaxis]\n\n            return ioas\n        else:\n            area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1])\n            union = area1[:, np.newaxis] + area2[np.newaxis, :] - intersection\n            intersection[area1 <= 0 + np.finfo('float').eps, :] = 0\n            intersection[:, area2 <= 0 + np.finfo('float').eps] = 0\n            intersection[union <= 0 + np.finfo('float').eps] = 0\n            union[union <= 0 + np.finfo('float').eps] = 1\n            ious = intersection / union\n            return ious\n\n    @staticmethod\n    def _calculate_euclidean_similarity(dets1, dets2, zero_distance=2.0):\n        \"\"\" Calculates the euclidean distance between two sets of detections, and then converts this into a similarity\n        measure with values between 0 and 1 using the following formula: sim = max(0, 1 - dist/zero_distance).\n        The default zero_distance of 2.0, corresponds to the default used in MOT15_3D, such that a 0.5 similarity\n        threshold corresponds to a 1m distance threshold for TPs.\n        \"\"\"\n        dist = np.linalg.norm(dets1[:, np.newaxis]-dets2[np.newaxis, :], axis=2)\n        sim = np.maximum(0, 1 - dist/zero_distance)\n        return sim\n\n    @staticmethod\n    def _check_unique_ids(data, after_preproc=False):\n        \"\"\"Check the requirement that the tracker_ids and gt_ids are unique per timestep\"\"\"\n        gt_ids = data['gt_ids']\n        tracker_ids = data['tracker_ids']\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(gt_ids, tracker_ids)):\n            if len(tracker_ids_t) > 0:\n                unique_ids, counts = np.unique(tracker_ids_t, return_counts=True)\n                if np.max(counts) != 1:\n                    duplicate_ids = unique_ids[counts > 1]\n                    exc_str_init = 'Tracker predicts the same ID more than once in a single timestep ' \\\n                                   '(seq: %s, frame: %i, ids:' % (data['seq'], t+1)\n                    exc_str = ' '.join([exc_str_init] + [str(d) for d in duplicate_ids]) + ')'\n                    if after_preproc:\n                        exc_str_init += '\\n Note that this error occurred after preprocessing (but not before), ' \\\n                                        'so ids may not be as in file, and something seems wrong with preproc.'\n                    raise TrackEvalException(exc_str)\n            if len(gt_ids_t) > 0:\n                unique_ids, counts = np.unique(gt_ids_t, return_counts=True)\n                if np.max(counts) != 1:\n                    duplicate_ids = unique_ids[counts > 1]\n                    exc_str_init = 'Ground-truth has the same ID more than once in a single timestep ' \\\n                                   '(seq: %s, frame: %i, ids:' % (data['seq'], t+1)\n                    exc_str = ' '.join([exc_str_init] + [str(d) for d in duplicate_ids]) + ')'\n                    if after_preproc:\n                        exc_str_init += '\\n Note that this error occurred after preprocessing (but not before), ' \\\n                                        'so ids may not be as in file, and something seems wrong with preproc.'\n                    raise TrackEvalException(exc_str)\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/bdd100k.py",
    "content": "\nimport os\nimport json\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ..utils import TrackEvalException\nfrom ._base_dataset import _BaseDataset\nfrom .. import utils\nfrom .. import _timing\n\n\nclass BDD100K(_BaseDataset):\n    \"\"\"Dataset class for BDD100K tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/bdd100k/bdd100k_val'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/bdd100k/bdd100k_val'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': ['pedestrian', 'rider', 'car', 'bus', 'truck', 'train', 'motorcycle', 'bicycle'],\n            # Valid: ['pedestrian', 'rider', 'car', 'bus', 'truck', 'train', 'motorcycle', 'bicycle']\n            'SPLIT_TO_EVAL': 'val',  # Valid: 'training', 'val',\n            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())\n        self.gt_fol = self.config['GT_FOLDER']\n        self.tracker_fol = self.config['TRACKERS_FOLDER']\n        self.should_classes_combine = True\n        self.use_super_categories = True\n\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']\n\n        # Get classes to eval\n        self.valid_classes = ['pedestrian', 'rider', 'car', 'bus', 'truck', 'train', 'motorcycle', 'bicycle']\n        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n                           for cls in self.config['CLASSES_TO_EVAL']]\n        if not all(self.class_list):\n            raise TrackEvalException('Attempted to evaluate an invalid class. Only classes [pedestrian, rider, car, '\n                                     'bus, truck, train, motorcycle, bicycle] are valid.')\n        self.super_categories = {\"HUMAN\": [cls for cls in [\"pedestrian\", \"rider\"] if cls in self.class_list],\n                                 \"VEHICLE\": [cls for cls in [\"car\", \"truck\", \"bus\", \"train\"] if cls in self.class_list],\n                                 \"BIKE\": [cls for cls in [\"motorcycle\", \"bicycle\"] if cls in self.class_list]}\n        self.distractor_classes = ['other person', 'trailer', 'other vehicle']\n        self.class_name_to_class_id = {'pedestrian': 1, 'rider': 2, 'other person': 3, 'car': 4, 'bus': 5, 'truck': 6,\n                                       'train': 7, 'trailer': 8, 'other vehicle': 9, 'motorcycle': 10, 'bicycle': 11}\n\n        # Get sequences to eval\n        self.seq_list = []\n        self.seq_lengths = {}\n\n        self.seq_list = [seq_file.replace('.json', '') for seq_file in os.listdir(self.gt_fol)]\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n        for tracker in self.tracker_list:\n            for seq in self.seq_list:\n                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.json')\n                if not os.path.isfile(curr_file):\n                    print('Tracker file not found: ' + curr_file)\n                    raise TrackEvalException(\n                        'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(\n                            curr_file))\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the BDD100K format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        \"\"\"\n        # File location\n        if is_gt:\n            file = os.path.join(self.gt_fol, seq + '.json')\n        else:\n            file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.json')\n\n        with open(file) as f:\n            data = json.load(f)\n\n        # sort data by frame index\n        data = sorted(data, key=lambda x: x['index'])\n\n        # check sequence length\n        if is_gt:\n            self.seq_lengths[seq] = len(data)\n            num_timesteps = len(data)\n        else:\n            num_timesteps = self.seq_lengths[seq]\n            if num_timesteps != len(data):\n                raise TrackEvalException('Number of ground truth and tracker timesteps do not match for sequence %s'\n                                         % seq)\n\n        # Convert data to required format\n        data_keys = ['ids', 'classes', 'dets']\n        if is_gt:\n            data_keys += ['gt_crowd_ignore_regions']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n        for t in range(num_timesteps):\n            ig_ids = []\n            keep_ids = []\n            for i in range(len(data[t]['labels'])):\n                ann = data[t]['labels'][i]\n                if is_gt and (ann['category'] in self.distractor_classes or 'attributes' in ann.keys()\n                              and ann['attributes']['Crowd']):\n                    ig_ids.append(i)\n                else:\n                    keep_ids.append(i)\n\n            if keep_ids:\n                raw_data['dets'][t] = np.atleast_2d([[data[t]['labels'][i]['box2d']['x1'],\n                                                      data[t]['labels'][i]['box2d']['y1'],\n                                                      data[t]['labels'][i]['box2d']['x2'],\n                                                      data[t]['labels'][i]['box2d']['y2']\n                                                      ] for i in keep_ids]).astype(float)\n                raw_data['ids'][t] = np.atleast_1d([data[t]['labels'][i]['id'] for i in keep_ids]).astype(int)\n                raw_data['classes'][t] = np.atleast_1d([self.class_name_to_class_id[data[t]['labels'][i]['category']]\n                                                        for i in keep_ids]).astype(int)\n            else:\n                raw_data['dets'][t] = np.empty((0, 4)).astype(float)\n                raw_data['ids'][t] = np.empty(0).astype(int)\n                raw_data['classes'][t] = np.empty(0).astype(int)\n\n            if is_gt:\n                if ig_ids:\n                    raw_data['gt_crowd_ignore_regions'][t] = np.atleast_2d([[data[t]['labels'][i]['box2d']['x1'],\n                                                                             data[t]['labels'][i]['box2d']['y1'],\n                                                                             data[t]['labels'][i]['box2d']['x2'],\n                                                                             data[t]['labels'][i]['box2d']['y2']\n                                                                             ] for i in ig_ids]).astype(float)\n                else:\n                    raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4)).astype(float)\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'classes': 'gt_classes',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'classes': 'tracker_classes',\n                       'dets': 'tracker_dets'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n        raw_data['num_timesteps'] = num_timesteps\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n\n        BDD100K:\n            In BDD100K, the 4 preproc steps are as follow:\n                1) There are eight classes (pedestrian, rider, car, bus, truck, train, motorcycle, bicycle)\n                    which are evaluated separately.\n                2) For BDD100K there is no removal of matched tracker dets.\n                3) Crowd ignore regions are used to remove unmatched detections.\n                4) No removal of gt dets.\n        \"\"\"\n        cls_id = self.class_name_to_class_id[cls]\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'similarity_scores']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n        for t in range(raw_data['num_timesteps']):\n\n            # Only extract relevant dets for this class for preproc and eval (cls)\n            gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)\n            gt_class_mask = gt_class_mask.astype(np.bool)\n            gt_ids = raw_data['gt_ids'][t][gt_class_mask]\n            gt_dets = raw_data['gt_dets'][t][gt_class_mask]\n\n            tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)\n            tracker_class_mask = tracker_class_mask.astype(np.bool)\n            tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]\n            tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]\n            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]\n\n            # Match tracker and gt dets (with hungarian algorithm)\n            unmatched_indices = np.arange(tracker_ids.shape[0])\n            if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n                matching_scores = similarity_scores.copy()\n                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0\n                match_rows, match_cols = linear_sum_assignment(-matching_scores)\n                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\n                match_cols = match_cols[actually_matched_mask]\n                unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)\n\n            # For unmatched tracker dets, remove those that are greater than 50% within a crowd ignore region.\n            unmatched_tracker_dets = tracker_dets[unmatched_indices, :]\n            crowd_ignore_regions = raw_data['gt_crowd_ignore_regions'][t]\n            intersection_with_ignore_region = self._calculate_box_ious(unmatched_tracker_dets, crowd_ignore_regions,\n                                                                       box_format='x0y0x1y1', do_ioa=True)\n            is_within_crowd_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps,\n                                                   axis=1)\n\n            # Apply preprocessing to remove unwanted tracker dets.\n            to_remove_tracker = unmatched_indices[is_within_crowd_ignore_region]\n            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n            data['gt_ids'][t] = gt_ids\n            data['gt_dets'][t] = gt_dets\n            data['similarity_scores'][t] = similarity_scores\n\n            unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n            num_tracker_dets += len(data['tracker_ids'][t])\n            num_gt_dets += len(data['gt_ids'][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = len(unique_tracker_ids)\n        data['num_gt_ids'] = len(unique_gt_ids)\n        data['num_timesteps'] = raw_data['num_timesteps']\n\n        # Ensure that ids are unique per timestep.\n        self._check_unique_ids(data)\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t, box_format='x0y0x1y1')\n        return similarity_scores\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/davis.py",
    "content": "import os\nimport csv\nimport numpy as np\nfrom ._base_dataset import _BaseDataset\nfrom ..utils import TrackEvalException\nfrom .. import utils\nfrom .. import _timing\n\n\nclass DAVIS(_BaseDataset):\n    \"\"\"Dataset class for DAVIS tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/davis/davis_unsupervised_val/'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/davis/davis_unsupervised_val/'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'SPLIT_TO_EVAL': 'val',  # Valid: 'val', 'train'\n            'CLASSES_TO_EVAL': ['general'],\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n            'SEQMAP_FILE': None,  # Specify seqmap file\n            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n            # '{gt_folder}/Annotations_unsupervised/480p/{seq}'\n            'MAX_DETECTIONS': 0  # Maximum number of allowed detections per sequence (0 for no threshold)\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())\n        # defining a default class since there are no classes in DAVIS\n        self.should_classes_combine = False\n        self.use_super_categories = False\n\n        self.gt_fol = self.config['GT_FOLDER']\n        self.tracker_fol = self.config['TRACKERS_FOLDER']\n\n        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.config['TRACKERS_FOLDER']\n\n        self.max_det = self.config['MAX_DETECTIONS']\n\n        # Get classes to eval\n        self.valid_classes = ['general']\n        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n                           for cls in self.config['CLASSES_TO_EVAL']]\n        if not all(self.class_list):\n            raise TrackEvalException('Attempted to evaluate an invalid class. Only general class is valid.')\n\n        # Get sequences to eval\n        if self.config[\"SEQ_INFO\"]:\n            self.seq_list = list(self.config[\"SEQ_INFO\"].keys())\n            self.seq_lengths = self.config[\"SEQ_INFO\"]\n        elif self.config[\"SEQMAP_FILE\"]:\n            self.seq_list = []\n            seqmap_file = self.config[\"SEQMAP_FILE\"]\n            if not os.path.isfile(seqmap_file):\n                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))\n            with open(seqmap_file) as fp:\n                reader = csv.reader(fp)\n                for i, row in enumerate(reader):\n                    if row[0] == '':\n                        continue\n                    seq = row[0]\n                    self.seq_list.append(seq)\n        else:\n            self.seq_list = os.listdir(self.gt_fol)\n\n        self.seq_lengths = {seq: len(os.listdir(os.path.join(self.gt_fol, seq))) for seq in self.seq_list}\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n        for tracker in self.tracker_list:\n            for seq in self.seq_list:\n                curr_dir = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq)\n                if not os.path.isdir(curr_dir):\n                    print('Tracker directory not found: ' + curr_dir)\n                    raise TrackEvalException('Tracker directory not found: ' +\n                                             os.path.join(tracker, self.tracker_sub_fol, seq))\n                tr_timesteps = len(os.listdir(curr_dir))\n                if self.seq_lengths[seq] != tr_timesteps:\n                    raise TrackEvalException('GT folder and tracker folder have a different number'\n                                             'timesteps for tracker %s and sequence %s' % (tracker, seq))\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the DAVIS format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets]: list (for each timestep) of lists of detections.\n        [masks_void]: list of masks with void pixels (pixels to be ignored during evaluation)\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n        from PIL import Image\n\n        # File location\n        if is_gt:\n            seq_dir = os.path.join(self.gt_fol, seq)\n        else:\n            seq_dir = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq)\n\n        num_timesteps = self.seq_lengths[seq]\n        data_keys = ['ids', 'dets', 'masks_void']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n\n        # read frames\n        frames = [os.path.join(seq_dir, im_name) for im_name in sorted(os.listdir(seq_dir))]\n\n        id_list = []\n        for t in range(num_timesteps):\n            frame = np.array(Image.open(frames[t]))\n            if is_gt:\n                void = frame == 255\n                frame[void] = 0\n                raw_data['masks_void'][t] = mask_utils.encode(np.asfortranarray(void.astype(np.uint8)))\n            id_values = np.unique(frame)\n            id_values = id_values[id_values != 0]\n            id_list += list(id_values)\n            tmp = np.ones((len(id_values), *frame.shape))\n            tmp = tmp * id_values[:, None, None]\n            masks = np.array(tmp == frame[None, ...]).astype(np.uint8)\n            raw_data['dets'][t] = mask_utils.encode(np.array(np.transpose(masks, (1, 2, 0)), order='F'))\n            raw_data['ids'][t] = id_values.astype(int)\n        num_objects = len(np.unique(id_list))\n\n        if not is_gt and num_objects > self.max_det > 0:\n            raise Exception('Number of proposals (%i) for sequence %s exceeds number of maximum allowed proposals (%i).'\n                            % (num_objects, seq, self.max_det))\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'dets': 'tracker_dets'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n        raw_data[\"num_timesteps\"] = num_timesteps\n        raw_data['mask_shape'] = np.array(Image.open(frames[0])).shape\n        if is_gt:\n            raw_data['num_gt_ids'] = num_objects\n        else:\n            raw_data['num_tracker_ids'] = num_objects\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detection masks.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n\n        DAVIS:\n            In DAVIS, the 4 preproc steps are as follow:\n                1) There are no classes, all detections are evaluated jointly\n                2) No matched tracker detections are removed.\n                3) No unmatched tracker detections are removed.\n                4) There are no ground truth detections (e.g. those of distractor classes) to be removed.\n            Preprocessing special to DAVIS: Pixels which are marked as void in the ground truth are set to zero in the\n                tracker detections since they are not considered during evaluation.\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'similarity_scores']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        num_gt_dets = 0\n        num_tracker_dets = 0\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_timesteps = raw_data['num_timesteps']\n\n        # count detections\n        for t in range(num_timesteps):\n            num_gt_dets += len(raw_data['gt_dets'][t])\n            num_tracker_dets += len(raw_data['tracker_dets'][t])\n            unique_gt_ids += list(np.unique(raw_data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(raw_data['tracker_ids'][t]))\n\n        data['gt_ids'] = raw_data['gt_ids']\n        data['gt_dets'] = raw_data['gt_dets']\n        data['similarity_scores'] = raw_data['similarity_scores']\n        data['tracker_ids'] = raw_data['tracker_ids']\n\n        # set void pixels in tracker detections to zero\n        for t in range(num_timesteps):\n            void_mask = raw_data['masks_void'][t]\n            if mask_utils.area(void_mask) > 0:\n                void_mask_ious = np.atleast_1d(mask_utils.iou(raw_data['tracker_dets'][t], [void_mask], [False]))\n                if void_mask_ious.any():\n                    rows, columns = np.where(void_mask_ious > 0)\n                    for r in rows:\n                        det = mask_utils.decode(raw_data['tracker_dets'][t][r])\n                        void = mask_utils.decode(void_mask).astype(np.bool)\n                        det[void] = 0\n                        det = mask_utils.encode(np.array(det, order='F').astype(np.uint8))\n                        raw_data['tracker_dets'][t][r] = det\n        data['tracker_dets'] = raw_data['tracker_dets']\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = raw_data['num_tracker_ids']\n        data['num_gt_ids'] = raw_data['num_gt_ids']\n        data['mask_shape'] = raw_data['mask_shape']\n        data['num_timesteps'] = num_timesteps\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_mask_ious(gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False)\n        return similarity_scores\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/head_tracking_challenge.py",
    "content": "import os\nimport csv\nimport configparser\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_dataset import _BaseDataset\nfrom .. import utils\nfrom .. import _timing\nfrom ..utils import TrackEvalException\n\n\nclass HeadTrackingChallenge(_BaseDataset):\n    \"\"\"Dataset class for Head Tracking Challenge - 2D bounding box tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']\n            'BENCHMARK': 'HT',  # Valid: 'HT'. Refers to \"Head Tracking or the dataset CroHD\"\n            'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'\n            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'DO_PREPROC': True,  # Whether to perform preprocessing (never done for MOT15)\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/seqmaps)\n            'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/benchmark-split_to_eval)\n            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n            'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt',  # '{gt_folder}/{seq}/gt/gt.txt'\n            'SKIP_SPLIT_FOL': False,  # If False, data is in GT_FOLDER/BENCHMARK-SPLIT_TO_EVAL/ and in\n                                      # TRACKERS_FOLDER/BENCHMARK-SPLIT_TO_EVAL/tracker/\n                                      # If True, then the middle 'benchmark-split' folder is skipped for both.\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())\n\n        self.benchmark = self.config['BENCHMARK']\n        gt_set = self.config['BENCHMARK'] + '-' + self.config['SPLIT_TO_EVAL']\n        self.gt_set = gt_set\n        if not self.config['SKIP_SPLIT_FOL']:\n            split_fol = gt_set\n        else:\n            split_fol = ''\n        self.gt_fol = os.path.join(self.config['GT_FOLDER'], split_fol)\n        self.tracker_fol = os.path.join(self.config['TRACKERS_FOLDER'], split_fol)\n        self.should_classes_combine = False\n        self.use_super_categories = False\n        self.data_is_zipped = self.config['INPUT_AS_ZIP']\n        self.do_preproc = self.config['DO_PREPROC']\n\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']\n\n        # Get classes to eval\n        self.valid_classes = ['pedestrian']\n        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n                           for cls in self.config['CLASSES_TO_EVAL']]\n        if not all(self.class_list):\n            raise TrackEvalException('Attempted to evaluate an invalid class. Only pedestrian class is valid.')\n        self.class_name_to_class_id = {'pedestrian': 1, 'static': 2, 'ignore': 3, 'person_on_vehicle': 4}\n        self.valid_class_numbers = list(self.class_name_to_class_id.values())\n\n        # Get sequences to eval and check gt files exist\n        self.seq_list, self.seq_lengths = self._get_seq_info()\n        if len(self.seq_list) < 1:\n            raise TrackEvalException('No sequences are selected to be evaluated.')\n\n        # Check gt files exist\n        for seq in self.seq_list:\n            if not self.data_is_zipped:\n                curr_file = self.config[\"GT_LOC_FORMAT\"].format(gt_folder=self.gt_fol, seq=seq)\n                if not os.path.isfile(curr_file):\n                    print('GT file not found ' + curr_file)\n                    raise TrackEvalException('GT file not found for sequence: ' + seq)\n        if self.data_is_zipped:\n            curr_file = os.path.join(self.gt_fol, 'data.zip')\n            if not os.path.isfile(curr_file):\n                print('GT file not found ' + curr_file)\n                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n        for tracker in self.tracker_list:\n            if self.data_is_zipped:\n                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')\n                if not os.path.isfile(curr_file):\n                    print('Tracker file not found: ' + curr_file)\n                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))\n            else:\n                for seq in self.seq_list:\n                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')\n                    if not os.path.isfile(curr_file):\n                        print('Tracker file not found: ' + curr_file)\n                        raise TrackEvalException(\n                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(\n                                curr_file))\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _get_seq_info(self):\n        seq_list = []\n        seq_lengths = {}\n        if self.config[\"SEQ_INFO\"]:\n            seq_list = list(self.config[\"SEQ_INFO\"].keys())\n            seq_lengths = self.config[\"SEQ_INFO\"]\n\n            # If sequence length is 'None' tries to read sequence length from .ini files.\n            for seq, seq_length in seq_lengths.items():\n                if seq_length is None:\n                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')\n                    if not os.path.isfile(ini_file):\n                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))\n                    ini_data = configparser.ConfigParser()\n                    ini_data.read(ini_file)\n                    seq_lengths[seq] = int(ini_data['Sequence']['seqLength'])\n\n        else:\n            if self.config[\"SEQMAP_FILE\"]:\n                seqmap_file = self.config[\"SEQMAP_FILE\"]\n            else:\n                if self.config[\"SEQMAP_FOLDER\"] is None:\n                    seqmap_file = os.path.join(self.config['GT_FOLDER'], 'seqmaps', self.gt_set + '.txt')\n                else:\n                    seqmap_file = os.path.join(self.config[\"SEQMAP_FOLDER\"], self.gt_set + '.txt')\n            if not os.path.isfile(seqmap_file):\n                print('no seqmap found: ' + seqmap_file)\n                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))\n            with open(seqmap_file) as fp:\n                reader = csv.reader(fp)\n                for i, row in enumerate(reader):\n                    if i == 0 or row[0] == '':\n                        continue\n                    seq = row[0]\n                    seq_list.append(seq)\n                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')\n                    if not os.path.isfile(ini_file):\n                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))\n                    ini_data = configparser.ConfigParser()\n                    ini_data.read(ini_file)\n                    seq_lengths[seq] = int(ini_data['Sequence']['seqLength'])\n        return seq_list, seq_lengths\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the MOT Challenge 2D box format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.\n        [gt_extras] : list (for each timestep) of dicts (for each extra) of 1D NDArrays (for each det).\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        \"\"\"\n        # File location\n        if self.data_is_zipped:\n            if is_gt:\n                zip_file = os.path.join(self.gt_fol, 'data.zip')\n            else:\n                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')\n            file = seq + '.txt'\n        else:\n            zip_file = None\n            if is_gt:\n                file = self.config[\"GT_LOC_FORMAT\"].format(gt_folder=self.gt_fol, seq=seq)\n            else:\n                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')\n\n        # Load raw data from text file\n        read_data, ignore_data = self._load_simple_text_file(file, is_zipped=self.data_is_zipped, zip_file=zip_file)\n\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq]\n        data_keys = ['ids', 'classes', 'dets']\n        if is_gt:\n            data_keys += ['gt_crowd_ignore_regions', 'gt_extras']\n        else:\n            data_keys += ['tracker_confidences']\n\n        if self.benchmark == 'HT':\n            data_keys += ['visibility']\n            data_keys += ['gt_conf']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n\n        # Check for any extra time keys\n        current_time_keys = [str( t+ 1) for t in range(num_timesteps)]\n        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]\n        if len(extra_time_keys) > 0:\n            if is_gt:\n                text = 'Ground-truth'\n            else:\n                text = 'Tracking'\n            raise TrackEvalException(\n                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(\n                    [str(x) + ', ' for x in extra_time_keys]))\n\n        for t in range(num_timesteps):\n            time_key = str(t+1)\n            if time_key in read_data.keys():\n                try:\n                    time_data = np.asarray(read_data[time_key], dtype=np.float)\n                except ValueError:\n                    if is_gt:\n                        raise TrackEvalException(\n                            'Cannot convert gt data for sequence %s to float. Is data corrupted?' % seq)\n                    else:\n                        raise TrackEvalException(\n                            'Cannot convert tracking data from tracker %s, sequence %s to float. Is data corrupted?' % (\n                                tracker, seq))\n                try:\n                    raw_data['dets'][t] = np.atleast_2d(time_data[:, 2:6])\n                    raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)\n                except IndexError:\n                    if is_gt:\n                        err = 'Cannot load gt data from sequence %s, because there is not enough ' \\\n                              'columns in the data.' % seq\n                        raise TrackEvalException(err)\n                    else:\n                        err = 'Cannot load tracker data from tracker %s, sequence %s, because there is not enough ' \\\n                              'columns in the data.' % (tracker, seq)\n                        raise TrackEvalException(err)\n                if time_data.shape[1] >= 8:\n                    raw_data['gt_conf'][t] = np.atleast_1d(time_data[:, 6]).astype(float)\n                    raw_data['visibility'][t] = np.atleast_1d(time_data[:, 8]).astype(float)\n                    raw_data['classes'][t] = np.atleast_1d(time_data[:, 7]).astype(int)\n                else:\n                    if not is_gt:\n                        raw_data['classes'][t] = np.ones_like(raw_data['ids'][t])\n                    else:\n                        raise TrackEvalException(\n                            'GT data is not in a valid format, there is not enough rows in seq %s, timestep %i.' % (\n                                seq, t))\n                if is_gt:\n                    gt_extras_dict = {'zero_marked': np.atleast_1d(time_data[:, 6].astype(int))}\n                    raw_data['gt_extras'][t] = gt_extras_dict\n                else:\n                    raw_data['tracker_confidences'][t] = np.atleast_1d(time_data[:, 6])\n            else:\n                raw_data['dets'][t] = np.empty((0, 4))\n                raw_data['ids'][t] = np.empty(0).astype(int)\n                raw_data['classes'][t] = np.empty(0).astype(int)\n                if is_gt:\n                    gt_extras_dict = {'zero_marked': np.empty(0)}\n                    raw_data['gt_extras'][t] = gt_extras_dict\n                else:\n                    raw_data['tracker_confidences'][t] = np.empty(0)\n            if is_gt:\n                raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4))\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'classes': 'gt_classes',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'classes': 'tracker_classes',\n                       'dets': 'tracker_dets'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n        raw_data['num_timesteps'] = num_timesteps\n        raw_data['seq'] = seq\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n\n        MOT Challenge:\n            In MOT Challenge, the 4 preproc steps are as follow:\n                1) There is only one class (pedestrian) to be evaluated, but all other classes are used for preproc.\n                2) Predictions are matched against all gt boxes (regardless of class), those matching with distractor\n                    objects are removed.\n                3) There is no crowd ignore regions.\n                4) All gt dets except pedestrian are removed, also removes pedestrian gt dets marked with zero_marked.\n        \"\"\"\n        # Check that input data has unique ids\n        self._check_unique_ids(raw_data)\n\n        # 'static': 2, 'ignore': 3, 'person_on_vehicle':\n\n        distractor_class_names = ['static', 'ignore', 'person_on_vehicle']\n\n        distractor_classes = [self.class_name_to_class_id[x] for x in distractor_class_names]\n        cls_id = self.class_name_to_class_id[cls]\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences',\n                     'similarity_scores', 'gt_visibility']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n        for t in range(raw_data['num_timesteps']):\n\n            # Get all data\n            gt_ids = raw_data['gt_ids'][t]\n            gt_dets = raw_data['gt_dets'][t]\n            gt_classes = raw_data['gt_classes'][t]\n            gt_visibility = raw_data['visibility'][t]\n            gt_conf = raw_data['gt_conf'][t]\n\n            gt_zero_marked = raw_data['gt_extras'][t]['zero_marked']\n\n            tracker_ids = raw_data['tracker_ids'][t]\n            tracker_dets = raw_data['tracker_dets'][t]\n            tracker_classes = raw_data['tracker_classes'][t]\n            tracker_confidences = raw_data['tracker_confidences'][t]\n            similarity_scores = raw_data['similarity_scores'][t]\n\n            # Evaluation is ONLY valid for pedestrian class\n            if len(tracker_classes) > 0 and np.max(tracker_classes) > 1:\n                raise TrackEvalException(\n                    'Evaluation is only valid for pedestrian class. Non pedestrian class (%i) found in sequence %s at '\n                    'timestep %i.' % (np.max(tracker_classes), raw_data['seq'], t))\n\n            # Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets\n            # which are labeled as belonging to a distractor class.\n            to_remove_tracker = np.array([], np.int)\n            if self.do_preproc and self.benchmark != 'MOT15' and gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n\n                # Check all classes are valid:\n                invalid_classes = np.setdiff1d(np.unique(gt_classes), self.valid_class_numbers)\n                if len(invalid_classes) > 0:\n                    print(' '.join([str(x) for x in invalid_classes]))\n                    raise(TrackEvalException('Attempting to evaluate using invalid gt classes. '\n                                             'This warning only triggers if preprocessing is performed, '\n                                             'e.g. not for MOT15 or where prepropressing is explicitly disabled. '\n                                             'Please either check your gt data, or disable preprocessing. '\n                                             'The following invalid classes were found in timestep ' + str(t) + ': ' +\n                                             ' '.join([str(x) for x in invalid_classes])))\n\n                matching_scores = similarity_scores.copy()\n\n                matching_scores[matching_scores < 0.4 - np.finfo('float').eps] = 0\n\n                match_rows, match_cols = linear_sum_assignment(-matching_scores)\n                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\n                match_rows = match_rows[actually_matched_mask]\n                match_cols = match_cols[actually_matched_mask]\n\n                is_distractor_class = np.logical_not(np.isin(gt_classes[match_rows], cls_id))\n                if self.benchmark == 'HT':\n                    is_invisible_class = gt_visibility[match_rows] < np.finfo('float').eps\n                    low_conf_class = gt_conf[match_rows] < np.finfo('float').eps\n                    are_distractors = np.logical_or(is_invisible_class, is_distractor_class, low_conf_class)\n                    to_remove_tracker = match_cols[are_distractors]\n                else:\n                    to_remove_tracker = match_cols[is_distractor_class]\n\n            # Apply preprocessing to remove all unwanted tracker dets.\n            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)\n            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n            # Remove gt detections marked as to remove (zero marked), and also remove gt detections not in pedestrian\n            if self.do_preproc and self.benchmark == 'HT':\n                gt_to_keep_mask = (np.not_equal(gt_zero_marked, 0)) & \\\n                                  (np.equal(gt_classes, cls_id)) & \\\n                                  (gt_visibility > 0.) & \\\n                                  (gt_conf > 0.)\n\n            else:\n                # There are no classes for MOT15\n                gt_to_keep_mask = np.not_equal(gt_zero_marked, 0)\n            data['gt_ids'][t] = gt_ids[gt_to_keep_mask]\n            data['gt_dets'][t] = gt_dets[gt_to_keep_mask, :]\n            data['similarity_scores'][t] = similarity_scores[gt_to_keep_mask]\n            data['gt_visibility'][t] = gt_visibility # No mask!\n\n\n            unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n            num_tracker_dets += len(data['tracker_ids'][t])\n            num_gt_dets += len(data['gt_ids'][t])\n\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = len(unique_tracker_ids)\n        data['num_gt_ids'] = len(unique_gt_ids)\n        data['num_timesteps'] = raw_data['num_timesteps']\n        data['seq'] = raw_data['seq']\n\n        # Ensure again that ids are unique per timestep after preproc.\n        self._check_unique_ids(data, after_preproc=True)\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t, box_format='xywh')\n        return similarity_scores\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/kitti_2d_box.py",
    "content": "\nimport os\nimport csv\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_dataset import _BaseDataset\nfrom .. import utils\nfrom ..utils import TrackEvalException\nfrom .. import _timing\n\n\nclass Kitti2DBox(_BaseDataset):\n    \"\"\"Dataset class for KITTI 2D bounding box tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/kitti/kitti_2d_box_train'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_2d_box_train/'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': ['car', 'pedestrian'],  # Valid: ['car', 'pedestrian']\n            'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val', 'training_minus_val', 'test'\n            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())\n        self.gt_fol = self.config['GT_FOLDER']\n        self.tracker_fol = self.config['TRACKERS_FOLDER']\n        self.should_classes_combine = False\n        self.use_super_categories = False\n        self.data_is_zipped = self.config['INPUT_AS_ZIP']\n\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']\n\n        self.max_occlusion = 2\n        self.max_truncation = 0\n        self.min_height = 25\n\n        # Get classes to eval\n        self.valid_classes = ['car', 'pedestrian']\n        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n                           for cls in self.config['CLASSES_TO_EVAL']]\n        if not all(self.class_list):\n            raise TrackEvalException('Attempted to evaluate an invalid class. Only classes [car, pedestrian] are valid.')\n        self.class_name_to_class_id = {'car': 1, 'van': 2, 'truck': 3, 'pedestrian': 4, 'person': 5,  # person sitting\n                                       'cyclist': 6, 'tram': 7, 'misc': 8, 'dontcare': 9, 'car_2': 1}\n\n        # Get sequences to eval and check gt files exist\n        self.seq_list = []\n        self.seq_lengths = {}\n        seqmap_name = 'evaluate_tracking.seqmap.' + self.config['SPLIT_TO_EVAL']\n        seqmap_file = os.path.join(self.gt_fol, seqmap_name)\n        if not os.path.isfile(seqmap_file):\n            raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))\n        with open(seqmap_file) as fp:\n            dialect = csv.Sniffer().sniff(fp.read(1024))\n            fp.seek(0)\n            reader = csv.reader(fp, dialect)\n            for row in reader:\n                if len(row) >= 4:\n                    seq = row[0]\n                    self.seq_list.append(seq)\n                    self.seq_lengths[seq] = int(row[3])\n                    if not self.data_is_zipped:\n                        curr_file = os.path.join(self.gt_fol, 'label_02', seq + '.txt')\n                        if not os.path.isfile(curr_file):\n                            raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))\n            if self.data_is_zipped:\n                curr_file = os.path.join(self.gt_fol, 'data.zip')\n                if not os.path.isfile(curr_file):\n                    raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n        for tracker in self.tracker_list:\n            if self.data_is_zipped:\n                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')\n                if not os.path.isfile(curr_file):\n                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))\n            else:\n                for seq in self.seq_list:\n                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')\n                    if not os.path.isfile(curr_file):\n                        raise TrackEvalException(\n                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(\n                                curr_file))\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the kitti 2D box format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.\n        [gt_extras] : list (for each timestep) of dicts (for each extra) of 1D NDArrays (for each det).\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        \"\"\"\n        # File location\n        if self.data_is_zipped:\n            if is_gt:\n                zip_file = os.path.join(self.gt_fol, 'data.zip')\n            else:\n                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')\n            file = seq + '.txt'\n        else:\n            zip_file = None\n            if is_gt:\n                file = os.path.join(self.gt_fol, 'label_02', seq + '.txt')\n            else:\n                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')\n\n        # Ignore regions\n        if is_gt:\n            crowd_ignore_filter = {2: ['dontcare']}\n        else:\n            crowd_ignore_filter = None\n\n        # Valid classes\n        valid_filter = {2: [x for x in self.class_list]}\n        if is_gt:\n            if 'car' in self.class_list:\n                valid_filter[2].append('van')\n            if 'pedestrian' in self.class_list:\n                valid_filter[2] += ['person']\n\n        # Convert kitti class strings to class ids\n        convert_filter = {2: self.class_name_to_class_id}\n\n        # Load raw data from text file\n        read_data, ignore_data = self._load_simple_text_file(file, time_col=0, id_col=1, remove_negative_ids=True,\n                                                             valid_filter=valid_filter,\n                                                             crowd_ignore_filter=crowd_ignore_filter,\n                                                             convert_filter=convert_filter,\n                                                             is_zipped=self.data_is_zipped, zip_file=zip_file)\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq]\n        data_keys = ['ids', 'classes', 'dets']\n        if is_gt:\n            data_keys += ['gt_crowd_ignore_regions', 'gt_extras']\n        else:\n            data_keys += ['tracker_confidences']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n\n        # Check for any extra time keys\n        current_time_keys = [str(t) for t in range(num_timesteps)]\n        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]\n        if len(extra_time_keys) > 0:\n            if is_gt:\n                text = 'Ground-truth'\n            else:\n                text = 'Tracking'\n            raise TrackEvalException(\n                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(\n                    [str(x) + ', ' for x in extra_time_keys]))\n\n        for t in range(num_timesteps):\n            time_key = str(t)\n            if time_key in read_data.keys():\n                time_data = np.asarray(read_data[time_key], dtype=np.float)\n                raw_data['dets'][t] = np.atleast_2d(time_data[:, 6:10])\n                raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)\n                raw_data['classes'][t] = np.atleast_1d(time_data[:, 2]).astype(int)\n                if is_gt:\n                    gt_extras_dict = {'truncation': np.atleast_1d(time_data[:, 3].astype(int)),\n                                      'occlusion': np.atleast_1d(time_data[:, 4].astype(int))}\n                    raw_data['gt_extras'][t] = gt_extras_dict\n                else:\n                    if time_data.shape[1] > 17:\n                        raw_data['tracker_confidences'][t] = np.atleast_1d(time_data[:, 17])\n                    else:\n                        raw_data['tracker_confidences'][t] = np.ones(time_data.shape[0])\n            else:\n                raw_data['dets'][t] = np.empty((0, 4))\n                raw_data['ids'][t] = np.empty(0).astype(int)\n                raw_data['classes'][t] = np.empty(0).astype(int)\n                if is_gt:\n                    gt_extras_dict = {'truncation': np.empty(0),\n                                      'occlusion': np.empty(0)}\n                    raw_data['gt_extras'][t] = gt_extras_dict\n                else:\n                    raw_data['tracker_confidences'][t] = np.empty(0)\n            if is_gt:\n                if time_key in ignore_data.keys():\n                    time_ignore = np.asarray(ignore_data[time_key], dtype=np.float)\n                    raw_data['gt_crowd_ignore_regions'][t] = np.atleast_2d(time_ignore[:, 6:10])\n                else:\n                    raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4))\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'classes': 'gt_classes',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'classes': 'tracker_classes',\n                       'dets': 'tracker_dets'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n        raw_data['num_timesteps'] = num_timesteps\n        raw_data['seq'] = seq\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n\n        KITTI:\n            In KITTI, the 4 preproc steps are as follow:\n                1) There are two classes (pedestrian and car) which are evaluated separately.\n                2) For the pedestrian class, the 'person' class is distractor objects (people sitting).\n                    For the car class, the 'van' class are distractor objects.\n                    GT boxes marked as having occlusion level > 2 or truncation level > 0 are also treated as\n                        distractors.\n                3) Crowd ignore regions are used to remove unmatched detections. Also unmatched detections with\n                    height <= 25 pixels are removed.\n                4) Distractor gt dets (including truncated and occluded) are removed.\n        \"\"\"\n        if cls == 'pedestrian':\n            distractor_classes = [self.class_name_to_class_id['person']]\n        elif cls == 'car':\n            distractor_classes = [self.class_name_to_class_id['van']]\n        else:\n            raise (TrackEvalException('Class %s is not evaluatable' % cls))\n        cls_id = self.class_name_to_class_id[cls]\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n        for t in range(raw_data['num_timesteps']):\n\n            # Only extract relevant dets for this class for preproc and eval (cls + distractor classes)\n            gt_class_mask = np.sum([raw_data['gt_classes'][t] == c for c in [cls_id] + distractor_classes], axis=0)\n            gt_class_mask = gt_class_mask.astype(np.bool)\n            gt_ids = raw_data['gt_ids'][t][gt_class_mask]\n            gt_dets = raw_data['gt_dets'][t][gt_class_mask]\n            gt_classes = raw_data['gt_classes'][t][gt_class_mask]\n            gt_occlusion = raw_data['gt_extras'][t]['occlusion'][gt_class_mask]\n            gt_truncation = raw_data['gt_extras'][t]['truncation'][gt_class_mask]\n\n            tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)\n            tracker_class_mask = tracker_class_mask.astype(np.bool)\n            tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]\n            tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]\n            tracker_confidences = raw_data['tracker_confidences'][t][tracker_class_mask]\n            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]\n\n            # Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets\n            # which are labeled as truncated, occluded, or belonging to a distractor class.\n            to_remove_matched = np.array([], np.int)\n            unmatched_indices = np.arange(tracker_ids.shape[0])\n            if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n                matching_scores = similarity_scores.copy()\n                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0\n                match_rows, match_cols = linear_sum_assignment(-matching_scores)\n                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\n                match_rows = match_rows[actually_matched_mask]\n                match_cols = match_cols[actually_matched_mask]\n\n                is_distractor_class = np.isin(gt_classes[match_rows], distractor_classes)\n                is_occluded_or_truncated = np.logical_or(\n                    gt_occlusion[match_rows] > self.max_occlusion + np.finfo('float').eps,\n                    gt_truncation[match_rows] > self.max_truncation + np.finfo('float').eps)\n                to_remove_matched = np.logical_or(is_distractor_class, is_occluded_or_truncated)\n                to_remove_matched = match_cols[to_remove_matched]\n                unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)\n\n            # For unmatched tracker dets, also remove those smaller than a minimum height.\n            unmatched_tracker_dets = tracker_dets[unmatched_indices, :]\n            unmatched_heights = unmatched_tracker_dets[:, 3] - unmatched_tracker_dets[:, 1]\n            is_too_small = unmatched_heights <= self.min_height + np.finfo('float').eps\n\n            # For unmatched tracker dets, also remove those that are greater than 50% within a crowd ignore region.\n            crowd_ignore_regions = raw_data['gt_crowd_ignore_regions'][t]\n            intersection_with_ignore_region = self._calculate_box_ious(unmatched_tracker_dets, crowd_ignore_regions,\n                                                                       box_format='x0y0x1y1', do_ioa=True)\n            is_within_crowd_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps, axis=1)\n\n            # Apply preprocessing to remove all unwanted tracker dets.\n            to_remove_unmatched = unmatched_indices[np.logical_or(is_too_small, is_within_crowd_ignore_region)]\n            to_remove_tracker = np.concatenate((to_remove_matched, to_remove_unmatched), axis=0)\n            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)\n            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n            # Also remove gt dets that were only useful for preprocessing and are not needed for evaluation.\n            # These are those that are occluded, truncated and from distractor objects.\n            gt_to_keep_mask = (np.less_equal(gt_occlusion, self.max_occlusion)) & \\\n                              (np.less_equal(gt_truncation, self.max_truncation)) & \\\n                              (np.equal(gt_classes, cls_id))\n            data['gt_ids'][t] = gt_ids[gt_to_keep_mask]\n            data['gt_dets'][t] = gt_dets[gt_to_keep_mask, :]\n            data['similarity_scores'][t] = similarity_scores[gt_to_keep_mask]\n\n            unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n            num_tracker_dets += len(data['tracker_ids'][t])\n            num_gt_dets += len(data['gt_ids'][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = len(unique_tracker_ids)\n        data['num_gt_ids'] = len(unique_gt_ids)\n        data['num_timesteps'] = raw_data['num_timesteps']\n        data['seq'] = raw_data['seq']\n\n        # Ensure that ids are unique per timestep.\n        self._check_unique_ids(data)\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t, box_format='x0y0x1y1')\n        return similarity_scores\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/kitti_mots.py",
    "content": "import os\nimport csv\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_dataset import _BaseDataset\nfrom .. import utils\nfrom .. import _timing\nfrom ..utils import TrackEvalException\n\n\nclass KittiMOTS(_BaseDataset):\n    \"\"\"Dataset class for KITTI MOTS tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/kitti/kitti_mots_val'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_mots_val'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': ['car', 'pedestrian'],  # Valid: ['car', 'pedestrian']\n            'SPLIT_TO_EVAL': 'val',  # Valid: 'training', 'val'\n            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER)\n            'SEQMAP_FILE': None,    # Directly specify seqmap file (if none use seqmap_folder/split_to_eval.seqmap)\n            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n            'GT_LOC_FORMAT': '{gt_folder}/label_02/{seq}.txt',  # format of gt localization\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())\n        self.gt_fol = self.config['GT_FOLDER']\n        self.tracker_fol = self.config['TRACKERS_FOLDER']\n        self.split_to_eval = self.config['SPLIT_TO_EVAL']\n        self.should_classes_combine = False\n        self.use_super_categories = False\n        self.data_is_zipped = self.config['INPUT_AS_ZIP']\n\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']\n\n        # Get classes to eval\n        self.valid_classes = ['car', 'pedestrian']\n        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n                           for cls in self.config['CLASSES_TO_EVAL']]\n        if not all(self.class_list):\n            raise TrackEvalException('Attempted to evaluate an invalid class. '\n                                     'Only classes [car, pedestrian] are valid.')\n        self.class_name_to_class_id = {'car': '1', 'pedestrian': '2', 'ignore': '10'}\n\n        # Get sequences to eval and check gt files exist\n        self.seq_list, self.seq_lengths = self._get_seq_info()\n        if len(self.seq_list) < 1:\n            raise TrackEvalException('No sequences are selected to be evaluated.')\n\n        # Check gt files exist\n        for seq in self.seq_list:\n            if not self.data_is_zipped:\n                curr_file = self.config[\"GT_LOC_FORMAT\"].format(gt_folder=self.gt_fol, seq=seq)\n                if not os.path.isfile(curr_file):\n                    print('GT file not found ' + curr_file)\n                    raise TrackEvalException('GT file not found for sequence: ' + seq)\n        if self.data_is_zipped:\n            curr_file = os.path.join(self.gt_fol, 'data.zip')\n            if not os.path.isfile(curr_file):\n                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n        for tracker in self.tracker_list:\n            if self.data_is_zipped:\n                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')\n                if not os.path.isfile(curr_file):\n                    print('Tracker file not found: ' + curr_file)\n                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))\n            else:\n                for seq in self.seq_list:\n                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')\n                    if not os.path.isfile(curr_file):\n                        print('Tracker file not found: ' + curr_file)\n                        raise TrackEvalException(\n                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(\n                                curr_file))\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _get_seq_info(self):\n        seq_list = []\n        seq_lengths = {}\n        seqmap_name = 'evaluate_mots.seqmap.' + self.config['SPLIT_TO_EVAL']\n\n        if self.config[\"SEQ_INFO\"]:\n            seq_list = list(self.config[\"SEQ_INFO\"].keys())\n            seq_lengths = self.config[\"SEQ_INFO\"]\n        else:\n            if self.config[\"SEQMAP_FILE\"]:\n                seqmap_file = self.config[\"SEQMAP_FILE\"]\n            else:\n                if self.config[\"SEQMAP_FOLDER\"] is None:\n                    seqmap_file = os.path.join(self.config['GT_FOLDER'], seqmap_name)\n                else:\n                    seqmap_file = os.path.join(self.config[\"SEQMAP_FOLDER\"], seqmap_name)\n            if not os.path.isfile(seqmap_file):\n                print('no seqmap found: ' + seqmap_file)\n                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))\n            with open(seqmap_file) as fp:\n                reader = csv.reader(fp)\n                for i, _ in enumerate(reader):\n                    dialect = csv.Sniffer().sniff(fp.read(1024))\n                    fp.seek(0)\n                    reader = csv.reader(fp, dialect)\n                    for row in reader:\n                        if len(row) >= 4:\n                            seq = \"%04d\" % int(row[0])\n                            seq_list.append(seq)\n                            seq_lengths[seq] = int(row[3]) + 1\n        return seq_list, seq_lengths\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the KITTI MOTS format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets]: list (for each timestep) of lists of detections.\n        [gt_ignore_region]: list (for each timestep) of masks for the ignore regions\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        # File location\n        if self.data_is_zipped:\n            if is_gt:\n                zip_file = os.path.join(self.gt_fol, 'data.zip')\n            else:\n                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')\n            file = seq + '.txt'\n        else:\n            zip_file = None\n            if is_gt:\n                file = self.config[\"GT_LOC_FORMAT\"].format(gt_folder=self.gt_fol, seq=seq)\n            else:\n                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')\n\n        # Ignore regions\n        if is_gt:\n            crowd_ignore_filter = {2: ['10']}\n        else:\n            crowd_ignore_filter = None\n\n        # Load raw data from text file\n        read_data, ignore_data = self._load_simple_text_file(file, crowd_ignore_filter=crowd_ignore_filter,\n                                                             is_zipped=self.data_is_zipped, zip_file=zip_file,\n                                                             force_delimiters=' ')\n\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq]\n        data_keys = ['ids', 'classes', 'dets']\n        if is_gt:\n            data_keys += ['gt_ignore_region']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n\n        # Check for any extra time keys\n        current_time_keys = [str(t) for t in range(num_timesteps)]\n        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]\n        if len(extra_time_keys) > 0:\n            if is_gt:\n                text = 'Ground-truth'\n            else:\n                text = 'Tracking'\n            raise TrackEvalException(\n                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(\n                    [str(x) + ', ' for x in extra_time_keys]))\n\n        for t in range(num_timesteps):\n            time_key = str(t)\n            # list to collect all masks of a timestep to check for overlapping areas\n            all_masks = []\n            if time_key in read_data.keys():\n                try:\n                    raw_data['dets'][t] = [{'size': [int(region[3]), int(region[4])],\n                                            'counts': region[5].encode(encoding='UTF-8')}\n                                           for region in read_data[time_key]]\n                    raw_data['ids'][t] = np.atleast_1d([region[1] for region in read_data[time_key]]).astype(int)\n                    raw_data['classes'][t] = np.atleast_1d([region[2] for region in read_data[time_key]]).astype(int)\n                    all_masks += raw_data['dets'][t]\n                except IndexError:\n                    self._raise_index_error(is_gt, tracker, seq)\n                except ValueError:\n                    self._raise_value_error(is_gt, tracker, seq)\n            else:\n                raw_data['dets'][t] = []\n                raw_data['ids'][t] = np.empty(0).astype(int)\n                raw_data['classes'][t] = np.empty(0).astype(int)\n            if is_gt:\n                if time_key in ignore_data.keys():\n                    try:\n                        time_ignore = [{'size': [int(region[3]), int(region[4])],\n                                        'counts': region[5].encode(encoding='UTF-8')}\n                                       for region in ignore_data[time_key]]\n                        raw_data['gt_ignore_region'][t] = mask_utils.merge([mask for mask in time_ignore],\n                                                                           intersect=False)\n                        all_masks += [raw_data['gt_ignore_region'][t]]\n                    except IndexError:\n                        self._raise_index_error(is_gt, tracker, seq)\n                    except ValueError:\n                        self._raise_value_error(is_gt, tracker, seq)\n                else:\n                    raw_data['gt_ignore_region'][t] = mask_utils.merge([], intersect=False)\n\n            # check for overlapping masks\n            if all_masks:\n                masks_merged = all_masks[0]\n                for mask in all_masks[1:]:\n                    if mask_utils.area(mask_utils.merge([masks_merged, mask], intersect=True)) != 0.0:\n                        raise TrackEvalException(\n                            'Tracker has overlapping masks. Tracker: ' + tracker + ' Seq: ' + seq + ' Timestep: ' + str(\n                                t))\n                    masks_merged = mask_utils.merge([masks_merged, mask], intersect=False)\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'classes': 'gt_classes',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'classes': 'tracker_classes',\n                       'dets': 'tracker_dets'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n        raw_data[\"num_timesteps\"] = num_timesteps\n        raw_data['seq'] = seq\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detection masks.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n\n        KITTI MOTS:\n            In KITTI MOTS, the 4 preproc steps are as follow:\n                1) There are two classes (car and pedestrian) which are evaluated separately.\n                2) There are no ground truth detections marked as to be removed/distractor classes.\n                    Therefore also no matched tracker detections are removed.\n                3) Ignore regions are used to remove unmatched detections (at least 50% overlap with ignore region).\n                4) There are no ground truth detections (e.g. those of distractor classes) to be removed.\n        \"\"\"\n        # Check that input data has unique ids\n        self._check_unique_ids(raw_data)\n\n        cls_id = int(self.class_name_to_class_id[cls])\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'similarity_scores']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n        for t in range(raw_data['num_timesteps']):\n\n            # Only extract relevant dets for this class for preproc and eval (cls)\n            gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)\n            gt_class_mask = gt_class_mask.astype(np.bool)\n            gt_ids = raw_data['gt_ids'][t][gt_class_mask]\n            gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]\n\n            tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)\n            tracker_class_mask = tracker_class_mask.astype(np.bool)\n            tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]\n            tracker_dets = [raw_data['tracker_dets'][t][ind] for ind in range(len(tracker_class_mask)) if\n                            tracker_class_mask[ind]]\n            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]\n\n            # Match tracker and gt dets (with hungarian algorithm)\n            unmatched_indices = np.arange(tracker_ids.shape[0])\n            if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n                matching_scores = similarity_scores.copy()\n                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = -10000\n                match_rows, match_cols = linear_sum_assignment(-matching_scores)\n                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\n                match_cols = match_cols[actually_matched_mask]\n\n                unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)\n\n            # For unmatched tracker dets, remove those that are greater than 50% within a crowd ignore region.\n            unmatched_tracker_dets = [tracker_dets[i] for i in range(len(tracker_dets)) if i in unmatched_indices]\n            ignore_region = raw_data['gt_ignore_region'][t]\n            intersection_with_ignore_region = self._calculate_mask_ious(unmatched_tracker_dets, [ignore_region],\n                                                                        is_encoded=True, do_ioa=True)\n            is_within_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps, axis=1)\n\n            # Apply preprocessing to remove unwanted tracker dets.\n            to_remove_tracker = unmatched_indices[is_within_ignore_region]\n            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n            # Keep all ground truth detections\n            data['gt_ids'][t] = gt_ids\n            data['gt_dets'][t] = gt_dets\n            data['similarity_scores'][t] = similarity_scores\n\n            unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n            num_tracker_dets += len(data['tracker_ids'][t])\n            num_gt_dets += len(data['gt_ids'][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = len(unique_tracker_ids)\n        data['num_gt_ids'] = len(unique_gt_ids)\n        data['num_timesteps'] = raw_data['num_timesteps']\n        data['seq'] = raw_data['seq']\n        data['cls'] = cls\n\n        # Ensure again that ids are unique per timestep after preproc.\n        self._check_unique_ids(data, after_preproc=True)\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_mask_ious(gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False)\n        return similarity_scores\n\n    @staticmethod\n    def _raise_index_error(is_gt, tracker, seq):\n        \"\"\"\n        Auxiliary method to raise an evaluation error in case of an index error while reading files.\n        :param is_gt: whether gt or tracker data is read\n        :param tracker: the name of the tracker\n        :param seq: the name of the seq\n        :return: None\n        \"\"\"\n        if is_gt:\n            err = 'Cannot load gt data from sequence %s, because there are not enough ' \\\n                  'columns in the data.' % seq\n            raise TrackEvalException(err)\n        else:\n            err = 'Cannot load tracker data from tracker %s, sequence %s, because there are not enough ' \\\n                  'columns in the data.' % (tracker, seq)\n            raise TrackEvalException(err)\n\n    @staticmethod\n    def _raise_value_error(is_gt, tracker, seq):\n        \"\"\"\n        Auxiliary method to raise an evaluation error in case of an value error while reading files.\n        :param is_gt: whether gt or tracker data is read\n        :param tracker: the name of the tracker\n        :param seq: the name of the seq\n        :return: None\n        \"\"\"\n        if is_gt:\n            raise TrackEvalException(\n                'GT data for sequence %s cannot be converted to the right format. Is data corrupted?' % seq)\n        else:\n            raise TrackEvalException(\n                'Tracking data from tracker %s, sequence %s cannot be converted to the right format. '\n                'Is data corrupted?' % (tracker, seq))\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/mot_challenge_2d_box.py",
    "content": "import os\nimport csv\nimport configparser\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_dataset import _BaseDataset\nfrom .. import utils\nfrom .. import _timing\nfrom ..utils import TrackEvalException\n\n\nclass MotChallenge2DBox(_BaseDataset):\n    \"\"\"Dataset class for MOT Challenge 2D bounding box tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']\n            'BENCHMARK': 'MOT17',  # Valid: 'MOT17', 'MOT16', 'MOT20', 'MOT15'\n            'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'\n            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'DO_PREPROC': True,  # Whether to perform preprocessing (never done for MOT15)\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/seqmaps)\n            'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/benchmark-split_to_eval)\n            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n            'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt',  # '{gt_folder}/{seq}/gt/gt.txt'\n            'SKIP_SPLIT_FOL': False,  # If False, data is in GT_FOLDER/BENCHMARK-SPLIT_TO_EVAL/ and in\n                                      # TRACKERS_FOLDER/BENCHMARK-SPLIT_TO_EVAL/tracker/\n                                      # If True, then the middle 'benchmark-split' folder is skipped for both.\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())\n\n        self.benchmark = self.config['BENCHMARK']\n        gt_set = self.config['SPLIT_TO_EVAL']       # TODO: [hgx 0401]: delete \"self.config['BENCHMARK'] + '-' +\"\n        self.gt_set = gt_set\n        if not self.config['SKIP_SPLIT_FOL']:\n            split_fol = gt_set\n        else:\n            split_fol = ''\n        self.gt_fol = os.path.join(self.config['GT_FOLDER'], split_fol)\n        self.tracker_fol = os.path.join(self.config['TRACKERS_FOLDER'])         # TODO: [hgx 0401]: delete \"split_fol\"\n        self.should_classes_combine = False\n        self.use_super_categories = False\n        self.data_is_zipped = self.config['INPUT_AS_ZIP']\n        self.do_preproc = self.config['DO_PREPROC']\n\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']\n\n        # Get classes to eval\n        self.valid_classes = ['pedestrian']\n        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n                           for cls in self.config['CLASSES_TO_EVAL']]\n        if not all(self.class_list):\n            raise TrackEvalException('Attempted to evaluate an invalid class. Only pedestrian class is valid.')\n        self.class_name_to_class_id = {'pedestrian': 1, 'person_on_vehicle': 2, 'car': 3, 'bicycle': 4, 'motorbike': 5,\n                                       'non_mot_vehicle': 6, 'static_person': 7, 'distractor': 8, 'occluder': 9,\n                                       'occluder_on_ground': 10, 'occluder_full': 11, 'reflection': 12, 'crowd': 13}\n        self.valid_class_numbers = list(self.class_name_to_class_id.values())\n\n        # Get sequences to eval and check gt files exist\n        self.seq_list, self.seq_lengths = self._get_seq_info()\n        if len(self.seq_list) < 1:\n            raise TrackEvalException('No sequences are selected to be evaluated.')\n\n        # Check gt files exist\n        for seq in self.seq_list:\n            if not self.data_is_zipped:\n                curr_file = self.config[\"GT_LOC_FORMAT\"].format(gt_folder=self.gt_fol, seq=seq)\n                if not os.path.isfile(curr_file):\n                    print('GT file not found ' + curr_file)\n                    raise TrackEvalException('GT file not found for sequence: ' + seq)\n        if self.data_is_zipped:\n            curr_file = os.path.join(self.gt_fol, 'data.zip')\n            if not os.path.isfile(curr_file):\n                print('GT file not found ' + curr_file)\n                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n        for tracker in self.tracker_list:\n            if self.data_is_zipped:\n                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')\n                if not os.path.isfile(curr_file):\n                    print('Tracker file not found: ' + curr_file)\n                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))\n            else:\n                for seq in self.seq_list:\n                    curr_file = os.path.join(self.tracker_fol, seq + '.txt')        # TODO: [hgx 0401], delete \"tracker, self.tracker_sub_fol\"\n                    if not os.path.isfile(curr_file):\n                        print('Tracker file not found: ' + curr_file)\n                        raise TrackEvalException(\n                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(\n                                curr_file))\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _get_seq_info(self):\n        seq_list = []\n        seq_lengths = {}\n        if self.config[\"SEQ_INFO\"]:\n            seq_list = list(self.config[\"SEQ_INFO\"].keys())\n            seq_lengths = self.config[\"SEQ_INFO\"]\n\n            # If sequence length is 'None' tries to read sequence length from .ini files.\n            for seq, seq_length in seq_lengths.items():\n                if seq_length is None:\n                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')\n                    if not os.path.isfile(ini_file):\n                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))\n                    ini_data = configparser.ConfigParser()\n                    ini_data.read(ini_file)\n                    seq_lengths[seq] = int(ini_data['Sequence']['seqLength'])\n\n        else:\n            if self.config[\"SEQMAP_FILE\"]:\n                seqmap_file = self.config[\"SEQMAP_FILE\"]\n            else:\n                if self.config[\"SEQMAP_FOLDER\"] is None:\n                    seqmap_file = os.path.join(self.config['GT_FOLDER'], 'seqmaps', self.gt_set + '.txt')\n                else:\n                    seqmap_file = os.path.join(self.config[\"SEQMAP_FOLDER\"], self.gt_set + '.txt')\n            if not os.path.isfile(seqmap_file):\n                print('no seqmap found: ' + seqmap_file)\n                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))\n            with open(seqmap_file) as fp:\n                reader = csv.reader(fp)\n                for i, row in enumerate(reader):\n                    if i == 0 or row[0] == '':\n                        continue\n                    seq = row[0]\n                    seq_list.append(seq)\n                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')\n                    if not os.path.isfile(ini_file):\n                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))\n                    ini_data = configparser.ConfigParser()\n                    ini_data.read(ini_file)\n                    seq_lengths[seq] = int(ini_data['Sequence']['seqLength'])\n        return seq_list, seq_lengths\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the MOT Challenge 2D box format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.\n        [gt_extras] : list (for each timestep) of dicts (for each extra) of 1D NDArrays (for each det).\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        \"\"\"\n        # File location\n        if self.data_is_zipped:\n            if is_gt:\n                zip_file = os.path.join(self.gt_fol, 'data.zip')\n            else:\n                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')\n            file = seq + '.txt'\n        else:\n            zip_file = None\n            if is_gt:\n                file = self.config[\"GT_LOC_FORMAT\"].format(gt_folder=self.gt_fol, seq=seq)\n            else:\n                file = os.path.join(self.tracker_fol, seq + '.txt')         # TODO: [hgx 0401], delete \"tracker, self.tracker_sub_fol\"\n\n        # Load raw data from text file\n        read_data, ignore_data = self._load_simple_text_file(file, is_zipped=self.data_is_zipped, zip_file=zip_file)\n\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq]\n        data_keys = ['ids', 'classes', 'dets']\n        if is_gt:\n            data_keys += ['gt_crowd_ignore_regions', 'gt_extras']\n        else:\n            data_keys += ['tracker_confidences']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n\n        # Check for any extra time keys\n        current_time_keys = [str( t+ 1) for t in range(num_timesteps)]\n        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]\n        if len(extra_time_keys) > 0:\n            if is_gt:\n                text = 'Ground-truth'\n            else:\n                text = 'Tracking'\n            raise TrackEvalException(\n                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(\n                    [str(x) + ', ' for x in extra_time_keys]))\n\n        for t in range(num_timesteps):\n            time_key = str(t+1)\n            if time_key in read_data.keys():\n                try:\n                    time_data = np.asarray(read_data[time_key], dtype=np.float)\n                except ValueError:\n                    if is_gt:\n                        raise TrackEvalException(\n                            'Cannot convert gt data for sequence %s to float. Is data corrupted?' % seq)\n                    else:\n                        raise TrackEvalException(\n                            'Cannot convert tracking data from tracker %s, sequence %s to float. Is data corrupted?' % (\n                                tracker, seq))\n                try:\n                    raw_data['dets'][t] = np.atleast_2d(time_data[:, 2:6])\n                    raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)\n                except IndexError:\n                    if is_gt:\n                        err = 'Cannot load gt data from sequence %s, because there is not enough ' \\\n                              'columns in the data.' % seq\n                        raise TrackEvalException(err)\n                    else:\n                        err = 'Cannot load tracker data from tracker %s, sequence %s, because there is not enough ' \\\n                              'columns in the data.' % (tracker, seq)\n                        raise TrackEvalException(err)\n                if time_data.shape[1] >= 8:\n                    raw_data['classes'][t] = np.atleast_1d(time_data[:, 7]).astype(int)\n                else:\n                    if not is_gt:\n                        raw_data['classes'][t] = np.ones_like(raw_data['ids'][t])\n                    else:\n                        raise TrackEvalException(\n                            'GT data is not in a valid format, there is not enough rows in seq %s, timestep %i.' % (\n                                seq, t))\n                if is_gt:\n                    gt_extras_dict = {'zero_marked': np.atleast_1d(time_data[:, 6].astype(int))}\n                    raw_data['gt_extras'][t] = gt_extras_dict\n                else:\n                    raw_data['tracker_confidences'][t] = np.atleast_1d(time_data[:, 6])\n            else:\n                raw_data['dets'][t] = np.empty((0, 4))\n                raw_data['ids'][t] = np.empty(0).astype(int)\n                raw_data['classes'][t] = np.empty(0).astype(int)\n                if is_gt:\n                    gt_extras_dict = {'zero_marked': np.empty(0)}\n                    raw_data['gt_extras'][t] = gt_extras_dict\n                else:\n                    raw_data['tracker_confidences'][t] = np.empty(0)\n            if is_gt:\n                raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4))\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'classes': 'gt_classes',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'classes': 'tracker_classes',\n                       'dets': 'tracker_dets'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n        raw_data['num_timesteps'] = num_timesteps\n        raw_data['seq'] = seq\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n\n        MOT Challenge:\n            In MOT Challenge, the 4 preproc steps are as follow:\n                1) There is only one class (pedestrian) to be evaluated, but all other classes are used for preproc.\n                2) Predictions are matched against all gt boxes (regardless of class), those matching with distractor\n                    objects are removed.\n                3) There is no crowd ignore regions.\n                4) All gt dets except pedestrian are removed, also removes pedestrian gt dets marked with zero_marked.\n        \"\"\"\n        # Check that input data has unique ids\n        self._check_unique_ids(raw_data)\n\n        distractor_class_names = ['person_on_vehicle', 'static_person', 'distractor', 'reflection']\n        if self.benchmark == 'MOT20':\n            distractor_class_names.append('non_mot_vehicle')\n        distractor_classes = [self.class_name_to_class_id[x] for x in distractor_class_names]\n        cls_id = self.class_name_to_class_id[cls]\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n        for t in range(raw_data['num_timesteps']):\n\n            # Get all data\n            gt_ids = raw_data['gt_ids'][t]\n            gt_dets = raw_data['gt_dets'][t]\n            gt_classes = raw_data['gt_classes'][t]\n            gt_zero_marked = raw_data['gt_extras'][t]['zero_marked']\n\n            tracker_ids = raw_data['tracker_ids'][t]\n            tracker_dets = raw_data['tracker_dets'][t]\n            tracker_classes = raw_data['tracker_classes'][t]\n            tracker_confidences = raw_data['tracker_confidences'][t]\n            similarity_scores = raw_data['similarity_scores'][t]\n\n            # Evaluation is ONLY valid for pedestrian class\n            if len(tracker_classes) > 0 and np.max(tracker_classes) > 1:\n                raise TrackEvalException(\n                    'Evaluation is only valid for pedestrian class. Non pedestrian class (%i) found in sequence %s at '\n                    'timestep %i.' % (np.max(tracker_classes), raw_data['seq'], t))\n\n            # Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets\n            # which are labeled as belonging to a distractor class.\n            to_remove_tracker = np.array([], np.int)\n            if self.do_preproc and self.benchmark != 'MOT15' and gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n\n                # Check all classes are valid:\n                invalid_classes = np.setdiff1d(np.unique(gt_classes), self.valid_class_numbers)\n                if len(invalid_classes) > 0:\n                    print(' '.join([str(x) for x in invalid_classes]))\n                    raise(TrackEvalException('Attempting to evaluate using invalid gt classes. '\n                                             'This warning only triggers if preprocessing is performed, '\n                                             'e.g. not for MOT15 or where prepropressing is explicitly disabled. '\n                                             'Please either check your gt data, or disable preprocessing. '\n                                             'The following invalid classes were found in timestep ' + str(t) + ': ' +\n                                             ' '.join([str(x) for x in invalid_classes])))\n\n                matching_scores = similarity_scores.copy()\n                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0\n                match_rows, match_cols = linear_sum_assignment(-matching_scores)\n                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\n                match_rows = match_rows[actually_matched_mask]\n                match_cols = match_cols[actually_matched_mask]\n\n                is_distractor_class = np.isin(gt_classes[match_rows], distractor_classes)\n                to_remove_tracker = match_cols[is_distractor_class]\n\n            # Apply preprocessing to remove all unwanted tracker dets.\n            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)\n            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n            # Remove gt detections marked as to remove (zero marked), and also remove gt detections not in pedestrian\n            # class (not applicable for MOT15)\n            if self.do_preproc and self.benchmark != 'MOT15':\n                gt_to_keep_mask = (np.not_equal(gt_zero_marked, 0)) & \\\n                                  (np.equal(gt_classes, cls_id))\n            else:\n                # There are no classes for MOT15\n                gt_to_keep_mask = np.not_equal(gt_zero_marked, 0)\n            data['gt_ids'][t] = gt_ids[gt_to_keep_mask]\n            data['gt_dets'][t] = gt_dets[gt_to_keep_mask, :]\n            data['similarity_scores'][t] = similarity_scores[gt_to_keep_mask]\n\n            unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n            num_tracker_dets += len(data['tracker_ids'][t])\n            num_gt_dets += len(data['gt_ids'][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = len(unique_tracker_ids)\n        data['num_gt_ids'] = len(unique_gt_ids)\n        data['num_timesteps'] = raw_data['num_timesteps']\n        data['seq'] = raw_data['seq']\n\n        # Ensure again that ids are unique per timestep after preproc.\n        self._check_unique_ids(data, after_preproc=True)\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t, box_format='xywh')\n        return similarity_scores\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/mots_challenge.py",
    "content": "import os\nimport csv\nimport configparser\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_dataset import _BaseDataset\nfrom .. import utils\nfrom .. import _timing\nfrom ..utils import TrackEvalException\n\n\nclass MOTSChallenge(_BaseDataset):\n    \"\"\"Dataset class for MOTS Challenge tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']\n            'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test'\n            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/seqmaps)\n            'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/MOTS-split_to_eval)\n            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n            'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt',  # '{gt_folder}/{seq}/gt/gt.txt'\n            'SKIP_SPLIT_FOL': False,  # If False, data is in GT_FOLDER/MOTS-SPLIT_TO_EVAL/ and in\n                                      # TRACKERS_FOLDER/MOTS-SPLIT_TO_EVAL/tracker/\n                                      # If True, then the middle 'MOTS-split' folder is skipped for both.\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())\n\n        self.benchmark = 'MOTS'\n        self.gt_set = self.benchmark + '-' + self.config['SPLIT_TO_EVAL']\n        if not self.config['SKIP_SPLIT_FOL']:\n            split_fol = self.gt_set\n        else:\n            split_fol = ''\n        self.gt_fol = os.path.join(self.config['GT_FOLDER'], split_fol)\n        self.tracker_fol = os.path.join(self.config['TRACKERS_FOLDER'], split_fol)\n        self.should_classes_combine = False\n        self.use_super_categories = False\n        self.data_is_zipped = self.config['INPUT_AS_ZIP']\n\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']\n\n        # Get classes to eval\n        self.valid_classes = ['pedestrian']\n        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n                           for cls in self.config['CLASSES_TO_EVAL']]\n        if not all(self.class_list):\n            raise TrackEvalException('Attempted to evaluate an invalid class. Only pedestrian class is valid.')\n        self.class_name_to_class_id = {'pedestrian': '2', 'ignore': '10'}\n\n        # Get sequences to eval and check gt files exist\n        self.seq_list, self.seq_lengths = self._get_seq_info()\n        if len(self.seq_list) < 1:\n            raise TrackEvalException('No sequences are selected to be evaluated.')\n\n        # Check gt files exist\n        for seq in self.seq_list:\n            if not self.data_is_zipped:\n                curr_file = self.config[\"GT_LOC_FORMAT\"].format(gt_folder=self.gt_fol, seq=seq)\n                if not os.path.isfile(curr_file):\n                    print('GT file not found ' + curr_file)\n                    raise TrackEvalException('GT file not found for sequence: ' + seq)\n        if self.data_is_zipped:\n            curr_file = os.path.join(self.gt_fol, 'data.zip')\n            if not os.path.isfile(curr_file):\n                print('GT file not found ' + curr_file)\n                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n        for tracker in self.tracker_list:\n            if self.data_is_zipped:\n                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')\n                if not os.path.isfile(curr_file):\n                    print('Tracker file not found: ' + curr_file)\n                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))\n            else:\n                for seq in self.seq_list:\n                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')\n                    if not os.path.isfile(curr_file):\n                        print('Tracker file not found: ' + curr_file)\n                        raise TrackEvalException(\n                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(\n                                curr_file))\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _get_seq_info(self):\n        seq_list = []\n        seq_lengths = {}\n        if self.config[\"SEQ_INFO\"]:\n            seq_list = list(self.config[\"SEQ_INFO\"].keys())\n            seq_lengths = self.config[\"SEQ_INFO\"]\n\n            # If sequence length is 'None' tries to read sequence length from .ini files.\n            for seq, seq_length in seq_lengths.items():\n                if seq_length is None:\n                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')\n                    if not os.path.isfile(ini_file):\n                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))\n                    ini_data = configparser.ConfigParser()\n                    ini_data.read(ini_file)\n                    seq_lengths[seq] = int(ini_data['Sequence']['seqLength'])\n\n        else:\n            if self.config[\"SEQMAP_FILE\"]:\n                seqmap_file = self.config[\"SEQMAP_FILE\"]\n            else:\n                if self.config[\"SEQMAP_FOLDER\"] is None:\n                    seqmap_file = os.path.join(self.config['GT_FOLDER'], 'seqmaps', self.gt_set + '.txt')\n                else:\n                    seqmap_file = os.path.join(self.config[\"SEQMAP_FOLDER\"], self.gt_set + '.txt')\n            if not os.path.isfile(seqmap_file):\n                print('no seqmap found: ' + seqmap_file)\n                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))\n            with open(seqmap_file) as fp:\n                reader = csv.reader(fp)\n                for i, row in enumerate(reader):\n                    if i == 0 or row[0] == '':\n                        continue\n                    seq = row[0]\n                    seq_list.append(seq)\n                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')\n                    if not os.path.isfile(ini_file):\n                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))\n                    ini_data = configparser.ConfigParser()\n                    ini_data.read(ini_file)\n                    seq_lengths[seq] = int(ini_data['Sequence']['seqLength'])\n        return seq_list, seq_lengths\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the MOTS Challenge format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets]: list (for each timestep) of lists of detections.\n        [gt_ignore_region]: list (for each timestep) of masks for the ignore regions\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        # File location\n        if self.data_is_zipped:\n            if is_gt:\n                zip_file = os.path.join(self.gt_fol, 'data.zip')\n            else:\n                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')\n            file = seq + '.txt'\n        else:\n            zip_file = None\n            if is_gt:\n                file = self.config[\"GT_LOC_FORMAT\"].format(gt_folder=self.gt_fol, seq=seq)\n            else:\n                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')\n\n        # Ignore regions\n        if is_gt:\n            crowd_ignore_filter = {2: ['10']}\n        else:\n            crowd_ignore_filter = None\n\n        # Load raw data from text file\n        read_data, ignore_data = self._load_simple_text_file(file, crowd_ignore_filter=crowd_ignore_filter,\n                                                             is_zipped=self.data_is_zipped, zip_file=zip_file,\n                                                             force_delimiters=' ')\n\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq]\n        data_keys = ['ids', 'classes', 'dets']\n        if is_gt:\n            data_keys += ['gt_ignore_region']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n\n        # Check for any extra time keys\n        current_time_keys = [str(t + 1) for t in range(num_timesteps)]\n        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]\n        if len(extra_time_keys) > 0:\n            if is_gt:\n                text = 'Ground-truth'\n            else:\n                text = 'Tracking'\n            raise TrackEvalException(\n                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(\n                    [str(x) + ', ' for x in extra_time_keys]))\n\n        for t in range(num_timesteps):\n            time_key = str(t+1)\n            # list to collect all masks of a timestep to check for overlapping areas\n            all_masks = []\n            if time_key in read_data.keys():\n                try:\n                    raw_data['dets'][t] = [{'size': [int(region[3]), int(region[4])],\n                                            'counts': region[5].encode(encoding='UTF-8')}\n                                           for region in read_data[time_key]]\n                    raw_data['ids'][t] = np.atleast_1d([region[1] for region in read_data[time_key]]).astype(int)\n                    raw_data['classes'][t] = np.atleast_1d([region[2] for region in read_data[time_key]]).astype(int)\n                    all_masks += raw_data['dets'][t]\n                except IndexError:\n                    self._raise_index_error(is_gt, tracker, seq)\n                except ValueError:\n                    self._raise_value_error(is_gt, tracker, seq)\n            else:\n                raw_data['dets'][t] = []\n                raw_data['ids'][t] = np.empty(0).astype(int)\n                raw_data['classes'][t] = np.empty(0).astype(int)\n            if is_gt:\n                if time_key in ignore_data.keys():\n                    try:\n                        time_ignore = [{'size': [int(region[3]), int(region[4])],\n                                        'counts': region[5].encode(encoding='UTF-8')}\n                                       for region in ignore_data[time_key]]\n                        raw_data['gt_ignore_region'][t] = mask_utils.merge([mask for mask in time_ignore],\n                                                                           intersect=False)\n                        all_masks += [raw_data['gt_ignore_region'][t]]\n                    except IndexError:\n                        self._raise_index_error(is_gt, tracker, seq)\n                    except ValueError:\n                        self._raise_value_error(is_gt, tracker, seq)\n                else:\n                    raw_data['gt_ignore_region'][t] = mask_utils.merge([], intersect=False)\n\n            # check for overlapping masks\n            if all_masks:\n                masks_merged = all_masks[0]\n                for mask in all_masks[1:]:\n                    if mask_utils.area(mask_utils.merge([masks_merged, mask], intersect=True)) != 0.0:\n                        raise TrackEvalException(\n                            'Tracker has overlapping masks. Tracker: ' + tracker + ' Seq: ' + seq + ' Timestep: ' + str(\n                                t))\n                    masks_merged = mask_utils.merge([masks_merged, mask], intersect=False)\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'classes': 'gt_classes',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'classes': 'tracker_classes',\n                       'dets': 'tracker_dets'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n        raw_data['num_timesteps'] = num_timesteps\n        raw_data['seq'] = seq\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detection masks.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n\n        MOTS Challenge:\n            In MOTS Challenge, the 4 preproc steps are as follow:\n                1) There is only one class (pedestrians) to be evaluated.\n                2) There are no ground truth detections marked as to be removed/distractor classes.\n                    Therefore also no matched tracker detections are removed.\n                3) Ignore regions are used to remove unmatched detections (at least 50% overlap with ignore region).\n                4) There are no ground truth detections (e.g. those of distractor classes) to be removed.\n        \"\"\"\n        # Check that input data has unique ids\n        self._check_unique_ids(raw_data)\n\n        cls_id = int(self.class_name_to_class_id[cls])\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'similarity_scores']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n        for t in range(raw_data['num_timesteps']):\n\n            # Only extract relevant dets for this class for preproc and eval (cls)\n            gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)\n            gt_class_mask = gt_class_mask.astype(np.bool)\n            gt_ids = raw_data['gt_ids'][t][gt_class_mask]\n            gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]\n\n            tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)\n            tracker_class_mask = tracker_class_mask.astype(np.bool)\n            tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]\n            tracker_dets = [raw_data['tracker_dets'][t][ind] for ind in range(len(tracker_class_mask)) if\n                            tracker_class_mask[ind]]\n            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]\n\n            # Match tracker and gt dets (with hungarian algorithm)\n            unmatched_indices = np.arange(tracker_ids.shape[0])\n            if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n                matching_scores = similarity_scores.copy()\n                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = -10000\n                match_rows, match_cols = linear_sum_assignment(-matching_scores)\n                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\n                match_cols = match_cols[actually_matched_mask]\n\n                unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)\n\n            # For unmatched tracker dets, remove those that are greater than 50% within a crowd ignore region.\n            unmatched_tracker_dets = [tracker_dets[i] for i in range(len(tracker_dets)) if i in unmatched_indices]\n            ignore_region = raw_data['gt_ignore_region'][t]\n            intersection_with_ignore_region = self._calculate_mask_ious(unmatched_tracker_dets, [ignore_region],\n                                                                        is_encoded=True, do_ioa=True)\n            is_within_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps, axis=1)\n\n            # Apply preprocessing to remove unwanted tracker dets.\n            to_remove_tracker = unmatched_indices[is_within_ignore_region]\n            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n            # Keep all ground truth detections\n            data['gt_ids'][t] = gt_ids\n            data['gt_dets'][t] = gt_dets\n            data['similarity_scores'][t] = similarity_scores\n\n            unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n            num_tracker_dets += len(data['tracker_ids'][t])\n            num_gt_dets += len(data['gt_ids'][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = len(unique_tracker_ids)\n        data['num_gt_ids'] = len(unique_gt_ids)\n        data['num_timesteps'] = raw_data['num_timesteps']\n        data['seq'] = raw_data['seq']\n\n        # Ensure again that ids are unique per timestep after preproc.\n        self._check_unique_ids(data, after_preproc=True)\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_mask_ious(gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False)\n        return similarity_scores\n\n    @staticmethod\n    def _raise_index_error(is_gt, tracker, seq):\n        \"\"\"\n        Auxiliary method to raise an evaluation error in case of an index error while reading files.\n        :param is_gt: whether gt or tracker data is read\n        :param tracker: the name of the tracker\n        :param seq: the name of the seq\n        :return: None\n        \"\"\"\n        if is_gt:\n            err = 'Cannot load gt data from sequence %s, because there are not enough ' \\\n                  'columns in the data.' % seq\n            raise TrackEvalException(err)\n        else:\n            err = 'Cannot load tracker data from tracker %s, sequence %s, because there are not enough ' \\\n                  'columns in the data.' % (tracker, seq)\n            raise TrackEvalException(err)\n\n    @staticmethod\n    def _raise_value_error(is_gt, tracker, seq):\n        \"\"\"\n        Auxiliary method to raise an evaluation error in case of an value error while reading files.\n        :param is_gt: whether gt or tracker data is read\n        :param tracker: the name of the tracker\n        :param seq: the name of the seq\n        :return: None\n        \"\"\"\n        if is_gt:\n            raise TrackEvalException(\n                'GT data for sequence %s cannot be converted to the right format. Is data corrupted?' % seq)\n        else:\n            raise TrackEvalException(\n                'Tracking data from tracker %s, sequence %s cannot be converted to the right format. '\n                'Is data corrupted?' % (tracker, seq))\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/rob_mots.py",
    "content": "\nimport os\nimport csv\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_dataset import _BaseDataset\nfrom .. import utils\nfrom ..utils import TrackEvalException\nfrom .. import _timing\nfrom ..datasets.rob_mots_classmap import cls_id_to_name\n\n\nclass RobMOTS(_BaseDataset):\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/rob_mots'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/rob_mots'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'SUB_BENCHMARK': None,  # REQUIRED. Sub-benchmark to eval. If None, then error.\n            # ['mots_challenge', 'kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'waymo', 'tao']\n            'CLASSES_TO_EVAL': None,  # List of classes to eval. If None, then it does all COCO classes.\n            'SPLIT_TO_EVAL': 'train',  # valid: ['train', 'val', 'test']\n            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'OUTPUT_SUB_FOLDER': 'results',  # Output files are saved in OUTPUT_FOLDER/DATA_LOC_FORMAT/OUTPUT_SUB_FOLDER\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/DATA_LOC_FORMAT/TRACKER_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/dataset_subfolder/seqmaps)\n            'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use SEQMAP_FOLDER/BENCHMARK_SPLIT_TO_EVAL)\n            'CLSMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/dataset_subfolder/clsmaps)\n            'CLSMAP_FILE': None,  # Directly specify seqmap file (if none use CLSMAP_FOLDER/BENCHMARK_SPLIT_TO_EVAL)\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config())\n\n        self.split = self.config['SPLIT_TO_EVAL']\n        valid_benchmarks = ['mots_challenge', 'kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'waymo', 'tao']\n        self.box_gt_benchmarks = ['waymo', 'tao']\n\n        self.sub_benchmark = self.config['SUB_BENCHMARK']\n        if not self.sub_benchmark:\n            raise TrackEvalException('SUB_BENCHMARK config input is required (there is no default value)' +\n                                     ', '.join(valid_benchmarks) + ' are valid.')\n        if self.sub_benchmark not in valid_benchmarks:\n            raise TrackEvalException('Attempted to evaluate an invalid benchmark: ' + self.sub_benchmark + '. Only benchmarks ' +\n                                     ', '.join(valid_benchmarks) + ' are valid.')\n\n        self.gt_fol = self.config['GT_FOLDER']\n        self.tracker_fol = os.path.join(self.config['TRACKERS_FOLDER'], self.config['SPLIT_TO_EVAL'])\n        self.data_is_zipped = self.config['INPUT_AS_ZIP']\n\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n        self.output_sub_fol = os.path.join(self.config['OUTPUT_SUB_FOLDER'], self.sub_benchmark)\n\n        # Loops through all sub-benchmarks, and reads in seqmaps to info on all sequences to eval.\n        self._get_seq_info()\n\n        if len(self.seq_list) < 1:\n            raise TrackEvalException('No sequences are selected to be evaluated.')\n\n        valid_class_ids = np.atleast_1d(np.genfromtxt(os.path.join(self.gt_fol, self.split, self.sub_benchmark,\n                                                                   'clsmap.txt')))\n        valid_classes = [cls_id_to_name[int(x)] for x in valid_class_ids] + ['all']\n        self.valid_class_ids = valid_class_ids\n        self.class_name_to_class_id = {cls_name: cls_id for cls_id, cls_name in cls_id_to_name.items()}\n        self.class_name_to_class_id['all'] = -1\n        if not self.config['CLASSES_TO_EVAL']:\n            self.class_list = valid_classes\n        else:\n            self.class_list = [cls if cls in valid_classes else None\n                               for cls in self.config['CLASSES_TO_EVAL']]\n            if not all(self.class_list):\n                raise TrackEvalException('Attempted to evaluate an invalid class. Only classes ' +\n                                         ', '.join(valid_classes) + ' are valid.')\n\n        # Check gt files exist\n        for seq in self.seq_list:\n            if not self.data_is_zipped:\n                curr_file = os.path.join(self.gt_fol, self.split, self.sub_benchmark, 'data', seq + '.txt')\n                if not os.path.isfile(curr_file):\n                    print('GT file not found ' + curr_file)\n                    raise TrackEvalException('GT file not found for sequence: ' + seq)\n        if self.data_is_zipped:\n            curr_file = os.path.join(self.gt_fol, self.split, self.sub_benchmark, 'data.zip')\n            if not os.path.isfile(curr_file):\n                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n        for tracker in self.tracker_list:\n            if self.data_is_zipped:\n                curr_file = os.path.join(self.tracker_fol, tracker, 'data.zip')\n                if not os.path.isfile(curr_file):\n                    raise TrackEvalException('Tracker file not found: ' + os.path.basename(curr_file))\n            else:\n                for seq in self.seq_list:\n                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, self.sub_benchmark, seq\n                                             + '.txt')\n                    if not os.path.isfile(curr_file):\n                        print('Tracker file not found: ' + curr_file)\n                        raise TrackEvalException(\n                            'Tracker file not found: ' + self.sub_benchmark + '/' + os.path.basename(curr_file))\n\n    def get_name(self):\n        return self.get_class_name() + '.' + self.sub_benchmark\n\n    def _get_seq_info(self):\n        self.seq_list = []\n        self.seq_lengths = {}\n        self.seq_sizes = {}\n        self.seq_ignore_class_ids = {}\n        if self.config[\"SEQMAP_FILE\"]:\n            seqmap_file = self.config[\"SEQMAP_FILE\"]\n        else:\n            if self.config[\"SEQMAP_FOLDER\"] is None:\n                seqmap_file = os.path.join(self.gt_fol, self.split, self.sub_benchmark, 'seqmap.txt')\n            else:\n                seqmap_file = os.path.join(self.config[\"SEQMAP_FOLDER\"], self.split + '.seqmap')\n        if not os.path.isfile(seqmap_file):\n            print('no seqmap found: ' + seqmap_file)\n            raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))\n        with open(seqmap_file) as fp:\n            dialect = csv.Sniffer().sniff(fp.readline(), delimiters=' ')\n            fp.seek(0)\n            reader = csv.reader(fp, dialect)\n            for i, row in enumerate(reader):\n                if len(row) >= 4:\n                    # first col: sequence, second col: sequence length, third and fourth col: sequence height/width\n                    # The rest of the columns list the 'sequence ignore class ids' which are classes not penalized as\n                    # FPs for this sequence.\n                    seq = row[0]\n                    self.seq_list.append(seq)\n                    self.seq_lengths[seq] = int(row[1])\n                    self.seq_sizes[seq] = (int(row[2]), int(row[3]))\n                    self.seq_ignore_class_ids[seq] = [int(row[x]) for x in range(4, len(row))]\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the unified RobMOTS format.\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        \"\"\"\n        # import to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        # File location\n        if self.data_is_zipped:\n            if is_gt:\n                zip_file = os.path.join(self.gt_fol, self.split, self.sub_benchmark, 'data.zip')\n            else:\n                zip_file = os.path.join(self.tracker_fol, tracker, 'data.zip')\n            file = seq + '.txt'\n        else:\n            zip_file = None\n            if is_gt:\n                file = os.path.join(self.gt_fol, self.split, self.sub_benchmark, 'data', seq + '.txt')\n            else:\n                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, self.sub_benchmark, seq + '.txt')\n\n        # Load raw data from text file\n        read_data, ignore_data = self._load_simple_text_file(file, is_zipped=self.data_is_zipped, zip_file=zip_file,\n                                                             force_delimiters=' ')\n\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq]\n        data_keys = ['ids', 'classes', 'dets']\n        if not is_gt:\n            data_keys += ['tracker_confidences']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n        for t in range(num_timesteps):\n            time_key = str(t)\n            # list to collect all masks of a timestep to check for overlapping areas (for segmentation datasets)\n            all_valid_masks = []\n            if time_key in read_data.keys():\n                try:\n                    raw_data['ids'][t] = np.atleast_1d([det[1] for det in read_data[time_key]]).astype(int)\n                    raw_data['classes'][t] = np.atleast_1d([det[2] for det in read_data[time_key]]).astype(int)\n                    if (not is_gt) or (self.sub_benchmark not in self.box_gt_benchmarks):\n                        raw_data['dets'][t] = [{'size': [int(region[4]), int(region[5])],\n                                                'counts': region[6].encode(encoding='UTF-8')}\n                                               for region in read_data[time_key]]\n                        all_valid_masks += [mask for mask, cls in zip(raw_data['dets'][t], raw_data['classes'][t]) if\n                                      cls < 100]\n                    else:\n                        raw_data['dets'][t] = np.atleast_2d([det[4:8] for det in read_data[time_key]]).astype(float)\n\n                    if not is_gt:\n                        raw_data['tracker_confidences'][t] = np.atleast_1d([det[3] for det\n                                                                            in read_data[time_key]]).astype(float)\n                except IndexError:\n                    self._raise_index_error(is_gt, self.sub_benchmark, seq)\n                except ValueError:\n                    self._raise_value_error(is_gt, self.sub_benchmark, seq)\n            # no detection in this timestep\n            else:\n                if (not is_gt) or (self.sub_benchmark not in self.box_gt_benchmarks):\n                    raw_data['dets'][t] = []\n                else:\n                    raw_data['dets'][t] = np.empty((0, 4)).astype(float)\n                raw_data['ids'][t] = np.empty(0).astype(int)\n                raw_data['classes'][t] = np.empty(0).astype(int)\n                if not is_gt:\n                    raw_data['tracker_confidences'][t] = np.empty(0).astype(float)\n\n            # check for overlapping masks\n            if all_valid_masks:\n                masks_merged = all_valid_masks[0]\n                for mask in all_valid_masks[1:]:\n                    if mask_utils.area(mask_utils.merge([masks_merged, mask], intersect=True)) != 0.0:\n                        err = 'Overlapping masks in frame %d' % t\n                        raise TrackEvalException(err)\n                    masks_merged = mask_utils.merge([masks_merged, mask], intersect=False)\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'classes': 'gt_classes',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'classes': 'tracker_classes',\n                       'dets': 'tracker_dets'}\n\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        raw_data['num_timesteps'] = num_timesteps\n        raw_data['frame_size'] = self.seq_sizes[seq]\n        raw_data['seq'] = seq\n        return raw_data\n\n    @staticmethod\n    def _raise_index_error(is_gt, sub_benchmark, seq):\n        \"\"\"\n        Auxiliary method to raise an evaluation error in case of an index error while reading files.\n        :param is_gt: whether gt or tracker data is read\n        :param tracker: the name of the tracker\n        :param seq: the name of the seq\n        :return: None\n        \"\"\"\n        if is_gt:\n            err = 'Cannot load gt data from sequence %s, because there are not enough ' \\\n                  'columns in the data.' % seq\n            raise TrackEvalException(err)\n        else:\n            err = 'Cannot load tracker data from benchmark %s, sequence %s, because there are not enough ' \\\n                  'columns in the data.' % (sub_benchmark, seq)\n            raise TrackEvalException(err)\n\n    @staticmethod\n    def _raise_value_error(is_gt, sub_benchmark, seq):\n        \"\"\"\n        Auxiliary method to raise an evaluation error in case of an value error while reading files.\n        :param is_gt: whether gt or tracker data is read\n        :param tracker: the name of the tracker\n        :param seq: the name of the seq\n        :return: None\n        \"\"\"\n        if is_gt:\n            raise TrackEvalException(\n                'GT data for sequence %s cannot be converted to the right format. Is data corrupted?' % seq)\n        else:\n            raise TrackEvalException(\n                'Tracking data from benchmark %s, sequence %s cannot be converted to the right format. '\n                'Is data corrupted?' % (sub_benchmark, seq))\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            Preprocessing (preproc) occurs in 3 steps.\n                1) Extract only detections relevant for the class to be evaluated.\n                2) Match gt dets and tracker dets. Tracker dets that are to a gt det (TPs) are marked as not to be\n                    removed.\n                3) Remove unmatched tracker dets if they fall within an ignore region or are too small, or if that class\n                    is marked as an ignore class for that sequence.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n            Note that there is a special 'all' class, which evaluates all of the COCO classes together in a\n                'class agnostic' fashion.\n        \"\"\"\n        # import to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        # Check that input data has unique ids\n        self._check_unique_ids(raw_data)\n\n        cls_id = self.class_name_to_class_id[cls]\n        ignore_class_id = cls_id+100\n        seq = raw_data['seq']\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n\n        for t in range(raw_data['num_timesteps']):\n\n            # Only extract relevant dets for this class\n            if cls == 'all':\n                gt_class_mask = raw_data['gt_classes'][t] < 100\n            # For waymo, combine predictions for [car, truck, bus, motorcycle] into car, because they are all annotated\n            # together as one 'vehicle' class.\n            elif self.sub_benchmark == 'waymo' and cls == 'car':\n                waymo_vehicle_classes = np.array([3, 4, 6, 8])\n                gt_class_mask = np.isin(raw_data['gt_classes'][t], waymo_vehicle_classes)\n            else:\n                gt_class_mask = raw_data['gt_classes'][t] == cls_id\n            gt_class_mask = gt_class_mask.astype(np.bool)\n            gt_ids = raw_data['gt_ids'][t][gt_class_mask]\n            if cls == 'all':\n                ignore_regions_mask = raw_data['gt_classes'][t] >= 100\n            else:\n                ignore_regions_mask = raw_data['gt_classes'][t] == ignore_class_id\n                ignore_regions_mask = np.logical_or(ignore_regions_mask, raw_data['gt_classes'][t] == 100)\n            if self.sub_benchmark in self.box_gt_benchmarks:\n                gt_dets = raw_data['gt_dets'][t][gt_class_mask]\n                ignore_regions_box = raw_data['gt_dets'][t][ignore_regions_mask]\n                if len(ignore_regions_box) > 0:\n                    ignore_regions_box[:, 2] = ignore_regions_box[:, 2] - ignore_regions_box[:, 0]\n                    ignore_regions_box[:, 3] = ignore_regions_box[:, 3] - ignore_regions_box[:, 1]\n                    ignore_regions = mask_utils.frPyObjects(ignore_regions_box, self.seq_sizes[seq][0], self.seq_sizes[seq][1])\n                else:\n                    ignore_regions = []\n            else:\n                gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]\n                ignore_regions = [raw_data['gt_dets'][t][ind] for ind in range(len(ignore_regions_mask)) if\n                                  ignore_regions_mask[ind]]\n\n            if cls == 'all':\n                tracker_class_mask = np.ones_like(raw_data['tracker_classes'][t])\n            else:\n                tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)\n            tracker_class_mask = tracker_class_mask.astype(np.bool)\n            tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]\n            tracker_dets = [raw_data['tracker_dets'][t][ind] for ind in range(len(tracker_class_mask)) if\n                            tracker_class_mask[ind]]\n            tracker_confidences = raw_data['tracker_confidences'][t][tracker_class_mask]\n            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]\n            tracker_classes = raw_data['tracker_classes'][t][tracker_class_mask]\n\n            # Only do preproc if there are ignore regions defined to remove\n            if tracker_ids.shape[0] > 0:\n\n                # Match tracker and gt dets (with hungarian algorithm)\n                unmatched_indices = np.arange(tracker_ids.shape[0])\n                if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n                    matching_scores = similarity_scores.copy()\n                    matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0\n                    match_rows, match_cols = linear_sum_assignment(-matching_scores)\n                    actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\n                    # match_rows = match_rows[actually_matched_mask]\n                    match_cols = match_cols[actually_matched_mask]\n                    unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)\n\n                # For unmatched tracker dets remove those that are greater than 50% within an ignore region.\n                # unmatched_tracker_dets = tracker_dets[unmatched_indices, :]\n                # crowd_ignore_regions = raw_data['gt_ignore_regions'][t]\n                # intersection_with_ignore_region = self. \\\n                #     _calculate_box_ious(unmatched_tracker_dets, crowd_ignore_regions, box_format='x0y0x1y1',\n                #                         do_ioa=True)\n\n\n                if cls_id in self.seq_ignore_class_ids[seq]:\n                    # Remove unmatched detections for classes that are marked as 'ignore' for the whole sequence.\n                    to_remove_tracker = unmatched_indices\n                else:\n                    unmatched_tracker_dets = [tracker_dets[i] for i in range(len(tracker_dets)) if\n                                              i in unmatched_indices]\n\n                    # For unmatched tracker dets remove those that are too small.\n                    tracker_boxes_t = mask_utils.toBbox(unmatched_tracker_dets)\n                    unmatched_widths = tracker_boxes_t[:, 2]\n                    unmatched_heights = tracker_boxes_t[:, 3]\n                    unmatched_size = np.maximum(unmatched_heights, unmatched_widths)\n                    min_size = np.min(self.seq_sizes[seq])/8\n                    is_too_small = unmatched_size <= min_size + np.finfo('float').eps\n\n                    # For unmatched tracker dets remove those that are greater than 50% within an ignore region.\n                    if ignore_regions:\n                        ignore_region_merged = ignore_regions[0]\n                        for mask in ignore_regions[1:]:\n                            ignore_region_merged = mask_utils.merge([ignore_region_merged, mask], intersect=False)\n                        intersection_with_ignore_region = self. \\\n                            _calculate_mask_ious(unmatched_tracker_dets, [ignore_region_merged], is_encoded=True, do_ioa=True)\n                        is_within_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps, axis=1)\n                        to_remove_tracker = unmatched_indices[np.logical_or(is_too_small, is_within_ignore_region)]\n                    else:\n                        to_remove_tracker = unmatched_indices[is_too_small]\n\n                # For the special 'all' class, you need to remove unmatched detections from all ignore classes and\n                #   non-evaluated classes.\n                if cls == 'all':\n                    unmatched_tracker_classes = [tracker_classes[i] for i in range(len(tracker_classes)) if\n                                              i in unmatched_indices]\n                    is_ignore_class = np.isin(unmatched_tracker_classes, self.seq_ignore_class_ids[seq])\n                    is_not_evaled_class = np.logical_not(np.isin(unmatched_tracker_classes, self.valid_class_ids))\n                    to_remove_all = unmatched_indices[np.logical_or(is_ignore_class, is_not_evaled_class)]\n                    to_remove_tracker = np.concatenate([to_remove_tracker, to_remove_all], axis=0)\n            else:\n                to_remove_tracker = np.array([], dtype=np.int)\n\n            # remove all unwanted tracker detections\n            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)\n            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n            # keep all ground truth detections\n            data['gt_ids'][t] = gt_ids\n            data['gt_dets'][t] = gt_dets\n            data['similarity_scores'][t] = similarity_scores\n\n            unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n            num_tracker_dets += len(data['tracker_ids'][t])\n            num_gt_dets += len(data['gt_ids'][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = len(unique_tracker_ids)\n        data['num_gt_ids'] = len(unique_gt_ids)\n        data['num_timesteps'] = raw_data['num_timesteps']\n        data['seq'] = raw_data['seq']\n        data['frame_size'] = raw_data['frame_size']\n\n        # Ensure that ids are unique per timestep.\n        self._check_unique_ids(data, after_preproc=True)\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        if self.sub_benchmark in self.box_gt_benchmarks:\n            # Convert tracker masks to bboxes (for benchmarks with only bbox ground-truth),\n            # and then convert to x0y0x1y1 format.\n            tracker_boxes_t = mask_utils.toBbox(tracker_dets_t)\n            tracker_boxes_t[:, 2] = tracker_boxes_t[:, 0] + tracker_boxes_t[:, 2]\n            tracker_boxes_t[:, 3] = tracker_boxes_t[:, 1] + tracker_boxes_t[:, 3]\n            similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_boxes_t, box_format='x0y0x1y1')\n        else:\n            similarity_scores = self._calculate_mask_ious(gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False)\n        return similarity_scores\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/rob_mots_classmap.py",
    "content": "cls_id_to_name = {\n 1: 'person',\n 2: 'bicycle',\n 3: 'car',\n 4: 'motorcycle',\n 5: 'airplane',\n 6: 'bus',\n 7: 'train',\n 8: 'truck',\n 9: 'boat',\n 10: 'traffic light',\n 11: 'fire hydrant',\n 12: 'stop sign',\n 13: 'parking meter',\n 14: 'bench',\n 15: 'bird',\n 16: 'cat',\n 17: 'dog',\n 18: 'horse',\n 19: 'sheep',\n 20: 'cow',\n 21: 'elephant',\n 22: 'bear',\n 23: 'zebra',\n 24: 'giraffe',\n 25: 'backpack',\n 26: 'umbrella',\n 27: 'handbag',\n 28: 'tie',\n 29: 'suitcase',\n 30: 'frisbee',\n 31: 'skis',\n 32: 'snowboard',\n 33: 'sports ball',\n 34: 'kite',\n 35: 'baseball bat',\n 36: 'baseball glove',\n 37: 'skateboard',\n 38: 'surfboard',\n 39: 'tennis racket',\n 40: 'bottle',\n 41: 'wine glass',\n 42: 'cup',\n 43: 'fork',\n 44: 'knife',\n 45: 'spoon',\n 46: 'bowl',\n 47: 'banana',\n 48: 'apple',\n 49: 'sandwich',\n 50: 'orange',\n 51: 'broccoli',\n 52: 'carrot',\n 53: 'hot dog',\n 54: 'pizza',\n 55: 'donut',\n 56: 'cake',\n 57: 'chair',\n 58: 'couch',\n 59: 'potted plant',\n 60: 'bed',\n 61: 'dining table',\n 62: 'toilet',\n 63: 'tv',\n 64: 'laptop',\n 65: 'mouse',\n 66: 'remote',\n 67: 'keyboard',\n 68: 'cell phone',\n 69: 'microwave',\n 70: 'oven',\n 71: 'toaster',\n 72: 'sink',\n 73: 'refrigerator',\n 74: 'book',\n 75: 'clock',\n 76: 'vase',\n 77: 'scissors',\n 78: 'teddy bear',\n 79: 'hair drier',\n 80: 'toothbrush'}"
  },
  {
    "path": "TrackEval/trackeval/datasets/run_rob_mots.py",
    "content": "\n# python3 scripts\\run_rob_mots.py --ROBMOTS_SPLIT val --TRACKERS_TO_EVAL tracker_name (e.g. STP) --USE_PARALLEL True --NUM_PARALLEL_CORES 4\n\nimport sys\nimport os\nimport csv\nimport numpy as np\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\nfrom trackeval import utils\ncode_path = utils.get_code_path()\n\nif __name__ == '__main__':\n    freeze_support()\n\n    script_config = {\n        'ROBMOTS_SPLIT': 'train',  # 'train',  # valid: 'train', 'val', 'test', 'test_live', 'test_post', 'test_all'\n        'BENCHMARKS': ['kitti_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'tao'], # 'bdd_mots' coming soon\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/rob_mots'),\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/rob_mots'),\n    }\n\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = True\n    default_dataset_config = trackeval.datasets.RobMOTS.get_default_dataset_config()\n    config = {**default_eval_config, **default_dataset_config, **script_config}\n\n    # Command line interface:\n    config = utils.update_config(config)\n\n    if config['ROBMOTS_SPLIT'] == 'val':\n        config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis',\n                                       'tao', 'mots_challenge']\n        config['SPLIT_TO_EVAL'] = 'val'\n    elif config['ROBMOTS_SPLIT'] == 'test' or config['SPLIT_TO_EVAL'] == 'test_live':\n        config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'tao']\n        config['SPLIT_TO_EVAL'] = 'test'\n    elif config['ROBMOTS_SPLIT'] == 'test_post':\n        config['BENCHMARKS'] = ['mots_challenge', 'waymo']\n        config['SPLIT_TO_EVAL'] = 'test'\n    elif config['ROBMOTS_SPLIT'] == 'test_all':\n        config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis',\n                                       'tao', 'mots_challenge', 'waymo']\n        config['SPLIT_TO_EVAL'] = 'test'\n    elif config['ROBMOTS_SPLIT'] == 'train':\n        config['BENCHMARKS'] = ['kitti_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'tao']  # 'bdd_mots' coming soon\n        config['SPLIT_TO_EVAL'] = 'train'\n\n    metrics_config = {'METRICS': ['HOTA']}\n    # metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}\n    eval_config = {k: v for k, v in config.items() if k in config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in config.keys()}\n\n    # Run code\n    dataset_list = []\n    for bench in config['BENCHMARKS']:\n        dataset_config['SUB_BENCHMARK'] = bench\n        dataset_list.append(trackeval.datasets.RobMOTS(dataset_config))\n    evaluator = trackeval.Evaluator(eval_config)\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    output_res, output_msg = evaluator.evaluate(dataset_list, metrics_list)\n\n\n    # For each benchmark, combine the 'all' score with the 'cls_averaged' using geometric mean.\n    metrics_to_calc = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA']\n    trackers = list(output_res['RobMOTS.' + config['BENCHMARKS'][0]].keys())\n    for tracker in trackers:\n        # final_results[benchmark][result_type][metric]\n        final_results = {}\n        res = {bench: output_res['RobMOTS.' + bench][tracker]['COMBINED_SEQ'] for bench in config['BENCHMARKS']}\n        for bench in config['BENCHMARKS']:\n            final_results[bench] = {'cls_av': {}, 'det_av': {}, 'final': {}}\n            for metric in metrics_to_calc:\n                final_results[bench]['cls_av'][metric] = np.mean(res[bench]['cls_comb_cls_av']['HOTA'][metric])\n                final_results[bench]['det_av'][metric] = np.mean(res[bench]['all']['HOTA'][metric])\n                final_results[bench]['final'][metric] = \\\n                    np.sqrt(final_results[bench]['cls_av'][metric] * final_results[bench]['det_av'][metric])\n\n        # Take the arithmetic mean over all the benchmarks\n        final_results['overall'] = {'cls_av': {}, 'det_av': {}, 'final': {}}\n        for metric in metrics_to_calc:\n            final_results['overall']['cls_av'][metric] = \\\n                np.mean([final_results[bench]['cls_av'][metric] for bench in config['BENCHMARKS']])\n            final_results['overall']['det_av'][metric] = \\\n                np.mean([final_results[bench]['det_av'][metric] for bench in config['BENCHMARKS']])\n            final_results['overall']['final'][metric] = \\\n                np.mean([final_results[bench]['final'][metric] for bench in config['BENCHMARKS']])\n\n        # Save out result\n        headers = [config['SPLIT_TO_EVAL']] + [x + '___' + metric for x in ['f', 'c', 'd'] for metric in metrics_to_calc]\n\n        def rowify(d):\n            return [d[x][metric] for x in ['final', 'cls_av', 'det_av'] for metric in metrics_to_calc]\n\n        out_file = os.path.join(script_config['TRACKERS_FOLDER'], script_config['ROBMOTS_SPLIT'], tracker,\n                                'final_results.csv')\n\n        with open(out_file, 'w', newline='') as f:\n            writer = csv.writer(f, delimiter=',')\n            writer.writerow(headers)\n            writer.writerow(['overall'] + rowify(final_results['overall']))\n            for bench in config['BENCHMARKS']:\n                if bench == 'overall':\n                    continue\n                writer.writerow([bench] + rowify(final_results[bench]))\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/tao.py",
    "content": "import os\nimport numpy as np\nimport json\nimport itertools\nfrom collections import defaultdict\nfrom scipy.optimize import linear_sum_assignment\nfrom ..utils import TrackEvalException\nfrom ._base_dataset import _BaseDataset\nfrom .. import utils\nfrom .. import _timing\n\n\nclass TAO(_BaseDataset):\n    \"\"\"Dataset class for TAO tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/tao/tao_training'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/tao/tao_training'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)\n            'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val'\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n            'MAX_DETECTIONS': 300,  # Number of maximal allowed detections per image (0 for unlimited)\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())\n        self.gt_fol = self.config['GT_FOLDER']\n        self.tracker_fol = self.config['TRACKERS_FOLDER']\n        self.should_classes_combine = True\n        self.use_super_categories = False\n\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']\n\n        gt_dir_files = [file for file in os.listdir(self.gt_fol) if file.endswith('.json')]\n        if len(gt_dir_files) != 1:\n            raise TrackEvalException(self.gt_fol + ' does not contain exactly one json file.')\n\n        with open(os.path.join(self.gt_fol, gt_dir_files[0])) as f:\n            self.gt_data = json.load(f)\n\n        # merge categories marked with a merged tag in TAO dataset\n        self._merge_categories(self.gt_data['annotations'] + self.gt_data['tracks'])\n\n        # Get sequences to eval and sequence information\n        self.seq_list = [vid['name'].replace('/', '-') for vid in self.gt_data['videos']]\n        self.seq_name_to_seq_id = {vid['name'].replace('/', '-'): vid['id'] for vid in self.gt_data['videos']}\n        # compute mappings from videos to annotation data\n        self.videos_to_gt_tracks, self.videos_to_gt_images = self._compute_vid_mappings(self.gt_data['annotations'])\n        # compute sequence lengths\n        self.seq_lengths = {vid['id']: 0 for vid in self.gt_data['videos']}\n        for img in self.gt_data['images']:\n            self.seq_lengths[img['video_id']] += 1\n        self.seq_to_images_to_timestep = self._compute_image_to_timestep_mappings()\n        self.seq_to_classes = {vid['id']: {'pos_cat_ids': list({track['category_id'] for track\n                                                                in self.videos_to_gt_tracks[vid['id']]}),\n                                           'neg_cat_ids': vid['neg_category_ids'],\n                                           'not_exhaustively_labeled_cat_ids': vid['not_exhaustive_category_ids']}\n                               for vid in self.gt_data['videos']}\n\n        # Get classes to eval\n        considered_vid_ids = [self.seq_name_to_seq_id[vid] for vid in self.seq_list]\n        seen_cats = set([cat_id for vid_id in considered_vid_ids for cat_id\n                         in self.seq_to_classes[vid_id]['pos_cat_ids']])\n        # only classes with ground truth are evaluated in TAO\n        self.valid_classes = [cls['name'] for cls in self.gt_data['categories'] if cls['id'] in seen_cats]\n        cls_name_to_cls_id_map = {cls['name']: cls['id'] for cls in self.gt_data['categories']}\n\n        if self.config['CLASSES_TO_EVAL']:\n            self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n                               for cls in self.config['CLASSES_TO_EVAL']]\n            if not all(self.class_list):\n                raise TrackEvalException('Attempted to evaluate an invalid class. Only classes ' +\n                                         ', '.join(self.valid_classes) +\n                                         ' are valid (classes present in ground truth data).')\n        else:\n            self.class_list = [cls for cls in self.valid_classes]\n        self.class_name_to_class_id = {k: v for k, v in cls_name_to_cls_id_map.items() if k in self.class_list}\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n        self.tracker_data = {tracker: dict() for tracker in self.tracker_list}\n\n        for tracker in self.tracker_list:\n            tr_dir_files = [file for file in os.listdir(os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol))\n                            if file.endswith('.json')]\n            if len(tr_dir_files) != 1:\n                raise TrackEvalException(os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol)\n                                         + ' does not contain exactly one json file.')\n            with open(os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, tr_dir_files[0])) as f:\n                curr_data = json.load(f)\n\n            # limit detections if MAX_DETECTIONS > 0\n            if self.config['MAX_DETECTIONS']:\n                curr_data = self._limit_dets_per_image(curr_data)\n\n            # fill missing video ids\n            self._fill_video_ids_inplace(curr_data)\n\n            # make track ids unique over whole evaluation set\n            self._make_track_ids_unique(curr_data)\n\n            # merge categories marked with a merged tag in TAO dataset\n            self._merge_categories(curr_data)\n\n            # get tracker sequence information\n            curr_videos_to_tracker_tracks, curr_videos_to_tracker_images = self._compute_vid_mappings(curr_data)\n            self.tracker_data[tracker]['vids_to_tracks'] = curr_videos_to_tracker_tracks\n            self.tracker_data[tracker]['vids_to_images'] = curr_videos_to_tracker_images\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the TAO format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets]: list (for each timestep) of lists of detections.\n        [classes_to_gt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as\n                                keys and corresponding segmentations as values) for each track\n        [classes_to_gt_track_ids, classes_to_gt_track_areas, classes_to_gt_track_lengths]: dictionary with class values\n                                as keys and lists (for each track) as values\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        [classes_to_dt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as\n                                keys and corresponding segmentations as values) for each track\n        [classes_to_dt_track_ids, classes_to_dt_track_areas, classes_to_dt_track_lengths]: dictionary with class values\n                                                                                           as keys and lists as values\n        [classes_to_dt_track_scores]: dictionary with class values as keys and 1D numpy arrays as values\n        \"\"\"\n        seq_id = self.seq_name_to_seq_id[seq]\n        # File location\n        if is_gt:\n            imgs = self.videos_to_gt_images[seq_id]\n        else:\n            imgs = self.tracker_data[tracker]['vids_to_images'][seq_id]\n\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq_id]\n        img_to_timestep = self.seq_to_images_to_timestep[seq_id]\n        data_keys = ['ids', 'classes', 'dets']\n        if not is_gt:\n            data_keys += ['tracker_confidences']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n        for img in imgs:\n            # some tracker data contains images without any ground truth information, these are ignored\n            try:\n                t = img_to_timestep[img['id']]\n            except KeyError:\n                continue\n            annotations = img['annotations']\n            raw_data['dets'][t] = np.atleast_2d([ann['bbox'] for ann in annotations]).astype(float)\n            raw_data['ids'][t] = np.atleast_1d([ann['track_id'] for ann in annotations]).astype(int)\n            raw_data['classes'][t] = np.atleast_1d([ann['category_id'] for ann in annotations]).astype(int)\n            if not is_gt:\n                raw_data['tracker_confidences'][t] = np.atleast_1d([ann['score'] for ann in annotations]).astype(float)\n\n        for t, d in enumerate(raw_data['dets']):\n            if d is None:\n                raw_data['dets'][t] = np.empty((0, 4)).astype(float)\n                raw_data['ids'][t] = np.empty(0).astype(int)\n                raw_data['classes'][t] = np.empty(0).astype(int)\n                if not is_gt:\n                    raw_data['tracker_confidences'][t] = np.empty(0)\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'classes': 'gt_classes',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'classes': 'tracker_classes',\n                       'dets': 'tracker_dets'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        all_classes = [self.class_name_to_class_id[cls] for cls in self.class_list]\n        if is_gt:\n            classes_to_consider = all_classes\n            all_tracks = self.videos_to_gt_tracks[seq_id]\n        else:\n            classes_to_consider = self.seq_to_classes[seq_id]['pos_cat_ids'] \\\n                                  + self.seq_to_classes[seq_id]['neg_cat_ids']\n            all_tracks = self.tracker_data[tracker]['vids_to_tracks'][seq_id]\n\n        classes_to_tracks = {cls: [track for track in all_tracks if track['category_id'] == cls]\n                             if cls in classes_to_consider else [] for cls in all_classes}\n\n        # mapping from classes to track information\n        raw_data['classes_to_tracks'] = {cls: [{det['image_id']: np.atleast_1d(det['bbox'])\n                                                for det in track['annotations']} for track in tracks]\n                                         for cls, tracks in classes_to_tracks.items()}\n        raw_data['classes_to_track_ids'] = {cls: [track['id'] for track in tracks]\n                                            for cls, tracks in classes_to_tracks.items()}\n        raw_data['classes_to_track_areas'] = {cls: [track['area'] for track in tracks]\n                                              for cls, tracks in classes_to_tracks.items()}\n        raw_data['classes_to_track_lengths'] = {cls: [len(track['annotations']) for track in tracks]\n                                                for cls, tracks in classes_to_tracks.items()}\n\n        if not is_gt:\n            raw_data['classes_to_dt_track_scores'] = {cls: np.array([np.mean([float(x['score'])\n                                                                              for x in track['annotations']])\n                                                                     for track in tracks])\n                                                      for cls, tracks in classes_to_tracks.items()}\n\n        if is_gt:\n            key_map = {'classes_to_tracks': 'classes_to_gt_tracks',\n                       'classes_to_track_ids': 'classes_to_gt_track_ids',\n                       'classes_to_track_lengths': 'classes_to_gt_track_lengths',\n                       'classes_to_track_areas': 'classes_to_gt_track_areas'}\n        else:\n            key_map = {'classes_to_tracks': 'classes_to_dt_tracks',\n                       'classes_to_track_ids': 'classes_to_dt_track_ids',\n                       'classes_to_track_lengths': 'classes_to_dt_track_lengths',\n                       'classes_to_track_areas': 'classes_to_dt_track_areas'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        raw_data['num_timesteps'] = num_timesteps\n        raw_data['neg_cat_ids'] = self.seq_to_classes[seq_id]['neg_cat_ids']\n        raw_data['not_exhaustively_labeled_cls'] = self.seq_to_classes[seq_id]['not_exhaustively_labeled_cat_ids']\n        raw_data['seq'] = seq\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n        TAO:\n            In TAO, the 4 preproc steps are as follow:\n                1) All classes present in the ground truth data are evaluated separately.\n                2) No matched tracker detections are removed.\n                3) Unmatched tracker detections are removed if there is not ground truth data and the class does not\n                    belong to the categories marked as negative for this sequence. Additionally, unmatched tracker\n                    detections for classes which are marked as not exhaustively labeled are removed.\n                4) No gt detections are removed.\n            Further, for TrackMAP computation track representations for the given class are accessed from a dictionary\n            and the tracks from the tracker data are sorted according to the tracker confidence.\n        \"\"\"\n        cls_id = self.class_name_to_class_id[cls]\n        is_not_exhaustively_labeled = cls_id in raw_data['not_exhaustively_labeled_cls']\n        is_neg_category = cls_id in raw_data['neg_cat_ids']\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n        for t in range(raw_data['num_timesteps']):\n\n            # Only extract relevant dets for this class for preproc and eval (cls)\n            gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)\n            gt_class_mask = gt_class_mask.astype(np.bool)\n            gt_ids = raw_data['gt_ids'][t][gt_class_mask]\n            gt_dets = raw_data['gt_dets'][t][gt_class_mask]\n\n            tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)\n            tracker_class_mask = tracker_class_mask.astype(np.bool)\n            tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]\n            tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]\n            tracker_confidences = raw_data['tracker_confidences'][t][tracker_class_mask]\n            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]\n\n            # Match tracker and gt dets (with hungarian algorithm).\n            unmatched_indices = np.arange(tracker_ids.shape[0])\n            if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n                matching_scores = similarity_scores.copy()\n                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0\n                match_rows, match_cols = linear_sum_assignment(-matching_scores)\n                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\n                match_cols = match_cols[actually_matched_mask]\n                unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)\n\n            if gt_ids.shape[0] == 0 and not is_neg_category:\n                to_remove_tracker = unmatched_indices\n            elif is_not_exhaustively_labeled:\n                to_remove_tracker = unmatched_indices\n            else:\n                to_remove_tracker = np.array([], dtype=np.int)\n\n            # remove all unwanted unmatched tracker detections\n            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)\n            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n            data['gt_ids'][t] = gt_ids\n            data['gt_dets'][t] = gt_dets\n            data['similarity_scores'][t] = similarity_scores\n\n            unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n            num_tracker_dets += len(data['tracker_ids'][t])\n            num_gt_dets += len(data['gt_ids'][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = len(unique_tracker_ids)\n        data['num_gt_ids'] = len(unique_gt_ids)\n        data['num_timesteps'] = raw_data['num_timesteps']\n        data['seq'] = raw_data['seq']\n\n        # get track representations\n        data['gt_tracks'] = raw_data['classes_to_gt_tracks'][cls_id]\n        data['gt_track_ids'] = raw_data['classes_to_gt_track_ids'][cls_id]\n        data['gt_track_lengths'] = raw_data['classes_to_gt_track_lengths'][cls_id]\n        data['gt_track_areas'] = raw_data['classes_to_gt_track_areas'][cls_id]\n        data['dt_tracks'] = raw_data['classes_to_dt_tracks'][cls_id]\n        data['dt_track_ids'] = raw_data['classes_to_dt_track_ids'][cls_id]\n        data['dt_track_lengths'] = raw_data['classes_to_dt_track_lengths'][cls_id]\n        data['dt_track_areas'] = raw_data['classes_to_dt_track_areas'][cls_id]\n        data['dt_track_scores'] = raw_data['classes_to_dt_track_scores'][cls_id]\n        data['not_exhaustively_labeled'] = is_not_exhaustively_labeled\n        data['iou_type'] = 'bbox'\n\n        # sort tracker data tracks by tracker confidence scores\n        if data['dt_tracks']:\n            idx = np.argsort([-score for score in data['dt_track_scores']], kind=\"mergesort\")\n            data['dt_track_scores'] = [data['dt_track_scores'][i] for i in idx]\n            data['dt_tracks'] = [data['dt_tracks'][i] for i in idx]\n            data['dt_track_ids'] = [data['dt_track_ids'][i] for i in idx]\n            data['dt_track_lengths'] = [data['dt_track_lengths'][i] for i in idx]\n            data['dt_track_areas'] = [data['dt_track_areas'][i] for i in idx]\n        # Ensure that ids are unique per timestep.\n        self._check_unique_ids(data)\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t)\n        return similarity_scores\n\n    def _merge_categories(self, annotations):\n        \"\"\"\n        Merges categories with a merged tag. Adapted from https://github.com/TAO-Dataset\n        :param annotations: the annotations in which the classes should be merged\n        :return: None\n        \"\"\"\n        merge_map = {}\n        for category in self.gt_data['categories']:\n            if 'merged' in category:\n                for to_merge in category['merged']:\n                    merge_map[to_merge['id']] = category['id']\n\n        for ann in annotations:\n            ann['category_id'] = merge_map.get(ann['category_id'], ann['category_id'])\n\n    def _compute_vid_mappings(self, annotations):\n        \"\"\"\n        Computes mappings from Videos to corresponding tracks and images.\n        :param annotations: the annotations for which the mapping should be generated\n        :return: the video-to-track-mapping, the video-to-image-mapping\n        \"\"\"\n        vids_to_tracks = {}\n        vids_to_imgs = {}\n        vid_ids = [vid['id'] for vid in self.gt_data['videos']]\n\n        # compute an mapping from image IDs to images\n        images = {}\n        for image in self.gt_data['images']:\n            images[image['id']] = image\n\n        for ann in annotations:\n            ann[\"area\"] = ann[\"bbox\"][2] * ann[\"bbox\"][3]\n\n            vid = ann[\"video_id\"]\n            if ann[\"video_id\"] not in vids_to_tracks.keys():\n                vids_to_tracks[ann[\"video_id\"]] = list()\n            if ann[\"video_id\"] not in vids_to_imgs.keys():\n                vids_to_imgs[ann[\"video_id\"]] = list()\n\n            # Fill in vids_to_tracks\n            tid = ann[\"track_id\"]\n            exist_tids = [track[\"id\"] for track in vids_to_tracks[vid]]\n            try:\n                index1 = exist_tids.index(tid)\n            except ValueError:\n                index1 = -1\n            if tid not in exist_tids:\n                curr_track = {\"id\": tid, \"category_id\": ann['category_id'],\n                              \"video_id\": vid, \"annotations\": [ann]}\n                vids_to_tracks[vid].append(curr_track)\n            else:\n                vids_to_tracks[vid][index1][\"annotations\"].append(ann)\n\n            # Fill in vids_to_imgs\n            img_id = ann['image_id']\n            exist_img_ids = [img[\"id\"] for img in vids_to_imgs[vid]]\n            try:\n                index2 = exist_img_ids.index(img_id)\n            except ValueError:\n                index2 = -1\n            if index2 == -1:\n                curr_img = {\"id\": img_id, \"annotations\": [ann]}\n                vids_to_imgs[vid].append(curr_img)\n            else:\n                vids_to_imgs[vid][index2][\"annotations\"].append(ann)\n\n        # sort annotations by frame index and compute track area\n        for vid, tracks in vids_to_tracks.items():\n            for track in tracks:\n                track[\"annotations\"] = sorted(\n                    track['annotations'],\n                    key=lambda x: images[x['image_id']]['frame_index'])\n                # Computer average area\n                track[\"area\"] = (sum(x['area'] for x in track['annotations']) / len(track['annotations']))\n\n        # Ensure all videos are present\n        for vid_id in vid_ids:\n            if vid_id not in vids_to_tracks.keys():\n                vids_to_tracks[vid_id] = []\n            if vid_id not in vids_to_imgs.keys():\n                vids_to_imgs[vid_id] = []\n\n        return vids_to_tracks, vids_to_imgs\n\n    def _compute_image_to_timestep_mappings(self):\n        \"\"\"\n        Computes a mapping from images to the corresponding timestep in the sequence.\n        :return: the image-to-timestep-mapping\n        \"\"\"\n        images = {}\n        for image in self.gt_data['images']:\n            images[image['id']] = image\n\n        seq_to_imgs_to_timestep = {vid['id']: dict() for vid in self.gt_data['videos']}\n        for vid in seq_to_imgs_to_timestep:\n            curr_imgs = [img['id'] for img in self.videos_to_gt_images[vid]]\n            curr_imgs = sorted(curr_imgs, key=lambda x: images[x]['frame_index'])\n            seq_to_imgs_to_timestep[vid] = {curr_imgs[i]: i for i in range(len(curr_imgs))}\n\n        return seq_to_imgs_to_timestep\n\n    def _limit_dets_per_image(self, annotations):\n        \"\"\"\n        Limits the number of detections for each image to config['MAX_DETECTIONS']. Adapted from\n        https://github.com/TAO-Dataset/\n        :param annotations: the annotations in which the detections should be limited\n        :return: the annotations with limited detections\n        \"\"\"\n        max_dets = self.config['MAX_DETECTIONS']\n        img_ann = defaultdict(list)\n        for ann in annotations:\n            img_ann[ann[\"image_id\"]].append(ann)\n\n        for img_id, _anns in img_ann.items():\n            if len(_anns) <= max_dets:\n                continue\n            _anns = sorted(_anns, key=lambda x: x[\"score\"], reverse=True)\n            img_ann[img_id] = _anns[:max_dets]\n\n        return [ann for anns in img_ann.values() for ann in anns]\n\n    def _fill_video_ids_inplace(self, annotations):\n        \"\"\"\n        Fills in missing video IDs inplace. Adapted from https://github.com/TAO-Dataset/\n        :param annotations: the annotations for which the videos IDs should be filled inplace\n        :return: None\n        \"\"\"\n        missing_video_id = [x for x in annotations if 'video_id' not in x]\n        if missing_video_id:\n            image_id_to_video_id = {\n                x['id']: x['video_id'] for x in self.gt_data['images']\n            }\n            for x in missing_video_id:\n                x['video_id'] = image_id_to_video_id[x['image_id']]\n\n    @staticmethod\n    def _make_track_ids_unique(annotations):\n        \"\"\"\n        Makes the track IDs unqiue over the whole annotation set. Adapted from https://github.com/TAO-Dataset/\n        :param annotations: the annotation set\n        :return: the number of updated IDs\n        \"\"\"\n        track_id_videos = {}\n        track_ids_to_update = set()\n        max_track_id = 0\n        for ann in annotations:\n            t = ann['track_id']\n            if t not in track_id_videos:\n                track_id_videos[t] = ann['video_id']\n\n            if ann['video_id'] != track_id_videos[t]:\n                # Track id is assigned to multiple videos\n                track_ids_to_update.add(t)\n            max_track_id = max(max_track_id, t)\n\n        if track_ids_to_update:\n            print('true')\n            next_id = itertools.count(max_track_id + 1)\n            new_track_ids = defaultdict(lambda: next(next_id))\n            for ann in annotations:\n                t = ann['track_id']\n                v = ann['video_id']\n                if t in track_ids_to_update:\n                    ann['track_id'] = new_track_ids[t, v]\n        return len(track_ids_to_update)\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/tao_ow.py",
    "content": "import os\nimport numpy as np\nimport json\nimport itertools\nfrom collections import defaultdict\nfrom scipy.optimize import linear_sum_assignment\nfrom ..utils import TrackEvalException\nfrom ._base_dataset import _BaseDataset\nfrom .. import utils\nfrom .. import _timing\n\n\nclass TAO_OW(_BaseDataset):\n    \"\"\"Dataset class for TAO tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/tao/tao_training'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/tao/tao_training'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)\n            'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val'\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n            'MAX_DETECTIONS': 300,  # Number of maximal allowed detections per image (0 for unlimited)\n            'SUBSET': 'all'\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())\n        self.gt_fol = self.config['GT_FOLDER']\n        self.tracker_fol = self.config['TRACKERS_FOLDER']\n        self.should_classes_combine = True\n        self.use_super_categories = False\n\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']\n\n        gt_dir_files = [file for file in os.listdir(self.gt_fol) if file.endswith('.json')]\n        if len(gt_dir_files) != 1:\n            raise TrackEvalException(self.gt_fol + ' does not contain exactly one json file.')\n\n        with open(os.path.join(self.gt_fol, gt_dir_files[0])) as f:\n            self.gt_data = json.load(f)\n\n        self.subset = self.config['SUBSET']\n        if self.subset != 'all':\n            # Split GT data into `known`, `unknown` or `distractor`\n            self._split_known_unknown_distractor()\n            self.gt_data = self._filter_gt_data(self.gt_data)\n\n        # merge categories marked with a merged tag in TAO dataset\n        self._merge_categories(self.gt_data['annotations'] + self.gt_data['tracks'])\n\n        # Get sequences to eval and sequence information\n        self.seq_list = [vid['name'].replace('/', '-') for vid in self.gt_data['videos']]\n        self.seq_name_to_seq_id = {vid['name'].replace('/', '-'): vid['id'] for vid in self.gt_data['videos']}\n        # compute mappings from videos to annotation data\n        self.videos_to_gt_tracks, self.videos_to_gt_images = self._compute_vid_mappings(self.gt_data['annotations'])\n        # compute sequence lengths\n        self.seq_lengths = {vid['id']: 0 for vid in self.gt_data['videos']}\n        for img in self.gt_data['images']:\n            self.seq_lengths[img['video_id']] += 1\n        self.seq_to_images_to_timestep = self._compute_image_to_timestep_mappings()\n        self.seq_to_classes = {vid['id']: {'pos_cat_ids': list({track['category_id'] for track\n                                                                in self.videos_to_gt_tracks[vid['id']]}),\n                                           'neg_cat_ids': vid['neg_category_ids'],\n                                           'not_exhaustively_labeled_cat_ids': vid['not_exhaustive_category_ids']}\n                               for vid in self.gt_data['videos']}\n\n        # Get classes to eval\n        considered_vid_ids = [self.seq_name_to_seq_id[vid] for vid in self.seq_list]\n        seen_cats = set([cat_id for vid_id in considered_vid_ids for cat_id\n                         in self.seq_to_classes[vid_id]['pos_cat_ids']])\n        # only classes with ground truth are evaluated in TAO\n        self.valid_classes = [cls['name'] for cls in self.gt_data['categories'] if cls['id'] in seen_cats]\n        # cls_name_to_cls_id_map = {cls['name']: cls['id'] for cls in self.gt_data['categories']}\n\n        if self.config['CLASSES_TO_EVAL']:\n            # self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n            #                    for cls in self.config['CLASSES_TO_EVAL']]\n            self.class_list = [\"object\"]  # class-agnostic\n            if not all(self.class_list):\n                raise TrackEvalException('Attempted to evaluate an invalid class. Only classes ' +\n                                         ', '.join(self.valid_classes) +\n                                         ' are valid (classes present in ground truth data).')\n        else:\n            # self.class_list = [cls for cls in self.valid_classes]\n            self.class_list = [\"object\"]  # class-agnostic\n        # self.class_name_to_class_id = {k: v for k, v in cls_name_to_cls_id_map.items() if k in self.class_list}\n        self.class_name_to_class_id = {\"object\": 1}  # class-agnostic\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n        self.tracker_data = {tracker: dict() for tracker in self.tracker_list}\n\n        for tracker in self.tracker_list:\n            tr_dir_files = [file for file in os.listdir(os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol))\n                            if file.endswith('.json')]\n            if len(tr_dir_files) != 1:\n                raise TrackEvalException(os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol)\n                                         + ' does not contain exactly one json file.')\n            with open(os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, tr_dir_files[0])) as f:\n                curr_data = json.load(f)\n\n            # limit detections if MAX_DETECTIONS > 0\n            if self.config['MAX_DETECTIONS']:\n                curr_data = self._limit_dets_per_image(curr_data)\n\n            # fill missing video ids\n            self._fill_video_ids_inplace(curr_data)\n\n            # make track ids unique over whole evaluation set\n            self._make_track_ids_unique(curr_data)\n\n            # merge categories marked with a merged tag in TAO dataset\n            self._merge_categories(curr_data)\n\n            # get tracker sequence information\n            curr_videos_to_tracker_tracks, curr_videos_to_tracker_images = self._compute_vid_mappings(curr_data)\n            self.tracker_data[tracker]['vids_to_tracks'] = curr_videos_to_tracker_tracks\n            self.tracker_data[tracker]['vids_to_images'] = curr_videos_to_tracker_images\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the TAO format\n\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets]: list (for each timestep) of lists of detections.\n        [classes_to_gt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as\n                                keys and corresponding segmentations as values) for each track\n        [classes_to_gt_track_ids, classes_to_gt_track_areas, classes_to_gt_track_lengths]: dictionary with class values\n                                as keys and lists (for each track) as values\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        [classes_to_dt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as\n                                keys and corresponding segmentations as values) for each track\n        [classes_to_dt_track_ids, classes_to_dt_track_areas, classes_to_dt_track_lengths]: dictionary with class values\n                                                                                           as keys and lists as values\n        [classes_to_dt_track_scores]: dictionary with class values as keys and 1D numpy arrays as values\n        \"\"\"\n        seq_id = self.seq_name_to_seq_id[seq]\n        # File location\n        if is_gt:\n            imgs = self.videos_to_gt_images[seq_id]\n        else:\n            imgs = self.tracker_data[tracker]['vids_to_images'][seq_id]\n\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq_id]\n        img_to_timestep = self.seq_to_images_to_timestep[seq_id]\n        data_keys = ['ids', 'classes', 'dets']\n        if not is_gt:\n            data_keys += ['tracker_confidences']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n        for img in imgs:\n            # some tracker data contains images without any ground truth information, these are ignored\n            try:\n                t = img_to_timestep[img['id']]\n            except KeyError:\n                continue\n            annotations = img['annotations']\n            raw_data['dets'][t] = np.atleast_2d([ann['bbox'] for ann in annotations]).astype(float)\n            raw_data['ids'][t] = np.atleast_1d([ann['track_id'] for ann in annotations]).astype(int)\n            raw_data['classes'][t] = np.atleast_1d([1 for _ in annotations]).astype(int)   # class-agnostic\n            if not is_gt:\n                raw_data['tracker_confidences'][t] = np.atleast_1d([ann['score'] for ann in annotations]).astype(float)\n\n        for t, d in enumerate(raw_data['dets']):\n            if d is None:\n                raw_data['dets'][t] = np.empty((0, 4)).astype(float)\n                raw_data['ids'][t] = np.empty(0).astype(int)\n                raw_data['classes'][t] = np.empty(0).astype(int)\n                if not is_gt:\n                    raw_data['tracker_confidences'][t] = np.empty(0)\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'classes': 'gt_classes',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'classes': 'tracker_classes',\n                       'dets': 'tracker_dets'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        # all_classes = [self.class_name_to_class_id[cls] for cls in self.class_list]\n        all_classes = [1]  # class-agnostic\n\n        if is_gt:\n            classes_to_consider = all_classes\n            all_tracks = self.videos_to_gt_tracks[seq_id]\n        else:\n            # classes_to_consider = self.seq_to_classes[seq_id]['pos_cat_ids'] \\\n            #                       + self.seq_to_classes[seq_id]['neg_cat_ids']\n            classes_to_consider = all_classes  # class-agnostic\n            all_tracks = self.tracker_data[tracker]['vids_to_tracks'][seq_id]\n\n        # classes_to_tracks = {cls: [track for track in all_tracks if track['category_id'] == cls]\n        #                      if cls in classes_to_consider else [] for cls in all_classes}\n        classes_to_tracks = {cls: [track for track in all_tracks]\n        if cls in classes_to_consider else [] for cls in all_classes}  # class-agnostic\n\n        # mapping from classes to track information\n        raw_data['classes_to_tracks'] = {cls: [{det['image_id']: np.atleast_1d(det['bbox'])\n                                                for det in track['annotations']} for track in tracks]\n                                         for cls, tracks in classes_to_tracks.items()}\n        raw_data['classes_to_track_ids'] = {cls: [track['id'] for track in tracks]\n                                            for cls, tracks in classes_to_tracks.items()}\n        raw_data['classes_to_track_areas'] = {cls: [track['area'] for track in tracks]\n                                              for cls, tracks in classes_to_tracks.items()}\n        raw_data['classes_to_track_lengths'] = {cls: [len(track['annotations']) for track in tracks]\n                                                for cls, tracks in classes_to_tracks.items()}\n\n        if not is_gt:\n            raw_data['classes_to_dt_track_scores'] = {cls: np.array([np.mean([float(x['score'])\n                                                                              for x in track['annotations']])\n                                                                     for track in tracks])\n                                                      for cls, tracks in classes_to_tracks.items()}\n\n        if is_gt:\n            key_map = {'classes_to_tracks': 'classes_to_gt_tracks',\n                       'classes_to_track_ids': 'classes_to_gt_track_ids',\n                       'classes_to_track_lengths': 'classes_to_gt_track_lengths',\n                       'classes_to_track_areas': 'classes_to_gt_track_areas'}\n        else:\n            key_map = {'classes_to_tracks': 'classes_to_dt_tracks',\n                       'classes_to_track_ids': 'classes_to_dt_track_ids',\n                       'classes_to_track_lengths': 'classes_to_dt_track_lengths',\n                       'classes_to_track_areas': 'classes_to_dt_track_areas'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        raw_data['num_timesteps'] = num_timesteps\n        raw_data['neg_cat_ids'] = self.seq_to_classes[seq_id]['neg_cat_ids']\n        raw_data['not_exhaustively_labeled_cls'] = self.seq_to_classes[seq_id]['not_exhaustively_labeled_cat_ids']\n        raw_data['seq'] = seq\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n        TAO:\n            In TAO, the 4 preproc steps are as follow:\n                1) All classes present in the ground truth data are evaluated separately.\n                2) No matched tracker detections are removed.\n                3) Unmatched tracker detections are removed if there is not ground truth data and the class does not\n                    belong to the categories marked as negative for this sequence. Additionally, unmatched tracker\n                    detections for classes which are marked as not exhaustively labeled are removed.\n                4) No gt detections are removed.\n            Further, for TrackMAP computation track representations for the given class are accessed from a dictionary\n            and the tracks from the tracker data are sorted according to the tracker confidence.\n        \"\"\"\n        cls_id = self.class_name_to_class_id[cls]\n        is_not_exhaustively_labeled = cls_id in raw_data['not_exhaustively_labeled_cls']\n        is_neg_category = cls_id in raw_data['neg_cat_ids']\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n        for t in range(raw_data['num_timesteps']):\n\n            # Only extract relevant dets for this class for preproc and eval (cls)\n            gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)\n            gt_class_mask = gt_class_mask.astype(np.bool)\n            gt_ids = raw_data['gt_ids'][t][gt_class_mask]\n            gt_dets = raw_data['gt_dets'][t][gt_class_mask]\n\n            tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)\n            tracker_class_mask = tracker_class_mask.astype(np.bool)\n            tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]\n            tracker_dets = raw_data['tracker_dets'][t][tracker_class_mask]\n            tracker_confidences = raw_data['tracker_confidences'][t][tracker_class_mask]\n            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]\n\n            # Match tracker and gt dets (with hungarian algorithm).\n            unmatched_indices = np.arange(tracker_ids.shape[0])\n            if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n                matching_scores = similarity_scores.copy()\n                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0\n                match_rows, match_cols = linear_sum_assignment(-matching_scores)\n                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\n                match_cols = match_cols[actually_matched_mask]\n                unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)\n\n            if gt_ids.shape[0] == 0 and not is_neg_category:\n                to_remove_tracker = unmatched_indices\n            elif is_not_exhaustively_labeled:\n                to_remove_tracker = unmatched_indices\n            else:\n                to_remove_tracker = np.array([], dtype=np.int)\n\n            # remove all unwanted unmatched tracker detections\n            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)\n            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n            data['gt_ids'][t] = gt_ids\n            data['gt_dets'][t] = gt_dets\n            data['similarity_scores'][t] = similarity_scores\n\n            unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n            num_tracker_dets += len(data['tracker_ids'][t])\n            num_gt_dets += len(data['gt_ids'][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = len(unique_tracker_ids)\n        data['num_gt_ids'] = len(unique_gt_ids)\n        data['num_timesteps'] = raw_data['num_timesteps']\n        data['seq'] = raw_data['seq']\n\n        # get track representations\n        data['gt_tracks'] = raw_data['classes_to_gt_tracks'][cls_id]\n        data['gt_track_ids'] = raw_data['classes_to_gt_track_ids'][cls_id]\n        data['gt_track_lengths'] = raw_data['classes_to_gt_track_lengths'][cls_id]\n        data['gt_track_areas'] = raw_data['classes_to_gt_track_areas'][cls_id]\n        data['dt_tracks'] = raw_data['classes_to_dt_tracks'][cls_id]\n        data['dt_track_ids'] = raw_data['classes_to_dt_track_ids'][cls_id]\n        data['dt_track_lengths'] = raw_data['classes_to_dt_track_lengths'][cls_id]\n        data['dt_track_areas'] = raw_data['classes_to_dt_track_areas'][cls_id]\n        data['dt_track_scores'] = raw_data['classes_to_dt_track_scores'][cls_id]\n        data['not_exhaustively_labeled'] = is_not_exhaustively_labeled\n        data['iou_type'] = 'bbox'\n\n        # sort tracker data tracks by tracker confidence scores\n        if data['dt_tracks']:\n            idx = np.argsort([-score for score in data['dt_track_scores']], kind=\"mergesort\")\n            data['dt_track_scores'] = [data['dt_track_scores'][i] for i in idx]\n            data['dt_tracks'] = [data['dt_tracks'][i] for i in idx]\n            data['dt_track_ids'] = [data['dt_track_ids'][i] for i in idx]\n            data['dt_track_lengths'] = [data['dt_track_lengths'][i] for i in idx]\n            data['dt_track_areas'] = [data['dt_track_areas'][i] for i in idx]\n        # Ensure that ids are unique per timestep.\n        self._check_unique_ids(data)\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t)\n        return similarity_scores\n\n    def _merge_categories(self, annotations):\n        \"\"\"\n        Merges categories with a merged tag. Adapted from https://github.com/TAO-Dataset\n        :param annotations: the annotations in which the classes should be merged\n        :return: None\n        \"\"\"\n        merge_map = {}\n        for category in self.gt_data['categories']:\n            if 'merged' in category:\n                for to_merge in category['merged']:\n                    merge_map[to_merge['id']] = category['id']\n\n        for ann in annotations:\n            ann['category_id'] = merge_map.get(ann['category_id'], ann['category_id'])\n\n    def _compute_vid_mappings(self, annotations):\n        \"\"\"\n        Computes mappings from Videos to corresponding tracks and images.\n        :param annotations: the annotations for which the mapping should be generated\n        :return: the video-to-track-mapping, the video-to-image-mapping\n        \"\"\"\n        vids_to_tracks = {}\n        vids_to_imgs = {}\n        vid_ids = [vid['id'] for vid in self.gt_data['videos']]\n\n        # compute an mapping from image IDs to images\n        images = {}\n        for image in self.gt_data['images']:\n            images[image['id']] = image\n\n        for ann in annotations:\n            ann[\"area\"] = ann[\"bbox\"][2] * ann[\"bbox\"][3]\n\n            vid = ann[\"video_id\"]\n            if ann[\"video_id\"] not in vids_to_tracks.keys():\n                vids_to_tracks[ann[\"video_id\"]] = list()\n            if ann[\"video_id\"] not in vids_to_imgs.keys():\n                vids_to_imgs[ann[\"video_id\"]] = list()\n\n            # Fill in vids_to_tracks\n            tid = ann[\"track_id\"]\n            exist_tids = [track[\"id\"] for track in vids_to_tracks[vid]]\n            try:\n                index1 = exist_tids.index(tid)\n            except ValueError:\n                index1 = -1\n            if tid not in exist_tids:\n                curr_track = {\"id\": tid, \"category_id\": ann['category_id'],\n                              \"video_id\": vid, \"annotations\": [ann]}\n                vids_to_tracks[vid].append(curr_track)\n            else:\n                vids_to_tracks[vid][index1][\"annotations\"].append(ann)\n\n            # Fill in vids_to_imgs\n            img_id = ann['image_id']\n            exist_img_ids = [img[\"id\"] for img in vids_to_imgs[vid]]\n            try:\n                index2 = exist_img_ids.index(img_id)\n            except ValueError:\n                index2 = -1\n            if index2 == -1:\n                curr_img = {\"id\": img_id, \"annotations\": [ann]}\n                vids_to_imgs[vid].append(curr_img)\n            else:\n                vids_to_imgs[vid][index2][\"annotations\"].append(ann)\n\n        # sort annotations by frame index and compute track area\n        for vid, tracks in vids_to_tracks.items():\n            for track in tracks:\n                track[\"annotations\"] = sorted(\n                    track['annotations'],\n                    key=lambda x: images[x['image_id']]['frame_index'])\n                # Computer average area\n                track[\"area\"] = (sum(x['area'] for x in track['annotations']) / len(track['annotations']))\n\n        # Ensure all videos are present\n        for vid_id in vid_ids:\n            if vid_id not in vids_to_tracks.keys():\n                vids_to_tracks[vid_id] = []\n            if vid_id not in vids_to_imgs.keys():\n                vids_to_imgs[vid_id] = []\n\n        return vids_to_tracks, vids_to_imgs\n\n    def _compute_image_to_timestep_mappings(self):\n        \"\"\"\n        Computes a mapping from images to the corresponding timestep in the sequence.\n        :return: the image-to-timestep-mapping\n        \"\"\"\n        images = {}\n        for image in self.gt_data['images']:\n            images[image['id']] = image\n\n        seq_to_imgs_to_timestep = {vid['id']: dict() for vid in self.gt_data['videos']}\n        for vid in seq_to_imgs_to_timestep:\n            curr_imgs = [img['id'] for img in self.videos_to_gt_images[vid]]\n            curr_imgs = sorted(curr_imgs, key=lambda x: images[x]['frame_index'])\n            seq_to_imgs_to_timestep[vid] = {curr_imgs[i]: i for i in range(len(curr_imgs))}\n\n        return seq_to_imgs_to_timestep\n\n    def _limit_dets_per_image(self, annotations):\n        \"\"\"\n        Limits the number of detections for each image to config['MAX_DETECTIONS']. Adapted from\n        https://github.com/TAO-Dataset/\n        :param annotations: the annotations in which the detections should be limited\n        :return: the annotations with limited detections\n        \"\"\"\n        max_dets = self.config['MAX_DETECTIONS']\n        img_ann = defaultdict(list)\n        for ann in annotations:\n            img_ann[ann[\"image_id\"]].append(ann)\n\n        for img_id, _anns in img_ann.items():\n            if len(_anns) <= max_dets:\n                continue\n            _anns = sorted(_anns, key=lambda x: x[\"score\"], reverse=True)\n            img_ann[img_id] = _anns[:max_dets]\n\n        return [ann for anns in img_ann.values() for ann in anns]\n\n    def _fill_video_ids_inplace(self, annotations):\n        \"\"\"\n        Fills in missing video IDs inplace. Adapted from https://github.com/TAO-Dataset/\n        :param annotations: the annotations for which the videos IDs should be filled inplace\n        :return: None\n        \"\"\"\n        missing_video_id = [x for x in annotations if 'video_id' not in x]\n        if missing_video_id:\n            image_id_to_video_id = {\n                x['id']: x['video_id'] for x in self.gt_data['images']\n            }\n            for x in missing_video_id:\n                x['video_id'] = image_id_to_video_id[x['image_id']]\n\n    @staticmethod\n    def _make_track_ids_unique(annotations):\n        \"\"\"\n        Makes the track IDs unqiue over the whole annotation set. Adapted from https://github.com/TAO-Dataset/\n        :param annotations: the annotation set\n        :return: the number of updated IDs\n        \"\"\"\n        track_id_videos = {}\n        track_ids_to_update = set()\n        max_track_id = 0\n        for ann in annotations:\n            t = ann['track_id']\n            if t not in track_id_videos:\n                track_id_videos[t] = ann['video_id']\n\n            if ann['video_id'] != track_id_videos[t]:\n                # Track id is assigned to multiple videos\n                track_ids_to_update.add(t)\n            max_track_id = max(max_track_id, t)\n\n        if track_ids_to_update:\n            print('true')\n            next_id = itertools.count(max_track_id + 1)\n            new_track_ids = defaultdict(lambda: next(next_id))\n            for ann in annotations:\n                t = ann['track_id']\n                v = ann['video_id']\n                if t in track_ids_to_update:\n                    ann['track_id'] = new_track_ids[t, v]\n        return len(track_ids_to_update)\n\n    def _split_known_unknown_distractor(self):\n        all_ids = set([i for i in range(1, 2000)])  # 2000 is larger than the max category id in TAO-OW.\n        # `knowns` includes 78 TAO_category_ids that corresponds to 78 COCO classes.\n        # (The other 2 COCO classes do not have corresponding classes in TAO).\n        self.knowns = {4, 13, 1038, 544, 1057, 34, 35, 36, 41, 45, 58, 60, 579, 1091, 1097, 1099, 78, 79, 81, 91, 1115,\n                     1117, 95, 1122, 99, 1132, 621, 1135, 625, 118, 1144, 126, 642, 1155, 133, 1162, 139, 154, 174, 185,\n                     699, 1215, 714, 717, 1229, 211, 729, 221, 229, 747, 235, 237, 779, 276, 805, 299, 829, 852, 347,\n                     371, 382, 896, 392, 926, 937, 428, 429, 961, 452, 979, 980, 982, 475, 480, 993, 1001, 502, 1018}\n        # `distractors` is defined as in the paper \"Opening up Open-World Tracking\"\n        self.distractors = {20, 63, 108, 180, 188, 204, 212, 247, 303, 403, 407, 415, 490, 504, 507, 513, 529, 567,\n                            569, 588, 672, 691, 702, 708, 711, 720, 736, 737, 798, 813, 815, 827, 831, 851, 877, 883,\n                            912, 971, 976, 1130, 1133, 1134, 1169, 1184, 1220}\n        self.unknowns = all_ids.difference(self.knowns.union(self.distractors))\n\n    def _filter_gt_data(self, raw_gt_data):\n        \"\"\"\n        Filter out irrelevant data in the raw_gt_data\n        Args:\n            raw_gt_data: directly loaded from json.\n\n        Returns:\n            filtered gt_data\n        \"\"\"\n        valid_cat_ids = list()\n        if self.subset == \"known\":\n            valid_cat_ids = self.knowns\n        elif self.subset == \"distractor\":\n            valid_cat_ids = self.distractors\n        elif self.subset == \"unknown\":\n            valid_cat_ids = self.unknowns\n        # elif self.subset == \"test_only_unknowns\":\n        #     valid_cat_ids = test_only_unknowns\n        else:\n            raise Exception(\"The parameter `SUBSET` is incorrect\")\n\n        filtered = dict()\n        filtered[\"videos\"] = raw_gt_data[\"videos\"]\n        # filtered[\"videos\"] = list()\n        unwanted_vid = set()\n        # for video in raw_gt_data[\"videos\"]:\n        #     datasrc = video[\"name\"].split('/')[1]\n        #     if datasrc in data_srcs:\n        #         filtered[\"videos\"].append(video)\n        #     else:\n        #         unwanted_vid.add(video[\"id\"])\n\n        filtered[\"annotations\"] = list()\n        for ann in raw_gt_data[\"annotations\"]:\n            if (ann[\"video_id\"] not in unwanted_vid) and (ann[\"category_id\"] in valid_cat_ids):\n                filtered[\"annotations\"].append(ann)\n\n        filtered[\"tracks\"] = list()\n        for track in raw_gt_data[\"tracks\"]:\n            if (track[\"video_id\"] not in unwanted_vid) and (track[\"category_id\"] in valid_cat_ids):\n                filtered[\"tracks\"].append(track)\n\n        filtered[\"images\"] = list()\n        for image in raw_gt_data[\"images\"]:\n            if image[\"video_id\"] not in unwanted_vid:\n                filtered[\"images\"].append(image)\n\n        filtered[\"categories\"] = list()\n        for cat in raw_gt_data[\"categories\"]:\n            if cat[\"id\"] in valid_cat_ids:\n                filtered[\"categories\"].append(cat)\n\n        filtered[\"info\"] = raw_gt_data[\"info\"]\n        filtered[\"licenses\"] = raw_gt_data[\"licenses\"]\n\n        return filtered\n"
  },
  {
    "path": "TrackEval/trackeval/datasets/youtube_vis.py",
    "content": "import os\nimport numpy as np\nimport json\nfrom ._base_dataset import _BaseDataset\nfrom ..utils import TrackEvalException\nfrom .. import utils\nfrom .. import _timing\n\n\nclass YouTubeVIS(_BaseDataset):\n    \"\"\"Dataset class for YouTubeVIS tracking\"\"\"\n\n    @staticmethod\n    def get_default_dataset_config():\n        \"\"\"Default class config values\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/youtube_vis/'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/youtube_vis/'),\n            # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)\n            'SPLIT_TO_EVAL': 'train_sub_split',  # Valid: 'train', 'val', 'train_sub_split'\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise dataset, checking that all required files are present\"\"\"\n        super().__init__()\n        # Fill non-given config values with defaults\n        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())\n        self.gt_fol = self.config['GT_FOLDER'] + 'youtube_vis_' + self.config['SPLIT_TO_EVAL']\n        self.tracker_fol = self.config['TRACKERS_FOLDER'] + 'youtube_vis_' + self.config['SPLIT_TO_EVAL']\n        self.use_super_categories = False\n        self.should_classes_combine = True\n\n        self.output_fol = self.config['OUTPUT_FOLDER']\n        if self.output_fol is None:\n            self.output_fol = self.tracker_fol\n        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']\n        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']\n\n        if not os.path.exists(self.gt_fol):\n            print(\"GT folder not found: \" + self.gt_fol)\n            raise TrackEvalException(\"GT folder not found: \" + os.path.basename(self.gt_fol))\n        gt_dir_files = [file for file in os.listdir(self.gt_fol) if file.endswith('.json')]\n        if len(gt_dir_files) != 1:\n            raise TrackEvalException(self.gt_fol + ' does not contain exactly one json file.')\n\n        with open(os.path.join(self.gt_fol, gt_dir_files[0])) as f:\n            self.gt_data = json.load(f)\n\n        # Get classes to eval\n        self.valid_classes = [cls['name'] for cls in self.gt_data['categories']]\n        cls_name_to_cls_id_map = {cls['name']: cls['id'] for cls in self.gt_data['categories']}\n\n        if self.config['CLASSES_TO_EVAL']:\n            self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None\n                               for cls in self.config['CLASSES_TO_EVAL']]\n            if not all(self.class_list):\n                raise TrackEvalException('Attempted to evaluate an invalid class. Only classes ' +\n                                         ', '.join(self.valid_classes) + ' are valid.')\n        else:\n            self.class_list = [cls['name'] for cls in self.gt_data['categories']]\n        self.class_name_to_class_id = {k: v for k, v in cls_name_to_cls_id_map.items() if k in self.class_list}\n\n        # Get sequences to eval and check gt files exist\n        self.seq_list = [vid['file_names'][0].split('/')[0] for vid in self.gt_data['videos']]\n        self.seq_name_to_seq_id = {vid['file_names'][0].split('/')[0]: vid['id'] for vid in self.gt_data['videos']}\n        self.seq_lengths = {vid['id']: len(vid['file_names']) for vid in self.gt_data['videos']}\n\n        # encode masks and compute track areas\n        self._prepare_gt_annotations()\n\n        # Get trackers to eval\n        if self.config['TRACKERS_TO_EVAL'] is None:\n            self.tracker_list = os.listdir(self.tracker_fol)\n        else:\n            self.tracker_list = self.config['TRACKERS_TO_EVAL']\n\n        if self.config['TRACKER_DISPLAY_NAMES'] is None:\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))\n        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (\n                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):\n            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))\n        else:\n            raise TrackEvalException('List of tracker files and tracker display names do not match.')\n\n        # counter for globally unique track IDs\n        self.global_tid_counter = 0\n\n        self.tracker_data = dict()\n        for tracker in self.tracker_list:\n            tracker_dir_path = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol)\n            tr_dir_files = [file for file in os.listdir(tracker_dir_path) if file.endswith('.json')]\n            if len(tr_dir_files) != 1:\n                raise TrackEvalException(tracker_dir_path + ' does not contain exactly one json file.')\n\n            with open(os.path.join(tracker_dir_path, tr_dir_files[0])) as f:\n                curr_data = json.load(f)\n\n            self.tracker_data[tracker] = curr_data\n\n    def get_display_name(self, tracker):\n        return self.tracker_to_disp[tracker]\n\n    def _load_raw_file(self, tracker, seq, is_gt):\n        \"\"\"Load a file (gt or tracker) in the YouTubeVIS format\n        If is_gt, this returns a dict which contains the fields:\n        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).\n        [gt_dets]: list (for each timestep) of lists of detections.\n        [classes_to_gt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as\n                                keys and corresponding segmentations as values) for each track\n        [classes_to_gt_track_ids, classes_to_gt_track_areas, classes_to_gt_track_iscrowd]: dictionary with class values\n                                as keys and lists (for each track) as values\n\n        if not is_gt, this returns a dict which contains the fields:\n        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).\n        [tracker_dets]: list (for each timestep) of lists of detections.\n        [classes_to_dt_tracks]: dictionary with class values as keys and list of dictionaries (with frame indices as\n                                keys and corresponding segmentations as values) for each track\n        [classes_to_dt_track_ids, classes_to_dt_track_areas]: dictionary with class values as keys and lists as values\n        [classes_to_dt_track_scores]: dictionary with class values as keys and 1D numpy arrays as values\n        \"\"\"\n        # select sequence tracks\n        seq_id = self.seq_name_to_seq_id[seq]\n        if is_gt:\n            tracks = [ann for ann in self.gt_data['annotations'] if ann['video_id'] == seq_id]\n        else:\n            tracks = self._get_tracker_seq_tracks(tracker, seq_id)\n\n        # Convert data to required format\n        num_timesteps = self.seq_lengths[seq_id]\n        data_keys = ['ids', 'classes', 'dets']\n        if not is_gt:\n            data_keys += ['tracker_confidences']\n        raw_data = {key: [None] * num_timesteps for key in data_keys}\n        for t in range(num_timesteps):\n            raw_data['dets'][t] = [track['segmentations'][t] for track in tracks if track['segmentations'][t]]\n            raw_data['ids'][t] = np.atleast_1d([track['id'] for track in tracks\n                                                if track['segmentations'][t]]).astype(int)\n            raw_data['classes'][t] = np.atleast_1d([track['category_id'] for track in tracks\n                                                    if track['segmentations'][t]]).astype(int)\n            if not is_gt:\n                raw_data['tracker_confidences'][t] = np.atleast_1d([track['score'] for track in tracks\n                                                                    if track['segmentations'][t]]).astype(float)\n\n        if is_gt:\n            key_map = {'ids': 'gt_ids',\n                       'classes': 'gt_classes',\n                       'dets': 'gt_dets'}\n        else:\n            key_map = {'ids': 'tracker_ids',\n                       'classes': 'tracker_classes',\n                       'dets': 'tracker_dets'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        all_cls_ids = {self.class_name_to_class_id[cls] for cls in self.class_list}\n        classes_to_tracks = {cls: [track for track in tracks if track['category_id'] == cls] for cls in all_cls_ids}\n\n        # mapping from classes to track representations and track information\n        raw_data['classes_to_tracks'] = {cls: [{i: track['segmentations'][i]\n                                                for i in range(len(track['segmentations']))} for track in tracks]\n                                         for cls, tracks in classes_to_tracks.items()}\n        raw_data['classes_to_track_ids'] = {cls: [track['id'] for track in tracks]\n                                            for cls, tracks in classes_to_tracks.items()}\n        raw_data['classes_to_track_areas'] = {cls: [track['area'] for track in tracks]\n                                              for cls, tracks in classes_to_tracks.items()}\n\n        if is_gt:\n            raw_data['classes_to_gt_track_iscrowd'] = {cls: [track['iscrowd'] for track in tracks]\n                                                       for cls, tracks in classes_to_tracks.items()}\n        else:\n            raw_data['classes_to_dt_track_scores'] = {cls: np.array([track['score'] for track in tracks])\n                                                      for cls, tracks in classes_to_tracks.items()}\n\n        if is_gt:\n            key_map = {'classes_to_tracks': 'classes_to_gt_tracks',\n                       'classes_to_track_ids': 'classes_to_gt_track_ids',\n                       'classes_to_track_areas': 'classes_to_gt_track_areas'}\n        else:\n            key_map = {'classes_to_tracks': 'classes_to_dt_tracks',\n                       'classes_to_track_ids': 'classes_to_dt_track_ids',\n                       'classes_to_track_areas': 'classes_to_dt_track_areas'}\n        for k, v in key_map.items():\n            raw_data[v] = raw_data.pop(k)\n\n        raw_data['num_timesteps'] = num_timesteps\n        raw_data['seq'] = seq\n        return raw_data\n\n    @_timing.time\n    def get_preprocessed_seq_data(self, raw_data, cls):\n        \"\"\" Preprocess data for a single sequence for a single class ready for evaluation.\n        Inputs:\n             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().\n             - cls is the class to be evaluated.\n        Outputs:\n             - data is a dict containing all of the information that metrics need to perform evaluation.\n                It contains the following fields:\n                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.\n                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).\n                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.\n                    [similarity_scores]: list (for each timestep) of 2D NDArrays.\n        Notes:\n            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.\n                1) Extract only detections relevant for the class to be evaluated (including distractor detections).\n                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a\n                    distractor class, or otherwise marked as to be removed.\n                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain\n                    other criteria (e.g. are too small).\n                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.\n            After the above preprocessing steps, this function also calculates the number of gt and tracker detections\n                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are\n                unique within each timestep.\n        YouTubeVIS:\n            In YouTubeVIS, the 4 preproc steps are as follow:\n                1) There are 40 classes which are evaluated separately.\n                2) No matched tracker dets are removed.\n                3) No unmatched tracker dets are removed.\n                4) No gt dets are removed.\n            Further, for TrackMAP computation track representations for the given class are accessed from a dictionary\n            and the tracks from the tracker data are sorted according to the tracker confidence.\n        \"\"\"\n        cls_id = self.class_name_to_class_id[cls]\n\n        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'similarity_scores']\n        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n        unique_gt_ids = []\n        unique_tracker_ids = []\n        num_gt_dets = 0\n        num_tracker_dets = 0\n\n        for t in range(raw_data['num_timesteps']):\n\n            # Only extract relevant dets for this class for eval (cls)\n            gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)\n            gt_class_mask = gt_class_mask.astype(np.bool)\n            gt_ids = raw_data['gt_ids'][t][gt_class_mask]\n            gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]\n\n            tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)\n            tracker_class_mask = tracker_class_mask.astype(np.bool)\n            tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]\n            tracker_dets = [raw_data['tracker_dets'][t][ind] for ind in range(len(tracker_class_mask)) if\n                            tracker_class_mask[ind]]\n            similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]\n\n            data['tracker_ids'][t] = tracker_ids\n            data['tracker_dets'][t] = tracker_dets\n            data['gt_ids'][t] = gt_ids\n            data['gt_dets'][t] = gt_dets\n            data['similarity_scores'][t] = similarity_scores\n\n            unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n            num_tracker_dets += len(data['tracker_ids'][t])\n            num_gt_dets += len(data['gt_ids'][t])\n\n        # Re-label IDs such that there are no empty IDs\n        if len(unique_gt_ids) > 0:\n            unique_gt_ids = np.unique(unique_gt_ids)\n            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['gt_ids'][t]) > 0:\n                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n        if len(unique_tracker_ids) > 0:\n            unique_tracker_ids = np.unique(unique_tracker_ids)\n            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n            for t in range(raw_data['num_timesteps']):\n                if len(data['tracker_ids'][t]) > 0:\n                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n        # Ensure that ids are unique per timestep.\n        self._check_unique_ids(data)\n\n        # Record overview statistics.\n        data['num_tracker_dets'] = num_tracker_dets\n        data['num_gt_dets'] = num_gt_dets\n        data['num_tracker_ids'] = len(unique_tracker_ids)\n        data['num_gt_ids'] = len(unique_gt_ids)\n        data['num_timesteps'] = raw_data['num_timesteps']\n        data['seq'] = raw_data['seq']\n\n        # get track representations\n        data['gt_tracks'] = raw_data['classes_to_gt_tracks'][cls_id]\n        data['gt_track_ids'] = raw_data['classes_to_gt_track_ids'][cls_id]\n        data['gt_track_areas'] = raw_data['classes_to_gt_track_areas'][cls_id]\n        data['gt_track_iscrowd'] = raw_data['classes_to_gt_track_iscrowd'][cls_id]\n        data['dt_tracks'] = raw_data['classes_to_dt_tracks'][cls_id]\n        data['dt_track_ids'] = raw_data['classes_to_dt_track_ids'][cls_id]\n        data['dt_track_areas'] = raw_data['classes_to_dt_track_areas'][cls_id]\n        data['dt_track_scores'] = raw_data['classes_to_dt_track_scores'][cls_id]\n        data['iou_type'] = 'mask'\n\n        # sort tracker data tracks by tracker confidence scores\n        if data['dt_tracks']:\n            idx = np.argsort([-score for score in data['dt_track_scores']], kind=\"mergesort\")\n            data['dt_track_scores'] = [data['dt_track_scores'][i] for i in idx]\n            data['dt_tracks'] = [data['dt_tracks'][i] for i in idx]\n            data['dt_track_ids'] = [data['dt_track_ids'][i] for i in idx]\n            data['dt_track_areas'] = [data['dt_track_areas'][i] for i in idx]\n\n        return data\n\n    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):\n        similarity_scores = self._calculate_mask_ious(gt_dets_t, tracker_dets_t, is_encoded=True, do_ioa=False)\n        return similarity_scores\n\n    def _prepare_gt_annotations(self):\n        \"\"\"\n        Prepares GT data by rle encoding segmentations and computing the average track area.\n        :return: None\n        \"\"\"\n        # only loaded when needed to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        for track in self.gt_data['annotations']:\n            h = track['height']\n            w = track['width']\n            for i, seg in enumerate(track['segmentations']):\n                if seg:\n                    track['segmentations'][i] = mask_utils.frPyObjects(seg, h, w)\n            areas = [a for a in track['areas'] if a]\n            if len(areas) == 0:\n                track['area'] = 0\n            else:\n                track['area'] = np.array(areas).mean()\n\n    def _get_tracker_seq_tracks(self, tracker, seq_id):\n        \"\"\"\n        Prepares tracker data for a given sequence. Extracts all annotations for given sequence ID, computes\n        average track area and assigns a track ID.\n        :param tracker: the given tracker\n        :param seq_id: the sequence ID\n        :return: the extracted tracks\n        \"\"\"\n        # only loaded when needed to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        tracks = [ann for ann in self.tracker_data[tracker] if ann['video_id'] == seq_id]\n        for track in tracks:\n            track['areas'] = []\n            for seg in track['segmentations']:\n                if seg:\n                    track['areas'].append(mask_utils.area(seg))\n                else:\n                    track['areas'].append(None)\n            areas = [a for a in track['areas'] if a]\n            if len(areas) == 0:\n                track['area'] = 0\n            else:\n                track['area'] = np.array(areas).mean()\n            track['id'] = self.global_tid_counter\n            self.global_tid_counter += 1\n        return tracks\n"
  },
  {
    "path": "TrackEval/trackeval/eval.py",
    "content": "import time\nimport traceback\nfrom multiprocessing.pool import Pool\nfrom functools import partial\nimport os\nfrom . import utils\nfrom .utils import TrackEvalException\nfrom . import _timing\nfrom .metrics import Count\n\n\nclass Evaluator:\n    \"\"\"Evaluator class for evaluating different metrics for different datasets\"\"\"\n\n    @staticmethod\n    def get_default_eval_config():\n        \"\"\"Returns the default config values for evaluation\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'USE_PARALLEL': False,\n            'NUM_PARALLEL_CORES': 8,\n            'BREAK_ON_ERROR': True,  # Raises exception and exits with error\n            'RETURN_ON_ERROR': False,  # if not BREAK_ON_ERROR, then returns from function on error\n            'LOG_ON_ERROR': os.path.join(code_path, 'error_log.txt'),  # if not None, save any errors into a log file.\n\n            'PRINT_RESULTS': True,\n            'PRINT_ONLY_COMBINED': False,\n            'PRINT_CONFIG': True,\n            'TIME_PROGRESS': True,\n            'DISPLAY_LESS_PROGRESS': True,\n\n            'OUTPUT_SUMMARY': True,\n            'OUTPUT_EMPTY_CLASSES': True,  # If False, summary files are not output for classes with no detections\n            'OUTPUT_DETAILED': True,\n            'PLOT_CURVES': True,\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        \"\"\"Initialise the evaluator with a config file\"\"\"\n        self.config = utils.init_config(config, self.get_default_eval_config(), 'Eval')\n        # Only run timing analysis if not run in parallel.\n        if self.config['TIME_PROGRESS'] and not self.config['USE_PARALLEL']:\n            _timing.DO_TIMING = True\n            if self.config['DISPLAY_LESS_PROGRESS']:\n                _timing.DISPLAY_LESS_PROGRESS = True\n\n    @_timing.time\n    def evaluate(self, dataset_list, metrics_list):\n        \"\"\"Evaluate a set of metrics on a set of datasets\"\"\"\n        config = self.config\n        metrics_list = metrics_list + [Count()]  # Count metrics are always run\n        metric_names = utils.validate_metrics_list(metrics_list)\n        dataset_names = [dataset.get_name() for dataset in dataset_list]\n        output_res = {}\n        output_msg = {}\n\n        for dataset, dataset_name in zip(dataset_list, dataset_names):\n            # Get dataset info about what to evaluate\n            output_res[dataset_name] = {}\n            output_msg[dataset_name] = {}\n            tracker_list, seq_list, class_list = dataset.get_eval_info()\n            print('\\nEvaluating %i tracker(s) on %i sequence(s) for %i class(es) on %s dataset using the following '\n                  'metrics: %s\\n' % (len(tracker_list), len(seq_list), len(class_list), dataset_name,\n                                     ', '.join(metric_names)))\n\n            # Evaluate each tracker\n            for tracker in tracker_list:\n                # if not config['BREAK_ON_ERROR'] then go to next tracker without breaking\n                try:\n                    # Evaluate each sequence in parallel or in series.\n                    # returns a nested dict (res), indexed like: res[seq][class][metric_name][sub_metric field]\n                    # e.g. res[seq_0001][pedestrian][hota][DetA]\n                    print('\\nEvaluating %s\\n' % tracker)\n                    time_start = time.time()\n                    if config['USE_PARALLEL']:\n                        with Pool(config['NUM_PARALLEL_CORES']) as pool:\n                            _eval_sequence = partial(eval_sequence, dataset=dataset, tracker=tracker,\n                                                     class_list=class_list, metrics_list=metrics_list,\n                                                     metric_names=metric_names)\n                            results = pool.map(_eval_sequence, seq_list)\n                            res = dict(zip(seq_list, results))\n                    else:\n                        res = {}\n                        for curr_seq in sorted(seq_list):\n                            res[curr_seq] = eval_sequence(curr_seq, dataset, tracker, class_list, metrics_list,\n                                                          metric_names)\n\n                    # Combine results over all sequences and then over all classes\n\n                    # collecting combined cls keys (cls averaged, det averaged, super classes)\n                    combined_cls_keys = []\n                    res['COMBINED_SEQ'] = {}\n                    # combine sequences for each class\n                    for c_cls in class_list:\n                        res['COMBINED_SEQ'][c_cls] = {}\n                        for metric, metric_name in zip(metrics_list, metric_names):\n                            curr_res = {seq_key: seq_value[c_cls][metric_name] for seq_key, seq_value in res.items() if\n                                        seq_key != 'COMBINED_SEQ'}\n                            res['COMBINED_SEQ'][c_cls][metric_name] = metric.combine_sequences(curr_res)\n                    # combine classes\n                    if dataset.should_classes_combine:\n                        combined_cls_keys += ['cls_comb_cls_av', 'cls_comb_det_av', 'all']\n                        res['COMBINED_SEQ']['cls_comb_cls_av'] = {}\n                        res['COMBINED_SEQ']['cls_comb_det_av'] = {}\n                        for metric, metric_name in zip(metrics_list, metric_names):\n                            cls_res = {cls_key: cls_value[metric_name] for cls_key, cls_value in\n                                       res['COMBINED_SEQ'].items() if cls_key not in combined_cls_keys}\n                            res['COMBINED_SEQ']['cls_comb_cls_av'][metric_name] = \\\n                                metric.combine_classes_class_averaged(cls_res)\n                            res['COMBINED_SEQ']['cls_comb_det_av'][metric_name] = \\\n                                metric.combine_classes_det_averaged(cls_res)\n                    # combine classes to super classes\n                    if dataset.use_super_categories:\n                        for cat, sub_cats in dataset.super_categories.items():\n                            combined_cls_keys.append(cat)\n                            res['COMBINED_SEQ'][cat] = {}\n                            for metric, metric_name in zip(metrics_list, metric_names):\n                                cat_res = {cls_key: cls_value[metric_name] for cls_key, cls_value in\n                                           res['COMBINED_SEQ'].items() if cls_key in sub_cats}\n                                res['COMBINED_SEQ'][cat][metric_name] = metric.combine_classes_det_averaged(cat_res)\n\n                    # Print and output results in various formats\n                    if config['TIME_PROGRESS']:\n                        print('\\nAll sequences for %s finished in %.2f seconds' % (tracker, time.time() - time_start))\n                    output_fol = dataset.get_output_fol(tracker)\n                    tracker_display_name = dataset.get_display_name(tracker)\n                    for c_cls in res['COMBINED_SEQ'].keys():  # class_list + combined classes if calculated\n                        summaries = []\n                        details = []\n                        num_dets = res['COMBINED_SEQ'][c_cls]['Count']['Dets']\n                        if config['OUTPUT_EMPTY_CLASSES'] or num_dets > 0:\n                            for metric, metric_name in zip(metrics_list, metric_names):\n                                # for combined classes there is no per sequence evaluation\n                                if c_cls in combined_cls_keys:\n                                    table_res = {'COMBINED_SEQ': res['COMBINED_SEQ'][c_cls][metric_name]}\n                                else:\n                                    table_res = {seq_key: seq_value[c_cls][metric_name] for seq_key, seq_value\n                                                 in res.items()}\n\n                                if config['PRINT_RESULTS'] and config['PRINT_ONLY_COMBINED']:\n                                    dont_print = dataset.should_classes_combine and c_cls not in combined_cls_keys\n                                    if not dont_print:\n                                        metric.print_table({'COMBINED_SEQ': table_res['COMBINED_SEQ']},\n                                                           tracker_display_name, c_cls, output_fol)     # TODO: [hgx 0403], add 'output_fol'\n                                elif config['PRINT_RESULTS']:\n                                    metric.print_table(table_res, tracker_display_name, c_cls, output_fol)      # TODO: [hgx 0403], add 'output_fol'\n                                if config['OUTPUT_SUMMARY']:\n                                    summaries.append(metric.summary_results(table_res))\n                                if config['OUTPUT_DETAILED']:\n                                    details.append(metric.detailed_results(table_res))\n                                if config['PLOT_CURVES']:\n                                    metric.plot_single_tracker_results(table_res, tracker_display_name, c_cls,\n                                                                       output_fol)\n                            if config['OUTPUT_SUMMARY']:\n                                utils.write_summary_results(summaries, c_cls, output_fol)\n                            if config['OUTPUT_DETAILED']:\n                                utils.write_detailed_results(details, c_cls, output_fol)\n\n                    # Output for returning from function\n                    output_res[dataset_name][tracker] = res\n                    output_msg[dataset_name][tracker] = 'Success'\n\n                except Exception as err:\n                    output_res[dataset_name][tracker] = None\n                    if type(err) == TrackEvalException:\n                        output_msg[dataset_name][tracker] = str(err)\n                    else:\n                        output_msg[dataset_name][tracker] = 'Unknown error occurred.'\n                    print('Tracker %s was unable to be evaluated.' % tracker)\n                    print(err)\n                    traceback.print_exc()\n                    if config['LOG_ON_ERROR'] is not None:\n                        with open(config['LOG_ON_ERROR'], 'a') as f:\n                            print(dataset_name, file=f)\n                            print(tracker, file=f)\n                            print(traceback.format_exc(), file=f)\n                            print('\\n\\n\\n', file=f)\n                    if config['BREAK_ON_ERROR']:\n                        raise err\n                    elif config['RETURN_ON_ERROR']:\n                        return output_res, output_msg\n\n        return output_res, output_msg\n\n\n@_timing.time\ndef eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names):\n    \"\"\"Function for evaluating a single sequence\"\"\"\n\n    raw_data = dataset.get_raw_seq_data(tracker, seq)\n    seq_res = {}\n    for cls in class_list:\n        seq_res[cls] = {}\n        data = dataset.get_preprocessed_seq_data(raw_data, cls)\n        for metric, met_name in zip(metrics_list, metric_names):\n            seq_res[cls][met_name] = metric.eval_sequence(data)\n    return seq_res\n"
  },
  {
    "path": "TrackEval/trackeval/metrics/__init__.py",
    "content": "from .hota import HOTA\nfrom .clear import CLEAR\nfrom .identity import Identity\nfrom .count import Count\nfrom .j_and_f import JAndF\nfrom .track_map import TrackMAP\nfrom .vace import VACE\nfrom .ideucl import IDEucl"
  },
  {
    "path": "TrackEval/trackeval/metrics/_base_metric.py",
    "content": "\nimport numpy as np\nfrom abc import ABC, abstractmethod\nfrom .. import _timing\nfrom ..utils import TrackEvalException\nimport os\n\nclass _BaseMetric(ABC):\n    @abstractmethod\n    def __init__(self):\n        self.plottable = False\n        self.integer_fields = []\n        self.float_fields = []\n        self.array_labels = []\n        self.integer_array_fields = []\n        self.float_array_fields = []\n        self.fields = []\n        self.summary_fields = []\n        self.registered = False\n\n    #####################################################################\n    # Abstract functions for subclasses to implement\n\n    @_timing.time\n    @abstractmethod\n    def eval_sequence(self, data):\n        ...\n\n    @abstractmethod\n    def combine_sequences(self, all_res):\n        ...\n\n    @abstractmethod\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        ...\n\n    @ abstractmethod\n    def combine_classes_det_averaged(self, all_res):\n        ...\n\n    def plot_single_tracker_results(self, all_res, tracker, output_folder, cls):\n        \"\"\"Plot results of metrics, only valid for metrics with self.plottable\"\"\"\n        if self.plottable:\n            raise NotImplementedError('plot_results is not implemented for metric %s' % self.get_name())\n        else:\n            pass\n\n    #####################################################################\n    # Helper functions which are useful for all metrics:\n\n    @classmethod\n    def get_name(cls):\n        return cls.__name__\n\n    @staticmethod\n    def _combine_sum(all_res, field):\n        \"\"\"Combine sequence results via sum\"\"\"\n        return sum([all_res[k][field] for k in all_res.keys()])\n\n    @staticmethod\n    def _combine_weighted_av(all_res, field, comb_res, weight_field):\n        \"\"\"Combine sequence results via weighted average\"\"\"\n        return sum([all_res[k][field] * all_res[k][weight_field] for k in all_res.keys()]) / np.maximum(1.0, comb_res[\n            weight_field])\n\n    def print_table(self, table_res, tracker, cls, output_fol=None):\n        \"\"\"Prints table of results for all sequences\"\"\"\n\n        # TODO: [hgx 0403], make file folder for 'val_log.txt'\n        if output_fol is not None:\n            out_file = os.path.join(output_fol, 'val_log.txt')\n            os.makedirs(os.path.dirname(out_file), exist_ok=True)\n        else:\n            out_file = None\n\n        print('')\n        metric_name = self.get_name()\n        self._row_print(out_file, [metric_name + ': ' + tracker + '-' + cls] + self.summary_fields)       # TODO: [hgx 0403], add 'output_fol'\n        for seq, results in sorted(table_res.items()):\n            if seq == 'COMBINED_SEQ':\n                continue\n            summary_res = self._summary_row(results)\n            self._row_print(out_file, [seq] + summary_res)            # TODO: [hgx 0403], add 'output_fol'\n        summary_res = self._summary_row(table_res['COMBINED_SEQ'])\n        self._row_print(out_file, ['COMBINED'] + summary_res)         # TODO: [hgx 0403], add 'output_fol'\n\n    def _summary_row(self, results_):\n        vals = []\n        for h in self.summary_fields:\n            if h in self.float_array_fields:\n                vals.append(\"{0:1.5g}\".format(100 * np.mean(results_[h])))\n            elif h in self.float_fields:\n                vals.append(\"{0:1.5g}\".format(100 * float(results_[h])))\n            elif h in self.integer_fields:\n                vals.append(\"{0:d}\".format(int(results_[h])))\n            else:\n                raise NotImplementedError(\"Summary function not implemented for this field type.\")\n        return vals\n\n    @staticmethod\n    def _row_print(out_file, *argv):\n        \"\"\"Prints results in an evenly spaced rows, with more space in first row\"\"\"\n        if len(argv) == 1:\n            argv = argv[0]\n        to_print = '%-35s' % argv[0]\n        for v in argv[1:]:\n            to_print += '%-10s' % str(v)\n        print(to_print)\n        if out_file is not None:        # TODO: [hgx 0403], write terminal outputs to txt file\n            with open(out_file, 'a+', newline='') as f:\n                print(to_print, file=f)\n\n    def summary_results(self, table_res):\n        \"\"\"Returns a simple summary of final results for a tracker\"\"\"\n        return dict(zip(self.summary_fields, self._summary_row(table_res['COMBINED_SEQ'])))\n\n    def detailed_results(self, table_res):\n        \"\"\"Returns detailed final results for a tracker\"\"\"\n        # Get detailed field information\n        detailed_fields = self.float_fields + self.integer_fields\n        for h in self.float_array_fields + self.integer_array_fields:\n            for alpha in [int(100*x) for x in self.array_labels]:\n                detailed_fields.append(h + '___' + str(alpha))\n            detailed_fields.append(h + '___AUC')\n\n        # Get detailed results\n        detailed_results = {}\n        for seq, res in table_res.items():\n            detailed_row = self._detailed_row(res)\n            if len(detailed_row) != len(detailed_fields):\n                raise TrackEvalException(\n                    'Field names and data have different sizes (%i and %i)' % (len(detailed_row), len(detailed_fields)))\n            detailed_results[seq] = dict(zip(detailed_fields, detailed_row))\n        return detailed_results\n\n    def _detailed_row(self, res):\n        detailed_row = []\n        for h in self.float_fields + self.integer_fields:\n            detailed_row.append(res[h])\n        for h in self.float_array_fields + self.integer_array_fields:\n            for i, alpha in enumerate([int(100 * x) for x in self.array_labels]):\n                detailed_row.append(res[h][i])\n            detailed_row.append(np.mean(res[h]))\n        return detailed_row\n"
  },
  {
    "path": "TrackEval/trackeval/metrics/clear.py",
    "content": "\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\nfrom .. import utils\n\nclass CLEAR(_BaseMetric):\n    \"\"\"Class which implements the CLEAR metrics\"\"\"\n\n    @staticmethod\n    def get_default_config():\n        \"\"\"Default class config values\"\"\"\n        default_config = {\n            'THRESHOLD': 0.5,  # Similarity score threshold required for a TP match. Default 0.5.\n            'PRINT_CONFIG': True,  # Whether to print the config information on init. Default: False.\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        super().__init__()\n        main_integer_fields = ['CLR_TP', 'CLR_FN', 'CLR_FP', 'IDSW', 'MT', 'PT', 'ML', 'Frag']\n        extra_integer_fields = ['CLR_Frames']\n        self.integer_fields = main_integer_fields + extra_integer_fields\n        main_float_fields = ['MOTA', 'MOTP', 'MODA', 'CLR_Re', 'CLR_Pr', 'MTR', 'PTR', 'MLR', 'sMOTA']\n        extra_float_fields = ['CLR_F1', 'FP_per_frame', 'MOTAL', 'MOTP_sum']\n        self.float_fields = main_float_fields + extra_float_fields\n        self.fields = self.float_fields + self.integer_fields\n        self.summed_fields = self.integer_fields + ['MOTP_sum']\n        self.summary_fields = main_float_fields + main_integer_fields\n\n        # Configuration options:\n        self.config = utils.init_config(config, self.get_default_config(), self.get_name())\n        self.threshold = float(self.config['THRESHOLD'])\n\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates CLEAR metrics for one sequence\"\"\"\n        # Initialise results\n        res = {}\n        for field in self.fields:\n            res[field] = 0\n\n        # Return result quickly if tracker or gt sequence is empty\n        if data['num_tracker_dets'] == 0:\n            res['CLR_FN'] = data['num_gt_dets']\n            res['ML'] = data['num_gt_ids']\n            res['MLR'] = 1.0\n            return res\n        if data['num_gt_dets'] == 0:\n            res['CLR_FP'] = data['num_tracker_dets']\n            res['MLR'] = 1.0\n            return res\n\n        # Variables counting global association\n        num_gt_ids = data['num_gt_ids']\n        gt_id_count = np.zeros(num_gt_ids)  # For MT/ML/PT\n        gt_matched_count = np.zeros(num_gt_ids)  # For MT/ML/PT\n        gt_frag_count = np.zeros(num_gt_ids)  # For Frag\n\n        # Note that IDSWs are counted based on the last time each gt_id was present (any number of frames previously),\n        # but are only used in matching to continue current tracks based on the gt_id in the single previous timestep.\n        prev_tracker_id = np.nan * np.zeros(num_gt_ids)  # For scoring IDSW\n        prev_timestep_tracker_id = np.nan * np.zeros(num_gt_ids)  # For matching IDSW\n\n        # Calculate scores for each timestep\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            # Deal with the case that there are no gt_det/tracker_det in a timestep.\n            if len(gt_ids_t) == 0:\n                res['CLR_FP'] += len(tracker_ids_t)\n                continue\n            if len(tracker_ids_t) == 0:\n                res['CLR_FN'] += len(gt_ids_t)\n                gt_id_count[gt_ids_t] += 1\n                continue\n\n            # Calc score matrix to first minimise IDSWs from previous frame, and then maximise MOTP secondarily\n            similarity = data['similarity_scores'][t]\n            score_mat = (tracker_ids_t[np.newaxis, :] == prev_timestep_tracker_id[gt_ids_t[:, np.newaxis]])\n            score_mat = 1000 * score_mat + similarity\n            score_mat[similarity < self.threshold - np.finfo('float').eps] = 0\n\n            # Hungarian algorithm to find best matches\n            match_rows, match_cols = linear_sum_assignment(-score_mat)\n            actually_matched_mask = score_mat[match_rows, match_cols] > 0 + np.finfo('float').eps\n            match_rows = match_rows[actually_matched_mask]\n            match_cols = match_cols[actually_matched_mask]\n\n            matched_gt_ids = gt_ids_t[match_rows]\n            matched_tracker_ids = tracker_ids_t[match_cols]\n\n            # Calc IDSW for MOTA\n            prev_matched_tracker_ids = prev_tracker_id[matched_gt_ids]\n            is_idsw = (np.logical_not(np.isnan(prev_matched_tracker_ids))) & (\n                np.not_equal(matched_tracker_ids, prev_matched_tracker_ids))\n            res['IDSW'] += np.sum(is_idsw)\n\n            # Update counters for MT/ML/PT/Frag and record for IDSW/Frag for next timestep\n            gt_id_count[gt_ids_t] += 1\n            gt_matched_count[matched_gt_ids] += 1\n            not_previously_tracked = np.isnan(prev_timestep_tracker_id)\n            prev_tracker_id[matched_gt_ids] = matched_tracker_ids\n            prev_timestep_tracker_id[:] = np.nan\n            prev_timestep_tracker_id[matched_gt_ids] = matched_tracker_ids\n            currently_tracked = np.logical_not(np.isnan(prev_timestep_tracker_id))\n            gt_frag_count += np.logical_and(not_previously_tracked, currently_tracked)\n\n            # Calculate and accumulate basic statistics\n            num_matches = len(matched_gt_ids)\n            res['CLR_TP'] += num_matches\n            res['CLR_FN'] += len(gt_ids_t) - num_matches\n            res['CLR_FP'] += len(tracker_ids_t) - num_matches\n            if num_matches > 0:\n                res['MOTP_sum'] += sum(similarity[match_rows, match_cols])\n\n        # Calculate MT/ML/PT/Frag/MOTP\n        tracked_ratio = gt_matched_count[gt_id_count > 0] / gt_id_count[gt_id_count > 0]\n        res['MT'] = np.sum(np.greater(tracked_ratio, 0.8))\n        res['PT'] = np.sum(np.greater_equal(tracked_ratio, 0.2)) - res['MT']\n        res['ML'] = num_gt_ids - res['MT'] - res['PT']\n        res['Frag'] = np.sum(np.subtract(gt_frag_count[gt_frag_count > 0], 1))\n        res['MOTP'] = res['MOTP_sum'] / np.maximum(1.0, res['CLR_TP'])\n\n        res['CLR_Frames'] = data['num_timesteps']\n\n        # Calculate final CLEAR scores\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.summed_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.summed_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n        for field in self.integer_fields:\n            if ignore_empty_classes:\n                res[field] = self._combine_sum(\n                    {k: v for k, v in all_res.items() if v['CLR_TP'] + v['CLR_FN'] + v['CLR_FP'] > 0}, field)\n            else:\n                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)\n        for field in self.float_fields:\n            if ignore_empty_classes:\n                res[field] = np.mean(\n                    [v[field] for v in all_res.values() if v['CLR_TP'] + v['CLR_FN'] + v['CLR_FP'] > 0], axis=0)\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    @staticmethod\n    def _compute_final_fields(res):\n        \"\"\"Calculate sub-metric ('field') values which only depend on other sub-metric values.\n        This function is used both for both per-sequence calculation, and in combining values across sequences.\n        \"\"\"\n        num_gt_ids = res['MT'] + res['ML'] + res['PT']\n        res['MTR'] = res['MT'] / np.maximum(1.0, num_gt_ids)\n        res['MLR'] = res['ML'] / np.maximum(1.0, num_gt_ids)\n        res['PTR'] = res['PT'] / np.maximum(1.0, num_gt_ids)\n        res['CLR_Re'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])\n        res['CLR_Pr'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + res['CLR_FP'])\n        res['MODA'] = (res['CLR_TP'] - res['CLR_FP']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])\n        res['MOTA'] = (res['CLR_TP'] - res['CLR_FP'] - res['IDSW']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])\n        res['MOTP'] = res['MOTP_sum'] / np.maximum(1.0, res['CLR_TP'])\n        res['sMOTA'] = (res['MOTP_sum'] - res['CLR_FP'] - res['IDSW']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])\n\n        res['CLR_F1'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + 0.5*res['CLR_FN'] + 0.5*res['CLR_FP'])\n        res['FP_per_frame'] = res['CLR_FP'] / np.maximum(1.0, res['CLR_Frames'])\n        safe_log_idsw = np.log10(res['IDSW']) if res['IDSW'] > 0 else res['IDSW']\n        res['MOTAL'] = (res['CLR_TP'] - res['CLR_FP'] - safe_log_idsw) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])\n        return res\n"
  },
  {
    "path": "TrackEval/trackeval/metrics/count.py",
    "content": "\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\n\n\nclass Count(_BaseMetric):\n    \"\"\"Class which simply counts the number of tracker and gt detections and ids.\"\"\"\n    def __init__(self, config=None):\n        super().__init__()\n        self.integer_fields = ['Dets', 'GT_Dets', 'IDs', 'GT_IDs']\n        self.fields = self.integer_fields\n        self.summary_fields = self.fields\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Returns counts for one sequence\"\"\"\n        # Get results\n        res = {'Dets': data['num_tracker_dets'],\n               'GT_Dets': data['num_gt_dets'],\n               'IDs': data['num_tracker_ids'],\n               'GT_IDs': data['num_gt_ids'],\n               'Frames': data['num_timesteps']}\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=None):\n        \"\"\"Combines metrics across all classes by averaging over the class values\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        return res\n"
  },
  {
    "path": "TrackEval/trackeval/metrics/hota.py",
    "content": "\nimport os\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\n\n\nclass HOTA(_BaseMetric):\n    \"\"\"Class which implements the HOTA metrics.\n    See: https://link.springer.com/article/10.1007/s11263-020-01375-2\n    \"\"\"\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.plottable = True\n        self.array_labels = np.arange(0.05, 0.99, 0.05)\n        self.integer_array_fields = ['HOTA_TP', 'HOTA_FN', 'HOTA_FP']\n        self.float_array_fields = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'RHOTA']\n        self.float_fields = ['HOTA(0)', 'LocA(0)', 'HOTALocA(0)']\n        self.fields = self.float_array_fields + self.integer_array_fields + self.float_fields\n        self.summary_fields = self.float_array_fields + self.float_fields\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates the HOTA metrics for one sequence\"\"\"\n\n        # Initialise results\n        res = {}\n        for field in self.float_array_fields + self.integer_array_fields:\n            res[field] = np.zeros((len(self.array_labels)), dtype=np.float)\n        for field in self.float_fields:\n            res[field] = 0\n\n        # Return result quickly if tracker or gt sequence is empty\n        if data['num_tracker_dets'] == 0:\n            res['HOTA_FN'] = data['num_gt_dets'] * np.ones((len(self.array_labels)), dtype=np.float)\n            res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float)\n            res['LocA(0)'] = 1.0\n            return res\n        if data['num_gt_dets'] == 0:\n            res['HOTA_FP'] = data['num_tracker_dets'] * np.ones((len(self.array_labels)), dtype=np.float)\n            res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float)\n            res['LocA(0)'] = 1.0\n            return res\n\n        # Variables counting global association\n        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))\n        gt_id_count = np.zeros((data['num_gt_ids'], 1))\n        tracker_id_count = np.zeros((1, data['num_tracker_ids']))\n\n        # First loop through each timestep and accumulate global track information.\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            # Count the potential matches between ids in each timestep\n            # These are normalised, weighted by the match similarity.\n            similarity = data['similarity_scores'][t]\n            sim_iou_denom = similarity.sum(0)[np.newaxis, :] + similarity.sum(1)[:, np.newaxis] - similarity\n            sim_iou = np.zeros_like(similarity)\n            sim_iou_mask = sim_iou_denom > 0 + np.finfo('float').eps\n            sim_iou[sim_iou_mask] = similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]\n            potential_matches_count[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] += sim_iou\n\n            # Calculate the total number of dets for each gt_id and tracker_id.\n            gt_id_count[gt_ids_t] += 1\n            tracker_id_count[0, tracker_ids_t] += 1\n\n        # Calculate overall jaccard alignment score (before unique matching) between IDs\n        global_alignment_score = potential_matches_count / (gt_id_count + tracker_id_count - potential_matches_count)\n        matches_counts = [np.zeros_like(potential_matches_count) for _ in self.array_labels]\n\n        # Calculate scores for each timestep\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            # Deal with the case that there are no gt_det/tracker_det in a timestep.\n            if len(gt_ids_t) == 0:\n                for a, alpha in enumerate(self.array_labels):\n                    res['HOTA_FP'][a] += len(tracker_ids_t)\n                continue\n            if len(tracker_ids_t) == 0:\n                for a, alpha in enumerate(self.array_labels):\n                    res['HOTA_FN'][a] += len(gt_ids_t)\n                continue\n\n            # Get matching scores between pairs of dets for optimizing HOTA\n            similarity = data['similarity_scores'][t]\n            score_mat = global_alignment_score[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] * similarity\n\n            # Hungarian algorithm to find best matches\n            match_rows, match_cols = linear_sum_assignment(-score_mat)\n\n            # Calculate and accumulate basic statistics\n            for a, alpha in enumerate(self.array_labels):\n                actually_matched_mask = similarity[match_rows, match_cols] >= alpha - np.finfo('float').eps\n                alpha_match_rows = match_rows[actually_matched_mask]\n                alpha_match_cols = match_cols[actually_matched_mask]\n                num_matches = len(alpha_match_rows)\n                res['HOTA_TP'][a] += num_matches\n                res['HOTA_FN'][a] += len(gt_ids_t) - num_matches\n                res['HOTA_FP'][a] += len(tracker_ids_t) - num_matches\n                if num_matches > 0:\n                    res['LocA'][a] += sum(similarity[alpha_match_rows, alpha_match_cols])\n                    matches_counts[a][gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]] += 1\n\n        # Calculate association scores (AssA, AssRe, AssPr) for the alpha value.\n        # First calculate scores per gt_id/tracker_id combo and then average over the number of detections.\n        for a, alpha in enumerate(self.array_labels):\n            matches_count = matches_counts[a]\n            ass_a = matches_count / np.maximum(1, gt_id_count + tracker_id_count - matches_count)\n            res['AssA'][a] = np.sum(matches_count * ass_a) / np.maximum(1, res['HOTA_TP'][a])\n            ass_re = matches_count / np.maximum(1, gt_id_count)\n            res['AssRe'][a] = np.sum(matches_count * ass_re) / np.maximum(1, res['HOTA_TP'][a])\n            ass_pr = matches_count / np.maximum(1, tracker_id_count)\n            res['AssPr'][a] = np.sum(matches_count * ass_pr) / np.maximum(1, res['HOTA_TP'][a])\n\n        # Calculate final scores\n        res['LocA'] = np.maximum(1e-10, res['LocA']) / np.maximum(1e-10, res['HOTA_TP'])\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            res[field] = self._combine_sum(all_res, field)\n        for field in ['AssRe', 'AssPr', 'AssA']:\n            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')\n        loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])\n        res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            if ignore_empty_classes:\n                res[field] = self._combine_sum(\n                    {k: v for k, v in all_res.items()\n                     if (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()}, field)\n            else:\n                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)\n\n        for field in self.float_fields + self.float_array_fields:\n            if ignore_empty_classes:\n                res[field] = np.mean([v[field] for v in all_res.values() if\n                                      (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()],\n                                     axis=0)\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            res[field] = self._combine_sum(all_res, field)\n        for field in ['AssRe', 'AssPr', 'AssA']:\n            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')\n        loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])\n        res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])\n        res = self._compute_final_fields(res)\n        return res\n\n    @staticmethod\n    def _compute_final_fields(res):\n        \"\"\"Calculate sub-metric ('field') values which only depend on other sub-metric values.\n        This function is used both for both per-sequence calculation, and in combining values across sequences.\n        \"\"\"\n        res['DetRe'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FN'])\n        res['DetPr'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FP'])\n        res['DetA'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FN'] + res['HOTA_FP'])\n        res['HOTA'] = np.sqrt(res['DetA'] * res['AssA'])\n        res['RHOTA'] = np.sqrt(res['DetRe'] * res['AssA'])\n\n        res['HOTA(0)'] = res['HOTA'][0]\n        res['LocA(0)'] = res['LocA'][0]\n        res['HOTALocA(0)'] = res['HOTA(0)']*res['LocA(0)']\n        return res\n\n    def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):\n        \"\"\"Create plot of results\"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from matplotlib import pyplot as plt\n\n        res = table_res['COMBINED_SEQ']\n        styles_to_plot = ['r', 'b', 'g', 'b--', 'b:', 'g--', 'g:', 'm']\n        for name, style in zip(self.float_array_fields, styles_to_plot):\n            plt.plot(self.array_labels, res[name], style)\n        plt.xlabel('alpha')\n        plt.ylabel('score')\n        plt.title(tracker + ' - ' + cls)\n        plt.axis([0, 1, 0, 1])\n        legend = []\n        for name in self.float_array_fields:\n            legend += [name + ' (' + str(np.round(np.mean(res[name]), 2)) + ')']\n        plt.legend(legend, loc='lower left')\n        out_file = os.path.join(output_folder, cls + '_plot.pdf')\n        os.makedirs(os.path.dirname(out_file), exist_ok=True)\n        plt.savefig(out_file)\n        plt.savefig(out_file.replace('.pdf', '.png'))\n        plt.clf()\n"
  },
  {
    "path": "TrackEval/trackeval/metrics/identity.py",
    "content": "import numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\nfrom .. import utils\n\n\nclass Identity(_BaseMetric):\n    \"\"\"Class which implements the ID metrics\"\"\"\n\n    @staticmethod\n    def get_default_config():\n        \"\"\"Default class config values\"\"\"\n        default_config = {\n            'THRESHOLD': 0.5,  # Similarity score threshold required for a IDTP match. Default 0.5.\n            'PRINT_CONFIG': True,  # Whether to print the config information on init. Default: False.\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.integer_fields = ['IDTP', 'IDFN', 'IDFP']\n        self.float_fields = ['IDF1', 'IDR', 'IDP']\n        self.fields = self.float_fields + self.integer_fields\n        self.summary_fields = self.fields\n\n        # Configuration options:\n        self.config = utils.init_config(config, self.get_default_config(), self.get_name())\n        self.threshold = float(self.config['THRESHOLD'])\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates ID metrics for one sequence\"\"\"\n        # Initialise results\n        res = {}\n        for field in self.fields:\n            res[field] = 0\n\n        # Return result quickly if tracker or gt sequence is empty\n        if data['num_tracker_dets'] == 0:\n            res['IDFN'] = data['num_gt_dets']\n            return res\n        if data['num_gt_dets'] == 0:\n            res['IDFP'] = data['num_tracker_dets']\n            return res\n\n        # Variables counting global association\n        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))\n        gt_id_count = np.zeros(data['num_gt_ids'])\n        tracker_id_count = np.zeros(data['num_tracker_ids'])\n\n        # First loop through each timestep and accumulate global track information.\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            # Count the potential matches between ids in each timestep\n            matches_mask = np.greater_equal(data['similarity_scores'][t], self.threshold)\n            match_idx_gt, match_idx_tracker = np.nonzero(matches_mask)\n            potential_matches_count[gt_ids_t[match_idx_gt], tracker_ids_t[match_idx_tracker]] += 1\n\n            # Calculate the total number of dets for each gt_id and tracker_id.\n            gt_id_count[gt_ids_t] += 1\n            tracker_id_count[tracker_ids_t] += 1\n\n        # Calculate optimal assignment cost matrix for ID metrics\n        num_gt_ids = data['num_gt_ids']\n        num_tracker_ids = data['num_tracker_ids']\n        fp_mat = np.zeros((num_gt_ids + num_tracker_ids, num_gt_ids + num_tracker_ids))\n        fn_mat = np.zeros((num_gt_ids + num_tracker_ids, num_gt_ids + num_tracker_ids))\n        fp_mat[num_gt_ids:, :num_tracker_ids] = 1e10\n        fn_mat[:num_gt_ids, num_tracker_ids:] = 1e10\n        for gt_id in range(num_gt_ids):\n            fn_mat[gt_id, :num_tracker_ids] = gt_id_count[gt_id]\n            fn_mat[gt_id, num_tracker_ids + gt_id] = gt_id_count[gt_id]\n        for tracker_id in range(num_tracker_ids):\n            fp_mat[:num_gt_ids, tracker_id] = tracker_id_count[tracker_id]\n            fp_mat[tracker_id + num_gt_ids, tracker_id] = tracker_id_count[tracker_id]\n        fn_mat[:num_gt_ids, :num_tracker_ids] -= potential_matches_count\n        fp_mat[:num_gt_ids, :num_tracker_ids] -= potential_matches_count\n\n        # Hungarian algorithm\n        match_rows, match_cols = linear_sum_assignment(fn_mat + fp_mat)\n\n        # Accumulate basic statistics\n        res['IDFN'] = fn_mat[match_rows, match_cols].sum().astype(np.int)\n        res['IDFP'] = fp_mat[match_rows, match_cols].sum().astype(np.int)\n        res['IDTP'] = (gt_id_count.sum() - res['IDFN']).astype(np.int)\n\n        # Calculate final ID scores\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n        for field in self.integer_fields:\n            if ignore_empty_classes:\n                res[field] = self._combine_sum({k: v for k, v in all_res.items()\n                                                if v['IDTP'] + v['IDFN'] + v['IDFP'] > 0 + np.finfo('float').eps},\n                                               field)\n            else:\n                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)\n        for field in self.float_fields:\n            if ignore_empty_classes:\n                res[field] = np.mean([v[field] for v in all_res.values()\n                                      if v['IDTP'] + v['IDFN'] + v['IDFP'] > 0 + np.finfo('float').eps], axis=0)\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res)\n        return res\n\n    @staticmethod\n    def _compute_final_fields(res):\n        \"\"\"Calculate sub-metric ('field') values which only depend on other sub-metric values.\n        This function is used both for both per-sequence calculation, and in combining values across sequences.\n        \"\"\"\n        res['IDR'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + res['IDFN'])\n        res['IDP'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + res['IDFP'])\n        res['IDF1'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + 0.5 * res['IDFP'] + 0.5 * res['IDFN'])\n        return res\n"
  },
  {
    "path": "TrackEval/trackeval/metrics/ideucl.py",
    "content": "import numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\nfrom collections import defaultdict\nfrom .. import utils\n\n\nclass IDEucl(_BaseMetric):\n    \"\"\"Class which implements the ID metrics\"\"\"\n\n    @staticmethod\n    def get_default_config():\n        \"\"\"Default class config values\"\"\"\n        default_config = {\n            'THRESHOLD': 0.4,  # Similarity score threshold required for a IDTP match. 0.4 for IDEucl.\n            'PRINT_CONFIG': True,  # Whether to print the config information on init. Default: False.\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.fields = ['IDEucl']\n        self.float_fields = self.fields\n        self.summary_fields = self.fields\n\n        # Configuration options:\n        self.config = utils.init_config(config, self.get_default_config(), self.get_name())\n        self.threshold = float(self.config['THRESHOLD'])\n\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates IDEucl metrics for all frames\"\"\"\n        # Initialise results\n        res = {'IDEucl' : 0}\n\n        # Return result quickly if tracker or gt sequence is empty\n        if data['num_tracker_dets'] == 0 or data['num_gt_dets'] == 0.:\n            return res\n\n        data['centroid'] = []\n        for t, gt_det in enumerate(data['gt_dets']):\n            # import pdb;pdb.set_trace()\n            data['centroid'].append(self._compute_centroid(gt_det))\n\n        oid_hid_cent = defaultdict(list)\n        oid_cent = defaultdict(list)\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            matches_mask = np.greater_equal(data['similarity_scores'][t], self.threshold)\n\n            # I hope the orders of ids and boxes are maintained in `data`\n            for ind, gid in enumerate(gt_ids_t):\n                oid_cent[gid].append(data['centroid'][t][ind])\n\n            match_idx_gt, match_idx_tracker = np.nonzero(matches_mask)\n            for m_gid, m_tid in zip(match_idx_gt, match_idx_tracker):\n                oid_hid_cent[gt_ids_t[m_gid], tracker_ids_t[m_tid]].append(data['centroid'][t][m_gid])\n\n        oid_hid_dist = {k : np.sum(np.linalg.norm(np.diff(np.array(v), axis=0), axis=1)) for k, v in oid_hid_cent.items()}\n        oid_dist = {int(k) : np.sum(np.linalg.norm(np.diff(np.array(v), axis=0), axis=1)) for k, v in oid_cent.items()}\n\n        unique_oid = np.unique([i[0] for i in oid_hid_dist.keys()]).tolist()\n        unique_hid = np.unique([i[1] for i in oid_hid_dist.keys()]).tolist()\n        o_len = len(unique_oid)\n        h_len = len(unique_hid)\n        dist_matrix = np.zeros((o_len, h_len))\n        for ((oid, hid), dist) in oid_hid_dist.items():\n            oid_ind = unique_oid.index(oid)\n            hid_ind = unique_hid.index(hid)\n            dist_matrix[oid_ind, hid_ind] = dist\n\n        # opt_hyp_dist contains GT ID : max dist covered by track\n        opt_hyp_dist = dict.fromkeys(oid_dist.keys(), 0.)\n        cost_matrix = np.max(dist_matrix) - dist_matrix\n        rows, cols = linear_sum_assignment(cost_matrix)\n        for (row, col) in zip(rows, cols):\n            value = dist_matrix[row, col]\n            opt_hyp_dist[int(unique_oid[row])] = value\n\n        assert len(opt_hyp_dist.keys()) == len(oid_dist.keys())\n        hyp_length = np.sum(list(opt_hyp_dist.values()))\n        gt_length = np.sum(list(oid_dist.values()))\n        id_eucl =np.mean([np.divide(a, b, out=np.zeros_like(a), where=b!=0) for a, b in zip(opt_hyp_dist.values(), oid_dist.values())])\n        res['IDEucl'] = np.divide(hyp_length, gt_length, out=np.zeros_like(hyp_length), where=gt_length!=0)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n\n        for field in self.float_fields:\n            if ignore_empty_classes:\n                res[field] = np.mean([v[field] for v in all_res.values()\n                                      if v['IDEucl'] > 0 + np.finfo('float').eps], axis=0)\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.float_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res, len(all_res))\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.float_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res, len(all_res))\n        return res\n\n\n    @staticmethod\n    def _compute_centroid(box):\n        box = np.array(box)\n        if len(box.shape) == 1:\n            centroid = (box[0:2] + box[2:4])/2\n        else:\n            centroid = (box[:, 0:2] + box[:, 2:4])/2\n        return  np.flip(centroid, axis=1)\n\n\n    @staticmethod\n    def _compute_final_fields(res, res_len):\n        \"\"\"\n        Exists only to match signature with the original Identiy class.\n\n        \"\"\"\n        return {k:v/res_len for k,v in res.items()}\n"
  },
  {
    "path": "TrackEval/trackeval/metrics/j_and_f.py",
    "content": "\nimport numpy as np\nimport math\nfrom scipy.optimize import linear_sum_assignment\nfrom ..utils import TrackEvalException\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\n\n\nclass JAndF(_BaseMetric):\n    \"\"\"Class which implements the J&F metrics\"\"\"\n    def __init__(self, config=None):\n        super().__init__()\n        self.integer_fields = ['num_gt_tracks']\n        self.float_fields = ['J-Mean', 'J-Recall', 'J-Decay', 'F-Mean', 'F-Recall', 'F-Decay', 'J&F']\n        self.fields = self.float_fields + self.integer_fields\n        self.summary_fields = self.float_fields\n        self.optim_type = 'J'  # possible values J, J&F\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Returns J&F metrics for one sequence\"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        num_timesteps = data['num_timesteps']\n        num_tracker_ids = data['num_tracker_ids']\n        num_gt_ids = data['num_gt_ids']\n        gt_dets = data['gt_dets']\n        tracker_dets = data['tracker_dets']\n        gt_ids = data['gt_ids']\n        tracker_ids = data['tracker_ids']\n\n        # get shape of frames\n        frame_shape = None\n        if num_gt_ids > 0:\n            for t in range(num_timesteps):\n                if len(gt_ids[t]) > 0:\n                    frame_shape = gt_dets[t][0]['size']\n                    break\n        elif num_tracker_ids > 0:\n            for t in range(num_timesteps):\n                if len(tracker_ids[t]) > 0:\n                    frame_shape = tracker_dets[t][0]['size']\n                    break\n\n        if frame_shape:\n            # append all zero masks for timesteps in which tracks do not have a detection\n            zero_padding = np.zeros((frame_shape), order= 'F').astype(np.uint8)\n            padding_mask = mask_utils.encode(zero_padding)\n            for t in range(num_timesteps):\n                gt_id_det_mapping = {gt_ids[t][i]: gt_dets[t][i] for i in range(len(gt_ids[t]))}\n                gt_dets[t] = [gt_id_det_mapping[index] if index in gt_ids[t] else padding_mask for index\n                              in range(num_gt_ids)]\n                tracker_id_det_mapping = {tracker_ids[t][i]: tracker_dets[t][i] for i in range(len(tracker_ids[t]))}\n                tracker_dets[t] = [tracker_id_det_mapping[index] if index in tracker_ids[t] else padding_mask for index\n                                   in range(num_tracker_ids)]\n            # also perform zero padding if number of tracker IDs < number of ground truth IDs\n            if num_tracker_ids < num_gt_ids:\n                diff = num_gt_ids - num_tracker_ids\n                for t in range(num_timesteps):\n                    tracker_dets[t] = tracker_dets[t] + [padding_mask for _ in range(diff)]\n                num_tracker_ids += diff\n\n        j = self._compute_j(gt_dets, tracker_dets, num_gt_ids, num_tracker_ids, num_timesteps)\n\n        # boundary threshold for F computation\n        bound_th = 0.008\n\n        # perform matching\n        if self.optim_type == 'J&F':\n            f = np.zeros_like(j)\n            for k in range(num_tracker_ids):\n                for i in range(num_gt_ids):\n                    f[k, i, :] = self._compute_f(gt_dets, tracker_dets, k, i, bound_th)\n            optim_metrics = (np.mean(j, axis=2) + np.mean(f, axis=2)) / 2\n            row_ind, col_ind = linear_sum_assignment(- optim_metrics)\n            j_m = j[row_ind, col_ind, :]\n            f_m = f[row_ind, col_ind, :]\n        elif self.optim_type == 'J':\n            optim_metrics = np.mean(j, axis=2)\n            row_ind, col_ind = linear_sum_assignment(- optim_metrics)\n            j_m = j[row_ind, col_ind, :]\n            f_m = np.zeros_like(j_m)\n            for i, (tr_ind, gt_ind) in enumerate(zip(row_ind, col_ind)):\n                f_m[i] = self._compute_f(gt_dets, tracker_dets, tr_ind, gt_ind, bound_th)\n        else:\n            raise TrackEvalException('Unsupported optimization type %s for J&F metric.' % self.optim_type)\n\n        # append zeros for false negatives\n        if j_m.shape[0] < data['num_gt_ids']:\n            diff = data['num_gt_ids'] - j_m.shape[0]\n            j_m = np.concatenate((j_m, np.zeros((diff, j_m.shape[1]))), axis=0)\n            f_m = np.concatenate((f_m, np.zeros((diff, f_m.shape[1]))), axis=0)\n\n        # compute the metrics for each ground truth track\n        res = {\n            'J-Mean': [np.nanmean(j_m[i, :]) for i in range(j_m.shape[0])],\n            'J-Recall': [np.nanmean(j_m[i, :] > 0.5 + np.finfo('float').eps) for i in range(j_m.shape[0])],\n            'F-Mean': [np.nanmean(f_m[i, :]) for i in range(f_m.shape[0])],\n            'F-Recall': [np.nanmean(f_m[i, :] > 0.5 + np.finfo('float').eps) for i in range(f_m.shape[0])],\n            'J-Decay': [],\n            'F-Decay': []\n        }\n        n_bins = 4\n        ids = np.round(np.linspace(1, data['num_timesteps'], n_bins + 1) + 1e-10) - 1\n        ids = ids.astype(np.uint8)\n\n        for k in range(j_m.shape[0]):\n            d_bins_j = [j_m[k][ids[i]:ids[i + 1] + 1] for i in range(0, n_bins)]\n            res['J-Decay'].append(np.nanmean(d_bins_j[0]) - np.nanmean(d_bins_j[3]))\n        for k in range(f_m.shape[0]):\n            d_bins_f = [f_m[k][ids[i]:ids[i + 1] + 1] for i in range(0, n_bins)]\n            res['F-Decay'].append(np.nanmean(d_bins_f[0]) - np.nanmean(d_bins_f[3]))\n\n        # count number of tracks for weighting of the result\n        res['num_gt_tracks'] = len(res['J-Mean'])\n        for field in ['J-Mean', 'J-Recall', 'J-Decay', 'F-Mean', 'F-Recall', 'F-Decay']:\n            res[field] = np.mean(res[field])\n        res['J&F'] = (res['J-Mean'] + res['F-Mean']) / 2\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {'num_gt_tracks': self._combine_sum(all_res, 'num_gt_tracks')}\n        for field in self.summary_fields:\n            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='num_gt_tracks')\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values\n        'ignore empty classes' is not yet implemented here.\n        \"\"\"\n        res = {'num_gt_tracks': self._combine_sum(all_res, 'num_gt_tracks')}\n        for field in self.float_fields:\n            res[field] = np.mean([v[field] for v in all_res.values()])\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {'num_gt_tracks': self._combine_sum(all_res, 'num_gt_tracks')}\n        for field in self.float_fields:\n            res[field] = np.mean([v[field] for v in all_res.values()])\n        return res\n\n    @staticmethod\n    def _seg2bmap(seg, width=None, height=None):\n        \"\"\"\n        From a segmentation, compute a binary boundary map with 1 pixel wide\n        boundaries.  The boundary pixels are offset by 1/2 pixel towards the\n        origin from the actual segment boundary.\n        Arguments:\n            seg     : Segments labeled from 1..k.\n            width\t  :\tWidth of desired bmap  <= seg.shape[1]\n            height  :\tHeight of desired bmap <= seg.shape[0]\n        Returns:\n            bmap (ndarray):\tBinary boundary map.\n         David Martin <dmartin@eecs.berkeley.edu>\n         January 2003\n        \"\"\"\n\n        seg = seg.astype(np.bool)\n        seg[seg > 0] = 1\n\n        assert np.atleast_3d(seg).shape[2] == 1\n\n        width = seg.shape[1] if width is None else width\n        height = seg.shape[0] if height is None else height\n\n        h, w = seg.shape[:2]\n\n        ar1 = float(width) / float(height)\n        ar2 = float(w) / float(h)\n\n        assert not (\n                width > w | height > h | abs(ar1 - ar2) > 0.01\n        ), \"Can\" \"t convert %dx%d seg to %dx%d bmap.\" % (w, h, width, height)\n\n        e = np.zeros_like(seg)\n        s = np.zeros_like(seg)\n        se = np.zeros_like(seg)\n\n        e[:, :-1] = seg[:, 1:]\n        s[:-1, :] = seg[1:, :]\n        se[:-1, :-1] = seg[1:, 1:]\n\n        b = seg ^ e | seg ^ s | seg ^ se\n        b[-1, :] = seg[-1, :] ^ e[-1, :]\n        b[:, -1] = seg[:, -1] ^ s[:, -1]\n        b[-1, -1] = 0\n\n        if w == width and h == height:\n            bmap = b\n        else:\n            bmap = np.zeros((height, width))\n            for x in range(w):\n                for y in range(h):\n                    if b[y, x]:\n                        j = 1 + math.floor((y - 1) + height / h)\n                        i = 1 + math.floor((x - 1) + width / h)\n                        bmap[j, i] = 1\n\n        return bmap\n\n    @staticmethod\n    def _compute_f(gt_data, tracker_data, tracker_data_id, gt_id, bound_th):\n        \"\"\"\n        Perform F computation for a given gt and a given tracker ID. Adapted from\n        https://github.com/davisvideochallenge/davis2017-evaluation\n        :param gt_data: the encoded gt masks\n        :param tracker_data: the encoded tracker masks\n        :param tracker_data_id: the tracker ID\n        :param gt_id: the ground truth ID\n        :param bound_th: boundary threshold parameter\n        :return: the F value for the given tracker and gt ID\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n        from skimage.morphology import disk\n        import cv2\n\n        f = np.zeros(len(gt_data))\n\n        for t, (gt_masks, tracker_masks) in enumerate(zip(gt_data, tracker_data)):\n            curr_tracker_mask = mask_utils.decode(tracker_masks[tracker_data_id])\n            curr_gt_mask = mask_utils.decode(gt_masks[gt_id])\n            \n            bound_pix = bound_th if bound_th >= 1 - np.finfo('float').eps else \\\n                np.ceil(bound_th * np.linalg.norm(curr_tracker_mask.shape))\n\n            # Get the pixel boundaries of both masks\n            fg_boundary = JAndF._seg2bmap(curr_tracker_mask)\n            gt_boundary = JAndF._seg2bmap(curr_gt_mask)\n\n            # fg_dil = binary_dilation(fg_boundary, disk(bound_pix))\n            fg_dil = cv2.dilate(fg_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8))\n            # gt_dil = binary_dilation(gt_boundary, disk(bound_pix))\n            gt_dil = cv2.dilate(gt_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8))\n\n            # Get the intersection\n            gt_match = gt_boundary * fg_dil\n            fg_match = fg_boundary * gt_dil\n\n            # Area of the intersection\n            n_fg = np.sum(fg_boundary)\n            n_gt = np.sum(gt_boundary)\n\n            # % Compute precision and recall\n            if n_fg == 0 and n_gt > 0:\n                precision = 1\n                recall = 0\n            elif n_fg > 0 and n_gt == 0:\n                precision = 0\n                recall = 1\n            elif n_fg == 0 and n_gt == 0:\n                precision = 1\n                recall = 1\n            else:\n                precision = np.sum(fg_match) / float(n_fg)\n                recall = np.sum(gt_match) / float(n_gt)\n\n            # Compute F measure\n            if precision + recall == 0:\n                f_val = 0\n            else:\n                f_val = 2 * precision * recall / (precision + recall)\n\n            f[t] = f_val\n\n        return f\n\n    @staticmethod\n    def _compute_j(gt_data, tracker_data, num_gt_ids, num_tracker_ids, num_timesteps):\n        \"\"\"\n        Computation of J value for all ground truth IDs and all tracker IDs in the given sequence. Adapted from\n        https://github.com/davisvideochallenge/davis2017-evaluation\n        :param gt_data: the ground truth masks\n        :param tracker_data: the tracker masks\n        :param num_gt_ids: the number of ground truth IDs\n        :param num_tracker_ids: the number of tracker IDs\n        :param num_timesteps: the number of timesteps\n        :return: the J values\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        j = np.zeros((num_tracker_ids, num_gt_ids, num_timesteps))\n\n        for t, (time_gt, time_data) in enumerate(zip(gt_data, tracker_data)):\n            # run length encoded masks with pycocotools\n            area_gt = mask_utils.area(time_gt)\n            time_data = list(time_data)\n            area_tr = mask_utils.area(time_data)\n\n            area_tr = np.repeat(area_tr[:, np.newaxis], len(area_gt), axis=1)\n            area_gt = np.repeat(area_gt[np.newaxis, :], len(area_tr), axis=0)\n\n            # mask iou computation with pycocotools\n            ious = np.atleast_2d(mask_utils.iou(time_data, time_gt, [0]*len(time_gt)))\n            # set iou to 1 if both masks are close to 0 (no ground truth and no predicted mask in timestep)\n            ious[np.isclose(area_tr, 0) & np.isclose(area_gt, 0)] = 1\n            assert (ious >= 0 - np.finfo('float').eps).all()\n            assert (ious <= 1 + np.finfo('float').eps).all()\n\n            j[..., t] = ious\n\n        return j\n"
  },
  {
    "path": "TrackEval/trackeval/metrics/track_map.py",
    "content": "import numpy as np\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\nfrom functools import partial\nfrom .. import utils\nfrom ..utils import TrackEvalException\n\n\nclass TrackMAP(_BaseMetric):\n    \"\"\"Class which implements the TrackMAP metrics\"\"\"\n\n    @staticmethod\n    def get_default_metric_config():\n        \"\"\"Default class config values\"\"\"\n        default_config = {\n            'USE_AREA_RANGES': True,  # whether to evaluate for certain area ranges\n            'AREA_RANGES': [[0 ** 2, 32 ** 2],  # additional area range sets for which TrackMAP is evaluated\n                            [32 ** 2, 96 ** 2],  # (all area range always included), default values for TAO\n                            [96 ** 2, 1e5 ** 2]],  # evaluation\n            'AREA_RANGE_LABELS': [\"area_s\", \"area_m\", \"area_l\"],  # the labels for the area ranges\n            'USE_TIME_RANGES': True,  # whether to evaluate for certain time ranges (length of tracks)\n            'TIME_RANGES': [[0, 3], [3, 10], [10, 1e5]],  # additional time range sets for which TrackMAP is evaluated\n            # (all time range always included) , default values for TAO evaluation\n            'TIME_RANGE_LABELS': [\"time_s\", \"time_m\", \"time_l\"],  # the labels for the time ranges\n            'IOU_THRESHOLDS': np.arange(0.5, 0.96, 0.05),  # the IoU thresholds\n            'RECALL_THRESHOLDS': np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01) + 1), endpoint=True),\n            # recall thresholds at which precision is evaluated\n            'MAX_DETECTIONS': 0,  # limit the maximum number of considered tracks per sequence (0 for unlimited)\n            'PRINT_CONFIG': True\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.config = utils.init_config(config, self.get_default_metric_config(), self.get_name())\n\n        self.num_ig_masks = 1\n        self.lbls = ['all']\n        self.use_area_rngs = self.config['USE_AREA_RANGES']\n        if self.use_area_rngs:\n            self.area_rngs = self.config['AREA_RANGES']\n            self.area_rng_lbls = self.config['AREA_RANGE_LABELS']\n            self.num_ig_masks += len(self.area_rng_lbls)\n            self.lbls += self.area_rng_lbls\n\n        self.use_time_rngs = self.config['USE_TIME_RANGES']\n        if self.use_time_rngs:\n            self.time_rngs = self.config['TIME_RANGES']\n            self.time_rng_lbls = self.config['TIME_RANGE_LABELS']\n            self.num_ig_masks += len(self.time_rng_lbls)\n            self.lbls += self.time_rng_lbls\n\n        self.array_labels = self.config['IOU_THRESHOLDS']\n        self.rec_thrs = self.config['RECALL_THRESHOLDS']\n\n        self.maxDet = self.config['MAX_DETECTIONS']\n        self.float_array_fields = ['AP_' + lbl for lbl in self.lbls] + ['AR_' + lbl for lbl in self.lbls]\n        self.fields = self.float_array_fields\n        self.summary_fields = self.float_array_fields\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates GT and Tracker matches for one sequence for TrackMAP metrics. Adapted from\n        https://github.com/TAO-Dataset/\"\"\"\n\n        # Initialise results to zero for each sequence as the fields are only defined over the set of all sequences\n        res = {}\n        for field in self.fields:\n            res[field] = [0 for _ in self.array_labels]\n\n        gt_ids, dt_ids = data['gt_track_ids'], data['dt_track_ids']\n\n        if len(gt_ids) == 0 and len(dt_ids) == 0:\n            for idx in range(self.num_ig_masks):\n                res[idx] = None\n            return res\n\n        # get track data\n        gt_tr_areas = data.get('gt_track_areas', None) if self.use_area_rngs else None\n        gt_tr_lengths = data.get('gt_track_lengths', None) if self.use_time_rngs else None\n        gt_tr_iscrowd = data.get('gt_track_iscrowd', None)\n        dt_tr_areas = data.get('dt_track_areas', None) if self.use_area_rngs else None\n        dt_tr_lengths = data.get('dt_track_lengths', None) if self.use_time_rngs else None\n        is_nel = data.get('not_exhaustively_labeled', False)\n\n        # compute ignore masks for different track sets to eval\n        gt_ig_masks = self._compute_track_ig_masks(len(gt_ids), track_lengths=gt_tr_lengths, track_areas=gt_tr_areas,\n                                                   iscrowd=gt_tr_iscrowd)\n        dt_ig_masks = self._compute_track_ig_masks(len(dt_ids), track_lengths=dt_tr_lengths, track_areas=dt_tr_areas,\n                                                   is_not_exhaustively_labeled=is_nel, is_gt=False)\n\n        boxformat = data.get('boxformat', 'xywh')\n        ious = self._compute_track_ious(data['dt_tracks'], data['gt_tracks'], iou_function=data['iou_type'],\n                                        boxformat=boxformat)\n\n        for mask_idx in range(self.num_ig_masks):\n            gt_ig_mask = gt_ig_masks[mask_idx]\n\n            # Sort gt ignore last\n            gt_idx = np.argsort([g for g in gt_ig_mask], kind=\"mergesort\")\n            gt_ids = [gt_ids[i] for i in gt_idx]\n\n            ious_sorted = ious[:, gt_idx] if len(ious) > 0 else ious\n\n            num_thrs = len(self.array_labels)\n            num_gt = len(gt_ids)\n            num_dt = len(dt_ids)\n\n            # Array to store the \"id\" of the matched dt/gt\n            gt_m = np.zeros((num_thrs, num_gt)) - 1\n            dt_m = np.zeros((num_thrs, num_dt)) - 1\n\n            gt_ig = np.array([gt_ig_mask[idx] for idx in gt_idx])\n            dt_ig = np.zeros((num_thrs, num_dt))\n\n            for iou_thr_idx, iou_thr in enumerate(self.array_labels):\n                if len(ious_sorted) == 0:\n                    break\n\n                for dt_idx, _dt in enumerate(dt_ids):\n                    iou = min([iou_thr, 1 - 1e-10])\n                    # information about best match so far (m=-1 -> unmatched)\n                    # store the gt_idx which matched for _dt\n                    m = -1\n                    for gt_idx, _ in enumerate(gt_ids):\n                        # if this gt already matched continue\n                        if gt_m[iou_thr_idx, gt_idx] > 0:\n                            continue\n                        # if _dt matched to reg gt, and on ignore gt, stop\n                        if m > -1 and gt_ig[m] == 0 and gt_ig[gt_idx] == 1:\n                            break\n                        # continue to next gt unless better match made\n                        if ious_sorted[dt_idx, gt_idx] < iou - np.finfo('float').eps:\n                            continue\n                        # if match successful and best so far, store appropriately\n                        iou = ious_sorted[dt_idx, gt_idx]\n                        m = gt_idx\n\n                    # No match found for _dt, go to next _dt\n                    if m == -1:\n                        continue\n\n                    # if gt to ignore for some reason update dt_ig.\n                    # Should not be used in evaluation.\n                    dt_ig[iou_thr_idx, dt_idx] = gt_ig[m]\n                    # _dt match found, update gt_m, and dt_m with \"id\"\n                    dt_m[iou_thr_idx, dt_idx] = gt_ids[m]\n                    gt_m[iou_thr_idx, m] = _dt\n\n            dt_ig_mask = dt_ig_masks[mask_idx]\n\n            dt_ig_mask = np.array(dt_ig_mask).reshape((1, num_dt))  # 1 X num_dt\n            dt_ig_mask = np.repeat(dt_ig_mask, num_thrs, 0)  # num_thrs X num_dt\n\n            # Based on dt_ig_mask ignore any unmatched detection by updating dt_ig\n            dt_ig = np.logical_or(dt_ig, np.logical_and(dt_m == -1, dt_ig_mask))\n            # store results for given video and category\n            res[mask_idx] = {\n                \"dt_ids\": dt_ids,\n                \"gt_ids\": gt_ids,\n                \"dt_matches\": dt_m,\n                \"gt_matches\": gt_m,\n                \"dt_scores\": data['dt_track_scores'],\n                \"gt_ignore\": gt_ig,\n                \"dt_ignore\": dt_ig,\n            }\n\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences. Computes precision and recall values based on track matches.\n        Adapted from https://github.com/TAO-Dataset/\n        \"\"\"\n        num_thrs = len(self.array_labels)\n        num_recalls = len(self.rec_thrs)\n\n        # -1 for absent categories\n        precision = -np.ones(\n            (num_thrs, num_recalls, self.num_ig_masks)\n        )\n        recall = -np.ones((num_thrs, self.num_ig_masks))\n\n        for ig_idx in range(self.num_ig_masks):\n            ig_idx_results = [res[ig_idx] for res in all_res.values() if res[ig_idx] is not None]\n\n            # Remove elements which are None\n            if len(ig_idx_results) == 0:\n                continue\n\n            # Append all scores: shape (N,)\n            # limit considered tracks for each sequence if maxDet > 0\n            if self.maxDet == 0:\n                dt_scores = np.concatenate([res[\"dt_scores\"] for res in ig_idx_results], axis=0)\n\n                dt_idx = np.argsort(-dt_scores, kind=\"mergesort\")\n\n                dt_m = np.concatenate([e[\"dt_matches\"] for e in ig_idx_results],\n                                      axis=1)[:, dt_idx]\n                dt_ig = np.concatenate([e[\"dt_ignore\"] for e in ig_idx_results],\n                                       axis=1)[:, dt_idx]\n            elif self.maxDet > 0:\n                dt_scores = np.concatenate([res[\"dt_scores\"][0:self.maxDet] for res in ig_idx_results], axis=0)\n\n                dt_idx = np.argsort(-dt_scores, kind=\"mergesort\")\n\n                dt_m = np.concatenate([e[\"dt_matches\"][:, 0:self.maxDet] for e in ig_idx_results],\n                                      axis=1)[:, dt_idx]\n                dt_ig = np.concatenate([e[\"dt_ignore\"][:, 0:self.maxDet] for e in ig_idx_results],\n                                       axis=1)[:, dt_idx]\n            else:\n                raise Exception(\"Number of maximum detections must be >= 0, but is set to %i\" % self.maxDet)\n\n            gt_ig = np.concatenate([res[\"gt_ignore\"] for res in ig_idx_results])\n            # num gt anns to consider\n            num_gt = np.count_nonzero(gt_ig == 0)\n\n            if num_gt == 0:\n                continue\n\n            tps = np.logical_and(dt_m != -1, np.logical_not(dt_ig))\n            fps = np.logical_and(dt_m == -1, np.logical_not(dt_ig))\n\n            tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)\n            fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)\n\n            for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):\n                tp = np.array(tp)\n                fp = np.array(fp)\n                num_tp = len(tp)\n                rc = tp / num_gt\n                if num_tp:\n                    recall[iou_thr_idx, ig_idx] = rc[-1]\n                else:\n                    recall[iou_thr_idx, ig_idx] = 0\n\n                # np.spacing(1) ~= eps\n                pr = tp / (fp + tp + np.spacing(1))\n                pr = pr.tolist()\n\n                # Ensure precision values are monotonically decreasing\n                for i in range(num_tp - 1, 0, -1):\n                    if pr[i] > pr[i - 1]:\n                        pr[i - 1] = pr[i]\n\n                # find indices at the predefined recall values\n                rec_thrs_insert_idx = np.searchsorted(rc, self.rec_thrs, side=\"left\")\n\n                pr_at_recall = [0.0] * num_recalls\n\n                try:\n                    for _idx, pr_idx in enumerate(rec_thrs_insert_idx):\n                        pr_at_recall[_idx] = pr[pr_idx]\n                except IndexError:\n                    pass\n\n                precision[iou_thr_idx, :, ig_idx] = (np.array(pr_at_recall))\n\n        res = {'precision': precision, 'recall': recall}\n\n        # compute the precision and recall averages for the respective alpha thresholds and ignore masks\n        for lbl in self.lbls:\n            res['AP_' + lbl] = np.zeros((len(self.array_labels)), dtype=np.float)\n            res['AR_' + lbl] = np.zeros((len(self.array_labels)), dtype=np.float)\n\n        for a_id, alpha in enumerate(self.array_labels):\n            for lbl_idx, lbl in enumerate(self.lbls):\n                p = precision[a_id, :, lbl_idx]\n                if len(p[p > -1]) == 0:\n                    mean_p = -1\n                else:\n                    mean_p = np.mean(p[p > -1])\n                res['AP_' + lbl][a_id] = mean_p\n                res['AR_' + lbl][a_id] = recall[a_id, lbl_idx]\n\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=True):\n        \"\"\"Combines metrics across all classes by averaging over the class values\n        Note mAP is not well defined for 'empty classes' so 'ignore empty classes' is always true here.\n        \"\"\"\n        res = {}\n        for field in self.fields:\n            res[field] = np.zeros((len(self.array_labels)), dtype=np.float)\n            field_stacked = np.array([res[field] for res in all_res.values()])\n\n            for a_id, alpha in enumerate(self.array_labels):\n                values = field_stacked[:, a_id]\n                if len(values[values > -1]) == 0:\n                    mean = -1\n                else:\n                    mean = np.mean(values[values > -1])\n                res[field][a_id] = mean\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n\n        res = {}\n        for field in self.fields:\n            res[field] = np.zeros((len(self.array_labels)), dtype=np.float)\n            field_stacked = np.array([res[field] for res in all_res.values()])\n\n            for a_id, alpha in enumerate(self.array_labels):\n                values = field_stacked[:, a_id]\n                if len(values[values > -1]) == 0:\n                    mean = -1\n                else:\n                    mean = np.mean(values[values > -1])\n                res[field][a_id] = mean\n        return res\n\n    def _compute_track_ig_masks(self, num_ids, track_lengths=None, track_areas=None, iscrowd=None,\n                                is_not_exhaustively_labeled=False, is_gt=True):\n        \"\"\"\n        Computes ignore masks for different track sets to evaluate\n        :param num_ids: the number of track IDs\n        :param track_lengths: the lengths of the tracks (number of timesteps)\n        :param track_areas: the average area of a track\n        :param iscrowd: whether a track is marked as crowd\n        :param is_not_exhaustively_labeled: whether the track category is not exhaustively labeled\n        :param is_gt: whether it is gt\n        :return: the track ignore masks\n        \"\"\"\n        # for TAO tracks for classes which are not exhaustively labeled are not evaluated\n        if not is_gt and is_not_exhaustively_labeled:\n            track_ig_masks = [[1 for _ in range(num_ids)] for i in range(self.num_ig_masks)]\n        else:\n            # consider all tracks\n            track_ig_masks = [[0 for _ in range(num_ids)]]\n\n            # consider tracks with certain area\n            if self.use_area_rngs:\n                for rng in self.area_rngs:\n                    track_ig_masks.append([0 if rng[0] - np.finfo('float').eps <= area <= rng[1] + np.finfo('float').eps\n                                           else 1 for area in track_areas])\n\n            # consider tracks with certain duration\n            if self.use_time_rngs:\n                for rng in self.time_rngs:\n                    track_ig_masks.append([0 if rng[0] - np.finfo('float').eps <= length\n                                                <= rng[1] + np.finfo('float').eps else 1 for length in track_lengths])\n\n        # for YouTubeVIS evaluation tracks with crowd tag are not evaluated\n        if is_gt and iscrowd:\n            track_ig_masks = [np.logical_or(mask, iscrowd) for mask in track_ig_masks]\n\n        return track_ig_masks\n\n    @staticmethod\n    def _compute_bb_track_iou(dt_track, gt_track, boxformat='xywh'):\n        \"\"\"\n        Calculates the track IoU for one detected track and one ground truth track for bounding boxes\n        :param dt_track: the detected track (format: dictionary with frame index as keys and\n                            numpy arrays as values)\n        :param gt_track: the ground truth track (format: dictionary with frame index as keys and\n                        numpy array as values)\n        :param boxformat: the format of the boxes\n        :return: the track IoU\n        \"\"\"\n        intersect = 0\n        union = 0\n        image_ids = set(gt_track.keys()) | set(dt_track.keys())\n        for image in image_ids:\n            g = gt_track.get(image, None)\n            d = dt_track.get(image, None)\n            if boxformat == 'xywh':\n                if d is not None and g is not None:\n                    dx, dy, dw, dh = d\n                    gx, gy, gw, gh = g\n                    w = max(min(dx + dw, gx + gw) - max(dx, gx), 0)\n                    h = max(min(dy + dh, gy + gh) - max(dy, gy), 0)\n                    i = w * h\n                    u = dw * dh + gw * gh - i\n                    intersect += i\n                    union += u\n                elif d is None and g is not None:\n                    union += g[2] * g[3]\n                elif d is not None and g is None:\n                    union += d[2] * d[3]\n            elif boxformat == 'x0y0x1y1':\n                if d is not None and g is not None:\n                    dx0, dy0, dx1, dy1 = d\n                    gx0, gy0, gx1, gy1 = g\n                    w = max(min(dx1, gx1) - max(dx0, gx0), 0)\n                    h = max(min(dy1, gy1) - max(dy0, gy0), 0)\n                    i = w * h\n                    u = (dx1 - dx0) * (dy1 - dy0) + (gx1 - gx0) * (gy1 - gy0) - i\n                    intersect += i\n                    union += u\n                elif d is None and g is not None:\n                    union += (g[2] - g[0]) * (g[3] - g[1])\n                elif d is not None and g is None:\n                    union += (d[2] - d[0]) * (d[3] - d[1])\n            else:\n                raise TrackEvalException('BoxFormat not implemented')\n        if intersect > union:\n            raise TrackEvalException(\"Intersection value > union value. Are the box values corrupted?\")\n        return intersect / union if union > 0 else 0\n\n    @staticmethod\n    def _compute_mask_track_iou(dt_track, gt_track):\n        \"\"\"\n        Calculates the track IoU for one detected track and one ground truth track for segmentation masks\n        :param dt_track: the detected track (format: dictionary with frame index as keys and\n                            pycocotools rle encoded masks as values)\n        :param gt_track: the ground truth track (format: dictionary with frame index as keys and\n                            pycocotools rle encoded masks as values)\n        :return: the track IoU\n        \"\"\"\n        # only loaded when needed to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        intersect = .0\n        union = .0\n        image_ids = set(gt_track.keys()) | set(dt_track.keys())\n        for image in image_ids:\n            g = gt_track.get(image, None)\n            d = dt_track.get(image, None)\n            if d and g:\n                intersect += mask_utils.area(mask_utils.merge([d, g], True))\n                union += mask_utils.area(mask_utils.merge([d, g], False))\n            elif not d and g:\n                union += mask_utils.area(g)\n            elif d and not g:\n                union += mask_utils.area(d)\n        if union < 0.0 - np.finfo('float').eps:\n            raise TrackEvalException(\"Union value < 0. Are the segmentaions corrupted?\")\n        if intersect > union:\n            raise TrackEvalException(\"Intersection value > union value. Are the segmentations corrupted?\")\n        iou = intersect / union if union > 0.0 + np.finfo('float').eps else 0.0\n        return iou\n\n    @staticmethod\n    def _compute_track_ious(dt, gt, iou_function='bbox', boxformat='xywh'):\n        \"\"\"\n        Calculate track IoUs for a set of ground truth tracks and a set of detected tracks\n        \"\"\"\n\n        if len(gt) == 0 and len(dt) == 0:\n            return []\n\n        if iou_function == 'bbox':\n            track_iou_function = partial(TrackMAP._compute_bb_track_iou, boxformat=boxformat)\n        elif iou_function == 'mask':\n            track_iou_function = partial(TrackMAP._compute_mask_track_iou)\n        else:\n            raise Exception('IoU function not implemented')\n\n        ious = np.zeros([len(dt), len(gt)])\n        for i, j in np.ndindex(ious.shape):\n            ious[i, j] = track_iou_function(dt[i], gt[j])\n        return ious\n\n    @staticmethod\n    def _row_print(*argv):\n        \"\"\"Prints results in an evenly spaced rows, with more space in first row\"\"\"\n        if len(argv) == 1:\n            argv = argv[0]\n        to_print = '%-40s' % argv[0]\n        for v in argv[1:]:\n            to_print += '%-12s' % str(v)\n        print(to_print)\n"
  },
  {
    "path": "TrackEval/trackeval/metrics/vace.py",
    "content": "import numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\n\n\nclass VACE(_BaseMetric):\n    \"\"\"Class which implements the VACE metrics.\n\n    The metrics are described in:\n    Manohar et al. (2006) \"Performance Evaluation of Object Detection and Tracking in Video\"\n    https://link.springer.com/chapter/10.1007/11612704_16\n\n    This implementation uses the \"relaxed\" variant of the metrics,\n    where an overlap threshold is applied in each frame.\n    \"\"\"\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.integer_fields = ['VACE_IDs', 'VACE_GT_IDs', 'num_non_empty_timesteps']\n        self.float_fields = ['STDA', 'ATA', 'FDA', 'SFDA']\n        self.fields = self.integer_fields + self.float_fields\n        self.summary_fields = ['SFDA', 'ATA']\n\n        # Fields that are accumulated over multiple videos.\n        self._additive_fields = self.integer_fields + ['STDA', 'FDA']\n\n        self.threshold = 0.5\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates VACE metrics for one sequence.\n\n        Depends on the fields:\n            data['num_gt_ids']\n            data['num_tracker_ids']\n            data['gt_ids']\n            data['tracker_ids']\n            data['similarity_scores']\n        \"\"\"\n        res = {}\n\n        # Obtain Average Tracking Accuracy (ATA) using track correspondence.\n        # Obtain counts necessary to compute temporal IOU.\n        # Assume that integer counts can be represented exactly as floats.\n        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))\n        gt_id_count = np.zeros(data['num_gt_ids'])\n        tracker_id_count = np.zeros(data['num_tracker_ids'])\n        both_present_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            # Count the number of frames in which two tracks satisfy the overlap criterion.\n            matches_mask = np.greater_equal(data['similarity_scores'][t], self.threshold)\n            match_idx_gt, match_idx_tracker = np.nonzero(matches_mask)\n            potential_matches_count[gt_ids_t[match_idx_gt], tracker_ids_t[match_idx_tracker]] += 1\n            # Count the number of frames in which the tracks are present.\n            gt_id_count[gt_ids_t] += 1\n            tracker_id_count[tracker_ids_t] += 1\n            both_present_count[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] += 1\n        # Number of frames in which either track is present (union of the two sets of frames).\n        union_count = (gt_id_count[:, np.newaxis]\n                       + tracker_id_count[np.newaxis, :]\n                       - both_present_count)\n        # The denominator should always be non-zero if all tracks are non-empty.\n        with np.errstate(divide='raise', invalid='raise'):\n            temporal_iou = potential_matches_count / union_count\n        # Find assignment that maximizes temporal IOU.\n        match_rows, match_cols = linear_sum_assignment(-temporal_iou)\n        res['STDA'] = temporal_iou[match_rows, match_cols].sum()\n        res['VACE_IDs'] = data['num_tracker_ids']\n        res['VACE_GT_IDs'] = data['num_gt_ids']\n\n        # Obtain Frame Detection Accuracy (FDA) using per-frame correspondence.\n        non_empty_count = 0\n        fda = 0\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            n_g = len(gt_ids_t)\n            n_d = len(tracker_ids_t)\n            if not (n_g or n_d):\n                continue\n            # n_g > 0 or n_d > 0\n            non_empty_count += 1\n            if not (n_g and n_d):\n                continue\n            # n_g > 0 and n_d > 0\n            spatial_overlap = data['similarity_scores'][t]\n            match_rows, match_cols = linear_sum_assignment(-spatial_overlap)\n            overlap_ratio = spatial_overlap[match_rows, match_cols].sum()\n            fda += overlap_ratio / (0.5 * (n_g + n_d))\n        res['FDA'] = fda\n        res['num_non_empty_timesteps'] = non_empty_count\n\n        res.update(self._compute_final_fields(res))\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=True):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n        for field in self.fields:\n            if ignore_empty_classes:\n                res[field] = np.mean([v[field] for v in all_res.values()\n                                  if v['VACE_GT_IDs'] > 0 or v['VACE_IDs'] > 0], axis=0)\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self._additive_fields:\n            res[field] = _BaseMetric._combine_sum(all_res, field)\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for header in self._additive_fields:\n            res[header] = _BaseMetric._combine_sum(all_res, header)\n        res.update(self._compute_final_fields(res))\n        return res\n\n    @staticmethod\n    def _compute_final_fields(additive):\n        final = {}\n        with np.errstate(invalid='ignore'):  # Permit nan results.\n            final['ATA'] = (additive['STDA'] /\n                            (0.5 * (additive['VACE_IDs'] + additive['VACE_GT_IDs'])))\n            final['SFDA'] = additive['FDA'] / additive['num_non_empty_timesteps']\n        return final\n"
  },
  {
    "path": "TrackEval/trackeval/plotting.py",
    "content": "\nimport os\nimport numpy as np\nfrom .utils import TrackEvalException\n\n\ndef plot_compare_trackers(tracker_folder, tracker_list, cls, output_folder, plots_list=None):\n    \"\"\"Create plots which compare metrics across different trackers.\"\"\"\n    # Define what to plot\n    if plots_list is None:\n        plots_list = get_default_plots_list()\n\n    # Load data\n    data = load_multiple_tracker_summaries(tracker_folder, tracker_list, cls)\n    out_loc = os.path.join(output_folder, cls)\n\n    # Plot\n    for args in plots_list:\n        create_comparison_plot(data, out_loc, *args)\n\n\ndef get_default_plots_list():\n    # y_label, x_label, sort_label, bg_label, bg_function\n    plots_list = [\n        ['AssA', 'DetA', 'HOTA', 'HOTA', 'geometric_mean'],\n        ['AssPr', 'AssRe', 'HOTA', 'AssA', 'jaccard'],\n        ['DetPr', 'DetRe', 'HOTA', 'DetA', 'jaccard'],\n        ['HOTA(0)', 'LocA(0)', 'HOTA', 'HOTALocA(0)', 'multiplication'],\n        ['HOTA', 'LocA', 'HOTA', None, None],\n\n        ['HOTA', 'MOTA', 'HOTA', None, None],\n        ['HOTA', 'IDF1', 'HOTA', None, None],\n        ['IDF1', 'MOTA', 'HOTA', None, None],\n    ]\n    return plots_list\n\n\ndef load_multiple_tracker_summaries(tracker_folder, tracker_list, cls):\n    \"\"\"Loads summary data for multiple trackers.\"\"\"\n    data = {}\n    for tracker in tracker_list:\n        with open(os.path.join(tracker_folder, tracker, cls + '_summary.txt')) as f:\n            keys = next(f).split(' ')\n            done = False\n            while not done:\n                values = next(f).split(' ')\n                if len(values) == len(keys):\n                    done = True\n            data[tracker] = dict(zip(keys, map(float, values)))\n    return data\n\n\ndef create_comparison_plot(data, out_loc, y_label, x_label, sort_label, bg_label=None, bg_function=None, settings=None):\n    \"\"\" Creates a scatter plot comparing multiple trackers between two metric fields, with one on the x-axis and the\n    other on the y axis. Adds pareto optical lines and (optionally) a background contour.\n\n    Inputs:\n        data: dict of dicts such that data[tracker_name][metric_field_name] = float\n        y_label: the metric_field_name to be plotted on the y-axis\n        x_label: the metric_field_name to be plotted on the x-axis\n        sort_label: the metric_field_name by which trackers are ordered and ranked\n        bg_label: the metric_field_name by which (optional) background contours are plotted\n        bg_function: the (optional) function bg_function(x,y) which converts the x_label / y_label values into bg_label.\n        settings: dict of plot settings with keys:\n            'gap_val': gap between axis ticks and bg curves.\n            'num_to_plot': maximum number of trackers to plot\n    \"\"\"\n\n    # Only loaded when run to reduce minimum requirements\n    from matplotlib import pyplot as plt\n\n    # Get plot settings\n    if settings is None:\n        gap_val = 2\n        num_to_plot = 20\n    else:\n        gap_val = settings['gap_val']\n        num_to_plot = settings['num_to_plot']\n\n    if (bg_label is None) != (bg_function is None):\n        raise TrackEvalException('bg_function and bg_label must either be both given or neither given.')\n\n    # Extract data\n    tracker_names = np.array(list(data.keys()))\n    sort_index = np.array([data[t][sort_label] for t in tracker_names]).argsort()[::-1]\n    x_values = np.array([data[t][x_label] for t in tracker_names])[sort_index][:num_to_plot]\n    y_values = np.array([data[t][y_label] for t in tracker_names])[sort_index][:num_to_plot]\n\n    # Print info on what is being plotted\n    tracker_names = tracker_names[sort_index][:num_to_plot]\n    print('\\nPlotting %s vs %s, for the following (ordered) trackers:' % (y_label, x_label))\n    for i, name in enumerate(tracker_names):\n        print('%i: %s' % (i+1, name))\n\n    # Find best fitting boundaries for data\n    boundaries = _get_boundaries(x_values, y_values, round_val=gap_val/2)\n\n    fig = plt.figure()\n\n    # Plot background contour\n    if bg_function is not None:\n        _plot_bg_contour(bg_function, boundaries, gap_val)\n\n    # Plot pareto optimal lines\n    _plot_pareto_optimal_lines(x_values, y_values)\n\n    # Plot data points with number labels\n    labels = np.arange(len(y_values)) + 1\n    plt.plot(x_values, y_values, 'b.', markersize=15)\n    for xx, yy, l in zip(x_values, y_values, labels):\n        plt.text(xx, yy, str(l), color=\"red\", fontsize=15)\n\n    # Add extra explanatory text to plots\n    plt.text(0, -0.11, 'label order:\\nHOTA', horizontalalignment='left', verticalalignment='center',\n             transform=fig.axes[0].transAxes, color=\"red\", fontsize=12)\n    if bg_label is not None:\n        plt.text(1, -0.11, 'curve values:\\n' + bg_label, horizontalalignment='right', verticalalignment='center',\n                 transform=fig.axes[0].transAxes, color=\"grey\", fontsize=12)\n\n    plt.xlabel(x_label, fontsize=15)\n    plt.ylabel(y_label, fontsize=15)\n    title = y_label + ' vs ' + x_label\n    if bg_label is not None:\n        title += ' (' + bg_label + ')'\n    plt.title(title, fontsize=17)\n    plt.xticks(np.arange(0, 100, gap_val))\n    plt.yticks(np.arange(0, 100, gap_val))\n    min_x, max_x, min_y, max_y = boundaries\n    plt.xlim(min_x, max_x)\n    plt.ylim(min_y, max_y)\n    plt.gca().set_aspect('equal', adjustable='box')\n    plt.tight_layout()\n\n    os.makedirs(out_loc, exist_ok=True)\n    filename = os.path.join(out_loc, title.replace(' ', '_'))\n    plt.savefig(filename + '.pdf', bbox_inches='tight', pad_inches=0.05)\n    plt.savefig(filename + '.png', bbox_inches='tight', pad_inches=0.05)\n\n\ndef _get_boundaries(x_values, y_values, round_val):\n    x1 = np.min(np.floor((x_values - 0.5) / round_val) * round_val)\n    x2 = np.max(np.ceil((x_values + 0.5) / round_val) * round_val)\n    y1 = np.min(np.floor((y_values - 0.5) / round_val) * round_val)\n    y2 = np.max(np.ceil((y_values + 0.5) / round_val) * round_val)\n    x_range = x2 - x1\n    y_range = y2 - y1\n    max_range = max(x_range, y_range)\n    x_center = (x1 + x2) / 2\n    y_center = (y1 + y2) / 2\n    min_x = max(x_center - max_range / 2, 0)\n    max_x = min(x_center + max_range / 2, 100)\n    min_y = max(y_center - max_range / 2, 0)\n    max_y = min(y_center + max_range / 2, 100)\n    return min_x, max_x, min_y, max_y\n\n\ndef geometric_mean(x, y):\n    return np.sqrt(x * y)\n\n\ndef jaccard(x, y):\n    x = x / 100\n    y = y / 100\n    return 100 * (x * y) / (x + y - x * y)\n\n\ndef multiplication(x, y):\n    return x * y / 100\n\n\nbg_function_dict = {\n    \"geometric_mean\": geometric_mean,\n    \"jaccard\": jaccard,\n    \"multiplication\": multiplication,\n    }\n\n\ndef _plot_bg_contour(bg_function, plot_boundaries, gap_val):\n    \"\"\" Plot background contour. \"\"\"\n\n    # Only loaded when run to reduce minimum requirements\n    from matplotlib import pyplot as plt\n\n    # Plot background contour\n    min_x, max_x, min_y, max_y = plot_boundaries\n    x = np.arange(min_x, max_x, 0.1)\n    y = np.arange(min_y, max_y, 0.1)\n    x_grid, y_grid = np.meshgrid(x, y)\n    if bg_function in bg_function_dict.keys():\n        z_grid = bg_function_dict[bg_function](x_grid, y_grid)\n    else:\n        raise TrackEvalException(\"background plotting function '%s' is not defined.\" % bg_function)\n    levels = np.arange(0, 100, gap_val)\n    con = plt.contour(x_grid, y_grid, z_grid, levels, colors='grey')\n\n    def bg_format(val):\n        s = '{:1f}'.format(val)\n        return '{:.0f}'.format(val) if s[-1] == '0' else s\n\n    con.levels = [bg_format(val) for val in con.levels]\n    plt.clabel(con, con.levels, inline=True, fmt='%r', fontsize=8)\n\n\ndef _plot_pareto_optimal_lines(x_values, y_values):\n    \"\"\" Plot pareto optimal lines \"\"\"\n\n    # Only loaded when run to reduce minimum requirements\n    from matplotlib import pyplot as plt\n\n    # Plot pareto optimal lines\n    cxs = x_values\n    cys = y_values\n    best_y = np.argmax(cys)\n    x_pareto = [0, cxs[best_y]]\n    y_pareto = [cys[best_y], cys[best_y]]\n    t = 2\n    remaining = cxs > x_pareto[t - 1]\n    cys = cys[remaining]\n    cxs = cxs[remaining]\n    while len(cxs) > 0 and len(cys) > 0:\n        best_y = np.argmax(cys)\n        x_pareto += [x_pareto[t - 1], cxs[best_y]]\n        y_pareto += [cys[best_y], cys[best_y]]\n        t += 2\n        remaining = cxs > x_pareto[t - 1]\n        cys = cys[remaining]\n        cxs = cxs[remaining]\n    x_pareto.append(x_pareto[t - 1])\n    y_pareto.append(0)\n    plt.plot(np.array(x_pareto), np.array(y_pareto), '--r')\n"
  },
  {
    "path": "TrackEval/trackeval/utils.py",
    "content": "\nimport os\nimport csv\nimport argparse\nfrom collections import OrderedDict\n\n\ndef init_config(config, default_config, name=None):\n    \"\"\"Initialise non-given config values with defaults\"\"\"\n    if config is None:\n        config = default_config\n    else:\n        for k in default_config.keys():\n            if k not in config.keys():\n                config[k] = default_config[k]\n    if name and config['PRINT_CONFIG']:\n        print('\\n%s Config:' % name)\n        for c in config.keys():\n            print('%-20s : %-30s' % (c, config[c]))\n    return config\n\n\ndef update_config(config):\n    \"\"\"\n    Parse the arguments of a script and updates the config values for a given value if specified in the arguments.\n    :param config: the config to update\n    :return: the updated config\n    \"\"\"\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    return config\n\n\ndef get_code_path():\n    \"\"\"Get base path where code is\"\"\"\n    return os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))\n\n\ndef validate_metrics_list(metrics_list):\n    \"\"\"Get names of metric class and ensures they are unique, further checks that the fields within each metric class\n    do not have overlapping names.\n    \"\"\"\n    metric_names = [metric.get_name() for metric in metrics_list]\n    # check metric names are unique\n    if len(metric_names) != len(set(metric_names)):\n        raise TrackEvalException('Code being run with multiple metrics of the same name')\n    fields = []\n    for m in metrics_list:\n        fields += m.fields\n    # check metric fields are unique\n    if len(fields) != len(set(fields)):\n        raise TrackEvalException('Code being run with multiple metrics with fields of the same name')\n    return metric_names\n\n\ndef write_summary_results(summaries, cls, output_folder):\n    \"\"\"Write summary results to file\"\"\"\n\n    fields = sum([list(s.keys()) for s in summaries], [])\n    values = sum([list(s.values()) for s in summaries], [])\n\n    # In order to remain consistent upon new fields being adding, for each of the following fields if they are present\n    # they will be output in the summary first in the order below. Any further fields will be output in the order each\n    # metric family is called, and within each family either in the order they were added to the dict (python >= 3.6) or\n    # randomly (python < 3.6).\n    default_order = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'RHOTA', 'HOTA(0)', 'LocA(0)',\n                     'HOTALocA(0)', 'MOTA', 'MOTP', 'MODA', 'CLR_Re', 'CLR_Pr', 'MTR', 'PTR', 'MLR', 'CLR_TP', 'CLR_FN',\n                     'CLR_FP', 'IDSW', 'MT', 'PT', 'ML', 'Frag', 'sMOTA', 'IDF1', 'IDR', 'IDP', 'IDTP', 'IDFN', 'IDFP',\n                     'Dets', 'GT_Dets', 'IDs', 'GT_IDs']\n    default_ordered_dict = OrderedDict(zip(default_order, [None for _ in default_order]))\n    for f, v in zip(fields, values):\n        default_ordered_dict[f] = v\n    for df in default_order:\n        if default_ordered_dict[df] is None:\n            del default_ordered_dict[df]\n    fields = list(default_ordered_dict.keys())\n    values = list(default_ordered_dict.values())\n\n    out_file = os.path.join(output_folder, cls + '_summary.txt')\n    os.makedirs(os.path.dirname(out_file), exist_ok=True)\n    with open(out_file, 'w', newline='') as f:\n        writer = csv.writer(f, delimiter=' ')\n        writer.writerow(fields)\n        writer.writerow(values)\n\n\ndef write_detailed_results(details, cls, output_folder):\n    \"\"\"Write detailed results to file\"\"\"\n    sequences = details[0].keys()\n    fields = ['seq'] + sum([list(s['COMBINED_SEQ'].keys()) for s in details], [])\n    out_file = os.path.join(output_folder, cls + '_detailed.csv')\n    os.makedirs(os.path.dirname(out_file), exist_ok=True)\n    with open(out_file, 'w', newline='') as f:\n        writer = csv.writer(f)\n        writer.writerow(fields)\n        for seq in sorted(sequences):\n            if seq == 'COMBINED_SEQ':\n                continue\n            writer.writerow([seq] + sum([list(s[seq].values()) for s in details], []))\n        writer.writerow(['COMBINED'] + sum([list(s['COMBINED_SEQ'].values()) for s in details], []))\n\n\ndef load_detail(file):\n    \"\"\"Loads detailed data for a tracker.\"\"\"\n    data = {}\n    with open(file) as f:\n        for i, row_text in enumerate(f):\n            row = row_text.replace('\\r', '').replace('\\n', '').split(',')\n            if i == 0:\n                keys = row[1:]\n                continue\n            current_values = row[1:]\n            seq = row[0]\n            if seq == 'COMBINED':\n                seq = 'COMBINED_SEQ'\n            if (len(current_values) == len(keys)) and seq != '':\n                data[seq] = {}\n                for key, value in zip(keys, current_values):\n                    data[seq][key] = float(value)\n    return data\n\n\nclass TrackEvalException(Exception):\n    \"\"\"Custom exception for catching expected errors.\"\"\"\n    ...\n"
  },
  {
    "path": "deploy/ONNXRuntime/README.md",
    "content": "## ByteTrack-ONNXRuntime in Python\n\nThis doc introduces how to convert your pytorch model into onnx, and how to run an onnxruntime demo to verify your convertion.\n\n### Convert Your Model to ONNX\n\n```shell\ncd <ByteTrack_HOME>\npython3 tools/export_onnx.py --output-name bytetrack_s.onnx -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_s_mot17.pth.tar\n```\n\n### ONNXRuntime Demo\n\nYou can run onnx demo with **16 FPS** (96-core Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz):\n\n```shell\ncd <ByteTrack_HOME>/deploy/ONNXRuntime\npython3 onnx_inference.py\n```\n"
  },
  {
    "path": "deploy/ONNXRuntime/onnx_inference.py",
    "content": "import argparse\nimport os\n\nimport cv2\nimport numpy as np\nfrom loguru import logger\n\nimport onnxruntime\n\nfrom yolox.data.data_augment import preproc as preprocess\nfrom yolox.utils import mkdir, multiclass_nms, demo_postprocess, vis\nfrom yolox.utils.visualize import plot_tracking\nfrom trackers.ocsort_tracker.ocsort import OCSort\nfrom trackers.tracking_utils.timer import Timer\n\n\ndef make_parser():\n    parser = argparse.ArgumentParser(\"onnxruntime inference sample\")\n    parser.add_argument(\n        \"-m\",\n        \"--model\",\n        type=str,\n        default=\"../../ocsort.onnx\",\n        help=\"Input your onnx model.\",\n    )\n    parser.add_argument(\n        \"-i\",\n        \"--video_path\",\n        type=str,\n        default='../../videos/dance_demo.mp4',\n        help=\"Path to your input image.\",\n    )\n    parser.add_argument(\n        \"-o\",\n        \"--output_dir\",\n        type=str,\n        default='demo_output',\n        help=\"Path to your output directory.\",\n    )\n    parser.add_argument(\n        \"-s\",\n        \"--score_thr\",\n        type=float,\n        default=0.1,\n        help=\"Score threshould to filter the result.\",\n    )\n    parser.add_argument(\n        \"-n\",\n        \"--nms_thr\",\n        type=float,\n        default=0.7,\n        help=\"NMS threshould.\",\n    )\n    parser.add_argument(\n        \"--input_shape\",\n        type=str,\n        default=\"800,1440\",\n        help=\"Specify an input shape for inference.\",\n    )\n    parser.add_argument(\n        \"--with_p6\",\n        action=\"store_true\",\n        help=\"Whether your model uses p6 in FPN/PAN.\",\n    )\n    # tracking args\n    parser.add_argument(\"--track_thresh\", type=float, default=0.6, help=\"tracking confidence threshold\")\n    parser.add_argument(\"--iou_thresh\", type=float, default=0.3, help=\"tracking confidence threshold\")\n    parser.add_argument(\"--track_buffer\", type=int, default=30, help=\"the frames for keep lost tracks\")\n    parser.add_argument(\"--match_thresh\", type=float, default=0.8, help=\"matching threshold for tracking\")\n    parser.add_argument('--min-box-area', type=float, default=10, help='filter out tiny boxes')\n    parser.add_argument(\"--mot20\", dest=\"mot20\", default=False, action=\"store_true\", help=\"test mot20.\")\n    return parser\n\n\nclass Predictor(object):\n    def __init__(self, args):\n        self.rgb_means = (0.485, 0.456, 0.406)\n        self.std = (0.229, 0.224, 0.225)\n        self.args = args\n        self.session = onnxruntime.InferenceSession(args.model)\n        self.input_shape = tuple(map(int, args.input_shape.split(',')))\n\n    def inference(self, ori_img, timer):\n        img_info = {\"id\": 0}\n        height, width = ori_img.shape[:2]\n        img_info[\"height\"] = height\n        img_info[\"width\"] = width\n        img_info[\"raw_img\"] = ori_img\n\n        img, ratio = preprocess(ori_img, self.input_shape, self.rgb_means, self.std)\n        img_info[\"ratio\"] = ratio\n        ort_inputs = {self.session.get_inputs()[0].name: img[None, :, :, :]}\n        timer.tic()\n        output = self.session.run(None, ort_inputs)\n        predictions = demo_postprocess(output[0], self.input_shape, p6=self.args.with_p6)[0]\n\n        boxes = predictions[:, :4]\n        scores = predictions[:, 4:5] * predictions[:, 5:]\n\n        boxes_xyxy = np.ones_like(boxes)\n        boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2.\n        boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2.\n        boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2.\n        boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2.\n        boxes_xyxy /= ratio\n        dets = multiclass_nms(boxes_xyxy, scores, nms_thr=self.args.nms_thr, score_thr=self.args.score_thr)\n        return dets[:, :-1], img_info\n\n\ndef imageflow_demo(predictor, args):\n    cap = cv2.VideoCapture(args.video_path)\n    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float\n    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float\n    fps = cap.get(cv2.CAP_PROP_FPS)\n    save_folder = args.output_dir\n    os.makedirs(save_folder, exist_ok=True)\n    save_path = os.path.join(save_folder, args.video_path.split(\"/\")[-1])\n    logger.info(f\"video save_path is {save_path}\")\n    vid_writer = cv2.VideoWriter(\n        save_path, cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (int(width), int(height))\n    )\n    tracker = OCSort(det_thresh=args.track_thresh, iou_threshold=args.iou_thresh)\n    timer = Timer()\n    frame_id = 0\n    results = []\n    while True:\n        if frame_id % 20 == 0:\n            logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))\n        ret_val, frame = cap.read()\n        if ret_val:\n            outputs, img_info = predictor.inference(frame, timer)\n            online_targets = tracker.update(outputs, [img_info['height'], img_info['width']], [img_info['height'], img_info['width']])\n            online_tlwhs = []\n            online_ids = []\n            # online_scores = []\n            for t in online_targets:\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                vertical = tlwh[2] / tlwh[3] > 1.6\n                if tlwh[2] * tlwh[3] > args.min_box_area and not vertical:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    # online_scores.append(t.score)\n            timer.toc()\n            results.append((frame_id + 1, online_tlwhs, online_ids))\n            online_im = plot_tracking(img_info['raw_img'], online_tlwhs, online_ids, frame_id=frame_id + 1,\n                                      fps=1. / timer.average_time)\n            vid_writer.write(online_im)\n            ch = cv2.waitKey(1)\n            if ch == 27 or ch == ord(\"q\") or ch == ord(\"Q\"):\n                break\n        else:\n            break\n        frame_id += 1\n\n\nif __name__ == '__main__':\n    args = make_parser().parse_args()\n\n    predictor = Predictor(args)\n    imageflow_demo(predictor, args)\n"
  },
  {
    "path": "deploy/TensorRT/cpp/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 2.6)\n\nproject(bytetrack)\n\nadd_definitions(-std=c++11)\n\noption(CUDA_USE_STATIC_CUDA_RUNTIME OFF)\nset(CMAKE_CXX_STANDARD 11)\nset(CMAKE_BUILD_TYPE Debug)\n\nfind_package(CUDA REQUIRED)\n\ninclude_directories(${PROJECT_SOURCE_DIR}/include)\ninclude_directories(/usr/local/include/eigen3)\nlink_directories(${PROJECT_SOURCE_DIR}/include)\n# include and link dirs of cuda and tensorrt, you need adapt them if yours are different\n# cuda\ninclude_directories(/usr/local/cuda/include)\nlink_directories(/usr/local/cuda/lib64)\n# cudnn\ninclude_directories(/data/cuda/cuda-10.2/cudnn/v8.0.4/include)\nlink_directories(/data/cuda/cuda-10.2/cudnn/v8.0.4/lib64)\n# tensorrt\ninclude_directories(/opt/tiger/demo/TensorRT-7.2.3.4/include)\nlink_directories(/opt/tiger/demo/TensorRT-7.2.3.4/lib)\n\nset(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED\")\n\nfind_package(OpenCV)\ninclude_directories(${OpenCV_INCLUDE_DIRS})\n\nfile(GLOB My_Source_Files ${PROJECT_SOURCE_DIR}/src/*.cpp)\nadd_executable(bytetrack ${My_Source_Files})\ntarget_link_libraries(bytetrack nvinfer)\ntarget_link_libraries(bytetrack cudart)\ntarget_link_libraries(bytetrack ${OpenCV_LIBS})\n\nadd_definitions(-O2 -pthread)\n\n"
  },
  {
    "path": "deploy/TensorRT/cpp/README.md",
    "content": "# ByteTrack-TensorRT in C++\n\n## Installation\n\nInstall opencv with ```sudo apt-get install libopencv-dev``` (we don't need a higher version of opencv like v3.3+).\n\nInstall eigen-3.3.9 [[google]](https://drive.google.com/file/d/1rqO74CYCNrmRAg8Rra0JP3yZtJ-rfket/view?usp=sharing), [[baidu(code:ueq4)]](https://pan.baidu.com/s/15kEfCxpy-T7tz60msxxExg).\n\n```shell\nunzip eigen-3.3.9.zip\ncd eigen-3.3.9\nmkdir build\ncd build\ncmake ..\nsudo make install\n```\n\n## Prepare serialized engine file\n\nFollow the TensorRT Python demo to convert and save the serialized engine file.\n\nCheck the 'model_trt.engine' file, which will be automatically saved at the YOLOX_output dir.\n\n## Build the demo\n\nYou should set the TensorRT path and CUDA path in CMakeLists.txt.\n\nFor bytetrack_s model, we set the input frame size 1088 x 608. For bytetrack_m, bytetrack_l, bytetrack_x models, we set the input frame size 1440 x 800. You can modify the INPUT_W and INPUT_H in src/bytetrack.cpp\n\n```c++\nstatic const int INPUT_W = 1088;\nstatic const int INPUT_H = 608;\n```\n\nYou can first build the demo:\n\n```shell\ncd <ByteTrack_HOME>/deploy/TensorRT/cpp\nmkdir build\ncd build\ncmake ..\nmake\n```\n\nThen you can run the demo with **200 FPS**:\n\n```shell\n./bytetrack ../../../../YOLOX_outputs/yolox_s_mix_det/model_trt.engine -i ../../../../videos/palace.mp4\n```\n\n(If you find the output video lose some frames, you can convert the input video by running:\n\n```shell\ncd <ByteTrack_HOME>\npython3 tools/convert_video.py\n```\nto generate an appropriate input video for TensorRT C++ demo. )\n\n"
  },
  {
    "path": "deploy/TensorRT/cpp/include/BYTETracker.h",
    "content": "#pragma once\r\n\r\n#include \"STrack.h\"\r\n\r\nstruct Object\r\n{\r\n    cv::Rect_<float> rect;\r\n    int label;\r\n    float prob;\r\n};\r\n\r\nclass BYTETracker\r\n{\r\npublic:\r\n\tBYTETracker(int frame_rate = 30, int track_buffer = 30);\r\n\t~BYTETracker();\r\n\r\n\tvector<STrack> update(const vector<Object>& objects);\r\n\tScalar get_color(int idx);\r\n\r\nprivate:\r\n\tvector<STrack*> joint_stracks(vector<STrack*> &tlista, vector<STrack> &tlistb);\r\n\tvector<STrack> joint_stracks(vector<STrack> &tlista, vector<STrack> &tlistb);\r\n\r\n\tvector<STrack> sub_stracks(vector<STrack> &tlista, vector<STrack> &tlistb);\r\n\tvoid remove_duplicate_stracks(vector<STrack> &resa, vector<STrack> &resb, vector<STrack> &stracksa, vector<STrack> &stracksb);\r\n\r\n\tvoid linear_assignment(vector<vector<float> > &cost_matrix, int cost_matrix_size, int cost_matrix_size_size, float thresh,\r\n\t\tvector<vector<int> > &matches, vector<int> &unmatched_a, vector<int> &unmatched_b);\r\n\tvector<vector<float> > iou_distance(vector<STrack*> &atracks, vector<STrack> &btracks, int &dist_size, int &dist_size_size);\r\n\tvector<vector<float> > iou_distance(vector<STrack> &atracks, vector<STrack> &btracks);\r\n\tvector<vector<float> > ious(vector<vector<float> > &atlbrs, vector<vector<float> > &btlbrs);\r\n\r\n\tdouble lapjv(const vector<vector<float> > &cost, vector<int> &rowsol, vector<int> &colsol, \r\n\t\tbool extend_cost = false, float cost_limit = LONG_MAX, bool return_cost = true);\r\n\r\nprivate:\r\n\r\n\tfloat track_thresh;\r\n\tfloat high_thresh;\r\n\tfloat match_thresh;\r\n\tint frame_id;\r\n\tint max_time_lost;\r\n\r\n\tvector<STrack> tracked_stracks;\r\n\tvector<STrack> lost_stracks;\r\n\tvector<STrack> removed_stracks;\r\n\tbyte_kalman::KalmanFilter kalman_filter;\r\n};"
  },
  {
    "path": "deploy/TensorRT/cpp/include/STrack.h",
    "content": "#pragma once\r\n\r\n#include <opencv2/opencv.hpp>\r\n#include \"kalmanFilter.h\"\r\n\r\nusing namespace cv;\r\nusing namespace std;\r\n\r\nenum TrackState { New = 0, Tracked, Lost, Removed };\r\n\r\nclass STrack\r\n{\r\npublic:\r\n\tSTrack(vector<float> tlwh_, float score);\r\n\t~STrack();\r\n\r\n\tvector<float> static tlbr_to_tlwh(vector<float> &tlbr);\r\n\tvoid static multi_predict(vector<STrack*> &stracks, byte_kalman::KalmanFilter &kalman_filter);\r\n\tvoid static_tlwh();\r\n\tvoid static_tlbr();\r\n\tvector<float> tlwh_to_xyah(vector<float> tlwh_tmp);\r\n\tvector<float> to_xyah();\r\n\tvoid mark_lost();\r\n\tvoid mark_removed();\r\n\tint next_id();\r\n\tint end_frame();\r\n\t\r\n\tvoid activate(byte_kalman::KalmanFilter &kalman_filter, int frame_id);\r\n\tvoid re_activate(STrack &new_track, int frame_id, bool new_id = false);\r\n\tvoid update(STrack &new_track, int frame_id);\r\n\r\npublic:\r\n\tbool is_activated;\r\n\tint track_id;\r\n\tint state;\r\n\r\n\tvector<float> _tlwh;\r\n\tvector<float> tlwh;\r\n\tvector<float> tlbr;\r\n\tint frame_id;\r\n\tint tracklet_len;\r\n\tint start_frame;\r\n\r\n\tKAL_MEAN mean;\r\n\tKAL_COVA covariance;\r\n\tfloat score;\r\n\r\nprivate:\r\n\tbyte_kalman::KalmanFilter kalman_filter;\r\n};"
  },
  {
    "path": "deploy/TensorRT/cpp/include/dataType.h",
    "content": "#pragma once\r\n\r\n#include <cstddef>\r\n#include <vector>\r\n\r\n#include <Eigen/Core>\r\n#include <Eigen/Dense>\r\ntypedef Eigen::Matrix<float, 1, 4, Eigen::RowMajor> DETECTBOX;\r\ntypedef Eigen::Matrix<float, -1, 4, Eigen::RowMajor> DETECTBOXSS;\r\ntypedef Eigen::Matrix<float, 1, 128, Eigen::RowMajor> FEATURE;\r\ntypedef Eigen::Matrix<float, Eigen::Dynamic, 128, Eigen::RowMajor> FEATURESS;\r\n//typedef std::vector<FEATURE> FEATURESS;\r\n\r\n//Kalmanfilter\r\n//typedef Eigen::Matrix<float, 8, 8, Eigen::RowMajor> KAL_FILTER;\r\ntypedef Eigen::Matrix<float, 1, 8, Eigen::RowMajor> KAL_MEAN;\r\ntypedef Eigen::Matrix<float, 8, 8, Eigen::RowMajor> KAL_COVA;\r\ntypedef Eigen::Matrix<float, 1, 4, Eigen::RowMajor> KAL_HMEAN;\r\ntypedef Eigen::Matrix<float, 4, 4, Eigen::RowMajor> KAL_HCOVA;\r\nusing KAL_DATA = std::pair<KAL_MEAN, KAL_COVA>;\r\nusing KAL_HDATA = std::pair<KAL_HMEAN, KAL_HCOVA>;\r\n\r\n//main\r\nusing RESULT_DATA = std::pair<int, DETECTBOX>;\r\n\r\n//tracker:\r\nusing TRACKER_DATA = std::pair<int, FEATURESS>;\r\nusing MATCH_DATA = std::pair<int, int>;\r\ntypedef struct t {\r\n\tstd::vector<MATCH_DATA> matches;\r\n\tstd::vector<int> unmatched_tracks;\r\n\tstd::vector<int> unmatched_detections;\r\n}TRACHER_MATCHD;\r\n\r\n//linear_assignment:\r\ntypedef Eigen::Matrix<float, -1, -1, Eigen::RowMajor> DYNAMICM;"
  },
  {
    "path": "deploy/TensorRT/cpp/include/kalmanFilter.h",
    "content": "#pragma once\r\n\r\n#include \"dataType.h\"\r\n\r\nnamespace byte_kalman\r\n{\r\n\tclass KalmanFilter\r\n\t{\r\n\tpublic:\r\n\t\tstatic const double chi2inv95[10];\r\n\t\tKalmanFilter();\r\n\t\tKAL_DATA initiate(const DETECTBOX& measurement);\r\n\t\tvoid predict(KAL_MEAN& mean, KAL_COVA& covariance);\r\n\t\tKAL_HDATA project(const KAL_MEAN& mean, const KAL_COVA& covariance);\r\n\t\tKAL_DATA update(const KAL_MEAN& mean,\r\n\t\t\tconst KAL_COVA& covariance,\r\n\t\t\tconst DETECTBOX& measurement);\r\n\r\n\t\tEigen::Matrix<float, 1, -1> gating_distance(\r\n\t\t\tconst KAL_MEAN& mean,\r\n\t\t\tconst KAL_COVA& covariance,\r\n\t\t\tconst std::vector<DETECTBOX>& measurements,\r\n\t\t\tbool only_position = false);\r\n\r\n\tprivate:\r\n\t\tEigen::Matrix<float, 8, 8, Eigen::RowMajor> _motion_mat;\r\n\t\tEigen::Matrix<float, 4, 8, Eigen::RowMajor> _update_mat;\r\n\t\tfloat _std_weight_position;\r\n\t\tfloat _std_weight_velocity;\r\n\t};\r\n}"
  },
  {
    "path": "deploy/TensorRT/cpp/include/lapjv.h",
    "content": "#ifndef LAPJV_H\r\n#define LAPJV_H\r\n\r\n#define LARGE 1000000\r\n\r\n#if !defined TRUE\r\n#define TRUE 1\r\n#endif\r\n#if !defined FALSE\r\n#define FALSE 0\r\n#endif\r\n\r\n#define NEW(x, t, n) if ((x = (t *)malloc(sizeof(t) * (n))) == 0) { return -1; }\r\n#define FREE(x) if (x != 0) { free(x); x = 0; }\r\n#define SWAP_INDICES(a, b) { int_t _temp_index = a; a = b; b = _temp_index; }\r\n\r\n#if 0\r\n#include <assert.h>\r\n#define ASSERT(cond) assert(cond)\r\n#define PRINTF(fmt, ...) printf(fmt, ##__VA_ARGS__)\r\n#define PRINT_COST_ARRAY(a, n) \\\r\n    while (1) { \\\r\n        printf(#a\" = [\"); \\\r\n        if ((n) > 0) { \\\r\n            printf(\"%f\", (a)[0]); \\\r\n            for (uint_t j = 1; j < n; j++) { \\\r\n                printf(\", %f\", (a)[j]); \\\r\n            } \\\r\n        } \\\r\n        printf(\"]\\n\"); \\\r\n        break; \\\r\n    }\r\n#define PRINT_INDEX_ARRAY(a, n) \\\r\n    while (1) { \\\r\n        printf(#a\" = [\"); \\\r\n        if ((n) > 0) { \\\r\n            printf(\"%d\", (a)[0]); \\\r\n            for (uint_t j = 1; j < n; j++) { \\\r\n                printf(\", %d\", (a)[j]); \\\r\n            } \\\r\n        } \\\r\n        printf(\"]\\n\"); \\\r\n        break; \\\r\n    }\r\n#else\r\n#define ASSERT(cond)\r\n#define PRINTF(fmt, ...)\r\n#define PRINT_COST_ARRAY(a, n)\r\n#define PRINT_INDEX_ARRAY(a, n)\r\n#endif\r\n\r\n\r\ntypedef signed int int_t;\r\ntypedef unsigned int uint_t;\r\ntypedef double cost_t;\r\ntypedef char boolean;\r\ntypedef enum fp_t { FP_1 = 1, FP_2 = 2, FP_DYNAMIC = 3 } fp_t;\r\n\r\nextern int_t lapjv_internal(\r\n\tconst uint_t n, cost_t *cost[],\r\n\tint_t *x, int_t *y);\r\n\r\n#endif // LAPJV_H"
  },
  {
    "path": "deploy/TensorRT/cpp/include/logging.h",
    "content": "/*\n * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *     http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n#ifndef TENSORRT_LOGGING_H\n#define TENSORRT_LOGGING_H\n\n#include \"NvInferRuntimeCommon.h\"\n#include <cassert>\n#include <ctime>\n#include <iomanip>\n#include <iostream>\n#include <ostream>\n#include <sstream>\n#include <string>\n\nusing Severity = nvinfer1::ILogger::Severity;\n\nclass LogStreamConsumerBuffer : public std::stringbuf\n{\npublic:\n    LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)\n        : mOutput(stream)\n        , mPrefix(prefix)\n        , mShouldLog(shouldLog)\n    {\n    }\n\n    LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)\n        : mOutput(other.mOutput)\n    {\n    }\n\n    ~LogStreamConsumerBuffer()\n    {\n        // std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence\n        // std::streambuf::pptr() gives a pointer to the current position of the output sequence\n        // if the pointer to the beginning is not equal to the pointer to the current position,\n        // call putOutput() to log the output to the stream\n        if (pbase() != pptr())\n        {\n            putOutput();\n        }\n    }\n\n    // synchronizes the stream buffer and returns 0 on success\n    // synchronizing the stream buffer consists of inserting the buffer contents into the stream,\n    // resetting the buffer and flushing the stream\n    virtual int sync()\n    {\n        putOutput();\n        return 0;\n    }\n\n    void putOutput()\n    {\n        if (mShouldLog)\n        {\n            // prepend timestamp\n            std::time_t timestamp = std::time(nullptr);\n            tm* tm_local = std::localtime(&timestamp);\n            std::cout << \"[\";\n            std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << \"/\";\n            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << \"/\";\n            std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << \"-\";\n            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << \":\";\n            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << \":\";\n            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << \"] \";\n            // std::stringbuf::str() gets the string contents of the buffer\n            // insert the buffer contents pre-appended by the appropriate prefix into the stream\n            mOutput << mPrefix << str();\n            // set the buffer to empty\n            str(\"\");\n            // flush the stream\n            mOutput.flush();\n        }\n    }\n\n    void setShouldLog(bool shouldLog)\n    {\n        mShouldLog = shouldLog;\n    }\n\nprivate:\n    std::ostream& mOutput;\n    std::string mPrefix;\n    bool mShouldLog;\n};\n\n//!\n//! \\class LogStreamConsumerBase\n//! \\brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer\n//!\nclass LogStreamConsumerBase\n{\npublic:\n    LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)\n        : mBuffer(stream, prefix, shouldLog)\n    {\n    }\n\nprotected:\n    LogStreamConsumerBuffer mBuffer;\n};\n\n//!\n//! \\class LogStreamConsumer\n//! \\brief Convenience object used to facilitate use of C++ stream syntax when logging messages.\n//!  Order of base classes is LogStreamConsumerBase and then std::ostream.\n//!  This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field\n//!  in LogStreamConsumer and then the address of the buffer is passed to std::ostream.\n//!  This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.\n//!  Please do not change the order of the parent classes.\n//!\nclass LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream\n{\npublic:\n    //! \\brief Creates a LogStreamConsumer which logs messages with level severity.\n    //!  Reportable severity determines if the messages are severe enough to be logged.\n    LogStreamConsumer(Severity reportableSeverity, Severity severity)\n        : LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)\n        , std::ostream(&mBuffer) // links the stream buffer with the stream\n        , mShouldLog(severity <= reportableSeverity)\n        , mSeverity(severity)\n    {\n    }\n\n    LogStreamConsumer(LogStreamConsumer&& other)\n        : LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)\n        , std::ostream(&mBuffer) // links the stream buffer with the stream\n        , mShouldLog(other.mShouldLog)\n        , mSeverity(other.mSeverity)\n    {\n    }\n\n    void setReportableSeverity(Severity reportableSeverity)\n    {\n        mShouldLog = mSeverity <= reportableSeverity;\n        mBuffer.setShouldLog(mShouldLog);\n    }\n\nprivate:\n    static std::ostream& severityOstream(Severity severity)\n    {\n        return severity >= Severity::kINFO ? std::cout : std::cerr;\n    }\n\n    static std::string severityPrefix(Severity severity)\n    {\n        switch (severity)\n        {\n        case Severity::kINTERNAL_ERROR: return \"[F] \";\n        case Severity::kERROR: return \"[E] \";\n        case Severity::kWARNING: return \"[W] \";\n        case Severity::kINFO: return \"[I] \";\n        case Severity::kVERBOSE: return \"[V] \";\n        default: assert(0); return \"\";\n        }\n    }\n\n    bool mShouldLog;\n    Severity mSeverity;\n};\n\n//! \\class Logger\n//!\n//! \\brief Class which manages logging of TensorRT tools and samples\n//!\n//! \\details This class provides a common interface for TensorRT tools and samples to log information to the console,\n//! and supports logging two types of messages:\n//!\n//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)\n//! - Test pass/fail messages\n//!\n//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is\n//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.\n//!\n//! In the future, this class could be extended to support dumping test results to a file in some standard format\n//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).\n//!\n//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger\n//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT\n//! library and messages coming from the sample.\n//!\n//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the\n//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger\n//! object.\n\nclass Logger : public nvinfer1::ILogger\n{\npublic:\n    Logger(Severity severity = Severity::kWARNING)\n        : mReportableSeverity(severity)\n    {\n    }\n\n    //!\n    //! \\enum TestResult\n    //! \\brief Represents the state of a given test\n    //!\n    enum class TestResult\n    {\n        kRUNNING, //!< The test is running\n        kPASSED,  //!< The test passed\n        kFAILED,  //!< The test failed\n        kWAIVED   //!< The test was waived\n    };\n\n    //!\n    //! \\brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger\n    //! \\return The nvinfer1::ILogger associated with this Logger\n    //!\n    //! TODO Once all samples are updated to use this method to register the logger with TensorRT,\n    //! we can eliminate the inheritance of Logger from ILogger\n    //!\n    nvinfer1::ILogger& getTRTLogger()\n    {\n        return *this;\n    }\n\n    //!\n    //! \\brief Implementation of the nvinfer1::ILogger::log() virtual method\n    //!\n    //! Note samples should not be calling this function directly; it will eventually go away once we eliminate the\n    //! inheritance from nvinfer1::ILogger\n    //!\n    void log(Severity severity, const char* msg) noexcept override\n    {\n        LogStreamConsumer(mReportableSeverity, severity) << \"[TRT] \" << std::string(msg) << std::endl;\n    }\n\n    //!\n    //! \\brief Method for controlling the verbosity of logging output\n    //!\n    //! \\param severity The logger will only emit messages that have severity of this level or higher.\n    //!\n    void setReportableSeverity(Severity severity)\n    {\n        mReportableSeverity = severity;\n    }\n\n    //!\n    //! \\brief Opaque handle that holds logging information for a particular test\n    //!\n    //! This object is an opaque handle to information used by the Logger to print test results.\n    //! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used\n    //! with Logger::reportTest{Start,End}().\n    //!\n    class TestAtom\n    {\n    public:\n        TestAtom(TestAtom&&) = default;\n\n    private:\n        friend class Logger;\n\n        TestAtom(bool started, const std::string& name, const std::string& cmdline)\n            : mStarted(started)\n            , mName(name)\n            , mCmdline(cmdline)\n        {\n        }\n\n        bool mStarted;\n        std::string mName;\n        std::string mCmdline;\n    };\n\n    //!\n    //! \\brief Define a test for logging\n    //!\n    //! \\param[in] name The name of the test.  This should be a string starting with\n    //!                  \"TensorRT\" and containing dot-separated strings containing\n    //!                  the characters [A-Za-z0-9_].\n    //!                  For example, \"TensorRT.sample_googlenet\"\n    //! \\param[in] cmdline The command line used to reproduce the test\n    //\n    //! \\return a TestAtom that can be used in Logger::reportTest{Start,End}().\n    //!\n    static TestAtom defineTest(const std::string& name, const std::string& cmdline)\n    {\n        return TestAtom(false, name, cmdline);\n    }\n\n    //!\n    //! \\brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments\n    //!        as input\n    //!\n    //! \\param[in] name The name of the test\n    //! \\param[in] argc The number of command-line arguments\n    //! \\param[in] argv The array of command-line arguments (given as C strings)\n    //!\n    //! \\return a TestAtom that can be used in Logger::reportTest{Start,End}().\n    static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)\n    {\n        auto cmdline = genCmdlineString(argc, argv);\n        return defineTest(name, cmdline);\n    }\n\n    //!\n    //! \\brief Report that a test has started.\n    //!\n    //! \\pre reportTestStart() has not been called yet for the given testAtom\n    //!\n    //! \\param[in] testAtom The handle to the test that has started\n    //!\n    static void reportTestStart(TestAtom& testAtom)\n    {\n        reportTestResult(testAtom, TestResult::kRUNNING);\n        assert(!testAtom.mStarted);\n        testAtom.mStarted = true;\n    }\n\n    //!\n    //! \\brief Report that a test has ended.\n    //!\n    //! \\pre reportTestStart() has been called for the given testAtom\n    //!\n    //! \\param[in] testAtom The handle to the test that has ended\n    //! \\param[in] result The result of the test. Should be one of TestResult::kPASSED,\n    //!                   TestResult::kFAILED, TestResult::kWAIVED\n    //!\n    static void reportTestEnd(const TestAtom& testAtom, TestResult result)\n    {\n        assert(result != TestResult::kRUNNING);\n        assert(testAtom.mStarted);\n        reportTestResult(testAtom, result);\n    }\n\n    static int reportPass(const TestAtom& testAtom)\n    {\n        reportTestEnd(testAtom, TestResult::kPASSED);\n        return EXIT_SUCCESS;\n    }\n\n    static int reportFail(const TestAtom& testAtom)\n    {\n        reportTestEnd(testAtom, TestResult::kFAILED);\n        return EXIT_FAILURE;\n    }\n\n    static int reportWaive(const TestAtom& testAtom)\n    {\n        reportTestEnd(testAtom, TestResult::kWAIVED);\n        return EXIT_SUCCESS;\n    }\n\n    static int reportTest(const TestAtom& testAtom, bool pass)\n    {\n        return pass ? reportPass(testAtom) : reportFail(testAtom);\n    }\n\n    Severity getReportableSeverity() const\n    {\n        return mReportableSeverity;\n    }\n\nprivate:\n    //!\n    //! \\brief returns an appropriate string for prefixing a log message with the given severity\n    //!\n    static const char* severityPrefix(Severity severity)\n    {\n        switch (severity)\n        {\n        case Severity::kINTERNAL_ERROR: return \"[F] \";\n        case Severity::kERROR: return \"[E] \";\n        case Severity::kWARNING: return \"[W] \";\n        case Severity::kINFO: return \"[I] \";\n        case Severity::kVERBOSE: return \"[V] \";\n        default: assert(0); return \"\";\n        }\n    }\n\n    //!\n    //! \\brief returns an appropriate string for prefixing a test result message with the given result\n    //!\n    static const char* testResultString(TestResult result)\n    {\n        switch (result)\n        {\n        case TestResult::kRUNNING: return \"RUNNING\";\n        case TestResult::kPASSED: return \"PASSED\";\n        case TestResult::kFAILED: return \"FAILED\";\n        case TestResult::kWAIVED: return \"WAIVED\";\n        default: assert(0); return \"\";\n        }\n    }\n\n    //!\n    //! \\brief returns an appropriate output stream (cout or cerr) to use with the given severity\n    //!\n    static std::ostream& severityOstream(Severity severity)\n    {\n        return severity >= Severity::kINFO ? std::cout : std::cerr;\n    }\n\n    //!\n    //! \\brief method that implements logging test results\n    //!\n    static void reportTestResult(const TestAtom& testAtom, TestResult result)\n    {\n        severityOstream(Severity::kINFO) << \"&&&& \" << testResultString(result) << \" \" << testAtom.mName << \" # \"\n                                         << testAtom.mCmdline << std::endl;\n    }\n\n    //!\n    //! \\brief generate a command line string from the given (argc, argv) values\n    //!\n    static std::string genCmdlineString(int argc, char const* const* argv)\n    {\n        std::stringstream ss;\n        for (int i = 0; i < argc; i++)\n        {\n            if (i > 0)\n                ss << \" \";\n            ss << argv[i];\n        }\n        return ss.str();\n    }\n\n    Severity mReportableSeverity;\n};\n\nnamespace\n{\n\n//!\n//! \\brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE\n//!\n//! Example usage:\n//!\n//!     LOG_VERBOSE(logger) << \"hello world\" << std::endl;\n//!\ninline LogStreamConsumer LOG_VERBOSE(const Logger& logger)\n{\n    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);\n}\n\n//!\n//! \\brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO\n//!\n//! Example usage:\n//!\n//!     LOG_INFO(logger) << \"hello world\" << std::endl;\n//!\ninline LogStreamConsumer LOG_INFO(const Logger& logger)\n{\n    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);\n}\n\n//!\n//! \\brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING\n//!\n//! Example usage:\n//!\n//!     LOG_WARN(logger) << \"hello world\" << std::endl;\n//!\ninline LogStreamConsumer LOG_WARN(const Logger& logger)\n{\n    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);\n}\n\n//!\n//! \\brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR\n//!\n//! Example usage:\n//!\n//!     LOG_ERROR(logger) << \"hello world\" << std::endl;\n//!\ninline LogStreamConsumer LOG_ERROR(const Logger& logger)\n{\n    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);\n}\n\n//!\n//! \\brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR\n//         (\"fatal\" severity)\n//!\n//! Example usage:\n//!\n//!     LOG_FATAL(logger) << \"hello world\" << std::endl;\n//!\ninline LogStreamConsumer LOG_FATAL(const Logger& logger)\n{\n    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);\n}\n\n} // anonymous namespace\n\n#endif // TENSORRT_LOGGING_H\n"
  },
  {
    "path": "deploy/TensorRT/cpp/src/BYTETracker.cpp",
    "content": "#include \"BYTETracker.h\"\r\n#include <fstream>\r\n\r\nBYTETracker::BYTETracker(int frame_rate, int track_buffer)\r\n{\r\n\ttrack_thresh = 0.5;\r\n\thigh_thresh = 0.6;\r\n\tmatch_thresh = 0.8;\r\n\r\n\tframe_id = 0;\r\n\tmax_time_lost = int(frame_rate / 30.0 * track_buffer);\r\n\tcout << \"Init ByteTrack!\" << endl;\r\n}\r\n\r\nBYTETracker::~BYTETracker()\r\n{\r\n}\r\n\r\nvector<STrack> BYTETracker::update(const vector<Object>& objects)\r\n{\r\n\r\n\t////////////////// Step 1: Get detections //////////////////\r\n\tthis->frame_id++;\r\n\tvector<STrack> activated_stracks;\r\n\tvector<STrack> refind_stracks;\r\n\tvector<STrack> removed_stracks;\r\n\tvector<STrack> lost_stracks;\r\n\tvector<STrack> detections;\r\n\tvector<STrack> detections_low;\r\n\r\n\tvector<STrack> detections_cp;\r\n\tvector<STrack> tracked_stracks_swap;\r\n\tvector<STrack> resa, resb;\r\n\tvector<STrack> output_stracks;\r\n\r\n\tvector<STrack*> unconfirmed;\r\n\tvector<STrack*> tracked_stracks;\r\n\tvector<STrack*> strack_pool;\r\n\tvector<STrack*> r_tracked_stracks;\r\n\r\n\tif (objects.size() > 0)\r\n\t{\r\n\t\tfor (int i = 0; i < objects.size(); i++)\r\n\t\t{\r\n\t\t\tvector<float> tlbr_;\r\n\t\t\ttlbr_.resize(4);\r\n\t\t\ttlbr_[0] = objects[i].rect.x;\r\n\t\t\ttlbr_[1] = objects[i].rect.y;\r\n\t\t\ttlbr_[2] = objects[i].rect.x + objects[i].rect.width;\r\n\t\t\ttlbr_[3] = objects[i].rect.y + objects[i].rect.height;\r\n\r\n\t\t\tfloat score = objects[i].prob;\r\n\r\n\t\t\tSTrack strack(STrack::tlbr_to_tlwh(tlbr_), score);\r\n\t\t\tif (score >= track_thresh)\r\n\t\t\t{\r\n\t\t\t\tdetections.push_back(strack);\r\n\t\t\t}\r\n\t\t\telse\r\n\t\t\t{\r\n\t\t\t\tdetections_low.push_back(strack);\r\n\t\t\t}\r\n\t\t\t\r\n\t\t}\r\n\t}\r\n\r\n\t// Add newly detected tracklets to tracked_stracks\r\n\tfor (int i = 0; i < this->tracked_stracks.size(); i++)\r\n\t{\r\n\t\tif (!this->tracked_stracks[i].is_activated)\r\n\t\t\tunconfirmed.push_back(&this->tracked_stracks[i]);\r\n\t\telse\r\n\t\t\ttracked_stracks.push_back(&this->tracked_stracks[i]);\r\n\t}\r\n\r\n\t////////////////// Step 2: First association, with IoU //////////////////\r\n\tstrack_pool = joint_stracks(tracked_stracks, this->lost_stracks);\r\n\tSTrack::multi_predict(strack_pool, this->kalman_filter);\r\n\r\n\tvector<vector<float> > dists;\r\n\tint dist_size = 0, dist_size_size = 0;\r\n\tdists = iou_distance(strack_pool, detections, dist_size, dist_size_size);\r\n\r\n\tvector<vector<int> > matches;\r\n\tvector<int> u_track, u_detection;\r\n\tlinear_assignment(dists, dist_size, dist_size_size, match_thresh, matches, u_track, u_detection);\r\n\r\n\tfor (int i = 0; i < matches.size(); i++)\r\n\t{\r\n\t\tSTrack *track = strack_pool[matches[i][0]];\r\n\t\tSTrack *det = &detections[matches[i][1]];\r\n\t\tif (track->state == TrackState::Tracked)\r\n\t\t{\r\n\t\t\ttrack->update(*det, this->frame_id);\r\n\t\t\tactivated_stracks.push_back(*track);\r\n\t\t}\r\n\t\telse\r\n\t\t{\r\n\t\t\ttrack->re_activate(*det, this->frame_id, false);\r\n\t\t\trefind_stracks.push_back(*track);\r\n\t\t}\r\n\t}\r\n\r\n\t////////////////// Step 3: Second association, using low score dets //////////////////\r\n\tfor (int i = 0; i < u_detection.size(); i++)\r\n\t{\r\n\t\tdetections_cp.push_back(detections[u_detection[i]]);\r\n\t}\r\n\tdetections.clear();\r\n\tdetections.assign(detections_low.begin(), detections_low.end());\r\n\t\r\n\tfor (int i = 0; i < u_track.size(); i++)\r\n\t{\r\n\t\tif (strack_pool[u_track[i]]->state == TrackState::Tracked)\r\n\t\t{\r\n\t\t\tr_tracked_stracks.push_back(strack_pool[u_track[i]]);\r\n\t\t}\r\n\t}\r\n\r\n\tdists.clear();\r\n\tdists = iou_distance(r_tracked_stracks, detections, dist_size, dist_size_size);\r\n\r\n\tmatches.clear();\r\n\tu_track.clear();\r\n\tu_detection.clear();\r\n\tlinear_assignment(dists, dist_size, dist_size_size, 0.5, matches, u_track, u_detection);\r\n\r\n\tfor (int i = 0; i < matches.size(); i++)\r\n\t{\r\n\t\tSTrack *track = r_tracked_stracks[matches[i][0]];\r\n\t\tSTrack *det = &detections[matches[i][1]];\r\n\t\tif (track->state == TrackState::Tracked)\r\n\t\t{\r\n\t\t\ttrack->update(*det, this->frame_id);\r\n\t\t\tactivated_stracks.push_back(*track);\r\n\t\t}\r\n\t\telse\r\n\t\t{\r\n\t\t\ttrack->re_activate(*det, this->frame_id, false);\r\n\t\t\trefind_stracks.push_back(*track);\r\n\t\t}\r\n\t}\r\n\r\n\tfor (int i = 0; i < u_track.size(); i++)\r\n\t{\r\n\t\tSTrack *track = r_tracked_stracks[u_track[i]];\r\n\t\tif (track->state != TrackState::Lost)\r\n\t\t{\r\n\t\t\ttrack->mark_lost();\r\n\t\t\tlost_stracks.push_back(*track);\r\n\t\t}\r\n\t}\r\n\r\n\t// Deal with unconfirmed tracks, usually tracks with only one beginning frame\r\n\tdetections.clear();\r\n\tdetections.assign(detections_cp.begin(), detections_cp.end());\r\n\r\n\tdists.clear();\r\n\tdists = iou_distance(unconfirmed, detections, dist_size, dist_size_size);\r\n\r\n\tmatches.clear();\r\n\tvector<int> u_unconfirmed;\r\n\tu_detection.clear();\r\n\tlinear_assignment(dists, dist_size, dist_size_size, 0.7, matches, u_unconfirmed, u_detection);\r\n\r\n\tfor (int i = 0; i < matches.size(); i++)\r\n\t{\r\n\t\tunconfirmed[matches[i][0]]->update(detections[matches[i][1]], this->frame_id);\r\n\t\tactivated_stracks.push_back(*unconfirmed[matches[i][0]]);\r\n\t}\r\n\r\n\tfor (int i = 0; i < u_unconfirmed.size(); i++)\r\n\t{\r\n\t\tSTrack *track = unconfirmed[u_unconfirmed[i]];\r\n\t\ttrack->mark_removed();\r\n\t\tremoved_stracks.push_back(*track);\r\n\t}\r\n\r\n\t////////////////// Step 4: Init new stracks //////////////////\r\n\tfor (int i = 0; i < u_detection.size(); i++)\r\n\t{\r\n\t\tSTrack *track = &detections[u_detection[i]];\r\n\t\tif (track->score < this->high_thresh)\r\n\t\t\tcontinue;\r\n\t\ttrack->activate(this->kalman_filter, this->frame_id);\r\n\t\tactivated_stracks.push_back(*track);\r\n\t}\r\n\r\n\t////////////////// Step 5: Update state //////////////////\r\n\tfor (int i = 0; i < this->lost_stracks.size(); i++)\r\n\t{\r\n\t\tif (this->frame_id - this->lost_stracks[i].end_frame() > this->max_time_lost)\r\n\t\t{\r\n\t\t\tthis->lost_stracks[i].mark_removed();\r\n\t\t\tremoved_stracks.push_back(this->lost_stracks[i]);\r\n\t\t}\r\n\t}\r\n\t\r\n\tfor (int i = 0; i < this->tracked_stracks.size(); i++)\r\n\t{\r\n\t\tif (this->tracked_stracks[i].state == TrackState::Tracked)\r\n\t\t{\r\n\t\t\ttracked_stracks_swap.push_back(this->tracked_stracks[i]);\r\n\t\t}\r\n\t}\r\n\tthis->tracked_stracks.clear();\r\n\tthis->tracked_stracks.assign(tracked_stracks_swap.begin(), tracked_stracks_swap.end());\r\n\r\n\tthis->tracked_stracks = joint_stracks(this->tracked_stracks, activated_stracks);\r\n\tthis->tracked_stracks = joint_stracks(this->tracked_stracks, refind_stracks);\r\n\r\n\t//std::cout << activated_stracks.size() << std::endl;\r\n\r\n\tthis->lost_stracks = sub_stracks(this->lost_stracks, this->tracked_stracks);\r\n\tfor (int i = 0; i < lost_stracks.size(); i++)\r\n\t{\r\n\t\tthis->lost_stracks.push_back(lost_stracks[i]);\r\n\t}\r\n\r\n\tthis->lost_stracks = sub_stracks(this->lost_stracks, this->removed_stracks);\r\n\tfor (int i = 0; i < removed_stracks.size(); i++)\r\n\t{\r\n\t\tthis->removed_stracks.push_back(removed_stracks[i]);\r\n\t}\r\n\t\r\n\tremove_duplicate_stracks(resa, resb, this->tracked_stracks, this->lost_stracks);\r\n\r\n\tthis->tracked_stracks.clear();\r\n\tthis->tracked_stracks.assign(resa.begin(), resa.end());\r\n\tthis->lost_stracks.clear();\r\n\tthis->lost_stracks.assign(resb.begin(), resb.end());\r\n\t\r\n\tfor (int i = 0; i < this->tracked_stracks.size(); i++)\r\n\t{\r\n\t\tif (this->tracked_stracks[i].is_activated)\r\n\t\t{\r\n\t\t\toutput_stracks.push_back(this->tracked_stracks[i]);\r\n\t\t}\r\n\t}\r\n\treturn output_stracks;\r\n}"
  },
  {
    "path": "deploy/TensorRT/cpp/src/STrack.cpp",
    "content": "#include \"STrack.h\"\r\n\r\nSTrack::STrack(vector<float> tlwh_, float score)\r\n{\r\n\t_tlwh.resize(4);\r\n\t_tlwh.assign(tlwh_.begin(), tlwh_.end());\r\n\r\n\tis_activated = false;\r\n\ttrack_id = 0;\r\n\tstate = TrackState::New;\r\n\t\r\n\ttlwh.resize(4);\r\n\ttlbr.resize(4);\r\n\r\n\tstatic_tlwh();\r\n\tstatic_tlbr();\r\n\tframe_id = 0;\r\n\ttracklet_len = 0;\r\n\tthis->score = score;\r\n\tstart_frame = 0;\r\n}\r\n\r\nSTrack::~STrack()\r\n{\r\n}\r\n\r\nvoid STrack::activate(byte_kalman::KalmanFilter &kalman_filter, int frame_id)\r\n{\r\n\tthis->kalman_filter = kalman_filter;\r\n\tthis->track_id = this->next_id();\r\n\r\n\tvector<float> _tlwh_tmp(4);\r\n\t_tlwh_tmp[0] = this->_tlwh[0];\r\n\t_tlwh_tmp[1] = this->_tlwh[1];\r\n\t_tlwh_tmp[2] = this->_tlwh[2];\r\n\t_tlwh_tmp[3] = this->_tlwh[3];\r\n\tvector<float> xyah = tlwh_to_xyah(_tlwh_tmp);\r\n\tDETECTBOX xyah_box;\r\n\txyah_box[0] = xyah[0];\r\n\txyah_box[1] = xyah[1];\r\n\txyah_box[2] = xyah[2];\r\n\txyah_box[3] = xyah[3];\r\n\tauto mc = this->kalman_filter.initiate(xyah_box);\r\n\tthis->mean = mc.first;\r\n\tthis->covariance = mc.second;\r\n\r\n\tstatic_tlwh();\r\n\tstatic_tlbr();\r\n\r\n\tthis->tracklet_len = 0;\r\n\tthis->state = TrackState::Tracked;\r\n\tif (frame_id == 1)\r\n\t{\r\n\t\tthis->is_activated = true;\r\n\t}\r\n\t//this->is_activated = true;\r\n\tthis->frame_id = frame_id;\r\n\tthis->start_frame = frame_id;\r\n}\r\n\r\nvoid STrack::re_activate(STrack &new_track, int frame_id, bool new_id)\r\n{\r\n\tvector<float> xyah = tlwh_to_xyah(new_track.tlwh);\r\n\tDETECTBOX xyah_box;\r\n\txyah_box[0] = xyah[0];\r\n\txyah_box[1] = xyah[1];\r\n\txyah_box[2] = xyah[2];\r\n\txyah_box[3] = xyah[3];\r\n\tauto mc = this->kalman_filter.update(this->mean, this->covariance, xyah_box);\r\n\tthis->mean = mc.first;\r\n\tthis->covariance = mc.second;\r\n\r\n\tstatic_tlwh();\r\n\tstatic_tlbr();\r\n\r\n\tthis->tracklet_len = 0;\r\n\tthis->state = TrackState::Tracked;\r\n\tthis->is_activated = true;\r\n\tthis->frame_id = frame_id;\r\n\tthis->score = new_track.score;\r\n\tif (new_id)\r\n\t\tthis->track_id = next_id();\r\n}\r\n\r\nvoid STrack::update(STrack &new_track, int frame_id)\r\n{\r\n\tthis->frame_id = frame_id;\r\n\tthis->tracklet_len++;\r\n\r\n\tvector<float> xyah = tlwh_to_xyah(new_track.tlwh);\r\n\tDETECTBOX xyah_box;\r\n\txyah_box[0] = xyah[0];\r\n\txyah_box[1] = xyah[1];\r\n\txyah_box[2] = xyah[2];\r\n\txyah_box[3] = xyah[3];\r\n\r\n\tauto mc = this->kalman_filter.update(this->mean, this->covariance, xyah_box);\r\n\tthis->mean = mc.first;\r\n\tthis->covariance = mc.second;\r\n\r\n\tstatic_tlwh();\r\n\tstatic_tlbr();\r\n\r\n\tthis->state = TrackState::Tracked;\r\n\tthis->is_activated = true;\r\n\r\n\tthis->score = new_track.score;\r\n}\r\n\r\nvoid STrack::static_tlwh()\r\n{\r\n\tif (this->state == TrackState::New)\r\n\t{\r\n\t\ttlwh[0] = _tlwh[0];\r\n\t\ttlwh[1] = _tlwh[1];\r\n\t\ttlwh[2] = _tlwh[2];\r\n\t\ttlwh[3] = _tlwh[3];\r\n\t\treturn;\r\n\t}\r\n\r\n\ttlwh[0] = mean[0];\r\n\ttlwh[1] = mean[1];\r\n\ttlwh[2] = mean[2];\r\n\ttlwh[3] = mean[3];\r\n\r\n\ttlwh[2] *= tlwh[3];\r\n\ttlwh[0] -= tlwh[2] / 2;\r\n\ttlwh[1] -= tlwh[3] / 2;\r\n}\r\n\r\nvoid STrack::static_tlbr()\r\n{\r\n\ttlbr.clear();\r\n\ttlbr.assign(tlwh.begin(), tlwh.end());\r\n\ttlbr[2] += tlbr[0];\r\n\ttlbr[3] += tlbr[1];\r\n}\r\n\r\nvector<float> STrack::tlwh_to_xyah(vector<float> tlwh_tmp)\r\n{\r\n\tvector<float> tlwh_output = tlwh_tmp;\r\n\ttlwh_output[0] += tlwh_output[2] / 2;\r\n\ttlwh_output[1] += tlwh_output[3] / 2;\r\n\ttlwh_output[2] /= tlwh_output[3];\r\n\treturn tlwh_output;\r\n}\r\n\r\nvector<float> STrack::to_xyah()\r\n{\r\n\treturn tlwh_to_xyah(tlwh);\r\n}\r\n\r\nvector<float> STrack::tlbr_to_tlwh(vector<float> &tlbr)\r\n{\r\n\ttlbr[2] -= tlbr[0];\r\n\ttlbr[3] -= tlbr[1];\r\n\treturn tlbr;\r\n}\r\n\r\nvoid STrack::mark_lost()\r\n{\r\n\tstate = TrackState::Lost;\r\n}\r\n\r\nvoid STrack::mark_removed()\r\n{\r\n\tstate = TrackState::Removed;\r\n}\r\n\r\nint STrack::next_id()\r\n{\r\n\tstatic int _count = 0;\r\n\t_count++;\r\n\treturn _count;\r\n}\r\n\r\nint STrack::end_frame()\r\n{\r\n\treturn this->frame_id;\r\n}\r\n\r\nvoid STrack::multi_predict(vector<STrack*> &stracks, byte_kalman::KalmanFilter &kalman_filter)\r\n{\r\n\tfor (int i = 0; i < stracks.size(); i++)\r\n\t{\r\n\t\tif (stracks[i]->state != TrackState::Tracked)\r\n\t\t{\r\n\t\t\tstracks[i]->mean[7] = 0;\r\n\t\t}\r\n\t\tkalman_filter.predict(stracks[i]->mean, stracks[i]->covariance);\r\n\t}\r\n}"
  },
  {
    "path": "deploy/TensorRT/cpp/src/bytetrack.cpp",
    "content": "#include <fstream>\n#include <iostream>\n#include <sstream>\n#include <numeric>\n#include <chrono>\n#include <vector>\n#include <opencv2/opencv.hpp>\n#include <dirent.h>\n#include \"NvInfer.h\"\n#include \"cuda_runtime_api.h\"\n#include \"logging.h\"\n#include \"BYTETracker.h\"\n\n#define CHECK(status) \\\n    do\\\n    {\\\n        auto ret = (status);\\\n        if (ret != 0)\\\n        {\\\n            cerr << \"Cuda failure: \" << ret << endl;\\\n            abort();\\\n        }\\\n    } while (0)\n\n#define DEVICE 0  // GPU id\n#define NMS_THRESH 0.7\n#define BBOX_CONF_THRESH 0.1\n\nusing namespace nvinfer1;\n\n// stuff we know about the network and the input/output blobs\nstatic const int INPUT_W = 1088;\nstatic const int INPUT_H = 608;\nconst char* INPUT_BLOB_NAME = \"input_0\";\nconst char* OUTPUT_BLOB_NAME = \"output_0\";\nstatic Logger gLogger;\n\nMat static_resize(Mat& img) {\n    float r = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));\n    // r = std::min(r, 1.0f);\n    int unpad_w = r * img.cols;\n    int unpad_h = r * img.rows;\n    Mat re(unpad_h, unpad_w, CV_8UC3);\n    resize(img, re, re.size());\n    Mat out(INPUT_H, INPUT_W, CV_8UC3, Scalar(114, 114, 114));\n    re.copyTo(out(Rect(0, 0, re.cols, re.rows)));\n    return out;\n}\n\nstruct GridAndStride\n{\n    int grid0;\n    int grid1;\n    int stride;\n};\n\nstatic void generate_grids_and_stride(const int target_w, const int target_h, vector<int>& strides, vector<GridAndStride>& grid_strides)\n{\n    for (auto stride : strides)\n    {\n        int num_grid_w = target_w / stride;\n        int num_grid_h = target_h / stride;\n        for (int g1 = 0; g1 < num_grid_h; g1++)\n        {\n            for (int g0 = 0; g0 < num_grid_w; g0++)\n            {\n                grid_strides.push_back((GridAndStride){g0, g1, stride});\n            }\n        }\n    }\n}\n\nstatic inline float intersection_area(const Object& a, const Object& b)\n{\n    Rect_<float> inter = a.rect & b.rect;\n    return inter.area();\n}\n\nstatic void qsort_descent_inplace(vector<Object>& faceobjects, int left, int right)\n{\n    int i = left;\n    int j = right;\n    float p = faceobjects[(left + right) / 2].prob;\n\n    while (i <= j)\n    {\n        while (faceobjects[i].prob > p)\n            i++;\n\n        while (faceobjects[j].prob < p)\n            j--;\n\n        if (i <= j)\n        {\n            // swap\n            swap(faceobjects[i], faceobjects[j]);\n\n            i++;\n            j--;\n        }\n    }\n\n    #pragma omp parallel sections\n    {\n        #pragma omp section\n        {\n            if (left < j) qsort_descent_inplace(faceobjects, left, j);\n        }\n        #pragma omp section\n        {\n            if (i < right) qsort_descent_inplace(faceobjects, i, right);\n        }\n    }\n}\n\nstatic void qsort_descent_inplace(vector<Object>& objects)\n{\n    if (objects.empty())\n        return;\n\n    qsort_descent_inplace(objects, 0, objects.size() - 1);\n}\n\nstatic void nms_sorted_bboxes(const vector<Object>& faceobjects, vector<int>& picked, float nms_threshold)\n{\n    picked.clear();\n\n    const int n = faceobjects.size();\n\n    vector<float> areas(n);\n    for (int i = 0; i < n; i++)\n    {\n        areas[i] = faceobjects[i].rect.area();\n    }\n\n    for (int i = 0; i < n; i++)\n    {\n        const Object& a = faceobjects[i];\n\n        int keep = 1;\n        for (int j = 0; j < (int)picked.size(); j++)\n        {\n            const Object& b = faceobjects[picked[j]];\n\n            // intersection over union\n            float inter_area = intersection_area(a, b);\n            float union_area = areas[i] + areas[picked[j]] - inter_area;\n            // float IoU = inter_area / union_area\n            if (inter_area / union_area > nms_threshold)\n                keep = 0;\n        }\n\n        if (keep)\n            picked.push_back(i);\n    }\n}\n\n\nstatic void generate_yolox_proposals(vector<GridAndStride> grid_strides, float* feat_blob, float prob_threshold, vector<Object>& objects)\n{\n    const int num_class = 1;\n\n    const int num_anchors = grid_strides.size();\n\n    for (int anchor_idx = 0; anchor_idx < num_anchors; anchor_idx++)\n    {\n        const int grid0 = grid_strides[anchor_idx].grid0;\n        const int grid1 = grid_strides[anchor_idx].grid1;\n        const int stride = grid_strides[anchor_idx].stride;\n\n        const int basic_pos = anchor_idx * (num_class + 5);\n\n        // yolox/models/yolo_head.py decode logic\n        float x_center = (feat_blob[basic_pos+0] + grid0) * stride;\n        float y_center = (feat_blob[basic_pos+1] + grid1) * stride;\n        float w = exp(feat_blob[basic_pos+2]) * stride;\n        float h = exp(feat_blob[basic_pos+3]) * stride;\n        float x0 = x_center - w * 0.5f;\n        float y0 = y_center - h * 0.5f;\n\n        float box_objectness = feat_blob[basic_pos+4];\n        for (int class_idx = 0; class_idx < num_class; class_idx++)\n        {\n            float box_cls_score = feat_blob[basic_pos + 5 + class_idx];\n            float box_prob = box_objectness * box_cls_score;\n            if (box_prob > prob_threshold)\n            {\n                Object obj;\n                obj.rect.x = x0;\n                obj.rect.y = y0;\n                obj.rect.width = w;\n                obj.rect.height = h;\n                obj.label = class_idx;\n                obj.prob = box_prob;\n\n                objects.push_back(obj);\n            }\n\n        } // class loop\n\n    } // point anchor loop\n}\n\nfloat* blobFromImage(Mat& img){\n    cvtColor(img, img, COLOR_BGR2RGB);\n\n    float* blob = new float[img.total()*3];\n    int channels = 3;\n    int img_h = img.rows;\n    int img_w = img.cols;\n    vector<float> mean = {0.485, 0.456, 0.406};\n    vector<float> std = {0.229, 0.224, 0.225};\n    for (size_t c = 0; c < channels; c++) \n    {\n        for (size_t  h = 0; h < img_h; h++) \n        {\n            for (size_t w = 0; w < img_w; w++) \n            {\n                blob[c * img_w * img_h + h * img_w + w] =\n                    (((float)img.at<Vec3b>(h, w)[c]) / 255.0f - mean[c]) / std[c];\n            }\n        }\n    }\n    return blob;\n}\n\n\nstatic void decode_outputs(float* prob, vector<Object>& objects, float scale, const int img_w, const int img_h) {\n        vector<Object> proposals;\n        vector<int> strides = {8, 16, 32};\n        vector<GridAndStride> grid_strides;\n        generate_grids_and_stride(INPUT_W, INPUT_H, strides, grid_strides);\n        generate_yolox_proposals(grid_strides, prob,  BBOX_CONF_THRESH, proposals);\n        //std::cout << \"num of boxes before nms: \" << proposals.size() << std::endl;\n\n        qsort_descent_inplace(proposals);\n\n        vector<int> picked;\n        nms_sorted_bboxes(proposals, picked, NMS_THRESH);\n\n\n        int count = picked.size();\n\n        //std::cout << \"num of boxes: \" << count << std::endl;\n\n        objects.resize(count);\n        for (int i = 0; i < count; i++)\n        {\n            objects[i] = proposals[picked[i]];\n\n            // adjust offset to original unpadded\n            float x0 = (objects[i].rect.x) / scale;\n            float y0 = (objects[i].rect.y) / scale;\n            float x1 = (objects[i].rect.x + objects[i].rect.width) / scale;\n            float y1 = (objects[i].rect.y + objects[i].rect.height) / scale;\n\n            // clip\n            // x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);\n            // y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);\n            // x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);\n            // y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);\n\n            objects[i].rect.x = x0;\n            objects[i].rect.y = y0;\n            objects[i].rect.width = x1 - x0;\n            objects[i].rect.height = y1 - y0;\n        }\n}\n\nconst float color_list[80][3] =\n{\n    {0.000, 0.447, 0.741},\n    {0.850, 0.325, 0.098},\n    {0.929, 0.694, 0.125},\n    {0.494, 0.184, 0.556},\n    {0.466, 0.674, 0.188},\n    {0.301, 0.745, 0.933},\n    {0.635, 0.078, 0.184},\n    {0.300, 0.300, 0.300},\n    {0.600, 0.600, 0.600},\n    {1.000, 0.000, 0.000},\n    {1.000, 0.500, 0.000},\n    {0.749, 0.749, 0.000},\n    {0.000, 1.000, 0.000},\n    {0.000, 0.000, 1.000},\n    {0.667, 0.000, 1.000},\n    {0.333, 0.333, 0.000},\n    {0.333, 0.667, 0.000},\n    {0.333, 1.000, 0.000},\n    {0.667, 0.333, 0.000},\n    {0.667, 0.667, 0.000},\n    {0.667, 1.000, 0.000},\n    {1.000, 0.333, 0.000},\n    {1.000, 0.667, 0.000},\n    {1.000, 1.000, 0.000},\n    {0.000, 0.333, 0.500},\n    {0.000, 0.667, 0.500},\n    {0.000, 1.000, 0.500},\n    {0.333, 0.000, 0.500},\n    {0.333, 0.333, 0.500},\n    {0.333, 0.667, 0.500},\n    {0.333, 1.000, 0.500},\n    {0.667, 0.000, 0.500},\n    {0.667, 0.333, 0.500},\n    {0.667, 0.667, 0.500},\n    {0.667, 1.000, 0.500},\n    {1.000, 0.000, 0.500},\n    {1.000, 0.333, 0.500},\n    {1.000, 0.667, 0.500},\n    {1.000, 1.000, 0.500},\n    {0.000, 0.333, 1.000},\n    {0.000, 0.667, 1.000},\n    {0.000, 1.000, 1.000},\n    {0.333, 0.000, 1.000},\n    {0.333, 0.333, 1.000},\n    {0.333, 0.667, 1.000},\n    {0.333, 1.000, 1.000},\n    {0.667, 0.000, 1.000},\n    {0.667, 0.333, 1.000},\n    {0.667, 0.667, 1.000},\n    {0.667, 1.000, 1.000},\n    {1.000, 0.000, 1.000},\n    {1.000, 0.333, 1.000},\n    {1.000, 0.667, 1.000},\n    {0.333, 0.000, 0.000},\n    {0.500, 0.000, 0.000},\n    {0.667, 0.000, 0.000},\n    {0.833, 0.000, 0.000},\n    {1.000, 0.000, 0.000},\n    {0.000, 0.167, 0.000},\n    {0.000, 0.333, 0.000},\n    {0.000, 0.500, 0.000},\n    {0.000, 0.667, 0.000},\n    {0.000, 0.833, 0.000},\n    {0.000, 1.000, 0.000},\n    {0.000, 0.000, 0.167},\n    {0.000, 0.000, 0.333},\n    {0.000, 0.000, 0.500},\n    {0.000, 0.000, 0.667},\n    {0.000, 0.000, 0.833},\n    {0.000, 0.000, 1.000},\n    {0.000, 0.000, 0.000},\n    {0.143, 0.143, 0.143},\n    {0.286, 0.286, 0.286},\n    {0.429, 0.429, 0.429},\n    {0.571, 0.571, 0.571},\n    {0.714, 0.714, 0.714},\n    {0.857, 0.857, 0.857},\n    {0.000, 0.447, 0.741},\n    {0.314, 0.717, 0.741},\n    {0.50, 0.5, 0}\n};\n\nvoid doInference(IExecutionContext& context, float* input, float* output, const int output_size, Size input_shape) {\n    const ICudaEngine& engine = context.getEngine();\n\n    // Pointers to input and output device buffers to pass to engine.\n    // Engine requires exactly IEngine::getNbBindings() number of buffers.\n    assert(engine.getNbBindings() == 2);\n    void* buffers[2];\n\n    // In order to bind the buffers, we need to know the names of the input and output tensors.\n    // Note that indices are guaranteed to be less than IEngine::getNbBindings()\n    const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);\n\n    assert(engine.getBindingDataType(inputIndex) == nvinfer1::DataType::kFLOAT);\n    const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);\n    assert(engine.getBindingDataType(outputIndex) == nvinfer1::DataType::kFLOAT);\n    int mBatchSize = engine.getMaxBatchSize();\n\n    // Create GPU buffers on device\n    CHECK(cudaMalloc(&buffers[inputIndex], 3 * input_shape.height * input_shape.width * sizeof(float)));\n    CHECK(cudaMalloc(&buffers[outputIndex], output_size*sizeof(float)));\n\n    // Create stream\n    cudaStream_t stream;\n    CHECK(cudaStreamCreate(&stream));\n\n    // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host\n    CHECK(cudaMemcpyAsync(buffers[inputIndex], input, 3 * input_shape.height * input_shape.width * sizeof(float), cudaMemcpyHostToDevice, stream));\n    context.enqueue(1, buffers, stream, nullptr);\n    CHECK(cudaMemcpyAsync(output, buffers[outputIndex], output_size * sizeof(float), cudaMemcpyDeviceToHost, stream));\n    cudaStreamSynchronize(stream);\n\n    // Release stream and buffers\n    cudaStreamDestroy(stream);\n    CHECK(cudaFree(buffers[inputIndex]));\n    CHECK(cudaFree(buffers[outputIndex]));\n}\n\nint main(int argc, char** argv) {\n    cudaSetDevice(DEVICE);\n    \n    // create a model using the API directly and serialize it to a stream\n    char *trtModelStream{nullptr};\n    size_t size{0};\n\n    if (argc == 4 && string(argv[2]) == \"-i\") {\n        const string engine_file_path {argv[1]};\n        ifstream file(engine_file_path, ios::binary);\n        if (file.good()) {\n            file.seekg(0, file.end);\n            size = file.tellg();\n            file.seekg(0, file.beg);\n            trtModelStream = new char[size];\n            assert(trtModelStream);\n            file.read(trtModelStream, size);\n            file.close();\n        }\n    } else {\n        cerr << \"arguments not right!\" << endl;\n        cerr << \"run 'python3 tools/trt.py -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_s_mot17.pth.tar' to serialize model first!\" << std::endl;\n        cerr << \"Then use the following command:\" << endl;\n        cerr << \"cd demo/TensorRT/cpp/build\" << endl;\n        cerr << \"./bytetrack ../../../../YOLOX_outputs/yolox_s_mix_det/model_trt.engine -i ../../../../videos/palace.mp4  // deserialize file and run inference\" << std::endl;\n        return -1;\n    }\n    const string input_video_path {argv[3]};\n\n    IRuntime* runtime = createInferRuntime(gLogger);\n    assert(runtime != nullptr);\n    ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);\n    assert(engine != nullptr); \n    IExecutionContext* context = engine->createExecutionContext();\n    assert(context != nullptr);\n    delete[] trtModelStream;\n    auto out_dims = engine->getBindingDimensions(1);\n    auto output_size = 1;\n    for(int j=0;j<out_dims.nbDims;j++) {\n        output_size *= out_dims.d[j];\n    }\n    static float* prob = new float[output_size];\n\n    VideoCapture cap(input_video_path);\n\tif (!cap.isOpened())\n\t\treturn 0;\n\n\tint img_w = cap.get(CAP_PROP_FRAME_WIDTH);\n\tint img_h = cap.get(CAP_PROP_FRAME_HEIGHT);\n    int fps = cap.get(CAP_PROP_FPS);\n    long nFrame = static_cast<long>(cap.get(CAP_PROP_FRAME_COUNT));\n    cout << \"Total frames: \" << nFrame << endl;\n\n    VideoWriter writer(\"demo.mp4\", VideoWriter::fourcc('m', 'p', '4', 'v'), fps, Size(img_w, img_h));\n\n    Mat img;\n    BYTETracker tracker(fps, 30);\n    int num_frames = 0;\n    int total_ms = 0;\n\twhile (true)\n    {\n        if(!cap.read(img))\n            break;\n        num_frames ++;\n        if (num_frames % 20 == 0)\n        {\n            cout << \"Processing frame \" << num_frames << \" (\" << num_frames * 1000000 / total_ms << \" fps)\" << endl;\n        }\n\t\tif (img.empty())\n\t\t\tbreak;\n        Mat pr_img = static_resize(img);\n        \n        float* blob;\n        blob = blobFromImage(pr_img);\n        float scale = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));\n        \n        // run inference\n        auto start = chrono::system_clock::now();\n        doInference(*context, blob, prob, output_size, pr_img.size());\n        vector<Object> objects;\n        decode_outputs(prob, objects, scale, img_w, img_h);\n        vector<STrack> output_stracks = tracker.update(objects);\n        auto end = chrono::system_clock::now();\n        total_ms = total_ms + chrono::duration_cast<chrono::microseconds>(end - start).count();\n\n        for (int i = 0; i < output_stracks.size(); i++)\n\t\t{\n\t\t\tvector<float> tlwh = output_stracks[i].tlwh;\n\t\t\tbool vertical = tlwh[2] / tlwh[3] > 1.6;\n\t\t\tif (tlwh[2] * tlwh[3] > 20 && !vertical)\n\t\t\t{\n\t\t\t\tScalar s = tracker.get_color(output_stracks[i].track_id);\n\t\t\t\tputText(img, format(\"%d\", output_stracks[i].track_id), Point(tlwh[0], tlwh[1] - 5), \n                        0, 0.6, Scalar(0, 0, 255), 2, LINE_AA);\n                rectangle(img, Rect(tlwh[0], tlwh[1], tlwh[2], tlwh[3]), s, 2);\n\t\t\t}\n\t\t}\n        putText(img, format(\"frame: %d fps: %d num: %d\", num_frames, num_frames * 1000000 / total_ms, output_stracks.size()), \n                Point(0, 30), 0, 0.6, Scalar(0, 0, 255), 2, LINE_AA);\n        writer.write(img);\n\n        delete blob;\n        char c = waitKey(1);\n        if (c > 0)\n        {\n            break;\n        }\n    }\n    cap.release();\n    cout << \"FPS: \" << num_frames * 1000000 / total_ms << endl;\n    // destroy the engine\n    context->destroy();\n    engine->destroy();\n    runtime->destroy();\n    return 0;\n}\n"
  },
  {
    "path": "deploy/TensorRT/cpp/src/kalmanFilter.cpp",
    "content": "#include \"kalmanFilter.h\"\r\n#include <Eigen/Cholesky>\r\n\r\nnamespace byte_kalman\r\n{\r\n\tconst double KalmanFilter::chi2inv95[10] = {\r\n\t0,\r\n\t3.8415,\r\n\t5.9915,\r\n\t7.8147,\r\n\t9.4877,\r\n\t11.070,\r\n\t12.592,\r\n\t14.067,\r\n\t15.507,\r\n\t16.919\r\n\t};\r\n\tKalmanFilter::KalmanFilter()\r\n\t{\r\n\t\tint ndim = 4;\r\n\t\tdouble dt = 1.;\r\n\r\n\t\t_motion_mat = Eigen::MatrixXf::Identity(8, 8);\r\n\t\tfor (int i = 0; i < ndim; i++) {\r\n\t\t\t_motion_mat(i, ndim + i) = dt;\r\n\t\t}\r\n\t\t_update_mat = Eigen::MatrixXf::Identity(4, 8);\r\n\r\n\t\tthis->_std_weight_position = 1. / 20;\r\n\t\tthis->_std_weight_velocity = 1. / 160;\r\n\t}\r\n\r\n\tKAL_DATA KalmanFilter::initiate(const DETECTBOX &measurement)\r\n\t{\r\n\t\tDETECTBOX mean_pos = measurement;\r\n\t\tDETECTBOX mean_vel;\r\n\t\tfor (int i = 0; i < 4; i++) mean_vel(i) = 0;\r\n\r\n\t\tKAL_MEAN mean;\r\n\t\tfor (int i = 0; i < 8; i++) {\r\n\t\t\tif (i < 4) mean(i) = mean_pos(i);\r\n\t\t\telse mean(i) = mean_vel(i - 4);\r\n\t\t}\r\n\r\n\t\tKAL_MEAN std;\r\n\t\tstd(0) = 2 * _std_weight_position * measurement[3];\r\n\t\tstd(1) = 2 * _std_weight_position * measurement[3];\r\n\t\tstd(2) = 1e-2;\r\n\t\tstd(3) = 2 * _std_weight_position * measurement[3];\r\n\t\tstd(4) = 10 * _std_weight_velocity * measurement[3];\r\n\t\tstd(5) = 10 * _std_weight_velocity * measurement[3];\r\n\t\tstd(6) = 1e-5;\r\n\t\tstd(7) = 10 * _std_weight_velocity * measurement[3];\r\n\r\n\t\tKAL_MEAN tmp = std.array().square();\r\n\t\tKAL_COVA var = tmp.asDiagonal();\r\n\t\treturn std::make_pair(mean, var);\r\n\t}\r\n\r\n\tvoid KalmanFilter::predict(KAL_MEAN &mean, KAL_COVA &covariance)\r\n\t{\r\n\t\t//revise the data;\r\n\t\tDETECTBOX std_pos;\r\n\t\tstd_pos << _std_weight_position * mean(3),\r\n\t\t\t_std_weight_position * mean(3),\r\n\t\t\t1e-2,\r\n\t\t\t_std_weight_position * mean(3);\r\n\t\tDETECTBOX std_vel;\r\n\t\tstd_vel << _std_weight_velocity * mean(3),\r\n\t\t\t_std_weight_velocity * mean(3),\r\n\t\t\t1e-5,\r\n\t\t\t_std_weight_velocity * mean(3);\r\n\t\tKAL_MEAN tmp;\r\n\t\ttmp.block<1, 4>(0, 0) = std_pos;\r\n\t\ttmp.block<1, 4>(0, 4) = std_vel;\r\n\t\ttmp = tmp.array().square();\r\n\t\tKAL_COVA motion_cov = tmp.asDiagonal();\r\n\t\tKAL_MEAN mean1 = this->_motion_mat * mean.transpose();\r\n\t\tKAL_COVA covariance1 = this->_motion_mat * covariance *(_motion_mat.transpose());\r\n\t\tcovariance1 += motion_cov;\r\n\r\n\t\tmean = mean1;\r\n\t\tcovariance = covariance1;\r\n\t}\r\n\r\n\tKAL_HDATA KalmanFilter::project(const KAL_MEAN &mean, const KAL_COVA &covariance)\r\n\t{\r\n\t\tDETECTBOX std;\r\n\t\tstd << _std_weight_position * mean(3), _std_weight_position * mean(3),\r\n\t\t\t1e-1, _std_weight_position * mean(3);\r\n\t\tKAL_HMEAN mean1 = _update_mat * mean.transpose();\r\n\t\tKAL_HCOVA covariance1 = _update_mat * covariance * (_update_mat.transpose());\r\n\t\tEigen::Matrix<float, 4, 4> diag = std.asDiagonal();\r\n\t\tdiag = diag.array().square().matrix();\r\n\t\tcovariance1 += diag;\r\n\t\t//    covariance1.diagonal() << diag;\r\n\t\treturn std::make_pair(mean1, covariance1);\r\n\t}\r\n\r\n\tKAL_DATA\r\n\t\tKalmanFilter::update(\r\n\t\t\tconst KAL_MEAN &mean,\r\n\t\t\tconst KAL_COVA &covariance,\r\n\t\t\tconst DETECTBOX &measurement)\r\n\t{\r\n\t\tKAL_HDATA pa = project(mean, covariance);\r\n\t\tKAL_HMEAN projected_mean = pa.first;\r\n\t\tKAL_HCOVA projected_cov = pa.second;\r\n\r\n\t\t//chol_factor, lower =\r\n\t\t//scipy.linalg.cho_factor(projected_cov, lower=True, check_finite=False)\r\n\t\t//kalmain_gain =\r\n\t\t//scipy.linalg.cho_solve((cho_factor, lower),\r\n\t\t//np.dot(covariance, self._upadte_mat.T).T,\r\n\t\t//check_finite=False).T\r\n\t\tEigen::Matrix<float, 4, 8> B = (covariance * (_update_mat.transpose())).transpose();\r\n\t\tEigen::Matrix<float, 8, 4> kalman_gain = (projected_cov.llt().solve(B)).transpose(); // eg.8x4\r\n\t\tEigen::Matrix<float, 1, 4> innovation = measurement - projected_mean; //eg.1x4\r\n\t\tauto tmp = innovation * (kalman_gain.transpose());\r\n\t\tKAL_MEAN new_mean = (mean.array() + tmp.array()).matrix();\r\n\t\tKAL_COVA new_covariance = covariance - kalman_gain * projected_cov*(kalman_gain.transpose());\r\n\t\treturn std::make_pair(new_mean, new_covariance);\r\n\t}\r\n\r\n\tEigen::Matrix<float, 1, -1>\r\n\t\tKalmanFilter::gating_distance(\r\n\t\t\tconst KAL_MEAN &mean,\r\n\t\t\tconst KAL_COVA &covariance,\r\n\t\t\tconst std::vector<DETECTBOX> &measurements,\r\n\t\t\tbool only_position)\r\n\t{\r\n\t\tKAL_HDATA pa = this->project(mean, covariance);\r\n\t\tif (only_position) {\r\n\t\t\tprintf(\"not implement!\");\r\n\t\t\texit(0);\r\n\t\t}\r\n\t\tKAL_HMEAN mean1 = pa.first;\r\n\t\tKAL_HCOVA covariance1 = pa.second;\r\n\r\n\t\t//    Eigen::Matrix<float, -1, 4, Eigen::RowMajor> d(size, 4);\r\n\t\tDETECTBOXSS d(measurements.size(), 4);\r\n\t\tint pos = 0;\r\n\t\tfor (DETECTBOX box : measurements) {\r\n\t\t\td.row(pos++) = box - mean1;\r\n\t\t}\r\n\t\tEigen::Matrix<float, -1, -1, Eigen::RowMajor> factor = covariance1.llt().matrixL();\r\n\t\tEigen::Matrix<float, -1, -1> z = factor.triangularView<Eigen::Lower>().solve<Eigen::OnTheRight>(d).transpose();\r\n\t\tauto zz = ((z.array())*(z.array())).matrix();\r\n\t\tauto square_maha = zz.colwise().sum();\r\n\t\treturn square_maha;\r\n\t}\r\n}"
  },
  {
    "path": "deploy/TensorRT/cpp/src/lapjv.cpp",
    "content": "#include <stdio.h>\r\n#include <stdlib.h>\r\n#include <string.h>\r\n\r\n#include \"lapjv.h\"\r\n\r\n/** Column-reduction and reduction transfer for a dense cost matrix.\r\n */\r\nint_t _ccrrt_dense(const uint_t n, cost_t *cost[],\r\n\tint_t *free_rows, int_t *x, int_t *y, cost_t *v)\r\n{\r\n\tint_t n_free_rows;\r\n\tboolean *unique;\r\n\r\n\tfor (uint_t i = 0; i < n; i++) {\r\n\t\tx[i] = -1;\r\n\t\tv[i] = LARGE;\r\n\t\ty[i] = 0;\r\n\t}\r\n\tfor (uint_t i = 0; i < n; i++) {\r\n\t\tfor (uint_t j = 0; j < n; j++) {\r\n\t\t\tconst cost_t c = cost[i][j];\r\n\t\t\tif (c < v[j]) {\r\n\t\t\t\tv[j] = c;\r\n\t\t\t\ty[j] = i;\r\n\t\t\t}\r\n\t\t\tPRINTF(\"i=%d, j=%d, c[i,j]=%f, v[j]=%f y[j]=%d\\n\", i, j, c, v[j], y[j]);\r\n\t\t}\r\n\t}\r\n\tPRINT_COST_ARRAY(v, n);\r\n\tPRINT_INDEX_ARRAY(y, n);\r\n\tNEW(unique, boolean, n);\r\n\tmemset(unique, TRUE, n);\r\n\t{\r\n\t\tint_t j = n;\r\n\t\tdo {\r\n\t\t\tj--;\r\n\t\t\tconst int_t i = y[j];\r\n\t\t\tif (x[i] < 0) {\r\n\t\t\t\tx[i] = j;\r\n\t\t\t}\r\n\t\t\telse {\r\n\t\t\t\tunique[i] = FALSE;\r\n\t\t\t\ty[j] = -1;\r\n\t\t\t}\r\n\t\t} while (j > 0);\r\n\t}\r\n\tn_free_rows = 0;\r\n\tfor (uint_t i = 0; i < n; i++) {\r\n\t\tif (x[i] < 0) {\r\n\t\t\tfree_rows[n_free_rows++] = i;\r\n\t\t}\r\n\t\telse if (unique[i]) {\r\n\t\t\tconst int_t j = x[i];\r\n\t\t\tcost_t min = LARGE;\r\n\t\t\tfor (uint_t j2 = 0; j2 < n; j2++) {\r\n\t\t\t\tif (j2 == (uint_t)j) {\r\n\t\t\t\t\tcontinue;\r\n\t\t\t\t}\r\n\t\t\t\tconst cost_t c = cost[i][j2] - v[j2];\r\n\t\t\t\tif (c < min) {\r\n\t\t\t\t\tmin = c;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\tPRINTF(\"v[%d] = %f - %f\\n\", j, v[j], min);\r\n\t\t\tv[j] -= min;\r\n\t\t}\r\n\t}\r\n\tFREE(unique);\r\n\treturn n_free_rows;\r\n}\r\n\r\n\r\n/** Augmenting row reduction for a dense cost matrix.\r\n */\r\nint_t _carr_dense(\r\n\tconst uint_t n, cost_t *cost[],\r\n\tconst uint_t n_free_rows,\r\n\tint_t *free_rows, int_t *x, int_t *y, cost_t *v)\r\n{\r\n\tuint_t current = 0;\r\n\tint_t new_free_rows = 0;\r\n\tuint_t rr_cnt = 0;\r\n\tPRINT_INDEX_ARRAY(x, n);\r\n\tPRINT_INDEX_ARRAY(y, n);\r\n\tPRINT_COST_ARRAY(v, n);\r\n\tPRINT_INDEX_ARRAY(free_rows, n_free_rows);\r\n\twhile (current < n_free_rows) {\r\n\t\tint_t i0;\r\n\t\tint_t j1, j2;\r\n\t\tcost_t v1, v2, v1_new;\r\n\t\tboolean v1_lowers;\r\n\r\n\t\trr_cnt++;\r\n\t\tPRINTF(\"current = %d rr_cnt = %d\\n\", current, rr_cnt);\r\n\t\tconst int_t free_i = free_rows[current++];\r\n\t\tj1 = 0;\r\n\t\tv1 = cost[free_i][0] - v[0];\r\n\t\tj2 = -1;\r\n\t\tv2 = LARGE;\r\n\t\tfor (uint_t j = 1; j < n; j++) {\r\n\t\t\tPRINTF(\"%d = %f %d = %f\\n\", j1, v1, j2, v2);\r\n\t\t\tconst cost_t c = cost[free_i][j] - v[j];\r\n\t\t\tif (c < v2) {\r\n\t\t\t\tif (c >= v1) {\r\n\t\t\t\t\tv2 = c;\r\n\t\t\t\t\tj2 = j;\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tv2 = v1;\r\n\t\t\t\t\tv1 = c;\r\n\t\t\t\t\tj2 = j1;\r\n\t\t\t\t\tj1 = j;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\ti0 = y[j1];\r\n\t\tv1_new = v[j1] - (v2 - v1);\r\n\t\tv1_lowers = v1_new < v[j1];\r\n\t\tPRINTF(\"%d %d 1=%d,%f 2=%d,%f v1'=%f(%d,%g) \\n\", free_i, i0, j1, v1, j2, v2, v1_new, v1_lowers, v[j1] - v1_new);\r\n\t\tif (rr_cnt < current * n) {\r\n\t\t\tif (v1_lowers) {\r\n\t\t\t\tv[j1] = v1_new;\r\n\t\t\t}\r\n\t\t\telse if (i0 >= 0 && j2 >= 0) {\r\n\t\t\t\tj1 = j2;\r\n\t\t\t\ti0 = y[j2];\r\n\t\t\t}\r\n\t\t\tif (i0 >= 0) {\r\n\t\t\t\tif (v1_lowers) {\r\n\t\t\t\t\tfree_rows[--current] = i0;\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tfree_rows[new_free_rows++] = i0;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\telse {\r\n\t\t\tPRINTF(\"rr_cnt=%d >= %d (current=%d * n=%d)\\n\", rr_cnt, current * n, current, n);\r\n\t\t\tif (i0 >= 0) {\r\n\t\t\t\tfree_rows[new_free_rows++] = i0;\r\n\t\t\t}\r\n\t\t}\r\n\t\tx[free_i] = j1;\r\n\t\ty[j1] = free_i;\r\n\t}\r\n\treturn new_free_rows;\r\n}\r\n\r\n\r\n/** Find columns with minimum d[j] and put them on the SCAN list.\r\n */\r\nuint_t _find_dense(const uint_t n, uint_t lo, cost_t *d, int_t *cols, int_t *y)\r\n{\r\n\tuint_t hi = lo + 1;\r\n\tcost_t mind = d[cols[lo]];\r\n\tfor (uint_t k = hi; k < n; k++) {\r\n\t\tint_t j = cols[k];\r\n\t\tif (d[j] <= mind) {\r\n\t\t\tif (d[j] < mind) {\r\n\t\t\t\thi = lo;\r\n\t\t\t\tmind = d[j];\r\n\t\t\t}\r\n\t\t\tcols[k] = cols[hi];\r\n\t\t\tcols[hi++] = j;\r\n\t\t}\r\n\t}\r\n\treturn hi;\r\n}\r\n\r\n\r\n// Scan all columns in TODO starting from arbitrary column in SCAN\r\n// and try to decrease d of the TODO columns using the SCAN column.\r\nint_t _scan_dense(const uint_t n, cost_t *cost[],\r\n\tuint_t *plo, uint_t*phi,\r\n\tcost_t *d, int_t *cols, int_t *pred,\r\n\tint_t *y, cost_t *v)\r\n{\r\n\tuint_t lo = *plo;\r\n\tuint_t hi = *phi;\r\n\tcost_t h, cred_ij;\r\n\r\n\twhile (lo != hi) {\r\n\t\tint_t j = cols[lo++];\r\n\t\tconst int_t i = y[j];\r\n\t\tconst cost_t mind = d[j];\r\n\t\th = cost[i][j] - v[j] - mind;\r\n\t\tPRINTF(\"i=%d j=%d h=%f\\n\", i, j, h);\r\n\t\t// For all columns in TODO\r\n\t\tfor (uint_t k = hi; k < n; k++) {\r\n\t\t\tj = cols[k];\r\n\t\t\tcred_ij = cost[i][j] - v[j] - h;\r\n\t\t\tif (cred_ij < d[j]) {\r\n\t\t\t\td[j] = cred_ij;\r\n\t\t\t\tpred[j] = i;\r\n\t\t\t\tif (cred_ij == mind) {\r\n\t\t\t\t\tif (y[j] < 0) {\r\n\t\t\t\t\t\treturn j;\r\n\t\t\t\t\t}\r\n\t\t\t\t\tcols[k] = cols[hi];\r\n\t\t\t\t\tcols[hi++] = j;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\t*plo = lo;\r\n\t*phi = hi;\r\n\treturn -1;\r\n}\r\n\r\n\r\n/** Single iteration of modified Dijkstra shortest path algorithm as explained in the JV paper.\r\n *\r\n * This is a dense matrix version.\r\n *\r\n * \\return The closest free column index.\r\n */\r\nint_t find_path_dense(\r\n\tconst uint_t n, cost_t *cost[],\r\n\tconst int_t start_i,\r\n\tint_t *y, cost_t *v,\r\n\tint_t *pred)\r\n{\r\n\tuint_t lo = 0, hi = 0;\r\n\tint_t final_j = -1;\r\n\tuint_t n_ready = 0;\r\n\tint_t *cols;\r\n\tcost_t *d;\r\n\r\n\tNEW(cols, int_t, n);\r\n\tNEW(d, cost_t, n);\r\n\r\n\tfor (uint_t i = 0; i < n; i++) {\r\n\t\tcols[i] = i;\r\n\t\tpred[i] = start_i;\r\n\t\td[i] = cost[start_i][i] - v[i];\r\n\t}\r\n\tPRINT_COST_ARRAY(d, n);\r\n\twhile (final_j == -1) {\r\n\t\t// No columns left on the SCAN list.\r\n\t\tif (lo == hi) {\r\n\t\t\tPRINTF(\"%d..%d -> find\\n\", lo, hi);\r\n\t\t\tn_ready = lo;\r\n\t\t\thi = _find_dense(n, lo, d, cols, y);\r\n\t\t\tPRINTF(\"check %d..%d\\n\", lo, hi);\r\n\t\t\tPRINT_INDEX_ARRAY(cols, n);\r\n\t\t\tfor (uint_t k = lo; k < hi; k++) {\r\n\t\t\t\tconst int_t j = cols[k];\r\n\t\t\t\tif (y[j] < 0) {\r\n\t\t\t\t\tfinal_j = j;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\tif (final_j == -1) {\r\n\t\t\tPRINTF(\"%d..%d -> scan\\n\", lo, hi);\r\n\t\t\tfinal_j = _scan_dense(\r\n\t\t\t\tn, cost, &lo, &hi, d, cols, pred, y, v);\r\n\t\t\tPRINT_COST_ARRAY(d, n);\r\n\t\t\tPRINT_INDEX_ARRAY(cols, n);\r\n\t\t\tPRINT_INDEX_ARRAY(pred, n);\r\n\t\t}\r\n\t}\r\n\r\n\tPRINTF(\"found final_j=%d\\n\", final_j);\r\n\tPRINT_INDEX_ARRAY(cols, n);\r\n\t{\r\n\t\tconst cost_t mind = d[cols[lo]];\r\n\t\tfor (uint_t k = 0; k < n_ready; k++) {\r\n\t\t\tconst int_t j = cols[k];\r\n\t\t\tv[j] += d[j] - mind;\r\n\t\t}\r\n\t}\r\n\r\n\tFREE(cols);\r\n\tFREE(d);\r\n\r\n\treturn final_j;\r\n}\r\n\r\n\r\n/** Augment for a dense cost matrix.\r\n */\r\nint_t _ca_dense(\r\n\tconst uint_t n, cost_t *cost[],\r\n\tconst uint_t n_free_rows,\r\n\tint_t *free_rows, int_t *x, int_t *y, cost_t *v)\r\n{\r\n\tint_t *pred;\r\n\r\n\tNEW(pred, int_t, n);\r\n\r\n\tfor (int_t *pfree_i = free_rows; pfree_i < free_rows + n_free_rows; pfree_i++) {\r\n\t\tint_t i = -1, j;\r\n\t\tuint_t k = 0;\r\n\r\n\t\tPRINTF(\"looking at free_i=%d\\n\", *pfree_i);\r\n\t\tj = find_path_dense(n, cost, *pfree_i, y, v, pred);\r\n\t\tASSERT(j >= 0);\r\n\t\tASSERT(j < n);\r\n\t\twhile (i != *pfree_i) {\r\n\t\t\tPRINTF(\"augment %d\\n\", j);\r\n\t\t\tPRINT_INDEX_ARRAY(pred, n);\r\n\t\t\ti = pred[j];\r\n\t\t\tPRINTF(\"y[%d]=%d -> %d\\n\", j, y[j], i);\r\n\t\t\ty[j] = i;\r\n\t\t\tPRINT_INDEX_ARRAY(x, n);\r\n\t\t\tSWAP_INDICES(j, x[i]);\r\n\t\t\tk++;\r\n\t\t\tif (k >= n) {\r\n\t\t\t\tASSERT(FALSE);\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\tFREE(pred);\r\n\treturn 0;\r\n}\r\n\r\n\r\n/** Solve dense sparse LAP.\r\n */\r\nint lapjv_internal(\r\n\tconst uint_t n, cost_t *cost[],\r\n\tint_t *x, int_t *y)\r\n{\r\n\tint ret;\r\n\tint_t *free_rows;\r\n\tcost_t *v;\r\n\r\n\tNEW(free_rows, int_t, n);\r\n\tNEW(v, cost_t, n);\r\n\tret = _ccrrt_dense(n, cost, free_rows, x, y, v);\r\n\tint i = 0;\r\n\twhile (ret > 0 && i < 2) {\r\n\t\tret = _carr_dense(n, cost, ret, free_rows, x, y, v);\r\n\t\ti++;\r\n\t}\r\n\tif (ret > 0) {\r\n\t\tret = _ca_dense(n, cost, ret, free_rows, x, y, v);\r\n\t}\r\n\tFREE(v);\r\n\tFREE(free_rows);\r\n\treturn ret;\r\n}"
  },
  {
    "path": "deploy/TensorRT/cpp/src/utils.cpp",
    "content": "#include \"BYTETracker.h\"\r\n#include \"lapjv.h\"\r\n\r\nvector<STrack*> BYTETracker::joint_stracks(vector<STrack*> &tlista, vector<STrack> &tlistb)\r\n{\r\n\tmap<int, int> exists;\r\n\tvector<STrack*> res;\r\n\tfor (int i = 0; i < tlista.size(); i++)\r\n\t{\r\n\t\texists.insert(pair<int, int>(tlista[i]->track_id, 1));\r\n\t\tres.push_back(tlista[i]);\r\n\t}\r\n\tfor (int i = 0; i < tlistb.size(); i++)\r\n\t{\r\n\t\tint tid = tlistb[i].track_id;\r\n\t\tif (!exists[tid] || exists.count(tid) == 0)\r\n\t\t{\r\n\t\t\texists[tid] = 1;\r\n\t\t\tres.push_back(&tlistb[i]);\r\n\t\t}\r\n\t}\r\n\treturn res;\r\n}\r\n\r\nvector<STrack> BYTETracker::joint_stracks(vector<STrack> &tlista, vector<STrack> &tlistb)\r\n{\r\n\tmap<int, int> exists;\r\n\tvector<STrack> res;\r\n\tfor (int i = 0; i < tlista.size(); i++)\r\n\t{\r\n\t\texists.insert(pair<int, int>(tlista[i].track_id, 1));\r\n\t\tres.push_back(tlista[i]);\r\n\t}\r\n\tfor (int i = 0; i < tlistb.size(); i++)\r\n\t{\r\n\t\tint tid = tlistb[i].track_id;\r\n\t\tif (!exists[tid] || exists.count(tid) == 0)\r\n\t\t{\r\n\t\t\texists[tid] = 1;\r\n\t\t\tres.push_back(tlistb[i]);\r\n\t\t}\r\n\t}\r\n\treturn res;\r\n}\r\n\r\nvector<STrack> BYTETracker::sub_stracks(vector<STrack> &tlista, vector<STrack> &tlistb)\r\n{\r\n\tmap<int, STrack> stracks;\r\n\tfor (int i = 0; i < tlista.size(); i++)\r\n\t{\r\n\t\tstracks.insert(pair<int, STrack>(tlista[i].track_id, tlista[i]));\r\n\t}\r\n\tfor (int i = 0; i < tlistb.size(); i++)\r\n\t{\r\n\t\tint tid = tlistb[i].track_id;\r\n\t\tif (stracks.count(tid) != 0)\r\n\t\t{\r\n\t\t\tstracks.erase(tid);\r\n\t\t}\r\n\t}\r\n\r\n\tvector<STrack> res;\r\n\tstd::map<int, STrack>::iterator  it;\r\n\tfor (it = stracks.begin(); it != stracks.end(); ++it)\r\n\t{\r\n\t\tres.push_back(it->second);\r\n\t}\r\n\r\n\treturn res;\r\n}\r\n\r\nvoid BYTETracker::remove_duplicate_stracks(vector<STrack> &resa, vector<STrack> &resb, vector<STrack> &stracksa, vector<STrack> &stracksb)\r\n{\r\n\tvector<vector<float> > pdist = iou_distance(stracksa, stracksb);\r\n\tvector<pair<int, int> > pairs;\r\n\tfor (int i = 0; i < pdist.size(); i++)\r\n\t{\r\n\t\tfor (int j = 0; j < pdist[i].size(); j++)\r\n\t\t{\r\n\t\t\tif (pdist[i][j] < 0.15)\r\n\t\t\t{\r\n\t\t\t\tpairs.push_back(pair<int, int>(i, j));\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\r\n\tvector<int> dupa, dupb;\r\n\tfor (int i = 0; i < pairs.size(); i++)\r\n\t{\r\n\t\tint timep = stracksa[pairs[i].first].frame_id - stracksa[pairs[i].first].start_frame;\r\n\t\tint timeq = stracksb[pairs[i].second].frame_id - stracksb[pairs[i].second].start_frame;\r\n\t\tif (timep > timeq)\r\n\t\t\tdupb.push_back(pairs[i].second);\r\n\t\telse\r\n\t\t\tdupa.push_back(pairs[i].first);\r\n\t}\r\n\r\n\tfor (int i = 0; i < stracksa.size(); i++)\r\n\t{\r\n\t\tvector<int>::iterator iter = find(dupa.begin(), dupa.end(), i);\r\n\t\tif (iter == dupa.end())\r\n\t\t{\r\n\t\t\tresa.push_back(stracksa[i]);\r\n\t\t}\r\n\t}\r\n\r\n\tfor (int i = 0; i < stracksb.size(); i++)\r\n\t{\r\n\t\tvector<int>::iterator iter = find(dupb.begin(), dupb.end(), i);\r\n\t\tif (iter == dupb.end())\r\n\t\t{\r\n\t\t\tresb.push_back(stracksb[i]);\r\n\t\t}\r\n\t}\r\n}\r\n\r\nvoid BYTETracker::linear_assignment(vector<vector<float> > &cost_matrix, int cost_matrix_size, int cost_matrix_size_size, float thresh,\r\n\tvector<vector<int> > &matches, vector<int> &unmatched_a, vector<int> &unmatched_b)\r\n{\r\n\tif (cost_matrix.size() == 0)\r\n\t{\r\n\t\tfor (int i = 0; i < cost_matrix_size; i++)\r\n\t\t{\r\n\t\t\tunmatched_a.push_back(i);\r\n\t\t}\r\n\t\tfor (int i = 0; i < cost_matrix_size_size; i++)\r\n\t\t{\r\n\t\t\tunmatched_b.push_back(i);\r\n\t\t}\r\n\t\treturn;\r\n\t}\r\n\r\n\tvector<int> rowsol; vector<int> colsol;\r\n\tfloat c = lapjv(cost_matrix, rowsol, colsol, true, thresh);\r\n\tfor (int i = 0; i < rowsol.size(); i++)\r\n\t{\r\n\t\tif (rowsol[i] >= 0)\r\n\t\t{\r\n\t\t\tvector<int> match;\r\n\t\t\tmatch.push_back(i);\r\n\t\t\tmatch.push_back(rowsol[i]);\r\n\t\t\tmatches.push_back(match);\r\n\t\t}\r\n\t\telse\r\n\t\t{\r\n\t\t\tunmatched_a.push_back(i);\r\n\t\t}\r\n\t}\r\n\r\n\tfor (int i = 0; i < colsol.size(); i++)\r\n\t{\r\n\t\tif (colsol[i] < 0)\r\n\t\t{\r\n\t\t\tunmatched_b.push_back(i);\r\n\t\t}\r\n\t}\r\n}\r\n\r\nvector<vector<float> > BYTETracker::ious(vector<vector<float> > &atlbrs, vector<vector<float> > &btlbrs)\r\n{\r\n\tvector<vector<float> > ious;\r\n\tif (atlbrs.size()*btlbrs.size() == 0)\r\n\t\treturn ious;\r\n\r\n\tious.resize(atlbrs.size());\r\n\tfor (int i = 0; i < ious.size(); i++)\r\n\t{\r\n\t\tious[i].resize(btlbrs.size());\r\n\t}\r\n\r\n\t//bbox_ious\r\n\tfor (int k = 0; k < btlbrs.size(); k++)\r\n\t{\r\n\t\tvector<float> ious_tmp;\r\n\t\tfloat box_area = (btlbrs[k][2] - btlbrs[k][0] + 1)*(btlbrs[k][3] - btlbrs[k][1] + 1);\r\n\t\tfor (int n = 0; n < atlbrs.size(); n++)\r\n\t\t{\r\n\t\t\tfloat iw = min(atlbrs[n][2], btlbrs[k][2]) - max(atlbrs[n][0], btlbrs[k][0]) + 1;\r\n\t\t\tif (iw > 0)\r\n\t\t\t{\r\n\t\t\t\tfloat ih = min(atlbrs[n][3], btlbrs[k][3]) - max(atlbrs[n][1], btlbrs[k][1]) + 1;\r\n\t\t\t\tif(ih > 0)\r\n\t\t\t\t{\r\n\t\t\t\t\tfloat ua = (atlbrs[n][2] - atlbrs[n][0] + 1)*(atlbrs[n][3] - atlbrs[n][1] + 1) + box_area - iw * ih;\r\n\t\t\t\t\tious[n][k] = iw * ih / ua;\r\n\t\t\t\t}\r\n\t\t\t\telse\r\n\t\t\t\t{\r\n\t\t\t\t\tious[n][k] = 0.0;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\telse\r\n\t\t\t{\r\n\t\t\t\tious[n][k] = 0.0;\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\r\n\treturn ious;\r\n}\r\n\r\nvector<vector<float> > BYTETracker::iou_distance(vector<STrack*> &atracks, vector<STrack> &btracks, int &dist_size, int &dist_size_size)\r\n{\r\n\tvector<vector<float> > cost_matrix;\r\n\tif (atracks.size() * btracks.size() == 0)\r\n\t{\r\n\t\tdist_size = atracks.size();\r\n\t\tdist_size_size = btracks.size();\r\n\t\treturn cost_matrix;\r\n\t}\r\n\tvector<vector<float> > atlbrs, btlbrs;\r\n\tfor (int i = 0; i < atracks.size(); i++)\r\n\t{\r\n\t\tatlbrs.push_back(atracks[i]->tlbr);\r\n\t}\r\n\tfor (int i = 0; i < btracks.size(); i++)\r\n\t{\r\n\t\tbtlbrs.push_back(btracks[i].tlbr);\r\n\t}\r\n\r\n\tdist_size = atracks.size();\r\n\tdist_size_size = btracks.size();\r\n\r\n\tvector<vector<float> > _ious = ious(atlbrs, btlbrs);\r\n\t\r\n\tfor (int i = 0; i < _ious.size();i++)\r\n\t{\r\n\t\tvector<float> _iou;\r\n\t\tfor (int j = 0; j < _ious[i].size(); j++)\r\n\t\t{\r\n\t\t\t_iou.push_back(1 - _ious[i][j]);\r\n\t\t}\r\n\t\tcost_matrix.push_back(_iou);\r\n\t}\r\n\r\n\treturn cost_matrix;\r\n}\r\n\r\nvector<vector<float> > BYTETracker::iou_distance(vector<STrack> &atracks, vector<STrack> &btracks)\r\n{\r\n\tvector<vector<float> > atlbrs, btlbrs;\r\n\tfor (int i = 0; i < atracks.size(); i++)\r\n\t{\r\n\t\tatlbrs.push_back(atracks[i].tlbr);\r\n\t}\r\n\tfor (int i = 0; i < btracks.size(); i++)\r\n\t{\r\n\t\tbtlbrs.push_back(btracks[i].tlbr);\r\n\t}\r\n\r\n\tvector<vector<float> > _ious = ious(atlbrs, btlbrs);\r\n\tvector<vector<float> > cost_matrix;\r\n\tfor (int i = 0; i < _ious.size(); i++)\r\n\t{\r\n\t\tvector<float> _iou;\r\n\t\tfor (int j = 0; j < _ious[i].size(); j++)\r\n\t\t{\r\n\t\t\t_iou.push_back(1 - _ious[i][j]);\r\n\t\t}\r\n\t\tcost_matrix.push_back(_iou);\r\n\t}\r\n\r\n\treturn cost_matrix;\r\n}\r\n\r\ndouble BYTETracker::lapjv(const vector<vector<float> > &cost, vector<int> &rowsol, vector<int> &colsol,\r\n\tbool extend_cost, float cost_limit, bool return_cost)\r\n{\r\n\tvector<vector<float> > cost_c;\r\n\tcost_c.assign(cost.begin(), cost.end());\r\n\r\n\tvector<vector<float> > cost_c_extended;\r\n\r\n\tint n_rows = cost.size();\r\n\tint n_cols = cost[0].size();\r\n\trowsol.resize(n_rows);\r\n\tcolsol.resize(n_cols);\r\n\r\n\tint n = 0;\r\n\tif (n_rows == n_cols)\r\n\t{\r\n\t\tn = n_rows;\r\n\t}\r\n\telse\r\n\t{\r\n\t\tif (!extend_cost)\r\n\t\t{\r\n\t\t\tcout << \"set extend_cost=True\" << endl;\r\n\t\t\tsystem(\"pause\");\r\n\t\t\texit(0);\r\n\t\t}\r\n\t}\r\n\t\t\r\n\tif (extend_cost || cost_limit < LONG_MAX)\r\n\t{\r\n\t\tn = n_rows + n_cols;\r\n\t\tcost_c_extended.resize(n);\r\n\t\tfor (int i = 0; i < cost_c_extended.size(); i++)\r\n\t\t\tcost_c_extended[i].resize(n);\r\n\r\n\t\tif (cost_limit < LONG_MAX)\r\n\t\t{\r\n\t\t\tfor (int i = 0; i < cost_c_extended.size(); i++)\r\n\t\t\t{\r\n\t\t\t\tfor (int j = 0; j < cost_c_extended[i].size(); j++)\r\n\t\t\t\t{\r\n\t\t\t\t\tcost_c_extended[i][j] = cost_limit / 2.0;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\telse\r\n\t\t{\r\n\t\t\tfloat cost_max = -1;\r\n\t\t\tfor (int i = 0; i < cost_c.size(); i++)\r\n\t\t\t{\r\n\t\t\t\tfor (int j = 0; j < cost_c[i].size(); j++)\r\n\t\t\t\t{\r\n\t\t\t\t\tif (cost_c[i][j] > cost_max)\r\n\t\t\t\t\t\tcost_max = cost_c[i][j];\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\tfor (int i = 0; i < cost_c_extended.size(); i++)\r\n\t\t\t{\r\n\t\t\t\tfor (int j = 0; j < cost_c_extended[i].size(); j++)\r\n\t\t\t\t{\r\n\t\t\t\t\tcost_c_extended[i][j] = cost_max + 1;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\tfor (int i = n_rows; i < cost_c_extended.size(); i++)\r\n\t\t{\r\n\t\t\tfor (int j = n_cols; j < cost_c_extended[i].size(); j++)\r\n\t\t\t{\r\n\t\t\t\tcost_c_extended[i][j] = 0;\r\n\t\t\t}\r\n\t\t}\r\n\t\tfor (int i = 0; i < n_rows; i++)\r\n\t\t{\r\n\t\t\tfor (int j = 0; j < n_cols; j++)\r\n\t\t\t{\r\n\t\t\t\tcost_c_extended[i][j] = cost_c[i][j];\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\tcost_c.clear();\r\n\t\tcost_c.assign(cost_c_extended.begin(), cost_c_extended.end());\r\n\t}\r\n\r\n\tdouble **cost_ptr;\r\n\tcost_ptr = new double *[sizeof(double *) * n];\r\n\tfor (int i = 0; i < n; i++)\r\n\t\tcost_ptr[i] = new double[sizeof(double) * n];\r\n\r\n\tfor (int i = 0; i < n; i++)\r\n\t{\r\n\t\tfor (int j = 0; j < n; j++)\r\n\t\t{\r\n\t\t\tcost_ptr[i][j] = cost_c[i][j];\r\n\t\t}\r\n\t}\r\n\r\n\tint* x_c = new int[sizeof(int) * n];\r\n\tint *y_c = new int[sizeof(int) * n];\r\n\r\n\tint ret = lapjv_internal(n, cost_ptr, x_c, y_c);\r\n\tif (ret != 0)\r\n\t{\r\n\t\tcout << \"Calculate Wrong!\" << endl;\r\n\t\tsystem(\"pause\");\r\n\t\texit(0);\r\n\t}\r\n\r\n\tdouble opt = 0.0;\r\n\r\n\tif (n != n_rows)\r\n\t{\r\n\t\tfor (int i = 0; i < n; i++)\r\n\t\t{\r\n\t\t\tif (x_c[i] >= n_cols)\r\n\t\t\t\tx_c[i] = -1;\r\n\t\t\tif (y_c[i] >= n_rows)\r\n\t\t\t\ty_c[i] = -1;\r\n\t\t}\r\n\t\tfor (int i = 0; i < n_rows; i++)\r\n\t\t{\r\n\t\t\trowsol[i] = x_c[i];\r\n\t\t}\r\n\t\tfor (int i = 0; i < n_cols; i++)\r\n\t\t{\r\n\t\t\tcolsol[i] = y_c[i];\r\n\t\t}\r\n\r\n\t\tif (return_cost)\r\n\t\t{\r\n\t\t\tfor (int i = 0; i < rowsol.size(); i++)\r\n\t\t\t{\r\n\t\t\t\tif (rowsol[i] != -1)\r\n\t\t\t\t{\r\n\t\t\t\t\t//cout << i << \"\\t\" << rowsol[i] << \"\\t\" << cost_ptr[i][rowsol[i]] << endl;\r\n\t\t\t\t\topt += cost_ptr[i][rowsol[i]];\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\telse if (return_cost)\r\n\t{\r\n\t\tfor (int i = 0; i < rowsol.size(); i++)\r\n\t\t{\r\n\t\t\topt += cost_ptr[i][rowsol[i]];\r\n\t\t}\r\n\t}\r\n\r\n\tfor (int i = 0; i < n; i++)\r\n\t{\r\n\t\tdelete[]cost_ptr[i];\r\n\t}\r\n\tdelete[]cost_ptr;\r\n\tdelete[]x_c;\r\n\tdelete[]y_c;\r\n\r\n\treturn opt;\r\n}\r\n\r\nScalar BYTETracker::get_color(int idx)\r\n{\r\n\tidx += 3;\r\n\treturn Scalar(37 * idx % 255, 17 * idx % 255, 29 * idx % 255);\r\n}"
  },
  {
    "path": "deploy/TensorRT/python/README.md",
    "content": "# ByteTrack-TensorRT in Python\n\n## Install TensorRT Toolkit\nPlease follow the [TensorRT Installation Guide](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) and [torch2trt gitrepo](https://github.com/NVIDIA-AI-IOT/torch2trt) to install TensorRT (Version 7 recommended) and torch2trt.\n\n## Convert model\n\nYou can convert the Pytorch model “bytetrack_s_mot17” to TensorRT model by running:\n\n```shell\ncd <ByteTrack_HOME>\npython3 tools/trt.py -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_s_mot17.pth.tar\n```\n\n## Run TensorRT demo\n\nYou can use the converted model_trt.pth to run TensorRT demo with **130 FPS**:\n\n```shell\ncd <ByteTrack_HOME>\npython3 tools/demo_track.py video -f exps/example/mot/yolox_s_mix_det.py --trt --save_result\n```\n"
  },
  {
    "path": "deploy/ncnn/cpp/CMakeLists.txt",
    "content": "macro(ncnn_add_example name)\n    add_executable(${name} ${name}.cpp)\n    if(OpenCV_FOUND)\n        target_include_directories(${name} PRIVATE ${OpenCV_INCLUDE_DIRS})\n        target_link_libraries(${name} PRIVATE ncnn ${OpenCV_LIBS})\n    elseif(NCNN_SIMPLEOCV)\n        target_compile_definitions(${name} PUBLIC USE_NCNN_SIMPLEOCV)\n        target_link_libraries(${name} PRIVATE ncnn)\n    endif()\n\n    # add test to a virtual project group\n    set_property(TARGET ${name} PROPERTY FOLDER \"examples\")\nendmacro()\n\nif(NCNN_PIXEL)\n    find_package(OpenCV QUIET COMPONENTS opencv_world)\n    # for opencv 2.4 on ubuntu 16.04, there is no opencv_world but OpenCV_FOUND will be TRUE\n    if(\"${OpenCV_LIBS}\" STREQUAL \"\")\n        set(OpenCV_FOUND FALSE)\n    endif()\n    if(NOT OpenCV_FOUND)\n        find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs videoio)\n    endif()\n    if(NOT OpenCV_FOUND)\n        find_package(OpenCV QUIET COMPONENTS core highgui imgproc)\n    endif()\n\n    if(OpenCV_FOUND OR NCNN_SIMPLEOCV)\n        if(OpenCV_FOUND)\n            message(STATUS \"OpenCV library: ${OpenCV_INSTALL_PATH}\")\n            message(STATUS \"    version: ${OpenCV_VERSION}\")\n            message(STATUS \"    libraries: ${OpenCV_LIBS}\")\n            message(STATUS \"    include path: ${OpenCV_INCLUDE_DIRS}\")\n\n            if(${OpenCV_VERSION_MAJOR} GREATER 3)\n                set(CMAKE_CXX_STANDARD 11)\n            endif()\n        endif()\n\n        include_directories(${CMAKE_CURRENT_SOURCE_DIR}/../src)\n        include_directories(${CMAKE_CURRENT_BINARY_DIR}/../src)\n        include_directories(include)\n        include_directories(/usr/local/include/eigen3)\n\n        ncnn_add_example(squeezenet)\n        ncnn_add_example(squeezenet_c_api)\n        ncnn_add_example(fasterrcnn)\n        ncnn_add_example(rfcn)\n        ncnn_add_example(yolov2)\n        ncnn_add_example(yolov3)\n        if(OpenCV_FOUND)\n            ncnn_add_example(yolov4)\n        endif()\n        ncnn_add_example(yolov5)\n        ncnn_add_example(yolox)\n        ncnn_add_example(mobilenetv2ssdlite)\n        ncnn_add_example(mobilenetssd)\n        ncnn_add_example(squeezenetssd)\n        ncnn_add_example(shufflenetv2)\n        ncnn_add_example(peleenetssd_seg)\n        ncnn_add_example(simplepose)\n        ncnn_add_example(retinaface)\n        ncnn_add_example(yolact)\n        ncnn_add_example(nanodet)\n        ncnn_add_example(scrfd)\n        ncnn_add_example(scrfd_crowdhuman)\n        ncnn_add_example(rvm)\n        file(GLOB My_Source_Files src/*.cpp)\n        add_executable(bytetrack ${My_Source_Files})\n        if(OpenCV_FOUND)\n            target_include_directories(bytetrack PRIVATE ${OpenCV_INCLUDE_DIRS})\n            target_link_libraries(bytetrack PRIVATE ncnn ${OpenCV_LIBS})\n        elseif(NCNN_SIMPLEOCV)\n            target_compile_definitions(bytetrack PUBLIC USE_NCNN_SIMPLEOCV)\n            target_link_libraries(bytetrack PRIVATE ncnn)\n        endif()\n        # add test to a virtual project group\n        set_property(TARGET bytetrack PROPERTY FOLDER \"examples\")\n    else()\n        message(WARNING \"OpenCV not found and NCNN_SIMPLEOCV disabled, examples won't be built\")\n    endif()\nelse()\n    message(WARNING \"NCNN_PIXEL not enabled, examples won't be built\")\nendif()\n"
  },
  {
    "path": "deploy/ncnn/cpp/README.md",
    "content": "# ByteTrack-CPP-ncnn\n\n## Installation\n\nClone [ncnn](https://github.com/Tencent/ncnn) first, then please following [build tutorial of ncnn](https://github.com/Tencent/ncnn/wiki/how-to-build) to build on your own device.\n\nInstall eigen-3.3.9 [[google]](https://drive.google.com/file/d/1rqO74CYCNrmRAg8Rra0JP3yZtJ-rfket/view?usp=sharing), [[baidu(code:ueq4)]](https://pan.baidu.com/s/15kEfCxpy-T7tz60msxxExg).\n\n```shell\nunzip eigen-3.3.9.zip\ncd eigen-3.3.9\nmkdir build\ncd build\ncmake ..\nsudo make install\n```\n\n## Generate onnx file\nUse provided tools to generate onnx file.\nFor example, if you want to generate onnx file of bytetrack_s_mot17.pth, please run the following command:\n```shell\ncd <ByteTrack_HOME>\npython3 tools/export_onnx.py -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_s_mot17.pth.tar\n```\nThen, a bytetrack_s.onnx file is generated under <ByteTrack_HOME>.\n\n## Generate ncnn param and bin file\nPut bytetrack_s.onnx under ncnn/build/tools/onnx and then run: \n\n```shell\ncd ncnn/build/tools/onnx\n./onnx2ncnn bytetrack_s.onnx bytetrack_s.param bytetrack_s.bin\n```\n\nSince Focus module is not supported in ncnn. Warnings like:\n```shell\nUnsupported slice step ! \n```\nwill be printed. However, don't  worry!  C++ version of Focus layer is already implemented in src/bytetrack.cpp.\n  \n## Modify param file\nOpen **bytetrack_s.param**, and modify it.\nBefore (just an example):\n```\n235 268\nInput            images                   0 1 images\nSplit            splitncnn_input0         1 4 images images_splitncnn_0 images_splitncnn_1 images_splitncnn_2 images_splitncnn_3\nCrop             Slice_4                  1 1 images_splitncnn_3 467 -23309=1,0 -23310=1,2147483647 -23311=1,1\nCrop             Slice_9                  1 1 467 472 -23309=1,0 -23310=1,2147483647 -23311=1,2\nCrop             Slice_14                 1 1 images_splitncnn_2 477 -23309=1,0 -23310=1,2147483647 -23311=1,1\nCrop             Slice_19                 1 1 477 482 -23309=1,1 -23310=1,2147483647 -23311=1,2\nCrop             Slice_24                 1 1 images_splitncnn_1 487 -23309=1,1 -23310=1,2147483647 -23311=1,1\nCrop             Slice_29                 1 1 487 492 -23309=1,0 -23310=1,2147483647 -23311=1,2\nCrop             Slice_34                 1 1 images_splitncnn_0 497 -23309=1,1 -23310=1,2147483647 -23311=1,1\nCrop             Slice_39                 1 1 497 502 -23309=1,1 -23310=1,2147483647 -23311=1,2\nConcat           Concat_40                4 1 472 492 482 502 503 0=0\n...\n```\n* Change first number for 235 to 235 - 9 = 226(since we will remove 10 layers and add 1 layers, total layers number should minus 9). \n* Then remove 10 lines of code from Split to Concat, but remember the last but 2nd number: 503.\n* Add YoloV5Focus layer After Input (using previous number 503):\n```\nYoloV5Focus      focus                    1 1 images 503\n```\nAfter(just an exmaple):\n```\n226 328\nInput            images                   0 1 images\nYoloV5Focus      focus                    1 1 images 503\n...\n```\n\n## Use ncnn_optimize to generate new param and bin\n```shell\n# suppose you are still under ncnn/build/tools/onnx dir.\n../ncnnoptimize bytetrack_s.param bytetrack_s.bin bytetrack_s_op.param bytetrack_s_op.bin 65536\n```\n\n## Copy files and build ByteTrack\nCopy or move 'src', 'include' folders and 'CMakeLists.txt' file into ncnn/examples. Copy bytetrack_s_op.param, bytetrack_s_op.bin and <ByteTrack_HOME>/videos/palace.mp4 into ncnn/build/examples. Then, build ByteTrack:\n\n```shell\ncd ncnn/build/examples\ncmake ..\nmake\n```\n\n## Run the demo\nYou can run the ncnn demo with **5 FPS** (96-core Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz):\n```shell\n./bytetrack palace.mp4\n```\n\nYou can modify 'num_threads' to optimize the running speed in [bytetrack.cpp](https://github.com/ifzhang/ByteTrack/blob/2e9a67895da6b47b948015f6861bba0bacd4e72f/deploy/ncnn/cpp/src/bytetrack.cpp#L309) according to the number of your CPU cores:\n\n```\nyolox.opt.num_threads = 20;\n```\n\n\n## Acknowledgement\n\n* [ncnn](https://github.com/Tencent/ncnn)\n"
  },
  {
    "path": "deploy/ncnn/cpp/include/BYTETracker.h",
    "content": "#pragma once\r\n\r\n#include \"STrack.h\"\r\n\r\nstruct Object\r\n{\r\n    cv::Rect_<float> rect;\r\n    int label;\r\n    float prob;\r\n};\r\n\r\nclass BYTETracker\r\n{\r\npublic:\r\n\tBYTETracker(int frame_rate = 30, int track_buffer = 30);\r\n\t~BYTETracker();\r\n\r\n\tvector<STrack> update(const vector<Object>& objects);\r\n\tScalar get_color(int idx);\r\n\r\nprivate:\r\n\tvector<STrack*> joint_stracks(vector<STrack*> &tlista, vector<STrack> &tlistb);\r\n\tvector<STrack> joint_stracks(vector<STrack> &tlista, vector<STrack> &tlistb);\r\n\r\n\tvector<STrack> sub_stracks(vector<STrack> &tlista, vector<STrack> &tlistb);\r\n\tvoid remove_duplicate_stracks(vector<STrack> &resa, vector<STrack> &resb, vector<STrack> &stracksa, vector<STrack> &stracksb);\r\n\r\n\tvoid linear_assignment(vector<vector<float> > &cost_matrix, int cost_matrix_size, int cost_matrix_size_size, float thresh,\r\n\t\tvector<vector<int> > &matches, vector<int> &unmatched_a, vector<int> &unmatched_b);\r\n\tvector<vector<float> > iou_distance(vector<STrack*> &atracks, vector<STrack> &btracks, int &dist_size, int &dist_size_size);\r\n\tvector<vector<float> > iou_distance(vector<STrack> &atracks, vector<STrack> &btracks);\r\n\tvector<vector<float> > ious(vector<vector<float> > &atlbrs, vector<vector<float> > &btlbrs);\r\n\r\n\tdouble lapjv(const vector<vector<float> > &cost, vector<int> &rowsol, vector<int> &colsol, \r\n\t\tbool extend_cost = false, float cost_limit = LONG_MAX, bool return_cost = true);\r\n\r\nprivate:\r\n\r\n\tfloat track_thresh;\r\n\tfloat high_thresh;\r\n\tfloat match_thresh;\r\n\tint frame_id;\r\n\tint max_time_lost;\r\n\r\n\tvector<STrack> tracked_stracks;\r\n\tvector<STrack> lost_stracks;\r\n\tvector<STrack> removed_stracks;\r\n\tbyte_kalman::KalmanFilter kalman_filter;\r\n};"
  },
  {
    "path": "deploy/ncnn/cpp/include/STrack.h",
    "content": "#pragma once\r\n\r\n#include <opencv2/opencv.hpp>\r\n#include \"kalmanFilter.h\"\r\n\r\nusing namespace cv;\r\nusing namespace std;\r\n\r\nenum TrackState { New = 0, Tracked, Lost, Removed };\r\n\r\nclass STrack\r\n{\r\npublic:\r\n\tSTrack(vector<float> tlwh_, float score);\r\n\t~STrack();\r\n\r\n\tvector<float> static tlbr_to_tlwh(vector<float> &tlbr);\r\n\tvoid static multi_predict(vector<STrack*> &stracks, byte_kalman::KalmanFilter &kalman_filter);\r\n\tvoid static_tlwh();\r\n\tvoid static_tlbr();\r\n\tvector<float> tlwh_to_xyah(vector<float> tlwh_tmp);\r\n\tvector<float> to_xyah();\r\n\tvoid mark_lost();\r\n\tvoid mark_removed();\r\n\tint next_id();\r\n\tint end_frame();\r\n\t\r\n\tvoid activate(byte_kalman::KalmanFilter &kalman_filter, int frame_id);\r\n\tvoid re_activate(STrack &new_track, int frame_id, bool new_id = false);\r\n\tvoid update(STrack &new_track, int frame_id);\r\n\r\npublic:\r\n\tbool is_activated;\r\n\tint track_id;\r\n\tint state;\r\n\r\n\tvector<float> _tlwh;\r\n\tvector<float> tlwh;\r\n\tvector<float> tlbr;\r\n\tint frame_id;\r\n\tint tracklet_len;\r\n\tint start_frame;\r\n\r\n\tKAL_MEAN mean;\r\n\tKAL_COVA covariance;\r\n\tfloat score;\r\n\r\nprivate:\r\n\tbyte_kalman::KalmanFilter kalman_filter;\r\n};"
  },
  {
    "path": "deploy/ncnn/cpp/include/dataType.h",
    "content": "#pragma once\r\n\r\n#include <cstddef>\r\n#include <vector>\r\n\r\n#include <Eigen/Core>\r\n#include <Eigen/Dense>\r\ntypedef Eigen::Matrix<float, 1, 4, Eigen::RowMajor> DETECTBOX;\r\ntypedef Eigen::Matrix<float, -1, 4, Eigen::RowMajor> DETECTBOXSS;\r\ntypedef Eigen::Matrix<float, 1, 128, Eigen::RowMajor> FEATURE;\r\ntypedef Eigen::Matrix<float, Eigen::Dynamic, 128, Eigen::RowMajor> FEATURESS;\r\n//typedef std::vector<FEATURE> FEATURESS;\r\n\r\n//Kalmanfilter\r\n//typedef Eigen::Matrix<float, 8, 8, Eigen::RowMajor> KAL_FILTER;\r\ntypedef Eigen::Matrix<float, 1, 8, Eigen::RowMajor> KAL_MEAN;\r\ntypedef Eigen::Matrix<float, 8, 8, Eigen::RowMajor> KAL_COVA;\r\ntypedef Eigen::Matrix<float, 1, 4, Eigen::RowMajor> KAL_HMEAN;\r\ntypedef Eigen::Matrix<float, 4, 4, Eigen::RowMajor> KAL_HCOVA;\r\nusing KAL_DATA = std::pair<KAL_MEAN, KAL_COVA>;\r\nusing KAL_HDATA = std::pair<KAL_HMEAN, KAL_HCOVA>;\r\n\r\n//main\r\nusing RESULT_DATA = std::pair<int, DETECTBOX>;\r\n\r\n//tracker:\r\nusing TRACKER_DATA = std::pair<int, FEATURESS>;\r\nusing MATCH_DATA = std::pair<int, int>;\r\ntypedef struct t {\r\n\tstd::vector<MATCH_DATA> matches;\r\n\tstd::vector<int> unmatched_tracks;\r\n\tstd::vector<int> unmatched_detections;\r\n}TRACHER_MATCHD;\r\n\r\n//linear_assignment:\r\ntypedef Eigen::Matrix<float, -1, -1, Eigen::RowMajor> DYNAMICM;"
  },
  {
    "path": "deploy/ncnn/cpp/include/kalmanFilter.h",
    "content": "#pragma once\r\n\r\n#include \"dataType.h\"\r\n\r\nnamespace byte_kalman\r\n{\r\n\tclass KalmanFilter\r\n\t{\r\n\tpublic:\r\n\t\tstatic const double chi2inv95[10];\r\n\t\tKalmanFilter();\r\n\t\tKAL_DATA initiate(const DETECTBOX& measurement);\r\n\t\tvoid predict(KAL_MEAN& mean, KAL_COVA& covariance);\r\n\t\tKAL_HDATA project(const KAL_MEAN& mean, const KAL_COVA& covariance);\r\n\t\tKAL_DATA update(const KAL_MEAN& mean,\r\n\t\t\tconst KAL_COVA& covariance,\r\n\t\t\tconst DETECTBOX& measurement);\r\n\r\n\t\tEigen::Matrix<float, 1, -1> gating_distance(\r\n\t\t\tconst KAL_MEAN& mean,\r\n\t\t\tconst KAL_COVA& covariance,\r\n\t\t\tconst std::vector<DETECTBOX>& measurements,\r\n\t\t\tbool only_position = false);\r\n\r\n\tprivate:\r\n\t\tEigen::Matrix<float, 8, 8, Eigen::RowMajor> _motion_mat;\r\n\t\tEigen::Matrix<float, 4, 8, Eigen::RowMajor> _update_mat;\r\n\t\tfloat _std_weight_position;\r\n\t\tfloat _std_weight_velocity;\r\n\t};\r\n}"
  },
  {
    "path": "deploy/ncnn/cpp/include/lapjv.h",
    "content": "#ifndef LAPJV_H\r\n#define LAPJV_H\r\n\r\n#define LARGE 1000000\r\n\r\n#if !defined TRUE\r\n#define TRUE 1\r\n#endif\r\n#if !defined FALSE\r\n#define FALSE 0\r\n#endif\r\n\r\n#define NEW(x, t, n) if ((x = (t *)malloc(sizeof(t) * (n))) == 0) { return -1; }\r\n#define FREE(x) if (x != 0) { free(x); x = 0; }\r\n#define SWAP_INDICES(a, b) { int_t _temp_index = a; a = b; b = _temp_index; }\r\n\r\n#if 0\r\n#include <assert.h>\r\n#define ASSERT(cond) assert(cond)\r\n#define PRINTF(fmt, ...) printf(fmt, ##__VA_ARGS__)\r\n#define PRINT_COST_ARRAY(a, n) \\\r\n    while (1) { \\\r\n        printf(#a\" = [\"); \\\r\n        if ((n) > 0) { \\\r\n            printf(\"%f\", (a)[0]); \\\r\n            for (uint_t j = 1; j < n; j++) { \\\r\n                printf(\", %f\", (a)[j]); \\\r\n            } \\\r\n        } \\\r\n        printf(\"]\\n\"); \\\r\n        break; \\\r\n    }\r\n#define PRINT_INDEX_ARRAY(a, n) \\\r\n    while (1) { \\\r\n        printf(#a\" = [\"); \\\r\n        if ((n) > 0) { \\\r\n            printf(\"%d\", (a)[0]); \\\r\n            for (uint_t j = 1; j < n; j++) { \\\r\n                printf(\", %d\", (a)[j]); \\\r\n            } \\\r\n        } \\\r\n        printf(\"]\\n\"); \\\r\n        break; \\\r\n    }\r\n#else\r\n#define ASSERT(cond)\r\n#define PRINTF(fmt, ...)\r\n#define PRINT_COST_ARRAY(a, n)\r\n#define PRINT_INDEX_ARRAY(a, n)\r\n#endif\r\n\r\n\r\ntypedef signed int int_t;\r\ntypedef unsigned int uint_t;\r\ntypedef double cost_t;\r\ntypedef char boolean;\r\ntypedef enum fp_t { FP_1 = 1, FP_2 = 2, FP_DYNAMIC = 3 } fp_t;\r\n\r\nextern int_t lapjv_internal(\r\n\tconst uint_t n, cost_t *cost[],\r\n\tint_t *x, int_t *y);\r\n\r\n#endif // LAPJV_H"
  },
  {
    "path": "deploy/ncnn/cpp/src/BYTETracker.cpp",
    "content": "#include \"BYTETracker.h\"\r\n#include <fstream>\r\n\r\nBYTETracker::BYTETracker(int frame_rate, int track_buffer)\r\n{\r\n\ttrack_thresh = 0.5;\r\n\thigh_thresh = 0.6;\r\n\tmatch_thresh = 0.8;\r\n\r\n\tframe_id = 0;\r\n\tmax_time_lost = int(frame_rate / 30.0 * track_buffer);\r\n\tcout << \"Init ByteTrack!\" << endl;\r\n}\r\n\r\nBYTETracker::~BYTETracker()\r\n{\r\n}\r\n\r\nvector<STrack> BYTETracker::update(const vector<Object>& objects)\r\n{\r\n\r\n\t////////////////// Step 1: Get detections //////////////////\r\n\tthis->frame_id++;\r\n\tvector<STrack> activated_stracks;\r\n\tvector<STrack> refind_stracks;\r\n\tvector<STrack> removed_stracks;\r\n\tvector<STrack> lost_stracks;\r\n\tvector<STrack> detections;\r\n\tvector<STrack> detections_low;\r\n\r\n\tvector<STrack> detections_cp;\r\n\tvector<STrack> tracked_stracks_swap;\r\n\tvector<STrack> resa, resb;\r\n\tvector<STrack> output_stracks;\r\n\r\n\tvector<STrack*> unconfirmed;\r\n\tvector<STrack*> tracked_stracks;\r\n\tvector<STrack*> strack_pool;\r\n\tvector<STrack*> r_tracked_stracks;\r\n\r\n\tif (objects.size() > 0)\r\n\t{\r\n\t\tfor (int i = 0; i < objects.size(); i++)\r\n\t\t{\r\n\t\t\tvector<float> tlbr_;\r\n\t\t\ttlbr_.resize(4);\r\n\t\t\ttlbr_[0] = objects[i].rect.x;\r\n\t\t\ttlbr_[1] = objects[i].rect.y;\r\n\t\t\ttlbr_[2] = objects[i].rect.x + objects[i].rect.width;\r\n\t\t\ttlbr_[3] = objects[i].rect.y + objects[i].rect.height;\r\n\r\n\t\t\tfloat score = objects[i].prob;\r\n\r\n\t\t\tSTrack strack(STrack::tlbr_to_tlwh(tlbr_), score);\r\n\t\t\tif (score >= track_thresh)\r\n\t\t\t{\r\n\t\t\t\tdetections.push_back(strack);\r\n\t\t\t}\r\n\t\t\telse\r\n\t\t\t{\r\n\t\t\t\tdetections_low.push_back(strack);\r\n\t\t\t}\r\n\t\t\t\r\n\t\t}\r\n\t}\r\n\r\n\t// Add newly detected tracklets to tracked_stracks\r\n\tfor (int i = 0; i < this->tracked_stracks.size(); i++)\r\n\t{\r\n\t\tif (!this->tracked_stracks[i].is_activated)\r\n\t\t\tunconfirmed.push_back(&this->tracked_stracks[i]);\r\n\t\telse\r\n\t\t\ttracked_stracks.push_back(&this->tracked_stracks[i]);\r\n\t}\r\n\r\n\t////////////////// Step 2: First association, with IoU //////////////////\r\n\tstrack_pool = joint_stracks(tracked_stracks, this->lost_stracks);\r\n\tSTrack::multi_predict(strack_pool, this->kalman_filter);\r\n\r\n\tvector<vector<float> > dists;\r\n\tint dist_size = 0, dist_size_size = 0;\r\n\tdists = iou_distance(strack_pool, detections, dist_size, dist_size_size);\r\n\r\n\tvector<vector<int> > matches;\r\n\tvector<int> u_track, u_detection;\r\n\tlinear_assignment(dists, dist_size, dist_size_size, match_thresh, matches, u_track, u_detection);\r\n\r\n\tfor (int i = 0; i < matches.size(); i++)\r\n\t{\r\n\t\tSTrack *track = strack_pool[matches[i][0]];\r\n\t\tSTrack *det = &detections[matches[i][1]];\r\n\t\tif (track->state == TrackState::Tracked)\r\n\t\t{\r\n\t\t\ttrack->update(*det, this->frame_id);\r\n\t\t\tactivated_stracks.push_back(*track);\r\n\t\t}\r\n\t\telse\r\n\t\t{\r\n\t\t\ttrack->re_activate(*det, this->frame_id, false);\r\n\t\t\trefind_stracks.push_back(*track);\r\n\t\t}\r\n\t}\r\n\r\n\t////////////////// Step 3: Second association, using low score dets //////////////////\r\n\tfor (int i = 0; i < u_detection.size(); i++)\r\n\t{\r\n\t\tdetections_cp.push_back(detections[u_detection[i]]);\r\n\t}\r\n\tdetections.clear();\r\n\tdetections.assign(detections_low.begin(), detections_low.end());\r\n\t\r\n\tfor (int i = 0; i < u_track.size(); i++)\r\n\t{\r\n\t\tif (strack_pool[u_track[i]]->state == TrackState::Tracked)\r\n\t\t{\r\n\t\t\tr_tracked_stracks.push_back(strack_pool[u_track[i]]);\r\n\t\t}\r\n\t}\r\n\r\n\tdists.clear();\r\n\tdists = iou_distance(r_tracked_stracks, detections, dist_size, dist_size_size);\r\n\r\n\tmatches.clear();\r\n\tu_track.clear();\r\n\tu_detection.clear();\r\n\tlinear_assignment(dists, dist_size, dist_size_size, 0.5, matches, u_track, u_detection);\r\n\r\n\tfor (int i = 0; i < matches.size(); i++)\r\n\t{\r\n\t\tSTrack *track = r_tracked_stracks[matches[i][0]];\r\n\t\tSTrack *det = &detections[matches[i][1]];\r\n\t\tif (track->state == TrackState::Tracked)\r\n\t\t{\r\n\t\t\ttrack->update(*det, this->frame_id);\r\n\t\t\tactivated_stracks.push_back(*track);\r\n\t\t}\r\n\t\telse\r\n\t\t{\r\n\t\t\ttrack->re_activate(*det, this->frame_id, false);\r\n\t\t\trefind_stracks.push_back(*track);\r\n\t\t}\r\n\t}\r\n\r\n\tfor (int i = 0; i < u_track.size(); i++)\r\n\t{\r\n\t\tSTrack *track = r_tracked_stracks[u_track[i]];\r\n\t\tif (track->state != TrackState::Lost)\r\n\t\t{\r\n\t\t\ttrack->mark_lost();\r\n\t\t\tlost_stracks.push_back(*track);\r\n\t\t}\r\n\t}\r\n\r\n\t// Deal with unconfirmed tracks, usually tracks with only one beginning frame\r\n\tdetections.clear();\r\n\tdetections.assign(detections_cp.begin(), detections_cp.end());\r\n\r\n\tdists.clear();\r\n\tdists = iou_distance(unconfirmed, detections, dist_size, dist_size_size);\r\n\r\n\tmatches.clear();\r\n\tvector<int> u_unconfirmed;\r\n\tu_detection.clear();\r\n\tlinear_assignment(dists, dist_size, dist_size_size, 0.7, matches, u_unconfirmed, u_detection);\r\n\r\n\tfor (int i = 0; i < matches.size(); i++)\r\n\t{\r\n\t\tunconfirmed[matches[i][0]]->update(detections[matches[i][1]], this->frame_id);\r\n\t\tactivated_stracks.push_back(*unconfirmed[matches[i][0]]);\r\n\t}\r\n\r\n\tfor (int i = 0; i < u_unconfirmed.size(); i++)\r\n\t{\r\n\t\tSTrack *track = unconfirmed[u_unconfirmed[i]];\r\n\t\ttrack->mark_removed();\r\n\t\tremoved_stracks.push_back(*track);\r\n\t}\r\n\r\n\t////////////////// Step 4: Init new stracks //////////////////\r\n\tfor (int i = 0; i < u_detection.size(); i++)\r\n\t{\r\n\t\tSTrack *track = &detections[u_detection[i]];\r\n\t\tif (track->score < this->high_thresh)\r\n\t\t\tcontinue;\r\n\t\ttrack->activate(this->kalman_filter, this->frame_id);\r\n\t\tactivated_stracks.push_back(*track);\r\n\t}\r\n\r\n\t////////////////// Step 5: Update state //////////////////\r\n\tfor (int i = 0; i < this->lost_stracks.size(); i++)\r\n\t{\r\n\t\tif (this->frame_id - this->lost_stracks[i].end_frame() > this->max_time_lost)\r\n\t\t{\r\n\t\t\tthis->lost_stracks[i].mark_removed();\r\n\t\t\tremoved_stracks.push_back(this->lost_stracks[i]);\r\n\t\t}\r\n\t}\r\n\t\r\n\tfor (int i = 0; i < this->tracked_stracks.size(); i++)\r\n\t{\r\n\t\tif (this->tracked_stracks[i].state == TrackState::Tracked)\r\n\t\t{\r\n\t\t\ttracked_stracks_swap.push_back(this->tracked_stracks[i]);\r\n\t\t}\r\n\t}\r\n\tthis->tracked_stracks.clear();\r\n\tthis->tracked_stracks.assign(tracked_stracks_swap.begin(), tracked_stracks_swap.end());\r\n\r\n\tthis->tracked_stracks = joint_stracks(this->tracked_stracks, activated_stracks);\r\n\tthis->tracked_stracks = joint_stracks(this->tracked_stracks, refind_stracks);\r\n\r\n\t//std::cout << activated_stracks.size() << std::endl;\r\n\r\n\tthis->lost_stracks = sub_stracks(this->lost_stracks, this->tracked_stracks);\r\n\tfor (int i = 0; i < lost_stracks.size(); i++)\r\n\t{\r\n\t\tthis->lost_stracks.push_back(lost_stracks[i]);\r\n\t}\r\n\r\n\tthis->lost_stracks = sub_stracks(this->lost_stracks, this->removed_stracks);\r\n\tfor (int i = 0; i < removed_stracks.size(); i++)\r\n\t{\r\n\t\tthis->removed_stracks.push_back(removed_stracks[i]);\r\n\t}\r\n\t\r\n\tremove_duplicate_stracks(resa, resb, this->tracked_stracks, this->lost_stracks);\r\n\r\n\tthis->tracked_stracks.clear();\r\n\tthis->tracked_stracks.assign(resa.begin(), resa.end());\r\n\tthis->lost_stracks.clear();\r\n\tthis->lost_stracks.assign(resb.begin(), resb.end());\r\n\t\r\n\tfor (int i = 0; i < this->tracked_stracks.size(); i++)\r\n\t{\r\n\t\tif (this->tracked_stracks[i].is_activated)\r\n\t\t{\r\n\t\t\toutput_stracks.push_back(this->tracked_stracks[i]);\r\n\t\t}\r\n\t}\r\n\treturn output_stracks;\r\n}"
  },
  {
    "path": "deploy/ncnn/cpp/src/STrack.cpp",
    "content": "#include \"STrack.h\"\r\n\r\nSTrack::STrack(vector<float> tlwh_, float score)\r\n{\r\n\t_tlwh.resize(4);\r\n\t_tlwh.assign(tlwh_.begin(), tlwh_.end());\r\n\r\n\tis_activated = false;\r\n\ttrack_id = 0;\r\n\tstate = TrackState::New;\r\n\t\r\n\ttlwh.resize(4);\r\n\ttlbr.resize(4);\r\n\r\n\tstatic_tlwh();\r\n\tstatic_tlbr();\r\n\tframe_id = 0;\r\n\ttracklet_len = 0;\r\n\tthis->score = score;\r\n\tstart_frame = 0;\r\n}\r\n\r\nSTrack::~STrack()\r\n{\r\n}\r\n\r\nvoid STrack::activate(byte_kalman::KalmanFilter &kalman_filter, int frame_id)\r\n{\r\n\tthis->kalman_filter = kalman_filter;\r\n\tthis->track_id = this->next_id();\r\n\r\n\tvector<float> _tlwh_tmp(4);\r\n\t_tlwh_tmp[0] = this->_tlwh[0];\r\n\t_tlwh_tmp[1] = this->_tlwh[1];\r\n\t_tlwh_tmp[2] = this->_tlwh[2];\r\n\t_tlwh_tmp[3] = this->_tlwh[3];\r\n\tvector<float> xyah = tlwh_to_xyah(_tlwh_tmp);\r\n\tDETECTBOX xyah_box;\r\n\txyah_box[0] = xyah[0];\r\n\txyah_box[1] = xyah[1];\r\n\txyah_box[2] = xyah[2];\r\n\txyah_box[3] = xyah[3];\r\n\tauto mc = this->kalman_filter.initiate(xyah_box);\r\n\tthis->mean = mc.first;\r\n\tthis->covariance = mc.second;\r\n\r\n\tstatic_tlwh();\r\n\tstatic_tlbr();\r\n\r\n\tthis->tracklet_len = 0;\r\n\tthis->state = TrackState::Tracked;\r\n\tif (frame_id == 1)\r\n\t{\r\n\t\tthis->is_activated = true;\r\n\t}\r\n\t//this->is_activated = true;\r\n\tthis->frame_id = frame_id;\r\n\tthis->start_frame = frame_id;\r\n}\r\n\r\nvoid STrack::re_activate(STrack &new_track, int frame_id, bool new_id)\r\n{\r\n\tvector<float> xyah = tlwh_to_xyah(new_track.tlwh);\r\n\tDETECTBOX xyah_box;\r\n\txyah_box[0] = xyah[0];\r\n\txyah_box[1] = xyah[1];\r\n\txyah_box[2] = xyah[2];\r\n\txyah_box[3] = xyah[3];\r\n\tauto mc = this->kalman_filter.update(this->mean, this->covariance, xyah_box);\r\n\tthis->mean = mc.first;\r\n\tthis->covariance = mc.second;\r\n\r\n\tstatic_tlwh();\r\n\tstatic_tlbr();\r\n\r\n\tthis->tracklet_len = 0;\r\n\tthis->state = TrackState::Tracked;\r\n\tthis->is_activated = true;\r\n\tthis->frame_id = frame_id;\r\n\tthis->score = new_track.score;\r\n\tif (new_id)\r\n\t\tthis->track_id = next_id();\r\n}\r\n\r\nvoid STrack::update(STrack &new_track, int frame_id)\r\n{\r\n\tthis->frame_id = frame_id;\r\n\tthis->tracklet_len++;\r\n\r\n\tvector<float> xyah = tlwh_to_xyah(new_track.tlwh);\r\n\tDETECTBOX xyah_box;\r\n\txyah_box[0] = xyah[0];\r\n\txyah_box[1] = xyah[1];\r\n\txyah_box[2] = xyah[2];\r\n\txyah_box[3] = xyah[3];\r\n\r\n\tauto mc = this->kalman_filter.update(this->mean, this->covariance, xyah_box);\r\n\tthis->mean = mc.first;\r\n\tthis->covariance = mc.second;\r\n\r\n\tstatic_tlwh();\r\n\tstatic_tlbr();\r\n\r\n\tthis->state = TrackState::Tracked;\r\n\tthis->is_activated = true;\r\n\r\n\tthis->score = new_track.score;\r\n}\r\n\r\nvoid STrack::static_tlwh()\r\n{\r\n\tif (this->state == TrackState::New)\r\n\t{\r\n\t\ttlwh[0] = _tlwh[0];\r\n\t\ttlwh[1] = _tlwh[1];\r\n\t\ttlwh[2] = _tlwh[2];\r\n\t\ttlwh[3] = _tlwh[3];\r\n\t\treturn;\r\n\t}\r\n\r\n\ttlwh[0] = mean[0];\r\n\ttlwh[1] = mean[1];\r\n\ttlwh[2] = mean[2];\r\n\ttlwh[3] = mean[3];\r\n\r\n\ttlwh[2] *= tlwh[3];\r\n\ttlwh[0] -= tlwh[2] / 2;\r\n\ttlwh[1] -= tlwh[3] / 2;\r\n}\r\n\r\nvoid STrack::static_tlbr()\r\n{\r\n\ttlbr.clear();\r\n\ttlbr.assign(tlwh.begin(), tlwh.end());\r\n\ttlbr[2] += tlbr[0];\r\n\ttlbr[3] += tlbr[1];\r\n}\r\n\r\nvector<float> STrack::tlwh_to_xyah(vector<float> tlwh_tmp)\r\n{\r\n\tvector<float> tlwh_output = tlwh_tmp;\r\n\ttlwh_output[0] += tlwh_output[2] / 2;\r\n\ttlwh_output[1] += tlwh_output[3] / 2;\r\n\ttlwh_output[2] /= tlwh_output[3];\r\n\treturn tlwh_output;\r\n}\r\n\r\nvector<float> STrack::to_xyah()\r\n{\r\n\treturn tlwh_to_xyah(tlwh);\r\n}\r\n\r\nvector<float> STrack::tlbr_to_tlwh(vector<float> &tlbr)\r\n{\r\n\ttlbr[2] -= tlbr[0];\r\n\ttlbr[3] -= tlbr[1];\r\n\treturn tlbr;\r\n}\r\n\r\nvoid STrack::mark_lost()\r\n{\r\n\tstate = TrackState::Lost;\r\n}\r\n\r\nvoid STrack::mark_removed()\r\n{\r\n\tstate = TrackState::Removed;\r\n}\r\n\r\nint STrack::next_id()\r\n{\r\n\tstatic int _count = 0;\r\n\t_count++;\r\n\treturn _count;\r\n}\r\n\r\nint STrack::end_frame()\r\n{\r\n\treturn this->frame_id;\r\n}\r\n\r\nvoid STrack::multi_predict(vector<STrack*> &stracks, byte_kalman::KalmanFilter &kalman_filter)\r\n{\r\n\tfor (int i = 0; i < stracks.size(); i++)\r\n\t{\r\n\t\tif (stracks[i]->state != TrackState::Tracked)\r\n\t\t{\r\n\t\t\tstracks[i]->mean[7] = 0;\r\n\t\t}\r\n\t\tkalman_filter.predict(stracks[i]->mean, stracks[i]->covariance);\r\n\t}\r\n}"
  },
  {
    "path": "deploy/ncnn/cpp/src/bytetrack.cpp",
    "content": "#include \"layer.h\"\n#include \"net.h\"\n\n#if defined(USE_NCNN_SIMPLEOCV)\n#include \"simpleocv.h\"\n#include <opencv2/opencv.hpp>\n#else\n#include <opencv2/core/core.hpp>\n#include <opencv2/highgui/highgui.hpp>\n#include <opencv2/imgproc/imgproc.hpp>\n#include <opencv2/opencv.hpp>\n#endif\n#include <float.h>\n#include <stdio.h>\n#include <vector>\n#include <chrono>\n#include \"BYTETracker.h\"\n\n#define YOLOX_NMS_THRESH  0.7 // nms threshold\n#define YOLOX_CONF_THRESH 0.1 // threshold of bounding box prob\n#define INPUT_W 1088  // target image size w after resize\n#define INPUT_H 608   // target image size h after resize\n\nMat static_resize(Mat& img) {\n    float r = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));\n    // r = std::min(r, 1.0f);\n    int unpad_w = r * img.cols;\n    int unpad_h = r * img.rows;\n    Mat re(unpad_h, unpad_w, CV_8UC3);\n    resize(img, re, re.size());\n    Mat out(INPUT_H, INPUT_W, CV_8UC3, Scalar(114, 114, 114));\n    re.copyTo(out(Rect(0, 0, re.cols, re.rows)));\n    return out;\n}\n\n// YOLOX use the same focus in yolov5\nclass YoloV5Focus : public ncnn::Layer\n{\npublic:\n    YoloV5Focus()\n    {\n        one_blob_only = true;\n    }\n\n    virtual int forward(const ncnn::Mat& bottom_blob, ncnn::Mat& top_blob, const ncnn::Option& opt) const\n    {\n        int w = bottom_blob.w;\n        int h = bottom_blob.h;\n        int channels = bottom_blob.c;\n\n        int outw = w / 2;\n        int outh = h / 2;\n        int outc = channels * 4;\n\n        top_blob.create(outw, outh, outc, 4u, 1, opt.blob_allocator);\n        if (top_blob.empty())\n            return -100;\n\n        #pragma omp parallel for num_threads(opt.num_threads)\n        for (int p = 0; p < outc; p++)\n        {\n            const float* ptr = bottom_blob.channel(p % channels).row((p / channels) % 2) + ((p / channels) / 2);\n            float* outptr = top_blob.channel(p);\n\n            for (int i = 0; i < outh; i++)\n            {\n                for (int j = 0; j < outw; j++)\n                {\n                    *outptr = *ptr;\n\n                    outptr += 1;\n                    ptr += 2;\n                }\n\n                ptr += w;\n            }\n        }\n\n        return 0;\n    }\n};\n\nDEFINE_LAYER_CREATOR(YoloV5Focus)\n\nstruct GridAndStride\n{\n    int grid0;\n    int grid1;\n    int stride;\n};\n\nstatic inline float intersection_area(const Object& a, const Object& b)\n{\n    cv::Rect_<float> inter = a.rect & b.rect;\n    return inter.area();\n}\n\nstatic void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right)\n{\n    int i = left;\n    int j = right;\n    float p = faceobjects[(left + right) / 2].prob;\n\n    while (i <= j)\n    {\n        while (faceobjects[i].prob > p)\n            i++;\n\n        while (faceobjects[j].prob < p)\n            j--;\n\n        if (i <= j)\n        {\n            // swap\n            std::swap(faceobjects[i], faceobjects[j]);\n\n            i++;\n            j--;\n        }\n    }\n\n    #pragma omp parallel sections\n    {\n        #pragma omp section\n        {\n            if (left < j) qsort_descent_inplace(faceobjects, left, j);\n        }\n        #pragma omp section\n        {\n            if (i < right) qsort_descent_inplace(faceobjects, i, right);\n        }\n    }\n}\n\nstatic void qsort_descent_inplace(std::vector<Object>& objects)\n{\n    if (objects.empty())\n        return;\n\n    qsort_descent_inplace(objects, 0, objects.size() - 1);\n}\n\nstatic void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold)\n{\n    picked.clear();\n\n    const int n = faceobjects.size();\n\n    std::vector<float> areas(n);\n    for (int i = 0; i < n; i++)\n    {\n        areas[i] = faceobjects[i].rect.area();\n    }\n\n    for (int i = 0; i < n; i++)\n    {\n        const Object& a = faceobjects[i];\n\n        int keep = 1;\n        for (int j = 0; j < (int)picked.size(); j++)\n        {\n            const Object& b = faceobjects[picked[j]];\n\n            // intersection over union\n            float inter_area = intersection_area(a, b);\n            float union_area = areas[i] + areas[picked[j]] - inter_area;\n            // float IoU = inter_area / union_area\n            if (inter_area / union_area > nms_threshold)\n                keep = 0;\n        }\n\n        if (keep)\n            picked.push_back(i);\n    }\n}\n\nstatic void generate_grids_and_stride(const int target_w, const int target_h, std::vector<int>& strides, std::vector<GridAndStride>& grid_strides)\n{\n    for (int i = 0; i < (int)strides.size(); i++)\n    {\n        int stride = strides[i];\n        int num_grid_w = target_w / stride;\n        int num_grid_h = target_h / stride;\n        for (int g1 = 0; g1 < num_grid_h; g1++)\n        {\n            for (int g0 = 0; g0 < num_grid_w; g0++)\n            {\n                GridAndStride gs;\n                gs.grid0 = g0;\n                gs.grid1 = g1;\n                gs.stride = stride;\n                grid_strides.push_back(gs);\n            }\n        }\n    }\n}\n\nstatic void generate_yolox_proposals(std::vector<GridAndStride> grid_strides, const ncnn::Mat& feat_blob, float prob_threshold, std::vector<Object>& objects)\n{\n    const int num_grid = feat_blob.h;\n    const int num_class = feat_blob.w - 5;\n    const int num_anchors = grid_strides.size();\n\n    const float* feat_ptr = feat_blob.channel(0);\n    for (int anchor_idx = 0; anchor_idx < num_anchors; anchor_idx++)\n    {\n        const int grid0 = grid_strides[anchor_idx].grid0;\n        const int grid1 = grid_strides[anchor_idx].grid1;\n        const int stride = grid_strides[anchor_idx].stride;\n\n        // yolox/models/yolo_head.py decode logic\n        //  outputs[..., :2] = (outputs[..., :2] + grids) * strides\n        //  outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides\n        float x_center = (feat_ptr[0] + grid0) * stride;\n        float y_center = (feat_ptr[1] + grid1) * stride;\n        float w = exp(feat_ptr[2]) * stride;\n        float h = exp(feat_ptr[3]) * stride;\n        float x0 = x_center - w * 0.5f;\n        float y0 = y_center - h * 0.5f;\n\n        float box_objectness = feat_ptr[4];\n        for (int class_idx = 0; class_idx < num_class; class_idx++)\n        {\n            float box_cls_score = feat_ptr[5 + class_idx];\n            float box_prob = box_objectness * box_cls_score;\n            if (box_prob > prob_threshold)\n            {\n                Object obj;\n                obj.rect.x = x0;\n                obj.rect.y = y0;\n                obj.rect.width = w;\n                obj.rect.height = h;\n                obj.label = class_idx;\n                obj.prob = box_prob;\n\n                objects.push_back(obj);\n            }\n\n        } // class loop\n        feat_ptr += feat_blob.w;\n\n    } // point anchor loop\n}\n\nstatic int detect_yolox(ncnn::Mat& in_pad, std::vector<Object>& objects, ncnn::Extractor ex, float scale)\n{\n\n    ex.input(\"images\", in_pad);\n    \n    std::vector<Object> proposals;\n\n    {\n        ncnn::Mat out;\n        ex.extract(\"output\", out);\n\n        static const int stride_arr[] = {8, 16, 32}; // might have stride=64 in YOLOX\n        std::vector<int> strides(stride_arr, stride_arr + sizeof(stride_arr) / sizeof(stride_arr[0]));\n        std::vector<GridAndStride> grid_strides;\n        generate_grids_and_stride(INPUT_W, INPUT_H, strides, grid_strides);\n        generate_yolox_proposals(grid_strides, out, YOLOX_CONF_THRESH, proposals);\n    }\n    // sort all proposals by score from highest to lowest\n    qsort_descent_inplace(proposals);\n\n    // apply nms with nms_threshold\n    std::vector<int> picked;\n    nms_sorted_bboxes(proposals, picked, YOLOX_NMS_THRESH);\n\n    int count = picked.size();\n\n    objects.resize(count);\n    for (int i = 0; i < count; i++)\n    {\n        objects[i] = proposals[picked[i]];\n\n        // adjust offset to original unpadded\n        float x0 = (objects[i].rect.x) / scale;\n        float y0 = (objects[i].rect.y) / scale;\n        float x1 = (objects[i].rect.x + objects[i].rect.width) / scale;\n        float y1 = (objects[i].rect.y + objects[i].rect.height) / scale;\n\n        // clip\n        // x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);\n        // y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);\n        // x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);\n        // y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);\n\n        objects[i].rect.x = x0;\n        objects[i].rect.y = y0;\n        objects[i].rect.width = x1 - x0;\n        objects[i].rect.height = y1 - y0;\n    }\n\n    return 0;\n}\n\nint main(int argc, char** argv)\n{\n    if (argc != 2)\n    {\n        fprintf(stderr, \"Usage: %s [videopath]\\n\", argv[0]);\n        return -1;\n    }\n\n    ncnn::Net yolox;\n\n    //yolox.opt.use_vulkan_compute = true;\n    //yolox.opt.use_bf16_storage = true;\n    yolox.opt.num_threads = 20;\n    //ncnn::set_cpu_powersave(0);\n\n    //ncnn::set_omp_dynamic(0);\n    //ncnn::set_omp_num_threads(20);\n\n    // Focus in yolov5\n    yolox.register_custom_layer(\"YoloV5Focus\", YoloV5Focus_layer_creator);\n\n    yolox.load_param(\"bytetrack_s_op.param\");\n    yolox.load_model(\"bytetrack_s_op.bin\");\n    \n    ncnn::Extractor ex = yolox.create_extractor();\n\n    const char* videopath = argv[1];\n\n    VideoCapture cap(videopath);\n\tif (!cap.isOpened())\n\t\treturn 0;\n\n\tint img_w = cap.get(CV_CAP_PROP_FRAME_WIDTH);\n\tint img_h = cap.get(CV_CAP_PROP_FRAME_HEIGHT);\n    int fps = cap.get(CV_CAP_PROP_FPS);\n    long nFrame = static_cast<long>(cap.get(CV_CAP_PROP_FRAME_COUNT));\n    cout << \"Total frames: \" << nFrame << endl;\n\n    VideoWriter writer(\"demo.mp4\", CV_FOURCC('m', 'p', '4', 'v'), fps, Size(img_w, img_h));\n\n    Mat img;\n    BYTETracker tracker(fps, 30);\n    int num_frames = 0;\n    int total_ms = 1;\n\tfor (;;)\n    {\n        if(!cap.read(img))\n            break;\n        num_frames ++;\n        if (num_frames % 20 == 0)\n        {\n            cout << \"Processing frame \" << num_frames << \" (\" << num_frames * 1000000 / total_ms << \" fps)\" << endl;\n        }\n\t\tif (img.empty())\n\t\t\tbreak;\n\n        float scale = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));\n        Mat pr_img = static_resize(img);\n        ncnn::Mat in_pad = ncnn::Mat::from_pixels_resize(pr_img.data, ncnn::Mat::PIXEL_BGR2RGB, INPUT_W, INPUT_H, INPUT_W, INPUT_H);\n    \n        // python 0-1 input tensor with rgb_means = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225)\n        // so for 0-255 input image, rgb_mean should multiply 255 and norm should div by std.\n        const float mean_vals[3] = {255.f * 0.485f, 255.f * 0.456, 255.f * 0.406f};\n        const float norm_vals[3] = {1 / (255.f * 0.229f), 1 / (255.f * 0.224f), 1 / (255.f * 0.225f)};\n\n        in_pad.substract_mean_normalize(mean_vals, norm_vals);\n\n        std::vector<Object> objects;\n        auto start = chrono::system_clock::now();\n        //detect_yolox(img, objects);\n        detect_yolox(in_pad, objects, ex, scale);\n        vector<STrack> output_stracks = tracker.update(objects);\n        auto end = chrono::system_clock::now();\n        total_ms = total_ms + chrono::duration_cast<chrono::microseconds>(end - start).count();\n        for (int i = 0; i < output_stracks.size(); i++)\n\t\t{\n\t\t\tvector<float> tlwh = output_stracks[i].tlwh;\n\t\t\tbool vertical = tlwh[2] / tlwh[3] > 1.6;\n\t\t\tif (tlwh[2] * tlwh[3] > 20 && !vertical)\n\t\t\t{\n\t\t\t\tScalar s = tracker.get_color(output_stracks[i].track_id);\n\t\t\t\tputText(img, format(\"%d\", output_stracks[i].track_id), Point(tlwh[0], tlwh[1] - 5), \n                        0, 0.6, Scalar(0, 0, 255), 2, LINE_AA);\n                rectangle(img, Rect(tlwh[0], tlwh[1], tlwh[2], tlwh[3]), s, 2);\n\t\t\t}\n\t\t}\n        putText(img, format(\"frame: %d fps: %d num: %d\", num_frames, num_frames * 1000000 / total_ms, output_stracks.size()), \n                Point(0, 30), 0, 0.6, Scalar(0, 0, 255), 2, LINE_AA);\n        writer.write(img);\n        char c = waitKey(1);\n        if (c > 0)\n        {\n            break;\n        }\n    }\n    cap.release();\n    cout << \"FPS: \" << num_frames * 1000000 / total_ms << endl;\n\n    return 0;\n}\n"
  },
  {
    "path": "deploy/ncnn/cpp/src/kalmanFilter.cpp",
    "content": "#include \"kalmanFilter.h\"\r\n#include <Eigen/Cholesky>\r\n\r\nnamespace byte_kalman\r\n{\r\n\tconst double KalmanFilter::chi2inv95[10] = {\r\n\t0,\r\n\t3.8415,\r\n\t5.9915,\r\n\t7.8147,\r\n\t9.4877,\r\n\t11.070,\r\n\t12.592,\r\n\t14.067,\r\n\t15.507,\r\n\t16.919\r\n\t};\r\n\tKalmanFilter::KalmanFilter()\r\n\t{\r\n\t\tint ndim = 4;\r\n\t\tdouble dt = 1.;\r\n\r\n\t\t_motion_mat = Eigen::MatrixXf::Identity(8, 8);\r\n\t\tfor (int i = 0; i < ndim; i++) {\r\n\t\t\t_motion_mat(i, ndim + i) = dt;\r\n\t\t}\r\n\t\t_update_mat = Eigen::MatrixXf::Identity(4, 8);\r\n\r\n\t\tthis->_std_weight_position = 1. / 20;\r\n\t\tthis->_std_weight_velocity = 1. / 160;\r\n\t}\r\n\r\n\tKAL_DATA KalmanFilter::initiate(const DETECTBOX &measurement)\r\n\t{\r\n\t\tDETECTBOX mean_pos = measurement;\r\n\t\tDETECTBOX mean_vel;\r\n\t\tfor (int i = 0; i < 4; i++) mean_vel(i) = 0;\r\n\r\n\t\tKAL_MEAN mean;\r\n\t\tfor (int i = 0; i < 8; i++) {\r\n\t\t\tif (i < 4) mean(i) = mean_pos(i);\r\n\t\t\telse mean(i) = mean_vel(i - 4);\r\n\t\t}\r\n\r\n\t\tKAL_MEAN std;\r\n\t\tstd(0) = 2 * _std_weight_position * measurement[3];\r\n\t\tstd(1) = 2 * _std_weight_position * measurement[3];\r\n\t\tstd(2) = 1e-2;\r\n\t\tstd(3) = 2 * _std_weight_position * measurement[3];\r\n\t\tstd(4) = 10 * _std_weight_velocity * measurement[3];\r\n\t\tstd(5) = 10 * _std_weight_velocity * measurement[3];\r\n\t\tstd(6) = 1e-5;\r\n\t\tstd(7) = 10 * _std_weight_velocity * measurement[3];\r\n\r\n\t\tKAL_MEAN tmp = std.array().square();\r\n\t\tKAL_COVA var = tmp.asDiagonal();\r\n\t\treturn std::make_pair(mean, var);\r\n\t}\r\n\r\n\tvoid KalmanFilter::predict(KAL_MEAN &mean, KAL_COVA &covariance)\r\n\t{\r\n\t\t//revise the data;\r\n\t\tDETECTBOX std_pos;\r\n\t\tstd_pos << _std_weight_position * mean(3),\r\n\t\t\t_std_weight_position * mean(3),\r\n\t\t\t1e-2,\r\n\t\t\t_std_weight_position * mean(3);\r\n\t\tDETECTBOX std_vel;\r\n\t\tstd_vel << _std_weight_velocity * mean(3),\r\n\t\t\t_std_weight_velocity * mean(3),\r\n\t\t\t1e-5,\r\n\t\t\t_std_weight_velocity * mean(3);\r\n\t\tKAL_MEAN tmp;\r\n\t\ttmp.block<1, 4>(0, 0) = std_pos;\r\n\t\ttmp.block<1, 4>(0, 4) = std_vel;\r\n\t\ttmp = tmp.array().square();\r\n\t\tKAL_COVA motion_cov = tmp.asDiagonal();\r\n\t\tKAL_MEAN mean1 = this->_motion_mat * mean.transpose();\r\n\t\tKAL_COVA covariance1 = this->_motion_mat * covariance *(_motion_mat.transpose());\r\n\t\tcovariance1 += motion_cov;\r\n\r\n\t\tmean = mean1;\r\n\t\tcovariance = covariance1;\r\n\t}\r\n\r\n\tKAL_HDATA KalmanFilter::project(const KAL_MEAN &mean, const KAL_COVA &covariance)\r\n\t{\r\n\t\tDETECTBOX std;\r\n\t\tstd << _std_weight_position * mean(3), _std_weight_position * mean(3),\r\n\t\t\t1e-1, _std_weight_position * mean(3);\r\n\t\tKAL_HMEAN mean1 = _update_mat * mean.transpose();\r\n\t\tKAL_HCOVA covariance1 = _update_mat * covariance * (_update_mat.transpose());\r\n\t\tEigen::Matrix<float, 4, 4> diag = std.asDiagonal();\r\n\t\tdiag = diag.array().square().matrix();\r\n\t\tcovariance1 += diag;\r\n\t\t//    covariance1.diagonal() << diag;\r\n\t\treturn std::make_pair(mean1, covariance1);\r\n\t}\r\n\r\n\tKAL_DATA\r\n\t\tKalmanFilter::update(\r\n\t\t\tconst KAL_MEAN &mean,\r\n\t\t\tconst KAL_COVA &covariance,\r\n\t\t\tconst DETECTBOX &measurement)\r\n\t{\r\n\t\tKAL_HDATA pa = project(mean, covariance);\r\n\t\tKAL_HMEAN projected_mean = pa.first;\r\n\t\tKAL_HCOVA projected_cov = pa.second;\r\n\r\n\t\t//chol_factor, lower =\r\n\t\t//scipy.linalg.cho_factor(projected_cov, lower=True, check_finite=False)\r\n\t\t//kalmain_gain =\r\n\t\t//scipy.linalg.cho_solve((cho_factor, lower),\r\n\t\t//np.dot(covariance, self._upadte_mat.T).T,\r\n\t\t//check_finite=False).T\r\n\t\tEigen::Matrix<float, 4, 8> B = (covariance * (_update_mat.transpose())).transpose();\r\n\t\tEigen::Matrix<float, 8, 4> kalman_gain = (projected_cov.llt().solve(B)).transpose(); // eg.8x4\r\n\t\tEigen::Matrix<float, 1, 4> innovation = measurement - projected_mean; //eg.1x4\r\n\t\tauto tmp = innovation * (kalman_gain.transpose());\r\n\t\tKAL_MEAN new_mean = (mean.array() + tmp.array()).matrix();\r\n\t\tKAL_COVA new_covariance = covariance - kalman_gain * projected_cov*(kalman_gain.transpose());\r\n\t\treturn std::make_pair(new_mean, new_covariance);\r\n\t}\r\n\r\n\tEigen::Matrix<float, 1, -1>\r\n\t\tKalmanFilter::gating_distance(\r\n\t\t\tconst KAL_MEAN &mean,\r\n\t\t\tconst KAL_COVA &covariance,\r\n\t\t\tconst std::vector<DETECTBOX> &measurements,\r\n\t\t\tbool only_position)\r\n\t{\r\n\t\tKAL_HDATA pa = this->project(mean, covariance);\r\n\t\tif (only_position) {\r\n\t\t\tprintf(\"not implement!\");\r\n\t\t\texit(0);\r\n\t\t}\r\n\t\tKAL_HMEAN mean1 = pa.first;\r\n\t\tKAL_HCOVA covariance1 = pa.second;\r\n\r\n\t\t//    Eigen::Matrix<float, -1, 4, Eigen::RowMajor> d(size, 4);\r\n\t\tDETECTBOXSS d(measurements.size(), 4);\r\n\t\tint pos = 0;\r\n\t\tfor (DETECTBOX box : measurements) {\r\n\t\t\td.row(pos++) = box - mean1;\r\n\t\t}\r\n\t\tEigen::Matrix<float, -1, -1, Eigen::RowMajor> factor = covariance1.llt().matrixL();\r\n\t\tEigen::Matrix<float, -1, -1> z = factor.triangularView<Eigen::Lower>().solve<Eigen::OnTheRight>(d).transpose();\r\n\t\tauto zz = ((z.array())*(z.array())).matrix();\r\n\t\tauto square_maha = zz.colwise().sum();\r\n\t\treturn square_maha;\r\n\t}\r\n}"
  },
  {
    "path": "deploy/ncnn/cpp/src/lapjv.cpp",
    "content": "#include <stdio.h>\r\n#include <stdlib.h>\r\n#include <string.h>\r\n\r\n#include \"lapjv.h\"\r\n\r\n/** Column-reduction and reduction transfer for a dense cost matrix.\r\n */\r\nint_t _ccrrt_dense(const uint_t n, cost_t *cost[],\r\n\tint_t *free_rows, int_t *x, int_t *y, cost_t *v)\r\n{\r\n\tint_t n_free_rows;\r\n\tboolean *unique;\r\n\r\n\tfor (uint_t i = 0; i < n; i++) {\r\n\t\tx[i] = -1;\r\n\t\tv[i] = LARGE;\r\n\t\ty[i] = 0;\r\n\t}\r\n\tfor (uint_t i = 0; i < n; i++) {\r\n\t\tfor (uint_t j = 0; j < n; j++) {\r\n\t\t\tconst cost_t c = cost[i][j];\r\n\t\t\tif (c < v[j]) {\r\n\t\t\t\tv[j] = c;\r\n\t\t\t\ty[j] = i;\r\n\t\t\t}\r\n\t\t\tPRINTF(\"i=%d, j=%d, c[i,j]=%f, v[j]=%f y[j]=%d\\n\", i, j, c, v[j], y[j]);\r\n\t\t}\r\n\t}\r\n\tPRINT_COST_ARRAY(v, n);\r\n\tPRINT_INDEX_ARRAY(y, n);\r\n\tNEW(unique, boolean, n);\r\n\tmemset(unique, TRUE, n);\r\n\t{\r\n\t\tint_t j = n;\r\n\t\tdo {\r\n\t\t\tj--;\r\n\t\t\tconst int_t i = y[j];\r\n\t\t\tif (x[i] < 0) {\r\n\t\t\t\tx[i] = j;\r\n\t\t\t}\r\n\t\t\telse {\r\n\t\t\t\tunique[i] = FALSE;\r\n\t\t\t\ty[j] = -1;\r\n\t\t\t}\r\n\t\t} while (j > 0);\r\n\t}\r\n\tn_free_rows = 0;\r\n\tfor (uint_t i = 0; i < n; i++) {\r\n\t\tif (x[i] < 0) {\r\n\t\t\tfree_rows[n_free_rows++] = i;\r\n\t\t}\r\n\t\telse if (unique[i]) {\r\n\t\t\tconst int_t j = x[i];\r\n\t\t\tcost_t min = LARGE;\r\n\t\t\tfor (uint_t j2 = 0; j2 < n; j2++) {\r\n\t\t\t\tif (j2 == (uint_t)j) {\r\n\t\t\t\t\tcontinue;\r\n\t\t\t\t}\r\n\t\t\t\tconst cost_t c = cost[i][j2] - v[j2];\r\n\t\t\t\tif (c < min) {\r\n\t\t\t\t\tmin = c;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\tPRINTF(\"v[%d] = %f - %f\\n\", j, v[j], min);\r\n\t\t\tv[j] -= min;\r\n\t\t}\r\n\t}\r\n\tFREE(unique);\r\n\treturn n_free_rows;\r\n}\r\n\r\n\r\n/** Augmenting row reduction for a dense cost matrix.\r\n */\r\nint_t _carr_dense(\r\n\tconst uint_t n, cost_t *cost[],\r\n\tconst uint_t n_free_rows,\r\n\tint_t *free_rows, int_t *x, int_t *y, cost_t *v)\r\n{\r\n\tuint_t current = 0;\r\n\tint_t new_free_rows = 0;\r\n\tuint_t rr_cnt = 0;\r\n\tPRINT_INDEX_ARRAY(x, n);\r\n\tPRINT_INDEX_ARRAY(y, n);\r\n\tPRINT_COST_ARRAY(v, n);\r\n\tPRINT_INDEX_ARRAY(free_rows, n_free_rows);\r\n\twhile (current < n_free_rows) {\r\n\t\tint_t i0;\r\n\t\tint_t j1, j2;\r\n\t\tcost_t v1, v2, v1_new;\r\n\t\tboolean v1_lowers;\r\n\r\n\t\trr_cnt++;\r\n\t\tPRINTF(\"current = %d rr_cnt = %d\\n\", current, rr_cnt);\r\n\t\tconst int_t free_i = free_rows[current++];\r\n\t\tj1 = 0;\r\n\t\tv1 = cost[free_i][0] - v[0];\r\n\t\tj2 = -1;\r\n\t\tv2 = LARGE;\r\n\t\tfor (uint_t j = 1; j < n; j++) {\r\n\t\t\tPRINTF(\"%d = %f %d = %f\\n\", j1, v1, j2, v2);\r\n\t\t\tconst cost_t c = cost[free_i][j] - v[j];\r\n\t\t\tif (c < v2) {\r\n\t\t\t\tif (c >= v1) {\r\n\t\t\t\t\tv2 = c;\r\n\t\t\t\t\tj2 = j;\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tv2 = v1;\r\n\t\t\t\t\tv1 = c;\r\n\t\t\t\t\tj2 = j1;\r\n\t\t\t\t\tj1 = j;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\ti0 = y[j1];\r\n\t\tv1_new = v[j1] - (v2 - v1);\r\n\t\tv1_lowers = v1_new < v[j1];\r\n\t\tPRINTF(\"%d %d 1=%d,%f 2=%d,%f v1'=%f(%d,%g) \\n\", free_i, i0, j1, v1, j2, v2, v1_new, v1_lowers, v[j1] - v1_new);\r\n\t\tif (rr_cnt < current * n) {\r\n\t\t\tif (v1_lowers) {\r\n\t\t\t\tv[j1] = v1_new;\r\n\t\t\t}\r\n\t\t\telse if (i0 >= 0 && j2 >= 0) {\r\n\t\t\t\tj1 = j2;\r\n\t\t\t\ti0 = y[j2];\r\n\t\t\t}\r\n\t\t\tif (i0 >= 0) {\r\n\t\t\t\tif (v1_lowers) {\r\n\t\t\t\t\tfree_rows[--current] = i0;\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tfree_rows[new_free_rows++] = i0;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\telse {\r\n\t\t\tPRINTF(\"rr_cnt=%d >= %d (current=%d * n=%d)\\n\", rr_cnt, current * n, current, n);\r\n\t\t\tif (i0 >= 0) {\r\n\t\t\t\tfree_rows[new_free_rows++] = i0;\r\n\t\t\t}\r\n\t\t}\r\n\t\tx[free_i] = j1;\r\n\t\ty[j1] = free_i;\r\n\t}\r\n\treturn new_free_rows;\r\n}\r\n\r\n\r\n/** Find columns with minimum d[j] and put them on the SCAN list.\r\n */\r\nuint_t _find_dense(const uint_t n, uint_t lo, cost_t *d, int_t *cols, int_t *y)\r\n{\r\n\tuint_t hi = lo + 1;\r\n\tcost_t mind = d[cols[lo]];\r\n\tfor (uint_t k = hi; k < n; k++) {\r\n\t\tint_t j = cols[k];\r\n\t\tif (d[j] <= mind) {\r\n\t\t\tif (d[j] < mind) {\r\n\t\t\t\thi = lo;\r\n\t\t\t\tmind = d[j];\r\n\t\t\t}\r\n\t\t\tcols[k] = cols[hi];\r\n\t\t\tcols[hi++] = j;\r\n\t\t}\r\n\t}\r\n\treturn hi;\r\n}\r\n\r\n\r\n// Scan all columns in TODO starting from arbitrary column in SCAN\r\n// and try to decrease d of the TODO columns using the SCAN column.\r\nint_t _scan_dense(const uint_t n, cost_t *cost[],\r\n\tuint_t *plo, uint_t*phi,\r\n\tcost_t *d, int_t *cols, int_t *pred,\r\n\tint_t *y, cost_t *v)\r\n{\r\n\tuint_t lo = *plo;\r\n\tuint_t hi = *phi;\r\n\tcost_t h, cred_ij;\r\n\r\n\twhile (lo != hi) {\r\n\t\tint_t j = cols[lo++];\r\n\t\tconst int_t i = y[j];\r\n\t\tconst cost_t mind = d[j];\r\n\t\th = cost[i][j] - v[j] - mind;\r\n\t\tPRINTF(\"i=%d j=%d h=%f\\n\", i, j, h);\r\n\t\t// For all columns in TODO\r\n\t\tfor (uint_t k = hi; k < n; k++) {\r\n\t\t\tj = cols[k];\r\n\t\t\tcred_ij = cost[i][j] - v[j] - h;\r\n\t\t\tif (cred_ij < d[j]) {\r\n\t\t\t\td[j] = cred_ij;\r\n\t\t\t\tpred[j] = i;\r\n\t\t\t\tif (cred_ij == mind) {\r\n\t\t\t\t\tif (y[j] < 0) {\r\n\t\t\t\t\t\treturn j;\r\n\t\t\t\t\t}\r\n\t\t\t\t\tcols[k] = cols[hi];\r\n\t\t\t\t\tcols[hi++] = j;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\t*plo = lo;\r\n\t*phi = hi;\r\n\treturn -1;\r\n}\r\n\r\n\r\n/** Single iteration of modified Dijkstra shortest path algorithm as explained in the JV paper.\r\n *\r\n * This is a dense matrix version.\r\n *\r\n * \\return The closest free column index.\r\n */\r\nint_t find_path_dense(\r\n\tconst uint_t n, cost_t *cost[],\r\n\tconst int_t start_i,\r\n\tint_t *y, cost_t *v,\r\n\tint_t *pred)\r\n{\r\n\tuint_t lo = 0, hi = 0;\r\n\tint_t final_j = -1;\r\n\tuint_t n_ready = 0;\r\n\tint_t *cols;\r\n\tcost_t *d;\r\n\r\n\tNEW(cols, int_t, n);\r\n\tNEW(d, cost_t, n);\r\n\r\n\tfor (uint_t i = 0; i < n; i++) {\r\n\t\tcols[i] = i;\r\n\t\tpred[i] = start_i;\r\n\t\td[i] = cost[start_i][i] - v[i];\r\n\t}\r\n\tPRINT_COST_ARRAY(d, n);\r\n\twhile (final_j == -1) {\r\n\t\t// No columns left on the SCAN list.\r\n\t\tif (lo == hi) {\r\n\t\t\tPRINTF(\"%d..%d -> find\\n\", lo, hi);\r\n\t\t\tn_ready = lo;\r\n\t\t\thi = _find_dense(n, lo, d, cols, y);\r\n\t\t\tPRINTF(\"check %d..%d\\n\", lo, hi);\r\n\t\t\tPRINT_INDEX_ARRAY(cols, n);\r\n\t\t\tfor (uint_t k = lo; k < hi; k++) {\r\n\t\t\t\tconst int_t j = cols[k];\r\n\t\t\t\tif (y[j] < 0) {\r\n\t\t\t\t\tfinal_j = j;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\tif (final_j == -1) {\r\n\t\t\tPRINTF(\"%d..%d -> scan\\n\", lo, hi);\r\n\t\t\tfinal_j = _scan_dense(\r\n\t\t\t\tn, cost, &lo, &hi, d, cols, pred, y, v);\r\n\t\t\tPRINT_COST_ARRAY(d, n);\r\n\t\t\tPRINT_INDEX_ARRAY(cols, n);\r\n\t\t\tPRINT_INDEX_ARRAY(pred, n);\r\n\t\t}\r\n\t}\r\n\r\n\tPRINTF(\"found final_j=%d\\n\", final_j);\r\n\tPRINT_INDEX_ARRAY(cols, n);\r\n\t{\r\n\t\tconst cost_t mind = d[cols[lo]];\r\n\t\tfor (uint_t k = 0; k < n_ready; k++) {\r\n\t\t\tconst int_t j = cols[k];\r\n\t\t\tv[j] += d[j] - mind;\r\n\t\t}\r\n\t}\r\n\r\n\tFREE(cols);\r\n\tFREE(d);\r\n\r\n\treturn final_j;\r\n}\r\n\r\n\r\n/** Augment for a dense cost matrix.\r\n */\r\nint_t _ca_dense(\r\n\tconst uint_t n, cost_t *cost[],\r\n\tconst uint_t n_free_rows,\r\n\tint_t *free_rows, int_t *x, int_t *y, cost_t *v)\r\n{\r\n\tint_t *pred;\r\n\r\n\tNEW(pred, int_t, n);\r\n\r\n\tfor (int_t *pfree_i = free_rows; pfree_i < free_rows + n_free_rows; pfree_i++) {\r\n\t\tint_t i = -1, j;\r\n\t\tuint_t k = 0;\r\n\r\n\t\tPRINTF(\"looking at free_i=%d\\n\", *pfree_i);\r\n\t\tj = find_path_dense(n, cost, *pfree_i, y, v, pred);\r\n\t\tASSERT(j >= 0);\r\n\t\tASSERT(j < n);\r\n\t\twhile (i != *pfree_i) {\r\n\t\t\tPRINTF(\"augment %d\\n\", j);\r\n\t\t\tPRINT_INDEX_ARRAY(pred, n);\r\n\t\t\ti = pred[j];\r\n\t\t\tPRINTF(\"y[%d]=%d -> %d\\n\", j, y[j], i);\r\n\t\t\ty[j] = i;\r\n\t\t\tPRINT_INDEX_ARRAY(x, n);\r\n\t\t\tSWAP_INDICES(j, x[i]);\r\n\t\t\tk++;\r\n\t\t\tif (k >= n) {\r\n\t\t\t\tASSERT(FALSE);\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\tFREE(pred);\r\n\treturn 0;\r\n}\r\n\r\n\r\n/** Solve dense sparse LAP.\r\n */\r\nint lapjv_internal(\r\n\tconst uint_t n, cost_t *cost[],\r\n\tint_t *x, int_t *y)\r\n{\r\n\tint ret;\r\n\tint_t *free_rows;\r\n\tcost_t *v;\r\n\r\n\tNEW(free_rows, int_t, n);\r\n\tNEW(v, cost_t, n);\r\n\tret = _ccrrt_dense(n, cost, free_rows, x, y, v);\r\n\tint i = 0;\r\n\twhile (ret > 0 && i < 2) {\r\n\t\tret = _carr_dense(n, cost, ret, free_rows, x, y, v);\r\n\t\ti++;\r\n\t}\r\n\tif (ret > 0) {\r\n\t\tret = _ca_dense(n, cost, ret, free_rows, x, y, v);\r\n\t}\r\n\tFREE(v);\r\n\tFREE(free_rows);\r\n\treturn ret;\r\n}"
  },
  {
    "path": "deploy/ncnn/cpp/src/utils.cpp",
    "content": "#include \"BYTETracker.h\"\r\n#include \"lapjv.h\"\r\n\r\nvector<STrack*> BYTETracker::joint_stracks(vector<STrack*> &tlista, vector<STrack> &tlistb)\r\n{\r\n\tmap<int, int> exists;\r\n\tvector<STrack*> res;\r\n\tfor (int i = 0; i < tlista.size(); i++)\r\n\t{\r\n\t\texists.insert(pair<int, int>(tlista[i]->track_id, 1));\r\n\t\tres.push_back(tlista[i]);\r\n\t}\r\n\tfor (int i = 0; i < tlistb.size(); i++)\r\n\t{\r\n\t\tint tid = tlistb[i].track_id;\r\n\t\tif (!exists[tid] || exists.count(tid) == 0)\r\n\t\t{\r\n\t\t\texists[tid] = 1;\r\n\t\t\tres.push_back(&tlistb[i]);\r\n\t\t}\r\n\t}\r\n\treturn res;\r\n}\r\n\r\nvector<STrack> BYTETracker::joint_stracks(vector<STrack> &tlista, vector<STrack> &tlistb)\r\n{\r\n\tmap<int, int> exists;\r\n\tvector<STrack> res;\r\n\tfor (int i = 0; i < tlista.size(); i++)\r\n\t{\r\n\t\texists.insert(pair<int, int>(tlista[i].track_id, 1));\r\n\t\tres.push_back(tlista[i]);\r\n\t}\r\n\tfor (int i = 0; i < tlistb.size(); i++)\r\n\t{\r\n\t\tint tid = tlistb[i].track_id;\r\n\t\tif (!exists[tid] || exists.count(tid) == 0)\r\n\t\t{\r\n\t\t\texists[tid] = 1;\r\n\t\t\tres.push_back(tlistb[i]);\r\n\t\t}\r\n\t}\r\n\treturn res;\r\n}\r\n\r\nvector<STrack> BYTETracker::sub_stracks(vector<STrack> &tlista, vector<STrack> &tlistb)\r\n{\r\n\tmap<int, STrack> stracks;\r\n\tfor (int i = 0; i < tlista.size(); i++)\r\n\t{\r\n\t\tstracks.insert(pair<int, STrack>(tlista[i].track_id, tlista[i]));\r\n\t}\r\n\tfor (int i = 0; i < tlistb.size(); i++)\r\n\t{\r\n\t\tint tid = tlistb[i].track_id;\r\n\t\tif (stracks.count(tid) != 0)\r\n\t\t{\r\n\t\t\tstracks.erase(tid);\r\n\t\t}\r\n\t}\r\n\r\n\tvector<STrack> res;\r\n\tstd::map<int, STrack>::iterator  it;\r\n\tfor (it = stracks.begin(); it != stracks.end(); ++it)\r\n\t{\r\n\t\tres.push_back(it->second);\r\n\t}\r\n\r\n\treturn res;\r\n}\r\n\r\nvoid BYTETracker::remove_duplicate_stracks(vector<STrack> &resa, vector<STrack> &resb, vector<STrack> &stracksa, vector<STrack> &stracksb)\r\n{\r\n\tvector<vector<float> > pdist = iou_distance(stracksa, stracksb);\r\n\tvector<pair<int, int> > pairs;\r\n\tfor (int i = 0; i < pdist.size(); i++)\r\n\t{\r\n\t\tfor (int j = 0; j < pdist[i].size(); j++)\r\n\t\t{\r\n\t\t\tif (pdist[i][j] < 0.15)\r\n\t\t\t{\r\n\t\t\t\tpairs.push_back(pair<int, int>(i, j));\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\r\n\tvector<int> dupa, dupb;\r\n\tfor (int i = 0; i < pairs.size(); i++)\r\n\t{\r\n\t\tint timep = stracksa[pairs[i].first].frame_id - stracksa[pairs[i].first].start_frame;\r\n\t\tint timeq = stracksb[pairs[i].second].frame_id - stracksb[pairs[i].second].start_frame;\r\n\t\tif (timep > timeq)\r\n\t\t\tdupb.push_back(pairs[i].second);\r\n\t\telse\r\n\t\t\tdupa.push_back(pairs[i].first);\r\n\t}\r\n\r\n\tfor (int i = 0; i < stracksa.size(); i++)\r\n\t{\r\n\t\tvector<int>::iterator iter = find(dupa.begin(), dupa.end(), i);\r\n\t\tif (iter == dupa.end())\r\n\t\t{\r\n\t\t\tresa.push_back(stracksa[i]);\r\n\t\t}\r\n\t}\r\n\r\n\tfor (int i = 0; i < stracksb.size(); i++)\r\n\t{\r\n\t\tvector<int>::iterator iter = find(dupb.begin(), dupb.end(), i);\r\n\t\tif (iter == dupb.end())\r\n\t\t{\r\n\t\t\tresb.push_back(stracksb[i]);\r\n\t\t}\r\n\t}\r\n}\r\n\r\nvoid BYTETracker::linear_assignment(vector<vector<float> > &cost_matrix, int cost_matrix_size, int cost_matrix_size_size, float thresh,\r\n\tvector<vector<int> > &matches, vector<int> &unmatched_a, vector<int> &unmatched_b)\r\n{\r\n\tif (cost_matrix.size() == 0)\r\n\t{\r\n\t\tfor (int i = 0; i < cost_matrix_size; i++)\r\n\t\t{\r\n\t\t\tunmatched_a.push_back(i);\r\n\t\t}\r\n\t\tfor (int i = 0; i < cost_matrix_size_size; i++)\r\n\t\t{\r\n\t\t\tunmatched_b.push_back(i);\r\n\t\t}\r\n\t\treturn;\r\n\t}\r\n\r\n\tvector<int> rowsol; vector<int> colsol;\r\n\tfloat c = lapjv(cost_matrix, rowsol, colsol, true, thresh);\r\n\tfor (int i = 0; i < rowsol.size(); i++)\r\n\t{\r\n\t\tif (rowsol[i] >= 0)\r\n\t\t{\r\n\t\t\tvector<int> match;\r\n\t\t\tmatch.push_back(i);\r\n\t\t\tmatch.push_back(rowsol[i]);\r\n\t\t\tmatches.push_back(match);\r\n\t\t}\r\n\t\telse\r\n\t\t{\r\n\t\t\tunmatched_a.push_back(i);\r\n\t\t}\r\n\t}\r\n\r\n\tfor (int i = 0; i < colsol.size(); i++)\r\n\t{\r\n\t\tif (colsol[i] < 0)\r\n\t\t{\r\n\t\t\tunmatched_b.push_back(i);\r\n\t\t}\r\n\t}\r\n}\r\n\r\nvector<vector<float> > BYTETracker::ious(vector<vector<float> > &atlbrs, vector<vector<float> > &btlbrs)\r\n{\r\n\tvector<vector<float> > ious;\r\n\tif (atlbrs.size()*btlbrs.size() == 0)\r\n\t\treturn ious;\r\n\r\n\tious.resize(atlbrs.size());\r\n\tfor (int i = 0; i < ious.size(); i++)\r\n\t{\r\n\t\tious[i].resize(btlbrs.size());\r\n\t}\r\n\r\n\t//bbox_ious\r\n\tfor (int k = 0; k < btlbrs.size(); k++)\r\n\t{\r\n\t\tvector<float> ious_tmp;\r\n\t\tfloat box_area = (btlbrs[k][2] - btlbrs[k][0] + 1)*(btlbrs[k][3] - btlbrs[k][1] + 1);\r\n\t\tfor (int n = 0; n < atlbrs.size(); n++)\r\n\t\t{\r\n\t\t\tfloat iw = min(atlbrs[n][2], btlbrs[k][2]) - max(atlbrs[n][0], btlbrs[k][0]) + 1;\r\n\t\t\tif (iw > 0)\r\n\t\t\t{\r\n\t\t\t\tfloat ih = min(atlbrs[n][3], btlbrs[k][3]) - max(atlbrs[n][1], btlbrs[k][1]) + 1;\r\n\t\t\t\tif(ih > 0)\r\n\t\t\t\t{\r\n\t\t\t\t\tfloat ua = (atlbrs[n][2] - atlbrs[n][0] + 1)*(atlbrs[n][3] - atlbrs[n][1] + 1) + box_area - iw * ih;\r\n\t\t\t\t\tious[n][k] = iw * ih / ua;\r\n\t\t\t\t}\r\n\t\t\t\telse\r\n\t\t\t\t{\r\n\t\t\t\t\tious[n][k] = 0.0;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\telse\r\n\t\t\t{\r\n\t\t\t\tious[n][k] = 0.0;\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\r\n\treturn ious;\r\n}\r\n\r\nvector<vector<float> > BYTETracker::iou_distance(vector<STrack*> &atracks, vector<STrack> &btracks, int &dist_size, int &dist_size_size)\r\n{\r\n\tvector<vector<float> > cost_matrix;\r\n\tif (atracks.size() * btracks.size() == 0)\r\n\t{\r\n\t\tdist_size = atracks.size();\r\n\t\tdist_size_size = btracks.size();\r\n\t\treturn cost_matrix;\r\n\t}\r\n\tvector<vector<float> > atlbrs, btlbrs;\r\n\tfor (int i = 0; i < atracks.size(); i++)\r\n\t{\r\n\t\tatlbrs.push_back(atracks[i]->tlbr);\r\n\t}\r\n\tfor (int i = 0; i < btracks.size(); i++)\r\n\t{\r\n\t\tbtlbrs.push_back(btracks[i].tlbr);\r\n\t}\r\n\r\n\tdist_size = atracks.size();\r\n\tdist_size_size = btracks.size();\r\n\r\n\tvector<vector<float> > _ious = ious(atlbrs, btlbrs);\r\n\t\r\n\tfor (int i = 0; i < _ious.size();i++)\r\n\t{\r\n\t\tvector<float> _iou;\r\n\t\tfor (int j = 0; j < _ious[i].size(); j++)\r\n\t\t{\r\n\t\t\t_iou.push_back(1 - _ious[i][j]);\r\n\t\t}\r\n\t\tcost_matrix.push_back(_iou);\r\n\t}\r\n\r\n\treturn cost_matrix;\r\n}\r\n\r\nvector<vector<float> > BYTETracker::iou_distance(vector<STrack> &atracks, vector<STrack> &btracks)\r\n{\r\n\tvector<vector<float> > atlbrs, btlbrs;\r\n\tfor (int i = 0; i < atracks.size(); i++)\r\n\t{\r\n\t\tatlbrs.push_back(atracks[i].tlbr);\r\n\t}\r\n\tfor (int i = 0; i < btracks.size(); i++)\r\n\t{\r\n\t\tbtlbrs.push_back(btracks[i].tlbr);\r\n\t}\r\n\r\n\tvector<vector<float> > _ious = ious(atlbrs, btlbrs);\r\n\tvector<vector<float> > cost_matrix;\r\n\tfor (int i = 0; i < _ious.size(); i++)\r\n\t{\r\n\t\tvector<float> _iou;\r\n\t\tfor (int j = 0; j < _ious[i].size(); j++)\r\n\t\t{\r\n\t\t\t_iou.push_back(1 - _ious[i][j]);\r\n\t\t}\r\n\t\tcost_matrix.push_back(_iou);\r\n\t}\r\n\r\n\treturn cost_matrix;\r\n}\r\n\r\ndouble BYTETracker::lapjv(const vector<vector<float> > &cost, vector<int> &rowsol, vector<int> &colsol,\r\n\tbool extend_cost, float cost_limit, bool return_cost)\r\n{\r\n\tvector<vector<float> > cost_c;\r\n\tcost_c.assign(cost.begin(), cost.end());\r\n\r\n\tvector<vector<float> > cost_c_extended;\r\n\r\n\tint n_rows = cost.size();\r\n\tint n_cols = cost[0].size();\r\n\trowsol.resize(n_rows);\r\n\tcolsol.resize(n_cols);\r\n\r\n\tint n = 0;\r\n\tif (n_rows == n_cols)\r\n\t{\r\n\t\tn = n_rows;\r\n\t}\r\n\telse\r\n\t{\r\n\t\tif (!extend_cost)\r\n\t\t{\r\n\t\t\tcout << \"set extend_cost=True\" << endl;\r\n\t\t\tsystem(\"pause\");\r\n\t\t\texit(0);\r\n\t\t}\r\n\t}\r\n\t\t\r\n\tif (extend_cost || cost_limit < LONG_MAX)\r\n\t{\r\n\t\tn = n_rows + n_cols;\r\n\t\tcost_c_extended.resize(n);\r\n\t\tfor (int i = 0; i < cost_c_extended.size(); i++)\r\n\t\t\tcost_c_extended[i].resize(n);\r\n\r\n\t\tif (cost_limit < LONG_MAX)\r\n\t\t{\r\n\t\t\tfor (int i = 0; i < cost_c_extended.size(); i++)\r\n\t\t\t{\r\n\t\t\t\tfor (int j = 0; j < cost_c_extended[i].size(); j++)\r\n\t\t\t\t{\r\n\t\t\t\t\tcost_c_extended[i][j] = cost_limit / 2.0;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\telse\r\n\t\t{\r\n\t\t\tfloat cost_max = -1;\r\n\t\t\tfor (int i = 0; i < cost_c.size(); i++)\r\n\t\t\t{\r\n\t\t\t\tfor (int j = 0; j < cost_c[i].size(); j++)\r\n\t\t\t\t{\r\n\t\t\t\t\tif (cost_c[i][j] > cost_max)\r\n\t\t\t\t\t\tcost_max = cost_c[i][j];\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\tfor (int i = 0; i < cost_c_extended.size(); i++)\r\n\t\t\t{\r\n\t\t\t\tfor (int j = 0; j < cost_c_extended[i].size(); j++)\r\n\t\t\t\t{\r\n\t\t\t\t\tcost_c_extended[i][j] = cost_max + 1;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\tfor (int i = n_rows; i < cost_c_extended.size(); i++)\r\n\t\t{\r\n\t\t\tfor (int j = n_cols; j < cost_c_extended[i].size(); j++)\r\n\t\t\t{\r\n\t\t\t\tcost_c_extended[i][j] = 0;\r\n\t\t\t}\r\n\t\t}\r\n\t\tfor (int i = 0; i < n_rows; i++)\r\n\t\t{\r\n\t\t\tfor (int j = 0; j < n_cols; j++)\r\n\t\t\t{\r\n\t\t\t\tcost_c_extended[i][j] = cost_c[i][j];\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\tcost_c.clear();\r\n\t\tcost_c.assign(cost_c_extended.begin(), cost_c_extended.end());\r\n\t}\r\n\r\n\tdouble **cost_ptr;\r\n\tcost_ptr = new double *[sizeof(double *) * n];\r\n\tfor (int i = 0; i < n; i++)\r\n\t\tcost_ptr[i] = new double[sizeof(double) * n];\r\n\r\n\tfor (int i = 0; i < n; i++)\r\n\t{\r\n\t\tfor (int j = 0; j < n; j++)\r\n\t\t{\r\n\t\t\tcost_ptr[i][j] = cost_c[i][j];\r\n\t\t}\r\n\t}\r\n\r\n\tint* x_c = new int[sizeof(int) * n];\r\n\tint *y_c = new int[sizeof(int) * n];\r\n\r\n\tint ret = lapjv_internal(n, cost_ptr, x_c, y_c);\r\n\tif (ret != 0)\r\n\t{\r\n\t\tcout << \"Calculate Wrong!\" << endl;\r\n\t\tsystem(\"pause\");\r\n\t\texit(0);\r\n\t}\r\n\r\n\tdouble opt = 0.0;\r\n\r\n\tif (n != n_rows)\r\n\t{\r\n\t\tfor (int i = 0; i < n; i++)\r\n\t\t{\r\n\t\t\tif (x_c[i] >= n_cols)\r\n\t\t\t\tx_c[i] = -1;\r\n\t\t\tif (y_c[i] >= n_rows)\r\n\t\t\t\ty_c[i] = -1;\r\n\t\t}\r\n\t\tfor (int i = 0; i < n_rows; i++)\r\n\t\t{\r\n\t\t\trowsol[i] = x_c[i];\r\n\t\t}\r\n\t\tfor (int i = 0; i < n_cols; i++)\r\n\t\t{\r\n\t\t\tcolsol[i] = y_c[i];\r\n\t\t}\r\n\r\n\t\tif (return_cost)\r\n\t\t{\r\n\t\t\tfor (int i = 0; i < rowsol.size(); i++)\r\n\t\t\t{\r\n\t\t\t\tif (rowsol[i] != -1)\r\n\t\t\t\t{\r\n\t\t\t\t\t//cout << i << \"\\t\" << rowsol[i] << \"\\t\" << cost_ptr[i][rowsol[i]] << endl;\r\n\t\t\t\t\topt += cost_ptr[i][rowsol[i]];\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\telse if (return_cost)\r\n\t{\r\n\t\tfor (int i = 0; i < rowsol.size(); i++)\r\n\t\t{\r\n\t\t\topt += cost_ptr[i][rowsol[i]];\r\n\t\t}\r\n\t}\r\n\r\n\tfor (int i = 0; i < n; i++)\r\n\t{\r\n\t\tdelete[]cost_ptr[i];\r\n\t}\r\n\tdelete[]cost_ptr;\r\n\tdelete[]x_c;\r\n\tdelete[]y_c;\r\n\r\n\treturn opt;\r\n}\r\n\r\nScalar BYTETracker::get_color(int idx)\r\n{\r\n\tidx += 3;\r\n\treturn Scalar(37 * idx % 255, 17 * idx % 255, 29 * idx % 255);\r\n}"
  },
  {
    "path": "deploy/scripts/export_onnx.py",
    "content": "from loguru import logger\n\nimport torch\nfrom torch import nn\n\nfrom yolox.exp import get_exp\nfrom yolox.models.network_blocks import SiLU\nfrom yolox.utils import replace_module\n\nimport argparse\nimport os\n\n\ndef make_parser():\n    parser = argparse.ArgumentParser(\"YOLOX onnx deploy\")\n    parser.add_argument(\n        \"--output-name\", type=str, default=\"ocsort.onnx\", help=\"output name of models\"\n    )\n    parser.add_argument(\n        \"--input\", default=\"images\", type=str, help=\"input node name of onnx model\"\n    )\n    parser.add_argument(\n        \"--output\", default=\"output\", type=str, help=\"output node name of onnx model\"\n    )\n    parser.add_argument(\n        \"-o\", \"--opset\", default=11, type=int, help=\"onnx opset version\"\n    )\n    parser.add_argument(\"--no-onnxsim\", action=\"store_true\", help=\"use onnxsim or not\")\n    parser.add_argument(\n        \"-f\",\n        \"--exp_file\",\n        default=None,\n        type=str,\n        help=\"expriment description file\",\n    )\n    parser.add_argument(\"-expn\", \"--experiment-name\", type=str, default=None)\n    parser.add_argument(\"-n\", \"--name\", type=str, default=None, help=\"model name\")\n    parser.add_argument(\"-c\", \"--ckpt\", default=None, type=str, help=\"ckpt path\")\n    parser.add_argument(\n        \"opts\",\n        help=\"Modify config options using the command-line\",\n        default=None,\n        nargs=argparse.REMAINDER,\n    )\n\n    return parser\n\n\n@logger.catch\ndef main():\n    args = make_parser().parse_args()\n    logger.info(\"args value: {}\".format(args))\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n\n    if not args.experiment_name:\n        args.experiment_name = exp.exp_name\n\n    model = exp.get_model()\n    if args.ckpt is None:\n        file_name = os.path.join(exp.output_dir, args.experiment_name)\n        ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n    else:\n        ckpt_file = args.ckpt\n\n    # load the model state dict\n    ckpt = torch.load(ckpt_file, map_location=\"cpu\")\n\n    model.eval()\n    if \"model\" in ckpt:\n        ckpt = ckpt[\"model\"]\n    model.load_state_dict(ckpt)\n    model = replace_module(model, nn.SiLU, SiLU)\n    model.head.decode_in_inference = False\n\n    logger.info(\"loading checkpoint done.\")\n    dummy_input = torch.randn(1, 3, exp.test_size[0], exp.test_size[1])\n    torch.onnx._export(\n        model,\n        dummy_input,\n        args.output_name,\n        input_names=[args.input],\n        output_names=[args.output],\n        opset_version=args.opset,\n    )\n    logger.info(\"generated onnx model named {}\".format(args.output_name))\n\n    if not args.no_onnxsim:\n        import onnx\n\n        from onnxsim import simplify\n\n        # use onnxsimplify to reduce reduent model.\n        onnx_model = onnx.load(args.output_name)\n        model_simp, check = simplify(onnx_model)\n        assert check, \"Simplified ONNX model could not be validated\"\n        onnx.save(model_simp, args.output_name)\n        logger.info(\"generated simplified onnx model named {}\".format(args.output_name))\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "deploy/scripts/trt.py",
    "content": "from loguru import logger\n\nimport tensorrt as trt\nimport torch\nfrom torch2trt import torch2trt\n\nfrom yolox.exp import get_exp\n\nimport argparse\nimport os\nimport shutil\n\n\ndef make_parser():\n    parser = argparse.ArgumentParser(\"YOLOX ncnn deploy\")\n    parser.add_argument(\"-expn\", \"--experiment-name\", type=str, default=None)\n    parser.add_argument(\"-n\", \"--name\", type=str, default=None, help=\"model name\")\n\n    parser.add_argument(\n        \"-f\",\n        \"--exp_file\",\n        default=None,\n        type=str,\n        help=\"pls input your expriment description file\",\n    )\n    parser.add_argument(\"-c\", \"--ckpt\", default=None, type=str, help=\"ckpt path\")\n    return parser\n\n\n@logger.catch\ndef main():\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    if not args.experiment_name:\n        args.experiment_name = exp.exp_name\n\n    model = exp.get_model()\n    file_name = os.path.join(exp.output_dir, args.experiment_name)\n    os.makedirs(file_name, exist_ok=True)\n    if args.ckpt is None:\n        ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n    else:\n        ckpt_file = args.ckpt\n\n    ckpt = torch.load(ckpt_file, map_location=\"cpu\")\n    # load the model state dict\n\n    model.load_state_dict(ckpt[\"model\"])\n    logger.info(\"loaded checkpoint done.\")\n    model.eval()\n    model.cuda()\n    model.head.decode_in_inference = False\n    x = torch.ones(1, 3, exp.test_size[0], exp.test_size[1]).cuda()\n    model_trt = torch2trt(\n        model,\n        [x],\n        fp16_mode=True,\n        log_level=trt.Logger.INFO,\n        max_workspace_size=(1 << 32),\n    )\n    torch.save(model_trt.state_dict(), os.path.join(file_name, \"model_trt.pth\"))\n    logger.info(\"Converted TensorRT model done.\")\n    engine_file = os.path.join(file_name, \"model_trt.engine\")\n    engine_file_demo = os.path.join(\"deploy\", \"TensorRT\", \"cpp\", \"model_trt.engine\")\n    with open(engine_file, \"wb\") as f:\n        f.write(model_trt.engine.serialize())\n\n    shutil.copyfile(engine_file, engine_file_demo)\n\n    logger.info(\"Converted TensorRT model engine file is saved for C++ inference.\")\n\n\nif __name__ == \"__main__\":\n    main()\n"
  },
  {
    "path": "docs/DEPLOY.md",
    "content": "# Deployment \n\nWe provide support to some popular deployment tools. This part is built upon the implementation of [YOLOX Deployment](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo) and [the adaptation by ByteTrack](https://github.com/ifzhang/ByteTrack/tree/main/deploy).\n\n\n## ONNX support \n\n1. convert the pytorch model to onnx checkpoints, we provide an example here. \n    ```python\n    # In pratice you may want smaller model for faster inference.\n    python deploy/scripts/export_onnx.py --output-name  ocsort.onnx -f exps/example/mot/yolox_x_mix_det.py -c pretrained/bytetrack_x_mot17.pth.tar\n    ```\n\n2. run on the provided model video by\n    ```shell\n    cd $OCSORT_HOME/deploy/ONNXRuntime\n    python onnx_inference.py\n    ```\n\n## TensorRT support (Python)\n\n1. Follow [TensorRT Installation Guide](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) and [torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt) to install TensorRT (Version 7 recommended) and torch2trt.\n\n2. Convert Model\n    ```python\n    # you have to download checkpoint bytetrack_s_mot17.pth.tar from model zoo of ByteTrack\n    python3 deploy/scripts/trt.py -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_s_mot17.pth.tar\n    ```\n\n3. Run on a demo video\n    ```python\n    python3 tools/demo_track.py video -f exps/example/mot/yolox_s_mix_det.py --trt --save_result\n    ```\n\n*Note: We haven't validated the C++ support for TensorRT yet, please refer to [ByteTrack guidance](https://github.com/ifzhang/ByteTrack/tree/main/deploy/TensorRT/cpp) for adaptation for now.*\n\n## ncnn support\nPlease follow the [guidelines](https://github.com/ifzhang/ByteTrack/tree/main/deploy/ncnn/cpp) from ByteTrack to deploy by support from ncnn."
  },
  {
    "path": "exps/default/nano.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\nimport torch.nn as nn\n\nfrom yolox.exp import Exp as MyExp\n\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.depth = 0.33\n        self.width = 0.25\n        self.scale = (0.5, 1.5)\n        self.random_size = (10, 20)\n        self.test_size = (416, 416)\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.enable_mixup = False\n\n    def get_model(self, sublinear=False):\n\n        def init_yolo(M):\n            for m in M.modules():\n                if isinstance(m, nn.BatchNorm2d):\n                    m.eps = 1e-3\n                    m.momentum = 0.03\n        if \"model\" not in self.__dict__:\n            from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead\n            in_channels = [256, 512, 1024]\n            # NANO model use depthwise = True, which is main difference.\n            backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels, depthwise=True)\n            head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels, depthwise=True)\n            self.model = YOLOX(backbone, head)\n\n        self.model.apply(init_yolo)\n        self.model.head.initialize_biases(1e-2)\n        return self.model\n"
  },
  {
    "path": "exps/default/yolov3.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\nimport torch\nimport torch.nn as nn\n\nfrom yolox.exp import Exp as MyExp\n\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.depth = 1.0\n        self.width = 1.0\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n\n    def get_model(self, sublinear=False):\n        def init_yolo(M):\n            for m in M.modules():\n                if isinstance(m, nn.BatchNorm2d):\n                    m.eps = 1e-3\n                    m.momentum = 0.03\n        if \"model\" not in self.__dict__:\n            from yolox.models import YOLOX, YOLOFPN, YOLOXHead\n            backbone = YOLOFPN()\n            head = YOLOXHead(self.num_classes, self.width, in_channels=[128, 256, 512], act=\"lrelu\")\n            self.model = YOLOX(backbone, head)\n        self.model.apply(init_yolo)\n        self.model.head.initialize_biases(1e-2)\n\n        return self.model\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from data.datasets.cocodataset import COCODataset\n        from data.datasets.mosaicdetection import MosaicDetection\n        from data.datasets.data_augment import TrainTransform\n        from data.datasets.dataloading import YoloBatchSampler, DataLoader, InfiniteSampler\n        import torch.distributed as dist\n\n        dataset = COCODataset(\n                data_dir='data/COCO/',\n                json_file=self.train_ann,\n                img_size=self.input_size,\n                preproc=TrainTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                    max_labels=50\n                ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=120\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = InfiniteSampler(len(self.dataset), seed=self.seed if self.seed else 0)\n        else:\n            sampler = torch.utils.data.RandomSampler(self.dataset)\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n"
  },
  {
    "path": "exps/default/yolox_l.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\n\nfrom yolox.exp import Exp as MyExp\n\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.depth = 1.0\n        self.width = 1.0\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n"
  },
  {
    "path": "exps/default/yolox_m.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\n\nfrom yolox.exp import Exp as MyExp\n\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.depth = 0.67\n        self.width = 0.75\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n"
  },
  {
    "path": "exps/default/yolox_s.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\n\nfrom yolox.exp import Exp as MyExp\n\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.depth = 0.33\n        self.width = 0.50\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n"
  },
  {
    "path": "exps/default/yolox_tiny.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\n\nfrom yolox.exp import Exp as MyExp\n\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.depth = 0.33\n        self.width = 0.375\n        self.scale = (0.5, 1.5)\n        self.random_size = (10, 20)\n        self.test_size = (416, 416)\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.enable_mixup = False\n"
  },
  {
    "path": "exps/default/yolox_x.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\n\nfrom yolox.exp import Exp as MyExp\n\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n"
  },
  {
    "path": "exps/example/mot/yolox_dancetrack_test.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val.json\"\n        self.test_ann = \"test.json\"\n        \n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 8\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 1\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n            json_file=self.train_ann,\n            name='train',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n        \n        if testdev:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.test_ann,\n                img_size=self.test_size,\n                name='test',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n        else:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.val_ann,\n                img_size=self.test_size,\n                name='val',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator"
  },
  {
    "path": "exps/example/mot/yolox_dancetrack_test_hybrid_sort.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val.json\"\n        self.test_ann = \"test.json\"\n        \n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 8\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 1\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n        # tracking params for Hybrid-SORT\n        self.ckpt = \"pretrained/bytetrack_dance_model.pth.tar\"\n        self.use_byte = True\n        self.dataset = \"dancetrack\"\n        self.inertia = 0.05\n        self.iou_thresh = 0.15\n        self.asso = \"Height_Modulated_IoU\"\n        self.TCM_first_step = True\n        self.TCM_byte_step = True\n        self.TCM_first_step_weight = 1.5\n        self.TCM_byte_step_weight = 1.0\n        self.hybrid_sort_with_reid = False\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n            json_file=self.train_ann,\n            name='train',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n        \n        if testdev:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.test_ann,\n                img_size=self.test_size,\n                name='test',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n        else:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.val_ann,\n                img_size=self.test_size,\n                name='val',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator"
  },
  {
    "path": "exps/example/mot/yolox_dancetrack_test_hybrid_sort_reid.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val.json\"\n        self.test_ann = \"test.json\"\n        \n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 8\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 1\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n        # tracking params for Hybrid-SORT-ReID\n        self.ckpt = \"pretrained/bytetrack_dance_model.pth.tar\"\n        self.use_byte = True\n        self.dataset = \"dancetrack\"\n        self.inertia = 0.05\n        self.iou_thresh = 0.15\n        self.asso = \"Height_Modulated_IoU\"\n        self.TCM_first_step = True\n        self.TCM_byte_step = True\n        self.TCM_first_step_weight = 1.5\n        self.TCM_byte_step_weight = 1.0\n        self.hybrid_sort_with_reid = True\n        self.with_fastreid =True\n        self.EG_weight_high_score= 2.8\n        self.EG_weight_low_score= 1.4\n        self.fast_reid_config = \"fast_reid/configs/CUHKSYSU_DanceTrack/sbs_S50.yml\"\n        self.fast_reid_weights = \"pretrained/dancetrack_sbs_S50.pth\"\n        self.with_longterm_reid_correction = True\n        self.longterm_reid_correction_thresh = 0.20\n        self.longterm_reid_correction_thresh_low = 1.0\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n            json_file=self.train_ann,\n            name='train',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n        \n        if testdev:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.test_ann,\n                img_size=self.test_size,\n                name='test',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n        else:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.val_ann,\n                img_size=self.test_size,\n                name='val',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator"
  },
  {
    "path": "exps/example/mot/yolox_dancetrack_val.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val.json\"\n        self.test_ann = \"val.json\"\n        \n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 8\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 1\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n            json_file=self.train_ann,\n            name='train',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n        \n        if testdev:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.test_ann,\n                img_size=self.test_size,\n                name='test',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n        else:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.val_ann,\n                img_size=self.test_size,\n                name='val',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator"
  },
  {
    "path": "exps/example/mot/yolox_dancetrack_val_hybrid_sort.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val.json\"\n        self.test_ann = \"val.json\"\n        \n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 8\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 1\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n        # tracking params for Hybrid-SORT\n        self.ckpt = \"pretrained/bytetrack_dance_model.pth.tar\"\n        self.use_byte = True\n        self.dataset = \"dancetrack\"\n        self.inertia = 0.05\n        self.iou_thresh = 0.15\n        self.asso = \"Height_Modulated_IoU\"\n        self.TCM_first_step = True\n        self.TCM_byte_step = True\n        self.TCM_first_step_weight = 1.0\n        self.TCM_byte_step_weight = 1.0\n        self.hybrid_sort_with_reid = False\n\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n            json_file=self.train_ann,\n            name='train',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n        \n        if testdev:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.test_ann,\n                img_size=self.test_size,\n                name='test',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n        else:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.val_ann,\n                img_size=self.test_size,\n                name='val',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator"
  },
  {
    "path": "exps/example/mot/yolox_dancetrack_val_hybrid_sort_reid.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val.json\"\n        self.test_ann = \"val.json\"\n        \n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 8\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 1\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n        # tracking params for Hybrid-SORT-ReID\n        self.ckpt = \"pretrained/bytetrack_dance_model.pth.tar\"\n        self.use_byte = True\n        self.dataset = \"dancetrack\"\n        self.inertia = 0.05\n        self.iou_thresh = 0.15\n        self.asso = \"Height_Modulated_IoU\"\n        self.TCM_first_step = True\n        self.TCM_byte_step = True\n        self.TCM_first_step_weight = 1.0\n        self.TCM_byte_step_weight = 1.0\n        self.hybrid_sort_with_reid = True\n        self.with_fastreid =True\n        self.EG_weight_high_score= 4.0\n        self.EG_weight_low_score= 4.4\n        self.fast_reid_config = \"fast_reid/configs/CUHKSYSU_DanceTrack/sbs_S50.yml\"\n        self.fast_reid_weights = \"pretrained/dancetrack_sbs_S50.pth\"\n        self.with_longterm_reid_correction = False\n        self.longterm_reid_correction_thresh = 1.0\n        self.longterm_reid_correction_thresh_low = 1.0\n\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n            json_file=self.train_ann,\n            name='train',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n        \n        if testdev:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.test_ann,\n                img_size=self.test_size,\n                name='test',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n        else:\n            valdataset = MOTDataset(\n                data_dir=os.path.join(get_yolox_datadir(), \"dancetrack\"),\n                json_file=self.val_ann,\n                img_size=self.test_size,\n                name='val',\n                preproc=ValTransform(\n                    rgb_means=(0.485, 0.456, 0.406),\n                    std=(0.229, 0.224, 0.225),\n                ),\n                run_tracking=run_tracking\n            )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator"
  },
  {
    "path": "exps/example/mot/yolox_l_mix_det.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.0\n        self.width = 1.0\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"train.json\"\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_det\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False):\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_m_mix_det.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 0.67\n        self.width = 0.75\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"train.json\"\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_det\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False):\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_nano_mix_det.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 0.33\n        self.width = 0.25\n        self.scale = (0.5, 1.5)\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"train.json\"\n        self.input_size = (608, 1088)\n        self.test_size = (608, 1088)\n        self.random_size = (12, 26)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_model(self, sublinear=False):\n\n        def init_yolo(M):\n            for m in M.modules():\n                if isinstance(m, nn.BatchNorm2d):\n                    m.eps = 1e-3\n                    m.momentum = 0.03\n        if \"model\" not in self.__dict__:\n            from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead\n            in_channels = [256, 512, 1024]\n            # NANO model use depthwise = True, which is main difference.\n            backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels, depthwise=True)\n            head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels, depthwise=True)\n            self.model = YOLOX(backbone, head)\n\n        self.model.apply(init_yolo)\n        self.model.head.initialize_biases(1e-2)\n        return self.model\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_det\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False):\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_s_mix_det.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 0.33\n        self.width = 0.50\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"train.json\"\n        self.input_size = (608, 1088)\n        self.test_size = (608, 1088)\n        self.random_size = (12, 26)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_det\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False):\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_tiny_mix_det.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 0.33\n        self.width = 0.375\n        self.scale = (0.5, 1.5)\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"train.json\"\n        self.input_size = (608, 1088)\n        self.test_size = (608, 1088)\n        self.random_size = (12, 26)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_det\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False):\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_ablation.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val_half.json\"\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_mot_ch\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_ablation_hybrid_sort.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val_half.json\"\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n        # tracking params for Hybrid-SORT\n        self.ckpt = \"pretrained/ocsort_mot17_ablation.pth.tar\"\n        self.use_byte = True\n        self.dataset = \"mot17\"\n        self.inertia = 0.05\n        self.iou_thresh = 0.25\n        self.asso = \"Height_Modulated_IoU\"\n        self.TCM_first_step = True\n        self.TCM_byte_step = True\n        self.TCM_first_step_weight = 1.0\n        self.TCM_byte_step_weight = 1.0\n        self.hybrid_sort_with_reid = False\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_mot_ch\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_ablation_hybrid_sort_reid.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val_half.json\"\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n        # tracking params for Hybrid-SORT-ReID\n        self.ckpt = \"pretrained/ocsort_mot17_ablation.pth.tar\"\n        self.use_byte = True\n        self.dataset = \"mot17\"\n        self.inertia = 0.05\n        self.iou_thresh = 0.25\n        self.asso = \"Height_Modulated_IoU\"\n        self.TCM_first_step = True\n        self.TCM_byte_step = True\n        self.TCM_first_step_weight = 1.0\n        self.TCM_byte_step_weight = 1.0\n        self.hybrid_sort_with_reid = True\n        self.with_fastreid =True\n        self.EG_weight_high_score= 1.3\n        self.EG_weight_low_score= 1.2\n        self.fast_reid_config = \"fast_reid/configs/MOT17/sbs_S50.yml\"\n        self.fast_reid_weights = \"pretrained/mot17_sbs_S50.pth\"\n        self.with_longterm_reid_correction = True\n        self.longterm_reid_correction_thresh = 0.4\n        self.longterm_reid_correction_thresh_low = 0.4\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_mot_ch\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_ch.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val_half.json\"\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"ch_all\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False):\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mix_det.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"test.json\"    # change to train.json when running on training set\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_det\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='test',   # change to train when running on training set\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mix_det_hybrid_sort.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"test.json\"    # change to train.json when running on training set\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n        # tracking params for Hybrid-SORT\n        self.ckpt = \"pretrained/ocsort_x_mot17.pth.tar\"\n        self.use_byte = True\n        self.dataset = \"mot17\"\n        self.inertia = 0.05\n        self.iou_thresh = 0.25\n        self.asso = \"Height_Modulated_IoU\"\n        self.TCM_first_step = True\n        self.TCM_byte_step = True\n        self.TCM_first_step_weight = 1.0\n        self.TCM_byte_step_weight = 1.0\n        self.hybrid_sort_with_reid = False\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_det\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='test',   # change to train when running on training set\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mix_det_hybrid_sort_reid.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"test.json\"    # change to train.json when running on training set\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n        # tracking params for Hybrid-SORT-ReID\n        self.ckpt = \"pretrained/ocsort_x_mot17.pth.tar\"\n        self.use_byte = True\n        self.dataset = \"mot17\"\n        self.inertia = 0.05\n        self.iou_thresh = 0.25\n        self.asso = \"Height_Modulated_IoU\"\n        self.TCM_first_step = True\n        self.TCM_byte_step = True\n        self.TCM_first_step_weight = 1.0\n        self.TCM_byte_step_weight = 1.0\n        self.hybrid_sort_with_reid = True\n        self.with_fastreid =True\n        self.EG_weight_high_score= 1.3\n        self.EG_weight_low_score= 1.2\n        self.fast_reid_config = \"fast_reid/configs/MOT17/sbs_S50.yml\"\n        self.fast_reid_weights = \"pretrained/mot17_sbs_S50.pth\"\n        self.with_longterm_reid_correction = True\n        self.longterm_reid_correction_thresh = 0.4\n        self.longterm_reid_correction_thresh_low = 0.4\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_det\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='test',   # change to train when running on training set\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mix_det_train.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"train.json\"    # change to train.json when running on training set\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_det\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',   # change to train when running on training set\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mix_mot20_ch.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"test.json\"   # change to train.json when running on training set\n        self.input_size = (896, 1600)\n        self.test_size = (896, 1600)\n        #self.test_size = (736, 1920)\n        self.random_size = (20, 36)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_mot20_ch\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=600,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1200,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"MOT20\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='test', # change to train when running on training set\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mix_mot20_ch_hybrid_sort.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"test.json\"   # change to train.json when running on training set\n        self.input_size = (896, 1600)\n        self.test_size = (896, 1600)\n        #self.test_size = (736, 1920)\n        self.random_size = (20, 36)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n        # tracking params for Hybrid-SORT\n        self.ckpt = \"pretrained/ocsort_x_mot20.pth.tar\"\n        self.use_byte = True\n        self.dataset = \"mot20\"\n        self.track_thresh = 0.4\n        self.inertia = 0.05\n        self.iou_thresh = 0.25\n        self.asso = \"Height_Modulated_IoU\"\n        self.TCM_first_step = True\n        self.TCM_byte_step = True\n        self.TCM_first_step_weight = 1.0\n        self.TCM_byte_step_weight = 1.0\n        self.hybrid_sort_with_reid = False\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_mot20_ch\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=600,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1200,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"MOT20\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='test', # change to train when running on training set\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mix_mot20_ch_hybrid_sort_reid.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"test.json\"   # change to train.json when running on training set\n        self.input_size = (896, 1600)\n        self.test_size = (896, 1600)\n        #self.test_size = (736, 1920)\n        self.random_size = (20, 36)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n        # tracking params for Hybrid-SORT-ReID\n        self.ckpt = \"pretrained/ocsort_x_mot20.pth.tar\"\n        self.use_byte = True\n        self.dataset = \"mot20\"\n        self.track_thresh = 0.4\n        self.inertia = 0.05\n        self.iou_thresh = 0.25\n        self.asso = \"Height_Modulated_IoU\"\n        self.TCM_first_step = True\n        self.TCM_byte_step = True\n        self.TCM_first_step_weight = 1.0\n        self.TCM_byte_step_weight = 1.0\n        self.hybrid_sort_with_reid = True\n        self.with_fastreid =True\n        self.EG_weight_high_score= 4.6\n        self.EG_weight_low_score= 2.4\n        self.fast_reid_config = \"fast_reid/configs/MOT20/sbs_S50.yml\"\n        self.fast_reid_weights = \"pretrained/mot20_sbs_S50.pth\"\n        self.with_longterm_reid_correction = False\n        self.longterm_reid_correction_thresh = 1.0\n        self.longterm_reid_correction_thresh_low = 1.0\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_mot20_ch\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=600,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1200,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"MOT20\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='test', # change to train when running on training set\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mix_mot20_ch_train.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"train.json\"   # change to train.json when running on training set\n        self.input_size = (896, 1600)\n        self.test_size = (896, 1600)\n        #self.test_size = (736, 1920)\n        self.random_size = (20, 36)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_mot20_ch\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=600,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1200,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"MOT20\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train', # change to train when running on training set\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mix_mot20_ch_valhalf.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val_half.json\"   # change to val_half.json when running on training set\n        self.input_size = (896, 1600)\n        self.test_size = (896, 1600)\n        #self.test_size = (736, 1920)\n        self.random_size = (20, 36)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.001\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_mot20_ch\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=600,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1200,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False, run_tracking=False):   # [hgx0411] dataloader related\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"MOT20\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train', # change to train when running on training set\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n            run_tracking=run_tracking\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev, run_tracking=False)      # [hgx0411] dataloader related\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mot17_ablation_half_train.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"train_half.json\"\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mix_mot_ch\"),\n            json_file=self.train_ann,\n            name='',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False):\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mot17_half.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"val_half.json\"\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.train_ann,\n            name='train',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False):\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/example/mot/yolox_x_mot17_train.py",
    "content": "# encoding: utf-8\nimport os\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\n\nfrom yolox.exp import Exp as MyExp\nfrom yolox.data import get_yolox_datadir\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.num_classes = 1\n        self.depth = 1.33\n        self.width = 1.25\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n        self.train_ann = \"train.json\"\n        self.val_ann = \"train.json\"\n        self.input_size = (800, 1440)\n        self.test_size = (800, 1440)\n        self.random_size = (18, 32)\n        self.max_epoch = 80\n        self.print_interval = 20\n        self.eval_interval = 5\n        self.test_conf = 0.1\n        self.nmsthre = 0.7\n        self.no_aug_epochs = 10\n        self.basic_lr_per_img = 0.001 / 64.0\n        self.warmup_epochs = 1\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            MOTDataset,\n            TrainTransform,\n            YoloBatchSampler,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n        )\n\n        dataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.train_ann,\n            name='train',\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=500,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=1000,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(\n            len(self.dataset), seed=self.seed if self.seed else 0\n        )\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False):\n        from yolox.data import MOTDataset, ValTransform\n\n        valdataset = MOTDataset(\n            data_dir=os.path.join(get_yolox_datadir(), \"mot\"),\n            json_file=self.val_ann,\n            img_size=self.test_size,\n            name='train',\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n            ),\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0000.txt",
    "content": "0 1 Car -1 -1 -1 720.30 170.64 914.77 310.69 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n0 2 Car -1 -1 -1 676.65 173.45 793.68 258.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n0 3 Van -1 -1 -1 167.54 136.85 449.33 335.32 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n0 4 Car -1 -1 -1 626.85 173.56 652.13 195.94 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n0 5 Car -1 -1 -1 643.38 166.57 682.57 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n0 6 Car -1 -1 -1 658.34 175.54 719.42 219.53 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n0 7 Car -1 -1 -1 1.12 213.18 57.59 367.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n0 8 Car -1 -1 -1 669.43 176.70 739.41 227.67 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n0 9 Car -1 -1 -1 404.72 176.59 515.39 234.17 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n1 1 Car -1 -1 -1 728.80 171.89 945.69 324.77 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1 2 Car -1 -1 -1 679.35 172.55 806.64 262.64 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1 4 Car -1 -1 -1 627.02 174.84 652.19 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1 8 Car -1 -1 -1 670.19 177.43 746.17 232.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n1 5 Car -1 -1 -1 643.70 165.68 686.84 204.78 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n1 6 Car -1 -1 -1 660.40 177.71 724.36 223.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n1 9 Car -1 -1 -1 388.03 178.72 516.01 238.35 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n1 3 Van -1 -1 -1 106.65 134.38 440.12 353.05 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n2 1 Car -1 -1 -1 737.39 172.70 983.71 345.68 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n2 2 Car -1 -1 -1 685.06 174.38 817.27 268.35 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n2 8 Car -1 -1 -1 671.18 179.73 746.60 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n2 5 Car -1 -1 -1 644.26 166.47 688.92 206.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n2 6 Car -1 -1 -1 661.66 178.67 725.53 225.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n2 9 Car -1 -1 -1 378.80 182.10 510.18 244.95 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n2 4 Car -1 -1 -1 627.25 175.21 655.12 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n2 3 Van -1 -1 -1 19.37 128.65 419.86 366.54 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n2 10 Car -1 -1 -1 480.97 179.55 531.78 213.68 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n2 11 Car -1 -1 -1 617.17 174.08 643.58 194.19 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n3 1 Car -1 -1 -1 749.18 171.53 1031.98 369.10 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n3 2 Car -1 -1 -1 687.92 174.62 831.39 274.50 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n3 8 Car -1 -1 -1 675.11 178.51 755.28 238.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n3 6 Car -1 -1 -1 662.68 178.20 730.37 226.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n3 5 Car -1 -1 -1 644.40 166.56 689.43 206.57 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n3 9 Car -1 -1 -1 360.31 183.48 505.28 249.83 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n3 4 Car -1 -1 -1 628.80 176.52 655.66 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n3 10 Car -1 -1 -1 480.72 180.55 530.77 213.17 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n3 11 Car -1 -1 -1 617.03 173.64 644.63 194.43 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n4 1 Car -1 -1 -1 763.40 170.88 1088.08 372.20 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n4 2 Car -1 -1 -1 693.19 173.21 847.53 277.91 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n4 5 Car -1 -1 -1 645.44 165.88 693.26 206.45 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n4 8 Car -1 -1 -1 676.54 177.92 761.44 239.71 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n4 6 Car -1 -1 -1 663.00 177.19 736.48 227.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n4 4 Car -1 -1 -1 627.84 175.29 656.02 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n4 9 Car -1 -1 -1 342.78 182.85 499.46 251.95 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n4 10 Car -1 -1 -1 476.33 180.51 528.02 214.87 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n5 1 Car -1 -1 -1 774.65 169.84 1170.40 371.11 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n5 2 Car -1 -1 -1 698.27 172.05 865.22 286.16 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n5 8 Car -1 -1 -1 679.27 177.51 766.30 241.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n5 5 Car -1 -1 -1 646.12 165.27 694.48 206.76 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n5 6 Car -1 -1 -1 665.78 176.56 736.19 227.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n5 4 Car -1 -1 -1 628.54 174.84 657.26 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n5 10 Car -1 -1 -1 471.02 180.08 525.70 215.85 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n5 9 Car -1 -1 -1 324.39 185.56 494.24 256.56 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n5 12 Car -1 -1 -1 641.58 174.11 679.74 203.61 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n6 1 Car -1 -1 -1 799.66 166.95 1236.96 368.74 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n6 2 Car -1 -1 -1 704.04 173.11 891.42 297.13 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n6 8 Car -1 -1 -1 680.27 177.50 774.41 245.42 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n6 6 Car -1 -1 -1 667.18 176.48 740.57 228.52 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n6 5 Car -1 -1 -1 647.98 164.61 695.83 207.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n6 4 Car -1 -1 -1 629.00 175.23 656.35 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n6 10 Car -1 -1 -1 458.62 180.53 522.64 219.14 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n6 9 Car -1 -1 -1 327.25 184.78 491.86 257.28 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n6 12 Car -1 -1 -1 641.97 175.15 679.15 204.46 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n6 13 Car -1 -1 -1 295.24 188.48 484.66 260.36 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n7 1 Car -1 -1 -1 819.73 171.58 1233.46 369.25 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n7 2 Car -1 -1 -1 708.53 172.90 918.85 307.26 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n7 8 Car -1 -1 -1 683.82 176.44 785.93 250.61 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n7 6 Car -1 -1 -1 670.31 177.66 744.31 231.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n7 5 Car -1 -1 -1 647.69 165.28 696.90 208.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n7 10 Car -1 -1 -1 452.65 181.40 521.53 221.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n7 4 Car -1 -1 -1 628.23 176.22 657.20 200.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n7 9 Car -1 -1 -1 310.50 187.06 484.50 261.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n7 12 Car -1 -1 -1 642.53 174.68 679.36 206.48 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n8 1 Car -1 -1 -1 844.98 163.75 1237.61 370.47 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n8 2 Car -1 -1 -1 711.76 171.70 953.55 322.19 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n8 8 Car -1 -1 -1 687.07 176.09 792.25 255.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n8 5 Car -1 -1 -1 648.25 165.35 698.84 211.09 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n8 6 Car -1 -1 -1 673.49 177.86 748.82 234.84 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n8 4 Car -1 -1 -1 628.30 176.39 658.06 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n8 10 Car -1 -1 -1 448.14 182.46 517.79 222.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n8 9 Car -1 -1 -1 268.95 190.67 472.31 275.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n8 12 Car -1 -1 -1 643.88 177.05 679.89 207.01 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n8 14 Car -1 -1 -1 3.70 205.70 177.85 289.16 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n8 15 Car -1 -1 -1 505.39 180.25 561.78 213.79 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n9 2 Car -1 -1 -1 719.55 169.18 991.50 334.72 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n9 1 Car -1 -1 -1 873.13 166.52 1241.44 368.80 -1 -1 -1 -1000 -1000 -1000 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0.95\n10 1 Car -1 -1 -1 924.88 175.03 1236.38 367.43 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 5 Car -1 -1 -1 649.77 164.14 703.69 212.74 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 9 Car -1 -1 -1 191.66 190.76 455.59 303.33 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n10 4 Car -1 -1 -1 627.40 176.02 659.15 202.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n10 10 Car -1 -1 -1 432.50 182.60 511.86 226.21 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n10 15 Car -1 -1 -1 494.09 180.26 557.41 214.35 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n10 14 Car -1 -1 -1 -0.47 210.27 151.53 300.92 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n10 12 Car -1 -1 -1 643.57 176.56 681.65 207.80 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n11 2 Car -1 -1 -1 737.93 172.41 1098.02 370.15 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n11 8 Car -1 -1 -1 695.86 176.61 828.19 272.50 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n11 6 Car -1 -1 -1 679.91 180.40 759.65 243.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n11 5 Car -1 -1 -1 649.56 165.57 705.13 215.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n11 9 Car -1 -1 -1 152.42 190.41 449.38 319.18 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n11 10 Car -1 -1 -1 423.37 183.95 511.36 228.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n11 4 Car -1 -1 -1 627.46 177.38 658.94 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n11 1 Car -1 -1 -1 998.23 200.62 1241.13 364.60 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n11 14 Car -1 -1 -1 -1.26 206.59 121.24 304.55 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n11 12 Car -1 -1 -1 644.15 177.22 684.90 210.52 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n11 15 Car -1 -1 -1 489.78 182.78 554.39 217.61 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n11 16 Car -1 -1 -1 258.27 185.15 429.12 256.21 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n12 2 Car -1 -1 -1 750.00 173.48 1178.14 369.19 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n12 8 Car -1 -1 -1 701.47 179.27 839.61 279.29 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n12 6 Car -1 -1 -1 683.55 182.41 765.94 249.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n12 9 Car -1 -1 -1 118.37 192.83 444.66 333.17 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n12 5 Car -1 -1 -1 650.83 167.17 706.40 218.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n12 10 Car -1 -1 -1 413.42 186.59 507.57 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n12 4 Car -1 -1 -1 628.77 179.76 658.36 204.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n12 12 Car -1 -1 -1 643.64 178.77 685.47 214.14 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n12 14 Car -1 -1 -1 0.09 218.28 95.85 308.46 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n12 15 Car -1 -1 -1 483.81 184.22 552.55 219.95 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n13 2 Car -1 -1 -1 761.35 174.68 1237.85 366.93 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n13 8 Car -1 -1 -1 705.22 180.70 858.47 289.78 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n13 10 Car -1 -1 -1 401.67 187.85 502.92 237.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n13 6 Car -1 -1 -1 685.96 183.58 776.29 255.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n13 5 Car -1 -1 -1 651.87 167.40 708.64 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n13 9 Car -1 -1 -1 70.65 191.55 437.78 351.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n13 4 Car -1 -1 -1 628.02 181.56 659.35 207.12 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n13 12 Car -1 -1 -1 643.65 179.88 686.31 215.61 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n13 14 Car -1 -1 -1 0.32 222.19 66.94 319.38 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n13 15 Car -1 -1 -1 477.95 185.67 542.56 223.26 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n13 17 Car -1 -1 -1 400.26 186.36 457.24 217.97 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n13 18 Car -1 -1 -1 616.26 176.83 645.00 201.05 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n14 2 Car -1 -1 -1 779.47 172.90 1235.00 368.86 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n14 8 Car -1 -1 -1 710.44 180.12 878.04 299.09 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n14 6 Car -1 -1 -1 688.27 182.75 784.05 258.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n14 10 Car -1 -1 -1 396.91 187.58 498.12 238.43 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n14 5 Car -1 -1 -1 652.34 166.38 712.50 222.38 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 9 Car -1 -1 -1 14.20 187.30 431.68 370.19 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 4 Car -1 -1 -1 628.29 180.87 659.31 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n14 12 Car -1 -1 -1 644.57 178.93 687.67 215.91 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n14 15 Car -1 -1 -1 484.96 185.37 550.82 223.07 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n14 14 Car -1 -1 -1 -0.10 216.45 41.87 333.10 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n14 17 Car -1 -1 -1 396.19 185.46 453.87 217.75 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n15 2 Car -1 -1 -1 801.76 171.82 1234.07 370.77 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n15 8 Car -1 -1 -1 716.91 180.10 908.51 307.61 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n15 6 Car -1 -1 -1 691.11 180.42 795.81 260.96 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n15 5 Car -1 -1 -1 652.86 163.42 716.93 221.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n15 10 Car -1 -1 -1 391.94 186.56 494.63 237.49 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n15 9 Car -1 -1 -1 7.94 188.30 414.69 362.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n15 4 Car -1 -1 -1 628.68 178.56 661.12 205.30 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n15 15 Car -1 -1 -1 472.82 184.50 548.33 223.22 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n15 12 Car -1 -1 -1 645.51 178.34 691.89 214.60 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n15 17 Car -1 -1 -1 391.79 184.09 450.40 216.94 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n16 8 Car -1 -1 -1 721.51 178.86 928.72 318.16 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n16 2 Car -1 -1 -1 823.14 179.40 1237.11 369.66 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n16 10 Car -1 -1 -1 381.45 185.19 491.78 238.85 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n16 6 Car -1 -1 -1 694.07 179.76 806.25 267.19 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n16 9 Car -1 -1 -1 3.46 189.68 388.55 367.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n16 5 Car -1 -1 -1 654.76 162.49 720.93 221.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n16 12 Car -1 -1 -1 645.41 176.87 694.56 215.49 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n16 15 Car -1 -1 -1 467.15 183.01 546.25 221.83 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n16 4 Car -1 -1 -1 626.61 177.68 660.31 204.12 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n16 17 Car -1 -1 -1 392.64 182.42 449.42 213.59 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n17 8 Car -1 -1 -1 727.58 179.91 968.99 337.00 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n17 6 Car -1 -1 -1 695.48 180.68 814.20 270.30 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n17 9 Car -1 -1 -1 1.49 191.09 373.17 366.47 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n17 10 Car -1 -1 -1 373.44 184.59 485.99 240.75 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n17 2 Car -1 -1 -1 846.94 189.38 1237.86 367.56 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n17 5 Car -1 -1 -1 655.23 163.11 723.82 223.16 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n17 15 Car -1 -1 -1 467.84 182.82 544.93 222.08 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n17 4 Car -1 -1 -1 628.10 178.48 661.76 205.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n17 17 Car -1 -1 -1 387.86 181.65 446.63 214.05 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n17 12 Car -1 -1 -1 646.54 177.38 694.61 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n18 8 Car -1 -1 -1 738.68 179.43 1010.99 353.87 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n18 6 Car -1 -1 -1 700.17 182.28 825.82 276.25 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n18 9 Car -1 -1 -1 2.36 195.67 349.34 367.43 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n18 10 Car -1 -1 -1 365.69 185.30 482.24 242.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n18 2 Car -1 -1 -1 891.47 193.84 1239.61 371.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n18 5 Car -1 -1 -1 656.60 162.45 727.89 224.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n18 4 Car -1 -1 -1 626.96 178.51 660.28 206.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 17 Car -1 -1 -1 384.52 181.01 442.02 214.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n18 15 Car -1 -1 -1 462.18 183.72 543.26 224.40 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n18 12 Car -1 -1 -1 646.70 178.50 698.38 217.36 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n18 19 Car -1 -1 -1 275.84 190.59 357.23 229.09 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n19 8 Car -1 -1 -1 751.49 181.24 1061.64 370.01 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n19 6 Car -1 -1 -1 705.22 184.08 842.49 282.20 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n19 10 Car -1 -1 -1 355.15 186.56 477.10 246.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n19 9 Car -1 -1 -1 5.74 196.62 316.52 367.64 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n19 4 Car -1 -1 -1 628.05 179.44 662.21 208.00 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n19 5 Car -1 -1 -1 659.77 161.84 732.92 227.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n19 19 Car -1 -1 -1 274.14 192.04 349.56 231.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n19 17 Car -1 -1 -1 377.48 180.96 434.03 214.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n19 15 Car -1 -1 -1 456.40 184.49 541.61 225.92 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n19 12 Car -1 -1 -1 647.59 179.83 700.43 219.38 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n19 2 Car -1 -1 -1 962.80 214.09 1237.58 366.46 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n19 20 Car -1 -1 -1 331.58 187.07 402.41 223.93 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n19 21 Car -1 -1 -1 619.80 176.47 650.29 202.11 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n20 8 Car -1 -1 -1 761.35 186.05 1135.72 369.61 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n20 6 Car -1 -1 -1 711.17 183.94 859.80 293.88 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n20 9 Car -1 -1 -1 4.91 197.11 285.39 367.61 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n20 10 Car -1 -1 -1 345.65 188.32 468.20 249.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n20 4 Car -1 -1 -1 628.30 179.85 662.79 208.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n20 19 Car -1 -1 -1 260.23 193.16 342.77 234.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n20 5 Car -1 -1 -1 660.34 162.82 735.88 230.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n20 12 Car -1 -1 -1 649.86 179.26 702.99 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n20 17 Car -1 -1 -1 372.64 184.17 431.04 217.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n20 15 Car -1 -1 -1 444.53 186.02 537.68 229.95 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n20 21 Car -1 -1 -1 619.18 176.42 649.85 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n20 20 Car -1 -1 -1 331.10 187.56 402.42 224.08 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n21 8 Car -1 -1 -1 775.28 183.16 1223.92 368.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n21 6 Car -1 -1 -1 716.38 186.08 879.59 302.44 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n21 10 Car -1 -1 -1 336.28 188.66 464.55 253.96 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n21 9 Car -1 -1 -1 2.84 199.33 240.96 366.76 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n21 19 Car -1 -1 -1 249.73 193.63 336.46 237.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n21 5 Car -1 -1 -1 662.29 162.66 741.31 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n21 15 Car -1 -1 -1 434.67 185.46 539.66 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 4 Car -1 -1 -1 629.89 181.24 664.07 210.27 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 12 Car -1 -1 -1 650.65 179.82 705.84 222.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 17 Car -1 -1 -1 367.91 184.38 428.01 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n21 20 Car -1 -1 -1 322.42 189.23 396.37 227.67 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n22 8 Car -1 -1 -1 794.72 186.72 1235.75 369.26 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n22 6 Car -1 -1 -1 723.99 189.45 903.10 313.72 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n22 10 Car -1 -1 -1 323.94 189.35 458.26 257.25 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n22 4 Car -1 -1 -1 627.91 180.87 664.03 211.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n22 19 Car -1 -1 -1 238.26 193.99 329.95 237.43 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n22 15 Car -1 -1 -1 425.29 185.62 541.63 234.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n22 9 Car -1 -1 -1 -0.54 206.67 175.61 366.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n22 12 Car -1 -1 -1 651.58 181.78 709.91 225.49 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n22 17 Car -1 -1 -1 362.28 184.53 425.86 217.67 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n22 20 Car -1 -1 -1 316.64 188.96 393.52 227.74 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n22 5 Car -1 -1 -1 664.80 162.49 746.79 236.62 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n23 8 Car -1 -1 -1 817.83 188.73 1234.34 369.28 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n23 10 Car -1 -1 -1 312.33 189.08 451.85 260.27 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n23 6 Car -1 -1 -1 732.00 187.58 941.06 329.58 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n23 19 Car -1 -1 -1 227.31 193.30 321.37 238.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n23 15 Car -1 -1 -1 419.37 186.25 540.22 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n23 12 Car -1 -1 -1 652.52 180.03 709.64 224.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n23 4 Car -1 -1 -1 628.46 180.44 665.42 210.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n23 17 Car -1 -1 -1 356.95 184.22 423.54 218.05 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n23 5 Car -1 -1 -1 666.77 160.70 752.75 236.19 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n23 20 Car -1 -1 -1 308.89 189.61 386.48 226.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n24 8 Car -1 -1 -1 837.00 193.07 1238.80 370.38 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n24 6 Car -1 -1 -1 740.98 186.07 971.28 341.09 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n24 10 Car -1 -1 -1 300.20 188.17 443.05 263.26 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n24 19 Car -1 -1 -1 214.85 192.66 315.67 239.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n24 12 Car -1 -1 -1 653.40 179.26 710.41 224.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n24 15 Car -1 -1 -1 414.84 184.78 537.48 235.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 4 Car -1 -1 -1 630.64 178.67 667.50 209.96 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n24 20 Car -1 -1 -1 303.84 189.95 383.22 226.85 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n24 17 Car -1 -1 -1 351.73 183.55 420.85 218.55 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n24 5 Car -1 -1 -1 668.07 157.84 759.27 237.76 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n25 6 Car -1 -1 -1 750.98 183.02 1015.02 359.02 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n25 8 Car -1 -1 -1 868.19 195.48 1238.79 369.15 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n25 10 Car -1 -1 -1 285.42 187.11 434.72 266.90 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n25 19 Car -1 -1 -1 200.69 191.56 307.50 241.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n25 4 Car -1 -1 -1 629.80 176.80 669.97 208.70 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n25 15 Car -1 -1 -1 408.04 183.14 535.65 236.86 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n25 12 Car -1 -1 -1 654.07 177.24 714.69 224.28 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n25 5 Car -1 -1 -1 671.59 155.25 766.32 238.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n25 17 Car -1 -1 -1 346.65 182.49 418.42 219.55 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n25 20 Car -1 -1 -1 298.09 189.91 381.07 227.26 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n26 10 Car -1 -1 -1 270.13 185.69 426.16 270.98 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n26 6 Car -1 -1 -1 762.25 182.84 1065.83 373.84 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n26 8 Car -1 -1 -1 904.80 198.82 1241.47 366.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 19 Car -1 -1 -1 186.78 190.59 299.75 241.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n26 5 Car -1 -1 -1 673.32 153.20 772.05 239.43 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n26 15 Car -1 -1 -1 395.33 181.57 532.88 237.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n26 4 Car -1 -1 -1 629.35 175.33 671.64 208.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n26 12 Car -1 -1 -1 654.75 175.67 716.11 224.02 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n26 17 Car -1 -1 -1 342.91 182.43 414.10 219.68 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n27 10 Car -1 -1 -1 254.46 188.24 415.51 278.23 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n27 6 Car -1 -1 -1 776.27 186.09 1136.85 370.91 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n27 19 Car -1 -1 -1 173.09 190.82 295.86 244.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n27 12 Car -1 -1 -1 655.82 175.12 721.27 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n27 15 Car -1 -1 -1 390.66 181.98 529.25 242.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n27 4 Car -1 -1 -1 632.13 175.96 673.34 208.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n27 5 Car -1 -1 -1 675.51 152.24 779.49 243.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n27 8 Car -1 -1 -1 969.29 214.88 1239.03 366.18 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n27 17 Car -1 -1 -1 337.63 182.44 411.51 219.62 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n28 10 Car -1 -1 -1 235.53 190.95 405.29 287.82 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n28 6 Car -1 -1 -1 792.04 183.19 1221.87 368.41 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n28 4 Car -1 -1 -1 631.83 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682.58 152.83 796.81 250.09 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n29 17 Car -1 -1 -1 330.50 184.16 403.31 225.17 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n30 10 Car -1 -1 -1 191.07 193.13 381.39 303.72 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n30 6 Car -1 -1 -1 835.54 187.60 1239.05 369.47 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n30 15 Car -1 -1 -1 351.26 182.16 521.95 251.78 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n30 19 Car -1 -1 -1 123.92 194.44 260.49 255.15 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 12 Car -1 -1 -1 662.82 175.66 728.52 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 4 Car -1 -1 -1 635.19 175.76 677.65 211.43 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n30 17 Car -1 -1 -1 321.50 182.75 396.95 226.31 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n30 5 Car -1 -1 -1 683.97 149.61 808.43 253.33 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n30 23 Van -1 -1 -1 683.30 150.03 806.14 252.83 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n31 6 Car -1 -1 -1 862.87 188.57 1236.82 369.31 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n31 10 Car -1 -1 -1 169.66 191.72 367.69 311.64 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n31 15 Car -1 -1 -1 336.02 180.27 514.98 253.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n31 12 Car -1 -1 -1 664.56 173.10 736.61 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n31 4 Car -1 -1 -1 635.59 174.30 679.52 210.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 19 Car -1 -1 -1 104.78 192.91 248.39 255.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n31 23 Van -1 -1 -1 687.30 146.00 820.87 256.31 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n31 17 Car -1 -1 -1 317.44 181.38 393.20 222.18 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n32 10 Car -1 -1 -1 135.21 188.91 349.78 322.67 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n32 6 Car -1 -1 -1 903.07 197.88 1234.94 368.00 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n32 12 Car -1 -1 -1 665.37 171.69 742.73 231.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n32 15 Car -1 -1 -1 317.19 180.03 510.31 254.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n32 19 Car -1 -1 -1 87.08 192.99 227.93 255.90 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n32 4 Car -1 -1 -1 635.70 172.47 682.35 209.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n32 23 Van -1 -1 -1 690.26 143.35 833.78 260.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n32 17 Car -1 -1 -1 312.07 180.28 390.44 222.07 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n33 10 Car -1 -1 -1 106.18 192.35 324.71 334.24 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n33 12 Car -1 -1 -1 666.96 171.73 750.06 235.49 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n33 15 Car -1 -1 -1 298.29 179.79 506.13 260.31 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n33 6 Car -1 -1 -1 953.28 205.15 1239.29 368.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n33 4 Car -1 -1 -1 636.95 172.76 684.98 211.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n33 19 Car -1 -1 -1 65.46 192.76 218.26 257.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n33 23 Van -1 -1 -1 692.60 140.43 847.42 264.20 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n33 17 Car -1 -1 -1 302.75 180.02 384.68 221.95 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n33 24 Car -1 -1 -1 693.15 142.74 846.72 265.21 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n34 10 Car -1 -1 -1 68.02 196.08 300.89 352.88 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n34 12 Car -1 -1 -1 671.05 173.21 755.57 237.96 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n34 15 Car -1 -1 -1 275.35 180.91 505.49 266.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n34 4 Car -1 -1 -1 638.40 173.85 686.93 212.56 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n34 19 Car -1 -1 -1 43.06 194.06 208.85 263.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n34 23 Van -1 -1 -1 698.27 140.08 863.87 271.83 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n34 17 Car -1 -1 -1 295.45 181.13 384.15 221.64 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n34 24 Car -1 -1 -1 697.99 142.96 860.22 272.15 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n35 10 Car -1 -1 -1 22.41 197.33 276.48 366.47 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n35 15 Car -1 -1 -1 259.02 182.01 498.17 275.08 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n35 12 Car -1 -1 -1 672.20 174.90 766.11 243.06 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n35 4 Car -1 -1 -1 638.67 175.65 690.71 216.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n35 19 Car -1 -1 -1 12.56 195.23 185.42 269.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n35 17 Car -1 -1 -1 279.18 182.20 384.87 243.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n35 23 Van -1 -1 -1 704.74 139.40 881.51 280.61 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n35 24 Car -1 -1 -1 705.19 142.14 880.92 282.15 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n35 25 Car -1 -1 -1 164.21 193.04 274.33 247.24 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n36 10 Car -1 -1 -1 -2.16 196.89 245.42 369.83 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n36 12 Car -1 -1 -1 675.68 175.97 772.48 249.41 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n36 15 Car -1 -1 -1 239.37 183.50 493.58 282.07 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n36 4 Car -1 -1 -1 639.79 177.77 692.30 219.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n36 25 Car -1 -1 -1 147.88 194.28 260.32 248.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n36 17 Car -1 -1 -1 261.11 187.43 370.88 246.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n36 24 Car -1 -1 -1 709.37 141.64 901.27 292.45 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n36 23 Van -1 -1 -1 709.30 143.74 901.06 294.74 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n37 10 Car -1 -1 -1 0.01 199.31 197.80 367.24 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n37 12 Car -1 -1 -1 678.45 178.06 783.88 255.07 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n37 15 Car -1 -1 -1 217.99 185.66 491.11 292.05 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n37 4 Car -1 -1 -1 641.17 179.40 695.27 221.63 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n37 25 Car -1 -1 -1 130.58 195.69 253.06 254.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n37 17 Car -1 -1 -1 234.72 189.14 351.28 253.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n37 19 Car -1 -1 -1 2.86 197.96 171.88 274.66 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n37 24 Car -1 -1 -1 713.95 138.20 928.00 304.57 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n37 23 Van -1 -1 -1 713.95 138.20 928.00 304.57 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n38 12 Car -1 -1 -1 681.40 180.28 791.48 260.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n38 4 Car -1 -1 -1 641.86 180.54 697.50 224.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n38 15 Car -1 -1 -1 187.59 189.98 484.43 299.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n38 10 Car -1 -1 -1 -1.98 211.45 144.89 368.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n38 19 Car -1 -1 -1 3.19 202.11 147.86 284.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n38 25 Car -1 -1 -1 112.99 197.58 239.79 260.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n38 17 Car -1 -1 -1 206.82 193.68 333.37 256.54 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n38 24 Car -1 -1 -1 720.30 138.78 960.08 316.59 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n38 23 Van -1 -1 -1 722.27 138.44 963.66 316.81 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n39 12 Car -1 -1 -1 685.43 180.65 807.74 267.03 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n39 15 Car -1 -1 -1 160.37 193.27 478.70 309.38 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n39 4 Car -1 -1 -1 644.14 181.93 700.81 225.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n39 25 Car -1 -1 -1 90.80 198.30 230.71 265.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n39 19 Car -1 -1 -1 1.74 201.54 132.73 292.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n39 17 Car -1 -1 -1 178.35 196.68 322.33 267.21 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n39 10 Car -1 -1 -1 -3.31 219.18 70.12 369.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n39 23 Van -1 -1 -1 728.36 135.15 997.38 335.26 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n39 24 Car -1 -1 -1 728.03 134.15 992.87 331.19 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n40 15 Car -1 -1 -1 124.73 192.98 469.61 324.80 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n40 12 Car -1 -1 -1 688.62 181.58 820.58 273.83 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n40 4 Car -1 -1 -1 646.81 182.79 704.84 228.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n40 17 Car -1 -1 -1 144.10 198.30 302.19 273.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n40 19 Car -1 -1 -1 2.24 203.47 109.39 300.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n40 25 Car -1 -1 -1 70.19 200.50 213.53 269.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n40 24 Car -1 -1 -1 735.87 130.80 1039.59 356.03 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n40 23 Van -1 -1 -1 739.21 128.93 1048.13 357.64 -1 -1 -1 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-1000 -1000 -1000 -10 0.71\n42 19 Car -1 -1 -1 0.28 203.97 64.56 314.30 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n42 27 Car -1 -1 -1 218.70 189.67 336.48 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n42 24 Car -1 -1 -1 754.47 126.14 1175.16 368.06 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n42 28 Van -1 -1 -1 751.90 110.46 1191.33 369.42 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n42 29 Pedestrian -1 -1 -1 639.73 178.28 659.03 232.35 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n42 30 Car -1 -1 -1 358.45 188.61 413.50 214.38 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n43 12 Car -1 -1 -1 705.80 182.24 865.29 291.36 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n43 15 Car -1 -1 -1 -5.23 193.97 436.35 364.61 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n43 4 Car -1 -1 -1 650.13 182.93 717.87 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n43 25 Car -1 -1 -1 2.77 204.03 171.84 275.52 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n43 17 Car -1 -1 -1 3.77 204.36 233.00 307.23 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n43 30 Car -1 -1 -1 349.34 186.75 415.90 222.70 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n43 19 Car -1 -1 -1 1.01 205.26 34.52 313.26 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n43 28 Van -1 -1 -1 774.24 96.43 1238.76 367.63 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n44 15 Car -1 -1 -1 -5.21 193.55 420.37 370.68 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n44 12 Car -1 -1 -1 715.04 181.71 887.77 298.48 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n44 4 Car -1 -1 -1 652.78 181.85 722.98 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n44 17 Car -1 -1 -1 2.91 206.05 187.04 312.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n44 25 Car -1 -1 -1 -3.54 205.05 147.37 281.97 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n44 30 Car -1 -1 -1 351.37 186.12 414.29 221.42 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n44 31 Car -1 -1 -1 787.91 64.91 1240.80 368.59 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n45 15 Car -1 -1 -1 -0.51 195.73 401.35 369.52 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n45 12 Car -1 -1 -1 721.19 181.87 912.63 306.05 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n45 4 Car -1 -1 -1 655.53 180.62 728.71 234.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n45 17 Car -1 -1 -1 1.26 202.41 134.43 324.17 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n45 30 Car -1 -1 -1 352.68 184.19 411.98 218.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n45 25 Car -1 -1 -1 2.90 206.14 118.03 288.98 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n45 32 Pedestrian -1 -1 -1 648.41 175.28 668.55 233.95 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n46 15 Car -1 -1 -1 -3.07 197.98 386.24 367.91 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n46 12 Car -1 -1 -1 729.40 181.03 943.63 315.75 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n46 4 Car -1 -1 -1 660.42 180.14 734.21 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n46 30 Car -1 -1 -1 348.00 183.09 408.29 219.14 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n46 27 Car -1 -1 -1 115.71 186.66 400.43 324.51 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n46 32 Pedestrian -1 -1 -1 651.84 173.12 672.70 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n46 25 Car -1 -1 -1 -5.98 201.70 87.55 285.40 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n47 15 Car -1 -1 -1 1.77 205.58 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Car -1 -1 -1 782.16 187.23 1139.26 368.49 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n50 4 Car -1 -1 -1 677.62 181.40 768.25 249.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n50 27 Car -1 -1 -1 5.85 192.96 339.56 371.80 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n50 30 Car -1 -1 -1 332.63 182.24 401.23 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n50 36 Car -1 -1 -1 653.06 175.05 685.81 203.62 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n50 32 Pedestrian -1 -1 -1 667.90 172.60 693.26 246.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n50 15 Car -1 -1 -1 0.98 210.21 212.28 370.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n50 33 Car -1 -1 -1 141.43 185.64 281.78 246.88 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n50 37 Car -1 -1 -1 387.56 184.14 532.22 240.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n51 12 Car -1 -1 -1 796.97 182.47 1225.30 367.46 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n51 27 Car -1 -1 -1 0.20 190.05 314.16 368.70 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n51 4 Car -1 -1 -1 684.94 181.75 776.31 251.08 -1 -1 -1 -1000 -1000 -1000 -10 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-1000 -1000 -1000 -10 0.73\n206 111 Car -1 -1 -1 385.28 169.63 519.04 217.47 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n206 114 Car -1 -1 -1 640.43 177.26 668.99 200.93 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n206 106 Car -1 -1 -1 425.55 166.02 540.60 210.32 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n206 112 Car -1 -1 -1 485.35 174.85 558.95 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n206 107 Car -1 -1 -1 513.71 169.93 576.51 195.85 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n206 115 Car -1 -1 -1 633.13 176.22 659.33 195.93 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n207 92 Car -1 -1 -1 686.33 180.60 790.15 251.89 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n207 102 Car -1 -1 -1 100.88 177.71 407.29 292.41 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n207 105 Car -1 -1 -1 660.86 178.26 707.71 216.02 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n207 104 Car -1 -1 -1 334.60 177.69 508.28 234.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n207 109 Car -1 -1 -1 647.01 178.08 681.90 207.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n207 106 Car -1 -1 -1 411.18 165.22 540.10 213.86 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n207 107 Car -1 -1 -1 514.35 171.43 575.31 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n207 111 Car -1 -1 -1 368.18 170.68 513.33 223.09 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n207 114 Car -1 -1 -1 640.13 178.36 669.53 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n207 112 Car -1 -1 -1 486.01 175.87 557.92 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n208 92 Car -1 -1 -1 690.09 182.63 801.33 259.02 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n208 102 Car -1 -1 -1 49.22 178.60 390.53 303.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n208 104 Car -1 -1 -1 322.25 180.04 504.29 238.57 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n208 105 Car -1 -1 -1 662.27 179.88 710.35 219.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n208 111 Car -1 -1 -1 355.60 171.55 510.45 224.71 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n208 109 Car -1 -1 -1 647.52 179.92 682.24 209.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n208 106 Car -1 -1 -1 405.77 167.42 537.44 217.02 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n208 114 Car -1 -1 -1 643.24 180.14 672.79 206.69 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n208 107 Car -1 -1 -1 509.01 172.39 574.01 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n208 112 Car -1 -1 -1 471.66 174.40 556.89 205.47 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n208 116 Car -1 -1 -1 634.67 179.90 659.57 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n209 92 Car -1 -1 -1 693.99 184.64 813.70 265.58 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n209 102 Car -1 -1 -1 -2.29 180.24 371.85 317.04 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n209 105 Car -1 -1 -1 663.64 181.96 714.30 222.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n209 109 Car -1 -1 -1 648.15 181.69 683.08 212.31 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n209 104 Car -1 -1 -1 311.29 180.68 499.36 242.84 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n209 111 Car -1 -1 -1 343.86 173.04 506.22 229.08 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n209 106 Car -1 -1 -1 399.10 167.45 535.86 219.01 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n209 112 Car -1 -1 -1 464.97 177.65 556.25 208.32 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643.10 182.91 673.05 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n210 119 Car -1 -1 -1 636.16 181.90 663.51 203.63 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n211 92 Car -1 -1 -1 703.27 186.56 843.99 278.86 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n211 102 Car -1 -1 -1 1.99 185.42 321.26 348.27 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n211 105 Car -1 -1 -1 664.46 182.26 722.89 225.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n211 104 Car -1 -1 -1 271.30 183.73 484.80 248.89 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n211 109 Car -1 -1 -1 648.36 181.86 685.07 214.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n211 111 Car -1 -1 -1 318.61 173.47 492.68 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n211 106 Car -1 -1 -1 381.76 167.88 530.97 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n211 112 Car -1 -1 -1 468.90 181.11 551.44 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n211 107 Car -1 -1 -1 503.01 176.13 571.45 201.27 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n211 118 Car -1 -1 -1 643.48 182.49 674.05 209.78 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n211 119 Car -1 -1 -1 635.99 182.09 663.97 203.59 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n212 92 Car -1 -1 -1 708.89 186.89 862.07 285.79 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n212 102 Car -1 -1 -1 0.57 186.28 289.84 364.32 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n212 105 Car -1 -1 -1 666.27 182.24 726.29 226.74 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n212 104 Car -1 -1 -1 247.10 183.88 477.56 255.59 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n212 109 Car -1 -1 -1 649.61 181.71 687.68 215.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n212 106 Car -1 -1 -1 380.82 167.83 530.98 224.72 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n212 111 Car -1 -1 -1 306.88 173.93 488.85 237.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n212 112 Car -1 -1 -1 464.08 180.98 549.97 211.97 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n212 107 Car -1 -1 -1 496.87 175.81 569.96 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n212 119 Car -1 -1 -1 635.90 181.94 664.05 204.14 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n212 118 Car -1 -1 -1 643.71 182.46 674.21 210.08 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n213 92 Car -1 -1 -1 715.42 189.31 882.04 296.11 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n213 102 Car -1 -1 -1 3.81 189.12 242.02 367.84 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n213 105 Car -1 -1 -1 668.76 183.41 730.89 228.73 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n213 104 Car -1 -1 -1 222.44 184.05 470.50 259.38 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n213 109 Car -1 -1 -1 650.70 183.20 689.36 216.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n213 111 Car -1 -1 -1 295.98 173.96 483.86 243.53 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n213 106 Car -1 -1 -1 375.81 167.95 528.44 226.12 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n213 112 Car -1 -1 -1 464.91 181.94 547.95 212.46 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n213 107 Car -1 -1 -1 496.95 175.69 569.62 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n213 118 Car -1 -1 -1 644.44 183.70 674.00 210.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n213 119 Car -1 -1 -1 636.08 182.34 664.27 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n213 120 Car 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-1000 -10 0.63\n215 92 Car -1 -1 -1 731.01 192.02 942.36 320.41 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n215 104 Car -1 -1 -1 165.68 187.62 457.07 275.31 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n215 105 Car -1 -1 -1 672.04 184.92 741.71 235.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n215 109 Car -1 -1 -1 651.93 184.86 693.38 219.30 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n215 111 Car -1 -1 -1 262.79 174.31 470.37 251.93 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n215 112 Car -1 -1 -1 452.99 183.58 545.13 217.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n215 102 Car -1 -1 -1 -1.83 204.51 130.75 368.46 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n215 118 Car -1 -1 -1 646.10 184.45 679.39 215.42 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n215 106 Car -1 -1 -1 363.33 167.71 525.13 232.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n215 107 Car -1 -1 -1 491.67 177.62 568.39 205.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n215 119 Car -1 -1 -1 636.04 183.58 664.50 206.21 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n215 121 Car -1 -1 -1 485.37 185.05 550.78 207.21 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n216 92 Car -1 -1 -1 738.73 191.83 981.65 335.83 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n216 104 Car -1 -1 -1 133.13 188.04 445.67 282.42 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n216 105 Car -1 -1 -1 674.53 185.08 746.88 237.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n216 109 Car -1 -1 -1 652.95 185.15 694.38 220.03 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n216 112 Car -1 -1 -1 453.23 184.16 543.64 217.80 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n216 111 Car -1 -1 -1 239.55 173.30 462.71 259.03 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n216 118 Car -1 -1 -1 646.21 184.57 683.32 216.73 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n216 107 Car -1 -1 -1 490.08 176.79 568.49 206.80 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n216 106 Car -1 -1 -1 357.75 166.89 523.11 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n216 121 Car -1 -1 -1 480.51 185.42 548.17 207.71 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n216 119 Car -1 -1 -1 636.93 183.53 665.10 206.23 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n217 92 Car -1 -1 -1 751.02 193.32 1029.51 355.49 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n217 104 Car -1 -1 -1 93.59 186.25 437.81 288.04 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n217 105 Car -1 -1 -1 676.14 183.64 750.96 239.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n217 112 Car -1 -1 -1 449.37 183.20 541.26 217.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n217 111 Car -1 -1 -1 205.94 171.56 457.66 261.58 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n217 109 Car -1 -1 -1 654.26 184.64 697.81 220.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n217 107 Car -1 -1 -1 485.15 174.77 567.31 206.18 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n217 118 Car -1 -1 -1 646.53 183.24 684.46 216.79 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n217 106 Car -1 -1 -1 347.51 165.44 517.96 234.34 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n217 119 Car -1 -1 -1 636.90 182.68 666.16 206.29 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n217 121 Car -1 -1 -1 475.47 184.10 545.39 208.53 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n218 92 Car -1 -1 -1 766.99 195.00 1084.16 369.63 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n218 104 Car -1 -1 -1 52.68 187.09 424.15 299.04 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n218 105 Car -1 -1 -1 679.67 182.22 757.89 240.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n218 109 Car -1 -1 -1 655.65 183.26 700.37 219.49 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n218 107 Car -1 -1 -1 484.47 172.93 567.36 206.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n218 112 Car -1 -1 -1 448.24 182.20 539.86 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n218 111 Car -1 -1 -1 174.39 170.08 450.30 269.33 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n218 118 Car -1 -1 -1 647.25 182.03 685.61 214.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n218 106 Car -1 -1 -1 343.36 164.82 514.38 234.72 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n218 119 Car -1 -1 -1 637.61 181.09 669.23 206.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n218 121 Car -1 -1 -1 475.88 182.73 545.25 206.08 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n219 92 Car -1 -1 -1 775.04 194.62 1176.20 370.29 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n219 104 Car -1 -1 -1 3.05 187.48 411.02 309.95 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n219 105 Car -1 -1 -1 683.51 180.78 765.32 241.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n219 109 Car -1 -1 -1 657.73 181.78 704.02 219.08 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n219 112 Car -1 -1 -1 440.18 178.67 540.79 218.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n219 118 Car -1 -1 -1 648.96 179.99 689.16 214.99 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n219 111 Car -1 -1 -1 160.45 170.53 440.72 270.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n219 107 Car -1 -1 -1 482.99 171.83 567.84 205.14 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n219 106 Car -1 -1 -1 332.33 163.75 509.91 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n219 119 Car -1 -1 -1 639.43 180.20 669.45 204.99 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n219 121 Car -1 -1 -1 469.91 181.63 543.84 206.45 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n220 92 Car -1 -1 -1 797.65 197.27 1239.46 367.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n220 104 Car -1 -1 -1 3.88 187.63 394.42 322.58 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n220 105 Car -1 -1 -1 687.11 179.35 774.03 244.82 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n220 109 Car -1 -1 -1 659.54 180.88 707.95 219.43 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n220 112 Car -1 -1 -1 435.54 177.79 538.75 218.83 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n220 111 Car -1 -1 -1 136.31 168.92 433.81 280.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n220 118 Car -1 -1 -1 650.48 179.75 690.49 214.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n220 107 Car -1 -1 -1 476.08 170.46 567.61 205.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n220 106 Car -1 -1 -1 318.94 162.44 507.90 237.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n220 121 Car -1 -1 -1 469.41 180.73 543.29 206.32 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n220 119 Car -1 -1 -1 640.84 178.77 672.31 204.64 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n221 104 Car -1 -1 -1 3.12 186.94 374.36 332.84 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n221 92 Car -1 -1 -1 824.09 197.46 1234.92 367.97 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n221 105 Car -1 -1 -1 690.62 178.66 782.89 246.83 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-1000 -1000 -10 0.79\n226 121 Car -1 -1 -1 445.59 176.85 535.98 211.26 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n226 107 Car -1 -1 -1 466.50 166.83 562.90 207.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n226 106 Car -1 -1 -1 202.64 154.30 484.24 253.55 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n226 104 Car -1 -1 -1 1.14 195.43 204.96 370.20 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n226 119 Car -1 -1 -1 647.99 177.25 681.16 206.33 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n226 122 Car -1 -1 -1 30.11 167.67 369.33 304.57 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n227 105 Car -1 -1 -1 720.94 178.87 863.61 276.67 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n227 111 Car -1 -1 -1 0.74 166.40 329.71 329.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n227 109 Car -1 -1 -1 671.10 178.02 743.49 229.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n227 122 Car -1 -1 -1 -3.61 155.15 310.22 371.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n227 112 Car -1 -1 -1 407.88 180.85 527.08 223.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n227 118 Car -1 -1 -1 659.43 178.17 712.34 219.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n227 121 Car -1 -1 -1 440.91 176.60 532.97 211.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n227 107 Car -1 -1 -1 466.75 166.31 561.21 207.34 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n227 106 Car -1 -1 -1 181.98 151.46 473.63 256.27 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n227 119 Car -1 -1 -1 648.48 176.60 681.53 205.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n228 105 Car -1 -1 -1 727.81 179.89 882.01 284.29 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n228 111 Car -1 -1 -1 2.80 161.42 304.51 349.70 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n228 109 Car -1 -1 -1 673.19 178.19 748.51 230.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n228 107 Car -1 -1 -1 462.62 165.97 558.80 207.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n228 118 Car -1 -1 -1 659.50 177.48 717.07 220.01 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n228 121 Car -1 -1 -1 434.83 174.94 531.54 212.21 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n228 119 Car -1 -1 -1 649.17 176.34 682.57 205.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n228 112 Car -1 -1 -1 403.01 180.41 523.79 224.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n228 122 Car -1 -1 -1 -2.61 151.56 270.41 374.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n228 106 Car -1 -1 -1 128.36 148.07 472.80 261.65 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n228 123 Car -1 -1 -1 637.54 179.65 662.69 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n229 105 Car -1 -1 -1 735.67 180.98 905.81 292.58 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n229 109 Car -1 -1 -1 676.54 178.75 754.11 232.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n229 111 Car -1 -1 -1 -0.18 166.82 274.70 351.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n229 107 Car -1 -1 -1 457.08 165.74 557.34 207.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n229 112 Car -1 -1 -1 392.88 180.79 519.08 226.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n229 118 Car -1 -1 -1 660.91 177.89 722.19 222.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n229 121 Car -1 -1 -1 429.38 174.60 528.47 213.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n229 119 Car -1 -1 -1 649.73 176.51 683.17 205.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n229 123 Car -1 -1 -1 637.66 179.58 662.03 197.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n229 122 Car -1 -1 -1 -4.13 153.13 210.07 373.41 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n229 106 Car -1 -1 -1 82.77 145.11 471.74 271.00 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n230 105 Car -1 -1 -1 743.62 183.07 937.44 304.51 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n230 109 Car -1 -1 -1 677.85 179.46 761.18 236.46 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n230 118 Car -1 -1 -1 662.66 178.76 724.24 223.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n230 111 Car -1 -1 -1 3.25 174.13 240.92 367.49 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n230 112 Car -1 -1 -1 384.11 181.30 513.37 228.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n230 119 Car -1 -1 -1 650.04 176.42 686.81 208.21 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n230 121 Car -1 -1 -1 417.34 174.81 526.81 217.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n230 107 Car -1 -1 -1 456.27 165.39 556.40 208.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n230 123 Car -1 -1 -1 638.68 180.37 663.21 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n231 105 Car -1 -1 -1 754.15 184.84 971.62 318.90 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n231 109 Car -1 -1 -1 681.97 179.70 767.97 239.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n231 111 Car -1 -1 -1 -0.81 180.95 190.56 368.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n231 119 Car -1 -1 -1 651.42 177.13 688.97 209.26 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n231 118 Car -1 -1 -1 664.56 179.17 729.01 225.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n231 112 Car -1 -1 -1 371.78 181.09 509.47 230.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n231 121 Car -1 -1 -1 411.76 174.36 523.48 218.57 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n231 107 Car -1 -1 -1 451.08 165.52 554.91 210.56 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n231 123 Car -1 -1 -1 638.88 180.58 664.04 199.93 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n232 105 Car -1 -1 -1 765.57 186.25 1014.74 339.28 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n232 109 Car -1 -1 -1 684.88 181.35 777.04 245.04 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n232 118 Car -1 -1 -1 664.57 181.23 733.99 229.18 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n232 121 Car -1 -1 -1 407.95 175.46 519.47 220.38 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n232 111 Car -1 -1 -1 -0.41 189.15 128.16 368.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n232 112 Car -1 -1 -1 361.80 181.15 503.74 234.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n232 107 Car -1 -1 -1 444.92 165.83 552.33 212.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n232 119 Car -1 -1 -1 651.55 178.40 689.80 210.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n232 123 Car -1 -1 -1 638.56 182.15 666.88 202.03 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n232 124 Car -1 -1 -1 -1.93 135.42 432.09 290.24 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n233 105 Car -1 -1 -1 782.55 189.21 1068.52 361.20 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n233 109 Car -1 -1 -1 687.22 182.64 785.92 250.50 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n233 119 Car -1 -1 -1 651.73 179.40 693.34 213.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n233 112 Car -1 -1 -1 347.88 182.56 501.94 237.68 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n233 121 Car -1 -1 -1 395.55 176.36 517.14 224.57 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n233 118 Car -1 -1 -1 666.44 182.83 737.01 232.17 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n233 107 Car -1 -1 -1 437.15 165.94 552.40 214.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n233 123 Car -1 -1 -1 639.04 183.44 667.57 204.08 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n233 124 Car -1 -1 -1 3.06 133.83 412.08 299.72 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n233 125 Car -1 -1 -1 203.82 189.56 420.83 266.42 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n234 105 Car -1 -1 -1 795.66 191.40 1149.50 367.87 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n234 109 Car -1 -1 -1 691.13 183.38 796.58 256.18 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n234 118 Car -1 -1 -1 667.60 183.04 742.94 235.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n234 119 Car -1 -1 -1 653.06 180.24 695.69 215.30 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n234 124 Car -1 -1 -1 -2.74 128.96 393.92 312.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n234 112 Car -1 -1 -1 332.27 183.14 493.94 241.32 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n234 121 Car -1 -1 -1 390.04 177.79 513.86 226.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n234 107 Car -1 -1 -1 428.89 166.47 553.37 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n234 123 Car -1 -1 -1 639.74 184.06 668.57 205.22 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n234 125 Car -1 -1 -1 185.59 190.19 407.92 273.43 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n235 105 Car -1 -1 -1 816.10 190.57 1238.21 368.27 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n235 109 Car -1 -1 -1 694.76 184.54 808.54 261.73 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n235 118 Car -1 -1 -1 670.40 182.90 747.54 237.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n235 112 Car -1 -1 -1 317.18 183.51 485.99 244.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n235 121 Car -1 -1 -1 379.42 178.85 509.44 229.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n235 107 Car -1 -1 -1 424.06 167.29 551.15 219.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n235 124 Car -1 -1 -1 0.19 126.50 367.70 323.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n235 119 Car -1 -1 -1 653.72 180.45 698.01 216.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n235 125 Car -1 -1 -1 148.96 192.78 390.29 285.87 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n235 123 Car -1 -1 -1 640.50 184.32 669.41 205.24 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n236 105 Car -1 -1 -1 842.07 193.71 1235.18 369.42 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n236 109 Car -1 -1 -1 701.24 184.94 821.40 266.03 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n236 112 Car -1 -1 -1 306.80 184.79 481.16 250.31 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n236 118 Car -1 -1 -1 672.52 182.53 753.85 241.05 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n236 119 Car -1 -1 -1 655.16 180.43 700.17 216.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n236 121 Car -1 -1 -1 369.25 179.78 504.73 231.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n236 107 Car -1 -1 -1 417.83 167.51 549.48 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n236 124 Car -1 -1 -1 2.78 120.21 342.86 343.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n236 125 Car -1 -1 -1 80.33 192.54 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-1000 -10 0.79\n410 181 Car -1 -1 -1 378.79 176.20 510.20 226.05 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n410 191 Car -1 -1 -1 328.60 180.18 497.66 238.34 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n410 193 Car -1 -1 -1 417.93 177.39 532.87 218.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n410 188 Car -1 -1 -1 51.74 181.14 417.80 299.58 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n410 186 Car -1 -1 -1 441.60 168.85 524.60 205.19 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n410 174 Car -1 -1 -1 699.40 154.99 835.93 262.75 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n410 184 Car -1 -1 -1 571.08 170.90 595.16 190.00 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n410 195 Car -1 -1 -1 307.94 180.83 495.44 242.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n410 196 Car -1 -1 -1 360.17 177.51 505.18 232.42 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n411 157 Car -1 -1 -1 1.98 190.44 359.01 365.99 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n411 183 Car -1 -1 -1 674.49 178.23 735.94 218.02 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n411 170 Car -1 -1 -1 634.07 174.08 676.19 203.84 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n411 187 Car -1 -1 -1 802.44 193.65 1149.88 370.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n411 188 Car -1 -1 -1 10.91 184.55 404.01 318.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n411 176 Car -1 -1 -1 177.02 177.92 454.85 279.60 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n411 178 Car -1 -1 -1 619.74 172.32 656.06 200.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n411 191 Car -1 -1 -1 304.51 180.98 490.58 243.99 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n411 193 Car -1 -1 -1 412.61 177.43 529.80 219.29 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n411 186 Car -1 -1 -1 435.63 168.81 523.12 205.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n411 181 Car -1 -1 -1 373.63 176.22 507.27 226.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n411 196 Car -1 -1 -1 356.73 177.40 500.78 232.77 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n411 195 Car -1 -1 -1 337.14 179.66 497.32 236.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n411 184 Car -1 -1 -1 569.22 169.35 593.48 189.39 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n411 174 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-1000 -1000 -10 0.81\n412 196 Car -1 -1 -1 345.81 179.94 496.32 237.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n412 186 Car -1 -1 -1 426.40 169.11 523.57 209.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n412 184 Car -1 -1 -1 567.82 169.31 592.68 189.51 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n412 197 Van -1 -1 -1 708.67 149.91 872.31 270.40 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n412 181 Car -1 -1 -1 376.47 177.90 512.57 225.79 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n412 174 Car -1 -1 -1 708.25 151.62 872.56 272.29 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n412 198 Car -1 -1 -1 609.83 172.64 644.01 196.70 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n413 187 Car -1 -1 -1 847.36 194.54 1237.75 368.95 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n413 188 Car -1 -1 -1 1.33 181.35 351.98 360.44 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n413 183 Car -1 -1 -1 679.31 179.80 746.45 223.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n413 178 Car -1 -1 -1 619.95 175.14 655.75 203.76 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n413 170 Car -1 -1 -1 634.84 175.98 680.39 208.37 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n413 157 Car -1 -1 -1 -4.55 211.45 288.02 368.42 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n413 184 Car -1 -1 -1 566.88 171.42 592.04 191.78 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n413 193 Car -1 -1 -1 385.90 179.63 526.20 228.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n413 176 Car -1 -1 -1 91.67 178.28 439.99 309.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n413 196 Car -1 -1 -1 341.22 180.82 492.98 238.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n413 191 Car -1 -1 -1 265.23 185.87 475.99 253.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n413 186 Car -1 -1 -1 414.01 170.22 522.02 216.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n413 198 Car -1 -1 -1 609.31 173.80 644.02 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n413 174 Car -1 -1 -1 714.32 152.55 895.83 282.48 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n414 187 Car -1 -1 -1 877.87 196.45 1237.49 369.15 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n414 183 Car -1 -1 -1 681.08 184.68 752.48 230.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n414 170 Car -1 -1 -1 635.10 180.27 682.42 213.84 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n414 193 Car -1 -1 -1 384.22 185.16 520.40 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n414 178 Car -1 -1 -1 619.68 179.44 657.66 208.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n414 188 Car -1 -1 -1 -0.48 193.00 322.07 364.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n414 184 Car -1 -1 -1 566.90 175.39 591.75 196.48 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n414 196 Car -1 -1 -1 324.08 186.37 487.25 247.53 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n414 186 Car -1 -1 -1 413.35 174.79 520.67 220.15 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n414 176 Car -1 -1 -1 42.58 182.78 426.83 328.39 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n414 191 Car -1 -1 -1 233.90 189.81 468.58 266.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n414 157 Car -1 -1 -1 -2.25 200.81 246.85 372.37 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n414 198 Car -1 -1 -1 610.19 178.51 644.34 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n414 199 Car -1 -1 -1 248.99 189.45 468.90 260.48 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n414 200 Van -1 -1 -1 721.27 154.78 926.75 299.60 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n415 187 Car -1 -1 -1 923.10 204.98 1238.13 367.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n415 176 Car -1 -1 -1 -8.65 185.00 408.26 349.29 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n415 178 Car -1 -1 -1 620.50 181.61 657.59 211.40 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n415 183 Car -1 -1 -1 683.59 185.89 761.37 234.54 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n415 186 Car -1 -1 -1 406.83 177.01 520.17 223.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n415 196 Car -1 -1 -1 312.77 187.26 482.60 253.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n415 193 Car -1 -1 -1 373.85 186.63 514.65 237.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n415 188 Car -1 -1 -1 0.21 201.09 283.51 372.16 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n415 170 Car -1 -1 -1 634.50 182.34 684.03 216.91 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n415 200 Van -1 -1 -1 729.05 152.94 959.69 312.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n415 191 Car -1 -1 -1 215.81 192.29 455.48 272.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n415 198 Car -1 -1 -1 610.28 181.01 644.99 206.00 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n415 184 Car -1 -1 -1 565.71 177.53 591.86 199.29 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n416 176 Car -1 -1 -1 2.47 183.14 372.17 351.75 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n416 183 Car -1 -1 -1 685.85 183.96 764.29 234.56 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n416 170 Car -1 -1 -1 634.31 179.21 687.02 215.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n416 178 Car -1 -1 -1 618.58 177.94 659.75 210.04 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n416 196 Car -1 -1 -1 303.76 186.88 475.57 255.34 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n416 187 Car -1 -1 -1 979.71 204.24 1236.81 368.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n416 186 Car -1 -1 -1 395.64 174.59 516.99 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n416 193 Car -1 -1 -1 368.80 183.87 511.61 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n416 184 Car -1 -1 -1 565.09 175.21 590.12 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n416 191 Car -1 -1 -1 202.20 191.98 445.59 273.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n416 188 Car -1 -1 -1 0.15 216.62 229.24 371.66 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n416 198 Car -1 -1 -1 609.17 179.85 644.46 205.07 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n416 200 Van -1 -1 -1 736.09 146.80 1005.18 326.59 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n416 201 Car -1 -1 -1 734.44 147.89 1002.16 330.19 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n417 176 Car -1 -1 -1 4.29 184.11 341.32 364.75 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n417 183 Car -1 -1 -1 688.39 182.06 772.20 234.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n417 193 Car -1 -1 -1 353.66 181.62 504.10 236.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n417 170 Car -1 -1 -1 635.84 177.27 689.42 213.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n417 196 Car -1 -1 -1 273.81 186.39 467.44 260.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n417 186 Car -1 -1 -1 391.99 171.31 512.97 222.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n417 178 Car -1 -1 -1 618.55 175.59 660.06 208.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n417 184 Car -1 -1 -1 563.23 171.21 591.17 194.59 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n417 191 Car -1 -1 -1 165.99 188.42 435.38 282.08 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n417 201 Car -1 -1 -1 743.42 144.77 1055.18 348.69 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n417 198 Car -1 -1 -1 610.88 175.42 650.22 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n417 188 Car -1 -1 -1 -1.50 210.16 168.72 370.62 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n418 183 Car -1 -1 -1 692.57 181.96 779.20 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n418 176 Car -1 -1 -1 2.18 181.43 312.66 369.13 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n418 196 Car -1 -1 -1 246.85 184.38 455.33 264.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n418 193 Car -1 -1 -1 347.01 180.28 495.15 237.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n418 170 Car -1 -1 -1 637.82 177.03 691.65 214.37 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n418 184 Car -1 -1 -1 562.45 170.54 590.83 194.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n418 186 Car 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-1000 -1000 -10 0.82\n419 178 Car -1 -1 -1 619.87 176.52 662.01 209.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n419 191 Car -1 -1 -1 78.14 188.68 414.75 306.22 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n419 184 Car -1 -1 -1 562.59 173.04 588.50 195.74 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n419 204 Van -1 -1 -1 770.32 125.11 1227.24 370.72 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n419 198 Car -1 -1 -1 608.06 175.67 647.17 203.61 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n419 201 Car -1 -1 -1 766.33 136.32 1217.65 372.93 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n420 183 Car -1 -1 -1 700.31 183.24 793.88 244.18 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n420 170 Car -1 -1 -1 637.46 178.16 696.52 218.07 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n420 191 Car -1 -1 -1 15.48 189.04 399.77 322.49 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n420 178 Car -1 -1 -1 620.33 176.97 663.03 211.37 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n420 196 Car -1 -1 -1 202.67 188.22 437.14 276.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n420 193 Car -1 -1 -1 307.46 181.01 488.11 245.93 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n420 176 Car -1 -1 -1 4.70 188.07 209.01 370.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n420 186 Car -1 -1 -1 373.23 171.35 508.27 224.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n420 184 Car -1 -1 -1 561.99 173.30 588.19 196.36 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n420 198 Car -1 -1 -1 607.79 176.01 647.33 204.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n420 201 Car -1 -1 -1 791.32 125.85 1237.87 368.65 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n420 204 Van -1 -1 -1 793.20 117.49 1242.48 369.82 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n421 183 Car -1 -1 -1 703.51 181.99 805.23 246.43 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n421 191 Car -1 -1 -1 0.70 189.39 382.15 337.06 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n421 170 Car -1 -1 -1 637.40 176.63 699.87 218.44 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n421 178 Car -1 -1 -1 620.22 175.63 664.94 210.66 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n421 193 Car -1 -1 -1 282.35 181.43 481.83 250.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n421 196 Car -1 -1 -1 150.77 187.54 427.55 291.02 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n421 186 Car -1 -1 -1 362.15 171.01 503.24 224.79 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n421 198 Car -1 -1 -1 608.17 174.71 651.72 204.70 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n421 176 Car -1 -1 -1 1.38 201.97 127.11 370.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n421 184 Car -1 -1 -1 560.04 171.19 587.74 195.28 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n422 191 Car -1 -1 -1 -0.22 190.48 353.06 344.65 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n422 170 Car -1 -1 -1 637.55 175.17 702.66 217.62 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n422 183 Car -1 -1 -1 707.59 180.58 817.01 249.79 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n422 178 Car -1 -1 -1 619.39 173.87 665.76 210.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n422 184 Car -1 -1 -1 558.75 169.58 586.95 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n422 193 Car -1 -1 -1 279.49 178.14 469.05 248.41 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n422 186 Car -1 -1 -1 356.79 168.95 500.05 224.54 -1 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219.44 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n429 186 Car -1 -1 -1 234.77 161.91 444.02 241.95 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n429 198 Car -1 -1 -1 601.52 171.31 653.49 206.31 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n429 184 Car -1 -1 -1 546.97 167.89 577.41 194.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n429 206 Car -1 -1 -1 291.31 172.02 465.74 236.55 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n429 207 Car -1 -1 -1 355.93 173.24 477.89 220.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n430 183 Car -1 -1 -1 763.29 187.81 995.54 317.30 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n430 193 Car -1 -1 -1 5.25 173.40 378.90 307.66 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n430 170 Car -1 -1 -1 641.90 175.84 733.18 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n430 178 Car -1 -1 -1 617.99 173.99 680.51 222.24 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n430 184 Car -1 -1 -1 545.55 168.59 577.95 196.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n430 207 Car -1 -1 -1 344.93 172.79 473.63 222.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n430 196 Car 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-1000 -1000 -10 0.77\n431 206 Car -1 -1 -1 272.60 176.39 452.86 242.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n432 183 Car -1 -1 -1 796.71 195.35 1101.40 355.85 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n432 193 Car -1 -1 -1 0.81 178.26 336.18 339.70 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n432 170 Car -1 -1 -1 647.22 178.11 748.23 246.44 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n432 178 Car -1 -1 -1 620.36 175.81 687.35 228.22 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n432 184 Car -1 -1 -1 543.86 170.31 578.07 200.71 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n432 198 Car -1 -1 -1 602.24 174.87 660.04 213.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n432 186 Car -1 -1 -1 153.16 164.19 409.56 261.61 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n432 207 Car -1 -1 -1 331.86 175.99 463.32 226.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n432 206 Car -1 -1 -1 251.45 177.46 443.06 248.25 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n433 183 Car -1 -1 -1 809.31 197.98 1190.09 368.05 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n433 193 Car -1 -1 -1 -1.21 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0.94\n434 184 Car -1 -1 -1 543.86 169.87 579.08 201.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n434 198 Car -1 -1 -1 606.00 174.63 665.06 217.43 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n434 207 Car -1 -1 -1 307.81 176.66 456.11 232.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n434 186 Car -1 -1 -1 95.17 163.16 382.05 276.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n434 206 Car -1 -1 -1 215.03 177.40 425.30 256.12 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n435 183 Car -1 -1 -1 866.03 197.63 1233.60 369.31 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n435 170 Car -1 -1 -1 658.82 177.69 780.27 258.21 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n435 178 Car -1 -1 -1 626.97 173.82 706.50 234.98 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n435 186 Car -1 -1 -1 59.58 159.15 363.84 283.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n435 207 Car -1 -1 -1 297.32 176.66 451.35 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n435 198 Car -1 -1 -1 607.95 173.19 669.93 218.34 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n435 193 Car -1 -1 -1 1.05 172.39 228.29 369.89 -1 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Car -1 -1 -1 615.53 169.59 678.73 227.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n455 210 Car -1 -1 -1 31.99 187.49 374.90 323.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n455 209 Car -1 -1 -1 2.01 203.10 165.49 370.12 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n455 211 Car -1 -1 -1 409.52 179.84 448.43 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n456 198 Car -1 -1 -1 767.91 175.80 1053.27 335.00 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n456 178 Car -1 -1 -1 932.99 190.81 1235.75 367.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n456 210 Car -1 -1 -1 8.95 187.95 358.43 331.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n456 184 Car -1 -1 -1 619.57 167.95 687.14 228.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n456 209 Car -1 -1 -1 -1.73 208.53 107.07 372.01 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n456 211 Car -1 -1 -1 413.65 178.44 451.37 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n456 212 Car -1 -1 -1 288.86 181.88 336.55 212.01 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n457 198 Car -1 -1 -1 784.77 176.56 1113.13 350.76 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n457 210 Car -1 -1 -1 -0.17 188.99 346.22 345.53 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n457 184 Car -1 -1 -1 623.69 167.23 693.17 228.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n457 178 Car -1 -1 -1 985.62 199.67 1238.50 365.41 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n457 212 Car -1 -1 -1 282.08 181.39 335.25 215.48 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n457 211 Car -1 -1 -1 412.79 177.90 452.34 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n458 198 Car -1 -1 -1 799.04 178.39 1184.87 365.39 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n458 210 Car -1 -1 -1 0.08 191.15 330.31 357.48 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n458 184 Car -1 -1 -1 627.32 168.07 698.03 231.32 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n458 212 Car -1 -1 -1 278.12 182.56 332.52 217.16 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n458 211 Car -1 -1 -1 413.25 178.39 453.29 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n459 198 Car -1 -1 -1 814.25 175.10 1238.81 368.38 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n459 210 Car -1 -1 -1 0.40 193.27 307.06 364.17 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n459 184 Car -1 -1 -1 631.20 167.41 707.10 234.24 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n459 212 Car -1 -1 -1 275.51 182.39 331.95 218.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n459 211 Car -1 -1 -1 417.22 178.86 455.21 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n460 198 Car -1 -1 -1 837.75 179.29 1237.79 369.31 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n460 210 Car -1 -1 -1 -2.11 197.95 284.07 367.35 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n460 184 Car -1 -1 -1 636.26 168.61 716.07 238.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n460 212 Car -1 -1 -1 272.30 183.89 323.18 218.78 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n460 211 Car -1 -1 -1 421.60 179.11 457.51 199.57 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n460 213 Car -1 -1 -1 319.05 175.88 354.46 200.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n461 198 Car -1 -1 -1 866.83 181.40 1233.04 367.19 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n461 210 Car -1 -1 -1 2.71 198.88 250.39 366.48 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n461 184 Car -1 -1 -1 640.82 168.17 722.41 240.25 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n461 212 Car -1 -1 -1 270.51 183.91 323.95 218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n461 211 Car -1 -1 -1 422.09 179.38 458.83 199.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n461 213 Car -1 -1 -1 320.09 175.52 357.04 201.19 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n462 198 Car -1 -1 -1 895.28 181.67 1235.87 368.77 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n462 184 Car -1 -1 -1 646.20 167.24 732.68 242.58 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n462 210 Car -1 -1 -1 0.30 198.95 220.54 367.54 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n462 212 Car -1 -1 -1 267.82 183.56 324.31 219.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n462 211 Car -1 -1 -1 426.73 179.12 461.83 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n462 213 Car -1 -1 -1 321.39 175.80 359.23 200.70 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n463 198 Car -1 -1 -1 927.80 187.67 1235.21 368.32 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n463 184 Car -1 -1 -1 651.79 166.66 743.35 244.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n463 210 Car -1 -1 -1 -1.35 204.65 183.24 368.96 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n463 212 Car -1 -1 -1 265.33 183.92 321.58 219.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n463 211 Car -1 -1 -1 425.26 178.38 463.68 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n463 213 Car -1 -1 -1 324.27 173.94 362.14 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n464 198 Car -1 -1 -1 969.93 188.92 1239.58 367.86 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n464 184 Car -1 -1 -1 657.74 165.57 753.17 246.86 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n464 212 Car -1 -1 -1 263.96 183.55 320.74 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n464 210 Car -1 -1 -1 -1.22 211.32 144.64 370.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n464 211 Car -1 -1 -1 428.27 178.58 466.67 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n464 213 Car -1 -1 -1 325.35 173.87 362.86 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0001.txt",
    "content": "0 1 Car -1 -1 -1 483.81 173.31 658.93 242.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n0 2 Car -1 -1 -1 1194.45 161.21 1236.85 217.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n0 3 Car -1 -1 -1 -0.20 192.65 44.39 256.60 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n1 1 Car -1 -1 -1 538.72 171.69 714.61 239.16 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n1 3 Car -1 -1 -1 -0.95 189.76 97.57 260.09 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n2 3 Car -1 -1 -1 0.43 189.24 150.57 258.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n2 1 Car -1 -1 -1 593.42 170.96 769.27 237.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n3 3 Car -1 -1 -1 0.37 185.97 203.20 257.39 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n3 1 Car -1 -1 -1 651.31 168.97 825.34 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n4 3 Car -1 -1 -1 41.72 184.46 250.44 255.42 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n4 1 Car -1 -1 -1 701.95 167.28 879.81 234.11 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n5 3 Car -1 -1 -1 98.18 181.99 303.15 252.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n5 1 Car -1 -1 -1 753.03 166.24 936.76 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n6 3 Car -1 -1 -1 153.44 180.62 354.49 250.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n6 1 Car -1 -1 -1 807.79 164.00 990.77 230.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n7 1 Car -1 -1 -1 858.90 163.17 1048.53 228.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n7 3 Car -1 -1 -1 206.87 180.21 410.45 250.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n8 1 Car -1 -1 -1 913.65 161.27 1100.26 227.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n8 3 Car -1 -1 -1 261.55 178.83 457.51 248.49 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n9 3 Car -1 -1 -1 322.14 176.00 506.43 248.06 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n9 1 Car -1 -1 -1 964.92 159.64 1158.51 227.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n10 1 Car -1 -1 -1 1021.83 157.66 1216.27 226.65 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n10 3 Car -1 -1 -1 371.77 175.71 563.08 243.38 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n11 1 Car -1 -1 -1 1068.77 155.54 1234.25 225.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n11 3 Car -1 -1 -1 430.40 172.84 613.58 242.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n12 3 Car -1 -1 -1 482.87 172.66 663.88 241.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n12 1 Car -1 -1 -1 1122.09 155.21 1236.08 224.78 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n13 3 Car -1 -1 -1 534.55 170.45 719.66 239.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n13 1 Car -1 -1 -1 1174.99 153.59 1237.57 219.41 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n14 3 Car -1 -1 -1 587.88 169.74 774.39 237.90 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n14 1 Car -1 -1 -1 1225.64 152.43 1237.91 218.60 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n15 3 Car -1 -1 -1 642.65 167.77 826.78 236.75 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 4 Car -1 -1 -1 1.05 204.56 34.68 283.45 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n16 3 Car -1 -1 -1 690.56 166.37 882.92 235.04 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n16 4 Car -1 -1 -1 0.25 191.57 51.70 287.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n17 3 Car -1 -1 -1 740.88 165.03 938.37 234.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n17 4 Car -1 -1 -1 -2.13 185.11 74.03 293.82 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n18 3 Car -1 -1 -1 790.47 163.44 989.99 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n18 4 Car -1 -1 -1 -2.27 182.41 90.12 296.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n19 3 Car -1 -1 -1 844.27 161.98 1045.61 230.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n19 4 Car -1 -1 -1 -2.78 178.58 106.15 301.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n20 3 Car -1 -1 -1 896.40 159.94 1095.58 229.38 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n20 4 Car -1 -1 -1 -2.30 177.43 115.09 303.38 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n21 3 Car -1 -1 -1 948.26 158.19 1150.30 229.59 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n21 4 Car -1 -1 -1 -2.59 180.63 128.75 307.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n21 5 Car -1 -1 -1 1.85 183.83 134.64 257.72 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n22 3 Car -1 -1 -1 999.99 156.46 1201.49 228.34 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n22 4 Car -1 -1 -1 0.15 179.41 134.86 314.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n22 5 Car -1 -1 -1 28.55 195.02 192.98 254.75 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n23 3 Car -1 -1 -1 1051.32 156.95 1233.78 227.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n23 4 Car -1 -1 -1 -0.51 176.02 143.03 319.28 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n23 5 Car -1 -1 -1 91.68 194.73 245.53 251.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n24 5 Car -1 -1 -1 147.80 191.95 299.09 248.87 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n24 4 Car -1 -1 -1 -0.18 173.49 145.04 323.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n24 3 Car -1 -1 -1 1097.17 154.76 1235.85 229.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n24 6 Van -1 -1 -1 1097.30 153.63 1235.99 227.38 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n25 3 Car -1 -1 -1 1148.15 153.78 1238.02 224.81 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n25 5 Car -1 -1 -1 206.54 191.22 349.24 247.18 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n25 4 Car -1 -1 -1 -1.34 174.47 146.29 329.12 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n26 4 Car -1 -1 -1 -1.96 171.46 152.31 339.57 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n26 5 Car -1 -1 -1 264.71 188.22 406.86 246.42 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n26 3 Car -1 -1 -1 1198.27 153.14 1236.77 218.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n27 5 Car -1 -1 -1 325.13 184.91 462.62 246.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n27 4 Car -1 -1 -1 0.35 174.04 143.95 345.33 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n28 4 Car -1 -1 -1 1.12 172.98 135.79 354.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n28 5 Car -1 -1 -1 361.52 184.53 519.96 243.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n28 7 Car -1 -1 -1 4.31 193.25 201.54 270.94 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n29 5 Car -1 -1 -1 439.70 184.67 571.35 240.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n29 4 Car -1 -1 -1 0.24 169.26 127.74 366.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n29 7 Car -1 -1 -1 36.20 194.28 247.10 268.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n30 5 Car -1 -1 -1 495.72 182.17 625.18 238.61 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n30 4 Car -1 -1 -1 1.06 168.89 119.58 372.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 7 Car -1 -1 -1 69.29 193.56 291.64 265.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 5 Car -1 -1 -1 552.50 180.53 678.82 237.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n31 7 Car -1 -1 -1 123.92 191.71 338.21 264.60 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n31 4 Car -1 -1 -1 1.39 170.35 118.94 371.14 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n32 5 Car -1 -1 -1 609.04 179.92 736.07 235.48 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n32 7 Car -1 -1 -1 176.98 190.80 378.48 263.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n32 4 Car -1 -1 -1 -1.51 171.79 121.70 369.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n33 7 Car -1 -1 -1 226.79 187.64 430.29 263.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n33 5 Car -1 -1 -1 662.65 178.21 792.52 234.24 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n33 4 Car -1 -1 -1 -0.74 171.42 120.15 369.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n34 5 Car -1 -1 -1 714.92 176.76 849.39 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n34 7 Car -1 -1 -1 281.52 183.12 475.40 259.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n34 4 Car -1 -1 -1 -1.41 171.30 115.37 370.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n35 5 Car -1 -1 -1 765.67 174.83 906.72 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n35 7 Car -1 -1 -1 335.19 182.65 529.27 259.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n35 4 Car -1 -1 -1 -2.74 170.73 114.37 370.39 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n36 5 Car -1 -1 -1 816.70 173.14 962.77 230.97 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n36 7 Car -1 -1 -1 376.21 183.21 582.44 257.23 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n36 4 Car -1 -1 -1 -0.62 165.32 97.03 368.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n37 7 Car -1 -1 -1 438.41 181.96 630.55 257.04 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n37 5 Car -1 -1 -1 871.36 171.80 1016.98 229.47 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n37 4 Car -1 -1 -1 0.73 170.03 66.19 371.36 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n38 7 Car -1 -1 -1 491.83 180.62 682.43 252.85 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n38 5 Car -1 -1 -1 921.91 170.22 1075.30 226.86 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n38 4 Car -1 -1 -1 -2.06 170.30 44.55 371.58 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n38 8 Car -1 -1 -1 -0.63 195.76 52.58 253.72 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n39 7 Car -1 -1 -1 546.50 178.99 736.42 252.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n39 5 Car -1 -1 -1 972.32 168.63 1128.33 226.87 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n39 8 Car -1 -1 -1 -0.22 191.42 97.72 258.16 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n40 8 Car -1 -1 -1 0.19 191.31 142.59 256.58 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n40 7 Car -1 -1 -1 600.40 177.96 791.15 249.66 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n40 5 Car -1 -1 -1 1025.19 166.49 1183.16 225.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n41 8 Car -1 -1 -1 2.22 189.93 186.25 257.34 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n41 7 Car -1 -1 -1 654.11 177.33 842.28 248.53 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n41 5 Car -1 -1 -1 1074.78 165.37 1233.17 223.02 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n42 8 Car -1 -1 -1 34.52 187.83 225.27 254.54 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n42 7 Car -1 -1 -1 704.32 175.26 900.68 248.40 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n42 5 Car -1 -1 -1 1128.30 164.57 1233.71 222.35 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n43 7 Car -1 -1 -1 757.48 173.08 955.06 247.12 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n43 8 Car -1 -1 -1 80.05 185.83 271.47 253.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n43 5 Car -1 -1 -1 1175.96 162.41 1236.45 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n44 7 Car -1 -1 -1 810.63 173.18 1010.96 246.05 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n44 8 Car -1 -1 -1 128.70 184.00 311.86 249.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n44 5 Car -1 -1 -1 1224.59 154.52 1238.16 224.59 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n45 7 Car -1 -1 -1 863.23 172.62 1067.48 243.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n45 8 Car -1 -1 -1 175.99 183.07 356.49 248.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n46 7 Car -1 -1 -1 919.14 170.17 1125.46 241.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n46 8 Car -1 -1 -1 222.67 182.20 409.60 248.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n47 7 Car -1 -1 -1 971.39 168.68 1183.26 241.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n47 8 Car -1 -1 -1 270.36 180.79 448.76 246.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n48 8 Car -1 -1 -1 323.47 178.37 494.36 246.65 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n48 7 Car -1 -1 -1 1025.46 167.27 1236.87 242.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n49 7 Car -1 -1 -1 1074.16 166.14 1235.77 242.81 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n49 8 Car -1 -1 -1 354.93 177.08 541.24 242.82 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n50 8 Car -1 -1 -1 423.63 176.74 587.22 241.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n50 7 Car -1 -1 -1 1128.82 165.39 1236.71 239.38 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n51 8 Car -1 -1 -1 459.54 174.96 636.97 240.53 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n51 7 Car -1 -1 -1 1183.68 167.26 1236.39 234.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n52 8 Car -1 -1 -1 519.54 172.88 685.23 239.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n53 8 Car -1 -1 -1 567.85 172.09 733.79 237.06 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n54 8 Car -1 -1 -1 617.33 169.75 783.69 235.23 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n55 8 Car -1 -1 -1 663.50 168.59 836.90 233.74 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n56 8 Car -1 -1 -1 711.65 167.08 885.43 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n57 8 Car -1 -1 -1 760.67 166.88 936.65 232.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n58 8 Car -1 -1 -1 809.80 164.65 986.99 230.11 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n59 8 Car -1 -1 -1 858.00 163.28 1039.28 228.42 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n60 8 Car -1 -1 -1 906.97 161.52 1090.55 227.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n61 8 Car -1 -1 -1 956.05 160.25 1137.59 226.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n62 8 Car -1 -1 -1 1005.41 158.33 1194.82 226.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n63 8 Car -1 -1 -1 1050.91 157.14 1234.01 224.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n64 8 Car -1 -1 -1 1104.24 157.54 1234.18 223.28 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n65 8 Car -1 -1 -1 1148.41 155.96 1238.44 222.18 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n66 8 Car -1 -1 -1 1197.31 154.81 1235.82 217.15 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n86 10 Car -1 -1 -1 1202.14 156.05 1238.51 192.06 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n87 10 Car -1 -1 -1 1172.36 157.13 1237.04 192.33 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n88 10 Car -1 -1 -1 1135.15 158.96 1238.16 194.08 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n89 10 Car -1 -1 -1 1104.70 160.61 1220.96 195.23 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n90 10 Car -1 -1 -1 1073.05 161.73 1182.25 196.36 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n91 10 Car -1 -1 -1 1047.17 162.37 1152.13 195.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n92 10 Car -1 -1 -1 1007.16 163.93 1116.63 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n93 10 Car -1 -1 -1 974.93 164.82 1085.05 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n94 10 Car -1 -1 -1 941.99 166.28 1055.12 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n95 10 Car -1 -1 -1 910.21 166.22 1018.21 199.93 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n96 10 Car -1 -1 -1 883.02 168.35 982.44 201.38 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n97 10 Car -1 -1 -1 845.69 169.86 953.44 202.19 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n98 10 Car -1 -1 -1 816.51 171.73 917.91 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n99 10 Car -1 -1 -1 784.77 173.02 885.65 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n100 10 Car -1 -1 -1 754.98 173.68 853.46 205.49 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n101 10 Car -1 -1 -1 721.43 173.82 819.88 207.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n102 10 Car -1 -1 -1 691.59 176.53 788.74 208.18 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n103 10 Car -1 -1 -1 659.72 178.20 756.60 209.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n104 10 Car -1 -1 -1 629.27 179.42 724.13 210.21 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n105 10 Car -1 -1 -1 596.15 180.64 690.94 212.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n106 10 Car -1 -1 -1 565.93 182.70 659.04 212.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n107 10 Car -1 -1 -1 535.46 184.45 627.03 214.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n108 10 Car -1 -1 -1 502.33 185.85 596.72 216.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n109 10 Car -1 -1 -1 474.15 187.09 564.08 216.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n110 10 Car -1 -1 -1 432.24 187.60 532.91 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n111 10 Car -1 -1 -1 405.77 189.53 500.32 220.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n112 10 Car -1 -1 -1 376.00 190.11 473.39 221.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n113 10 Car -1 -1 -1 342.60 188.57 436.79 227.45 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n114 10 Car -1 -1 -1 310.23 190.05 408.34 226.92 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n115 10 Car -1 -1 -1 281.96 194.83 373.75 224.75 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n116 10 Car -1 -1 -1 246.58 196.54 340.75 226.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n117 10 Car -1 -1 -1 212.16 197.92 313.04 228.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n118 10 Car -1 -1 -1 179.60 198.59 280.09 228.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n119 10 Car -1 -1 -1 145.78 199.87 245.71 231.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n120 10 Car -1 -1 -1 112.22 201.26 211.44 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n121 10 Car -1 -1 -1 78.36 201.44 183.12 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n122 10 Car -1 -1 -1 44.81 202.78 152.73 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n123 10 Car -1 -1 -1 9.72 204.68 124.09 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n124 10 Car -1 -1 -1 1.17 206.41 86.91 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n125 10 Car -1 -1 -1 -0.10 207.47 56.93 238.89 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n126 10 Car -1 -1 -1 0.69 201.13 26.03 240.43 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0002.txt",
    "content": "0 1 Car -1 -1 -1 436.32 183.45 498.72 231.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n0 2 Car -1 -1 -1 555.20 179.74 591.20 214.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n0 3 Car -1 -1 -1 495.47 184.61 517.27 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n0 4 Car -1 -1 -1 140.31 185.56 217.98 219.29 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n0 5 Car -1 -1 -1 530.46 184.56 547.71 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n0 6 Van -1 -1 -1 -1.94 180.78 100.16 230.51 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n0 7 Car -1 -1 -1 -0.10 183.78 104.01 232.17 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n1 1 Car -1 -1 -1 418.36 185.76 491.31 240.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 2 Car -1 -1 -1 555.43 180.30 591.30 215.56 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n1 4 Car -1 -1 -1 120.74 188.76 203.82 223.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n1 3 Car -1 -1 -1 495.06 185.43 518.87 203.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n1 5 Car -1 -1 -1 530.49 184.93 549.21 200.88 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n1 6 Van -1 -1 -1 -2.34 176.29 77.48 235.10 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n2 1 Car -1 -1 -1 389.58 187.21 478.20 253.43 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n2 4 Car -1 -1 -1 100.22 191.87 190.55 228.05 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n2 2 Car -1 -1 -1 554.41 180.72 591.04 216.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n2 3 Car -1 -1 -1 493.74 185.90 520.18 205.54 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n2 5 Car -1 -1 -1 531.18 186.25 549.01 201.13 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n2 8 Car -1 -1 -1 479.83 187.47 502.31 201.17 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n3 1 Car -1 -1 -1 349.59 187.01 462.44 268.76 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n3 2 Car -1 -1 -1 553.78 179.44 591.33 215.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n3 4 Car -1 -1 -1 77.12 191.53 175.76 228.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n3 5 Car -1 -1 -1 531.62 184.67 549.39 200.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n3 3 Car -1 -1 -1 493.87 185.44 520.57 205.70 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n3 8 Car -1 -1 -1 481.43 186.71 502.85 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n3 9 Car -1 -1 -1 323.35 182.61 348.79 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n4 1 Car -1 -1 -1 278.17 185.27 440.65 294.93 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n4 4 Car -1 -1 -1 55.63 190.64 156.87 229.12 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n4 2 Car -1 -1 -1 552.86 177.62 591.38 213.84 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 3 Car -1 -1 -1 492.46 183.39 520.85 204.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n4 5 Car -1 -1 -1 531.83 183.70 549.51 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n4 9 Car -1 -1 -1 320.53 181.22 344.44 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n5 1 Car -1 -1 -1 154.07 186.96 401.93 340.09 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n5 2 Car -1 -1 -1 552.84 176.57 591.25 212.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n5 3 Car -1 -1 -1 489.91 182.39 521.02 205.47 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n5 4 Car -1 -1 -1 27.92 189.54 136.59 229.90 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n5 5 Car -1 -1 -1 531.58 182.30 549.50 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n6 1 Car -1 -1 -1 -0.39 196.60 330.54 368.01 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n6 2 Car -1 -1 -1 552.15 177.14 590.48 212.51 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n6 4 Car -1 -1 -1 2.27 191.15 110.61 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n6 3 Car -1 -1 -1 487.20 182.44 520.29 207.14 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n6 5 Car -1 -1 -1 531.79 183.12 549.47 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n6 10 Car -1 -1 -1 315.06 180.14 342.67 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n7 3 Car -1 -1 -1 483.75 183.39 519.89 211.30 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n7 1 Car -1 -1 -1 -3.40 211.43 170.51 369.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n7 4 Car -1 -1 -1 -0.48 192.98 91.31 238.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n7 5 Car -1 -1 -1 532.46 184.10 550.49 200.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n7 2 Car -1 -1 -1 552.28 177.94 589.99 213.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n7 10 Car -1 -1 -1 313.75 180.70 342.04 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n8 3 Car -1 -1 -1 479.42 182.44 519.54 214.35 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n8 2 Car -1 -1 -1 553.05 177.11 590.70 212.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n8 4 Car -1 -1 -1 -0.84 194.25 68.33 240.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 5 Car -1 -1 -1 533.41 183.89 551.56 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n8 10 Car -1 -1 -1 312.48 181.09 341.35 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n9 2 Car -1 -1 -1 553.23 176.65 590.84 211.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n9 3 Car -1 -1 -1 474.72 180.96 518.13 216.70 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n9 4 Car -1 -1 -1 -0.15 197.31 37.70 243.62 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n9 5 Car -1 -1 -1 534.39 183.19 553.79 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n9 10 Car -1 -1 -1 309.83 180.80 339.82 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n10 2 Car -1 -1 -1 553.78 175.87 592.07 210.51 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n10 3 Car -1 -1 -1 466.33 181.52 517.49 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n10 5 Car -1 -1 -1 535.40 182.79 555.12 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n10 4 Car -1 -1 -1 -1.44 197.81 12.09 249.98 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n10 10 Car -1 -1 -1 307.16 180.97 339.57 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n11 2 Car -1 -1 -1 554.68 174.83 592.53 209.78 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n11 3 Car -1 -1 -1 456.08 182.17 515.27 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n11 5 Car -1 -1 -1 536.83 181.71 556.28 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n11 10 Car -1 -1 -1 301.47 181.61 333.08 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n11 11 Car -1 -1 -1 494.95 183.39 520.06 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n12 3 Car -1 -1 -1 440.37 181.65 511.04 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n12 2 Car -1 -1 -1 554.75 174.44 592.68 209.17 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n12 11 Car -1 -1 -1 493.64 182.29 522.22 203.30 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n12 5 Car -1 -1 -1 538.35 181.02 558.90 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n13 3 Car -1 -1 -1 418.43 182.63 503.63 242.55 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n13 2 Car -1 -1 -1 554.62 172.93 593.42 207.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n13 11 Car -1 -1 -1 492.25 180.83 523.63 203.35 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n14 3 Car -1 -1 -1 386.19 185.12 492.00 257.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n14 2 Car -1 -1 -1 556.64 173.54 593.78 207.33 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n14 11 Car -1 -1 -1 492.87 180.65 525.43 205.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n15 3 Car -1 -1 -1 333.48 190.41 477.05 282.27 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n15 2 Car -1 -1 -1 557.33 175.46 593.57 209.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n15 11 Car -1 -1 -1 491.54 182.49 526.94 209.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n16 3 Car -1 -1 -1 239.05 194.82 454.77 324.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n16 11 Car -1 -1 -1 488.19 183.79 526.48 213.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n16 2 Car -1 -1 -1 557.81 177.10 593.31 211.35 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 12 Car -1 -1 -1 293.31 180.56 331.32 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n17 3 Car -1 -1 -1 23.55 196.50 407.01 369.42 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n17 11 Car -1 -1 -1 483.73 184.03 526.32 215.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n17 12 Car -1 -1 -1 287.87 181.50 321.06 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n17 2 Car -1 -1 -1 557.49 177.84 592.50 211.56 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n18 3 Car -1 -1 -1 -0.76 204.45 308.14 369.94 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n18 2 Car -1 -1 -1 556.53 176.84 590.97 210.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n18 11 Car -1 -1 -1 476.19 180.64 523.83 216.73 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n18 12 Car -1 -1 -1 281.43 177.93 314.31 195.39 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n19 11 Car -1 -1 -1 465.55 180.33 518.00 219.31 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n19 2 Car -1 -1 -1 554.16 175.55 589.70 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n19 12 Car -1 -1 -1 275.52 175.37 310.86 194.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n19 13 Car -1 -1 -1 497.52 181.01 522.71 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n20 11 Car -1 -1 -1 451.42 180.11 513.16 224.40 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n20 2 Car -1 -1 -1 551.49 176.47 587.59 209.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n20 13 Car -1 -1 -1 496.32 180.93 523.13 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n20 12 Car -1 -1 -1 268.08 173.87 309.70 192.17 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n21 11 Car -1 -1 -1 432.66 179.00 507.80 231.50 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n21 2 Car -1 -1 -1 550.82 176.29 585.59 208.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n21 12 Car -1 -1 -1 261.61 171.43 300.64 192.11 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n21 13 Car -1 -1 -1 497.16 180.54 523.26 200.75 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n21 14 Car -1 -1 -1 285.13 172.29 316.35 189.40 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n22 11 Car -1 -1 -1 404.38 180.78 497.71 243.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n22 13 Car -1 -1 -1 496.02 181.30 524.45 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n22 2 Car -1 -1 -1 549.87 177.63 584.37 210.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n22 12 Car -1 -1 -1 254.21 172.78 294.06 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n22 14 Car -1 -1 -1 281.84 173.92 313.12 190.95 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n23 11 Car -1 -1 -1 364.45 185.66 483.19 262.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n23 2 Car -1 -1 -1 548.39 180.17 583.91 211.96 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n23 13 Car -1 -1 -1 496.40 184.51 525.61 207.10 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n23 12 Car -1 -1 -1 246.89 177.78 289.97 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n24 11 Car -1 -1 -1 294.39 188.73 464.31 292.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n24 2 Car -1 -1 -1 547.86 180.86 583.89 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n24 13 Car -1 -1 -1 495.66 186.23 526.39 210.37 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n24 12 Car -1 -1 -1 239.76 183.08 285.60 204.80 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n24 15 Car -1 -1 -1 485.13 184.73 506.64 200.70 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n25 11 Car -1 -1 -1 162.67 193.96 431.94 348.00 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n25 13 Car -1 -1 -1 493.83 185.52 527.29 211.46 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n25 2 Car -1 -1 -1 548.62 179.99 583.08 211.38 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n25 12 Car -1 -1 -1 233.34 184.99 283.53 207.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n25 15 Car -1 -1 -1 485.80 183.14 509.83 200.84 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n25 16 Car -1 -1 -1 272.63 183.80 306.48 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n25 17 Cyclist -1 -1 -1 92.45 186.08 144.66 229.94 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n26 11 Car -1 -1 -1 0.17 205.18 368.60 368.46 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n26 13 Car -1 -1 -1 492.58 188.05 528.13 216.27 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n26 2 Car -1 -1 -1 548.82 182.18 583.01 213.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n26 12 Car -1 -1 -1 226.34 189.28 274.58 213.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n26 16 Car -1 -1 -1 269.36 188.74 300.96 206.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n26 18 Car -1 -1 -1 468.53 182.77 484.84 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n27 13 Car -1 -1 -1 488.45 193.80 530.38 225.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n27 11 Car -1 -1 -1 -2.98 227.99 224.60 369.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n27 16 Car -1 -1 -1 265.82 195.70 298.00 213.47 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n27 2 Car -1 -1 -1 549.29 189.56 584.61 220.13 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n27 12 Car -1 -1 -1 220.40 195.39 270.13 220.05 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n28 13 Car -1 -1 -1 482.68 195.90 529.06 230.54 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n28 2 Car -1 -1 -1 548.40 190.80 582.34 221.54 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n28 12 Car -1 -1 -1 209.77 194.89 261.95 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 16 Car -1 -1 -1 260.07 195.15 292.32 214.00 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n29 2 Car -1 -1 -1 546.65 188.78 581.64 219.22 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n29 13 Car -1 -1 -1 475.54 192.98 526.43 231.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n29 12 Car -1 -1 -1 199.64 189.17 253.72 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n29 16 Car -1 -1 -1 253.28 189.90 286.01 209.36 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n29 19 Car -1 -1 -1 566.64 189.45 591.87 210.43 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n30 13 Car -1 -1 -1 465.25 191.17 522.85 234.24 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n30 2 Car -1 -1 -1 546.94 185.75 580.63 217.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 12 Car -1 -1 -1 188.24 185.70 244.40 213.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n30 19 Car -1 -1 -1 566.72 188.19 592.02 208.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 16 Car -1 -1 -1 245.75 186.03 280.96 206.47 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n31 13 Car -1 -1 -1 450.88 192.23 517.43 242.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n31 12 Car -1 -1 -1 174.68 187.45 234.35 216.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n31 2 Car -1 -1 -1 546.14 186.28 578.55 216.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 19 Car -1 -1 -1 567.55 188.09 591.85 207.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n31 16 Car -1 -1 -1 238.02 187.82 275.51 208.37 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 20 Car -1 -1 -1 486.37 191.14 517.23 217.44 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n32 13 Car -1 -1 -1 429.50 192.62 511.40 249.54 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n32 12 Car -1 -1 -1 161.21 186.40 224.32 216.51 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n32 2 Car -1 -1 -1 544.75 184.39 577.90 215.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n32 20 Car -1 -1 -1 486.49 189.45 517.40 212.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n32 19 Car -1 -1 -1 567.77 186.34 592.22 206.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n32 16 Car -1 -1 -1 229.24 186.22 269.13 208.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n32 21 Car -1 -1 -1 474.43 184.01 499.87 203.90 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n33 13 Car -1 -1 -1 398.98 192.20 499.64 262.46 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n33 2 Car -1 -1 -1 543.96 183.36 575.74 213.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n33 20 Car -1 -1 -1 483.51 188.58 516.46 213.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n33 12 Car -1 -1 -1 145.75 184.94 212.39 216.24 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n33 16 Car -1 -1 -1 219.00 185.06 260.53 207.24 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n33 21 Car -1 -1 -1 473.72 183.28 498.78 203.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n33 19 Car -1 -1 -1 568.92 185.06 592.65 204.54 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n34 13 Car -1 -1 -1 352.63 195.35 483.08 282.67 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n34 12 Car -1 -1 -1 129.06 183.11 200.16 213.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n34 20 Car -1 -1 -1 480.19 190.61 516.37 217.19 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n34 16 Car -1 -1 -1 209.92 183.35 251.72 205.96 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n34 19 Car -1 -1 -1 569.33 186.92 593.33 206.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n34 2 Car -1 -1 -1 542.93 185.25 575.76 214.59 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n34 21 Car -1 -1 -1 476.15 184.46 498.02 201.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n35 13 Car -1 -1 -1 266.88 195.22 460.57 316.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n35 2 Car -1 -1 -1 539.34 186.40 573.39 215.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n35 12 Car -1 -1 -1 107.72 181.71 185.76 213.60 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n35 16 Car -1 -1 -1 196.66 182.69 242.74 205.46 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n35 20 Car -1 -1 -1 474.01 192.10 513.35 220.10 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n35 19 Car -1 -1 -1 567.64 187.22 593.00 206.67 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n35 21 Car -1 -1 -1 473.85 184.83 500.54 207.31 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n36 12 Car -1 -1 -1 87.55 176.37 172.02 209.62 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n36 13 Car -1 -1 -1 97.97 202.18 425.46 370.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n36 20 Car -1 -1 -1 466.81 189.66 509.76 221.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n36 16 Car -1 -1 -1 185.49 177.63 231.65 201.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n36 19 Car -1 -1 -1 568.42 186.31 592.85 205.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n36 21 Car -1 -1 -1 472.47 183.06 499.39 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n36 2 Car -1 -1 -1 538.62 184.15 570.45 213.38 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n37 13 Car -1 -1 -1 -2.44 195.12 354.80 370.11 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n37 12 Car -1 -1 -1 64.04 172.80 157.14 208.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n37 2 Car -1 -1 -1 537.78 183.32 570.15 211.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n37 20 Car -1 -1 -1 458.58 188.97 506.86 223.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n37 19 Car -1 -1 -1 569.30 183.53 593.68 203.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n37 16 Car -1 -1 -1 171.91 174.93 222.21 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n37 21 Car -1 -1 -1 471.17 181.02 500.08 203.94 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n38 20 Car -1 -1 -1 447.77 186.18 501.88 226.40 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n38 2 Car -1 -1 -1 536.62 179.73 568.81 207.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n38 12 Car -1 -1 -1 40.92 169.27 139.31 204.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n38 16 Car -1 -1 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0.58\n56 24 Car -1 -1 -1 495.17 169.19 507.72 179.19 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n57 19 Car -1 -1 -1 606.22 172.08 629.64 191.95 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n57 2 Car -1 -1 -1 556.42 171.03 583.66 195.07 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n57 23 Pedestrian -1 -1 -1 242.90 158.23 256.56 188.03 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n57 24 Car -1 -1 -1 495.56 167.47 509.31 178.11 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n58 2 Car -1 -1 -1 560.22 168.61 586.06 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n58 24 Car -1 -1 -1 497.19 165.68 510.77 175.75 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n58 19 Car -1 -1 -1 610.34 169.25 632.28 188.78 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n59 2 Car -1 -1 -1 563.98 166.92 589.26 191.04 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n59 24 Car -1 -1 -1 498.95 163.80 513.47 175.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n59 19 Car -1 -1 -1 613.37 167.54 635.99 187.51 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n59 25 Pedestrian -1 -1 -1 229.28 153.98 242.36 183.44 -1 -1 -1 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Pedestrian -1 -1 -1 169.01 158.52 184.97 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n66 2 Car -1 -1 -1 588.47 176.06 611.56 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n66 19 Car -1 -1 -1 636.17 176.39 659.64 195.49 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n66 24 Car -1 -1 -1 502.65 174.05 522.61 188.36 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n66 28 Car -1 -1 -1 537.59 173.23 553.84 184.87 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n67 19 Car -1 -1 -1 638.36 176.49 661.11 195.04 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n67 24 Car -1 -1 -1 501.46 174.15 520.89 189.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n67 2 Car -1 -1 -1 590.10 175.96 613.93 196.97 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n67 28 Car -1 -1 -1 538.02 173.38 553.68 185.11 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n67 29 Pedestrian -1 -1 -1 138.05 161.47 153.88 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n68 28 Car -1 -1 -1 538.36 171.84 553.46 183.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n68 2 Car -1 -1 -1 592.11 173.92 615.65 194.73 -1 -1 -1 -1000 -1000 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Pedestrian -1 -1 -1 1079.93 180.41 1096.05 227.29 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n161 78 Pedestrian -1 -1 -1 1015.03 173.67 1032.07 219.94 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n161 80 Car -1 -1 -1 782.82 171.64 820.68 186.37 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n161 74 Car -1 -1 -1 403.77 169.37 438.94 183.57 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n161 75 Pedestrian -1 -1 -1 1022.91 181.37 1045.29 222.75 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n162 19 Car -1 -1 -1 791.49 175.97 864.67 216.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n162 53 Car -1 -1 -1 740.79 174.39 779.10 199.49 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n162 2 Car -1 -1 -1 619.41 172.16 639.95 189.65 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n162 68 Pedestrian -1 -1 -1 1209.09 169.37 1233.37 242.80 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n162 79 Pedestrian -1 -1 -1 1085.55 177.00 1108.34 232.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n162 73 Pedestrian -1 -1 -1 1108.92 172.12 1130.63 230.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n162 78 Pedestrian 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-10 0.90\n167 74 Car -1 -1 -1 315.21 170.60 357.80 185.36 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n167 53 Car -1 -1 -1 747.47 177.08 791.21 206.35 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n167 2 Car -1 -1 -1 606.45 172.96 630.74 193.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n167 80 Car -1 -1 -1 866.55 173.99 924.85 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n167 78 Pedestrian -1 -1 -1 1221.46 177.43 1237.24 256.05 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n168 19 Car -1 -1 -1 816.39 178.46 911.69 226.56 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n168 53 Car -1 -1 -1 748.93 176.14 794.44 205.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n168 2 Car -1 -1 -1 602.77 171.91 628.65 192.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n168 80 Car -1 -1 -1 884.57 173.67 945.10 190.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n168 74 Car -1 -1 -1 301.40 168.55 345.12 184.41 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n169 53 Car -1 -1 -1 751.67 175.07 797.97 204.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n169 19 Car -1 -1 -1 821.98 177.13 922.53 227.90 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n169 2 Car -1 -1 -1 598.52 171.15 626.16 191.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n169 80 Car -1 -1 -1 904.75 171.77 968.58 190.99 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n169 74 Car -1 -1 -1 287.78 167.51 329.94 183.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n170 19 Car -1 -1 -1 829.86 177.62 933.06 230.58 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n170 53 Car -1 -1 -1 753.65 175.01 802.62 205.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n170 80 Car -1 -1 -1 923.68 172.24 991.31 190.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n170 74 Car -1 -1 -1 270.28 168.11 313.75 182.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n170 2 Car -1 -1 -1 596.86 171.16 623.91 191.05 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n171 19 Car -1 -1 -1 836.24 178.37 945.14 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n171 53 Car -1 -1 -1 756.71 175.23 806.03 206.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n171 2 Car -1 -1 -1 593.66 171.91 622.01 192.11 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n171 80 Car -1 -1 -1 945.32 172.66 1016.44 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n171 74 Car -1 -1 -1 254.26 169.04 298.53 183.80 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n172 19 Car -1 -1 -1 843.34 179.57 959.64 236.74 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n172 53 Car -1 -1 -1 759.39 176.04 811.33 208.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n172 2 Car -1 -1 -1 588.82 171.86 618.62 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n172 80 Car -1 -1 -1 970.26 173.58 1044.92 195.14 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n172 74 Car -1 -1 -1 236.22 169.15 281.43 184.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n173 19 Car -1 -1 -1 851.75 179.75 974.70 239.25 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n173 53 Car -1 -1 -1 762.53 176.15 815.85 210.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n173 80 Car -1 -1 -1 998.39 175.03 1070.05 195.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n173 2 Car -1 -1 -1 582.26 171.70 615.02 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n173 74 Car -1 -1 -1 218.61 168.03 265.64 184.93 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n174 19 Car -1 -1 -1 859.80 179.66 991.08 242.92 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n174 53 Car -1 -1 -1 765.06 176.65 821.13 211.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n174 80 Car -1 -1 -1 1023.85 174.21 1100.06 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n174 2 Car -1 -1 -1 575.73 171.86 608.80 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n174 74 Car -1 -1 -1 200.45 168.43 245.84 184.84 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n175 19 Car -1 -1 -1 868.13 179.83 1008.12 245.48 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n175 53 Car -1 -1 -1 768.44 176.26 825.97 212.55 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n175 80 Car -1 -1 -1 1052.76 173.92 1125.97 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n175 2 Car -1 -1 -1 573.19 171.05 604.15 193.10 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n175 74 Car -1 -1 -1 182.84 168.97 229.82 185.28 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n176 19 Car -1 -1 -1 878.31 179.88 1026.78 247.73 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n176 53 Car -1 -1 -1 772.08 175.96 830.49 212.63 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n176 2 Car -1 -1 -1 570.05 170.88 599.75 193.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n176 74 Car -1 -1 -1 161.62 168.71 209.88 185.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n176 80 Car -1 -1 -1 1080.13 173.96 1152.97 196.73 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n177 19 Car -1 -1 -1 889.14 178.80 1047.33 251.56 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n177 53 Car -1 -1 -1 775.91 174.91 836.64 212.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n177 2 Car -1 -1 -1 554.43 170.69 591.32 192.64 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n177 74 Car -1 -1 -1 142.03 167.56 190.47 184.76 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n177 80 Car -1 -1 -1 1107.69 172.27 1186.22 195.99 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n177 85 Car -1 -1 -1 4.08 164.68 53.11 185.23 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n178 19 Car -1 -1 -1 899.28 177.30 1070.78 254.60 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n178 53 Car -1 -1 -1 778.88 174.26 842.04 212.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n178 2 Car -1 -1 -1 545.15 169.94 586.03 192.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n178 74 Car -1 -1 -1 121.08 167.05 169.89 183.91 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n178 80 Car -1 -1 -1 1142.60 171.34 1221.54 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n178 85 Car -1 -1 -1 1.60 165.57 34.76 184.61 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n179 19 Car -1 -1 -1 910.74 179.01 1097.62 259.44 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n179 53 Car -1 -1 -1 783.25 174.39 848.94 214.40 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n179 2 Car -1 -1 -1 535.32 169.65 578.88 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n179 74 Car -1 -1 -1 96.98 167.17 149.76 185.28 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n179 80 Car -1 -1 -1 1179.93 170.83 1238.06 194.68 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n179 85 Car -1 -1 -1 -0.26 166.72 28.18 187.86 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n180 19 Car -1 -1 -1 925.04 179.58 1126.29 263.72 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n180 53 Car -1 -1 -1 787.00 175.21 855.84 216.25 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n180 2 Car -1 -1 -1 526.06 170.21 571.40 194.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n180 74 Car -1 -1 -1 76.99 167.43 127.07 185.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n181 19 Car -1 -1 -1 940.19 179.39 1157.62 269.31 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n181 53 Car -1 -1 -1 791.44 174.56 862.79 217.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n181 2 Car -1 -1 -1 516.13 169.92 561.74 193.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n181 74 Car -1 -1 -1 51.60 167.02 106.12 186.27 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n182 19 Car -1 -1 -1 953.65 179.09 1193.89 275.60 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n182 53 Car -1 -1 -1 795.79 173.84 870.32 218.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n182 2 Car -1 -1 -1 505.61 169.80 553.30 194.22 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n182 74 Car -1 -1 -1 26.45 167.53 84.02 186.09 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n183 19 Car -1 -1 -1 972.55 178.98 1233.82 283.33 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n183 53 Car -1 -1 -1 800.88 173.32 879.01 220.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n183 74 Car -1 -1 -1 2.96 167.52 61.67 187.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n183 2 Car -1 -1 -1 494.88 169.78 544.54 194.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n184 19 Car -1 -1 -1 990.99 178.10 1238.04 287.34 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n184 53 Car -1 -1 -1 806.29 172.86 888.25 221.72 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n184 2 Car -1 -1 -1 483.98 169.50 534.90 194.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n184 74 Car -1 -1 -1 -0.49 167.60 36.61 186.96 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n185 19 Car -1 -1 -1 1009.99 178.73 1238.01 294.54 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n185 53 Car -1 -1 -1 811.20 171.77 899.03 223.55 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n185 2 Car -1 -1 -1 470.52 169.10 525.48 194.61 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n185 74 Car -1 -1 -1 0.13 164.63 9.62 189.03 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n186 19 Car -1 -1 -1 1035.11 179.32 1235.93 302.10 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n186 53 Car -1 -1 -1 818.02 172.13 909.16 224.95 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n186 2 Car -1 -1 -1 459.06 169.08 514.67 195.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n187 19 Car -1 -1 -1 1063.01 179.44 1237.03 315.78 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n187 53 Car -1 -1 -1 824.44 172.82 920.66 227.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n187 2 Car -1 -1 -1 445.33 169.20 502.83 196.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n188 53 Car -1 -1 -1 832.10 172.63 933.75 229.59 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n188 19 Car -1 -1 -1 1088.77 183.17 1237.51 320.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n188 2 Car -1 -1 -1 431.39 169.54 490.80 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n189 53 Car -1 -1 -1 840.18 172.49 948.15 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n189 2 Car -1 -1 -1 418.28 169.67 477.67 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n189 19 Car -1 -1 -1 1124.50 189.70 1238.62 329.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n190 53 Car -1 -1 -1 848.91 172.50 963.23 235.40 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n190 2 Car -1 -1 -1 403.39 170.41 464.70 196.16 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n190 19 Car -1 -1 -1 1165.30 192.93 1237.77 333.66 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n191 53 Car -1 -1 -1 860.29 173.40 980.79 237.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n191 2 Car -1 -1 -1 388.32 170.34 451.96 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n192 53 Car -1 -1 -1 870.05 173.07 999.10 242.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n192 2 Car -1 -1 -1 374.51 170.99 437.81 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n193 53 Car -1 -1 -1 880.71 173.02 1019.47 245.69 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n193 2 Car -1 -1 -1 359.62 170.44 423.34 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n194 53 Car -1 -1 -1 895.11 173.37 1040.94 249.85 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n194 2 Car -1 -1 -1 343.24 170.01 408.15 196.51 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n195 53 Car -1 -1 -1 909.32 173.79 1064.00 253.69 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n195 2 Car -1 -1 -1 326.46 170.39 392.53 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n196 53 Car -1 -1 -1 923.62 174.73 1090.51 258.46 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n196 2 Car -1 -1 -1 306.59 170.67 375.01 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n197 53 Car -1 -1 -1 938.54 175.90 1116.65 264.02 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n197 2 Car -1 -1 -1 288.47 170.47 358.52 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n198 53 Car -1 -1 -1 954.92 177.52 1146.84 269.57 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n198 2 Car -1 -1 -1 268.18 171.59 340.61 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n199 53 Car -1 -1 -1 972.74 175.96 1182.04 273.92 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n199 2 Car -1 -1 -1 249.14 171.37 322.81 199.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n200 53 Car -1 -1 -1 990.56 176.06 1219.58 280.67 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n200 2 Car -1 -1 -1 228.64 170.39 302.77 200.86 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n201 53 Car -1 -1 -1 1008.48 175.49 1238.28 289.46 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n201 2 Car -1 -1 -1 204.48 169.28 286.00 200.28 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n202 53 Car -1 -1 -1 1030.84 177.01 1238.01 295.75 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n202 2 Car -1 -1 -1 185.36 169.33 262.00 199.10 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n203 53 Car -1 -1 -1 1051.40 178.29 1236.79 302.03 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n203 2 Car -1 -1 -1 161.47 169.53 240.53 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n204 53 Car -1 -1 -1 1074.31 179.98 1236.94 314.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n204 2 Car -1 -1 -1 136.82 168.72 216.55 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n205 53 Car -1 -1 -1 1098.81 183.64 1239.83 319.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n205 2 Car -1 -1 -1 108.30 167.86 190.97 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n206 53 Car -1 -1 -1 1124.81 190.17 1240.23 328.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n206 2 Car -1 -1 -1 81.45 167.77 163.01 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n207 2 Car -1 -1 -1 55.30 167.90 142.35 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n207 53 Car -1 -1 -1 1157.02 194.49 1239.02 339.66 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n208 2 Car -1 -1 -1 24.14 166.64 113.45 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n208 53 Car -1 -1 -1 1187.95 188.56 1238.65 353.52 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n209 2 Car -1 -1 -1 -0.89 166.57 84.38 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n210 2 Car -1 -1 -1 -1.53 165.20 59.73 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n211 2 Car -1 -1 -1 0.27 161.27 33.69 195.23 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n241 86 Car -1 -1 -1 0.72 175.72 56.71 249.41 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n242 86 Car -1 -1 -1 -0.30 172.26 89.99 253.34 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0003.txt",
    "content": "0 1 Car -1 -1 -1 -2.54 209.60 356.33 369.89 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n0 2 Car -1 -1 -1 323.26 197.18 449.98 282.75 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n0 3 Car -1 -1 -1 402.97 173.64 507.90 237.57 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n0 4 Car -1 -1 -1 482.01 182.34 513.92 203.75 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n1 1 Car -1 -1 -1 -3.47 210.38 331.90 369.00 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1 2 Car -1 -1 -1 304.49 195.49 444.41 286.00 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1 3 Car -1 -1 -1 399.13 172.72 505.03 237.54 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n1 4 Car -1 -1 -1 477.40 180.97 511.32 203.21 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n2 1 Car -1 -1 -1 -1.17 210.69 306.20 370.24 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n2 2 Car -1 -1 -1 280.84 194.62 437.97 294.08 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n2 3 Car -1 -1 -1 393.28 171.81 502.59 239.40 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n2 4 Car -1 -1 -1 475.22 179.82 507.84 202.10 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n3 2 Car -1 -1 -1 264.80 194.56 428.87 301.03 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n3 1 Car -1 -1 -1 1.05 213.19 273.58 369.16 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n3 3 Car -1 -1 -1 388.95 170.61 500.35 241.30 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n3 4 Car -1 -1 -1 470.30 178.95 505.93 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n3 5 Cyclist -1 -1 -1 685.01 163.25 698.95 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n4 2 Car -1 -1 -1 241.47 195.23 420.17 309.59 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n4 1 Car -1 -1 -1 0.11 218.73 229.05 370.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n4 3 Car -1 -1 -1 383.23 170.79 497.73 244.28 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 4 Car -1 -1 -1 468.08 178.97 504.95 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n5 2 Car -1 -1 -1 208.04 197.24 410.22 319.69 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n5 3 Car -1 -1 -1 377.46 168.79 495.14 247.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n5 4 Car -1 -1 -1 464.51 178.32 502.32 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n5 1 Car -1 -1 -1 -0.18 224.03 181.87 372.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n5 6 Pedestrian -1 -1 -1 687.01 161.85 699.22 196.41 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n6 2 Car -1 -1 -1 178.03 198.02 399.76 328.40 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n6 3 Car -1 -1 -1 372.00 167.92 491.67 249.50 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n6 4 Car -1 -1 -1 460.74 177.94 500.75 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n6 1 Car -1 -1 -1 -2.98 230.63 123.58 373.52 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n6 6 Pedestrian -1 -1 -1 689.01 161.83 702.19 196.32 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n7 2 Car -1 -1 -1 139.96 197.00 390.46 338.65 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n7 3 Car -1 -1 -1 363.83 166.03 486.36 252.41 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n7 4 Car -1 -1 -1 459.02 176.65 498.65 201.96 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n8 2 Car -1 -1 -1 92.77 195.54 377.85 355.07 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n8 3 Car -1 -1 -1 354.95 164.73 485.34 254.89 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n8 4 Car -1 -1 -1 454.87 175.30 497.30 201.30 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n8 7 Car -1 -1 -1 971.79 161.65 1043.01 200.27 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n8 8 Car -1 -1 -1 488.72 173.18 515.13 191.66 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n9 2 Car -1 -1 -1 40.91 195.03 366.42 370.16 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n9 3 Car -1 -1 -1 348.27 162.47 478.76 257.69 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n9 7 Car -1 -1 -1 976.77 160.32 1061.57 202.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n9 4 Car -1 -1 -1 452.78 173.74 495.73 200.39 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n9 8 Car -1 -1 -1 488.04 171.73 515.87 191.09 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n10 2 Car -1 -1 -1 0.01 195.85 346.45 370.03 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n10 3 Car -1 -1 -1 337.36 160.78 474.27 263.36 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n10 4 Car -1 -1 -1 448.67 173.30 494.58 200.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n10 7 Car -1 -1 -1 988.92 160.47 1072.86 201.78 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n10 8 Car -1 -1 -1 488.03 171.38 515.76 191.22 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n10 9 Car -1 -1 -1 1059.95 156.82 1141.20 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n10 10 Car -1 -1 -1 1112.50 156.35 1220.78 200.59 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n11 2 Car -1 -1 -1 -5.31 197.49 329.33 369.46 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n11 3 Car -1 -1 -1 327.09 158.63 468.69 269.02 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n11 7 Car -1 -1 -1 996.36 159.60 1088.39 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n11 4 Car -1 -1 -1 444.81 174.00 492.73 202.14 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n11 10 Car -1 -1 -1 1127.21 157.47 1236.53 204.55 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n11 8 Car -1 -1 -1 487.90 171.53 515.89 191.54 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n11 9 Car -1 -1 -1 1074.74 157.47 1157.27 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n12 2 Car -1 -1 -1 -5.32 202.98 310.01 369.95 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n12 3 Car -1 -1 -1 316.55 158.80 463.45 276.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n12 7 Car -1 -1 -1 1007.50 159.96 1100.14 204.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n12 4 Car -1 -1 -1 441.31 174.10 491.93 203.39 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n12 9 Car -1 -1 -1 1088.00 156.74 1175.73 205.51 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n12 10 Car -1 -1 -1 1141.85 157.85 1236.63 205.05 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n12 8 Car -1 -1 -1 487.60 171.40 515.92 192.05 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n13 2 Car -1 -1 -1 -2.12 204.77 284.48 369.93 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n13 3 Car -1 -1 -1 303.41 158.71 454.54 284.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n13 4 Car -1 -1 -1 437.15 175.05 489.93 205.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n13 7 Car -1 -1 -1 1015.63 161.48 1115.12 207.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n13 9 Car -1 -1 -1 1098.63 157.96 1187.97 206.16 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n13 8 Car -1 -1 -1 487.53 171.78 515.94 192.92 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n13 10 Car -1 -1 -1 1165.60 159.78 1236.25 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n14 3 Car -1 -1 -1 292.18 164.06 448.38 291.94 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n14 2 Car -1 -1 -1 1.18 209.96 251.95 369.37 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n14 7 Car -1 -1 -1 1025.64 161.46 1128.38 208.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n14 4 Car -1 -1 -1 431.87 175.49 487.23 205.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n14 9 Car -1 -1 -1 1101.74 158.24 1206.95 211.99 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n14 8 Car -1 -1 -1 486.60 172.26 516.15 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n15 3 Car -1 -1 -1 277.68 164.31 439.64 300.07 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n15 2 Car -1 -1 -1 1.15 210.81 219.57 371.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 7 Car -1 -1 -1 1034.26 160.97 1143.61 210.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 4 Car -1 -1 -1 428.89 175.58 484.20 206.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n15 9 Car -1 -1 -1 1120.68 158.12 1219.25 206.79 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n15 8 Car -1 -1 -1 486.39 172.49 516.08 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n15 11 Car -1 -1 -1 353.11 171.52 426.75 221.64 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n16 3 Car -1 -1 -1 260.87 164.00 426.45 309.41 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n16 7 Car -1 -1 -1 1043.55 160.91 1157.36 211.91 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n16 2 Car -1 -1 -1 -2.24 217.76 177.30 370.57 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n16 4 Car -1 -1 -1 424.14 176.01 480.51 207.84 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n16 9 Car -1 -1 -1 1125.26 158.19 1231.10 212.53 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n16 11 Car -1 -1 -1 341.63 170.15 423.16 225.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n16 8 Car -1 -1 -1 485.10 172.45 514.80 193.97 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n17 3 Car -1 -1 -1 243.01 167.88 412.40 320.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n17 7 Car -1 -1 -1 1053.04 159.72 1171.62 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n17 4 Car -1 -1 -1 419.49 176.26 477.07 208.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n17 2 Car -1 -1 -1 -2.07 218.30 130.68 370.56 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n17 11 Car -1 -1 -1 336.28 170.58 420.79 229.76 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n17 8 Car -1 -1 -1 484.18 173.42 514.09 194.55 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n17 12 Car -1 -1 -1 239.53 154.85 385.17 278.48 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n17 13 Pedestrian -1 -1 -1 1168.67 150.45 1203.29 214.70 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n18 3 Car -1 -1 -1 215.28 166.07 400.77 336.18 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n18 4 Car -1 -1 -1 414.88 176.21 473.54 208.91 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n18 7 Car -1 -1 -1 1063.69 160.10 1184.38 212.75 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n18 11 Car -1 -1 -1 332.92 170.53 416.42 229.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n18 8 Car -1 -1 -1 481.93 173.38 513.85 194.87 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 2 Car -1 -1 -1 -1.15 206.59 74.54 373.95 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n18 12 Car -1 -1 -1 217.31 156.76 369.26 276.36 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n18 14 Car -1 -1 -1 1194.80 158.12 1239.83 213.38 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n19 3 Car -1 -1 -1 191.76 166.22 385.88 351.55 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n19 4 Car -1 -1 -1 409.89 176.82 470.13 210.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n19 7 Car -1 -1 -1 1072.68 158.65 1198.95 214.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n19 11 Car -1 -1 -1 329.79 170.68 411.45 230.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n19 8 Car -1 -1 -1 481.28 173.34 513.24 195.28 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n19 12 Car -1 -1 -1 200.25 160.32 354.96 272.48 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n20 3 Car -1 -1 -1 160.95 162.22 370.71 364.99 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n20 4 Car -1 -1 -1 404.87 177.46 467.22 211.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n20 7 Car -1 -1 -1 1082.06 157.85 1218.20 214.95 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n20 11 Car -1 -1 -1 324.93 171.83 407.02 231.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n20 8 Car -1 -1 -1 479.99 173.98 511.90 196.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n21 3 Car -1 -1 -1 121.44 161.36 356.70 372.54 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n21 7 Car -1 -1 -1 1091.29 158.20 1231.74 215.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n21 8 Car -1 -1 -1 479.00 174.20 511.95 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n21 4 Car -1 -1 -1 399.73 177.34 464.13 212.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n21 11 Car -1 -1 -1 314.60 171.99 401.91 235.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n21 15 Car -1 -1 -1 1181.40 155.48 1236.41 215.19 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n22 3 Car -1 -1 -1 82.85 157.01 339.60 370.05 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n22 7 Car -1 -1 -1 1101.40 159.00 1237.27 217.75 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n22 11 Car -1 -1 -1 301.29 171.70 395.07 238.43 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n22 4 Car -1 -1 -1 393.63 177.89 461.40 213.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n22 8 Car -1 -1 -1 477.75 174.80 510.30 197.24 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 15 Car -1 -1 -1 1201.95 155.73 1238.92 214.51 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n23 3 Car -1 -1 -1 35.68 157.60 318.08 368.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n23 7 Car -1 -1 -1 1109.99 158.75 1239.60 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n23 11 Car -1 -1 -1 292.69 173.59 392.96 242.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n23 4 Car -1 -1 -1 388.06 179.09 459.30 216.30 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n23 8 Car -1 -1 -1 477.21 175.54 510.32 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n24 3 Car -1 -1 -1 -4.65 150.82 296.82 368.80 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n24 11 Car -1 -1 -1 285.08 175.56 386.97 244.43 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n24 7 Car -1 -1 -1 1123.39 157.31 1238.14 220.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n24 4 Car -1 -1 -1 384.48 179.71 455.20 217.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 8 Car -1 -1 -1 476.95 177.06 507.88 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n25 3 Car -1 -1 -1 -2.32 149.18 269.77 368.93 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n25 11 Car -1 -1 -1 275.13 177.19 382.95 247.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n25 7 Car -1 -1 -1 1131.69 156.59 1239.45 216.52 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n25 4 Car -1 -1 -1 378.45 180.78 451.13 219.60 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n25 8 Car -1 -1 -1 475.83 177.41 508.18 200.75 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n26 11 Car -1 -1 -1 266.44 177.23 379.34 249.91 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n26 3 Car -1 -1 -1 1.64 150.58 243.33 367.91 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n26 8 Car -1 -1 -1 474.66 177.11 507.25 201.00 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 7 Car -1 -1 -1 1140.66 154.51 1239.32 214.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n26 4 Car -1 -1 -1 373.38 181.19 448.41 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n27 11 Car -1 -1 -1 256.92 177.67 374.95 253.56 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n27 3 Car -1 -1 -1 1.17 148.75 226.17 369.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n27 4 Car -1 -1 -1 370.18 181.26 445.92 222.25 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n27 8 Car -1 -1 -1 473.30 177.07 507.10 200.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n27 7 Car -1 -1 -1 1149.80 151.52 1238.36 211.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n28 11 Car -1 -1 -1 247.48 178.83 371.44 256.30 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n28 3 Car -1 -1 -1 1.90 149.80 202.33 368.88 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n28 4 Car -1 -1 -1 366.19 182.57 442.18 224.51 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n28 8 Car -1 -1 -1 472.15 177.25 507.18 201.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 7 Car -1 -1 -1 1163.34 148.89 1237.49 207.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n29 11 Car -1 -1 -1 239.79 180.39 367.57 260.65 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n29 4 Car -1 -1 -1 361.21 184.40 439.53 227.10 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n29 7 Car -1 -1 -1 1166.10 148.45 1238.31 208.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n29 3 Car -1 -1 -1 1.68 147.63 173.76 363.21 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n29 8 Car -1 -1 -1 472.02 178.86 506.93 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n30 11 Car -1 -1 -1 230.81 186.09 363.60 268.39 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n30 4 Car -1 -1 -1 357.08 190.47 435.92 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n30 8 Car -1 -1 -1 470.81 185.30 505.97 209.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n30 7 Car -1 -1 -1 1174.47 155.03 1236.75 216.75 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n30 3 Car -1 -1 -1 4.26 162.09 147.53 356.76 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n31 11 Car -1 -1 -1 221.97 195.70 358.06 278.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n31 4 Car -1 -1 -1 351.99 199.22 434.21 243.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n31 8 Car -1 -1 -1 469.91 193.90 505.84 218.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n31 7 Car -1 -1 -1 1187.22 165.16 1237.59 230.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n31 3 Car -1 -1 -1 -0.20 164.12 143.37 362.26 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n32 11 Car -1 -1 -1 214.34 205.45 354.26 290.75 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n32 4 Car -1 -1 -1 347.25 209.01 430.87 253.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n32 8 Car -1 -1 -1 468.91 203.08 505.68 227.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n32 3 Car -1 -1 -1 -1.01 172.99 113.48 368.96 -1 -1 -1 -1000 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0.87\n68 4 Car -1 -1 -1 410.54 194.58 610.60 294.77 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n68 8 Car -1 -1 -1 704.32 176.37 750.82 209.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n68 11 Car -1 -1 -1 2.03 216.62 195.89 372.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n69 4 Car -1 -1 -1 408.04 189.63 614.60 292.86 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n69 8 Car -1 -1 -1 711.32 171.97 757.72 206.39 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n69 11 Car -1 -1 -1 -0.24 216.28 136.46 372.03 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n70 4 Car -1 -1 -1 403.97 185.96 618.48 294.54 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n70 8 Car -1 -1 -1 716.89 168.44 764.00 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n71 4 Car -1 -1 -1 398.38 183.68 622.45 297.18 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n71 8 Car -1 -1 -1 723.24 165.55 770.04 200.43 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n72 4 Car -1 -1 -1 390.35 182.75 624.20 304.26 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n72 8 Car -1 -1 -1 726.21 163.88 776.35 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n73 4 Car -1 -1 -1 380.45 185.40 623.56 315.77 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n73 8 Car -1 -1 -1 730.20 163.15 780.99 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n74 4 Car -1 -1 -1 364.73 187.57 623.25 323.88 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n74 8 Car -1 -1 -1 732.84 163.47 782.78 199.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n75 4 Car -1 -1 -1 343.60 188.57 622.28 336.44 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n75 8 Car -1 -1 -1 733.99 164.92 784.05 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n76 4 Car -1 -1 -1 318.88 189.72 617.73 345.89 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n76 8 Car -1 -1 -1 732.34 166.62 784.53 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n77 4 Car -1 -1 -1 285.66 192.28 612.18 357.83 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n77 8 Car -1 -1 -1 729.75 168.67 782.57 207.16 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n78 4 Car -1 -1 -1 246.64 195.50 603.85 370.11 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n78 8 Car -1 -1 -1 728.31 168.54 779.88 207.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n79 4 Car -1 -1 -1 193.07 196.56 595.18 368.84 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n79 8 Car -1 -1 -1 724.85 167.50 777.87 205.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n80 4 Car -1 -1 -1 133.93 197.17 584.77 369.05 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n80 8 Car -1 -1 -1 722.07 166.58 775.15 205.42 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n81 4 Car -1 -1 -1 67.02 199.42 566.09 367.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n81 8 Car -1 -1 -1 718.87 166.35 772.92 206.03 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n82 4 Car -1 -1 -1 4.93 204.01 558.04 369.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n82 8 Car -1 -1 -1 715.15 167.32 768.54 208.94 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n83 4 Car -1 -1 -1 -6.17 210.64 538.32 370.40 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n83 8 Car -1 -1 -1 709.43 171.13 764.30 212.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n84 4 Car -1 -1 -1 -3.42 217.20 518.86 370.60 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n84 8 Car -1 -1 -1 705.61 175.23 758.56 217.41 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n85 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-1 -1 590.09 184.67 650.62 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n94 8 Car -1 -1 -1 576.07 185.30 636.34 234.89 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n95 8 Car -1 -1 -1 560.82 186.66 622.87 237.64 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n96 8 Car -1 -1 -1 547.98 185.71 611.11 238.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n97 8 Car -1 -1 -1 536.21 182.46 600.41 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n98 8 Car -1 -1 -1 525.37 176.95 590.76 231.22 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n99 8 Car -1 -1 -1 516.35 167.18 583.29 221.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n100 8 Car -1 -1 -1 506.55 155.35 577.05 210.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n101 8 Car -1 -1 -1 498.33 150.69 571.26 207.68 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n102 8 Car -1 -1 -1 491.96 157.46 565.99 216.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n102 16 Pedestrian -1 -1 -1 802.33 146.79 815.02 179.72 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n103 8 Car -1 -1 -1 484.15 168.52 560.53 228.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n103 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172.64 818.80 206.31 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n108 8 Car -1 -1 -1 446.81 170.12 536.13 238.44 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n108 16 Pedestrian -1 -1 -1 807.04 162.44 819.33 196.06 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n109 8 Car -1 -1 -1 440.45 168.66 531.91 236.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n109 19 Cyclist -1 -1 -1 809.41 161.28 822.78 195.88 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n110 8 Car -1 -1 -1 432.12 174.36 527.85 246.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n110 20 Pedestrian -1 -1 -1 813.26 165.81 826.30 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n111 8 Car -1 -1 -1 423.55 183.77 525.26 257.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n111 21 Cyclist -1 -1 -1 832.62 174.93 847.91 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n111 22 Cyclist -1 -1 -1 814.18 173.69 828.92 208.17 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n112 8 Car -1 -1 -1 413.69 188.71 518.94 266.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n112 22 Cyclist -1 -1 -1 817.03 177.65 832.75 214.77 -1 -1 -1 -1000 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Pedestrian -1 -1 -1 1069.03 177.72 1100.16 241.25 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n133 8 Car -1 -1 -1 0.60 219.92 251.10 369.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n133 29 Pedestrian -1 -1 -1 1055.16 172.10 1091.94 252.67 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n133 32 Pedestrian -1 -1 -1 1093.16 176.81 1130.37 248.85 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n134 8 Car -1 -1 -1 1.96 235.20 181.17 368.86 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n134 29 Pedestrian -1 -1 -1 1089.69 168.78 1126.46 258.47 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n135 29 Pedestrian -1 -1 -1 1128.44 170.69 1165.01 263.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n135 33 Pedestrian -1 -1 -1 1164.16 173.17 1207.07 260.09 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n136 29 Pedestrian -1 -1 -1 1165.94 170.63 1205.97 269.75 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n136 33 Pedestrian -1 -1 -1 1201.38 165.82 1240.03 267.73 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n136 34 Car -1 -1 -1 564.24 183.43 586.35 200.44 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n137 29 Pedestrian -1 -1 -1 1208.85 173.13 1239.93 276.22 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n139 35 Car -1 -1 -1 562.72 181.99 587.28 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n140 35 Car -1 -1 -1 564.05 181.93 588.13 199.44 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n141 35 Car -1 -1 -1 563.90 181.92 588.17 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n142 35 Car -1 -1 -1 563.22 181.91 587.37 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n143 35 Car -1 -1 -1 563.00 181.44 586.88 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n144 35 Car -1 -1 -1 560.70 181.29 586.05 198.96 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n145 35 Car -1 -1 -1 559.39 180.27 584.13 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n146 35 Car -1 -1 -1 557.57 180.16 582.70 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n147 35 Car -1 -1 -1 557.21 179.82 582.44 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n148 35 Car -1 -1 -1 556.58 179.57 581.82 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n149 35 Car -1 -1 -1 555.94 180.09 581.79 198.62 -1 -1 -1 -1000 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-1 -1 -1 -1000 -1000 -1000 -10 0.42\n244 47 Cyclist -1 -1 -1 404.79 179.16 437.64 248.15 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n244 44 Car -1 -1 -1 578.29 181.06 598.28 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n244 48 Cyclist -1 -1 -1 846.90 165.32 866.73 206.47 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n244 50 Pedestrian -1 -1 -1 848.31 164.99 868.89 206.62 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n245 47 Cyclist -1 -1 -1 392.90 173.51 425.02 251.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n245 44 Car -1 -1 -1 577.13 176.85 598.45 194.56 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n245 48 Cyclist -1 -1 -1 855.43 162.30 881.05 207.06 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n246 44 Car -1 -1 -1 576.75 176.01 599.32 194.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n246 47 Cyclist -1 -1 -1 371.90 172.88 413.70 258.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n246 48 Cyclist -1 -1 -1 869.19 161.71 896.79 207.68 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n246 51 Pedestrian -1 -1 -1 869.19 161.71 896.79 207.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n246 52 Pedestrian -1 -1 -1 758.21 165.30 768.43 190.53 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n247 44 Car -1 -1 -1 576.64 176.46 599.20 194.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n247 47 Cyclist -1 -1 -1 348.91 173.91 399.64 264.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n247 52 Pedestrian -1 -1 -1 761.60 165.61 773.35 190.69 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n247 48 Cyclist -1 -1 -1 884.46 159.75 911.68 206.26 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n248 44 Car -1 -1 -1 575.99 177.20 599.12 195.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n248 47 Cyclist -1 -1 -1 321.32 172.02 382.88 274.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n248 52 Pedestrian -1 -1 -1 765.88 165.69 777.35 191.40 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n248 53 Pedestrian -1 -1 -1 902.21 158.16 925.62 208.06 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n249 44 Car -1 -1 -1 575.06 177.14 599.05 195.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n249 47 Cyclist -1 -1 -1 298.36 170.87 357.38 285.87 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n249 53 Pedestrian -1 -1 -1 918.56 157.19 940.40 208.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n249 52 Pedestrian -1 -1 -1 769.70 165.45 781.16 191.75 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n249 54 Cyclist -1 -1 -1 992.91 153.42 1020.87 210.76 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n250 44 Car -1 -1 -1 574.91 177.05 598.59 195.53 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n250 53 Pedestrian -1 -1 -1 934.85 155.94 962.45 210.15 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n250 52 Pedestrian -1 -1 -1 774.13 164.89 784.90 191.73 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n250 47 Cyclist -1 -1 -1 252.23 170.62 333.64 300.53 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n251 47 Cyclist -1 -1 -1 198.16 173.60 303.54 314.30 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n251 52 Pedestrian -1 -1 -1 778.05 164.15 788.02 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n251 44 Car -1 -1 -1 574.15 178.24 598.68 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n251 55 Cyclist -1 -1 -1 949.71 157.32 985.40 212.33 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n251 56 Cyclist -1 -1 -1 778.05 164.15 788.02 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n252 44 Car -1 -1 -1 572.25 179.76 598.26 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n252 55 Cyclist -1 -1 -1 967.20 160.19 1010.10 217.19 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n252 52 Pedestrian -1 -1 -1 781.08 164.10 791.65 194.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n252 57 Pedestrian -1 -1 -1 159.90 171.78 269.84 338.82 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n253 44 Car -1 -1 -1 570.03 180.07 597.16 200.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n253 55 Cyclist -1 -1 -1 990.53 157.67 1038.83 221.55 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n253 52 Pedestrian -1 -1 -1 783.84 165.10 795.53 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n253 58 Cyclist -1 -1 -1 50.24 170.39 217.75 365.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n254 44 Car -1 -1 -1 569.24 179.22 596.47 201.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n254 58 Cyclist -1 -1 -1 -6.66 162.67 142.63 372.80 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n254 55 Cyclist -1 -1 -1 1010.37 154.76 1066.92 224.56 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n254 52 Pedestrian -1 -1 -1 787.18 164.16 798.75 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n255 44 Car -1 -1 -1 567.12 178.03 596.18 200.48 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n255 55 Cyclist -1 -1 -1 1041.25 155.64 1097.92 229.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n255 52 Pedestrian -1 -1 -1 790.63 162.35 802.24 195.90 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n256 44 Car -1 -1 -1 566.57 176.80 595.30 200.15 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n256 55 Cyclist -1 -1 -1 1071.85 153.37 1144.16 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0004.txt",
    "content": "0 1 Car -1 -1 -1 193.61 191.06 437.96 342.77 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n0 2 Car -1 -1 -1 628.99 172.36 658.62 199.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n1 1 Car -1 -1 -1 -1.44 197.68 370.44 367.51 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1 2 Car -1 -1 -1 629.79 171.99 659.98 199.24 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n2 1 Car -1 -1 -1 -2.55 211.69 223.88 369.76 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n2 2 Car -1 -1 -1 629.65 171.96 659.94 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n3 2 Car -1 -1 -1 629.53 171.76 659.93 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n4 2 Car -1 -1 -1 629.60 171.32 659.92 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n5 2 Car -1 -1 -1 629.71 171.99 659.79 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n6 2 Car -1 -1 -1 629.75 172.34 660.59 200.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n7 2 Car -1 -1 -1 629.37 171.25 660.76 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n8 2 Car -1 -1 -1 629.81 170.25 660.73 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n9 2 Car -1 -1 -1 629.86 168.66 661.76 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n10 2 Car -1 -1 -1 630.83 168.86 662.00 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n11 2 Car -1 -1 -1 631.82 168.56 662.65 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n12 2 Car -1 -1 -1 633.03 168.10 664.26 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n12 3 Pedestrian -1 -1 -1 740.84 156.91 754.54 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n13 2 Car -1 -1 -1 634.79 168.18 666.40 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n14 2 Car -1 -1 -1 636.31 169.89 668.83 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n15 2 Car -1 -1 -1 638.92 168.79 670.71 197.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n16 2 Car -1 -1 -1 640.56 168.59 672.66 197.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n17 2 Car -1 -1 -1 642.46 170.45 673.82 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 2 Car -1 -1 -1 642.69 172.04 675.12 200.39 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n19 2 Car -1 -1 -1 644.21 173.89 677.74 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n20 2 Car -1 -1 -1 644.70 172.87 678.40 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n21 2 Car -1 -1 -1 646.42 171.92 678.19 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n22 2 Car -1 -1 -1 645.50 168.92 680.14 195.66 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n23 2 Car -1 -1 -1 646.25 168.93 680.03 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n24 2 Car -1 -1 -1 648.25 172.32 681.07 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n25 2 Car -1 -1 -1 648.25 172.23 681.08 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n26 2 Car -1 -1 -1 647.85 168.48 680.70 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n27 2 Car -1 -1 -1 648.54 167.49 680.14 195.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n28 2 Car -1 -1 -1 648.15 167.85 680.72 196.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n29 2 Car -1 -1 -1 648.74 169.70 681.71 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n30 2 Car -1 -1 -1 649.05 168.34 681.48 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n31 2 Car -1 -1 -1 649.70 168.26 682.77 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n32 2 Car -1 -1 -1 649.92 168.33 683.06 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n33 2 Car -1 -1 -1 650.45 167.94 683.61 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n34 2 Car -1 -1 -1 651.69 167.48 684.85 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n35 2 Car -1 -1 -1 652.01 167.92 685.75 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n36 2 Car -1 -1 -1 652.85 169.19 685.65 199.29 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n37 2 Car -1 -1 -1 652.98 167.87 686.74 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n38 2 Car -1 -1 -1 653.13 167.61 687.20 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n39 2 Car -1 -1 -1 653.64 167.76 687.06 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n40 2 Car -1 -1 -1 653.22 167.53 687.15 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n41 2 Car -1 -1 -1 652.82 167.94 686.69 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n42 2 Car -1 -1 -1 652.94 167.92 686.37 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n42 4 Car -1 -1 -1 702.54 170.08 724.51 184.12 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n43 2 Car -1 -1 -1 652.61 167.04 685.87 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n43 4 Car -1 -1 -1 696.77 168.40 718.95 182.55 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n44 2 Car -1 -1 -1 653.44 167.21 686.28 196.85 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n44 4 Car -1 -1 -1 691.29 168.89 711.74 183.84 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n45 2 Car -1 -1 -1 652.85 167.89 686.70 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n46 2 Car -1 -1 -1 653.33 171.07 687.12 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n47 2 Car -1 -1 -1 653.62 170.32 687.83 200.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n48 2 Car -1 -1 -1 655.17 168.87 689.67 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n49 2 Car -1 -1 -1 656.53 167.13 690.34 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n50 2 Car -1 -1 -1 656.24 169.07 692.08 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n51 2 Car -1 -1 -1 659.36 170.94 694.00 200.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n51 5 Car -1 -1 -1 642.84 176.02 671.11 194.14 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n52 2 Car -1 -1 -1 660.90 171.92 695.16 201.42 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n52 5 Car -1 -1 -1 635.17 177.32 666.27 196.18 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n53 5 Car -1 -1 -1 627.23 178.91 658.77 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n53 2 Car -1 -1 -1 662.23 173.62 694.35 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n54 5 Car -1 -1 -1 617.56 179.03 652.16 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n54 2 Car -1 -1 -1 661.32 172.46 694.85 201.73 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n55 2 Car -1 -1 -1 661.58 169.77 695.86 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n55 5 Car -1 -1 -1 607.92 175.88 643.92 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n56 5 Car -1 -1 -1 595.96 171.83 636.49 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n56 2 Car -1 -1 -1 661.07 165.60 696.37 195.55 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n57 5 Car -1 -1 -1 584.91 172.15 628.21 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n57 2 Car -1 -1 -1 661.25 165.82 695.88 195.48 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n58 5 Car -1 -1 -1 574.05 175.57 618.12 204.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n58 2 Car -1 -1 -1 660.82 167.41 695.52 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n59 5 Car -1 -1 -1 559.69 177.80 608.43 209.63 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n59 2 Car -1 -1 -1 660.39 169.69 695.69 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n60 5 Car -1 -1 -1 545.31 177.12 598.30 212.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n60 2 Car -1 -1 -1 660.22 167.73 695.71 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n61 5 Car -1 -1 -1 527.21 177.57 586.04 216.38 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n61 2 Car -1 -1 -1 659.48 167.61 695.78 198.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n62 5 Car -1 -1 -1 506.04 178.36 574.26 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n62 2 Car -1 -1 -1 659.50 167.24 695.75 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n63 5 Car -1 -1 -1 482.52 178.03 560.48 227.03 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n63 2 Car -1 -1 -1 659.62 168.64 695.17 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n64 5 Car -1 -1 -1 451.67 180.75 543.55 238.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n64 2 Car -1 -1 -1 658.89 169.97 695.49 201.56 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n65 5 Car -1 -1 -1 411.77 184.23 524.23 251.25 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n65 2 Car -1 -1 -1 658.78 172.83 696.01 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n66 5 Car -1 -1 -1 357.25 185.37 499.77 270.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n66 2 Car -1 -1 -1 658.66 172.57 695.64 204.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n67 5 Car -1 -1 -1 272.05 188.70 468.64 299.12 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n67 2 Car -1 -1 -1 658.40 170.64 695.93 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n68 5 Car -1 -1 -1 107.06 193.00 425.26 356.18 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n68 2 Car -1 -1 -1 658.78 169.87 695.75 202.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n69 5 Car -1 -1 -1 -0.87 189.73 353.06 369.13 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n69 2 Car -1 -1 -1 659.19 169.22 696.22 201.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n70 2 Car -1 -1 -1 659.05 169.10 696.69 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n70 5 Car -1 -1 -1 -6.01 201.86 204.56 370.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n71 2 Car -1 -1 -1 660.16 168.85 696.67 200.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n72 2 Car -1 -1 -1 660.71 168.43 697.01 200.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n73 2 Car -1 -1 -1 662.05 165.93 697.88 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n74 2 Car -1 -1 -1 662.02 162.79 698.34 195.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n75 2 Car -1 -1 -1 661.91 162.67 699.68 195.70 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n76 2 Car -1 -1 -1 662.39 166.12 699.16 199.09 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n77 2 Car -1 -1 -1 662.58 168.96 701.20 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n78 2 Car -1 -1 -1 662.64 169.67 701.46 203.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n79 2 Car -1 -1 -1 663.02 169.13 701.16 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n80 2 Car -1 -1 -1 663.10 167.04 701.43 200.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n81 2 Car -1 -1 -1 662.48 163.58 700.37 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n82 2 Car -1 -1 -1 661.42 160.90 699.23 194.27 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n83 2 Car -1 -1 -1 660.70 160.72 698.98 194.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n84 2 Car -1 -1 -1 660.03 164.94 699.09 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n85 2 Car -1 -1 -1 658.30 169.24 698.69 204.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n86 2 Car -1 -1 -1 658.78 173.25 697.96 207.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n87 2 Car -1 -1 -1 657.71 171.93 697.64 205.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n88 2 Car -1 -1 -1 656.39 166.99 695.54 200.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n89 2 Car -1 -1 -1 655.12 163.21 694.23 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n90 2 Car -1 -1 -1 654.31 162.10 692.99 196.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n91 2 Car -1 -1 -1 653.93 164.20 691.02 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n92 2 Car -1 -1 -1 652.03 164.22 690.12 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n93 2 Car -1 -1 -1 651.42 164.19 690.45 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n94 2 Car -1 -1 -1 650.93 165.46 689.66 200.62 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n95 2 Car -1 -1 -1 650.29 168.92 689.69 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n96 2 Car -1 -1 -1 650.71 172.00 689.73 207.99 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n97 2 Car -1 -1 -1 648.15 170.14 689.58 206.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n98 2 Car -1 -1 -1 647.73 167.85 688.43 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n99 2 Car -1 -1 -1 646.33 167.46 687.65 203.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n100 2 Car -1 -1 -1 645.32 167.91 686.25 203.64 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n101 2 Car -1 -1 -1 643.75 167.80 684.62 203.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n102 2 Car -1 -1 -1 641.93 167.58 682.07 203.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n103 2 Car -1 -1 -1 640.49 168.68 681.39 204.50 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n104 2 Car -1 -1 -1 640.77 172.16 680.39 207.35 -1 -1 -1 -1000 -1000 -1000 -10 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0.95\n363 2 Car -1 -1 -1 567.35 166.54 634.91 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n364 2 Car -1 -1 -1 568.20 166.49 635.96 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n365 2 Car -1 -1 -1 568.25 167.65 636.64 234.86 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n366 2 Car -1 -1 -1 568.41 170.16 636.76 237.63 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n367 2 Car -1 -1 -1 568.96 174.80 637.13 242.06 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n368 2 Car -1 -1 -1 569.76 174.91 637.88 243.28 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n369 2 Car -1 -1 -1 570.37 171.14 638.48 240.66 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n370 2 Car -1 -1 -1 570.74 169.40 638.45 238.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n371 2 Car -1 -1 -1 570.34 168.08 638.52 236.45 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n372 2 Car -1 -1 -1 569.49 168.06 638.64 236.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n373 2 Car -1 -1 -1 568.91 169.67 637.02 238.28 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n374 2 Car -1 -1 -1 568.53 169.21 636.45 238.22 -1 -1 -1 -1000 -1000 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218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n383 11 Cyclist -1 -1 -1 892.89 164.64 918.06 220.36 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n384 2 Car -1 -1 -1 566.50 168.67 634.29 239.79 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n384 10 Cyclist -1 -1 -1 872.08 166.70 901.75 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n384 11 Cyclist -1 -1 -1 913.71 162.23 945.09 218.77 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n385 2 Car -1 -1 -1 566.61 169.47 634.39 240.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n385 11 Cyclist -1 -1 -1 934.62 159.80 972.59 226.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n385 10 Cyclist -1 -1 -1 895.70 165.16 926.78 223.01 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n386 2 Car -1 -1 -1 565.75 172.30 634.92 242.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n386 10 Cyclist -1 -1 -1 920.30 168.37 955.83 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n386 11 Cyclist -1 -1 -1 966.92 162.86 1006.88 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n387 2 Car -1 -1 -1 564.85 174.03 635.47 246.32 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n387 10 Cyclist -1 -1 -1 953.13 169.27 991.16 239.39 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n387 11 Cyclist -1 -1 -1 1002.98 163.38 1042.62 240.82 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n388 2 Car -1 -1 -1 565.31 177.05 634.64 245.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n388 10 Cyclist -1 -1 -1 984.51 169.01 1038.03 247.65 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n388 11 Cyclist -1 -1 -1 1044.84 162.12 1099.84 248.51 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n389 2 Car -1 -1 -1 565.56 173.04 633.05 241.67 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n389 11 Cyclist -1 -1 -1 1095.64 156.27 1159.70 252.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n389 10 Cyclist -1 -1 -1 1031.34 162.26 1098.57 254.57 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n390 2 Car -1 -1 -1 565.02 167.73 633.58 236.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n391 2 Car -1 -1 -1 565.02 166.96 632.98 236.70 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n392 2 Car -1 -1 -1 564.56 167.55 633.16 237.16 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n393 2 Car -1 -1 -1 565.09 167.88 633.33 237.07 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n394 2 Car -1 -1 -1 564.81 170.75 634.21 239.96 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n395 2 Car -1 -1 -1 565.40 171.21 634.70 240.15 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n396 2 Car -1 -1 -1 566.03 169.10 634.59 238.99 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n397 2 Car -1 -1 -1 566.13 167.48 635.05 236.95 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n398 2 Car -1 -1 -1 566.34 167.21 635.11 237.24 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n399 2 Car -1 -1 -1 566.81 167.46 635.03 237.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n400 2 Car -1 -1 -1 566.75 167.61 635.17 237.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n401 2 Car -1 -1 -1 567.48 166.51 636.83 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n402 2 Car -1 -1 -1 568.23 167.65 636.36 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n403 2 Car -1 -1 -1 568.15 170.26 638.10 241.04 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n404 2 Car -1 -1 -1 568.98 173.75 636.48 241.44 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n405 2 Car -1 -1 -1 569.23 171.58 637.33 239.57 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n406 2 Car -1 -1 -1 570.11 168.71 637.51 235.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n407 2 Car -1 -1 -1 570.31 168.14 638.43 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n408 2 Car -1 -1 -1 568.86 170.24 639.38 238.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n409 2 Car -1 -1 -1 569.84 167.52 639.64 237.43 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n409 12 Car -1 -1 -1 553.64 174.85 575.23 191.35 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n410 2 Car -1 -1 -1 569.80 168.54 638.41 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n410 12 Car -1 -1 -1 553.51 174.37 573.96 190.51 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n411 2 Car -1 -1 -1 569.78 168.61 638.27 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n411 12 Car -1 -1 -1 551.93 174.14 575.87 190.09 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n412 2 Car -1 -1 -1 570.83 168.76 637.74 235.53 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n412 12 Car -1 -1 -1 549.19 174.93 573.97 191.66 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n413 2 Car -1 -1 -1 570.38 170.46 637.96 236.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n413 12 Car -1 -1 -1 547.71 177.92 574.82 194.05 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n414 2 Car -1 -1 -1 570.80 172.24 637.09 238.71 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n414 12 Car -1 -1 -1 545.17 180.64 574.88 196.73 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n415 2 Car -1 -1 -1 571.80 172.93 637.09 238.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n415 12 Car -1 -1 -1 540.08 180.91 567.64 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n416 2 Car -1 -1 -1 571.51 171.12 636.83 236.44 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n416 12 Car -1 -1 -1 537.38 178.65 568.83 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n417 2 Car -1 -1 -1 571.61 168.44 636.61 232.42 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n417 12 Car -1 -1 -1 534.78 175.99 563.46 196.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n418 2 Car -1 -1 -1 572.20 169.69 636.63 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n418 12 Car -1 -1 -1 532.01 177.72 560.32 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n419 2 Car -1 -1 -1 571.71 172.15 637.27 237.23 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n419 12 Car -1 -1 -1 528.55 181.35 557.42 203.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n420 2 Car -1 -1 -1 572.76 173.65 636.35 237.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n420 12 Car -1 -1 -1 524.57 181.48 555.13 205.61 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0005.txt",
    "content": "0 1 Car -1 -1 -1 223.46 196.73 331.60 245.55 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n0 2 Car -1 -1 -1 258.53 191.84 320.19 223.87 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n1 1 Car -1 -1 -1 249.45 196.94 351.70 238.14 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n1 2 Car -1 -1 -1 225.25 195.74 289.80 221.53 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n2 1 Car -1 -1 -1 272.12 196.24 367.10 237.75 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n2 2 Car -1 -1 -1 179.89 196.88 243.32 223.15 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n3 2 Car -1 -1 -1 114.33 195.91 200.43 234.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n3 1 Car -1 -1 -1 285.56 192.62 369.45 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n4 1 Car -1 -1 -1 313.22 192.88 382.78 224.98 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n4 2 Car -1 -1 -1 43.10 197.13 138.45 235.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n5 1 Car -1 -1 -1 324.12 190.98 393.90 225.78 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n6 1 Car -1 -1 -1 332.87 189.66 401.83 226.34 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n7 1 Car -1 -1 -1 344.69 189.96 410.79 225.72 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n8 1 Car -1 -1 -1 355.77 191.17 417.49 224.55 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n9 1 Car -1 -1 -1 370.00 189.26 424.76 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n9 3 Car -1 -1 -1 426.96 185.02 452.84 200.71 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n10 1 Car -1 -1 -1 380.16 189.13 431.70 220.29 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n11 1 Car -1 -1 -1 388.91 188.55 439.10 219.99 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n12 1 Car -1 -1 -1 399.70 190.56 444.64 214.00 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n12 4 Car -1 -1 -1 389.37 187.78 423.40 205.20 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n13 1 Car -1 -1 -1 407.29 190.17 451.29 213.12 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n13 4 Car -1 -1 -1 377.33 188.24 408.77 206.07 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n13 5 Car -1 -1 -1 464.01 184.55 487.93 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n14 1 Car -1 -1 -1 413.18 189.02 454.85 212.73 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n14 4 Car -1 -1 -1 356.67 188.74 393.14 207.91 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n14 5 Car -1 -1 -1 454.46 184.30 481.02 199.57 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n15 1 Car -1 -1 -1 420.34 188.55 459.16 212.46 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n15 4 Car -1 -1 -1 339.06 188.57 377.08 208.11 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n15 6 Car -1 -1 -1 476.92 180.83 498.34 195.53 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n16 1 Car -1 -1 -1 426.70 187.60 461.89 209.11 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n16 4 Car -1 -1 -1 318.89 189.45 359.29 212.06 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n16 6 Car -1 -1 -1 468.32 180.64 491.44 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n17 4 Car -1 -1 -1 292.61 191.65 333.11 212.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n17 1 Car -1 -1 -1 421.64 186.03 451.82 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n17 7 Car -1 -1 -1 0.40 208.66 166.50 294.66 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 7 Car -1 -1 -1 7.88 206.38 205.36 288.96 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n18 4 Car -1 -1 -1 260.05 193.14 310.25 216.60 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n18 1 Car -1 -1 -1 410.64 186.42 438.45 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n18 8 Car -1 -1 -1 436.08 188.31 470.31 207.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n19 7 Car -1 -1 -1 64.57 207.28 227.31 273.55 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n19 1 Car -1 -1 -1 397.42 187.60 428.27 205.76 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n19 4 Car -1 -1 -1 224.52 195.66 283.82 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n19 8 Car -1 -1 -1 439.46 182.06 466.91 199.21 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n20 7 Car -1 -1 -1 111.47 202.72 250.72 269.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n20 4 Car -1 -1 -1 182.70 195.71 248.20 228.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n20 1 Car -1 -1 -1 382.43 187.97 415.62 205.58 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n20 8 Car -1 -1 -1 429.95 181.73 456.99 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n21 7 Car -1 -1 -1 151.02 201.66 280.02 262.43 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n21 1 Car -1 -1 -1 362.80 188.85 403.43 207.42 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n21 4 Car -1 -1 -1 131.16 199.13 207.49 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n21 8 Car -1 -1 -1 417.84 182.49 445.91 201.53 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n21 9 Car -1 -1 -1 449.72 185.27 480.31 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n22 7 Car -1 -1 -1 185.06 199.66 299.48 255.17 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n22 4 Car -1 -1 -1 66.51 201.35 161.84 241.31 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n22 1 Car -1 -1 -1 347.29 189.70 388.01 209.57 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n22 9 Car -1 -1 -1 443.20 184.59 476.30 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n22 10 Truck -1 -1 -1 405.97 182.54 434.41 201.50 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n23 7 Car -1 -1 -1 209.57 200.06 316.04 248.17 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n23 1 Car -1 -1 -1 327.53 191.05 368.92 211.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n23 4 Car -1 -1 -1 -1.50 202.82 98.23 253.67 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n24 7 Car -1 -1 -1 234.51 196.90 326.56 244.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n24 1 Car -1 -1 -1 301.42 191.08 348.46 216.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n24 11 Car -1 -1 -1 375.46 184.28 406.57 203.96 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n25 1 Car -1 -1 -1 283.00 193.05 325.70 217.17 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n25 7 Car -1 -1 -1 251.53 195.79 335.33 239.63 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n25 12 Car -1 -1 -1 410.61 188.48 439.50 204.72 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n25 13 Truck -1 -1 -1 358.82 185.33 390.86 204.53 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n26 1 Car -1 -1 -1 251.59 195.71 302.54 220.50 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n26 12 Car -1 -1 -1 398.64 189.78 425.75 205.26 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n26 7 Car -1 -1 -1 273.89 198.53 351.09 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n26 14 Car -1 -1 -1 339.17 187.19 373.17 207.30 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n27 12 Car -1 -1 -1 383.76 190.96 411.37 205.59 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n27 1 Car -1 -1 -1 219.04 198.97 271.83 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n27 14 Car -1 -1 -1 315.75 188.05 356.98 211.99 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n28 1 Car -1 -1 -1 175.29 199.73 238.98 227.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n28 12 Car -1 -1 -1 366.02 191.51 398.06 209.63 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n28 14 Car -1 -1 -1 295.37 189.92 336.12 212.58 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n28 15 Car -1 -1 -1 417.57 187.83 445.90 204.49 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n28 16 Car -1 -1 -1 302.40 198.29 369.87 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n29 1 Car -1 -1 -1 126.55 202.07 203.33 236.72 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n29 12 Car -1 -1 -1 349.74 193.45 382.89 211.59 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n29 16 Car -1 -1 -1 314.22 199.11 373.87 227.07 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n29 15 Car -1 -1 -1 403.37 188.66 437.12 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n29 17 Truck -1 -1 -1 270.16 190.56 314.45 214.23 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n30 12 Car -1 -1 -1 330.90 193.53 370.41 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n30 1 Car -1 -1 -1 66.40 204.30 154.87 245.44 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n30 15 Car -1 -1 -1 389.55 190.77 423.19 206.61 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n30 18 Car -1 -1 -1 242.41 192.17 288.10 217.38 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n31 1 Car -1 -1 -1 1.61 208.24 102.52 255.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n31 18 Car -1 -1 -1 207.99 194.21 263.78 221.49 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n31 12 Car -1 -1 -1 308.57 197.22 348.84 214.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n31 15 Car -1 -1 -1 379.93 191.35 408.81 209.12 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n32 15 Car -1 -1 -1 363.61 192.01 400.63 211.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n32 18 Car -1 -1 -1 170.65 195.73 234.52 223.79 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n32 12 Car -1 -1 -1 284.91 197.38 331.59 219.98 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n33 12 Car -1 -1 -1 256.59 201.61 306.15 222.13 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n33 18 Car -1 -1 -1 125.96 197.10 196.81 230.13 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n33 15 Car -1 -1 -1 348.49 194.21 385.78 213.26 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n33 19 Car -1 -1 -1 385.64 190.86 411.63 208.52 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n34 12 Car -1 -1 -1 222.42 202.41 277.34 224.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n34 15 Car -1 -1 -1 332.48 195.13 370.55 214.98 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n34 19 Car -1 -1 -1 368.71 191.88 402.88 210.25 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n34 18 Car -1 -1 -1 71.63 197.22 157.82 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n34 20 Van -1 -1 -1 71.63 197.22 157.82 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n35 12 Car -1 -1 -1 180.27 203.14 249.70 230.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n35 19 Car -1 -1 -1 356.96 191.92 389.99 211.47 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n35 15 Car -1 -1 -1 313.33 196.22 351.61 214.94 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n35 20 Van -1 -1 -1 -0.80 197.94 105.74 243.75 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n36 19 Car -1 -1 -1 343.12 192.15 373.98 210.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n36 15 Car -1 -1 -1 292.35 197.15 331.17 214.28 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n36 12 Car -1 -1 -1 137.56 204.08 207.91 234.58 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n37 15 Car -1 -1 -1 264.48 198.26 314.12 219.04 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n37 19 Car -1 -1 -1 324.58 192.96 361.73 211.46 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n37 12 Car -1 -1 -1 75.62 207.69 161.01 240.57 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n38 19 Car -1 -1 -1 304.88 195.77 343.97 213.03 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n38 15 Car -1 -1 -1 237.04 200.30 288.89 223.00 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n38 12 Car -1 -1 -1 9.54 210.48 103.62 251.55 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n39 19 Car -1 -1 -1 285.18 197.48 325.64 214.03 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n39 15 Car -1 -1 -1 204.28 202.05 266.03 228.74 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n40 19 Car -1 -1 -1 262.87 199.08 308.43 218.24 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n40 15 Car -1 -1 -1 171.70 206.26 235.04 229.64 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n41 19 Car -1 -1 -1 238.09 200.59 286.21 219.11 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n41 15 Car -1 -1 -1 129.81 209.24 200.63 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n42 19 Car -1 -1 -1 211.45 201.91 265.00 223.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n42 15 Car -1 -1 -1 81.69 210.39 162.80 243.97 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n43 19 Car -1 -1 -1 178.62 204.08 237.64 227.30 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n43 15 Car -1 -1 -1 21.66 213.12 113.43 251.06 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n44 19 Car -1 -1 -1 143.13 204.57 210.36 229.88 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n45 19 Car -1 -1 -1 108.55 208.22 173.97 232.06 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n60 21 Car -1 -1 -1 99.52 219.25 167.63 244.90 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n61 21 Car -1 -1 -1 48.56 223.59 118.81 249.68 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n258 22 Car -1 -1 -1 555.61 178.39 595.76 199.09 -1 -1 -1 -1000 -1000 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-1000 -1000 -10 0.77\n788 96 Car -1 -1 -1 571.98 169.15 590.99 188.00 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n789 95 Truck -1 -1 -1 195.24 131.51 328.62 216.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n789 96 Car -1 -1 -1 571.96 169.24 590.76 188.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n789 99 Car -1 -1 -1 406.75 174.18 433.96 192.32 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n790 95 Truck -1 -1 -1 108.14 121.95 276.05 226.41 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n790 99 Car -1 -1 -1 390.91 174.68 422.93 194.87 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n790 96 Car -1 -1 -1 572.23 169.49 590.49 188.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n791 99 Car -1 -1 -1 371.99 174.03 408.35 196.30 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n791 96 Car -1 -1 -1 572.09 168.80 590.34 187.70 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n791 95 Truck -1 -1 -1 -1.67 107.82 223.10 247.81 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n792 95 Truck -1 -1 -1 1.39 91.71 126.40 256.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n792 99 Car -1 -1 -1 348.73 172.04 390.57 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n792 96 Car -1 -1 -1 571.99 167.42 589.86 186.27 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n793 99 Car -1 -1 -1 318.91 172.05 368.44 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n793 96 Car -1 -1 -1 571.80 167.20 589.32 185.60 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n794 99 Car -1 -1 -1 279.29 171.48 339.98 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n794 96 Car -1 -1 -1 571.25 167.78 588.91 185.89 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n795 99 Car -1 -1 -1 227.47 172.51 302.91 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n795 96 Car -1 -1 -1 571.23 168.53 588.46 185.94 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n796 96 Car -1 -1 -1 570.76 168.35 588.48 185.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n796 99 Car -1 -1 -1 154.74 171.81 250.96 213.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n797 99 Car -1 -1 -1 35.86 171.87 177.16 228.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n797 96 Car -1 -1 -1 570.48 168.54 588.32 186.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n798 96 Car -1 -1 -1 570.60 169.28 587.96 186.47 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n798 99 Car -1 -1 -1 -2.41 175.95 60.60 234.46 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n799 96 Car -1 -1 -1 570.14 168.17 588.04 185.73 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n800 96 Car -1 -1 -1 570.43 168.27 587.91 185.37 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n801 96 Car -1 -1 -1 570.22 167.89 588.43 185.31 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n802 96 Car -1 -1 -1 570.76 168.02 588.91 185.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n803 96 Car -1 -1 -1 570.61 168.35 588.94 185.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n804 96 Car -1 -1 -1 570.35 168.29 588.64 185.67 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n805 96 Car -1 -1 -1 570.34 168.27 588.33 185.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n806 96 Car -1 -1 -1 570.23 168.98 588.29 185.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n807 96 Car -1 -1 -1 570.21 170.71 588.02 187.12 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n807 100 Car -1 -1 -1 439.71 175.26 457.16 188.22 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n808 100 Car -1 -1 -1 429.79 177.19 449.12 190.68 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n808 96 Car -1 -1 -1 570.61 172.08 587.30 188.08 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0006.txt",
    "content": "0 1 Car -1 -1 -1 370.69 168.99 489.39 255.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n1 1 Car -1 -1 -1 391.62 170.00 497.52 248.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n2 1 Car -1 -1 -1 408.28 171.98 503.30 244.04 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n3 1 Car -1 -1 -1 421.41 174.10 508.52 238.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n4 1 Car -1 -1 -1 435.57 173.88 513.01 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n5 1 Car -1 -1 -1 446.56 172.58 517.06 228.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n6 1 Car -1 -1 -1 454.66 172.10 521.96 224.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n7 1 Car -1 -1 -1 463.10 172.41 525.46 221.59 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n8 1 Car -1 -1 -1 470.44 172.28 529.22 219.28 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n9 1 Car -1 -1 -1 478.04 173.06 532.02 216.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n10 1 Car -1 -1 -1 484.55 172.92 534.84 214.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n11 1 Car -1 -1 -1 490.32 172.14 538.25 212.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n12 1 Car -1 -1 -1 495.55 172.32 541.28 209.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n13 1 Car -1 -1 -1 501.14 173.05 543.56 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 1 Car -1 -1 -1 505.38 173.02 546.26 207.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n15 1 Car -1 -1 -1 509.40 172.67 549.48 206.60 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n16 1 Car -1 -1 -1 513.46 171.66 551.65 204.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n17 1 Car -1 -1 -1 516.96 171.46 553.12 202.35 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n18 1 Car -1 -1 -1 519.91 172.12 555.29 202.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n19 1 Car -1 -1 -1 522.80 173.68 557.13 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n20 1 Car -1 -1 -1 524.68 173.97 557.67 201.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n21 1 Car -1 -1 -1 526.30 174.59 558.52 201.42 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n21 2 Car -1 -1 -1 428.52 176.82 453.07 192.33 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n22 2 Car -1 -1 -1 416.12 176.94 442.59 193.17 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n22 1 Car -1 -1 -1 528.34 174.77 559.29 201.00 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n23 2 Car -1 -1 -1 402.67 176.21 430.83 194.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n23 1 Car -1 -1 -1 530.04 174.27 560.51 199.50 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n23 3 Car -1 -1 -1 465.87 175.79 484.88 188.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n24 1 Car -1 -1 -1 531.42 174.06 561.34 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n24 2 Car -1 -1 -1 384.95 175.95 417.93 195.12 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n24 3 Car -1 -1 -1 457.96 176.32 480.13 189.44 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n25 1 Car -1 -1 -1 533.40 173.69 562.23 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n25 2 Car -1 -1 -1 365.21 175.51 400.67 195.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n25 3 Car -1 -1 -1 451.55 175.93 473.76 189.84 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n26 1 Car -1 -1 -1 534.49 173.54 562.80 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n26 2 Car -1 -1 -1 341.86 175.41 382.04 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n26 3 Car -1 -1 -1 442.75 176.00 466.70 190.08 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n27 1 Car -1 -1 -1 535.49 173.49 563.49 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n27 2 Car -1 -1 -1 307.66 174.53 355.16 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n27 3 Car -1 -1 -1 432.14 175.67 458.62 190.68 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n28 2 Car -1 -1 -1 270.22 173.88 323.06 204.02 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n28 1 Car -1 -1 -1 537.10 173.28 563.66 195.21 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n28 3 Car -1 -1 -1 421.29 174.84 449.51 191.72 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n29 2 Car -1 -1 -1 212.28 173.65 282.18 206.79 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n29 3 Car -1 -1 -1 409.19 174.25 438.99 192.12 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n29 1 Car -1 -1 -1 538.27 173.23 564.55 195.05 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n30 3 Car -1 -1 -1 392.91 174.77 427.01 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n30 1 Car -1 -1 -1 538.98 173.26 564.80 194.66 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n30 2 Car -1 -1 -1 136.78 173.97 223.69 214.27 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n30 4 Van -1 -1 -1 472.45 164.65 495.78 185.06 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n31 3 Car -1 -1 -1 374.09 174.41 412.31 195.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 1 Car -1 -1 -1 539.97 173.19 565.46 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n31 2 Car -1 -1 -1 12.74 171.06 146.05 230.89 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n31 4 Van -1 -1 -1 467.09 164.38 492.17 185.87 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n32 3 Car -1 -1 -1 349.99 174.92 393.94 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n32 1 Car -1 -1 -1 540.96 173.92 566.10 194.04 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n32 4 Van -1 -1 -1 460.41 163.83 488.53 186.17 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n33 3 Car -1 -1 -1 321.80 175.56 371.69 200.59 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n33 1 Car -1 -1 -1 542.05 173.54 566.44 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n34 3 Car -1 -1 -1 281.79 175.33 345.66 204.14 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n34 1 Car -1 -1 -1 542.81 173.62 566.58 192.76 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n35 3 Car -1 -1 -1 232.19 175.16 309.34 208.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n35 1 Car -1 -1 -1 544.09 174.13 567.09 192.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n35 5 Van -1 -1 -1 439.64 161.46 473.02 188.14 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n36 3 Car -1 -1 -1 160.94 174.60 263.97 213.22 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n36 5 Van -1 -1 -1 428.98 158.62 467.71 189.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n36 1 Car -1 -1 -1 544.73 174.35 566.94 192.06 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n37 3 Car -1 -1 -1 58.08 172.79 194.84 222.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n37 1 Car -1 -1 -1 545.12 173.52 566.90 191.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n37 5 Van -1 -1 -1 417.99 158.25 457.30 189.34 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n38 1 Car -1 -1 -1 545.03 172.80 567.15 190.28 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n38 3 Car -1 -1 -1 -0.25 173.23 95.45 236.91 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n38 5 Van -1 -1 -1 404.11 155.96 447.85 189.80 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n39 1 Car -1 -1 -1 545.18 172.61 566.82 189.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n39 5 Van -1 -1 -1 390.01 154.88 436.37 190.57 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n40 1 Car -1 -1 -1 545.03 172.45 566.55 189.30 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n40 5 Van -1 -1 -1 375.19 153.80 419.31 192.12 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n40 6 Truck -1 -1 -1 375.19 153.80 419.31 192.12 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n41 1 Car -1 -1 -1 545.15 172.92 566.68 189.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n41 6 Truck -1 -1 -1 352.94 150.08 403.51 195.32 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n41 5 Van -1 -1 -1 352.94 150.08 403.51 195.32 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n41 7 Car -1 -1 -1 499.43 170.90 522.37 182.55 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n42 1 Car -1 -1 -1 545.40 173.24 566.72 189.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n42 6 Truck -1 -1 -1 327.41 148.61 382.86 197.27 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n42 7 Car -1 -1 -1 494.57 170.75 520.21 183.86 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n43 1 Car -1 -1 -1 545.72 173.45 566.87 189.72 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n43 7 Car -1 -1 -1 489.91 170.91 513.98 185.07 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n43 6 Truck -1 -1 -1 294.27 147.23 369.00 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n44 1 Car -1 -1 -1 545.99 174.09 566.47 189.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n44 6 Truck -1 -1 -1 252.06 144.18 325.98 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n44 7 Car -1 -1 -1 484.22 171.37 507.87 186.06 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n45 1 Car -1 -1 -1 546.31 174.13 566.23 189.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 6 Truck -1 -1 -1 192.94 137.51 284.16 209.66 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n45 7 Car -1 -1 -1 478.31 171.04 503.48 186.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n46 1 Car -1 -1 -1 546.59 173.98 566.46 189.49 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n46 7 Car -1 -1 -1 470.06 171.29 494.09 187.34 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n46 6 Truck -1 -1 -1 108.91 129.84 228.95 217.26 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n47 1 Car -1 -1 -1 546.55 173.81 566.40 189.10 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n47 7 Car -1 -1 -1 461.04 170.90 487.07 187.89 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n47 8 Van -1 -1 -1 0.19 119.20 134.98 228.13 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n48 1 Car -1 -1 -1 546.45 173.70 566.26 188.76 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n48 7 Car -1 -1 -1 450.37 169.45 478.18 189.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n49 1 Car -1 -1 -1 546.18 173.62 565.95 188.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n49 7 Car -1 -1 -1 437.97 168.88 468.34 189.68 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n50 1 Car -1 -1 -1 546.40 173.31 565.57 187.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n50 7 Car -1 -1 -1 422.17 168.17 457.14 190.63 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n51 1 Car -1 -1 -1 546.27 173.30 565.77 187.70 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n51 7 Car -1 -1 -1 404.40 168.73 442.70 194.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n51 9 Car -1 -1 -1 -1.30 188.96 182.89 368.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n52 1 Car -1 -1 -1 546.78 174.08 565.74 188.23 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n52 9 Car -1 -1 -1 -3.45 124.41 279.47 371.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n52 7 Car -1 -1 -1 378.73 168.30 424.54 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n53 7 Car -1 -1 -1 347.17 166.69 402.10 204.74 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n53 1 Car -1 -1 -1 547.60 174.60 565.90 188.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n53 9 Car -1 -1 -1 1.63 131.97 336.98 369.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n54 1 Car -1 -1 -1 548.54 174.18 566.23 188.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n54 7 Car -1 -1 -1 300.96 165.71 371.44 206.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n54 9 Car -1 -1 -1 7.56 128.91 384.43 367.82 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n54 10 Van -1 -1 -1 5.51 140.29 386.33 369.42 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n55 1 Car -1 -1 -1 549.24 173.84 567.22 188.14 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n55 10 Van -1 -1 -1 59.86 147.44 423.76 370.88 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n55 9 Car -1 -1 -1 50.53 149.92 420.11 374.18 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n56 9 Car -1 -1 -1 165.56 154.22 443.87 341.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n56 1 Car -1 -1 -1 550.18 173.54 568.33 187.32 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n57 9 Car -1 -1 -1 243.84 155.48 465.06 316.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n57 1 Car -1 -1 -1 550.49 173.63 568.51 187.61 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n58 9 Car -1 -1 -1 296.15 157.28 483.51 298.53 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n58 1 Car -1 -1 -1 550.98 173.78 568.46 187.55 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n58 11 Van -1 -1 -1 296.15 157.28 483.51 298.53 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n59 9 Car -1 -1 -1 338.07 158.89 498.08 282.46 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n59 1 Car -1 -1 -1 551.11 173.95 568.40 187.48 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n60 9 Car -1 -1 -1 371.13 159.94 511.47 271.46 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n60 1 Car -1 -1 -1 551.82 172.37 567.54 185.99 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n61 9 Car -1 -1 -1 398.13 159.50 522.05 260.35 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n61 1 Car -1 -1 -1 551.71 172.05 567.80 185.28 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n62 9 Car -1 -1 -1 420.11 161.89 531.24 253.85 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n62 1 Car -1 -1 -1 551.66 171.50 568.11 184.99 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n63 9 Car -1 -1 -1 438.05 163.70 538.55 247.73 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n63 1 Car -1 -1 -1 551.07 171.34 568.27 184.96 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n64 9 Car -1 -1 -1 453.44 166.10 544.81 243.70 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n65 9 Car -1 -1 -1 468.23 168.76 549.65 239.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n66 9 Car -1 -1 -1 479.56 168.40 555.34 234.19 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n67 9 Car -1 -1 -1 490.51 166.65 560.38 229.45 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n67 12 Car -1 -1 -1 2.66 198.50 210.44 367.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n67 13 Car -1 -1 -1 548.61 171.44 565.51 184.37 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n68 12 Car -1 -1 -1 1.01 204.17 283.47 368.33 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n68 9 Car -1 -1 -1 500.22 165.85 564.35 225.90 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n68 13 Car -1 -1 -1 547.08 171.41 565.97 184.15 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n69 12 Car -1 -1 -1 -0.81 196.93 338.13 369.27 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n69 9 Car -1 -1 -1 507.46 165.10 567.63 221.80 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n70 12 Car -1 -1 -1 3.11 194.58 373.00 370.53 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n70 9 Car -1 -1 -1 514.12 165.63 570.90 218.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n71 12 Car -1 -1 -1 116.30 192.54 400.36 342.16 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n71 9 Car -1 -1 -1 519.50 165.05 573.61 215.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n72 12 Car -1 -1 -1 196.52 187.79 420.98 309.69 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n72 9 Car -1 -1 -1 524.94 165.32 575.98 214.17 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n73 12 Car -1 -1 -1 253.88 186.55 440.39 292.60 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n73 9 Car -1 -1 -1 530.61 166.28 578.21 212.21 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n73 14 Car -1 -1 -1 455.82 169.98 487.45 185.64 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n74 12 Car -1 -1 -1 296.99 184.89 454.54 278.67 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n74 9 Car -1 -1 -1 535.91 167.45 580.69 211.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n74 14 Car -1 -1 -1 451.57 171.21 482.61 186.91 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n75 12 Car -1 -1 -1 330.94 184.53 465.75 266.35 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n75 9 Car -1 -1 -1 540.46 168.38 583.41 209.74 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n75 14 Car -1 -1 -1 445.17 172.46 474.09 188.59 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n76 12 Car -1 -1 -1 358.06 183.13 477.67 258.14 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n76 9 Car -1 -1 -1 544.64 168.68 585.52 207.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n76 14 Car -1 -1 -1 433.18 172.91 465.32 190.00 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n77 12 Car -1 -1 -1 380.80 182.05 486.71 251.17 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n77 9 Car -1 -1 -1 547.71 170.06 587.21 207.04 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n77 14 Car -1 -1 -1 416.98 173.01 454.37 196.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n78 12 Car -1 -1 -1 398.34 181.90 495.53 245.51 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n78 14 Car -1 -1 -1 390.22 174.27 436.82 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n78 9 Car -1 -1 -1 551.12 170.87 590.35 207.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n79 12 Car -1 -1 -1 415.04 182.29 502.15 240.44 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n79 9 Car -1 -1 -1 554.07 170.95 591.96 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n79 14 Car -1 -1 -1 357.60 174.73 413.94 201.67 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n80 12 Car -1 -1 -1 427.36 183.58 508.25 236.56 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n80 14 Car -1 -1 -1 312.79 176.66 381.35 204.79 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n80 9 Car -1 -1 -1 557.13 171.51 592.78 205.83 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n80 15 Car -1 -1 -1 393.60 175.46 423.26 195.17 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n81 12 Car -1 -1 -1 439.84 184.14 513.38 234.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n81 9 Car -1 -1 -1 559.53 172.42 594.05 205.69 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n81 14 Car -1 -1 -1 246.77 177.16 338.40 215.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n81 15 Car -1 -1 -1 375.01 175.89 411.05 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n82 12 Car -1 -1 -1 450.62 183.25 517.50 229.46 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n82 14 Car -1 -1 -1 130.56 175.34 269.56 227.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n82 9 Car -1 -1 -1 561.80 172.36 595.85 204.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n82 15 Car -1 -1 -1 356.12 176.79 394.39 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n83 12 Car -1 -1 -1 459.93 183.57 522.18 226.68 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n83 9 Car -1 -1 -1 564.14 171.73 596.58 201.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n83 14 Car -1 -1 -1 1.99 174.78 148.47 242.04 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n83 15 Car -1 -1 -1 332.32 176.02 375.72 200.89 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n84 12 Car -1 -1 -1 467.48 183.25 524.91 224.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n84 15 Car -1 -1 -1 300.98 176.09 352.45 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n84 9 Car -1 -1 -1 565.68 171.45 597.35 201.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n85 12 Car -1 -1 -1 474.85 183.39 528.30 220.70 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n85 15 Car -1 -1 -1 262.92 176.42 320.93 208.72 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n85 9 Car -1 -1 -1 567.14 172.19 598.24 201.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n86 12 Car -1 -1 -1 481.11 182.91 530.41 218.97 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n86 15 Car -1 -1 -1 208.94 175.57 283.67 213.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n86 9 Car -1 -1 -1 569.12 172.56 598.35 200.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n87 15 Car -1 -1 -1 137.81 175.57 233.05 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n87 9 Car -1 -1 -1 570.47 171.97 599.50 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n87 12 Car -1 -1 -1 485.88 182.46 533.78 216.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n88 12 Car -1 -1 -1 491.24 180.25 536.08 212.98 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n88 15 Car -1 -1 -1 35.61 173.48 161.56 228.45 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n88 9 Car -1 -1 -1 570.91 171.19 599.59 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n89 12 Car -1 -1 -1 495.67 178.03 537.71 208.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n89 9 Car -1 -1 -1 572.11 169.69 599.22 195.29 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n89 15 Car -1 -1 -1 -1.03 174.68 53.10 236.16 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n90 9 Car -1 -1 -1 572.99 168.48 600.12 194.13 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n90 12 Car -1 -1 -1 499.68 176.88 539.42 204.57 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n91 12 Car -1 -1 -1 502.95 176.53 541.82 203.92 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n91 9 Car -1 -1 -1 573.49 168.53 600.20 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n92 9 Car -1 -1 -1 574.00 168.85 600.21 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n92 12 Car -1 -1 -1 506.29 176.76 542.88 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n93 12 Car -1 -1 -1 509.05 177.10 545.00 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n93 9 Car -1 -1 -1 574.57 169.52 600.02 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n94 12 Car -1 -1 -1 512.69 177.55 546.47 201.97 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n94 9 Car -1 -1 -1 575.29 170.20 599.95 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n94 16 Car -1 -1 -1 428.43 172.60 453.32 189.81 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n95 9 Car -1 -1 -1 576.12 170.88 599.98 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n95 12 Car -1 -1 -1 514.73 177.77 547.55 201.83 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n95 16 Car -1 -1 -1 411.50 173.53 439.66 190.86 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n96 12 Car -1 -1 -1 517.60 178.21 549.09 201.05 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n96 9 Car -1 -1 -1 576.83 171.23 600.15 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n96 16 Car -1 -1 -1 391.29 174.27 425.88 194.24 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n97 9 Car -1 -1 -1 577.76 171.65 600.25 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n97 16 Car -1 -1 -1 366.79 173.93 404.92 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n97 12 Car -1 -1 -1 519.89 178.13 550.43 200.18 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n98 12 Car -1 -1 -1 522.56 177.69 552.66 198.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n98 9 Car -1 -1 -1 577.89 171.69 600.45 192.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n98 16 Car -1 -1 -1 333.41 174.24 378.59 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n99 16 Car -1 -1 -1 288.47 174.82 346.08 204.49 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n99 9 Car -1 -1 -1 577.81 172.54 600.45 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n99 12 Car -1 -1 -1 523.99 178.05 554.04 199.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n100 12 Car -1 -1 -1 526.53 179.04 556.07 199.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n100 9 Car -1 -1 -1 578.07 174.28 600.05 193.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n100 16 Car -1 -1 -1 218.87 174.73 298.75 217.42 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n101 12 Car -1 -1 -1 528.80 179.82 556.86 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n101 9 Car -1 -1 -1 578.55 174.49 600.32 193.76 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n101 16 Car -1 -1 -1 118.40 175.52 227.12 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n102 12 Car -1 -1 -1 531.74 179.18 557.75 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n102 9 Car -1 -1 -1 578.83 173.57 600.40 192.61 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n103 12 Car -1 -1 -1 533.76 177.77 558.52 195.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n103 9 Car -1 -1 -1 578.78 172.10 600.32 191.16 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n104 12 Car -1 -1 -1 534.01 176.35 559.74 194.41 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n104 9 Car -1 -1 -1 578.93 171.41 600.36 190.08 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n105 12 Car -1 -1 -1 536.22 175.91 560.63 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n105 9 Car -1 -1 -1 579.56 171.14 601.00 189.70 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n106 12 Car -1 -1 -1 537.93 176.03 561.75 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n106 9 Car -1 -1 -1 579.46 171.20 601.01 189.51 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n107 12 Car -1 -1 -1 538.85 176.71 562.55 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n107 9 Car -1 -1 -1 579.02 172.08 600.19 189.92 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n108 12 Car -1 -1 -1 540.39 178.62 563.07 194.11 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n108 9 Car -1 -1 -1 579.30 173.27 599.84 190.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n109 12 Car -1 -1 -1 542.69 179.58 563.50 194.13 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n109 9 Car -1 -1 -1 580.42 173.87 600.07 191.18 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n110 12 Car -1 -1 -1 544.17 179.71 564.11 194.09 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n110 9 Car -1 -1 -1 580.48 174.18 600.05 191.49 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n111 9 Car -1 -1 -1 580.98 173.86 599.87 191.07 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n111 12 Car -1 -1 -1 545.46 179.63 565.14 194.12 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n112 9 Car -1 -1 -1 581.50 173.75 599.91 190.11 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n112 12 Car -1 -1 -1 547.26 179.00 565.48 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n113 9 Car -1 -1 -1 581.67 173.26 600.49 189.72 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n113 12 Car -1 -1 -1 547.99 178.25 567.17 192.91 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0007.txt",
    "content": "0 1 Car -1 -1 -1 644.01 186.16 701.50 231.62 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n0 2 Car -1 -1 -1 854.72 182.94 890.33 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n1 1 Car -1 -1 -1 647.24 185.91 706.60 232.13 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1 2 Car -1 -1 -1 870.75 183.16 905.03 201.69 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n2 1 Car -1 -1 -1 649.83 185.68 711.39 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n2 2 Car -1 -1 -1 882.68 182.75 920.62 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n3 1 Car -1 -1 -1 653.66 184.70 715.08 232.51 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n3 2 Car -1 -1 -1 895.71 181.48 933.65 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n4 1 Car -1 -1 -1 655.53 183.63 719.73 232.26 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n4 2 Car -1 -1 -1 907.23 179.30 945.32 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n5 1 Car -1 -1 -1 658.64 181.57 724.00 230.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n5 2 Car -1 -1 -1 918.55 177.55 955.28 195.78 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n6 1 Car -1 -1 -1 660.46 180.08 727.39 230.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n6 2 Car -1 -1 -1 925.96 175.24 966.02 194.54 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n7 1 Car -1 -1 -1 662.54 179.40 731.25 230.48 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n7 2 Car -1 -1 -1 938.82 173.01 975.12 192.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n8 1 Car -1 -1 -1 664.60 179.68 734.93 231.52 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n8 2 Car -1 -1 -1 949.67 172.69 986.13 192.20 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n9 1 Car -1 -1 -1 665.97 179.75 737.83 232.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n9 2 Car -1 -1 -1 957.68 174.14 992.41 190.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n10 1 Car -1 -1 -1 668.62 178.94 740.99 232.49 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n10 2 Car -1 -1 -1 966.41 172.84 998.68 190.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n11 1 Car -1 -1 -1 670.48 177.86 744.63 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n11 2 Car -1 -1 -1 971.15 171.82 1005.14 188.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n12 1 Car -1 -1 -1 672.08 177.08 747.28 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n12 2 Car -1 -1 -1 979.03 170.69 1010.34 186.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n13 1 Car -1 -1 -1 674.19 176.29 751.43 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n13 2 Car -1 -1 -1 984.82 169.49 1016.33 186.71 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n14 1 Car -1 -1 -1 676.67 175.78 755.68 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n14 2 Car -1 -1 -1 987.98 168.30 1021.20 186.30 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n15 1 Car -1 -1 -1 679.56 175.68 759.67 234.95 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n15 2 Car -1 -1 -1 995.42 167.09 1025.52 185.49 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n16 1 Car -1 -1 -1 682.75 175.11 763.88 234.72 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n16 2 Car -1 -1 -1 999.72 165.93 1030.46 184.09 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n17 1 Car -1 -1 -1 686.14 174.13 768.17 234.97 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n17 2 Car -1 -1 -1 1003.79 164.88 1032.97 182.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n18 1 Car -1 -1 -1 689.35 175.42 772.45 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n18 2 Car -1 -1 -1 1006.98 165.10 1036.18 182.43 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n18 3 Car -1 -1 -1 -0.28 184.54 50.41 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n19 1 Car -1 -1 -1 692.06 175.78 776.35 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n19 3 Car -1 -1 -1 2.34 181.04 103.85 238.00 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n19 2 Car -1 -1 -1 1008.17 164.69 1038.55 181.88 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n20 3 Car -1 -1 -1 0.96 181.87 164.41 237.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n20 1 Car -1 -1 -1 694.51 176.21 780.88 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n20 2 Car -1 -1 -1 1011.49 164.49 1040.95 181.76 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n21 1 Car -1 -1 -1 697.03 176.28 783.80 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n21 3 Car -1 -1 -1 46.16 180.43 213.15 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n21 2 Car -1 -1 -1 1012.44 164.35 1039.64 180.98 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n22 1 Car -1 -1 -1 699.83 176.47 785.55 236.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n22 3 Car -1 -1 -1 103.61 179.17 265.81 232.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n22 2 Car -1 -1 -1 1010.14 163.08 1036.77 179.73 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n23 3 Car -1 -1 -1 161.93 178.67 313.52 231.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n23 1 Car -1 -1 -1 700.77 177.42 788.30 237.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n23 2 Car -1 -1 -1 1008.17 162.98 1032.08 179.64 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n24 1 Car -1 -1 -1 702.18 177.76 791.13 237.95 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n24 3 Car -1 -1 -1 213.44 178.86 356.44 228.75 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n24 2 Car -1 -1 -1 1004.37 163.73 1028.01 179.49 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n25 1 Car -1 -1 -1 703.01 178.87 791.50 239.16 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n25 3 Car -1 -1 -1 258.44 178.97 397.76 228.76 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n25 2 Car -1 -1 -1 1001.80 165.10 1025.72 181.46 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n26 1 Car -1 -1 -1 702.43 179.75 790.67 240.47 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n26 3 Car -1 -1 -1 305.81 179.50 434.36 228.44 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n26 2 Car -1 -1 -1 994.77 166.31 1019.96 182.49 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n27 1 Car -1 -1 -1 701.59 180.73 790.15 242.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n27 3 Car -1 -1 -1 345.44 179.93 465.90 227.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n27 2 Car -1 -1 -1 987.74 166.27 1013.39 182.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n27 4 Car -1 -1 -1 909.02 166.18 934.42 182.35 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n28 1 Car -1 -1 -1 701.79 181.89 790.04 243.30 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n28 3 Car -1 -1 -1 383.33 180.94 495.52 226.10 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n28 2 Car -1 -1 -1 981.85 165.54 1006.84 182.75 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n28 5 Car -1 -1 -1 698.00 179.52 740.34 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n29 1 Car -1 -1 -1 701.49 181.62 790.37 244.26 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n29 3 Car -1 -1 -1 419.75 180.60 525.69 223.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 5 Car -1 -1 -1 671.61 181.13 720.86 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n29 2 Car -1 -1 -1 974.96 166.13 999.14 182.98 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n29 6 Car -1 -1 -1 879.04 168.42 904.53 185.10 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n30 1 Car -1 -1 -1 701.19 182.18 791.05 244.98 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n30 3 Car -1 -1 -1 452.77 181.19 552.04 222.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n30 5 Car -1 -1 -1 647.71 182.37 697.09 201.97 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n30 6 Car -1 -1 -1 865.30 169.20 891.13 185.79 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n30 2 Car -1 -1 -1 966.54 165.32 992.95 182.98 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n30 7 Car -1 -1 -1 805.10 173.57 836.40 189.52 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n31 1 Car -1 -1 -1 700.79 183.90 791.73 246.60 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n31 3 Car -1 -1 -1 481.87 181.97 576.38 222.56 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n31 5 Car -1 -1 -1 621.08 184.18 672.52 204.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n31 6 Car -1 -1 -1 849.74 170.62 876.87 186.70 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n31 7 Car -1 -1 -1 788.57 174.18 819.84 191.75 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n31 2 Car -1 -1 -1 960.34 166.35 985.20 183.32 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n32 1 Car -1 -1 -1 701.54 185.27 790.59 248.04 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n32 3 Car -1 -1 -1 509.17 183.13 596.69 221.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n32 5 Car -1 -1 -1 592.43 186.37 646.87 206.05 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n32 6 Car -1 -1 -1 834.65 172.47 861.74 188.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n32 7 Car -1 -1 -1 770.88 175.94 802.81 194.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n32 2 Car -1 -1 -1 955.01 168.29 976.74 185.22 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n33 1 Car -1 -1 -1 700.64 188.14 790.71 250.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n33 3 Car -1 -1 -1 533.50 184.95 617.36 223.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n33 7 Car -1 -1 -1 753.56 179.56 785.92 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n33 5 Car -1 -1 -1 569.12 188.06 622.91 211.26 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n33 2 Car -1 -1 -1 949.44 170.77 970.07 185.93 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n33 6 Car -1 -1 -1 818.39 174.65 848.07 191.67 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n34 1 Car -1 -1 -1 700.81 190.68 790.67 252.26 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n34 3 Car -1 -1 -1 554.68 187.85 634.14 224.77 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n34 6 Car -1 -1 -1 801.26 177.45 832.51 194.85 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n34 5 Car -1 -1 -1 525.93 193.72 588.77 216.14 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n34 7 Car -1 -1 -1 733.47 181.89 769.45 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n35 1 Car -1 -1 -1 700.01 192.05 789.42 254.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n35 5 Car -1 -1 -1 485.49 195.56 559.23 222.50 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n35 3 Car -1 -1 -1 571.90 188.70 648.53 223.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n35 6 Car -1 -1 -1 786.32 178.78 815.10 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n35 7 Car -1 -1 -1 715.13 182.90 750.13 205.38 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n36 1 Car -1 -1 -1 699.62 189.97 788.55 252.16 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n36 3 Car -1 -1 -1 588.63 185.75 662.85 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n36 5 Car -1 -1 -1 442.49 195.99 525.61 223.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n36 7 Car -1 -1 -1 697.17 182.52 732.89 203.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n36 6 Car -1 -1 -1 770.39 177.83 799.04 195.79 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n36 8 Car -1 -1 -1 923.74 171.26 945.93 186.63 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n37 1 Car -1 -1 -1 699.53 186.78 789.22 248.68 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n37 3 Car -1 -1 -1 605.68 182.06 673.01 215.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n37 5 Car -1 -1 -1 395.05 194.58 487.45 223.68 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n37 7 Car -1 -1 -1 677.06 179.40 713.95 200.42 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n37 6 Car -1 -1 -1 751.09 174.69 784.61 194.73 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n38 1 Car -1 -1 -1 701.11 184.94 790.04 246.49 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n38 3 Car -1 -1 -1 619.81 179.59 685.33 212.39 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n38 5 Car -1 -1 -1 343.91 194.00 442.62 228.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n38 6 Car -1 -1 -1 734.96 172.26 769.28 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n38 7 Car -1 -1 -1 651.88 176.75 695.86 201.21 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n39 1 Car -1 -1 -1 701.69 183.69 790.04 244.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n39 3 Car -1 -1 -1 632.79 177.44 692.58 208.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n39 5 Car -1 -1 -1 278.89 195.28 393.61 230.99 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n39 6 Car -1 -1 -1 718.46 171.88 754.89 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n40 1 Car -1 -1 -1 702.54 183.48 790.71 244.35 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n40 5 Car -1 -1 -1 204.70 197.05 335.31 237.52 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n40 3 Car -1 -1 -1 643.51 176.87 701.36 206.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n40 6 Car -1 -1 -1 703.67 171.97 738.90 192.36 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n40 9 Car -1 -1 -1 613.10 177.56 655.80 200.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n41 1 Car -1 -1 -1 704.44 182.95 791.40 243.34 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n41 3 Car -1 -1 -1 654.04 176.01 706.63 204.51 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n41 9 Car -1 -1 -1 589.63 177.46 635.03 201.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 5 Car -1 -1 -1 112.48 198.88 270.12 244.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n41 6 Car -1 -1 -1 684.35 171.52 723.85 193.98 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n42 1 Car -1 -1 -1 705.30 182.50 791.65 243.10 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n42 5 Car -1 -1 -1 3.42 202.11 185.81 254.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n42 9 Car -1 -1 -1 564.67 177.57 613.01 203.69 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n42 3 Car -1 -1 -1 662.32 175.38 712.81 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n43 1 Car -1 -1 -1 706.52 183.11 792.39 243.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n43 3 Car -1 -1 -1 667.85 175.20 717.51 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n43 9 Car -1 -1 -1 536.92 178.65 589.36 206.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n43 5 Car -1 -1 -1 -2.70 203.20 84.33 262.02 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n43 10 Car -1 -1 -1 651.43 173.44 689.45 195.29 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n44 1 Car -1 -1 -1 705.82 183.22 791.20 243.97 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n44 3 Car -1 -1 -1 672.68 174.93 718.61 201.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n44 10 Car -1 -1 -1 631.95 174.08 670.16 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n44 9 Car -1 -1 -1 505.91 179.62 564.21 209.06 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n45 1 Car -1 -1 -1 705.50 183.22 789.64 243.15 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n45 10 Car -1 -1 -1 611.00 173.94 651.70 197.08 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n45 9 Car -1 -1 -1 472.48 180.26 534.96 211.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n45 3 Car -1 -1 -1 675.70 174.77 717.67 200.95 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n46 1 Car -1 -1 -1 704.51 182.42 787.56 241.85 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n46 3 Car -1 -1 -1 676.53 173.14 716.73 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n46 9 Car -1 -1 -1 435.24 179.71 504.96 212.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n46 10 Car -1 -1 -1 586.74 173.41 631.15 197.27 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n47 1 Car -1 -1 -1 701.50 181.25 784.72 241.17 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n47 9 Car -1 -1 -1 393.24 179.71 469.62 215.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n47 3 Car -1 -1 -1 676.41 171.78 716.56 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n47 10 Car -1 -1 -1 563.00 172.17 610.00 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n48 1 Car -1 -1 -1 698.43 180.36 782.05 240.08 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n48 9 Car -1 -1 -1 346.15 181.09 432.78 220.71 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n48 3 Car -1 -1 -1 675.89 171.94 714.78 196.44 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n48 10 Car -1 -1 -1 537.60 173.74 587.00 201.81 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n49 10 Car -1 -1 -1 509.83 174.64 563.44 205.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n49 1 Car -1 -1 -1 696.75 181.40 777.32 239.32 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n49 9 Car -1 -1 -1 291.59 184.51 389.92 226.39 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n49 3 Car -1 -1 -1 673.47 172.04 712.19 196.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n50 1 Car -1 -1 -1 694.33 181.27 774.33 238.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n50 9 Car -1 -1 -1 227.60 186.57 341.80 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n50 10 Car -1 -1 -1 480.11 175.39 538.04 208.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n50 3 Car -1 -1 -1 671.74 172.32 707.05 195.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n51 1 Car -1 -1 -1 691.00 180.82 770.50 238.13 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n51 10 Car -1 -1 -1 448.22 176.77 511.16 211.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n51 9 Car -1 -1 -1 151.43 189.56 285.75 241.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n51 3 Car -1 -1 -1 668.58 172.51 701.38 195.39 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n52 1 Car -1 -1 -1 688.43 181.18 766.55 238.36 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n52 10 Car -1 -1 -1 413.57 178.36 481.88 215.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n52 9 Car -1 -1 -1 59.60 192.17 216.69 250.72 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n52 3 Car -1 -1 -1 664.88 172.92 696.70 195.03 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n53 1 Car -1 -1 -1 685.51 182.06 761.98 238.58 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n53 10 Car -1 -1 -1 374.45 179.57 450.74 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n53 3 Car -1 -1 -1 662.07 173.45 693.13 195.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n53 9 Car -1 -1 -1 2.44 197.51 134.97 264.94 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n54 1 Car -1 -1 -1 683.38 182.92 758.57 239.60 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n54 10 Car -1 -1 -1 331.51 182.14 415.75 225.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n54 3 Car -1 -1 -1 656.72 173.92 688.04 195.51 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n54 9 Car -1 -1 -1 -2.32 202.90 36.81 276.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n55 1 Car -1 -1 -1 680.96 182.70 754.03 237.76 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n55 10 Car -1 -1 -1 281.81 183.57 379.50 231.52 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n55 3 Car -1 -1 -1 653.15 174.31 683.98 195.43 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n56 1 Car -1 -1 -1 679.24 180.43 750.47 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n56 10 Car -1 -1 -1 223.88 183.19 337.75 235.45 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n56 3 Car -1 -1 -1 649.75 172.03 679.57 192.67 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n56 11 Car -1 -1 -1 626.95 173.14 651.32 190.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n57 1 Car -1 -1 -1 677.25 176.49 746.77 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n57 10 Car -1 -1 -1 157.19 182.15 289.92 238.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n57 3 Car -1 -1 -1 646.27 169.11 675.21 188.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n57 11 Car -1 -1 -1 613.91 170.62 639.40 187.42 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n58 1 Car -1 -1 -1 673.94 174.01 744.11 229.32 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n58 11 Car -1 -1 -1 600.74 168.70 626.96 187.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n58 10 Car -1 -1 -1 74.56 181.01 232.65 246.16 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n58 3 Car -1 -1 -1 642.75 167.97 669.93 186.49 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n58 12 Van -1 -1 -1 74.56 181.01 232.65 246.16 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n59 1 Car -1 -1 -1 671.95 173.17 739.15 228.14 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n59 10 Car -1 -1 -1 3.67 183.67 162.15 255.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n59 3 Car -1 -1 -1 639.79 167.43 665.41 185.44 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n59 11 Car -1 -1 -1 587.15 169.24 614.85 187.32 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n60 1 Car -1 -1 -1 669.06 174.28 736.98 228.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n60 10 Car -1 -1 -1 -0.43 189.25 79.99 266.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n60 11 Car -1 -1 -1 573.95 171.05 602.14 189.33 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n60 3 Car -1 -1 -1 636.45 168.64 660.47 185.90 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n61 1 Car -1 -1 -1 665.54 175.48 733.33 231.49 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n61 3 Car -1 -1 -1 629.96 170.16 656.52 187.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n61 11 Car -1 -1 -1 559.90 173.36 590.01 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n61 13 Car -1 -1 -1 599.23 171.70 617.39 184.06 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n62 1 Car -1 -1 -1 662.70 177.82 729.38 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n62 3 Car -1 -1 -1 626.40 172.96 650.14 190.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n62 11 Car -1 -1 -1 545.22 176.33 576.43 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n62 13 Car -1 -1 -1 586.86 174.48 606.31 186.95 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n62 14 Car -1 -1 -1 -3.88 201.47 147.48 370.88 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n63 1 Car -1 -1 -1 659.26 177.93 724.94 231.47 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n63 14 Car -1 -1 -1 -3.31 193.96 202.28 370.46 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n63 3 Car -1 -1 -1 622.16 171.58 646.28 189.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n63 13 Car -1 -1 -1 574.73 174.23 595.84 187.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n63 11 Car -1 -1 -1 530.53 176.98 562.56 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n63 15 Car -1 -1 -1 605.99 172.37 626.48 184.93 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n64 14 Car -1 -1 -1 -0.82 186.87 252.90 369.61 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n64 1 Car -1 -1 -1 655.65 175.87 719.47 228.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n64 15 Car -1 -1 -1 595.30 169.70 616.93 184.61 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n64 3 Car -1 -1 -1 617.55 169.06 641.00 186.83 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n64 11 Car -1 -1 -1 514.21 175.73 548.25 195.65 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n64 13 Car -1 -1 -1 562.61 172.20 585.33 186.23 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n65 14 Car -1 -1 -1 4.16 184.06 293.33 359.73 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n65 1 Car -1 -1 -1 651.56 174.90 713.18 226.57 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n65 11 Car -1 -1 -1 497.03 174.75 533.51 195.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n65 3 Car -1 -1 -1 612.32 168.33 634.90 185.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n65 15 Car -1 -1 -1 584.13 169.02 605.57 183.83 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n65 13 Car -1 -1 -1 549.92 171.57 573.77 186.00 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n66 14 Car -1 -1 -1 51.64 184.47 325.18 348.99 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n66 1 Car -1 -1 -1 646.09 176.60 709.60 228.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n66 15 Car -1 -1 -1 572.86 171.13 595.33 186.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n66 13 Car -1 -1 -1 537.01 173.61 561.91 189.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n66 11 Car -1 -1 -1 478.66 176.31 516.89 200.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n66 3 Car -1 -1 -1 606.93 171.07 629.27 187.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n67 14 Car -1 -1 -1 114.47 184.06 348.22 336.38 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n67 1 Car -1 -1 -1 640.02 181.60 704.69 233.78 -1 -1 -1 -1000 -1000 -1000 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0.79\n119 30 Car -1 -1 -1 446.61 181.26 486.90 208.57 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n120 26 Car -1 -1 -1 4.59 195.03 240.05 300.09 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n120 14 Car -1 -1 -1 578.79 178.15 611.49 208.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n120 1 Car -1 -1 -1 632.39 180.61 688.67 224.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n120 30 Car -1 -1 -1 430.84 181.40 475.06 210.20 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n121 30 Car -1 -1 -1 413.21 181.41 462.43 212.30 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n121 26 Car -1 -1 -1 0.43 196.51 150.31 329.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n121 1 Car -1 -1 -1 628.91 180.66 685.21 224.53 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n121 14 Car -1 -1 -1 577.22 178.02 609.32 208.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n122 1 Car -1 -1 -1 626.16 182.61 681.54 226.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n122 14 Car -1 -1 -1 575.33 178.66 607.10 208.47 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n122 30 Car -1 -1 -1 393.59 183.26 447.54 216.18 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n122 34 Car -1 -1 -1 56.39 198.86 118.64 220.74 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n123 1 Car -1 -1 -1 622.80 182.50 678.69 226.38 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n123 30 Car -1 -1 -1 371.01 184.21 430.85 219.47 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n123 14 Car -1 -1 -1 573.54 179.20 604.83 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n123 34 Car -1 -1 -1 33.01 198.29 103.06 222.10 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n124 30 Car -1 -1 -1 343.99 184.97 411.60 222.95 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n124 1 Car -1 -1 -1 620.60 182.83 674.63 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n124 14 Car -1 -1 -1 572.18 179.01 602.69 208.10 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n124 34 Car -1 -1 -1 17.80 199.44 88.44 224.11 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n125 30 Car -1 -1 -1 312.27 184.49 391.58 227.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n125 1 Car -1 -1 -1 617.71 182.60 672.10 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n125 14 Car -1 -1 -1 570.33 178.44 600.32 206.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n126 30 Car -1 -1 -1 274.20 185.34 365.12 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n126 1 Car -1 -1 -1 615.15 182.07 668.60 225.12 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n126 14 Car -1 -1 -1 568.52 178.46 598.59 206.57 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n126 35 Car -1 -1 -1 3.39 200.57 54.09 225.21 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n127 30 Car -1 -1 -1 228.52 187.55 335.47 238.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n127 1 Car -1 -1 -1 613.01 182.23 665.83 224.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n127 14 Car -1 -1 -1 566.74 178.55 596.36 205.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n127 35 Car -1 -1 -1 -0.47 202.71 37.10 228.91 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n128 30 Car -1 -1 -1 171.04 189.25 297.80 249.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n128 14 Car -1 -1 -1 565.11 178.72 594.41 205.52 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n128 1 Car -1 -1 -1 610.34 181.31 663.45 223.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n128 35 Car -1 -1 -1 0.52 196.18 19.61 236.76 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n129 30 Car -1 -1 -1 95.05 190.95 251.32 260.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n129 1 Car -1 -1 -1 607.96 181.13 659.97 223.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n129 14 Car -1 -1 -1 564.39 178.90 592.98 205.42 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n130 30 Car -1 -1 -1 1.01 193.77 190.68 277.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n130 1 Car -1 -1 -1 605.40 181.16 658.40 223.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n130 14 Car -1 -1 -1 562.52 178.72 591.29 204.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n131 1 Car -1 -1 -1 603.80 180.06 656.11 221.89 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n131 30 Car -1 -1 -1 -0.67 195.63 113.48 292.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n131 14 Car -1 -1 -1 561.65 177.27 590.07 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 1 Car -1 -1 -1 601.26 179.57 653.15 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n132 14 Car -1 -1 -1 560.49 176.72 589.00 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n133 1 Car -1 -1 -1 598.52 180.67 649.89 221.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n133 14 Car -1 -1 -1 558.19 177.05 587.15 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n133 36 Car -1 -1 -1 -0.49 197.23 35.33 228.35 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n134 1 Car -1 -1 -1 596.67 180.89 647.97 222.42 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n134 14 Car -1 -1 -1 557.49 177.59 585.84 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n135 1 Car -1 -1 -1 594.69 181.74 644.82 222.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n135 14 Car -1 -1 -1 556.34 178.48 584.01 202.27 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n136 1 Car -1 -1 -1 593.52 183.52 642.51 223.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n136 14 Car -1 -1 -1 556.15 179.73 583.15 203.89 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n137 1 Car -1 -1 -1 592.32 184.29 640.78 224.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n137 14 Car -1 -1 -1 556.13 180.43 582.55 204.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n138 1 Car -1 -1 -1 591.29 184.45 640.61 225.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n138 14 Car -1 -1 -1 556.28 180.64 582.87 204.95 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n139 1 Car -1 -1 -1 590.77 183.88 639.07 223.66 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n139 14 Car -1 -1 -1 556.36 179.92 582.43 203.75 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n140 1 Car -1 -1 -1 590.05 182.67 637.99 222.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n140 14 Car -1 -1 -1 556.22 178.09 582.87 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n141 1 Car -1 -1 -1 588.48 182.39 636.92 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n141 14 Car -1 -1 -1 556.25 177.45 582.72 201.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n142 1 Car -1 -1 -1 588.13 181.92 636.05 219.95 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n142 14 Car -1 -1 -1 556.83 176.80 582.62 200.25 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n143 1 Car -1 -1 -1 587.67 182.97 634.92 220.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n143 14 Car -1 -1 -1 556.57 176.81 582.58 200.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n144 1 Car -1 -1 -1 586.38 182.68 634.14 220.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n144 14 Car -1 -1 -1 556.53 176.47 582.36 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n145 1 Car -1 -1 -1 585.61 182.55 633.78 219.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n145 14 Car -1 -1 -1 556.78 176.22 582.10 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n146 1 Car -1 -1 -1 584.97 182.68 632.58 219.56 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n146 14 Car -1 -1 -1 557.17 176.57 581.84 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n147 1 Car -1 -1 -1 584.92 182.78 631.86 219.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n147 14 Car -1 -1 -1 557.09 177.01 581.81 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n148 14 Car -1 -1 -1 556.78 178.61 581.62 201.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n148 1 Car -1 -1 -1 584.94 184.12 631.73 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n149 1 Car -1 -1 -1 584.89 185.14 631.27 222.71 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n149 14 Car -1 -1 -1 556.98 180.48 581.10 201.57 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n150 14 Car -1 -1 -1 556.89 179.42 581.23 200.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n150 1 Car -1 -1 -1 584.44 184.58 631.06 222.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n151 1 Car -1 -1 -1 583.80 182.80 630.78 220.60 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n151 14 Car -1 -1 -1 556.62 177.81 581.26 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n152 1 Car -1 -1 -1 584.22 182.83 630.05 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n152 14 Car -1 -1 -1 556.59 177.93 581.13 199.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n153 1 Car -1 -1 -1 584.51 183.38 629.60 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n153 14 Car -1 -1 -1 556.14 178.86 581.21 200.73 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n154 14 Car -1 -1 -1 555.87 179.29 581.30 201.03 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n154 1 Car -1 -1 -1 584.33 183.82 629.56 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n155 1 Car -1 -1 -1 584.00 182.89 629.48 220.44 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n155 14 Car -1 -1 -1 556.44 178.64 580.80 200.05 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n156 1 Car -1 -1 -1 583.50 182.82 629.45 220.32 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n156 14 Car -1 -1 -1 556.47 178.47 580.79 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n157 1 Car -1 -1 -1 583.09 182.40 629.53 220.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n157 14 Car -1 -1 -1 555.82 178.18 580.79 199.50 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n158 1 Car -1 -1 -1 582.45 180.92 629.56 218.76 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n158 14 Car -1 -1 -1 555.94 177.16 580.19 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n158 37 Cyclist -1 -1 -1 212.22 180.35 233.49 220.20 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n159 1 Car -1 -1 -1 582.65 178.49 629.14 216.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n159 14 Car -1 -1 -1 555.83 174.49 580.36 195.23 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n160 1 Car -1 -1 -1 582.82 177.70 628.86 215.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n160 14 Car -1 -1 -1 555.98 173.77 580.20 194.74 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n160 38 Cyclist -1 -1 -1 150.98 176.98 177.31 226.53 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n161 1 Car -1 -1 -1 582.23 179.92 627.91 216.56 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n161 14 Car -1 -1 -1 554.70 175.37 580.19 196.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n161 38 Cyclist -1 -1 -1 111.63 178.17 141.26 231.63 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n162 1 Car -1 -1 -1 582.24 180.16 627.80 216.78 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n162 14 Car -1 -1 -1 555.15 176.33 580.01 196.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n163 1 Car -1 -1 -1 581.85 180.23 627.79 216.83 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n163 14 Car -1 -1 -1 555.45 176.37 580.20 195.81 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n164 1 Car -1 -1 -1 581.14 180.18 627.65 216.84 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n164 14 Car -1 -1 -1 555.07 176.53 580.82 196.04 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n165 1 Car -1 -1 -1 580.95 181.00 626.62 218.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n165 14 Car -1 -1 -1 555.08 177.32 580.73 196.36 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n166 1 Car -1 -1 -1 580.48 181.81 626.80 219.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n166 14 Car -1 -1 -1 555.12 178.24 580.21 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n167 1 Car -1 -1 -1 579.97 181.47 626.89 219.45 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n167 14 Car -1 -1 -1 555.29 177.93 579.87 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n168 1 Car -1 -1 -1 579.83 181.16 626.82 219.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n168 14 Car -1 -1 -1 555.31 177.31 579.62 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n169 1 Car -1 -1 -1 579.98 180.36 626.67 218.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n169 14 Car -1 -1 -1 555.96 176.84 580.58 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n170 1 Car -1 -1 -1 580.35 179.34 627.46 217.49 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n170 14 Car -1 -1 -1 556.76 175.45 582.64 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n171 1 Car -1 -1 -1 580.21 178.54 627.83 216.93 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n171 14 Car -1 -1 -1 557.76 174.31 584.32 196.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n172 1 Car -1 -1 -1 581.10 178.20 627.86 216.43 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n172 14 Car -1 -1 -1 558.32 174.36 582.19 195.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n173 1 Car -1 -1 -1 581.15 178.74 628.17 216.57 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n173 14 Car -1 -1 -1 558.42 174.54 581.23 194.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n174 1 Car -1 -1 -1 581.08 178.69 629.05 216.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n174 14 Car -1 -1 -1 558.72 174.89 581.18 194.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n175 1 Car -1 -1 -1 582.59 178.93 629.19 216.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n175 14 Car -1 -1 -1 559.11 175.09 581.28 194.48 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n176 1 Car -1 -1 -1 582.78 179.07 629.17 217.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n176 14 Car -1 -1 -1 559.05 175.07 581.53 194.55 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n177 1 Car -1 -1 -1 582.92 178.51 629.44 216.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n177 14 Car -1 -1 -1 559.77 174.73 580.79 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n178 1 Car -1 -1 -1 582.98 177.30 629.81 215.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n178 14 Car -1 -1 -1 559.31 173.71 581.27 192.27 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n179 1 Car -1 -1 -1 583.16 177.74 630.06 215.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n179 14 Car -1 -1 -1 559.19 173.37 581.29 192.29 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n180 1 Car -1 -1 -1 583.82 178.34 630.92 216.65 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n180 14 Car -1 -1 -1 559.86 173.54 581.94 192.28 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n181 1 Car -1 -1 -1 584.43 178.55 631.12 217.18 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n181 14 Car -1 -1 -1 560.59 173.87 582.30 192.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n182 1 Car -1 -1 -1 584.59 179.53 631.04 217.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n182 14 Car -1 -1 -1 561.35 175.15 582.57 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n183 1 Car -1 -1 -1 584.74 180.33 630.80 219.05 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n183 14 Car -1 -1 -1 561.24 175.50 582.57 194.35 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n184 1 Car -1 -1 -1 584.82 180.24 631.13 218.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n184 14 Car -1 -1 -1 562.10 175.38 582.70 194.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n185 1 Car -1 -1 -1 585.18 180.49 631.81 219.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n185 14 Car -1 -1 -1 562.04 174.74 582.53 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n186 1 Car -1 -1 -1 585.29 180.30 631.92 218.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n186 14 Car -1 -1 -1 561.83 173.98 582.68 192.12 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n187 1 Car -1 -1 -1 584.95 179.41 632.26 217.29 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n187 14 Car -1 -1 -1 562.28 173.12 583.22 191.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n188 14 Car -1 -1 -1 562.73 172.70 583.61 191.26 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n188 1 Car -1 -1 -1 585.21 179.46 632.44 217.29 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n189 14 Car -1 -1 -1 562.85 173.75 583.97 192.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n189 1 Car -1 -1 -1 585.55 180.44 632.28 218.57 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n190 1 Car -1 -1 -1 585.48 181.67 634.12 220.38 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n190 14 Car -1 -1 -1 563.71 175.30 583.65 193.18 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n191 1 Car -1 -1 -1 586.10 182.18 634.02 221.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n191 14 Car -1 -1 -1 564.11 175.45 583.95 193.68 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n192 1 Car -1 -1 -1 586.73 180.58 634.95 219.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n192 14 Car -1 -1 -1 563.87 174.14 585.71 192.14 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n193 1 Car -1 -1 -1 586.62 179.83 635.82 219.44 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n193 14 Car -1 -1 -1 564.32 173.68 585.35 191.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n194 1 Car -1 -1 -1 587.26 179.72 636.05 219.40 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n194 14 Car -1 -1 -1 564.84 173.66 586.02 191.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n195 1 Car -1 -1 -1 588.04 180.26 636.41 220.37 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n195 14 Car -1 -1 -1 565.12 174.25 586.11 191.96 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n196 1 Car -1 -1 -1 587.95 181.92 636.53 221.40 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n196 14 Car -1 -1 -1 565.40 175.59 585.68 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n197 1 Car -1 -1 -1 588.12 180.36 636.41 220.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n197 14 Car -1 -1 -1 565.35 174.61 585.75 191.98 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n198 1 Car -1 -1 -1 588.32 179.40 635.98 218.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n198 14 Car -1 -1 -1 565.33 173.88 585.47 191.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n199 1 Car -1 -1 -1 587.64 178.20 636.32 217.45 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n199 14 Car -1 -1 -1 564.79 172.14 585.46 190.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n200 1 Car -1 -1 -1 587.31 177.92 636.02 217.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n200 14 Car -1 -1 -1 564.87 171.97 585.10 189.96 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n201 1 Car -1 -1 -1 587.04 177.11 635.45 216.48 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n201 14 Car -1 -1 -1 564.65 171.56 585.15 189.69 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n202 1 Car -1 -1 -1 586.62 177.55 635.77 216.95 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n202 14 Car -1 -1 -1 564.38 172.34 585.10 189.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n203 1 Car -1 -1 -1 586.68 177.65 635.71 217.15 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n203 14 Car -1 -1 -1 564.20 173.15 583.85 190.08 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n204 1 Car -1 -1 -1 586.65 177.59 635.27 217.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n204 14 Car -1 -1 -1 563.94 173.97 583.63 190.68 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n205 1 Car -1 -1 -1 586.50 178.79 634.60 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n205 14 Car -1 -1 -1 563.13 174.42 583.49 190.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n206 1 Car -1 -1 -1 585.64 177.90 634.29 217.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n206 14 Car -1 -1 -1 562.37 174.03 583.22 190.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n207 14 Car -1 -1 -1 562.31 173.39 582.78 189.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n207 1 Car -1 -1 -1 585.36 177.16 634.05 216.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n208 14 Car -1 -1 -1 562.51 172.77 582.70 189.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n208 1 Car -1 -1 -1 584.47 176.92 633.52 216.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n209 14 Car -1 -1 -1 562.54 172.21 582.57 188.90 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n209 1 Car -1 -1 -1 584.50 176.24 633.45 215.97 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n210 1 Car -1 -1 -1 584.26 175.67 633.57 215.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n210 14 Car -1 -1 -1 562.80 172.01 582.22 188.44 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n211 1 Car -1 -1 -1 584.11 175.64 633.61 215.85 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n211 14 Car -1 -1 -1 562.74 171.97 582.30 188.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n212 14 Car -1 -1 -1 563.19 172.07 582.36 188.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n212 1 Car -1 -1 -1 584.19 175.72 633.85 215.68 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n213 14 Car -1 -1 -1 562.95 172.59 582.50 189.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n213 1 Car -1 -1 -1 584.53 176.23 633.10 215.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n214 14 Car -1 -1 -1 563.18 172.39 582.65 189.71 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n214 1 Car -1 -1 -1 584.61 176.10 632.95 215.50 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0008.txt",
    "content": "0 1 Car -1 -1 -1 842.19 176.14 1038.64 334.22 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n0 2 Car -1 -1 -1 716.49 178.83 846.64 232.33 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n1 1 Car -1 -1 -1 839.71 175.37 1028.79 329.20 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1 2 Car -1 -1 -1 689.98 179.12 818.61 231.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n2 1 Car -1 -1 -1 836.22 175.33 1016.69 329.01 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n2 2 Car -1 -1 -1 662.58 179.49 791.29 232.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n3 1 Car -1 -1 -1 828.50 174.86 1005.29 329.74 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n3 2 Car -1 -1 -1 632.45 179.69 760.21 231.35 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n4 1 Car -1 -1 -1 820.37 175.24 990.12 327.55 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n4 2 Car -1 -1 -1 600.71 178.22 729.79 230.65 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n5 1 Car -1 -1 -1 809.80 173.04 977.22 323.51 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n5 2 Car -1 -1 -1 569.57 177.19 697.83 227.76 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n5 3 Pedestrian -1 -1 -1 1182.53 155.34 1235.07 293.50 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n6 1 Car -1 -1 -1 797.17 172.86 961.44 315.69 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n6 2 Car -1 -1 -1 534.64 175.80 665.47 227.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n6 3 Pedestrian -1 -1 -1 1166.26 155.19 1228.59 294.11 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n6 4 Car -1 -1 -1 1163.60 175.51 1238.99 211.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n7 1 Car -1 -1 -1 782.75 171.54 945.70 314.97 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n7 2 Car -1 -1 -1 501.62 175.53 633.29 228.01 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n7 4 Car -1 -1 -1 1135.50 174.01 1227.15 211.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n7 3 Pedestrian -1 -1 -1 1156.83 151.53 1222.11 305.01 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n8 1 Car -1 -1 -1 767.17 169.96 929.48 311.64 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n8 2 Car -1 -1 -1 465.45 175.07 599.91 227.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n8 3 Pedestrian -1 -1 -1 1144.68 156.44 1203.46 292.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n8 4 Car -1 -1 -1 1101.88 172.70 1199.29 208.83 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n9 1 Car -1 -1 -1 747.58 169.33 910.32 311.41 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n9 2 Car -1 -1 -1 424.84 174.75 565.88 228.56 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n9 4 Car -1 -1 -1 1076.16 172.88 1170.16 208.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n9 3 Pedestrian -1 -1 -1 1132.67 153.88 1191.83 295.47 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n10 1 Car -1 -1 -1 724.34 170.77 887.52 307.99 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n10 4 Car -1 -1 -1 1049.12 176.00 1128.46 208.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 2 Car -1 -1 -1 388.17 175.96 529.61 228.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n10 3 Pedestrian -1 -1 -1 1120.25 155.08 1180.74 300.78 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n11 1 Car -1 -1 -1 698.59 171.91 867.06 308.58 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n11 2 Car -1 -1 -1 347.32 176.93 493.21 232.20 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n11 4 Car -1 -1 -1 1020.63 178.16 1100.70 211.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n11 3 Pedestrian -1 -1 -1 1107.33 156.40 1170.80 300.68 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n12 1 Car -1 -1 -1 674.01 174.16 843.62 312.02 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n12 2 Car -1 -1 -1 304.07 178.13 454.38 237.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n12 4 Car -1 -1 -1 997.37 182.43 1070.58 214.46 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n12 3 Pedestrian -1 -1 -1 1098.17 159.37 1164.47 312.54 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n13 1 Car -1 -1 -1 647.21 174.83 817.42 306.87 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n13 2 Car -1 -1 -1 256.98 180.03 414.66 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n13 4 Car -1 -1 -1 972.04 184.24 1042.23 216.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n13 3 Pedestrian -1 -1 -1 1086.85 161.94 1152.73 325.51 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n14 1 Car -1 -1 -1 615.47 175.29 792.56 310.48 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n14 4 Car -1 -1 -1 946.22 185.42 1016.02 216.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n14 2 Car -1 -1 -1 206.83 179.93 372.45 238.17 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 3 Pedestrian -1 -1 -1 1075.87 159.82 1148.23 335.00 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n15 1 Car -1 -1 -1 585.16 173.72 763.77 306.35 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n15 4 Car -1 -1 -1 922.99 185.06 991.03 215.55 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n15 2 Car -1 -1 -1 147.54 179.73 330.62 238.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n15 3 Pedestrian -1 -1 -1 1066.45 159.88 1142.42 336.02 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n16 1 Car -1 -1 -1 553.29 172.49 739.03 306.46 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n16 4 Car -1 -1 -1 901.18 183.82 967.30 213.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n16 2 Car -1 -1 -1 100.63 180.12 283.51 244.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n16 3 Pedestrian -1 -1 -1 1061.89 154.18 1146.24 356.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n17 1 Car -1 -1 -1 522.26 172.59 710.12 306.67 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n17 4 Car -1 -1 -1 880.71 183.42 947.39 213.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n17 2 Car -1 -1 -1 36.51 179.28 239.90 245.26 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n17 3 Pedestrian -1 -1 -1 1060.31 154.89 1140.68 364.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 1 Car -1 -1 -1 491.76 171.27 683.57 307.77 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n18 2 Car -1 -1 -1 -1.78 179.97 193.83 244.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n18 4 Car -1 -1 -1 862.63 182.59 928.31 213.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n18 3 Pedestrian -1 -1 -1 1060.11 154.77 1148.50 365.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n19 1 Car -1 -1 -1 461.75 169.93 658.42 308.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n19 2 Car -1 -1 -1 -1.76 179.15 145.10 245.95 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n19 4 Car -1 -1 -1 847.35 181.19 912.43 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n19 3 Pedestrian -1 -1 -1 1066.06 153.25 1165.57 371.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n20 1 Car -1 -1 -1 432.37 169.32 633.66 308.66 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n20 4 Car -1 -1 -1 834.31 179.48 900.42 210.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n20 2 Car -1 -1 -1 0.51 177.06 97.71 247.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n20 3 Pedestrian -1 -1 -1 1078.87 150.00 1183.89 369.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n20 5 Car -1 -1 -1 1034.43 191.39 1111.59 217.94 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n21 1 Car -1 -1 -1 405.08 169.88 608.91 308.37 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n21 2 Car -1 -1 -1 0.43 173.70 58.53 251.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n21 4 Car -1 -1 -1 825.80 179.05 890.78 210.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 3 Pedestrian -1 -1 -1 1096.13 149.30 1213.30 369.25 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n21 5 Car -1 -1 -1 1024.30 191.09 1099.14 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n22 1 Car -1 -1 -1 378.98 168.43 587.87 310.59 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n22 5 Car -1 -1 -1 1015.16 191.49 1090.93 217.57 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n22 4 Car -1 -1 -1 817.34 179.87 883.94 211.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n22 3 Pedestrian -1 -1 -1 1122.50 150.78 1232.97 366.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n22 2 Car -1 -1 -1 0.04 179.46 13.04 245.99 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n22 6 Car -1 -1 -1 1074.09 193.33 1149.08 218.81 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n23 1 Car -1 -1 -1 352.42 168.58 566.73 310.80 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n23 4 Car -1 -1 -1 812.39 179.80 878.56 211.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n23 5 Car -1 -1 -1 1009.53 191.85 1084.33 217.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n23 3 Pedestrian -1 -1 -1 1152.23 144.31 1234.85 366.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n23 6 Car -1 -1 -1 1066.96 193.77 1142.07 218.93 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n24 1 Car -1 -1 -1 326.27 168.03 544.60 311.36 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n24 4 Car -1 -1 -1 807.63 179.72 875.45 211.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n24 5 Car -1 -1 -1 1007.95 191.93 1083.37 218.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n24 6 Car -1 -1 -1 1062.84 194.37 1138.38 218.54 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n25 1 Car -1 -1 -1 300.50 167.33 524.82 313.00 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n25 4 Car -1 -1 -1 806.50 179.04 873.41 210.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n25 5 Car -1 -1 -1 1004.08 191.36 1081.11 217.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n25 6 Car -1 -1 -1 1063.87 194.48 1136.80 218.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n26 1 Car -1 -1 -1 274.61 170.02 504.95 316.75 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n26 4 Car -1 -1 -1 806.77 178.86 874.96 210.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n26 5 Car -1 -1 -1 1003.11 190.65 1082.62 217.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n26 6 Car -1 -1 -1 1062.82 193.63 1138.81 217.96 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n27 1 Car -1 -1 -1 249.83 169.87 483.63 318.64 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n27 4 Car -1 -1 -1 808.04 178.65 877.36 211.08 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n27 6 Car -1 -1 -1 1070.96 193.33 1144.40 217.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n27 5 Car -1 -1 -1 1004.66 190.35 1087.77 217.64 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n28 1 Car -1 -1 -1 224.83 171.14 463.68 323.74 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n28 4 Car -1 -1 -1 812.08 179.65 882.21 212.51 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n28 6 Car -1 -1 -1 1079.04 193.64 1152.06 218.29 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n28 5 Car -1 -1 -1 1018.98 190.89 1095.82 217.81 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n29 1 Car -1 -1 -1 201.74 171.35 447.26 324.37 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n29 4 Car -1 -1 -1 817.77 181.06 888.54 213.79 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n29 6 Car -1 -1 -1 1084.21 195.40 1163.18 220.17 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n29 5 Car -1 -1 -1 1029.98 192.47 1107.98 218.85 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n30 1 Car -1 -1 -1 178.75 174.59 429.68 328.45 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n30 4 Car -1 -1 -1 824.92 182.53 896.93 215.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n30 5 Car -1 -1 -1 1036.94 193.89 1117.18 221.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n30 6 Car -1 -1 -1 1096.20 196.98 1174.58 222.47 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n31 1 Car -1 -1 -1 156.92 177.61 412.37 332.50 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n31 4 Car -1 -1 -1 834.34 183.58 906.90 217.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 5 Car -1 -1 -1 1052.98 195.85 1131.50 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n31 6 Car -1 -1 -1 1110.54 198.79 1191.20 224.89 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n32 1 Car -1 -1 -1 135.66 176.77 396.78 335.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n32 4 Car -1 -1 -1 845.69 184.12 920.40 218.90 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n32 6 Car -1 -1 -1 1128.62 199.97 1211.09 225.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n32 5 Car -1 -1 -1 1066.84 196.61 1149.46 224.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n33 1 Car -1 -1 -1 118.40 179.20 382.72 338.26 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n33 4 Car -1 -1 -1 858.52 184.36 936.17 219.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n33 6 Car -1 -1 -1 1145.25 200.39 1233.82 226.59 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n33 5 Car -1 -1 -1 1084.29 197.66 1171.72 226.14 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n34 1 Car -1 -1 -1 101.59 180.04 369.34 339.10 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n34 4 Car -1 -1 -1 873.13 184.67 954.52 220.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n34 5 Car -1 -1 -1 1107.18 197.70 1195.06 226.83 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n34 6 Car -1 -1 -1 1173.60 200.42 1237.13 227.67 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n35 1 Car -1 -1 -1 87.73 180.64 358.03 339.65 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n35 4 Car -1 -1 -1 891.05 184.91 974.23 222.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n35 5 Car -1 -1 -1 1130.31 197.54 1226.76 227.98 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n35 6 Car -1 -1 -1 1200.83 202.51 1239.92 229.05 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n36 1 Car -1 -1 -1 74.13 180.94 347.41 344.37 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n36 4 Car -1 -1 -1 910.07 184.61 995.77 222.89 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n36 5 Car -1 -1 -1 1161.69 197.34 1238.50 229.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n37 1 Car -1 -1 -1 62.22 182.17 337.69 343.17 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n37 4 Car -1 -1 -1 931.41 184.34 1020.72 223.33 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n37 5 Car -1 -1 -1 1188.70 196.34 1239.20 229.80 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n38 1 Car -1 -1 -1 53.61 183.15 330.73 344.30 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n38 4 Car -1 -1 -1 956.56 184.27 1049.56 224.38 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n38 5 Car -1 -1 -1 1224.96 192.00 1240.00 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n39 1 Car -1 -1 -1 44.86 183.31 323.35 344.57 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n39 4 Car -1 -1 -1 982.49 184.56 1080.22 225.62 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n40 1 Car -1 -1 -1 38.18 184.54 315.28 348.24 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n40 4 Car -1 -1 -1 1010.35 184.87 1113.41 226.67 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n41 1 Car -1 -1 -1 29.23 185.61 310.10 347.65 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n41 4 Car -1 -1 -1 1042.85 184.11 1149.78 227.35 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n42 1 Car -1 -1 -1 25.82 185.71 305.64 348.29 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n42 4 Car -1 -1 -1 1075.10 183.84 1190.10 227.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n43 1 Car -1 -1 -1 23.74 184.44 305.46 348.91 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n43 4 Car -1 -1 -1 1113.49 182.41 1236.57 228.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n44 1 Car -1 -1 -1 24.48 184.13 304.80 348.51 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n44 4 Car -1 -1 -1 1156.74 181.91 1237.74 229.43 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n44 7 Car -1 -1 -1 10.52 186.56 57.11 207.11 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n44 8 Car -1 -1 -1 0.19 186.28 13.26 213.47 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n45 1 Car -1 -1 -1 26.71 185.32 304.60 348.16 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n45 7 Car -1 -1 -1 2.98 184.64 48.25 211.84 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n45 4 Car -1 -1 -1 1210.09 187.54 1237.56 230.81 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n45 9 Car -1 -1 -1 48.09 186.23 94.72 206.67 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n46 1 Car -1 -1 -1 31.87 186.54 305.99 347.36 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n46 7 Car -1 -1 -1 31.70 185.25 88.36 211.62 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n47 1 Car -1 -1 -1 38.51 187.02 306.85 345.78 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n47 7 Car -1 -1 -1 70.00 185.09 121.40 210.22 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n47 10 Car -1 -1 -1 1.23 194.98 42.75 239.59 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n47 11 Van -1 -1 -1 2.36 178.50 65.25 217.19 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n48 1 Car -1 -1 -1 48.82 185.49 311.69 342.19 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n48 10 Car -1 -1 -1 1.56 190.09 81.05 236.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n48 11 Van -1 -1 -1 28.29 177.50 100.69 215.68 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n49 1 Car -1 -1 -1 59.38 183.49 316.61 341.20 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n49 10 Car -1 -1 -1 24.61 185.76 126.30 232.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n49 11 Van -1 -1 -1 64.78 174.05 140.64 214.10 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n50 1 Car -1 -1 -1 71.63 181.75 322.07 336.59 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n50 10 Car -1 -1 -1 70.34 182.63 159.33 227.28 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n50 12 Car -1 -1 -1 20.77 183.86 91.90 218.14 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n51 1 Car -1 -1 -1 83.60 178.70 330.56 332.95 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n51 12 Car -1 -1 -1 59.22 181.05 131.06 214.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n51 10 Car -1 -1 -1 111.34 178.99 187.89 216.24 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n51 13 Car -1 -1 -1 0.05 187.62 12.65 229.51 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n52 1 Car -1 -1 -1 98.74 176.01 338.10 327.88 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n52 13 Car -1 -1 -1 1.30 183.88 47.35 225.04 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n52 12 Car -1 -1 -1 95.43 177.86 163.86 210.22 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n52 14 Van -1 -1 -1 165.96 165.61 241.01 205.54 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n53 1 Car -1 -1 -1 115.61 175.55 344.40 326.02 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n53 13 Car -1 -1 -1 1.12 181.26 78.88 227.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n53 12 Car -1 -1 -1 129.76 176.25 192.02 208.90 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n53 15 Car -1 -1 -1 314.37 169.93 350.29 188.35 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n54 1 Car -1 -1 -1 130.61 173.97 352.59 321.78 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n54 13 Car -1 -1 -1 2.04 181.44 108.97 226.74 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n54 15 Car -1 -1 -1 304.81 170.32 343.75 192.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n54 12 Car -1 -1 -1 167.10 175.23 224.79 204.38 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n54 16 Van -1 -1 -1 378.76 158.08 417.51 184.22 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n54 17 Van -1 -1 -1 234.86 165.15 297.63 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n54 18 Car -1 -1 -1 343.89 169.49 379.14 187.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n55 1 Car -1 -1 -1 146.85 174.00 361.02 321.01 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n55 13 Car -1 -1 -1 4.96 179.68 138.04 228.35 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n55 16 Van -1 -1 -1 406.12 157.13 443.61 184.11 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n55 18 Car -1 -1 -1 371.90 168.79 406.26 186.84 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n55 15 Car -1 -1 -1 331.66 169.19 373.09 191.62 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n55 17 Van -1 -1 -1 267.53 164.66 333.13 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n55 12 Car -1 -1 -1 188.01 175.35 250.56 204.41 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n55 20 Car -1 -1 -1 284.92 174.42 346.69 206.79 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n56 1 Car -1 -1 -1 163.43 173.94 369.80 319.76 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n56 13 Car -1 -1 -1 32.54 178.77 165.71 226.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n56 20 Car -1 -1 -1 316.49 173.19 383.83 207.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n56 18 Car -1 -1 -1 399.42 168.35 434.37 186.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n56 15 Car -1 -1 -1 358.94 169.19 400.15 191.21 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n56 16 Van -1 -1 -1 433.45 157.30 471.65 183.05 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n56 21 Car -1 -1 -1 288.39 162.85 360.19 200.34 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n57 1 Car -1 -1 -1 181.84 172.60 381.90 315.49 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n57 20 Car -1 -1 -1 345.80 172.89 417.19 206.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n57 13 Car -1 -1 -1 59.44 178.57 192.99 225.17 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n57 18 Car -1 -1 -1 426.30 168.46 461.36 185.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n57 21 Car -1 -1 -1 323.46 162.51 385.63 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n57 12 Car -1 -1 -1 215.30 175.11 285.65 203.74 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n57 15 Car -1 -1 -1 386.94 168.90 426.81 189.77 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n57 16 Van -1 -1 -1 460.00 156.65 497.87 182.58 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n58 1 Car -1 -1 -1 201.65 172.06 392.04 313.85 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n58 20 Car -1 -1 -1 378.48 172.73 447.14 205.31 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n58 13 Car -1 -1 -1 88.48 178.09 217.01 223.23 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n58 21 Car -1 -1 -1 349.67 162.34 408.56 195.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n58 18 Car -1 -1 -1 453.48 168.26 486.98 185.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n58 15 Car -1 -1 -1 414.00 168.29 453.12 189.72 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n58 12 Car -1 -1 -1 231.80 175.77 299.91 203.60 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n59 1 Car -1 -1 -1 220.29 171.32 403.75 309.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n59 20 Car -1 -1 -1 412.38 171.97 477.14 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n59 18 Car -1 -1 -1 477.69 168.24 511.98 185.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n59 13 Car -1 -1 -1 114.42 177.32 245.76 222.49 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n59 21 Car -1 -1 -1 375.89 162.16 436.34 194.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n59 15 Car -1 -1 -1 442.18 167.65 477.28 189.59 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n59 12 Car -1 -1 -1 249.63 174.74 313.35 204.88 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n60 1 Car -1 -1 -1 236.87 171.58 412.82 307.43 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n60 20 Car -1 -1 -1 445.82 171.12 506.48 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n60 13 Car -1 -1 -1 133.86 177.54 259.04 223.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n60 18 Car -1 -1 -1 502.12 168.40 536.61 185.30 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n60 15 Car -1 -1 -1 466.73 168.35 502.09 188.83 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n60 21 Car -1 -1 -1 400.61 161.62 459.55 195.06 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n60 23 Truck -1 -1 -1 535.41 155.89 571.25 182.24 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n61 1 Car -1 -1 -1 255.85 173.63 424.64 306.40 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n61 20 Car -1 -1 -1 476.91 170.58 535.58 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n61 13 Car -1 -1 -1 155.21 178.81 283.19 224.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n61 18 Car -1 -1 -1 526.75 168.48 561.29 187.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n61 22 Car -1 -1 -1 358.28 173.63 429.38 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n61 21 Car -1 -1 -1 424.96 162.62 485.86 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n61 24 Van -1 -1 -1 563.01 157.00 595.44 183.24 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n62 1 Car -1 -1 -1 276.81 174.79 434.65 305.61 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n62 20 Car -1 -1 -1 507.44 171.10 562.50 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n62 18 Car -1 -1 -1 551.20 169.48 584.21 187.66 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n62 22 Car -1 -1 -1 384.08 174.85 450.03 204.29 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n62 13 Car -1 -1 -1 173.62 180.18 304.18 224.23 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n62 21 Car -1 -1 -1 449.22 164.12 509.00 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n62 25 Truck -1 -1 -1 584.70 158.05 621.02 183.99 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n63 1 Car -1 -1 -1 298.09 175.16 449.16 304.17 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n63 20 Car -1 -1 -1 537.81 171.33 591.51 201.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n63 22 Car -1 -1 -1 408.66 174.61 472.76 204.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n63 13 Car -1 -1 -1 193.19 180.88 323.37 227.58 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n63 18 Car -1 -1 -1 576.86 168.93 606.25 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488.91 183.88 562.31 231.40 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n157 63 Car -1 -1 -1 542.82 181.43 585.61 215.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n157 1 Car -1 -1 -1 597.74 175.95 658.88 231.10 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n157 68 Car -1 -1 -1 575.20 171.02 609.99 200.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n157 61 Car -1 -1 -1 570.48 179.07 602.95 206.39 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n157 64 Car -1 -1 -1 651.50 176.56 670.47 191.99 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n158 60 Car -1 -1 -1 482.30 183.04 561.04 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n158 59 Car -1 -1 -1 2.02 190.18 381.96 366.33 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n158 1 Car -1 -1 -1 600.12 174.58 660.15 230.26 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n158 61 Car -1 -1 -1 567.55 178.80 601.93 206.76 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n158 63 Car -1 -1 -1 539.41 180.78 584.99 215.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n158 68 Car -1 -1 -1 574.67 170.78 609.36 200.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n158 64 Car -1 -1 -1 651.64 174.69 671.77 191.23 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n159 59 Car -1 -1 -1 1.40 193.70 350.57 369.21 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n159 1 Car -1 -1 -1 601.86 174.22 660.66 228.82 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n159 60 Car -1 -1 -1 475.52 182.79 558.71 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n159 63 Car -1 -1 -1 537.77 180.04 584.34 215.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n159 61 Car -1 -1 -1 565.93 178.33 601.80 206.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n159 68 Car -1 -1 -1 574.66 169.81 609.91 200.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n159 64 Car -1 -1 -1 652.50 174.03 672.39 190.27 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n160 59 Car -1 -1 -1 -1.53 191.93 309.36 366.52 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n160 60 Car -1 -1 -1 467.34 182.64 556.12 238.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n160 1 Car -1 -1 -1 603.88 173.61 662.30 227.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n160 63 Car -1 -1 -1 536.00 179.46 583.34 216.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n160 61 Car -1 -1 -1 564.96 177.79 601.43 206.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n160 68 Car -1 -1 -1 573.68 169.02 609.33 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n160 64 Car -1 -1 -1 651.60 173.37 673.26 189.40 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n161 59 Car -1 -1 -1 2.12 196.75 256.92 367.58 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n161 60 Car -1 -1 -1 458.31 182.79 552.31 241.84 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n161 1 Car -1 -1 -1 604.35 173.57 662.83 227.22 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n161 63 Car -1 -1 -1 531.83 179.46 582.28 217.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n161 61 Car -1 -1 -1 561.47 177.58 601.42 207.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n161 68 Car -1 -1 -1 572.87 168.47 609.40 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n162 60 Car -1 -1 -1 445.59 182.99 546.42 245.59 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n162 1 Car -1 -1 -1 604.16 173.39 662.38 226.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n162 63 Car -1 -1 -1 526.80 178.65 580.39 218.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n162 59 Car -1 -1 -1 0.30 198.49 189.39 367.54 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n162 61 Car -1 -1 -1 558.38 178.36 599.97 208.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n162 68 Car -1 -1 -1 568.06 168.92 607.83 201.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n163 60 Car -1 -1 -1 429.08 183.48 539.68 251.44 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n163 1 Car -1 -1 -1 602.59 173.07 661.23 226.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n163 61 Car -1 -1 -1 554.80 178.69 597.33 209.13 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n163 63 Car -1 -1 -1 520.00 179.90 576.13 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n163 68 Car -1 -1 -1 563.59 168.77 605.93 201.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n164 60 Car -1 -1 -1 411.03 184.09 531.92 256.92 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n164 1 Car -1 -1 -1 601.55 173.06 659.79 226.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n164 63 Car -1 -1 -1 512.15 179.70 572.74 222.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n164 61 Car -1 -1 -1 549.67 178.47 594.80 210.08 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n164 68 Car -1 -1 -1 558.77 168.54 603.07 202.23 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0009.txt",
    "content": "0 1 Car -1 -1 -1 487.70 168.95 509.64 180.98 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n1 1 Car -1 -1 -1 487.33 168.65 509.76 181.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n2 1 Car -1 -1 -1 486.53 169.19 508.79 181.42 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n2 2 Car -1 -1 -1 479.68 168.41 502.17 180.90 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n3 1 Car -1 -1 -1 485.26 170.94 506.16 183.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n3 2 Car -1 -1 -1 476.81 170.59 496.47 182.61 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n4 1 Car -1 -1 -1 483.30 172.92 504.33 185.11 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n4 2 Car -1 -1 -1 473.31 171.56 492.73 183.99 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n5 1 Car -1 -1 -1 479.60 171.59 502.24 185.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n5 2 Car -1 -1 -1 468.61 170.44 490.30 183.52 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n6 1 Car -1 -1 -1 474.28 170.23 499.85 184.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n6 2 Car -1 -1 -1 463.15 168.97 486.77 183.38 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n7 1 Car -1 -1 -1 470.11 170.20 498.16 183.58 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n7 2 Car -1 -1 -1 458.66 169.10 483.07 181.86 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n8 1 Car -1 -1 -1 469.67 170.67 496.18 184.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 2 Car -1 -1 -1 453.27 168.98 476.37 183.31 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n9 2 Car -1 -1 -1 448.75 169.60 470.34 184.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n9 1 Car -1 -1 -1 467.37 171.69 494.22 185.53 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n10 1 Car -1 -1 -1 465.39 172.00 492.80 186.09 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n10 2 Car -1 -1 -1 444.25 169.79 465.64 184.44 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n11 2 Car -1 -1 -1 439.65 166.99 459.98 181.88 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n11 1 Car -1 -1 -1 462.26 169.20 493.70 183.64 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n12 2 Car -1 -1 -1 434.27 167.17 455.58 182.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n12 1 Car -1 -1 -1 461.75 168.98 494.08 184.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n13 2 Car -1 -1 -1 429.24 168.60 451.12 183.98 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n13 1 Car -1 -1 -1 460.88 170.32 494.77 185.85 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n14 1 Car -1 -1 -1 460.93 168.25 496.33 184.33 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n14 2 Car -1 -1 -1 424.16 167.04 446.47 182.53 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n15 2 Car -1 -1 -1 418.59 166.02 441.76 182.04 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n15 1 Car -1 -1 -1 461.29 167.76 499.85 184.50 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n16 1 Car -1 -1 -1 461.87 169.93 505.67 187.05 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n16 2 Car -1 -1 -1 414.49 168.13 438.10 185.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n17 1 Car -1 -1 -1 463.59 173.41 510.26 191.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n17 2 Car -1 -1 -1 409.94 172.34 434.28 189.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n18 2 Car -1 -1 -1 404.47 173.48 431.59 191.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n18 1 Car -1 -1 -1 465.85 174.31 514.80 192.33 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n19 1 Car -1 -1 -1 469.75 173.31 521.89 192.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n19 2 Car -1 -1 -1 400.65 172.56 428.68 191.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n20 1 Car -1 -1 -1 476.65 172.40 527.03 192.41 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n20 2 Car -1 -1 -1 397.96 171.19 426.08 191.21 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n21 1 Car -1 -1 -1 484.82 172.26 535.89 191.74 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n21 2 Car -1 -1 -1 395.80 171.02 425.73 191.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n22 2 Car -1 -1 -1 394.63 170.51 426.28 191.15 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n22 1 Car -1 -1 -1 493.85 171.67 547.67 191.95 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n23 1 Car -1 -1 -1 504.51 170.51 560.90 192.83 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n23 2 Car -1 -1 -1 394.50 170.31 427.09 191.10 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n24 2 Car -1 -1 -1 395.60 171.67 429.67 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n24 1 Car -1 -1 -1 513.55 171.76 574.82 194.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n25 1 Car -1 -1 -1 530.12 172.81 590.63 195.34 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n25 2 Car -1 -1 -1 396.42 171.76 433.26 193.81 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n26 1 Car -1 -1 -1 546.51 170.80 608.11 194.58 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n26 2 Car -1 -1 -1 399.95 170.32 439.32 193.16 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n27 1 Car -1 -1 -1 565.64 171.35 625.85 194.47 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n27 2 Car -1 -1 -1 403.26 170.26 445.00 193.95 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n27 3 Pedestrian -1 -1 -1 515.06 171.22 529.67 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n28 1 Car -1 -1 -1 584.76 173.05 645.68 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n28 2 Car -1 -1 -1 406.29 172.46 451.42 197.05 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n29 2 Car -1 -1 -1 409.97 173.79 458.07 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n29 1 Car -1 -1 -1 603.95 174.27 666.96 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n29 4 Pedestrian -1 -1 -1 517.96 174.96 534.83 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n30 1 Car -1 -1 -1 625.18 173.44 689.20 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n30 2 Car -1 -1 -1 414.32 173.03 465.68 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n31 1 Car -1 -1 -1 647.41 171.90 713.01 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 2 Car -1 -1 -1 418.52 171.33 473.03 199.20 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n32 2 Car -1 -1 -1 423.26 170.66 482.40 199.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n32 1 Car -1 -1 -1 668.39 170.63 738.30 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n33 2 Car -1 -1 -1 428.01 170.26 490.90 200.55 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n33 1 Car -1 -1 -1 692.27 169.51 764.18 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n34 2 Car -1 -1 -1 433.71 170.00 501.50 200.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n34 1 Car -1 -1 -1 717.48 169.47 790.64 198.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n35 2 Car -1 -1 -1 439.33 170.23 511.68 202.20 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n35 1 Car -1 -1 -1 744.13 169.33 818.55 200.41 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n36 2 Car -1 -1 -1 446.11 169.28 522.52 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n36 1 Car -1 -1 -1 771.62 169.16 847.36 200.69 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n37 2 Car -1 -1 -1 452.95 168.36 534.33 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n37 1 Car -1 -1 -1 799.72 168.39 879.49 200.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n38 2 Car -1 -1 -1 462.14 166.41 545.60 204.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n38 1 Car -1 -1 -1 829.91 167.79 910.78 200.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n39 2 Car -1 -1 -1 470.90 167.17 562.57 205.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n39 1 Car -1 -1 -1 861.04 166.91 943.40 201.27 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n40 2 Car -1 -1 -1 479.53 166.60 578.03 206.50 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n40 1 Car -1 -1 -1 893.93 165.63 979.22 199.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n41 2 Car -1 -1 -1 489.96 165.59 594.34 206.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n41 1 Car -1 -1 -1 927.65 164.11 1015.58 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n42 2 Car -1 -1 -1 502.39 163.96 613.51 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n42 1 Car -1 -1 -1 961.86 162.08 1052.12 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n43 2 Car -1 -1 -1 515.15 161.90 630.56 208.09 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n43 1 Car -1 -1 -1 998.29 160.37 1092.40 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n44 2 Car -1 -1 -1 527.06 161.58 650.01 209.89 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n44 1 Car -1 -1 -1 1034.00 159.48 1126.11 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n45 2 Car -1 -1 -1 540.17 160.38 667.62 211.38 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n45 1 Car -1 -1 -1 1067.76 157.51 1162.87 196.87 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n46 2 Car -1 -1 -1 554.69 159.34 688.57 212.84 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n46 1 Car -1 -1 -1 1099.63 156.16 1195.97 194.62 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n47 2 Car -1 -1 -1 567.82 157.89 706.35 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n47 1 Car -1 -1 -1 1128.75 153.37 1228.20 192.56 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n48 2 Car -1 -1 -1 581.66 156.16 724.80 214.04 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n48 1 Car -1 -1 -1 1153.76 150.52 1240.49 191.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n49 2 Car -1 -1 -1 596.65 154.01 743.54 215.11 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n49 1 Car -1 -1 -1 1182.59 147.02 1240.70 187.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n50 2 Car -1 -1 -1 609.55 153.05 765.68 217.75 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n50 1 Car -1 -1 -1 1204.63 145.94 1239.08 185.92 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n51 2 Car -1 -1 -1 624.92 153.30 783.99 218.31 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n51 1 Car -1 -1 -1 1220.40 143.75 1238.94 180.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n52 2 Car -1 -1 -1 639.33 151.37 802.73 219.30 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n53 2 Car -1 -1 -1 656.47 149.21 822.79 218.96 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n54 2 Car -1 -1 -1 672.46 145.53 845.25 217.77 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n55 2 Car -1 -1 -1 688.80 143.29 865.04 218.47 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n55 5 Car -1 -1 -1 1211.14 115.74 1240.62 141.81 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n56 2 Car -1 -1 -1 706.32 142.54 882.13 218.37 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n56 5 Car -1 -1 -1 1178.93 115.02 1237.41 141.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n57 2 Car -1 -1 -1 723.34 141.39 897.35 218.89 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n57 5 Car -1 -1 -1 1143.17 114.57 1203.93 142.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n58 2 Car -1 -1 -1 738.93 138.99 912.11 216.96 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n58 5 Car -1 -1 -1 1110.21 113.24 1167.86 143.00 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n59 2 Car -1 -1 -1 752.68 135.88 926.25 214.75 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n59 5 Car -1 -1 -1 1073.88 114.55 1131.65 142.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n60 2 Car -1 -1 -1 766.79 133.75 934.56 213.88 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n60 5 Car -1 -1 -1 1036.77 115.14 1095.03 144.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n60 6 Car -1 -1 -1 1233.85 107.97 1239.35 138.51 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n61 2 Car -1 -1 -1 777.13 133.83 942.39 213.15 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n61 5 Car -1 -1 -1 997.91 117.56 1057.82 147.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n61 6 Car -1 -1 -1 1216.64 106.90 1239.61 138.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n62 2 Car -1 -1 -1 786.73 133.30 946.61 213.30 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n62 6 Car -1 -1 -1 1196.08 108.59 1239.24 139.10 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n62 5 Car -1 -1 -1 959.94 120.84 1020.43 151.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n63 2 Car -1 -1 -1 795.48 133.31 949.04 213.44 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n63 5 Car -1 -1 -1 919.86 125.22 983.02 155.41 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n63 6 Car -1 -1 -1 1176.19 111.50 1224.81 140.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n64 2 Car -1 -1 -1 800.37 134.48 948.54 213.99 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n64 6 Car -1 -1 -1 1151.85 114.31 1195.61 143.34 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n64 5 Car -1 -1 -1 881.05 128.98 945.68 161.81 -1 -1 -1 -1000 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187.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n204 9 Car -1 -1 -1 519.88 171.60 549.72 184.44 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n205 2 Car -1 -1 -1 604.71 173.19 624.25 187.89 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n205 9 Car -1 -1 -1 520.63 170.77 552.27 184.68 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n206 2 Car -1 -1 -1 607.89 173.41 627.73 188.10 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n206 9 Car -1 -1 -1 520.92 170.40 553.12 184.09 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n207 2 Car -1 -1 -1 611.97 174.41 630.74 187.88 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n207 9 Car -1 -1 -1 521.04 170.32 553.33 184.19 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n208 2 Car -1 -1 -1 614.86 174.76 633.26 187.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n208 9 Car -1 -1 -1 521.42 170.60 553.88 184.61 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n209 2 Car -1 -1 -1 618.48 174.35 636.49 187.44 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n209 9 Car -1 -1 -1 521.27 170.51 554.59 184.17 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n210 2 Car -1 -1 -1 621.47 173.77 640.57 187.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n210 9 Car -1 -1 -1 521.22 170.56 556.19 184.28 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n211 2 Car -1 -1 -1 624.04 173.37 643.33 188.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n211 9 Car -1 -1 -1 521.04 171.20 556.23 184.69 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n212 2 Car -1 -1 -1 626.27 173.46 645.83 187.41 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n212 9 Car -1 -1 -1 519.86 171.05 556.50 184.57 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n213 9 Car -1 -1 -1 519.24 170.50 556.32 184.44 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n213 2 Car -1 -1 -1 630.09 173.27 649.49 187.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n214 9 Car -1 -1 -1 519.25 170.79 557.17 184.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n214 2 Car -1 -1 -1 633.27 172.51 653.02 186.36 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n214 10 Van -1 -1 -1 604.06 166.58 626.20 182.42 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n215 9 Car -1 -1 -1 518.19 171.16 556.98 185.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n215 2 Car -1 -1 -1 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0.84\n228 13 Car -1 -1 -1 434.35 176.81 483.54 194.49 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n228 2 Car -1 -1 -1 673.48 170.50 690.28 183.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n228 12 Car -1 -1 -1 605.88 172.68 626.54 187.68 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n228 18 Truck -1 -1 -1 573.76 163.35 601.28 191.59 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n228 9 Car -1 -1 -1 495.45 173.46 545.90 193.08 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n229 9 Car -1 -1 -1 491.51 171.65 544.51 192.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n229 2 Car -1 -1 -1 675.39 167.05 693.08 180.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n229 12 Car -1 -1 -1 602.66 170.46 625.37 186.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n229 13 Car -1 -1 -1 430.90 175.46 480.71 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n229 17 Car -1 -1 -1 632.90 171.26 650.68 181.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n229 11 Car -1 -1 -1 562.01 174.06 591.51 193.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n229 18 Truck -1 -1 -1 570.29 160.74 598.57 190.19 -1 -1 -1 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0.97\n248 13 Car -1 -1 -1 292.09 184.41 372.91 217.42 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n248 17 Car -1 -1 -1 550.63 179.39 599.31 207.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n248 9 Car -1 -1 -1 388.83 177.19 476.84 216.39 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n248 2 Car -1 -1 -1 721.40 158.35 744.07 174.25 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n249 12 Car -1 -1 -1 320.50 177.95 488.60 293.23 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n249 17 Car -1 -1 -1 539.60 180.61 592.41 211.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n249 13 Car -1 -1 -1 277.36 185.61 360.65 219.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n249 21 Car -1 -1 -1 405.02 182.14 491.95 213.37 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n250 12 Car -1 -1 -1 193.40 179.55 447.72 340.74 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n250 9 Car -1 -1 -1 346.23 177.97 457.03 225.00 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n250 17 Car -1 -1 -1 525.25 181.12 583.47 215.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n250 21 Car -1 -1 -1 390.02 180.07 483.25 215.42 -1 -1 -1 -1000 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-1 -1 175.74 171.97 333.71 252.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n259 13 Car -1 -1 -1 51.41 182.09 186.78 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n259 21 Car -1 -1 -1 259.88 176.05 403.00 228.29 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n260 9 Car -1 -1 -1 147.54 173.00 311.99 259.72 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n260 13 Car -1 -1 -1 20.24 184.33 163.26 241.50 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n260 21 Car -1 -1 -1 233.89 177.04 390.71 232.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n261 9 Car -1 -1 -1 113.12 173.70 286.90 268.65 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n261 13 Car -1 -1 -1 -1.88 186.85 131.32 246.79 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n261 21 Car -1 -1 -1 208.80 177.36 377.07 238.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n262 9 Car -1 -1 -1 72.64 173.87 258.79 281.16 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n262 13 Car -1 -1 -1 -0.98 188.68 97.65 252.25 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n262 21 Car -1 -1 -1 174.75 178.27 365.08 241.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n263 9 Car -1 -1 -1 29.03 172.65 223.68 292.38 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n263 21 Car -1 -1 -1 145.11 178.54 347.76 246.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n263 13 Car -1 -1 -1 -0.45 185.02 65.83 256.22 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n264 9 Car -1 -1 -1 -1.37 172.91 185.01 307.25 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n264 21 Car -1 -1 -1 123.80 180.10 330.21 251.28 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n264 13 Car -1 -1 -1 -3.20 188.65 30.50 252.74 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n265 9 Car -1 -1 -1 -0.20 173.22 136.07 322.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n265 21 Car -1 -1 -1 79.83 180.28 311.53 255.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n266 21 Car -1 -1 -1 30.82 180.84 291.70 265.74 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n266 9 Car -1 -1 -1 0.74 175.94 72.02 343.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n267 21 Car -1 -1 -1 -2.29 183.32 270.66 274.75 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n268 21 Car -1 -1 -1 0.06 189.13 244.52 284.17 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n269 21 Car -1 -1 -1 2.64 191.09 216.96 294.45 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n270 21 Car -1 -1 -1 1.83 189.86 187.17 305.08 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n271 21 Car -1 -1 -1 -1.09 191.41 158.55 318.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n272 21 Car -1 -1 -1 -1.10 193.84 127.81 332.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n273 21 Car -1 -1 -1 0.02 190.70 89.63 351.06 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n274 21 Car -1 -1 -1 0.03 194.04 57.66 355.68 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n346 22 Car -1 -1 -1 700.73 180.83 720.54 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n347 22 Car -1 -1 -1 679.24 179.03 699.53 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n348 22 Car -1 -1 -1 658.36 176.27 679.34 193.88 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0010.txt",
    "content": "0 1 Car -1 -1 -1 530.56 179.79 731.32 254.72 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n0 2 Car -1 -1 -1 73.74 184.36 257.33 239.53 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n0 3 Van -1 -1 -1 660.11 164.49 731.73 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n0 4 Car -1 -1 -1 918.39 173.26 1088.20 222.89 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n0 5 Car -1 -1 -1 503.10 177.73 525.25 191.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n0 6 Car -1 -1 -1 539.00 176.08 590.44 209.63 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n0 7 Car -1 -1 -1 886.08 170.55 973.04 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n0 8 Car -1 -1 -1 911.30 173.32 1033.06 215.32 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n0 9 Car -1 -1 -1 966.73 162.12 1094.22 208.55 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n0 10 Car -1 -1 -1 531.91 176.70 567.35 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n1 1 Car -1 -1 -1 572.75 179.47 775.13 254.89 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1 2 Car -1 -1 -1 105.04 184.13 279.61 238.72 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 6 Car -1 -1 -1 546.28 174.97 598.69 211.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n1 4 Car -1 -1 -1 917.67 173.39 1089.56 222.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n1 3 Van -1 -1 -1 660.64 164.22 730.96 200.64 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n1 5 Car -1 -1 -1 501.72 177.44 524.47 191.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n1 10 Car -1 -1 -1 535.03 177.09 564.23 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n1 9 Car -1 -1 -1 967.41 162.07 1093.21 208.29 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n1 7 Car -1 -1 -1 880.83 170.08 963.12 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n1 8 Car -1 -1 -1 911.20 172.56 1041.07 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n2 1 Car -1 -1 -1 618.24 180.07 819.75 255.60 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n2 2 Car -1 -1 -1 133.32 183.67 305.42 236.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n2 6 Car -1 -1 -1 553.05 175.47 610.01 210.39 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n2 4 Car -1 -1 -1 917.50 173.16 1090.03 223.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n2 5 Car -1 -1 -1 499.40 177.43 522.24 191.87 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n2 7 Car -1 -1 -1 874.93 173.55 952.32 203.26 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n2 9 Car -1 -1 -1 967.09 162.31 1093.75 207.97 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n2 3 Van -1 -1 -1 659.89 164.63 725.92 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n2 10 Car -1 -1 -1 537.34 177.07 565.93 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n2 8 Car -1 -1 -1 913.37 172.67 1039.17 215.77 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n3 1 Car -1 -1 -1 660.21 179.62 867.03 255.55 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n3 2 Car -1 -1 -1 163.59 183.71 328.41 235.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n3 6 Car -1 -1 -1 560.68 175.60 614.26 209.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n3 4 Car -1 -1 -1 917.65 173.42 1090.02 223.06 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n3 7 Car -1 -1 -1 868.99 174.04 943.41 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n3 5 Car -1 -1 -1 497.09 177.29 521.24 191.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n3 9 Car -1 -1 -1 967.38 162.16 1093.76 207.74 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n3 3 Van -1 -1 -1 656.57 166.57 715.58 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n3 10 Car -1 -1 -1 539.25 176.62 565.28 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n3 8 Car -1 -1 -1 912.76 172.54 1039.61 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n4 1 Car -1 -1 -1 706.65 180.71 913.40 255.09 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n4 2 Car -1 -1 -1 192.05 183.61 349.59 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n4 6 Car -1 -1 -1 566.73 175.71 616.30 208.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n4 4 Car -1 -1 -1 917.84 173.53 1089.81 223.17 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 5 Car -1 -1 -1 495.49 177.16 518.82 192.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n4 7 Car -1 -1 -1 864.32 174.42 939.28 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 3 Van -1 -1 -1 655.23 165.68 722.13 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n4 9 Car -1 -1 -1 967.23 162.16 1094.08 207.66 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n4 10 Car -1 -1 -1 540.82 176.52 566.39 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n4 8 Car -1 -1 -1 913.63 172.44 1039.04 216.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n5 1 Car -1 -1 -1 752.30 179.84 966.80 255.69 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n5 2 Car -1 -1 -1 224.12 183.46 371.03 232.62 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n5 4 Car -1 -1 -1 918.02 173.63 1089.55 223.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n5 6 Car -1 -1 -1 571.57 175.94 618.31 208.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n5 3 Van -1 -1 -1 654.38 166.23 722.89 199.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n5 5 Car -1 -1 -1 493.55 176.98 517.61 192.47 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n5 7 Car -1 -1 -1 862.47 174.06 934.56 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n5 9 Car -1 -1 -1 967.19 162.20 1094.23 207.98 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n5 10 Car -1 -1 -1 541.67 176.48 567.40 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n5 8 Car -1 -1 -1 909.98 173.40 1049.99 219.12 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n6 1 Car -1 -1 -1 799.33 181.60 1019.77 256.80 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n6 2 Car -1 -1 -1 247.53 182.90 394.44 231.74 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n6 5 Car -1 -1 -1 491.43 177.14 515.05 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n6 3 Van -1 -1 -1 653.26 166.28 722.84 199.34 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n6 4 Car -1 -1 -1 923.03 174.67 1090.77 225.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n6 6 Car -1 -1 -1 576.30 176.22 620.10 207.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n6 10 Car -1 -1 -1 543.21 176.75 568.42 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n6 9 Car -1 -1 -1 966.62 162.20 1094.70 208.61 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n6 7 Car -1 -1 -1 859.44 173.92 929.24 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n6 8 Car -1 -1 -1 908.66 174.00 1051.17 219.11 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n7 1 Car -1 -1 -1 848.54 182.61 1071.27 256.09 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n7 2 Car -1 -1 -1 270.55 182.22 416.95 229.65 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n7 5 Car -1 -1 -1 489.85 177.60 513.68 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n7 10 Car -1 -1 -1 544.54 176.71 568.76 196.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n7 3 Van -1 -1 -1 652.64 166.29 722.56 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n7 4 Car -1 -1 -1 926.24 175.33 1088.67 224.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n7 7 Car -1 -1 -1 845.16 173.12 928.01 203.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n7 6 Car -1 -1 -1 579.87 175.70 619.36 206.48 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n7 9 Car -1 -1 -1 966.27 162.14 1094.82 208.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n7 11 Car -1 -1 -1 1233.03 175.59 1237.89 211.18 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n8 2 Car -1 -1 -1 294.31 181.71 437.44 228.55 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n8 1 Car -1 -1 -1 898.39 182.98 1123.89 255.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n8 5 Car -1 -1 -1 487.83 177.47 511.16 193.70 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n8 10 Car -1 -1 -1 545.93 176.66 569.23 196.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n8 4 Car -1 -1 -1 926.82 174.79 1095.51 225.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n8 7 Car -1 -1 -1 839.54 174.31 916.82 201.76 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n8 6 Car -1 -1 -1 583.26 175.38 620.75 206.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n8 3 Van -1 -1 -1 652.50 166.23 722.26 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n8 9 Car -1 -1 -1 959.60 161.86 1093.71 208.94 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n8 11 Car -1 -1 -1 1220.87 173.53 1238.38 213.43 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n8 12 Car -1 -1 -1 858.23 169.46 930.24 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n9 1 Car -1 -1 -1 948.47 182.15 1174.73 256.63 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n9 2 Car -1 -1 -1 318.00 181.12 454.17 227.45 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n9 6 Car -1 -1 -1 585.68 175.44 621.22 205.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n9 7 Car -1 -1 -1 833.99 174.92 910.39 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n9 5 Car -1 -1 -1 485.76 177.52 509.54 194.03 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n9 10 Car -1 -1 -1 546.71 176.63 569.68 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n9 3 Van -1 -1 -1 651.28 166.04 720.65 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n9 4 Car -1 -1 -1 925.58 174.90 1096.13 226.15 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n9 11 Car -1 -1 -1 1212.01 173.51 1237.46 213.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n9 9 Car -1 -1 -1 962.78 163.37 1090.54 208.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n9 12 Car -1 -1 -1 851.84 168.95 928.99 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n9 13 Car -1 -1 -1 -0.59 195.62 25.81 244.85 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n10 1 Car -1 -1 -1 993.43 180.18 1232.07 258.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n10 2 Car -1 -1 -1 337.33 181.15 472.53 225.75 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 6 Car -1 -1 -1 587.74 175.54 621.49 204.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n10 5 Car -1 -1 -1 482.83 177.16 507.59 194.24 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n10 7 Car -1 -1 -1 829.76 173.98 904.80 201.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n10 3 Van -1 -1 -1 651.23 165.87 721.36 199.99 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n10 11 Car -1 -1 -1 1203.03 173.63 1237.90 212.89 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n10 9 Car -1 -1 -1 956.47 162.88 1088.72 209.13 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n10 4 Car -1 -1 -1 913.99 172.98 1077.33 223.56 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n10 10 Car -1 -1 -1 547.23 176.73 569.87 195.68 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n10 13 Car -1 -1 -1 2.96 196.12 53.30 243.91 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n10 14 Car -1 -1 -1 352.27 182.68 412.13 203.81 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n11 1 Car -1 -1 -1 1049.51 178.78 1238.52 255.01 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n11 2 Car -1 -1 -1 355.87 180.63 487.39 223.66 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n11 7 Car -1 -1 -1 825.67 173.46 899.83 200.74 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n11 13 Car -1 -1 -1 -1.20 184.35 81.73 242.27 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n11 4 Car -1 -1 -1 915.60 174.36 1091.26 225.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n11 6 Car -1 -1 -1 589.26 175.79 620.96 204.01 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n11 3 Van -1 -1 -1 651.11 166.03 721.16 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n11 5 Car -1 -1 -1 480.99 177.48 506.07 194.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n11 10 Car -1 -1 -1 548.40 177.18 570.35 195.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n11 9 Car -1 -1 -1 956.76 163.35 1088.53 209.34 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n11 11 Car -1 -1 -1 1195.01 173.49 1238.49 213.59 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n11 15 Car -1 -1 -1 581.76 177.91 600.84 192.54 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n11 16 Car -1 -1 -1 906.14 173.49 1046.13 219.04 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n12 2 Car -1 -1 -1 376.68 180.15 502.20 222.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n12 1 Car -1 -1 -1 1102.55 179.77 1237.50 254.57 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 10 Car -1 -1 -1 549.45 177.07 570.85 194.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 4 Car -1 -1 -1 917.03 173.06 1090.01 223.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 13 Car -1 -1 -1 -1.50 185.35 106.87 241.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n12 5 Car -1 -1 -1 479.05 177.91 503.69 195.16 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n12 6 Car -1 -1 -1 590.57 175.52 621.61 203.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n12 7 Car -1 -1 -1 821.78 173.37 895.29 200.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n12 3 Van -1 -1 -1 651.10 166.04 720.76 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n12 9 Car -1 -1 -1 968.88 162.20 1091.48 208.22 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n12 15 Car -1 -1 -1 582.37 177.80 600.88 191.87 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n12 11 Car -1 -1 -1 1194.17 172.90 1238.76 213.48 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n12 16 Car -1 -1 -1 907.68 173.44 1045.13 218.61 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n13 2 Car -1 -1 -1 394.92 179.70 515.45 220.93 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n13 13 Car -1 -1 -1 0.04 183.29 135.42 241.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n13 10 Car -1 -1 -1 550.48 176.98 571.51 194.22 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n13 4 Car -1 -1 -1 917.22 173.32 1089.92 223.36 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n13 1 Car -1 -1 -1 1149.41 178.09 1239.63 253.93 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n13 6 Car -1 -1 -1 590.60 175.41 621.40 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n13 3 Van -1 -1 -1 650.75 166.08 720.43 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n13 7 Car -1 -1 -1 816.07 173.34 890.28 201.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n13 5 Car -1 -1 -1 477.28 177.90 502.57 195.50 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n13 9 Car -1 -1 -1 968.40 162.32 1092.43 208.37 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n13 15 Car -1 -1 -1 582.66 177.88 601.06 191.71 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n13 16 Car -1 -1 -1 907.59 173.41 1052.28 218.87 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n13 11 Car -1 -1 -1 1185.64 171.99 1239.77 214.33 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n14 13 Car -1 -1 -1 0.85 182.96 164.29 241.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n14 2 Car -1 -1 -1 413.31 179.64 528.00 219.84 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n14 4 Car -1 -1 -1 916.78 173.24 1090.34 223.20 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 6 Car -1 -1 -1 591.20 175.50 621.18 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n14 10 Car -1 -1 -1 551.56 177.06 572.18 194.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n14 7 Car -1 -1 -1 812.44 173.61 885.71 200.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n14 9 Car -1 -1 -1 967.52 162.27 1093.60 208.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n14 3 Van -1 -1 -1 650.33 166.32 719.86 199.57 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n14 5 Car -1 -1 -1 473.46 177.93 501.76 196.00 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n14 11 Car -1 -1 -1 1178.80 168.70 1238.00 211.27 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n14 1 Car -1 -1 -1 1205.33 170.54 1237.47 254.88 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n14 16 Car -1 -1 -1 907.34 173.23 1052.61 219.06 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n15 13 Car -1 -1 -1 -1.20 182.36 189.91 241.55 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n15 2 Car -1 -1 -1 429.52 178.95 538.69 218.38 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n15 4 Car -1 -1 -1 916.81 173.21 1090.50 223.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n15 11 Car -1 -1 -1 1173.25 167.40 1237.93 212.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n15 6 Car -1 -1 -1 591.11 175.63 621.01 201.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n15 9 Car -1 -1 -1 966.66 162.26 1094.72 208.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n15 5 Car -1 -1 -1 469.74 177.93 503.15 196.22 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n15 3 Van -1 -1 -1 650.12 166.40 719.75 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n15 7 Car -1 -1 -1 807.13 173.69 883.48 201.82 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n15 10 Car -1 -1 -1 552.22 177.10 572.67 194.03 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n15 16 Car -1 -1 -1 907.40 173.21 1052.49 219.05 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n16 2 Car -1 -1 -1 445.73 179.41 549.92 217.17 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n16 13 Car -1 -1 -1 12.60 180.51 216.12 240.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n16 4 Car -1 -1 -1 916.84 173.32 1090.52 223.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n16 10 Car -1 -1 -1 553.28 177.90 574.06 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n16 11 Car -1 -1 -1 1170.81 167.87 1238.26 213.25 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n16 9 Car -1 -1 -1 966.58 162.39 1095.06 209.06 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n16 6 Car -1 -1 -1 591.21 175.97 620.57 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n16 7 Car -1 -1 -1 805.82 173.39 880.51 200.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n16 3 Van -1 -1 -1 649.83 166.43 719.51 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n16 16 Car -1 -1 -1 907.41 173.18 1052.51 219.19 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n17 13 Car -1 -1 -1 43.35 180.23 239.63 239.42 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n17 2 Car -1 -1 -1 460.84 178.80 557.71 216.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n17 4 Car -1 -1 -1 916.41 173.17 1090.61 223.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n17 10 Car -1 -1 -1 554.29 177.88 575.03 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n17 11 Car -1 -1 -1 1166.56 170.01 1237.65 214.19 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n17 9 Car -1 -1 -1 972.62 162.51 1095.09 208.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n17 7 Car -1 -1 -1 801.34 173.88 877.46 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n17 3 Van -1 -1 -1 649.50 166.38 719.53 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n17 6 Car -1 -1 -1 591.17 176.01 620.49 200.95 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n17 5 Car -1 -1 -1 467.18 178.39 496.73 195.52 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n17 16 Car -1 -1 -1 908.00 173.18 1051.95 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n18 13 Car -1 -1 -1 77.06 179.67 262.29 237.95 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n18 2 Car -1 -1 -1 474.81 178.53 566.55 215.70 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n18 4 Car -1 -1 -1 916.67 173.26 1090.62 223.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n18 11 Car -1 -1 -1 1165.02 170.28 1237.55 213.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n18 7 Car -1 -1 -1 798.72 174.24 872.50 201.55 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n18 9 Car -1 -1 -1 972.61 162.56 1095.17 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n18 10 Car -1 -1 -1 555.43 178.08 576.11 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n18 3 Van -1 -1 -1 649.40 166.35 719.58 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n18 5 Car -1 -1 -1 463.77 179.03 494.33 196.72 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 6 Car -1 -1 -1 591.37 176.19 620.25 200.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n18 16 Car -1 -1 -1 907.91 173.01 1052.15 219.36 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n19 2 Car -1 -1 -1 487.02 178.37 573.76 214.78 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n19 13 Car -1 -1 -1 108.18 179.17 289.94 236.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n19 4 Car -1 -1 -1 916.55 173.28 1090.55 222.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n19 7 Car -1 -1 -1 795.93 174.60 867.97 201.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n19 11 Car -1 -1 -1 1164.00 170.22 1238.09 214.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n19 9 Car -1 -1 -1 972.41 162.61 1095.63 208.49 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n19 3 Van -1 -1 -1 649.73 166.34 719.30 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n19 6 Car -1 -1 -1 591.04 176.33 618.85 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n19 5 Car -1 -1 -1 461.08 178.52 491.65 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n19 10 Car -1 -1 -1 556.03 178.43 576.08 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n19 16 Car -1 -1 -1 908.16 172.95 1051.88 219.34 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n20 13 Car -1 -1 -1 139.00 178.94 313.90 234.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n20 2 Car -1 -1 -1 498.38 178.59 579.08 213.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n20 4 Car -1 -1 -1 916.75 173.25 1090.30 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n20 11 Car -1 -1 -1 1163.39 170.42 1238.41 214.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n20 7 Car -1 -1 -1 792.70 174.46 865.07 201.27 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n20 9 Car -1 -1 -1 972.67 162.59 1095.42 208.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n20 3 Van -1 -1 -1 649.56 166.32 719.31 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n20 6 Car -1 -1 -1 590.60 176.26 618.25 200.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 5 Car -1 -1 -1 458.42 178.29 490.65 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n20 10 Car -1 -1 -1 557.41 178.79 577.11 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n20 16 Car -1 -1 -1 908.11 172.83 1051.96 219.39 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n21 13 Car -1 -1 -1 168.84 179.35 338.27 232.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n21 4 Car -1 -1 -1 916.45 173.29 1090.62 223.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n21 11 Car -1 -1 -1 1162.60 170.86 1238.66 213.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n21 7 Car -1 -1 -1 789.65 174.42 861.73 201.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n21 2 Car -1 -1 -1 509.84 178.70 583.59 212.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n21 9 Car -1 -1 -1 973.17 162.76 1094.77 208.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n21 3 Van -1 -1 -1 649.48 166.26 719.29 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n21 5 Car -1 -1 -1 456.25 178.14 488.64 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n21 6 Car -1 -1 -1 590.17 176.34 617.56 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n21 10 Car -1 -1 -1 557.72 178.89 578.12 193.91 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n21 16 Car -1 -1 -1 908.13 172.87 1051.92 219.36 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n22 13 Car -1 -1 -1 196.51 179.38 359.99 230.90 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n22 4 Car -1 -1 -1 916.91 173.34 1090.20 223.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n22 2 Car -1 -1 -1 520.76 178.11 586.85 211.44 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n22 11 Car -1 -1 -1 1162.31 170.87 1238.65 213.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n22 9 Car -1 -1 -1 972.95 162.66 1094.96 208.50 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n22 7 Car -1 -1 -1 785.70 174.62 858.86 201.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n22 3 Van -1 -1 -1 649.48 166.34 719.22 199.86 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n22 5 Car -1 -1 -1 453.77 178.14 486.89 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n22 6 Car -1 -1 -1 589.01 176.24 616.88 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n22 10 Car -1 -1 -1 557.58 179.06 579.29 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n22 16 Car -1 -1 -1 908.39 172.84 1051.65 219.29 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n23 13 Car -1 -1 -1 224.90 178.72 382.87 229.86 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n23 4 Car -1 -1 -1 916.77 173.28 1090.28 223.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n23 2 Car -1 -1 -1 529.05 177.84 590.08 211.26 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n23 5 Car -1 -1 -1 451.96 178.37 484.92 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n23 11 Car -1 -1 -1 1161.70 171.17 1238.93 213.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n23 9 Car -1 -1 -1 973.19 162.61 1094.64 208.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n23 7 Car -1 -1 -1 784.50 174.01 855.64 200.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n23 3 Van -1 -1 -1 649.22 166.50 718.79 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n23 6 Car -1 -1 -1 588.67 176.57 616.05 199.21 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n23 10 Car -1 -1 -1 557.80 179.42 579.55 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n23 16 Car -1 -1 -1 908.19 172.92 1051.79 219.24 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n24 13 Car -1 -1 -1 249.51 178.88 404.84 228.58 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n24 2 Car -1 -1 -1 536.05 177.93 593.48 210.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n24 4 Car -1 -1 -1 916.91 173.35 1090.18 223.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n24 7 Car -1 -1 -1 780.66 174.55 853.62 201.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n24 5 Car -1 -1 -1 449.80 178.32 483.03 198.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 11 Car -1 -1 -1 1158.18 172.43 1238.22 213.53 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 9 Car -1 -1 -1 973.34 162.49 1094.55 208.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n24 3 Van -1 -1 -1 649.09 166.56 718.59 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n24 6 Car -1 -1 -1 588.11 175.94 614.32 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n24 16 Car -1 -1 -1 908.36 172.88 1051.72 219.24 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n25 13 Car -1 -1 -1 274.80 177.94 422.31 226.47 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n25 4 Car -1 -1 -1 916.73 173.34 1090.12 223.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n25 5 Car -1 -1 -1 447.88 178.05 481.01 199.52 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n25 2 Car -1 -1 -1 543.63 178.19 596.07 209.17 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n25 7 Car -1 -1 -1 777.43 174.65 851.40 200.99 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n25 9 Car -1 -1 -1 967.12 162.40 1094.41 209.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n25 11 Car -1 -1 -1 1161.85 168.49 1239.50 212.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n25 3 Van -1 -1 -1 649.06 166.61 718.20 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n25 6 Car -1 -1 -1 588.43 176.31 613.37 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n25 16 Car -1 -1 -1 908.57 172.86 1051.53 219.27 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n26 13 Car -1 -1 -1 300.66 177.64 440.92 225.18 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n26 4 Car -1 -1 -1 917.10 173.46 1089.91 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n26 2 Car -1 -1 -1 548.84 178.17 598.48 207.94 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n26 11 Car -1 -1 -1 1156.55 168.54 1239.20 211.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n26 7 Car -1 -1 -1 776.34 174.82 849.10 200.94 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n26 5 Car -1 -1 -1 445.51 178.03 479.68 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n26 9 Car -1 -1 -1 967.27 162.40 1094.13 209.13 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n26 6 Car -1 -1 -1 588.05 176.52 612.88 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n26 3 Van -1 -1 -1 649.12 166.57 717.97 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n26 16 Car -1 -1 -1 908.42 172.91 1051.62 219.24 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n27 13 Car -1 -1 -1 324.27 177.10 458.02 223.33 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n27 2 Car -1 -1 -1 553.22 178.15 600.77 207.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n27 4 Car -1 -1 -1 917.20 173.45 1089.57 222.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n27 5 Car -1 -1 -1 443.71 177.80 477.88 200.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n27 11 Car -1 -1 -1 1153.22 168.03 1239.82 210.88 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n27 7 Car -1 -1 -1 774.32 174.76 846.71 201.12 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n27 9 Car -1 -1 -1 967.21 162.37 1094.30 209.10 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n27 6 Car -1 -1 -1 588.03 176.80 612.37 197.22 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n27 3 Van -1 -1 -1 648.25 166.23 716.37 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n27 16 Car -1 -1 -1 908.45 172.93 1051.55 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n27 17 Car -1 -1 -1 1178.46 170.75 1239.25 214.31 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n28 2 Car -1 -1 -1 558.30 178.00 601.99 206.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n28 13 Car -1 -1 -1 347.00 177.38 477.06 222.23 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n28 4 Car -1 -1 -1 917.50 173.40 1089.35 223.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n28 11 Car -1 -1 -1 1147.08 167.42 1238.96 210.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n28 5 Car -1 -1 -1 442.05 177.88 476.75 200.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n28 7 Car -1 -1 -1 773.14 174.59 844.15 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 9 Car -1 -1 -1 967.52 162.50 1094.05 209.07 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n28 6 Car -1 -1 -1 587.40 176.99 611.15 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n28 3 Van -1 -1 -1 648.43 166.22 715.97 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n28 17 Car -1 -1 -1 1177.37 169.97 1240.02 214.84 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n28 16 Car -1 -1 -1 908.47 172.97 1051.41 219.19 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n29 13 Car -1 -1 -1 367.65 177.27 491.14 220.26 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n29 4 Car -1 -1 -1 917.68 173.44 1089.30 223.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n29 11 Car -1 -1 -1 1143.53 167.54 1236.79 211.20 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n29 7 Car -1 -1 -1 770.78 174.94 842.40 201.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n29 2 Car -1 -1 -1 562.81 177.99 602.85 206.00 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n29 9 Car -1 -1 -1 967.29 162.53 1094.29 209.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n29 5 Car -1 -1 -1 442.18 177.84 476.10 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n29 6 Car -1 -1 -1 586.95 176.73 610.35 196.75 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n29 3 Van -1 -1 -1 648.35 166.09 715.95 200.19 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n29 17 Car -1 -1 -1 1177.41 169.54 1239.78 215.15 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n29 16 Car -1 -1 -1 908.31 172.94 1051.63 219.23 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n30 13 Car -1 -1 -1 388.59 175.52 505.43 219.54 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n30 4 Car -1 -1 -1 917.61 173.33 1089.35 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 11 Car -1 -1 -1 1140.43 167.44 1237.04 211.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n30 5 Car -1 -1 -1 437.92 178.87 475.83 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n30 7 Car -1 -1 -1 769.67 175.03 840.61 201.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n30 9 Car -1 -1 -1 967.34 162.47 1094.20 209.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 2 Car -1 -1 -1 566.17 178.09 603.07 205.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n30 3 Van -1 -1 -1 648.35 166.05 715.58 200.10 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n30 6 Car -1 -1 -1 586.75 176.73 609.71 196.68 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 17 Car -1 -1 -1 1177.60 169.38 1239.89 215.32 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n30 16 Car -1 -1 -1 908.34 172.96 1051.60 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n31 13 Car -1 -1 -1 407.03 175.42 518.42 218.31 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n31 5 Car -1 -1 -1 436.05 177.55 477.02 200.20 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n31 4 Car -1 -1 -1 917.57 173.31 1089.42 223.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n31 11 Car -1 -1 -1 1137.20 167.53 1236.27 211.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n31 7 Car -1 -1 -1 769.41 175.30 838.73 201.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n31 9 Car -1 -1 -1 967.36 162.44 1094.04 209.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 2 Car -1 -1 -1 569.01 177.27 603.93 204.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 17 Car -1 -1 -1 1177.99 169.33 1239.83 215.32 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n31 3 Van -1 -1 -1 648.49 166.30 714.57 200.18 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n31 6 Car -1 -1 -1 586.55 176.88 609.67 196.39 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n31 16 Car -1 -1 -1 908.54 173.01 1051.36 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n32 13 Car -1 -1 -1 423.81 175.53 529.63 216.76 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n32 11 Car -1 -1 -1 1134.95 167.44 1236.28 211.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n32 2 Car -1 -1 -1 571.30 177.26 604.70 203.85 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n32 4 Car -1 -1 -1 917.71 173.46 1089.31 222.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n32 7 Car -1 -1 -1 766.78 175.48 837.72 201.26 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n32 9 Car -1 -1 -1 967.44 162.28 1094.03 209.30 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n32 17 Car -1 -1 -1 1179.23 167.43 1239.58 213.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n32 6 Car -1 -1 -1 583.97 177.00 609.98 196.75 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n32 3 Van -1 -1 -1 648.61 166.26 714.39 200.33 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n32 16 Car -1 -1 -1 908.37 172.97 1051.49 219.16 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n33 13 Car -1 -1 -1 440.04 175.46 540.38 215.86 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n33 4 Car -1 -1 -1 917.37 173.47 1089.66 222.94 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n33 2 Car -1 -1 -1 572.61 177.04 605.31 203.32 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n33 7 Car -1 -1 -1 766.05 175.72 835.94 201.18 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n33 11 Car -1 -1 -1 1132.96 167.87 1236.20 211.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n33 5 Car -1 -1 -1 429.41 177.88 472.80 202.06 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n33 9 Car -1 -1 -1 967.75 162.27 1093.66 209.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n33 17 Car -1 -1 -1 1178.41 169.33 1239.51 215.42 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n33 6 Car -1 -1 -1 583.43 177.18 609.40 196.32 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n33 3 Van -1 -1 -1 648.52 166.26 714.52 200.18 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n33 16 Car -1 -1 -1 908.32 173.00 1051.53 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n34 11 Car -1 -1 -1 1129.42 167.77 1235.22 211.23 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n34 13 Car -1 -1 -1 454.96 175.26 549.80 214.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n34 4 Car -1 -1 -1 917.44 173.56 1089.70 222.91 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n34 7 Car -1 -1 -1 765.70 175.33 834.76 201.06 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n34 5 Car -1 -1 -1 430.41 177.96 468.21 202.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n34 9 Car -1 -1 -1 967.74 162.29 1093.64 209.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n34 2 Car -1 -1 -1 574.07 177.17 606.44 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n34 17 Car -1 -1 -1 1177.91 170.10 1239.35 214.91 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n34 3 Van -1 -1 -1 648.17 166.18 714.57 200.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n34 16 Car -1 -1 -1 908.27 172.98 1051.60 219.18 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n35 13 Car -1 -1 -1 469.65 175.27 559.47 214.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n35 5 Car -1 -1 -1 428.40 178.01 467.30 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n35 11 Car -1 -1 -1 1127.54 167.88 1235.32 210.99 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n35 4 Car -1 -1 -1 917.09 173.40 1089.84 223.00 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n35 2 Car -1 -1 -1 575.24 176.84 606.92 202.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n35 9 Car -1 -1 -1 967.76 162.28 1093.59 209.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n35 7 Car -1 -1 -1 766.35 175.12 833.72 201.04 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n35 17 Car -1 -1 -1 1171.31 170.64 1239.24 215.19 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n35 3 Van -1 -1 -1 647.91 166.12 714.82 200.10 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n35 20 Car -1 -1 -1 475.43 180.08 513.25 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n35 16 Car -1 -1 -1 908.31 173.03 1051.54 219.12 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n36 13 Car -1 -1 -1 482.24 175.85 567.88 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n36 2 Car -1 -1 -1 576.70 176.87 607.03 201.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n36 4 Car -1 -1 -1 916.98 173.28 1089.82 223.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n36 11 Car -1 -1 -1 1125.68 167.99 1235.91 211.10 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n36 7 Car -1 -1 -1 763.82 174.94 833.33 201.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n36 5 Car -1 -1 -1 425.80 178.07 465.88 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n36 9 Car -1 -1 -1 967.85 162.29 1093.42 209.44 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n36 17 Car -1 -1 -1 1170.46 170.16 1239.54 215.28 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n36 3 Van -1 -1 -1 647.68 166.21 714.49 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n36 20 Car -1 -1 -1 487.79 179.30 525.00 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n36 16 Car -1 -1 -1 912.30 171.94 1047.37 217.08 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n37 13 Car -1 -1 -1 493.75 175.63 574.31 212.35 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n37 5 Car -1 -1 -1 424.40 178.12 465.04 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n37 2 Car -1 -1 -1 577.89 177.07 606.76 200.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n37 4 Car -1 -1 -1 917.14 173.32 1089.79 223.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n37 11 Car -1 -1 -1 1121.90 168.14 1235.23 210.86 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n37 7 Car -1 -1 -1 764.00 174.91 832.34 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n37 9 Car -1 -1 -1 967.65 162.25 1093.70 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n37 17 Car -1 -1 -1 1170.21 170.26 1239.48 215.30 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n37 3 Van -1 -1 -1 647.41 166.10 714.58 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n37 20 Car -1 -1 -1 498.21 179.06 537.84 200.37 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n37 16 Car -1 -1 -1 912.26 171.99 1047.36 217.05 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n38 11 Car -1 -1 -1 1121.33 168.37 1234.96 210.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n38 13 Car -1 -1 -1 506.16 175.49 579.55 211.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n38 5 Car -1 -1 -1 422.75 178.05 464.51 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n38 4 Car -1 -1 -1 917.45 173.39 1089.62 223.00 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n38 7 Car -1 -1 -1 763.03 174.99 831.39 201.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n38 2 Car -1 -1 -1 578.27 177.20 606.85 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n38 9 Car -1 -1 -1 967.71 162.20 1093.56 209.55 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n38 17 Car -1 -1 -1 1170.24 170.26 1239.48 215.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n38 3 Van -1 -1 -1 647.10 166.14 714.69 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n38 20 Car -1 -1 -1 509.22 178.41 549.51 200.46 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n38 16 Car -1 -1 -1 908.12 172.93 1051.77 219.19 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n39 13 Car -1 -1 -1 516.29 175.45 584.50 210.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n39 11 Car -1 -1 -1 1120.33 168.54 1234.78 210.32 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n39 4 Car -1 -1 -1 917.26 173.34 1089.74 223.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n39 7 Car -1 -1 -1 762.57 174.80 830.50 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n39 2 Car -1 -1 -1 578.06 177.23 607.04 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n39 5 Car -1 -1 -1 421.97 178.10 464.00 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n39 9 Car -1 -1 -1 967.88 162.20 1093.41 209.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n39 17 Car -1 -1 -1 1169.80 170.14 1239.99 215.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n39 3 Van -1 -1 -1 647.08 166.31 714.43 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n39 20 Car -1 -1 -1 519.14 178.67 555.64 200.25 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n39 16 Car -1 -1 -1 908.34 173.01 1051.47 219.09 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n40 13 Car -1 -1 -1 525.12 176.09 589.25 209.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n40 4 Car -1 -1 -1 917.49 173.43 1089.73 222.96 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n40 11 Car -1 -1 -1 1119.31 168.63 1234.79 210.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n40 7 Car -1 -1 -1 759.48 174.57 830.52 201.55 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n40 5 Car -1 -1 -1 420.94 177.89 462.88 203.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n40 9 Car -1 -1 -1 967.82 162.16 1093.45 209.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n40 2 Car -1 -1 -1 578.54 177.38 606.88 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n40 17 Car -1 -1 -1 1169.64 169.95 1240.16 215.11 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n40 3 Van -1 -1 -1 647.28 166.47 713.77 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n40 16 Car -1 -1 -1 908.33 173.03 1051.44 219.00 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n41 13 Car -1 -1 -1 534.86 176.15 592.39 208.76 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n41 5 Car -1 -1 -1 419.44 177.95 462.39 203.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n41 7 Car -1 -1 -1 758.71 174.76 829.76 201.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n41 4 Car -1 -1 -1 917.18 173.31 1089.86 223.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 11 Car -1 -1 -1 1116.71 168.56 1232.83 210.20 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 9 Car -1 -1 -1 967.79 162.16 1093.54 209.63 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n41 17 Car -1 -1 -1 1169.36 169.73 1240.50 215.18 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n41 3 Van -1 -1 -1 647.08 166.53 713.71 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n41 2 Car -1 -1 -1 581.04 177.45 607.60 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n41 16 Car -1 -1 -1 908.28 173.09 1051.51 219.00 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n42 11 Car -1 -1 -1 1116.41 168.83 1232.40 209.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n42 13 Car -1 -1 -1 541.04 176.21 595.42 207.83 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n42 4 Car -1 -1 -1 917.48 173.31 1089.49 223.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n42 7 Car -1 -1 -1 758.05 174.87 828.08 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n42 5 Car -1 -1 -1 418.51 177.82 461.58 204.12 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n42 9 Car -1 -1 -1 967.94 162.22 1093.39 209.65 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n42 17 Car -1 -1 -1 1169.25 169.65 1240.66 215.13 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n42 3 Van -1 -1 -1 647.14 166.51 713.22 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n42 2 Car -1 -1 -1 581.37 177.50 607.37 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n42 16 Car -1 -1 -1 908.49 173.08 1051.30 219.02 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n42 21 Car -1 -1 -1 513.29 175.14 537.82 189.57 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n43 11 Car -1 -1 -1 1116.04 168.78 1232.23 209.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n43 4 Car -1 -1 -1 917.28 173.27 1089.61 223.06 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n43 13 Car -1 -1 -1 546.94 176.22 597.17 207.31 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n43 7 Car -1 -1 -1 757.72 174.80 827.07 201.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n43 5 Car -1 -1 -1 417.80 177.67 460.85 204.24 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n43 9 Car -1 -1 -1 967.96 162.24 1093.44 209.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n43 17 Car -1 -1 -1 1169.22 169.75 1240.85 215.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n43 3 Van -1 -1 -1 647.01 166.55 713.02 199.78 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n43 21 Car -1 -1 -1 509.01 175.29 534.22 189.23 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n43 2 Car -1 -1 -1 579.97 176.76 606.51 197.62 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n43 16 Car -1 -1 -1 908.36 173.03 1051.43 219.05 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n44 11 Car -1 -1 -1 1114.96 168.81 1232.57 209.91 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n44 4 Car -1 -1 -1 917.45 173.25 1089.61 223.06 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n44 7 Car -1 -1 -1 755.12 174.54 826.62 201.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n44 13 Car -1 -1 -1 552.67 175.27 597.75 206.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n44 9 Car -1 -1 -1 967.95 162.29 1093.27 209.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n44 5 Car -1 -1 -1 416.40 177.40 459.99 204.52 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n44 17 Car -1 -1 -1 1169.25 169.64 1240.77 214.98 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n44 21 Car -1 -1 -1 504.43 175.38 530.75 189.60 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n44 3 Van -1 -1 -1 646.98 166.53 712.84 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n44 2 Car -1 -1 -1 580.08 176.72 605.78 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n44 16 Car -1 -1 -1 908.41 172.98 1051.45 219.09 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n45 4 Car -1 -1 -1 917.39 173.11 1089.71 223.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n45 7 Car -1 -1 -1 755.02 174.31 826.03 201.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 11 Car -1 -1 -1 1113.55 168.67 1232.98 210.04 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n45 5 Car -1 -1 -1 415.91 177.41 459.57 204.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n45 13 Car -1 -1 -1 558.06 175.46 599.65 206.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n45 9 Car -1 -1 -1 967.88 162.22 1093.41 209.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n45 21 Car -1 -1 -1 500.90 175.54 526.59 189.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n45 17 Car -1 -1 -1 1168.84 167.01 1241.20 213.73 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n45 3 Van -1 -1 -1 646.81 166.47 712.62 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n45 2 Car -1 -1 -1 579.80 176.75 605.83 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n45 16 Car -1 -1 -1 908.51 172.90 1051.45 219.18 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n46 4 Car -1 -1 -1 917.24 173.14 1089.80 223.10 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n46 7 Car -1 -1 -1 754.63 174.30 824.71 201.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n46 11 Car -1 -1 -1 1111.79 168.46 1230.11 210.39 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n46 5 Car -1 -1 -1 415.23 177.46 458.76 204.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n46 13 Car -1 -1 -1 562.83 175.69 599.62 204.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n46 9 Car -1 -1 -1 967.98 162.18 1093.30 209.71 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n46 21 Car -1 -1 -1 498.08 175.63 523.05 189.52 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n46 17 Car -1 -1 -1 1163.03 171.10 1239.47 213.79 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n46 2 Car -1 -1 -1 577.69 177.13 605.49 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n46 3 Van -1 -1 -1 646.75 166.53 712.52 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n46 16 Car -1 -1 -1 908.68 172.89 1051.33 219.19 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n47 13 Car -1 -1 -1 566.36 175.86 601.54 204.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n47 11 Car -1 -1 -1 1112.21 168.56 1228.99 210.26 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n47 4 Car -1 -1 -1 917.45 173.18 1089.73 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n47 5 Car -1 -1 -1 414.74 177.58 458.33 204.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n47 7 Car -1 -1 -1 754.48 174.15 823.72 201.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n47 9 Car -1 -1 -1 967.95 162.18 1093.37 209.88 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n47 21 Car -1 -1 -1 494.13 175.34 519.39 189.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n47 17 Car -1 -1 -1 1168.98 167.08 1241.04 213.61 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n47 2 Car -1 -1 -1 577.41 177.20 605.08 199.36 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n47 3 Van -1 -1 -1 646.35 166.26 710.78 199.99 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n47 16 Car -1 -1 -1 908.31 172.81 1051.74 219.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n48 11 Car -1 -1 -1 1111.36 168.56 1229.17 210.38 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n48 4 Car -1 -1 -1 917.63 173.15 1089.59 223.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n48 5 Car -1 -1 -1 414.29 177.53 458.14 204.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n48 7 Car -1 -1 -1 754.18 174.23 823.08 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n48 13 Car -1 -1 -1 569.57 176.21 603.74 203.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n48 9 Car -1 -1 -1 967.88 162.15 1093.45 209.88 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n48 17 Car -1 -1 -1 1169.40 166.97 1240.88 213.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n48 21 Car -1 -1 -1 490.38 175.18 515.98 189.48 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n48 3 Van -1 -1 -1 646.55 166.32 710.14 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n48 16 Car -1 -1 -1 908.22 172.88 1051.76 219.23 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n49 13 Car -1 -1 -1 571.23 176.03 604.53 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n49 4 Car -1 -1 -1 917.64 173.15 1089.53 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n49 11 Car -1 -1 -1 1109.67 168.49 1230.22 210.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n49 5 Car -1 -1 -1 414.21 177.60 457.82 204.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n49 9 Car -1 -1 -1 967.91 162.11 1093.41 209.88 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n49 21 Car -1 -1 -1 487.45 175.18 512.09 189.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n49 7 Car -1 -1 -1 752.65 174.11 822.05 201.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n49 17 Car -1 -1 -1 1169.70 167.03 1240.90 213.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n49 3 Van -1 -1 -1 646.55 166.41 710.27 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n49 16 Car -1 -1 -1 908.47 172.91 1051.53 219.18 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n49 22 Car -1 -1 -1 0.61 195.30 36.30 236.95 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n50 13 Car -1 -1 -1 573.13 176.18 604.70 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n50 4 Car -1 -1 -1 917.27 172.99 1089.59 223.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n50 11 Car -1 -1 -1 1108.18 168.52 1231.12 210.41 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n50 7 Car -1 -1 -1 751.94 174.28 821.75 201.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n50 9 Car -1 -1 -1 967.96 162.09 1093.30 209.90 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n50 5 Car -1 -1 -1 413.90 177.62 457.50 204.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n50 22 Car -1 -1 -1 -1.05 185.19 69.15 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n50 17 Car -1 -1 -1 1169.96 166.99 1240.73 213.47 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n50 21 Car -1 -1 -1 483.55 175.35 508.42 189.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n50 3 Van -1 -1 -1 646.34 166.34 710.44 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n50 16 Car -1 -1 -1 908.65 172.84 1051.34 219.25 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n51 4 Car -1 -1 -1 917.25 173.00 1089.56 223.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n51 7 Car -1 -1 -1 751.19 174.35 821.13 201.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n51 11 Car -1 -1 -1 1107.82 168.42 1231.23 210.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n51 22 Car -1 -1 -1 -1.50 185.08 100.36 234.09 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n51 13 Car -1 -1 -1 574.08 176.18 604.79 201.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n51 9 Car -1 -1 -1 967.95 162.06 1093.46 209.94 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n51 17 Car -1 -1 -1 1176.62 166.62 1240.34 213.35 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n51 5 Car -1 -1 -1 412.65 178.23 458.27 205.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n51 21 Car -1 -1 -1 480.62 175.88 504.38 189.86 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n51 3 Van -1 -1 -1 646.15 166.33 710.60 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n51 16 Car -1 -1 -1 908.44 172.75 1051.69 219.38 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n52 22 Car -1 -1 -1 3.12 184.32 132.56 233.93 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n52 7 Car -1 -1 -1 750.89 174.48 820.26 201.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n52 4 Car -1 -1 -1 916.84 172.75 1089.61 223.55 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n52 11 Car -1 -1 -1 1105.82 168.23 1228.29 210.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n52 13 Car -1 -1 -1 575.28 176.17 605.91 201.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n52 9 Car -1 -1 -1 967.79 161.98 1093.49 209.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n52 17 Car -1 -1 -1 1169.51 167.22 1241.00 213.37 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n52 5 Car -1 -1 -1 413.29 177.52 457.37 204.91 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n52 21 Car -1 -1 -1 477.10 175.47 502.48 189.99 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n52 3 Van -1 -1 -1 646.13 166.34 710.63 199.98 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n52 16 Car -1 -1 -1 913.21 171.88 1046.41 217.14 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n53 22 Car -1 -1 -1 -1.63 182.55 166.35 234.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n53 11 Car -1 -1 -1 1105.27 168.33 1228.24 210.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n53 4 Car -1 -1 -1 916.40 172.66 1089.26 223.66 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n53 13 Car -1 -1 -1 576.04 176.05 606.36 200.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n53 7 Car -1 -1 -1 750.88 174.59 819.07 201.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n53 9 Car -1 -1 -1 967.56 161.90 1093.74 209.86 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n53 17 Car -1 -1 -1 1169.38 167.17 1241.08 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n53 21 Car -1 -1 -1 474.14 175.47 497.95 190.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n53 5 Car -1 -1 -1 412.47 178.23 458.06 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n53 3 Van -1 -1 -1 646.16 166.39 710.31 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n53 16 Car -1 -1 -1 914.02 172.19 1045.53 216.82 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n54 22 Car -1 -1 -1 27.34 181.41 193.23 234.62 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n54 11 Car -1 -1 -1 1104.52 168.29 1228.52 210.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n54 4 Car -1 -1 -1 913.01 172.96 1086.06 223.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n54 13 Car -1 -1 -1 577.18 176.08 606.41 200.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n54 7 Car -1 -1 -1 750.94 174.53 818.51 201.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n54 21 Car -1 -1 -1 471.49 175.11 494.72 190.26 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n54 9 Car -1 -1 -1 967.39 161.95 1094.06 209.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n54 17 Car -1 -1 -1 1169.20 167.15 1241.15 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n54 5 Car -1 -1 -1 412.46 178.22 458.14 205.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n54 3 Van -1 -1 -1 645.89 166.43 710.15 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n54 16 Car -1 -1 -1 914.10 172.12 1039.18 216.72 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n54 23 Car -1 -1 -1 563.06 175.34 580.83 188.40 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n55 22 Car -1 -1 -1 61.90 181.79 221.39 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n55 11 Car -1 -1 -1 1104.01 168.31 1228.89 210.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n55 4 Car -1 -1 -1 910.79 172.90 1081.57 224.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n55 9 Car -1 -1 -1 967.16 161.88 1094.44 210.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n55 7 Car -1 -1 -1 750.82 174.54 818.32 201.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n55 21 Car -1 -1 -1 468.69 175.18 491.71 190.39 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n55 17 Car -1 -1 -1 1169.34 167.17 1241.05 213.23 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n55 13 Car -1 -1 -1 577.86 176.20 606.50 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n55 5 Car -1 -1 -1 412.43 178.23 458.06 205.05 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n55 3 Van -1 -1 -1 645.62 166.41 710.15 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n55 23 Car -1 -1 -1 559.52 175.66 577.95 188.14 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n55 16 Car -1 -1 -1 915.88 172.12 1037.27 216.55 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n56 22 Car -1 -1 -1 94.43 180.85 249.59 230.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n56 11 Car -1 -1 -1 1104.08 168.41 1228.68 210.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n56 4 Car -1 -1 -1 909.99 172.86 1079.74 224.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n56 7 Car -1 -1 -1 748.92 174.57 817.37 201.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n56 9 Car -1 -1 -1 966.92 161.91 1094.04 210.98 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n56 17 Car -1 -1 -1 1169.30 167.26 1241.21 213.17 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n56 13 Car -1 -1 -1 578.51 175.79 606.20 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n56 5 Car -1 -1 -1 412.50 178.25 457.96 205.10 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n56 21 Car -1 -1 -1 466.03 174.92 489.99 190.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n56 3 Van -1 -1 -1 645.49 166.54 710.09 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n56 23 Car -1 -1 -1 555.77 175.95 574.22 188.14 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n57 22 Car -1 -1 -1 127.51 180.93 274.14 229.09 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n57 11 Car -1 -1 -1 1103.79 168.52 1228.68 210.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n57 7 Car -1 -1 -1 748.22 174.39 817.18 201.51 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n57 4 Car -1 -1 -1 905.40 172.82 1071.89 224.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n57 9 Car -1 -1 -1 966.44 161.85 1094.19 211.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n57 13 Car -1 -1 -1 578.78 175.93 606.13 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n57 17 Car -1 -1 -1 1169.22 167.32 1241.21 213.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n57 21 Car -1 -1 -1 462.89 175.02 486.60 190.88 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n57 5 Car -1 -1 -1 412.57 178.22 457.91 205.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n57 3 Van -1 -1 -1 645.16 166.57 710.12 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n57 23 Car -1 -1 -1 552.51 176.10 570.56 188.19 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n58 22 Car -1 -1 -1 163.34 180.30 299.95 227.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n58 7 Car -1 -1 -1 747.88 174.29 817.05 201.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n58 11 Car -1 -1 -1 1102.83 168.49 1229.28 210.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n58 4 Car -1 -1 -1 902.73 173.07 1071.24 226.24 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n58 9 Car -1 -1 -1 965.10 163.44 1095.02 212.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n58 13 Car -1 -1 -1 578.99 176.03 606.06 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n58 17 Car -1 -1 -1 1169.04 167.18 1241.11 213.19 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n58 21 Car -1 -1 -1 460.56 174.90 484.14 190.98 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n58 5 Car -1 -1 -1 412.66 178.24 458.01 205.13 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n58 3 Van -1 -1 -1 644.97 166.66 709.36 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n58 23 Car -1 -1 -1 547.89 175.83 566.94 188.15 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n59 22 Car -1 -1 -1 193.37 180.04 325.34 224.92 -1 -1 -1 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163.67 1094.24 212.72 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n62 11 Car -1 -1 -1 1100.08 168.33 1225.92 210.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n62 5 Car -1 -1 -1 412.44 178.34 458.58 205.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n62 7 Car -1 -1 -1 747.31 174.81 813.90 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n62 17 Car -1 -1 -1 1168.39 167.29 1241.39 213.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n62 3 Van -1 -1 -1 644.27 166.67 709.61 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n62 24 Car -1 -1 -1 269.10 181.86 324.50 210.75 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n62 21 Car -1 -1 -1 451.96 174.64 475.27 191.58 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n62 23 Car -1 -1 -1 531.72 175.73 553.56 188.57 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n63 4 Car -1 -1 -1 872.56 174.04 1042.58 226.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n63 13 Car -1 -1 -1 580.22 176.46 603.70 195.69 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n63 22 Car -1 -1 -1 299.99 178.55 415.41 219.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n63 9 Car -1 -1 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Car -1 -1 -1 745.54 174.60 812.74 201.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n64 9 Car -1 -1 -1 961.25 163.91 1092.78 212.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n64 11 Car -1 -1 -1 1099.39 168.27 1226.53 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n64 17 Car -1 -1 -1 1168.07 167.25 1241.39 213.39 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n64 5 Car -1 -1 -1 412.20 178.38 458.59 205.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n64 24 Car -1 -1 -1 319.63 181.37 367.24 206.14 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n64 23 Car -1 -1 -1 525.33 175.10 547.71 188.28 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n64 3 Van -1 -1 -1 644.22 166.90 708.82 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n64 25 Car -1 -1 -1 928.11 174.04 1054.87 218.29 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n65 22 Car -1 -1 -1 340.57 177.30 449.07 217.11 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n65 4 Car -1 -1 -1 859.66 175.05 1024.74 225.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n65 13 Car -1 -1 -1 580.49 176.01 602.82 194.53 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n65 7 Car -1 -1 -1 745.38 174.52 812.68 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n65 9 Car -1 -1 -1 961.08 163.81 1092.77 213.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n65 11 Car -1 -1 -1 1098.96 168.27 1226.96 210.62 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n65 17 Car -1 -1 -1 1162.23 167.78 1240.80 212.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n65 5 Car -1 -1 -1 414.61 178.20 457.26 203.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n65 25 Car -1 -1 -1 928.44 174.33 1054.50 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n65 23 Car -1 -1 -1 520.79 175.41 544.54 188.36 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n65 3 Van -1 -1 -1 644.35 166.90 708.06 199.93 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n66 22 Car -1 -1 -1 360.46 177.73 465.18 215.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n66 4 Car -1 -1 -1 852.55 175.17 1016.35 225.97 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n66 13 Car -1 -1 -1 580.38 175.90 602.64 194.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n66 7 Car -1 -1 -1 745.15 174.56 812.38 201.39 -1 -1 -1 -1000 -1000 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210.62 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n68 17 Car -1 -1 -1 1161.75 167.82 1241.02 212.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n68 25 Car -1 -1 -1 921.98 173.88 1052.77 218.05 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n68 23 Car -1 -1 -1 510.23 175.53 533.30 189.03 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n68 27 Van -1 -1 -1 642.72 166.87 709.04 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n69 22 Car -1 -1 -1 413.89 175.63 504.62 212.73 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n69 4 Car -1 -1 -1 829.94 175.80 990.35 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n69 7 Car -1 -1 -1 743.77 174.78 811.40 201.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n69 13 Car -1 -1 -1 580.81 175.96 602.73 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n69 9 Car -1 -1 -1 960.09 163.70 1093.65 213.41 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n69 11 Car -1 -1 -1 1098.27 168.41 1227.62 210.66 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n69 24 Car -1 -1 -1 415.05 178.85 457.98 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n69 25 Car -1 -1 -1 923.62 173.90 1051.97 217.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n69 17 Car -1 -1 -1 1161.75 167.72 1240.94 213.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n69 23 Car -1 -1 -1 507.49 175.29 530.41 188.75 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n69 27 Van -1 -1 -1 642.45 166.99 709.03 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n70 4 Car -1 -1 -1 822.21 175.98 982.24 225.47 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n70 22 Car -1 -1 -1 430.25 176.04 515.52 211.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n70 13 Car -1 -1 -1 580.95 176.22 602.66 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n70 7 Car -1 -1 -1 743.50 174.88 811.25 201.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n70 9 Car -1 -1 -1 960.27 163.68 1093.59 213.25 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n70 11 Car -1 -1 -1 1098.46 168.43 1227.48 210.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n70 25 Car -1 -1 -1 924.08 173.06 1051.66 216.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n70 17 Car -1 -1 -1 1161.87 167.79 1240.81 212.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n70 24 Car -1 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24 Car -1 -1 -1 413.84 177.80 458.17 204.32 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n71 28 Car -1 -1 -1 436.99 179.31 482.64 200.78 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n71 27 Van -1 -1 -1 642.11 166.79 707.14 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n72 4 Car -1 -1 -1 802.73 174.47 963.53 226.23 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n72 22 Car -1 -1 -1 458.07 175.75 533.79 209.96 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n72 9 Car -1 -1 -1 960.90 163.75 1093.03 213.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n72 13 Car -1 -1 -1 581.36 176.48 601.95 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n72 7 Car -1 -1 -1 743.17 175.03 810.83 201.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n72 25 Car -1 -1 -1 923.68 172.91 1051.62 216.49 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n72 11 Car -1 -1 -1 1098.17 168.36 1227.72 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n72 24 Car -1 -1 -1 413.31 177.74 457.72 204.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n72 17 Car -1 -1 -1 1162.21 167.81 1240.42 212.92 -1 -1 -1 -1000 -1000 -1000 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-1000 -10 0.73\n104 22 Car -1 -1 -1 564.19 177.73 587.78 194.58 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n105 4 Car -1 -1 -1 425.32 178.47 603.45 248.84 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n105 25 Car -1 -1 -1 670.17 175.25 801.83 216.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n105 17 Car -1 -1 -1 760.83 176.61 927.99 225.07 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n105 11 Car -1 -1 -1 1003.33 169.11 1157.45 219.37 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n105 40 Car -1 -1 -1 1100.38 166.39 1231.90 212.10 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n105 9 Car -1 -1 -1 932.84 163.56 1067.16 213.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n105 29 Car -1 -1 -1 325.96 176.22 381.57 205.44 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n105 24 Car -1 -1 -1 412.88 177.17 455.69 204.93 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n105 22 Car -1 -1 -1 564.09 177.20 587.96 194.94 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n105 42 Van -1 -1 -1 600.15 168.97 646.84 199.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n106 4 Car -1 -1 -1 407.71 177.99 590.91 250.42 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11 Car -1 -1 -1 967.00 170.71 1124.21 217.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n108 9 Car -1 -1 -1 922.03 163.17 1060.57 213.68 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n108 24 Car -1 -1 -1 414.36 178.11 457.34 205.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n108 42 Van -1 -1 -1 595.79 169.23 637.16 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n108 45 Car -1 -1 -1 569.13 176.75 589.64 191.68 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n108 46 Car -1 -1 -1 407.02 176.86 443.34 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n109 4 Car -1 -1 -1 351.03 179.41 553.23 259.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n109 17 Car -1 -1 -1 680.94 175.62 846.42 227.56 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n109 25 Car -1 -1 -1 605.95 176.77 731.23 216.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n109 11 Car -1 -1 -1 953.84 170.86 1107.76 217.95 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n109 40 Car -1 -1 -1 1096.40 166.99 1227.56 210.59 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n109 9 Car -1 -1 -1 918.05 163.74 1050.04 213.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n109 24 Car -1 -1 -1 415.40 178.54 456.63 205.39 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n109 29 Car -1 -1 -1 308.88 176.21 369.58 206.04 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n109 44 Car -1 -1 -1 697.79 178.89 742.36 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n109 42 Van -1 -1 -1 595.49 169.16 634.99 196.72 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n109 43 Car -1 -1 -1 690.00 179.99 733.99 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n109 45 Car -1 -1 -1 570.06 176.92 589.27 191.34 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n109 47 Car -1 -1 -1 675.32 180.32 733.23 204.56 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n110 4 Car -1 -1 -1 332.91 179.25 537.94 261.94 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n110 25 Car -1 -1 -1 589.59 176.01 711.55 217.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n110 11 Car -1 -1 -1 944.96 170.22 1099.09 217.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n110 17 Car -1 -1 -1 664.55 176.23 827.52 227.67 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n110 40 Car -1 -1 -1 1094.66 167.04 1223.81 210.45 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n110 24 Car -1 -1 -1 415.07 178.80 457.41 205.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n110 9 Car -1 -1 -1 913.04 163.51 1047.29 213.26 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n110 29 Car -1 -1 -1 305.01 176.47 367.17 206.98 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n110 42 Van -1 -1 -1 596.41 169.03 633.52 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n110 45 Car -1 -1 -1 570.22 176.88 589.63 191.41 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n110 44 Car -1 -1 -1 691.59 178.94 732.69 199.70 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n111 4 Car -1 -1 -1 309.51 178.35 523.40 265.30 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n111 17 Car -1 -1 -1 642.98 176.13 805.87 227.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n111 25 Car -1 -1 -1 573.45 176.76 696.50 216.95 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n111 11 Car -1 -1 -1 931.09 171.00 1084.05 216.91 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n111 40 Car -1 -1 -1 1093.38 167.18 1223.38 210.45 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n111 24 Car -1 -1 -1 414.96 178.90 457.58 205.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n111 9 Car -1 -1 -1 903.50 163.71 1041.30 213.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n111 29 Car -1 -1 -1 300.60 176.33 364.28 207.21 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n111 42 Van -1 -1 -1 596.95 169.27 631.91 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n112 4 Car -1 -1 -1 285.79 178.90 508.27 270.40 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n112 17 Car -1 -1 -1 625.88 176.22 788.55 228.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n112 11 Car -1 -1 -1 919.77 171.94 1069.76 216.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n112 25 Car -1 -1 -1 559.15 176.96 678.20 216.25 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n112 40 Car -1 -1 -1 1088.95 167.43 1221.83 210.27 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n112 24 Car -1 -1 -1 415.01 178.80 457.45 205.10 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n112 9 Car -1 -1 -1 895.65 164.49 1041.05 212.36 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n112 29 Car -1 -1 -1 296.17 176.63 358.17 207.19 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n112 42 Van -1 -1 -1 594.06 170.00 629.62 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n112 48 Car -1 -1 -1 402.61 176.97 440.37 195.98 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n113 4 Car -1 -1 -1 260.07 179.41 494.53 275.47 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n113 17 Car -1 -1 -1 607.01 176.30 769.03 228.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n113 11 Car -1 -1 -1 906.06 173.01 1053.49 215.58 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n113 25 Car -1 -1 -1 542.95 177.07 662.30 216.74 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n113 40 Car -1 -1 -1 1087.52 168.00 1221.24 210.18 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n113 24 Car -1 -1 -1 415.23 178.95 457.06 205.11 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n113 29 Car -1 -1 -1 292.88 176.95 353.53 207.21 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n113 9 Car -1 -1 -1 888.59 165.42 1032.53 211.52 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n113 48 Car -1 -1 -1 402.96 176.79 438.44 195.54 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n113 42 Van -1 -1 -1 593.59 170.28 629.20 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n114 4 Car -1 -1 -1 232.20 179.23 478.38 279.18 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n114 17 Car -1 -1 -1 586.44 176.18 752.39 228.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n114 11 Car -1 -1 -1 891.02 172.48 1037.58 216.26 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n114 40 Car -1 -1 -1 1083.17 168.41 1218.63 209.91 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n114 25 Car -1 -1 -1 526.93 176.68 642.89 216.95 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n114 29 Car -1 -1 -1 288.78 177.64 351.16 207.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n114 24 Car -1 -1 -1 415.47 179.05 456.78 205.79 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n114 48 Car -1 -1 -1 399.36 177.47 436.83 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n114 42 Van -1 -1 -1 593.81 170.60 628.55 195.50 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n115 4 Car -1 -1 -1 202.90 180.37 460.82 284.66 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n115 25 Car -1 -1 -1 507.98 177.47 627.98 216.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n115 17 Car -1 -1 -1 567.77 176.18 733.72 229.04 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n115 11 Car -1 -1 -1 874.70 170.34 1022.29 217.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n115 40 Car -1 -1 -1 1079.37 168.22 1215.14 209.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n115 29 Car -1 -1 -1 285.74 177.23 346.92 207.72 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n115 24 Car -1 -1 -1 415.67 178.52 457.09 205.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n115 48 Car -1 -1 -1 399.70 176.39 434.96 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n116 4 Car -1 -1 -1 171.13 180.85 444.88 290.85 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n116 17 Car -1 -1 -1 548.95 176.17 718.33 231.59 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n116 25 Car -1 -1 -1 491.71 177.00 608.27 216.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n116 11 Car -1 -1 -1 867.83 164.84 999.30 213.53 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n116 29 Car -1 -1 -1 282.15 178.14 342.64 207.83 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n116 40 Car -1 -1 -1 1077.40 167.96 1210.23 209.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n116 24 Car 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569.73 210.46 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n408 102 Car -1 -1 -1 508.92 172.11 563.09 216.44 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n409 102 Car -1 -1 -1 490.34 171.99 552.49 222.49 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n410 102 Car -1 -1 -1 465.57 171.26 539.42 230.28 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n411 102 Car -1 -1 -1 431.65 168.83 525.63 240.95 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n412 102 Car -1 -1 -1 382.56 167.76 504.05 257.58 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n413 102 Car -1 -1 -1 295.57 166.06 477.00 289.55 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n414 102 Car -1 -1 -1 111.28 168.72 427.76 358.12 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n415 102 Car -1 -1 -1 -4.69 156.94 333.83 370.46 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n425 104 Car -1 -1 -1 637.19 167.65 663.27 180.27 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n426 104 Car -1 -1 -1 638.30 167.79 663.37 180.90 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n427 104 Car -1 -1 -1 640.17 166.99 666.45 180.58 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n428 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649.79 164.62 672.20 177.22 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n456 107 Car -1 -1 -1 649.02 165.33 669.45 177.50 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n457 107 Car -1 -1 -1 649.68 167.82 668.80 179.17 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n458 107 Car -1 -1 -1 649.63 168.28 668.69 179.60 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n459 107 Car -1 -1 -1 648.85 167.92 667.45 179.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n460 107 Car -1 -1 -1 648.05 167.24 667.04 178.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n461 107 Car -1 -1 -1 647.15 166.68 665.79 177.85 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n462 107 Car -1 -1 -1 645.83 167.09 663.79 178.25 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n463 107 Car -1 -1 -1 643.89 165.34 662.32 177.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n464 107 Car -1 -1 -1 641.16 164.93 658.97 176.49 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n465 107 Car -1 -1 -1 639.70 167.10 655.49 178.57 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n466 107 Car -1 -1 -1 636.72 169.89 654.10 182.81 -1 -1 -1 -1000 -1000 -1000 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189.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n492 108 Car -1 -1 -1 566.16 176.83 588.36 192.23 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n493 108 Car -1 -1 -1 565.17 176.71 586.72 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n494 108 Car -1 -1 -1 562.66 176.14 585.01 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n495 108 Car -1 -1 -1 560.55 174.90 583.47 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n496 108 Car -1 -1 -1 556.88 174.18 580.46 193.94 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n497 108 Car -1 -1 -1 552.21 173.75 578.00 194.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n498 108 Car -1 -1 -1 547.49 174.07 574.67 196.46 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n499 108 Car -1 -1 -1 541.99 175.16 571.66 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n500 108 Car -1 -1 -1 535.57 175.60 567.77 201.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n501 108 Car -1 -1 -1 528.36 175.63 563.64 204.30 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n502 108 Car -1 -1 -1 521.05 175.69 559.62 206.14 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n503 108 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-1000 -1000 -1000 -10 0.76\n510 108 Car -1 -1 -1 379.23 175.22 493.93 248.16 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n510 110 Car -1 -1 -1 515.19 176.99 539.55 196.70 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n511 108 Car -1 -1 -1 330.47 176.50 472.80 265.18 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n511 110 Car -1 -1 -1 513.55 178.57 538.39 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n512 108 Car -1 -1 -1 246.55 176.26 449.04 294.78 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n512 110 Car -1 -1 -1 509.89 179.12 536.94 201.65 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n513 108 Car -1 -1 -1 102.61 174.79 406.85 337.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n513 110 Car -1 -1 -1 508.45 177.73 535.66 202.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n514 108 Car -1 -1 -1 -2.18 170.71 346.64 370.70 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n514 110 Car -1 -1 -1 504.99 176.32 534.39 202.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n515 110 Car -1 -1 -1 503.00 175.42 533.12 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n515 108 Car -1 -1 -1 -3.41 172.74 217.68 369.57 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n516 110 Car -1 -1 -1 499.37 174.84 531.77 204.43 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n517 110 Car -1 -1 -1 496.10 174.00 529.42 205.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n518 110 Car -1 -1 -1 489.43 172.83 528.83 207.87 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n519 110 Car -1 -1 -1 483.17 173.28 526.71 211.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n520 110 Car -1 -1 -1 474.44 175.93 521.14 217.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n521 110 Car -1 -1 -1 463.02 177.22 517.48 223.72 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n522 110 Car -1 -1 -1 447.93 174.51 510.57 228.09 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n522 112 Car -1 -1 -1 484.79 180.60 518.48 207.26 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n523 110 Car -1 -1 -1 425.93 171.49 500.49 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n523 112 Car -1 -1 -1 481.55 178.81 513.94 206.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n523 113 Car -1 -1 -1 513.21 177.58 533.38 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n524 110 Car -1 -1 -1 395.80 173.13 486.77 245.27 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n524 112 Car -1 -1 -1 472.23 180.23 511.78 209.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n524 113 Car -1 -1 -1 510.84 178.67 531.41 194.31 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n525 110 Car -1 -1 -1 352.42 173.27 467.30 262.41 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n525 112 Car -1 -1 -1 464.86 182.31 507.93 214.62 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n525 113 Car -1 -1 -1 509.37 180.99 528.11 194.92 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n526 110 Car -1 -1 -1 279.73 170.88 444.04 293.56 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n526 112 Car -1 -1 -1 455.56 182.77 502.90 218.53 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n526 113 Car -1 -1 -1 506.64 180.41 526.53 195.47 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n527 110 Car -1 -1 -1 138.91 172.09 401.35 340.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n527 112 Car -1 -1 -1 443.12 182.05 497.20 221.84 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n527 113 Car -1 -1 -1 503.14 178.77 525.08 194.15 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0.85\n914 163 Car -1 -1 -1 950.67 141.92 976.96 158.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n915 161 Car -1 -1 -1 2.90 195.09 287.05 339.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n915 158 Cyclist -1 -1 -1 829.26 170.13 911.94 340.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n915 163 Car -1 -1 -1 939.06 142.49 963.91 158.98 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n916 161 Car -1 -1 -1 1.08 195.34 197.77 347.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n916 158 Cyclist -1 -1 -1 829.78 171.24 911.16 340.62 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n916 163 Car -1 -1 -1 927.22 142.58 952.89 158.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n916 166 Car -1 -1 -1 979.84 133.05 1011.50 160.65 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n917 158 Cyclist -1 -1 -1 828.46 172.46 920.25 346.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n917 163 Car -1 -1 -1 916.25 142.90 942.73 159.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n917 161 Car -1 -1 -1 4.18 187.83 116.53 354.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n918 158 Cyclist -1 -1 -1 835.27 169.30 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-1000 -1000 -10 0.78\n1018 184 Car -1 -1 -1 584.82 179.82 648.52 213.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n1018 194 Car -1 -1 -1 724.67 172.21 764.47 193.70 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n1019 184 Car -1 -1 -1 584.35 178.61 650.68 214.48 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1019 194 Car -1 -1 -1 727.02 171.43 767.79 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n1020 184 Car -1 -1 -1 583.20 176.67 651.86 212.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n1020 194 Car -1 -1 -1 728.95 169.25 771.41 192.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n1021 184 Car -1 -1 -1 581.45 174.83 651.54 211.50 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n1021 194 Car -1 -1 -1 731.45 167.40 772.76 190.14 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n1022 184 Car -1 -1 -1 579.92 175.22 651.68 213.75 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1022 194 Car -1 -1 -1 731.21 168.79 776.04 191.61 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n1023 184 Car -1 -1 -1 577.01 181.22 652.21 221.74 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1023 194 Car -1 -1 -1 731.86 174.83 777.08 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n1024 184 Car -1 -1 -1 571.87 184.33 651.79 226.96 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n1024 194 Car -1 -1 -1 731.59 177.73 777.41 201.88 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n1025 184 Car -1 -1 -1 567.12 185.19 648.70 229.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1025 194 Car -1 -1 -1 730.11 177.68 777.69 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n1026 184 Car -1 -1 -1 561.28 182.07 644.88 227.39 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1026 194 Car -1 -1 -1 728.72 174.29 774.61 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1027 184 Car -1 -1 -1 553.67 180.46 640.59 227.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1027 194 Car -1 -1 -1 724.82 172.56 772.35 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n1028 184 Car -1 -1 -1 546.33 180.67 636.92 229.86 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1028 194 Car -1 -1 -1 722.86 172.84 770.36 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1029 184 Car -1 -1 -1 536.68 183.42 630.10 234.85 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1029 194 Car -1 -1 -1 716.96 174.85 766.71 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n1030 184 Car -1 -1 -1 524.59 183.06 625.49 237.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n1030 194 Car -1 -1 -1 711.86 175.32 761.04 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n1031 184 Car -1 -1 -1 513.26 182.76 616.27 240.62 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1031 194 Car -1 -1 -1 706.49 174.50 756.66 201.29 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1032 184 Car -1 -1 -1 501.01 183.74 610.39 243.69 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n1032 194 Car -1 -1 -1 701.11 173.86 752.05 201.94 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n1033 184 Car -1 -1 -1 488.94 186.09 601.87 248.28 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1033 194 Car -1 -1 -1 695.41 174.79 747.36 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n1034 184 Car -1 -1 -1 475.38 187.29 593.99 253.44 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1034 194 Car -1 -1 -1 690.34 175.15 742.81 204.09 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n1035 184 Car -1 -1 -1 460.43 188.09 585.36 258.86 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1035 194 Car -1 -1 -1 684.34 175.96 738.03 205.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n1036 184 Car -1 -1 -1 443.52 188.87 576.07 265.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1036 194 Car -1 -1 -1 678.18 177.49 733.80 207.13 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n1037 184 Car -1 -1 -1 424.26 188.21 566.21 268.13 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1037 194 Car -1 -1 -1 672.33 176.80 728.12 207.07 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n1038 184 Car -1 -1 -1 402.73 186.29 556.12 271.48 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1038 194 Car -1 -1 -1 666.30 175.19 722.19 206.05 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n1039 184 Car -1 -1 -1 376.71 185.72 544.76 277.10 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1039 194 Car -1 -1 -1 659.35 174.06 717.03 206.36 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1040 184 Car -1 -1 -1 348.84 186.93 532.62 286.16 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1040 194 Car -1 -1 -1 652.46 175.55 711.25 208.69 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n1041 184 Car -1 -1 -1 316.49 189.76 518.69 297.60 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1041 194 Car -1 -1 -1 645.59 177.00 706.26 210.91 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n1042 184 Car -1 -1 -1 278.30 191.59 502.48 309.70 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1042 194 Car -1 -1 -1 637.90 179.40 700.42 212.34 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n1043 184 Car -1 -1 -1 231.87 193.75 485.22 325.44 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1043 194 Car -1 -1 -1 630.18 179.71 693.80 214.83 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n1044 184 Car -1 -1 -1 166.42 193.21 467.08 349.39 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1044 194 Car -1 -1 -1 622.13 179.63 686.57 216.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1045 184 Car -1 -1 -1 86.56 195.65 444.60 369.53 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1045 194 Car -1 -1 -1 612.82 179.56 680.24 217.66 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n1046 184 Car -1 -1 -1 -1.30 194.37 416.71 369.83 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1046 194 Car -1 -1 -1 603.92 178.73 672.67 217.55 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n1047 184 Car -1 -1 -1 -3.43 196.43 381.37 369.16 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1047 194 Car -1 -1 -1 595.17 177.11 665.03 217.15 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n1048 184 Car -1 -1 -1 -2.63 196.62 341.03 370.02 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1048 194 Car -1 -1 -1 585.68 177.30 657.56 219.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n1049 184 Car -1 -1 -1 3.34 202.94 286.58 369.53 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1049 194 Car -1 -1 -1 576.05 179.75 649.37 222.17 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n1050 194 Car -1 -1 -1 565.56 182.49 641.89 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n1050 184 Car -1 -1 -1 1.53 205.63 219.47 368.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n1051 194 Car -1 -1 -1 554.67 183.05 634.49 227.46 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n1051 184 Car -1 -1 -1 0.02 203.76 127.92 369.15 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n1052 194 Car -1 -1 -1 543.40 182.16 626.11 229.21 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1053 194 Car -1 -1 -1 530.88 182.89 616.61 232.30 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n1054 194 Car -1 -1 -1 519.51 184.93 608.56 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n1055 194 Car -1 -1 -1 506.84 186.15 600.67 240.73 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1056 194 Car -1 -1 -1 494.23 186.56 593.42 243.86 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1057 194 Car -1 -1 -1 480.42 185.14 584.36 245.54 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n1058 194 Car -1 -1 -1 466.15 184.98 575.50 247.70 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1059 194 Car -1 -1 -1 450.34 185.47 564.71 250.67 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1060 194 Car -1 -1 -1 434.36 187.24 555.13 256.16 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1061 194 Car -1 -1 -1 414.52 188.73 544.30 262.02 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1062 194 Car -1 -1 -1 394.02 188.93 532.38 267.63 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1063 194 Car -1 -1 -1 370.74 189.39 520.03 273.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1064 194 Car -1 -1 -1 344.09 190.89 505.88 280.78 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1065 194 Car -1 -1 -1 312.75 192.33 490.86 289.00 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1066 194 Car -1 -1 -1 276.99 193.49 473.08 299.93 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1067 194 Car -1 -1 -1 235.25 191.54 453.45 310.69 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1068 194 Car -1 -1 -1 185.64 188.93 432.59 322.62 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1069 194 Car -1 -1 -1 122.87 189.33 410.19 338.92 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1070 194 Car -1 -1 -1 48.43 189.44 381.32 361.25 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1071 194 Car -1 -1 -1 -0.05 189.46 351.68 368.74 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n1071 195 Car -1 -1 -1 270.23 181.92 323.46 205.10 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n1071 196 Car -1 -1 -1 311.85 183.95 367.03 205.45 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n1072 194 Car -1 -1 -1 -2.75 190.84 315.71 366.87 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1072 196 Car -1 -1 -1 294.05 182.61 347.05 210.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n1072 195 Car -1 -1 -1 250.13 181.57 304.39 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n1073 194 Car -1 -1 -1 -1.67 194.40 268.73 369.69 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1073 195 Car -1 -1 -1 230.43 181.30 285.48 206.99 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n1073 196 Car -1 -1 -1 277.32 184.03 332.73 209.26 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n1074 194 Car -1 -1 -1 1.91 196.96 210.55 368.58 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n1074 195 Car -1 -1 -1 210.86 181.48 266.90 207.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n1074 196 Car -1 -1 -1 261.30 183.65 323.86 208.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n1075 194 Car -1 -1 -1 -2.27 196.09 130.56 369.77 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n1075 196 Car -1 -1 -1 238.93 181.93 301.08 206.18 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n1075 195 Car -1 -1 -1 188.50 180.15 250.18 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n1076 195 Car -1 -1 -1 165.09 176.25 227.89 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n1076 196 Car -1 -1 -1 216.23 179.28 277.10 204.94 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n1077 195 Car -1 -1 -1 137.77 170.52 205.77 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1077 196 Car -1 -1 -1 197.54 175.44 264.33 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n1078 196 Car -1 -1 -1 175.34 175.13 248.01 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n1078 195 Car -1 -1 -1 109.45 170.88 180.68 201.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n1079 195 Car -1 -1 -1 80.21 171.29 148.52 205.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n1079 196 Car -1 -1 -1 149.25 176.18 227.17 205.04 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n1080 195 Car -1 -1 -1 47.59 172.08 118.56 207.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n1080 196 Car -1 -1 -1 123.31 178.61 200.11 205.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n1081 196 Car -1 -1 -1 103.02 175.09 181.26 204.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n1081 195 Car -1 -1 -1 14.01 168.15 90.59 204.61 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n1082 196 Car -1 -1 -1 71.78 172.98 156.69 204.02 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1082 195 Car -1 -1 -1 1.82 169.02 57.12 200.34 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n1083 196 Car -1 -1 -1 32.61 174.51 111.64 204.64 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n1083 197 Car -1 -1 -1 42.65 174.69 131.76 205.40 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n1083 198 Car -1 -1 -1 1.11 169.12 34.18 201.86 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n1084 197 Car -1 -1 -1 10.18 178.27 102.58 206.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n1085 197 Car -1 -1 -1 -0.71 180.08 66.15 207.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n1086 197 Car -1 -1 -1 -0.79 178.17 28.30 208.79 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0011.txt",
    "content": "0 1 Car -1 -1 -1 847.09 187.81 1235.58 369.99 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n0 2 Car -1 -1 -1 718.03 189.50 900.05 303.95 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n0 3 Car -1 -1 -1 549.32 186.72 641.55 263.06 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n0 4 Car -1 -1 -1 672.64 183.87 767.69 255.80 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n0 5 Car -1 -1 -1 370.70 191.48 501.63 282.93 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n0 6 Car -1 -1 -1 485.75 185.50 547.87 223.58 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n0 7 Car -1 -1 -1 75.27 193.26 162.12 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n0 8 Car -1 -1 -1 652.68 182.19 715.62 228.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n0 9 Car -1 -1 -1 573.15 175.41 640.96 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n0 10 Car -1 -1 -1 217.12 193.49 289.94 218.89 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n0 11 Car -1 -1 -1 317.07 185.57 385.98 211.54 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n0 12 Car -1 -1 -1 639.05 177.10 667.69 201.58 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n0 13 Car -1 -1 -1 144.26 190.73 208.84 212.58 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n1 2 Car -1 -1 -1 721.98 190.40 911.96 307.00 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1 1 Car -1 -1 -1 855.45 188.52 1237.14 369.66 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1 4 Car -1 -1 -1 674.76 185.58 771.94 257.20 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1 5 Car -1 -1 -1 338.60 194.93 489.74 301.39 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1 3 Car -1 -1 -1 552.98 187.43 641.04 262.71 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1 6 Car -1 -1 -1 477.61 186.15 543.83 226.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n1 10 Car -1 -1 -1 213.68 195.05 288.04 221.88 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n1 8 Car -1 -1 -1 653.72 184.72 717.29 230.77 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n1 9 Car -1 -1 -1 574.35 176.06 641.88 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n1 11 Car -1 -1 -1 317.09 187.97 386.76 215.09 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1 7 Car -1 -1 -1 71.10 193.86 159.09 226.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n1 12 Car -1 -1 -1 640.87 178.41 668.43 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n2 2 Car -1 -1 -1 726.79 192.01 923.76 312.42 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n2 3 Car -1 -1 -1 555.11 187.73 643.10 261.98 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n2 1 Car -1 -1 -1 871.39 189.32 1236.96 369.29 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n2 5 Car -1 -1 -1 296.64 198.87 475.68 320.34 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n2 4 Car -1 -1 -1 677.31 186.60 776.56 260.29 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n2 6 Car -1 -1 -1 470.51 188.05 539.82 230.25 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n2 8 Car -1 -1 -1 655.59 184.78 720.21 232.36 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n2 10 Car -1 -1 -1 212.05 195.49 286.54 221.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n2 9 Car -1 -1 -1 578.29 176.48 642.93 231.95 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n2 7 Car -1 -1 -1 68.73 196.78 154.15 226.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n2 11 Car -1 -1 -1 318.85 187.99 385.62 214.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n2 12 Car -1 -1 -1 643.56 178.66 672.23 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n2 14 Car -1 -1 -1 501.75 184.01 548.41 207.84 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n2 15 Car -1 -1 -1 138.66 192.79 207.02 214.40 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n2 16 Car -1 -1 -1 467.73 183.87 507.75 210.13 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n3 2 Car -1 -1 -1 729.59 191.79 942.22 319.64 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n3 4 Car -1 -1 -1 679.02 186.80 782.51 262.78 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n3 1 Car -1 -1 -1 885.20 189.62 1237.67 369.20 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n3 3 Car -1 -1 -1 557.11 186.80 643.87 260.25 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n3 5 Car -1 -1 -1 237.10 201.98 457.98 348.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n3 8 Car -1 -1 -1 658.96 185.74 719.98 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n3 9 Car -1 -1 -1 578.78 176.99 643.66 232.19 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n3 10 Car -1 -1 -1 209.55 194.76 284.48 221.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n3 6 Car -1 -1 -1 460.53 188.22 534.90 232.40 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n3 7 Car -1 -1 -1 66.10 196.38 153.29 227.04 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n3 11 Car -1 -1 -1 317.68 187.60 385.32 214.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n3 14 Car -1 -1 -1 491.78 183.64 543.45 208.77 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n3 12 Car -1 -1 -1 647.31 178.52 674.68 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n3 16 Car -1 -1 -1 464.58 183.65 509.90 210.79 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n3 15 Car -1 -1 -1 140.90 191.94 205.08 212.73 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n3 17 Car -1 -1 -1 653.37 183.41 708.43 224.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n3 18 Car -1 -1 -1 564.47 180.76 604.02 214.83 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n4 2 Car -1 -1 -1 734.35 193.25 959.94 326.75 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n4 4 Car -1 -1 -1 681.43 188.27 789.41 265.96 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n4 1 Car -1 -1 -1 904.96 196.87 1233.02 368.36 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n4 5 Car -1 -1 -1 139.41 205.76 438.92 368.27 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n4 3 Car -1 -1 -1 559.88 187.82 645.30 259.67 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n4 6 Car -1 -1 -1 449.90 189.15 529.77 237.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n4 8 Car -1 -1 -1 661.03 187.10 722.79 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n4 9 Car -1 -1 -1 578.91 177.85 645.64 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n4 7 Car -1 -1 -1 61.72 195.96 150.30 227.36 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 17 Car -1 -1 -1 655.15 184.39 707.45 225.23 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 10 Car -1 -1 -1 206.83 195.19 283.96 221.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 11 Car -1 -1 -1 316.39 187.93 384.76 214.15 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n4 14 Car -1 -1 -1 484.01 185.13 535.27 209.88 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n4 18 Car -1 -1 -1 562.10 181.59 605.23 218.88 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n4 16 Car -1 -1 -1 465.51 184.71 507.60 210.67 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n4 12 Car -1 -1 -1 649.70 179.74 675.99 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n4 15 Car -1 -1 -1 132.98 191.88 197.07 213.21 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n5 2 Car -1 -1 -1 741.64 192.72 977.46 334.97 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n5 4 Car -1 -1 -1 683.74 188.28 795.87 268.11 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n5 5 Car -1 -1 -1 -6.62 210.09 406.21 369.74 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n5 3 Car -1 -1 -1 561.75 187.97 646.48 258.89 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n5 1 Car -1 -1 -1 928.62 203.88 1233.75 367.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n5 6 Car -1 -1 -1 437.28 189.83 523.90 241.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n5 8 Car -1 -1 -1 661.83 186.98 725.09 236.48 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n5 9 Car -1 -1 -1 584.15 178.34 646.03 231.94 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n5 10 Car -1 -1 -1 203.71 195.66 280.31 222.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n5 7 Car -1 -1 -1 56.30 195.54 147.89 228.10 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n5 11 Car -1 -1 -1 313.05 188.40 382.27 215.00 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n5 17 Car -1 -1 -1 656.26 184.51 713.00 226.61 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n5 18 Car -1 -1 -1 559.02 182.44 600.09 213.46 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n5 16 Car -1 -1 -1 463.29 184.98 510.22 210.84 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n5 14 Car -1 -1 -1 470.68 185.20 527.32 211.38 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n5 12 Car -1 -1 -1 652.86 179.26 680.22 204.75 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n5 15 Car -1 -1 -1 128.01 191.92 194.73 213.52 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n6 2 Car -1 -1 -1 747.34 192.59 1001.63 343.16 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n6 4 Car -1 -1 -1 686.95 187.47 801.23 269.51 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n6 5 Car -1 -1 -1 -0.88 210.36 354.51 370.74 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n6 6 Car -1 -1 -1 424.43 189.89 517.49 246.08 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n6 1 Car -1 -1 -1 956.65 204.22 1236.34 368.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n6 3 Car -1 -1 -1 563.81 187.41 648.45 256.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n6 8 Car -1 -1 -1 663.88 186.80 728.89 237.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n6 18 Car -1 -1 -1 557.46 182.57 596.08 212.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n6 10 Car -1 -1 -1 200.22 196.44 277.97 222.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n6 9 Car -1 -1 -1 583.45 177.87 649.03 232.21 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n6 7 Car -1 -1 -1 48.88 195.13 142.78 229.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n6 11 Car -1 -1 -1 308.98 188.49 379.97 215.39 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n6 16 Car -1 -1 -1 460.79 184.30 514.45 211.07 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n6 17 Car -1 -1 -1 657.58 183.98 718.88 227.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n6 15 Car -1 -1 -1 126.47 192.84 195.26 214.06 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n6 12 Car -1 -1 -1 657.18 178.38 689.01 206.04 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n7 2 Car -1 -1 -1 756.44 193.58 1026.01 355.02 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n7 4 Car -1 -1 -1 690.20 187.38 810.78 271.24 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n7 3 Car -1 -1 -1 565.38 186.73 648.51 255.21 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n7 6 Car -1 -1 -1 407.93 191.05 509.96 252.40 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n7 5 Car -1 -1 -1 -0.55 218.92 267.99 370.14 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n7 10 Car -1 -1 -1 195.53 195.66 274.05 222.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n7 1 Car -1 -1 -1 988.59 205.45 1235.81 368.15 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n7 9 Car -1 -1 -1 587.47 178.35 649.65 231.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n7 8 Car -1 -1 -1 666.15 186.09 733.12 237.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n7 11 Car -1 -1 -1 307.96 188.27 378.57 215.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n7 7 Car -1 -1 -1 43.36 195.02 140.57 230.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n7 18 Car -1 -1 -1 555.51 182.93 595.02 212.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n7 17 Car -1 -1 -1 659.01 184.05 718.87 227.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n7 16 Car -1 -1 -1 456.26 184.08 510.66 211.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n7 15 Car -1 -1 -1 118.79 192.42 188.31 214.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n7 12 Car -1 -1 -1 660.24 177.23 701.93 208.49 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n8 2 Car -1 -1 -1 763.52 194.10 1058.09 365.04 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n8 3 Car -1 -1 -1 566.42 186.72 649.13 254.19 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n8 4 Car -1 -1 -1 693.10 188.33 818.26 275.35 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n8 6 Car -1 -1 -1 388.78 192.73 501.37 258.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n8 8 Car -1 -1 -1 669.46 185.94 732.65 237.54 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n8 18 Car -1 -1 -1 553.27 183.23 591.99 212.58 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n8 10 Car -1 -1 -1 192.37 195.79 270.22 223.64 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n8 17 Car -1 -1 -1 660.26 184.02 719.11 227.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n8 7 Car -1 -1 -1 37.05 195.55 136.82 231.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 9 Car -1 -1 -1 592.31 179.33 651.15 229.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 11 Car -1 -1 -1 304.98 188.32 375.77 215.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 16 Car -1 -1 -1 447.60 184.70 504.60 209.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n8 1 Car -1 -1 -1 1033.15 206.65 1237.21 366.47 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n8 15 Car -1 -1 -1 114.58 191.83 184.74 216.13 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n8 12 Car -1 -1 -1 661.57 177.13 692.79 203.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n9 2 Car -1 -1 -1 771.60 196.38 1096.22 370.05 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n9 6 Car -1 -1 -1 367.28 194.66 491.24 267.83 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n9 4 Car -1 -1 -1 696.08 190.03 827.41 279.72 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n9 3 Car -1 -1 -1 568.81 187.39 650.91 253.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n9 8 Car -1 -1 -1 670.93 186.05 737.09 239.38 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n9 9 Car -1 -1 -1 592.83 179.58 653.20 229.21 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n9 17 Car -1 -1 -1 661.89 184.09 723.13 228.57 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n9 10 Car -1 -1 -1 187.40 195.77 266.54 224.35 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n9 7 Car -1 -1 -1 30.06 196.11 130.13 231.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n9 16 Car -1 -1 -1 431.49 185.80 496.16 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n9 18 Car -1 -1 -1 550.48 183.78 592.39 213.75 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n9 11 Car -1 -1 -1 304.18 188.50 373.86 216.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n9 15 Car -1 -1 -1 109.80 191.89 181.22 216.64 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n9 12 Car -1 -1 -1 663.35 178.09 691.52 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n9 1 Car -1 -1 -1 1075.94 203.57 1241.21 369.68 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n10 2 Car -1 -1 -1 781.61 195.78 1140.44 370.35 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n10 4 Car -1 -1 -1 700.58 189.55 838.17 283.43 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n10 3 Car -1 -1 -1 570.50 187.51 650.88 253.31 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n10 6 Car -1 -1 -1 336.89 196.19 480.89 276.39 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n10 8 Car -1 -1 -1 673.13 186.31 737.85 240.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 18 Car -1 -1 -1 548.50 184.06 590.71 215.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n10 9 Car -1 -1 -1 596.35 178.26 655.30 225.50 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n10 10 Car -1 -1 -1 182.81 195.57 262.29 224.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n10 16 Car -1 -1 -1 407.34 185.80 482.68 215.13 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n10 7 Car -1 -1 -1 25.44 196.19 124.59 231.63 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n10 17 Car -1 -1 -1 664.77 185.19 722.06 229.93 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n10 11 Car -1 -1 -1 301.83 189.45 370.09 217.64 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n10 15 Car -1 -1 -1 106.67 191.93 176.80 216.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n10 12 Car -1 -1 -1 665.21 178.55 695.45 205.73 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n11 2 Car -1 -1 -1 788.80 196.43 1210.26 368.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n11 4 Car -1 -1 -1 703.78 190.26 845.43 287.57 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n11 3 Car -1 -1 -1 571.91 186.95 651.56 252.57 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n11 6 Car -1 -1 -1 302.77 197.31 468.54 287.93 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n11 8 Car -1 -1 -1 675.93 185.88 742.01 241.32 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n11 9 Car -1 -1 -1 596.82 178.25 657.44 225.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n11 18 Car -1 -1 -1 544.82 183.56 590.04 215.97 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n11 7 Car -1 -1 -1 18.31 195.53 116.94 231.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n11 17 Car -1 -1 -1 665.83 183.21 726.14 229.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n11 16 Car -1 -1 -1 391.69 185.39 467.88 214.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n11 10 Car -1 -1 -1 177.41 195.06 259.37 225.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n11 11 Car -1 -1 -1 297.45 188.96 367.64 215.92 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n11 12 Car -1 -1 -1 668.23 177.54 695.03 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n11 15 Car -1 -1 -1 102.21 191.65 172.98 217.10 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n12 2 Car -1 -1 -1 806.53 196.62 1237.32 367.86 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n12 4 Car -1 -1 -1 708.88 189.78 856.79 291.46 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n12 6 Car -1 -1 -1 258.87 197.95 451.03 303.74 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n12 8 Car -1 -1 -1 678.64 185.22 746.58 241.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n12 3 Car -1 -1 -1 572.96 185.48 652.26 250.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n12 18 Car -1 -1 -1 540.96 183.25 588.07 216.17 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n12 9 Car -1 -1 -1 599.65 177.11 659.24 226.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n12 17 Car -1 -1 -1 668.27 182.58 730.97 229.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 7 Car -1 -1 -1 9.13 195.13 111.15 232.35 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n12 16 Car -1 -1 -1 375.33 184.40 458.56 212.96 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n12 10 Car -1 -1 -1 172.19 194.49 256.04 225.70 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n12 11 Car -1 -1 -1 294.45 188.46 362.72 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n12 12 Car -1 -1 -1 670.96 176.51 697.05 200.90 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n12 15 Car -1 -1 -1 96.98 191.24 170.84 217.10 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n13 2 Car -1 -1 -1 819.15 197.51 1234.15 368.46 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n13 4 Car -1 -1 -1 712.69 189.76 874.63 297.79 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n13 6 Car -1 -1 -1 197.97 199.69 428.06 327.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n13 3 Car -1 -1 -1 573.99 184.59 654.06 250.44 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n13 8 Car -1 -1 -1 679.93 184.45 751.15 243.34 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n13 9 Car -1 -1 -1 599.61 176.29 661.15 226.61 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n13 18 Car -1 -1 -1 536.70 182.64 586.70 217.16 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n13 17 Car -1 -1 -1 670.40 182.23 731.23 229.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n13 16 Car -1 -1 -1 358.97 184.81 443.33 215.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n13 7 Car -1 -1 -1 1.20 195.22 102.61 232.23 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n13 10 Car -1 -1 -1 165.29 194.98 249.31 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n13 11 Car -1 -1 -1 288.88 187.64 360.24 213.69 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n13 15 Car -1 -1 -1 88.36 191.44 164.31 217.18 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n13 12 Car -1 -1 -1 672.78 176.11 698.74 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n14 4 Car -1 -1 -1 716.35 189.77 893.60 306.34 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n14 2 Car -1 -1 -1 834.73 198.13 1233.79 368.76 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n14 3 Car -1 -1 -1 575.15 185.09 654.42 250.63 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n14 8 Car -1 -1 -1 682.26 185.71 756.45 246.00 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n14 6 Car -1 -1 -1 110.82 202.87 404.91 361.46 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n14 18 Car -1 -1 -1 532.48 183.03 583.27 219.53 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n14 9 Car -1 -1 -1 600.09 176.69 663.20 227.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n14 16 Car -1 -1 -1 340.10 185.45 426.01 216.28 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 17 Car -1 -1 -1 671.78 183.28 736.94 232.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 7 Car -1 -1 -1 -1.41 198.90 91.73 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n14 10 Car -1 -1 -1 157.62 195.03 243.79 224.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n14 11 Car -1 -1 -1 283.12 187.78 356.69 213.20 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n14 12 Car -1 -1 -1 675.03 176.20 702.25 200.46 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n14 15 Car -1 -1 -1 82.57 191.35 161.98 216.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n14 19 Car -1 -1 -1 -2.45 203.19 21.27 237.65 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n15 4 Car -1 -1 -1 721.64 192.47 912.36 316.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n15 2 Car -1 -1 -1 855.81 203.05 1234.40 368.92 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n15 3 Car -1 -1 -1 576.11 186.84 655.28 252.85 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n15 6 Car -1 -1 -1 -1.00 205.71 369.74 369.03 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n15 8 Car -1 -1 -1 683.75 187.72 762.26 250.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n15 18 Car -1 -1 -1 526.86 184.45 579.75 223.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 17 Car -1 -1 -1 673.28 184.68 741.28 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n15 9 Car -1 -1 -1 603.04 178.65 663.52 229.47 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n15 16 Car -1 -1 -1 323.99 186.98 409.02 217.68 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n15 12 Car -1 -1 -1 676.36 178.33 700.47 200.42 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n15 7 Car -1 -1 -1 -0.88 200.38 83.18 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n15 10 Car -1 -1 -1 148.08 195.79 237.02 227.71 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n15 11 Car -1 -1 -1 278.09 189.39 354.11 214.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n15 15 Car -1 -1 -1 74.99 192.57 154.63 217.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n16 4 Car -1 -1 -1 726.45 193.12 938.64 326.31 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n16 6 Car -1 -1 -1 -1.72 211.79 323.40 369.28 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n16 2 Car -1 -1 -1 871.95 204.40 1236.41 368.97 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n16 3 Car -1 -1 -1 576.58 186.42 655.33 253.31 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n16 8 Car -1 -1 -1 686.52 188.35 766.58 253.82 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n16 9 Car -1 -1 -1 602.21 178.59 664.77 230.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n16 18 Car -1 -1 -1 519.61 185.19 576.65 226.70 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n16 17 Car -1 -1 -1 674.86 186.03 741.67 237.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n16 16 Car -1 -1 -1 303.15 188.91 390.89 219.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n16 12 Car -1 -1 -1 677.15 179.24 701.37 201.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n16 11 Car -1 -1 -1 261.27 190.39 356.59 222.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n16 10 Car -1 -1 -1 141.88 197.98 233.59 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 7 Car -1 -1 -1 -0.72 200.60 74.66 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n16 15 Car -1 -1 -1 68.47 193.64 152.61 218.90 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n16 20 Car -1 -1 -1 586.93 182.65 619.40 210.38 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n17 4 Car -1 -1 -1 731.45 191.20 965.87 336.32 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n17 2 Car -1 -1 -1 907.35 204.53 1237.92 369.56 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n17 3 Car -1 -1 -1 576.99 186.62 655.78 252.75 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n17 6 Car -1 -1 -1 2.36 220.93 249.88 368.53 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n17 9 Car -1 -1 -1 603.24 178.56 666.58 230.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n17 8 Car -1 -1 -1 688.02 187.93 769.73 254.99 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n17 17 Car -1 -1 -1 676.02 185.57 746.75 238.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n17 18 Car -1 -1 -1 511.64 185.40 573.29 229.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n17 10 Car -1 -1 -1 129.55 198.28 224.47 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n17 16 Car -1 -1 -1 276.84 188.98 371.13 222.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n17 7 Car -1 -1 -1 0.53 202.39 65.18 238.78 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n17 20 Car -1 -1 -1 582.67 181.68 618.10 211.40 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n17 11 Car -1 -1 -1 249.18 190.74 359.32 225.12 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n17 12 Car -1 -1 -1 677.95 178.10 702.16 200.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n17 15 Car -1 -1 -1 56.47 195.30 142.57 222.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n18 4 Car -1 -1 -1 740.13 190.84 1001.52 351.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n18 3 Car -1 -1 -1 578.13 186.82 657.31 252.51 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n18 8 Car -1 -1 -1 690.69 187.32 777.60 259.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n18 18 Car -1 -1 -1 503.69 185.71 569.68 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n18 2 Car -1 -1 -1 938.19 209.73 1239.58 370.83 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n18 17 Car -1 -1 -1 677.86 185.51 748.05 239.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n18 9 Car -1 -1 -1 606.46 178.62 668.41 229.46 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n18 11 Car -1 -1 -1 248.12 191.43 347.25 226.83 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n18 10 Car -1 -1 -1 120.51 199.46 215.67 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n18 20 Car -1 -1 -1 578.82 181.74 618.73 213.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n18 12 Car -1 -1 -1 679.63 177.74 704.50 200.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n18 7 Car -1 -1 -1 0.26 202.95 52.08 239.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n18 6 Car -1 -1 -1 -2.46 234.50 138.92 369.76 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 15 Car -1 -1 -1 49.67 196.96 139.83 225.89 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n19 4 Car -1 -1 -1 748.29 190.94 1042.01 367.93 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n19 8 Car -1 -1 -1 692.73 187.84 783.89 263.00 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n19 3 Car -1 -1 -1 578.17 186.73 657.54 252.55 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n19 18 Car -1 -1 -1 494.17 184.86 565.63 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n19 9 Car -1 -1 -1 606.61 178.74 669.72 229.45 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n19 10 Car -1 -1 -1 109.02 199.38 206.26 234.94 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n19 17 Car -1 -1 -1 680.73 185.66 756.84 242.29 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n19 20 Car -1 -1 -1 575.37 181.02 614.67 212.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n19 11 Car -1 -1 -1 240.55 191.51 336.99 226.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n19 12 Car -1 -1 -1 680.94 177.57 705.30 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n19 2 Car -1 -1 -1 998.35 213.33 1233.39 367.20 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n19 7 Car -1 -1 -1 0.41 205.88 41.76 242.84 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n19 15 Car -1 -1 -1 38.26 196.31 129.41 223.94 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n20 4 Car -1 -1 -1 759.61 195.36 1098.60 370.67 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n20 3 Car -1 -1 -1 579.66 187.22 659.03 253.68 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n20 8 Car -1 -1 -1 695.83 189.56 792.26 268.57 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n20 18 Car -1 -1 -1 483.28 186.51 561.25 241.30 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n20 17 Car -1 -1 -1 684.62 187.42 760.72 245.21 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n20 20 Car -1 -1 -1 571.95 181.90 613.09 212.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n20 9 Car -1 -1 -1 606.23 179.61 672.79 230.83 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n20 10 Car -1 -1 -1 97.95 199.18 194.94 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n20 11 Car -1 -1 -1 226.77 191.80 329.93 227.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n20 12 Car -1 -1 -1 682.00 178.83 705.90 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 15 Car -1 -1 -1 28.79 195.89 121.84 224.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n20 7 Car -1 -1 -1 1.96 206.29 30.80 242.74 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n21 4 Car -1 -1 -1 767.52 197.03 1176.74 369.14 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n21 8 Car -1 -1 -1 699.85 191.30 803.74 275.20 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n21 18 Car -1 -1 -1 472.16 189.86 556.75 249.38 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n21 3 Car -1 -1 -1 580.21 189.50 662.98 257.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n21 17 Car -1 -1 -1 686.61 190.72 762.41 249.71 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n21 9 Car -1 -1 -1 609.85 182.51 674.97 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n21 11 Car -1 -1 -1 219.40 194.60 327.31 231.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n21 20 Car -1 -1 -1 568.96 184.86 609.18 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n21 10 Car -1 -1 -1 86.46 202.94 187.96 240.09 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 15 Car -1 -1 -1 18.23 197.65 117.97 229.11 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n21 12 Car -1 -1 -1 682.80 180.62 708.61 201.06 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n21 7 Car -1 -1 -1 -0.44 209.54 17.95 247.00 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n21 21 Car -1 -1 -1 452.98 188.57 490.73 207.81 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n21 22 Car -1 -1 -1 167.88 196.72 278.49 235.01 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n21 23 Car -1 -1 -1 668.65 181.47 693.04 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n21 24 Car -1 -1 -1 762.80 177.39 794.59 203.34 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n22 4 Car -1 -1 -1 791.32 200.26 1237.50 366.96 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n22 3 Car -1 -1 -1 582.50 190.26 664.97 259.55 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n22 8 Car -1 -1 -1 707.31 193.57 815.68 281.12 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n22 18 Car -1 -1 -1 459.01 192.86 551.31 256.83 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n22 9 Car -1 -1 -1 613.53 183.16 679.79 235.49 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n22 11 Car -1 -1 -1 219.76 197.23 319.21 234.26 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n22 20 Car -1 -1 -1 565.18 186.23 608.87 217.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n22 17 Car -1 -1 -1 690.64 192.81 766.39 254.41 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n22 12 Car -1 -1 -1 685.66 182.34 709.31 203.20 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n22 15 Car -1 -1 -1 4.38 199.59 108.71 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 10 Car -1 -1 -1 75.58 206.92 177.59 241.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n22 21 Car -1 -1 -1 452.67 191.06 490.86 209.67 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n22 22 Car -1 -1 -1 136.55 200.08 255.13 235.24 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n22 7 Car -1 -1 -1 -0.94 208.45 4.65 248.33 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n23 8 Car -1 -1 -1 711.95 192.89 828.00 287.11 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n23 18 Car -1 -1 -1 443.83 192.41 545.06 264.76 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n23 3 Car -1 -1 -1 584.56 188.44 669.33 259.55 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n23 4 Car -1 -1 -1 803.28 201.35 1235.09 370.40 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n23 17 Car -1 -1 -1 694.30 190.30 776.50 256.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n23 20 Car -1 -1 -1 562.90 185.64 606.08 218.75 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n23 11 Car -1 -1 -1 208.72 197.60 314.36 235.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n23 9 Car -1 -1 -1 621.79 182.64 684.42 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n23 22 Car -1 -1 -1 101.18 200.36 221.37 240.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n23 12 Car -1 -1 -1 688.75 180.49 712.49 201.30 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n23 15 Car -1 -1 -1 0.11 202.22 104.36 236.79 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n23 10 Car -1 -1 -1 65.14 208.50 171.81 245.60 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n23 21 Car -1 -1 -1 451.59 190.96 491.87 210.94 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n23 25 Car -1 -1 -1 669.38 181.44 695.05 200.01 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n23 26 Car -1 -1 -1 135.80 200.90 263.91 240.40 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n24 4 Car -1 -1 -1 823.46 203.13 1237.31 369.24 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n24 8 Car -1 -1 -1 718.65 190.84 842.99 290.35 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n24 18 Car -1 -1 -1 422.78 189.43 537.21 272.37 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n24 3 Car -1 -1 -1 586.41 186.57 669.89 256.34 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n24 17 Car -1 -1 -1 697.49 186.40 782.22 256.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n24 9 Car -1 -1 -1 621.21 178.53 686.76 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n24 11 Car -1 -1 -1 200.45 194.01 308.30 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n24 20 Car -1 -1 -1 558.77 182.25 603.98 216.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 10 Car -1 -1 -1 51.49 205.33 162.05 244.02 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n24 21 Car -1 -1 -1 449.34 188.88 492.59 213.44 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n24 15 Car -1 -1 -1 1.80 200.30 87.25 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n24 12 Car -1 -1 -1 692.17 177.03 713.96 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n24 22 Car -1 -1 -1 78.86 200.62 189.37 239.15 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n25 18 Car -1 -1 -1 397.45 187.44 528.99 279.23 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n25 8 Car -1 -1 -1 725.78 188.24 859.23 293.87 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n25 3 Car -1 -1 -1 587.57 183.78 673.68 254.61 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n25 4 Car -1 -1 -1 853.34 205.47 1237.03 369.01 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n25 17 Car -1 -1 -1 700.20 181.76 792.82 258.23 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n25 9 Car -1 -1 -1 624.58 174.43 690.67 228.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n25 20 Car -1 -1 -1 553.07 178.86 601.21 215.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n25 12 Car -1 -1 -1 692.64 175.01 716.09 194.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n25 11 Car -1 -1 -1 188.28 191.42 302.85 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n25 21 Car -1 -1 -1 444.86 187.90 490.64 213.52 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n25 10 Car -1 -1 -1 37.55 202.61 152.19 245.38 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n25 15 Car -1 -1 -1 2.29 201.10 71.85 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n26 8 Car -1 -1 -1 731.99 187.94 877.75 301.64 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n26 18 Car -1 -1 -1 364.73 188.02 518.01 293.45 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n26 4 Car -1 -1 -1 881.53 213.38 1234.54 367.79 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n26 17 Car -1 -1 -1 704.53 181.21 803.10 261.28 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n26 3 Car -1 -1 -1 589.44 182.19 677.41 254.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n26 20 Car -1 -1 -1 547.69 177.28 598.33 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n26 9 Car -1 -1 -1 627.85 173.65 693.64 227.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 11 Car -1 -1 -1 178.31 187.56 293.39 229.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n26 10 Car -1 -1 -1 12.31 196.10 139.85 238.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n26 12 Car -1 -1 -1 695.11 173.06 719.05 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n26 21 Car -1 -1 -1 439.32 184.27 488.90 210.21 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n26 27 Car -1 -1 -1 777.73 170.44 810.95 195.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n26 28 Car -1 -1 -1 600.41 167.80 645.95 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n27 8 Car -1 -1 -1 739.87 188.80 900.66 314.66 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n27 17 Car -1 -1 -1 709.25 183.60 809.30 265.81 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n27 18 Car -1 -1 -1 320.69 189.44 504.13 314.56 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n27 3 Car -1 -1 -1 591.50 182.74 680.13 255.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n27 20 Car -1 -1 -1 543.16 177.24 596.38 217.52 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n27 9 Car -1 -1 -1 628.16 173.43 696.28 228.49 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n27 4 Car -1 -1 -1 922.42 227.53 1239.83 368.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n27 11 Car -1 -1 -1 169.15 187.00 286.40 230.25 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n27 10 Car -1 -1 -1 1.44 195.82 134.15 239.17 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n27 12 Car -1 -1 -1 697.48 172.88 720.22 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n27 27 Car -1 -1 -1 781.28 170.17 815.05 195.73 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n27 28 Car -1 -1 -1 597.84 167.41 641.44 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n28 8 Car -1 -1 -1 746.00 186.66 926.35 324.15 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n28 17 Car -1 -1 -1 714.36 181.66 819.35 268.30 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n28 3 Car -1 -1 -1 594.40 180.91 683.59 255.02 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n28 18 Car -1 -1 -1 252.77 190.23 488.69 350.22 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n28 20 Car -1 -1 -1 536.43 176.78 593.98 219.39 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n28 9 Car -1 -1 -1 631.35 172.39 700.43 228.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n28 11 Car -1 -1 -1 159.50 187.87 277.19 232.44 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 10 Car -1 -1 -1 3.83 199.67 117.21 241.68 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n28 12 Car -1 -1 -1 698.41 171.11 720.85 191.74 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n28 27 Car -1 -1 -1 782.64 168.70 818.25 195.78 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n28 28 Car -1 -1 -1 595.98 167.16 635.44 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n28 4 Car -1 -1 -1 991.74 225.61 1239.65 363.04 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n28 29 Car -1 -1 -1 -2.01 206.46 21.83 249.54 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n28 30 Car -1 -1 -1 736.99 171.19 763.82 191.37 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n29 3 Car -1 -1 -1 597.39 178.64 687.18 254.48 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n29 8 Car -1 -1 -1 756.25 184.10 956.15 335.79 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n29 17 Car -1 -1 -1 719.40 179.32 830.19 270.20 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n29 18 Car -1 -1 -1 150.11 194.19 466.89 369.84 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n29 20 Car -1 -1 -1 529.75 175.50 590.86 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n29 9 Car -1 -1 -1 639.09 169.80 705.86 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n29 11 Car -1 -1 -1 146.16 189.54 268.29 234.37 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n29 12 Car -1 -1 -1 701.08 168.88 722.96 188.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n29 10 Car -1 -1 -1 2.01 199.52 109.30 242.99 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n29 30 Car -1 -1 -1 738.78 167.69 763.96 188.09 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n29 28 Car -1 -1 -1 593.29 165.10 637.09 201.04 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n29 27 Car -1 -1 -1 785.25 165.31 823.34 195.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n30 8 Car -1 -1 -1 766.39 183.47 993.01 351.42 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n30 3 Car -1 -1 -1 599.88 176.88 691.69 255.55 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n30 18 Car -1 -1 -1 -2.20 196.12 433.30 369.74 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n30 17 Car -1 -1 -1 725.31 178.36 844.34 272.85 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n30 9 Car -1 -1 -1 639.15 168.54 709.51 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n30 20 Car -1 -1 -1 522.42 174.41 588.51 222.84 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n30 11 Car -1 -1 -1 137.41 188.78 261.08 236.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n30 10 Car -1 -1 -1 0.63 198.39 95.09 244.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n30 28 Car -1 -1 -1 590.15 164.57 632.56 201.22 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n30 12 Car -1 -1 -1 703.33 167.41 726.23 187.61 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n30 30 Car -1 -1 -1 741.01 166.60 768.98 188.63 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n30 27 Car -1 -1 -1 787.44 163.50 828.81 194.03 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n31 8 Car -1 -1 -1 777.19 186.03 1037.00 370.06 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n31 17 Car -1 -1 -1 731.77 179.87 856.36 283.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n31 3 Car -1 -1 -1 601.81 177.22 696.11 257.39 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n31 18 Car -1 -1 -1 -4.09 199.32 393.98 373.22 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n31 20 Car -1 -1 -1 512.65 174.49 584.28 226.48 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n31 9 Car -1 -1 -1 644.31 169.10 715.95 224.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 12 Car -1 -1 -1 704.81 167.81 727.04 187.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n31 11 Car -1 -1 -1 125.45 187.64 249.94 236.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n31 28 Car -1 -1 -1 586.39 163.97 630.54 201.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n31 27 Car -1 -1 -1 791.32 163.67 833.28 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n31 10 Car -1 -1 -1 -0.11 197.21 81.20 245.05 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n31 30 Car -1 -1 -1 742.66 167.48 768.54 188.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n32 17 Car -1 -1 -1 741.43 181.31 876.56 291.87 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n32 8 Car -1 -1 -1 793.56 189.69 1096.42 369.68 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n32 3 Car -1 -1 -1 604.01 179.38 701.04 261.55 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n32 20 Car -1 -1 -1 502.16 176.43 580.84 234.46 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n32 18 Car -1 -1 -1 -0.13 217.93 298.73 371.01 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n32 9 Car -1 -1 -1 647.37 171.04 721.53 228.59 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n32 11 Car -1 -1 -1 106.62 187.83 245.23 238.56 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n32 12 Car -1 -1 -1 708.50 169.44 730.75 188.74 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n32 10 Car -1 -1 -1 1.45 198.63 64.42 250.13 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n32 27 Car -1 -1 -1 794.85 165.11 837.42 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n32 28 Car -1 -1 -1 584.74 164.82 631.50 205.25 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n32 30 Car -1 -1 -1 744.81 169.83 770.12 190.58 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n32 31 Car -1 -1 -1 675.06 176.35 726.27 217.22 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n33 17 Car -1 -1 -1 747.88 184.25 895.24 303.84 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n33 3 Car -1 -1 -1 606.52 182.37 706.17 265.55 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n33 20 Car -1 -1 -1 489.94 180.61 576.36 243.77 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n33 8 Car -1 -1 -1 810.39 193.89 1172.85 369.90 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n33 9 Car -1 -1 -1 649.56 172.55 727.59 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n33 11 Car -1 -1 -1 92.20 193.79 235.89 246.40 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n33 12 Car -1 -1 -1 707.61 172.44 731.59 192.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n33 30 Car -1 -1 -1 746.92 171.55 772.70 192.00 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n33 27 Car -1 -1 -1 799.71 167.71 840.58 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n33 10 Car -1 -1 -1 0.35 201.31 42.53 255.95 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n33 31 Car -1 -1 -1 683.11 177.19 732.89 217.85 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n33 32 Van -1 -1 -1 581.73 165.26 633.82 208.61 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n34 8 Car -1 -1 -1 833.36 197.45 1242.90 368.03 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n34 17 Car -1 -1 -1 755.46 183.21 924.13 313.51 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n34 20 Car -1 -1 -1 473.46 177.85 570.09 249.64 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n34 3 Car -1 -1 -1 608.22 181.22 709.62 265.53 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n34 9 Car -1 -1 -1 653.59 171.08 732.70 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n34 11 Car -1 -1 -1 73.58 192.46 225.54 246.91 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n34 27 Car -1 -1 -1 803.13 167.44 844.27 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n34 30 Car -1 -1 -1 747.89 170.52 775.34 192.57 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n34 12 Car -1 -1 -1 711.61 172.35 734.96 192.13 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n34 10 Car -1 -1 -1 0.14 203.98 26.49 253.12 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n34 33 Car -1 -1 -1 577.42 164.34 631.10 209.51 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n35 20 Car -1 -1 -1 456.07 177.20 562.08 256.78 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n35 3 Car -1 -1 -1 611.14 179.07 713.10 264.89 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n35 17 Car -1 -1 -1 763.25 182.90 955.79 322.13 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n35 8 Car -1 -1 -1 857.09 194.29 1241.20 370.63 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n35 11 Car -1 -1 -1 55.83 191.45 212.51 248.07 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n35 9 Car -1 -1 -1 659.95 170.04 741.31 230.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n35 27 Car -1 -1 -1 803.58 166.58 847.81 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n35 12 Car -1 -1 -1 704.38 171.66 736.50 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n35 30 Car -1 -1 -1 749.05 169.96 775.70 191.00 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n35 33 Car -1 -1 -1 571.63 163.10 628.79 210.39 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n36 20 Car -1 -1 -1 429.69 176.01 553.72 265.71 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n36 8 Car -1 -1 -1 883.26 196.98 1239.47 368.98 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n36 3 Car -1 -1 -1 612.60 178.71 717.16 265.07 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n36 17 Car -1 -1 -1 771.15 182.01 987.41 335.83 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n36 11 Car -1 -1 -1 37.34 191.14 199.15 249.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n36 9 Car -1 -1 -1 667.16 168.26 749.02 228.66 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n36 27 Car -1 -1 -1 806.28 164.78 851.09 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n36 30 Car -1 -1 -1 748.67 167.25 776.09 189.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n36 12 Car -1 -1 -1 708.01 171.26 739.48 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n36 33 Car -1 -1 -1 565.35 161.79 626.30 211.71 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n37 3 Car -1 -1 -1 613.59 177.12 719.81 264.60 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n37 20 Car -1 -1 -1 398.96 174.07 541.64 276.65 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n37 17 Car -1 -1 -1 779.32 177.47 1018.60 342.67 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n37 8 Car -1 -1 -1 910.22 202.80 1235.66 368.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n37 11 Car -1 -1 -1 19.35 190.84 185.61 249.99 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n37 9 Car -1 -1 -1 674.54 164.60 756.11 224.04 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n37 27 Car -1 -1 -1 807.72 160.89 850.97 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n37 33 Car -1 -1 -1 557.59 159.63 621.27 210.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n37 30 Car -1 -1 -1 748.56 163.39 776.42 186.67 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n38 17 Car -1 -1 -1 789.36 182.13 1069.55 360.37 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n38 20 Car -1 -1 -1 355.41 175.09 527.56 297.05 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n38 3 Car -1 -1 -1 613.95 178.91 723.26 267.68 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n38 9 Car -1 -1 -1 675.09 165.10 764.37 227.96 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n38 8 Car -1 -1 -1 945.25 212.54 1239.92 367.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n38 11 Car -1 -1 -1 1.78 190.44 172.16 252.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n38 33 Car -1 -1 -1 550.09 159.61 617.00 213.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n38 27 Car -1 -1 -1 807.44 162.30 855.48 195.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n38 30 Car -1 -1 -1 748.10 165.82 776.24 188.22 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n38 34 Car -1 -1 -1 635.20 171.24 671.15 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n39 3 Car -1 -1 -1 615.42 183.17 724.43 274.42 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n39 17 Car -1 -1 -1 799.76 187.16 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0.96\n652 183 Car -1 -1 -1 3.21 156.65 242.85 369.53 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n652 184 Car -1 -1 -1 232.79 212.10 274.91 242.04 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n653 183 Car -1 -1 -1 -2.44 148.20 223.90 370.01 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n653 184 Car -1 -1 -1 231.76 209.86 275.19 240.65 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n654 184 Car -1 -1 -1 231.04 208.98 276.61 241.92 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n654 183 Car -1 -1 -1 -1.30 140.79 191.53 369.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n655 184 Car -1 -1 -1 231.33 209.42 278.13 241.93 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n655 183 Car -1 -1 -1 -1.01 140.50 153.11 370.27 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n656 184 Car -1 -1 -1 230.29 208.94 280.29 242.35 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n656 183 Car -1 -1 -1 -1.91 136.26 107.89 367.06 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n657 184 Car -1 -1 -1 232.78 208.33 281.43 242.86 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n658 184 Car -1 -1 -1 233.53 207.89 282.22 242.74 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n659 184 Car -1 -1 -1 233.38 207.89 282.97 243.03 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n660 184 Car -1 -1 -1 234.10 206.45 283.27 243.30 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n661 184 Car -1 -1 -1 236.30 206.40 285.24 243.61 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n662 184 Car -1 -1 -1 236.67 206.83 287.19 244.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n663 184 Car -1 -1 -1 238.50 207.78 287.41 246.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n664 184 Car -1 -1 -1 239.97 207.94 289.23 246.78 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n665 184 Car -1 -1 -1 242.30 207.76 289.06 246.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n666 184 Car -1 -1 -1 243.17 204.86 290.77 244.09 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n667 184 Car -1 -1 -1 244.56 203.50 292.74 244.53 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n668 184 Car -1 -1 -1 245.30 207.23 293.89 247.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n669 184 Car -1 -1 -1 245.20 209.31 295.43 249.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n670 184 Car -1 -1 -1 248.59 211.23 296.56 252.58 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n671 184 Car -1 -1 -1 250.39 214.59 297.92 255.31 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n672 184 Car -1 -1 -1 251.32 215.85 298.51 257.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n673 184 Car -1 -1 -1 251.20 217.32 301.43 259.74 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n674 184 Car -1 -1 -1 252.41 217.81 301.92 260.82 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n675 184 Car -1 -1 -1 253.16 218.00 302.82 260.65 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n676 184 Car -1 -1 -1 254.93 218.04 305.10 261.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n677 184 Car -1 -1 -1 254.40 218.08 306.59 261.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n678 184 Car -1 -1 -1 255.01 219.10 308.08 262.29 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n679 184 Car -1 -1 -1 258.95 221.86 309.11 265.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n680 184 Car -1 -1 -1 260.81 223.90 312.07 266.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n681 184 Car -1 -1 -1 264.14 224.85 313.91 268.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n682 184 Car -1 -1 -1 268.68 225.09 317.56 268.66 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n683 184 Car -1 -1 -1 269.46 225.81 318.50 268.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n684 184 Car -1 -1 -1 269.15 226.67 318.46 268.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n685 184 Car -1 -1 -1 266.76 227.21 317.73 269.63 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n686 184 Car -1 -1 -1 262.13 226.90 314.71 270.02 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n687 184 Car -1 -1 -1 259.27 225.81 308.78 268.72 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n688 184 Car -1 -1 -1 255.58 225.77 306.19 268.28 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n689 184 Car -1 -1 -1 251.90 224.85 301.99 267.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n690 184 Car -1 -1 -1 247.08 224.83 299.39 267.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n691 184 Car -1 -1 -1 242.13 223.41 295.43 266.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n692 184 Car -1 -1 -1 237.32 224.86 288.40 268.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n693 184 Car -1 -1 -1 230.91 225.24 283.08 268.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n694 184 Car -1 -1 -1 223.91 225.29 275.86 269.97 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n695 184 Car -1 -1 -1 215.76 225.28 267.74 270.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n696 184 Car -1 -1 -1 205.18 225.07 258.78 271.94 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n697 184 Car -1 -1 -1 194.92 224.51 250.23 272.57 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n698 184 Car -1 -1 -1 183.09 226.80 240.42 274.39 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n699 184 Car -1 -1 -1 169.63 226.69 229.21 276.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n700 184 Car -1 -1 -1 154.57 225.15 215.63 276.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n701 184 Car -1 -1 -1 140.39 222.83 203.26 274.94 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n702 184 Car -1 -1 -1 123.74 221.49 182.18 272.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n703 184 Car -1 -1 -1 102.40 217.33 165.32 271.31 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n704 184 Car -1 -1 -1 80.60 217.33 149.99 270.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n705 184 Car -1 -1 -1 56.28 217.04 127.34 270.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n706 184 Car -1 -1 -1 34.58 212.02 110.56 267.01 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n707 184 Car -1 -1 -1 11.30 209.04 86.42 262.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n708 184 Car -1 -1 -1 -0.79 203.26 74.03 262.27 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n709 184 Car -1 -1 -1 1.39 201.76 49.20 262.52 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0012.txt",
    "content": "0 1 Car -1 -1 -1 532.35 192.59 715.14 357.48 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n0 2 Car -1 -1 -1 408.03 173.87 537.04 269.58 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n0 3 Car -1 -1 -1 1059.06 149.13 1196.63 192.38 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n0 4 Car -1 -1 -1 593.36 174.84 665.83 241.85 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n0 5 Car -1 -1 -1 238.47 194.56 299.31 214.94 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n0 6 Car -1 -1 -1 551.32 179.43 584.39 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n0 7 Car -1 -1 -1 508.95 180.28 543.17 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n0 8 Car -1 -1 -1 24.31 199.99 119.00 232.38 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n0 9 Car -1 -1 -1 351.85 188.57 413.26 207.77 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n0 10 Car -1 -1 -1 712.46 172.89 736.53 192.10 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n0 11 Car -1 -1 -1 116.80 199.33 181.83 225.16 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n1 1 Car -1 -1 -1 535.33 192.60 711.72 351.17 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n1 2 Car -1 -1 -1 412.21 174.23 538.27 268.63 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1 3 Car -1 -1 -1 1059.76 149.00 1196.16 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1 4 Car -1 -1 -1 593.25 174.73 665.45 241.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n1 5 Car -1 -1 -1 239.94 194.76 300.34 214.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n1 8 Car -1 -1 -1 24.84 199.68 118.19 232.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n1 6 Car -1 -1 -1 550.17 179.67 581.99 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n1 7 Car -1 -1 -1 504.95 180.80 540.22 201.22 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n1 10 Car -1 -1 -1 712.53 172.80 737.18 192.16 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n1 9 Car -1 -1 -1 350.95 189.11 406.77 207.84 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n2 1 Car -1 -1 -1 535.85 192.44 710.68 347.55 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n2 2 Car -1 -1 -1 417.02 174.31 538.83 266.92 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n2 4 Car -1 -1 -1 592.53 174.58 663.17 237.43 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n2 3 Car -1 -1 -1 1061.17 149.59 1195.35 192.52 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n2 6 Car -1 -1 -1 548.45 179.57 581.19 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n2 5 Car -1 -1 -1 242.09 195.36 303.55 214.51 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n2 8 Car -1 -1 -1 19.49 199.88 117.29 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n2 7 Car -1 -1 -1 504.20 181.40 538.03 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n2 9 Car -1 -1 -1 352.24 188.98 411.51 207.82 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n2 10 Car -1 -1 -1 713.71 172.69 739.74 192.04 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n3 1 Car -1 -1 -1 538.57 192.75 706.59 342.62 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n3 2 Car -1 -1 -1 420.17 174.58 539.88 265.24 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n3 4 Car -1 -1 -1 593.12 174.55 662.74 236.66 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n3 3 Car -1 -1 -1 1061.87 149.42 1199.10 192.48 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n3 8 Car -1 -1 -1 25.28 199.73 117.61 232.24 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n3 5 Car -1 -1 -1 243.83 195.22 305.12 214.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n3 7 Car -1 -1 -1 500.92 181.33 536.36 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n3 6 Car -1 -1 -1 546.66 179.66 579.76 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n3 9 Car -1 -1 -1 352.57 188.82 411.37 207.78 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n3 10 Car -1 -1 -1 713.71 172.70 739.64 192.06 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n4 1 Car -1 -1 -1 542.31 192.83 701.78 336.00 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n4 2 Car -1 -1 -1 422.82 174.62 541.46 263.95 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n4 4 Car -1 -1 -1 592.93 174.08 662.00 236.30 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n4 3 Car -1 -1 -1 1062.08 149.30 1199.11 192.56 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n4 6 Car -1 -1 -1 544.29 179.81 577.70 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n4 5 Car -1 -1 -1 248.92 195.31 307.10 214.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n4 8 Car -1 -1 -1 20.12 199.81 116.83 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n4 7 Car -1 -1 -1 499.59 181.62 536.51 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n4 9 Car -1 -1 -1 352.56 188.88 411.51 207.86 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n4 10 Car -1 -1 -1 713.76 172.94 739.35 192.09 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n4 12 Car -1 -1 -1 120.60 200.47 178.63 223.81 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n5 1 Car -1 -1 -1 543.61 192.80 700.26 332.64 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n5 2 Car -1 -1 -1 426.27 175.46 542.01 262.78 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n5 4 Car -1 -1 -1 593.32 173.73 661.55 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n5 3 Car -1 -1 -1 1062.78 149.31 1199.00 192.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n5 5 Car -1 -1 -1 253.68 195.29 308.92 214.17 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n5 8 Car -1 -1 -1 20.18 199.96 116.54 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n5 7 Car -1 -1 -1 495.93 181.42 534.03 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n5 6 Car -1 -1 -1 542.59 179.99 574.43 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n5 9 Car -1 -1 -1 350.55 189.11 407.42 207.99 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n5 10 Car -1 -1 -1 714.13 173.11 739.49 192.04 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n5 12 Car -1 -1 -1 120.30 200.73 178.84 224.01 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n6 1 Car -1 -1 -1 546.99 193.34 696.71 326.67 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n6 2 Car -1 -1 -1 429.54 175.68 542.87 260.42 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n6 4 Car -1 -1 -1 594.01 173.68 660.76 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n6 3 Car -1 -1 -1 1064.13 149.29 1198.70 192.46 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n6 6 Car -1 -1 -1 540.10 180.30 572.76 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n6 5 Car -1 -1 -1 257.18 195.62 311.23 214.38 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n6 7 Car -1 -1 -1 495.96 181.36 532.46 204.28 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n6 8 Car -1 -1 -1 20.81 200.30 115.55 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n6 9 Car -1 -1 -1 350.34 189.20 407.12 207.98 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n6 10 Car -1 -1 -1 714.07 173.26 739.88 192.41 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n7 1 Car -1 -1 -1 548.23 193.14 695.26 323.00 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n7 2 Car -1 -1 -1 432.37 175.55 543.84 259.72 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n7 4 Car -1 -1 -1 594.13 174.92 660.66 235.53 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n7 3 Car -1 -1 -1 1064.49 149.79 1199.36 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n7 5 Car -1 -1 -1 259.23 196.10 312.12 214.54 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n7 6 Car -1 -1 -1 537.24 181.16 570.85 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n7 7 Car -1 -1 -1 492.35 181.36 528.77 205.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n7 8 Car -1 -1 -1 21.37 200.22 115.03 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n7 9 Car -1 -1 -1 350.31 190.10 407.22 209.11 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n7 10 Car -1 -1 -1 714.74 175.35 739.80 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n7 13 Car -1 -1 -1 120.20 202.07 178.57 224.49 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n8 1 Car -1 -1 -1 549.12 193.53 691.48 318.18 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n8 2 Car -1 -1 -1 434.80 176.29 545.30 259.50 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n8 4 Car -1 -1 -1 593.87 175.29 660.89 236.42 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n8 3 Car -1 -1 -1 1064.12 151.30 1204.78 194.34 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n8 5 Car -1 -1 -1 262.23 196.63 313.76 215.43 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n8 7 Car -1 -1 -1 492.46 182.24 527.85 204.78 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n8 8 Car -1 -1 -1 21.17 200.86 114.79 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n8 9 Car -1 -1 -1 350.45 190.56 406.22 209.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n8 6 Car -1 -1 -1 535.33 181.83 568.61 200.33 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n8 10 Car -1 -1 -1 714.39 175.35 739.16 194.38 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n8 13 Car -1 -1 -1 135.44 203.85 210.12 227.05 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n8 14 Car -1 -1 -1 118.64 202.12 180.31 225.10 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n9 1 Car -1 -1 -1 550.71 194.35 689.78 315.32 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n9 2 Car -1 -1 -1 437.29 177.09 545.58 259.02 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n9 4 Car -1 -1 -1 594.11 175.86 660.98 236.33 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n9 3 Car -1 -1 -1 1065.46 152.31 1205.12 194.97 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n9 6 Car -1 -1 -1 533.90 182.77 566.22 201.77 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n9 7 Car -1 -1 -1 488.14 181.63 525.15 205.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n9 5 Car -1 -1 -1 263.61 197.37 314.88 215.74 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n9 8 Car -1 -1 -1 20.75 201.38 114.95 234.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n9 9 Car -1 -1 -1 350.53 191.28 406.30 210.57 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n9 10 Car -1 -1 -1 714.39 176.20 738.73 194.85 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n9 13 Car -1 -1 -1 137.22 204.51 208.57 227.05 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n9 14 Car -1 -1 -1 118.60 204.33 180.50 226.72 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n10 1 Car -1 -1 -1 554.15 195.16 686.56 309.97 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n10 2 Car -1 -1 -1 439.33 177.51 545.28 258.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n10 4 Car -1 -1 -1 594.87 176.09 661.17 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n10 3 Car -1 -1 -1 1067.62 153.15 1208.85 195.56 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 8 Car -1 -1 -1 20.60 202.87 114.26 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n10 5 Car -1 -1 -1 266.35 198.40 316.86 217.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n10 7 Car -1 -1 -1 486.14 183.31 526.20 209.81 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n10 6 Car -1 -1 -1 531.18 183.74 564.48 202.39 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n10 13 Car -1 -1 -1 137.13 205.24 208.85 227.87 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n10 9 Car -1 -1 -1 350.61 192.13 405.75 211.37 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n10 14 Car -1 -1 -1 114.11 204.02 177.14 226.97 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n11 1 Car -1 -1 -1 557.11 194.54 686.01 306.51 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n11 2 Car -1 -1 -1 441.26 178.51 545.94 257.35 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n11 3 Car -1 -1 -1 1069.88 153.21 1209.04 196.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n11 4 Car -1 -1 -1 595.42 176.02 661.12 235.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n11 5 Car -1 -1 -1 268.07 198.75 317.02 217.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n11 6 Car -1 -1 -1 528.73 183.96 562.30 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n11 7 Car -1 -1 -1 481.69 182.98 523.89 210.37 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n11 8 Car -1 -1 -1 19.14 203.28 109.71 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n11 13 Car -1 -1 -1 136.99 205.68 209.40 228.02 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n11 9 Car -1 -1 -1 350.93 192.49 405.65 211.63 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n11 14 Car -1 -1 -1 114.81 204.12 176.29 226.88 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n12 2 Car -1 -1 -1 442.40 178.68 546.73 256.77 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n12 1 Car -1 -1 -1 559.63 194.37 683.53 301.84 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n12 3 Car -1 -1 -1 1072.62 152.54 1212.76 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n12 4 Car -1 -1 -1 597.65 176.14 661.11 234.59 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n12 5 Car -1 -1 -1 269.61 199.00 317.92 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 8 Car -1 -1 -1 19.89 203.41 108.30 236.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n12 13 Car -1 -1 -1 133.86 205.92 211.82 229.22 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n12 6 Car -1 -1 -1 527.91 184.01 559.87 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n12 9 Car -1 -1 -1 350.80 192.57 405.35 211.91 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n12 14 Car -1 -1 -1 113.91 204.36 177.17 227.40 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n12 15 Car -1 -1 -1 713.47 177.49 739.30 196.16 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n12 16 Car -1 -1 -1 570.76 181.05 599.04 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n13 2 Car -1 -1 -1 444.02 179.10 547.54 256.17 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n13 1 Car -1 -1 -1 561.44 194.31 681.85 299.16 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n13 4 Car -1 -1 -1 598.25 176.36 660.82 234.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n13 3 Car -1 -1 -1 1073.83 152.56 1218.45 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n13 8 Car -1 -1 -1 18.53 203.55 108.21 237.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n13 6 Car -1 -1 -1 524.81 184.23 556.93 203.63 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n13 5 Car -1 -1 -1 270.71 199.14 318.17 217.90 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n13 13 Car -1 -1 -1 134.64 206.00 209.90 229.15 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n13 16 Car -1 -1 -1 568.76 181.64 599.20 200.00 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n13 14 Car -1 -1 -1 113.52 204.66 177.21 227.41 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n13 15 Car -1 -1 -1 713.75 177.52 741.02 195.91 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n13 9 Car -1 -1 -1 350.31 192.84 405.29 211.99 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n13 17 Car -1 -1 -1 204.08 200.77 258.29 222.25 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n14 1 Car -1 -1 -1 564.01 194.18 679.71 295.78 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n14 2 Car -1 -1 -1 445.15 180.10 547.19 255.57 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n14 4 Car -1 -1 -1 598.68 176.60 660.80 234.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n14 3 Car -1 -1 -1 1076.36 153.21 1218.67 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 8 Car -1 -1 -1 15.55 203.91 106.16 237.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n14 5 Car -1 -1 -1 271.34 199.85 319.85 217.93 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n14 6 Car -1 -1 -1 522.67 184.33 554.61 204.21 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n14 13 Car -1 -1 -1 130.70 206.81 206.59 229.41 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n14 15 Car -1 -1 -1 713.69 177.98 740.96 196.16 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n14 9 Car -1 -1 -1 349.32 193.81 400.39 213.91 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n14 16 Car -1 -1 -1 567.38 182.85 598.25 201.28 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n14 17 Car -1 -1 -1 204.47 200.91 257.08 222.76 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n14 14 Car -1 -1 -1 110.63 204.99 172.56 227.76 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n15 1 Car -1 -1 -1 564.84 193.73 678.99 293.45 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n15 2 Car -1 -1 -1 446.79 180.57 548.22 254.69 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n15 4 Car -1 -1 -1 599.72 176.64 660.57 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n15 8 Car -1 -1 -1 12.90 204.10 106.83 237.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n15 3 Car -1 -1 -1 1079.57 153.55 1222.97 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n15 5 Car -1 -1 -1 272.44 200.23 318.78 218.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n15 6 Car -1 -1 -1 522.69 184.53 552.31 204.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n15 16 Car -1 -1 -1 564.04 183.64 595.58 202.17 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n15 13 Car -1 -1 -1 127.77 207.91 201.85 230.44 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n15 14 Car -1 -1 -1 106.58 205.78 169.46 227.91 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n15 17 Car -1 -1 -1 200.68 201.40 254.54 223.37 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n15 15 Car -1 -1 -1 714.63 178.95 742.32 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n15 9 Car -1 -1 -1 347.88 194.05 400.89 214.29 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n16 1 Car -1 -1 -1 566.07 193.33 678.51 291.63 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n16 2 Car -1 -1 -1 447.78 181.25 546.99 254.42 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n16 4 Car -1 -1 -1 600.34 176.70 660.83 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n16 3 Car -1 -1 -1 1082.95 154.36 1226.91 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n16 8 Car -1 -1 -1 9.56 204.68 103.85 238.50 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n16 16 Car -1 -1 -1 561.00 184.32 593.62 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n16 13 Car -1 -1 -1 127.56 207.72 202.28 230.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n16 5 Car -1 -1 -1 273.47 200.68 317.46 218.28 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n16 15 Car -1 -1 -1 714.72 178.68 741.88 197.41 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n16 6 Car -1 -1 -1 519.26 184.70 549.84 204.91 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n16 9 Car -1 -1 -1 345.48 194.61 396.57 214.98 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n16 14 Car -1 -1 -1 104.96 205.56 170.31 228.30 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n16 18 Car -1 -1 -1 444.21 187.75 498.47 215.55 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n17 1 Car -1 -1 -1 568.21 192.93 677.10 288.79 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n17 2 Car -1 -1 -1 448.12 181.06 547.04 254.64 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n17 4 Car -1 -1 -1 601.36 176.26 661.43 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n17 3 Car -1 -1 -1 1087.59 154.25 1230.29 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n17 8 Car -1 -1 -1 7.49 204.50 103.34 239.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n17 5 Car -1 -1 -1 274.11 200.51 314.54 218.54 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n17 18 Car -1 -1 -1 437.29 188.43 490.75 216.47 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n17 16 Car -1 -1 -1 558.08 184.42 592.89 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n17 6 Car -1 -1 -1 519.26 185.93 547.24 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n17 13 Car -1 -1 -1 124.16 208.17 198.49 230.80 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n17 9 Car -1 -1 -1 342.47 194.89 391.49 214.60 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n17 15 Car -1 -1 -1 715.14 178.28 742.01 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n17 14 Car -1 -1 -1 102.29 206.47 165.47 228.11 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n17 19 Car -1 -1 -1 117.79 209.75 181.14 230.62 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n18 1 Car -1 -1 -1 569.39 192.81 676.45 286.51 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n18 2 Car -1 -1 -1 448.27 180.34 546.55 254.54 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n18 4 Car -1 -1 -1 601.58 175.83 661.91 232.52 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n18 3 Car -1 -1 -1 1091.29 153.28 1234.38 197.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n18 8 Car -1 -1 -1 5.10 204.08 101.19 239.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n18 5 Car -1 -1 -1 273.80 200.45 313.57 218.44 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n18 16 Car -1 -1 -1 555.22 184.13 590.31 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n18 18 Car -1 -1 -1 432.18 188.69 481.50 218.58 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n18 6 Car -1 -1 -1 514.12 186.00 545.66 206.02 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n18 13 Car -1 -1 -1 122.59 208.12 199.01 230.86 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n18 15 Car -1 -1 -1 715.48 178.07 741.92 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n18 9 Car -1 -1 -1 341.71 194.74 391.69 215.04 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n18 14 Car -1 -1 -1 97.67 206.31 163.14 228.90 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n18 20 Car -1 -1 -1 193.54 201.39 252.49 224.02 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n19 1 Car -1 -1 -1 570.43 192.87 676.47 285.19 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n19 2 Car -1 -1 -1 448.47 180.63 545.99 254.38 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n19 3 Car -1 -1 -1 1095.72 153.58 1236.29 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n19 4 Car -1 -1 -1 605.76 176.75 661.02 227.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n19 5 Car -1 -1 -1 273.77 200.56 313.03 218.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n19 8 Car -1 -1 -1 2.94 204.75 99.74 241.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n19 18 Car -1 -1 -1 424.21 189.36 478.85 219.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n19 13 Car -1 -1 -1 119.18 208.36 195.10 231.12 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n19 16 Car -1 -1 -1 552.51 184.15 587.71 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n19 15 Car -1 -1 -1 715.78 177.71 741.50 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n19 6 Car -1 -1 -1 513.33 186.96 544.54 206.37 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n19 14 Car -1 -1 -1 97.91 206.31 162.37 228.37 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n19 9 Car -1 -1 -1 338.98 195.67 387.83 215.04 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n20 1 Car -1 -1 -1 571.77 192.98 676.13 283.81 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n20 2 Car -1 -1 -1 448.90 180.91 545.36 254.77 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n20 4 Car -1 -1 -1 607.16 176.40 661.81 226.74 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n20 3 Car -1 -1 -1 1098.68 153.91 1235.68 198.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n20 8 Car -1 -1 -1 2.42 204.91 94.42 241.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n20 5 Car -1 -1 -1 273.55 200.80 311.65 218.95 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n20 16 Car -1 -1 -1 549.38 184.06 585.72 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n20 18 Car -1 -1 -1 417.84 189.71 472.97 219.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n20 13 Car -1 -1 -1 117.85 208.25 195.87 230.96 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n20 6 Car -1 -1 -1 504.49 186.94 540.39 206.75 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n20 15 Car -1 -1 -1 716.39 178.27 744.00 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n20 9 Car -1 -1 -1 336.62 195.54 389.05 215.58 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n20 14 Car -1 -1 -1 94.00 206.55 159.22 228.97 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n21 2 Car -1 -1 -1 448.15 180.87 544.04 254.84 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n21 1 Car -1 -1 -1 573.72 192.67 674.93 282.02 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n21 4 Car -1 -1 -1 608.19 176.20 662.53 226.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n21 3 Car -1 -1 -1 1103.87 154.06 1235.57 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n21 18 Car -1 -1 -1 410.84 189.96 469.91 220.78 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n21 16 Car -1 -1 -1 546.32 184.37 583.34 203.51 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n21 8 Car -1 -1 -1 2.28 204.73 92.97 241.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n21 13 Car -1 -1 -1 114.35 208.75 192.43 230.90 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n21 15 Car -1 -1 -1 717.22 177.94 744.03 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n21 6 Car -1 -1 -1 499.88 187.23 536.97 207.14 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n21 14 Car -1 -1 -1 90.29 207.26 155.33 228.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n21 5 Car -1 -1 -1 273.12 200.91 308.12 219.46 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n21 9 Car -1 -1 -1 333.52 195.27 385.65 216.11 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n22 1 Car -1 -1 -1 576.00 193.06 675.41 281.29 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n22 2 Car -1 -1 -1 447.59 181.69 543.16 256.31 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n22 3 Car -1 -1 -1 1109.81 154.29 1236.70 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n22 4 Car -1 -1 -1 608.85 176.45 662.97 226.00 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n22 8 Car -1 -1 -1 2.08 205.45 87.37 242.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n22 18 Car -1 -1 -1 402.35 190.16 468.43 222.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n22 15 Car -1 -1 -1 717.29 178.11 744.16 198.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n22 13 Car -1 -1 -1 111.92 208.51 193.70 231.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n22 16 Car -1 -1 -1 543.34 185.05 580.59 204.12 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n22 6 Car -1 -1 -1 495.14 186.67 533.50 207.93 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n22 5 Car -1 -1 -1 272.83 201.63 306.00 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n22 14 Car -1 -1 -1 88.62 206.80 155.19 229.01 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n22 9 Car -1 -1 -1 330.93 195.36 386.50 216.38 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n23 2 Car -1 -1 -1 446.31 182.13 541.69 257.17 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n23 1 Car -1 -1 -1 576.60 193.47 674.87 280.47 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n23 3 Car -1 -1 -1 1113.66 154.65 1235.66 200.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n23 4 Car -1 -1 -1 609.31 177.06 662.45 225.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n23 18 Car -1 -1 -1 393.63 191.48 461.80 224.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n23 8 Car -1 -1 -1 2.43 205.97 85.29 242.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n23 15 Car -1 -1 -1 717.05 178.64 744.09 199.27 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n23 5 Car -1 -1 -1 270.96 201.88 305.90 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n23 13 Car -1 -1 -1 107.21 209.22 191.05 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n23 6 Car -1 -1 -1 490.82 186.07 529.84 208.62 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n23 16 Car -1 -1 -1 539.54 186.17 577.20 205.33 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n23 14 Car -1 -1 -1 85.46 208.00 151.20 230.50 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n23 9 Car -1 -1 -1 323.18 197.85 379.56 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n24 1 Car -1 -1 -1 576.82 193.38 674.40 279.99 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n24 2 Car -1 -1 -1 444.69 182.60 539.70 258.01 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n24 3 Car -1 -1 -1 1118.72 154.51 1235.78 200.74 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n24 18 Car -1 -1 -1 383.72 192.46 450.48 226.59 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n24 15 Car -1 -1 -1 716.64 178.84 744.62 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n24 4 Car -1 -1 -1 609.81 177.39 662.25 224.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n24 5 Car -1 -1 -1 269.18 202.35 302.52 221.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 8 Car -1 -1 -1 2.49 207.36 78.03 241.65 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n24 16 Car -1 -1 -1 535.94 186.69 572.87 205.73 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n24 13 Car -1 -1 -1 104.34 209.99 186.10 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n24 14 Car -1 -1 -1 81.24 208.26 147.78 231.81 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n24 9 Car -1 -1 -1 319.88 198.14 375.26 218.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n24 21 Car -1 -1 -1 171.37 204.39 236.37 227.90 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n25 2 Car -1 -1 -1 442.42 183.12 538.06 258.21 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n25 1 Car -1 -1 -1 576.91 192.54 674.03 279.52 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n25 18 Car -1 -1 -1 373.89 192.47 442.92 227.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n25 3 Car -1 -1 -1 1122.20 153.91 1236.03 200.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n25 4 Car -1 -1 -1 612.17 177.28 662.41 223.99 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n25 15 Car -1 -1 -1 716.60 178.25 744.52 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n25 5 Car -1 -1 -1 265.55 202.55 299.36 223.13 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n25 16 Car -1 -1 -1 532.18 186.96 568.70 206.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n25 8 Car -1 -1 -1 2.20 208.48 70.42 241.27 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n25 13 Car -1 -1 -1 101.88 210.04 181.06 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n25 14 Car -1 -1 -1 77.37 208.75 143.26 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n25 9 Car -1 -1 -1 316.57 198.73 371.07 218.28 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n26 2 Car -1 -1 -1 439.52 182.76 536.04 259.46 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n26 1 Car -1 -1 -1 577.38 192.66 673.65 278.73 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n26 18 Car -1 -1 -1 361.81 193.64 433.78 230.00 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n26 3 Car -1 -1 -1 1128.58 153.46 1236.88 200.96 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n26 4 Car -1 -1 -1 613.02 177.84 662.37 224.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n26 15 Car -1 -1 -1 716.57 178.37 744.77 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n26 16 Car -1 -1 -1 526.51 187.33 565.63 206.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n26 8 Car -1 -1 -1 2.77 209.31 64.60 245.64 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n26 5 Car -1 -1 -1 261.36 203.23 296.15 223.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n26 13 Car -1 -1 -1 96.42 211.49 170.54 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n26 9 Car -1 -1 -1 315.61 198.54 371.41 218.63 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n26 14 Car -1 -1 -1 65.39 210.12 140.30 236.53 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n27 1 Car -1 -1 -1 577.64 192.28 673.76 278.04 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n27 2 Car -1 -1 -1 437.52 182.48 534.13 259.82 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n27 18 Car -1 -1 -1 348.71 193.07 424.67 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n27 4 Car -1 -1 -1 613.63 177.76 662.73 224.20 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n27 15 Car -1 -1 -1 717.09 177.47 745.29 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n27 16 Car -1 -1 -1 521.22 186.94 561.58 206.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n27 3 Car -1 -1 -1 1136.41 153.09 1237.14 200.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n27 13 Car -1 -1 -1 87.21 211.30 164.45 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n27 5 Car -1 -1 -1 257.39 202.69 292.21 224.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n27 14 Car -1 -1 -1 61.50 210.00 136.23 236.59 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n27 8 Car -1 -1 -1 1.50 209.26 58.67 245.91 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n27 9 Car -1 -1 -1 313.01 197.52 366.68 219.24 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n27 6 Car -1 -1 -1 473.21 184.68 516.45 209.98 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n27 23 Car -1 -1 -1 165.05 204.35 227.08 228.05 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n28 2 Car -1 -1 -1 433.99 180.81 532.03 259.79 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n28 1 Car -1 -1 -1 578.49 190.77 673.49 275.61 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n28 18 Car -1 -1 -1 338.57 192.18 412.91 232.50 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n28 4 Car -1 -1 -1 613.91 176.47 664.04 224.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n28 3 Car -1 -1 -1 1144.22 150.07 1237.15 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 16 Car -1 -1 -1 516.32 186.07 557.61 206.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n28 5 Car -1 -1 -1 251.75 201.81 287.07 223.59 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n28 15 Car -1 -1 -1 716.45 177.01 744.72 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n28 13 Car -1 -1 -1 77.69 210.70 158.59 236.88 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n28 8 Car -1 -1 -1 0.94 208.15 50.90 241.72 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n28 7 Car -1 -1 -1 449.72 186.06 501.06 216.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n28 14 Car -1 -1 -1 56.28 209.63 126.28 236.59 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n28 9 Car -1 -1 -1 308.88 197.21 362.86 219.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n29 2 Car -1 -1 -1 429.55 179.68 529.85 259.77 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n29 1 Car -1 -1 -1 577.80 189.04 674.16 273.29 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n29 18 Car -1 -1 -1 323.94 191.86 402.88 233.54 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n29 15 Car -1 -1 -1 716.87 174.92 745.73 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n29 3 Car -1 -1 -1 1156.44 149.04 1236.49 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n29 4 Car -1 -1 -1 614.98 175.23 664.53 220.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n29 16 Car -1 -1 -1 511.91 184.08 553.51 205.41 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n29 5 Car -1 -1 -1 247.30 200.86 285.86 222.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n29 13 Car -1 -1 -1 72.84 210.29 155.87 236.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n29 8 Car -1 -1 -1 1.39 207.99 42.12 241.08 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n29 7 Car -1 -1 -1 447.43 185.55 503.43 216.46 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n29 14 Car -1 -1 -1 53.05 208.35 121.13 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n29 24 Car -1 -1 -1 152.51 203.21 217.48 227.75 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n30 1 Car -1 -1 -1 578.22 187.33 673.54 269.95 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n30 2 Car -1 -1 -1 426.10 177.17 526.49 258.44 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n30 18 Car -1 -1 -1 301.96 190.55 390.73 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n30 15 Car -1 -1 -1 716.77 172.89 745.87 195.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 3 Car -1 -1 -1 1164.94 146.09 1236.23 195.49 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n30 5 Car -1 -1 -1 241.85 199.00 281.66 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n30 4 Car -1 -1 -1 617.26 173.45 664.70 219.69 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n30 16 Car -1 -1 -1 508.03 182.54 548.89 204.05 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n30 8 Car -1 -1 -1 0.93 205.85 35.51 242.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n30 7 Car -1 -1 -1 443.70 182.53 499.46 212.57 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n30 14 Car -1 -1 -1 50.81 207.59 115.45 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n30 13 Car -1 -1 -1 68.82 207.97 144.72 234.33 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n30 24 Car -1 -1 -1 153.31 203.19 214.70 224.53 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n31 1 Car -1 -1 -1 578.31 185.74 674.00 268.81 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n31 2 Car -1 -1 -1 422.09 176.25 523.81 259.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n31 18 Car -1 -1 -1 282.16 190.01 378.90 237.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n31 4 Car -1 -1 -1 618.13 172.54 665.38 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n31 15 Car -1 -1 -1 717.29 171.44 746.11 194.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n31 3 Car -1 -1 -1 1173.03 146.12 1236.59 194.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n31 16 Car -1 -1 -1 502.08 181.95 544.36 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n31 5 Car -1 -1 -1 234.85 198.74 279.97 221.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 8 Car -1 -1 -1 -0.26 204.81 28.79 242.56 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n31 14 Car -1 -1 -1 40.34 206.83 103.47 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n31 13 Car -1 -1 -1 61.01 206.72 136.98 234.85 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n31 24 Car -1 -1 -1 146.60 202.50 207.66 224.58 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n32 1 Car -1 -1 -1 578.70 185.76 673.54 267.87 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n32 2 Car -1 -1 -1 418.15 177.55 522.03 260.63 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n32 18 Car -1 -1 -1 258.75 190.19 364.86 243.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n32 4 Car -1 -1 -1 618.91 172.07 666.94 215.32 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n32 16 Car -1 -1 -1 498.03 182.12 539.54 204.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n32 15 Car -1 -1 -1 717.53 171.58 746.13 194.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n32 3 Car -1 -1 -1 1179.99 147.23 1236.99 194.41 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n32 5 Car -1 -1 -1 228.42 199.84 278.57 222.73 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n32 8 Car -1 -1 -1 -1.72 203.84 22.66 239.10 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n32 7 Car -1 -1 -1 422.22 184.23 474.51 216.43 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n32 13 Car -1 -1 -1 52.09 206.77 130.71 235.30 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n32 14 Car -1 -1 -1 36.61 207.13 99.40 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n32 24 Car -1 -1 -1 140.99 202.35 204.42 224.99 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n33 2 Car -1 -1 -1 413.67 177.27 519.22 262.20 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n33 1 Car -1 -1 -1 578.87 186.17 673.46 267.39 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n33 18 Car -1 -1 -1 232.61 191.32 351.04 248.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n33 4 Car -1 -1 -1 620.70 172.19 669.05 214.56 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n33 16 Car -1 -1 -1 492.39 182.38 534.91 205.78 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n33 15 Car -1 -1 -1 717.65 171.73 746.55 194.66 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n33 7 Car -1 -1 -1 408.91 184.68 471.44 217.67 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n33 5 Car -1 -1 -1 223.60 199.63 275.94 223.87 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n33 14 Car -1 -1 -1 28.90 207.15 91.78 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n33 13 Car -1 -1 -1 41.76 206.76 125.88 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n33 3 Car -1 -1 -1 1186.49 146.99 1239.03 194.56 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n33 24 Car -1 -1 -1 136.60 202.33 200.69 225.09 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n33 25 Car -1 -1 -1 563.26 178.84 596.16 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n34 1 Car -1 -1 -1 580.31 185.22 672.99 265.17 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n34 2 Car -1 -1 -1 407.96 176.63 517.22 262.99 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n34 18 Car -1 -1 -1 206.55 191.72 331.29 252.03 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n34 4 Car -1 -1 -1 621.48 171.93 670.03 214.54 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n34 5 Car -1 -1 -1 216.49 199.64 268.31 226.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n34 16 Car -1 -1 -1 484.39 182.48 529.50 206.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n34 7 Car -1 -1 -1 397.89 185.74 459.58 218.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n34 15 Car -1 -1 -1 718.04 171.90 746.55 194.68 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n34 25 Car -1 -1 -1 560.17 178.76 594.05 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n34 3 Car -1 -1 -1 1192.68 149.94 1240.86 197.21 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n34 13 Car -1 -1 -1 40.64 206.59 118.13 235.53 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n34 14 Car -1 -1 -1 11.33 215.87 61.86 234.87 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n35 1 Car -1 -1 -1 581.29 184.94 672.77 265.14 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n35 2 Car -1 -1 -1 403.61 176.37 513.61 265.02 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n35 18 Car -1 -1 -1 171.66 193.98 312.31 256.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n35 4 Car -1 -1 -1 622.47 172.12 671.42 214.00 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n35 5 Car -1 -1 -1 208.46 199.46 268.32 226.84 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n35 7 Car -1 -1 -1 386.03 185.96 454.30 221.40 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n35 16 Car -1 -1 -1 479.50 182.91 524.86 206.58 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n35 15 Car -1 -1 -1 718.53 172.10 746.07 194.55 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n35 25 Car -1 -1 -1 556.90 179.29 590.03 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n35 3 Car -1 -1 -1 1211.11 150.79 1237.76 196.14 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n36 2 Car -1 -1 -1 398.33 178.02 511.15 268.83 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n36 1 Car -1 -1 -1 581.82 185.48 674.22 265.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n36 18 Car -1 -1 -1 131.57 197.00 292.15 266.70 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n36 4 Car -1 -1 -1 625.65 173.64 671.87 213.20 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n36 7 Car -1 -1 -1 372.72 187.73 444.66 224.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n36 15 Car -1 -1 -1 718.75 173.93 745.80 195.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n36 16 Car -1 -1 -1 471.00 184.49 519.97 208.78 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n36 25 Car -1 -1 -1 553.12 181.42 586.13 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n36 3 Car -1 -1 -1 1220.69 150.45 1237.54 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n36 27 Car -1 -1 -1 268.41 194.58 334.20 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n36 28 Car -1 -1 -1 23.21 210.20 105.32 237.60 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n37 1 Car -1 -1 -1 584.61 187.27 677.68 267.71 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n37 2 Car -1 -1 -1 390.91 178.71 508.22 272.22 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n37 18 Car -1 -1 -1 84.53 199.16 267.63 280.11 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n37 7 Car -1 -1 -1 360.00 190.12 433.89 227.88 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n37 4 Car -1 -1 -1 625.80 174.89 671.75 213.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n37 27 Car -1 -1 -1 268.53 195.97 333.29 219.61 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n37 25 Car -1 -1 -1 548.98 182.74 586.87 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n37 16 Car -1 -1 -1 466.01 186.11 514.73 210.37 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n37 15 Car -1 -1 -1 718.82 175.41 749.81 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n38 2 Car -1 -1 -1 384.64 180.42 504.57 274.73 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n38 1 Car -1 -1 -1 586.69 187.62 683.19 267.11 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n38 18 Car -1 -1 -1 25.65 202.74 227.53 290.56 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n38 25 Car -1 -1 -1 544.63 183.51 583.64 203.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n38 27 Car -1 -1 -1 264.18 195.82 330.77 220.01 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n38 4 Car -1 -1 -1 627.27 175.38 673.29 212.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n38 7 Car -1 -1 -1 344.54 190.20 420.12 230.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n38 16 Car -1 -1 -1 458.79 187.07 508.12 212.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n38 15 Car -1 -1 -1 719.15 176.83 746.61 199.49 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n39 2 Car -1 -1 -1 375.80 180.00 499.13 278.05 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n39 1 Car -1 -1 -1 589.31 188.13 687.39 266.61 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n39 18 Car -1 -1 -1 1.12 201.49 195.50 303.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n39 7 Car -1 -1 -1 327.16 191.45 407.93 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n39 27 Car -1 -1 -1 258.58 196.74 328.54 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n39 4 Car -1 -1 -1 628.10 175.26 672.55 211.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n39 25 Car -1 -1 -1 539.57 184.55 579.77 203.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n39 15 Car -1 -1 -1 718.84 177.01 746.95 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n39 16 Car -1 -1 -1 456.36 188.01 501.22 213.49 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n40 2 Car -1 -1 -1 365.17 180.42 493.55 281.51 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n40 1 Car -1 -1 -1 592.06 187.79 691.62 266.21 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n40 7 Car -1 -1 -1 307.08 192.41 395.13 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n40 18 Car -1 -1 -1 1.08 203.21 148.55 322.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n40 27 Car -1 -1 -1 251.41 197.00 327.10 222.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n40 15 Car -1 -1 -1 718.66 177.26 747.07 200.29 -1 -1 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-1 -1000 -1000 -1000 -10 0.62\n42 2 Car -1 -1 -1 343.08 180.92 482.21 289.57 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n42 1 Car -1 -1 -1 599.41 187.64 703.02 266.40 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n42 7 Car -1 -1 -1 257.17 195.20 361.89 245.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n42 25 Car -1 -1 -1 525.02 186.21 564.64 206.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n42 27 Car -1 -1 -1 239.76 198.35 315.46 226.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n42 15 Car -1 -1 -1 717.46 178.24 748.24 202.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n42 16 Car -1 -1 -1 437.74 189.64 480.55 213.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n42 4 Car -1 -1 -1 627.64 176.79 672.03 211.18 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n42 18 Car -1 -1 -1 0.18 213.24 17.85 336.26 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n42 29 Car -1 -1 -1 126.47 206.01 196.14 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n42 30 Car -1 -1 -1 571.32 181.79 605.70 200.21 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n43 2 Car -1 -1 -1 328.10 179.81 475.13 294.68 -1 -1 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-1 -1000 -1000 -1000 -10 0.87\n44 25 Car -1 -1 -1 511.61 187.54 555.18 209.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n44 15 Car -1 -1 -1 715.95 179.16 748.28 204.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n44 30 Car -1 -1 -1 561.98 184.03 597.15 203.86 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n44 16 Car -1 -1 -1 412.82 191.25 461.18 218.47 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n44 4 Car -1 -1 -1 629.24 178.96 671.67 215.63 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n45 2 Car -1 -1 -1 289.92 183.41 458.73 309.95 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n45 1 Car -1 -1 -1 616.48 189.16 728.59 269.72 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n45 7 Car -1 -1 -1 143.14 200.85 292.99 265.40 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n45 15 Car -1 -1 -1 713.86 180.21 747.88 206.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n45 29 Car -1 -1 -1 52.00 209.81 146.15 241.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n45 25 Car -1 -1 -1 504.35 189.08 550.03 211.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n45 16 Car -1 -1 -1 398.79 189.88 451.55 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n45 30 Car -1 -1 -1 557.08 184.69 593.07 204.71 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n46 2 Car -1 -1 -1 262.98 183.23 447.64 319.97 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n46 1 Car -1 -1 -1 622.11 190.66 738.69 271.81 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n46 7 Car -1 -1 -1 87.23 203.72 259.78 275.76 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n46 29 Car -1 -1 -1 25.11 212.98 125.76 244.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n46 25 Car -1 -1 -1 496.00 190.26 542.85 213.55 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n46 15 Car -1 -1 -1 711.42 181.53 745.59 207.83 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n46 30 Car -1 -1 -1 549.65 186.68 586.04 207.13 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n46 16 Car -1 -1 -1 372.42 191.36 439.05 226.17 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n46 32 Car -1 -1 -1 603.28 184.56 635.36 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n46 33 Car -1 -1 -1 628.93 183.52 664.50 211.91 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n47 1 Car -1 -1 -1 629.32 190.95 750.36 272.64 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-1 -1 -1000 -1000 -1000 -10 0.94\n48 33 Car -1 -1 -1 625.38 183.87 669.25 220.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n48 25 Car -1 -1 -1 478.94 192.03 528.54 215.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n48 30 Car -1 -1 -1 538.06 188.38 574.88 208.50 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n48 29 Car -1 -1 -1 2.26 214.13 63.27 250.68 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n48 32 Car -1 -1 -1 592.73 185.22 627.37 203.99 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n48 31 Car -1 -1 -1 182.84 202.98 270.99 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n48 15 Car -1 -1 -1 706.31 183.54 741.91 209.87 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n49 1 Car -1 -1 -1 649.94 188.12 780.69 271.06 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n49 2 Car -1 -1 -1 161.07 184.17 408.57 358.55 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n49 33 Car -1 -1 -1 624.87 182.34 669.31 220.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n49 25 Car -1 -1 -1 470.39 191.68 521.53 215.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n49 7 Car -1 -1 -1 0.82 207.70 120.61 318.71 -1 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Car -1 -1 -1 477.98 165.11 521.75 196.83 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n516 234 Car -1 -1 -1 730.98 171.52 825.07 200.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n516 233 Cyclist -1 -1 -1 858.27 142.12 1023.79 368.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n516 235 Car -1 -1 -1 532.12 174.08 599.98 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n516 229 Car -1 -1 -1 478.15 165.11 521.74 196.89 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n517 234 Car -1 -1 -1 722.67 171.51 817.59 201.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n517 233 Cyclist -1 -1 -1 858.63 142.51 1017.09 367.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n517 235 Car -1 -1 -1 515.12 174.26 585.87 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n517 229 Car -1 -1 -1 478.18 165.06 521.71 196.90 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n518 234 Car -1 -1 -1 716.89 171.21 807.64 201.32 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n518 233 Cyclist -1 -1 -1 860.60 141.63 1020.54 369.23 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n518 235 Car -1 -1 -1 500.22 176.14 568.29 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n518 229 Car -1 -1 -1 478.45 165.04 521.36 196.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n519 234 Car -1 -1 -1 702.78 171.16 798.15 202.05 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n519 235 Car -1 -1 -1 485.35 176.43 552.62 200.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n519 233 Cyclist -1 -1 -1 859.07 142.13 1016.65 368.37 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n519 229 Car -1 -1 -1 478.93 165.44 521.22 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n520 234 Car -1 -1 -1 690.86 171.05 789.69 202.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n520 235 Car -1 -1 -1 469.53 176.10 537.00 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n520 233 Cyclist -1 -1 -1 857.86 141.84 1017.42 368.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n520 229 Car -1 -1 -1 478.66 165.31 521.45 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n520 236 Car -1 -1 -1 876.09 168.49 959.81 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n521 235 Car -1 -1 -1 453.80 176.61 520.54 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n521 234 Car -1 -1 -1 681.96 171.77 779.26 201.89 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n521 229 Car -1 -1 -1 478.40 165.25 520.60 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n521 233 Cyclist -1 -1 -1 858.11 140.89 1017.01 369.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n521 236 Car -1 -1 -1 860.19 168.16 952.46 196.66 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n522 234 Car -1 -1 -1 672.39 172.31 769.52 201.38 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n522 235 Car -1 -1 -1 438.52 176.98 506.17 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n522 229 Car -1 -1 -1 477.55 164.89 522.19 197.38 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n522 233 Cyclist -1 -1 -1 856.63 141.19 1018.45 369.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n522 236 Car -1 -1 -1 843.21 168.06 954.26 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n523 234 Car -1 -1 -1 660.66 172.52 758.45 201.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n523 235 Car -1 -1 -1 421.44 177.62 488.95 203.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n523 233 Cyclist -1 -1 -1 854.00 142.01 1020.68 368.59 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n523 229 Car -1 -1 -1 477.62 164.93 521.80 197.13 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n523 236 Car -1 -1 -1 841.41 168.47 947.71 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n524 233 Cyclist -1 -1 -1 853.49 142.65 1021.25 368.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n524 234 Car -1 -1 -1 651.09 172.68 748.00 201.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n524 229 Car -1 -1 -1 477.33 164.95 522.54 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n524 235 Car -1 -1 -1 402.43 177.93 473.01 203.94 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n524 236 Car -1 -1 -1 833.81 168.15 940.04 201.85 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n525 234 Car -1 -1 -1 639.53 172.21 736.76 201.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n525 233 Cyclist -1 -1 -1 852.75 142.39 1022.14 368.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n525 235 Car -1 -1 -1 385.53 178.82 458.00 205.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n525 229 Car -1 -1 -1 477.69 165.01 522.41 197.10 -1 -1 -1 -1000 -1000 -1000 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703.55 203.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n528 233 Cyclist -1 -1 -1 852.09 141.36 1022.50 369.02 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n528 229 Car -1 -1 -1 478.14 165.17 521.95 196.92 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n528 235 Car -1 -1 -1 339.13 177.94 411.77 207.27 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n528 236 Car -1 -1 -1 806.14 168.15 890.23 196.18 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n529 234 Car -1 -1 -1 592.21 172.43 692.48 204.41 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n529 233 Cyclist -1 -1 -1 852.87 141.37 1021.37 368.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n529 235 Car -1 -1 -1 322.61 179.53 404.13 208.28 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n529 236 Car -1 -1 -1 797.95 167.53 883.77 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n529 229 Car -1 -1 -1 478.20 165.17 522.01 196.85 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n530 234 Car -1 -1 -1 579.35 172.65 680.74 204.70 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n530 233 Cyclist -1 -1 -1 854.16 141.48 1020.09 368.44 -1 -1 -1 -1000 -1000 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164.88 521.55 196.85 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n532 235 Car -1 -1 -1 270.96 183.20 346.70 210.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n533 234 Car -1 -1 -1 542.24 172.31 644.03 206.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n533 236 Car -1 -1 -1 754.89 169.85 854.13 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n533 233 Cyclist -1 -1 -1 854.98 142.45 1019.55 367.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n533 229 Car -1 -1 -1 478.48 164.75 521.49 196.90 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n533 235 Car -1 -1 -1 255.12 184.35 332.02 210.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n534 234 Car -1 -1 -1 528.76 172.13 632.32 207.43 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n534 236 Car -1 -1 -1 748.64 171.33 846.18 200.09 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n534 233 Cyclist -1 -1 -1 853.47 143.10 1021.30 367.15 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n534 229 Car -1 -1 -1 478.52 164.77 521.45 196.84 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n534 235 Car -1 -1 -1 237.60 184.64 310.74 210.07 -1 -1 -1 -1000 -1000 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Cyclist -1 -1 -1 856.30 142.91 1018.60 367.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n646 237 Cyclist -1 -1 -1 253.68 173.97 287.98 214.00 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n646 239 Car -1 -1 -1 -0.68 191.73 17.84 241.12 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n647 229 Car -1 -1 -1 477.73 164.89 522.55 196.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n647 233 Cyclist -1 -1 -1 856.40 142.98 1018.39 366.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n647 237 Cyclist -1 -1 -1 254.29 173.89 290.75 214.03 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n647 239 Car -1 -1 -1 -0.72 191.82 18.04 241.01 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n648 229 Car -1 -1 -1 477.75 164.93 522.49 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n648 233 Cyclist -1 -1 -1 856.72 143.47 1018.17 366.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n648 237 Cyclist -1 -1 -1 253.84 174.11 287.79 213.73 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n648 239 Car -1 -1 -1 -0.64 191.93 17.94 241.03 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n649 229 Car -1 -1 -1 477.75 164.95 522.44 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n649 233 Cyclist -1 -1 -1 861.74 140.28 1012.63 364.69 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n649 237 Cyclist -1 -1 -1 253.95 174.04 287.67 213.86 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n649 239 Car -1 -1 -1 -0.79 191.89 18.02 240.94 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n650 229 Car -1 -1 -1 477.77 164.93 522.49 197.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n650 233 Cyclist -1 -1 -1 855.97 143.50 1018.94 366.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n650 237 Cyclist -1 -1 -1 253.98 174.04 287.66 213.89 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n650 239 Car -1 -1 -1 -0.63 191.98 17.90 240.95 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n651 229 Car -1 -1 -1 477.80 164.93 522.36 196.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n651 233 Cyclist -1 -1 -1 857.10 143.10 1017.79 366.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n651 237 Cyclist -1 -1 -1 254.01 174.16 287.44 213.78 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n651 239 Car -1 -1 -1 -0.66 191.77 17.90 241.11 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n652 229 Car -1 -1 -1 477.80 164.82 522.46 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n652 233 Cyclist -1 -1 -1 857.42 142.90 1017.48 367.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n652 237 Cyclist -1 -1 -1 254.32 174.06 287.13 213.92 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n652 239 Car -1 -1 -1 -0.73 191.81 18.03 240.99 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n653 229 Car -1 -1 -1 477.83 164.87 522.26 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n653 233 Cyclist -1 -1 -1 856.24 143.29 1018.50 366.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n653 237 Cyclist -1 -1 -1 254.02 174.01 287.38 213.96 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n653 239 Car -1 -1 -1 -0.69 191.98 17.99 240.83 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n654 229 Car -1 -1 -1 477.85 164.93 522.14 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n654 233 Cyclist -1 -1 -1 856.74 143.13 1017.99 366.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n654 237 Cyclist -1 -1 -1 254.01 173.93 287.46 214.05 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n654 239 Car -1 -1 -1 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-1 -1000 -1000 -1000 -10 0.56\n657 239 Car -1 -1 -1 -0.76 192.07 18.07 240.82 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n658 229 Car -1 -1 -1 477.85 164.84 522.02 196.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n658 233 Cyclist -1 -1 -1 856.61 143.13 1017.92 367.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n658 237 Cyclist -1 -1 -1 254.02 173.97 287.51 213.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n658 239 Car -1 -1 -1 -0.63 191.88 17.83 240.98 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n659 229 Car -1 -1 -1 477.98 164.85 521.88 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n659 233 Cyclist -1 -1 -1 855.61 143.07 1019.04 367.07 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n659 237 Cyclist -1 -1 -1 254.03 174.14 287.64 213.78 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n659 239 Car -1 -1 -1 -0.69 191.86 17.92 241.00 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n660 229 Car -1 -1 -1 478.02 164.87 521.82 196.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n660 233 Cyclist -1 -1 -1 855.77 142.89 1018.90 367.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n660 237 Cyclist -1 -1 -1 253.92 173.68 291.23 214.16 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n660 239 Car -1 -1 -1 -0.72 191.79 17.89 241.02 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n661 229 Car -1 -1 -1 477.90 164.87 521.97 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n661 233 Cyclist -1 -1 -1 856.72 142.97 1017.78 367.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n661 237 Cyclist -1 -1 -1 253.54 173.98 288.07 213.89 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n661 239 Car -1 -1 -1 -0.74 191.78 17.95 241.03 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n662 229 Car -1 -1 -1 477.90 164.86 521.95 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n662 233 Cyclist -1 -1 -1 855.84 142.80 1018.88 367.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n662 237 Cyclist -1 -1 -1 253.97 173.98 287.50 214.01 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n662 239 Car -1 -1 -1 -0.66 191.69 17.79 241.11 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n663 229 Car -1 -1 -1 477.86 164.81 521.97 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n663 233 Cyclist -1 -1 -1 856.58 143.00 1018.09 367.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n663 237 Cyclist -1 -1 -1 253.72 173.86 287.90 214.14 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n663 239 Car -1 -1 -1 -0.55 191.92 17.73 240.99 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n664 229 Car -1 -1 -1 477.78 164.82 522.01 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n664 233 Cyclist -1 -1 -1 856.27 143.23 1018.31 367.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n664 237 Cyclist -1 -1 -1 254.82 173.64 290.30 214.34 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n664 239 Car -1 -1 -1 -0.70 191.67 17.88 241.18 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n665 229 Car -1 -1 -1 477.96 164.86 521.92 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n665 233 Cyclist -1 -1 -1 855.88 143.01 1018.76 367.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n665 237 Cyclist -1 -1 -1 254.84 173.66 290.47 214.39 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n665 239 Car -1 -1 -1 -0.76 191.85 17.96 241.02 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n666 229 Car -1 -1 -1 477.87 164.83 521.84 197.05 -1 -1 -1 -1000 -1000 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-1 -1 -1000 -1000 -1000 -10 0.45\n672 233 Cyclist -1 -1 -1 855.76 143.21 1018.99 367.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n672 229 Car -1 -1 -1 477.92 164.85 521.88 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n672 237 Cyclist -1 -1 -1 256.32 174.00 291.33 213.86 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n672 239 Car -1 -1 -1 -0.70 191.71 17.84 241.14 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n673 229 Car -1 -1 -1 477.93 164.89 521.81 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n673 233 Cyclist -1 -1 -1 856.00 142.90 1018.80 367.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n673 237 Cyclist -1 -1 -1 255.72 173.92 292.42 213.56 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n673 239 Car -1 -1 -1 -0.70 191.82 17.82 241.11 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n674 233 Cyclist -1 -1 -1 855.93 143.07 1018.76 367.23 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n674 229 Car -1 -1 -1 477.81 164.85 521.98 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n674 237 Cyclist -1 -1 -1 258.06 173.87 290.53 213.61 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n674 239 Car -1 -1 -1 -0.67 191.80 17.83 241.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n675 229 Car -1 -1 -1 477.82 164.86 521.94 196.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n675 233 Cyclist -1 -1 -1 855.98 142.83 1018.76 367.26 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n675 237 Cyclist -1 -1 -1 261.35 173.40 292.24 213.52 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n675 239 Car -1 -1 -1 -0.60 191.84 17.78 241.04 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n676 229 Car -1 -1 -1 477.90 164.86 521.93 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n676 233 Cyclist -1 -1 -1 856.60 142.62 1018.28 367.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n676 237 Cyclist -1 -1 -1 263.32 173.03 291.74 213.29 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n676 239 Car -1 -1 -1 -0.61 191.79 17.70 241.12 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n677 229 Car -1 -1 -1 477.88 164.89 521.82 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n677 233 Cyclist -1 -1 -1 856.48 142.81 1018.11 367.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n677 237 Cyclist -1 -1 -1 263.53 172.99 292.04 212.83 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n677 239 Car -1 -1 -1 -0.73 191.76 17.84 241.07 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n678 229 Car -1 -1 -1 477.79 164.87 521.83 197.02 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n678 233 Cyclist -1 -1 -1 856.31 142.73 1018.37 367.20 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n678 237 Cyclist -1 -1 -1 267.76 172.39 293.49 213.19 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n678 239 Car -1 -1 -1 -0.72 192.09 17.89 240.75 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n679 233 Cyclist -1 -1 -1 856.00 143.28 1018.53 366.73 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n679 229 Car -1 -1 -1 477.82 164.86 521.97 197.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n679 237 Cyclist -1 -1 -1 268.69 172.32 294.09 212.95 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n679 239 Car -1 -1 -1 -0.69 192.03 17.85 240.85 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n680 229 Car -1 -1 -1 477.92 164.85 521.81 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n680 233 Cyclist -1 -1 -1 855.47 143.09 1019.17 366.89 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n680 237 Cyclist -1 -1 -1 269.16 172.03 294.50 213.02 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n680 239 Car -1 -1 -1 -0.65 192.08 17.80 240.79 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n681 229 Car -1 -1 -1 477.87 164.88 521.91 197.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n681 233 Cyclist -1 -1 -1 856.10 142.46 1018.20 367.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n681 239 Car -1 -1 -1 -0.68 192.29 17.84 240.64 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n682 229 Car -1 -1 -1 477.87 164.93 521.87 197.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n682 233 Cyclist -1 -1 -1 855.00 142.54 1019.62 367.49 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n682 239 Car -1 -1 -1 -0.74 192.19 17.87 240.69 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n683 233 Cyclist -1 -1 -1 855.62 142.60 1018.97 367.43 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n683 229 Car -1 -1 -1 477.80 164.76 522.07 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n683 239 Car -1 -1 -1 -0.68 192.33 17.80 240.55 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n684 229 Car -1 -1 -1 477.87 164.88 521.99 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n684 233 Cyclist -1 -1 -1 856.14 142.58 1018.63 367.45 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n684 239 Car -1 -1 -1 -0.73 192.25 17.87 240.65 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n685 233 Cyclist -1 -1 -1 855.64 142.63 1019.03 367.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n685 229 Car -1 -1 -1 477.92 164.83 521.96 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n685 239 Car -1 -1 -1 -0.66 192.09 17.80 240.77 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n686 233 Cyclist -1 -1 -1 855.14 142.54 1019.46 367.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n686 229 Car -1 -1 -1 477.80 164.88 521.92 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n686 239 Car -1 -1 -1 -0.64 192.01 17.77 240.85 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n687 229 Car -1 -1 -1 477.88 164.86 521.84 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n687 233 Cyclist -1 -1 -1 855.89 142.93 1018.84 367.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n687 239 Car -1 -1 -1 -0.65 192.24 17.77 240.66 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n688 229 Car -1 -1 -1 478.04 164.94 521.70 196.97 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n688 233 Cyclist -1 -1 -1 855.63 142.85 1019.14 367.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n688 239 Car -1 -1 -1 -0.59 192.14 17.71 240.71 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n689 229 Car -1 -1 -1 477.97 164.98 521.77 197.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n689 233 Cyclist -1 -1 -1 855.27 142.90 1019.44 367.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n689 239 Car -1 -1 -1 -0.64 192.15 17.74 240.69 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n690 229 Car -1 -1 -1 477.91 164.90 521.95 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n690 233 Cyclist -1 -1 -1 855.49 142.69 1019.19 367.20 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n690 239 Car -1 -1 -1 -0.67 192.30 17.76 240.54 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n691 229 Car -1 -1 -1 477.85 164.99 522.02 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n691 233 Cyclist -1 -1 -1 856.05 142.90 1018.70 366.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n691 239 Car -1 -1 -1 -0.73 192.36 17.79 240.49 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n692 229 Car -1 -1 -1 477.91 164.93 521.92 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n692 233 Cyclist -1 -1 -1 855.69 142.92 1019.12 366.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n692 239 Car -1 -1 -1 -0.72 192.32 17.86 240.57 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n693 229 Car -1 -1 -1 477.90 164.92 521.93 197.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n693 233 Cyclist -1 -1 -1 856.21 142.71 1018.48 367.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n693 239 Car -1 -1 -1 -0.75 192.09 17.85 240.69 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0013.txt",
    "content": "0 1 Car -1 -1 -1 773.43 182.57 868.92 240.18 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n0 2 Van -1 -1 -1 398.45 168.18 450.33 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n0 3 Car -1 -1 -1 1164.71 145.85 1236.73 192.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n0 4 Car -1 -1 -1 924.55 147.84 971.45 167.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n0 5 Car -1 -1 -1 549.44 171.18 571.00 187.05 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n0 6 Car -1 -1 -1 1111.47 140.57 1182.26 168.77 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n0 7 Car -1 -1 -1 1146.38 142.67 1232.70 172.91 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n0 8 Car -1 -1 -1 987.25 150.25 1057.59 180.16 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n0 9 Car -1 -1 -1 850.08 154.65 907.57 176.12 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n0 10 Car -1 -1 -1 627.13 168.99 644.28 183.55 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n0 11 Car -1 -1 -1 1020.85 145.37 1086.25 170.74 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n0 12 Car -1 -1 -1 1079.14 141.53 1136.64 166.01 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n1 1 Car -1 -1 -1 772.54 179.96 866.27 237.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n1 2 Van -1 -1 -1 394.01 167.07 447.06 201.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1 10 Car -1 -1 -1 626.83 166.75 644.85 182.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n1 6 Car -1 -1 -1 1121.36 139.69 1195.47 168.33 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n1 5 Car -1 -1 -1 547.51 170.63 569.11 185.86 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n1 7 Car -1 -1 -1 1167.27 138.88 1235.26 171.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n1 3 Car -1 -1 -1 1196.38 143.40 1236.71 195.83 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n1 4 Car -1 -1 -1 931.13 147.22 977.20 166.50 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n1 8 Car -1 -1 -1 999.84 148.75 1069.42 177.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n1 11 Car -1 -1 -1 1035.68 142.43 1102.49 167.12 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n1 9 Car -1 -1 -1 857.16 152.53 909.71 174.22 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n1 12 Car -1 -1 -1 1086.61 140.83 1153.23 165.76 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n1 13 Car -1 -1 -1 997.53 142.99 1055.28 166.11 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n1 14 Car -1 -1 -1 1138.74 133.16 1209.05 160.31 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n2 1 Car -1 -1 -1 770.38 178.06 862.12 234.49 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n2 5 Car -1 -1 -1 545.52 170.21 567.31 185.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n2 2 Van -1 -1 -1 390.33 166.02 441.83 199.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n2 6 Car -1 -1 -1 1134.52 140.02 1206.04 168.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n2 8 Car -1 -1 -1 1012.21 149.64 1087.08 176.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n2 10 Car -1 -1 -1 627.37 165.60 644.71 180.93 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n2 4 Car -1 -1 -1 934.57 147.15 984.11 166.44 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n2 12 Car -1 -1 -1 1101.92 141.37 1160.83 164.93 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n2 7 Car -1 -1 -1 1181.51 139.45 1236.62 171.24 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n2 9 Car -1 -1 -1 860.05 152.01 913.56 174.36 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n2 13 Car -1 -1 -1 1011.19 143.71 1066.05 165.87 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n2 11 Car -1 -1 -1 1050.76 142.53 1110.83 165.21 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n2 3 Car -1 -1 -1 1220.92 142.58 1236.64 190.74 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n3 1 Car -1 -1 -1 767.11 178.47 857.46 234.56 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n3 4 Car -1 -1 -1 939.96 147.89 989.06 167.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n3 10 Car -1 -1 -1 625.90 167.28 644.92 182.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n3 6 Car -1 -1 -1 1145.05 140.53 1219.34 169.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n3 5 Car -1 -1 -1 540.74 171.26 564.83 187.17 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n3 2 Van -1 -1 -1 382.21 166.70 436.91 201.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n3 9 Car -1 -1 -1 863.15 151.87 918.47 175.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n3 12 Car -1 -1 -1 1114.07 142.33 1172.89 165.84 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n3 8 Car -1 -1 -1 1029.50 152.13 1101.11 178.14 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n3 7 Car -1 -1 -1 1203.50 143.88 1236.68 170.98 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n3 11 Car -1 -1 -1 1046.54 146.64 1107.50 168.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n3 13 Car -1 -1 -1 1020.12 144.32 1072.29 166.16 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n4 1 Car -1 -1 -1 764.49 181.48 852.06 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n4 5 Car -1 -1 -1 537.15 173.83 559.65 190.57 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n4 9 Car -1 -1 -1 865.84 154.45 922.50 178.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 10 Car -1 -1 -1 624.87 169.72 642.62 184.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n4 6 Car -1 -1 -1 1160.98 142.52 1233.41 172.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n4 4 Car -1 -1 -1 944.90 150.34 992.96 171.02 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n4 13 Car -1 -1 -1 1032.06 146.72 1083.26 168.39 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n4 11 Car -1 -1 -1 1065.22 145.87 1128.18 169.34 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n4 2 Van -1 -1 -1 375.25 168.31 430.53 204.99 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n4 7 Car -1 -1 -1 1211.76 142.66 1238.79 173.86 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n4 8 Car -1 -1 -1 1041.57 151.45 1127.57 180.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n4 12 Car -1 -1 -1 1123.84 143.42 1186.32 167.64 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n4 15 Car -1 -1 -1 452.88 174.89 481.29 190.41 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n5 1 Car -1 -1 -1 760.23 181.72 845.83 234.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n5 5 Car -1 -1 -1 533.54 176.02 555.67 192.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n5 4 Car -1 -1 -1 948.64 150.40 997.17 171.61 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n5 10 Car -1 -1 -1 623.43 171.07 640.68 186.73 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n5 9 Car -1 -1 -1 866.97 155.45 924.44 179.07 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n5 6 Car -1 -1 -1 1164.71 141.82 1237.46 174.10 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n5 13 Car -1 -1 -1 1037.39 146.92 1086.17 168.62 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n5 11 Car -1 -1 -1 1075.52 145.80 1140.70 170.36 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n5 2 Van -1 -1 -1 367.48 172.11 426.06 207.53 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n5 8 Car -1 -1 -1 1060.13 150.75 1147.82 182.56 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n5 15 Car -1 -1 -1 450.61 177.08 478.05 192.40 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n5 16 Car -1 -1 -1 367.48 172.11 426.06 207.53 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n5 17 Car -1 -1 -1 1052.16 147.19 1117.98 171.61 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n6 1 Car -1 -1 -1 756.58 180.04 841.27 231.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n6 10 Car -1 -1 -1 622.81 170.97 640.09 186.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n6 4 Car -1 -1 -1 956.62 148.73 1004.46 169.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n6 5 Car -1 -1 -1 528.27 175.63 552.49 193.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n6 9 Car -1 -1 -1 872.65 153.84 933.19 177.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n6 2 Van -1 -1 -1 360.27 171.67 419.05 208.20 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n6 13 Car -1 -1 -1 1046.56 144.29 1099.58 167.16 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n6 6 Car -1 -1 -1 1189.26 140.74 1235.30 170.12 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n6 17 Car -1 -1 -1 1063.65 146.83 1122.40 169.00 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n6 11 Car -1 -1 -1 1074.32 145.21 1149.64 171.60 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n6 18 Car -1 -1 -1 685.94 169.01 716.20 180.53 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n7 1 Car -1 -1 -1 753.60 177.09 836.81 227.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n7 10 Car -1 -1 -1 621.87 169.70 638.39 184.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n7 5 Car -1 -1 -1 523.48 172.71 549.35 191.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n7 6 Car -1 -1 -1 1202.97 133.58 1236.79 168.76 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n7 4 Car -1 -1 -1 958.81 145.17 1008.59 166.65 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n7 9 Car -1 -1 -1 877.26 150.95 937.43 174.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n7 17 Car -1 -1 -1 1074.04 141.28 1150.06 168.20 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n7 13 Car -1 -1 -1 1052.83 141.59 1109.96 164.60 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n7 2 Van -1 -1 -1 354.67 170.14 414.40 207.02 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n7 19 Car -1 -1 -1 560.91 171.11 582.25 185.70 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n7 20 Car -1 -1 -1 446.94 176.73 474.81 191.92 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n7 21 Pedestrian -1 -1 -1 320.87 178.94 332.82 206.44 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n8 1 Car -1 -1 -1 750.79 174.59 831.59 224.30 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n8 17 Car -1 -1 -1 1087.07 138.30 1160.65 163.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n8 13 Car -1 -1 -1 1062.38 138.03 1122.92 160.89 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n8 5 Car -1 -1 -1 519.56 171.20 545.06 190.04 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n8 10 Car -1 -1 -1 621.55 167.09 637.64 181.93 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n8 9 Car -1 -1 -1 884.80 148.30 944.66 171.15 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n8 2 Van -1 -1 -1 346.81 167.75 407.92 205.59 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n8 20 Car -1 -1 -1 444.33 174.15 473.97 189.72 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n8 19 Car -1 -1 -1 555.08 170.03 575.36 184.13 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n8 6 Car -1 -1 -1 1160.26 134.60 1234.64 160.67 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n8 4 Car -1 -1 -1 974.81 143.01 1023.49 163.53 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n8 22 Truck -1 -1 -1 509.45 151.62 543.79 179.40 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n8 23 Car -1 -1 -1 1107.94 142.35 1217.22 181.57 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n9 1 Car -1 -1 -1 748.33 172.17 826.37 220.07 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n9 17 Car -1 -1 -1 1105.12 136.72 1173.56 162.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n9 9 Car -1 -1 -1 891.64 145.49 951.07 169.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n9 5 Car -1 -1 -1 515.15 169.37 541.69 188.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n9 4 Car -1 -1 -1 968.50 140.03 1022.79 161.71 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n9 13 Car -1 -1 -1 1070.57 135.87 1129.21 158.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n9 2 Van -1 -1 -1 337.51 164.78 402.92 204.43 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n9 23 Car -1 -1 -1 1127.00 138.56 1236.35 178.51 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n9 20 Car -1 -1 -1 441.19 169.75 471.35 187.28 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n9 10 Car -1 -1 -1 619.75 164.99 637.10 180.07 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n9 19 Car -1 -1 -1 553.51 167.08 576.35 181.47 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n9 6 Car -1 -1 -1 1167.34 133.35 1235.53 160.12 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n9 24 Pedestrian -1 -1 -1 304.04 175.45 315.01 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n9 25 Car -1 -1 -1 586.05 164.54 603.84 177.76 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n10 1 Car -1 -1 -1 744.67 170.74 822.45 218.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n10 5 Car -1 -1 -1 508.74 168.55 537.40 188.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n10 4 Car -1 -1 -1 975.51 139.43 1032.21 161.13 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n10 13 Car -1 -1 -1 1077.65 135.06 1138.71 158.09 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n10 17 Car -1 -1 -1 1115.48 135.17 1192.86 161.14 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n10 19 Car -1 -1 -1 549.31 166.83 571.04 181.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n10 9 Car -1 -1 -1 898.62 144.62 960.01 167.48 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n10 10 Car -1 -1 -1 618.78 163.54 637.54 179.27 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n10 20 Car -1 -1 -1 439.98 170.12 470.11 186.66 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n10 23 Car -1 -1 -1 1157.74 138.53 1236.73 177.98 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n10 24 Pedestrian -1 -1 -1 294.91 174.26 306.91 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n10 26 Car -1 -1 -1 328.62 164.80 395.84 204.20 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n11 1 Car -1 -1 -1 743.20 170.82 818.58 218.01 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n11 4 Car -1 -1 -1 983.20 139.84 1039.91 162.00 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n11 9 Car -1 -1 -1 900.49 145.62 967.26 168.70 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n11 17 Car -1 -1 -1 1120.21 135.53 1212.92 163.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n11 13 Car -1 -1 -1 1086.02 135.12 1146.37 158.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n11 5 Car -1 -1 -1 503.06 169.67 533.45 190.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n11 10 Car -1 -1 -1 618.37 164.59 637.71 180.55 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n11 19 Car -1 -1 -1 546.03 168.10 567.36 182.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n11 20 Car -1 -1 -1 436.97 172.79 467.86 187.85 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n11 23 Car -1 -1 -1 1160.75 138.88 1233.73 169.32 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n11 24 Pedestrian -1 -1 -1 284.67 173.98 296.27 203.52 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n11 26 Car -1 -1 -1 319.75 163.70 390.40 205.55 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n11 27 Van -1 -1 -1 319.75 163.70 390.40 205.55 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n11 28 Car -1 -1 -1 863.00 146.58 919.34 169.38 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n12 1 Car -1 -1 -1 741.06 173.26 814.42 219.42 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n12 5 Car -1 -1 -1 497.30 172.22 530.16 194.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n12 10 Car -1 -1 -1 618.21 166.54 638.21 182.87 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n12 17 Car -1 -1 -1 1135.84 136.85 1228.69 165.89 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n12 9 Car -1 -1 -1 908.19 147.61 975.48 171.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n12 13 Car -1 -1 -1 1099.16 137.42 1163.91 161.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n12 4 Car -1 -1 -1 987.34 142.03 1042.13 164.60 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n12 19 Car -1 -1 -1 544.70 169.56 566.17 185.36 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n12 20 Car -1 -1 -1 435.81 174.17 467.00 190.09 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n12 24 Pedestrian -1 -1 -1 276.11 178.34 287.33 207.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n12 27 Van -1 -1 -1 311.65 165.19 384.57 208.89 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n12 28 Car -1 -1 -1 868.73 148.90 922.20 170.16 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n12 23 Car -1 -1 -1 1188.68 140.82 1235.74 176.00 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n12 29 Pedestrian -1 -1 -1 285.70 174.74 297.83 206.86 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n12 30 Car -1 -1 -1 519.44 169.93 539.00 184.49 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n13 1 Car -1 -1 -1 739.31 175.69 811.44 221.54 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n13 19 Car -1 -1 -1 542.90 173.56 565.03 189.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n13 10 Car -1 -1 -1 618.30 169.28 637.83 186.99 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n13 17 Car -1 -1 -1 1156.96 140.27 1237.16 167.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n13 13 Car -1 -1 -1 1104.80 139.12 1173.96 164.45 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n13 4 Car -1 -1 -1 993.92 144.43 1053.33 167.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n13 9 Car -1 -1 -1 916.03 151.16 983.61 175.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n13 5 Car -1 -1 -1 491.67 175.68 526.51 198.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n13 28 Car -1 -1 -1 876.47 150.47 928.27 172.86 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n13 20 Car -1 -1 -1 433.88 177.14 464.49 194.16 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n13 30 Car -1 -1 -1 518.26 174.23 538.53 188.77 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n13 27 Van -1 -1 -1 302.35 169.80 378.61 214.93 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n13 24 Pedestrian -1 -1 -1 266.14 181.16 278.93 212.65 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n13 23 Car -1 -1 -1 1203.89 134.55 1237.42 166.84 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n13 29 Pedestrian -1 -1 -1 275.21 178.89 288.93 212.74 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n14 1 Car -1 -1 -1 737.78 177.45 808.35 222.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n14 4 Car -1 -1 -1 1003.36 145.66 1065.45 169.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n14 5 Car -1 -1 -1 485.61 178.72 521.04 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n14 13 Car -1 -1 -1 1114.41 140.48 1187.62 166.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n14 17 Car -1 -1 -1 1170.47 141.38 1238.24 169.04 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n14 10 Car -1 -1 -1 618.51 172.40 637.93 189.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n14 24 Pedestrian -1 -1 -1 255.38 184.20 267.75 216.39 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n14 19 Car -1 -1 -1 539.75 176.42 561.34 192.78 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n14 27 Van -1 -1 -1 292.41 170.34 373.27 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n14 9 Car -1 -1 -1 924.80 152.60 989.27 178.56 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n14 30 Car -1 -1 -1 514.80 175.54 536.34 191.16 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n14 20 Car -1 -1 -1 431.93 180.21 463.47 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n14 31 Car -1 -1 -1 601.48 172.60 621.35 185.30 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n15 1 Car -1 -1 -1 735.98 177.93 804.67 221.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n15 4 Car -1 -1 -1 1009.74 145.68 1076.35 169.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n15 17 Car -1 -1 -1 1179.41 140.47 1238.35 168.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n15 27 Van -1 -1 -1 282.63 169.97 366.09 219.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n15 24 Pedestrian -1 -1 -1 244.35 184.07 256.30 217.91 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n15 10 Car -1 -1 -1 618.92 172.43 637.73 189.74 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n15 13 Car -1 -1 -1 1124.74 139.92 1201.06 166.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n15 20 Car -1 -1 -1 429.19 180.19 460.93 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n15 19 Car -1 -1 -1 536.29 176.59 559.86 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n15 9 Car -1 -1 -1 936.49 151.64 999.91 180.02 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n15 30 Car -1 -1 -1 510.78 176.38 533.37 191.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n15 5 Car -1 -1 -1 477.08 179.56 515.58 203.71 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n15 31 Car -1 -1 -1 598.92 172.28 618.40 185.58 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n15 32 Pedestrian -1 -1 -1 254.58 182.62 268.62 217.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n15 33 Car -1 -1 -1 1209.97 141.24 1238.80 168.67 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n15 34 Car -1 -1 -1 880.82 153.63 939.69 177.67 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n15 35 Truck -1 -1 -1 497.81 160.04 529.41 187.71 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n16 1 Car -1 -1 -1 735.03 176.44 801.03 219.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n16 27 Van -1 -1 -1 272.17 168.33 358.48 218.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n16 17 Car -1 -1 -1 1195.82 137.68 1237.55 171.16 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n16 5 Car -1 -1 -1 468.72 178.37 510.71 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n16 4 Car -1 -1 -1 1018.26 145.09 1083.54 168.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n16 30 Car -1 -1 -1 507.04 174.77 531.14 191.17 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 31 Car -1 -1 -1 595.79 170.87 618.45 185.11 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n16 9 Car -1 -1 -1 941.79 150.79 1010.55 179.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n16 19 Car -1 -1 -1 533.06 175.56 556.99 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n16 10 Car -1 -1 -1 618.20 170.64 638.34 188.02 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n16 13 Car -1 -1 -1 1140.78 138.27 1223.29 165.67 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n16 24 Pedestrian -1 -1 -1 232.17 183.21 244.78 216.61 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n16 20 Car -1 -1 -1 428.29 179.38 459.33 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n16 32 Pedestrian -1 -1 -1 242.88 180.10 257.63 215.77 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n17 1 Car -1 -1 -1 734.47 176.31 798.31 219.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n17 27 Van -1 -1 -1 259.59 167.90 350.44 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n17 4 Car -1 -1 -1 1029.07 143.14 1093.23 168.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n17 31 Car -1 -1 -1 591.01 170.48 617.51 184.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n17 30 Car -1 -1 -1 501.09 174.78 527.47 191.00 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n17 10 Car -1 -1 -1 617.90 170.77 637.96 187.82 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n17 9 Car -1 -1 -1 952.94 150.01 1021.98 177.00 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n17 19 Car -1 -1 -1 529.94 175.13 554.89 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n17 5 Car -1 -1 -1 459.35 178.63 504.20 205.08 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n17 13 Car -1 -1 -1 1161.57 137.69 1233.69 165.06 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n17 24 Pedestrian -1 -1 -1 217.92 183.38 230.13 217.68 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n17 17 Car -1 -1 -1 1210.90 135.88 1237.98 166.36 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n17 20 Car -1 -1 -1 424.37 179.69 456.14 196.38 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n17 32 Pedestrian -1 -1 -1 230.76 180.61 245.30 216.78 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n17 36 Car -1 -1 -1 1149.94 137.65 1222.16 165.45 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n18 5 Car -1 -1 -1 448.77 179.64 495.87 207.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n18 1 Car -1 -1 -1 732.62 176.27 795.89 219.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n18 4 Car -1 -1 -1 1033.96 142.60 1106.22 167.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n18 13 Car -1 -1 -1 1164.18 136.52 1238.97 163.49 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 9 Car -1 -1 -1 958.51 149.13 1033.88 176.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n18 19 Car -1 -1 -1 526.89 174.76 553.64 194.16 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n18 31 Car -1 -1 -1 588.03 170.91 613.60 184.92 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n18 30 Car -1 -1 -1 497.30 175.63 524.53 192.33 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n18 24 Pedestrian -1 -1 -1 204.19 183.48 217.20 220.70 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n18 27 Van -1 -1 -1 249.02 168.22 342.66 221.55 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n18 10 Car -1 -1 -1 618.39 171.28 637.94 188.83 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n18 20 Car -1 -1 -1 421.91 180.55 452.93 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n18 32 Pedestrian -1 -1 -1 218.28 180.74 233.38 219.99 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n18 17 Car -1 -1 -1 1226.83 132.25 1237.57 162.79 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n19 1 Car -1 -1 -1 731.36 176.46 793.96 218.67 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n19 4 Car -1 -1 -1 1046.93 142.93 1116.14 167.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n19 27 Van -1 -1 -1 235.50 169.27 334.13 225.41 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n19 5 Car -1 -1 -1 436.77 181.43 489.17 210.49 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n19 13 Car -1 -1 -1 1179.91 135.62 1237.54 163.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n19 9 Car -1 -1 -1 969.98 148.02 1045.12 175.76 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n19 10 Car -1 -1 -1 617.95 171.22 637.90 189.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n19 24 Pedestrian -1 -1 -1 188.83 185.74 203.53 222.81 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n19 30 Car -1 -1 -1 494.41 176.02 520.61 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n19 19 Car -1 -1 -1 521.52 176.06 548.82 195.56 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n19 32 Pedestrian -1 -1 -1 204.29 182.48 219.06 221.70 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n19 31 Car -1 -1 -1 583.08 171.48 610.09 185.86 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n19 20 Car -1 -1 -1 418.97 181.05 452.28 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n19 37 Car -1 -1 -1 469.71 180.29 499.05 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n19 38 Car -1 -1 -1 877.05 153.76 936.02 177.52 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n19 39 Car -1 -1 -1 914.70 149.86 975.61 174.79 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n20 1 Car -1 -1 -1 730.04 176.61 792.48 218.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n20 5 Car -1 -1 -1 424.50 181.90 478.64 213.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n20 30 Car -1 -1 -1 489.96 176.58 516.46 195.20 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n20 10 Car -1 -1 -1 618.21 171.94 637.84 189.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n20 19 Car -1 -1 -1 518.71 176.42 547.13 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n20 4 Car -1 -1 -1 1058.44 141.30 1127.04 167.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n20 9 Car -1 -1 -1 980.11 146.90 1058.22 175.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n20 27 Van -1 -1 -1 218.65 169.49 323.06 227.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 32 Pedestrian -1 -1 -1 188.83 183.26 204.30 224.44 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n20 37 Car -1 -1 -1 463.46 180.86 494.18 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n20 24 Pedestrian -1 -1 -1 174.47 185.68 188.89 225.21 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n20 13 Car -1 -1 -1 1195.20 134.08 1237.91 161.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n20 31 Car -1 -1 -1 579.31 172.23 604.32 186.09 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n20 38 Car -1 -1 -1 885.53 150.92 942.57 174.88 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n20 40 Car -1 -1 -1 591.49 170.79 613.76 185.86 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n20 41 Car -1 -1 -1 1119.26 140.77 1168.39 159.84 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n20 42 Car -1 -1 -1 1154.94 138.18 1216.55 163.23 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n21 1 Car -1 -1 -1 729.18 177.12 790.45 218.10 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n21 10 Car -1 -1 -1 617.23 172.86 637.86 190.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n21 27 Van -1 -1 -1 202.78 170.13 314.39 230.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n21 4 Car -1 -1 -1 1063.97 141.75 1137.65 168.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n21 9 Car -1 -1 -1 989.95 146.71 1072.04 176.16 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n21 5 Car -1 -1 -1 408.91 182.57 469.22 217.39 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n21 24 Pedestrian -1 -1 -1 156.46 188.08 172.74 228.19 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n21 32 Pedestrian -1 -1 -1 172.51 184.67 188.29 227.05 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n21 30 Car -1 -1 -1 485.61 177.74 513.09 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n21 19 Car -1 -1 -1 524.93 176.80 548.89 194.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n21 37 Car -1 -1 -1 455.26 182.05 489.46 201.98 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n21 31 Car -1 -1 -1 575.05 172.38 600.71 187.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n21 40 Car -1 -1 -1 583.21 171.50 608.96 187.06 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n21 38 Car -1 -1 -1 888.36 153.03 947.84 177.89 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n21 43 Car -1 -1 -1 514.23 178.56 542.58 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n21 44 Car -1 -1 -1 709.31 169.09 743.88 182.04 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n22 1 Car -1 -1 -1 727.41 178.31 787.92 218.39 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n22 27 Van -1 -1 -1 185.02 170.40 301.32 232.29 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n22 43 Car -1 -1 -1 507.77 179.23 536.69 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n22 5 Car -1 -1 -1 389.79 183.52 457.32 221.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n22 9 Car -1 -1 -1 999.91 147.19 1084.16 178.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n22 10 Car -1 -1 -1 616.27 173.93 636.98 191.98 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n22 44 Car -1 -1 -1 700.62 171.01 731.09 183.52 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n22 37 Car -1 -1 -1 445.99 182.61 483.02 203.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n22 4 Car -1 -1 -1 1076.26 142.29 1148.43 169.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 19 Car -1 -1 -1 520.89 176.91 545.14 195.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n22 24 Pedestrian -1 -1 -1 137.27 188.08 153.97 231.23 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n22 30 Car -1 -1 -1 478.63 179.08 508.47 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n22 32 Pedestrian -1 -1 -1 153.06 185.75 170.26 229.64 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n22 40 Car -1 -1 -1 583.30 171.90 605.56 188.19 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n22 38 Car -1 -1 -1 892.02 153.32 952.39 178.36 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n22 45 Car -1 -1 -1 929.57 151.88 999.57 180.65 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n23 5 Car -1 -1 -1 366.77 184.72 442.69 226.70 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n23 27 Van -1 -1 -1 165.25 170.61 289.00 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n23 1 Car -1 -1 -1 725.71 180.26 783.66 218.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n23 4 Car -1 -1 -1 1087.82 143.39 1160.39 170.94 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n23 37 Car -1 -1 -1 436.95 183.55 475.41 205.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n23 44 Car -1 -1 -1 688.83 172.20 720.75 184.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n23 30 Car -1 -1 -1 471.80 179.89 500.73 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n23 32 Pedestrian -1 -1 -1 133.70 185.40 150.55 232.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n23 9 Car -1 -1 -1 1011.71 147.62 1096.40 179.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n23 40 Car -1 -1 -1 576.61 173.61 600.81 189.90 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n23 19 Car -1 -1 -1 517.29 177.48 541.90 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n23 43 Car -1 -1 -1 501.61 180.42 532.48 201.29 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n23 10 Car -1 -1 -1 615.47 175.15 635.51 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n23 24 Pedestrian -1 -1 -1 117.98 189.33 134.24 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n23 38 Car -1 -1 -1 901.08 153.64 958.19 178.18 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n23 45 Car -1 -1 -1 943.46 153.09 1008.65 179.59 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n23 46 Car -1 -1 -1 1190.18 141.38 1236.82 167.52 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n23 47 Car -1 -1 -1 1145.75 143.79 1203.12 163.65 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n24 5 Car -1 -1 -1 340.02 186.49 426.74 231.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n24 4 Car -1 -1 -1 1096.41 143.05 1174.66 172.46 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n24 27 Van -1 -1 -1 144.43 168.78 276.53 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 1 Car -1 -1 -1 722.90 180.82 780.02 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 30 Car -1 -1 -1 463.56 179.93 494.89 200.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n24 24 Pedestrian -1 -1 -1 94.55 189.25 112.22 234.50 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 40 Car -1 -1 -1 571.64 173.70 596.24 190.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 9 Car -1 -1 -1 1018.12 147.22 1112.67 183.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n24 46 Car -1 -1 -1 1201.71 141.45 1238.84 168.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n24 37 Car -1 -1 -1 425.90 182.68 468.23 206.50 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n24 10 Car -1 -1 -1 613.41 174.58 635.13 193.69 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n24 43 Car -1 -1 -1 494.26 180.88 526.09 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n24 32 Pedestrian -1 -1 -1 112.66 186.17 129.77 232.11 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n24 44 Car -1 -1 -1 678.82 172.57 709.30 184.59 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n24 47 Car -1 -1 -1 1151.37 144.05 1212.87 164.88 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n24 45 Car -1 -1 -1 946.69 153.09 1012.52 180.06 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n24 19 Car -1 -1 -1 512.71 177.58 536.34 196.79 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n24 38 Car -1 -1 -1 909.21 153.73 965.68 178.26 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n25 5 Car -1 -1 -1 307.98 186.57 404.88 236.60 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n25 1 Car -1 -1 -1 720.11 179.82 776.93 217.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n25 4 Car -1 -1 -1 1108.19 143.31 1185.89 172.42 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n25 27 Van -1 -1 -1 120.30 166.83 263.41 236.47 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n25 30 Car -1 -1 -1 455.95 178.62 488.83 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n25 37 Car -1 -1 -1 415.14 182.27 459.34 206.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n25 43 Car -1 -1 -1 486.89 178.98 520.26 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n25 32 Pedestrian -1 -1 -1 89.56 184.56 106.57 232.52 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n25 9 Car -1 -1 -1 1028.62 147.69 1126.25 184.25 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n25 24 Pedestrian -1 -1 -1 70.79 187.20 88.82 235.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n25 10 Car -1 -1 -1 611.24 173.52 633.64 192.76 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n25 46 Car -1 -1 -1 1212.53 142.09 1238.53 167.84 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n25 19 Car -1 -1 -1 506.27 178.47 531.34 197.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n25 47 Car -1 -1 -1 1161.54 144.05 1225.10 165.74 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n25 40 Car -1 -1 -1 564.88 172.52 590.76 189.66 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n25 45 Car -1 -1 -1 951.31 153.26 1017.11 180.38 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n25 44 Car -1 -1 -1 669.83 171.29 698.20 185.50 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n26 5 Car -1 -1 -1 266.36 187.29 382.62 243.27 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n26 1 Car -1 -1 -1 718.06 179.27 773.87 216.01 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 30 Car -1 -1 -1 447.71 178.47 481.70 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n26 24 Pedestrian -1 -1 -1 42.99 186.28 62.80 237.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n26 37 Car -1 -1 -1 402.44 181.69 449.77 207.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n26 27 Van -1 -1 -1 95.79 166.47 248.18 238.01 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n26 4 Car -1 -1 -1 1115.77 142.31 1200.87 172.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n26 9 Car -1 -1 -1 1040.73 147.09 1144.36 184.42 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n26 43 Car -1 -1 -1 478.60 178.63 513.48 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n26 32 Pedestrian -1 -1 -1 62.63 185.01 79.92 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n26 10 Car -1 -1 -1 609.28 173.31 632.04 192.67 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n26 19 Car -1 -1 -1 501.21 178.76 525.87 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n26 40 Car -1 -1 -1 563.76 172.27 586.16 188.22 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n26 47 Car -1 -1 -1 1161.64 143.22 1233.75 166.12 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n26 46 Car -1 -1 -1 1227.73 140.63 1238.37 167.91 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n26 45 Car -1 -1 -1 957.60 152.61 1026.38 180.71 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n26 48 Car -1 -1 -1 551.54 174.29 571.88 189.39 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n26 49 Car -1 -1 -1 918.98 153.72 979.51 179.43 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n27 5 Car -1 -1 -1 215.68 187.73 354.04 251.66 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n27 1 Car -1 -1 -1 714.42 178.58 769.97 215.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n27 30 Car -1 -1 -1 438.87 177.72 473.67 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n27 10 Car -1 -1 -1 607.57 173.15 630.40 192.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n27 43 Car -1 -1 -1 467.79 177.91 505.93 204.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n27 27 Van -1 -1 -1 67.67 163.05 231.81 241.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n27 24 Pedestrian -1 -1 -1 13.98 185.62 34.40 240.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n27 37 Car -1 -1 -1 388.62 181.91 439.83 209.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n27 32 Pedestrian -1 -1 -1 34.22 181.68 53.81 237.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n27 48 Car -1 -1 -1 545.35 172.35 567.37 189.04 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n27 4 Car -1 -1 -1 1127.75 142.35 1212.60 171.90 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n27 47 Car -1 -1 -1 1172.02 142.67 1238.33 165.46 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n27 9 Car -1 -1 -1 1054.86 146.22 1161.09 184.34 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n27 19 Car -1 -1 -1 493.63 177.67 521.28 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n27 45 Car -1 -1 -1 967.94 150.80 1039.05 181.08 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n27 49 Car -1 -1 -1 920.98 152.91 985.57 179.42 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n27 40 Car -1 -1 -1 558.29 171.43 580.36 188.97 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n28 5 Car -1 -1 -1 148.07 187.20 320.02 261.82 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n28 1 Car -1 -1 -1 712.29 177.90 766.23 214.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n28 37 Car -1 -1 -1 375.17 179.93 427.39 209.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n28 30 Car -1 -1 -1 429.71 177.05 465.86 200.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n28 43 Car -1 -1 -1 457.88 177.43 498.35 204.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n28 4 Car -1 -1 -1 1135.79 140.95 1227.70 170.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n28 27 Van -1 -1 -1 35.72 160.47 209.97 243.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n28 9 Car -1 -1 -1 1070.75 144.25 1175.68 181.97 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n28 32 Pedestrian -1 -1 -1 3.86 180.01 24.91 239.31 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n28 10 Car -1 -1 -1 605.51 171.74 628.25 191.90 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n28 19 Car -1 -1 -1 487.66 176.07 515.64 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n28 47 Car -1 -1 -1 1178.33 142.79 1240.39 164.36 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n28 45 Car -1 -1 -1 974.58 147.65 1056.23 178.88 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n28 48 Car -1 -1 -1 539.97 171.20 565.71 189.03 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n28 49 Car -1 -1 -1 926.58 151.47 995.99 179.19 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n28 24 Pedestrian -1 -1 -1 -0.96 186.79 6.54 245.88 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n28 40 Car -1 -1 -1 552.36 171.46 575.95 186.86 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n29 5 Car -1 -1 -1 55.78 188.39 273.51 275.80 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n29 1 Car -1 -1 -1 709.92 176.61 763.50 212.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n29 37 Car -1 -1 -1 358.22 179.35 415.67 210.29 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n29 43 Car -1 -1 -1 446.22 177.09 489.85 204.40 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n29 10 Car -1 -1 -1 604.17 170.44 627.26 190.99 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n29 19 Car -1 -1 -1 479.97 175.07 508.93 197.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n29 4 Car -1 -1 -1 1151.13 139.00 1235.95 170.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n29 48 Car -1 -1 -1 534.55 170.12 557.57 187.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n29 27 Van -1 -1 -1 3.05 156.30 195.10 247.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n29 30 Car -1 -1 -1 419.76 174.95 458.50 201.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n29 47 Car -1 -1 -1 1195.71 142.35 1238.54 165.29 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n29 45 Car -1 -1 -1 985.27 146.47 1068.73 177.81 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n29 9 Car -1 -1 -1 1087.49 142.63 1190.50 182.17 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n29 49 Car -1 -1 -1 937.96 149.52 1005.94 177.47 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n29 51 Car -1 -1 -1 583.55 172.00 614.85 185.17 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n30 5 Car -1 -1 -1 2.44 188.73 211.59 299.16 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n30 1 Car -1 -1 -1 707.77 176.37 760.56 211.59 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 37 Car -1 -1 -1 338.73 179.53 403.79 212.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n30 19 Car -1 -1 -1 470.87 174.81 502.99 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n30 43 Car -1 -1 -1 432.67 177.64 481.01 206.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n30 27 Van -1 -1 -1 -1.31 155.80 184.12 255.02 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n30 30 Car -1 -1 -1 408.83 174.32 450.67 201.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 48 Car -1 -1 -1 528.90 169.91 553.47 187.88 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n30 10 Car -1 -1 -1 602.65 170.06 626.56 190.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n30 51 Car -1 -1 -1 575.99 171.25 607.72 184.50 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n30 4 Car -1 -1 -1 1165.87 138.40 1237.14 170.65 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n30 9 Car -1 -1 -1 1107.70 141.83 1209.36 181.38 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n30 47 Car -1 -1 -1 1202.88 142.20 1239.16 166.27 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n30 49 Car -1 -1 -1 942.04 148.96 1010.91 177.40 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n30 45 Car -1 -1 -1 996.58 145.13 1080.16 178.06 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n31 43 Car -1 -1 -1 418.62 178.24 471.30 209.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 1 Car -1 -1 -1 705.72 177.16 757.51 212.01 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 37 Car -1 -1 -1 319.32 180.43 389.21 215.81 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n31 27 Van -1 -1 -1 3.12 156.70 149.29 253.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n31 5 Car -1 -1 -1 0.89 195.68 133.56 322.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n31 30 Car -1 -1 -1 397.22 175.17 443.08 204.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n31 19 Car -1 -1 -1 461.46 176.27 496.61 200.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n31 10 Car -1 -1 -1 600.36 170.13 625.21 191.50 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 9 Car -1 -1 -1 1127.43 140.56 1236.44 183.87 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n31 48 Car -1 -1 -1 522.81 169.60 549.88 188.52 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n31 51 Car -1 -1 -1 569.64 170.68 599.17 186.21 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n31 4 Car -1 -1 -1 1180.88 140.16 1237.50 170.70 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n31 47 Car -1 -1 -1 1218.56 143.73 1238.14 165.26 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n31 49 Car -1 -1 -1 949.19 148.35 1019.18 177.52 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n31 45 Car -1 -1 -1 1009.95 145.02 1089.61 178.07 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n32 37 Car -1 -1 -1 295.77 182.32 374.11 219.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n32 27 Van -1 -1 -1 3.30 154.78 124.59 255.20 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n32 43 Car -1 -1 -1 402.96 181.04 461.42 212.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n32 10 Car -1 -1 -1 599.62 171.09 624.75 193.18 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n32 19 Car -1 -1 -1 452.71 178.36 489.81 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n32 30 Car -1 -1 -1 385.66 175.46 433.37 206.46 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n32 1 Car -1 -1 -1 703.04 178.19 755.57 212.96 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n32 51 Car -1 -1 -1 563.92 172.57 594.95 187.84 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n32 48 Car -1 -1 -1 517.92 169.47 547.16 189.25 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n32 9 Car -1 -1 -1 1159.93 141.79 1234.66 182.95 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n32 4 Car -1 -1 -1 1196.67 144.51 1237.12 171.54 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n32 45 Car -1 -1 -1 1020.26 144.64 1102.70 178.02 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n32 47 Car -1 -1 -1 1226.51 144.11 1237.92 165.23 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n32 49 Car -1 -1 -1 958.85 149.24 1031.77 177.67 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-1 -1000 -1000 -1000 -10 0.50\n139 81 Car -1 -1 -1 1.89 188.48 406.38 368.58 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n139 10 Car -1 -1 -1 488.51 160.09 710.03 356.98 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n139 100 Car -1 -1 -1 482.92 174.22 515.05 196.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n139 110 Car -1 -1 -1 501.15 170.49 529.20 192.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n139 116 Car -1 -1 -1 103.77 186.99 148.48 207.03 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n139 114 Car -1 -1 -1 231.21 182.42 275.19 204.51 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n140 81 Car -1 -1 -1 2.59 188.61 405.08 368.68 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n140 10 Car -1 -1 -1 488.13 160.14 710.47 357.42 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n140 100 Car -1 -1 -1 482.94 174.25 515.00 196.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n140 110 Car -1 -1 -1 500.93 170.58 529.11 192.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n140 116 Car -1 -1 -1 103.45 187.27 148.30 207.10 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n140 114 Car -1 -1 -1 231.52 182.45 274.94 204.42 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n141 81 Car -1 -1 -1 3.23 188.89 403.88 368.36 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n141 10 Car -1 -1 -1 487.78 160.13 710.80 357.85 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n141 100 Car -1 -1 -1 482.89 174.28 514.90 196.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n141 110 Car -1 -1 -1 500.73 170.49 529.30 192.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n141 114 Car -1 -1 -1 231.67 182.55 274.69 204.30 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n141 116 Car -1 -1 -1 103.73 187.26 147.77 207.09 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n142 10 Car -1 -1 -1 487.49 159.86 710.90 358.72 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n142 81 Car -1 -1 -1 2.72 188.97 403.92 368.54 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n142 100 Car -1 -1 -1 482.80 174.32 514.92 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n142 110 Car -1 -1 -1 500.78 170.47 529.36 192.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n142 114 Car -1 -1 -1 231.60 182.57 274.70 204.31 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n142 116 Car -1 -1 -1 103.37 187.28 147.55 207.24 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n143 81 Car -1 -1 -1 2.86 189.12 404.04 368.23 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n143 10 Car -1 -1 -1 487.60 159.95 711.02 358.83 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n143 100 Car -1 -1 -1 482.81 174.38 514.93 196.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n143 110 Car -1 -1 -1 501.03 170.60 529.35 192.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n143 116 Car -1 -1 -1 101.48 187.65 144.73 207.20 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n143 114 Car -1 -1 -1 229.51 183.02 273.12 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n144 10 Car -1 -1 -1 487.13 159.88 711.33 359.27 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n144 81 Car -1 -1 -1 2.05 189.37 404.17 368.21 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n144 100 Car -1 -1 -1 482.83 174.42 515.03 196.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n144 110 Car -1 -1 -1 501.17 170.62 529.54 192.72 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n144 116 Car -1 -1 -1 101.95 187.88 144.49 207.53 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n144 114 Car -1 -1 -1 229.43 183.11 273.08 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n145 10 Car -1 -1 -1 486.79 160.05 711.58 359.25 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n145 81 Car -1 -1 -1 2.41 189.76 403.46 368.15 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n145 100 Car -1 -1 -1 482.80 174.49 515.18 196.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n145 110 Car -1 -1 -1 501.13 170.71 529.64 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n145 116 Car -1 -1 -1 102.10 187.99 144.43 207.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n145 114 Car -1 -1 -1 229.30 183.12 273.12 204.00 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n146 10 Car -1 -1 -1 486.79 160.09 711.56 359.25 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n146 81 Car -1 -1 -1 2.39 189.66 403.36 368.26 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n146 100 Car -1 -1 -1 482.74 174.48 515.24 196.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n146 110 Car -1 -1 -1 501.09 170.79 529.80 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n146 116 Car -1 -1 -1 102.02 187.94 144.35 207.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n146 114 Car -1 -1 -1 229.08 183.19 273.26 203.93 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n147 10 Car -1 -1 -1 486.57 159.88 711.86 359.66 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n147 81 Car -1 -1 -1 2.95 189.63 402.93 368.24 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n147 100 Car -1 -1 -1 482.68 174.45 515.17 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n147 110 Car -1 -1 -1 501.12 170.73 529.79 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n147 116 Car -1 -1 -1 102.05 188.04 144.38 207.99 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n147 114 Car -1 -1 -1 228.98 183.27 273.33 203.84 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n148 10 Car -1 -1 -1 486.55 159.76 711.79 359.59 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n148 81 Car -1 -1 -1 3.14 189.69 402.81 368.44 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n148 100 Car -1 -1 -1 482.74 174.45 515.15 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n148 110 Car -1 -1 -1 501.24 170.67 529.78 192.80 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n148 116 Car -1 -1 -1 101.98 188.01 144.52 207.98 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n148 114 Car -1 -1 -1 228.77 183.24 273.49 203.90 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n149 10 Car -1 -1 -1 486.35 159.68 711.84 359.78 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n149 81 Car -1 -1 -1 2.84 189.48 402.95 368.58 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n149 100 Car -1 -1 -1 482.76 174.46 515.18 197.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n149 110 Car -1 -1 -1 501.15 170.67 529.64 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n149 116 Car -1 -1 -1 102.20 187.99 144.24 207.97 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n149 114 Car -1 -1 -1 228.73 183.26 273.47 203.90 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n150 81 Car -1 -1 -1 3.78 189.61 402.44 368.61 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n150 10 Car -1 -1 -1 486.73 160.07 711.92 359.61 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n150 100 Car -1 -1 -1 483.06 174.41 515.41 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n150 110 Car -1 -1 -1 501.12 170.83 529.74 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n150 116 Car -1 -1 -1 102.32 188.07 144.29 208.23 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n150 114 Car -1 -1 -1 228.82 183.25 273.41 203.87 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n151 81 Car -1 -1 -1 3.37 189.73 403.04 368.58 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n151 10 Car -1 -1 -1 486.61 160.30 712.08 359.54 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n151 100 Car -1 -1 -1 483.06 174.46 515.50 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n151 110 Car -1 -1 -1 501.05 170.95 529.75 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n151 116 Car -1 -1 -1 102.31 188.12 144.31 208.18 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n151 114 Car -1 -1 -1 228.93 183.28 273.47 203.82 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n151 117 Car -1 -1 -1 54.04 188.76 97.26 208.60 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0014.txt",
    "content": "0 1 Car -1 -1 -1 2.16 197.46 383.05 369.37 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n0 2 Car -1 -1 -1 525.07 173.60 649.34 285.03 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n0 3 Car -1 -1 -1 870.40 169.77 974.49 210.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n0 4 Truck -1 -1 -1 1149.90 133.08 1236.87 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n0 5 Van -1 -1 -1 989.19 145.47 1144.05 209.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n0 6 Car -1 -1 -1 302.18 188.97 353.54 210.61 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n0 7 Car -1 -1 -1 658.37 180.38 694.29 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n0 8 Cyclist -1 -1 -1 832.62 178.03 870.10 248.67 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n0 9 Car -1 -1 -1 374.17 187.66 406.81 205.89 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n0 10 Car -1 -1 -1 77.31 199.78 128.42 218.74 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n0 11 Car -1 -1 -1 278.62 191.67 331.22 212.92 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n0 12 Car -1 -1 -1 528.44 183.33 549.29 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n0 13 Car -1 -1 -1 -0.20 199.59 11.37 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n0 14 Car -1 -1 -1 357.13 189.49 392.94 207.45 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n0 15 Car -1 -1 -1 212.58 194.22 263.23 215.65 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n0 16 Car -1 -1 -1 330.51 190.79 371.63 209.53 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n1 1 Car -1 -1 -1 3.60 197.66 381.00 369.29 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n1 2 Car -1 -1 -1 524.64 174.13 645.80 284.73 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1 8 Cyclist -1 -1 -1 832.62 178.43 870.31 248.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n1 3 Car -1 -1 -1 874.25 169.70 978.19 210.91 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n1 4 Truck -1 -1 -1 1150.53 133.18 1238.06 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n1 6 Car -1 -1 -1 301.77 189.64 352.43 211.32 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n1 15 Car -1 -1 -1 208.60 194.85 260.87 216.31 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n1 7 Car -1 -1 -1 656.55 181.98 695.58 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n1 10 Car -1 -1 -1 74.57 200.57 123.21 219.34 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n1 12 Car -1 -1 -1 528.07 183.85 549.65 200.22 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n1 5 Van -1 -1 -1 997.47 146.00 1148.58 210.15 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n1 9 Car -1 -1 -1 372.57 188.06 406.63 206.33 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n1 11 Car -1 -1 -1 278.11 192.36 331.07 212.83 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n1 14 Car -1 -1 -1 355.57 189.83 393.34 207.58 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n1 16 Car -1 -1 -1 330.68 190.82 372.54 209.93 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n2 1 Car -1 -1 -1 3.30 201.58 379.91 369.16 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n2 2 Car -1 -1 -1 525.39 174.97 644.01 284.11 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n2 3 Car -1 -1 -1 877.97 169.45 983.08 211.70 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n2 8 Cyclist -1 -1 -1 837.24 179.87 867.93 247.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n2 4 Truck -1 -1 -1 1157.64 133.26 1238.38 201.27 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n2 6 Car -1 -1 -1 299.48 190.34 350.08 212.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n2 10 Car -1 -1 -1 68.93 201.26 119.70 223.62 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n2 11 Car -1 -1 -1 274.18 194.25 326.69 214.42 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n2 15 Car -1 -1 -1 206.68 195.31 255.98 216.13 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n2 7 Car -1 -1 -1 655.02 182.09 693.27 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n2 12 Car -1 -1 -1 528.46 183.81 552.09 201.18 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n2 9 Car -1 -1 -1 368.48 188.43 404.52 207.01 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n2 14 Car -1 -1 -1 355.81 190.51 393.33 208.95 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n2 17 Truck -1 -1 -1 1002.77 145.92 1156.97 210.26 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n2 18 Car -1 -1 -1 11.33 201.93 70.62 230.53 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n3 1 Car -1 -1 -1 1.89 202.16 380.30 369.39 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n3 2 Car -1 -1 -1 524.42 175.55 642.56 282.39 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n3 3 Car -1 -1 -1 881.01 168.81 987.97 211.35 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n3 4 Truck -1 -1 -1 1164.70 133.05 1238.74 200.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n3 8 Cyclist -1 -1 -1 840.47 178.98 871.26 247.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n3 6 Car -1 -1 -1 299.55 190.56 348.51 212.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n3 11 Car -1 -1 -1 270.19 194.37 324.28 214.47 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n3 10 Car -1 -1 -1 62.04 201.07 114.00 223.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n3 17 Truck -1 -1 -1 1007.11 144.87 1163.31 210.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n3 7 Car -1 -1 -1 654.68 181.72 693.18 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n3 12 Car -1 -1 -1 528.23 184.04 552.68 202.00 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n3 14 Car -1 -1 -1 328.10 191.77 366.87 210.18 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n3 9 Car -1 -1 -1 347.24 190.78 386.18 209.31 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n3 15 Car -1 -1 -1 202.52 195.80 252.93 216.93 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n3 18 Car -1 -1 -1 5.48 201.93 68.43 230.26 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n3 19 Car -1 -1 -1 368.94 188.40 402.39 206.83 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n3 20 Car -1 -1 -1 821.27 182.99 889.04 209.74 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n4 1 Car -1 -1 -1 2.44 202.24 375.30 369.53 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n4 2 Car -1 -1 -1 523.15 175.84 639.01 280.53 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n4 4 Truck -1 -1 -1 1170.96 132.80 1238.69 200.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n4 3 Car -1 -1 -1 886.79 168.77 993.48 212.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 8 Cyclist -1 -1 -1 842.81 179.10 875.54 248.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 10 Car -1 -1 -1 58.31 201.22 109.51 223.31 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n4 6 Car -1 -1 -1 296.15 190.46 344.75 211.84 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n4 11 Car -1 -1 -1 269.59 194.19 323.66 214.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n4 7 Car -1 -1 -1 656.94 181.44 697.76 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n4 20 Car -1 -1 -1 824.15 183.44 888.83 210.10 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n4 9 Car -1 -1 -1 343.25 190.67 382.85 209.54 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n4 14 Car -1 -1 -1 323.48 191.66 363.96 210.31 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n4 17 Truck -1 -1 -1 1014.51 144.22 1169.69 210.92 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n4 12 Car -1 -1 -1 528.67 184.07 552.94 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n4 15 Car -1 -1 -1 196.45 196.06 250.90 219.42 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n4 19 Car -1 -1 -1 364.56 188.59 399.87 206.94 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n4 18 Car -1 -1 -1 3.10 202.55 56.25 229.56 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n5 1 Car -1 -1 -1 3.21 203.74 372.36 369.07 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n5 2 Car -1 -1 -1 521.00 176.32 633.85 279.67 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n5 3 Car -1 -1 -1 890.96 169.20 999.24 212.49 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n5 8 Cyclist -1 -1 -1 845.35 180.12 881.82 251.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n5 4 Truck -1 -1 -1 1176.77 132.45 1239.12 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n5 11 Car -1 -1 -1 265.13 195.02 321.21 215.91 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n5 10 Car -1 -1 -1 51.48 202.64 106.40 224.30 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n5 20 Car -1 -1 -1 826.11 182.90 900.44 211.63 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n5 17 Truck -1 -1 -1 1018.12 145.14 1180.59 211.19 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n5 12 Car -1 -1 -1 527.80 184.86 553.58 201.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n5 9 Car -1 -1 -1 338.41 191.35 380.57 210.77 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n5 15 Car -1 -1 -1 197.12 197.09 247.44 220.01 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n5 6 Car -1 -1 -1 294.78 190.95 344.98 213.55 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n5 7 Car -1 -1 -1 656.76 181.70 697.40 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n5 14 Car -1 -1 -1 313.81 192.34 358.73 212.71 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n5 18 Car -1 -1 -1 1.56 203.48 49.73 229.69 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n6 1 Car -1 -1 -1 3.27 205.27 366.42 369.21 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n6 2 Car -1 -1 -1 515.72 176.36 630.70 280.08 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n6 8 Cyclist -1 -1 -1 848.06 180.73 886.06 252.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n6 3 Car -1 -1 -1 891.70 169.79 1004.82 213.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n6 4 Truck -1 -1 -1 1178.72 132.01 1239.65 201.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n6 17 Truck -1 -1 -1 1022.06 144.16 1186.41 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n6 11 Car -1 -1 -1 261.47 197.27 315.96 217.77 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n6 10 Car -1 -1 -1 45.73 203.74 97.73 227.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n6 12 Car -1 -1 -1 527.63 184.81 554.04 202.05 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n6 7 Car -1 -1 -1 657.01 182.92 697.88 200.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n6 15 Car -1 -1 -1 190.58 196.99 241.81 221.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n6 6 Car -1 -1 -1 288.78 193.63 336.43 215.52 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n6 9 Car -1 -1 -1 348.55 193.22 384.64 211.65 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n6 20 Car -1 -1 -1 829.55 183.56 905.51 212.02 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n6 14 Car -1 -1 -1 314.99 194.94 355.62 213.66 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n6 21 Car -1 -1 -1 633.05 181.41 665.17 196.36 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n6 22 Car -1 -1 -1 365.60 190.75 398.11 209.31 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n7 1 Car -1 -1 -1 3.71 210.06 364.62 368.91 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n7 2 Car -1 -1 -1 510.21 178.38 625.71 280.03 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n7 3 Car -1 -1 -1 897.18 169.96 1009.27 214.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n7 8 Cyclist -1 -1 -1 853.61 182.66 888.78 251.43 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n7 4 Truck -1 -1 -1 1186.04 129.68 1239.66 203.57 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n7 15 Car -1 -1 -1 185.99 201.27 235.93 223.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n7 11 Car -1 -1 -1 256.64 199.34 307.27 219.89 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n7 6 Car -1 -1 -1 287.49 194.64 337.88 217.24 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n7 17 Truck -1 -1 -1 1029.10 143.83 1193.85 211.97 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n7 12 Car -1 -1 -1 527.57 184.98 554.00 202.15 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n7 9 Car -1 -1 -1 344.98 195.59 379.58 213.59 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n7 10 Car -1 -1 -1 38.18 206.60 88.88 229.25 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n7 7 Car -1 -1 -1 656.08 183.80 697.96 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n7 20 Car -1 -1 -1 831.32 183.64 911.65 212.64 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n7 22 Car -1 -1 -1 361.21 192.56 393.93 211.41 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n7 14 Car -1 -1 -1 310.89 196.40 352.88 215.75 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n8 1 Car -1 -1 -1 2.47 211.22 364.53 369.03 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n8 2 Car -1 -1 -1 503.01 179.24 620.19 279.98 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n8 8 Cyclist -1 -1 -1 855.59 183.66 894.34 254.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 3 Car -1 -1 -1 900.18 169.63 1015.16 215.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n8 9 Car -1 -1 -1 340.98 195.53 377.15 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n8 15 Car -1 -1 -1 179.19 202.83 229.93 224.34 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n8 4 Truck -1 -1 -1 1193.92 128.19 1238.84 204.75 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n8 17 Truck -1 -1 -1 1034.96 142.45 1203.90 212.90 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n8 11 Car -1 -1 -1 252.80 201.13 302.11 221.53 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n8 10 Car -1 -1 -1 27.90 208.06 78.16 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n8 7 Car -1 -1 -1 656.34 184.63 697.79 201.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n8 6 Car -1 -1 -1 282.97 195.83 334.65 218.76 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n8 20 Car -1 -1 -1 834.66 182.82 922.48 213.57 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n8 14 Car -1 -1 -1 327.95 195.54 367.86 215.54 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n8 22 Car -1 -1 -1 358.43 194.62 391.76 212.39 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n8 23 Car -1 -1 -1 306.93 197.95 349.16 217.43 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n9 1 Car -1 -1 -1 0.01 211.60 360.21 369.25 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n9 2 Car -1 -1 -1 494.72 179.71 613.54 278.58 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n9 3 Car -1 -1 -1 905.51 168.49 1023.49 217.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n9 8 Cyclist -1 -1 -1 857.07 184.10 899.62 255.26 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n9 11 Car -1 -1 -1 248.04 201.35 299.15 222.25 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n9 6 Car -1 -1 -1 277.31 196.00 331.73 219.57 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n9 17 Truck -1 -1 -1 1042.01 140.71 1214.04 214.08 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n9 9 Car -1 -1 -1 336.40 195.26 374.38 215.35 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n9 15 Car -1 -1 -1 172.95 203.75 225.77 226.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n9 10 Car -1 -1 -1 17.78 208.25 72.71 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n9 7 Car -1 -1 -1 655.57 184.59 697.83 202.35 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n9 4 Truck -1 -1 -1 1202.56 126.43 1238.11 205.29 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n9 20 Car -1 -1 -1 838.26 182.37 927.26 214.28 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n9 22 Car -1 -1 -1 354.13 194.68 388.30 212.74 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n9 14 Car -1 -1 -1 327.29 195.23 367.55 215.85 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n9 23 Car -1 -1 -1 301.21 198.26 346.99 217.84 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n10 1 Car -1 -1 -1 -0.83 212.06 353.48 369.21 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n10 2 Car -1 -1 -1 486.05 180.51 606.33 277.70 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n10 8 Cyclist -1 -1 -1 859.99 182.78 905.50 256.64 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n10 3 Car -1 -1 -1 912.06 168.17 1032.23 216.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n10 17 Truck -1 -1 -1 1048.13 140.41 1223.43 213.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n10 11 Car -1 -1 -1 243.98 201.20 294.68 222.07 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n10 15 Car -1 -1 -1 164.69 203.30 220.23 227.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n10 6 Car -1 -1 -1 272.05 195.61 328.57 219.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n10 20 Car -1 -1 -1 841.47 181.88 931.91 214.22 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n10 14 Car -1 -1 -1 322.53 195.14 364.42 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n10 7 Car -1 -1 -1 655.30 183.72 697.68 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n10 10 Car -1 -1 -1 8.42 209.56 72.64 236.28 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n10 9 Car -1 -1 -1 330.99 194.85 371.05 215.34 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n10 22 Car -1 -1 -1 349.73 193.79 385.24 213.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n10 4 Truck -1 -1 -1 1211.15 126.63 1237.22 205.26 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n10 23 Car -1 -1 -1 297.81 197.52 343.56 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n10 24 Car -1 -1 -1 371.39 192.42 401.83 210.70 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n11 2 Car -1 -1 -1 475.28 180.99 599.07 277.18 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n11 1 Car -1 -1 -1 -0.06 212.68 346.02 369.42 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n11 8 Cyclist -1 -1 -1 864.15 181.02 911.57 259.62 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n11 15 Car -1 -1 -1 156.60 202.79 213.27 227.96 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n11 17 Truck -1 -1 -1 1058.44 139.25 1234.03 215.23 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n11 3 Car -1 -1 -1 916.92 166.87 1041.24 217.53 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n11 6 Car -1 -1 -1 264.90 195.62 321.45 219.77 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n11 7 Car -1 -1 -1 653.74 183.49 694.38 201.93 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n11 11 Car -1 -1 -1 239.72 200.82 290.56 221.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n11 14 Car -1 -1 -1 318.53 194.85 361.41 215.60 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n11 20 Car -1 -1 -1 846.41 181.80 935.21 214.83 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n11 10 Car -1 -1 -1 4.36 207.52 54.34 234.75 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n11 22 Car -1 -1 -1 348.36 193.05 384.30 212.26 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n12 1 Car -1 -1 -1 0.40 214.03 342.90 368.49 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n12 2 Car -1 -1 -1 464.15 181.44 590.34 277.11 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n12 8 Cyclist -1 -1 -1 870.12 180.38 918.49 263.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n12 6 Car -1 -1 -1 259.05 195.74 318.87 220.48 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n12 15 Car -1 -1 -1 148.43 203.35 205.81 228.17 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n12 17 Truck -1 -1 -1 1071.07 138.85 1235.89 215.02 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n12 11 Car -1 -1 -1 233.39 201.22 284.20 222.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n12 14 Car -1 -1 -1 313.67 194.98 357.88 216.59 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n12 3 Car -1 -1 -1 925.31 166.86 1048.52 218.02 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n12 7 Car -1 -1 -1 653.71 183.50 693.81 201.86 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n12 20 Car -1 -1 -1 850.28 181.86 939.40 214.90 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n12 22 Car -1 -1 -1 344.83 193.91 381.87 213.67 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n12 10 Car -1 -1 -1 0.32 206.98 43.08 234.79 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n12 25 Car -1 -1 -1 293.23 197.68 339.70 217.40 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n13 2 Car -1 -1 -1 452.49 180.14 582.13 276.51 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n13 1 Car -1 -1 -1 1.39 214.49 335.06 367.90 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n13 17 Truck -1 -1 -1 1079.13 137.26 1237.24 212.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n13 15 Car -1 -1 -1 140.13 204.01 198.07 228.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n13 6 Car -1 -1 -1 255.66 195.72 314.21 221.03 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n13 11 Car -1 -1 -1 228.46 201.56 278.89 222.87 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n13 8 Cyclist -1 -1 -1 878.25 178.69 925.83 268.48 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n13 20 Car -1 -1 -1 859.31 181.46 944.78 214.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n13 3 Car -1 -1 -1 931.99 165.46 1057.57 216.21 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n13 14 Car -1 -1 -1 309.78 194.65 354.53 216.53 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n13 7 Car -1 -1 -1 652.93 183.06 693.76 201.36 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n13 22 Car -1 -1 -1 318.30 194.49 361.01 215.79 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n13 25 Car -1 -1 -1 290.90 196.87 334.03 217.93 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n13 10 Car -1 -1 -1 1.91 210.99 33.04 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n13 26 Car -1 -1 -1 340.47 193.25 378.11 214.25 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n14 2 Car -1 -1 -1 439.02 180.63 572.53 277.09 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n14 1 Car -1 -1 -1 2.67 219.09 325.91 368.58 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n14 3 Car -1 -1 -1 935.85 163.87 1064.40 217.45 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n14 15 Car -1 -1 -1 130.74 204.18 191.79 229.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n14 17 Truck -1 -1 -1 1088.60 133.89 1235.29 214.65 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n14 6 Car -1 -1 -1 247.10 195.83 309.06 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n14 11 Car -1 -1 -1 222.39 201.33 276.85 223.16 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n14 7 Car -1 -1 -1 652.19 182.90 694.80 200.85 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n14 26 Car -1 -1 -1 336.34 193.18 374.12 214.38 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n14 14 Car -1 -1 -1 303.98 194.71 351.35 216.71 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n14 22 Car -1 -1 -1 318.22 194.41 360.49 215.27 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n14 10 Car -1 -1 -1 0.09 210.53 19.79 237.01 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n14 25 Car -1 -1 -1 284.32 197.24 333.39 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n15 1 Car -1 -1 -1 2.68 220.28 317.69 368.16 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n15 2 Car -1 -1 -1 424.51 180.30 559.31 277.36 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n15 3 Car -1 -1 -1 939.81 162.83 1074.60 217.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 15 Car -1 -1 -1 121.25 204.67 184.26 229.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n15 17 Truck -1 -1 -1 1097.32 130.79 1235.67 215.50 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n15 6 Car -1 -1 -1 241.44 195.69 304.89 221.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n15 11 Car -1 -1 -1 216.48 201.20 269.17 224.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n15 20 Car -1 -1 -1 866.24 179.47 953.59 215.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n15 7 Car -1 -1 -1 652.19 181.14 695.41 200.60 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n15 14 Car -1 -1 -1 299.63 194.71 348.38 216.68 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n15 26 Car -1 -1 -1 330.83 193.40 371.13 214.38 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n15 10 Car -1 -1 -1 1.11 211.50 3.79 237.04 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n15 28 Car -1 -1 -1 387.15 187.17 416.90 207.66 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n15 29 Car -1 -1 -1 533.44 183.04 555.65 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n16 2 Car -1 -1 -1 408.15 178.13 547.88 276.29 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n16 1 Car -1 -1 -1 3.35 219.79 304.43 368.09 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n16 3 Car -1 -1 -1 943.20 160.10 1086.22 218.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n16 6 Car -1 -1 -1 232.35 194.24 300.23 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n16 17 Truck -1 -1 -1 1109.98 126.64 1235.99 212.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n16 15 Car -1 -1 -1 112.88 202.97 177.79 229.08 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 7 Car -1 -1 -1 652.13 178.77 695.65 199.02 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n16 26 Car -1 -1 -1 321.84 190.64 366.16 213.00 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n16 11 Car -1 -1 -1 210.77 199.90 265.34 222.99 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n16 29 Car -1 -1 -1 533.38 180.04 554.59 196.08 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n16 14 Car -1 -1 -1 294.43 192.78 346.20 215.90 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n16 30 Car -1 -1 -1 303.10 191.37 353.01 213.79 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n16 31 Car -1 -1 -1 278.72 193.42 330.81 217.12 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n16 32 Van -1 -1 -1 1106.52 124.83 1235.68 210.34 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n17 2 Car -1 -1 -1 391.65 175.39 534.43 274.29 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n17 1 Car -1 -1 -1 2.39 219.09 295.59 368.21 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n17 6 Car -1 -1 -1 227.91 191.87 294.94 220.01 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n17 7 Car -1 -1 -1 652.75 174.29 701.85 196.27 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n17 11 Car -1 -1 -1 203.13 197.24 259.30 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n17 17 Truck -1 -1 -1 1119.17 120.09 1237.63 210.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n17 20 Car -1 -1 -1 872.54 170.04 993.92 210.27 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n17 26 Car -1 -1 -1 317.09 188.54 362.53 211.84 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n17 29 Car -1 -1 -1 531.77 176.97 554.19 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n17 15 Car -1 -1 -1 100.31 203.23 167.79 229.26 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n17 30 Car -1 -1 -1 295.64 190.07 345.16 213.08 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n17 32 Van -1 -1 -1 1120.66 118.79 1236.48 207.90 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n18 1 Car -1 -1 -1 1.29 214.10 281.67 367.99 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n18 2 Car -1 -1 -1 374.34 173.67 519.82 272.34 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n18 3 Car -1 -1 -1 960.29 156.53 1093.19 214.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n18 29 Car -1 -1 -1 514.31 176.28 537.73 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n18 7 Car -1 -1 -1 653.54 171.22 702.66 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 20 Car -1 -1 -1 876.98 167.65 1005.36 210.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 6 Car -1 -1 -1 221.71 189.27 287.25 218.48 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 26 Car -1 -1 -1 313.96 185.77 358.27 208.78 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 15 Car -1 -1 -1 98.61 199.56 160.21 226.06 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n18 30 Car -1 -1 -1 296.62 187.05 343.97 209.70 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n18 11 Car -1 -1 -1 199.48 193.02 255.55 218.83 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n18 17 Truck -1 -1 -1 1140.63 114.92 1237.91 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n18 33 Car -1 -1 -1 532.72 174.41 555.30 190.02 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n18 34 Car -1 -1 -1 631.07 171.97 661.69 188.77 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n18 35 Car -1 -1 -1 172.40 197.42 227.33 221.43 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n19 2 Car -1 -1 -1 354.71 170.92 504.92 270.78 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n19 1 Car -1 -1 -1 0.56 213.40 266.17 368.74 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n19 6 Car -1 -1 -1 215.95 186.60 285.57 216.31 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n19 20 Car -1 -1 -1 888.10 165.29 1024.63 207.67 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n19 29 Car -1 -1 -1 514.30 174.78 538.67 191.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n19 33 Car -1 -1 -1 533.69 172.78 556.22 187.86 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n19 7 Car -1 -1 -1 656.94 169.36 705.87 191.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n19 3 Car -1 -1 -1 975.65 146.40 1147.90 210.98 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n19 15 Car -1 -1 -1 92.62 198.31 150.68 224.40 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n19 26 Car -1 -1 -1 307.72 184.78 355.92 207.26 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n19 35 Car -1 -1 -1 166.40 197.43 218.62 219.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n19 30 Car -1 -1 -1 286.76 185.61 338.02 209.32 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n19 36 Cyclist -1 -1 -1 932.95 167.20 994.59 265.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n19 38 Car -1 -1 -1 68.93 205.32 128.99 227.29 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n19 39 Car -1 -1 -1 342.80 181.80 383.60 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n20 2 Car -1 -1 -1 334.85 170.78 489.96 272.41 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n20 1 Car -1 -1 -1 0.29 213.61 250.45 368.62 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n20 36 Cyclist -1 -1 -1 935.75 164.33 1010.01 269.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n20 20 Car -1 -1 -1 897.25 163.04 1039.03 208.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n20 6 Car -1 -1 -1 211.35 187.37 281.41 217.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n20 3 Car -1 -1 -1 985.22 144.10 1154.76 211.92 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 15 Car -1 -1 -1 84.79 198.19 144.17 226.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 33 Car -1 -1 -1 535.08 172.78 557.70 187.74 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n20 7 Car -1 -1 -1 660.95 169.71 709.45 190.89 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n20 29 Car -1 -1 -1 514.75 174.25 540.04 191.48 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n20 30 Car -1 -1 -1 287.35 184.96 337.70 210.40 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n20 35 Car -1 -1 -1 161.35 197.72 214.84 221.92 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n20 17 Truck -1 -1 -1 1172.66 103.76 1237.43 204.59 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n20 26 Car -1 -1 -1 301.82 184.45 353.71 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n20 39 Car -1 -1 -1 342.14 181.65 383.72 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n20 40 Van -1 -1 -1 1169.01 100.98 1235.62 201.35 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n20 41 Car -1 -1 -1 118.26 200.92 172.57 224.18 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n20 42 Car -1 -1 -1 189.84 191.43 248.69 219.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n21 2 Car -1 -1 -1 313.84 173.99 472.87 274.48 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n21 1 Car -1 -1 -1 1.56 218.62 227.82 368.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n21 36 Cyclist -1 -1 -1 945.93 164.85 1030.86 274.56 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n21 6 Car -1 -1 -1 206.08 189.02 278.75 219.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n21 3 Car -1 -1 -1 998.33 144.32 1163.65 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n21 20 Car -1 -1 -1 905.22 162.68 1054.23 208.33 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n21 33 Car -1 -1 -1 535.57 173.01 560.59 188.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n21 15 Car -1 -1 -1 75.18 200.67 138.95 230.06 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n21 7 Car -1 -1 -1 666.24 169.12 718.57 191.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n21 29 Car -1 -1 -1 516.79 175.37 541.76 192.61 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n21 26 Car -1 -1 -1 301.33 184.89 353.98 209.79 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n21 35 Car -1 -1 -1 157.98 199.51 211.43 223.72 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n21 30 Car -1 -1 -1 285.25 185.67 339.81 211.25 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n21 39 Car -1 -1 -1 335.70 181.48 383.09 203.50 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n21 42 Car -1 -1 -1 185.31 194.62 245.19 221.54 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n21 40 Van -1 -1 -1 1188.14 101.55 1239.55 206.58 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n21 43 Car -1 -1 -1 637.92 170.91 662.76 186.17 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n21 44 Car -1 -1 -1 247.76 188.74 306.86 214.56 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n22 2 Car -1 -1 -1 291.57 174.97 455.44 276.20 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n22 1 Car -1 -1 -1 0.76 219.42 211.73 368.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n22 36 Cyclist -1 -1 -1 970.35 163.63 1044.19 277.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n22 6 Car -1 -1 -1 201.42 189.76 277.09 221.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n22 20 Car -1 -1 -1 917.37 162.38 1066.00 208.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 33 Car -1 -1 -1 538.75 173.24 561.65 188.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 3 Car -1 -1 -1 1016.39 141.48 1199.10 211.59 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n22 15 Car -1 -1 -1 69.32 201.66 134.99 232.00 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n22 29 Car -1 -1 -1 518.47 175.72 543.56 192.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n22 7 Car -1 -1 -1 666.86 169.92 719.20 191.12 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n22 30 Car -1 -1 -1 284.14 187.39 340.60 213.27 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n22 42 Car -1 -1 -1 182.46 195.04 241.49 223.49 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n22 35 Car -1 -1 -1 153.12 201.11 208.83 225.99 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n22 43 Car -1 -1 -1 639.63 171.01 662.48 186.51 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n22 26 Car -1 -1 -1 295.96 185.15 352.19 210.88 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n22 45 Car -1 -1 -1 107.94 204.67 167.62 228.42 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n23 2 Car -1 -1 -1 269.50 176.84 438.95 278.84 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n23 1 Car -1 -1 -1 -2.38 212.64 199.14 368.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n23 36 Cyclist -1 -1 -1 984.79 163.20 1075.51 284.54 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n23 15 Car -1 -1 -1 63.78 201.62 127.85 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n23 20 Car -1 -1 -1 933.36 161.31 1088.67 208.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n23 3 Car -1 -1 -1 1032.69 138.76 1223.89 210.40 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n23 6 Car -1 -1 -1 201.21 189.67 275.09 221.99 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n23 33 Car -1 -1 -1 542.31 172.87 566.17 187.92 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n23 30 Car -1 -1 -1 280.23 187.62 336.98 214.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n23 29 Car -1 -1 -1 521.80 175.73 547.59 192.28 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n23 7 Car -1 -1 -1 672.76 169.55 728.28 190.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n23 35 Car -1 -1 -1 152.64 201.34 208.75 226.10 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n23 42 Car -1 -1 -1 181.10 194.53 241.81 224.58 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n23 43 Car -1 -1 -1 645.19 170.43 669.80 185.52 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n23 46 Car -1 -1 -1 636.67 168.63 663.77 185.10 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n24 2 Car -1 -1 -1 248.12 176.67 420.79 280.72 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n24 3 Car -1 -1 -1 1056.32 136.27 1237.24 211.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n24 1 Car -1 -1 -1 -0.78 210.53 174.91 369.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 20 Car -1 -1 -1 948.06 159.21 1119.89 209.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n24 36 Cyclist -1 -1 -1 1009.86 161.73 1098.44 287.34 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n24 6 Car -1 -1 -1 199.97 189.73 276.92 222.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n24 15 Car -1 -1 -1 62.71 202.95 126.13 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n24 7 Car -1 -1 -1 687.18 168.47 736.60 190.10 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n24 33 Car -1 -1 -1 548.04 172.78 571.36 187.45 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n24 43 Car -1 -1 -1 650.68 169.98 674.15 185.52 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n24 29 Car -1 -1 -1 528.33 175.96 552.17 192.10 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n24 35 Car -1 -1 -1 150.65 202.69 210.12 227.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n24 42 Car -1 -1 -1 180.88 196.95 241.80 226.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n24 47 Car -1 -1 -1 678.09 169.93 730.57 190.74 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n25 2 Car -1 -1 -1 225.68 178.69 404.37 283.51 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n25 1 Car -1 -1 -1 1.07 217.81 150.06 370.31 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n25 3 Car -1 -1 -1 1081.16 133.75 1237.26 214.21 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n25 15 Car -1 -1 -1 60.00 204.01 123.88 236.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n25 20 Car -1 -1 -1 964.89 158.78 1150.39 209.84 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n25 33 Car -1 -1 -1 553.93 173.05 577.73 187.75 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n25 6 Car -1 -1 -1 199.89 191.40 277.15 223.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n25 29 Car -1 -1 -1 532.93 175.51 558.44 192.59 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n25 7 Car -1 -1 -1 694.98 167.93 746.01 190.72 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n25 43 Car -1 -1 -1 657.62 170.27 683.67 185.93 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n25 36 Cyclist -1 -1 -1 1048.84 161.20 1128.33 293.39 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n25 35 Car -1 -1 -1 148.15 203.76 205.33 229.32 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n25 47 Car -1 -1 -1 687.22 170.20 730.48 190.81 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n25 42 Car -1 -1 -1 178.90 197.59 237.42 226.55 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n25 48 Car -1 -1 -1 388.02 179.55 423.74 200.43 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n25 49 Car -1 -1 -1 370.78 184.54 408.85 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n25 50 Car -1 -1 -1 357.79 183.83 398.37 204.72 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n26 2 Car -1 -1 -1 204.96 179.56 386.75 284.91 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n26 1 Car -1 -1 -1 -0.79 218.23 129.45 369.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n26 6 Car -1 -1 -1 201.32 191.85 275.53 224.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n26 3 Car -1 -1 -1 1116.38 131.25 1240.00 216.37 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n26 20 Car -1 -1 -1 988.60 157.67 1180.98 211.69 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n26 43 Car -1 -1 -1 668.73 169.60 694.78 185.82 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n26 15 Car -1 -1 -1 61.79 203.66 127.82 237.14 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n26 29 Car -1 -1 -1 540.80 175.34 567.10 192.66 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n26 7 Car -1 -1 -1 701.94 167.73 755.11 191.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n26 33 Car -1 -1 -1 561.02 172.85 586.58 187.92 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n26 36 Cyclist -1 -1 -1 1069.99 160.78 1177.24 301.91 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n26 50 Car -1 -1 -1 362.52 183.70 402.32 206.06 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n26 48 Car -1 -1 -1 399.68 179.63 432.62 200.43 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n26 49 Car -1 -1 -1 380.26 185.39 415.26 204.48 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n27 2 Car -1 -1 -1 183.34 179.79 370.22 285.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n27 20 Car -1 -1 -1 1011.98 156.15 1219.64 213.22 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n27 29 Car -1 -1 -1 550.33 173.80 577.52 191.87 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n27 33 Car -1 -1 -1 571.32 171.30 595.99 187.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n27 6 Car -1 -1 -1 201.41 192.57 275.62 223.95 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n27 36 Cyclist -1 -1 -1 1107.24 154.71 1225.29 309.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n27 1 Car -1 -1 -1 -1.92 211.83 113.76 369.08 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n27 15 Car -1 -1 -1 62.32 202.93 128.78 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n27 7 Car -1 -1 -1 718.46 166.41 767.05 190.46 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n27 48 Car -1 -1 -1 407.17 179.16 443.48 200.54 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n27 3 Car -1 -1 -1 1147.70 130.39 1240.19 217.48 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n27 50 Car -1 -1 -1 362.46 184.21 403.07 207.08 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n27 49 Car -1 -1 -1 388.47 183.89 422.26 203.56 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n27 43 Car -1 -1 -1 679.79 168.40 704.55 184.68 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n27 51 Car -1 -1 -1 331.76 185.73 379.65 209.38 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n27 53 Car -1 -1 -1 180.32 197.88 235.06 227.53 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n28 2 Car -1 -1 -1 161.45 181.63 353.05 288.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n28 20 Car -1 -1 -1 1046.11 155.96 1232.85 213.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n28 33 Car -1 -1 -1 582.60 170.89 607.73 187.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 29 Car -1 -1 -1 561.51 173.93 588.59 191.93 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n28 7 Car -1 -1 -1 726.04 166.41 775.66 190.24 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n28 15 Car -1 -1 -1 69.39 203.32 136.64 237.80 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n28 48 Car -1 -1 -1 418.76 179.26 452.58 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n28 1 Car -1 -1 -1 -1.39 218.17 89.79 370.38 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n28 50 Car -1 -1 -1 374.35 184.29 412.14 207.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n28 43 Car -1 -1 -1 690.73 168.10 716.52 184.60 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n28 51 Car -1 -1 -1 339.34 185.86 387.39 210.73 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n28 3 Car -1 -1 -1 1188.63 137.92 1244.97 210.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n28 49 Car -1 -1 -1 396.16 183.65 430.51 203.54 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n28 54 Car -1 -1 -1 -1.57 212.29 21.52 245.04 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n28 55 Car -1 -1 -1 130.56 205.13 192.00 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n28 56 Car -1 -1 -1 713.32 168.88 743.35 186.26 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n29 2 Car -1 -1 -1 140.57 179.49 336.81 286.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n29 20 Car -1 -1 -1 1080.91 152.49 1235.65 213.27 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n29 33 Car -1 -1 -1 594.89 169.76 619.08 185.07 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n29 29 Car -1 -1 -1 573.21 172.43 599.86 190.15 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n29 48 Car -1 -1 -1 426.71 177.59 462.58 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n29 51 Car -1 -1 -1 355.20 183.40 401.33 208.74 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n29 15 Car -1 -1 -1 74.83 202.54 146.27 237.17 -1 -1 -1 -1000 -1000 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-1 -1000 -1000 -1000 -10 0.91\n54 2 Car -1 -1 -1 97.38 182.56 211.39 250.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n54 81 Car -1 -1 -1 487.15 182.19 587.56 228.53 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n54 69 Car -1 -1 -1 620.19 181.20 696.69 230.51 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n54 100 Car -1 -1 -1 403.14 184.21 494.52 223.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n54 91 Car -1 -1 -1 365.31 182.80 453.95 218.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n54 73 Car -1 -1 -1 722.34 181.68 804.34 215.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n54 83 Car -1 -1 -1 442.92 184.11 531.05 224.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n54 109 Car -1 -1 -1 254.41 179.67 346.93 213.57 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n54 105 Car -1 -1 -1 338.64 183.95 426.13 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n54 76 Car -1 -1 -1 889.05 165.50 1039.95 219.12 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n54 114 Car -1 -1 -1 606.65 188.26 662.26 213.54 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n54 48 Car -1 -1 -1 971.17 162.87 1105.55 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126.61 179.85 232.30 246.17 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n55 69 Car -1 -1 -1 657.06 178.76 734.86 231.05 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n55 100 Car -1 -1 -1 434.89 180.96 524.73 221.34 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n55 81 Car -1 -1 -1 515.33 180.51 621.97 227.58 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n55 116 Car -1 -1 -1 1.42 199.23 85.78 335.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n55 91 Car -1 -1 -1 396.65 179.36 484.69 215.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n55 73 Car -1 -1 -1 759.36 179.67 851.37 215.08 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n55 118 Car -1 -1 -1 815.76 176.64 903.30 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n55 83 Car -1 -1 -1 471.14 181.03 564.48 222.71 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n55 105 Car -1 -1 -1 368.19 180.05 451.24 214.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n55 95 Car -1 -1 -1 345.21 180.97 427.03 213.66 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n55 109 Car -1 -1 -1 293.11 177.22 378.05 209.47 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n55 110 Car -1 -1 -1 1128.21 157.86 1235.41 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n55 113 Car -1 -1 -1 1069.90 157.29 1223.93 206.17 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n55 76 Car -1 -1 -1 939.22 164.79 1098.50 220.00 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n55 48 Car -1 -1 -1 1018.73 160.36 1166.43 212.31 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n55 112 Car -1 -1 -1 618.29 185.06 674.80 209.84 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n55 117 Car -1 -1 -1 715.61 181.54 793.41 214.79 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n56 58 Car -1 -1 -1 907.71 164.65 1068.81 228.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n56 116 Car -1 -1 -1 0.53 194.20 113.19 332.51 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n56 69 Car -1 -1 -1 690.84 176.97 771.64 231.71 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n56 2 Car -1 -1 -1 152.99 176.66 252.29 242.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n56 81 Car -1 -1 -1 546.15 177.24 652.42 226.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n56 100 Car -1 -1 -1 466.95 178.79 553.34 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n56 73 Car -1 -1 -1 793.26 178.40 887.54 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n56 109 Car -1 -1 -1 316.16 173.79 410.03 207.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n56 83 Car -1 -1 -1 505.92 179.65 592.58 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n56 118 Car -1 -1 -1 852.16 175.75 944.73 216.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n56 91 Car -1 -1 -1 426.87 176.64 516.38 216.43 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n56 48 Car -1 -1 -1 1074.65 157.56 1234.91 214.24 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n56 105 Car -1 -1 -1 398.16 178.38 483.30 214.43 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n56 117 Car -1 -1 -1 748.20 180.65 831.30 214.90 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n56 76 Car -1 -1 -1 991.86 162.79 1162.20 221.97 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n56 95 Car -1 -1 -1 374.45 176.62 461.02 211.56 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n56 112 Car -1 -1 -1 649.37 183.26 712.63 209.24 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n56 113 Car -1 -1 -1 1102.80 158.60 1237.53 211.21 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n56 119 Car -1 -1 -1 671.84 185.09 737.53 210.31 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n57 116 Car -1 -1 -1 0.84 194.22 140.78 325.72 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n57 58 Car -1 -1 -1 954.17 161.46 1137.30 231.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n57 2 Car -1 -1 -1 180.06 177.90 272.07 241.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n57 69 Car -1 -1 -1 727.94 176.90 811.53 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n57 81 Car -1 -1 -1 574.63 177.12 687.22 227.56 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n57 100 Car -1 -1 -1 495.14 178.49 580.63 221.99 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n57 73 Car -1 -1 -1 831.53 176.97 933.81 215.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n57 118 Car -1 -1 -1 890.77 173.40 992.06 215.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n57 109 Car -1 -1 -1 348.46 174.37 439.34 207.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n57 105 Car -1 -1 -1 427.33 178.42 516.14 214.52 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n57 83 Car -1 -1 -1 533.83 179.93 619.72 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n57 119 Car -1 -1 -1 707.11 182.41 780.33 210.94 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n57 91 Car -1 -1 -1 456.82 176.84 556.59 216.24 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n57 48 Car -1 -1 -1 1129.40 156.79 1234.02 214.81 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n57 76 Car -1 -1 -1 1046.19 161.16 1232.15 223.26 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n57 95 Car -1 -1 -1 402.06 177.59 487.32 211.57 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n57 117 Car -1 -1 -1 785.83 179.28 870.85 214.92 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n57 112 Car -1 -1 -1 678.78 182.18 745.85 210.12 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n58 116 Car -1 -1 -1 -0.46 196.90 166.23 329.63 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n58 69 Car -1 -1 -1 764.28 177.83 853.70 239.48 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n58 2 Car -1 -1 -1 206.08 182.11 293.15 243.83 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n58 58 Car -1 -1 -1 1006.98 161.12 1209.80 234.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n58 81 Car -1 -1 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246.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n60 100 Car -1 -1 -1 578.90 182.06 675.59 227.78 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n60 109 Car -1 -1 -1 435.76 176.78 530.78 211.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n60 73 Car -1 -1 -1 958.80 180.62 1079.52 227.39 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n60 105 Car -1 -1 -1 541.59 180.91 634.58 221.43 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n60 83 Car -1 -1 -1 623.36 181.93 723.49 229.41 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n60 119 Car -1 -1 -1 807.95 187.62 889.08 216.71 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n60 95 Car -1 -1 -1 489.09 182.29 570.81 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n60 91 Car -1 -1 -1 513.63 181.26 600.69 219.60 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n60 76 Car -1 -1 -1 1103.18 171.77 1236.73 229.77 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n60 112 Car -1 -1 -1 776.24 185.26 850.14 216.92 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n61 116 Car -1 -1 -1 45.78 194.41 246.18 302.01 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n61 69 Car -1 -1 -1 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0.54\n61 91 Car -1 -1 -1 541.30 179.65 627.04 220.41 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n61 112 Car -1 -1 -1 813.22 184.22 890.85 217.30 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n61 122 Car -1 -1 -1 0.55 198.86 40.28 242.71 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n61 123 Car -1 -1 -1 384.80 179.86 412.59 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n61 124 Car -1 -1 -1 885.28 179.37 996.33 229.62 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n61 125 Car -1 -1 -1 881.26 185.30 970.29 224.43 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n62 116 Car -1 -1 -1 90.89 195.55 276.30 297.84 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n62 69 Car -1 -1 -1 950.50 176.25 1078.49 258.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n62 2 Car -1 -1 -1 317.67 181.32 384.21 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n62 81 Car -1 -1 -1 733.76 178.82 884.18 244.90 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n62 122 Car -1 -1 -1 0.63 199.34 74.35 249.77 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n62 100 Car -1 -1 -1 641.90 180.74 744.62 229.84 -1 -1 -1 -1000 -1000 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1030.25 221.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n63 91 Car -1 -1 -1 596.94 179.54 688.23 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n63 126 Car -1 -1 -1 0.43 202.78 56.13 284.23 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n63 95 Car -1 -1 -1 573.96 179.09 656.30 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n63 124 Car -1 -1 -1 972.89 182.97 1088.11 225.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n63 123 Car -1 -1 -1 442.12 179.23 469.98 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n63 127 Car -1 -1 -1 75.70 203.07 128.72 230.21 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n63 58 Car -1 -1 -1 1217.61 185.68 1238.01 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n64 116 Car -1 -1 -1 165.32 194.99 327.50 287.20 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n64 69 Car -1 -1 -1 1066.13 173.69 1234.67 276.41 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n64 81 Car -1 -1 -1 810.14 178.13 979.64 252.54 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n64 2 Car -1 -1 -1 368.51 182.42 427.95 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n64 122 Car -1 -1 -1 42.53 199.13 134.29 244.06 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n64 100 Car -1 -1 -1 703.68 180.94 821.42 231.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n64 105 Car -1 -1 -1 655.16 177.82 762.53 224.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n64 83 Car -1 -1 -1 752.33 179.22 881.15 239.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n64 109 Car -1 -1 -1 551.45 175.98 639.01 210.56 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n64 124 Car -1 -1 -1 1020.81 183.65 1132.94 227.02 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n64 119 Car -1 -1 -1 973.07 183.38 1079.58 224.89 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n64 127 Car -1 -1 -1 107.50 203.57 159.78 229.71 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n64 91 Car -1 -1 -1 623.98 179.52 722.16 221.06 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n64 126 Car -1 -1 -1 0.44 206.10 64.50 280.77 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n64 95 Car -1 -1 -1 601.14 179.56 690.97 217.28 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n64 73 Car -1 -1 -1 1168.04 181.24 1241.90 235.45 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n64 123 Car -1 -1 -1 468.57 179.58 496.67 198.88 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n64 128 Car -1 -1 -1 576.02 179.59 654.21 217.07 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n65 116 Car -1 -1 -1 199.05 196.43 350.48 284.49 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n65 2 Car -1 -1 -1 392.82 183.67 448.89 232.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n65 81 Car -1 -1 -1 851.28 178.60 1045.38 257.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n65 122 Car -1 -1 -1 68.95 199.90 161.97 247.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n65 109 Car -1 -1 -1 573.40 176.19 665.55 211.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 100 Car -1 -1 -1 731.64 181.66 856.38 235.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n65 126 Car -1 -1 -1 -1.05 208.43 89.92 271.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n65 83 Car -1 -1 -1 786.80 179.67 924.64 244.48 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n65 124 Car -1 -1 -1 1070.39 183.36 1192.41 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n65 105 Car -1 -1 -1 683.78 177.65 802.52 226.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n65 95 Car -1 -1 -1 625.97 181.11 713.86 219.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n65 91 Car -1 -1 -1 650.80 179.98 750.62 222.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n65 69 Car -1 -1 -1 1133.16 172.60 1239.10 284.24 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n65 119 Car -1 -1 -1 1020.04 183.14 1126.09 228.10 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n65 127 Car -1 -1 -1 134.80 204.14 188.54 230.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n65 73 Car -1 -1 -1 1201.15 182.93 1240.21 234.06 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n66 116 Car -1 -1 -1 230.55 195.38 373.31 279.21 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n66 122 Car -1 -1 -1 94.49 197.72 187.30 245.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n66 81 Car -1 -1 -1 895.19 180.19 1096.35 266.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n66 124 Car -1 -1 -1 1128.30 187.81 1234.65 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n66 2 Car -1 -1 -1 415.10 181.81 468.47 230.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n66 109 Car -1 -1 -1 601.40 176.70 690.69 212.45 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n66 126 Car -1 -1 -1 3.93 207.02 115.11 272.31 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n66 83 Car -1 -1 -1 823.37 181.53 965.76 249.98 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n66 119 Car -1 -1 -1 1061.04 185.61 1186.56 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n66 105 Car -1 -1 -1 712.58 179.95 828.06 230.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n66 100 Car -1 -1 -1 762.33 183.22 887.71 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n66 127 Car -1 -1 -1 161.13 202.50 215.49 229.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n66 95 Car -1 -1 -1 651.14 181.18 742.39 221.73 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n66 91 Car -1 -1 -1 679.55 182.18 783.74 226.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n66 69 Car -1 -1 -1 1188.06 189.74 1238.77 242.97 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n66 129 Car -1 -1 -1 516.51 180.20 543.71 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n67 116 Car -1 -1 -1 259.19 192.91 395.46 273.71 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n67 81 Car -1 -1 -1 943.26 180.11 1164.18 274.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n67 126 Car -1 -1 -1 4.46 203.81 131.05 261.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n67 122 Car -1 -1 -1 115.20 195.27 208.99 243.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n67 2 Car -1 -1 -1 436.48 180.94 488.07 226.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n67 109 Car -1 -1 -1 624.38 175.86 714.82 212.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n67 100 Car -1 -1 -1 794.28 182.51 932.72 241.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n67 95 Car -1 -1 -1 673.97 179.84 773.67 222.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n67 127 Car -1 -1 -1 186.74 199.91 236.62 225.34 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n67 83 Car -1 -1 -1 860.94 181.02 1006.39 252.26 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n67 105 Car -1 -1 -1 738.67 177.75 872.17 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n67 124 Car -1 -1 -1 1177.95 190.25 1239.60 241.73 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n67 129 Car -1 -1 -1 539.96 178.92 567.50 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n67 91 Car 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0.74\n68 127 Car -1 -1 -1 205.26 198.79 257.82 224.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n68 95 Car -1 -1 -1 698.46 179.65 796.24 222.91 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n68 129 Car -1 -1 -1 561.51 178.16 589.27 199.25 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n68 130 Car -1 -1 -1 593.60 176.26 644.42 193.88 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n68 131 Car -1 -1 -1 675.61 181.91 757.14 219.75 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n69 116 Car -1 -1 -1 308.82 193.30 432.92 270.00 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n69 81 Car -1 -1 -1 1048.59 177.20 1236.85 296.17 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n69 2 Car -1 -1 -1 471.17 180.20 520.15 224.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n69 122 Car -1 -1 -1 146.65 195.19 243.22 243.05 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n69 126 Car -1 -1 -1 24.53 202.99 156.73 261.52 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n69 109 Car -1 -1 -1 661.20 175.88 755.89 212.01 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n69 91 Car -1 -1 -1 752.08 179.66 873.26 230.40 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n69 100 Car -1 -1 -1 855.52 183.01 1011.55 250.44 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n69 83 Car -1 -1 -1 934.56 182.38 1110.79 265.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n69 127 Car -1 -1 -1 224.32 198.95 275.37 224.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n69 95 Car -1 -1 -1 721.30 179.42 827.11 224.91 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n69 105 Car -1 -1 -1 791.94 178.02 935.50 238.80 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n69 129 Car -1 -1 -1 578.86 178.77 605.95 199.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n69 131 Car -1 -1 -1 693.70 180.51 778.54 222.76 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n69 130 Car -1 -1 -1 609.41 177.48 652.23 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n70 116 Car -1 -1 -1 329.14 194.25 449.52 268.48 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n70 126 Car -1 -1 -1 31.75 204.73 164.87 265.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n70 122 Car -1 -1 -1 157.17 195.79 255.74 244.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n70 2 Car -1 -1 -1 486.25 180.74 534.03 223.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n70 91 Car -1 -1 -1 775.22 180.50 897.16 230.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n70 109 Car -1 -1 -1 681.06 175.84 775.60 212.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n70 127 Car -1 -1 -1 240.91 199.54 289.38 225.98 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n70 129 Car -1 -1 -1 594.26 178.24 621.38 200.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n70 100 Car -1 -1 -1 885.50 184.59 1059.03 255.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n70 105 Car -1 -1 -1 819.06 177.72 977.96 241.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n70 95 Car -1 -1 -1 742.24 178.82 852.48 225.69 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n70 131 Car -1 -1 -1 713.93 180.24 803.35 222.96 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n70 83 Car -1 -1 -1 971.61 181.05 1189.95 275.40 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n70 81 Car -1 -1 -1 1110.82 176.99 1237.68 303.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n70 130 Car -1 -1 -1 627.31 177.41 672.65 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n70 132 Car -1 -1 -1 0.44 219.15 34.87 268.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n71 116 Car -1 -1 -1 350.86 195.48 465.40 269.08 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n71 122 Car -1 -1 -1 162.78 197.85 268.86 249.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n71 126 Car -1 -1 -1 32.74 207.54 172.53 270.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n71 83 Car -1 -1 -1 1018.97 179.17 1236.61 279.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n71 100 Car -1 -1 -1 923.77 182.92 1105.63 259.27 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n71 109 Car -1 -1 -1 698.86 176.22 795.83 216.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n71 2 Car -1 -1 -1 501.19 182.64 548.23 224.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n71 105 Car -1 -1 -1 842.69 177.59 1016.53 246.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n71 91 Car -1 -1 -1 796.53 178.99 930.44 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n71 95 Car -1 -1 -1 762.27 180.64 871.69 228.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n71 127 Car -1 -1 -1 251.20 201.77 302.99 229.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n71 132 Car -1 -1 -1 1.61 222.27 41.43 273.47 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n71 129 Car -1 -1 -1 609.52 179.57 638.05 201.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n71 131 Car -1 -1 -1 734.67 179.45 822.80 224.80 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n71 130 Car -1 -1 -1 642.80 177.68 688.25 193.75 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n71 81 Car -1 -1 -1 1177.90 174.86 1240.48 313.16 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n71 133 Car -1 -1 -1 591.25 180.52 615.23 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n72 116 Car -1 -1 -1 368.66 196.21 480.76 267.95 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n72 122 Car -1 -1 -1 169.70 198.35 277.70 251.12 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n72 126 Car -1 -1 -1 34.36 209.01 177.40 272.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n72 83 Car -1 -1 -1 1071.17 178.06 1238.99 287.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n72 2 Car -1 -1 -1 513.97 182.48 560.83 224.66 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n72 105 Car -1 -1 -1 869.57 176.64 1052.24 249.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n72 127 Car -1 -1 -1 263.02 202.29 314.47 229.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n72 109 Car -1 -1 -1 717.08 176.71 816.85 216.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n72 91 Car -1 -1 -1 820.15 178.78 961.13 237.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n72 100 Car -1 -1 -1 960.29 182.55 1162.41 265.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n72 129 Car -1 -1 -1 625.24 178.98 652.19 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n72 95 Car -1 -1 -1 785.34 180.09 903.04 230.55 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n72 131 Car -1 -1 -1 755.71 179.07 854.31 225.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n72 132 Car -1 -1 -1 1.49 224.27 43.02 277.96 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n72 130 Car -1 -1 -1 659.75 177.96 702.90 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n72 134 Car -1 -1 -1 73.35 199.67 147.77 225.86 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n73 116 Car -1 -1 -1 386.69 197.01 496.44 266.40 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n73 126 Car -1 -1 -1 32.44 209.80 179.30 276.48 -1 -1 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-1 869.52 178.36 1043.63 245.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n74 131 Car -1 -1 -1 793.21 179.71 903.21 229.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n74 129 Car -1 -1 -1 652.27 178.40 680.18 202.21 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n74 132 Car -1 -1 -1 0.72 225.81 49.08 283.89 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n74 130 Car -1 -1 -1 693.02 177.19 738.80 193.23 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n74 134 Car -1 -1 -1 107.11 199.76 175.41 226.33 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n75 116 Car -1 -1 -1 419.39 195.04 523.07 260.91 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n75 126 Car -1 -1 -1 16.48 212.08 180.15 283.32 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n75 122 Car -1 -1 -1 184.14 199.13 292.32 255.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n75 2 Car -1 -1 -1 549.98 180.57 594.66 220.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n75 109 Car -1 -1 -1 764.75 172.80 877.80 215.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n75 127 Car -1 -1 -1 289.11 203.02 342.32 230.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n75 100 Car -1 -1 -1 1103.16 173.76 1236.75 290.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n75 95 Car -1 -1 -1 848.44 176.85 994.76 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n75 105 Car -1 -1 -1 968.83 171.88 1200.66 268.13 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n75 129 Car -1 -1 -1 663.23 176.95 692.19 200.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n75 131 Car -1 -1 -1 811.63 177.00 923.45 226.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n75 91 Car -1 -1 -1 896.15 175.30 1071.67 244.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n75 130 Car -1 -1 -1 701.68 176.83 746.27 192.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n75 132 Car -1 -1 -1 1.07 226.06 48.24 283.85 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n75 135 Car -1 -1 -1 432.68 192.95 456.15 209.25 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n75 136 Car -1 -1 -1 238.90 205.10 308.39 237.49 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n76 116 Car -1 -1 -1 432.65 194.74 534.32 260.17 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n76 126 Car -1 -1 -1 4.05 213.42 176.92 288.26 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n76 122 Car -1 -1 -1 181.80 200.16 293.31 257.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n76 105 Car -1 -1 -1 1007.23 169.40 1239.32 273.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n76 2 Car -1 -1 -1 559.03 180.48 602.70 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n76 127 Car -1 -1 -1 294.31 203.06 347.52 231.08 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n76 109 Car -1 -1 -1 777.06 171.04 904.31 217.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n76 91 Car -1 -1 -1 920.92 174.24 1116.66 250.93 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n76 95 Car -1 -1 -1 870.74 174.07 1034.84 237.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n76 131 Car -1 -1 -1 829.12 174.85 952.66 229.28 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n76 129 Car -1 -1 -1 670.96 176.12 704.41 200.40 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n76 136 Car -1 -1 -1 243.44 207.05 312.10 239.33 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n76 130 Car -1 -1 -1 711.84 175.91 759.43 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n76 132 Car -1 -1 -1 0.22 227.21 42.55 283.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n76 100 Car -1 -1 -1 1160.24 167.56 1234.14 296.94 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n76 135 Car -1 -1 -1 438.38 193.59 463.50 209.05 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n77 116 Car -1 -1 -1 444.79 193.74 543.41 257.69 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n77 126 Car -1 -1 -1 0.44 213.17 167.01 291.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n77 105 Car -1 -1 -1 1047.30 167.85 1239.54 280.86 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n77 122 Car -1 -1 -1 174.95 199.90 292.66 259.20 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n77 127 Car -1 -1 -1 296.22 203.11 350.36 231.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n77 2 Car -1 -1 -1 566.10 180.50 609.76 218.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n77 109 Car -1 -1 -1 792.69 172.40 919.75 219.79 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n77 131 Car -1 -1 -1 846.77 173.61 980.68 230.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n77 95 Car -1 -1 -1 885.88 173.46 1073.92 238.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n77 91 Car -1 -1 -1 946.47 173.88 1153.34 253.93 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n77 129 Car -1 -1 -1 680.26 175.94 712.73 200.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n77 136 Car -1 -1 -1 243.30 207.10 312.72 239.64 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n77 135 Car -1 -1 -1 441.65 193.45 468.10 209.49 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n77 130 Car -1 -1 -1 723.11 175.43 771.19 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n77 132 Car -1 -1 -1 -0.78 227.73 36.05 283.59 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n78 116 Car -1 -1 -1 453.54 194.10 550.12 257.31 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n78 126 Car -1 -1 -1 1.69 213.34 149.54 299.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n78 122 Car -1 -1 -1 163.89 200.53 288.36 262.58 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n78 105 Car -1 -1 -1 1087.38 165.80 1238.37 290.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n78 2 Car -1 -1 -1 570.81 181.26 614.41 219.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n78 109 Car -1 -1 -1 801.89 172.56 933.90 221.61 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n78 131 Car 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0.92\n79 122 Car -1 -1 -1 147.74 200.82 277.16 264.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n79 91 Car -1 -1 -1 1007.48 175.77 1239.83 273.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n79 2 Car -1 -1 -1 573.62 180.99 616.42 218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n79 95 Car -1 -1 -1 926.96 177.14 1126.10 250.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n79 109 Car -1 -1 -1 812.91 172.08 953.29 223.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n79 131 Car -1 -1 -1 880.93 176.73 1025.02 235.20 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n79 129 Car -1 -1 -1 687.79 177.31 721.84 203.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n79 127 Car -1 -1 -1 290.84 203.58 347.43 232.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n79 105 Car -1 -1 -1 1138.79 163.75 1239.19 300.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n79 137 Car -1 -1 -1 16.38 210.74 150.90 270.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n79 136 Car -1 -1 -1 234.43 208.12 304.71 240.77 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n79 138 Car -1 -1 -1 668.51 179.19 694.28 197.73 -1 -1 -1 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227.32 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n80 95 Car -1 -1 -1 946.78 178.57 1153.36 256.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n80 131 Car -1 -1 -1 892.30 179.14 1059.35 240.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n80 129 Car -1 -1 -1 690.87 177.53 725.01 203.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n80 136 Car -1 -1 -1 217.13 208.90 299.99 245.25 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n80 135 Car -1 -1 -1 439.12 194.73 464.32 212.67 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n80 130 Car -1 -1 -1 746.71 174.20 802.30 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n80 138 Car -1 -1 -1 670.32 179.54 694.46 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n80 139 Car -1 -1 -1 682.99 184.05 709.38 202.00 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n81 122 Car -1 -1 -1 104.46 202.16 257.49 270.59 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n81 116 Car -1 -1 -1 469.89 193.09 561.31 253.46 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n81 91 Car -1 -1 -1 1075.91 172.68 1240.53 291.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n81 109 Car -1 -1 -1 827.60 172.75 985.53 229.95 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n81 137 Car -1 -1 -1 -1.51 212.48 113.83 281.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n81 131 Car -1 -1 -1 906.33 179.48 1092.35 245.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n81 2 Car -1 -1 -1 576.05 181.73 618.01 218.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n81 129 Car -1 -1 -1 694.70 177.31 728.37 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n81 95 Car -1 -1 -1 968.87 179.60 1192.80 262.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n81 136 Car -1 -1 -1 207.98 208.28 292.74 245.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n81 127 Car -1 -1 -1 273.73 204.38 337.87 234.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n81 126 Car -1 -1 -1 -1.76 214.37 75.20 319.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n81 135 Car -1 -1 -1 435.52 194.44 461.55 212.58 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n81 138 Car -1 -1 -1 672.58 179.32 697.23 196.99 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n81 130 Car -1 -1 -1 750.42 173.82 806.82 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n81 139 Car -1 -1 -1 686.05 182.57 712.33 201.39 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n82 116 Car -1 -1 -1 474.09 190.40 563.63 250.54 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n82 122 Car -1 -1 -1 80.30 200.56 241.48 271.83 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n82 109 Car -1 -1 -1 833.87 170.16 1001.42 231.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n82 2 Car -1 -1 -1 576.28 180.32 617.50 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n82 91 Car -1 -1 -1 1115.41 171.29 1240.45 300.45 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n82 127 Car -1 -1 -1 264.49 202.11 330.71 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n82 135 Car -1 -1 -1 430.47 192.43 458.17 211.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n82 95 Car -1 -1 -1 1000.29 177.23 1231.60 265.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n82 137 Car -1 -1 -1 -0.87 210.18 96.41 284.99 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n82 129 Car -1 -1 -1 696.46 175.30 729.16 201.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n82 131 Car -1 -1 -1 925.32 177.27 1127.35 247.49 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n82 136 Car -1 -1 -1 195.64 206.73 282.88 243.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n82 126 Car -1 -1 -1 0.64 214.48 35.90 319.83 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n82 139 Car -1 -1 -1 685.96 180.96 713.81 199.61 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n82 138 Car -1 -1 -1 673.36 176.32 697.71 195.36 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n83 116 Car -1 -1 -1 477.74 185.95 565.93 244.89 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n83 2 Car -1 -1 -1 576.35 175.99 616.97 211.38 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n83 122 Car -1 -1 -1 51.10 197.05 225.88 272.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n83 109 Car -1 -1 -1 845.01 166.25 1014.34 228.21 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n83 95 Car -1 -1 -1 1032.00 172.17 1237.37 270.52 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n83 135 Car -1 -1 -1 426.05 187.78 453.92 207.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n83 127 Car -1 -1 -1 255.91 197.78 323.76 229.85 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n83 131 Car -1 -1 -1 945.06 176.47 1154.48 248.21 -1 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737.07 172.51 795.17 212.49 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n99 127 Car -1 -1 -1 1.45 224.89 93.29 286.96 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n99 143 Car -1 -1 -1 949.12 166.29 1080.44 219.69 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n99 2 Car -1 -1 -1 580.78 181.59 616.74 214.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n99 141 Car -1 -1 -1 807.59 167.29 866.39 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n99 145 Car -1 -1 -1 715.69 178.88 761.82 209.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n99 138 Car -1 -1 -1 694.60 176.06 727.89 201.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n99 147 Car -1 -1 -1 785.96 173.97 832.13 200.19 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n100 116 Car -1 -1 -1 535.35 186.59 604.16 236.68 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n100 129 Car -1 -1 -1 740.88 168.84 801.79 210.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n100 135 Car -1 -1 -1 228.32 203.56 316.60 247.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n100 127 Car -1 -1 -1 0.59 221.23 65.41 290.08 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n100 2 Car -1 -1 -1 580.90 177.59 615.85 210.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n100 143 Car -1 -1 -1 970.05 161.80 1129.22 222.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n100 141 Car -1 -1 -1 816.95 164.43 877.96 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n100 145 Car -1 -1 -1 718.87 174.92 768.39 205.69 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n100 138 Car -1 -1 -1 697.70 171.80 729.18 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n100 147 Car -1 -1 -1 789.30 171.83 837.70 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n101 116 Car -1 -1 -1 538.38 183.85 606.31 232.48 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n101 129 Car -1 -1 -1 746.56 165.84 808.74 207.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n101 135 Car -1 -1 -1 198.91 202.18 299.45 251.67 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n101 143 Car -1 -1 -1 991.51 156.78 1163.36 220.67 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n101 138 Car -1 -1 -1 700.19 168.99 731.97 194.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n101 141 Car -1 -1 -1 819.29 159.52 885.84 196.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n101 145 Car -1 -1 -1 723.39 175.64 763.03 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n101 127 Car -1 -1 -1 -0.59 222.59 35.25 288.13 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n101 2 Car -1 -1 -1 580.90 174.75 615.62 206.99 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n101 147 Car -1 -1 -1 793.72 169.08 841.99 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n102 135 Car -1 -1 -1 166.21 202.93 279.91 255.79 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n102 129 Car -1 -1 -1 750.04 164.41 816.65 207.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n102 143 Car -1 -1 -1 1012.82 152.02 1227.55 226.08 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n102 116 Car -1 -1 -1 540.71 183.30 608.88 231.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n102 141 Car -1 -1 -1 826.21 157.56 893.89 195.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n102 2 Car -1 -1 -1 581.16 173.68 615.44 205.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n102 145 Car -1 -1 -1 727.50 174.04 773.04 202.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n102 138 Car -1 -1 -1 703.12 167.86 735.01 193.98 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n102 147 Car -1 -1 -1 800.41 167.59 850.30 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n103 116 Car -1 -1 -1 544.05 184.10 609.82 232.75 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n103 135 Car -1 -1 -1 128.43 206.75 255.39 263.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n103 129 Car -1 -1 -1 756.38 164.27 824.24 209.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n103 143 Car -1 -1 -1 1049.62 150.86 1235.07 228.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n103 141 Car -1 -1 -1 834.75 157.34 900.91 195.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n103 145 Car -1 -1 -1 730.80 173.56 777.27 203.68 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n103 2 Car -1 -1 -1 581.17 174.43 615.32 206.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n103 147 Car -1 -1 -1 806.19 167.47 858.32 194.25 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n103 138 Car -1 -1 -1 707.16 167.90 738.55 195.22 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n104 129 Car -1 -1 -1 761.08 165.47 833.36 212.40 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n104 135 Car -1 -1 -1 80.44 208.79 227.63 272.79 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n104 116 Car -1 -1 -1 546.22 185.24 611.43 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n104 141 Car -1 -1 -1 841.69 158.32 908.66 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n104 145 Car -1 -1 -1 736.15 173.97 786.78 206.35 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n104 143 Car -1 -1 -1 1077.12 149.40 1232.40 236.88 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n104 147 Car -1 -1 -1 808.26 168.45 864.95 196.75 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n104 138 Car -1 -1 -1 709.85 169.42 743.15 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n104 2 Car -1 -1 -1 581.20 175.66 615.14 206.37 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n105 129 Car -1 -1 -1 766.63 165.76 843.72 215.41 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n105 135 Car -1 -1 -1 23.38 211.96 196.49 284.18 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n105 116 Car -1 -1 -1 549.10 186.91 613.57 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n105 141 Car -1 -1 -1 849.12 159.48 922.98 201.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n105 143 Car -1 -1 -1 1112.56 144.07 1235.15 250.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n105 138 Car -1 -1 -1 711.20 170.42 746.60 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n105 145 Car -1 -1 -1 741.24 173.64 796.88 208.20 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n105 2 Car -1 -1 -1 578.77 176.46 615.75 208.76 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n105 147 Car -1 -1 -1 812.11 168.71 876.17 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n106 129 Car -1 -1 -1 773.52 166.79 853.90 218.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n106 135 Car -1 -1 -1 3.07 213.90 157.10 294.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n106 116 Car -1 -1 -1 551.82 187.58 614.36 235.37 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n106 145 Car -1 -1 -1 745.18 173.55 802.80 211.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n106 141 Car -1 -1 -1 856.43 160.05 934.40 203.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n106 138 Car -1 -1 -1 713.50 171.30 751.64 200.37 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n106 147 Car -1 -1 -1 817.18 169.16 886.83 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n106 2 Car -1 -1 -1 578.96 176.70 615.55 209.01 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n106 143 Car -1 -1 -1 1150.13 137.84 1237.10 256.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n107 129 Car -1 -1 -1 779.36 166.35 868.04 220.77 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n107 141 Car -1 -1 -1 863.79 159.68 947.74 204.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n107 116 Car -1 -1 -1 553.45 188.06 616.89 235.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n107 145 Car -1 -1 -1 749.53 174.20 812.48 212.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n107 135 Car -1 -1 -1 1.43 217.96 116.98 307.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n107 138 Car -1 -1 -1 716.15 171.63 755.93 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n107 147 Car -1 -1 -1 824.64 169.30 894.74 200.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n107 2 Car -1 -1 -1 580.63 176.67 615.75 208.91 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n108 129 Car -1 -1 -1 788.50 165.53 881.96 222.64 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n108 116 Car -1 -1 -1 555.72 186.55 618.76 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n108 141 Car -1 -1 -1 870.63 159.31 959.02 205.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n108 145 Car -1 -1 -1 754.76 173.00 818.49 212.89 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n108 138 Car -1 -1 -1 719.60 171.07 760.77 201.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n108 135 Car -1 -1 -1 0.16 220.59 64.42 312.99 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n108 2 Car -1 -1 -1 580.81 176.36 615.53 208.19 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n109 116 Car -1 -1 -1 558.59 186.18 619.48 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n109 141 Car -1 -1 -1 883.02 158.56 974.43 206.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n109 129 Car -1 -1 -1 797.04 164.50 896.36 227.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n109 145 Car -1 -1 -1 760.72 173.26 828.66 214.26 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n109 138 Car -1 -1 -1 721.47 170.43 766.62 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n109 2 Car -1 -1 -1 581.49 176.39 615.18 207.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n110 129 Car -1 -1 -1 806.14 163.95 913.03 228.84 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n110 141 Car -1 -1 -1 893.70 158.09 988.61 207.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n110 116 Car -1 -1 -1 560.77 185.93 621.26 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n110 145 Car -1 -1 -1 769.53 173.65 835.04 215.04 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n110 138 Car -1 -1 -1 756.47 172.79 806.74 204.97 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n110 2 Car -1 -1 -1 581.80 177.05 615.20 208.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n110 148 Car -1 -1 -1 728.08 170.43 773.75 205.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n111 129 Car -1 -1 -1 815.00 161.92 933.85 230.88 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n111 116 Car -1 -1 -1 562.92 184.92 623.33 232.39 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n111 141 Car -1 -1 -1 899.54 155.54 1006.85 208.52 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n111 145 Car -1 -1 -1 775.83 173.23 849.31 216.34 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n111 148 Car -1 -1 -1 734.68 168.88 780.78 204.77 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n111 2 Car -1 -1 -1 581.71 176.92 615.66 208.50 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n111 138 Car -1 -1 -1 760.41 171.34 812.17 204.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n112 129 Car -1 -1 -1 825.22 156.21 955.32 229.95 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n112 116 Car -1 -1 -1 565.98 180.43 623.59 228.78 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n112 145 Car -1 -1 -1 783.57 170.00 859.68 216.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n112 141 Car -1 -1 -1 912.93 150.58 1024.67 206.59 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n112 148 Car -1 -1 -1 732.96 165.03 784.24 203.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n112 2 Car -1 -1 -1 581.14 175.42 617.01 209.02 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n112 138 Car -1 -1 -1 765.16 167.60 815.36 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n113 129 Car -1 -1 -1 837.46 150.14 980.49 227.35 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n113 145 Car -1 -1 -1 790.20 162.74 875.37 214.15 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n113 141 Car -1 -1 -1 919.95 144.87 1049.04 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n113 148 Car -1 -1 -1 735.96 159.15 791.03 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n113 116 Car -1 -1 -1 567.77 176.18 624.94 223.17 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n113 2 Car -1 -1 -1 579.72 171.90 618.80 206.59 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n113 138 Car -1 -1 -1 766.51 162.10 820.45 194.89 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n114 129 Car -1 -1 -1 849.75 144.46 1007.59 229.10 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n114 145 Car -1 -1 -1 796.86 159.44 893.15 211.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n114 141 Car -1 -1 -1 943.11 139.24 1070.47 200.90 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n114 116 Car -1 -1 -1 569.78 172.35 627.89 219.71 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n114 148 Car -1 -1 -1 741.37 155.92 800.44 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n114 2 Car -1 -1 -1 580.91 164.20 618.03 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n114 138 Car -1 -1 -1 757.87 155.27 821.69 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n115 129 Car -1 -1 -1 864.60 140.71 1039.08 232.53 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n115 145 Car -1 -1 -1 805.33 157.54 906.73 211.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n115 116 Car -1 -1 -1 572.29 171.50 629.00 217.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n115 141 Car -1 -1 -1 957.37 137.80 1095.90 201.06 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n115 148 Car -1 -1 -1 745.49 154.62 803.94 195.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n115 2 Car -1 -1 -1 581.22 164.23 617.90 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n115 138 Car -1 -1 -1 771.61 154.80 831.91 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n116 129 Car -1 -1 -1 882.19 139.72 1076.22 237.85 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n116 145 Car -1 -1 -1 813.67 156.80 921.84 214.95 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n116 116 Car -1 -1 -1 573.61 172.35 631.29 219.32 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n116 141 Car -1 -1 -1 977.51 137.06 1122.18 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n116 148 Car -1 -1 -1 751.47 156.15 811.96 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n117 116 Car -1 -1 -1 576.07 174.44 631.82 220.84 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n117 145 Car -1 -1 -1 824.66 156.54 940.99 217.24 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n117 129 Car -1 -1 -1 905.26 137.48 1116.06 243.30 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n117 141 Car -1 -1 -1 996.53 134.78 1157.67 204.59 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n117 148 Car -1 -1 -1 755.65 157.15 817.11 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n118 145 Car -1 -1 -1 837.69 158.06 959.69 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n118 129 Car -1 -1 -1 927.84 134.88 1164.41 250.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n118 148 Car -1 -1 -1 764.43 156.78 830.41 204.74 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n118 116 Car -1 -1 -1 578.20 175.43 631.67 221.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n118 141 Car -1 -1 -1 1019.60 133.93 1188.82 205.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n119 145 Car -1 -1 -1 849.50 155.73 986.06 222.74 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n119 116 Car -1 -1 -1 580.27 174.72 634.07 221.90 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n119 148 Car -1 -1 -1 775.17 156.00 841.79 206.13 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n119 129 Car -1 -1 -1 954.40 129.07 1224.06 256.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n119 141 Car -1 -1 -1 1046.66 130.88 1216.33 208.41 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n120 145 Car -1 -1 -1 863.45 153.22 1012.63 226.47 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n120 116 Car -1 -1 -1 581.03 173.02 636.38 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n120 148 Car -1 -1 -1 781.82 155.78 852.09 206.01 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n120 129 Car -1 -1 -1 984.76 121.50 1238.27 266.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n120 141 Car -1 -1 -1 1075.66 129.19 1225.98 211.01 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n120 149 Van -1 -1 -1 984.76 121.50 1238.27 266.23 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n121 145 Car -1 -1 -1 876.46 151.79 1051.68 232.35 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n121 116 Car -1 -1 -1 582.40 172.82 638.91 219.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n121 129 Car -1 -1 -1 1018.85 119.73 1236.84 275.89 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n121 148 Car -1 -1 -1 796.52 153.27 868.04 208.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n121 150 Car -1 -1 -1 789.82 154.51 844.02 183.69 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n121 151 Car -1 -1 -1 863.91 154.04 940.11 192.50 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n122 145 Car -1 -1 -1 895.64 151.83 1094.37 236.66 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n122 116 Car -1 -1 -1 583.99 173.34 639.80 219.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n122 148 Car -1 -1 -1 794.98 154.66 877.36 209.80 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n122 129 Car -1 -1 -1 1058.36 117.66 1236.51 284.32 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n122 151 Car -1 -1 -1 871.49 154.39 948.40 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n122 150 Car -1 -1 -1 795.36 155.77 854.21 183.15 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n122 152 Van -1 -1 -1 1056.68 115.35 1238.17 280.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n122 153 Car -1 -1 -1 1006.56 145.62 1162.86 208.61 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n123 145 Car -1 -1 -1 913.32 151.35 1132.98 243.95 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n123 116 Car -1 -1 -1 584.62 174.06 640.10 220.34 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n123 148 Car -1 -1 -1 799.10 152.12 888.82 212.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n123 129 Car -1 -1 -1 1105.37 117.33 1235.71 292.99 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n123 151 Car -1 -1 -1 884.93 155.45 958.81 193.85 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n123 153 Car -1 -1 -1 1025.05 143.38 1198.98 212.51 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n123 150 Car -1 -1 -1 803.80 156.12 860.84 184.14 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n123 154 Car -1 -1 -1 582.29 169.80 617.15 201.96 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n124 145 Car -1 -1 -1 938.01 146.82 1185.26 249.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n124 116 Car -1 -1 -1 584.91 173.26 640.51 219.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n124 148 Car -1 -1 -1 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575.13 181.06 633.48 227.49 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n136 148 Car -1 -1 -1 1003.16 136.26 1237.72 290.02 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n136 156 Car -1 -1 -1 916.89 164.10 1025.68 208.89 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n136 154 Car -1 -1 -1 556.74 174.94 588.92 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n136 157 Cyclist -1 -1 -1 132.74 190.15 164.70 229.16 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n137 116 Car -1 -1 -1 574.26 181.08 632.54 226.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n137 156 Car -1 -1 -1 929.08 162.10 1044.71 208.45 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n137 148 Car -1 -1 -1 1034.60 131.33 1236.74 294.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n137 154 Car -1 -1 -1 554.62 174.60 587.91 203.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n137 157 Cyclist -1 -1 -1 114.04 190.88 147.15 234.08 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n137 158 Car -1 -1 -1 595.42 175.01 613.15 190.76 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n138 156 Car -1 -1 -1 939.44 160.95 1061.34 208.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n138 116 Car -1 -1 -1 573.23 181.37 631.15 226.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n138 154 Car -1 -1 -1 553.08 174.60 585.51 203.30 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n138 148 Car -1 -1 -1 1077.11 123.39 1233.80 309.84 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n138 158 Car -1 -1 -1 595.21 175.34 613.01 190.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n138 151 Car -1 -1 -1 1087.92 122.09 1228.59 280.17 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n139 156 Car -1 -1 -1 952.27 160.84 1084.55 211.65 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n139 116 Car -1 -1 -1 572.32 182.77 629.75 228.32 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n139 154 Car -1 -1 -1 551.85 176.12 584.63 204.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n139 158 Car -1 -1 -1 594.81 176.76 612.36 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n139 148 Car -1 -1 -1 1130.11 129.42 1234.56 304.04 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n139 160 Cyclist -1 -1 -1 75.60 196.54 107.40 238.66 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n140 156 Car -1 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Pedestrian -1 -1 -1 173.23 158.72 188.86 196.19 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n337 204 Car -1 -1 -1 1217.72 182.87 1239.60 226.78 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n338 154 Car -1 -1 -1 725.68 165.22 837.58 261.53 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n338 206 Car -1 -1 -1 940.65 178.29 1072.02 224.64 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n338 208 Car -1 -1 -1 355.95 169.96 410.23 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n338 205 Car -1 -1 -1 522.83 168.67 557.98 194.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n338 215 Car -1 -1 -1 739.19 166.89 864.01 212.68 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n338 210 Car -1 -1 -1 635.43 172.11 662.23 192.27 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n338 211 Car -1 -1 -1 310.84 172.63 362.57 195.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n338 202 Car -1 -1 -1 1065.25 170.62 1197.84 214.29 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n338 222 Pedestrian -1 -1 -1 159.92 161.10 177.05 201.32 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n338 216 Pedestrian -1 -1 -1 184.15 158.94 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Car -1 -1 -1 390.52 175.23 428.55 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n342 202 Car -1 -1 -1 1166.42 168.66 1243.96 218.83 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n342 225 Car -1 -1 -1 590.76 171.50 609.18 184.04 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n342 226 Pedestrian -1 -1 -1 74.48 159.25 92.20 209.66 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n342 227 Car -1 -1 -1 538.47 168.24 568.29 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n343 154 Car -1 -1 -1 828.45 165.90 984.18 296.56 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n343 210 Car -1 -1 -1 646.29 178.49 676.37 201.18 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n343 211 Car -1 -1 -1 276.61 180.23 338.00 208.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n343 208 Car -1 -1 -1 346.56 177.57 400.93 204.55 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n343 206 Car -1 -1 -1 1162.74 181.28 1237.30 245.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n343 215 Car -1 -1 -1 785.95 172.23 941.34 228.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n343 205 Car -1 -1 -1 519.10 176.28 556.49 205.43 -1 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-1000 -10 0.57\n409 311 Car -1 -1 -1 661.93 172.29 699.29 201.57 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n409 312 Car -1 -1 -1 894.25 180.23 987.58 215.17 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n410 255 Car -1 -1 -1 252.33 152.86 464.42 311.16 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n410 288 Car -1 -1 -1 -2.79 182.66 370.79 368.95 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n410 269 Car -1 -1 -1 436.32 165.40 529.03 235.46 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n410 271 Car -1 -1 -1 679.86 175.63 736.67 212.66 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n410 305 Car -1 -1 -1 479.03 171.82 540.29 217.08 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n410 302 Car -1 -1 -1 2.91 169.50 156.13 240.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n410 295 Car -1 -1 -1 670.60 174.36 713.59 205.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n410 304 Car -1 -1 -1 117.82 168.10 242.92 219.81 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n410 284 Car -1 -1 -1 540.07 168.99 568.82 192.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n410 296 Car -1 -1 -1 551.94 165.51 578.88 187.79 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n410 297 Car -1 -1 -1 217.37 156.93 353.49 230.32 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n410 311 Car -1 -1 -1 661.70 171.13 701.71 200.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n410 246 Car -1 -1 -1 1049.17 180.14 1237.08 369.75 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n410 313 Pedestrian -1 -1 -1 650.81 166.76 665.96 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n410 314 Car -1 -1 -1 650.81 166.76 665.96 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n411 288 Car -1 -1 -1 3.84 182.25 316.97 368.67 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n411 255 Car -1 -1 -1 197.22 150.25 451.32 330.61 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n411 269 Car -1 -1 -1 423.12 164.98 524.97 242.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n411 284 Car -1 -1 -1 539.29 170.23 565.87 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n411 305 Car -1 -1 -1 472.62 172.30 538.15 220.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n411 271 Car -1 -1 -1 681.61 176.32 741.61 215.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n411 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-1000 -1000 -1000 -10 0.89\n412 295 Car -1 -1 -1 674.61 176.74 719.96 211.50 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n412 311 Car -1 -1 -1 665.55 173.75 705.85 204.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n412 302 Car -1 -1 -1 0.97 170.12 102.70 239.41 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n412 284 Car -1 -1 -1 535.73 171.19 564.19 194.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n412 296 Car -1 -1 -1 548.24 166.95 575.12 190.42 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n412 304 Car -1 -1 -1 61.75 164.19 206.08 223.64 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n412 297 Car -1 -1 -1 140.09 158.46 298.40 228.31 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n412 315 Pedestrian -1 -1 -1 654.12 168.73 667.67 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n413 269 Car -1 -1 -1 389.23 167.01 512.42 257.63 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n413 271 Car -1 -1 -1 684.94 179.37 753.45 222.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n413 255 Car -1 -1 -1 16.31 139.58 413.61 370.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n413 305 Car -1 -1 -1 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496.13 277.12 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n415 295 Car -1 -1 -1 681.08 181.49 735.77 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n415 305 Car -1 -1 -1 439.85 176.98 521.22 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n415 271 Car -1 -1 -1 692.42 182.88 768.17 229.72 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n415 311 Car -1 -1 -1 668.30 177.32 717.00 211.83 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n415 296 Car -1 -1 -1 543.33 170.33 572.47 195.21 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n415 284 Car -1 -1 -1 529.81 174.19 559.54 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n415 297 Car -1 -1 -1 3.89 59.92 334.33 365.73 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n415 320 Car -1 -1 -1 1197.45 197.25 1238.52 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n415 318 Car -1 -1 -1 314.93 170.68 362.04 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n415 317 Car -1 -1 -1 1096.06 184.65 1228.39 226.85 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n415 321 Pedestrian -1 -1 -1 658.43 171.92 673.97 206.76 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n415 316 Car -1 -1 -1 648.65 175.20 674.85 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n415 322 Car -1 -1 -1 227.84 172.46 296.43 206.69 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n416 269 Car -1 -1 -1 317.49 167.26 487.51 289.75 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n416 305 Car -1 -1 -1 427.91 178.12 516.61 241.54 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n416 271 Car -1 -1 -1 696.65 185.26 776.90 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n416 297 Car -1 -1 -1 -0.77 119.92 292.22 367.74 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n416 295 Car -1 -1 -1 684.92 183.62 739.98 224.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n416 322 Car -1 -1 -1 212.04 172.97 286.94 208.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n416 311 Car -1 -1 -1 671.13 179.50 722.22 215.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n416 318 Car -1 -1 -1 306.79 171.45 358.30 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n416 284 Car -1 -1 -1 527.11 175.52 558.87 201.95 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n416 296 Car -1 -1 -1 543.36 171.01 572.27 195.54 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786.84 240.81 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n421 284 Car -1 -1 -1 518.77 176.40 554.02 207.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n421 296 Car -1 -1 -1 537.71 171.56 571.20 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n421 321 Pedestrian -1 -1 -1 671.92 168.72 692.06 223.43 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n421 311 Car -1 -1 -1 688.43 182.70 759.05 228.61 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n421 255 Car -1 -1 -1 1.67 178.63 87.35 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n421 325 Pedestrian -1 -1 -1 597.81 173.94 607.24 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n422 269 Car -1 -1 -1 0.62 150.02 383.55 369.01 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n422 271 Car -1 -1 -1 734.08 189.02 863.59 261.48 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n422 305 Car -1 -1 -1 303.18 180.76 477.84 286.01 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n422 295 Car -1 -1 -1 711.95 184.48 799.34 241.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n422 284 Car -1 -1 -1 516.34 172.55 553.69 205.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n422 296 Car -1 -1 -1 537.50 168.35 570.71 196.47 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n422 311 Car -1 -1 -1 695.99 180.74 766.74 228.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n422 321 Pedestrian -1 -1 -1 675.64 165.39 696.75 222.92 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n422 318 Car -1 -1 -1 243.38 167.09 319.80 204.52 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n422 325 Pedestrian -1 -1 -1 599.35 171.01 608.77 194.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n422 326 Car -1 -1 -1 656.66 177.02 691.34 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n423 271 Car -1 -1 -1 742.21 193.29 886.06 270.61 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n423 269 Car -1 -1 -1 3.98 135.45 341.23 367.94 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n423 295 Car -1 -1 -1 716.69 186.04 816.08 247.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n423 305 Car -1 -1 -1 267.92 181.33 466.16 300.48 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n423 284 Car -1 -1 -1 513.55 172.78 553.22 206.56 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n423 296 Car -1 -1 -1 536.68 168.65 570.48 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n423 318 Car -1 -1 -1 235.66 168.14 305.92 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n423 311 Car -1 -1 -1 698.72 182.07 774.04 230.29 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n423 321 Pedestrian -1 -1 -1 680.40 165.29 702.91 227.40 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n423 325 Pedestrian -1 -1 -1 602.86 171.07 611.57 194.04 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n423 326 Car -1 -1 -1 658.62 177.64 697.35 206.28 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n424 271 Car -1 -1 -1 752.95 197.91 913.25 281.98 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n424 295 Car -1 -1 -1 723.60 190.18 831.95 252.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n424 269 Car -1 -1 -1 1.17 158.24 290.67 368.50 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n424 305 Car -1 -1 -1 233.67 186.70 452.20 323.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n424 321 Pedestrian -1 -1 -1 683.95 168.47 709.52 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n424 296 Car -1 -1 -1 535.83 171.81 570.20 201.48 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n424 318 Car -1 -1 -1 225.12 171.09 298.89 206.52 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n424 284 Car -1 -1 -1 511.39 175.98 551.11 211.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n424 311 Car -1 -1 -1 704.45 185.96 790.08 238.49 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n424 322 Car -1 -1 -1 54.40 173.61 182.25 227.00 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n424 325 Pedestrian -1 -1 -1 604.97 174.34 612.60 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n425 271 Car -1 -1 -1 768.74 201.71 944.78 295.30 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n425 295 Car -1 -1 -1 731.77 193.44 848.66 262.78 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n425 305 Car -1 -1 -1 179.46 188.36 436.32 346.60 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n425 284 Car -1 -1 -1 509.05 177.25 550.47 214.89 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n425 321 Pedestrian -1 -1 -1 690.42 172.01 717.29 238.10 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n425 296 Car -1 -1 -1 533.86 172.97 569.99 205.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n425 322 Car -1 -1 -1 24.70 172.28 159.24 223.42 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n425 318 Car -1 -1 -1 214.43 171.22 292.02 208.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n425 269 Car -1 -1 -1 -4.04 180.00 210.35 370.07 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n425 325 Pedestrian -1 -1 -1 606.46 176.53 616.98 200.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n425 311 Car -1 -1 -1 704.73 189.55 797.25 242.93 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n425 327 Car -1 -1 -1 473.21 173.59 493.40 188.94 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n425 328 Car -1 -1 -1 664.94 184.63 703.24 212.43 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n426 271 Car -1 -1 -1 782.83 204.05 989.42 312.39 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n426 305 Car -1 -1 -1 94.49 192.69 421.95 370.94 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n426 295 Car -1 -1 -1 741.67 194.91 875.89 270.98 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n426 284 Car -1 -1 -1 507.39 178.00 550.09 216.96 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n426 322 Car -1 -1 -1 5.51 174.70 137.95 228.30 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n426 296 Car -1 -1 -1 534.31 173.94 569.78 205.96 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n426 318 Car -1 -1 -1 202.88 172.08 281.12 213.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n426 321 Pedestrian -1 -1 -1 698.03 172.68 725.30 242.50 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n426 311 Car -1 -1 -1 706.96 187.93 801.93 246.33 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n426 328 Car -1 -1 -1 665.44 185.90 705.16 214.08 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n426 325 Pedestrian -1 -1 -1 608.27 176.70 621.29 201.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n426 327 Car -1 -1 -1 471.34 174.59 492.18 190.41 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n427 305 Car -1 -1 -1 -3.37 190.42 396.41 368.61 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n427 271 Car -1 -1 -1 804.48 206.10 1038.69 327.55 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n427 295 Car -1 -1 -1 753.15 195.67 897.11 278.17 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n427 296 Car -1 -1 -1 534.49 174.01 570.04 206.65 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n427 321 Pedestrian -1 -1 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-1000 -1000 -10 0.93\n428 321 Pedestrian -1 -1 -1 716.34 166.82 746.75 248.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n428 311 Car -1 -1 -1 733.06 186.27 838.41 253.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n428 318 Car -1 -1 -1 171.95 170.11 261.07 215.17 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n428 296 Car -1 -1 -1 533.48 171.40 571.45 204.24 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n428 328 Car -1 -1 -1 672.75 182.24 714.81 212.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n428 322 Car -1 -1 -1 2.36 170.89 96.22 246.60 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n428 327 Car -1 -1 -1 467.65 171.13 490.37 187.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n428 325 Pedestrian -1 -1 -1 618.09 174.81 627.40 201.64 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n429 271 Car -1 -1 -1 852.75 202.34 1192.63 364.17 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n429 305 Car -1 -1 -1 1.27 196.55 320.33 367.20 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n429 284 Car -1 -1 -1 501.08 171.60 550.41 214.23 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n429 318 Car -1 -1 -1 157.34 167.11 249.69 211.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n429 295 Car -1 -1 -1 781.13 191.59 961.07 289.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n429 311 Car -1 -1 -1 734.48 179.19 852.44 254.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n429 321 Pedestrian -1 -1 -1 727.02 160.55 760.78 248.81 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n429 296 Car -1 -1 -1 534.23 166.45 571.43 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n429 328 Car -1 -1 -1 674.87 177.20 720.33 209.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n429 322 Car -1 -1 -1 1.96 169.68 72.05 248.07 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n429 325 Pedestrian -1 -1 -1 622.32 170.03 632.06 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n429 327 Car -1 -1 -1 465.62 166.59 490.31 184.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n430 271 Car -1 -1 -1 881.98 205.50 1234.82 368.72 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n430 295 Car -1 -1 -1 795.44 191.74 1002.11 302.23 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n430 305 Car -1 -1 -1 0.28 197.47 259.75 368.49 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n430 318 Car -1 -1 -1 139.35 167.36 237.30 213.30 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n430 284 Car -1 -1 -1 497.80 171.91 548.83 214.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n430 311 Car -1 -1 -1 746.40 179.53 879.93 260.96 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n430 296 Car -1 -1 -1 532.69 165.89 572.49 200.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n430 328 Car -1 -1 -1 678.31 176.47 725.38 209.64 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n430 322 Car -1 -1 -1 -0.18 174.66 42.61 242.41 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n430 325 Pedestrian -1 -1 -1 625.63 171.27 636.88 196.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n430 327 Car -1 -1 -1 462.82 167.71 489.24 184.75 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n430 321 Pedestrian -1 -1 -1 736.52 160.65 778.62 254.92 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n431 271 Car -1 -1 -1 926.25 211.06 1236.06 369.81 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n431 295 Car -1 -1 -1 813.01 196.18 1053.64 322.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n431 311 Car -1 -1 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-1000 -1000 -1000 -10 0.92\n432 284 Car -1 -1 -1 488.56 180.86 545.31 228.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n432 271 Car -1 -1 -1 971.24 219.88 1237.24 368.95 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n432 318 Car -1 -1 -1 94.54 176.54 206.06 226.24 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n432 296 Car -1 -1 -1 529.07 175.32 570.85 211.66 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n432 327 Car -1 -1 -1 455.58 175.53 481.09 194.29 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n432 328 Car -1 -1 -1 684.49 186.74 734.15 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n432 321 Pedestrian -1 -1 -1 764.67 170.89 807.37 277.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n432 325 Pedestrian -1 -1 -1 631.85 179.67 643.07 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n433 295 Car -1 -1 -1 862.71 210.06 1199.26 369.85 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n433 311 Car -1 -1 -1 776.47 193.59 951.54 293.29 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n433 318 Car -1 -1 -1 70.74 177.54 188.99 229.78 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n433 284 Car 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-1 -1 -1 -1000 -1000 -1000 -10 0.88\n434 318 Car -1 -1 -1 40.49 175.78 166.49 229.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n434 296 Car -1 -1 -1 523.49 175.38 566.29 214.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n434 325 Pedestrian -1 -1 -1 636.33 180.84 648.38 208.70 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n434 327 Car -1 -1 -1 445.20 176.05 471.64 195.88 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n434 321 Pedestrian -1 -1 -1 801.57 171.17 856.35 302.35 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n434 329 Car -1 -1 -1 566.04 181.15 611.38 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n435 295 Car -1 -1 -1 934.45 220.50 1236.10 369.70 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n435 318 Car -1 -1 -1 5.14 174.51 146.19 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n435 328 Car -1 -1 -1 687.72 190.58 746.31 228.68 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n435 296 Car -1 -1 -1 518.04 175.57 563.51 216.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n435 284 Car -1 -1 -1 467.37 183.46 535.08 236.95 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n435 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228.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n844 491 Car -1 -1 -1 230.96 189.40 291.47 219.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n844 476 Car -1 -1 -1 -1.43 197.09 75.47 259.63 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n844 495 Car -1 -1 -1 664.16 181.21 721.86 223.13 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n844 498 Car -1 -1 -1 656.11 182.07 700.14 217.20 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n844 496 Car -1 -1 -1 414.13 182.52 442.14 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n844 481 Car -1 -1 -1 289.94 187.46 341.43 214.56 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n844 490 Car -1 -1 -1 370.49 184.87 406.66 204.43 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n844 499 Car -1 -1 -1 444.69 182.34 469.39 196.05 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n844 482 Car -1 -1 -1 598.46 179.53 619.52 196.59 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n844 500 Car -1 -1 -1 424.30 182.39 450.92 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n845 470 Car -1 -1 -1 774.21 188.62 1054.42 353.67 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n845 493 Car -1 -1 -1 712.30 185.39 858.85 273.28 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n845 492 Car -1 -1 -1 678.93 183.86 759.63 238.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n845 486 Car -1 -1 -1 31.85 196.30 166.72 244.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n845 484 Car -1 -1 -1 147.20 191.86 236.74 232.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n845 482 Car -1 -1 -1 597.39 179.76 619.23 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n845 481 Car -1 -1 -1 282.52 187.44 335.78 215.10 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n845 495 Car -1 -1 -1 667.11 181.86 727.21 225.22 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n845 498 Car -1 -1 -1 656.63 181.27 704.64 218.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n845 496 Car -1 -1 -1 410.20 183.08 438.44 200.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n845 476 Car -1 -1 -1 -0.44 201.64 49.70 262.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n845 490 Car -1 -1 -1 363.40 184.53 402.42 205.34 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n845 499 Car -1 -1 -1 443.74 182.73 467.47 196.58 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n845 500 Car -1 -1 -1 423.74 182.16 448.63 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n845 491 Car -1 -1 -1 220.63 189.20 280.76 222.04 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n846 470 Car -1 -1 -1 789.63 193.29 1147.17 370.36 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n846 493 Car -1 -1 -1 721.40 187.11 880.74 283.42 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n846 492 Car -1 -1 -1 683.23 183.33 769.86 242.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n846 495 Car -1 -1 -1 669.12 181.36 732.74 228.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n846 496 Car -1 -1 -1 407.16 184.25 435.16 201.24 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n846 486 Car -1 -1 -1 5.99 198.30 145.74 249.41 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n846 482 Car -1 -1 -1 596.67 180.26 619.15 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n846 490 Car -1 -1 -1 358.32 186.22 397.71 206.57 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n846 491 Car -1 -1 -1 204.01 191.24 272.46 224.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n846 481 Car -1 -1 -1 269.93 187.54 329.01 217.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n846 498 Car -1 -1 -1 658.07 182.07 706.47 219.82 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n846 484 Car -1 -1 -1 132.74 192.13 219.61 234.85 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n846 499 Car -1 -1 -1 439.78 183.05 463.99 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n846 476 Car -1 -1 -1 -0.21 201.06 17.57 270.72 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n847 470 Car -1 -1 -1 815.11 196.26 1237.14 368.10 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n847 492 Car -1 -1 -1 684.72 184.87 778.51 246.63 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n847 493 Car -1 -1 -1 729.77 188.31 911.58 293.40 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n847 495 Car -1 -1 -1 670.42 183.14 738.27 231.98 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n847 496 Car -1 -1 -1 402.59 184.18 432.29 201.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n847 482 Car -1 -1 -1 596.22 181.15 617.99 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n847 481 Car -1 -1 -1 260.74 189.00 318.67 218.75 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n847 486 Car -1 -1 -1 -3.32 201.03 130.23 253.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n847 491 Car -1 -1 -1 188.06 190.87 259.39 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n847 484 Car -1 -1 -1 110.05 194.00 204.91 234.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n847 490 Car -1 -1 -1 352.14 186.63 394.30 207.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n847 498 Car -1 -1 -1 659.32 182.16 710.71 222.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n847 499 Car -1 -1 -1 436.23 183.40 460.95 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n847 501 Car -1 -1 -1 423.93 184.78 450.32 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n848 470 Car -1 -1 -1 842.71 198.07 1235.27 367.87 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n848 492 Car -1 -1 -1 689.62 185.12 788.85 250.59 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n848 493 Car -1 -1 -1 737.97 189.55 957.75 307.76 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n848 495 Car -1 -1 -1 673.63 183.29 744.52 234.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n848 484 Car -1 -1 -1 86.35 191.26 189.82 235.41 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n848 486 Car -1 -1 -1 -2.26 196.54 104.79 253.61 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n848 482 Car -1 -1 -1 595.30 180.61 618.18 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n848 496 Car -1 -1 -1 398.54 182.59 429.28 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n848 491 Car -1 -1 -1 173.02 187.76 247.72 224.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n848 490 Car -1 -1 -1 345.89 184.78 389.51 206.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n848 498 Car -1 -1 -1 662.66 182.83 714.19 224.45 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n848 481 Car -1 -1 -1 246.06 186.76 311.43 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n848 499 Car -1 -1 -1 431.09 182.36 457.67 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n849 470 Car -1 -1 -1 885.81 205.16 1236.51 368.87 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n849 492 Car -1 -1 -1 692.75 186.11 803.04 255.56 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n849 493 Car -1 -1 -1 750.71 191.25 1008.56 328.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n849 484 Car -1 -1 -1 63.26 190.12 172.16 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n849 481 Car -1 -1 -1 234.00 185.78 303.32 217.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n849 495 Car -1 -1 -1 674.94 183.25 751.33 236.49 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n849 491 Car -1 -1 -1 156.20 187.29 234.49 224.76 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n849 482 Car -1 -1 -1 594.78 179.67 617.91 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n849 490 Car -1 -1 -1 340.01 183.20 384.25 206.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n849 498 Car -1 -1 -1 663.84 182.60 719.50 226.43 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n849 486 Car -1 -1 -1 -0.72 195.40 76.27 254.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n849 496 Car -1 -1 -1 394.65 181.52 426.30 200.09 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n849 499 Car -1 -1 -1 414.24 180.26 442.98 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0015.txt",
    "content": "0 1 Car -1 -1 -1 604.56 197.13 982.57 361.01 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n0 2 Car -1 -1 -1 1066.70 175.05 1235.53 249.40 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n0 3 Car -1 -1 -1 478.72 207.03 513.71 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n0 4 Cyclist -1 -1 -1 448.84 195.36 477.45 262.48 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n1 1 Car -1 -1 -1 522.31 198.91 918.83 368.21 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1 2 Car -1 -1 -1 1065.00 172.26 1235.83 248.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n1 3 Car -1 -1 -1 476.95 207.22 511.96 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n1 4 Cyclist -1 -1 -1 445.74 194.98 474.71 261.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n2 1 Car -1 -1 -1 436.50 204.37 849.18 370.26 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n2 2 Car -1 -1 -1 1064.37 173.98 1235.17 250.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n2 4 Cyclist -1 -1 -1 443.23 195.81 470.72 261.80 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n2 3 Car -1 -1 -1 474.27 208.03 510.48 237.79 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n3 1 Car -1 -1 -1 337.76 206.04 775.77 368.34 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n3 2 Car -1 -1 -1 1059.59 173.81 1236.09 251.11 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n3 3 Car -1 -1 -1 471.68 208.01 509.18 238.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n3 4 Cyclist -1 -1 -1 438.77 196.62 467.22 266.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n4 2 Car -1 -1 -1 1058.77 174.23 1235.85 251.28 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n4 1 Car -1 -1 -1 226.22 212.35 693.36 369.81 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n4 3 Car -1 -1 -1 468.50 208.37 506.77 238.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n4 4 Cyclist -1 -1 -1 438.73 196.41 464.51 266.28 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n5 2 Car -1 -1 -1 1056.94 175.13 1235.81 251.96 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n5 1 Car -1 -1 -1 94.50 218.14 600.26 369.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n5 3 Car -1 -1 -1 466.83 208.92 506.16 238.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n5 4 Cyclist -1 -1 -1 435.74 197.62 459.74 266.17 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n6 2 Car -1 -1 -1 1054.34 177.99 1237.08 253.53 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n6 1 Car -1 -1 -1 -5.33 224.82 505.11 370.40 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n6 3 Car -1 -1 -1 460.36 211.21 500.10 243.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n6 4 Cyclist -1 -1 -1 426.70 197.73 455.26 266.08 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n7 2 Car -1 -1 -1 1048.17 179.11 1237.39 255.95 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n7 1 Car -1 -1 -1 -0.02 229.78 392.69 368.18 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n7 3 Car -1 -1 -1 456.07 212.88 495.88 245.70 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n7 4 Cyclist -1 -1 -1 417.58 198.82 453.50 281.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n8 2 Car -1 -1 -1 1043.01 181.64 1236.67 257.86 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n8 1 Car -1 -1 -1 0.72 234.60 275.80 370.37 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n8 3 Car -1 -1 -1 449.30 215.02 491.55 248.93 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n8 4 Cyclist -1 -1 -1 407.78 201.55 447.54 286.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n9 2 Car -1 -1 -1 1035.65 183.15 1236.96 259.37 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n9 3 Car -1 -1 -1 441.57 215.90 485.42 250.78 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n9 4 Cyclist -1 -1 -1 396.85 201.55 439.05 288.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n9 1 Car -1 -1 -1 -3.37 248.69 139.57 370.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n10 2 Car -1 -1 -1 1027.61 183.30 1237.50 260.01 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n10 3 Car -1 -1 -1 431.53 217.53 478.18 253.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n10 4 Cyclist -1 -1 -1 386.41 201.71 430.12 292.30 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n11 2 Car -1 -1 -1 1022.18 183.52 1238.18 262.57 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n11 4 Cyclist -1 -1 -1 374.42 199.98 418.89 296.93 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n11 3 Car -1 -1 -1 422.21 217.94 468.80 254.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n12 2 Car -1 -1 -1 1012.76 183.50 1236.09 263.41 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n12 4 Cyclist -1 -1 -1 355.42 199.56 407.68 303.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n12 3 Car -1 -1 -1 411.10 219.73 460.55 257.17 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n12 5 Car -1 -1 -1 1128.47 183.48 1235.05 250.65 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n13 2 Car -1 -1 -1 1004.71 185.24 1232.92 264.09 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n13 3 Car -1 -1 -1 399.26 220.64 449.07 260.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n13 4 Cyclist -1 -1 -1 338.36 202.76 392.65 307.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n13 5 Car -1 -1 -1 1109.01 187.17 1231.18 254.25 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n14 2 Car -1 -1 -1 994.82 186.47 1220.91 264.41 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n14 4 Cyclist -1 -1 -1 317.44 203.34 377.05 315.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n14 3 Car -1 -1 -1 386.76 221.70 438.52 263.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 5 Car -1 -1 -1 1092.23 187.76 1232.69 259.66 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n15 2 Car -1 -1 -1 988.21 188.14 1211.76 267.72 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n15 3 Car -1 -1 -1 373.25 222.24 428.50 265.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n15 4 Cyclist -1 -1 -1 293.78 201.16 363.49 325.01 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n15 5 Car -1 -1 -1 1084.69 190.33 1232.44 259.57 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n16 2 Car -1 -1 -1 979.83 191.34 1205.13 271.43 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n16 4 Cyclist -1 -1 -1 272.69 202.59 346.20 332.17 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n16 3 Car -1 -1 -1 360.29 222.53 417.30 267.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n16 5 Car -1 -1 -1 1076.67 193.37 1232.49 263.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n17 2 Car -1 -1 -1 970.86 195.76 1198.32 274.58 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n17 4 Cyclist -1 -1 -1 251.62 202.07 326.16 339.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n17 3 Car -1 -1 -1 345.53 224.83 405.66 272.42 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n17 5 Car -1 -1 -1 1061.76 196.15 1231.88 267.96 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n18 2 Car -1 -1 -1 965.94 196.09 1195.27 276.11 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n18 3 Car -1 -1 -1 331.16 227.50 394.58 276.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n18 4 Cyclist -1 -1 -1 231.23 203.10 308.50 347.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n18 5 Car -1 -1 -1 1051.11 196.49 1235.37 268.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n19 2 Car -1 -1 -1 960.53 194.68 1193.19 276.27 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n19 3 Car -1 -1 -1 318.09 228.26 384.56 281.36 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n19 4 Cyclist -1 -1 -1 204.01 204.14 290.54 359.72 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n19 5 Car -1 -1 -1 1042.64 196.06 1236.13 268.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n20 3 Car -1 -1 -1 303.54 227.86 375.63 284.51 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n20 2 Car -1 -1 -1 957.17 193.39 1195.42 275.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n20 4 Cyclist -1 -1 -1 175.46 200.93 269.98 371.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n20 5 Car -1 -1 -1 1042.42 194.63 1236.11 267.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n21 3 Car -1 -1 -1 290.29 226.45 365.70 285.52 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n21 2 Car -1 -1 -1 957.36 191.82 1194.81 275.18 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n21 4 Cyclist -1 -1 -1 138.88 202.41 253.45 370.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n21 5 Car -1 -1 -1 1044.31 193.06 1233.95 264.97 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n22 2 Car -1 -1 -1 953.85 192.66 1200.59 277.81 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n22 3 Car -1 -1 -1 275.46 225.88 356.27 286.80 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n22 4 Cyclist -1 -1 -1 96.47 199.71 233.59 372.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n22 5 Car -1 -1 -1 1035.14 194.02 1235.62 269.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n23 2 Car -1 -1 -1 954.89 193.49 1206.20 280.65 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n23 4 Cyclist -1 -1 -1 59.79 197.68 199.77 369.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n23 3 Car -1 -1 -1 257.84 223.79 345.74 289.68 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n23 5 Car -1 -1 -1 1049.89 194.91 1235.97 270.18 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n24 2 Car -1 -1 -1 955.25 194.09 1214.87 283.63 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n24 3 Car -1 -1 -1 242.33 224.28 334.84 294.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n24 4 Cyclist -1 -1 -1 8.01 194.73 159.32 364.83 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n24 5 Car -1 -1 -1 1066.68 197.88 1234.68 272.74 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n25 2 Car -1 -1 -1 964.67 191.18 1227.14 281.80 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n25 3 Car -1 -1 -1 227.17 223.88 326.50 300.67 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n25 4 Cyclist -1 -1 -1 -10.56 189.15 130.31 370.03 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n25 5 Car -1 -1 -1 1066.48 195.70 1235.04 268.73 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n26 2 Car -1 -1 -1 974.28 186.43 1234.45 278.70 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n26 3 Car -1 -1 -1 213.36 220.62 318.72 300.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n26 6 Pedestrian -1 -1 -1 0.84 197.92 49.80 366.55 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n27 2 Car -1 -1 -1 987.49 182.28 1235.63 276.47 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n27 3 Car -1 -1 -1 200.95 215.37 314.93 301.42 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n28 2 Car -1 -1 -1 1007.34 180.82 1238.97 282.71 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n28 3 Car -1 -1 -1 189.22 210.60 313.09 302.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n29 2 Car -1 -1 -1 1032.05 180.65 1239.16 291.66 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n29 3 Car -1 -1 -1 177.10 208.47 314.97 308.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n30 3 Car -1 -1 -1 161.72 205.55 316.60 313.41 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n30 2 Car -1 -1 -1 1064.78 179.91 1238.05 291.73 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n31 3 Car -1 -1 -1 144.37 205.97 316.57 327.57 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n31 2 Car -1 -1 -1 1103.45 175.30 1237.49 289.36 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n32 3 Car -1 -1 -1 120.91 209.76 317.35 340.98 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n32 2 Car -1 -1 -1 1146.89 180.87 1239.36 290.95 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n33 3 Car -1 -1 -1 89.51 213.31 317.18 360.14 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n33 2 Car -1 -1 -1 1197.10 177.24 1236.94 294.94 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n34 3 Car -1 -1 -1 36.99 217.76 315.94 369.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n35 3 Car -1 -1 -1 -0.68 214.63 307.83 367.92 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n36 3 Car -1 -1 -1 -1.11 220.36 301.34 368.15 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n37 3 Car -1 -1 -1 -1.40 220.80 284.70 367.74 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n38 3 Car -1 -1 -1 0.93 219.50 259.42 369.09 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n39 3 Car -1 -1 -1 3.43 188.47 202.58 368.92 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n57 7 Pedestrian -1 -1 -1 826.68 168.00 845.26 218.61 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n60 8 Pedestrian -1 -1 -1 861.35 164.89 882.75 222.63 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n61 8 Pedestrian -1 -1 -1 875.52 163.76 898.20 224.26 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n62 8 Pedestrian -1 -1 -1 886.35 162.45 911.98 224.82 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n63 8 Pedestrian -1 -1 -1 905.36 162.65 929.74 229.69 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n64 8 Pedestrian -1 -1 -1 923.74 160.21 949.09 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n65 8 Pedestrian -1 -1 -1 943.68 161.31 971.67 234.47 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n66 8 Pedestrian -1 -1 -1 971.15 157.11 1002.81 243.78 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n67 8 Pedestrian -1 -1 -1 998.59 159.06 1032.95 249.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n68 8 Pedestrian -1 -1 -1 1032.04 154.19 1073.86 263.30 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n69 8 Pedestrian -1 -1 -1 1072.08 153.26 1119.44 263.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n71 9 Pedestrian -1 -1 -1 1171.17 144.43 1239.08 281.28 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n86 10 Car -1 -1 -1 976.84 158.08 1044.57 177.34 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n87 10 Car -1 -1 -1 978.99 158.30 1041.79 177.01 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n88 10 Car -1 -1 -1 988.40 159.25 1049.69 178.82 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n88 11 Car -1 -1 -1 926.32 161.26 979.58 178.01 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n89 11 Car -1 -1 -1 934.07 159.71 993.98 178.63 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n89 10 Car -1 -1 -1 997.26 158.09 1055.33 179.80 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n90 10 Car -1 -1 -1 1004.16 155.85 1064.02 177.60 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n90 11 Car -1 -1 -1 939.20 158.52 997.74 176.47 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n91 10 Car -1 -1 -1 1011.44 152.50 1074.31 172.94 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n91 11 Car -1 -1 -1 948.01 155.85 1005.29 174.57 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n92 11 Car -1 -1 -1 954.01 153.28 1012.81 171.71 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n92 10 Car -1 -1 -1 1021.12 148.11 1086.33 170.64 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n93 11 Car -1 -1 -1 961.17 151.55 1021.94 171.00 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n93 10 Car -1 -1 -1 1030.87 148.15 1099.67 169.54 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n94 11 Car -1 -1 -1 972.29 150.96 1033.24 171.43 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n94 10 Car -1 -1 -1 1041.23 145.82 1112.86 171.09 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n95 11 Car -1 -1 -1 979.76 152.18 1042.89 172.16 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n95 10 Car -1 -1 -1 1052.14 148.26 1118.25 170.96 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n96 11 Car -1 -1 -1 988.35 152.52 1055.64 174.66 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n96 10 Car -1 -1 -1 1063.16 150.16 1130.10 173.05 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n97 11 Car -1 -1 -1 994.82 155.37 1065.56 176.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n97 10 Car -1 -1 -1 1073.87 152.22 1142.43 174.74 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n98 10 Car -1 -1 -1 1084.60 153.65 1155.72 176.94 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n98 11 Car -1 -1 -1 1006.41 156.95 1076.36 177.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n99 10 Car -1 -1 -1 1099.27 153.74 1169.83 177.03 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n99 11 Car -1 -1 -1 1012.99 157.12 1086.06 177.90 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n100 10 Car -1 -1 -1 1105.42 152.09 1181.90 175.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n100 11 Car -1 -1 -1 1024.33 154.99 1097.62 177.03 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n101 10 Car -1 -1 -1 1113.77 149.35 1202.66 173.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n101 11 Car -1 -1 -1 1033.56 154.13 1106.43 176.45 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n101 12 Car -1 -1 -1 1162.10 146.34 1232.72 170.93 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n102 10 Car -1 -1 -1 1127.43 147.72 1214.17 171.97 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n102 13 Car -1 -1 -1 1216.30 147.53 1238.81 168.80 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n102 14 Pedestrian -1 -1 -1 778.76 154.62 795.08 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n103 10 Car -1 -1 -1 1144.44 146.98 1233.62 171.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n103 14 Pedestrian -1 -1 -1 785.00 157.05 804.07 205.74 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n104 10 Car -1 -1 -1 1161.57 146.37 1238.91 170.53 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 10 Car -1 -1 -1 1187.95 146.85 1237.65 169.07 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n106 10 Car -1 -1 -1 1201.40 148.71 1240.00 167.21 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n107 10 Car -1 -1 -1 1219.16 149.16 1238.39 167.58 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n107 15 Pedestrian -1 -1 -1 817.41 148.33 838.85 209.04 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n108 15 Pedestrian -1 -1 -1 826.33 149.28 848.09 208.31 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n108 16 Car -1 -1 -1 1116.31 150.92 1192.51 175.65 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n109 15 Pedestrian -1 -1 -1 837.68 153.07 858.83 210.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n109 16 Car -1 -1 -1 1132.84 151.50 1214.50 175.62 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n110 15 Pedestrian -1 -1 -1 846.06 147.41 871.52 217.50 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n110 16 Car -1 -1 -1 1148.57 153.01 1230.73 177.95 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n111 15 Pedestrian -1 -1 -1 857.37 146.73 882.52 218.52 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n111 16 Car -1 -1 -1 1148.91 148.05 1237.45 176.99 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n112 17 Car -1 -1 -1 346.56 177.48 434.03 203.76 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n113 17 Car -1 -1 -1 353.92 180.37 425.92 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n113 18 Pedestrian -1 -1 -1 883.49 140.58 911.86 224.05 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n114 18 Pedestrian -1 -1 -1 900.87 145.14 927.80 226.95 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n114 17 Car -1 -1 -1 342.25 180.64 422.10 207.39 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n114 19 Car -1 -1 -1 1201.07 144.98 1239.87 172.42 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n115 18 Pedestrian -1 -1 -1 916.47 140.71 948.47 231.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n115 17 Car -1 -1 -1 333.24 182.43 416.03 210.57 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n116 18 Pedestrian -1 -1 -1 938.88 137.99 968.40 232.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n116 17 Car -1 -1 -1 331.09 183.45 410.69 210.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n117 18 Pedestrian -1 -1 -1 962.00 133.64 991.60 237.47 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n117 17 Car -1 -1 -1 321.21 185.09 404.32 214.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n118 17 Car -1 -1 -1 318.69 186.56 398.79 215.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n119 17 Car -1 -1 -1 315.52 187.90 394.79 214.98 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n119 20 Pedestrian -1 -1 -1 1017.93 135.18 1058.90 236.72 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n120 20 Pedestrian -1 -1 -1 1052.54 125.42 1118.19 246.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n120 17 Car -1 -1 -1 309.53 187.95 392.79 214.54 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n121 20 Pedestrian -1 -1 -1 1100.61 120.98 1169.14 256.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n121 17 Car -1 -1 -1 305.36 185.21 389.26 215.58 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n122 20 Pedestrian -1 -1 -1 1148.56 112.87 1222.86 266.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n122 17 Car -1 -1 -1 291.67 182.99 379.69 217.08 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n123 17 Car -1 -1 -1 278.38 182.19 376.59 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 20 Pedestrian -1 -1 -1 1205.28 108.56 1236.08 271.03 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n124 17 Car -1 -1 -1 263.19 184.95 369.99 223.53 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n125 17 Car -1 -1 -1 250.92 189.77 358.76 227.32 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n126 17 Car -1 -1 -1 239.63 192.07 354.85 228.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n127 17 Car -1 -1 -1 225.69 195.31 345.47 231.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n128 17 Car -1 -1 -1 211.08 197.70 335.81 235.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n129 17 Car -1 -1 -1 196.68 198.50 325.52 237.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n130 17 Car -1 -1 -1 178.72 200.04 313.71 240.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n131 17 Car -1 -1 -1 158.57 200.06 302.16 243.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n131 21 Car -1 -1 -1 563.48 179.48 590.89 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n131 22 Car -1 -1 -1 503.48 182.47 540.91 203.45 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n132 17 Car -1 -1 -1 138.22 199.57 290.74 248.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n132 21 Car -1 -1 -1 562.39 178.97 590.42 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n132 22 Car -1 -1 -1 500.75 182.21 535.82 203.43 -1 -1 -1 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-1000 -1000 -1000 -10 0.86\n401 32 Car -1 -1 -1 1157.50 165.01 1237.90 236.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n401 69 Pedestrian -1 -1 -1 288.04 171.89 313.24 240.59 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n401 71 Car -1 -1 -1 263.10 172.19 354.70 228.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n401 70 Pedestrian -1 -1 -1 301.53 167.66 332.10 242.33 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n402 31 Car -1 -1 -1 695.35 168.59 893.08 239.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n402 35 Car -1 -1 -1 933.32 167.67 1152.16 237.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n402 60 Car -1 -1 -1 590.73 169.99 679.31 210.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n402 61 Car -1 -1 -1 664.54 168.09 768.33 208.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n402 32 Car -1 -1 -1 1158.17 164.70 1237.37 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n402 71 Car -1 -1 -1 260.42 172.62 357.39 228.42 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n402 69 Pedestrian -1 -1 -1 287.90 172.16 313.97 240.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n402 70 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237.27 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n404 60 Car -1 -1 -1 590.77 169.92 679.29 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n404 61 Car -1 -1 -1 664.86 168.06 768.51 209.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n404 32 Car -1 -1 -1 1157.81 165.03 1237.80 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n404 71 Car -1 -1 -1 258.57 172.75 359.48 227.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n404 69 Pedestrian -1 -1 -1 287.99 171.77 313.29 240.72 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n404 70 Pedestrian -1 -1 -1 301.03 167.91 332.31 241.73 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n405 31 Car -1 -1 -1 695.95 168.80 892.52 238.98 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n405 35 Car -1 -1 -1 932.67 167.12 1152.73 237.48 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n405 60 Car -1 -1 -1 590.39 169.80 679.55 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n405 61 Car -1 -1 -1 664.75 168.11 768.48 209.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n405 32 Car -1 -1 -1 1157.48 160.83 1238.13 234.48 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n405 69 Pedestrian -1 -1 -1 287.79 171.53 313.40 240.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n405 71 Car -1 -1 -1 261.22 172.51 356.87 228.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n405 70 Pedestrian -1 -1 -1 300.88 167.71 332.45 242.15 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n406 31 Car -1 -1 -1 695.65 168.86 892.79 239.02 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n406 35 Car -1 -1 -1 931.81 167.07 1153.48 237.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n406 60 Car -1 -1 -1 590.32 169.89 679.63 210.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n406 61 Car -1 -1 -1 664.73 167.99 768.08 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n406 32 Car -1 -1 -1 1158.14 165.17 1237.64 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n406 71 Car -1 -1 -1 260.52 172.52 357.25 228.31 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n406 69 Pedestrian -1 -1 -1 287.56 171.60 314.10 240.74 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n406 70 Pedestrian -1 -1 -1 301.11 168.15 332.23 241.73 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n407 31 Car -1 -1 -1 695.94 168.89 892.43 239.02 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n407 35 Car -1 -1 -1 932.21 167.18 1152.82 237.67 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n407 60 Car -1 -1 -1 590.61 169.83 679.65 210.88 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n407 61 Car -1 -1 -1 664.55 168.03 768.02 208.97 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n407 71 Car -1 -1 -1 260.15 172.86 357.37 227.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n407 32 Car -1 -1 -1 1158.46 164.94 1237.41 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n407 69 Pedestrian -1 -1 -1 287.93 171.66 314.39 240.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n407 70 Pedestrian -1 -1 -1 301.50 168.53 332.30 241.75 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n408 31 Car -1 -1 -1 696.07 168.79 892.40 239.05 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n408 35 Car -1 -1 -1 933.31 167.46 1152.10 237.34 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n408 60 Car -1 -1 -1 590.84 169.91 679.61 210.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n408 61 Car -1 -1 -1 664.46 168.13 768.39 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n408 71 Car -1 -1 -1 260.32 172.19 357.16 228.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n408 32 Car -1 -1 -1 1158.14 164.71 1237.65 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n408 69 Pedestrian -1 -1 -1 287.51 171.82 315.01 240.46 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n408 70 Pedestrian -1 -1 -1 301.63 168.13 332.33 241.89 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n409 31 Car -1 -1 -1 695.46 168.66 892.72 239.00 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n409 35 Car -1 -1 -1 933.40 167.23 1152.17 237.42 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n409 60 Car -1 -1 -1 590.78 169.91 679.38 210.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n409 61 Car -1 -1 -1 664.35 167.97 768.08 208.96 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n409 32 Car -1 -1 -1 1157.76 164.95 1238.12 236.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n409 69 Pedestrian -1 -1 -1 287.97 171.30 314.31 240.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n409 71 Car -1 -1 -1 261.25 172.06 356.68 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n409 70 Pedestrian -1 -1 -1 301.53 168.41 332.69 241.92 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n410 31 Car -1 -1 -1 695.97 168.49 892.19 239.06 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n410 35 Car -1 -1 -1 931.92 167.14 1153.38 237.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n410 60 Car -1 -1 -1 590.59 170.01 679.59 210.82 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n410 61 Car -1 -1 -1 664.07 167.94 768.04 208.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n410 32 Car -1 -1 -1 1157.80 164.97 1238.02 236.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n410 69 Pedestrian -1 -1 -1 287.55 170.94 314.81 240.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n410 71 Car -1 -1 -1 260.47 172.20 357.46 228.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n410 70 Pedestrian -1 -1 -1 301.79 168.25 332.69 242.07 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n411 31 Car -1 -1 -1 695.56 168.60 893.03 239.04 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n411 35 Car -1 -1 -1 932.30 166.84 1153.08 237.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n411 60 Car -1 -1 -1 590.59 170.04 679.25 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n411 61 Car -1 -1 -1 664.13 167.82 767.73 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n411 71 Car -1 -1 -1 258.38 172.98 359.28 227.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n411 32 Car -1 -1 -1 1158.02 164.54 1237.92 236.57 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n411 69 Pedestrian -1 -1 -1 287.31 171.59 315.33 240.43 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n411 70 Pedestrian -1 -1 -1 301.42 168.10 333.00 242.20 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n412 31 Car -1 -1 -1 695.60 168.70 893.11 238.97 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n412 35 Car -1 -1 -1 932.60 166.97 1152.79 237.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n412 60 Car -1 -1 -1 590.32 169.99 679.75 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n412 61 Car -1 -1 -1 664.29 167.82 767.99 209.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n412 71 Car -1 -1 -1 259.31 172.30 358.48 228.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n412 32 Car -1 -1 -1 1157.30 160.77 1238.12 234.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n412 69 Pedestrian -1 -1 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-1000 -1000 -1000 -10 0.95\n414 60 Car -1 -1 -1 590.52 170.05 679.07 210.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n414 61 Car -1 -1 -1 664.65 167.97 767.88 209.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n414 32 Car -1 -1 -1 1157.27 161.03 1238.21 234.27 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n414 71 Car -1 -1 -1 258.58 173.20 358.76 227.71 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n414 69 Pedestrian -1 -1 -1 287.09 171.73 315.47 240.35 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n414 70 Pedestrian -1 -1 -1 301.89 168.71 332.39 241.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n415 31 Car -1 -1 -1 695.75 168.64 893.01 238.99 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n415 35 Car -1 -1 -1 934.04 167.37 1151.54 237.36 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n415 60 Car -1 -1 -1 590.55 170.02 679.15 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n415 61 Car -1 -1 -1 664.34 168.00 768.05 209.08 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n415 32 Car -1 -1 -1 1158.27 165.26 1237.72 236.19 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n415 71 Car -1 -1 -1 259.69 172.47 357.70 228.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n415 69 Pedestrian -1 -1 -1 287.09 171.88 314.84 240.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n415 70 Pedestrian -1 -1 -1 301.84 169.04 332.43 241.74 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n416 31 Car -1 -1 -1 696.38 168.70 892.07 238.86 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n416 35 Car -1 -1 -1 933.05 167.28 1152.64 237.44 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n416 60 Car -1 -1 -1 590.59 170.03 679.25 210.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n416 61 Car -1 -1 -1 664.64 167.90 767.97 209.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n416 71 Car -1 -1 -1 259.00 172.83 358.16 227.89 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n416 32 Car -1 -1 -1 1158.18 165.41 1237.61 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n416 69 Pedestrian -1 -1 -1 286.84 171.60 315.13 240.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n416 70 Pedestrian -1 -1 -1 301.55 168.74 332.76 241.65 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n417 31 Car -1 -1 -1 695.65 168.75 892.78 238.95 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n417 35 Car -1 -1 -1 932.71 166.89 1152.70 237.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n417 60 Car -1 -1 -1 590.39 169.99 679.59 210.86 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n417 61 Car -1 -1 -1 664.31 167.80 767.59 209.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n417 32 Car -1 -1 -1 1157.65 165.32 1238.17 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n417 71 Car -1 -1 -1 259.24 172.58 358.08 228.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n417 69 Pedestrian -1 -1 -1 286.88 171.97 315.10 240.25 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n417 70 Pedestrian -1 -1 -1 301.48 168.71 332.59 241.91 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n418 31 Car -1 -1 -1 696.03 168.66 892.60 238.91 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n418 35 Car -1 -1 -1 932.86 167.17 1152.41 237.61 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n418 60 Car -1 -1 -1 590.63 170.11 679.03 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n418 61 Car -1 -1 -1 664.90 167.90 768.07 209.13 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n418 32 Car -1 -1 -1 1157.75 165.57 1238.02 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n418 71 Car -1 -1 -1 259.55 172.48 357.88 228.42 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n418 69 Pedestrian -1 -1 -1 287.10 171.59 315.13 240.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n418 70 Pedestrian -1 -1 -1 301.41 168.65 332.52 241.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n419 31 Car -1 -1 -1 696.14 168.63 892.31 238.78 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n419 35 Car -1 -1 -1 932.73 167.38 1152.90 237.38 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n419 60 Car -1 -1 -1 590.74 170.05 679.40 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n419 61 Car -1 -1 -1 664.70 168.05 768.34 209.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n419 32 Car -1 -1 -1 1157.51 165.16 1238.03 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n419 71 Car -1 -1 -1 260.64 172.02 356.99 228.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n419 69 Pedestrian -1 -1 -1 287.70 171.60 314.70 240.45 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n419 70 Pedestrian -1 -1 -1 301.14 168.77 332.55 242.00 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n420 31 Car -1 -1 -1 696.31 168.91 892.16 238.88 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n420 35 Car -1 -1 -1 932.56 167.48 1153.21 237.29 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n420 60 Car -1 -1 -1 590.71 170.19 678.92 210.74 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n420 61 Car -1 -1 -1 664.66 168.14 768.27 209.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n420 32 Car -1 -1 -1 1157.28 165.07 1238.27 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n420 71 Car -1 -1 -1 260.29 172.23 357.29 228.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n420 69 Pedestrian -1 -1 -1 287.72 170.96 314.67 240.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n420 70 Pedestrian -1 -1 -1 300.86 168.27 332.78 242.27 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n421 31 Car -1 -1 -1 695.95 168.84 892.58 238.95 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n421 35 Car -1 -1 -1 931.70 167.13 1153.93 237.60 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n421 60 Car -1 -1 -1 590.52 170.13 678.97 210.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n421 61 Car -1 -1 -1 664.75 168.08 768.12 209.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n421 32 Car -1 -1 -1 1157.30 165.05 1238.11 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n421 71 Car -1 -1 -1 260.00 171.88 357.29 229.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n421 69 Pedestrian -1 -1 -1 287.08 171.63 315.46 240.28 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n421 70 Pedestrian -1 -1 -1 300.96 168.17 332.48 242.54 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n422 31 Car -1 -1 -1 695.64 168.97 893.02 238.78 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n422 35 Car -1 -1 -1 933.18 167.40 1152.27 237.35 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n422 60 Car -1 -1 -1 590.68 169.88 679.23 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n422 61 Car -1 -1 -1 664.55 167.98 768.45 209.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n422 32 Car -1 -1 -1 1157.29 160.84 1238.07 234.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n422 71 Car -1 -1 -1 260.42 171.89 356.89 228.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n422 69 Pedestrian -1 -1 -1 286.87 171.48 315.54 240.19 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n422 70 Pedestrian -1 -1 -1 301.19 168.39 332.62 242.38 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n423 31 Car -1 -1 -1 695.57 168.88 892.90 238.99 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n423 35 Car -1 -1 -1 933.86 167.20 1151.66 237.59 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n423 60 Car -1 -1 -1 590.85 170.17 678.26 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n423 61 Car -1 -1 -1 664.65 167.96 768.16 209.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n423 71 Car -1 -1 -1 259.89 172.57 357.18 228.26 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n423 69 Pedestrian -1 -1 -1 287.47 171.13 315.34 240.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n423 32 Car -1 -1 -1 1158.49 162.19 1236.56 239.57 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n423 70 Pedestrian -1 -1 -1 301.39 168.19 332.55 242.53 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n424 31 Car -1 -1 -1 696.17 169.04 892.13 238.60 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n424 60 Car -1 -1 -1 590.44 170.08 679.03 210.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n424 32 Car -1 -1 -1 1077.35 165.59 1238.85 253.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n424 35 Car -1 -1 -1 936.61 168.89 1154.79 238.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n424 61 Car -1 -1 -1 664.88 167.97 767.79 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n424 69 Pedestrian -1 -1 -1 287.82 170.67 314.79 240.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n424 71 Car -1 -1 -1 262.02 171.69 355.41 229.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n424 70 Pedestrian -1 -1 -1 301.06 167.98 332.60 242.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n424 81 Car -1 -1 -1 1160.22 160.23 1234.81 234.84 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n425 31 Car -1 -1 -1 696.19 168.90 892.08 238.75 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n425 60 Car -1 -1 -1 590.73 170.09 678.63 210.70 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n425 32 Car -1 -1 -1 1005.08 169.04 1235.67 254.87 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n425 61 Car -1 -1 -1 664.47 167.98 767.89 209.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n425 35 Car -1 -1 -1 936.73 169.50 1155.51 239.25 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n425 71 Car -1 -1 -1 260.88 172.18 356.37 228.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n425 69 Pedestrian -1 -1 -1 288.07 171.40 314.65 240.16 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n425 70 Pedestrian -1 -1 -1 301.01 168.17 332.44 242.45 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n425 81 Car -1 -1 -1 1166.61 159.36 1234.67 235.43 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n425 82 Car -1 -1 -1 1197.92 161.32 1236.13 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n426 31 Car -1 -1 -1 696.49 168.83 891.84 238.72 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n426 60 Car -1 -1 -1 590.51 170.15 679.31 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n426 32 Car -1 -1 -1 929.84 166.74 1209.63 256.40 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n426 61 Car -1 -1 -1 664.95 167.86 767.93 209.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n426 35 Car -1 -1 -1 922.08 168.02 1169.90 239.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n426 71 Car -1 -1 -1 261.69 171.77 355.61 229.12 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n426 69 Pedestrian -1 -1 -1 287.38 171.32 315.13 240.21 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n426 70 Pedestrian -1 -1 -1 300.76 167.86 332.55 242.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n426 81 Car -1 -1 -1 1163.79 160.23 1238.37 234.92 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n427 31 Car -1 -1 -1 696.59 169.26 891.45 238.99 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n427 32 Car -1 -1 -1 865.32 167.55 1125.33 256.54 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n427 60 Car -1 -1 -1 590.81 169.99 679.18 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n427 61 Car -1 -1 -1 664.72 167.86 768.22 209.00 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n427 35 Car -1 -1 -1 944.81 170.45 1146.60 238.17 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n427 71 Car -1 -1 -1 260.87 172.12 356.47 228.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n427 81 Car -1 -1 -1 1158.31 164.63 1236.83 236.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n427 69 Pedestrian -1 -1 -1 287.43 171.38 314.84 240.22 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n427 70 Pedestrian -1 -1 -1 300.83 167.72 332.26 242.49 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n428 32 Car -1 -1 -1 791.84 169.20 1044.47 256.70 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n428 31 Car -1 -1 -1 698.67 170.95 888.70 238.29 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n428 60 Car -1 -1 -1 590.72 170.08 679.19 210.68 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n428 61 Car -1 -1 -1 664.76 167.68 768.23 209.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n428 69 Pedestrian -1 -1 -1 288.20 171.55 313.81 239.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n428 81 Car -1 -1 -1 1157.93 163.05 1237.63 238.04 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n428 71 Car -1 -1 -1 262.90 171.94 354.57 228.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n428 35 Car -1 -1 -1 955.71 168.45 1144.18 240.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n428 70 Pedestrian -1 -1 -1 301.25 167.45 332.42 242.32 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n429 32 Car -1 -1 -1 721.98 169.45 966.92 256.69 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n429 60 Car -1 -1 -1 590.75 170.11 679.55 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n429 31 Car -1 -1 -1 699.52 171.55 888.38 238.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n429 61 Car -1 -1 -1 664.98 167.94 768.35 209.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n429 35 Car -1 -1 -1 940.52 167.39 1150.79 240.31 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n429 81 Car -1 -1 -1 1158.27 164.25 1237.29 237.03 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n429 69 Pedestrian -1 -1 -1 287.52 171.03 314.71 239.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n429 71 Car -1 -1 -1 263.38 171.54 353.97 229.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n429 70 Pedestrian -1 -1 -1 300.96 167.55 332.01 242.32 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n430 32 Car -1 -1 -1 652.57 168.32 894.65 256.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n430 60 Car -1 -1 -1 590.51 169.90 679.38 210.56 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n430 31 Car -1 -1 -1 695.15 170.73 892.56 238.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n430 35 Car -1 -1 -1 936.68 168.12 1154.08 239.29 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n430 61 Car -1 -1 -1 663.62 167.97 768.39 208.83 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n430 81 Car -1 -1 -1 1157.83 165.19 1237.64 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n430 69 Pedestrian -1 -1 -1 287.68 170.97 314.19 240.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n430 71 Car -1 -1 -1 264.85 171.23 352.62 229.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n430 70 Pedestrian -1 -1 -1 300.85 167.32 332.28 242.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n431 32 Car -1 -1 -1 585.49 172.68 814.39 258.02 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n431 60 Car -1 -1 -1 590.46 169.82 678.92 210.29 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n431 35 Car -1 -1 -1 932.06 166.80 1153.40 237.98 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n431 31 Car -1 -1 -1 696.72 170.23 891.51 240.26 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n431 61 Car -1 -1 -1 663.43 168.01 768.57 208.46 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n431 81 Car -1 -1 -1 1158.41 165.03 1237.07 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n431 69 Pedestrian -1 -1 -1 287.67 171.25 314.14 239.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n431 71 Car -1 -1 -1 265.24 170.84 352.09 230.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n431 70 Pedestrian -1 -1 -1 300.81 167.21 332.29 242.59 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n432 32 Car -1 -1 -1 517.17 173.93 743.63 257.82 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n432 35 Car -1 -1 -1 932.47 167.43 1153.14 237.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n432 60 Car -1 -1 -1 591.35 170.12 678.44 209.43 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n432 31 Car -1 -1 -1 696.87 169.68 890.77 239.54 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n432 81 Car -1 -1 -1 1158.05 165.21 1237.38 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n432 71 Car -1 -1 -1 261.54 171.31 355.82 229.75 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n432 61 Car -1 -1 -1 663.75 167.68 768.67 208.75 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n432 69 Pedestrian -1 -1 -1 287.48 171.41 315.18 239.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n432 70 Pedestrian -1 -1 -1 301.04 167.69 332.28 242.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n433 32 Car -1 -1 -1 449.73 175.44 672.99 257.45 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n433 31 Car -1 -1 -1 695.81 169.21 892.72 239.12 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n433 35 Car -1 -1 -1 932.36 167.51 1153.27 237.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n433 60 Car -1 -1 -1 592.12 169.90 677.75 209.49 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n433 61 Car -1 -1 -1 664.88 167.93 768.27 208.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n433 81 Car -1 -1 -1 1158.33 164.82 1237.30 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n433 71 Car -1 -1 -1 259.85 172.01 357.40 229.04 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n433 69 Pedestrian -1 -1 -1 287.13 171.74 315.04 239.86 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n433 70 Pedestrian -1 -1 -1 301.56 168.35 331.96 242.36 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n434 32 Car -1 -1 -1 384.51 176.44 605.71 257.95 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n434 31 Car -1 -1 -1 695.93 168.57 892.44 239.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n434 35 Car -1 -1 -1 936.62 167.72 1153.79 237.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n434 60 Car -1 -1 -1 592.16 169.68 677.25 210.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n434 61 Car -1 -1 -1 665.63 167.93 767.14 209.29 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n434 71 Car -1 -1 -1 260.60 172.20 356.45 229.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n434 81 Car -1 -1 -1 1158.21 164.72 1237.35 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n434 69 Pedestrian -1 -1 -1 287.73 171.21 314.67 240.33 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n434 70 Pedestrian -1 -1 -1 301.24 168.42 332.27 242.46 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n435 31 Car -1 -1 -1 695.47 168.75 892.53 239.23 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n435 32 Car -1 -1 -1 321.02 177.67 537.66 257.70 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n435 35 Car -1 -1 -1 933.02 167.23 1152.50 237.55 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n435 60 Car -1 -1 -1 591.38 169.75 678.59 210.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n435 61 Car -1 -1 -1 665.21 168.17 766.73 209.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n435 81 Car -1 -1 -1 1157.68 164.45 1237.62 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n435 71 Car -1 -1 -1 259.09 172.44 358.30 229.42 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n435 69 Pedestrian -1 -1 -1 285.87 171.55 315.28 240.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n435 70 Pedestrian -1 -1 -1 298.70 167.81 333.86 242.42 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n436 31 Car -1 -1 -1 695.19 168.90 893.18 239.00 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n436 35 Car -1 -1 -1 932.26 167.17 1153.05 237.56 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n436 32 Car -1 -1 -1 256.50 179.18 477.16 260.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n436 60 Car -1 -1 -1 591.09 169.79 678.69 210.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n436 61 Car -1 -1 -1 665.07 168.00 767.42 209.16 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n436 81 Car -1 -1 -1 1157.20 164.85 1237.99 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n436 71 Car -1 -1 -1 265.43 172.67 351.41 229.16 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n436 70 Pedestrian -1 -1 -1 299.62 167.48 333.94 242.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n436 69 Pedestrian -1 -1 -1 284.26 171.73 318.11 239.16 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n437 31 Car -1 -1 -1 695.99 168.73 892.22 239.23 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n437 35 Car -1 -1 -1 932.75 167.04 1152.28 237.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n437 32 Car -1 -1 -1 194.62 179.96 414.80 260.35 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n437 60 Car -1 -1 -1 590.84 169.80 678.81 210.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n437 61 Car -1 -1 -1 664.77 167.97 767.70 209.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n437 81 Car -1 -1 -1 1157.54 164.87 1237.86 236.22 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n437 70 Pedestrian -1 -1 -1 298.93 166.69 334.58 241.75 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n437 69 Pedestrian -1 -1 -1 285.45 171.22 316.86 239.00 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n437 71 Car -1 -1 -1 274.26 170.23 342.21 231.53 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n438 31 Car -1 -1 -1 695.24 168.51 893.08 239.28 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n438 35 Car -1 -1 -1 932.91 167.30 1152.54 237.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n438 60 Car -1 -1 -1 590.66 169.86 678.29 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n438 32 Car -1 -1 -1 131.98 182.86 352.43 260.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n438 61 Car -1 -1 -1 664.65 167.91 767.47 209.09 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n438 81 Car -1 -1 -1 1157.47 164.71 1238.05 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n438 71 Car -1 -1 -1 270.28 171.80 339.28 229.95 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n438 70 Pedestrian -1 -1 -1 292.63 167.58 340.61 241.05 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n439 31 Car -1 -1 -1 695.75 168.87 892.48 239.16 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n439 35 Car -1 -1 -1 932.75 167.26 1152.52 237.44 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n439 60 Car -1 -1 -1 590.61 169.80 678.98 210.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n439 32 Car -1 -1 -1 67.52 185.50 294.61 261.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n439 61 Car -1 -1 -1 664.65 167.79 767.48 209.01 -1 -1 -1 -1000 -1000 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Car -1 -1 -1 664.21 167.99 768.79 208.87 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n497 31 Car -1 -1 -1 694.92 171.08 900.36 244.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n497 71 Car -1 -1 -1 257.48 173.44 359.71 227.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n497 89 Car -1 -1 -1 1158.56 164.65 1237.22 236.56 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n497 70 Pedestrian -1 -1 -1 286.79 171.99 315.02 240.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n497 90 Car -1 -1 -1 951.90 167.72 1148.00 240.29 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n497 83 Pedestrian -1 -1 -1 301.63 168.10 332.45 242.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n498 87 Car -1 -1 -1 704.72 171.33 1036.31 284.75 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n498 60 Car -1 -1 -1 591.39 170.09 678.88 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n498 61 Car -1 -1 -1 664.67 167.84 768.81 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n498 31 Car -1 -1 -1 694.77 170.67 900.45 245.29 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n498 70 Pedestrian -1 -1 -1 286.88 171.44 314.44 240.44 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n498 71 Car -1 -1 -1 259.44 173.03 357.72 227.98 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n498 89 Car -1 -1 -1 1158.58 165.10 1237.14 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n498 90 Car -1 -1 -1 951.92 167.78 1147.86 240.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n498 83 Pedestrian -1 -1 -1 301.26 168.11 332.65 242.40 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n499 87 Car -1 -1 -1 705.21 171.22 1035.76 284.57 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n499 60 Car -1 -1 -1 591.12 170.08 678.90 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n499 61 Car -1 -1 -1 664.76 167.81 768.72 208.87 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n499 31 Car -1 -1 -1 694.98 170.55 899.74 245.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n499 70 Pedestrian -1 -1 -1 287.14 171.34 313.97 240.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n499 71 Car -1 -1 -1 260.63 172.24 356.78 229.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n499 89 Car -1 -1 -1 1158.80 164.32 1237.01 236.89 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n499 90 Car -1 -1 -1 951.87 167.10 1148.12 237.39 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n499 83 Pedestrian -1 -1 -1 300.89 168.02 332.06 242.73 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n500 87 Car -1 -1 -1 704.96 171.30 1035.75 284.76 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n500 60 Car -1 -1 -1 590.89 170.16 679.04 210.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n500 61 Car -1 -1 -1 664.36 167.82 768.79 208.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n500 31 Car -1 -1 -1 694.75 170.61 900.12 245.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n500 89 Car -1 -1 -1 1158.62 164.49 1237.05 236.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n500 70 Pedestrian -1 -1 -1 286.81 171.75 314.17 240.59 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n500 71 Car -1 -1 -1 262.37 172.22 354.90 229.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n500 90 Car -1 -1 -1 951.78 167.14 1148.08 237.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n500 83 Pedestrian -1 -1 -1 301.24 167.99 331.58 242.88 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n501 87 Car -1 -1 -1 705.05 170.88 1036.00 284.78 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n501 60 Car -1 -1 -1 591.27 170.14 678.81 210.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n501 61 Car -1 -1 -1 664.64 167.74 768.42 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n501 31 Car -1 -1 -1 694.65 170.40 900.28 245.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n501 70 Pedestrian -1 -1 -1 287.13 171.61 314.36 240.61 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n501 71 Car -1 -1 -1 261.76 172.01 355.65 229.22 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n501 89 Car -1 -1 -1 1158.76 164.33 1236.96 236.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n501 90 Car -1 -1 -1 952.00 167.22 1148.02 237.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n501 83 Pedestrian -1 -1 -1 300.84 167.64 331.86 242.87 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n502 87 Car -1 -1 -1 705.96 170.84 1035.57 284.72 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n502 60 Car -1 -1 -1 591.46 170.15 678.49 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n502 61 Car -1 -1 -1 664.89 167.74 768.22 208.88 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n502 31 Car -1 -1 -1 694.52 170.39 900.28 245.56 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n502 71 Car -1 -1 -1 260.84 172.05 356.87 229.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n502 70 Pedestrian -1 -1 -1 287.60 171.91 314.10 240.31 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n502 89 Car -1 -1 -1 1158.37 164.59 1237.24 236.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n502 90 Car -1 -1 -1 953.24 167.75 1146.51 240.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n502 83 Pedestrian -1 -1 -1 300.64 167.38 331.97 242.86 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n503 87 Car -1 -1 -1 706.18 170.97 1035.51 284.49 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n503 60 Car -1 -1 -1 591.18 170.12 678.89 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n503 61 Car -1 -1 -1 664.95 167.76 768.54 208.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n503 31 Car -1 -1 -1 694.52 170.34 900.35 245.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n503 70 Pedestrian -1 -1 -1 287.98 171.42 313.76 240.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n503 71 Car -1 -1 -1 262.20 171.79 355.51 229.02 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n503 90 Car -1 -1 -1 952.00 167.17 1147.89 237.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n503 89 Car -1 -1 -1 1157.51 160.75 1238.03 234.72 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n503 83 Pedestrian -1 -1 -1 301.00 167.25 331.55 242.70 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n504 87 Car -1 -1 -1 706.02 170.89 1035.71 284.58 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n504 60 Car -1 -1 -1 591.44 170.22 678.85 210.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n504 61 Car -1 -1 -1 664.93 167.80 768.24 208.79 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n504 31 Car -1 -1 -1 694.64 170.58 900.39 245.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n504 71 Car -1 -1 -1 261.03 172.30 356.68 228.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n504 70 Pedestrian -1 -1 -1 287.32 171.62 314.44 240.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n504 90 Car -1 -1 -1 952.32 167.07 1147.62 237.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n504 89 Car -1 -1 -1 1157.64 159.84 1237.93 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n504 83 Pedestrian -1 -1 -1 300.91 167.62 331.44 242.62 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n505 87 Car -1 -1 -1 706.72 171.24 1034.86 284.68 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n505 60 Car -1 -1 -1 591.15 170.00 679.17 210.70 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n505 61 Car -1 -1 -1 664.69 167.71 768.53 208.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n505 31 Car -1 -1 -1 693.73 170.45 901.28 245.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n505 70 Pedestrian -1 -1 -1 287.74 171.38 314.14 240.66 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n505 71 Car -1 -1 -1 262.34 171.84 355.46 229.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n505 89 Car -1 -1 -1 1157.48 160.18 1237.99 235.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n505 90 Car -1 -1 -1 952.14 167.19 1147.81 237.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n505 83 Pedestrian -1 -1 -1 300.65 167.36 331.61 242.67 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n506 87 Car -1 -1 -1 706.63 171.04 1035.07 284.63 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n506 60 Car -1 -1 -1 590.88 169.99 679.01 210.68 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n506 61 Car -1 -1 -1 664.95 167.78 768.22 208.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n506 70 Pedestrian -1 -1 -1 287.47 171.48 314.25 240.26 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n506 31 Car -1 -1 -1 694.22 170.43 900.69 245.62 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n506 71 Car -1 -1 -1 262.90 171.72 354.82 229.58 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n506 89 Car -1 -1 -1 1158.50 164.80 1237.42 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n506 90 Car -1 -1 -1 953.38 167.65 1146.56 240.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n506 83 Pedestrian -1 -1 -1 300.26 167.21 331.98 242.91 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n507 87 Car -1 -1 -1 706.45 171.01 1035.23 284.55 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n507 60 Car -1 -1 -1 591.11 170.02 678.82 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n507 61 Car -1 -1 -1 665.06 167.81 767.84 208.80 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n507 31 Car -1 -1 -1 695.03 170.38 900.06 245.72 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n507 70 Pedestrian -1 -1 -1 287.61 170.96 314.14 240.28 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n507 71 Car -1 -1 -1 262.89 171.34 354.68 229.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n507 89 Car -1 -1 -1 1157.70 159.72 1237.75 235.42 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n507 90 Car -1 -1 -1 951.88 166.94 1148.06 237.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n507 83 Pedestrian -1 -1 -1 300.49 167.13 332.10 242.97 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n508 87 Car -1 -1 -1 706.33 171.10 1035.22 284.55 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n508 60 Car -1 -1 -1 591.17 169.92 679.00 210.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n508 61 Car -1 -1 -1 665.13 167.77 768.15 208.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n508 31 Car -1 -1 -1 695.22 170.22 899.64 245.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n508 70 Pedestrian -1 -1 -1 287.11 171.52 314.95 240.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n508 71 Car -1 -1 -1 261.61 171.51 355.91 229.60 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n508 89 Car -1 -1 -1 1158.37 164.69 1237.36 236.51 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n508 90 Car -1 -1 -1 952.90 167.51 1147.02 240.27 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n508 83 Pedestrian -1 -1 -1 300.23 167.32 332.23 242.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n508 91 Car -1 -1 -1 -3.69 193.20 53.44 324.94 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n509 87 Car -1 -1 -1 706.84 171.18 1034.76 284.60 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n509 60 Car -1 -1 -1 591.12 170.07 679.06 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n509 91 Car -1 -1 -1 -0.89 184.16 190.58 334.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n509 61 Car -1 -1 -1 664.97 167.71 768.23 208.83 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n509 31 Car -1 -1 -1 694.99 170.31 899.99 245.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n509 70 Pedestrian -1 -1 -1 287.55 171.19 314.36 240.20 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n509 89 Car -1 -1 -1 1157.88 160.29 1237.62 234.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n509 90 Car -1 -1 -1 953.35 167.58 1146.53 240.16 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n509 71 Car -1 -1 -1 264.16 171.27 353.49 229.81 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n509 83 Pedestrian -1 -1 -1 300.68 167.24 331.93 242.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n510 87 Car -1 -1 -1 706.43 170.99 1035.21 284.45 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n510 91 Car -1 -1 -1 4.76 183.42 333.27 334.61 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n510 60 Car -1 -1 -1 591.14 170.14 678.87 210.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n510 61 Car -1 -1 -1 664.89 167.82 768.36 208.85 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n510 31 Car -1 -1 -1 695.60 170.38 899.47 245.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n510 89 Car -1 -1 -1 1157.52 160.30 1237.94 234.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n510 71 Car -1 -1 -1 259.61 172.19 357.85 229.42 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n510 90 Car -1 -1 -1 951.56 167.09 1148.47 237.43 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n510 70 Pedestrian -1 -1 -1 287.59 171.62 314.74 239.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n510 83 Pedestrian -1 -1 -1 300.69 167.44 331.97 242.86 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n510 92 Car -1 -1 -1 0.39 193.70 150.82 371.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n511 87 Car -1 -1 -1 707.04 171.19 1034.59 284.57 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n511 92 Car -1 -1 -1 -1.56 205.89 363.39 366.87 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n511 60 Car -1 -1 -1 591.25 169.95 679.37 210.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n511 61 Car -1 -1 -1 664.83 167.74 768.53 208.96 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n511 31 Car -1 -1 -1 695.27 170.24 899.85 245.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n511 91 Car -1 -1 -1 65.28 182.09 482.19 337.43 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n511 89 Car -1 -1 -1 1157.78 160.26 1237.78 235.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n511 90 Car -1 -1 -1 953.11 167.30 1147.00 237.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n511 83 Pedestrian -1 -1 -1 301.11 167.69 331.84 242.29 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n511 71 Car -1 -1 -1 255.51 173.79 362.03 223.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n511 70 Pedestrian -1 -1 -1 287.81 172.89 314.69 237.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n512 87 Car -1 -1 -1 707.08 171.02 1034.56 284.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n512 92 Car -1 -1 -1 17.03 205.66 592.49 367.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n512 60 Car -1 -1 -1 591.24 169.60 679.57 210.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n512 61 Car -1 -1 -1 664.91 167.62 768.64 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n512 31 Car -1 -1 -1 695.59 170.46 899.84 245.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n512 89 Car -1 -1 -1 1157.62 159.07 1237.84 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n512 91 Car -1 -1 -1 274.19 179.21 653.63 331.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n512 90 Car -1 -1 -1 953.20 167.56 1146.84 240.25 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n512 83 Pedestrian -1 -1 -1 298.88 167.29 333.68 242.78 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n512 71 Car -1 -1 -1 265.71 172.48 351.84 229.34 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n512 70 Pedestrian -1 -1 -1 285.73 172.66 316.99 237.84 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-1000 -1000 -1000 -10 0.86\n515 70 Pedestrian -1 -1 -1 288.62 170.50 312.90 240.21 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n515 87 Car -1 -1 -1 700.94 175.00 1034.21 288.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n515 90 Car -1 -1 -1 947.37 167.17 1152.27 237.09 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n515 31 Car -1 -1 -1 691.66 170.01 903.19 247.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n515 71 Car -1 -1 -1 266.12 170.65 351.93 230.05 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n515 89 Car -1 -1 -1 1160.28 163.50 1235.30 238.02 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n515 83 Pedestrian -1 -1 -1 301.00 167.67 331.90 241.69 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n515 93 Car -1 -1 -1 823.02 178.20 1206.93 324.58 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n516 87 Car -1 -1 -1 706.80 172.02 1036.06 284.08 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n516 60 Car -1 -1 -1 591.21 169.88 679.00 210.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n516 93 Car -1 -1 -1 1005.04 170.93 1234.66 331.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n516 61 Car -1 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-1000 -1000 -1000 -10 0.87\n517 31 Car -1 -1 -1 695.97 170.03 898.94 246.20 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n517 70 Pedestrian -1 -1 -1 287.81 170.84 313.67 240.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n517 90 Car -1 -1 -1 952.63 167.55 1147.06 240.22 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n517 71 Car -1 -1 -1 265.25 170.66 352.56 229.89 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n517 89 Car -1 -1 -1 1155.84 162.96 1240.00 238.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n517 83 Pedestrian -1 -1 -1 300.73 167.27 331.73 242.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n517 93 Car -1 -1 -1 1197.13 165.79 1237.55 322.09 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n518 87 Car -1 -1 -1 707.82 171.10 1033.82 284.21 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n518 60 Car -1 -1 -1 590.87 170.13 679.13 210.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n518 61 Car -1 -1 -1 665.20 167.67 767.82 208.91 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n518 31 Car -1 -1 -1 696.07 170.25 899.12 245.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n518 90 Car -1 -1 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-1 -1000 -1000 -1000 -10 0.76\n519 71 Car -1 -1 -1 262.94 170.88 354.93 229.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n519 83 Pedestrian -1 -1 -1 300.53 167.39 332.11 242.44 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n520 87 Car -1 -1 -1 707.24 170.94 1034.35 284.58 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n520 60 Car -1 -1 -1 590.83 170.09 679.03 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n520 61 Car -1 -1 -1 665.05 167.76 767.90 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n520 31 Car -1 -1 -1 694.94 170.22 900.05 245.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n520 70 Pedestrian -1 -1 -1 288.05 171.15 313.12 240.46 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n520 89 Car -1 -1 -1 1157.63 159.80 1237.46 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n520 90 Car -1 -1 -1 952.35 166.61 1147.64 237.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n520 71 Car -1 -1 -1 264.03 170.67 353.78 229.89 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n520 83 Pedestrian -1 -1 -1 301.05 167.27 332.22 242.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n521 87 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-1 -1000 -1000 -1000 -10 0.84\n522 31 Car -1 -1 -1 695.70 170.18 899.16 245.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n522 89 Car -1 -1 -1 1157.61 160.28 1237.91 234.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n522 90 Car -1 -1 -1 952.12 166.84 1147.85 237.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n522 70 Pedestrian -1 -1 -1 287.62 171.38 313.07 240.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n522 71 Car -1 -1 -1 263.27 171.08 354.57 229.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n522 83 Pedestrian -1 -1 -1 300.75 167.68 332.24 242.13 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n523 87 Car -1 -1 -1 707.29 171.09 1034.30 284.35 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n523 60 Car -1 -1 -1 590.96 170.00 679.07 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n523 61 Car -1 -1 -1 665.23 167.87 768.28 208.87 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n523 31 Car -1 -1 -1 695.22 170.36 899.79 245.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n523 70 Pedestrian -1 -1 -1 287.17 171.05 313.36 240.29 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n523 90 Car -1 -1 -1 952.32 166.91 1147.63 237.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n523 89 Car -1 -1 -1 1159.14 164.43 1236.91 236.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n523 71 Car -1 -1 -1 264.94 170.73 352.75 230.12 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n523 83 Pedestrian -1 -1 -1 300.29 167.27 332.28 242.37 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n524 87 Car -1 -1 -1 707.43 170.84 1034.32 284.47 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n524 60 Car -1 -1 -1 591.28 170.04 678.62 210.70 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n524 61 Car -1 -1 -1 664.78 167.89 768.45 208.79 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n524 31 Car -1 -1 -1 695.18 170.20 899.69 245.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n524 90 Car -1 -1 -1 953.11 167.03 1146.84 237.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n524 70 Pedestrian -1 -1 -1 287.20 171.22 313.83 240.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n524 89 Car -1 -1 -1 1158.03 159.42 1237.56 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n524 71 Car -1 -1 -1 263.16 171.18 354.19 229.66 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n524 83 Pedestrian -1 -1 -1 301.07 167.45 331.76 242.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n525 87 Car -1 -1 -1 707.10 171.00 1034.50 284.58 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n525 60 Car -1 -1 -1 591.48 170.12 679.05 210.74 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n525 61 Car -1 -1 -1 664.82 167.82 768.61 208.87 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n525 31 Car -1 -1 -1 695.16 170.16 899.78 245.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n525 90 Car -1 -1 -1 952.13 166.79 1147.77 237.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n525 89 Car -1 -1 -1 1157.51 159.54 1237.86 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n525 71 Car -1 -1 -1 262.95 171.09 354.48 229.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n525 70 Pedestrian -1 -1 -1 286.43 171.45 314.12 240.53 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n525 83 Pedestrian -1 -1 -1 300.08 167.38 332.03 242.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n526 87 Car -1 -1 -1 707.28 170.82 1034.26 284.64 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n526 60 Car -1 -1 -1 591.53 170.02 678.88 210.70 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n526 61 Car -1 -1 -1 664.97 167.84 768.39 208.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n526 31 Car -1 -1 -1 695.39 170.15 899.58 245.67 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n526 89 Car -1 -1 -1 1157.56 158.94 1237.68 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n526 71 Car -1 -1 -1 262.01 171.17 355.56 229.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n526 90 Car -1 -1 -1 953.07 166.88 1146.76 237.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n526 70 Pedestrian -1 -1 -1 286.48 171.37 313.94 240.79 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n526 83 Pedestrian -1 -1 -1 300.90 167.17 331.80 242.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n527 87 Car -1 -1 -1 707.35 170.93 1034.43 284.51 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n527 60 Car -1 -1 -1 591.35 170.06 679.15 210.70 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n527 61 Car -1 -1 -1 664.80 167.79 768.42 208.80 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n527 31 Car -1 -1 -1 696.17 170.16 898.91 245.75 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n527 90 Car -1 -1 -1 953.53 166.84 1146.42 237.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n527 89 Car -1 -1 -1 1157.51 159.12 1237.59 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n527 71 Car -1 -1 -1 262.26 171.49 355.19 229.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n527 70 Pedestrian -1 -1 -1 286.16 171.53 313.67 240.62 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n527 83 Pedestrian -1 -1 -1 300.82 167.56 331.75 242.75 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n528 87 Car -1 -1 -1 707.51 170.62 1034.20 284.57 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n528 60 Car -1 -1 -1 591.35 170.11 678.91 210.74 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n528 61 Car -1 -1 -1 664.95 167.71 768.14 208.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n528 31 Car -1 -1 -1 696.05 170.19 899.03 245.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n528 70 Pedestrian -1 -1 -1 287.04 170.96 313.15 240.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n528 90 Car -1 -1 -1 952.92 166.65 1147.04 237.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n528 89 Car -1 -1 -1 1157.08 159.33 1238.13 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n528 71 Car -1 -1 -1 262.97 171.09 354.65 229.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n528 83 Pedestrian -1 -1 -1 301.38 167.36 332.07 242.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n529 87 Car -1 -1 -1 707.70 170.72 1034.20 284.46 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n529 60 Car -1 -1 -1 590.90 170.08 679.11 210.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n529 61 Car -1 -1 -1 664.98 167.77 768.27 208.79 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n529 31 Car -1 -1 -1 695.34 170.14 899.81 245.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n529 90 Car -1 -1 -1 953.42 166.77 1146.41 237.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n529 89 Car -1 -1 -1 1157.79 159.61 1237.97 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n529 71 Car -1 -1 -1 263.24 171.40 354.31 229.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n529 70 Pedestrian -1 -1 -1 286.53 171.26 313.29 240.78 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n529 83 Pedestrian -1 -1 -1 301.54 167.89 331.23 242.36 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n530 87 Car -1 -1 -1 707.39 170.74 1034.34 284.66 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n530 60 Car -1 -1 -1 591.33 170.04 679.11 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n530 61 Car -1 -1 -1 664.88 167.72 768.57 208.84 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n530 31 Car -1 -1 -1 695.38 170.00 899.57 245.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n530 71 Car -1 -1 -1 260.82 171.84 356.94 228.78 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n530 90 Car -1 -1 -1 952.97 166.96 1146.79 237.55 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n530 89 Car -1 -1 -1 1157.75 159.47 1237.92 235.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n530 70 Pedestrian -1 -1 -1 286.65 171.65 313.04 240.64 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n530 83 Pedestrian -1 -1 -1 301.53 167.66 331.86 242.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n531 87 Car -1 -1 -1 707.60 171.23 1034.09 284.48 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n531 60 Car -1 -1 -1 591.51 170.02 679.11 210.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n531 61 Car -1 -1 -1 664.69 167.79 768.30 208.81 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n531 31 Car -1 -1 -1 695.58 170.27 899.62 245.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n531 90 Car -1 -1 -1 953.24 166.71 1146.63 237.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n531 71 Car -1 -1 -1 260.42 172.19 357.51 228.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n531 89 Car -1 -1 -1 1159.07 163.75 1236.81 237.42 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n531 70 Pedestrian -1 -1 -1 286.69 171.72 312.78 240.50 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n531 83 Pedestrian -1 -1 -1 301.40 167.57 331.64 242.70 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n532 87 Car -1 -1 -1 707.71 171.13 1034.27 284.39 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n532 60 Car -1 -1 -1 591.23 170.27 679.24 210.77 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n532 61 Car -1 -1 -1 664.79 167.76 768.27 208.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n532 31 Car -1 -1 -1 695.49 170.09 899.83 245.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n532 90 Car -1 -1 -1 953.09 166.75 1146.89 237.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n532 89 Car -1 -1 -1 1157.79 159.20 1237.56 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n532 71 Car -1 -1 -1 261.89 172.50 355.96 228.58 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n532 70 Pedestrian -1 -1 -1 286.43 171.68 313.45 240.70 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n532 83 Pedestrian -1 -1 -1 301.52 167.88 331.72 242.31 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n533 87 Car -1 -1 -1 707.44 171.05 1034.43 284.67 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n533 60 Car -1 -1 -1 591.26 170.14 678.93 210.68 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n533 61 Car -1 -1 -1 664.85 167.65 767.89 208.86 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n533 31 Car -1 -1 -1 695.62 170.13 899.58 245.85 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n533 71 Car -1 -1 -1 259.89 172.85 357.94 228.12 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n533 90 Car -1 -1 -1 952.81 166.85 1147.18 237.60 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n533 89 Car -1 -1 -1 1157.38 159.19 1238.03 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n533 70 Pedestrian -1 -1 -1 286.74 171.71 313.11 240.80 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n533 83 Pedestrian -1 -1 -1 301.59 168.30 331.48 242.07 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n534 87 Car -1 -1 -1 707.13 171.09 1034.82 284.65 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n534 60 Car -1 -1 -1 591.21 170.14 679.04 210.68 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n534 61 Car -1 -1 -1 665.10 167.76 768.15 208.94 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n534 31 Car -1 -1 -1 695.00 170.05 900.22 245.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n534 71 Car -1 -1 -1 260.33 172.28 357.40 228.52 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n534 90 Car -1 -1 -1 953.21 166.79 1146.68 237.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n534 89 Car -1 -1 -1 1157.71 159.89 1237.90 235.35 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n534 70 Pedestrian -1 -1 -1 286.63 171.35 313.30 241.13 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n534 83 Pedestrian -1 -1 -1 301.21 168.05 331.60 242.70 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n535 87 Car -1 -1 -1 707.14 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-1000 -1000 -1000 -10 0.78\n546 71 Car -1 -1 -1 260.92 171.88 356.54 228.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n546 89 Car -1 -1 -1 1158.17 160.01 1237.50 235.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n546 70 Pedestrian -1 -1 -1 283.62 170.66 312.64 241.58 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n546 83 Pedestrian -1 -1 -1 300.93 167.56 331.55 242.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n547 87 Car -1 -1 -1 706.93 171.01 1034.77 284.27 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n547 60 Car -1 -1 -1 591.31 169.94 678.96 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n547 61 Car -1 -1 -1 665.07 167.73 768.38 208.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n547 31 Car -1 -1 -1 695.63 170.21 899.27 245.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n547 90 Car -1 -1 -1 953.72 166.76 1146.20 237.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n547 71 Car -1 -1 -1 261.32 171.25 356.46 229.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n547 89 Car -1 -1 -1 1157.57 160.70 1238.02 234.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n547 70 Pedestrian 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0.72\n552 71 Car -1 -1 -1 253.59 172.94 364.24 224.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n553 87 Car -1 -1 -1 706.90 171.35 1034.77 284.43 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n553 60 Car -1 -1 -1 591.18 170.04 679.39 210.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n553 61 Car -1 -1 -1 665.01 167.83 768.41 208.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n553 70 Pedestrian -1 -1 -1 289.22 170.28 318.85 241.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n553 31 Car -1 -1 -1 695.76 170.20 899.26 245.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n553 90 Car -1 -1 -1 953.17 166.94 1146.86 237.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n553 89 Car -1 -1 -1 1158.65 164.32 1237.15 236.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n553 83 Pedestrian -1 -1 -1 300.81 167.31 331.47 241.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n553 71 Car -1 -1 -1 254.37 172.95 363.52 224.39 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n554 87 Car -1 -1 -1 706.82 171.50 1034.67 284.45 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n554 60 Car -1 -1 -1 591.37 170.10 679.05 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0.95\n588 90 Car -1 -1 -1 935.40 169.06 1150.31 238.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n588 60 Car -1 -1 -1 590.93 169.27 678.61 210.04 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n588 61 Car -1 -1 -1 665.32 167.80 767.51 209.08 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n588 99 Pedestrian -1 -1 -1 301.42 168.86 332.38 240.71 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n588 102 Car -1 -1 -1 1156.96 165.82 1239.30 235.48 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n588 83 Pedestrian -1 -1 -1 288.60 171.59 314.73 239.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n588 71 Car -1 -1 -1 260.84 172.66 356.98 228.66 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n588 103 Car -1 -1 -1 1197.00 160.01 1237.40 210.87 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n589 87 Car -1 -1 -1 197.47 176.91 590.83 333.88 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n589 101 Car -1 -1 -1 695.91 168.97 892.29 238.58 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n589 90 Car -1 -1 -1 934.32 167.56 1151.12 237.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n589 60 Car -1 -1 -1 591.00 169.57 678.72 210.32 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n589 61 Car -1 -1 -1 665.16 167.86 767.90 209.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n589 83 Pedestrian -1 -1 -1 287.09 172.74 315.79 238.40 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n589 99 Pedestrian -1 -1 -1 301.06 168.55 332.84 240.60 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n589 71 Car -1 -1 -1 258.81 174.16 358.71 227.39 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n589 102 Car -1 -1 -1 1155.46 162.49 1240.19 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n589 103 Car -1 -1 -1 1198.45 159.96 1236.05 204.28 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n590 87 Car -1 -1 -1 152.70 177.88 564.67 341.83 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n590 101 Car -1 -1 -1 695.71 168.87 892.49 238.76 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n590 60 Car -1 -1 -1 591.06 169.73 678.61 210.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n590 90 Car -1 -1 -1 935.32 169.78 1156.40 238.37 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n590 61 Car -1 -1 -1 664.97 167.94 767.94 209.07 -1 -1 -1 -1000 -1000 -1000 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Truck -1 -1 -1 4.43 88.31 434.10 266.55 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n613 101 Car -1 -1 -1 696.21 169.49 891.54 238.92 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n613 90 Car -1 -1 -1 936.86 168.61 1154.41 238.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n613 60 Car -1 -1 -1 591.85 170.26 678.45 211.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n613 61 Car -1 -1 -1 665.32 168.22 767.11 209.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n613 102 Car -1 -1 -1 1158.28 164.81 1237.53 236.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n613 113 Car -1 -1 -1 1204.22 160.60 1237.11 203.59 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n614 101 Car -1 -1 -1 696.02 169.47 892.39 239.29 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n614 90 Car -1 -1 -1 936.68 168.72 1154.61 238.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n614 60 Car -1 -1 -1 591.56 170.36 678.33 211.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n614 61 Car -1 -1 -1 665.30 168.24 767.15 209.51 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n614 102 Car -1 -1 -1 1158.28 165.04 1237.79 236.57 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n614 71 Car -1 -1 -1 267.62 171.19 350.02 230.07 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n614 83 Pedestrian -1 -1 -1 283.56 170.17 325.43 239.26 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n614 114 Truck -1 -1 -1 6.44 100.29 345.26 263.45 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n615 101 Car -1 -1 -1 695.89 169.39 892.81 239.24 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n615 90 Car -1 -1 -1 937.29 168.62 1154.07 238.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n615 60 Car -1 -1 -1 591.50 170.43 678.03 211.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n615 61 Car -1 -1 -1 664.80 168.19 766.82 209.54 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n615 102 Car -1 -1 -1 1158.50 164.66 1237.40 236.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n615 83 Pedestrian -1 -1 -1 288.24 169.37 322.58 241.97 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n615 71 Car -1 -1 -1 264.72 170.03 352.36 232.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n615 114 Truck -1 -1 -1 5.26 98.59 292.11 264.63 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n615 115 Pedestrian -1 -1 -1 299.54 167.66 333.49 242.50 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n615 116 Van -1 -1 -1 4.88 93.37 285.35 263.94 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n615 117 Car -1 -1 -1 1204.53 160.83 1236.99 203.44 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n616 101 Car -1 -1 -1 695.47 169.26 893.04 239.51 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n616 90 Car -1 -1 -1 936.67 168.66 1154.70 238.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n616 60 Car -1 -1 -1 591.43 170.41 678.65 211.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n616 61 Car -1 -1 -1 665.31 168.19 767.14 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n616 102 Car -1 -1 -1 1158.28 165.61 1237.85 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n616 83 Pedestrian -1 -1 -1 291.60 169.14 323.76 241.76 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n616 71 Car -1 -1 -1 269.18 170.09 347.63 232.49 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n616 115 Pedestrian -1 -1 -1 299.52 167.02 333.65 242.40 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n616 114 Truck -1 -1 -1 3.36 99.04 247.37 270.70 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n616 116 Van -1 -1 -1 5.08 96.59 238.98 267.75 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n616 117 Car -1 -1 -1 1204.30 160.70 1236.62 203.43 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n617 101 Car -1 -1 -1 695.78 169.38 893.21 239.41 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n617 90 Car -1 -1 -1 937.17 168.68 1153.85 238.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n617 60 Car -1 -1 -1 591.66 170.36 678.68 211.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n617 61 Car -1 -1 -1 665.56 168.14 767.23 209.47 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n617 102 Car -1 -1 -1 1159.02 164.76 1237.24 236.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n617 83 Pedestrian -1 -1 -1 291.44 169.32 323.93 241.95 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n617 71 Car -1 -1 -1 265.02 170.96 352.24 231.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n617 115 Pedestrian -1 -1 -1 299.24 167.48 334.27 241.87 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n617 116 Van -1 -1 -1 4.07 96.66 185.93 267.84 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n617 114 Truck -1 -1 -1 4.04 99.66 192.59 270.86 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n618 101 Car -1 -1 -1 697.86 169.21 895.49 239.50 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n618 90 Car -1 -1 -1 936.45 168.55 1155.44 238.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n618 60 Car -1 -1 -1 591.85 170.36 678.74 211.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n618 61 Car -1 -1 -1 665.79 168.07 767.44 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n618 114 Truck -1 -1 -1 0.94 94.64 143.22 269.41 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n618 102 Car -1 -1 -1 1158.02 164.80 1238.19 236.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n618 83 Pedestrian -1 -1 -1 292.56 170.00 323.08 241.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n618 71 Car -1 -1 -1 265.44 171.41 351.62 231.15 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n618 115 Pedestrian -1 -1 -1 300.08 167.95 333.93 241.50 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n619 101 Car -1 -1 -1 697.69 169.13 895.90 239.64 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n619 90 Car -1 -1 -1 936.47 168.58 1155.37 238.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n619 60 Car -1 -1 -1 592.07 170.34 678.67 211.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n619 61 Car -1 -1 -1 665.66 168.15 767.35 209.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n619 102 Car -1 -1 -1 1162.90 165.65 1237.54 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n619 83 Pedestrian -1 -1 -1 292.00 170.25 323.71 241.00 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n619 71 Car -1 -1 -1 265.66 172.09 351.18 230.69 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n619 115 Pedestrian -1 -1 -1 300.23 168.43 333.45 241.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n619 114 Truck -1 -1 -1 -0.06 110.06 96.74 268.52 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n620 101 Car -1 -1 -1 698.14 169.29 896.04 239.39 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n620 90 Car -1 -1 -1 935.03 167.29 1156.72 237.58 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n620 60 Car -1 -1 -1 591.62 170.34 679.08 211.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n620 61 Car -1 -1 -1 665.64 168.07 767.57 209.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n620 102 Car -1 -1 -1 1163.07 165.35 1237.37 236.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n620 83 Pedestrian -1 -1 -1 291.69 169.74 323.90 241.39 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n620 115 Pedestrian -1 -1 -1 299.98 168.06 333.60 241.98 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n620 71 Car -1 -1 -1 266.85 171.24 350.20 231.47 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n621 101 Car -1 -1 -1 698.45 169.44 895.34 239.43 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n621 90 Car -1 -1 -1 936.97 168.52 1154.93 238.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n621 60 Car -1 -1 -1 591.88 170.46 678.98 211.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n621 61 Car -1 -1 -1 665.97 168.26 767.60 209.53 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n621 102 Car -1 -1 -1 1163.00 165.98 1237.38 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n621 115 Pedestrian -1 -1 -1 304.12 167.98 334.38 242.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n621 83 Pedestrian -1 -1 -1 292.31 169.48 323.16 241.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n621 71 Car -1 -1 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-1000 -1000 -1000 -10 0.94\n623 60 Car -1 -1 -1 592.23 170.49 678.94 211.13 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n623 61 Car -1 -1 -1 666.06 168.38 767.38 209.35 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n623 102 Car -1 -1 -1 1163.04 165.82 1237.42 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n623 83 Pedestrian -1 -1 -1 293.09 170.30 323.54 241.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n623 115 Pedestrian -1 -1 -1 304.02 168.07 334.49 242.64 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n623 71 Car -1 -1 -1 268.44 170.98 348.52 231.81 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n624 101 Car -1 -1 -1 698.14 169.38 895.99 239.45 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n624 90 Car -1 -1 -1 936.62 168.22 1155.76 239.15 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n624 60 Car -1 -1 -1 592.45 170.46 678.78 211.12 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n624 61 Car -1 -1 -1 666.03 168.28 767.57 209.40 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n624 102 Car -1 -1 -1 1162.90 165.77 1237.69 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n624 83 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1237.02 203.54 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n626 101 Car -1 -1 -1 697.87 168.99 896.35 239.63 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n626 90 Car -1 -1 -1 936.46 168.15 1155.85 239.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n626 60 Car -1 -1 -1 592.11 170.37 682.50 211.25 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n626 61 Car -1 -1 -1 665.76 168.12 772.52 209.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n626 102 Car -1 -1 -1 1162.96 165.93 1237.83 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n626 83 Pedestrian -1 -1 -1 292.57 170.83 323.89 240.86 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n626 71 Car -1 -1 -1 267.09 172.18 349.93 230.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n626 115 Pedestrian -1 -1 -1 299.78 169.23 333.98 241.52 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n626 119 Car -1 -1 -1 1205.14 160.63 1236.95 203.53 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n627 101 Car -1 -1 -1 698.28 169.00 896.28 239.80 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n627 90 Car -1 -1 -1 936.94 168.16 1155.44 239.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n627 60 Car -1 -1 -1 592.55 170.38 682.42 211.30 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n627 61 Car -1 -1 -1 666.36 168.26 772.39 209.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n627 102 Car -1 -1 -1 1163.67 165.85 1237.19 236.24 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n627 83 Pedestrian -1 -1 -1 292.98 171.02 323.79 240.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n627 115 Pedestrian -1 -1 -1 299.80 168.84 334.02 241.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n627 71 Car -1 -1 -1 270.15 171.25 347.19 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n627 119 Car -1 -1 -1 1205.15 161.13 1236.64 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n628 101 Car -1 -1 -1 698.50 168.99 896.25 239.90 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n628 90 Car -1 -1 -1 934.85 166.66 1157.46 238.22 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n628 61 Car -1 -1 -1 666.49 168.35 772.48 209.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n628 60 Car -1 -1 -1 592.51 170.44 682.29 211.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n628 102 Car -1 -1 -1 1163.40 165.95 1238.22 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n628 83 Pedestrian -1 -1 -1 292.69 170.55 323.86 240.69 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n628 71 Car -1 -1 -1 268.05 171.22 349.15 231.52 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n628 115 Pedestrian -1 -1 -1 299.73 169.33 334.09 241.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n628 119 Car -1 -1 -1 1205.27 160.80 1236.87 203.41 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n629 101 Car -1 -1 -1 698.64 169.07 896.46 240.06 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n629 90 Car -1 -1 -1 935.38 166.83 1157.71 238.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n629 60 Car -1 -1 -1 592.80 170.33 682.71 211.34 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n629 61 Car -1 -1 -1 666.34 168.36 773.11 210.10 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n629 102 Car -1 -1 -1 1164.12 166.40 1238.09 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n629 83 Pedestrian -1 -1 -1 293.53 170.29 323.47 240.80 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n629 115 Pedestrian -1 -1 -1 303.57 168.18 335.70 242.95 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n629 71 Car -1 -1 -1 269.65 170.85 347.72 232.14 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n629 119 Car -1 -1 -1 1209.54 160.73 1238.33 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n630 101 Car -1 -1 -1 698.53 169.14 897.59 239.97 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n630 90 Car -1 -1 -1 939.19 168.00 1160.31 239.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n630 60 Car -1 -1 -1 593.13 170.45 682.62 211.29 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n630 61 Car -1 -1 -1 667.32 168.38 772.43 210.00 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n630 102 Car -1 -1 -1 1164.56 166.51 1238.21 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n630 83 Pedestrian -1 -1 -1 292.99 170.29 323.31 241.17 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n630 115 Pedestrian -1 -1 -1 303.92 168.29 335.63 243.03 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n630 71 Car -1 -1 -1 269.25 171.03 355.85 230.84 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n630 119 Car -1 -1 -1 1210.05 161.14 1238.33 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n631 101 Car -1 -1 -1 700.30 169.76 901.27 240.10 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n631 90 Car -1 -1 -1 939.56 168.06 1160.83 239.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n631 60 Car -1 -1 -1 594.03 170.54 682.48 211.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n631 61 Car -1 -1 -1 667.75 168.43 771.92 209.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n631 83 Pedestrian -1 -1 -1 293.03 170.27 323.88 241.26 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n631 102 Car -1 -1 -1 1165.51 165.50 1238.05 237.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n631 71 Car -1 -1 -1 270.81 171.18 354.19 230.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n631 115 Pedestrian -1 -1 -1 304.04 168.46 336.26 242.47 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n631 119 Car -1 -1 -1 1210.36 161.84 1238.35 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n632 101 Car -1 -1 -1 701.22 169.33 901.42 240.11 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n632 90 Car -1 -1 -1 942.67 168.51 1164.37 240.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n632 60 Car -1 -1 -1 594.72 170.51 682.41 211.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n632 61 Car -1 -1 -1 668.74 168.45 771.55 210.21 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n632 102 Car -1 -1 -1 1171.55 165.71 1236.67 237.06 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n632 83 Pedestrian -1 -1 -1 293.45 170.41 324.55 240.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n632 71 Car -1 -1 -1 268.99 171.79 355.98 230.23 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n632 115 Pedestrian -1 -1 -1 303.65 168.94 337.34 242.38 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n632 119 Car -1 -1 -1 1211.23 161.03 1238.07 203.22 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n633 101 Car -1 -1 -1 702.26 169.82 901.94 239.90 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n633 90 Car -1 -1 -1 945.26 168.85 1168.76 239.97 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n633 60 Car -1 -1 -1 595.44 170.67 682.38 211.26 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n633 61 Car -1 -1 -1 669.44 168.44 771.18 210.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n633 71 Car -1 -1 -1 266.53 172.22 358.66 230.04 -1 -1 -1 -1000 -1000 -1000 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241.10 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n639 90 Car -1 -1 -1 963.81 170.13 1189.63 241.49 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n639 60 Car -1 -1 -1 604.54 171.11 695.23 212.69 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n639 61 Car -1 -1 -1 680.96 168.66 782.43 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n639 115 Pedestrian -1 -1 -1 310.79 167.19 344.99 243.80 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n639 83 Pedestrian -1 -1 -1 299.74 169.35 331.46 242.26 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n639 102 Car -1 -1 -1 1197.08 167.65 1236.96 241.83 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n639 71 Car -1 -1 -1 283.06 169.50 357.53 232.58 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n640 101 Car -1 -1 -1 716.98 170.18 922.85 241.40 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n640 90 Car -1 -1 -1 966.91 169.72 1195.08 241.51 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n640 61 Car -1 -1 -1 682.98 168.37 786.85 210.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n640 60 Car -1 -1 -1 607.37 170.88 698.52 212.88 -1 -1 -1 -1000 -1000 -1000 -10 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0.58\n644 71 Car -1 -1 -1 287.91 169.64 368.02 231.92 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n645 101 Car -1 -1 -1 730.75 173.34 942.29 246.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n645 60 Car -1 -1 -1 618.51 171.61 711.74 214.55 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n645 90 Car -1 -1 -1 988.54 174.72 1226.22 248.35 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n645 61 Car -1 -1 -1 697.06 169.58 803.86 212.07 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n645 115 Pedestrian -1 -1 -1 315.42 168.63 357.09 243.34 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n645 71 Car -1 -1 -1 282.25 171.89 373.68 230.81 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n645 83 Pedestrian -1 -1 -1 311.39 173.68 336.74 242.46 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n646 101 Car -1 -1 -1 734.50 174.96 946.17 248.07 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n646 90 Car -1 -1 -1 994.29 175.11 1230.02 249.69 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n646 60 Car -1 -1 -1 621.43 172.57 714.89 215.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n646 61 Car -1 -1 -1 698.32 171.12 805.03 214.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n646 115 Pedestrian -1 -1 -1 322.19 169.37 356.75 245.77 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n646 83 Pedestrian -1 -1 -1 311.77 173.66 337.64 243.01 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n646 71 Car -1 -1 -1 285.49 171.75 378.23 231.18 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n646 120 Pedestrian -1 -1 -1 549.96 169.72 564.76 201.05 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n647 101 Car -1 -1 -1 737.34 175.17 951.35 248.64 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n647 90 Car -1 -1 -1 998.93 174.41 1233.43 251.11 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n647 61 Car -1 -1 -1 701.61 171.63 809.35 215.58 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n647 60 Car -1 -1 -1 624.69 173.47 715.89 216.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n647 115 Pedestrian -1 -1 -1 323.00 169.69 357.22 246.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n647 83 Pedestrian -1 -1 -1 313.89 174.24 341.26 244.14 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n647 71 Car -1 -1 -1 286.78 172.72 376.91 231.30 -1 -1 -1 -1000 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Pedestrian -1 -1 -1 322.93 172.05 364.54 247.50 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n649 83 Pedestrian -1 -1 -1 319.09 177.47 343.24 247.61 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n649 122 Car -1 -1 -1 299.67 175.79 386.51 235.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n650 101 Car -1 -1 -1 750.60 176.28 968.69 251.40 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n650 90 Car -1 -1 -1 1017.95 177.39 1235.88 254.41 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n650 60 Car -1 -1 -1 632.27 174.76 727.66 218.65 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n650 61 Car -1 -1 -1 711.81 173.36 820.78 218.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n650 83 Pedestrian -1 -1 -1 319.46 177.90 343.91 247.95 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n650 115 Pedestrian -1 -1 -1 323.72 171.03 364.82 248.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n650 122 Car -1 -1 -1 303.09 174.31 384.41 236.61 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n651 90 Car -1 -1 -1 1025.98 178.81 1236.11 255.39 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n651 101 Car -1 -1 -1 755.21 176.67 978.19 253.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n651 60 Car -1 -1 -1 634.85 175.09 729.54 219.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n651 61 Car -1 -1 -1 714.83 173.44 824.97 218.75 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n651 115 Pedestrian -1 -1 -1 327.20 170.39 368.40 248.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n651 83 Pedestrian -1 -1 -1 320.04 177.03 345.03 247.26 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n651 122 Car -1 -1 -1 306.26 173.10 388.52 237.85 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n652 101 Car -1 -1 -1 759.24 177.13 983.99 254.64 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n652 90 Car -1 -1 -1 1033.24 178.69 1234.95 255.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n652 60 Car -1 -1 -1 639.80 174.68 735.87 219.63 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n652 61 Car -1 -1 -1 719.22 173.74 829.46 218.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n652 83 Pedestrian -1 -1 -1 323.54 177.12 348.86 248.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n652 122 Car -1 -1 -1 302.20 175.15 399.48 235.51 -1 -1 -1 -1000 -1000 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-1 -1 345.36 177.29 394.28 257.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n658 125 Cyclist -1 -1 -1 580.16 176.28 596.66 212.42 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n659 101 Car -1 -1 -1 802.05 183.67 1056.65 267.80 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n659 60 Car -1 -1 -1 672.58 181.27 774.32 228.60 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n659 90 Car -1 -1 -1 1109.62 188.47 1236.67 269.40 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n659 61 Car -1 -1 -1 758.51 180.19 868.89 227.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n659 122 Car -1 -1 -1 322.55 182.93 432.59 242.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n659 83 Pedestrian -1 -1 -1 346.43 182.22 370.84 257.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n659 115 Pedestrian -1 -1 -1 350.94 176.60 396.28 258.29 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n659 125 Cyclist -1 -1 -1 584.18 176.22 600.01 213.37 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n659 126 Car -1 -1 -1 1079.78 175.94 1159.57 218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n660 101 Car -1 -1 -1 813.37 184.95 1074.90 269.86 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n660 60 Car -1 -1 -1 679.56 181.52 781.37 228.83 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n660 61 Car -1 -1 -1 765.11 179.90 877.64 227.94 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n660 122 Car -1 -1 -1 323.06 182.45 439.58 242.05 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n660 83 Pedestrian -1 -1 -1 350.38 181.38 376.07 258.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n660 90 Car -1 -1 -1 1122.08 191.02 1234.41 266.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n660 115 Pedestrian -1 -1 -1 360.89 176.69 394.13 261.82 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n661 101 Car -1 -1 -1 822.47 184.88 1089.52 272.06 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n661 90 Car -1 -1 -1 1077.96 190.79 1237.87 305.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n661 60 Car -1 -1 -1 685.90 181.31 790.72 229.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n661 61 Car -1 -1 -1 772.59 180.00 885.97 228.44 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n661 122 Car -1 -1 -1 327.00 182.29 444.29 241.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n661 83 Pedestrian -1 -1 -1 354.15 182.13 379.31 257.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n661 115 Pedestrian -1 -1 -1 362.29 177.04 396.66 261.20 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n662 90 Car -1 -1 -1 990.80 187.33 1240.95 308.62 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n662 101 Car -1 -1 -1 830.56 187.28 1106.14 275.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n662 60 Car -1 -1 -1 692.48 182.37 795.45 230.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n662 122 Car -1 -1 -1 328.56 183.67 450.91 241.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n662 61 Car -1 -1 -1 779.23 181.13 900.44 229.73 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n662 83 Pedestrian -1 -1 -1 357.17 180.43 385.09 261.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n662 115 Pedestrian -1 -1 -1 367.54 176.48 403.27 263.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n663 90 Car -1 -1 -1 918.01 188.45 1235.06 314.78 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n663 60 Car -1 -1 -1 699.88 184.71 807.66 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n663 61 Car -1 -1 -1 786.99 183.28 910.20 232.41 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n663 122 Car -1 -1 -1 331.89 185.57 455.54 242.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n663 115 Pedestrian -1 -1 -1 369.84 178.45 408.80 264.37 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n663 83 Pedestrian -1 -1 -1 364.58 183.81 390.44 262.46 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n663 101 Car -1 -1 -1 843.49 189.78 1124.44 276.31 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n664 90 Car -1 -1 -1 836.22 190.36 1230.96 318.73 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n664 60 Car -1 -1 -1 708.65 186.35 814.73 234.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n664 61 Car -1 -1 -1 796.71 185.49 921.52 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n664 122 Car -1 -1 -1 340.35 186.49 455.80 247.32 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n664 83 Pedestrian -1 -1 -1 368.05 184.11 394.95 265.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n664 115 Pedestrian -1 -1 -1 376.89 180.47 411.43 266.74 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n665 90 Car -1 -1 -1 753.18 193.10 1128.56 323.95 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818.83 188.14 923.70 229.70 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n667 131 Pedestrian -1 -1 -1 629.24 179.12 646.61 226.30 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n668 90 Car -1 -1 -1 510.23 194.39 844.27 323.56 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n668 128 Car -1 -1 -1 922.84 193.58 1231.28 299.24 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n668 122 Car -1 -1 -1 374.71 192.10 498.61 250.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n668 61 Car -1 -1 -1 851.54 187.59 985.52 239.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n668 60 Car -1 -1 -1 757.64 189.72 868.81 233.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n668 115 Pedestrian -1 -1 -1 407.53 183.08 442.29 273.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n668 83 Pedestrian -1 -1 -1 398.13 186.33 427.53 271.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n668 131 Pedestrian -1 -1 -1 640.70 180.09 658.90 228.31 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n669 90 Car -1 -1 -1 436.99 195.87 763.58 325.31 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n669 128 Car -1 -1 -1 946.99 194.45 1236.77 308.06 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0.51\n670 132 Cyclist -1 -1 -1 668.50 183.66 692.41 232.38 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n671 90 Car -1 -1 -1 318.02 200.70 625.71 326.93 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n671 128 Car -1 -1 -1 1020.84 199.69 1235.61 317.46 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n671 60 Car -1 -1 -1 826.96 192.24 953.67 249.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n671 61 Car -1 -1 -1 933.07 190.28 1096.42 251.41 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n671 83 Pedestrian -1 -1 -1 459.28 189.80 492.58 267.29 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n671 132 Cyclist -1 -1 -1 689.78 184.71 709.85 234.20 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n672 90 Car -1 -1 -1 267.14 204.10 567.21 328.40 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n672 128 Car -1 -1 -1 1070.73 199.27 1238.66 319.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n672 60 Car -1 -1 -1 858.59 191.98 991.75 251.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n672 61 Car -1 -1 -1 971.83 190.31 1128.74 252.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n672 132 Cyclist -1 -1 -1 710.76 187.02 736.59 236.67 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n672 83 Pedestrian -1 -1 -1 484.84 189.94 520.67 266.59 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n672 133 Car -1 -1 -1 459.50 200.00 576.78 249.84 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n672 134 Car -1 -1 -1 1103.99 191.93 1228.91 256.80 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n673 90 Car -1 -1 -1 233.33 204.89 515.44 323.95 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n673 60 Car -1 -1 -1 897.82 190.57 1032.64 252.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n673 61 Car -1 -1 -1 1016.17 188.75 1184.75 253.46 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n673 128 Car -1 -1 -1 1132.81 199.50 1237.74 327.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n673 133 Car -1 -1 -1 473.10 197.30 609.78 253.86 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n673 83 Pedestrian -1 -1 -1 499.40 192.50 529.91 285.26 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n673 134 Car -1 -1 -1 1171.31 191.71 1238.02 256.45 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n673 132 Cyclist -1 -1 -1 738.05 186.13 765.29 237.39 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n673 135 Pedestrian -1 -1 -1 509.06 186.99 544.32 287.05 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n674 90 Car -1 -1 -1 202.62 203.83 468.71 321.64 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n674 60 Car -1 -1 -1 939.27 189.11 1084.01 253.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n674 61 Car -1 -1 -1 1068.98 187.45 1239.05 259.91 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n674 133 Car -1 -1 -1 507.12 195.80 638.81 254.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n674 83 Pedestrian -1 -1 -1 528.20 189.05 561.30 284.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n674 135 Pedestrian -1 -1 -1 535.35 185.39 572.37 287.30 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n674 132 Cyclist -1 -1 -1 770.00 182.45 795.90 237.27 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n675 90 Car -1 -1 -1 181.18 203.97 429.40 315.38 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n675 60 Car -1 -1 -1 988.42 189.25 1143.31 257.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n675 61 Car -1 -1 -1 1125.88 187.77 1239.69 261.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n675 133 Car -1 -1 -1 536.75 192.84 670.88 255.96 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n675 83 Pedestrian -1 -1 -1 559.39 187.62 592.77 285.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n675 135 Pedestrian -1 -1 -1 569.95 182.82 606.01 290.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n675 132 Cyclist -1 -1 -1 804.75 180.71 831.01 238.40 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n676 90 Car -1 -1 -1 165.30 203.70 399.25 312.68 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n676 60 Car -1 -1 -1 1040.47 187.17 1215.23 261.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n676 133 Car -1 -1 -1 568.83 191.31 708.50 256.45 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n676 83 Pedestrian -1 -1 -1 590.98 185.66 624.49 288.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n676 61 Car -1 -1 -1 1197.32 185.75 1236.86 262.74 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n676 135 Pedestrian -1 -1 -1 600.87 181.43 638.73 292.31 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n676 132 Cyclist -1 -1 -1 843.21 179.11 868.93 240.31 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n677 90 Car -1 -1 -1 158.08 203.33 374.23 304.90 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n677 60 Car -1 -1 -1 1102.59 186.33 1237.19 264.69 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n677 133 Car -1 -1 -1 599.35 188.48 746.91 254.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n677 83 Pedestrian -1 -1 -1 623.21 181.67 662.16 291.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n677 135 Pedestrian -1 -1 -1 633.40 178.15 675.49 295.27 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n677 132 Cyclist -1 -1 -1 878.87 177.46 911.71 242.32 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n678 90 Car -1 -1 -1 159.28 203.85 358.18 298.55 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n678 133 Car -1 -1 -1 635.45 189.66 780.49 257.84 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n678 135 Pedestrian -1 -1 -1 672.32 179.14 713.32 299.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n678 60 Car -1 -1 -1 1167.38 185.76 1236.46 270.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n678 83 Pedestrian -1 -1 -1 660.71 183.52 694.95 295.35 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n678 136 Pedestrian -1 -1 -1 929.03 176.16 953.57 244.13 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n679 90 Car -1 -1 -1 166.32 204.43 348.80 293.59 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n679 133 Car -1 -1 -1 670.63 188.84 822.69 260.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n679 135 Pedestrian -1 -1 -1 714.69 179.86 755.73 305.03 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n679 83 Pedestrian -1 -1 -1 698.40 185.67 734.46 300.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n679 137 Cyclist -1 -1 -1 971.93 176.76 1005.41 248.46 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n680 90 Car -1 -1 -1 176.75 204.97 341.56 290.11 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n680 133 Car -1 -1 -1 704.36 187.32 867.80 261.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n680 83 Pedestrian -1 -1 -1 738.88 183.84 779.17 303.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n680 135 Pedestrian -1 -1 -1 755.07 177.65 800.39 308.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n680 137 Cyclist -1 -1 -1 1026.39 173.45 1059.64 251.16 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n681 90 Car -1 -1 -1 192.09 205.84 340.21 287.37 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n681 133 Car -1 -1 -1 742.42 185.10 914.86 263.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n681 135 Pedestrian -1 -1 -1 801.25 173.59 848.25 314.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n681 83 Pedestrian -1 -1 -1 782.52 184.94 821.61 309.46 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n681 138 Pedestrian -1 -1 -1 1084.12 169.54 1118.10 250.85 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n682 90 Car -1 -1 -1 208.76 205.80 344.38 281.67 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n682 133 Car -1 -1 -1 786.23 184.55 963.51 264.47 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n682 135 Pedestrian -1 -1 -1 844.59 172.45 899.03 321.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n682 83 Pedestrian -1 -1 -1 829.47 182.67 875.03 313.82 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n682 139 Cyclist -1 -1 -1 1143.21 165.39 1188.72 253.89 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n683 90 Car -1 -1 -1 227.45 205.14 350.16 276.04 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n683 133 Car -1 -1 -1 829.91 184.10 1013.33 264.81 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n683 135 Pedestrian -1 -1 -1 906.79 168.57 960.26 333.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n683 83 Pedestrian -1 -1 -1 881.91 181.28 930.40 320.57 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n683 140 Pedestrian -1 -1 -1 1211.48 155.21 1238.16 262.84 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n683 141 Van -1 -1 -1 65.39 191.07 102.71 220.29 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n684 90 Car -1 -1 -1 244.79 202.59 358.93 270.10 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n684 133 Car -1 -1 -1 875.99 184.75 1076.35 263.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n684 135 Pedestrian -1 -1 -1 967.15 162.17 1031.35 342.27 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n684 83 Pedestrian -1 -1 -1 941.99 175.83 994.80 328.14 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n685 90 Car -1 -1 -1 267.21 198.95 367.70 263.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n685 133 Car -1 -1 -1 922.87 181.05 1138.84 267.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n685 135 Pedestrian -1 -1 -1 1038.04 149.53 1116.67 361.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n685 83 Pedestrian -1 -1 -1 1004.94 170.69 1072.28 345.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n686 90 Car -1 -1 -1 288.36 194.78 381.98 256.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n686 133 Car -1 -1 -1 971.36 173.07 1229.56 268.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n686 83 Pedestrian -1 -1 -1 1078.72 163.15 1160.94 356.80 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n686 135 Pedestrian -1 -1 -1 1116.42 148.41 1215.99 369.28 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n687 133 Car -1 -1 -1 1018.98 161.81 1236.94 265.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n687 90 Car -1 -1 -1 308.99 192.61 395.70 250.49 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n687 135 Pedestrian -1 -1 -1 1184.14 152.03 1240.25 373.22 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n687 142 Car -1 -1 -1 124.06 197.49 151.37 214.47 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n688 90 Car -1 -1 -1 329.94 189.35 409.63 246.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n688 133 Car -1 -1 -1 1079.58 156.69 1238.07 268.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n688 142 Car -1 -1 -1 156.91 195.71 182.20 213.77 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n689 90 Car -1 -1 -1 346.96 187.65 419.95 242.80 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n689 142 Car -1 -1 -1 186.61 193.83 210.08 210.66 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n689 133 Car -1 -1 -1 1150.31 151.71 1235.96 273.82 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n689 143 Car -1 -1 -1 288.01 176.56 312.15 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n690 90 Car -1 -1 -1 362.73 186.41 431.93 238.12 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n690 142 Car -1 -1 -1 209.83 192.65 234.76 209.70 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n690 143 Car -1 -1 -1 312.43 172.47 335.22 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n691 90 Car -1 -1 -1 376.62 187.06 442.23 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n691 142 Car -1 -1 -1 228.83 191.50 256.42 210.00 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n691 143 Car -1 -1 -1 332.50 172.78 357.00 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n692 90 Car -1 -1 -1 390.68 185.94 451.79 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n692 142 Car -1 -1 -1 246.10 191.22 275.68 210.11 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n693 90 Car -1 -1 -1 403.32 185.18 461.35 232.21 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n693 142 Car -1 -1 -1 263.00 191.95 290.45 211.10 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n694 90 Car -1 -1 -1 415.28 185.47 471.07 229.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n694 142 Car -1 -1 -1 276.28 190.78 307.23 210.68 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n695 90 Car -1 -1 -1 428.59 185.18 481.74 227.31 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n695 142 Car -1 -1 -1 290.91 190.97 320.64 211.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n696 90 Car -1 -1 -1 443.27 184.80 493.82 226.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n696 142 Car -1 -1 -1 304.79 192.00 336.75 213.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n696 144 Van -1 -1 -1 425.79 169.82 446.37 194.84 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n697 90 Car -1 -1 -1 459.01 185.79 507.23 226.63 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n697 142 Car -1 -1 -1 317.69 193.34 352.26 215.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n698 142 Car -1 -1 -1 328.71 194.28 365.64 217.27 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n698 90 Car -1 -1 -1 473.15 186.21 519.19 225.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n699 90 Car -1 -1 -1 487.05 186.02 531.66 224.80 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n699 142 Car -1 -1 -1 337.61 195.21 378.11 219.45 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n700 90 Car -1 -1 -1 498.95 186.72 542.46 223.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n700 142 Car -1 -1 -1 344.67 196.03 382.24 221.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n700 145 Car -1 -1 -1 418.41 185.45 441.23 201.71 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0016.txt",
    "content": "0 1 Cyclist -1 -1 -1 871.26 155.94 972.71 246.42 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n0 2 Pedestrian -1 -1 -1 334.65 163.19 367.53 260.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n0 3 Pedestrian -1 -1 -1 276.62 167.85 309.28 259.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n0 4 Car -1 -1 -1 456.05 178.40 484.16 200.44 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n0 5 Pedestrian -1 -1 -1 10.32 190.24 47.12 256.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n0 6 Car -1 -1 -1 1108.45 155.27 1239.29 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n0 7 Pedestrian -1 -1 -1 358.74 167.15 397.66 243.07 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n0 8 Pedestrian -1 -1 -1 300.53 166.26 324.94 244.00 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n1 1 Cyclist -1 -1 -1 888.90 153.69 985.10 249.28 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n1 4 Car -1 -1 -1 454.20 178.14 483.43 200.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n1 2 Pedestrian -1 -1 -1 321.77 162.27 358.40 263.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n1 3 Pedestrian -1 -1 -1 264.18 169.35 298.15 261.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n1 6 Car -1 -1 -1 1123.62 155.51 1240.39 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n1 5 Pedestrian -1 -1 -1 -0.12 189.39 21.06 260.58 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n1 8 Pedestrian -1 -1 -1 287.22 165.91 314.59 245.01 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n1 7 Pedestrian -1 -1 -1 347.14 169.35 394.46 245.72 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n2 4 Car -1 -1 -1 452.95 178.56 481.71 201.28 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n2 1 Cyclist -1 -1 -1 901.58 155.01 1004.48 253.56 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n2 2 Pedestrian -1 -1 -1 311.11 163.95 345.28 268.48 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n2 3 Pedestrian -1 -1 -1 248.85 169.91 282.30 265.17 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n2 6 Car -1 -1 -1 1127.51 154.49 1236.92 200.28 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n2 7 Pedestrian -1 -1 -1 340.04 167.03 379.11 250.63 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n2 8 Pedestrian -1 -1 -1 269.54 165.64 301.49 260.75 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n2 9 Truck -1 -1 -1 570.05 79.78 728.89 276.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n3 1 Cyclist -1 -1 -1 920.57 153.80 1025.06 257.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n3 2 Pedestrian -1 -1 -1 303.21 163.72 336.93 271.11 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n3 3 Pedestrian -1 -1 -1 231.79 171.23 268.16 271.30 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n3 4 Car -1 -1 -1 451.92 179.67 480.69 202.01 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n3 8 Pedestrian -1 -1 -1 257.32 165.40 289.46 260.70 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n3 7 Pedestrian -1 -1 -1 324.60 168.10 371.80 255.39 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n3 6 Car -1 -1 -1 1149.01 154.92 1237.16 200.72 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n3 10 Van -1 -1 -1 577.25 73.59 736.68 282.29 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n3 11 Cyclist -1 -1 -1 329.05 167.79 380.05 252.69 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n4 1 Cyclist -1 -1 -1 937.14 153.77 1047.03 263.23 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n4 7 Pedestrian -1 -1 -1 317.37 168.18 362.09 258.53 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n4 4 Car -1 -1 -1 449.97 179.90 479.41 203.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n4 3 Pedestrian -1 -1 -1 214.10 172.95 247.79 276.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n4 8 Pedestrian -1 -1 -1 237.44 167.43 271.27 273.43 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n4 6 Car -1 -1 -1 1157.64 151.74 1237.40 204.20 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n4 2 Pedestrian -1 -1 -1 292.60 167.38 324.33 265.43 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n4 10 Van -1 -1 -1 582.93 76.51 738.94 279.17 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n4 12 Pedestrian -1 -1 -1 277.83 170.89 307.68 264.22 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n4 13 Pedestrian -1 -1 -1 285.31 170.95 316.55 277.60 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n5 1 Cyclist -1 -1 -1 952.69 153.44 1077.72 270.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n5 4 Car -1 -1 -1 447.32 180.59 477.66 204.06 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n5 8 Pedestrian -1 -1 -1 219.95 167.73 256.76 279.92 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n5 3 Pedestrian -1 -1 -1 190.77 172.29 232.36 283.23 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n5 12 Pedestrian -1 -1 -1 261.69 172.88 293.37 269.31 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n5 7 Pedestrian -1 -1 -1 296.23 166.41 353.23 261.27 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n5 13 Pedestrian -1 -1 -1 268.83 170.08 309.14 285.80 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n5 10 Van -1 -1 -1 583.95 76.39 747.49 285.42 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n5 14 Cyclist -1 -1 -1 298.92 168.30 364.05 259.43 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n5 15 Car -1 -1 -1 295.87 170.24 360.56 260.57 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n5 16 Truck -1 -1 -1 583.95 76.39 747.49 285.42 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n5 17 Van -1 -1 -1 1170.14 143.63 1239.56 205.00 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n6 4 Car -1 -1 -1 444.67 180.56 475.09 204.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n6 1 Cyclist -1 -1 -1 980.44 153.13 1096.22 274.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n6 3 Pedestrian -1 -1 -1 166.52 171.49 209.48 292.16 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n6 8 Pedestrian -1 -1 -1 196.93 169.45 234.77 286.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n6 16 Truck -1 -1 -1 585.26 72.63 754.08 289.32 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n6 13 Pedestrian -1 -1 -1 254.14 168.82 300.46 294.47 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n6 7 Pedestrian -1 -1 -1 281.25 168.66 344.51 264.20 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n6 12 Pedestrian -1 -1 -1 243.65 172.19 280.43 275.83 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n6 15 Car -1 -1 -1 280.57 169.13 360.54 262.66 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n6 14 Cyclist -1 -1 -1 284.37 168.53 362.59 262.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n6 18 Car -1 -1 -1 1187.68 146.21 1239.10 209.25 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n7 1 Cyclist -1 -1 -1 1001.52 152.24 1129.59 281.53 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n7 4 Car -1 -1 -1 441.73 180.12 472.88 204.39 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n7 13 Pedestrian -1 -1 -1 234.24 163.41 289.06 300.55 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n7 8 Pedestrian -1 -1 -1 169.92 165.75 214.73 290.84 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n7 12 Pedestrian -1 -1 -1 223.52 171.13 262.12 278.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n7 3 Pedestrian -1 -1 -1 143.47 171.06 186.38 294.76 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n7 7 Pedestrian -1 -1 -1 270.62 166.40 330.78 268.04 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n7 14 Cyclist -1 -1 -1 275.91 167.09 347.66 266.53 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n7 16 Truck -1 -1 -1 595.48 71.03 757.66 284.40 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n7 19 Van -1 -1 -1 595.48 71.03 757.66 284.40 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n8 1 Cyclist -1 -1 -1 1024.85 148.79 1175.33 291.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n8 16 Truck -1 -1 -1 596.65 65.86 766.05 281.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 3 Pedestrian -1 -1 -1 109.85 169.03 158.44 302.78 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n8 13 Pedestrian -1 -1 -1 206.43 165.16 270.65 307.50 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n8 7 Pedestrian -1 -1 -1 257.46 165.78 305.92 273.32 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n8 12 Pedestrian -1 -1 -1 201.69 171.75 244.78 285.22 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n8 8 Pedestrian -1 -1 -1 141.71 165.04 189.15 299.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n8 4 Car -1 -1 -1 437.87 178.43 469.45 203.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n8 20 Pedestrian -1 -1 -1 217.84 164.04 267.17 284.13 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n8 21 Pedestrian -1 -1 -1 512.18 176.42 522.14 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n9 1 Cyclist -1 -1 -1 1048.50 145.17 1214.36 296.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n9 4 Car -1 -1 -1 434.98 177.69 466.46 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n9 13 Pedestrian -1 -1 -1 180.01 159.24 251.19 319.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n9 16 Truck -1 -1 -1 602.09 65.39 769.54 275.38 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n9 7 Pedestrian -1 -1 -1 237.41 163.20 286.66 278.57 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n9 12 Pedestrian -1 -1 -1 175.49 166.30 224.42 297.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n9 3 Pedestrian -1 -1 -1 77.17 167.49 128.41 311.56 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n9 8 Pedestrian -1 -1 -1 113.07 164.55 163.27 306.51 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n9 22 Car -1 -1 -1 233.02 167.84 330.01 271.20 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n10 1 Cyclist -1 -1 -1 1080.16 138.36 1237.08 310.68 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n10 4 Car -1 -1 -1 430.87 176.79 463.00 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n10 16 Truck -1 -1 -1 608.12 66.00 778.08 281.60 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n10 13 Pedestrian -1 -1 -1 151.22 159.47 224.91 327.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n10 7 Pedestrian -1 -1 -1 217.83 163.14 267.23 285.30 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n10 12 Pedestrian -1 -1 -1 145.54 164.91 199.92 299.76 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n10 3 Pedestrian -1 -1 -1 36.76 167.70 91.66 319.50 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n10 8 Pedestrian -1 -1 -1 80.07 161.53 133.32 311.00 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n10 22 Car -1 -1 -1 212.23 167.10 320.19 274.02 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n11 16 Truck -1 -1 -1 610.83 66.81 783.85 280.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n11 4 Car -1 -1 -1 426.40 176.55 459.55 203.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n11 13 Pedestrian -1 -1 -1 119.53 156.30 195.10 339.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n11 1 Cyclist -1 -1 -1 1126.06 137.12 1237.84 326.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n11 7 Pedestrian -1 -1 -1 190.95 160.69 239.84 289.57 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n11 3 Pedestrian -1 -1 -1 3.50 167.73 54.27 327.76 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n11 22 Car -1 -1 -1 189.59 166.65 304.03 279.87 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n11 8 Pedestrian -1 -1 -1 34.29 162.77 94.23 324.94 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n11 12 Pedestrian -1 -1 -1 115.81 165.86 174.98 306.09 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n11 23 Cyclist -1 -1 -1 193.33 157.94 306.73 277.13 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n12 4 Car -1 -1 -1 420.69 176.99 455.50 204.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n12 16 Truck -1 -1 -1 615.33 62.22 791.68 285.37 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n12 7 Pedestrian -1 -1 -1 157.20 156.18 212.26 301.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n12 23 Cyclist -1 -1 -1 173.74 160.89 288.35 281.59 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n12 13 Pedestrian -1 -1 -1 87.62 156.56 156.77 354.76 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n12 12 Pedestrian -1 -1 -1 92.14 162.45 160.15 309.78 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n12 3 Pedestrian -1 -1 -1 -1.81 190.50 36.85 335.39 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n13 4 Car -1 -1 -1 415.56 178.71 450.58 205.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n13 16 Truck -1 -1 -1 619.73 61.42 795.76 285.53 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n13 7 Pedestrian -1 -1 -1 122.14 155.32 184.78 310.22 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n13 23 Cyclist -1 -1 -1 152.70 163.12 269.52 292.52 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n13 13 Pedestrian -1 -1 -1 53.80 150.43 128.80 369.33 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n14 4 Car -1 -1 -1 407.99 179.49 444.53 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n14 23 Cyclist -1 -1 -1 106.78 160.46 253.56 303.77 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n14 7 Pedestrian -1 -1 -1 79.37 154.25 149.15 325.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n14 13 Pedestrian -1 -1 -1 12.44 152.37 100.35 373.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n14 16 Truck -1 -1 -1 621.35 57.77 801.05 290.30 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n15 4 Car -1 -1 -1 400.02 180.49 436.60 207.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n15 16 Truck -1 -1 -1 618.90 56.89 804.11 297.44 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n15 7 Pedestrian -1 -1 -1 28.02 152.19 108.41 336.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n15 23 Cyclist -1 -1 -1 68.32 161.21 230.18 311.19 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n16 16 Truck -1 -1 -1 618.79 55.07 804.35 293.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n16 4 Car -1 -1 -1 389.40 180.25 428.05 209.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n16 23 Cyclist -1 -1 -1 26.25 158.70 202.25 321.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n16 7 Pedestrian -1 -1 -1 3.02 153.27 63.25 349.56 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n17 16 Truck -1 -1 -1 618.13 54.68 804.41 293.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n17 4 Car -1 -1 -1 378.65 181.40 417.21 210.07 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n17 23 Cyclist -1 -1 -1 1.75 151.08 157.92 335.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n17 24 Car -1 -1 -1 601.79 174.60 622.25 191.22 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n17 25 Car -1 -1 -1 563.89 175.31 583.44 189.63 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n18 4 Car -1 -1 -1 365.09 182.05 405.84 211.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n18 16 Truck -1 -1 -1 615.30 54.35 800.18 294.27 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n18 24 Car -1 -1 -1 592.47 177.04 611.94 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n18 23 Cyclist -1 -1 -1 -1.36 147.72 113.45 347.90 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n18 25 Car -1 -1 -1 556.46 175.85 574.99 190.17 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n19 4 Car -1 -1 -1 352.17 182.47 393.84 212.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n19 24 Car -1 -1 -1 583.59 177.59 602.52 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n19 16 Truck -1 -1 -1 613.93 55.29 800.29 293.64 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n19 25 Car -1 -1 -1 546.63 177.14 567.20 191.03 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n20 16 Truck -1 -1 -1 604.01 55.24 795.70 293.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n20 4 Car -1 -1 -1 335.90 181.83 379.43 212.27 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n20 24 Car -1 -1 -1 572.62 176.62 592.77 192.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n20 25 Car -1 -1 -1 537.05 175.73 555.70 190.23 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n21 4 Car -1 -1 -1 318.94 180.48 365.09 211.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n21 16 Truck -1 -1 -1 600.24 56.84 791.21 290.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n21 25 Car -1 -1 -1 524.88 174.21 544.40 188.85 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n21 24 Car -1 -1 -1 560.99 174.89 581.21 191.27 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n21 26 Van -1 -1 -1 597.26 50.47 782.77 289.88 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n22 4 Car -1 -1 -1 301.60 178.63 348.11 210.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n22 16 Truck -1 -1 -1 586.99 51.84 783.16 294.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 24 Car -1 -1 -1 549.20 173.51 569.71 189.76 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n22 25 Car -1 -1 -1 514.10 171.20 537.03 186.70 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n23 4 Car -1 -1 -1 283.76 177.90 331.92 210.36 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n23 16 Truck -1 -1 -1 575.93 50.81 778.32 295.44 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n23 24 Car -1 -1 -1 537.85 170.46 557.76 187.92 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n23 25 Car -1 -1 -1 503.53 170.66 524.28 185.62 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n24 4 Car -1 -1 -1 264.23 177.96 313.85 211.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n24 16 Truck -1 -1 -1 567.16 50.18 770.55 296.64 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n24 24 Car -1 -1 -1 525.20 172.23 545.07 188.62 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n24 25 Car -1 -1 -1 490.62 170.24 514.18 185.99 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n25 4 Car -1 -1 -1 242.59 180.54 295.66 215.54 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n25 24 Car -1 -1 -1 512.32 173.94 532.20 190.72 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n25 16 Truck -1 -1 -1 556.86 48.26 757.69 298.80 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n26 4 Car -1 -1 -1 219.61 183.35 274.92 218.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 16 Truck -1 -1 -1 541.83 47.02 749.21 301.00 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n26 24 Car -1 -1 -1 498.59 176.40 519.18 192.49 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n27 4 Car -1 -1 -1 192.72 186.99 251.48 222.29 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n27 24 Car -1 -1 -1 483.60 178.04 503.22 194.29 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n27 27 Van -1 -1 -1 523.81 48.75 730.78 306.23 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n27 28 Car -1 -1 -1 450.75 178.05 470.85 192.36 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n28 4 Car -1 -1 -1 164.22 187.77 226.26 223.84 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n28 28 Car -1 -1 -1 434.18 178.79 453.05 193.80 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n28 24 Car -1 -1 -1 467.52 180.14 485.82 195.63 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n28 27 Van -1 -1 -1 507.06 44.77 714.12 303.84 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n28 29 Truck -1 -1 -1 507.17 47.59 714.03 307.58 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n29 4 Car -1 -1 -1 132.27 188.99 198.34 226.14 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n29 24 Car -1 -1 -1 449.46 180.24 469.34 196.00 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n29 29 Truck -1 -1 -1 488.35 46.88 695.29 308.03 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n29 27 Van -1 -1 -1 489.61 42.43 693.88 299.13 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n30 4 Car -1 -1 -1 97.86 188.56 169.02 227.81 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n30 24 Car -1 -1 -1 430.09 179.95 449.95 196.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 29 Truck -1 -1 -1 467.83 45.03 676.58 309.95 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n30 30 Car -1 -1 -1 398.45 177.97 418.48 194.00 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n31 4 Car -1 -1 -1 62.28 189.27 136.11 230.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n31 29 Truck -1 -1 -1 438.86 38.02 658.47 310.16 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n31 24 Car -1 -1 -1 411.03 179.71 430.26 196.59 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n31 30 Car -1 -1 -1 379.98 177.70 399.54 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n32 4 Car -1 -1 -1 20.38 191.44 101.41 234.79 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n32 24 Car -1 -1 -1 392.20 181.11 410.95 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n32 29 Truck -1 -1 -1 416.44 44.34 633.60 311.37 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n32 30 Car -1 -1 -1 361.14 181.56 378.39 195.97 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n33 4 Car -1 -1 -1 0.63 195.31 64.63 239.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n33 24 Car -1 -1 -1 370.89 180.88 393.29 199.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n33 29 Truck -1 -1 -1 385.83 40.96 610.05 314.93 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n33 30 Car -1 -1 -1 340.89 181.69 360.25 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n34 29 Truck -1 -1 -1 356.14 39.46 585.41 316.59 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n34 4 Car -1 -1 -1 -0.11 195.70 26.44 246.40 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n34 24 Car -1 -1 -1 351.81 178.70 381.59 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n35 29 Truck -1 -1 -1 323.41 36.16 556.15 327.54 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n35 31 Van -1 -1 -1 314.34 39.58 552.63 338.44 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n49 33 Car -1 -1 -1 1208.55 162.54 1239.22 309.66 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n50 33 Car -1 -1 -1 1179.90 177.19 1238.93 310.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n50 34 Car -1 -1 -1 1188.06 165.03 1237.77 244.85 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n51 33 Car -1 -1 -1 1155.03 181.05 1239.95 314.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n51 34 Car -1 -1 -1 1152.86 172.97 1241.01 259.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n52 33 Car -1 -1 -1 1131.72 181.34 1237.55 315.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n52 34 Car -1 -1 -1 1116.91 175.81 1230.65 250.77 -1 -1 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253.55 185.39 394.18 271.29 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n296 103 Car -1 -1 -1 474.37 181.65 513.79 209.65 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n296 116 Pedestrian -1 -1 -1 979.28 141.20 1035.57 259.61 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n296 114 Car -1 -1 -1 518.35 178.83 543.29 199.70 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n297 78 Car -1 -1 -1 -2.07 210.22 394.47 369.74 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n297 109 Car -1 -1 -1 387.27 187.95 460.64 240.80 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n297 103 Car -1 -1 -1 472.25 186.74 511.79 216.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n297 83 Car -1 -1 -1 232.67 191.58 376.64 281.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n297 116 Pedestrian -1 -1 -1 1021.86 142.44 1070.06 267.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n297 114 Car -1 -1 -1 493.58 184.94 527.16 211.52 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n297 117 Pedestrian -1 -1 -1 349.76 189.96 392.12 281.96 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n297 118 Car -1 -1 -1 518.61 184.19 542.89 205.06 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n298 78 Car -1 -1 -1 -1.02 211.91 375.74 369.07 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n298 109 Car -1 -1 -1 377.36 190.48 457.29 245.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n298 103 Car -1 -1 -1 469.13 188.84 510.89 219.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n298 116 Pedestrian -1 -1 -1 1055.15 141.75 1106.74 275.87 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n298 118 Car -1 -1 -1 517.31 186.73 541.90 207.69 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n298 83 Car -1 -1 -1 189.18 195.27 350.25 292.64 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n298 117 Pedestrian -1 -1 -1 322.64 192.62 372.06 288.15 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n299 78 Car -1 -1 -1 -3.05 212.59 348.22 369.76 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n299 109 Car -1 -1 -1 368.84 187.34 451.67 244.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n299 103 Car -1 -1 -1 467.03 185.44 508.94 216.76 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n299 116 Pedestrian -1 -1 -1 1100.43 140.20 1162.17 285.14 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n299 117 Pedestrian -1 -1 -1 285.55 191.06 346.24 303.62 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n299 83 Car -1 -1 -1 174.48 196.91 356.82 298.53 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n299 118 Car -1 -1 -1 517.25 183.37 540.47 205.79 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n300 78 Car -1 -1 -1 0.35 214.21 312.93 367.85 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n300 109 Car -1 -1 -1 358.69 182.07 446.83 242.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n300 103 Car -1 -1 -1 464.48 180.17 507.59 213.26 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n300 83 Car -1 -1 -1 136.37 190.17 341.14 306.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n300 116 Pedestrian -1 -1 -1 1146.39 141.36 1217.24 291.90 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n300 118 Car -1 -1 -1 516.27 178.68 540.63 201.63 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n300 119 Cyclist -1 -1 -1 232.74 180.14 322.40 315.99 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n300 120 Pedestrian -1 -1 -1 353.84 178.46 371.78 230.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n301 109 Car -1 -1 -1 348.05 179.27 441.54 243.36 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n301 103 Car -1 -1 -1 461.73 177.35 505.82 210.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n301 78 Car -1 -1 -1 1.41 218.62 258.01 369.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n301 119 Cyclist -1 -1 -1 169.40 173.40 292.57 353.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n301 83 Car -1 -1 -1 115.00 180.14 323.92 323.02 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n301 120 Pedestrian -1 -1 -1 346.80 175.41 365.78 227.42 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n301 118 Car -1 -1 -1 514.72 176.20 539.74 199.70 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n302 109 Car -1 -1 -1 335.83 178.43 435.40 245.43 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n302 103 Car -1 -1 -1 459.47 176.78 504.60 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n302 83 Car -1 -1 -1 52.62 183.07 300.93 328.16 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n302 119 Cyclist -1 -1 -1 86.65 173.32 243.42 368.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n302 118 Car -1 -1 -1 514.46 174.88 539.83 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n302 78 Car -1 -1 -1 1.86 217.75 180.65 371.11 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n302 120 Pedestrian -1 -1 -1 341.81 174.45 360.59 227.05 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n302 121 Cyclist -1 -1 -1 1025.84 138.15 1113.00 257.76 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n303 109 Car -1 -1 -1 321.81 177.56 428.28 249.04 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n303 83 Car -1 -1 -1 -3.62 182.96 286.11 351.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n303 103 Car -1 -1 -1 455.83 176.19 501.89 210.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n303 118 Car -1 -1 -1 513.90 173.91 539.16 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n303 120 Pedestrian -1 -1 -1 333.93 172.69 355.10 228.09 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n303 119 Cyclist -1 -1 -1 -6.77 172.48 188.91 369.59 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n304 83 Car -1 -1 -1 -2.34 182.89 262.40 359.06 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n304 109 Car -1 -1 -1 306.24 175.51 422.04 251.10 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n304 103 Car -1 -1 -1 452.08 174.40 499.92 209.67 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n304 118 Car -1 -1 -1 512.08 172.55 538.42 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n304 120 Pedestrian -1 -1 -1 327.30 171.76 350.16 229.21 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n305 83 Car -1 -1 -1 -4.16 181.67 233.59 369.06 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n305 109 Car -1 -1 -1 292.12 175.05 415.75 253.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n305 103 Car -1 -1 -1 449.94 172.87 498.86 209.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n305 120 Pedestrian -1 -1 -1 321.30 171.37 342.02 229.94 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n305 118 Car -1 -1 -1 511.96 171.21 537.76 195.32 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n306 109 Car -1 -1 -1 272.27 175.70 406.79 260.01 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n306 83 Car -1 -1 -1 -4.25 187.85 201.23 370.74 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n306 103 Car -1 -1 -1 446.60 173.77 495.68 210.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n306 120 Pedestrian -1 -1 -1 313.07 171.32 335.24 230.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n306 118 Car -1 -1 -1 510.63 171.72 538.74 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n307 109 Car -1 -1 -1 250.77 178.75 397.82 268.18 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n307 103 Car -1 -1 -1 442.69 174.56 493.44 213.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n307 83 Car -1 -1 -1 -3.69 197.29 154.62 368.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n307 118 Car -1 -1 -1 507.65 172.78 536.69 196.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n307 120 Pedestrian -1 -1 -1 305.37 173.66 327.67 229.28 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n308 109 Car -1 -1 -1 228.91 182.42 386.18 274.89 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n308 103 Car -1 -1 -1 439.03 177.68 490.57 217.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n308 83 Car -1 -1 -1 -0.54 197.20 98.42 368.21 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n308 118 Car -1 -1 -1 508.39 174.38 536.57 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n308 120 Pedestrian -1 -1 -1 308.70 171.65 330.46 231.25 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n308 122 Pedestrian -1 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496.26 175.47 531.41 205.96 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n317 125 Pedestrian -1 -1 -1 182.12 181.84 225.52 274.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n317 124 Pedestrian -1 -1 -1 473.54 173.28 491.65 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n317 123 Pedestrian -1 -1 -1 347.38 175.62 417.59 296.47 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n318 103 Car -1 -1 -1 385.43 179.04 462.69 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n318 109 Car -1 -1 -1 -1.35 186.84 175.49 370.21 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n318 125 Pedestrian -1 -1 -1 165.18 181.37 210.61 280.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n318 118 Car -1 -1 -1 495.09 174.99 530.41 206.07 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n318 124 Pedestrian -1 -1 -1 469.86 172.88 489.37 222.79 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n318 126 Car -1 -1 -1 465.82 176.52 497.82 219.30 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n319 103 Car -1 -1 -1 376.49 179.35 458.41 235.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n319 109 Car -1 -1 -1 -2.68 187.01 130.59 370.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n319 125 Pedestrian -1 -1 -1 144.49 179.55 193.89 286.09 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n319 118 Car -1 -1 -1 493.95 174.84 529.74 206.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n319 124 Pedestrian -1 -1 -1 467.12 171.64 485.38 225.11 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n319 127 Pedestrian -1 -1 -1 616.90 173.53 627.99 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n320 103 Car -1 -1 -1 369.58 180.16 455.37 238.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n320 118 Car -1 -1 -1 492.12 175.66 530.28 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n320 124 Pedestrian -1 -1 -1 463.29 174.07 481.32 227.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n320 125 Pedestrian -1 -1 -1 126.83 177.44 172.23 293.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n320 109 Car -1 -1 -1 -0.96 197.19 67.23 367.74 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n320 127 Pedestrian -1 -1 -1 617.91 174.13 629.41 203.78 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n320 128 Cyclist -1 -1 -1 42.89 180.73 286.83 369.53 -1 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0.69\n322 124 Pedestrian -1 -1 -1 456.54 176.59 473.11 230.75 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n322 131 Car -1 -1 -1 511.53 180.36 542.07 205.42 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n322 130 Pedestrian -1 -1 -1 441.00 170.91 462.31 229.87 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n323 103 Car -1 -1 -1 341.12 180.61 440.78 246.98 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n323 118 Car -1 -1 -1 485.70 175.97 528.33 210.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n323 125 Pedestrian -1 -1 -1 39.22 180.94 105.08 312.39 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n323 131 Car -1 -1 -1 510.54 180.67 541.10 205.70 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n323 124 Pedestrian -1 -1 -1 452.42 174.70 469.38 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n323 129 Car -1 -1 -1 448.30 182.89 488.06 225.27 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n323 130 Pedestrian -1 -1 -1 433.04 169.41 456.89 231.21 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n324 103 Car -1 -1 -1 329.76 181.08 434.79 250.58 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n324 118 Car -1 -1 -1 484.59 176.02 527.42 210.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n324 125 Pedestrian -1 -1 -1 19.41 179.22 77.61 316.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n324 124 Pedestrian -1 -1 -1 447.80 175.91 465.63 234.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n324 129 Car -1 -1 -1 442.66 182.87 486.58 227.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n324 131 Car -1 -1 -1 509.69 180.90 540.94 206.27 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n324 130 Pedestrian -1 -1 -1 428.86 169.66 452.54 232.46 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n325 103 Car -1 -1 -1 313.83 181.21 429.48 254.59 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n325 118 Car -1 -1 -1 484.59 176.58 526.51 211.77 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n325 129 Car -1 -1 -1 435.86 185.02 490.18 227.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n325 124 Pedestrian -1 -1 -1 443.32 174.94 461.44 237.50 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n325 125 Pedestrian -1 -1 -1 2.93 180.97 55.42 323.22 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n325 131 Car -1 -1 -1 509.07 181.21 540.74 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n326 103 Car -1 -1 -1 299.55 180.83 424.87 258.51 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n326 118 Car -1 -1 -1 481.63 176.04 525.98 212.70 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n326 129 Car -1 -1 -1 428.52 184.20 489.28 228.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n326 124 Pedestrian -1 -1 -1 437.79 173.77 457.21 237.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n326 131 Car -1 -1 -1 505.78 180.23 540.41 207.41 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n326 125 Pedestrian -1 -1 -1 0.13 177.27 19.27 333.77 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n326 132 Pedestrian -1 -1 -1 418.59 168.26 440.85 234.39 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n327 103 Car -1 -1 -1 283.88 180.40 416.73 262.06 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n327 118 Car -1 -1 -1 480.02 176.11 524.95 212.83 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n327 131 Car -1 -1 -1 505.11 180.11 539.55 207.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n327 129 Car -1 -1 -1 422.96 182.47 482.62 230.29 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n327 124 Pedestrian -1 -1 -1 430.73 171.56 451.10 241.02 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n327 132 Pedestrian -1 -1 -1 413.62 167.12 436.29 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n327 133 Pedestrian -1 -1 -1 623.92 175.09 635.52 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n328 103 Car -1 -1 -1 265.18 180.40 407.30 266.59 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n328 129 Car -1 -1 -1 415.10 183.19 482.12 232.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n328 118 Car -1 -1 -1 476.27 175.79 523.96 213.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n328 131 Car -1 -1 -1 503.79 180.03 537.96 207.63 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n328 124 Pedestrian -1 -1 -1 424.98 172.92 446.24 243.57 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n328 132 Pedestrian -1 -1 -1 406.52 166.69 429.27 235.63 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n328 133 Pedestrian -1 -1 -1 625.45 175.33 637.03 203.36 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n329 103 Car -1 -1 -1 245.81 180.59 400.31 270.71 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n329 129 Car -1 -1 -1 410.04 184.52 479.62 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n329 118 Car -1 -1 -1 474.10 175.75 522.26 213.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n329 124 Pedestrian -1 -1 -1 416.95 171.05 440.42 247.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n329 131 Car -1 -1 -1 500.65 180.01 537.34 208.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n329 132 Pedestrian -1 -1 -1 398.96 167.81 421.78 241.59 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n330 103 Car -1 -1 -1 223.91 180.17 391.80 278.01 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n330 129 Car -1 -1 -1 404.96 183.96 475.79 235.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n330 131 Car -1 -1 -1 499.71 179.91 536.11 209.39 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n330 124 Pedestrian -1 -1 -1 409.15 173.46 433.17 250.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n330 118 Car -1 -1 -1 470.81 175.44 521.39 214.49 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n330 132 Pedestrian -1 -1 -1 390.26 170.97 413.61 239.91 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n330 134 Pedestrian -1 -1 -1 629.48 175.41 641.94 205.11 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n331 103 Car -1 -1 -1 197.14 180.61 381.15 285.12 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n331 118 Car -1 -1 -1 467.95 175.93 519.75 215.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n331 124 Pedestrian -1 -1 -1 400.55 171.88 426.75 255.28 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n331 129 Car -1 -1 -1 395.36 183.77 472.05 236.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n331 131 Car -1 -1 -1 498.73 179.83 535.86 209.60 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n331 132 Pedestrian -1 -1 -1 381.59 167.65 407.50 249.90 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n331 134 Pedestrian -1 -1 -1 631.32 175.27 644.03 205.66 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n331 135 Pedestrian -1 -1 -1 654.59 175.69 666.67 209.65 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n332 103 Car -1 -1 -1 168.35 182.82 369.68 294.97 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n332 118 Car -1 -1 -1 463.63 176.41 517.70 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n332 129 Car -1 -1 -1 388.06 184.60 468.62 241.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n332 124 Pedestrian -1 -1 -1 392.19 173.04 419.59 258.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n332 132 Pedestrian -1 -1 -1 372.24 166.76 398.69 252.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n332 135 Pedestrian -1 -1 -1 655.85 175.80 667.92 210.23 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n332 131 Car -1 -1 -1 495.04 180.26 535.94 211.35 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n333 103 Car -1 -1 -1 135.87 182.46 355.53 304.68 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n333 129 Car -1 -1 -1 378.42 184.94 463.64 243.47 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n333 118 Car -1 -1 -1 460.58 177.33 515.84 219.04 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n333 131 Car -1 -1 -1 494.00 181.22 535.34 212.14 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n333 132 Pedestrian -1 -1 -1 360.40 165.68 389.29 260.30 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n333 124 Pedestrian -1 -1 -1 379.63 171.17 410.13 263.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n333 135 Pedestrian -1 -1 -1 656.79 176.96 669.51 211.48 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n333 136 Pedestrian -1 -1 -1 859.17 159.09 876.52 210.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n333 137 Pedestrian -1 -1 -1 871.30 159.30 888.08 209.75 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n334 103 Car -1 -1 -1 96.43 182.30 342.13 314.65 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n334 129 Car -1 -1 -1 369.30 186.78 458.57 248.25 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n334 131 Car -1 -1 -1 491.57 182.01 534.44 213.42 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n334 132 Pedestrian -1 -1 -1 346.41 165.44 379.45 265.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n334 118 Car -1 -1 -1 457.47 177.99 514.11 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n334 124 Pedestrian -1 -1 -1 369.65 172.51 401.33 269.11 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n334 135 Pedestrian -1 -1 -1 658.23 177.47 671.75 214.02 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n334 136 Pedestrian -1 -1 -1 866.99 159.95 884.84 212.73 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n334 137 Pedestrian -1 -1 -1 878.57 159.92 896.41 211.74 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n334 138 Pedestrian -1 -1 -1 634.91 176.50 647.43 208.35 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n335 103 Car -1 -1 -1 53.09 183.75 321.90 326.96 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n335 118 Car -1 -1 -1 453.71 178.23 512.27 221.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n335 129 Car -1 -1 -1 364.38 187.30 452.31 252.41 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n335 131 Car -1 -1 -1 489.05 182.46 532.42 214.27 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n335 124 Pedestrian -1 -1 -1 357.98 174.90 390.85 274.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n335 132 Pedestrian -1 -1 -1 331.44 167.02 363.77 271.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n335 135 Pedestrian -1 -1 -1 659.89 178.23 672.79 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n335 137 Pedestrian -1 -1 -1 891.42 157.53 913.01 213.54 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n335 138 Pedestrian -1 -1 -1 635.76 176.73 647.98 207.90 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n335 136 Pedestrian -1 -1 -1 877.64 160.20 897.18 213.53 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n336 103 Car -1 -1 -1 1.30 182.66 304.01 342.20 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n336 129 Car -1 -1 -1 347.82 187.37 446.88 255.46 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n336 118 Car -1 -1 -1 449.72 177.73 510.30 223.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n336 131 Car -1 -1 -1 487.93 182.12 531.36 214.69 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n336 132 Pedestrian -1 -1 -1 314.03 164.13 349.43 278.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n336 124 Pedestrian -1 -1 -1 338.30 179.42 371.39 283.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n336 135 Pedestrian -1 -1 -1 661.75 178.06 674.70 215.17 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n336 137 Pedestrian -1 -1 -1 899.19 157.20 920.21 214.02 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n336 138 Pedestrian -1 -1 -1 636.42 175.72 648.57 205.67 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n336 139 Pedestrian -1 -1 -1 341.25 168.70 376.13 271.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n337 103 Car -1 -1 -1 -3.75 181.90 286.50 353.91 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n337 118 Car -1 -1 -1 444.15 176.72 508.28 223.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n337 129 Car -1 -1 -1 333.58 187.30 440.46 258.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n337 131 Car -1 -1 -1 483.43 181.18 530.11 214.49 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n337 124 Pedestrian -1 -1 -1 320.32 176.38 358.21 289.86 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n337 132 Pedestrian -1 -1 -1 295.37 163.26 330.02 284.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n337 139 Pedestrian -1 -1 -1 323.43 167.99 363.97 279.20 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n337 135 Pedestrian -1 -1 -1 663.07 176.21 676.21 215.41 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n337 137 Pedestrian -1 -1 -1 907.24 155.61 929.84 215.00 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n337 138 Pedestrian -1 -1 -1 637.16 175.00 649.34 205.84 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n337 140 Pedestrian -1 -1 -1 894.63 157.87 917.59 214.94 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n337 141 Pedestrian -1 -1 -1 678.68 177.32 692.45 216.82 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n338 103 Car -1 -1 -1 -1.65 182.12 260.26 367.09 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n338 129 Car -1 -1 -1 317.77 186.17 432.80 260.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n338 118 Car -1 -1 -1 439.23 174.96 505.08 222.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n338 131 Car -1 -1 -1 478.72 178.48 528.12 213.45 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n338 124 Pedestrian -1 -1 -1 299.97 177.74 339.37 294.21 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n338 132 Pedestrian -1 -1 -1 274.18 161.40 311.53 289.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n338 135 Pedestrian -1 -1 -1 664.19 175.17 677.51 213.76 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n338 139 Pedestrian -1 -1 -1 308.07 165.61 347.64 276.86 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n338 138 Pedestrian -1 -1 -1 637.34 174.00 649.87 206.35 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n338 140 Pedestrian -1 -1 -1 902.11 155.75 926.89 214.22 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n338 137 Pedestrian -1 -1 -1 917.94 152.09 942.38 214.12 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n338 142 Pedestrian -1 -1 -1 993.76 142.37 1019.60 214.08 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n338 143 Pedestrian -1 -1 -1 948.15 152.23 973.78 217.12 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n338 144 Pedestrian -1 -1 -1 872.17 155.77 893.50 215.89 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n339 103 Car -1 -1 -1 0.91 179.31 228.41 369.53 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n339 129 Car -1 -1 -1 304.69 184.39 427.24 262.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n339 118 Car -1 -1 -1 431.86 172.58 502.55 221.96 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n339 131 Car -1 -1 -1 477.37 177.03 524.89 211.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n339 142 Pedestrian -1 -1 -1 1007.33 139.31 1039.00 221.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n339 135 Pedestrian -1 -1 -1 665.83 173.23 679.56 213.57 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n339 124 Pedestrian -1 -1 -1 277.58 173.68 315.56 300.40 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n339 132 Pedestrian -1 -1 -1 249.42 156.59 289.80 298.10 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n339 138 Pedestrian -1 -1 -1 639.53 171.21 651.88 206.36 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n339 139 Pedestrian -1 -1 -1 284.73 160.88 332.50 287.49 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n339 137 Pedestrian -1 -1 -1 928.17 150.84 953.44 214.17 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n339 144 Pedestrian -1 -1 -1 879.76 153.90 902.31 216.47 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n339 140 Pedestrian -1 -1 -1 911.96 154.82 940.26 213.87 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n339 143 Pedestrian -1 -1 -1 953.95 152.74 983.61 216.92 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n340 129 Car -1 -1 -1 277.97 182.94 417.51 265.81 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n340 118 Car -1 -1 -1 425.34 171.49 499.75 222.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n340 103 Car -1 -1 -1 0.46 179.02 196.33 370.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n340 131 Car -1 -1 -1 472.80 175.86 523.24 212.10 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n340 142 Pedestrian -1 -1 -1 1024.03 137.68 1058.80 223.24 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n340 135 Pedestrian -1 -1 -1 666.50 171.87 681.64 213.91 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n340 124 Pedestrian -1 -1 -1 251.82 171.83 294.97 309.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n340 132 Pedestrian -1 -1 -1 221.17 154.15 264.55 304.02 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n340 137 Pedestrian -1 -1 -1 935.79 149.43 962.32 214.58 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n340 138 Pedestrian -1 -1 -1 639.60 169.60 652.09 203.68 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n340 139 Pedestrian -1 -1 -1 264.51 158.61 313.89 291.30 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n340 140 Pedestrian -1 -1 -1 922.76 151.89 951.29 213.89 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n340 143 Pedestrian -1 -1 -1 968.63 151.10 999.75 217.96 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n340 145 Pedestrian -1 -1 -1 684.60 171.00 700.20 216.01 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n341 129 Car -1 -1 -1 255.25 183.61 408.98 272.73 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n341 131 Car -1 -1 -1 469.68 175.35 521.01 212.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n341 118 Car -1 -1 -1 418.30 170.52 496.59 224.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n341 103 Car -1 -1 -1 -3.18 179.07 154.56 371.36 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n341 142 Pedestrian -1 -1 -1 1044.25 135.58 1078.38 226.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n341 135 Pedestrian -1 -1 -1 668.39 171.10 684.34 215.20 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n341 137 Pedestrian -1 -1 -1 943.06 148.26 970.99 216.18 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n341 124 Pedestrian -1 -1 -1 217.06 172.58 268.36 323.12 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n341 132 Pedestrian -1 -1 -1 187.77 149.01 242.99 315.91 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n341 139 Pedestrian -1 -1 -1 234.59 157.96 289.45 299.52 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n341 145 Pedestrian -1 -1 -1 685.05 170.69 701.13 215.58 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n341 138 Pedestrian -1 -1 -1 639.22 169.10 651.74 203.34 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n341 143 Pedestrian -1 -1 -1 982.75 148.22 1015.24 220.47 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n341 146 Pedestrian -1 -1 -1 1103.95 149.39 1127.97 215.12 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n341 147 Pedestrian -1 -1 -1 1092.28 149.90 1116.34 214.11 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n342 129 Car -1 -1 -1 233.98 185.54 397.88 278.13 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n342 118 Car -1 -1 -1 412.67 171.17 493.15 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n342 131 Car -1 -1 -1 465.17 175.62 518.54 212.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n342 142 Pedestrian -1 -1 -1 1061.91 133.60 1100.82 230.66 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n342 135 Pedestrian -1 -1 -1 668.74 171.27 685.18 215.57 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n342 145 Pedestrian -1 -1 -1 688.21 170.55 704.92 217.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n342 132 Pedestrian -1 -1 -1 150.05 151.63 211.21 313.46 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n342 103 Car -1 -1 -1 -2.99 186.64 100.84 370.64 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n342 124 Pedestrian -1 -1 -1 176.32 170.58 231.33 339.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n342 137 Pedestrian -1 -1 -1 933.69 152.47 957.03 212.54 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n342 143 Pedestrian -1 -1 -1 1019.12 149.48 1049.17 219.98 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n342 139 Pedestrian -1 -1 -1 200.77 157.71 261.25 315.15 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n342 147 Pedestrian -1 -1 -1 1106.40 149.27 1133.12 213.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n342 138 Pedestrian -1 -1 -1 638.78 168.72 652.08 203.94 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n342 146 Pedestrian -1 -1 -1 1117.76 148.27 1145.04 216.85 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n342 148 Pedestrian -1 -1 -1 943.58 150.92 970.73 214.65 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n342 149 Pedestrian -1 -1 -1 965.16 147.75 994.95 216.82 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n342 150 Pedestrian -1 -1 -1 958.51 148.25 986.21 216.87 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n342 151 Pedestrian -1 -1 -1 698.20 167.85 717.69 219.60 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n342 152 Pedestrian -1 -1 -1 995.90 147.69 1025.75 216.84 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n342 153 Pedestrian -1 -1 -1 1201.25 139.65 1232.49 231.35 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n342 154 Pedestrian -1 -1 -1 1144.65 149.57 1172.57 220.25 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n343 129 Car -1 -1 -1 199.02 185.83 386.55 286.82 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n343 118 Car -1 -1 -1 405.28 171.20 489.03 228.53 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n343 131 Car -1 -1 -1 459.09 176.76 515.25 215.45 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n343 142 Pedestrian -1 -1 -1 1087.48 133.16 1128.19 237.01 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n343 143 Pedestrian -1 -1 -1 1032.86 149.75 1059.37 222.40 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n343 132 Pedestrian -1 -1 -1 101.38 146.49 173.91 325.89 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n343 145 Pedestrian -1 -1 -1 688.70 172.29 705.61 219.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n343 135 Pedestrian -1 -1 -1 669.53 171.90 685.82 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n343 154 Pedestrian -1 -1 -1 1163.35 149.23 1192.72 223.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n343 152 Pedestrian -1 -1 -1 1012.41 146.80 1041.04 222.92 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n343 146 Pedestrian -1 -1 -1 1135.96 149.27 1166.53 221.23 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n343 149 Pedestrian -1 -1 -1 973.33 147.50 1002.62 221.38 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n343 137 Pedestrian -1 -1 -1 945.18 152.10 968.97 213.30 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n343 139 Pedestrian -1 -1 -1 164.13 157.07 227.70 337.60 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n343 151 Pedestrian -1 -1 -1 699.26 169.11 717.33 219.75 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n343 147 Pedestrian -1 -1 -1 1119.88 147.19 1150.66 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n343 155 Pedestrian -1 -1 -1 848.34 147.47 878.69 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n344 118 Car -1 -1 -1 396.33 171.62 484.01 231.68 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n344 129 Car -1 -1 -1 158.34 187.46 373.81 298.68 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n344 131 Car -1 -1 -1 454.53 177.62 512.11 217.46 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n344 142 Pedestrian -1 -1 -1 1112.32 128.37 1158.85 241.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n344 135 Pedestrian -1 -1 -1 671.62 172.53 688.82 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n344 139 Pedestrian -1 -1 -1 112.25 154.62 186.75 349.38 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n344 132 Pedestrian -1 -1 -1 27.68 141.23 124.08 368.97 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n344 145 Pedestrian -1 -1 -1 690.43 171.85 709.62 222.14 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n344 152 Pedestrian -1 -1 -1 1024.96 146.67 1059.61 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n344 149 Pedestrian -1 -1 -1 991.24 146.73 1022.70 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n344 143 Pedestrian -1 -1 -1 1044.65 149.68 1078.60 227.21 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n344 154 Pedestrian -1 -1 -1 1183.78 147.97 1218.96 225.22 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n344 146 Pedestrian -1 -1 -1 1154.41 147.88 1186.90 224.13 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n344 137 Pedestrian -1 -1 -1 958.19 151.87 986.56 219.26 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n344 155 Pedestrian -1 -1 -1 859.52 148.79 891.11 231.62 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n344 156 Pedestrian -1 -1 -1 64.73 173.53 141.22 366.93 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n344 157 Pedestrian -1 -1 -1 924.00 149.53 950.69 215.14 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n345 129 Car -1 -1 -1 115.79 189.21 362.20 307.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n345 118 Car -1 -1 -1 387.49 171.78 479.65 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n345 131 Car -1 -1 -1 449.18 177.44 508.19 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n345 135 Pedestrian -1 -1 -1 671.69 172.62 689.60 223.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n345 145 Pedestrian -1 -1 -1 691.23 172.05 710.33 223.82 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n345 139 Pedestrian -1 -1 -1 51.62 147.62 146.14 369.87 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n345 146 Pedestrian -1 -1 -1 1148.88 132.61 1191.23 245.13 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n345 143 Pedestrian -1 -1 -1 1063.89 149.04 1098.16 228.52 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n345 149 Pedestrian -1 -1 -1 1010.23 146.23 1042.73 224.68 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n345 137 Pedestrian -1 -1 -1 990.46 148.88 1023.87 223.14 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n345 157 Pedestrian -1 -1 -1 938.51 146.87 974.85 225.29 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n345 154 Pedestrian -1 -1 -1 1202.39 149.88 1231.14 227.84 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n345 156 Pedestrian -1 -1 -1 1.43 172.96 80.24 367.73 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n345 152 Pedestrian -1 -1 -1 1044.76 147.10 1078.47 224.63 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n345 132 Pedestrian -1 -1 -1 1.40 136.17 64.96 358.64 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n345 158 Pedestrian -1 -1 -1 969.23 151.59 999.12 220.13 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n346 118 Car -1 -1 -1 377.97 171.68 474.09 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n346 129 Car -1 -1 -1 71.99 189.32 343.90 320.62 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n346 131 Car -1 -1 -1 444.60 176.95 505.12 219.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n346 135 Pedestrian -1 -1 -1 672.82 172.83 690.92 223.52 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n346 146 Pedestrian -1 -1 -1 1178.09 119.37 1232.32 251.21 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n346 145 Pedestrian -1 -1 -1 694.89 171.77 713.30 224.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n346 139 Pedestrian -1 -1 -1 1.90 145.73 87.64 372.83 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n346 149 Pedestrian -1 -1 -1 1026.02 144.41 1058.57 225.77 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n346 137 Pedestrian -1 -1 -1 1007.68 147.78 1038.13 224.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n346 143 Pedestrian -1 -1 -1 1085.22 146.80 1122.73 231.95 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n346 158 Pedestrian -1 -1 -1 980.24 147.82 1011.12 224.25 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n346 152 Pedestrian -1 -1 -1 1110.47 145.83 1144.63 225.48 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n347 129 Car -1 -1 -1 21.69 187.61 324.26 337.66 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n347 118 Car -1 -1 -1 368.75 171.96 468.37 238.76 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n347 131 Car -1 -1 -1 436.13 177.37 501.46 222.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n347 135 Pedestrian -1 -1 -1 674.35 171.76 693.71 225.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n347 145 Pedestrian -1 -1 -1 695.31 172.04 714.32 225.25 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n347 149 Pedestrian -1 -1 -1 1039.09 144.93 1076.61 226.76 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n347 137 Pedestrian -1 -1 -1 1018.47 151.49 1050.91 226.69 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n347 146 Pedestrian -1 -1 -1 1190.81 137.93 1227.25 234.14 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n347 143 Pedestrian -1 -1 -1 1115.33 145.83 1155.06 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n347 159 Pedestrian -1 -1 -1 928.78 146.55 953.71 216.80 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n348 118 Car -1 -1 -1 357.47 173.34 462.92 243.19 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n348 129 Car -1 -1 -1 -6.04 190.14 306.00 358.97 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n348 131 Car -1 -1 -1 435.15 178.97 497.87 224.30 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n348 135 Pedestrian -1 -1 -1 674.79 172.90 695.20 227.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n348 145 Pedestrian -1 -1 -1 697.20 173.04 719.37 227.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n348 143 Pedestrian -1 -1 -1 1101.46 142.77 1145.76 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n348 137 Pedestrian -1 -1 -1 988.94 139.97 1033.28 246.79 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n348 146 Pedestrian -1 -1 -1 1212.69 142.30 1236.46 236.30 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n348 159 Pedestrian -1 -1 -1 949.59 143.37 971.55 205.48 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n348 160 Pedestrian -1 -1 -1 638.74 171.47 652.07 206.80 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n349 129 Car -1 -1 -1 -3.61 188.79 280.09 369.99 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n349 118 Car -1 -1 -1 344.42 174.51 457.85 248.51 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n349 131 Car -1 -1 -1 427.49 180.63 494.88 226.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n349 135 Pedestrian -1 -1 -1 677.88 173.20 698.69 230.41 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n349 145 Pedestrian -1 -1 -1 700.91 173.39 722.53 231.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n349 143 Pedestrian -1 -1 -1 1160.39 143.90 1203.80 241.99 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n349 160 Pedestrian -1 -1 -1 638.67 172.01 651.96 205.84 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n349 137 Pedestrian -1 -1 -1 1014.90 140.20 1069.40 247.30 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n350 129 Car -1 -1 -1 -0.91 190.34 251.97 367.95 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n350 118 Car -1 -1 -1 331.56 173.51 450.96 250.93 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n350 131 Car -1 -1 -1 419.05 180.49 490.38 228.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n350 145 Pedestrian -1 -1 -1 702.59 174.64 723.19 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n350 135 Pedestrian -1 -1 -1 678.89 172.96 699.31 232.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n350 137 Pedestrian -1 -1 -1 1034.89 136.13 1088.18 258.76 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n350 143 Pedestrian -1 -1 -1 1184.06 139.57 1226.43 247.88 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n350 160 Pedestrian -1 -1 -1 638.82 172.03 651.96 206.34 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n350 161 Pedestrian -1 -1 -1 982.28 144.22 1016.41 234.42 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n350 162 Pedestrian -1 -1 -1 1147.92 140.83 1192.38 239.89 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n350 163 Car -1 -1 -1 261.46 180.96 324.86 203.51 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n350 164 Pedestrian -1 -1 -1 1104.75 142.76 1150.53 244.49 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n350 165 Pedestrian -1 -1 -1 1086.87 141.85 1129.63 237.79 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n351 118 Car -1 -1 -1 315.59 171.73 443.78 255.03 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n351 129 Car -1 -1 -1 -0.29 189.64 221.03 368.99 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n351 131 Car -1 -1 -1 408.43 180.42 486.89 229.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n351 145 Pedestrian -1 -1 -1 705.56 174.84 726.87 235.63 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n351 135 Pedestrian -1 -1 -1 681.37 172.82 702.67 235.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n351 161 Pedestrian -1 -1 -1 1003.85 142.56 1042.23 243.56 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n351 137 Pedestrian -1 -1 -1 1058.37 136.29 1126.80 265.70 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n351 160 Pedestrian -1 -1 -1 638.40 171.27 652.14 207.80 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n351 166 Pedestrian -1 -1 -1 719.49 168.98 736.17 219.01 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n352 118 Car -1 -1 -1 301.11 173.37 437.26 259.46 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n352 131 Car -1 -1 -1 399.47 180.98 483.18 231.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n352 129 Car -1 -1 -1 -1.50 196.10 183.69 368.73 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n352 135 Pedestrian -1 -1 -1 682.58 173.19 704.39 237.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n352 145 Pedestrian -1 -1 -1 708.96 174.87 730.91 237.89 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n352 166 Pedestrian -1 -1 -1 722.78 168.23 740.38 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n353 118 Car -1 -1 -1 282.75 174.45 428.32 265.19 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n353 131 Car -1 -1 -1 394.28 181.62 477.27 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n353 129 Car -1 -1 -1 -4.28 197.45 139.55 368.51 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n353 135 Pedestrian -1 -1 -1 684.69 173.79 708.51 240.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n353 145 Pedestrian -1 -1 -1 712.78 175.23 735.89 241.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n353 166 Pedestrian -1 -1 -1 726.30 170.42 745.02 222.09 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n353 167 Pedestrian -1 -1 -1 799.53 162.69 819.87 221.48 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n353 168 Pedestrian -1 -1 -1 1063.20 137.47 1106.72 249.55 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n353 169 Pedestrian -1 -1 -1 1122.43 138.55 1171.81 255.91 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n353 170 Pedestrian -1 -1 -1 636.61 171.60 649.70 207.32 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n354 118 Car -1 -1 -1 264.40 173.83 420.00 269.84 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n354 131 Car -1 -1 -1 385.56 182.29 473.66 238.27 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n354 135 Pedestrian -1 -1 -1 686.74 174.42 713.84 245.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n354 145 Pedestrian -1 -1 -1 717.63 173.42 744.36 246.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n354 167 Pedestrian -1 -1 -1 809.53 163.77 831.06 224.94 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n354 129 Car -1 -1 -1 -1.86 203.65 76.49 369.12 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n354 166 Pedestrian -1 -1 -1 732.43 169.96 753.63 226.51 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n354 169 Pedestrian -1 -1 -1 1181.32 137.58 1229.11 249.25 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n355 118 Car -1 -1 -1 242.71 173.99 412.26 275.80 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n355 131 Car -1 -1 -1 373.66 183.25 470.38 240.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n355 135 Pedestrian -1 -1 -1 690.57 174.29 717.80 250.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n355 145 Pedestrian -1 -1 -1 720.91 172.82 749.94 251.28 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n355 167 Pedestrian -1 -1 -1 812.70 166.95 836.00 227.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n355 166 Pedestrian -1 -1 -1 734.20 171.74 760.23 228.89 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n355 169 Pedestrian -1 -1 -1 1194.57 137.35 1239.51 249.36 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n355 171 Pedestrian -1 -1 -1 746.53 168.64 770.80 231.96 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n355 172 Pedestrian -1 -1 -1 826.76 164.10 846.84 223.92 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n356 118 Car -1 -1 -1 219.66 174.09 403.41 282.26 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n356 131 Car -1 -1 -1 365.84 182.65 466.91 243.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n356 135 Pedestrian -1 -1 -1 695.20 174.51 723.19 252.69 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n356 145 Pedestrian -1 -1 -1 726.81 172.92 758.42 254.91 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n356 172 Pedestrian -1 -1 -1 852.03 163.52 874.35 228.57 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n356 166 Pedestrian -1 -1 -1 737.12 171.60 764.14 230.97 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n356 167 Pedestrian -1 -1 -1 819.53 167.72 844.52 227.36 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n356 171 Pedestrian -1 -1 -1 749.11 167.75 777.32 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n357 118 Car -1 -1 -1 192.31 174.18 392.68 290.42 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n357 131 Car -1 -1 -1 350.14 181.84 462.39 246.22 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n357 135 Pedestrian -1 -1 -1 701.59 171.48 730.12 256.75 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n357 145 Pedestrian -1 -1 -1 734.85 174.37 765.83 258.21 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n357 172 Pedestrian -1 -1 -1 860.61 164.38 883.17 228.97 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n357 166 Pedestrian -1 -1 -1 741.70 170.34 767.46 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n357 173 Cyclist -1 -1 -1 760.32 166.20 787.86 235.13 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n358 118 Car -1 -1 -1 160.00 171.46 380.14 299.92 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n358 131 Car -1 -1 -1 338.41 181.09 455.71 251.03 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n358 135 Pedestrian -1 -1 -1 705.54 172.60 735.38 260.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n358 145 Pedestrian -1 -1 -1 738.64 172.72 771.97 262.81 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n358 172 Pedestrian -1 -1 -1 869.76 161.44 896.31 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n358 166 Pedestrian -1 -1 -1 746.41 168.90 771.99 234.69 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n358 173 Cyclist -1 -1 -1 766.64 165.15 796.97 236.61 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n358 175 Pedestrian -1 -1 -1 895.70 157.12 939.99 261.12 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n358 176 Pedestrian -1 -1 -1 637.58 171.39 653.20 210.22 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n359 118 Car -1 -1 -1 122.98 170.03 368.30 307.63 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n359 131 Car -1 -1 -1 322.88 179.46 449.62 252.17 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n359 135 Pedestrian -1 -1 -1 710.59 171.09 744.18 263.16 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n359 145 Pedestrian -1 -1 -1 747.99 169.14 784.53 265.01 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n359 172 Pedestrian -1 -1 -1 883.06 155.89 913.17 238.78 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n359 166 Pedestrian -1 -1 -1 774.26 161.94 805.65 238.14 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n359 176 Pedestrian -1 -1 -1 638.52 170.22 654.04 209.58 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n359 173 Cyclist -1 -1 -1 774.26 161.94 805.65 238.14 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n359 177 Pedestrian -1 -1 -1 754.63 167.66 785.77 235.03 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n360 118 Car -1 -1 -1 78.80 167.53 352.69 314.24 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n360 131 Car -1 -1 -1 306.54 176.53 443.00 256.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n360 135 Pedestrian -1 -1 -1 717.51 168.65 752.42 266.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n360 145 Pedestrian -1 -1 -1 754.73 165.95 794.15 269.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n360 166 Pedestrian -1 -1 -1 781.68 158.84 814.06 238.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n360 172 Pedestrian -1 -1 -1 893.90 155.43 926.02 238.59 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n360 177 Pedestrian -1 -1 -1 762.80 166.50 793.65 234.80 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n360 176 Pedestrian -1 -1 -1 641.16 167.52 656.95 205.73 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n360 178 Pedestrian -1 -1 -1 943.16 145.55 993.18 287.23 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n361 118 Car -1 -1 -1 29.63 169.05 338.07 332.86 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n361 131 Car -1 -1 -1 290.11 177.61 436.21 261.94 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n361 135 Pedestrian -1 -1 -1 725.46 167.52 762.14 271.38 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n361 145 Pedestrian -1 -1 -1 766.46 165.76 805.07 276.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n361 166 Pedestrian -1 -1 -1 792.27 158.33 825.74 242.66 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n361 178 Pedestrian -1 -1 -1 970.69 142.25 1028.00 298.26 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n361 177 Pedestrian -1 -1 -1 768.49 164.60 803.39 238.06 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n361 172 Pedestrian -1 -1 -1 911.75 157.26 939.11 236.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n361 176 Pedestrian -1 -1 -1 641.73 167.87 657.90 204.68 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n361 179 Pedestrian -1 -1 -1 888.29 152.82 915.39 219.25 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n361 180 Pedestrian -1 -1 -1 826.13 161.63 854.77 239.91 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n361 181 Pedestrian -1 -1 -1 1023.78 145.84 1076.20 271.39 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n361 182 Pedestrian -1 -1 -1 896.55 151.30 916.45 203.45 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n361 183 Pedestrian -1 -1 -1 807.44 157.97 834.94 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n362 118 Car -1 -1 -1 -1.77 173.82 322.27 352.47 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n362 131 Car -1 -1 -1 271.38 181.61 430.29 268.95 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n362 135 Pedestrian -1 -1 -1 734.21 166.95 775.19 279.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n362 145 Pedestrian -1 -1 -1 777.54 166.28 817.77 284.42 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n362 178 Pedestrian -1 -1 -1 1005.88 141.83 1062.64 306.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n362 166 Pedestrian -1 -1 -1 801.60 158.65 833.33 244.41 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n362 177 Pedestrian -1 -1 -1 728.79 166.37 764.91 267.47 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n362 180 Pedestrian -1 -1 -1 836.44 161.37 867.49 242.61 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n362 181 Pedestrian -1 -1 -1 1055.82 138.28 1113.79 278.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n362 183 Pedestrian -1 -1 -1 776.43 165.33 818.67 244.14 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n362 176 Pedestrian -1 -1 -1 645.73 167.77 660.59 205.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n362 179 Pedestrian -1 -1 -1 899.91 152.33 928.41 219.76 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n362 172 Pedestrian -1 -1 -1 919.22 152.90 955.95 242.13 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n362 184 Pedestrian -1 -1 -1 818.53 156.38 846.40 237.66 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n363 118 Car -1 -1 -1 -1.85 174.59 301.42 368.30 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n363 131 Car -1 -1 -1 249.34 183.97 422.18 278.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n363 135 Pedestrian -1 -1 -1 745.61 168.86 788.12 288.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n363 145 Pedestrian -1 -1 -1 791.45 169.74 834.66 294.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n363 177 Pedestrian -1 -1 -1 738.12 168.66 772.40 272.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n363 180 Pedestrian -1 -1 -1 854.24 161.64 888.75 248.53 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n363 172 Pedestrian -1 -1 -1 939.99 150.45 980.93 259.63 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n363 166 Pedestrian -1 -1 -1 810.44 159.34 847.00 249.42 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n363 181 Pedestrian -1 -1 -1 1105.85 134.25 1164.57 298.30 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n363 183 Pedestrian -1 -1 -1 782.89 166.17 820.88 245.35 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n363 178 Pedestrian -1 -1 -1 1044.22 139.88 1117.61 317.26 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n363 179 Pedestrian -1 -1 -1 912.81 152.64 946.88 234.96 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n363 184 Pedestrian -1 -1 -1 828.98 156.48 858.99 237.94 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n363 185 Cyclist -1 -1 -1 647.03 169.41 666.35 210.17 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n363 186 Pedestrian -1 -1 -1 1090.70 138.64 1148.93 279.08 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n363 187 Pedestrian -1 -1 -1 913.34 152.12 938.86 211.97 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n364 118 Car -1 -1 -1 -0.27 174.90 275.67 368.82 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n364 131 Car -1 -1 -1 233.53 183.55 413.34 282.59 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n364 135 Pedestrian -1 -1 -1 756.79 168.86 799.59 294.92 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n364 145 Pedestrian -1 -1 -1 808.79 170.24 855.52 301.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n364 177 Pedestrian -1 -1 -1 746.50 168.82 780.17 273.43 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n364 166 Pedestrian -1 -1 -1 820.70 157.23 860.37 254.04 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n364 172 Pedestrian -1 -1 -1 959.82 151.27 1000.10 259.61 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n364 180 Pedestrian -1 -1 -1 868.49 158.34 905.44 252.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n364 183 Pedestrian -1 -1 -1 797.44 163.42 836.54 254.05 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n364 185 Cyclist -1 -1 -1 649.84 168.59 667.92 209.53 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n364 178 Pedestrian -1 -1 -1 1097.28 133.31 1173.48 338.80 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n364 184 Pedestrian -1 -1 -1 839.64 153.73 872.63 239.37 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n364 179 Pedestrian -1 -1 -1 928.67 148.47 961.76 231.27 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n364 181 Pedestrian -1 -1 -1 1147.61 128.94 1215.81 304.72 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n365 118 Car -1 -1 -1 -1.21 172.39 252.14 370.56 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n365 131 Car -1 -1 -1 199.26 182.38 402.53 290.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n365 145 Pedestrian -1 -1 -1 827.76 170.57 875.61 309.84 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n365 135 Pedestrian -1 -1 -1 771.11 164.12 816.97 302.18 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n365 180 Pedestrian -1 -1 -1 884.28 154.72 928.84 256.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n365 166 Pedestrian -1 -1 -1 834.50 153.12 877.30 257.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n365 177 Pedestrian -1 -1 -1 756.19 166.17 793.75 277.32 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n365 172 Pedestrian -1 -1 -1 978.87 146.02 1020.97 263.21 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n365 179 Pedestrian -1 -1 -1 946.05 143.69 983.59 241.52 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n365 183 Pedestrian -1 -1 -1 807.89 160.11 842.71 251.50 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n365 178 Pedestrian -1 -1 -1 1058.50 150.18 1119.19 282.82 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n365 184 Pedestrian -1 -1 -1 851.33 150.80 883.93 235.28 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n365 181 Pedestrian -1 -1 -1 1122.22 142.21 1225.95 344.90 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n365 188 Pedestrian -1 -1 -1 930.19 153.51 967.64 248.80 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n365 189 Pedestrian -1 -1 -1 1177.70 128.85 1240.21 312.11 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n365 190 Pedestrian -1 -1 -1 922.94 150.03 951.13 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n365 191 Pedestrian -1 -1 -1 930.87 147.12 960.70 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n366 131 Car -1 -1 -1 165.20 180.20 389.99 299.76 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n366 118 Car -1 -1 -1 0.02 170.17 214.27 371.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n366 135 Pedestrian -1 -1 -1 788.44 163.42 837.20 310.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n366 145 Pedestrian -1 -1 -1 845.14 166.72 897.76 319.25 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n366 166 Pedestrian -1 -1 -1 847.69 149.55 895.34 260.85 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n366 177 Pedestrian -1 -1 -1 766.40 164.01 805.88 279.57 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n366 179 Pedestrian -1 -1 -1 970.54 141.23 1005.44 239.79 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n366 180 Pedestrian -1 -1 -1 902.16 151.26 949.37 259.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n366 172 Pedestrian -1 -1 -1 1004.34 145.56 1041.80 256.06 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n366 188 Pedestrian -1 -1 -1 948.04 150.87 988.51 250.42 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n366 183 Pedestrian -1 -1 -1 817.01 157.36 856.50 253.47 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n366 178 Pedestrian -1 -1 -1 1087.52 140.31 1152.59 284.59 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n366 192 Pedestrian -1 -1 -1 994.49 140.07 1027.50 240.32 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n366 193 Cyclist -1 -1 -1 657.44 163.38 675.87 206.95 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n367 131 Car -1 -1 -1 129.45 181.31 378.12 312.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n367 118 Car -1 -1 -1 -3.75 178.73 177.93 371.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n367 135 Pedestrian -1 -1 -1 805.78 162.36 858.88 323.97 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n367 145 Pedestrian -1 -1 -1 874.83 163.92 930.33 332.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n367 177 Pedestrian -1 -1 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Pedestrian -1 -1 -1 1113.95 144.92 1156.28 256.67 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n380 207 Pedestrian -1 -1 -1 1046.02 149.34 1092.37 268.26 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n380 218 Pedestrian -1 -1 -1 1107.88 143.84 1147.54 251.22 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n380 204 Car -1 -1 -1 -0.72 192.21 4.89 249.53 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n380 220 Cyclist -1 -1 -1 956.46 144.79 1010.86 266.25 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n380 221 Pedestrian -1 -1 -1 750.03 168.12 762.43 203.21 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n381 214 Cyclist -1 -1 -1 1002.38 136.67 1073.59 265.78 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n381 201 Car -1 -1 -1 554.66 177.97 576.79 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n381 207 Pedestrian -1 -1 -1 1069.28 145.75 1123.59 270.61 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n381 206 Pedestrian -1 -1 -1 1194.47 133.13 1239.21 254.07 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n381 135 Pedestrian -1 -1 -1 1133.91 143.09 1191.28 259.39 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n381 213 Pedestrian -1 -1 -1 522.74 173.16 537.15 210.69 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n381 218 Pedestrian -1 -1 -1 1114.94 144.26 1155.43 249.63 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n381 222 Pedestrian -1 -1 -1 982.39 140.11 1039.11 271.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n381 223 Cyclist -1 -1 -1 832.15 161.48 849.09 204.35 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n381 224 Car -1 -1 -1 -1.04 190.59 27.53 220.19 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n382 207 Pedestrian -1 -1 -1 1100.60 146.82 1154.77 271.32 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n382 135 Pedestrian -1 -1 -1 1169.88 136.29 1225.12 273.59 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n382 222 Pedestrian -1 -1 -1 1031.54 134.85 1106.41 274.98 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n382 201 Car -1 -1 -1 553.11 178.87 575.46 194.03 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n382 224 Car -1 -1 -1 -2.01 191.51 19.93 217.82 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n382 214 Cyclist -1 -1 -1 1004.68 140.49 1064.90 278.12 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n382 218 Pedestrian -1 -1 -1 1140.98 142.62 1191.56 260.50 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n382 225 Cyclist -1 -1 -1 517.41 172.76 534.12 212.75 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n382 226 Pedestrian -1 -1 -1 1007.90 136.63 1068.64 275.02 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n382 227 Pedestrian -1 -1 -1 836.52 162.61 853.21 205.22 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n382 228 Cyclist -1 -1 -1 804.02 162.74 823.17 206.87 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n382 229 Cyclist -1 -1 -1 738.25 170.42 754.75 206.59 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n383 229 Cyclist -1 -1 -1 741.26 169.70 760.99 209.22 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n383 227 Pedestrian -1 -1 -1 841.90 161.32 860.28 207.23 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n383 225 Cyclist -1 -1 -1 512.53 174.08 529.56 215.29 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n383 207 Pedestrian -1 -1 -1 1126.36 135.36 1190.91 274.08 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n383 226 Pedestrian -1 -1 -1 1037.98 136.44 1116.03 288.48 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n383 218 Pedestrian -1 -1 -1 1147.61 140.92 1200.58 285.21 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n383 222 Pedestrian -1 -1 -1 1051.30 135.41 1149.11 287.67 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n383 214 Cyclist -1 -1 -1 1055.59 132.45 1145.17 285.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n383 135 Pedestrian -1 -1 -1 1185.32 138.19 1232.60 271.55 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n383 201 Car -1 -1 -1 551.81 180.96 571.73 195.56 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n383 228 Cyclist -1 -1 -1 808.11 163.75 826.61 206.69 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n383 230 Pedestrian -1 -1 -1 319.25 179.00 334.98 221.63 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n383 231 Pedestrian -1 -1 -1 1062.93 131.60 1137.76 271.89 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n384 226 Pedestrian -1 -1 -1 1070.40 129.00 1161.62 297.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n384 218 Pedestrian -1 -1 -1 1171.46 133.37 1230.67 276.68 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n384 230 Pedestrian -1 -1 -1 309.38 179.77 325.63 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n384 229 Cyclist -1 -1 -1 745.76 168.95 765.69 211.12 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n384 225 Cyclist -1 -1 -1 506.01 173.61 524.68 216.02 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n384 201 Car -1 -1 -1 549.61 180.67 570.98 195.38 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n384 231 Pedestrian -1 -1 -1 1099.43 126.94 1193.63 290.19 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n385 226 Pedestrian -1 -1 -1 1119.26 123.40 1205.62 316.71 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n385 230 Pedestrian -1 -1 -1 299.01 179.88 316.70 222.01 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n385 229 Cyclist -1 -1 -1 748.31 167.44 769.75 212.87 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n385 225 Cyclist -1 -1 -1 500.50 172.46 519.15 217.45 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n385 218 Pedestrian -1 -1 -1 1195.67 135.99 1238.01 274.00 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n385 201 Car -1 -1 -1 546.33 178.73 569.42 194.87 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n385 232 Pedestrian -1 -1 -1 849.46 161.32 870.47 207.93 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n386 225 Cyclist -1 -1 -1 490.17 172.70 515.61 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n386 232 Pedestrian -1 -1 -1 857.08 155.38 877.26 210.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n386 230 Pedestrian -1 -1 -1 288.36 180.57 305.64 222.35 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n386 226 Pedestrian -1 -1 -1 1171.73 119.51 1238.50 329.76 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n387 230 Pedestrian -1 -1 -1 277.90 183.16 294.11 226.37 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n387 225 Cyclist -1 -1 -1 484.36 175.12 507.81 225.19 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n387 232 Pedestrian -1 -1 -1 864.37 157.69 884.86 212.04 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n387 233 Car -1 -1 -1 543.46 182.43 565.30 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n388 233 Car -1 -1 -1 542.06 185.09 564.75 201.00 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n388 225 Cyclist -1 -1 -1 477.15 178.90 502.59 231.20 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n388 230 Pedestrian -1 -1 -1 265.87 187.07 282.98 223.94 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n388 234 Pedestrian -1 -1 -1 834.34 166.72 852.42 219.15 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n389 225 Cyclist -1 -1 -1 464.86 182.88 498.80 235.13 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n389 230 Pedestrian -1 -1 -1 254.45 189.28 269.75 227.18 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n389 233 Car -1 -1 -1 539.62 187.73 563.73 203.75 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n389 234 Pedestrian -1 -1 -1 839.68 166.40 857.83 225.41 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n389 235 Cyclist -1 -1 -1 875.27 162.58 899.39 218.43 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n389 236 Cyclist -1 -1 -1 761.66 173.54 794.37 223.83 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n390 225 Cyclist -1 -1 -1 458.89 180.78 485.89 239.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n390 233 Car -1 -1 -1 537.72 188.09 560.96 204.05 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n390 236 Cyclist -1 -1 -1 768.54 174.42 796.69 226.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n390 230 Pedestrian -1 -1 -1 240.56 190.47 258.84 233.83 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n390 234 Pedestrian -1 -1 -1 846.17 165.35 866.40 223.77 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n390 237 Pedestrian -1 -1 -1 884.99 161.35 906.00 220.01 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n390 238 Pedestrian -1 -1 -1 708.76 179.05 718.02 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n391 237 Pedestrian -1 -1 -1 893.97 158.96 918.74 225.21 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n391 236 Cyclist -1 -1 -1 772.25 173.61 801.27 226.83 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n391 225 Cyclist -1 -1 -1 450.36 179.12 477.17 241.37 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n391 230 Pedestrian -1 -1 -1 225.96 190.66 244.48 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n391 238 Pedestrian -1 -1 -1 699.60 176.02 709.93 204.57 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n391 233 Car -1 -1 -1 535.45 186.53 560.17 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n391 234 Pedestrian -1 -1 -1 853.74 166.02 874.62 226.30 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n392 236 Cyclist -1 -1 -1 772.74 171.09 806.40 225.47 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n392 233 Car -1 -1 -1 534.53 184.59 558.31 201.79 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n392 225 Cyclist -1 -1 -1 435.19 177.83 471.41 244.96 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n392 237 Pedestrian -1 -1 -1 904.33 158.45 925.61 223.09 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n392 230 Pedestrian -1 -1 -1 210.91 190.12 229.52 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n392 234 Pedestrian -1 -1 -1 863.19 164.89 886.35 226.81 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n392 238 Pedestrian -1 -1 -1 699.86 173.78 710.03 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n393 236 Cyclist -1 -1 -1 775.49 168.71 810.66 224.20 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n393 237 Pedestrian -1 -1 -1 916.29 155.54 941.97 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n393 225 Cyclist -1 -1 -1 426.07 175.32 460.08 247.37 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n393 234 Pedestrian -1 -1 -1 874.13 158.83 897.41 225.66 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n393 233 Car -1 -1 -1 533.80 183.54 557.27 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n393 230 Pedestrian -1 -1 -1 197.59 189.05 215.41 229.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n393 238 Pedestrian -1 -1 -1 711.68 172.98 721.22 199.20 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n393 239 Pedestrian -1 -1 -1 702.14 171.45 712.40 199.78 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n394 225 Cyclist -1 -1 -1 407.33 173.44 451.27 247.23 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n394 237 Pedestrian -1 -1 -1 928.42 150.53 952.91 219.15 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n394 233 Car -1 -1 -1 533.07 179.90 556.18 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n394 236 Cyclist -1 -1 -1 780.13 163.55 813.08 220.44 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n394 230 Pedestrian -1 -1 -1 181.54 186.50 200.72 228.90 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n394 239 Pedestrian -1 -1 -1 702.57 168.30 715.25 197.24 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n394 238 Pedestrian -1 -1 -1 711.47 169.23 722.97 196.71 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n394 240 Cyclist -1 -1 -1 880.38 158.92 906.93 221.99 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n395 225 Cyclist -1 -1 -1 390.23 169.86 435.69 250.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n395 236 Cyclist -1 -1 -1 783.85 161.17 812.45 217.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n395 237 Pedestrian -1 -1 -1 941.35 146.91 966.51 219.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n395 238 Pedestrian -1 -1 -1 714.77 166.15 725.22 195.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n395 230 Pedestrian -1 -1 -1 163.53 184.43 182.52 225.86 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n395 233 Car -1 -1 -1 531.68 176.65 556.31 195.13 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n395 241 Pedestrian -1 -1 -1 896.38 151.51 918.10 222.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n396 225 Cyclist -1 -1 -1 368.18 167.75 425.28 259.17 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n396 236 Cyclist -1 -1 -1 786.95 160.89 816.40 217.73 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n396 233 Car -1 -1 -1 530.26 176.31 555.18 194.46 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n396 241 Pedestrian -1 -1 -1 910.38 149.87 932.14 223.77 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n396 238 Pedestrian -1 -1 -1 715.54 165.79 725.63 194.67 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n396 230 Pedestrian -1 -1 -1 145.22 183.03 168.61 228.16 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n396 242 Cyclist -1 -1 -1 949.24 157.99 987.05 206.77 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n397 233 Car -1 -1 -1 529.50 177.73 554.41 195.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n397 236 Cyclist -1 -1 -1 788.48 161.79 821.02 219.01 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n397 225 Cyclist -1 -1 -1 343.86 169.88 407.54 269.35 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n397 241 Pedestrian -1 -1 -1 923.71 148.94 949.57 227.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n397 238 Pedestrian -1 -1 -1 707.67 164.69 718.62 196.26 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n397 230 Pedestrian -1 -1 -1 128.22 184.17 148.12 232.01 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n397 243 Pedestrian -1 -1 -1 971.45 144.83 1002.99 224.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n397 244 Pedestrian -1 -1 -1 716.84 166.44 728.44 196.39 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n398 236 Cyclist -1 -1 -1 791.50 164.36 825.44 223.41 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n398 233 Car -1 -1 -1 527.71 180.13 553.25 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n398 225 Cyclist -1 -1 -1 313.06 168.57 388.15 279.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n398 243 Pedestrian -1 -1 -1 987.56 147.69 1020.76 228.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n398 241 Pedestrian -1 -1 -1 936.84 152.56 962.82 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n398 244 Pedestrian -1 -1 -1 717.72 168.56 729.86 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n398 238 Pedestrian -1 -1 -1 708.90 166.87 720.69 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n398 230 Pedestrian -1 -1 -1 112.05 183.97 132.77 234.51 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n399 243 Pedestrian -1 -1 -1 1007.97 149.16 1044.59 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n399 241 Pedestrian -1 -1 -1 952.75 155.84 982.60 238.50 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n399 236 Cyclist -1 -1 -1 795.74 167.53 828.37 227.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n399 225 Cyclist -1 -1 -1 274.54 172.48 365.39 299.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n399 238 Pedestrian -1 -1 -1 709.64 170.35 721.93 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n399 233 Car -1 -1 -1 526.06 183.01 552.29 201.40 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n399 244 Pedestrian -1 -1 -1 719.46 172.05 730.56 201.81 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n400 233 Car -1 -1 -1 525.43 184.52 551.85 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n400 241 Pedestrian -1 -1 -1 970.22 158.74 999.41 243.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n400 244 Pedestrian -1 -1 -1 721.59 173.28 732.84 204.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n400 243 Pedestrian -1 -1 -1 1029.36 151.25 1064.62 240.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n400 225 Cyclist -1 -1 -1 224.82 174.29 338.59 320.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n400 236 Cyclist -1 -1 -1 798.39 167.92 830.38 229.48 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n400 238 Pedestrian -1 -1 -1 709.98 171.06 723.07 205.60 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n401 241 Pedestrian -1 -1 -1 990.75 155.13 1022.87 248.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n401 243 Pedestrian -1 -1 -1 1052.69 148.23 1092.32 244.30 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n401 225 Cyclist -1 -1 -1 164.66 169.18 298.18 343.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n401 233 Car -1 -1 -1 523.49 183.84 550.11 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n401 236 Cyclist -1 -1 -1 802.02 168.30 834.64 228.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n401 244 Pedestrian -1 -1 -1 723.68 172.99 734.54 204.99 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n401 238 Pedestrian -1 -1 -1 702.09 172.72 713.49 204.85 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n401 245 Pedestrian -1 -1 -1 42.00 193.46 71.09 253.84 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n402 243 Pedestrian -1 -1 -1 1076.26 145.42 1117.71 248.50 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n402 225 Cyclist -1 -1 -1 83.43 168.52 247.13 367.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n402 241 Pedestrian -1 -1 -1 1011.21 155.15 1043.76 252.29 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n402 236 Cyclist -1 -1 -1 806.46 166.55 840.51 229.37 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n402 238 Pedestrian -1 -1 -1 702.85 172.05 714.67 204.00 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n402 245 Pedestrian -1 -1 -1 17.02 192.23 47.96 255.68 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n402 244 Pedestrian -1 -1 -1 724.74 171.63 736.79 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n402 233 Car -1 -1 -1 522.61 182.38 549.29 201.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n403 241 Pedestrian -1 -1 -1 1036.23 151.54 1072.64 257.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n403 233 Car -1 -1 -1 520.41 180.42 547.96 200.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n403 225 Cyclist -1 -1 -1 -7.22 163.40 173.53 371.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n403 243 Pedestrian -1 -1 -1 1105.20 146.76 1150.36 253.19 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n403 236 Cyclist -1 -1 -1 809.95 167.28 844.77 229.67 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n403 238 Pedestrian -1 -1 -1 704.09 170.18 715.43 203.94 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n403 245 Pedestrian -1 -1 -1 1.31 189.24 17.82 259.50 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n404 241 Pedestrian -1 -1 -1 1065.27 149.37 1103.27 262.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n404 233 Car -1 -1 -1 519.66 180.46 545.89 200.39 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n404 243 Pedestrian -1 -1 -1 1137.58 143.98 1186.86 257.90 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n404 236 Cyclist -1 -1 -1 814.26 166.66 848.51 230.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n404 238 Pedestrian -1 -1 -1 705.67 169.68 716.95 203.94 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n404 246 Pedestrian -1 -1 -1 717.70 167.15 730.29 204.33 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n405 241 Pedestrian -1 -1 -1 1097.99 143.84 1140.97 267.33 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n405 233 Car -1 -1 -1 516.92 182.64 544.71 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n405 243 Pedestrian -1 -1 -1 1173.58 139.87 1228.44 261.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n405 236 Cyclist -1 -1 -1 818.35 166.37 852.42 230.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n405 238 Pedestrian -1 -1 -1 706.08 170.96 718.75 205.50 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n405 246 Pedestrian -1 -1 -1 719.24 168.19 731.42 205.20 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n405 247 Pedestrian -1 -1 -1 732.32 166.33 745.39 203.96 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n406 233 Car -1 -1 -1 516.23 183.76 544.31 203.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n406 241 Pedestrian -1 -1 -1 1135.94 144.08 1181.59 274.94 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n406 238 Pedestrian -1 -1 -1 709.59 171.82 721.38 207.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n406 236 Cyclist -1 -1 -1 824.99 168.33 856.42 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n406 246 Pedestrian -1 -1 -1 722.14 170.45 733.96 206.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n406 247 Pedestrian -1 -1 -1 732.18 172.25 744.53 206.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n406 243 Pedestrian -1 -1 -1 1217.49 132.71 1238.67 270.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n406 248 Pedestrian -1 -1 -1 1199.81 130.80 1241.48 247.91 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n407 233 Car -1 -1 -1 515.62 183.64 543.46 204.44 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n407 236 Cyclist -1 -1 -1 831.90 167.26 861.85 234.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n407 241 Pedestrian -1 -1 -1 1167.97 141.71 1226.99 284.98 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n407 247 Pedestrian -1 -1 -1 733.83 172.62 746.71 207.30 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n407 246 Pedestrian -1 -1 -1 724.42 170.77 737.20 208.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n407 238 Pedestrian -1 -1 -1 712.62 172.08 724.35 208.80 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n408 233 Car -1 -1 -1 515.36 182.89 543.90 203.68 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n408 247 Pedestrian -1 -1 -1 737.98 172.51 750.54 206.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n408 236 Cyclist -1 -1 -1 841.45 166.33 869.73 234.83 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n408 238 Pedestrian -1 -1 -1 715.40 171.22 727.01 208.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n408 246 Pedestrian -1 -1 -1 728.18 170.47 740.17 208.42 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n409 233 Car -1 -1 -1 515.29 181.40 543.30 202.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n409 247 Pedestrian -1 -1 -1 741.85 170.11 754.57 205.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n409 236 Cyclist -1 -1 -1 850.31 163.41 879.26 231.98 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n409 246 Pedestrian -1 -1 -1 730.68 167.25 743.00 205.41 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n409 238 Pedestrian -1 -1 -1 718.44 168.03 731.13 206.08 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n410 236 Cyclist -1 -1 -1 856.44 163.91 895.42 232.59 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n410 233 Car -1 -1 -1 514.96 181.12 543.34 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n410 246 Pedestrian -1 -1 -1 734.33 166.30 747.28 206.58 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n410 238 Pedestrian -1 -1 -1 721.54 168.92 735.85 206.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n410 247 Pedestrian -1 -1 -1 745.47 170.03 759.49 206.79 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n410 249 Cyclist -1 -1 -1 846.47 164.36 865.23 209.01 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n411 236 Cyclist -1 -1 -1 864.88 164.98 908.67 237.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n411 238 Pedestrian -1 -1 -1 725.18 170.98 738.82 208.23 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n411 233 Car -1 -1 -1 514.08 181.48 543.18 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n411 246 Pedestrian -1 -1 -1 738.13 168.07 751.02 207.86 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n411 247 Pedestrian -1 -1 -1 749.75 171.66 765.00 207.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n412 236 Cyclist -1 -1 -1 878.41 165.41 919.54 238.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n412 233 Car -1 -1 -1 513.88 181.65 543.52 204.04 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n412 247 Pedestrian -1 -1 -1 754.29 171.60 769.38 209.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n412 238 Pedestrian -1 -1 -1 729.77 171.07 742.54 210.55 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n412 246 Pedestrian -1 -1 -1 741.99 169.77 755.19 209.87 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n413 233 Car -1 -1 -1 514.25 181.46 544.39 204.19 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n413 247 Pedestrian -1 -1 -1 759.51 171.18 774.43 209.04 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n413 236 Cyclist -1 -1 -1 887.74 165.67 934.72 239.02 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n413 246 Pedestrian -1 -1 -1 747.45 168.02 760.87 210.48 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n413 238 Pedestrian -1 -1 -1 734.15 170.45 747.07 210.23 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n414 233 Car -1 -1 -1 514.06 181.36 544.60 204.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n414 247 Pedestrian -1 -1 -1 764.36 170.92 778.32 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n414 238 Pedestrian -1 -1 -1 739.83 169.38 753.19 210.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n414 246 Pedestrian -1 -1 -1 752.30 167.07 766.45 209.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n414 236 Cyclist -1 -1 -1 909.14 165.26 941.50 238.26 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n414 250 Cyclist -1 -1 -1 864.50 166.31 886.68 213.08 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n415 233 Car -1 -1 -1 513.80 182.71 545.57 205.84 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n415 236 Cyclist -1 -1 -1 927.34 162.06 963.70 240.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n415 238 Pedestrian -1 -1 -1 744.39 169.73 757.99 211.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n415 246 Pedestrian -1 -1 -1 757.83 166.93 772.71 211.59 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n415 250 Cyclist -1 -1 -1 868.18 166.29 895.98 213.95 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n415 247 Pedestrian -1 -1 -1 769.41 171.02 784.55 210.55 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n416 233 Car -1 -1 -1 512.73 182.65 545.86 206.20 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n416 246 Pedestrian -1 -1 -1 762.31 166.70 778.73 211.82 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n416 250 Cyclist -1 -1 -1 871.79 165.34 903.18 214.23 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n416 247 Pedestrian -1 -1 -1 772.43 169.20 789.32 211.53 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n416 238 Pedestrian -1 -1 -1 748.43 168.89 762.92 212.79 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n416 236 Cyclist -1 -1 -1 946.24 160.51 981.03 240.19 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n417 233 Car -1 -1 -1 512.16 181.29 545.95 205.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n417 247 Pedestrian -1 -1 -1 777.40 167.18 794.27 209.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n417 250 Cyclist -1 -1 -1 876.71 163.88 910.34 213.71 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n417 238 Pedestrian -1 -1 -1 753.77 167.16 768.92 212.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n417 246 Pedestrian -1 -1 -1 766.71 165.55 784.52 210.93 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n417 236 Cyclist -1 -1 -1 961.38 159.29 1000.79 234.78 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n418 246 Pedestrian -1 -1 -1 772.58 161.76 790.37 208.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n418 247 Pedestrian -1 -1 -1 782.63 164.51 798.93 207.85 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n418 238 Pedestrian -1 -1 -1 757.95 163.87 775.16 209.95 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n418 250 Cyclist -1 -1 -1 882.03 161.65 912.63 211.15 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n418 236 Cyclist -1 -1 -1 980.65 156.37 1020.10 232.01 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n418 233 Car -1 -1 -1 511.17 178.38 545.51 203.40 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n419 250 Cyclist -1 -1 -1 887.58 161.05 917.62 210.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n419 238 Pedestrian -1 -1 -1 762.68 162.83 779.61 209.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n419 246 Pedestrian -1 -1 -1 777.93 160.01 795.07 209.26 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n419 236 Cyclist -1 -1 -1 1000.00 152.58 1037.81 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n419 247 Pedestrian -1 -1 -1 789.16 164.34 804.55 208.13 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n419 233 Car -1 -1 -1 509.89 178.10 544.98 203.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n420 233 Car -1 -1 -1 509.06 179.94 545.49 205.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n420 236 Cyclist -1 -1 -1 1017.61 153.42 1059.73 239.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n420 246 Pedestrian -1 -1 -1 784.23 160.98 802.39 211.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n420 250 Cyclist -1 -1 -1 892.27 162.08 921.84 211.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n420 238 Pedestrian -1 -1 -1 769.25 164.53 785.10 211.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n420 247 Pedestrian -1 -1 -1 794.15 165.25 810.38 211.17 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n421 250 Cyclist -1 -1 -1 894.82 163.54 932.01 216.01 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n421 233 Car -1 -1 -1 508.49 181.18 545.90 208.20 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n421 238 Pedestrian -1 -1 -1 774.71 166.82 790.55 214.47 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n421 247 Pedestrian -1 -1 -1 800.87 166.98 817.89 213.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n421 246 Pedestrian -1 -1 -1 789.34 163.67 808.19 214.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n421 236 Cyclist -1 -1 -1 1037.29 154.57 1077.79 241.76 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n422 233 Car -1 -1 -1 507.71 181.88 544.88 209.27 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n422 247 Pedestrian -1 -1 -1 807.59 166.39 826.92 214.32 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n422 236 Cyclist -1 -1 -1 1053.44 154.80 1107.50 246.22 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n422 246 Pedestrian -1 -1 -1 796.20 163.82 816.20 215.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n422 238 Pedestrian -1 -1 -1 780.62 166.66 797.94 217.31 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n422 250 Cyclist -1 -1 -1 905.00 162.85 936.65 218.91 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n422 251 Car -1 -1 -1 536.27 181.24 559.79 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n423 233 Car -1 -1 -1 506.28 180.78 543.92 208.34 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n423 236 Cyclist -1 -1 -1 1068.19 153.71 1125.48 247.95 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n423 246 Pedestrian -1 -1 -1 803.23 162.88 823.15 216.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n423 250 Cyclist -1 -1 -1 910.39 162.71 941.16 219.11 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n423 247 Pedestrian -1 -1 -1 815.10 165.58 833.27 214.87 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n423 238 Pedestrian -1 -1 -1 786.61 166.81 802.49 215.21 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n424 233 Car -1 -1 -1 503.28 179.49 542.99 208.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n424 247 Pedestrian -1 -1 -1 819.54 164.53 838.64 215.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n424 238 Pedestrian -1 -1 -1 791.57 164.45 808.98 215.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n424 236 Cyclist -1 -1 -1 1087.68 154.41 1151.12 247.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n424 246 Pedestrian -1 -1 -1 810.28 163.58 831.20 215.08 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n424 250 Cyclist -1 -1 -1 916.42 162.18 945.34 218.46 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n424 252 Pedestrian -1 -1 -1 917.89 161.61 946.80 219.24 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n424 253 Car -1 -1 -1 533.79 177.46 558.85 196.61 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n425 233 Car -1 -1 -1 502.56 178.53 542.18 207.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n425 250 Cyclist -1 -1 -1 920.30 160.84 953.50 218.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n425 238 Pedestrian -1 -1 -1 797.39 163.12 815.12 215.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n425 236 Cyclist -1 -1 -1 1102.69 152.19 1175.78 249.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n425 247 Pedestrian -1 -1 -1 817.39 157.74 838.96 216.44 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n425 253 Car -1 -1 -1 533.78 177.06 558.28 196.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n426 250 Cyclist -1 -1 -1 924.93 159.98 959.31 218.66 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n426 233 Car -1 -1 -1 499.09 178.25 540.12 207.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n426 238 Pedestrian -1 -1 -1 802.78 162.03 822.62 217.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n426 236 Cyclist -1 -1 -1 1120.30 150.84 1196.90 250.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n426 247 Pedestrian -1 -1 -1 822.04 157.55 844.28 218.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n426 253 Car -1 -1 -1 532.29 176.96 557.57 196.20 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n426 254 Pedestrian -1 -1 -1 830.52 161.47 851.80 217.16 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n427 233 Car -1 -1 -1 496.42 179.63 538.55 209.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n427 250 Cyclist -1 -1 -1 931.26 159.45 967.56 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n427 247 Pedestrian -1 -1 -1 829.51 160.33 852.49 220.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n427 238 Pedestrian -1 -1 -1 810.44 163.45 830.58 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n427 253 Car -1 -1 -1 532.00 178.99 556.29 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n427 236 Cyclist -1 -1 -1 1141.93 150.13 1206.16 251.42 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n427 255 Pedestrian -1 -1 -1 26.75 184.71 53.88 234.77 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n428 233 Car -1 -1 -1 492.81 182.38 537.11 214.18 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n428 236 Cyclist -1 -1 -1 1158.79 153.32 1228.07 255.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n428 247 Pedestrian -1 -1 -1 837.15 162.28 860.43 224.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n428 238 Pedestrian -1 -1 -1 817.26 165.22 839.37 224.46 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n428 250 Cyclist -1 -1 -1 939.40 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528.52 212.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n433 253 Car -1 -1 -1 522.92 174.67 551.04 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n433 247 Pedestrian -1 -1 -1 883.74 152.78 913.27 227.39 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n433 238 Pedestrian -1 -1 -1 861.76 156.94 887.80 227.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n433 250 Cyclist -1 -1 -1 970.42 155.03 1015.29 223.58 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n433 256 Pedestrian -1 -1 -1 897.76 156.43 924.14 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n433 258 Car -1 -1 -1 631.25 170.37 650.86 185.27 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n433 257 Pedestrian -1 -1 -1 1233.93 149.99 1238.95 252.80 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n434 233 Car -1 -1 -1 472.47 175.82 526.23 213.58 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n434 238 Pedestrian -1 -1 -1 872.51 156.96 899.89 229.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n434 250 Cyclist -1 -1 -1 978.52 155.10 1026.16 224.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n434 256 Pedestrian -1 -1 -1 912.73 157.29 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653.82 192.35 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n448 262 Cyclist -1 -1 -1 1100.61 152.76 1201.63 257.93 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n449 233 Car -1 -1 -1 352.70 185.20 471.64 253.69 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n449 253 Car -1 -1 -1 479.71 181.18 522.63 212.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n449 262 Cyclist -1 -1 -1 1119.19 154.35 1220.65 262.24 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n449 261 Car -1 -1 -1 632.25 177.80 653.61 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n450 233 Car -1 -1 -1 336.59 185.26 465.91 257.07 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n450 253 Car -1 -1 -1 476.39 180.51 519.71 212.82 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n450 262 Cyclist -1 -1 -1 1132.90 151.34 1230.44 265.36 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n450 261 Car -1 -1 -1 633.00 176.59 654.19 192.65 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n451 233 Car -1 -1 -1 321.84 185.73 459.01 261.92 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n451 253 Car -1 -1 -1 472.06 180.48 518.34 213.83 -1 -1 -1 -1000 -1000 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200.64 424.81 300.38 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n455 253 Car -1 -1 -1 457.15 190.83 507.93 227.39 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n455 261 Car -1 -1 -1 633.74 184.45 658.66 201.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n456 233 Car -1 -1 -1 202.32 198.07 413.05 306.68 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n456 253 Car -1 -1 -1 452.60 186.45 506.78 225.87 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n456 261 Car -1 -1 -1 634.41 179.81 660.53 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n457 233 Car -1 -1 -1 162.76 196.74 400.13 320.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n457 253 Car -1 -1 -1 448.26 185.19 502.76 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n457 261 Car -1 -1 -1 635.55 176.30 663.81 196.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n458 233 Car -1 -1 -1 119.21 194.89 389.01 332.46 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n458 253 Car -1 -1 -1 443.49 184.65 500.11 226.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n458 261 Car -1 -1 -1 636.16 175.81 664.71 195.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n459 233 Car -1 -1 -1 70.23 196.34 374.82 352.02 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n459 253 Car -1 -1 -1 437.49 184.51 499.01 227.82 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n459 261 Car -1 -1 -1 636.45 175.42 664.82 195.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n460 233 Car -1 -1 -1 1.53 195.05 358.55 369.43 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n460 253 Car -1 -1 -1 431.93 184.82 497.16 229.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n460 261 Car -1 -1 -1 638.59 175.48 667.02 195.82 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n461 233 Car -1 -1 -1 -1.13 196.56 339.01 367.96 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n461 253 Car -1 -1 -1 425.93 184.79 493.96 231.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n461 261 Car -1 -1 -1 639.23 174.97 668.60 195.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n462 233 Car -1 -1 -1 -4.14 196.41 319.55 369.06 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n462 253 Car -1 -1 -1 419.64 184.84 490.75 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n462 261 Car -1 -1 -1 640.39 175.41 668.28 195.77 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n463 233 Car -1 -1 -1 1.94 197.86 290.05 367.95 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n463 253 Car -1 -1 -1 410.89 184.97 486.40 235.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n463 261 Car -1 -1 -1 641.13 175.08 668.86 195.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n464 233 Car -1 -1 -1 0.64 200.02 251.52 366.58 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n464 253 Car -1 -1 -1 400.77 185.86 482.50 237.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n464 261 Car -1 -1 -1 640.91 176.22 669.50 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n465 253 Car -1 -1 -1 392.72 186.76 478.85 241.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n465 233 Car -1 -1 -1 -1.29 205.24 207.19 368.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n465 261 Car -1 -1 -1 641.85 176.40 671.43 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n466 253 Car -1 -1 -1 383.49 188.08 473.85 244.38 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n466 233 Car -1 -1 -1 -1.68 213.16 152.69 368.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n466 261 Car -1 -1 -1 641.57 177.59 671.60 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n467 253 Car -1 -1 -1 373.21 188.98 467.77 247.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n467 261 Car -1 -1 -1 641.55 177.92 672.20 200.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n467 233 Car -1 -1 -1 -0.04 223.64 74.01 372.26 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n468 253 Car -1 -1 -1 357.94 190.21 462.15 252.07 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n468 261 Car -1 -1 -1 642.32 178.51 673.04 201.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n469 253 Car -1 -1 -1 347.90 190.87 455.66 256.27 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n469 261 Car -1 -1 -1 642.18 178.89 674.35 202.18 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n470 253 Car -1 -1 -1 331.59 192.41 449.10 261.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n470 261 Car -1 -1 -1 642.78 181.07 673.96 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n471 253 Car -1 -1 -1 313.47 194.45 441.87 267.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n471 261 Car -1 -1 -1 643.53 180.61 674.84 203.74 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n472 253 Car -1 -1 -1 292.89 195.60 431.51 271.39 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n472 261 Car -1 -1 -1 644.46 179.84 676.14 203.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n473 253 Car -1 -1 -1 266.58 196.05 421.45 278.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n473 261 Car -1 -1 -1 643.55 178.63 677.02 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n474 253 Car -1 -1 -1 243.26 196.35 410.98 284.74 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n474 261 Car -1 -1 -1 642.12 178.49 676.11 203.70 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n475 253 Car -1 -1 -1 210.75 197.01 398.39 292.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n475 261 Car -1 -1 -1 642.14 178.59 675.81 203.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n476 253 Car -1 -1 -1 173.95 197.78 382.03 303.36 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n476 261 Car -1 -1 -1 640.74 177.70 675.48 203.83 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n477 253 Car -1 -1 -1 128.18 195.94 365.30 313.77 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n477 261 Car -1 -1 -1 638.11 176.43 675.29 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n478 253 Car -1 -1 -1 76.39 194.01 345.39 326.00 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n478 261 Car -1 -1 -1 635.35 174.43 673.44 201.46 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n479 253 Car -1 -1 -1 10.20 193.55 318.63 347.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n479 261 Car -1 -1 -1 634.32 173.45 671.37 200.75 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n480 253 Car -1 -1 -1 -4.92 194.74 295.93 362.50 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n480 261 Car -1 -1 -1 631.34 173.01 669.85 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n481 253 Car -1 -1 -1 0.80 196.07 257.64 367.97 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n481 261 Car -1 -1 -1 628.83 174.56 668.67 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n481 263 Car -1 -1 -1 738.23 171.78 770.57 186.34 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n482 253 Car -1 -1 -1 2.97 197.09 211.54 367.41 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n482 261 Car -1 -1 -1 625.86 174.53 666.23 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n483 253 Car -1 -1 -1 -1.79 204.79 162.51 368.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n483 261 Car -1 -1 -1 622.49 175.69 663.60 205.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n484 261 Car -1 -1 -1 620.93 177.39 661.18 207.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n484 253 Car -1 -1 -1 -4.52 195.39 109.79 369.74 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n484 264 Car -1 -1 -1 733.98 174.88 761.03 190.19 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n485 261 Car -1 -1 -1 616.67 177.53 658.05 208.21 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n486 261 Car -1 -1 -1 612.86 177.62 654.26 208.90 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n486 265 Car -1 -1 -1 728.46 174.96 757.20 190.43 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n486 266 Car -1 -1 -1 807.73 169.71 864.00 191.37 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n487 261 Car -1 -1 -1 609.17 177.55 650.91 209.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n487 265 Car -1 -1 -1 724.73 174.77 753.24 190.45 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n487 267 Pedestrian -1 -1 -1 971.05 161.74 996.75 234.63 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n488 267 Pedestrian -1 -1 -1 978.26 161.64 1005.63 239.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n488 261 Car -1 -1 -1 603.97 178.36 646.90 210.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n488 265 Car -1 -1 -1 722.10 175.21 749.64 190.94 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n488 268 Car -1 -1 -1 801.54 170.98 855.05 191.64 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n489 261 Car -1 -1 -1 597.02 179.69 642.92 212.69 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n489 267 Pedestrian -1 -1 -1 985.19 161.50 1019.10 243.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n489 268 Car -1 -1 -1 796.88 171.57 853.95 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n489 265 Car -1 -1 -1 717.84 177.58 745.06 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n490 261 Car -1 -1 -1 590.96 180.47 638.58 214.65 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n490 267 Pedestrian -1 -1 -1 996.38 162.49 1034.79 248.49 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n490 265 Car -1 -1 -1 712.89 178.06 741.58 193.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n490 268 Car -1 -1 -1 793.74 171.94 854.07 193.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n491 261 Car -1 -1 -1 585.99 179.92 634.77 215.20 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n491 267 Pedestrian -1 -1 -1 1012.60 162.55 1053.81 253.26 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n491 268 Car -1 -1 -1 790.03 171.81 851.27 193.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n491 265 Car -1 -1 -1 709.54 178.46 737.55 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n492 267 Pedestrian -1 -1 -1 1033.42 160.58 1074.96 255.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n492 261 Car -1 -1 -1 579.94 179.05 630.04 215.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n492 268 Car -1 -1 -1 786.60 170.41 848.88 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n492 265 Car -1 -1 -1 706.31 176.52 734.04 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n493 261 Car -1 -1 -1 574.83 179.16 625.89 215.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n493 267 Pedestrian -1 -1 -1 1058.14 158.96 1101.83 265.78 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n493 268 Car -1 -1 -1 782.40 170.99 845.43 194.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n493 265 Car -1 -1 -1 701.40 177.38 724.05 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n494 267 Pedestrian -1 -1 -1 1081.63 158.76 1128.44 273.43 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n494 268 Car -1 -1 -1 780.61 172.55 845.59 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n494 261 Car -1 -1 -1 568.33 180.11 621.27 217.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n494 265 Car -1 -1 -1 697.07 178.80 719.78 195.14 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n495 261 Car -1 -1 -1 561.82 185.06 616.31 223.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n495 267 Pedestrian -1 -1 -1 1116.58 163.88 1160.81 286.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n495 268 Car -1 -1 -1 778.25 176.43 842.75 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n495 265 Car -1 -1 -1 692.81 183.31 714.20 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n495 269 Car -1 -1 -1 701.17 181.63 722.81 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n496 267 Pedestrian -1 -1 -1 1149.63 169.73 1205.83 304.35 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n496 261 Car -1 -1 -1 554.91 189.56 610.40 229.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n496 268 Car -1 -1 -1 775.13 180.55 837.30 204.90 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n496 265 Car -1 -1 -1 688.22 187.59 708.08 205.04 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n497 261 Car -1 -1 -1 547.61 190.87 604.32 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n497 267 Pedestrian -1 -1 -1 1193.17 169.53 1239.19 324.80 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n497 268 Car -1 -1 -1 771.86 181.48 832.88 205.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n497 265 Car -1 -1 -1 682.91 188.35 704.80 206.10 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n498 261 Car -1 -1 -1 540.31 188.66 598.03 230.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n498 265 Car -1 -1 -1 678.05 186.54 701.42 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n498 268 Car -1 -1 -1 766.94 179.79 829.97 205.21 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n498 270 Car -1 -1 -1 684.20 185.15 709.80 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n499 261 Car -1 -1 -1 532.97 185.39 593.55 229.21 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n499 265 Car -1 -1 -1 673.43 181.45 696.94 199.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n499 268 Car -1 -1 -1 761.01 173.62 828.27 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n500 261 Car -1 -1 -1 525.01 183.79 587.50 228.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n500 265 Car -1 -1 -1 668.46 179.38 693.43 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n500 268 Car -1 -1 -1 757.46 171.73 827.87 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n501 261 Car -1 -1 -1 518.58 183.36 581.31 228.39 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n501 268 Car -1 -1 -1 756.11 169.91 823.41 195.56 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n501 265 Car -1 -1 -1 664.59 177.73 689.93 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n502 261 Car -1 -1 -1 511.17 180.61 576.66 227.28 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n502 265 Car -1 -1 -1 661.81 175.63 685.72 194.14 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n502 268 Car -1 -1 -1 753.16 167.93 820.48 194.53 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n503 261 Car -1 -1 -1 503.12 175.91 570.69 224.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n503 265 Car -1 -1 -1 657.35 171.12 681.16 190.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n503 268 Car -1 -1 -1 750.46 163.03 821.41 192.32 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n504 261 Car -1 -1 -1 493.77 173.64 564.97 223.43 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n504 265 Car -1 -1 -1 652.15 169.13 676.78 187.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n504 268 Car -1 -1 -1 748.37 160.64 817.05 188.09 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n504 271 Car -1 -1 -1 661.89 167.83 686.64 186.74 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n505 261 Car -1 -1 -1 485.28 171.53 558.87 224.23 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n505 265 Car -1 -1 -1 648.60 167.75 672.92 186.28 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n505 268 Car -1 -1 -1 744.66 160.20 813.85 188.45 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n506 261 Car -1 -1 -1 476.63 170.97 551.79 225.37 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n506 265 Car -1 -1 -1 644.39 167.72 668.61 185.82 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n506 268 Car -1 -1 -1 742.16 160.27 813.78 188.52 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n506 272 Car -1 -1 -1 653.34 165.99 678.89 184.47 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n507 261 Car -1 -1 -1 466.15 171.52 544.93 228.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n507 265 Car -1 -1 -1 640.29 167.85 665.15 186.64 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n507 268 Car -1 -1 -1 736.64 160.87 810.89 191.90 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n508 261 Car -1 -1 -1 454.47 172.90 537.64 231.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n508 265 Car -1 -1 -1 635.30 168.93 663.13 188.64 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n508 268 Car -1 -1 -1 732.60 160.97 809.11 192.57 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n508 273 Car -1 -1 -1 645.53 168.34 670.73 186.70 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n509 261 Car -1 -1 -1 444.60 176.02 530.61 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n509 268 Car -1 -1 -1 730.51 162.53 804.74 193.86 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n509 265 Car -1 -1 -1 631.82 170.97 658.61 190.45 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0017.txt",
    "content": "0 1 Car -1 -1 -1 682.96 172.39 903.26 246.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n0 2 Car -1 -1 -1 998.47 163.51 1139.47 210.50 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n0 3 Car -1 -1 -1 447.19 164.83 484.98 191.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n0 4 Cyclist -1 -1 -1 500.03 159.07 535.20 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n0 5 Car -1 -1 -1 931.24 165.73 1054.09 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n1 1 Car -1 -1 -1 723.47 172.31 939.42 245.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n1 2 Car -1 -1 -1 998.77 163.76 1139.15 210.28 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n1 3 Car -1 -1 -1 447.19 164.82 485.23 191.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1 4 Cyclist -1 -1 -1 506.62 158.80 539.64 192.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n1 5 Car -1 -1 -1 933.33 165.36 1059.48 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n2 1 Car -1 -1 -1 762.38 170.69 977.39 243.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n2 2 Car -1 -1 -1 993.54 164.01 1137.89 210.48 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n2 4 Cyclist -1 -1 -1 512.95 159.28 548.51 192.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n2 3 Car -1 -1 -1 447.32 164.78 485.09 191.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n2 5 Car -1 -1 -1 934.95 166.70 1065.60 204.84 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n3 1 Car -1 -1 -1 799.48 172.90 1010.79 244.52 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n3 4 Cyclist -1 -1 -1 522.28 159.35 554.80 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n3 2 Car -1 -1 -1 993.15 163.87 1137.77 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n3 3 Car -1 -1 -1 447.38 164.72 485.08 191.52 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n3 5 Car -1 -1 -1 939.55 166.43 1068.41 205.40 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n3 6 Car -1 -1 -1 476.16 166.89 501.08 184.42 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n4 1 Car -1 -1 -1 839.50 173.20 1045.78 244.80 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 4 Cyclist -1 -1 -1 527.37 159.63 562.72 192.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 2 Car -1 -1 -1 999.72 163.53 1137.58 210.86 -1 -1 -1 -1000 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-1 -1000 -1000 -1000 -10 0.52\n12 1 Car -1 -1 -1 1146.96 167.66 1220.52 252.26 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n13 2 Car -1 -1 -1 954.33 165.35 1106.26 209.16 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n13 8 Car -1 -1 -1 903.24 164.82 1028.19 203.73 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n13 3 Car -1 -1 -1 447.16 165.05 485.30 191.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n13 6 Car -1 -1 -1 476.13 167.66 501.46 184.21 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n14 2 Car -1 -1 -1 943.80 165.77 1095.35 209.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n14 3 Car -1 -1 -1 447.12 165.12 485.35 191.60 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n14 8 Car -1 -1 -1 895.02 164.59 1021.98 204.14 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n14 6 Car -1 -1 -1 475.90 167.67 501.62 184.19 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n14 9 Car -1 -1 -1 1123.65 170.27 1214.05 204.39 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n14 10 Cyclist -1 -1 -1 595.19 159.24 633.98 192.42 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n15 2 Car -1 -1 -1 937.34 166.20 1086.66 209.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n15 3 Car -1 -1 -1 447.21 165.23 485.30 191.66 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n15 8 Car -1 -1 -1 888.24 164.83 1020.45 206.48 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n15 6 Car -1 -1 -1 475.89 167.69 501.53 184.10 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n15 9 Car -1 -1 -1 1118.59 169.36 1211.63 204.45 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n16 2 Car -1 -1 -1 932.05 166.38 1076.06 208.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n16 3 Car -1 -1 -1 447.34 165.15 485.31 191.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n16 8 Car -1 -1 -1 876.48 165.10 1009.07 206.08 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n16 6 Car -1 -1 -1 475.78 167.69 501.45 184.15 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n16 9 Car -1 -1 -1 1116.93 169.50 1212.01 205.19 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n17 2 Car -1 -1 -1 921.68 165.53 1063.61 208.70 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n17 3 Car -1 -1 -1 447.27 165.19 485.30 191.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n17 8 Car -1 -1 -1 865.94 164.86 996.85 203.71 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n17 9 Car -1 -1 -1 1109.94 170.12 1212.05 205.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n17 6 Car -1 -1 -1 475.64 167.73 501.49 184.16 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n18 2 Car -1 -1 -1 911.11 164.69 1050.87 208.34 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n18 8 Car -1 -1 -1 857.79 164.83 980.86 203.43 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 3 Car -1 -1 -1 447.31 165.18 485.34 191.57 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n18 9 Car -1 -1 -1 1104.27 170.27 1210.40 205.44 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n18 6 Car -1 -1 -1 475.46 167.66 501.53 184.18 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n19 2 Car -1 -1 -1 896.45 164.06 1042.79 208.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n19 8 Car -1 -1 -1 848.12 165.09 968.09 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n19 9 Car -1 -1 -1 1096.23 167.34 1210.41 207.23 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n19 3 Car -1 -1 -1 448.48 165.32 485.56 191.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n19 6 Car -1 -1 -1 475.43 167.67 501.62 184.20 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n20 2 Car -1 -1 -1 885.76 164.49 1030.99 209.04 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n20 8 Car -1 -1 -1 836.37 164.49 956.72 203.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n20 9 Car -1 -1 -1 1088.00 165.04 1210.51 208.04 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n20 3 Car -1 -1 -1 448.50 165.37 485.54 191.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n20 6 Car -1 -1 -1 475.26 167.70 501.68 184.27 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n21 2 Car -1 -1 -1 872.05 164.30 1021.03 208.88 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n21 9 Car -1 -1 -1 1076.67 163.84 1208.08 208.63 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n21 3 Car -1 -1 -1 447.26 165.26 485.43 191.50 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n21 8 Car -1 -1 -1 823.61 165.60 947.39 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n21 6 Car -1 -1 -1 475.47 167.73 501.66 184.28 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n22 2 Car -1 -1 -1 861.74 163.45 1007.76 209.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n22 9 Car -1 -1 -1 1067.38 162.55 1208.17 209.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n22 3 Car -1 -1 -1 448.41 165.40 485.61 191.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n22 8 Car -1 -1 -1 809.91 165.68 937.47 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n22 6 Car -1 -1 -1 475.46 167.72 501.46 184.24 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n23 2 Car -1 -1 -1 849.57 163.52 989.28 209.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n23 9 Car -1 -1 -1 1055.15 158.82 1205.65 208.63 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n23 3 Car -1 -1 -1 448.39 165.41 485.60 191.49 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n23 6 Car -1 -1 -1 475.56 167.72 501.39 184.12 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n23 8 Car -1 -1 -1 797.51 166.43 927.78 202.19 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n24 2 Car -1 -1 -1 832.81 163.19 977.15 210.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n24 3 Car -1 -1 -1 447.24 165.26 485.35 191.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n24 9 Car -1 -1 -1 1046.10 160.74 1198.94 210.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n24 6 Car -1 -1 -1 475.74 167.75 501.39 183.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n24 8 Car -1 -1 -1 780.70 165.72 905.98 202.05 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n25 2 Car -1 -1 -1 818.34 163.21 959.16 209.13 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n25 3 Car -1 -1 -1 447.28 165.27 485.19 191.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n25 9 Car -1 -1 -1 1032.48 161.60 1190.02 210.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n25 6 Car -1 -1 -1 475.81 167.77 501.15 183.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n25 8 Car -1 -1 -1 766.07 165.39 889.72 201.76 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n26 2 Car -1 -1 -1 801.68 163.48 939.47 208.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n26 3 Car -1 -1 -1 447.32 165.20 485.15 191.58 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n26 8 Car -1 -1 -1 750.78 165.10 874.64 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n26 9 Car -1 -1 -1 1026.26 164.05 1172.71 209.49 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n26 6 Car -1 -1 -1 475.92 167.75 501.00 183.97 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n27 2 Car -1 -1 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-1 697.53 164.91 827.57 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n29 3 Car -1 -1 -1 447.05 165.39 485.41 191.73 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n29 6 Car -1 -1 -1 475.98 167.91 501.02 183.91 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n29 11 Car -1 -1 -1 1024.41 164.53 1151.79 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n30 2 Car -1 -1 -1 733.17 164.02 868.31 208.97 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n30 9 Car -1 -1 -1 966.20 165.15 1118.42 209.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n30 8 Car -1 -1 -1 680.61 165.60 798.85 200.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n30 3 Car -1 -1 -1 447.13 165.42 485.47 191.72 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n30 6 Car -1 -1 -1 475.87 167.81 501.21 183.93 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n30 11 Car -1 -1 -1 1007.50 163.87 1138.51 204.41 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n31 2 Car -1 -1 -1 719.12 163.65 851.90 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n31 8 Car -1 -1 -1 662.41 164.84 772.64 200.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n31 9 Car -1 -1 -1 950.57 165.23 1103.28 209.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n31 3 Car -1 -1 -1 446.96 165.39 485.59 191.69 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n31 11 Car -1 -1 -1 999.06 163.78 1123.48 204.67 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n31 6 Car -1 -1 -1 475.92 167.88 501.30 183.94 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n32 2 Car -1 -1 -1 702.05 163.88 833.77 207.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n32 8 Car -1 -1 -1 640.59 164.44 756.65 200.84 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n32 9 Car -1 -1 -1 938.54 165.79 1085.55 210.13 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n32 3 Car -1 -1 -1 446.95 165.33 485.63 191.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n32 11 Car -1 -1 -1 991.34 164.50 1108.09 204.01 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n32 6 Car -1 -1 -1 475.94 167.82 501.31 184.04 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n33 2 Car -1 -1 -1 685.07 163.84 817.63 207.89 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n33 8 Car -1 -1 -1 622.61 164.45 735.67 200.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n33 3 Car 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Car -1 -1 -1 881.42 164.25 1028.05 208.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n35 3 Car -1 -1 -1 447.92 165.62 486.18 191.83 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n35 6 Car -1 -1 -1 475.94 167.81 501.49 184.24 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n35 11 Car -1 -1 -1 941.37 162.73 1066.72 204.32 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n36 2 Car -1 -1 -1 631.51 163.02 763.92 208.38 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n36 9 Car -1 -1 -1 863.42 163.86 1007.11 208.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n36 8 Car -1 -1 -1 561.73 163.79 681.86 200.90 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n36 3 Car -1 -1 -1 448.01 165.54 486.14 191.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n36 6 Car -1 -1 -1 475.63 167.77 501.78 184.38 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n37 2 Car -1 -1 -1 614.20 162.66 742.37 206.37 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n37 9 Car -1 -1 -1 845.93 164.42 979.92 208.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n37 8 Car -1 -1 -1 541.46 163.91 662.16 201.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n37 3 Car -1 -1 -1 448.03 165.57 486.29 191.76 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n37 6 Car -1 -1 -1 475.30 167.54 501.87 184.49 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n37 11 Car -1 -1 -1 920.83 166.24 1041.10 201.51 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n38 9 Car -1 -1 -1 824.01 164.18 961.28 208.59 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n38 8 Car -1 -1 -1 522.07 164.51 637.96 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n38 2 Car -1 -1 -1 593.77 163.05 723.42 208.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n38 3 Car -1 -1 -1 448.04 165.61 486.34 191.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n38 11 Car -1 -1 -1 908.65 166.60 1030.48 201.90 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n38 6 Car -1 -1 -1 474.68 167.41 502.31 184.58 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n39 8 Car -1 -1 -1 500.45 164.47 619.88 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n39 9 Car -1 -1 -1 802.84 163.97 938.53 208.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n39 2 Car -1 -1 -1 572.14 162.67 700.29 206.36 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n39 3 Car -1 -1 -1 448.02 165.48 486.32 191.78 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n39 11 Car -1 -1 -1 890.94 165.03 1009.51 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n39 6 Car -1 -1 -1 474.21 167.36 502.60 184.71 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n40 9 Car -1 -1 -1 782.19 163.08 919.64 209.22 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n40 2 Car -1 -1 -1 549.69 163.16 679.32 208.15 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n40 8 Car -1 -1 -1 479.38 164.59 596.34 200.96 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n40 3 Car -1 -1 -1 447.90 165.60 486.68 191.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n40 11 Car -1 -1 -1 880.96 166.25 988.28 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n40 6 Car -1 -1 -1 473.49 167.47 503.13 185.16 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n41 2 Car -1 -1 -1 527.78 163.23 654.68 207.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n41 8 Car -1 -1 -1 454.58 164.43 575.74 201.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n41 9 Car -1 -1 -1 762.37 163.83 899.73 208.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n41 3 Car -1 -1 -1 448.26 165.81 486.39 191.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n41 11 Car -1 -1 -1 864.30 167.11 974.40 201.71 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n41 6 Car -1 -1 -1 472.04 167.94 504.46 184.89 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n42 2 Car -1 -1 -1 504.36 163.81 630.84 207.47 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n42 8 Car -1 -1 -1 434.70 165.21 549.01 201.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n42 3 Car -1 -1 -1 448.39 166.63 486.25 190.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n42 9 Car -1 -1 -1 741.76 164.12 874.80 207.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n42 11 Car -1 -1 -1 850.85 168.22 958.23 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n42 12 Car -1 -1 -1 1107.70 158.73 1206.86 201.03 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n43 3 Car -1 -1 -1 442.05 166.27 492.70 191.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n43 9 Car -1 -1 -1 721.16 163.65 857.27 207.70 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n43 2 Car -1 -1 -1 480.94 163.59 606.88 207.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n43 11 Car -1 -1 -1 831.67 167.17 947.60 204.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n43 12 Car -1 -1 -1 1100.57 162.73 1198.49 203.56 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n44 3 Car -1 -1 -1 443.80 167.35 487.76 190.46 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n44 9 Car -1 -1 -1 700.24 164.01 834.73 207.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n44 2 Car -1 -1 -1 456.74 163.18 581.28 207.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n44 11 Car -1 -1 -1 822.91 167.79 926.14 203.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n44 12 Car -1 -1 -1 1084.09 161.98 1206.49 203.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n44 13 Car -1 -1 -1 387.85 164.96 509.69 202.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n45 2 Car -1 -1 -1 433.51 162.82 556.97 206.35 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n45 3 Car -1 -1 -1 443.19 165.91 488.24 192.40 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n45 9 Car -1 -1 -1 680.51 164.59 814.04 207.37 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n45 11 Car -1 -1 -1 807.00 168.11 909.49 203.57 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n45 13 Car -1 -1 -1 366.60 165.20 484.76 202.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n45 12 Car -1 -1 -1 1069.31 156.40 1206.22 203.54 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n46 3 Car -1 -1 -1 445.82 166.61 488.95 190.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n46 9 Car -1 -1 -1 659.30 163.69 792.25 207.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n46 11 Car -1 -1 -1 788.29 168.09 892.99 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n46 13 Car -1 -1 -1 340.85 164.57 465.00 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n46 12 Car -1 -1 -1 1052.92 156.73 1192.72 203.59 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n47 9 Car -1 -1 -1 640.10 164.07 771.38 207.39 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n47 3 Car -1 -1 -1 378.10 162.05 506.30 206.80 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n47 11 Car -1 -1 -1 772.92 166.39 877.24 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n47 13 Car -1 -1 -1 314.65 164.82 437.89 202.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n47 12 Car -1 -1 -1 1032.64 156.12 1182.06 204.58 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n47 14 Car -1 -1 -1 446.17 166.57 484.73 191.39 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n48 3 Car -1 -1 -1 350.56 162.06 479.73 206.82 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n48 9 Car -1 -1 -1 619.71 163.74 753.02 207.71 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n48 11 Car -1 -1 -1 757.49 165.98 858.50 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n48 13 Car -1 -1 -1 289.40 164.51 410.18 202.40 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n48 14 Car -1 -1 -1 445.91 165.19 485.13 191.78 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n48 12 Car -1 -1 -1 1017.08 157.54 1159.29 206.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n49 3 Car -1 -1 -1 320.76 161.67 454.26 207.10 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n49 11 Car -1 -1 -1 741.52 166.51 837.02 201.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n49 9 Car -1 -1 -1 597.52 164.07 731.85 207.40 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n49 13 Car -1 -1 -1 258.95 164.50 386.96 201.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n49 14 Car -1 -1 -1 449.33 165.67 485.15 191.61 -1 -1 -1 -1000 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-1000 -1000 -1000 -10 0.79\n51 12 Car -1 -1 -1 976.51 158.82 1106.75 206.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n51 13 Car -1 -1 -1 212.77 163.80 326.10 201.65 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n51 15 Car -1 -1 -1 474.81 167.64 501.91 184.84 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n52 11 Car -1 -1 -1 685.63 165.70 779.38 201.00 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n52 3 Car -1 -1 -1 229.81 161.71 364.09 207.07 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n52 9 Car -1 -1 -1 535.84 162.92 668.14 206.23 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n52 12 Car -1 -1 -1 953.80 159.39 1085.98 204.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n52 14 Car -1 -1 -1 446.96 165.56 485.52 192.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n52 13 Car -1 -1 -1 174.09 163.53 288.72 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n52 15 Car -1 -1 -1 474.75 167.49 501.83 184.81 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n53 11 Car -1 -1 -1 665.95 165.42 758.55 201.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n53 3 Car -1 -1 -1 202.70 161.37 335.07 206.54 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n53 9 Car -1 -1 -1 512.99 164.05 645.83 207.14 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n53 14 Car -1 -1 -1 448.16 165.52 486.03 191.87 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n53 13 Car -1 -1 -1 140.69 162.68 253.70 201.10 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n53 12 Car -1 -1 -1 940.29 157.53 1066.95 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n53 15 Car -1 -1 -1 474.83 167.23 502.11 184.83 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n54 11 Car -1 -1 -1 645.12 165.64 737.75 200.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n54 3 Car -1 -1 -1 169.23 159.50 307.51 207.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n54 9 Car -1 -1 -1 490.20 164.11 621.91 207.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n54 14 Car -1 -1 -1 447.80 165.50 486.48 191.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n54 12 Car -1 -1 -1 925.65 158.85 1044.22 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n54 13 Car -1 -1 -1 109.19 163.20 223.29 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n55 11 Car -1 -1 -1 624.56 165.00 717.02 200.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n55 3 Car -1 -1 -1 133.32 159.80 274.93 206.60 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n55 9 Car -1 -1 -1 467.63 163.10 599.27 206.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n55 12 Car -1 -1 -1 907.40 157.67 1032.75 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n55 14 Car -1 -1 -1 447.91 165.82 486.47 191.78 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n55 13 Car -1 -1 -1 72.85 162.42 197.91 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n56 11 Car -1 -1 -1 602.44 165.66 695.91 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n56 9 Car -1 -1 -1 442.19 163.14 577.04 207.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n56 14 Car -1 -1 -1 448.60 166.29 486.00 191.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n56 3 Car -1 -1 -1 96.68 159.46 242.84 206.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n56 12 Car -1 -1 -1 888.40 157.11 1020.95 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n56 13 Car -1 -1 -1 36.65 162.83 158.10 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n57 12 Car -1 -1 -1 872.75 157.84 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0.81\n271 56 Car -1 -1 -1 514.19 167.98 539.32 182.52 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n271 40 Car -1 -1 -1 470.71 167.75 504.21 184.65 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n272 50 Car -1 -1 -1 494.74 159.67 646.63 239.35 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n272 23 Car -1 -1 -1 461.94 165.68 498.83 189.73 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n272 56 Car -1 -1 -1 515.58 167.93 543.76 182.44 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n273 50 Car -1 -1 -1 483.12 161.26 637.97 242.75 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n273 23 Car -1 -1 -1 462.62 165.77 499.22 189.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n273 56 Car -1 -1 -1 515.91 167.79 543.95 182.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n274 50 Car -1 -1 -1 472.98 160.99 630.05 244.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n274 23 Car -1 -1 -1 462.97 165.89 499.29 189.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n274 56 Car -1 -1 -1 516.53 167.82 543.72 182.00 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n275 50 Car -1 -1 -1 459.93 163.04 620.41 247.55 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n275 23 Car -1 -1 -1 463.49 165.96 499.17 189.50 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n276 50 Car -1 -1 -1 447.30 162.74 610.87 250.92 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n276 23 Car -1 -1 -1 464.74 166.09 501.88 189.35 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n277 50 Car -1 -1 -1 435.20 163.52 600.36 254.39 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n277 23 Car -1 -1 -1 465.17 165.35 502.01 188.71 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n278 50 Car -1 -1 -1 425.35 162.33 590.07 257.46 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n278 23 Car -1 -1 -1 465.33 166.14 502.29 189.46 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n279 50 Car -1 -1 -1 410.79 162.36 581.33 259.28 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n279 23 Car -1 -1 -1 465.37 166.72 502.71 189.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n280 50 Car -1 -1 -1 399.44 165.12 573.73 263.22 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n280 23 Car -1 -1 -1 464.94 165.65 503.18 187.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n281 50 Car -1 -1 -1 385.40 165.37 564.99 267.92 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n282 50 Car -1 -1 -1 367.72 166.20 555.70 275.11 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n283 50 Car -1 -1 -1 354.06 166.21 546.29 278.69 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n284 50 Car -1 -1 -1 338.48 166.61 537.83 284.40 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n285 50 Car -1 -1 -1 324.19 166.82 527.82 290.92 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n285 60 Car -1 -1 -1 512.93 167.09 539.44 183.11 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n286 50 Car -1 -1 -1 304.41 166.87 518.56 298.49 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n286 60 Car -1 -1 -1 515.75 167.62 541.20 182.36 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n287 50 Car -1 -1 -1 283.77 166.21 509.41 305.73 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n287 60 Car -1 -1 -1 516.20 167.54 540.69 182.46 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n288 50 Car -1 -1 -1 261.96 165.44 500.16 314.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n288 60 Car -1 -1 -1 515.61 167.35 538.83 182.60 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n288 23 Car -1 -1 -1 466.74 165.90 499.93 187.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n289 50 Car -1 -1 -1 238.61 165.71 491.41 323.08 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n289 60 Car -1 -1 -1 515.12 167.13 538.50 182.84 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n289 23 Car -1 -1 -1 463.70 165.62 498.14 188.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n290 50 Car -1 -1 -1 215.58 166.28 482.57 331.38 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n290 60 Car -1 -1 -1 514.92 167.09 538.32 182.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n290 23 Car -1 -1 -1 466.08 165.63 499.31 188.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n291 50 Car -1 -1 -1 182.86 166.09 471.10 345.78 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n291 60 Car -1 -1 -1 514.95 167.10 538.22 182.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n291 23 Car -1 -1 -1 466.05 165.50 499.56 188.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n292 50 Car -1 -1 -1 147.70 165.96 460.32 360.81 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n292 60 Car -1 -1 -1 514.85 167.09 538.24 183.04 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n292 23 Car -1 -1 -1 465.80 165.61 499.39 188.21 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n293 50 Car -1 -1 -1 107.41 168.22 447.95 365.94 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n293 60 Car -1 -1 -1 514.81 167.15 538.18 183.18 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n293 23 Car -1 -1 -1 465.99 165.66 499.24 188.38 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n294 50 Car -1 -1 -1 65.84 168.37 434.62 365.15 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n294 60 Car -1 -1 -1 514.77 167.21 538.09 183.23 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n294 23 Car -1 -1 -1 466.30 165.81 499.21 188.34 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n295 50 Car -1 -1 -1 4.96 166.38 427.41 366.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n295 60 Car -1 -1 -1 514.74 167.35 537.87 183.18 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n295 23 Car -1 -1 -1 466.74 165.68 499.62 188.52 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n296 50 Car -1 -1 -1 -0.53 169.41 410.77 364.00 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n296 60 Car -1 -1 -1 514.45 167.30 537.93 183.20 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n296 23 Car -1 -1 -1 467.57 165.56 500.51 188.58 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n297 50 Car -1 -1 -1 -3.16 164.67 398.30 363.18 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n297 60 Car -1 -1 -1 514.29 167.34 537.80 183.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n297 23 Car -1 -1 -1 468.16 165.32 501.04 188.80 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n298 50 Car -1 -1 -1 -4.94 160.95 384.21 366.24 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n298 60 Car -1 -1 -1 514.25 167.39 537.70 183.22 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n298 23 Car -1 -1 -1 468.81 165.24 501.56 188.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n299 50 Car -1 -1 -1 -0.96 154.31 362.92 366.11 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n299 60 Car -1 -1 -1 514.33 167.44 537.70 183.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n299 23 Car -1 -1 -1 470.12 165.96 502.60 189.46 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n300 50 Car -1 -1 -1 0.14 154.15 339.26 366.09 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n300 60 Car -1 -1 -1 514.27 167.42 537.70 183.12 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n300 23 Car -1 -1 -1 470.21 165.42 503.47 188.76 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n301 50 Car -1 -1 -1 2.86 155.04 306.68 364.90 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n301 60 Car -1 -1 -1 514.40 167.43 537.64 183.03 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n301 23 Car -1 -1 -1 470.32 165.31 504.33 188.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n302 50 Car -1 -1 -1 1.90 154.92 277.54 365.03 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n302 60 Car -1 -1 -1 514.34 167.39 537.78 183.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n302 23 Car -1 -1 -1 470.91 165.82 505.62 189.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n303 60 Car -1 -1 -1 514.36 167.42 537.72 183.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n303 50 Car -1 -1 -1 -1.73 160.65 235.02 366.31 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n303 23 Car -1 -1 -1 471.31 165.82 506.75 189.63 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n304 60 Car -1 -1 -1 514.57 167.39 537.69 182.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n304 50 Car -1 -1 -1 -1.56 161.10 196.35 366.17 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n304 23 Car -1 -1 -1 472.36 165.70 507.99 189.76 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0018.txt",
    "content": "0 1 Car -1 -1 -1 246.26 185.91 386.63 259.15 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n0 2 Car -1 -1 -1 381.09 172.92 457.72 226.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n0 3 Pedestrian -1 -1 -1 1106.07 137.40 1153.32 251.76 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n0 4 Car -1 -1 -1 441.13 177.38 486.71 211.20 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n0 5 Pedestrian -1 -1 -1 895.76 141.68 943.60 260.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n0 6 Pedestrian -1 -1 -1 951.47 135.53 1002.61 259.27 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n0 7 Car -1 -1 -1 525.86 162.98 566.77 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n0 8 Pedestrian -1 -1 -1 1008.94 134.44 1059.91 254.66 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n0 9 Pedestrian -1 -1 -1 300.68 169.25 321.38 226.23 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n0 10 Pedestrian -1 -1 -1 1041.27 131.98 1089.74 248.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n0 11 Pedestrian -1 -1 -1 705.64 155.38 718.62 196.68 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n0 12 Pedestrian -1 -1 -1 318.57 170.55 336.50 224.61 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n0 13 Car -1 -1 -1 477.96 174.30 514.04 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n0 14 Pedestrian -1 -1 -1 675.40 159.79 687.53 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n0 15 Van -1 -1 -1 558.73 119.65 638.94 216.28 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n1 1 Car -1 -1 -1 240.24 187.53 382.70 262.97 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1 2 Car -1 -1 -1 378.76 174.29 457.02 228.82 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n1 4 Car -1 -1 -1 441.20 178.38 486.70 212.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n1 5 Pedestrian -1 -1 -1 902.27 141.56 952.19 263.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n1 7 Car -1 -1 -1 524.90 164.12 566.49 200.39 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n1 6 Pedestrian -1 -1 -1 960.48 138.18 1015.91 264.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n1 3 Pedestrian -1 -1 -1 1115.46 138.37 1168.52 257.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1 9 Pedestrian -1 -1 -1 297.68 169.79 318.56 226.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n1 8 Pedestrian -1 -1 -1 1016.13 133.84 1067.72 256.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n1 12 Pedestrian -1 -1 -1 317.76 171.73 335.44 224.19 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n1 10 Pedestrian -1 -1 -1 1051.55 133.28 1101.51 254.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n1 11 Pedestrian -1 -1 -1 707.17 156.42 720.19 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n1 14 Pedestrian -1 -1 -1 677.54 160.91 689.30 196.84 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n1 16 Cyclist -1 -1 -1 494.99 168.90 511.98 211.41 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n2 1 Car -1 -1 -1 232.78 189.43 380.40 266.57 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n2 2 Car -1 -1 -1 374.51 175.73 455.86 231.16 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n2 3 Pedestrian -1 -1 -1 1128.38 142.27 1184.78 260.62 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n2 4 Car -1 -1 -1 441.02 180.60 486.73 214.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n2 6 Pedestrian -1 -1 -1 968.24 139.50 1023.74 265.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n2 7 Car -1 -1 -1 524.42 165.43 565.84 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n2 5 Pedestrian -1 -1 -1 905.05 141.81 951.04 264.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n2 8 Pedestrian -1 -1 -1 1021.06 135.73 1071.66 259.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n2 11 Pedestrian -1 -1 -1 709.17 159.03 724.33 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n2 10 Pedestrian -1 -1 -1 1058.20 137.43 1110.85 257.17 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n2 9 Pedestrian -1 -1 -1 294.54 171.14 316.03 227.49 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n2 12 Pedestrian -1 -1 -1 314.57 173.51 332.78 224.71 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n2 14 Pedestrian -1 -1 -1 680.68 163.29 692.90 200.65 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n2 16 Cyclist -1 -1 -1 493.53 172.13 510.45 216.66 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n3 1 Car -1 -1 -1 223.09 190.63 375.48 269.39 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n3 2 Car -1 -1 -1 373.20 176.99 454.49 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n3 5 Pedestrian -1 -1 -1 907.69 142.40 955.75 271.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n3 4 Car -1 -1 -1 437.88 181.89 486.40 216.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n3 6 Pedestrian -1 -1 -1 976.82 139.88 1031.96 270.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n3 7 Car -1 -1 -1 521.79 166.98 563.27 205.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n3 3 Pedestrian -1 -1 -1 1140.09 141.79 1189.84 263.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n3 10 Pedestrian -1 -1 -1 1067.50 136.15 1117.23 260.96 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n3 8 Pedestrian -1 -1 -1 1025.90 136.45 1081.04 262.46 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n3 11 Pedestrian -1 -1 -1 710.71 159.90 725.09 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n3 9 Pedestrian -1 -1 -1 292.70 170.93 314.26 228.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n3 12 Pedestrian -1 -1 -1 310.66 174.66 330.37 229.38 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n3 16 Cyclist -1 -1 -1 489.43 172.06 508.64 219.31 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n3 14 Pedestrian -1 -1 -1 682.92 165.06 696.69 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n3 17 Truck -1 -1 -1 563.51 124.62 639.71 219.27 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n4 1 Car -1 -1 -1 213.19 191.89 370.23 273.00 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n4 2 Car -1 -1 -1 369.97 177.32 452.68 234.96 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n4 7 Car -1 -1 -1 519.46 167.32 563.23 206.86 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n4 5 Pedestrian -1 -1 -1 912.14 142.57 964.63 271.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n4 6 Pedestrian -1 -1 -1 984.59 138.80 1038.80 272.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n4 4 Car -1 -1 -1 439.02 182.39 485.26 216.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n4 10 Pedestrian -1 -1 -1 1075.72 135.46 1124.19 262.33 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n4 8 Pedestrian -1 -1 -1 1037.84 137.56 1092.29 268.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n4 3 Pedestrian -1 -1 -1 1155.72 141.33 1203.78 265.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n4 16 Cyclist -1 -1 -1 484.30 174.04 506.83 220.95 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n4 9 Pedestrian -1 -1 -1 289.42 170.27 310.89 231.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n4 11 Pedestrian -1 -1 -1 712.53 160.05 726.96 204.09 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n4 12 Pedestrian -1 -1 -1 309.13 175.23 328.52 229.72 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n4 14 Pedestrian -1 -1 -1 684.72 166.90 697.43 204.32 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n4 17 Truck -1 -1 -1 564.51 125.07 639.02 219.99 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n5 1 Car -1 -1 -1 201.71 192.29 366.10 275.10 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n5 2 Car -1 -1 -1 366.07 176.93 450.49 235.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n5 5 Pedestrian -1 -1 -1 920.23 144.00 972.61 275.46 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n5 4 Car -1 -1 -1 438.85 182.26 482.72 216.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n5 6 Pedestrian -1 -1 -1 993.72 139.10 1045.47 272.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n5 9 Pedestrian -1 -1 -1 286.31 170.38 307.81 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n5 11 Pedestrian -1 -1 -1 714.21 159.59 729.08 204.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n5 7 Car -1 -1 -1 517.28 167.08 562.65 207.02 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n5 8 Pedestrian -1 -1 -1 1044.58 136.72 1100.77 269.62 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n5 12 Pedestrian -1 -1 -1 306.46 175.46 326.26 230.04 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n5 3 Pedestrian -1 -1 -1 1160.50 139.02 1215.07 271.24 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n5 10 Pedestrian -1 -1 -1 1082.39 137.10 1139.86 265.61 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n5 17 Truck -1 -1 -1 564.59 125.33 639.11 218.90 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n5 16 Cyclist -1 -1 -1 480.23 174.03 503.17 221.72 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n5 14 Pedestrian -1 -1 -1 686.20 165.13 699.50 205.86 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n5 18 Car -1 -1 -1 479.26 178.02 516.99 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n6 1 Car -1 -1 -1 187.68 193.38 361.15 278.56 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n6 2 Car -1 -1 -1 360.30 176.90 449.24 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n6 16 Cyclist -1 -1 -1 476.19 172.63 500.89 224.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n6 11 Pedestrian -1 -1 -1 716.45 160.92 732.29 204.50 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n6 17 Truck -1 -1 -1 564.06 125.70 640.49 218.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n6 5 Pedestrian -1 -1 -1 928.96 143.12 979.63 278.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n6 4 Car -1 -1 -1 437.56 182.16 482.02 217.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n6 7 Car -1 -1 -1 514.61 166.98 563.03 207.82 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n6 14 Pedestrian -1 -1 -1 688.10 164.61 701.10 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n6 6 Pedestrian -1 -1 -1 1007.75 142.44 1061.61 275.07 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n6 3 Pedestrian -1 -1 -1 1175.56 140.91 1221.61 270.41 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n6 8 Pedestrian -1 -1 -1 1056.18 135.63 1112.43 270.72 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n6 9 Pedestrian -1 -1 -1 284.16 171.27 306.54 232.61 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n6 10 Pedestrian -1 -1 -1 1093.51 135.58 1159.11 268.84 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n6 12 Pedestrian -1 -1 -1 304.15 175.37 324.87 229.69 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n6 18 Car -1 -1 -1 478.35 178.57 517.48 208.35 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n6 19 Pedestrian -1 -1 -1 962.57 143.72 992.93 223.85 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n7 1 Car -1 -1 -1 174.92 193.49 355.34 282.17 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n7 2 Car -1 -1 -1 355.42 177.39 445.92 237.23 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n7 7 Car -1 -1 -1 512.05 166.44 563.91 208.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n7 16 Cyclist -1 -1 -1 472.11 172.01 497.00 226.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n7 5 Pedestrian -1 -1 -1 937.41 143.22 986.41 277.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n7 6 Pedestrian -1 -1 -1 1016.99 140.00 1075.27 277.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n7 4 Car -1 -1 -1 433.63 181.97 480.81 217.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n7 11 Pedestrian -1 -1 -1 718.22 161.13 733.48 205.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n7 17 Truck -1 -1 -1 564.05 126.11 640.25 218.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n7 9 Pedestrian -1 -1 -1 281.25 171.73 303.40 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n7 8 Pedestrian -1 -1 -1 1072.50 135.32 1126.34 275.29 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n7 18 Car -1 -1 -1 475.39 179.16 516.96 208.86 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n7 10 Pedestrian -1 -1 -1 1106.36 135.46 1169.66 270.23 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n7 12 Pedestrian -1 -1 -1 301.08 175.96 321.51 230.24 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n7 3 Pedestrian -1 -1 -1 1192.43 145.49 1221.84 267.56 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n7 14 Pedestrian -1 -1 -1 690.27 165.78 703.21 205.40 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n8 1 Car -1 -1 -1 158.31 194.72 349.93 286.68 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n8 2 Car -1 -1 -1 352.87 177.22 444.09 239.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n8 16 Cyclist -1 -1 -1 467.31 171.91 492.12 227.76 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n8 5 Pedestrian -1 -1 -1 947.69 137.96 998.67 280.48 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n8 18 Car -1 -1 -1 475.08 179.14 516.28 208.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 7 Car -1 -1 -1 510.30 165.47 563.18 209.34 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 4 Car -1 -1 -1 434.14 181.37 480.30 218.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n8 11 Pedestrian -1 -1 -1 720.58 160.26 737.03 205.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n8 6 Pedestrian -1 -1 -1 1029.41 137.72 1085.46 280.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n8 9 Pedestrian -1 -1 -1 276.89 170.61 300.47 234.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n8 17 Truck -1 -1 -1 564.04 125.97 640.77 218.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n8 10 Pedestrian -1 -1 -1 1122.17 132.00 1176.98 273.01 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n8 8 Pedestrian -1 -1 -1 1083.07 136.41 1139.20 276.20 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n8 14 Pedestrian -1 -1 -1 692.00 165.14 704.64 204.00 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n8 12 Pedestrian -1 -1 -1 295.67 175.64 319.19 235.17 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n8 3 Pedestrian -1 -1 -1 1215.20 143.28 1221.67 275.60 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n9 1 Car -1 -1 -1 142.74 193.61 341.40 289.70 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n9 2 Car -1 -1 -1 348.06 176.91 441.40 240.48 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n9 5 Pedestrian -1 -1 -1 952.39 137.70 1010.21 281.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n9 17 Truck -1 -1 -1 564.27 126.09 641.46 217.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n9 7 Car -1 -1 -1 508.18 164.84 561.35 209.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n9 18 Car -1 -1 -1 474.48 178.44 514.87 208.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n9 4 Car -1 -1 -1 433.90 181.40 481.02 217.91 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n9 6 Pedestrian -1 -1 -1 1043.53 137.87 1094.61 280.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n9 9 Pedestrian -1 -1 -1 271.89 169.64 297.28 235.37 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n9 16 Cyclist -1 -1 -1 463.80 172.33 486.69 226.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n9 10 Pedestrian -1 -1 -1 1136.94 131.47 1192.19 272.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n9 11 Pedestrian -1 -1 -1 723.94 158.82 738.94 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n9 14 Pedestrian -1 -1 -1 694.35 164.46 707.69 204.34 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n9 12 Pedestrian -1 -1 -1 290.85 175.47 316.47 237.45 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n9 8 Pedestrian -1 -1 -1 1094.59 136.64 1158.13 276.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n9 20 Pedestrian -1 -1 -1 709.28 161.81 723.33 203.41 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n10 1 Car -1 -1 -1 124.06 193.90 332.63 293.44 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n10 2 Car -1 -1 -1 339.90 176.14 438.37 240.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n10 7 Car -1 -1 -1 505.98 164.14 560.20 210.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n10 5 Pedestrian -1 -1 -1 965.41 136.56 1026.60 285.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n10 6 Pedestrian -1 -1 -1 1055.44 135.05 1112.77 283.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n10 17 Truck -1 -1 -1 564.64 125.74 641.13 217.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n10 9 Pedestrian -1 -1 -1 264.73 168.60 291.66 236.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n10 4 Car -1 -1 -1 433.22 180.70 480.54 217.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n10 14 Pedestrian -1 -1 -1 696.34 163.86 709.65 204.75 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n10 16 Cyclist -1 -1 -1 454.98 171.13 484.08 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n10 18 Car -1 -1 -1 474.69 178.17 513.42 207.72 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n10 12 Pedestrian -1 -1 -1 286.31 174.28 312.47 239.40 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n10 11 Pedestrian -1 -1 -1 726.46 158.72 740.90 204.57 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n10 8 Pedestrian -1 -1 -1 1104.96 139.27 1163.65 279.32 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n10 20 Pedestrian -1 -1 -1 711.22 161.42 725.49 203.77 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n11 1 Car -1 -1 -1 105.27 193.40 325.44 297.55 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n11 2 Car -1 -1 -1 334.89 175.61 435.50 241.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n11 5 Pedestrian -1 -1 -1 977.00 137.03 1038.62 289.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n11 7 Car -1 -1 -1 503.05 164.05 559.07 210.94 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n11 16 Cyclist -1 -1 -1 448.25 170.92 480.27 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n11 6 Pedestrian -1 -1 -1 1069.49 135.73 1130.14 284.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n11 9 Pedestrian -1 -1 -1 259.75 168.62 287.57 237.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n11 14 Pedestrian -1 -1 -1 698.45 163.29 712.56 205.53 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n11 18 Car -1 -1 -1 472.95 177.18 511.73 207.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n11 4 Car -1 -1 -1 433.78 180.50 479.81 217.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n11 12 Pedestrian -1 -1 -1 280.61 173.30 306.17 240.31 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n11 17 Truck -1 -1 -1 564.80 125.54 641.30 217.60 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n11 11 Pedestrian -1 -1 -1 729.48 158.39 744.52 205.12 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n11 8 Pedestrian -1 -1 -1 1119.61 133.62 1187.05 284.51 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n11 10 Pedestrian -1 -1 -1 1160.73 131.90 1214.49 280.11 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n12 1 Car -1 -1 -1 83.10 193.97 316.78 303.85 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n12 2 Car -1 -1 -1 328.69 175.31 433.11 242.77 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n12 7 Car -1 -1 -1 501.88 163.47 559.12 211.52 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n12 5 Pedestrian -1 -1 -1 982.95 133.06 1047.84 293.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n12 16 Cyclist -1 -1 -1 438.32 170.92 475.70 239.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n12 18 Car -1 -1 -1 472.75 176.86 511.48 207.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n12 6 Pedestrian -1 -1 -1 1084.48 134.58 1153.31 292.72 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n12 9 Pedestrian -1 -1 -1 253.59 168.30 284.31 243.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n12 12 Pedestrian -1 -1 -1 276.03 171.64 302.38 239.94 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n12 4 Car -1 -1 -1 430.52 180.23 476.29 218.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n12 11 Pedestrian -1 -1 -1 732.77 158.24 747.81 205.24 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n12 8 Pedestrian -1 -1 -1 1131.22 130.51 1191.15 287.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n12 14 Pedestrian -1 -1 -1 701.20 162.79 715.22 206.35 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n12 17 Truck -1 -1 -1 568.60 125.55 642.09 217.54 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n12 10 Pedestrian -1 -1 -1 1187.57 132.06 1217.42 280.54 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n13 2 Car -1 -1 -1 322.44 174.85 430.31 244.34 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n13 1 Car -1 -1 -1 56.48 194.64 307.77 310.28 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n13 5 Pedestrian -1 -1 -1 992.97 135.32 1061.72 292.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n13 7 Car -1 -1 -1 499.91 163.02 558.00 212.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n13 16 Cyclist -1 -1 -1 431.78 170.80 466.79 242.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n13 14 Pedestrian -1 -1 -1 702.98 163.30 717.58 207.94 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n13 6 Pedestrian -1 -1 -1 1103.22 136.84 1172.41 297.17 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n13 9 Pedestrian -1 -1 -1 249.07 167.19 280.51 243.16 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n13 18 Car -1 -1 -1 471.35 176.48 510.40 206.80 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n13 12 Pedestrian -1 -1 -1 271.37 171.22 299.72 239.74 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n13 8 Pedestrian -1 -1 -1 1144.72 131.55 1207.94 288.78 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n13 11 Pedestrian -1 -1 -1 736.53 158.72 751.83 205.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n13 4 Car -1 -1 -1 423.74 179.06 474.79 219.72 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n13 17 Truck -1 -1 -1 569.41 125.57 641.94 217.12 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n13 21 Pedestrian -1 -1 -1 725.36 162.18 739.69 204.68 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n14 1 Car -1 -1 -1 28.43 195.02 297.27 316.65 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n14 2 Car -1 -1 -1 316.63 173.94 428.24 245.52 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n14 5 Pedestrian -1 -1 -1 1002.03 135.44 1081.90 298.53 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 16 Cyclist -1 -1 -1 424.44 169.74 460.63 245.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n14 14 Pedestrian -1 -1 -1 705.08 161.21 721.53 207.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n14 7 Car -1 -1 -1 496.84 161.74 557.44 213.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n14 9 Pedestrian -1 -1 -1 244.26 165.66 274.11 244.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n14 6 Pedestrian -1 -1 -1 1120.54 135.76 1186.16 298.12 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n14 12 Pedestrian -1 -1 -1 266.75 171.17 295.30 240.44 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n14 11 Pedestrian -1 -1 -1 740.40 158.12 756.25 203.48 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n14 4 Car -1 -1 -1 409.93 177.72 474.24 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n14 8 Pedestrian -1 -1 -1 1167.47 131.50 1215.44 295.73 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n14 18 Car -1 -1 -1 471.76 175.34 510.62 206.69 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n14 17 Truck -1 -1 -1 569.34 124.50 642.67 217.10 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n15 1 Car -1 -1 -1 0.14 196.08 287.31 323.29 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n15 2 Car -1 -1 -1 311.31 174.24 425.52 246.82 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n15 16 Cyclist -1 -1 -1 414.04 170.23 452.76 249.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n15 5 Pedestrian -1 -1 -1 1021.86 135.22 1101.33 307.20 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n15 9 Pedestrian -1 -1 -1 239.42 166.30 270.23 244.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n15 14 Pedestrian -1 -1 -1 707.53 160.67 724.74 208.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n15 7 Car -1 -1 -1 495.49 162.16 554.89 214.15 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n15 6 Pedestrian -1 -1 -1 1131.06 131.91 1213.97 302.06 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n15 11 Pedestrian -1 -1 -1 743.66 157.63 760.67 205.84 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n15 12 Pedestrian -1 -1 -1 263.36 172.13 289.68 240.46 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n15 4 Car -1 -1 -1 409.56 179.50 472.78 223.08 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n15 18 Car -1 -1 -1 471.02 174.33 511.50 207.74 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n15 8 Pedestrian -1 -1 -1 1179.67 130.90 1218.60 303.98 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n15 17 Truck -1 -1 -1 568.20 122.65 644.79 214.60 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n15 22 Pedestrian -1 -1 -1 729.96 161.46 744.52 203.19 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n16 1 Car -1 -1 -1 -3.79 196.97 276.18 336.72 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n16 2 Car -1 -1 -1 302.34 176.71 422.22 252.38 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n16 4 Car -1 -1 -1 408.78 182.63 473.56 224.17 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n16 16 Cyclist -1 -1 -1 402.49 174.37 443.69 253.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n16 14 Pedestrian -1 -1 -1 709.88 162.94 726.56 212.06 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n16 9 Pedestrian -1 -1 -1 234.79 168.51 265.37 243.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n16 5 Pedestrian -1 -1 -1 1033.41 132.34 1112.36 311.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n16 11 Pedestrian -1 -1 -1 745.85 158.98 763.69 208.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 7 Car -1 -1 -1 492.20 163.78 553.71 218.48 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n16 22 Pedestrian -1 -1 -1 732.60 162.95 748.33 208.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n16 12 Pedestrian -1 -1 -1 257.90 174.51 283.51 243.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n16 18 Car -1 -1 -1 467.34 176.82 507.61 209.45 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n16 6 Pedestrian -1 -1 -1 1137.13 130.86 1223.13 304.53 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n16 8 Pedestrian -1 -1 -1 1191.38 132.75 1221.95 302.73 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n16 17 Truck -1 -1 -1 567.27 125.64 646.34 218.21 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n17 1 Car -1 -1 -1 0.53 202.60 263.48 347.33 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n17 2 Car -1 -1 -1 296.91 179.77 416.73 256.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n17 14 Pedestrian -1 -1 -1 711.94 166.23 730.03 214.72 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n17 4 Car -1 -1 -1 406.20 185.68 471.53 227.36 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n17 16 Cyclist -1 -1 -1 390.10 176.33 432.84 260.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n17 5 Pedestrian -1 -1 -1 1055.31 129.50 1128.71 313.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n17 22 Pedestrian -1 -1 -1 735.49 164.74 751.66 210.64 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n17 9 Pedestrian -1 -1 -1 227.16 171.70 260.13 249.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n17 7 Car -1 -1 -1 489.48 165.74 552.56 222.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n17 11 Pedestrian -1 -1 -1 750.28 161.52 767.85 211.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n17 12 Pedestrian -1 -1 -1 254.94 174.98 282.09 251.05 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n17 6 Pedestrian -1 -1 -1 1156.43 133.40 1218.33 308.84 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n17 18 Car -1 -1 -1 466.74 178.41 508.89 212.20 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n18 1 Car -1 -1 -1 -1.76 205.18 251.26 359.27 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n18 2 Car -1 -1 -1 291.17 180.66 409.30 259.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n18 9 Pedestrian -1 -1 -1 221.54 173.67 256.84 254.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n18 4 Car -1 -1 -1 404.72 186.06 470.09 229.18 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n18 14 Pedestrian -1 -1 -1 714.98 166.62 733.03 215.94 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n18 16 Cyclist -1 -1 -1 367.44 178.27 425.62 272.95 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n18 12 Pedestrian -1 -1 -1 249.21 177.65 275.97 255.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 5 Pedestrian -1 -1 -1 1075.74 131.80 1154.21 318.37 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n18 22 Pedestrian -1 -1 -1 738.44 164.39 754.64 211.88 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n18 11 Pedestrian -1 -1 -1 754.47 162.86 773.20 212.80 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n18 6 Pedestrian -1 -1 -1 1184.93 131.63 1221.05 318.56 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n18 18 Car -1 -1 -1 471.71 179.31 511.95 215.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n18 7 Car -1 -1 -1 486.35 166.60 550.83 225.01 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n18 23 Van -1 -1 -1 486.35 166.60 550.83 225.01 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 24 Truck -1 -1 -1 571.92 130.71 647.93 221.68 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n19 1 Car -1 -1 -1 -3.47 209.38 237.25 364.02 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n19 4 Car -1 -1 -1 400.31 187.43 468.33 232.25 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n19 2 Car -1 -1 -1 280.01 182.76 403.87 262.57 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n19 16 Cyclist -1 -1 -1 352.38 179.76 409.25 280.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n19 12 Pedestrian -1 -1 -1 239.93 180.28 269.72 255.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n19 9 Pedestrian -1 -1 -1 216.89 174.87 247.29 254.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n19 14 Pedestrian -1 -1 -1 718.05 166.49 737.30 217.67 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n19 5 Pedestrian -1 -1 -1 1090.59 130.74 1178.31 326.61 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n19 22 Pedestrian -1 -1 -1 740.16 163.15 757.91 213.59 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n19 7 Car -1 -1 -1 481.52 166.62 549.98 228.47 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n19 18 Car -1 -1 -1 468.09 181.29 507.40 215.18 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n19 11 Pedestrian -1 -1 -1 758.27 163.73 777.67 215.47 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n19 23 Van -1 -1 -1 483.59 166.02 549.98 226.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n19 24 Truck -1 -1 -1 570.93 133.06 649.84 223.52 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n20 1 Car -1 -1 -1 -2.30 209.56 221.06 364.74 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n20 4 Car -1 -1 -1 395.39 188.72 465.25 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n20 2 Car -1 -1 -1 269.34 183.95 401.16 267.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n20 16 Cyclist -1 -1 -1 335.31 175.59 393.35 290.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n20 9 Pedestrian -1 -1 -1 209.35 175.18 239.07 259.79 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n20 7 Car -1 -1 -1 475.84 168.00 548.28 230.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n20 5 Pedestrian -1 -1 -1 1108.10 129.47 1213.82 335.58 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n20 22 Pedestrian -1 -1 -1 743.95 164.62 761.89 215.93 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 11 Pedestrian -1 -1 -1 763.89 162.34 785.49 218.49 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n20 12 Pedestrian -1 -1 -1 234.22 182.64 264.56 260.03 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n20 14 Pedestrian -1 -1 -1 720.84 165.88 741.47 218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n20 18 Car -1 -1 -1 467.93 181.81 507.35 215.90 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n20 24 Truck -1 -1 -1 574.40 133.24 651.15 224.36 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n21 1 Car -1 -1 -1 -1.17 213.56 202.80 365.10 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n21 4 Car -1 -1 -1 390.52 188.12 463.46 236.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n21 2 Car -1 -1 -1 257.70 185.04 397.32 267.38 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n21 7 Car -1 -1 -1 470.68 167.89 545.93 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n21 16 Cyclist -1 -1 -1 298.79 171.31 379.16 309.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n21 9 Pedestrian -1 -1 -1 202.13 172.70 236.57 262.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n21 22 Pedestrian -1 -1 -1 746.95 164.53 765.87 218.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 14 Pedestrian -1 -1 -1 724.08 166.43 743.63 220.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n21 5 Pedestrian -1 -1 -1 1129.72 129.85 1222.90 336.52 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n21 12 Pedestrian -1 -1 -1 225.95 179.16 254.43 263.10 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n21 11 Pedestrian -1 -1 -1 768.52 159.67 789.95 220.73 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n21 18 Car -1 -1 -1 465.40 182.62 503.40 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n21 24 Truck -1 -1 -1 571.42 133.20 649.91 224.45 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n21 25 Pedestrian -1 -1 -1 1087.60 137.37 1127.53 245.79 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n22 1 Car -1 -1 -1 -0.88 213.00 180.55 366.06 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n22 4 Car -1 -1 -1 386.00 187.85 460.38 236.84 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n22 14 Pedestrian -1 -1 -1 726.21 166.05 747.32 222.20 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n22 2 Car -1 -1 -1 244.73 184.41 394.75 273.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n22 7 Car -1 -1 -1 464.89 166.36 543.98 235.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n22 22 Pedestrian -1 -1 -1 750.18 164.49 769.58 218.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n22 16 Cyclist -1 -1 -1 257.17 171.38 365.26 325.56 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n22 12 Pedestrian -1 -1 -1 215.81 178.23 247.91 263.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n22 9 Pedestrian -1 -1 -1 189.63 172.24 226.60 269.72 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 11 Pedestrian -1 -1 -1 773.99 158.91 796.35 220.93 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n22 5 Pedestrian -1 -1 -1 1153.26 120.66 1221.30 345.32 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n22 24 Truck -1 -1 -1 575.11 132.40 650.65 224.35 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n22 25 Pedestrian -1 -1 -1 1096.74 136.61 1141.10 246.77 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n23 7 Car -1 -1 -1 457.88 165.19 541.71 237.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n23 4 Car -1 -1 -1 380.34 187.42 457.52 237.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n23 2 Car -1 -1 -1 242.15 184.58 388.13 275.26 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n23 1 Car -1 -1 -1 -2.10 201.29 158.98 364.23 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n23 22 Pedestrian -1 -1 -1 753.24 162.06 772.10 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n23 14 Pedestrian -1 -1 -1 729.21 164.38 750.47 221.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n23 9 Pedestrian -1 -1 -1 180.12 174.02 215.30 268.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n23 12 Pedestrian -1 -1 -1 205.96 177.75 240.81 266.16 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n23 11 Pedestrian -1 -1 -1 776.93 158.54 800.87 220.84 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n23 16 Cyclist -1 -1 -1 219.56 175.81 333.71 344.01 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n23 5 Pedestrian -1 -1 -1 1164.54 121.69 1218.91 344.12 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n23 24 Truck -1 -1 -1 571.03 130.09 650.51 221.87 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n23 25 Pedestrian -1 -1 -1 1106.59 136.89 1154.16 250.98 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n24 2 Car -1 -1 -1 222.09 182.98 378.93 282.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n24 4 Car -1 -1 -1 374.51 187.99 453.91 237.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n24 7 Car -1 -1 -1 451.03 163.77 538.24 239.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n24 16 Cyclist -1 -1 -1 162.02 175.55 299.79 367.39 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n24 22 Pedestrian -1 -1 -1 756.62 160.38 776.53 218.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 14 Pedestrian -1 -1 -1 731.34 161.47 754.51 222.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n24 9 Pedestrian -1 -1 -1 166.27 173.37 205.92 269.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n24 1 Car -1 -1 -1 -2.32 201.45 135.16 363.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n24 11 Pedestrian -1 -1 -1 780.26 158.15 801.09 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n24 12 Pedestrian -1 -1 -1 196.25 181.62 229.08 262.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n24 24 Truck -1 -1 -1 570.36 129.57 651.09 221.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n24 25 Pedestrian -1 -1 -1 1115.06 135.19 1168.64 253.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n24 5 Pedestrian -1 -1 -1 1187.34 118.43 1218.16 347.49 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n25 2 Car -1 -1 -1 197.71 184.31 372.72 283.12 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n25 7 Car -1 -1 -1 441.95 162.56 534.35 243.52 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n25 4 Car -1 -1 -1 366.76 187.83 449.51 238.92 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n25 16 Cyclist -1 -1 -1 77.76 174.76 255.07 368.21 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n25 14 Pedestrian -1 -1 -1 733.68 161.08 756.17 222.15 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n25 9 Pedestrian -1 -1 -1 155.33 173.81 193.85 269.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n25 11 Pedestrian -1 -1 -1 783.10 157.21 805.70 218.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n25 22 Pedestrian -1 -1 -1 759.23 159.40 780.45 219.46 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n25 12 Pedestrian -1 -1 -1 187.39 181.88 221.33 267.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n25 1 Car -1 -1 -1 -3.27 202.01 106.10 363.02 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n25 24 Truck -1 -1 -1 570.39 128.93 650.52 222.03 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n25 25 Pedestrian -1 -1 -1 1124.23 129.26 1182.49 259.33 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n25 26 Car -1 -1 -1 638.37 172.41 682.03 192.67 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n26 2 Car -1 -1 -1 183.39 184.21 363.96 288.48 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n26 7 Car -1 -1 -1 431.07 162.62 530.60 248.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n26 4 Car -1 -1 -1 359.57 187.89 446.04 240.59 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n26 14 Pedestrian -1 -1 -1 736.18 159.81 760.47 222.00 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 11 Pedestrian -1 -1 -1 785.61 155.28 809.95 220.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n26 22 Pedestrian -1 -1 -1 762.14 159.09 784.45 220.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n26 16 Cyclist -1 -1 -1 -3.80 178.23 198.29 363.28 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n26 9 Pedestrian -1 -1 -1 145.69 172.14 185.88 276.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n26 12 Pedestrian -1 -1 -1 173.16 181.13 212.09 269.87 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n26 26 Car -1 -1 -1 637.44 172.47 682.25 192.08 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n27 2 Car -1 -1 -1 162.38 183.45 355.09 292.37 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n27 7 Car -1 -1 -1 418.92 161.10 526.52 252.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n27 11 Pedestrian -1 -1 -1 789.59 153.28 813.61 220.73 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n27 4 Car -1 -1 -1 349.45 188.00 443.35 241.63 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n27 14 Pedestrian -1 -1 -1 739.30 161.18 764.63 223.23 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n27 9 Pedestrian -1 -1 -1 132.59 174.60 170.64 275.03 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n27 22 Pedestrian -1 -1 -1 765.57 159.24 789.35 221.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n27 26 Car -1 -1 -1 637.85 172.10 680.72 191.80 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n27 12 Pedestrian -1 -1 -1 160.95 182.39 195.92 269.44 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n28 2 Car -1 -1 -1 144.80 183.56 347.35 298.49 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n28 7 Car -1 -1 -1 406.15 160.66 522.12 257.41 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n28 11 Pedestrian -1 -1 -1 793.44 152.46 818.32 221.46 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n28 4 Car -1 -1 -1 341.32 187.50 442.59 242.66 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n28 14 Pedestrian -1 -1 -1 742.36 160.88 767.44 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n28 9 Pedestrian -1 -1 -1 120.09 176.70 158.79 272.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 22 Pedestrian -1 -1 -1 768.70 158.25 793.18 221.99 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n28 26 Car -1 -1 -1 636.58 172.22 680.66 191.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n28 12 Pedestrian -1 -1 -1 147.35 183.83 185.06 272.61 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n28 27 Pedestrian -1 -1 -1 723.77 164.70 735.14 194.85 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n29 2 Car -1 -1 -1 124.34 183.78 337.26 306.14 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n29 7 Car -1 -1 -1 390.66 158.94 518.19 263.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 14 Pedestrian -1 -1 -1 746.90 160.72 771.66 227.31 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n29 22 Pedestrian -1 -1 -1 772.97 156.50 797.47 222.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n29 4 Car -1 -1 -1 336.76 187.86 439.07 245.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n29 11 Pedestrian -1 -1 -1 798.23 152.02 826.97 224.62 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n29 9 Pedestrian -1 -1 -1 107.47 177.64 147.95 274.33 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n29 12 Pedestrian -1 -1 -1 135.25 184.16 173.80 274.39 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n29 26 Car -1 -1 -1 637.21 172.30 679.96 191.74 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n29 27 Pedestrian -1 -1 -1 738.06 160.30 751.87 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n30 2 Car -1 -1 -1 97.51 184.29 327.63 312.87 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n30 7 Car -1 -1 -1 376.45 157.65 512.90 271.24 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n30 14 Pedestrian -1 -1 -1 750.59 159.73 776.77 228.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 22 Pedestrian -1 -1 -1 777.17 157.07 802.90 225.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 11 Pedestrian -1 -1 -1 803.63 153.25 831.00 226.95 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n30 4 Car -1 -1 -1 329.37 188.95 432.80 246.89 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n30 9 Pedestrian -1 -1 -1 92.89 175.76 133.58 280.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n30 12 Pedestrian -1 -1 -1 121.02 183.97 157.68 274.53 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n30 26 Car -1 -1 -1 635.98 172.03 681.00 191.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n30 27 Pedestrian -1 -1 -1 725.44 164.40 736.93 195.42 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n30 28 Pedestrian -1 -1 -1 738.05 160.96 751.39 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n31 2 Car -1 -1 -1 69.56 185.44 315.80 319.53 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n31 7 Car -1 -1 -1 354.44 155.88 508.30 280.17 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n31 11 Pedestrian -1 -1 -1 809.15 151.84 838.72 228.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 4 Car -1 -1 -1 322.84 188.51 430.03 248.25 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n31 14 Pedestrian -1 -1 -1 754.47 158.12 781.64 230.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n31 22 Pedestrian -1 -1 -1 781.48 155.51 808.25 226.47 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n31 9 Pedestrian -1 -1 -1 79.01 174.18 116.87 281.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n31 27 Pedestrian -1 -1 -1 726.29 163.81 737.93 195.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n31 12 Pedestrian -1 -1 -1 102.94 182.18 139.18 274.94 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n31 26 Car -1 -1 -1 633.37 171.81 680.86 192.08 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n31 28 Pedestrian -1 -1 -1 738.64 161.01 751.32 195.28 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n32 7 Car -1 -1 -1 335.69 155.57 499.88 288.25 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n32 2 Car -1 -1 -1 34.94 184.30 304.65 328.80 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n32 4 Car -1 -1 -1 311.19 187.30 427.46 248.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n32 22 Pedestrian -1 -1 -1 787.33 154.95 815.48 227.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n32 11 Pedestrian -1 -1 -1 817.23 149.64 846.80 229.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n32 14 Pedestrian -1 -1 -1 759.36 159.99 788.01 234.34 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n32 27 Pedestrian -1 -1 -1 726.51 163.26 738.17 194.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n32 9 Pedestrian -1 -1 -1 61.15 172.86 103.16 283.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n32 12 Pedestrian -1 -1 -1 86.33 178.65 124.23 272.62 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n32 26 Car -1 -1 -1 636.46 171.77 681.28 191.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n32 28 Pedestrian -1 -1 -1 741.69 160.58 755.00 195.49 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n33 2 Car -1 -1 -1 2.40 183.87 292.06 336.45 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n33 7 Car -1 -1 -1 306.88 152.96 494.02 298.99 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n33 14 Pedestrian -1 -1 -1 762.34 158.62 794.84 236.98 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n33 11 Pedestrian -1 -1 -1 823.90 148.61 854.84 231.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n33 22 Pedestrian -1 -1 -1 793.02 152.53 823.19 230.47 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n33 9 Pedestrian -1 -1 -1 42.18 174.06 84.24 283.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n33 27 Pedestrian -1 -1 -1 726.39 163.40 739.86 196.40 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n33 26 Car -1 -1 -1 636.78 171.90 681.08 191.98 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n33 12 Pedestrian -1 -1 -1 73.34 181.67 106.49 275.48 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n33 28 Pedestrian -1 -1 -1 743.99 159.66 757.10 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n34 2 Car -1 -1 -1 -1.61 184.38 280.56 344.19 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n34 7 Car -1 -1 -1 275.39 150.19 486.37 309.60 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n34 11 Pedestrian -1 -1 -1 830.09 147.73 863.84 234.36 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n34 14 Pedestrian -1 -1 -1 767.91 156.10 801.89 239.89 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n34 22 Pedestrian -1 -1 -1 799.30 153.05 827.92 229.32 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n34 9 Pedestrian -1 -1 -1 25.73 175.68 69.33 288.89 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n34 28 Pedestrian -1 -1 -1 744.47 158.61 757.42 194.38 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n34 27 Pedestrian -1 -1 -1 727.69 163.51 742.26 196.90 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n34 26 Car -1 -1 -1 637.08 171.32 681.49 191.85 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n34 12 Pedestrian -1 -1 -1 60.06 184.10 89.34 274.20 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n35 2 Car -1 -1 -1 0.08 183.49 270.66 358.43 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n35 7 Car -1 -1 -1 241.45 152.77 472.35 321.59 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n35 4 Car -1 -1 -1 287.05 183.66 421.02 266.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n35 14 Pedestrian -1 -1 -1 771.53 153.10 806.04 240.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n35 11 Pedestrian -1 -1 -1 836.78 147.01 874.81 236.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n35 27 Pedestrian -1 -1 -1 728.96 162.69 742.97 195.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n35 22 Pedestrian -1 -1 -1 804.45 151.76 836.82 231.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n35 9 Pedestrian -1 -1 -1 9.12 175.41 48.09 282.99 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n35 28 Pedestrian -1 -1 -1 746.49 158.74 758.88 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n35 26 Car -1 -1 -1 644.14 169.63 683.06 189.37 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n35 12 Pedestrian -1 -1 -1 42.78 181.67 75.67 276.31 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n35 29 Car -1 -1 -1 401.03 183.08 483.27 226.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n36 2 Car -1 -1 -1 1.33 183.92 247.78 364.75 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n36 14 Pedestrian -1 -1 -1 776.99 152.49 811.41 242.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n36 7 Car -1 -1 -1 189.89 148.14 464.14 347.62 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n36 29 Car -1 -1 -1 396.76 181.47 480.55 225.10 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n36 11 Pedestrian -1 -1 -1 847.77 147.02 884.45 239.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n36 28 Pedestrian -1 -1 -1 748.28 157.55 762.36 193.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n36 9 Pedestrian -1 -1 -1 1.38 174.52 32.41 283.87 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n36 26 Car -1 -1 -1 644.08 168.94 682.73 189.17 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n36 27 Pedestrian -1 -1 -1 730.60 161.43 743.20 195.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n36 22 Pedestrian -1 -1 -1 811.57 151.08 844.27 230.92 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n37 7 Car -1 -1 -1 131.35 144.06 452.81 367.03 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n37 2 Car -1 -1 -1 3.51 185.19 221.98 363.79 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n37 29 Car -1 -1 -1 397.03 182.11 477.97 227.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n37 14 Pedestrian -1 -1 -1 783.77 154.30 820.02 247.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n37 11 Pedestrian -1 -1 -1 854.82 147.14 894.63 243.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n37 22 Pedestrian -1 -1 -1 819.45 150.82 853.29 239.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n37 27 Pedestrian -1 -1 -1 729.77 161.69 743.99 195.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n37 28 Pedestrian -1 -1 -1 751.27 158.33 766.09 195.36 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n37 26 Car -1 -1 -1 638.49 169.86 681.25 189.93 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n37 9 Pedestrian -1 -1 -1 -0.91 176.22 12.90 289.05 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n38 22 Pedestrian -1 -1 -1 829.18 150.00 865.30 245.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n38 29 Car -1 -1 -1 394.12 181.57 474.09 226.10 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n38 11 Pedestrian -1 -1 -1 866.60 143.73 905.10 247.05 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n38 14 Pedestrian -1 -1 -1 791.93 156.05 827.70 250.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n38 2 Car -1 -1 -1 3.65 183.85 206.42 358.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n38 7 Car -1 -1 -1 40.18 138.73 437.19 364.62 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n38 26 Car -1 -1 -1 639.28 169.99 680.82 190.03 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n38 27 Pedestrian -1 -1 -1 732.35 161.65 745.72 196.05 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n38 28 Pedestrian -1 -1 -1 753.06 158.72 767.17 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n38 31 Pedestrian -1 -1 -1 416.14 171.15 429.43 210.29 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n38 32 Car -1 -1 -1 66.60 141.81 411.24 339.90 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n39 29 Car -1 -1 -1 388.85 180.80 472.09 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n39 14 Pedestrian -1 -1 -1 801.35 154.59 839.78 255.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n39 11 Pedestrian -1 -1 -1 877.18 143.30 918.14 251.04 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n39 22 Pedestrian -1 -1 -1 839.12 149.74 877.00 248.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n39 7 Car -1 -1 -1 -4.15 125.78 420.94 363.58 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n39 28 Pedestrian -1 -1 -1 755.78 157.44 770.18 196.08 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n39 26 Car -1 -1 -1 645.17 168.88 683.30 189.62 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n39 27 Pedestrian -1 -1 -1 732.15 161.56 746.48 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n39 31 Pedestrian -1 -1 -1 414.84 169.99 428.35 209.96 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n39 32 Car -1 -1 -1 38.20 134.20 393.13 331.99 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n40 29 Car -1 -1 -1 385.52 181.11 469.15 229.39 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n40 14 Pedestrian -1 -1 -1 811.69 151.96 851.58 260.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n40 11 Pedestrian -1 -1 -1 891.41 141.76 939.69 255.18 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n40 22 Pedestrian -1 -1 -1 850.20 148.81 888.54 254.01 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n40 28 Pedestrian -1 -1 -1 759.13 156.04 773.64 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n40 7 Car -1 -1 -1 -4.99 122.90 398.33 364.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n40 26 Car -1 -1 -1 645.79 168.95 682.85 189.79 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n40 32 Car -1 -1 -1 0.99 101.00 392.63 365.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n40 31 Pedestrian -1 -1 -1 411.80 169.54 426.51 211.35 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n40 27 Pedestrian -1 -1 -1 732.08 161.69 747.16 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n40 36 Pedestrian -1 -1 -1 384.64 172.60 400.78 217.90 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n41 29 Car -1 -1 -1 382.05 182.29 465.48 230.82 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n41 11 Pedestrian -1 -1 -1 903.59 142.72 952.30 259.50 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 14 Pedestrian -1 -1 -1 821.75 149.37 865.79 265.22 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n41 22 Pedestrian -1 -1 -1 861.26 148.04 901.51 257.75 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n41 32 Car -1 -1 -1 4.01 92.98 360.93 365.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n41 31 Pedestrian -1 -1 -1 410.53 170.23 426.13 212.11 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n41 26 Car -1 -1 -1 645.52 169.50 682.71 190.45 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n41 36 Pedestrian -1 -1 -1 381.86 172.84 400.11 223.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n41 28 Pedestrian -1 -1 -1 759.80 156.88 774.46 196.76 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n41 27 Pedestrian -1 -1 -1 733.34 162.15 747.71 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n42 29 Car -1 -1 -1 375.84 182.46 464.04 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n42 14 Pedestrian -1 -1 -1 835.02 148.56 882.39 271.91 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n42 11 Pedestrian -1 -1 -1 920.67 140.11 973.18 265.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n42 22 Pedestrian -1 -1 -1 871.82 146.75 916.02 265.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n42 4 Car -1 -1 -1 205.54 195.59 364.03 270.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n42 32 Car -1 -1 -1 2.87 87.69 323.21 362.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n42 36 Pedestrian -1 -1 -1 379.91 172.57 398.49 225.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n42 26 Car -1 -1 -1 645.19 170.06 683.05 190.73 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n42 28 Pedestrian -1 -1 -1 757.80 159.67 775.96 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n42 31 Pedestrian -1 -1 -1 409.74 170.35 425.54 212.85 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n42 27 Pedestrian -1 -1 -1 733.72 164.40 748.18 194.52 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n43 29 Car -1 -1 -1 374.07 182.61 462.07 232.45 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n43 11 Pedestrian -1 -1 -1 940.40 139.30 991.08 271.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n43 22 Pedestrian -1 -1 -1 886.48 148.56 937.85 269.43 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n43 4 Car -1 -1 -1 190.59 195.53 356.44 277.45 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n43 14 Pedestrian -1 -1 -1 848.04 149.78 899.41 278.62 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n43 36 Pedestrian -1 -1 -1 378.66 172.48 397.55 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n43 27 Pedestrian -1 -1 -1 736.65 163.65 753.70 201.04 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n43 26 Car -1 -1 -1 645.09 170.37 683.22 191.17 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n43 31 Pedestrian -1 -1 -1 409.44 169.81 425.85 213.63 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n44 4 Car -1 -1 -1 167.34 196.97 349.19 281.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n44 29 Car -1 -1 -1 367.69 182.98 460.44 234.92 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n44 11 Pedestrian -1 -1 -1 961.99 135.90 1015.58 277.95 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n44 14 Pedestrian -1 -1 -1 861.48 150.43 917.46 286.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n44 22 Pedestrian -1 -1 -1 902.08 148.78 954.01 276.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n44 27 Pedestrian -1 -1 -1 740.31 161.84 756.42 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n44 36 Pedestrian -1 -1 -1 378.73 172.71 396.23 224.87 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n44 26 Car -1 -1 -1 649.07 170.28 686.60 191.29 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n44 31 Pedestrian -1 -1 -1 407.93 169.75 424.09 214.05 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n44 38 Pedestrian -1 -1 -1 766.76 157.52 783.54 200.15 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n45 29 Car -1 -1 -1 362.10 183.36 458.27 237.12 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n45 4 Car -1 -1 -1 141.74 197.29 344.00 285.96 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n45 14 Pedestrian -1 -1 -1 876.93 146.85 932.71 294.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n45 11 Pedestrian -1 -1 -1 986.55 133.76 1045.35 287.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 22 Pedestrian -1 -1 -1 923.83 148.10 977.27 286.32 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 27 Pedestrian -1 -1 -1 742.17 162.23 759.85 203.50 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n45 38 Pedestrian -1 -1 -1 770.64 157.21 787.88 200.47 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n45 36 Pedestrian -1 -1 -1 376.72 173.58 393.60 224.70 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n45 26 Car -1 -1 -1 653.32 170.75 687.97 192.58 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n45 31 Pedestrian -1 -1 -1 407.10 169.79 424.25 217.11 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n45 39 Car -1 -1 -1 -3.84 179.98 83.84 362.02 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n46 4 Car -1 -1 -1 119.52 199.00 336.75 291.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n46 29 Car -1 -1 -1 357.58 183.98 455.53 238.66 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n46 14 Pedestrian -1 -1 -1 896.40 146.19 958.93 302.31 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n46 22 Pedestrian -1 -1 -1 945.26 147.42 1001.61 293.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n46 11 Pedestrian -1 -1 -1 1013.91 135.91 1077.86 297.53 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n46 27 Pedestrian -1 -1 -1 746.54 162.50 762.73 204.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n46 26 Car -1 -1 -1 653.84 171.52 688.23 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n46 31 Pedestrian -1 -1 -1 407.22 169.61 424.05 217.86 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n46 38 Pedestrian -1 -1 -1 773.29 158.59 791.89 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n47 4 Car -1 -1 -1 95.18 200.03 330.25 298.13 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n47 29 Car -1 -1 -1 351.89 185.19 453.85 241.47 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n47 14 Pedestrian -1 -1 -1 922.94 146.18 993.25 311.78 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n47 22 Pedestrian -1 -1 -1 969.59 143.52 1031.63 299.52 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n47 11 Pedestrian -1 -1 -1 1040.47 130.92 1113.84 304.97 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n47 27 Pedestrian -1 -1 -1 749.84 162.23 767.17 205.93 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n47 26 Car -1 -1 -1 653.33 171.33 689.67 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n47 31 Pedestrian -1 -1 -1 407.03 169.63 423.67 218.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n47 40 Car -1 -1 -1 563.43 174.38 589.45 193.98 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n48 29 Car -1 -1 -1 346.78 185.03 452.09 242.85 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n48 4 Car -1 -1 -1 73.63 199.78 321.36 305.71 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n48 14 Pedestrian -1 -1 -1 949.66 142.22 1027.57 324.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n48 11 Pedestrian -1 -1 -1 1074.69 130.20 1155.68 313.66 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n48 22 Pedestrian -1 -1 -1 996.59 139.83 1073.16 311.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n48 27 Pedestrian -1 -1 -1 754.06 161.95 770.31 206.57 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n48 31 Pedestrian -1 -1 -1 407.35 169.85 423.76 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n48 26 Car -1 -1 -1 652.60 171.83 690.83 194.18 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n48 40 Car -1 -1 -1 564.88 173.94 591.97 194.05 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n48 41 Pedestrian -1 -1 -1 783.93 156.80 802.46 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n48 42 Pedestrian -1 -1 -1 538.18 173.46 550.98 206.15 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n49 4 Car -1 -1 -1 52.10 200.39 312.17 312.31 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n49 29 Car -1 -1 -1 341.10 185.05 449.27 244.66 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n49 14 Pedestrian -1 -1 -1 979.66 139.93 1067.15 340.20 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n49 11 Pedestrian -1 -1 -1 1121.95 122.29 1207.26 327.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n49 22 Pedestrian -1 -1 -1 1032.78 136.20 1120.74 328.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n49 31 Pedestrian -1 -1 -1 406.70 169.85 424.13 217.67 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n49 41 Pedestrian -1 -1 -1 788.78 157.49 805.98 203.59 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n49 27 Pedestrian -1 -1 -1 757.49 162.27 774.57 206.90 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n49 26 Car -1 -1 -1 657.61 172.02 693.97 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n49 42 Pedestrian -1 -1 -1 538.26 173.80 550.50 205.76 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n50 4 Car -1 -1 -1 28.77 202.70 302.70 318.11 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n50 29 Car -1 -1 -1 336.24 185.50 447.28 246.98 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n50 14 Pedestrian -1 -1 -1 1020.13 132.11 1110.54 356.25 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n50 41 Pedestrian -1 -1 -1 792.72 157.22 810.63 204.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n50 31 Pedestrian -1 -1 -1 407.34 170.60 424.52 218.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n50 27 Pedestrian -1 -1 -1 761.68 161.85 779.38 207.42 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n50 42 Pedestrian -1 -1 -1 538.06 174.58 550.08 205.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n50 26 Car -1 -1 -1 660.69 172.60 696.83 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n50 22 Pedestrian -1 -1 -1 1060.46 133.89 1161.97 340.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n50 11 Pedestrian -1 -1 -1 1162.18 114.90 1220.30 342.76 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n51 4 Car -1 -1 -1 1.11 203.26 292.38 329.42 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n51 29 Car -1 -1 -1 330.23 186.10 445.31 249.95 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n51 14 Pedestrian -1 -1 -1 1062.78 132.03 1174.72 363.23 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n51 41 Pedestrian -1 -1 -1 797.17 158.00 815.21 205.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n51 22 Pedestrian -1 -1 -1 1114.91 130.58 1207.29 358.06 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n51 27 Pedestrian -1 -1 -1 766.15 162.10 784.86 208.47 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n51 31 Pedestrian -1 -1 -1 407.62 171.20 424.62 219.36 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n51 42 Pedestrian -1 -1 -1 537.93 175.63 550.65 207.07 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n51 43 Pedestrian -1 -1 -1 382.14 170.22 402.84 226.48 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n51 44 Truck -1 -1 -1 605.45 127.77 691.52 214.89 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n52 4 Car -1 -1 -1 -3.63 203.14 282.75 337.53 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n52 29 Car -1 -1 -1 322.64 187.02 443.80 253.32 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n52 14 Pedestrian -1 -1 -1 1104.86 131.69 1224.30 363.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n52 27 Pedestrian -1 -1 -1 770.58 162.76 788.98 210.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n52 41 Pedestrian -1 -1 -1 800.88 159.54 819.77 207.58 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n52 31 Pedestrian -1 -1 -1 409.16 171.83 426.16 219.63 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n52 42 Pedestrian -1 -1 -1 537.97 176.36 550.62 207.65 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n52 43 Pedestrian -1 -1 -1 384.89 170.75 405.14 225.78 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n52 45 Car -1 -1 -1 566.29 176.08 592.72 195.35 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n53 4 Car -1 -1 -1 -1.06 205.14 266.37 345.33 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n53 29 Car -1 -1 -1 314.26 188.48 440.33 255.22 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n53 27 Pedestrian -1 -1 -1 776.83 164.32 795.00 211.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n53 41 Pedestrian -1 -1 -1 805.59 159.12 825.63 210.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n53 42 Pedestrian -1 -1 -1 538.59 175.04 551.35 208.67 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n53 31 Pedestrian -1 -1 -1 409.39 171.63 427.45 223.09 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n53 14 Pedestrian -1 -1 -1 1160.10 141.63 1223.10 362.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n53 43 Pedestrian -1 -1 -1 381.80 170.88 403.82 226.39 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n53 45 Car -1 -1 -1 567.03 176.80 593.64 196.20 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n54 4 Car -1 -1 -1 0.41 207.90 254.54 356.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n54 29 Car -1 -1 -1 309.41 189.50 436.42 258.45 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n54 41 Pedestrian -1 -1 -1 808.74 159.53 830.51 212.32 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n54 27 Pedestrian -1 -1 -1 781.24 165.79 800.17 213.38 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n54 31 Pedestrian -1 -1 -1 409.46 172.15 427.02 219.36 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n54 42 Pedestrian -1 -1 -1 538.75 177.20 551.63 209.67 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n54 43 Pedestrian -1 -1 -1 383.00 171.56 406.89 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n55 4 Car -1 -1 -1 -1.66 211.28 240.91 361.07 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n55 29 Car -1 -1 -1 303.30 190.24 433.20 260.68 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n55 41 Pedestrian -1 -1 -1 812.63 160.57 834.55 212.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n55 27 Pedestrian -1 -1 -1 786.91 165.69 806.56 213.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n55 42 Pedestrian -1 -1 -1 538.43 177.01 550.90 209.88 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n55 31 Pedestrian -1 -1 -1 408.62 172.62 427.57 221.94 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n56 4 Car -1 -1 -1 -0.54 215.67 219.76 362.76 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n56 29 Car -1 -1 -1 291.63 191.77 429.44 264.97 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n56 27 Pedestrian -1 -1 -1 790.60 165.03 811.47 215.23 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n56 41 Pedestrian -1 -1 -1 817.84 161.08 838.56 213.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n56 42 Pedestrian -1 -1 -1 537.96 177.12 550.74 209.87 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n56 31 Pedestrian -1 -1 -1 405.99 173.18 425.87 222.32 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n57 4 Car -1 -1 -1 1.36 216.10 200.26 363.85 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n57 29 Car -1 -1 -1 280.50 192.77 425.25 267.26 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n57 41 Pedestrian -1 -1 -1 821.81 159.44 843.21 215.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n57 27 Pedestrian -1 -1 -1 797.01 163.69 818.40 216.17 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n57 42 Pedestrian -1 -1 -1 537.19 178.14 550.78 210.45 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n57 31 Pedestrian -1 -1 -1 407.00 173.10 424.79 223.62 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n58 29 Car -1 -1 -1 266.49 193.86 419.64 270.88 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n58 4 Car -1 -1 -1 -0.05 216.92 179.04 363.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n58 27 Pedestrian -1 -1 -1 801.69 163.54 822.93 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n58 41 Pedestrian -1 -1 -1 827.98 159.72 849.23 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n58 42 Pedestrian -1 -1 -1 535.76 178.06 549.40 210.97 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n59 29 Car -1 -1 -1 254.45 193.45 414.68 274.01 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n59 4 Car -1 -1 -1 -1.10 215.91 156.56 364.66 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n59 27 Pedestrian -1 -1 -1 806.95 163.47 828.39 218.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n59 41 Pedestrian -1 -1 -1 832.90 160.74 854.61 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n59 42 Pedestrian -1 -1 -1 535.44 178.70 548.96 210.95 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n59 47 Pedestrian -1 -1 -1 403.63 174.18 427.77 236.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n60 29 Car -1 -1 -1 237.01 193.82 410.29 278.09 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n60 27 Pedestrian -1 -1 -1 813.27 165.01 834.95 221.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n60 4 Car -1 -1 -1 -4.13 208.64 131.31 364.29 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n60 41 Pedestrian -1 -1 -1 839.75 161.20 860.28 220.03 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n60 47 Pedestrian -1 -1 -1 402.55 175.06 426.14 234.89 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n60 42 Pedestrian -1 -1 -1 534.95 178.03 549.30 211.71 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n60 48 Pedestrian -1 -1 -1 786.86 170.96 799.28 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n61 29 Car -1 -1 -1 225.18 194.98 404.72 283.77 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n61 27 Pedestrian -1 -1 -1 820.06 164.85 843.28 223.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n61 41 Pedestrian -1 -1 -1 845.50 159.62 866.21 221.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n61 4 Car -1 -1 -1 -2.70 208.35 104.91 364.30 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n61 47 Pedestrian -1 -1 -1 397.42 175.20 425.85 236.57 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n61 42 Pedestrian -1 -1 -1 534.46 177.79 548.90 212.09 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n61 48 Pedestrian -1 -1 -1 800.00 163.67 812.15 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n61 49 Van -1 -1 -1 634.37 134.29 722.84 216.35 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n61 50 Pedestrian -1 -1 -1 790.03 170.39 802.40 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n62 29 Car -1 -1 -1 210.30 195.35 397.22 287.97 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n62 27 Pedestrian -1 -1 -1 827.15 164.10 849.99 223.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n62 41 Pedestrian -1 -1 -1 851.47 158.21 873.43 222.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n62 47 Pedestrian -1 -1 -1 397.10 175.73 424.88 238.87 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n62 4 Car -1 -1 -1 -0.99 200.28 66.70 364.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n62 48 Pedestrian -1 -1 -1 816.43 165.27 826.77 202.00 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n62 50 Pedestrian -1 -1 -1 805.59 165.65 817.60 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n62 51 Truck -1 -1 -1 641.65 133.87 723.46 215.31 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n63 29 Car -1 -1 -1 193.35 196.85 390.31 292.83 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n63 27 Pedestrian -1 -1 -1 835.18 161.06 858.71 223.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n63 41 Pedestrian -1 -1 -1 857.80 157.45 880.53 222.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n63 47 Pedestrian -1 -1 -1 398.46 174.78 423.36 243.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n63 50 Pedestrian -1 -1 -1 809.29 164.27 822.34 201.72 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n63 48 Pedestrian -1 -1 -1 818.84 163.81 831.31 201.26 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n64 29 Car -1 -1 -1 174.72 196.46 381.38 297.63 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n64 41 Pedestrian -1 -1 -1 862.64 156.97 886.88 224.34 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n64 27 Pedestrian -1 -1 -1 843.01 161.25 866.84 225.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n64 47 Pedestrian -1 -1 -1 394.38 173.14 421.66 244.57 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n64 48 Pedestrian -1 -1 -1 822.00 164.37 835.08 201.33 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n64 50 Pedestrian -1 -1 -1 810.09 164.37 823.61 201.59 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n65 29 Car -1 -1 -1 151.03 195.46 373.81 302.29 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n65 41 Pedestrian -1 -1 -1 869.34 155.57 894.79 224.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n65 47 Pedestrian -1 -1 -1 394.66 170.11 420.05 244.43 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n65 27 Pedestrian -1 -1 -1 849.40 159.11 875.45 225.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n65 50 Pedestrian -1 -1 -1 814.06 162.13 827.72 202.19 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n65 48 Pedestrian -1 -1 -1 825.64 163.94 839.03 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n65 52 Van -1 -1 -1 650.09 132.95 732.34 211.90 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n65 53 Car -1 -1 -1 565.30 174.94 595.56 197.23 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n66 29 Car -1 -1 -1 128.21 194.90 363.36 309.20 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n66 47 Pedestrian -1 -1 -1 392.36 169.18 421.01 244.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n66 27 Pedestrian -1 -1 -1 855.68 158.94 883.28 227.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n66 41 Pedestrian -1 -1 -1 876.83 154.12 902.79 225.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n66 48 Pedestrian -1 -1 -1 828.94 163.61 842.73 201.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n66 50 Pedestrian -1 -1 -1 817.93 162.32 831.99 201.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n66 54 Truck -1 -1 -1 653.11 132.03 735.80 212.59 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n67 29 Car -1 -1 -1 101.48 195.38 351.60 316.30 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n67 47 Pedestrian -1 -1 -1 387.63 168.41 419.29 245.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n67 54 Truck -1 -1 -1 656.63 132.69 738.68 211.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n67 41 Pedestrian -1 -1 -1 887.21 152.85 912.81 226.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n67 27 Pedestrian -1 -1 -1 864.62 157.17 893.01 229.54 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n67 48 Pedestrian -1 -1 -1 832.60 163.57 846.98 201.00 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n67 50 Pedestrian -1 -1 -1 821.43 161.79 834.99 201.67 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n67 55 Pedestrian -1 -1 -1 806.81 165.65 820.66 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n67 56 Cyclist -1 -1 -1 354.24 167.81 384.76 228.90 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n68 29 Car -1 -1 -1 64.67 195.06 338.15 325.17 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n68 54 Truck -1 -1 -1 658.03 132.36 744.96 210.41 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n68 47 Pedestrian -1 -1 -1 382.40 169.00 416.27 248.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n68 41 Pedestrian -1 -1 -1 892.76 152.03 924.84 229.20 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n68 27 Pedestrian -1 -1 -1 874.24 156.21 903.98 230.66 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n68 48 Pedestrian -1 -1 -1 836.30 163.20 851.26 201.81 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n68 50 Pedestrian -1 -1 -1 823.61 161.31 838.58 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n68 55 Pedestrian -1 -1 -1 808.55 166.61 824.34 204.68 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n69 29 Car -1 -1 -1 31.31 196.48 324.05 336.26 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n69 47 Pedestrian -1 -1 -1 373.52 169.24 410.74 250.41 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n69 41 Pedestrian -1 -1 -1 904.79 151.03 936.26 232.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n69 27 Pedestrian -1 -1 -1 883.46 155.11 912.25 232.16 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n69 54 Truck -1 -1 -1 662.67 132.62 746.27 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n69 48 Pedestrian -1 -1 -1 839.63 161.29 854.45 202.21 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n69 50 Pedestrian -1 -1 -1 824.01 161.14 840.33 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n69 55 Pedestrian -1 -1 -1 811.09 166.83 827.68 205.21 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n70 29 Car -1 -1 -1 -0.26 196.34 309.58 346.38 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n70 27 Pedestrian -1 -1 -1 895.20 155.38 923.03 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n70 47 Pedestrian -1 -1 -1 366.20 169.03 403.63 251.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n70 41 Pedestrian -1 -1 -1 915.41 149.95 948.75 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n70 54 Truck -1 -1 -1 662.45 132.55 749.65 210.61 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n70 50 Pedestrian -1 -1 -1 828.59 161.18 843.52 203.70 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n70 55 Pedestrian -1 -1 -1 812.74 166.29 828.20 205.14 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n70 48 Pedestrian -1 -1 -1 843.35 161.14 857.96 202.28 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n71 29 Car -1 -1 -1 -1.58 197.48 294.75 359.53 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n71 27 Pedestrian -1 -1 -1 905.32 153.75 933.68 236.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n71 47 Pedestrian -1 -1 -1 364.77 168.07 397.68 253.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n71 41 Pedestrian -1 -1 -1 927.53 147.51 959.99 236.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n71 54 Truck -1 -1 -1 665.09 133.11 752.58 209.77 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n71 50 Pedestrian -1 -1 -1 828.50 161.04 844.55 204.79 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n71 48 Pedestrian -1 -1 -1 844.66 162.23 858.91 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n71 55 Pedestrian -1 -1 -1 815.38 163.86 831.28 204.92 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n71 57 Cyclist -1 -1 -1 334.42 166.71 372.83 232.28 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n72 29 Car -1 -1 -1 -0.47 200.70 271.54 364.00 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n72 47 Pedestrian -1 -1 -1 360.52 168.06 391.77 257.23 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n72 27 Pedestrian -1 -1 -1 915.13 152.57 946.80 241.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n72 41 Pedestrian -1 -1 -1 941.99 146.53 972.68 240.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n72 54 Truck -1 -1 -1 667.72 133.57 750.79 209.10 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n72 50 Pedestrian -1 -1 -1 831.19 162.00 847.09 205.17 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n72 48 Pedestrian -1 -1 -1 847.66 161.21 862.38 203.97 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n72 55 Pedestrian -1 -1 -1 815.31 165.56 831.86 205.78 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n72 57 Cyclist -1 -1 -1 326.31 168.36 366.53 234.34 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n72 58 Car -1 -1 -1 645.97 171.86 695.78 197.13 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n73 29 Car -1 -1 -1 1.80 200.43 247.68 364.35 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n73 27 Pedestrian -1 -1 -1 927.86 151.23 957.33 245.60 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n73 47 Pedestrian -1 -1 -1 350.97 168.56 387.40 260.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n73 41 Pedestrian 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975.99 250.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n74 58 Car -1 -1 -1 638.69 173.58 695.50 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n74 57 Cyclist -1 -1 -1 311.24 167.89 358.19 236.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n74 50 Pedestrian -1 -1 -1 834.88 161.41 851.90 207.71 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n74 54 Truck -1 -1 -1 667.83 134.48 752.09 209.82 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n74 48 Pedestrian -1 -1 -1 848.25 162.09 862.81 205.56 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n74 55 Pedestrian -1 -1 -1 817.28 166.02 833.19 208.16 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n75 29 Car -1 -1 -1 1.66 200.58 193.01 365.54 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n75 47 Pedestrian -1 -1 -1 326.03 169.59 373.35 271.34 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n75 41 Pedestrian -1 -1 -1 981.13 148.64 1020.62 255.31 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n75 27 Pedestrian -1 -1 -1 950.38 151.93 988.50 255.19 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n75 58 Car -1 -1 -1 633.49 174.34 693.31 201.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n75 57 Cyclist -1 -1 -1 303.11 167.27 349.76 239.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n75 50 Pedestrian -1 -1 -1 837.68 161.86 855.88 209.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n75 55 Pedestrian -1 -1 -1 819.32 167.11 835.74 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n75 48 Pedestrian -1 -1 -1 851.39 161.85 865.94 206.33 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n75 54 Truck -1 -1 -1 670.10 134.79 756.23 210.30 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n76 41 Pedestrian -1 -1 -1 998.49 147.93 1041.34 263.65 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n76 29 Car -1 -1 -1 -2.00 206.78 159.52 365.37 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n76 47 Pedestrian -1 -1 -1 311.27 171.01 358.91 276.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n76 27 Pedestrian -1 -1 -1 963.93 153.72 1005.06 263.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n76 58 Car -1 -1 -1 630.81 174.24 690.14 201.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n76 50 Pedestrian -1 -1 -1 838.81 162.34 856.17 211.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n76 55 Pedestrian -1 -1 -1 820.07 167.40 837.29 211.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n76 48 Pedestrian -1 -1 -1 853.97 163.16 869.74 209.05 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n76 57 Cyclist -1 -1 -1 295.32 166.94 342.63 244.90 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n76 54 Truck -1 -1 -1 669.73 138.16 754.98 211.18 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n76 60 Pedestrian -1 -1 -1 591.75 174.37 604.65 208.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n77 47 Pedestrian -1 -1 -1 303.41 168.84 350.26 281.55 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n77 41 Pedestrian -1 -1 -1 1018.12 144.09 1066.60 269.03 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n77 27 Pedestrian -1 -1 -1 978.74 151.66 1023.18 269.26 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n77 58 Car -1 -1 -1 627.08 175.42 683.89 203.47 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n77 55 Pedestrian -1 -1 -1 822.41 167.25 840.39 215.33 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n77 29 Car -1 -1 -1 -3.72 200.25 122.26 364.99 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n77 50 Pedestrian -1 -1 -1 842.02 163.43 860.42 213.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n77 48 Pedestrian -1 -1 -1 854.46 164.62 871.84 211.07 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n77 54 Truck -1 -1 -1 668.30 138.99 752.47 211.52 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n77 60 Pedestrian -1 -1 -1 587.62 174.43 600.02 209.12 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n77 57 Cyclist -1 -1 -1 290.23 166.73 331.94 245.60 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n77 61 Car -1 -1 -1 496.66 179.06 531.54 200.48 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n77 62 Cyclist -1 -1 -1 477.35 176.27 504.88 226.39 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n77 63 Pedestrian -1 -1 -1 606.27 173.33 616.90 200.96 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n78 41 Pedestrian -1 -1 -1 1040.52 143.19 1090.94 274.79 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n78 47 Pedestrian -1 -1 -1 283.65 168.20 338.64 287.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n78 58 Car -1 -1 -1 624.52 175.43 680.19 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n78 27 Pedestrian -1 -1 -1 997.97 148.45 1047.85 276.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n78 54 Truck -1 -1 -1 668.75 138.70 751.99 211.80 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n78 50 Pedestrian -1 -1 -1 845.50 164.29 863.08 214.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n78 60 Pedestrian -1 -1 -1 583.72 174.86 596.21 208.75 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n78 62 Cyclist -1 -1 -1 466.45 175.63 502.01 227.81 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n78 48 Pedestrian -1 -1 -1 857.39 165.55 874.65 212.99 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n78 61 Car -1 -1 -1 493.43 179.25 527.67 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n78 29 Car -1 -1 -1 -1.25 209.02 73.44 363.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n78 63 Pedestrian -1 -1 -1 602.01 172.90 612.94 201.43 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n78 57 Cyclist -1 -1 -1 277.16 167.00 322.94 251.40 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n78 55 Pedestrian -1 -1 -1 825.65 169.51 844.18 217.11 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n79 47 Pedestrian -1 -1 -1 259.30 167.12 318.99 292.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n79 41 Pedestrian -1 -1 -1 1068.05 139.89 1130.57 281.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n79 27 Pedestrian -1 -1 -1 1019.17 149.98 1066.64 282.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n79 58 Car -1 -1 -1 619.56 174.91 679.28 203.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n79 50 Pedestrian -1 -1 -1 845.18 163.59 863.79 216.06 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n79 54 Truck -1 -1 -1 670.18 138.33 754.57 211.51 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n79 55 Pedestrian -1 -1 -1 826.06 168.58 844.35 218.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n79 61 Car -1 -1 -1 486.13 179.42 521.35 200.10 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n79 48 Pedestrian -1 -1 -1 858.48 164.40 875.39 211.98 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n79 60 Pedestrian -1 -1 -1 579.55 174.55 591.93 208.99 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n79 63 Pedestrian -1 -1 -1 597.85 172.90 608.66 201.48 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n79 64 Pedestrian -1 -1 -1 265.35 168.00 312.68 250.96 -1 -1 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-1000 -10 0.53\n80 60 Pedestrian -1 -1 -1 574.28 174.17 586.86 209.62 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n80 65 Car -1 -1 -1 520.13 175.01 554.48 200.46 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n80 66 Cyclist -1 -1 -1 453.99 174.83 482.97 229.45 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n81 58 Car -1 -1 -1 610.85 174.31 677.99 204.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n81 47 Pedestrian -1 -1 -1 217.60 167.21 275.46 308.29 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n81 27 Pedestrian -1 -1 -1 1070.82 148.07 1136.28 301.25 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n81 41 Pedestrian -1 -1 -1 1128.34 137.23 1208.97 304.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n81 48 Pedestrian -1 -1 -1 860.53 165.01 879.30 215.95 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n81 54 Truck -1 -1 -1 669.92 138.91 748.57 211.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n81 55 Pedestrian -1 -1 -1 826.46 167.83 845.66 218.95 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n81 50 Pedestrian -1 -1 -1 848.62 163.59 868.80 216.64 -1 -1 -1 -1000 -1000 -1000 -10 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-1000 -1000 -10 0.43\n102 91 Pedestrian -1 -1 -1 423.81 171.49 452.01 241.28 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n103 58 Car -1 -1 -1 491.62 179.24 584.04 222.22 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n103 77 Pedestrian -1 -1 -1 787.15 167.54 800.03 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n103 50 Pedestrian -1 -1 -1 1072.74 151.88 1142.20 314.57 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n103 87 Pedestrian -1 -1 -1 763.46 168.56 775.76 205.87 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n103 55 Pedestrian -1 -1 -1 1026.42 161.16 1088.97 320.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n103 83 Pedestrian -1 -1 -1 983.05 144.79 1040.52 290.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n103 91 Pedestrian -1 -1 -1 409.23 175.09 435.15 246.68 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n103 68 Car -1 -1 -1 377.15 179.79 444.62 210.95 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n103 89 Car -1 -1 -1 621.44 178.04 653.43 195.64 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n103 92 Pedestrian -1 -1 -1 804.21 163.67 816.34 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n104 58 Car -1 -1 -1 486.49 177.30 579.66 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n104 50 Pedestrian -1 -1 -1 1108.45 149.53 1190.72 324.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n104 68 Car -1 -1 -1 367.38 179.58 431.75 209.78 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n104 87 Pedestrian -1 -1 -1 762.75 166.72 776.31 204.44 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n104 83 Pedestrian -1 -1 -1 1004.33 144.88 1065.14 297.52 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n104 77 Pedestrian -1 -1 -1 787.51 167.41 800.02 203.35 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n104 55 Pedestrian -1 -1 -1 1057.48 160.55 1134.46 328.54 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n104 89 Car -1 -1 -1 617.30 176.96 649.57 195.83 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n104 91 Pedestrian -1 -1 -1 394.68 179.01 420.79 246.66 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n105 58 Car -1 -1 -1 479.26 175.46 575.24 220.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n105 77 Pedestrian -1 -1 -1 787.80 164.46 799.83 201.76 -1 -1 -1 -1000 -1000 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0.73\n107 89 Car -1 -1 -1 606.60 170.42 638.38 190.36 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n107 83 Pedestrian -1 -1 -1 1090.41 134.10 1177.77 316.46 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n107 93 Pedestrian -1 -1 -1 771.39 161.50 783.42 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n107 95 Pedestrian -1 -1 -1 798.50 156.21 812.86 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n107 96 Cyclist -1 -1 -1 335.77 165.88 373.38 238.06 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n108 58 Car -1 -1 -1 459.80 170.96 562.69 219.50 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n108 77 Pedestrian -1 -1 -1 787.82 159.10 801.63 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n108 96 Cyclist -1 -1 -1 312.22 166.30 358.64 237.75 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n108 89 Car -1 -1 -1 604.14 169.23 636.53 190.41 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n108 83 Pedestrian -1 -1 -1 1127.19 127.07 1217.68 338.86 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n108 87 Pedestrian -1 -1 -1 759.96 159.66 775.20 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n108 95 Pedestrian -1 -1 -1 797.33 154.99 812.36 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n108 93 Pedestrian -1 -1 -1 768.38 160.34 782.87 200.74 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n109 58 Car -1 -1 -1 453.56 170.33 557.95 220.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n109 89 Car -1 -1 -1 600.76 169.38 634.21 190.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n109 96 Cyclist -1 -1 -1 293.73 167.90 337.74 237.82 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n109 77 Pedestrian -1 -1 -1 788.44 157.85 804.01 201.50 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n109 87 Pedestrian -1 -1 -1 758.86 159.58 774.29 201.72 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n109 93 Pedestrian -1 -1 -1 770.08 160.69 784.34 200.19 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n109 83 Pedestrian -1 -1 -1 1163.42 136.90 1219.43 359.21 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n110 58 Car -1 -1 -1 448.13 169.36 555.03 220.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n110 77 Pedestrian -1 -1 -1 789.37 157.75 804.45 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n110 87 Pedestrian -1 -1 -1 758.94 159.55 773.59 200.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n110 93 Pedestrian -1 -1 -1 769.91 159.98 784.67 200.55 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n110 96 Cyclist -1 -1 -1 263.61 168.77 329.25 242.04 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n110 89 Car -1 -1 -1 597.90 169.01 631.15 188.68 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n110 97 Car -1 -1 -1 312.42 170.34 380.70 204.47 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n111 58 Car -1 -1 -1 441.27 168.41 551.63 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n111 77 Pedestrian -1 -1 -1 789.77 157.35 805.37 201.44 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n111 93 Pedestrian -1 -1 -1 770.37 159.62 785.70 200.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n111 96 Cyclist -1 -1 -1 241.12 166.37 305.54 244.64 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n111 87 Pedestrian -1 -1 -1 757.90 158.65 773.47 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n111 89 Car -1 -1 -1 594.57 167.49 627.84 188.18 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n111 98 Pedestrian -1 -1 -1 794.77 151.31 809.45 194.13 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n111 99 Truck -1 -1 -1 641.23 129.60 699.95 192.39 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n112 58 Car -1 -1 -1 435.57 168.49 548.38 221.90 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n112 87 Pedestrian -1 -1 -1 758.26 157.56 774.16 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n112 77 Pedestrian -1 -1 -1 790.35 156.96 807.17 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n112 89 Car -1 -1 -1 592.32 168.14 626.99 188.09 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n112 96 Cyclist -1 -1 -1 221.15 165.83 278.48 248.00 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n112 93 Pedestrian -1 -1 -1 771.94 159.62 787.18 201.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n112 99 Truck -1 -1 -1 642.61 128.87 699.49 192.22 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n112 100 Car -1 -1 -1 619.12 163.98 648.44 185.97 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n113 58 Car -1 -1 -1 429.53 168.89 545.42 222.91 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n113 77 Pedestrian -1 -1 -1 791.46 156.47 809.51 203.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n113 96 Cyclist -1 -1 -1 185.32 167.00 255.71 252.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n113 89 Car -1 -1 -1 590.66 168.26 623.61 188.53 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n113 87 Pedestrian -1 -1 -1 759.14 158.51 775.44 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n113 93 Pedestrian -1 -1 -1 774.38 159.86 789.19 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n113 100 Car -1 -1 -1 618.81 163.82 647.69 186.16 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n113 101 Car -1 -1 -1 635.97 161.85 691.97 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n113 102 Car -1 -1 -1 276.26 168.88 355.01 206.03 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n114 58 Car -1 -1 -1 422.87 168.76 542.83 226.45 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n114 96 Cyclist -1 -1 -1 152.41 164.46 233.18 261.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n114 89 Car -1 -1 -1 589.07 168.61 622.01 189.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n114 93 Pedestrian -1 -1 -1 775.60 159.75 790.34 203.98 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n114 77 Pedestrian -1 -1 -1 795.14 155.28 813.24 205.84 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n114 100 Car -1 -1 -1 618.89 164.54 646.28 186.74 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n114 87 Pedestrian -1 -1 -1 760.15 159.27 776.23 204.21 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n114 101 Car -1 -1 -1 636.88 162.25 690.63 203.36 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n114 102 Car -1 -1 -1 289.95 168.07 364.40 207.25 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n115 58 Car -1 -1 -1 416.00 170.29 538.99 228.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n115 89 Car -1 -1 -1 586.43 168.91 619.80 190.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n115 93 Pedestrian -1 -1 -1 777.63 160.41 793.23 205.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n115 77 Pedestrian -1 -1 -1 797.23 157.94 815.53 207.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n115 96 Cyclist -1 -1 -1 110.58 165.87 205.75 268.67 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n115 87 Pedestrian -1 -1 -1 760.16 160.25 778.48 206.23 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n115 100 Car -1 -1 -1 615.85 165.29 643.17 187.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n115 102 Car -1 -1 -1 283.06 169.68 356.04 210.64 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n115 103 Pedestrian -1 -1 -1 225.46 177.50 252.40 247.97 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n116 58 Car -1 -1 -1 409.20 171.98 535.12 231.69 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n116 96 Cyclist -1 -1 -1 69.87 166.30 169.84 276.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n116 100 Car -1 -1 -1 613.67 167.05 642.77 190.58 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n116 77 Pedestrian -1 -1 -1 799.77 160.43 818.40 210.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n116 89 Car -1 -1 -1 585.77 169.51 618.58 191.99 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n116 102 Car -1 -1 -1 275.12 171.18 340.52 211.68 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n116 93 Pedestrian -1 -1 -1 779.02 160.56 795.61 207.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n116 103 Pedestrian -1 -1 -1 205.96 179.73 233.31 252.85 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n116 87 Pedestrian -1 -1 -1 761.04 161.78 778.84 209.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n116 104 Van -1 -1 -1 630.82 132.20 696.97 202.04 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n117 58 Car -1 -1 -1 402.70 172.62 531.82 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n117 100 Car -1 -1 -1 611.51 167.87 639.72 191.77 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n117 96 Cyclist -1 -1 -1 20.45 168.49 128.93 283.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n117 93 Pedestrian -1 -1 -1 781.55 162.28 797.78 208.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n117 102 Car -1 -1 -1 264.40 172.15 336.89 215.62 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n117 77 Pedestrian -1 -1 -1 803.59 160.19 822.26 209.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n117 89 Car -1 -1 -1 584.23 171.32 614.64 192.69 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n117 87 Pedestrian -1 -1 -1 761.37 161.78 779.59 209.30 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n117 103 Pedestrian -1 -1 -1 185.88 183.50 215.44 257.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n117 105 Pedestrian -1 -1 -1 798.99 156.23 813.52 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n117 106 Car -1 -1 -1 717.30 164.35 777.50 187.10 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n118 58 Car -1 -1 -1 394.99 172.72 527.26 234.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n118 102 Car -1 -1 -1 256.01 172.84 329.63 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n118 87 Pedestrian -1 -1 -1 761.57 161.99 780.68 210.52 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n118 77 Pedestrian -1 -1 -1 806.39 158.43 825.92 210.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n118 93 Pedestrian -1 -1 -1 784.86 161.79 801.68 209.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n118 89 Car -1 -1 -1 582.12 171.04 614.06 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n118 100 Car -1 -1 -1 609.86 167.66 638.31 191.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n118 103 Pedestrian -1 -1 -1 163.16 179.19 192.37 262.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n118 96 Cyclist -1 -1 -1 -1.74 169.23 88.47 295.61 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n118 106 Car -1 -1 -1 715.40 164.61 773.17 187.34 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n118 105 Pedestrian -1 -1 -1 798.21 155.67 813.69 200.89 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n118 107 Car -1 -1 -1 169.27 178.39 255.34 219.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n119 58 Car -1 -1 -1 388.17 172.25 523.73 237.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n119 102 Car -1 -1 -1 247.42 172.29 322.10 216.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n119 100 Car -1 -1 -1 608.08 166.69 636.10 189.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n119 107 Car -1 -1 -1 164.30 178.99 245.81 218.50 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n119 77 Pedestrian -1 -1 -1 810.68 156.92 828.82 211.29 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n119 106 Car -1 -1 -1 710.33 164.60 776.81 187.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n119 87 Pedestrian -1 -1 -1 763.32 159.68 783.20 211.67 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n119 89 Car -1 -1 -1 579.32 170.95 611.59 192.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n119 103 Pedestrian -1 -1 -1 135.66 178.95 166.79 265.07 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n119 93 Pedestrian -1 -1 -1 787.52 159.73 805.30 209.26 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n119 105 Pedestrian -1 -1 -1 800.15 154.46 816.15 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n120 58 Car -1 -1 -1 378.83 172.24 519.31 238.09 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n120 87 Pedestrian -1 -1 -1 764.43 159.43 784.32 212.31 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n120 102 Car -1 -1 -1 237.67 172.98 310.84 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n120 77 Pedestrian -1 -1 -1 814.58 156.09 832.94 211.02 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n120 107 Car -1 -1 -1 152.14 179.06 235.62 219.77 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n120 100 Car -1 -1 -1 606.12 166.48 635.25 189.38 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n120 93 Pedestrian -1 -1 -1 789.23 159.42 807.70 209.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n120 106 Car -1 -1 -1 709.90 163.63 776.20 186.66 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n120 89 Car -1 -1 -1 576.55 169.08 610.73 192.35 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n120 103 Pedestrian -1 -1 -1 105.99 179.93 142.63 271.84 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n120 105 Pedestrian -1 -1 -1 800.38 153.79 816.40 199.52 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n120 108 Car -1 -1 -1 635.93 161.00 689.96 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n121 58 Car -1 -1 -1 368.43 172.50 515.59 239.67 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n121 102 Car -1 -1 -1 227.42 172.60 303.81 217.99 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n121 107 Car -1 -1 -1 138.75 179.18 226.09 224.10 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n121 106 Car -1 -1 -1 707.35 163.62 774.62 187.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n121 100 Car -1 -1 -1 603.47 166.37 634.05 189.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n121 87 Pedestrian -1 -1 -1 765.60 158.51 785.29 212.83 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n121 93 Pedestrian -1 -1 -1 791.75 158.65 810.01 210.31 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n121 89 Car -1 -1 -1 573.21 168.78 608.76 192.10 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n121 108 Car -1 -1 -1 635.76 161.39 690.34 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n121 77 Pedestrian -1 -1 -1 817.51 155.98 837.07 212.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n121 103 Pedestrian -1 -1 -1 70.78 182.20 109.14 281.70 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n122 58 Car -1 -1 -1 359.82 173.43 509.97 241.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n122 107 Car -1 -1 -1 124.22 179.22 217.52 225.91 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n122 106 Car -1 -1 -1 705.79 163.81 774.80 187.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n122 87 Pedestrian -1 -1 -1 767.39 157.76 787.82 213.84 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n122 102 Car -1 -1 -1 215.05 172.90 295.21 218.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n122 108 Car -1 -1 -1 635.47 161.52 689.57 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n122 77 Pedestrian -1 -1 -1 819.13 156.06 839.58 215.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n122 100 Car -1 -1 -1 603.44 165.98 631.80 188.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n122 89 Car -1 -1 -1 570.66 168.90 605.59 192.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n122 93 Pedestrian -1 -1 -1 795.14 159.53 812.90 211.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n122 103 Pedestrian -1 -1 -1 31.84 181.52 71.71 285.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n122 109 Pedestrian -1 -1 -1 800.84 152.89 816.38 199.46 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n123 58 Car -1 -1 -1 351.31 173.19 507.13 245.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n123 107 Car -1 -1 -1 111.91 179.99 207.10 226.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n123 106 Car -1 -1 -1 705.86 164.07 773.89 187.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n123 102 Car -1 -1 -1 205.85 173.76 287.66 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n123 77 Pedestrian -1 -1 -1 822.48 155.49 842.94 216.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n123 87 Pedestrian -1 -1 -1 770.44 157.84 791.17 216.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n123 108 Car -1 -1 -1 632.84 161.22 687.65 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n123 89 Car -1 -1 -1 568.33 170.41 603.78 193.50 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n123 93 Pedestrian -1 -1 -1 796.32 159.18 815.50 212.87 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n123 100 Car -1 -1 -1 600.09 167.05 629.13 188.59 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n123 103 Pedestrian -1 -1 -1 0.67 185.12 26.90 288.53 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n124 58 Car -1 -1 -1 340.33 173.87 503.24 247.41 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n124 107 Car -1 -1 -1 96.33 180.90 198.00 230.24 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n124 102 Car -1 -1 -1 193.38 174.80 277.97 221.89 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n124 89 Car -1 -1 -1 565.36 170.88 601.31 194.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n124 100 Car -1 -1 -1 597.79 167.58 628.34 189.16 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n124 77 Pedestrian -1 -1 -1 827.14 154.66 849.61 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n124 106 Car -1 -1 -1 704.54 164.79 773.87 187.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n124 108 Car -1 -1 -1 632.24 160.98 687.86 203.64 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n124 87 Pedestrian -1 -1 -1 772.66 158.84 794.14 217.85 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n124 93 Pedestrian -1 -1 -1 801.45 158.96 822.32 217.37 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n124 110 Car -1 -1 -1 11.08 180.27 107.84 230.79 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n125 58 Car -1 -1 -1 330.42 174.37 497.45 250.63 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n125 107 Car -1 -1 -1 82.91 181.17 186.76 231.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n125 102 Car -1 -1 -1 182.88 174.90 271.38 223.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n125 100 Car -1 -1 -1 595.99 167.78 625.13 189.17 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n125 106 Car -1 -1 -1 703.53 165.67 769.55 186.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n125 77 Pedestrian -1 -1 -1 831.19 155.52 853.93 219.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n125 87 Pedestrian -1 -1 -1 775.55 157.78 798.66 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n125 89 Car -1 -1 -1 563.26 171.36 600.62 195.15 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n125 110 Car -1 -1 -1 2.97 180.51 92.85 231.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n125 93 Pedestrian -1 -1 -1 804.22 159.30 823.89 216.54 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n125 108 Car -1 -1 -1 632.21 161.40 687.25 203.70 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n126 58 Car -1 -1 -1 316.50 174.73 491.86 253.54 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n126 107 Car -1 -1 -1 66.29 181.36 174.87 232.49 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n126 106 Car -1 -1 -1 704.54 165.52 767.48 186.58 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n126 102 Car -1 -1 -1 169.93 175.55 261.56 227.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n126 89 Car -1 -1 -1 560.56 171.80 596.36 195.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n126 87 Pedestrian -1 -1 -1 778.93 157.08 803.22 221.89 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n126 100 Car -1 -1 -1 591.85 167.64 622.17 189.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n126 93 Pedestrian -1 -1 -1 807.37 158.87 827.66 217.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n126 110 Car -1 -1 -1 1.12 180.78 78.47 231.60 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n126 77 Pedestrian -1 -1 -1 836.50 156.00 858.52 220.70 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n126 108 Car -1 -1 -1 632.94 161.52 686.14 203.48 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n126 111 Van -1 -1 -1 627.22 131.41 686.65 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n127 58 Car -1 -1 -1 303.79 173.89 488.51 255.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n127 107 Car -1 -1 -1 48.87 181.19 162.91 237.07 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n127 100 Car -1 -1 -1 590.71 167.42 620.29 189.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n127 87 Pedestrian -1 -1 -1 782.30 156.89 807.64 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n127 93 Pedestrian -1 -1 -1 809.62 159.05 832.83 220.50 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n127 106 Car -1 -1 -1 704.49 165.72 766.48 186.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n127 89 Car -1 -1 -1 557.89 171.81 593.45 195.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n127 102 Car -1 -1 -1 155.16 175.49 246.68 228.05 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n127 77 Pedestrian -1 -1 -1 840.67 154.24 867.35 224.86 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n127 110 Car -1 -1 -1 -1.75 180.87 65.65 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n127 112 Truck -1 -1 -1 626.74 129.56 686.36 201.04 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n128 58 Car -1 -1 -1 292.65 173.65 483.10 259.77 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n128 107 Car -1 -1 -1 32.49 180.94 153.01 237.66 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n128 102 Car -1 -1 -1 139.53 174.88 239.84 228.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n128 100 Car -1 -1 -1 588.57 168.04 617.41 189.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n128 106 Car -1 -1 -1 701.91 165.16 764.66 185.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n128 87 Pedestrian -1 -1 -1 785.64 157.25 811.92 225.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n128 93 Pedestrian -1 -1 -1 813.37 159.37 836.98 223.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n128 77 Pedestrian -1 -1 -1 848.79 153.62 874.90 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n128 112 Truck -1 -1 -1 625.00 128.87 686.25 201.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n128 89 Car -1 -1 -1 554.58 171.74 591.06 195.40 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n128 110 Car -1 -1 -1 1.66 180.93 49.07 237.69 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n129 58 Car -1 -1 -1 276.52 174.13 477.43 262.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n129 107 Car -1 -1 -1 15.46 181.40 140.50 237.35 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n129 102 Car -1 -1 -1 126.08 174.66 229.40 231.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n129 100 Car -1 -1 -1 587.30 167.63 616.04 188.88 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n129 106 Car -1 -1 -1 703.12 165.63 763.34 185.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n129 77 Pedestrian -1 -1 -1 854.65 153.21 878.42 226.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n129 89 Car -1 -1 -1 551.85 171.04 590.23 195.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n129 87 Pedestrian -1 -1 -1 790.29 156.47 817.44 227.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n129 112 Truck -1 -1 -1 624.72 128.31 685.64 202.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n129 93 Pedestrian -1 -1 -1 819.91 159.16 842.67 225.15 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n129 110 Car 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-1 -1 -1000 -1000 -1000 -10 0.70\n130 113 Car -1 -1 -1 696.03 161.51 752.61 181.72 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n130 114 Pedestrian -1 -1 -1 792.48 155.48 809.49 203.26 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n131 58 Car -1 -1 -1 251.35 173.47 464.21 269.62 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n131 106 Car -1 -1 -1 699.93 164.75 765.97 186.49 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n131 102 Car -1 -1 -1 92.37 174.93 210.69 231.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n131 107 Car -1 -1 -1 -1.06 181.82 112.95 237.35 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n131 112 Truck -1 -1 -1 620.83 128.26 683.81 201.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n131 89 Car -1 -1 -1 544.67 171.15 583.72 195.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n131 77 Pedestrian -1 -1 -1 866.57 150.88 895.05 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n131 87 Pedestrian -1 -1 -1 800.14 155.07 830.90 231.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n131 100 Car -1 -1 -1 582.69 166.98 612.62 188.46 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n131 93 Pedestrian -1 -1 -1 828.53 157.78 852.93 228.43 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n131 114 Pedestrian -1 -1 -1 793.84 155.62 809.79 203.34 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n131 113 Car -1 -1 -1 695.58 161.64 746.91 181.73 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n132 58 Car -1 -1 -1 235.03 174.45 458.61 273.80 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n132 87 Pedestrian -1 -1 -1 806.08 154.08 835.64 234.06 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n132 93 Pedestrian -1 -1 -1 834.56 157.20 859.06 230.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 100 Car -1 -1 -1 580.78 167.24 609.36 188.94 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 77 Pedestrian -1 -1 -1 873.68 152.10 903.14 234.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n132 106 Car -1 -1 -1 699.44 164.87 764.94 186.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n132 102 Car -1 -1 -1 75.17 175.63 204.62 234.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n132 107 Car -1 -1 -1 -1.18 181.84 98.17 237.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n132 112 Truck -1 -1 -1 620.92 128.20 682.42 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n132 89 Car -1 -1 -1 542.13 171.86 579.67 195.80 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n132 113 Car -1 -1 -1 694.56 161.34 747.43 181.90 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n132 115 Van -1 -1 -1 620.60 128.91 682.93 203.46 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n133 58 Car -1 -1 -1 216.73 173.87 452.58 277.38 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n133 87 Pedestrian -1 -1 -1 810.91 152.42 843.59 237.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n133 77 Pedestrian -1 -1 -1 882.08 151.18 911.81 237.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n133 102 Car -1 -1 -1 59.67 176.17 196.12 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n133 112 Truck -1 -1 -1 620.59 127.67 682.51 201.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n133 107 Car -1 -1 -1 1.02 181.66 80.55 238.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n133 93 Pedestrian -1 -1 -1 841.33 156.36 868.32 234.59 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n133 106 Car -1 -1 -1 697.80 164.46 761.48 186.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n133 89 Car -1 -1 -1 538.21 172.22 576.47 195.83 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n133 100 Car -1 -1 -1 577.12 166.78 606.74 188.88 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n133 116 Pedestrian -1 -1 -1 794.78 156.11 809.43 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n134 58 Car -1 -1 -1 196.09 174.11 442.66 282.66 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n134 112 Truck -1 -1 -1 617.89 127.67 680.40 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n134 106 Car -1 -1 -1 697.90 164.06 760.46 186.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n134 77 Pedestrian -1 -1 -1 892.79 149.30 921.92 239.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n134 102 Car -1 -1 -1 41.22 176.16 184.05 237.21 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n134 87 Pedestrian -1 -1 -1 818.76 152.22 851.54 239.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n134 93 Pedestrian -1 -1 -1 848.91 155.79 875.44 239.60 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n134 100 Car -1 -1 -1 575.16 166.79 605.14 188.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n134 107 Car -1 -1 -1 0.66 180.98 64.96 239.71 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n134 89 Car -1 -1 -1 535.68 171.45 575.30 196.16 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n134 116 Pedestrian -1 -1 -1 796.74 155.70 811.34 194.88 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n134 117 Car -1 -1 -1 692.41 161.51 741.81 181.34 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n134 118 Pedestrian -1 -1 -1 815.47 154.53 832.10 205.58 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n134 119 Car -1 -1 -1 593.41 164.50 617.28 183.66 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n135 58 Car -1 -1 -1 171.62 173.83 435.63 290.17 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n135 87 Pedestrian -1 -1 -1 825.56 151.38 861.98 244.19 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n135 112 Truck -1 -1 -1 616.74 127.60 679.71 201.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n135 106 Car -1 -1 -1 697.62 163.81 759.77 186.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n135 100 Car -1 -1 -1 571.91 166.63 602.51 189.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n135 77 Pedestrian -1 -1 -1 901.32 147.38 932.71 242.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n135 102 Car -1 -1 -1 18.28 177.47 168.47 240.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n135 93 Pedestrian -1 -1 -1 856.15 154.39 885.01 242.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n135 89 Car -1 -1 -1 532.75 171.87 573.00 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n135 116 Pedestrian -1 -1 -1 797.60 155.02 812.65 195.39 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n135 118 Pedestrian -1 -1 -1 814.49 147.88 832.72 205.35 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n135 107 Car -1 -1 -1 0.44 185.05 49.48 241.27 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n135 119 Car -1 -1 -1 589.90 164.79 615.75 183.52 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n135 117 Car -1 -1 -1 692.25 162.10 735.16 181.65 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n136 58 Car -1 -1 -1 144.07 173.98 426.54 297.42 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n136 106 Car -1 -1 -1 696.55 163.97 760.40 186.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n136 87 Pedestrian -1 -1 -1 835.38 149.67 873.00 246.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n136 102 Car -1 -1 -1 5.30 177.05 150.66 242.48 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n136 77 Pedestrian -1 -1 -1 911.75 147.68 949.37 247.38 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n136 112 Truck -1 -1 -1 615.81 127.46 679.22 201.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n136 93 Pedestrian -1 -1 -1 867.51 153.66 895.53 245.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n136 100 Car -1 -1 -1 569.06 166.91 600.35 190.32 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n136 118 Pedestrian -1 -1 -1 814.84 148.71 834.03 208.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n136 89 Car -1 -1 -1 530.40 171.02 572.65 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n136 116 Pedestrian -1 -1 -1 798.40 154.14 813.66 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n136 107 Car -1 -1 -1 -0.45 186.02 42.36 241.04 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n136 119 Car -1 -1 -1 589.06 164.36 614.13 184.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n136 117 Car -1 -1 -1 691.82 162.78 735.47 181.40 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n137 58 Car -1 -1 -1 120.79 175.24 416.96 304.23 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n137 106 Car -1 -1 -1 695.85 164.33 759.66 187.15 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n137 100 Car -1 -1 -1 567.51 167.42 597.87 191.02 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n137 112 Truck -1 -1 -1 612.70 127.17 677.43 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n137 87 Pedestrian -1 -1 -1 844.33 148.10 882.68 251.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n137 77 Pedestrian -1 -1 -1 924.70 148.20 967.20 251.09 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n137 93 Pedestrian -1 -1 -1 876.69 153.58 908.51 249.37 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n137 102 Car -1 -1 -1 -0.15 177.63 141.31 243.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n137 89 Car -1 -1 -1 526.82 170.95 570.46 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n137 118 Pedestrian -1 -1 -1 816.83 147.78 837.75 208.91 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n137 116 Pedestrian -1 -1 -1 798.18 154.68 814.39 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n137 119 Car -1 -1 -1 586.65 165.28 611.21 184.75 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n137 117 Car -1 -1 -1 688.03 163.06 739.08 181.90 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n137 107 Car -1 -1 -1 0.89 186.33 33.71 240.43 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n138 58 Car -1 -1 -1 87.97 176.08 405.41 311.58 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n138 100 Car -1 -1 -1 564.58 167.64 595.50 191.97 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n138 87 Pedestrian -1 -1 -1 853.98 148.94 895.75 256.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n138 106 Car -1 -1 -1 695.17 164.53 759.65 187.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n138 77 Pedestrian -1 -1 -1 939.39 147.29 983.88 257.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n138 112 Truck -1 -1 -1 611.68 126.58 676.96 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n138 89 Car -1 -1 -1 523.47 172.34 567.55 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n138 93 Pedestrian -1 -1 -1 884.99 155.31 924.09 250.28 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n138 118 Pedestrian -1 -1 -1 817.61 147.24 837.99 208.98 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n138 102 Car -1 -1 -1 0.32 177.92 125.27 243.59 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n138 116 Pedestrian -1 -1 -1 799.84 154.58 815.90 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n138 117 Car -1 -1 -1 687.73 163.38 738.75 182.42 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n138 119 Car -1 -1 -1 585.89 165.63 609.89 185.05 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n139 58 Car -1 -1 -1 52.02 175.86 395.40 320.85 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n139 106 Car -1 -1 -1 692.87 164.60 756.89 188.28 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n139 87 Pedestrian -1 -1 -1 867.56 149.09 911.03 262.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n139 77 Pedestrian -1 -1 -1 954.22 144.51 1000.89 262.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n139 89 Car -1 -1 -1 519.71 172.50 564.02 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n139 102 Car -1 -1 -1 0.95 178.93 102.89 248.68 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n139 100 Car -1 -1 -1 562.66 168.04 593.49 192.01 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n139 112 Truck -1 -1 -1 608.75 126.80 674.53 203.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n139 118 Pedestrian -1 -1 -1 818.80 147.26 839.26 209.27 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n139 93 Pedestrian -1 -1 -1 898.78 157.06 941.72 254.83 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n139 116 Pedestrian -1 -1 -1 800.16 154.83 816.71 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n139 119 Car -1 -1 -1 582.56 166.14 606.66 185.63 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n139 117 Car -1 -1 -1 684.56 163.15 727.68 181.62 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n140 58 Car -1 -1 -1 16.02 176.79 384.27 332.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n140 77 Pedestrian -1 -1 -1 972.93 141.99 1020.95 268.56 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n140 118 Pedestrian -1 -1 -1 821.06 147.48 843.40 210.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n140 112 Truck -1 -1 -1 607.48 128.63 674.22 205.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n140 100 Car -1 -1 -1 559.85 168.06 591.29 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n140 89 Car -1 -1 -1 515.47 173.69 560.87 201.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n140 87 Pedestrian -1 -1 -1 879.37 147.28 923.40 266.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n140 102 Car -1 -1 -1 0.77 178.86 87.31 256.05 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n140 106 Car -1 -1 -1 690.82 165.26 756.90 188.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n140 116 Pedestrian -1 -1 -1 800.55 156.00 817.46 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n140 93 Pedestrian -1 -1 -1 918.89 153.81 959.28 259.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n140 119 Car -1 -1 -1 578.56 166.53 604.21 185.75 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n140 117 Car -1 -1 -1 686.59 165.23 732.84 183.26 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n141 58 Car -1 -1 -1 -1.13 176.64 373.01 342.99 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n141 77 Pedestrian -1 -1 -1 998.30 140.20 1047.48 278.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n141 87 Pedestrian -1 -1 -1 894.35 146.14 945.95 274.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n141 112 Truck -1 -1 -1 607.05 128.84 674.43 205.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n141 118 Pedestrian -1 -1 -1 823.00 147.68 847.16 211.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n141 89 Car -1 -1 -1 512.39 173.44 559.85 201.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n141 106 Car -1 -1 -1 690.83 165.76 753.33 188.07 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n141 100 Car -1 -1 -1 557.62 168.14 588.62 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n141 93 Pedestrian -1 -1 -1 936.87 149.88 979.29 270.89 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n141 102 Car -1 -1 -1 -0.07 182.31 72.76 260.50 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n141 117 Car -1 -1 -1 685.42 165.61 733.71 183.41 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n141 119 Car -1 -1 -1 578.40 166.31 602.44 186.50 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n141 116 Pedestrian -1 -1 -1 801.68 154.68 817.21 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n142 58 Car -1 -1 -1 1.23 174.55 355.30 358.53 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n142 89 Car -1 -1 -1 508.64 172.70 557.25 202.13 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n142 77 Pedestrian -1 -1 -1 1022.48 139.89 1076.41 280.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n142 87 Pedestrian -1 -1 -1 918.03 143.82 967.11 282.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n142 106 Car -1 -1 -1 691.19 165.08 752.81 188.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n142 100 Car -1 -1 -1 554.83 167.91 587.20 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n142 112 Truck -1 -1 -1 604.89 128.45 670.97 205.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n142 118 Pedestrian -1 -1 -1 826.47 146.70 851.03 212.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n142 93 Pedestrian -1 -1 -1 959.62 150.21 1002.67 278.18 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n142 117 Car -1 -1 -1 686.09 165.99 733.07 183.81 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n142 119 Car -1 -1 -1 574.58 166.30 599.69 186.27 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n142 116 Pedestrian -1 -1 -1 801.97 154.85 817.66 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n143 58 Car -1 -1 -1 0.87 176.24 341.01 365.01 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n143 112 Truck -1 -1 -1 604.03 128.49 670.49 205.46 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n143 89 Car -1 -1 -1 504.04 172.76 554.68 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n143 77 Pedestrian -1 -1 -1 1046.65 137.56 1108.01 291.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n143 118 Pedestrian -1 -1 -1 828.08 146.26 851.68 212.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n143 87 Pedestrian -1 -1 -1 936.40 141.75 994.98 292.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n143 100 Car -1 -1 -1 551.61 167.46 584.29 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n143 106 Car -1 -1 -1 689.95 165.21 753.53 188.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n143 93 Pedestrian -1 -1 -1 979.52 150.01 1029.02 291.83 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n143 117 Car -1 -1 -1 685.34 166.12 733.81 184.29 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n143 116 Pedestrian -1 -1 -1 804.08 154.45 819.98 199.10 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n143 119 Car -1 -1 -1 574.15 165.99 598.55 186.07 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n143 120 Car -1 -1 -1 162.84 160.02 246.39 184.62 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n143 121 Car -1 -1 -1 183.59 159.03 263.40 184.18 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n144 58 Car -1 -1 -1 2.13 178.27 322.03 363.22 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n144 112 Truck -1 -1 -1 602.33 127.49 670.51 205.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n144 89 Car -1 -1 -1 500.36 172.95 551.50 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n144 77 Pedestrian -1 -1 -1 1081.69 136.01 1148.59 300.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n144 106 Car -1 -1 -1 686.63 165.66 754.67 189.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n144 87 Pedestrian -1 -1 -1 957.81 142.96 1027.75 299.51 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n144 118 Pedestrian -1 -1 -1 829.38 145.78 851.80 213.46 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n144 100 Car -1 -1 -1 548.48 167.29 581.68 194.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n144 93 Pedestrian -1 -1 -1 1003.17 150.04 1058.42 299.99 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n144 121 Car -1 -1 -1 179.41 159.13 260.09 183.30 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n144 116 Pedestrian -1 -1 -1 804.14 154.91 819.63 199.00 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n144 119 Car -1 -1 -1 570.46 166.36 595.09 186.47 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n144 117 Car -1 -1 -1 682.80 165.51 729.11 183.63 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n144 120 Car -1 -1 -1 156.80 161.10 236.63 184.06 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n145 58 Car -1 -1 -1 5.68 177.92 302.30 362.99 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n145 89 Car -1 -1 -1 497.05 172.41 548.72 203.40 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n145 112 Truck -1 -1 -1 603.36 126.48 667.97 204.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n145 87 Pedestrian -1 -1 -1 984.79 139.43 1062.06 310.89 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n145 100 Car -1 -1 -1 546.35 167.37 579.69 194.08 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n145 118 Pedestrian -1 -1 -1 833.03 146.00 855.60 213.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n145 77 Pedestrian -1 -1 -1 1116.07 128.76 1197.90 321.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n145 106 Car -1 -1 -1 685.17 165.03 751.34 189.06 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n145 121 Car -1 -1 -1 170.23 158.38 253.32 184.31 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n145 116 Pedestrian -1 -1 -1 805.73 155.82 821.05 199.93 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n145 117 Car -1 -1 -1 681.96 165.60 729.81 183.90 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n145 93 Pedestrian -1 -1 -1 1036.88 148.11 1093.41 309.59 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n145 119 Car -1 -1 -1 567.17 166.44 592.45 186.24 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n145 120 Car -1 -1 -1 143.98 159.83 226.74 185.60 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n145 122 Pedestrian -1 -1 -1 821.09 155.54 836.85 201.89 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n146 58 Car -1 -1 -1 2.74 173.12 283.16 362.64 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n146 112 Truck -1 -1 -1 600.52 126.32 666.51 204.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n146 106 Car -1 -1 -1 683.35 165.42 751.21 190.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n146 100 Car -1 -1 -1 542.84 167.15 576.70 194.29 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n146 89 Car -1 -1 -1 492.68 172.55 546.36 203.99 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n146 87 Pedestrian -1 -1 -1 1018.65 132.31 1104.58 333.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n146 121 Car -1 -1 -1 160.72 158.38 241.30 184.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n146 118 Pedestrian -1 -1 -1 836.99 146.82 859.06 213.38 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n146 122 Pedestrian -1 -1 -1 823.24 154.93 839.12 203.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n146 119 Car -1 -1 -1 566.18 165.94 591.69 186.31 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n146 116 Pedestrian -1 -1 -1 807.95 155.99 823.29 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n146 117 Car -1 -1 -1 679.46 165.29 724.93 183.43 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n146 77 Pedestrian -1 -1 -1 1162.55 123.90 1220.32 334.26 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n146 120 Car -1 -1 -1 129.15 158.80 218.29 186.76 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n146 93 Pedestrian -1 -1 -1 1075.16 137.79 1139.56 335.01 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n147 58 Car -1 -1 -1 0.95 177.66 261.50 362.95 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n147 112 Truck -1 -1 -1 598.59 127.78 665.95 205.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n147 106 Car -1 -1 -1 683.71 165.40 750.30 190.37 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n147 100 Car -1 -1 -1 540.08 168.68 573.87 194.52 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n147 89 Car -1 -1 -1 489.66 172.43 544.44 204.18 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n147 118 Pedestrian -1 -1 -1 839.77 147.81 862.74 216.51 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n147 87 Pedestrian -1 -1 -1 1063.05 130.39 1151.52 349.94 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n147 122 Pedestrian -1 -1 -1 823.65 153.71 840.60 204.53 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n147 116 Pedestrian -1 -1 -1 808.16 154.39 823.77 199.50 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n147 121 Car -1 -1 -1 149.36 157.74 237.81 185.48 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n147 119 Car -1 -1 -1 562.42 165.78 589.73 187.02 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n147 117 Car -1 -1 -1 679.42 165.47 724.59 183.87 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n147 120 Car -1 -1 -1 112.11 159.00 205.61 190.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n147 93 Pedestrian -1 -1 -1 1122.38 136.31 1199.44 336.63 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n148 58 Car -1 -1 -1 0.83 177.99 232.23 362.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n148 112 Truck -1 -1 -1 596.23 128.51 664.25 205.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n148 106 Car -1 -1 -1 681.94 165.40 751.54 191.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n148 87 Pedestrian -1 -1 -1 1101.29 127.65 1220.57 360.98 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n148 100 Car -1 -1 -1 537.33 168.77 570.98 195.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n148 89 Car -1 -1 -1 484.38 174.51 538.69 205.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n148 118 Pedestrian -1 -1 -1 843.48 146.39 867.51 217.82 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n148 121 Car -1 -1 -1 145.28 157.67 232.89 186.39 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n148 116 Pedestrian -1 -1 -1 809.51 153.57 824.55 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n148 122 Pedestrian -1 -1 -1 825.30 151.79 844.36 205.95 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n148 120 Car -1 -1 -1 106.09 159.70 195.80 189.02 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n148 117 Car -1 -1 -1 678.93 165.72 724.83 184.47 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n148 119 Car -1 -1 -1 561.67 165.58 588.76 187.16 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n149 58 Car -1 -1 -1 -1.13 179.00 203.66 363.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n149 112 Truck -1 -1 -1 595.42 128.49 663.57 206.89 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n149 106 Car -1 -1 -1 680.61 165.27 752.15 192.27 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n149 100 Car -1 -1 -1 534.61 168.28 568.89 195.79 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n149 89 Car -1 -1 -1 481.57 174.28 537.38 205.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n149 120 Car -1 -1 -1 95.27 159.60 183.96 189.47 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n149 118 Pedestrian -1 -1 -1 847.01 143.95 870.34 214.81 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n149 122 Pedestrian -1 -1 -1 827.02 151.17 845.43 206.21 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n149 121 Car -1 -1 -1 140.63 158.38 222.83 184.83 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n149 116 Pedestrian -1 -1 -1 809.69 153.28 825.17 199.93 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n149 87 Pedestrian -1 -1 -1 1144.72 125.40 1222.99 363.65 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n149 117 Car -1 -1 -1 675.67 165.00 720.87 183.98 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n149 119 Car -1 -1 -1 558.41 165.82 584.93 187.10 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n150 100 Car -1 -1 -1 531.38 168.58 565.99 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n150 58 Car -1 -1 -1 -4.01 178.49 168.27 364.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n150 106 Car -1 -1 -1 679.83 166.00 748.43 192.44 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n150 112 Truck -1 -1 -1 594.00 127.93 662.16 207.93 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n150 89 Car -1 -1 -1 476.88 174.39 534.58 206.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n150 121 Car -1 -1 -1 129.95 158.98 211.30 185.00 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n150 118 Pedestrian -1 -1 -1 850.39 141.97 874.66 214.88 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n150 122 Pedestrian -1 -1 -1 827.30 151.79 846.21 207.49 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n150 117 Car -1 -1 -1 674.40 164.74 721.95 185.18 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n150 120 Car -1 -1 -1 74.21 158.89 166.64 190.46 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n150 116 Pedestrian -1 -1 -1 811.75 153.49 827.75 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n150 119 Car -1 -1 -1 558.03 165.68 585.17 187.40 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n150 123 Pedestrian -1 -1 -1 851.07 154.01 873.08 219.08 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n150 124 Car -1 -1 -1 105.67 159.01 188.95 186.86 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n151 112 Truck -1 -1 -1 590.23 128.78 661.20 208.49 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n151 100 Car -1 -1 -1 528.22 168.72 563.46 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n151 106 Car -1 -1 -1 678.77 166.29 749.09 192.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n151 89 Car -1 -1 -1 472.72 174.46 531.27 207.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n151 58 Car -1 -1 -1 -4.16 185.95 121.81 363.48 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n151 118 Pedestrian -1 -1 -1 853.84 142.92 879.62 216.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n151 122 Pedestrian -1 -1 -1 829.46 151.99 849.15 207.94 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n151 121 Car -1 -1 -1 124.50 159.79 207.29 185.53 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n151 116 Pedestrian -1 -1 -1 811.91 155.09 829.12 201.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n151 120 Car -1 -1 -1 63.48 159.95 161.94 191.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n151 117 Car -1 -1 -1 672.81 165.12 723.40 186.29 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n151 124 Car -1 -1 -1 81.49 160.52 174.78 188.67 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n151 119 Car -1 -1 -1 551.00 168.55 578.68 187.74 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n152 112 Truck -1 -1 -1 589.63 128.41 659.68 208.57 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n152 106 Car -1 -1 -1 677.59 167.06 748.98 193.08 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n152 89 Car -1 -1 -1 467.99 174.88 528.38 208.67 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n152 118 Pedestrian -1 -1 -1 857.00 143.26 883.49 221.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n152 100 Car -1 -1 -1 524.52 169.34 560.92 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n152 120 Car -1 -1 -1 42.37 159.28 145.09 192.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n152 122 Pedestrian -1 -1 -1 829.98 152.10 849.56 207.44 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n152 121 Car -1 -1 -1 113.88 160.23 203.59 188.08 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n152 116 Pedestrian -1 -1 -1 812.75 154.88 829.00 201.58 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n152 124 Car -1 -1 -1 73.35 161.59 167.50 188.81 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n152 58 Car -1 -1 -1 -3.85 186.83 61.49 362.61 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n152 117 Car -1 -1 -1 669.43 164.40 719.37 185.70 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n152 119 Car -1 -1 -1 550.03 168.21 577.84 188.08 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n152 125 Car -1 -1 -1 673.04 167.49 730.29 188.90 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n153 112 Truck -1 -1 -1 587.28 128.35 657.93 209.50 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n153 106 Car -1 -1 -1 677.04 167.25 748.87 193.81 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n153 89 Car -1 -1 -1 463.74 174.47 525.14 209.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n153 118 Pedestrian -1 -1 -1 860.85 144.22 888.30 222.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n153 100 Car -1 -1 -1 521.95 169.08 558.14 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n153 120 Car -1 -1 -1 27.84 159.18 136.43 193.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n153 121 Car -1 -1 -1 102.82 160.89 192.00 187.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n153 124 Car -1 -1 -1 65.42 161.60 159.62 189.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n153 122 Pedestrian -1 -1 -1 831.15 154.48 848.89 204.97 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n153 116 Pedestrian -1 -1 -1 813.15 155.20 829.49 201.35 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n153 119 Car -1 -1 -1 546.37 168.24 574.97 189.11 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n153 117 Car -1 -1 -1 667.58 164.46 720.92 186.66 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n154 118 Pedestrian -1 -1 -1 863.80 143.47 893.31 223.50 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n154 112 Truck -1 -1 -1 586.34 128.51 656.41 209.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n154 89 Car -1 -1 -1 459.62 176.30 521.07 211.10 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n154 100 Car -1 -1 -1 519.61 171.46 555.10 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n154 106 Car -1 -1 -1 675.65 168.09 749.67 195.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n154 116 Pedestrian -1 -1 -1 815.21 154.54 832.60 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n154 120 Car -1 -1 -1 15.04 158.40 127.63 195.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n154 121 Car -1 -1 -1 88.99 161.06 182.80 188.05 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n154 122 Pedestrian -1 -1 -1 831.41 154.75 849.16 203.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n154 124 Car -1 -1 -1 50.06 160.47 151.74 190.85 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n154 117 Car -1 -1 -1 670.20 168.31 726.71 190.05 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n154 119 Car -1 -1 -1 542.66 168.73 571.66 190.05 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n154 126 Car -1 -1 -1 533.64 172.64 562.61 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n155 106 Car -1 -1 -1 673.76 168.57 746.42 195.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n155 100 Car -1 -1 -1 516.63 171.96 552.17 200.43 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n155 118 Pedestrian -1 -1 -1 867.32 142.16 897.38 226.36 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n155 89 Car -1 -1 -1 455.30 176.91 517.38 212.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n155 116 Pedestrian -1 -1 -1 816.03 155.52 832.98 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n155 120 Car -1 -1 -1 6.28 160.09 113.17 196.27 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n155 121 Car -1 -1 -1 80.67 161.73 174.92 189.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n155 112 Truck -1 -1 -1 583.64 129.48 653.44 211.76 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n155 122 Pedestrian -1 -1 -1 833.66 154.71 852.15 203.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n155 124 Car -1 -1 -1 38.72 161.16 140.31 191.57 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n155 117 Car -1 -1 -1 667.00 168.37 729.35 191.55 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n155 119 Car -1 -1 -1 541.75 168.86 570.38 190.56 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n155 126 Car -1 -1 -1 529.10 172.88 560.30 195.10 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n155 127 Car -1 -1 -1 659.70 165.38 705.75 186.12 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n156 106 Car -1 -1 -1 672.77 168.60 747.08 195.65 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n156 120 Car -1 -1 -1 2.72 159.46 107.50 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n156 89 Car -1 -1 -1 450.20 176.70 514.97 213.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n156 100 Car -1 -1 -1 512.92 172.23 549.50 201.01 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n156 118 Pedestrian -1 -1 -1 873.50 141.56 903.79 229.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n156 121 Car -1 -1 -1 67.69 161.36 165.23 189.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n156 117 Car -1 -1 -1 663.74 168.71 725.95 190.92 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n156 122 Pedestrian -1 -1 -1 835.16 155.38 852.39 204.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n156 124 Car -1 -1 -1 16.94 159.44 132.23 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n156 112 Truck -1 -1 -1 583.23 130.07 651.84 211.55 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n156 116 Pedestrian -1 -1 -1 816.88 156.35 834.21 204.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n156 119 Car -1 -1 -1 538.46 169.25 566.64 191.34 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n157 106 Car -1 -1 -1 670.24 168.65 748.29 196.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n157 118 Pedestrian -1 -1 -1 879.30 142.41 908.08 230.76 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n157 100 Car -1 -1 -1 510.21 172.39 546.92 201.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n157 120 Car -1 -1 -1 2.45 159.66 91.76 197.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n157 89 Car -1 -1 -1 444.21 177.70 509.47 214.13 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n157 124 Car -1 -1 -1 4.72 158.71 121.76 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n157 121 Car -1 -1 -1 59.92 161.02 157.34 190.01 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n157 117 Car -1 -1 -1 661.66 168.39 727.40 192.15 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n157 116 Pedestrian -1 -1 -1 818.66 156.03 837.06 205.58 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n157 122 Pedestrian -1 -1 -1 837.16 155.30 855.70 205.16 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n157 112 Truck -1 -1 -1 581.56 125.62 652.18 211.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n157 119 Car -1 -1 -1 525.29 173.30 555.83 195.62 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n158 106 Car -1 -1 -1 669.40 169.52 748.18 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n158 100 Car -1 -1 -1 505.69 172.77 544.33 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n158 89 Car -1 -1 -1 439.43 179.26 505.99 215.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n158 118 Pedestrian -1 -1 -1 884.81 141.47 916.79 234.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n158 120 Car -1 -1 -1 1.64 160.65 79.08 198.69 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n158 124 Car -1 -1 -1 0.26 157.48 110.60 195.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n158 112 Truck -1 -1 -1 576.81 127.90 650.71 213.44 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n158 122 Pedestrian -1 -1 -1 838.58 155.50 856.06 205.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n158 121 Car -1 -1 -1 46.21 161.06 148.24 191.55 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n158 116 Pedestrian -1 -1 -1 821.56 155.61 840.09 205.48 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n158 117 Car -1 -1 -1 662.83 169.92 725.92 194.10 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n158 119 Car -1 -1 -1 534.19 171.51 562.03 192.10 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n158 128 Car -1 -1 -1 522.20 175.26 552.23 196.79 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n159 89 Car -1 -1 -1 434.83 178.70 502.11 216.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n159 118 Pedestrian -1 -1 -1 888.59 142.56 922.15 237.23 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n159 100 Car -1 -1 -1 502.35 172.85 541.38 203.62 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n159 106 Car -1 -1 -1 668.51 170.04 748.22 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n159 124 Car -1 -1 -1 2.12 161.79 94.13 195.12 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n159 120 Car -1 -1 -1 0.76 162.17 65.10 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n159 116 Pedestrian -1 -1 -1 822.28 154.88 841.30 205.74 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n159 112 Truck -1 -1 -1 572.10 127.30 649.17 213.83 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n159 117 Car -1 -1 -1 659.68 170.62 722.51 193.85 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n159 122 Pedestrian -1 -1 -1 840.87 154.75 859.64 206.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n159 121 Car -1 -1 -1 31.01 161.91 133.56 191.56 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n159 119 Car -1 -1 -1 530.85 171.84 559.51 192.63 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n159 129 Car -1 -1 -1 654.40 167.61 711.08 190.63 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n160 89 Car -1 -1 -1 429.31 178.43 499.48 218.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n160 118 Pedestrian -1 -1 -1 893.81 141.87 930.36 238.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n160 100 Car -1 -1 -1 498.82 174.24 538.05 204.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n160 106 Car -1 -1 -1 667.05 169.90 746.37 197.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n160 120 Car -1 -1 -1 1.19 165.44 56.18 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n160 124 Car -1 -1 -1 1.60 162.44 86.42 196.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n160 122 Pedestrian -1 -1 -1 842.02 153.74 859.94 205.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n160 121 Car -1 -1 -1 10.25 161.59 123.41 194.97 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n160 116 Pedestrian -1 -1 -1 822.95 154.25 841.82 206.13 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n160 117 Car -1 -1 -1 657.63 170.20 723.64 194.12 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n160 119 Car -1 -1 -1 527.75 172.12 555.61 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n160 112 Truck -1 -1 -1 571.17 129.06 648.08 212.96 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n161 89 Car -1 -1 -1 425.44 178.40 495.32 219.12 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n161 100 Car -1 -1 -1 494.82 173.97 535.03 205.09 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n161 106 Car -1 -1 -1 666.57 169.68 746.83 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n161 118 Pedestrian -1 -1 -1 900.23 137.97 939.89 238.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n161 121 Car -1 -1 -1 1.88 162.47 108.43 195.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n161 120 Car -1 -1 -1 -0.99 164.88 49.53 200.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n161 122 Pedestrian -1 -1 -1 844.57 152.53 863.86 205.69 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n161 116 Pedestrian -1 -1 -1 824.23 153.69 845.32 206.92 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n161 124 Car -1 -1 -1 0.53 163.97 72.28 196.71 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n161 119 Car -1 -1 -1 527.28 172.01 553.92 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n161 117 Car -1 -1 -1 656.16 169.95 724.27 194.41 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n162 100 Car -1 -1 -1 491.33 174.34 531.54 205.92 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n162 106 Car -1 -1 -1 666.61 170.29 746.55 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n162 89 Car -1 -1 -1 417.72 178.99 490.70 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n162 118 Pedestrian -1 -1 -1 908.29 138.02 946.37 238.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n162 122 Pedestrian -1 -1 -1 845.23 152.92 863.95 206.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n162 121 Car -1 -1 -1 3.68 162.83 91.52 196.58 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n162 124 Car -1 -1 -1 0.44 167.30 49.19 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n162 116 Pedestrian -1 -1 -1 825.85 154.70 845.97 209.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n162 117 Car -1 -1 -1 655.27 171.09 724.56 195.13 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n162 119 Car -1 -1 -1 523.26 172.29 551.51 194.04 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n163 89 Car -1 -1 -1 411.74 179.66 486.83 222.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n163 106 Car -1 -1 -1 663.17 171.10 749.91 200.70 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n163 118 Pedestrian -1 -1 -1 913.75 140.54 949.89 240.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n163 100 Car -1 -1 -1 487.11 174.93 529.19 207.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n163 116 Pedestrian -1 -1 -1 825.99 155.59 846.87 209.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n163 117 Car -1 -1 -1 653.00 171.30 720.99 195.98 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n163 124 Car -1 -1 -1 0.16 162.27 49.72 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n163 122 Pedestrian -1 -1 -1 846.02 153.79 864.71 207.14 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n163 121 Car -1 -1 -1 4.15 161.96 75.59 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n163 119 Car -1 -1 -1 519.33 172.52 548.15 195.56 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n164 89 Car -1 -1 -1 406.04 179.10 482.98 223.69 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n164 118 Pedestrian -1 -1 -1 919.71 141.15 957.95 242.15 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n164 106 Car -1 -1 -1 665.92 171.31 750.80 200.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n164 100 Car -1 -1 -1 483.20 175.09 525.62 207.48 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n164 122 Pedestrian -1 -1 -1 847.91 154.40 868.71 210.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n164 116 Pedestrian -1 -1 -1 828.38 155.92 848.79 209.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n164 124 Car -1 -1 -1 -0.53 161.54 43.46 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n164 117 Car -1 -1 -1 650.93 171.35 722.45 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n164 119 Car -1 -1 -1 515.95 173.00 545.21 195.33 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n164 130 Car -1 -1 -1 501.79 176.28 535.80 200.01 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n165 89 Car -1 -1 -1 399.47 179.12 478.25 225.00 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n165 100 Car -1 -1 -1 480.38 174.64 523.56 207.70 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n165 118 Pedestrian -1 -1 -1 926.20 140.99 965.95 246.70 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n165 106 Car -1 -1 -1 665.75 171.20 751.08 200.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n165 122 Pedestrian -1 -1 -1 848.61 155.13 869.06 210.33 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n165 124 Car -1 -1 -1 -0.20 162.04 35.21 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n165 116 Pedestrian -1 -1 -1 829.53 155.01 849.00 208.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n165 117 Car -1 -1 -1 648.44 171.27 724.12 196.83 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n165 119 Car -1 -1 -1 515.43 173.14 543.29 195.19 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n165 130 Car -1 -1 -1 497.17 175.68 532.45 200.38 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n166 89 Car -1 -1 -1 393.20 179.76 474.05 226.56 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n166 100 Car -1 -1 -1 477.07 174.91 520.21 208.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n166 118 Pedestrian -1 -1 -1 933.53 138.75 974.14 248.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n166 106 Car -1 -1 -1 664.74 171.35 751.88 201.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n166 117 Car -1 -1 -1 646.79 170.92 719.52 197.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n166 122 Pedestrian -1 -1 -1 849.30 155.32 869.60 210.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n166 116 Pedestrian -1 -1 -1 832.71 155.12 852.75 209.82 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n166 124 Car -1 -1 -1 -0.42 165.58 19.06 200.81 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n166 119 Car -1 -1 -1 511.81 173.22 540.87 195.44 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n166 130 Car -1 -1 -1 496.15 177.04 531.49 201.86 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n167 106 Car -1 -1 -1 662.21 171.76 750.93 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n167 89 Car -1 -1 -1 388.42 181.26 470.00 228.91 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n167 100 Car -1 -1 -1 473.20 176.61 518.13 210.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n167 118 Pedestrian -1 -1 -1 938.41 136.31 978.71 247.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n167 122 Pedestrian -1 -1 -1 852.12 154.28 871.41 210.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n167 117 Car -1 -1 -1 646.10 171.07 719.40 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n167 124 Car -1 -1 -1 -1.96 166.17 13.20 200.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n167 116 Pedestrian -1 -1 -1 832.82 156.02 852.78 210.15 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n167 130 Car -1 -1 -1 491.93 177.61 529.27 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n167 119 Car -1 -1 -1 510.47 174.82 539.67 196.74 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n168 106 Car -1 -1 -1 661.46 171.68 750.13 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n168 89 Car -1 -1 -1 381.45 181.38 465.87 230.56 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n168 100 Car -1 -1 -1 470.01 176.46 514.64 210.53 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n168 118 Pedestrian -1 -1 -1 946.73 135.66 986.01 252.24 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n168 116 Pedestrian -1 -1 -1 832.70 153.83 855.52 212.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n168 122 Pedestrian -1 -1 -1 852.48 155.78 871.84 210.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n168 117 Car -1 -1 -1 646.59 172.85 719.07 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n168 130 Car -1 -1 -1 488.46 177.82 526.46 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n168 119 Car -1 -1 -1 507.17 175.07 536.61 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n168 131 Car -1 -1 -1 -1.75 167.22 13.21 198.97 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n169 106 Car -1 -1 -1 660.95 171.51 750.26 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n169 89 Car -1 -1 -1 376.22 180.83 462.30 230.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n169 118 Pedestrian -1 -1 -1 955.71 139.34 997.90 251.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n169 116 Pedestrian -1 -1 -1 835.49 154.40 858.14 211.83 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n169 100 Car -1 -1 -1 467.11 176.03 513.13 210.39 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n169 117 Car -1 -1 -1 642.89 171.12 714.96 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n169 122 Pedestrian -1 -1 -1 852.38 156.28 872.37 210.60 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n169 119 Car -1 -1 -1 503.20 174.86 534.02 196.77 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n169 130 Car -1 -1 -1 488.28 177.76 524.46 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n170 89 Car -1 -1 -1 371.05 180.45 458.67 231.22 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n170 106 Car -1 -1 -1 658.92 171.37 751.19 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n170 118 Pedestrian -1 -1 -1 962.25 139.57 1006.13 257.36 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n170 100 Car -1 -1 -1 464.09 175.57 510.09 210.62 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n170 116 Pedestrian -1 -1 -1 836.79 154.19 858.44 212.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n170 117 Car -1 -1 -1 641.78 171.41 709.13 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n170 122 Pedestrian -1 -1 -1 852.94 155.86 872.50 211.28 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n170 130 Car -1 -1 -1 484.41 176.99 521.45 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n170 119 Car -1 -1 -1 503.18 174.53 531.72 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n170 132 Pedestrian -1 -1 -1 977.17 147.13 1015.96 234.79 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n171 89 Car -1 -1 -1 364.20 180.83 455.98 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n171 100 Car -1 -1 -1 461.18 175.76 507.55 211.49 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n171 106 Car -1 -1 -1 658.45 171.75 751.26 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n171 118 Pedestrian -1 -1 -1 970.36 139.78 1014.38 257.59 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n171 116 Pedestrian -1 -1 -1 836.86 153.75 859.62 213.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n171 122 Pedestrian -1 -1 -1 856.29 154.51 877.15 212.80 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n171 119 Car -1 -1 -1 499.67 174.84 530.40 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n171 117 Car -1 -1 -1 642.35 172.77 715.15 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n171 130 Car -1 -1 -1 479.65 177.45 519.79 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n172 89 Car -1 -1 -1 359.31 182.64 452.88 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n172 118 Pedestrian -1 -1 -1 976.90 140.03 1023.88 256.53 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n172 100 Car -1 -1 -1 458.08 177.22 504.76 213.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n172 106 Car -1 -1 -1 657.86 173.24 751.18 206.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n172 116 Pedestrian -1 -1 -1 839.49 156.05 862.18 215.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n172 119 Car -1 -1 -1 499.41 175.54 528.42 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n172 117 Car -1 -1 -1 639.85 173.48 710.65 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n172 130 Car -1 -1 -1 480.79 178.58 517.40 205.61 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n172 122 Pedestrian -1 -1 -1 858.48 154.30 880.34 213.89 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n173 89 Car -1 -1 -1 353.07 183.94 448.55 237.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n173 106 Car -1 -1 -1 655.77 174.33 750.45 207.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n173 118 Pedestrian -1 -1 -1 984.93 137.90 1030.51 259.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n173 100 Car -1 -1 -1 456.30 178.81 503.17 214.98 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n173 116 Pedestrian -1 -1 -1 839.97 157.03 862.13 216.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n173 130 Car -1 -1 -1 475.57 179.99 515.35 207.21 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n173 119 Car -1 -1 -1 496.27 176.56 526.12 199.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n173 122 Pedestrian -1 -1 -1 858.75 155.40 880.79 215.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n173 117 Car -1 -1 -1 638.25 174.36 711.95 200.65 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n173 133 Car -1 -1 -1 632.05 171.49 688.26 195.08 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n174 89 Car -1 -1 -1 347.91 184.20 445.42 238.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n174 106 Car -1 -1 -1 653.76 174.43 751.64 208.07 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n174 100 Car -1 -1 -1 453.81 179.26 500.49 214.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n174 116 Pedestrian -1 -1 -1 840.29 158.18 861.81 216.24 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n174 118 Pedestrian -1 -1 -1 991.24 139.22 1039.29 259.21 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n174 122 Pedestrian -1 -1 -1 858.51 155.37 881.35 216.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n174 130 Car -1 -1 -1 475.65 179.55 513.47 208.19 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n174 119 Car -1 -1 -1 495.09 176.90 524.97 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n174 117 Car -1 -1 -1 636.43 174.88 713.21 201.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n175 89 Car -1 -1 -1 343.48 184.35 441.55 241.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n175 100 Car -1 -1 -1 451.46 179.09 498.84 215.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n175 106 Car -1 -1 -1 654.83 174.72 751.36 209.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n175 130 Car -1 -1 -1 472.01 180.29 511.99 208.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n175 118 Pedestrian -1 -1 -1 992.77 139.30 1045.75 264.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n175 116 Pedestrian -1 -1 -1 841.20 157.46 862.49 216.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n175 122 Pedestrian -1 -1 -1 859.81 156.11 880.94 217.08 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n175 119 Car -1 -1 -1 494.17 177.62 524.82 200.89 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n175 117 Car -1 -1 -1 636.34 176.37 713.21 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n175 134 Car -1 -1 -1 621.69 168.85 675.76 190.97 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n176 89 Car -1 -1 -1 338.05 184.43 439.13 241.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n176 100 Car -1 -1 -1 448.85 178.95 495.95 215.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n176 106 Car -1 -1 -1 656.03 174.71 752.68 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n176 118 Pedestrian -1 -1 -1 1000.03 139.55 1053.98 271.49 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n176 116 Pedestrian -1 -1 -1 840.92 156.53 862.46 217.31 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n176 130 Car -1 -1 -1 471.48 180.03 509.90 208.71 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n176 122 Pedestrian -1 -1 -1 860.08 155.64 880.83 217.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n176 119 Car -1 -1 -1 491.56 177.90 522.99 200.67 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n176 117 Car -1 -1 -1 634.87 176.17 714.70 203.25 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n176 134 Car -1 -1 -1 621.30 168.74 675.88 191.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n177 89 Car -1 -1 -1 332.80 184.53 435.61 243.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n177 106 Car -1 -1 -1 654.46 174.73 751.82 209.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n177 100 Car -1 -1 -1 446.94 179.11 494.91 217.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n177 118 Pedestrian -1 -1 -1 1003.67 139.65 1064.89 270.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n177 116 Pedestrian -1 -1 -1 840.69 156.44 862.78 217.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n177 130 Car -1 -1 -1 468.08 180.45 507.76 209.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n177 122 Pedestrian -1 -1 -1 859.41 156.04 881.81 217.10 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n177 119 Car -1 -1 -1 490.53 177.85 521.80 201.06 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n177 117 Car -1 -1 -1 634.38 176.28 715.23 203.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n178 100 Car -1 -1 -1 444.14 179.45 492.66 217.95 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n178 89 Car -1 -1 -1 327.68 185.56 432.25 243.98 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n178 106 Car -1 -1 -1 654.61 175.05 751.82 209.27 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n178 116 Pedestrian -1 -1 -1 840.82 157.63 862.47 218.02 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n178 118 Pedestrian -1 -1 -1 1012.45 137.69 1071.06 272.94 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n178 119 Car -1 -1 -1 487.70 178.21 519.77 201.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n178 130 Car -1 -1 -1 467.19 180.59 506.56 210.12 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n178 122 Pedestrian -1 -1 -1 859.70 156.71 881.72 217.17 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n178 117 Car -1 -1 -1 634.27 176.85 708.77 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n178 135 Truck -1 -1 -1 537.73 129.07 621.57 222.95 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n178 136 Pedestrian -1 -1 -1 973.35 154.39 1004.74 225.29 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n179 89 Car -1 -1 -1 324.04 186.00 427.68 244.19 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n179 100 Car -1 -1 -1 441.68 179.42 490.45 218.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n179 136 Pedestrian -1 -1 -1 972.13 153.18 1005.59 226.86 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n179 106 Car -1 -1 -1 654.06 175.62 751.98 210.73 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n179 119 Car -1 -1 -1 486.66 177.97 518.91 201.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n179 130 Car -1 -1 -1 463.59 180.76 505.17 210.61 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n179 117 Car -1 -1 -1 632.68 177.10 709.98 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n179 122 Pedestrian -1 -1 -1 860.46 156.49 881.54 217.46 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n179 116 Pedestrian -1 -1 -1 841.19 158.18 862.43 218.33 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n179 118 Pedestrian -1 -1 -1 1020.39 140.76 1071.90 264.25 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0019.txt",
    "content": "0 1 Cyclist -1 -1 -1 459.29 160.61 547.91 336.61 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n0 2 Cyclist -1 -1 -1 311.16 158.54 442.16 322.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n0 3 Car -1 -1 -1 1115.32 188.23 1222.38 226.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n0 4 Car -1 -1 -1 876.78 184.26 945.99 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n0 5 Pedestrian -1 -1 -1 578.05 173.30 588.12 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n0 6 Pedestrian -1 -1 -1 974.20 170.84 1026.47 271.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n0 7 Car -1 -1 -1 985.09 185.67 1068.70 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n0 8 Car -1 -1 -1 607.95 176.22 633.24 199.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n0 9 Pedestrian -1 -1 -1 272.06 159.92 290.12 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n0 10 Cyclist -1 -1 -1 294.38 153.76 360.60 303.99 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n0 11 Car -1 -1 -1 929.62 184.27 1010.29 221.26 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n0 12 Pedestrian -1 -1 -1 405.46 165.04 418.34 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n1 1 Cyclist -1 -1 -1 473.47 162.16 554.39 326.61 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n1 3 Car -1 -1 -1 1115.71 188.25 1222.34 226.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n1 2 Cyclist -1 -1 -1 342.08 158.89 441.68 307.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n1 4 Car -1 -1 -1 876.78 184.38 945.47 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n1 6 Pedestrian -1 -1 -1 986.97 172.67 1036.11 268.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1 11 Car -1 -1 -1 930.55 184.34 1008.53 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n1 7 Car -1 -1 -1 985.55 185.71 1068.31 219.58 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n1 8 Car -1 -1 -1 608.10 176.35 633.46 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n1 9 Pedestrian -1 -1 -1 271.84 160.07 289.71 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n1 10 Cyclist -1 -1 -1 311.99 153.59 373.28 296.86 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n1 5 Pedestrian -1 -1 -1 578.82 172.85 589.67 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n1 13 Pedestrian -1 -1 -1 1001.70 173.13 1045.10 267.89 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n1 14 Pedestrian -1 -1 -1 0.97 165.99 26.76 283.68 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n2 3 Car -1 -1 -1 1115.44 188.45 1222.29 225.91 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n2 1 Cyclist -1 -1 -1 489.13 159.97 561.33 315.67 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n2 4 Car -1 -1 -1 876.72 184.42 945.40 217.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n2 2 Cyclist -1 -1 -1 364.65 157.89 449.69 300.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n2 10 Cyclist -1 -1 -1 328.16 155.34 387.12 294.53 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n2 6 Pedestrian -1 -1 -1 991.61 172.37 1039.93 268.57 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n2 11 Car -1 -1 -1 929.83 184.20 1010.76 220.43 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n2 8 Car -1 -1 -1 607.68 176.31 633.68 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n2 14 Pedestrian -1 -1 -1 1.23 162.22 40.04 281.66 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n2 9 Pedestrian -1 -1 -1 271.69 160.25 289.80 197.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n2 13 Pedestrian -1 -1 -1 1012.95 171.84 1055.40 268.68 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n2 5 Pedestrian -1 -1 -1 580.80 173.21 591.88 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n2 7 Car -1 -1 -1 988.54 185.99 1072.42 218.49 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n2 15 Pedestrian -1 -1 -1 368.62 160.10 386.23 208.43 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n3 3 Car -1 -1 -1 1115.74 188.56 1221.87 225.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n3 1 Cyclist -1 -1 -1 500.81 161.92 565.61 306.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n3 4 Car -1 -1 -1 876.84 184.51 945.51 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n3 11 Car -1 -1 -1 929.71 184.67 1010.94 220.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n3 2 Cyclist -1 -1 -1 380.79 161.00 457.25 288.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n3 8 Car -1 -1 -1 607.27 176.25 634.08 200.00 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n3 14 Pedestrian -1 -1 -1 0.64 160.58 49.54 281.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n3 6 Pedestrian -1 -1 -1 1008.19 171.36 1045.83 269.24 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n3 9 Pedestrian -1 -1 -1 271.92 160.29 289.38 197.13 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n3 10 Cyclist -1 -1 -1 345.97 156.77 399.22 285.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n3 13 Pedestrian -1 -1 -1 1019.81 171.17 1057.22 266.18 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n3 7 Car -1 -1 -1 995.05 185.81 1074.11 217.99 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n3 5 Pedestrian -1 -1 -1 583.81 173.72 595.13 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n3 15 Pedestrian -1 -1 -1 368.87 161.50 385.34 206.93 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n4 3 Car -1 -1 -1 1115.87 188.73 1221.74 225.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n4 1 Cyclist -1 -1 -1 509.07 166.61 572.28 298.85 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n4 2 Cyclist -1 -1 -1 394.25 158.94 465.82 285.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n4 11 Car -1 -1 -1 930.00 184.65 1010.82 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 6 Pedestrian -1 -1 -1 1016.16 170.41 1060.42 270.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n4 4 Car -1 -1 -1 876.49 184.46 945.72 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n4 8 Car -1 -1 -1 607.56 176.26 633.91 199.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n4 10 Cyclist -1 -1 -1 357.25 156.43 409.99 278.99 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n4 9 Pedestrian -1 -1 -1 271.93 160.34 289.58 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n4 7 Car -1 -1 -1 988.17 185.71 1065.87 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n4 14 Pedestrian -1 -1 -1 3.67 161.43 53.35 280.32 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n4 15 Pedestrian -1 -1 -1 367.92 161.34 385.54 206.48 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n5 3 Car -1 -1 -1 1116.41 188.73 1221.22 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n5 6 Pedestrian -1 -1 -1 1017.72 169.11 1074.21 272.89 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n5 11 Car -1 -1 -1 930.59 184.77 1010.46 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n5 2 Cyclist -1 -1 -1 404.72 161.03 472.19 280.15 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n5 4 Car -1 -1 -1 876.82 184.42 945.49 217.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n5 1 Cyclist -1 -1 -1 518.71 164.03 576.80 288.46 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n5 8 Car -1 -1 -1 607.72 176.30 634.11 199.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n5 10 Cyclist -1 -1 -1 365.82 159.03 419.52 275.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n5 9 Pedestrian -1 -1 -1 272.16 160.02 289.47 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n5 7 Car -1 -1 -1 987.23 185.07 1066.07 220.98 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n5 14 Pedestrian -1 -1 -1 7.06 159.46 65.30 276.40 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n5 15 Pedestrian -1 -1 -1 368.53 161.68 386.56 206.77 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n5 16 Cyclist -1 -1 -1 -3.54 162.08 259.39 365.63 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n5 17 Pedestrian -1 -1 -1 404.33 164.83 418.35 202.11 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n6 2 Cyclist -1 -1 -1 418.28 159.85 479.89 275.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n6 3 Car -1 -1 -1 1115.65 188.59 1221.37 225.72 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n6 11 Car -1 -1 -1 930.34 184.95 1010.54 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n6 1 Cyclist -1 -1 -1 525.17 163.38 580.79 281.75 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n6 4 Car -1 -1 -1 876.67 184.39 945.58 217.86 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n6 6 Pedestrian -1 -1 -1 1024.00 170.51 1090.70 273.49 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n6 10 Cyclist -1 -1 -1 378.47 158.39 427.74 269.27 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n6 7 Car -1 -1 -1 985.75 185.34 1067.40 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n6 8 Car -1 -1 -1 607.68 176.34 633.68 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n6 16 Cyclist -1 -1 -1 26.70 162.36 336.40 364.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n6 9 Pedestrian -1 -1 -1 271.92 160.27 289.33 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n6 15 Pedestrian -1 -1 -1 368.20 161.62 387.18 206.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n6 14 Pedestrian -1 -1 -1 14.19 160.02 65.92 282.27 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n6 18 Cyclist -1 -1 -1 6.91 151.82 195.89 360.62 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n7 2 Cyclist -1 -1 -1 424.40 162.04 488.45 272.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n7 16 Cyclist -1 -1 -1 148.68 169.90 360.34 364.21 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n7 11 Car -1 -1 -1 929.76 184.94 1010.80 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n7 3 Car -1 -1 -1 1115.43 188.65 1221.55 225.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n7 1 Cyclist -1 -1 -1 530.79 163.49 583.28 278.31 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n7 4 Car -1 -1 -1 876.59 184.32 945.78 217.91 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n7 7 Car -1 -1 -1 982.04 185.36 1065.64 220.66 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n7 6 Pedestrian -1 -1 -1 1031.50 170.81 1092.00 272.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n7 8 Car -1 -1 -1 607.51 176.24 633.84 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n7 10 Cyclist -1 -1 -1 385.49 157.84 437.24 269.06 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n7 15 Pedestrian -1 -1 -1 368.61 161.22 386.39 206.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n7 14 Pedestrian -1 -1 -1 17.71 160.63 77.88 274.50 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n7 18 Cyclist -1 -1 -1 40.72 156.81 269.17 361.64 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n7 9 Pedestrian -1 -1 -1 272.29 160.44 289.22 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n7 19 Pedestrian -1 -1 -1 2.07 157.24 39.41 271.23 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n8 16 Cyclist -1 -1 -1 202.08 169.41 398.84 365.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n8 11 Car -1 -1 -1 929.60 184.93 1010.70 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n8 2 Cyclist -1 -1 -1 431.85 164.31 490.97 269.65 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n8 3 Car -1 -1 -1 1114.77 188.65 1221.78 225.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n8 4 Car -1 -1 -1 876.58 184.26 945.74 217.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n8 1 Cyclist -1 -1 -1 536.16 163.15 586.39 273.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n8 6 Pedestrian -1 -1 -1 1050.38 168.74 1094.54 275.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n8 7 Car -1 -1 -1 983.42 185.34 1068.49 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n8 10 Cyclist -1 -1 -1 393.92 159.01 445.55 262.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 8 Car -1 -1 -1 607.14 176.19 633.88 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n8 15 Pedestrian -1 -1 -1 368.52 161.64 386.30 206.70 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n8 18 Cyclist -1 -1 -1 98.75 153.29 326.27 365.47 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n8 14 Pedestrian -1 -1 -1 23.16 160.91 80.05 273.64 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n8 9 Pedestrian -1 -1 -1 270.61 160.92 290.70 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n8 19 Pedestrian -1 -1 -1 3.20 156.42 38.92 270.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n9 16 Cyclist -1 -1 -1 265.16 164.51 419.20 364.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n9 18 Cyclist -1 -1 -1 164.57 153.05 351.72 367.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n9 11 Car -1 -1 -1 929.53 184.89 1010.32 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n9 2 Cyclist -1 -1 -1 442.78 163.83 493.60 264.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n9 3 Car -1 -1 -1 1114.27 188.58 1221.97 225.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n9 7 Car -1 -1 -1 982.88 185.28 1069.11 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n9 1 Cyclist -1 -1 -1 541.52 164.08 588.73 264.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n9 4 Car -1 -1 -1 876.44 184.31 945.86 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n9 10 Cyclist -1 -1 -1 403.02 161.08 457.27 258.88 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n9 6 Pedestrian -1 -1 -1 1063.06 168.90 1105.52 278.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n9 14 Pedestrian -1 -1 -1 31.20 161.85 87.13 272.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n9 8 Car -1 -1 -1 607.06 176.14 633.79 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n9 19 Pedestrian -1 -1 -1 0.20 153.59 42.21 272.78 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n9 15 Pedestrian -1 -1 -1 368.82 163.15 386.60 208.25 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n9 9 Pedestrian -1 -1 -1 271.92 160.41 290.52 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n9 20 Pedestrian -1 -1 -1 403.39 165.61 417.19 201.06 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n10 16 Cyclist -1 -1 -1 296.61 162.88 449.21 364.46 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n10 11 Car -1 -1 -1 929.26 184.85 1010.79 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 7 Car -1 -1 -1 983.28 185.36 1069.00 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n10 18 Cyclist -1 -1 -1 214.48 153.51 378.71 367.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n10 4 Car -1 -1 -1 876.55 184.28 945.77 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n10 6 Pedestrian -1 -1 -1 1069.97 169.03 1122.57 278.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n10 3 Car -1 -1 -1 1114.98 188.92 1222.05 225.66 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n10 14 Pedestrian -1 -1 -1 37.12 162.52 96.90 271.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n10 1 Cyclist -1 -1 -1 546.82 163.36 588.00 259.04 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n10 2 Cyclist -1 -1 -1 449.59 163.59 501.13 262.30 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n10 8 Car -1 -1 -1 606.83 175.99 633.93 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n10 15 Pedestrian -1 -1 -1 368.36 163.48 386.37 208.73 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n10 9 Pedestrian -1 -1 -1 272.47 158.99 290.22 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n10 19 Pedestrian -1 -1 -1 2.78 151.34 47.11 269.88 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n10 20 Pedestrian -1 -1 -1 403.41 164.37 417.61 201.01 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n10 10 Cyclist -1 -1 -1 416.15 159.97 460.14 253.64 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n11 16 Cyclist -1 -1 -1 327.45 165.01 471.66 361.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n11 11 Car -1 -1 -1 929.26 184.83 1010.59 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n11 18 Cyclist -1 -1 -1 254.10 156.94 398.96 362.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n11 2 Cyclist -1 -1 -1 454.27 163.36 507.07 257.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n11 7 Car -1 -1 -1 983.47 185.36 1068.71 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n11 1 Cyclist -1 -1 -1 547.76 164.18 590.25 258.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n11 4 Car -1 -1 -1 876.70 184.31 945.67 217.90 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n11 6 Pedestrian -1 -1 -1 1076.00 169.86 1139.20 279.96 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n11 14 Pedestrian -1 -1 -1 44.16 164.41 97.72 269.25 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n11 3 Car -1 -1 -1 1114.82 189.16 1222.32 225.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n11 8 Car -1 -1 -1 606.86 175.97 634.30 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n11 19 Pedestrian -1 -1 -1 12.85 151.86 59.32 268.29 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n11 15 Pedestrian -1 -1 -1 368.13 162.70 386.89 209.53 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n11 9 Pedestrian -1 -1 -1 272.10 159.58 289.15 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n11 10 Cyclist -1 -1 -1 426.45 162.10 465.85 243.88 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n12 11 Car -1 -1 -1 929.41 184.83 1010.53 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n12 16 Cyclist -1 -1 -1 357.03 164.86 479.77 354.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n12 1 Cyclist -1 -1 -1 551.82 166.17 591.16 253.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n12 7 Car -1 -1 -1 983.45 185.48 1068.75 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n12 6 Pedestrian -1 -1 -1 1083.29 170.30 1154.52 280.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 4 Car -1 -1 -1 876.73 184.32 945.58 217.96 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 14 Pedestrian -1 -1 -1 55.23 164.31 100.91 268.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n12 18 Cyclist -1 -1 -1 286.00 159.85 421.20 352.49 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n12 19 Pedestrian -1 -1 -1 16.56 153.85 64.50 267.35 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n12 8 Car -1 -1 -1 606.47 176.19 634.30 199.64 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n12 3 Car -1 -1 -1 1115.17 189.38 1221.47 225.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n12 2 Cyclist -1 -1 -1 463.46 164.41 505.80 253.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n12 15 Pedestrian -1 -1 -1 371.34 163.24 388.52 207.93 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n12 9 Pedestrian -1 -1 -1 271.30 159.77 289.38 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n12 10 Cyclist -1 -1 -1 434.96 162.13 470.96 242.60 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n13 11 Car -1 -1 -1 929.34 184.75 1010.61 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n13 7 Car -1 -1 -1 983.34 185.29 1068.75 221.47 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n13 16 Cyclist -1 -1 -1 387.84 164.68 486.88 340.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n13 4 Car -1 -1 -1 876.79 184.34 945.58 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n13 2 Cyclist -1 -1 -1 472.78 161.88 511.54 249.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n13 14 Pedestrian -1 -1 -1 67.89 161.98 112.18 266.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n13 19 Pedestrian -1 -1 -1 22.32 154.83 73.92 265.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n13 1 Cyclist -1 -1 -1 556.12 166.70 590.05 246.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n13 18 Cyclist -1 -1 -1 315.41 161.47 431.13 335.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n13 8 Car -1 -1 -1 606.87 176.27 634.39 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n13 3 Car -1 -1 -1 1116.08 189.31 1220.75 225.43 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n13 6 Pedestrian -1 -1 -1 1100.51 170.15 1152.94 279.78 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n13 9 Pedestrian -1 -1 -1 272.01 159.94 288.89 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n13 15 Pedestrian -1 -1 -1 372.04 163.77 388.44 207.54 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n13 21 Pedestrian -1 -1 -1 402.71 163.70 417.23 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n14 11 Car -1 -1 -1 929.27 184.76 1010.84 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n14 14 Pedestrian -1 -1 -1 74.27 162.38 121.58 266.00 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 7 Car -1 -1 -1 983.30 185.45 1068.60 221.43 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 4 Car -1 -1 -1 876.86 184.38 945.71 217.88 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 2 Cyclist -1 -1 -1 480.54 162.11 517.08 244.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n14 19 Pedestrian -1 -1 -1 31.79 154.73 78.46 265.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n14 16 Cyclist -1 -1 -1 400.09 165.00 499.19 332.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n14 6 Pedestrian -1 -1 -1 1117.07 169.98 1158.92 279.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n14 18 Cyclist -1 -1 -1 340.25 164.08 443.76 324.45 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n14 1 Cyclist -1 -1 -1 557.67 165.78 592.54 245.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n14 8 Car -1 -1 -1 606.86 175.97 634.25 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n14 3 Car -1 -1 -1 1116.68 189.13 1221.31 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n14 9 Pedestrian -1 -1 -1 272.14 160.02 289.19 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n14 15 Pedestrian -1 -1 -1 371.72 163.20 389.00 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n14 21 Pedestrian -1 -1 -1 400.93 164.21 416.01 200.59 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n14 22 Pedestrian -1 -1 -1 365.41 162.70 380.36 203.26 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n14 23 Cyclist -1 -1 -1 448.61 163.94 478.92 225.75 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n15 11 Car -1 -1 -1 929.55 184.77 1010.73 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n15 4 Car -1 -1 -1 877.00 184.40 945.49 217.95 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n15 14 Pedestrian -1 -1 -1 79.94 163.80 129.37 264.68 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n15 6 Pedestrian -1 -1 -1 1125.24 169.97 1173.66 280.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n15 7 Car -1 -1 -1 979.20 185.53 1069.11 221.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n15 18 Cyclist -1 -1 -1 364.57 162.74 451.01 311.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n15 2 Cyclist -1 -1 -1 485.14 163.53 521.80 241.43 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n15 1 Cyclist -1 -1 -1 558.90 166.97 592.84 243.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n15 19 Pedestrian -1 -1 -1 43.21 154.31 82.34 264.93 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n15 16 Cyclist -1 -1 -1 422.36 166.80 505.82 316.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n15 8 Car -1 -1 -1 606.96 175.94 634.42 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n15 9 Pedestrian -1 -1 -1 272.37 160.15 289.22 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n15 15 Pedestrian -1 -1 -1 372.36 162.60 389.95 209.09 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n15 3 Car -1 -1 -1 1115.85 188.89 1221.93 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n15 22 Pedestrian -1 -1 -1 365.27 162.14 379.85 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n16 11 Car -1 -1 -1 929.73 184.82 1010.68 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n16 14 Pedestrian -1 -1 -1 84.10 164.51 133.18 263.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n16 18 Cyclist -1 -1 -1 381.73 163.01 462.13 303.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n16 4 Car -1 -1 -1 877.10 184.39 945.32 217.95 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n16 7 Car -1 -1 -1 982.80 185.47 1069.11 221.45 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n16 1 Cyclist -1 -1 -1 560.47 167.07 593.07 240.05 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n16 19 Pedestrian -1 -1 -1 51.56 155.71 89.29 263.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n16 6 Pedestrian -1 -1 -1 1136.53 171.75 1192.71 280.28 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n16 8 Car -1 -1 -1 607.18 175.87 634.18 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n16 16 Cyclist -1 -1 -1 436.69 167.74 514.93 305.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n16 2 Cyclist -1 -1 -1 492.73 164.21 526.56 234.71 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n16 9 Pedestrian -1 -1 -1 272.16 160.25 289.05 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n16 3 Car -1 -1 -1 1114.46 188.59 1223.04 225.83 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n16 15 Pedestrian -1 -1 -1 374.10 162.15 392.28 209.30 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n16 22 Pedestrian -1 -1 -1 365.03 161.79 379.86 203.19 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n17 11 Car -1 -1 -1 929.70 184.86 1010.28 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n17 18 Cyclist -1 -1 -1 397.56 162.39 470.44 297.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n17 14 Pedestrian -1 -1 -1 90.49 163.92 135.79 262.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n17 4 Car -1 -1 -1 877.11 184.44 945.26 217.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n17 7 Car -1 -1 -1 982.94 185.57 1068.98 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n17 16 Cyclist -1 -1 -1 448.19 169.24 520.52 296.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n17 19 Pedestrian -1 -1 -1 55.97 155.27 100.38 262.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n17 1 Cyclist -1 -1 -1 563.09 166.72 593.44 238.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n17 6 Pedestrian -1 -1 -1 1132.93 174.21 1211.88 283.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n17 2 Cyclist -1 -1 -1 495.40 163.96 527.97 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n17 8 Car -1 -1 -1 607.36 175.72 634.30 200.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n17 9 Pedestrian -1 -1 -1 272.25 160.26 288.79 196.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n17 22 Pedestrian -1 -1 -1 364.58 160.99 379.83 203.71 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n17 3 Car -1 -1 -1 1116.22 189.02 1220.71 225.31 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n17 15 Pedestrian -1 -1 -1 373.96 161.92 392.46 209.44 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n17 24 Pedestrian -1 -1 -1 400.51 163.60 416.19 201.61 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n18 11 Car -1 -1 -1 929.69 184.87 1010.37 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n18 14 Pedestrian -1 -1 -1 99.78 163.96 140.25 261.79 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n18 7 Car -1 -1 -1 979.24 185.59 1069.00 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n18 4 Car -1 -1 -1 877.16 184.48 945.16 217.96 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n18 18 Cyclist -1 -1 -1 410.16 164.33 479.25 288.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n18 16 Cyclist -1 -1 -1 458.02 170.30 527.03 289.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n18 19 Pedestrian -1 -1 -1 58.20 157.18 106.38 260.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n18 1 Cyclist -1 -1 -1 564.20 167.41 592.88 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n18 6 Pedestrian -1 -1 -1 1150.41 172.63 1209.61 284.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n18 8 Car -1 -1 -1 607.21 175.70 634.31 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n18 3 Car -1 -1 -1 1117.42 188.95 1219.27 225.28 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n18 2 Cyclist -1 -1 -1 502.26 164.91 532.66 230.81 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n18 22 Pedestrian -1 -1 -1 364.22 161.05 379.74 203.71 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n18 9 Pedestrian -1 -1 -1 272.09 160.30 288.73 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n18 15 Pedestrian -1 -1 -1 372.24 161.87 391.23 209.81 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n18 24 Pedestrian -1 -1 -1 399.71 164.17 415.53 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n18 25 Cyclist -1 -1 -1 464.85 163.89 493.77 225.98 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n19 11 Car -1 -1 -1 929.65 184.85 1010.49 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n19 18 Cyclist -1 -1 -1 417.34 165.23 489.61 283.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n19 16 Cyclist -1 -1 -1 472.72 170.44 533.28 282.17 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n19 7 Car -1 -1 -1 979.01 185.69 1069.13 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n19 4 Car -1 -1 -1 877.06 184.44 945.35 217.93 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n19 14 Pedestrian -1 -1 -1 104.92 165.10 143.07 260.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n19 19 Pedestrian -1 -1 -1 61.44 158.04 111.59 260.28 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n19 1 Cyclist -1 -1 -1 566.63 167.45 591.65 227.90 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n19 15 Pedestrian -1 -1 -1 373.65 162.57 393.92 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n19 8 Car -1 -1 -1 607.12 175.80 634.14 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n19 3 Car -1 -1 -1 1119.17 188.87 1218.34 225.21 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n19 6 Pedestrian -1 -1 -1 1168.22 171.76 1214.63 285.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n19 9 Pedestrian -1 -1 -1 271.99 160.18 289.15 196.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n19 2 Cyclist -1 -1 -1 508.20 166.56 534.03 224.84 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n19 22 Pedestrian -1 -1 -1 364.35 161.57 379.56 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n19 24 Pedestrian -1 -1 -1 399.47 164.11 415.34 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n19 25 Cyclist -1 -1 -1 466.63 163.34 494.49 226.81 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n20 11 Car -1 -1 -1 929.61 184.88 1010.63 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n20 18 Cyclist -1 -1 -1 428.62 165.47 495.41 278.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n20 4 Car -1 -1 -1 877.08 184.45 945.39 217.90 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n20 7 Car -1 -1 -1 979.29 185.68 1068.98 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n20 14 Pedestrian -1 -1 -1 105.98 165.69 151.55 259.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n20 16 Cyclist -1 -1 -1 476.40 171.83 544.05 277.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n20 15 Pedestrian -1 -1 -1 373.80 162.90 394.05 210.93 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n20 6 Pedestrian -1 -1 -1 1177.62 171.33 1220.01 287.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n20 8 Car -1 -1 -1 607.00 175.94 634.54 200.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n20 3 Car -1 -1 -1 1118.12 189.09 1218.95 225.16 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 1 Cyclist -1 -1 -1 567.24 168.19 591.69 226.78 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n20 19 Pedestrian -1 -1 -1 69.19 158.05 111.67 259.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n20 2 Cyclist -1 -1 -1 512.15 165.99 538.58 224.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n20 9 Pedestrian -1 -1 -1 272.01 160.31 288.84 196.57 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n20 22 Pedestrian -1 -1 -1 364.12 162.06 379.40 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n20 24 Pedestrian -1 -1 -1 398.60 163.93 415.44 203.53 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n20 25 Cyclist -1 -1 -1 473.58 163.58 500.14 226.79 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n21 11 Car -1 -1 -1 929.80 184.81 1010.37 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n21 18 Cyclist -1 -1 -1 438.14 165.99 501.38 271.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n21 4 Car -1 -1 -1 877.15 184.46 945.41 217.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n21 7 Car -1 -1 -1 979.69 185.63 1068.63 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n21 16 Cyclist -1 -1 -1 491.52 168.90 547.17 274.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n21 14 Pedestrian -1 -1 -1 110.71 166.18 159.74 258.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n21 3 Car -1 -1 -1 1116.98 189.00 1219.27 225.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n21 19 Pedestrian -1 -1 -1 82.80 156.35 119.23 256.87 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n21 15 Pedestrian -1 -1 -1 373.64 162.68 394.31 211.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n21 8 Car -1 -1 -1 607.03 175.97 634.35 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n21 6 Pedestrian -1 -1 -1 1184.97 170.91 1219.70 287.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n21 1 Cyclist -1 -1 -1 566.93 168.19 590.70 226.44 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n21 9 Pedestrian -1 -1 -1 271.98 160.23 289.08 196.75 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n21 24 Pedestrian -1 -1 -1 399.36 163.61 415.47 203.22 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n21 2 Cyclist -1 -1 -1 513.47 165.39 540.17 224.03 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n21 22 Pedestrian -1 -1 -1 363.99 161.72 379.36 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n21 25 Cyclist -1 -1 -1 478.50 164.57 503.20 223.56 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n22 11 Car -1 -1 -1 929.61 184.77 1010.43 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n22 18 Cyclist -1 -1 -1 447.33 166.20 506.61 268.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n22 4 Car -1 -1 -1 877.06 184.47 945.24 217.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n22 14 Pedestrian -1 -1 -1 116.89 165.46 163.34 256.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n22 7 Car -1 -1 -1 983.45 185.69 1068.41 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n22 16 Cyclist -1 -1 -1 502.76 169.34 554.62 267.75 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n22 3 Car -1 -1 -1 1116.75 188.93 1218.86 225.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n22 19 Pedestrian -1 -1 -1 90.25 157.16 126.58 256.45 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n22 15 Pedestrian -1 -1 -1 373.28 162.46 394.45 211.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n22 24 Pedestrian -1 -1 -1 398.52 163.28 415.56 203.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n22 1 Cyclist -1 -1 -1 566.79 168.34 591.41 223.60 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n22 8 Car -1 -1 -1 606.97 176.12 634.12 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n22 9 Pedestrian -1 -1 -1 271.93 160.27 288.87 196.75 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n22 22 Pedestrian -1 -1 -1 362.45 161.62 377.96 202.28 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n22 2 Cyclist -1 -1 -1 518.17 165.44 542.01 223.20 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n22 6 Pedestrian -1 -1 -1 1192.22 179.42 1220.73 285.55 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n22 25 Cyclist -1 -1 -1 481.01 164.94 502.96 217.64 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n23 18 Cyclist -1 -1 -1 458.41 166.32 508.92 263.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n23 11 Car -1 -1 -1 929.58 184.77 1010.34 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n23 19 Pedestrian -1 -1 -1 93.82 157.64 132.19 255.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n23 3 Car -1 -1 -1 1116.23 188.73 1219.55 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n23 7 Car -1 -1 -1 979.82 185.64 1068.48 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n23 4 Car -1 -1 -1 876.91 184.48 945.33 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n23 16 Cyclist -1 -1 -1 509.22 169.21 556.76 265.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n23 14 Pedestrian -1 -1 -1 128.51 164.75 167.15 255.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n23 15 Pedestrian -1 -1 -1 373.40 162.60 394.39 211.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n23 1 Cyclist -1 -1 -1 567.29 168.67 591.67 222.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n23 22 Pedestrian -1 -1 -1 362.10 161.74 377.59 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n23 8 Car -1 -1 -1 606.73 176.02 634.17 200.21 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n23 24 Pedestrian -1 -1 -1 397.86 163.43 414.93 203.61 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n23 9 Pedestrian -1 -1 -1 272.06 160.31 288.77 196.59 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n23 2 Cyclist -1 -1 -1 523.20 167.10 543.59 216.14 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n23 25 Cyclist -1 -1 -1 485.88 164.78 506.35 217.30 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n23 26 Pedestrian -1 -1 -1 183.08 158.86 202.05 209.14 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n24 11 Car -1 -1 -1 929.57 184.80 1010.57 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n24 18 Cyclist -1 -1 -1 464.74 166.39 512.30 260.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n24 3 Car -1 -1 -1 1116.92 188.56 1220.19 225.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 7 Car -1 -1 -1 979.75 185.64 1068.53 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n24 14 Pedestrian -1 -1 -1 138.35 163.57 179.07 255.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n24 4 Car -1 -1 -1 876.93 184.52 945.29 217.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n24 16 Cyclist -1 -1 -1 517.39 169.12 558.58 259.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n24 15 Pedestrian -1 -1 -1 374.13 162.77 394.71 212.20 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 22 Pedestrian -1 -1 -1 361.31 162.30 377.53 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n24 19 Pedestrian -1 -1 -1 96.86 158.50 137.01 255.48 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n24 1 Cyclist -1 -1 -1 568.28 168.66 591.90 219.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 9 Pedestrian -1 -1 -1 272.01 160.22 289.02 196.66 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n24 24 Pedestrian -1 -1 -1 395.47 163.71 413.10 204.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n24 8 Car -1 -1 -1 606.74 176.11 633.89 200.23 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n24 25 Cyclist -1 -1 -1 489.26 165.08 510.12 216.04 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n24 2 Cyclist -1 -1 -1 523.56 166.99 543.66 214.87 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n25 11 Car -1 -1 -1 929.64 184.89 1010.72 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n25 18 Cyclist -1 -1 -1 472.39 165.09 517.38 257.52 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n25 3 Car -1 -1 -1 1115.86 188.83 1220.67 225.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n25 4 Car -1 -1 -1 877.05 184.57 945.33 217.86 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n25 7 Car -1 -1 -1 983.15 185.68 1068.75 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n25 19 Pedestrian -1 -1 -1 100.65 157.14 140.15 256.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n25 1 Cyclist -1 -1 -1 568.31 168.94 591.82 220.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n25 15 Pedestrian -1 -1 -1 374.83 162.73 395.43 212.31 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n25 14 Pedestrian -1 -1 -1 145.39 163.89 187.48 256.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n25 22 Pedestrian -1 -1 -1 360.67 162.48 376.33 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n25 16 Cyclist -1 -1 -1 522.44 169.56 565.78 256.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n25 8 Car -1 -1 -1 607.12 175.94 633.88 200.25 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n25 24 Pedestrian -1 -1 -1 395.39 163.45 413.16 204.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n25 9 Pedestrian -1 -1 -1 271.99 160.21 289.37 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n25 27 Cyclist -1 -1 -1 -2.48 135.94 273.76 361.47 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n26 11 Car -1 -1 -1 929.59 184.84 1010.59 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 18 Cyclist -1 -1 -1 477.02 166.04 521.33 254.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n26 3 Car -1 -1 -1 1116.10 188.75 1220.49 225.87 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n26 7 Car -1 -1 -1 979.64 185.63 1068.61 221.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n26 4 Car -1 -1 -1 877.01 184.57 945.26 217.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n26 1 Cyclist -1 -1 -1 567.87 168.82 591.91 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n26 19 Pedestrian -1 -1 -1 108.04 159.60 147.59 253.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n26 16 Cyclist -1 -1 -1 527.15 169.16 565.15 252.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n26 15 Pedestrian -1 -1 -1 374.35 162.37 396.33 212.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n26 14 Pedestrian -1 -1 -1 152.67 163.15 194.90 256.39 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n26 27 Cyclist -1 -1 -1 64.15 134.96 314.60 362.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n26 24 Pedestrian -1 -1 -1 395.37 163.38 413.48 204.28 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n26 8 Car -1 -1 -1 606.95 175.90 633.65 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n26 9 Pedestrian -1 -1 -1 271.75 160.19 289.52 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n26 22 Pedestrian -1 -1 -1 359.62 161.89 375.75 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n26 28 Pedestrian -1 -1 -1 75.51 156.05 226.67 363.46 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n27 11 Car -1 -1 -1 929.48 184.84 1010.68 221.02 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n27 3 Car -1 -1 -1 1115.88 189.00 1220.87 225.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n27 7 Car -1 -1 -1 979.63 185.66 1068.70 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n27 27 Cyclist -1 -1 -1 136.68 146.61 364.60 365.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n27 4 Car -1 -1 -1 876.89 184.58 945.36 217.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n27 19 Pedestrian -1 -1 -1 117.84 158.19 152.85 254.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n27 1 Cyclist -1 -1 -1 567.80 168.76 591.83 219.49 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n27 18 Cyclist -1 -1 -1 483.18 166.17 527.98 251.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n27 16 Cyclist -1 -1 -1 531.03 169.66 569.14 250.25 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n27 24 Pedestrian -1 -1 -1 395.16 163.14 413.67 204.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n27 14 Pedestrian -1 -1 -1 149.99 164.90 190.96 254.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n27 15 Pedestrian -1 -1 -1 375.12 162.11 396.31 213.06 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n27 8 Car -1 -1 -1 607.05 176.02 633.51 200.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n27 9 Pedestrian -1 -1 -1 272.34 160.47 289.34 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n27 22 Pedestrian -1 -1 -1 358.19 161.40 374.46 201.86 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n28 27 Cyclist -1 -1 -1 194.59 147.55 390.86 364.75 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n28 11 Car -1 -1 -1 929.43 184.84 1010.64 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n28 18 Cyclist -1 -1 -1 489.73 166.20 530.71 248.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n28 3 Car -1 -1 -1 1115.86 188.85 1220.73 225.75 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n28 14 Pedestrian -1 -1 -1 156.25 163.97 200.07 254.49 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n28 7 Car -1 -1 -1 979.62 185.66 1068.66 221.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n28 19 Pedestrian -1 -1 -1 120.34 159.29 160.11 252.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n28 4 Car -1 -1 -1 877.04 184.55 945.20 217.57 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n28 16 Cyclist -1 -1 -1 537.00 169.79 570.89 244.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n28 15 Pedestrian -1 -1 -1 377.67 163.43 396.74 211.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n28 22 Pedestrian -1 -1 -1 358.32 161.51 374.42 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n28 1 Cyclist -1 -1 -1 568.44 168.81 591.37 218.63 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n28 24 Pedestrian -1 -1 -1 394.89 163.46 413.29 205.16 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n28 8 Car -1 -1 -1 606.94 175.97 633.53 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n28 9 Pedestrian -1 -1 -1 272.54 160.52 289.21 197.42 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n29 27 Cyclist -1 -1 -1 253.97 147.65 415.47 365.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 11 Car -1 -1 -1 929.50 184.80 1010.71 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n29 18 Cyclist -1 -1 -1 495.26 166.20 533.22 246.50 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n29 3 Car -1 -1 -1 1116.67 188.86 1220.79 225.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n29 19 Pedestrian -1 -1 -1 124.74 161.53 168.14 250.68 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n29 7 Car -1 -1 -1 983.15 185.64 1068.69 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n29 14 Pedestrian -1 -1 -1 163.35 164.80 201.30 252.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n29 4 Car -1 -1 -1 877.03 184.45 945.43 217.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n29 16 Cyclist -1 -1 -1 541.19 170.11 572.74 242.17 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n29 1 Cyclist -1 -1 -1 568.79 169.25 589.40 213.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n29 22 Pedestrian -1 -1 -1 358.20 161.92 374.57 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n29 15 Pedestrian -1 -1 -1 378.28 164.08 397.23 211.76 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n29 8 Car -1 -1 -1 607.02 175.82 633.37 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n29 9 Pedestrian -1 -1 -1 272.40 160.67 289.44 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n29 24 Pedestrian -1 -1 -1 395.20 163.84 412.81 205.44 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n29 29 Cyclist -1 -1 -1 535.20 167.01 552.84 215.29 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n30 27 Cyclist -1 -1 -1 284.28 149.89 445.68 362.42 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n30 11 Car -1 -1 -1 929.56 184.80 1010.85 220.98 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n30 3 Car -1 -1 -1 1117.03 188.79 1220.98 225.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n30 4 Car -1 -1 -1 877.02 184.35 945.57 217.66 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n30 7 Car -1 -1 -1 982.95 185.63 1068.90 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n30 19 Pedestrian -1 -1 -1 127.62 162.09 168.43 250.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n30 18 Cyclist -1 -1 -1 498.33 167.75 536.75 243.52 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n30 14 Pedestrian -1 -1 -1 166.21 165.47 205.59 251.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n30 22 Pedestrian -1 -1 -1 357.90 162.74 374.34 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n30 8 Car -1 -1 -1 607.17 175.83 633.38 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n30 15 Pedestrian -1 -1 -1 378.81 164.79 396.96 211.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n30 1 Cyclist -1 -1 -1 568.69 168.97 588.66 213.49 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 16 Cyclist -1 -1 -1 545.22 170.39 574.69 239.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 9 Pedestrian -1 -1 -1 271.41 160.76 289.65 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n30 29 Cyclist -1 -1 -1 537.08 166.62 554.64 214.78 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n30 24 Pedestrian -1 -1 -1 395.33 164.97 413.92 206.23 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n31 11 Car -1 -1 -1 929.57 184.84 1010.60 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 3 Car -1 -1 -1 1117.15 188.70 1220.96 225.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n31 27 Cyclist -1 -1 -1 320.38 150.04 463.33 361.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n31 4 Car -1 -1 -1 877.14 184.26 945.69 217.70 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n31 7 Car -1 -1 -1 979.54 185.58 1068.83 221.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n31 19 Pedestrian -1 -1 -1 136.24 160.27 172.51 250.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n31 14 Pedestrian -1 -1 -1 170.37 165.78 209.07 249.15 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n31 22 Pedestrian -1 -1 -1 357.79 161.80 373.40 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n31 15 Pedestrian -1 -1 -1 379.17 164.73 397.12 211.83 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n31 16 Cyclist -1 -1 -1 548.85 169.25 578.11 237.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n31 8 Car -1 -1 -1 607.00 175.83 633.51 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 24 Pedestrian -1 -1 -1 396.08 165.56 413.09 205.97 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n31 18 Cyclist -1 -1 -1 505.26 166.31 537.11 240.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n31 9 Pedestrian -1 -1 -1 272.03 160.76 289.26 196.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n31 1 Cyclist -1 -1 -1 569.01 169.12 588.03 212.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n31 29 Cyclist -1 -1 -1 539.11 167.40 556.64 214.60 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n32 11 Car -1 -1 -1 929.40 184.83 1010.72 220.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n32 3 Car -1 -1 -1 1116.69 188.75 1221.04 225.98 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n32 27 Cyclist -1 -1 -1 350.46 150.28 478.75 354.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n32 7 Car -1 -1 -1 979.47 185.54 1068.89 221.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n32 4 Car -1 -1 -1 876.93 184.31 945.64 217.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n32 19 Pedestrian -1 -1 -1 145.82 160.00 179.18 249.95 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n32 14 Pedestrian -1 -1 -1 173.80 165.71 213.71 248.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n32 16 Cyclist -1 -1 -1 551.94 170.78 578.77 233.54 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n32 18 Cyclist -1 -1 -1 509.84 165.99 539.75 238.27 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n32 15 Pedestrian -1 -1 -1 379.36 163.81 397.22 212.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n32 8 Car -1 -1 -1 606.89 175.68 633.66 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n32 24 Pedestrian -1 -1 -1 396.29 165.49 412.66 206.41 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n32 22 Pedestrian -1 -1 -1 358.20 161.11 373.91 202.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n32 9 Pedestrian -1 -1 -1 271.69 160.78 289.49 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n32 29 Cyclist -1 -1 -1 540.28 167.49 558.22 214.80 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n32 1 Cyclist -1 -1 -1 569.38 169.26 587.64 211.90 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n32 30 Cyclist -1 -1 -1 396.29 165.49 412.66 206.41 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n33 11 Car -1 -1 -1 929.58 184.87 1010.61 220.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n33 3 Car -1 -1 -1 1116.51 188.78 1220.78 225.72 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n33 7 Car -1 -1 -1 983.11 185.60 1068.75 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n33 4 Car -1 -1 -1 876.95 184.33 945.48 217.75 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n33 14 Pedestrian -1 -1 -1 180.61 164.86 219.98 248.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n33 19 Pedestrian -1 -1 -1 149.02 160.38 183.79 249.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n33 27 Cyclist -1 -1 -1 376.59 154.10 491.13 335.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n33 18 Cyclist -1 -1 -1 511.75 165.71 540.71 236.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n33 16 Cyclist -1 -1 -1 554.02 170.82 581.74 232.63 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n33 8 Car -1 -1 -1 606.91 175.72 634.02 199.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n33 22 Pedestrian -1 -1 -1 358.01 161.51 374.02 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n33 24 Pedestrian -1 -1 -1 396.37 164.64 412.51 207.50 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n33 9 Pedestrian -1 -1 -1 271.35 160.65 289.51 196.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n33 15 Pedestrian -1 -1 -1 378.78 163.00 399.24 213.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n33 1 Cyclist -1 -1 -1 569.66 169.28 586.96 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n33 29 Cyclist -1 -1 -1 541.95 167.74 558.58 212.99 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n34 11 Car -1 -1 -1 929.73 184.90 1010.43 220.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n34 3 Car -1 -1 -1 1116.36 188.69 1220.72 225.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n34 14 Pedestrian -1 -1 -1 185.72 164.53 222.75 248.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n34 4 Car -1 -1 -1 877.07 184.34 945.39 217.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n34 7 Car -1 -1 -1 983.21 185.59 1068.65 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n34 22 Pedestrian -1 -1 -1 357.42 161.93 374.02 203.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n34 19 Pedestrian -1 -1 -1 150.95 161.13 189.13 248.48 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n34 27 Cyclist -1 -1 -1 400.00 155.82 497.30 325.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n34 16 Cyclist -1 -1 -1 555.10 170.52 582.85 232.36 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n34 8 Car -1 -1 -1 606.80 175.78 633.92 199.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n34 15 Pedestrian -1 -1 -1 381.64 162.73 401.97 213.58 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n34 29 Cyclist -1 -1 -1 543.17 166.71 561.93 213.87 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n34 9 Pedestrian -1 -1 -1 271.48 160.52 289.45 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n34 18 Cyclist -1 -1 -1 515.94 165.44 542.79 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n34 24 Pedestrian -1 -1 -1 396.25 162.53 412.76 206.70 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n34 1 Cyclist -1 -1 -1 569.65 168.75 587.48 210.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n35 11 Car -1 -1 -1 929.65 184.92 1010.58 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n35 14 Pedestrian -1 -1 -1 189.91 165.12 227.45 248.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n35 3 Car -1 -1 -1 1116.06 188.74 1220.90 225.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n35 7 Car -1 -1 -1 979.62 185.57 1068.73 221.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n35 4 Car -1 -1 -1 876.98 184.36 945.48 217.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n35 22 Pedestrian -1 -1 -1 356.71 162.08 373.45 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n35 16 Cyclist -1 -1 -1 556.91 169.47 586.01 229.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n35 19 Pedestrian -1 -1 -1 155.85 160.81 193.05 248.92 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n35 27 Cyclist -1 -1 -1 413.65 157.00 506.49 315.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n35 8 Car -1 -1 -1 606.96 175.87 633.52 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n35 15 Pedestrian -1 -1 -1 382.09 162.47 403.09 214.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n35 18 Cyclist -1 -1 -1 518.52 165.38 543.62 231.50 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n35 9 Pedestrian -1 -1 -1 269.93 160.49 286.50 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n35 24 Pedestrian -1 -1 -1 396.24 162.26 412.39 207.03 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n35 1 Cyclist -1 -1 -1 569.45 168.87 587.82 211.12 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n35 29 Cyclist -1 -1 -1 545.61 166.65 561.96 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n35 31 Pedestrian -1 -1 -1 545.61 166.65 561.96 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n35 32 Pedestrian -1 -1 -1 376.97 162.94 393.35 204.67 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n36 3 Car -1 -1 -1 1115.80 188.67 1220.78 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n36 11 Car -1 -1 -1 929.66 184.92 1010.71 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n36 14 Pedestrian -1 -1 -1 193.39 166.12 231.95 247.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n36 7 Car -1 -1 -1 983.20 185.59 1068.71 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n36 4 Car -1 -1 -1 877.08 184.35 945.38 217.91 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n36 16 Cyclist -1 -1 -1 558.71 169.74 585.80 228.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n36 27 Cyclist -1 -1 -1 427.98 158.05 516.02 307.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n36 22 Pedestrian -1 -1 -1 356.02 162.12 372.87 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n36 18 Cyclist -1 -1 -1 521.05 165.88 546.80 229.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n36 19 Pedestrian -1 -1 -1 165.29 160.75 197.13 248.84 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n36 15 Pedestrian -1 -1 -1 383.76 162.21 406.01 216.48 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n36 8 Car -1 -1 -1 606.80 175.90 633.41 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n36 9 Pedestrian -1 -1 -1 271.52 160.67 289.31 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n36 31 Pedestrian -1 -1 -1 547.16 167.17 563.91 213.14 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n36 24 Pedestrian -1 -1 -1 395.98 162.17 413.08 206.86 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n36 32 Pedestrian -1 -1 -1 376.54 161.93 392.61 204.64 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n36 1 Cyclist -1 -1 -1 565.89 169.05 587.36 211.08 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n37 11 Car -1 -1 -1 929.86 184.92 1010.55 220.98 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n37 3 Car -1 -1 -1 1116.93 188.45 1221.12 225.99 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n37 14 Pedestrian -1 -1 -1 198.28 166.43 235.12 246.47 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n37 19 Pedestrian -1 -1 -1 169.86 158.80 202.04 247.70 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n37 7 Car -1 -1 -1 983.24 185.64 1068.68 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n37 4 Car -1 -1 -1 876.96 184.33 945.36 217.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n37 16 Cyclist -1 -1 -1 559.14 170.20 586.99 227.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n37 27 Cyclist -1 -1 -1 444.59 160.44 521.73 298.15 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n37 18 Cyclist -1 -1 -1 522.10 166.37 546.80 228.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n37 22 Pedestrian -1 -1 -1 355.67 161.38 372.42 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n37 8 Car -1 -1 -1 606.65 175.73 633.59 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n37 15 Pedestrian -1 -1 -1 383.79 162.41 407.19 216.07 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n37 9 Pedestrian -1 -1 -1 271.78 160.48 289.35 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n37 31 Pedestrian -1 -1 -1 547.69 167.72 564.46 212.65 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n37 1 Cyclist -1 -1 -1 567.16 169.45 585.86 211.19 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n37 32 Pedestrian -1 -1 -1 376.49 162.50 392.02 204.01 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n37 24 Pedestrian -1 -1 -1 395.33 162.09 413.51 206.70 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n37 33 Cyclist -1 -1 -1 516.86 166.11 535.22 213.00 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n38 11 Car -1 -1 -1 929.76 184.97 1010.72 221.00 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n38 3 Car -1 -1 -1 1117.17 188.31 1221.07 226.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n38 14 Pedestrian -1 -1 -1 202.10 165.53 237.89 245.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n38 19 Pedestrian -1 -1 -1 172.46 159.52 207.51 246.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n38 4 Car -1 -1 -1 877.04 184.36 945.46 217.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n38 7 Car -1 -1 -1 983.24 185.69 1068.66 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n38 16 Cyclist -1 -1 -1 561.08 170.89 588.19 226.28 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n38 27 Cyclist -1 -1 -1 457.78 166.97 524.12 290.48 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n38 22 Pedestrian -1 -1 -1 353.89 161.65 371.18 203.46 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n38 8 Car -1 -1 -1 606.62 175.83 633.66 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n38 15 Pedestrian -1 -1 -1 383.36 162.97 407.53 215.56 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n38 9 Pedestrian -1 -1 -1 271.87 160.45 289.27 196.68 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n38 18 Cyclist -1 -1 -1 523.23 167.13 546.51 227.36 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n38 1 Cyclist -1 -1 -1 566.29 169.50 585.99 212.04 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n38 32 Pedestrian -1 -1 -1 376.76 163.26 392.22 203.80 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n38 24 Pedestrian -1 -1 -1 394.65 162.84 413.60 208.38 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n38 33 Cyclist -1 -1 -1 517.97 167.01 536.12 212.62 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n38 31 Pedestrian -1 -1 -1 547.91 168.25 565.48 212.06 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n38 34 Cyclist -1 -1 -1 547.91 168.25 565.48 212.06 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n39 11 Car -1 -1 -1 929.82 184.98 1010.61 220.98 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n39 3 Car -1 -1 -1 1117.17 188.41 1221.13 225.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n39 19 Pedestrian -1 -1 -1 173.93 161.06 213.18 244.18 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n39 4 Car -1 -1 -1 876.90 184.38 945.56 217.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n39 7 Car -1 -1 -1 983.08 185.64 1068.85 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n39 14 Pedestrian -1 -1 -1 206.99 166.12 238.55 244.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n39 8 Car -1 -1 -1 606.82 175.76 633.74 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n39 16 Cyclist -1 -1 -1 562.40 170.95 588.32 224.31 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n39 22 Pedestrian -1 -1 -1 354.13 162.19 371.18 203.96 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n39 18 Cyclist -1 -1 -1 527.13 166.09 549.64 223.69 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n39 15 Pedestrian -1 -1 -1 384.29 163.50 408.45 215.97 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n39 9 Pedestrian -1 -1 -1 271.90 160.62 289.02 196.44 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n39 27 Cyclist -1 -1 -1 471.90 167.55 532.24 282.60 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n39 31 Pedestrian -1 -1 -1 549.22 168.16 565.23 211.66 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n39 32 Pedestrian -1 -1 -1 376.60 163.10 392.26 204.09 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n40 11 Car -1 -1 -1 929.74 184.96 1010.24 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n40 3 Car -1 -1 -1 1116.79 188.41 1221.11 226.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n40 19 Pedestrian -1 -1 -1 178.45 162.18 215.78 241.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n40 14 Pedestrian -1 -1 -1 209.21 166.17 244.30 245.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n40 4 Car -1 -1 -1 876.92 184.40 945.47 218.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n40 7 Car -1 -1 -1 983.18 185.69 1068.68 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n40 22 Pedestrian -1 -1 -1 355.81 162.35 372.22 204.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n40 16 Cyclist -1 -1 -1 562.92 169.62 588.65 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n40 8 Car -1 -1 -1 606.50 175.77 633.91 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n40 18 Cyclist -1 -1 -1 531.00 166.93 552.85 221.64 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n40 9 Pedestrian -1 -1 -1 271.84 160.71 289.19 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n40 15 Pedestrian -1 -1 -1 387.99 163.53 410.75 217.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n40 27 Cyclist -1 -1 -1 480.62 163.85 542.19 278.74 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n40 32 Pedestrian -1 -1 -1 378.33 164.92 398.19 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n40 31 Pedestrian -1 -1 -1 549.87 168.14 565.74 211.25 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n40 35 Cyclist -1 -1 -1 549.87 168.14 565.74 211.25 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n41 11 Car -1 -1 -1 929.59 184.94 1010.29 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n41 3 Car -1 -1 -1 1116.33 188.48 1221.07 225.93 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n41 19 Pedestrian -1 -1 -1 182.89 162.07 218.59 241.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 14 Pedestrian -1 -1 -1 211.52 167.20 244.80 243.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n41 4 Car -1 -1 -1 876.98 184.45 945.46 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n41 7 Car -1 -1 -1 983.02 185.61 1068.89 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n41 22 Pedestrian -1 -1 -1 356.12 162.31 372.73 204.10 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n41 16 Cyclist -1 -1 -1 564.04 169.25 588.85 220.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n41 8 Car -1 -1 -1 606.66 175.71 633.97 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n41 15 Pedestrian -1 -1 -1 392.45 163.32 413.40 217.38 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n41 27 Cyclist -1 -1 -1 493.07 163.90 551.41 272.30 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n41 9 Pedestrian -1 -1 -1 271.54 160.86 289.55 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n41 18 Cyclist -1 -1 -1 533.63 167.23 554.60 220.34 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n41 32 Pedestrian -1 -1 -1 376.24 162.70 392.83 204.68 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n41 36 Pedestrian -1 -1 -1 378.77 166.92 398.71 212.62 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n42 11 Car -1 -1 -1 929.71 184.94 1010.35 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n42 3 Car -1 -1 -1 1116.11 188.45 1221.35 226.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n42 7 Car -1 -1 -1 982.97 185.65 1068.97 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n42 4 Car -1 -1 -1 876.91 184.45 945.56 217.93 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n42 19 Pedestrian -1 -1 -1 191.68 161.56 224.13 242.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n42 15 Pedestrian -1 -1 -1 392.72 163.02 414.62 217.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n42 14 Pedestrian -1 -1 -1 216.24 167.13 248.19 242.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n42 22 Pedestrian -1 -1 -1 355.92 162.08 372.52 204.04 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n42 27 Cyclist -1 -1 -1 502.95 162.91 557.40 266.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n42 8 Car -1 -1 -1 606.70 175.81 634.06 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n42 16 Cyclist -1 -1 -1 564.42 169.38 588.52 219.22 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n42 9 Pedestrian -1 -1 -1 271.71 160.88 289.45 196.31 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n42 18 Cyclist -1 -1 -1 533.77 167.47 556.07 219.81 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n42 36 Pedestrian -1 -1 -1 379.07 167.20 398.74 212.43 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n42 32 Pedestrian -1 -1 -1 375.78 162.58 392.80 205.04 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n42 37 Cyclist -1 -1 -1 552.10 168.71 566.91 208.03 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n43 11 Car -1 -1 -1 929.66 184.92 1010.41 220.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n43 3 Car -1 -1 -1 1116.65 188.42 1220.90 225.95 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n43 19 Pedestrian -1 -1 -1 194.95 162.40 228.45 240.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n43 4 Car -1 -1 -1 876.97 184.45 945.49 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n43 7 Car -1 -1 -1 982.95 185.66 1068.96 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n43 14 Pedestrian -1 -1 -1 221.96 166.29 256.81 241.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n43 15 Pedestrian -1 -1 -1 393.15 163.58 415.99 218.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n43 22 Pedestrian -1 -1 -1 356.06 162.16 372.59 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n43 8 Car -1 -1 -1 606.53 175.92 634.14 199.98 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n43 27 Cyclist -1 -1 -1 512.07 164.71 564.40 262.98 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n43 18 Cyclist -1 -1 -1 534.50 168.13 556.10 219.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n43 9 Pedestrian -1 -1 -1 272.07 160.77 289.40 196.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n43 16 Cyclist -1 -1 -1 566.43 169.18 587.32 214.73 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n43 32 Pedestrian -1 -1 -1 372.38 162.68 388.27 205.10 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n43 36 Pedestrian -1 -1 -1 379.17 167.25 398.59 212.49 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n43 37 Cyclist -1 -1 -1 553.02 168.84 566.53 206.39 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n43 38 Pedestrian -1 -1 -1 553.02 168.84 566.53 206.39 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n44 11 Car -1 -1 -1 929.61 184.90 1010.47 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n44 3 Car -1 -1 -1 1117.09 188.34 1221.45 226.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n44 4 Car -1 -1 -1 876.93 184.43 945.54 218.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n44 7 Car -1 -1 -1 982.95 185.66 1068.97 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n44 15 Pedestrian -1 -1 -1 395.49 164.44 417.86 218.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n44 19 Pedestrian -1 -1 -1 196.03 162.38 230.35 240.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n44 27 Cyclist -1 -1 -1 519.53 164.04 570.88 258.16 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n44 14 Pedestrian -1 -1 -1 228.46 166.58 258.74 242.45 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n44 8 Car -1 -1 -1 606.67 176.01 634.15 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n44 22 Pedestrian -1 -1 -1 355.86 162.53 372.40 205.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n44 16 Cyclist -1 -1 -1 566.78 169.62 587.33 214.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n44 9 Pedestrian -1 -1 -1 272.13 160.81 289.30 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n44 18 Cyclist -1 -1 -1 538.84 168.88 557.66 213.07 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n44 36 Pedestrian -1 -1 -1 379.29 167.70 398.69 213.36 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n44 32 Pedestrian -1 -1 -1 370.28 162.96 385.58 204.75 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n44 37 Cyclist -1 -1 -1 553.33 168.92 566.86 205.56 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n44 38 Pedestrian -1 -1 -1 553.33 168.92 566.86 205.56 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n44 39 Pedestrian -1 -1 -1 387.95 165.70 404.70 207.89 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n45 11 Car -1 -1 -1 929.66 185.01 1010.30 220.84 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n45 3 Car -1 -1 -1 1117.01 188.54 1221.20 225.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n45 4 Car -1 -1 -1 876.91 184.42 945.55 218.00 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 7 Car -1 -1 -1 982.91 185.62 1069.04 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 14 Pedestrian -1 -1 -1 232.45 167.28 261.08 242.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n45 15 Pedestrian -1 -1 -1 396.83 164.66 419.50 218.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n45 19 Pedestrian -1 -1 -1 200.26 162.07 233.92 239.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n45 27 Cyclist -1 -1 -1 526.74 164.57 573.37 256.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n45 22 Pedestrian -1 -1 -1 356.30 163.13 372.92 205.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n45 8 Car -1 -1 -1 606.69 176.07 634.27 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n45 18 Cyclist -1 -1 -1 538.95 169.10 558.49 212.70 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n45 9 Pedestrian -1 -1 -1 271.91 160.88 289.25 196.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n45 36 Pedestrian -1 -1 -1 379.21 167.80 399.00 213.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n45 16 Cyclist -1 -1 -1 567.05 170.09 587.13 212.94 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n45 37 Cyclist -1 -1 -1 553.11 169.00 566.93 205.42 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n45 32 Pedestrian -1 -1 -1 370.00 163.80 385.09 205.01 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n45 39 Pedestrian -1 -1 -1 388.02 166.11 404.69 206.96 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n45 38 Pedestrian -1 -1 -1 553.11 169.00 566.93 205.42 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n46 11 Car -1 -1 -1 929.66 184.94 1010.22 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n46 3 Car -1 -1 -1 1116.84 188.44 1221.36 226.05 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n46 4 Car -1 -1 -1 876.91 184.40 945.64 218.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n46 7 Car -1 -1 -1 983.13 185.68 1068.78 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n46 15 Pedestrian -1 -1 -1 399.66 164.80 422.79 218.75 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n46 14 Pedestrian -1 -1 -1 235.49 167.92 264.58 241.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n46 27 Cyclist -1 -1 -1 533.79 166.14 580.89 253.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n46 19 Pedestrian -1 -1 -1 207.98 162.13 237.56 239.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n46 22 Pedestrian -1 -1 -1 356.00 163.18 372.89 205.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n46 8 Car -1 -1 -1 606.85 176.22 634.13 199.86 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n46 16 Cyclist -1 -1 -1 569.30 170.10 588.06 212.02 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n46 9 Pedestrian -1 -1 -1 272.03 160.81 289.13 196.16 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n46 36 Pedestrian -1 -1 -1 379.37 167.39 399.33 213.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n46 18 Cyclist -1 -1 -1 540.93 171.20 558.10 204.15 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n46 37 Cyclist -1 -1 -1 553.34 169.22 567.06 204.99 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n46 39 Pedestrian -1 -1 -1 387.72 166.29 404.63 207.14 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n46 32 Pedestrian -1 -1 -1 369.66 165.08 385.60 205.90 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n46 38 Pedestrian -1 -1 -1 553.34 169.22 567.06 204.99 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n46 40 Cyclist -1 -1 -1 526.94 169.78 539.50 204.40 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n47 11 Car -1 -1 -1 929.76 184.89 1010.43 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n47 3 Car -1 -1 -1 1116.94 188.57 1221.27 225.92 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n47 7 Car -1 -1 -1 982.92 185.58 1069.00 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n47 4 Car -1 -1 -1 876.89 184.37 945.59 217.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n47 27 Cyclist -1 -1 -1 539.03 164.50 581.85 250.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n47 14 Pedestrian -1 -1 -1 239.66 167.62 268.12 239.76 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n47 22 Pedestrian -1 -1 -1 355.96 162.52 372.82 205.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n47 19 Pedestrian -1 -1 -1 212.45 161.70 241.38 239.99 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n47 8 Car -1 -1 -1 606.92 176.15 634.11 199.93 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n47 15 Pedestrian -1 -1 -1 402.84 164.35 426.02 219.07 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n47 9 Pedestrian -1 -1 -1 272.27 160.71 289.14 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n47 16 Cyclist -1 -1 -1 570.94 170.66 588.67 210.68 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n47 36 Pedestrian -1 -1 -1 381.06 167.80 400.65 214.22 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n47 40 Cyclist -1 -1 -1 528.30 169.97 540.37 204.66 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n47 32 Pedestrian -1 -1 -1 368.76 164.99 384.65 205.95 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n47 37 Cyclist -1 -1 -1 553.68 169.61 567.19 204.71 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n47 38 Pedestrian -1 -1 -1 553.68 169.61 567.19 204.71 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n47 18 Cyclist -1 -1 -1 541.91 170.67 563.10 210.66 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n47 41 Car -1 -1 -1 553.68 169.61 567.19 204.71 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n48 11 Car -1 -1 -1 929.56 184.93 1010.47 220.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n48 3 Car -1 -1 -1 1116.52 188.59 1221.20 225.85 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n48 4 Car -1 -1 -1 876.89 184.33 945.62 218.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n48 7 Car -1 -1 -1 983.17 185.65 1068.66 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n48 27 Cyclist -1 -1 -1 542.44 165.90 585.29 247.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n48 14 Pedestrian -1 -1 -1 244.76 167.34 270.49 237.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n48 22 Pedestrian -1 -1 -1 356.67 162.45 372.93 205.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n48 19 Pedestrian -1 -1 -1 215.75 161.13 244.81 237.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n48 15 Pedestrian -1 -1 -1 404.18 164.38 426.96 218.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n48 8 Car -1 -1 -1 606.82 176.15 634.04 199.99 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n48 32 Pedestrian -1 -1 -1 368.87 163.56 384.44 205.06 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n48 9 Pedestrian -1 -1 -1 272.37 160.57 289.09 196.25 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n48 36 Pedestrian -1 -1 -1 380.69 167.26 401.36 215.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n48 16 Cyclist -1 -1 -1 571.11 171.29 588.36 209.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n48 37 Cyclist -1 -1 -1 554.23 169.88 567.48 204.66 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n48 40 Cyclist -1 -1 -1 530.36 169.51 542.24 205.70 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n48 18 Cyclist -1 -1 -1 542.73 171.29 562.52 210.77 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n48 41 Car -1 -1 -1 554.23 169.88 567.48 204.66 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n48 38 Pedestrian -1 -1 -1 554.23 169.88 567.48 204.66 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n49 11 Car -1 -1 -1 929.93 184.86 1010.49 220.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n49 19 Pedestrian -1 -1 -1 216.06 160.96 247.90 236.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n49 3 Car -1 -1 -1 1116.85 188.45 1221.14 226.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n49 4 Car -1 -1 -1 877.03 184.32 945.55 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n49 7 Car -1 -1 -1 983.10 185.56 1068.78 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n49 14 Pedestrian -1 -1 -1 248.54 167.27 274.81 238.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n49 27 Cyclist -1 -1 -1 546.90 166.03 589.07 245.34 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n49 15 Pedestrian -1 -1 -1 406.53 163.85 429.54 219.48 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n49 22 Pedestrian -1 -1 -1 357.66 162.82 373.89 206.29 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n49 8 Car -1 -1 -1 606.76 176.04 633.92 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n49 32 Pedestrian -1 -1 -1 368.55 163.58 384.32 205.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n49 9 Pedestrian -1 -1 -1 272.66 160.50 289.30 196.25 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n49 16 Cyclist -1 -1 -1 571.02 171.21 587.57 210.27 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n49 36 Pedestrian -1 -1 -1 387.82 164.84 405.15 206.86 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n49 40 Cyclist -1 -1 -1 530.82 169.89 542.66 205.91 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n49 37 Cyclist -1 -1 -1 553.64 169.81 568.20 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n49 41 Car -1 -1 -1 553.64 169.81 568.20 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n50 11 Car -1 -1 -1 930.00 184.76 1010.43 220.78 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n50 19 Pedestrian -1 -1 -1 220.00 161.74 251.66 236.69 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n50 3 Car -1 -1 -1 1116.86 188.54 1221.21 225.80 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n50 7 Car -1 -1 -1 982.85 185.51 1068.98 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n50 14 Pedestrian -1 -1 -1 251.79 167.05 279.99 238.44 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n50 4 Car -1 -1 -1 877.00 184.26 945.55 218.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n50 27 Cyclist -1 -1 -1 553.00 164.43 590.89 241.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n50 8 Car -1 -1 -1 606.56 175.89 633.96 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n50 15 Pedestrian -1 -1 -1 408.47 164.45 431.28 219.68 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n50 36 Pedestrian -1 -1 -1 388.39 165.12 405.50 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n50 9 Pedestrian -1 -1 -1 272.67 160.73 289.53 196.11 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n50 32 Pedestrian -1 -1 -1 368.06 163.79 384.05 205.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n50 16 Cyclist -1 -1 -1 571.30 171.39 587.46 210.03 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n50 40 Cyclist -1 -1 -1 531.65 169.83 543.51 204.38 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n50 22 Pedestrian -1 -1 -1 358.55 162.88 374.74 206.46 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n50 41 Car -1 -1 -1 566.12 172.26 585.92 202.13 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n50 42 Pedestrian -1 -1 -1 399.81 164.89 416.10 208.45 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n50 43 Pedestrian -1 -1 -1 546.50 169.54 560.76 212.27 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n51 19 Pedestrian -1 -1 -1 223.00 161.31 255.09 237.25 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n51 3 Car -1 -1 -1 1116.71 188.42 1221.25 225.84 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n51 7 Car -1 -1 -1 982.95 185.51 1069.01 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n51 11 Car -1 -1 -1 930.27 184.75 1010.54 220.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n51 4 Car -1 -1 -1 877.05 184.17 945.51 217.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n51 14 Pedestrian -1 -1 -1 254.91 167.36 283.29 238.36 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n51 27 Cyclist -1 -1 -1 556.82 164.84 593.13 239.21 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n51 42 Pedestrian -1 -1 -1 399.83 164.47 416.13 208.62 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n51 8 Car -1 -1 -1 606.68 175.82 633.88 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n51 36 Pedestrian -1 -1 -1 381.76 167.62 401.23 214.92 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n51 15 Pedestrian -1 -1 -1 412.29 164.90 433.31 219.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n51 9 Pedestrian -1 -1 -1 272.83 160.71 289.17 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n51 22 Pedestrian -1 -1 -1 359.42 162.67 377.22 206.64 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n51 16 Cyclist -1 -1 -1 571.33 172.20 587.79 209.42 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n51 40 Cyclist -1 -1 -1 532.74 169.56 543.68 203.25 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n51 32 Pedestrian -1 -1 -1 368.24 164.42 384.02 204.84 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n51 41 Car -1 -1 -1 565.52 172.30 585.97 201.92 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n52 3 Car -1 -1 -1 1116.43 188.33 1221.32 225.70 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n52 19 Pedestrian -1 -1 -1 228.05 161.44 256.66 236.71 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n52 7 Car -1 -1 -1 982.97 185.44 1068.93 221.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n52 4 Car -1 -1 -1 877.00 184.01 945.56 217.98 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n52 11 Car -1 -1 -1 930.51 184.58 1010.69 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n52 14 Pedestrian -1 -1 -1 257.20 167.53 283.99 237.25 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n52 15 Pedestrian -1 -1 -1 414.90 164.28 436.44 219.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n52 8 Car -1 -1 -1 606.60 175.72 634.16 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n52 36 Pedestrian -1 -1 -1 381.83 167.36 402.05 214.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n52 27 Cyclist -1 -1 -1 560.51 164.80 592.42 232.59 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n52 42 Pedestrian -1 -1 -1 399.77 163.26 415.97 208.42 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n52 22 Pedestrian -1 -1 -1 359.96 162.56 377.93 206.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n52 9 Pedestrian -1 -1 -1 273.15 160.60 288.86 196.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n52 32 Pedestrian -1 -1 -1 368.28 164.80 383.98 204.47 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n52 16 Cyclist -1 -1 -1 548.18 168.83 564.38 211.27 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n52 41 Car -1 -1 -1 565.11 171.63 586.17 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n52 44 Pedestrian -1 -1 -1 548.18 168.83 564.38 211.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n53 3 Car -1 -1 -1 1116.58 188.41 1220.92 225.52 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n53 7 Car -1 -1 -1 983.13 185.48 1068.82 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n53 19 Pedestrian -1 -1 -1 233.38 161.15 259.52 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n53 4 Car -1 -1 -1 877.12 184.02 945.47 217.84 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n53 11 Car -1 -1 -1 934.35 184.49 1010.45 220.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n53 14 Pedestrian -1 -1 -1 260.80 167.15 288.19 237.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n53 15 Pedestrian -1 -1 -1 414.09 163.66 439.46 220.48 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n53 8 Car -1 -1 -1 606.53 175.67 634.28 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n53 36 Pedestrian -1 -1 -1 382.71 167.41 402.72 214.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n53 22 Pedestrian -1 -1 -1 360.59 162.42 378.78 206.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n53 44 Pedestrian -1 -1 -1 549.55 168.58 564.62 211.38 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n53 9 Pedestrian -1 -1 -1 273.36 160.33 289.19 196.49 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n53 42 Pedestrian -1 -1 -1 400.00 163.26 416.41 207.96 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n53 32 Pedestrian -1 -1 -1 368.43 164.28 384.02 204.54 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n53 27 Cyclist -1 -1 -1 565.93 166.25 591.45 224.67 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n53 41 Car -1 -1 -1 564.83 171.91 586.39 195.37 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n53 45 Pedestrian -1 -1 -1 1.09 163.05 56.23 355.96 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n53 46 Pedestrian -1 -1 -1 534.05 169.86 545.44 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n54 3 Car -1 -1 -1 1116.32 188.31 1221.03 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n54 19 Pedestrian -1 -1 -1 235.52 162.13 263.89 235.40 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n54 7 Car -1 -1 -1 983.29 185.49 1068.63 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n54 4 Car -1 -1 -1 877.19 184.03 945.36 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n54 11 Car -1 -1 -1 930.92 184.58 1010.35 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n54 8 Car -1 -1 -1 606.61 175.60 634.27 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n54 45 Pedestrian -1 -1 -1 2.09 160.74 78.75 358.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n54 14 Pedestrian -1 -1 -1 265.38 167.52 291.07 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n54 15 Pedestrian -1 -1 -1 413.58 163.39 441.32 221.02 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n54 42 Pedestrian -1 -1 -1 399.81 163.63 416.66 208.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n54 46 Pedestrian -1 -1 -1 533.98 169.94 545.76 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n54 27 Cyclist -1 -1 -1 566.32 165.13 592.30 224.26 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n54 44 Pedestrian -1 -1 -1 550.84 168.12 564.35 211.31 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n54 36 Pedestrian -1 -1 -1 385.19 167.63 404.22 214.20 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n54 9 Pedestrian -1 -1 -1 273.85 160.28 288.50 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n54 32 Pedestrian -1 -1 -1 368.74 164.17 384.28 204.48 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n54 22 Pedestrian -1 -1 -1 361.00 162.34 379.25 206.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n54 41 Car -1 -1 -1 564.24 172.15 587.17 195.08 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n55 3 Car -1 -1 -1 1116.55 188.24 1220.90 225.41 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n55 7 Car -1 -1 -1 983.16 185.47 1068.83 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n55 19 Pedestrian -1 -1 -1 238.43 162.72 267.94 234.20 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n55 4 Car -1 -1 -1 877.06 184.02 945.46 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n55 11 Car -1 -1 -1 930.98 184.61 1010.35 220.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n55 8 Car -1 -1 -1 606.72 175.62 634.35 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n55 15 Pedestrian -1 -1 -1 417.09 163.59 443.59 222.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n55 45 Pedestrian -1 -1 -1 6.47 162.59 97.00 357.52 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n55 36 Pedestrian -1 -1 -1 385.71 167.60 405.14 214.02 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n55 14 Pedestrian -1 -1 -1 271.91 167.25 296.22 234.60 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n55 46 Pedestrian -1 -1 -1 534.28 169.98 545.76 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n55 42 Pedestrian -1 -1 -1 399.64 163.84 416.83 208.20 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n55 9 Pedestrian -1 -1 -1 273.71 160.43 288.27 196.66 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n55 27 Cyclist -1 -1 -1 566.65 164.50 591.70 226.15 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n55 32 Pedestrian -1 -1 -1 362.58 163.39 380.61 207.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n55 44 Pedestrian -1 -1 -1 550.74 167.93 564.83 210.88 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n55 41 Car -1 -1 -1 564.55 171.33 587.46 196.26 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n56 3 Car -1 -1 -1 1116.42 188.31 1220.98 225.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n56 45 Pedestrian -1 -1 -1 10.84 161.55 100.38 357.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n56 7 Car -1 -1 -1 983.34 185.53 1068.53 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n56 19 Pedestrian -1 -1 -1 242.16 162.29 271.79 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n56 4 Car -1 -1 -1 877.11 184.06 945.52 217.73 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n56 11 Car -1 -1 -1 931.09 184.58 1010.13 220.46 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n56 8 Car -1 -1 -1 606.82 175.62 634.39 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n56 15 Pedestrian -1 -1 -1 421.56 164.22 445.52 222.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n56 36 Pedestrian -1 -1 -1 386.39 166.91 405.73 214.20 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n56 32 Pedestrian -1 -1 -1 362.26 163.15 381.13 208.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n56 42 Pedestrian -1 -1 -1 399.44 163.96 416.57 209.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n56 14 Pedestrian -1 -1 -1 275.24 166.91 300.66 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n56 9 Pedestrian -1 -1 -1 273.09 160.24 288.45 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n56 27 Cyclist -1 -1 -1 567.40 165.10 591.61 226.35 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n56 46 Pedestrian -1 -1 -1 534.80 170.51 546.50 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n56 44 Pedestrian -1 -1 -1 552.04 168.11 567.17 210.52 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n57 3 Car -1 -1 -1 1116.52 188.22 1220.94 225.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n57 7 Car -1 -1 -1 983.36 185.50 1068.57 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n57 4 Car -1 -1 -1 877.14 184.07 945.41 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n57 11 Car -1 -1 -1 931.17 184.55 1010.07 220.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n57 45 Pedestrian -1 -1 -1 46.24 157.19 118.28 355.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n57 8 Car -1 -1 -1 606.92 175.68 634.27 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n57 19 Pedestrian -1 -1 -1 247.40 162.40 274.13 232.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n57 14 Pedestrian -1 -1 -1 276.16 167.94 302.46 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n57 15 Pedestrian -1 -1 -1 426.60 163.68 448.58 222.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n57 42 Pedestrian -1 -1 -1 398.86 163.10 417.02 209.91 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n57 36 Pedestrian -1 -1 -1 385.70 164.17 404.86 210.54 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n57 32 Pedestrian -1 -1 -1 362.45 162.20 381.38 207.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n57 9 Pedestrian -1 -1 -1 273.08 160.19 288.77 197.19 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n57 27 Cyclist -1 -1 -1 567.20 165.46 591.99 225.52 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n57 46 Pedestrian -1 -1 -1 535.36 170.52 547.05 201.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n57 44 Pedestrian -1 -1 -1 552.82 167.88 567.06 208.64 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n58 3 Car -1 -1 -1 1116.88 188.39 1220.86 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n58 45 Pedestrian -1 -1 -1 54.41 160.50 149.02 352.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n58 7 Car -1 -1 -1 983.32 185.48 1068.56 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n58 19 Pedestrian -1 -1 -1 250.45 161.78 274.46 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n58 11 Car -1 -1 -1 931.02 184.47 1010.08 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n58 4 Car -1 -1 -1 877.19 184.00 945.24 217.77 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n58 8 Car -1 -1 -1 606.73 175.56 634.10 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n58 15 Pedestrian -1 -1 -1 428.88 164.14 453.52 222.60 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n58 32 Pedestrian -1 -1 -1 362.78 162.24 382.30 208.66 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n58 42 Pedestrian -1 -1 -1 398.50 162.96 417.46 211.02 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n58 46 Pedestrian -1 -1 -1 536.00 170.27 547.65 201.98 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n58 36 Pedestrian -1 -1 -1 385.97 163.61 404.61 209.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n58 14 Pedestrian -1 -1 -1 277.87 167.83 305.66 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n58 9 Pedestrian -1 -1 -1 273.20 159.95 289.24 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n58 44 Pedestrian -1 -1 -1 553.66 168.10 567.81 207.97 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n58 27 Cyclist -1 -1 -1 568.93 166.19 591.53 216.25 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n59 3 Car -1 -1 -1 1117.00 188.22 1220.81 225.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n59 45 Pedestrian -1 -1 -1 61.42 164.52 172.33 354.19 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n59 7 Car -1 -1 -1 983.12 185.45 1068.92 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n59 11 Car -1 -1 -1 934.75 184.40 1010.47 220.50 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n59 4 Car -1 -1 -1 877.24 184.02 945.30 217.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n59 8 Car -1 -1 -1 606.84 175.65 634.31 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n59 19 Pedestrian -1 -1 -1 252.89 162.13 278.52 231.77 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n59 32 Pedestrian -1 -1 -1 363.35 162.18 382.90 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n59 36 Pedestrian -1 -1 -1 386.16 163.47 404.92 210.05 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n59 14 Pedestrian -1 -1 -1 277.49 166.30 306.59 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n59 27 Cyclist -1 -1 -1 569.64 165.61 591.20 216.75 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n59 42 Pedestrian -1 -1 -1 398.61 163.53 417.92 211.56 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n59 44 Pedestrian -1 -1 -1 553.86 168.27 567.98 207.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n59 9 Pedestrian -1 -1 -1 273.10 159.92 288.40 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n59 15 Pedestrian -1 -1 -1 430.50 164.58 458.64 223.41 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n59 46 Pedestrian -1 -1 -1 537.18 170.14 548.14 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n60 3 Car -1 -1 -1 1117.11 188.10 1220.70 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n60 45 Pedestrian -1 -1 -1 78.94 166.41 185.20 352.36 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n60 7 Car -1 -1 -1 983.20 185.44 1068.83 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n60 19 Pedestrian -1 -1 -1 253.06 162.39 284.96 232.66 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n60 11 Car -1 -1 -1 934.72 184.34 1010.38 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n60 4 Car -1 -1 -1 877.41 183.95 945.44 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n60 8 Car -1 -1 -1 606.80 175.59 634.29 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n60 32 Pedestrian -1 -1 -1 364.61 162.73 383.47 208.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n60 36 Pedestrian -1 -1 -1 386.43 163.85 404.50 210.32 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n60 42 Pedestrian -1 -1 -1 401.13 163.61 419.81 212.32 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n60 14 Pedestrian -1 -1 -1 284.26 167.65 310.18 233.93 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n60 15 Pedestrian -1 -1 -1 432.73 164.77 459.99 223.14 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n60 46 Pedestrian -1 -1 -1 538.73 169.87 549.61 201.75 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n60 9 Pedestrian -1 -1 -1 272.59 159.42 288.89 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n60 27 Cyclist -1 -1 -1 569.63 165.05 591.43 216.52 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n60 44 Pedestrian -1 -1 -1 554.62 168.40 568.28 207.69 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n61 3 Car -1 -1 -1 1117.51 188.14 1220.47 225.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n61 7 Car -1 -1 -1 983.33 185.45 1068.79 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n61 45 Pedestrian -1 -1 -1 112.16 162.52 190.75 350.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n61 4 Car -1 -1 -1 877.47 183.99 945.46 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n61 8 Car -1 -1 -1 606.97 175.56 634.40 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n61 11 Car -1 -1 -1 934.76 184.42 1010.11 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n61 19 Pedestrian -1 -1 -1 256.49 162.67 288.52 232.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n61 14 Pedestrian -1 -1 -1 286.35 165.38 314.93 234.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n61 42 Pedestrian -1 -1 -1 402.06 163.84 419.90 211.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n61 46 Pedestrian -1 -1 -1 539.88 170.78 550.65 201.73 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n61 36 Pedestrian -1 -1 -1 386.57 163.91 404.79 210.31 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n61 32 Pedestrian -1 -1 -1 365.07 162.32 383.75 208.68 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n61 15 Pedestrian -1 -1 -1 436.64 163.92 460.55 224.18 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n61 9 Pedestrian -1 -1 -1 272.12 159.68 289.01 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n61 27 Cyclist -1 -1 -1 571.65 164.97 592.89 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n61 44 Pedestrian -1 -1 -1 556.40 168.82 569.82 207.55 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n62 3 Car -1 -1 -1 1117.46 188.28 1220.33 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n62 7 Car -1 -1 -1 983.36 185.51 1068.74 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n62 45 Pedestrian -1 -1 -1 141.41 160.44 207.32 351.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n62 4 Car -1 -1 -1 877.39 184.05 945.37 217.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n62 11 Car -1 -1 -1 934.86 184.38 1010.15 220.46 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n62 19 Pedestrian -1 -1 -1 258.42 163.60 290.31 232.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n62 8 Car -1 -1 -1 606.99 175.62 634.38 199.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n62 15 Pedestrian -1 -1 -1 441.01 163.76 463.27 223.93 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n62 14 Pedestrian -1 -1 -1 289.00 167.82 317.76 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n62 42 Pedestrian -1 -1 -1 402.46 163.28 420.46 211.84 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n62 36 Pedestrian -1 -1 -1 387.24 163.89 405.20 210.53 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n62 27 Cyclist -1 -1 -1 571.96 164.90 593.14 216.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n62 32 Pedestrian -1 -1 -1 366.14 161.81 384.73 208.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n62 44 Pedestrian -1 -1 -1 556.69 168.54 569.89 207.74 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n62 9 Pedestrian -1 -1 -1 272.40 159.68 288.73 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n62 46 Pedestrian -1 -1 -1 540.92 171.40 551.28 201.69 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n62 47 Pedestrian -1 -1 -1 0.14 163.92 72.63 363.61 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n63 3 Car -1 -1 -1 1116.81 188.25 1220.93 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n63 7 Car -1 -1 -1 983.31 185.45 1068.63 221.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n63 45 Pedestrian -1 -1 -1 148.32 162.42 238.24 348.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n63 15 Pedestrian -1 -1 -1 441.22 163.59 466.87 223.64 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n63 4 Car -1 -1 -1 877.42 184.01 945.27 217.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n63 11 Car -1 -1 -1 931.35 184.35 1009.96 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n63 8 Car -1 -1 -1 607.08 175.56 634.19 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n63 42 Pedestrian -1 -1 -1 402.49 163.07 420.67 211.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n63 32 Pedestrian -1 -1 -1 366.45 161.80 385.71 208.95 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n63 19 Pedestrian -1 -1 -1 265.35 163.83 290.77 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n63 14 Pedestrian -1 -1 -1 291.26 166.75 319.04 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n63 47 Pedestrian -1 -1 -1 0.22 167.30 103.01 366.68 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n63 36 Pedestrian -1 -1 -1 387.66 163.70 406.30 211.58 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n63 27 Cyclist -1 -1 -1 572.16 165.64 592.76 214.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n63 44 Pedestrian -1 -1 -1 557.21 168.74 570.00 207.37 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n63 9 Pedestrian -1 -1 -1 273.25 160.04 287.82 199.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n63 46 Pedestrian -1 -1 -1 542.13 170.67 552.72 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n64 3 Car -1 -1 -1 1117.00 188.25 1220.93 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n64 15 Pedestrian -1 -1 -1 443.52 164.06 469.83 224.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n64 45 Pedestrian -1 -1 -1 158.75 164.24 258.00 347.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n64 7 Car -1 -1 -1 983.40 185.43 1068.51 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n64 32 Pedestrian -1 -1 -1 366.71 162.21 387.05 209.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n64 11 Car -1 -1 -1 934.85 184.34 1010.16 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n64 4 Car -1 -1 -1 877.40 183.99 945.27 217.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n64 8 Car -1 -1 -1 607.07 175.55 634.17 199.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n64 14 Pedestrian -1 -1 -1 295.67 166.80 322.15 232.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n64 36 Pedestrian -1 -1 -1 389.06 163.40 409.15 212.64 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n64 47 Pedestrian -1 -1 -1 1.53 169.97 132.34 364.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n64 19 Pedestrian -1 -1 -1 269.22 159.77 294.94 230.34 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n64 42 Pedestrian -1 -1 -1 402.20 163.36 420.92 212.53 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n64 27 Cyclist -1 -1 -1 572.65 165.94 592.42 213.80 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n64 9 Pedestrian -1 -1 -1 272.60 159.51 288.60 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n64 46 Pedestrian -1 -1 -1 542.30 170.83 552.96 200.64 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n64 44 Pedestrian -1 -1 -1 557.88 169.05 570.20 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n65 3 Car -1 -1 -1 1117.19 188.31 1220.64 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n65 45 Pedestrian -1 -1 -1 173.46 164.58 273.18 347.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n65 7 Car -1 -1 -1 983.18 185.41 1068.69 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n65 32 Pedestrian -1 -1 -1 367.48 163.04 387.27 210.77 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n65 47 Pedestrian -1 -1 -1 6.69 170.26 142.18 364.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n65 8 Car -1 -1 -1 607.34 175.51 634.23 199.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n65 11 Car -1 -1 -1 934.87 184.32 1010.18 220.49 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n65 15 Pedestrian -1 -1 -1 444.60 164.04 471.68 224.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 4 Car -1 -1 -1 877.48 183.94 945.34 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 14 Pedestrian -1 -1 -1 299.01 166.83 324.28 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 19 Pedestrian -1 -1 -1 276.28 164.57 299.67 229.69 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n65 36 Pedestrian -1 -1 -1 389.42 164.04 408.62 212.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n65 27 Cyclist -1 -1 -1 570.44 166.03 590.83 216.71 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n65 42 Pedestrian -1 -1 -1 402.57 163.89 421.11 212.77 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n65 46 Pedestrian -1 -1 -1 543.23 171.07 553.21 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n65 9 Pedestrian -1 -1 -1 272.53 159.37 289.20 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n65 44 Pedestrian -1 -1 -1 557.90 168.70 570.83 206.54 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n66 3 Car -1 -1 -1 1117.11 188.31 1220.62 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n66 45 Pedestrian -1 -1 -1 193.68 162.57 276.62 348.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n66 7 Car -1 -1 -1 983.46 185.41 1068.45 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n66 8 Car -1 -1 -1 607.22 175.56 634.13 199.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n66 4 Car -1 -1 -1 877.45 183.92 945.35 217.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n66 15 Pedestrian -1 -1 -1 449.89 163.83 473.86 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n66 11 Car -1 -1 -1 931.45 184.35 1009.85 220.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n66 47 Pedestrian -1 -1 -1 28.43 171.55 143.50 364.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n66 32 Pedestrian -1 -1 -1 367.95 162.90 387.76 210.93 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n66 36 Pedestrian -1 -1 -1 389.56 163.90 408.49 212.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n66 19 Pedestrian -1 -1 -1 276.90 164.57 302.12 229.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n66 14 Pedestrian -1 -1 -1 301.55 167.68 327.13 231.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n66 27 Cyclist -1 -1 -1 570.77 166.46 589.84 216.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n66 42 Pedestrian -1 -1 -1 397.55 166.78 417.01 215.51 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n66 9 Pedestrian -1 -1 -1 273.03 159.09 289.35 200.20 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n66 44 Pedestrian -1 -1 -1 558.56 168.87 571.29 206.51 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n66 46 Pedestrian -1 -1 -1 543.59 170.92 553.34 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n67 45 Pedestrian -1 -1 -1 228.10 162.22 289.32 342.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n67 3 Car -1 -1 -1 1116.92 188.29 1220.92 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n67 7 Car -1 -1 -1 983.42 185.42 1068.54 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n67 11 Car -1 -1 -1 931.42 184.39 1009.74 220.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n67 14 Pedestrian -1 -1 -1 303.24 167.90 328.07 230.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n67 47 Pedestrian -1 -1 -1 62.99 168.68 162.67 365.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n67 4 Car -1 -1 -1 877.54 183.91 945.44 217.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n67 32 Pedestrian -1 -1 -1 369.76 162.67 389.12 211.21 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n67 8 Car -1 -1 -1 607.29 175.62 634.01 199.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n67 36 Pedestrian -1 -1 -1 389.85 164.04 408.92 211.55 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n67 15 Pedestrian -1 -1 -1 454.38 163.46 476.67 224.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n67 19 Pedestrian -1 -1 -1 280.59 164.23 305.30 229.83 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n67 27 Cyclist -1 -1 -1 570.92 167.01 589.80 215.19 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n67 42 Pedestrian -1 -1 -1 397.75 166.68 417.92 216.17 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n67 44 Pedestrian -1 -1 -1 558.84 169.09 571.33 205.92 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n67 9 Pedestrian -1 -1 -1 273.32 159.19 289.11 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n68 3 Car -1 -1 -1 1116.96 188.32 1220.82 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n68 45 Pedestrian -1 -1 -1 242.78 162.45 310.81 340.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n68 7 Car -1 -1 -1 983.60 185.44 1068.29 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n68 32 Pedestrian -1 -1 -1 370.54 162.55 390.12 211.56 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n68 11 Car -1 -1 -1 931.23 184.43 1009.85 220.46 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n68 15 Pedestrian -1 -1 -1 457.52 163.47 480.95 224.90 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n68 47 Pedestrian -1 -1 -1 86.15 170.30 185.56 363.74 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n68 8 Car -1 -1 -1 607.39 175.54 634.26 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n68 4 Car -1 -1 -1 877.63 183.92 945.36 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n68 36 Pedestrian -1 -1 -1 390.70 163.69 409.32 211.36 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n68 19 Pedestrian -1 -1 -1 285.03 163.72 308.12 227.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n68 14 Pedestrian -1 -1 -1 304.91 167.52 328.58 229.95 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n68 42 Pedestrian -1 -1 -1 404.84 163.21 423.82 213.05 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n68 44 Pedestrian -1 -1 -1 559.25 169.64 571.48 205.18 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n68 9 Pedestrian -1 -1 -1 273.31 159.42 289.53 200.61 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n68 27 Cyclist -1 -1 -1 571.03 166.74 589.30 213.52 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n68 48 Pedestrian -1 -1 -1 398.30 168.88 418.57 217.50 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n68 49 Pedestrian -1 -1 -1 571.03 166.74 589.30 213.52 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n69 45 Pedestrian -1 -1 -1 244.59 163.61 333.21 340.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n69 3 Car -1 -1 -1 1117.35 188.32 1220.56 225.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n69 47 Pedestrian -1 -1 -1 95.21 170.32 214.85 364.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n69 7 Car -1 -1 -1 983.37 185.45 1068.46 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n69 15 Pedestrian -1 -1 -1 461.09 165.24 484.56 224.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n69 8 Car -1 -1 -1 607.26 175.56 634.41 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n69 32 Pedestrian -1 -1 -1 371.31 162.85 390.97 211.75 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n69 4 Car -1 -1 -1 877.57 183.96 945.35 217.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n69 11 Car -1 -1 -1 934.77 184.41 1010.10 220.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n69 14 Pedestrian -1 -1 -1 306.59 166.95 330.89 229.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n69 19 Pedestrian -1 -1 -1 287.41 163.91 311.39 227.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n69 36 Pedestrian -1 -1 -1 391.72 164.16 409.25 211.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n69 48 Pedestrian -1 -1 -1 401.15 166.22 421.43 214.81 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n69 27 Cyclist -1 -1 -1 570.89 166.52 589.41 213.17 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n69 44 Pedestrian -1 -1 -1 559.45 169.68 571.15 203.81 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n69 9 Pedestrian -1 -1 -1 273.40 159.65 289.30 200.28 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n70 3 Car -1 -1 -1 1117.07 188.26 1220.44 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n70 45 Pedestrian -1 -1 -1 253.08 165.18 347.41 340.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n70 7 Car -1 -1 -1 983.23 185.34 1068.67 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n70 47 Pedestrian -1 -1 -1 108.44 171.94 231.80 364.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n70 8 Car -1 -1 -1 607.25 175.48 634.40 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n70 4 Car -1 -1 -1 877.60 183.95 945.30 217.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n70 11 Car -1 -1 -1 934.74 184.41 1010.07 220.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n70 14 Pedestrian -1 -1 -1 306.71 166.53 333.35 229.33 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n70 15 Pedestrian -1 -1 -1 463.46 164.09 486.62 225.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n70 32 Pedestrian -1 -1 -1 371.58 163.20 391.72 212.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n70 19 Pedestrian -1 -1 -1 289.09 162.72 313.21 228.22 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n70 48 Pedestrian -1 -1 -1 400.49 169.67 420.44 218.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n70 36 Pedestrian -1 -1 -1 392.22 164.45 409.85 211.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n70 44 Pedestrian -1 -1 -1 572.15 166.58 589.12 212.90 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n70 9 Pedestrian -1 -1 -1 272.79 160.37 289.00 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n70 27 Cyclist -1 -1 -1 572.29 166.62 588.92 209.99 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n70 50 Pedestrian -1 -1 -1 559.53 169.58 571.24 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n71 45 Pedestrian -1 -1 -1 272.83 166.38 350.83 338.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n71 3 Car -1 -1 -1 1116.29 188.71 1219.68 225.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n71 7 Car -1 -1 -1 983.22 185.36 1068.65 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n71 47 Pedestrian -1 -1 -1 131.04 174.20 239.29 366.29 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n71 8 Car -1 -1 -1 607.34 175.52 634.43 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n71 11 Car -1 -1 -1 934.75 184.35 1010.03 220.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n71 4 Car -1 -1 -1 877.62 183.96 945.36 217.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n71 32 Pedestrian -1 -1 -1 373.18 163.02 393.94 212.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n71 19 Pedestrian -1 -1 -1 291.87 162.82 316.23 228.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n71 14 Pedestrian -1 -1 -1 309.53 166.57 337.32 228.87 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n71 15 Pedestrian -1 -1 -1 465.69 163.33 489.12 226.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n71 36 Pedestrian -1 -1 -1 393.76 164.44 412.26 212.11 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n71 48 Pedestrian -1 -1 -1 401.47 169.78 421.15 218.19 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n71 50 Pedestrian -1 -1 -1 560.72 168.77 573.41 203.64 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n71 44 Pedestrian -1 -1 -1 572.79 166.55 588.86 212.20 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n71 9 Pedestrian -1 -1 -1 272.14 160.47 289.18 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n72 3 Car -1 -1 -1 1118.16 188.53 1218.93 225.33 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n72 45 Pedestrian -1 -1 -1 297.80 164.59 363.27 339.78 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n72 47 Pedestrian -1 -1 -1 168.73 172.55 248.25 363.92 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n72 7 Car -1 -1 -1 983.10 185.35 1068.71 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n72 32 Pedestrian -1 -1 -1 373.18 162.20 394.54 212.43 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n72 8 Car -1 -1 -1 607.32 175.57 634.10 199.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n72 11 Car -1 -1 -1 931.32 184.43 1009.94 220.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n72 19 Pedestrian -1 -1 -1 293.88 162.71 320.84 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n72 4 Car -1 -1 -1 877.30 183.93 945.40 217.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n72 15 Pedestrian -1 -1 -1 469.03 162.99 493.53 226.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n72 36 Pedestrian -1 -1 -1 394.67 163.90 413.17 212.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n72 14 Pedestrian -1 -1 -1 314.40 165.71 340.78 229.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n72 48 Pedestrian -1 -1 -1 405.13 165.03 425.43 214.35 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n72 44 Pedestrian -1 -1 -1 572.45 166.79 588.86 211.97 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n72 50 Pedestrian -1 -1 -1 560.82 168.45 573.23 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n72 9 Pedestrian -1 -1 -1 269.95 160.35 286.42 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n72 51 Pedestrian -1 -1 -1 1184.78 167.20 1221.75 274.94 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n73 45 Pedestrian -1 -1 -1 313.13 162.97 379.31 339.97 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n73 47 Pedestrian -1 -1 -1 181.43 170.84 274.34 364.37 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n73 7 Car -1 -1 -1 979.37 185.31 1068.97 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n73 3 Car -1 -1 -1 1118.76 188.54 1217.79 225.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n73 32 Pedestrian -1 -1 -1 374.06 162.46 394.87 212.83 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n73 8 Car -1 -1 -1 607.28 175.55 634.17 199.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n73 11 Car -1 -1 -1 931.42 184.48 1009.75 220.58 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n73 15 Pedestrian -1 -1 -1 471.57 163.22 498.42 226.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n73 4 Car -1 -1 -1 877.35 183.92 945.32 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n73 51 Pedestrian -1 -1 -1 1178.89 167.70 1220.20 274.22 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n73 19 Pedestrian -1 -1 -1 296.42 161.79 321.15 227.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n73 48 Pedestrian -1 -1 -1 405.52 165.34 425.23 216.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n73 36 Pedestrian -1 -1 -1 394.95 164.77 414.12 213.91 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n73 50 Pedestrian -1 -1 -1 561.06 168.65 573.29 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n73 44 Pedestrian -1 -1 -1 572.36 167.31 588.49 211.47 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n73 14 Pedestrian -1 -1 -1 315.32 166.41 345.73 228.53 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n73 9 Pedestrian -1 -1 -1 269.30 160.08 286.95 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n74 45 Pedestrian -1 -1 -1 316.84 162.99 406.61 339.45 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n74 47 Pedestrian -1 -1 -1 194.12 171.34 307.40 363.38 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n74 7 Car -1 -1 -1 982.75 185.23 1069.11 221.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n74 15 Pedestrian -1 -1 -1 473.35 162.66 501.93 228.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n74 3 Car -1 -1 -1 1119.85 188.80 1216.72 224.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n74 11 Car -1 -1 -1 931.21 184.50 1009.82 220.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n74 19 Pedestrian -1 -1 -1 301.51 161.79 322.94 226.89 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n74 51 Pedestrian -1 -1 -1 1167.97 167.57 1207.12 273.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n74 4 Car -1 -1 -1 877.13 183.97 945.31 217.70 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n74 8 Car -1 -1 -1 607.21 175.64 633.97 199.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n74 32 Pedestrian -1 -1 -1 375.75 163.20 394.79 213.19 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n74 48 Pedestrian -1 -1 -1 405.76 168.97 424.84 218.34 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n74 36 Pedestrian -1 -1 -1 396.86 165.35 416.13 214.75 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n74 14 Pedestrian -1 -1 -1 318.27 166.58 344.99 228.14 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n74 44 Pedestrian -1 -1 -1 572.35 165.44 588.61 210.00 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n74 50 Pedestrian -1 -1 -1 561.12 168.77 572.99 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n74 9 Pedestrian -1 -1 -1 269.41 160.52 286.90 196.67 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n75 45 Pedestrian -1 -1 -1 328.80 166.81 424.04 337.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n75 47 Pedestrian -1 -1 -1 201.95 170.93 322.16 364.97 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n75 7 Car -1 -1 -1 982.82 185.33 1069.10 221.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n75 51 Pedestrian -1 -1 -1 1142.07 166.16 1202.80 274.76 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n75 11 Car -1 -1 -1 931.19 184.50 1009.86 220.73 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n75 4 Car -1 -1 -1 877.09 183.91 945.46 217.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n75 19 Pedestrian -1 -1 -1 304.79 161.10 325.66 227.01 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n75 8 Car -1 -1 -1 607.22 175.77 634.06 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n75 3 Car -1 -1 -1 1118.71 188.96 1217.58 224.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n75 15 Pedestrian -1 -1 -1 476.37 162.71 504.12 228.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n75 48 Pedestrian -1 -1 -1 405.49 169.36 426.25 218.95 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n75 36 Pedestrian -1 -1 -1 397.29 165.77 416.28 213.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n75 32 Pedestrian -1 -1 -1 374.36 163.94 396.27 215.39 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n75 14 Pedestrian -1 -1 -1 320.85 166.35 347.68 228.39 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n75 50 Pedestrian -1 -1 -1 559.74 169.52 571.29 202.08 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n75 9 Pedestrian -1 -1 -1 272.28 160.35 288.28 196.87 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n75 44 Pedestrian -1 -1 -1 572.19 165.56 588.84 209.67 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n75 52 Cyclist -1 -1 -1 572.19 165.56 588.84 209.67 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n76 47 Pedestrian -1 -1 -1 221.88 171.18 325.87 363.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n76 45 Pedestrian -1 -1 -1 343.40 166.68 425.73 336.90 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n76 7 Car -1 -1 -1 982.54 185.34 1069.43 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n76 15 Pedestrian -1 -1 -1 482.37 162.41 506.96 228.41 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n76 11 Car -1 -1 -1 931.14 184.50 1009.94 220.76 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n76 4 Car -1 -1 -1 876.91 183.85 945.46 217.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n76 8 Car -1 -1 -1 607.27 175.75 634.09 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n76 19 Pedestrian -1 -1 -1 307.89 162.99 328.89 226.37 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n76 51 Pedestrian -1 -1 -1 1124.95 168.00 1203.74 273.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n76 3 Car -1 -1 -1 1118.40 188.92 1218.43 224.61 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n76 48 Pedestrian -1 -1 -1 408.96 169.64 428.32 219.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n76 36 Pedestrian -1 -1 -1 396.84 165.70 417.61 215.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n76 32 Pedestrian -1 -1 -1 376.09 163.31 398.69 216.92 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n76 14 Pedestrian -1 -1 -1 322.36 164.92 348.91 226.44 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n76 52 Cyclist -1 -1 -1 571.87 165.58 588.65 209.15 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n76 50 Pedestrian -1 -1 -1 559.86 169.73 571.16 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n76 9 Pedestrian -1 -1 -1 272.27 160.37 288.28 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n76 44 Pedestrian -1 -1 -1 571.87 165.58 588.65 209.15 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n77 47 Pedestrian -1 -1 -1 258.44 170.18 334.85 363.46 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n77 45 Pedestrian -1 -1 -1 366.02 163.46 433.32 339.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n77 7 Car -1 -1 -1 982.23 185.21 1069.79 221.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n77 11 Car -1 -1 -1 931.17 184.56 1009.83 220.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n77 4 Car -1 -1 -1 876.79 183.85 945.56 218.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n77 15 Pedestrian -1 -1 -1 486.53 162.40 510.82 229.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n77 8 Car -1 -1 -1 607.11 175.79 634.19 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n77 51 Pedestrian -1 -1 -1 1118.76 165.71 1180.98 271.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n77 19 Pedestrian -1 -1 -1 308.95 162.81 331.41 227.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n77 3 Car -1 -1 -1 1117.51 188.18 1220.31 226.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n77 48 Pedestrian -1 -1 -1 409.54 169.37 429.44 218.82 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n77 14 Pedestrian -1 -1 -1 326.13 166.94 351.64 228.60 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n77 32 Pedestrian -1 -1 -1 378.80 163.26 399.31 215.58 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n77 36 Pedestrian -1 -1 -1 397.04 165.92 417.75 215.32 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n77 52 Cyclist -1 -1 -1 573.03 166.59 588.47 207.95 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n77 9 Pedestrian -1 -1 -1 271.98 160.26 289.21 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n77 50 Pedestrian -1 -1 -1 561.58 168.94 572.38 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n77 44 Pedestrian -1 -1 -1 573.03 166.59 588.47 207.95 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n78 47 Pedestrian -1 -1 -1 282.45 172.90 363.23 360.12 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n78 7 Car -1 -1 -1 982.64 185.19 1069.39 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n78 45 Pedestrian -1 -1 -1 389.04 164.98 447.91 333.66 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n78 11 Car -1 -1 -1 931.16 184.52 1009.74 220.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n78 4 Car -1 -1 -1 876.80 183.80 945.59 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n78 19 Pedestrian -1 -1 -1 311.94 162.73 334.64 227.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n78 8 Car -1 -1 -1 607.13 175.77 634.28 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n78 51 Pedestrian -1 -1 -1 1114.12 165.09 1155.29 268.28 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n78 36 Pedestrian -1 -1 -1 398.50 165.12 416.67 216.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n78 14 Pedestrian -1 -1 -1 330.03 166.68 355.18 227.88 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n78 48 Pedestrian -1 -1 -1 412.80 168.74 432.15 219.73 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n78 3 Car -1 -1 -1 1118.35 189.08 1219.30 225.27 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n78 15 Pedestrian -1 -1 -1 489.88 163.04 514.13 228.43 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n78 32 Pedestrian -1 -1 -1 379.52 164.75 399.13 213.86 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n78 50 Pedestrian -1 -1 -1 561.84 169.46 572.62 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n78 9 Pedestrian -1 -1 -1 271.95 160.12 288.91 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n78 52 Cyclist -1 -1 -1 574.82 166.95 589.81 208.20 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n78 44 Pedestrian -1 -1 -1 574.82 166.95 589.81 208.20 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n79 45 Pedestrian -1 -1 -1 396.04 166.09 472.94 332.45 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n79 7 Car -1 -1 -1 982.77 185.17 1069.49 221.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n79 47 Pedestrian -1 -1 -1 287.51 172.42 389.48 362.23 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n79 11 Car -1 -1 -1 931.04 184.49 1009.71 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n79 4 Car -1 -1 -1 876.65 183.79 945.62 218.05 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n79 51 Pedestrian -1 -1 -1 1101.93 164.94 1142.35 267.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n79 19 Pedestrian -1 -1 -1 314.45 162.42 338.65 227.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n79 8 Car -1 -1 -1 606.94 175.83 634.26 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n79 3 Car -1 -1 -1 1116.87 188.98 1220.31 225.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n79 36 Pedestrian -1 -1 -1 399.82 165.71 416.26 214.63 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n79 32 Pedestrian -1 -1 -1 381.66 164.89 400.69 214.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n79 14 Pedestrian -1 -1 -1 333.38 168.32 357.72 227.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n79 48 Pedestrian -1 -1 -1 413.02 168.09 434.00 220.24 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n79 15 Pedestrian -1 -1 -1 492.82 163.18 519.20 228.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n79 50 Pedestrian -1 -1 -1 562.11 169.73 572.38 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n79 44 Pedestrian -1 -1 -1 574.98 167.10 590.58 208.03 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n79 9 Pedestrian -1 -1 -1 270.29 159.90 286.10 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n79 52 Cyclist -1 -1 -1 574.98 167.10 590.58 208.03 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n79 53 Pedestrian -1 -1 -1 375.09 164.16 392.65 211.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n80 45 Pedestrian -1 -1 -1 400.69 169.02 490.65 334.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n80 7 Car -1 -1 -1 983.09 185.23 1069.30 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n80 47 Pedestrian -1 -1 -1 293.76 169.04 398.99 364.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n80 11 Car -1 -1 -1 930.91 184.47 1009.85 220.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n80 51 Pedestrian -1 -1 -1 1083.08 162.68 1137.96 266.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n80 4 Car -1 -1 -1 876.81 183.82 945.53 218.07 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n80 3 Car -1 -1 -1 1116.83 188.80 1220.55 225.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n80 32 Pedestrian -1 -1 -1 382.89 165.29 402.11 215.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n80 8 Car -1 -1 -1 607.02 175.81 634.40 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n80 15 Pedestrian -1 -1 -1 496.77 162.56 522.94 231.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n80 36 Pedestrian -1 -1 -1 399.69 165.84 416.90 214.67 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n80 19 Pedestrian -1 -1 -1 316.20 162.54 339.76 226.71 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n80 48 Pedestrian -1 -1 -1 415.96 168.23 435.58 220.71 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n80 53 Pedestrian -1 -1 -1 375.58 164.20 393.54 212.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n80 14 Pedestrian -1 -1 -1 332.87 168.44 360.17 227.93 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n80 50 Pedestrian -1 -1 -1 562.56 169.64 572.88 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n80 44 Pedestrian -1 -1 -1 575.62 166.19 590.95 207.58 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n80 52 Cyclist -1 -1 -1 575.62 166.19 590.95 207.58 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n80 9 Pedestrian -1 -1 -1 272.27 160.05 288.54 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n81 45 Pedestrian -1 -1 -1 407.28 167.44 498.78 336.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n81 7 Car -1 -1 -1 983.53 185.36 1069.01 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n81 47 Pedestrian -1 -1 -1 313.84 169.86 402.33 358.66 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n81 11 Car -1 -1 -1 930.87 184.55 1009.96 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n81 3 Car -1 -1 -1 1116.36 188.93 1220.00 225.47 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n81 51 Pedestrian -1 -1 -1 1069.47 165.14 1130.19 263.96 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n81 4 Car -1 -1 -1 876.72 183.81 945.58 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n81 8 Car -1 -1 -1 607.04 175.73 634.45 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n81 19 Pedestrian -1 -1 -1 317.74 163.65 342.59 224.83 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n81 15 Pedestrian -1 -1 -1 499.24 162.64 523.91 231.15 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n81 36 Pedestrian -1 -1 -1 401.95 165.52 419.11 214.35 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n81 32 Pedestrian -1 -1 -1 385.47 164.46 404.46 214.95 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n81 48 Pedestrian -1 -1 -1 414.68 167.44 437.18 221.33 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n81 52 Cyclist -1 -1 -1 575.68 166.45 591.36 207.19 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n81 14 Pedestrian -1 -1 -1 338.18 164.93 362.70 225.81 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n81 50 Pedestrian -1 -1 -1 562.81 170.01 572.65 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n81 9 Pedestrian -1 -1 -1 272.38 160.27 288.24 196.76 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n81 44 Pedestrian -1 -1 -1 575.68 166.45 591.36 207.19 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n81 53 Pedestrian -1 -1 -1 376.52 165.02 393.98 211.47 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n82 7 Car -1 -1 -1 983.70 185.36 1068.72 221.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n82 45 Pedestrian -1 -1 -1 426.81 166.72 501.90 335.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n82 47 Pedestrian -1 -1 -1 347.41 172.35 413.98 356.28 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n82 11 Car -1 -1 -1 930.81 184.58 1009.96 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n82 3 Car -1 -1 -1 1115.68 188.62 1220.77 225.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n82 4 Car -1 -1 -1 876.55 183.77 945.66 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n82 19 Pedestrian -1 -1 -1 317.90 163.81 344.87 224.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n82 8 Car -1 -1 -1 606.88 175.60 634.52 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n82 51 Pedestrian -1 -1 -1 1063.74 166.20 1111.93 263.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n82 36 Pedestrian -1 -1 -1 403.05 165.69 420.17 214.17 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n82 14 Pedestrian -1 -1 -1 339.60 162.16 365.92 225.70 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n82 32 Pedestrian -1 -1 -1 385.89 163.23 406.38 215.69 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n82 15 Pedestrian -1 -1 -1 503.12 162.82 528.24 231.13 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n82 48 Pedestrian -1 -1 -1 415.13 165.31 439.33 217.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n82 52 Cyclist -1 -1 -1 576.14 166.96 591.15 206.76 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n82 9 Pedestrian -1 -1 -1 272.99 160.37 287.89 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n82 50 Pedestrian -1 -1 -1 563.16 169.99 572.81 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n82 44 Pedestrian -1 -1 -1 576.14 166.96 591.15 206.76 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n83 47 Pedestrian -1 -1 -1 361.27 173.96 445.30 354.55 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n83 7 Car -1 -1 -1 983.78 185.32 1068.57 220.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n83 11 Car -1 -1 -1 930.78 184.55 1009.96 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n83 3 Car -1 -1 -1 1115.50 188.39 1221.35 225.91 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n83 4 Car -1 -1 -1 876.56 183.75 945.73 218.23 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n83 45 Pedestrian -1 -1 -1 454.61 166.84 512.70 331.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n83 8 Car -1 -1 -1 606.70 175.62 634.60 199.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n83 51 Pedestrian -1 -1 -1 1060.22 166.86 1092.50 262.97 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n83 14 Pedestrian -1 -1 -1 340.53 163.32 366.52 225.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n83 15 Pedestrian -1 -1 -1 506.26 164.02 532.31 229.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n83 36 Pedestrian -1 -1 -1 402.49 165.56 421.25 215.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n83 19 Pedestrian -1 -1 -1 320.19 163.65 347.05 224.50 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n83 32 Pedestrian -1 -1 -1 386.33 162.92 407.05 215.94 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n83 48 Pedestrian -1 -1 -1 418.31 165.14 441.04 217.91 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n83 52 Cyclist -1 -1 -1 576.17 167.82 591.14 205.79 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n83 50 Pedestrian -1 -1 -1 563.12 169.92 573.21 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n83 9 Pedestrian -1 -1 -1 272.86 160.35 288.01 196.33 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n83 44 Pedestrian -1 -1 -1 576.17 167.82 591.14 205.79 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n83 54 Pedestrian -1 -1 -1 378.70 165.13 396.71 213.72 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n84 47 Pedestrian -1 -1 -1 368.05 172.80 469.45 355.37 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n84 45 Pedestrian -1 -1 -1 462.60 164.12 528.22 332.80 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n84 3 Car -1 -1 -1 1115.96 188.39 1221.24 225.81 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n84 7 Car -1 -1 -1 983.92 185.27 1068.18 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n84 15 Pedestrian -1 -1 -1 507.80 165.60 536.85 229.46 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n84 4 Car -1 -1 -1 876.80 183.76 945.73 218.21 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n84 11 Car -1 -1 -1 930.74 184.53 1010.52 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n84 51 Pedestrian -1 -1 -1 1041.25 165.87 1079.90 261.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n84 8 Car -1 -1 -1 606.49 175.68 634.55 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n84 19 Pedestrian -1 -1 -1 321.91 163.38 347.78 223.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n84 32 Pedestrian -1 -1 -1 388.78 163.73 409.45 216.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n84 14 Pedestrian -1 -1 -1 342.09 164.16 367.22 225.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n84 36 Pedestrian -1 -1 -1 402.32 165.72 421.49 215.04 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n84 48 Pedestrian -1 -1 -1 419.60 168.54 440.64 220.10 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n84 52 Cyclist -1 -1 -1 576.40 167.79 590.90 205.34 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n84 54 Pedestrian -1 -1 -1 379.16 165.85 396.09 210.94 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n84 9 Pedestrian -1 -1 -1 272.83 160.28 287.93 196.32 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n84 44 Pedestrian -1 -1 -1 576.40 167.79 590.90 205.34 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n85 47 Pedestrian -1 -1 -1 370.60 171.03 483.23 356.55 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n85 45 Pedestrian -1 -1 -1 466.44 164.59 554.68 332.58 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n85 7 Car -1 -1 -1 984.25 185.08 1068.23 221.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n85 3 Car -1 -1 -1 1116.19 188.57 1221.78 225.86 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n85 51 Pedestrian -1 -1 -1 1025.10 166.50 1074.77 260.57 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n85 11 Car -1 -1 -1 930.74 184.51 1010.46 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n85 4 Car -1 -1 -1 876.79 183.78 945.56 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n85 32 Pedestrian -1 -1 -1 389.84 164.19 409.72 216.97 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n85 15 Pedestrian -1 -1 -1 512.86 165.77 539.49 230.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n85 8 Car -1 -1 -1 606.46 175.54 634.60 199.77 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n85 19 Pedestrian -1 -1 -1 325.20 163.92 350.79 223.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n85 14 Pedestrian -1 -1 -1 346.83 163.91 370.04 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n85 36 Pedestrian -1 -1 -1 404.99 164.96 423.71 216.88 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n85 48 Pedestrian -1 -1 -1 418.64 164.45 443.61 217.48 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n85 52 Cyclist -1 -1 -1 576.45 168.03 591.24 204.85 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n85 54 Pedestrian -1 -1 -1 379.50 165.60 397.10 213.00 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n85 9 Pedestrian -1 -1 -1 272.64 160.49 288.14 196.40 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n86 47 Pedestrian -1 -1 -1 395.63 170.68 487.44 356.49 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n86 45 Pedestrian -1 -1 -1 473.29 165.41 563.88 331.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n86 3 Car -1 -1 -1 1116.37 188.65 1221.37 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n86 51 Pedestrian -1 -1 -1 1011.87 168.08 1066.28 260.40 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n86 7 Car -1 -1 -1 984.94 185.31 1067.42 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n86 4 Car -1 -1 -1 876.65 183.74 945.76 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n86 11 Car -1 -1 -1 934.18 184.32 1010.58 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n86 8 Car -1 -1 -1 606.60 175.69 634.48 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n86 14 Pedestrian -1 -1 -1 349.67 163.94 373.06 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n86 36 Pedestrian -1 -1 -1 406.14 165.52 423.45 216.65 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n86 19 Pedestrian -1 -1 -1 330.45 163.72 352.07 223.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n86 32 Pedestrian -1 -1 -1 390.97 164.25 410.63 216.88 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n86 15 Pedestrian -1 -1 -1 517.72 165.58 542.09 230.53 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n86 54 Pedestrian -1 -1 -1 379.89 166.05 397.94 213.37 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n86 48 Pedestrian -1 -1 -1 423.17 166.29 444.30 221.55 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n86 52 Cyclist -1 -1 -1 576.30 168.36 591.55 204.55 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n86 9 Pedestrian -1 -1 -1 270.70 160.59 285.71 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n87 45 Pedestrian -1 -1 -1 486.38 166.97 566.10 328.79 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n87 3 Car -1 -1 -1 1116.04 188.58 1221.98 225.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n87 47 Pedestrian -1 -1 -1 424.35 172.49 490.57 353.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n87 7 Car -1 -1 -1 985.12 184.76 1067.94 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n87 51 Pedestrian -1 -1 -1 1005.47 170.14 1048.35 256.85 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n87 4 Car -1 -1 -1 876.60 183.80 945.87 218.26 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n87 11 Car -1 -1 -1 930.82 184.40 1010.33 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n87 19 Pedestrian -1 -1 -1 331.31 164.57 354.45 222.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n87 32 Pedestrian -1 -1 -1 393.26 163.81 413.44 217.25 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n87 8 Car -1 -1 -1 606.81 175.73 634.47 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n87 48 Pedestrian -1 -1 -1 423.84 168.68 445.53 221.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n87 36 Pedestrian -1 -1 -1 405.98 166.01 424.85 217.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n87 15 Pedestrian -1 -1 -1 521.17 165.86 546.04 230.16 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n87 14 Pedestrian -1 -1 -1 351.97 163.77 376.64 224.53 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n87 54 Pedestrian -1 -1 -1 380.31 166.11 398.26 213.97 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n87 52 Cyclist -1 -1 -1 576.37 168.72 592.14 204.44 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n87 9 Pedestrian -1 -1 -1 272.15 160.38 288.55 196.86 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n88 45 Pedestrian -1 -1 -1 509.71 165.17 573.58 330.38 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n88 47 Pedestrian -1 -1 -1 440.19 174.62 519.48 351.03 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n88 3 Car -1 -1 -1 1116.57 188.83 1221.54 225.63 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n88 7 Car -1 -1 -1 984.19 184.25 1068.58 221.69 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n88 4 Car -1 -1 -1 876.46 183.71 945.90 218.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n88 11 Car -1 -1 -1 933.52 184.46 1011.47 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n88 19 Pedestrian -1 -1 -1 333.98 164.67 356.36 222.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n88 32 Pedestrian -1 -1 -1 392.79 163.21 415.59 217.28 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n88 8 Car -1 -1 -1 606.88 175.72 634.34 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n88 14 Pedestrian -1 -1 -1 353.77 164.36 378.33 224.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n88 51 Pedestrian -1 -1 -1 999.76 167.07 1025.46 258.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n88 48 Pedestrian -1 -1 -1 426.06 168.89 448.13 222.68 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n88 54 Pedestrian -1 -1 -1 381.84 166.06 400.08 214.18 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n88 15 Pedestrian -1 -1 -1 525.26 165.35 549.78 229.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n88 36 Pedestrian -1 -1 -1 407.94 165.73 427.86 218.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n88 52 Cyclist -1 -1 -1 579.01 168.12 594.03 204.34 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n88 9 Pedestrian -1 -1 -1 270.54 160.60 285.82 196.33 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n89 45 Pedestrian -1 -1 -1 526.87 164.14 586.35 330.86 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n89 3 Car -1 -1 -1 1116.73 188.70 1221.35 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n89 47 Pedestrian -1 -1 -1 442.85 174.35 540.52 352.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n89 4 Car -1 -1 -1 876.32 183.72 946.13 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n89 51 Pedestrian -1 -1 -1 983.34 165.72 1018.82 256.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n89 7 Car -1 -1 -1 979.11 184.59 1068.75 221.38 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n89 32 Pedestrian -1 -1 -1 395.22 164.06 417.69 218.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n89 11 Car -1 -1 -1 933.48 184.60 1011.59 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n89 19 Pedestrian -1 -1 -1 336.28 164.68 357.95 222.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n89 8 Car -1 -1 -1 606.75 175.74 634.30 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n89 54 Pedestrian -1 -1 -1 383.29 165.65 401.21 213.79 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n89 48 Pedestrian -1 -1 -1 427.71 170.47 449.89 224.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n89 15 Pedestrian -1 -1 -1 528.19 165.38 554.56 230.66 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n89 14 Pedestrian -1 -1 -1 356.47 164.30 381.01 223.78 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n89 36 Pedestrian -1 -1 -1 408.93 165.57 429.18 218.74 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n89 52 Cyclist -1 -1 -1 579.21 168.85 594.28 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n89 9 Pedestrian -1 -1 -1 270.72 160.60 285.85 196.11 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n90 47 Pedestrian -1 -1 -1 452.33 174.37 546.31 353.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n90 45 Pedestrian -1 -1 -1 533.58 165.61 610.42 329.60 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n90 3 Car -1 -1 -1 1115.86 188.78 1221.49 225.48 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n90 7 Car -1 -1 -1 978.52 184.77 1068.89 221.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n90 32 Pedestrian -1 -1 -1 396.35 164.50 418.98 219.30 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n90 4 Car -1 -1 -1 876.33 183.69 945.93 218.16 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n90 51 Pedestrian -1 -1 -1 975.81 170.98 1016.24 255.84 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n90 11 Car -1 -1 -1 930.79 184.53 1009.94 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n90 19 Pedestrian -1 -1 -1 339.58 164.14 360.29 222.43 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n90 8 Car -1 -1 -1 606.61 175.66 634.26 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n90 36 Pedestrian -1 -1 -1 412.50 165.69 431.78 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n90 15 Pedestrian -1 -1 -1 530.66 164.74 558.48 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n90 54 Pedestrian -1 -1 -1 383.87 165.66 402.34 214.20 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n90 48 Pedestrian -1 -1 -1 429.50 171.11 452.14 224.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n90 14 Pedestrian -1 -1 -1 361.07 166.16 382.97 215.77 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n90 9 Pedestrian -1 -1 -1 270.65 160.75 285.74 195.88 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n90 52 Cyclist -1 -1 -1 579.22 169.98 593.88 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n91 45 Pedestrian -1 -1 -1 540.87 166.50 625.21 329.69 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n91 47 Pedestrian -1 -1 -1 472.32 174.72 549.36 350.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n91 3 Car -1 -1 -1 1115.87 188.74 1221.57 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n91 7 Car -1 -1 -1 983.08 185.18 1068.93 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n91 4 Car -1 -1 -1 876.08 183.59 946.08 218.31 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n91 11 Car -1 -1 -1 930.50 184.66 1010.02 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n91 8 Car -1 -1 -1 606.40 175.64 634.13 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n91 54 Pedestrian -1 -1 -1 385.74 166.54 404.22 214.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n91 32 Pedestrian -1 -1 -1 398.75 163.75 421.81 219.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n91 48 Pedestrian -1 -1 -1 429.64 170.42 453.97 224.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n91 19 Pedestrian -1 -1 -1 342.80 164.09 362.43 221.92 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n91 51 Pedestrian -1 -1 -1 959.83 169.36 1002.41 252.93 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n91 36 Pedestrian -1 -1 -1 412.98 165.60 432.29 217.80 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n91 14 Pedestrian -1 -1 -1 362.52 165.92 384.61 221.23 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n91 15 Pedestrian -1 -1 -1 536.66 165.35 560.47 232.66 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n91 9 Pedestrian -1 -1 -1 270.54 160.72 285.67 195.81 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n91 52 Cyclist -1 -1 -1 579.69 169.38 594.75 203.69 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n92 45 Pedestrian -1 -1 -1 551.20 164.74 629.93 331.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n92 3 Car -1 -1 -1 1115.57 188.65 1221.58 225.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n92 47 Pedestrian -1 -1 -1 497.22 175.98 562.54 349.54 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n92 7 Car -1 -1 -1 983.47 185.28 1068.65 221.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n92 4 Car -1 -1 -1 876.03 183.46 946.24 218.48 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n92 11 Car -1 -1 -1 930.02 184.26 1010.51 220.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n92 32 Pedestrian -1 -1 -1 400.48 163.90 422.38 219.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n92 54 Pedestrian -1 -1 -1 386.53 166.51 404.70 215.56 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n92 48 Pedestrian -1 -1 -1 429.03 167.12 455.68 221.93 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n92 8 Car -1 -1 -1 606.85 175.66 634.18 199.70 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n92 14 Pedestrian -1 -1 -1 363.39 166.39 388.57 221.37 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n92 19 Pedestrian -1 -1 -1 343.65 164.72 365.40 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n92 51 Pedestrian -1 -1 -1 953.44 169.29 986.43 252.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n92 36 Pedestrian -1 -1 -1 413.13 165.19 432.67 218.47 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n92 9 Pedestrian -1 -1 -1 270.69 160.93 285.51 195.71 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n92 15 Pedestrian -1 -1 -1 538.99 165.04 565.60 232.65 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n92 52 Cyclist -1 -1 -1 580.86 168.95 594.59 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n93 47 Pedestrian -1 -1 -1 510.95 172.12 587.19 348.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n93 45 Pedestrian -1 -1 -1 569.97 165.73 634.64 331.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n93 3 Car -1 -1 -1 1115.94 188.54 1221.61 225.76 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n93 7 Car -1 -1 -1 983.18 185.21 1068.90 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n93 4 Car -1 -1 -1 876.64 183.56 945.92 218.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n93 19 Pedestrian -1 -1 -1 345.50 165.91 368.46 220.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n93 32 Pedestrian -1 -1 -1 404.58 163.24 425.00 220.36 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n93 51 Pedestrian -1 -1 -1 943.45 167.29 974.04 253.45 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n93 11 Car -1 -1 -1 930.89 183.18 1009.55 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n93 8 Car -1 -1 -1 605.95 175.60 634.95 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n93 54 Pedestrian -1 -1 -1 387.60 166.55 405.91 216.33 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n93 14 Pedestrian -1 -1 -1 365.08 165.50 389.91 221.79 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n93 48 Pedestrian -1 -1 -1 434.48 170.42 457.39 225.83 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n93 36 Pedestrian -1 -1 -1 416.30 165.37 435.78 218.68 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n93 15 Pedestrian -1 -1 -1 543.40 166.31 567.72 231.32 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n93 9 Pedestrian -1 -1 -1 270.93 161.10 285.58 195.57 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n93 55 Cyclist -1 -1 -1 431.88 166.09 459.42 217.11 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n94 47 Pedestrian -1 -1 -1 517.57 171.11 610.17 349.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n94 3 Car -1 -1 -1 1116.32 188.54 1221.30 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n94 45 Pedestrian -1 -1 -1 588.44 164.26 647.45 332.08 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n94 7 Car -1 -1 -1 982.76 185.14 1069.28 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n94 51 Pedestrian -1 -1 -1 932.40 166.56 969.69 252.52 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n94 19 Pedestrian -1 -1 -1 347.08 166.05 369.71 221.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n94 4 Car -1 -1 -1 876.55 183.51 945.72 218.31 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n94 11 Car -1 -1 -1 930.63 183.90 1009.63 220.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n94 32 Pedestrian -1 -1 -1 405.34 163.72 425.80 220.15 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n94 14 Pedestrian -1 -1 -1 367.17 164.44 392.41 223.01 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n94 8 Car -1 -1 -1 606.02 175.95 634.91 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n94 54 Pedestrian -1 -1 -1 387.94 165.78 405.93 216.00 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n94 48 Pedestrian -1 -1 -1 433.42 166.22 459.34 221.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n94 36 Pedestrian -1 -1 -1 417.79 165.02 436.71 218.85 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n94 9 Pedestrian -1 -1 -1 270.70 161.04 285.47 195.61 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n94 15 Pedestrian -1 -1 -1 547.17 168.21 572.44 229.61 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n95 47 Pedestrian -1 -1 -1 526.70 172.26 617.83 347.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n95 3 Car -1 -1 -1 1116.37 188.60 1221.16 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n95 45 Pedestrian -1 -1 -1 595.74 164.08 662.88 331.10 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n95 51 Pedestrian -1 -1 -1 917.27 166.22 968.50 252.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n95 7 Car -1 -1 -1 982.95 185.37 1069.10 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n95 4 Car -1 -1 -1 876.46 183.49 946.55 218.44 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n95 19 Pedestrian -1 -1 -1 350.01 165.70 371.73 221.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n95 14 Pedestrian -1 -1 -1 368.93 164.35 392.98 223.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n95 11 Car -1 -1 -1 930.82 183.87 1009.33 221.06 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n95 54 Pedestrian -1 -1 -1 389.76 165.27 408.15 216.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n95 8 Car -1 -1 -1 606.45 175.96 634.14 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n95 48 Pedestrian -1 -1 -1 436.80 169.92 462.25 225.41 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n95 32 Pedestrian -1 -1 -1 407.49 164.77 428.28 221.64 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n95 36 Pedestrian -1 -1 -1 421.50 166.10 439.39 220.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n95 9 Pedestrian -1 -1 -1 270.62 160.96 285.29 195.70 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n96 3 Car -1 -1 -1 1116.62 188.49 1221.16 225.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n96 47 Pedestrian -1 -1 -1 547.25 171.23 619.84 348.40 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n96 45 Pedestrian -1 -1 -1 598.87 165.67 682.16 329.92 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n96 7 Car -1 -1 -1 982.96 185.34 1069.00 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n96 4 Car -1 -1 -1 877.33 183.53 945.98 218.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n96 51 Pedestrian -1 -1 -1 909.97 166.02 959.44 251.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n96 11 Car -1 -1 -1 930.89 183.99 1009.57 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n96 32 Pedestrian -1 -1 -1 409.22 165.20 429.72 222.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n96 54 Pedestrian -1 -1 -1 391.48 165.62 409.29 216.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n96 48 Pedestrian -1 -1 -1 438.60 170.45 467.30 224.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n96 14 Pedestrian -1 -1 -1 369.91 165.23 393.05 222.80 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n96 19 Pedestrian -1 -1 -1 353.52 165.09 375.43 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n96 8 Car -1 -1 -1 607.09 176.03 633.65 199.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n96 36 Pedestrian -1 -1 -1 423.51 167.70 443.18 219.87 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n96 9 Pedestrian -1 -1 -1 270.49 161.16 285.33 195.65 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n96 56 Pedestrian -1 -1 -1 556.06 164.33 580.03 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n97 3 Car -1 -1 -1 1116.13 188.51 1221.32 225.66 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n97 7 Car -1 -1 -1 983.27 185.35 1068.64 221.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n97 45 Pedestrian -1 -1 -1 606.73 166.56 689.75 329.84 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n97 11 Car -1 -1 -1 930.73 184.23 1009.84 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n97 4 Car -1 -1 -1 877.07 183.68 946.45 218.30 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n97 51 Pedestrian -1 -1 -1 904.76 168.40 942.03 249.38 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n97 47 Pedestrian -1 -1 -1 569.35 170.21 635.28 349.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n97 56 Pedestrian -1 -1 -1 565.17 163.41 585.93 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n97 14 Pedestrian -1 -1 -1 373.15 166.23 394.71 222.25 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n97 32 Pedestrian -1 -1 -1 411.35 164.82 432.44 221.91 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n97 48 Pedestrian -1 -1 -1 443.05 171.19 469.89 225.20 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n97 54 Pedestrian -1 -1 -1 391.24 166.03 410.09 217.08 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n97 8 Car -1 -1 -1 606.34 176.15 634.56 200.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n97 36 Pedestrian -1 -1 -1 424.73 167.66 445.16 220.92 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n97 19 Pedestrian -1 -1 -1 356.20 165.40 379.59 220.69 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n97 9 Pedestrian -1 -1 -1 270.53 161.01 285.42 195.75 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n98 3 Car -1 -1 -1 1116.32 188.57 1221.01 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n98 47 Pedestrian -1 -1 -1 579.91 171.76 663.65 348.92 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n98 7 Car -1 -1 -1 982.99 185.20 1068.91 221.00 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n98 11 Car -1 -1 -1 931.01 184.40 1009.76 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n98 56 Pedestrian -1 -1 -1 567.48 165.27 592.36 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n98 4 Car -1 -1 -1 879.94 180.65 943.10 221.83 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n98 48 Pedestrian -1 -1 -1 443.52 171.36 471.63 226.26 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n98 45 Pedestrian -1 -1 -1 625.15 166.31 694.27 329.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n98 19 Pedestrian -1 -1 -1 357.71 165.89 380.44 220.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n98 14 Pedestrian -1 -1 -1 374.32 166.48 394.92 221.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n98 51 Pedestrian -1 -1 -1 896.81 169.04 926.65 248.27 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n98 54 Pedestrian -1 -1 -1 393.44 166.02 412.29 217.59 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n98 36 Pedestrian -1 -1 -1 428.66 167.68 447.95 220.73 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n98 32 Pedestrian -1 -1 -1 412.87 164.63 433.36 221.57 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n98 8 Car -1 -1 -1 606.12 176.09 635.35 200.67 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n98 9 Pedestrian -1 -1 -1 270.68 161.11 285.41 195.63 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n99 3 Car -1 -1 -1 1116.72 188.59 1220.81 225.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n99 47 Pedestrian -1 -1 -1 588.59 174.61 678.00 350.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n99 7 Car -1 -1 -1 982.65 185.21 1069.29 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n99 11 Car -1 -1 -1 931.10 184.62 1009.44 220.75 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n99 51 Pedestrian -1 -1 -1 879.61 166.52 923.50 250.70 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n99 45 Pedestrian -1 -1 -1 644.66 165.14 705.45 329.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n99 8 Car -1 -1 -1 607.28 175.64 634.19 200.27 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n99 48 Pedestrian -1 -1 -1 449.86 170.55 472.69 226.98 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n99 4 Car -1 -1 -1 876.87 181.45 946.74 220.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n99 54 Pedestrian -1 -1 -1 394.78 165.56 413.39 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n99 56 Pedestrian -1 -1 -1 567.83 166.12 600.08 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n99 14 Pedestrian -1 -1 -1 374.10 165.77 395.68 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n99 32 Pedestrian -1 -1 -1 413.18 164.53 434.37 221.66 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n99 36 Pedestrian -1 -1 -1 428.77 166.98 447.59 220.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n99 19 Pedestrian -1 -1 -1 359.19 165.25 381.10 219.33 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n99 9 Pedestrian -1 -1 -1 270.70 161.07 285.34 195.66 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n99 57 Pedestrian -1 -1 -1 442.36 166.92 464.54 220.45 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n100 47 Pedestrian -1 -1 -1 597.19 173.86 683.74 351.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n100 3 Car -1 -1 -1 1116.83 188.58 1220.62 225.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n100 7 Car -1 -1 -1 982.81 185.26 1069.18 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n100 11 Car -1 -1 -1 931.01 184.66 1009.44 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n100 45 Pedestrian -1 -1 -1 658.01 162.97 722.32 326.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n100 51 Pedestrian -1 -1 -1 868.48 165.69 918.64 248.83 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n100 32 Pedestrian -1 -1 -1 415.24 165.49 436.54 221.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n100 48 Pedestrian -1 -1 -1 452.29 170.56 475.77 227.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n100 8 Car -1 -1 -1 607.48 175.88 633.31 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n100 4 Car -1 -1 -1 871.32 181.64 947.26 220.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n100 54 Pedestrian -1 -1 -1 397.02 165.36 415.35 217.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n100 56 Pedestrian -1 -1 -1 570.94 165.19 602.95 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n100 19 Pedestrian -1 -1 -1 361.46 165.83 382.91 220.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n100 36 Pedestrian -1 -1 -1 431.69 167.06 451.18 221.36 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n100 14 Pedestrian -1 -1 -1 374.36 165.51 396.07 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n100 9 Pedestrian -1 -1 -1 270.68 161.09 285.31 195.50 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n101 3 Car -1 -1 -1 1116.64 188.69 1220.73 225.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n101 47 Pedestrian -1 -1 -1 616.70 169.90 687.47 350.17 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n101 7 Car -1 -1 -1 982.64 185.28 1069.44 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n101 11 Car -1 -1 -1 930.76 184.73 1009.63 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n101 51 Pedestrian -1 -1 -1 862.27 165.51 908.33 247.87 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n101 45 Pedestrian -1 -1 -1 661.13 165.29 742.63 330.29 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n101 4 Car -1 -1 -1 872.08 182.66 946.04 219.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n101 48 Pedestrian -1 -1 -1 453.49 171.10 477.59 227.09 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n101 54 Pedestrian -1 -1 -1 397.54 165.14 415.76 218.30 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n101 32 Pedestrian -1 -1 -1 416.38 165.36 438.43 222.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n101 8 Car -1 -1 -1 607.65 175.92 633.17 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n101 56 Pedestrian -1 -1 -1 581.31 164.45 608.02 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n101 36 Pedestrian -1 -1 -1 432.42 167.61 452.05 221.60 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n101 14 Pedestrian -1 -1 -1 376.94 165.48 398.45 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n101 19 Pedestrian -1 -1 -1 363.18 165.26 384.54 218.82 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n101 9 Pedestrian -1 -1 -1 270.88 161.07 285.50 195.43 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n101 58 Pedestrian -1 -1 -1 447.30 166.84 468.32 216.61 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n102 3 Car -1 -1 -1 1116.89 188.71 1220.85 225.73 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n102 7 Car -1 -1 -1 982.72 185.34 1069.21 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n102 11 Car -1 -1 -1 930.58 184.68 1009.64 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n102 47 Pedestrian -1 -1 -1 638.20 169.90 704.13 349.35 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n102 32 Pedestrian -1 -1 -1 419.03 165.06 441.17 222.72 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n102 48 Pedestrian -1 -1 -1 456.01 170.49 480.99 227.28 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n102 51 Pedestrian -1 -1 -1 859.79 163.70 894.74 247.45 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n102 4 Car -1 -1 -1 876.98 182.86 946.19 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n102 56 Pedestrian -1 -1 -1 586.72 162.38 610.35 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n102 8 Car -1 -1 -1 607.93 176.00 633.32 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n102 45 Pedestrian -1 -1 -1 668.43 165.34 750.71 331.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n102 19 Pedestrian -1 -1 -1 365.82 164.44 387.56 219.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n102 36 Pedestrian -1 -1 -1 435.97 167.65 455.67 221.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n102 54 Pedestrian -1 -1 -1 398.57 165.76 417.25 218.55 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n102 14 Pedestrian -1 -1 -1 376.60 163.37 400.61 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n102 9 Pedestrian -1 -1 -1 271.04 161.00 285.51 195.48 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n103 3 Car -1 -1 -1 1117.22 188.51 1220.40 225.79 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n103 11 Car -1 -1 -1 930.35 184.56 1009.64 220.69 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n103 7 Car -1 -1 -1 982.94 185.30 1068.99 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n103 47 Pedestrian -1 -1 -1 647.46 168.23 733.39 351.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n103 56 Pedestrian -1 -1 -1 587.79 162.73 618.22 235.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n103 8 Car -1 -1 -1 608.41 176.14 633.26 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n103 4 Car -1 -1 -1 876.77 183.09 946.03 218.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n103 45 Pedestrian -1 -1 -1 686.04 166.66 755.66 330.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n103 32 Pedestrian -1 -1 -1 423.03 164.51 443.31 223.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n103 19 Pedestrian -1 -1 -1 366.16 164.11 388.67 219.70 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n103 48 Pedestrian -1 -1 -1 457.18 172.00 481.87 226.64 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n103 51 Pedestrian -1 -1 -1 849.41 161.70 882.47 247.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n103 14 Pedestrian -1 -1 -1 380.89 164.15 403.67 219.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n103 36 Pedestrian -1 -1 -1 436.54 166.50 456.17 222.32 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n103 54 Pedestrian -1 -1 -1 400.54 166.42 419.51 219.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n103 9 Pedestrian -1 -1 -1 271.13 160.89 285.42 195.43 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n104 3 Car -1 -1 -1 1116.93 188.56 1220.32 225.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n104 11 Car -1 -1 -1 930.25 184.50 1009.90 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n104 7 Car -1 -1 -1 982.76 185.27 1069.12 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n104 51 Pedestrian -1 -1 -1 834.49 159.71 875.68 246.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n104 4 Car -1 -1 -1 876.77 183.19 946.29 218.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n104 47 Pedestrian -1 -1 -1 652.98 170.08 743.35 350.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n104 8 Car -1 -1 -1 608.57 176.17 633.27 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n104 56 Pedestrian -1 -1 -1 589.62 161.43 624.27 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n104 32 Pedestrian -1 -1 -1 424.84 164.52 445.59 223.63 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n104 45 Pedestrian -1 -1 -1 702.17 165.45 762.64 332.15 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n104 36 Pedestrian -1 -1 -1 439.33 166.12 459.13 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n104 54 Pedestrian -1 -1 -1 400.80 166.44 420.68 220.10 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n104 19 Pedestrian -1 -1 -1 367.83 163.61 392.43 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n104 48 Pedestrian -1 -1 -1 455.56 166.93 475.13 221.77 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n104 14 Pedestrian -1 -1 -1 384.46 166.42 406.58 220.49 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n104 9 Pedestrian -1 -1 -1 271.28 160.81 285.33 195.45 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n104 59 Pedestrian -1 -1 -1 463.03 172.74 484.35 226.60 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n105 3 Car -1 -1 -1 1117.06 188.62 1220.41 225.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n105 11 Car -1 -1 -1 930.30 184.43 1009.81 220.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n105 7 Car -1 -1 -1 982.72 185.21 1069.23 221.41 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n105 32 Pedestrian -1 -1 -1 427.65 165.17 448.00 223.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n105 4 Car -1 -1 -1 876.92 183.28 946.39 218.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n105 47 Pedestrian -1 -1 -1 665.56 169.56 753.56 350.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n105 8 Car -1 -1 -1 608.13 175.82 633.30 199.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n105 45 Pedestrian -1 -1 -1 714.20 162.25 774.13 334.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n105 51 Pedestrian -1 -1 -1 827.43 160.85 873.66 244.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n105 59 Pedestrian -1 -1 -1 466.32 170.57 487.39 227.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 36 Pedestrian -1 -1 -1 440.12 166.44 460.17 223.24 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 56 Pedestrian -1 -1 -1 593.23 160.30 627.33 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n105 14 Pedestrian -1 -1 -1 385.15 167.00 406.72 220.24 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n105 54 Pedestrian -1 -1 -1 405.01 165.55 423.75 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n105 19 Pedestrian -1 -1 -1 369.57 164.41 393.70 219.68 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n105 9 Pedestrian -1 -1 -1 271.21 160.73 285.34 195.40 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n106 3 Car -1 -1 -1 1117.05 188.62 1220.13 225.81 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n106 11 Car -1 -1 -1 930.27 184.48 1009.63 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n106 7 Car -1 -1 -1 979.28 185.23 1069.04 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n106 47 Pedestrian -1 -1 -1 688.49 170.13 760.59 349.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n106 8 Car -1 -1 -1 607.94 175.89 633.39 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n106 4 Car -1 -1 -1 876.98 183.27 946.11 218.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n106 45 Pedestrian -1 -1 -1 723.29 163.25 794.91 326.39 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n106 36 Pedestrian -1 -1 -1 442.61 167.32 464.15 223.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n106 51 Pedestrian -1 -1 -1 819.93 161.84 860.64 241.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n106 32 Pedestrian -1 -1 -1 430.13 166.12 451.27 223.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n106 56 Pedestrian -1 -1 -1 603.10 160.59 630.51 236.46 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n106 59 Pedestrian -1 -1 -1 466.50 170.52 493.42 226.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n106 19 Pedestrian -1 -1 -1 374.29 165.15 396.44 218.92 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n106 54 Pedestrian -1 -1 -1 408.66 165.02 427.24 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n106 14 Pedestrian -1 -1 -1 385.54 166.64 407.77 220.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n106 9 Pedestrian -1 -1 -1 271.24 160.73 285.50 195.55 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n106 60 Pedestrian -1 -1 -1 461.80 167.14 484.15 222.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n107 3 Car -1 -1 -1 1117.30 188.54 1220.53 225.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n107 11 Car -1 -1 -1 930.20 184.41 1009.86 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n107 47 Pedestrian -1 -1 -1 704.65 167.88 775.57 352.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n107 7 Car -1 -1 -1 979.12 185.15 1069.12 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n107 32 Pedestrian -1 -1 -1 431.33 165.86 453.25 224.20 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n107 4 Car -1 -1 -1 876.96 183.22 946.22 218.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n107 8 Car -1 -1 -1 607.49 176.11 633.74 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n107 56 Pedestrian -1 -1 -1 609.10 161.62 634.29 236.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n107 54 Pedestrian -1 -1 -1 408.72 166.39 430.31 220.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n107 14 Pedestrian -1 -1 -1 390.12 167.36 410.05 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n107 51 Pedestrian -1 -1 -1 816.67 160.18 848.31 242.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n107 45 Pedestrian -1 -1 -1 731.82 163.30 809.21 331.99 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n107 36 Pedestrian -1 -1 -1 447.04 167.72 465.69 222.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n107 59 Pedestrian -1 -1 -1 463.83 167.95 487.43 222.50 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n107 60 Pedestrian -1 -1 -1 468.36 170.94 497.73 227.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n107 19 Pedestrian -1 -1 -1 378.98 165.43 399.12 218.74 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n107 9 Pedestrian -1 -1 -1 271.29 160.80 285.44 195.37 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n108 11 Car -1 -1 -1 930.08 184.46 1010.06 220.91 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n108 3 Car -1 -1 -1 1117.15 188.88 1219.67 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n108 47 Pedestrian -1 -1 -1 714.77 171.47 796.53 348.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n108 7 Car -1 -1 -1 978.66 185.09 1069.62 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n108 54 Pedestrian -1 -1 -1 410.09 167.16 435.46 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n108 4 Car -1 -1 -1 876.63 183.16 946.65 218.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n108 56 Pedestrian -1 -1 -1 613.11 162.27 644.37 236.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n108 32 Pedestrian -1 -1 -1 434.14 164.61 455.73 224.43 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n108 19 Pedestrian -1 -1 -1 381.79 164.64 402.52 219.16 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n108 8 Car -1 -1 -1 607.51 176.19 633.72 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n108 60 Pedestrian -1 -1 -1 470.71 170.01 498.81 227.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n108 36 Pedestrian -1 -1 -1 447.98 167.03 466.66 222.51 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n108 59 Pedestrian -1 -1 -1 465.82 167.60 486.83 222.53 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n108 45 Pedestrian -1 -1 -1 747.83 165.29 816.75 329.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n108 14 Pedestrian -1 -1 -1 392.73 168.69 413.31 219.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n108 51 Pedestrian -1 -1 -1 809.89 159.41 837.44 240.10 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n108 9 Pedestrian -1 -1 -1 271.33 160.82 285.35 195.29 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n109 11 Car -1 -1 -1 930.10 184.51 1010.28 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n109 7 Car -1 -1 -1 978.64 185.21 1069.56 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n109 3 Car -1 -1 -1 1116.78 189.11 1219.38 225.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n109 47 Pedestrian -1 -1 -1 728.37 172.01 813.14 348.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n109 4 Car -1 -1 -1 876.59 183.17 946.52 218.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n109 32 Pedestrian -1 -1 -1 435.69 164.69 456.86 224.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n109 54 Pedestrian -1 -1 -1 413.48 167.54 438.28 221.64 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n109 8 Car -1 -1 -1 607.95 175.85 633.86 200.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n109 60 Pedestrian -1 -1 -1 474.69 169.62 501.40 228.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n109 56 Pedestrian -1 -1 -1 616.63 163.10 650.59 236.30 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n109 36 Pedestrian -1 -1 -1 450.87 166.44 469.39 223.27 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n109 19 Pedestrian -1 -1 -1 384.53 165.15 405.50 218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n109 59 Pedestrian -1 -1 -1 466.98 167.49 486.84 222.95 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n109 45 Pedestrian -1 -1 -1 775.83 165.95 834.23 323.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n109 14 Pedestrian -1 -1 -1 397.10 169.20 416.43 219.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n109 51 Pedestrian -1 -1 -1 799.79 160.31 832.70 238.67 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n109 9 Pedestrian -1 -1 -1 271.15 160.87 285.40 195.35 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n109 61 Pedestrian -1 -1 -1 1179.29 176.77 1217.95 273.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n110 47 Pedestrian -1 -1 -1 737.30 170.58 826.38 350.14 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n110 11 Car -1 -1 -1 930.18 184.51 1010.16 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n110 7 Car -1 -1 -1 978.44 185.23 1069.79 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n110 3 Car -1 -1 -1 1117.84 189.01 1218.37 225.31 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n110 4 Car -1 -1 -1 876.42 183.14 946.43 218.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n110 8 Car -1 -1 -1 607.84 175.67 633.40 200.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n110 54 Pedestrian -1 -1 -1 420.44 167.51 440.42 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n110 60 Pedestrian -1 -1 -1 479.71 169.36 503.73 228.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n110 56 Pedestrian -1 -1 -1 620.83 162.11 653.05 237.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n110 32 Pedestrian -1 -1 -1 437.75 165.19 459.08 224.78 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n110 14 Pedestrian -1 -1 -1 398.05 169.62 416.85 219.12 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n110 45 Pedestrian -1 -1 -1 780.48 163.31 837.47 326.13 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n110 61 Pedestrian -1 -1 -1 1176.00 174.92 1214.08 274.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n110 36 Pedestrian -1 -1 -1 451.47 167.00 470.94 223.95 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n110 19 Pedestrian -1 -1 -1 385.17 167.20 407.90 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n110 59 Pedestrian -1 -1 -1 468.95 167.37 490.32 223.82 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n110 9 Pedestrian -1 -1 -1 271.01 160.87 285.37 195.42 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n111 47 Pedestrian -1 -1 -1 755.67 173.18 824.57 347.36 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n111 11 Car -1 -1 -1 930.11 184.58 1009.78 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n111 7 Car -1 -1 -1 978.65 185.28 1069.55 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n111 3 Car -1 -1 -1 1118.68 189.10 1217.77 225.09 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n111 4 Car -1 -1 -1 876.54 183.33 946.31 218.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n111 8 Car -1 -1 -1 607.70 175.73 633.31 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n111 32 Pedestrian -1 -1 -1 438.61 165.67 461.00 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n111 54 Pedestrian -1 -1 -1 428.16 166.76 446.92 221.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n111 19 Pedestrian -1 -1 -1 388.64 167.29 411.19 219.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n111 60 Pedestrian -1 -1 -1 483.13 169.40 506.01 228.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n111 45 Pedestrian -1 -1 -1 789.71 163.58 850.77 326.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n111 14 Pedestrian -1 -1 -1 401.09 169.73 420.09 220.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n111 56 Pedestrian -1 -1 -1 631.36 161.40 658.32 238.44 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n111 61 Pedestrian -1 -1 -1 1159.84 171.82 1200.72 276.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n111 59 Pedestrian -1 -1 -1 470.59 166.81 495.94 223.72 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n111 36 Pedestrian -1 -1 -1 454.41 167.50 473.65 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n111 9 Pedestrian -1 -1 -1 271.09 160.87 285.44 195.33 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n112 47 Pedestrian -1 -1 -1 777.79 171.93 840.40 347.21 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n112 11 Car -1 -1 -1 930.13 184.58 1009.57 220.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n112 7 Car -1 -1 -1 978.27 185.28 1069.83 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n112 8 Car -1 -1 -1 607.59 175.75 633.95 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n112 61 Pedestrian -1 -1 -1 1142.42 173.20 1194.91 271.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n112 3 Car -1 -1 -1 1119.22 189.49 1217.37 224.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n112 4 Car -1 -1 -1 876.84 183.27 946.05 218.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n112 32 Pedestrian -1 -1 -1 441.94 165.53 464.21 225.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n112 59 Pedestrian -1 -1 -1 472.40 166.83 495.44 224.00 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n112 54 Pedestrian -1 -1 -1 430.61 166.32 452.28 222.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n112 14 Pedestrian -1 -1 -1 401.63 169.46 420.83 219.37 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n112 45 Pedestrian -1 -1 -1 796.93 165.46 866.88 329.98 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n112 19 Pedestrian -1 -1 -1 393.17 167.07 414.49 219.36 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n112 56 Pedestrian -1 -1 -1 636.78 161.17 661.28 238.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n112 36 Pedestrian -1 -1 -1 458.52 166.59 477.23 222.99 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n112 60 Pedestrian -1 -1 -1 486.69 169.51 510.07 229.44 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n112 9 Pedestrian -1 -1 -1 271.17 160.95 285.67 195.36 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n113 11 Car -1 -1 -1 930.05 184.58 1009.55 220.95 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n113 7 Car -1 -1 -1 978.25 185.37 1069.96 221.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n113 8 Car -1 -1 -1 607.67 175.93 634.08 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n113 61 Pedestrian -1 -1 -1 1129.85 172.93 1191.39 271.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n113 3 Car -1 -1 -1 1119.00 189.13 1217.81 225.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n113 4 Car -1 -1 -1 877.24 183.38 945.86 218.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n113 47 Pedestrian -1 -1 -1 785.76 172.78 855.42 346.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n113 56 Pedestrian -1 -1 -1 638.81 161.20 674.57 240.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n113 19 Pedestrian -1 -1 -1 397.03 164.47 418.18 219.49 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n113 45 Pedestrian -1 -1 -1 809.79 166.19 884.40 330.76 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n113 32 Pedestrian -1 -1 -1 446.07 165.66 467.43 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n113 36 Pedestrian -1 -1 -1 459.25 166.68 479.12 224.03 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n113 54 Pedestrian -1 -1 -1 432.81 166.55 457.52 222.52 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n113 59 Pedestrian -1 -1 -1 474.61 168.11 499.22 222.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n113 60 Pedestrian -1 -1 -1 489.49 171.34 514.70 230.88 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n113 9 Pedestrian -1 -1 -1 272.55 160.73 287.96 195.77 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n113 62 Pedestrian -1 -1 -1 772.78 159.38 800.55 237.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n114 11 Car -1 -1 -1 929.77 184.67 1009.86 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n114 7 Car -1 -1 -1 978.45 185.42 1069.74 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n114 8 Car -1 -1 -1 607.77 175.88 634.18 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n114 47 Pedestrian -1 -1 -1 792.71 174.35 879.04 345.40 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n114 4 Car -1 -1 -1 877.77 183.70 945.12 218.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n114 3 Car -1 -1 -1 1118.38 188.85 1219.42 225.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n114 61 Pedestrian -1 -1 -1 1123.44 171.91 1174.24 271.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n114 59 Pedestrian -1 -1 -1 477.49 167.58 498.52 222.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n114 56 Pedestrian -1 -1 -1 642.16 161.69 679.28 240.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n114 45 Pedestrian -1 -1 -1 822.75 168.13 894.73 335.50 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n114 36 Pedestrian -1 -1 -1 461.98 167.23 482.55 224.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n114 32 Pedestrian -1 -1 -1 449.71 166.27 470.94 225.12 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n114 60 Pedestrian -1 -1 -1 491.89 172.15 515.95 230.64 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n114 19 Pedestrian -1 -1 -1 400.18 165.20 420.31 219.13 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n114 62 Pedestrian -1 -1 -1 759.24 160.80 798.29 234.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n114 54 Pedestrian -1 -1 -1 409.66 168.89 428.75 220.12 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n114 9 Pedestrian -1 -1 -1 270.96 160.83 285.68 195.61 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n115 11 Car -1 -1 -1 929.58 184.66 1010.10 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n115 7 Car -1 -1 -1 978.67 185.37 1069.39 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n115 8 Car -1 -1 -1 607.80 175.82 634.04 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n115 4 Car -1 -1 -1 877.82 183.54 945.90 218.50 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n115 47 Pedestrian -1 -1 -1 807.81 173.31 886.67 346.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n115 56 Pedestrian -1 -1 -1 645.43 160.34 682.38 239.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n115 3 Car -1 -1 -1 1118.25 189.13 1219.34 225.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n115 61 Pedestrian -1 -1 -1 1117.02 170.06 1151.09 271.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n115 62 Pedestrian -1 -1 -1 753.40 160.87 795.80 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n115 32 Pedestrian -1 -1 -1 449.99 165.92 472.65 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n115 45 Pedestrian -1 -1 -1 839.63 166.19 900.71 337.90 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n115 19 Pedestrian -1 -1 -1 400.56 166.24 423.00 218.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n115 59 Pedestrian -1 -1 -1 480.38 167.37 500.93 223.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n115 60 Pedestrian -1 -1 -1 499.22 170.33 520.53 229.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n115 36 Pedestrian -1 -1 -1 462.28 166.91 483.52 224.48 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n115 9 Pedestrian -1 -1 -1 272.35 160.57 288.45 195.96 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n116 11 Car -1 -1 -1 929.50 184.69 1010.32 221.03 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n116 47 Pedestrian -1 -1 -1 824.89 170.80 893.18 348.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n116 7 Car -1 -1 -1 978.60 185.28 1069.59 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n116 8 Car -1 -1 -1 607.72 175.66 634.14 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n116 4 Car -1 -1 -1 877.52 183.51 946.59 218.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n116 61 Pedestrian -1 -1 -1 1102.04 171.08 1142.52 270.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n116 3 Car -1 -1 -1 1117.75 189.03 1219.96 225.50 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n116 32 Pedestrian -1 -1 -1 452.47 166.16 476.82 224.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n116 56 Pedestrian -1 -1 -1 655.34 161.60 686.50 241.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n116 62 Pedestrian -1 -1 -1 748.47 160.16 786.64 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n116 19 Pedestrian -1 -1 -1 406.74 167.70 430.33 219.06 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n116 60 Pedestrian -1 -1 -1 501.91 170.08 525.46 229.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n116 59 Pedestrian -1 -1 -1 482.34 167.19 501.88 223.53 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n116 36 Pedestrian -1 -1 -1 466.27 167.31 487.05 224.04 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n116 45 Pedestrian -1 -1 -1 855.25 166.78 915.83 335.75 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n116 9 Pedestrian -1 -1 -1 270.70 160.53 285.57 195.54 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n117 47 Pedestrian -1 -1 -1 837.90 171.94 909.63 348.00 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n117 11 Car -1 -1 -1 929.59 184.68 1010.17 221.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n117 7 Car -1 -1 -1 982.53 185.25 1069.52 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n117 8 Car -1 -1 -1 607.55 175.63 634.14 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n117 3 Car -1 -1 -1 1117.35 188.91 1219.69 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n117 4 Car -1 -1 -1 877.38 183.80 946.62 218.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n117 61 Pedestrian -1 -1 -1 1085.75 171.45 1136.55 269.34 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n117 32 Pedestrian -1 -1 -1 456.51 165.57 479.43 225.73 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n117 60 Pedestrian -1 -1 -1 502.94 171.50 531.46 230.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n117 19 Pedestrian -1 -1 -1 411.15 167.19 433.57 219.45 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n117 36 Pedestrian -1 -1 -1 470.10 167.18 490.41 224.09 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n117 62 Pedestrian -1 -1 -1 748.66 158.53 776.33 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n117 56 Pedestrian -1 -1 -1 665.34 159.75 693.18 243.67 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n117 45 Pedestrian -1 -1 -1 863.90 165.01 930.27 332.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n117 59 Pedestrian -1 -1 -1 485.17 168.17 504.01 221.65 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n117 9 Pedestrian -1 -1 -1 273.15 160.92 288.16 195.32 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n117 63 Pedestrian -1 -1 -1 396.44 165.86 408.71 194.49 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n118 7 Car -1 -1 -1 982.17 185.26 1069.98 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n118 11 Car -1 -1 -1 929.40 184.78 1009.93 220.95 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n118 3 Car -1 -1 -1 1117.52 188.94 1219.60 225.51 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n118 4 Car -1 -1 -1 877.12 183.84 946.68 218.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n118 8 Car -1 -1 -1 607.48 175.65 634.29 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n118 60 Pedestrian -1 -1 -1 502.31 172.61 534.33 230.73 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n118 61 Pedestrian -1 -1 -1 1079.15 170.68 1127.74 266.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n118 32 Pedestrian -1 -1 -1 456.65 164.99 480.49 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n118 47 Pedestrian -1 -1 -1 847.67 177.15 923.07 342.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n118 19 Pedestrian -1 -1 -1 415.61 164.31 436.35 219.11 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n118 36 Pedestrian -1 -1 -1 473.66 166.84 494.24 224.80 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n118 45 Pedestrian -1 -1 -1 864.57 166.76 944.76 336.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n118 59 Pedestrian -1 -1 -1 485.51 168.80 504.72 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n118 62 Pedestrian -1 -1 -1 739.76 157.90 770.10 236.22 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n118 56 Pedestrian -1 -1 -1 668.52 159.68 704.57 245.63 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n118 9 Pedestrian -1 -1 -1 273.29 161.05 288.38 195.32 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n118 63 Pedestrian -1 -1 -1 396.01 165.39 408.48 195.33 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n119 7 Car -1 -1 -1 982.28 185.43 1069.88 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n119 3 Car -1 -1 -1 1116.49 188.86 1220.29 225.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n119 11 Car -1 -1 -1 929.92 185.07 1009.55 220.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n119 8 Car -1 -1 -1 607.53 175.56 634.24 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n119 4 Car -1 -1 -1 877.18 183.80 946.55 218.44 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n119 60 Pedestrian -1 -1 -1 505.95 172.57 537.81 231.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n119 47 Pedestrian -1 -1 -1 858.28 175.17 943.51 344.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n119 32 Pedestrian -1 -1 -1 458.56 163.99 485.69 226.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n119 61 Pedestrian -1 -1 -1 1074.65 171.29 1109.41 266.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n119 19 Pedestrian -1 -1 -1 416.51 165.36 436.73 218.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n119 59 Pedestrian -1 -1 -1 484.75 168.06 505.76 221.85 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n119 62 Pedestrian -1 -1 -1 728.18 158.34 768.08 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n119 45 Pedestrian -1 -1 -1 877.44 166.66 962.64 337.12 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n119 56 Pedestrian -1 -1 -1 668.72 161.07 712.44 243.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n119 9 Pedestrian -1 -1 -1 273.40 160.74 288.55 195.80 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n119 63 Pedestrian -1 -1 -1 395.97 165.39 408.61 195.47 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n120 3 Car -1 -1 -1 1115.96 188.64 1220.54 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n120 7 Car -1 -1 -1 981.97 185.30 1070.30 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n120 47 Pedestrian -1 -1 -1 871.71 173.38 953.04 345.45 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n120 8 Car -1 -1 -1 607.48 175.56 634.26 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n120 11 Car -1 -1 -1 930.09 185.00 1009.21 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n120 61 Pedestrian -1 -1 -1 1064.24 170.80 1096.61 265.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n120 60 Pedestrian -1 -1 -1 512.93 171.23 538.76 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n120 32 Pedestrian -1 -1 -1 463.45 163.44 488.82 227.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n120 4 Car -1 -1 -1 878.61 183.78 944.43 217.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n120 56 Pedestrian -1 -1 -1 673.46 160.08 715.20 244.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n120 62 Pedestrian -1 -1 -1 717.65 159.66 763.08 234.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n120 59 Pedestrian -1 -1 -1 479.75 167.80 503.32 227.06 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n120 19 Pedestrian -1 -1 -1 418.37 167.79 440.91 218.43 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n120 45 Pedestrian -1 -1 -1 901.28 166.10 969.14 336.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n120 9 Pedestrian -1 -1 -1 273.56 160.48 288.82 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n120 63 Pedestrian -1 -1 -1 396.04 165.68 408.46 195.77 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n120 64 Pedestrian -1 -1 -1 425.83 170.41 451.07 216.74 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n121 3 Car -1 -1 -1 1115.52 188.40 1221.16 225.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n121 7 Car -1 -1 -1 982.44 185.41 1070.00 220.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n121 47 Pedestrian -1 -1 -1 891.58 171.29 963.44 346.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n121 8 Car -1 -1 -1 607.44 175.66 634.08 199.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n121 11 Car -1 -1 -1 929.90 184.76 1009.40 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n121 61 Pedestrian -1 -1 -1 1048.04 169.59 1091.23 264.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n121 60 Pedestrian -1 -1 -1 518.63 171.41 540.43 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n121 4 Car -1 -1 -1 878.54 183.60 944.70 217.98 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n121 56 Pedestrian -1 -1 -1 686.50 157.95 716.94 246.98 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n121 32 Pedestrian -1 -1 -1 467.31 164.49 491.89 226.70 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n121 62 Pedestrian -1 -1 -1 714.91 159.26 750.65 235.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n121 19 Pedestrian -1 -1 -1 420.57 167.66 441.68 218.60 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n121 59 Pedestrian -1 -1 -1 484.08 168.47 506.58 226.80 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n121 9 Pedestrian -1 -1 -1 273.66 160.57 288.78 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n121 45 Pedestrian -1 -1 -1 910.68 167.01 982.83 336.39 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n121 64 Pedestrian -1 -1 -1 431.32 170.14 453.39 217.48 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n121 63 Pedestrian -1 -1 -1 396.10 165.68 408.19 195.82 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n122 3 Car -1 -1 -1 1115.70 188.49 1221.36 225.76 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n122 11 Car -1 -1 -1 929.35 184.88 1009.92 220.44 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n122 47 Pedestrian -1 -1 -1 905.64 171.49 972.43 346.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n122 7 Car -1 -1 -1 981.87 185.39 1070.30 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n122 8 Car -1 -1 -1 607.50 175.63 634.25 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n122 61 Pedestrian -1 -1 -1 1037.94 172.12 1084.97 262.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n122 60 Pedestrian -1 -1 -1 521.53 171.96 544.15 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n122 56 Pedestrian -1 -1 -1 694.18 157.57 724.59 246.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n122 4 Car -1 -1 -1 878.40 183.45 944.30 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n122 32 Pedestrian -1 -1 -1 470.98 164.90 495.97 227.11 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n122 59 Pedestrian -1 -1 -1 488.27 167.94 509.69 228.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n122 19 Pedestrian -1 -1 -1 424.26 167.10 445.34 217.04 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n122 45 Pedestrian -1 -1 -1 934.96 164.95 996.48 332.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n122 62 Pedestrian -1 -1 -1 710.25 158.71 739.61 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n122 63 Pedestrian -1 -1 -1 394.22 164.76 407.03 196.00 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n122 9 Pedestrian -1 -1 -1 273.84 160.45 288.82 196.51 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n122 64 Pedestrian -1 -1 -1 434.79 169.43 455.52 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n123 11 Car -1 -1 -1 929.48 184.81 1009.72 220.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n123 3 Car -1 -1 -1 1115.99 188.51 1221.36 225.70 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n123 7 Car -1 -1 -1 982.48 185.33 1069.76 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n123 8 Car -1 -1 -1 607.30 175.48 634.40 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n123 47 Pedestrian -1 -1 -1 917.28 174.06 991.35 344.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n123 62 Pedestrian -1 -1 -1 704.02 158.20 737.39 237.44 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 61 Pedestrian -1 -1 -1 1031.44 173.75 1068.88 260.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 32 Pedestrian -1 -1 -1 475.65 165.08 499.45 229.46 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n123 60 Pedestrian -1 -1 -1 523.71 171.79 549.21 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n123 59 Pedestrian -1 -1 -1 491.94 167.70 513.01 227.65 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n123 4 Car -1 -1 -1 877.99 183.36 944.33 218.17 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n123 19 Pedestrian -1 -1 -1 429.21 167.24 447.31 216.97 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n123 9 Pedestrian -1 -1 -1 273.66 160.27 288.73 196.44 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n123 45 Pedestrian -1 -1 -1 932.47 163.84 1014.47 333.50 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n123 63 Pedestrian -1 -1 -1 394.07 165.00 407.22 196.26 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n123 64 Pedestrian -1 -1 -1 438.60 169.65 459.64 218.36 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n124 11 Car -1 -1 -1 930.49 184.60 1008.77 220.40 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n124 3 Car -1 -1 -1 1116.42 188.61 1220.81 225.41 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n124 7 Car -1 -1 -1 981.65 185.66 1070.90 220.41 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n124 8 Car -1 -1 -1 607.35 175.62 634.27 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n124 62 Pedestrian -1 -1 -1 697.87 157.99 736.68 239.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n124 61 Pedestrian -1 -1 -1 1024.78 172.21 1051.50 261.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n124 4 Car -1 -1 -1 877.71 183.34 944.66 218.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n124 60 Pedestrian -1 -1 -1 527.12 171.58 553.76 231.63 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n124 47 Pedestrian -1 -1 -1 931.29 171.36 1015.67 340.70 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n124 32 Pedestrian -1 -1 -1 479.55 165.16 502.62 229.36 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n124 59 Pedestrian -1 -1 -1 493.00 168.17 514.13 227.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n124 19 Pedestrian -1 -1 -1 432.52 167.61 452.13 216.81 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n124 45 Pedestrian -1 -1 -1 944.69 166.42 1033.12 337.33 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n124 64 Pedestrian -1 -1 -1 443.76 170.64 463.75 218.70 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n124 9 Pedestrian -1 -1 -1 273.52 159.94 288.69 196.58 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n124 63 Pedestrian -1 -1 -1 393.83 165.11 407.70 196.10 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n125 3 Car -1 -1 -1 1116.28 188.76 1221.19 225.25 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n125 7 Car -1 -1 -1 983.40 185.95 1069.36 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n125 8 Car -1 -1 -1 607.57 175.68 634.40 199.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n125 11 Car -1 -1 -1 930.09 184.33 1008.98 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n125 32 Pedestrian -1 -1 -1 483.64 165.62 505.92 229.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n125 62 Pedestrian -1 -1 -1 709.20 159.60 747.07 246.66 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n125 47 Pedestrian -1 -1 -1 944.08 170.89 1025.59 341.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n125 4 Car -1 -1 -1 874.86 183.72 944.06 217.87 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n125 59 Pedestrian -1 -1 -1 496.61 168.38 517.23 227.82 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n125 60 Pedestrian -1 -1 -1 530.70 172.05 553.89 232.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n125 45 Pedestrian -1 -1 -1 977.86 173.24 1045.21 337.82 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n125 64 Pedestrian -1 -1 -1 446.92 170.75 468.96 217.73 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n125 61 Pedestrian -1 -1 -1 1008.78 174.64 1044.61 259.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n125 19 Pedestrian -1 -1 -1 435.28 168.49 456.08 216.09 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n125 9 Pedestrian -1 -1 -1 273.57 159.96 288.68 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n125 63 Pedestrian -1 -1 -1 393.75 165.71 407.57 195.55 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n125 65 Pedestrian -1 -1 -1 691.50 160.61 728.31 234.93 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n126 3 Car -1 -1 -1 1116.83 188.71 1221.29 225.41 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n126 47 Pedestrian -1 -1 -1 958.48 171.36 1034.48 347.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n126 7 Car -1 -1 -1 981.92 186.06 1071.16 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n126 8 Car -1 -1 -1 607.52 175.55 634.13 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n126 11 Car -1 -1 -1 930.23 184.41 1008.90 220.58 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n126 32 Pedestrian -1 -1 -1 486.78 166.04 510.51 230.47 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n126 62 Pedestrian -1 -1 -1 711.51 158.05 746.51 246.52 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n126 59 Pedestrian -1 -1 -1 500.02 167.98 521.05 228.88 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n126 4 Car -1 -1 -1 874.50 183.73 944.55 217.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n126 60 Pedestrian -1 -1 -1 535.21 172.08 556.84 232.16 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n126 64 Pedestrian -1 -1 -1 454.26 170.83 474.06 217.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n126 65 Pedestrian -1 -1 -1 687.98 161.05 723.62 235.24 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n126 19 Pedestrian -1 -1 -1 437.60 168.19 459.39 216.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n126 9 Pedestrian -1 -1 -1 273.33 160.12 288.78 196.57 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n126 63 Pedestrian -1 -1 -1 393.45 165.54 407.30 195.88 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n126 61 Pedestrian -1 -1 -1 1002.03 175.34 1036.31 259.03 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n127 3 Car -1 -1 -1 1116.61 188.69 1221.11 225.32 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n127 7 Car -1 -1 -1 982.26 186.00 1070.88 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n127 47 Pedestrian -1 -1 -1 971.59 167.75 1052.35 350.32 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n127 8 Car -1 -1 -1 607.26 175.58 634.42 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n127 11 Car -1 -1 -1 930.08 184.48 1009.45 220.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n127 62 Pedestrian -1 -1 -1 725.21 156.36 754.22 247.66 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n127 60 Pedestrian -1 -1 -1 539.06 170.67 564.78 232.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n127 32 Pedestrian -1 -1 -1 489.87 167.07 514.91 230.54 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n127 59 Pedestrian -1 -1 -1 503.19 167.99 524.57 228.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n127 4 Car -1 -1 -1 874.36 183.73 944.93 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n127 65 Pedestrian -1 -1 -1 687.14 160.11 709.65 231.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n127 19 Pedestrian -1 -1 -1 438.40 168.42 461.68 215.82 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n127 64 Pedestrian -1 -1 -1 455.26 170.37 474.98 217.18 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n127 63 Pedestrian -1 -1 -1 393.14 165.36 407.65 196.22 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n127 9 Pedestrian -1 -1 -1 273.09 159.99 288.70 196.61 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n128 3 Car -1 -1 -1 1116.40 188.56 1221.39 225.32 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n128 7 Car -1 -1 -1 982.61 185.97 1070.26 219.95 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n128 8 Car -1 -1 -1 607.33 175.59 634.44 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n128 62 Pedestrian -1 -1 -1 728.17 157.19 767.21 246.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n128 47 Pedestrian -1 -1 -1 991.39 172.20 1077.93 347.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n128 11 Car -1 -1 -1 929.73 184.26 1010.02 220.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n128 32 Pedestrian -1 -1 -1 495.11 167.38 518.48 230.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n128 60 Pedestrian -1 -1 -1 540.62 170.97 571.48 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n128 4 Car -1 -1 -1 876.30 183.22 945.95 218.36 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n128 59 Pedestrian -1 -1 -1 507.29 167.92 529.71 228.86 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n128 64 Pedestrian -1 -1 -1 459.01 170.44 477.38 217.66 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n128 19 Pedestrian -1 -1 -1 446.19 169.92 466.50 217.34 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n128 9 Pedestrian -1 -1 -1 273.06 159.93 288.58 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n128 63 Pedestrian -1 -1 -1 392.88 165.55 407.07 196.18 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n128 65 Pedestrian -1 -1 -1 676.07 160.01 706.17 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n129 3 Car -1 -1 -1 1116.57 188.64 1220.88 225.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n129 8 Car -1 -1 -1 607.42 175.73 634.20 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n129 62 Pedestrian -1 -1 -1 729.75 158.89 774.71 246.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n129 47 Pedestrian -1 -1 -1 993.01 172.54 1091.99 346.86 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n129 7 Car -1 -1 -1 982.25 185.81 1071.88 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n129 60 Pedestrian -1 -1 -1 540.85 171.41 573.31 231.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n129 11 Car -1 -1 -1 929.28 184.22 1010.49 220.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n129 64 Pedestrian -1 -1 -1 464.08 169.95 481.58 218.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n129 4 Car -1 -1 -1 874.03 183.65 945.28 217.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n129 32 Pedestrian -1 -1 -1 499.76 166.35 522.76 231.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n129 59 Pedestrian -1 -1 -1 510.41 167.12 533.76 228.81 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n129 19 Pedestrian -1 -1 -1 447.55 169.83 468.13 217.15 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n129 9 Pedestrian -1 -1 -1 272.99 159.96 288.47 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n129 63 Pedestrian -1 -1 -1 392.53 165.33 407.93 196.33 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n130 3 Car -1 -1 -1 1116.33 188.69 1220.92 225.15 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n130 47 Pedestrian -1 -1 -1 1008.09 171.18 1100.00 348.16 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n130 62 Pedestrian -1 -1 -1 735.46 158.50 776.66 247.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n130 7 Car -1 -1 -1 984.25 185.53 1069.04 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n130 60 Pedestrian -1 -1 -1 545.95 172.34 575.72 232.26 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n130 8 Car -1 -1 -1 607.30 175.71 634.22 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n130 11 Car -1 -1 -1 928.57 184.46 1010.64 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n130 64 Pedestrian -1 -1 -1 464.93 171.56 486.89 216.17 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n130 4 Car -1 -1 -1 875.75 183.16 946.53 218.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n130 32 Pedestrian -1 -1 -1 501.44 166.06 526.65 231.08 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n130 59 Pedestrian -1 -1 -1 510.25 167.74 534.47 229.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n130 19 Pedestrian -1 -1 -1 451.63 170.17 472.08 217.03 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n130 63 Pedestrian -1 -1 -1 391.98 165.36 408.19 196.22 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n130 9 Pedestrian -1 -1 -1 272.96 160.07 288.48 196.50 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n131 47 Pedestrian -1 -1 -1 1035.53 170.13 1109.66 349.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n131 3 Car -1 -1 -1 1116.18 188.99 1220.88 225.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n131 7 Car -1 -1 -1 983.85 185.59 1068.81 220.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n131 11 Car -1 -1 -1 928.73 184.52 1010.89 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n131 8 Car -1 -1 -1 607.32 175.79 634.19 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n131 4 Car -1 -1 -1 875.74 183.08 946.53 218.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n131 60 Pedestrian -1 -1 -1 553.63 172.02 576.69 232.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n131 32 Pedestrian -1 -1 -1 502.79 166.56 527.45 231.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n131 62 Pedestrian -1 -1 -1 748.22 158.12 779.19 248.88 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n131 64 Pedestrian -1 -1 -1 469.78 171.51 490.44 216.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n131 19 Pedestrian -1 -1 -1 453.63 168.90 476.85 214.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n131 59 Pedestrian -1 -1 -1 515.18 167.32 537.66 230.80 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n131 63 Pedestrian -1 -1 -1 391.82 165.34 407.54 196.42 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n131 9 Pedestrian -1 -1 -1 278.25 159.94 292.57 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n131 66 Pedestrian -1 -1 -1 953.66 173.07 992.35 255.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n131 67 Pedestrian -1 -1 -1 661.83 162.92 689.86 229.30 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n132 47 Pedestrian -1 -1 -1 1049.17 167.76 1127.45 350.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n132 7 Car -1 -1 -1 983.74 185.30 1068.88 220.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n132 3 Car -1 -1 -1 1115.72 189.05 1221.39 225.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n132 11 Car -1 -1 -1 929.47 183.62 1009.52 220.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n132 66 Pedestrian -1 -1 -1 945.68 172.83 979.69 253.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n132 8 Car -1 -1 -1 607.37 175.59 634.06 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n132 64 Pedestrian -1 -1 -1 474.06 171.88 493.23 217.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n132 4 Car -1 -1 -1 875.97 182.99 946.57 218.70 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n132 60 Pedestrian -1 -1 -1 557.90 172.52 579.57 232.52 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n132 62 Pedestrian -1 -1 -1 755.12 158.68 787.16 248.45 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n132 19 Pedestrian -1 -1 -1 457.34 171.86 479.48 216.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n132 32 Pedestrian -1 -1 -1 504.23 167.24 532.61 231.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n132 59 Pedestrian -1 -1 -1 517.78 168.88 541.59 230.05 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n132 9 Pedestrian -1 -1 -1 278.24 159.77 292.69 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n132 63 Pedestrian -1 -1 -1 391.99 165.70 407.48 195.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n133 7 Car -1 -1 -1 982.97 185.28 1069.25 220.38 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n133 47 Pedestrian -1 -1 -1 1056.43 168.72 1150.82 350.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n133 11 Car -1 -1 -1 929.18 183.84 1009.79 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n133 3 Car -1 -1 -1 1116.08 189.23 1221.61 225.32 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n133 8 Car -1 -1 -1 607.17 175.64 634.31 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n133 62 Pedestrian -1 -1 -1 760.08 159.22 803.16 250.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n133 4 Car -1 -1 -1 875.94 182.99 946.60 218.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n133 64 Pedestrian -1 -1 -1 478.19 170.23 496.44 218.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n133 60 Pedestrian -1 -1 -1 560.51 172.44 585.17 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n133 66 Pedestrian -1 -1 -1 939.25 170.75 967.88 255.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n133 32 Pedestrian -1 -1 -1 506.65 167.36 537.51 231.84 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n133 59 Pedestrian -1 -1 -1 517.88 168.04 548.41 229.95 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n133 19 Pedestrian -1 -1 -1 462.67 169.20 480.93 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n133 9 Pedestrian -1 -1 -1 278.38 160.14 292.60 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n133 63 Pedestrian -1 -1 -1 391.97 165.80 407.95 195.76 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n133 68 Pedestrian -1 -1 -1 652.24 161.52 681.24 229.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n134 7 Car -1 -1 -1 983.04 185.17 1069.33 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n134 66 Pedestrian -1 -1 -1 925.71 167.59 960.75 253.35 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n134 62 Pedestrian -1 -1 -1 761.46 158.57 810.08 251.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n134 3 Car -1 -1 -1 1116.16 189.21 1221.02 225.18 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n134 60 Pedestrian -1 -1 -1 562.23 171.83 590.26 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n134 47 Pedestrian -1 -1 -1 1067.50 170.90 1170.28 348.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n134 4 Car -1 -1 -1 876.48 183.14 946.31 218.56 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n134 68 Pedestrian -1 -1 -1 643.67 162.15 677.62 228.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n134 8 Car -1 -1 -1 607.03 175.82 634.17 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n134 64 Pedestrian -1 -1 -1 480.49 170.47 501.14 217.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n134 59 Pedestrian -1 -1 -1 529.22 167.49 552.44 229.13 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n134 11 Car -1 -1 -1 929.57 184.30 1009.33 220.27 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n134 32 Pedestrian -1 -1 -1 512.61 166.24 540.24 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n134 19 Pedestrian -1 -1 -1 465.02 169.86 482.12 216.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n134 9 Pedestrian -1 -1 -1 278.79 160.24 292.70 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n134 63 Pedestrian -1 -1 -1 391.79 165.85 407.98 195.48 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n134 69 Pedestrian -1 -1 -1 271.03 160.50 285.54 195.91 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n135 47 Pedestrian -1 -1 -1 1077.05 171.81 1176.04 348.34 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n135 66 Pedestrian -1 -1 -1 913.07 167.50 958.13 253.90 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n135 7 Car -1 -1 -1 983.05 185.31 1069.08 220.70 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n135 60 Pedestrian -1 -1 -1 565.65 170.94 593.82 234.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n135 4 Car -1 -1 -1 876.22 183.03 946.80 218.88 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n135 62 Pedestrian -1 -1 -1 767.32 159.00 812.45 250.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n135 3 Car -1 -1 -1 1115.44 189.40 1222.01 225.17 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n135 68 Pedestrian -1 -1 -1 640.30 163.30 673.24 228.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n135 8 Car -1 -1 -1 606.84 175.71 634.19 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n135 11 Car -1 -1 -1 929.38 184.41 1009.78 220.15 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n135 59 Pedestrian -1 -1 -1 533.90 166.67 556.02 229.80 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n135 32 Pedestrian -1 -1 -1 520.01 164.96 546.29 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n135 64 Pedestrian -1 -1 -1 483.34 170.60 505.82 216.77 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n135 19 Pedestrian -1 -1 -1 465.91 169.02 486.95 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n135 9 Pedestrian -1 -1 -1 278.77 160.31 292.97 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n135 63 Pedestrian -1 -1 -1 387.64 164.95 404.48 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n135 69 Pedestrian -1 -1 -1 270.93 160.52 285.59 195.85 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n136 62 Pedestrian -1 -1 -1 779.61 156.91 814.68 249.64 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n136 7 Car -1 -1 -1 983.35 185.27 1068.72 220.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n136 47 Pedestrian -1 -1 -1 1094.64 170.34 1189.31 348.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n136 11 Car -1 -1 -1 928.83 184.44 1009.75 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n136 68 Pedestrian -1 -1 -1 638.72 163.17 665.57 227.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n136 4 Car -1 -1 -1 876.72 183.12 946.71 218.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n136 60 Pedestrian -1 -1 -1 571.20 171.16 595.83 234.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n136 66 Pedestrian -1 -1 -1 906.99 170.09 948.64 251.99 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n136 8 Car -1 -1 -1 606.74 175.58 634.45 199.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n136 32 Pedestrian -1 -1 -1 520.25 165.28 547.48 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n136 3 Car -1 -1 -1 1115.11 189.31 1222.35 225.30 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n136 59 Pedestrian -1 -1 -1 540.74 167.41 563.35 229.22 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n136 19 Pedestrian -1 -1 -1 468.56 170.95 491.07 215.48 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n136 64 Pedestrian -1 -1 -1 486.04 169.80 506.02 216.82 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n136 9 Pedestrian -1 -1 -1 278.95 160.43 292.96 193.00 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n136 69 Pedestrian -1 -1 -1 271.06 160.69 285.71 195.81 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n136 63 Pedestrian -1 -1 -1 391.57 165.49 408.01 195.83 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n137 7 Car -1 -1 -1 983.40 185.24 1068.65 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n137 11 Car -1 -1 -1 928.99 184.47 1010.36 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n137 4 Car -1 -1 -1 876.51 182.67 947.11 218.99 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n137 47 Pedestrian -1 -1 -1 1116.34 171.64 1205.33 348.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n137 3 Car -1 -1 -1 1115.93 189.44 1221.34 225.23 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n137 66 Pedestrian -1 -1 -1 905.05 169.68 933.30 251.46 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n137 60 Pedestrian -1 -1 -1 575.17 171.34 599.82 235.27 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n137 32 Pedestrian -1 -1 -1 524.45 165.63 551.46 232.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n137 8 Car -1 -1 -1 606.62 175.58 634.07 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n137 62 Pedestrian -1 -1 -1 789.99 156.54 821.03 252.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n137 59 Pedestrian -1 -1 -1 547.63 167.50 571.59 227.96 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n137 64 Pedestrian -1 -1 -1 489.45 170.56 508.29 216.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n137 68 Pedestrian -1 -1 -1 635.11 162.65 655.84 227.99 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n137 19 Pedestrian -1 -1 -1 472.20 171.70 493.34 215.19 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n137 63 Pedestrian -1 -1 -1 387.71 166.39 403.30 196.99 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n137 9 Pedestrian -1 -1 -1 278.70 161.13 293.04 194.54 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n138 7 Car -1 -1 -1 982.83 185.10 1069.35 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n138 66 Pedestrian -1 -1 -1 893.68 170.77 923.55 249.23 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n138 11 Car -1 -1 -1 929.46 184.45 1010.28 220.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n138 62 Pedestrian -1 -1 -1 796.07 159.02 836.92 250.84 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n138 60 Pedestrian -1 -1 -1 576.66 170.83 606.20 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n138 3 Car -1 -1 -1 1116.97 189.35 1220.29 225.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n138 32 Pedestrian -1 -1 -1 527.71 165.75 554.65 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n138 4 Car -1 -1 -1 876.77 182.41 946.35 219.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n138 59 Pedestrian -1 -1 -1 552.72 168.21 576.57 227.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n138 68 Pedestrian -1 -1 -1 628.52 163.29 654.26 227.01 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n138 8 Car -1 -1 -1 606.80 175.81 633.81 199.46 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n138 47 Pedestrian -1 -1 -1 1121.81 167.25 1215.23 352.50 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n138 64 Pedestrian -1 -1 -1 493.65 170.42 511.32 216.57 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n138 19 Pedestrian -1 -1 -1 475.19 171.38 494.38 215.11 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n138 9 Pedestrian -1 -1 -1 278.55 161.39 292.77 194.51 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n138 63 Pedestrian -1 -1 -1 387.49 166.47 402.80 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n138 70 Pedestrian -1 -1 -1 540.28 167.61 566.01 230.27 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n138 71 Pedestrian -1 -1 -1 396.02 166.51 409.31 195.10 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n139 11 Car -1 -1 -1 929.73 184.60 1010.07 220.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n139 66 Pedestrian -1 -1 -1 880.12 168.63 921.88 250.56 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n139 7 Car -1 -1 -1 982.71 185.26 1069.37 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n139 62 Pedestrian -1 -1 -1 800.23 160.62 847.08 250.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n139 60 Pedestrian -1 -1 -1 578.04 171.30 611.12 234.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n139 32 Pedestrian -1 -1 -1 533.34 165.78 557.37 232.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n139 4 Car -1 -1 -1 876.17 181.91 947.52 219.50 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n139 64 Pedestrian -1 -1 -1 497.03 171.29 515.36 215.62 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n139 3 Car -1 -1 -1 1118.18 189.57 1218.44 224.86 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n139 59 Pedestrian -1 -1 -1 556.06 167.88 581.07 228.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n139 19 Pedestrian -1 -1 -1 480.76 169.46 496.36 214.15 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n139 8 Car -1 -1 -1 606.33 175.51 634.06 200.05 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n139 47 Pedestrian -1 -1 -1 1132.03 167.67 1220.35 351.31 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n139 70 Pedestrian -1 -1 -1 547.64 166.91 572.43 230.68 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n139 68 Pedestrian -1 -1 -1 622.20 163.82 651.30 226.25 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n139 9 Pedestrian -1 -1 -1 278.52 161.98 292.69 194.51 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n139 71 Pedestrian -1 -1 -1 396.27 166.42 409.20 195.13 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n139 63 Pedestrian -1 -1 -1 387.50 166.35 401.94 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n140 62 Pedestrian -1 -1 -1 804.81 159.34 852.16 252.87 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n140 7 Car -1 -1 -1 982.37 185.18 1069.71 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n140 11 Car -1 -1 -1 929.98 184.74 1010.00 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n140 4 Car -1 -1 -1 877.42 182.47 946.98 219.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n140 66 Pedestrian -1 -1 -1 872.33 168.05 914.59 250.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n140 19 Pedestrian -1 -1 -1 483.88 169.74 500.66 213.48 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n140 60 Pedestrian -1 -1 -1 581.79 170.92 613.87 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n140 32 Pedestrian -1 -1 -1 540.84 163.37 564.25 234.10 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n140 3 Car -1 -1 -1 1118.79 189.41 1217.50 224.75 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n140 59 Pedestrian -1 -1 -1 564.64 166.55 586.43 229.20 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n140 64 Pedestrian -1 -1 -1 498.58 172.35 516.99 214.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n140 70 Pedestrian -1 -1 -1 553.22 165.73 575.37 230.83 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n140 47 Pedestrian -1 -1 -1 1146.21 170.53 1221.09 348.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n140 8 Car -1 -1 -1 605.25 175.66 632.87 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n140 71 Pedestrian -1 -1 -1 397.29 166.91 409.77 194.52 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n140 68 Pedestrian -1 -1 -1 616.92 165.48 643.80 225.45 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n140 63 Pedestrian -1 -1 -1 387.88 166.67 401.71 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n140 9 Pedestrian -1 -1 -1 278.36 162.43 292.75 194.48 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n141 7 Car -1 -1 -1 982.19 185.19 1069.87 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n141 11 Car -1 -1 -1 929.98 184.70 1010.14 220.74 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n141 62 Pedestrian -1 -1 -1 819.11 158.20 852.03 254.44 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n141 32 Pedestrian -1 -1 -1 539.97 163.46 566.11 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n141 4 Car -1 -1 -1 878.77 183.05 944.92 218.76 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n141 19 Pedestrian -1 -1 -1 485.18 170.27 505.37 213.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n141 3 Car -1 -1 -1 1119.16 189.08 1216.83 224.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n141 66 Pedestrian -1 -1 -1 868.72 169.14 902.98 248.34 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n141 60 Pedestrian -1 -1 -1 589.03 170.58 615.59 236.44 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n141 64 Pedestrian -1 -1 -1 500.65 172.15 519.45 214.66 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n141 59 Pedestrian -1 -1 -1 567.14 166.85 593.06 228.93 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n141 8 Car -1 -1 -1 604.98 176.58 632.93 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n141 70 Pedestrian -1 -1 -1 557.11 166.44 579.41 230.47 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n141 47 Pedestrian -1 -1 -1 1163.49 169.06 1219.49 349.97 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n141 9 Pedestrian -1 -1 -1 272.93 161.08 288.12 195.19 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n141 63 Pedestrian -1 -1 -1 387.49 166.46 401.69 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n141 71 Pedestrian -1 -1 -1 396.97 167.64 410.35 195.81 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n141 72 Cyclist -1 -1 -1 609.23 165.67 636.13 225.50 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n142 7 Car -1 -1 -1 982.41 185.21 1069.62 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n142 11 Car -1 -1 -1 930.07 184.60 1010.21 220.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n142 62 Pedestrian -1 -1 -1 829.11 157.28 858.01 254.98 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n142 4 Car -1 -1 -1 878.72 182.91 945.61 219.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n142 3 Car -1 -1 -1 1118.65 188.81 1217.29 224.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n142 32 Pedestrian -1 -1 -1 543.45 163.62 570.52 234.26 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n142 64 Pedestrian -1 -1 -1 504.41 171.87 523.79 215.71 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n142 8 Car -1 -1 -1 604.44 176.26 633.38 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n142 60 Pedestrian -1 -1 -1 594.66 170.52 618.29 236.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n142 59 Pedestrian -1 -1 -1 575.19 168.63 599.88 228.37 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n142 70 Pedestrian -1 -1 -1 562.98 166.55 587.59 230.97 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n142 66 Pedestrian -1 -1 -1 865.00 167.94 889.59 246.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n142 19 Pedestrian -1 -1 -1 488.10 171.11 507.70 213.29 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n142 9 Pedestrian -1 -1 -1 272.83 161.15 288.34 195.46 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n142 63 Pedestrian -1 -1 -1 386.11 166.26 400.54 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n142 47 Pedestrian -1 -1 -1 1186.97 165.53 1218.79 345.59 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n142 73 Pedestrian -1 -1 -1 609.77 164.83 633.68 224.97 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n143 11 Car -1 -1 -1 929.90 184.57 1010.06 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n143 7 Car -1 -1 -1 982.73 185.24 1069.16 221.34 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n143 3 Car -1 -1 -1 1118.31 188.51 1218.56 224.94 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n143 4 Car -1 -1 -1 878.71 183.23 945.23 218.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n143 62 Pedestrian -1 -1 -1 834.93 159.67 875.05 253.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n143 8 Car -1 -1 -1 605.06 175.78 632.74 199.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n143 32 Pedestrian -1 -1 -1 546.57 164.06 574.11 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n143 64 Pedestrian -1 -1 -1 506.55 171.74 524.28 215.61 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n143 66 Pedestrian -1 -1 -1 849.92 165.87 882.87 248.47 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n143 19 Pedestrian -1 -1 -1 491.53 170.95 507.40 213.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n143 70 Pedestrian -1 -1 -1 567.09 166.62 591.04 231.05 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n143 60 Pedestrian -1 -1 -1 597.75 170.16 623.45 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n143 63 Pedestrian -1 -1 -1 385.78 166.40 400.27 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n143 9 Pedestrian -1 -1 -1 272.69 161.09 288.33 195.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n143 59 Pedestrian -1 -1 -1 583.60 169.77 604.81 228.31 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n143 47 Pedestrian -1 -1 -1 1193.71 168.19 1220.23 343.04 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n143 73 Pedestrian -1 -1 -1 605.08 165.75 631.15 224.92 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n143 74 Cyclist -1 -1 -1 605.08 165.75 631.15 224.92 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n143 75 Cyclist -1 -1 -1 -9.34 129.25 173.73 359.80 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n143 76 Pedestrian -1 -1 -1 397.36 167.72 410.12 195.83 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n144 66 Pedestrian -1 -1 -1 837.33 159.79 887.46 254.47 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n144 11 Car -1 -1 -1 929.99 184.55 1010.17 220.46 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n144 3 Car -1 -1 -1 1117.89 188.58 1219.45 225.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n144 7 Car -1 -1 -1 982.50 185.26 1069.39 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n144 4 Car -1 -1 -1 878.47 183.42 945.33 218.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n144 64 Pedestrian -1 -1 -1 509.70 171.80 527.31 215.49 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n144 8 Car -1 -1 -1 605.83 175.89 631.49 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n144 32 Pedestrian -1 -1 -1 549.94 164.38 578.53 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n144 75 Cyclist -1 -1 -1 -7.69 129.18 247.99 361.05 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n144 74 Cyclist -1 -1 -1 597.60 166.68 630.76 224.81 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n144 9 Pedestrian -1 -1 -1 272.68 160.91 288.44 195.74 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n144 19 Pedestrian -1 -1 -1 494.21 170.49 509.49 214.00 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n144 59 Pedestrian -1 -1 -1 588.04 168.84 607.79 229.05 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n144 70 Pedestrian -1 -1 -1 571.68 167.05 594.52 231.00 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n144 60 Pedestrian -1 -1 -1 599.08 171.95 630.57 234.59 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n144 63 Pedestrian -1 -1 -1 386.00 166.55 400.02 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n144 76 Pedestrian -1 -1 -1 398.40 167.81 410.44 195.92 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n144 47 Pedestrian -1 -1 -1 1194.54 172.44 1219.65 339.48 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n144 77 Pedestrian -1 -1 -1 481.18 169.29 493.40 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n145 75 Cyclist -1 -1 -1 47.38 137.51 285.45 360.02 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n145 11 Car -1 -1 -1 930.05 184.55 1010.21 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n145 3 Car -1 -1 -1 1117.59 188.71 1220.07 225.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n145 7 Car -1 -1 -1 982.37 185.26 1069.50 221.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n145 4 Car -1 -1 -1 878.88 183.49 945.44 218.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n145 66 Pedestrian -1 -1 -1 843.94 161.95 888.35 256.44 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n145 64 Pedestrian -1 -1 -1 511.35 172.10 530.99 215.17 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n145 8 Car -1 -1 -1 604.51 175.54 632.60 200.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n145 32 Pedestrian -1 -1 -1 555.67 164.64 580.29 234.33 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n145 59 Pedestrian -1 -1 -1 591.97 168.54 612.87 228.94 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n145 9 Pedestrian -1 -1 -1 272.27 160.79 288.60 196.05 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n145 70 Pedestrian -1 -1 -1 575.99 166.90 597.65 231.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n145 60 Pedestrian -1 -1 -1 601.30 172.59 635.04 237.06 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n145 63 Pedestrian -1 -1 -1 386.36 166.41 400.09 198.69 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n145 19 Pedestrian -1 -1 -1 496.51 170.40 510.96 213.56 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n145 76 Pedestrian -1 -1 -1 398.06 168.09 410.91 195.68 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n145 74 Cyclist -1 -1 -1 602.12 166.56 624.93 224.45 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n145 78 Pedestrian -1 -1 -1 291.91 164.50 307.68 194.94 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n146 75 Cyclist -1 -1 -1 120.83 142.34 311.29 361.62 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n146 11 Car -1 -1 -1 929.95 184.58 1010.13 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n146 3 Car -1 -1 -1 1117.61 188.58 1220.07 225.73 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n146 7 Car -1 -1 -1 982.31 185.18 1069.61 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n146 4 Car -1 -1 -1 878.89 183.46 944.92 218.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n146 64 Pedestrian -1 -1 -1 513.38 172.65 531.93 214.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n146 32 Pedestrian -1 -1 -1 560.51 166.87 583.68 235.61 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n146 8 Car -1 -1 -1 605.06 175.87 632.00 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n146 66 Pedestrian -1 -1 -1 856.15 160.84 892.39 254.08 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n146 59 Pedestrian -1 -1 -1 595.28 168.66 617.56 228.51 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n146 70 Pedestrian -1 -1 -1 579.34 166.46 602.38 232.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n146 9 Pedestrian -1 -1 -1 272.05 160.81 289.10 196.22 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n146 60 Pedestrian -1 -1 -1 607.58 172.43 636.37 237.24 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n146 63 Pedestrian -1 -1 -1 386.65 165.88 399.96 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n146 19 Pedestrian -1 -1 -1 497.88 170.17 516.67 213.83 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n146 76 Pedestrian -1 -1 -1 398.41 169.11 410.68 195.93 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n146 79 Pedestrian -1 -1 -1 833.43 166.26 861.54 245.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n147 75 Cyclist -1 -1 -1 165.34 140.88 343.51 363.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n147 11 Car -1 -1 -1 930.08 184.61 1010.05 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n147 7 Car -1 -1 -1 982.13 185.13 1069.89 221.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n147 3 Car -1 -1 -1 1117.29 188.66 1220.03 225.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n147 4 Car -1 -1 -1 879.23 183.15 945.03 218.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n147 79 Pedestrian -1 -1 -1 826.28 166.92 853.01 243.26 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n147 32 Pedestrian -1 -1 -1 563.34 166.91 588.51 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n147 66 Pedestrian -1 -1 -1 869.69 161.06 901.79 257.47 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n147 8 Car -1 -1 -1 606.15 176.20 631.22 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n147 63 Pedestrian -1 -1 -1 386.51 165.99 399.81 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n147 64 Pedestrian -1 -1 -1 516.54 172.12 534.45 214.34 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n147 19 Pedestrian -1 -1 -1 500.04 170.81 520.22 213.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n147 70 Pedestrian -1 -1 -1 581.58 166.26 608.70 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n147 59 Pedestrian -1 -1 -1 595.48 168.18 624.93 230.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n147 9 Pedestrian -1 -1 -1 272.23 160.92 289.47 196.69 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n147 60 Pedestrian -1 -1 -1 613.63 171.40 639.01 238.23 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n147 76 Pedestrian -1 -1 -1 400.62 168.93 411.97 196.13 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n147 80 Pedestrian -1 -1 -1 553.62 171.47 574.19 231.27 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n148 75 Cyclist -1 -1 -1 206.91 144.32 371.08 366.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n148 3 Car -1 -1 -1 1116.62 188.57 1220.33 225.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n148 7 Car -1 -1 -1 982.20 185.16 1069.88 221.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n148 11 Car -1 -1 -1 930.14 184.74 1010.17 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n148 66 Pedestrian -1 -1 -1 875.75 161.82 919.35 256.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n148 4 Car -1 -1 -1 879.51 183.53 945.24 218.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n148 8 Car -1 -1 -1 606.32 176.35 631.20 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n148 64 Pedestrian -1 -1 -1 520.46 172.85 536.70 215.59 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n148 79 Pedestrian -1 -1 -1 814.90 165.65 849.66 241.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n148 63 Pedestrian -1 -1 -1 386.35 166.46 399.64 199.22 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n148 32 Pedestrian -1 -1 -1 566.83 167.33 593.48 236.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n148 60 Pedestrian -1 -1 -1 621.09 170.03 645.74 236.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n148 70 Pedestrian -1 -1 -1 584.69 166.33 612.61 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n148 9 Pedestrian -1 -1 -1 272.35 161.19 289.19 196.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n148 59 Pedestrian -1 -1 -1 600.23 168.56 627.93 230.14 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n148 80 Pedestrian -1 -1 -1 557.58 172.47 578.36 231.77 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n148 19 Pedestrian -1 -1 -1 504.68 170.89 521.93 212.86 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n148 76 Pedestrian -1 -1 -1 401.12 168.93 412.59 196.20 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n149 75 Cyclist -1 -1 -1 246.36 145.92 392.13 365.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n149 3 Car -1 -1 -1 1117.09 188.57 1219.99 225.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n149 11 Car -1 -1 -1 930.28 184.78 1010.12 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n149 7 Car -1 -1 -1 982.33 185.17 1069.59 221.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n149 66 Pedestrian -1 -1 -1 880.33 163.15 935.38 257.26 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n149 64 Pedestrian -1 -1 -1 522.59 172.73 538.31 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n149 79 Pedestrian -1 -1 -1 810.07 165.62 845.69 241.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n149 8 Car -1 -1 -1 605.28 175.91 632.28 200.29 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n149 4 Car -1 -1 -1 879.76 182.05 944.87 219.57 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n149 32 Pedestrian -1 -1 -1 573.93 166.28 601.01 231.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n149 63 Pedestrian -1 -1 -1 386.60 166.47 399.57 199.34 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n149 60 Pedestrian -1 -1 -1 623.07 171.05 651.55 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n149 70 Pedestrian -1 -1 -1 594.11 166.50 618.25 232.29 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n149 9 Pedestrian -1 -1 -1 272.62 160.99 288.85 196.79 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n149 19 Pedestrian -1 -1 -1 509.58 170.31 524.69 213.81 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n149 59 Pedestrian -1 -1 -1 606.05 167.84 630.24 230.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n149 76 Pedestrian -1 -1 -1 401.11 168.53 412.71 196.14 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n150 3 Car -1 -1 -1 1116.73 188.57 1220.16 225.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n150 7 Car -1 -1 -1 982.29 185.20 1069.62 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n150 75 Cyclist -1 -1 -1 272.72 145.42 404.65 366.54 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n150 11 Car -1 -1 -1 930.01 184.81 1010.48 220.69 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n150 66 Pedestrian -1 -1 -1 890.03 163.05 941.26 258.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n150 79 Pedestrian -1 -1 -1 805.41 165.99 835.38 240.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n150 4 Car -1 -1 -1 876.56 181.67 948.03 219.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n150 8 Car -1 -1 -1 607.48 175.52 634.13 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n150 64 Pedestrian -1 -1 -1 524.23 173.34 540.51 214.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n150 32 Pedestrian -1 -1 -1 578.46 165.65 604.38 232.26 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n150 19 Pedestrian -1 -1 -1 511.38 171.11 526.46 213.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n150 60 Pedestrian -1 -1 -1 624.27 170.77 657.28 235.46 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n150 70 Pedestrian -1 -1 -1 598.09 165.22 622.49 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n150 63 Pedestrian -1 -1 -1 386.94 166.28 399.38 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n150 59 Pedestrian -1 -1 -1 572.52 167.69 594.45 231.06 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n150 9 Pedestrian -1 -1 -1 272.09 160.67 289.53 196.00 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n150 76 Pedestrian -1 -1 -1 401.88 168.55 412.81 196.14 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n150 81 Pedestrian -1 -1 -1 609.83 167.08 633.27 230.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n151 3 Car -1 -1 -1 1116.71 188.62 1220.87 225.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n151 11 Car -1 -1 -1 930.04 184.77 1010.32 220.69 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n151 7 Car -1 -1 -1 982.25 185.15 1069.64 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n151 4 Car -1 -1 -1 876.96 181.84 947.97 220.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n151 64 Pedestrian -1 -1 -1 524.95 173.05 542.38 214.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n151 66 Pedestrian -1 -1 -1 905.21 162.24 942.79 259.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n151 79 Pedestrian -1 -1 -1 799.55 166.16 826.53 239.95 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n151 32 Pedestrian -1 -1 -1 580.92 165.05 609.22 237.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n151 75 Cyclist -1 -1 -1 302.94 147.35 419.68 349.48 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n151 8 Car -1 -1 -1 607.84 175.43 633.79 200.42 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n151 70 Pedestrian -1 -1 -1 600.18 165.45 627.24 232.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n151 63 Pedestrian -1 -1 -1 386.55 166.07 399.34 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n151 9 Pedestrian -1 -1 -1 271.95 160.53 289.42 195.83 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n151 81 Pedestrian -1 -1 -1 614.72 168.16 643.45 229.22 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n151 60 Pedestrian -1 -1 -1 628.90 169.52 660.65 236.45 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n151 76 Pedestrian -1 -1 -1 401.47 168.45 412.30 195.97 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n151 19 Pedestrian -1 -1 -1 514.13 171.11 529.45 213.17 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n152 3 Car -1 -1 -1 1116.69 188.51 1220.92 225.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n152 7 Car -1 -1 -1 981.97 185.14 1069.91 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n152 11 Car -1 -1 -1 930.47 184.94 1009.76 220.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n152 79 Pedestrian -1 -1 -1 793.18 166.19 824.05 239.49 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n152 4 Car -1 -1 -1 878.78 182.57 946.43 219.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n152 66 Pedestrian -1 -1 -1 918.93 160.83 952.81 261.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n152 8 Car -1 -1 -1 607.02 174.99 634.66 200.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n152 75 Cyclist -1 -1 -1 324.10 151.38 430.40 337.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n152 64 Pedestrian -1 -1 -1 527.69 173.00 545.39 214.98 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n152 32 Pedestrian -1 -1 -1 589.10 164.03 615.62 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n152 60 Pedestrian -1 -1 -1 634.07 170.99 661.37 239.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n152 70 Pedestrian -1 -1 -1 604.55 165.82 631.02 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n152 9 Pedestrian -1 -1 -1 272.65 160.47 289.17 195.97 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n152 63 Pedestrian -1 -1 -1 386.65 166.68 399.10 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n152 76 Pedestrian -1 -1 -1 401.20 168.82 412.30 196.06 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n152 81 Pedestrian -1 -1 -1 619.89 168.43 646.37 229.92 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n152 19 Pedestrian -1 -1 -1 515.03 172.01 531.40 212.20 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n152 82 Pedestrian -1 -1 -1 575.69 168.36 599.08 229.79 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n152 83 Pedestrian -1 -1 -1 489.91 173.08 501.38 200.73 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n152 84 Cyclist -1 -1 -1 515.03 172.01 531.40 212.20 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n153 3 Car -1 -1 -1 1116.24 188.49 1221.01 225.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n153 7 Car -1 -1 -1 981.80 185.08 1070.25 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n153 66 Pedestrian -1 -1 -1 922.93 159.20 976.74 262.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n153 79 Pedestrian -1 -1 -1 783.61 166.53 819.73 239.10 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n153 11 Car -1 -1 -1 930.00 184.47 1009.67 220.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n153 4 Car -1 -1 -1 877.64 182.52 947.51 219.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n153 75 Cyclist -1 -1 -1 345.21 149.59 445.72 324.92 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n153 64 Pedestrian -1 -1 -1 529.51 172.32 546.17 214.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n153 32 Pedestrian -1 -1 -1 592.47 165.29 620.13 232.35 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n153 9 Pedestrian -1 -1 -1 272.95 160.48 289.08 195.88 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n153 82 Pedestrian -1 -1 -1 562.59 166.92 588.71 223.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n153 60 Pedestrian -1 -1 -1 639.65 171.21 663.43 239.27 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n153 70 Pedestrian -1 -1 -1 606.86 166.16 636.40 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n153 19 Pedestrian -1 -1 -1 517.71 171.03 533.98 212.49 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n153 63 Pedestrian -1 -1 -1 386.34 166.68 399.36 198.90 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n153 81 Pedestrian -1 -1 -1 626.64 167.68 654.38 234.95 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n153 85 Pedestrian -1 -1 -1 576.24 169.43 597.76 229.04 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n154 3 Car -1 -1 -1 1116.65 188.56 1221.27 225.72 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n154 7 Car -1 -1 -1 982.03 185.07 1070.04 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n154 11 Car -1 -1 -1 929.47 184.26 1010.86 220.39 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n154 66 Pedestrian -1 -1 -1 925.85 163.98 983.63 261.89 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n154 4 Car -1 -1 -1 876.40 182.45 947.24 219.23 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n154 82 Pedestrian -1 -1 -1 558.36 167.28 586.53 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n154 79 Pedestrian -1 -1 -1 777.37 167.03 811.26 238.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n154 75 Cyclist -1 -1 -1 365.91 151.89 455.49 314.19 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n154 32 Pedestrian -1 -1 -1 593.84 164.58 626.28 233.91 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n154 9 Pedestrian -1 -1 -1 272.97 160.45 288.95 195.95 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n154 60 Pedestrian -1 -1 -1 642.66 170.97 668.80 240.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n154 63 Pedestrian -1 -1 -1 386.84 166.57 398.28 199.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n154 64 Pedestrian -1 -1 -1 531.81 172.09 549.86 214.84 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n154 70 Pedestrian -1 -1 -1 610.94 167.04 640.10 235.47 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n154 81 Pedestrian -1 -1 -1 630.53 167.97 658.06 234.27 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n154 19 Pedestrian -1 -1 -1 521.92 170.42 535.53 212.87 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n154 86 Cyclist -1 -1 -1 351.95 162.86 455.24 325.52 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n154 87 Cyclist -1 -1 -1 521.92 170.42 535.53 212.87 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n155 3 Car -1 -1 -1 1116.49 188.54 1221.30 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n155 7 Car -1 -1 -1 982.24 185.22 1069.88 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n155 11 Car -1 -1 -1 929.53 184.37 1010.81 219.96 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n155 66 Pedestrian -1 -1 -1 940.13 164.79 990.16 262.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n155 82 Pedestrian -1 -1 -1 556.62 167.02 580.64 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n155 4 Car -1 -1 -1 876.07 182.62 947.26 218.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n155 79 Pedestrian -1 -1 -1 776.53 165.21 804.18 238.07 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n155 75 Cyclist -1 -1 -1 380.37 151.55 463.78 307.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n155 9 Pedestrian -1 -1 -1 273.07 160.28 289.10 196.12 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n155 32 Pedestrian -1 -1 -1 598.45 165.00 629.68 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n155 63 Pedestrian -1 -1 -1 386.77 166.27 399.36 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n155 19 Pedestrian -1 -1 -1 522.88 170.27 536.94 213.06 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n155 60 Pedestrian -1 -1 -1 644.79 171.95 675.27 239.32 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n155 64 Pedestrian -1 -1 -1 533.01 172.34 550.89 214.11 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n155 70 Pedestrian -1 -1 -1 615.37 166.09 643.95 236.47 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n155 81 Pedestrian -1 -1 -1 638.76 166.80 664.60 232.46 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n155 87 Cyclist -1 -1 -1 522.88 170.27 536.94 213.06 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n156 3 Car -1 -1 -1 1116.24 188.62 1221.41 225.71 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n156 66 Pedestrian -1 -1 -1 953.59 161.87 993.28 264.86 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n156 7 Car -1 -1 -1 982.50 185.31 1069.46 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n156 11 Car -1 -1 -1 929.48 183.66 1010.21 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n156 4 Car -1 -1 -1 875.93 182.72 947.01 218.79 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n156 79 Pedestrian -1 -1 -1 772.89 165.44 797.82 237.20 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n156 82 Pedestrian -1 -1 -1 553.66 165.35 573.96 221.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n156 75 Cyclist -1 -1 -1 395.83 151.73 471.89 299.45 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n156 63 Pedestrian -1 -1 -1 386.27 165.61 399.39 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n156 9 Pedestrian -1 -1 -1 272.84 160.37 289.02 196.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n156 19 Pedestrian -1 -1 -1 524.32 170.61 540.61 212.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n156 32 Pedestrian -1 -1 -1 603.05 165.50 632.26 239.08 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n156 60 Pedestrian -1 -1 -1 648.53 171.71 679.72 239.99 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n156 70 Pedestrian -1 -1 -1 622.62 165.98 651.26 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n156 64 Pedestrian -1 -1 -1 536.08 171.76 552.49 212.43 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n156 81 Pedestrian -1 -1 -1 641.49 167.64 669.47 231.15 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n157 3 Car -1 -1 -1 1116.47 188.61 1221.35 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n157 7 Car -1 -1 -1 982.11 185.30 1069.80 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n157 11 Car -1 -1 -1 929.28 184.42 1010.88 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n157 4 Car -1 -1 -1 875.89 183.03 946.80 218.52 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n157 79 Pedestrian -1 -1 -1 762.84 166.09 793.73 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n157 75 Cyclist -1 -1 -1 408.15 153.18 476.59 290.94 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n157 19 Pedestrian -1 -1 -1 524.75 170.88 540.96 212.07 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n157 32 Pedestrian -1 -1 -1 607.49 165.19 635.92 239.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n157 63 Pedestrian -1 -1 -1 386.13 165.82 399.56 200.36 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n157 82 Pedestrian -1 -1 -1 549.25 165.41 570.78 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n157 9 Pedestrian -1 -1 -1 272.88 160.50 288.98 195.98 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n157 66 Pedestrian -1 -1 -1 970.16 169.90 1005.70 266.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n157 64 Pedestrian -1 -1 -1 538.09 171.15 553.58 212.37 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n157 70 Pedestrian -1 -1 -1 625.04 166.33 656.25 236.69 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n157 81 Pedestrian -1 -1 -1 640.98 167.54 671.54 234.81 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n157 60 Pedestrian -1 -1 -1 655.34 170.54 685.64 241.02 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n158 3 Car -1 -1 -1 1117.24 188.69 1221.06 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n158 7 Car -1 -1 -1 978.33 185.10 1069.65 221.02 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n158 66 Pedestrian -1 -1 -1 972.85 163.17 1020.75 265.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n158 11 Car -1 -1 -1 929.98 184.58 1010.50 220.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n158 4 Car -1 -1 -1 875.75 183.12 946.67 218.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n158 32 Pedestrian -1 -1 -1 610.50 166.74 641.18 238.35 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n158 82 Pedestrian -1 -1 -1 543.72 165.69 568.03 220.66 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n158 63 Pedestrian -1 -1 -1 385.80 166.27 399.52 200.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n158 75 Cyclist -1 -1 -1 416.65 155.77 490.25 287.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n158 79 Pedestrian -1 -1 -1 754.56 166.09 787.59 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n158 9 Pedestrian -1 -1 -1 272.81 160.63 288.88 195.85 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n158 8 Car -1 -1 -1 606.44 175.30 630.55 201.42 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n158 19 Pedestrian -1 -1 -1 526.77 170.53 542.60 211.82 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n158 60 Pedestrian -1 -1 -1 660.70 169.77 688.77 243.01 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n158 70 Pedestrian -1 -1 -1 599.88 166.36 635.44 230.51 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n158 81 Pedestrian -1 -1 -1 634.09 165.73 662.24 236.65 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n158 88 Pedestrian -1 -1 -1 398.54 168.82 410.28 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n158 89 Pedestrian -1 -1 -1 645.52 166.64 674.42 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n159 3 Car -1 -1 -1 1117.25 188.78 1221.15 225.47 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n159 7 Car -1 -1 -1 977.73 185.16 1070.48 220.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n159 82 Pedestrian -1 -1 -1 540.46 167.12 565.29 220.44 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n159 66 Pedestrian -1 -1 -1 974.66 163.61 1034.31 269.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n159 19 Pedestrian -1 -1 -1 529.94 169.27 545.35 212.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n159 11 Car -1 -1 -1 929.92 184.37 1010.10 220.56 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n159 75 Cyclist -1 -1 -1 428.95 156.30 494.13 280.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n159 4 Car -1 -1 -1 873.36 183.68 945.73 217.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n159 32 Pedestrian -1 -1 -1 614.76 167.74 644.23 237.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n159 63 Pedestrian -1 -1 -1 385.92 166.41 399.50 200.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n159 79 Pedestrian -1 -1 -1 752.62 164.55 782.40 234.79 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n159 60 Pedestrian -1 -1 -1 664.47 170.06 694.22 242.12 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n159 8 Car -1 -1 -1 606.27 175.34 630.85 200.86 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n159 9 Pedestrian -1 -1 -1 272.70 160.58 288.85 195.87 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n159 88 Pedestrian -1 -1 -1 397.95 167.66 410.47 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n159 70 Pedestrian -1 -1 -1 605.88 166.42 638.09 231.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n159 89 Pedestrian -1 -1 -1 648.76 166.79 678.23 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n159 81 Pedestrian -1 -1 -1 634.75 167.40 662.05 235.35 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n160 3 Car -1 -1 -1 1117.13 188.84 1220.98 225.45 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n160 7 Car -1 -1 -1 982.17 185.10 1070.25 221.00 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n160 66 Pedestrian -1 -1 -1 987.07 165.10 1037.06 268.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n160 11 Car -1 -1 -1 929.92 184.24 1010.63 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n160 4 Car -1 -1 -1 875.55 183.24 946.77 218.28 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n160 75 Cyclist -1 -1 -1 436.91 160.12 501.86 276.31 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n160 63 Pedestrian -1 -1 -1 385.59 165.79 399.85 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n160 32 Pedestrian -1 -1 -1 618.15 167.19 648.74 238.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n160 9 Pedestrian -1 -1 -1 272.60 160.61 288.80 195.93 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n160 79 Pedestrian -1 -1 -1 750.76 164.02 776.25 237.63 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n160 82 Pedestrian -1 -1 -1 539.83 168.24 563.70 220.05 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n160 60 Pedestrian -1 -1 -1 666.01 169.76 700.37 242.03 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n160 8 Car -1 -1 -1 607.66 175.31 634.22 201.12 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n160 81 Pedestrian -1 -1 -1 640.43 166.16 671.07 236.76 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n160 88 Pedestrian -1 -1 -1 397.24 168.08 410.29 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n160 19 Pedestrian -1 -1 -1 532.69 169.79 549.13 212.71 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n160 89 Pedestrian -1 -1 -1 657.29 167.25 685.26 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n160 90 Cyclist -1 -1 -1 537.43 167.71 562.21 219.97 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n160 91 Car -1 -1 -1 617.37 167.50 648.23 223.04 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n160 92 Cyclist -1 -1 -1 532.69 169.79 549.13 212.71 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n161 3 Car -1 -1 -1 1117.24 188.66 1220.88 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n161 7 Car -1 -1 -1 982.17 185.18 1070.81 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n161 66 Pedestrian -1 -1 -1 1000.31 166.85 1039.34 267.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n161 11 Car -1 -1 -1 930.11 184.20 1010.38 220.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n161 4 Car -1 -1 -1 875.53 183.37 946.74 218.17 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n161 79 Pedestrian -1 -1 -1 740.48 164.21 771.51 234.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n161 75 Cyclist -1 -1 -1 443.96 161.90 509.46 273.48 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n161 63 Pedestrian -1 -1 -1 385.26 165.86 399.89 200.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n161 9 Pedestrian -1 -1 -1 272.70 160.52 288.72 195.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n161 32 Pedestrian -1 -1 -1 627.24 165.92 654.39 239.21 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n161 90 Cyclist -1 -1 -1 534.29 168.51 557.06 219.34 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n161 81 Pedestrian -1 -1 -1 644.23 166.33 674.94 237.12 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n161 60 Pedestrian -1 -1 -1 670.78 169.47 708.78 243.02 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n161 88 Pedestrian -1 -1 -1 396.97 168.31 409.93 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n161 8 Car -1 -1 -1 606.88 176.56 630.61 199.47 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n161 82 Pedestrian -1 -1 -1 542.33 172.29 563.70 215.19 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n161 19 Pedestrian -1 -1 -1 534.29 168.51 557.06 219.34 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n162 3 Car -1 -1 -1 1117.95 188.69 1220.19 225.39 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n162 7 Car -1 -1 -1 983.95 184.78 1069.18 220.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n162 66 Pedestrian -1 -1 -1 1013.41 161.14 1056.23 272.30 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n162 11 Car -1 -1 -1 930.13 184.20 1010.75 220.60 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n162 4 Car -1 -1 -1 875.52 183.37 946.66 218.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n162 75 Cyclist -1 -1 -1 452.70 162.48 515.72 266.36 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n162 79 Pedestrian -1 -1 -1 735.78 164.23 768.70 234.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n162 63 Pedestrian -1 -1 -1 384.45 166.16 400.51 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n162 32 Pedestrian -1 -1 -1 631.14 165.29 658.27 240.13 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n162 9 Pedestrian -1 -1 -1 272.56 160.43 288.76 196.01 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n162 60 Pedestrian -1 -1 -1 676.39 170.10 710.94 243.07 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n162 82 Pedestrian -1 -1 -1 548.10 172.03 564.18 211.78 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n162 81 Pedestrian -1 -1 -1 621.71 165.37 645.11 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n162 8 Car -1 -1 -1 607.43 176.21 633.63 200.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n162 19 Pedestrian -1 -1 -1 528.89 167.18 554.80 219.71 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n162 90 Cyclist -1 -1 -1 529.82 166.45 554.15 217.78 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n162 93 Pedestrian -1 -1 -1 647.40 166.72 679.34 237.87 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n162 94 Car -1 -1 -1 623.08 166.96 657.78 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n163 3 Car -1 -1 -1 1117.37 188.68 1220.33 225.33 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n163 66 Pedestrian -1 -1 -1 1019.71 160.97 1079.55 272.21 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n163 7 Car -1 -1 -1 984.24 184.33 1069.21 221.73 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n163 11 Car -1 -1 -1 930.30 184.28 1010.63 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n163 4 Car -1 -1 -1 875.55 183.37 946.73 218.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n163 79 Pedestrian -1 -1 -1 731.67 164.79 765.22 234.56 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n163 75 Cyclist -1 -1 -1 459.83 162.91 521.49 265.21 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n163 8 Car -1 -1 -1 607.27 175.88 633.70 200.46 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n163 9 Pedestrian -1 -1 -1 272.51 160.44 288.76 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n163 63 Pedestrian -1 -1 -1 383.97 166.40 401.25 200.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n163 81 Pedestrian -1 -1 -1 623.32 165.23 650.90 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n163 60 Pedestrian -1 -1 -1 683.18 169.31 712.22 244.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n163 19 Pedestrian -1 -1 -1 524.06 167.05 552.41 219.69 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n163 32 Pedestrian -1 -1 -1 634.31 167.06 662.67 237.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n163 82 Pedestrian -1 -1 -1 548.39 171.80 565.59 211.72 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n163 93 Pedestrian -1 -1 -1 651.71 166.50 683.54 238.76 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n163 90 Cyclist -1 -1 -1 526.67 166.80 554.11 220.05 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n163 95 Pedestrian -1 -1 -1 391.58 167.78 408.43 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n164 3 Car -1 -1 -1 1116.64 188.58 1220.46 225.31 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n164 7 Car -1 -1 -1 984.58 184.89 1068.38 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n164 66 Pedestrian -1 -1 -1 1025.21 160.63 1090.31 273.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n164 11 Car -1 -1 -1 930.57 184.50 1010.45 220.47 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n164 4 Car -1 -1 -1 875.94 183.45 946.62 218.09 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n164 8 Car -1 -1 -1 606.81 175.50 633.94 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n164 75 Cyclist -1 -1 -1 466.85 165.92 524.12 262.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n164 79 Pedestrian -1 -1 -1 731.98 165.22 757.23 234.40 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n164 9 Pedestrian -1 -1 -1 272.61 160.46 288.79 196.19 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n164 19 Pedestrian -1 -1 -1 523.27 166.89 549.92 219.90 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n164 82 Pedestrian -1 -1 -1 552.00 171.29 568.59 211.97 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n164 32 Pedestrian -1 -1 -1 636.56 166.07 667.81 238.97 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n164 81 Pedestrian -1 -1 -1 627.02 163.94 654.98 234.19 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n164 63 Pedestrian -1 -1 -1 385.67 166.41 400.36 201.32 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n164 60 Pedestrian -1 -1 -1 687.56 168.95 716.04 244.62 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n164 93 Pedestrian -1 -1 -1 660.12 165.53 690.91 239.14 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n164 95 Pedestrian -1 -1 -1 392.36 167.67 407.82 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n164 96 Pedestrian -1 -1 -1 540.83 170.18 556.49 211.79 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n165 3 Car -1 -1 -1 1116.70 188.69 1220.14 225.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n165 66 Pedestrian -1 -1 -1 1033.78 161.23 1096.60 274.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n165 7 Car -1 -1 -1 984.14 185.10 1067.97 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n165 11 Car -1 -1 -1 930.56 184.59 1010.22 220.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n165 4 Car -1 -1 -1 875.80 183.41 946.65 218.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n165 8 Car -1 -1 -1 606.87 175.29 633.64 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n165 96 Pedestrian -1 -1 -1 544.17 170.01 560.24 212.26 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n165 9 Pedestrian -1 -1 -1 272.58 160.49 288.66 196.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n165 82 Pedestrian -1 -1 -1 551.67 172.00 569.81 211.09 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n165 79 Pedestrian -1 -1 -1 725.37 166.03 748.15 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n165 63 Pedestrian -1 -1 -1 385.34 166.13 400.70 201.17 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n165 32 Pedestrian -1 -1 -1 640.68 165.86 671.50 239.25 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n165 75 Cyclist -1 -1 -1 472.48 168.70 527.19 259.67 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n165 19 Pedestrian -1 -1 -1 521.37 166.27 544.76 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n165 93 Pedestrian -1 -1 -1 666.58 166.31 699.14 238.89 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n165 60 Pedestrian -1 -1 -1 691.84 169.42 720.35 244.56 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n166 3 Car -1 -1 -1 1116.68 188.59 1220.55 225.45 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n166 7 Car -1 -1 -1 983.69 184.94 1068.63 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n166 66 Pedestrian -1 -1 -1 1049.10 161.58 1097.29 274.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n166 11 Car -1 -1 -1 930.52 184.69 1010.20 220.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n166 4 Car -1 -1 -1 875.75 183.44 946.71 218.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n166 8 Car -1 -1 -1 606.66 175.15 633.76 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n166 63 Pedestrian -1 -1 -1 385.19 166.06 400.49 201.17 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n166 96 Pedestrian -1 -1 -1 545.46 170.20 561.34 212.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n166 9 Pedestrian -1 -1 -1 272.48 160.50 288.75 196.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n166 32 Pedestrian -1 -1 -1 645.20 164.83 674.90 240.43 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n166 79 Pedestrian -1 -1 -1 714.97 165.46 743.53 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n166 75 Cyclist -1 -1 -1 479.21 172.01 532.54 254.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n166 93 Pedestrian -1 -1 -1 670.37 164.73 702.63 240.72 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n166 82 Pedestrian -1 -1 -1 556.16 171.76 572.61 211.10 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n166 60 Pedestrian -1 -1 -1 693.27 169.80 726.62 243.93 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n166 19 Pedestrian -1 -1 -1 522.95 166.17 541.92 217.88 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n167 7 Car -1 -1 -1 983.36 184.93 1069.03 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n167 3 Car -1 -1 -1 1116.39 188.71 1220.56 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n167 11 Car -1 -1 -1 930.46 184.74 1010.14 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n167 66 Pedestrian -1 -1 -1 1068.04 159.94 1109.03 276.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n167 4 Car -1 -1 -1 875.70 183.40 946.65 218.23 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n167 8 Car -1 -1 -1 606.71 175.20 633.64 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n167 96 Pedestrian -1 -1 -1 547.64 170.76 563.90 211.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n167 75 Cyclist -1 -1 -1 488.26 170.02 533.55 251.98 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n167 63 Pedestrian -1 -1 -1 385.09 166.12 400.37 201.47 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n167 9 Pedestrian -1 -1 -1 272.62 160.43 288.82 196.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n167 32 Pedestrian -1 -1 -1 650.02 166.54 677.67 239.35 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n167 93 Pedestrian -1 -1 -1 675.11 164.51 706.07 240.83 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n167 82 Pedestrian -1 -1 -1 556.09 172.05 573.08 210.24 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n167 60 Pedestrian -1 -1 -1 697.73 173.52 735.78 244.35 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n167 79 Pedestrian -1 -1 -1 700.85 168.91 741.69 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n167 97 Cyclist -1 -1 -1 516.49 167.62 537.67 216.48 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n168 7 Car -1 -1 -1 983.14 184.88 1069.71 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n168 11 Car -1 -1 -1 930.16 184.53 1010.11 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n168 3 Car -1 -1 -1 1116.54 188.93 1220.78 225.38 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n168 66 Pedestrian -1 -1 -1 1073.45 160.19 1134.00 276.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n168 4 Car -1 -1 -1 875.69 183.39 946.73 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n168 96 Pedestrian -1 -1 -1 548.18 170.18 565.37 211.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n168 8 Car -1 -1 -1 606.26 175.09 634.16 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n168 75 Cyclist -1 -1 -1 491.73 171.62 537.94 249.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n168 9 Pedestrian -1 -1 -1 272.70 160.40 288.79 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n168 63 Pedestrian -1 -1 -1 385.22 166.27 400.66 201.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n168 82 Pedestrian -1 -1 -1 560.61 172.03 575.60 210.11 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n168 32 Pedestrian -1 -1 -1 657.06 166.84 685.53 243.90 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n168 93 Pedestrian -1 -1 -1 641.75 163.38 671.08 234.43 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n168 97 Cyclist -1 -1 -1 515.01 168.02 536.01 216.02 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n168 79 Pedestrian -1 -1 -1 705.61 166.08 736.51 232.02 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n168 60 Pedestrian -1 -1 -1 679.67 164.72 709.68 240.50 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n168 98 Pedestrian -1 -1 -1 704.75 172.63 736.42 245.06 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n169 7 Car -1 -1 -1 983.53 185.01 1069.68 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n169 11 Car -1 -1 -1 930.30 184.56 1010.13 220.60 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n169 3 Car -1 -1 -1 1116.46 188.95 1221.16 225.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n169 66 Pedestrian -1 -1 -1 1075.92 160.61 1146.51 280.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n169 4 Car -1 -1 -1 875.72 183.48 946.64 218.09 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n169 96 Pedestrian -1 -1 -1 551.29 170.02 568.34 210.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n169 8 Car -1 -1 -1 606.59 175.29 633.93 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n169 82 Pedestrian -1 -1 -1 563.93 170.07 579.91 210.44 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n169 75 Cyclist -1 -1 -1 497.34 173.84 538.47 246.24 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n169 63 Pedestrian -1 -1 -1 385.09 165.84 400.87 202.14 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n169 9 Pedestrian -1 -1 -1 272.81 160.43 288.62 196.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n169 93 Pedestrian -1 -1 -1 647.10 162.55 679.69 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n169 32 Pedestrian -1 -1 -1 660.45 167.15 690.23 243.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n169 97 Cyclist -1 -1 -1 511.26 168.99 534.19 213.70 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n169 98 Pedestrian -1 -1 -1 714.47 171.78 741.48 246.32 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n169 60 Pedestrian -1 -1 -1 681.96 165.80 714.54 239.33 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n169 79 Pedestrian -1 -1 -1 697.22 165.21 729.76 238.29 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n170 7 Car -1 -1 -1 983.62 185.19 1069.23 220.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n170 11 Car -1 -1 -1 930.40 184.61 1009.94 220.57 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n170 66 Pedestrian -1 -1 -1 1086.07 159.97 1152.01 282.46 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n170 3 Car -1 -1 -1 1116.95 188.99 1220.85 225.32 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n170 4 Car -1 -1 -1 875.85 183.57 946.86 218.01 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n170 96 Pedestrian -1 -1 -1 553.72 169.30 568.77 211.77 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n170 8 Car -1 -1 -1 606.54 175.43 634.03 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n170 82 Pedestrian -1 -1 -1 565.14 170.45 580.36 210.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n170 9 Pedestrian -1 -1 -1 272.81 160.48 288.70 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n170 98 Pedestrian -1 -1 -1 717.94 170.48 746.88 247.35 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n170 63 Pedestrian -1 -1 -1 384.85 165.66 401.35 202.08 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n170 32 Pedestrian -1 -1 -1 666.19 164.40 699.10 246.91 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n170 60 Pedestrian -1 -1 -1 689.01 165.65 722.58 238.30 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n170 97 Cyclist -1 -1 -1 507.61 170.44 529.47 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n170 93 Pedestrian -1 -1 -1 651.83 162.62 683.21 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n170 75 Cyclist -1 -1 -1 501.29 173.49 541.06 244.89 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n170 99 Cyclist -1 -1 -1 689.01 165.65 722.58 238.30 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n171 7 Car -1 -1 -1 983.25 185.29 1069.47 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n171 66 Pedestrian -1 -1 -1 1100.04 160.49 1153.23 281.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n171 11 Car -1 -1 -1 930.16 184.54 1010.16 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n171 3 Car -1 -1 -1 1116.96 188.73 1221.11 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n171 4 Car -1 -1 -1 875.78 183.59 946.84 217.94 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n171 8 Car -1 -1 -1 606.55 175.45 634.12 200.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n171 96 Pedestrian -1 -1 -1 556.51 169.57 570.93 211.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n171 82 Pedestrian -1 -1 -1 567.61 170.73 582.84 210.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n171 98 Pedestrian -1 -1 -1 718.36 170.83 755.26 247.19 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n171 32 Pedestrian -1 -1 -1 669.01 163.93 703.89 247.55 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n171 9 Pedestrian -1 -1 -1 272.87 160.44 288.75 196.33 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n171 63 Pedestrian -1 -1 -1 387.03 166.59 403.45 201.52 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n171 60 Pedestrian -1 -1 -1 692.90 164.14 726.11 239.48 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n171 75 Cyclist -1 -1 -1 505.13 172.75 544.64 241.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n171 93 Pedestrian -1 -1 -1 660.20 160.86 690.42 236.89 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n171 97 Cyclist -1 -1 -1 508.64 170.00 528.82 210.30 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n172 7 Car -1 -1 -1 982.92 185.40 1069.68 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n172 11 Car -1 -1 -1 930.20 184.53 1010.10 220.71 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n172 66 Pedestrian -1 -1 -1 1122.83 161.36 1168.35 282.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n172 98 Pedestrian -1 -1 -1 720.03 170.90 761.11 247.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n172 8 Car -1 -1 -1 606.58 175.40 634.32 200.18 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n172 4 Car -1 -1 -1 875.55 183.55 946.95 217.99 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n172 3 Car -1 -1 -1 1116.80 188.69 1221.16 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n172 82 Pedestrian -1 -1 -1 567.49 171.36 584.26 210.23 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n172 96 Pedestrian -1 -1 -1 557.72 169.75 571.98 210.99 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n172 9 Pedestrian -1 -1 -1 272.89 160.45 288.71 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n172 75 Cyclist -1 -1 -1 505.46 173.05 545.21 239.92 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n172 32 Pedestrian -1 -1 -1 674.65 166.03 706.36 245.59 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n172 60 Pedestrian -1 -1 -1 692.48 163.53 726.87 240.55 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n172 97 Cyclist -1 -1 -1 505.63 166.74 529.98 215.06 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n172 63 Pedestrian -1 -1 -1 387.19 167.06 403.09 201.79 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n172 93 Pedestrian -1 -1 -1 663.74 160.13 694.99 237.20 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n173 7 Car -1 -1 -1 982.68 185.40 1069.86 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n173 11 Car -1 -1 -1 930.18 184.49 1010.13 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n173 66 Pedestrian -1 -1 -1 1129.04 161.44 1192.60 282.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n173 4 Car -1 -1 -1 875.51 183.51 947.19 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n173 8 Car -1 -1 -1 606.57 175.32 634.28 200.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n173 98 Pedestrian -1 -1 -1 729.41 170.73 765.15 248.63 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n173 3 Car -1 -1 -1 1115.91 188.49 1221.85 225.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n173 96 Pedestrian -1 -1 -1 559.21 169.98 576.14 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n173 9 Pedestrian -1 -1 -1 272.86 160.37 288.75 196.23 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n173 82 Pedestrian -1 -1 -1 570.83 171.61 587.32 210.60 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n173 75 Cyclist -1 -1 -1 513.24 173.85 546.01 238.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n173 63 Pedestrian -1 -1 -1 387.30 167.09 403.63 201.83 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n173 32 Pedestrian -1 -1 -1 678.79 166.06 710.25 246.76 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n173 60 Pedestrian -1 -1 -1 701.78 162.61 733.15 241.89 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n173 97 Cyclist -1 -1 -1 504.14 167.78 525.74 216.05 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n173 100 Cyclist -1 -1 -1 559.21 169.98 576.14 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n174 7 Car -1 -1 -1 982.89 185.46 1069.32 220.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n174 11 Car -1 -1 -1 930.49 184.42 1009.91 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n174 8 Car -1 -1 -1 606.48 175.14 634.45 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n174 4 Car -1 -1 -1 875.57 183.58 947.08 217.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n174 96 Pedestrian -1 -1 -1 560.73 169.58 576.23 210.27 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n174 3 Car 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Car -1 -1 -1 982.95 185.38 1069.27 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n175 11 Car -1 -1 -1 930.54 184.45 1010.08 220.79 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n175 66 Pedestrian -1 -1 -1 1147.55 162.34 1211.77 287.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n175 8 Car -1 -1 -1 606.61 174.98 634.50 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n175 4 Car -1 -1 -1 875.84 183.62 946.84 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n175 3 Car -1 -1 -1 1117.93 188.71 1219.40 225.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n175 96 Pedestrian -1 -1 -1 563.70 169.86 578.57 210.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n175 9 Pedestrian -1 -1 -1 272.92 160.36 288.91 196.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n175 63 Pedestrian -1 -1 -1 387.00 166.51 403.39 202.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n175 98 Pedestrian -1 -1 -1 742.80 170.13 769.39 248.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n175 75 Cyclist -1 -1 -1 522.24 172.45 551.59 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n175 82 Pedestrian -1 -1 -1 574.29 172.19 591.04 209.38 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n175 32 Pedestrian -1 -1 -1 688.72 163.09 730.04 241.69 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n175 60 Pedestrian -1 -1 -1 670.77 162.83 702.58 240.11 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n175 101 Pedestrian -1 -1 -1 714.45 164.63 750.68 240.89 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n175 97 Cyclist -1 -1 -1 501.94 165.79 520.05 215.52 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n175 102 Pedestrian -1 -1 -1 501.94 165.79 520.05 215.52 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n176 7 Car -1 -1 -1 983.01 185.31 1069.16 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n176 11 Car -1 -1 -1 930.66 184.41 1009.89 220.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n176 8 Car -1 -1 -1 606.77 174.99 634.39 200.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n176 4 Car -1 -1 -1 875.94 183.72 946.86 217.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n176 3 Car -1 -1 -1 1118.71 188.66 1218.56 225.27 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n176 66 Pedestrian -1 -1 -1 1167.59 162.15 1214.44 287.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n176 98 Pedestrian -1 -1 -1 749.02 170.58 775.91 249.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n176 96 Pedestrian -1 -1 -1 564.11 169.09 580.74 210.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n176 9 Pedestrian -1 -1 -1 272.94 160.37 288.81 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n176 63 Pedestrian -1 -1 -1 386.98 166.65 403.03 201.72 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n176 32 Pedestrian -1 -1 -1 693.53 164.64 733.11 240.62 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n176 82 Pedestrian -1 -1 -1 575.29 172.69 591.69 208.90 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n176 75 Cyclist -1 -1 -1 524.36 172.50 551.75 232.21 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n176 102 Pedestrian -1 -1 -1 500.81 166.39 518.27 215.49 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n176 101 Pedestrian -1 -1 -1 723.97 163.81 755.90 241.41 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n176 103 Cyclist -1 -1 -1 662.20 167.18 696.64 235.44 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n176 104 Cyclist -1 -1 -1 575.29 172.69 591.69 208.90 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n176 105 Cyclist -1 -1 -1 565.95 169.78 583.41 209.90 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n177 7 Car -1 -1 -1 982.95 185.32 1069.20 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n177 11 Car -1 -1 -1 930.62 184.38 1009.97 220.70 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n177 3 Car -1 -1 -1 1118.56 188.67 1218.72 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n177 8 Car -1 -1 -1 606.98 175.27 634.29 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n177 4 Car -1 -1 -1 875.96 183.75 946.91 217.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n177 98 Pedestrian -1 -1 -1 749.07 169.75 783.89 250.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n177 66 Pedestrian -1 -1 -1 1189.26 161.55 1217.53 288.41 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n177 9 Pedestrian -1 -1 -1 273.06 160.34 288.80 196.27 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n177 63 Pedestrian -1 -1 -1 387.14 166.85 403.58 201.57 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n177 102 Pedestrian -1 -1 -1 499.86 166.66 515.65 215.71 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n177 96 Pedestrian -1 -1 -1 564.77 169.00 580.86 210.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n177 32 Pedestrian -1 -1 -1 701.40 165.59 740.13 245.98 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n177 75 Cyclist -1 -1 -1 528.16 169.87 554.23 228.59 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n177 101 Pedestrian -1 -1 -1 686.24 161.84 717.59 242.80 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n177 82 Pedestrian -1 -1 -1 575.73 172.35 592.42 208.87 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n177 104 Cyclist -1 -1 -1 575.73 172.35 592.42 208.87 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n177 106 Pedestrian -1 -1 -1 664.02 167.13 691.99 231.11 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n177 107 Pedestrian -1 -1 -1 728.51 163.07 759.16 242.32 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n178 11 Car -1 -1 -1 930.53 184.32 1009.86 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n178 7 Car -1 -1 -1 983.17 185.30 1068.98 220.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n178 3 Car -1 -1 -1 1118.69 188.75 1218.45 225.41 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n178 4 Car -1 -1 -1 876.04 183.73 946.90 217.78 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n178 98 Pedestrian -1 -1 -1 753.24 169.57 788.94 250.85 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n178 8 Car -1 -1 -1 606.86 175.20 634.03 200.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n178 102 Pedestrian -1 -1 -1 499.48 167.61 515.26 215.84 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n178 9 Pedestrian -1 -1 -1 273.13 160.24 288.67 196.28 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n178 63 Pedestrian -1 -1 -1 387.45 167.15 403.99 201.50 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n178 32 Pedestrian -1 -1 -1 709.01 163.02 740.80 247.76 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n178 96 Pedestrian -1 -1 -1 566.13 168.96 585.16 209.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n178 75 Cyclist -1 -1 -1 528.88 171.33 555.57 226.85 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n178 106 Pedestrian -1 -1 -1 659.57 166.87 681.94 230.18 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n178 104 Cyclist -1 -1 -1 578.72 172.07 595.40 209.65 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n178 101 Pedestrian -1 -1 -1 685.81 160.93 718.65 237.62 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n178 82 Pedestrian -1 -1 -1 578.72 172.07 595.40 209.65 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n178 107 Pedestrian -1 -1 -1 735.89 162.84 767.21 242.93 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n178 66 Pedestrian -1 -1 -1 1200.77 169.15 1219.22 287.94 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n179 7 Car -1 -1 -1 983.14 185.28 1069.10 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n179 11 Car -1 -1 -1 930.55 184.35 1009.94 220.70 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n179 3 Car -1 -1 -1 1118.41 188.87 1218.87 225.49 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n179 4 Car -1 -1 -1 876.12 183.78 946.80 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n179 106 Pedestrian -1 -1 -1 651.67 167.30 676.41 228.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n179 8 Car -1 -1 -1 606.96 175.23 634.09 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n179 102 Pedestrian -1 -1 -1 497.84 167.42 514.50 214.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n179 98 Pedestrian -1 -1 -1 758.78 168.33 790.50 252.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n179 75 Cyclist -1 -1 -1 530.91 170.54 558.67 226.21 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n179 9 Pedestrian -1 -1 -1 272.94 160.37 288.58 196.06 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n179 63 Pedestrian -1 -1 -1 387.29 166.97 404.66 202.14 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n179 96 Pedestrian -1 -1 -1 567.40 168.95 585.95 209.83 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n179 32 Pedestrian -1 -1 -1 716.02 162.95 749.26 247.85 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n179 101 Pedestrian -1 -1 -1 687.40 163.33 724.89 239.59 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n179 82 Pedestrian -1 -1 -1 581.78 170.96 598.34 209.65 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n179 107 Pedestrian -1 -1 -1 737.77 163.44 773.00 242.50 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n180 3 Car -1 -1 -1 1117.78 188.67 1219.53 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n180 7 Car -1 -1 -1 983.13 185.24 1068.99 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n180 11 Car -1 -1 -1 930.42 184.24 1010.22 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n180 106 Pedestrian -1 -1 -1 643.56 167.67 674.99 228.28 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n180 4 Car -1 -1 -1 876.34 183.74 946.64 217.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n180 8 Car -1 -1 -1 606.92 175.26 634.06 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n180 9 Pedestrian -1 -1 -1 273.02 160.38 288.53 196.10 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n180 98 Pedestrian -1 -1 -1 763.92 169.18 793.86 251.31 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n180 63 Pedestrian -1 -1 -1 387.14 166.28 404.76 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n180 32 Pedestrian -1 -1 -1 718.12 164.19 746.15 240.57 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n180 107 Pedestrian -1 -1 -1 722.95 162.96 757.75 247.81 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n180 102 Pedestrian -1 -1 -1 495.58 166.95 513.06 213.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n180 101 Pedestrian -1 -1 -1 692.91 163.78 727.03 239.22 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n180 75 Cyclist -1 -1 -1 534.08 168.74 557.88 222.26 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n180 96 Pedestrian -1 -1 -1 571.20 169.43 587.99 209.12 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n180 82 Pedestrian -1 -1 -1 583.35 170.92 599.73 209.10 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n180 108 Cyclist -1 -1 -1 583.35 170.92 599.73 209.10 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n180 109 Cyclist -1 -1 -1 571.72 167.56 587.82 209.00 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n181 3 Car -1 -1 -1 1117.88 188.61 1219.87 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n181 11 Car -1 -1 -1 930.40 184.27 1010.25 220.75 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n181 7 Car -1 -1 -1 983.20 185.22 1068.76 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n181 4 Car -1 -1 -1 876.40 183.75 946.65 217.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n181 106 Pedestrian -1 -1 -1 639.84 167.63 671.12 228.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n181 8 Car -1 -1 -1 606.81 175.19 634.06 199.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n181 102 Pedestrian -1 -1 -1 494.89 166.48 512.76 213.88 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n181 98 Pedestrian -1 -1 -1 769.40 169.57 802.17 251.29 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n181 75 Cyclist -1 -1 -1 536.08 171.25 562.39 225.34 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n181 9 Pedestrian -1 -1 -1 273.00 160.43 288.62 196.10 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n181 63 Pedestrian -1 -1 -1 387.65 166.48 404.35 204.54 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n181 32 Pedestrian -1 -1 -1 720.73 163.33 751.57 242.22 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n181 101 Pedestrian -1 -1 -1 700.97 162.37 733.40 240.35 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n181 96 Pedestrian -1 -1 -1 574.92 170.04 590.75 208.84 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n181 107 Pedestrian -1 -1 -1 736.75 162.47 774.09 248.20 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n181 82 Pedestrian -1 -1 -1 585.62 171.02 602.55 208.95 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n182 3 Car -1 -1 -1 1117.47 188.69 1220.07 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n182 7 Car -1 -1 -1 983.06 185.12 1069.06 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n182 11 Car -1 -1 -1 930.40 184.30 1010.48 220.80 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n182 4 Car -1 -1 -1 876.41 183.82 946.63 217.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n182 106 Pedestrian -1 -1 -1 639.40 166.64 664.11 228.99 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n182 8 Car -1 -1 -1 606.43 175.05 634.46 199.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n182 102 Pedestrian -1 -1 -1 494.33 166.75 512.04 213.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n182 63 Pedestrian -1 -1 -1 387.82 166.91 404.64 204.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n182 9 Pedestrian -1 -1 -1 273.03 160.39 288.64 196.18 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n182 98 Pedestrian -1 -1 -1 774.14 170.63 812.49 250.98 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n182 96 Pedestrian -1 -1 -1 576.40 170.63 591.09 208.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n182 75 Cyclist -1 -1 -1 538.81 171.51 564.41 224.63 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n182 32 Pedestrian -1 -1 -1 716.72 162.26 748.45 241.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n182 107 Pedestrian -1 -1 -1 749.56 162.55 784.36 249.02 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n182 82 Pedestrian -1 -1 -1 587.03 171.31 603.32 208.40 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n182 110 Cyclist -1 -1 -1 540.12 168.80 551.26 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n183 3 Car -1 -1 -1 1117.22 188.56 1220.60 225.77 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n183 7 Car -1 -1 -1 982.83 185.14 1069.30 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n183 11 Car -1 -1 -1 930.43 184.33 1010.42 220.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n183 4 Car -1 -1 -1 876.59 183.85 946.60 217.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n183 106 Pedestrian -1 -1 -1 635.81 167.54 654.86 227.44 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n183 63 Pedestrian -1 -1 -1 388.04 167.39 404.39 204.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n183 8 Car -1 -1 -1 606.07 175.05 634.38 199.50 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n183 9 Pedestrian -1 -1 -1 272.92 160.44 288.78 196.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n183 102 Pedestrian -1 -1 -1 493.29 167.22 510.24 213.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n183 32 Pedestrian -1 -1 -1 714.76 162.44 743.26 240.65 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n183 75 Cyclist -1 -1 -1 540.39 172.53 564.03 223.66 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n183 98 Pedestrian -1 -1 -1 779.21 170.03 822.33 254.88 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n183 107 Pedestrian -1 -1 -1 730.10 162.99 757.72 241.58 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n183 96 Pedestrian -1 -1 -1 576.68 171.11 592.06 209.16 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n183 110 Cyclist -1 -1 -1 542.13 167.52 555.62 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n183 111 Pedestrian -1 -1 -1 754.05 161.19 787.35 250.03 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n183 112 Cyclist -1 -1 -1 576.94 171.00 595.36 209.09 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n184 3 Car -1 -1 -1 1117.20 188.45 1220.67 225.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n184 7 Car -1 -1 -1 982.90 185.13 1069.27 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n184 11 Car -1 -1 -1 930.40 184.26 1010.34 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n184 4 Car -1 -1 -1 876.45 183.84 946.65 217.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n184 106 Pedestrian -1 -1 -1 627.61 167.97 653.27 226.66 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n184 102 Pedestrian -1 -1 -1 491.20 166.38 508.44 213.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n184 63 Pedestrian -1 -1 -1 388.16 166.85 404.70 204.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n184 9 Pedestrian -1 -1 -1 273.07 160.44 288.68 196.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n184 8 Car -1 -1 -1 605.87 175.21 634.42 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n184 32 Pedestrian -1 -1 -1 719.09 162.78 753.64 240.41 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n184 98 Pedestrian -1 -1 -1 780.49 169.76 822.69 251.76 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n184 75 Cyclist -1 -1 -1 541.01 169.55 565.98 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n184 111 Pedestrian -1 -1 -1 756.84 161.95 791.91 249.99 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n184 107 Pedestrian -1 -1 -1 732.54 163.18 763.18 241.46 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n184 112 Cyclist -1 -1 -1 577.65 170.66 595.56 209.37 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n184 96 Pedestrian -1 -1 -1 577.65 170.66 595.56 209.37 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n184 110 Cyclist -1 -1 -1 543.51 168.15 555.55 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n185 3 Car -1 -1 -1 1116.97 188.44 1220.74 225.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n185 7 Car -1 -1 -1 982.75 185.10 1069.40 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n185 11 Car -1 -1 -1 930.31 184.29 1010.29 220.76 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n185 4 Car -1 -1 -1 876.41 183.73 946.66 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n185 106 Pedestrian -1 -1 -1 624.53 168.24 650.26 226.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n185 102 Pedestrian -1 -1 -1 490.68 166.61 506.77 212.48 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n185 9 Pedestrian -1 -1 -1 273.18 160.37 288.85 196.14 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n185 63 Pedestrian -1 -1 -1 388.58 166.45 404.63 204.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n185 8 Car -1 -1 -1 604.28 174.94 634.05 200.59 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n185 98 Pedestrian -1 -1 -1 792.45 168.00 825.96 252.81 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n185 32 Pedestrian -1 -1 -1 721.28 162.03 758.67 241.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n185 111 Pedestrian -1 -1 -1 765.45 163.59 806.24 248.88 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n185 75 Cyclist -1 -1 -1 544.75 171.10 566.32 220.38 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n185 107 Pedestrian -1 -1 -1 738.18 163.14 772.56 242.04 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n185 96 Pedestrian -1 -1 -1 579.75 170.73 595.72 209.25 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n185 110 Cyclist -1 -1 -1 545.93 167.80 559.09 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n185 112 Cyclist -1 -1 -1 581.20 170.89 598.45 209.01 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n186 3 Car -1 -1 -1 1117.11 188.50 1220.60 225.77 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n186 7 Car -1 -1 -1 983.00 185.16 1069.05 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n186 11 Car -1 -1 -1 930.23 184.28 1010.41 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n186 4 Car -1 -1 -1 876.44 183.76 946.81 217.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n186 106 Pedestrian -1 -1 -1 620.04 169.33 645.98 226.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n186 63 Pedestrian -1 -1 -1 388.17 166.88 404.73 205.35 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n186 102 Pedestrian -1 -1 -1 490.02 167.33 506.00 212.81 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n186 9 Pedestrian -1 -1 -1 273.25 160.49 288.87 196.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n186 8 Car -1 -1 -1 604.60 175.21 633.63 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n186 32 Pedestrian -1 -1 -1 728.80 162.63 759.20 241.09 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n186 98 Pedestrian -1 -1 -1 802.10 167.93 830.38 253.77 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n186 111 Pedestrian -1 -1 -1 769.17 164.96 810.49 248.08 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n186 96 Pedestrian -1 -1 -1 581.51 171.26 595.33 209.17 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n186 75 Cyclist -1 -1 -1 545.57 171.71 566.60 219.98 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n186 112 Cyclist -1 -1 -1 582.70 171.04 597.98 209.19 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n186 110 Cyclist -1 -1 -1 547.66 167.94 559.71 200.09 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n187 3 Car -1 -1 -1 1117.06 188.57 1220.56 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n187 7 Car -1 -1 -1 982.75 185.10 1069.30 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n187 11 Car -1 -1 -1 930.16 184.30 1010.16 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n187 4 Car -1 -1 -1 876.53 183.75 946.58 217.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n187 63 Pedestrian -1 -1 -1 388.09 167.03 404.71 205.96 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n187 106 Pedestrian -1 -1 -1 615.20 168.54 637.48 227.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n187 9 Pedestrian -1 -1 -1 273.14 160.49 288.91 196.13 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n187 102 Pedestrian -1 -1 -1 490.10 168.20 505.47 212.27 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n187 8 Car -1 -1 -1 605.80 175.42 634.40 200.29 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n187 98 Pedestrian -1 -1 -1 804.75 170.84 837.17 254.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n187 75 Cyclist -1 -1 -1 545.38 171.71 568.10 219.28 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n187 32 Pedestrian -1 -1 -1 737.59 161.61 765.63 241.85 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n187 111 Pedestrian -1 -1 -1 742.75 162.95 775.84 242.67 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n187 96 Pedestrian -1 -1 -1 583.49 170.86 599.01 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n187 112 Cyclist -1 -1 -1 583.49 170.86 599.01 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n187 110 Cyclist -1 -1 -1 548.10 166.97 565.04 206.46 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n187 113 Pedestrian -1 -1 -1 773.62 163.78 813.61 249.88 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n188 3 Car -1 -1 -1 1116.92 188.61 1220.43 225.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n188 7 Car -1 -1 -1 982.67 185.06 1069.44 221.21 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n188 11 Car -1 -1 -1 930.16 184.26 1010.25 220.88 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n188 4 Car -1 -1 -1 876.68 183.70 946.47 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n188 102 Pedestrian -1 -1 -1 488.17 168.70 504.35 212.75 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n188 32 Pedestrian -1 -1 -1 742.40 160.25 775.91 243.69 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n188 98 Pedestrian -1 -1 -1 808.21 171.50 847.24 254.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n188 8 Car -1 -1 -1 605.60 175.63 634.41 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n188 9 Pedestrian -1 -1 -1 273.08 160.47 288.94 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n188 106 Pedestrian -1 -1 -1 611.75 168.18 633.64 226.25 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n188 63 Pedestrian -1 -1 -1 388.60 166.93 405.40 205.59 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n188 75 Cyclist -1 -1 -1 546.42 170.31 568.13 218.56 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n188 113 Pedestrian -1 -1 -1 785.93 162.09 824.21 251.33 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n188 96 Pedestrian -1 -1 -1 583.40 170.75 599.89 208.48 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n189 3 Car -1 -1 -1 1116.50 188.67 1220.35 225.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n189 11 Car -1 -1 -1 930.13 184.30 1010.16 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n189 7 Car -1 -1 -1 982.52 185.05 1069.57 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n189 4 Car -1 -1 -1 876.56 183.70 946.50 217.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n189 102 Pedestrian -1 -1 -1 486.74 167.89 503.61 212.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n189 98 Pedestrian -1 -1 -1 811.27 171.04 852.25 256.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n189 8 Car -1 -1 -1 606.04 174.80 634.46 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n189 9 Pedestrian -1 -1 -1 273.10 160.54 288.82 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n189 106 Pedestrian -1 -1 -1 606.83 168.29 630.74 224.03 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n189 32 Pedestrian -1 -1 -1 741.66 160.49 777.25 243.39 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n189 63 Pedestrian -1 -1 -1 388.49 166.55 405.64 205.47 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n189 113 Pedestrian -1 -1 -1 796.43 162.49 836.80 250.91 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n189 75 Cyclist -1 -1 -1 548.89 166.52 569.30 215.17 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n189 96 Pedestrian -1 -1 -1 586.16 171.01 602.91 208.69 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n189 114 Cyclist -1 -1 -1 586.16 171.01 602.91 208.69 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n190 3 Car -1 -1 -1 1116.78 188.55 1220.62 225.74 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n190 11 Car -1 -1 -1 930.22 184.30 1010.10 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n190 7 Car -1 -1 -1 982.65 185.08 1069.42 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n190 4 Car -1 -1 -1 876.62 183.69 946.52 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n190 8 Car -1 -1 -1 606.30 174.54 634.62 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n190 102 Pedestrian -1 -1 -1 486.20 167.46 502.93 211.27 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n190 32 Pedestrian -1 -1 -1 744.19 162.27 781.54 242.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n190 106 Pedestrian -1 -1 -1 604.30 167.64 629.58 224.81 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n190 9 Pedestrian -1 -1 -1 273.00 160.37 288.92 196.20 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n190 113 Pedestrian -1 -1 -1 796.17 162.96 837.50 251.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n190 98 Pedestrian -1 -1 -1 817.65 170.94 854.08 256.04 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n190 63 Pedestrian -1 -1 -1 388.40 166.21 405.53 205.87 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n190 96 Pedestrian -1 -1 -1 586.95 171.18 602.90 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n190 75 Cyclist -1 -1 -1 548.93 166.39 569.42 214.40 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n191 3 Car -1 -1 -1 1117.08 188.52 1220.41 225.73 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n191 11 Car -1 -1 -1 930.19 184.27 1010.06 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n191 7 Car -1 -1 -1 982.62 185.12 1069.37 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n191 4 Car -1 -1 -1 876.62 183.66 946.59 217.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n191 106 Pedestrian -1 -1 -1 602.29 167.58 625.04 224.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n191 9 Pedestrian -1 -1 -1 273.04 160.38 288.91 196.27 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n191 63 Pedestrian -1 -1 -1 388.35 166.57 405.39 206.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n191 98 Pedestrian -1 -1 -1 827.17 170.49 859.53 257.35 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n191 8 Car -1 -1 -1 607.46 174.99 633.71 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n191 113 Pedestrian -1 -1 -1 801.88 162.77 846.61 250.57 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n191 32 Pedestrian -1 -1 -1 751.73 162.75 789.75 242.08 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n191 102 Pedestrian -1 -1 -1 484.07 167.63 501.81 211.58 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n191 96 Pedestrian -1 -1 -1 590.64 171.28 605.35 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n191 75 Cyclist -1 -1 -1 550.91 166.13 565.10 209.23 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n192 3 Car -1 -1 -1 1116.91 188.50 1220.45 225.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n192 11 Car -1 -1 -1 930.15 184.32 1010.01 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n192 7 Car -1 -1 -1 982.52 185.18 1069.45 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n192 4 Car -1 -1 -1 876.81 183.77 946.23 217.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n192 102 Pedestrian -1 -1 -1 482.81 168.51 500.52 212.12 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n192 98 Pedestrian -1 -1 -1 834.04 171.18 867.44 256.08 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n192 9 Pedestrian -1 -1 -1 273.12 160.37 288.93 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n192 113 Pedestrian -1 -1 -1 808.85 162.23 855.13 251.83 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n192 32 Pedestrian -1 -1 -1 765.30 163.80 799.14 241.32 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n192 8 Car -1 -1 -1 607.77 175.36 633.21 199.77 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n192 63 Pedestrian -1 -1 -1 388.41 166.99 405.61 206.79 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n192 106 Pedestrian -1 -1 -1 600.25 169.46 618.19 224.52 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n192 96 Pedestrian -1 -1 -1 592.29 170.81 606.47 208.40 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n192 75 Cyclist -1 -1 -1 551.82 166.21 566.81 209.11 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n192 115 Pedestrian -1 -1 -1 552.29 172.16 567.94 215.94 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n193 3 Car -1 -1 -1 1117.24 188.54 1220.38 225.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n193 11 Car -1 -1 -1 930.11 184.38 1009.98 220.79 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n193 7 Car -1 -1 -1 982.39 185.18 1069.53 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n193 4 Car -1 -1 -1 876.85 183.88 946.15 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n193 106 Pedestrian -1 -1 -1 595.43 170.32 615.65 223.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n193 98 Pedestrian -1 -1 -1 839.12 171.19 877.88 257.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n193 9 Pedestrian -1 -1 -1 273.05 160.40 288.81 196.23 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n193 63 Pedestrian -1 -1 -1 388.32 166.98 405.64 206.68 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n193 113 Pedestrian -1 -1 -1 822.09 159.84 864.46 253.76 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n193 32 Pedestrian -1 -1 -1 773.63 166.07 805.58 245.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n193 8 Car -1 -1 -1 608.60 175.08 632.82 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n193 102 Pedestrian -1 -1 -1 481.61 168.53 498.72 211.62 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n193 75 Cyclist -1 -1 -1 551.94 166.19 567.30 209.64 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n193 116 Pedestrian -1 -1 -1 -0.45 161.11 18.94 236.89 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n194 3 Car -1 -1 -1 1117.22 188.54 1220.68 225.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n194 11 Car -1 -1 -1 929.99 184.37 1010.23 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n194 7 Car -1 -1 -1 982.31 185.19 1069.64 221.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n194 4 Car -1 -1 -1 877.09 184.00 946.50 217.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n194 106 Pedestrian -1 -1 -1 590.77 170.60 614.10 223.53 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n194 63 Pedestrian -1 -1 -1 387.89 166.63 405.66 206.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n194 32 Pedestrian -1 -1 -1 776.35 162.03 810.86 243.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n194 98 Pedestrian -1 -1 -1 843.60 172.26 888.51 256.95 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n194 113 Pedestrian -1 -1 -1 826.17 160.29 876.42 251.99 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n194 9 Pedestrian -1 -1 -1 273.03 160.37 288.83 196.32 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n194 102 Pedestrian -1 -1 -1 479.17 167.95 496.92 211.56 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n194 116 Pedestrian -1 -1 -1 0.84 159.54 25.59 237.34 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n194 8 Car -1 -1 -1 609.08 174.56 632.16 200.27 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n194 75 Cyclist -1 -1 -1 552.34 166.77 567.33 209.15 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n195 3 Car -1 -1 -1 1116.75 188.69 1220.57 225.73 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n195 11 Car -1 -1 -1 930.14 184.40 1010.28 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n195 7 Car -1 -1 -1 982.43 185.22 1069.46 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n195 4 Car -1 -1 -1 877.65 183.99 946.81 218.06 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n195 106 Pedestrian -1 -1 -1 587.95 171.12 609.91 223.53 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n195 63 Pedestrian -1 -1 -1 388.12 166.39 405.45 207.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n195 32 Pedestrian -1 -1 -1 778.03 163.89 816.76 246.89 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n195 113 Pedestrian -1 -1 -1 832.78 161.14 884.37 251.76 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n195 116 Pedestrian -1 -1 -1 1.52 158.33 27.08 238.36 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n195 9 Pedestrian -1 -1 -1 272.99 160.49 288.85 196.30 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n195 102 Pedestrian -1 -1 -1 478.85 167.83 494.86 211.60 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n195 98 Pedestrian -1 -1 -1 847.91 173.51 892.06 259.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n195 8 Car -1 -1 -1 610.06 174.40 631.82 200.76 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n195 75 Cyclist -1 -1 -1 553.07 166.37 567.85 209.37 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n195 117 Cyclist -1 -1 -1 610.45 173.00 627.43 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n196 3 Car -1 -1 -1 1116.88 188.55 1220.46 225.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n196 11 Car -1 -1 -1 930.01 184.38 1010.27 220.91 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n196 7 Car -1 -1 -1 982.32 185.16 1069.63 221.46 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n196 4 Car -1 -1 -1 877.84 184.23 946.10 218.15 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n196 116 Pedestrian -1 -1 -1 1.85 158.29 31.31 237.63 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n196 106 Pedestrian -1 -1 -1 584.34 170.20 605.06 224.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n196 32 Pedestrian -1 -1 -1 780.23 164.33 822.43 247.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n196 113 Pedestrian -1 -1 -1 835.55 159.95 889.73 253.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n196 63 Pedestrian -1 -1 -1 388.22 166.71 405.67 207.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n196 9 Pedestrian -1 -1 -1 273.04 160.50 288.80 196.33 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n196 98 Pedestrian -1 -1 -1 858.12 172.84 897.23 260.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n196 102 Pedestrian -1 -1 -1 478.65 168.15 494.18 211.18 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n196 8 Car -1 -1 -1 609.94 174.19 631.74 200.88 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n196 75 Cyclist -1 -1 -1 554.34 166.55 567.36 208.79 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n196 117 Cyclist -1 -1 -1 609.78 173.38 628.05 201.91 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n196 118 Pedestrian -1 -1 -1 554.59 173.38 568.09 214.63 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n197 3 Car -1 -1 -1 1117.38 188.60 1220.17 225.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n197 11 Car -1 -1 -1 930.12 184.44 1010.24 220.91 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n197 7 Car -1 -1 -1 982.59 185.19 1069.34 221.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n197 4 Car -1 -1 -1 877.44 183.94 945.81 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n197 116 Pedestrian -1 -1 -1 1.28 158.47 34.65 237.19 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n197 32 Pedestrian -1 -1 -1 787.94 163.89 830.02 246.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n197 102 Pedestrian -1 -1 -1 476.15 168.39 492.47 211.43 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n197 106 Pedestrian -1 -1 -1 581.00 170.07 602.05 223.83 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n197 113 Pedestrian -1 -1 -1 845.51 161.87 894.59 251.80 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n197 9 Pedestrian -1 -1 -1 273.01 160.46 289.03 196.40 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n197 63 Pedestrian -1 -1 -1 388.41 166.78 405.72 208.26 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n197 98 Pedestrian -1 -1 -1 868.04 171.94 903.20 261.38 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n197 8 Car -1 -1 -1 611.23 173.06 630.91 201.79 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n197 117 Cyclist -1 -1 -1 611.23 173.06 630.91 201.79 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n197 75 Cyclist -1 -1 -1 554.46 165.92 568.40 209.81 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n197 118 Pedestrian -1 -1 -1 554.91 168.45 570.55 212.59 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n198 3 Car -1 -1 -1 1116.81 188.58 1220.53 225.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n198 11 Car -1 -1 -1 930.33 184.54 1009.78 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n198 7 Car -1 -1 -1 982.22 185.07 1069.86 221.50 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n198 116 Pedestrian -1 -1 -1 2.31 158.74 39.64 236.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n198 4 Car -1 -1 -1 877.02 183.68 946.82 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n198 106 Pedestrian -1 -1 -1 575.88 169.14 599.38 222.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n198 63 Pedestrian -1 -1 -1 390.21 166.85 406.88 207.88 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n198 32 Pedestrian -1 -1 -1 794.32 163.16 831.12 247.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n198 102 Pedestrian -1 -1 -1 474.93 168.26 491.20 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n198 98 Pedestrian -1 -1 -1 874.11 173.02 911.79 260.23 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n198 9 Pedestrian -1 -1 -1 273.02 160.53 288.92 196.21 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n198 113 Pedestrian -1 -1 -1 854.04 160.47 902.39 253.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n198 117 Cyclist -1 -1 -1 611.61 173.41 631.26 202.07 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n198 118 Pedestrian -1 -1 -1 555.10 167.87 570.56 211.86 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n199 3 Car -1 -1 -1 1116.76 188.54 1220.46 225.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n199 7 Car -1 -1 -1 982.19 185.09 1069.89 221.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n199 11 Car -1 -1 -1 930.29 184.66 1009.90 220.74 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n199 4 Car -1 -1 -1 877.02 183.69 946.68 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n199 116 Pedestrian -1 -1 -1 4.56 158.23 43.92 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n199 32 Pedestrian -1 -1 -1 799.15 162.09 841.08 249.38 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n199 63 Pedestrian -1 -1 -1 390.07 166.34 407.29 208.23 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n199 106 Pedestrian -1 -1 -1 572.46 170.38 596.50 223.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n199 98 Pedestrian -1 -1 -1 881.25 173.46 920.17 260.13 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n199 9 Pedestrian -1 -1 -1 273.09 160.54 288.71 196.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n199 113 Pedestrian -1 -1 -1 864.68 162.90 914.05 255.23 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n199 102 Pedestrian -1 -1 -1 472.93 168.00 489.82 210.48 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n199 118 Pedestrian -1 -1 -1 555.56 167.78 570.92 211.15 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n200 3 Car -1 -1 -1 1116.94 188.36 1220.98 226.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n200 7 Car -1 -1 -1 982.35 185.11 1069.79 221.41 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n200 11 Car -1 -1 -1 930.37 184.63 1010.04 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n200 4 Car -1 -1 -1 876.92 182.99 946.71 218.94 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n200 106 Pedestrian -1 -1 -1 571.69 169.09 593.62 223.01 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n200 116 Pedestrian -1 -1 -1 9.51 157.93 46.44 236.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n200 32 Pedestrian -1 -1 -1 803.55 163.97 844.20 248.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n200 63 Pedestrian -1 -1 -1 389.74 166.44 407.78 208.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n200 98 Pedestrian -1 -1 -1 882.34 174.55 927.61 260.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n200 9 Pedestrian -1 -1 -1 273.15 160.51 288.72 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n200 113 Pedestrian -1 -1 -1 864.17 164.58 921.63 253.59 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n200 102 Pedestrian -1 -1 -1 471.57 168.25 489.72 210.21 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n200 118 Pedestrian -1 -1 -1 556.39 169.02 570.95 210.05 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n200 119 Cyclist -1 -1 -1 612.29 172.55 630.83 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n200 120 Cyclist -1 -1 -1 556.57 167.17 570.79 209.09 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n201 3 Car -1 -1 -1 1116.74 188.44 1221.10 226.11 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n201 7 Car -1 -1 -1 982.47 185.18 1069.63 221.38 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n201 11 Car -1 -1 -1 930.47 184.66 1010.13 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n201 4 Car -1 -1 -1 876.61 182.27 947.28 220.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n201 116 Pedestrian -1 -1 -1 15.77 157.12 48.45 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n201 106 Pedestrian -1 -1 -1 569.64 169.37 588.49 221.84 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n201 32 Pedestrian -1 -1 -1 807.56 163.45 849.55 248.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n201 63 Pedestrian -1 -1 -1 390.46 166.77 408.55 209.21 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n201 9 Pedestrian -1 -1 -1 273.15 160.52 288.79 196.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n201 98 Pedestrian -1 -1 -1 891.00 174.75 933.77 260.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n201 113 Pedestrian -1 -1 -1 872.94 163.95 921.47 257.11 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n201 102 Pedestrian -1 -1 -1 470.66 168.81 488.04 210.75 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n201 119 Cyclist -1 -1 -1 612.76 172.38 631.02 203.90 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n201 120 Cyclist -1 -1 -1 557.64 167.24 570.62 208.92 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n201 121 Cyclist -1 -1 -1 602.47 170.98 618.35 205.13 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n202 3 Car -1 -1 -1 1116.51 188.47 1220.93 226.05 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n202 7 Car -1 -1 -1 982.13 185.03 1070.03 221.40 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n202 11 Car -1 -1 -1 930.19 184.68 1009.96 220.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n202 4 Car -1 -1 -1 877.88 182.45 945.89 219.87 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n202 116 Pedestrian -1 -1 -1 17.63 157.22 54.97 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n202 32 Pedestrian -1 -1 -1 819.04 163.83 860.02 248.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n202 63 Pedestrian -1 -1 -1 391.45 167.12 408.51 209.24 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n202 113 Pedestrian -1 -1 -1 883.92 168.96 933.35 257.83 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n202 106 Pedestrian -1 -1 -1 568.04 169.92 585.30 222.23 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n202 9 Pedestrian -1 -1 -1 273.03 160.51 288.71 196.19 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n202 102 Pedestrian -1 -1 -1 467.87 168.82 486.08 210.25 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n202 119 Cyclist -1 -1 -1 613.79 172.17 630.18 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n202 122 Pedestrian -1 -1 -1 558.69 170.81 571.38 209.95 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n203 3 Car -1 -1 -1 1116.48 188.40 1221.17 226.13 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n203 7 Car -1 -1 -1 982.27 185.14 1070.00 221.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n203 11 Car -1 -1 -1 930.37 184.80 1009.80 220.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n203 116 Pedestrian -1 -1 -1 21.34 157.56 58.20 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n203 4 Car -1 -1 -1 878.20 181.95 945.81 219.66 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n203 32 Pedestrian -1 -1 -1 823.45 164.16 863.42 249.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n203 63 Pedestrian -1 -1 -1 391.95 166.74 408.93 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n203 9 Pedestrian -1 -1 -1 272.94 160.60 288.72 196.20 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n203 106 Pedestrian -1 -1 -1 564.89 170.19 584.45 222.02 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n203 113 Pedestrian -1 -1 -1 905.55 171.47 949.19 263.89 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n203 119 Cyclist -1 -1 -1 605.83 171.04 621.74 205.47 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n203 102 Pedestrian -1 -1 -1 467.29 168.75 483.96 209.76 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n204 3 Car -1 -1 -1 1116.70 188.38 1220.88 226.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n204 7 Car -1 -1 -1 982.26 185.22 1070.00 221.26 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n204 11 Car -1 -1 -1 930.89 184.82 1009.22 220.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n204 116 Pedestrian -1 -1 -1 28.03 158.23 59.21 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n204 4 Car -1 -1 -1 877.66 182.63 947.12 219.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n204 106 Pedestrian -1 -1 -1 561.22 171.89 581.55 222.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n204 32 Pedestrian -1 -1 -1 832.21 163.36 870.66 250.56 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n204 9 Pedestrian -1 -1 -1 272.91 160.46 288.90 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n204 113 Pedestrian -1 -1 -1 911.81 173.02 958.76 263.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n204 63 Pedestrian -1 -1 -1 393.98 166.60 410.46 209.62 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n204 119 Cyclist -1 -1 -1 606.52 170.71 621.75 205.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n204 102 Pedestrian -1 -1 -1 465.61 168.73 481.15 209.57 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n204 123 Pedestrian -1 -1 -1 899.54 169.27 941.06 252.71 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n204 124 Pedestrian -1 -1 -1 582.91 173.65 592.85 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n205 3 Car -1 -1 -1 1116.57 188.36 1221.08 226.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n205 7 Car -1 -1 -1 982.19 185.20 1070.18 221.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n205 116 Pedestrian -1 -1 -1 35.77 157.68 66.07 231.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n205 11 Car -1 -1 -1 930.26 184.67 1009.38 220.32 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n205 4 Car -1 -1 -1 877.66 183.26 946.72 218.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n205 32 Pedestrian -1 -1 -1 837.46 166.70 879.33 252.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n205 9 Pedestrian -1 -1 -1 272.96 160.53 288.73 196.26 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n205 123 Pedestrian -1 -1 -1 905.70 171.03 949.45 254.47 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n205 113 Pedestrian -1 -1 -1 918.87 173.90 967.28 262.68 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n205 63 Pedestrian -1 -1 -1 393.90 166.76 410.79 209.90 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n205 106 Pedestrian -1 -1 -1 560.15 172.44 577.83 221.34 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n205 119 Cyclist -1 -1 -1 607.28 170.79 621.87 205.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n205 102 Pedestrian -1 -1 -1 465.46 167.64 480.56 208.64 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n205 124 Pedestrian -1 -1 -1 585.10 173.22 594.77 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n206 3 Car -1 -1 -1 1117.16 188.52 1220.77 226.02 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n206 7 Car -1 -1 -1 982.23 185.28 1070.26 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n206 116 Pedestrian -1 -1 -1 39.10 157.71 70.94 231.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n206 11 Car -1 -1 -1 929.91 184.58 1009.73 220.24 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n206 4 Car -1 -1 -1 877.27 183.35 946.62 218.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n206 32 Pedestrian -1 -1 -1 842.70 165.13 882.24 253.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n206 9 Pedestrian -1 -1 -1 273.08 160.53 288.83 196.38 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n206 123 Pedestrian -1 -1 -1 911.35 170.59 966.59 256.04 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n206 113 Pedestrian -1 -1 -1 918.63 172.37 975.31 264.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n206 63 Pedestrian -1 -1 -1 393.97 166.65 411.55 212.22 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n206 102 Pedestrian -1 -1 -1 464.79 169.01 479.83 209.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n206 106 Pedestrian -1 -1 -1 558.38 170.58 577.03 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n206 119 Cyclist -1 -1 -1 607.72 170.94 622.30 205.57 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n206 125 Cyclist -1 -1 -1 558.38 170.58 577.03 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n207 3 Car -1 -1 -1 1116.68 188.41 1220.75 225.89 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n207 116 Pedestrian -1 -1 -1 42.95 158.32 74.61 230.82 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n207 7 Car -1 -1 -1 982.61 185.40 1069.79 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n207 11 Car -1 -1 -1 929.79 184.80 1010.20 220.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n207 106 Pedestrian -1 -1 -1 553.53 170.47 576.15 220.28 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n207 32 Pedestrian -1 -1 -1 853.78 165.16 894.13 253.49 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n207 4 Car -1 -1 -1 877.76 183.46 946.18 218.59 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n207 9 Pedestrian -1 -1 -1 273.02 160.57 288.76 196.40 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n207 63 Pedestrian -1 -1 -1 394.15 166.52 411.72 212.52 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n207 123 Pedestrian -1 -1 -1 920.41 170.27 965.58 258.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n207 113 Pedestrian -1 -1 -1 936.64 172.57 979.85 268.07 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n207 102 Pedestrian -1 -1 -1 462.97 168.35 477.09 208.46 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n207 119 Cyclist -1 -1 -1 609.81 170.80 624.47 205.80 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n208 3 Car -1 -1 -1 1116.58 188.36 1221.36 225.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n208 7 Car -1 -1 -1 982.66 185.47 1069.57 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n208 11 Car -1 -1 -1 929.37 184.78 1010.30 219.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n208 116 Pedestrian -1 -1 -1 46.80 159.14 77.44 230.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n208 4 Car -1 -1 -1 878.30 183.51 945.60 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n208 106 Pedestrian -1 -1 -1 552.85 170.79 573.89 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n208 32 Pedestrian -1 -1 -1 860.23 165.71 903.30 253.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n208 9 Pedestrian -1 -1 -1 273.02 160.69 288.67 196.36 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n208 63 Pedestrian -1 -1 -1 394.25 166.37 412.09 212.22 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n208 113 Pedestrian -1 -1 -1 946.05 172.09 985.58 268.88 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n208 123 Pedestrian -1 -1 -1 932.92 169.07 969.02 259.28 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n208 102 Pedestrian -1 -1 -1 462.24 169.00 476.68 209.31 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n208 119 Cyclist -1 -1 -1 610.67 170.93 626.02 205.73 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n208 126 Pedestrian -1 -1 -1 962.89 166.33 999.09 262.13 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n209 3 Car -1 -1 -1 1116.71 188.31 1221.61 225.96 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n209 7 Car -1 -1 -1 982.92 185.41 1069.36 221.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n209 116 Pedestrian -1 -1 -1 50.93 158.68 82.01 230.20 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n209 11 Car -1 -1 -1 930.09 184.95 1009.64 219.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n209 106 Pedestrian -1 -1 -1 550.64 170.75 571.21 220.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n209 4 Car -1 -1 -1 878.69 183.38 945.88 218.39 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n209 32 Pedestrian -1 -1 -1 868.74 166.84 910.16 253.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n209 113 Pedestrian -1 -1 -1 954.79 173.14 991.30 270.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n209 9 Pedestrian -1 -1 -1 273.05 160.59 288.72 196.47 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n209 63 Pedestrian -1 -1 -1 394.71 166.44 412.98 212.00 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n209 102 Pedestrian -1 -1 -1 462.14 168.73 476.21 208.16 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n209 119 Cyclist -1 -1 -1 610.51 170.81 626.10 205.39 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n209 123 Pedestrian -1 -1 -1 941.04 171.67 975.47 256.23 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n209 126 Pedestrian -1 -1 -1 970.22 168.45 1007.29 266.40 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n210 3 Car -1 -1 -1 1116.99 188.31 1221.75 225.98 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n210 11 Car -1 -1 -1 930.79 184.80 1008.75 219.92 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n210 7 Car -1 -1 -1 982.55 185.62 1069.43 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n210 116 Pedestrian -1 -1 -1 55.05 158.62 84.78 229.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n210 4 Car -1 -1 -1 879.85 183.55 945.55 218.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n210 32 Pedestrian -1 -1 -1 875.48 167.53 918.44 253.15 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n210 9 Pedestrian -1 -1 -1 273.03 160.51 288.73 196.46 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n210 63 Pedestrian -1 -1 -1 394.91 166.66 414.15 212.54 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n210 106 Pedestrian -1 -1 -1 549.14 169.63 566.57 219.64 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n210 102 Pedestrian -1 -1 -1 460.17 168.68 475.82 207.66 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n210 113 Pedestrian -1 -1 -1 958.39 174.31 996.77 269.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n210 119 Cyclist -1 -1 -1 610.85 171.48 625.62 205.13 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n210 123 Pedestrian -1 -1 -1 946.17 171.30 984.68 257.63 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n210 126 Pedestrian -1 -1 -1 976.89 166.22 1015.80 267.31 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n211 3 Car -1 -1 -1 1116.88 188.37 1221.63 225.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n211 7 Car -1 -1 -1 982.79 185.41 1069.43 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n211 11 Car -1 -1 -1 931.98 184.29 1007.58 219.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n211 116 Pedestrian -1 -1 -1 59.25 158.73 88.00 228.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n211 4 Car -1 -1 -1 881.65 183.29 943.27 218.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n211 9 Pedestrian -1 -1 -1 273.01 160.51 288.92 196.63 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n211 63 Pedestrian -1 -1 -1 397.44 167.60 415.47 212.70 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n211 32 Pedestrian -1 -1 -1 890.07 169.17 926.71 252.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n211 126 Pedestrian -1 -1 -1 987.71 166.85 1028.47 266.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n211 106 Pedestrian -1 -1 -1 546.51 170.51 564.01 220.15 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n211 102 Pedestrian -1 -1 -1 458.30 168.64 473.71 208.08 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n211 123 Pedestrian -1 -1 -1 948.02 170.45 998.83 263.92 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n211 113 Pedestrian -1 -1 -1 965.94 176.27 1011.45 272.23 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n211 119 Cyclist -1 -1 -1 610.25 171.57 626.16 204.79 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n211 127 Pedestrian -1 -1 -1 878.02 173.70 916.51 253.94 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n211 128 Pedestrian -1 -1 -1 958.71 174.01 1003.98 269.95 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n211 129 Pedestrian -1 -1 -1 567.59 168.25 579.25 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n212 3 Car -1 -1 -1 1116.79 188.34 1221.91 226.01 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n212 7 Car -1 -1 -1 983.12 185.40 1069.19 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n212 106 Pedestrian -1 -1 -1 542.62 170.91 561.75 219.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n212 116 Pedestrian -1 -1 -1 61.04 159.28 94.17 227.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n212 11 Car -1 -1 -1 932.29 184.06 1007.49 220.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n212 4 Car -1 -1 -1 880.14 182.05 944.41 220.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n212 63 Pedestrian -1 -1 -1 398.22 168.09 416.77 212.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n212 127 Pedestrian -1 -1 -1 887.76 170.12 929.35 256.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n212 9 Pedestrian -1 -1 -1 273.01 160.51 288.85 196.54 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n212 102 Pedestrian -1 -1 -1 457.57 168.21 472.12 208.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n212 126 Pedestrian -1 -1 -1 997.65 166.03 1040.28 268.27 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n212 113 Pedestrian -1 -1 -1 980.04 175.64 1020.58 273.80 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n212 123 Pedestrian -1 -1 -1 960.50 170.11 1001.18 264.87 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n212 119 Cyclist -1 -1 -1 610.53 171.79 626.56 204.74 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n212 129 Pedestrian -1 -1 -1 568.76 169.47 580.21 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n212 130 Pedestrian -1 -1 -1 595.46 174.41 604.19 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n213 3 Car -1 -1 -1 1116.52 188.32 1221.88 225.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n213 7 Car -1 -1 -1 983.10 185.67 1069.39 220.20 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n213 116 Pedestrian -1 -1 -1 61.96 159.65 96.42 227.40 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n213 4 Car -1 -1 -1 877.59 182.29 946.87 219.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n213 106 Pedestrian -1 -1 -1 539.60 171.12 559.80 220.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n213 11 Car -1 -1 -1 932.67 184.17 1013.24 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n213 127 Pedestrian -1 -1 -1 899.84 164.50 947.38 255.50 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n213 9 Pedestrian -1 -1 -1 272.99 160.49 288.81 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n213 113 Pedestrian -1 -1 -1 983.68 172.68 1023.96 275.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n213 126 Pedestrian -1 -1 -1 1009.67 167.40 1044.43 267.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n213 102 Pedestrian -1 -1 -1 456.99 168.38 470.55 208.01 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n213 63 Pedestrian -1 -1 -1 401.92 167.49 418.03 212.99 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n213 129 Pedestrian -1 -1 -1 569.21 170.15 580.66 202.12 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n213 119 Cyclist -1 -1 -1 613.14 171.60 627.93 205.31 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n214 7 Car -1 -1 -1 982.13 185.40 1070.31 220.58 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n214 3 Car -1 -1 -1 1116.30 188.24 1221.60 225.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n214 116 Pedestrian -1 -1 -1 66.52 160.15 97.36 227.07 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n214 63 Pedestrian -1 -1 -1 403.18 167.15 418.71 212.35 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n214 4 Car -1 -1 -1 878.52 181.89 945.78 220.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n214 113 Pedestrian -1 -1 -1 989.10 173.22 1033.86 275.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n214 11 Car -1 -1 -1 932.35 184.61 1013.64 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n214 9 Pedestrian -1 -1 -1 272.89 160.40 288.91 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n214 127 Pedestrian -1 -1 -1 906.65 164.32 948.94 257.11 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n214 102 Pedestrian -1 -1 -1 455.55 168.13 468.50 207.70 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n214 126 Pedestrian -1 -1 -1 1015.45 165.78 1053.98 268.65 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n214 106 Pedestrian -1 -1 -1 538.80 171.65 556.70 219.54 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n214 129 Pedestrian -1 -1 -1 569.93 170.73 580.53 201.56 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n214 119 Cyclist -1 -1 -1 613.91 171.71 628.93 205.04 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n214 131 Pedestrian -1 -1 -1 395.46 168.20 409.57 204.72 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n215 7 Car -1 -1 -1 982.63 185.41 1069.89 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n215 3 Car -1 -1 -1 1116.47 188.28 1221.41 225.78 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n215 116 Pedestrian -1 -1 -1 75.94 159.77 102.02 227.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n215 63 Pedestrian -1 -1 -1 404.38 167.69 419.33 212.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n215 102 Pedestrian -1 -1 -1 454.74 168.37 467.73 207.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n215 127 Pedestrian -1 -1 -1 919.92 166.87 965.24 258.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n215 113 Pedestrian -1 -1 -1 996.15 173.54 1042.82 276.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n215 9 Pedestrian -1 -1 -1 272.90 160.38 288.88 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n215 11 Car -1 -1 -1 932.78 184.74 1013.40 220.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n215 4 Car -1 -1 -1 879.37 182.44 945.07 219.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n215 126 Pedestrian -1 -1 -1 1021.55 165.20 1070.41 269.52 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n215 131 Pedestrian -1 -1 -1 395.45 168.97 409.01 204.22 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n215 119 Cyclist -1 -1 -1 614.56 171.55 629.96 205.09 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n215 106 Pedestrian -1 -1 -1 537.76 171.00 553.98 219.71 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n215 129 Pedestrian -1 -1 -1 570.97 171.13 581.18 201.21 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n216 7 Car -1 -1 -1 982.78 185.41 1069.68 220.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n216 3 Car -1 -1 -1 1116.43 188.20 1221.57 225.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n216 116 Pedestrian -1 -1 -1 80.03 160.17 105.56 226.96 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n216 102 Pedestrian -1 -1 -1 454.00 168.33 466.61 207.32 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n216 9 Pedestrian -1 -1 -1 273.07 160.38 288.95 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n216 127 Pedestrian -1 -1 -1 925.99 166.76 968.14 258.65 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n216 106 Pedestrian -1 -1 -1 532.68 170.95 552.55 218.46 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n216 4 Car -1 -1 -1 878.48 182.64 945.09 219.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n216 11 Car -1 -1 -1 932.94 184.46 1013.78 219.59 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n216 113 Pedestrian -1 -1 -1 1001.94 174.73 1052.47 275.92 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n216 63 Pedestrian -1 -1 -1 406.26 167.96 421.19 214.26 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n216 126 Pedestrian -1 -1 -1 1028.99 165.49 1085.31 268.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n216 131 Pedestrian -1 -1 -1 393.68 169.67 407.31 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n216 119 Cyclist -1 -1 -1 614.48 172.22 629.94 204.53 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n216 129 Pedestrian -1 -1 -1 570.92 170.96 581.50 201.23 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n217 7 Car -1 -1 -1 982.98 185.00 1069.52 220.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n217 3 Car -1 -1 -1 1116.50 188.22 1221.14 225.76 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n217 106 Pedestrian -1 -1 -1 530.91 171.29 550.12 218.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n217 116 Pedestrian -1 -1 -1 82.95 159.81 110.52 226.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n217 63 Pedestrian -1 -1 -1 407.08 167.88 422.20 214.10 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n217 4 Car -1 -1 -1 877.35 182.66 945.67 219.12 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n217 11 Car -1 -1 -1 937.43 183.75 1017.01 220.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n217 127 Pedestrian -1 -1 -1 936.38 167.80 979.14 254.20 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n217 9 Pedestrian -1 -1 -1 273.02 160.37 288.96 196.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n217 131 Pedestrian -1 -1 -1 392.52 169.08 406.31 203.82 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n217 113 Pedestrian -1 -1 -1 1007.84 174.31 1061.92 276.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n217 126 Pedestrian -1 -1 -1 1038.66 165.04 1091.83 271.36 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n217 102 Pedestrian -1 -1 -1 453.25 167.59 465.89 206.52 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n217 119 Cyclist -1 -1 -1 613.95 173.02 629.90 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n218 7 Car -1 -1 -1 983.84 184.90 1068.83 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n218 3 Car -1 -1 -1 1116.19 188.26 1221.71 225.69 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n218 116 Pedestrian -1 -1 -1 83.01 160.20 113.70 226.85 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n218 4 Car -1 -1 -1 876.82 182.73 945.85 219.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n218 63 Pedestrian -1 -1 -1 406.97 167.54 423.85 214.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n218 11 Car -1 -1 -1 939.38 183.68 1015.57 220.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n218 127 Pedestrian -1 -1 -1 945.00 169.69 986.73 257.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n218 106 Pedestrian -1 -1 -1 529.20 170.95 547.78 218.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n218 9 Pedestrian -1 -1 -1 273.02 160.42 288.90 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n218 113 Pedestrian -1 -1 -1 1022.66 171.58 1069.49 278.67 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n218 131 Pedestrian -1 -1 -1 392.38 169.05 405.93 203.82 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n218 102 Pedestrian -1 -1 -1 451.27 166.34 464.82 205.62 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n218 126 Pedestrian -1 -1 -1 1050.23 164.39 1095.63 272.84 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n218 132 Pedestrian -1 -1 -1 619.60 174.79 631.05 204.49 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n218 133 Pedestrian -1 -1 -1 634.40 176.02 644.72 204.37 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n219 3 Car -1 -1 -1 1115.33 188.40 1222.09 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n219 116 Pedestrian -1 -1 -1 86.80 160.40 116.88 226.44 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n219 7 Car -1 -1 -1 984.60 184.17 1068.16 220.23 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n219 4 Car -1 -1 -1 876.59 182.84 946.02 219.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n219 102 Pedestrian -1 -1 -1 450.57 167.36 464.35 206.21 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n219 106 Pedestrian -1 -1 -1 528.56 170.24 544.15 217.81 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n219 11 Car -1 -1 -1 938.87 184.05 1015.94 219.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n219 127 Pedestrian -1 -1 -1 946.62 166.04 993.09 261.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n219 9 Pedestrian -1 -1 -1 273.08 160.46 288.91 196.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n219 63 Pedestrian -1 -1 -1 406.56 167.97 425.77 213.95 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n219 113 Pedestrian -1 -1 -1 1033.63 172.33 1074.36 277.85 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n219 131 Pedestrian -1 -1 -1 392.21 169.54 405.73 203.61 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n219 126 Pedestrian -1 -1 -1 1064.29 164.74 1103.89 272.77 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n219 132 Pedestrian -1 -1 -1 619.96 175.28 631.75 204.34 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n219 134 Pedestrian -1 -1 -1 1019.63 171.37 1057.74 263.50 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n219 135 Pedestrian -1 -1 -1 573.40 170.09 583.96 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n219 136 Car -1 -1 -1 607.39 175.88 629.77 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n220 3 Car -1 -1 -1 1115.91 188.31 1221.93 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n220 106 Pedestrian -1 -1 -1 525.47 171.19 542.71 218.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n220 7 Car -1 -1 -1 985.37 184.01 1067.72 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n220 4 Car -1 -1 -1 876.32 182.78 946.31 219.40 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n220 127 Pedestrian -1 -1 -1 960.31 166.97 1001.60 261.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n220 63 Pedestrian -1 -1 -1 408.16 169.15 429.08 214.79 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n220 102 Pedestrian -1 -1 -1 450.14 168.23 463.13 206.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n220 116 Pedestrian -1 -1 -1 93.08 159.61 118.73 224.79 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n220 11 Car -1 -1 -1 934.20 183.97 1012.38 219.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n220 9 Pedestrian -1 -1 -1 273.23 160.50 288.91 196.57 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n220 126 Pedestrian -1 -1 -1 1071.70 164.63 1120.58 272.13 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n220 113 Pedestrian -1 -1 -1 1045.04 173.57 1085.55 278.24 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n220 131 Pedestrian -1 -1 -1 392.10 169.80 405.82 203.68 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n220 134 Pedestrian -1 -1 -1 1024.73 170.10 1067.48 265.67 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n220 135 Pedestrian -1 -1 -1 574.28 171.94 584.70 200.27 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n221 7 Car -1 -1 -1 986.91 184.18 1066.83 220.93 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n221 3 Car -1 -1 -1 1116.20 188.47 1222.13 225.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n221 116 Pedestrian -1 -1 -1 97.87 159.78 121.38 224.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n221 4 Car -1 -1 -1 876.15 182.79 946.02 219.34 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n221 11 Car -1 -1 -1 934.62 184.32 1012.06 220.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n221 126 Pedestrian -1 -1 -1 1081.20 165.63 1141.14 276.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n221 106 Pedestrian -1 -1 -1 523.70 171.58 540.84 217.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n221 9 Pedestrian -1 -1 -1 273.16 160.43 288.88 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n221 127 Pedestrian -1 -1 -1 970.34 173.66 1006.77 261.15 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n221 113 Pedestrian -1 -1 -1 1052.88 174.13 1100.26 282.28 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n221 63 Pedestrian -1 -1 -1 409.54 169.13 430.29 215.35 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n221 102 Pedestrian -1 -1 -1 447.27 168.46 461.60 206.50 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n221 131 Pedestrian -1 -1 -1 391.81 169.48 405.92 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n221 134 Pedestrian -1 -1 -1 1029.94 172.93 1078.17 263.91 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n221 137 Car -1 -1 -1 606.88 175.73 629.31 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n222 106 Pedestrian -1 -1 -1 520.70 171.21 537.90 217.73 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n222 116 Pedestrian -1 -1 -1 99.38 160.34 127.42 223.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n222 7 Car -1 -1 -1 985.06 184.75 1067.00 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n222 3 Car -1 -1 -1 1115.99 188.65 1221.98 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n222 102 Pedestrian -1 -1 -1 446.47 167.53 460.73 205.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n222 4 Car -1 -1 -1 876.12 182.78 945.97 219.20 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n222 11 Car -1 -1 -1 934.51 184.34 1012.74 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n222 63 Pedestrian -1 -1 -1 412.76 168.62 431.38 215.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n222 9 Pedestrian -1 -1 -1 272.98 160.47 289.02 196.49 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n222 126 Pedestrian -1 -1 -1 1091.16 166.56 1146.47 276.74 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n222 113 Pedestrian -1 -1 -1 1051.98 172.78 1108.91 284.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n222 127 Pedestrian -1 -1 -1 982.08 169.76 1018.30 263.06 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n222 131 Pedestrian -1 -1 -1 391.33 169.12 405.69 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n222 137 Car -1 -1 -1 606.44 176.28 630.31 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n222 138 Pedestrian -1 -1 -1 362.55 164.55 373.98 192.43 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n222 139 Pedestrian -1 -1 -1 575.46 173.05 585.08 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n223 116 Pedestrian -1 -1 -1 101.45 161.13 131.38 223.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n223 7 Car -1 -1 -1 984.89 184.40 1067.39 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n223 3 Car -1 -1 -1 1116.57 188.51 1221.04 225.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n223 4 Car -1 -1 -1 876.06 182.84 946.17 219.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n223 102 Pedestrian -1 -1 -1 446.00 167.35 459.52 205.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n223 106 Pedestrian -1 -1 -1 518.05 171.12 535.22 218.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n223 11 Car -1 -1 -1 933.74 184.35 1012.80 220.80 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n223 127 Pedestrian -1 -1 -1 987.79 168.15 1026.82 260.55 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n223 9 Pedestrian -1 -1 -1 272.93 160.51 288.97 196.48 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n223 63 Pedestrian -1 -1 -1 415.13 167.75 432.58 215.35 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n223 126 Pedestrian -1 -1 -1 1103.25 166.13 1150.06 276.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n223 137 Car -1 -1 -1 606.40 176.24 631.40 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n223 113 Pedestrian -1 -1 -1 1062.55 173.90 1113.99 283.29 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n223 131 Pedestrian -1 -1 -1 390.94 168.63 405.36 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n223 138 Pedestrian -1 -1 -1 363.36 164.99 374.75 192.35 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n223 139 Pedestrian -1 -1 -1 575.87 173.22 585.33 200.25 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n224 7 Car -1 -1 -1 984.43 184.18 1068.71 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n224 4 Car -1 -1 -1 875.92 182.87 946.18 219.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n224 63 Pedestrian -1 -1 -1 417.19 167.94 435.38 215.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n224 3 Car -1 -1 -1 1116.72 188.33 1220.90 225.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n224 126 Pedestrian -1 -1 -1 1117.54 164.14 1166.02 279.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n224 127 Pedestrian -1 -1 -1 993.69 167.92 1030.65 261.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n224 11 Car -1 -1 -1 933.80 184.21 1012.48 220.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n224 102 Pedestrian -1 -1 -1 445.38 167.37 459.43 205.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n224 116 Pedestrian -1 -1 -1 103.99 161.63 131.65 222.45 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n224 106 Pedestrian -1 -1 -1 517.27 171.50 533.50 217.18 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n224 113 Pedestrian -1 -1 -1 1078.74 173.94 1120.11 283.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n224 9 Pedestrian -1 -1 -1 273.08 160.47 289.00 196.49 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n224 137 Car -1 -1 -1 606.58 176.34 631.61 199.14 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n224 131 Pedestrian -1 -1 -1 389.65 168.56 404.81 203.33 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n224 138 Pedestrian -1 -1 -1 363.97 164.49 375.58 192.63 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n224 140 Pedestrian -1 -1 -1 1059.10 169.65 1102.29 271.81 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n225 7 Car -1 -1 -1 985.00 184.32 1069.15 221.70 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n225 116 Pedestrian -1 -1 -1 111.92 161.34 135.22 221.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n225 4 Car -1 -1 -1 875.92 182.86 946.17 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n225 11 Car -1 -1 -1 933.47 184.07 1013.13 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n225 127 Pedestrian -1 -1 -1 997.60 166.41 1041.14 260.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n225 3 Car -1 -1 -1 1115.47 188.49 1222.09 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n225 106 Pedestrian -1 -1 -1 513.75 171.93 532.53 216.38 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n225 102 Pedestrian -1 -1 -1 442.63 166.84 458.32 207.95 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n225 131 Pedestrian -1 -1 -1 389.23 168.85 404.35 203.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n225 113 Pedestrian -1 -1 -1 1088.50 174.51 1133.57 283.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n225 9 Pedestrian -1 -1 -1 272.97 160.51 288.92 196.49 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n225 137 Car -1 -1 -1 606.93 176.12 633.38 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n225 126 Pedestrian -1 -1 -1 1131.09 164.55 1183.14 279.02 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n225 63 Pedestrian -1 -1 -1 418.07 168.17 436.95 215.94 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n225 140 Pedestrian -1 -1 -1 1070.05 170.12 1113.85 270.97 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n226 7 Car -1 -1 -1 984.63 184.59 1069.58 220.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n226 3 Car -1 -1 -1 1115.16 188.75 1222.49 225.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n226 11 Car -1 -1 -1 933.67 184.01 1012.44 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n226 4 Car -1 -1 -1 875.98 182.89 946.12 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n226 106 Pedestrian -1 -1 -1 512.09 172.15 530.68 215.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n226 116 Pedestrian -1 -1 -1 114.16 160.92 139.20 222.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n226 131 Pedestrian -1 -1 -1 388.50 169.60 403.07 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n226 127 Pedestrian -1 -1 -1 1007.19 167.52 1054.33 261.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n226 102 Pedestrian -1 -1 -1 442.42 166.76 458.35 208.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n226 113 Pedestrian -1 -1 -1 1093.92 173.47 1144.17 285.05 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n226 9 Pedestrian -1 -1 -1 272.86 160.50 288.76 196.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n226 63 Pedestrian -1 -1 -1 420.50 168.43 439.73 215.74 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n226 137 Car -1 -1 -1 606.79 176.39 631.57 199.47 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n226 126 Pedestrian -1 -1 -1 1136.49 167.12 1200.28 281.81 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n226 140 Pedestrian -1 -1 -1 1073.83 171.08 1125.89 270.35 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n226 141 Pedestrian -1 -1 -1 365.92 164.59 377.76 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n227 116 Pedestrian -1 -1 -1 115.93 160.92 141.87 221.91 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n227 11 Car -1 -1 -1 934.20 184.04 1011.50 220.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n227 7 Car -1 -1 -1 986.23 183.99 1067.57 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n227 4 Car -1 -1 -1 875.99 182.91 946.10 219.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n227 3 Car -1 -1 -1 1116.79 188.71 1220.70 225.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n227 102 Pedestrian -1 -1 -1 442.07 166.61 458.03 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n227 127 Pedestrian -1 -1 -1 1010.27 163.80 1059.00 269.18 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n227 113 Pedestrian -1 -1 -1 1095.76 172.43 1157.51 287.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n227 131 Pedestrian -1 -1 -1 387.19 169.16 402.25 204.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n227 137 Car -1 -1 -1 606.56 176.33 631.49 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n227 106 Pedestrian -1 -1 -1 510.44 171.30 528.44 215.19 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n227 9 Pedestrian -1 -1 -1 272.75 160.48 288.55 196.42 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n227 126 Pedestrian -1 -1 -1 1148.29 168.88 1204.09 281.09 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n227 63 Pedestrian -1 -1 -1 423.88 168.40 442.21 215.00 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n227 140 Pedestrian -1 -1 -1 1083.14 171.24 1139.42 271.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n228 116 Pedestrian -1 -1 -1 117.41 161.73 145.63 221.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n228 11 Car -1 -1 -1 934.18 183.96 1011.47 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n228 4 Car -1 -1 -1 876.04 182.92 946.08 219.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n228 7 Car -1 -1 -1 987.17 183.73 1066.18 222.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n228 3 Car -1 -1 -1 1117.95 188.83 1219.62 225.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n228 102 Pedestrian -1 -1 -1 442.18 166.73 457.67 208.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n228 106 Pedestrian -1 -1 -1 509.57 171.34 526.32 215.09 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n228 113 Pedestrian -1 -1 -1 1103.20 173.64 1165.29 285.87 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n228 127 Pedestrian -1 -1 -1 1019.81 165.08 1072.45 267.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n228 9 Pedestrian -1 -1 -1 272.56 160.43 288.62 196.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n228 137 Car -1 -1 -1 606.72 176.36 631.70 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n228 131 Pedestrian -1 -1 -1 385.63 168.25 401.14 203.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n228 126 Pedestrian -1 -1 -1 1159.14 168.06 1208.52 281.79 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n228 63 Pedestrian -1 -1 -1 423.57 168.32 445.84 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n229 116 Pedestrian -1 -1 -1 121.62 161.94 148.53 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n229 7 Car -1 -1 -1 986.88 184.42 1066.33 221.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n229 11 Car -1 -1 -1 934.44 183.98 1011.42 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n229 4 Car -1 -1 -1 876.18 183.01 945.91 218.99 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n229 102 Pedestrian -1 -1 -1 440.92 166.38 457.14 208.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n229 3 Car -1 -1 -1 1118.98 188.76 1218.82 225.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n229 9 Pedestrian -1 -1 -1 272.40 160.24 288.81 196.63 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n229 106 Pedestrian -1 -1 -1 506.64 171.39 524.21 214.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n229 113 Pedestrian -1 -1 -1 1124.53 177.47 1174.09 287.64 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n229 131 Pedestrian -1 -1 -1 385.02 167.72 400.79 203.56 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n229 137 Car -1 -1 -1 606.85 176.38 631.58 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n229 127 Pedestrian -1 -1 -1 1034.78 167.25 1080.78 266.68 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n229 126 Pedestrian -1 -1 -1 1172.86 167.09 1217.37 282.71 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n229 63 Pedestrian -1 -1 -1 424.16 168.14 449.76 218.81 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n229 142 Pedestrian -1 -1 -1 367.99 164.91 378.77 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n229 143 Pedestrian -1 -1 -1 1104.18 171.41 1149.09 272.53 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n229 144 Pedestrian -1 -1 -1 578.32 173.72 588.09 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n230 7 Car -1 -1 -1 986.53 184.70 1065.48 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n230 4 Car -1 -1 -1 876.30 183.00 945.84 219.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n230 11 Car -1 -1 -1 934.45 184.09 1010.70 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n230 116 Pedestrian -1 -1 -1 126.38 162.00 150.34 220.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n230 131 Pedestrian -1 -1 -1 384.35 168.14 400.39 204.25 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n230 3 Car -1 -1 -1 1120.33 188.89 1217.53 225.29 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n230 106 Pedestrian -1 -1 -1 505.32 171.70 522.06 214.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n230 127 Pedestrian -1 -1 -1 1033.64 168.54 1089.61 267.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n230 63 Pedestrian -1 -1 -1 426.46 168.82 450.87 219.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n230 113 Pedestrian -1 -1 -1 1133.10 174.77 1181.43 289.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n230 9 Pedestrian -1 -1 -1 272.82 160.15 288.80 196.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n230 102 Pedestrian -1 -1 -1 438.02 166.30 455.24 209.19 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n230 137 Car -1 -1 -1 607.06 176.37 631.47 199.61 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n230 126 Pedestrian -1 -1 -1 1178.38 166.42 1219.87 283.74 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n230 143 Pedestrian -1 -1 -1 1116.27 171.02 1159.39 272.72 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n231 116 Pedestrian -1 -1 -1 129.40 161.83 150.98 219.88 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n231 7 Car -1 -1 -1 983.55 184.73 1064.31 221.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n231 4 Car -1 -1 -1 876.43 183.10 945.78 219.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n231 11 Car -1 -1 -1 934.37 184.13 1010.70 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n231 131 Pedestrian -1 -1 -1 384.14 168.48 399.59 204.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n231 127 Pedestrian -1 -1 -1 1045.62 166.75 1100.47 269.30 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n231 106 Pedestrian -1 -1 -1 502.76 171.45 520.75 215.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n231 3 Car -1 -1 -1 1120.13 189.15 1217.46 224.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n231 137 Car -1 -1 -1 606.75 176.23 631.63 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n231 113 Pedestrian -1 -1 -1 1147.99 176.89 1197.17 289.09 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n231 63 Pedestrian -1 -1 -1 428.64 168.63 454.20 220.33 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n231 9 Pedestrian -1 -1 -1 272.71 160.39 288.46 196.51 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n231 102 Pedestrian -1 -1 -1 437.72 166.31 454.46 209.15 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n231 143 Pedestrian -1 -1 -1 1121.01 171.23 1170.42 273.32 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n231 126 Pedestrian -1 -1 -1 1191.35 165.49 1221.30 286.09 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n232 7 Car -1 -1 -1 985.13 184.40 1066.92 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n232 116 Pedestrian -1 -1 -1 131.04 161.06 154.39 219.23 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n232 11 Car -1 -1 -1 930.90 184.26 1010.14 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n232 4 Car -1 -1 -1 876.47 183.15 945.60 218.96 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n232 131 Pedestrian -1 -1 -1 383.65 168.45 398.76 204.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n232 3 Car -1 -1 -1 1120.50 188.84 1217.00 224.91 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n232 63 Pedestrian -1 -1 -1 431.62 167.32 453.80 220.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n232 127 Pedestrian -1 -1 -1 1054.65 168.68 1106.50 273.01 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n232 106 Pedestrian -1 -1 -1 501.62 171.34 518.93 215.08 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n232 137 Car -1 -1 -1 606.67 176.18 631.85 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n232 113 Pedestrian -1 -1 -1 1148.90 178.70 1211.23 288.48 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n232 9 Pedestrian -1 -1 -1 270.50 160.22 285.68 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n232 143 Pedestrian -1 -1 -1 1122.82 174.55 1183.85 275.73 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n232 145 Pedestrian -1 -1 -1 370.17 165.25 380.90 194.33 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n233 7 Car -1 -1 -1 984.44 184.38 1067.94 221.60 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n233 11 Car -1 -1 -1 931.01 184.36 1009.94 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n233 4 Car -1 -1 -1 876.47 183.25 945.56 218.94 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n233 116 Pedestrian -1 -1 -1 132.39 161.38 156.71 218.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n233 3 Car -1 -1 -1 1120.43 188.72 1217.03 225.38 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n233 127 Pedestrian -1 -1 -1 1059.96 171.22 1108.89 271.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n233 113 Pedestrian -1 -1 -1 1153.30 177.86 1214.32 293.73 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n233 63 Pedestrian -1 -1 -1 435.38 166.32 455.02 216.45 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n233 106 Pedestrian -1 -1 -1 502.14 171.63 518.50 214.36 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n233 131 Pedestrian -1 -1 -1 383.36 168.34 398.05 204.54 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n233 137 Car -1 -1 -1 607.27 175.72 632.78 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n233 9 Pedestrian -1 -1 -1 270.39 160.37 285.91 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n233 143 Pedestrian -1 -1 -1 1141.32 175.12 1203.23 282.12 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n233 145 Pedestrian -1 -1 -1 370.66 165.88 381.46 194.49 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n234 7 Car -1 -1 -1 983.93 184.59 1068.36 221.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n234 116 Pedestrian -1 -1 -1 134.57 161.22 159.12 219.00 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n234 11 Car -1 -1 -1 931.07 184.26 1009.96 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n234 4 Car -1 -1 -1 876.44 183.25 945.65 218.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n234 127 Pedestrian -1 -1 -1 1067.30 168.15 1124.28 273.86 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n234 63 Pedestrian -1 -1 -1 435.61 167.87 456.97 220.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n234 3 Car -1 -1 -1 1120.77 189.18 1216.65 224.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n234 137 Car -1 -1 -1 606.78 176.05 631.87 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n234 131 Pedestrian -1 -1 -1 382.14 168.25 396.94 204.52 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n234 113 Pedestrian -1 -1 -1 1166.18 178.02 1216.64 294.56 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n234 9 Pedestrian -1 -1 -1 272.67 160.23 288.77 196.36 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n234 106 Pedestrian -1 -1 -1 501.33 171.78 517.88 214.15 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n234 145 Pedestrian -1 -1 -1 371.28 165.49 382.24 194.45 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n234 146 Pedestrian -1 -1 -1 183.46 159.41 202.07 209.32 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n235 7 Car -1 -1 -1 983.40 184.61 1068.84 221.67 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n235 11 Car -1 -1 -1 931.04 184.30 1010.15 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n235 63 Pedestrian -1 -1 -1 438.49 168.15 460.28 221.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n235 4 Car -1 -1 -1 876.51 183.23 945.62 218.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n235 127 Pedestrian -1 -1 -1 1087.33 163.58 1143.21 277.49 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n235 116 Pedestrian -1 -1 -1 138.22 160.45 161.78 218.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n235 131 Pedestrian -1 -1 -1 380.72 168.58 397.02 204.86 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n235 3 Car -1 -1 -1 1120.91 189.13 1216.51 224.55 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n235 137 Car -1 -1 -1 607.65 175.78 632.52 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n235 9 Pedestrian -1 -1 -1 272.61 159.99 289.04 196.41 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n235 106 Pedestrian -1 -1 -1 499.90 172.12 515.74 213.85 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n235 113 Pedestrian -1 -1 -1 1185.39 176.93 1220.72 290.00 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n235 145 Pedestrian -1 -1 -1 371.22 165.45 382.95 194.55 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n235 146 Pedestrian -1 -1 -1 183.41 159.46 201.91 209.21 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n235 147 Pedestrian -1 -1 -1 1164.15 173.92 1203.65 275.65 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n236 7 Car -1 -1 -1 983.53 184.64 1068.69 221.65 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n236 11 Car -1 -1 -1 931.12 184.31 1009.99 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n236 4 Car -1 -1 -1 876.44 183.25 945.76 218.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n236 116 Pedestrian -1 -1 -1 139.23 159.98 162.46 218.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n236 127 Pedestrian -1 -1 -1 1091.38 164.15 1154.35 278.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n236 131 Pedestrian -1 -1 -1 379.83 168.83 396.16 204.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n236 63 Pedestrian -1 -1 -1 442.40 168.05 462.07 221.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n236 3 Car -1 -1 -1 1120.56 189.38 1217.06 224.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n236 9 Pedestrian -1 -1 -1 272.43 159.90 288.84 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n236 106 Pedestrian -1 -1 -1 498.84 171.14 514.29 213.74 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n236 137 Car -1 -1 -1 607.66 175.82 632.38 199.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n236 147 Pedestrian -1 -1 -1 1167.96 173.70 1215.16 276.66 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n236 113 Pedestrian -1 -1 -1 1200.37 177.93 1220.59 294.57 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n236 146 Pedestrian -1 -1 -1 183.34 159.25 201.83 209.35 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n236 145 Pedestrian -1 -1 -1 371.11 165.72 383.19 194.67 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n237 7 Car -1 -1 -1 983.18 184.75 1068.98 221.60 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n237 11 Car -1 -1 -1 931.00 184.24 1010.01 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n237 4 Car -1 -1 -1 876.52 183.27 945.63 218.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n237 127 Pedestrian -1 -1 -1 1097.15 164.59 1163.50 279.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n237 63 Pedestrian -1 -1 -1 443.66 167.15 464.88 222.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n237 116 Pedestrian -1 -1 -1 140.80 159.73 163.66 218.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n237 9 Pedestrian -1 -1 -1 272.42 160.03 288.60 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n237 131 Pedestrian -1 -1 -1 378.58 168.67 395.27 204.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n237 106 Pedestrian -1 -1 -1 497.09 170.67 511.71 213.50 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n237 3 Car -1 -1 -1 1120.23 189.52 1217.56 224.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n237 137 Car -1 -1 -1 607.57 175.96 632.48 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n237 146 Pedestrian -1 -1 -1 183.40 159.14 201.61 209.35 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n237 147 Pedestrian -1 -1 -1 1174.14 172.86 1216.07 278.79 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n237 148 Pedestrian -1 -1 -1 281.57 160.62 295.88 192.48 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n238 7 Car -1 -1 -1 983.08 184.82 1068.96 221.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n238 11 Car -1 -1 -1 930.98 184.25 1010.00 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n238 4 Car -1 -1 -1 876.57 183.30 945.62 218.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n238 127 Pedestrian -1 -1 -1 1107.69 163.73 1168.47 279.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n238 106 Pedestrian -1 -1 -1 495.66 170.75 510.78 213.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n238 131 Pedestrian -1 -1 -1 376.71 168.51 393.91 204.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n238 63 Pedestrian -1 -1 -1 447.33 168.25 468.96 222.63 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n238 116 Pedestrian -1 -1 -1 142.25 159.83 165.46 218.68 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n238 9 Pedestrian -1 -1 -1 272.40 159.84 289.03 197.45 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n238 137 Car -1 -1 -1 607.42 176.00 632.80 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n238 3 Car -1 -1 -1 1120.19 189.40 1217.87 224.86 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n238 146 Pedestrian -1 -1 -1 183.27 159.13 201.36 209.33 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n238 147 Pedestrian -1 -1 -1 1180.26 171.27 1218.29 286.67 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n239 7 Car -1 -1 -1 982.76 184.87 1069.29 221.65 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n239 11 Car -1 -1 -1 931.11 184.27 1009.99 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n239 4 Car -1 -1 -1 876.68 183.32 945.59 218.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n239 63 Pedestrian -1 -1 -1 448.97 168.88 472.80 223.16 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n239 131 Pedestrian -1 -1 -1 376.55 168.08 393.49 204.24 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n239 116 Pedestrian -1 -1 -1 142.35 159.84 166.08 218.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n239 127 Pedestrian -1 -1 -1 1120.18 163.07 1178.28 279.45 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n239 9 Pedestrian -1 -1 -1 272.90 159.83 289.18 197.50 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n239 106 Pedestrian -1 -1 -1 494.64 170.97 509.56 213.58 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n239 137 Car -1 -1 -1 607.40 176.10 632.77 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n239 3 Car -1 -1 -1 1119.90 188.78 1217.99 225.19 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n239 146 Pedestrian -1 -1 -1 183.36 159.00 201.48 209.39 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n239 147 Pedestrian -1 -1 -1 1199.84 171.84 1220.98 285.72 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n239 149 Pedestrian -1 -1 -1 437.87 165.44 453.88 209.43 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n240 7 Car -1 -1 -1 982.84 184.98 1069.20 221.59 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n240 11 Car -1 -1 -1 931.22 184.36 1009.78 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n240 4 Car -1 -1 -1 876.60 183.40 945.60 218.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n240 116 Pedestrian -1 -1 -1 144.20 159.69 167.15 216.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n240 131 Pedestrian -1 -1 -1 376.17 168.18 392.79 204.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n240 63 Pedestrian -1 -1 -1 452.03 169.52 475.54 224.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n240 127 Pedestrian -1 -1 -1 1140.72 165.18 1196.19 277.73 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n240 9 Pedestrian -1 -1 -1 273.09 160.02 289.14 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n240 137 Car -1 -1 -1 607.22 176.08 632.95 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n240 106 Pedestrian -1 -1 -1 493.00 170.82 507.76 213.52 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n240 3 Car -1 -1 -1 1119.63 188.32 1218.17 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n240 146 Pedestrian -1 -1 -1 183.22 158.98 201.65 209.41 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n240 149 Pedestrian -1 -1 -1 437.08 164.92 453.40 209.50 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n241 7 Car -1 -1 -1 982.64 184.96 1069.37 221.57 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n241 11 Car -1 -1 -1 931.27 184.38 1009.85 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n241 4 Car -1 -1 -1 876.67 183.44 945.65 218.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n241 116 Pedestrian -1 -1 -1 146.77 159.80 169.15 215.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n241 106 Pedestrian -1 -1 -1 491.95 170.37 507.34 212.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n241 131 Pedestrian -1 -1 -1 375.44 168.61 392.89 204.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n241 127 Pedestrian -1 -1 -1 1143.79 166.22 1208.43 278.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n241 137 Car -1 -1 -1 607.16 176.07 633.02 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n241 63 Pedestrian -1 -1 -1 456.54 168.43 478.01 223.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n241 9 Pedestrian -1 -1 -1 273.27 160.05 289.18 196.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n241 3 Car -1 -1 -1 1119.42 188.53 1217.55 225.01 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n241 146 Pedestrian -1 -1 -1 183.11 159.03 201.44 209.33 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n241 149 Pedestrian -1 -1 -1 433.59 163.27 452.31 210.93 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n242 7 Car -1 -1 -1 982.81 185.04 1069.16 221.49 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n242 116 Pedestrian -1 -1 -1 147.89 160.05 169.83 215.51 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n242 11 Car -1 -1 -1 931.29 184.35 1009.79 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n242 106 Pedestrian -1 -1 -1 491.06 170.14 506.75 212.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n242 4 Car -1 -1 -1 876.73 183.45 945.56 218.50 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n242 131 Pedestrian -1 -1 -1 374.95 168.58 393.16 205.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n242 63 Pedestrian -1 -1 -1 459.72 167.91 479.84 223.75 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n242 137 Car -1 -1 -1 606.97 175.97 633.39 200.29 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n242 127 Pedestrian -1 -1 -1 1149.69 167.53 1217.49 282.09 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n242 3 Car -1 -1 -1 1120.82 188.56 1216.32 224.79 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n242 9 Pedestrian -1 -1 -1 273.35 160.08 289.26 196.84 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n242 149 Pedestrian -1 -1 -1 433.07 162.97 452.58 210.62 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n242 146 Pedestrian -1 -1 -1 183.12 158.99 201.38 209.31 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n243 7 Car -1 -1 -1 982.98 185.10 1069.08 221.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n243 4 Car -1 -1 -1 876.89 183.52 945.59 218.44 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n243 116 Pedestrian -1 -1 -1 148.99 159.91 170.16 215.31 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n243 11 Car -1 -1 -1 931.34 184.39 1009.92 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n243 106 Pedestrian -1 -1 -1 490.29 170.60 506.22 212.82 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n243 131 Pedestrian -1 -1 -1 375.10 167.78 392.99 205.91 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n243 3 Car -1 -1 -1 1120.31 188.51 1217.02 224.94 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n243 137 Car -1 -1 -1 607.37 175.93 633.04 200.12 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n243 127 Pedestrian -1 -1 -1 1159.11 167.57 1216.67 282.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n243 63 Pedestrian -1 -1 -1 464.65 168.84 485.05 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n243 9 Pedestrian -1 -1 -1 277.64 160.04 292.30 193.05 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n243 149 Pedestrian -1 -1 -1 432.99 163.04 452.40 210.56 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n243 146 Pedestrian -1 -1 -1 183.09 158.86 201.52 209.46 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n244 7 Car -1 -1 -1 982.93 185.06 1068.97 221.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n244 11 Car -1 -1 -1 931.40 184.35 1009.84 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n244 4 Car -1 -1 -1 876.80 183.47 945.39 218.34 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n244 116 Pedestrian -1 -1 -1 150.84 160.19 171.74 214.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n244 63 Pedestrian -1 -1 -1 465.91 168.64 487.25 223.37 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n244 3 Car -1 -1 -1 1119.54 188.49 1217.51 224.85 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n244 106 Pedestrian -1 -1 -1 488.19 170.34 504.61 212.77 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n244 137 Car -1 -1 -1 607.24 175.92 633.08 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n244 131 Pedestrian -1 -1 -1 374.52 166.37 392.85 206.08 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n244 127 Pedestrian -1 -1 -1 1170.75 165.62 1219.00 285.36 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n244 9 Pedestrian -1 -1 -1 277.76 160.09 292.29 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n244 149 Pedestrian -1 -1 -1 432.92 163.15 452.83 211.34 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n244 146 Pedestrian -1 -1 -1 183.19 158.87 201.54 209.45 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n245 116 Pedestrian -1 -1 -1 151.89 160.01 172.19 214.12 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n245 7 Car -1 -1 -1 979.73 185.06 1068.65 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n245 11 Car -1 -1 -1 931.40 184.30 1009.76 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n245 4 Car -1 -1 -1 876.75 183.44 945.43 218.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n245 3 Car -1 -1 -1 1118.36 188.66 1218.31 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n245 106 Pedestrian -1 -1 -1 487.29 170.64 503.21 212.50 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n245 131 Pedestrian -1 -1 -1 375.21 166.20 392.81 206.11 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n245 63 Pedestrian -1 -1 -1 468.66 168.39 490.66 223.85 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n245 137 Car -1 -1 -1 607.40 176.07 632.94 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n245 9 Pedestrian -1 -1 -1 277.84 160.12 292.13 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n245 146 Pedestrian -1 -1 -1 183.14 158.91 201.44 209.35 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n245 127 Pedestrian -1 -1 -1 1183.78 167.04 1221.66 284.95 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n245 149 Pedestrian -1 -1 -1 432.75 163.07 453.07 211.92 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n246 116 Pedestrian -1 -1 -1 152.07 159.75 172.81 214.05 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n246 7 Car -1 -1 -1 982.94 184.98 1069.00 221.40 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n246 3 Car -1 -1 -1 1118.32 188.77 1218.70 224.64 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n246 11 Car -1 -1 -1 931.33 184.33 1009.74 220.91 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n246 4 Car -1 -1 -1 876.76 183.43 945.42 218.36 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n246 131 Pedestrian -1 -1 -1 375.79 166.43 393.17 206.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n246 137 Car -1 -1 -1 607.33 176.03 633.14 200.00 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n246 63 Pedestrian -1 -1 -1 474.01 168.22 495.37 223.87 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n246 106 Pedestrian -1 -1 -1 486.91 170.52 502.28 212.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n246 9 Pedestrian -1 -1 -1 277.93 160.20 292.14 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n246 146 Pedestrian -1 -1 -1 183.20 158.71 201.57 209.51 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n246 127 Pedestrian -1 -1 -1 1200.01 175.42 1220.19 282.73 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n246 149 Pedestrian -1 -1 -1 433.10 163.18 452.98 211.71 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n247 116 Pedestrian -1 -1 -1 152.32 159.49 172.83 213.96 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n247 7 Car -1 -1 -1 983.03 185.07 1068.83 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n247 3 Car -1 -1 -1 1118.10 188.67 1219.46 225.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n247 11 Car -1 -1 -1 931.33 184.33 1009.64 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n247 4 Car -1 -1 -1 876.67 183.41 945.49 218.31 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n247 63 Pedestrian -1 -1 -1 476.67 167.23 497.98 224.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n247 131 Pedestrian -1 -1 -1 375.80 167.07 393.24 206.52 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n247 137 Car -1 -1 -1 607.38 176.10 632.98 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n247 9 Pedestrian -1 -1 -1 278.08 160.31 292.37 192.97 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n247 146 Pedestrian -1 -1 -1 183.20 158.81 201.50 209.51 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n247 127 Pedestrian -1 -1 -1 1208.99 176.32 1220.11 282.29 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n248 116 Pedestrian -1 -1 -1 152.45 159.53 172.43 214.02 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n248 3 Car -1 -1 -1 1117.82 188.53 1220.16 225.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n248 7 Car -1 -1 -1 982.94 185.07 1068.95 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n248 11 Car -1 -1 -1 931.36 184.37 1009.71 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n248 4 Car -1 -1 -1 876.70 183.45 945.48 218.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n248 63 Pedestrian -1 -1 -1 477.10 167.55 505.24 226.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n248 137 Car -1 -1 -1 607.37 176.01 633.26 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n248 131 Pedestrian -1 -1 -1 375.62 167.27 393.27 206.39 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n248 9 Pedestrian -1 -1 -1 278.01 160.23 292.18 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n248 146 Pedestrian -1 -1 -1 183.18 158.79 201.45 209.47 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n249 116 Pedestrian -1 -1 -1 151.75 159.80 172.52 214.36 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n249 3 Car -1 -1 -1 1117.64 188.41 1220.42 225.12 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n249 7 Car -1 -1 -1 983.15 185.10 1068.70 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n249 11 Car -1 -1 -1 931.33 184.34 1009.65 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n249 4 Car -1 -1 -1 876.69 183.47 945.51 218.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n249 63 Pedestrian -1 -1 -1 481.05 167.99 507.30 227.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n249 137 Car -1 -1 -1 607.26 176.03 633.47 200.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n249 131 Pedestrian -1 -1 -1 375.60 166.74 393.05 206.33 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n249 9 Pedestrian -1 -1 -1 278.05 160.13 292.21 192.97 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n249 146 Pedestrian -1 -1 -1 183.26 158.85 201.36 209.45 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n250 116 Pedestrian -1 -1 -1 152.35 159.34 172.55 214.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n250 3 Car -1 -1 -1 1117.85 188.59 1220.23 225.31 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n250 7 Car -1 -1 -1 983.05 185.07 1068.79 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n250 11 Car -1 -1 -1 931.35 184.41 1009.67 220.76 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n250 4 Car -1 -1 -1 876.85 183.42 945.43 218.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n250 137 Car -1 -1 -1 607.30 176.02 633.61 199.86 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n250 63 Pedestrian -1 -1 -1 484.51 168.27 507.83 226.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n250 131 Pedestrian -1 -1 -1 375.51 166.69 393.17 206.70 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n250 9 Pedestrian -1 -1 -1 278.12 160.10 292.36 193.13 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n250 146 Pedestrian -1 -1 -1 183.20 158.73 201.76 209.65 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n250 150 Pedestrian -1 -1 -1 480.63 169.63 496.66 212.76 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n251 116 Pedestrian -1 -1 -1 152.07 159.09 172.81 213.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n251 3 Car -1 -1 -1 1117.82 188.48 1220.05 225.19 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n251 7 Car -1 -1 -1 983.23 185.13 1068.64 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n251 11 Car -1 -1 -1 931.35 184.37 1009.82 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n251 4 Car -1 -1 -1 876.75 183.46 945.49 218.27 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n251 137 Car -1 -1 -1 607.31 175.97 633.73 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n251 63 Pedestrian -1 -1 -1 491.80 167.54 512.00 228.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n251 131 Pedestrian -1 -1 -1 375.85 167.08 393.44 206.83 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n251 150 Pedestrian -1 -1 -1 480.85 170.14 496.04 211.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n251 9 Pedestrian -1 -1 -1 278.17 160.06 292.35 193.10 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n251 146 Pedestrian -1 -1 -1 183.13 158.72 201.93 209.74 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n251 151 Pedestrian -1 -1 -1 431.65 163.33 453.74 211.40 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n252 3 Car -1 -1 -1 1117.76 188.40 1220.09 225.39 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n252 116 Pedestrian -1 -1 -1 151.93 158.97 172.60 213.47 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n252 7 Car -1 -1 -1 979.60 185.15 1068.88 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n252 11 Car -1 -1 -1 931.34 184.35 1009.75 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n252 4 Car -1 -1 -1 876.69 183.48 945.62 218.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n252 137 Car -1 -1 -1 607.42 175.97 633.64 199.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n252 150 Pedestrian -1 -1 -1 480.35 170.18 495.75 211.17 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n252 131 Pedestrian -1 -1 -1 375.76 166.40 394.03 207.13 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n252 63 Pedestrian -1 -1 -1 495.14 168.90 517.58 227.69 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n252 9 Pedestrian -1 -1 -1 278.06 160.01 292.45 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n252 146 Pedestrian -1 -1 -1 182.92 158.79 201.87 209.64 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n252 151 Pedestrian -1 -1 -1 431.70 163.14 453.91 211.69 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n253 3 Car -1 -1 -1 1117.88 188.37 1220.43 225.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n253 116 Pedestrian -1 -1 -1 150.72 159.45 173.03 213.49 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n253 7 Car -1 -1 -1 979.59 185.15 1068.87 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n253 11 Car -1 -1 -1 931.36 184.33 1009.74 220.79 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n253 4 Car -1 -1 -1 876.62 183.41 945.61 218.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n253 137 Car -1 -1 -1 607.21 175.83 633.83 200.01 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n253 63 Pedestrian -1 -1 -1 496.18 169.41 524.59 227.82 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n253 150 Pedestrian -1 -1 -1 479.29 170.65 494.47 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n253 131 Pedestrian -1 -1 -1 376.65 167.44 394.16 207.43 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n253 9 Pedestrian -1 -1 -1 278.06 159.91 292.46 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n253 146 Pedestrian -1 -1 -1 183.13 158.77 201.60 209.61 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n253 151 Pedestrian -1 -1 -1 432.22 163.83 453.52 211.22 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n254 3 Car -1 -1 -1 1117.13 188.40 1220.57 225.28 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n254 7 Car -1 -1 -1 979.48 185.04 1068.90 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n254 11 Car -1 -1 -1 931.20 184.33 1009.73 220.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n254 116 Pedestrian -1 -1 -1 150.14 159.51 172.62 213.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n254 4 Car -1 -1 -1 876.55 183.45 945.65 218.29 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n254 137 Car -1 -1 -1 607.22 175.73 633.80 200.09 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n254 150 Pedestrian -1 -1 -1 477.43 170.30 492.18 210.59 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n254 63 Pedestrian -1 -1 -1 497.62 169.12 529.41 228.81 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n254 9 Pedestrian -1 -1 -1 278.06 160.03 292.65 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n254 131 Pedestrian -1 -1 -1 378.05 168.03 395.86 207.44 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n254 146 Pedestrian -1 -1 -1 183.28 158.73 201.59 209.59 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n254 151 Pedestrian -1 -1 -1 432.13 163.91 453.31 211.16 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n255 3 Car -1 -1 -1 1117.03 188.47 1220.54 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n255 116 Pedestrian -1 -1 -1 148.12 158.75 170.48 213.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n255 7 Car -1 -1 -1 979.23 185.17 1069.17 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n255 11 Car -1 -1 -1 931.25 184.36 1009.79 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n255 4 Car -1 -1 -1 876.64 183.48 945.57 218.23 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n255 150 Pedestrian -1 -1 -1 475.72 170.18 490.62 210.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n255 137 Car -1 -1 -1 607.28 175.80 633.32 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n255 131 Pedestrian -1 -1 -1 378.27 168.08 396.01 207.58 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n255 9 Pedestrian -1 -1 -1 278.07 160.10 292.54 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n255 63 Pedestrian -1 -1 -1 504.39 169.74 532.27 228.30 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n255 146 Pedestrian -1 -1 -1 183.32 158.74 201.47 209.59 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n255 151 Pedestrian -1 -1 -1 431.38 163.96 453.63 211.08 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n256 3 Car -1 -1 -1 1116.93 188.58 1220.56 225.38 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n256 7 Car -1 -1 -1 979.50 185.16 1068.97 221.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n256 11 Car -1 -1 -1 931.37 184.35 1009.72 220.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n256 116 Pedestrian -1 -1 -1 147.64 158.18 169.22 213.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n256 4 Car -1 -1 -1 876.57 183.50 945.65 218.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n256 137 Car -1 -1 -1 607.22 175.82 633.47 199.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n256 131 Pedestrian -1 -1 -1 378.16 167.97 396.40 207.55 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n256 150 Pedestrian -1 -1 -1 473.79 170.70 488.85 210.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n256 151 Pedestrian -1 -1 -1 431.17 163.52 453.26 211.49 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n256 9 Pedestrian -1 -1 -1 278.06 160.13 292.53 193.17 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n256 63 Pedestrian -1 -1 -1 511.16 169.36 534.37 228.71 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n256 146 Pedestrian -1 -1 -1 183.20 158.65 201.76 209.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n257 3 Car -1 -1 -1 1116.72 188.61 1220.48 225.52 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n257 7 Car -1 -1 -1 979.49 185.16 1068.92 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n257 11 Car -1 -1 -1 931.47 184.39 1009.60 220.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n257 63 Pedestrian -1 -1 -1 512.59 169.12 540.95 228.83 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n257 4 Car -1 -1 -1 876.53 183.55 945.66 218.23 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n257 150 Pedestrian -1 -1 -1 473.15 171.42 487.90 210.37 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n257 137 Car -1 -1 -1 607.28 175.90 633.41 199.93 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n257 116 Pedestrian -1 -1 -1 145.36 158.23 165.81 212.93 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n257 131 Pedestrian -1 -1 -1 378.23 168.35 396.77 207.71 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n257 151 Pedestrian -1 -1 -1 431.37 163.95 452.32 210.67 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n257 9 Pedestrian -1 -1 -1 277.85 160.28 292.61 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n257 146 Pedestrian -1 -1 -1 183.13 158.63 201.96 209.78 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n258 3 Car -1 -1 -1 1116.87 188.34 1220.49 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n258 7 Car -1 -1 -1 979.45 185.18 1068.95 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n258 11 Car -1 -1 -1 931.23 184.38 1009.69 220.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n258 4 Car -1 -1 -1 876.37 183.55 945.82 218.27 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n258 63 Pedestrian -1 -1 -1 515.86 169.79 545.37 228.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n258 137 Car -1 -1 -1 607.30 175.79 633.73 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n258 150 Pedestrian -1 -1 -1 472.82 171.50 487.69 210.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n258 116 Pedestrian -1 -1 -1 143.43 158.48 165.52 212.51 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n258 131 Pedestrian -1 -1 -1 378.86 168.33 396.67 207.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n258 9 Pedestrian -1 -1 -1 277.97 160.21 292.52 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n258 151 Pedestrian -1 -1 -1 431.51 164.05 451.61 210.69 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n258 146 Pedestrian -1 -1 -1 183.23 158.71 202.05 209.79 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n259 3 Car -1 -1 -1 1116.75 188.37 1220.56 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n259 7 Car -1 -1 -1 982.82 185.22 1069.03 221.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n259 11 Car -1 -1 -1 931.31 184.42 1009.63 220.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n259 4 Car -1 -1 -1 876.49 183.56 945.73 218.28 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n259 150 Pedestrian -1 -1 -1 472.86 171.09 487.09 209.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n259 63 Pedestrian -1 -1 -1 520.32 170.27 548.04 229.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n259 137 Car -1 -1 -1 607.17 175.80 633.62 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n259 116 Pedestrian -1 -1 -1 143.46 159.09 163.83 211.99 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n259 131 Pedestrian -1 -1 -1 378.65 168.40 396.60 207.97 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n259 9 Pedestrian -1 -1 -1 277.89 160.26 292.40 193.10 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n259 151 Pedestrian -1 -1 -1 431.61 163.97 451.18 210.61 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n259 146 Pedestrian -1 -1 -1 183.25 158.81 201.98 209.71 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n260 3 Car -1 -1 -1 1117.47 188.48 1220.50 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n260 7 Car -1 -1 -1 982.71 185.21 1069.11 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n260 11 Car -1 -1 -1 931.16 184.35 1009.78 220.76 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n260 4 Car -1 -1 -1 876.55 183.57 945.72 218.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n260 63 Pedestrian -1 -1 -1 527.03 169.81 549.68 229.13 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n260 137 Car -1 -1 -1 607.16 175.95 633.64 199.86 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n260 150 Pedestrian -1 -1 -1 472.13 170.89 486.32 209.46 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n260 131 Pedestrian -1 -1 -1 378.77 167.97 396.49 208.22 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n260 116 Pedestrian -1 -1 -1 139.99 159.69 161.58 211.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n260 9 Pedestrian -1 -1 -1 278.03 160.16 292.56 193.18 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n260 146 Pedestrian -1 -1 -1 183.35 158.98 201.80 209.60 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n260 151 Pedestrian -1 -1 -1 429.28 163.91 448.61 209.73 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n261 3 Car -1 -1 -1 1117.55 188.42 1220.61 225.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n261 7 Car -1 -1 -1 982.91 185.20 1069.03 221.32 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n261 11 Car -1 -1 -1 931.08 184.35 1009.76 220.79 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n261 4 Car -1 -1 -1 876.50 183.59 945.71 218.26 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n261 137 Car -1 -1 -1 607.13 175.84 633.62 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n261 116 Pedestrian -1 -1 -1 137.95 158.16 157.54 210.90 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n261 131 Pedestrian -1 -1 -1 379.51 168.29 396.77 208.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n261 9 Pedestrian -1 -1 -1 278.12 160.19 292.63 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n261 63 Pedestrian -1 -1 -1 533.96 170.63 555.71 228.52 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n261 150 Pedestrian -1 -1 -1 469.39 170.76 485.69 209.64 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n261 146 Pedestrian -1 -1 -1 183.33 158.91 201.76 209.59 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n261 151 Pedestrian -1 -1 -1 429.87 164.10 447.75 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n262 3 Car -1 -1 -1 1116.97 188.38 1221.00 225.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n262 11 Car -1 -1 -1 931.06 184.34 1009.69 220.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n262 7 Car -1 -1 -1 983.03 185.23 1068.82 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n262 4 Car -1 -1 -1 876.56 183.64 945.66 218.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n262 116 Pedestrian -1 -1 -1 136.89 158.31 156.68 210.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n262 137 Car -1 -1 -1 607.14 175.85 633.75 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n262 63 Pedestrian -1 -1 -1 538.11 168.18 564.93 228.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n262 150 Pedestrian -1 -1 -1 468.68 171.29 485.15 209.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n262 131 Pedestrian -1 -1 -1 379.77 167.47 397.37 209.08 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n262 9 Pedestrian -1 -1 -1 278.20 160.16 292.64 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n262 146 Pedestrian -1 -1 -1 183.31 158.92 201.71 209.57 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n262 151 Pedestrian -1 -1 -1 430.05 164.05 447.34 209.38 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n263 3 Car -1 -1 -1 1116.64 188.45 1220.90 225.71 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n263 7 Car -1 -1 -1 983.11 185.26 1068.73 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n263 11 Car -1 -1 -1 931.14 184.40 1009.89 220.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n263 4 Car -1 -1 -1 876.65 183.65 945.57 218.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n263 137 Car -1 -1 -1 607.14 175.86 633.90 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n263 63 Pedestrian -1 -1 -1 539.33 168.77 571.95 229.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n263 116 Pedestrian -1 -1 -1 134.78 158.32 154.27 209.99 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n263 150 Pedestrian -1 -1 -1 467.39 171.32 482.99 209.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n263 131 Pedestrian -1 -1 -1 379.82 167.19 397.73 209.30 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n263 9 Pedestrian -1 -1 -1 278.15 160.24 292.74 192.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n263 146 Pedestrian -1 -1 -1 183.41 158.92 201.53 209.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n263 151 Pedestrian -1 -1 -1 430.06 164.34 446.91 209.32 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n264 3 Car -1 -1 -1 1117.23 188.43 1220.61 225.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n264 7 Car -1 -1 -1 982.97 185.24 1068.91 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n264 11 Car -1 -1 -1 931.16 184.39 1009.69 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n264 4 Car -1 -1 -1 876.63 183.62 945.68 218.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n264 137 Car -1 -1 -1 607.33 176.08 633.79 199.77 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n264 116 Pedestrian -1 -1 -1 134.05 158.56 153.99 210.39 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n264 150 Pedestrian -1 -1 -1 466.31 171.17 479.99 208.26 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n264 63 Pedestrian -1 -1 -1 544.03 169.46 574.98 229.12 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n264 9 Pedestrian -1 -1 -1 278.06 160.09 292.71 193.08 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n264 131 Pedestrian -1 -1 -1 380.45 169.20 398.17 210.41 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n264 146 Pedestrian -1 -1 -1 183.35 158.91 201.62 209.65 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n264 151 Pedestrian -1 -1 -1 430.07 164.58 446.42 209.13 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n265 3 Car -1 -1 -1 1117.28 188.56 1220.85 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n265 7 Car -1 -1 -1 982.99 185.26 1068.86 221.26 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n265 11 Car -1 -1 -1 931.26 184.40 1009.75 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n265 4 Car -1 -1 -1 876.84 183.68 945.50 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n265 137 Car -1 -1 -1 607.31 175.92 633.63 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n265 116 Pedestrian -1 -1 -1 132.94 158.94 152.13 209.65 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n265 150 Pedestrian -1 -1 -1 464.31 171.00 478.26 208.40 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n265 9 Pedestrian -1 -1 -1 277.75 159.93 292.64 193.10 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n265 131 Pedestrian -1 -1 -1 381.79 168.93 399.56 210.63 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n265 63 Pedestrian -1 -1 -1 553.62 169.54 575.57 229.21 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n265 146 Pedestrian -1 -1 -1 183.41 158.88 201.53 209.71 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n265 151 Pedestrian -1 -1 -1 429.98 164.58 446.18 208.49 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n266 3 Car -1 -1 -1 1117.21 188.54 1220.80 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n266 7 Car -1 -1 -1 982.90 185.19 1068.97 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n266 11 Car -1 -1 -1 931.05 184.41 1009.87 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n266 4 Car -1 -1 -1 876.80 183.68 945.57 218.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n266 63 Pedestrian -1 -1 -1 556.62 168.57 580.69 229.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n266 150 Pedestrian -1 -1 -1 461.27 171.53 476.21 208.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n266 137 Car -1 -1 -1 607.19 175.91 633.71 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n266 116 Pedestrian -1 -1 -1 130.74 159.22 149.74 208.90 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n266 131 Pedestrian -1 -1 -1 381.93 168.63 400.68 210.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n266 9 Pedestrian -1 -1 -1 277.62 160.02 292.40 193.10 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n266 146 Pedestrian -1 -1 -1 183.53 158.76 201.76 209.90 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n266 151 Pedestrian -1 -1 -1 430.37 164.96 444.94 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n267 3 Car -1 -1 -1 1116.92 188.66 1221.17 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n267 7 Car -1 -1 -1 982.80 185.24 1068.98 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n267 11 Car -1 -1 -1 931.16 184.42 1009.78 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n267 4 Car -1 -1 -1 876.79 183.66 945.56 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n267 63 Pedestrian -1 -1 -1 559.73 167.99 589.05 230.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n267 137 Car -1 -1 -1 607.13 175.98 633.68 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n267 131 Pedestrian -1 -1 -1 382.76 168.31 400.95 210.69 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n267 150 Pedestrian -1 -1 -1 460.40 170.97 474.03 208.17 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n267 9 Pedestrian -1 -1 -1 277.57 160.03 292.45 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n267 146 Pedestrian -1 -1 -1 183.46 158.76 202.02 209.93 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n267 151 Pedestrian -1 -1 -1 430.51 165.53 443.93 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n268 3 Car -1 -1 -1 1116.94 188.56 1221.08 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n268 7 Car -1 -1 -1 982.87 185.23 1068.96 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n268 11 Car -1 -1 -1 931.09 184.43 1009.80 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n268 4 Car -1 -1 -1 876.75 183.64 945.53 218.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n268 63 Pedestrian -1 -1 -1 563.51 168.43 593.01 230.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n268 131 Pedestrian -1 -1 -1 383.28 168.85 401.36 210.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n268 137 Car -1 -1 -1 607.12 175.95 633.71 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n268 9 Pedestrian -1 -1 -1 277.59 160.08 292.25 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n268 150 Pedestrian -1 -1 -1 458.82 170.26 472.41 208.17 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n268 146 Pedestrian -1 -1 -1 183.45 158.93 202.06 209.73 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n268 151 Pedestrian -1 -1 -1 430.46 165.70 443.54 200.01 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n269 3 Car -1 -1 -1 1117.19 188.56 1220.78 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n269 7 Car -1 -1 -1 982.94 185.21 1068.90 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n269 11 Car -1 -1 -1 931.04 184.45 1009.93 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n269 4 Car -1 -1 -1 876.75 183.63 945.58 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n269 131 Pedestrian -1 -1 -1 383.15 169.44 401.39 211.41 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n269 63 Pedestrian -1 -1 -1 566.27 169.59 593.84 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n269 137 Car -1 -1 -1 607.14 175.89 633.63 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n269 150 Pedestrian -1 -1 -1 457.79 169.25 470.80 207.53 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n269 9 Pedestrian -1 -1 -1 277.54 160.14 292.25 193.01 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n269 146 Pedestrian -1 -1 -1 183.27 158.98 202.07 209.68 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n270 3 Car -1 -1 -1 1117.04 188.58 1220.80 225.41 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n270 11 Car -1 -1 -1 931.02 184.39 1009.81 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n270 7 Car -1 -1 -1 982.79 185.24 1069.00 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n270 4 Car -1 -1 -1 876.79 183.68 945.54 218.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n270 131 Pedestrian -1 -1 -1 383.02 169.18 401.73 211.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n270 63 Pedestrian -1 -1 -1 576.76 171.55 596.76 231.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n270 137 Car -1 -1 -1 607.17 175.91 633.60 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n270 9 Pedestrian -1 -1 -1 277.73 160.20 291.90 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n270 150 Pedestrian -1 -1 -1 456.50 169.24 470.47 207.46 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n270 146 Pedestrian -1 -1 -1 183.29 158.79 202.20 209.85 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n270 152 Pedestrian -1 -1 -1 1.29 156.63 33.74 270.18 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n271 3 Car -1 -1 -1 1116.94 188.57 1220.77 225.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n271 7 Car -1 -1 -1 979.24 185.14 1069.25 221.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n271 11 Car -1 -1 -1 930.99 184.46 1009.98 220.70 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n271 63 Pedestrian -1 -1 -1 581.44 169.55 607.27 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n271 4 Car -1 -1 -1 876.66 183.63 945.68 218.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n271 131 Pedestrian -1 -1 -1 383.19 169.10 401.77 211.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n271 137 Car -1 -1 -1 607.26 175.82 633.35 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n271 150 Pedestrian -1 -1 -1 454.07 169.05 468.79 207.85 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n271 9 Pedestrian -1 -1 -1 277.72 160.18 292.03 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n271 152 Pedestrian -1 -1 -1 1.67 159.29 39.89 268.28 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n271 146 Pedestrian -1 -1 -1 183.35 158.89 202.09 209.73 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n272 3 Car -1 -1 -1 1116.71 188.47 1220.74 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n272 11 Car -1 -1 -1 931.03 184.43 1009.89 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n272 7 Car -1 -1 -1 979.22 185.18 1069.28 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n272 4 Car -1 -1 -1 876.65 183.67 945.66 218.23 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n272 131 Pedestrian -1 -1 -1 383.30 168.99 402.06 211.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n272 63 Pedestrian -1 -1 -1 582.54 169.81 614.64 231.73 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n272 137 Car -1 -1 -1 607.46 176.00 633.09 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n272 150 Pedestrian -1 -1 -1 453.43 168.15 466.70 207.48 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n272 9 Pedestrian -1 -1 -1 277.67 160.12 292.12 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n272 146 Pedestrian -1 -1 -1 183.12 158.47 202.13 210.02 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n273 3 Car -1 -1 -1 1116.46 188.55 1220.82 225.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n273 7 Car -1 -1 -1 982.76 185.15 1069.02 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n273 11 Car -1 -1 -1 931.12 184.43 1009.85 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n273 4 Car -1 -1 -1 876.71 183.69 945.51 218.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n273 131 Pedestrian -1 -1 -1 383.20 169.47 402.37 212.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n273 63 Pedestrian -1 -1 -1 585.26 171.13 620.82 231.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n273 150 Pedestrian -1 -1 -1 451.42 168.12 464.22 207.16 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n273 137 Car -1 -1 -1 607.60 176.07 633.17 199.27 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n273 9 Pedestrian -1 -1 -1 277.63 160.09 292.08 193.05 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n273 146 Pedestrian -1 -1 -1 182.74 158.52 201.59 209.97 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n273 153 Pedestrian -1 -1 -1 0.16 154.51 48.59 266.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n273 154 Pedestrian -1 -1 -1 428.83 166.12 441.12 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n274 3 Car -1 -1 -1 1116.63 188.46 1220.85 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n274 7 Car -1 -1 -1 982.83 185.24 1068.98 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n274 11 Car -1 -1 -1 931.20 184.48 1009.88 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n274 4 Car -1 -1 -1 876.67 183.65 945.53 218.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n274 153 Pedestrian -1 -1 -1 1.77 155.40 46.90 262.74 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n274 131 Pedestrian -1 -1 -1 383.08 170.00 402.39 212.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n274 137 Car -1 -1 -1 607.50 176.04 633.32 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n274 150 Pedestrian -1 -1 -1 450.03 168.44 463.71 207.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n274 9 Pedestrian -1 -1 -1 273.30 160.63 289.68 196.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n274 154 Pedestrian -1 -1 -1 429.09 166.57 440.72 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n274 155 Cyclist -1 -1 -1 55.08 142.16 346.22 361.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n274 156 Pedestrian -1 -1 -1 380.47 165.90 394.01 199.10 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n274 157 Cyclist -1 -1 -1 596.98 168.93 622.72 231.34 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n275 3 Car -1 -1 -1 1116.68 188.46 1221.02 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n275 7 Car -1 -1 -1 982.85 185.15 1069.04 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n275 11 Car -1 -1 -1 931.23 184.46 1009.82 220.57 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n275 4 Car -1 -1 -1 876.65 183.60 945.66 218.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n275 155 Cyclist -1 -1 -1 127.62 146.83 388.49 364.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n275 153 Pedestrian -1 -1 -1 5.63 156.06 50.88 258.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n275 137 Car -1 -1 -1 607.53 175.81 633.68 199.46 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n275 131 Pedestrian -1 -1 -1 386.02 169.85 403.16 213.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n275 9 Pedestrian -1 -1 -1 273.69 161.24 289.78 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n275 150 Pedestrian -1 -1 -1 448.95 168.78 462.76 207.96 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n275 154 Pedestrian -1 -1 -1 429.00 166.61 440.63 196.88 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n275 158 Pedestrian -1 -1 -1 604.33 168.72 624.75 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n276 155 Cyclist -1 -1 -1 194.30 154.51 414.27 363.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n276 3 Car -1 -1 -1 1116.54 188.64 1220.85 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n276 11 Car -1 -1 -1 931.36 184.50 1009.63 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n276 7 Car -1 -1 -1 979.43 185.16 1069.04 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n276 4 Car -1 -1 -1 876.69 183.63 945.55 218.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n276 158 Pedestrian -1 -1 -1 604.80 169.20 632.56 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n276 137 Car -1 -1 -1 607.09 176.08 633.49 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n276 131 Pedestrian -1 -1 -1 386.33 170.24 403.86 213.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n276 153 Pedestrian -1 -1 -1 6.65 157.77 57.70 259.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n276 150 Pedestrian -1 -1 -1 446.28 168.22 461.52 208.35 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n276 9 Pedestrian -1 -1 -1 273.47 160.87 289.25 197.45 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n276 154 Pedestrian -1 -1 -1 429.07 165.72 440.13 195.58 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n276 159 Pedestrian -1 -1 -1 378.36 166.67 390.98 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n277 155 Cyclist -1 -1 -1 237.22 153.52 439.78 366.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n277 3 Car -1 -1 -1 1116.83 188.48 1221.00 225.62 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n277 7 Car -1 -1 -1 982.46 185.16 1069.33 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n277 11 Car -1 -1 -1 931.29 184.45 1009.89 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n277 4 Car -1 -1 -1 876.78 183.61 945.43 218.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n277 158 Pedestrian -1 -1 -1 608.17 170.17 640.65 234.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n277 131 Pedestrian -1 -1 -1 387.22 170.37 404.16 213.09 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n277 137 Car -1 -1 -1 607.07 176.36 633.43 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n277 153 Pedestrian -1 -1 -1 11.70 158.44 59.85 255.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n277 9 Pedestrian -1 -1 -1 272.44 160.59 290.05 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n277 150 Pedestrian -1 -1 -1 444.65 168.06 460.59 208.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n277 159 Pedestrian -1 -1 -1 378.09 166.74 390.18 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n277 154 Pedestrian -1 -1 -1 429.19 165.75 440.38 195.38 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n277 160 Pedestrian -1 -1 -1 1.61 158.80 25.06 254.97 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n278 3 Car -1 -1 -1 1116.45 188.48 1221.22 225.60 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n278 7 Car -1 -1 -1 982.81 185.16 1069.06 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n278 11 Car -1 -1 -1 931.27 184.43 1009.80 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n278 155 Cyclist -1 -1 -1 275.32 154.70 455.02 365.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n278 4 Car -1 -1 -1 876.63 183.59 945.62 218.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n278 137 Car -1 -1 -1 607.34 176.19 633.53 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n278 153 Pedestrian -1 -1 -1 17.22 157.83 62.98 254.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n278 131 Pedestrian -1 -1 -1 388.55 170.65 404.00 212.72 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n278 158 Pedestrian -1 -1 -1 613.62 169.55 642.76 235.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n278 160 Pedestrian -1 -1 -1 2.20 160.99 31.52 252.65 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n278 9 Pedestrian -1 -1 -1 272.52 160.31 290.17 196.90 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n278 150 Pedestrian -1 -1 -1 442.01 167.33 458.79 209.21 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n278 154 Pedestrian -1 -1 -1 429.02 166.16 440.09 195.00 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n278 159 Pedestrian -1 -1 -1 378.08 166.89 390.44 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n279 3 Car -1 -1 -1 1116.71 188.39 1220.44 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n279 7 Car -1 -1 -1 982.86 185.16 1069.13 221.26 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n279 155 Cyclist -1 -1 -1 308.96 154.05 474.02 364.50 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n279 11 Car -1 -1 -1 931.24 184.50 1009.95 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n279 4 Car -1 -1 -1 876.72 183.55 945.69 218.34 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n279 153 Pedestrian -1 -1 -1 26.64 156.12 69.70 255.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n279 137 Car -1 -1 -1 607.81 176.37 632.89 200.00 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n279 160 Pedestrian -1 -1 -1 2.01 163.43 32.30 250.04 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n279 158 Pedestrian -1 -1 -1 620.45 170.65 644.82 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n279 131 Pedestrian -1 -1 -1 388.72 170.58 403.84 212.71 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n279 150 Pedestrian -1 -1 -1 441.53 167.06 458.49 209.35 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n279 9 Pedestrian -1 -1 -1 277.28 160.05 292.61 193.13 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n279 154 Pedestrian -1 -1 -1 428.28 167.41 439.25 195.82 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n280 3 Car -1 -1 -1 1116.95 188.38 1220.67 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n280 7 Car -1 -1 -1 983.01 185.14 1069.00 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n280 11 Car -1 -1 -1 931.38 184.53 1009.73 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n280 153 Pedestrian -1 -1 -1 34.22 156.82 77.49 253.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n280 4 Car -1 -1 -1 876.78 183.61 945.56 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n280 155 Cyclist -1 -1 -1 341.54 153.91 487.85 359.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n280 158 Pedestrian -1 -1 -1 629.33 168.01 651.16 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n280 160 Pedestrian -1 -1 -1 3.07 162.33 38.69 250.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n280 137 Car -1 -1 -1 607.81 176.06 632.70 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n280 131 Pedestrian -1 -1 -1 388.75 170.83 404.03 212.41 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n280 9 Pedestrian -1 -1 -1 273.47 160.71 289.51 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n280 150 Pedestrian -1 -1 -1 442.88 167.72 457.61 208.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n280 154 Pedestrian -1 -1 -1 428.23 167.54 438.37 195.84 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n280 161 Pedestrian -1 -1 -1 377.57 167.08 390.32 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n281 3 Car -1 -1 -1 1116.93 188.40 1220.62 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n281 7 Car -1 -1 -1 982.98 185.18 1068.97 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n281 11 Car -1 -1 -1 931.33 184.55 1009.86 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n281 153 Pedestrian -1 -1 -1 38.21 157.79 80.48 252.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n281 4 Car -1 -1 -1 876.79 183.59 945.60 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n281 158 Pedestrian -1 -1 -1 632.50 167.45 663.26 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n281 137 Car -1 -1 -1 607.52 176.05 633.05 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n281 155 Cyclist -1 -1 -1 361.05 155.62 499.98 363.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n281 160 Pedestrian -1 -1 -1 5.49 160.96 43.77 252.21 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n281 131 Pedestrian -1 -1 -1 387.21 170.18 403.50 213.64 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n281 150 Pedestrian -1 -1 -1 443.17 168.46 457.05 207.50 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n281 9 Pedestrian -1 -1 -1 273.59 160.63 289.77 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n281 162 Pedestrian -1 -1 -1 -0.49 155.82 19.39 249.33 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n282 3 Car -1 -1 -1 1117.00 188.48 1220.62 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n282 7 Car -1 -1 -1 983.23 185.25 1068.70 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n282 11 Car -1 -1 -1 931.39 184.60 1009.84 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n282 4 Car -1 -1 -1 876.75 183.62 945.62 218.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n282 158 Pedestrian -1 -1 -1 634.83 168.84 670.09 235.05 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n282 137 Car -1 -1 -1 607.45 175.94 633.73 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n282 153 Pedestrian -1 -1 -1 40.49 156.79 85.88 254.10 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n282 131 Pedestrian -1 -1 -1 386.51 169.25 403.66 214.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n282 155 Cyclist -1 -1 -1 383.19 154.97 507.70 356.58 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n282 160 Pedestrian -1 -1 -1 8.39 161.15 48.75 251.68 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n282 150 Pedestrian -1 -1 -1 442.14 168.27 457.05 207.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n282 9 Pedestrian -1 -1 -1 277.56 160.24 291.72 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n283 3 Car -1 -1 -1 1117.06 188.54 1220.62 225.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n283 7 Car -1 -1 -1 983.14 185.33 1068.83 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n283 11 Car -1 -1 -1 931.48 184.54 1009.56 220.47 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n283 4 Car -1 -1 -1 876.84 183.63 945.66 218.08 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n283 137 Car -1 -1 -1 607.66 176.05 633.70 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n283 158 Pedestrian -1 -1 -1 641.23 168.75 677.11 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n283 155 Cyclist -1 -1 -1 405.65 154.33 514.31 343.35 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n283 131 Pedestrian -1 -1 -1 386.24 169.38 403.74 214.93 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n283 153 Pedestrian -1 -1 -1 44.09 154.24 89.18 256.36 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n283 9 Pedestrian -1 -1 -1 277.56 160.22 291.72 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n283 150 Pedestrian -1 -1 -1 442.18 168.02 457.69 207.52 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n283 160 Pedestrian -1 -1 -1 7.51 160.07 49.85 252.59 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n284 3 Car -1 -1 -1 1117.07 188.52 1220.67 225.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n284 7 Car -1 -1 -1 982.97 185.35 1069.08 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n284 11 Car -1 -1 -1 931.35 184.60 1009.84 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n284 137 Car -1 -1 -1 607.97 176.00 634.01 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n284 4 Car -1 -1 -1 876.67 183.64 945.62 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n284 155 Cyclist -1 -1 -1 417.81 157.98 519.52 332.36 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n284 153 Pedestrian -1 -1 -1 52.10 156.01 90.10 250.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n284 160 Pedestrian -1 -1 -1 16.56 160.76 56.59 250.69 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n284 131 Pedestrian -1 -1 -1 386.85 169.42 404.73 215.25 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n284 9 Pedestrian -1 -1 -1 273.22 160.62 289.90 197.05 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n284 158 Pedestrian -1 -1 -1 653.83 166.90 679.18 238.33 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n284 150 Pedestrian -1 -1 -1 442.38 168.21 455.26 207.52 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n284 163 Pedestrian -1 -1 -1 3.29 156.21 39.42 248.30 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n284 164 Pedestrian -1 -1 -1 426.21 166.28 437.01 194.67 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n285 3 Car -1 -1 -1 1117.01 188.52 1220.72 225.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n285 7 Car -1 -1 -1 983.07 185.37 1068.85 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n285 11 Car -1 -1 -1 931.34 184.57 1009.74 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n285 4 Car -1 -1 -1 876.78 183.62 945.74 218.06 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n285 137 Car -1 -1 -1 607.85 176.10 634.04 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n285 155 Cyclist -1 -1 -1 431.33 158.27 527.34 324.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n285 163 Pedestrian -1 -1 -1 6.20 156.25 50.61 249.05 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n285 131 Pedestrian -1 -1 -1 386.46 170.56 405.23 216.65 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n285 153 Pedestrian -1 -1 -1 55.02 157.94 95.04 251.65 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n285 158 Pedestrian -1 -1 -1 658.48 167.52 683.82 236.89 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n285 160 Pedestrian -1 -1 -1 19.14 161.88 61.72 249.51 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n285 9 Pedestrian -1 -1 -1 273.58 160.81 289.74 196.77 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n285 150 Pedestrian -1 -1 -1 437.86 167.17 453.21 211.47 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n285 164 Pedestrian -1 -1 -1 425.81 165.90 437.11 194.42 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n285 165 Cyclist -1 -1 -1 74.84 142.39 357.33 361.12 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n285 166 Pedestrian -1 -1 -1 374.23 166.40 387.20 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n286 3 Car -1 -1 -1 1117.20 188.51 1220.64 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n286 7 Car -1 -1 -1 982.98 185.39 1068.93 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n286 11 Car -1 -1 -1 931.38 184.57 1009.74 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n286 165 Cyclist -1 -1 -1 157.63 131.95 373.95 365.88 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n286 137 Car -1 -1 -1 607.86 176.15 634.07 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n286 4 Car -1 -1 -1 876.72 183.65 945.64 218.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n286 155 Cyclist -1 -1 -1 444.07 160.72 531.49 314.31 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n286 158 Pedestrian -1 -1 -1 661.09 167.75 690.77 236.50 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n286 131 Pedestrian -1 -1 -1 387.57 170.82 405.33 217.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n286 153 Pedestrian -1 -1 -1 57.99 159.69 99.57 250.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n286 163 Pedestrian -1 -1 -1 8.05 155.87 49.64 246.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n286 160 Pedestrian -1 -1 -1 27.67 162.31 67.74 248.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n286 9 Pedestrian -1 -1 -1 273.65 161.38 290.02 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n286 164 Pedestrian -1 -1 -1 425.76 165.45 437.29 194.49 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n286 150 Pedestrian -1 -1 -1 433.29 166.07 452.26 212.81 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n286 166 Pedestrian -1 -1 -1 373.44 166.26 385.88 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n287 3 Car -1 -1 -1 1117.15 188.48 1220.60 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n287 165 Cyclist -1 -1 -1 195.66 140.49 412.62 364.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n287 7 Car -1 -1 -1 983.04 185.44 1068.86 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n287 11 Car -1 -1 -1 931.30 184.59 1009.78 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n287 137 Car -1 -1 -1 607.67 176.02 634.06 199.77 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n287 4 Car -1 -1 -1 876.65 183.70 945.72 217.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n287 155 Cyclist -1 -1 -1 454.25 160.77 537.07 305.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n287 158 Pedestrian -1 -1 -1 662.27 168.10 696.93 237.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n287 153 Pedestrian -1 -1 -1 60.93 160.67 103.67 248.77 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n287 163 Pedestrian -1 -1 -1 13.56 154.69 51.39 244.60 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n287 9 Pedestrian -1 -1 -1 273.37 160.39 289.15 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n287 131 Pedestrian -1 -1 -1 387.52 170.93 406.21 216.56 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n287 160 Pedestrian -1 -1 -1 33.99 162.31 69.73 247.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n287 164 Pedestrian -1 -1 -1 425.64 166.34 437.16 194.23 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n287 166 Pedestrian -1 -1 -1 373.34 166.38 385.59 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n287 150 Pedestrian -1 -1 -1 433.40 166.82 452.27 212.98 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n288 3 Car -1 -1 -1 1116.83 188.52 1220.78 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n288 165 Cyclist -1 -1 -1 235.27 147.91 433.35 364.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n288 7 Car -1 -1 -1 983.02 185.37 1068.85 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n288 11 Car -1 -1 -1 931.28 184.54 1009.98 220.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n288 137 Car -1 -1 -1 607.64 175.81 634.18 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n288 155 Cyclist -1 -1 -1 469.04 161.01 537.14 299.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n288 4 Car -1 -1 -1 876.77 183.73 945.70 217.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n288 153 Pedestrian -1 -1 -1 67.26 160.19 105.90 245.97 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n288 160 Pedestrian -1 -1 -1 40.42 161.35 77.25 248.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n288 158 Pedestrian -1 -1 -1 667.45 167.86 699.65 238.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n288 163 Pedestrian -1 -1 -1 18.53 155.60 54.49 246.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n288 9 Pedestrian -1 -1 -1 273.24 160.32 289.54 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n288 131 Pedestrian -1 -1 -1 389.21 170.85 407.73 216.47 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n288 150 Pedestrian -1 -1 -1 432.48 167.41 452.87 213.99 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n288 164 Pedestrian -1 -1 -1 425.56 166.51 436.99 194.52 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n288 166 Pedestrian -1 -1 -1 373.29 166.67 385.51 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n289 3 Car -1 -1 -1 1117.15 188.45 1220.82 225.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n289 7 Car -1 -1 -1 982.84 185.31 1069.06 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n289 165 Cyclist -1 -1 -1 267.27 145.02 454.79 367.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n289 11 Car -1 -1 -1 931.33 184.56 1009.88 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n289 137 Car -1 -1 -1 607.39 175.80 634.29 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n289 4 Car -1 -1 -1 876.95 183.81 945.63 217.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n289 153 Pedestrian -1 -1 -1 77.46 158.39 110.91 247.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n289 155 Cyclist -1 -1 -1 475.20 162.80 544.75 295.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n289 163 Pedestrian -1 -1 -1 21.95 156.92 58.40 245.22 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n289 160 Pedestrian -1 -1 -1 41.34 162.96 77.63 246.85 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n289 158 Pedestrian -1 -1 -1 676.93 166.64 704.84 240.14 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n289 131 Pedestrian -1 -1 -1 388.72 170.88 409.19 217.14 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n289 9 Pedestrian -1 -1 -1 272.67 159.97 289.69 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n289 164 Pedestrian -1 -1 -1 425.00 165.87 436.48 194.46 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n289 150 Pedestrian -1 -1 -1 432.61 166.99 453.01 213.57 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n290 3 Car -1 -1 -1 1117.21 188.59 1220.83 225.43 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n290 165 Cyclist -1 -1 -1 303.41 146.63 456.98 365.84 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n290 153 Pedestrian -1 -1 -1 84.05 158.30 118.13 246.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n290 7 Car -1 -1 -1 982.91 185.37 1068.92 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n290 137 Car -1 -1 -1 607.36 175.63 634.61 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n290 11 Car -1 -1 -1 931.33 184.56 1009.94 220.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n290 4 Car -1 -1 -1 876.83 183.85 945.61 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n290 155 Cyclist -1 -1 -1 483.51 162.03 547.88 289.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n290 160 Pedestrian -1 -1 -1 50.05 163.05 84.16 243.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n290 158 Pedestrian -1 -1 -1 686.45 166.07 716.55 240.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n290 131 Pedestrian -1 -1 -1 390.60 171.06 409.32 217.27 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n290 163 Pedestrian -1 -1 -1 25.17 155.15 63.78 243.99 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n290 164 Pedestrian -1 -1 -1 424.79 165.94 436.15 194.32 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n290 9 Pedestrian -1 -1 -1 277.39 159.49 292.08 193.80 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n290 150 Pedestrian -1 -1 -1 432.12 166.43 453.01 213.82 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n291 3 Car -1 -1 -1 1117.28 188.54 1220.82 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n291 165 Cyclist -1 -1 -1 325.65 146.94 465.38 365.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n291 153 Pedestrian -1 -1 -1 86.13 159.59 124.30 245.20 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n291 7 Car -1 -1 -1 983.07 185.38 1068.87 221.21 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n291 11 Car -1 -1 -1 931.34 184.56 1009.90 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n291 137 Car -1 -1 -1 607.29 175.72 634.38 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n291 155 Cyclist -1 -1 -1 488.92 162.54 554.53 287.08 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n291 4 Car -1 -1 -1 876.68 183.78 945.68 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n291 158 Pedestrian -1 -1 -1 686.60 166.38 725.14 238.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n291 163 Pedestrian -1 -1 -1 31.37 155.79 70.82 243.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n291 131 Pedestrian -1 -1 -1 391.24 171.25 408.61 216.95 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n291 160 Pedestrian -1 -1 -1 58.45 162.49 89.39 244.04 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n291 9 Pedestrian -1 -1 -1 272.98 160.38 289.75 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n291 164 Pedestrian -1 -1 -1 424.53 166.42 435.93 194.35 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n292 3 Car -1 -1 -1 1117.26 188.61 1220.71 225.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n292 7 Car -1 -1 -1 983.08 185.42 1068.86 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n292 153 Pedestrian -1 -1 -1 89.23 159.46 128.66 245.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n292 165 Cyclist -1 -1 -1 347.65 150.18 474.83 361.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n292 11 Car -1 -1 -1 931.36 184.58 1009.85 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n292 137 Car -1 -1 -1 607.28 175.70 634.62 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n292 155 Cyclist -1 -1 -1 494.50 162.75 558.39 280.18 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n292 4 Car -1 -1 -1 876.68 183.84 945.82 217.66 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n292 158 Pedestrian -1 -1 -1 689.18 166.19 729.65 240.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n292 163 Pedestrian -1 -1 -1 37.89 156.53 72.48 241.68 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n292 160 Pedestrian -1 -1 -1 60.14 162.43 90.03 244.18 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n292 131 Pedestrian -1 -1 -1 393.68 170.94 412.41 218.16 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n292 9 Pedestrian -1 -1 -1 273.19 160.37 289.79 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n292 167 Pedestrian -1 -1 -1 371.78 166.12 384.01 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n293 3 Car -1 -1 -1 1117.33 188.63 1220.51 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n293 153 Pedestrian -1 -1 -1 95.15 158.84 129.94 244.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n293 7 Car -1 -1 -1 983.01 185.43 1068.92 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n293 11 Car -1 -1 -1 931.22 184.52 1009.90 220.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n293 137 Car -1 -1 -1 607.27 175.82 634.78 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n293 155 Cyclist -1 -1 -1 504.96 161.62 560.30 275.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n293 4 Car -1 -1 -1 876.59 183.81 945.84 217.70 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n293 158 Pedestrian -1 -1 -1 699.90 166.20 733.48 240.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n293 165 Cyclist -1 -1 -1 368.95 150.62 482.88 354.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n293 160 Pedestrian -1 -1 -1 65.16 161.83 93.13 243.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n293 163 Pedestrian -1 -1 -1 44.43 156.54 74.65 241.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n293 9 Pedestrian -1 -1 -1 273.11 160.47 289.85 197.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n293 131 Pedestrian -1 -1 -1 391.74 170.38 408.72 218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n293 167 Pedestrian -1 -1 -1 373.09 165.63 385.67 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n294 3 Car -1 -1 -1 1116.85 188.57 1220.78 225.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n294 7 Car -1 -1 -1 982.91 185.43 1068.99 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n294 11 Car -1 -1 -1 930.99 184.51 1010.23 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n294 137 Car -1 -1 -1 607.11 175.85 634.77 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n294 4 Car -1 -1 -1 876.55 183.79 945.91 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n294 153 Pedestrian -1 -1 -1 100.81 158.41 132.09 243.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n294 160 Pedestrian -1 -1 -1 67.13 162.75 98.57 242.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n294 158 Pedestrian -1 -1 -1 711.63 165.00 737.24 241.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n294 131 Pedestrian -1 -1 -1 393.20 170.81 412.55 218.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n294 155 Cyclist -1 -1 -1 509.51 161.86 559.69 272.08 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n294 163 Pedestrian -1 -1 -1 50.19 157.43 82.56 240.59 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n294 165 Cyclist -1 -1 -1 389.54 152.40 493.44 336.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n294 9 Pedestrian -1 -1 -1 273.07 160.59 289.90 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n294 167 Pedestrian -1 -1 -1 373.28 165.92 386.06 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n295 3 Car -1 -1 -1 1117.07 188.55 1220.73 225.60 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n295 7 Car -1 -1 -1 982.93 185.37 1069.16 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n295 11 Car -1 -1 -1 930.97 184.48 1010.24 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n295 155 Cyclist -1 -1 -1 513.86 162.32 563.46 266.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n295 137 Car -1 -1 -1 607.15 175.89 634.60 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n295 153 Pedestrian -1 -1 -1 105.34 159.85 136.69 242.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n295 4 Car -1 -1 -1 876.51 183.83 945.87 217.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n295 165 Cyclist -1 -1 -1 403.15 153.55 503.40 328.01 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n295 131 Pedestrian -1 -1 -1 392.51 171.43 414.15 219.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n295 160 Pedestrian -1 -1 -1 73.34 162.53 105.34 242.09 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n295 163 Pedestrian -1 -1 -1 52.89 157.67 87.42 240.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n295 158 Pedestrian -1 -1 -1 715.30 166.34 748.65 241.01 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n295 9 Pedestrian -1 -1 -1 273.13 160.65 289.93 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n295 168 Pedestrian -1 -1 -1 423.69 166.23 434.58 194.07 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n296 3 Car -1 -1 -1 1117.27 188.61 1220.75 225.66 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n296 7 Car -1 -1 -1 983.12 185.44 1068.80 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n296 153 Pedestrian -1 -1 -1 106.63 160.88 141.72 241.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n296 11 Car -1 -1 -1 930.93 184.45 1010.24 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n296 155 Cyclist -1 -1 -1 518.41 163.60 565.75 264.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n296 137 Car -1 -1 -1 607.23 175.81 634.53 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n296 4 Car -1 -1 -1 876.54 183.80 946.04 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n296 163 Pedestrian -1 -1 -1 56.22 158.38 92.08 238.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n296 158 Pedestrian -1 -1 -1 716.69 166.56 755.95 243.02 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n296 131 Pedestrian -1 -1 -1 393.35 171.43 414.15 219.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n296 160 Pedestrian -1 -1 -1 74.23 162.81 106.50 241.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n296 165 Cyclist -1 -1 -1 417.90 155.32 511.11 317.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n296 9 Pedestrian -1 -1 -1 273.17 160.75 290.04 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n296 169 Pedestrian -1 -1 -1 370.78 165.36 385.06 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n297 3 Car -1 -1 -1 1117.12 188.49 1220.86 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n297 7 Car -1 -1 -1 983.05 185.37 1068.93 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n297 11 Car -1 -1 -1 930.84 184.41 1010.21 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n297 153 Pedestrian -1 -1 -1 110.44 160.18 145.12 241.79 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n297 137 Car -1 -1 -1 607.06 175.73 634.62 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n297 155 Cyclist -1 -1 -1 523.79 162.77 566.58 259.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n297 4 Car -1 -1 -1 876.48 183.86 945.99 217.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n297 158 Pedestrian -1 -1 -1 720.10 166.02 760.87 243.51 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n297 165 Cyclist -1 -1 -1 425.42 154.30 519.97 311.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n297 131 Pedestrian -1 -1 -1 396.75 170.91 417.58 219.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n297 163 Pedestrian -1 -1 -1 61.13 158.23 95.37 237.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n297 160 Pedestrian -1 -1 -1 81.00 162.39 112.87 240.40 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n297 9 Pedestrian -1 -1 -1 273.18 160.57 290.37 197.17 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n297 170 Pedestrian -1 -1 -1 422.73 167.04 433.12 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n298 3 Car -1 -1 -1 1116.97 188.56 1220.81 225.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n298 7 Car -1 -1 -1 983.03 185.45 1068.86 221.00 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n298 11 Car -1 -1 -1 930.79 184.42 1010.16 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n298 137 Car -1 -1 -1 607.35 175.74 634.41 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n298 155 Cyclist -1 -1 -1 528.43 163.34 568.25 256.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n298 158 Pedestrian -1 -1 -1 730.92 166.72 763.32 243.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n298 4 Car -1 -1 -1 876.35 183.79 946.12 217.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n298 165 Cyclist -1 -1 -1 441.44 154.55 526.32 303.17 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n298 160 Pedestrian -1 -1 -1 83.55 162.76 118.09 240.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n298 163 Pedestrian -1 -1 -1 63.27 157.04 94.49 238.98 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n298 153 Pedestrian -1 -1 -1 111.89 161.43 145.72 240.58 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n298 131 Pedestrian -1 -1 -1 400.33 170.72 420.17 219.50 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n298 9 Pedestrian -1 -1 -1 273.10 160.53 290.39 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n299 3 Car -1 -1 -1 1117.04 188.60 1220.78 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n299 7 Car -1 -1 -1 983.01 185.28 1068.93 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n299 11 Car -1 -1 -1 930.87 184.41 1010.11 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n299 165 Cyclist -1 -1 -1 452.80 154.99 531.11 296.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n299 137 Car -1 -1 -1 607.37 175.70 634.31 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n299 4 Car -1 -1 -1 876.41 183.78 946.09 217.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n299 153 Pedestrian -1 -1 -1 119.44 160.14 151.70 239.36 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n299 163 Pedestrian -1 -1 -1 67.43 156.24 97.13 239.18 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n299 155 Cyclist -1 -1 -1 531.49 165.13 571.90 254.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n299 158 Pedestrian -1 -1 -1 741.29 165.92 768.43 245.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n299 160 Pedestrian -1 -1 -1 88.23 162.88 120.68 239.71 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n299 131 Pedestrian -1 -1 -1 402.22 170.97 422.18 219.19 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n299 9 Pedestrian -1 -1 -1 273.30 160.57 290.24 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n299 171 Pedestrian -1 -1 -1 372.57 164.91 386.31 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n300 3 Car -1 -1 -1 1117.37 188.61 1220.51 225.56 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n300 7 Car -1 -1 -1 982.95 185.23 1069.16 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n300 11 Car -1 -1 -1 930.70 184.42 1010.08 220.49 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n300 153 Pedestrian -1 -1 -1 123.92 160.23 155.98 239.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n300 165 Cyclist -1 -1 -1 462.41 157.61 537.16 292.44 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n300 163 Pedestrian -1 -1 -1 69.39 156.89 101.75 239.17 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n300 137 Car -1 -1 -1 607.35 175.71 634.21 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n300 4 Car -1 -1 -1 876.41 183.81 946.14 217.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n300 155 Cyclist -1 -1 -1 535.31 164.83 571.50 248.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n300 158 Pedestrian -1 -1 -1 745.81 166.40 780.75 244.56 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n300 160 Pedestrian -1 -1 -1 95.06 162.40 124.11 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n300 131 Pedestrian -1 -1 -1 403.74 171.41 425.05 219.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n300 9 Pedestrian -1 -1 -1 273.50 160.72 290.22 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n300 171 Pedestrian -1 -1 -1 373.19 165.23 386.80 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n301 3 Car -1 -1 -1 1117.55 188.61 1220.43 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n301 7 Car -1 -1 -1 982.91 185.33 1069.01 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n301 11 Car -1 -1 -1 930.79 184.44 1010.15 220.50 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n301 163 Pedestrian -1 -1 -1 72.53 157.48 107.08 237.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n301 153 Pedestrian -1 -1 -1 127.02 161.06 158.73 237.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n301 158 Pedestrian -1 -1 -1 748.82 166.90 791.89 245.11 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n301 165 Cyclist -1 -1 -1 474.37 160.22 538.69 284.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n301 160 Pedestrian -1 -1 -1 98.90 161.65 128.08 236.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n301 137 Car -1 -1 -1 607.37 175.74 634.11 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n301 4 Car -1 -1 -1 876.41 183.79 946.07 217.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n301 155 Cyclist -1 -1 -1 540.52 164.58 573.26 241.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n301 9 Pedestrian -1 -1 -1 273.39 160.64 290.27 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n301 171 Pedestrian -1 -1 -1 373.20 165.40 387.15 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n301 131 Pedestrian -1 -1 -1 405.45 172.06 426.57 219.95 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n301 172 Pedestrian -1 -1 -1 183.83 159.20 202.16 209.24 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n302 3 Car -1 -1 -1 1117.06 188.61 1220.68 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n302 7 Car -1 -1 -1 982.91 185.24 1069.12 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n302 11 Car -1 -1 -1 930.78 184.44 1010.05 220.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n302 158 Pedestrian -1 -1 -1 754.15 167.70 795.32 245.76 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n302 153 Pedestrian -1 -1 -1 130.05 161.34 163.24 237.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n302 160 Pedestrian -1 -1 -1 102.69 161.24 131.44 237.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n302 4 Car -1 -1 -1 876.25 183.78 946.19 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n302 165 Cyclist -1 -1 -1 480.57 161.54 542.55 281.68 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n302 137 Car -1 -1 -1 607.11 175.68 633.86 199.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n302 163 Pedestrian -1 -1 -1 75.77 158.00 110.51 236.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n302 155 Cyclist -1 -1 -1 540.95 164.23 574.66 241.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n302 9 Pedestrian -1 -1 -1 273.12 160.56 290.27 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n302 171 Pedestrian -1 -1 -1 372.77 165.49 387.50 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n302 131 Pedestrian -1 -1 -1 408.01 172.17 427.58 219.88 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n302 172 Pedestrian -1 -1 -1 183.62 159.26 201.91 209.18 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n303 3 Car -1 -1 -1 1117.31 188.51 1220.59 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n303 7 Car -1 -1 -1 983.28 185.35 1068.64 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n303 11 Car -1 -1 -1 930.86 184.41 1009.93 220.41 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n303 158 Pedestrian -1 -1 -1 766.05 166.34 798.15 247.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n303 155 Cyclist -1 -1 -1 543.70 164.80 576.26 240.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n303 4 Car -1 -1 -1 876.39 183.76 946.22 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n303 160 Pedestrian -1 -1 -1 106.68 161.86 134.05 236.56 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n303 137 Car -1 -1 -1 607.12 175.64 633.77 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n303 153 Pedestrian -1 -1 -1 132.60 161.72 163.69 237.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n303 131 Pedestrian -1 -1 -1 409.61 171.99 428.47 219.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n303 165 Cyclist -1 -1 -1 485.61 160.65 548.98 276.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n303 163 Pedestrian -1 -1 -1 81.24 157.29 114.32 234.65 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n303 171 Pedestrian -1 -1 -1 372.82 165.66 387.67 199.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n303 9 Pedestrian -1 -1 -1 273.18 160.55 290.19 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n303 172 Pedestrian -1 -1 -1 183.64 159.02 202.06 209.34 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n304 3 Car -1 -1 -1 1117.30 188.62 1220.44 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n304 7 Car -1 -1 -1 983.14 185.33 1068.79 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n304 11 Car -1 -1 -1 930.85 184.44 1009.96 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n304 153 Pedestrian -1 -1 -1 136.04 161.40 165.78 236.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n304 4 Car -1 -1 -1 876.38 183.77 946.31 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n304 155 Cyclist -1 -1 -1 545.52 165.42 577.29 238.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n304 158 Pedestrian -1 -1 -1 771.21 166.96 801.98 246.29 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n304 137 Car -1 -1 -1 607.00 175.58 633.65 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n304 165 Cyclist -1 -1 -1 492.61 168.80 545.50 272.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n304 163 Pedestrian -1 -1 -1 86.40 158.72 117.85 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n304 171 Pedestrian -1 -1 -1 372.81 165.33 387.86 199.61 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n304 160 Pedestrian -1 -1 -1 110.55 162.77 136.03 236.59 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n304 9 Pedestrian -1 -1 -1 273.18 160.55 290.14 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n304 131 Pedestrian -1 -1 -1 410.91 171.49 429.07 219.86 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n304 172 Pedestrian -1 -1 -1 183.45 158.91 202.15 209.39 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n305 3 Car -1 -1 -1 1117.67 188.47 1220.63 225.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n305 11 Car -1 -1 -1 930.93 184.47 1009.87 220.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n305 7 Car -1 -1 -1 983.24 185.35 1068.65 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n305 153 Pedestrian -1 -1 -1 140.15 160.64 168.98 236.76 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n305 4 Car -1 -1 -1 876.13 183.74 946.42 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n305 131 Pedestrian -1 -1 -1 410.94 170.29 434.67 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n305 160 Pedestrian -1 -1 -1 111.54 163.41 137.91 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n305 158 Pedestrian -1 -1 -1 779.68 167.18 816.17 247.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n305 137 Car -1 -1 -1 607.08 175.62 633.74 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n305 163 Pedestrian -1 -1 -1 91.67 157.90 124.70 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n305 165 Cyclist -1 -1 -1 498.91 168.16 550.50 268.00 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n305 155 Cyclist -1 -1 -1 549.92 166.22 578.59 232.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n305 171 Pedestrian -1 -1 -1 372.62 165.13 388.01 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n305 9 Pedestrian -1 -1 -1 273.11 160.69 290.16 197.38 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n305 172 Pedestrian -1 -1 -1 183.32 158.97 202.07 209.32 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n306 3 Car -1 -1 -1 1117.18 188.52 1220.62 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n306 11 Car -1 -1 -1 930.87 184.43 1009.85 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n306 7 Car -1 -1 -1 982.80 185.23 1069.11 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n306 153 Pedestrian -1 -1 -1 141.25 160.73 174.56 237.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n306 4 Car -1 -1 -1 876.24 183.76 946.26 217.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n306 160 Pedestrian -1 -1 -1 114.47 163.76 141.13 234.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n306 137 Car -1 -1 -1 607.01 175.62 633.68 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n306 158 Pedestrian -1 -1 -1 782.16 168.68 827.25 248.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n306 163 Pedestrian -1 -1 -1 93.49 158.30 126.18 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n306 131 Pedestrian -1 -1 -1 412.95 169.96 437.59 221.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n306 171 Pedestrian -1 -1 -1 372.55 164.98 388.19 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n306 165 Cyclist -1 -1 -1 504.27 168.91 553.53 265.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n306 155 Cyclist -1 -1 -1 549.99 167.27 578.35 236.78 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n306 9 Pedestrian -1 -1 -1 272.92 160.60 290.27 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n306 172 Pedestrian -1 -1 -1 183.26 159.14 201.89 209.20 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n307 3 Car -1 -1 -1 1117.15 188.47 1220.61 225.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n307 7 Car -1 -1 -1 982.96 185.26 1068.95 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n307 11 Car -1 -1 -1 930.81 184.43 1010.04 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n307 153 Pedestrian -1 -1 -1 143.06 161.48 175.32 236.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n307 158 Pedestrian -1 -1 -1 790.55 166.09 828.46 248.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n307 4 Car -1 -1 -1 876.12 183.73 946.38 217.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n307 160 Pedestrian -1 -1 -1 118.92 163.15 144.42 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n307 163 Pedestrian -1 -1 -1 97.28 158.42 128.45 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n307 137 Car -1 -1 -1 607.02 175.70 633.62 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n307 131 Pedestrian -1 -1 -1 414.88 170.49 438.34 220.92 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n307 171 Pedestrian -1 -1 -1 372.54 164.82 388.16 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n307 155 Cyclist -1 -1 -1 550.96 168.40 578.61 235.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n307 9 Pedestrian -1 -1 -1 272.65 160.42 290.35 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n307 165 Cyclist -1 -1 -1 510.16 167.02 551.87 261.25 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n307 172 Pedestrian -1 -1 -1 183.20 159.09 201.58 209.08 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n307 173 Cyclist -1 -1 -1 556.89 167.92 580.24 223.56 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n308 3 Car -1 -1 -1 1117.24 188.51 1220.33 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n308 11 Car -1 -1 -1 930.71 184.50 1009.85 220.60 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n308 7 Car -1 -1 -1 982.76 185.32 1069.12 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n308 160 Pedestrian -1 -1 -1 120.76 163.19 150.13 234.35 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n308 153 Pedestrian -1 -1 -1 147.01 161.19 177.55 235.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n308 158 Pedestrian -1 -1 -1 801.81 166.38 831.67 251.40 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n308 4 Car -1 -1 -1 876.00 183.76 946.48 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n308 163 Pedestrian -1 -1 -1 101.28 158.70 131.81 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n308 171 Pedestrian -1 -1 -1 373.08 164.94 388.21 200.13 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n308 137 Car -1 -1 -1 607.05 175.71 633.61 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n308 165 Cyclist -1 -1 -1 513.94 167.76 559.47 259.77 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n308 131 Pedestrian -1 -1 -1 418.90 170.06 440.48 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n308 9 Pedestrian -1 -1 -1 272.85 160.43 290.32 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n308 155 Cyclist -1 -1 -1 551.15 168.45 579.27 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n308 172 Pedestrian -1 -1 -1 183.46 159.26 201.17 209.14 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n309 3 Car -1 -1 -1 1116.86 188.57 1220.53 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n309 11 Car -1 -1 -1 930.65 184.54 1009.90 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n309 153 Pedestrian -1 -1 -1 152.43 161.13 181.14 235.00 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n309 7 Car -1 -1 -1 982.92 185.35 1068.94 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n309 158 Pedestrian -1 -1 -1 811.27 166.05 845.68 252.29 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n309 4 Car -1 -1 -1 876.25 183.77 946.41 217.84 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n309 160 Pedestrian -1 -1 -1 123.14 163.10 154.27 234.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n309 171 Pedestrian -1 -1 -1 373.63 165.45 388.40 200.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n309 163 Pedestrian -1 -1 -1 105.63 157.70 135.64 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n309 165 Cyclist -1 -1 -1 518.16 164.75 562.34 256.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n309 137 Car -1 -1 -1 607.04 175.70 633.57 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n309 9 Pedestrian -1 -1 -1 272.68 160.43 290.40 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n309 131 Pedestrian -1 -1 -1 424.06 170.20 442.83 221.81 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n309 155 Cyclist -1 -1 -1 554.58 170.04 579.50 229.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n309 172 Pedestrian -1 -1 -1 183.93 159.10 201.62 209.34 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n309 174 Cyclist -1 -1 -1 562.73 169.83 581.05 214.79 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n310 3 Car -1 -1 -1 1116.85 188.67 1220.43 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n310 11 Car -1 -1 -1 930.52 184.49 1009.87 220.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n310 7 Car -1 -1 -1 982.75 185.31 1069.12 221.23 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n310 153 Pedestrian -1 -1 -1 157.91 160.78 183.81 234.91 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n310 160 Pedestrian -1 -1 -1 125.87 163.26 155.09 234.15 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n310 158 Pedestrian -1 -1 -1 815.43 166.61 857.18 251.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n310 163 Pedestrian -1 -1 -1 108.73 157.88 138.95 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n310 4 Car -1 -1 -1 876.15 183.83 946.25 217.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n310 165 Cyclist -1 -1 -1 522.05 169.10 566.82 252.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n310 137 Car -1 -1 -1 607.11 175.80 633.52 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n310 171 Pedestrian -1 -1 -1 374.59 165.53 388.33 200.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n310 155 Cyclist -1 -1 -1 555.85 169.95 580.19 228.85 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n310 9 Pedestrian -1 -1 -1 272.62 160.44 290.39 197.45 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n310 172 Pedestrian -1 -1 -1 184.04 159.28 201.87 209.22 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n310 131 Pedestrian -1 -1 -1 425.17 171.12 445.57 223.12 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n311 3 Car -1 -1 -1 1117.07 188.58 1220.42 225.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n311 153 Pedestrian -1 -1 -1 160.54 161.31 187.85 234.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n311 11 Car -1 -1 -1 930.46 184.48 1010.08 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n311 7 Car -1 -1 -1 978.95 185.33 1069.39 221.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n311 158 Pedestrian -1 -1 -1 818.49 167.11 867.23 252.75 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n311 163 Pedestrian -1 -1 -1 111.46 158.01 142.99 232.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n311 160 Pedestrian -1 -1 -1 134.03 164.28 159.09 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n311 4 Car -1 -1 -1 876.10 183.85 946.19 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n311 137 Car -1 -1 -1 607.25 175.86 633.53 199.78 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n311 171 Pedestrian -1 -1 -1 374.73 165.21 388.60 200.64 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n311 165 Cyclist -1 -1 -1 526.42 169.45 565.75 249.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n311 9 Pedestrian -1 -1 -1 272.64 160.35 290.40 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n311 155 Cyclist -1 -1 -1 556.45 170.29 580.60 227.86 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n311 131 Pedestrian -1 -1 -1 427.83 173.21 450.60 222.23 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n311 172 Pedestrian -1 -1 -1 183.82 158.99 202.17 209.57 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n312 153 Pedestrian -1 -1 -1 162.97 161.39 192.08 234.95 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n312 3 Car -1 -1 -1 1116.96 188.48 1220.58 225.80 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n312 11 Car -1 -1 -1 930.37 184.45 1010.16 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n312 158 Pedestrian -1 -1 -1 827.78 166.00 872.82 253.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n312 7 Car -1 -1 -1 979.24 185.25 1069.18 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n312 163 Pedestrian -1 -1 -1 113.15 158.75 144.30 231.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n312 160 Pedestrian -1 -1 -1 139.04 164.11 161.75 232.30 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n312 4 Car -1 -1 -1 876.03 183.88 946.24 217.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n312 165 Cyclist -1 -1 -1 533.40 167.22 571.24 246.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n312 137 Car -1 -1 -1 607.22 175.90 633.62 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n312 171 Pedestrian -1 -1 -1 374.70 164.85 388.93 200.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n312 9 Pedestrian -1 -1 -1 272.62 160.40 290.44 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n312 155 Cyclist -1 -1 -1 561.36 168.24 584.15 220.07 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n312 131 Pedestrian -1 -1 -1 430.16 173.68 453.63 223.01 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n312 172 Pedestrian -1 -1 -1 183.58 159.07 202.22 209.56 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n312 175 Cyclist -1 -1 -1 430.16 173.68 453.63 223.01 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n313 3 Car -1 -1 -1 1117.15 188.50 1220.25 225.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n313 11 Car -1 -1 -1 930.30 184.39 1010.48 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n313 7 Car -1 -1 -1 982.72 185.25 1069.15 221.26 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n313 158 Pedestrian -1 -1 -1 842.50 167.01 875.49 254.17 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n313 153 Pedestrian -1 -1 -1 166.42 161.20 194.65 235.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n313 4 Car -1 -1 -1 876.32 183.91 946.52 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n313 163 Pedestrian -1 -1 -1 115.92 160.11 146.14 231.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n313 160 Pedestrian -1 -1 -1 142.83 164.48 164.98 232.22 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n313 165 Cyclist -1 -1 -1 537.04 168.00 574.81 244.78 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n313 137 Car -1 -1 -1 607.20 175.92 633.49 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n313 171 Pedestrian -1 -1 -1 374.21 164.61 389.25 200.85 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n313 155 Cyclist -1 -1 -1 561.67 168.48 584.12 221.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n313 9 Pedestrian -1 -1 -1 272.74 160.47 290.38 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n314 3 Car -1 -1 -1 1117.01 188.47 1220.68 225.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n314 11 Car -1 -1 -1 930.32 184.48 1010.26 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n314 7 Car -1 -1 -1 982.77 185.26 1069.08 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n314 4 Car -1 -1 -1 876.77 183.99 946.26 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n314 160 Pedestrian -1 -1 -1 143.84 164.76 167.60 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n314 158 Pedestrian -1 -1 -1 850.12 168.59 890.95 256.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n314 163 Pedestrian -1 -1 -1 118.76 160.27 146.62 230.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n314 153 Pedestrian -1 -1 -1 168.72 161.36 196.06 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n314 165 Cyclist -1 -1 -1 540.42 167.63 578.15 244.20 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n314 137 Car -1 -1 -1 607.20 175.87 633.57 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n314 171 Pedestrian -1 -1 -1 374.07 164.67 389.35 201.24 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n314 155 Cyclist -1 -1 -1 564.13 167.24 586.10 222.14 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n314 9 Pedestrian -1 -1 -1 272.93 160.57 290.25 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n315 3 Car -1 -1 -1 1116.98 188.53 1220.80 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n315 7 Car -1 -1 -1 982.56 185.27 1069.30 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n315 11 Car -1 -1 -1 930.35 184.60 1010.50 220.56 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n315 158 Pedestrian -1 -1 -1 852.84 168.12 904.39 257.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n315 160 Pedestrian -1 -1 -1 146.17 164.34 170.54 231.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n315 153 Pedestrian -1 -1 -1 172.57 161.43 198.76 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n315 4 Car -1 -1 -1 877.25 184.02 946.10 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n315 163 Pedestrian -1 -1 -1 126.67 159.61 151.56 230.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n315 165 Cyclist -1 -1 -1 543.17 166.47 577.94 239.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n315 137 Car -1 -1 -1 607.34 175.94 633.69 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n315 155 Cyclist -1 -1 -1 564.55 167.06 586.69 221.76 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n315 171 Pedestrian -1 -1 -1 374.21 164.71 389.70 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n315 9 Pedestrian -1 -1 -1 272.90 160.66 290.11 196.97 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n316 3 Car -1 -1 -1 1116.90 188.44 1220.94 225.63 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n316 158 Pedestrian -1 -1 -1 858.50 169.10 913.09 258.73 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n316 7 Car -1 -1 -1 982.92 185.23 1069.02 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n316 11 Car -1 -1 -1 930.58 184.64 1010.17 220.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n316 4 Car -1 -1 -1 877.49 183.65 946.09 218.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n316 160 Pedestrian -1 -1 -1 150.20 164.24 173.61 230.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n316 153 Pedestrian -1 -1 -1 174.46 161.18 203.42 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n316 137 Car -1 -1 -1 607.25 175.91 633.54 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n316 163 Pedestrian -1 -1 -1 128.42 159.65 152.26 229.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n316 165 Cyclist -1 -1 -1 546.46 165.89 580.60 238.45 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n316 155 Cyclist -1 -1 -1 565.14 166.89 586.91 221.65 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n316 171 Pedestrian -1 -1 -1 373.81 164.65 390.11 201.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n316 9 Pedestrian -1 -1 -1 273.03 160.67 289.99 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n316 176 Pedestrian -1 -1 -1 438.39 171.64 466.09 223.44 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n317 3 Car -1 -1 -1 1116.97 188.46 1220.62 225.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n317 7 Car -1 -1 -1 982.72 185.27 1069.27 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n317 11 Car -1 -1 -1 930.59 184.67 1010.36 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n317 158 Pedestrian -1 -1 -1 874.13 169.65 912.98 259.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n317 4 Car -1 -1 -1 877.38 183.58 946.71 218.45 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n317 163 Pedestrian -1 -1 -1 132.12 160.94 155.49 229.19 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n317 176 Pedestrian -1 -1 -1 438.70 172.28 469.32 222.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n317 160 Pedestrian -1 -1 -1 154.31 163.71 177.02 230.73 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n317 137 Car -1 -1 -1 607.04 175.90 633.49 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n317 153 Pedestrian -1 -1 -1 176.45 161.23 207.84 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n317 165 Cyclist -1 -1 -1 549.38 165.73 580.56 237.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n317 171 Pedestrian -1 -1 -1 374.11 164.57 389.81 201.36 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n317 9 Pedestrian -1 -1 -1 273.16 160.68 289.85 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n317 155 Cyclist -1 -1 -1 565.33 166.81 586.38 221.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n318 3 Car -1 -1 -1 1116.70 188.48 1220.83 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n318 7 Car -1 -1 -1 982.91 185.31 1069.06 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n318 11 Car -1 -1 -1 930.67 184.70 1010.31 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n318 4 Car -1 -1 -1 877.54 183.59 946.24 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n318 158 Pedestrian -1 -1 -1 889.55 169.40 920.17 259.07 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n318 163 Pedestrian -1 -1 -1 134.75 161.63 157.93 227.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n318 153 Pedestrian -1 -1 -1 179.03 161.73 207.78 232.22 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n318 160 Pedestrian -1 -1 -1 155.98 162.11 182.31 229.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n318 137 Car -1 -1 -1 607.11 175.95 633.41 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n318 176 Pedestrian -1 -1 -1 445.19 171.57 469.21 223.61 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n318 171 Pedestrian -1 -1 -1 373.99 164.51 389.86 201.49 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n318 155 Cyclist -1 -1 -1 564.11 167.11 586.94 222.93 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n318 165 Cyclist -1 -1 -1 551.05 165.61 579.38 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n318 9 Pedestrian -1 -1 -1 273.27 160.71 289.87 196.74 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n319 3 Car -1 -1 -1 1116.61 188.52 1221.02 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n319 7 Car -1 -1 -1 982.75 185.31 1069.20 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n319 158 Pedestrian -1 -1 -1 894.34 167.97 938.30 260.81 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n319 11 Car -1 -1 -1 930.83 184.78 1010.21 220.57 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n319 4 Car -1 -1 -1 877.83 183.53 945.51 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n319 160 Pedestrian -1 -1 -1 156.65 164.30 185.21 230.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n319 163 Pedestrian -1 -1 -1 135.99 161.33 159.70 228.42 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n319 153 Pedestrian -1 -1 -1 184.41 162.33 208.94 229.78 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n319 137 Car -1 -1 -1 607.14 175.96 633.45 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n319 176 Pedestrian -1 -1 -1 453.72 171.88 474.03 224.52 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n319 165 Cyclist -1 -1 -1 552.52 165.69 582.94 232.02 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n319 171 Pedestrian -1 -1 -1 375.28 164.85 390.74 201.58 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n319 155 Cyclist -1 -1 -1 563.29 167.12 588.72 222.82 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n319 9 Pedestrian -1 -1 -1 276.95 160.07 292.40 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n320 3 Car -1 -1 -1 1116.47 188.50 1221.20 225.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n320 7 Car -1 -1 -1 982.84 185.34 1069.07 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n320 158 Pedestrian -1 -1 -1 897.87 169.98 950.48 263.20 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n320 11 Car -1 -1 -1 931.12 184.87 1009.67 220.28 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n320 160 Pedestrian -1 -1 -1 159.50 164.44 188.85 230.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n320 4 Car -1 -1 -1 878.47 183.48 945.92 218.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n320 163 Pedestrian -1 -1 -1 138.10 160.64 163.85 228.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n320 153 Pedestrian -1 -1 -1 187.39 162.10 213.03 230.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n320 165 Cyclist -1 -1 -1 552.82 165.30 583.45 232.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n320 137 Car -1 -1 -1 607.28 176.06 633.25 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n320 176 Pedestrian -1 -1 -1 455.82 172.83 479.15 224.53 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n320 171 Pedestrian -1 -1 -1 375.30 164.93 390.82 201.71 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n320 155 Cyclist -1 -1 -1 562.78 167.62 588.83 222.05 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n320 9 Pedestrian -1 -1 -1 276.69 159.89 292.46 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n321 3 Car -1 -1 -1 1116.67 188.53 1220.99 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n321 158 Pedestrian -1 -1 -1 905.03 169.86 958.56 265.25 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n321 7 Car -1 -1 -1 982.52 185.39 1069.41 220.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n321 11 Car -1 -1 -1 930.88 185.00 1009.95 219.98 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n321 176 Pedestrian -1 -1 -1 455.97 172.92 482.08 224.38 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n321 163 Pedestrian -1 -1 -1 141.46 159.10 167.52 228.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n321 4 Car -1 -1 -1 877.75 183.54 945.80 218.29 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n321 160 Pedestrian -1 -1 -1 163.86 163.25 190.33 228.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n321 165 Cyclist -1 -1 -1 553.61 166.11 583.55 230.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n321 137 Car -1 -1 -1 607.31 175.89 633.46 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n321 153 Pedestrian -1 -1 -1 190.04 162.51 217.19 229.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n321 171 Pedestrian -1 -1 -1 375.29 164.87 391.21 201.77 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n321 155 Cyclist -1 -1 -1 562.42 168.11 588.76 220.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n321 9 Pedestrian -1 -1 -1 276.55 159.87 292.28 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n322 3 Car -1 -1 -1 1116.80 188.52 1220.95 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n322 7 Car -1 -1 -1 982.34 185.31 1069.67 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n322 158 Pedestrian -1 -1 -1 921.82 169.87 963.57 264.76 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n322 176 Pedestrian -1 -1 -1 459.11 172.54 485.04 224.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n322 153 Pedestrian -1 -1 -1 192.00 162.73 218.87 228.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n322 11 Car -1 -1 -1 930.54 184.74 1010.03 220.31 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n322 163 Pedestrian -1 -1 -1 143.82 158.50 172.32 228.49 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n322 4 Car -1 -1 -1 877.29 183.32 945.71 218.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n322 160 Pedestrian -1 -1 -1 169.10 163.12 192.77 228.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n322 171 Pedestrian -1 -1 -1 375.83 164.70 391.46 201.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n322 137 Car -1 -1 -1 607.32 175.99 633.43 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n322 165 Cyclist -1 -1 -1 556.12 167.11 580.36 223.80 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n322 155 Cyclist -1 -1 -1 562.27 168.28 588.84 220.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n322 9 Pedestrian -1 -1 -1 276.49 159.86 292.35 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n323 3 Car -1 -1 -1 1116.34 188.65 1221.16 225.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n323 7 Car -1 -1 -1 982.48 185.40 1069.57 221.03 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n323 163 Pedestrian -1 -1 -1 144.47 159.43 174.64 228.28 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n323 153 Pedestrian -1 -1 -1 194.59 162.42 220.96 229.17 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n323 11 Car -1 -1 -1 930.54 184.54 1010.13 220.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n323 158 Pedestrian -1 -1 -1 936.65 171.12 972.12 265.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n323 4 Car -1 -1 -1 877.25 183.46 945.15 218.31 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n323 171 Pedestrian -1 -1 -1 375.66 165.13 391.43 202.17 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n323 160 Pedestrian -1 -1 -1 170.39 163.19 195.22 228.71 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n323 137 Car -1 -1 -1 607.21 175.94 633.45 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n323 176 Pedestrian -1 -1 -1 462.14 172.99 485.05 224.49 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n323 165 Cyclist -1 -1 -1 557.08 166.79 580.02 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n323 9 Pedestrian -1 -1 -1 276.77 159.86 292.46 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n323 155 Cyclist -1 -1 -1 573.04 167.29 591.17 213.88 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n323 177 Pedestrian -1 -1 -1 428.36 168.08 440.20 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n324 3 Car -1 -1 -1 1116.09 188.58 1221.17 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n324 7 Car -1 -1 -1 982.97 185.41 1069.13 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n324 163 Pedestrian -1 -1 -1 146.38 160.03 178.27 227.81 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n324 158 Pedestrian -1 -1 -1 943.59 170.84 988.30 265.60 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n324 11 Car -1 -1 -1 930.69 184.40 1010.25 220.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n324 4 Car -1 -1 -1 877.30 183.59 945.42 218.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n324 153 Pedestrian -1 -1 -1 196.02 162.27 221.07 228.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n324 160 Pedestrian -1 -1 -1 173.87 163.70 197.77 228.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n324 171 Pedestrian -1 -1 -1 376.04 165.45 391.31 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n324 137 Car -1 -1 -1 607.26 175.91 633.37 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n324 176 Pedestrian -1 -1 -1 469.43 172.35 488.19 225.11 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n324 165 Cyclist -1 -1 -1 558.73 166.47 584.67 223.28 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n324 9 Pedestrian -1 -1 -1 276.87 159.82 292.44 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n324 155 Cyclist -1 -1 -1 572.99 167.67 591.22 214.44 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n325 3 Car -1 -1 -1 1116.04 188.47 1221.72 225.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n325 7 Car -1 -1 -1 982.86 185.42 1069.48 221.00 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n325 153 Pedestrian -1 -1 -1 199.26 161.86 223.57 228.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n325 11 Car -1 -1 -1 930.58 184.57 1009.97 220.30 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n325 4 Car -1 -1 -1 876.89 183.66 945.41 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n325 158 Pedestrian -1 -1 -1 945.83 171.59 1001.24 269.69 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n325 176 Pedestrian -1 -1 -1 471.56 172.11 490.33 225.01 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n325 160 Pedestrian -1 -1 -1 176.68 164.10 199.96 227.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n325 163 Pedestrian -1 -1 -1 150.35 160.42 180.63 226.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n325 137 Car -1 -1 -1 607.13 175.81 633.47 199.70 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n325 171 Pedestrian -1 -1 -1 375.60 165.31 390.96 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n325 165 Cyclist -1 -1 -1 557.83 168.55 586.57 226.94 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n325 9 Pedestrian -1 -1 -1 277.10 159.88 292.75 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n325 155 Cyclist -1 -1 -1 572.45 167.59 591.68 214.17 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n325 178 Pedestrian -1 -1 -1 428.21 168.11 439.70 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n326 3 Car -1 -1 -1 1116.54 188.46 1221.43 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n326 7 Car -1 -1 -1 982.82 185.48 1069.41 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n326 176 Pedestrian -1 -1 -1 471.97 171.54 495.32 224.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n326 160 Pedestrian -1 -1 -1 178.32 163.89 201.78 226.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n326 4 Car -1 -1 -1 876.79 183.58 945.57 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n326 11 Car -1 -1 -1 930.42 184.58 1010.70 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n326 153 Pedestrian -1 -1 -1 200.67 161.88 226.22 228.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n326 137 Car -1 -1 -1 607.29 175.84 633.73 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n326 165 Cyclist -1 -1 -1 560.35 165.96 589.07 224.55 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n326 163 Pedestrian -1 -1 -1 154.59 161.01 179.90 225.93 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n326 158 Pedestrian -1 -1 -1 960.55 179.53 1008.96 270.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n326 171 Pedestrian -1 -1 -1 375.56 165.11 390.64 202.32 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n326 155 Cyclist -1 -1 -1 572.94 167.51 591.76 213.74 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n326 9 Pedestrian -1 -1 -1 277.09 159.88 292.87 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n326 178 Pedestrian -1 -1 -1 427.74 168.52 439.06 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n327 3 Car -1 -1 -1 1116.85 188.64 1221.31 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n327 153 Pedestrian -1 -1 -1 202.89 162.21 228.87 227.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n327 7 Car -1 -1 -1 982.60 185.56 1069.38 220.80 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n327 160 Pedestrian -1 -1 -1 181.11 164.05 204.30 226.14 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n327 4 Car -1 -1 -1 877.33 183.58 945.76 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n327 176 Pedestrian -1 -1 -1 473.45 169.98 500.15 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n327 11 Car -1 -1 -1 930.34 184.77 1010.46 220.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n327 137 Car -1 -1 -1 607.32 175.85 633.73 199.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n327 165 Cyclist -1 -1 -1 561.45 165.92 588.58 223.17 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n327 158 Pedestrian -1 -1 -1 980.86 171.59 1012.07 271.14 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n327 163 Pedestrian -1 -1 -1 161.94 160.54 184.62 225.98 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n327 171 Pedestrian -1 -1 -1 375.58 164.95 390.66 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n327 155 Cyclist -1 -1 -1 573.07 166.93 591.49 213.94 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n327 9 Pedestrian -1 -1 -1 277.14 159.76 293.00 193.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n327 178 Pedestrian -1 -1 -1 427.61 168.51 438.73 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n328 3 Car -1 -1 -1 1116.50 188.60 1221.59 225.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n328 7 Car -1 -1 -1 982.73 185.44 1069.49 220.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n328 160 Pedestrian -1 -1 -1 182.34 163.74 205.70 225.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n328 158 Pedestrian -1 -1 -1 988.15 171.31 1027.19 271.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n328 4 Car -1 -1 -1 877.47 183.61 945.82 217.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n328 176 Pedestrian -1 -1 -1 474.86 170.58 502.02 226.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n328 11 Car -1 -1 -1 930.77 184.55 1009.58 220.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n328 153 Pedestrian -1 -1 -1 206.59 161.66 231.68 227.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n328 137 Car -1 -1 -1 607.27 175.93 633.65 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n328 163 Pedestrian -1 -1 -1 162.97 160.75 187.09 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n328 171 Pedestrian -1 -1 -1 375.47 165.17 390.70 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n328 165 Cyclist -1 -1 -1 560.53 166.75 584.42 221.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n328 9 Pedestrian -1 -1 -1 277.19 159.69 293.07 193.86 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n328 155 Cyclist -1 -1 -1 572.91 167.32 591.40 213.84 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n329 3 Car -1 -1 -1 1116.64 188.59 1221.54 225.51 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n329 158 Pedestrian -1 -1 -1 991.96 172.37 1046.56 271.65 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n329 7 Car -1 -1 -1 983.48 185.40 1069.28 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n329 176 Pedestrian -1 -1 -1 477.83 169.60 504.81 226.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n329 153 Pedestrian -1 -1 -1 207.57 162.96 231.45 227.06 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n329 4 Car -1 -1 -1 877.43 183.67 945.58 217.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n329 11 Car -1 -1 -1 931.07 184.46 1009.67 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n329 160 Pedestrian -1 -1 -1 183.10 164.44 208.76 225.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n329 137 Car -1 -1 -1 607.32 175.87 633.59 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n329 171 Pedestrian -1 -1 -1 375.93 164.82 390.88 203.54 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n329 163 Pedestrian -1 -1 -1 166.42 161.51 190.08 224.81 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n329 165 Cyclist -1 -1 -1 560.29 167.21 584.85 222.08 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n329 9 Pedestrian -1 -1 -1 277.34 159.76 292.85 193.81 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n329 155 Cyclist -1 -1 -1 572.94 168.12 591.57 213.23 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n330 3 Car -1 -1 -1 1116.59 188.59 1221.54 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n330 158 Pedestrian -1 -1 -1 996.04 170.20 1058.73 274.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n330 7 Car -1 -1 -1 983.25 185.34 1070.01 220.70 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n330 160 Pedestrian -1 -1 -1 185.19 164.51 209.47 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n330 176 Pedestrian -1 -1 -1 484.64 169.95 506.82 227.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n330 11 Car -1 -1 -1 930.96 184.34 1009.92 220.77 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n330 4 Car -1 -1 -1 877.25 183.65 945.53 217.93 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n330 153 Pedestrian -1 -1 -1 210.22 162.55 234.76 227.25 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n330 163 Pedestrian -1 -1 -1 169.37 161.86 194.00 225.01 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n330 171 Pedestrian -1 -1 -1 376.39 164.92 391.19 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n330 137 Car -1 -1 -1 607.35 175.97 633.34 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n330 165 Cyclist -1 -1 -1 561.44 167.25 584.13 221.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n330 9 Pedestrian -1 -1 -1 277.49 159.66 292.89 193.91 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n330 155 Cyclist -1 -1 -1 572.77 167.96 591.77 213.04 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n331 3 Car -1 -1 -1 1116.70 188.64 1221.20 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n331 153 Pedestrian -1 -1 -1 211.28 162.51 236.72 226.96 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n331 158 Pedestrian -1 -1 -1 1007.39 170.78 1070.00 277.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n331 7 Car -1 -1 -1 984.04 185.19 1069.42 220.91 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n331 160 Pedestrian -1 -1 -1 188.54 163.61 212.50 225.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n331 4 Car -1 -1 -1 877.60 183.63 945.68 217.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n331 11 Car -1 -1 -1 931.25 184.61 1009.83 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n331 171 Pedestrian -1 -1 -1 376.78 164.55 391.88 203.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n331 176 Pedestrian -1 -1 -1 489.53 171.43 509.83 227.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n331 137 Car -1 -1 -1 607.40 176.02 633.15 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n331 165 Cyclist -1 -1 -1 562.13 167.66 583.23 220.49 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n331 163 Pedestrian -1 -1 -1 171.59 162.19 197.84 224.38 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n331 9 Pedestrian -1 -1 -1 277.44 159.83 292.64 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n331 155 Cyclist -1 -1 -1 573.05 168.14 591.39 212.25 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n332 3 Car -1 -1 -1 1116.31 188.58 1221.22 225.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n332 7 Car -1 -1 -1 984.69 185.54 1067.62 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n332 158 Pedestrian -1 -1 -1 1032.25 171.23 1075.31 278.15 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n332 153 Pedestrian -1 -1 -1 214.10 162.53 239.30 226.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n332 160 Pedestrian -1 -1 -1 192.85 163.58 215.56 224.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n332 4 Car -1 -1 -1 877.53 183.58 945.37 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n332 11 Car -1 -1 -1 931.45 184.61 1009.79 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n332 163 Pedestrian -1 -1 -1 171.14 160.60 199.04 223.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n332 171 Pedestrian -1 -1 -1 377.04 164.55 392.23 204.02 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n332 137 Car -1 -1 -1 607.40 175.93 633.26 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n332 176 Pedestrian -1 -1 -1 491.10 173.14 514.37 228.43 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n332 165 Cyclist -1 -1 -1 562.91 166.99 582.37 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n332 9 Pedestrian -1 -1 -1 277.44 159.73 292.70 193.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n332 179 Pedestrian -1 -1 -1 427.46 168.49 438.23 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n333 3 Car -1 -1 -1 1116.26 188.57 1221.17 225.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n333 7 Car -1 -1 -1 982.58 185.12 1065.66 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n333 153 Pedestrian -1 -1 -1 215.61 162.91 241.52 226.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n333 158 Pedestrian -1 -1 -1 1046.74 169.51 1092.12 278.31 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n333 4 Car -1 -1 -1 877.36 183.60 945.59 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n333 11 Car -1 -1 -1 934.58 184.57 1010.15 220.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n333 163 Pedestrian -1 -1 -1 174.85 159.90 201.94 223.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n333 160 Pedestrian -1 -1 -1 194.03 163.68 217.12 224.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n333 176 Pedestrian -1 -1 -1 495.10 172.82 517.96 226.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n333 137 Car -1 -1 -1 607.34 176.01 633.31 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n333 171 Pedestrian -1 -1 -1 376.96 164.57 392.76 204.41 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n333 165 Cyclist -1 -1 -1 563.49 167.40 582.19 213.65 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n333 9 Pedestrian -1 -1 -1 277.56 159.77 292.93 193.86 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n333 180 Cyclist -1 -1 -1 573.56 168.18 590.83 212.36 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n334 3 Car -1 -1 -1 1115.66 188.66 1221.00 225.22 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n334 158 Pedestrian -1 -1 -1 1045.71 169.69 1115.42 280.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n334 7 Car -1 -1 -1 984.18 185.10 1068.13 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n334 160 Pedestrian -1 -1 -1 196.70 164.03 219.70 224.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n334 4 Car -1 -1 -1 877.38 183.59 945.46 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n334 11 Car -1 -1 -1 931.15 184.66 1010.12 220.74 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n334 163 Pedestrian -1 -1 -1 176.08 160.48 204.46 223.15 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n334 153 Pedestrian -1 -1 -1 217.72 162.75 242.86 225.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n334 176 Pedestrian -1 -1 -1 497.10 173.49 521.90 228.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n334 137 Car -1 -1 -1 607.38 175.92 633.35 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n334 171 Pedestrian -1 -1 -1 377.10 164.59 392.86 204.47 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n334 9 Pedestrian -1 -1 -1 277.84 159.81 292.92 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n334 165 Cyclist -1 -1 -1 563.69 166.87 582.24 213.76 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n334 180 Cyclist -1 -1 -1 573.39 168.35 591.10 212.09 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n335 3 Car -1 -1 -1 1115.42 188.72 1221.37 225.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n335 7 Car -1 -1 -1 983.33 185.11 1069.00 220.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n335 158 Pedestrian -1 -1 -1 1053.68 169.99 1123.20 281.80 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n335 11 Car -1 -1 -1 931.07 184.62 1009.94 220.79 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n335 4 Car -1 -1 -1 877.10 183.57 945.60 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n335 163 Pedestrian -1 -1 -1 178.27 161.19 208.05 223.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n335 153 Pedestrian -1 -1 -1 219.35 162.26 243.11 224.54 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n335 160 Pedestrian -1 -1 -1 197.68 164.28 221.08 224.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n335 176 Pedestrian -1 -1 -1 503.13 172.34 524.15 227.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n335 137 Car -1 -1 -1 607.35 175.93 633.17 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n335 171 Pedestrian -1 -1 -1 376.65 164.46 393.48 204.59 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n335 165 Cyclist -1 -1 -1 564.48 166.67 585.21 215.04 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n335 9 Pedestrian -1 -1 -1 277.63 159.69 292.99 193.84 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n335 180 Cyclist -1 -1 -1 573.00 168.33 591.76 212.00 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n335 181 Pedestrian -1 -1 -1 427.59 168.61 438.25 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n336 7 Car -1 -1 -1 983.25 185.05 1069.07 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n336 3 Car -1 -1 -1 1115.62 188.78 1221.47 225.26 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n336 4 Car -1 -1 -1 877.18 183.59 945.62 218.21 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n336 11 Car -1 -1 -1 931.00 184.63 1010.12 220.74 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n336 160 Pedestrian -1 -1 -1 201.15 163.68 224.09 223.38 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n336 158 Pedestrian -1 -1 -1 1077.10 170.79 1130.22 284.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n336 163 Pedestrian -1 -1 -1 181.90 161.35 210.95 222.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n336 153 Pedestrian -1 -1 -1 222.75 161.24 246.13 224.97 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n336 137 Car -1 -1 -1 607.28 175.98 633.17 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n336 165 Cyclist -1 -1 -1 565.31 167.07 585.35 215.59 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n336 171 Pedestrian -1 -1 -1 376.94 164.34 393.46 204.87 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n336 9 Pedestrian -1 -1 -1 277.94 159.72 292.68 193.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n336 181 Pedestrian -1 -1 -1 426.22 168.08 437.14 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n336 176 Pedestrian -1 -1 -1 507.60 172.03 526.27 229.36 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n337 7 Car -1 -1 -1 983.43 185.17 1068.94 220.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n337 3 Car -1 -1 -1 1115.63 188.82 1221.87 225.26 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n337 4 Car -1 -1 -1 877.17 183.63 945.70 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n337 11 Car -1 -1 -1 931.03 184.61 1010.12 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n337 153 Pedestrian -1 -1 -1 221.96 162.46 248.99 224.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n337 163 Pedestrian -1 -1 -1 184.60 162.15 210.56 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n337 158 Pedestrian -1 -1 -1 1099.62 169.62 1137.26 286.36 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n337 160 Pedestrian -1 -1 -1 201.83 164.52 224.45 222.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n337 137 Car -1 -1 -1 607.28 175.95 633.08 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n337 176 Pedestrian -1 -1 -1 508.39 171.71 530.64 227.77 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n337 171 Pedestrian -1 -1 -1 377.23 164.22 393.50 204.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n337 165 Cyclist -1 -1 -1 565.69 166.83 584.95 214.09 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n337 9 Pedestrian -1 -1 -1 278.11 159.76 292.87 193.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n337 181 Pedestrian -1 -1 -1 425.68 168.32 437.28 196.49 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n337 182 Cyclist -1 -1 -1 574.15 169.36 592.52 210.09 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n338 7 Car -1 -1 -1 982.78 185.36 1069.50 220.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n338 158 Pedestrian -1 -1 -1 1108.77 169.58 1166.63 287.94 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n338 3 Car -1 -1 -1 1116.25 188.92 1221.34 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n338 4 Car -1 -1 -1 877.39 183.66 945.70 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n338 11 Car -1 -1 -1 931.09 184.59 1010.13 220.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n338 160 Pedestrian -1 -1 -1 204.21 164.64 227.76 222.28 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n338 153 Pedestrian -1 -1 -1 222.71 162.99 250.05 223.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n338 176 Pedestrian -1 -1 -1 510.17 173.54 535.94 229.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n338 163 Pedestrian -1 -1 -1 190.26 160.20 213.21 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n338 137 Car -1 -1 -1 607.33 175.94 633.07 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n338 165 Cyclist -1 -1 -1 565.83 167.78 584.74 213.01 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n338 171 Pedestrian -1 -1 -1 377.28 163.67 393.93 205.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n338 9 Pedestrian -1 -1 -1 278.02 159.76 293.06 193.84 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n338 181 Pedestrian -1 -1 -1 425.90 168.73 436.83 196.70 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n338 182 Cyclist -1 -1 -1 573.38 168.83 592.11 210.58 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n339 7 Car -1 -1 -1 982.63 185.29 1069.74 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n339 4 Car -1 -1 -1 877.20 183.70 945.69 217.98 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n339 11 Car -1 -1 -1 931.17 184.58 1010.10 220.69 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n339 3 Car -1 -1 -1 1115.75 188.87 1222.13 225.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n339 158 Pedestrian -1 -1 -1 1108.98 172.05 1190.01 287.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n339 153 Pedestrian -1 -1 -1 227.06 162.58 252.32 224.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n339 176 Pedestrian -1 -1 -1 512.42 173.54 538.95 229.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n339 163 Pedestrian -1 -1 -1 194.41 160.41 216.54 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n339 160 Pedestrian -1 -1 -1 204.65 164.76 228.65 221.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n339 137 Car -1 -1 -1 607.24 175.89 633.09 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n339 165 Cyclist -1 -1 -1 565.71 167.67 585.14 213.50 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n339 9 Pedestrian -1 -1 -1 277.77 159.94 293.45 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n339 171 Pedestrian -1 -1 -1 378.58 163.92 395.58 205.33 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n339 181 Pedestrian -1 -1 -1 425.60 168.78 437.00 196.73 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n340 7 Car -1 -1 -1 982.61 185.33 1069.66 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n340 158 Pedestrian -1 -1 -1 1120.74 171.85 1200.84 293.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n340 11 Car -1 -1 -1 931.22 184.57 1010.01 220.76 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n340 4 Car -1 -1 -1 877.08 183.72 945.60 217.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n340 163 Pedestrian -1 -1 -1 195.76 160.86 220.10 221.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n340 3 Car -1 -1 -1 1116.49 189.04 1220.99 224.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n340 176 Pedestrian -1 -1 -1 513.68 173.79 540.33 228.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n340 160 Pedestrian -1 -1 -1 207.02 164.52 231.37 221.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n340 137 Car -1 -1 -1 607.26 175.96 633.14 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n340 165 Cyclist -1 -1 -1 566.70 168.14 583.97 213.02 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n340 153 Pedestrian -1 -1 -1 233.77 161.33 256.87 223.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n340 171 Pedestrian -1 -1 -1 379.07 164.01 396.53 205.27 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n340 181 Pedestrian -1 -1 -1 426.01 168.50 436.92 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n340 9 Pedestrian -1 -1 -1 272.57 160.13 288.95 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n341 7 Car -1 -1 -1 982.76 185.34 1069.31 220.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n341 158 Pedestrian -1 -1 -1 1143.81 170.41 1207.86 294.92 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n341 11 Car -1 -1 -1 931.34 184.58 1009.96 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n341 4 Car -1 -1 -1 876.97 183.68 945.68 217.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n341 163 Pedestrian -1 -1 -1 196.12 161.34 221.13 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n341 3 Car -1 -1 -1 1117.55 188.99 1219.79 224.72 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n341 176 Pedestrian -1 -1 -1 519.47 173.59 541.51 229.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n341 153 Pedestrian -1 -1 -1 235.37 161.17 260.25 223.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n341 137 Car -1 -1 -1 607.40 176.03 633.09 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n341 160 Pedestrian -1 -1 -1 211.05 163.53 234.77 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n341 165 Cyclist -1 -1 -1 567.61 167.79 584.59 212.45 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n341 171 Pedestrian -1 -1 -1 380.01 164.50 396.37 206.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n341 181 Pedestrian -1 -1 -1 425.81 168.52 437.07 196.31 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n341 9 Pedestrian -1 -1 -1 272.62 159.95 288.91 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n342 7 Car -1 -1 -1 982.86 185.38 1069.08 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n342 3 Car -1 -1 -1 1118.43 189.00 1219.04 224.79 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n342 11 Car -1 -1 -1 931.37 184.57 1009.98 220.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n342 4 Car -1 -1 -1 876.86 183.70 945.69 217.94 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n342 163 Pedestrian -1 -1 -1 199.89 161.71 224.02 219.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n342 158 Pedestrian -1 -1 -1 1170.46 168.77 1212.31 297.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n342 153 Pedestrian -1 -1 -1 237.24 161.64 263.47 223.03 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n342 171 Pedestrian -1 -1 -1 380.82 164.65 396.72 206.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n342 160 Pedestrian -1 -1 -1 212.96 163.16 236.19 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n342 137 Car -1 -1 -1 607.34 175.96 633.10 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n342 176 Pedestrian -1 -1 -1 526.44 173.83 545.72 228.88 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n342 181 Pedestrian -1 -1 -1 425.96 168.81 437.10 196.22 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n342 165 Cyclist -1 -1 -1 567.89 167.74 584.99 212.54 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n342 9 Pedestrian -1 -1 -1 277.48 161.41 293.13 195.41 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n342 183 Pedestrian -1 -1 -1 270.27 160.53 285.90 196.34 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n343 7 Car -1 -1 -1 982.85 185.32 1069.11 221.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n343 3 Car -1 -1 -1 1116.96 189.11 1219.27 224.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n343 11 Car -1 -1 -1 931.45 184.58 1009.80 220.77 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n343 4 Car -1 -1 -1 876.84 183.69 945.71 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n343 153 Pedestrian -1 -1 -1 238.85 163.27 264.29 223.28 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n343 158 Pedestrian -1 -1 -1 1178.95 171.00 1218.74 295.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n343 160 Pedestrian -1 -1 -1 215.50 163.03 237.84 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n343 176 Pedestrian -1 -1 -1 527.42 174.93 549.64 228.72 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n343 163 Pedestrian -1 -1 -1 201.46 161.75 224.90 218.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n343 137 Car -1 -1 -1 607.32 175.98 633.14 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n343 171 Pedestrian -1 -1 -1 381.23 164.63 397.58 206.80 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n343 165 Cyclist -1 -1 -1 569.47 168.80 588.69 211.00 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n343 181 Pedestrian -1 -1 -1 426.04 168.79 437.25 195.98 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n343 183 Pedestrian -1 -1 -1 270.01 160.45 285.88 196.28 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n344 7 Car -1 -1 -1 982.87 185.27 1069.15 221.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n344 3 Car -1 -1 -1 1116.88 189.19 1219.24 224.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n344 11 Car -1 -1 -1 931.45 184.54 1009.82 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n344 4 Car -1 -1 -1 876.91 183.73 945.60 218.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n344 163 Pedestrian -1 -1 -1 203.84 161.17 228.17 219.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n344 160 Pedestrian -1 -1 -1 216.23 163.54 238.68 220.30 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n344 176 Pedestrian -1 -1 -1 529.93 174.19 553.88 227.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n344 153 Pedestrian -1 -1 -1 241.71 162.39 265.76 222.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n344 171 Pedestrian -1 -1 -1 380.89 164.62 398.11 207.07 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n344 137 Car -1 -1 -1 607.25 175.91 633.06 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n344 165 Cyclist -1 -1 -1 569.85 169.06 588.89 211.13 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n344 183 Pedestrian -1 -1 -1 270.39 160.26 285.73 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n344 181 Pedestrian -1 -1 -1 425.73 168.60 437.34 195.96 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n344 158 Pedestrian -1 -1 -1 1176.88 176.76 1221.35 296.73 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n344 184 Pedestrian -1 -1 -1 282.11 162.56 296.10 194.33 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n345 3 Car -1 -1 -1 1117.46 188.90 1219.77 225.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n345 7 Car -1 -1 -1 983.12 185.39 1068.78 221.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n345 11 Car -1 -1 -1 931.41 184.57 1009.86 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n345 4 Car -1 -1 -1 876.80 183.69 945.71 218.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n345 163 Pedestrian -1 -1 -1 206.67 160.30 232.01 220.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n345 153 Pedestrian -1 -1 -1 242.74 162.80 266.09 221.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n345 176 Pedestrian -1 -1 -1 533.02 174.24 556.01 227.38 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n345 137 Car -1 -1 -1 607.26 175.80 633.32 199.98 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n345 160 Pedestrian -1 -1 -1 217.02 163.57 240.39 220.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n345 171 Pedestrian -1 -1 -1 380.99 165.04 398.11 207.41 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n345 165 Cyclist -1 -1 -1 570.86 169.25 589.63 210.52 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n345 181 Pedestrian -1 -1 -1 425.32 168.32 437.24 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n345 183 Pedestrian -1 -1 -1 270.48 160.43 285.42 195.88 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n345 184 Pedestrian -1 -1 -1 282.16 162.52 295.98 194.49 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n346 3 Car -1 -1 -1 1117.51 188.64 1220.31 225.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n346 7 Car -1 -1 -1 982.97 185.36 1069.01 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n346 163 Pedestrian -1 -1 -1 208.02 161.83 233.40 219.13 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n346 4 Car -1 -1 -1 876.97 183.67 945.65 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n346 11 Car -1 -1 -1 931.42 184.60 1009.92 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n346 160 Pedestrian -1 -1 -1 220.39 164.13 243.71 219.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n346 153 Pedestrian -1 -1 -1 245.90 161.93 267.85 221.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n346 176 Pedestrian -1 -1 -1 539.06 173.65 558.57 228.18 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n346 137 Car -1 -1 -1 607.27 175.97 633.28 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n346 171 Pedestrian -1 -1 -1 381.30 165.17 398.00 207.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n346 181 Pedestrian -1 -1 -1 425.18 168.13 436.68 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n346 183 Pedestrian -1 -1 -1 270.50 160.33 285.29 195.98 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n346 165 Cyclist -1 -1 -1 571.90 169.41 589.54 210.44 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n346 184 Pedestrian -1 -1 -1 282.18 162.69 296.27 194.50 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n347 3 Car -1 -1 -1 1117.18 188.65 1220.45 225.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n347 7 Car -1 -1 -1 983.12 185.36 1068.85 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n347 4 Car -1 -1 -1 876.83 183.63 945.75 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n347 163 Pedestrian -1 -1 -1 208.98 161.45 237.31 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n347 11 Car -1 -1 -1 934.84 184.48 1009.85 220.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n347 153 Pedestrian -1 -1 -1 247.04 162.47 270.04 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n347 160 Pedestrian -1 -1 -1 223.08 163.53 245.36 219.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n347 137 Car -1 -1 -1 607.26 175.80 633.25 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n347 176 Pedestrian -1 -1 -1 542.48 172.91 560.67 228.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n347 171 Pedestrian -1 -1 -1 382.54 164.97 398.73 207.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n347 183 Pedestrian -1 -1 -1 270.25 160.36 285.31 195.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n347 181 Pedestrian -1 -1 -1 424.35 168.47 436.47 196.50 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n347 165 Cyclist -1 -1 -1 573.66 171.11 591.74 209.32 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n347 184 Pedestrian -1 -1 -1 282.18 162.72 296.43 194.42 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n348 3 Car -1 -1 -1 1117.16 188.56 1220.53 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n348 7 Car -1 -1 -1 982.93 185.30 1069.01 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n348 160 Pedestrian -1 -1 -1 224.26 163.57 246.82 219.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n348 11 Car -1 -1 -1 931.45 184.50 1009.85 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n348 4 Car -1 -1 -1 876.83 183.60 945.72 218.10 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n348 153 Pedestrian -1 -1 -1 249.37 162.33 272.60 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n348 163 Pedestrian -1 -1 -1 211.95 161.71 237.24 218.45 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n348 171 Pedestrian -1 -1 -1 382.99 165.56 399.14 207.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n348 137 Car -1 -1 -1 607.16 175.78 633.55 199.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n348 176 Pedestrian -1 -1 -1 542.19 172.98 565.38 228.45 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n348 181 Pedestrian -1 -1 -1 423.98 168.69 436.11 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n348 183 Pedestrian -1 -1 -1 269.99 160.44 285.63 195.72 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n348 165 Cyclist -1 -1 -1 573.67 168.65 590.95 208.14 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n348 185 Pedestrian -1 -1 -1 527.23 171.06 537.03 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n349 3 Car -1 -1 -1 1117.24 188.51 1220.69 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n349 7 Car -1 -1 -1 982.86 185.34 1069.02 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n349 4 Car -1 -1 -1 876.87 183.61 945.66 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n349 11 Car -1 -1 -1 931.41 184.50 1009.94 220.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n349 160 Pedestrian -1 -1 -1 226.30 163.93 249.62 218.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n349 171 Pedestrian -1 -1 -1 383.80 164.92 400.39 207.26 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n349 153 Pedestrian -1 -1 -1 250.13 162.63 273.89 220.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n349 176 Pedestrian -1 -1 -1 545.71 172.83 572.69 229.23 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n349 137 Car -1 -1 -1 607.20 175.76 633.36 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n349 163 Pedestrian -1 -1 -1 218.17 160.35 242.44 215.95 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n349 165 Cyclist -1 -1 -1 574.27 169.13 591.41 207.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n349 183 Pedestrian -1 -1 -1 269.85 160.38 286.22 195.85 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n349 181 Pedestrian -1 -1 -1 424.15 168.48 435.85 196.09 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n350 3 Car -1 -1 -1 1117.28 188.50 1220.68 225.51 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n350 7 Car -1 -1 -1 982.80 185.32 1069.08 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n350 160 Pedestrian -1 -1 -1 227.55 164.03 251.24 219.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n350 11 Car -1 -1 -1 931.34 184.49 1009.79 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n350 4 Car -1 -1 -1 876.84 183.66 945.70 218.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n350 163 Pedestrian -1 -1 -1 220.14 161.31 242.90 217.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n350 176 Pedestrian -1 -1 -1 546.26 172.77 574.21 229.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n350 153 Pedestrian -1 -1 -1 254.21 162.30 275.31 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n350 171 Pedestrian -1 -1 -1 384.29 164.64 401.43 207.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n350 137 Car -1 -1 -1 607.14 175.83 633.51 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n350 165 Cyclist -1 -1 -1 574.01 168.84 591.20 207.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n350 183 Pedestrian -1 -1 -1 269.72 160.17 286.16 196.00 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n350 181 Pedestrian -1 -1 -1 424.23 168.71 435.67 195.64 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n350 186 Pedestrian -1 -1 -1 523.41 170.85 534.89 201.81 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n351 3 Car -1 -1 -1 1117.17 188.62 1220.65 225.51 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n351 7 Car -1 -1 -1 982.70 185.30 1069.25 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n351 11 Car -1 -1 -1 931.34 184.47 1009.85 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n351 4 Car -1 -1 -1 876.99 183.65 945.74 218.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n351 160 Pedestrian -1 -1 -1 231.63 164.28 253.59 218.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n351 176 Pedestrian -1 -1 -1 549.82 173.75 576.91 229.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n351 153 Pedestrian -1 -1 -1 256.51 162.66 277.46 220.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n351 137 Car -1 -1 -1 607.29 175.85 633.57 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n351 163 Pedestrian -1 -1 -1 222.67 161.86 245.85 216.94 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n351 165 Cyclist -1 -1 -1 574.35 169.03 590.77 207.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n351 171 Pedestrian -1 -1 -1 384.72 164.08 401.93 207.07 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n351 181 Pedestrian -1 -1 -1 424.24 168.73 435.54 195.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n351 183 Pedestrian -1 -1 -1 269.80 159.85 285.93 196.34 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n351 187 Cyclist -1 -1 -1 522.73 171.02 534.02 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n352 3 Car -1 -1 -1 1116.94 188.46 1220.72 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n352 153 Pedestrian -1 -1 -1 257.77 162.72 280.89 220.00 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n352 7 Car -1 -1 -1 982.82 185.35 1069.06 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n352 11 Car -1 -1 -1 931.28 184.44 1009.80 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n352 4 Car -1 -1 -1 876.97 183.66 945.65 218.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n352 160 Pedestrian -1 -1 -1 236.08 164.59 256.09 219.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n352 163 Pedestrian -1 -1 -1 224.14 162.09 247.48 216.68 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n352 137 Car -1 -1 -1 607.24 175.96 633.29 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n352 165 Cyclist -1 -1 -1 574.07 169.55 591.43 206.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n352 176 Pedestrian -1 -1 -1 556.67 173.52 578.55 229.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n352 171 Pedestrian -1 -1 -1 386.09 163.91 403.03 207.49 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n352 181 Pedestrian -1 -1 -1 424.59 168.42 435.94 195.68 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n352 183 Pedestrian -1 -1 -1 272.50 160.11 289.17 196.34 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n353 3 Car -1 -1 -1 1117.04 188.52 1220.82 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n353 7 Car -1 -1 -1 982.79 185.36 1069.03 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n353 11 Car -1 -1 -1 931.15 184.41 1009.94 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n353 4 Car -1 -1 -1 876.99 183.66 945.73 218.09 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n353 153 Pedestrian -1 -1 -1 259.46 163.08 281.87 219.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n353 163 Pedestrian -1 -1 -1 227.31 162.49 249.70 217.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n353 176 Pedestrian -1 -1 -1 562.47 173.61 582.38 230.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n353 160 Pedestrian -1 -1 -1 237.40 164.25 257.86 218.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n353 171 Pedestrian -1 -1 -1 386.12 164.89 403.15 208.06 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n353 137 Car -1 -1 -1 607.31 176.02 633.13 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n353 165 Cyclist -1 -1 -1 575.02 169.97 591.55 206.36 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n353 181 Pedestrian -1 -1 -1 424.71 168.47 435.56 195.41 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n353 183 Pedestrian -1 -1 -1 272.74 160.10 288.90 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n354 3 Car -1 -1 -1 1117.31 188.41 1220.72 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n354 7 Car -1 -1 -1 982.86 185.39 1068.96 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n354 11 Car -1 -1 -1 931.29 184.48 1009.87 220.60 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n354 4 Car -1 -1 -1 876.95 183.65 945.63 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n354 153 Pedestrian -1 -1 -1 261.14 162.61 283.80 220.27 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n354 171 Pedestrian -1 -1 -1 386.62 165.34 402.90 208.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n354 160 Pedestrian -1 -1 -1 239.87 165.31 259.38 218.27 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n354 163 Pedestrian -1 -1 -1 228.37 162.19 250.44 217.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n354 137 Car -1 -1 -1 607.25 175.97 633.31 199.77 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n354 176 Pedestrian -1 -1 -1 563.35 173.48 587.56 230.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n354 183 Pedestrian -1 -1 -1 273.40 159.88 288.98 196.87 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n354 181 Pedestrian -1 -1 -1 424.64 168.37 435.13 195.01 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n354 165 Cyclist -1 -1 -1 575.48 171.99 591.38 207.73 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n354 188 Cyclist -1 -1 -1 519.14 170.33 530.99 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n355 3 Car -1 -1 -1 1116.74 188.46 1220.87 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n355 176 Pedestrian -1 -1 -1 565.76 173.54 592.10 230.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n355 7 Car -1 -1 -1 979.48 185.33 1068.98 221.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n355 11 Car -1 -1 -1 931.18 184.44 1009.82 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n355 4 Car -1 -1 -1 876.96 183.68 945.67 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n355 153 Pedestrian -1 -1 -1 261.63 162.59 283.83 219.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n355 160 Pedestrian -1 -1 -1 241.17 165.44 261.52 217.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n355 171 Pedestrian -1 -1 -1 386.36 165.47 403.16 208.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n355 137 Car -1 -1 -1 607.33 176.01 633.37 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n355 163 Pedestrian -1 -1 -1 230.02 162.00 254.00 216.98 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n355 183 Pedestrian -1 -1 -1 273.71 160.07 289.28 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n355 181 Pedestrian -1 -1 -1 424.38 168.46 434.98 194.87 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n355 165 Cyclist -1 -1 -1 575.39 170.58 591.70 205.28 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n355 189 Pedestrian -1 -1 -1 183.01 159.09 202.31 209.22 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n356 3 Car -1 -1 -1 1117.35 188.43 1220.59 225.56 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n356 11 Car -1 -1 -1 931.14 184.49 1009.59 220.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n356 7 Car -1 -1 -1 982.85 185.36 1068.97 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n356 4 Car -1 -1 -1 876.99 183.72 945.73 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n356 160 Pedestrian -1 -1 -1 244.46 165.23 263.46 217.49 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n356 153 Pedestrian -1 -1 -1 262.83 162.44 283.92 219.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n356 176 Pedestrian -1 -1 -1 568.51 172.98 595.58 231.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n356 137 Car -1 -1 -1 607.33 175.84 633.52 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n356 163 Pedestrian -1 -1 -1 231.27 160.60 255.18 216.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n356 171 Pedestrian -1 -1 -1 386.43 165.02 402.98 208.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n356 183 Pedestrian -1 -1 -1 273.81 159.88 289.43 196.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n356 165 Cyclist -1 -1 -1 574.68 170.97 592.13 204.85 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n356 181 Pedestrian -1 -1 -1 424.15 168.48 434.48 194.96 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n356 189 Pedestrian -1 -1 -1 183.08 158.81 202.45 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n356 190 Pedestrian -1 -1 -1 517.04 170.00 528.84 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n357 3 Car -1 -1 -1 1116.94 188.64 1220.84 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n357 11 Car -1 -1 -1 930.97 184.48 1009.74 220.48 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n357 7 Car -1 -1 -1 982.82 185.37 1068.97 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n357 4 Car -1 -1 -1 876.86 183.71 945.75 217.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n357 171 Pedestrian -1 -1 -1 386.84 165.10 403.51 208.47 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n357 163 Pedestrian -1 -1 -1 233.97 161.08 258.75 217.54 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n357 176 Pedestrian -1 -1 -1 573.33 172.42 595.40 231.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n357 137 Car -1 -1 -1 607.44 175.97 633.28 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n357 160 Pedestrian -1 -1 -1 245.46 165.65 265.15 215.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n357 153 Pedestrian -1 -1 -1 264.93 162.10 287.80 219.75 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n357 183 Pedestrian -1 -1 -1 273.65 159.91 288.98 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n357 165 Cyclist -1 -1 -1 575.34 171.44 591.48 204.45 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n357 181 Pedestrian -1 -1 -1 423.80 168.62 434.14 195.07 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n357 189 Pedestrian -1 -1 -1 183.12 158.76 202.31 209.52 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n357 190 Pedestrian -1 -1 -1 515.39 170.42 527.62 203.87 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n357 191 Cyclist -1 -1 -1 515.39 170.42 527.62 203.87 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n358 3 Car -1 -1 -1 1117.22 188.49 1220.81 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n358 11 Car -1 -1 -1 930.74 184.41 1009.97 220.44 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n358 7 Car -1 -1 -1 982.82 185.38 1068.98 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n358 4 Car -1 -1 -1 876.77 183.69 945.81 217.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n358 171 Pedestrian -1 -1 -1 387.69 164.68 404.54 208.74 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n358 160 Pedestrian -1 -1 -1 248.41 164.50 268.20 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n358 137 Car -1 -1 -1 607.70 176.10 632.97 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n358 153 Pedestrian -1 -1 -1 266.79 162.08 288.16 219.64 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n358 163 Pedestrian -1 -1 -1 235.50 161.54 259.32 215.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n358 176 Pedestrian -1 -1 -1 578.54 171.91 597.79 231.71 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n358 183 Pedestrian -1 -1 -1 273.42 159.59 288.49 196.85 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n358 165 Cyclist -1 -1 -1 575.90 171.87 591.03 204.02 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n358 191 Cyclist -1 -1 -1 514.47 170.70 526.84 203.45 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n358 181 Pedestrian -1 -1 -1 422.52 168.36 433.36 194.99 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n358 189 Pedestrian -1 -1 -1 183.13 158.80 202.20 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n359 3 Car -1 -1 -1 1117.12 188.37 1220.76 225.73 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n359 7 Car -1 -1 -1 982.73 185.38 1069.06 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n359 11 Car -1 -1 -1 930.81 184.40 1010.09 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n359 4 Car -1 -1 -1 876.88 183.63 945.82 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n359 171 Pedestrian -1 -1 -1 388.34 165.27 405.29 209.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n359 176 Pedestrian -1 -1 -1 580.68 171.97 603.62 231.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n359 160 Pedestrian -1 -1 -1 250.43 164.66 271.39 216.95 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n359 153 Pedestrian -1 -1 -1 269.82 160.88 292.29 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n359 137 Car -1 -1 -1 607.57 176.05 633.01 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n359 163 Pedestrian -1 -1 -1 238.07 162.29 261.98 214.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n359 191 Cyclist -1 -1 -1 511.48 169.55 525.58 205.48 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n359 183 Pedestrian -1 -1 -1 273.23 159.65 288.90 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n359 165 Cyclist -1 -1 -1 576.09 171.15 590.46 204.76 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n359 181 Pedestrian -1 -1 -1 422.40 168.31 433.48 195.03 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n360 3 Car -1 -1 -1 1117.28 188.55 1220.58 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n360 7 Car -1 -1 -1 982.57 185.40 1069.30 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n360 11 Car -1 -1 -1 931.01 184.41 1009.92 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n360 160 Pedestrian -1 -1 -1 251.80 164.93 272.29 216.45 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n360 4 Car -1 -1 -1 876.74 183.57 945.87 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n360 176 Pedestrian -1 -1 -1 582.54 172.95 609.06 232.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n360 153 Pedestrian -1 -1 -1 274.08 161.86 295.66 220.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n360 163 Pedestrian -1 -1 -1 241.97 161.91 264.91 214.41 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n360 171 Pedestrian -1 -1 -1 389.66 165.18 407.16 209.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n360 137 Car -1 -1 -1 607.57 176.11 632.92 199.47 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n360 183 Pedestrian -1 -1 -1 273.06 159.67 289.20 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n360 191 Cyclist -1 -1 -1 510.72 169.81 524.15 206.67 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n360 181 Pedestrian -1 -1 -1 422.60 168.37 433.25 194.94 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n360 165 Cyclist -1 -1 -1 574.81 171.64 586.26 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n361 3 Car -1 -1 -1 1117.06 188.62 1220.76 225.60 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n361 7 Car -1 -1 -1 982.64 185.41 1069.20 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n361 11 Car -1 -1 -1 931.20 184.43 1009.98 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n361 4 Car -1 -1 -1 876.75 183.59 945.82 218.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n361 176 Pedestrian -1 -1 -1 582.93 172.55 613.16 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n361 171 Pedestrian -1 -1 -1 390.15 164.98 408.38 209.75 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n361 153 Pedestrian -1 -1 -1 273.75 161.10 297.61 220.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n361 163 Pedestrian -1 -1 -1 244.13 161.73 265.76 214.53 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n361 160 Pedestrian -1 -1 -1 255.56 164.80 274.33 215.55 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n361 137 Car -1 -1 -1 607.92 176.09 632.67 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n361 183 Pedestrian -1 -1 -1 273.10 159.69 289.20 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n361 181 Pedestrian -1 -1 -1 422.50 168.43 433.18 194.93 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n361 191 Cyclist -1 -1 -1 508.26 168.51 522.36 208.05 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n361 165 Cyclist -1 -1 -1 574.85 171.34 586.19 204.09 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n362 3 Car -1 -1 -1 1116.97 188.58 1220.78 225.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n362 7 Car -1 -1 -1 982.84 185.41 1068.93 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n362 11 Car -1 -1 -1 935.13 184.38 1009.86 220.69 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n362 4 Car -1 -1 -1 876.76 183.56 945.72 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n362 171 Pedestrian -1 -1 -1 391.04 165.14 408.91 210.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n362 160 Pedestrian -1 -1 -1 256.56 164.28 275.24 215.36 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n362 153 Pedestrian -1 -1 -1 276.72 162.39 299.54 218.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n362 176 Pedestrian -1 -1 -1 587.51 172.18 615.32 232.30 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n362 163 Pedestrian -1 -1 -1 247.10 162.05 267.17 214.18 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n362 137 Car -1 -1 -1 608.28 176.23 632.47 199.20 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n362 183 Pedestrian -1 -1 -1 273.30 159.56 288.54 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n362 181 Pedestrian -1 -1 -1 422.57 168.47 433.10 194.85 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n362 165 Cyclist -1 -1 -1 575.91 171.27 589.65 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n362 191 Cyclist -1 -1 -1 506.64 168.78 520.49 208.31 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n363 3 Car -1 -1 -1 1117.32 188.51 1220.89 225.72 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n363 7 Car -1 -1 -1 982.83 185.41 1069.03 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n363 11 Car -1 -1 -1 935.05 184.40 1009.89 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n363 4 Car -1 -1 -1 876.89 183.54 945.76 218.06 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n363 171 Pedestrian -1 -1 -1 391.64 165.26 409.06 210.49 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n363 163 Pedestrian -1 -1 -1 247.55 162.44 267.80 214.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n363 160 Pedestrian -1 -1 -1 259.10 163.97 277.89 215.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n363 153 Pedestrian -1 -1 -1 278.96 163.40 301.03 217.11 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n363 137 Car -1 -1 -1 608.40 176.25 632.71 199.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n363 183 Pedestrian -1 -1 -1 273.39 159.92 288.06 197.42 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n363 181 Pedestrian -1 -1 -1 422.85 168.59 432.82 194.64 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n363 176 Pedestrian -1 -1 -1 594.90 173.06 617.17 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n363 191 Cyclist -1 -1 -1 503.85 169.05 519.85 210.10 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n363 165 Cyclist -1 -1 -1 576.27 171.13 589.76 203.61 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n364 3 Car -1 -1 -1 1117.20 188.66 1220.71 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n364 7 Car -1 -1 -1 982.69 185.37 1069.20 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n364 4 Car -1 -1 -1 876.87 183.56 945.69 218.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n364 160 Pedestrian -1 -1 -1 261.06 164.40 279.23 215.93 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n364 11 Car -1 -1 -1 935.07 184.47 1009.70 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n364 153 Pedestrian -1 -1 -1 280.78 163.46 303.11 216.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n364 171 Pedestrian -1 -1 -1 391.92 165.21 409.32 210.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n364 163 Pedestrian -1 -1 -1 248.62 162.24 268.40 213.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n364 137 Car -1 -1 -1 608.23 175.90 632.96 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n364 191 Cyclist -1 -1 -1 501.68 169.19 518.74 210.38 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n364 181 Pedestrian -1 -1 -1 422.82 168.51 432.36 194.61 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n364 183 Pedestrian -1 -1 -1 274.13 160.07 287.11 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n364 165 Cyclist -1 -1 -1 576.46 170.89 589.20 203.80 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n365 3 Car -1 -1 -1 1117.00 188.62 1220.55 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n365 7 Car -1 -1 -1 982.71 185.31 1069.09 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n365 11 Car -1 -1 -1 931.46 184.39 1009.62 220.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n365 4 Car -1 -1 -1 876.78 183.60 945.69 218.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n365 153 Pedestrian -1 -1 -1 281.92 163.42 303.30 215.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n365 160 Pedestrian -1 -1 -1 261.03 164.67 280.21 215.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n365 137 Car -1 -1 -1 607.66 175.64 633.55 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n365 171 Pedestrian -1 -1 -1 392.08 164.81 409.97 210.38 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n365 163 Pedestrian -1 -1 -1 251.07 162.41 271.17 213.79 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n365 191 Cyclist -1 -1 -1 497.74 168.83 518.07 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n365 165 Cyclist -1 -1 -1 576.71 170.84 589.97 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n365 181 Pedestrian -1 -1 -1 422.11 168.37 432.30 194.90 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n365 183 Pedestrian -1 -1 -1 271.14 160.26 284.69 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n365 192 Cyclist -1 -1 -1 602.21 174.80 626.44 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n366 3 Car -1 -1 -1 1116.89 188.58 1220.72 225.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n366 11 Car -1 -1 -1 931.32 184.42 1009.59 220.47 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n366 7 Car -1 -1 -1 982.83 185.38 1068.91 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n366 4 Car -1 -1 -1 876.84 183.63 945.68 218.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n366 163 Pedestrian -1 -1 -1 251.22 161.07 271.91 214.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n366 171 Pedestrian -1 -1 -1 393.10 164.62 411.33 210.28 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n366 160 Pedestrian -1 -1 -1 260.38 165.17 280.82 214.77 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n366 191 Cyclist -1 -1 -1 496.54 168.53 516.05 211.29 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n366 137 Car -1 -1 -1 607.23 175.73 634.03 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n366 153 Pedestrian -1 -1 -1 283.06 163.67 304.64 215.16 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n366 165 Cyclist -1 -1 -1 577.36 171.22 589.86 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n366 181 Pedestrian -1 -1 -1 421.65 168.45 431.96 194.84 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n366 183 Pedestrian -1 -1 -1 273.48 160.04 287.14 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n366 193 Pedestrian -1 -1 -1 602.75 174.84 630.92 234.83 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n367 3 Car -1 -1 -1 1116.58 188.55 1220.99 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n367 7 Car -1 -1 -1 982.84 185.29 1069.00 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n367 11 Car -1 -1 -1 931.05 184.37 1009.75 220.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n367 4 Car -1 -1 -1 876.89 183.64 945.52 217.94 -1 -1 -1 -1000 -1000 -1000 -10 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428.44 214.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n382 193 Pedestrian -1 -1 -1 653.53 173.43 687.25 240.57 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n382 191 Cyclist -1 -1 -1 445.51 165.18 477.97 229.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n382 183 Pedestrian -1 -1 -1 271.86 158.63 290.63 207.61 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n382 163 Pedestrian -1 -1 -1 280.53 162.10 299.29 210.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n382 153 Pedestrian -1 -1 -1 292.09 165.93 309.78 210.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n382 196 Pedestrian -1 -1 -1 308.90 163.75 328.51 213.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n382 195 Cyclist -1 -1 -1 482.11 169.40 498.95 210.19 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n383 3 Car -1 -1 -1 1116.46 188.68 1220.61 225.51 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n383 7 Car -1 -1 -1 982.92 185.33 1068.99 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n383 11 Car -1 -1 -1 931.26 184.67 1009.76 220.41 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n383 4 Car -1 -1 -1 878.31 183.72 945.50 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n383 191 Cyclist -1 -1 -1 440.43 164.90 475.47 231.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n383 137 Car -1 -1 -1 607.88 176.10 633.85 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n383 171 Pedestrian -1 -1 -1 409.25 165.32 428.39 214.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n383 163 Pedestrian -1 -1 -1 283.80 162.13 301.59 209.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n383 153 Pedestrian -1 -1 -1 295.01 165.29 312.19 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n383 196 Pedestrian -1 -1 -1 309.38 164.92 329.42 213.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n383 195 Cyclist -1 -1 -1 480.43 169.60 500.07 210.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n383 193 Pedestrian -1 -1 -1 656.41 173.84 687.32 240.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n383 183 Pedestrian -1 -1 -1 271.61 158.78 289.82 206.68 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n384 3 Car -1 -1 -1 1116.87 188.52 1220.72 225.63 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n384 7 Car -1 -1 -1 982.90 185.32 1069.09 221.21 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n384 11 Car -1 -1 -1 930.95 184.59 1009.75 220.41 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n384 191 Cyclist -1 -1 -1 436.51 164.82 471.42 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n384 4 Car -1 -1 -1 878.12 183.79 945.33 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n384 137 Car -1 -1 -1 607.70 176.04 633.93 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n384 171 Pedestrian -1 -1 -1 409.55 165.82 429.73 215.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n384 153 Pedestrian -1 -1 -1 295.69 165.59 312.41 209.80 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n384 196 Pedestrian -1 -1 -1 309.94 163.96 329.89 212.87 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n384 195 Cyclist -1 -1 -1 478.61 169.56 499.64 210.69 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n384 163 Pedestrian -1 -1 -1 285.15 162.25 302.43 209.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n384 183 Pedestrian -1 -1 -1 271.53 158.80 289.55 206.17 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n384 197 Cyclist -1 -1 -1 663.21 173.39 696.29 240.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n385 3 Car -1 -1 -1 1116.85 188.54 1220.79 225.63 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n385 7 Car -1 -1 -1 983.01 185.38 1068.88 221.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n385 11 Car -1 -1 -1 931.01 184.53 1009.77 220.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n385 4 Car -1 -1 -1 878.06 183.84 945.45 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n385 191 Cyclist -1 -1 -1 431.86 164.57 468.17 234.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n385 137 Car -1 -1 -1 607.78 176.03 633.78 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n385 195 Cyclist -1 -1 -1 478.39 169.39 499.11 211.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n385 163 Pedestrian -1 -1 -1 286.15 163.71 304.79 209.75 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n385 153 Pedestrian -1 -1 -1 295.64 165.73 313.42 210.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n385 171 Pedestrian -1 -1 -1 412.06 166.45 431.28 215.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n385 196 Pedestrian -1 -1 -1 310.80 164.18 330.08 212.27 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n385 183 Pedestrian -1 -1 -1 271.57 158.74 288.94 201.53 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n385 197 Cyclist -1 -1 -1 668.01 172.48 699.62 245.89 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n386 3 Car -1 -1 -1 1116.56 188.62 1220.68 225.48 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n386 7 Car -1 -1 -1 983.07 185.42 1068.82 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n386 11 Car -1 -1 -1 931.05 184.59 1009.80 220.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n386 4 Car -1 -1 -1 878.04 183.92 945.36 217.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n386 191 Cyclist -1 -1 -1 426.19 164.78 465.96 238.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n386 137 Car -1 -1 -1 607.66 176.00 633.64 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n386 163 Pedestrian -1 -1 -1 286.70 164.20 305.12 209.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n386 196 Pedestrian -1 -1 -1 313.14 164.51 331.69 211.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n386 195 Cyclist -1 -1 -1 478.19 169.26 499.28 212.54 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n386 171 Pedestrian -1 -1 -1 413.15 166.03 432.85 215.69 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n386 197 Cyclist -1 -1 -1 672.89 172.19 706.37 246.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n386 153 Pedestrian -1 -1 -1 298.41 166.02 315.93 211.00 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n386 183 Pedestrian -1 -1 -1 271.66 159.08 288.98 200.99 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n387 3 Car -1 -1 -1 1116.84 188.61 1220.70 225.51 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n387 11 Car -1 -1 -1 930.96 184.60 1009.83 220.46 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n387 7 Car -1 -1 -1 983.03 185.35 1068.73 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n387 4 Car -1 -1 -1 877.88 183.97 945.43 217.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n387 191 Cyclist -1 -1 -1 416.65 165.77 465.43 238.12 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n387 137 Car -1 -1 -1 607.67 175.97 633.55 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n387 195 Cyclist -1 -1 -1 477.18 169.27 500.07 213.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n387 163 Pedestrian -1 -1 -1 287.40 163.69 305.41 209.71 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n387 196 Pedestrian -1 -1 -1 313.43 164.66 331.68 211.19 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n387 153 Pedestrian -1 -1 -1 298.41 166.05 316.34 210.75 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n387 183 Pedestrian -1 -1 -1 271.74 159.05 288.76 200.47 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n387 197 Cyclist -1 -1 -1 673.69 174.13 713.08 244.88 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n387 198 Cyclist -1 -1 -1 413.78 166.77 436.77 215.85 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n388 3 Car -1 -1 -1 1117.14 188.63 1220.91 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n388 11 Car -1 -1 -1 930.82 184.63 1009.83 220.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n388 7 Car -1 -1 -1 982.96 185.33 1068.86 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n388 4 Car -1 -1 -1 877.95 183.96 945.46 217.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n388 137 Car -1 -1 -1 607.56 175.86 633.96 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n388 191 Cyclist -1 -1 -1 413.46 165.49 461.01 239.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n388 163 Pedestrian -1 -1 -1 288.22 162.75 305.96 209.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n388 196 Pedestrian -1 -1 -1 314.05 164.73 332.59 211.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n388 195 Cyclist -1 -1 -1 477.29 169.26 500.10 213.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n388 153 Pedestrian -1 -1 -1 299.71 166.22 317.90 210.49 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n388 183 Pedestrian -1 -1 -1 271.86 159.32 288.46 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n388 198 Cyclist -1 -1 -1 415.94 167.47 437.29 215.21 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n388 199 Pedestrian -1 -1 -1 677.83 173.91 717.39 245.00 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n389 3 Car -1 -1 -1 1116.80 188.55 1220.92 225.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n389 11 Car -1 -1 -1 930.84 184.61 1009.74 220.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n389 7 Car -1 -1 -1 982.89 185.31 1068.90 221.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n389 191 Cyclist -1 -1 -1 405.65 166.22 455.57 240.35 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n389 4 Car -1 -1 -1 877.96 183.92 945.64 217.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n389 137 Car -1 -1 -1 607.55 175.97 633.93 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n389 163 Pedestrian -1 -1 -1 290.57 162.82 307.83 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n389 199 Pedestrian -1 -1 -1 685.96 172.53 717.57 245.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n389 196 Pedestrian -1 -1 -1 316.52 164.25 335.69 211.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n389 153 Pedestrian -1 -1 -1 301.53 166.14 319.96 210.97 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n389 195 Cyclist -1 -1 -1 480.01 169.08 500.81 214.51 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n389 183 Pedestrian -1 -1 -1 270.60 159.31 286.22 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n390 3 Car -1 -1 -1 1116.42 188.75 1220.99 225.56 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n390 11 Car -1 -1 -1 930.88 184.59 1009.80 220.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n390 7 Car -1 -1 -1 982.97 185.31 1068.91 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n390 137 Car -1 -1 -1 607.54 175.96 633.79 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n390 4 Car -1 -1 -1 878.89 183.93 944.64 216.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n390 191 Cyclist -1 -1 -1 400.62 164.26 451.05 242.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n390 163 Pedestrian -1 -1 -1 291.59 163.39 310.02 208.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n390 196 Pedestrian -1 -1 -1 317.93 165.06 337.08 211.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n390 153 Pedestrian -1 -1 -1 303.36 166.59 321.40 210.00 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n390 199 Pedestrian -1 -1 -1 696.67 171.10 720.39 243.71 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n390 195 Cyclist -1 -1 -1 480.68 169.21 503.01 214.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n390 183 Pedestrian -1 -1 -1 272.16 159.60 288.45 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n391 3 Car -1 -1 -1 1116.76 188.68 1220.83 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n391 11 Car -1 -1 -1 930.93 184.63 1009.80 220.27 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n391 7 Car -1 -1 -1 983.12 185.28 1068.73 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n391 4 Car -1 -1 -1 878.95 183.92 944.72 216.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n391 137 Car -1 -1 -1 607.58 176.03 633.59 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n391 191 Cyclist -1 -1 -1 393.79 164.68 445.76 247.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n391 199 Pedestrian -1 -1 -1 700.78 170.82 725.20 246.17 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n391 163 Pedestrian -1 -1 -1 292.70 164.00 310.09 208.46 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n391 196 Pedestrian -1 -1 -1 320.17 164.56 338.89 211.28 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n391 153 Pedestrian -1 -1 -1 304.50 166.32 321.58 209.79 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n391 195 Cyclist -1 -1 -1 483.17 170.20 505.11 216.56 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n391 183 Pedestrian -1 -1 -1 272.20 159.76 288.61 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n392 3 Car -1 -1 -1 1116.41 188.75 1220.81 225.52 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n392 7 Car -1 -1 -1 983.06 185.33 1068.81 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n392 11 Car -1 -1 -1 930.88 184.66 1009.92 220.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n392 191 Cyclist -1 -1 -1 384.68 163.31 440.02 250.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n392 4 Car -1 -1 -1 879.02 183.89 944.78 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n392 137 Car -1 -1 -1 607.46 175.96 633.90 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n392 199 Pedestrian -1 -1 -1 700.89 171.42 734.68 245.84 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n392 153 Pedestrian -1 -1 -1 307.26 165.97 323.83 210.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n392 196 Pedestrian -1 -1 -1 320.20 164.82 340.24 211.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n392 195 Cyclist -1 -1 -1 483.06 170.28 508.61 217.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n392 163 Pedestrian -1 -1 -1 293.82 163.84 311.72 208.45 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n392 183 Pedestrian -1 -1 -1 272.37 160.11 288.98 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n393 3 Car -1 -1 -1 1116.76 188.64 1220.82 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n393 7 Car -1 -1 -1 983.04 185.36 1068.81 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n393 11 Car -1 -1 -1 930.95 184.66 1009.86 220.34 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n393 199 Pedestrian -1 -1 -1 702.69 172.25 740.65 246.26 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n393 4 Car -1 -1 -1 879.19 183.93 944.66 216.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n393 191 Cyclist -1 -1 -1 375.89 163.19 433.42 251.36 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n393 137 Car -1 -1 -1 607.46 176.02 633.82 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n393 195 Cyclist -1 -1 -1 485.23 169.93 512.37 218.33 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n393 163 Pedestrian -1 -1 -1 293.68 163.57 312.56 208.41 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n393 196 Pedestrian -1 -1 -1 320.99 164.81 340.40 211.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n393 153 Pedestrian -1 -1 -1 309.65 166.12 326.52 210.11 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n393 183 Pedestrian -1 -1 -1 272.83 160.22 288.91 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n394 3 Car -1 -1 -1 1116.87 188.59 1221.08 225.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n394 7 Car -1 -1 -1 983.05 185.31 1068.92 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n394 11 Car -1 -1 -1 931.01 184.70 1009.84 220.32 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n394 199 Pedestrian -1 -1 -1 704.94 171.83 745.66 246.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n394 191 Cyclist -1 -1 -1 367.92 161.31 425.65 252.66 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n394 4 Car -1 -1 -1 878.29 183.86 945.69 217.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n394 137 Car -1 -1 -1 607.44 176.12 633.86 199.62 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n394 195 Cyclist -1 -1 -1 486.07 169.97 514.19 218.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n394 196 Pedestrian -1 -1 -1 322.29 165.06 340.45 210.64 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n394 163 Pedestrian -1 -1 -1 294.70 163.52 312.28 208.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n394 153 Pedestrian -1 -1 -1 309.76 166.31 327.57 210.22 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n394 183 Pedestrian -1 -1 -1 272.87 160.01 289.10 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n394 200 Cyclist -1 -1 -1 423.42 166.88 444.41 216.54 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n395 3 Car -1 -1 -1 1117.03 188.68 1221.00 225.56 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n395 11 Car -1 -1 -1 930.77 184.61 1009.79 220.29 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n395 7 Car -1 -1 -1 982.91 185.35 1068.99 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n395 191 Cyclist -1 -1 -1 357.52 159.77 420.69 260.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n395 4 Car -1 -1 -1 877.87 183.89 945.59 217.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n395 195 Cyclist -1 -1 -1 488.91 169.72 518.44 219.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n395 137 Car -1 -1 -1 607.50 176.13 633.67 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n395 199 Pedestrian -1 -1 -1 712.47 170.50 746.24 246.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n395 163 Pedestrian -1 -1 -1 296.05 163.59 312.50 208.09 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n395 153 Pedestrian -1 -1 -1 310.81 166.21 326.98 209.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n395 196 Pedestrian -1 -1 -1 322.53 164.69 341.16 210.85 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n395 183 Pedestrian -1 -1 -1 272.99 159.94 289.09 197.19 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n395 201 Pedestrian -1 -1 -1 424.01 165.48 445.18 217.98 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n396 3 Car -1 -1 -1 1116.75 188.71 1221.18 225.56 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n396 11 Car -1 -1 -1 930.72 184.59 1009.94 220.34 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n396 7 Car -1 -1 -1 982.98 185.35 1068.86 221.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n396 191 Cyclist -1 -1 -1 349.66 158.98 410.13 261.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n396 4 Car -1 -1 -1 878.80 183.87 944.63 216.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n396 195 Cyclist -1 -1 -1 493.95 170.26 521.71 219.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n396 137 Car -1 -1 -1 607.43 176.23 633.68 199.57 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n396 199 Pedestrian -1 -1 -1 723.65 170.32 748.71 247.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n396 153 Pedestrian -1 -1 -1 310.78 166.01 327.91 209.55 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n396 163 Pedestrian -1 -1 -1 296.12 163.79 313.06 207.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n396 196 Pedestrian -1 -1 -1 322.33 164.78 341.40 210.61 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n396 201 Pedestrian -1 -1 -1 423.98 165.54 446.60 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n396 183 Pedestrian -1 -1 -1 272.61 160.13 289.09 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n397 3 Car -1 -1 -1 1117.06 188.74 1221.04 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n397 11 Car -1 -1 -1 930.65 184.56 1009.96 220.39 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n397 7 Car -1 -1 -1 982.89 185.39 1069.01 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n397 195 Cyclist -1 -1 -1 496.95 170.43 525.95 220.49 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n397 199 Pedestrian -1 -1 -1 729.58 171.70 757.99 247.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n397 191 Cyclist -1 -1 -1 336.15 158.66 403.18 268.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n397 4 Car -1 -1 -1 878.62 183.75 944.69 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n397 137 Car -1 -1 -1 607.53 176.13 633.68 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n397 163 Pedestrian -1 -1 -1 296.37 163.80 313.24 207.85 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n397 153 Pedestrian -1 -1 -1 311.92 165.46 328.89 209.35 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n397 201 Pedestrian -1 -1 -1 426.46 167.11 449.18 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n397 196 Pedestrian -1 -1 -1 324.79 165.00 343.42 210.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n397 183 Pedestrian -1 -1 -1 272.70 160.20 289.14 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n398 3 Car -1 -1 -1 1117.14 188.73 1221.03 225.51 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n398 7 Car -1 -1 -1 982.83 185.32 1069.12 221.03 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n398 11 Car -1 -1 -1 930.64 184.62 1010.14 220.35 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n398 199 Pedestrian -1 -1 -1 729.03 173.38 766.35 248.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n398 4 Car -1 -1 -1 878.53 183.83 944.78 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n398 195 Cyclist -1 -1 -1 499.82 170.22 531.39 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n398 191 Cyclist -1 -1 -1 325.38 158.16 395.76 270.47 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n398 137 Car -1 -1 -1 607.66 176.19 633.49 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n398 201 Pedestrian -1 -1 -1 428.66 167.65 449.32 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n398 153 Pedestrian -1 -1 -1 313.79 165.13 330.87 208.61 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n398 196 Pedestrian -1 -1 -1 326.66 165.18 343.73 209.94 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n398 163 Pedestrian -1 -1 -1 296.93 163.75 313.11 207.47 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n398 183 Pedestrian -1 -1 -1 272.51 160.24 289.31 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n399 7 Car -1 -1 -1 982.73 185.34 1069.36 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n399 3 Car -1 -1 -1 1116.93 188.75 1221.39 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n399 11 Car -1 -1 -1 930.77 184.62 1009.87 220.31 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n399 191 Cyclist -1 -1 -1 313.77 160.87 385.18 275.61 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n399 199 Pedestrian -1 -1 -1 732.48 172.83 770.95 249.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n399 4 Car -1 -1 -1 877.60 183.66 945.84 217.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n399 195 Cyclist -1 -1 -1 503.90 169.56 535.44 221.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n399 137 Car -1 -1 -1 607.63 176.15 633.43 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n399 153 Pedestrian -1 -1 -1 314.44 165.02 331.78 208.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n399 201 Pedestrian -1 -1 -1 430.49 167.46 451.73 218.87 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n399 196 Pedestrian -1 -1 -1 327.13 165.56 344.01 209.46 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n399 163 Pedestrian -1 -1 -1 296.74 162.56 313.44 206.62 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n399 183 Pedestrian -1 -1 -1 272.69 160.15 289.22 196.92 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n400 7 Car -1 -1 -1 982.81 185.31 1069.23 220.95 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n400 3 Car -1 -1 -1 1117.10 188.63 1221.26 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n400 11 Car -1 -1 -1 930.75 184.65 1009.85 220.25 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n400 195 Cyclist -1 -1 -1 509.66 168.81 539.97 221.74 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n400 199 Pedestrian -1 -1 -1 740.28 172.78 771.05 249.07 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n400 4 Car -1 -1 -1 878.76 183.83 944.76 216.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n400 191 Cyclist -1 -1 -1 296.33 158.81 374.73 283.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n400 137 Car -1 -1 -1 607.47 175.96 633.63 199.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n400 153 Pedestrian -1 -1 -1 315.03 164.83 331.92 208.72 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n400 163 Pedestrian -1 -1 -1 299.28 162.65 315.49 206.30 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n400 201 Pedestrian -1 -1 -1 432.02 166.98 452.86 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n400 196 Pedestrian -1 -1 -1 329.04 165.83 346.95 209.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n400 183 Pedestrian -1 -1 -1 272.78 160.11 289.27 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n401 7 Car -1 -1 -1 982.81 185.33 1069.31 220.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n401 3 Car -1 -1 -1 1116.94 188.59 1220.83 225.56 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n401 11 Car -1 -1 -1 930.78 184.68 1009.84 220.26 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n401 191 Cyclist -1 -1 -1 281.28 158.15 365.15 286.36 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n401 195 Cyclist -1 -1 -1 514.24 169.50 545.81 221.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n401 4 Car -1 -1 -1 877.93 183.77 945.86 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n401 199 Pedestrian -1 -1 -1 746.85 170.87 772.54 249.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n401 137 Car -1 -1 -1 607.56 175.96 633.57 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n401 163 Pedestrian -1 -1 -1 299.00 162.94 315.80 208.38 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n401 153 Pedestrian -1 -1 -1 314.71 164.56 332.48 209.19 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n401 196 Pedestrian -1 -1 -1 328.75 164.77 348.82 210.39 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n401 183 Pedestrian -1 -1 -1 272.96 160.17 289.05 196.72 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n401 202 Cyclist -1 -1 -1 430.59 166.14 455.22 217.82 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n402 3 Car -1 -1 -1 1116.96 188.54 1221.13 225.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n402 7 Car -1 -1 -1 982.75 185.32 1069.43 221.00 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n402 11 Car -1 -1 -1 930.84 184.67 1010.06 220.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n402 199 Pedestrian -1 -1 -1 754.26 171.45 779.56 249.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n402 191 Cyclist -1 -1 -1 262.02 156.91 347.78 293.76 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n402 4 Car -1 -1 -1 877.85 183.79 945.84 217.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n402 137 Car -1 -1 -1 607.24 175.93 633.71 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n402 195 Cyclist -1 -1 -1 519.79 170.25 549.21 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n402 153 Pedestrian -1 -1 -1 314.84 164.48 332.49 209.33 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n402 163 Pedestrian -1 -1 -1 298.36 163.01 316.16 208.05 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n402 202 Cyclist -1 -1 -1 433.68 165.98 456.67 217.80 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n402 183 Pedestrian -1 -1 -1 272.98 160.55 288.72 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n402 196 Pedestrian -1 -1 -1 329.66 164.56 348.91 210.42 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n403 3 Car -1 -1 -1 1116.72 188.61 1221.05 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n403 7 Car -1 -1 -1 982.89 185.33 1069.19 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n403 11 Car -1 -1 -1 931.00 184.66 1009.84 220.27 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n403 4 Car -1 -1 -1 877.54 183.80 946.01 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n403 199 Pedestrian -1 -1 -1 753.07 172.00 788.96 250.16 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n403 191 Cyclist -1 -1 -1 243.43 154.55 335.88 303.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n403 137 Car -1 -1 -1 607.11 176.00 633.75 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n403 195 Cyclist -1 -1 -1 522.19 171.26 558.98 224.55 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n403 153 Pedestrian -1 -1 -1 318.24 165.32 334.30 208.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n403 202 Cyclist -1 -1 -1 432.98 167.54 459.19 219.94 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n403 196 Pedestrian -1 -1 -1 332.07 165.33 350.37 209.64 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n403 163 Pedestrian -1 -1 -1 299.18 162.51 317.61 208.89 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n403 183 Pedestrian -1 -1 -1 273.39 160.68 288.33 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0020.txt",
    "content": "0 1 Cyclist -1 -1 -1 851.42 167.11 987.90 307.31 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n0 2 Car -1 -1 -1 1094.47 184.10 1220.51 235.01 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n0 3 Pedestrian -1 -1 -1 258.06 151.61 289.23 223.28 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n0 4 Car -1 -1 -1 1029.30 183.73 1155.83 231.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n0 5 Car -1 -1 -1 950.14 182.46 1066.86 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n0 6 Pedestrian -1 -1 -1 222.79 154.15 248.58 222.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n0 7 Pedestrian -1 -1 -1 480.61 159.26 511.47 254.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n0 8 Car -1 -1 -1 601.69 173.13 636.84 201.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n0 9 Cyclist -1 -1 -1 563.90 163.76 584.98 212.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n0 10 Pedestrian -1 -1 -1 218.99 155.21 234.63 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n0 11 Pedestrian -1 -1 -1 192.14 159.55 208.62 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n0 12 Pedestrian -1 -1 -1 336.76 159.91 348.96 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n0 13 Pedestrian -1 -1 -1 349.09 161.53 363.77 194.39 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n0 14 Pedestrian -1 -1 -1 365.60 162.81 377.92 193.80 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n1 1 Cyclist -1 -1 -1 815.59 166.37 947.18 301.02 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n1 2 Car -1 -1 -1 1094.65 183.85 1221.14 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 4 Car -1 -1 -1 1028.47 183.09 1157.41 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n1 5 Car -1 -1 -1 952.92 182.89 1068.14 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1 3 Pedestrian -1 -1 -1 261.81 152.75 291.05 222.46 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n1 7 Pedestrian -1 -1 -1 475.19 158.82 506.98 254.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n1 6 Pedestrian -1 -1 -1 225.72 154.60 251.35 221.98 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n1 8 Car -1 -1 -1 602.90 172.77 637.10 202.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n1 9 Cyclist -1 -1 -1 564.39 164.65 584.62 213.63 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n1 10 Pedestrian -1 -1 -1 219.60 155.25 234.85 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n1 14 Pedestrian -1 -1 -1 366.28 163.27 377.71 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n1 13 Pedestrian -1 -1 -1 349.29 161.92 365.47 193.91 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n1 11 Pedestrian -1 -1 -1 192.55 159.59 208.20 199.21 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n1 12 Pedestrian -1 -1 -1 336.80 159.93 349.34 193.50 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n2 2 Car -1 -1 -1 1094.79 183.90 1221.07 235.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n2 7 Pedestrian -1 -1 -1 465.36 157.21 502.47 253.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n2 5 Car -1 -1 -1 953.93 183.13 1067.05 231.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n2 4 Car -1 -1 -1 1028.25 183.56 1157.51 231.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n2 1 Cyclist -1 -1 -1 783.58 166.22 904.36 299.48 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n2 6 Pedestrian -1 -1 -1 225.99 154.66 251.99 219.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n2 3 Pedestrian -1 -1 -1 263.25 153.83 292.61 222.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n2 8 Car -1 -1 -1 601.88 173.16 636.88 202.13 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n2 10 Pedestrian -1 -1 -1 219.03 155.55 235.24 196.57 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n2 9 Cyclist -1 -1 -1 563.83 164.52 584.91 211.53 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n2 11 Pedestrian -1 -1 -1 192.27 159.79 208.35 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n2 12 Pedestrian -1 -1 -1 336.82 160.12 349.37 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n2 14 Pedestrian -1 -1 -1 366.44 163.29 378.56 192.40 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n2 13 Pedestrian -1 -1 -1 350.31 160.79 365.78 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n3 2 Car -1 -1 -1 1094.85 183.94 1221.02 235.38 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n3 7 Pedestrian -1 -1 -1 459.10 158.01 500.48 253.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n3 5 Car -1 -1 -1 954.24 182.90 1066.97 231.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n3 4 Car -1 -1 -1 1028.79 183.49 1157.32 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n3 1 Cyclist -1 -1 -1 749.79 164.70 869.15 299.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n3 3 Pedestrian -1 -1 -1 266.88 154.12 293.42 221.15 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n3 6 Pedestrian -1 -1 -1 227.71 154.36 252.16 218.62 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n3 8 Car -1 -1 -1 602.07 173.17 636.73 201.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n3 9 Cyclist -1 -1 -1 564.02 164.46 584.92 210.70 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n3 10 Pedestrian -1 -1 -1 219.02 155.47 235.01 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n3 11 Pedestrian -1 -1 -1 192.06 160.04 208.56 199.36 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n3 13 Pedestrian -1 -1 -1 350.89 160.47 366.03 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n3 14 Pedestrian -1 -1 -1 366.60 163.33 378.15 192.48 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n3 12 Pedestrian -1 -1 -1 336.65 160.24 349.46 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n4 2 Car -1 -1 -1 1094.87 183.84 1221.08 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n4 5 Car -1 -1 -1 954.09 182.94 1066.98 231.94 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n4 7 Pedestrian -1 -1 -1 456.39 158.12 496.47 253.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n4 6 Pedestrian -1 -1 -1 230.18 155.21 256.30 218.56 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n4 4 Car -1 -1 -1 1028.24 183.26 1157.72 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n4 1 Cyclist -1 -1 -1 715.87 162.29 840.99 296.19 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n4 3 Pedestrian -1 -1 -1 269.29 152.91 293.70 220.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n4 8 Car -1 -1 -1 602.80 172.98 637.21 202.38 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n4 9 Cyclist -1 -1 -1 564.39 164.52 584.92 210.34 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n4 10 Pedestrian -1 -1 -1 218.85 155.26 234.84 196.87 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n4 11 Pedestrian -1 -1 -1 192.13 160.09 208.51 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n4 14 Pedestrian -1 -1 -1 366.12 163.37 377.91 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n4 13 Pedestrian -1 -1 -1 350.73 160.74 366.31 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n4 12 Pedestrian -1 -1 -1 336.93 160.27 349.29 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n4 15 Car -1 -1 -1 599.37 173.63 620.71 192.42 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n5 2 Car -1 -1 -1 1095.26 184.09 1220.67 235.15 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n5 5 Car -1 -1 -1 954.04 183.09 1066.99 231.76 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n5 4 Car -1 -1 -1 1028.78 183.65 1156.84 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n5 1 Cyclist -1 -1 -1 692.15 165.12 810.38 291.32 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n5 6 Pedestrian -1 -1 -1 232.27 155.96 259.06 218.89 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n5 7 Pedestrian -1 -1 -1 454.85 158.46 491.00 252.68 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n5 3 Pedestrian -1 -1 -1 271.62 152.92 296.50 220.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n5 8 Car -1 -1 -1 601.94 173.08 636.91 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n5 10 Pedestrian -1 -1 -1 219.33 155.21 234.99 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n5 9 Cyclist -1 -1 -1 564.42 164.74 584.70 209.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n5 11 Pedestrian -1 -1 -1 192.19 160.12 208.68 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n5 15 Car -1 -1 -1 599.13 173.65 620.51 192.42 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n5 13 Pedestrian -1 -1 -1 350.07 162.10 367.60 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n6 2 Car -1 -1 -1 1094.91 183.87 1221.09 235.39 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n6 5 Car -1 -1 -1 954.14 183.10 1066.88 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n6 4 Car -1 -1 -1 1028.94 183.64 1156.81 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n6 1 Cyclist -1 -1 -1 663.44 163.14 784.92 293.22 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n6 3 Pedestrian -1 -1 -1 272.20 153.47 298.06 219.81 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n6 6 Pedestrian -1 -1 -1 236.37 155.76 262.34 218.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n6 7 Pedestrian -1 -1 -1 454.38 158.07 482.14 252.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n6 8 Car -1 -1 -1 602.77 173.05 637.24 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n6 9 Cyclist -1 -1 -1 564.47 164.86 584.40 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n6 10 Pedestrian -1 -1 -1 219.09 155.41 234.67 196.77 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n6 11 Pedestrian -1 -1 -1 192.34 160.35 208.56 199.45 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n6 13 Pedestrian -1 -1 -1 338.96 161.37 351.09 191.70 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n6 15 Car -1 -1 -1 599.28 173.60 620.62 192.75 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n6 16 Pedestrian -1 -1 -1 350.85 161.30 366.48 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n6 17 Pedestrian -1 -1 -1 366.01 163.24 378.35 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n7 2 Car -1 -1 -1 1095.31 183.83 1220.47 235.18 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n7 5 Car -1 -1 -1 954.00 183.09 1067.11 231.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n7 4 Car -1 -1 -1 1028.62 183.70 1157.24 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n7 1 Cyclist -1 -1 -1 641.26 162.09 755.01 288.27 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n7 3 Pedestrian -1 -1 -1 274.28 153.42 302.78 219.75 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n7 6 Pedestrian -1 -1 -1 238.15 155.94 264.07 217.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n7 7 Pedestrian -1 -1 -1 450.61 158.50 477.79 251.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n7 8 Car -1 -1 -1 601.70 172.72 637.19 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n7 9 Cyclist -1 -1 -1 564.60 165.20 584.19 208.90 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n7 10 Pedestrian -1 -1 -1 218.86 155.32 234.40 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n7 11 Pedestrian -1 -1 -1 192.30 160.34 208.54 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n7 13 Pedestrian -1 -1 -1 339.13 161.07 351.31 191.91 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n7 17 Pedestrian -1 -1 -1 366.25 162.92 378.13 192.71 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n7 16 Pedestrian -1 -1 -1 350.75 160.68 366.57 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n8 5 Car -1 -1 -1 954.19 183.01 1066.89 232.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n8 2 Car -1 -1 -1 1095.33 184.48 1220.01 234.24 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n8 4 Car -1 -1 -1 1029.19 183.57 1156.12 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n8 3 Pedestrian -1 -1 -1 274.35 153.69 303.49 219.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n8 1 Cyclist -1 -1 -1 616.64 160.85 732.37 287.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n8 6 Pedestrian -1 -1 -1 242.41 154.63 266.24 216.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n8 8 Car -1 -1 -1 601.51 172.72 637.32 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n8 7 Pedestrian -1 -1 -1 442.39 158.72 473.37 250.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n8 10 Pedestrian -1 -1 -1 216.80 155.17 232.32 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n8 9 Cyclist -1 -1 -1 563.95 165.13 582.60 207.93 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n8 11 Pedestrian -1 -1 -1 192.34 160.14 208.57 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n8 13 Pedestrian -1 -1 -1 339.36 161.12 351.47 191.69 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n8 16 Pedestrian -1 -1 -1 351.48 160.24 366.01 193.71 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n8 17 Pedestrian -1 -1 -1 366.03 163.09 378.69 192.54 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n8 18 Cyclist -1 -1 -1 1073.74 184.94 1224.85 325.71 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n9 5 Car -1 -1 -1 954.28 182.93 1066.91 232.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n9 2 Car -1 -1 -1 1094.48 184.40 1219.98 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n9 18 Cyclist -1 -1 -1 1033.02 181.76 1204.67 321.45 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n9 3 Pedestrian -1 -1 -1 276.30 153.73 301.48 218.33 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n9 1 Cyclist -1 -1 -1 593.66 162.00 702.00 282.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n9 7 Pedestrian -1 -1 -1 441.82 159.46 471.31 250.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n9 4 Car -1 -1 -1 1030.36 183.79 1153.46 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n9 8 Car -1 -1 -1 601.57 172.90 637.04 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n9 6 Pedestrian -1 -1 -1 244.31 155.14 266.62 216.81 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n9 10 Pedestrian -1 -1 -1 216.59 155.07 232.24 196.74 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n9 9 Cyclist -1 -1 -1 564.30 165.05 582.29 207.14 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n9 11 Pedestrian -1 -1 -1 192.36 160.26 208.35 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n9 17 Pedestrian -1 -1 -1 366.08 163.05 379.00 192.59 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n9 13 Pedestrian -1 -1 -1 339.91 160.89 351.75 191.62 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n9 16 Pedestrian -1 -1 -1 352.89 160.61 368.00 193.13 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n10 2 Car -1 -1 -1 1094.18 183.77 1220.96 235.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n10 5 Car -1 -1 -1 954.51 182.96 1066.62 232.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n10 4 Car -1 -1 -1 1034.70 183.46 1157.04 233.96 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n10 7 Pedestrian -1 -1 -1 438.47 159.75 467.21 250.34 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n10 18 Cyclist -1 -1 -1 992.50 180.00 1154.00 316.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n10 6 Pedestrian -1 -1 -1 247.12 155.94 268.54 216.74 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n10 3 Pedestrian -1 -1 -1 277.64 153.56 301.34 217.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n10 8 Car -1 -1 -1 602.64 172.59 637.75 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n10 1 Cyclist -1 -1 -1 570.86 167.17 673.06 277.14 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n10 10 Pedestrian -1 -1 -1 216.83 155.38 232.26 196.83 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n10 11 Pedestrian -1 -1 -1 192.30 160.31 208.15 199.96 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n10 9 Cyclist -1 -1 -1 564.93 164.97 581.79 207.08 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n10 17 Pedestrian -1 -1 -1 366.69 162.76 379.18 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n10 16 Pedestrian -1 -1 -1 353.32 160.66 367.83 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n10 13 Pedestrian -1 -1 -1 340.39 160.82 352.00 191.63 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n11 5 Car -1 -1 -1 955.57 183.08 1066.14 231.96 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n11 2 Car -1 -1 -1 1095.65 184.05 1219.71 234.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n11 4 Car -1 -1 -1 1030.35 183.57 1154.32 231.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n11 8 Car -1 -1 -1 601.31 172.80 637.21 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n11 1 Cyclist -1 -1 -1 548.88 168.40 648.71 274.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n11 3 Pedestrian -1 -1 -1 277.10 154.01 302.46 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n11 7 Pedestrian -1 -1 -1 431.48 160.11 460.79 249.25 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n11 18 Cyclist -1 -1 -1 952.87 180.91 1109.10 314.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n11 6 Pedestrian -1 -1 -1 249.24 156.32 272.76 216.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n11 10 Pedestrian -1 -1 -1 216.61 155.32 232.31 196.89 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n11 9 Cyclist -1 -1 -1 566.10 165.36 582.45 207.10 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n11 17 Pedestrian -1 -1 -1 366.94 162.77 379.30 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n11 11 Pedestrian -1 -1 -1 192.14 160.27 208.04 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n11 13 Pedestrian -1 -1 -1 340.98 160.62 352.17 191.54 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n11 16 Pedestrian -1 -1 -1 353.26 160.24 367.88 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n11 19 Cyclist -1 -1 -1 1096.86 184.98 1209.44 350.23 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n11 20 Car -1 -1 -1 597.59 172.80 622.74 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n12 2 Car -1 -1 -1 1094.11 183.73 1220.27 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n12 5 Car -1 -1 -1 956.63 182.97 1065.88 231.66 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n12 3 Pedestrian -1 -1 -1 279.12 154.65 304.93 217.95 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n12 8 Car -1 -1 -1 601.29 173.12 636.80 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 7 Pedestrian -1 -1 -1 425.22 159.81 458.22 247.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 19 Cyclist -1 -1 -1 1018.77 184.90 1203.18 342.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 4 Car -1 -1 -1 1029.20 183.75 1155.85 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n12 1 Cyclist -1 -1 -1 529.32 167.62 627.61 274.06 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n12 6 Pedestrian -1 -1 -1 249.96 156.07 274.10 215.27 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n12 10 Pedestrian -1 -1 -1 216.66 155.33 232.30 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n12 18 Cyclist -1 -1 -1 914.88 177.68 1070.18 311.24 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n12 9 Cyclist -1 -1 -1 564.74 165.61 581.51 206.16 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n12 11 Pedestrian -1 -1 -1 192.12 160.34 207.97 200.00 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n12 17 Pedestrian -1 -1 -1 367.55 162.56 379.71 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n12 16 Pedestrian -1 -1 -1 353.46 160.38 367.84 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n12 20 Car -1 -1 -1 598.14 173.55 622.10 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n13 2 Car -1 -1 -1 1094.40 183.99 1221.03 235.45 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n13 7 Pedestrian -1 -1 -1 419.77 159.91 456.03 246.79 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n13 5 Car -1 -1 -1 954.25 182.96 1063.35 231.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n13 4 Car -1 -1 -1 1035.22 183.83 1155.55 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n13 1 Cyclist -1 -1 -1 508.42 166.25 603.02 270.50 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n13 3 Pedestrian -1 -1 -1 280.90 155.41 305.66 217.06 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n13 8 Car -1 -1 -1 601.55 173.20 636.57 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n13 19 Cyclist -1 -1 -1 966.60 187.19 1148.74 339.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n13 10 Pedestrian -1 -1 -1 216.57 155.33 232.26 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n13 18 Cyclist -1 -1 -1 879.74 174.96 1029.02 306.66 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n13 6 Pedestrian -1 -1 -1 250.96 155.98 275.17 215.12 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n13 9 Cyclist -1 -1 -1 565.35 165.09 581.29 207.75 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n13 11 Pedestrian -1 -1 -1 192.15 160.41 207.80 199.96 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n13 17 Pedestrian -1 -1 -1 367.59 162.46 379.87 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n13 20 Car -1 -1 -1 598.60 173.67 621.02 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n13 16 Pedestrian -1 -1 -1 353.63 160.29 367.62 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n14 2 Car -1 -1 -1 1094.79 184.07 1220.95 235.10 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n14 5 Car -1 -1 -1 952.63 183.26 1065.08 231.40 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n14 7 Pedestrian -1 -1 -1 416.63 160.37 451.80 246.16 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n14 1 Cyclist -1 -1 -1 487.63 167.69 579.67 268.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n14 4 Car -1 -1 -1 1031.26 184.02 1154.29 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 19 Cyclist -1 -1 -1 933.50 182.81 1090.54 328.93 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 18 Cyclist -1 -1 -1 848.98 179.00 976.13 301.45 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n14 8 Car -1 -1 -1 601.81 173.17 636.80 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n14 3 Pedestrian -1 -1 -1 283.20 154.35 308.52 216.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n14 6 Pedestrian -1 -1 -1 253.80 155.14 276.88 213.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n14 10 Pedestrian -1 -1 -1 216.46 155.20 232.27 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n14 9 Cyclist -1 -1 -1 564.55 165.17 581.44 208.55 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n14 11 Pedestrian -1 -1 -1 191.95 160.41 207.83 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n14 17 Pedestrian -1 -1 -1 367.88 161.84 379.81 192.08 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n14 20 Car -1 -1 -1 598.68 173.48 621.20 192.93 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n14 16 Pedestrian -1 -1 -1 354.13 159.83 367.75 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n15 2 Car -1 -1 -1 1094.63 184.13 1221.09 234.98 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n15 5 Car -1 -1 -1 955.63 182.42 1065.98 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n15 7 Pedestrian -1 -1 -1 414.13 161.15 447.83 244.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n15 4 Car -1 -1 -1 1030.56 184.18 1155.11 232.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n15 1 Cyclist -1 -1 -1 468.74 168.62 558.16 266.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n15 19 Cyclist -1 -1 -1 902.78 180.13 1036.44 330.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n15 18 Cyclist -1 -1 -1 819.89 172.49 935.87 301.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n15 8 Car -1 -1 -1 602.06 173.34 636.59 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n15 10 Pedestrian -1 -1 -1 216.54 155.42 232.33 196.97 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n15 3 Pedestrian -1 -1 -1 284.59 154.46 310.45 216.24 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n15 6 Pedestrian -1 -1 -1 256.55 156.43 280.28 214.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n15 9 Cyclist -1 -1 -1 564.37 165.38 581.36 206.13 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n15 11 Pedestrian -1 -1 -1 192.00 160.48 208.01 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n15 20 Car -1 -1 -1 598.85 173.53 621.09 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n15 17 Pedestrian -1 -1 -1 367.98 161.45 380.05 192.25 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n16 2 Car -1 -1 -1 1094.89 184.13 1220.94 235.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n16 5 Car -1 -1 -1 954.56 182.72 1066.91 232.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n16 7 Pedestrian -1 -1 -1 411.28 160.34 442.49 245.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n16 4 Car -1 -1 -1 1029.22 183.46 1156.77 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n16 1 Cyclist -1 -1 -1 447.63 169.00 535.80 265.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n16 3 Pedestrian -1 -1 -1 287.37 154.05 313.01 215.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n16 19 Cyclist -1 -1 -1 869.64 179.34 977.33 324.45 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n16 6 Pedestrian -1 -1 -1 258.24 156.86 281.95 214.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n16 8 Car -1 -1 -1 602.13 173.18 636.73 202.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n16 10 Pedestrian -1 -1 -1 216.45 155.29 232.36 197.13 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 18 Cyclist -1 -1 -1 783.30 174.15 903.74 300.31 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 11 Pedestrian -1 -1 -1 191.85 160.42 208.27 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n16 9 Cyclist -1 -1 -1 564.85 165.69 580.46 203.43 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n16 20 Car -1 -1 -1 598.94 173.58 621.01 192.45 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n16 17 Pedestrian -1 -1 -1 368.13 161.28 380.01 191.93 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n16 21 Pedestrian -1 -1 -1 389.63 163.62 403.95 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n17 2 Car -1 -1 -1 1095.16 184.10 1220.86 235.35 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n17 5 Car -1 -1 -1 954.97 182.89 1066.72 231.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n17 4 Car -1 -1 -1 1029.22 183.40 1156.89 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n17 7 Pedestrian -1 -1 -1 407.74 160.71 436.98 244.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n17 1 Cyclist -1 -1 -1 427.52 170.96 522.25 264.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n17 19 Cyclist -1 -1 -1 825.80 178.57 929.70 318.07 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n17 8 Car -1 -1 -1 601.93 173.20 636.80 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n17 10 Pedestrian -1 -1 -1 216.64 155.32 232.25 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n17 6 Pedestrian -1 -1 -1 262.43 157.16 284.23 213.65 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n17 3 Pedestrian -1 -1 -1 289.03 154.56 312.97 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n17 18 Cyclist -1 -1 -1 765.46 174.29 867.28 299.68 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n17 21 Pedestrian -1 -1 -1 391.90 164.29 405.39 203.85 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n17 11 Pedestrian -1 -1 -1 192.11 160.58 208.10 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n17 9 Cyclist -1 -1 -1 564.77 166.69 580.22 205.05 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n17 20 Car -1 -1 -1 598.73 173.52 621.25 192.65 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n17 17 Pedestrian -1 -1 -1 367.92 161.00 380.20 191.81 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n18 2 Car -1 -1 -1 1095.29 183.99 1220.64 235.39 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n18 5 Car -1 -1 -1 954.67 182.94 1066.68 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n18 4 Car -1 -1 -1 1028.79 183.29 1157.24 232.01 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n18 19 Cyclist -1 -1 -1 783.25 178.21 895.58 318.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n18 1 Cyclist -1 -1 -1 407.02 168.02 499.83 261.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n18 8 Car -1 -1 -1 602.07 172.99 636.76 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n18 3 Pedestrian -1 -1 -1 291.89 155.08 314.50 216.01 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n18 10 Pedestrian -1 -1 -1 216.67 155.35 232.23 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n18 6 Pedestrian -1 -1 -1 266.63 156.00 286.11 213.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n18 21 Pedestrian -1 -1 -1 393.17 163.83 406.47 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n18 7 Pedestrian -1 -1 -1 402.98 161.72 436.06 243.39 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n18 11 Pedestrian -1 -1 -1 192.05 160.54 208.37 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n18 18 Cyclist -1 -1 -1 735.73 172.87 836.38 294.39 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n18 9 Cyclist -1 -1 -1 564.74 166.81 580.06 204.78 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n18 20 Car -1 -1 -1 598.70 173.37 621.01 192.56 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n19 2 Car -1 -1 -1 1095.19 184.10 1220.67 235.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n19 5 Car -1 -1 -1 954.62 182.98 1066.67 231.83 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n19 4 Car -1 -1 -1 1028.81 183.25 1157.34 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n19 19 Cyclist -1 -1 -1 740.21 178.17 854.35 318.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n19 1 Cyclist -1 -1 -1 387.38 165.74 480.12 262.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n19 3 Pedestrian -1 -1 -1 292.60 155.32 315.32 215.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n19 8 Car -1 -1 -1 602.06 173.00 636.83 202.30 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n19 10 Pedestrian -1 -1 -1 216.84 155.21 232.23 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n19 6 Pedestrian -1 -1 -1 267.76 156.23 288.31 213.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n19 21 Pedestrian -1 -1 -1 393.75 163.98 406.86 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n19 7 Pedestrian -1 -1 -1 397.99 162.29 432.43 241.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n19 11 Pedestrian -1 -1 -1 192.08 160.46 208.32 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n19 20 Car -1 -1 -1 598.79 173.41 620.96 192.36 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n19 9 Cyclist -1 -1 -1 565.58 166.86 579.62 202.00 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n20 2 Car -1 -1 -1 1095.42 184.14 1220.60 235.23 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n20 5 Car -1 -1 -1 954.66 183.12 1066.69 231.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n20 4 Car -1 -1 -1 1029.52 183.91 1156.36 232.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n20 1 Cyclist -1 -1 -1 373.52 165.98 457.25 260.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n20 8 Car -1 -1 -1 601.81 173.00 636.86 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n20 3 Pedestrian -1 -1 -1 293.52 154.64 315.87 214.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n20 19 Cyclist -1 -1 -1 705.76 178.13 827.22 311.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n20 10 Pedestrian -1 -1 -1 216.80 154.96 232.06 197.17 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 6 Pedestrian -1 -1 -1 270.35 156.82 290.06 214.10 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n20 7 Pedestrian -1 -1 -1 392.87 162.69 429.90 241.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n20 11 Pedestrian -1 -1 -1 191.95 160.16 208.28 200.09 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n20 21 Pedestrian -1 -1 -1 393.62 163.87 407.55 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n20 9 Cyclist -1 -1 -1 566.00 166.72 579.51 201.86 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n20 20 Car -1 -1 -1 598.70 173.39 621.13 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n21 2 Car -1 -1 -1 1095.17 184.12 1220.78 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n21 5 Car -1 -1 -1 954.52 182.98 1066.83 231.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n21 4 Car -1 -1 -1 1029.58 183.88 1156.29 232.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n21 1 Cyclist -1 -1 -1 356.88 164.04 441.62 257.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n21 8 Car -1 -1 -1 601.87 172.85 637.01 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 10 Pedestrian -1 -1 -1 216.78 154.73 232.02 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n21 6 Pedestrian -1 -1 -1 271.03 157.47 291.30 213.82 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n21 3 Pedestrian -1 -1 -1 294.10 154.46 316.24 214.39 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n21 9 Cyclist -1 -1 -1 566.27 166.41 579.55 201.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n21 7 Pedestrian -1 -1 -1 386.26 161.58 428.20 241.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n21 11 Pedestrian -1 -1 -1 191.76 160.03 208.44 200.21 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n21 21 Pedestrian -1 -1 -1 394.69 163.12 411.00 203.36 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n21 20 Car -1 -1 -1 598.84 173.29 620.78 192.70 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n21 19 Cyclist -1 -1 -1 673.41 177.28 784.38 303.75 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n22 2 Car -1 -1 -1 1095.16 184.09 1220.83 235.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n22 5 Car -1 -1 -1 954.56 183.00 1066.84 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n22 4 Car -1 -1 -1 1029.51 183.86 1156.30 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n22 19 Cyclist -1 -1 -1 637.00 174.61 758.93 299.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 8 Car -1 -1 -1 601.68 172.55 637.29 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 1 Cyclist -1 -1 -1 340.22 167.54 422.42 254.42 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n22 10 Pedestrian -1 -1 -1 216.66 154.94 232.03 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n22 3 Pedestrian -1 -1 -1 296.23 154.89 317.93 214.15 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n22 9 Cyclist -1 -1 -1 566.30 166.30 579.76 200.98 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n22 6 Pedestrian -1 -1 -1 272.04 156.42 291.99 212.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n22 11 Pedestrian -1 -1 -1 192.03 159.95 208.22 200.34 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n22 20 Car -1 -1 -1 598.61 173.29 620.62 192.71 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n22 21 Pedestrian -1 -1 -1 395.87 162.95 410.19 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n23 2 Car -1 -1 -1 1095.27 184.08 1220.61 235.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n23 5 Car -1 -1 -1 954.34 182.99 1067.07 231.95 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n23 4 Car -1 -1 -1 1029.68 183.90 1156.09 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n23 1 Cyclist -1 -1 -1 326.86 166.60 403.44 255.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n23 8 Car -1 -1 -1 601.59 172.47 637.29 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n23 21 Pedestrian -1 -1 -1 382.94 161.44 415.32 242.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n23 6 Pedestrian -1 -1 -1 275.29 156.27 295.20 212.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n23 10 Pedestrian -1 -1 -1 216.61 154.94 232.05 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n23 19 Cyclist -1 -1 -1 611.70 168.95 729.69 298.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n23 3 Pedestrian -1 -1 -1 298.02 155.09 319.22 214.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n23 9 Cyclist -1 -1 -1 566.48 166.48 579.95 201.26 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n23 11 Pedestrian -1 -1 -1 191.86 159.85 208.31 200.33 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n23 20 Car -1 -1 -1 598.36 173.40 620.33 192.50 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n24 2 Car -1 -1 -1 1095.06 184.10 1220.81 235.44 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n24 5 Car -1 -1 -1 954.25 183.06 1067.07 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n24 4 Car -1 -1 -1 1029.52 183.92 1156.20 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n24 8 Car -1 -1 -1 601.67 172.77 636.69 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 21 Pedestrian -1 -1 -1 377.82 161.72 412.48 241.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 19 Cyclist -1 -1 -1 590.12 174.35 698.71 292.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 1 Cyclist -1 -1 -1 311.95 167.81 388.72 253.41 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 10 Pedestrian -1 -1 -1 216.35 154.75 232.27 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n24 3 Pedestrian -1 -1 -1 299.92 155.55 321.21 215.27 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n24 6 Pedestrian -1 -1 -1 276.18 156.96 296.43 212.35 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n24 11 Pedestrian -1 -1 -1 191.72 159.76 208.29 200.44 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n24 20 Car -1 -1 -1 598.18 173.24 620.80 192.36 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n24 9 Cyclist -1 -1 -1 566.79 166.48 580.08 201.09 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n24 22 Pedestrian -1 -1 -1 567.67 167.04 581.11 200.53 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n25 2 Car -1 -1 -1 1094.92 184.09 1220.90 235.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n25 5 Car -1 -1 -1 954.47 183.08 1066.89 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n25 4 Car -1 -1 -1 1029.61 183.97 1156.13 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n25 21 Pedestrian -1 -1 -1 376.41 161.19 407.55 241.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n25 19 Cyclist -1 -1 -1 566.63 173.81 676.00 291.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n25 8 Car -1 -1 -1 601.17 172.79 637.49 202.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n25 1 Cyclist -1 -1 -1 303.85 167.80 371.76 253.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n25 6 Pedestrian -1 -1 -1 277.69 157.99 299.09 212.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n25 10 Pedestrian -1 -1 -1 216.28 154.81 232.26 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n25 3 Pedestrian -1 -1 -1 302.12 155.68 321.88 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n25 11 Pedestrian -1 -1 -1 191.52 159.93 208.23 200.32 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n25 20 Car -1 -1 -1 597.71 173.24 621.17 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n25 22 Pedestrian -1 -1 -1 568.14 167.06 581.10 200.52 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n26 2 Car -1 -1 -1 1094.88 184.13 1220.96 235.39 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n26 5 Car -1 -1 -1 954.43 183.12 1066.82 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n26 4 Car -1 -1 -1 1029.39 183.96 1156.26 232.66 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n26 1 Cyclist -1 -1 -1 287.56 166.30 358.04 248.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n26 21 Pedestrian -1 -1 -1 374.74 160.41 401.22 241.27 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n26 8 Car -1 -1 -1 601.30 172.62 637.53 202.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n26 10 Pedestrian -1 -1 -1 216.29 154.75 232.14 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n26 6 Pedestrian -1 -1 -1 279.23 156.99 300.41 212.15 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n26 3 Pedestrian -1 -1 -1 302.91 155.76 322.70 212.79 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n26 19 Cyclist -1 -1 -1 542.52 176.34 655.52 289.04 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n26 11 Pedestrian -1 -1 -1 191.51 159.76 208.14 200.38 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n26 22 Pedestrian -1 -1 -1 568.69 167.19 580.99 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n26 20 Car -1 -1 -1 597.06 172.73 622.22 192.76 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n26 23 Pedestrian -1 -1 -1 396.79 163.86 410.42 202.17 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n26 24 Cyclist -1 -1 -1 568.69 167.19 580.99 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n27 2 Car -1 -1 -1 1094.88 184.10 1220.96 235.38 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n27 5 Car -1 -1 -1 954.45 183.19 1066.73 231.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n27 4 Car -1 -1 -1 1029.40 184.03 1156.25 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n27 8 Car -1 -1 -1 601.33 172.95 636.93 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n27 21 Pedestrian -1 -1 -1 372.15 161.07 397.09 240.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n27 10 Pedestrian -1 -1 -1 216.27 154.76 232.19 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n27 1 Cyclist -1 -1 -1 272.22 166.28 345.31 248.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n27 3 Pedestrian -1 -1 -1 304.79 156.15 325.26 212.13 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n27 23 Pedestrian -1 -1 -1 396.83 163.89 410.90 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n27 6 Pedestrian -1 -1 -1 281.31 157.24 303.28 211.70 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n27 11 Pedestrian -1 -1 -1 191.70 159.85 208.07 200.23 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n27 20 Car -1 -1 -1 597.72 173.12 621.68 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n27 19 Cyclist -1 -1 -1 525.03 177.65 626.02 287.01 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n27 22 Pedestrian -1 -1 -1 569.30 168.88 581.81 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n27 24 Cyclist -1 -1 -1 569.30 168.88 581.81 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n28 2 Car -1 -1 -1 1094.82 184.09 1220.98 235.35 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n28 5 Car -1 -1 -1 954.37 183.20 1066.79 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n28 4 Car -1 -1 -1 1029.43 184.02 1156.31 232.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n28 1 Cyclist -1 -1 -1 259.84 165.10 333.16 248.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n28 8 Car -1 -1 -1 601.53 173.11 636.52 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n28 10 Pedestrian -1 -1 -1 216.27 154.83 232.15 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n28 21 Pedestrian -1 -1 -1 367.91 161.55 395.39 240.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n28 3 Pedestrian -1 -1 -1 304.83 155.00 326.71 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n28 19 Cyclist -1 -1 -1 505.54 174.84 598.57 282.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n28 23 Pedestrian -1 -1 -1 397.85 163.52 411.20 202.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n28 24 Cyclist -1 -1 -1 569.63 168.09 582.20 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n28 6 Pedestrian -1 -1 -1 281.72 158.32 302.69 209.31 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n28 20 Car -1 -1 -1 598.10 173.31 621.09 192.81 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n28 11 Pedestrian -1 -1 -1 191.58 160.03 207.90 200.18 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n28 22 Pedestrian -1 -1 -1 569.63 168.09 582.20 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n29 2 Car -1 -1 -1 1094.90 184.13 1220.89 235.34 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n29 5 Car -1 -1 -1 954.22 183.19 1066.88 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 4 Car -1 -1 -1 1029.43 184.01 1156.31 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n29 8 Car -1 -1 -1 602.02 173.17 636.24 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n29 1 Cyclist -1 -1 -1 249.87 164.81 318.74 246.00 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n29 10 Pedestrian -1 -1 -1 216.21 154.96 232.26 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n29 21 Pedestrian -1 -1 -1 366.58 162.44 392.94 239.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n29 3 Pedestrian -1 -1 -1 308.13 155.92 328.66 212.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n29 24 Cyclist -1 -1 -1 569.61 168.40 582.38 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n29 23 Pedestrian -1 -1 -1 398.07 163.35 411.45 201.96 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n29 19 Cyclist -1 -1 -1 478.05 177.72 580.64 279.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n29 6 Pedestrian -1 -1 -1 284.91 157.93 307.09 209.46 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n29 11 Pedestrian -1 -1 -1 191.65 160.27 207.78 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n29 20 Car -1 -1 -1 598.49 173.34 621.09 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n30 2 Car -1 -1 -1 1094.96 184.14 1220.83 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n30 5 Car -1 -1 -1 954.27 183.17 1066.85 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n30 4 Car -1 -1 -1 1029.55 184.03 1156.22 232.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n30 1 Cyclist -1 -1 -1 242.19 162.28 305.03 244.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n30 21 Pedestrian -1 -1 -1 362.65 161.75 389.38 237.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n30 8 Car -1 -1 -1 602.30 173.25 636.39 202.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n30 3 Pedestrian -1 -1 -1 309.00 155.56 329.57 212.41 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n30 10 Pedestrian -1 -1 -1 216.29 155.11 232.40 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 6 Pedestrian -1 -1 -1 285.76 158.69 307.89 209.35 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n30 19 Cyclist -1 -1 -1 467.45 177.46 545.36 274.25 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 24 Cyclist -1 -1 -1 569.81 168.44 582.57 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n30 11 Pedestrian -1 -1 -1 191.48 160.26 208.05 200.09 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n30 23 Pedestrian -1 -1 -1 399.59 163.97 412.36 202.28 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n30 20 Car -1 -1 -1 598.48 173.43 621.08 193.00 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n30 25 Pedestrian -1 -1 -1 569.81 168.44 582.57 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n31 2 Car -1 -1 -1 1094.89 184.18 1220.90 235.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n31 5 Car -1 -1 -1 954.28 183.17 1066.75 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n31 4 Car -1 -1 -1 1029.49 183.98 1156.09 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 21 Pedestrian -1 -1 -1 359.57 160.63 386.18 236.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n31 3 Pedestrian -1 -1 -1 309.79 155.17 329.99 212.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n31 1 Cyclist -1 -1 -1 234.47 160.79 296.81 242.50 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n31 10 Pedestrian -1 -1 -1 216.09 154.95 232.53 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 8 Car -1 -1 -1 602.18 173.05 636.73 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 6 Pedestrian -1 -1 -1 289.10 157.20 309.34 209.41 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 19 Cyclist -1 -1 -1 444.18 177.02 523.93 274.61 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n31 24 Cyclist -1 -1 -1 570.31 168.21 582.50 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n31 11 Pedestrian -1 -1 -1 191.39 160.02 208.25 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n31 23 Pedestrian -1 -1 -1 399.27 162.99 412.91 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n31 20 Car -1 -1 -1 598.80 173.34 620.96 192.78 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n32 2 Car -1 -1 -1 1094.95 184.20 1220.95 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n32 5 Car -1 -1 -1 954.30 183.13 1066.75 231.98 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n32 4 Car -1 -1 -1 1029.50 183.99 1156.01 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n32 21 Pedestrian -1 -1 -1 355.54 159.56 384.26 237.07 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n32 6 Pedestrian -1 -1 -1 288.97 156.57 310.81 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n32 1 Cyclist -1 -1 -1 224.77 161.91 284.41 241.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n32 8 Car -1 -1 -1 602.19 173.00 636.63 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n32 10 Pedestrian -1 -1 -1 216.27 155.02 232.57 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n32 3 Pedestrian -1 -1 -1 312.33 154.38 331.99 211.02 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n32 24 Cyclist -1 -1 -1 570.35 168.33 582.74 197.05 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n32 23 Pedestrian -1 -1 -1 399.76 162.63 413.29 201.90 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n32 11 Pedestrian -1 -1 -1 191.36 160.00 208.44 200.34 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n32 19 Cyclist -1 -1 -1 429.55 177.12 500.04 273.73 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n32 20 Car -1 -1 -1 598.79 173.33 620.77 192.62 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n33 2 Car -1 -1 -1 1095.16 184.22 1220.78 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n33 5 Car -1 -1 -1 954.45 183.15 1066.68 231.94 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n33 4 Car -1 -1 -1 1029.60 184.02 1156.02 232.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n33 21 Pedestrian -1 -1 -1 350.34 160.25 382.03 237.39 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n33 6 Pedestrian -1 -1 -1 291.27 155.93 310.71 209.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n33 8 Car -1 -1 -1 602.02 172.75 636.82 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n33 3 Pedestrian -1 -1 -1 312.75 154.37 332.54 210.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n33 10 Pedestrian -1 -1 -1 216.33 154.75 232.61 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n33 1 Cyclist -1 -1 -1 217.66 162.82 275.07 239.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n33 23 Pedestrian -1 -1 -1 400.30 162.92 413.85 201.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n33 20 Car -1 -1 -1 598.86 173.19 620.75 192.57 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n33 11 Pedestrian -1 -1 -1 191.14 159.76 208.81 200.54 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n33 19 Cyclist -1 -1 -1 445.84 171.82 498.72 263.76 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n33 24 Cyclist -1 -1 -1 570.37 168.16 583.25 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n33 26 Cyclist -1 -1 -1 410.38 174.21 481.20 270.75 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n34 2 Car -1 -1 -1 1095.23 184.19 1220.80 235.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n34 21 Pedestrian -1 -1 -1 349.87 161.64 380.39 236.40 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n34 5 Car -1 -1 -1 954.28 183.14 1066.83 231.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n34 4 Car -1 -1 -1 1029.42 183.99 1156.22 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n34 1 Cyclist -1 -1 -1 212.08 162.47 266.73 237.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n34 8 Car -1 -1 -1 601.92 172.71 636.88 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n34 3 Pedestrian -1 -1 -1 313.91 155.30 334.03 211.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n34 6 Pedestrian -1 -1 -1 293.33 156.61 312.55 209.04 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n34 10 Pedestrian -1 -1 -1 216.39 154.70 232.59 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n34 23 Pedestrian -1 -1 -1 400.41 162.70 414.16 201.47 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n34 19 Cyclist -1 -1 -1 430.11 173.47 483.61 261.21 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n34 20 Car -1 -1 -1 598.90 173.19 620.83 192.43 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n34 11 Pedestrian -1 -1 -1 191.07 159.63 208.90 200.57 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n34 24 Cyclist -1 -1 -1 570.94 168.21 582.88 197.19 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n34 27 Pedestrian -1 -1 -1 572.49 168.46 584.45 197.22 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n35 2 Car -1 -1 -1 1095.19 184.21 1220.89 235.28 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n35 5 Car -1 -1 -1 954.21 183.11 1066.86 231.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n35 4 Car -1 -1 -1 1029.46 183.94 1156.10 232.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n35 21 Pedestrian -1 -1 -1 347.31 161.65 377.57 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n35 1 Cyclist -1 -1 -1 211.04 161.92 258.43 236.48 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n35 6 Pedestrian -1 -1 -1 293.79 157.24 313.39 209.57 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n35 8 Car -1 -1 -1 601.83 172.85 636.94 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n35 3 Pedestrian -1 -1 -1 316.76 155.37 335.57 211.11 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n35 10 Pedestrian -1 -1 -1 216.23 154.90 232.65 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n35 23 Pedestrian -1 -1 -1 400.02 163.25 414.52 200.72 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n35 20 Car -1 -1 -1 598.76 173.37 620.43 192.37 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n35 19 Cyclist -1 -1 -1 371.38 176.01 442.36 266.68 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n35 11 Pedestrian -1 -1 -1 191.36 159.65 208.61 200.30 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n35 27 Pedestrian -1 -1 -1 572.75 168.24 584.29 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n35 28 Cyclist -1 -1 -1 408.63 174.81 467.58 259.31 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n36 2 Car -1 -1 -1 1095.25 184.25 1220.79 235.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n36 5 Car -1 -1 -1 954.16 183.15 1066.89 231.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n36 4 Car -1 -1 -1 1029.65 183.96 1156.06 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n36 21 Pedestrian -1 -1 -1 347.79 160.91 374.36 235.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n36 1 Cyclist -1 -1 -1 208.88 161.39 252.57 234.28 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n36 3 Pedestrian -1 -1 -1 317.03 155.25 336.34 210.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n36 8 Car -1 -1 -1 601.73 172.87 637.00 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n36 6 Pedestrian -1 -1 -1 296.09 157.44 314.23 209.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n36 23 Pedestrian -1 -1 -1 400.54 162.91 415.46 202.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n36 10 Pedestrian -1 -1 -1 216.19 155.18 232.70 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n36 28 Cyclist -1 -1 -1 393.46 173.27 451.81 256.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n36 20 Car -1 -1 -1 598.62 173.41 620.50 192.32 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n36 11 Pedestrian -1 -1 -1 191.52 159.45 208.78 200.27 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n36 27 Pedestrian -1 -1 -1 572.63 168.12 584.44 197.02 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n36 29 Pedestrian -1 -1 -1 677.39 168.21 689.69 204.30 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n37 2 Car -1 -1 -1 1095.02 184.26 1220.95 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n37 5 Car -1 -1 -1 954.15 183.13 1066.88 231.94 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n37 4 Car -1 -1 -1 1029.73 183.96 1155.89 232.70 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n37 21 Pedestrian -1 -1 -1 344.40 161.73 372.28 235.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n37 6 Pedestrian -1 -1 -1 298.48 157.71 316.69 208.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n37 8 Car -1 -1 -1 601.59 172.86 637.14 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n37 3 Pedestrian -1 -1 -1 318.65 155.34 337.58 210.33 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n37 10 Pedestrian -1 -1 -1 215.74 154.96 233.15 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n37 1 Cyclist -1 -1 -1 206.61 160.45 246.59 231.42 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n37 28 Cyclist -1 -1 -1 380.45 172.53 433.67 254.72 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n37 23 Pedestrian -1 -1 -1 401.18 163.02 415.63 201.88 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n37 29 Pedestrian -1 -1 -1 677.23 167.44 689.69 204.94 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n37 20 Car -1 -1 -1 598.53 173.39 620.39 192.23 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n37 11 Pedestrian -1 -1 -1 191.66 159.36 208.74 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n37 27 Pedestrian -1 -1 -1 572.72 168.28 585.26 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n37 30 Cyclist -1 -1 -1 572.72 168.28 585.26 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n38 2 Car -1 -1 -1 1095.06 184.23 1220.79 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n38 5 Car -1 -1 -1 954.18 183.02 1066.87 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n38 4 Car -1 -1 -1 1029.64 183.96 1155.84 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n38 21 Pedestrian -1 -1 -1 343.05 161.63 371.76 234.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n38 6 Pedestrian -1 -1 -1 299.62 157.67 317.67 208.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n38 8 Car -1 -1 -1 601.85 172.87 636.91 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n38 1 Cyclist -1 -1 -1 202.43 158.77 243.88 231.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n38 3 Pedestrian -1 -1 -1 320.79 156.24 339.60 209.54 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n38 10 Pedestrian -1 -1 -1 215.79 154.91 233.06 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n38 29 Pedestrian -1 -1 -1 677.36 166.91 689.70 205.26 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n38 23 Pedestrian -1 -1 -1 403.28 164.64 416.57 201.44 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n38 28 Cyclist -1 -1 -1 323.18 167.19 391.82 261.99 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n38 20 Car -1 -1 -1 598.37 173.25 620.24 192.19 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n38 27 Pedestrian -1 -1 -1 572.60 168.50 585.54 196.84 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n38 11 Pedestrian -1 -1 -1 191.49 159.57 208.65 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n38 31 Cyclist -1 -1 -1 366.88 171.95 417.83 250.76 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n39 2 Car -1 -1 -1 1095.32 184.23 1220.66 235.22 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n39 5 Car -1 -1 -1 954.28 183.04 1066.83 232.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n39 4 Car -1 -1 -1 1029.76 184.01 1155.85 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n39 8 Car -1 -1 -1 601.77 172.78 637.10 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n39 3 Pedestrian -1 -1 -1 321.48 156.50 341.09 210.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n39 21 Pedestrian -1 -1 -1 337.81 161.39 369.67 234.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n39 6 Pedestrian -1 -1 -1 301.39 157.85 319.67 208.61 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n39 23 Pedestrian -1 -1 -1 403.44 164.60 417.17 200.82 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n39 10 Pedestrian -1 -1 -1 215.70 154.86 233.15 197.42 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n39 1 Cyclist -1 -1 -1 202.51 159.57 238.47 228.04 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n39 29 Pedestrian -1 -1 -1 677.28 166.94 690.12 205.57 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n39 28 Cyclist -1 -1 -1 311.70 162.08 381.13 259.29 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n39 20 Car -1 -1 -1 598.37 173.13 620.50 192.25 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n39 27 Pedestrian -1 -1 -1 573.00 168.16 586.20 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n39 11 Pedestrian -1 -1 -1 191.58 159.47 208.53 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n40 2 Car -1 -1 -1 1095.05 184.19 1220.84 235.20 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n40 5 Car -1 -1 -1 954.11 183.10 1066.90 232.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n40 4 Car -1 -1 -1 1029.63 183.97 1155.91 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n40 6 Pedestrian -1 -1 -1 301.63 157.92 321.03 208.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n40 8 Car -1 -1 -1 601.60 172.74 637.19 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n40 23 Pedestrian -1 -1 -1 404.35 164.41 417.24 201.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n40 3 Pedestrian -1 -1 -1 321.30 156.71 341.40 210.63 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n40 10 Pedestrian -1 -1 -1 215.84 154.83 233.01 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n40 21 Pedestrian -1 -1 -1 337.99 160.36 368.23 235.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n40 1 Cyclist -1 -1 -1 203.59 161.00 235.95 226.83 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n40 20 Car -1 -1 -1 598.30 173.12 620.64 192.34 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n40 28 Cyclist -1 -1 -1 327.17 168.60 396.71 252.86 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n40 11 Pedestrian -1 -1 -1 192.04 159.66 208.38 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n40 29 Pedestrian -1 -1 -1 677.50 167.04 690.29 205.39 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n40 27 Pedestrian -1 -1 -1 573.45 167.80 587.20 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n40 32 Cyclist -1 -1 -1 306.97 171.46 354.81 257.64 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n41 2 Car -1 -1 -1 1095.07 184.28 1220.78 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n41 5 Car -1 -1 -1 954.12 183.19 1066.91 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n41 4 Car -1 -1 -1 1029.85 183.97 1155.77 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n41 6 Pedestrian -1 -1 -1 302.39 158.39 322.15 208.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n41 8 Car -1 -1 -1 601.66 172.82 637.19 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n41 23 Pedestrian -1 -1 -1 404.43 164.23 417.28 201.30 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n41 3 Pedestrian -1 -1 -1 321.28 157.06 341.73 210.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n41 10 Pedestrian -1 -1 -1 215.70 154.60 232.85 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n41 21 Pedestrian -1 -1 -1 334.89 161.25 366.26 234.89 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n41 20 Car -1 -1 -1 598.54 173.15 620.56 192.40 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n41 11 Pedestrian -1 -1 -1 192.37 159.40 208.41 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n41 28 Cyclist -1 -1 -1 329.08 166.80 378.02 253.62 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n41 33 Pedestrian -1 -1 -1 205.01 162.10 233.61 224.86 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n41 34 Pedestrian -1 -1 -1 371.44 161.33 383.10 188.41 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n42 2 Car -1 -1 -1 1095.09 184.24 1220.70 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n42 5 Car -1 -1 -1 954.25 183.18 1066.73 231.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n42 4 Car -1 -1 -1 1029.84 184.00 1155.82 232.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n42 8 Car -1 -1 -1 601.61 172.91 637.12 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n42 23 Pedestrian -1 -1 -1 404.65 164.18 418.28 201.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n42 10 Pedestrian -1 -1 -1 215.50 154.36 232.83 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n42 6 Pedestrian -1 -1 -1 303.00 158.17 322.53 208.85 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n42 3 Pedestrian -1 -1 -1 322.44 157.13 341.35 209.86 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n42 33 Pedestrian -1 -1 -1 202.36 161.21 231.11 222.62 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n42 20 Car -1 -1 -1 598.39 173.18 620.74 192.52 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n42 34 Pedestrian -1 -1 -1 371.97 161.18 383.14 188.18 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n42 21 Pedestrian -1 -1 -1 332.73 161.20 367.12 234.90 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n42 11 Pedestrian -1 -1 -1 192.47 159.58 208.42 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n42 35 Pedestrian -1 -1 -1 318.95 170.70 366.56 248.92 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n42 36 Pedestrian -1 -1 -1 577.32 168.20 589.51 195.75 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n43 2 Car -1 -1 -1 1095.06 184.18 1220.80 235.18 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n43 4 Car -1 -1 -1 1029.61 183.95 1155.97 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n43 5 Car -1 -1 -1 954.19 183.11 1066.80 231.95 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n43 8 Car -1 -1 -1 601.62 172.76 637.11 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n43 6 Pedestrian -1 -1 -1 304.88 158.14 324.46 207.59 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n43 10 Pedestrian -1 -1 -1 215.62 154.54 232.95 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n43 23 Pedestrian -1 -1 -1 404.46 164.15 418.05 200.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n43 3 Pedestrian -1 -1 -1 323.91 157.53 343.31 208.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n43 21 Pedestrian -1 -1 -1 332.12 162.05 361.94 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n43 20 Car -1 -1 -1 598.61 173.12 620.59 192.45 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n43 11 Pedestrian -1 -1 -1 192.71 159.75 208.29 198.97 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n43 34 Pedestrian -1 -1 -1 371.73 161.21 383.19 187.99 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n43 33 Pedestrian -1 -1 -1 202.76 161.63 230.70 221.70 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n44 2 Car -1 -1 -1 1095.11 184.20 1220.75 235.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n44 5 Car -1 -1 -1 954.18 183.07 1066.83 231.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n44 4 Car -1 -1 -1 1029.64 183.98 1156.05 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n44 8 Car -1 -1 -1 601.64 172.83 637.06 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n44 21 Pedestrian -1 -1 -1 334.48 161.06 359.40 229.87 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n44 6 Pedestrian -1 -1 -1 306.06 158.56 324.69 206.99 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n44 23 Pedestrian -1 -1 -1 405.06 164.03 418.68 200.66 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n44 10 Pedestrian -1 -1 -1 215.86 154.68 232.77 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n44 3 Pedestrian -1 -1 -1 324.28 157.49 343.13 207.75 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n44 33 Pedestrian -1 -1 -1 204.87 161.83 229.09 220.46 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n44 20 Car -1 -1 -1 598.30 173.10 620.56 192.39 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n44 11 Pedestrian -1 -1 -1 192.71 159.71 208.44 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n44 37 Pedestrian -1 -1 -1 680.27 168.06 693.23 206.65 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n45 2 Car -1 -1 -1 1095.08 184.19 1220.53 235.22 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n45 5 Car -1 -1 -1 954.13 183.23 1066.92 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n45 4 Car -1 -1 -1 1029.72 183.97 1155.92 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n45 21 Pedestrian -1 -1 -1 333.00 160.35 358.16 228.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n45 8 Car -1 -1 -1 601.45 172.76 637.17 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n45 10 Pedestrian -1 -1 -1 215.70 154.64 232.31 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n45 23 Pedestrian -1 -1 -1 405.17 163.85 418.79 200.84 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n45 3 Pedestrian -1 -1 -1 324.83 156.75 344.35 209.02 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n45 6 Pedestrian -1 -1 -1 306.88 157.99 324.83 207.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n45 33 Pedestrian -1 -1 -1 204.68 161.52 229.57 219.66 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n45 20 Car -1 -1 -1 598.32 173.16 620.77 192.45 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n45 11 Pedestrian -1 -1 -1 192.48 159.88 208.32 199.47 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n45 37 Pedestrian -1 -1 -1 680.42 167.82 693.84 206.77 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n45 38 Pedestrian -1 -1 -1 578.96 167.73 588.90 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n46 2 Car -1 -1 -1 1095.09 184.18 1220.68 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n46 5 Car -1 -1 -1 954.15 183.17 1066.87 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n46 4 Car -1 -1 -1 1029.82 184.06 1155.75 232.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n46 21 Pedestrian -1 -1 -1 332.83 161.18 358.05 228.18 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n46 8 Car -1 -1 -1 601.29 172.64 637.26 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n46 10 Pedestrian -1 -1 -1 215.57 154.34 232.54 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n46 23 Pedestrian -1 -1 -1 404.85 164.04 419.48 201.10 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n46 6 Pedestrian -1 -1 -1 309.15 158.22 328.95 207.36 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n46 3 Pedestrian -1 -1 -1 327.77 157.16 347.07 208.15 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n46 33 Pedestrian -1 -1 -1 207.57 160.59 230.68 218.78 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n46 37 Pedestrian -1 -1 -1 680.67 167.12 694.14 207.23 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n46 20 Car -1 -1 -1 598.23 173.07 620.54 192.14 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n46 11 Pedestrian -1 -1 -1 192.76 159.95 208.35 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n46 39 Cyclist -1 -1 -1 293.46 172.25 328.72 237.21 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n46 40 Pedestrian -1 -1 -1 204.17 158.38 220.15 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n47 2 Car -1 -1 -1 1094.97 184.21 1220.83 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n47 5 Car -1 -1 -1 954.06 183.18 1066.97 231.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n47 4 Car -1 -1 -1 1029.78 184.04 1155.80 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n47 21 Pedestrian -1 -1 -1 333.09 160.87 357.32 227.27 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n47 8 Car -1 -1 -1 601.34 172.69 637.31 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n47 6 Pedestrian -1 -1 -1 309.68 158.18 328.65 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n47 10 Pedestrian -1 -1 -1 215.68 154.18 232.33 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n47 23 Pedestrian -1 -1 -1 406.85 164.52 420.63 200.70 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n47 3 Pedestrian -1 -1 -1 329.24 156.56 347.85 207.22 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n47 33 Pedestrian -1 -1 -1 209.12 158.51 231.57 216.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n47 37 Pedestrian -1 -1 -1 680.65 167.03 694.34 207.47 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n47 20 Car -1 -1 -1 598.19 173.00 620.56 192.20 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n47 11 Pedestrian -1 -1 -1 192.82 159.72 208.33 198.96 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n47 39 Cyclist -1 -1 -1 282.98 170.02 323.61 234.99 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n48 2 Car -1 -1 -1 1094.98 184.15 1220.84 235.22 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n48 5 Car -1 -1 -1 954.19 183.24 1066.82 231.84 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n48 4 Car -1 -1 -1 1029.49 183.99 1156.05 232.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n48 8 Car -1 -1 -1 601.38 172.84 637.21 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n48 21 Pedestrian -1 -1 -1 330.16 161.15 356.65 226.77 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n48 6 Pedestrian -1 -1 -1 310.78 158.36 328.63 206.02 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n48 23 Pedestrian -1 -1 -1 407.08 164.49 421.01 200.47 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n48 10 Pedestrian -1 -1 -1 215.80 154.15 232.39 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n48 39 Cyclist -1 -1 -1 277.79 170.51 316.77 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n48 37 Pedestrian -1 -1 -1 680.62 167.42 694.28 207.72 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n48 20 Car -1 -1 -1 598.14 172.99 620.51 192.35 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n48 11 Pedestrian -1 -1 -1 192.70 159.71 208.34 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n48 3 Pedestrian -1 -1 -1 329.47 156.29 349.05 207.62 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n48 41 Cyclist -1 -1 -1 211.21 158.64 234.98 214.62 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n49 2 Car -1 -1 -1 1095.01 184.16 1220.67 235.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n49 4 Car -1 -1 -1 1029.47 184.00 1156.14 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n49 5 Car -1 -1 -1 954.27 183.23 1066.71 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n49 21 Pedestrian -1 -1 -1 332.86 161.51 357.46 226.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n49 8 Car -1 -1 -1 601.68 172.92 637.07 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n49 6 Pedestrian -1 -1 -1 311.46 158.82 329.33 206.00 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n49 23 Pedestrian -1 -1 -1 407.41 164.68 421.02 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n49 10 Pedestrian -1 -1 -1 215.87 154.22 232.61 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n49 39 Cyclist -1 -1 -1 275.14 170.64 309.89 232.24 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n49 37 Pedestrian -1 -1 -1 680.51 167.71 693.91 207.97 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n49 20 Car -1 -1 -1 598.14 173.15 620.26 192.22 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n49 11 Pedestrian -1 -1 -1 192.46 159.62 208.43 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n49 41 Cyclist -1 -1 -1 212.24 158.02 235.55 214.99 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n50 2 Car -1 -1 -1 1094.98 184.14 1220.77 235.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n50 5 Car -1 -1 -1 954.25 183.25 1066.78 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n50 4 Car -1 -1 -1 1029.55 184.00 1156.08 232.66 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n50 8 Car -1 -1 -1 601.78 172.98 637.11 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n50 21 Pedestrian -1 -1 -1 333.25 161.39 357.07 225.92 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n50 23 Pedestrian -1 -1 -1 408.46 164.62 421.36 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n50 6 Pedestrian -1 -1 -1 311.14 159.04 329.79 205.33 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n50 37 Pedestrian -1 -1 -1 680.80 167.51 693.67 207.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n50 10 Pedestrian -1 -1 -1 216.19 154.36 233.16 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n50 39 Cyclist -1 -1 -1 269.30 167.16 306.63 230.01 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n50 20 Car -1 -1 -1 598.23 173.24 620.26 192.14 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n50 11 Pedestrian -1 -1 -1 192.38 159.81 208.80 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n50 41 Cyclist -1 -1 -1 211.33 162.12 251.84 234.96 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n51 2 Car -1 -1 -1 1095.04 184.23 1220.62 235.28 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n51 5 Car -1 -1 -1 954.18 183.28 1066.83 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n51 4 Car -1 -1 -1 1029.60 184.02 1156.02 232.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n51 8 Car -1 -1 -1 601.75 173.05 637.10 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n51 23 Pedestrian -1 -1 -1 408.44 164.77 421.70 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n51 21 Pedestrian -1 -1 -1 333.61 160.66 357.24 225.62 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n51 37 Pedestrian -1 -1 -1 681.05 167.44 693.64 208.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n51 6 Pedestrian -1 -1 -1 312.94 158.77 331.01 205.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n51 20 Car -1 -1 -1 597.93 173.31 620.18 191.99 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n51 10 Pedestrian -1 -1 -1 216.38 154.33 233.04 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n51 11 Pedestrian -1 -1 -1 192.15 159.71 208.74 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n51 39 Cyclist -1 -1 -1 264.76 162.88 303.73 227.82 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n52 2 Car -1 -1 -1 1095.11 184.27 1220.67 235.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n52 4 Car -1 -1 -1 1029.52 184.00 1156.12 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n52 5 Car -1 -1 -1 954.20 183.27 1066.81 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n52 23 Pedestrian -1 -1 -1 408.80 164.81 421.86 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n52 8 Car -1 -1 -1 601.75 173.00 637.11 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n52 6 Pedestrian -1 -1 -1 313.75 158.44 332.14 205.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n52 21 Pedestrian -1 -1 -1 332.88 158.64 358.38 225.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n52 37 Pedestrian -1 -1 -1 681.06 167.34 693.93 208.02 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n52 10 Pedestrian -1 -1 -1 216.54 154.36 232.77 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n52 39 Cyclist -1 -1 -1 261.15 163.72 300.08 226.77 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n52 20 Car -1 -1 -1 597.93 173.10 620.18 192.17 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n52 11 Pedestrian -1 -1 -1 192.11 159.63 208.57 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n52 42 Cyclist -1 -1 -1 217.80 158.91 251.62 231.08 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n53 2 Car -1 -1 -1 1095.14 184.28 1220.64 235.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n53 5 Car -1 -1 -1 954.24 183.21 1066.78 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n53 4 Car -1 -1 -1 1029.67 184.03 1156.08 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n53 8 Car -1 -1 -1 601.75 173.04 637.17 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n53 23 Pedestrian -1 -1 -1 408.83 165.02 422.17 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n53 21 Pedestrian -1 -1 -1 333.29 159.13 357.23 224.95 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n53 6 Pedestrian -1 -1 -1 315.29 158.20 332.61 205.33 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n53 37 Pedestrian -1 -1 -1 681.29 167.19 694.18 208.19 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n53 10 Pedestrian -1 -1 -1 216.15 154.28 232.68 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n53 20 Car -1 -1 -1 597.76 173.15 620.17 192.24 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n53 11 Pedestrian -1 -1 -1 192.00 159.57 208.64 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n54 2 Car -1 -1 -1 1095.36 184.36 1220.42 235.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n54 5 Car -1 -1 -1 954.35 183.21 1066.70 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n54 4 Car -1 -1 -1 1029.61 184.04 1156.06 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n54 8 Car -1 -1 -1 601.51 172.98 637.20 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n54 23 Pedestrian -1 -1 -1 408.96 164.70 421.78 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n54 21 Pedestrian -1 -1 -1 334.29 160.89 356.15 225.20 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n54 10 Pedestrian -1 -1 -1 218.71 154.18 235.30 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n54 6 Pedestrian -1 -1 -1 315.94 158.16 332.47 205.30 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n54 37 Pedestrian -1 -1 -1 682.60 167.25 695.59 208.39 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n54 11 Pedestrian -1 -1 -1 192.13 159.69 208.17 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n54 20 Car -1 -1 -1 597.89 173.15 620.24 192.27 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n54 43 Cyclist -1 -1 -1 210.61 158.49 245.41 230.69 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n55 2 Car -1 -1 -1 1095.28 184.23 1220.36 235.22 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n55 4 Car -1 -1 -1 1029.49 184.04 1156.19 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n55 5 Car -1 -1 -1 954.18 183.30 1066.80 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n55 8 Car -1 -1 -1 601.42 172.98 637.23 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n55 37 Pedestrian -1 -1 -1 683.04 167.72 696.04 208.55 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n55 21 Pedestrian -1 -1 -1 331.08 159.68 355.95 224.69 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n55 10 Pedestrian -1 -1 -1 219.07 154.31 235.55 197.08 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n55 23 Pedestrian -1 -1 -1 409.30 164.48 421.89 198.89 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n55 20 Car -1 -1 -1 598.00 173.25 620.25 192.17 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n55 11 Pedestrian -1 -1 -1 192.44 159.57 208.08 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n55 6 Pedestrian -1 -1 -1 317.57 156.29 334.23 205.21 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n56 2 Car -1 -1 -1 1095.18 184.23 1220.65 235.20 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n56 5 Car -1 -1 -1 954.38 183.27 1066.66 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n56 4 Car -1 -1 -1 1029.68 184.06 1156.02 232.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n56 21 Pedestrian -1 -1 -1 331.28 159.75 355.10 223.41 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n56 8 Car -1 -1 -1 601.49 173.00 637.12 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n56 37 Pedestrian -1 -1 -1 683.27 167.70 696.53 208.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n56 6 Pedestrian -1 -1 -1 318.95 158.45 334.47 205.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n56 23 Pedestrian -1 -1 -1 409.30 163.45 422.27 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n56 10 Pedestrian -1 -1 -1 244.14 158.65 265.07 208.70 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n56 20 Car -1 -1 -1 597.97 173.19 620.46 192.24 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n56 11 Pedestrian -1 -1 -1 192.56 159.75 207.99 198.58 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n56 44 Pedestrian -1 -1 -1 219.18 154.46 236.16 196.83 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n56 45 Pedestrian -1 -1 -1 200.13 155.00 216.57 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n57 2 Car -1 -1 -1 1095.24 184.23 1220.55 235.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n57 5 Car -1 -1 -1 954.26 183.20 1066.73 231.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n57 4 Car -1 -1 -1 1029.63 184.08 1156.04 232.54 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n57 8 Car -1 -1 -1 601.56 173.03 636.95 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n57 21 Pedestrian -1 -1 -1 331.65 159.43 354.94 222.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n57 6 Pedestrian -1 -1 -1 319.84 158.79 334.94 205.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n57 37 Pedestrian -1 -1 -1 683.52 167.44 697.12 208.82 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n57 23 Pedestrian -1 -1 -1 409.40 163.72 422.24 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n57 20 Car -1 -1 -1 597.84 173.18 620.61 192.49 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n57 10 Pedestrian -1 -1 -1 247.80 158.64 267.40 208.27 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n57 44 Pedestrian -1 -1 -1 218.72 154.48 236.26 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n57 11 Pedestrian -1 -1 -1 192.56 160.04 207.95 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n57 46 Cyclist -1 -1 -1 210.62 157.04 244.99 225.71 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n57 47 Pedestrian -1 -1 -1 257.50 162.17 283.86 218.42 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n58 2 Car -1 -1 -1 1095.24 184.28 1220.66 235.18 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n58 5 Car -1 -1 -1 954.20 183.13 1066.82 231.96 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n58 4 Car -1 -1 -1 1029.92 184.12 1155.84 232.50 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n58 21 Pedestrian -1 -1 -1 332.11 159.70 354.43 222.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n58 8 Car -1 -1 -1 601.44 173.07 637.14 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n58 37 Pedestrian -1 -1 -1 683.65 167.31 697.71 208.71 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n58 6 Pedestrian -1 -1 -1 320.14 159.01 336.00 205.36 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n58 20 Car -1 -1 -1 597.93 173.17 620.74 192.52 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n58 23 Pedestrian -1 -1 -1 409.77 163.71 422.43 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n58 11 Pedestrian -1 -1 -1 192.80 160.43 207.97 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n58 44 Pedestrian -1 -1 -1 218.99 154.43 235.42 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n58 46 Cyclist -1 -1 -1 214.17 159.58 247.91 223.31 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n58 10 Pedestrian -1 -1 -1 251.50 158.15 273.44 208.82 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n59 2 Car -1 -1 -1 1095.20 184.29 1220.72 235.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n59 5 Car -1 -1 -1 954.25 183.14 1066.82 231.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n59 4 Car -1 -1 -1 1029.86 184.12 1155.92 232.49 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n59 8 Car -1 -1 -1 601.47 173.07 637.01 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n59 21 Pedestrian -1 -1 -1 331.90 160.04 354.70 222.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n59 37 Pedestrian -1 -1 -1 684.83 167.13 697.84 208.75 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n59 6 Pedestrian -1 -1 -1 321.37 159.34 337.88 204.82 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n59 20 Car -1 -1 -1 597.94 173.05 620.85 192.49 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n59 46 Cyclist -1 -1 -1 215.55 159.68 247.56 222.75 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n59 44 Pedestrian -1 -1 -1 218.68 154.58 235.80 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n59 11 Pedestrian -1 -1 -1 192.66 160.44 208.40 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n59 23 Pedestrian -1 -1 -1 409.98 163.53 422.64 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n59 10 Pedestrian -1 -1 -1 256.83 163.07 284.56 217.77 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n60 2 Car -1 -1 -1 1095.22 184.27 1220.74 235.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n60 5 Car -1 -1 -1 954.33 183.25 1066.72 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n60 4 Car -1 -1 -1 1029.96 184.14 1155.77 232.50 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n60 8 Car -1 -1 -1 601.57 173.07 636.98 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n60 21 Pedestrian -1 -1 -1 332.37 159.71 354.81 221.76 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n60 6 Pedestrian -1 -1 -1 321.41 159.78 338.52 205.18 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n60 20 Car -1 -1 -1 598.06 173.19 620.79 192.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n60 37 Pedestrian -1 -1 -1 685.16 167.26 698.18 208.84 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n60 11 Pedestrian -1 -1 -1 192.30 160.23 208.66 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n60 44 Pedestrian -1 -1 -1 218.73 154.78 235.50 197.23 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n60 10 Pedestrian -1 -1 -1 257.74 163.94 283.79 216.88 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n60 23 Pedestrian -1 -1 -1 411.13 163.88 423.71 197.28 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n60 46 Cyclist -1 -1 -1 217.52 162.07 247.28 221.60 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n61 2 Car -1 -1 -1 1095.29 184.30 1220.65 235.42 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n61 5 Car -1 -1 -1 954.27 183.24 1066.81 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n61 4 Car -1 -1 -1 1029.94 184.12 1155.82 232.54 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n61 8 Car -1 -1 -1 601.46 172.91 637.08 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n61 6 Pedestrian -1 -1 -1 321.76 160.11 338.67 205.14 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n61 21 Pedestrian -1 -1 -1 333.59 159.68 356.76 221.64 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n61 20 Car -1 -1 -1 598.07 173.12 620.89 192.53 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n61 23 Pedestrian -1 -1 -1 411.20 163.95 424.00 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n61 11 Pedestrian -1 -1 -1 192.35 160.34 208.58 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n61 44 Pedestrian -1 -1 -1 218.31 154.75 235.77 197.19 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n61 37 Pedestrian -1 -1 -1 686.12 167.40 699.74 209.20 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n62 2 Car -1 -1 -1 1095.76 184.19 1219.59 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n62 4 Car -1 -1 -1 1029.96 184.18 1155.59 232.52 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n62 5 Car -1 -1 -1 954.12 183.26 1066.91 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n62 8 Car -1 -1 -1 601.46 173.00 636.97 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n62 21 Pedestrian -1 -1 -1 333.84 159.44 356.95 221.05 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n62 6 Pedestrian -1 -1 -1 322.88 159.92 338.61 204.15 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n62 20 Car -1 -1 -1 597.96 173.21 620.77 192.61 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n62 11 Pedestrian -1 -1 -1 192.72 160.25 208.38 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n62 23 Pedestrian -1 -1 -1 411.12 164.08 423.86 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n62 44 Pedestrian -1 -1 -1 215.98 154.39 232.97 197.27 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n62 37 Pedestrian -1 -1 -1 686.21 167.64 700.06 209.49 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n62 48 Pedestrian -1 -1 -1 1157.66 152.60 1217.69 329.04 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n63 2 Car -1 -1 -1 1095.75 184.38 1220.15 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n63 5 Car -1 -1 -1 954.19 183.24 1066.86 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n63 4 Car -1 -1 -1 1030.17 184.03 1155.56 232.75 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n63 48 Pedestrian -1 -1 -1 1141.06 155.80 1218.60 325.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n63 8 Car -1 -1 -1 601.48 172.90 636.98 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n63 21 Pedestrian -1 -1 -1 334.58 159.66 356.62 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n63 44 Pedestrian -1 -1 -1 216.09 154.16 232.86 197.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n63 20 Car -1 -1 -1 598.08 173.29 620.47 192.50 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n63 6 Pedestrian -1 -1 -1 323.79 159.39 338.90 204.00 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n63 37 Pedestrian -1 -1 -1 686.79 167.61 700.59 209.63 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n63 11 Pedestrian -1 -1 -1 192.32 160.02 208.41 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n63 23 Pedestrian -1 -1 -1 411.15 164.37 423.73 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n63 49 Cyclist -1 -1 -1 224.68 156.97 251.87 216.93 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n64 2 Car -1 -1 -1 1096.53 184.08 1219.14 234.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n64 4 Car -1 -1 -1 1029.94 183.88 1155.42 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n64 5 Car -1 -1 -1 954.34 183.12 1066.68 232.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n64 48 Pedestrian -1 -1 -1 1127.72 158.45 1209.20 329.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n64 21 Pedestrian -1 -1 -1 335.04 160.21 356.34 220.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n64 8 Car -1 -1 -1 601.56 172.95 637.09 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n64 44 Pedestrian -1 -1 -1 218.95 154.74 234.43 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n64 20 Car -1 -1 -1 598.08 173.22 620.55 192.64 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n64 6 Pedestrian -1 -1 -1 324.37 159.69 339.90 203.95 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n64 23 Pedestrian -1 -1 -1 411.31 164.40 423.63 196.92 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n64 37 Pedestrian -1 -1 -1 687.31 167.59 700.67 209.62 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n64 11 Pedestrian -1 -1 -1 192.16 159.92 208.43 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n64 49 Cyclist -1 -1 -1 225.69 157.33 252.63 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n64 50 Cyclist -1 -1 -1 277.28 158.86 300.14 205.04 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n65 2 Car -1 -1 -1 1094.99 184.13 1220.60 235.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n65 4 Car -1 -1 -1 1030.38 183.82 1154.50 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n65 5 Car -1 -1 -1 954.07 183.09 1066.96 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n65 48 Pedestrian -1 -1 -1 1123.93 154.81 1189.47 327.15 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n65 8 Car -1 -1 -1 601.57 172.99 637.08 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n65 21 Pedestrian -1 -1 -1 335.10 160.18 355.44 219.63 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n65 44 Pedestrian -1 -1 -1 219.02 154.88 234.32 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n65 6 Pedestrian -1 -1 -1 325.66 159.94 341.23 203.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n65 23 Pedestrian -1 -1 -1 411.49 163.88 423.62 196.92 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n65 20 Car -1 -1 -1 597.86 173.29 620.62 192.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n65 49 Cyclist -1 -1 -1 225.68 158.22 254.46 215.35 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n65 37 Pedestrian -1 -1 -1 687.92 167.39 701.23 209.71 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n65 11 Pedestrian -1 -1 -1 192.00 159.67 208.63 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n65 51 Pedestrian -1 -1 -1 262.99 163.59 284.68 210.02 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n65 52 Pedestrian -1 -1 -1 280.76 159.87 303.44 203.88 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n66 2 Car -1 -1 -1 1095.35 183.73 1219.78 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n66 5 Car -1 -1 -1 954.14 183.07 1066.89 232.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n66 4 Car -1 -1 -1 1031.05 183.97 1153.25 232.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n66 48 Pedestrian -1 -1 -1 1094.12 154.54 1166.55 324.76 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n66 8 Car -1 -1 -1 601.56 173.03 636.87 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n66 6 Pedestrian -1 -1 -1 326.61 160.27 342.43 203.48 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n66 21 Pedestrian -1 -1 -1 334.86 160.51 355.56 218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n66 44 Pedestrian -1 -1 -1 219.23 154.81 234.43 196.73 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n66 49 Cyclist -1 -1 -1 227.96 158.08 257.76 214.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n66 51 Pedestrian -1 -1 -1 264.42 162.98 284.72 209.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n66 23 Pedestrian -1 -1 -1 411.65 163.77 423.32 196.72 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n66 20 Car -1 -1 -1 598.08 173.35 620.63 192.62 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n66 37 Pedestrian -1 -1 -1 688.45 168.14 702.32 210.33 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n66 11 Pedestrian -1 -1 -1 192.13 159.67 208.69 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n66 52 Pedestrian -1 -1 -1 282.48 159.95 304.70 203.69 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n66 53 Pedestrian -1 -1 -1 341.87 156.51 359.49 204.29 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n67 2 Car -1 -1 -1 1095.41 183.88 1219.98 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n67 5 Car -1 -1 -1 954.28 183.14 1066.81 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n67 48 Pedestrian -1 -1 -1 1070.58 155.67 1159.27 319.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n67 4 Car -1 -1 -1 1030.78 184.06 1154.69 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n67 8 Car -1 -1 -1 601.57 172.95 636.89 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n67 44 Pedestrian -1 -1 -1 219.31 154.80 234.49 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n67 51 Pedestrian -1 -1 -1 266.44 162.56 287.35 208.65 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n67 20 Car -1 -1 -1 597.87 173.23 620.54 192.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n67 6 Pedestrian -1 -1 -1 327.80 160.07 343.45 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n67 23 Pedestrian -1 -1 -1 410.55 163.46 422.31 196.88 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n67 21 Pedestrian -1 -1 -1 334.95 160.36 355.33 218.15 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n67 11 Pedestrian -1 -1 -1 192.16 159.83 208.64 199.06 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n67 52 Pedestrian -1 -1 -1 286.18 160.16 306.83 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n67 37 Pedestrian -1 -1 -1 688.46 168.08 702.74 210.50 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n67 49 Cyclist -1 -1 -1 230.74 154.84 257.11 212.05 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n67 53 Pedestrian -1 -1 -1 343.91 156.73 362.65 206.08 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n68 2 Car -1 -1 -1 1094.51 183.92 1220.57 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n68 5 Car -1 -1 -1 955.22 183.19 1065.85 231.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n68 4 Car -1 -1 -1 1030.95 183.96 1154.34 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n68 48 Pedestrian -1 -1 -1 1054.27 157.63 1144.90 316.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n68 8 Car -1 -1 -1 601.56 172.99 636.92 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n68 44 Pedestrian -1 -1 -1 219.44 154.77 234.36 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n68 51 Pedestrian -1 -1 -1 266.24 159.94 289.68 208.17 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n68 21 Pedestrian -1 -1 -1 334.78 159.51 355.84 217.22 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n68 37 Pedestrian -1 -1 -1 690.20 168.52 703.69 210.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n68 20 Car -1 -1 -1 597.89 173.45 620.31 192.47 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n68 23 Pedestrian -1 -1 -1 411.71 164.05 423.04 196.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n68 6 Pedestrian -1 -1 -1 328.97 159.24 345.22 203.71 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n68 53 Pedestrian -1 -1 -1 345.22 157.51 363.26 203.42 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n68 11 Pedestrian -1 -1 -1 192.02 159.95 208.42 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n68 52 Pedestrian -1 -1 -1 290.06 158.23 309.44 201.86 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n69 2 Car -1 -1 -1 1094.05 184.07 1220.32 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n69 5 Car -1 -1 -1 954.52 183.21 1064.00 231.96 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n69 4 Car -1 -1 -1 1030.38 183.97 1155.08 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n69 48 Pedestrian -1 -1 -1 1039.44 155.96 1121.14 315.77 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n69 8 Car -1 -1 -1 601.61 173.02 636.74 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n69 44 Pedestrian -1 -1 -1 219.21 154.92 234.24 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n69 6 Pedestrian -1 -1 -1 329.28 159.30 345.97 204.13 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n69 23 Pedestrian -1 -1 -1 411.75 163.99 423.32 196.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n69 21 Pedestrian -1 -1 -1 335.11 160.98 355.54 217.45 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n69 20 Car -1 -1 -1 597.90 173.21 620.55 192.66 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n69 51 Pedestrian -1 -1 -1 269.56 162.72 291.40 208.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n69 53 Pedestrian -1 -1 -1 345.48 157.61 363.73 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n69 11 Pedestrian -1 -1 -1 192.12 160.15 208.17 199.29 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n69 37 Pedestrian -1 -1 -1 690.27 168.62 704.48 211.64 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n69 52 Pedestrian -1 -1 -1 293.28 158.16 312.64 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n69 54 Cyclist -1 -1 -1 237.12 154.29 262.41 209.55 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n70 2 Car -1 -1 -1 1094.62 184.14 1220.71 235.61 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n70 5 Car -1 -1 -1 955.48 183.12 1065.75 231.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n70 4 Car -1 -1 -1 1034.83 183.93 1156.64 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n70 48 Pedestrian -1 -1 -1 1031.37 153.90 1090.66 312.86 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n70 8 Car -1 -1 -1 601.43 172.98 636.90 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n70 23 Pedestrian -1 -1 -1 411.71 163.82 423.50 196.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n70 44 Pedestrian -1 -1 -1 218.75 154.97 234.45 196.47 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n70 6 Pedestrian -1 -1 -1 329.76 159.48 345.83 203.79 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n70 21 Pedestrian -1 -1 -1 335.32 161.25 355.84 217.19 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n70 37 Pedestrian -1 -1 -1 690.38 168.46 704.78 211.44 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n70 20 Car -1 -1 -1 597.59 173.26 620.42 192.68 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n70 51 Pedestrian -1 -1 -1 271.41 162.84 292.40 207.88 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n70 11 Pedestrian -1 -1 -1 192.15 160.40 207.94 199.20 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n70 53 Pedestrian -1 -1 -1 345.20 157.31 364.39 203.72 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n70 52 Pedestrian -1 -1 -1 295.50 158.69 314.70 201.74 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n70 54 Cyclist -1 -1 -1 239.45 152.63 263.06 208.29 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n70 55 Cyclist -1 -1 -1 295.50 158.69 314.70 201.74 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n71 2 Car -1 -1 -1 1094.96 184.23 1220.64 235.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n71 48 Pedestrian -1 -1 -1 1002.37 152.81 1073.70 311.08 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n71 4 Car -1 -1 -1 1034.65 184.13 1155.70 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n71 5 Car -1 -1 -1 956.33 183.23 1064.78 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n71 8 Car -1 -1 -1 601.56 173.09 636.87 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n71 23 Pedestrian -1 -1 -1 411.58 163.77 423.59 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n71 21 Pedestrian -1 -1 -1 335.43 161.35 356.41 217.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n71 44 Pedestrian -1 -1 -1 218.88 154.92 234.33 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n71 37 Pedestrian -1 -1 -1 691.10 168.22 705.26 211.56 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n71 6 Pedestrian -1 -1 -1 329.92 159.54 345.74 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n71 20 Car -1 -1 -1 597.92 173.25 620.36 192.63 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n71 11 Pedestrian -1 -1 -1 192.37 160.52 207.88 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n71 51 Pedestrian -1 -1 -1 273.95 161.31 293.94 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n71 52 Pedestrian -1 -1 -1 298.75 159.51 318.02 200.90 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n71 53 Pedestrian -1 -1 -1 345.20 157.65 364.72 203.48 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n71 55 Cyclist -1 -1 -1 298.75 159.51 318.02 200.90 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n72 2 Car -1 -1 -1 1094.98 184.21 1220.95 235.47 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n72 5 Car -1 -1 -1 956.09 183.31 1065.14 231.09 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n72 4 Car -1 -1 -1 1034.36 184.00 1155.57 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n72 48 Pedestrian -1 -1 -1 974.84 155.93 1064.16 309.60 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n72 8 Car -1 -1 -1 601.76 172.96 636.90 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n72 51 Pedestrian -1 -1 -1 275.51 161.08 294.80 205.11 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n72 21 Pedestrian -1 -1 -1 335.07 160.84 357.18 215.79 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n72 44 Pedestrian -1 -1 -1 216.29 155.01 232.30 196.58 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n72 37 Pedestrian -1 -1 -1 691.91 168.18 705.81 211.51 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n72 23 Pedestrian -1 -1 -1 411.38 163.74 423.50 196.09 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n72 6 Pedestrian -1 -1 -1 329.92 159.40 346.27 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n72 20 Car -1 -1 -1 597.73 173.34 620.29 192.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n72 11 Pedestrian -1 -1 -1 192.39 160.80 208.19 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n72 53 Pedestrian -1 -1 -1 344.99 157.72 364.59 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n72 52 Pedestrian -1 -1 -1 301.93 159.49 320.18 200.98 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n72 56 Pedestrian -1 -1 -1 249.10 157.92 274.11 206.37 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n73 2 Car -1 -1 -1 1095.39 184.38 1220.67 235.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n73 5 Car -1 -1 -1 955.43 183.14 1065.84 231.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n73 4 Car -1 -1 -1 1030.99 184.16 1154.73 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n73 48 Pedestrian -1 -1 -1 965.56 157.53 1050.36 307.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n73 8 Car -1 -1 -1 601.70 172.84 636.84 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n73 21 Pedestrian -1 -1 -1 334.83 160.26 357.70 215.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n73 44 Pedestrian -1 -1 -1 216.37 155.13 232.24 196.38 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n73 52 Pedestrian -1 -1 -1 306.02 159.67 323.81 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n73 51 Pedestrian -1 -1 -1 276.12 161.03 296.01 204.22 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n73 23 Pedestrian -1 -1 -1 411.52 163.71 423.41 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n73 11 Pedestrian -1 -1 -1 191.99 160.64 208.57 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n73 20 Car -1 -1 -1 597.64 173.26 620.18 192.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n73 37 Pedestrian -1 -1 -1 692.16 168.15 706.36 212.12 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n73 6 Pedestrian -1 -1 -1 330.51 159.13 346.77 203.79 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n73 56 Pedestrian -1 -1 -1 254.14 160.46 275.77 205.47 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n73 53 Pedestrian -1 -1 -1 347.19 157.73 366.55 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n74 2 Car -1 -1 -1 1095.56 184.40 1220.38 235.23 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n74 4 Car -1 -1 -1 1030.67 184.15 1154.80 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n74 5 Car -1 -1 -1 955.78 183.03 1065.48 231.50 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n74 8 Car -1 -1 -1 601.75 172.95 636.72 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n74 48 Pedestrian -1 -1 -1 958.09 156.08 1033.96 307.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n74 44 Pedestrian -1 -1 -1 216.17 154.96 232.37 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n74 51 Pedestrian -1 -1 -1 277.88 161.63 299.33 204.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n74 21 Pedestrian -1 -1 -1 336.71 160.34 357.65 215.60 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n74 11 Pedestrian -1 -1 -1 191.96 160.60 208.75 198.97 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n74 52 Pedestrian -1 -1 -1 310.20 159.40 326.40 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n74 23 Pedestrian -1 -1 -1 410.36 163.36 422.44 196.28 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n74 20 Car -1 -1 -1 597.71 173.19 620.27 192.53 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n74 53 Pedestrian -1 -1 -1 347.86 157.91 366.81 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n74 37 Pedestrian -1 -1 -1 692.22 168.25 706.52 212.73 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n74 6 Pedestrian -1 -1 -1 331.27 158.82 346.81 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n74 56 Pedestrian -1 -1 -1 255.27 160.10 275.58 204.34 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n75 2 Car -1 -1 -1 1095.43 184.34 1220.52 235.32 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n75 5 Car -1 -1 -1 955.35 182.77 1066.02 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n75 4 Car -1 -1 -1 1030.58 184.09 1155.14 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n75 48 Pedestrian -1 -1 -1 949.22 151.89 1005.89 304.51 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n75 8 Car -1 -1 -1 601.66 172.92 636.82 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n75 23 Pedestrian -1 -1 -1 410.22 163.55 422.12 196.09 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n75 44 Pedestrian -1 -1 -1 216.07 154.93 232.36 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n75 11 Pedestrian -1 -1 -1 192.03 160.75 208.67 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n75 52 Pedestrian -1 -1 -1 312.31 159.01 328.19 198.97 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n75 20 Car -1 -1 -1 597.70 173.15 620.32 192.56 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n75 21 Pedestrian -1 -1 -1 336.97 160.44 357.58 215.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n75 51 Pedestrian -1 -1 -1 281.85 162.44 301.01 204.20 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n75 37 Pedestrian -1 -1 -1 693.17 168.41 707.94 213.97 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n75 53 Pedestrian -1 -1 -1 348.10 157.43 366.69 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n75 56 Pedestrian -1 -1 -1 255.38 159.92 276.34 204.52 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n75 6 Pedestrian -1 -1 -1 331.74 158.55 346.79 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n76 2 Car -1 -1 -1 1095.20 184.40 1220.59 235.24 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n76 5 Car -1 -1 -1 955.14 182.80 1066.18 231.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n76 4 Car -1 -1 -1 1030.29 183.98 1155.41 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n76 48 Pedestrian -1 -1 -1 923.41 150.65 992.82 305.11 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n76 8 Car -1 -1 -1 601.77 172.92 636.74 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n76 51 Pedestrian -1 -1 -1 283.32 163.01 303.23 203.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n76 23 Pedestrian -1 -1 -1 409.98 163.54 422.09 196.08 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n76 21 Pedestrian -1 -1 -1 336.69 160.56 357.35 215.06 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n76 44 Pedestrian -1 -1 -1 216.08 155.02 232.34 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n76 56 Pedestrian -1 -1 -1 258.58 160.41 278.28 204.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n76 37 Pedestrian -1 -1 -1 693.43 168.38 708.02 213.79 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n76 11 Pedestrian -1 -1 -1 191.96 160.80 208.79 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n76 20 Car -1 -1 -1 597.68 173.31 620.28 192.59 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n76 52 Pedestrian -1 -1 -1 314.70 158.46 332.35 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n76 53 Pedestrian -1 -1 -1 348.05 157.62 367.33 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n76 6 Pedestrian -1 -1 -1 333.13 158.14 349.62 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n77 2 Car -1 -1 -1 1094.63 184.31 1221.04 235.22 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n77 5 Car -1 -1 -1 954.25 182.74 1067.15 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n77 4 Car -1 -1 -1 1030.33 183.96 1155.60 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n77 48 Pedestrian -1 -1 -1 898.21 152.24 987.74 303.67 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n77 8 Car -1 -1 -1 601.80 173.03 636.66 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n77 23 Pedestrian -1 -1 -1 409.84 163.42 421.74 195.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n77 21 Pedestrian -1 -1 -1 336.32 160.42 357.32 214.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n77 51 Pedestrian -1 -1 -1 286.57 163.75 304.85 202.43 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n77 44 Pedestrian -1 -1 -1 218.96 155.04 234.33 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n77 56 Pedestrian -1 -1 -1 259.68 160.15 279.86 204.18 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n77 37 Pedestrian -1 -1 -1 693.72 168.15 708.08 213.34 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n77 11 Pedestrian -1 -1 -1 192.00 160.75 208.80 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n77 20 Car -1 -1 -1 597.65 173.39 620.27 192.57 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n77 52 Pedestrian -1 -1 -1 317.86 158.74 335.76 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n77 53 Pedestrian -1 -1 -1 348.32 157.72 367.46 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n77 6 Pedestrian -1 -1 -1 333.41 158.66 350.11 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n78 2 Car -1 -1 -1 1094.75 184.29 1220.82 235.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n78 5 Car -1 -1 -1 953.45 182.92 1067.72 231.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n78 4 Car -1 -1 -1 1030.44 183.94 1155.46 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n78 48 Pedestrian -1 -1 -1 890.24 152.64 979.90 298.70 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n78 8 Car -1 -1 -1 601.79 173.07 636.70 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n78 23 Pedestrian -1 -1 -1 409.63 163.29 421.55 195.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n78 21 Pedestrian -1 -1 -1 336.11 160.12 357.39 213.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n78 44 Pedestrian -1 -1 -1 218.98 154.92 234.35 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n78 37 Pedestrian -1 -1 -1 693.91 167.95 708.13 213.38 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n78 51 Pedestrian -1 -1 -1 287.47 162.47 307.23 201.91 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n78 11 Pedestrian -1 -1 -1 192.06 160.77 208.82 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n78 20 Car -1 -1 -1 597.69 173.40 620.20 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n78 56 Pedestrian -1 -1 -1 262.96 159.86 281.75 203.55 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n78 6 Pedestrian -1 -1 -1 333.38 158.46 350.72 202.20 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n78 53 Pedestrian -1 -1 -1 348.38 157.52 367.95 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n78 52 Pedestrian -1 -1 -1 320.52 158.63 339.29 198.89 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n79 2 Car -1 -1 -1 1094.71 184.22 1220.79 235.34 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n79 5 Car -1 -1 -1 953.88 183.03 1067.34 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n79 4 Car -1 -1 -1 1030.52 183.95 1155.43 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n79 48 Pedestrian -1 -1 -1 882.35 151.26 957.35 298.49 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n79 23 Pedestrian -1 -1 -1 409.17 163.38 421.34 195.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n79 8 Car -1 -1 -1 601.76 172.97 636.84 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n79 21 Pedestrian -1 -1 -1 336.47 159.85 357.13 213.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n79 37 Pedestrian -1 -1 -1 694.28 167.88 708.47 213.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n79 44 Pedestrian -1 -1 -1 219.12 154.91 234.38 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n79 20 Car -1 -1 -1 597.76 173.12 620.33 192.71 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n79 11 Pedestrian -1 -1 -1 192.14 160.68 208.83 198.89 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n79 51 Pedestrian -1 -1 -1 290.30 162.37 308.96 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n79 52 Pedestrian -1 -1 -1 325.07 159.07 343.21 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n79 6 Pedestrian -1 -1 -1 332.67 158.34 350.85 201.90 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n79 56 Pedestrian -1 -1 -1 264.46 159.74 284.67 203.68 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n79 53 Pedestrian -1 -1 -1 349.04 157.84 367.78 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n80 2 Car -1 -1 -1 1094.62 184.15 1221.01 235.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n80 5 Car -1 -1 -1 954.22 183.12 1067.13 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n80 4 Car -1 -1 -1 1030.69 183.95 1155.17 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n80 48 Pedestrian -1 -1 -1 878.12 150.71 931.00 297.93 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n80 23 Pedestrian -1 -1 -1 409.70 163.55 421.55 195.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n80 21 Pedestrian -1 -1 -1 336.78 159.70 357.10 213.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n80 8 Car -1 -1 -1 601.63 173.01 636.98 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n80 37 Pedestrian -1 -1 -1 694.48 168.12 709.12 213.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n80 44 Pedestrian -1 -1 -1 219.20 154.97 234.34 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n80 11 Pedestrian -1 -1 -1 192.17 160.95 208.78 198.97 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n80 20 Car -1 -1 -1 597.58 173.16 620.27 192.67 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n80 51 Pedestrian -1 -1 -1 291.91 162.27 311.05 201.92 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n80 6 Pedestrian -1 -1 -1 332.83 158.40 351.10 201.63 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n80 56 Pedestrian -1 -1 -1 266.84 160.30 288.90 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n80 53 Pedestrian -1 -1 -1 352.59 157.54 370.39 202.05 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n81 2 Car -1 -1 -1 1094.96 184.23 1220.77 235.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n81 5 Car -1 -1 -1 954.39 183.24 1066.90 231.81 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n81 4 Car -1 -1 -1 1033.36 183.71 1156.50 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n81 48 Pedestrian -1 -1 -1 853.85 150.68 917.97 297.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n81 23 Pedestrian -1 -1 -1 409.78 163.44 421.50 195.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n81 37 Pedestrian -1 -1 -1 694.72 168.61 709.31 213.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n81 8 Car -1 -1 -1 601.68 172.97 636.99 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n81 21 Pedestrian -1 -1 -1 336.24 159.87 357.74 213.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n81 44 Pedestrian -1 -1 -1 219.26 155.06 234.33 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n81 11 Pedestrian -1 -1 -1 192.55 161.19 208.67 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n81 51 Pedestrian -1 -1 -1 295.85 163.36 314.65 201.80 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n81 20 Car -1 -1 -1 597.48 173.19 620.20 192.61 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n81 6 Pedestrian -1 -1 -1 332.70 158.37 351.11 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n81 56 Pedestrian -1 -1 -1 271.75 158.42 290.28 201.16 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n82 2 Car -1 -1 -1 1094.87 184.24 1220.75 235.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n82 5 Car -1 -1 -1 954.41 183.28 1066.87 231.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n82 4 Car -1 -1 -1 1030.30 183.99 1155.68 232.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n82 48 Pedestrian -1 -1 -1 833.87 150.66 913.96 293.41 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n82 23 Pedestrian -1 -1 -1 409.57 163.38 421.51 194.50 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n82 8 Car -1 -1 -1 601.67 173.00 636.78 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n82 37 Pedestrian -1 -1 -1 695.42 168.94 710.04 214.11 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n82 51 Pedestrian -1 -1 -1 297.91 164.20 317.00 201.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n82 44 Pedestrian -1 -1 -1 219.40 155.06 234.32 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n82 6 Pedestrian -1 -1 -1 333.98 158.47 351.33 201.54 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n82 21 Pedestrian -1 -1 -1 336.81 160.26 357.55 212.73 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n82 11 Pedestrian -1 -1 -1 192.51 161.17 208.60 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n82 20 Car -1 -1 -1 597.63 173.06 620.31 192.72 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n82 56 Pedestrian -1 -1 -1 274.95 159.15 292.38 200.92 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n83 2 Car -1 -1 -1 1094.88 184.14 1220.62 235.12 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n83 5 Car -1 -1 -1 954.28 183.27 1067.04 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n83 4 Car -1 -1 -1 1030.25 183.96 1155.68 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n83 48 Pedestrian -1 -1 -1 825.14 151.54 907.18 293.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n83 23 Pedestrian -1 -1 -1 409.51 163.27 421.37 194.23 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n83 8 Car -1 -1 -1 601.64 172.95 636.92 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n83 56 Pedestrian -1 -1 -1 276.03 159.36 293.81 201.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n83 44 Pedestrian -1 -1 -1 219.55 155.09 234.17 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n83 11 Pedestrian -1 -1 -1 192.23 161.18 208.55 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n83 37 Pedestrian -1 -1 -1 696.36 168.54 712.38 214.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n83 6 Pedestrian -1 -1 -1 335.42 157.84 354.68 201.70 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n83 21 Pedestrian -1 -1 -1 338.98 159.43 358.64 212.57 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n83 20 Car -1 -1 -1 597.58 173.02 620.27 192.57 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n83 51 Pedestrian -1 -1 -1 302.30 164.38 318.94 200.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n83 57 Pedestrian -1 -1 -1 353.98 157.75 371.26 200.28 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n84 2 Car -1 -1 -1 1095.05 184.25 1220.45 235.11 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n84 5 Car -1 -1 -1 954.18 183.16 1066.99 231.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n84 4 Car -1 -1 -1 1030.04 183.97 1155.84 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n84 48 Pedestrian -1 -1 -1 822.32 153.05 888.29 291.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n84 23 Pedestrian -1 -1 -1 409.51 163.44 421.28 194.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n84 8 Car -1 -1 -1 601.50 172.98 636.81 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n84 6 Pedestrian -1 -1 -1 336.01 158.26 354.71 201.54 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n84 37 Pedestrian -1 -1 -1 696.29 168.59 712.77 214.38 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n84 44 Pedestrian -1 -1 -1 219.48 155.05 234.35 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n84 11 Pedestrian -1 -1 -1 192.25 160.96 208.39 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n84 21 Pedestrian -1 -1 -1 339.46 159.55 358.54 212.09 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n84 20 Car -1 -1 -1 597.53 173.03 620.45 192.68 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n84 51 Pedestrian -1 -1 -1 303.55 164.89 320.84 200.71 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n84 56 Pedestrian -1 -1 -1 279.73 159.41 295.29 200.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n84 57 Pedestrian -1 -1 -1 356.24 158.08 373.13 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n84 58 Pedestrian -1 -1 -1 320.68 157.47 333.03 187.83 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n85 2 Car -1 -1 -1 1095.16 184.29 1220.38 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n85 5 Car -1 -1 -1 954.26 183.20 1066.90 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n85 4 Car -1 -1 -1 1029.92 183.95 1155.93 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n85 48 Pedestrian -1 -1 -1 816.27 151.74 863.73 291.02 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n85 23 Pedestrian -1 -1 -1 409.40 163.65 421.11 194.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n85 8 Car -1 -1 -1 601.41 172.89 637.07 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n85 21 Pedestrian -1 -1 -1 339.41 159.87 358.84 212.51 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n85 37 Pedestrian -1 -1 -1 696.75 168.35 712.99 214.71 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n85 44 Pedestrian -1 -1 -1 219.62 155.07 234.47 197.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n85 6 Pedestrian -1 -1 -1 336.82 158.64 354.63 201.97 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n85 11 Pedestrian -1 -1 -1 192.34 161.06 208.51 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n85 51 Pedestrian -1 -1 -1 306.13 165.02 322.92 200.26 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n85 20 Car -1 -1 -1 597.45 173.03 620.53 192.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n85 57 Pedestrian -1 -1 -1 356.28 158.14 372.73 200.46 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n85 58 Pedestrian -1 -1 -1 320.90 158.22 333.44 187.40 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n85 56 Pedestrian -1 -1 -1 280.38 158.68 297.06 200.54 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n86 2 Car -1 -1 -1 1095.20 184.21 1220.48 235.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n86 48 Pedestrian -1 -1 -1 794.10 151.34 853.64 290.37 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n86 5 Car -1 -1 -1 954.55 183.10 1066.65 231.98 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n86 4 Car -1 -1 -1 1032.87 183.76 1156.98 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n86 8 Car -1 -1 -1 601.44 172.91 637.02 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n86 37 Pedestrian -1 -1 -1 697.68 168.22 713.24 214.68 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n86 23 Pedestrian -1 -1 -1 409.20 163.35 420.95 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n86 21 Pedestrian -1 -1 -1 339.22 160.15 358.70 212.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n86 44 Pedestrian -1 -1 -1 219.71 155.05 234.56 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n86 57 Pedestrian -1 -1 -1 356.32 158.35 372.61 201.05 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n86 11 Pedestrian -1 -1 -1 192.16 160.89 208.63 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n86 20 Car -1 -1 -1 597.41 173.15 620.43 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n86 6 Pedestrian -1 -1 -1 337.36 158.89 354.83 201.75 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n86 51 Pedestrian -1 -1 -1 308.37 164.55 324.33 200.07 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n86 58 Pedestrian -1 -1 -1 321.27 158.64 333.57 187.19 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n87 2 Car -1 -1 -1 1095.09 184.23 1220.56 235.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n87 48 Pedestrian -1 -1 -1 775.00 152.64 850.10 289.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n87 5 Car -1 -1 -1 954.41 183.13 1066.79 231.94 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n87 4 Car -1 -1 -1 1033.11 183.88 1156.87 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n87 37 Pedestrian -1 -1 -1 698.36 168.48 713.64 214.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n87 8 Car -1 -1 -1 601.64 173.04 636.93 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n87 23 Pedestrian -1 -1 -1 409.01 163.46 420.78 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n87 44 Pedestrian -1 -1 -1 219.83 155.18 234.53 197.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n87 57 Pedestrian -1 -1 -1 356.71 158.40 372.03 200.86 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n87 21 Pedestrian -1 -1 -1 337.29 158.42 356.47 208.40 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n87 20 Car -1 -1 -1 597.47 173.28 620.39 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n87 11 Pedestrian -1 -1 -1 192.10 160.97 208.65 198.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n87 51 Pedestrian -1 -1 -1 310.93 164.86 326.25 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n87 59 Pedestrian -1 -1 -1 283.35 159.40 303.18 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n88 2 Car -1 -1 -1 1095.15 184.33 1220.53 235.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n88 5 Car -1 -1 -1 954.36 183.15 1066.90 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n88 48 Pedestrian -1 -1 -1 764.64 154.10 838.72 288.62 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n88 4 Car -1 -1 -1 1033.42 183.91 1156.46 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n88 8 Car -1 -1 -1 601.54 173.03 636.87 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n88 37 Pedestrian -1 -1 -1 698.29 168.67 713.89 215.20 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n88 23 Pedestrian -1 -1 -1 408.71 163.44 420.68 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n88 57 Pedestrian -1 -1 -1 356.79 158.19 372.25 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n88 44 Pedestrian -1 -1 -1 219.87 155.30 234.92 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n88 21 Pedestrian -1 -1 -1 338.79 158.26 358.94 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n88 11 Pedestrian -1 -1 -1 191.99 160.83 208.83 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n88 20 Car -1 -1 -1 596.99 173.66 618.16 191.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n88 51 Pedestrian -1 -1 -1 312.47 165.05 327.53 199.48 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n88 59 Pedestrian -1 -1 -1 286.40 159.85 305.55 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n89 2 Car -1 -1 -1 1095.18 184.34 1220.36 235.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n89 5 Car -1 -1 -1 954.49 183.20 1066.71 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n89 48 Pedestrian -1 -1 -1 757.35 153.44 822.47 287.63 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n89 4 Car -1 -1 -1 1033.35 183.98 1156.57 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n89 8 Car -1 -1 -1 601.46 173.02 636.84 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n89 37 Pedestrian -1 -1 -1 698.30 168.68 714.16 215.35 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n89 57 Pedestrian -1 -1 -1 357.48 158.23 372.72 201.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n89 23 Pedestrian -1 -1 -1 408.75 163.55 420.50 193.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n89 44 Pedestrian -1 -1 -1 219.77 155.30 234.99 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n89 21 Pedestrian -1 -1 -1 339.11 159.22 358.90 211.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n89 20 Car -1 -1 -1 597.65 173.29 620.46 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n89 11 Pedestrian -1 -1 -1 192.07 160.66 208.90 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n89 51 Pedestrian -1 -1 -1 315.20 166.09 329.40 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n90 2 Car -1 -1 -1 1095.49 184.44 1220.19 235.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n90 5 Car -1 -1 -1 954.61 183.11 1066.67 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n90 4 Car -1 -1 -1 1030.25 184.15 1155.72 232.62 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n90 48 Pedestrian -1 -1 -1 754.83 151.83 802.10 285.02 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n90 8 Car -1 -1 -1 601.51 173.10 636.81 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n90 21 Pedestrian -1 -1 -1 339.80 159.68 359.21 211.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n90 37 Pedestrian -1 -1 -1 698.51 168.54 714.18 215.48 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n90 57 Pedestrian -1 -1 -1 358.22 158.63 373.21 201.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n90 23 Pedestrian -1 -1 -1 408.67 163.62 420.41 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n90 44 Pedestrian -1 -1 -1 219.77 155.37 235.09 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n90 20 Car -1 -1 -1 597.58 173.23 620.32 192.83 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n90 11 Pedestrian -1 -1 -1 192.26 160.64 208.92 199.17 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n90 51 Pedestrian -1 -1 -1 315.95 165.69 330.44 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n91 2 Car -1 -1 -1 1095.37 184.42 1220.17 235.17 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n91 5 Car -1 -1 -1 954.73 183.23 1066.63 231.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n91 48 Pedestrian -1 -1 -1 738.32 150.90 794.81 284.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n91 4 Car -1 -1 -1 1033.03 183.91 1156.86 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n91 21 Pedestrian -1 -1 -1 340.12 159.86 360.42 211.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n91 8 Car -1 -1 -1 601.70 173.10 636.73 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n91 57 Pedestrian -1 -1 -1 358.20 158.59 373.99 200.47 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n91 37 Pedestrian -1 -1 -1 698.75 168.32 714.22 215.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n91 44 Pedestrian -1 -1 -1 219.82 155.30 235.01 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n91 23 Pedestrian -1 -1 -1 409.12 163.60 420.64 192.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n91 20 Car -1 -1 -1 597.76 173.29 620.31 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n91 11 Pedestrian -1 -1 -1 192.35 160.56 208.82 199.14 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n92 2 Car -1 -1 -1 1095.45 184.37 1220.30 235.28 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n92 5 Car -1 -1 -1 954.78 183.27 1066.50 231.75 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n92 4 Car -1 -1 -1 1029.92 184.09 1156.08 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n92 48 Pedestrian -1 -1 -1 723.38 153.14 788.39 281.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n92 8 Car -1 -1 -1 601.67 173.13 636.82 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n92 57 Pedestrian -1 -1 -1 358.13 158.65 374.21 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n92 44 Pedestrian -1 -1 -1 219.80 155.44 234.95 197.22 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n92 21 Pedestrian -1 -1 -1 340.41 159.70 361.01 211.36 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n92 23 Pedestrian -1 -1 -1 409.19 163.64 420.75 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n92 37 Pedestrian -1 -1 -1 698.78 168.07 714.74 215.96 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n92 20 Car -1 -1 -1 597.67 173.25 620.33 192.91 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n92 11 Pedestrian -1 -1 -1 192.36 160.43 208.79 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n93 2 Car -1 -1 -1 1095.48 184.25 1220.33 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n93 5 Car -1 -1 -1 954.72 183.36 1066.57 231.71 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n93 4 Car -1 -1 -1 1033.07 183.80 1156.82 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n93 48 Pedestrian -1 -1 -1 716.06 155.21 779.94 281.20 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n93 8 Car -1 -1 -1 601.57 172.95 636.84 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n93 57 Pedestrian -1 -1 -1 357.40 158.53 374.14 200.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n93 44 Pedestrian -1 -1 -1 219.79 155.33 234.92 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n93 21 Pedestrian -1 -1 -1 340.99 159.55 361.14 211.40 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n93 20 Car -1 -1 -1 597.54 173.27 620.29 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n93 23 Pedestrian -1 -1 -1 408.79 163.57 420.90 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n93 37 Pedestrian -1 -1 -1 698.67 168.10 715.13 216.24 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n93 11 Pedestrian -1 -1 -1 192.40 160.30 208.60 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n93 60 Pedestrian -1 -1 -1 319.57 160.80 336.25 196.34 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n94 2 Car -1 -1 -1 1096.34 184.04 1219.04 234.47 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n94 5 Car -1 -1 -1 954.54 183.30 1066.61 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n94 4 Car -1 -1 -1 1029.88 184.04 1156.04 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n94 48 Pedestrian -1 -1 -1 713.98 155.22 767.40 280.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n94 8 Car -1 -1 -1 601.80 172.98 636.79 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n94 57 Pedestrian -1 -1 -1 357.78 158.93 374.52 200.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n94 44 Pedestrian -1 -1 -1 219.82 155.23 234.87 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n94 23 Pedestrian -1 -1 -1 409.11 163.63 420.93 192.70 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n94 20 Car -1 -1 -1 597.57 173.22 620.26 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n94 21 Pedestrian -1 -1 -1 341.22 159.52 360.74 211.35 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n94 11 Pedestrian -1 -1 -1 192.40 160.27 208.61 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n94 37 Pedestrian -1 -1 -1 699.06 168.01 714.90 216.28 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n94 60 Pedestrian -1 -1 -1 322.41 160.54 338.06 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n94 61 Cyclist -1 -1 -1 1125.80 155.20 1218.45 340.87 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n95 2 Car -1 -1 -1 1095.27 184.51 1220.14 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n95 5 Car -1 -1 -1 954.16 183.18 1067.11 232.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n95 4 Car -1 -1 -1 1030.04 183.85 1155.00 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n95 61 Cyclist -1 -1 -1 1061.68 146.18 1214.12 349.79 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n95 48 Pedestrian -1 -1 -1 704.76 153.25 746.08 279.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n95 8 Car -1 -1 -1 601.70 172.98 636.95 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n95 57 Pedestrian -1 -1 -1 357.91 159.27 374.65 200.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n95 23 Pedestrian -1 -1 -1 409.37 163.80 421.01 192.80 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n95 44 Pedestrian -1 -1 -1 219.73 155.08 234.81 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n95 20 Car -1 -1 -1 597.85 173.26 620.30 192.97 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n95 21 Pedestrian -1 -1 -1 341.08 158.60 360.68 210.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n95 37 Pedestrian -1 -1 -1 701.39 168.29 716.38 216.09 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n95 11 Pedestrian -1 -1 -1 192.31 160.05 208.54 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n95 60 Pedestrian -1 -1 -1 323.92 161.65 338.71 195.70 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n96 2 Car -1 -1 -1 1093.06 183.47 1221.87 235.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n96 5 Car -1 -1 -1 955.10 183.12 1066.07 231.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n96 4 Car -1 -1 -1 1030.62 183.17 1154.50 231.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n96 48 Pedestrian -1 -1 -1 685.76 151.69 741.96 278.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n96 61 Cyclist -1 -1 -1 994.69 147.48 1173.94 333.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n96 8 Car -1 -1 -1 601.70 172.93 637.14 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n96 57 Pedestrian -1 -1 -1 358.02 159.28 374.64 199.78 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n96 23 Pedestrian -1 -1 -1 409.13 163.88 420.85 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n96 44 Pedestrian -1 -1 -1 219.47 155.05 234.91 196.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n96 20 Car -1 -1 -1 597.96 173.26 620.35 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n96 21 Pedestrian -1 -1 -1 341.42 158.95 360.61 209.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n96 37 Pedestrian -1 -1 -1 700.42 170.00 718.38 216.62 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n96 11 Pedestrian -1 -1 -1 192.31 160.04 208.41 199.14 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n97 2 Car -1 -1 -1 1095.36 183.57 1220.27 235.61 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n97 5 Car -1 -1 -1 956.29 183.88 1065.15 230.95 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n97 48 Pedestrian -1 -1 -1 673.31 154.34 738.36 279.00 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n97 4 Car -1 -1 -1 1029.75 184.29 1155.68 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n97 61 Cyclist -1 -1 -1 938.64 148.29 1108.00 325.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n97 8 Car -1 -1 -1 601.95 172.87 637.02 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n97 57 Pedestrian -1 -1 -1 358.32 159.12 374.28 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n97 44 Pedestrian -1 -1 -1 219.43 154.85 234.94 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n97 23 Pedestrian -1 -1 -1 409.01 163.74 420.86 193.01 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n97 20 Car -1 -1 -1 598.10 173.31 620.24 193.00 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n97 21 Pedestrian -1 -1 -1 341.61 158.96 360.53 209.87 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n97 11 Pedestrian -1 -1 -1 192.17 159.79 208.51 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n97 37 Pedestrian -1 -1 -1 699.50 170.80 719.39 215.99 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n97 62 Pedestrian -1 -1 -1 327.23 162.89 341.92 195.36 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n98 2 Car -1 -1 -1 1095.63 183.90 1219.74 235.38 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n98 61 Cyclist -1 -1 -1 885.87 145.59 1046.06 320.98 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n98 48 Pedestrian -1 -1 -1 668.58 154.55 727.87 278.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n98 5 Car -1 -1 -1 957.13 183.30 1064.16 231.43 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n98 4 Car -1 -1 -1 1030.77 184.10 1154.89 232.78 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n98 8 Car -1 -1 -1 602.35 172.46 637.58 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n98 57 Pedestrian -1 -1 -1 358.41 159.13 373.95 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n98 44 Pedestrian -1 -1 -1 219.24 155.07 235.33 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n98 20 Car -1 -1 -1 598.13 173.22 620.31 192.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n98 23 Pedestrian -1 -1 -1 408.89 163.68 420.69 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n98 21 Pedestrian -1 -1 -1 341.92 159.16 360.54 209.56 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n98 11 Pedestrian -1 -1 -1 191.89 159.89 208.51 199.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n99 2 Car -1 -1 -1 1095.20 184.10 1220.09 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n99 4 Car -1 -1 -1 1030.83 183.94 1154.75 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n99 5 Car -1 -1 -1 955.22 183.16 1065.92 231.81 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n99 61 Cyclist -1 -1 -1 835.45 146.93 988.46 318.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n99 48 Pedestrian -1 -1 -1 667.62 155.31 713.44 277.39 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n99 8 Car -1 -1 -1 601.90 172.66 637.09 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n99 44 Pedestrian -1 -1 -1 219.00 155.05 235.33 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n99 21 Pedestrian -1 -1 -1 343.14 158.88 362.10 209.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n99 57 Pedestrian -1 -1 -1 360.72 159.19 375.23 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n99 20 Car -1 -1 -1 597.98 173.16 620.33 192.65 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n99 23 Pedestrian -1 -1 -1 408.90 163.75 420.56 192.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n99 11 Pedestrian -1 -1 -1 192.06 159.98 208.36 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n100 2 Car -1 -1 -1 1095.54 184.17 1219.52 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n100 5 Car -1 -1 -1 954.63 183.05 1066.41 231.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n100 4 Car -1 -1 -1 1030.21 184.04 1155.54 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n100 61 Cyclist -1 -1 -1 789.31 150.04 927.99 309.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n100 48 Pedestrian -1 -1 -1 661.78 155.02 702.29 274.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n100 8 Car -1 -1 -1 601.76 172.87 637.23 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n100 57 Pedestrian -1 -1 -1 361.17 158.71 375.26 200.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n100 44 Pedestrian -1 -1 -1 219.72 155.08 235.64 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n100 21 Pedestrian -1 -1 -1 343.32 159.02 361.91 209.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n100 20 Car -1 -1 -1 597.90 173.21 620.45 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n100 23 Pedestrian -1 -1 -1 408.69 163.96 420.58 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n100 11 Pedestrian -1 -1 -1 192.30 160.11 208.41 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n100 63 Pedestrian -1 -1 -1 704.86 166.85 721.46 217.28 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n100 64 Pedestrian -1 -1 -1 308.77 157.71 323.84 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n101 2 Car -1 -1 -1 1096.41 184.30 1218.59 234.98 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n101 5 Car -1 -1 -1 954.65 183.02 1066.38 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n101 48 Pedestrian -1 -1 -1 640.92 153.81 694.18 273.46 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n101 4 Car -1 -1 -1 1029.98 184.02 1155.85 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n101 61 Cyclist -1 -1 -1 740.75 148.27 877.77 309.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n101 21 Pedestrian -1 -1 -1 344.10 159.20 361.92 208.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n101 8 Car -1 -1 -1 602.39 172.63 637.66 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n101 57 Pedestrian -1 -1 -1 361.07 158.49 376.04 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n101 44 Pedestrian -1 -1 -1 220.28 155.14 235.79 197.42 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n101 20 Car -1 -1 -1 597.90 173.32 620.37 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n101 63 Pedestrian -1 -1 -1 704.97 167.94 722.85 218.23 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n101 23 Pedestrian -1 -1 -1 408.51 163.93 420.47 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n101 11 Pedestrian -1 -1 -1 192.44 159.86 208.48 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n102 2 Car -1 -1 -1 1094.58 184.55 1220.52 234.06 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n102 48 Pedestrian -1 -1 -1 629.05 155.31 690.26 272.43 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n102 4 Car -1 -1 -1 1030.68 183.78 1154.21 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n102 5 Car -1 -1 -1 954.34 183.14 1066.74 232.01 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n102 61 Cyclist -1 -1 -1 704.92 152.99 829.68 304.87 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n102 21 Pedestrian -1 -1 -1 344.80 158.79 362.13 208.15 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n102 8 Car -1 -1 -1 601.93 172.96 636.93 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n102 57 Pedestrian -1 -1 -1 361.45 157.95 376.20 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n102 20 Car -1 -1 -1 598.09 173.32 620.30 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n102 44 Pedestrian -1 -1 -1 220.90 155.26 235.90 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n102 63 Pedestrian -1 -1 -1 705.15 168.50 723.46 217.88 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n102 23 Pedestrian -1 -1 -1 408.61 163.72 420.56 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n102 11 Pedestrian -1 -1 -1 192.09 159.94 208.58 199.26 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n102 65 Cyclist -1 -1 -1 1077.52 156.72 1221.10 346.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n102 66 Pedestrian -1 -1 -1 185.16 159.78 201.17 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n103 4 Car -1 -1 -1 1030.88 184.05 1154.09 231.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n103 2 Car -1 -1 -1 1092.79 183.47 1221.55 235.50 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n103 5 Car -1 -1 -1 954.90 182.92 1066.54 232.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n103 48 Pedestrian -1 -1 -1 624.92 157.30 679.07 271.47 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n103 65 Cyclist -1 -1 -1 1022.09 152.86 1192.47 328.60 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n103 61 Cyclist -1 -1 -1 667.17 153.34 789.12 298.57 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n103 8 Car -1 -1 -1 601.75 172.59 636.95 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n103 57 Pedestrian -1 -1 -1 361.31 158.14 377.32 199.50 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n103 21 Pedestrian -1 -1 -1 345.45 158.60 362.70 207.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n103 44 Pedestrian -1 -1 -1 223.48 154.99 237.54 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n103 20 Car -1 -1 -1 597.89 173.33 620.57 193.00 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n103 63 Pedestrian -1 -1 -1 706.09 169.18 726.48 218.15 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n103 11 Pedestrian -1 -1 -1 192.38 160.46 208.15 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n103 23 Pedestrian -1 -1 -1 408.62 163.47 420.43 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n103 66 Pedestrian -1 -1 -1 185.58 159.72 200.82 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n104 2 Car -1 -1 -1 1094.33 183.76 1220.50 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n104 5 Car -1 -1 -1 956.78 183.44 1065.58 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n104 4 Car -1 -1 -1 1029.70 183.88 1155.69 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n104 65 Cyclist -1 -1 -1 964.39 149.70 1128.20 325.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n104 61 Cyclist -1 -1 -1 628.78 153.72 745.42 296.55 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n104 8 Car -1 -1 -1 601.86 172.70 636.72 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n104 21 Pedestrian -1 -1 -1 345.90 158.42 362.26 207.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n104 57 Pedestrian -1 -1 -1 361.44 158.23 377.69 199.26 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n104 48 Pedestrian -1 -1 -1 624.01 158.54 665.11 270.95 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n104 44 Pedestrian -1 -1 -1 223.46 154.97 237.67 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n104 20 Car -1 -1 -1 597.98 173.39 620.69 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n104 66 Pedestrian -1 -1 -1 185.47 159.96 200.77 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n104 23 Pedestrian -1 -1 -1 408.48 163.38 420.20 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n105 2 Car -1 -1 -1 1095.69 183.93 1219.76 235.56 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n105 5 Car -1 -1 -1 957.26 183.64 1064.89 231.33 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n105 4 Car -1 -1 -1 1030.24 184.02 1155.32 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n105 65 Cyclist -1 -1 -1 915.98 155.56 1068.44 317.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n105 21 Pedestrian -1 -1 -1 345.68 159.01 362.41 207.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n105 61 Cyclist -1 -1 -1 595.67 154.01 709.05 290.45 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n105 8 Car -1 -1 -1 601.80 173.49 636.52 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n105 57 Pedestrian -1 -1 -1 361.98 158.64 377.72 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n105 44 Pedestrian -1 -1 -1 223.64 155.04 238.16 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n105 48 Pedestrian -1 -1 -1 611.68 164.82 654.99 269.38 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n105 20 Car -1 -1 -1 597.99 173.34 621.03 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n105 66 Pedestrian -1 -1 -1 185.46 160.29 200.86 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n105 67 Pedestrian -1 -1 -1 705.41 168.33 722.06 217.94 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n105 68 Pedestrian -1 -1 -1 192.36 160.93 208.10 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n106 2 Car -1 -1 -1 1094.75 184.07 1220.21 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n106 5 Car -1 -1 -1 956.82 182.76 1065.55 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n106 4 Car -1 -1 -1 1031.04 183.91 1154.61 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n106 65 Cyclist -1 -1 -1 865.79 152.46 1013.09 314.34 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n106 8 Car -1 -1 -1 602.19 174.17 635.67 201.74 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n106 61 Cyclist -1 -1 -1 573.62 155.34 669.97 288.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n106 21 Pedestrian -1 -1 -1 345.08 159.56 363.19 207.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n106 57 Pedestrian -1 -1 -1 362.03 158.84 377.95 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n106 44 Pedestrian -1 -1 -1 223.83 155.18 237.93 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n106 20 Car -1 -1 -1 597.05 173.22 621.17 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n106 66 Pedestrian -1 -1 -1 185.49 160.48 200.97 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n106 68 Pedestrian -1 -1 -1 192.24 161.48 207.96 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n106 67 Pedestrian -1 -1 -1 705.51 168.35 722.32 217.88 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n106 69 Pedestrian -1 -1 -1 213.24 155.39 228.28 196.14 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n107 2 Car -1 -1 -1 1094.71 183.94 1220.55 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n107 5 Car -1 -1 -1 956.01 183.13 1065.40 231.81 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n107 4 Car -1 -1 -1 1030.34 183.81 1155.20 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n107 8 Car -1 -1 -1 602.48 173.62 635.71 201.70 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n107 65 Cyclist -1 -1 -1 818.66 157.44 960.01 308.28 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n107 61 Cyclist -1 -1 -1 536.45 155.65 651.61 287.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n107 21 Pedestrian -1 -1 -1 345.15 159.41 363.27 207.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n107 44 Pedestrian -1 -1 -1 223.98 155.28 237.89 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n107 57 Pedestrian -1 -1 -1 361.88 158.76 378.02 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n107 68 Pedestrian -1 -1 -1 192.14 160.95 208.14 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n107 67 Pedestrian -1 -1 -1 705.78 168.32 722.57 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n107 48 Pedestrian -1 -1 -1 591.52 158.21 643.47 270.91 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n107 69 Pedestrian -1 -1 -1 213.27 155.61 228.08 196.05 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n108 2 Car -1 -1 -1 1095.22 184.18 1219.81 235.10 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n108 5 Car -1 -1 -1 955.09 183.01 1066.04 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n108 4 Car -1 -1 -1 1029.87 183.88 1155.88 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n108 65 Cyclist -1 -1 -1 778.43 156.55 909.65 302.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n108 61 Cyclist -1 -1 -1 512.35 154.61 607.84 281.11 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n108 44 Pedestrian -1 -1 -1 223.94 155.36 237.81 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n108 8 Car -1 -1 -1 601.68 173.33 636.78 202.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n108 21 Pedestrian -1 -1 -1 344.96 159.23 363.81 206.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n108 57 Pedestrian -1 -1 -1 362.42 158.83 377.73 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n108 48 Pedestrian -1 -1 -1 576.51 159.10 637.53 270.11 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n108 20 Car -1 -1 -1 595.98 172.68 622.61 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n108 68 Pedestrian -1 -1 -1 191.85 160.51 208.25 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n108 67 Pedestrian -1 -1 -1 707.57 168.22 724.12 218.40 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n109 2 Car -1 -1 -1 1095.16 184.21 1220.16 235.25 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n109 5 Car -1 -1 -1 954.95 183.12 1066.16 231.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n109 4 Car -1 -1 -1 1032.68 183.59 1157.25 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n109 48 Pedestrian -1 -1 -1 576.85 158.72 620.72 268.64 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n109 61 Cyclist -1 -1 -1 486.33 156.97 579.48 277.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n109 65 Cyclist -1 -1 -1 739.33 153.89 863.46 298.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n109 8 Car -1 -1 -1 600.92 173.29 637.16 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n109 21 Pedestrian -1 -1 -1 345.51 159.05 363.66 205.90 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n109 44 Pedestrian -1 -1 -1 223.77 155.36 237.87 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n109 57 Pedestrian -1 -1 -1 362.39 159.04 377.34 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n109 67 Pedestrian -1 -1 -1 708.41 168.58 725.30 218.39 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n109 20 Car -1 -1 -1 596.19 172.77 622.47 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n109 68 Pedestrian -1 -1 -1 191.50 160.47 208.68 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n110 2 Car -1 -1 -1 1095.35 184.25 1220.38 235.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n110 5 Car -1 -1 -1 954.75 183.17 1066.37 231.84 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n110 4 Car -1 -1 -1 1029.57 183.88 1156.34 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n110 65 Cyclist -1 -1 -1 698.26 155.73 820.01 296.32 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n110 61 Cyclist -1 -1 -1 461.36 155.87 553.69 272.91 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n110 48 Pedestrian -1 -1 -1 572.90 157.43 610.19 267.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n110 8 Car -1 -1 -1 603.66 173.02 636.87 201.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n110 44 Pedestrian -1 -1 -1 223.49 155.22 237.95 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n110 21 Pedestrian -1 -1 -1 346.19 159.25 363.84 205.90 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n110 57 Pedestrian -1 -1 -1 362.99 159.33 377.57 197.68 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n110 20 Car -1 -1 -1 597.15 172.78 622.02 192.75 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n110 67 Pedestrian -1 -1 -1 709.09 168.74 726.06 218.44 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n110 68 Pedestrian -1 -1 -1 191.08 160.10 208.97 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n110 70 Pedestrian -1 -1 -1 213.29 155.82 228.11 196.13 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n111 2 Car -1 -1 -1 1095.22 184.12 1220.53 235.28 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n111 5 Car -1 -1 -1 954.83 183.15 1066.38 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n111 4 Car -1 -1 -1 1029.59 183.92 1156.25 232.86 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n111 48 Pedestrian -1 -1 -1 558.18 156.70 607.30 265.32 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n111 61 Cyclist -1 -1 -1 440.20 156.37 528.86 271.16 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n111 65 Cyclist -1 -1 -1 656.23 156.22 779.12 295.82 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n111 8 Car -1 -1 -1 601.37 172.60 637.13 202.37 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n111 44 Pedestrian -1 -1 -1 223.58 155.17 237.70 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n111 21 Pedestrian -1 -1 -1 347.63 159.30 365.39 205.57 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n111 67 Pedestrian -1 -1 -1 709.23 169.23 726.49 218.15 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n111 20 Car -1 -1 -1 597.37 172.77 622.07 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n111 57 Pedestrian -1 -1 -1 364.80 159.53 378.76 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n111 68 Pedestrian -1 -1 -1 191.37 160.36 209.09 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n112 2 Car -1 -1 -1 1095.34 184.22 1220.36 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n112 5 Car -1 -1 -1 954.66 183.28 1066.54 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n112 4 Car -1 -1 -1 1029.79 183.91 1155.96 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n112 61 Cyclist -1 -1 -1 421.95 155.18 507.35 270.96 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n112 65 Cyclist -1 -1 -1 622.10 153.89 736.60 295.40 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n112 48 Pedestrian -1 -1 -1 548.01 157.42 603.35 264.44 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n112 8 Car -1 -1 -1 602.89 172.39 637.33 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n112 44 Pedestrian -1 -1 -1 223.70 155.19 237.55 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n112 67 Pedestrian -1 -1 -1 711.04 168.12 728.97 218.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n112 57 Pedestrian -1 -1 -1 365.25 159.16 379.62 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n112 21 Pedestrian -1 -1 -1 347.57 159.09 366.15 205.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n112 20 Car -1 -1 -1 597.93 172.96 621.51 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n112 68 Pedestrian -1 -1 -1 191.53 160.24 209.16 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n112 71 Pedestrian -1 -1 -1 213.11 155.65 227.79 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n113 2 Car -1 -1 -1 1095.34 184.18 1220.42 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n113 5 Car -1 -1 -1 954.51 183.36 1066.65 231.76 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n113 61 Cyclist -1 -1 -1 404.85 156.01 486.45 265.45 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n113 4 Car -1 -1 -1 1030.05 183.96 1155.70 232.70 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n113 65 Cyclist -1 -1 -1 589.26 156.74 699.74 292.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n113 48 Pedestrian -1 -1 -1 545.33 157.11 598.66 265.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n113 8 Car -1 -1 -1 601.48 172.79 637.23 202.17 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n113 67 Pedestrian -1 -1 -1 712.17 168.14 728.85 218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n113 44 Pedestrian -1 -1 -1 223.66 155.19 237.38 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n113 20 Car -1 -1 -1 598.50 173.12 621.26 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n113 68 Pedestrian -1 -1 -1 191.51 160.04 209.31 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n113 57 Pedestrian -1 -1 -1 365.11 158.69 379.90 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n113 21 Pedestrian -1 -1 -1 347.23 159.53 367.23 205.13 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n113 72 Cyclist -1 -1 -1 347.23 159.53 367.23 205.13 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n113 73 Pedestrian -1 -1 -1 320.16 157.71 332.94 187.60 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n114 2 Car -1 -1 -1 1095.48 184.23 1220.27 235.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n114 5 Car -1 -1 -1 954.47 183.21 1066.68 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n114 61 Cyclist -1 -1 -1 390.05 155.84 462.88 263.60 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n114 4 Car -1 -1 -1 1030.17 183.98 1155.71 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n114 65 Cyclist -1 -1 -1 559.51 156.99 661.88 287.27 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n114 8 Car -1 -1 -1 601.61 173.42 636.88 201.24 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n114 48 Pedestrian -1 -1 -1 542.19 157.32 585.77 263.76 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n114 67 Pedestrian -1 -1 -1 712.83 167.93 729.79 219.24 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n114 44 Pedestrian -1 -1 -1 223.52 155.06 237.23 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n114 20 Car -1 -1 -1 597.55 173.23 621.78 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n114 68 Pedestrian -1 -1 -1 191.39 159.87 209.39 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n114 21 Pedestrian -1 -1 -1 347.32 159.57 366.96 204.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n114 57 Pedestrian -1 -1 -1 365.66 158.64 380.05 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n115 2 Car -1 -1 -1 1095.47 184.27 1220.23 235.23 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n115 5 Car -1 -1 -1 954.46 183.22 1066.67 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n115 4 Car -1 -1 -1 1030.05 184.02 1155.70 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n115 65 Cyclist -1 -1 -1 518.19 157.82 634.23 286.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n115 61 Cyclist -1 -1 -1 373.80 157.34 446.75 261.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n115 8 Car -1 -1 -1 601.79 173.16 636.44 201.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n115 67 Pedestrian -1 -1 -1 713.62 168.21 730.21 219.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n115 21 Pedestrian -1 -1 -1 348.19 159.33 367.56 204.75 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n115 44 Pedestrian -1 -1 -1 223.54 155.06 237.24 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n115 57 Pedestrian -1 -1 -1 366.30 158.60 380.56 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n115 20 Car -1 -1 -1 597.66 173.24 621.51 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n115 68 Pedestrian -1 -1 -1 191.45 159.80 209.15 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n115 48 Pedestrian -1 -1 -1 533.93 157.58 579.06 263.85 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n116 2 Car -1 -1 -1 1095.38 184.21 1220.37 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n116 5 Car -1 -1 -1 954.37 183.28 1066.73 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n116 4 Car -1 -1 -1 1030.11 184.07 1155.80 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n116 61 Cyclist -1 -1 -1 361.94 157.27 429.48 256.91 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n116 8 Car -1 -1 -1 601.61 173.28 636.70 202.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n116 65 Cyclist -1 -1 -1 499.05 156.73 592.39 279.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n116 44 Pedestrian -1 -1 -1 223.69 155.15 237.32 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n116 67 Pedestrian -1 -1 -1 713.98 168.52 730.62 219.86 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n116 57 Pedestrian -1 -1 -1 366.58 158.98 380.72 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n116 21 Pedestrian -1 -1 -1 349.25 159.44 367.88 204.63 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n116 48 Pedestrian -1 -1 -1 521.87 159.42 575.07 261.22 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n116 20 Car -1 -1 -1 598.27 173.23 621.47 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n116 68 Pedestrian -1 -1 -1 191.41 159.75 209.21 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n117 2 Car -1 -1 -1 1095.45 184.19 1220.42 235.05 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n117 5 Car -1 -1 -1 954.40 183.27 1066.64 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n117 61 Cyclist -1 -1 -1 345.11 157.00 416.53 255.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n117 4 Car -1 -1 -1 1033.09 183.83 1156.84 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n117 8 Car -1 -1 -1 601.61 173.57 636.68 202.28 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n117 65 Cyclist -1 -1 -1 466.09 157.49 578.69 279.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n117 67 Pedestrian -1 -1 -1 715.75 168.41 732.13 220.45 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n117 44 Pedestrian -1 -1 -1 223.78 155.19 237.48 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n117 57 Pedestrian -1 -1 -1 366.22 158.92 380.67 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n117 21 Pedestrian -1 -1 -1 349.59 159.71 367.07 204.98 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n117 20 Car -1 -1 -1 598.34 173.43 621.42 193.29 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n117 68 Pedestrian -1 -1 -1 191.29 159.45 209.49 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n117 48 Pedestrian -1 -1 -1 515.47 162.15 566.64 258.66 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n117 74 Pedestrian -1 -1 -1 319.94 157.68 332.93 187.47 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n118 2 Car -1 -1 -1 1095.46 184.30 1220.12 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n118 5 Car -1 -1 -1 954.50 183.29 1066.56 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n118 4 Car -1 -1 -1 1030.19 184.01 1155.60 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n118 67 Pedestrian -1 -1 -1 716.49 168.10 733.18 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n118 65 Cyclist -1 -1 -1 454.79 160.09 541.98 275.83 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n118 61 Cyclist -1 -1 -1 338.84 158.78 405.30 252.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n118 8 Car -1 -1 -1 602.05 173.62 636.47 202.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n118 44 Pedestrian -1 -1 -1 223.75 155.09 237.81 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n118 48 Pedestrian -1 -1 -1 510.62 159.45 557.24 261.64 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n118 57 Pedestrian -1 -1 -1 366.36 158.88 380.39 197.21 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n118 20 Car -1 -1 -1 598.57 173.49 621.11 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n118 68 Pedestrian -1 -1 -1 191.10 159.70 209.97 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n118 21 Pedestrian -1 -1 -1 349.12 159.71 366.90 204.19 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n118 74 Pedestrian -1 -1 -1 320.00 157.86 332.83 187.26 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n119 2 Car -1 -1 -1 1095.44 184.25 1220.35 235.28 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n119 5 Car -1 -1 -1 954.54 183.26 1066.55 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n119 4 Car -1 -1 -1 1030.05 184.03 1155.77 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n119 65 Cyclist -1 -1 -1 430.95 159.62 519.68 274.47 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n119 61 Cyclist -1 -1 -1 326.89 158.68 390.53 248.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n119 67 Pedestrian -1 -1 -1 717.34 167.90 733.53 221.08 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n119 8 Car -1 -1 -1 602.05 173.54 636.65 202.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n119 48 Pedestrian -1 -1 -1 504.49 158.87 547.06 259.01 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n119 44 Pedestrian -1 -1 -1 223.52 154.93 237.73 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n119 20 Car -1 -1 -1 598.59 173.48 621.33 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n119 21 Pedestrian -1 -1 -1 348.85 160.16 367.17 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n119 68 Pedestrian -1 -1 -1 191.03 159.51 209.95 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n119 57 Pedestrian -1 -1 -1 366.34 158.73 381.16 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n119 74 Pedestrian -1 -1 -1 320.02 157.85 332.95 187.41 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n120 2 Car -1 -1 -1 1095.49 184.18 1220.33 235.22 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n120 5 Car -1 -1 -1 954.63 183.25 1066.51 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n120 61 Cyclist -1 -1 -1 319.18 159.21 379.52 247.44 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n120 4 Car -1 -1 -1 1030.41 184.08 1155.51 232.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n120 65 Cyclist -1 -1 -1 413.48 158.06 494.59 270.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n120 48 Pedestrian -1 -1 -1 500.61 158.36 537.65 256.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n120 8 Car -1 -1 -1 601.76 173.34 637.04 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n120 44 Pedestrian -1 -1 -1 223.25 154.78 237.72 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n120 67 Pedestrian -1 -1 -1 718.49 167.55 736.21 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n120 20 Car -1 -1 -1 598.34 173.62 621.47 192.76 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n120 21 Pedestrian -1 -1 -1 349.50 160.41 366.67 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n120 68 Pedestrian -1 -1 -1 190.42 159.26 210.04 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n120 74 Pedestrian -1 -1 -1 320.20 157.90 333.08 187.87 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n120 57 Pedestrian -1 -1 -1 368.20 159.59 383.29 196.21 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n121 2 Car -1 -1 -1 1095.42 184.24 1220.44 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n121 5 Car -1 -1 -1 954.58 183.30 1066.49 231.83 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n121 4 Car -1 -1 -1 1030.24 184.08 1155.70 232.62 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n121 65 Cyclist -1 -1 -1 393.86 160.50 475.89 267.53 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n121 48 Pedestrian -1 -1 -1 493.09 158.86 535.50 255.90 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n121 67 Pedestrian -1 -1 -1 718.84 167.65 737.98 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n121 8 Car -1 -1 -1 601.83 173.21 637.03 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n121 61 Cyclist -1 -1 -1 309.61 158.03 369.57 246.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n121 44 Pedestrian -1 -1 -1 223.24 154.75 237.82 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n121 20 Car -1 -1 -1 598.33 173.61 621.16 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n121 68 Pedestrian -1 -1 -1 189.94 158.72 210.12 199.57 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n121 21 Pedestrian -1 -1 -1 348.64 159.69 367.32 203.99 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n121 57 Pedestrian -1 -1 -1 369.02 159.62 382.70 195.87 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n122 2 Car -1 -1 -1 1095.62 184.27 1220.15 235.20 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n122 5 Car -1 -1 -1 954.70 183.34 1066.46 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n122 4 Car -1 -1 -1 1033.27 183.82 1156.56 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n122 65 Cyclist -1 -1 -1 384.68 160.56 452.05 260.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n122 67 Pedestrian -1 -1 -1 718.88 168.26 739.33 221.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n122 61 Cyclist -1 -1 -1 307.84 157.96 359.53 241.07 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n122 48 Pedestrian -1 -1 -1 487.86 159.02 532.20 255.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n122 8 Car -1 -1 -1 601.83 173.22 637.03 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n122 44 Pedestrian -1 -1 -1 222.97 154.59 237.86 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n122 20 Car -1 -1 -1 598.12 173.52 621.19 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n122 21 Pedestrian -1 -1 -1 348.92 159.70 367.93 204.34 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n122 68 Pedestrian -1 -1 -1 187.18 158.85 207.86 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n122 57 Pedestrian -1 -1 -1 369.41 159.66 382.45 195.77 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n123 2 Car -1 -1 -1 1095.65 184.27 1220.20 235.18 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n123 5 Car -1 -1 -1 954.66 183.26 1066.56 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n123 4 Car -1 -1 -1 1030.29 184.03 1155.59 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n123 61 Cyclist -1 -1 -1 303.52 157.41 350.69 239.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n123 65 Cyclist -1 -1 -1 370.23 160.07 435.83 258.97 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n123 48 Pedestrian -1 -1 -1 484.91 159.06 527.53 255.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n123 8 Car -1 -1 -1 601.87 173.03 637.07 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n123 67 Pedestrian -1 -1 -1 720.85 167.95 741.39 222.05 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n123 44 Pedestrian -1 -1 -1 222.87 154.52 237.70 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n123 20 Car -1 -1 -1 598.06 173.46 621.15 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n123 57 Pedestrian -1 -1 -1 370.50 159.53 383.10 195.82 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n123 68 Pedestrian -1 -1 -1 186.78 158.36 208.23 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n123 21 Pedestrian -1 -1 -1 349.53 160.04 367.75 203.65 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n124 2 Car -1 -1 -1 1095.67 184.18 1220.26 235.12 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n124 5 Car -1 -1 -1 954.31 183.21 1066.73 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n124 4 Car -1 -1 -1 1030.16 184.05 1155.72 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n124 65 Cyclist -1 -1 -1 355.44 159.07 422.76 255.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n124 48 Pedestrian -1 -1 -1 480.13 159.22 517.11 255.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n124 61 Cyclist -1 -1 -1 300.17 158.20 345.92 237.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n124 8 Car -1 -1 -1 601.87 172.99 636.93 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n124 67 Pedestrian -1 -1 -1 720.71 167.85 742.17 222.12 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n124 20 Car -1 -1 -1 598.25 173.61 621.06 192.80 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n124 44 Pedestrian -1 -1 -1 220.79 154.36 236.15 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n124 68 Pedestrian -1 -1 -1 186.91 158.55 207.90 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n125 2 Car -1 -1 -1 1095.59 184.26 1220.16 235.22 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n125 5 Car -1 -1 -1 954.38 183.29 1066.70 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n125 4 Car -1 -1 -1 1030.05 184.03 1155.73 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n125 48 Pedestrian -1 -1 -1 470.22 158.82 512.34 254.45 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n125 65 Cyclist -1 -1 -1 342.75 159.88 410.22 254.54 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n125 67 Pedestrian -1 -1 -1 721.20 167.84 742.91 222.12 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n125 8 Car -1 -1 -1 601.97 173.04 636.83 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n125 61 Cyclist -1 -1 -1 298.23 158.04 339.18 234.09 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n125 20 Car -1 -1 -1 598.25 173.55 621.10 192.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n125 44 Pedestrian -1 -1 -1 220.77 154.28 236.06 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n125 68 Pedestrian -1 -1 -1 186.85 158.27 207.76 199.96 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n125 75 Pedestrian -1 -1 -1 370.31 159.05 383.95 194.80 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n126 2 Car -1 -1 -1 1095.75 184.27 1220.02 235.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n126 4 Car -1 -1 -1 1030.00 184.05 1155.79 232.75 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n126 5 Car -1 -1 -1 954.44 183.31 1066.57 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n126 48 Pedestrian -1 -1 -1 464.90 158.97 509.38 253.31 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n126 65 Cyclist -1 -1 -1 330.19 160.52 402.31 253.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n126 61 Cyclist -1 -1 -1 294.34 159.51 335.82 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n126 8 Car -1 -1 -1 601.99 173.10 636.74 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n126 67 Pedestrian -1 -1 -1 725.02 167.87 745.77 222.27 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n126 44 Pedestrian -1 -1 -1 220.90 154.36 236.03 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n126 20 Car -1 -1 -1 598.00 173.62 621.03 192.78 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n126 68 Pedestrian -1 -1 -1 187.10 157.92 207.34 200.24 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n126 75 Pedestrian -1 -1 -1 370.14 159.07 384.83 194.55 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n127 2 Car -1 -1 -1 1095.75 184.26 1220.23 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n127 4 Car -1 -1 -1 1030.01 184.05 1155.77 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n127 5 Car -1 -1 -1 954.42 183.28 1066.58 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n127 65 Cyclist -1 -1 -1 326.58 161.93 388.05 251.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n127 48 Pedestrian -1 -1 -1 458.09 159.04 503.70 253.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n127 67 Pedestrian -1 -1 -1 727.63 167.57 746.36 222.56 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n127 61 Cyclist -1 -1 -1 291.72 158.79 332.03 229.66 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n127 8 Car -1 -1 -1 602.03 173.03 636.65 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n127 44 Pedestrian -1 -1 -1 220.50 154.38 235.86 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n127 20 Car -1 -1 -1 597.97 173.45 621.20 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n127 75 Pedestrian -1 -1 -1 369.98 158.73 384.77 194.98 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n127 68 Pedestrian -1 -1 -1 187.08 157.93 207.24 200.15 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n128 2 Car -1 -1 -1 1095.81 184.25 1220.10 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n128 5 Car -1 -1 -1 954.38 183.30 1066.69 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n128 4 Car -1 -1 -1 1030.23 184.10 1155.58 232.62 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n128 48 Pedestrian -1 -1 -1 456.28 160.13 496.57 252.49 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n128 65 Cyclist -1 -1 -1 321.95 162.83 377.40 248.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n128 67 Pedestrian -1 -1 -1 730.62 167.31 748.46 222.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n128 8 Car -1 -1 -1 602.04 172.99 636.62 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n128 44 Pedestrian -1 -1 -1 220.57 154.49 235.82 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n128 61 Cyclist -1 -1 -1 293.29 157.68 327.74 226.61 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n128 20 Car -1 -1 -1 598.06 173.39 621.01 192.78 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n128 75 Pedestrian -1 -1 -1 372.29 158.90 386.52 194.73 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n128 68 Pedestrian -1 -1 -1 186.95 153.80 207.33 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n128 76 Pedestrian -1 -1 -1 352.52 160.75 370.36 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n128 77 Pedestrian -1 -1 -1 402.26 160.91 413.79 188.90 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n129 2 Car -1 -1 -1 1095.77 184.26 1220.10 235.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n129 5 Car -1 -1 -1 954.43 183.34 1066.66 231.82 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n129 4 Car -1 -1 -1 1030.08 184.06 1155.65 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n129 48 Pedestrian -1 -1 -1 452.07 157.99 486.43 252.18 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n129 8 Car -1 -1 -1 601.77 172.99 636.75 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n129 67 Pedestrian -1 -1 -1 731.16 167.92 750.32 223.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n129 65 Cyclist -1 -1 -1 316.35 161.44 370.83 244.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n129 61 Cyclist -1 -1 -1 291.95 157.86 324.92 225.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n129 44 Pedestrian -1 -1 -1 220.07 154.41 235.92 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n129 20 Car -1 -1 -1 598.07 173.40 621.12 192.76 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n129 75 Pedestrian -1 -1 -1 371.15 158.56 384.79 194.22 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n129 77 Pedestrian -1 -1 -1 402.13 160.69 413.72 188.94 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n129 68 Pedestrian -1 -1 -1 186.71 153.58 207.51 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n130 2 Car -1 -1 -1 1095.70 184.26 1220.08 235.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n130 4 Car -1 -1 -1 1030.04 184.06 1155.71 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n130 5 Car -1 -1 -1 954.51 183.32 1066.56 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n130 65 Cyclist -1 -1 -1 315.66 161.74 361.24 242.71 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n130 48 Pedestrian -1 -1 -1 445.68 157.30 482.48 252.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n130 8 Car -1 -1 -1 601.75 172.96 636.86 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n130 44 Pedestrian -1 -1 -1 219.30 154.59 236.09 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n130 20 Car -1 -1 -1 597.87 173.31 621.31 193.00 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n130 61 Cyclist -1 -1 -1 293.04 157.94 323.10 222.44 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n130 67 Pedestrian -1 -1 -1 732.68 168.41 752.72 223.72 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n130 75 Pedestrian -1 -1 -1 370.90 158.60 384.87 194.12 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n130 68 Pedestrian -1 -1 -1 187.19 153.58 207.31 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n130 77 Pedestrian -1 -1 -1 402.07 161.30 413.83 188.59 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n130 78 Pedestrian -1 -1 -1 352.40 159.70 370.99 203.47 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n131 2 Car -1 -1 -1 1095.75 184.23 1220.10 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n131 5 Car -1 -1 -1 954.55 183.38 1066.62 231.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n131 4 Car -1 -1 -1 1030.28 184.12 1155.61 232.63 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n131 48 Pedestrian -1 -1 -1 438.35 159.03 477.61 251.10 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n131 65 Cyclist -1 -1 -1 313.57 161.35 355.16 238.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n131 8 Car -1 -1 -1 601.84 172.96 636.92 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n131 44 Pedestrian -1 -1 -1 219.02 154.57 236.06 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n131 67 Pedestrian -1 -1 -1 733.18 169.21 753.52 224.60 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n131 20 Car -1 -1 -1 597.82 173.30 621.30 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n131 75 Pedestrian -1 -1 -1 370.82 158.75 384.86 194.20 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n131 61 Cyclist -1 -1 -1 294.49 157.35 322.51 218.65 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n131 78 Pedestrian -1 -1 -1 353.04 159.36 371.50 203.86 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n131 68 Pedestrian -1 -1 -1 187.27 153.46 207.45 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n131 77 Pedestrian -1 -1 -1 401.70 160.98 413.21 188.23 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n132 2 Car -1 -1 -1 1095.49 184.20 1220.20 235.39 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n132 4 Car -1 -1 -1 1030.20 184.04 1155.58 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n132 5 Car -1 -1 -1 954.51 183.35 1066.59 231.82 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n132 48 Pedestrian -1 -1 -1 434.29 159.66 473.69 250.41 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n132 8 Car -1 -1 -1 601.70 172.92 637.05 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n132 65 Cyclist -1 -1 -1 309.78 161.39 350.53 236.60 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n132 44 Pedestrian -1 -1 -1 218.94 154.53 235.99 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n132 20 Car -1 -1 -1 598.00 173.25 621.30 192.71 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n132 67 Pedestrian -1 -1 -1 738.01 168.51 755.72 225.23 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n132 61 Cyclist -1 -1 -1 294.69 158.15 323.09 217.34 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n132 75 Pedestrian -1 -1 -1 372.36 159.02 386.54 194.60 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n132 78 Pedestrian -1 -1 -1 355.43 158.56 372.99 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n132 68 Pedestrian -1 -1 -1 190.52 153.33 209.57 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n132 77 Pedestrian -1 -1 -1 401.20 161.19 412.75 188.48 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n133 2 Car -1 -1 -1 1095.68 184.23 1220.07 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n133 4 Car -1 -1 -1 1030.07 184.07 1155.73 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n133 5 Car -1 -1 -1 954.51 183.32 1066.54 231.82 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n133 48 Pedestrian -1 -1 -1 431.66 159.99 468.88 249.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n133 8 Car -1 -1 -1 601.82 172.98 637.04 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n133 65 Cyclist -1 -1 -1 305.33 161.47 349.61 234.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n133 44 Pedestrian -1 -1 -1 218.79 154.68 235.75 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n133 20 Car -1 -1 -1 598.01 173.33 621.30 192.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n133 67 Pedestrian -1 -1 -1 739.93 168.81 757.88 225.04 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n133 61 Cyclist -1 -1 -1 296.38 158.69 325.76 216.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n133 75 Pedestrian -1 -1 -1 372.40 158.82 387.45 193.97 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n133 77 Pedestrian -1 -1 -1 401.14 161.61 412.19 187.92 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n133 68 Pedestrian -1 -1 -1 187.49 153.67 207.08 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n133 78 Pedestrian -1 -1 -1 355.21 158.36 373.47 201.61 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n134 2 Car -1 -1 -1 1095.58 184.20 1220.05 235.37 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n134 4 Car -1 -1 -1 1029.93 184.04 1155.74 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n134 5 Car -1 -1 -1 954.53 183.33 1066.57 231.84 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n134 48 Pedestrian -1 -1 -1 428.21 159.66 462.17 247.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n134 8 Car -1 -1 -1 601.69 172.88 637.03 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n134 44 Pedestrian -1 -1 -1 218.64 154.72 235.70 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n134 67 Pedestrian -1 -1 -1 741.77 168.53 761.24 225.31 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n134 20 Car -1 -1 -1 597.93 173.33 621.43 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n134 61 Cyclist -1 -1 -1 298.05 157.87 326.13 216.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n134 75 Pedestrian -1 -1 -1 372.61 158.42 388.38 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n134 65 Cyclist -1 -1 -1 306.53 161.19 345.94 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n134 68 Pedestrian -1 -1 -1 187.48 153.51 207.08 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n134 77 Pedestrian -1 -1 -1 400.70 161.69 411.88 188.12 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n135 2 Car -1 -1 -1 1095.55 184.16 1220.25 235.18 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n135 5 Car -1 -1 -1 954.59 183.32 1066.53 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n135 4 Car -1 -1 -1 1030.19 184.13 1155.66 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n135 48 Pedestrian -1 -1 -1 421.82 158.67 460.29 248.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n135 8 Car -1 -1 -1 601.61 172.89 637.14 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n135 44 Pedestrian -1 -1 -1 218.71 154.66 235.64 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n135 67 Pedestrian -1 -1 -1 742.92 168.81 767.06 225.05 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n135 20 Car -1 -1 -1 598.01 173.32 621.34 192.69 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n135 75 Pedestrian -1 -1 -1 373.36 158.23 388.92 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n135 68 Pedestrian -1 -1 -1 187.62 153.66 206.69 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n135 77 Pedestrian -1 -1 -1 398.96 160.81 410.67 189.36 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n135 79 Pedestrian -1 -1 -1 309.96 159.10 343.01 229.77 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n136 2 Car -1 -1 -1 1095.61 184.17 1220.27 235.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n136 5 Car -1 -1 -1 954.58 183.25 1066.62 231.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n136 4 Car -1 -1 -1 1030.15 184.07 1155.67 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n136 48 Pedestrian -1 -1 -1 416.42 159.74 458.83 247.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n136 8 Car -1 -1 -1 601.69 172.81 636.99 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n136 67 Pedestrian -1 -1 -1 742.58 169.64 769.13 224.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n136 44 Pedestrian -1 -1 -1 218.96 154.71 235.23 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n136 20 Car -1 -1 -1 597.89 173.24 621.30 192.58 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n136 75 Pedestrian -1 -1 -1 373.71 157.91 388.90 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n136 68 Pedestrian -1 -1 -1 190.49 152.81 210.34 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n136 77 Pedestrian -1 -1 -1 399.10 161.00 410.20 188.80 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n136 80 Pedestrian -1 -1 -1 356.33 158.71 373.51 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n137 2 Car -1 -1 -1 1095.74 184.26 1220.00 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n137 4 Car -1 -1 -1 1029.94 184.02 1155.74 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n137 5 Car -1 -1 -1 954.60 183.39 1066.49 231.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n137 48 Pedestrian -1 -1 -1 410.88 160.74 451.37 246.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n137 8 Car -1 -1 -1 601.80 172.88 636.99 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n137 67 Pedestrian -1 -1 -1 743.56 169.97 772.88 224.86 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n137 44 Pedestrian -1 -1 -1 219.23 154.67 234.83 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n137 20 Car -1 -1 -1 598.08 173.21 621.38 192.68 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n137 75 Pedestrian -1 -1 -1 374.21 157.97 388.65 194.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n137 80 Pedestrian -1 -1 -1 356.79 158.78 373.81 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n137 77 Pedestrian -1 -1 -1 399.53 161.07 409.95 188.35 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n137 81 Cyclist -1 -1 -1 310.93 158.13 343.68 226.34 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n138 2 Car -1 -1 -1 1095.63 184.14 1220.13 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n138 5 Car -1 -1 -1 954.61 183.34 1066.57 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n138 4 Car -1 -1 -1 1029.96 184.01 1155.86 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n138 48 Pedestrian -1 -1 -1 408.16 160.98 445.71 246.12 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n138 67 Pedestrian -1 -1 -1 744.07 169.92 773.31 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n138 8 Car -1 -1 -1 601.80 172.86 636.94 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n138 44 Pedestrian -1 -1 -1 219.29 154.63 234.90 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n138 20 Car -1 -1 -1 598.09 173.19 621.32 192.61 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n138 75 Pedestrian -1 -1 -1 374.46 157.94 388.57 194.16 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n138 77 Pedestrian -1 -1 -1 399.61 161.00 409.84 188.16 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n138 80 Pedestrian -1 -1 -1 356.75 158.86 374.33 199.44 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n138 82 Pedestrian -1 -1 -1 187.92 158.94 205.89 199.25 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n139 2 Car -1 -1 -1 1095.75 184.21 1220.09 235.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n139 5 Car -1 -1 -1 954.60 183.39 1066.52 231.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n139 4 Car -1 -1 -1 1029.95 183.98 1155.84 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n139 48 Pedestrian -1 -1 -1 407.21 159.55 439.99 245.72 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n139 67 Pedestrian -1 -1 -1 746.48 169.33 772.17 226.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n139 8 Car -1 -1 -1 601.69 172.80 636.97 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n139 44 Pedestrian -1 -1 -1 219.05 154.58 235.03 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n139 20 Car -1 -1 -1 597.70 173.23 621.38 192.69 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n139 75 Pedestrian -1 -1 -1 374.59 157.86 388.54 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n139 77 Pedestrian -1 -1 -1 399.55 161.08 409.65 188.21 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n139 80 Pedestrian -1 -1 -1 357.48 158.43 374.49 200.19 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n139 82 Pedestrian -1 -1 -1 185.11 158.52 201.78 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n140 2 Car -1 -1 -1 1095.56 184.17 1220.01 235.39 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n140 4 Car -1 -1 -1 1029.94 183.97 1155.74 232.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n140 48 Pedestrian -1 -1 -1 402.83 159.14 436.22 245.39 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n140 5 Car -1 -1 -1 954.61 183.34 1066.46 231.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n140 67 Pedestrian -1 -1 -1 752.09 168.71 773.51 227.18 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n140 8 Car -1 -1 -1 601.70 172.85 636.95 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n140 44 Pedestrian -1 -1 -1 219.30 154.68 234.91 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n140 20 Car -1 -1 -1 597.65 173.28 621.28 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n140 75 Pedestrian -1 -1 -1 374.07 157.44 388.99 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n140 80 Pedestrian -1 -1 -1 358.16 158.62 374.22 200.56 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n140 77 Pedestrian -1 -1 -1 399.44 160.99 409.33 188.02 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n140 83 Cyclist -1 -1 -1 318.02 158.14 352.83 221.65 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n141 2 Car -1 -1 -1 1095.63 184.22 1220.33 235.38 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n141 4 Car -1 -1 -1 1030.03 184.00 1155.78 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n141 5 Car -1 -1 -1 954.59 183.37 1066.46 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n141 48 Pedestrian -1 -1 -1 397.99 161.13 433.50 244.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n141 67 Pedestrian -1 -1 -1 756.09 168.82 776.12 227.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n141 8 Car -1 -1 -1 601.62 172.80 636.98 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n141 44 Pedestrian -1 -1 -1 219.30 154.64 234.76 197.62 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n141 20 Car -1 -1 -1 597.64 173.31 621.37 192.71 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n141 75 Pedestrian -1 -1 -1 373.78 157.64 389.28 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n141 80 Pedestrian -1 -1 -1 360.63 158.65 376.03 200.95 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n141 83 Cyclist -1 -1 -1 322.00 156.88 354.57 219.28 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n141 84 Cyclist -1 -1 -1 319.12 156.85 342.80 211.55 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n142 2 Car -1 -1 -1 1095.55 184.22 1220.03 235.32 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n142 4 Car -1 -1 -1 1030.02 183.99 1155.76 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n142 5 Car -1 -1 -1 954.51 183.39 1066.59 231.76 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n142 48 Pedestrian -1 -1 -1 394.41 162.35 429.65 244.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n142 8 Car -1 -1 -1 601.53 172.76 637.13 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n142 67 Pedestrian -1 -1 -1 757.81 169.53 777.26 227.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n142 44 Pedestrian -1 -1 -1 219.23 154.77 234.78 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n142 20 Car -1 -1 -1 597.88 173.30 621.48 192.80 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n142 83 Cyclist -1 -1 -1 326.68 158.01 358.80 218.28 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n142 75 Pedestrian -1 -1 -1 376.06 157.98 390.96 192.53 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n142 80 Pedestrian -1 -1 -1 360.76 158.89 376.75 200.81 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n142 84 Cyclist -1 -1 -1 321.55 157.46 347.07 210.99 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n142 85 Pedestrian -1 -1 -1 191.19 154.45 210.08 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n143 2 Car -1 -1 -1 1095.32 184.11 1220.46 235.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n143 5 Car -1 -1 -1 954.60 183.33 1066.51 231.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n143 4 Car -1 -1 -1 1030.25 184.03 1155.68 232.77 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n143 48 Pedestrian -1 -1 -1 393.56 161.96 427.18 244.70 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n143 8 Car -1 -1 -1 601.56 172.86 636.90 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n143 44 Pedestrian -1 -1 -1 219.19 154.83 234.72 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n143 20 Car -1 -1 -1 597.94 173.17 621.37 192.50 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n143 83 Cyclist -1 -1 -1 329.67 158.38 363.28 216.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n143 67 Pedestrian -1 -1 -1 758.41 170.41 777.61 227.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n143 75 Pedestrian -1 -1 -1 376.94 158.06 390.93 192.43 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n143 85 Pedestrian -1 -1 -1 191.01 154.06 209.86 196.04 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n143 80 Pedestrian -1 -1 -1 361.17 159.19 376.87 200.33 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n143 84 Cyclist -1 -1 -1 326.01 157.42 351.59 210.37 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n144 2 Car -1 -1 -1 1095.64 184.24 1220.16 235.40 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n144 5 Car -1 -1 -1 954.50 183.29 1066.63 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n144 4 Car -1 -1 -1 1030.01 183.98 1155.83 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n144 48 Pedestrian -1 -1 -1 390.85 160.62 422.07 243.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n144 8 Car -1 -1 -1 601.72 172.89 636.85 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n144 83 Cyclist -1 -1 -1 334.92 158.49 366.41 216.00 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n144 67 Pedestrian -1 -1 -1 761.25 171.04 778.79 228.21 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n144 44 Pedestrian -1 -1 -1 219.19 154.75 234.70 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n144 20 Car -1 -1 -1 598.09 173.21 621.43 192.58 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n144 80 Pedestrian -1 -1 -1 361.73 159.76 376.67 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n144 85 Pedestrian -1 -1 -1 188.33 155.08 206.28 196.16 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n144 75 Pedestrian -1 -1 -1 377.26 158.05 391.68 192.44 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n144 84 Cyclist -1 -1 -1 330.49 159.06 355.01 207.20 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n145 2 Car -1 -1 -1 1095.47 184.18 1220.45 235.39 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n145 5 Car -1 -1 -1 954.43 183.26 1066.66 231.84 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n145 4 Car -1 -1 -1 1030.07 183.99 1155.81 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n145 48 Pedestrian -1 -1 -1 385.83 160.80 420.71 243.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n145 8 Car -1 -1 -1 601.85 172.84 636.74 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n145 67 Pedestrian -1 -1 -1 762.61 171.14 779.37 228.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n145 44 Pedestrian -1 -1 -1 219.25 154.79 234.67 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n145 20 Car -1 -1 -1 597.99 173.18 621.51 192.68 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n145 83 Cyclist -1 -1 -1 339.01 158.66 370.62 214.73 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n145 85 Pedestrian -1 -1 -1 188.50 154.89 206.25 196.20 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n145 80 Pedestrian -1 -1 -1 362.30 160.46 377.42 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n145 75 Pedestrian -1 -1 -1 377.80 158.47 391.45 192.06 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n146 2 Car -1 -1 -1 1095.68 184.19 1220.19 235.18 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n146 5 Car -1 -1 -1 954.43 183.31 1066.68 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n146 4 Car -1 -1 -1 1030.07 184.02 1155.87 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n146 48 Pedestrian -1 -1 -1 384.06 161.67 421.04 242.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n146 8 Car -1 -1 -1 601.81 172.93 636.81 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n146 67 Pedestrian -1 -1 -1 762.80 170.64 780.22 228.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n146 44 Pedestrian -1 -1 -1 219.41 154.82 234.66 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n146 20 Car -1 -1 -1 597.92 173.17 621.45 192.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n146 83 Cyclist -1 -1 -1 345.42 159.16 375.95 214.60 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n146 85 Pedestrian -1 -1 -1 188.59 154.95 206.11 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n146 75 Pedestrian -1 -1 -1 378.59 158.63 391.66 191.82 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n146 80 Pedestrian -1 -1 -1 362.68 160.66 377.65 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n146 86 Pedestrian -1 -1 -1 320.51 157.20 332.65 187.55 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n147 2 Car -1 -1 -1 1095.49 184.22 1220.38 235.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n147 4 Car -1 -1 -1 1029.92 183.95 1155.83 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n147 5 Car -1 -1 -1 954.43 183.39 1066.66 231.75 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n147 48 Pedestrian -1 -1 -1 381.88 162.52 417.65 242.36 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n147 8 Car -1 -1 -1 601.66 172.94 636.91 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n147 83 Cyclist -1 -1 -1 350.10 159.56 380.78 214.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n147 44 Pedestrian -1 -1 -1 219.39 154.91 234.67 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n147 67 Pedestrian -1 -1 -1 762.85 170.65 780.46 228.34 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n147 20 Car -1 -1 -1 597.80 173.19 621.43 192.68 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n147 85 Pedestrian -1 -1 -1 191.75 154.02 209.28 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n147 86 Pedestrian -1 -1 -1 320.56 157.02 332.95 187.65 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n147 75 Pedestrian -1 -1 -1 379.51 158.98 391.51 191.89 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n147 87 Cyclist -1 -1 -1 344.21 158.37 369.10 206.19 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n148 2 Car -1 -1 -1 1095.44 184.24 1220.37 235.28 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n148 5 Car -1 -1 -1 954.56 183.37 1066.52 231.71 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n148 4 Car -1 -1 -1 1029.96 183.98 1155.83 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n148 48 Pedestrian -1 -1 -1 380.21 161.63 413.38 240.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n148 8 Car -1 -1 -1 601.65 172.97 636.91 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n148 44 Pedestrian -1 -1 -1 219.37 154.84 234.94 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n148 67 Pedestrian -1 -1 -1 764.35 170.13 782.64 229.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n148 20 Car -1 -1 -1 597.89 173.33 621.37 192.69 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n148 83 Cyclist -1 -1 -1 355.79 160.13 382.49 212.17 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n148 87 Cyclist -1 -1 -1 348.65 159.86 373.24 205.07 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n148 85 Pedestrian -1 -1 -1 191.98 154.22 209.23 196.40 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n148 86 Pedestrian -1 -1 -1 320.24 157.11 332.82 187.37 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n148 75 Pedestrian -1 -1 -1 380.96 159.23 393.81 191.19 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n149 2 Car -1 -1 -1 1095.52 184.29 1220.43 235.23 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n149 5 Car -1 -1 -1 954.46 183.32 1066.62 231.75 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n149 4 Car -1 -1 -1 1030.11 184.02 1155.79 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n149 8 Car -1 -1 -1 601.64 172.91 636.90 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n149 48 Pedestrian -1 -1 -1 379.19 160.46 411.34 239.47 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n149 83 Cyclist -1 -1 -1 358.23 160.03 386.55 212.41 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n149 67 Pedestrian -1 -1 -1 764.75 169.82 783.27 228.95 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n149 44 Pedestrian -1 -1 -1 219.50 154.79 234.95 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n149 20 Car -1 -1 -1 597.88 173.37 621.38 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n149 86 Pedestrian -1 -1 -1 320.08 157.21 332.73 187.37 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n149 85 Pedestrian -1 -1 -1 192.03 154.59 209.06 196.23 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n150 2 Car -1 -1 -1 1095.58 184.32 1220.25 235.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n150 5 Car -1 -1 -1 954.62 183.38 1066.51 231.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n150 4 Car -1 -1 -1 1029.97 183.97 1155.78 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n150 8 Car -1 -1 -1 601.78 172.99 636.77 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n150 48 Pedestrian -1 -1 -1 375.81 160.52 407.87 239.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n150 44 Pedestrian -1 -1 -1 219.72 154.73 234.93 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n150 67 Pedestrian -1 -1 -1 764.96 170.22 784.43 229.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n150 20 Car -1 -1 -1 597.89 173.39 621.29 192.88 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n150 86 Pedestrian -1 -1 -1 320.14 157.20 332.84 187.58 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n150 83 Cyclist -1 -1 -1 363.83 160.03 388.31 212.45 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n150 85 Pedestrian -1 -1 -1 192.29 154.80 208.71 196.21 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n151 2 Car -1 -1 -1 1095.64 184.24 1220.17 235.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n151 5 Car -1 -1 -1 954.77 183.40 1066.39 231.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n151 4 Car -1 -1 -1 1029.88 183.97 1155.89 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n151 48 Pedestrian -1 -1 -1 373.71 162.51 404.88 239.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n151 8 Car -1 -1 -1 601.57 172.94 636.99 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n151 44 Pedestrian -1 -1 -1 219.86 154.75 234.95 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n151 67 Pedestrian -1 -1 -1 765.39 169.98 785.60 229.40 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n151 20 Car -1 -1 -1 597.83 173.36 621.45 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n151 86 Pedestrian -1 -1 -1 320.27 157.36 332.81 187.50 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n151 83 Cyclist -1 -1 -1 366.81 160.29 388.61 207.42 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n151 85 Pedestrian -1 -1 -1 193.00 160.08 207.83 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n152 2 Car -1 -1 -1 1095.64 184.31 1220.15 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n152 5 Car -1 -1 -1 954.66 183.38 1066.47 231.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n152 4 Car -1 -1 -1 1029.83 183.96 1155.92 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n152 48 Pedestrian -1 -1 -1 373.31 161.84 403.26 239.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n152 8 Car -1 -1 -1 601.62 172.98 636.81 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n152 44 Pedestrian -1 -1 -1 220.02 154.93 234.80 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n152 67 Pedestrian -1 -1 -1 767.07 169.96 788.03 229.69 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n152 20 Car -1 -1 -1 597.94 173.45 621.38 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n152 86 Pedestrian -1 -1 -1 320.40 157.34 333.05 187.54 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n152 85 Pedestrian -1 -1 -1 192.97 160.43 207.83 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n153 2 Car -1 -1 -1 1095.65 184.28 1220.09 235.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n153 5 Car -1 -1 -1 954.64 183.44 1066.56 231.68 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n153 4 Car -1 -1 -1 1029.69 183.98 1156.09 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n153 48 Pedestrian -1 -1 -1 373.30 160.23 401.43 237.39 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n153 8 Car -1 -1 -1 601.57 172.94 636.90 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n153 67 Pedestrian -1 -1 -1 767.36 169.81 788.86 229.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n153 44 Pedestrian -1 -1 -1 219.86 154.83 235.08 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n153 86 Pedestrian -1 -1 -1 320.27 157.16 333.08 187.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n153 20 Car -1 -1 -1 597.73 173.43 621.47 192.88 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n153 85 Pedestrian -1 -1 -1 193.00 160.64 207.70 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n153 88 Pedestrian -1 -1 -1 375.11 162.37 393.40 203.73 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n154 2 Car -1 -1 -1 1095.63 184.20 1220.18 235.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n154 5 Car -1 -1 -1 954.59 183.40 1066.57 231.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n154 4 Car -1 -1 -1 1029.79 183.96 1156.02 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n154 48 Pedestrian -1 -1 -1 372.25 160.03 402.16 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n154 8 Car -1 -1 -1 601.64 173.10 636.77 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n154 67 Pedestrian -1 -1 -1 768.47 169.52 788.97 229.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n154 44 Pedestrian -1 -1 -1 220.26 154.79 235.15 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n154 86 Pedestrian -1 -1 -1 320.36 157.21 333.09 187.59 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n154 20 Car -1 -1 -1 597.82 173.41 621.58 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n154 85 Pedestrian -1 -1 -1 192.99 160.98 207.74 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n154 89 Pedestrian -1 -1 -1 200.15 154.56 217.47 195.98 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n155 2 Car -1 -1 -1 1095.70 184.27 1220.15 235.23 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n155 5 Car -1 -1 -1 954.71 183.42 1066.41 231.63 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n155 4 Car -1 -1 -1 1029.96 184.05 1155.92 232.66 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n155 48 Pedestrian -1 -1 -1 369.29 160.32 401.97 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n155 8 Car -1 -1 -1 601.54 173.04 636.95 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n155 67 Pedestrian -1 -1 -1 769.02 169.33 789.30 230.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n155 44 Pedestrian -1 -1 -1 220.37 154.79 235.27 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n155 20 Car -1 -1 -1 597.90 173.43 621.60 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n155 86 Pedestrian -1 -1 -1 320.50 157.30 333.14 187.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n155 89 Pedestrian -1 -1 -1 199.39 153.84 217.56 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n155 85 Pedestrian -1 -1 -1 185.20 159.09 200.86 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n155 90 Pedestrian -1 -1 -1 382.60 162.48 402.60 203.50 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n156 2 Car -1 -1 -1 1095.64 184.21 1220.21 235.07 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n156 5 Car -1 -1 -1 954.68 183.36 1066.55 231.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n156 4 Car -1 -1 -1 1029.92 184.02 1155.90 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n156 8 Car -1 -1 -1 601.55 172.97 637.03 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n156 48 Pedestrian -1 -1 -1 368.10 161.61 402.98 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n156 67 Pedestrian -1 -1 -1 769.29 169.17 789.33 230.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n156 44 Pedestrian -1 -1 -1 220.21 154.88 235.10 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n156 20 Car -1 -1 -1 597.92 173.37 621.71 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n156 86 Pedestrian -1 -1 -1 320.83 157.26 333.29 187.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n156 89 Pedestrian -1 -1 -1 199.18 153.89 217.72 196.77 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n156 90 Pedestrian -1 -1 -1 386.85 162.56 404.23 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n156 85 Pedestrian -1 -1 -1 185.09 159.26 200.65 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n157 2 Car -1 -1 -1 1095.85 184.32 1220.06 235.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n157 5 Car -1 -1 -1 954.67 183.44 1066.55 231.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n157 4 Car -1 -1 -1 1029.82 184.02 1156.05 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n157 48 Pedestrian -1 -1 -1 368.93 162.16 400.87 234.93 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n157 8 Car -1 -1 -1 601.80 173.16 636.90 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n157 44 Pedestrian -1 -1 -1 220.10 154.85 234.98 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n157 86 Pedestrian -1 -1 -1 320.43 157.33 333.34 187.70 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n157 20 Car -1 -1 -1 597.90 173.48 621.53 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n157 67 Pedestrian -1 -1 -1 770.90 169.82 791.27 231.80 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n157 90 Pedestrian -1 -1 -1 395.53 162.07 409.54 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n157 89 Pedestrian -1 -1 -1 199.27 154.02 217.89 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n157 85 Pedestrian -1 -1 -1 185.07 159.20 200.62 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n158 2 Car -1 -1 -1 1095.72 184.25 1220.17 235.03 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n158 5 Car -1 -1 -1 954.62 183.39 1066.57 231.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n158 48 Pedestrian -1 -1 -1 370.25 161.80 398.05 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n158 4 Car -1 -1 -1 1029.86 184.06 1156.09 232.65 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n158 8 Car -1 -1 -1 601.88 173.05 636.86 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n158 67 Pedestrian -1 -1 -1 771.15 169.79 791.33 231.92 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n158 44 Pedestrian -1 -1 -1 220.05 154.97 234.93 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n158 86 Pedestrian -1 -1 -1 320.40 157.41 333.24 187.69 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n158 20 Car -1 -1 -1 597.96 173.57 621.43 192.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n158 89 Pedestrian -1 -1 -1 199.55 154.20 217.82 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n158 90 Pedestrian -1 -1 -1 399.11 161.70 413.23 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n158 85 Pedestrian -1 -1 -1 185.06 159.17 200.51 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n159 2 Car -1 -1 -1 1095.68 184.29 1220.13 235.11 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n159 5 Car -1 -1 -1 954.77 183.45 1066.45 231.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n159 4 Car -1 -1 -1 1029.72 184.04 1156.04 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n159 48 Pedestrian -1 -1 -1 370.59 161.54 396.35 232.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n159 8 Car -1 -1 -1 601.71 173.00 637.10 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n159 67 Pedestrian -1 -1 -1 770.85 169.69 791.94 231.98 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n159 86 Pedestrian -1 -1 -1 320.38 157.29 333.27 187.81 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n159 44 Pedestrian -1 -1 -1 220.11 155.00 234.87 197.41 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n159 90 Pedestrian -1 -1 -1 399.65 161.03 414.88 200.39 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n159 20 Car -1 -1 -1 597.86 173.63 621.37 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n159 89 Pedestrian -1 -1 -1 199.47 154.65 217.79 196.25 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n159 85 Pedestrian -1 -1 -1 185.08 159.27 200.40 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n160 2 Car -1 -1 -1 1095.58 184.31 1220.12 235.13 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n160 5 Car -1 -1 -1 954.70 183.38 1066.54 231.59 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n160 4 Car -1 -1 -1 1029.85 184.10 1156.00 232.63 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n160 8 Car -1 -1 -1 601.80 172.96 636.98 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n160 48 Pedestrian -1 -1 -1 368.50 161.62 394.33 232.50 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n160 67 Pedestrian -1 -1 -1 771.10 169.20 792.48 232.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n160 44 Pedestrian -1 -1 -1 219.78 154.93 234.69 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n160 86 Pedestrian -1 -1 -1 320.37 157.37 333.45 187.83 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n160 20 Car -1 -1 -1 597.95 173.62 621.37 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n160 90 Pedestrian -1 -1 -1 401.11 161.36 415.87 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n160 89 Pedestrian -1 -1 -1 199.50 154.84 217.74 196.09 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n160 85 Pedestrian -1 -1 -1 184.97 159.37 200.35 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n160 91 Pedestrian -1 -1 -1 192.49 160.64 208.22 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n161 2 Car -1 -1 -1 1095.55 184.27 1220.25 234.99 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n161 5 Car -1 -1 -1 954.76 183.49 1066.49 231.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n161 4 Car -1 -1 -1 1032.87 183.87 1157.01 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n161 48 Pedestrian -1 -1 -1 368.74 162.27 393.91 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n161 8 Car -1 -1 -1 601.89 173.14 636.94 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n161 67 Pedestrian -1 -1 -1 770.58 169.29 793.15 232.42 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n161 86 Pedestrian -1 -1 -1 320.32 157.33 333.49 187.90 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n161 44 Pedestrian -1 -1 -1 219.50 154.95 234.70 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n161 90 Pedestrian -1 -1 -1 403.88 161.49 418.72 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n161 20 Car -1 -1 -1 597.82 173.67 621.46 193.05 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n161 89 Pedestrian -1 -1 -1 199.73 154.99 217.49 195.95 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n161 85 Pedestrian -1 -1 -1 184.81 159.18 200.63 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n161 91 Pedestrian -1 -1 -1 192.36 160.52 208.32 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n161 92 Pedestrian -1 -1 -1 391.85 161.45 406.54 200.05 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n162 2 Car -1 -1 -1 1095.53 184.32 1220.12 235.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n162 5 Car -1 -1 -1 954.75 183.45 1066.44 231.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n162 4 Car -1 -1 -1 1029.87 184.03 1155.97 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n162 48 Pedestrian -1 -1 -1 367.78 162.66 392.73 231.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n162 67 Pedestrian -1 -1 -1 770.95 169.41 793.49 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n162 8 Car -1 -1 -1 601.94 173.22 636.92 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n162 44 Pedestrian -1 -1 -1 219.68 155.05 234.50 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n162 92 Pedestrian -1 -1 -1 393.51 161.77 407.47 202.05 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n162 86 Pedestrian -1 -1 -1 320.33 157.42 333.50 187.89 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n162 90 Pedestrian -1 -1 -1 407.39 162.39 420.99 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n162 20 Car -1 -1 -1 597.58 173.91 621.31 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n162 85 Pedestrian -1 -1 -1 184.71 158.90 200.90 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n162 91 Pedestrian -1 -1 -1 192.17 160.64 208.39 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n163 2 Car -1 -1 -1 1095.63 184.34 1220.12 235.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n163 5 Car -1 -1 -1 954.74 183.41 1066.52 231.59 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n163 4 Car -1 -1 -1 1029.91 184.07 1155.91 232.70 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n163 8 Car -1 -1 -1 601.90 173.06 636.82 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n163 67 Pedestrian -1 -1 -1 771.67 169.68 794.05 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n163 90 Pedestrian -1 -1 -1 408.24 163.19 422.53 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n163 48 Pedestrian -1 -1 -1 365.19 162.43 390.82 231.49 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n163 44 Pedestrian -1 -1 -1 219.70 155.12 234.61 197.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n163 86 Pedestrian -1 -1 -1 320.26 157.50 333.49 187.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n163 20 Car -1 -1 -1 597.55 173.86 621.28 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n163 92 Pedestrian -1 -1 -1 394.99 161.89 409.40 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n163 91 Pedestrian -1 -1 -1 192.00 160.43 208.45 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n163 85 Pedestrian -1 -1 -1 184.70 158.75 201.04 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n164 2 Car -1 -1 -1 1095.55 184.31 1220.24 235.13 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n164 5 Car -1 -1 -1 954.67 183.49 1066.53 231.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n164 4 Car -1 -1 -1 1029.84 184.11 1156.01 232.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n164 8 Car -1 -1 -1 601.96 173.01 636.80 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n164 48 Pedestrian -1 -1 -1 365.09 162.03 390.83 229.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n164 67 Pedestrian -1 -1 -1 773.40 169.77 796.24 234.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n164 86 Pedestrian -1 -1 -1 320.42 157.42 333.55 187.98 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n164 44 Pedestrian -1 -1 -1 219.64 155.16 234.64 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n164 90 Pedestrian -1 -1 -1 410.58 162.60 424.72 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n164 92 Pedestrian -1 -1 -1 396.67 161.99 410.92 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n164 20 Car -1 -1 -1 597.62 173.70 621.34 192.92 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n164 91 Pedestrian -1 -1 -1 191.83 160.49 208.67 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n165 2 Car -1 -1 -1 1095.47 184.31 1220.35 235.11 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n165 5 Car -1 -1 -1 954.67 183.48 1066.53 231.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n165 4 Car -1 -1 -1 1029.85 184.08 1155.98 232.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n165 67 Pedestrian -1 -1 -1 773.89 169.72 796.71 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n165 8 Car -1 -1 -1 601.85 173.05 636.96 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n165 48 Pedestrian -1 -1 -1 364.17 162.38 391.08 229.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n165 90 Pedestrian -1 -1 -1 411.97 162.79 426.11 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n165 86 Pedestrian -1 -1 -1 320.57 157.36 333.40 188.03 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n165 44 Pedestrian -1 -1 -1 219.59 155.15 234.52 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n165 20 Car -1 -1 -1 597.81 173.60 621.32 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n165 92 Pedestrian -1 -1 -1 398.10 161.50 411.75 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n165 91 Pedestrian -1 -1 -1 191.90 160.47 208.54 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n165 93 Pedestrian -1 -1 -1 184.59 158.73 201.37 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n165 94 Pedestrian -1 -1 -1 203.73 160.72 220.48 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n166 2 Car -1 -1 -1 1095.67 184.42 1220.15 235.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n166 5 Car -1 -1 -1 954.74 183.48 1066.51 231.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n166 4 Car -1 -1 -1 1029.74 184.08 1156.04 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n166 67 Pedestrian -1 -1 -1 774.48 169.26 797.27 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n166 8 Car -1 -1 -1 602.00 172.99 636.94 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n166 48 Pedestrian -1 -1 -1 363.23 162.88 391.02 228.78 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n166 90 Pedestrian -1 -1 -1 412.89 162.83 426.78 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n166 86 Pedestrian -1 -1 -1 320.61 157.35 333.38 187.98 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n166 44 Pedestrian -1 -1 -1 219.48 155.04 234.57 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n166 92 Pedestrian -1 -1 -1 399.43 161.81 413.68 199.86 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n166 20 Car -1 -1 -1 597.56 173.52 621.34 192.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n166 94 Pedestrian -1 -1 -1 203.76 160.95 220.39 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n166 93 Pedestrian -1 -1 -1 184.72 158.84 201.15 198.65 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n167 2 Car -1 -1 -1 1095.90 184.49 1219.99 235.07 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n167 5 Car -1 -1 -1 954.67 183.50 1066.46 231.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n167 4 Car -1 -1 -1 1029.75 184.10 1156.10 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n167 67 Pedestrian -1 -1 -1 774.34 168.57 797.07 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n167 8 Car -1 -1 -1 601.85 172.95 636.94 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n167 48 Pedestrian -1 -1 -1 361.57 163.18 390.37 227.38 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n167 92 Pedestrian -1 -1 -1 400.83 161.77 414.79 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n167 86 Pedestrian -1 -1 -1 320.52 157.32 333.36 187.99 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n167 44 Pedestrian -1 -1 -1 219.42 155.22 234.72 197.02 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n167 90 Pedestrian -1 -1 -1 413.09 163.22 427.20 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n167 20 Car -1 -1 -1 597.69 173.34 621.44 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n167 93 Pedestrian -1 -1 -1 184.66 158.91 201.09 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n167 94 Pedestrian -1 -1 -1 200.19 155.59 217.51 195.60 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n168 2 Car -1 -1 -1 1095.81 184.42 1219.98 235.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n168 5 Car -1 -1 -1 954.78 183.45 1066.50 231.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n168 4 Car -1 -1 -1 1029.93 184.08 1155.90 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n168 67 Pedestrian -1 -1 -1 774.89 168.06 797.66 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n168 8 Car -1 -1 -1 601.84 173.00 637.03 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n168 90 Pedestrian -1 -1 -1 415.41 163.30 430.01 194.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n168 48 Pedestrian -1 -1 -1 358.58 161.82 387.73 227.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n168 92 Pedestrian -1 -1 -1 401.51 161.26 415.65 199.45 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n168 86 Pedestrian -1 -1 -1 320.38 157.38 333.46 188.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n168 44 Pedestrian -1 -1 -1 219.46 155.28 234.66 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n168 20 Car -1 -1 -1 597.85 173.52 621.50 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n168 93 Pedestrian -1 -1 -1 184.82 159.09 200.76 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n168 94 Pedestrian -1 -1 -1 199.99 155.34 217.70 195.85 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n169 2 Car -1 -1 -1 1095.61 184.34 1220.24 235.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n169 5 Car -1 -1 -1 954.77 183.40 1066.46 231.61 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n169 4 Car -1 -1 -1 1029.75 184.04 1155.96 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n169 8 Car -1 -1 -1 601.86 172.89 636.99 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n169 92 Pedestrian -1 -1 -1 403.16 161.86 416.97 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n169 67 Pedestrian -1 -1 -1 775.46 167.90 798.12 234.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n169 86 Pedestrian -1 -1 -1 320.44 157.35 333.42 188.02 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n169 44 Pedestrian -1 -1 -1 219.62 155.34 234.59 197.22 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n169 20 Car -1 -1 -1 597.75 173.52 621.35 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n169 90 Pedestrian -1 -1 -1 417.32 163.45 430.49 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n169 48 Pedestrian -1 -1 -1 358.04 161.27 385.71 227.02 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n169 93 Pedestrian -1 -1 -1 185.17 159.84 200.53 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n169 94 Pedestrian -1 -1 -1 199.89 155.18 217.65 195.93 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n170 2 Car -1 -1 -1 1095.82 184.45 1220.01 235.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n170 5 Car -1 -1 -1 954.78 183.41 1066.41 231.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n170 4 Car -1 -1 -1 1029.72 183.97 1156.05 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n170 92 Pedestrian -1 -1 -1 403.97 162.04 417.62 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n170 67 Pedestrian -1 -1 -1 778.32 167.86 800.06 234.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n170 8 Car -1 -1 -1 602.06 172.96 636.91 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n170 44 Pedestrian -1 -1 -1 219.71 155.29 234.62 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n170 86 Pedestrian -1 -1 -1 320.30 157.36 333.46 188.08 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n170 90 Pedestrian -1 -1 -1 419.66 163.50 432.46 192.81 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n170 48 Pedestrian -1 -1 -1 355.15 161.33 384.77 228.13 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n170 20 Car -1 -1 -1 597.85 173.59 621.32 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n170 93 Pedestrian -1 -1 -1 185.34 160.09 200.37 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n170 94 Pedestrian -1 -1 -1 199.71 155.00 217.82 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n171 2 Car -1 -1 -1 1095.66 184.37 1220.32 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n171 5 Car -1 -1 -1 954.89 183.32 1066.43 231.67 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n171 4 Car -1 -1 -1 1029.90 183.99 1155.94 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n171 67 Pedestrian -1 -1 -1 778.82 168.08 800.72 235.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n171 92 Pedestrian -1 -1 -1 404.94 161.89 418.79 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n171 8 Car -1 -1 -1 602.07 173.01 636.83 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n171 48 Pedestrian -1 -1 -1 354.31 161.59 382.42 227.77 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n171 44 Pedestrian -1 -1 -1 219.70 155.01 234.78 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n171 86 Pedestrian -1 -1 -1 320.21 157.27 333.41 188.10 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n171 90 Pedestrian -1 -1 -1 420.59 163.05 434.52 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n171 20 Car -1 -1 -1 597.94 173.56 621.29 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n171 93 Pedestrian -1 -1 -1 185.40 160.21 200.62 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n171 94 Pedestrian -1 -1 -1 199.22 154.64 218.27 196.22 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n172 2 Car -1 -1 -1 1095.98 184.41 1219.89 235.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n172 5 Car -1 -1 -1 954.84 183.41 1066.42 231.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n172 4 Car -1 -1 -1 1029.91 184.00 1155.82 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n172 67 Pedestrian -1 -1 -1 779.84 167.91 801.44 235.24 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n172 8 Car -1 -1 -1 601.95 172.99 636.66 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n172 92 Pedestrian -1 -1 -1 407.17 162.16 420.38 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n172 44 Pedestrian -1 -1 -1 219.68 154.86 234.78 197.41 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n172 48 Pedestrian -1 -1 -1 352.17 161.52 380.23 228.05 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n172 86 Pedestrian -1 -1 -1 320.31 157.22 333.40 188.08 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n172 90 Pedestrian -1 -1 -1 422.94 162.82 436.17 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n172 20 Car -1 -1 -1 597.68 173.61 621.30 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n172 93 Pedestrian -1 -1 -1 185.49 160.24 200.62 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n172 94 Pedestrian -1 -1 -1 199.24 154.62 218.37 196.28 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0021.txt",
    "content": "0 1 Car -1 -1 -1 1095.11 184.42 1220.57 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n0 2 Car -1 -1 -1 953.84 182.53 1067.92 232.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n0 3 Car -1 -1 -1 1031.56 183.09 1158.39 234.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n0 4 Pedestrian -1 -1 -1 739.03 161.48 802.75 297.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n0 5 Pedestrian -1 -1 -1 87.88 151.67 131.41 247.58 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n0 6 Car -1 -1 -1 601.83 172.66 636.67 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n0 7 Pedestrian -1 -1 -1 220.68 155.17 234.79 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n0 8 Pedestrian -1 -1 -1 351.57 158.50 370.51 202.01 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n0 9 Pedestrian -1 -1 -1 193.09 154.99 208.65 196.25 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n0 10 Car -1 -1 -1 598.63 173.75 621.13 192.43 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n1 1 Car -1 -1 -1 1094.57 184.25 1221.47 236.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 2 Car -1 -1 -1 953.33 182.55 1068.19 232.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 3 Car -1 -1 -1 1031.57 182.99 1158.33 234.25 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n1 4 Pedestrian -1 -1 -1 732.64 160.12 785.98 297.12 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n1 5 Pedestrian -1 -1 -1 98.08 150.84 136.65 248.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n1 6 Car -1 -1 -1 601.66 172.65 636.89 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n1 7 Pedestrian -1 -1 -1 220.59 155.06 234.93 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n1 9 Pedestrian -1 -1 -1 193.22 155.16 208.32 196.39 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n1 8 Pedestrian -1 -1 -1 351.42 158.80 370.56 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n1 10 Car -1 -1 -1 598.98 173.40 621.28 192.97 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n2 1 Car -1 -1 -1 1094.98 184.37 1221.08 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n2 2 Car -1 -1 -1 953.81 182.87 1067.51 232.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n2 3 Car -1 -1 -1 1028.80 183.60 1157.06 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n2 4 Pedestrian -1 -1 -1 717.09 160.09 771.33 295.90 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n2 5 Pedestrian -1 -1 -1 105.35 151.82 144.55 247.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n2 6 Car -1 -1 -1 601.50 172.57 637.08 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n2 7 Pedestrian -1 -1 -1 220.95 155.38 234.92 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n2 9 Pedestrian -1 -1 -1 193.34 155.39 208.18 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n2 10 Car -1 -1 -1 598.41 173.29 621.45 192.83 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n2 8 Pedestrian -1 -1 -1 352.10 159.75 370.99 201.74 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n3 1 Car -1 -1 -1 1094.75 184.42 1221.16 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n3 2 Car -1 -1 -1 953.72 183.05 1067.67 232.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n3 3 Car -1 -1 -1 1028.91 183.55 1156.87 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n3 5 Pedestrian -1 -1 -1 105.30 151.16 159.79 246.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n3 4 Pedestrian -1 -1 -1 698.78 160.61 766.70 295.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n3 6 Car -1 -1 -1 601.73 172.73 637.04 203.46 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n3 7 Pedestrian -1 -1 -1 220.90 155.46 234.84 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n3 8 Pedestrian -1 -1 -1 352.53 159.88 371.35 200.84 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n3 9 Pedestrian -1 -1 -1 193.23 155.32 208.10 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n3 10 Car -1 -1 -1 598.88 173.34 621.52 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n4 1 Car -1 -1 -1 1094.22 184.29 1221.43 236.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n4 2 Car -1 -1 -1 953.68 183.09 1067.59 232.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n4 3 Car -1 -1 -1 1028.69 183.38 1157.19 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n4 4 Pedestrian -1 -1 -1 692.84 161.92 757.65 295.28 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n4 5 Pedestrian -1 -1 -1 110.06 151.10 167.53 247.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 6 Car -1 -1 -1 602.47 172.70 637.40 203.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n4 7 Pedestrian -1 -1 -1 220.70 155.46 234.79 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n4 8 Pedestrian -1 -1 -1 352.87 159.98 371.50 200.51 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n4 9 Pedestrian -1 -1 -1 193.24 155.28 208.09 196.11 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n4 10 Car -1 -1 -1 598.98 173.46 621.16 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n5 1 Car -1 -1 -1 1094.57 184.33 1221.25 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n5 2 Car -1 -1 -1 953.62 182.99 1067.65 232.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n5 3 Car -1 -1 -1 1028.63 183.19 1157.17 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n5 5 Pedestrian -1 -1 -1 113.77 151.64 173.13 246.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n5 4 Pedestrian -1 -1 -1 689.65 161.36 743.40 294.80 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n5 6 Car -1 -1 -1 601.75 172.52 637.27 203.29 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n5 7 Pedestrian -1 -1 -1 220.60 155.49 234.87 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n5 8 Pedestrian -1 -1 -1 353.01 160.05 371.72 200.14 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n5 9 Pedestrian -1 -1 -1 193.15 155.56 208.26 196.00 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n5 10 Car -1 -1 -1 599.11 173.42 621.26 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n5 11 Pedestrian -1 -1 -1 185.65 159.02 200.83 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n5 12 Pedestrian -1 -1 -1 203.39 155.51 220.29 196.75 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n6 1 Car -1 -1 -1 1094.66 184.40 1221.05 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n6 2 Car -1 -1 -1 953.62 182.88 1067.67 232.24 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n6 3 Car -1 -1 -1 1028.88 183.32 1156.90 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n6 5 Pedestrian -1 -1 -1 126.88 150.77 174.91 246.18 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n6 4 Pedestrian -1 -1 -1 679.14 160.15 730.71 292.64 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n6 6 Car -1 -1 -1 602.33 172.50 637.63 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n6 7 Pedestrian -1 -1 -1 220.74 155.64 234.79 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n6 8 Pedestrian -1 -1 -1 353.17 160.27 371.64 200.18 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n6 10 Car -1 -1 -1 599.25 173.32 620.98 192.77 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n6 11 Pedestrian -1 -1 -1 185.46 159.11 200.35 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n6 9 Pedestrian -1 -1 -1 193.18 155.94 208.27 195.79 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n6 12 Pedestrian -1 -1 -1 203.58 159.40 220.38 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n7 1 Car -1 -1 -1 1094.58 184.48 1221.15 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n7 2 Car -1 -1 -1 953.37 182.91 1067.95 232.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n7 3 Car -1 -1 -1 1028.90 183.46 1156.93 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n7 4 Pedestrian -1 -1 -1 661.27 159.66 720.49 291.39 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n7 5 Pedestrian -1 -1 -1 139.83 151.20 178.39 247.26 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n7 6 Car -1 -1 -1 602.43 172.53 637.50 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n7 7 Pedestrian -1 -1 -1 220.95 155.82 234.86 197.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n7 10 Car -1 -1 -1 598.93 173.38 621.34 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n7 8 Pedestrian -1 -1 -1 353.79 160.04 371.52 200.09 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n7 9 Pedestrian -1 -1 -1 193.79 156.40 208.18 195.40 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n7 11 Pedestrian -1 -1 -1 185.92 159.26 200.31 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n7 12 Pedestrian -1 -1 -1 203.99 159.68 220.31 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n7 13 Pedestrian -1 -1 -1 176.86 153.98 192.69 195.97 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n7 14 Pedestrian -1 -1 -1 848.06 162.91 877.36 246.82 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n8 1 Car -1 -1 -1 1094.73 184.42 1220.91 236.28 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n8 2 Car -1 -1 -1 953.36 182.87 1067.81 232.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n8 3 Car -1 -1 -1 1028.85 183.42 1156.97 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n8 4 Pedestrian -1 -1 -1 646.44 160.11 712.31 289.92 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n8 5 Pedestrian -1 -1 -1 146.52 152.06 187.12 247.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n8 6 Car -1 -1 -1 602.42 172.68 637.56 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n8 7 Pedestrian -1 -1 -1 221.04 155.77 235.01 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n8 8 Pedestrian -1 -1 -1 354.12 159.74 371.37 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n8 13 Pedestrian -1 -1 -1 172.07 153.52 192.51 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n8 10 Car -1 -1 -1 599.05 173.27 621.52 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n8 14 Pedestrian -1 -1 -1 848.22 163.13 877.27 246.56 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n8 11 Pedestrian -1 -1 -1 186.01 159.46 200.31 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n8 9 Pedestrian -1 -1 -1 193.90 156.63 207.97 195.35 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n8 12 Pedestrian -1 -1 -1 203.84 159.48 220.65 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n9 1 Car -1 -1 -1 1094.68 184.38 1221.00 236.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n9 3 Car -1 -1 -1 1028.87 183.30 1156.86 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n9 2 Car -1 -1 -1 953.23 182.76 1067.82 232.37 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n9 4 Pedestrian -1 -1 -1 640.89 160.63 702.97 289.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n9 5 Pedestrian -1 -1 -1 148.37 158.23 200.88 247.14 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n9 6 Car -1 -1 -1 601.70 172.69 637.27 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n9 13 Pedestrian -1 -1 -1 176.30 153.75 193.82 196.89 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n9 8 Pedestrian -1 -1 -1 355.93 160.17 372.45 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n9 7 Pedestrian -1 -1 -1 221.21 155.84 234.95 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n9 9 Pedestrian -1 -1 -1 193.47 161.43 206.71 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n9 10 Car -1 -1 -1 598.71 173.38 621.43 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n9 14 Pedestrian -1 -1 -1 848.47 163.16 877.24 246.64 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n9 12 Pedestrian -1 -1 -1 203.99 159.77 220.17 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n10 1 Car -1 -1 -1 1094.74 184.47 1221.07 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n10 3 Car -1 -1 -1 1028.35 183.28 1157.27 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n10 4 Pedestrian -1 -1 -1 637.61 162.03 690.47 288.03 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 2 Car -1 -1 -1 953.19 182.76 1067.81 232.36 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 5 Pedestrian -1 -1 -1 153.73 156.98 209.11 248.45 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n10 6 Car -1 -1 -1 601.97 172.76 636.96 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n10 13 Pedestrian -1 -1 -1 185.95 154.96 200.79 196.70 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n10 7 Pedestrian -1 -1 -1 221.37 155.85 234.99 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n10 8 Pedestrian -1 -1 -1 355.40 160.09 373.39 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n10 12 Pedestrian -1 -1 -1 204.52 160.51 219.89 196.99 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n10 10 Car -1 -1 -1 599.00 173.48 621.57 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n10 14 Pedestrian -1 -1 -1 848.82 163.25 877.03 246.70 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n11 1 Car -1 -1 -1 1094.80 184.46 1220.99 236.24 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n11 3 Car -1 -1 -1 1028.39 183.29 1157.24 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n11 2 Car -1 -1 -1 953.19 182.76 1067.87 232.34 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n11 5 Pedestrian -1 -1 -1 163.75 156.15 208.06 249.30 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n11 4 Pedestrian -1 -1 -1 632.02 160.71 678.55 288.09 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n11 6 Car -1 -1 -1 601.74 172.72 636.81 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n11 13 Pedestrian -1 -1 -1 190.01 154.31 205.26 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n11 7 Pedestrian -1 -1 -1 221.50 155.72 234.87 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n11 8 Pedestrian -1 -1 -1 354.88 160.03 374.17 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n11 12 Pedestrian -1 -1 -1 204.29 160.77 220.09 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n11 10 Car -1 -1 -1 598.64 173.52 621.72 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n12 1 Car -1 -1 -1 1094.74 184.51 1220.89 236.35 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n12 3 Car -1 -1 -1 1028.41 183.39 1157.22 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n12 2 Car -1 -1 -1 953.33 182.65 1067.79 232.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n12 4 Pedestrian -1 -1 -1 619.80 160.45 669.40 284.78 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n12 6 Car -1 -1 -1 601.83 172.99 636.72 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n12 5 Pedestrian -1 -1 -1 178.17 157.53 215.44 249.56 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n12 7 Pedestrian -1 -1 -1 221.75 155.83 234.92 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n12 13 Pedestrian -1 -1 -1 195.97 154.41 213.52 196.73 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n12 8 Pedestrian -1 -1 -1 355.75 159.98 373.97 199.10 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n12 10 Car -1 -1 -1 598.82 173.43 621.81 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n12 15 Pedestrian -1 -1 -1 177.29 153.78 194.31 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n13 1 Car -1 -1 -1 1094.78 184.47 1220.78 236.30 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n13 3 Car -1 -1 -1 1028.58 183.42 1157.08 233.54 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n13 2 Car -1 -1 -1 953.49 182.68 1067.65 232.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n13 4 Pedestrian -1 -1 -1 610.11 162.79 663.58 285.27 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n13 5 Pedestrian -1 -1 -1 185.42 159.98 223.11 249.58 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n13 6 Car -1 -1 -1 602.48 173.20 638.10 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n13 7 Pedestrian -1 -1 -1 221.50 155.55 235.09 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n13 8 Pedestrian -1 -1 -1 355.89 159.87 374.08 199.93 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n13 13 Pedestrian -1 -1 -1 202.83 154.21 221.11 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n13 15 Pedestrian -1 -1 -1 176.95 153.85 194.35 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n14 1 Car -1 -1 -1 1094.63 184.49 1221.22 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n14 2 Car -1 -1 -1 953.58 182.60 1067.67 232.48 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n14 3 Car -1 -1 -1 1028.68 183.49 1157.02 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n14 4 Pedestrian -1 -1 -1 603.98 162.62 653.94 285.94 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n14 5 Pedestrian -1 -1 -1 190.09 157.27 234.67 248.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 6 Car -1 -1 -1 601.84 173.22 638.62 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n14 7 Pedestrian -1 -1 -1 221.27 155.39 235.13 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n14 8 Pedestrian -1 -1 -1 356.54 159.88 374.35 200.68 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n14 13 Pedestrian -1 -1 -1 207.96 155.50 224.64 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n14 15 Pedestrian -1 -1 -1 193.60 159.70 208.47 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n14 16 Car -1 -1 -1 598.91 173.39 621.75 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n15 1 Car -1 -1 -1 1094.68 184.54 1221.12 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n15 2 Car -1 -1 -1 953.84 182.63 1067.30 232.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 3 Car -1 -1 -1 1028.83 183.55 1156.96 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 5 Pedestrian -1 -1 -1 190.89 157.66 242.86 248.59 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n15 4 Pedestrian -1 -1 -1 598.68 161.54 643.35 282.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n15 6 Car -1 -1 -1 602.67 172.95 638.49 202.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n15 7 Pedestrian -1 -1 -1 219.88 154.97 235.26 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n15 8 Pedestrian -1 -1 -1 357.48 160.37 374.65 200.55 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n15 15 Pedestrian -1 -1 -1 194.38 160.47 207.92 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n15 16 Car -1 -1 -1 598.08 173.55 622.65 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n16 1 Car -1 -1 -1 1094.63 184.41 1221.08 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n16 3 Car -1 -1 -1 1028.96 183.61 1156.84 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n16 2 Car -1 -1 -1 953.42 182.77 1067.60 232.33 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n16 4 Pedestrian -1 -1 -1 589.47 161.19 632.29 282.04 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n16 5 Pedestrian -1 -1 -1 196.93 158.26 249.66 248.60 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n16 6 Car -1 -1 -1 603.03 172.47 638.06 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n16 7 Pedestrian -1 -1 -1 216.53 154.63 232.17 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n16 15 Pedestrian -1 -1 -1 194.24 161.40 206.79 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n16 8 Pedestrian -1 -1 -1 357.98 160.48 375.16 200.49 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n17 1 Car -1 -1 -1 1094.67 184.48 1221.10 236.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n17 2 Car -1 -1 -1 953.55 182.82 1067.55 232.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n17 3 Car -1 -1 -1 1028.76 183.63 1157.08 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n17 4 Pedestrian -1 -1 -1 574.75 162.71 629.90 280.28 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n17 5 Pedestrian -1 -1 -1 206.75 158.05 249.83 247.83 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n17 6 Car -1 -1 -1 602.96 172.80 637.52 201.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n17 7 Pedestrian -1 -1 -1 220.00 154.99 235.89 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n17 8 Pedestrian -1 -1 -1 359.80 161.49 375.79 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n17 15 Pedestrian -1 -1 -1 193.74 160.82 207.24 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n18 1 Car -1 -1 -1 1094.47 184.41 1221.21 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n18 3 Car -1 -1 -1 1028.61 183.60 1157.19 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n18 2 Car -1 -1 -1 953.58 182.92 1067.49 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n18 4 Pedestrian -1 -1 -1 567.71 163.01 623.23 279.84 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n18 5 Pedestrian -1 -1 -1 220.89 161.83 250.95 247.98 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n18 6 Car -1 -1 -1 600.61 172.73 637.92 202.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n18 7 Pedestrian -1 -1 -1 221.07 154.52 235.92 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n18 8 Pedestrian -1 -1 -1 359.42 161.40 376.28 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n18 16 Car -1 -1 -1 597.31 173.34 622.59 192.33 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n18 15 Pedestrian -1 -1 -1 193.41 159.67 208.14 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n19 1 Car -1 -1 -1 1094.19 184.44 1221.55 236.18 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n19 4 Pedestrian -1 -1 -1 566.19 162.70 615.84 279.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n19 2 Car -1 -1 -1 953.97 182.93 1067.11 232.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n19 3 Car -1 -1 -1 1028.80 183.64 1157.10 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n19 6 Car -1 -1 -1 600.53 172.66 637.52 202.37 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n19 5 Pedestrian -1 -1 -1 228.41 162.29 258.58 249.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n19 8 Pedestrian -1 -1 -1 359.50 161.52 376.20 199.46 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n19 7 Pedestrian -1 -1 -1 221.17 154.66 235.92 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n19 16 Car -1 -1 -1 597.13 173.21 622.98 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n19 15 Pedestrian -1 -1 -1 192.65 159.18 208.70 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n20 1 Car -1 -1 -1 1094.26 184.40 1221.50 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n20 2 Car -1 -1 -1 954.03 182.90 1067.16 232.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n20 4 Pedestrian -1 -1 -1 561.24 162.52 605.97 279.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n20 3 Car -1 -1 -1 1029.20 183.65 1156.69 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n20 5 Pedestrian -1 -1 -1 230.62 160.36 270.48 250.96 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n20 6 Car -1 -1 -1 600.70 172.57 637.35 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n20 7 Pedestrian -1 -1 -1 221.41 154.57 235.64 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n20 8 Pedestrian -1 -1 -1 357.71 160.88 375.30 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n20 16 Car -1 -1 -1 597.72 173.45 622.61 192.97 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n20 15 Pedestrian -1 -1 -1 192.42 159.09 208.81 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n21 1 Car -1 -1 -1 1094.36 184.44 1221.33 236.20 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n21 2 Car -1 -1 -1 954.11 182.93 1067.10 232.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n21 3 Car -1 -1 -1 1032.62 183.50 1157.27 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n21 5 Pedestrian -1 -1 -1 235.79 159.98 278.87 251.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n21 4 Pedestrian -1 -1 -1 557.02 162.15 595.49 278.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n21 6 Car -1 -1 -1 601.10 172.56 637.20 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n21 7 Pedestrian -1 -1 -1 221.28 155.21 235.30 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n21 8 Pedestrian -1 -1 -1 359.39 161.49 376.25 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n21 16 Car -1 -1 -1 598.44 173.60 622.14 192.66 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n21 15 Pedestrian -1 -1 -1 192.52 159.11 208.57 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n22 1 Car -1 -1 -1 1094.34 184.33 1221.31 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n22 2 Car -1 -1 -1 954.35 183.04 1066.82 232.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n22 3 Car -1 -1 -1 1032.75 183.45 1157.15 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n22 5 Pedestrian -1 -1 -1 241.24 160.61 281.13 251.54 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n22 4 Pedestrian -1 -1 -1 546.98 163.32 589.75 274.31 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n22 6 Car -1 -1 -1 601.27 172.74 637.04 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n22 7 Pedestrian -1 -1 -1 220.94 155.46 235.01 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n22 8 Pedestrian -1 -1 -1 359.65 161.32 376.10 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n22 16 Car -1 -1 -1 599.00 173.53 621.78 192.67 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n22 15 Pedestrian -1 -1 -1 192.13 159.09 208.50 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n23 1 Car -1 -1 -1 1094.39 184.39 1221.42 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n23 2 Car -1 -1 -1 954.22 183.13 1067.07 232.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n23 3 Car -1 -1 -1 1029.38 183.72 1156.57 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n23 4 Pedestrian -1 -1 -1 541.83 163.00 585.57 273.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n23 5 Pedestrian -1 -1 -1 249.72 160.26 283.40 251.77 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n23 6 Car -1 -1 -1 601.63 172.83 636.84 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n23 8 Pedestrian -1 -1 -1 360.48 161.22 376.21 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n23 7 Pedestrian -1 -1 -1 220.62 155.33 235.02 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n23 16 Car -1 -1 -1 599.26 173.51 621.75 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n23 15 Pedestrian -1 -1 -1 192.01 159.09 208.36 199.99 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n24 1 Car -1 -1 -1 1094.31 184.43 1221.25 236.25 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n24 2 Car -1 -1 -1 954.38 183.15 1067.00 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n24 3 Car -1 -1 -1 1028.98 183.73 1156.80 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n24 4 Pedestrian -1 -1 -1 535.86 162.66 577.46 274.30 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n24 6 Car -1 -1 -1 601.58 172.74 636.83 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n24 5 Pedestrian -1 -1 -1 258.80 159.74 289.77 250.85 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 8 Pedestrian -1 -1 -1 360.69 161.46 376.33 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n24 7 Pedestrian -1 -1 -1 220.30 155.35 234.95 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n24 16 Car -1 -1 -1 599.22 173.46 621.76 192.66 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n24 15 Pedestrian -1 -1 -1 188.56 158.79 206.36 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n25 1 Car -1 -1 -1 1094.54 184.38 1221.23 236.22 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n25 3 Car -1 -1 -1 1029.10 183.75 1156.72 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n25 2 Car -1 -1 -1 954.38 183.10 1066.92 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n25 4 Pedestrian -1 -1 -1 529.12 162.00 568.84 273.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n25 5 Pedestrian -1 -1 -1 260.05 155.70 301.58 251.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n25 6 Car -1 -1 -1 601.51 172.88 636.92 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n25 8 Pedestrian -1 -1 -1 361.40 161.56 376.46 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n25 7 Pedestrian -1 -1 -1 220.12 155.39 234.86 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n25 15 Pedestrian -1 -1 -1 188.24 158.68 206.42 200.33 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n25 16 Car -1 -1 -1 599.10 173.41 621.86 192.81 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n26 1 Car -1 -1 -1 1094.87 184.46 1220.95 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n26 2 Car -1 -1 -1 954.42 183.12 1066.97 232.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n26 3 Car -1 -1 -1 1029.35 183.86 1156.68 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 5 Pedestrian -1 -1 -1 264.14 159.13 312.05 252.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n26 4 Pedestrian -1 -1 -1 519.47 161.37 562.13 272.58 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n26 6 Car -1 -1 -1 601.69 172.92 636.89 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n26 8 Pedestrian -1 -1 -1 361.78 161.62 377.34 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n26 7 Pedestrian -1 -1 -1 219.75 155.25 234.91 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n26 15 Pedestrian -1 -1 -1 188.34 158.80 206.40 200.31 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n26 16 Car -1 -1 -1 598.95 173.36 621.92 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n27 1 Car -1 -1 -1 1094.60 184.52 1221.29 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n27 5 Pedestrian -1 -1 -1 267.89 158.87 315.76 252.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n27 2 Car -1 -1 -1 954.27 183.14 1066.97 232.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n27 3 Car -1 -1 -1 1029.25 183.82 1156.73 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n27 4 Pedestrian -1 -1 -1 512.88 162.76 560.56 270.61 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n27 8 Pedestrian -1 -1 -1 361.63 161.30 377.31 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n27 6 Car -1 -1 -1 601.49 172.94 637.21 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n27 7 Pedestrian -1 -1 -1 219.61 155.28 234.85 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n27 15 Pedestrian -1 -1 -1 188.29 159.02 206.45 200.22 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n27 16 Car -1 -1 -1 598.79 173.43 622.09 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n28 1 Car -1 -1 -1 1094.89 184.56 1220.85 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n28 3 Car -1 -1 -1 1029.27 183.77 1156.60 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n28 2 Car -1 -1 -1 954.17 183.07 1067.13 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n28 4 Pedestrian -1 -1 -1 505.03 164.05 554.38 269.77 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n28 5 Pedestrian -1 -1 -1 275.23 160.22 316.68 252.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n28 8 Pedestrian -1 -1 -1 362.07 161.04 377.88 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n28 6 Car -1 -1 -1 601.50 173.01 637.12 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 7 Pedestrian -1 -1 -1 219.45 155.09 234.83 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n28 15 Pedestrian -1 -1 -1 187.96 158.56 206.67 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n28 16 Car -1 -1 -1 598.86 173.47 622.36 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n29 1 Car -1 -1 -1 1094.78 184.51 1220.92 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n29 3 Car -1 -1 -1 1029.38 183.79 1156.59 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 2 Car -1 -1 -1 954.06 183.11 1067.12 232.08 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n29 5 Pedestrian -1 -1 -1 282.78 160.40 319.30 252.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n29 4 Pedestrian -1 -1 -1 504.43 162.39 546.67 266.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n29 6 Car -1 -1 -1 601.32 172.95 637.37 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n29 8 Pedestrian -1 -1 -1 361.76 161.05 378.08 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n29 7 Pedestrian -1 -1 -1 219.11 154.99 234.92 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n29 15 Pedestrian -1 -1 -1 187.76 158.23 206.99 200.49 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n29 16 Car -1 -1 -1 598.84 173.51 622.44 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n30 1 Car -1 -1 -1 1094.57 184.47 1221.20 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n30 3 Car -1 -1 -1 1032.64 183.53 1157.32 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n30 2 Car -1 -1 -1 954.20 183.11 1066.87 232.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n30 5 Pedestrian -1 -1 -1 292.48 159.62 323.48 252.40 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 4 Pedestrian -1 -1 -1 500.38 161.22 536.46 267.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n30 8 Pedestrian -1 -1 -1 361.82 161.06 378.21 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n30 6 Car -1 -1 -1 601.62 173.16 637.29 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n30 7 Pedestrian -1 -1 -1 219.04 154.94 235.14 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n30 16 Car -1 -1 -1 598.68 173.57 622.48 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n30 15 Pedestrian -1 -1 -1 187.92 158.03 206.77 200.33 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n31 1 Car -1 -1 -1 1094.82 184.54 1221.00 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n31 5 Pedestrian -1 -1 -1 298.28 159.10 332.96 253.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n31 3 Car -1 -1 -1 1032.57 183.59 1157.34 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 2 Car -1 -1 -1 954.13 183.03 1066.94 232.12 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 4 Pedestrian -1 -1 -1 496.98 161.00 530.34 265.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n31 6 Car -1 -1 -1 601.95 173.09 637.02 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n31 8 Pedestrian -1 -1 -1 362.15 160.80 378.35 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 7 Pedestrian -1 -1 -1 219.11 154.86 235.38 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 16 Car -1 -1 -1 598.92 173.60 622.27 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n31 15 Pedestrian -1 -1 -1 187.71 157.93 207.03 200.08 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n32 1 Car -1 -1 -1 1094.76 184.52 1221.09 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n32 3 Car -1 -1 -1 1029.48 183.83 1156.52 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n32 2 Car -1 -1 -1 954.15 183.02 1066.92 232.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n32 4 Pedestrian -1 -1 -1 489.18 162.57 525.43 264.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n32 5 Pedestrian -1 -1 -1 302.96 160.28 341.65 254.04 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n32 6 Car -1 -1 -1 601.51 172.84 637.44 203.19 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n32 7 Pedestrian -1 -1 -1 219.14 154.86 235.37 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n32 8 Pedestrian -1 -1 -1 362.74 160.57 378.23 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n32 16 Car -1 -1 -1 598.86 173.61 622.33 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n32 15 Pedestrian -1 -1 -1 187.78 158.20 207.23 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n33 1 Car -1 -1 -1 1094.75 184.53 1221.11 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n33 3 Car -1 -1 -1 1032.49 183.58 1157.35 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n33 2 Car -1 -1 -1 954.14 183.09 1066.93 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n33 4 Pedestrian -1 -1 -1 484.63 163.25 521.41 265.37 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n33 5 Pedestrian -1 -1 -1 305.40 161.56 347.30 256.37 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n33 6 Car -1 -1 -1 601.68 172.86 637.34 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n33 7 Pedestrian -1 -1 -1 219.13 154.93 235.31 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n33 8 Pedestrian -1 -1 -1 362.67 160.69 378.33 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n33 16 Car -1 -1 -1 598.85 173.56 622.22 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n33 15 Pedestrian -1 -1 -1 187.75 158.34 207.36 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n34 1 Car -1 -1 -1 1094.64 184.44 1221.04 236.18 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n34 3 Car -1 -1 -1 1032.53 183.56 1157.33 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n34 2 Car -1 -1 -1 954.24 183.05 1066.88 232.12 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n34 5 Pedestrian -1 -1 -1 312.04 158.89 349.56 255.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n34 4 Pedestrian -1 -1 -1 479.52 162.34 517.36 266.24 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n34 6 Car -1 -1 -1 601.67 172.84 637.17 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n34 7 Pedestrian -1 -1 -1 219.08 155.04 235.43 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n34 8 Pedestrian -1 -1 -1 364.57 161.30 379.40 195.86 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n34 16 Car -1 -1 -1 598.93 173.48 622.21 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n34 15 Pedestrian -1 -1 -1 190.77 158.30 209.33 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n35 1 Car -1 -1 -1 1094.70 184.50 1221.00 236.20 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n35 3 Car -1 -1 -1 1029.31 183.81 1156.56 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n35 2 Car -1 -1 -1 954.35 183.17 1066.83 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n35 4 Pedestrian -1 -1 -1 472.74 161.60 510.79 264.71 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n35 5 Pedestrian -1 -1 -1 320.83 159.11 355.21 255.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n35 6 Car -1 -1 -1 601.74 172.85 637.23 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n35 8 Pedestrian -1 -1 -1 364.96 161.38 379.54 195.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n35 7 Pedestrian -1 -1 -1 218.95 155.06 235.47 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n35 16 Car -1 -1 -1 598.87 173.48 622.10 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n35 15 Pedestrian -1 -1 -1 191.26 154.54 209.57 197.08 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n36 1 Car -1 -1 -1 1094.65 184.58 1221.21 236.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n36 3 Car -1 -1 -1 1029.35 183.84 1156.54 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n36 2 Car -1 -1 -1 954.25 183.04 1067.02 232.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n36 5 Pedestrian -1 -1 -1 323.48 159.27 361.59 255.53 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n36 4 Pedestrian -1 -1 -1 465.71 161.72 508.43 263.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n36 8 Pedestrian -1 -1 -1 365.57 161.33 379.93 195.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n36 6 Car -1 -1 -1 601.81 172.96 637.16 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n36 7 Pedestrian -1 -1 -1 219.19 154.97 235.44 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n36 16 Car -1 -1 -1 598.99 173.49 622.12 193.10 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n36 15 Pedestrian -1 -1 -1 191.45 154.49 209.32 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n37 1 Car -1 -1 -1 1094.71 184.59 1221.10 236.19 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n37 5 Pedestrian -1 -1 -1 328.20 160.71 371.74 257.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n37 3 Car -1 -1 -1 1029.14 183.77 1156.71 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n37 2 Car -1 -1 -1 954.38 183.09 1066.84 232.08 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n37 4 Pedestrian -1 -1 -1 461.86 161.93 504.44 260.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n37 8 Pedestrian -1 -1 -1 366.27 161.20 380.05 195.26 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n37 6 Car -1 -1 -1 601.61 172.72 637.24 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n37 7 Pedestrian -1 -1 -1 219.42 155.09 235.32 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n37 16 Car -1 -1 -1 598.72 173.50 622.01 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n37 15 Pedestrian -1 -1 -1 191.66 154.83 208.95 196.66 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n38 1 Car -1 -1 -1 1094.75 184.56 1220.99 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n38 5 Pedestrian -1 -1 -1 330.97 160.22 376.74 258.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n38 3 Car -1 -1 -1 1029.19 183.84 1156.77 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n38 2 Car -1 -1 -1 954.33 183.08 1066.85 232.08 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n38 4 Pedestrian -1 -1 -1 457.61 162.44 501.06 262.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n38 8 Pedestrian -1 -1 -1 366.27 161.01 380.72 195.53 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n38 6 Car -1 -1 -1 602.47 172.62 637.45 203.21 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n38 7 Pedestrian -1 -1 -1 219.71 155.08 235.31 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n38 16 Car -1 -1 -1 598.63 173.46 621.94 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n38 15 Pedestrian -1 -1 -1 192.21 155.07 208.85 196.42 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n38 17 Pedestrian -1 -1 -1 848.20 163.84 876.93 246.30 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n39 1 Car -1 -1 -1 1094.54 184.53 1221.01 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n39 3 Car -1 -1 -1 1029.30 183.84 1156.64 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n39 5 Pedestrian -1 -1 -1 336.10 161.17 378.99 258.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n39 2 Car -1 -1 -1 954.39 183.10 1066.81 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n39 4 Pedestrian -1 -1 -1 456.34 162.46 494.26 259.70 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n39 6 Car -1 -1 -1 601.67 173.02 637.27 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n39 7 Pedestrian -1 -1 -1 219.57 154.98 235.30 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n39 8 Pedestrian -1 -1 -1 366.08 160.99 381.27 195.26 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n39 16 Car -1 -1 -1 598.63 173.45 621.95 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n39 15 Pedestrian -1 -1 -1 192.33 154.92 208.81 196.42 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n40 1 Car -1 -1 -1 1094.62 184.47 1220.99 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n40 3 Car -1 -1 -1 1029.26 183.81 1156.72 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n40 2 Car -1 -1 -1 954.18 182.91 1067.08 232.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n40 5 Pedestrian -1 -1 -1 343.85 161.36 379.60 258.51 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n40 4 Pedestrian -1 -1 -1 451.71 161.98 486.11 259.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n40 6 Car -1 -1 -1 602.40 172.72 637.54 203.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n40 7 Pedestrian -1 -1 -1 219.63 155.04 235.34 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n40 8 Pedestrian -1 -1 -1 366.49 161.05 381.06 194.94 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n40 16 Car -1 -1 -1 598.63 173.33 622.06 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n40 15 Pedestrian -1 -1 -1 192.97 155.04 208.65 196.12 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n40 18 Pedestrian -1 -1 -1 848.36 163.74 876.71 246.46 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n40 19 Pedestrian -1 -1 -1 202.86 153.89 222.32 196.70 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n41 1 Car -1 -1 -1 1094.65 184.43 1220.88 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n41 3 Car -1 -1 -1 1029.37 183.81 1156.65 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n41 2 Car -1 -1 -1 954.05 182.96 1067.26 232.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n41 5 Pedestrian -1 -1 -1 351.64 161.39 385.53 258.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n41 4 Pedestrian -1 -1 -1 448.34 162.65 481.40 258.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n41 6 Car -1 -1 -1 602.43 172.66 637.55 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n41 7 Pedestrian -1 -1 -1 220.00 154.99 235.38 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n41 8 Pedestrian -1 -1 -1 366.34 161.14 381.44 194.99 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n41 16 Car -1 -1 -1 598.74 173.26 622.27 193.05 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n41 15 Pedestrian -1 -1 -1 193.31 155.46 208.66 195.75 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n41 18 Pedestrian -1 -1 -1 848.13 163.64 876.83 246.50 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n42 1 Car -1 -1 -1 1094.84 184.52 1220.75 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n42 3 Car -1 -1 -1 1029.21 183.83 1156.76 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n42 2 Car -1 -1 -1 953.86 182.89 1067.46 232.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n42 5 Pedestrian -1 -1 -1 355.92 160.54 390.20 259.98 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n42 4 Pedestrian -1 -1 -1 443.42 164.79 477.91 257.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n42 6 Car -1 -1 -1 602.62 172.75 637.35 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n42 7 Pedestrian -1 -1 -1 219.99 154.96 235.38 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n42 16 Car -1 -1 -1 598.74 173.40 622.15 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n42 8 Pedestrian -1 -1 -1 366.48 161.30 381.19 194.90 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n42 18 Pedestrian -1 -1 -1 848.56 163.58 876.68 246.40 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n42 15 Pedestrian -1 -1 -1 193.22 155.82 208.60 195.71 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n43 1 Car -1 -1 -1 1094.68 184.52 1220.96 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n43 3 Car -1 -1 -1 1029.16 183.85 1156.72 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n43 2 Car -1 -1 -1 953.81 182.84 1067.58 232.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n43 5 Pedestrian -1 -1 -1 359.39 161.69 395.14 259.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n43 4 Pedestrian -1 -1 -1 440.01 164.79 473.14 256.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n43 6 Car -1 -1 -1 601.73 172.93 637.23 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n43 7 Pedestrian -1 -1 -1 220.06 155.02 235.36 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n43 16 Car -1 -1 -1 598.64 173.34 622.31 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n43 8 Pedestrian -1 -1 -1 366.78 161.02 380.64 195.24 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n43 15 Pedestrian -1 -1 -1 193.84 155.98 208.44 195.60 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n43 18 Pedestrian -1 -1 -1 848.14 163.90 876.63 246.21 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n43 20 Pedestrian -1 -1 -1 207.22 154.77 225.02 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n43 21 Pedestrian -1 -1 -1 176.60 153.45 195.21 196.40 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n44 1 Car -1 -1 -1 1094.74 184.54 1220.87 236.42 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n44 3 Car -1 -1 -1 1029.09 183.83 1156.66 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n44 2 Car -1 -1 -1 953.93 182.94 1067.29 232.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n44 5 Pedestrian -1 -1 -1 362.39 160.40 398.53 261.23 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n44 4 Pedestrian -1 -1 -1 435.82 163.38 469.37 256.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n44 6 Car -1 -1 -1 601.63 172.95 637.35 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n44 7 Pedestrian -1 -1 -1 220.32 155.05 235.21 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n44 16 Car -1 -1 -1 598.60 173.44 622.38 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n44 15 Pedestrian -1 -1 -1 193.41 160.35 208.11 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n44 8 Pedestrian -1 -1 -1 366.46 160.77 380.94 195.62 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n44 20 Pedestrian -1 -1 -1 207.51 154.91 225.00 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n44 18 Pedestrian -1 -1 -1 848.16 163.84 877.05 246.24 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n44 21 Pedestrian -1 -1 -1 176.72 153.18 194.89 196.46 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n45 1 Car -1 -1 -1 1094.95 184.58 1220.71 236.33 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n45 3 Car -1 -1 -1 1029.10 183.90 1156.86 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n45 2 Car -1 -1 -1 953.95 183.05 1067.23 232.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n45 5 Pedestrian -1 -1 -1 366.74 160.66 401.61 261.34 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 4 Pedestrian -1 -1 -1 430.95 162.88 466.29 255.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n45 6 Car -1 -1 -1 601.75 172.97 637.20 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n45 7 Pedestrian -1 -1 -1 220.26 154.98 235.09 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n45 16 Car -1 -1 -1 598.88 173.41 622.22 192.83 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n45 15 Pedestrian -1 -1 -1 193.36 160.68 208.47 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n45 21 Pedestrian -1 -1 -1 176.61 153.48 194.76 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n45 20 Pedestrian -1 -1 -1 207.85 154.78 224.93 196.85 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n46 1 Car -1 -1 -1 1095.03 184.65 1220.48 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n46 3 Car -1 -1 -1 1029.11 183.84 1156.67 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n46 2 Car -1 -1 -1 953.80 183.00 1067.31 232.22 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n46 5 Pedestrian -1 -1 -1 371.69 161.35 404.66 264.43 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n46 4 Pedestrian -1 -1 -1 423.96 163.65 461.22 254.83 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n46 6 Car -1 -1 -1 602.49 172.63 637.41 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n46 7 Pedestrian -1 -1 -1 220.27 155.00 235.10 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n46 16 Car -1 -1 -1 598.62 173.53 622.07 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n46 15 Pedestrian -1 -1 -1 192.86 160.41 208.95 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n46 21 Pedestrian -1 -1 -1 176.75 153.50 194.84 196.46 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n46 20 Pedestrian -1 -1 -1 207.90 154.89 224.88 196.79 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n47 1 Car -1 -1 -1 1094.97 184.56 1220.67 236.40 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n47 3 Car -1 -1 -1 1029.11 183.88 1156.74 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n47 2 Car -1 -1 -1 953.81 182.92 1067.39 232.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n47 5 Pedestrian -1 -1 -1 374.42 161.18 410.21 263.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n47 4 Pedestrian -1 -1 -1 420.23 163.40 457.47 254.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n47 6 Car -1 -1 -1 601.97 173.04 636.99 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n47 7 Pedestrian -1 -1 -1 220.46 155.08 234.81 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n47 16 Car -1 -1 -1 598.80 173.51 622.07 193.08 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n47 15 Pedestrian -1 -1 -1 193.43 160.45 208.98 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n47 21 Pedestrian -1 -1 -1 177.08 153.91 194.58 196.34 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n47 20 Pedestrian -1 -1 -1 208.24 155.21 224.32 196.77 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n48 1 Car -1 -1 -1 1095.11 184.66 1220.70 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n48 3 Car -1 -1 -1 1029.15 183.96 1156.88 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n48 2 Car -1 -1 -1 954.25 182.94 1067.10 232.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n48 5 Pedestrian -1 -1 -1 379.20 161.94 418.83 263.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n48 4 Pedestrian -1 -1 -1 416.95 163.27 452.89 254.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n48 6 Car -1 -1 -1 602.59 172.69 637.33 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n48 7 Pedestrian -1 -1 -1 220.55 155.01 234.82 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n48 16 Car -1 -1 -1 598.60 173.40 622.26 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n48 21 Pedestrian -1 -1 -1 176.98 153.64 194.54 196.46 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n48 22 Pedestrian -1 -1 -1 368.94 161.50 382.27 194.32 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n49 1 Car -1 -1 -1 1094.93 184.66 1220.88 236.30 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n49 3 Car -1 -1 -1 1028.83 183.86 1156.91 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n49 2 Car -1 -1 -1 954.57 182.97 1066.60 232.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n49 5 Pedestrian -1 -1 -1 382.04 161.54 423.89 265.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n49 4 Pedestrian -1 -1 -1 417.85 162.67 449.39 251.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n49 6 Car -1 -1 -1 601.68 172.86 637.15 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n49 7 Pedestrian -1 -1 -1 220.61 155.08 234.74 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n49 16 Car -1 -1 -1 598.68 173.43 622.10 193.10 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n49 22 Pedestrian -1 -1 -1 369.03 161.81 381.96 194.00 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n49 21 Pedestrian -1 -1 -1 180.76 160.19 197.32 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n49 23 Pedestrian -1 -1 -1 208.31 155.10 224.03 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n50 1 Car -1 -1 -1 1095.06 184.72 1220.60 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n50 3 Car -1 -1 -1 1029.10 183.94 1156.81 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n50 5 Pedestrian -1 -1 -1 385.87 161.87 428.13 265.00 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n50 2 Car -1 -1 -1 954.16 182.78 1066.92 232.34 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n50 6 Car -1 -1 -1 601.89 172.88 636.92 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n50 4 Pedestrian -1 -1 -1 409.99 162.96 443.84 254.34 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n50 7 Pedestrian -1 -1 -1 220.74 155.11 234.84 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n50 22 Pedestrian -1 -1 -1 369.00 161.05 382.77 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n50 16 Car -1 -1 -1 598.84 173.44 621.94 192.92 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n50 23 Pedestrian -1 -1 -1 208.22 154.76 224.24 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n50 24 Pedestrian -1 -1 -1 847.66 164.09 877.54 247.12 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n51 1 Car -1 -1 -1 1094.93 184.75 1220.74 236.24 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n51 3 Car -1 -1 -1 1029.18 183.92 1156.57 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n51 2 Car -1 -1 -1 953.93 182.92 1067.08 232.22 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n51 5 Pedestrian -1 -1 -1 389.85 164.12 430.91 264.54 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n51 6 Car -1 -1 -1 601.89 172.99 636.95 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n51 4 Pedestrian -1 -1 -1 410.68 164.77 441.93 253.22 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n51 22 Pedestrian -1 -1 -1 369.11 160.63 382.28 192.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n51 7 Pedestrian -1 -1 -1 220.80 155.20 234.79 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n51 24 Pedestrian -1 -1 -1 846.99 164.17 877.06 246.96 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n51 16 Car -1 -1 -1 598.72 173.39 622.02 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n51 23 Pedestrian -1 -1 -1 208.35 154.73 224.40 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n52 1 Car -1 -1 -1 1094.87 184.80 1220.68 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n52 3 Car -1 -1 -1 1029.01 183.92 1156.82 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n52 2 Car -1 -1 -1 953.92 182.83 1067.21 232.32 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n52 5 Pedestrian -1 -1 -1 395.49 164.45 434.50 264.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n52 22 Pedestrian -1 -1 -1 369.33 160.70 382.23 191.93 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n52 6 Car -1 -1 -1 601.71 173.11 637.08 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n52 7 Pedestrian -1 -1 -1 220.84 155.20 234.87 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n52 16 Car -1 -1 -1 598.74 173.44 622.00 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n52 24 Pedestrian -1 -1 -1 846.97 164.31 876.48 246.95 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n52 23 Pedestrian -1 -1 -1 208.31 154.61 224.49 197.42 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n53 1 Car -1 -1 -1 1094.68 184.72 1220.94 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n53 3 Car -1 -1 -1 1029.14 183.92 1156.79 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n53 2 Car -1 -1 -1 953.76 182.78 1067.40 232.34 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n53 5 Pedestrian -1 -1 -1 405.01 159.63 438.87 259.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n53 6 Car -1 -1 -1 601.59 172.88 637.17 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n53 24 Pedestrian -1 -1 -1 843.07 164.25 875.15 248.02 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n53 22 Pedestrian -1 -1 -1 369.71 161.27 382.09 192.27 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n53 7 Pedestrian -1 -1 -1 220.63 155.25 234.80 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n53 16 Car -1 -1 -1 598.68 173.41 621.88 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n53 23 Pedestrian -1 -1 -1 212.90 155.60 227.18 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n53 25 Pedestrian -1 -1 -1 199.53 153.85 217.83 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n54 1 Car -1 -1 -1 1094.86 184.82 1220.72 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n54 3 Car -1 -1 -1 1029.30 183.88 1156.68 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n54 2 Car -1 -1 -1 953.84 182.93 1067.32 232.21 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n54 6 Car -1 -1 -1 601.66 172.85 637.07 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n54 24 Pedestrian -1 -1 -1 842.55 164.44 874.19 248.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n54 5 Pedestrian -1 -1 -1 408.04 160.79 443.52 268.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n54 7 Pedestrian -1 -1 -1 220.64 155.20 234.75 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n54 22 Pedestrian -1 -1 -1 369.86 161.11 382.52 192.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n54 16 Car -1 -1 -1 598.59 173.49 621.96 192.88 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n54 25 Pedestrian -1 -1 -1 199.54 154.31 217.87 196.88 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n54 26 Pedestrian -1 -1 -1 401.21 162.24 435.59 251.79 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n55 1 Car -1 -1 -1 1094.84 184.68 1220.91 236.20 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n55 3 Car -1 -1 -1 1029.23 183.88 1156.73 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n55 2 Car -1 -1 -1 953.76 182.83 1067.37 232.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n55 5 Pedestrian -1 -1 -1 410.64 164.05 449.39 270.60 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n55 6 Car -1 -1 -1 601.82 172.79 637.02 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n55 22 Pedestrian -1 -1 -1 370.26 161.26 382.66 191.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n55 26 Pedestrian -1 -1 -1 397.77 164.26 430.40 249.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n55 24 Pedestrian -1 -1 -1 839.25 163.51 871.43 249.79 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n55 7 Pedestrian -1 -1 -1 220.50 155.16 234.50 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n55 16 Car -1 -1 -1 598.65 173.38 621.95 193.08 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n55 25 Pedestrian -1 -1 -1 199.25 154.47 217.91 197.02 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n56 1 Car -1 -1 -1 1094.54 184.61 1221.13 236.25 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n56 3 Car -1 -1 -1 1029.10 183.82 1156.83 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n56 2 Car -1 -1 -1 953.70 182.90 1067.44 232.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n56 5 Pedestrian -1 -1 -1 414.96 163.84 452.12 271.61 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n56 26 Pedestrian -1 -1 -1 394.21 166.25 427.00 248.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n56 6 Car -1 -1 -1 601.82 172.77 636.97 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n56 22 Pedestrian -1 -1 -1 370.68 161.00 383.29 191.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n56 7 Pedestrian -1 -1 -1 220.59 155.21 234.50 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n56 24 Pedestrian -1 -1 -1 839.42 163.76 869.39 249.74 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n56 16 Car -1 -1 -1 598.65 173.35 621.96 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n56 25 Pedestrian -1 -1 -1 199.19 154.37 217.90 197.08 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n57 1 Car -1 -1 -1 1094.65 184.69 1221.00 236.25 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n57 3 Car -1 -1 -1 1029.15 183.80 1156.58 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n57 2 Car -1 -1 -1 954.16 182.98 1067.10 232.20 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n57 5 Pedestrian -1 -1 -1 419.58 162.10 456.24 272.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n57 26 Pedestrian -1 -1 -1 390.41 165.22 424.44 247.99 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n57 22 Pedestrian -1 -1 -1 370.63 160.71 383.21 191.31 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n57 6 Car -1 -1 -1 601.75 172.81 637.12 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n57 7 Pedestrian -1 -1 -1 220.61 155.24 234.66 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n57 16 Car -1 -1 -1 598.69 173.39 622.09 192.93 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n57 24 Pedestrian -1 -1 -1 836.42 163.31 866.32 250.54 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n57 25 Pedestrian -1 -1 -1 198.97 154.39 217.86 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n58 1 Car -1 -1 -1 1094.67 184.64 1221.16 236.28 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n58 3 Car -1 -1 -1 1029.26 183.84 1156.59 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n58 2 Car -1 -1 -1 954.18 183.01 1067.20 232.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n58 26 Pedestrian -1 -1 -1 387.39 163.11 419.56 247.15 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n58 5 Pedestrian -1 -1 -1 423.82 159.89 460.88 273.41 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n58 6 Car -1 -1 -1 601.64 172.89 637.14 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n58 22 Pedestrian -1 -1 -1 370.16 161.16 383.02 191.32 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n58 7 Pedestrian -1 -1 -1 220.87 155.28 234.55 197.68 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n58 24 Pedestrian -1 -1 -1 836.20 163.97 864.96 249.36 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n58 16 Car -1 -1 -1 598.74 173.49 622.09 192.88 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n58 25 Pedestrian -1 -1 -1 199.41 154.57 217.54 196.92 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n59 1 Car -1 -1 -1 1094.68 184.62 1221.20 236.25 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n59 3 Car -1 -1 -1 1029.18 183.87 1156.74 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n59 2 Car -1 -1 -1 954.26 183.03 1067.13 232.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n59 5 Pedestrian -1 -1 -1 424.88 157.82 468.06 271.74 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n59 26 Pedestrian -1 -1 -1 385.05 162.92 414.68 246.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n59 24 Pedestrian -1 -1 -1 834.90 163.45 861.40 250.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n59 6 Car -1 -1 -1 601.80 173.00 637.05 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n59 22 Pedestrian -1 -1 -1 370.24 160.87 383.23 191.37 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n59 7 Pedestrian -1 -1 -1 220.81 155.32 234.65 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n59 16 Car -1 -1 -1 598.67 173.41 622.04 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n59 25 Pedestrian -1 -1 -1 199.53 154.29 217.37 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n60 1 Car -1 -1 -1 1094.67 184.71 1221.17 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n60 3 Car -1 -1 -1 1029.23 183.89 1156.65 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n60 2 Car -1 -1 -1 954.38 183.14 1066.98 232.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n60 24 Pedestrian -1 -1 -1 833.50 163.48 860.31 249.20 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n60 5 Pedestrian -1 -1 -1 429.46 159.21 475.45 273.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n60 26 Pedestrian -1 -1 -1 381.56 164.01 411.93 246.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n60 22 Pedestrian -1 -1 -1 369.96 160.31 383.03 190.64 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n60 6 Car -1 -1 -1 601.75 172.96 637.13 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n60 7 Pedestrian -1 -1 -1 220.54 155.41 234.54 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n60 16 Car -1 -1 -1 598.55 173.33 621.91 192.77 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n60 25 Pedestrian -1 -1 -1 199.59 154.29 217.42 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n61 1 Car -1 -1 -1 1094.78 184.68 1221.13 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n61 3 Car -1 -1 -1 1028.91 183.91 1156.92 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n61 2 Car -1 -1 -1 954.51 183.08 1066.86 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n61 5 Pedestrian -1 -1 -1 432.33 159.87 480.04 274.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n61 26 Pedestrian -1 -1 -1 379.39 164.98 410.97 245.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n61 24 Pedestrian -1 -1 -1 828.45 163.28 859.32 248.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n61 6 Car -1 -1 -1 601.76 172.92 637.10 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n61 22 Pedestrian -1 -1 -1 370.17 160.26 383.01 190.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n61 7 Pedestrian -1 -1 -1 220.77 155.40 234.48 197.45 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n61 16 Car -1 -1 -1 598.53 173.49 621.92 192.76 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n61 25 Pedestrian -1 -1 -1 199.87 154.33 217.18 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n62 1 Car -1 -1 -1 1094.89 184.66 1221.39 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n62 3 Car -1 -1 -1 1028.84 183.91 1157.12 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n62 2 Car -1 -1 -1 954.39 183.12 1067.03 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n62 5 Pedestrian -1 -1 -1 438.20 161.51 482.48 274.32 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n62 24 Pedestrian -1 -1 -1 828.40 164.13 857.90 248.43 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n62 26 Pedestrian -1 -1 -1 376.54 164.65 407.02 245.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n62 6 Car -1 -1 -1 601.81 173.03 637.00 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n62 7 Pedestrian -1 -1 -1 220.43 155.44 234.70 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n62 22 Pedestrian -1 -1 -1 370.39 160.59 383.10 190.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n62 16 Car -1 -1 -1 598.47 173.42 622.06 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n62 25 Pedestrian -1 -1 -1 199.59 154.29 217.49 197.02 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n63 1 Car -1 -1 -1 1094.28 184.63 1221.63 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n63 3 Car -1 -1 -1 1029.11 183.97 1156.83 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n63 2 Car -1 -1 -1 954.17 183.14 1067.11 232.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n63 24 Pedestrian -1 -1 -1 826.81 163.13 853.92 249.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n63 26 Pedestrian -1 -1 -1 373.29 163.96 403.57 245.48 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n63 5 Pedestrian -1 -1 -1 444.35 165.39 483.52 275.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n63 6 Car -1 -1 -1 601.71 172.92 637.14 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n63 7 Pedestrian -1 -1 -1 220.64 155.33 234.85 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n63 22 Pedestrian -1 -1 -1 370.58 160.46 383.79 190.60 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n63 16 Car -1 -1 -1 598.55 173.58 621.99 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n63 25 Pedestrian -1 -1 -1 199.55 154.09 217.47 197.22 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n63 27 Pedestrian -1 -1 -1 1168.63 138.33 1222.00 358.01 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n64 1 Car -1 -1 -1 1095.18 184.59 1220.25 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n64 2 Car -1 -1 -1 954.92 183.43 1066.64 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n64 3 Car -1 -1 -1 1028.90 184.01 1157.19 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n64 24 Pedestrian -1 -1 -1 827.31 163.45 851.84 248.50 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n64 5 Pedestrian -1 -1 -1 450.10 160.56 488.47 276.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n64 26 Pedestrian -1 -1 -1 369.42 163.18 399.75 243.89 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n64 6 Car -1 -1 -1 601.84 173.01 637.02 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n64 27 Pedestrian -1 -1 -1 1139.93 137.75 1219.63 365.77 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n64 7 Pedestrian -1 -1 -1 220.24 155.36 234.92 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n64 16 Car -1 -1 -1 598.67 173.52 622.19 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n64 22 Pedestrian -1 -1 -1 370.31 160.64 384.32 190.04 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n64 25 Pedestrian -1 -1 -1 199.13 153.67 217.87 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n65 1 Car -1 -1 -1 1093.83 184.41 1221.99 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n65 3 Car -1 -1 -1 1029.34 183.91 1156.42 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n65 2 Car -1 -1 -1 955.04 183.46 1066.50 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n65 24 Pedestrian -1 -1 -1 822.26 163.19 850.73 248.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n65 26 Pedestrian -1 -1 -1 364.86 163.79 397.55 243.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 5 Pedestrian -1 -1 -1 455.94 159.60 494.31 277.09 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n65 27 Pedestrian -1 -1 -1 1107.14 138.64 1221.96 365.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n65 6 Car -1 -1 -1 601.63 172.97 637.07 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n65 7 Pedestrian -1 -1 -1 220.06 155.31 234.86 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n65 16 Car -1 -1 -1 598.42 173.54 622.12 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n65 22 Pedestrian -1 -1 -1 370.38 160.46 384.83 189.48 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n65 25 Pedestrian -1 -1 -1 194.87 153.34 214.53 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n66 1 Car -1 -1 -1 1093.04 184.12 1222.64 235.43 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n66 3 Car -1 -1 -1 1029.35 184.05 1156.23 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n66 2 Car -1 -1 -1 955.12 183.34 1066.39 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n66 5 Pedestrian -1 -1 -1 458.54 158.97 500.39 278.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n66 24 Pedestrian -1 -1 -1 821.79 162.92 848.73 248.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n66 26 Pedestrian -1 -1 -1 364.45 164.79 395.76 242.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n66 6 Car -1 -1 -1 601.59 172.87 637.21 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n66 27 Pedestrian -1 -1 -1 1092.22 139.85 1221.70 363.59 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n66 7 Pedestrian -1 -1 -1 219.77 155.31 234.85 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n66 16 Car -1 -1 -1 598.41 173.53 622.25 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n66 25 Pedestrian -1 -1 -1 194.64 153.14 214.11 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n67 1 Car -1 -1 -1 1094.27 184.36 1221.10 235.34 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n67 2 Car -1 -1 -1 955.09 183.36 1066.44 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n67 3 Car -1 -1 -1 1030.14 184.15 1154.98 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n67 26 Pedestrian -1 -1 -1 362.09 164.45 392.33 241.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n67 27 Pedestrian -1 -1 -1 1079.90 136.77 1210.87 365.97 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n67 5 Pedestrian -1 -1 -1 463.07 161.43 503.28 280.76 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n67 24 Pedestrian -1 -1 -1 815.48 163.27 847.14 247.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n67 6 Car -1 -1 -1 601.77 172.88 637.11 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n67 7 Pedestrian -1 -1 -1 219.77 155.27 234.82 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n67 16 Car -1 -1 -1 598.39 173.55 622.26 192.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n67 25 Pedestrian -1 -1 -1 192.43 154.36 209.93 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n67 28 Pedestrian -1 -1 -1 182.07 161.68 196.72 197.28 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n68 1 Car -1 -1 -1 1094.04 183.95 1221.29 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n68 2 Car -1 -1 -1 955.11 183.01 1066.42 232.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n68 5 Pedestrian -1 -1 -1 467.60 159.91 506.58 282.86 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n68 24 Pedestrian -1 -1 -1 811.11 163.21 845.31 247.90 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n68 3 Car -1 -1 -1 1030.36 184.00 1154.89 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n68 26 Pedestrian -1 -1 -1 360.72 163.90 390.81 240.08 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n68 6 Car -1 -1 -1 601.53 172.76 637.07 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n68 27 Pedestrian -1 -1 -1 1076.76 141.10 1191.42 354.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n68 7 Pedestrian -1 -1 -1 219.64 155.29 234.80 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n68 16 Car -1 -1 -1 598.18 173.50 622.18 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n68 25 Pedestrian -1 -1 -1 192.77 160.12 208.90 198.59 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n68 28 Pedestrian -1 -1 -1 182.15 161.84 196.71 197.13 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n69 1 Car -1 -1 -1 1093.55 183.78 1220.58 236.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n69 2 Car -1 -1 -1 955.63 182.92 1065.96 232.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n69 3 Car -1 -1 -1 1029.78 184.01 1155.62 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n69 24 Pedestrian -1 -1 -1 811.19 163.47 843.67 248.07 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n69 5 Pedestrian -1 -1 -1 473.48 158.98 511.20 282.73 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n69 6 Car -1 -1 -1 601.52 172.78 637.07 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n69 26 Pedestrian -1 -1 -1 358.93 164.22 386.70 240.44 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n69 27 Pedestrian -1 -1 -1 1053.29 140.26 1153.85 349.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n69 7 Pedestrian -1 -1 -1 219.74 155.27 234.83 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n69 16 Car -1 -1 -1 598.11 173.47 622.08 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n69 25 Pedestrian -1 -1 -1 192.66 160.43 209.22 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n69 28 Pedestrian -1 -1 -1 182.05 161.99 196.57 197.19 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n70 2 Car -1 -1 -1 956.13 183.04 1065.57 232.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n70 5 Pedestrian -1 -1 -1 477.44 161.00 519.86 282.98 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n70 3 Car -1 -1 -1 1029.25 184.04 1155.99 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n70 24 Pedestrian -1 -1 -1 808.76 162.58 840.81 248.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n70 1 Car -1 -1 -1 1094.23 184.13 1219.18 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n70 26 Pedestrian -1 -1 -1 357.24 165.02 382.80 240.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n70 6 Car -1 -1 -1 601.71 172.86 636.88 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n70 27 Pedestrian -1 -1 -1 1024.00 145.29 1129.59 344.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n70 7 Pedestrian -1 -1 -1 220.22 155.29 234.81 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n70 16 Car -1 -1 -1 598.43 173.47 622.03 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n70 25 Pedestrian -1 -1 -1 192.45 161.02 208.68 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n70 28 Pedestrian -1 -1 -1 182.09 162.29 196.43 197.13 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n70 29 Pedestrian -1 -1 -1 1048.94 142.85 1150.26 337.77 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n71 5 Pedestrian -1 -1 -1 478.39 159.67 527.58 284.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n71 2 Car -1 -1 -1 954.44 182.80 1067.68 234.28 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n71 3 Car -1 -1 -1 1030.04 184.42 1155.11 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n71 24 Pedestrian -1 -1 -1 809.03 162.65 839.86 248.73 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n71 1 Car -1 -1 -1 1093.17 184.15 1220.53 236.90 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n71 26 Pedestrian -1 -1 -1 355.43 164.56 381.09 239.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n71 6 Car -1 -1 -1 601.55 172.94 637.12 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n71 27 Pedestrian -1 -1 -1 1000.50 143.67 1122.25 345.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n71 7 Pedestrian -1 -1 -1 219.91 155.24 234.81 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n71 16 Car -1 -1 -1 598.28 173.52 622.26 193.08 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n71 25 Pedestrian -1 -1 -1 192.45 161.17 208.21 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n71 28 Pedestrian -1 -1 -1 182.05 162.45 196.22 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n72 5 Pedestrian -1 -1 -1 479.96 158.09 534.71 284.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n72 3 Car -1 -1 -1 1029.69 183.90 1155.61 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n72 2 Car -1 -1 -1 955.20 182.86 1067.95 234.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n72 1 Car -1 -1 -1 1093.58 184.25 1220.72 236.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n72 24 Pedestrian -1 -1 -1 808.06 162.42 838.82 249.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n72 26 Pedestrian -1 -1 -1 353.18 163.90 378.94 238.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n72 6 Car -1 -1 -1 601.35 172.83 637.41 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n72 27 Pedestrian -1 -1 -1 990.19 143.90 1094.48 345.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n72 7 Pedestrian -1 -1 -1 219.78 155.18 235.16 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n72 16 Car -1 -1 -1 597.88 173.56 622.55 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n72 25 Pedestrian -1 -1 -1 192.61 161.28 207.96 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n72 28 Pedestrian -1 -1 -1 182.06 162.11 196.47 197.24 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n72 30 Pedestrian -1 -1 -1 1006.76 141.86 1108.56 330.73 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n73 3 Car -1 -1 -1 1033.30 182.78 1157.52 234.99 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n73 2 Car -1 -1 -1 954.99 182.29 1067.09 232.41 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n73 1 Car -1 -1 -1 1094.07 183.78 1220.41 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n73 5 Pedestrian -1 -1 -1 483.74 164.06 538.41 285.38 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n73 6 Car -1 -1 -1 601.64 172.89 636.81 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n73 24 Pedestrian -1 -1 -1 806.70 161.70 836.42 249.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n73 26 Pedestrian -1 -1 -1 351.65 164.20 377.53 237.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n73 27 Pedestrian -1 -1 -1 985.22 147.01 1060.67 341.00 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n73 7 Pedestrian -1 -1 -1 220.00 155.09 234.94 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n73 16 Car -1 -1 -1 597.97 173.60 622.37 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n73 25 Pedestrian -1 -1 -1 192.66 161.12 207.83 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n73 30 Pedestrian -1 -1 -1 1015.62 145.20 1091.95 319.99 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n73 28 Pedestrian -1 -1 -1 181.84 161.97 196.57 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n74 2 Car -1 -1 -1 954.02 182.02 1067.89 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n74 24 Pedestrian -1 -1 -1 805.70 162.19 836.14 250.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n74 5 Pedestrian -1 -1 -1 493.02 163.11 542.59 286.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n74 1 Car -1 -1 -1 1093.60 183.11 1220.69 236.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n74 3 Car -1 -1 -1 1031.73 183.11 1158.84 235.50 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n74 27 Pedestrian -1 -1 -1 960.08 141.90 1040.38 346.24 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n74 26 Pedestrian -1 -1 -1 347.40 164.09 375.49 237.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n74 6 Car -1 -1 -1 601.90 173.00 636.68 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n74 7 Pedestrian -1 -1 -1 219.96 155.03 235.14 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n74 16 Car -1 -1 -1 598.13 173.43 622.32 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n74 25 Pedestrian -1 -1 -1 192.06 160.79 207.99 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n74 30 Pedestrian -1 -1 -1 1002.27 146.01 1089.77 319.68 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n75 2 Car -1 -1 -1 954.81 182.21 1066.82 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n75 24 Pedestrian -1 -1 -1 804.81 162.81 835.81 250.19 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n75 5 Pedestrian -1 -1 -1 501.30 162.48 544.06 287.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n75 1 Car -1 -1 -1 1095.38 182.89 1219.33 237.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n75 3 Car -1 -1 -1 1028.50 183.56 1156.52 235.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n75 27 Pedestrian -1 -1 -1 930.60 143.28 1031.32 338.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n75 6 Car -1 -1 -1 601.84 172.90 636.68 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n75 26 Pedestrian -1 -1 -1 346.86 162.94 374.39 237.03 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n75 7 Pedestrian -1 -1 -1 220.20 154.91 234.95 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n75 16 Car -1 -1 -1 598.32 173.48 622.32 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n75 25 Pedestrian -1 -1 -1 192.39 160.84 207.90 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n75 30 Pedestrian -1 -1 -1 982.72 146.87 1071.11 318.60 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n75 31 Pedestrian -1 -1 -1 374.89 160.02 388.22 189.72 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n76 2 Car -1 -1 -1 955.33 181.88 1066.40 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n76 1 Car -1 -1 -1 1095.97 183.30 1219.41 237.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n76 3 Car -1 -1 -1 1028.72 183.21 1156.72 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n76 24 Pedestrian -1 -1 -1 803.64 163.04 835.64 250.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n76 26 Pedestrian -1 -1 -1 344.76 163.04 372.23 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n76 5 Pedestrian -1 -1 -1 509.02 162.41 551.59 289.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n76 27 Pedestrian -1 -1 -1 913.70 143.92 1018.00 338.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n76 6 Car -1 -1 -1 601.71 173.02 637.01 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n76 7 Pedestrian -1 -1 -1 219.86 154.92 234.85 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n76 16 Car -1 -1 -1 598.39 173.51 622.31 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n76 25 Pedestrian -1 -1 -1 192.63 160.99 208.02 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n76 30 Pedestrian -1 -1 -1 967.97 150.67 1047.67 307.94 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n76 31 Pedestrian -1 -1 -1 374.91 160.25 387.65 190.23 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n77 1 Car -1 -1 -1 1094.34 183.93 1220.69 237.34 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n77 3 Car -1 -1 -1 1029.55 182.57 1155.23 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n77 5 Pedestrian -1 -1 -1 512.83 160.34 562.32 290.69 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n77 2 Car -1 -1 -1 954.33 182.57 1067.83 234.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n77 24 Pedestrian -1 -1 -1 801.30 162.57 833.88 251.34 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n77 27 Pedestrian -1 -1 -1 903.82 144.41 997.29 336.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n77 6 Car -1 -1 -1 601.72 172.80 636.79 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n77 26 Pedestrian -1 -1 -1 343.74 163.81 370.56 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n77 7 Pedestrian -1 -1 -1 219.55 154.97 234.59 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n77 16 Car -1 -1 -1 598.39 173.46 622.38 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n77 25 Pedestrian -1 -1 -1 192.60 161.04 208.30 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n77 30 Pedestrian -1 -1 -1 961.77 145.11 1030.62 313.00 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n77 31 Pedestrian -1 -1 -1 374.44 160.36 387.79 190.37 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n77 32 Pedestrian -1 -1 -1 931.51 148.04 1023.10 317.27 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n78 1 Car -1 -1 -1 1093.93 184.15 1221.68 237.03 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n78 5 Pedestrian -1 -1 -1 518.16 159.81 571.13 291.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n78 24 Pedestrian -1 -1 -1 800.91 162.36 833.31 251.41 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n78 3 Car -1 -1 -1 1027.79 182.26 1157.56 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n78 2 Car -1 -1 -1 954.64 182.05 1067.61 234.73 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n78 6 Car -1 -1 -1 601.69 172.90 636.86 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n78 27 Pedestrian -1 -1 -1 899.22 142.28 971.22 332.27 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n78 26 Pedestrian -1 -1 -1 343.57 164.13 369.07 234.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n78 7 Pedestrian -1 -1 -1 219.88 155.13 234.70 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n78 16 Car -1 -1 -1 598.33 173.54 622.43 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n78 25 Pedestrian -1 -1 -1 192.48 160.98 208.33 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n78 30 Pedestrian -1 -1 -1 944.49 145.10 1017.47 312.74 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n78 31 Pedestrian -1 -1 -1 374.52 160.47 387.27 189.98 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n79 1 Car -1 -1 -1 1094.57 184.56 1221.27 236.53 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n79 5 Pedestrian -1 -1 -1 522.66 157.91 574.20 293.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n79 3 Car -1 -1 -1 1033.00 182.62 1158.00 235.43 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n79 24 Pedestrian -1 -1 -1 800.00 162.37 832.10 251.37 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n79 2 Car -1 -1 -1 957.11 182.33 1064.65 234.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n79 6 Car -1 -1 -1 601.47 172.88 637.06 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n79 26 Pedestrian -1 -1 -1 341.25 164.68 367.84 234.50 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n79 27 Pedestrian -1 -1 -1 882.43 142.62 949.80 330.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n79 7 Pedestrian -1 -1 -1 220.27 155.16 234.53 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n79 25 Pedestrian -1 -1 -1 192.53 160.95 208.37 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n79 16 Car -1 -1 -1 598.36 173.73 622.47 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n79 30 Pedestrian -1 -1 -1 915.94 149.75 992.63 316.33 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n79 31 Pedestrian -1 -1 -1 374.87 160.35 387.88 189.55 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n80 1 Car -1 -1 -1 1094.55 184.62 1221.52 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n80 5 Pedestrian -1 -1 -1 532.38 158.11 579.59 293.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n80 3 Car -1 -1 -1 1032.61 183.60 1157.69 234.47 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n80 6 Car -1 -1 -1 601.29 172.96 637.05 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n80 2 Car -1 -1 -1 955.19 182.49 1067.81 235.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n80 24 Pedestrian -1 -1 -1 797.29 161.96 829.42 252.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n80 26 Pedestrian -1 -1 -1 340.72 165.16 365.70 234.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n80 27 Pedestrian -1 -1 -1 856.03 142.83 945.51 329.63 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n80 7 Pedestrian -1 -1 -1 220.32 155.10 234.70 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n80 25 Pedestrian -1 -1 -1 192.42 161.03 208.30 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n80 16 Car -1 -1 -1 598.59 173.89 622.44 193.29 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n80 30 Pedestrian -1 -1 -1 908.13 152.29 985.03 312.93 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n80 31 Pedestrian -1 -1 -1 375.22 160.03 387.99 189.47 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n81 1 Car -1 -1 -1 1098.83 184.78 1220.86 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n81 3 Car -1 -1 -1 1033.18 183.72 1156.95 234.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n81 5 Pedestrian -1 -1 -1 538.54 153.89 588.89 297.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n81 2 Car -1 -1 -1 955.38 182.37 1067.40 235.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n81 6 Car -1 -1 -1 601.69 172.95 636.81 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n81 24 Pedestrian -1 -1 -1 797.29 162.27 829.50 252.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n81 26 Pedestrian -1 -1 -1 340.59 164.94 365.03 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n81 27 Pedestrian -1 -1 -1 842.19 144.40 936.34 322.18 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n81 7 Pedestrian -1 -1 -1 220.35 155.09 234.71 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n81 25 Pedestrian -1 -1 -1 192.47 161.19 208.30 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n81 16 Car -1 -1 -1 598.78 173.86 622.19 193.17 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n81 30 Pedestrian -1 -1 -1 899.03 146.76 986.52 303.53 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n81 33 Pedestrian -1 -1 -1 896.17 151.99 974.48 306.22 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n82 1 Car -1 -1 -1 1094.06 184.64 1221.87 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n82 3 Car -1 -1 -1 1033.04 183.53 1157.21 234.49 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n82 5 Pedestrian -1 -1 -1 543.65 154.61 598.78 296.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n82 6 Car -1 -1 -1 601.60 172.88 636.78 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n82 2 Car -1 -1 -1 956.16 182.23 1066.26 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n82 27 Pedestrian -1 -1 -1 829.36 145.77 919.02 320.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n82 24 Pedestrian -1 -1 -1 800.17 162.78 831.85 254.43 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n82 7 Pedestrian -1 -1 -1 220.30 155.12 234.87 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n82 25 Pedestrian -1 -1 -1 192.83 161.46 208.26 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n82 30 Pedestrian -1 -1 -1 893.36 144.47 969.30 305.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n82 16 Car -1 -1 -1 599.03 173.85 621.83 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n82 26 Pedestrian -1 -1 -1 337.69 164.65 364.76 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n83 1 Car -1 -1 -1 1094.14 184.57 1221.32 236.57 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n83 3 Car -1 -1 -1 1032.81 183.67 1157.64 234.49 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n83 5 Pedestrian -1 -1 -1 545.41 156.03 607.03 295.56 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n83 2 Car -1 -1 -1 957.27 181.09 1065.50 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n83 6 Car -1 -1 -1 601.17 172.75 636.79 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n83 24 Pedestrian -1 -1 -1 800.99 162.49 832.30 255.10 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n83 27 Pedestrian -1 -1 -1 824.72 145.51 900.46 321.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n83 26 Pedestrian -1 -1 -1 337.71 164.05 362.07 231.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n83 25 Pedestrian -1 -1 -1 192.75 161.49 208.49 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n83 7 Pedestrian -1 -1 -1 220.36 155.09 234.81 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n83 16 Car -1 -1 -1 598.84 173.67 621.92 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n83 30 Pedestrian -1 -1 -1 886.10 143.87 953.52 298.96 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n84 1 Car -1 -1 -1 1094.02 184.43 1221.26 236.73 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n84 5 Pedestrian -1 -1 -1 551.38 159.34 615.85 298.49 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n84 3 Car -1 -1 -1 1032.85 183.74 1157.15 234.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n84 2 Car -1 -1 -1 952.60 181.31 1064.55 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n84 6 Car -1 -1 -1 601.29 172.78 636.94 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n84 27 Pedestrian -1 -1 -1 813.67 143.19 873.47 322.98 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n84 24 Pedestrian -1 -1 -1 801.20 162.94 833.25 256.08 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n84 16 Car -1 -1 -1 598.98 173.54 622.11 193.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n84 25 Pedestrian -1 -1 -1 192.62 161.39 208.53 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n84 26 Pedestrian -1 -1 -1 337.47 164.34 361.70 231.28 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n84 7 Pedestrian -1 -1 -1 220.25 155.18 234.65 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n84 30 Pedestrian -1 -1 -1 866.84 149.98 934.54 307.82 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n84 34 Pedestrian -1 -1 -1 871.78 146.04 945.10 295.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n84 35 Pedestrian -1 -1 -1 378.09 160.52 389.91 188.34 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n85 1 Car -1 -1 -1 1094.56 184.55 1220.87 236.54 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n85 3 Car -1 -1 -1 1032.53 183.79 1157.55 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n85 2 Car -1 -1 -1 956.83 182.05 1066.54 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n85 5 Pedestrian -1 -1 -1 558.03 159.38 617.32 299.79 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n85 27 Pedestrian -1 -1 -1 788.55 146.29 860.52 318.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n85 6 Car -1 -1 -1 603.65 172.57 636.69 201.86 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n85 24 Pedestrian -1 -1 -1 800.74 160.97 839.65 259.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n85 16 Car -1 -1 -1 598.01 173.54 622.95 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n85 25 Pedestrian -1 -1 -1 192.40 161.45 208.55 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n85 30 Pedestrian -1 -1 -1 853.82 151.72 924.41 306.03 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n85 7 Pedestrian -1 -1 -1 219.93 155.15 234.54 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n85 26 Pedestrian -1 -1 -1 336.73 164.91 360.53 231.24 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n85 35 Pedestrian -1 -1 -1 378.38 160.58 389.56 188.44 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n85 36 Cyclist -1 -1 -1 520.34 166.84 532.79 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n86 1 Car -1 -1 -1 1094.39 184.74 1220.82 236.42 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n86 2 Car -1 -1 -1 956.43 182.70 1066.51 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n86 3 Car -1 -1 -1 1032.71 183.79 1157.21 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n86 5 Pedestrian -1 -1 -1 568.56 160.24 622.15 299.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n86 27 Pedestrian -1 -1 -1 768.87 146.93 863.80 318.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n86 6 Car -1 -1 -1 603.84 172.22 637.56 201.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n86 25 Pedestrian -1 -1 -1 192.51 161.41 208.45 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n86 26 Pedestrian -1 -1 -1 334.96 164.56 358.85 230.42 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n86 24 Pedestrian -1 -1 -1 800.93 162.21 839.33 258.20 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n86 16 Car -1 -1 -1 596.87 173.47 623.12 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n86 7 Pedestrian -1 -1 -1 219.92 155.21 234.43 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n86 30 Pedestrian -1 -1 -1 834.15 154.46 906.16 303.66 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n86 35 Pedestrian -1 -1 -1 378.21 161.26 389.30 188.28 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n86 36 Cyclist -1 -1 -1 519.17 167.14 531.13 198.65 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n86 37 Pedestrian -1 -1 -1 519.17 167.14 531.13 198.65 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n87 1 Car -1 -1 -1 1094.17 184.57 1221.34 236.49 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n87 2 Car -1 -1 -1 956.26 182.84 1066.35 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n87 3 Car -1 -1 -1 1032.47 183.78 1157.55 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n87 27 Pedestrian -1 -1 -1 759.21 148.89 843.70 316.86 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n87 5 Pedestrian -1 -1 -1 579.77 161.20 625.80 302.98 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n87 6 Car -1 -1 -1 602.87 172.36 638.78 201.53 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n87 16 Car -1 -1 -1 596.42 173.05 624.31 194.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n87 26 Pedestrian -1 -1 -1 334.35 164.25 357.37 229.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n87 7 Pedestrian -1 -1 -1 219.85 155.13 234.57 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n87 25 Pedestrian -1 -1 -1 192.40 161.43 208.26 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n87 24 Pedestrian -1 -1 -1 797.55 163.72 836.14 256.32 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n87 30 Pedestrian -1 -1 -1 829.32 154.86 895.63 303.14 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n87 35 Pedestrian -1 -1 -1 378.42 161.32 389.26 188.04 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n87 36 Cyclist -1 -1 -1 518.63 167.07 530.65 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n87 38 Pedestrian -1 -1 -1 496.18 169.29 507.59 198.90 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n88 1 Car -1 -1 -1 1094.33 184.47 1221.06 236.47 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n88 2 Car -1 -1 -1 955.99 182.97 1066.14 233.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n88 3 Car -1 -1 -1 1032.67 183.88 1157.25 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n88 5 Pedestrian -1 -1 -1 583.85 158.58 637.36 305.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n88 27 Pedestrian -1 -1 -1 756.02 147.80 831.07 316.47 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n88 6 Car -1 -1 -1 600.88 173.18 636.90 201.17 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n88 26 Pedestrian -1 -1 -1 334.70 164.48 355.97 229.23 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n88 25 Pedestrian -1 -1 -1 192.40 161.29 208.32 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n88 7 Pedestrian -1 -1 -1 220.07 155.10 234.44 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n88 30 Pedestrian -1 -1 -1 823.78 151.52 878.25 305.42 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n88 16 Car -1 -1 -1 597.35 173.25 622.95 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n88 24 Pedestrian -1 -1 -1 799.88 163.67 834.12 256.62 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n88 35 Pedestrian -1 -1 -1 378.24 161.10 389.27 187.76 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n89 1 Car -1 -1 -1 1094.73 184.52 1220.73 236.55 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n89 2 Car -1 -1 -1 955.61 182.98 1066.29 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n89 3 Car -1 -1 -1 1032.48 183.84 1157.39 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n89 5 Pedestrian -1 -1 -1 590.14 156.98 652.58 308.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n89 27 Pedestrian -1 -1 -1 754.30 146.94 810.11 310.51 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n89 6 Car -1 -1 -1 600.52 173.52 637.27 201.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n89 7 Pedestrian -1 -1 -1 220.05 155.19 234.37 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n89 25 Pedestrian -1 -1 -1 192.41 161.28 208.14 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n89 26 Pedestrian -1 -1 -1 334.19 164.69 355.53 229.09 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n89 30 Pedestrian -1 -1 -1 808.60 151.35 863.16 305.84 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n89 16 Car -1 -1 -1 597.75 173.70 622.74 193.70 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n89 24 Pedestrian -1 -1 -1 802.73 163.08 837.91 257.51 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n89 39 Cyclist -1 -1 -1 516.60 166.38 530.09 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n90 1 Car -1 -1 -1 1094.79 184.47 1220.72 236.53 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n90 2 Car -1 -1 -1 955.46 182.92 1066.58 234.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n90 3 Car -1 -1 -1 1032.32 183.80 1157.59 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n90 5 Pedestrian -1 -1 -1 593.77 158.08 657.39 307.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n90 27 Pedestrian -1 -1 -1 734.57 145.37 799.75 311.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n90 6 Car -1 -1 -1 601.04 173.64 636.81 202.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n90 26 Pedestrian -1 -1 -1 334.06 165.43 355.95 229.19 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n90 30 Pedestrian -1 -1 -1 792.80 154.20 855.91 303.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n90 7 Pedestrian -1 -1 -1 220.07 155.26 234.36 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n90 25 Pedestrian -1 -1 -1 192.62 161.50 207.98 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n90 16 Car -1 -1 -1 598.36 173.62 622.14 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n90 24 Pedestrian -1 -1 -1 800.30 162.25 840.49 258.13 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n90 39 Cyclist -1 -1 -1 516.39 166.92 529.53 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n90 40 Pedestrian -1 -1 -1 492.41 170.65 504.67 200.78 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n91 1 Car -1 -1 -1 1094.68 184.43 1221.03 236.51 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n91 2 Car -1 -1 -1 955.12 183.05 1066.67 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n91 3 Car -1 -1 -1 1031.81 183.69 1158.10 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n91 5 Pedestrian -1 -1 -1 603.58 156.48 661.55 309.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n91 6 Car -1 -1 -1 601.49 173.72 636.72 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n91 27 Pedestrian -1 -1 -1 720.24 146.08 797.99 305.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n91 30 Pedestrian -1 -1 -1 782.97 152.98 850.09 304.02 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n91 7 Pedestrian -1 -1 -1 220.25 155.33 234.44 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n91 25 Pedestrian -1 -1 -1 192.82 161.46 207.85 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n91 26 Pedestrian -1 -1 -1 334.66 164.95 355.51 228.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n91 16 Car -1 -1 -1 598.89 173.90 622.04 192.97 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n91 24 Pedestrian -1 -1 -1 797.06 161.17 843.49 259.02 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n91 40 Pedestrian -1 -1 -1 491.53 170.56 504.12 201.30 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n91 41 Pedestrian -1 -1 -1 516.04 166.23 528.45 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n92 1 Car -1 -1 -1 1094.96 184.47 1220.89 236.50 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n92 3 Car -1 -1 -1 1028.52 183.81 1157.39 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n92 2 Car -1 -1 -1 955.17 183.15 1066.51 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n92 5 Pedestrian -1 -1 -1 614.28 156.74 666.81 308.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n92 6 Car -1 -1 -1 601.61 173.24 636.50 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n92 27 Pedestrian -1 -1 -1 712.52 147.24 790.14 304.56 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n92 26 Pedestrian -1 -1 -1 334.38 163.87 356.15 227.55 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n92 30 Pedestrian -1 -1 -1 775.83 154.65 834.97 302.33 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n92 7 Pedestrian -1 -1 -1 220.18 155.30 234.48 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n92 25 Pedestrian -1 -1 -1 192.90 161.43 207.80 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n92 16 Car -1 -1 -1 598.67 174.00 621.59 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n92 24 Pedestrian -1 -1 -1 790.54 148.67 850.06 278.70 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n92 41 Pedestrian -1 -1 -1 515.03 166.94 527.82 200.30 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n92 42 Cyclist -1 -1 -1 515.03 166.94 527.82 200.30 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n93 1 Car -1 -1 -1 1095.28 184.40 1220.63 236.50 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n93 3 Car -1 -1 -1 1028.53 183.89 1157.37 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n93 2 Car -1 -1 -1 955.02 183.12 1066.69 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n93 5 Pedestrian -1 -1 -1 624.65 153.68 679.32 312.28 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n93 27 Pedestrian -1 -1 -1 707.25 147.29 779.78 304.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n93 6 Car -1 -1 -1 602.05 173.13 636.45 202.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n93 30 Pedestrian -1 -1 -1 769.69 150.18 825.42 300.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n93 26 Pedestrian -1 -1 -1 334.32 162.89 355.79 227.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n93 7 Pedestrian -1 -1 -1 220.20 155.34 234.57 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n93 25 Pedestrian -1 -1 -1 193.02 161.39 207.59 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n93 16 Car -1 -1 -1 598.70 173.77 621.74 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n93 24 Pedestrian -1 -1 -1 804.56 155.97 851.37 262.71 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n93 41 Pedestrian -1 -1 -1 513.06 166.53 526.38 201.35 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n93 43 Pedestrian -1 -1 -1 488.13 169.97 500.98 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n93 44 Pedestrian -1 -1 -1 796.09 153.97 844.53 265.07 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n94 1 Car -1 -1 -1 1095.50 184.46 1220.23 236.45 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n94 3 Car -1 -1 -1 1028.61 183.83 1157.26 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n94 2 Car -1 -1 -1 954.78 183.08 1067.00 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n94 5 Pedestrian -1 -1 -1 631.22 154.01 695.31 311.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n94 27 Pedestrian -1 -1 -1 703.17 148.54 755.05 302.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n94 6 Car -1 -1 -1 601.77 172.90 637.04 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n94 30 Pedestrian -1 -1 -1 762.13 150.69 817.16 299.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n94 26 Pedestrian -1 -1 -1 334.28 163.41 355.33 226.76 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n94 7 Pedestrian -1 -1 -1 220.36 155.26 234.56 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n94 25 Pedestrian -1 -1 -1 193.01 161.38 207.57 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n94 16 Car -1 -1 -1 598.74 173.70 621.38 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n94 24 Pedestrian -1 -1 -1 806.09 157.75 850.02 261.58 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n94 43 Pedestrian -1 -1 -1 486.44 169.26 499.53 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n94 45 Cyclist -1 -1 -1 511.94 166.81 525.17 201.28 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n95 1 Car -1 -1 -1 1095.45 184.49 1220.39 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n95 2 Car -1 -1 -1 954.86 183.20 1066.82 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n95 3 Car -1 -1 -1 1031.41 183.62 1158.43 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n95 6 Car -1 -1 -1 603.06 172.51 637.36 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n95 26 Pedestrian -1 -1 -1 333.01 163.65 353.94 225.81 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n95 5 Pedestrian -1 -1 -1 635.38 155.83 707.19 310.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n95 30 Pedestrian -1 -1 -1 744.26 153.48 805.02 296.23 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n95 27 Pedestrian -1 -1 -1 691.54 146.74 742.76 302.63 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n95 7 Pedestrian -1 -1 -1 220.32 155.28 234.58 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n95 24 Pedestrian -1 -1 -1 811.10 159.29 852.47 260.99 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n95 25 Pedestrian -1 -1 -1 193.08 161.55 207.48 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n95 16 Car -1 -1 -1 598.62 173.55 621.52 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n95 43 Pedestrian -1 -1 -1 485.76 169.11 498.73 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n95 45 Cyclist -1 -1 -1 510.67 166.89 523.79 201.19 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n96 1 Car -1 -1 -1 1095.49 184.61 1220.38 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n96 3 Car -1 -1 -1 1031.57 183.70 1158.29 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n96 2 Car -1 -1 -1 954.94 183.24 1066.73 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n96 6 Car -1 -1 -1 602.79 172.19 637.59 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n96 26 Pedestrian -1 -1 -1 333.19 162.71 353.50 224.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n96 5 Pedestrian -1 -1 -1 645.00 156.70 720.25 309.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n96 24 Pedestrian -1 -1 -1 815.10 158.87 855.80 262.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n96 27 Pedestrian -1 -1 -1 678.27 145.80 732.81 297.36 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n96 30 Pedestrian -1 -1 -1 734.04 152.74 799.61 296.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n96 7 Pedestrian -1 -1 -1 220.16 155.15 234.55 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n96 25 Pedestrian -1 -1 -1 192.82 161.48 207.32 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n96 16 Car -1 -1 -1 598.64 173.33 621.68 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n96 43 Pedestrian -1 -1 -1 484.15 168.72 497.54 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n96 46 Pedestrian -1 -1 -1 507.57 167.53 521.74 203.48 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n97 1 Car -1 -1 -1 1095.36 184.59 1220.47 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n97 3 Car -1 -1 -1 1028.35 183.81 1157.53 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n97 2 Car -1 -1 -1 954.94 183.27 1066.62 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n97 6 Car -1 -1 -1 602.53 172.31 637.84 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n97 26 Pedestrian -1 -1 -1 332.94 162.83 353.47 224.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n97 24 Pedestrian -1 -1 -1 817.73 160.37 855.23 265.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n97 5 Pedestrian -1 -1 -1 651.30 156.13 722.59 309.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n97 27 Pedestrian -1 -1 -1 654.79 149.46 725.97 293.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n97 30 Pedestrian -1 -1 -1 723.97 156.08 787.02 293.64 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n97 7 Pedestrian -1 -1 -1 220.15 155.21 234.65 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n97 25 Pedestrian -1 -1 -1 192.80 161.35 207.46 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n97 16 Car -1 -1 -1 598.54 173.31 621.76 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n97 46 Pedestrian -1 -1 -1 506.73 167.61 520.33 203.86 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n97 43 Pedestrian -1 -1 -1 483.48 169.53 496.90 203.45 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n97 47 Pedestrian -1 -1 -1 733.59 148.57 800.37 279.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n98 1 Car -1 -1 -1 1095.25 184.53 1220.50 236.45 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n98 3 Car -1 -1 -1 1028.48 183.79 1157.44 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n98 2 Car -1 -1 -1 954.85 183.17 1066.67 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n98 24 Pedestrian -1 -1 -1 820.42 160.01 857.83 265.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n98 6 Car -1 -1 -1 602.66 172.41 637.68 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n98 26 Pedestrian -1 -1 -1 332.88 163.24 353.64 223.60 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n98 27 Pedestrian -1 -1 -1 658.53 148.46 721.86 294.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n98 7 Pedestrian -1 -1 -1 220.11 155.16 234.76 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n98 43 Pedestrian -1 -1 -1 481.36 168.61 495.58 203.41 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n98 25 Pedestrian -1 -1 -1 192.88 161.42 207.55 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n98 5 Pedestrian -1 -1 -1 665.70 160.82 723.29 312.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n98 16 Car -1 -1 -1 598.60 173.29 621.55 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n98 30 Pedestrian -1 -1 -1 720.59 156.50 774.65 293.35 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n98 46 Pedestrian -1 -1 -1 504.74 166.47 519.34 204.46 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n98 47 Pedestrian -1 -1 -1 735.46 145.23 790.43 274.97 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n98 48 Pedestrian -1 -1 -1 181.74 160.90 196.55 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n99 1 Car -1 -1 -1 1095.08 184.52 1220.69 236.44 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n99 3 Car -1 -1 -1 1028.33 183.65 1157.42 233.70 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n99 2 Car -1 -1 -1 954.98 183.14 1066.63 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n99 24 Pedestrian -1 -1 -1 820.86 159.82 858.62 265.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n99 6 Car -1 -1 -1 602.63 172.49 637.71 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n99 27 Pedestrian -1 -1 -1 653.72 147.13 711.85 296.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n99 26 Pedestrian -1 -1 -1 333.20 164.07 354.30 223.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n99 7 Pedestrian -1 -1 -1 220.10 155.16 234.76 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n99 43 Pedestrian -1 -1 -1 480.55 169.46 494.64 204.85 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n99 25 Pedestrian -1 -1 -1 192.92 161.32 207.56 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n99 16 Car -1 -1 -1 598.68 173.39 621.23 192.80 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n99 5 Pedestrian -1 -1 -1 680.99 164.48 730.63 316.03 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n99 46 Pedestrian -1 -1 -1 504.81 165.70 518.54 203.20 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n99 30 Pedestrian -1 -1 -1 703.24 152.44 754.21 291.41 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n99 47 Pedestrian -1 -1 -1 728.87 145.94 781.76 282.02 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n99 48 Pedestrian -1 -1 -1 181.73 160.76 196.87 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n100 1 Car -1 -1 -1 1095.08 184.43 1220.75 236.47 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n100 3 Car -1 -1 -1 1028.38 183.66 1157.43 233.70 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n100 2 Car -1 -1 -1 954.91 183.16 1066.40 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n100 24 Pedestrian -1 -1 -1 821.66 160.00 858.82 265.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n100 27 Pedestrian -1 -1 -1 646.83 147.75 696.73 295.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n100 6 Car -1 -1 -1 602.89 172.56 637.37 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n100 5 Pedestrian -1 -1 -1 695.72 159.13 754.25 320.69 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n100 7 Pedestrian -1 -1 -1 220.09 155.10 234.75 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n100 25 Pedestrian -1 -1 -1 192.82 161.34 207.60 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n100 26 Pedestrian -1 -1 -1 334.55 163.92 355.12 222.19 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n100 16 Car -1 -1 -1 598.82 173.26 621.08 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n100 47 Pedestrian -1 -1 -1 702.53 146.30 777.95 281.23 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n100 43 Pedestrian -1 -1 -1 479.14 169.40 493.62 205.14 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n100 30 Pedestrian -1 -1 -1 688.28 152.38 745.91 290.75 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n100 46 Pedestrian -1 -1 -1 503.88 165.95 517.64 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n101 1 Car -1 -1 -1 1095.07 184.43 1220.84 236.47 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n101 3 Car -1 -1 -1 1028.47 183.66 1157.45 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n101 2 Car -1 -1 -1 954.81 183.14 1066.51 233.83 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n101 27 Pedestrian -1 -1 -1 631.09 147.14 696.14 295.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n101 24 Pedestrian -1 -1 -1 820.66 158.34 860.39 263.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n101 5 Pedestrian -1 -1 -1 695.90 160.25 761.92 320.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n101 6 Car -1 -1 -1 602.80 172.44 637.38 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n101 26 Pedestrian -1 -1 -1 335.44 163.69 355.62 222.34 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n101 7 Pedestrian -1 -1 -1 220.18 155.15 234.82 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n101 30 Pedestrian -1 -1 -1 686.04 156.39 740.36 285.75 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n101 25 Pedestrian -1 -1 -1 192.95 161.45 207.46 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n101 16 Car -1 -1 -1 598.64 173.17 621.09 192.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n101 47 Pedestrian -1 -1 -1 717.21 156.06 770.55 278.89 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n101 43 Pedestrian -1 -1 -1 476.72 169.34 491.47 206.02 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n101 46 Pedestrian -1 -1 -1 502.64 166.23 516.05 204.81 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n102 1 Car -1 -1 -1 1094.77 184.39 1221.02 236.59 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n102 3 Car -1 -1 -1 1028.39 183.60 1157.43 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n102 2 Car -1 -1 -1 954.72 183.11 1066.68 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n102 5 Pedestrian -1 -1 -1 702.54 156.31 777.03 324.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n102 27 Pedestrian -1 -1 -1 619.48 147.27 691.83 295.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n102 24 Pedestrian -1 -1 -1 824.33 159.39 861.77 265.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n102 6 Car -1 -1 -1 601.74 172.69 636.97 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n102 26 Pedestrian -1 -1 -1 335.85 163.00 356.52 221.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n102 7 Pedestrian -1 -1 -1 220.27 155.11 234.83 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n102 25 Pedestrian -1 -1 -1 192.78 161.53 207.60 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n102 16 Car -1 -1 -1 598.71 173.28 621.45 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n102 30 Pedestrian -1 -1 -1 676.82 157.33 734.54 285.73 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n102 46 Pedestrian -1 -1 -1 500.69 165.69 515.42 205.34 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n102 43 Pedestrian -1 -1 -1 475.31 168.56 490.46 207.18 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n102 47 Pedestrian -1 -1 -1 699.25 152.89 765.68 282.57 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n102 49 Pedestrian -1 -1 -1 181.26 161.19 196.79 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n103 1 Car -1 -1 -1 1094.72 184.28 1221.06 236.56 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n103 3 Car -1 -1 -1 1028.22 183.55 1157.64 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n103 2 Car -1 -1 -1 954.72 183.03 1066.74 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n103 27 Pedestrian -1 -1 -1 608.85 147.61 680.61 294.70 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n103 6 Car -1 -1 -1 602.02 172.82 636.58 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n103 5 Pedestrian -1 -1 -1 715.94 160.31 779.06 327.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n103 24 Pedestrian -1 -1 -1 824.71 159.71 863.43 265.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n103 26 Pedestrian -1 -1 -1 335.77 163.82 356.17 222.44 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n103 7 Pedestrian -1 -1 -1 220.15 155.05 235.04 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n103 30 Pedestrian -1 -1 -1 672.84 156.73 723.51 286.29 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n103 25 Pedestrian -1 -1 -1 192.86 161.63 207.70 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n103 46 Pedestrian -1 -1 -1 499.41 165.97 513.88 205.48 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n103 43 Pedestrian -1 -1 -1 473.42 168.25 488.24 207.46 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n103 16 Car -1 -1 -1 598.61 173.38 621.38 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n103 47 Pedestrian -1 -1 -1 701.18 147.84 755.98 278.92 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n103 49 Pedestrian -1 -1 -1 181.43 161.26 196.65 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n104 1 Car -1 -1 -1 1095.03 184.43 1220.79 236.50 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n104 3 Car -1 -1 -1 1028.17 183.61 1157.67 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n104 2 Car -1 -1 -1 954.69 183.03 1066.76 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n104 27 Pedestrian -1 -1 -1 606.63 147.94 666.95 293.15 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n104 24 Pedestrian -1 -1 -1 826.46 159.84 867.40 267.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n104 5 Pedestrian -1 -1 -1 732.25 157.85 786.27 329.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n104 6 Car -1 -1 -1 602.67 173.44 635.44 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n104 26 Pedestrian -1 -1 -1 335.46 163.92 356.44 222.17 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n104 7 Pedestrian -1 -1 -1 220.09 155.00 234.93 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n104 25 Pedestrian -1 -1 -1 192.85 161.46 207.67 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n104 30 Pedestrian -1 -1 -1 666.42 155.13 714.39 287.62 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n104 16 Car -1 -1 -1 598.87 173.23 621.11 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n104 47 Pedestrian -1 -1 -1 697.01 150.12 736.75 277.24 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n104 46 Pedestrian -1 -1 -1 498.25 166.11 512.86 205.90 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n104 49 Pedestrian -1 -1 -1 181.33 161.18 196.70 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n104 50 Pedestrian -1 -1 -1 366.12 161.02 378.26 187.15 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n105 1 Car -1 -1 -1 1094.89 184.38 1220.82 236.44 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n105 3 Car -1 -1 -1 1028.30 183.64 1157.56 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n105 2 Car -1 -1 -1 954.49 183.06 1066.97 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n105 27 Pedestrian -1 -1 -1 601.27 148.08 650.69 293.75 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n105 5 Pedestrian -1 -1 -1 738.90 153.12 802.45 328.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n105 24 Pedestrian -1 -1 -1 826.61 159.99 868.91 268.28 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n105 6 Car -1 -1 -1 602.91 174.02 635.20 202.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 7 Pedestrian -1 -1 -1 220.35 154.95 234.84 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n105 30 Pedestrian -1 -1 -1 651.12 155.63 699.07 287.29 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n105 47 Pedestrian -1 -1 -1 681.70 147.74 729.71 279.19 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n105 26 Pedestrian -1 -1 -1 335.55 163.74 357.07 220.50 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n105 25 Pedestrian -1 -1 -1 192.76 161.57 207.69 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n105 46 Pedestrian -1 -1 -1 495.64 165.17 512.05 206.59 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n105 16 Car -1 -1 -1 598.98 173.44 620.86 194.11 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n105 49 Pedestrian -1 -1 -1 181.28 161.36 196.55 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n106 1 Car -1 -1 -1 1094.83 184.34 1220.74 236.35 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n106 3 Car -1 -1 -1 1028.49 183.67 1157.41 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n106 2 Car -1 -1 -1 954.55 183.06 1066.86 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n106 5 Pedestrian -1 -1 -1 749.17 152.31 830.13 329.61 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n106 27 Pedestrian -1 -1 -1 590.79 150.22 645.05 291.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n106 6 Car -1 -1 -1 602.27 174.01 635.74 201.93 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n106 24 Pedestrian -1 -1 -1 824.54 159.07 871.14 269.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n106 30 Pedestrian -1 -1 -1 637.59 156.59 697.21 286.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n106 26 Pedestrian -1 -1 -1 335.76 163.45 357.19 219.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n106 7 Pedestrian -1 -1 -1 220.33 154.97 235.01 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n106 47 Pedestrian -1 -1 -1 677.73 147.84 725.48 278.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n106 25 Pedestrian -1 -1 -1 192.90 161.59 207.65 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n106 16 Car -1 -1 -1 598.30 173.53 621.48 194.09 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n106 49 Pedestrian -1 -1 -1 181.41 161.44 196.41 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n106 46 Pedestrian -1 -1 -1 493.92 165.36 510.51 207.10 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n106 51 Cyclist -1 -1 -1 467.44 167.90 484.17 208.49 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n106 52 Pedestrian -1 -1 -1 367.09 159.64 378.90 186.63 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n107 1 Car -1 -1 -1 1094.82 184.40 1220.90 236.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n107 3 Car -1 -1 -1 1031.29 183.36 1158.62 233.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n107 2 Car -1 -1 -1 954.28 182.84 1066.90 232.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n107 5 Pedestrian -1 -1 -1 753.46 153.72 841.37 334.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n107 27 Pedestrian -1 -1 -1 578.86 150.44 641.56 291.68 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n107 6 Car -1 -1 -1 602.21 173.95 635.89 201.97 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n107 26 Pedestrian -1 -1 -1 335.95 163.95 357.10 219.28 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n107 24 Pedestrian -1 -1 -1 823.85 159.26 871.28 273.71 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n107 30 Pedestrian -1 -1 -1 630.48 156.89 689.02 286.21 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n107 7 Pedestrian -1 -1 -1 220.46 155.01 235.07 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n107 25 Pedestrian -1 -1 -1 192.94 161.46 207.54 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n107 47 Pedestrian -1 -1 -1 665.51 149.55 715.44 277.05 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n107 51 Cyclist -1 -1 -1 463.76 167.26 482.34 209.45 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n107 49 Pedestrian -1 -1 -1 181.40 161.19 196.55 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n108 1 Car -1 -1 -1 1094.57 184.30 1221.06 236.49 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n108 3 Car -1 -1 -1 1031.47 183.38 1158.47 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n108 2 Car -1 -1 -1 954.24 182.88 1066.91 232.22 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n108 27 Pedestrian -1 -1 -1 575.65 150.33 636.28 291.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n108 5 Pedestrian -1 -1 -1 760.10 161.65 849.95 333.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n108 6 Car -1 -1 -1 602.22 173.96 635.83 201.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n108 26 Pedestrian -1 -1 -1 336.66 164.21 356.63 219.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n108 30 Pedestrian -1 -1 -1 628.02 156.22 684.07 286.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n108 24 Pedestrian -1 -1 -1 828.18 158.43 874.53 274.54 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n108 7 Pedestrian -1 -1 -1 220.64 155.11 235.04 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n108 25 Pedestrian -1 -1 -1 193.07 161.59 207.63 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n108 47 Pedestrian -1 -1 -1 668.72 148.61 711.55 272.46 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n108 51 Cyclist -1 -1 -1 460.97 168.38 482.06 210.42 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n108 49 Pedestrian -1 -1 -1 181.34 160.62 197.41 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n108 53 Pedestrian -1 -1 -1 490.12 163.83 508.19 208.65 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n109 1 Car -1 -1 -1 1094.67 184.21 1220.98 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n109 3 Car -1 -1 -1 1031.50 183.46 1158.41 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n109 2 Car -1 -1 -1 954.17 182.90 1067.01 232.24 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n109 27 Pedestrian -1 -1 -1 569.16 151.27 627.76 290.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n109 5 Pedestrian -1 -1 -1 769.58 157.77 848.93 331.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n109 30 Pedestrian -1 -1 -1 626.84 155.16 670.48 286.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n109 26 Pedestrian -1 -1 -1 336.85 164.35 356.78 218.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n109 6 Car -1 -1 -1 601.97 172.79 636.42 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n109 51 Cyclist -1 -1 -1 456.91 168.32 479.76 212.21 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n109 7 Pedestrian -1 -1 -1 220.83 155.27 234.94 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n109 47 Pedestrian -1 -1 -1 662.13 148.95 704.05 272.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n109 25 Pedestrian -1 -1 -1 193.18 161.63 207.64 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n109 24 Pedestrian -1 -1 -1 832.36 159.10 876.75 273.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n109 53 Pedestrian -1 -1 -1 489.41 163.00 506.97 210.48 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n109 54 Car -1 -1 -1 596.28 173.23 623.39 194.18 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n110 1 Car -1 -1 -1 1094.62 184.26 1221.05 236.55 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n110 3 Car -1 -1 -1 1031.26 183.46 1158.67 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n110 2 Car -1 -1 -1 954.67 183.08 1066.62 233.83 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n110 5 Pedestrian -1 -1 -1 786.10 155.35 854.84 334.61 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n110 27 Pedestrian -1 -1 -1 566.35 151.97 616.18 289.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n110 30 Pedestrian -1 -1 -1 618.48 155.29 662.49 285.74 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n110 26 Pedestrian -1 -1 -1 337.33 163.81 356.66 218.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n110 47 Pedestrian -1 -1 -1 651.85 149.05 698.92 272.58 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n110 51 Cyclist -1 -1 -1 453.76 167.42 477.77 213.17 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n110 6 Car -1 -1 -1 602.08 171.99 635.45 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n110 7 Pedestrian -1 -1 -1 220.70 155.30 235.21 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n110 25 Pedestrian -1 -1 -1 193.29 161.61 207.85 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n110 24 Pedestrian -1 -1 -1 833.21 158.02 876.63 275.44 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n110 54 Car -1 -1 -1 596.05 171.45 624.10 195.84 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n110 53 Pedestrian -1 -1 -1 486.40 162.31 505.88 211.70 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n111 1 Car -1 -1 -1 1094.60 184.24 1220.92 236.55 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n111 3 Car -1 -1 -1 1031.45 183.56 1158.37 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n111 2 Car -1 -1 -1 954.53 183.03 1066.73 233.91 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n111 5 Pedestrian -1 -1 -1 802.25 153.24 869.37 342.92 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n111 27 Pedestrian -1 -1 -1 563.15 152.29 610.43 288.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n111 30 Pedestrian -1 -1 -1 603.49 156.55 655.87 284.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n111 47 Pedestrian -1 -1 -1 646.30 151.10 696.85 270.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n111 6 Car -1 -1 -1 602.56 172.57 635.42 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n111 26 Pedestrian -1 -1 -1 337.64 163.00 356.65 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n111 51 Cyclist -1 -1 -1 450.75 167.56 473.88 214.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n111 7 Pedestrian -1 -1 -1 220.65 155.30 235.19 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n111 24 Pedestrian -1 -1 -1 831.17 157.55 878.65 276.44 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n111 25 Pedestrian -1 -1 -1 193.18 161.62 207.83 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n111 55 Cyclist -1 -1 -1 484.52 162.40 504.96 212.01 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n112 1 Car -1 -1 -1 1094.88 184.34 1220.68 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n112 3 Car -1 -1 -1 1031.63 183.56 1158.30 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n112 2 Car -1 -1 -1 954.37 182.91 1067.11 234.03 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n112 5 Pedestrian -1 -1 -1 814.49 157.36 887.90 339.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n112 27 Pedestrian -1 -1 -1 551.12 152.43 607.75 288.51 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n112 30 Pedestrian -1 -1 -1 597.17 157.33 653.30 283.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n112 6 Car -1 -1 -1 602.72 172.19 635.48 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n112 47 Pedestrian -1 -1 -1 641.91 152.83 693.02 272.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n112 51 Cyclist -1 -1 -1 449.42 167.73 471.29 215.20 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n112 24 Pedestrian -1 -1 -1 834.33 155.57 882.55 278.88 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n112 26 Pedestrian -1 -1 -1 337.50 163.45 357.27 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n112 7 Pedestrian -1 -1 -1 220.83 155.39 235.10 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n112 25 Pedestrian -1 -1 -1 193.05 161.70 207.68 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n112 55 Cyclist -1 -1 -1 484.74 162.10 503.33 212.10 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n112 56 Car -1 -1 -1 596.77 171.77 623.78 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n112 57 Pedestrian -1 -1 -1 181.50 161.25 197.06 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n113 1 Car -1 -1 -1 1094.73 184.38 1220.63 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n113 3 Car -1 -1 -1 1031.68 183.59 1158.24 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n113 2 Car -1 -1 -1 953.80 182.97 1067.44 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n113 5 Pedestrian -1 -1 -1 820.34 154.88 905.13 342.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n113 30 Pedestrian -1 -1 -1 592.18 158.48 643.81 282.94 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n113 27 Pedestrian -1 -1 -1 542.39 155.84 602.51 286.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n113 47 Pedestrian -1 -1 -1 636.69 153.28 684.10 272.29 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n113 51 Cyclist -1 -1 -1 445.74 167.56 467.95 215.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n113 6 Car -1 -1 -1 602.47 172.17 635.61 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n113 26 Pedestrian -1 -1 -1 337.62 163.73 356.98 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n113 7 Pedestrian -1 -1 -1 220.73 155.30 235.18 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n113 24 Pedestrian -1 -1 -1 831.85 155.64 885.66 278.90 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n113 25 Pedestrian -1 -1 -1 193.15 161.66 207.70 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n113 55 Cyclist -1 -1 -1 481.93 161.51 502.21 213.26 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n113 57 Pedestrian -1 -1 -1 181.75 161.03 196.96 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n113 58 Pedestrian -1 -1 -1 367.82 161.39 378.98 187.61 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n114 1 Car -1 -1 -1 1094.49 184.37 1220.57 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n114 3 Car -1 -1 -1 1031.91 183.59 1158.03 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n114 2 Car -1 -1 -1 953.95 182.94 1067.52 232.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n114 27 Pedestrian -1 -1 -1 538.67 151.92 597.70 284.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n114 5 Pedestrian -1 -1 -1 828.43 152.65 911.73 344.33 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n114 51 Cyclist -1 -1 -1 441.10 166.02 466.49 215.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n114 47 Pedestrian -1 -1 -1 631.82 151.58 672.68 269.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n114 26 Pedestrian -1 -1 -1 336.60 163.21 356.44 217.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n114 30 Pedestrian -1 -1 -1 587.59 157.18 632.06 280.12 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n114 6 Car -1 -1 -1 604.46 172.28 636.89 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n114 7 Pedestrian -1 -1 -1 220.52 155.20 235.36 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n114 25 Pedestrian -1 -1 -1 192.97 161.57 207.79 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n114 24 Pedestrian -1 -1 -1 836.18 154.98 888.73 279.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n114 55 Cyclist -1 -1 -1 479.99 161.12 500.73 213.92 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n114 58 Pedestrian -1 -1 -1 368.02 161.04 379.05 187.60 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n115 1 Car -1 -1 -1 1094.50 184.35 1220.71 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n115 3 Car -1 -1 -1 1032.19 183.59 1157.61 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n115 2 Car -1 -1 -1 953.87 183.05 1067.48 232.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n115 5 Pedestrian -1 -1 -1 847.06 155.43 915.65 347.99 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n115 27 Pedestrian -1 -1 -1 537.30 151.04 590.86 284.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n115 47 Pedestrian -1 -1 -1 623.69 151.84 665.25 267.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n115 30 Pedestrian -1 -1 -1 580.18 155.88 623.66 280.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n115 51 Cyclist -1 -1 -1 435.99 165.84 464.16 216.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n115 55 Cyclist -1 -1 -1 476.28 160.82 500.54 215.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n115 26 Pedestrian -1 -1 -1 337.13 162.51 355.71 216.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n115 6 Car -1 -1 -1 602.18 172.32 635.82 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n115 25 Pedestrian -1 -1 -1 192.86 161.64 207.73 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n115 7 Pedestrian -1 -1 -1 220.72 155.18 235.27 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n115 24 Pedestrian -1 -1 -1 840.72 156.03 891.83 279.52 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n115 58 Pedestrian -1 -1 -1 368.48 161.02 379.28 187.67 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n116 1 Car -1 -1 -1 1094.53 184.32 1220.84 236.42 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n116 3 Car -1 -1 -1 1032.28 183.44 1157.65 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n116 2 Car -1 -1 -1 954.00 183.05 1067.38 232.12 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n116 5 Pedestrian -1 -1 -1 860.39 152.55 926.10 350.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n116 30 Pedestrian -1 -1 -1 570.34 156.04 618.37 279.20 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n116 27 Pedestrian -1 -1 -1 533.72 152.56 578.98 281.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n116 47 Pedestrian -1 -1 -1 614.81 151.61 660.14 267.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n116 6 Car -1 -1 -1 602.42 172.41 635.74 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n116 51 Cyclist -1 -1 -1 433.39 164.74 460.04 218.64 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n116 7 Pedestrian -1 -1 -1 220.60 155.02 235.42 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n116 26 Pedestrian -1 -1 -1 336.50 160.64 356.33 216.19 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n116 25 Pedestrian -1 -1 -1 192.61 161.40 207.96 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n116 55 Cyclist -1 -1 -1 472.08 162.29 498.39 216.76 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n116 24 Pedestrian -1 -1 -1 842.49 156.92 890.22 278.44 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n117 1 Car -1 -1 -1 1094.40 184.34 1221.07 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n117 2 Car -1 -1 -1 954.05 182.95 1067.41 232.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n117 3 Car -1 -1 -1 1032.16 183.44 1157.73 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n117 5 Pedestrian -1 -1 -1 869.35 153.05 947.57 350.01 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n117 30 Pedestrian -1 -1 -1 565.26 158.58 615.89 277.17 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n117 47 Pedestrian -1 -1 -1 609.40 152.99 657.21 267.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n117 27 Pedestrian -1 -1 -1 525.75 154.54 571.98 281.38 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n117 55 Cyclist -1 -1 -1 469.42 162.36 497.12 217.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n117 6 Car -1 -1 -1 602.17 172.90 635.19 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n117 26 Pedestrian -1 -1 -1 336.95 162.89 356.01 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n117 7 Pedestrian -1 -1 -1 220.81 155.21 235.35 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n117 25 Pedestrian -1 -1 -1 192.60 161.44 207.80 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n117 24 Pedestrian -1 -1 -1 846.49 157.50 894.12 278.23 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n117 51 Cyclist -1 -1 -1 433.19 165.00 456.37 219.41 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n117 60 Cyclist -1 -1 -1 564.78 165.97 580.29 205.44 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n118 1 Car -1 -1 -1 1094.20 184.41 1221.33 236.34 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n118 2 Car -1 -1 -1 953.99 182.92 1067.53 232.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n118 3 Car -1 -1 -1 1032.26 183.40 1157.57 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n118 30 Pedestrian -1 -1 -1 562.03 159.93 612.77 276.70 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n118 5 Pedestrian -1 -1 -1 876.72 153.68 970.75 350.06 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n118 55 Cyclist -1 -1 -1 465.15 162.63 495.39 218.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n118 6 Car -1 -1 -1 602.21 172.99 635.12 202.03 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n118 47 Pedestrian -1 -1 -1 606.36 154.42 651.30 267.21 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n118 27 Pedestrian -1 -1 -1 516.83 154.41 566.41 280.90 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n118 25 Pedestrian -1 -1 -1 192.40 161.24 207.74 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n118 26 Pedestrian -1 -1 -1 337.54 163.64 356.57 216.44 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n118 7 Pedestrian -1 -1 -1 220.96 155.23 235.24 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n118 51 Cyclist -1 -1 -1 428.08 166.69 454.94 220.47 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n118 24 Pedestrian -1 -1 -1 853.24 156.86 902.42 279.98 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n118 60 Cyclist -1 -1 -1 559.53 164.04 576.79 207.35 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n119 1 Car -1 -1 -1 1093.85 184.41 1221.53 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n119 2 Car -1 -1 -1 953.69 182.93 1067.90 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n119 3 Car -1 -1 -1 1032.21 183.43 1157.57 233.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n119 27 Pedestrian -1 -1 -1 511.66 153.43 562.40 280.69 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n119 30 Pedestrian -1 -1 -1 560.96 160.31 605.65 275.62 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n119 55 Cyclist -1 -1 -1 460.21 161.63 492.41 221.47 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n119 5 Pedestrian -1 -1 -1 882.98 156.24 987.41 354.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n119 6 Car -1 -1 -1 601.70 173.01 635.68 201.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n119 47 Pedestrian -1 -1 -1 601.84 154.84 642.06 265.66 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n119 25 Pedestrian -1 -1 -1 192.43 161.04 207.75 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n119 7 Pedestrian -1 -1 -1 221.05 155.20 235.35 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n119 51 Cyclist -1 -1 -1 424.38 167.23 452.04 221.36 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n119 24 Pedestrian -1 -1 -1 852.84 156.21 903.02 280.55 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n119 26 Pedestrian -1 -1 -1 337.30 163.06 357.46 215.75 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n119 60 Cyclist -1 -1 -1 555.42 163.48 573.16 205.63 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n120 1 Car -1 -1 -1 1093.67 184.53 1221.72 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n120 3 Car -1 -1 -1 1032.22 183.61 1157.72 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n120 2 Car -1 -1 -1 954.64 182.99 1066.75 231.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n120 5 Pedestrian -1 -1 -1 888.75 155.49 989.89 355.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n120 55 Cyclist -1 -1 -1 456.17 161.14 488.98 221.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n120 30 Pedestrian -1 -1 -1 556.78 158.00 594.88 275.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n120 27 Pedestrian -1 -1 -1 508.23 153.18 557.66 276.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n120 6 Car -1 -1 -1 600.96 172.29 636.64 202.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n120 47 Pedestrian -1 -1 -1 598.88 153.25 636.28 265.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n120 24 Pedestrian -1 -1 -1 855.12 158.67 908.53 282.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n120 7 Pedestrian -1 -1 -1 221.01 155.12 235.35 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n120 25 Pedestrian -1 -1 -1 192.43 161.14 207.76 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n120 51 Cyclist -1 -1 -1 420.31 166.45 448.36 222.84 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n120 26 Pedestrian -1 -1 -1 339.43 161.35 359.54 214.75 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n120 60 Cyclist -1 -1 -1 551.88 165.07 568.01 203.35 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n121 1 Car -1 -1 -1 1094.09 184.54 1221.74 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n121 3 Car -1 -1 -1 1032.56 183.67 1157.41 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n121 5 Pedestrian -1 -1 -1 902.06 154.03 998.40 357.49 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n121 2 Car -1 -1 -1 954.92 183.02 1066.59 232.01 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n121 27 Pedestrian -1 -1 -1 503.43 156.81 548.23 277.27 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n121 47 Pedestrian -1 -1 -1 586.78 156.62 633.60 263.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n121 30 Pedestrian -1 -1 -1 547.27 158.94 589.53 273.73 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n121 55 Cyclist -1 -1 -1 451.14 161.81 484.97 221.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n121 24 Pedestrian -1 -1 -1 857.74 156.81 913.42 284.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n121 6 Car -1 -1 -1 600.59 171.76 636.62 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n121 7 Pedestrian -1 -1 -1 220.78 155.14 235.30 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n121 25 Pedestrian -1 -1 -1 192.21 161.07 207.64 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n121 26 Pedestrian -1 -1 -1 339.41 160.41 359.38 214.68 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n121 51 Cyclist -1 -1 -1 415.33 165.84 445.14 224.21 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n121 60 Cyclist -1 -1 -1 547.54 165.43 565.03 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n122 1 Car -1 -1 -1 1094.23 184.58 1221.60 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n122 5 Pedestrian -1 -1 -1 919.82 153.33 1004.15 358.48 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n122 3 Car -1 -1 -1 1032.77 183.77 1157.11 233.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n122 2 Car -1 -1 -1 955.07 182.65 1066.49 232.25 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n122 47 Pedestrian -1 -1 -1 582.22 157.39 630.68 262.45 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n122 30 Pedestrian -1 -1 -1 541.11 158.88 587.79 270.31 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n122 24 Pedestrian -1 -1 -1 866.70 155.05 918.89 286.87 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n122 27 Pedestrian -1 -1 -1 499.27 158.00 538.43 275.41 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n122 6 Car -1 -1 -1 604.32 171.91 636.75 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n122 55 Cyclist -1 -1 -1 447.60 161.74 480.75 221.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n122 7 Pedestrian -1 -1 -1 220.69 155.06 235.19 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n122 25 Pedestrian -1 -1 -1 192.30 161.14 207.63 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n122 51 Cyclist -1 -1 -1 408.28 164.24 439.23 226.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n122 26 Pedestrian -1 -1 -1 339.84 161.35 359.39 214.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n122 60 Cyclist -1 -1 -1 541.82 165.51 556.73 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n123 1 Car -1 -1 -1 1094.17 184.44 1221.69 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n123 2 Car -1 -1 -1 955.05 182.22 1066.89 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n123 3 Car -1 -1 -1 1029.87 183.93 1156.02 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n123 5 Pedestrian -1 -1 -1 943.38 154.65 1018.88 364.07 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n123 24 Pedestrian -1 -1 -1 870.93 155.33 923.44 287.24 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n123 27 Pedestrian -1 -1 -1 491.54 158.17 537.09 275.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n123 30 Pedestrian -1 -1 -1 535.90 159.97 584.20 269.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n123 47 Pedestrian -1 -1 -1 579.10 156.25 625.50 262.99 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n123 55 Cyclist -1 -1 -1 442.72 160.43 473.35 223.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 51 Cyclist -1 -1 -1 402.37 163.51 435.87 227.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n123 6 Car -1 -1 -1 601.79 171.84 636.34 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n123 7 Pedestrian -1 -1 -1 220.66 155.16 235.07 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n123 25 Pedestrian -1 -1 -1 192.18 161.28 207.50 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n123 26 Pedestrian -1 -1 -1 339.80 163.35 358.46 215.29 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n123 60 Cyclist -1 -1 -1 537.77 164.70 552.52 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n124 1 Car -1 -1 -1 1094.32 184.45 1221.82 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n124 3 Car -1 -1 -1 1033.06 183.90 1156.81 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n124 2 Car -1 -1 -1 954.47 182.29 1068.11 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n124 24 Pedestrian -1 -1 -1 874.70 155.82 927.61 287.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n124 5 Pedestrian -1 -1 -1 954.18 154.25 1046.36 366.10 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n124 47 Pedestrian -1 -1 -1 575.84 156.64 614.69 263.04 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n124 27 Pedestrian -1 -1 -1 479.37 153.64 535.61 275.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n124 30 Pedestrian -1 -1 -1 531.21 159.55 575.64 269.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n124 6 Car -1 -1 -1 604.25 172.63 636.74 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n124 26 Pedestrian -1 -1 -1 339.44 162.22 358.89 213.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n124 7 Pedestrian -1 -1 -1 220.62 155.10 235.09 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n124 25 Pedestrian -1 -1 -1 192.57 161.37 207.45 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n124 55 Cyclist -1 -1 -1 438.48 160.40 467.82 223.61 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n124 51 Cyclist -1 -1 -1 396.12 164.56 428.96 227.32 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n124 60 Cyclist -1 -1 -1 532.16 164.41 548.61 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n125 1 Car -1 -1 -1 1098.38 184.30 1221.20 236.53 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n125 3 Car -1 -1 -1 1032.77 183.97 1157.30 233.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n125 2 Car -1 -1 -1 955.22 182.43 1067.41 232.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n125 27 Pedestrian -1 -1 -1 475.57 154.04 530.66 274.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n125 24 Pedestrian -1 -1 -1 878.81 156.11 931.07 288.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n125 5 Pedestrian -1 -1 -1 958.77 158.54 1064.79 367.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n125 6 Car -1 -1 -1 601.26 171.99 636.43 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n125 30 Pedestrian -1 -1 -1 529.76 158.32 567.97 269.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n125 26 Pedestrian -1 -1 -1 340.08 160.93 358.91 212.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n125 47 Pedestrian -1 -1 -1 568.71 155.54 605.34 258.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n125 7 Pedestrian -1 -1 -1 220.34 155.05 235.14 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n125 55 Cyclist -1 -1 -1 429.52 163.04 463.15 224.60 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n125 25 Pedestrian -1 -1 -1 192.39 161.24 207.50 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n125 51 Cyclist -1 -1 -1 390.37 165.13 424.67 229.93 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n125 60 Cyclist -1 -1 -1 526.75 164.27 542.35 203.95 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n126 1 Car -1 -1 -1 1098.26 184.45 1221.41 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n126 3 Car -1 -1 -1 1033.53 184.12 1157.24 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n126 2 Car -1 -1 -1 956.52 183.08 1066.14 231.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n126 5 Pedestrian -1 -1 -1 974.11 158.51 1079.98 367.70 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n126 24 Pedestrian -1 -1 -1 883.03 155.23 934.69 289.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n126 6 Car -1 -1 -1 600.94 172.01 636.89 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n126 26 Pedestrian -1 -1 -1 340.47 161.02 359.32 211.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n126 27 Pedestrian -1 -1 -1 474.28 158.23 524.05 271.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n126 30 Pedestrian -1 -1 -1 522.70 157.40 561.09 268.99 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n126 51 Cyclist -1 -1 -1 379.63 164.32 420.71 231.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n126 7 Pedestrian -1 -1 -1 220.42 154.99 235.04 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n126 55 Cyclist -1 -1 -1 425.99 161.73 457.43 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n126 25 Pedestrian -1 -1 -1 192.06 160.91 207.57 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n126 47 Pedestrian -1 -1 -1 560.16 153.44 600.01 258.73 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n126 60 Cyclist -1 -1 -1 522.21 163.74 538.94 204.70 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n127 1 Car -1 -1 -1 1094.21 184.59 1221.92 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n127 3 Car -1 -1 -1 1033.77 184.37 1157.42 233.88 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n127 5 Pedestrian -1 -1 -1 989.60 152.63 1086.84 367.39 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n127 2 Car -1 -1 -1 956.54 183.28 1064.99 231.39 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n127 30 Pedestrian -1 -1 -1 515.41 158.20 559.90 268.09 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n127 24 Pedestrian -1 -1 -1 888.38 155.43 936.61 293.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n127 6 Car -1 -1 -1 601.53 172.68 636.57 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n127 51 Cyclist -1 -1 -1 369.95 164.48 416.01 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n127 27 Pedestrian -1 -1 -1 472.95 159.31 516.43 269.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n127 26 Pedestrian -1 -1 -1 340.97 161.69 360.23 211.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n127 55 Cyclist -1 -1 -1 420.29 161.11 454.40 228.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n127 47 Pedestrian -1 -1 -1 555.35 154.15 597.05 258.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n127 7 Pedestrian -1 -1 -1 220.14 155.10 235.03 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n127 25 Pedestrian -1 -1 -1 192.17 160.82 207.46 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n127 62 Car -1 -1 -1 599.26 173.02 621.58 193.82 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n128 1 Car -1 -1 -1 1093.81 184.51 1221.79 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n128 3 Car -1 -1 -1 1033.96 184.31 1157.53 234.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n128 5 Pedestrian -1 -1 -1 1009.26 152.11 1098.65 367.83 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n128 2 Car -1 -1 -1 955.18 183.27 1066.57 231.60 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n128 27 Pedestrian -1 -1 -1 466.35 158.23 509.61 269.83 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n128 24 Pedestrian -1 -1 -1 892.74 156.03 946.64 293.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n128 51 Cyclist -1 -1 -1 363.23 163.01 406.62 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n128 30 Pedestrian -1 -1 -1 511.98 159.94 555.94 266.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n128 55 Cyclist -1 -1 -1 413.52 160.43 447.11 229.29 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n128 6 Car -1 -1 -1 601.96 172.92 636.46 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n128 47 Pedestrian -1 -1 -1 550.47 156.56 593.56 256.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n128 26 Pedestrian -1 -1 -1 341.33 161.87 360.70 211.36 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n128 7 Pedestrian -1 -1 -1 220.14 155.16 234.92 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n128 25 Pedestrian -1 -1 -1 191.92 160.61 207.64 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n128 62 Car -1 -1 -1 599.92 173.29 621.09 193.82 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n129 1 Car -1 -1 -1 1093.61 184.38 1221.76 236.34 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n129 3 Car -1 -1 -1 1033.93 183.95 1158.05 234.38 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n129 2 Car -1 -1 -1 955.13 183.23 1066.62 231.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n129 55 Cyclist -1 -1 -1 409.31 160.52 443.20 229.48 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n129 51 Cyclist -1 -1 -1 354.97 162.61 399.74 236.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n129 30 Pedestrian -1 -1 -1 508.20 160.39 551.71 265.64 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n129 5 Pedestrian -1 -1 -1 1024.18 151.30 1121.91 368.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n129 24 Pedestrian -1 -1 -1 896.06 154.94 952.38 294.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n129 6 Car -1 -1 -1 601.86 172.96 636.40 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n129 27 Pedestrian -1 -1 -1 460.83 156.97 505.76 270.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n129 47 Pedestrian -1 -1 -1 548.77 155.98 588.28 256.91 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n129 7 Pedestrian -1 -1 -1 220.02 155.02 234.89 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n129 25 Pedestrian -1 -1 -1 191.88 160.49 207.70 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n129 26 Pedestrian -1 -1 -1 342.93 161.75 363.50 211.57 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n129 62 Car -1 -1 -1 599.91 173.41 620.89 193.88 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n130 1 Car -1 -1 -1 1093.43 184.38 1222.27 236.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n130 3 Car -1 -1 -1 1034.31 183.64 1157.17 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n130 2 Car -1 -1 -1 955.12 183.32 1066.55 231.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n130 24 Pedestrian -1 -1 -1 900.26 154.55 962.99 295.55 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n130 27 Pedestrian -1 -1 -1 458.27 156.57 500.43 269.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n130 6 Car -1 -1 -1 601.79 172.92 636.54 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n130 51 Cyclist -1 -1 -1 348.31 162.80 391.04 237.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n130 55 Cyclist -1 -1 -1 402.76 159.10 437.10 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n130 5 Pedestrian -1 -1 -1 1043.80 153.27 1155.57 365.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n130 47 Pedestrian -1 -1 -1 546.09 153.61 582.44 257.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n130 30 Pedestrian -1 -1 -1 507.03 159.08 544.02 266.06 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n130 7 Pedestrian -1 -1 -1 220.06 155.01 234.92 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n130 25 Pedestrian -1 -1 -1 191.91 160.59 207.75 199.04 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n130 62 Car -1 -1 -1 599.85 173.53 620.93 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n131 1 Car -1 -1 -1 1093.23 184.29 1222.18 236.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n131 2 Car -1 -1 -1 954.32 183.20 1067.62 231.80 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n131 3 Car -1 -1 -1 1035.07 183.88 1155.85 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n131 27 Pedestrian -1 -1 -1 454.11 155.20 496.90 270.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n131 24 Pedestrian -1 -1 -1 905.44 155.05 973.21 296.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n131 6 Car -1 -1 -1 601.51 172.89 636.75 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n131 5 Pedestrian -1 -1 -1 1044.79 151.85 1177.58 367.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n131 51 Cyclist -1 -1 -1 338.11 162.45 382.82 241.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n131 30 Pedestrian -1 -1 -1 500.34 157.73 536.62 263.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n131 47 Pedestrian -1 -1 -1 540.55 155.02 579.48 257.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n131 55 Cyclist -1 -1 -1 396.02 157.99 433.11 236.49 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n131 7 Pedestrian -1 -1 -1 220.33 155.02 234.87 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n131 25 Pedestrian -1 -1 -1 192.21 160.72 207.69 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n131 62 Car -1 -1 -1 599.62 173.39 621.45 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n131 63 Pedestrian -1 -1 -1 344.62 163.60 364.18 210.48 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n132 1 Car -1 -1 -1 1093.04 184.35 1222.70 236.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n132 51 Cyclist -1 -1 -1 324.32 161.87 375.74 243.19 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n132 5 Pedestrian -1 -1 -1 1055.17 152.65 1189.86 367.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n132 2 Car -1 -1 -1 955.01 183.40 1066.52 231.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n132 24 Pedestrian -1 -1 -1 907.96 154.23 978.52 297.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n132 27 Pedestrian -1 -1 -1 450.70 157.40 492.86 270.50 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n132 47 Pedestrian -1 -1 -1 531.38 157.69 575.36 254.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 3 Car -1 -1 -1 1034.95 184.27 1155.24 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 30 Pedestrian -1 -1 -1 494.09 158.88 534.48 261.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 6 Car -1 -1 -1 601.88 173.21 636.50 202.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n132 55 Cyclist -1 -1 -1 388.64 157.81 427.77 237.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n132 7 Pedestrian -1 -1 -1 219.79 154.81 234.78 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n132 25 Pedestrian -1 -1 -1 191.94 160.35 207.80 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n132 62 Car -1 -1 -1 599.57 173.51 621.98 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n132 63 Pedestrian -1 -1 -1 344.84 163.40 363.71 210.58 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n133 1 Car -1 -1 -1 1091.54 184.65 1224.24 236.24 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n133 24 Pedestrian -1 -1 -1 909.56 152.20 985.02 299.53 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n133 51 Cyclist -1 -1 -1 314.19 159.36 368.72 245.60 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n133 3 Car -1 -1 -1 1031.85 183.88 1153.37 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n133 55 Cyclist -1 -1 -1 383.31 157.78 423.22 239.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n133 5 Pedestrian -1 -1 -1 1071.56 154.50 1196.49 365.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n133 2 Car -1 -1 -1 950.19 183.06 1067.20 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n133 27 Pedestrian -1 -1 -1 445.42 156.01 485.45 270.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n133 30 Pedestrian -1 -1 -1 485.76 158.98 529.18 261.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n133 47 Pedestrian -1 -1 -1 527.57 161.06 570.92 253.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n133 6 Car -1 -1 -1 601.80 173.19 636.72 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n133 7 Pedestrian -1 -1 -1 219.59 154.46 234.81 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n133 25 Pedestrian -1 -1 -1 191.83 159.69 207.91 199.64 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n133 62 Car -1 -1 -1 599.60 173.59 622.34 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n134 1 Car -1 -1 -1 1091.91 184.34 1223.73 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n134 24 Pedestrian -1 -1 -1 919.07 152.71 997.31 303.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n134 3 Car -1 -1 -1 1031.91 183.57 1153.59 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n134 2 Car -1 -1 -1 951.53 183.17 1065.69 231.76 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n134 27 Pedestrian -1 -1 -1 439.10 156.16 483.95 270.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n134 51 Cyclist -1 -1 -1 303.18 160.53 357.49 246.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n134 5 Pedestrian -1 -1 -1 1095.58 155.46 1203.22 364.16 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n134 30 Pedestrian -1 -1 -1 482.44 159.98 524.30 261.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n134 55 Cyclist -1 -1 -1 374.85 157.12 417.55 241.24 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n134 47 Pedestrian -1 -1 -1 524.55 160.17 565.48 253.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n134 6 Car -1 -1 -1 601.98 173.10 636.80 202.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n134 7 Pedestrian -1 -1 -1 219.82 154.56 234.72 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n134 25 Pedestrian -1 -1 -1 191.83 159.83 207.98 199.62 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n134 62 Car -1 -1 -1 599.57 173.42 622.54 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n135 1 Car -1 -1 -1 1093.59 184.14 1221.83 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n135 24 Pedestrian -1 -1 -1 929.86 152.24 1008.77 305.26 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n135 3 Car -1 -1 -1 1033.94 183.45 1156.17 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n135 2 Car -1 -1 -1 951.98 183.02 1065.28 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n135 51 Cyclist -1 -1 -1 288.18 161.43 350.23 251.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n135 47 Pedestrian -1 -1 -1 520.11 157.26 555.29 255.06 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n135 55 Cyclist -1 -1 -1 366.70 157.81 410.28 245.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n135 30 Pedestrian -1 -1 -1 480.73 160.40 516.72 260.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n135 6 Car -1 -1 -1 601.92 173.09 636.87 202.21 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n135 27 Pedestrian -1 -1 -1 438.63 157.63 482.02 269.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n135 5 Pedestrian -1 -1 -1 1115.21 148.58 1214.27 363.72 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n135 7 Pedestrian -1 -1 -1 219.68 154.54 234.58 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n135 25 Pedestrian -1 -1 -1 191.83 159.94 207.90 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n135 62 Car -1 -1 -1 599.38 173.49 622.54 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n135 64 Pedestrian -1 -1 -1 347.11 161.52 366.96 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n136 1 Car -1 -1 -1 1095.78 184.28 1219.90 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n136 3 Car -1 -1 -1 1031.12 183.94 1154.47 233.54 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n136 24 Pedestrian -1 -1 -1 942.50 153.02 1012.79 304.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n136 2 Car -1 -1 -1 951.42 182.79 1065.85 232.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n136 51 Cyclist -1 -1 -1 274.04 160.12 342.96 253.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n136 47 Pedestrian -1 -1 -1 512.22 156.24 547.65 254.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n136 6 Car -1 -1 -1 601.80 173.15 636.93 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n136 30 Pedestrian -1 -1 -1 474.87 159.35 509.02 260.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n136 27 Pedestrian -1 -1 -1 434.43 156.86 478.60 264.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n136 55 Cyclist -1 -1 -1 358.29 157.82 404.77 248.09 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n136 5 Pedestrian -1 -1 -1 1134.26 147.33 1217.99 364.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n136 7 Pedestrian -1 -1 -1 219.79 154.64 234.43 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n136 25 Pedestrian -1 -1 -1 191.96 160.05 207.73 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n136 62 Car -1 -1 -1 599.38 173.28 622.76 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n136 64 Pedestrian -1 -1 -1 347.98 162.09 366.49 209.66 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n137 1 Car -1 -1 -1 1097.34 184.30 1218.14 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n137 3 Car -1 -1 -1 1030.27 184.10 1155.25 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n137 2 Car -1 -1 -1 955.78 182.71 1065.78 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n137 24 Pedestrian -1 -1 -1 949.05 154.73 1021.37 304.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n137 27 Pedestrian -1 -1 -1 429.61 157.31 470.16 262.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n137 51 Cyclist -1 -1 -1 267.90 161.04 331.73 253.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n137 30 Pedestrian -1 -1 -1 467.37 158.96 507.33 259.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n137 6 Car -1 -1 -1 601.66 173.30 637.09 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n137 47 Pedestrian -1 -1 -1 506.73 154.61 544.57 252.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n137 55 Cyclist -1 -1 -1 353.77 157.71 399.33 247.92 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n137 7 Pedestrian -1 -1 -1 219.67 154.43 234.53 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n137 62 Car -1 -1 -1 598.88 173.43 622.56 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n137 64 Pedestrian -1 -1 -1 348.98 162.45 367.14 210.83 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n137 25 Pedestrian -1 -1 -1 191.93 160.07 207.72 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n137 5 Pedestrian -1 -1 -1 1127.12 140.10 1225.35 363.63 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n137 65 Pedestrian -1 -1 -1 434.36 159.10 472.56 231.26 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n138 1 Car -1 -1 -1 1101.36 184.32 1218.80 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n138 3 Car -1 -1 -1 1030.09 184.17 1155.58 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n138 24 Pedestrian -1 -1 -1 955.97 157.49 1029.70 307.98 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n138 2 Car -1 -1 -1 955.66 182.61 1066.04 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n138 27 Pedestrian -1 -1 -1 426.18 154.48 465.66 259.88 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n138 51 Cyclist -1 -1 -1 248.43 161.03 323.03 259.35 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n138 47 Pedestrian -1 -1 -1 501.38 156.09 542.30 254.19 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n138 30 Pedestrian -1 -1 -1 459.80 160.75 501.26 257.68 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n138 6 Car -1 -1 -1 601.73 172.95 637.03 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n138 55 Cyclist -1 -1 -1 345.35 159.64 395.04 251.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n138 7 Pedestrian -1 -1 -1 219.92 154.74 234.36 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n138 5 Pedestrian -1 -1 -1 1116.55 138.65 1220.38 365.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n138 62 Car -1 -1 -1 598.84 173.52 622.03 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n138 25 Pedestrian -1 -1 -1 189.36 160.03 206.16 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n138 64 Pedestrian -1 -1 -1 351.37 163.00 370.18 210.76 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n138 66 Cyclist -1 -1 -1 352.72 159.22 392.78 238.07 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n139 1 Car -1 -1 -1 1095.76 184.33 1220.25 235.40 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n139 3 Car -1 -1 -1 1029.40 184.19 1156.34 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n139 24 Pedestrian -1 -1 -1 965.43 157.70 1034.52 308.33 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n139 2 Car -1 -1 -1 955.79 182.57 1065.67 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n139 27 Pedestrian -1 -1 -1 422.36 156.13 462.01 261.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n139 55 Cyclist -1 -1 -1 340.56 157.88 390.65 255.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n139 51 Cyclist -1 -1 -1 242.24 162.41 311.39 259.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n139 30 Pedestrian -1 -1 -1 459.16 160.32 500.12 258.42 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n139 6 Car -1 -1 -1 601.66 173.22 637.08 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n139 47 Pedestrian -1 -1 -1 494.47 156.26 535.50 253.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n139 5 Pedestrian -1 -1 -1 1091.58 141.03 1214.68 363.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n139 62 Car -1 -1 -1 598.57 173.60 621.92 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n139 7 Pedestrian -1 -1 -1 219.66 154.80 234.48 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n139 25 Pedestrian -1 -1 -1 189.25 159.91 206.38 200.26 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n139 64 Pedestrian -1 -1 -1 351.12 163.48 371.43 210.59 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n140 1 Car -1 -1 -1 1094.95 184.32 1220.72 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n140 3 Car -1 -1 -1 1029.67 184.17 1155.32 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n140 2 Car -1 -1 -1 954.82 182.65 1066.58 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n140 24 Pedestrian -1 -1 -1 973.10 157.43 1035.59 309.66 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n140 27 Pedestrian -1 -1 -1 416.86 155.62 458.21 258.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n140 51 Cyclist -1 -1 -1 228.95 160.83 302.42 266.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n140 6 Car -1 -1 -1 601.66 173.19 637.11 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n140 30 Pedestrian -1 -1 -1 457.06 159.39 494.26 258.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n140 5 Pedestrian -1 -1 -1 1077.43 145.86 1213.53 364.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n140 55 Cyclist -1 -1 -1 336.32 157.29 384.49 256.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n140 47 Pedestrian -1 -1 -1 493.77 155.95 528.24 251.09 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n140 7 Pedestrian -1 -1 -1 219.80 154.76 234.59 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n140 62 Car -1 -1 -1 598.50 173.72 621.66 192.97 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n140 25 Pedestrian -1 -1 -1 189.32 159.68 206.27 200.30 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n140 64 Pedestrian -1 -1 -1 350.58 162.92 372.89 211.12 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n141 1 Car -1 -1 -1 1095.77 184.01 1219.87 236.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n141 3 Car -1 -1 -1 1032.04 183.66 1158.49 234.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n141 2 Car -1 -1 -1 953.41 182.66 1064.40 231.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n141 27 Pedestrian -1 -1 -1 413.19 155.42 453.78 257.76 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n141 55 Cyclist -1 -1 -1 326.34 156.86 381.35 261.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n141 51 Cyclist -1 -1 -1 221.35 159.21 286.70 269.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n141 47 Pedestrian -1 -1 -1 489.92 155.36 524.05 251.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n141 24 Pedestrian -1 -1 -1 983.98 155.33 1047.19 316.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n141 6 Car -1 -1 -1 602.66 172.74 637.42 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n141 5 Pedestrian -1 -1 -1 1056.10 139.58 1181.41 364.04 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n141 30 Pedestrian -1 -1 -1 453.26 158.35 485.07 256.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n141 7 Pedestrian -1 -1 -1 220.54 154.90 234.46 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n141 62 Car -1 -1 -1 598.41 173.90 621.87 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n141 25 Pedestrian -1 -1 -1 191.99 159.49 207.85 200.07 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n142 1 Car -1 -1 -1 1094.86 184.05 1220.72 236.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n142 2 Car -1 -1 -1 955.20 182.46 1066.57 232.15 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n142 3 Car -1 -1 -1 1030.45 183.77 1154.79 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n142 27 Pedestrian -1 -1 -1 406.85 155.47 447.49 258.22 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n142 51 Cyclist -1 -1 -1 207.48 160.52 277.44 274.79 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n142 55 Cyclist -1 -1 -1 318.02 154.58 375.67 265.77 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n142 5 Pedestrian -1 -1 -1 1030.33 138.28 1153.73 364.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n142 47 Pedestrian -1 -1 -1 482.79 155.79 522.44 250.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n142 30 Pedestrian -1 -1 -1 447.43 159.35 482.38 255.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n142 6 Car -1 -1 -1 601.87 173.10 637.05 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n142 24 Pedestrian -1 -1 -1 991.59 155.60 1062.31 317.05 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n142 7 Pedestrian -1 -1 -1 219.91 155.05 234.65 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n142 62 Car -1 -1 -1 598.40 173.48 621.87 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n142 25 Pedestrian -1 -1 -1 192.19 159.71 207.73 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n143 1 Car -1 -1 -1 1093.66 184.29 1221.38 236.91 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n143 3 Car -1 -1 -1 1031.16 184.35 1153.79 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n143 2 Car -1 -1 -1 956.60 182.80 1065.85 234.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n143 5 Pedestrian -1 -1 -1 1005.21 140.58 1140.51 363.66 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n143 55 Cyclist -1 -1 -1 310.30 152.62 366.58 267.36 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n143 27 Pedestrian -1 -1 -1 404.21 155.05 441.63 257.98 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n143 47 Pedestrian -1 -1 -1 477.07 155.90 520.45 250.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n143 30 Pedestrian -1 -1 -1 442.02 161.58 479.76 255.62 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n143 6 Car -1 -1 -1 602.35 172.87 637.69 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n143 51 Cyclist -1 -1 -1 198.81 160.96 263.51 280.28 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n143 7 Pedestrian -1 -1 -1 218.84 154.73 235.54 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n143 25 Pedestrian -1 -1 -1 192.28 159.77 207.63 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n143 62 Car -1 -1 -1 598.40 173.33 622.31 194.31 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n143 24 Pedestrian -1 -1 -1 999.57 148.10 1100.34 333.17 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n144 1 Car -1 -1 -1 1093.85 184.39 1221.49 236.49 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n144 3 Car -1 -1 -1 1030.11 184.15 1155.49 234.28 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n144 2 Car -1 -1 -1 956.74 182.88 1065.17 234.32 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n144 5 Pedestrian -1 -1 -1 976.21 146.47 1123.55 364.42 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n144 27 Pedestrian -1 -1 -1 397.58 155.87 440.16 258.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n144 55 Cyclist -1 -1 -1 293.23 152.67 360.72 274.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n144 51 Cyclist -1 -1 -1 180.63 159.91 252.99 284.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n144 47 Pedestrian -1 -1 -1 471.04 158.97 513.10 250.73 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n144 30 Pedestrian -1 -1 -1 438.70 161.78 475.37 255.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n144 6 Car -1 -1 -1 602.16 172.69 637.87 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n144 7 Pedestrian -1 -1 -1 218.76 154.96 235.36 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n144 62 Car -1 -1 -1 597.16 172.95 622.51 194.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n144 25 Pedestrian -1 -1 -1 191.95 159.70 207.77 199.44 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n144 24 Pedestrian -1 -1 -1 1016.60 153.43 1113.59 334.81 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n145 1 Car -1 -1 -1 1093.43 184.40 1221.87 236.42 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n145 27 Pedestrian -1 -1 -1 392.10 155.50 438.41 257.48 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n145 3 Car -1 -1 -1 1029.85 184.23 1155.74 234.54 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n145 2 Car -1 -1 -1 956.24 182.95 1066.16 234.30 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n145 55 Cyclist -1 -1 -1 279.31 150.53 351.72 277.17 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n145 30 Pedestrian -1 -1 -1 436.18 160.41 470.14 253.74 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n145 51 Cyclist -1 -1 -1 160.28 161.84 247.04 295.23 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n145 47 Pedestrian -1 -1 -1 469.19 160.37 507.24 249.96 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n145 5 Pedestrian -1 -1 -1 967.80 147.35 1093.80 363.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n145 6 Car -1 -1 -1 601.03 172.69 637.89 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n145 7 Pedestrian -1 -1 -1 218.89 154.46 235.74 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n145 62 Car -1 -1 -1 596.74 172.95 623.13 194.42 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n145 25 Pedestrian -1 -1 -1 188.67 159.64 206.84 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n145 24 Pedestrian -1 -1 -1 1035.93 158.94 1102.04 322.50 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n146 1 Car -1 -1 -1 1093.33 184.44 1221.91 236.64 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n146 27 Pedestrian -1 -1 -1 388.31 155.23 433.87 256.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n146 3 Car -1 -1 -1 1034.74 183.99 1156.32 234.95 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n146 2 Car -1 -1 -1 956.37 183.54 1065.40 230.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n146 51 Cyclist -1 -1 -1 144.91 161.03 233.15 298.78 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n146 30 Pedestrian -1 -1 -1 432.79 158.70 466.06 254.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n146 55 Cyclist -1 -1 -1 263.74 149.74 343.56 285.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n146 5 Pedestrian -1 -1 -1 957.66 146.27 1058.08 364.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n146 6 Car -1 -1 -1 601.95 172.74 638.12 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n146 47 Pedestrian -1 -1 -1 467.69 158.04 500.20 248.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n146 7 Pedestrian -1 -1 -1 219.20 154.39 235.02 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n146 24 Pedestrian -1 -1 -1 1033.13 156.23 1105.03 324.06 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n146 62 Car -1 -1 -1 596.75 172.77 623.58 194.52 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n146 25 Pedestrian -1 -1 -1 188.92 160.00 206.38 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n146 67 Pedestrian -1 -1 -1 351.63 156.58 371.39 210.55 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n147 1 Car -1 -1 -1 1093.56 184.61 1221.49 236.69 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n147 3 Car -1 -1 -1 1034.14 183.95 1157.15 235.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n147 51 Cyclist -1 -1 -1 125.17 161.04 221.28 306.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n147 30 Pedestrian -1 -1 -1 428.16 159.42 463.25 253.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n147 2 Car -1 -1 -1 956.78 183.49 1065.40 231.39 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n147 27 Pedestrian -1 -1 -1 385.17 156.72 429.07 255.10 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n147 55 Cyclist -1 -1 -1 244.35 148.48 334.33 287.40 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n147 47 Pedestrian -1 -1 -1 460.33 157.43 492.49 248.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n147 5 Pedestrian -1 -1 -1 935.49 144.38 1034.24 365.43 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n147 24 Pedestrian -1 -1 -1 1038.91 155.79 1122.03 323.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n147 6 Car -1 -1 -1 601.02 172.67 637.97 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n147 7 Pedestrian -1 -1 -1 219.84 154.76 234.50 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n147 62 Car -1 -1 -1 596.54 172.72 623.56 194.63 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n147 25 Pedestrian -1 -1 -1 192.74 160.00 207.21 200.00 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n148 1 Car -1 -1 -1 1092.91 184.56 1221.45 236.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n148 30 Pedestrian -1 -1 -1 420.98 160.35 462.35 252.11 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n148 3 Car -1 -1 -1 1033.24 183.79 1157.66 234.73 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n148 51 Cyclist -1 -1 -1 94.17 159.10 215.44 322.40 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n148 2 Car -1 -1 -1 956.95 182.92 1065.37 231.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n148 5 Pedestrian -1 -1 -1 904.33 139.51 1019.72 364.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n148 24 Pedestrian -1 -1 -1 1056.38 152.96 1128.04 328.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n148 47 Pedestrian -1 -1 -1 456.05 156.92 489.58 248.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n148 55 Cyclist -1 -1 -1 227.12 146.97 320.90 295.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n148 6 Car -1 -1 -1 602.16 172.63 637.90 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n148 27 Pedestrian -1 -1 -1 381.71 156.32 418.64 253.81 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n148 7 Pedestrian -1 -1 -1 220.42 155.02 234.25 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n148 62 Car -1 -1 -1 597.25 172.60 623.19 193.93 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n148 25 Pedestrian -1 -1 -1 192.51 160.21 207.18 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n149 1 Car -1 -1 -1 1092.31 184.34 1221.15 236.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n149 3 Car -1 -1 -1 1034.39 183.28 1157.19 235.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n149 2 Car -1 -1 -1 956.11 182.68 1066.03 232.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n149 51 Cyclist -1 -1 -1 65.24 160.53 205.20 329.28 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n149 24 Pedestrian -1 -1 -1 1065.25 152.08 1149.17 335.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n149 30 Pedestrian -1 -1 -1 414.62 160.29 455.02 252.17 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n149 5 Pedestrian -1 -1 -1 887.41 140.57 1013.37 364.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n149 27 Pedestrian -1 -1 -1 378.25 154.64 413.74 255.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n149 47 Pedestrian -1 -1 -1 454.21 157.28 489.47 248.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n149 6 Car -1 -1 -1 602.31 172.56 637.72 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n149 55 Cyclist -1 -1 -1 198.52 146.79 318.12 311.59 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n149 7 Pedestrian -1 -1 -1 221.33 155.20 234.46 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n149 25 Pedestrian -1 -1 -1 192.90 160.79 207.17 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n149 62 Car -1 -1 -1 598.18 172.83 622.42 193.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n149 68 Pedestrian -1 -1 -1 664.32 167.84 678.81 205.25 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n150 1 Car -1 -1 -1 1091.16 184.01 1222.38 236.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n150 5 Pedestrian -1 -1 -1 870.97 143.76 999.41 366.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n150 2 Car -1 -1 -1 954.95 182.81 1066.51 232.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n150 51 Cyclist -1 -1 -1 29.30 160.35 187.84 344.47 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n150 30 Pedestrian -1 -1 -1 412.90 160.81 449.29 251.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n150 24 Pedestrian -1 -1 -1 1066.55 153.00 1156.05 336.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n150 27 Pedestrian -1 -1 -1 371.98 155.87 410.87 250.99 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n150 3 Car -1 -1 -1 1034.50 184.38 1156.01 234.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n150 47 Pedestrian -1 -1 -1 450.62 157.63 485.48 248.38 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n150 6 Car -1 -1 -1 602.64 172.85 637.58 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n150 55 Cyclist -1 -1 -1 170.46 149.60 307.10 323.35 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n150 7 Pedestrian -1 -1 -1 220.70 155.22 235.43 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n150 25 Pedestrian -1 -1 -1 193.06 161.11 207.18 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n150 62 Car -1 -1 -1 598.86 173.00 622.21 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n150 68 Pedestrian -1 -1 -1 643.49 167.89 661.43 203.75 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n150 69 Pedestrian -1 -1 -1 352.31 158.39 371.46 207.68 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n150 70 Cyclist -1 -1 -1 194.08 145.77 292.15 297.06 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n151 1 Car -1 -1 -1 1091.69 184.09 1222.44 236.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n151 2 Car -1 -1 -1 954.72 182.93 1066.67 232.15 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n151 5 Pedestrian -1 -1 -1 861.22 144.20 985.77 365.98 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n151 24 Pedestrian -1 -1 -1 1078.28 154.56 1166.54 335.46 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n151 27 Pedestrian -1 -1 -1 368.51 155.53 407.49 251.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n151 51 Cyclist -1 -1 -1 -4.30 159.09 175.21 359.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n151 3 Car -1 -1 -1 1031.82 183.80 1153.18 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n151 30 Pedestrian -1 -1 -1 410.93 159.84 443.28 250.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n151 47 Pedestrian -1 -1 -1 444.86 156.84 478.13 248.41 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n151 6 Car -1 -1 -1 601.84 172.92 636.85 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n151 7 Pedestrian -1 -1 -1 220.41 155.60 235.77 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n151 25 Pedestrian -1 -1 -1 192.94 161.25 207.75 198.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n151 69 Pedestrian -1 -1 -1 351.96 159.10 371.21 206.77 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n151 62 Car -1 -1 -1 598.63 173.23 621.62 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n151 55 Cyclist -1 -1 -1 139.38 147.54 293.50 333.16 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n151 68 Pedestrian -1 -1 -1 642.86 167.84 659.00 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n151 71 Pedestrian -1 -1 -1 655.43 169.13 673.96 204.41 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n152 1 Car -1 -1 -1 1091.35 184.13 1223.63 236.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n152 2 Car -1 -1 -1 955.42 182.89 1066.21 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n152 5 Pedestrian -1 -1 -1 857.43 144.98 951.72 365.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n152 3 Car -1 -1 -1 1032.14 183.70 1152.55 234.05 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n152 24 Pedestrian -1 -1 -1 1085.47 153.41 1167.76 342.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n152 47 Pedestrian -1 -1 -1 440.06 156.72 473.70 247.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n152 55 Cyclist -1 -1 -1 119.48 145.04 274.60 336.26 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n152 6 Car -1 -1 -1 602.01 173.01 636.57 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n152 27 Pedestrian -1 -1 -1 361.28 156.23 401.20 253.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n152 30 Pedestrian -1 -1 -1 407.13 159.66 439.03 250.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n152 51 Cyclist -1 -1 -1 -6.34 160.25 139.60 365.99 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n152 7 Pedestrian -1 -1 -1 220.69 155.91 235.37 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n152 62 Car -1 -1 -1 598.58 173.37 621.56 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n152 69 Pedestrian -1 -1 -1 351.98 159.68 370.26 206.50 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n152 25 Pedestrian -1 -1 -1 193.10 160.67 208.66 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n152 68 Pedestrian -1 -1 -1 639.96 169.54 654.86 204.62 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n152 71 Pedestrian -1 -1 -1 651.78 169.70 670.26 204.52 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n152 72 Pedestrian -1 -1 -1 573.09 169.04 583.51 196.47 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n153 2 Car -1 -1 -1 955.39 182.84 1066.43 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n153 1 Car -1 -1 -1 1090.62 184.20 1224.76 236.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n153 3 Car -1 -1 -1 1031.66 183.52 1152.92 234.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n153 5 Pedestrian -1 -1 -1 839.64 144.65 930.93 365.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n153 30 Pedestrian -1 -1 -1 401.39 161.50 436.50 248.51 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n153 55 Cyclist -1 -1 -1 83.31 144.18 256.84 344.90 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n153 24 Pedestrian -1 -1 -1 1102.73 154.72 1180.83 341.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n153 6 Car -1 -1 -1 602.13 172.93 636.58 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n153 27 Pedestrian -1 -1 -1 358.62 156.86 396.25 250.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n153 47 Pedestrian -1 -1 -1 434.56 155.31 470.87 247.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n153 69 Pedestrian -1 -1 -1 352.34 159.69 370.29 206.30 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n153 7 Pedestrian -1 -1 -1 221.51 155.84 235.30 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n153 72 Pedestrian -1 -1 -1 570.71 169.05 581.37 196.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n153 71 Pedestrian -1 -1 -1 652.94 169.60 667.32 203.36 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n153 62 Car -1 -1 -1 598.89 173.34 621.72 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n153 25 Pedestrian -1 -1 -1 193.51 160.90 208.71 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n154 2 Car -1 -1 -1 955.55 182.92 1066.38 234.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n154 1 Car -1 -1 -1 1089.90 184.23 1225.41 236.45 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n154 3 Car -1 -1 -1 1031.08 183.53 1153.82 234.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n154 5 Pedestrian -1 -1 -1 816.94 143.65 922.66 365.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n154 30 Pedestrian -1 -1 -1 396.07 162.09 433.90 248.46 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n154 55 Cyclist -1 -1 -1 43.08 144.11 236.42 360.15 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n154 47 Pedestrian -1 -1 -1 428.45 156.98 469.74 245.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n154 6 Car -1 -1 -1 601.87 172.60 636.43 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n154 24 Pedestrian -1 -1 -1 1118.58 156.48 1203.11 340.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n154 27 Pedestrian -1 -1 -1 355.39 157.25 391.35 249.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n154 7 Pedestrian -1 -1 -1 221.75 156.04 235.08 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n154 69 Pedestrian -1 -1 -1 352.50 160.13 370.14 206.35 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n154 25 Pedestrian -1 -1 -1 193.72 161.50 206.91 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n154 71 Pedestrian -1 -1 -1 650.18 168.84 663.38 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n154 62 Car -1 -1 -1 598.59 173.15 621.82 192.73 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n154 72 Pedestrian -1 -1 -1 566.75 169.48 581.99 196.84 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n154 73 Pedestrian -1 -1 -1 633.22 166.12 646.04 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n155 1 Car -1 -1 -1 1093.04 183.91 1221.92 236.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n155 2 Car -1 -1 -1 955.44 182.96 1066.43 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n155 3 Car -1 -1 -1 1030.37 183.62 1155.13 233.83 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n155 5 Pedestrian -1 -1 -1 795.37 147.10 907.12 364.41 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n155 55 Cyclist -1 -1 -1 6.43 142.69 212.26 368.81 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n155 47 Pedestrian -1 -1 -1 424.97 157.87 465.78 245.60 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n155 30 Pedestrian -1 -1 -1 393.99 161.98 427.81 248.60 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n155 6 Car -1 -1 -1 601.90 172.57 636.32 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n155 27 Pedestrian -1 -1 -1 350.00 158.75 389.56 251.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n155 24 Pedestrian -1 -1 -1 1121.76 154.73 1215.44 348.94 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n155 7 Pedestrian -1 -1 -1 221.66 155.76 234.87 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n155 25 Pedestrian -1 -1 -1 193.75 162.59 206.95 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n155 73 Pedestrian -1 -1 -1 630.15 167.12 644.95 203.59 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n155 71 Pedestrian -1 -1 -1 646.29 168.27 660.50 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n155 62 Car -1 -1 -1 598.59 173.15 621.69 192.63 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n155 69 Pedestrian -1 -1 -1 351.70 160.45 371.51 206.05 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n155 72 Pedestrian -1 -1 -1 562.94 169.99 580.27 196.67 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n155 74 Pedestrian -1 -1 -1 182.34 159.93 196.90 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n156 3 Car -1 -1 -1 1029.90 183.71 1155.45 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n156 2 Car -1 -1 -1 955.40 183.05 1066.29 233.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n156 1 Car -1 -1 -1 1095.57 184.06 1219.24 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n156 5 Pedestrian -1 -1 -1 781.75 149.15 889.60 363.79 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n156 47 Pedestrian -1 -1 -1 424.04 158.61 459.09 245.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n156 6 Car -1 -1 -1 601.87 172.74 636.32 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n156 27 Pedestrian -1 -1 -1 346.83 160.56 385.24 250.34 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n156 55 Cyclist -1 -1 -1 -8.20 135.93 180.04 369.06 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n156 30 Pedestrian -1 -1 -1 391.44 161.38 422.07 248.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n156 24 Pedestrian -1 -1 -1 1128.75 152.64 1215.58 351.77 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n156 7 Pedestrian -1 -1 -1 221.65 155.66 235.01 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n156 25 Pedestrian -1 -1 -1 193.56 163.05 206.82 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n156 71 Pedestrian -1 -1 -1 642.97 169.09 659.21 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n156 62 Car -1 -1 -1 598.82 173.20 621.70 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n156 72 Pedestrian -1 -1 -1 559.90 169.82 576.18 196.54 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n156 73 Pedestrian -1 -1 -1 627.16 167.52 640.51 204.34 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n156 69 Pedestrian -1 -1 -1 351.89 160.10 371.69 206.32 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n156 74 Pedestrian -1 -1 -1 181.86 160.27 197.46 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n157 1 Car -1 -1 -1 1096.44 184.38 1218.30 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n157 2 Car -1 -1 -1 955.20 183.15 1066.64 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n157 3 Car -1 -1 -1 1029.40 183.89 1156.20 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n157 5 Pedestrian -1 -1 -1 774.21 144.95 866.20 366.15 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n157 6 Car -1 -1 -1 601.73 172.49 636.23 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n157 27 Pedestrian -1 -1 -1 342.57 158.05 381.72 248.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n157 47 Pedestrian -1 -1 -1 419.85 160.00 450.28 243.98 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n157 30 Pedestrian -1 -1 -1 386.69 160.83 419.32 248.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n157 55 Cyclist -1 -1 -1 -11.40 133.92 144.74 370.31 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n157 7 Pedestrian -1 -1 -1 221.61 155.46 235.24 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n157 25 Pedestrian -1 -1 -1 193.69 162.97 206.65 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n157 24 Pedestrian -1 -1 -1 1134.03 155.46 1217.97 349.23 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n157 71 Pedestrian -1 -1 -1 638.93 168.70 656.41 203.51 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n157 72 Pedestrian -1 -1 -1 559.23 169.15 571.45 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n157 62 Car -1 -1 -1 598.80 173.22 621.70 193.18 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n157 69 Pedestrian -1 -1 -1 350.98 159.86 372.53 206.74 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n158 1 Car -1 -1 -1 1097.20 184.32 1217.96 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n158 6 Car -1 -1 -1 601.61 172.36 635.12 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n158 2 Car -1 -1 -1 954.79 183.07 1066.71 232.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n158 3 Car -1 -1 -1 1029.55 184.00 1156.29 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n158 5 Pedestrian -1 -1 -1 762.46 144.87 840.63 364.77 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n158 47 Pedestrian -1 -1 -1 416.17 159.80 444.97 244.01 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n158 27 Pedestrian -1 -1 -1 340.81 157.67 374.76 247.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n158 30 Pedestrian -1 -1 -1 378.55 161.59 414.58 247.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n158 7 Pedestrian -1 -1 -1 221.13 155.19 235.30 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n158 25 Pedestrian -1 -1 -1 193.00 162.56 207.11 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n158 24 Pedestrian -1 -1 -1 1148.20 161.92 1218.85 349.59 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n158 71 Pedestrian -1 -1 -1 636.93 168.02 653.58 203.88 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n158 62 Car -1 -1 -1 598.73 173.02 622.00 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n158 55 Cyclist -1 -1 -1 -2.37 133.20 90.80 370.76 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n158 72 Pedestrian -1 -1 -1 556.13 168.10 567.45 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n158 75 Cyclist -1 -1 -1 -11.81 141.70 129.92 369.30 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n159 1 Car -1 -1 -1 1096.62 184.38 1218.66 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n159 5 Pedestrian -1 -1 -1 752.99 143.67 834.06 361.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n159 2 Car -1 -1 -1 954.57 183.13 1066.87 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n159 3 Car -1 -1 -1 1029.42 184.01 1156.52 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n159 6 Car -1 -1 -1 601.10 172.34 633.98 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n159 27 Pedestrian -1 -1 -1 336.50 156.50 370.22 248.10 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n159 47 Pedestrian -1 -1 -1 410.74 158.45 443.20 244.53 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n159 30 Pedestrian -1 -1 -1 374.36 162.70 411.58 246.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n159 7 Pedestrian -1 -1 -1 221.19 155.15 235.38 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n159 25 Pedestrian -1 -1 -1 193.14 162.57 207.17 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n159 71 Pedestrian -1 -1 -1 636.61 167.99 650.28 203.52 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n159 24 Pedestrian -1 -1 -1 1176.68 166.01 1221.88 345.71 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n159 76 Cyclist -1 -1 -1 553.01 168.19 566.93 196.12 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n160 1 Car -1 -1 -1 1095.87 184.64 1219.61 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n160 2 Car -1 -1 -1 954.56 183.07 1066.94 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n160 3 Car -1 -1 -1 1032.31 183.80 1157.63 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n160 5 Pedestrian -1 -1 -1 740.20 143.40 831.48 362.12 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n160 6 Car -1 -1 -1 600.83 172.42 634.22 202.27 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n160 47 Pedestrian -1 -1 -1 406.64 159.13 439.87 243.74 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n160 27 Pedestrian -1 -1 -1 329.36 157.05 364.80 247.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n160 30 Pedestrian -1 -1 -1 373.42 162.47 409.50 246.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n160 7 Pedestrian -1 -1 -1 221.21 155.07 235.36 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n160 25 Pedestrian -1 -1 -1 192.96 162.23 207.33 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n160 71 Pedestrian -1 -1 -1 633.76 167.44 646.67 203.32 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n160 77 Pedestrian -1 -1 -1 546.75 168.75 566.17 195.99 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n161 1 Car -1 -1 -1 1095.57 184.74 1219.97 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n161 2 Car -1 -1 -1 954.51 183.13 1066.98 232.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n161 3 Car -1 -1 -1 1032.08 183.86 1157.91 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n161 5 Pedestrian -1 -1 -1 736.21 144.67 828.41 364.89 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n161 6 Car -1 -1 -1 600.32 172.08 634.12 202.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n161 47 Pedestrian -1 -1 -1 403.53 159.86 435.57 243.90 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n161 27 Pedestrian -1 -1 -1 324.61 157.26 361.75 246.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n161 30 Pedestrian -1 -1 -1 370.13 160.98 400.57 245.52 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n161 7 Pedestrian -1 -1 -1 221.12 154.99 235.42 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n161 25 Pedestrian -1 -1 -1 192.71 161.92 207.24 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n161 71 Pedestrian -1 -1 -1 630.73 167.46 643.89 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n161 78 Cyclist -1 -1 -1 547.61 168.20 562.65 196.39 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n162 1 Car -1 -1 -1 1095.46 184.85 1220.07 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n162 2 Car -1 -1 -1 954.33 183.06 1067.06 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n162 3 Car -1 -1 -1 1032.07 183.84 1157.76 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n162 5 Pedestrian -1 -1 -1 735.69 143.03 821.23 362.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n162 6 Car -1 -1 -1 600.18 172.20 635.50 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n162 47 Pedestrian -1 -1 -1 399.33 160.04 430.29 243.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n162 27 Pedestrian -1 -1 -1 320.80 155.86 358.06 247.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n162 30 Pedestrian -1 -1 -1 369.38 160.62 398.14 245.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n162 7 Pedestrian -1 -1 -1 221.21 155.10 235.34 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n162 25 Pedestrian -1 -1 -1 192.71 161.92 207.01 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n162 79 Pedestrian -1 -1 -1 546.11 168.22 557.37 196.05 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n162 80 Pedestrian -1 -1 -1 181.65 160.00 197.38 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n163 1 Car -1 -1 -1 1095.36 184.86 1220.37 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n163 2 Car -1 -1 -1 954.39 183.09 1067.20 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n163 3 Car -1 -1 -1 1031.94 183.77 1157.96 233.54 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n163 6 Car -1 -1 -1 600.51 172.11 635.53 201.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n163 5 Pedestrian -1 -1 -1 733.11 142.68 815.77 362.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n163 30 Pedestrian -1 -1 -1 364.78 162.03 395.26 244.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n163 27 Pedestrian -1 -1 -1 317.95 154.76 353.21 244.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n163 47 Pedestrian -1 -1 -1 394.13 158.52 426.70 243.44 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n163 7 Pedestrian -1 -1 -1 220.96 154.96 235.27 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n163 25 Pedestrian -1 -1 -1 192.82 161.89 206.90 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n163 79 Pedestrian -1 -1 -1 542.86 167.98 554.16 195.77 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n163 80 Pedestrian -1 -1 -1 181.21 159.64 197.35 199.17 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n164 1 Car -1 -1 -1 1095.08 184.77 1220.70 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n164 2 Car -1 -1 -1 954.45 183.06 1067.06 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n164 3 Car -1 -1 -1 1028.71 183.88 1157.12 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n164 6 Car -1 -1 -1 600.48 172.15 635.36 200.97 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n164 5 Pedestrian -1 -1 -1 729.47 144.21 803.99 365.43 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n164 30 Pedestrian -1 -1 -1 360.33 163.09 392.17 241.89 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n164 27 Pedestrian -1 -1 -1 316.53 155.07 351.01 243.93 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n164 47 Pedestrian -1 -1 -1 387.70 159.76 425.46 242.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n164 7 Pedestrian -1 -1 -1 220.85 154.83 235.05 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n164 25 Pedestrian -1 -1 -1 192.80 161.77 206.89 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n164 79 Pedestrian -1 -1 -1 539.30 168.55 553.84 196.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n164 80 Pedestrian -1 -1 -1 181.04 159.84 197.17 198.89 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n165 1 Car -1 -1 -1 1095.13 184.83 1220.59 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n165 2 Car -1 -1 -1 954.27 183.07 1067.19 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n165 3 Car -1 -1 -1 1031.76 183.63 1158.11 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n165 47 Pedestrian -1 -1 -1 383.55 158.09 422.47 241.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n165 27 Pedestrian -1 -1 -1 308.26 153.75 347.41 244.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n165 30 Pedestrian -1 -1 -1 356.64 163.24 388.50 242.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n165 6 Car -1 -1 -1 599.66 172.76 635.57 200.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n165 5 Pedestrian -1 -1 -1 725.79 145.00 792.47 364.49 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n165 7 Pedestrian -1 -1 -1 220.90 154.93 235.10 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n165 79 Pedestrian -1 -1 -1 536.35 167.76 552.18 195.90 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n165 25 Pedestrian -1 -1 -1 193.02 161.81 206.88 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n165 80 Pedestrian -1 -1 -1 181.16 159.63 197.30 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n166 1 Car -1 -1 -1 1094.85 184.67 1221.04 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n166 2 Car -1 -1 -1 954.32 183.08 1067.11 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n166 3 Car -1 -1 -1 1028.83 183.87 1157.11 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n166 5 Pedestrian -1 -1 -1 715.67 144.10 787.16 360.90 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n166 6 Car -1 -1 -1 599.31 172.81 636.77 201.28 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n166 27 Pedestrian -1 -1 -1 307.81 154.61 344.92 243.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n166 47 Pedestrian -1 -1 -1 381.15 158.54 417.50 240.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n166 30 Pedestrian -1 -1 -1 351.47 161.83 381.16 242.72 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n166 7 Pedestrian -1 -1 -1 220.97 154.94 235.03 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n166 79 Pedestrian -1 -1 -1 533.44 167.66 549.39 196.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n166 25 Pedestrian -1 -1 -1 193.17 161.87 206.94 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n166 80 Pedestrian -1 -1 -1 181.13 159.51 197.53 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n166 81 Pedestrian -1 -1 -1 594.98 164.88 609.81 207.60 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n167 1 Car -1 -1 -1 1095.12 184.62 1220.86 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n167 2 Car -1 -1 -1 954.38 183.06 1067.01 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n167 3 Car -1 -1 -1 1031.76 183.58 1158.12 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n167 5 Pedestrian -1 -1 -1 705.80 143.77 782.55 360.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n167 27 Pedestrian -1 -1 -1 303.86 155.65 341.24 241.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n167 6 Car -1 -1 -1 599.68 172.89 637.16 202.09 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n167 47 Pedestrian -1 -1 -1 380.52 157.97 410.31 240.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n167 30 Pedestrian -1 -1 -1 350.81 161.37 378.52 241.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n167 7 Pedestrian -1 -1 -1 220.98 154.96 235.00 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n167 79 Pedestrian -1 -1 -1 532.18 168.21 545.45 196.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n167 25 Pedestrian -1 -1 -1 193.12 161.59 206.97 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n167 81 Pedestrian -1 -1 -1 592.76 165.38 607.24 206.93 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n167 80 Pedestrian -1 -1 -1 180.31 158.76 197.71 199.50 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n168 1 Car -1 -1 -1 1094.98 184.67 1221.11 236.12 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n168 2 Car -1 -1 -1 954.10 183.07 1067.26 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n168 3 Car -1 -1 -1 1031.70 183.57 1158.15 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n168 5 Pedestrian -1 -1 -1 698.70 143.16 781.49 360.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n168 30 Pedestrian -1 -1 -1 345.40 161.48 377.18 241.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n168 27 Pedestrian -1 -1 -1 301.29 155.78 336.14 241.12 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n168 6 Car -1 -1 -1 599.28 172.25 637.56 202.15 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n168 47 Pedestrian -1 -1 -1 374.08 158.11 403.96 240.19 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n168 7 Pedestrian -1 -1 -1 221.07 154.93 234.90 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n168 81 Pedestrian -1 -1 -1 591.97 166.39 605.29 206.04 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n168 79 Pedestrian -1 -1 -1 530.48 168.06 542.59 196.06 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n168 25 Pedestrian -1 -1 -1 193.07 161.44 207.08 198.89 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n168 80 Pedestrian -1 -1 -1 180.19 158.64 197.66 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n169 1 Car -1 -1 -1 1095.14 184.62 1220.82 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n169 2 Car -1 -1 -1 954.17 183.10 1067.17 232.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n169 5 Pedestrian -1 -1 -1 695.41 144.48 778.09 360.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n169 3 Car -1 -1 -1 1028.63 183.80 1157.31 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n169 27 Pedestrian -1 -1 -1 298.18 155.26 332.10 241.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n169 30 Pedestrian -1 -1 -1 342.64 161.78 374.12 240.88 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n169 47 Pedestrian -1 -1 -1 372.82 158.51 402.46 239.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n169 6 Car -1 -1 -1 599.15 171.46 637.41 201.81 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n169 7 Pedestrian -1 -1 -1 220.97 154.93 234.87 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n169 79 Pedestrian -1 -1 -1 528.63 168.36 540.88 195.65 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n169 25 Pedestrian -1 -1 -1 192.98 161.37 207.00 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n169 81 Pedestrian -1 -1 -1 589.37 165.10 602.24 203.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n169 80 Pedestrian -1 -1 -1 180.30 158.78 197.50 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n170 1 Car -1 -1 -1 1095.13 184.60 1220.89 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n170 5 Pedestrian -1 -1 -1 694.19 144.41 778.38 360.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n170 3 Car -1 -1 -1 1028.81 183.82 1157.13 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n170 2 Car -1 -1 -1 954.07 183.17 1067.21 232.02 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n170 27 Pedestrian -1 -1 -1 294.15 155.71 329.38 241.33 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n170 47 Pedestrian -1 -1 -1 369.65 158.42 399.26 238.94 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n170 6 Car -1 -1 -1 599.63 171.64 637.99 201.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n170 30 Pedestrian -1 -1 -1 339.17 160.85 370.34 241.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n170 7 Pedestrian -1 -1 -1 220.86 155.06 234.83 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n170 79 Pedestrian -1 -1 -1 526.08 168.63 539.10 195.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n170 25 Pedestrian -1 -1 -1 192.90 161.28 207.10 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n170 81 Pedestrian -1 -1 -1 587.36 165.53 601.58 203.31 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n170 80 Pedestrian -1 -1 -1 180.33 158.89 197.17 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n171 1 Car -1 -1 -1 1095.14 184.59 1220.82 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n171 5 Pedestrian -1 -1 -1 690.14 143.11 775.05 361.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n171 3 Car -1 -1 -1 1028.85 183.81 1157.03 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n171 2 Car -1 -1 -1 953.95 183.16 1067.25 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n171 47 Pedestrian -1 -1 -1 365.68 159.35 395.63 238.76 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n171 27 Pedestrian -1 -1 -1 291.58 156.50 325.82 240.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n171 6 Car -1 -1 -1 603.41 172.46 638.16 200.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n171 30 Pedestrian -1 -1 -1 337.97 161.42 368.01 238.29 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n171 7 Pedestrian -1 -1 -1 220.81 155.06 235.03 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n171 79 Pedestrian -1 -1 -1 524.65 168.17 537.16 195.78 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n171 81 Pedestrian -1 -1 -1 583.20 167.57 599.76 204.12 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n171 25 Pedestrian -1 -1 -1 192.80 160.97 207.21 199.29 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n171 80 Pedestrian -1 -1 -1 180.49 159.35 196.77 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n172 1 Car -1 -1 -1 1095.01 184.54 1220.92 236.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n172 3 Car -1 -1 -1 1028.67 183.74 1157.17 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n172 2 Car -1 -1 -1 954.30 183.27 1066.95 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n172 5 Pedestrian -1 -1 -1 688.40 143.89 768.80 360.49 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n172 27 Pedestrian -1 -1 -1 291.05 155.37 324.52 240.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n172 30 Pedestrian -1 -1 -1 335.51 160.59 363.69 238.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n172 47 Pedestrian -1 -1 -1 362.13 159.65 391.31 236.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n172 6 Car -1 -1 -1 602.49 172.65 638.07 201.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n172 7 Pedestrian -1 -1 -1 220.89 155.05 235.00 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n172 79 Pedestrian -1 -1 -1 523.88 168.58 534.51 195.96 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n172 81 Pedestrian -1 -1 -1 580.72 168.81 596.20 204.18 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n172 25 Pedestrian -1 -1 -1 192.74 160.89 207.38 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n172 80 Pedestrian -1 -1 -1 180.47 159.05 197.11 199.77 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n172 82 Pedestrian -1 -1 -1 596.00 167.30 610.90 205.93 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n173 1 Car -1 -1 -1 1095.00 184.53 1221.15 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n173 3 Car -1 -1 -1 1028.75 183.71 1157.13 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n173 2 Car -1 -1 -1 954.50 183.24 1066.80 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n173 5 Pedestrian -1 -1 -1 683.99 145.07 758.09 359.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n173 6 Car -1 -1 -1 603.02 172.45 638.07 201.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n173 30 Pedestrian -1 -1 -1 331.32 160.97 359.98 238.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n173 47 Pedestrian -1 -1 -1 358.53 159.99 387.93 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n173 27 Pedestrian -1 -1 -1 288.62 155.31 321.16 238.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n173 7 Pedestrian -1 -1 -1 220.48 154.85 234.92 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n173 25 Pedestrian -1 -1 -1 192.52 160.84 207.46 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n173 81 Pedestrian -1 -1 -1 579.50 167.53 593.38 204.47 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n173 79 Pedestrian -1 -1 -1 521.56 168.24 533.13 195.32 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n173 80 Pedestrian -1 -1 -1 177.33 158.45 194.79 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n173 83 Cyclist -1 -1 -1 592.00 165.30 611.43 206.89 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n174 1 Car -1 -1 -1 1095.16 184.53 1220.82 236.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n174 3 Car -1 -1 -1 1028.73 183.73 1157.16 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n174 2 Car -1 -1 -1 954.38 183.20 1067.01 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n174 6 Car -1 -1 -1 603.21 172.46 637.89 202.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n174 30 Pedestrian -1 -1 -1 327.36 161.11 358.05 237.89 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n174 5 Pedestrian -1 -1 -1 680.78 145.97 746.40 358.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n174 27 Pedestrian -1 -1 -1 289.61 156.46 319.63 238.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n174 47 Pedestrian -1 -1 -1 353.78 157.04 386.45 238.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n174 81 Pedestrian -1 -1 -1 575.87 168.46 590.63 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n174 7 Pedestrian -1 -1 -1 220.26 154.96 234.87 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n174 25 Pedestrian -1 -1 -1 192.34 160.77 207.59 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n174 79 Pedestrian -1 -1 -1 519.40 168.15 532.03 195.13 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n174 80 Pedestrian -1 -1 -1 180.18 158.76 197.33 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n174 84 Pedestrian -1 -1 -1 591.30 166.46 607.36 205.49 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n175 1 Car -1 -1 -1 1094.88 184.43 1221.18 236.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n175 2 Car -1 -1 -1 954.70 183.21 1066.90 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n175 3 Car -1 -1 -1 1028.83 183.76 1157.12 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n175 6 Car -1 -1 -1 602.16 172.44 638.16 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n175 5 Pedestrian -1 -1 -1 673.75 146.20 745.00 358.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n175 47 Pedestrian -1 -1 -1 352.59 158.57 384.42 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n175 30 Pedestrian -1 -1 -1 324.13 161.00 354.65 237.34 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n175 27 Pedestrian -1 -1 -1 289.22 155.82 318.17 238.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n175 7 Pedestrian -1 -1 -1 220.42 154.92 234.86 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n175 81 Pedestrian -1 -1 -1 571.04 170.04 590.46 203.64 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n175 25 Pedestrian -1 -1 -1 192.24 160.81 207.62 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n175 84 Pedestrian -1 -1 -1 589.79 167.04 606.38 204.58 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n175 85 Pedestrian -1 -1 -1 0.92 148.39 25.55 249.43 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n176 1 Car -1 -1 -1 1094.85 184.47 1221.12 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n176 3 Car -1 -1 -1 1031.93 183.53 1157.96 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n176 2 Car -1 -1 -1 954.83 183.28 1066.76 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n176 5 Pedestrian -1 -1 -1 664.58 144.03 739.63 359.34 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n176 47 Pedestrian -1 -1 -1 348.32 159.72 381.67 235.44 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n176 6 Car -1 -1 -1 600.07 172.25 638.44 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n176 30 Pedestrian -1 -1 -1 324.09 161.20 351.58 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n176 27 Pedestrian -1 -1 -1 285.91 155.12 316.47 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n176 81 Pedestrian -1 -1 -1 570.52 169.83 588.58 204.06 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n176 7 Pedestrian -1 -1 -1 220.16 154.87 234.91 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n176 84 Pedestrian -1 -1 -1 587.74 168.35 601.92 204.61 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n176 85 Pedestrian -1 -1 -1 0.84 147.77 26.41 249.51 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n176 25 Pedestrian -1 -1 -1 192.44 161.08 207.42 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n177 1 Car -1 -1 -1 1095.08 184.50 1220.93 236.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n177 2 Car -1 -1 -1 954.76 183.32 1066.88 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n177 3 Car -1 -1 -1 1032.11 183.57 1157.72 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n177 5 Pedestrian -1 -1 -1 663.97 143.61 740.15 359.94 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n177 47 Pedestrian -1 -1 -1 346.18 161.55 377.86 235.13 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n177 30 Pedestrian -1 -1 -1 321.72 160.77 347.94 235.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n177 27 Pedestrian -1 -1 -1 286.15 157.61 314.37 236.93 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n177 6 Car -1 -1 -1 600.94 172.55 637.76 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n177 7 Pedestrian -1 -1 -1 220.18 154.82 234.76 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n177 81 Pedestrian -1 -1 -1 568.28 170.52 585.49 204.50 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n177 85 Pedestrian -1 -1 -1 1.09 147.24 33.30 249.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n177 84 Pedestrian -1 -1 -1 583.98 168.60 599.38 203.45 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n177 25 Pedestrian -1 -1 -1 192.33 161.22 207.51 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n178 1 Car -1 -1 -1 1095.03 184.48 1220.88 236.34 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n178 2 Car -1 -1 -1 954.91 183.31 1066.70 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n178 3 Car -1 -1 -1 1029.16 183.78 1156.80 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n178 5 Pedestrian -1 -1 -1 662.65 141.99 740.49 361.18 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n178 30 Pedestrian -1 -1 -1 317.17 160.47 346.23 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n178 27 Pedestrian -1 -1 -1 284.46 156.56 314.01 234.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n178 6 Car -1 -1 -1 600.90 172.68 637.79 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n178 47 Pedestrian -1 -1 -1 345.97 159.08 374.98 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n178 7 Pedestrian -1 -1 -1 219.95 154.81 234.78 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n178 81 Pedestrian -1 -1 -1 567.93 169.29 582.44 204.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n178 85 Pedestrian -1 -1 -1 1.46 146.43 40.88 251.43 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n178 25 Pedestrian -1 -1 -1 192.44 161.10 207.61 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n178 84 Pedestrian -1 -1 -1 579.37 168.38 596.84 203.96 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n179 1 Car -1 -1 -1 1095.03 184.50 1220.86 236.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n179 3 Car -1 -1 -1 1028.99 183.77 1156.78 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n179 2 Car -1 -1 -1 954.88 183.29 1066.61 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n179 5 Pedestrian -1 -1 -1 658.57 141.12 737.82 361.91 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n179 27 Pedestrian -1 -1 -1 282.16 155.97 311.40 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n179 30 Pedestrian -1 -1 -1 315.82 160.69 344.68 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n179 47 Pedestrian -1 -1 -1 343.15 159.16 372.44 232.50 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n179 6 Car -1 -1 -1 600.80 172.64 637.92 202.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n179 85 Pedestrian -1 -1 -1 2.38 148.45 47.45 249.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n179 81 Pedestrian -1 -1 -1 564.88 167.84 578.94 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n179 7 Pedestrian -1 -1 -1 219.74 154.78 234.81 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n179 84 Pedestrian -1 -1 -1 576.81 167.27 595.74 203.92 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n179 25 Pedestrian -1 -1 -1 192.44 161.24 207.43 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n179 86 Pedestrian -1 -1 -1 369.33 160.98 382.17 196.33 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n180 1 Car -1 -1 -1 1095.10 184.56 1220.88 236.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n180 2 Car -1 -1 -1 954.81 183.36 1066.76 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n180 3 Car -1 -1 -1 1029.09 183.80 1156.76 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n180 5 Pedestrian -1 -1 -1 658.82 141.88 737.10 361.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n180 27 Pedestrian -1 -1 -1 280.35 156.23 311.61 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n180 30 Pedestrian -1 -1 -1 313.19 160.97 340.61 234.67 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n180 6 Car -1 -1 -1 601.07 172.90 637.64 202.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n180 85 Pedestrian -1 -1 -1 5.22 147.91 52.83 248.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n180 47 Pedestrian -1 -1 -1 340.17 158.39 368.68 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n180 7 Pedestrian -1 -1 -1 219.74 154.88 234.81 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n180 84 Pedestrian -1 -1 -1 572.93 167.77 594.17 203.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n180 86 Pedestrian -1 -1 -1 369.49 161.58 382.11 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n180 81 Pedestrian -1 -1 -1 560.19 167.32 577.89 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n180 25 Pedestrian -1 -1 -1 192.50 161.24 207.42 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n181 1 Car -1 -1 -1 1095.19 184.66 1220.60 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n181 2 Car -1 -1 -1 954.97 183.37 1066.64 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n181 3 Car -1 -1 -1 1029.16 183.79 1156.65 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n181 5 Pedestrian -1 -1 -1 659.77 141.82 735.55 361.20 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n181 27 Pedestrian -1 -1 -1 281.08 156.48 310.52 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n181 85 Pedestrian -1 -1 -1 12.83 145.94 60.41 248.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n181 30 Pedestrian -1 -1 -1 310.53 160.25 337.80 234.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n181 6 Car -1 -1 -1 601.06 172.97 637.73 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n181 47 Pedestrian -1 -1 -1 336.75 158.82 364.89 232.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n181 81 Pedestrian -1 -1 -1 558.75 167.24 576.50 204.34 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n181 86 Pedestrian -1 -1 -1 369.75 161.18 382.59 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n181 7 Pedestrian -1 -1 -1 219.64 154.98 234.78 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n181 84 Pedestrian -1 -1 -1 571.09 168.16 590.05 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n181 25 Pedestrian -1 -1 -1 192.66 161.30 207.36 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n181 87 Cyclist -1 -1 -1 572.68 168.15 591.89 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n182 1 Car -1 -1 -1 1094.98 184.55 1220.95 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n182 2 Car -1 -1 -1 954.92 183.39 1066.56 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n182 3 Car -1 -1 -1 1029.06 183.80 1156.75 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n182 5 Pedestrian -1 -1 -1 657.32 142.08 731.71 360.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n182 85 Pedestrian -1 -1 -1 13.26 148.03 68.41 246.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n182 27 Pedestrian -1 -1 -1 278.73 156.32 308.06 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n182 47 Pedestrian -1 -1 -1 336.57 158.84 363.94 232.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n182 30 Pedestrian -1 -1 -1 310.03 159.93 336.41 232.31 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n182 6 Car -1 -1 -1 600.99 172.95 637.78 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n182 86 Pedestrian -1 -1 -1 369.87 161.45 382.90 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n182 7 Pedestrian -1 -1 -1 219.67 154.99 234.75 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n182 81 Pedestrian -1 -1 -1 558.10 167.20 572.90 205.14 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n182 25 Pedestrian -1 -1 -1 192.71 161.09 207.36 199.50 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n182 87 Cyclist -1 -1 -1 569.59 167.80 586.96 203.52 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n183 1 Car -1 -1 -1 1094.96 184.58 1220.85 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n183 2 Car -1 -1 -1 954.97 183.40 1066.48 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n183 3 Car -1 -1 -1 1028.98 183.77 1156.80 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n183 5 Pedestrian -1 -1 -1 657.16 142.57 730.96 360.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n183 27 Pedestrian -1 -1 -1 278.84 156.84 306.85 231.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n183 85 Pedestrian -1 -1 -1 18.79 149.23 75.32 245.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n183 30 Pedestrian -1 -1 -1 309.11 160.03 336.10 231.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n183 86 Pedestrian -1 -1 -1 370.28 162.53 383.17 197.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n183 6 Car -1 -1 -1 602.38 172.81 637.60 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n183 47 Pedestrian -1 -1 -1 334.22 162.22 360.19 232.49 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n183 7 Pedestrian -1 -1 -1 219.72 155.06 234.73 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n183 81 Pedestrian -1 -1 -1 557.19 167.52 570.14 204.41 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n183 25 Pedestrian -1 -1 -1 192.64 161.14 207.30 199.44 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n183 87 Cyclist -1 -1 -1 566.10 167.74 584.40 203.50 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n183 88 Pedestrian -1 -1 -1 566.10 167.74 584.40 203.50 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n183 89 Car -1 -1 -1 599.37 173.66 620.40 192.44 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n184 1 Car -1 -1 -1 1095.20 184.54 1220.70 236.40 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n184 2 Car -1 -1 -1 955.08 183.40 1066.36 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n184 3 Car -1 -1 -1 1029.09 183.76 1156.74 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n184 5 Pedestrian -1 -1 -1 654.51 143.33 726.95 359.71 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n184 27 Pedestrian -1 -1 -1 278.73 156.87 305.90 230.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n184 47 Pedestrian -1 -1 -1 333.60 163.23 359.37 230.98 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n184 85 Pedestrian -1 -1 -1 22.37 147.82 74.62 243.36 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n184 30 Pedestrian -1 -1 -1 305.44 160.94 334.28 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n184 6 Car -1 -1 -1 602.31 172.83 637.73 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n184 86 Pedestrian -1 -1 -1 369.98 162.51 383.38 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n184 88 Pedestrian -1 -1 -1 562.81 168.11 582.40 203.73 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n184 7 Pedestrian -1 -1 -1 219.65 154.99 234.62 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n184 25 Pedestrian -1 -1 -1 192.50 160.72 207.33 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n184 81 Pedestrian -1 -1 -1 554.21 167.39 567.85 203.90 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n184 89 Car -1 -1 -1 599.31 173.49 620.79 192.52 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n185 1 Car -1 -1 -1 1095.12 184.60 1220.95 236.34 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n185 2 Car -1 -1 -1 954.95 183.40 1066.44 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n185 3 Car -1 -1 -1 1029.02 183.76 1156.83 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n185 5 Pedestrian -1 -1 -1 652.66 144.70 721.42 357.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n185 85 Pedestrian -1 -1 -1 34.90 147.99 77.73 242.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n185 30 Pedestrian -1 -1 -1 305.28 161.03 332.93 231.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n185 86 Pedestrian -1 -1 -1 369.90 161.88 383.48 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n185 27 Pedestrian -1 -1 -1 278.35 156.68 304.93 230.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n185 47 Pedestrian -1 -1 -1 332.45 161.03 358.46 230.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n185 6 Car -1 -1 -1 601.23 173.00 637.67 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n185 88 Pedestrian -1 -1 -1 561.45 169.18 580.84 203.48 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n185 7 Pedestrian -1 -1 -1 219.68 154.99 234.58 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n185 81 Pedestrian -1 -1 -1 551.40 167.75 567.24 203.52 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n185 25 Pedestrian -1 -1 -1 192.40 160.74 207.39 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n185 89 Car -1 -1 -1 599.26 173.44 620.88 192.32 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n186 1 Car -1 -1 -1 1095.19 184.63 1220.85 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n186 2 Car -1 -1 -1 954.90 183.35 1066.52 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n186 3 Car -1 -1 -1 1029.19 183.78 1156.70 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n186 85 Pedestrian -1 -1 -1 42.54 147.72 82.93 242.79 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n186 5 Pedestrian -1 -1 -1 653.10 144.62 719.67 358.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n186 27 Pedestrian -1 -1 -1 275.56 156.38 303.65 230.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n186 6 Car -1 -1 -1 602.29 172.72 637.91 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n186 30 Pedestrian -1 -1 -1 305.53 160.22 331.13 229.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n186 86 Pedestrian -1 -1 -1 370.58 161.63 383.81 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n186 47 Pedestrian -1 -1 -1 330.48 161.33 356.60 230.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n186 88 Pedestrian -1 -1 -1 559.55 168.69 576.70 203.98 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n186 7 Pedestrian -1 -1 -1 219.58 154.91 234.62 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n186 81 Pedestrian -1 -1 -1 548.10 165.63 564.43 203.56 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n186 25 Pedestrian -1 -1 -1 192.32 160.82 207.41 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n186 89 Car -1 -1 -1 599.35 173.45 621.12 192.24 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n187 1 Car -1 -1 -1 1095.00 184.51 1220.83 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n187 2 Car -1 -1 -1 954.93 183.41 1066.57 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n187 3 Car -1 -1 -1 1029.19 183.76 1156.57 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n187 5 Pedestrian -1 -1 -1 648.99 145.39 716.68 358.35 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n187 85 Pedestrian -1 -1 -1 50.74 148.06 89.56 242.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n187 47 Pedestrian -1 -1 -1 330.04 161.87 355.55 229.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n187 27 Pedestrian -1 -1 -1 275.30 156.05 302.46 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n187 30 Pedestrian -1 -1 -1 304.11 159.28 328.65 229.19 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n187 6 Car -1 -1 -1 602.33 172.89 637.76 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n187 86 Pedestrian -1 -1 -1 371.08 161.73 384.06 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n187 7 Pedestrian -1 -1 -1 219.64 154.97 234.64 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n187 88 Pedestrian -1 -1 -1 557.10 168.47 572.16 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n187 81 Pedestrian -1 -1 -1 545.85 167.23 562.17 204.06 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n187 25 Pedestrian -1 -1 -1 192.47 161.02 207.40 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n187 89 Car -1 -1 -1 599.13 173.59 621.10 192.31 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n188 1 Car -1 -1 -1 1095.49 184.60 1220.50 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n188 2 Car -1 -1 -1 954.99 183.40 1066.71 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n188 5 Pedestrian -1 -1 -1 644.15 145.15 714.26 358.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n188 3 Car -1 -1 -1 1029.15 183.80 1156.70 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n188 85 Pedestrian -1 -1 -1 54.93 149.45 94.19 241.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n188 30 Pedestrian -1 -1 -1 303.81 160.07 327.96 228.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n188 47 Pedestrian -1 -1 -1 328.94 162.53 355.10 228.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n188 27 Pedestrian -1 -1 -1 274.44 156.04 301.33 227.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n188 6 Car -1 -1 -1 602.38 173.11 637.72 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n188 86 Pedestrian -1 -1 -1 371.17 162.19 383.98 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n188 7 Pedestrian -1 -1 -1 219.60 155.07 234.56 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n188 88 Pedestrian -1 -1 -1 553.12 169.26 569.15 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n188 81 Pedestrian -1 -1 -1 545.43 167.70 559.43 203.69 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n188 25 Pedestrian -1 -1 -1 192.36 160.73 207.51 199.70 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n189 1 Car -1 -1 -1 1095.39 184.55 1220.42 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n189 2 Car -1 -1 -1 955.00 183.35 1066.55 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n189 3 Car -1 -1 -1 1029.22 183.83 1156.66 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n189 5 Pedestrian -1 -1 -1 639.71 144.95 711.15 359.54 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n189 30 Pedestrian -1 -1 -1 303.65 160.78 326.72 228.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n189 85 Pedestrian -1 -1 -1 59.80 149.46 98.52 241.57 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n189 27 Pedestrian -1 -1 -1 272.12 155.16 299.78 227.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n189 47 Pedestrian -1 -1 -1 329.53 163.07 353.83 227.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n189 6 Car -1 -1 -1 602.26 173.09 637.75 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n189 86 Pedestrian -1 -1 -1 371.41 162.39 383.91 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n189 7 Pedestrian -1 -1 -1 219.67 155.01 234.46 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n189 81 Pedestrian -1 -1 -1 543.01 167.87 557.02 203.72 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n189 25 Pedestrian -1 -1 -1 192.22 160.42 207.44 200.01 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n189 88 Pedestrian -1 -1 -1 551.02 168.48 568.20 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n190 1 Car -1 -1 -1 1095.40 184.48 1220.47 236.35 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n190 2 Car -1 -1 -1 954.92 183.34 1066.68 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n190 3 Car -1 -1 -1 1029.40 183.81 1156.53 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n190 5 Pedestrian -1 -1 -1 638.44 144.20 711.56 360.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n190 85 Pedestrian -1 -1 -1 67.00 149.92 104.91 240.26 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n190 27 Pedestrian -1 -1 -1 271.89 155.40 299.46 227.31 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n190 30 Pedestrian -1 -1 -1 303.10 161.17 325.86 228.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n190 6 Car -1 -1 -1 602.27 172.98 637.71 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n190 86 Pedestrian -1 -1 -1 371.17 162.50 384.23 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n190 47 Pedestrian -1 -1 -1 329.14 163.57 353.57 228.10 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n190 7 Pedestrian -1 -1 -1 219.59 155.05 234.39 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n190 81 Pedestrian -1 -1 -1 541.50 168.19 554.92 203.31 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n190 25 Pedestrian -1 -1 -1 192.29 160.38 207.31 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n190 90 Cyclist -1 -1 -1 548.74 165.84 564.83 203.29 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n191 1 Car -1 -1 -1 1095.41 184.49 1220.52 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n191 2 Car -1 -1 -1 954.93 183.32 1066.61 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n191 3 Car -1 -1 -1 1032.38 183.56 1157.47 233.70 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n191 5 Pedestrian -1 -1 -1 634.62 144.09 708.01 361.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n191 85 Pedestrian -1 -1 -1 73.79 148.12 111.88 240.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n191 27 Pedestrian -1 -1 -1 272.19 156.01 298.55 226.58 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n191 30 Pedestrian -1 -1 -1 300.97 161.26 324.41 227.35 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n191 6 Car -1 -1 -1 602.49 172.94 637.60 202.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n191 86 Pedestrian -1 -1 -1 370.81 162.63 384.65 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n191 81 Pedestrian -1 -1 -1 538.19 168.99 552.75 203.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n191 47 Pedestrian -1 -1 -1 326.58 164.22 352.37 227.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n191 7 Pedestrian -1 -1 -1 219.62 155.02 234.30 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n191 25 Pedestrian -1 -1 -1 192.39 160.27 207.35 200.15 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n191 90 Cyclist -1 -1 -1 548.05 166.13 563.84 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n191 91 Pedestrian -1 -1 -1 547.60 167.68 565.06 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n192 1 Car -1 -1 -1 1095.39 184.55 1220.55 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n192 2 Car -1 -1 -1 954.95 183.35 1066.59 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n192 85 Pedestrian -1 -1 -1 76.63 149.23 118.41 238.54 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n192 3 Car -1 -1 -1 1032.53 183.59 1157.28 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n192 5 Pedestrian -1 -1 -1 630.17 146.02 704.90 363.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n192 27 Pedestrian -1 -1 -1 272.69 156.19 298.25 226.25 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n192 30 Pedestrian -1 -1 -1 300.72 160.44 323.97 226.88 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n192 47 Pedestrian -1 -1 -1 326.30 164.15 352.26 227.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n192 6 Car -1 -1 -1 601.42 172.92 637.41 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n192 86 Pedestrian -1 -1 -1 370.64 162.91 384.83 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n192 7 Pedestrian -1 -1 -1 219.63 154.98 234.41 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n192 81 Pedestrian -1 -1 -1 536.79 169.28 551.45 204.07 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n192 91 Pedestrian -1 -1 -1 544.89 167.53 561.94 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n192 25 Pedestrian -1 -1 -1 192.36 160.33 207.51 200.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n192 92 Car -1 -1 -1 -1.58 231.09 257.38 364.97 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n193 1 Car -1 -1 -1 1095.34 184.60 1220.58 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n193 92 Car -1 -1 -1 -1.25 231.14 287.18 365.06 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n193 85 Pedestrian -1 -1 -1 78.92 149.67 122.95 238.57 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n193 2 Car -1 -1 -1 954.87 183.32 1066.64 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n193 5 Pedestrian -1 -1 -1 627.03 144.99 707.79 365.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n193 3 Car -1 -1 -1 1029.38 183.82 1156.57 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n193 27 Pedestrian -1 -1 -1 272.49 155.79 297.55 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n193 47 Pedestrian -1 -1 -1 326.77 163.59 351.44 225.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n193 30 Pedestrian -1 -1 -1 300.58 160.04 323.79 226.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n193 6 Car -1 -1 -1 601.50 173.08 637.29 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n193 7 Pedestrian -1 -1 -1 219.69 155.05 234.33 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n193 86 Pedestrian -1 -1 -1 371.32 163.11 384.74 197.62 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n193 81 Pedestrian -1 -1 -1 534.81 167.42 549.49 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n193 25 Pedestrian -1 -1 -1 192.44 160.44 207.37 199.99 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n193 91 Pedestrian -1 -1 -1 544.70 167.47 559.58 203.26 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n194 92 Car -1 -1 -1 0.52 216.42 315.53 365.04 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n194 1 Car -1 -1 -1 1095.25 184.51 1220.68 236.42 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n194 85 Pedestrian -1 -1 -1 83.75 151.51 125.15 237.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n194 2 Car -1 -1 -1 954.88 183.32 1066.60 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n194 3 Car -1 -1 -1 1029.47 183.84 1156.51 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n194 5 Pedestrian -1 -1 -1 622.42 145.55 704.76 365.01 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n194 27 Pedestrian -1 -1 -1 272.89 155.59 298.35 224.52 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n194 47 Pedestrian -1 -1 -1 327.04 162.55 350.98 225.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n194 30 Pedestrian -1 -1 -1 300.12 160.57 324.22 225.93 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n194 6 Car -1 -1 -1 601.37 173.02 637.19 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n194 7 Pedestrian -1 -1 -1 219.73 154.98 234.50 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n194 86 Pedestrian -1 -1 -1 372.10 163.30 386.63 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n194 81 Pedestrian -1 -1 -1 533.74 167.94 547.45 203.75 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n194 91 Pedestrian -1 -1 -1 542.10 167.74 556.57 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n194 25 Pedestrian -1 -1 -1 192.39 160.37 207.36 200.00 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n194 93 Pedestrian -1 -1 -1 355.78 162.55 369.04 196.47 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n195 92 Car -1 -1 -1 3.09 216.05 330.70 364.23 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n195 1 Car -1 -1 -1 1095.28 184.48 1220.46 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n195 2 Car -1 -1 -1 954.87 183.31 1066.67 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n195 3 Car -1 -1 -1 1029.45 183.80 1156.47 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n195 85 Pedestrian -1 -1 -1 90.12 152.39 125.98 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n195 5 Pedestrian -1 -1 -1 615.54 146.86 689.32 364.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n195 27 Pedestrian -1 -1 -1 272.99 155.82 298.55 224.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n195 30 Pedestrian -1 -1 -1 299.74 160.87 324.06 225.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n195 6 Car -1 -1 -1 601.95 173.31 635.93 202.38 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n195 47 Pedestrian -1 -1 -1 326.80 162.04 351.46 224.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n195 7 Pedestrian -1 -1 -1 219.82 155.03 234.51 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n195 81 Pedestrian -1 -1 -1 530.49 167.75 543.40 203.74 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n195 86 Pedestrian -1 -1 -1 372.31 162.95 386.75 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n195 91 Pedestrian -1 -1 -1 541.63 167.68 556.06 203.34 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n195 93 Pedestrian -1 -1 -1 356.06 162.78 368.85 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n195 25 Pedestrian -1 -1 -1 190.25 160.42 205.62 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n196 92 Car -1 -1 -1 2.60 209.68 353.65 364.28 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n196 1 Car -1 -1 -1 1094.89 184.50 1220.85 236.46 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n196 2 Car -1 -1 -1 954.95 183.30 1066.45 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n196 3 Car -1 -1 -1 1029.44 183.77 1156.49 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n196 5 Pedestrian -1 -1 -1 606.49 146.20 674.88 364.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n196 27 Pedestrian -1 -1 -1 272.56 156.38 298.82 223.70 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n196 85 Pedestrian -1 -1 -1 93.07 153.37 127.09 234.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n196 30 Pedestrian -1 -1 -1 300.19 160.99 323.84 225.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n196 6 Car -1 -1 -1 602.16 173.76 635.94 202.15 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n196 7 Pedestrian -1 -1 -1 219.97 155.10 234.46 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n196 47 Pedestrian -1 -1 -1 326.96 162.08 351.81 224.67 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n196 86 Pedestrian -1 -1 -1 372.47 162.44 387.35 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n196 91 Pedestrian -1 -1 -1 540.58 168.25 555.90 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n196 81 Pedestrian -1 -1 -1 527.11 167.26 542.27 203.84 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n196 93 Pedestrian -1 -1 -1 355.91 162.94 368.58 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n196 25 Pedestrian -1 -1 -1 190.26 160.47 205.65 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n197 92 Car -1 -1 -1 0.47 208.30 370.82 363.39 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n197 1 Car -1 -1 -1 1094.87 184.47 1220.87 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n197 5 Pedestrian -1 -1 -1 591.78 146.96 674.54 363.90 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n197 3 Car -1 -1 -1 1029.43 183.75 1156.49 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n197 2 Car -1 -1 -1 954.89 183.26 1066.45 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n197 27 Pedestrian -1 -1 -1 272.54 156.36 298.38 223.92 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n197 30 Pedestrian -1 -1 -1 300.28 159.94 322.99 223.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n197 6 Car -1 -1 -1 601.96 174.24 635.71 201.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n197 47 Pedestrian -1 -1 -1 327.37 163.20 351.02 224.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n197 85 Pedestrian -1 -1 -1 96.86 153.83 130.80 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n197 7 Pedestrian -1 -1 -1 219.83 155.13 234.42 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n197 86 Pedestrian -1 -1 -1 372.77 162.06 387.64 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n197 91 Pedestrian -1 -1 -1 537.22 168.05 554.82 203.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n197 81 Pedestrian -1 -1 -1 525.35 166.65 540.94 204.36 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n197 25 Pedestrian -1 -1 -1 190.17 160.61 205.69 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n197 93 Pedestrian -1 -1 -1 355.80 162.75 368.76 196.49 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n198 92 Car -1 -1 -1 -1.38 202.04 388.80 363.41 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n198 1 Car -1 -1 -1 1094.78 184.42 1220.74 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n198 3 Car -1 -1 -1 1029.69 183.83 1156.25 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n198 5 Pedestrian -1 -1 -1 572.37 146.07 671.10 365.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n198 2 Car -1 -1 -1 954.81 183.19 1066.54 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n198 6 Car -1 -1 -1 601.45 173.59 636.54 201.83 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n198 30 Pedestrian -1 -1 -1 299.99 158.72 322.41 222.96 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n198 27 Pedestrian -1 -1 -1 272.56 156.46 297.90 223.20 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n198 47 Pedestrian -1 -1 -1 327.62 163.50 350.56 224.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n198 85 Pedestrian -1 -1 -1 101.13 154.19 134.24 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n198 7 Pedestrian -1 -1 -1 219.72 155.08 234.49 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n198 91 Pedestrian -1 -1 -1 536.44 168.39 553.02 203.63 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n198 86 Pedestrian -1 -1 -1 373.00 162.07 386.77 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n198 81 Pedestrian -1 -1 -1 522.84 167.37 537.17 204.12 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n198 25 Pedestrian -1 -1 -1 190.01 160.62 205.88 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n199 92 Car -1 -1 -1 -1.03 195.98 403.01 362.93 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n199 1 Car -1 -1 -1 1094.77 184.51 1220.95 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n199 5 Pedestrian -1 -1 -1 561.00 148.87 666.72 363.49 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n199 2 Car -1 -1 -1 954.84 183.17 1066.62 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n199 3 Car -1 -1 -1 1029.56 183.87 1156.45 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n199 30 Pedestrian -1 -1 -1 299.77 158.95 321.95 222.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n199 6 Car -1 -1 -1 601.51 173.57 637.06 201.58 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n199 47 Pedestrian -1 -1 -1 327.54 163.63 350.24 223.22 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n199 27 Pedestrian -1 -1 -1 272.14 156.23 297.28 222.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n199 85 Pedestrian -1 -1 -1 105.31 152.90 137.07 234.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n199 7 Pedestrian -1 -1 -1 219.51 155.05 234.70 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n199 81 Pedestrian -1 -1 -1 519.86 167.87 533.58 203.78 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n199 86 Pedestrian -1 -1 -1 372.83 162.34 386.55 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n199 91 Pedestrian -1 -1 -1 534.25 167.36 549.45 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n199 25 Pedestrian -1 -1 -1 189.24 160.44 206.38 200.20 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n200 92 Car -1 -1 -1 -0.37 195.94 416.14 361.74 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n200 1 Car -1 -1 -1 1094.98 184.51 1220.83 236.34 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n200 2 Car -1 -1 -1 954.91 183.19 1066.50 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n200 5 Pedestrian -1 -1 -1 556.28 152.22 656.51 365.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n200 3 Car -1 -1 -1 1032.40 183.55 1157.41 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n200 6 Car -1 -1 -1 603.75 173.19 636.71 201.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n200 85 Pedestrian -1 -1 -1 107.41 151.77 141.95 231.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n200 30 Pedestrian -1 -1 -1 299.56 159.04 321.52 222.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n200 47 Pedestrian -1 -1 -1 327.65 163.58 350.08 222.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n200 27 Pedestrian -1 -1 -1 271.99 155.87 296.95 223.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n200 7 Pedestrian -1 -1 -1 219.52 155.07 234.82 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n200 91 Pedestrian -1 -1 -1 533.64 167.77 547.09 203.47 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n200 86 Pedestrian -1 -1 -1 371.19 161.78 385.03 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n200 81 Pedestrian -1 -1 -1 518.20 167.54 531.57 203.39 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n200 25 Pedestrian -1 -1 -1 189.39 160.84 206.34 199.94 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n201 92 Car -1 -1 -1 2.77 194.69 427.54 362.54 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n201 1 Car -1 -1 -1 1095.00 184.49 1220.73 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n201 2 Car -1 -1 -1 954.97 183.23 1066.54 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n201 3 Car -1 -1 -1 1032.52 183.59 1157.28 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n201 5 Pedestrian -1 -1 -1 552.44 153.26 644.34 364.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n201 6 Car -1 -1 -1 601.50 173.00 636.84 201.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n201 85 Pedestrian -1 -1 -1 109.90 150.52 146.63 231.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n201 27 Pedestrian -1 -1 -1 272.28 155.94 296.61 223.45 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n201 30 Pedestrian -1 -1 -1 297.51 159.14 320.36 222.72 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n201 47 Pedestrian -1 -1 -1 327.34 162.45 349.96 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n201 7 Pedestrian -1 -1 -1 219.75 155.23 234.98 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n201 81 Pedestrian -1 -1 -1 513.83 167.55 529.54 203.62 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n201 91 Pedestrian -1 -1 -1 529.99 165.80 544.81 203.45 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n201 86 Pedestrian -1 -1 -1 370.92 161.74 385.24 198.65 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n201 25 Pedestrian -1 -1 -1 189.50 161.19 206.31 199.50 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n202 92 Car -1 -1 -1 0.77 193.56 438.77 363.01 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n202 1 Car -1 -1 -1 1094.78 184.56 1220.94 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n202 2 Car -1 -1 -1 954.93 183.29 1066.57 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n202 3 Car -1 -1 -1 1029.49 183.90 1156.37 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n202 5 Pedestrian -1 -1 -1 543.49 148.38 616.05 363.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n202 6 Car -1 -1 -1 601.56 173.32 636.64 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n202 30 Pedestrian -1 -1 -1 297.41 158.94 319.60 222.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n202 27 Pedestrian -1 -1 -1 272.54 156.20 296.14 223.62 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n202 85 Pedestrian -1 -1 -1 115.03 151.54 150.14 230.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n202 47 Pedestrian -1 -1 -1 328.12 163.78 349.28 219.13 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n202 7 Pedestrian -1 -1 -1 219.57 155.18 234.97 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n202 91 Pedestrian -1 -1 -1 529.30 168.07 543.88 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n202 81 Pedestrian -1 -1 -1 510.47 167.68 528.36 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n202 86 Pedestrian -1 -1 -1 372.30 162.33 386.63 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n202 94 Pedestrian -1 -1 -1 356.38 162.22 368.70 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n202 95 Pedestrian -1 -1 -1 339.76 162.36 354.73 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n202 96 Pedestrian -1 -1 -1 327.25 161.20 342.59 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0022.txt",
    "content": "0 1 Cyclist -1 -1 -1 389.76 158.78 547.61 361.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n0 2 Car -1 -1 -1 954.05 184.03 1067.69 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n0 3 Car -1 -1 -1 1095.41 185.18 1220.16 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n0 4 Car -1 -1 -1 1028.86 183.90 1156.48 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n0 5 Pedestrian -1 -1 -1 287.48 156.76 307.15 208.66 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n0 6 Pedestrian -1 -1 -1 307.69 158.19 325.11 208.98 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n0 7 Car -1 -1 -1 602.15 173.50 636.49 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n0 8 Pedestrian -1 -1 -1 224.47 155.65 239.02 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n0 9 Pedestrian -1 -1 -1 710.78 168.19 724.57 215.20 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n0 10 Pedestrian -1 -1 -1 329.25 164.07 345.41 210.42 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n0 11 Pedestrian -1 -1 -1 192.84 160.18 207.90 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n0 12 Car -1 -1 -1 597.93 173.93 622.10 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n1 1 Cyclist -1 -1 -1 415.74 159.75 558.35 366.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 2 Car -1 -1 -1 954.03 183.72 1067.64 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 3 Car -1 -1 -1 1095.09 184.88 1220.76 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n1 4 Car -1 -1 -1 1028.80 183.50 1156.80 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n1 5 Pedestrian -1 -1 -1 286.34 156.15 306.24 208.65 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n1 6 Pedestrian -1 -1 -1 307.22 157.74 324.68 208.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n1 10 Pedestrian -1 -1 -1 326.63 163.45 343.82 210.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1 7 Car -1 -1 -1 601.98 173.23 636.61 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n1 8 Pedestrian -1 -1 -1 224.17 155.54 239.25 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n1 9 Pedestrian -1 -1 -1 710.52 168.32 725.01 215.85 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n1 11 Pedestrian -1 -1 -1 192.90 160.44 207.58 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n1 13 Pedestrian -1 -1 -1 213.78 156.38 227.48 196.88 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n2 3 Car -1 -1 -1 1095.30 185.09 1220.64 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n2 2 Car -1 -1 -1 954.25 183.81 1067.32 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n2 4 Car -1 -1 -1 1029.15 183.72 1156.59 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n2 1 Cyclist -1 -1 -1 439.10 159.88 566.59 360.22 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n2 6 Pedestrian -1 -1 -1 306.56 158.13 323.87 208.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n2 10 Pedestrian -1 -1 -1 325.88 163.53 343.76 210.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n2 5 Pedestrian -1 -1 -1 283.35 156.11 304.32 208.39 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n2 7 Car -1 -1 -1 602.02 173.27 636.81 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n2 9 Pedestrian -1 -1 -1 710.53 168.25 725.37 216.25 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n2 8 Pedestrian -1 -1 -1 223.70 156.25 239.64 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n2 11 Pedestrian -1 -1 -1 192.91 161.07 207.36 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n2 13 Pedestrian -1 -1 -1 213.71 156.91 227.65 196.70 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n2 14 Car -1 -1 -1 598.30 173.98 621.79 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n3 3 Car -1 -1 -1 1094.99 185.01 1220.74 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n3 2 Car -1 -1 -1 954.30 183.79 1067.41 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n3 4 Car -1 -1 -1 1029.38 183.72 1156.38 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n3 5 Pedestrian -1 -1 -1 281.61 156.93 304.23 209.20 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n3 1 Cyclist -1 -1 -1 457.70 160.43 577.21 351.77 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n3 6 Pedestrian -1 -1 -1 305.68 158.46 323.65 209.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n3 10 Pedestrian -1 -1 -1 325.28 164.27 343.12 211.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n3 7 Car -1 -1 -1 601.79 173.28 637.00 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n3 9 Pedestrian -1 -1 -1 710.43 168.15 726.07 215.92 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n3 8 Pedestrian -1 -1 -1 223.52 156.26 239.46 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n3 11 Pedestrian -1 -1 -1 192.75 161.19 207.57 198.65 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n3 14 Car -1 -1 -1 598.18 173.88 621.97 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n3 13 Pedestrian -1 -1 -1 213.67 156.89 227.65 196.83 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n4 3 Car -1 -1 -1 1095.32 185.09 1220.58 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n4 2 Car -1 -1 -1 954.36 183.84 1067.39 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n4 4 Car -1 -1 -1 1029.22 183.73 1156.59 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n4 5 Pedestrian -1 -1 -1 281.08 157.55 303.17 209.84 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 1 Cyclist -1 -1 -1 479.61 157.67 579.62 339.73 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n4 6 Pedestrian -1 -1 -1 305.79 158.84 322.87 209.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n4 7 Car -1 -1 -1 601.73 173.37 636.94 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n4 10 Pedestrian -1 -1 -1 326.14 164.58 342.55 211.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n4 9 Pedestrian -1 -1 -1 711.57 167.90 727.79 216.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n4 11 Pedestrian -1 -1 -1 192.85 161.18 207.86 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n4 8 Pedestrian -1 -1 -1 223.40 156.38 239.28 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n4 14 Car -1 -1 -1 598.42 173.88 622.12 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n4 13 Pedestrian -1 -1 -1 213.69 157.01 227.59 196.77 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n5 3 Car -1 -1 -1 1095.39 185.15 1220.52 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n5 2 Car -1 -1 -1 954.35 183.86 1067.27 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n5 4 Car -1 -1 -1 1029.23 183.76 1156.54 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n5 1 Cyclist -1 -1 -1 491.09 157.58 590.81 332.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n5 6 Pedestrian -1 -1 -1 303.46 158.86 321.36 209.48 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n5 7 Car -1 -1 -1 601.79 173.25 636.75 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n5 9 Pedestrian -1 -1 -1 713.06 168.13 728.17 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n5 5 Pedestrian -1 -1 -1 279.98 157.42 300.16 208.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n5 10 Pedestrian -1 -1 -1 326.47 164.72 342.40 211.77 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n5 11 Pedestrian -1 -1 -1 193.10 161.42 207.71 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n5 8 Pedestrian -1 -1 -1 223.36 156.48 239.06 199.25 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n5 14 Car -1 -1 -1 598.51 173.69 621.98 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n6 3 Car -1 -1 -1 1095.42 185.25 1220.56 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n6 2 Car -1 -1 -1 954.27 183.85 1067.54 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n6 4 Car -1 -1 -1 1029.02 183.80 1156.89 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n6 6 Pedestrian -1 -1 -1 302.29 158.70 320.80 209.57 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n6 5 Pedestrian -1 -1 -1 279.39 157.15 299.02 208.52 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n6 7 Car -1 -1 -1 601.33 173.13 636.92 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n6 1 Cyclist -1 -1 -1 509.14 159.17 594.72 323.01 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n6 9 Pedestrian -1 -1 -1 713.64 168.11 728.36 215.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n6 10 Pedestrian -1 -1 -1 325.16 164.67 342.12 211.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n6 11 Pedestrian -1 -1 -1 192.99 161.28 207.69 198.65 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n6 8 Pedestrian -1 -1 -1 223.20 156.52 238.83 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n6 14 Car -1 -1 -1 598.42 173.68 622.07 193.23 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n7 3 Car -1 -1 -1 1095.22 185.20 1220.67 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n7 2 Car -1 -1 -1 954.25 183.89 1067.47 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n7 4 Car -1 -1 -1 1028.97 183.74 1156.75 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n7 1 Cyclist -1 -1 -1 521.76 159.31 600.05 315.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n7 7 Car -1 -1 -1 601.23 173.03 636.87 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n7 5 Pedestrian -1 -1 -1 278.95 157.19 297.45 208.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n7 6 Pedestrian -1 -1 -1 302.18 158.50 319.19 209.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n7 9 Pedestrian -1 -1 -1 714.23 168.05 729.41 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n7 10 Pedestrian -1 -1 -1 324.95 164.33 341.68 211.44 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n7 11 Pedestrian -1 -1 -1 192.82 161.21 207.81 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n7 8 Pedestrian -1 -1 -1 223.23 156.61 238.66 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n7 14 Car -1 -1 -1 598.64 173.88 622.02 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n8 3 Car -1 -1 -1 1095.17 185.10 1220.86 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n8 2 Car -1 -1 -1 954.23 183.87 1067.48 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n8 4 Car -1 -1 -1 1028.99 183.76 1156.84 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n8 1 Cyclist -1 -1 -1 530.80 160.06 607.39 307.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n8 7 Car -1 -1 -1 601.40 173.13 636.72 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 6 Pedestrian -1 -1 -1 299.83 158.36 318.16 210.21 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n8 5 Pedestrian -1 -1 -1 278.40 157.05 296.52 209.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n8 10 Pedestrian -1 -1 -1 323.18 164.31 340.73 212.05 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n8 11 Pedestrian -1 -1 -1 192.96 161.29 207.80 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n8 9 Pedestrian -1 -1 -1 714.49 167.90 729.92 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n8 8 Pedestrian -1 -1 -1 223.37 156.48 238.53 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n8 14 Car -1 -1 -1 598.98 173.85 621.77 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n9 3 Car -1 -1 -1 1095.48 185.12 1220.50 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n9 2 Car -1 -1 -1 954.36 183.85 1067.36 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n9 4 Car -1 -1 -1 1028.89 183.74 1156.99 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n9 1 Cyclist -1 -1 -1 537.65 161.17 612.78 304.87 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n9 7 Car -1 -1 -1 601.06 172.90 636.73 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n9 5 Pedestrian -1 -1 -1 276.07 157.15 295.20 209.50 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n9 6 Pedestrian -1 -1 -1 298.59 158.18 317.69 210.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n9 10 Pedestrian -1 -1 -1 322.57 164.51 340.40 211.79 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n9 11 Pedestrian -1 -1 -1 192.95 161.25 207.70 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n9 8 Pedestrian -1 -1 -1 223.50 156.31 238.34 199.25 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n9 9 Pedestrian -1 -1 -1 715.74 167.72 731.68 216.34 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n9 14 Car -1 -1 -1 599.12 173.97 621.73 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n10 3 Car -1 -1 -1 1095.12 184.99 1220.97 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n10 2 Car -1 -1 -1 954.43 183.85 1067.33 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n10 4 Car -1 -1 -1 1032.28 183.56 1157.57 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n10 1 Cyclist -1 -1 -1 546.96 161.78 614.28 298.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n10 7 Car -1 -1 -1 601.16 173.05 636.52 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n10 5 Pedestrian -1 -1 -1 275.31 156.98 294.79 209.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n10 6 Pedestrian -1 -1 -1 297.71 158.75 316.86 212.04 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n10 10 Pedestrian -1 -1 -1 321.84 164.67 339.58 212.28 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n10 11 Pedestrian -1 -1 -1 192.93 161.28 207.88 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n10 9 Pedestrian -1 -1 -1 716.78 167.66 731.96 216.34 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n10 8 Pedestrian -1 -1 -1 223.54 156.42 238.04 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n10 14 Car -1 -1 -1 598.62 173.81 622.00 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n10 15 Pedestrian -1 -1 -1 217.02 156.30 230.08 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n11 3 Car -1 -1 -1 1095.30 185.10 1220.94 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n11 2 Car -1 -1 -1 954.22 183.82 1067.40 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n11 4 Car -1 -1 -1 1029.15 183.71 1156.76 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n11 7 Car -1 -1 -1 600.75 173.09 636.78 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n11 5 Pedestrian -1 -1 -1 274.62 156.55 294.68 210.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n11 1 Cyclist -1 -1 -1 554.41 160.80 614.89 291.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n11 11 Pedestrian -1 -1 -1 192.64 161.03 207.93 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n11 6 Pedestrian -1 -1 -1 297.44 158.82 315.79 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n11 10 Pedestrian -1 -1 -1 319.26 164.04 337.23 212.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n11 9 Pedestrian -1 -1 -1 717.05 167.83 732.11 216.44 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n11 15 Pedestrian -1 -1 -1 217.06 156.01 230.34 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n11 14 Car -1 -1 -1 597.81 173.64 622.64 194.07 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n12 3 Car -1 -1 -1 1095.34 185.02 1220.93 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n12 2 Car -1 -1 -1 954.16 183.80 1067.55 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n12 4 Car -1 -1 -1 1029.35 183.72 1156.53 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n12 7 Car -1 -1 -1 600.44 172.98 636.65 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n12 1 Cyclist -1 -1 -1 559.26 160.91 620.47 284.39 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n12 5 Pedestrian -1 -1 -1 273.68 156.23 293.88 210.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n12 10 Pedestrian -1 -1 -1 318.87 165.10 336.87 213.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n12 6 Pedestrian -1 -1 -1 295.44 157.66 314.96 211.71 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n12 11 Pedestrian -1 -1 -1 192.49 160.94 208.04 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n12 9 Pedestrian -1 -1 -1 717.10 168.93 731.99 217.08 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n12 14 Car -1 -1 -1 597.79 173.60 622.31 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n12 15 Pedestrian -1 -1 -1 216.93 155.96 230.08 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n13 3 Car -1 -1 -1 1095.33 185.05 1220.85 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n13 2 Car -1 -1 -1 954.37 183.81 1067.30 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n13 4 Car -1 -1 -1 1029.13 183.70 1156.66 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n13 7 Car -1 -1 -1 600.14 172.97 637.07 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n13 1 Cyclist -1 -1 -1 561.51 162.14 626.02 280.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n13 5 Pedestrian -1 -1 -1 272.02 155.61 292.44 210.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n13 6 Pedestrian -1 -1 -1 294.92 157.37 314.81 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n13 10 Pedestrian -1 -1 -1 318.19 164.02 335.81 212.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n13 14 Car -1 -1 -1 597.84 173.79 622.40 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n13 9 Pedestrian -1 -1 -1 716.99 168.78 732.51 217.33 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n13 11 Pedestrian -1 -1 -1 192.81 160.97 207.99 198.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n13 15 Pedestrian -1 -1 -1 221.57 155.76 235.03 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n14 3 Car -1 -1 -1 1095.25 185.08 1220.85 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n14 2 Car -1 -1 -1 954.43 183.82 1067.36 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n14 4 Car -1 -1 -1 1029.19 183.68 1156.57 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n14 5 Pedestrian -1 -1 -1 271.31 155.71 292.12 211.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n14 7 Car -1 -1 -1 600.46 172.85 637.27 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n14 1 Cyclist -1 -1 -1 567.90 161.53 624.35 276.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n14 6 Pedestrian -1 -1 -1 294.81 157.29 314.21 211.56 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n14 10 Pedestrian -1 -1 -1 318.56 164.07 335.09 212.79 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n14 14 Car -1 -1 -1 597.98 173.52 622.68 193.30 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n14 9 Pedestrian -1 -1 -1 717.46 167.65 732.72 217.02 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n14 11 Pedestrian -1 -1 -1 193.14 161.12 207.85 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n14 15 Pedestrian -1 -1 -1 221.40 155.67 235.10 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n15 3 Car -1 -1 -1 1095.41 185.05 1220.76 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n15 2 Car -1 -1 -1 954.41 183.79 1067.35 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 4 Car -1 -1 -1 1029.25 183.72 1156.56 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 1 Cyclist -1 -1 -1 572.36 162.95 626.98 271.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n15 5 Pedestrian -1 -1 -1 269.73 156.23 291.56 211.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n15 7 Car -1 -1 -1 600.60 172.99 637.38 202.38 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n15 6 Pedestrian -1 -1 -1 293.92 157.71 312.94 211.61 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n15 10 Pedestrian -1 -1 -1 318.24 164.41 335.13 214.22 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n15 9 Pedestrian -1 -1 -1 717.64 167.66 733.90 216.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n15 11 Pedestrian -1 -1 -1 193.04 161.21 207.73 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n15 15 Pedestrian -1 -1 -1 221.41 155.77 235.10 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n16 3 Car -1 -1 -1 1095.34 185.10 1220.76 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n16 2 Car -1 -1 -1 954.57 183.84 1067.25 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n16 4 Car -1 -1 -1 1029.16 183.73 1156.68 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n16 1 Cyclist -1 -1 -1 579.81 162.55 624.03 266.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n16 7 Car -1 -1 -1 600.64 172.76 637.28 202.21 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n16 5 Pedestrian -1 -1 -1 269.38 156.63 290.28 212.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n16 10 Pedestrian -1 -1 -1 317.31 164.33 334.45 214.39 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n16 6 Pedestrian -1 -1 -1 291.86 157.94 311.30 212.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n16 11 Pedestrian -1 -1 -1 193.10 161.15 207.75 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n16 9 Pedestrian -1 -1 -1 718.98 167.84 736.05 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n16 15 Pedestrian -1 -1 -1 223.51 155.61 237.08 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n17 3 Car -1 -1 -1 1095.48 185.17 1220.57 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n17 2 Car -1 -1 -1 954.54 183.85 1067.21 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n17 4 Car -1 -1 -1 1029.35 183.76 1156.50 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n17 7 Car -1 -1 -1 600.47 172.62 637.42 202.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n17 6 Pedestrian -1 -1 -1 290.45 158.46 310.23 212.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n17 1 Cyclist -1 -1 -1 580.11 163.97 627.67 262.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n17 9 Pedestrian -1 -1 -1 719.29 167.07 737.86 217.55 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n17 5 Pedestrian -1 -1 -1 267.44 156.35 288.56 212.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n17 10 Pedestrian -1 -1 -1 315.31 163.97 333.83 214.80 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n17 11 Pedestrian -1 -1 -1 192.90 161.18 207.78 198.58 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n17 15 Pedestrian -1 -1 -1 223.55 155.50 237.03 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n18 3 Car -1 -1 -1 1095.33 185.11 1220.75 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n18 2 Car -1 -1 -1 954.57 183.92 1067.33 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n18 4 Car -1 -1 -1 1029.42 183.84 1156.45 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n18 1 Cyclist -1 -1 -1 581.90 164.65 629.45 257.47 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n18 7 Car -1 -1 -1 600.42 172.65 637.54 201.78 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n18 6 Pedestrian -1 -1 -1 289.28 158.41 309.17 212.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n18 9 Pedestrian -1 -1 -1 719.32 168.01 739.23 218.49 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 10 Pedestrian -1 -1 -1 314.74 164.56 332.91 214.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n18 5 Pedestrian -1 -1 -1 265.92 158.80 288.03 212.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n18 11 Pedestrian -1 -1 -1 192.93 161.14 207.71 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n18 15 Pedestrian -1 -1 -1 223.63 155.37 237.01 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n19 3 Car -1 -1 -1 1095.59 185.14 1220.61 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n19 2 Car -1 -1 -1 954.64 183.96 1067.30 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n19 4 Car -1 -1 -1 1029.43 183.81 1156.40 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n19 1 Cyclist -1 -1 -1 582.52 164.58 629.22 257.21 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n19 7 Car -1 -1 -1 600.42 172.82 637.60 201.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n19 10 Pedestrian -1 -1 -1 313.33 164.39 332.03 214.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n19 9 Pedestrian -1 -1 -1 719.87 168.33 739.54 218.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n19 6 Pedestrian -1 -1 -1 287.06 157.83 307.92 212.89 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n19 5 Pedestrian -1 -1 -1 263.69 155.15 285.74 213.12 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n19 11 Pedestrian -1 -1 -1 192.85 161.07 207.77 198.97 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n19 15 Pedestrian -1 -1 -1 223.50 155.30 237.03 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n20 3 Car -1 -1 -1 1095.41 185.09 1220.63 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n20 2 Car -1 -1 -1 954.78 184.01 1067.15 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n20 4 Car -1 -1 -1 1029.30 183.74 1156.50 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n20 7 Car -1 -1 -1 600.84 172.89 637.40 201.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n20 5 Pedestrian -1 -1 -1 263.40 155.15 285.03 213.11 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n20 9 Pedestrian -1 -1 -1 722.90 168.18 741.44 219.13 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n20 1 Cyclist -1 -1 -1 582.61 165.74 629.50 254.72 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n20 6 Pedestrian -1 -1 -1 285.81 157.53 306.90 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n20 10 Pedestrian -1 -1 -1 312.57 164.28 331.19 214.42 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n20 11 Pedestrian -1 -1 -1 192.85 161.01 207.78 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n20 15 Pedestrian -1 -1 -1 221.03 155.11 235.20 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n21 3 Car -1 -1 -1 1095.52 185.16 1220.58 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n21 2 Car -1 -1 -1 954.81 184.04 1067.09 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n21 4 Car -1 -1 -1 1029.26 183.76 1156.43 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n21 6 Pedestrian -1 -1 -1 285.68 157.46 306.45 214.01 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n21 5 Pedestrian -1 -1 -1 262.80 155.01 284.94 213.56 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n21 7 Car -1 -1 -1 600.85 172.77 637.46 201.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 9 Pedestrian -1 -1 -1 725.05 168.29 741.76 218.49 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n21 1 Cyclist -1 -1 -1 583.57 166.54 630.78 247.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n21 10 Pedestrian -1 -1 -1 310.79 164.01 329.62 214.51 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n21 15 Pedestrian -1 -1 -1 221.00 155.10 235.24 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n21 11 Pedestrian -1 -1 -1 192.98 161.00 207.63 199.21 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n22 3 Car -1 -1 -1 1095.33 185.04 1220.73 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n22 2 Car -1 -1 -1 954.89 184.02 1067.09 232.86 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n22 4 Car -1 -1 -1 1029.61 183.85 1156.21 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n22 6 Pedestrian -1 -1 -1 285.21 157.52 305.60 214.74 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n22 7 Car -1 -1 -1 600.55 172.55 637.78 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 5 Pedestrian -1 -1 -1 262.00 154.94 283.95 214.15 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n22 9 Pedestrian -1 -1 -1 727.26 168.24 743.56 219.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n22 10 Pedestrian -1 -1 -1 309.06 163.71 328.56 215.13 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n22 1 Cyclist -1 -1 -1 584.01 166.82 633.47 251.47 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n22 11 Pedestrian -1 -1 -1 192.96 161.16 207.70 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n22 15 Pedestrian -1 -1 -1 221.03 155.27 235.38 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n23 3 Car -1 -1 -1 1095.55 185.10 1220.63 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n23 2 Car -1 -1 -1 954.87 183.99 1067.09 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n23 4 Car -1 -1 -1 1029.55 183.85 1156.32 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n23 9 Pedestrian -1 -1 -1 728.16 168.98 744.81 219.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n23 6 Pedestrian -1 -1 -1 282.85 157.72 304.64 215.10 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n23 7 Car -1 -1 -1 600.83 172.63 637.69 202.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n23 1 Cyclist -1 -1 -1 586.22 166.73 631.38 247.77 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n23 10 Pedestrian -1 -1 -1 308.47 163.88 327.52 215.82 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n23 5 Pedestrian -1 -1 -1 260.15 154.28 281.63 214.85 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n23 15 Pedestrian -1 -1 -1 221.03 155.18 235.38 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n23 11 Pedestrian -1 -1 -1 192.97 161.12 207.59 199.26 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n24 3 Car -1 -1 -1 1095.39 185.08 1220.67 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n24 2 Car -1 -1 -1 954.83 184.03 1067.13 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n24 4 Car -1 -1 -1 1029.67 183.87 1156.21 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n24 5 Pedestrian -1 -1 -1 258.71 153.80 281.68 214.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n24 1 Cyclist -1 -1 -1 588.21 166.48 630.14 246.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 6 Pedestrian -1 -1 -1 282.88 157.72 304.43 214.81 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n24 9 Pedestrian -1 -1 -1 728.30 169.53 745.54 220.35 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 7 Car -1 -1 -1 603.59 173.00 637.04 201.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n24 10 Pedestrian -1 -1 -1 307.30 163.88 325.80 216.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n24 15 Pedestrian -1 -1 -1 221.15 155.31 235.29 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n24 11 Pedestrian -1 -1 -1 192.73 161.05 207.70 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n25 3 Car -1 -1 -1 1095.70 185.13 1220.46 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n25 2 Car -1 -1 -1 954.88 184.02 1067.10 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n25 4 Car -1 -1 -1 1029.72 183.86 1156.12 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n25 6 Pedestrian -1 -1 -1 281.79 157.38 303.92 214.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n25 10 Pedestrian -1 -1 -1 306.38 164.16 324.54 216.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n25 7 Car -1 -1 -1 603.92 172.84 636.99 201.82 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n25 5 Pedestrian -1 -1 -1 257.78 153.52 281.26 214.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n25 1 Cyclist -1 -1 -1 588.39 166.65 630.61 243.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n25 9 Pedestrian -1 -1 -1 728.23 169.84 745.80 220.22 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n25 15 Pedestrian -1 -1 -1 221.03 155.33 235.29 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n25 11 Pedestrian -1 -1 -1 192.77 161.12 207.61 199.20 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n26 3 Car -1 -1 -1 1095.52 185.13 1220.60 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n26 2 Car -1 -1 -1 954.87 184.02 1067.05 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n26 4 Car -1 -1 -1 1029.60 183.87 1156.27 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n26 1 Cyclist -1 -1 -1 591.04 165.21 627.93 240.47 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n26 6 Pedestrian -1 -1 -1 281.52 157.42 303.57 214.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n26 5 Pedestrian -1 -1 -1 257.14 153.30 280.87 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n26 7 Car -1 -1 -1 604.31 172.85 636.91 201.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n26 10 Pedestrian -1 -1 -1 305.66 164.18 323.82 216.45 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n26 9 Pedestrian -1 -1 -1 728.75 169.79 746.46 220.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n26 15 Pedestrian -1 -1 -1 221.18 155.45 235.24 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n26 11 Pedestrian -1 -1 -1 192.57 161.12 207.76 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n27 3 Car -1 -1 -1 1095.33 185.10 1220.72 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n27 2 Car -1 -1 -1 954.81 184.00 1067.06 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n27 4 Car -1 -1 -1 1029.52 183.88 1156.28 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n27 6 Pedestrian -1 -1 -1 282.00 158.01 302.84 214.89 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n27 1 Cyclist -1 -1 -1 591.88 165.96 626.86 238.77 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n27 9 Pedestrian -1 -1 -1 730.73 169.29 748.60 221.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n27 7 Car -1 -1 -1 604.53 172.72 636.57 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n27 10 Pedestrian -1 -1 -1 303.15 164.43 322.65 216.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n27 5 Pedestrian -1 -1 -1 256.51 155.76 280.33 215.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n27 11 Pedestrian -1 -1 -1 192.83 161.32 207.68 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n27 15 Pedestrian -1 -1 -1 221.20 155.46 235.18 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n27 14 Car -1 -1 -1 597.05 172.44 623.17 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n28 3 Car -1 -1 -1 1095.31 185.07 1220.89 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n28 2 Car -1 -1 -1 954.80 183.98 1067.08 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n28 4 Car -1 -1 -1 1029.60 183.89 1156.28 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n28 6 Pedestrian -1 -1 -1 281.17 158.68 302.09 215.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n28 5 Pedestrian -1 -1 -1 254.28 156.07 278.42 216.04 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n28 1 Cyclist -1 -1 -1 593.18 166.49 627.10 237.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n28 10 Pedestrian -1 -1 -1 302.87 164.26 321.39 216.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n28 9 Pedestrian -1 -1 -1 732.19 169.29 749.48 221.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n28 7 Car -1 -1 -1 604.67 172.73 636.28 201.38 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n28 15 Pedestrian -1 -1 -1 221.14 155.41 235.12 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n28 11 Pedestrian -1 -1 -1 192.71 161.28 207.73 199.02 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n28 14 Car -1 -1 -1 598.00 173.17 622.10 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n29 3 Car -1 -1 -1 1095.26 185.00 1220.77 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n29 2 Car -1 -1 -1 954.84 184.01 1066.88 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n29 4 Car -1 -1 -1 1029.11 183.89 1156.67 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 1 Cyclist -1 -1 -1 594.90 166.32 626.18 232.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n29 7 Car -1 -1 -1 604.73 172.78 636.08 201.33 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n29 10 Pedestrian -1 -1 -1 300.94 164.88 320.28 217.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n29 6 Pedestrian -1 -1 -1 279.21 158.49 300.82 215.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n29 9 Pedestrian -1 -1 -1 732.44 169.41 750.42 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n29 5 Pedestrian -1 -1 -1 250.90 154.84 275.38 216.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n29 15 Pedestrian -1 -1 -1 221.16 155.52 235.09 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n29 11 Pedestrian -1 -1 -1 192.75 161.24 207.65 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n29 14 Car -1 -1 -1 598.27 173.22 622.01 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n30 3 Car -1 -1 -1 1095.47 185.09 1220.57 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n30 2 Car -1 -1 -1 954.98 184.06 1066.81 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n30 4 Car -1 -1 -1 1029.28 183.91 1156.48 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n30 6 Pedestrian -1 -1 -1 278.90 158.60 300.13 215.72 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n30 7 Car -1 -1 -1 604.60 172.62 636.22 201.27 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n30 5 Pedestrian -1 -1 -1 250.51 153.43 274.71 215.53 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n30 10 Pedestrian -1 -1 -1 298.89 165.06 318.93 217.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 1 Cyclist -1 -1 -1 593.82 166.18 628.39 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n30 9 Pedestrian -1 -1 -1 734.05 169.38 752.16 222.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n30 15 Pedestrian -1 -1 -1 221.19 155.50 235.22 197.42 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n30 11 Pedestrian -1 -1 -1 192.68 161.22 207.60 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n30 14 Car -1 -1 -1 598.09 173.22 622.21 193.81 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n31 3 Car -1 -1 -1 1095.31 185.08 1220.67 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n31 2 Car -1 -1 -1 954.84 184.02 1067.04 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n31 4 Car -1 -1 -1 1029.41 183.91 1156.42 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n31 6 Pedestrian -1 -1 -1 278.06 158.20 298.58 215.93 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n31 10 Pedestrian -1 -1 -1 298.59 165.07 317.79 217.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n31 7 Car -1 -1 -1 605.06 172.66 636.29 201.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n31 5 Pedestrian -1 -1 -1 249.19 153.55 274.04 215.75 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n31 9 Pedestrian -1 -1 -1 734.84 169.12 753.36 222.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n31 1 Cyclist -1 -1 -1 596.32 167.11 624.66 228.72 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n31 15 Pedestrian -1 -1 -1 221.06 155.42 235.17 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n31 11 Pedestrian -1 -1 -1 192.62 161.16 207.58 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n31 14 Car -1 -1 -1 597.96 173.14 622.22 194.04 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n32 3 Car -1 -1 -1 1095.42 185.09 1220.55 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n32 2 Car -1 -1 -1 954.85 184.06 1066.93 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n32 4 Car -1 -1 -1 1029.41 183.92 1156.44 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n32 7 Car -1 -1 -1 605.10 172.86 636.08 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n32 6 Pedestrian -1 -1 -1 277.87 158.08 297.07 216.25 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n32 1 Cyclist -1 -1 -1 595.88 166.17 624.85 229.18 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n32 10 Pedestrian -1 -1 -1 297.32 164.88 315.93 217.58 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n32 5 Pedestrian -1 -1 -1 249.01 154.48 273.06 216.28 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n32 9 Pedestrian -1 -1 -1 736.03 169.31 754.22 222.37 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n32 15 Pedestrian -1 -1 -1 221.05 155.28 235.20 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n32 11 Pedestrian -1 -1 -1 192.63 161.05 207.54 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n32 14 Car -1 -1 -1 598.17 173.16 621.83 193.87 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n33 3 Car -1 -1 -1 1095.66 185.12 1220.42 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n33 2 Car -1 -1 -1 954.83 184.04 1066.97 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n33 4 Car -1 -1 -1 1029.34 183.89 1156.48 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n33 6 Pedestrian -1 -1 -1 275.24 157.94 295.92 216.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n33 5 Pedestrian -1 -1 -1 246.89 155.47 270.78 217.29 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n33 7 Car -1 -1 -1 604.64 172.80 636.06 201.16 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n33 10 Pedestrian -1 -1 -1 295.38 164.08 314.90 218.50 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n33 9 Pedestrian -1 -1 -1 737.89 168.44 755.57 223.11 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n33 15 Pedestrian -1 -1 -1 220.94 155.21 235.24 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n33 1 Cyclist -1 -1 -1 597.33 166.44 622.77 224.16 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n33 11 Pedestrian -1 -1 -1 192.66 161.03 207.63 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n33 14 Car -1 -1 -1 598.04 173.16 621.73 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n34 3 Car -1 -1 -1 1095.47 185.16 1220.46 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n34 2 Car -1 -1 -1 954.75 184.06 1066.83 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n34 4 Car -1 -1 -1 1029.71 183.90 1156.13 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n34 6 Pedestrian -1 -1 -1 274.03 158.02 295.18 217.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n34 5 Pedestrian -1 -1 -1 246.52 155.43 269.63 217.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n34 10 Pedestrian -1 -1 -1 295.25 164.09 314.49 218.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n34 7 Car -1 -1 -1 604.46 173.09 636.10 201.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n34 9 Pedestrian -1 -1 -1 738.10 169.04 756.37 224.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n34 15 Pedestrian -1 -1 -1 221.12 155.19 235.20 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n34 11 Pedestrian -1 -1 -1 192.52 160.89 207.68 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n34 1 Cyclist -1 -1 -1 598.20 167.49 621.91 222.65 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n34 14 Car -1 -1 -1 597.86 173.24 621.96 194.15 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n35 3 Car -1 -1 -1 1095.31 185.13 1220.55 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n35 2 Car -1 -1 -1 954.86 184.04 1066.88 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n35 4 Car -1 -1 -1 1029.56 183.90 1156.23 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n35 6 Pedestrian -1 -1 -1 273.08 157.87 294.64 217.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n35 5 Pedestrian -1 -1 -1 245.46 154.86 269.31 217.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n35 10 Pedestrian -1 -1 -1 295.13 163.82 314.31 219.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n35 7 Car -1 -1 -1 604.54 172.66 635.93 200.80 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n35 9 Pedestrian -1 -1 -1 738.54 169.20 756.58 224.88 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n35 15 Pedestrian -1 -1 -1 221.15 155.21 235.22 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n35 1 Cyclist -1 -1 -1 594.55 167.28 623.37 223.28 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n35 11 Pedestrian -1 -1 -1 192.49 160.90 207.58 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n35 14 Car -1 -1 -1 597.74 172.71 622.30 194.19 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n36 3 Car -1 -1 -1 1095.40 185.06 1220.51 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n36 2 Car -1 -1 -1 955.03 184.08 1066.73 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n36 4 Car -1 -1 -1 1029.58 183.92 1156.35 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n36 5 Pedestrian -1 -1 -1 243.10 154.35 266.66 217.21 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n36 7 Car -1 -1 -1 601.97 172.66 636.12 201.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n36 6 Pedestrian -1 -1 -1 273.02 157.97 294.29 217.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n36 10 Pedestrian -1 -1 -1 294.59 164.15 314.05 219.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n36 9 Pedestrian -1 -1 -1 738.35 169.89 758.26 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n36 15 Pedestrian -1 -1 -1 221.26 155.17 235.20 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n36 11 Pedestrian -1 -1 -1 192.51 160.87 207.67 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n36 1 Cyclist -1 -1 -1 595.34 167.38 622.84 222.67 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n37 3 Car -1 -1 -1 1095.51 185.16 1220.31 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n37 2 Car -1 -1 -1 954.99 184.08 1066.79 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n37 4 Car -1 -1 -1 1029.38 183.91 1156.45 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n37 6 Pedestrian -1 -1 -1 270.17 157.87 292.96 217.73 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n37 5 Pedestrian -1 -1 -1 241.87 154.08 265.66 217.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n37 7 Car -1 -1 -1 601.83 172.68 636.15 201.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n37 10 Pedestrian -1 -1 -1 294.02 164.07 313.21 220.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n37 9 Pedestrian -1 -1 -1 738.88 170.13 758.92 225.38 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n37 15 Pedestrian -1 -1 -1 221.25 155.09 235.20 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n37 11 Pedestrian -1 -1 -1 192.44 160.78 207.78 199.48 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n37 1 Cyclist -1 -1 -1 594.14 166.98 620.83 223.71 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n37 16 Car -1 -1 -1 549.97 168.52 566.09 181.67 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n38 3 Car -1 -1 -1 1095.51 185.12 1220.39 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n38 2 Car -1 -1 -1 955.01 184.11 1066.60 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n38 4 Car -1 -1 -1 1029.48 183.96 1156.46 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n38 6 Pedestrian -1 -1 -1 269.39 157.67 291.47 217.75 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n38 1 Cyclist -1 -1 -1 594.72 167.19 618.61 222.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n38 7 Car -1 -1 -1 601.42 172.84 636.38 202.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n38 9 Pedestrian -1 -1 -1 740.40 170.11 761.54 225.70 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n38 5 Pedestrian -1 -1 -1 239.25 154.19 264.35 218.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n38 10 Pedestrian -1 -1 -1 293.38 164.16 312.94 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n38 15 Pedestrian -1 -1 -1 221.29 155.12 235.29 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n38 11 Pedestrian -1 -1 -1 192.49 160.84 207.65 199.45 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n38 16 Car -1 -1 -1 550.00 168.46 566.26 181.98 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n39 3 Car -1 -1 -1 1095.55 185.17 1220.49 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n39 2 Car -1 -1 -1 954.92 184.12 1066.81 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n39 4 Car -1 -1 -1 1029.27 183.93 1156.47 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n39 7 Car -1 -1 -1 600.65 172.95 636.52 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n39 6 Pedestrian -1 -1 -1 268.97 157.76 290.72 217.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n39 9 Pedestrian -1 -1 -1 741.74 169.77 762.10 225.52 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n39 1 Cyclist -1 -1 -1 594.42 167.79 617.57 222.72 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n39 5 Pedestrian -1 -1 -1 238.42 155.12 264.06 218.83 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n39 10 Pedestrian -1 -1 -1 292.83 165.45 312.39 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n39 16 Car -1 -1 -1 549.67 168.25 566.26 182.21 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n39 15 Pedestrian -1 -1 -1 223.54 155.06 237.20 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n39 11 Pedestrian -1 -1 -1 192.69 160.81 207.48 199.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n40 3 Car -1 -1 -1 1095.59 185.18 1220.57 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n40 2 Car -1 -1 -1 955.06 184.09 1066.80 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n40 4 Car -1 -1 -1 1029.47 183.94 1156.38 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n40 7 Car -1 -1 -1 600.46 172.97 636.83 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n40 6 Pedestrian -1 -1 -1 266.56 158.44 289.65 217.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n40 9 Pedestrian -1 -1 -1 744.20 169.71 765.10 226.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n40 5 Pedestrian -1 -1 -1 238.24 155.84 262.96 218.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n40 10 Pedestrian -1 -1 -1 290.95 165.81 311.09 220.63 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n40 1 Cyclist -1 -1 -1 593.73 167.89 616.99 222.03 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n40 16 Car -1 -1 -1 549.46 168.08 566.09 182.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n40 15 Pedestrian -1 -1 -1 223.91 155.01 237.44 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n40 11 Pedestrian -1 -1 -1 192.74 160.95 207.61 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n41 3 Car -1 -1 -1 1095.83 185.24 1220.40 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n41 2 Car -1 -1 -1 955.02 184.07 1066.82 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n41 4 Car -1 -1 -1 1029.46 183.81 1156.35 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n41 6 Pedestrian -1 -1 -1 264.88 158.25 288.69 218.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n41 7 Car -1 -1 -1 600.51 172.91 637.36 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n41 9 Pedestrian -1 -1 -1 745.11 170.59 766.93 226.32 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n41 5 Pedestrian -1 -1 -1 235.70 155.06 259.89 218.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n41 10 Pedestrian -1 -1 -1 290.09 165.74 310.52 220.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n41 16 Car -1 -1 -1 548.84 168.00 566.01 182.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n41 1 Cyclist -1 -1 -1 591.52 167.22 614.62 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n41 15 Pedestrian -1 -1 -1 223.83 155.03 237.11 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n41 11 Pedestrian -1 -1 -1 192.66 160.82 207.55 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n42 3 Car -1 -1 -1 1095.62 185.25 1220.49 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n42 2 Car -1 -1 -1 955.09 184.07 1066.84 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n42 4 Car -1 -1 -1 1029.43 183.87 1156.40 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n42 5 Pedestrian -1 -1 -1 234.58 154.46 259.14 218.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n42 7 Car -1 -1 -1 600.74 172.89 637.26 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n42 16 Car -1 -1 -1 548.19 168.09 565.62 182.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n42 6 Pedestrian -1 -1 -1 264.64 158.24 287.32 218.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n42 9 Pedestrian -1 -1 -1 745.55 171.07 767.41 227.02 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n42 10 Pedestrian -1 -1 -1 287.83 165.39 309.83 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n42 1 Cyclist -1 -1 -1 589.42 167.48 613.67 220.16 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n42 15 Pedestrian -1 -1 -1 221.59 155.08 235.09 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n42 11 Pedestrian -1 -1 -1 192.63 160.67 207.55 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n43 3 Car -1 -1 -1 1095.64 185.26 1220.61 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n43 2 Car -1 -1 -1 955.06 184.07 1066.66 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n43 4 Car -1 -1 -1 1029.51 183.85 1156.32 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n43 6 Pedestrian -1 -1 -1 262.41 157.42 285.55 218.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n43 7 Car -1 -1 -1 600.80 172.80 637.05 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n43 5 Pedestrian -1 -1 -1 233.72 154.46 258.54 219.22 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n43 16 Car -1 -1 -1 547.56 168.14 565.12 182.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n43 9 Pedestrian -1 -1 -1 747.70 170.69 770.34 228.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n43 10 Pedestrian -1 -1 -1 286.38 165.31 309.39 220.95 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n43 15 Pedestrian -1 -1 -1 221.45 154.98 234.99 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n43 11 Pedestrian -1 -1 -1 192.57 160.61 207.53 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n43 1 Cyclist -1 -1 -1 588.54 167.62 610.90 216.43 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n44 3 Car -1 -1 -1 1095.54 185.20 1220.47 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n44 2 Car -1 -1 -1 955.07 184.08 1066.70 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n44 4 Car -1 -1 -1 1029.43 183.82 1156.44 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n44 7 Car -1 -1 -1 600.20 172.81 636.93 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n44 6 Pedestrian -1 -1 -1 261.24 157.03 284.14 219.37 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n44 16 Car -1 -1 -1 547.26 168.29 565.37 182.77 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n44 5 Pedestrian -1 -1 -1 231.63 154.97 256.25 220.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n44 10 Pedestrian -1 -1 -1 286.26 165.37 308.52 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n44 15 Pedestrian -1 -1 -1 221.33 155.08 235.15 197.41 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n44 1 Cyclist -1 -1 -1 587.49 168.69 610.36 218.64 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n44 9 Pedestrian -1 -1 -1 750.00 170.31 770.37 229.03 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n44 11 Pedestrian -1 -1 -1 192.50 160.49 207.51 199.57 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n45 3 Car -1 -1 -1 1095.76 185.20 1220.52 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n45 2 Car -1 -1 -1 955.14 184.07 1066.61 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n45 4 Car -1 -1 -1 1029.21 183.83 1156.67 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n45 7 Car -1 -1 -1 599.96 172.76 636.81 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n45 5 Pedestrian -1 -1 -1 231.37 155.31 256.06 220.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n45 16 Car -1 -1 -1 546.91 168.15 564.92 183.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n45 6 Pedestrian -1 -1 -1 260.63 156.64 283.80 220.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n45 10 Pedestrian -1 -1 -1 286.83 165.20 308.23 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n45 1 Cyclist -1 -1 -1 587.33 167.83 609.90 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n45 9 Pedestrian -1 -1 -1 753.67 171.44 771.89 230.05 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n45 15 Pedestrian -1 -1 -1 221.28 155.07 235.17 197.45 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n45 11 Pedestrian -1 -1 -1 192.49 160.42 207.51 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n46 3 Car -1 -1 -1 1095.86 185.26 1220.37 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n46 2 Car -1 -1 -1 954.96 184.06 1066.74 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n46 4 Car -1 -1 -1 1029.43 183.84 1156.42 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n46 7 Car -1 -1 -1 599.74 172.71 636.88 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n46 5 Pedestrian -1 -1 -1 230.98 155.38 255.53 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n46 16 Car -1 -1 -1 546.31 168.06 564.36 183.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n46 6 Pedestrian -1 -1 -1 258.42 157.49 283.12 221.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n46 10 Pedestrian -1 -1 -1 286.43 164.80 308.34 221.69 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n46 1 Cyclist -1 -1 -1 585.19 167.89 609.82 215.09 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n46 15 Pedestrian -1 -1 -1 221.05 155.05 235.23 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n46 9 Pedestrian -1 -1 -1 753.49 170.95 772.84 230.39 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n46 11 Pedestrian -1 -1 -1 192.46 160.39 207.63 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n47 3 Car -1 -1 -1 1095.79 185.18 1220.40 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n47 2 Car -1 -1 -1 955.11 184.03 1066.71 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n47 4 Car -1 -1 -1 1029.38 183.86 1156.43 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n47 7 Car -1 -1 -1 599.87 172.70 637.08 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n47 6 Pedestrian -1 -1 -1 257.39 157.42 282.49 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n47 5 Pedestrian -1 -1 -1 230.34 155.11 255.22 221.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n47 10 Pedestrian -1 -1 -1 286.00 164.45 307.71 221.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n47 1 Cyclist -1 -1 -1 584.44 168.15 605.90 213.90 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n47 16 Car -1 -1 -1 546.26 168.11 563.79 183.14 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n47 15 Pedestrian -1 -1 -1 221.20 155.37 235.15 197.24 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n47 11 Pedestrian -1 -1 -1 192.55 160.64 207.36 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n47 9 Pedestrian -1 -1 -1 753.36 170.72 774.75 230.72 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n47 17 Car -1 -1 -1 1.67 218.94 209.32 361.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n48 17 Car -1 -1 -1 -1.10 203.03 295.00 363.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n48 3 Car -1 -1 -1 1095.55 185.09 1220.61 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n48 2 Car -1 -1 -1 955.06 184.06 1066.76 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n48 4 Car -1 -1 -1 1029.57 183.87 1156.31 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n48 7 Car -1 -1 -1 599.99 172.71 637.03 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n48 1 Cyclist -1 -1 -1 583.38 168.28 605.79 213.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n48 6 Pedestrian -1 -1 -1 257.10 157.53 282.12 220.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n48 5 Pedestrian -1 -1 -1 230.04 154.40 254.42 221.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n48 10 Pedestrian -1 -1 -1 285.23 164.41 307.32 221.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n48 9 Pedestrian -1 -1 -1 755.45 170.26 776.10 231.54 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n48 16 Car -1 -1 -1 546.24 167.99 563.44 183.35 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n48 15 Pedestrian -1 -1 -1 221.13 155.38 234.81 197.22 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n48 11 Pedestrian -1 -1 -1 192.61 160.66 207.40 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n49 17 Car -1 -1 -1 2.81 194.93 344.69 363.97 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n49 3 Car -1 -1 -1 1095.41 185.09 1220.56 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n49 2 Car -1 -1 -1 955.17 184.11 1066.49 232.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n49 4 Car -1 -1 -1 1029.38 183.84 1156.57 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n49 7 Car -1 -1 -1 600.12 172.70 637.25 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n49 1 Cyclist -1 -1 -1 582.83 168.45 605.67 213.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n49 6 Pedestrian -1 -1 -1 257.01 157.21 281.63 221.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n49 10 Pedestrian -1 -1 -1 284.38 164.81 305.97 223.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n49 5 Pedestrian -1 -1 -1 229.69 154.20 254.18 222.23 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n49 9 Pedestrian -1 -1 -1 755.82 170.16 777.51 231.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n49 16 Car -1 -1 -1 545.22 168.15 563.27 183.67 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n49 15 Pedestrian -1 -1 -1 220.97 155.53 234.81 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n49 11 Pedestrian -1 -1 -1 192.67 160.75 207.28 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n50 17 Car -1 -1 -1 -0.83 188.46 387.17 362.61 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n50 3 Car -1 -1 -1 1095.19 185.06 1220.75 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n50 2 Car -1 -1 -1 955.23 184.09 1066.47 232.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n50 4 Car -1 -1 -1 1029.42 183.81 1156.54 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n50 7 Car -1 -1 -1 600.51 172.79 637.22 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n50 6 Pedestrian -1 -1 -1 257.05 157.10 280.78 219.34 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n50 5 Pedestrian -1 -1 -1 228.01 155.38 251.76 220.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n50 10 Pedestrian -1 -1 -1 282.35 164.97 305.41 223.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n50 1 Cyclist -1 -1 -1 582.77 168.67 604.72 211.72 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n50 16 Car -1 -1 -1 545.21 168.60 562.93 183.83 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n50 9 Pedestrian -1 -1 -1 757.53 169.89 778.36 231.59 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n50 15 Pedestrian -1 -1 -1 213.68 156.44 227.29 196.67 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n50 11 Pedestrian -1 -1 -1 192.83 161.14 207.12 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n50 19 Pedestrian -1 -1 -1 220.80 155.74 234.93 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n51 17 Car -1 -1 -1 1.52 185.13 421.92 363.94 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n51 3 Car -1 -1 -1 1095.58 185.12 1220.65 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n51 2 Car -1 -1 -1 955.28 184.08 1066.29 232.75 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n51 4 Car -1 -1 -1 1029.44 183.86 1156.55 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n51 7 Car -1 -1 -1 600.65 172.70 637.14 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n51 16 Car -1 -1 -1 544.71 168.64 562.61 183.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n51 10 Pedestrian -1 -1 -1 281.53 164.70 303.52 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n51 5 Pedestrian -1 -1 -1 228.80 156.72 251.27 219.31 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n51 1 Cyclist -1 -1 -1 582.90 168.81 604.21 211.92 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n51 9 Pedestrian -1 -1 -1 760.20 168.69 779.98 230.91 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n51 6 Pedestrian -1 -1 -1 256.25 156.93 280.52 221.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n51 19 Pedestrian -1 -1 -1 220.51 155.88 235.10 196.86 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n51 11 Pedestrian -1 -1 -1 192.43 162.71 207.29 202.39 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n51 18 Car -1 -1 -1 593.79 173.13 619.52 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n52 17 Car -1 -1 -1 8.09 183.06 447.59 365.56 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n52 3 Car -1 -1 -1 1095.53 185.10 1220.53 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n52 2 Car -1 -1 -1 955.38 184.08 1066.41 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n52 4 Car -1 -1 -1 1029.53 183.84 1156.43 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n52 7 Car -1 -1 -1 600.85 172.75 637.21 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n52 16 Car -1 -1 -1 544.44 168.69 561.84 183.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n52 5 Pedestrian -1 -1 -1 227.69 157.00 250.88 218.84 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n52 9 Pedestrian -1 -1 -1 761.46 168.67 780.79 231.26 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n52 10 Pedestrian -1 -1 -1 281.01 164.86 302.26 224.79 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n52 1 Cyclist -1 -1 -1 582.08 169.27 602.55 211.10 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n52 6 Pedestrian -1 -1 -1 254.20 156.63 280.03 222.45 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n52 19 Pedestrian -1 -1 -1 212.85 159.23 227.81 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n52 11 Pedestrian -1 -1 -1 192.48 162.66 207.68 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n52 18 Car -1 -1 -1 593.79 173.11 619.61 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n52 20 Pedestrian -1 -1 -1 220.93 156.22 235.17 196.54 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n53 17 Car -1 -1 -1 105.99 183.73 464.14 364.71 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n53 3 Car -1 -1 -1 1095.38 185.04 1220.63 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n53 2 Car -1 -1 -1 955.37 184.09 1066.36 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n53 4 Car -1 -1 -1 1029.53 183.84 1156.51 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n53 7 Car -1 -1 -1 600.39 172.66 637.45 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n53 16 Car -1 -1 -1 543.91 168.66 561.38 183.83 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n53 9 Pedestrian -1 -1 -1 761.63 169.64 781.12 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n53 1 Cyclist -1 -1 -1 582.01 169.26 602.25 210.86 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n53 5 Pedestrian -1 -1 -1 226.94 156.82 250.16 219.03 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n53 10 Pedestrian -1 -1 -1 277.82 164.56 301.39 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n53 11 Pedestrian -1 -1 -1 192.24 162.06 208.10 203.70 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n53 6 Pedestrian -1 -1 -1 254.20 156.36 279.01 224.12 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n53 19 Pedestrian -1 -1 -1 212.75 159.50 227.67 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n53 20 Pedestrian -1 -1 -1 220.72 156.32 235.20 196.49 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n54 17 Car -1 -1 -1 179.40 181.61 481.91 359.64 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n54 3 Car -1 -1 -1 1095.30 185.01 1220.76 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n54 2 Car -1 -1 -1 955.25 184.12 1066.55 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n54 4 Car -1 -1 -1 1032.49 183.58 1157.29 233.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n54 7 Car -1 -1 -1 600.56 172.67 637.54 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n54 16 Car -1 -1 -1 543.74 168.87 560.87 183.93 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n54 1 Cyclist -1 -1 -1 582.37 169.14 601.58 210.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n54 5 Pedestrian -1 -1 -1 226.62 156.60 250.31 219.20 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n54 9 Pedestrian -1 -1 -1 761.70 169.33 782.20 232.38 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n54 10 Pedestrian -1 -1 -1 277.07 164.76 302.36 230.95 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n54 6 Pedestrian -1 -1 -1 253.27 156.47 279.27 223.78 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n54 11 Pedestrian -1 -1 -1 191.98 161.54 208.42 203.34 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n54 20 Pedestrian -1 -1 -1 220.82 156.52 235.20 196.28 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n55 17 Car -1 -1 -1 233.83 178.49 495.75 340.93 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n55 3 Car -1 -1 -1 1095.29 185.14 1220.64 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n55 4 Car -1 -1 -1 1029.48 183.87 1156.41 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n55 2 Car -1 -1 -1 955.27 184.10 1066.36 232.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n55 6 Pedestrian -1 -1 -1 252.88 156.68 279.11 224.50 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n55 7 Car -1 -1 -1 600.78 172.66 637.81 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n55 9 Pedestrian -1 -1 -1 763.94 168.23 784.16 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n55 16 Car -1 -1 -1 543.17 168.75 560.39 184.00 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n55 5 Pedestrian -1 -1 -1 225.08 156.45 250.53 225.17 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n55 1 Cyclist -1 -1 -1 582.07 169.45 600.83 209.71 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n55 11 Pedestrian -1 -1 -1 193.05 161.12 207.74 199.29 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n55 10 Pedestrian -1 -1 -1 280.51 164.90 303.45 230.93 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n56 17 Car -1 -1 -1 276.70 178.27 507.81 323.72 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n56 3 Car -1 -1 -1 1095.23 185.14 1220.61 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n56 2 Car -1 -1 -1 955.11 184.09 1066.64 232.75 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n56 4 Car -1 -1 -1 1029.43 183.84 1156.48 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n56 6 Pedestrian -1 -1 -1 252.98 156.52 277.86 223.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n56 5 Pedestrian -1 -1 -1 222.98 156.77 248.75 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n56 7 Car -1 -1 -1 601.17 172.85 637.54 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n56 10 Pedestrian -1 -1 -1 280.74 165.95 305.81 228.63 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n56 9 Pedestrian -1 -1 -1 763.73 167.24 784.78 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n56 16 Car -1 -1 -1 542.82 168.47 560.03 184.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n56 1 Cyclist -1 -1 -1 582.80 169.22 599.18 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n56 11 Pedestrian -1 -1 -1 192.56 160.90 207.68 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n57 17 Car -1 -1 -1 312.44 176.89 518.31 306.42 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n57 3 Car -1 -1 -1 1095.41 185.16 1220.60 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n57 2 Car -1 -1 -1 955.20 184.08 1066.52 232.78 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n57 4 Car -1 -1 -1 1032.34 183.63 1157.43 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n57 6 Pedestrian -1 -1 -1 252.12 156.57 277.47 225.20 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n57 5 Pedestrian -1 -1 -1 222.30 157.10 247.68 226.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n57 9 Pedestrian -1 -1 -1 763.86 167.23 785.87 232.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n57 10 Pedestrian -1 -1 -1 281.47 165.57 305.37 225.95 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n57 7 Car -1 -1 -1 601.31 172.91 637.43 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n57 1 Cyclist -1 -1 -1 582.60 169.17 598.99 207.14 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n57 16 Car -1 -1 -1 541.59 167.89 559.25 184.49 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n57 11 Pedestrian -1 -1 -1 192.38 160.70 207.41 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n57 21 Pedestrian -1 -1 -1 222.36 157.43 240.77 208.02 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n58 17 Car -1 -1 -1 342.74 177.04 527.10 295.95 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n58 3 Car -1 -1 -1 1095.69 185.26 1220.52 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n58 2 Car -1 -1 -1 955.20 184.11 1066.50 232.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n58 4 Car -1 -1 -1 1032.30 183.62 1157.45 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n58 6 Pedestrian -1 -1 -1 251.47 157.32 277.93 225.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n58 5 Pedestrian -1 -1 -1 222.16 156.18 248.18 227.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n58 10 Pedestrian -1 -1 -1 280.99 165.16 304.23 226.76 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n58 7 Car -1 -1 -1 601.31 172.91 637.26 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n58 16 Car -1 -1 -1 541.11 167.82 559.40 184.83 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n58 1 Cyclist -1 -1 -1 582.89 169.35 598.81 207.40 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n58 9 Pedestrian -1 -1 -1 764.37 167.40 787.02 232.49 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n58 11 Pedestrian -1 -1 -1 192.49 160.77 207.46 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n59 17 Car -1 -1 -1 368.49 177.06 532.94 286.77 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n59 3 Car -1 -1 -1 1095.54 185.32 1220.67 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n59 2 Car -1 -1 -1 955.19 184.08 1066.67 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n59 4 Car -1 -1 -1 1029.37 183.83 1156.60 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n59 6 Pedestrian -1 -1 -1 251.42 157.32 278.59 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n59 5 Pedestrian -1 -1 -1 223.02 155.21 248.17 226.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n59 10 Pedestrian -1 -1 -1 280.18 164.89 304.29 227.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n59 16 Car -1 -1 -1 540.56 167.50 559.18 184.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n59 7 Car -1 -1 -1 601.44 173.10 637.07 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n59 9 Pedestrian -1 -1 -1 766.05 168.40 788.48 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n59 1 Cyclist -1 -1 -1 582.76 169.46 597.97 207.28 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n59 11 Pedestrian -1 -1 -1 192.31 160.61 207.41 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n60 17 Car -1 -1 -1 389.69 176.22 539.64 276.79 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n60 3 Car -1 -1 -1 1095.76 185.26 1220.48 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n60 2 Car -1 -1 -1 955.36 184.13 1066.29 232.75 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n60 4 Car -1 -1 -1 1029.39 183.84 1156.61 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n60 5 Pedestrian -1 -1 -1 222.60 154.82 248.20 226.35 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n60 6 Pedestrian -1 -1 -1 251.33 157.23 278.77 225.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n60 16 Car -1 -1 -1 539.93 167.76 558.97 185.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n60 10 Pedestrian -1 -1 -1 280.40 166.08 303.21 228.23 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n60 9 Pedestrian -1 -1 -1 766.45 167.65 788.82 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n60 7 Car -1 -1 -1 602.72 173.06 637.41 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n60 1 Cyclist -1 -1 -1 582.56 169.84 597.42 206.64 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n60 11 Pedestrian -1 -1 -1 192.21 160.52 207.36 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n61 17 Car -1 -1 -1 407.49 175.46 545.11 269.73 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n61 3 Car -1 -1 -1 1095.58 185.26 1220.60 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n61 2 Car -1 -1 -1 955.30 184.10 1066.39 232.78 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n61 4 Car -1 -1 -1 1029.23 183.85 1156.75 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n61 5 Pedestrian -1 -1 -1 222.03 154.47 248.59 226.40 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n61 10 Pedestrian -1 -1 -1 280.28 166.16 303.39 228.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n61 6 Pedestrian -1 -1 -1 251.36 157.11 278.64 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n61 9 Pedestrian -1 -1 -1 767.20 167.34 789.44 234.23 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n61 16 Car -1 -1 -1 539.57 168.36 558.41 185.16 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n61 7 Car -1 -1 -1 603.02 173.17 637.08 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n61 1 Cyclist -1 -1 -1 582.03 169.82 597.50 205.91 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n61 11 Pedestrian -1 -1 -1 192.34 160.50 207.40 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n62 17 Car -1 -1 -1 423.39 176.00 549.50 264.06 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n62 3 Car -1 -1 -1 1095.52 185.29 1220.45 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n62 2 Car -1 -1 -1 955.48 184.08 1066.28 232.78 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n62 4 Car -1 -1 -1 1029.35 183.88 1156.60 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n62 6 Pedestrian -1 -1 -1 251.45 157.09 278.58 226.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n62 5 Pedestrian -1 -1 -1 221.71 155.75 249.28 226.32 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n62 10 Pedestrian -1 -1 -1 279.94 166.50 302.94 229.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n62 16 Car -1 -1 -1 538.94 168.14 558.18 185.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n62 9 Pedestrian -1 -1 -1 767.82 166.23 790.31 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n62 7 Car -1 -1 -1 601.80 173.31 636.92 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n62 1 Cyclist -1 -1 -1 582.84 169.98 596.61 205.56 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n62 11 Pedestrian -1 -1 -1 192.51 160.61 207.27 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n63 17 Car -1 -1 -1 436.99 176.29 554.69 257.05 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n63 3 Car -1 -1 -1 1095.59 185.30 1220.34 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n63 2 Car -1 -1 -1 955.43 184.11 1066.28 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n63 4 Car -1 -1 -1 1029.41 183.92 1156.55 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n63 10 Pedestrian -1 -1 -1 279.64 166.36 305.18 229.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n63 6 Pedestrian -1 -1 -1 251.27 158.73 278.76 227.35 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n63 5 Pedestrian -1 -1 -1 222.18 156.47 248.55 227.45 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n63 16 Car -1 -1 -1 538.36 167.78 557.60 185.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n63 7 Car -1 -1 -1 601.71 173.30 636.84 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n63 9 Pedestrian -1 -1 -1 770.11 166.79 792.25 235.08 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n63 1 Cyclist -1 -1 -1 583.28 169.98 596.38 205.10 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n63 11 Pedestrian -1 -1 -1 192.22 160.48 207.31 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n64 17 Car -1 -1 -1 449.61 176.79 558.52 251.73 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n64 3 Car -1 -1 -1 1095.72 185.34 1220.22 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n64 2 Car -1 -1 -1 955.52 184.11 1066.15 232.78 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n64 4 Car -1 -1 -1 1029.42 183.91 1156.52 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n64 6 Pedestrian -1 -1 -1 250.85 158.98 278.86 228.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n64 9 Pedestrian -1 -1 -1 770.76 167.17 793.48 234.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n64 10 Pedestrian -1 -1 -1 280.85 167.01 306.66 229.05 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n64 5 Pedestrian -1 -1 -1 221.77 156.31 248.51 227.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n64 7 Car -1 -1 -1 601.55 173.15 636.99 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n64 16 Car -1 -1 -1 537.77 167.60 557.30 185.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n64 1 Cyclist -1 -1 -1 583.00 170.03 596.68 204.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n64 11 Pedestrian -1 -1 -1 192.22 160.59 207.19 198.69 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n64 22 Pedestrian -1 -1 -1 222.23 158.30 241.09 208.39 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n65 17 Car -1 -1 -1 460.14 175.80 561.51 246.96 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n65 3 Car -1 -1 -1 1095.70 185.31 1220.32 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n65 2 Car -1 -1 -1 955.54 184.13 1066.15 232.78 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n65 4 Car -1 -1 -1 1029.28 183.93 1156.64 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n65 6 Pedestrian -1 -1 -1 251.16 158.92 279.19 228.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n65 9 Pedestrian -1 -1 -1 771.45 167.31 794.46 235.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n65 10 Pedestrian -1 -1 -1 283.07 166.87 307.25 229.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n65 5 Pedestrian -1 -1 -1 221.19 155.75 248.47 228.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n65 7 Car -1 -1 -1 601.72 173.34 636.98 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n65 1 Cyclist -1 -1 -1 583.04 170.23 596.57 204.33 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n65 16 Car -1 -1 -1 537.83 167.45 556.85 185.12 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n65 22 Pedestrian -1 -1 -1 221.60 157.63 241.24 209.45 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n65 11 Pedestrian -1 -1 -1 192.02 160.38 207.49 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n66 17 Car -1 -1 -1 469.47 175.65 565.25 243.52 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n66 3 Car -1 -1 -1 1095.61 185.31 1220.33 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n66 4 Car -1 -1 -1 1029.01 183.83 1156.79 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n66 2 Car -1 -1 -1 955.51 184.09 1066.11 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n66 6 Pedestrian -1 -1 -1 251.15 158.68 279.61 228.49 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n66 10 Pedestrian -1 -1 -1 283.69 166.54 307.01 230.69 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n66 9 Pedestrian -1 -1 -1 773.20 167.13 797.76 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n66 5 Pedestrian -1 -1 -1 221.70 155.70 248.01 228.39 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n66 7 Car -1 -1 -1 601.92 173.42 636.69 202.37 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n66 1 Cyclist -1 -1 -1 582.92 170.45 596.33 204.00 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n66 16 Car -1 -1 -1 536.14 167.08 556.53 186.32 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n66 11 Pedestrian -1 -1 -1 189.44 159.84 206.41 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n66 22 Pedestrian -1 -1 -1 221.05 157.30 240.69 209.16 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n67 17 Car -1 -1 -1 477.93 174.74 566.89 240.16 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n67 3 Car -1 -1 -1 1095.54 185.36 1220.59 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n67 4 Car -1 -1 -1 1029.09 183.90 1156.73 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n67 2 Car -1 -1 -1 955.56 184.20 1066.03 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n67 10 Pedestrian -1 -1 -1 284.20 166.64 307.40 230.72 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n67 6 Pedestrian -1 -1 -1 251.22 158.34 280.66 228.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n67 9 Pedestrian -1 -1 -1 775.36 166.96 798.13 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n67 5 Pedestrian -1 -1 -1 221.61 156.38 247.71 229.86 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n67 7 Car -1 -1 -1 602.00 173.41 636.47 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n67 1 Cyclist -1 -1 -1 582.86 170.64 596.43 203.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n67 16 Car -1 -1 -1 535.36 167.16 556.46 186.61 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n67 22 Pedestrian -1 -1 -1 221.28 157.38 240.50 209.34 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n67 11 Pedestrian -1 -1 -1 191.91 160.00 207.60 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n68 17 Car -1 -1 -1 484.64 175.04 569.10 237.62 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n68 3 Car -1 -1 -1 1095.63 185.42 1220.50 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n68 4 Car -1 -1 -1 1029.03 183.87 1156.57 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n68 2 Car -1 -1 -1 955.57 184.15 1066.02 232.78 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n68 6 Pedestrian -1 -1 -1 251.25 158.46 281.73 228.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n68 10 Pedestrian -1 -1 -1 283.49 166.55 307.32 231.29 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n68 9 Pedestrian -1 -1 -1 778.03 166.60 800.16 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n68 5 Pedestrian -1 -1 -1 221.70 155.99 247.91 230.16 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n68 7 Car -1 -1 -1 601.97 173.41 636.48 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n68 1 Cyclist -1 -1 -1 581.66 170.59 595.07 203.93 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n68 16 Car -1 -1 -1 534.16 167.89 556.08 188.39 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n68 22 Pedestrian -1 -1 -1 217.86 156.83 238.18 208.42 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n68 11 Pedestrian -1 -1 -1 189.46 159.88 206.53 199.29 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n69 3 Car -1 -1 -1 1095.52 185.39 1220.41 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n69 4 Car -1 -1 -1 1029.26 183.93 1156.54 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n69 2 Car -1 -1 -1 955.58 184.14 1066.11 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n69 17 Car -1 -1 -1 491.25 175.46 570.73 234.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n69 6 Pedestrian -1 -1 -1 252.14 158.40 281.30 230.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n69 10 Pedestrian -1 -1 -1 283.24 166.44 307.74 231.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n69 9 Pedestrian -1 -1 -1 778.79 166.87 800.89 236.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n69 5 Pedestrian -1 -1 -1 222.42 156.17 247.74 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n69 7 Car -1 -1 -1 601.96 173.51 636.49 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n69 1 Cyclist -1 -1 -1 581.37 170.88 594.87 203.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n69 16 Car -1 -1 -1 533.17 167.75 555.78 188.83 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n69 22 Pedestrian -1 -1 -1 217.64 157.09 237.63 208.56 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n70 17 Car -1 -1 -1 496.83 175.22 572.76 231.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n70 3 Car -1 -1 -1 1095.68 185.45 1220.55 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n70 4 Car -1 -1 -1 1029.30 183.94 1156.58 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n70 2 Car -1 -1 -1 955.54 184.13 1066.11 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n70 5 Pedestrian -1 -1 -1 223.81 155.69 247.19 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n70 9 Pedestrian -1 -1 -1 778.20 167.47 801.76 237.06 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n70 6 Pedestrian -1 -1 -1 255.05 159.00 282.13 230.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n70 10 Pedestrian -1 -1 -1 283.07 166.92 308.05 232.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n70 7 Car -1 -1 -1 601.76 173.35 636.73 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n70 1 Cyclist -1 -1 -1 580.74 171.07 594.88 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n70 16 Car -1 -1 -1 532.86 167.71 555.80 189.01 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n70 22 Pedestrian -1 -1 -1 214.98 156.41 232.22 201.65 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n71 17 Car -1 -1 -1 502.67 174.79 573.74 229.59 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n71 3 Car -1 -1 -1 1095.57 185.41 1220.66 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n71 4 Car -1 -1 -1 1029.15 183.93 1156.59 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n71 2 Car -1 -1 -1 955.51 184.11 1066.08 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n71 6 Pedestrian -1 -1 -1 255.98 158.92 283.00 231.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n71 9 Pedestrian -1 -1 -1 778.04 167.70 801.85 237.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n71 5 Pedestrian -1 -1 -1 223.66 155.08 248.90 232.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n71 10 Pedestrian -1 -1 -1 282.54 166.63 308.34 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n71 7 Car -1 -1 -1 601.53 173.24 636.92 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n71 1 Cyclist -1 -1 -1 580.28 171.18 594.80 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n71 22 Pedestrian -1 -1 -1 216.41 156.30 231.52 196.28 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n71 16 Car -1 -1 -1 532.47 167.47 555.42 189.47 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n72 17 Car -1 -1 -1 506.79 174.44 575.97 227.14 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n72 3 Car -1 -1 -1 1095.67 185.46 1220.56 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n72 2 Car -1 -1 -1 955.48 184.11 1066.20 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n72 4 Car -1 -1 -1 1029.39 183.97 1156.44 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n72 6 Pedestrian -1 -1 -1 256.64 158.50 284.03 231.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n72 5 Pedestrian -1 -1 -1 226.03 153.98 251.63 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n72 10 Pedestrian -1 -1 -1 282.83 166.05 308.83 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n72 9 Pedestrian -1 -1 -1 778.30 167.06 802.30 237.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n72 7 Car -1 -1 -1 601.53 173.29 637.04 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n72 1 Cyclist -1 -1 -1 580.11 171.15 593.88 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n72 22 Pedestrian -1 -1 -1 216.05 156.64 230.58 196.12 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n73 17 Car -1 -1 -1 512.08 174.07 577.77 224.82 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n73 3 Car -1 -1 -1 1095.50 185.48 1220.77 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n73 2 Car -1 -1 -1 955.43 184.07 1066.24 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n73 4 Car -1 -1 -1 1029.27 183.90 1156.61 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n73 6 Pedestrian -1 -1 -1 256.49 157.78 284.88 232.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n73 5 Pedestrian -1 -1 -1 226.23 154.64 253.63 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n73 9 Pedestrian -1 -1 -1 778.89 165.96 802.53 237.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n73 7 Car -1 -1 -1 601.57 173.26 636.88 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n73 10 Pedestrian -1 -1 -1 284.47 166.69 309.59 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n73 22 Pedestrian -1 -1 -1 216.33 156.51 230.56 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n73 1 Cyclist -1 -1 -1 579.99 171.13 593.60 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n73 23 Car -1 -1 -1 530.58 167.01 553.59 190.86 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n74 3 Car -1 -1 -1 1095.62 185.46 1220.56 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n74 4 Car -1 -1 -1 1029.20 183.92 1156.62 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n74 2 Car -1 -1 -1 955.43 184.05 1066.13 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n74 17 Car -1 -1 -1 515.68 173.91 577.45 222.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n74 6 Pedestrian -1 -1 -1 256.20 157.51 285.32 232.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n74 5 Pedestrian -1 -1 -1 228.17 155.80 256.91 234.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n74 7 Car -1 -1 -1 601.35 173.23 636.89 203.21 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n74 9 Pedestrian -1 -1 -1 779.77 165.77 805.31 238.87 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n74 10 Pedestrian -1 -1 -1 288.29 168.04 309.77 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n74 23 Car -1 -1 -1 529.79 167.25 553.06 191.65 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n74 22 Pedestrian -1 -1 -1 216.53 156.52 230.39 196.11 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n75 17 Car -1 -1 -1 519.17 174.40 580.20 220.81 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n75 3 Car -1 -1 -1 1095.56 185.47 1220.54 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n75 2 Car -1 -1 -1 955.36 184.00 1066.29 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n75 4 Car -1 -1 -1 1029.31 183.96 1156.61 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n75 9 Pedestrian -1 -1 -1 781.09 165.93 805.34 239.17 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n75 10 Pedestrian -1 -1 -1 288.70 168.29 310.62 234.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n75 6 Pedestrian -1 -1 -1 257.89 158.52 287.02 232.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n75 7 Car -1 -1 -1 601.65 173.47 636.82 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n75 5 Pedestrian -1 -1 -1 228.64 156.12 256.85 235.25 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n75 23 Car -1 -1 -1 529.56 167.05 553.41 191.75 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n75 22 Pedestrian -1 -1 -1 216.95 156.36 230.67 195.96 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n75 24 Pedestrian -1 -1 -1 191.95 160.07 208.14 199.52 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n76 3 Car -1 -1 -1 1095.57 185.44 1220.56 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n76 2 Car -1 -1 -1 955.44 184.03 1066.23 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n76 4 Car -1 -1 -1 1029.29 183.93 1156.58 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n76 10 Pedestrian -1 -1 -1 289.56 168.78 312.01 234.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n76 9 Pedestrian -1 -1 -1 781.42 166.74 805.81 239.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n76 17 Car -1 -1 -1 521.99 174.45 580.52 219.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n76 6 Pedestrian -1 -1 -1 258.68 158.87 286.85 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n76 7 Car -1 -1 -1 601.88 173.46 636.64 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n76 5 Pedestrian -1 -1 -1 229.84 156.40 257.24 235.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n76 23 Car -1 -1 -1 529.36 166.85 554.09 191.32 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n76 22 Pedestrian -1 -1 -1 223.45 155.53 237.48 195.57 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n76 24 Pedestrian -1 -1 -1 192.10 160.16 208.10 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n76 25 Cyclist -1 -1 -1 580.18 171.14 593.41 202.08 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n77 3 Car -1 -1 -1 1095.49 185.47 1220.62 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n77 4 Car -1 -1 -1 1029.30 183.90 1156.53 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n77 2 Car -1 -1 -1 955.41 184.05 1066.12 232.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n77 17 Car -1 -1 -1 524.79 174.19 580.07 217.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n77 9 Pedestrian -1 -1 -1 781.92 166.58 806.74 239.54 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n77 6 Pedestrian -1 -1 -1 259.20 158.96 287.45 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n77 10 Pedestrian -1 -1 -1 291.69 167.86 314.04 235.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n77 23 Car -1 -1 -1 527.65 166.89 554.19 191.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n77 7 Car -1 -1 -1 601.84 173.55 636.67 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n77 5 Pedestrian -1 -1 -1 232.74 157.49 259.41 236.89 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n77 22 Pedestrian -1 -1 -1 223.66 155.43 237.73 196.44 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n77 24 Pedestrian -1 -1 -1 192.08 159.98 208.26 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n78 3 Car -1 -1 -1 1095.42 185.41 1220.64 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n78 4 Car -1 -1 -1 1029.21 183.87 1156.54 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n78 2 Car -1 -1 -1 955.49 184.01 1066.10 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n78 10 Pedestrian -1 -1 -1 290.84 167.51 316.29 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n78 17 Car -1 -1 -1 527.13 173.92 581.17 216.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n78 5 Pedestrian -1 -1 -1 233.92 156.90 259.68 237.41 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n78 6 Pedestrian -1 -1 -1 259.45 159.05 287.32 234.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n78 9 Pedestrian -1 -1 -1 783.89 165.42 809.11 240.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n78 7 Car -1 -1 -1 601.59 173.50 636.81 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n78 23 Car -1 -1 -1 525.98 166.39 554.37 190.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n78 22 Pedestrian -1 -1 -1 223.96 155.39 237.90 196.99 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n78 24 Pedestrian -1 -1 -1 191.97 159.93 208.12 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n79 17 Car -1 -1 -1 529.93 173.39 581.91 215.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n79 3 Car -1 -1 -1 1095.59 185.41 1220.57 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n79 4 Car -1 -1 -1 1029.33 183.91 1156.46 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n79 2 Car -1 -1 -1 955.41 184.00 1066.13 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n79 9 Pedestrian -1 -1 -1 785.06 164.99 809.67 240.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n79 10 Pedestrian -1 -1 -1 291.52 167.13 317.90 236.28 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n79 5 Pedestrian -1 -1 -1 232.59 156.52 262.01 237.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n79 6 Pedestrian -1 -1 -1 260.41 158.46 287.79 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n79 7 Car -1 -1 -1 601.54 173.57 636.87 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n79 23 Car -1 -1 -1 524.90 166.25 552.75 190.44 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n79 22 Pedestrian -1 -1 -1 224.33 155.34 237.66 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n79 24 Pedestrian -1 -1 -1 192.10 159.89 208.12 199.49 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n80 17 Car -1 -1 -1 532.08 173.42 583.03 214.28 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n80 3 Car -1 -1 -1 1095.78 185.35 1220.52 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n80 4 Car -1 -1 -1 1029.33 183.81 1156.43 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n80 2 Car -1 -1 -1 955.43 183.99 1066.22 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n80 5 Pedestrian -1 -1 -1 231.38 156.56 263.83 239.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n80 9 Pedestrian -1 -1 -1 785.77 164.60 810.70 240.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n80 6 Pedestrian -1 -1 -1 260.53 158.43 288.27 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n80 10 Pedestrian -1 -1 -1 291.66 167.43 318.67 237.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n80 7 Car -1 -1 -1 601.50 173.51 636.87 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n80 23 Car -1 -1 -1 524.61 166.31 552.67 191.20 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n80 22 Pedestrian -1 -1 -1 224.36 155.38 237.83 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n80 24 Pedestrian -1 -1 -1 192.09 160.01 208.26 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n81 3 Car -1 -1 -1 1095.80 185.42 1220.37 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n81 4 Car -1 -1 -1 1029.18 183.81 1156.56 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n81 2 Car -1 -1 -1 955.48 184.02 1066.13 232.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n81 6 Pedestrian -1 -1 -1 262.18 159.14 291.11 236.49 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n81 5 Pedestrian -1 -1 -1 235.41 156.84 266.14 239.79 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n81 17 Car -1 -1 -1 534.66 173.41 583.15 213.37 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n81 10 Pedestrian -1 -1 -1 293.63 167.80 320.04 237.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n81 23 Car -1 -1 -1 523.58 166.14 552.44 191.49 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n81 7 Car -1 -1 -1 601.62 173.54 636.79 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n81 9 Pedestrian -1 -1 -1 785.45 165.15 811.57 240.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n81 22 Pedestrian -1 -1 -1 224.03 155.37 237.73 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n81 24 Pedestrian -1 -1 -1 191.94 159.81 208.26 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n82 3 Car -1 -1 -1 1095.66 185.46 1220.63 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n82 4 Car -1 -1 -1 1029.33 183.88 1156.45 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n82 2 Car -1 -1 -1 955.47 183.98 1066.18 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n82 6 Pedestrian -1 -1 -1 263.98 159.39 291.82 237.65 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n82 5 Pedestrian -1 -1 -1 238.73 157.25 269.57 239.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n82 23 Car -1 -1 -1 522.91 165.85 551.87 191.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n82 17 Car -1 -1 -1 537.15 172.81 583.40 211.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n82 10 Pedestrian -1 -1 -1 294.37 168.39 320.96 238.15 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n82 7 Car -1 -1 -1 601.65 173.57 636.77 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n82 9 Pedestrian -1 -1 -1 784.69 165.65 811.98 241.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n82 22 Pedestrian -1 -1 -1 223.80 155.60 237.63 197.24 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n82 24 Pedestrian -1 -1 -1 192.31 160.15 208.14 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n83 3 Car -1 -1 -1 1095.83 185.51 1220.45 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n83 4 Car -1 -1 -1 1029.17 183.78 1156.47 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n83 2 Car -1 -1 -1 955.52 183.99 1066.07 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n83 17 Car -1 -1 -1 539.27 173.14 583.78 210.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n83 6 Pedestrian -1 -1 -1 266.58 159.68 293.88 238.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n83 5 Pedestrian -1 -1 -1 242.67 157.25 272.36 239.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n83 10 Pedestrian -1 -1 -1 295.94 167.86 322.31 238.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n83 7 Car -1 -1 -1 601.79 173.62 636.72 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n83 23 Car -1 -1 -1 521.63 165.64 550.58 191.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n83 9 Pedestrian -1 -1 -1 784.55 165.02 812.30 242.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n83 22 Pedestrian -1 -1 -1 224.05 155.44 237.77 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n83 24 Pedestrian -1 -1 -1 192.03 160.04 207.97 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n84 3 Car -1 -1 -1 1095.76 185.42 1220.40 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n84 4 Car -1 -1 -1 1029.29 183.80 1156.32 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n84 2 Car -1 -1 -1 955.45 184.00 1066.16 232.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n84 5 Pedestrian -1 -1 -1 242.36 156.35 275.30 240.28 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n84 6 Pedestrian -1 -1 -1 268.04 159.01 296.29 238.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n84 17 Car -1 -1 -1 541.21 173.45 584.23 209.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n84 23 Car -1 -1 -1 520.28 165.89 549.37 192.13 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n84 7 Car -1 -1 -1 601.58 173.61 636.78 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n84 10 Pedestrian -1 -1 -1 300.01 169.56 324.48 237.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n84 9 Pedestrian -1 -1 -1 784.80 165.74 812.64 243.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n84 22 Pedestrian -1 -1 -1 223.82 155.40 237.33 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n84 24 Pedestrian -1 -1 -1 192.07 159.98 207.89 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n85 3 Car -1 -1 -1 1095.66 185.47 1220.59 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n85 4 Car -1 -1 -1 1029.28 183.84 1156.44 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n85 2 Car -1 -1 -1 955.42 183.97 1066.14 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n85 17 Car -1 -1 -1 542.26 173.25 584.32 209.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n85 23 Car -1 -1 -1 518.36 166.05 549.47 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n85 6 Pedestrian -1 -1 -1 271.72 158.18 297.51 238.55 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n85 5 Pedestrian -1 -1 -1 246.22 156.62 277.94 240.75 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n85 7 Car -1 -1 -1 601.61 173.53 636.73 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n85 9 Pedestrian -1 -1 -1 784.65 165.67 812.82 244.08 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n85 10 Pedestrian -1 -1 -1 299.98 169.49 325.94 239.56 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n85 22 Pedestrian -1 -1 -1 220.69 154.90 235.40 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n85 24 Pedestrian -1 -1 -1 191.96 160.03 207.82 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n86 17 Car -1 -1 -1 542.95 173.34 584.46 208.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n86 3 Car -1 -1 -1 1095.56 185.38 1220.62 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n86 4 Car -1 -1 -1 1029.27 183.84 1156.49 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n86 2 Car -1 -1 -1 955.45 183.93 1066.14 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n86 23 Car -1 -1 -1 517.09 166.43 548.18 194.16 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n86 10 Pedestrian -1 -1 -1 302.45 169.63 327.73 239.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n86 7 Car -1 -1 -1 601.50 173.33 636.85 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n86 9 Pedestrian -1 -1 -1 784.61 162.78 812.44 243.77 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n86 5 Pedestrian -1 -1 -1 249.21 156.52 281.08 241.90 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n86 6 Pedestrian -1 -1 -1 272.25 158.21 299.22 237.25 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n86 22 Pedestrian -1 -1 -1 220.64 154.65 235.29 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n86 24 Pedestrian -1 -1 -1 192.06 160.04 207.73 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n87 17 Car -1 -1 -1 544.10 173.34 585.42 207.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n87 3 Car -1 -1 -1 1095.61 185.40 1220.57 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n87 4 Car -1 -1 -1 1029.44 183.85 1156.22 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n87 2 Car -1 -1 -1 955.46 183.94 1066.18 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n87 5 Pedestrian -1 -1 -1 250.25 157.17 282.29 241.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n87 23 Car -1 -1 -1 514.80 165.90 547.39 194.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n87 10 Pedestrian -1 -1 -1 303.83 169.41 329.02 240.56 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n87 7 Car -1 -1 -1 601.30 173.17 637.05 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n87 6 Pedestrian -1 -1 -1 275.04 159.71 301.47 237.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n87 9 Pedestrian -1 -1 -1 785.13 162.86 812.14 243.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n87 22 Pedestrian -1 -1 -1 220.55 154.60 235.37 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n87 24 Pedestrian -1 -1 -1 191.88 160.13 207.66 199.34 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n88 3 Car -1 -1 -1 1095.79 185.45 1220.40 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n88 4 Car -1 -1 -1 1029.40 183.85 1156.15 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n88 2 Car -1 -1 -1 955.53 183.97 1065.97 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n88 17 Car -1 -1 -1 545.38 173.38 585.49 207.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n88 23 Car -1 -1 -1 513.02 166.02 546.35 195.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n88 5 Pedestrian -1 -1 -1 254.18 157.36 285.80 242.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n88 6 Pedestrian -1 -1 -1 274.66 159.58 302.38 238.66 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n88 7 Car -1 -1 -1 601.20 173.32 636.97 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n88 9 Pedestrian -1 -1 -1 787.79 164.60 813.65 245.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n88 10 Pedestrian -1 -1 -1 308.20 169.39 332.73 241.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n88 22 Pedestrian -1 -1 -1 220.48 154.63 235.52 197.85 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n88 24 Pedestrian -1 -1 -1 191.83 160.19 207.76 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n89 3 Car -1 -1 -1 1095.79 185.44 1220.36 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n89 4 Car -1 -1 -1 1029.71 183.93 1156.04 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n89 2 Car -1 -1 -1 955.56 183.98 1065.95 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n89 10 Pedestrian -1 -1 -1 310.12 169.58 336.37 240.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n89 9 Pedestrian -1 -1 -1 787.49 164.56 815.41 245.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n89 6 Pedestrian -1 -1 -1 274.51 159.39 303.31 238.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n89 7 Car -1 -1 -1 601.27 173.41 636.86 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n89 17 Car -1 -1 -1 546.19 173.89 585.27 206.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n89 23 Car -1 -1 -1 512.03 165.79 545.35 195.47 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n89 5 Pedestrian -1 -1 -1 258.09 157.67 288.35 242.08 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n89 22 Pedestrian -1 -1 -1 220.28 154.50 235.61 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n89 24 Pedestrian -1 -1 -1 191.59 160.03 207.86 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n90 3 Car -1 -1 -1 1095.78 185.48 1220.45 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n90 4 Car -1 -1 -1 1029.95 183.96 1155.82 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n90 2 Car -1 -1 -1 955.56 183.99 1066.00 232.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n90 17 Car -1 -1 -1 547.49 173.84 586.12 205.74 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n90 10 Pedestrian -1 -1 -1 312.50 170.11 339.71 241.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n90 7 Car -1 -1 -1 601.19 173.38 636.99 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n90 9 Pedestrian -1 -1 -1 787.85 163.72 815.49 245.66 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n90 6 Pedestrian -1 -1 -1 275.01 158.70 304.00 239.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n90 5 Pedestrian -1 -1 -1 260.98 158.74 292.28 243.08 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n90 23 Car -1 -1 -1 509.75 165.19 544.31 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n90 22 Pedestrian -1 -1 -1 220.27 154.43 235.65 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n90 24 Pedestrian -1 -1 -1 191.62 159.94 207.90 199.53 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n91 3 Car -1 -1 -1 1095.58 185.38 1220.61 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n91 2 Car -1 -1 -1 955.53 183.98 1066.01 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n91 4 Car -1 -1 -1 1029.82 183.91 1156.03 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n91 10 Pedestrian -1 -1 -1 313.33 170.09 342.56 242.66 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n91 5 Pedestrian -1 -1 -1 260.42 158.21 294.37 244.34 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n91 17 Car -1 -1 -1 548.39 173.42 586.10 205.04 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n91 6 Pedestrian -1 -1 -1 278.69 157.89 307.31 240.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n91 7 Car -1 -1 -1 601.29 173.34 637.01 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n91 9 Pedestrian -1 -1 -1 787.49 161.30 816.23 245.74 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n91 23 Car -1 -1 -1 509.31 165.14 543.84 196.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n91 22 Pedestrian -1 -1 -1 220.36 154.53 235.56 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n91 24 Pedestrian -1 -1 -1 191.59 159.94 207.90 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n92 3 Car -1 -1 -1 1099.43 185.51 1220.15 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n92 4 Car -1 -1 -1 1029.84 183.89 1155.93 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n92 2 Car -1 -1 -1 955.64 184.01 1066.00 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n92 10 Pedestrian -1 -1 -1 316.67 170.58 344.07 242.96 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n92 5 Pedestrian -1 -1 -1 262.91 158.69 298.50 244.98 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n92 7 Car -1 -1 -1 601.12 173.26 637.16 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n92 17 Car -1 -1 -1 549.54 173.21 585.56 203.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n92 23 Car -1 -1 -1 508.46 166.01 542.95 197.27 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n92 9 Pedestrian -1 -1 -1 787.86 162.46 816.20 246.86 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n92 6 Pedestrian -1 -1 -1 277.99 157.82 308.72 241.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n92 22 Pedestrian -1 -1 -1 220.25 154.72 235.65 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n92 24 Pedestrian -1 -1 -1 191.80 160.20 207.93 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n93 3 Car -1 -1 -1 1099.52 185.61 1220.03 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n93 4 Car -1 -1 -1 1029.35 183.78 1156.32 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n93 2 Car -1 -1 -1 955.66 184.06 1065.81 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n93 17 Car -1 -1 -1 550.16 173.24 585.71 203.36 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n93 5 Pedestrian -1 -1 -1 267.86 159.35 301.06 244.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n93 7 Car -1 -1 -1 601.15 173.16 637.23 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n93 10 Pedestrian -1 -1 -1 320.89 170.99 345.93 242.41 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n93 23 Car -1 -1 -1 507.33 166.07 542.39 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n93 6 Pedestrian -1 -1 -1 281.27 157.41 312.09 242.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n93 9 Pedestrian -1 -1 -1 790.19 163.07 818.47 247.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n93 22 Pedestrian -1 -1 -1 220.25 154.80 235.75 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n93 24 Pedestrian -1 -1 -1 191.55 160.15 208.07 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n94 3 Car -1 -1 -1 1095.42 185.39 1220.89 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n94 4 Car -1 -1 -1 1029.43 183.76 1156.19 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n94 2 Car -1 -1 -1 955.54 184.03 1066.02 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n94 17 Car -1 -1 -1 550.62 173.14 585.84 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n94 23 Car -1 -1 -1 505.07 165.89 541.52 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n94 7 Car -1 -1 -1 601.41 173.23 636.94 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n94 9 Pedestrian -1 -1 -1 790.88 163.18 818.30 247.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n94 10 Pedestrian -1 -1 -1 322.29 170.40 349.24 243.61 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n94 5 Pedestrian -1 -1 -1 272.40 160.67 304.40 243.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n94 6 Pedestrian -1 -1 -1 284.06 158.18 314.84 243.62 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n94 22 Pedestrian -1 -1 -1 220.18 154.74 235.67 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n94 24 Pedestrian -1 -1 -1 191.67 160.30 207.98 199.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n95 3 Car -1 -1 -1 1095.48 185.39 1220.81 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n95 4 Car -1 -1 -1 1029.30 183.83 1156.29 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n95 17 Car -1 -1 -1 550.87 173.05 585.54 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n95 2 Car -1 -1 -1 955.60 184.01 1066.03 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n95 23 Car -1 -1 -1 503.64 165.44 540.95 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n95 10 Pedestrian -1 -1 -1 324.02 170.09 353.98 243.76 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n95 9 Pedestrian -1 -1 -1 790.46 162.83 818.88 248.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n95 5 Pedestrian -1 -1 -1 275.52 158.41 308.31 245.37 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n95 7 Car -1 -1 -1 601.34 173.13 637.18 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n95 6 Pedestrian -1 -1 -1 285.09 158.02 316.78 243.76 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n95 22 Pedestrian -1 -1 -1 220.33 154.78 235.63 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n95 24 Pedestrian -1 -1 -1 191.55 160.25 208.09 199.53 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n96 3 Car -1 -1 -1 1095.70 185.42 1220.58 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n96 17 Car -1 -1 -1 551.25 173.18 585.42 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n96 4 Car -1 -1 -1 1029.67 183.90 1156.08 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n96 2 Car -1 -1 -1 955.50 184.00 1066.11 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n96 23 Car -1 -1 -1 502.38 165.55 539.91 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n96 9 Pedestrian -1 -1 -1 790.41 162.25 818.88 249.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n96 10 Pedestrian -1 -1 -1 325.68 169.29 357.41 244.77 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n96 7 Car -1 -1 -1 601.37 173.19 637.15 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n96 6 Pedestrian -1 -1 -1 288.55 157.12 319.40 245.28 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n96 5 Pedestrian -1 -1 -1 276.57 159.00 309.34 245.29 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n96 22 Pedestrian -1 -1 -1 220.29 154.57 235.71 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n96 24 Pedestrian -1 -1 -1 191.64 160.18 208.04 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n97 3 Car -1 -1 -1 1095.53 185.43 1220.76 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n97 17 Car -1 -1 -1 551.51 173.25 585.39 201.67 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n97 4 Car -1 -1 -1 1029.66 183.90 1156.03 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n97 2 Car -1 -1 -1 955.50 183.99 1066.19 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n97 23 Car -1 -1 -1 499.64 165.34 538.71 200.38 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n97 9 Pedestrian -1 -1 -1 789.74 161.27 820.11 249.93 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n97 6 Pedestrian -1 -1 -1 293.13 156.61 322.49 245.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n97 10 Pedestrian -1 -1 -1 328.15 169.97 358.02 244.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n97 7 Car -1 -1 -1 601.34 173.17 637.07 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n97 5 Pedestrian -1 -1 -1 280.45 160.07 311.45 244.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n97 22 Pedestrian -1 -1 -1 220.17 154.64 235.69 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n97 24 Pedestrian -1 -1 -1 191.82 160.39 207.91 199.50 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n98 3 Car -1 -1 -1 1099.25 185.48 1220.28 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n98 17 Car -1 -1 -1 551.82 173.32 585.39 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n98 4 Car -1 -1 -1 1029.63 183.90 1156.01 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n98 2 Car -1 -1 -1 955.50 183.93 1066.15 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n98 23 Car -1 -1 -1 498.54 165.30 537.41 200.60 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n98 9 Pedestrian -1 -1 -1 790.85 160.53 820.34 250.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n98 6 Pedestrian -1 -1 -1 296.50 157.49 325.97 245.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n98 7 Car -1 -1 -1 601.29 173.21 637.06 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n98 10 Pedestrian -1 -1 -1 333.93 170.96 360.52 246.15 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n98 5 Pedestrian -1 -1 -1 283.53 160.09 315.76 245.70 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n98 22 Pedestrian -1 -1 -1 220.19 154.68 235.66 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n98 24 Pedestrian -1 -1 -1 191.95 160.26 207.98 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n98 26 Cyclist -1 -1 -1 -6.19 176.67 216.41 366.52 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n99 3 Car -1 -1 -1 1095.49 185.35 1220.76 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n99 17 Car -1 -1 -1 551.93 173.34 584.66 200.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n99 4 Car -1 -1 -1 1029.70 183.90 1155.98 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n99 2 Car -1 -1 -1 955.45 183.96 1066.19 232.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n99 23 Car -1 -1 -1 497.14 165.39 537.01 200.84 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n99 9 Pedestrian -1 -1 -1 790.20 161.76 821.81 250.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n99 26 Cyclist -1 -1 -1 21.49 177.19 296.02 364.78 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n99 7 Car -1 -1 -1 601.42 173.26 636.95 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n99 10 Pedestrian -1 -1 -1 339.08 170.99 366.96 246.01 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n99 6 Pedestrian -1 -1 -1 298.01 157.38 326.88 245.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n99 5 Pedestrian -1 -1 -1 285.28 160.44 316.97 246.55 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n99 22 Pedestrian -1 -1 -1 220.38 154.81 235.57 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n99 24 Pedestrian -1 -1 -1 192.06 160.72 208.09 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n100 3 Car -1 -1 -1 1095.38 185.40 1220.90 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n100 17 Car -1 -1 -1 551.71 173.34 584.19 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n100 4 Car -1 -1 -1 1029.61 183.88 1156.06 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n100 2 Car -1 -1 -1 955.38 183.93 1066.23 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n100 23 Car -1 -1 -1 494.52 165.23 535.30 201.63 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n100 9 Pedestrian -1 -1 -1 793.76 161.93 824.20 251.87 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n100 26 Cyclist -1 -1 -1 95.27 169.85 351.78 366.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n100 10 Pedestrian -1 -1 -1 340.49 171.48 372.95 246.99 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n100 6 Pedestrian -1 -1 -1 300.86 158.17 330.79 247.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n100 7 Car -1 -1 -1 601.20 173.34 637.17 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n100 5 Pedestrian -1 -1 -1 291.59 160.05 323.72 249.90 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n100 24 Pedestrian -1 -1 -1 192.74 160.88 207.74 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n100 22 Pedestrian -1 -1 -1 223.11 154.76 238.09 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n101 3 Car -1 -1 -1 1095.34 185.34 1220.86 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n101 4 Car -1 -1 -1 1029.75 183.91 1155.95 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n101 2 Car -1 -1 -1 955.49 183.98 1066.11 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n101 17 Car -1 -1 -1 551.28 173.26 583.74 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n101 23 Car -1 -1 -1 492.86 165.15 534.09 201.85 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n101 10 Pedestrian -1 -1 -1 341.71 171.87 375.36 248.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n101 9 Pedestrian -1 -1 -1 796.72 161.14 826.96 251.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n101 26 Cyclist -1 -1 -1 166.06 173.24 395.57 368.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n101 7 Car -1 -1 -1 601.30 173.36 637.08 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n101 6 Pedestrian -1 -1 -1 305.02 159.39 332.87 246.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n101 5 Pedestrian -1 -1 -1 291.41 160.33 324.40 250.21 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n101 24 Pedestrian -1 -1 -1 192.33 160.86 207.88 199.04 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n101 22 Pedestrian -1 -1 -1 220.29 155.10 235.83 197.85 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n102 3 Car -1 -1 -1 1098.86 185.51 1220.64 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n102 4 Car -1 -1 -1 1029.54 183.90 1156.05 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n102 2 Car -1 -1 -1 955.45 183.99 1065.97 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n102 26 Cyclist -1 -1 -1 237.50 175.79 416.75 366.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n102 9 Pedestrian -1 -1 -1 798.25 159.96 827.61 251.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n102 17 Car -1 -1 -1 550.97 173.22 583.01 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n102 23 Car -1 -1 -1 490.37 164.95 533.18 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n102 7 Car -1 -1 -1 601.23 173.37 637.21 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n102 10 Pedestrian -1 -1 -1 343.79 170.96 379.77 254.80 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n102 6 Pedestrian -1 -1 -1 307.02 159.76 338.02 245.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n102 24 Pedestrian -1 -1 -1 192.07 160.74 207.94 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n102 22 Pedestrian -1 -1 -1 220.33 155.12 235.55 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n103 3 Car -1 -1 -1 1099.15 185.50 1220.37 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n103 4 Car -1 -1 -1 1029.55 183.95 1156.19 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n103 2 Car -1 -1 -1 955.41 183.96 1066.04 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n103 23 Car -1 -1 -1 488.81 164.87 532.47 203.89 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n103 9 Pedestrian -1 -1 -1 799.44 159.34 831.56 253.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n103 6 Pedestrian -1 -1 -1 292.44 160.45 331.47 250.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n103 26 Cyclist -1 -1 -1 289.47 175.25 440.66 366.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n103 7 Car -1 -1 -1 601.20 173.34 637.46 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n103 17 Car -1 -1 -1 550.73 173.06 582.28 198.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n103 10 Pedestrian -1 -1 -1 307.55 158.48 339.81 247.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n103 22 Pedestrian -1 -1 -1 220.27 155.04 235.55 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n103 24 Pedestrian -1 -1 -1 192.17 160.74 207.89 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n103 27 Pedestrian -1 -1 -1 346.04 170.31 383.39 256.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n104 3 Car -1 -1 -1 1099.20 185.52 1220.35 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n104 4 Car -1 -1 -1 1029.72 183.97 1156.07 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n104 2 Car -1 -1 -1 955.28 183.97 1066.30 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n104 6 Pedestrian -1 -1 -1 296.91 160.14 333.92 252.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n104 26 Cyclist -1 -1 -1 325.12 175.15 466.26 365.75 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n104 9 Pedestrian -1 -1 -1 797.84 160.13 833.17 254.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n104 23 Car -1 -1 -1 485.74 164.68 530.35 204.50 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n104 7 Car -1 -1 -1 601.23 173.41 637.49 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n104 17 Car -1 -1 -1 550.28 173.17 580.82 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n104 10 Pedestrian -1 -1 -1 309.84 158.20 345.04 251.38 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n104 22 Pedestrian -1 -1 -1 220.32 155.14 235.47 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n104 24 Pedestrian -1 -1 -1 192.23 160.63 207.82 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n104 27 Pedestrian -1 -1 -1 356.55 171.33 380.56 248.70 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n105 3 Car -1 -1 -1 1099.01 185.50 1220.51 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n105 4 Car -1 -1 -1 1029.74 183.98 1156.09 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n105 2 Car -1 -1 -1 955.26 184.00 1066.38 232.91 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n105 9 Pedestrian -1 -1 -1 795.14 161.92 832.26 255.98 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n105 27 Pedestrian -1 -1 -1 357.35 171.09 388.09 248.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n105 26 Cyclist -1 -1 -1 348.89 172.18 488.83 361.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n105 23 Car -1 -1 -1 484.20 164.42 529.30 204.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n105 17 Car -1 -1 -1 549.58 173.14 579.85 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n105 10 Pedestrian -1 -1 -1 313.64 158.40 348.50 248.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n105 6 Pedestrian -1 -1 -1 301.48 160.26 336.56 251.14 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 7 Car -1 -1 -1 601.36 173.36 637.41 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n105 22 Pedestrian -1 -1 -1 220.50 155.40 235.38 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n105 24 Pedestrian -1 -1 -1 192.24 160.61 207.92 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n106 3 Car -1 -1 -1 1095.13 185.28 1221.06 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n106 4 Car -1 -1 -1 1029.57 184.01 1156.07 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n106 2 Car -1 -1 -1 955.30 184.01 1066.14 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n106 27 Pedestrian -1 -1 -1 359.41 169.99 392.92 250.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n106 9 Pedestrian -1 -1 -1 797.60 162.19 833.66 257.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n106 6 Pedestrian -1 -1 -1 302.46 160.16 337.26 251.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n106 26 Cyclist -1 -1 -1 371.14 171.67 505.37 355.94 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n106 10 Pedestrian -1 -1 -1 316.97 159.30 352.44 250.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n106 23 Car -1 -1 -1 482.34 164.05 528.78 205.13 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n106 17 Car -1 -1 -1 548.65 173.24 578.73 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n106 7 Car -1 -1 -1 601.36 173.57 637.38 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n106 24 Pedestrian -1 -1 -1 192.20 160.71 207.96 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n106 22 Pedestrian -1 -1 -1 220.76 155.55 235.26 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n107 3 Car -1 -1 -1 1095.11 185.36 1221.07 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n107 4 Car -1 -1 -1 1029.76 184.02 1155.85 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n107 2 Car -1 -1 -1 955.28 183.95 1066.22 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n107 9 Pedestrian -1 -1 -1 799.69 160.80 833.97 257.50 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n107 27 Pedestrian -1 -1 -1 361.32 170.42 393.69 249.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n107 6 Pedestrian -1 -1 -1 304.48 158.78 342.00 253.13 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n107 10 Pedestrian -1 -1 -1 320.57 158.38 356.25 252.26 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n107 23 Car -1 -1 -1 478.47 163.60 528.05 205.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n107 7 Car -1 -1 -1 601.34 173.61 637.40 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n107 26 Cyclist -1 -1 -1 398.07 171.54 522.99 340.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n107 17 Car -1 -1 -1 548.43 172.67 577.45 196.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n107 24 Pedestrian -1 -1 -1 192.11 160.82 207.99 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n107 22 Pedestrian -1 -1 -1 220.74 155.55 235.16 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n107 28 Pedestrian -1 -1 -1 686.82 169.65 699.54 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n108 3 Car -1 -1 -1 1095.18 185.38 1221.05 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n108 4 Car -1 -1 -1 1029.85 184.02 1155.71 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n108 2 Car -1 -1 -1 955.29 183.87 1066.17 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n108 23 Car -1 -1 -1 479.06 163.76 526.92 205.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n108 27 Pedestrian -1 -1 -1 368.80 170.39 397.69 250.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n108 9 Pedestrian -1 -1 -1 801.24 159.55 834.06 257.97 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n108 10 Pedestrian -1 -1 -1 324.23 158.06 359.20 252.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n108 7 Car -1 -1 -1 601.35 173.61 637.44 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n108 6 Pedestrian -1 -1 -1 305.53 159.79 342.15 253.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n108 17 Car -1 -1 -1 546.97 172.37 576.00 196.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n108 26 Cyclist -1 -1 -1 421.90 170.61 537.56 326.12 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n108 24 Pedestrian -1 -1 -1 192.19 161.04 207.89 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n108 22 Pedestrian -1 -1 -1 220.59 155.54 235.19 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n109 3 Car -1 -1 -1 1095.27 185.31 1221.06 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n109 4 Car -1 -1 -1 1029.88 184.00 1155.95 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n109 2 Car -1 -1 -1 955.25 183.80 1066.25 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n109 9 Pedestrian -1 -1 -1 804.29 159.80 836.82 258.55 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n109 27 Pedestrian -1 -1 -1 372.17 170.46 403.47 250.96 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n109 17 Car -1 -1 -1 545.72 172.16 574.77 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n109 26 Cyclist -1 -1 -1 447.58 168.18 548.99 313.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n109 10 Pedestrian -1 -1 -1 329.21 157.42 361.71 253.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n109 6 Pedestrian -1 -1 -1 309.18 159.35 345.54 254.72 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n109 23 Car -1 -1 -1 474.29 163.82 526.77 205.42 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n109 7 Car -1 -1 -1 601.34 173.51 637.49 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n109 22 Pedestrian -1 -1 -1 220.58 155.48 235.16 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n109 24 Pedestrian -1 -1 -1 192.26 161.08 207.73 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n110 3 Car -1 -1 -1 1098.91 185.42 1220.59 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n110 4 Car -1 -1 -1 1029.74 183.94 1156.03 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n110 2 Car -1 -1 -1 955.24 183.82 1066.33 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n110 23 Car -1 -1 -1 471.84 164.04 525.21 208.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n110 9 Pedestrian -1 -1 -1 805.17 159.92 837.64 258.79 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n110 26 Cyclist -1 -1 -1 464.74 168.64 562.39 305.69 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n110 27 Pedestrian -1 -1 -1 373.07 171.01 409.48 251.08 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n110 17 Car -1 -1 -1 544.93 171.79 573.84 195.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n110 10 Pedestrian -1 -1 -1 328.53 158.15 364.43 252.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n110 7 Car -1 -1 -1 602.42 173.21 637.59 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n110 6 Pedestrian -1 -1 -1 315.14 159.92 353.40 254.67 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n110 22 Pedestrian -1 -1 -1 220.63 155.54 235.31 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n110 24 Pedestrian -1 -1 -1 192.17 161.02 207.81 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n110 29 Pedestrian -1 -1 -1 687.93 171.84 699.07 206.69 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n111 3 Car -1 -1 -1 1095.34 185.31 1220.95 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n111 23 Car -1 -1 -1 469.53 163.88 523.25 209.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n111 4 Car -1 -1 -1 1029.94 183.98 1155.87 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n111 2 Car -1 -1 -1 955.26 183.84 1066.33 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n111 9 Pedestrian -1 -1 -1 806.80 160.66 840.38 259.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n111 26 Cyclist -1 -1 -1 480.38 167.67 570.51 299.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n111 27 Pedestrian -1 -1 -1 376.01 170.31 414.15 251.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n111 17 Car -1 -1 -1 545.06 171.54 573.27 195.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n111 10 Pedestrian -1 -1 -1 333.31 158.62 367.15 252.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n111 6 Pedestrian -1 -1 -1 317.03 159.39 353.01 254.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n111 7 Car -1 -1 -1 601.41 173.52 637.43 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n111 22 Pedestrian -1 -1 -1 220.48 155.22 235.39 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n111 24 Pedestrian -1 -1 -1 192.13 161.02 207.87 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n111 29 Pedestrian -1 -1 -1 688.06 171.96 699.32 206.74 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n111 30 Car -1 -1 -1 598.87 173.75 622.57 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n112 3 Car -1 -1 -1 1095.32 185.27 1220.89 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n112 2 Car -1 -1 -1 955.23 183.82 1066.37 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n112 4 Car -1 -1 -1 1029.99 184.01 1155.98 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n112 23 Car -1 -1 -1 466.97 163.68 521.20 209.92 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n112 9 Pedestrian -1 -1 -1 807.27 161.21 841.13 260.19 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n112 26 Cyclist -1 -1 -1 498.39 168.27 576.93 291.51 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n112 10 Pedestrian -1 -1 -1 336.84 159.18 370.82 253.43 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n112 27 Pedestrian -1 -1 -1 382.66 169.67 415.04 252.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n112 6 Pedestrian -1 -1 -1 321.16 159.32 355.41 258.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n112 17 Car -1 -1 -1 543.28 171.69 572.13 194.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n112 7 Car -1 -1 -1 602.31 173.13 637.67 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n112 22 Pedestrian -1 -1 -1 220.56 155.39 235.55 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n112 24 Pedestrian -1 -1 -1 192.11 161.25 207.83 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n112 29 Pedestrian -1 -1 -1 688.11 172.07 699.78 206.59 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n113 3 Car -1 -1 -1 1095.23 185.27 1221.02 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n113 23 Car -1 -1 -1 463.72 163.46 520.50 210.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n113 4 Car -1 -1 -1 1029.81 183.98 1156.06 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n113 2 Car -1 -1 -1 955.23 183.84 1066.17 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n113 26 Cyclist -1 -1 -1 512.63 167.99 585.91 284.87 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n113 10 Pedestrian -1 -1 -1 340.15 158.47 374.55 254.45 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n113 9 Pedestrian -1 -1 -1 807.27 160.75 842.93 261.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n113 27 Pedestrian -1 -1 -1 388.64 170.23 416.88 251.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n113 7 Car -1 -1 -1 601.33 173.23 637.37 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n113 17 Car -1 -1 -1 543.78 171.90 571.67 194.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n113 6 Pedestrian -1 -1 -1 322.06 158.65 355.47 256.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n113 22 Pedestrian -1 -1 -1 220.28 155.21 235.62 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n113 24 Pedestrian -1 -1 -1 192.07 161.25 207.73 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n113 29 Pedestrian -1 -1 -1 687.95 172.03 699.65 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n114 3 Car -1 -1 -1 1095.35 185.37 1220.95 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n114 23 Car -1 -1 -1 461.93 163.13 519.55 211.62 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n114 2 Car -1 -1 -1 955.21 183.76 1066.33 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n114 4 Car -1 -1 -1 1029.84 183.99 1156.03 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n114 9 Pedestrian -1 -1 -1 808.87 159.91 846.62 262.17 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n114 27 Pedestrian -1 -1 -1 392.01 169.70 421.99 252.52 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n114 10 Pedestrian -1 -1 -1 344.52 157.96 377.59 254.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n114 26 Cyclist -1 -1 -1 522.71 166.94 592.28 283.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n114 17 Car -1 -1 -1 543.15 172.04 571.59 194.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n114 6 Pedestrian -1 -1 -1 323.77 159.52 360.88 258.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n114 7 Car -1 -1 -1 601.16 173.07 637.37 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n114 22 Pedestrian -1 -1 -1 220.19 155.02 235.71 197.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n114 24 Pedestrian -1 -1 -1 191.97 161.37 207.71 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n114 29 Pedestrian -1 -1 -1 688.65 171.85 700.37 206.72 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n114 31 Pedestrian -1 -1 -1 -5.54 150.67 109.15 361.35 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n115 3 Car -1 -1 -1 1095.22 185.33 1220.93 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n115 23 Car -1 -1 -1 458.98 162.66 517.50 212.57 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n115 2 Car -1 -1 -1 955.17 183.76 1066.42 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n115 4 Car -1 -1 -1 1029.89 183.96 1155.95 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n115 9 Pedestrian -1 -1 -1 811.40 158.72 850.71 263.21 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n115 26 Cyclist -1 -1 -1 535.61 169.13 598.65 276.20 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n115 10 Pedestrian -1 -1 -1 347.95 157.17 381.55 255.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n115 27 Pedestrian -1 -1 -1 393.20 169.99 428.77 252.49 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n115 6 Pedestrian -1 -1 -1 323.00 159.06 362.52 260.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n115 17 Car -1 -1 -1 543.38 172.03 570.69 194.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n115 7 Car -1 -1 -1 601.21 173.19 637.29 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n115 31 Pedestrian -1 -1 -1 0.45 150.32 125.84 360.85 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n115 22 Pedestrian -1 -1 -1 220.22 155.07 235.63 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n115 24 Pedestrian -1 -1 -1 192.10 161.40 207.59 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n115 29 Pedestrian -1 -1 -1 688.97 171.67 701.24 206.90 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n116 3 Car -1 -1 -1 1095.35 185.36 1220.83 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n116 4 Car -1 -1 -1 1029.93 183.99 1155.88 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n116 23 Car -1 -1 -1 457.36 163.09 515.99 212.41 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n116 2 Car -1 -1 -1 955.11 183.80 1066.18 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n116 26 Cyclist -1 -1 -1 542.47 169.09 602.08 272.79 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n116 9 Pedestrian -1 -1 -1 813.62 159.47 850.88 265.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n116 10 Pedestrian -1 -1 -1 350.71 157.83 386.37 254.87 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n116 27 Pedestrian -1 -1 -1 395.90 170.31 433.89 254.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n116 6 Pedestrian -1 -1 -1 326.27 159.89 366.58 260.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n116 31 Pedestrian -1 -1 -1 8.28 148.67 133.24 361.96 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n116 7 Car -1 -1 -1 601.26 173.16 637.18 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n116 17 Car -1 -1 -1 544.91 172.00 569.81 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n116 22 Pedestrian -1 -1 -1 220.34 155.23 235.52 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n116 24 Pedestrian -1 -1 -1 192.27 161.56 207.41 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n117 3 Car -1 -1 -1 1095.29 185.40 1220.90 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n117 4 Car -1 -1 -1 1029.83 183.99 1156.08 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n117 23 Car -1 -1 -1 454.95 162.69 514.95 213.09 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n117 2 Car -1 -1 -1 954.91 183.73 1066.49 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n117 9 Pedestrian -1 -1 -1 816.68 159.95 853.67 265.73 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n117 27 Pedestrian -1 -1 -1 402.11 170.47 435.49 252.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n117 31 Pedestrian -1 -1 -1 17.31 140.80 154.80 363.20 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n117 6 Pedestrian -1 -1 -1 330.20 159.85 369.80 261.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n117 10 Pedestrian -1 -1 -1 353.08 158.30 391.65 255.20 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n117 7 Car -1 -1 -1 601.50 173.13 637.07 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n117 26 Cyclist -1 -1 -1 552.90 167.10 604.18 267.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n117 17 Car -1 -1 -1 545.66 171.86 569.62 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n117 22 Pedestrian -1 -1 -1 220.46 155.22 235.49 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n117 24 Pedestrian -1 -1 -1 192.38 161.63 207.32 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n117 32 Pedestrian -1 -1 -1 691.25 171.37 703.18 207.43 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n118 3 Car -1 -1 -1 1095.10 185.36 1221.16 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n118 23 Car -1 -1 -1 452.95 162.64 513.06 213.47 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n118 4 Car -1 -1 -1 1029.94 184.02 1155.99 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n118 2 Car -1 -1 -1 954.87 183.76 1066.42 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n118 31 Pedestrian -1 -1 -1 36.73 141.28 173.65 362.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n118 26 Cyclist -1 -1 -1 558.67 166.41 608.37 261.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n118 9 Pedestrian -1 -1 -1 817.18 159.44 856.16 266.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n118 10 Pedestrian -1 -1 -1 353.53 157.94 393.32 256.13 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n118 6 Pedestrian -1 -1 -1 335.90 159.14 371.68 260.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n118 27 Pedestrian -1 -1 -1 407.96 170.29 438.21 254.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n118 7 Car -1 -1 -1 601.17 173.06 637.50 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n118 17 Car -1 -1 -1 546.44 171.84 569.12 192.29 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n118 22 Pedestrian -1 -1 -1 220.66 155.37 235.33 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n118 24 Pedestrian -1 -1 -1 192.71 161.80 207.57 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n118 32 Pedestrian -1 -1 -1 673.60 171.97 685.81 207.44 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n119 3 Car -1 -1 -1 1095.22 185.31 1221.02 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n119 4 Car -1 -1 -1 1029.70 183.97 1156.19 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n119 2 Car -1 -1 -1 954.94 183.68 1066.37 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n119 23 Car -1 -1 -1 449.82 161.88 512.88 214.44 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n119 9 Pedestrian -1 -1 -1 821.12 159.00 859.83 267.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n119 31 Pedestrian -1 -1 -1 61.62 148.32 201.82 362.05 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n119 10 Pedestrian -1 -1 -1 357.05 158.70 397.06 259.04 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n119 7 Car -1 -1 -1 600.75 172.94 637.57 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n119 26 Cyclist -1 -1 -1 560.68 168.25 613.14 257.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n119 6 Pedestrian -1 -1 -1 341.31 157.90 375.06 262.23 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n119 27 Pedestrian -1 -1 -1 415.54 170.26 443.87 254.63 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n119 24 Pedestrian -1 -1 -1 192.74 161.83 207.45 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n119 17 Car -1 -1 -1 547.26 172.06 568.74 191.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n119 22 Pedestrian -1 -1 -1 220.64 155.31 235.33 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n120 3 Car -1 -1 -1 1095.35 185.48 1220.89 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n120 23 Car -1 -1 -1 446.75 161.90 511.70 214.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n120 4 Car -1 -1 -1 1030.04 183.99 1155.90 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n120 9 Pedestrian -1 -1 -1 824.40 158.37 863.31 268.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n120 2 Car -1 -1 -1 954.82 183.64 1066.35 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n120 27 Pedestrian -1 -1 -1 416.78 170.47 450.52 256.23 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n120 31 Pedestrian -1 -1 -1 90.46 149.60 234.49 362.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n120 10 Pedestrian -1 -1 -1 361.45 159.27 400.27 258.70 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n120 26 Cyclist -1 -1 -1 566.72 169.85 613.58 252.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n120 7 Car -1 -1 -1 600.59 173.04 637.56 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n120 6 Pedestrian -1 -1 -1 346.14 158.29 377.68 261.80 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n120 17 Car -1 -1 -1 548.60 172.29 569.39 191.36 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n120 24 Pedestrian -1 -1 -1 193.28 162.12 207.64 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n120 22 Pedestrian -1 -1 -1 220.72 155.17 235.41 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n120 33 Pedestrian -1 -1 -1 692.76 171.66 704.71 208.20 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n120 34 Pedestrian -1 -1 -1 675.68 171.72 687.72 207.32 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n121 3 Car -1 -1 -1 1098.93 185.49 1220.58 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n121 4 Car -1 -1 -1 1029.94 183.98 1156.01 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n121 2 Car -1 -1 -1 954.90 183.56 1066.39 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n121 9 Pedestrian -1 -1 -1 828.43 158.56 866.43 269.41 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n121 23 Car -1 -1 -1 442.93 161.63 511.55 215.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n121 31 Pedestrian -1 -1 -1 125.61 148.96 245.27 362.98 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n121 27 Pedestrian -1 -1 -1 420.44 170.66 454.50 257.06 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n121 7 Car -1 -1 -1 600.54 173.09 637.48 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n121 26 Cyclist -1 -1 -1 572.07 169.42 610.18 251.24 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n121 10 Pedestrian -1 -1 -1 367.34 159.30 402.32 259.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n121 6 Pedestrian -1 -1 -1 348.72 157.63 381.58 263.24 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n121 22 Pedestrian -1 -1 -1 223.63 155.19 237.32 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n121 17 Car -1 -1 -1 549.35 172.18 569.92 190.83 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n121 24 Pedestrian -1 -1 -1 192.95 161.27 208.71 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n121 34 Pedestrian -1 -1 -1 675.37 171.87 687.85 207.21 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n121 33 Pedestrian -1 -1 -1 693.12 171.54 705.06 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n121 35 Car -1 -1 -1 596.72 173.15 624.43 194.85 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n122 3 Car -1 -1 -1 1098.25 185.20 1221.37 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n122 2 Car -1 -1 -1 954.81 183.42 1066.58 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n122 4 Car -1 -1 -1 1032.53 183.72 1157.24 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n122 23 Car -1 -1 -1 440.43 161.56 511.34 217.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n122 9 Pedestrian -1 -1 -1 831.31 159.42 871.32 269.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n122 7 Car -1 -1 -1 600.51 173.11 637.51 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n122 27 Pedestrian -1 -1 -1 424.87 169.42 457.65 258.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n122 31 Pedestrian -1 -1 -1 143.42 147.96 273.66 363.41 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n122 17 Car -1 -1 -1 550.16 171.52 571.34 189.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n122 26 Cyclist -1 -1 -1 571.23 169.98 612.86 248.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n122 6 Pedestrian -1 -1 -1 351.23 157.45 386.02 263.49 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n122 10 Pedestrian -1 -1 -1 370.92 158.77 405.91 259.14 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n122 22 Pedestrian -1 -1 -1 222.13 154.65 239.05 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n122 34 Pedestrian -1 -1 -1 675.88 172.64 688.42 207.46 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n122 24 Pedestrian -1 -1 -1 192.86 161.26 208.64 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n122 33 Pedestrian -1 -1 -1 695.18 171.41 707.12 208.76 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n122 35 Car -1 -1 -1 596.75 173.17 624.69 194.89 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n122 36 Pedestrian -1 -1 -1 350.35 158.49 381.15 238.55 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n123 3 Car -1 -1 -1 1095.20 185.18 1220.59 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n123 2 Car -1 -1 -1 954.66 183.45 1066.99 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n123 4 Car -1 -1 -1 1029.75 184.09 1156.22 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n123 23 Car -1 -1 -1 441.83 161.46 510.70 217.86 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n123 7 Car -1 -1 -1 600.38 173.04 637.50 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n123 27 Pedestrian -1 -1 -1 430.99 168.83 460.10 258.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n123 9 Pedestrian -1 -1 -1 832.94 159.89 877.75 269.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n123 26 Cyclist -1 -1 -1 576.38 169.03 612.49 245.94 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 31 Pedestrian -1 -1 -1 168.46 140.42 301.85 364.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 17 Car -1 -1 -1 551.23 171.32 571.84 189.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n123 10 Pedestrian -1 -1 -1 374.78 159.10 409.84 259.38 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n123 6 Pedestrian -1 -1 -1 351.91 157.72 387.44 264.07 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n123 33 Pedestrian -1 -1 -1 695.29 171.27 707.55 208.52 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n123 22 Pedestrian -1 -1 -1 219.73 155.62 236.12 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n123 24 Pedestrian -1 -1 -1 193.24 162.12 207.77 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n123 34 Pedestrian -1 -1 -1 676.08 172.98 688.52 207.45 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n123 36 Pedestrian -1 -1 -1 353.41 159.06 385.47 238.61 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n123 37 Pedestrian -1 -1 -1 1155.28 145.94 1219.51 350.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n124 2 Car -1 -1 -1 954.88 183.39 1066.71 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n124 3 Car -1 -1 -1 1094.94 185.42 1220.91 235.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n124 4 Car -1 -1 -1 1032.11 183.78 1157.73 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n124 23 Car -1 -1 -1 441.27 161.42 510.46 218.60 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n124 9 Pedestrian -1 -1 -1 836.39 160.02 880.68 273.34 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n124 7 Car -1 -1 -1 600.08 172.90 637.70 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n124 27 Pedestrian -1 -1 -1 435.32 168.91 464.75 258.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n124 26 Cyclist -1 -1 -1 577.82 168.80 613.55 243.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n124 31 Pedestrian -1 -1 -1 185.95 146.02 330.55 364.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n124 10 Pedestrian -1 -1 -1 382.05 159.32 416.92 260.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n124 6 Pedestrian -1 -1 -1 359.73 161.54 394.04 265.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n124 37 Pedestrian -1 -1 -1 1130.58 160.74 1221.68 350.66 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n124 36 Pedestrian -1 -1 -1 360.94 159.70 391.19 238.33 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n124 24 Pedestrian -1 -1 -1 192.87 162.07 207.67 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n124 17 Car -1 -1 -1 552.62 171.38 572.65 189.05 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n124 33 Pedestrian -1 -1 -1 695.55 171.66 707.89 208.56 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n124 22 Pedestrian -1 -1 -1 220.85 156.20 235.32 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n124 34 Pedestrian -1 -1 -1 676.37 172.72 688.96 207.54 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n125 3 Car -1 -1 -1 1093.80 185.27 1222.02 235.21 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n125 4 Car -1 -1 -1 1029.31 183.82 1156.31 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n125 2 Car -1 -1 -1 955.01 183.40 1066.64 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n125 23 Car -1 -1 -1 435.70 161.51 508.47 220.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n125 9 Pedestrian -1 -1 -1 840.11 158.71 884.82 275.05 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n125 27 Pedestrian -1 -1 -1 436.31 167.69 470.83 258.85 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n125 31 Pedestrian -1 -1 -1 205.70 147.03 356.78 364.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n125 7 Car -1 -1 -1 600.10 172.88 637.79 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n125 17 Car -1 -1 -1 553.17 171.18 573.40 189.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n125 37 Pedestrian -1 -1 -1 1120.30 160.11 1216.83 344.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n125 10 Pedestrian -1 -1 -1 381.10 159.98 418.45 260.51 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n125 36 Pedestrian -1 -1 -1 361.29 159.00 391.45 236.61 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n125 6 Pedestrian -1 -1 -1 362.82 162.17 397.97 264.83 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n125 26 Cyclist -1 -1 -1 580.82 167.08 613.91 239.98 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n125 24 Pedestrian -1 -1 -1 192.27 161.53 207.73 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n125 22 Pedestrian -1 -1 -1 220.61 155.70 235.38 197.28 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n125 33 Pedestrian -1 -1 -1 695.80 171.25 708.63 209.14 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n125 34 Pedestrian -1 -1 -1 677.20 172.23 689.33 207.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n126 3 Car -1 -1 -1 1093.59 185.17 1222.10 235.34 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n126 4 Car -1 -1 -1 1030.36 183.78 1154.68 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n126 23 Car -1 -1 -1 431.39 161.29 507.50 220.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n126 2 Car -1 -1 -1 955.10 183.33 1066.61 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n126 9 Pedestrian -1 -1 -1 844.04 158.29 888.01 276.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n126 27 Pedestrian -1 -1 -1 439.12 167.90 474.61 259.57 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n126 31 Pedestrian -1 -1 -1 252.82 148.00 378.10 364.47 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n126 7 Car -1 -1 -1 600.18 172.75 637.74 203.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n126 17 Car -1 -1 -1 554.26 171.30 574.23 189.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n126 37 Pedestrian -1 -1 -1 1112.28 157.78 1201.76 345.35 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n126 10 Pedestrian -1 -1 -1 390.25 158.35 423.62 261.68 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n126 6 Pedestrian -1 -1 -1 360.25 158.96 401.61 268.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n126 26 Cyclist -1 -1 -1 581.86 167.22 613.00 238.41 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n126 36 Pedestrian -1 -1 -1 364.12 159.41 396.60 236.83 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n126 24 Pedestrian -1 -1 -1 192.53 161.76 207.82 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n126 22 Pedestrian -1 -1 -1 220.31 155.26 235.79 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n126 33 Pedestrian -1 -1 -1 696.03 171.06 709.21 209.59 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n126 34 Pedestrian -1 -1 -1 677.63 172.18 689.59 208.05 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n126 38 Pedestrian -1 -1 -1 -5.79 185.52 116.70 364.31 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n127 23 Car -1 -1 -1 428.75 161.41 506.96 221.34 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n127 3 Car -1 -1 -1 1093.90 185.12 1221.66 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n127 4 Car -1 -1 -1 1030.89 183.94 1154.17 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n127 2 Car -1 -1 -1 955.08 183.32 1066.54 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n127 9 Pedestrian -1 -1 -1 848.50 157.09 892.12 277.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n127 31 Pedestrian -1 -1 -1 283.96 147.66 392.98 363.95 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n127 7 Car -1 -1 -1 600.28 172.61 637.74 203.30 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n127 27 Pedestrian -1 -1 -1 439.00 168.86 476.52 260.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n127 37 Pedestrian -1 -1 -1 1100.91 154.97 1182.66 347.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n127 10 Pedestrian -1 -1 -1 394.36 157.90 427.86 262.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n127 17 Car -1 -1 -1 555.03 171.16 575.41 189.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n127 6 Pedestrian -1 -1 -1 363.89 158.64 404.58 268.70 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n127 22 Pedestrian -1 -1 -1 220.18 154.92 235.92 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n127 24 Pedestrian -1 -1 -1 192.53 161.70 207.76 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n127 36 Pedestrian -1 -1 -1 365.14 159.10 396.73 237.63 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n127 26 Cyclist -1 -1 -1 583.30 167.63 611.44 237.10 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n127 38 Pedestrian -1 -1 -1 -4.79 178.67 138.50 364.13 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n127 33 Pedestrian -1 -1 -1 696.17 170.77 709.56 209.88 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n127 34 Pedestrian -1 -1 -1 677.56 172.42 689.77 208.46 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n128 23 Car -1 -1 -1 423.44 160.80 506.86 222.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n128 3 Car -1 -1 -1 1094.27 184.75 1220.54 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n128 2 Car -1 -1 -1 954.98 183.31 1066.56 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n128 4 Car -1 -1 -1 1032.00 184.11 1152.85 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n128 9 Pedestrian -1 -1 -1 850.63 158.13 898.19 278.53 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n128 31 Pedestrian -1 -1 -1 306.11 145.75 409.67 364.28 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n128 7 Car -1 -1 -1 600.52 172.70 637.59 203.29 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n128 37 Pedestrian -1 -1 -1 1068.98 155.88 1176.30 347.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n128 27 Pedestrian -1 -1 -1 448.66 169.51 479.34 263.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n128 10 Pedestrian -1 -1 -1 396.07 158.47 434.39 261.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n128 6 Pedestrian -1 -1 -1 368.54 158.11 407.36 269.44 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n128 17 Car -1 -1 -1 555.94 170.68 575.34 188.65 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n128 22 Pedestrian -1 -1 -1 220.31 154.84 236.04 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n128 24 Pedestrian -1 -1 -1 192.72 161.95 207.71 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n128 26 Cyclist -1 -1 -1 581.44 168.69 610.14 234.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n128 36 Pedestrian -1 -1 -1 368.51 159.99 399.65 237.22 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n128 38 Pedestrian -1 -1 -1 -6.41 177.46 170.84 364.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n128 33 Pedestrian -1 -1 -1 698.35 170.35 711.25 210.29 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n129 3 Car -1 -1 -1 1094.37 184.78 1220.86 236.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n129 2 Car -1 -1 -1 954.90 183.28 1066.82 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n129 4 Car -1 -1 -1 1031.31 183.98 1154.14 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n129 23 Car -1 -1 -1 422.12 161.00 505.49 223.00 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n129 9 Pedestrian -1 -1 -1 853.10 159.40 902.74 281.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n129 31 Pedestrian -1 -1 -1 324.47 145.55 437.04 364.50 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n129 27 Pedestrian -1 -1 -1 454.28 169.92 482.59 263.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n129 7 Car -1 -1 -1 600.53 172.69 637.59 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n129 17 Car -1 -1 -1 557.09 170.98 577.08 188.02 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n129 37 Pedestrian -1 -1 -1 1049.87 159.07 1172.44 344.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n129 26 Cyclist -1 -1 -1 582.06 167.39 609.31 230.91 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n129 10 Pedestrian -1 -1 -1 401.96 158.54 442.35 262.13 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n129 24 Pedestrian -1 -1 -1 192.96 162.05 207.50 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n129 38 Pedestrian -1 -1 -1 2.76 177.73 192.30 364.08 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n129 6 Pedestrian -1 -1 -1 373.23 160.44 410.99 267.38 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n129 22 Pedestrian -1 -1 -1 220.61 155.27 235.75 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n129 36 Pedestrian -1 -1 -1 368.81 159.46 400.16 237.85 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n129 33 Pedestrian -1 -1 -1 698.44 170.48 711.47 210.66 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n129 39 Pedestrian -1 -1 -1 678.91 171.58 692.23 210.03 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n130 3 Car -1 -1 -1 1093.76 184.94 1221.09 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n130 23 Car -1 -1 -1 419.56 160.72 502.80 223.47 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n130 2 Car -1 -1 -1 954.62 183.15 1067.26 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n130 4 Car -1 -1 -1 1031.36 184.34 1154.12 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n130 7 Car -1 -1 -1 600.43 172.71 637.48 203.21 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n130 9 Pedestrian -1 -1 -1 859.77 157.14 909.44 280.25 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n130 31 Pedestrian -1 -1 -1 337.11 150.41 454.86 362.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n130 27 Pedestrian -1 -1 -1 457.03 170.32 488.69 263.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n130 17 Car -1 -1 -1 558.37 171.11 577.71 187.41 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n130 26 Cyclist -1 -1 -1 581.10 167.62 608.79 231.23 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n130 37 Pedestrian -1 -1 -1 1040.32 160.70 1120.81 327.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n130 10 Pedestrian -1 -1 -1 403.68 158.15 442.31 263.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n130 24 Pedestrian -1 -1 -1 193.21 162.21 207.68 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n130 22 Pedestrian -1 -1 -1 223.75 155.33 237.10 197.85 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n130 6 Pedestrian -1 -1 -1 376.30 163.11 415.58 265.00 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n130 33 Pedestrian -1 -1 -1 698.48 170.19 711.86 210.92 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n130 36 Pedestrian -1 -1 -1 380.29 158.62 412.15 238.40 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n130 39 Pedestrian -1 -1 -1 679.64 171.06 692.50 209.93 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n130 40 Pedestrian -1 -1 -1 181.58 159.37 198.66 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n131 3 Car -1 -1 -1 1093.37 185.02 1221.36 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n131 2 Car -1 -1 -1 954.55 183.19 1067.30 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n131 9 Pedestrian -1 -1 -1 864.80 156.47 914.37 280.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n131 4 Car -1 -1 -1 1031.04 184.68 1154.58 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n131 31 Pedestrian -1 -1 -1 360.74 149.67 477.08 363.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n131 7 Car -1 -1 -1 600.56 172.73 637.49 203.21 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n131 37 Pedestrian -1 -1 -1 1032.47 156.68 1098.32 325.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n131 27 Pedestrian -1 -1 -1 459.58 170.36 492.69 265.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n131 26 Cyclist -1 -1 -1 580.59 167.43 608.02 229.93 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n131 17 Car -1 -1 -1 559.96 171.20 578.08 187.13 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n131 23 Car -1 -1 -1 421.52 160.55 499.49 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n131 10 Pedestrian -1 -1 -1 412.54 160.84 447.63 260.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n131 24 Pedestrian -1 -1 -1 193.80 161.92 207.67 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n131 33 Pedestrian -1 -1 -1 698.42 169.45 712.30 211.88 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n131 22 Pedestrian -1 -1 -1 223.84 155.50 237.22 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n131 6 Pedestrian -1 -1 -1 379.72 155.79 420.67 256.56 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n131 39 Pedestrian -1 -1 -1 679.37 171.24 692.44 210.35 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n131 40 Pedestrian -1 -1 -1 184.41 159.73 200.95 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n131 41 Pedestrian -1 -1 -1 83.83 186.07 241.25 363.96 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n131 42 Car -1 -1 -1 596.61 172.78 624.88 195.12 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n132 3 Car -1 -1 -1 1093.55 185.15 1221.78 236.33 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n132 2 Car -1 -1 -1 954.63 183.29 1067.18 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n132 37 Pedestrian -1 -1 -1 1012.33 154.29 1087.76 326.91 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n132 4 Car -1 -1 -1 1030.34 184.65 1155.22 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n132 31 Pedestrian -1 -1 -1 386.33 149.55 497.63 363.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n132 7 Car -1 -1 -1 600.65 172.70 637.39 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n132 9 Pedestrian -1 -1 -1 872.42 157.38 920.72 284.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 26 Cyclist -1 -1 -1 580.89 167.33 606.80 229.32 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n132 23 Car -1 -1 -1 417.16 161.14 497.28 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n132 27 Pedestrian -1 -1 -1 461.91 170.90 496.86 265.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n132 6 Pedestrian -1 -1 -1 384.83 155.89 422.46 271.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n132 41 Pedestrian -1 -1 -1 109.35 185.11 261.83 364.36 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n132 33 Pedestrian -1 -1 -1 699.07 169.48 712.62 211.70 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n132 10 Pedestrian -1 -1 -1 418.32 161.56 457.12 258.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n132 24 Pedestrian -1 -1 -1 193.79 161.95 207.81 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n132 17 Car -1 -1 -1 560.80 171.30 577.97 186.77 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n132 22 Pedestrian -1 -1 -1 223.84 155.58 237.28 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n132 39 Pedestrian -1 -1 -1 679.37 171.47 692.88 211.00 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n132 42 Car -1 -1 -1 596.46 172.75 624.84 195.18 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n133 3 Car -1 -1 -1 1094.20 185.26 1221.53 236.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n133 2 Car -1 -1 -1 955.26 183.40 1067.26 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n133 4 Car -1 -1 -1 1030.20 184.33 1155.06 234.03 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n133 31 Pedestrian -1 -1 -1 413.44 147.39 516.53 364.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n133 9 Pedestrian -1 -1 -1 880.41 158.08 928.41 284.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n133 37 Pedestrian -1 -1 -1 984.59 155.55 1084.41 325.40 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n133 7 Car -1 -1 -1 600.84 172.80 637.38 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n133 10 Pedestrian -1 -1 -1 424.95 160.19 459.25 266.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n133 17 Car -1 -1 -1 561.59 172.05 579.50 186.32 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n133 6 Pedestrian -1 -1 -1 391.16 155.47 430.17 272.97 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n133 26 Cyclist -1 -1 -1 579.08 168.20 604.88 227.65 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n133 27 Pedestrian -1 -1 -1 463.34 171.21 496.69 265.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n133 41 Pedestrian -1 -1 -1 120.81 184.14 296.21 364.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n133 33 Pedestrian -1 -1 -1 699.45 170.16 713.23 211.57 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n133 24 Pedestrian -1 -1 -1 193.63 161.85 207.93 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n133 22 Pedestrian -1 -1 -1 223.53 155.54 238.09 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n133 23 Car -1 -1 -1 419.64 161.92 493.80 211.98 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n133 39 Pedestrian -1 -1 -1 679.93 171.65 693.78 211.91 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n133 42 Car -1 -1 -1 596.44 172.89 624.70 194.99 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n134 3 Car -1 -1 -1 1094.19 185.25 1221.42 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n134 4 Car -1 -1 -1 1030.53 184.22 1154.92 233.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n134 2 Car -1 -1 -1 956.45 183.43 1065.58 231.39 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n134 9 Pedestrian -1 -1 -1 879.83 158.79 937.36 284.28 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n134 31 Pedestrian -1 -1 -1 433.02 147.03 542.80 364.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n134 7 Car -1 -1 -1 600.84 172.66 637.22 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n134 37 Pedestrian -1 -1 -1 971.69 158.29 1074.64 322.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n134 23 Car -1 -1 -1 414.27 159.31 499.84 221.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n134 17 Car -1 -1 -1 563.11 172.30 579.80 185.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n134 10 Pedestrian -1 -1 -1 428.51 159.85 461.59 267.83 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n134 24 Pedestrian -1 -1 -1 193.61 162.18 207.44 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n134 6 Pedestrian -1 -1 -1 392.12 158.57 430.65 274.85 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n134 41 Pedestrian -1 -1 -1 148.31 186.09 329.53 363.29 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n134 26 Cyclist -1 -1 -1 580.23 168.75 603.27 226.27 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n134 22 Pedestrian -1 -1 -1 220.80 155.80 235.51 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n134 39 Pedestrian -1 -1 -1 680.90 171.57 694.19 212.12 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n134 33 Pedestrian -1 -1 -1 699.57 170.74 713.65 212.07 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n134 42 Car -1 -1 -1 596.38 172.60 624.26 195.20 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n135 3 Car -1 -1 -1 1095.90 185.36 1219.41 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n135 4 Car -1 -1 -1 1030.88 184.13 1154.46 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n135 23 Car -1 -1 -1 405.72 157.07 500.97 227.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n135 2 Car -1 -1 -1 956.45 183.62 1065.03 231.09 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n135 31 Pedestrian -1 -1 -1 452.25 146.69 569.11 364.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n135 7 Car -1 -1 -1 600.81 172.64 637.43 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n135 9 Pedestrian -1 -1 -1 884.57 157.39 947.76 287.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n135 10 Pedestrian -1 -1 -1 431.79 160.21 466.62 268.45 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n135 37 Pedestrian -1 -1 -1 965.27 159.40 1050.22 320.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n135 26 Cyclist -1 -1 -1 580.05 167.50 603.27 223.95 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n135 41 Pedestrian -1 -1 -1 200.48 185.06 361.75 365.10 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n135 24 Pedestrian -1 -1 -1 193.14 162.03 207.62 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n135 6 Pedestrian -1 -1 -1 400.76 165.67 436.91 276.72 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n135 17 Car -1 -1 -1 563.74 172.35 580.82 185.76 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n135 22 Pedestrian -1 -1 -1 220.54 155.54 235.46 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n135 33 Pedestrian -1 -1 -1 701.94 170.06 716.01 213.14 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n135 27 Pedestrian -1 -1 -1 470.53 174.01 505.76 267.92 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n135 39 Pedestrian -1 -1 -1 682.07 170.88 696.22 212.84 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n135 42 Car -1 -1 -1 596.69 172.61 624.43 195.17 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n135 43 Pedestrian -1 -1 -1 401.67 159.67 435.71 245.38 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n136 4 Car -1 -1 -1 1030.40 184.00 1154.79 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n136 3 Car -1 -1 -1 1095.94 185.32 1219.99 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n136 23 Car -1 -1 -1 406.72 156.56 501.02 231.33 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n136 2 Car -1 -1 -1 956.30 183.38 1065.46 231.47 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n136 9 Pedestrian -1 -1 -1 889.43 155.60 957.37 293.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n136 37 Pedestrian -1 -1 -1 958.32 156.03 1027.10 317.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n136 7 Car -1 -1 -1 601.15 172.83 637.24 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n136 31 Pedestrian -1 -1 -1 469.24 149.48 605.47 363.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n136 26 Cyclist -1 -1 -1 579.69 167.14 602.09 223.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n136 41 Pedestrian -1 -1 -1 237.19 184.19 386.31 365.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n136 24 Pedestrian -1 -1 -1 193.30 162.07 207.67 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n136 6 Pedestrian -1 -1 -1 402.59 159.14 443.14 277.78 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n136 10 Pedestrian -1 -1 -1 427.25 161.25 471.84 272.61 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n136 27 Pedestrian -1 -1 -1 476.88 170.36 513.57 266.36 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n136 33 Pedestrian -1 -1 -1 702.56 169.73 716.90 213.48 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n136 22 Pedestrian -1 -1 -1 220.66 155.43 235.52 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n136 39 Pedestrian -1 -1 -1 682.47 170.74 696.73 213.11 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n136 17 Car -1 -1 -1 563.51 172.31 581.52 185.44 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n136 43 Pedestrian -1 -1 -1 404.85 159.11 440.05 246.26 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n136 42 Car -1 -1 -1 596.90 172.72 624.04 195.17 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n137 4 Car -1 -1 -1 1030.00 183.92 1155.57 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n137 3 Car -1 -1 -1 1100.32 184.77 1219.73 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n137 23 Car -1 -1 -1 405.77 157.63 499.91 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n137 7 Car -1 -1 -1 600.92 172.73 637.16 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n137 2 Car -1 -1 -1 956.18 184.18 1065.55 232.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n137 31 Pedestrian -1 -1 -1 495.52 152.90 625.35 365.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n137 9 Pedestrian -1 -1 -1 900.11 154.93 962.53 294.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n137 37 Pedestrian -1 -1 -1 947.27 153.77 1014.34 317.83 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n137 41 Pedestrian -1 -1 -1 268.11 185.48 409.06 363.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n137 27 Pedestrian -1 -1 -1 476.08 170.19 515.55 267.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n137 26 Cyclist -1 -1 -1 580.62 167.75 600.88 221.39 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n137 6 Pedestrian -1 -1 -1 404.07 156.43 448.81 279.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n137 24 Pedestrian -1 -1 -1 193.35 162.10 208.17 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n137 10 Pedestrian -1 -1 -1 432.51 161.26 474.30 272.87 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n137 22 Pedestrian -1 -1 -1 220.54 155.74 235.64 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n137 17 Car -1 -1 -1 563.15 172.47 582.29 186.34 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n137 33 Pedestrian -1 -1 -1 703.25 170.28 716.38 213.10 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n137 39 Pedestrian -1 -1 -1 682.96 171.13 696.52 213.22 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n137 42 Car -1 -1 -1 596.40 172.71 624.31 195.07 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n137 43 Pedestrian -1 -1 -1 403.27 158.74 442.77 245.90 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n138 4 Car -1 -1 -1 1030.56 183.86 1154.85 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n138 3 Car -1 -1 -1 1095.65 185.01 1219.69 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n138 23 Car -1 -1 -1 399.34 158.03 498.66 231.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n138 2 Car -1 -1 -1 952.80 183.39 1064.76 231.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n138 6 Pedestrian -1 -1 -1 411.13 158.39 456.06 277.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n138 37 Pedestrian -1 -1 -1 916.75 152.84 1006.56 313.39 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n138 31 Pedestrian -1 -1 -1 537.41 148.80 644.51 364.18 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n138 7 Car -1 -1 -1 603.75 172.57 636.96 201.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n138 27 Pedestrian -1 -1 -1 484.41 168.51 519.71 269.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n138 9 Pedestrian -1 -1 -1 907.49 155.24 993.43 295.73 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n138 41 Pedestrian -1 -1 -1 303.11 191.92 435.04 364.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n138 10 Pedestrian -1 -1 -1 441.98 160.47 480.11 273.09 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n138 24 Pedestrian -1 -1 -1 193.53 162.30 208.30 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n138 26 Cyclist -1 -1 -1 580.90 167.68 600.81 221.24 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n138 22 Pedestrian -1 -1 -1 223.38 155.83 237.83 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n138 17 Car -1 -1 -1 565.25 172.57 583.51 185.45 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n138 39 Pedestrian -1 -1 -1 682.78 171.66 697.45 214.83 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n138 33 Pedestrian -1 -1 -1 703.26 170.39 716.30 213.33 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n139 4 Car -1 -1 -1 1030.90 183.78 1153.63 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n139 3 Car -1 -1 -1 1095.26 184.88 1220.00 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n139 2 Car -1 -1 -1 954.74 183.28 1066.61 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n139 31 Pedestrian -1 -1 -1 562.33 146.53 657.54 365.25 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n139 7 Car -1 -1 -1 600.94 173.10 637.20 201.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n139 37 Pedestrian -1 -1 -1 909.16 156.59 999.43 309.31 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n139 27 Pedestrian -1 -1 -1 490.41 169.07 524.28 268.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n139 41 Pedestrian -1 -1 -1 333.88 190.56 465.44 366.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n139 23 Car -1 -1 -1 391.14 156.71 492.69 231.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n139 6 Pedestrian -1 -1 -1 416.58 163.70 460.02 278.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n139 10 Pedestrian -1 -1 -1 449.23 160.27 486.96 273.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n139 24 Pedestrian -1 -1 -1 193.30 162.31 208.14 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n139 39 Pedestrian -1 -1 -1 683.02 171.53 697.72 215.16 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n139 22 Pedestrian -1 -1 -1 223.37 155.76 237.77 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n139 17 Car -1 -1 -1 566.38 172.18 584.18 185.14 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n139 26 Cyclist -1 -1 -1 579.82 169.25 596.67 213.64 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n139 33 Pedestrian -1 -1 -1 703.25 170.39 716.76 213.51 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n140 3 Car -1 -1 -1 1094.93 184.96 1220.15 236.66 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n140 4 Car -1 -1 -1 1031.11 183.74 1153.03 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n140 2 Car -1 -1 -1 951.10 182.91 1066.47 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n140 31 Pedestrian -1 -1 -1 578.01 146.17 680.53 364.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n140 23 Car -1 -1 -1 387.99 157.39 486.86 232.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n140 27 Pedestrian -1 -1 -1 494.14 169.78 533.87 270.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n140 7 Car -1 -1 -1 601.17 173.07 637.17 201.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n140 10 Pedestrian -1 -1 -1 450.85 160.22 493.22 272.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n140 41 Pedestrian -1 -1 -1 368.27 196.85 484.86 360.29 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n140 6 Pedestrian -1 -1 -1 422.56 164.49 468.35 278.41 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n140 37 Pedestrian -1 -1 -1 900.34 156.60 977.62 301.29 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n140 24 Pedestrian -1 -1 -1 193.12 162.23 208.15 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n140 39 Pedestrian -1 -1 -1 683.78 171.31 698.22 215.17 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n140 22 Pedestrian -1 -1 -1 220.86 155.80 235.50 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n140 26 Cyclist -1 -1 -1 578.42 168.26 597.55 215.24 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n140 17 Car -1 -1 -1 567.59 171.66 584.14 186.05 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n141 3 Car -1 -1 -1 1095.02 184.85 1220.71 236.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n141 4 Car -1 -1 -1 1030.69 183.45 1154.14 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n141 27 Pedestrian -1 -1 -1 497.30 170.63 539.33 270.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n141 2 Car -1 -1 -1 951.32 182.99 1066.24 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n141 23 Car -1 -1 -1 380.27 157.52 488.30 238.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n141 7 Car -1 -1 -1 601.05 172.67 637.46 202.32 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n141 31 Pedestrian -1 -1 -1 592.55 148.41 719.36 362.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n141 10 Pedestrian -1 -1 -1 458.05 159.70 500.89 269.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n141 41 Pedestrian -1 -1 -1 387.85 189.64 511.27 360.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n141 37 Pedestrian -1 -1 -1 931.57 155.92 999.42 301.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n141 24 Pedestrian -1 -1 -1 193.16 162.30 208.12 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n141 26 Cyclist -1 -1 -1 577.06 167.36 597.65 216.20 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n141 22 Pedestrian -1 -1 -1 223.36 155.71 237.77 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n141 6 Pedestrian -1 -1 -1 426.42 166.13 472.45 276.63 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n141 39 Pedestrian -1 -1 -1 684.25 171.48 698.50 214.77 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n141 44 Pedestrian -1 -1 -1 893.52 153.20 953.41 305.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n141 45 Pedestrian -1 -1 -1 427.21 157.72 472.42 261.97 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n141 46 Pedestrian -1 -1 -1 706.16 169.72 720.10 214.46 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n142 3 Car -1 -1 -1 1094.77 184.70 1220.88 236.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n142 4 Car -1 -1 -1 1031.49 183.13 1153.31 234.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n142 23 Car -1 -1 -1 379.27 155.52 488.88 241.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n142 2 Car -1 -1 -1 954.68 183.36 1067.40 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n142 44 Pedestrian -1 -1 -1 875.85 152.24 940.60 311.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n142 7 Car -1 -1 -1 602.84 172.37 637.59 202.38 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n142 31 Pedestrian -1 -1 -1 604.06 149.52 738.74 362.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n142 37 Pedestrian -1 -1 -1 939.84 155.15 1006.69 301.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n142 41 Pedestrian -1 -1 -1 414.36 188.53 545.60 362.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n142 27 Pedestrian -1 -1 -1 501.51 170.76 542.44 270.99 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n142 10 Pedestrian -1 -1 -1 462.59 157.55 511.91 277.07 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n142 6 Pedestrian -1 -1 -1 430.35 160.54 476.18 282.00 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n142 24 Pedestrian -1 -1 -1 193.18 162.41 208.00 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n142 26 Cyclist -1 -1 -1 577.65 167.92 597.14 215.21 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n142 22 Pedestrian -1 -1 -1 223.31 155.55 237.61 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n142 39 Pedestrian -1 -1 -1 685.53 172.00 700.64 214.64 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n142 46 Pedestrian -1 -1 -1 706.86 169.40 720.36 214.39 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n143 3 Car -1 -1 -1 1094.94 184.86 1220.72 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n143 23 Car -1 -1 -1 375.83 156.10 491.13 239.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n143 4 Car -1 -1 -1 1030.49 182.92 1154.89 234.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n143 2 Car -1 -1 -1 955.03 182.84 1067.13 232.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n143 44 Pedestrian -1 -1 -1 860.90 153.33 933.32 305.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n143 31 Pedestrian -1 -1 -1 634.01 149.33 769.68 362.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n143 41 Pedestrian -1 -1 -1 444.97 193.44 568.44 363.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n143 7 Car -1 -1 -1 602.90 172.52 637.43 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n143 37 Pedestrian -1 -1 -1 949.81 155.65 1012.32 301.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n143 27 Pedestrian -1 -1 -1 506.11 169.70 546.70 272.45 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n143 26 Cyclist -1 -1 -1 577.09 167.20 596.41 213.92 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n143 6 Pedestrian -1 -1 -1 437.43 165.04 477.00 284.52 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n143 24 Pedestrian -1 -1 -1 193.26 162.44 208.17 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n143 10 Pedestrian -1 -1 -1 464.10 159.02 511.40 274.78 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n143 22 Pedestrian -1 -1 -1 223.27 155.45 237.60 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n143 39 Pedestrian -1 -1 -1 686.07 171.72 700.88 215.41 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n144 23 Car -1 -1 -1 373.03 155.83 486.84 241.23 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n144 3 Car -1 -1 -1 1094.86 184.87 1220.85 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n144 4 Car -1 -1 -1 1031.05 183.31 1154.37 234.58 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n144 2 Car -1 -1 -1 954.74 182.50 1067.19 232.17 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n144 37 Pedestrian -1 -1 -1 968.85 156.49 1023.85 302.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n144 44 Pedestrian -1 -1 -1 846.83 158.13 923.97 306.56 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n144 31 Pedestrian -1 -1 -1 673.48 149.27 791.40 363.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n144 7 Car -1 -1 -1 602.70 172.63 637.58 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n144 6 Pedestrian -1 -1 -1 442.90 159.69 486.66 283.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n144 41 Pedestrian -1 -1 -1 469.47 185.89 605.08 364.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n144 10 Pedestrian -1 -1 -1 472.35 159.78 510.75 274.96 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n144 26 Cyclist -1 -1 -1 576.13 167.78 596.63 213.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n144 27 Pedestrian -1 -1 -1 513.57 169.37 552.88 273.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n144 24 Pedestrian -1 -1 -1 193.18 162.46 208.14 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n144 39 Pedestrian -1 -1 -1 685.54 171.81 700.92 215.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n144 22 Pedestrian -1 -1 -1 223.22 155.34 237.62 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n145 3 Car -1 -1 -1 1094.75 184.87 1221.06 236.27 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n145 23 Car -1 -1 -1 369.34 155.74 490.08 243.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n145 4 Car -1 -1 -1 1029.45 183.38 1155.04 234.72 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n145 2 Car -1 -1 -1 952.18 182.55 1071.05 234.61 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n145 7 Car -1 -1 -1 600.49 172.83 637.77 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n145 37 Pedestrian -1 -1 -1 979.65 159.00 1043.78 305.00 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n145 31 Pedestrian -1 -1 -1 692.39 146.16 818.40 365.62 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n145 27 Pedestrian -1 -1 -1 520.94 170.87 554.01 271.78 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n145 6 Pedestrian -1 -1 -1 445.43 157.58 492.52 285.04 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n145 44 Pedestrian -1 -1 -1 842.13 155.45 913.10 303.07 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n145 41 Pedestrian -1 -1 -1 492.41 182.93 635.98 365.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n145 39 Pedestrian -1 -1 -1 685.94 171.67 701.35 215.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n145 10 Pedestrian -1 -1 -1 477.42 159.89 513.72 275.68 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n145 24 Pedestrian -1 -1 -1 193.05 162.51 208.06 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n145 26 Cyclist -1 -1 -1 573.27 168.46 594.95 212.43 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n145 22 Pedestrian -1 -1 -1 220.74 155.46 235.75 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n145 47 Pedestrian -1 -1 -1 709.56 170.67 723.04 215.73 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n146 3 Car -1 -1 -1 1095.40 185.02 1220.64 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n146 23 Car -1 -1 -1 364.19 155.14 487.17 246.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n146 4 Car -1 -1 -1 1029.14 183.32 1156.35 234.45 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n146 2 Car -1 -1 -1 955.93 182.98 1066.79 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n146 37 Pedestrian -1 -1 -1 981.36 157.92 1065.40 307.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n146 7 Car -1 -1 -1 600.37 172.85 638.07 202.30 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n146 27 Pedestrian -1 -1 -1 522.76 171.25 560.83 272.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n146 31 Pedestrian -1 -1 -1 710.19 143.78 854.27 361.45 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n146 10 Pedestrian -1 -1 -1 483.84 157.78 521.22 277.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n146 44 Pedestrian -1 -1 -1 833.60 155.10 891.50 295.97 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n146 39 Pedestrian -1 -1 -1 687.16 171.29 702.08 215.24 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n146 6 Pedestrian -1 -1 -1 453.70 160.41 498.71 283.04 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n146 24 Pedestrian -1 -1 -1 193.11 162.64 207.74 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n146 41 Pedestrian -1 -1 -1 515.06 181.15 659.32 367.07 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n146 22 Pedestrian -1 -1 -1 223.29 155.42 237.67 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n146 26 Cyclist -1 -1 -1 577.75 168.47 595.24 211.31 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n146 47 Pedestrian -1 -1 -1 709.47 168.78 724.08 215.82 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n147 23 Car -1 -1 -1 360.37 155.34 479.19 247.51 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n147 3 Car -1 -1 -1 1095.11 184.99 1220.85 236.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n147 4 Car -1 -1 -1 1029.58 183.20 1155.97 234.31 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n147 2 Car -1 -1 -1 955.97 183.39 1066.64 231.56 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n147 37 Pedestrian -1 -1 -1 986.81 157.03 1074.54 309.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n147 7 Car -1 -1 -1 600.72 172.87 637.56 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n147 27 Pedestrian -1 -1 -1 525.52 170.83 565.43 273.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n147 31 Pedestrian -1 -1 -1 726.78 147.00 875.64 364.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n147 10 Pedestrian -1 -1 -1 485.90 157.93 527.60 277.67 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n147 39 Pedestrian -1 -1 -1 686.54 171.13 701.73 215.79 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n147 6 Pedestrian -1 -1 -1 461.54 166.09 505.93 284.78 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n147 41 Pedestrian -1 -1 -1 546.21 182.73 681.29 365.45 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n147 26 Cyclist -1 -1 -1 575.37 167.12 593.16 209.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n147 24 Pedestrian -1 -1 -1 192.80 162.13 207.66 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n147 44 Pedestrian -1 -1 -1 819.70 154.15 874.73 296.95 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n147 22 Pedestrian -1 -1 -1 223.10 155.21 237.76 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n147 47 Pedestrian -1 -1 -1 710.08 168.58 724.53 216.03 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n147 48 Pedestrian -1 -1 -1 181.17 159.63 198.98 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n148 23 Car -1 -1 -1 356.66 155.78 479.01 247.58 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n148 3 Car -1 -1 -1 1095.01 185.09 1220.96 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n148 4 Car -1 -1 -1 1033.57 183.70 1157.12 234.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n148 37 Pedestrian -1 -1 -1 997.62 156.78 1079.46 309.88 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n148 2 Car -1 -1 -1 956.52 183.65 1066.53 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n148 27 Pedestrian -1 -1 -1 529.62 169.21 568.91 275.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n148 7 Car -1 -1 -1 601.23 172.70 637.19 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n148 41 Pedestrian -1 -1 -1 562.14 177.67 726.96 364.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n148 31 Pedestrian -1 -1 -1 742.27 149.21 914.04 362.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n148 6 Pedestrian -1 -1 -1 469.76 165.31 513.37 285.16 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n148 10 Pedestrian -1 -1 -1 489.20 157.88 532.11 278.72 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n148 26 Cyclist -1 -1 -1 575.14 167.48 592.81 209.36 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n148 39 Pedestrian -1 -1 -1 686.16 171.41 701.84 216.34 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n148 44 Pedestrian -1 -1 -1 800.75 151.76 870.92 298.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n148 24 Pedestrian -1 -1 -1 192.72 162.09 207.85 198.59 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n148 22 Pedestrian -1 -1 -1 220.44 155.19 235.74 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n148 47 Pedestrian -1 -1 -1 709.74 170.19 725.41 216.16 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n148 48 Pedestrian -1 -1 -1 180.87 159.36 199.12 199.00 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n149 23 Car -1 -1 -1 350.97 156.26 477.05 248.49 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n149 3 Car -1 -1 -1 1094.79 184.86 1220.79 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n149 4 Car -1 -1 -1 1033.41 183.83 1158.17 234.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n149 37 Pedestrian -1 -1 -1 1015.78 154.61 1084.50 311.63 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n149 2 Car -1 -1 -1 956.89 183.81 1066.19 231.30 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n149 7 Car -1 -1 -1 603.17 172.39 637.14 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n149 27 Pedestrian -1 -1 -1 542.97 167.97 576.52 276.49 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n149 41 Pedestrian -1 -1 -1 600.02 178.45 750.32 364.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n149 6 Pedestrian -1 -1 -1 471.32 156.66 520.16 287.13 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n149 26 Cyclist -1 -1 -1 575.09 167.99 592.71 208.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n149 31 Pedestrian -1 -1 -1 784.88 147.81 932.63 364.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n149 44 Pedestrian -1 -1 -1 796.64 160.10 851.65 297.90 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n149 22 Pedestrian -1 -1 -1 220.48 155.17 235.80 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n149 24 Pedestrian -1 -1 -1 192.58 161.94 207.81 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n149 39 Pedestrian -1 -1 -1 686.58 171.85 702.66 216.38 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n149 10 Pedestrian -1 -1 -1 493.41 157.35 535.42 279.48 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n149 48 Pedestrian -1 -1 -1 180.75 159.50 198.81 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n150 23 Car -1 -1 -1 345.24 156.03 474.65 249.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n150 3 Car -1 -1 -1 1095.10 185.09 1219.19 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n150 4 Car -1 -1 -1 1033.62 184.06 1158.08 234.79 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n150 2 Car -1 -1 -1 956.84 183.30 1066.53 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n150 37 Pedestrian -1 -1 -1 1040.27 156.39 1097.91 310.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n150 27 Pedestrian -1 -1 -1 544.82 169.46 584.51 278.83 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n150 6 Pedestrian -1 -1 -1 476.23 158.22 529.66 290.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n150 7 Car -1 -1 -1 603.02 172.51 637.27 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n150 41 Pedestrian -1 -1 -1 633.82 177.96 769.80 364.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n150 26 Cyclist -1 -1 -1 575.33 167.46 592.62 208.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n150 31 Pedestrian -1 -1 -1 809.29 147.08 946.56 363.85 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n150 44 Pedestrian -1 -1 -1 788.02 158.05 837.32 299.76 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n150 39 Pedestrian -1 -1 -1 686.90 170.95 702.71 217.36 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n150 22 Pedestrian -1 -1 -1 220.34 155.18 235.95 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n150 10 Pedestrian -1 -1 -1 497.51 156.60 539.36 280.03 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n150 24 Pedestrian -1 -1 -1 192.94 161.80 207.84 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n150 48 Pedestrian -1 -1 -1 180.96 160.00 198.19 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n151 23 Car -1 -1 -1 339.60 156.06 473.26 253.44 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n151 3 Car -1 -1 -1 1093.74 184.79 1220.33 236.71 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n151 4 Car -1 -1 -1 1034.20 183.82 1157.21 234.61 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n151 2 Car -1 -1 -1 956.62 182.82 1066.67 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n151 27 Pedestrian -1 -1 -1 545.60 168.54 591.68 280.27 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n151 37 Pedestrian -1 -1 -1 1051.72 157.06 1124.42 315.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n151 6 Pedestrian -1 -1 -1 478.91 156.64 534.69 292.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n151 41 Pedestrian -1 -1 -1 647.48 168.93 802.27 366.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n151 7 Car -1 -1 -1 602.75 172.59 637.39 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n151 26 Cyclist -1 -1 -1 574.97 167.54 592.50 208.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n151 31 Pedestrian -1 -1 -1 833.75 141.32 983.08 363.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n151 39 Pedestrian -1 -1 -1 686.58 170.88 703.54 216.99 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n151 10 Pedestrian -1 -1 -1 502.56 156.30 548.37 284.81 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n151 24 Pedestrian -1 -1 -1 192.92 162.05 207.87 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n151 22 Pedestrian -1 -1 -1 220.40 155.47 235.98 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n151 44 Pedestrian -1 -1 -1 772.71 154.55 829.71 295.98 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n151 48 Pedestrian -1 -1 -1 180.79 159.65 198.45 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n151 49 Cyclist -1 -1 -1 944.89 177.73 978.78 220.14 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n152 23 Car -1 -1 -1 334.28 156.15 470.09 254.64 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n152 27 Pedestrian -1 -1 -1 551.19 167.97 599.36 282.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n152 4 Car -1 -1 -1 1034.87 183.71 1155.98 234.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n152 3 Car -1 -1 -1 1093.06 184.75 1221.18 237.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n152 2 Car -1 -1 -1 956.41 182.83 1066.74 232.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n152 37 Pedestrian -1 -1 -1 1059.68 158.22 1139.73 315.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n152 6 Pedestrian -1 -1 -1 477.60 155.89 536.57 292.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n152 7 Car -1 -1 -1 601.52 172.98 636.96 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n152 44 Pedestrian -1 -1 -1 753.65 153.27 818.28 297.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n152 31 Pedestrian -1 -1 -1 848.69 139.68 1021.63 365.20 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n152 26 Cyclist -1 -1 -1 574.97 167.84 592.19 207.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n152 39 Pedestrian -1 -1 -1 688.52 171.28 706.28 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n152 10 Pedestrian -1 -1 -1 506.13 154.87 552.95 281.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n152 22 Pedestrian -1 -1 -1 220.15 155.19 236.09 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n152 24 Pedestrian -1 -1 -1 193.46 162.26 207.69 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n152 41 Pedestrian -1 -1 -1 668.97 172.24 834.30 364.34 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n152 48 Pedestrian -1 -1 -1 180.54 159.55 198.24 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n152 50 Pedestrian -1 -1 -1 712.34 169.81 727.81 217.47 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n153 23 Car -1 -1 -1 326.36 154.95 467.79 257.72 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n153 4 Car -1 -1 -1 1034.72 183.48 1156.89 235.00 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n153 27 Pedestrian -1 -1 -1 558.58 168.95 601.23 282.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n153 2 Car -1 -1 -1 955.95 183.19 1067.64 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n153 3 Car -1 -1 -1 1093.88 184.86 1221.27 237.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n153 7 Car -1 -1 -1 601.51 172.95 636.91 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n153 37 Pedestrian -1 -1 -1 1067.58 159.12 1147.17 315.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n153 6 Pedestrian -1 -1 -1 489.55 164.49 539.47 293.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n153 31 Pedestrian -1 -1 -1 879.41 148.49 1029.35 363.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n153 10 Pedestrian -1 -1 -1 508.61 155.72 558.32 285.65 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n153 44 Pedestrian -1 -1 -1 746.23 161.07 810.60 296.27 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n153 26 Cyclist -1 -1 -1 575.14 167.21 592.35 207.53 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n153 24 Pedestrian -1 -1 -1 193.15 162.40 208.03 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n153 22 Pedestrian -1 -1 -1 220.17 155.05 236.15 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n153 39 Pedestrian -1 -1 -1 688.97 171.72 707.05 218.76 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n153 50 Pedestrian -1 -1 -1 712.99 170.31 728.20 216.93 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n153 48 Pedestrian -1 -1 -1 180.90 159.33 198.37 199.02 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n153 41 Pedestrian -1 -1 -1 722.46 185.47 879.56 363.56 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n153 51 Pedestrian -1 -1 -1 716.09 187.94 863.77 361.24 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n154 23 Car -1 -1 -1 319.62 154.47 465.21 259.31 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n154 27 Pedestrian -1 -1 -1 569.01 169.55 605.55 281.94 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n154 4 Car -1 -1 -1 1034.48 183.96 1156.39 234.51 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n154 2 Car -1 -1 -1 956.55 183.18 1066.80 231.47 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n154 37 Pedestrian -1 -1 -1 1082.18 158.67 1155.41 320.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n154 7 Car -1 -1 -1 601.41 172.78 636.92 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n154 3 Car -1 -1 -1 1093.70 184.61 1219.99 240.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n154 31 Pedestrian -1 -1 -1 912.50 147.70 1064.86 364.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n154 51 Pedestrian -1 -1 -1 740.47 177.32 908.27 364.57 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n154 10 Pedestrian -1 -1 -1 511.46 155.76 563.41 287.41 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n154 44 Pedestrian -1 -1 -1 741.88 160.85 799.36 296.28 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n154 6 Pedestrian -1 -1 -1 500.74 163.82 543.98 294.30 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n154 24 Pedestrian -1 -1 -1 193.03 162.36 207.96 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n154 22 Pedestrian -1 -1 -1 220.25 154.80 236.12 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n154 26 Cyclist -1 -1 -1 575.47 167.51 592.17 206.54 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n154 48 Pedestrian -1 -1 -1 180.38 158.94 198.59 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n154 39 Pedestrian -1 -1 -1 691.61 171.88 709.61 219.39 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n154 52 Cyclist -1 -1 -1 898.98 172.85 932.76 230.00 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n155 23 Car -1 -1 -1 311.11 154.22 463.21 262.89 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n155 3 Car -1 -1 -1 1094.87 184.81 1219.22 237.05 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n155 4 Car -1 -1 -1 1035.32 183.96 1155.59 234.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n155 2 Car -1 -1 -1 956.89 183.30 1066.36 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n155 7 Car -1 -1 -1 600.90 172.59 637.02 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n155 27 Pedestrian -1 -1 -1 573.74 168.73 616.03 282.51 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n155 37 Pedestrian -1 -1 -1 1101.11 158.30 1167.31 321.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n155 6 Pedestrian -1 -1 -1 507.14 161.00 552.65 297.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n155 31 Pedestrian -1 -1 -1 937.95 149.06 1108.47 362.70 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n155 51 Pedestrian -1 -1 -1 765.44 170.60 952.10 365.02 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n155 39 Pedestrian -1 -1 -1 692.21 171.59 710.00 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n155 24 Pedestrian -1 -1 -1 192.98 162.01 207.92 198.58 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n155 44 Pedestrian -1 -1 -1 737.34 157.93 788.60 293.13 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n155 22 Pedestrian -1 -1 -1 220.24 154.28 236.21 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n155 52 Cyclist -1 -1 -1 883.31 172.79 919.35 223.71 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n155 26 Cyclist -1 -1 -1 575.65 167.00 591.04 205.90 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n155 48 Pedestrian -1 -1 -1 180.10 158.54 198.62 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n155 53 Pedestrian -1 -1 -1 744.65 157.19 804.35 285.89 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n155 54 Pedestrian -1 -1 -1 713.60 170.31 729.29 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n156 23 Car -1 -1 -1 302.15 153.09 461.20 265.74 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n156 27 Pedestrian -1 -1 -1 575.42 167.91 622.49 283.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n156 2 Car -1 -1 -1 957.69 183.37 1064.01 231.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n156 3 Car -1 -1 -1 1094.62 184.61 1219.85 237.43 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n156 4 Car -1 -1 -1 1034.89 184.35 1156.15 234.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n156 7 Car -1 -1 -1 600.89 172.60 637.36 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n156 37 Pedestrian -1 -1 -1 1116.18 159.42 1190.21 321.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n156 44 Pedestrian -1 -1 -1 731.30 158.91 779.11 291.09 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n156 6 Pedestrian -1 -1 -1 518.02 171.94 564.27 294.86 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n156 39 Pedestrian -1 -1 -1 693.72 170.49 711.06 219.13 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n156 22 Pedestrian -1 -1 -1 219.87 154.05 236.22 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n156 24 Pedestrian -1 -1 -1 192.81 161.96 207.92 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n156 51 Pedestrian -1 -1 -1 781.52 178.20 974.47 363.03 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n156 54 Pedestrian -1 -1 -1 714.18 169.69 729.66 217.45 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n156 31 Pedestrian -1 -1 -1 963.67 148.31 1144.02 362.69 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n156 26 Cyclist -1 -1 -1 575.08 166.81 590.55 206.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n156 55 Pedestrian -1 -1 -1 516.52 155.51 566.23 279.98 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n156 56 Pedestrian -1 -1 -1 791.69 178.29 994.09 362.71 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n157 23 Car -1 -1 -1 294.05 152.98 458.79 268.51 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n157 3 Car -1 -1 -1 1094.51 184.37 1220.08 237.33 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n157 2 Car -1 -1 -1 956.00 183.33 1065.73 231.37 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n157 4 Car -1 -1 -1 1033.44 183.65 1157.46 234.58 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n157 27 Pedestrian -1 -1 -1 577.33 168.25 628.38 283.93 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n157 7 Car -1 -1 -1 600.05 172.53 637.04 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n157 55 Pedestrian -1 -1 -1 520.22 153.68 577.03 289.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n157 44 Pedestrian -1 -1 -1 722.50 160.61 772.66 289.14 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n157 56 Pedestrian -1 -1 -1 825.22 180.46 1014.63 362.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n157 6 Pedestrian -1 -1 -1 520.90 176.69 569.85 297.43 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n157 24 Pedestrian -1 -1 -1 192.57 161.75 208.12 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n157 22 Pedestrian -1 -1 -1 219.91 153.96 236.29 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n157 37 Pedestrian -1 -1 -1 1130.86 161.26 1205.94 320.15 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n157 31 Pedestrian -1 -1 -1 998.64 131.84 1177.63 365.09 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n157 54 Pedestrian -1 -1 -1 716.22 169.25 731.69 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n157 39 Pedestrian -1 -1 -1 695.56 170.41 713.48 219.11 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n157 57 Cyclist -1 -1 -1 849.92 169.46 890.06 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n158 23 Car -1 -1 -1 282.88 152.46 456.85 272.72 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n158 3 Car -1 -1 -1 1093.16 184.27 1221.31 237.37 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n158 2 Car -1 -1 -1 957.47 183.44 1064.39 231.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n158 4 Car -1 -1 -1 1033.82 183.82 1156.61 234.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n158 7 Car -1 -1 -1 600.24 172.50 637.56 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n158 55 Pedestrian -1 -1 -1 523.12 156.20 582.26 294.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n158 27 Pedestrian -1 -1 -1 587.90 170.12 630.79 285.99 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n158 44 Pedestrian -1 -1 -1 710.66 162.86 762.27 286.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n158 57 Cyclist -1 -1 -1 834.76 170.08 875.67 232.39 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n158 56 Pedestrian -1 -1 -1 865.49 178.98 1058.69 363.87 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n158 22 Pedestrian -1 -1 -1 219.95 153.73 236.29 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n158 24 Pedestrian -1 -1 -1 192.51 161.63 208.20 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n158 37 Pedestrian -1 -1 -1 1153.49 160.73 1213.99 327.41 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n158 31 Pedestrian -1 -1 -1 1032.56 141.36 1212.57 362.52 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n158 54 Pedestrian -1 -1 -1 716.95 169.96 732.04 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n158 39 Pedestrian -1 -1 -1 696.07 170.84 712.93 219.57 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n158 58 Pedestrian -1 -1 -1 179.79 157.84 199.12 200.59 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n159 23 Car -1 -1 -1 274.82 152.44 453.62 275.78 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n159 3 Car -1 -1 -1 1094.38 183.54 1219.25 237.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n159 4 Car -1 -1 -1 1033.51 183.67 1157.23 234.74 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n159 2 Car -1 -1 -1 954.70 183.69 1062.75 230.91 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n159 7 Car -1 -1 -1 601.12 172.81 636.38 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n159 27 Pedestrian -1 -1 -1 598.82 170.79 637.06 285.90 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n159 55 Pedestrian -1 -1 -1 530.38 155.11 591.10 295.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n159 57 Cyclist -1 -1 -1 818.36 170.08 862.02 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n159 44 Pedestrian -1 -1 -1 704.15 161.67 753.43 287.26 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n159 22 Pedestrian -1 -1 -1 219.78 153.60 236.36 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n159 56 Pedestrian -1 -1 -1 882.22 169.23 1103.01 366.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n159 37 Pedestrian -1 -1 -1 1160.44 158.59 1214.82 330.23 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n159 24 Pedestrian -1 -1 -1 192.08 161.42 208.14 199.32 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n159 31 Pedestrian -1 -1 -1 1074.52 140.83 1216.57 362.75 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n159 54 Pedestrian -1 -1 -1 716.86 170.12 732.45 217.70 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n159 39 Pedestrian -1 -1 -1 696.78 171.26 712.37 219.35 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n159 58 Pedestrian -1 -1 -1 180.02 157.65 199.19 200.94 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n159 59 Pedestrian -1 -1 -1 713.85 159.62 766.26 275.10 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n160 23 Car -1 -1 -1 261.31 152.15 451.82 280.82 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n160 2 Car -1 -1 -1 955.56 182.51 1066.82 232.00 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n160 27 Pedestrian -1 -1 -1 604.10 171.59 647.73 287.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n160 3 Car -1 -1 -1 1095.03 183.38 1219.40 237.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n160 55 Pedestrian -1 -1 -1 540.45 154.32 596.31 297.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n160 7 Car -1 -1 -1 601.31 172.53 636.43 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n160 4 Car -1 -1 -1 1031.45 184.20 1153.38 234.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n160 44 Pedestrian -1 -1 -1 696.03 160.48 738.44 283.58 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n160 57 Cyclist -1 -1 -1 800.68 168.33 848.59 234.34 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n160 56 Pedestrian -1 -1 -1 897.22 169.84 1156.92 365.12 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n160 59 Pedestrian -1 -1 -1 706.08 158.14 758.94 276.76 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n160 22 Pedestrian -1 -1 -1 219.75 153.74 236.37 198.97 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n160 24 Pedestrian -1 -1 -1 192.03 161.22 208.39 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n160 39 Pedestrian -1 -1 -1 697.68 171.66 712.73 219.39 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n160 54 Pedestrian -1 -1 -1 716.72 169.97 732.83 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n160 31 Pedestrian -1 -1 -1 1115.86 147.81 1221.13 363.68 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n160 37 Pedestrian -1 -1 -1 1156.02 152.65 1219.39 336.17 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n161 23 Car -1 -1 -1 249.57 151.65 448.25 284.46 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n161 27 Pedestrian -1 -1 -1 608.06 171.25 658.35 287.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n161 3 Car -1 -1 -1 1095.66 184.03 1219.08 237.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n161 7 Car -1 -1 -1 601.06 172.22 636.59 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n161 4 Car -1 -1 -1 1035.09 183.65 1155.73 234.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n161 55 Pedestrian -1 -1 -1 545.06 153.67 604.86 303.18 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n161 2 Car -1 -1 -1 956.27 183.56 1061.34 231.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n161 44 Pedestrian -1 -1 -1 684.93 159.97 727.35 282.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n161 57 Cyclist -1 -1 -1 780.74 165.55 837.99 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n161 59 Pedestrian -1 -1 -1 697.24 157.51 744.85 276.71 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n161 39 Pedestrian -1 -1 -1 697.89 171.53 713.27 219.56 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n161 22 Pedestrian -1 -1 -1 219.61 153.76 236.39 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n161 56 Pedestrian -1 -1 -1 922.13 168.93 1193.36 365.85 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n161 24 Pedestrian -1 -1 -1 191.75 161.08 208.65 199.56 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n161 37 Pedestrian -1 -1 -1 1178.68 158.76 1219.63 337.42 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n162 23 Car -1 -1 -1 234.37 151.46 443.16 289.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n162 3 Car -1 -1 -1 1094.93 184.06 1219.28 236.84 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n162 2 Car -1 -1 -1 955.71 183.31 1066.53 233.79 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-1 -1 -1000 -1000 -1000 -10 0.51\n163 23 Car -1 -1 -1 220.78 150.08 439.84 294.34 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n163 3 Car -1 -1 -1 1094.88 184.27 1219.39 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n163 2 Car -1 -1 -1 954.15 183.59 1063.74 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n163 4 Car -1 -1 -1 1033.26 183.38 1158.13 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n163 27 Pedestrian -1 -1 -1 623.11 170.25 667.11 289.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n163 55 Pedestrian -1 -1 -1 548.93 155.79 618.18 308.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n163 7 Car -1 -1 -1 601.65 171.97 636.83 201.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n163 44 Pedestrian -1 -1 -1 664.33 162.87 716.74 279.51 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n163 59 Pedestrian -1 -1 -1 680.84 156.44 730.20 272.29 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n163 56 Pedestrian -1 -1 -1 1022.95 178.35 1214.68 363.35 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n163 24 Pedestrian -1 -1 -1 192.32 161.53 208.26 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n163 22 Pedestrian -1 -1 -1 219.90 154.34 236.07 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n163 39 Pedestrian -1 -1 -1 696.34 170.26 715.91 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n163 57 Cyclist -1 -1 -1 754.33 163.00 802.29 234.71 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n164 23 Car -1 -1 -1 203.60 150.81 433.49 299.72 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n164 3 Car -1 -1 -1 1095.11 183.91 1219.16 236.44 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n164 2 Car -1 -1 -1 954.38 183.61 1063.04 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n164 4 Car -1 -1 -1 1034.99 182.93 1155.60 235.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n164 55 Pedestrian -1 -1 -1 556.13 156.01 625.20 308.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n164 27 Pedestrian -1 -1 -1 633.60 169.33 671.31 290.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n164 7 Car -1 -1 -1 604.20 172.09 636.37 201.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n164 44 Pedestrian -1 -1 -1 660.05 163.05 704.97 278.07 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n164 59 Pedestrian -1 -1 -1 671.81 159.59 724.14 274.27 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n164 24 Pedestrian -1 -1 -1 192.05 161.24 208.66 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n164 56 Pedestrian -1 -1 -1 1045.93 173.15 1222.31 361.43 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n164 22 Pedestrian -1 -1 -1 219.91 154.87 235.72 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n164 57 Cyclist -1 -1 -1 735.79 165.24 790.46 231.51 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n165 23 Car -1 -1 -1 181.26 150.16 427.94 306.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n165 3 Car -1 -1 -1 1096.61 183.68 1218.49 236.48 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n165 2 Car -1 -1 -1 954.10 183.66 1063.45 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n165 4 Car -1 -1 -1 1029.89 183.08 1155.40 235.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n165 55 Pedestrian -1 -1 -1 563.98 157.01 626.22 308.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n165 7 Car -1 -1 -1 604.24 172.70 636.64 201.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n165 27 Pedestrian -1 -1 -1 636.33 167.69 682.43 291.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n165 57 Cyclist -1 -1 -1 725.03 167.30 777.48 229.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n165 59 Pedestrian -1 -1 -1 664.73 158.89 716.02 275.26 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n165 24 Pedestrian -1 -1 -1 191.93 160.63 209.15 199.73 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n165 22 Pedestrian -1 -1 -1 220.17 157.25 235.34 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n165 44 Pedestrian -1 -1 -1 653.53 161.92 696.82 273.81 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n165 56 Pedestrian -1 -1 -1 1087.92 181.77 1218.41 360.25 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n166 23 Car -1 -1 -1 161.55 148.24 422.76 315.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n166 3 Car -1 -1 -1 1096.47 184.47 1219.00 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n166 4 Car -1 -1 -1 1033.82 183.36 1156.81 234.71 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n166 2 Car -1 -1 -1 953.25 183.71 1064.46 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n166 7 Car -1 -1 -1 603.67 172.87 636.73 201.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n166 55 Pedestrian -1 -1 -1 576.41 156.19 635.84 309.99 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n166 27 Pedestrian -1 -1 -1 639.88 165.37 687.68 294.11 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n166 57 Cyclist -1 -1 -1 712.14 166.35 759.55 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n166 44 Pedestrian -1 -1 -1 637.91 161.95 689.72 279.79 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n166 59 Pedestrian -1 -1 -1 661.02 159.53 704.31 273.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n166 22 Pedestrian -1 -1 -1 219.66 157.40 234.89 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n166 24 Pedestrian -1 -1 -1 192.21 160.37 209.17 200.10 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n167 23 Car -1 -1 -1 138.06 145.63 416.52 321.08 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n167 3 Car -1 -1 -1 1096.02 184.89 1219.70 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n167 4 Car -1 -1 -1 1029.74 183.65 1155.71 234.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n167 2 Car -1 -1 -1 953.07 183.37 1064.49 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n167 55 Pedestrian -1 -1 -1 586.10 152.94 649.09 313.57 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n167 7 Car -1 -1 -1 600.76 172.76 636.72 202.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n167 27 Pedestrian -1 -1 -1 646.34 170.60 695.67 293.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n167 59 Pedestrian -1 -1 -1 648.41 158.55 701.47 269.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n167 57 Cyclist -1 -1 -1 703.66 166.30 745.40 224.75 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n167 22 Pedestrian -1 -1 -1 219.63 157.85 235.26 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n167 44 Pedestrian -1 -1 -1 632.46 162.73 687.03 278.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n167 24 Pedestrian -1 -1 -1 193.08 163.05 207.73 201.85 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n168 3 Car -1 -1 -1 1095.76 185.03 1220.21 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n168 23 Car -1 -1 -1 112.68 144.20 409.69 329.68 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n168 4 Car -1 -1 -1 1030.18 183.66 1155.56 234.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n168 2 Car -1 -1 -1 952.98 183.39 1064.35 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n168 55 Pedestrian -1 -1 -1 591.14 155.51 658.50 317.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n168 7 Car -1 -1 -1 601.70 173.28 635.98 201.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n168 27 Pedestrian -1 -1 -1 651.93 171.63 698.11 292.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n168 59 Pedestrian -1 -1 -1 639.76 158.29 702.34 269.16 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n168 57 Cyclist -1 -1 -1 697.33 168.60 722.33 223.07 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n168 44 Pedestrian -1 -1 -1 627.85 164.00 676.25 271.54 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n168 24 Pedestrian -1 -1 -1 196.62 163.21 211.32 201.73 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n169 3 Car -1 -1 -1 1095.91 185.23 1220.16 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n169 4 Car -1 -1 -1 1029.90 183.76 1155.74 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n169 2 Car -1 -1 -1 952.82 183.35 1064.45 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n169 23 Car -1 -1 -1 80.54 142.63 403.85 339.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n169 55 Pedestrian -1 -1 -1 593.16 153.97 664.89 318.82 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n169 7 Car -1 -1 -1 601.26 172.87 636.33 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n169 27 Pedestrian -1 -1 -1 661.87 169.03 703.82 295.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n169 59 Pedestrian -1 -1 -1 635.56 159.00 691.13 269.47 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n169 44 Pedestrian -1 -1 -1 615.61 160.22 673.65 274.58 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n169 57 Cyclist -1 -1 -1 681.40 169.88 715.42 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n169 61 Cyclist -1 -1 -1 723.48 170.20 741.61 223.49 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n169 62 Cyclist -1 -1 -1 693.89 169.12 718.70 222.08 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n170 3 Car -1 -1 -1 1095.60 185.29 1220.47 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n170 4 Car -1 -1 -1 1029.65 183.66 1155.74 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n170 2 Car -1 -1 -1 951.84 182.15 1065.57 232.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n170 23 Car -1 -1 -1 43.93 142.27 394.80 354.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n170 7 Car -1 -1 -1 601.31 172.87 636.56 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n170 55 Pedestrian -1 -1 -1 606.74 157.10 674.20 316.35 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n170 27 Pedestrian -1 -1 -1 673.75 169.90 715.68 296.48 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n170 59 Pedestrian -1 -1 -1 629.15 157.07 681.96 271.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n171 3 Car -1 -1 -1 1095.55 185.36 1220.46 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n171 4 Car -1 -1 -1 1029.11 183.66 1156.32 233.83 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n171 2 Car -1 -1 -1 951.78 182.18 1065.06 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n171 23 Car -1 -1 -1 -1.46 142.78 388.47 367.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n171 7 Car -1 -1 -1 601.78 173.51 635.61 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n171 27 Pedestrian -1 -1 -1 676.70 172.06 726.86 299.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n171 55 Pedestrian -1 -1 -1 615.39 155.62 681.71 317.77 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n171 59 Pedestrian -1 -1 -1 608.22 159.47 657.44 274.16 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n171 63 Pedestrian -1 -1 -1 612.29 156.90 676.51 278.33 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n172 3 Car -1 -1 -1 1095.29 185.35 1220.77 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n172 4 Car -1 -1 -1 1029.06 183.59 1156.27 233.83 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n172 2 Car -1 -1 -1 950.70 182.41 1065.27 232.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n172 55 Pedestrian -1 -1 -1 621.69 155.54 690.71 317.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n172 27 Pedestrian -1 -1 -1 682.90 171.59 735.26 300.09 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n172 7 Car -1 -1 -1 601.89 173.98 635.65 202.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n172 23 Car -1 -1 -1 -2.23 143.60 380.46 367.30 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n172 59 Pedestrian -1 -1 -1 599.59 163.34 651.15 271.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n172 63 Pedestrian -1 -1 -1 615.96 156.68 672.99 276.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n173 3 Car -1 -1 -1 1094.70 185.15 1221.15 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n173 4 Car -1 -1 -1 1028.95 183.46 1156.58 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n173 2 Car -1 -1 -1 949.99 182.41 1066.00 232.57 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n173 7 Car -1 -1 -1 602.11 174.08 635.58 201.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n173 23 Car -1 -1 -1 -1.73 138.32 372.15 366.64 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n173 55 Pedestrian -1 -1 -1 630.14 151.39 704.77 322.22 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n173 27 Pedestrian -1 -1 -1 692.84 170.55 741.15 301.09 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n173 59 Pedestrian -1 -1 -1 592.04 165.01 635.92 269.58 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n173 63 Pedestrian -1 -1 -1 608.88 162.81 657.21 266.17 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n174 3 Car -1 -1 -1 1094.68 185.19 1221.22 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n174 23 Car -1 -1 -1 -2.79 137.14 364.45 366.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n174 4 Car -1 -1 -1 1029.22 183.66 1156.63 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n174 7 Car -1 -1 -1 602.45 173.65 635.41 201.76 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n174 2 Car -1 -1 -1 950.40 182.57 1067.28 232.57 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n174 55 Pedestrian -1 -1 -1 637.12 152.01 713.20 322.20 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n174 27 Pedestrian -1 -1 -1 704.49 170.20 746.13 301.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n174 59 Pedestrian -1 -1 -1 588.22 164.23 631.41 269.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n174 63 Pedestrian -1 -1 -1 605.96 163.17 652.36 265.71 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n175 23 Car -1 -1 -1 -2.75 130.94 349.29 365.73 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n175 3 Car -1 -1 -1 1094.71 185.16 1220.98 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n175 4 Car -1 -1 -1 1029.28 183.64 1156.53 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n175 7 Car -1 -1 -1 601.79 173.40 635.28 201.71 -1 -1 -1 -1000 -1000 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Pedestrian -1 -1 -1 715.55 170.20 772.61 303.18 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n176 59 Pedestrian -1 -1 -1 573.70 162.92 616.23 266.45 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n176 63 Pedestrian -1 -1 -1 594.56 159.55 633.40 262.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n176 64 Cyclist -1 -1 -1 917.08 174.03 954.67 223.39 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n177 23 Car -1 -1 -1 2.95 126.37 315.32 362.21 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n177 3 Car -1 -1 -1 1094.42 184.98 1221.20 236.24 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n177 4 Car -1 -1 -1 1029.31 183.56 1156.46 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n177 2 Car -1 -1 -1 950.99 183.51 1066.94 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n177 55 Pedestrian -1 -1 -1 664.68 152.17 739.12 328.87 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n177 7 Car -1 -1 -1 601.53 172.86 636.72 201.75 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n177 59 Pedestrian -1 -1 -1 559.02 163.02 608.33 265.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n177 63 Pedestrian -1 -1 -1 586.17 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0.49\n180 3 Car -1 -1 -1 1094.79 185.08 1220.78 236.20 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n180 4 Car -1 -1 -1 1029.60 183.70 1155.99 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n180 2 Car -1 -1 -1 954.83 183.53 1067.01 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n180 7 Car -1 -1 -1 600.48 172.78 636.93 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n180 23 Car -1 -1 -1 -1.41 96.23 257.95 361.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n180 55 Pedestrian -1 -1 -1 700.36 153.07 779.97 335.11 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n180 59 Pedestrian -1 -1 -1 545.27 162.48 583.15 265.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n180 63 Pedestrian -1 -1 -1 566.22 161.83 608.51 259.80 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n180 27 Pedestrian -1 -1 -1 746.10 169.05 803.33 311.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n180 64 Cyclist -1 -1 -1 883.75 173.20 926.32 223.35 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n181 3 Car -1 -1 -1 1095.05 185.09 1220.60 236.28 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n181 4 Car -1 -1 -1 1029.41 183.72 1156.36 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n181 2 Car -1 -1 -1 954.72 183.59 1067.30 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n181 7 Car -1 -1 -1 600.79 172.76 637.06 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n181 59 Pedestrian -1 -1 -1 533.91 162.47 579.90 265.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n181 23 Car -1 -1 -1 -2.91 99.88 229.55 365.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n181 55 Pedestrian -1 -1 -1 708.43 151.76 787.22 337.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n181 27 Pedestrian -1 -1 -1 753.50 170.38 818.51 311.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n181 63 Pedestrian -1 -1 -1 565.60 160.50 600.39 259.69 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n181 64 Cyclist -1 -1 -1 876.71 171.87 917.28 224.23 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n181 65 Pedestrian -1 -1 -1 190.09 156.03 204.55 196.90 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n181 66 Pedestrian -1 -1 -1 224.07 157.51 237.06 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n182 3 Car -1 -1 -1 1095.21 185.11 1220.62 236.28 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0.69\n183 23 Car -1 -1 -1 -3.37 92.62 146.45 357.79 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n183 27 Pedestrian -1 -1 -1 777.42 171.80 832.76 317.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n183 68 Pedestrian -1 -1 -1 733.77 169.85 752.56 226.28 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n183 64 Cyclist -1 -1 -1 861.00 169.16 902.47 226.49 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n183 66 Pedestrian -1 -1 -1 224.20 157.37 236.72 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n184 3 Car -1 -1 -1 1095.23 185.03 1220.39 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n184 4 Car -1 -1 -1 1029.48 183.77 1156.26 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n184 2 Car -1 -1 -1 954.87 183.90 1066.74 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n184 7 Car -1 -1 -1 601.27 172.89 637.09 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n184 55 Pedestrian -1 -1 -1 737.46 151.41 826.77 344.44 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n184 63 Pedestrian -1 -1 -1 546.97 161.66 588.44 258.27 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n184 59 Pedestrian -1 -1 -1 517.67 162.74 558.23 263.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n184 65 Pedestrian -1 -1 -1 192.25 154.86 209.76 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n184 67 Pedestrian -1 -1 -1 710.49 171.23 730.18 225.81 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n184 68 Pedestrian -1 -1 -1 734.26 169.74 752.98 225.87 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n184 27 Pedestrian -1 -1 -1 788.78 170.92 844.24 324.99 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n184 23 Car -1 -1 -1 -4.47 113.05 92.62 360.25 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n184 64 Cyclist -1 -1 -1 855.98 168.95 900.04 226.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n184 66 Pedestrian -1 -1 -1 223.76 157.29 237.31 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n184 69 Pedestrian -1 -1 -1 217.59 156.28 230.40 196.86 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n184 70 Car -1 -1 -1 599.19 173.63 621.60 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n185 3 Car -1 -1 -1 1095.63 185.12 1219.82 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n185 2 Car -1 -1 -1 954.98 183.91 1066.83 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n185 4 Car -1 -1 -1 1029.48 183.87 1156.34 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n185 7 Car -1 -1 -1 601.66 172.96 636.61 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n185 55 Pedestrian -1 -1 -1 741.83 152.06 837.71 344.20 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n185 67 Pedestrian -1 -1 -1 710.65 171.43 729.97 225.98 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n185 63 Pedestrian -1 -1 -1 541.85 162.11 579.95 257.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n185 59 Pedestrian -1 -1 -1 515.49 161.42 551.66 261.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n185 65 Pedestrian -1 -1 -1 192.81 154.53 211.21 198.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n185 68 Pedestrian -1 -1 -1 734.43 170.29 753.59 226.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n185 64 Cyclist -1 -1 -1 853.21 170.29 894.96 225.68 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n185 27 Pedestrian -1 -1 -1 792.11 171.57 856.53 324.34 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n185 66 Pedestrian -1 -1 -1 223.62 157.19 237.43 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n185 70 Car -1 -1 -1 599.24 173.57 621.39 193.13 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n186 2 Car -1 -1 -1 954.90 183.91 1066.93 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n186 3 Car -1 -1 -1 1094.49 185.14 1221.17 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n186 4 Car -1 -1 -1 1029.37 183.95 1156.54 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n186 7 Car -1 -1 -1 601.76 172.84 636.56 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n186 55 Pedestrian -1 -1 -1 750.93 148.79 851.39 347.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n186 59 Pedestrian -1 -1 -1 508.41 163.33 544.18 258.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n186 67 Pedestrian -1 -1 -1 710.90 171.56 729.92 226.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n186 63 Pedestrian -1 -1 -1 536.02 160.78 570.69 257.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n186 68 Pedestrian -1 -1 -1 733.79 170.70 753.79 226.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n186 65 Pedestrian -1 -1 -1 194.23 154.37 212.59 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n186 64 Cyclist -1 -1 -1 854.09 171.90 893.27 223.26 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n186 27 Pedestrian -1 -1 -1 802.06 168.94 869.37 326.71 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n186 66 Pedestrian -1 -1 -1 223.29 156.92 237.82 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n186 70 Car -1 -1 -1 599.10 173.47 620.97 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n186 71 Pedestrian -1 -1 -1 1153.93 139.60 1213.91 341.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n186 72 Pedestrian -1 -1 -1 216.69 157.69 230.24 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n187 2 Car -1 -1 -1 955.14 183.87 1066.64 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n187 3 Car -1 -1 -1 1094.86 184.59 1220.40 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n187 4 Car -1 -1 -1 1030.15 183.94 1155.73 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n187 7 Car -1 -1 -1 601.67 172.72 636.73 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n187 59 Pedestrian -1 -1 -1 503.23 164.07 540.67 257.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n187 55 Pedestrian -1 -1 -1 761.24 148.02 856.81 348.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n187 67 Pedestrian -1 -1 -1 711.46 171.47 730.28 227.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n187 65 Pedestrian -1 -1 -1 194.90 153.61 212.63 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n187 63 Pedestrian -1 -1 -1 532.43 161.00 566.21 254.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n187 68 Pedestrian -1 -1 -1 734.44 170.94 754.19 227.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n187 71 Pedestrian -1 -1 -1 1150.30 137.48 1217.18 334.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n187 27 Pedestrian -1 -1 -1 814.71 167.16 879.85 328.36 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n187 70 Car -1 -1 -1 598.73 173.23 621.02 193.18 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n187 66 Pedestrian -1 -1 -1 223.26 156.84 237.88 199.04 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n187 72 Pedestrian -1 -1 -1 216.75 157.67 230.50 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n187 73 Pedestrian -1 -1 -1 180.59 152.16 198.15 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n188 3 Car -1 -1 -1 1092.85 184.88 1222.24 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n188 2 Car -1 -1 -1 955.37 183.81 1066.54 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n188 4 Car -1 -1 -1 1029.82 183.84 1155.79 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n188 65 Pedestrian -1 -1 -1 195.02 153.54 213.18 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n188 7 Car -1 -1 -1 601.59 172.79 636.84 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n188 55 Pedestrian -1 -1 -1 779.58 149.94 868.58 347.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n188 59 Pedestrian -1 -1 -1 497.59 165.39 532.79 256.63 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n188 71 Pedestrian -1 -1 -1 1109.37 140.71 1204.62 331.68 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n188 63 Pedestrian -1 -1 -1 525.22 161.06 563.93 253.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n188 67 Pedestrian -1 -1 -1 711.36 172.05 730.24 227.50 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n188 68 Pedestrian -1 -1 -1 734.39 171.02 754.47 227.64 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n188 27 Pedestrian -1 -1 -1 837.90 169.93 886.90 320.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n188 70 Car -1 -1 -1 598.73 173.26 621.30 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n188 66 Pedestrian -1 -1 -1 223.21 156.73 237.73 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n189 2 Car -1 -1 -1 955.40 183.70 1066.39 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n189 3 Car -1 -1 -1 1094.03 184.25 1221.38 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n189 4 Car -1 -1 -1 1029.09 184.08 1156.26 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n189 65 Pedestrian -1 -1 -1 194.82 153.64 213.37 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n189 7 Car -1 -1 -1 601.71 172.89 636.80 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n189 59 Pedestrian -1 -1 -1 493.11 164.54 527.61 256.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n189 63 Pedestrian -1 -1 -1 517.86 161.98 557.92 252.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n189 71 Pedestrian -1 -1 -1 1081.85 143.70 1193.91 328.44 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n189 55 Pedestrian -1 -1 -1 795.90 153.92 875.83 349.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n189 68 Pedestrian -1 -1 -1 734.55 170.80 754.80 228.08 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n189 67 Pedestrian -1 -1 -1 711.90 172.60 730.32 227.45 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n189 70 Car -1 -1 -1 598.69 173.34 621.64 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n189 27 Pedestrian -1 -1 -1 846.01 169.00 901.38 326.82 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n189 66 Pedestrian -1 -1 -1 223.07 156.65 237.92 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n190 3 Car -1 -1 -1 1095.55 184.42 1219.81 236.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n190 2 Car -1 -1 -1 955.41 183.52 1066.50 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n190 4 Car -1 -1 -1 1027.86 183.61 1157.57 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n190 65 Pedestrian -1 -1 -1 195.28 153.83 213.01 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n190 7 Car -1 -1 -1 601.84 173.06 636.78 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n190 59 Pedestrian -1 -1 -1 486.26 163.49 521.37 257.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n190 55 Pedestrian -1 -1 -1 802.35 152.21 899.95 351.77 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n190 71 Pedestrian -1 -1 -1 1062.38 144.04 1175.29 328.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n190 63 Pedestrian -1 -1 -1 515.78 162.14 552.28 252.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n190 67 Pedestrian -1 -1 -1 711.84 173.01 730.97 228.67 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n190 68 Pedestrian -1 -1 -1 734.82 171.25 754.95 228.03 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n190 27 Pedestrian -1 -1 -1 849.84 167.24 913.68 329.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n190 70 Car -1 -1 -1 598.69 173.41 621.75 193.50 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n190 66 Pedestrian -1 -1 -1 220.58 157.45 235.74 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n191 3 Car -1 -1 -1 1094.68 184.91 1219.81 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n191 2 Car -1 -1 -1 955.04 183.20 1066.83 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n191 59 Pedestrian -1 -1 -1 478.80 163.49 518.73 257.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n191 4 Car -1 -1 -1 1027.89 183.49 1157.72 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n191 7 Car -1 -1 -1 601.60 172.85 636.94 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n191 65 Pedestrian -1 -1 -1 194.90 153.80 212.87 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n191 71 Pedestrian -1 -1 -1 1052.14 142.42 1131.54 316.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n191 63 Pedestrian -1 -1 -1 514.13 162.58 544.77 251.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n191 55 Pedestrian -1 -1 -1 813.89 149.32 919.08 355.07 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n191 67 Pedestrian -1 -1 -1 712.34 172.59 731.13 228.83 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n191 27 Pedestrian -1 -1 -1 853.38 165.28 933.17 331.70 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n191 70 Car -1 -1 -1 598.51 173.36 621.88 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n191 68 Pedestrian -1 -1 -1 736.70 170.92 756.84 228.90 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n191 66 Pedestrian -1 -1 -1 220.68 157.74 235.93 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n192 3 Car -1 -1 -1 1094.36 185.04 1220.58 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n192 2 Car -1 -1 -1 955.26 183.36 1066.88 231.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n192 4 Car -1 -1 -1 1032.14 183.29 1158.61 234.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n192 59 Pedestrian -1 -1 -1 475.25 165.24 514.74 256.34 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n192 63 Pedestrian -1 -1 -1 506.59 161.10 538.42 252.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n192 7 Car -1 -1 -1 601.51 172.86 637.08 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n192 65 Pedestrian -1 -1 -1 193.05 153.34 211.08 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n192 55 Pedestrian -1 -1 -1 836.53 151.63 934.34 360.96 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n192 71 Pedestrian -1 -1 -1 1031.83 148.39 1091.28 316.63 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n192 68 Pedestrian -1 -1 -1 736.79 171.69 757.40 230.18 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n192 67 Pedestrian -1 -1 -1 712.45 172.26 731.80 229.07 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n192 70 Car -1 -1 -1 598.50 173.27 622.04 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n192 66 Pedestrian -1 -1 -1 220.70 157.47 235.97 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n193 3 Car -1 -1 -1 1094.38 185.15 1221.46 236.35 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n193 2 Car -1 -1 -1 957.14 183.08 1065.88 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n193 4 Car -1 -1 -1 1029.09 183.61 1156.47 234.52 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n193 63 Pedestrian -1 -1 -1 499.59 160.90 536.59 251.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n193 59 Pedestrian -1 -1 -1 471.91 164.90 509.78 256.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n193 71 Pedestrian -1 -1 -1 992.68 142.59 1076.82 314.62 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n193 7 Car -1 -1 -1 601.58 172.94 636.90 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n193 65 Pedestrian -1 -1 -1 193.39 153.62 210.54 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n193 55 Pedestrian -1 -1 -1 853.34 155.12 948.17 363.81 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n193 68 Pedestrian -1 -1 -1 737.13 171.23 757.68 230.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n193 67 Pedestrian -1 -1 -1 713.61 172.29 733.65 230.15 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n193 70 Car -1 -1 -1 598.52 173.46 621.67 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n193 66 Pedestrian -1 -1 -1 220.61 157.40 235.87 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n193 74 Pedestrian -1 -1 -1 844.74 149.60 949.28 338.79 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n193 75 Pedestrian -1 -1 -1 883.38 165.27 956.55 331.54 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n194 3 Car -1 -1 -1 1098.73 185.10 1221.04 236.67 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n194 4 Car -1 -1 -1 1029.81 183.72 1155.91 234.42 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n194 2 Car -1 -1 -1 956.96 183.58 1065.80 231.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n194 59 Pedestrian -1 -1 -1 467.85 164.52 501.23 255.18 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n194 7 Car -1 -1 -1 601.40 172.82 637.12 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n194 63 Pedestrian -1 -1 -1 495.09 162.61 532.70 251.20 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n194 71 Pedestrian -1 -1 -1 975.42 143.73 1077.95 312.66 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n194 65 Pedestrian -1 -1 -1 193.30 154.08 210.10 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n194 67 Pedestrian -1 -1 -1 713.70 172.40 734.24 230.48 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n194 55 Pedestrian -1 -1 -1 867.09 147.83 949.92 363.58 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n194 68 Pedestrian -1 -1 -1 737.83 170.92 758.12 230.68 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n194 70 Car -1 -1 -1 598.59 173.46 621.68 193.76 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n194 66 Pedestrian -1 -1 -1 220.73 157.34 235.86 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n194 75 Pedestrian -1 -1 -1 903.29 164.39 967.03 332.17 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n195 3 Car -1 -1 -1 1098.68 185.13 1221.16 236.62 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n195 4 Car -1 -1 -1 1030.66 183.74 1154.89 234.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n195 2 Car -1 -1 -1 952.17 183.34 1070.11 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n195 63 Pedestrian -1 -1 -1 489.17 162.05 525.44 251.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n195 59 Pedestrian -1 -1 -1 463.73 164.64 497.22 255.52 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n195 7 Car -1 -1 -1 601.29 172.65 637.18 203.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n195 65 Pedestrian -1 -1 -1 192.89 154.38 209.30 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n195 67 Pedestrian -1 -1 -1 714.62 171.68 734.94 230.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n195 55 Pedestrian -1 -1 -1 887.87 152.96 975.17 366.34 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n195 68 Pedestrian -1 -1 -1 738.51 170.53 758.44 229.50 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n195 71 Pedestrian -1 -1 -1 953.48 148.65 1054.56 303.00 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n195 75 Pedestrian -1 -1 -1 875.45 147.10 972.23 341.50 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n195 70 Car -1 -1 -1 598.56 173.26 621.60 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n195 66 Pedestrian -1 -1 -1 220.79 157.50 235.90 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n195 76 Cyclist -1 -1 -1 832.85 170.14 869.46 232.51 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n196 3 Car -1 -1 -1 1094.78 185.20 1221.28 236.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n196 4 Car -1 -1 -1 1030.93 183.85 1154.75 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n196 63 Pedestrian -1 -1 -1 485.76 162.43 520.30 250.64 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n196 59 Pedestrian -1 -1 -1 460.54 165.59 492.46 253.66 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n196 2 Car -1 -1 -1 955.00 183.84 1066.80 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n196 7 Car -1 -1 -1 601.43 172.64 637.28 203.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n196 65 Pedestrian -1 -1 -1 191.89 154.35 208.55 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n196 55 Pedestrian -1 -1 -1 898.92 158.29 1009.73 361.91 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n196 67 Pedestrian -1 -1 -1 715.81 171.69 735.86 230.36 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n196 75 Pedestrian -1 -1 -1 882.77 147.52 987.78 341.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n196 68 Pedestrian -1 -1 -1 740.25 171.06 760.67 230.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n196 70 Car -1 -1 -1 598.56 173.35 621.53 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n196 66 Pedestrian -1 -1 -1 220.69 157.28 235.97 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n196 71 Pedestrian -1 -1 -1 942.28 149.72 1019.97 301.55 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n196 77 Pedestrian -1 -1 -1 207.77 155.04 224.59 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n197 3 Car -1 -1 -1 1095.07 185.23 1220.73 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n197 4 Car -1 -1 -1 1030.73 183.88 1155.04 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n197 59 Pedestrian -1 -1 -1 456.80 165.68 488.14 253.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n197 63 Pedestrian -1 -1 -1 478.20 161.29 513.23 250.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n197 68 Pedestrian -1 -1 -1 741.03 171.30 761.80 231.23 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n197 2 Car -1 -1 -1 956.07 184.33 1065.42 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n197 7 Car -1 -1 -1 601.55 172.67 637.10 203.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n197 75 Pedestrian -1 -1 -1 889.12 148.51 1004.69 347.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n197 55 Pedestrian -1 -1 -1 911.04 153.26 1028.38 366.19 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n197 67 Pedestrian -1 -1 -1 717.42 172.12 737.72 230.91 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n197 65 Pedestrian -1 -1 -1 191.32 154.44 207.74 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n197 70 Car -1 -1 -1 598.60 173.40 621.48 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n197 66 Pedestrian -1 -1 -1 220.74 157.51 235.82 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n197 78 Cyclist -1 -1 -1 833.03 170.20 868.74 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n198 3 Car -1 -1 -1 1095.13 185.27 1220.92 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n198 4 Car -1 -1 -1 1029.74 183.75 1155.84 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n198 2 Car -1 -1 -1 951.47 183.23 1065.74 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n198 7 Car -1 -1 -1 601.61 172.66 636.84 203.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n198 59 Pedestrian -1 -1 -1 453.18 165.71 483.39 253.21 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n198 63 Pedestrian -1 -1 -1 471.06 161.15 510.78 250.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n198 75 Pedestrian -1 -1 -1 896.00 142.69 1028.08 361.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n198 67 Pedestrian -1 -1 -1 717.60 172.21 737.78 231.40 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n198 68 Pedestrian -1 -1 -1 742.17 170.91 763.30 230.95 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n198 55 Pedestrian -1 -1 -1 919.85 152.72 1050.15 366.57 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n198 65 Pedestrian -1 -1 -1 191.74 154.03 207.58 199.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n198 70 Car -1 -1 -1 598.53 173.34 621.40 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n198 66 Pedestrian -1 -1 -1 223.41 157.00 237.76 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n198 78 Cyclist -1 -1 -1 833.27 169.74 868.56 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n198 79 Pedestrian -1 -1 -1 501.87 165.25 518.40 207.65 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n199 3 Car -1 -1 -1 1099.13 185.05 1220.56 236.62 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n199 4 Car -1 -1 -1 1032.73 183.69 1157.19 234.41 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n199 2 Car -1 -1 -1 954.43 183.72 1068.13 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n199 55 Pedestrian -1 -1 -1 933.39 142.49 1059.59 369.50 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n199 7 Car -1 -1 -1 601.52 172.65 636.94 203.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n199 67 Pedestrian -1 -1 -1 717.96 171.57 737.90 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n199 63 Pedestrian -1 -1 -1 467.53 162.49 506.82 249.10 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n199 68 Pedestrian -1 -1 -1 744.34 170.35 764.97 231.53 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n199 59 Pedestrian -1 -1 -1 448.60 165.46 479.74 252.31 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n199 65 Pedestrian -1 -1 -1 190.41 153.17 206.07 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n199 75 Pedestrian -1 -1 -1 889.33 149.39 981.11 293.72 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n199 70 Car -1 -1 -1 598.53 173.28 621.56 193.68 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n199 78 Cyclist -1 -1 -1 831.54 169.95 870.60 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n199 66 Pedestrian -1 -1 -1 223.50 156.92 237.76 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n199 79 Pedestrian -1 -1 -1 499.05 166.14 515.62 207.56 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n200 3 Car -1 -1 -1 1099.37 185.05 1220.69 236.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n200 2 Car -1 -1 -1 954.53 183.54 1067.84 231.33 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n200 4 Car -1 -1 -1 1033.15 183.72 1156.82 234.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n200 67 Pedestrian -1 -1 -1 718.33 171.17 737.98 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n200 7 Car -1 -1 -1 601.74 172.60 636.67 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n200 75 Pedestrian -1 -1 -1 871.92 141.98 959.87 301.49 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n200 63 Pedestrian -1 -1 -1 463.26 162.79 497.80 248.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n200 55 Pedestrian -1 -1 -1 955.24 147.17 1068.06 364.38 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n200 59 Pedestrian -1 -1 -1 443.59 164.65 477.15 250.39 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n200 65 Pedestrian -1 -1 -1 190.10 152.91 206.07 199.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n200 68 Pedestrian -1 -1 -1 744.86 169.89 765.62 231.57 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n200 70 Car -1 -1 -1 598.63 173.21 621.41 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n200 79 Pedestrian -1 -1 -1 495.57 167.22 512.49 207.85 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n200 78 Cyclist -1 -1 -1 832.02 167.79 870.46 235.03 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n200 66 Pedestrian -1 -1 -1 220.49 155.90 235.73 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n201 3 Car -1 -1 -1 1099.65 185.17 1220.44 236.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n201 2 Car -1 -1 -1 956.05 183.44 1066.30 231.43 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n201 4 Car -1 -1 -1 1033.86 184.01 1156.31 234.40 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n201 67 Pedestrian -1 -1 -1 718.96 171.41 738.76 232.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n201 75 Pedestrian -1 -1 -1 871.14 139.75 929.62 301.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n201 7 Car -1 -1 -1 601.47 172.61 637.13 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n201 59 Pedestrian -1 -1 -1 434.51 164.35 473.56 250.47 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n201 63 Pedestrian -1 -1 -1 459.55 160.38 492.39 249.63 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n201 65 Pedestrian -1 -1 -1 189.99 152.56 206.35 199.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n201 68 Pedestrian -1 -1 -1 745.61 170.01 765.96 231.83 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n201 55 Pedestrian -1 -1 -1 991.07 146.54 1086.23 365.65 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n201 70 Car -1 -1 -1 598.58 173.18 621.78 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n201 66 Pedestrian -1 -1 -1 220.50 155.76 235.75 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n201 78 Cyclist -1 -1 -1 833.89 168.10 868.86 234.70 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n201 80 Pedestrian -1 -1 -1 958.55 147.83 1057.18 355.86 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n202 3 Car -1 -1 -1 1094.74 184.89 1221.14 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n202 4 Car -1 -1 -1 1033.63 184.23 1156.49 234.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n202 75 Pedestrian -1 -1 -1 848.66 141.89 906.46 293.82 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n202 2 Car -1 -1 -1 957.62 182.62 1064.05 231.95 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n202 7 Car -1 -1 -1 601.60 172.93 637.01 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n202 67 Pedestrian -1 -1 -1 719.18 172.09 739.58 232.86 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n202 63 Pedestrian -1 -1 -1 456.17 160.31 488.77 249.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n202 59 Pedestrian -1 -1 -1 433.87 165.79 471.12 249.12 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n202 65 Pedestrian -1 -1 -1 189.37 152.47 206.49 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n202 55 Pedestrian -1 -1 -1 1011.32 143.02 1119.11 368.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n202 68 Pedestrian -1 -1 -1 745.66 170.30 766.56 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n202 70 Car -1 -1 -1 598.50 173.46 621.61 193.50 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n202 80 Pedestrian -1 -1 -1 965.29 146.17 1081.08 357.46 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n202 66 Pedestrian -1 -1 -1 220.59 155.88 235.77 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n203 3 Car -1 -1 -1 1094.10 184.81 1221.22 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n203 4 Car -1 -1 -1 1033.32 184.13 1156.95 234.55 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n203 2 Car -1 -1 -1 957.76 182.96 1064.26 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n203 63 Pedestrian -1 -1 -1 450.32 162.05 486.57 249.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n203 7 Car -1 -1 -1 601.65 172.87 636.87 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n203 67 Pedestrian -1 -1 -1 721.38 171.76 741.98 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n203 75 Pedestrian -1 -1 -1 815.19 143.35 902.94 291.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n203 59 Pedestrian -1 -1 -1 428.98 166.83 462.91 248.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n203 65 Pedestrian -1 -1 -1 189.35 152.01 206.33 198.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n203 68 Pedestrian -1 -1 -1 748.14 169.48 768.50 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n203 55 Pedestrian -1 -1 -1 1026.27 146.82 1157.59 364.96 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n203 80 Pedestrian -1 -1 -1 981.57 145.53 1103.03 358.17 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n203 70 Car -1 -1 -1 598.50 173.38 621.64 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n203 66 Pedestrian -1 -1 -1 220.23 155.71 235.94 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n203 81 Pedestrian -1 -1 -1 488.51 171.29 504.19 207.46 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n204 4 Car -1 -1 -1 1032.03 184.02 1158.46 234.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n204 3 Car -1 -1 -1 1094.46 184.57 1221.15 236.42 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n204 2 Car -1 -1 -1 957.44 183.53 1064.02 231.49 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n204 67 Pedestrian -1 -1 -1 722.12 171.39 742.90 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n204 63 Pedestrian -1 -1 -1 447.14 163.12 483.44 248.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n204 59 Pedestrian -1 -1 -1 426.05 165.63 456.61 248.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n204 65 Pedestrian -1 -1 -1 188.63 152.47 206.34 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n204 7 Car -1 -1 -1 601.60 172.73 636.99 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n204 68 Pedestrian -1 -1 -1 749.27 169.38 769.32 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n204 80 Pedestrian -1 -1 -1 991.10 145.99 1108.97 365.72 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n204 75 Pedestrian -1 -1 -1 803.84 144.63 891.11 291.02 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n204 66 Pedestrian -1 -1 -1 220.41 155.44 235.98 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n204 70 Car -1 -1 -1 598.30 173.33 621.61 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n204 55 Pedestrian -1 -1 -1 1044.98 154.20 1169.58 364.94 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n205 4 Car -1 -1 -1 1028.96 183.98 1156.74 234.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n205 3 Car -1 -1 -1 1093.93 185.01 1221.52 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n205 2 Car -1 -1 -1 956.59 183.17 1065.20 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n205 7 Car -1 -1 -1 601.47 172.59 637.07 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n205 63 Pedestrian -1 -1 -1 444.29 163.00 477.54 247.54 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n205 65 Pedestrian -1 -1 -1 188.40 152.71 206.15 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n205 75 Pedestrian -1 -1 -1 798.93 145.85 872.64 291.01 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n205 67 Pedestrian -1 -1 -1 723.05 171.16 743.37 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n205 68 Pedestrian -1 -1 -1 749.71 169.87 770.26 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n205 59 Pedestrian -1 -1 -1 421.94 165.41 452.41 248.69 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n205 80 Pedestrian -1 -1 -1 1025.29 145.11 1135.92 365.56 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n205 66 Pedestrian -1 -1 -1 220.20 155.45 235.78 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n205 70 Car -1 -1 -1 598.26 173.38 621.38 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n205 55 Pedestrian -1 -1 -1 1044.88 158.82 1185.12 367.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n205 82 Cyclist -1 -1 -1 479.94 169.02 500.64 204.64 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n206 4 Car -1 -1 -1 1033.96 183.87 1156.25 234.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n206 2 Car -1 -1 -1 956.70 183.38 1065.31 231.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n206 3 Car -1 -1 -1 1093.61 185.16 1221.72 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n206 59 Pedestrian -1 -1 -1 415.60 165.41 446.13 246.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n206 75 Pedestrian -1 -1 -1 795.40 144.91 845.63 290.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n206 7 Car -1 -1 -1 601.44 172.69 637.21 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n206 65 Pedestrian -1 -1 -1 188.53 152.56 206.12 199.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n206 63 Pedestrian -1 -1 -1 442.42 161.58 470.75 245.61 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n206 67 Pedestrian -1 -1 -1 724.70 171.53 746.15 235.08 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n206 68 Pedestrian -1 -1 -1 751.90 170.22 772.31 234.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n206 80 Pedestrian -1 -1 -1 1049.34 146.52 1134.84 365.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n206 66 Pedestrian -1 -1 -1 220.29 155.40 235.70 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n206 70 Car -1 -1 -1 598.34 173.30 621.64 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n206 55 Pedestrian -1 -1 -1 1083.60 167.34 1192.65 366.77 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n206 82 Cyclist -1 -1 -1 476.83 168.09 497.41 204.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n207 3 Car -1 -1 -1 1093.41 184.86 1221.96 236.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n207 2 Car -1 -1 -1 956.07 183.35 1065.61 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n207 75 Pedestrian -1 -1 -1 771.83 142.96 830.93 290.52 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n207 4 Car -1 -1 -1 1030.60 183.42 1154.67 234.65 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n207 7 Car -1 -1 -1 601.37 172.70 637.09 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n207 59 Pedestrian -1 -1 -1 411.55 165.88 442.89 247.26 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n207 63 Pedestrian -1 -1 -1 436.62 161.67 468.47 245.24 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n207 65 Pedestrian -1 -1 -1 188.75 152.48 206.20 198.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n207 67 Pedestrian -1 -1 -1 725.20 171.70 746.71 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n207 68 Pedestrian -1 -1 -1 753.31 169.85 772.69 234.46 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n207 82 Cyclist -1 -1 -1 474.22 167.97 494.22 204.89 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n207 80 Pedestrian -1 -1 -1 1056.56 142.44 1173.51 368.63 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n207 66 Pedestrian -1 -1 -1 220.60 155.30 235.75 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n207 70 Car -1 -1 -1 598.40 173.47 621.35 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n207 55 Pedestrian -1 -1 -1 1107.10 137.61 1214.34 366.49 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n207 83 Pedestrian -1 -1 -1 1095.40 159.82 1211.01 367.16 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n208 3 Car -1 -1 -1 1093.51 184.95 1221.88 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n208 2 Car -1 -1 -1 955.45 183.57 1066.17 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n208 4 Car -1 -1 -1 1031.78 183.55 1153.38 234.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n208 75 Pedestrian -1 -1 -1 749.63 145.34 829.79 289.12 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n208 59 Pedestrian -1 -1 -1 407.34 166.69 439.04 246.64 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n208 7 Car -1 -1 -1 601.80 172.97 636.71 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n208 67 Pedestrian -1 -1 -1 725.09 170.62 747.12 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n208 63 Pedestrian -1 -1 -1 431.62 163.15 466.57 244.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n208 65 Pedestrian -1 -1 -1 188.76 152.60 206.96 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n208 80 Pedestrian -1 -1 -1 1063.87 146.50 1196.75 365.58 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n208 68 Pedestrian -1 -1 -1 755.47 167.54 776.91 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n208 82 Cyclist -1 -1 -1 470.01 168.15 491.09 204.84 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n208 66 Pedestrian -1 -1 -1 220.35 155.17 235.58 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n208 70 Car -1 -1 -1 598.36 173.49 621.41 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n208 83 Pedestrian -1 -1 -1 1112.67 159.10 1216.54 367.57 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n209 3 Car -1 -1 -1 1093.32 184.87 1222.02 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n209 2 Car -1 -1 -1 955.33 183.61 1066.38 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n209 4 Car -1 -1 -1 1031.94 183.38 1152.92 234.35 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n209 75 Pedestrian -1 -1 -1 737.69 149.04 819.08 286.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n209 59 Pedestrian -1 -1 -1 403.78 165.55 433.69 246.26 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n209 67 Pedestrian -1 -1 -1 725.67 170.29 748.15 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n209 7 Car -1 -1 -1 601.95 173.02 636.71 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n209 65 Pedestrian -1 -1 -1 190.72 153.21 209.46 199.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n209 63 Pedestrian -1 -1 -1 427.90 163.02 463.46 246.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n209 80 Pedestrian -1 -1 -1 1085.75 150.28 1205.53 361.97 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n209 68 Pedestrian -1 -1 -1 754.06 164.97 786.28 239.21 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n209 82 Cyclist -1 -1 -1 464.71 169.34 488.52 204.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n209 70 Car -1 -1 -1 598.56 173.46 621.34 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n209 66 Pedestrian -1 -1 -1 220.32 155.15 235.73 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n210 3 Car -1 -1 -1 1094.16 184.46 1221.05 236.12 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n210 2 Car -1 -1 -1 955.33 183.63 1066.50 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n210 4 Car -1 -1 -1 1031.13 183.35 1154.32 234.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n210 65 Pedestrian -1 -1 -1 191.50 153.25 210.01 199.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n210 59 Pedestrian -1 -1 -1 396.89 163.94 431.94 246.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n210 7 Car -1 -1 -1 601.86 173.03 636.70 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n210 75 Pedestrian -1 -1 -1 734.70 144.12 791.90 285.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n210 63 Pedestrian -1 -1 -1 426.96 164.47 457.39 243.07 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n210 80 Pedestrian -1 -1 -1 1104.87 148.11 1216.85 363.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n210 67 Pedestrian -1 -1 -1 728.01 169.20 750.58 237.74 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n210 70 Car -1 -1 -1 598.23 173.47 621.35 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n210 66 Pedestrian -1 -1 -1 220.26 155.53 235.90 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n210 82 Cyclist -1 -1 -1 461.97 168.30 483.88 204.83 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n210 68 Pedestrian -1 -1 -1 754.24 165.80 779.35 238.04 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n210 84 Cyclist -1 -1 -1 833.24 172.14 869.90 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n211 4 Car -1 -1 -1 1031.13 183.43 1154.25 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n211 2 Car -1 -1 -1 955.31 183.67 1066.34 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n211 59 Pedestrian -1 -1 -1 385.23 164.97 429.95 246.37 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n211 3 Car -1 -1 -1 1095.97 184.39 1219.36 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n211 63 Pedestrian -1 -1 -1 423.16 163.32 451.36 242.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n211 75 Pedestrian -1 -1 -1 722.67 149.59 766.44 284.50 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n211 7 Car -1 -1 -1 601.65 172.87 636.96 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n211 65 Pedestrian -1 -1 -1 192.52 152.77 210.47 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n211 67 Pedestrian -1 -1 -1 728.96 167.97 751.96 241.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n211 80 Pedestrian -1 -1 -1 1123.37 145.96 1221.37 365.07 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n211 70 Car -1 -1 -1 598.23 173.51 621.38 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n211 66 Pedestrian -1 -1 -1 220.31 155.45 235.89 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n211 68 Pedestrian -1 -1 -1 753.35 165.80 780.89 239.36 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n211 82 Cyclist -1 -1 -1 458.16 167.93 480.32 204.97 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n211 84 Cyclist -1 -1 -1 833.34 172.40 869.30 232.65 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n212 2 Car -1 -1 -1 955.08 183.72 1066.65 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n212 4 Car -1 -1 -1 1030.13 183.57 1155.44 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n212 3 Car -1 -1 -1 1097.54 184.59 1217.65 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n212 59 Pedestrian -1 -1 -1 381.44 166.33 425.39 246.08 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n212 75 Pedestrian -1 -1 -1 699.17 146.54 758.99 282.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n212 63 Pedestrian -1 -1 -1 417.46 163.14 449.25 241.89 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n212 7 Car -1 -1 -1 601.59 172.93 637.10 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n212 65 Pedestrian -1 -1 -1 193.18 153.16 210.10 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n212 80 Pedestrian -1 -1 -1 1142.68 146.08 1224.81 365.19 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n212 70 Car -1 -1 -1 598.22 173.42 621.42 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n212 66 Pedestrian -1 -1 -1 220.34 155.50 235.71 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n212 67 Pedestrian -1 -1 -1 725.74 164.22 754.27 245.72 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n212 68 Pedestrian -1 -1 -1 758.08 167.66 782.47 237.05 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n212 84 Cyclist -1 -1 -1 831.40 172.31 871.56 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n212 85 Pedestrian -1 -1 -1 205.17 159.53 220.27 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n213 4 Car -1 -1 -1 1029.75 183.56 1155.82 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n213 2 Car -1 -1 -1 955.27 183.79 1066.33 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n213 3 Car -1 -1 -1 1097.38 184.78 1218.29 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n213 63 Pedestrian -1 -1 -1 412.89 163.97 446.28 241.62 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n213 75 Pedestrian -1 -1 -1 685.28 146.28 755.92 281.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n213 7 Car -1 -1 -1 601.55 173.01 637.08 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n213 59 Pedestrian -1 -1 -1 380.43 165.37 417.86 245.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n213 65 Pedestrian -1 -1 -1 195.09 153.02 212.43 199.06 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n213 68 Pedestrian -1 -1 -1 759.21 167.17 782.53 237.15 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n213 66 Pedestrian -1 -1 -1 220.06 155.53 235.93 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n213 70 Car -1 -1 -1 598.03 173.44 621.48 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n213 85 Pedestrian -1 -1 -1 205.48 159.49 220.43 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n213 67 Pedestrian -1 -1 -1 724.37 166.12 756.49 245.12 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n213 84 Cyclist -1 -1 -1 829.81 173.06 872.67 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n213 80 Pedestrian -1 -1 -1 1158.83 146.23 1224.01 365.43 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n214 3 Car -1 -1 -1 1096.84 184.80 1218.87 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n214 2 Car -1 -1 -1 955.11 183.82 1066.33 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n214 4 Car -1 -1 -1 1029.75 183.66 1156.05 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n214 59 Pedestrian -1 -1 -1 377.61 164.60 407.38 246.06 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n214 65 Pedestrian -1 -1 -1 195.46 153.18 212.93 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n214 63 Pedestrian -1 -1 -1 408.62 164.69 443.09 242.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n214 7 Car -1 -1 -1 601.62 172.87 637.08 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n214 75 Pedestrian -1 -1 -1 676.94 148.24 742.29 284.80 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n214 68 Pedestrian -1 -1 -1 759.61 168.35 783.31 237.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n214 67 Pedestrian -1 -1 -1 732.00 168.56 754.99 238.06 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n214 84 Cyclist -1 -1 -1 823.93 172.05 870.86 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n214 70 Car -1 -1 -1 598.00 173.28 621.42 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n214 66 Pedestrian -1 -1 -1 220.35 155.86 235.69 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n214 85 Pedestrian -1 -1 -1 205.74 159.51 220.61 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n215 3 Car -1 -1 -1 1096.39 184.97 1219.32 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n215 2 Car -1 -1 -1 955.05 183.88 1066.36 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n215 4 Car -1 -1 -1 1029.65 183.74 1156.18 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n215 59 Pedestrian -1 -1 -1 372.88 165.21 402.84 245.94 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n215 75 Pedestrian -1 -1 -1 675.11 145.74 727.77 281.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n215 63 Pedestrian -1 -1 -1 402.69 164.82 436.63 241.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n215 65 Pedestrian -1 -1 -1 196.04 153.10 213.07 199.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n215 7 Car -1 -1 -1 601.44 172.86 637.39 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n215 68 Pedestrian -1 -1 -1 761.99 167.82 785.76 238.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n215 67 Pedestrian -1 -1 -1 732.94 169.89 755.86 239.80 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n215 70 Car -1 -1 -1 597.96 173.17 621.44 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n215 84 Cyclist -1 -1 -1 823.36 171.53 871.45 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n215 66 Pedestrian -1 -1 -1 220.52 156.06 235.50 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n215 85 Pedestrian -1 -1 -1 205.89 159.52 220.76 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n216 3 Car -1 -1 -1 1095.74 185.10 1220.16 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n216 2 Car -1 -1 -1 955.05 183.85 1066.34 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n216 4 Car -1 -1 -1 1029.61 183.73 1156.20 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n216 63 Pedestrian -1 -1 -1 402.58 162.71 428.91 241.62 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n216 59 Pedestrian -1 -1 -1 368.78 165.73 398.76 244.49 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n216 75 Pedestrian -1 -1 -1 662.53 146.35 703.70 279.70 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n216 65 Pedestrian -1 -1 -1 196.27 153.07 213.07 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n216 7 Car -1 -1 -1 601.27 172.88 637.65 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n216 68 Pedestrian -1 -1 -1 762.79 167.81 787.11 238.75 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n216 67 Pedestrian -1 -1 -1 733.69 170.48 755.95 240.28 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n216 84 Cyclist -1 -1 -1 818.67 169.67 868.89 234.88 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n216 70 Car -1 -1 -1 597.81 173.25 621.38 193.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n216 66 Pedestrian -1 -1 -1 223.24 156.13 238.04 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n216 85 Pedestrian -1 -1 -1 208.05 158.46 222.95 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n217 3 Car -1 -1 -1 1095.80 185.16 1220.07 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n217 4 Car -1 -1 -1 1029.27 183.75 1156.45 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n217 2 Car -1 -1 -1 955.06 183.87 1066.25 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n217 63 Pedestrian -1 -1 -1 394.86 161.64 427.00 242.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n217 75 Pedestrian -1 -1 -1 642.17 146.57 700.54 278.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n217 65 Pedestrian -1 -1 -1 196.60 153.23 213.25 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n217 7 Car -1 -1 -1 601.21 172.65 637.57 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n217 67 Pedestrian -1 -1 -1 736.07 170.20 758.28 240.74 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n217 59 Pedestrian -1 -1 -1 365.32 165.09 394.75 242.36 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n217 68 Pedestrian -1 -1 -1 765.71 166.49 789.16 239.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n217 66 Pedestrian -1 -1 -1 223.28 156.84 238.22 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n217 84 Cyclist -1 -1 -1 816.16 170.25 870.89 234.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n217 70 Car -1 -1 -1 597.73 173.14 621.44 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n217 85 Pedestrian -1 -1 -1 208.53 158.95 222.84 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n218 3 Car -1 -1 -1 1095.61 185.23 1220.20 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n218 4 Car -1 -1 -1 1029.33 183.79 1156.42 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n218 2 Car -1 -1 -1 955.01 183.85 1066.30 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n218 75 Pedestrian -1 -1 -1 631.72 148.31 695.38 277.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n218 63 Pedestrian -1 -1 -1 388.73 162.72 425.64 241.35 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n218 7 Car -1 -1 -1 600.70 172.44 637.71 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n218 67 Pedestrian -1 -1 -1 736.52 169.62 759.32 240.83 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n218 65 Pedestrian -1 -1 -1 196.06 153.25 213.02 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n218 68 Pedestrian -1 -1 -1 765.76 165.39 791.67 239.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n218 59 Pedestrian -1 -1 -1 362.99 164.44 390.34 242.55 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n218 84 Cyclist -1 -1 -1 813.63 169.52 865.98 234.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n218 66 Pedestrian -1 -1 -1 223.12 156.75 238.60 199.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n218 70 Car -1 -1 -1 597.47 173.13 621.33 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n218 85 Pedestrian -1 -1 -1 208.58 159.14 223.19 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n219 3 Car -1 -1 -1 1095.49 185.25 1220.49 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n219 4 Car -1 -1 -1 1029.17 183.77 1156.60 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n219 2 Car -1 -1 -1 955.10 183.78 1066.38 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n219 75 Pedestrian -1 -1 -1 625.76 148.92 685.23 276.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n219 63 Pedestrian -1 -1 -1 386.64 163.12 420.59 240.60 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n219 7 Car -1 -1 -1 600.92 172.47 637.17 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n219 59 Pedestrian -1 -1 -1 358.46 164.49 387.31 242.26 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n219 65 Pedestrian -1 -1 -1 195.35 152.95 212.50 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n219 67 Pedestrian -1 -1 -1 737.45 170.61 763.45 241.13 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n219 68 Pedestrian -1 -1 -1 764.36 165.23 793.82 240.11 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n219 66 Pedestrian -1 -1 -1 223.55 157.00 239.05 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n219 84 Cyclist -1 -1 -1 810.01 168.15 862.74 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n219 70 Car -1 -1 -1 597.54 173.18 621.42 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n219 85 Pedestrian -1 -1 -1 208.53 158.57 223.69 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n220 3 Car -1 -1 -1 1095.57 185.23 1220.40 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n220 2 Car -1 -1 -1 955.08 183.83 1066.57 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n220 4 Car -1 -1 -1 1029.29 183.78 1156.52 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n220 75 Pedestrian -1 -1 -1 614.20 149.05 660.67 272.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n220 7 Car -1 -1 -1 601.41 172.76 636.71 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n220 63 Pedestrian -1 -1 -1 385.01 162.58 413.79 240.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n220 67 Pedestrian -1 -1 -1 737.86 171.32 764.22 242.16 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n220 59 Pedestrian -1 -1 -1 349.42 164.80 383.07 241.82 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n220 65 Pedestrian -1 -1 -1 194.96 152.93 212.46 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n220 68 Pedestrian -1 -1 -1 767.40 165.23 796.96 240.92 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n220 66 Pedestrian -1 -1 -1 223.99 156.98 239.53 199.21 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n220 84 Cyclist -1 -1 -1 809.39 167.72 862.59 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n220 70 Car -1 -1 -1 597.83 173.03 621.37 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n220 85 Pedestrian -1 -1 -1 211.85 157.47 227.98 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n221 3 Car -1 -1 -1 1095.46 185.23 1220.35 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n221 4 Car -1 -1 -1 1029.20 183.78 1156.58 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n221 2 Car -1 -1 -1 955.25 183.88 1066.29 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n221 75 Pedestrian -1 -1 -1 606.16 147.14 653.14 271.42 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n221 67 Pedestrian -1 -1 -1 739.25 171.05 765.03 242.84 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n221 59 Pedestrian -1 -1 -1 348.05 165.67 381.58 241.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n221 7 Car -1 -1 -1 601.80 172.97 636.05 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n221 65 Pedestrian -1 -1 -1 192.92 152.72 210.54 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n221 68 Pedestrian -1 -1 -1 767.78 164.99 797.09 241.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n221 66 Pedestrian -1 -1 -1 224.66 156.97 239.82 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n221 63 Pedestrian -1 -1 -1 380.05 162.03 405.83 239.46 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n221 84 Cyclist -1 -1 -1 805.89 168.87 858.14 234.00 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n221 70 Car -1 -1 -1 597.97 172.69 621.56 193.95 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n221 85 Pedestrian -1 -1 -1 208.02 158.07 224.27 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n222 3 Car -1 -1 -1 1095.56 185.22 1220.24 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n222 4 Car -1 -1 -1 1029.26 183.80 1156.52 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n222 2 Car -1 -1 -1 955.18 183.89 1066.52 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n222 75 Pedestrian -1 -1 -1 593.14 148.90 649.47 269.35 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n222 67 Pedestrian -1 -1 -1 741.91 170.17 767.42 243.37 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n222 59 Pedestrian -1 -1 -1 346.59 166.12 377.34 240.69 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n222 65 Pedestrian -1 -1 -1 192.77 152.87 210.25 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n222 7 Car -1 -1 -1 602.15 173.82 635.65 202.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n222 63 Pedestrian -1 -1 -1 373.15 162.82 403.61 238.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n222 68 Pedestrian -1 -1 -1 771.53 164.88 799.00 241.69 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n222 66 Pedestrian -1 -1 -1 224.58 156.96 239.85 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n222 84 Cyclist -1 -1 -1 805.69 169.38 858.28 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n222 70 Car -1 -1 -1 598.65 172.83 621.15 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n223 3 Car -1 -1 -1 1095.32 185.19 1220.58 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n223 4 Car -1 -1 -1 1029.31 183.76 1156.42 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n223 2 Car -1 -1 -1 955.28 183.84 1066.40 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n223 75 Pedestrian -1 -1 -1 585.02 151.85 643.74 267.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n223 7 Car -1 -1 -1 602.04 173.72 635.11 201.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n223 59 Pedestrian -1 -1 -1 344.50 165.05 372.01 239.40 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n223 63 Pedestrian -1 -1 -1 368.15 163.12 401.73 238.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n223 65 Pedestrian -1 -1 -1 192.25 153.31 209.72 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n223 67 Pedestrian -1 -1 -1 744.38 170.08 767.78 243.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n223 66 Pedestrian -1 -1 -1 224.48 157.09 239.70 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n223 68 Pedestrian -1 -1 -1 772.98 164.63 799.11 242.19 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n223 70 Car -1 -1 -1 597.53 173.00 622.04 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n223 84 Cyclist -1 -1 -1 807.03 169.95 857.24 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n224 3 Car -1 -1 -1 1095.51 185.25 1220.59 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n224 2 Car -1 -1 -1 955.28 183.84 1066.49 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n224 4 Car -1 -1 -1 1029.41 183.77 1156.34 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n224 75 Pedestrian -1 -1 -1 582.17 151.67 631.47 267.43 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n224 65 Pedestrian -1 -1 -1 191.86 153.44 209.62 199.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n224 63 Pedestrian -1 -1 -1 367.69 164.00 399.00 238.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n224 67 Pedestrian -1 -1 -1 746.04 170.43 771.10 243.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n224 7 Car -1 -1 -1 601.92 173.24 636.42 201.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n224 59 Pedestrian -1 -1 -1 341.00 164.97 368.26 238.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n224 66 Pedestrian -1 -1 -1 224.83 157.28 239.84 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n224 68 Pedestrian -1 -1 -1 775.71 165.95 801.78 243.83 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n224 70 Car -1 -1 -1 596.34 172.81 622.60 193.18 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n224 84 Cyclist -1 -1 -1 807.39 170.22 856.60 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n225 3 Car -1 -1 -1 1095.57 185.28 1220.56 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n225 2 Car -1 -1 -1 955.27 183.88 1066.51 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n225 4 Car -1 -1 -1 1029.55 183.86 1156.38 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n225 65 Pedestrian -1 -1 -1 191.30 153.44 209.15 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n225 68 Pedestrian -1 -1 -1 777.02 167.16 802.67 244.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n225 59 Pedestrian -1 -1 -1 336.45 165.53 364.72 238.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n225 7 Car -1 -1 -1 601.77 173.53 636.51 201.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n225 75 Pedestrian -1 -1 -1 574.72 149.87 615.40 267.56 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n225 63 Pedestrian -1 -1 -1 363.09 163.25 392.83 238.38 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n225 67 Pedestrian -1 -1 -1 746.90 170.68 772.95 246.42 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n225 70 Car -1 -1 -1 596.53 172.78 622.67 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n225 66 Pedestrian -1 -1 -1 226.08 157.76 242.19 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n225 84 Cyclist -1 -1 -1 806.26 171.58 856.97 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n226 3 Car -1 -1 -1 1095.32 185.23 1220.81 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n226 2 Car -1 -1 -1 955.24 183.91 1066.46 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n226 4 Car -1 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955.27 183.91 1066.48 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n227 4 Car -1 -1 -1 1029.42 183.88 1156.47 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n227 7 Car -1 -1 -1 600.72 172.76 637.29 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n227 63 Pedestrian -1 -1 -1 354.13 161.52 385.16 237.38 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n227 75 Pedestrian -1 -1 -1 552.53 152.88 606.88 264.68 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n227 65 Pedestrian -1 -1 -1 190.72 153.72 209.24 199.17 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n227 66 Pedestrian -1 -1 -1 226.74 157.25 242.79 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n227 68 Pedestrian -1 -1 -1 781.37 168.01 806.56 245.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n227 59 Pedestrian -1 -1 -1 329.88 165.25 356.63 237.87 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n227 67 Pedestrian -1 -1 -1 750.90 169.33 776.09 245.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n227 70 Car -1 -1 -1 596.67 172.61 623.46 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n227 84 Cyclist -1 -1 -1 794.38 170.10 847.16 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n228 3 Car -1 -1 -1 1095.18 185.27 1221.09 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n228 2 Car -1 -1 -1 955.21 183.89 1066.65 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n228 4 Car -1 -1 -1 1029.50 183.91 1156.37 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n228 7 Car -1 -1 -1 601.15 172.74 636.93 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n228 66 Pedestrian -1 -1 -1 226.77 157.62 243.60 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n228 63 Pedestrian -1 -1 -1 350.54 162.79 381.89 236.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n228 75 Pedestrian -1 -1 -1 551.05 153.24 599.48 264.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n228 65 Pedestrian -1 -1 -1 190.84 153.57 208.54 199.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n228 59 Pedestrian -1 -1 -1 328.91 163.74 355.26 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n228 67 Pedestrian -1 -1 -1 752.87 168.74 779.29 246.53 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n228 68 Pedestrian -1 -1 -1 783.91 168.36 809.58 244.89 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n228 70 Car -1 -1 -1 598.08 173.24 622.17 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n228 84 Cyclist -1 -1 -1 792.29 169.18 841.68 229.32 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n229 3 Car -1 -1 -1 1098.86 185.42 1220.73 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n229 2 Car -1 -1 -1 955.24 183.88 1066.60 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n229 4 Car -1 -1 -1 1032.23 183.68 1157.61 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n229 67 Pedestrian -1 -1 -1 753.99 170.31 780.90 248.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n229 7 Car -1 -1 -1 602.01 172.99 636.27 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n229 66 Pedestrian -1 -1 -1 227.39 157.57 243.88 200.43 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n229 75 Pedestrian -1 -1 -1 547.59 152.35 587.01 261.75 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n229 63 Pedestrian -1 -1 -1 350.13 163.70 378.55 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n229 65 Pedestrian -1 -1 -1 190.92 153.49 208.24 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n229 68 Pedestrian -1 -1 -1 783.99 168.97 810.95 244.44 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n229 59 Pedestrian -1 -1 -1 320.82 163.89 350.74 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n229 70 Car -1 -1 -1 598.70 173.55 621.36 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n229 84 Cyclist -1 -1 -1 785.87 168.13 840.78 230.31 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n230 3 Car -1 -1 -1 1099.05 185.45 1220.47 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n230 2 Car -1 -1 -1 955.36 183.85 1066.52 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n230 4 Car -1 -1 -1 1029.46 183.92 1156.41 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n230 75 Pedestrian -1 -1 -1 537.86 150.87 575.58 261.68 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n230 63 Pedestrian -1 -1 -1 348.47 162.49 373.84 234.94 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n230 7 Car -1 -1 -1 601.93 172.99 636.40 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n230 66 Pedestrian -1 -1 -1 227.61 157.27 244.31 200.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n230 65 Pedestrian -1 -1 -1 190.83 153.38 208.57 198.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n230 67 Pedestrian -1 -1 -1 753.77 171.56 782.04 249.77 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n230 59 Pedestrian -1 -1 -1 317.60 163.82 350.10 237.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n230 68 Pedestrian -1 -1 -1 781.59 168.22 814.45 245.51 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n230 70 Car -1 -1 -1 598.91 173.64 620.87 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n231 3 Car -1 -1 -1 1095.42 185.34 1220.90 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n231 2 Car -1 -1 -1 955.24 183.88 1066.60 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n231 4 Car -1 -1 -1 1029.56 183.92 1156.29 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n231 75 Pedestrian -1 -1 -1 524.55 151.51 572.94 258.36 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n231 63 Pedestrian -1 -1 -1 345.17 161.65 371.23 234.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n231 67 Pedestrian -1 -1 -1 757.82 170.48 785.05 249.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n231 7 Car -1 -1 -1 601.79 173.24 636.68 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n231 59 Pedestrian -1 -1 -1 313.92 164.19 346.62 235.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n231 65 Pedestrian -1 -1 -1 190.78 153.02 208.48 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n231 66 Pedestrian -1 -1 -1 227.32 156.71 245.10 200.46 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n231 68 Pedestrian -1 -1 -1 784.01 167.63 818.13 246.53 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n231 70 Car -1 -1 -1 598.55 173.56 621.06 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n232 3 Car -1 -1 -1 1095.56 185.31 1220.72 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n232 2 Car -1 -1 -1 955.29 183.84 1066.62 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n232 4 Car -1 -1 -1 1029.60 183.92 1156.31 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n232 75 Pedestrian -1 -1 -1 516.20 152.78 567.51 257.38 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n232 63 Pedestrian -1 -1 -1 340.37 162.49 368.94 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n232 7 Car -1 -1 -1 601.70 173.14 636.94 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n232 59 Pedestrian -1 -1 -1 311.68 164.04 340.70 234.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n232 67 Pedestrian -1 -1 -1 761.59 168.73 787.43 249.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n232 65 Pedestrian -1 -1 -1 190.69 153.00 208.56 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n232 66 Pedestrian -1 -1 -1 226.95 156.00 245.71 200.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n232 68 Pedestrian -1 -1 -1 785.34 167.04 817.69 247.04 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n232 70 Car -1 -1 -1 598.49 173.49 621.21 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n233 3 Car -1 -1 -1 1095.44 185.32 1220.73 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n233 2 Car -1 -1 -1 955.35 183.84 1066.62 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n233 4 Car -1 -1 -1 1029.53 183.92 1156.34 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n233 59 Pedestrian -1 -1 -1 308.60 162.56 336.70 234.25 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n233 63 Pedestrian -1 -1 -1 340.53 163.48 366.31 232.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n233 75 Pedestrian -1 -1 -1 513.03 155.01 561.21 257.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n233 7 Car -1 -1 -1 601.72 173.26 637.02 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n233 65 Pedestrian -1 -1 -1 190.57 152.85 208.67 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n233 67 Pedestrian -1 -1 -1 763.87 168.15 791.63 251.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n233 66 Pedestrian -1 -1 -1 228.95 155.89 247.37 200.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n233 68 Pedestrian -1 -1 -1 785.83 165.12 817.65 248.36 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n233 70 Car -1 -1 -1 598.35 173.65 621.64 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n234 3 Car -1 -1 -1 1095.16 185.20 1221.05 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n234 2 Car -1 -1 -1 955.31 183.88 1066.63 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n234 4 Car -1 -1 -1 1029.25 183.84 1156.56 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n234 75 Pedestrian -1 -1 -1 510.97 154.35 547.96 257.47 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n234 63 Pedestrian -1 -1 -1 336.84 162.86 363.63 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n234 59 Pedestrian -1 -1 -1 305.25 162.49 333.13 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n234 67 Pedestrian -1 -1 -1 764.42 168.14 792.50 252.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n234 7 Car -1 -1 -1 601.69 173.22 637.05 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n234 65 Pedestrian -1 -1 -1 188.48 152.52 207.26 198.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n234 66 Pedestrian -1 -1 -1 229.53 155.82 248.17 200.31 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n234 68 Pedestrian -1 -1 -1 789.86 165.17 819.62 248.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n234 70 Car -1 -1 -1 597.96 173.55 621.59 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n235 3 Car -1 -1 -1 1095.17 185.16 1221.04 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n235 2 Car -1 -1 -1 955.40 183.96 1066.48 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n235 4 Car -1 -1 -1 1029.41 183.86 1156.40 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n235 59 Pedestrian -1 -1 -1 302.85 163.29 329.09 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n235 65 Pedestrian -1 -1 -1 188.21 152.28 207.14 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n235 7 Car -1 -1 -1 601.80 172.90 636.88 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n235 75 Pedestrian -1 -1 -1 503.48 153.70 540.14 255.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n235 67 Pedestrian -1 -1 -1 765.39 167.84 797.70 253.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n235 63 Pedestrian -1 -1 -1 335.17 161.59 362.34 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n235 66 Pedestrian -1 -1 -1 229.97 156.15 248.50 200.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n235 68 Pedestrian -1 -1 -1 794.04 167.42 822.39 250.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n235 70 Car -1 -1 -1 598.05 173.56 621.59 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n236 3 Car -1 -1 -1 1095.32 185.23 1220.94 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n236 4 Car -1 -1 -1 1029.19 183.85 1156.48 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n236 2 Car -1 -1 -1 955.54 183.95 1066.30 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n236 65 Pedestrian -1 -1 -1 187.18 152.50 206.52 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n236 63 Pedestrian -1 -1 -1 333.77 161.41 359.89 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n236 75 Pedestrian -1 -1 -1 495.64 153.72 533.95 252.95 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n236 7 Car -1 -1 -1 601.72 172.92 637.09 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n236 59 Pedestrian -1 -1 -1 301.01 163.61 327.56 232.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n236 67 Pedestrian -1 -1 -1 770.43 167.05 800.24 254.20 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n236 66 Pedestrian -1 -1 -1 230.31 156.06 248.52 200.79 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n236 68 Pedestrian -1 -1 -1 795.57 165.93 823.70 251.15 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n236 70 Car -1 -1 -1 598.05 173.51 621.74 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n237 3 Car -1 -1 -1 1095.42 185.16 1220.83 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n237 2 Car -1 -1 -1 955.48 183.93 1066.43 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n237 4 Car -1 -1 -1 1029.25 183.90 1156.48 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n237 65 Pedestrian -1 -1 -1 186.93 152.26 206.39 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n237 7 Car -1 -1 -1 601.77 172.80 636.97 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n237 67 Pedestrian -1 -1 -1 771.34 166.56 801.86 255.04 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n237 66 Pedestrian -1 -1 -1 230.16 155.87 248.67 200.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n237 75 Pedestrian -1 -1 -1 491.38 154.97 529.65 252.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n237 59 Pedestrian -1 -1 -1 297.74 161.39 324.69 232.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n237 68 Pedestrian -1 -1 -1 799.19 164.80 826.58 252.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n237 63 Pedestrian -1 -1 -1 329.74 161.81 357.27 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n237 70 Car -1 -1 -1 598.28 173.51 621.80 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n238 3 Car -1 -1 -1 1095.58 185.25 1220.73 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n238 2 Car -1 -1 -1 955.53 183.96 1066.28 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n238 4 Car -1 -1 -1 1029.58 184.01 1156.22 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n238 63 Pedestrian -1 -1 -1 329.09 162.63 355.50 231.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n238 7 Car -1 -1 -1 601.84 172.86 636.93 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n238 65 Pedestrian -1 -1 -1 186.60 152.04 205.88 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n238 75 Pedestrian -1 -1 -1 488.34 156.13 524.29 253.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n238 67 Pedestrian -1 -1 -1 774.35 166.14 805.91 255.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n238 59 Pedestrian -1 -1 -1 295.16 161.51 322.36 232.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n238 66 Pedestrian -1 -1 -1 230.44 155.60 249.12 200.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n238 68 Pedestrian -1 -1 -1 801.53 165.03 830.29 252.23 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n238 70 Car -1 -1 -1 597.94 173.53 621.68 193.83 -1 -1 -1 -1000 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0.71\n239 70 Car -1 -1 -1 598.10 173.57 621.68 193.97 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n240 3 Car -1 -1 -1 1095.43 185.20 1220.89 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n240 2 Car -1 -1 -1 955.42 183.98 1066.29 232.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n240 4 Car -1 -1 -1 1029.30 183.96 1156.49 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n240 75 Pedestrian -1 -1 -1 473.89 153.34 510.11 252.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n240 59 Pedestrian -1 -1 -1 290.24 163.37 318.14 231.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n240 63 Pedestrian -1 -1 -1 327.13 162.35 350.94 229.40 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n240 67 Pedestrian -1 -1 -1 780.84 169.24 815.19 258.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n240 68 Pedestrian -1 -1 -1 804.86 166.05 836.02 254.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n240 65 Pedestrian -1 -1 -1 184.35 150.50 202.92 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n240 66 Pedestrian -1 -1 -1 232.71 155.84 251.50 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n240 7 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255.04 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n242 65 Pedestrian -1 -1 -1 183.00 148.27 202.79 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n242 7 Car -1 -1 -1 602.13 173.10 636.84 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n242 70 Car -1 -1 -1 598.29 173.69 621.65 193.92 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n243 3 Car -1 -1 -1 1095.44 185.17 1220.77 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n243 2 Car -1 -1 -1 955.19 183.97 1066.32 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n243 4 Car -1 -1 -1 1029.21 184.00 1156.68 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n243 75 Pedestrian -1 -1 -1 458.78 156.17 494.89 248.75 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n243 68 Pedestrian -1 -1 -1 807.68 164.37 842.13 255.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n243 59 Pedestrian -1 -1 -1 286.12 161.51 313.36 228.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n243 63 Pedestrian -1 -1 -1 320.67 162.04 346.22 228.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n243 67 Pedestrian -1 -1 -1 790.87 167.27 820.85 259.48 -1 -1 -1 -1000 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0.81\n244 65 Pedestrian -1 -1 -1 183.32 148.38 202.61 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n244 67 Pedestrian -1 -1 -1 792.47 168.21 824.91 261.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n244 7 Car -1 -1 -1 601.90 172.77 636.89 203.26 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n244 59 Pedestrian -1 -1 -1 285.43 162.15 313.56 228.20 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n244 70 Car -1 -1 -1 598.44 173.37 621.51 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n245 3 Car -1 -1 -1 1095.57 185.16 1220.51 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n245 2 Car -1 -1 -1 955.01 183.91 1066.65 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n245 4 Car -1 -1 -1 1029.06 183.95 1156.79 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n245 63 Pedestrian -1 -1 -1 318.50 161.61 343.22 227.31 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n245 75 Pedestrian -1 -1 -1 453.63 154.27 483.75 247.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n245 66 Pedestrian -1 -1 -1 237.62 155.31 255.85 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n245 65 Pedestrian -1 -1 -1 183.15 148.33 202.57 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n245 67 Pedestrian -1 -1 -1 793.70 168.67 825.54 264.17 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n245 7 Car -1 -1 -1 602.73 172.70 637.37 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n245 68 Pedestrian -1 -1 -1 811.44 165.42 845.76 256.73 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n245 59 Pedestrian -1 -1 -1 283.05 162.57 311.75 228.39 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n245 70 Car -1 -1 -1 598.50 173.48 621.64 194.02 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n245 86 Cyclist -1 -1 -1 710.34 167.21 770.43 229.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n246 3 Car -1 -1 -1 1095.18 185.26 1220.85 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n246 4 Car -1 -1 -1 1029.27 183.97 1156.59 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n246 2 Car -1 -1 -1 955.01 183.88 1066.49 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n246 63 Pedestrian -1 -1 -1 319.23 160.62 342.18 226.45 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n246 67 Pedestrian -1 -1 -1 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233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n247 67 Pedestrian -1 -1 -1 799.91 165.97 832.98 267.00 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n247 65 Pedestrian -1 -1 -1 183.35 148.39 202.29 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n247 75 Pedestrian -1 -1 -1 443.21 156.45 477.14 247.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n247 59 Pedestrian -1 -1 -1 286.21 162.15 311.96 226.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n247 66 Pedestrian -1 -1 -1 238.35 155.81 256.10 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n247 68 Pedestrian -1 -1 -1 819.59 162.90 852.46 258.36 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n247 86 Cyclist -1 -1 -1 700.88 163.77 763.78 231.16 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n247 7 Car -1 -1 -1 601.91 173.02 636.84 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n247 63 Pedestrian -1 -1 -1 316.81 160.72 339.77 226.01 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n247 70 Car -1 -1 -1 598.54 173.57 621.42 193.88 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n248 3 Car -1 -1 -1 1095.45 185.13 1220.04 236.32 -1 -1 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-1000 -1000 -10 0.69\n248 70 Car -1 -1 -1 598.65 173.41 621.54 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n249 3 Car -1 -1 -1 1096.05 185.09 1219.45 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n249 2 Car -1 -1 -1 954.93 183.78 1066.70 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n249 4 Car -1 -1 -1 1029.65 183.77 1156.24 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n249 63 Pedestrian -1 -1 -1 315.01 161.56 337.43 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n249 67 Pedestrian -1 -1 -1 800.21 168.92 839.77 267.60 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n249 59 Pedestrian -1 -1 -1 284.21 162.35 310.51 226.46 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n249 86 Cyclist -1 -1 -1 690.31 163.62 752.60 231.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n249 65 Pedestrian -1 -1 -1 183.99 148.73 201.98 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n249 75 Pedestrian -1 -1 -1 434.87 156.01 465.37 245.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n249 68 Pedestrian -1 -1 -1 825.35 163.65 862.37 262.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n249 7 Car -1 -1 -1 602.82 172.58 637.37 203.63 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n249 66 Pedestrian -1 -1 -1 240.63 155.98 257.92 203.58 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n249 70 Car -1 -1 -1 598.67 173.28 621.68 193.80 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n250 3 Car -1 -1 -1 1095.83 185.04 1219.84 236.46 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n250 4 Car -1 -1 -1 1029.16 183.69 1156.45 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n250 2 Car -1 -1 -1 954.88 183.70 1066.58 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n250 75 Pedestrian -1 -1 -1 429.09 156.14 463.67 243.31 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n250 59 Pedestrian -1 -1 -1 283.80 162.46 310.52 226.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n250 68 Pedestrian -1 -1 -1 827.99 163.78 867.18 263.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n250 66 Pedestrian -1 -1 -1 240.76 155.80 258.64 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n250 67 Pedestrian -1 -1 -1 802.59 168.42 844.71 269.19 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n250 65 Pedestrian -1 -1 -1 183.84 149.06 201.59 196.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n250 7 Car -1 -1 -1 602.85 172.71 637.41 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n250 63 Pedestrian -1 -1 -1 313.07 161.81 335.56 224.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n250 86 Cyclist -1 -1 -1 686.16 162.34 746.15 229.21 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n250 70 Car -1 -1 -1 598.75 173.29 621.80 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n251 4 Car -1 -1 -1 1029.86 183.45 1155.39 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n251 3 Car -1 -1 -1 1094.73 184.69 1220.49 236.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n251 2 Car -1 -1 -1 954.73 183.62 1066.79 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n251 63 Pedestrian -1 -1 -1 311.44 160.55 333.64 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n251 66 Pedestrian -1 -1 -1 241.28 156.23 259.32 203.73 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n251 86 Cyclist -1 -1 -1 678.91 164.55 740.60 230.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n251 59 Pedestrian -1 -1 -1 284.57 162.14 309.59 225.21 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n251 68 Pedestrian -1 -1 -1 832.74 162.94 870.36 265.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n251 67 Pedestrian -1 -1 -1 807.55 168.74 847.45 271.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n251 75 Pedestrian -1 -1 -1 427.44 157.32 461.41 242.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n251 7 Car -1 -1 -1 601.94 173.09 636.76 203.34 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n251 65 Pedestrian -1 -1 -1 183.90 149.19 201.45 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n251 70 Car -1 -1 -1 598.81 173.32 621.81 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n252 4 Car -1 -1 -1 1029.45 183.53 1155.28 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n252 2 Car -1 -1 -1 954.66 183.50 1066.80 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n252 3 Car -1 -1 -1 1094.32 184.14 1220.70 237.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n252 86 Cyclist -1 -1 -1 678.55 163.43 734.09 228.37 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n252 67 Pedestrian -1 -1 -1 814.27 166.09 850.77 270.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n252 75 Pedestrian -1 -1 -1 423.92 158.81 458.06 243.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n252 68 Pedestrian -1 -1 -1 837.64 162.82 872.85 265.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n252 66 Pedestrian -1 -1 -1 241.29 156.68 259.77 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n252 63 Pedestrian -1 -1 -1 308.86 160.33 332.35 222.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n252 65 Pedestrian -1 -1 -1 184.17 150.41 201.83 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n252 7 Car -1 -1 -1 602.69 172.97 637.46 203.59 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n252 59 Pedestrian -1 -1 -1 284.75 161.79 309.41 224.53 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n252 70 Car -1 -1 -1 598.76 173.32 622.27 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n253 3 Car -1 -1 -1 1095.76 184.61 1219.79 236.86 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n253 2 Car -1 -1 -1 954.57 183.48 1067.00 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n253 4 Car -1 -1 -1 1029.36 183.31 1155.57 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n253 86 Cyclist -1 -1 -1 671.62 163.01 730.41 228.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n253 63 Pedestrian -1 -1 -1 308.48 160.97 332.01 222.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n253 68 Pedestrian -1 -1 -1 838.82 163.36 878.57 265.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n253 75 Pedestrian -1 -1 -1 421.76 158.31 452.25 240.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n253 67 Pedestrian -1 -1 -1 819.78 165.45 857.86 272.10 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n253 66 Pedestrian -1 -1 -1 241.88 156.52 260.24 204.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n253 65 Pedestrian -1 -1 -1 184.58 149.24 201.60 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n253 7 Car -1 -1 -1 602.73 173.11 637.38 203.40 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n253 59 Pedestrian -1 -1 -1 284.87 161.90 309.92 224.20 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n253 70 Car -1 -1 -1 598.94 173.39 622.23 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n254 3 Car -1 -1 -1 1095.35 184.79 1219.71 236.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n254 4 Car -1 -1 -1 1028.98 183.09 1155.70 234.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n254 2 Car -1 -1 -1 954.51 183.44 1066.95 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n254 86 Cyclist -1 -1 -1 663.71 161.80 723.86 228.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n254 67 Pedestrian -1 -1 -1 819.68 165.51 859.70 275.66 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n254 63 Pedestrian -1 -1 -1 308.30 161.13 331.66 222.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n254 68 Pedestrian -1 -1 -1 842.05 164.11 883.26 265.81 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n254 75 Pedestrian -1 -1 -1 418.02 157.91 448.57 241.13 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n254 7 Car -1 -1 -1 602.74 172.88 637.48 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n254 66 Pedestrian -1 -1 -1 241.74 156.05 261.19 204.31 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n254 65 Pedestrian -1 -1 -1 184.32 149.00 202.17 197.21 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n254 70 Car -1 -1 -1 598.10 173.33 621.67 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n254 59 Pedestrian -1 -1 -1 287.74 162.72 311.18 223.37 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n255 3 Car -1 -1 -1 1094.95 184.95 1219.94 236.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n255 2 Car -1 -1 -1 954.30 183.35 1067.20 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n255 4 Car -1 -1 -1 1028.46 182.99 1156.54 234.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n255 63 Pedestrian -1 -1 -1 309.17 160.99 331.13 221.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n255 67 Pedestrian -1 -1 -1 823.00 166.79 864.73 276.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n255 86 Cyclist -1 -1 -1 660.68 162.34 717.73 227.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n255 68 Pedestrian -1 -1 -1 845.03 164.20 887.25 269.06 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n255 7 Car -1 -1 -1 602.84 172.89 637.44 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n255 65 Pedestrian -1 -1 -1 184.25 148.67 203.15 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n255 66 Pedestrian -1 -1 -1 243.39 155.99 262.83 203.83 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n255 59 Pedestrian -1 -1 -1 287.19 162.27 311.99 222.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n255 75 Pedestrian -1 -1 -1 416.16 160.27 444.03 241.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n255 70 Car -1 -1 -1 598.11 173.51 621.32 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n256 3 Car -1 -1 -1 1094.77 184.79 1220.50 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n256 4 Car -1 -1 -1 1030.91 182.46 1159.25 234.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n256 2 Car -1 -1 -1 954.84 183.32 1066.33 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n256 63 Pedestrian -1 -1 -1 309.59 160.36 330.83 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n256 59 Pedestrian -1 -1 -1 288.60 161.99 312.66 221.44 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n256 67 Pedestrian -1 -1 -1 827.41 166.39 867.86 277.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n256 86 Cyclist -1 -1 -1 654.80 160.08 713.30 228.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n256 75 Pedestrian -1 -1 -1 412.43 159.38 442.04 239.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n256 7 Car -1 -1 -1 602.80 173.01 637.58 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n256 66 Pedestrian -1 -1 -1 243.72 155.91 263.00 203.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n256 68 Pedestrian -1 -1 -1 850.78 162.72 890.02 270.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n256 65 Pedestrian -1 -1 -1 183.62 148.74 203.42 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n256 70 Car -1 -1 -1 597.53 173.76 621.19 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n257 3 Car -1 -1 -1 1094.58 184.79 1220.67 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n257 4 Car -1 -1 -1 1032.65 183.00 1157.78 234.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n257 2 Car -1 -1 -1 953.86 183.05 1067.37 234.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n257 86 Cyclist -1 -1 -1 648.51 161.93 709.59 227.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n257 67 Pedestrian -1 -1 -1 832.99 165.31 876.68 278.94 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n257 7 Car -1 -1 -1 603.11 172.85 637.63 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n257 63 Pedestrian -1 -1 -1 309.52 160.04 330.89 221.20 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n257 59 Pedestrian -1 -1 -1 289.37 161.57 312.33 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n257 66 Pedestrian -1 -1 -1 244.52 156.13 263.66 204.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n257 75 Pedestrian -1 -1 -1 411.55 158.76 439.52 238.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n257 68 Pedestrian -1 -1 -1 853.21 162.20 895.56 272.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n257 65 Pedestrian -1 -1 -1 184.07 150.03 204.10 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n257 70 Car -1 -1 -1 597.62 174.13 621.06 193.08 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n258 3 Car -1 -1 -1 1094.84 184.85 1220.95 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n258 4 Car -1 -1 -1 1032.78 183.39 1157.55 234.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n258 2 Car -1 -1 -1 954.22 183.21 1067.15 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n258 7 Car -1 -1 -1 603.14 172.76 637.49 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n258 86 Cyclist -1 -1 -1 643.88 163.59 704.76 227.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n258 63 Pedestrian -1 -1 -1 309.11 160.91 331.53 220.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n258 59 Pedestrian -1 -1 -1 289.03 161.55 311.97 219.94 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n258 65 Pedestrian -1 -1 -1 186.31 150.47 206.25 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n258 68 Pedestrian -1 -1 -1 859.23 162.52 904.21 272.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n258 66 Pedestrian -1 -1 -1 244.85 156.46 263.98 204.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n258 75 Pedestrian -1 -1 -1 405.54 158.17 434.03 238.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n258 67 Pedestrian -1 -1 -1 834.35 166.26 883.98 278.55 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n258 70 Car -1 -1 -1 597.48 173.59 621.42 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n259 3 Car -1 -1 -1 1094.56 184.89 1221.46 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n259 4 Car -1 -1 -1 1029.30 183.80 1156.51 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n259 2 Car -1 -1 -1 954.80 183.31 1066.49 231.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n259 65 Pedestrian -1 -1 -1 187.00 151.29 207.24 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n259 7 Car -1 -1 -1 603.13 172.74 637.34 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n259 75 Pedestrian -1 -1 -1 401.46 157.44 429.50 237.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n259 59 Pedestrian -1 -1 -1 288.29 161.98 312.21 220.35 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n259 63 Pedestrian -1 -1 -1 309.00 161.66 331.31 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n259 86 Cyclist -1 -1 -1 640.37 164.44 700.26 226.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n259 67 Pedestrian -1 -1 -1 839.02 166.68 894.08 282.15 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n259 66 Pedestrian -1 -1 -1 244.82 156.62 263.93 204.10 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n259 68 Pedestrian -1 -1 -1 866.34 163.22 911.49 273.43 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n259 70 Car -1 -1 -1 595.54 173.17 618.87 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n260 3 Car -1 -1 -1 1094.37 184.89 1221.55 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n260 4 Car -1 -1 -1 1029.99 183.80 1155.76 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n260 2 Car -1 -1 -1 954.22 183.20 1067.31 233.93 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n260 86 Cyclist -1 -1 -1 634.99 165.01 692.86 226.34 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n260 75 Pedestrian -1 -1 -1 395.95 158.06 427.90 236.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n260 65 Pedestrian -1 -1 -1 187.78 152.04 207.88 199.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n260 67 Pedestrian -1 -1 -1 844.31 166.08 903.27 283.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n260 63 Pedestrian -1 -1 -1 308.84 161.45 330.77 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n260 59 Pedestrian -1 -1 -1 287.57 161.76 311.94 220.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n260 66 Pedestrian -1 -1 -1 245.14 156.31 264.11 203.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n260 7 Car -1 -1 -1 602.72 172.68 637.28 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n260 68 Pedestrian -1 -1 -1 869.34 162.32 917.04 279.48 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n260 70 Car -1 -1 -1 596.67 172.90 622.50 194.82 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n261 3 Car -1 -1 -1 1094.37 185.02 1221.54 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n261 4 Car -1 -1 -1 1030.15 183.83 1155.45 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n261 2 Car -1 -1 -1 954.30 182.73 1067.42 234.44 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n261 67 Pedestrian -1 -1 -1 851.45 164.61 905.02 286.42 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n261 63 Pedestrian -1 -1 -1 309.22 161.07 330.26 220.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n261 75 Pedestrian -1 -1 -1 394.13 158.90 426.39 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n261 59 Pedestrian -1 -1 -1 287.98 161.73 311.70 219.89 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n261 7 Car -1 -1 -1 601.27 172.64 637.20 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n261 66 Pedestrian -1 -1 -1 245.80 156.13 264.33 204.44 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n261 65 Pedestrian -1 -1 -1 188.14 152.57 208.51 199.48 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n261 86 Cyclist -1 -1 -1 632.04 165.88 685.55 224.99 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n261 68 Pedestrian -1 -1 -1 873.43 162.40 921.13 279.88 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n261 70 Car -1 -1 -1 595.86 172.71 623.09 195.35 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n262 3 Car -1 -1 -1 1094.22 185.04 1221.50 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n262 4 Car -1 -1 -1 1029.91 183.92 1155.51 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n262 2 Car -1 -1 -1 951.69 183.13 1065.62 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n262 67 Pedestrian -1 -1 -1 866.15 163.59 912.71 287.23 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n262 59 Pedestrian -1 -1 -1 288.32 161.25 311.30 219.39 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n262 63 Pedestrian -1 -1 -1 309.57 160.95 329.92 219.15 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n262 7 Car -1 -1 -1 601.41 172.63 637.17 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n262 65 Pedestrian -1 -1 -1 189.69 153.04 209.58 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n262 86 Cyclist -1 -1 -1 624.40 165.31 682.06 225.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n262 75 Pedestrian -1 -1 -1 392.28 159.20 421.12 234.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n262 66 Pedestrian -1 -1 -1 245.84 156.42 264.81 204.87 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n262 68 Pedestrian -1 -1 -1 881.70 162.91 927.50 280.19 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n262 70 Car -1 -1 -1 596.72 172.62 623.10 194.40 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n263 3 Car -1 -1 -1 1094.25 185.03 1221.27 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n263 4 Car -1 -1 -1 1030.60 183.81 1155.10 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n263 2 Car -1 -1 -1 954.02 183.00 1067.82 234.00 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n263 67 Pedestrian -1 -1 -1 872.31 165.58 921.84 286.36 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n263 59 Pedestrian -1 -1 -1 288.73 161.58 311.68 219.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n263 63 Pedestrian -1 -1 -1 309.42 161.43 330.07 219.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n263 86 Cyclist -1 -1 -1 622.10 165.08 675.76 224.43 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n263 7 Car -1 -1 -1 601.82 172.77 636.53 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n263 65 Pedestrian -1 -1 -1 190.34 153.12 210.02 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n263 75 Pedestrian -1 -1 -1 389.80 157.28 416.04 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n263 68 Pedestrian -1 -1 -1 883.35 163.40 933.91 280.04 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n263 66 Pedestrian -1 -1 -1 247.71 156.89 266.05 204.87 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n263 70 Car -1 -1 -1 596.22 172.76 623.25 194.82 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n264 3 Car -1 -1 -1 1094.22 184.96 1221.35 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n264 4 Car -1 -1 -1 1030.45 183.86 1155.48 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n264 2 Car -1 -1 -1 954.25 183.08 1067.38 234.00 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n264 59 Pedestrian -1 -1 -1 289.11 161.93 311.65 219.02 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n264 63 Pedestrian -1 -1 -1 309.84 161.44 330.53 219.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n264 65 Pedestrian -1 -1 -1 190.72 153.32 210.00 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n264 7 Car -1 -1 -1 602.03 172.89 636.28 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n264 67 Pedestrian -1 -1 -1 879.14 166.01 937.42 290.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n264 75 Pedestrian -1 -1 -1 384.42 156.96 415.46 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n264 86 Cyclist -1 -1 -1 616.40 164.79 670.20 223.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n264 66 Pedestrian -1 -1 -1 247.96 156.74 266.59 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n264 70 Car -1 -1 -1 596.95 172.80 622.86 194.50 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n265 3 Car -1 -1 -1 1094.02 184.94 1221.52 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n265 4 Car -1 -1 -1 1030.35 183.76 1155.49 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n265 2 Car -1 -1 -1 953.86 183.11 1067.38 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n265 67 Pedestrian -1 -1 -1 881.23 164.82 943.62 292.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n265 59 Pedestrian -1 -1 -1 288.65 161.67 311.34 218.79 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n265 65 Pedestrian -1 -1 -1 190.77 153.29 209.48 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n265 86 Cyclist -1 -1 -1 609.92 165.82 664.27 222.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n265 63 Pedestrian -1 -1 -1 309.96 160.87 330.86 218.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n265 7 Car -1 -1 -1 602.15 173.29 636.10 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n265 66 Pedestrian -1 -1 -1 248.09 156.68 266.75 205.09 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n265 75 Pedestrian -1 -1 -1 379.01 158.82 410.88 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n265 70 Car -1 -1 -1 597.59 173.09 621.92 194.25 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n265 87 Cyclist -1 -1 -1 2.52 149.31 77.04 240.31 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n265 88 Pedestrian -1 -1 -1 902.22 165.01 953.40 285.43 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n266 3 Car -1 -1 -1 1094.01 184.90 1221.65 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n266 4 Car -1 -1 -1 1030.22 183.75 1155.63 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n266 2 Car -1 -1 -1 953.69 183.05 1067.69 233.96 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n266 67 Pedestrian -1 -1 -1 891.85 164.43 947.68 293.47 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n266 65 Pedestrian -1 -1 -1 190.65 153.65 209.24 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n266 59 Pedestrian -1 -1 -1 289.11 161.20 311.31 218.09 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n266 7 Car -1 -1 -1 602.32 173.67 635.95 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n266 66 Pedestrian -1 -1 -1 248.70 156.36 267.43 205.29 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n266 86 Cyclist -1 -1 -1 607.07 166.01 656.69 224.01 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n266 75 Pedestrian -1 -1 -1 375.12 159.94 408.68 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n266 63 Pedestrian -1 -1 -1 310.32 159.98 331.15 218.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n266 70 Car -1 -1 -1 597.99 173.07 621.60 193.99 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n266 87 Cyclist -1 -1 -1 13.90 150.69 80.88 238.80 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n267 3 Car -1 -1 -1 1094.07 185.05 1221.64 236.16 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n267 4 Car -1 -1 -1 1030.19 183.79 1155.79 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n267 2 Car -1 -1 -1 953.57 183.12 1067.77 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n267 67 Pedestrian -1 -1 -1 903.06 163.83 959.84 295.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n267 7 Car -1 -1 -1 601.57 173.92 636.51 201.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n267 65 Pedestrian -1 -1 -1 190.49 153.82 208.94 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n267 59 Pedestrian -1 -1 -1 289.08 161.34 310.53 217.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n267 63 Pedestrian -1 -1 -1 311.93 159.73 332.82 218.63 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n267 86 Cyclist -1 -1 -1 602.42 167.33 649.75 222.77 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n267 75 Pedestrian -1 -1 -1 373.64 158.81 403.69 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n267 66 Pedestrian -1 -1 -1 249.72 157.33 268.23 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n267 87 Cyclist -1 -1 -1 33.48 147.94 85.18 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n267 70 Car -1 -1 -1 598.20 173.34 621.35 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n267 89 Pedestrian -1 -1 -1 571.05 168.51 587.41 200.07 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n268 3 Car -1 -1 -1 1093.95 185.01 1221.45 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n268 4 Car -1 -1 -1 1030.20 183.82 1155.66 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n268 2 Car -1 -1 -1 953.60 183.10 1068.09 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n268 67 Pedestrian -1 -1 -1 909.80 163.11 976.09 296.48 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n268 7 Car -1 -1 -1 600.56 173.65 636.02 200.95 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n268 65 Pedestrian -1 -1 -1 190.36 153.64 208.92 199.00 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n268 63 Pedestrian -1 -1 -1 312.24 160.06 332.88 218.57 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n268 75 Pedestrian -1 -1 -1 372.72 156.73 398.18 231.61 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n268 66 Pedestrian -1 -1 -1 249.28 157.41 269.00 206.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n268 86 Cyclist -1 -1 -1 596.72 167.62 644.68 222.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n268 89 Pedestrian -1 -1 -1 566.78 167.81 585.63 200.62 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n268 59 Pedestrian -1 -1 -1 288.69 161.62 310.08 217.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n268 70 Car -1 -1 -1 597.69 173.31 621.77 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n268 87 Cyclist -1 -1 -1 51.90 148.04 96.74 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n268 90 Cyclist -1 -1 -1 869.17 168.13 900.92 229.26 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n269 3 Car -1 -1 -1 1093.58 184.91 1222.10 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n269 4 Car -1 -1 -1 1030.01 183.88 1155.92 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n269 2 Car -1 -1 -1 953.59 182.78 1068.05 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n269 67 Pedestrian -1 -1 -1 913.53 164.65 995.48 301.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n269 75 Pedestrian -1 -1 -1 367.90 157.84 394.94 230.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n269 7 Car -1 -1 -1 600.89 173.00 635.88 201.29 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n269 65 Pedestrian -1 -1 -1 190.31 153.64 208.86 199.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n269 63 Pedestrian -1 -1 -1 312.67 160.67 332.77 217.82 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n269 59 Pedestrian -1 -1 -1 288.35 161.95 310.17 216.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n269 87 Cyclist -1 -1 -1 62.26 148.97 116.93 230.83 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n269 66 Pedestrian -1 -1 -1 249.68 157.33 269.06 206.89 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n269 86 Cyclist -1 -1 -1 591.13 168.02 639.00 221.83 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n269 89 Pedestrian -1 -1 -1 565.23 168.52 584.60 200.29 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n269 90 Cyclist -1 -1 -1 846.75 169.46 893.77 226.55 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n270 3 Car -1 -1 -1 1093.75 184.99 1222.21 236.19 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n270 4 Car -1 -1 -1 1030.16 183.91 1155.62 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n270 67 Pedestrian -1 -1 -1 927.01 163.05 1004.22 303.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n270 75 Pedestrian -1 -1 -1 365.54 158.20 394.53 230.26 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n270 2 Car -1 -1 -1 953.80 182.91 1067.71 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n270 7 Car -1 -1 -1 600.93 172.90 636.08 201.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n270 65 Pedestrian -1 -1 -1 190.31 153.72 208.71 199.02 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n270 63 Pedestrian -1 -1 -1 312.69 160.18 333.15 216.77 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n270 59 Pedestrian -1 -1 -1 286.09 161.40 308.89 215.36 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n270 66 Pedestrian -1 -1 -1 251.15 157.41 270.17 206.87 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n270 86 Cyclist -1 -1 -1 587.00 168.01 633.24 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n270 90 Cyclist -1 -1 -1 823.23 167.40 887.17 230.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n270 87 Cyclist -1 -1 -1 71.00 151.71 124.67 228.52 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n270 89 Pedestrian -1 -1 -1 564.43 171.38 578.69 201.23 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n270 91 Car -1 -1 -1 596.86 173.58 622.67 194.13 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n271 3 Car -1 -1 -1 1097.85 184.95 1221.75 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n271 4 Car -1 -1 -1 1030.25 183.84 1155.45 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n271 67 Pedestrian -1 -1 -1 939.82 162.50 1006.91 304.20 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n271 2 Car -1 -1 -1 950.28 183.17 1067.20 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n271 75 Pedestrian -1 -1 -1 362.47 158.35 391.74 229.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n271 63 Pedestrian -1 -1 -1 312.20 160.23 332.72 216.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n271 7 Car -1 -1 -1 604.18 172.44 636.85 200.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n271 66 Pedestrian -1 -1 -1 251.02 156.86 271.26 207.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n271 65 Pedestrian -1 -1 -1 190.21 153.69 208.88 198.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n271 59 Pedestrian -1 -1 -1 286.37 161.26 308.44 215.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n271 90 Cyclist -1 -1 -1 807.66 166.30 865.32 231.76 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n271 89 Pedestrian -1 -1 -1 563.25 170.29 574.13 201.08 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n271 87 Cyclist -1 -1 -1 88.47 151.22 136.16 223.65 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n271 86 Cyclist -1 -1 -1 583.16 167.75 626.88 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n271 91 Car -1 -1 -1 595.76 173.21 623.54 194.67 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n272 3 Car -1 -1 -1 1098.25 185.10 1221.57 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n272 4 Car -1 -1 -1 1029.96 183.81 1155.60 233.54 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n272 2 Car -1 -1 -1 953.46 182.67 1068.10 232.22 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n272 67 Pedestrian -1 -1 -1 951.97 163.08 1018.32 302.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n272 63 Pedestrian -1 -1 -1 313.23 160.40 332.70 215.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n272 66 Pedestrian -1 -1 -1 251.72 156.87 271.73 207.87 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n272 7 Car -1 -1 -1 601.44 172.74 636.47 200.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n272 65 Pedestrian -1 -1 -1 190.16 153.67 209.01 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n272 90 Cyclist -1 -1 -1 794.19 167.24 847.08 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n272 59 Pedestrian -1 -1 -1 289.00 161.25 309.82 214.39 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n272 89 Pedestrian -1 -1 -1 557.63 170.52 571.74 200.81 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n272 87 Cyclist -1 -1 -1 93.00 155.20 141.53 226.52 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n272 91 Car -1 -1 -1 595.54 173.46 623.39 193.79 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n272 86 Cyclist -1 -1 -1 576.64 168.21 625.80 219.42 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n272 75 Pedestrian -1 -1 -1 359.10 157.46 388.75 229.09 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n273 3 Car -1 -1 -1 1098.31 185.19 1221.58 236.12 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n273 4 Car -1 -1 -1 1029.67 183.85 1155.76 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n273 2 Car -1 -1 -1 953.35 182.64 1068.34 232.20 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n273 66 Pedestrian -1 -1 -1 251.68 157.16 271.72 208.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n273 67 Pedestrian -1 -1 -1 970.01 164.59 1037.84 307.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n273 7 Car -1 -1 -1 601.56 172.83 636.24 201.17 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n273 63 Pedestrian -1 -1 -1 314.05 161.16 332.85 214.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n273 65 Pedestrian -1 -1 -1 190.38 153.90 208.88 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n273 90 Cyclist -1 -1 -1 774.68 165.54 829.37 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n273 86 Cyclist -1 -1 -1 568.00 167.74 621.90 219.05 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n273 59 Pedestrian -1 -1 -1 289.01 162.16 309.82 213.94 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n273 91 Car -1 -1 -1 596.67 172.98 623.57 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n273 87 Cyclist -1 -1 -1 103.46 151.43 151.98 224.53 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n273 89 Pedestrian -1 -1 -1 553.34 169.93 570.08 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n273 92 Cyclist -1 -1 -1 357.30 157.48 387.26 227.16 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n274 3 Car -1 -1 -1 1098.58 185.23 1221.43 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n274 4 Car -1 -1 -1 1029.71 183.96 1155.99 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n274 7 Car -1 -1 -1 600.68 172.70 636.87 201.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n274 67 Pedestrian -1 -1 -1 971.05 164.68 1052.36 309.02 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n274 2 Car -1 -1 -1 953.99 183.01 1067.32 231.92 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n274 66 Pedestrian -1 -1 -1 252.18 157.14 272.71 208.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n274 65 Pedestrian -1 -1 -1 190.44 154.04 208.69 198.89 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n274 63 Pedestrian -1 -1 -1 315.00 161.28 333.18 215.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n274 59 Pedestrian -1 -1 -1 289.24 162.25 309.78 213.97 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n274 89 Pedestrian -1 -1 -1 552.61 170.39 567.33 201.22 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n274 87 Cyclist -1 -1 -1 115.53 151.56 163.62 223.77 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n274 91 Car -1 -1 -1 597.01 173.27 623.41 193.94 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n274 86 Cyclist -1 -1 -1 562.62 167.11 614.23 219.72 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n274 90 Cyclist -1 -1 -1 760.76 163.00 811.30 233.95 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n274 92 Cyclist -1 -1 -1 356.24 157.37 382.30 227.32 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n275 3 Car -1 -1 -1 1098.68 185.26 1221.22 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n275 4 Car -1 -1 -1 1029.37 184.09 1156.41 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n275 7 Car -1 -1 -1 600.14 172.85 636.92 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n275 2 Car -1 -1 -1 954.18 183.31 1067.95 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n275 67 Pedestrian -1 -1 -1 984.72 162.63 1069.20 311.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n275 59 Pedestrian -1 -1 -1 289.81 161.74 310.21 213.37 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n275 65 Pedestrian -1 -1 -1 190.60 153.99 209.11 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n275 66 Pedestrian -1 -1 -1 252.61 156.75 273.11 207.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n275 86 Cyclist -1 -1 -1 559.16 167.76 608.12 219.62 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n275 89 Pedestrian -1 -1 -1 550.71 170.31 561.88 201.38 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n275 63 Pedestrian -1 -1 -1 315.79 161.01 333.19 214.78 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n275 91 Car -1 -1 -1 597.35 173.34 622.95 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n275 87 Cyclist -1 -1 -1 131.81 149.88 170.70 215.44 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n275 90 Cyclist -1 -1 -1 741.18 164.11 799.91 232.70 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n275 92 Cyclist -1 -1 -1 353.52 159.72 378.06 229.04 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n275 93 Pedestrian -1 -1 -1 353.52 159.72 378.06 229.04 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n276 3 Car -1 -1 -1 1099.35 185.04 1220.86 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n276 4 Car -1 -1 -1 1033.14 184.02 1158.02 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n276 2 Car -1 -1 -1 954.67 183.39 1067.35 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n276 7 Car -1 -1 -1 600.35 173.26 636.59 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n276 67 Pedestrian -1 -1 -1 1005.31 164.96 1071.27 315.03 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n276 59 Pedestrian -1 -1 -1 289.80 160.86 310.67 212.99 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n276 65 Pedestrian -1 -1 -1 190.95 154.00 209.29 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n276 86 Cyclist -1 -1 -1 558.20 166.16 603.09 217.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n276 93 Pedestrian -1 -1 -1 351.15 158.67 373.91 228.74 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n276 66 Pedestrian -1 -1 -1 252.94 156.48 273.19 208.00 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n276 63 Pedestrian -1 -1 -1 316.66 160.41 334.88 214.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n276 89 Pedestrian -1 -1 -1 547.83 170.24 558.95 201.18 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n276 91 Car -1 -1 -1 597.44 173.66 622.36 194.15 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n276 90 Cyclist -1 -1 -1 727.49 167.29 783.39 229.93 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n276 87 Cyclist -1 -1 -1 141.43 149.91 176.17 214.98 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n276 94 Pedestrian -1 -1 -1 147.17 150.61 175.40 210.18 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n277 3 Car -1 -1 -1 1099.44 185.20 1220.45 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n277 4 Car -1 -1 -1 1033.56 184.18 1157.70 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n277 7 Car -1 -1 -1 600.60 173.03 636.83 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n277 2 Car -1 -1 -1 953.65 183.56 1064.01 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n277 67 Pedestrian -1 -1 -1 1016.14 164.02 1084.47 315.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n277 59 Pedestrian -1 -1 -1 289.87 160.92 310.44 212.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n277 86 Cyclist -1 -1 -1 557.56 165.67 594.09 218.60 -1 -1 -1 -1000 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Pedestrian -1 -1 -1 289.63 161.39 310.66 212.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n278 93 Pedestrian -1 -1 -1 346.78 156.17 369.86 224.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n278 66 Pedestrian -1 -1 -1 254.87 156.94 274.99 209.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n278 63 Pedestrian -1 -1 -1 317.36 160.89 335.47 213.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n278 65 Pedestrian -1 -1 -1 191.35 154.26 209.22 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n278 67 Pedestrian -1 -1 -1 1030.41 167.70 1107.64 314.09 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n278 86 Cyclist -1 -1 -1 552.30 166.64 589.78 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n278 90 Cyclist -1 -1 -1 696.47 163.69 754.49 231.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n278 91 Car -1 -1 -1 598.49 173.93 621.46 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n278 87 Cyclist -1 -1 -1 159.50 152.04 187.91 206.79 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n278 95 Pedestrian -1 -1 -1 540.36 169.78 555.74 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n279 3 Car 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686.33 163.27 738.74 228.25 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n279 87 Cyclist -1 -1 -1 166.37 151.67 196.28 207.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n279 91 Car -1 -1 -1 598.79 173.94 621.20 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n279 96 Cyclist -1 -1 -1 537.31 170.61 553.30 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n280 3 Car -1 -1 -1 1093.27 185.10 1220.78 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n280 2 Car -1 -1 -1 954.58 183.37 1066.99 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n280 4 Car -1 -1 -1 1035.27 184.06 1155.78 234.41 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n280 7 Car -1 -1 -1 601.44 172.88 636.82 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n280 66 Pedestrian -1 -1 -1 255.18 157.12 275.33 209.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n280 90 Cyclist -1 -1 -1 668.99 161.44 728.01 229.17 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n280 59 Pedestrian -1 -1 -1 289.39 161.16 310.37 212.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n280 86 Cyclist -1 -1 -1 543.11 167.21 579.57 216.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n280 67 Pedestrian -1 -1 -1 1067.41 169.88 1139.77 318.50 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n280 93 Pedestrian -1 -1 -1 344.74 157.43 368.12 224.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n280 65 Pedestrian -1 -1 -1 192.01 153.87 209.67 199.27 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n280 63 Pedestrian -1 -1 -1 315.27 160.67 333.40 213.71 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n280 87 Cyclist -1 -1 -1 167.29 153.08 204.51 212.28 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n280 91 Car -1 -1 -1 598.79 173.93 621.26 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n281 3 Car -1 -1 -1 1093.54 184.85 1222.05 236.69 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n281 2 Car -1 -1 -1 954.65 183.44 1066.66 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n281 86 Cyclist -1 -1 -1 538.51 165.46 575.34 218.55 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n281 4 Car -1 -1 -1 1034.61 184.21 1156.27 234.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n281 67 Pedestrian -1 -1 -1 1077.75 169.58 1151.97 325.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n281 66 Pedestrian -1 -1 -1 255.71 156.82 275.64 209.39 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n281 7 Car -1 -1 -1 601.85 173.14 636.72 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n281 59 Pedestrian -1 -1 -1 289.73 160.26 310.27 212.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n281 90 Cyclist -1 -1 -1 659.27 159.86 712.06 229.04 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n281 93 Pedestrian -1 -1 -1 343.01 156.61 366.74 224.11 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n281 63 Pedestrian -1 -1 -1 315.07 160.29 333.80 213.24 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n281 65 Pedestrian -1 -1 -1 191.66 153.13 210.12 200.23 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n281 87 Cyclist -1 -1 -1 177.43 153.04 208.13 212.86 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n281 91 Car -1 -1 -1 599.05 174.02 621.20 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n282 2 Car -1 -1 -1 954.44 183.38 1066.88 233.96 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n282 3 Car -1 -1 -1 1098.99 185.31 1221.03 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n282 4 Car -1 -1 -1 1032.29 184.00 1152.70 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n282 66 Pedestrian -1 -1 -1 255.99 157.09 276.05 209.21 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n282 67 Pedestrian -1 -1 -1 1091.88 169.04 1169.03 326.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n282 7 Car -1 -1 -1 601.92 173.11 636.77 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n282 59 Pedestrian -1 -1 -1 289.91 160.48 310.49 211.86 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n282 86 Cyclist -1 -1 -1 535.30 165.75 570.80 218.42 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n282 93 Pedestrian -1 -1 -1 342.11 156.69 366.46 222.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n282 90 Cyclist -1 -1 -1 647.82 161.45 700.29 227.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n282 63 Pedestrian -1 -1 -1 317.53 160.16 334.25 212.74 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n282 87 Cyclist -1 -1 -1 190.01 153.07 211.32 204.64 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n282 91 Car -1 -1 -1 598.75 173.76 621.68 193.84 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n282 65 Pedestrian -1 -1 -1 195.51 153.42 212.90 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n283 2 Car -1 -1 -1 954.29 183.41 1067.10 233.91 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n283 3 Car -1 -1 -1 1093.03 185.19 1222.72 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n283 4 Car -1 -1 -1 1032.06 183.59 1152.77 234.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n283 90 Cyclist -1 -1 -1 638.97 163.89 689.24 224.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n283 86 Cyclist -1 -1 -1 528.71 165.78 567.75 217.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n283 66 Pedestrian -1 -1 -1 256.31 157.81 276.64 209.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n283 7 Car -1 -1 -1 601.48 172.91 637.25 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n283 67 Pedestrian -1 -1 -1 1107.30 170.73 1199.17 331.85 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n283 59 Pedestrian -1 -1 -1 290.24 161.26 310.56 211.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n283 63 Pedestrian -1 -1 -1 318.01 160.64 334.19 212.46 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n283 87 Cyclist -1 -1 -1 189.29 156.69 227.80 208.72 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n283 93 Pedestrian -1 -1 -1 341.71 156.73 365.62 221.99 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n283 65 Pedestrian -1 -1 -1 191.90 153.78 210.59 198.69 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n283 91 Car -1 -1 -1 598.56 173.82 621.91 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n284 2 Car -1 -1 -1 954.59 183.53 1066.86 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n284 4 Car -1 -1 -1 1031.46 183.69 1153.82 234.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n284 3 Car -1 -1 -1 1093.65 185.08 1222.04 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n284 67 Pedestrian -1 -1 -1 1111.23 166.21 1210.85 331.34 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n284 7 Car -1 -1 -1 601.38 173.02 637.31 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n284 86 Cyclist -1 -1 -1 526.62 165.71 561.93 218.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n284 59 Pedestrian -1 -1 -1 290.72 161.58 311.23 211.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n284 63 Pedestrian -1 -1 -1 319.00 160.84 334.44 212.26 -1 -1 -1 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0.77\n285 67 Pedestrian -1 -1 -1 1130.21 169.55 1214.41 334.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n285 63 Pedestrian -1 -1 -1 319.46 160.76 335.19 212.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n285 86 Cyclist -1 -1 -1 522.67 166.66 558.44 217.59 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n285 90 Cyclist -1 -1 -1 620.20 167.30 668.23 222.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n285 65 Pedestrian -1 -1 -1 192.08 154.08 210.36 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n285 59 Pedestrian -1 -1 -1 293.64 160.87 312.45 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n285 87 Cyclist -1 -1 -1 207.75 153.86 239.16 205.08 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n285 91 Car -1 -1 -1 598.63 173.50 622.02 193.81 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n285 93 Pedestrian -1 -1 -1 342.06 157.67 365.72 221.65 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n286 2 Car -1 -1 -1 954.84 183.54 1066.60 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n286 4 Car -1 -1 -1 1030.60 183.70 1154.56 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n286 3 Car 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183.66 1066.65 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n287 4 Car -1 -1 -1 1030.12 183.70 1155.42 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n287 3 Car -1 -1 -1 1095.96 185.05 1219.27 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n287 65 Pedestrian -1 -1 -1 191.24 153.77 210.44 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n287 86 Cyclist -1 -1 -1 515.68 166.30 550.71 217.31 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n287 87 Cyclist -1 -1 -1 219.53 154.35 251.86 205.31 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n287 66 Pedestrian -1 -1 -1 259.69 157.50 281.13 211.26 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n287 7 Car -1 -1 -1 602.01 174.06 636.35 202.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n287 63 Pedestrian -1 -1 -1 318.84 160.23 336.03 211.89 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n287 59 Pedestrian -1 -1 -1 293.78 160.54 313.24 210.24 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n287 67 Pedestrian -1 -1 -1 1176.51 168.28 1221.66 335.87 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n287 91 Car -1 -1 -1 598.83 173.61 621.94 193.90 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n287 98 Cyclist -1 -1 -1 610.88 166.01 648.88 217.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n288 3 Car -1 -1 -1 1095.66 185.11 1219.74 236.46 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n288 2 Car -1 -1 -1 954.92 183.60 1066.61 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n288 4 Car -1 -1 -1 1030.04 183.77 1155.64 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n288 65 Pedestrian -1 -1 -1 191.43 153.80 210.56 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n288 7 Car -1 -1 -1 601.40 174.09 635.99 201.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n288 86 Cyclist -1 -1 -1 512.93 166.60 549.21 216.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n288 87 Cyclist -1 -1 -1 228.73 154.12 257.95 204.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n288 63 Pedestrian -1 -1 -1 318.97 160.57 336.22 212.20 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n288 59 Pedestrian -1 -1 -1 294.33 161.27 314.00 209.81 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n288 98 Cyclist -1 -1 -1 600.45 170.08 642.08 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n288 66 Pedestrian -1 -1 -1 259.92 157.89 281.85 211.37 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n288 91 Car -1 -1 -1 598.76 173.57 621.90 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n288 67 Pedestrian -1 -1 -1 1184.47 173.31 1221.25 331.04 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n289 3 Car -1 -1 -1 1095.24 185.15 1220.56 236.46 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n289 2 Car -1 -1 -1 954.91 183.64 1066.62 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n289 4 Car -1 -1 -1 1030.05 183.80 1155.64 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n289 65 Pedestrian -1 -1 -1 191.47 153.81 210.73 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n289 87 Cyclist -1 -1 -1 235.75 154.62 266.02 204.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n289 86 Cyclist -1 -1 -1 511.63 166.58 545.72 215.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n289 59 Pedestrian -1 -1 -1 293.98 161.32 314.36 210.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n289 63 Pedestrian -1 -1 -1 318.86 161.08 336.40 211.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n289 7 Car -1 -1 -1 600.49 173.27 635.70 201.35 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n289 66 Pedestrian -1 -1 -1 262.38 158.67 283.36 212.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n289 98 Cyclist -1 -1 -1 597.63 167.96 637.75 218.55 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n289 99 Pedestrian -1 -1 -1 343.88 157.60 366.13 218.81 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n290 3 Car -1 -1 -1 1095.15 185.25 1220.80 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n290 2 Car -1 -1 -1 954.89 183.67 1066.70 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n290 4 Car -1 -1 -1 1029.97 183.84 1155.74 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n290 86 Cyclist -1 -1 -1 510.64 166.14 541.31 215.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n290 65 Pedestrian -1 -1 -1 191.77 153.85 210.77 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n290 7 Car -1 -1 -1 601.11 172.14 635.37 201.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n290 98 Cyclist -1 -1 -1 594.71 167.49 634.45 216.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n290 66 Pedestrian -1 -1 -1 262.80 157.75 283.68 213.36 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n290 63 Pedestrian -1 -1 -1 318.53 160.55 336.06 211.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n290 59 Pedestrian -1 -1 -1 294.87 161.26 314.18 210.10 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n290 99 Pedestrian -1 -1 -1 344.46 158.10 365.45 217.79 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n290 87 Cyclist -1 -1 -1 242.61 156.00 267.81 203.52 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n291 3 Car -1 -1 -1 1095.39 185.28 1220.70 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n291 2 Car -1 -1 -1 954.83 183.71 1066.79 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n291 4 Car -1 -1 -1 1029.96 183.86 1155.74 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n291 65 Pedestrian -1 -1 -1 191.67 153.84 210.96 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n291 98 Cyclist -1 -1 -1 592.75 166.05 620.54 216.31 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n291 86 Cyclist -1 -1 -1 507.12 166.62 539.24 214.72 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n291 7 Car -1 -1 -1 601.56 172.14 635.56 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n291 63 Pedestrian -1 -1 -1 318.47 160.47 335.94 210.59 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n291 66 Pedestrian -1 -1 -1 263.88 158.19 284.14 212.74 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n291 59 Pedestrian -1 -1 -1 295.02 160.01 314.01 208.83 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n291 99 Pedestrian -1 -1 -1 344.26 157.84 365.19 217.44 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n291 100 Pedestrian -1 -1 -1 249.89 155.27 275.04 203.76 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n292 3 Car -1 -1 -1 1095.31 185.37 1220.85 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n292 4 Car -1 -1 -1 1029.55 183.84 1156.10 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n292 2 Car -1 -1 -1 954.88 183.72 1066.57 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n292 86 Cyclist -1 -1 -1 507.30 166.58 536.49 215.95 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n292 65 Pedestrian -1 -1 -1 191.80 153.82 211.08 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n292 7 Car -1 -1 -1 602.01 172.34 635.86 201.06 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n292 98 Cyclist -1 -1 -1 590.00 165.15 615.79 215.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n292 66 Pedestrian -1 -1 -1 263.72 158.75 285.21 212.77 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n292 100 Pedestrian -1 -1 -1 257.16 154.84 280.58 204.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n292 63 Pedestrian -1 -1 -1 319.46 159.25 335.38 210.10 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n292 59 Pedestrian -1 -1 -1 295.77 160.46 314.43 208.19 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n292 99 Pedestrian -1 -1 -1 343.57 156.82 366.10 216.82 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n292 101 Car -1 -1 -1 595.72 171.71 624.24 195.25 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n293 3 Car -1 -1 -1 1095.33 185.29 1220.79 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n293 2 Car -1 -1 -1 954.91 183.68 1066.73 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n293 4 Car -1 -1 -1 1029.61 183.88 1156.10 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n293 65 Pedestrian -1 -1 -1 191.99 153.72 211.16 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n293 66 Pedestrian -1 -1 -1 265.50 158.96 287.45 213.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n293 86 Cyclist -1 -1 -1 504.08 167.17 535.29 214.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n293 7 Car -1 -1 -1 601.05 172.59 637.11 201.98 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n293 98 Cyclist -1 -1 -1 589.66 164.28 616.00 214.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n293 100 Pedestrian -1 -1 -1 263.37 155.93 284.19 203.85 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n293 59 Pedestrian -1 -1 -1 298.25 161.15 315.94 207.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n293 63 Pedestrian -1 -1 -1 319.52 160.96 335.65 210.14 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n293 99 Pedestrian -1 -1 -1 344.06 156.78 365.98 216.39 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n294 3 Car -1 -1 -1 1094.97 185.24 1221.05 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n294 2 Car -1 -1 -1 954.90 183.75 1066.77 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n294 4 Car -1 -1 -1 1029.87 183.92 1155.90 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n294 86 Cyclist -1 -1 -1 503.66 167.24 532.28 214.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n294 66 Pedestrian -1 -1 -1 267.54 159.61 288.82 213.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n294 65 Pedestrian -1 -1 -1 192.16 153.70 211.05 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n294 7 Car -1 -1 -1 601.29 172.91 637.29 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n294 100 Pedestrian -1 -1 -1 271.79 157.43 291.32 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n294 59 Pedestrian -1 -1 -1 298.35 161.20 316.88 207.94 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n294 63 Pedestrian -1 -1 -1 319.56 160.87 335.83 210.39 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n294 98 Cyclist -1 -1 -1 588.53 164.11 613.87 212.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n294 99 Pedestrian -1 -1 -1 344.16 157.59 365.98 216.25 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n295 3 Car -1 -1 -1 1095.20 185.31 1220.95 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n295 2 Car -1 -1 -1 955.05 183.75 1066.61 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n295 4 Car -1 -1 -1 1029.54 183.88 1156.18 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n295 66 Pedestrian -1 -1 -1 269.99 158.46 290.85 214.66 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n295 86 Cyclist -1 -1 -1 499.51 167.93 531.18 213.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n295 65 Pedestrian -1 -1 -1 192.20 153.65 211.24 199.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n295 7 Car -1 -1 -1 601.70 173.16 636.90 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n295 98 Cyclist -1 -1 -1 586.98 163.73 611.03 212.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n295 59 Pedestrian -1 -1 -1 299.04 160.59 317.71 208.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n295 100 Pedestrian -1 -1 -1 279.93 158.16 297.71 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n295 63 Pedestrian -1 -1 -1 320.03 160.84 335.75 210.05 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n295 99 Pedestrian -1 -1 -1 346.35 157.71 367.59 216.34 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n296 3 Car -1 -1 -1 1098.96 185.33 1220.56 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n296 2 Car -1 -1 -1 954.92 183.75 1066.84 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n296 4 Car -1 -1 -1 1029.76 183.87 1156.01 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n296 66 Pedestrian -1 -1 -1 270.61 157.61 292.15 215.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n296 7 Car -1 -1 -1 601.11 172.80 636.97 203.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n296 65 Pedestrian -1 -1 -1 192.47 153.77 211.36 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n296 86 Cyclist -1 -1 -1 500.09 167.54 526.30 212.50 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n296 59 Pedestrian -1 -1 -1 299.96 160.49 317.57 207.70 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n296 63 Pedestrian -1 -1 -1 320.18 159.55 335.69 209.26 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n296 98 Cyclist -1 -1 -1 586.65 164.17 608.61 211.74 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n296 100 Pedestrian -1 -1 -1 284.94 157.68 302.14 200.80 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n296 99 Pedestrian -1 -1 -1 346.37 157.58 368.46 216.05 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n297 3 Car -1 -1 -1 1095.12 185.28 1221.11 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n297 2 Car -1 -1 -1 954.99 183.79 1066.74 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n297 4 Car -1 -1 -1 1029.86 183.87 1155.97 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n297 7 Car -1 -1 -1 601.42 172.68 636.81 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n297 66 Pedestrian -1 -1 -1 271.25 158.08 293.02 215.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n297 65 Pedestrian -1 -1 -1 192.95 153.62 211.09 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n297 86 Cyclist -1 -1 -1 496.57 167.71 525.75 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n297 98 Cyclist -1 -1 -1 584.25 163.69 607.28 211.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n297 59 Pedestrian -1 -1 -1 300.54 160.83 317.26 206.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n297 100 Pedestrian -1 -1 -1 290.79 157.14 308.16 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n297 63 Pedestrian -1 -1 -1 319.78 159.45 336.08 209.26 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n297 99 Pedestrian -1 -1 -1 346.64 157.31 368.88 215.61 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n297 102 Cyclist -1 -1 -1 346.64 157.31 368.88 215.61 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n298 3 Car -1 -1 -1 1095.16 185.29 1220.97 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n298 2 Car -1 -1 -1 955.06 183.78 1066.73 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n298 4 Car -1 -1 -1 1029.88 183.91 1155.90 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n298 7 Car -1 -1 -1 601.76 172.92 636.53 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n298 66 Pedestrian -1 -1 -1 273.25 158.84 294.86 216.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n298 65 Pedestrian -1 -1 -1 192.81 153.47 211.13 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n298 98 Cyclist -1 -1 -1 583.24 164.23 606.85 211.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n298 99 Pedestrian -1 -1 -1 347.83 157.70 367.64 215.28 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n298 86 Cyclist -1 -1 -1 496.05 168.17 522.99 211.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n298 59 Pedestrian -1 -1 -1 302.21 160.86 319.08 206.61 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n298 100 Pedestrian -1 -1 -1 294.46 157.68 312.05 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n298 63 Pedestrian -1 -1 -1 319.86 159.81 336.07 209.29 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n299 3 Car -1 -1 -1 1094.98 185.30 1221.11 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n299 2 Car -1 -1 -1 955.09 183.82 1066.83 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n299 4 Car -1 -1 -1 1029.95 183.98 1155.94 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n299 7 Car -1 -1 -1 601.59 172.92 636.74 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n299 66 Pedestrian -1 -1 -1 274.90 159.00 296.31 217.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n299 99 Pedestrian -1 -1 -1 348.28 157.92 368.29 215.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n299 65 Pedestrian -1 -1 -1 192.64 153.27 211.17 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n299 59 Pedestrian -1 -1 -1 299.18 160.36 318.36 206.31 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n299 86 Cyclist -1 -1 -1 495.78 167.75 520.38 211.80 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n299 98 Cyclist -1 -1 -1 583.26 165.20 604.77 209.92 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n299 63 Pedestrian -1 -1 -1 319.57 159.74 336.33 209.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n300 3 Car -1 -1 -1 1095.04 185.29 1221.10 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n300 2 Car -1 -1 -1 955.06 183.81 1066.84 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n300 4 Car -1 -1 -1 1029.81 183.88 1155.96 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n300 7 Car -1 -1 -1 601.98 173.15 636.41 202.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n300 65 Pedestrian -1 -1 -1 192.53 153.27 210.94 199.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n300 99 Pedestrian -1 -1 -1 348.50 157.98 368.16 214.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n300 59 Pedestrian -1 -1 -1 301.50 159.78 320.79 207.36 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n300 66 Pedestrian -1 -1 -1 274.97 158.81 297.19 217.09 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n300 86 Cyclist -1 -1 -1 496.17 167.71 517.92 211.49 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n300 63 Pedestrian -1 -1 -1 319.54 160.04 336.56 208.60 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n300 98 Cyclist -1 -1 -1 581.77 165.48 602.66 209.70 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n300 103 Car -1 -1 -1 599.50 173.70 622.21 193.70 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n301 3 Car -1 -1 -1 1095.06 185.24 1221.07 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n301 2 Car -1 -1 -1 955.03 183.80 1066.87 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n301 4 Car -1 -1 -1 1029.83 183.89 1155.98 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n301 66 Pedestrian -1 -1 -1 277.17 157.96 299.67 218.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n301 7 Car -1 -1 -1 602.02 173.23 636.26 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n301 65 Pedestrian -1 -1 -1 192.22 153.40 210.79 199.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n301 59 Pedestrian -1 -1 -1 302.52 159.31 320.28 206.47 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n301 86 Cyclist -1 -1 -1 494.52 168.08 517.08 211.49 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n301 99 Pedestrian -1 -1 -1 348.15 157.60 368.36 213.76 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n301 98 Cyclist -1 -1 -1 581.92 165.83 602.31 208.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n301 63 Pedestrian -1 -1 -1 319.96 159.48 336.07 207.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n301 103 Car -1 -1 -1 599.31 173.66 621.90 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n302 3 Car -1 -1 -1 1094.97 185.22 1221.24 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n302 2 Car -1 -1 -1 954.92 183.85 1066.89 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n302 4 Car -1 -1 -1 1029.64 183.88 1156.15 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n302 7 Car -1 -1 -1 601.96 173.20 636.33 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n302 65 Pedestrian -1 -1 -1 191.85 153.44 210.77 199.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n302 66 Pedestrian -1 -1 -1 278.31 158.20 301.32 218.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n302 98 Cyclist -1 -1 -1 581.90 166.24 600.86 207.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n302 59 Pedestrian -1 -1 -1 302.69 159.91 320.49 206.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n302 86 Cyclist -1 -1 -1 491.51 168.39 515.85 210.58 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n302 99 Pedestrian -1 -1 -1 348.18 157.52 368.73 213.57 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n302 63 Pedestrian -1 -1 -1 321.58 160.38 337.72 207.13 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n302 103 Car -1 -1 -1 598.94 173.65 621.92 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n302 104 Pedestrian -1 -1 -1 310.59 157.32 327.89 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n303 3 Car -1 -1 -1 1095.09 185.24 1221.14 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n303 2 Car -1 -1 -1 955.01 183.79 1066.87 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n303 4 Car -1 -1 -1 1029.68 183.88 1156.12 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n303 7 Car -1 -1 -1 601.96 173.23 636.34 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n303 65 Pedestrian -1 -1 -1 191.32 153.68 210.41 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n303 66 Pedestrian -1 -1 -1 280.54 158.85 302.42 219.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n303 98 Cyclist -1 -1 -1 581.54 166.08 599.75 207.31 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n303 86 Cyclist -1 -1 -1 489.90 168.03 514.95 210.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n303 59 Pedestrian -1 -1 -1 303.87 160.54 321.17 206.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n303 63 Pedestrian -1 -1 -1 319.45 160.22 336.66 207.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n303 99 Pedestrian -1 -1 -1 348.33 158.13 368.87 213.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n303 103 Car -1 -1 -1 598.85 173.62 621.78 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n304 3 Car -1 -1 -1 1095.02 185.23 1221.11 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n304 2 Car -1 -1 -1 955.12 183.85 1066.90 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n304 4 Car -1 -1 -1 1029.91 183.92 1155.92 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n304 7 Car -1 -1 -1 601.55 173.22 636.61 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n304 66 Pedestrian -1 -1 -1 281.83 158.85 303.77 220.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n304 65 Pedestrian -1 -1 -1 190.87 153.97 209.49 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n304 63 Pedestrian -1 -1 -1 321.99 160.12 338.20 207.21 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n304 59 Pedestrian -1 -1 -1 306.25 160.44 322.88 206.73 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n304 98 Cyclist -1 -1 -1 581.41 166.40 598.77 206.42 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n304 86 Cyclist -1 -1 -1 488.92 169.20 511.00 209.84 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n304 99 Pedestrian -1 -1 -1 348.40 158.58 369.01 212.94 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n304 103 Car -1 -1 -1 598.66 173.61 621.56 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n304 105 Pedestrian -1 -1 -1 468.25 170.67 484.83 200.76 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n305 3 Car -1 -1 -1 1095.00 185.25 1221.12 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n305 2 Car -1 -1 -1 955.13 183.86 1066.80 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n305 4 Car -1 -1 -1 1029.97 183.96 1155.92 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n305 7 Car -1 -1 -1 601.41 173.15 636.74 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n305 63 Pedestrian -1 -1 -1 322.90 159.90 339.57 207.18 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n305 65 Pedestrian -1 -1 -1 189.15 153.57 207.41 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n305 59 Pedestrian -1 -1 -1 306.63 160.07 323.24 205.90 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n305 86 Cyclist -1 -1 -1 489.16 169.35 509.96 209.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n305 66 Pedestrian -1 -1 -1 283.15 158.76 304.80 219.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n305 98 Cyclist -1 -1 -1 581.58 166.54 597.91 205.13 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n305 99 Pedestrian -1 -1 -1 348.39 158.31 369.05 213.00 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n305 103 Car -1 -1 -1 598.61 173.61 621.41 192.92 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n306 3 Car -1 -1 -1 1095.12 185.25 1221.06 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n306 2 Car -1 -1 -1 955.21 183.79 1066.68 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n306 4 Car -1 -1 -1 1029.72 183.85 1155.94 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n306 65 Pedestrian -1 -1 -1 188.71 153.08 206.92 198.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n306 7 Car -1 -1 -1 601.70 173.30 636.58 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n306 63 Pedestrian -1 -1 -1 322.98 159.57 340.38 206.46 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n306 66 Pedestrian -1 -1 -1 285.51 158.37 306.31 220.01 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n306 59 Pedestrian -1 -1 -1 306.47 159.82 323.46 205.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n306 98 Cyclist -1 -1 -1 581.94 166.97 597.39 204.25 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n306 86 Cyclist -1 -1 -1 488.95 169.51 507.56 208.88 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n306 103 Car -1 -1 -1 598.51 173.74 621.23 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n306 99 Pedestrian -1 -1 -1 348.51 157.16 368.96 211.45 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n306 106 Pedestrian -1 -1 -1 462.16 168.21 476.05 200.41 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n307 3 Car -1 -1 -1 1095.32 185.32 1220.94 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n307 2 Car -1 -1 -1 955.12 183.85 1066.79 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n307 4 Car -1 -1 -1 1029.88 183.87 1155.78 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n307 65 Pedestrian -1 -1 -1 188.50 153.09 206.42 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n307 7 Car -1 -1 -1 601.64 173.39 636.62 202.37 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n307 66 Pedestrian -1 -1 -1 285.80 158.60 307.64 220.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n307 63 Pedestrian -1 -1 -1 322.97 159.95 340.29 206.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n307 59 Pedestrian -1 -1 -1 306.51 160.16 323.57 206.00 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n307 86 Cyclist -1 -1 -1 487.48 168.27 505.44 208.33 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n307 99 Pedestrian -1 -1 -1 348.73 157.22 368.82 211.02 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n307 103 Car -1 -1 -1 598.48 173.70 621.13 192.62 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n307 98 Cyclist -1 -1 -1 582.60 166.56 596.61 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n307 107 Pedestrian -1 -1 -1 331.21 159.04 346.93 199.26 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n308 3 Car -1 -1 -1 1098.81 185.31 1220.69 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n308 2 Car -1 -1 -1 955.17 183.86 1066.77 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n308 4 Car -1 -1 -1 1029.81 183.90 1156.05 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n308 65 Pedestrian -1 -1 -1 187.90 153.05 206.05 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n308 7 Car -1 -1 -1 601.90 173.38 636.60 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n308 66 Pedestrian -1 -1 -1 286.28 159.41 308.59 221.41 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n308 59 Pedestrian -1 -1 -1 307.29 160.71 323.75 205.49 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n308 63 Pedestrian -1 -1 -1 323.48 160.04 339.99 205.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n308 98 Cyclist -1 -1 -1 580.65 165.25 595.18 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n308 86 Cyclist -1 -1 -1 487.54 167.88 504.99 208.21 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n308 99 Pedestrian -1 -1 -1 351.76 157.74 370.18 210.67 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n308 103 Car -1 -1 -1 598.76 173.62 621.10 192.75 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n308 107 Pedestrian -1 -1 -1 334.44 159.65 349.05 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n309 3 Car -1 -1 -1 1098.81 185.35 1220.71 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n309 2 Car -1 -1 -1 955.22 183.81 1066.69 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n309 4 Car -1 -1 -1 1029.89 183.89 1155.92 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n309 66 Pedestrian -1 -1 -1 288.82 159.40 310.76 221.65 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n309 65 Pedestrian -1 -1 -1 187.54 152.71 206.22 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n309 7 Car -1 -1 -1 601.62 173.21 636.75 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n309 59 Pedestrian -1 -1 -1 307.51 160.97 323.80 205.52 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n309 63 Pedestrian -1 -1 -1 323.03 160.48 340.11 205.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n309 98 Cyclist -1 -1 -1 580.35 165.19 595.14 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n309 99 Pedestrian -1 -1 -1 352.10 157.76 370.30 210.51 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n309 103 Car -1 -1 -1 598.66 173.59 621.11 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n309 107 Pedestrian -1 -1 -1 335.20 159.43 350.43 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n309 86 Cyclist -1 -1 -1 487.26 167.72 504.24 207.71 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n309 108 Pedestrian -1 -1 -1 487.26 167.72 504.24 207.71 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n310 3 Car -1 -1 -1 1098.84 185.38 1220.66 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n310 2 Car -1 -1 -1 954.91 183.81 1066.74 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n310 4 Car -1 -1 -1 1029.59 183.85 1156.15 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n310 66 Pedestrian -1 -1 -1 289.90 158.76 311.94 221.52 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n310 65 Pedestrian -1 -1 -1 186.80 152.55 206.24 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n310 7 Car -1 -1 -1 601.82 173.20 636.61 202.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n310 63 Pedestrian -1 -1 -1 322.96 159.95 339.99 205.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n310 59 Pedestrian -1 -1 -1 306.80 160.57 323.45 204.92 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n310 98 Cyclist -1 -1 -1 579.94 164.81 595.05 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n310 99 Pedestrian -1 -1 -1 351.86 157.82 369.83 210.38 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n310 107 Pedestrian -1 -1 -1 338.53 159.68 352.48 196.39 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n310 103 Car -1 -1 -1 598.78 173.34 621.11 192.78 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n310 86 Cyclist -1 -1 -1 487.04 167.19 504.27 207.59 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n310 108 Pedestrian -1 -1 -1 487.04 167.19 504.27 207.59 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n311 3 Car -1 -1 -1 1095.20 185.29 1221.03 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n311 2 Car -1 -1 -1 954.90 183.83 1066.92 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n311 4 Car -1 -1 -1 1029.77 183.87 1155.98 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n311 7 Car -1 -1 -1 601.78 173.12 636.40 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n311 65 Pedestrian -1 -1 -1 186.26 152.72 206.17 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n311 66 Pedestrian -1 -1 -1 291.74 158.38 313.74 221.83 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n311 59 Pedestrian -1 -1 -1 307.40 160.44 323.61 204.27 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n311 63 Pedestrian -1 -1 -1 323.77 159.68 340.10 205.22 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n311 99 Pedestrian -1 -1 -1 351.64 157.30 369.77 210.25 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n311 98 Cyclist -1 -1 -1 579.72 165.56 593.75 201.24 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n311 107 Pedestrian -1 -1 -1 339.61 159.19 354.33 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n311 103 Car -1 -1 -1 598.85 173.53 621.25 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n312 3 Car -1 -1 -1 1095.07 185.30 1221.04 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n312 2 Car -1 -1 -1 955.03 183.83 1066.76 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n312 4 Car -1 -1 -1 1029.51 183.84 1156.14 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n312 66 Pedestrian -1 -1 -1 292.48 159.34 314.85 222.05 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n312 7 Car -1 -1 -1 601.80 173.11 636.55 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n312 65 Pedestrian -1 -1 -1 186.27 152.86 205.77 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n312 59 Pedestrian -1 -1 -1 307.98 160.92 324.25 203.74 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n312 99 Pedestrian -1 -1 -1 348.34 157.59 368.79 210.35 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n312 63 Pedestrian -1 -1 -1 324.14 159.61 340.01 204.69 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n312 98 Cyclist -1 -1 -1 579.87 165.91 593.10 200.51 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n312 103 Car -1 -1 -1 599.15 173.40 621.31 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n312 107 Pedestrian -1 -1 -1 341.56 158.26 356.40 195.62 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n313 3 Car -1 -1 -1 1095.13 185.29 1220.96 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n313 2 Car -1 -1 -1 955.17 183.81 1066.70 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n313 4 Car -1 -1 -1 1029.55 183.87 1156.03 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n313 66 Pedestrian -1 -1 -1 293.94 160.32 315.78 222.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n313 7 Car -1 -1 -1 601.83 173.15 636.53 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n313 65 Pedestrian -1 -1 -1 184.35 152.37 204.01 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n313 59 Pedestrian -1 -1 -1 308.16 161.30 324.74 203.79 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n313 63 Pedestrian -1 -1 -1 325.79 160.38 341.68 204.46 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n313 99 Pedestrian -1 -1 -1 350.92 158.15 370.07 210.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n313 107 Pedestrian -1 -1 -1 343.12 158.63 357.59 195.06 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n313 98 Cyclist -1 -1 -1 579.98 166.26 593.06 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n313 103 Car -1 -1 -1 599.08 173.51 621.36 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n314 3 Car -1 -1 -1 1095.20 185.24 1220.99 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n314 2 Car -1 -1 -1 955.07 183.85 1066.85 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n314 4 Car -1 -1 -1 1029.53 183.87 1156.13 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n314 66 Pedestrian -1 -1 -1 296.09 160.21 318.11 223.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n314 7 Car -1 -1 -1 601.99 173.15 636.34 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n314 65 Pedestrian -1 -1 -1 184.22 152.00 203.90 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n314 63 Pedestrian -1 -1 -1 326.47 160.38 342.30 204.68 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n314 59 Pedestrian -1 -1 -1 308.14 160.60 324.79 204.10 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n314 99 Pedestrian -1 -1 -1 350.82 158.01 370.12 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n314 107 Pedestrian -1 -1 -1 344.45 158.97 357.83 194.52 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n314 98 Cyclist -1 -1 -1 579.68 167.04 592.25 199.49 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n314 103 Car -1 -1 -1 599.29 173.55 621.20 193.05 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n314 109 Cyclist -1 -1 -1 484.02 168.00 500.77 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n314 110 Pedestrian -1 -1 -1 484.02 168.00 500.77 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n315 3 Car -1 -1 -1 1095.10 185.21 1221.06 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n315 2 Car -1 -1 -1 955.05 183.85 1066.93 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n315 4 Car -1 -1 -1 1029.59 183.88 1156.06 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n315 66 Pedestrian -1 -1 -1 296.93 159.90 319.23 223.21 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n315 7 Car -1 -1 -1 601.98 173.12 636.35 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n315 65 Pedestrian -1 -1 -1 184.31 151.84 203.92 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n315 63 Pedestrian -1 -1 -1 327.02 160.30 342.36 204.12 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n315 59 Pedestrian -1 -1 -1 308.17 159.94 325.06 204.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n315 107 Pedestrian -1 -1 -1 346.75 159.00 360.66 194.12 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n315 99 Pedestrian -1 -1 -1 348.49 157.94 369.00 207.90 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n315 103 Car -1 -1 -1 599.29 173.56 621.19 193.01 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n315 98 Cyclist -1 -1 -1 579.64 166.79 592.32 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n315 110 Pedestrian -1 -1 -1 483.70 168.18 499.61 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n315 109 Cyclist -1 -1 -1 483.70 168.18 499.61 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n316 3 Car -1 -1 -1 1095.18 185.22 1220.95 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n316 2 Car -1 -1 -1 955.24 183.89 1066.68 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n316 4 Car -1 -1 -1 1029.63 183.88 1156.09 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n316 66 Pedestrian -1 -1 -1 297.62 159.23 319.81 223.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n316 7 Car -1 -1 -1 601.94 173.01 636.33 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n316 65 Pedestrian -1 -1 -1 184.30 152.06 204.00 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n316 63 Pedestrian -1 -1 -1 327.44 160.08 342.82 203.78 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n316 59 Pedestrian -1 -1 -1 308.04 160.01 325.18 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n316 99 Pedestrian -1 -1 -1 348.35 157.85 369.00 207.26 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n316 103 Car -1 -1 -1 599.24 173.62 620.88 192.93 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n317 3 Car -1 -1 -1 1095.21 185.37 1221.01 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n317 2 Car -1 -1 -1 955.07 183.86 1066.81 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n317 4 Car -1 -1 -1 1029.63 183.95 1156.13 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n317 7 Car -1 -1 -1 601.88 172.99 636.53 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n317 65 Pedestrian -1 -1 -1 184.38 152.18 203.98 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n317 66 Pedestrian -1 -1 -1 298.54 160.14 322.39 223.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n317 63 Pedestrian -1 -1 -1 327.35 160.24 342.82 204.09 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n317 59 Pedestrian -1 -1 -1 307.74 160.27 325.18 204.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n317 99 Pedestrian -1 -1 -1 351.01 157.82 370.60 207.18 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n317 103 Car -1 -1 -1 599.09 173.53 620.88 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n318 3 Car -1 -1 -1 1095.06 185.27 1221.05 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n318 2 Car -1 -1 -1 955.17 183.88 1066.80 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n318 4 Car -1 -1 -1 1029.46 183.88 1156.13 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n318 7 Car -1 -1 -1 601.87 172.92 636.57 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n318 65 Pedestrian -1 -1 -1 184.18 152.18 204.12 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n318 63 Pedestrian -1 -1 -1 327.63 160.50 343.14 204.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n318 99 Pedestrian -1 -1 -1 351.21 158.12 370.47 207.31 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n318 59 Pedestrian -1 -1 -1 307.72 160.39 325.18 205.32 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n318 66 Pedestrian -1 -1 -1 299.02 160.67 322.26 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n318 103 Car -1 -1 -1 599.16 173.54 621.00 192.88 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n319 3 Car -1 -1 -1 1095.01 185.29 1221.08 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n319 2 Car -1 -1 -1 955.20 183.89 1066.81 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n319 4 Car -1 -1 -1 1029.59 183.89 1155.90 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n319 66 Pedestrian -1 -1 -1 299.17 160.38 323.44 226.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n319 65 Pedestrian -1 -1 -1 184.01 152.32 203.99 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n319 7 Car -1 -1 -1 601.93 173.03 636.52 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n319 63 Pedestrian -1 -1 -1 327.75 160.61 343.19 203.98 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n319 99 Pedestrian -1 -1 -1 351.40 158.13 370.45 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n319 59 Pedestrian -1 -1 -1 310.23 160.45 326.91 204.72 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n319 103 Car -1 -1 -1 599.12 173.48 621.21 192.80 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n319 111 Cyclist -1 -1 -1 576.83 167.74 590.62 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n320 3 Car -1 -1 -1 1095.00 185.22 1221.09 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n320 2 Car -1 -1 -1 955.13 183.88 1066.72 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n320 4 Car -1 -1 -1 1029.51 183.90 1155.91 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n320 7 Car -1 -1 -1 601.91 173.03 636.33 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n320 65 Pedestrian -1 -1 -1 183.76 152.29 203.98 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n320 66 Pedestrian -1 -1 -1 300.12 160.28 325.03 226.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n320 63 Pedestrian -1 -1 -1 327.51 160.39 343.06 203.57 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n320 59 Pedestrian -1 -1 -1 310.08 160.44 327.06 205.17 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n320 99 Pedestrian -1 -1 -1 351.58 157.81 369.79 205.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n320 111 Cyclist -1 -1 -1 576.04 167.81 590.11 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n320 103 Car -1 -1 -1 598.86 173.50 621.16 192.77 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n321 3 Car -1 -1 -1 1098.66 185.31 1220.83 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n321 2 Car -1 -1 -1 955.22 183.82 1066.78 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n321 4 Car -1 -1 -1 1029.69 183.90 1156.09 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n321 7 Car -1 -1 -1 601.87 172.88 636.28 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n321 65 Pedestrian -1 -1 -1 183.55 152.20 204.16 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n321 66 Pedestrian -1 -1 -1 302.96 159.54 326.12 227.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n321 59 Pedestrian -1 -1 -1 310.35 160.47 327.00 205.01 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n321 63 Pedestrian -1 -1 -1 327.80 160.30 342.73 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n321 99 Pedestrian -1 -1 -1 351.41 156.86 370.42 204.09 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n321 111 Cyclist -1 -1 -1 575.64 167.83 589.35 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n321 103 Car -1 -1 -1 598.93 173.52 621.17 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n322 3 Car -1 -1 -1 1095.37 185.33 1220.90 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n322 2 Car -1 -1 -1 955.24 183.82 1066.72 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n322 4 Car -1 -1 -1 1029.74 183.91 1155.91 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n322 66 Pedestrian -1 -1 -1 304.20 160.29 328.02 227.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n322 7 Car -1 -1 -1 601.99 172.99 636.28 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n322 65 Pedestrian -1 -1 -1 183.70 152.30 204.10 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n322 59 Pedestrian -1 -1 -1 310.62 160.36 327.18 205.21 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n322 63 Pedestrian -1 -1 -1 328.09 160.57 342.91 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n322 99 Pedestrian -1 -1 -1 351.56 157.93 369.95 205.70 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n322 103 Car -1 -1 -1 598.90 173.59 621.15 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n322 111 Cyclist -1 -1 -1 575.65 168.19 588.89 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n322 112 Pedestrian -1 -1 -1 481.12 169.91 496.31 204.89 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n322 113 Pedestrian -1 -1 -1 361.46 159.83 374.49 191.03 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n323 3 Car -1 -1 -1 1094.92 185.17 1221.16 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n323 2 Car -1 -1 -1 955.07 183.82 1066.86 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n323 4 Car -1 -1 -1 1029.57 183.90 1156.01 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n323 66 Pedestrian -1 -1 -1 306.00 160.70 330.49 228.57 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n323 7 Car -1 -1 -1 601.88 172.91 636.57 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n323 65 Pedestrian -1 -1 -1 184.14 151.97 203.88 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n323 59 Pedestrian -1 -1 -1 310.95 160.09 327.60 205.70 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n323 63 Pedestrian -1 -1 -1 328.11 160.66 343.05 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n323 99 Pedestrian -1 -1 -1 351.83 157.88 370.11 206.07 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n323 103 Car -1 -1 -1 598.84 173.59 621.16 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n323 112 Pedestrian -1 -1 -1 480.05 169.88 495.89 204.98 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n324 3 Car -1 -1 -1 1095.06 185.27 1221.16 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n324 2 Car -1 -1 -1 955.10 183.80 1066.76 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n324 4 Car -1 -1 -1 1029.53 183.91 1155.99 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n324 66 Pedestrian -1 -1 -1 307.61 160.42 331.97 228.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n324 7 Car -1 -1 -1 601.87 173.03 636.35 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n324 65 Pedestrian -1 -1 -1 184.35 151.94 203.75 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n324 59 Pedestrian -1 -1 -1 311.15 160.41 327.80 205.65 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n324 63 Pedestrian -1 -1 -1 327.94 160.53 343.41 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n324 99 Pedestrian -1 -1 -1 351.79 158.28 370.12 205.37 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n324 103 Car -1 -1 -1 598.86 173.56 621.24 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n325 3 Car -1 -1 -1 1095.30 185.24 1220.87 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n325 2 Car -1 -1 -1 955.08 183.82 1066.83 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n325 4 Car -1 -1 -1 1029.52 183.92 1156.04 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n325 66 Pedestrian -1 -1 -1 308.58 161.11 332.84 228.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n325 7 Car -1 -1 -1 601.79 172.97 636.77 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n325 65 Pedestrian -1 -1 -1 184.62 151.83 203.73 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n325 63 Pedestrian -1 -1 -1 328.12 160.32 343.30 203.20 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n325 59 Pedestrian -1 -1 -1 311.49 160.74 327.74 205.34 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n325 103 Car -1 -1 -1 598.88 173.49 621.24 193.00 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n325 99 Pedestrian -1 -1 -1 351.27 157.81 370.11 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n325 114 Pedestrian -1 -1 -1 412.22 163.64 426.60 197.19 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n326 3 Car -1 -1 -1 1095.49 185.29 1220.81 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n326 2 Car -1 -1 -1 955.06 183.77 1066.68 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n326 4 Car -1 -1 -1 1029.49 183.86 1156.06 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n326 66 Pedestrian -1 -1 -1 310.62 160.57 335.27 229.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n326 7 Car -1 -1 -1 601.88 173.01 636.68 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n326 65 Pedestrian -1 -1 -1 185.01 152.27 203.49 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n326 114 Pedestrian -1 -1 -1 409.53 163.07 422.84 197.41 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n326 63 Pedestrian -1 -1 -1 329.82 159.86 344.99 201.83 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n326 103 Car -1 -1 -1 598.71 173.53 621.30 193.08 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n326 99 Pedestrian -1 -1 -1 351.79 158.19 369.71 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n326 59 Pedestrian -1 -1 -1 311.91 161.05 327.84 205.02 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n326 115 Cyclist -1 -1 -1 474.51 170.40 491.05 204.64 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n326 116 Pedestrian -1 -1 -1 363.20 158.82 376.54 189.48 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n327 3 Car -1 -1 -1 1095.40 185.32 1220.78 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n327 2 Car -1 -1 -1 954.96 183.82 1066.85 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n327 4 Car -1 -1 -1 1029.55 183.87 1155.99 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n327 66 Pedestrian -1 -1 -1 311.28 161.49 336.71 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n327 7 Car -1 -1 -1 601.88 172.94 636.64 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n327 65 Pedestrian -1 -1 -1 184.79 152.22 203.22 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n327 114 Pedestrian -1 -1 -1 407.73 163.71 421.15 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n327 115 Cyclist -1 -1 -1 471.07 170.12 489.36 204.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n327 63 Pedestrian -1 -1 -1 329.99 161.15 345.20 202.18 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n327 103 Car -1 -1 -1 598.94 173.53 621.15 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n327 59 Pedestrian -1 -1 -1 312.51 161.35 327.59 205.09 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n327 99 Pedestrian -1 -1 -1 352.17 158.37 369.58 202.15 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n328 3 Car -1 -1 -1 1095.27 185.25 1220.92 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n328 2 Car -1 -1 -1 954.99 183.81 1066.84 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n328 4 Car -1 -1 -1 1029.43 183.89 1156.19 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n328 7 Car -1 -1 -1 601.86 172.92 636.74 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n328 65 Pedestrian -1 -1 -1 185.15 152.28 203.05 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n328 114 Pedestrian -1 -1 -1 402.65 164.03 419.90 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n328 66 Pedestrian -1 -1 -1 312.46 161.26 339.26 230.94 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n328 115 Cyclist -1 -1 -1 465.98 168.77 488.22 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n328 63 Pedestrian -1 -1 -1 330.00 160.90 345.32 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n328 103 Car -1 -1 -1 598.88 173.57 621.30 193.03 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n328 59 Pedestrian -1 -1 -1 312.55 161.00 327.91 205.33 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n329 3 Car -1 -1 -1 1095.22 185.27 1220.90 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n329 2 Car -1 -1 -1 955.03 183.84 1066.84 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n329 4 Car -1 -1 -1 1029.57 183.88 1156.02 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n329 66 Pedestrian -1 -1 -1 313.37 160.46 340.88 231.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n329 7 Car -1 -1 -1 601.70 172.84 636.76 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n329 65 Pedestrian -1 -1 -1 187.48 152.43 205.35 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n329 115 Cyclist -1 -1 -1 460.65 168.32 486.02 203.45 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n329 114 Pedestrian -1 -1 -1 401.28 163.77 418.45 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n329 63 Pedestrian -1 -1 -1 329.87 160.59 345.71 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n329 103 Car -1 -1 -1 598.92 173.59 621.30 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n329 59 Pedestrian -1 -1 -1 314.42 161.71 330.57 203.38 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n329 117 Pedestrian -1 -1 -1 365.18 158.55 378.49 187.82 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n330 3 Car -1 -1 -1 1095.27 185.30 1220.85 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n330 2 Car -1 -1 -1 955.08 183.85 1066.84 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n330 4 Car -1 -1 -1 1029.48 183.85 1156.22 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n330 65 Pedestrian -1 -1 -1 188.49 152.46 205.81 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n330 66 Pedestrian -1 -1 -1 316.39 160.31 342.87 230.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n330 7 Car -1 -1 -1 601.94 172.89 636.61 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n330 115 Cyclist -1 -1 -1 460.34 168.25 483.53 203.49 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n330 114 Pedestrian -1 -1 -1 399.76 164.05 415.48 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n330 59 Pedestrian -1 -1 -1 314.93 161.16 330.30 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n330 103 Car -1 -1 -1 598.84 173.43 621.23 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n330 63 Pedestrian -1 -1 -1 329.80 159.90 345.44 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n330 117 Pedestrian -1 -1 -1 363.67 158.56 376.72 187.55 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n331 3 Car -1 -1 -1 1095.15 185.32 1220.89 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n331 2 Car -1 -1 -1 955.16 183.86 1066.80 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n331 4 Car -1 -1 -1 1029.59 183.87 1155.95 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n331 66 Pedestrian -1 -1 -1 318.28 160.81 344.48 231.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n331 7 Car -1 -1 -1 602.09 172.93 636.44 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n331 65 Pedestrian -1 -1 -1 189.61 152.85 206.24 197.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n331 114 Pedestrian -1 -1 -1 398.88 163.32 410.64 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n331 115 Cyclist -1 -1 -1 460.06 168.34 482.53 203.26 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n331 59 Pedestrian -1 -1 -1 315.14 161.53 330.80 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n331 103 Car -1 -1 -1 598.63 173.51 621.16 192.91 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n331 63 Pedestrian -1 -1 -1 330.30 159.88 345.56 203.31 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n331 117 Pedestrian -1 -1 -1 363.66 158.65 376.01 187.04 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n332 3 Car -1 -1 -1 1095.01 185.26 1220.93 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n332 2 Car -1 -1 -1 955.06 183.79 1066.90 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n332 4 Car -1 -1 -1 1029.59 183.90 1156.09 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n332 66 Pedestrian -1 -1 -1 320.00 161.69 347.74 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n332 7 Car -1 -1 -1 602.07 172.93 636.40 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n332 114 Pedestrian -1 -1 -1 396.76 163.77 408.85 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n332 65 Pedestrian -1 -1 -1 191.42 153.24 207.93 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n332 59 Pedestrian -1 -1 -1 315.86 162.24 331.24 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n332 103 Car -1 -1 -1 598.53 173.46 621.26 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n332 63 Pedestrian -1 -1 -1 330.77 159.84 345.89 203.70 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n332 115 Cyclist -1 -1 -1 459.11 167.18 478.32 201.55 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n332 117 Pedestrian -1 -1 -1 363.53 158.64 375.82 187.20 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n333 3 Car -1 -1 -1 1095.08 185.25 1221.02 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n333 2 Car -1 -1 -1 955.12 183.75 1066.92 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n333 4 Car -1 -1 -1 1029.64 183.86 1156.08 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n333 66 Pedestrian -1 -1 -1 322.00 162.78 348.49 233.91 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n333 7 Car -1 -1 -1 602.09 172.95 636.32 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n333 114 Pedestrian -1 -1 -1 392.37 163.60 407.20 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n333 65 Pedestrian -1 -1 -1 191.98 153.56 207.86 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n333 115 Cyclist -1 -1 -1 454.10 167.13 477.10 204.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n333 59 Pedestrian -1 -1 -1 316.30 162.17 331.48 201.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n333 103 Car -1 -1 -1 598.66 173.48 621.36 192.97 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n333 63 Pedestrian -1 -1 -1 331.72 160.83 345.20 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n334 3 Car -1 -1 -1 1095.03 185.27 1221.17 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n334 2 Car -1 -1 -1 955.04 183.71 1067.02 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n334 4 Car -1 -1 -1 1029.96 183.91 1155.81 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n334 66 Pedestrian -1 -1 -1 325.16 163.34 351.08 234.17 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n334 7 Car -1 -1 -1 601.77 172.83 636.74 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n334 65 Pedestrian -1 -1 -1 192.65 153.72 208.44 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n334 59 Pedestrian -1 -1 -1 318.67 161.21 334.00 202.43 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n334 114 Pedestrian -1 -1 -1 389.15 163.45 404.86 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n334 103 Car -1 -1 -1 598.65 173.38 621.48 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n334 63 Pedestrian -1 -1 -1 331.51 161.41 345.58 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n335 3 Car -1 -1 -1 1095.13 185.32 1220.92 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n335 2 Car -1 -1 -1 954.90 183.79 1066.84 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n335 4 Car -1 -1 -1 1029.84 183.92 1155.81 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n335 66 Pedestrian -1 -1 -1 328.57 161.82 353.71 234.38 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n335 7 Car -1 -1 -1 601.79 172.87 636.74 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n335 65 Pedestrian -1 -1 -1 192.69 154.05 208.71 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n335 114 Pedestrian -1 -1 -1 386.28 162.96 400.12 196.86 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n335 59 Pedestrian -1 -1 -1 318.93 160.39 333.39 200.94 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n335 103 Car -1 -1 -1 598.36 173.42 621.40 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n335 118 Cyclist -1 -1 -1 449.53 167.02 470.74 204.97 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n336 3 Car -1 -1 -1 1094.91 185.31 1221.22 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n336 2 Car -1 -1 -1 954.89 183.78 1067.01 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n336 4 Car -1 -1 -1 1029.94 183.97 1155.75 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n336 66 Pedestrian -1 -1 -1 328.81 161.80 356.94 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n336 7 Car -1 -1 -1 602.00 172.92 636.44 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n336 65 Pedestrian -1 -1 -1 192.43 153.95 208.61 197.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n336 114 Pedestrian -1 -1 -1 384.41 163.03 397.80 196.17 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n336 59 Pedestrian -1 -1 -1 318.87 160.11 333.50 201.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n336 103 Car -1 -1 -1 598.51 173.53 621.23 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n336 118 Cyclist -1 -1 -1 445.97 166.35 467.71 205.32 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n336 119 Pedestrian -1 -1 -1 361.56 160.47 375.33 196.77 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n337 3 Car -1 -1 -1 1095.01 185.31 1221.18 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n337 2 Car -1 -1 -1 954.86 183.74 1067.01 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n337 4 Car -1 -1 -1 1029.67 183.93 1155.85 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n337 66 Pedestrian -1 -1 -1 330.91 162.97 360.11 234.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n337 7 Car -1 -1 -1 601.62 172.75 636.81 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n337 65 Pedestrian -1 -1 -1 192.38 153.83 208.47 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n337 114 Pedestrian -1 -1 -1 382.57 162.71 395.99 195.85 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n337 59 Pedestrian -1 -1 -1 319.27 160.54 333.87 200.97 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n337 119 Pedestrian -1 -1 -1 360.64 161.24 375.73 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n337 103 Car -1 -1 -1 598.37 173.49 621.55 193.10 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n337 118 Cyclist -1 -1 -1 441.85 166.28 465.32 206.41 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n338 3 Car -1 -1 -1 1094.85 185.22 1221.32 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n338 2 Car -1 -1 -1 954.80 183.75 1067.00 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n338 4 Car -1 -1 -1 1029.54 183.85 1155.88 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n338 7 Car -1 -1 -1 601.55 172.79 636.84 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n338 65 Pedestrian -1 -1 -1 192.44 153.73 208.34 197.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n338 66 Pedestrian -1 -1 -1 333.03 163.74 361.59 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n338 114 Pedestrian -1 -1 -1 380.89 163.54 394.72 196.06 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n338 119 Pedestrian -1 -1 -1 361.17 161.28 375.70 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n338 59 Pedestrian -1 -1 -1 319.36 160.52 333.58 200.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n338 103 Car -1 -1 -1 598.23 173.42 621.40 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n339 3 Car -1 -1 -1 1095.06 185.28 1221.08 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n339 2 Car -1 -1 -1 954.85 183.79 1066.96 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n339 4 Car -1 -1 -1 1029.38 183.87 1156.10 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n339 7 Car -1 -1 -1 601.74 172.89 636.64 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n339 66 Pedestrian -1 -1 -1 336.68 163.38 364.03 236.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n339 65 Pedestrian -1 -1 -1 192.30 153.78 208.45 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n339 59 Pedestrian -1 -1 -1 319.40 160.51 333.65 200.14 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n339 119 Pedestrian -1 -1 -1 361.55 161.02 375.05 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n339 114 Pedestrian -1 -1 -1 377.20 163.29 392.05 196.01 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n339 103 Car -1 -1 -1 598.25 173.45 621.33 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n340 3 Car -1 -1 -1 1095.24 185.27 1220.88 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n340 2 Car -1 -1 -1 954.84 183.79 1067.04 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n340 4 Car -1 -1 -1 1029.45 183.85 1156.10 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n340 66 Pedestrian -1 -1 -1 339.06 162.56 366.77 237.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n340 7 Car -1 -1 -1 601.97 172.97 636.53 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n340 65 Pedestrian -1 -1 -1 191.95 153.66 208.75 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n340 59 Pedestrian -1 -1 -1 319.69 160.33 334.41 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n340 119 Pedestrian -1 -1 -1 361.66 160.91 375.01 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n340 103 Car -1 -1 -1 598.49 173.45 621.11 193.17 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n340 114 Pedestrian -1 -1 -1 376.44 162.75 390.59 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n341 3 Car -1 -1 -1 1095.24 185.32 1220.98 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n341 2 Car -1 -1 -1 954.89 183.85 1067.04 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n341 4 Car -1 -1 -1 1029.55 183.87 1155.97 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n341 7 Car -1 -1 -1 601.86 173.03 636.51 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n341 66 Pedestrian -1 -1 -1 342.15 162.03 370.74 237.76 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n341 65 Pedestrian -1 -1 -1 191.94 153.57 208.59 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n341 59 Pedestrian -1 -1 -1 319.82 160.96 335.21 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n341 103 Car -1 -1 -1 598.46 173.44 621.29 193.17 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n341 119 Pedestrian -1 -1 -1 361.60 160.81 375.30 198.58 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n342 3 Car -1 -1 -1 1095.32 185.38 1220.79 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n342 2 Car -1 -1 -1 954.87 183.86 1066.98 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n342 4 Car -1 -1 -1 1029.55 183.92 1156.03 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n342 66 Pedestrian -1 -1 -1 342.68 162.54 373.27 239.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n342 7 Car -1 -1 -1 601.89 172.97 636.63 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n342 65 Pedestrian -1 -1 -1 192.33 153.57 208.71 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n342 59 Pedestrian -1 -1 -1 319.98 161.42 335.60 199.88 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n342 103 Car -1 -1 -1 598.70 173.48 621.24 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n342 119 Pedestrian -1 -1 -1 362.17 161.06 376.08 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n342 120 Pedestrian -1 -1 -1 369.64 162.12 383.60 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n342 121 Pedestrian -1 -1 -1 336.14 161.00 350.04 199.86 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n343 3 Car -1 -1 -1 1095.23 185.31 1220.89 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n343 2 Car -1 -1 -1 954.92 183.85 1066.91 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n343 4 Car -1 -1 -1 1029.64 183.92 1156.02 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n343 66 Pedestrian -1 -1 -1 346.09 163.16 374.42 239.89 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n343 7 Car -1 -1 -1 601.92 172.97 636.55 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n343 65 Pedestrian -1 -1 -1 192.54 153.54 208.45 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n343 59 Pedestrian -1 -1 -1 320.42 161.56 335.71 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n343 103 Car -1 -1 -1 598.62 173.47 621.24 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n343 120 Pedestrian -1 -1 -1 366.45 160.85 381.23 196.13 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n343 121 Pedestrian -1 -1 -1 336.54 161.04 350.15 199.93 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n344 3 Car -1 -1 -1 1095.26 185.37 1220.92 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n344 2 Car -1 -1 -1 954.96 183.85 1066.86 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n344 4 Car -1 -1 -1 1029.84 183.96 1155.93 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n344 66 Pedestrian -1 -1 -1 350.11 163.97 378.00 239.66 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n344 7 Car -1 -1 -1 601.81 172.88 636.61 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n344 65 Pedestrian -1 -1 -1 192.31 153.65 208.54 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n344 59 Pedestrian -1 -1 -1 322.25 161.49 337.24 199.57 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n344 121 Pedestrian -1 -1 -1 338.95 161.28 351.90 199.57 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n344 103 Car -1 -1 -1 598.51 173.46 621.24 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n344 120 Pedestrian -1 -1 -1 365.48 161.10 379.37 196.61 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n345 3 Car -1 -1 -1 1095.20 185.30 1220.97 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n345 2 Car -1 -1 -1 954.94 183.78 1067.06 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n345 4 Car -1 -1 -1 1029.91 183.96 1155.80 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n345 66 Pedestrian -1 -1 -1 352.10 163.06 380.03 239.86 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n345 7 Car -1 -1 -1 601.62 172.97 636.76 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n345 65 Pedestrian -1 -1 -1 191.75 153.78 208.55 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n345 59 Pedestrian -1 -1 -1 322.61 161.02 337.41 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n345 121 Pedestrian -1 -1 -1 339.05 160.98 351.84 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n345 103 Car -1 -1 -1 598.49 173.53 621.35 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n346 3 Car -1 -1 -1 1095.21 185.26 1221.01 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n346 2 Car -1 -1 -1 954.94 183.79 1067.07 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n346 4 Car -1 -1 -1 1029.89 183.98 1155.89 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n346 66 Pedestrian -1 -1 -1 355.45 163.48 382.37 240.04 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n346 7 Car -1 -1 -1 601.67 172.90 636.68 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n346 65 Pedestrian -1 -1 -1 191.90 153.89 208.40 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n346 59 Pedestrian -1 -1 -1 322.70 160.97 337.99 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n346 121 Pedestrian -1 -1 -1 339.41 160.74 352.91 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n346 103 Car -1 -1 -1 598.55 173.35 621.42 193.13 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n347 3 Car -1 -1 -1 1095.28 185.31 1220.98 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n347 2 Car -1 -1 -1 954.87 183.80 1067.16 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n347 4 Car -1 -1 -1 1029.95 183.94 1155.71 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n347 66 Pedestrian -1 -1 -1 357.35 164.88 387.57 240.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n347 7 Car -1 -1 -1 601.78 172.96 636.58 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n347 65 Pedestrian -1 -1 -1 191.98 153.87 208.41 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n347 59 Pedestrian -1 -1 -1 322.74 161.27 338.00 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n347 121 Pedestrian -1 -1 -1 340.06 161.01 353.68 199.65 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n347 103 Car -1 -1 -1 598.56 173.31 621.22 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n348 3 Car -1 -1 -1 1095.33 185.37 1220.91 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n348 2 Car -1 -1 -1 954.87 183.83 1067.07 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n348 4 Car -1 -1 -1 1029.77 183.92 1155.87 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n348 66 Pedestrian -1 -1 -1 361.56 165.15 390.21 241.08 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n348 7 Car -1 -1 -1 601.52 173.01 636.80 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n348 65 Pedestrian -1 -1 -1 192.09 154.08 208.17 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n348 121 Pedestrian -1 -1 -1 339.87 161.13 353.71 199.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n348 59 Pedestrian -1 -1 -1 322.63 161.35 338.04 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n348 103 Car -1 -1 -1 598.52 173.40 621.45 192.93 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n349 3 Car -1 -1 -1 1095.35 185.39 1220.94 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n349 2 Car -1 -1 -1 954.75 183.82 1067.11 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n349 4 Car -1 -1 -1 1029.68 183.89 1155.85 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n349 66 Pedestrian -1 -1 -1 363.37 164.25 392.26 242.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n349 7 Car -1 -1 -1 601.73 173.03 636.76 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n349 59 Pedestrian -1 -1 -1 322.92 161.08 338.19 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n349 65 Pedestrian -1 -1 -1 192.47 154.19 208.35 196.86 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n349 121 Pedestrian -1 -1 -1 340.34 161.46 353.74 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n349 103 Car -1 -1 -1 598.67 173.35 621.30 192.80 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n350 3 Car -1 -1 -1 1095.26 185.35 1220.97 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n350 2 Car -1 -1 -1 954.81 183.76 1067.11 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n350 4 Car -1 -1 -1 1029.85 183.92 1155.79 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n350 66 Pedestrian -1 -1 -1 367.50 163.55 395.20 241.93 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n350 7 Car -1 -1 -1 601.69 172.96 636.89 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n350 59 Pedestrian -1 -1 -1 323.76 161.06 338.85 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n350 121 Pedestrian -1 -1 -1 340.14 161.17 353.82 199.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n350 65 Pedestrian -1 -1 -1 192.49 154.51 209.02 196.71 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n350 103 Car -1 -1 -1 598.55 173.34 621.26 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n351 3 Car -1 -1 -1 1095.09 185.33 1221.13 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n351 2 Car -1 -1 -1 954.79 183.74 1067.06 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n351 4 Car -1 -1 -1 1029.77 183.94 1155.89 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n351 66 Pedestrian -1 -1 -1 369.43 164.19 400.44 242.30 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n351 7 Car -1 -1 -1 601.79 172.92 636.68 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n351 121 Pedestrian -1 -1 -1 340.40 161.13 353.93 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n351 65 Pedestrian -1 -1 -1 192.56 154.77 209.27 196.89 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n351 59 Pedestrian -1 -1 -1 323.73 161.48 339.59 199.09 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n351 103 Car -1 -1 -1 598.69 173.31 621.38 192.77 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n351 122 Pedestrian -1 -1 -1 184.22 153.13 202.44 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n352 3 Car -1 -1 -1 1095.20 185.34 1221.07 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n352 2 Car -1 -1 -1 954.82 183.78 1067.02 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n352 4 Car -1 -1 -1 1029.59 183.89 1156.09 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n352 66 Pedestrian -1 -1 -1 371.43 164.67 403.52 244.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n352 7 Car -1 -1 -1 601.64 172.93 636.90 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n352 65 Pedestrian -1 -1 -1 192.24 155.21 209.70 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n352 59 Pedestrian -1 -1 -1 323.98 162.01 339.79 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n352 121 Pedestrian -1 -1 -1 340.42 161.09 354.03 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n352 103 Car -1 -1 -1 598.64 173.38 621.47 192.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n353 3 Car -1 -1 -1 1095.15 185.38 1220.96 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n353 2 Car -1 -1 -1 954.84 183.74 1067.01 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n353 4 Car -1 -1 -1 1029.80 183.90 1155.84 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n353 66 Pedestrian -1 -1 -1 373.18 164.53 405.42 245.52 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n353 7 Car -1 -1 -1 601.88 173.06 636.71 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n353 59 Pedestrian -1 -1 -1 326.22 162.23 341.65 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n353 121 Pedestrian -1 -1 -1 341.55 160.85 356.71 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n353 65 Pedestrian -1 -1 -1 192.70 155.38 209.80 196.50 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n353 103 Car -1 -1 -1 598.66 173.42 621.51 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n353 123 Pedestrian -1 -1 -1 368.69 160.89 383.17 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n354 3 Car -1 -1 -1 1095.20 185.34 1221.06 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n354 2 Car -1 -1 -1 954.79 183.77 1066.95 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n354 4 Car -1 -1 -1 1029.76 183.92 1155.95 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n354 66 Pedestrian -1 -1 -1 380.97 164.94 409.13 244.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n354 7 Car -1 -1 -1 601.69 172.98 636.82 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n354 59 Pedestrian -1 -1 -1 327.12 161.29 341.79 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n354 121 Pedestrian -1 -1 -1 342.22 161.27 356.28 198.88 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n354 65 Pedestrian -1 -1 -1 192.59 155.45 209.96 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n354 103 Car -1 -1 -1 598.45 173.54 621.63 192.91 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n354 123 Pedestrian -1 -1 -1 368.99 160.00 382.49 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n355 3 Car -1 -1 -1 1094.96 185.32 1221.26 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n355 2 Car -1 -1 -1 954.83 183.77 1066.99 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n355 4 Car -1 -1 -1 1029.78 183.89 1155.92 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n355 7 Car -1 -1 -1 601.69 172.91 636.83 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n355 59 Pedestrian -1 -1 -1 327.63 160.81 342.02 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n355 121 Pedestrian -1 -1 -1 342.28 161.10 356.49 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n355 66 Pedestrian -1 -1 -1 380.25 163.93 414.20 245.78 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n355 65 Pedestrian -1 -1 -1 192.38 155.25 209.68 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n355 123 Pedestrian -1 -1 -1 368.83 160.19 382.63 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n355 103 Car -1 -1 -1 598.52 173.46 621.56 192.92 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n356 3 Car -1 -1 -1 1095.14 185.26 1221.05 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n356 2 Car -1 -1 -1 955.02 183.77 1066.90 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n356 4 Car -1 -1 -1 1029.76 183.93 1155.94 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n356 66 Pedestrian -1 -1 -1 386.83 163.88 418.19 246.17 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n356 7 Car -1 -1 -1 601.85 172.94 636.77 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n356 121 Pedestrian -1 -1 -1 342.95 161.52 356.96 198.54 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n356 59 Pedestrian -1 -1 -1 328.15 160.58 342.67 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n356 123 Pedestrian -1 -1 -1 369.30 160.71 382.95 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n356 65 Pedestrian -1 -1 -1 192.43 155.31 209.65 196.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n356 103 Car -1 -1 -1 598.43 173.41 621.45 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n357 3 Car -1 -1 -1 1098.73 185.38 1220.77 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n357 2 Car -1 -1 -1 954.83 183.84 1067.03 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n357 4 Car -1 -1 -1 1030.02 183.97 1155.76 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n357 66 Pedestrian -1 -1 -1 388.53 165.16 420.56 246.50 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n357 7 Car -1 -1 -1 601.90 172.96 636.72 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n357 121 Pedestrian -1 -1 -1 343.15 162.04 357.40 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n357 65 Pedestrian -1 -1 -1 192.19 155.41 209.61 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n357 59 Pedestrian -1 -1 -1 329.81 161.09 345.58 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n357 123 Pedestrian -1 -1 -1 369.23 160.78 382.54 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n357 103 Car -1 -1 -1 598.55 173.50 621.61 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n358 3 Car -1 -1 -1 1095.39 185.37 1220.89 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n358 2 Car -1 -1 -1 954.90 183.80 1067.01 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n358 4 Car -1 -1 -1 1029.78 183.91 1155.91 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n358 66 Pedestrian -1 -1 -1 394.33 165.33 425.86 247.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n358 7 Car -1 -1 -1 601.79 172.93 636.95 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n358 59 Pedestrian -1 -1 -1 327.23 161.17 343.69 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n358 123 Pedestrian -1 -1 -1 369.40 160.41 383.09 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n358 121 Pedestrian -1 -1 -1 343.63 162.12 357.83 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n358 65 Pedestrian -1 -1 -1 192.02 155.17 209.49 196.68 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n358 103 Car -1 -1 -1 598.52 173.45 621.57 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n359 3 Car -1 -1 -1 1098.94 185.50 1220.49 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n359 2 Car -1 -1 -1 954.99 183.85 1066.88 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n359 4 Car -1 -1 -1 1029.89 183.93 1155.84 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n359 66 Pedestrian -1 -1 -1 397.87 165.46 426.34 247.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n359 7 Car -1 -1 -1 601.75 172.83 636.95 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n359 123 Pedestrian -1 -1 -1 369.29 160.02 383.73 197.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n359 59 Pedestrian -1 -1 -1 327.15 160.77 343.42 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n359 121 Pedestrian -1 -1 -1 344.26 161.73 357.64 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n359 65 Pedestrian -1 -1 -1 192.26 155.37 208.95 196.58 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n359 103 Car -1 -1 -1 598.32 173.34 621.60 192.80 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n360 3 Car -1 -1 -1 1095.47 185.38 1220.84 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n360 2 Car -1 -1 -1 954.96 183.82 1066.91 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n360 4 Car -1 -1 -1 1029.80 183.88 1155.84 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n360 66 Pedestrian -1 -1 -1 401.55 164.70 429.60 247.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n360 7 Car -1 -1 -1 601.76 172.91 636.81 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n360 123 Pedestrian -1 -1 -1 369.73 160.25 383.72 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n360 121 Pedestrian -1 -1 -1 344.57 161.57 357.10 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n360 59 Pedestrian -1 -1 -1 327.86 160.95 343.73 197.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n360 65 Pedestrian -1 -1 -1 191.80 155.27 208.86 196.67 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n360 103 Car -1 -1 -1 598.46 173.57 621.73 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n361 3 Car -1 -1 -1 1095.26 185.26 1220.85 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n361 2 Car -1 -1 -1 954.97 183.81 1066.94 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n361 4 Car -1 -1 -1 1029.78 183.86 1155.96 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n361 66 Pedestrian -1 -1 -1 404.12 163.51 439.35 249.10 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n361 123 Pedestrian -1 -1 -1 370.86 160.44 384.00 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n361 7 Car -1 -1 -1 602.02 172.84 636.72 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n361 121 Pedestrian -1 -1 -1 344.36 161.45 357.00 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n361 59 Pedestrian -1 -1 -1 328.15 161.71 343.53 197.41 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n361 103 Car -1 -1 -1 598.54 173.45 621.68 192.91 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n361 65 Pedestrian -1 -1 -1 191.75 155.30 208.78 196.69 -1 -1 -1 -1000 -1000 -1000 -10 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193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n377 123 Pedestrian -1 -1 -1 371.98 160.15 383.63 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n377 103 Car -1 -1 -1 598.90 173.78 622.14 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n378 3 Car -1 -1 -1 1095.23 185.36 1220.98 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n378 2 Car -1 -1 -1 955.05 183.85 1066.76 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n378 4 Car -1 -1 -1 1029.95 183.91 1155.75 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n378 66 Pedestrian -1 -1 -1 483.04 162.21 524.64 259.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n378 7 Car -1 -1 -1 602.89 172.87 637.07 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n378 59 Pedestrian -1 -1 -1 332.62 161.46 346.10 195.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n378 125 Pedestrian -1 -1 -1 296.07 158.85 311.03 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n378 65 Pedestrian -1 -1 -1 193.70 162.23 207.81 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n378 123 Pedestrian -1 -1 -1 371.42 160.26 383.19 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n378 124 Pedestrian -1 -1 -1 186.09 161.81 200.54 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n378 121 Pedestrian -1 -1 -1 348.73 161.78 363.69 195.09 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n378 103 Car -1 -1 -1 598.88 173.84 622.05 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n379 3 Car -1 -1 -1 1095.30 185.30 1220.95 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n379 2 Car -1 -1 -1 955.07 183.84 1066.90 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n379 4 Car -1 -1 -1 1030.07 183.91 1155.74 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n379 66 Pedestrian -1 -1 -1 492.64 161.56 527.25 259.73 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n379 7 Car -1 -1 -1 601.92 173.01 636.86 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n379 125 Pedestrian -1 -1 -1 297.15 158.96 311.19 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n379 65 Pedestrian -1 -1 -1 193.79 162.22 207.96 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n379 123 Pedestrian -1 -1 -1 370.99 160.16 382.81 192.77 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n379 124 Pedestrian -1 -1 -1 186.13 161.95 200.68 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n379 59 Pedestrian -1 -1 -1 332.79 161.20 346.98 194.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n379 121 Pedestrian -1 -1 -1 349.51 161.68 363.13 194.60 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n379 103 Car -1 -1 -1 599.04 173.83 621.97 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n380 3 Car -1 -1 -1 1095.52 185.35 1220.70 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n380 2 Car -1 -1 -1 955.09 183.82 1066.74 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n380 4 Car -1 -1 -1 1030.22 183.94 1155.61 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n380 66 Pedestrian -1 -1 -1 496.43 161.64 531.18 260.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n380 7 Car -1 -1 -1 602.06 173.05 636.79 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n380 59 Pedestrian -1 -1 -1 334.29 161.82 347.84 194.51 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n380 65 Pedestrian -1 -1 -1 193.56 162.10 207.98 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n380 125 Pedestrian -1 -1 -1 296.87 158.63 311.23 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n380 124 Pedestrian -1 -1 -1 185.75 161.97 200.40 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n380 123 Pedestrian -1 -1 -1 371.11 160.37 382.60 192.66 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n380 121 Pedestrian -1 -1 -1 349.32 161.70 363.35 194.30 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n380 103 Car -1 -1 -1 598.59 173.76 621.98 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n381 3 Car -1 -1 -1 1095.44 185.30 1220.78 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n381 2 Car -1 -1 -1 954.96 183.79 1066.83 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n381 4 Car -1 -1 -1 1030.05 183.92 1155.69 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n381 66 Pedestrian -1 -1 -1 498.49 161.54 539.92 260.95 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n381 7 Car -1 -1 -1 601.88 173.00 636.79 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n381 59 Pedestrian -1 -1 -1 334.72 162.09 347.55 194.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n381 65 Pedestrian -1 -1 -1 193.42 161.92 208.05 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n381 124 Pedestrian -1 -1 -1 185.64 162.04 200.37 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n381 123 Pedestrian -1 -1 -1 371.09 160.48 382.37 192.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n381 121 Pedestrian -1 -1 -1 349.37 161.69 363.38 194.55 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n381 125 Pedestrian -1 -1 -1 296.91 158.26 311.24 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n381 103 Car -1 -1 -1 598.45 173.86 621.98 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n382 3 Car -1 -1 -1 1095.54 185.31 1220.66 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n382 2 Car -1 -1 -1 954.90 183.82 1066.90 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n382 4 Car -1 -1 -1 1030.03 183.94 1155.72 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n382 66 Pedestrian -1 -1 -1 503.48 161.58 546.83 263.49 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n382 7 Car -1 -1 -1 601.96 173.01 636.70 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n382 59 Pedestrian -1 -1 -1 335.51 161.84 348.93 194.56 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n382 65 Pedestrian -1 -1 -1 193.48 161.87 208.03 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n382 123 Pedestrian -1 -1 -1 370.27 160.45 381.99 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n382 125 Pedestrian -1 -1 -1 293.11 158.28 308.76 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n382 124 Pedestrian -1 -1 -1 185.86 162.05 200.45 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n382 121 Pedestrian -1 -1 -1 349.79 161.90 362.94 194.39 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n382 103 Car -1 -1 -1 598.61 173.74 622.04 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n383 3 Car -1 -1 -1 1095.45 185.25 1220.80 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n383 2 Car -1 -1 -1 955.02 183.81 1066.76 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n383 4 Car -1 -1 -1 1030.01 183.99 1155.80 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n383 66 Pedestrian -1 -1 -1 507.66 161.14 550.45 264.39 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n383 7 Car -1 -1 -1 601.95 172.96 636.89 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n383 123 Pedestrian -1 -1 -1 370.35 160.32 382.53 192.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n383 65 Pedestrian -1 -1 -1 193.33 161.84 208.03 197.62 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n383 59 Pedestrian -1 -1 -1 336.52 161.62 350.03 194.59 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n383 124 Pedestrian -1 -1 -1 185.77 161.89 200.39 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n383 125 Pedestrian -1 -1 -1 295.49 158.68 311.01 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n383 121 Pedestrian -1 -1 -1 350.11 162.09 363.53 194.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n383 103 Car -1 -1 -1 598.68 173.59 622.41 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n384 3 Car -1 -1 -1 1098.76 185.42 1220.74 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n384 2 Car -1 -1 -1 955.00 183.82 1066.80 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n384 4 Car -1 -1 -1 1030.07 183.96 1155.67 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n384 66 Pedestrian -1 -1 -1 514.38 160.78 553.98 264.58 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n384 7 Car -1 -1 -1 602.02 173.03 636.90 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n384 123 Pedestrian -1 -1 -1 371.08 160.24 383.35 192.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n384 65 Pedestrian -1 -1 -1 192.97 161.85 208.01 197.50 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n384 59 Pedestrian -1 -1 -1 336.92 161.17 350.01 194.97 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n384 125 Pedestrian -1 -1 -1 293.09 158.66 309.63 194.51 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n384 124 Pedestrian -1 -1 -1 185.84 161.95 200.79 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n384 121 Pedestrian -1 -1 -1 350.25 161.82 363.81 194.21 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n384 103 Car -1 -1 -1 598.69 173.66 622.38 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n385 3 Car -1 -1 -1 1098.93 185.38 1220.52 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n385 2 Car -1 -1 -1 955.00 183.78 1066.78 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n385 4 Car -1 -1 -1 1030.08 184.03 1155.82 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n385 66 Pedestrian -1 -1 -1 522.53 159.04 561.36 266.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n385 7 Car -1 -1 -1 601.82 172.84 636.78 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n385 123 Pedestrian -1 -1 -1 371.14 160.42 383.66 192.27 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n385 65 Pedestrian -1 -1 -1 193.00 161.68 208.05 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n385 125 Pedestrian -1 -1 -1 295.44 159.26 311.05 194.13 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n385 124 Pedestrian -1 -1 -1 185.87 162.06 201.03 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n385 59 Pedestrian -1 -1 -1 337.16 161.07 350.06 195.15 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n385 121 Pedestrian -1 -1 -1 350.20 161.75 363.26 193.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n385 103 Car -1 -1 -1 598.93 173.58 622.59 193.29 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n386 3 Car -1 -1 -1 1095.44 185.28 1220.70 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n386 2 Car -1 -1 -1 954.98 183.88 1066.70 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n386 4 Car -1 -1 -1 1029.90 183.96 1155.85 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n386 66 Pedestrian -1 -1 -1 530.77 159.73 573.28 266.93 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n386 7 Car -1 -1 -1 601.51 172.97 636.95 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n386 123 Pedestrian -1 -1 -1 371.09 160.69 383.06 191.76 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n386 125 Pedestrian -1 -1 -1 295.96 159.57 311.61 193.85 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n386 65 Pedestrian -1 -1 -1 193.15 161.73 208.02 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n386 121 Pedestrian -1 -1 -1 350.51 161.82 363.68 194.12 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n386 124 Pedestrian -1 -1 -1 185.85 161.97 201.06 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n386 59 Pedestrian -1 -1 -1 338.71 161.82 350.98 194.50 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n387 3 Car -1 -1 -1 1098.76 185.41 1220.73 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n387 2 Car -1 -1 -1 955.08 183.85 1066.86 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n387 66 Pedestrian -1 -1 -1 532.38 160.21 580.94 268.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n387 4 Car -1 -1 -1 1029.96 184.02 1155.88 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n387 7 Car -1 -1 -1 601.81 173.05 636.65 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n387 123 Pedestrian -1 -1 -1 370.72 160.27 382.55 191.21 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n387 125 Pedestrian -1 -1 -1 296.20 159.65 311.93 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n387 65 Pedestrian -1 -1 -1 193.40 161.81 207.84 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n387 121 Pedestrian -1 -1 -1 350.39 161.94 363.99 194.21 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n387 59 Pedestrian -1 -1 -1 339.00 161.87 351.33 194.43 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n387 124 Pedestrian -1 -1 -1 186.10 161.97 201.07 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n387 126 Car -1 -1 -1 599.30 173.74 622.37 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n388 3 Car -1 -1 -1 1095.45 185.39 1220.83 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n388 2 Car -1 -1 -1 954.93 183.88 1066.90 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n388 4 Car -1 -1 -1 1029.87 184.01 1155.94 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n388 66 Pedestrian -1 -1 -1 536.53 159.97 584.50 269.18 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n388 7 Car -1 -1 -1 601.71 173.08 636.61 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n388 123 Pedestrian -1 -1 -1 370.11 160.07 382.28 191.06 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n388 65 Pedestrian -1 -1 -1 193.37 161.84 207.93 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n388 125 Pedestrian -1 -1 -1 296.81 159.80 312.20 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n388 59 Pedestrian -1 -1 -1 339.11 161.91 351.82 194.01 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n388 121 Pedestrian -1 -1 -1 350.83 162.06 364.56 193.85 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n388 124 Pedestrian -1 -1 -1 186.18 161.93 201.22 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n388 126 Car -1 -1 -1 599.29 173.91 622.21 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n389 3 Car -1 -1 -1 1095.26 185.31 1221.01 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n389 2 Car -1 -1 -1 954.92 183.86 1066.90 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n389 4 Car -1 -1 -1 1029.77 183.98 1155.99 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n389 66 Pedestrian -1 -1 -1 548.81 159.52 587.01 270.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n389 7 Car -1 -1 -1 601.86 173.08 636.50 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n389 65 Pedestrian -1 -1 -1 193.46 161.92 207.93 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n389 123 Pedestrian -1 -1 -1 370.22 160.34 381.78 191.06 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n389 125 Pedestrian -1 -1 -1 296.90 159.54 311.99 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n389 59 Pedestrian -1 -1 -1 338.89 161.97 352.14 193.87 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n389 124 Pedestrian -1 -1 -1 186.09 161.84 201.17 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n389 121 Pedestrian -1 -1 -1 350.48 161.96 363.95 193.75 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n390 3 Car -1 -1 -1 1095.47 185.34 1220.75 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n390 2 Car -1 -1 -1 955.00 183.88 1066.97 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n390 4 Car -1 -1 -1 1029.66 183.98 1156.03 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n390 66 Pedestrian -1 -1 -1 552.57 160.31 592.11 272.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n390 7 Car -1 -1 -1 601.69 172.81 636.67 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n390 65 Pedestrian -1 -1 -1 193.99 162.06 207.80 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n390 59 Pedestrian -1 -1 -1 339.58 161.97 352.70 193.75 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n390 123 Pedestrian -1 -1 -1 370.45 160.42 381.91 191.15 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n390 125 Pedestrian -1 -1 -1 297.24 159.53 312.50 193.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n390 124 Pedestrian -1 -1 -1 186.36 162.03 200.99 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n390 121 Pedestrian -1 -1 -1 350.24 161.88 363.57 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n391 3 Car -1 -1 -1 1095.17 185.30 1221.13 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n391 2 Car -1 -1 -1 955.00 183.82 1066.95 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n391 4 Car -1 -1 -1 1029.90 183.97 1155.96 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n391 66 Pedestrian -1 -1 -1 558.17 161.21 607.21 272.46 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n391 7 Car -1 -1 -1 601.28 172.87 636.78 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n391 65 Pedestrian -1 -1 -1 194.22 162.19 207.83 197.50 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n391 123 Pedestrian -1 -1 -1 370.39 160.11 382.02 191.09 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n391 59 Pedestrian -1 -1 -1 340.34 161.86 353.29 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n391 124 Pedestrian -1 -1 -1 186.48 162.17 201.02 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n391 125 Pedestrian -1 -1 -1 297.05 159.58 312.71 194.05 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n391 121 Pedestrian -1 -1 -1 350.50 161.06 363.23 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n391 127 Car -1 -1 -1 598.52 173.62 622.70 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n392 3 Car -1 -1 -1 1095.27 185.40 1220.96 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n392 2 Car -1 -1 -1 954.80 183.78 1067.18 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n392 66 Pedestrian -1 -1 -1 559.73 160.64 613.83 273.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n392 4 Car -1 -1 -1 1029.95 184.01 1155.99 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n392 7 Car -1 -1 -1 601.28 172.88 636.71 202.28 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n392 65 Pedestrian -1 -1 -1 194.42 162.17 207.95 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n392 123 Pedestrian -1 -1 -1 370.42 159.83 382.71 190.96 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n392 124 Pedestrian -1 -1 -1 186.58 162.36 200.87 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n392 121 Pedestrian -1 -1 -1 350.83 161.19 363.47 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n392 125 Pedestrian -1 -1 -1 299.56 159.70 315.23 194.19 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n392 127 Car -1 -1 -1 598.19 173.35 623.17 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n392 59 Pedestrian -1 -1 -1 340.60 160.39 353.43 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n393 3 Car -1 -1 -1 1095.05 185.32 1221.26 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n393 2 Car -1 -1 -1 954.90 183.67 1067.16 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n393 4 Car -1 -1 -1 1029.75 183.95 1156.08 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n393 66 Pedestrian -1 -1 -1 564.83 161.04 617.16 274.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n393 7 Car -1 -1 -1 601.87 172.91 636.61 202.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n393 65 Pedestrian -1 -1 -1 194.44 162.22 207.92 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n393 121 Pedestrian -1 -1 -1 351.22 161.15 364.05 192.52 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n393 124 Pedestrian -1 -1 -1 186.76 162.53 200.73 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n393 123 Pedestrian -1 -1 -1 370.77 159.83 382.75 191.15 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n393 125 Pedestrian -1 -1 -1 299.82 159.62 315.11 194.07 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n393 59 Pedestrian -1 -1 -1 340.67 160.36 353.77 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n394 3 Car -1 -1 -1 1098.71 185.37 1220.75 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n394 2 Car -1 -1 -1 954.70 183.67 1067.23 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n394 66 Pedestrian -1 -1 -1 577.29 160.30 619.11 274.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n394 4 Car -1 -1 -1 1029.83 183.97 1155.93 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n394 7 Car -1 -1 -1 601.39 172.90 637.10 201.91 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n394 65 Pedestrian -1 -1 -1 194.34 162.10 207.97 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n394 125 Pedestrian -1 -1 -1 300.80 158.93 315.74 194.15 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n394 121 Pedestrian -1 -1 -1 351.63 160.89 364.42 192.01 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n394 124 Pedestrian -1 -1 -1 186.88 162.68 200.62 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n394 59 Pedestrian -1 -1 -1 342.27 160.55 355.14 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n394 123 Pedestrian -1 -1 -1 370.87 159.70 383.15 191.29 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n395 3 Car -1 -1 -1 1095.30 185.33 1220.82 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n395 2 Car -1 -1 -1 954.72 183.65 1067.17 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n395 4 Car -1 -1 -1 1029.62 183.91 1156.09 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n395 66 Pedestrian -1 -1 -1 586.26 159.25 627.39 275.66 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n395 7 Car -1 -1 -1 601.36 172.88 636.97 201.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n395 65 Pedestrian -1 -1 -1 193.98 162.15 207.72 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n395 123 Pedestrian -1 -1 -1 370.96 159.89 383.45 191.00 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n395 125 Pedestrian -1 -1 -1 303.16 159.36 318.29 194.25 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n395 121 Pedestrian -1 -1 -1 351.71 160.77 365.14 192.07 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n395 124 Pedestrian -1 -1 -1 186.85 162.77 200.54 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n395 59 Pedestrian -1 -1 -1 342.17 161.01 355.24 193.04 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n395 127 Car -1 -1 -1 596.21 172.13 624.80 194.73 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n396 3 Car -1 -1 -1 1095.22 185.31 1220.96 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n396 2 Car -1 -1 -1 954.81 183.63 1067.23 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n396 4 Car -1 -1 -1 1029.74 183.93 1156.01 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n396 66 Pedestrian -1 -1 -1 593.41 159.52 641.09 276.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n396 7 Car -1 -1 -1 601.11 173.03 636.58 201.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n396 65 Pedestrian -1 -1 -1 194.11 162.17 207.76 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n396 125 Pedestrian -1 -1 -1 303.82 159.09 319.25 194.64 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n396 123 Pedestrian -1 -1 -1 370.62 160.26 383.14 190.64 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n396 121 Pedestrian -1 -1 -1 353.96 160.61 368.00 191.55 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n396 124 Pedestrian -1 -1 -1 186.83 162.73 200.50 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n396 59 Pedestrian -1 -1 -1 342.15 161.08 355.29 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n396 127 Car -1 -1 -1 597.65 172.98 623.27 194.33 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n397 3 Car -1 -1 -1 1095.25 185.32 1220.78 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n397 2 Car -1 -1 -1 954.78 183.64 1067.02 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n397 4 Car -1 -1 -1 1029.48 183.90 1156.25 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n397 66 Pedestrian -1 -1 -1 595.75 159.17 648.68 278.33 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n397 7 Car -1 -1 -1 601.60 173.54 636.45 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n397 65 Pedestrian -1 -1 -1 193.98 162.13 207.63 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n397 123 Pedestrian -1 -1 -1 370.21 159.80 382.30 190.09 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n397 125 Pedestrian -1 -1 -1 304.62 159.15 319.14 194.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n397 121 Pedestrian -1 -1 -1 354.48 160.54 368.32 191.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n397 124 Pedestrian -1 -1 -1 186.76 162.67 200.43 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n397 59 Pedestrian -1 -1 -1 341.36 160.72 353.57 192.93 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n398 3 Car -1 -1 -1 1095.06 185.28 1220.92 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n398 2 Car -1 -1 -1 954.78 183.64 1067.05 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n398 4 Car -1 -1 -1 1029.70 183.91 1156.14 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n398 66 Pedestrian -1 -1 -1 603.57 160.03 653.92 280.82 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n398 7 Car -1 -1 -1 601.77 174.03 636.07 201.75 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n398 123 Pedestrian -1 -1 -1 370.49 160.30 382.28 190.06 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n398 65 Pedestrian -1 -1 -1 194.03 162.12 207.66 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n398 125 Pedestrian -1 -1 -1 304.90 158.73 319.36 194.94 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n398 121 Pedestrian -1 -1 -1 355.15 160.64 369.32 191.85 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n398 124 Pedestrian -1 -1 -1 186.75 162.55 200.39 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n398 59 Pedestrian -1 -1 -1 342.27 160.99 354.85 192.27 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n399 3 Car -1 -1 -1 1095.07 185.23 1221.06 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n399 2 Car -1 -1 -1 954.87 183.65 1067.03 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n399 4 Car -1 -1 -1 1029.86 183.92 1155.92 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n399 66 Pedestrian -1 -1 -1 612.09 159.09 654.43 281.53 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n399 7 Car -1 -1 -1 601.66 173.95 636.37 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n399 123 Pedestrian -1 -1 -1 370.46 160.58 382.09 189.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n399 65 Pedestrian -1 -1 -1 194.00 161.96 207.62 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n399 125 Pedestrian -1 -1 -1 304.44 158.74 319.49 194.60 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n399 124 Pedestrian -1 -1 -1 186.63 162.39 200.31 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n399 59 Pedestrian -1 -1 -1 342.34 161.22 354.76 192.21 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n399 121 Pedestrian -1 -1 -1 355.24 160.82 369.25 191.54 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n399 128 Pedestrian -1 -1 -1 425.17 165.23 437.04 200.79 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n400 3 Car -1 -1 -1 1095.02 185.17 1221.20 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n400 2 Car -1 -1 -1 954.80 183.66 1067.03 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n400 4 Car -1 -1 -1 1029.97 183.85 1155.83 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n400 66 Pedestrian -1 -1 -1 617.92 158.29 662.70 283.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n400 7 Car -1 -1 -1 601.73 173.43 636.42 202.38 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n400 65 Pedestrian -1 -1 -1 193.68 161.78 207.54 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n400 123 Pedestrian -1 -1 -1 370.82 160.61 382.33 189.97 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n400 125 Pedestrian -1 -1 -1 303.59 159.03 318.67 194.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n400 59 Pedestrian -1 -1 -1 342.09 161.63 355.48 192.37 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n400 124 Pedestrian -1 -1 -1 186.45 162.23 200.31 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n400 121 Pedestrian -1 -1 -1 354.71 161.13 369.07 191.07 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n400 129 Pedestrian -1 -1 -1 0.65 152.72 26.95 274.18 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n401 3 Car -1 -1 -1 1095.28 185.25 1220.88 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n401 2 Car -1 -1 -1 954.73 183.68 1067.09 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n401 4 Car -1 -1 -1 1029.85 183.84 1155.93 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n401 66 Pedestrian -1 -1 -1 623.49 158.76 680.50 283.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n401 7 Car -1 -1 -1 601.70 173.28 636.48 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n401 59 Pedestrian -1 -1 -1 342.54 161.64 355.78 192.11 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n401 65 Pedestrian -1 -1 -1 193.53 161.73 207.35 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n401 125 Pedestrian -1 -1 -1 303.50 158.89 318.38 194.31 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n401 129 Pedestrian -1 -1 -1 1.29 150.56 33.87 275.12 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n401 124 Pedestrian -1 -1 -1 186.61 162.24 200.10 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n401 123 Pedestrian -1 -1 -1 371.13 160.58 382.64 190.07 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n401 121 Pedestrian -1 -1 -1 355.05 160.70 369.23 191.55 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n402 3 Car -1 -1 -1 1095.29 185.28 1220.78 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n402 2 Car -1 -1 -1 954.82 183.68 1067.14 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n402 4 Car -1 -1 -1 1029.87 183.87 1155.94 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n402 66 Pedestrian -1 -1 -1 626.37 158.13 686.34 284.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n402 7 Car -1 -1 -1 601.51 173.21 637.06 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n402 59 Pedestrian -1 -1 -1 343.26 161.22 356.60 191.55 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n402 129 Pedestrian -1 -1 -1 2.16 147.22 40.47 273.09 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n402 65 Pedestrian -1 -1 -1 193.30 161.45 207.50 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n402 125 Pedestrian -1 -1 -1 303.39 158.67 318.96 194.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n402 124 Pedestrian -1 -1 -1 186.64 162.26 200.06 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n402 123 Pedestrian -1 -1 -1 371.11 160.59 383.01 189.87 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n402 130 Pedestrian -1 -1 -1 421.19 165.82 434.20 200.64 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n402 131 Car -1 -1 -1 598.49 173.84 622.03 193.01 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n403 3 Car -1 -1 -1 1095.29 185.31 1220.68 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n403 2 Car -1 -1 -1 954.80 183.65 1066.95 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n403 4 Car -1 -1 -1 1029.91 183.89 1155.91 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n403 66 Pedestrian -1 -1 -1 634.66 159.37 692.22 284.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n403 129 Pedestrian -1 -1 -1 3.38 145.69 54.29 272.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n403 7 Car -1 -1 -1 601.37 173.07 637.37 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n403 59 Pedestrian -1 -1 -1 343.66 160.78 356.85 191.65 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n403 65 Pedestrian -1 -1 -1 193.44 161.36 207.54 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n403 125 Pedestrian -1 -1 -1 303.48 158.62 319.13 194.54 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n403 130 Pedestrian -1 -1 -1 420.51 165.99 433.73 200.73 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n403 124 Pedestrian -1 -1 -1 186.73 162.20 200.05 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n403 123 Pedestrian -1 -1 -1 371.21 160.17 383.60 190.10 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n403 131 Car -1 -1 -1 598.20 173.86 622.14 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n404 3 Car -1 -1 -1 1095.14 185.21 1220.88 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n404 2 Car -1 -1 -1 954.79 183.61 1066.98 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n404 4 Car -1 -1 -1 1029.69 183.83 1155.96 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n404 66 Pedestrian -1 -1 -1 647.09 158.58 696.81 285.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n404 129 Pedestrian -1 -1 -1 3.66 146.39 61.74 272.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n404 7 Car -1 -1 -1 601.50 173.06 637.34 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n404 59 Pedestrian -1 -1 -1 343.77 160.48 356.84 191.67 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n404 125 Pedestrian -1 -1 -1 303.77 158.73 319.58 194.59 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n404 65 Pedestrian -1 -1 -1 193.42 161.35 207.58 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n404 124 Pedestrian -1 -1 -1 186.56 162.19 200.00 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n404 123 Pedestrian -1 -1 -1 371.60 160.09 383.48 189.68 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n404 130 Pedestrian -1 -1 -1 419.74 166.25 433.29 200.86 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n404 131 Car -1 -1 -1 598.33 173.77 622.14 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n405 3 Car -1 -1 -1 1095.28 185.24 1220.73 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n405 2 Car -1 -1 -1 954.59 183.66 1067.13 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n405 4 Car -1 -1 -1 1029.80 183.88 1156.00 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n405 66 Pedestrian -1 -1 -1 662.94 156.38 710.27 286.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n405 129 Pedestrian -1 -1 -1 5.25 148.57 66.89 270.07 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n405 7 Car -1 -1 -1 601.39 173.08 637.40 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n405 125 Pedestrian -1 -1 -1 303.68 158.51 319.76 194.61 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n405 59 Pedestrian -1 -1 -1 344.40 160.96 356.94 191.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n405 65 Pedestrian -1 -1 -1 193.50 161.34 207.48 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n405 123 Pedestrian -1 -1 -1 371.61 160.30 383.17 189.30 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n405 124 Pedestrian -1 -1 -1 186.66 162.13 200.18 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n405 130 Pedestrian -1 -1 -1 418.40 166.24 432.96 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n405 131 Car -1 -1 -1 598.21 173.73 622.21 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n406 3 Car -1 -1 -1 1095.30 185.23 1220.56 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n406 2 Car -1 -1 -1 954.62 183.76 1067.13 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n406 4 Car -1 -1 -1 1029.69 183.84 1156.07 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n406 66 Pedestrian -1 -1 -1 668.65 156.77 727.62 287.96 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n406 129 Pedestrian -1 -1 -1 8.26 150.07 71.93 268.43 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n406 7 Car -1 -1 -1 601.50 173.13 637.28 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n406 125 Pedestrian -1 -1 -1 304.25 158.14 319.93 194.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n406 59 Pedestrian -1 -1 -1 344.59 161.06 356.89 191.07 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n406 65 Pedestrian -1 -1 -1 192.98 161.18 207.47 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n406 131 Car -1 -1 -1 598.23 173.75 622.13 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n406 130 Pedestrian -1 -1 -1 418.41 166.33 432.01 201.03 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n406 123 Pedestrian -1 -1 -1 371.58 160.51 383.07 189.34 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n407 3 Car -1 -1 -1 1095.47 185.23 1220.59 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n407 2 Car -1 -1 -1 954.81 183.82 1067.06 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n407 4 Car -1 -1 -1 1029.61 183.81 1156.19 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n407 66 Pedestrian -1 -1 -1 672.15 158.23 738.82 290.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n407 129 Pedestrian -1 -1 -1 19.37 147.75 75.20 266.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n407 7 Car -1 -1 -1 601.60 173.35 637.09 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n407 125 Pedestrian -1 -1 -1 304.52 157.99 320.21 194.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n407 59 Pedestrian -1 -1 -1 344.85 160.72 357.01 191.19 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n407 65 Pedestrian -1 -1 -1 192.76 160.81 207.73 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n407 131 Car -1 -1 -1 598.27 173.65 621.94 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n407 130 Pedestrian -1 -1 -1 417.02 166.17 430.32 200.92 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n408 3 Car -1 -1 -1 1095.32 185.31 1220.81 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n408 2 Car -1 -1 -1 954.90 183.82 1067.08 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n408 66 Pedestrian -1 -1 -1 681.01 158.28 744.81 291.26 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n408 4 Car -1 -1 -1 1029.60 183.83 1156.14 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n408 129 Pedestrian -1 -1 -1 22.65 147.79 74.16 264.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n408 7 Car -1 -1 -1 601.49 173.20 637.20 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n408 125 Pedestrian -1 -1 -1 304.98 157.97 320.31 194.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n408 65 Pedestrian -1 -1 -1 192.63 160.88 207.65 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n408 59 Pedestrian -1 -1 -1 345.07 160.57 357.33 190.76 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n408 131 Car -1 -1 -1 597.87 173.61 621.99 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n408 130 Pedestrian -1 -1 -1 416.69 166.12 429.79 200.71 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n409 3 Car -1 -1 -1 1095.37 185.20 1220.75 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n409 2 Car -1 -1 -1 954.82 183.71 1066.98 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n409 4 Car -1 -1 -1 1029.58 183.75 1156.23 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n409 66 Pedestrian -1 -1 -1 696.91 157.87 743.97 292.36 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n409 129 Pedestrian -1 -1 -1 28.32 148.10 81.88 264.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n409 7 Car -1 -1 -1 601.50 173.12 637.12 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n409 125 Pedestrian -1 -1 -1 304.86 157.93 320.34 194.39 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n409 65 Pedestrian -1 -1 -1 192.66 161.03 207.82 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n409 59 Pedestrian -1 -1 -1 346.36 160.53 359.40 190.20 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n409 130 Pedestrian -1 -1 -1 414.97 165.87 429.19 201.61 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n409 131 Car -1 -1 -1 598.28 173.55 622.02 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n410 3 Car -1 -1 -1 1095.38 185.26 1220.72 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n410 2 Car -1 -1 -1 954.75 183.72 1067.10 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n410 4 Car -1 -1 -1 1029.59 183.75 1156.31 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n410 129 Pedestrian -1 -1 -1 34.43 147.99 91.34 263.18 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n410 66 Pedestrian -1 -1 -1 704.04 159.35 753.64 292.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n410 7 Car -1 -1 -1 601.64 173.13 636.93 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n410 125 Pedestrian -1 -1 -1 304.85 158.11 320.34 194.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n410 65 Pedestrian -1 -1 -1 192.71 161.01 208.03 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n410 59 Pedestrian -1 -1 -1 346.50 161.02 359.63 189.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n410 131 Car -1 -1 -1 598.28 173.65 621.97 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n410 130 Pedestrian -1 -1 -1 414.69 166.08 428.22 201.29 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n410 132 Pedestrian -1 -1 -1 185.10 159.77 201.03 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n411 3 Car -1 -1 -1 1095.41 185.25 1220.71 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n411 2 Car -1 -1 -1 954.94 183.71 1066.94 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n411 4 Car -1 -1 -1 1029.70 183.75 1156.22 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n411 129 Pedestrian -1 -1 -1 39.18 149.00 95.47 261.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n411 66 Pedestrian -1 -1 -1 712.54 158.86 775.51 293.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n411 7 Car -1 -1 -1 601.43 173.06 637.01 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n411 125 Pedestrian -1 -1 -1 304.50 158.18 319.80 194.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n411 59 Pedestrian -1 -1 -1 346.99 161.38 360.25 190.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n411 65 Pedestrian -1 -1 -1 191.91 159.44 208.34 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n411 131 Car -1 -1 -1 598.23 173.58 622.10 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n411 130 Pedestrian -1 -1 -1 413.02 166.16 426.62 200.84 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n412 3 Car -1 -1 -1 1095.60 185.31 1220.56 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n412 2 Car -1 -1 -1 955.01 183.77 1067.03 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n412 4 Car -1 -1 -1 1029.71 183.82 1156.23 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n412 66 Pedestrian -1 -1 -1 717.34 161.15 786.59 295.28 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n412 129 Pedestrian -1 -1 -1 47.31 148.51 101.11 258.40 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n412 7 Car -1 -1 -1 601.54 173.22 636.87 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n412 125 Pedestrian -1 -1 -1 304.30 158.34 319.90 194.18 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n412 59 Pedestrian -1 -1 -1 347.62 161.46 360.86 190.12 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n412 65 Pedestrian -1 -1 -1 189.54 154.99 206.05 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n412 130 Pedestrian -1 -1 -1 412.12 166.32 425.68 201.10 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n412 131 Car -1 -1 -1 598.23 173.69 622.05 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n413 3 Car -1 -1 -1 1095.45 185.32 1220.59 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n413 2 Car -1 -1 -1 954.94 183.80 1066.95 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n413 4 Car -1 -1 -1 1029.29 183.79 1156.50 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n413 129 Pedestrian -1 -1 -1 58.45 149.81 104.80 256.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n413 66 Pedestrian -1 -1 -1 726.02 161.21 792.89 296.54 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n413 7 Car -1 -1 -1 601.62 173.23 636.75 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n413 125 Pedestrian -1 -1 -1 304.21 158.26 320.16 194.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n413 65 Pedestrian -1 -1 -1 190.07 154.21 205.53 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n413 130 Pedestrian -1 -1 -1 411.70 166.27 425.66 201.52 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n413 59 Pedestrian -1 -1 -1 348.21 160.77 361.40 190.11 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n413 131 Car -1 -1 -1 598.20 173.64 621.92 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n414 3 Car -1 -1 -1 1095.56 185.43 1220.60 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n414 129 Pedestrian -1 -1 -1 67.54 149.15 111.59 255.57 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n414 2 Car -1 -1 -1 955.05 183.82 1066.95 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n414 4 Car -1 -1 -1 1029.27 183.82 1156.50 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n414 66 Pedestrian -1 -1 -1 742.15 158.01 798.44 299.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n414 7 Car -1 -1 -1 601.73 173.26 636.71 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n414 125 Pedestrian -1 -1 -1 304.18 158.28 320.04 194.22 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n414 65 Pedestrian -1 -1 -1 190.42 154.09 204.98 196.76 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n414 130 Pedestrian -1 -1 -1 411.36 166.30 424.97 201.86 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n414 59 Pedestrian -1 -1 -1 349.39 160.33 363.34 190.14 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n414 131 Car -1 -1 -1 598.21 173.60 622.02 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n415 3 Car -1 -1 -1 1095.31 185.25 1220.71 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n415 2 Car -1 -1 -1 955.16 183.79 1066.92 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n415 4 Car -1 -1 -1 1029.60 183.88 1156.31 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n415 129 Pedestrian -1 -1 -1 71.97 149.74 117.18 255.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n415 66 Pedestrian -1 -1 -1 759.48 156.40 811.82 301.15 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n415 7 Car -1 -1 -1 601.44 173.18 636.99 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n415 65 Pedestrian -1 -1 -1 190.76 153.92 204.87 196.76 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n415 125 Pedestrian -1 -1 -1 304.31 158.53 319.67 193.99 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n415 130 Pedestrian -1 -1 -1 411.09 166.35 424.46 202.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n415 59 Pedestrian -1 -1 -1 349.58 160.82 363.38 189.94 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n415 131 Car -1 -1 -1 598.02 173.63 622.36 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n416 3 Car -1 -1 -1 1095.19 185.25 1220.85 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n416 2 Car -1 -1 -1 955.06 183.82 1066.92 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n416 4 Car -1 -1 -1 1029.91 183.90 1156.01 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n416 129 Pedestrian -1 -1 -1 75.81 150.05 125.65 253.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n416 66 Pedestrian -1 -1 -1 767.77 159.15 834.54 299.43 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n416 7 Car -1 -1 -1 601.47 173.15 636.89 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n416 65 Pedestrian -1 -1 -1 190.58 153.77 204.73 196.74 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n416 125 Pedestrian -1 -1 -1 304.07 158.60 320.00 194.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n416 130 Pedestrian -1 -1 -1 409.38 165.86 423.12 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n416 59 Pedestrian -1 -1 -1 349.11 160.68 361.35 190.27 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n416 131 Car -1 -1 -1 598.00 173.61 622.22 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n417 3 Car -1 -1 -1 1095.17 185.26 1220.78 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n417 2 Car -1 -1 -1 955.09 183.83 1066.90 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n417 4 Car -1 -1 -1 1029.92 183.92 1155.97 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n417 129 Pedestrian -1 -1 -1 78.73 150.68 130.51 253.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n417 66 Pedestrian -1 -1 -1 771.02 158.70 847.07 300.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n417 7 Car -1 -1 -1 601.49 173.13 636.91 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n417 65 Pedestrian -1 -1 -1 190.63 153.69 205.01 196.83 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n417 125 Pedestrian -1 -1 -1 303.89 158.45 319.76 193.90 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n417 130 Pedestrian -1 -1 -1 408.81 165.89 422.74 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n417 59 Pedestrian -1 -1 -1 349.63 161.16 363.22 189.76 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n417 131 Car -1 -1 -1 598.07 173.62 622.09 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n417 133 Pedestrian -1 -1 -1 362.33 160.54 376.71 187.89 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n418 3 Car -1 -1 -1 1095.43 185.27 1220.66 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n418 2 Car -1 -1 -1 955.12 183.79 1066.98 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n418 4 Car -1 -1 -1 1032.60 183.64 1157.36 233.54 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n418 66 Pedestrian -1 -1 -1 784.20 159.64 855.89 304.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n418 129 Pedestrian -1 -1 -1 85.62 150.90 131.67 251.99 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n418 7 Car -1 -1 -1 601.71 173.07 636.93 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n418 65 Pedestrian -1 -1 -1 190.82 154.09 205.25 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n418 125 Pedestrian -1 -1 -1 304.31 158.53 319.04 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n418 130 Pedestrian -1 -1 -1 407.91 166.02 422.30 202.02 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n418 59 Pedestrian -1 -1 -1 349.24 160.76 363.64 189.64 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n418 133 Pedestrian -1 -1 -1 362.73 160.66 375.67 188.21 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n418 131 Car -1 -1 -1 598.03 173.58 622.13 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n419 3 Car -1 -1 -1 1095.25 185.29 1220.88 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n419 2 Car -1 -1 -1 955.19 183.78 1066.92 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n419 4 Car -1 -1 -1 1032.67 183.63 1157.18 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n419 66 Pedestrian -1 -1 -1 800.78 158.55 855.28 301.49 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n419 129 Pedestrian -1 -1 -1 86.57 150.10 131.96 249.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n419 7 Car -1 -1 -1 601.81 173.10 636.82 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n419 65 Pedestrian -1 -1 -1 190.78 153.98 205.25 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n419 125 Pedestrian -1 -1 -1 303.94 158.38 319.37 193.89 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n419 59 Pedestrian -1 -1 -1 349.54 161.18 363.18 189.44 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n419 131 Car -1 -1 -1 598.22 173.63 622.12 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n419 130 Pedestrian -1 -1 -1 407.06 166.09 421.61 202.06 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n419 133 Pedestrian -1 -1 -1 366.31 160.01 378.17 188.21 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n420 3 Car -1 -1 -1 1095.16 185.23 1220.90 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n420 2 Car -1 -1 -1 954.97 183.79 1066.86 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n420 4 Car -1 -1 -1 1029.71 183.90 1156.24 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n420 129 Pedestrian -1 -1 -1 94.10 150.27 138.21 249.48 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n420 66 Pedestrian -1 -1 -1 815.21 160.74 870.84 304.23 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n420 7 Car -1 -1 -1 601.84 173.11 636.92 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n420 65 Pedestrian -1 -1 -1 190.96 153.97 205.38 197.42 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n420 125 Pedestrian -1 -1 -1 303.97 158.87 319.12 193.75 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n420 59 Pedestrian -1 -1 -1 349.79 161.51 363.14 189.24 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n420 131 Car -1 -1 -1 598.34 173.71 622.04 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n420 133 Pedestrian -1 -1 -1 362.23 160.60 374.94 188.36 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n421 3 Car -1 -1 -1 1095.03 185.17 1220.82 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n421 2 Car -1 -1 -1 954.95 183.70 1066.84 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n421 4 Car -1 -1 -1 1030.05 183.93 1155.99 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n421 66 Pedestrian -1 -1 -1 821.26 156.99 896.29 307.54 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n421 129 Pedestrian -1 -1 -1 97.09 152.65 143.99 249.23 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n421 7 Car -1 -1 -1 601.58 172.98 636.87 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n421 65 Pedestrian -1 -1 -1 192.01 154.20 207.39 197.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n421 59 Pedestrian -1 -1 -1 350.06 161.47 363.16 189.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n421 125 Pedestrian -1 -1 -1 303.54 158.87 318.97 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n421 133 Pedestrian -1 -1 -1 362.12 160.51 374.77 188.08 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n421 131 Car -1 -1 -1 598.17 173.60 622.03 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n421 134 Pedestrian -1 -1 -1 311.38 160.98 325.73 195.25 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n421 135 Pedestrian -1 -1 -1 405.16 165.86 419.32 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n422 3 Car -1 -1 -1 1094.93 185.20 1220.90 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n422 2 Car -1 -1 -1 954.91 183.61 1066.97 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n422 4 Car -1 -1 -1 1030.10 183.93 1155.88 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n422 66 Pedestrian -1 -1 -1 831.18 156.51 909.05 307.93 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n422 129 Pedestrian -1 -1 -1 102.53 151.64 147.20 247.79 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n422 7 Car -1 -1 -1 601.51 172.91 636.93 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n422 65 Pedestrian -1 -1 -1 191.92 154.05 207.57 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n422 59 Pedestrian -1 -1 -1 349.97 161.82 364.06 188.77 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n422 125 Pedestrian -1 -1 -1 303.40 158.86 318.54 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n422 133 Pedestrian -1 -1 -1 363.08 161.04 375.29 187.85 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n422 135 Pedestrian -1 -1 -1 404.32 165.61 418.61 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n422 131 Car -1 -1 -1 598.06 173.52 622.15 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n422 134 Pedestrian -1 -1 -1 311.76 161.36 325.90 194.95 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n423 3 Car -1 -1 -1 1094.78 185.21 1220.76 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n423 2 Car -1 -1 -1 954.80 183.59 1067.16 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n423 4 Car -1 -1 -1 1030.17 183.87 1155.69 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n423 66 Pedestrian -1 -1 -1 843.28 155.48 919.84 309.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n423 129 Pedestrian -1 -1 -1 106.66 152.53 151.47 246.51 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n423 7 Car -1 -1 -1 601.52 172.96 636.78 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n423 65 Pedestrian -1 -1 -1 191.95 154.21 207.50 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n423 59 Pedestrian -1 -1 -1 349.55 161.62 365.14 188.56 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n423 125 Pedestrian -1 -1 -1 301.82 158.77 316.21 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n423 133 Pedestrian -1 -1 -1 363.61 160.85 375.76 187.67 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n423 134 Pedestrian -1 -1 -1 311.42 161.07 325.75 195.19 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n423 131 Car -1 -1 -1 598.08 173.61 621.96 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n423 135 Pedestrian -1 -1 -1 403.57 165.58 417.75 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n424 3 Car -1 -1 -1 1094.86 185.20 1220.76 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n424 129 Pedestrian -1 -1 -1 115.95 151.06 155.99 245.62 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n424 2 Car -1 -1 -1 954.49 183.52 1067.51 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n424 4 Car -1 -1 -1 1030.07 183.85 1155.68 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n424 66 Pedestrian -1 -1 -1 858.27 155.44 921.16 310.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n424 7 Car -1 -1 -1 601.59 172.95 636.71 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n424 65 Pedestrian -1 -1 -1 192.36 154.11 207.10 196.48 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n424 125 Pedestrian -1 -1 -1 301.44 158.67 316.28 193.08 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n424 133 Pedestrian -1 -1 -1 363.77 160.92 375.97 187.58 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n424 59 Pedestrian -1 -1 -1 349.30 161.54 364.85 187.94 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n424 134 Pedestrian -1 -1 -1 311.26 161.02 325.71 195.03 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n424 131 Car -1 -1 -1 598.06 173.69 621.92 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n424 136 Pedestrian -1 -1 -1 181.38 153.74 197.02 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n425 3 Car -1 -1 -1 1094.64 185.23 1221.11 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n425 2 Car -1 -1 -1 954.49 183.53 1067.53 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n425 4 Car -1 -1 -1 1030.44 183.87 1155.55 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n425 129 Pedestrian -1 -1 -1 122.84 150.55 161.98 245.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n425 66 Pedestrian -1 -1 -1 881.23 155.22 942.51 316.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n425 7 Car -1 -1 -1 601.78 172.99 636.78 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n425 65 Pedestrian -1 -1 -1 190.84 153.72 205.20 196.32 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n425 125 Pedestrian -1 -1 -1 300.73 158.44 316.18 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n425 133 Pedestrian -1 -1 -1 363.53 160.64 376.33 187.98 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n425 59 Pedestrian -1 -1 -1 349.00 161.50 365.15 187.74 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n425 136 Pedestrian -1 -1 -1 181.68 154.11 197.32 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n425 131 Car -1 -1 -1 598.23 173.65 621.90 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n425 134 Pedestrian -1 -1 -1 311.22 161.07 325.73 195.07 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n426 3 Car -1 -1 -1 1094.38 185.16 1221.41 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n426 2 Car -1 -1 -1 954.18 183.48 1067.59 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n426 4 Car -1 -1 -1 1030.28 183.76 1155.58 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n426 129 Pedestrian -1 -1 -1 125.29 151.51 167.99 244.72 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n426 66 Pedestrian -1 -1 -1 889.34 154.49 965.86 312.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n426 7 Car -1 -1 -1 601.77 172.89 636.75 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n426 65 Pedestrian -1 -1 -1 190.64 154.16 205.26 196.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n426 125 Pedestrian -1 -1 -1 300.42 158.51 315.34 192.44 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n426 133 Pedestrian -1 -1 -1 366.10 160.82 377.02 188.52 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n426 134 Pedestrian -1 -1 -1 311.00 161.20 325.91 195.05 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n426 136 Pedestrian -1 -1 -1 181.84 155.12 197.46 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n426 131 Car -1 -1 -1 598.15 173.73 621.88 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n426 59 Pedestrian -1 -1 -1 348.45 162.29 366.08 187.46 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n426 137 Cyclist -1 -1 -1 348.45 162.29 366.08 187.46 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n427 3 Car -1 -1 -1 1094.09 185.25 1221.66 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n427 4 Car -1 -1 -1 1030.14 183.81 1155.81 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n427 66 Pedestrian -1 -1 -1 895.66 156.47 982.51 316.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n427 2 Car -1 -1 -1 954.13 183.59 1067.49 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n427 129 Pedestrian -1 -1 -1 129.04 151.40 172.05 243.74 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n427 7 Car -1 -1 -1 601.85 172.93 636.56 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n427 65 Pedestrian -1 -1 -1 192.90 154.91 207.39 196.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n427 125 Pedestrian -1 -1 -1 300.35 158.51 315.12 192.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n427 133 Pedestrian -1 -1 -1 365.92 160.82 377.55 188.36 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n427 134 Pedestrian -1 -1 -1 311.26 161.37 325.81 195.18 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n427 59 Pedestrian -1 -1 -1 348.44 160.98 362.07 188.17 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n427 136 Pedestrian -1 -1 -1 182.28 155.48 197.72 196.83 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n427 131 Car -1 -1 -1 598.34 173.64 622.03 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n427 138 Pedestrian -1 -1 -1 399.67 164.72 414.86 203.55 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n428 3 Car -1 -1 -1 1094.23 185.24 1221.44 236.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n428 4 Car -1 -1 -1 1032.80 183.65 1157.04 233.83 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n428 2 Car -1 -1 -1 953.98 183.29 1067.75 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n428 66 Pedestrian -1 -1 -1 909.54 155.17 991.69 319.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n428 129 Pedestrian -1 -1 -1 133.41 150.91 175.01 243.89 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n428 7 Car -1 -1 -1 601.82 173.12 636.59 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n428 65 Pedestrian -1 -1 -1 192.74 155.03 207.90 196.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n428 125 Pedestrian -1 -1 -1 299.79 158.64 315.17 192.40 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n428 134 Pedestrian -1 -1 -1 311.64 161.63 325.91 195.17 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n428 133 Pedestrian -1 -1 -1 365.97 160.69 377.59 188.61 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n428 131 Car -1 -1 -1 598.20 173.59 622.05 193.29 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n428 138 Pedestrian -1 -1 -1 399.57 165.01 414.50 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n428 59 Pedestrian -1 -1 -1 348.98 161.98 364.67 187.20 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n429 3 Car -1 -1 -1 1094.62 185.22 1221.50 236.16 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n429 66 Pedestrian -1 -1 -1 930.44 155.19 994.48 319.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n429 4 Car -1 -1 -1 1032.75 183.62 1157.07 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n429 2 Car -1 -1 -1 954.30 183.68 1067.50 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n429 129 Pedestrian -1 -1 -1 138.87 149.56 177.65 241.75 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n429 7 Car -1 -1 -1 601.79 173.16 636.54 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n429 65 Pedestrian -1 -1 -1 192.57 155.11 207.98 195.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n429 125 Pedestrian -1 -1 -1 299.93 158.66 315.07 192.38 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n429 133 Pedestrian -1 -1 -1 366.30 160.39 377.17 188.34 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n429 134 Pedestrian -1 -1 -1 311.76 161.69 325.95 195.13 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n429 131 Car -1 -1 -1 598.16 173.66 622.12 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n429 59 Pedestrian -1 -1 -1 349.25 162.09 364.75 187.05 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n429 138 Pedestrian -1 -1 -1 399.17 164.62 413.71 203.51 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n430 3 Car -1 -1 -1 1098.48 185.26 1221.10 236.45 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n430 4 Car -1 -1 -1 1030.01 183.90 1155.68 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n430 2 Car -1 -1 -1 953.94 183.00 1067.94 232.07 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n430 66 Pedestrian -1 -1 -1 947.51 155.34 1014.12 318.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n430 129 Pedestrian -1 -1 -1 141.46 150.98 177.08 240.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n430 7 Car -1 -1 -1 601.70 173.10 636.77 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n430 65 Pedestrian -1 -1 -1 192.32 155.11 208.21 195.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n430 125 Pedestrian -1 -1 -1 300.14 158.51 315.12 192.55 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n430 134 Pedestrian -1 -1 -1 311.84 161.86 325.97 195.09 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n430 133 Pedestrian -1 -1 -1 366.14 160.81 376.99 187.98 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n430 131 Car -1 -1 -1 598.19 173.65 622.36 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n430 138 Pedestrian -1 -1 -1 398.63 164.68 413.47 203.46 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n430 59 Pedestrian -1 -1 -1 349.65 162.43 364.87 187.04 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n431 3 Car -1 -1 -1 1094.79 185.31 1221.34 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n431 4 Car -1 -1 -1 1029.91 183.86 1155.56 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n431 2 Car -1 -1 -1 953.74 182.58 1068.06 232.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n431 66 Pedestrian -1 -1 -1 960.07 156.05 1048.15 322.87 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n431 129 Pedestrian -1 -1 -1 144.21 151.57 182.73 239.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n431 7 Car -1 -1 -1 601.90 173.08 636.76 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n431 125 Pedestrian -1 -1 -1 300.86 158.65 315.32 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n431 65 Pedestrian -1 -1 -1 192.57 155.38 208.30 195.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n431 134 Pedestrian -1 -1 -1 311.89 161.63 325.77 195.17 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n431 133 Pedestrian -1 -1 -1 366.18 160.99 376.88 187.74 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n431 131 Car -1 -1 -1 598.31 173.73 622.10 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n431 59 Pedestrian -1 -1 -1 348.01 161.51 361.23 187.93 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n431 138 Pedestrian -1 -1 -1 396.98 164.56 412.01 204.13 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n432 3 Car -1 -1 -1 1098.39 185.41 1221.15 236.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n432 2 Car -1 -1 -1 954.84 182.87 1067.43 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n432 4 Car -1 -1 -1 1029.69 183.94 1156.00 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n432 66 Pedestrian -1 -1 -1 967.33 154.96 1064.07 326.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n432 129 Pedestrian -1 -1 -1 147.87 152.80 187.18 239.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n432 7 Car -1 -1 -1 601.75 173.15 636.53 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n432 125 Pedestrian -1 -1 -1 300.75 158.59 315.94 192.66 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n432 65 Pedestrian -1 -1 -1 192.11 155.35 208.52 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n432 134 Pedestrian -1 -1 -1 311.97 161.73 325.52 194.99 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n432 133 Pedestrian -1 -1 -1 366.46 161.07 376.65 187.37 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n432 131 Car -1 -1 -1 598.11 173.57 622.04 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n432 59 Pedestrian -1 -1 -1 347.79 161.80 360.51 187.72 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n432 138 Pedestrian -1 -1 -1 396.12 166.02 410.98 205.20 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n433 3 Car -1 -1 -1 1098.70 185.33 1220.95 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n433 4 Car -1 -1 -1 1028.55 183.75 1156.95 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n433 2 Car -1 -1 -1 955.54 183.49 1066.32 231.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n433 66 Pedestrian -1 -1 -1 980.77 154.25 1073.56 327.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n433 129 Pedestrian -1 -1 -1 151.40 153.42 191.30 238.49 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n433 7 Car -1 -1 -1 601.50 173.07 636.77 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n433 65 Pedestrian -1 -1 -1 192.28 155.22 208.66 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n433 125 Pedestrian -1 -1 -1 301.53 158.88 315.95 192.50 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n433 134 Pedestrian -1 -1 -1 311.90 161.95 325.51 194.86 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n433 133 Pedestrian -1 -1 -1 366.37 161.06 376.60 187.34 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n433 131 Car -1 -1 -1 597.99 173.73 622.05 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n433 59 Pedestrian -1 -1 -1 347.73 161.92 360.63 187.88 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n434 3 Car -1 -1 -1 1094.78 185.24 1221.44 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n434 4 Car -1 -1 -1 1032.96 183.93 1157.57 233.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n434 66 Pedestrian -1 -1 -1 1004.75 154.73 1079.20 327.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n434 2 Car -1 -1 -1 955.22 183.74 1066.25 231.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n434 129 Pedestrian -1 -1 -1 155.72 154.32 193.83 237.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n434 7 Car -1 -1 -1 601.67 173.09 636.67 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n434 65 Pedestrian -1 -1 -1 192.74 155.25 208.19 195.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n434 125 Pedestrian -1 -1 -1 301.34 159.05 316.02 192.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n434 134 Pedestrian -1 -1 -1 311.56 162.14 325.40 194.89 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n434 133 Pedestrian -1 -1 -1 366.51 161.10 376.58 187.27 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n434 131 Car -1 -1 -1 598.15 173.71 622.20 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n434 139 Pedestrian -1 -1 -1 395.08 166.17 410.02 205.06 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n435 3 Car -1 -1 -1 1093.90 185.06 1221.71 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n435 4 Car -1 -1 -1 1033.80 184.05 1157.41 234.15 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n435 2 Car -1 -1 -1 955.19 183.39 1066.94 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n435 129 Pedestrian -1 -1 -1 163.30 154.26 199.04 236.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n435 66 Pedestrian -1 -1 -1 1030.40 152.40 1099.73 335.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n435 7 Car -1 -1 -1 601.64 173.12 636.71 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n435 65 Pedestrian -1 -1 -1 193.34 155.34 208.43 195.80 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n435 125 Pedestrian -1 -1 -1 301.14 159.04 316.08 192.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n435 134 Pedestrian -1 -1 -1 311.64 162.09 325.30 194.88 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n435 139 Pedestrian -1 -1 -1 392.86 166.05 407.72 205.60 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n435 133 Pedestrian -1 -1 -1 366.34 161.11 376.85 187.56 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n435 131 Car -1 -1 -1 598.37 173.70 622.22 193.23 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0023.txt",
    "content": "0 1 Car -1 -1 -1 953.80 183.77 1068.13 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n0 2 Car -1 -1 -1 1095.17 185.12 1220.59 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n0 3 Car -1 -1 -1 1029.54 183.89 1156.03 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n0 4 Pedestrian -1 -1 -1 414.74 166.73 444.32 246.26 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n0 5 Car -1 -1 -1 601.89 173.09 636.60 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n0 6 Pedestrian -1 -1 -1 328.01 161.12 342.18 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n0 7 Pedestrian -1 -1 -1 363.25 162.39 376.85 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n0 8 Pedestrian -1 -1 -1 349.78 161.76 363.88 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n0 9 Pedestrian -1 -1 -1 297.07 159.42 310.57 194.54 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n0 10 Pedestrian -1 -1 -1 309.16 160.32 323.05 195.64 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n1 2 Car -1 -1 -1 1094.91 185.06 1221.03 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 1 Car -1 -1 -1 954.08 183.52 1067.66 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 3 Car -1 -1 -1 1029.11 183.58 1156.47 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n1 4 Pedestrian -1 -1 -1 416.11 166.28 450.73 246.98 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1 5 Car -1 -1 -1 602.67 172.82 637.24 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n1 6 Pedestrian -1 -1 -1 328.10 161.36 342.17 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n1 7 Pedestrian -1 -1 -1 364.85 162.78 378.80 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n1 8 Pedestrian -1 -1 -1 349.30 162.97 364.32 200.39 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n1 10 Pedestrian -1 -1 -1 308.97 160.29 323.19 195.75 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n1 9 Pedestrian -1 -1 -1 298.18 159.32 311.70 194.68 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n1 11 Pedestrian -1 -1 -1 190.85 154.79 209.59 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n2 2 Car -1 -1 -1 1094.83 185.27 1221.33 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n2 1 Car -1 -1 -1 954.44 183.72 1067.14 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n2 3 Car -1 -1 -1 1029.45 183.79 1156.20 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n2 4 Pedestrian -1 -1 -1 420.96 166.74 453.93 246.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n2 5 Car -1 -1 -1 601.86 173.02 636.98 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n2 7 Pedestrian -1 -1 -1 365.29 163.21 379.65 202.04 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n2 6 Pedestrian -1 -1 -1 327.58 161.57 342.18 199.04 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n2 8 Pedestrian -1 -1 -1 347.25 163.46 362.39 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n2 10 Pedestrian -1 -1 -1 311.99 161.55 324.87 195.16 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n2 9 Pedestrian -1 -1 -1 300.13 160.94 313.83 194.64 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n2 11 Pedestrian -1 -1 -1 190.73 155.09 209.81 196.71 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n3 2 Car -1 -1 -1 1094.96 185.32 1221.14 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n3 1 Car -1 -1 -1 954.20 183.62 1067.41 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n3 3 Car -1 -1 -1 1029.06 183.78 1156.61 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n3 4 Pedestrian -1 -1 -1 429.60 166.76 455.66 246.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n3 7 Pedestrian -1 -1 -1 365.57 163.08 379.71 202.25 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n3 5 Car -1 -1 -1 602.50 172.93 637.39 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n3 6 Pedestrian -1 -1 -1 327.36 161.90 341.95 199.00 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n3 8 Pedestrian -1 -1 -1 346.70 163.35 361.84 200.25 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n3 9 Pedestrian -1 -1 -1 300.88 161.05 315.88 194.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n3 10 Pedestrian -1 -1 -1 311.88 161.76 324.85 195.24 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n3 11 Pedestrian -1 -1 -1 190.12 154.96 210.36 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n3 12 Car -1 -1 -1 598.43 173.68 622.20 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n4 2 Car -1 -1 -1 1094.79 185.24 1221.10 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n4 3 Car -1 -1 -1 1029.25 183.79 1156.45 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n4 1 Car -1 -1 -1 954.25 183.62 1067.27 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n4 4 Pedestrian -1 -1 -1 435.06 165.70 462.13 247.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 7 Pedestrian -1 -1 -1 365.55 163.23 381.23 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n4 5 Car -1 -1 -1 601.90 173.04 637.02 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n4 6 Pedestrian -1 -1 -1 326.76 161.66 341.50 199.09 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n4 8 Pedestrian -1 -1 -1 346.60 162.88 361.39 200.19 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n4 10 Pedestrian -1 -1 -1 311.77 161.89 324.99 195.04 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n4 12 Car -1 -1 -1 598.47 173.64 622.01 192.92 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n4 11 Pedestrian -1 -1 -1 190.46 154.97 210.29 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n5 2 Car -1 -1 -1 1094.87 185.29 1221.20 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n5 1 Car -1 -1 -1 954.32 183.65 1067.26 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n5 3 Car -1 -1 -1 1029.63 183.83 1156.04 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n5 4 Pedestrian -1 -1 -1 436.20 165.77 472.41 247.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n5 5 Car -1 -1 -1 602.03 173.12 636.74 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n5 7 Pedestrian -1 -1 -1 366.36 163.31 381.71 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n5 6 Pedestrian -1 -1 -1 326.54 161.63 340.91 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n5 8 Pedestrian -1 -1 -1 346.30 162.90 361.00 200.23 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n5 10 Pedestrian -1 -1 -1 308.38 159.28 322.37 194.78 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n5 12 Car -1 -1 -1 598.66 173.67 621.95 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n5 11 Pedestrian -1 -1 -1 190.22 154.84 210.44 196.71 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n6 2 Car -1 -1 -1 1094.91 185.17 1221.15 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n6 1 Car -1 -1 -1 954.45 183.65 1067.34 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n6 3 Car -1 -1 -1 1029.48 183.84 1156.30 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n6 4 Pedestrian -1 -1 -1 439.90 165.30 479.40 248.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n6 5 Car -1 -1 -1 602.57 173.07 637.30 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n6 7 Pedestrian -1 -1 -1 368.40 162.90 383.36 201.62 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n6 8 Pedestrian -1 -1 -1 345.95 163.27 360.50 200.15 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n6 10 Pedestrian -1 -1 -1 308.77 160.80 323.48 195.35 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n6 6 Pedestrian -1 -1 -1 326.31 161.72 340.65 199.26 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n6 12 Car -1 -1 -1 598.76 173.69 622.05 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n6 11 Pedestrian -1 -1 -1 190.45 155.20 210.32 196.57 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n6 13 Cyclist -1 -1 -1 0.30 156.55 48.66 278.50 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n7 2 Car -1 -1 -1 1094.74 185.16 1221.25 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n7 1 Car -1 -1 -1 954.46 183.71 1067.32 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n7 3 Car -1 -1 -1 1029.57 183.81 1156.12 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n7 4 Pedestrian -1 -1 -1 443.67 165.93 479.35 249.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n7 5 Car -1 -1 -1 601.85 172.89 637.06 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n7 7 Pedestrian -1 -1 -1 369.78 162.43 383.83 201.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n7 8 Pedestrian -1 -1 -1 345.35 163.29 360.09 200.61 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n7 10 Pedestrian -1 -1 -1 308.74 161.03 322.98 195.03 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n7 6 Pedestrian -1 -1 -1 324.87 161.60 339.03 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n7 11 Pedestrian -1 -1 -1 190.66 155.60 210.35 196.21 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n7 12 Car -1 -1 -1 598.57 173.61 622.06 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n7 13 Cyclist -1 -1 -1 0.82 151.14 55.92 276.66 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n8 2 Car -1 -1 -1 1094.66 185.15 1221.11 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n8 1 Car -1 -1 -1 954.53 183.70 1067.27 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n8 3 Car -1 -1 -1 1029.61 183.84 1156.12 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n8 4 Pedestrian -1 -1 -1 452.34 165.88 482.71 249.30 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n8 5 Car -1 -1 -1 602.04 173.06 636.87 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n8 13 Cyclist -1 -1 -1 1.52 146.22 70.66 274.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n8 7 Pedestrian -1 -1 -1 369.94 162.60 385.24 200.65 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n8 6 Pedestrian -1 -1 -1 324.58 161.97 338.66 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n8 10 Pedestrian -1 -1 -1 308.19 159.47 321.93 194.56 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n8 8 Pedestrian -1 -1 -1 345.14 163.34 359.78 200.42 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n8 12 Car -1 -1 -1 598.60 173.65 622.13 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n8 11 Pedestrian -1 -1 -1 190.19 155.56 210.57 196.19 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n9 2 Car -1 -1 -1 1094.73 185.18 1221.24 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n9 1 Car -1 -1 -1 954.38 183.71 1067.32 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n9 3 Car -1 -1 -1 1029.55 183.87 1156.13 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n9 4 Pedestrian -1 -1 -1 459.71 166.88 486.54 250.04 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n9 5 Car -1 -1 -1 602.50 172.99 637.38 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n9 13 Cyclist -1 -1 -1 -10.50 121.37 205.53 360.40 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n9 8 Pedestrian -1 -1 -1 343.93 162.92 358.32 200.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n9 6 Pedestrian -1 -1 -1 323.81 161.89 338.13 199.24 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n9 7 Pedestrian -1 -1 -1 369.94 162.85 386.07 200.67 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n9 10 Pedestrian -1 -1 -1 308.15 159.39 322.10 194.41 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n9 12 Car -1 -1 -1 598.72 173.65 622.34 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n9 11 Pedestrian -1 -1 -1 190.66 155.72 210.29 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n10 2 Car -1 -1 -1 1094.72 185.17 1221.20 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n10 1 Car -1 -1 -1 954.52 183.77 1067.14 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n10 3 Car -1 -1 -1 1029.44 183.77 1156.14 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n10 4 Pedestrian -1 -1 -1 461.90 165.69 497.66 249.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n10 5 Car -1 -1 -1 602.63 173.08 637.30 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n10 8 Pedestrian -1 -1 -1 343.40 162.64 357.93 200.69 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n10 10 Pedestrian -1 -1 -1 305.38 159.23 319.60 194.04 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n10 7 Pedestrian -1 -1 -1 371.83 162.93 387.07 200.56 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n10 13 Cyclist -1 -1 -1 -9.40 129.20 288.18 360.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n10 6 Pedestrian -1 -1 -1 322.79 161.77 337.53 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n10 12 Car -1 -1 -1 598.72 173.82 622.35 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n10 11 Pedestrian -1 -1 -1 191.03 156.06 209.93 195.99 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n11 2 Car -1 -1 -1 1094.86 185.16 1221.26 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n11 3 Car -1 -1 -1 1029.44 183.75 1156.22 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n11 1 Car -1 -1 -1 954.56 183.72 1067.03 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n11 13 Cyclist -1 -1 -1 34.65 137.30 328.79 367.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n11 4 Pedestrian -1 -1 -1 464.80 166.14 503.51 251.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n11 5 Car -1 -1 -1 602.54 172.96 637.50 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n11 6 Pedestrian -1 -1 -1 319.43 160.67 335.49 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n11 8 Pedestrian -1 -1 -1 342.25 162.34 357.54 200.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n11 10 Pedestrian -1 -1 -1 307.63 159.43 321.82 194.20 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n11 7 Pedestrian -1 -1 -1 371.82 162.82 387.34 200.44 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n11 11 Pedestrian -1 -1 -1 190.24 155.52 211.05 196.38 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n11 12 Car -1 -1 -1 598.85 173.73 622.40 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n11 14 Cyclist -1 -1 -1 27.85 149.53 135.97 269.41 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n12 2 Car -1 -1 -1 1094.84 185.18 1221.11 236.12 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n12 3 Car -1 -1 -1 1029.46 183.80 1156.22 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n12 1 Car -1 -1 -1 954.56 183.76 1066.98 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n12 14 Cyclist -1 -1 -1 45.23 152.23 142.62 259.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n12 13 Cyclist -1 -1 -1 110.03 138.61 360.42 365.63 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n12 4 Pedestrian -1 -1 -1 472.04 165.99 504.88 251.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n12 5 Car -1 -1 -1 602.56 172.93 637.38 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n12 6 Pedestrian -1 -1 -1 318.64 160.67 334.97 200.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n12 8 Pedestrian -1 -1 -1 341.47 162.56 357.03 200.95 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n12 7 Pedestrian -1 -1 -1 372.04 162.90 387.54 200.42 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n12 10 Pedestrian -1 -1 -1 307.99 159.51 321.60 194.38 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n12 12 Car -1 -1 -1 598.72 173.72 622.41 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n13 2 Car -1 -1 -1 1094.75 185.14 1221.10 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n13 13 Cyclist -1 -1 -1 180.49 140.82 381.85 364.64 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n13 1 Car -1 -1 -1 954.61 183.75 1066.92 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n13 3 Car -1 -1 -1 1029.41 183.83 1156.33 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n13 14 Cyclist -1 -1 -1 61.58 154.06 155.08 258.51 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n13 4 Pedestrian -1 -1 -1 484.38 166.98 512.45 251.98 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n13 8 Pedestrian -1 -1 -1 342.20 162.81 357.03 201.05 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n13 5 Car -1 -1 -1 602.67 172.93 637.21 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n13 6 Pedestrian -1 -1 -1 318.71 160.73 334.31 200.48 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n13 10 Pedestrian -1 -1 -1 308.66 161.06 322.17 195.68 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n13 7 Pedestrian -1 -1 -1 373.30 162.71 387.63 200.61 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n13 12 Car -1 -1 -1 598.82 173.68 622.51 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n14 2 Car -1 -1 -1 1094.63 185.11 1221.31 236.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n14 13 Cyclist -1 -1 -1 225.71 149.27 405.06 361.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n14 1 Car -1 -1 -1 954.67 183.72 1066.90 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n14 3 Car -1 -1 -1 1029.45 183.80 1156.30 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n14 14 Cyclist -1 -1 -1 72.38 155.08 169.32 256.15 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 4 Pedestrian -1 -1 -1 485.52 165.65 521.73 252.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n14 8 Pedestrian -1 -1 -1 342.40 162.98 356.78 201.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n14 5 Car -1 -1 -1 602.55 172.97 637.36 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n14 6 Pedestrian -1 -1 -1 318.24 163.49 334.35 200.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n14 7 Pedestrian -1 -1 -1 373.31 162.85 388.17 200.53 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n14 10 Pedestrian -1 -1 -1 307.71 160.87 323.01 196.18 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n14 12 Car -1 -1 -1 598.73 173.72 622.33 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n15 2 Car -1 -1 -1 1094.86 185.16 1221.04 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n15 1 Car -1 -1 -1 954.64 183.68 1067.07 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n15 3 Car -1 -1 -1 1029.42 183.90 1156.39 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n15 13 Cyclist -1 -1 -1 258.23 148.47 426.26 363.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 14 Cyclist -1 -1 -1 97.81 151.94 174.58 253.90 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n15 4 Pedestrian -1 -1 -1 489.46 165.21 531.47 253.02 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n15 5 Car -1 -1 -1 602.53 172.89 637.37 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n15 8 Pedestrian -1 -1 -1 341.87 163.82 357.14 200.66 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n15 6 Pedestrian -1 -1 -1 315.79 164.17 332.11 200.30 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n15 7 Pedestrian -1 -1 -1 374.54 162.55 387.59 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n15 10 Pedestrian -1 -1 -1 305.25 160.68 319.21 195.89 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n15 12 Car -1 -1 -1 598.59 173.81 622.30 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n15 15 Pedestrian -1 -1 -1 131.66 153.04 148.57 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n15 16 Pedestrian -1 -1 -1 185.88 153.98 208.79 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n16 2 Car -1 -1 -1 1094.59 185.15 1221.29 236.18 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n16 1 Car -1 -1 -1 954.73 183.75 1067.05 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n16 3 Car -1 -1 -1 1029.47 183.87 1156.24 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n16 13 Cyclist -1 -1 -1 291.48 145.62 438.03 366.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n16 4 Pedestrian -1 -1 -1 492.57 165.49 535.76 253.57 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n16 14 Cyclist -1 -1 -1 109.20 151.71 185.99 252.72 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n16 5 Car -1 -1 -1 602.67 172.83 637.23 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n16 6 Pedestrian -1 -1 -1 314.85 160.03 330.32 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n16 8 Pedestrian -1 -1 -1 341.47 163.28 356.52 201.19 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n16 7 Pedestrian -1 -1 -1 374.97 162.96 387.87 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n16 12 Car -1 -1 -1 598.79 173.66 622.23 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n16 10 Pedestrian -1 -1 -1 305.54 160.01 319.39 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n17 2 Car -1 -1 -1 1094.51 185.21 1221.50 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n17 1 Car -1 -1 -1 954.77 183.78 1067.11 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n17 3 Car -1 -1 -1 1029.53 183.89 1156.23 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n17 13 Cyclist -1 -1 -1 313.90 144.66 447.30 367.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n17 4 Pedestrian -1 -1 -1 498.97 166.10 537.76 254.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n17 14 Cyclist -1 -1 -1 125.45 152.91 197.89 250.50 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n17 5 Car -1 -1 -1 602.67 172.81 637.32 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n17 6 Pedestrian -1 -1 -1 312.09 159.91 328.50 200.23 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n17 7 Pedestrian -1 -1 -1 376.72 163.56 390.80 200.99 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n17 8 Pedestrian -1 -1 -1 341.85 162.87 355.84 200.63 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n17 12 Car -1 -1 -1 598.82 173.59 622.52 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n17 17 Cyclist -1 -1 -1 376.72 163.56 390.80 200.99 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n18 2 Car -1 -1 -1 1094.70 185.17 1221.21 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n18 1 Car -1 -1 -1 954.73 183.79 1066.89 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n18 3 Car -1 -1 -1 1029.45 183.88 1156.28 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n18 14 Cyclist -1 -1 -1 139.88 152.96 206.56 245.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n18 13 Cyclist -1 -1 -1 339.71 145.68 451.40 359.32 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n18 4 Pedestrian -1 -1 -1 510.83 163.87 540.65 256.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n18 5 Car -1 -1 -1 602.59 172.80 637.43 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n18 7 Pedestrian -1 -1 -1 377.35 163.46 391.19 200.93 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n18 8 Pedestrian -1 -1 -1 339.32 162.12 355.11 202.14 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n18 6 Pedestrian -1 -1 -1 314.37 160.10 330.05 200.80 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n18 12 Car -1 -1 -1 598.90 173.54 622.40 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n18 18 Pedestrian -1 -1 -1 192.28 155.07 209.25 196.04 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n19 2 Car -1 -1 -1 1094.70 185.18 1221.15 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n19 1 Car -1 -1 -1 954.76 183.82 1066.92 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n19 3 Car -1 -1 -1 1029.55 183.92 1156.17 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n19 14 Cyclist -1 -1 -1 151.64 153.39 213.16 244.41 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n19 4 Pedestrian -1 -1 -1 515.89 164.41 550.46 255.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n19 5 Car -1 -1 -1 602.48 172.70 637.55 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n19 13 Cyclist -1 -1 -1 357.08 154.37 456.72 349.80 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n19 8 Pedestrian -1 -1 -1 339.29 162.27 355.13 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n19 6 Pedestrian -1 -1 -1 311.58 160.01 328.70 200.70 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n19 12 Car -1 -1 -1 598.77 173.47 622.42 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n19 7 Pedestrian -1 -1 -1 378.84 162.83 389.69 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n19 18 Pedestrian -1 -1 -1 193.49 156.08 208.76 195.64 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n20 2 Car -1 -1 -1 1094.80 185.18 1220.93 236.12 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n20 3 Car -1 -1 -1 1029.46 183.90 1156.12 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n20 1 Car -1 -1 -1 954.86 183.81 1066.72 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n20 4 Pedestrian -1 -1 -1 517.64 164.75 558.02 256.53 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n20 14 Cyclist -1 -1 -1 160.08 154.91 219.84 241.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n20 13 Cyclist -1 -1 -1 373.55 150.33 470.21 331.75 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n20 5 Car -1 -1 -1 602.95 172.83 637.12 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n20 6 Pedestrian -1 -1 -1 311.26 159.86 328.13 200.70 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 8 Pedestrian -1 -1 -1 339.40 162.35 354.83 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n20 12 Car -1 -1 -1 598.99 173.54 622.16 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n20 7 Pedestrian -1 -1 -1 378.95 162.50 390.01 203.30 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n21 2 Car -1 -1 -1 1095.04 185.25 1220.86 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n21 1 Car -1 -1 -1 954.83 183.82 1066.98 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n21 3 Car -1 -1 -1 1029.52 183.88 1156.08 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n21 4 Pedestrian -1 -1 -1 524.39 163.85 564.13 257.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n21 5 Car -1 -1 -1 602.83 172.80 637.26 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n21 14 Cyclist -1 -1 -1 169.60 155.60 233.16 239.04 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n21 13 Cyclist -1 -1 -1 387.91 153.18 474.04 321.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n21 8 Pedestrian -1 -1 -1 338.90 162.05 354.70 202.02 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n21 6 Pedestrian -1 -1 -1 307.65 160.59 324.53 199.60 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n21 7 Pedestrian -1 -1 -1 374.61 160.34 394.24 219.88 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n21 12 Car -1 -1 -1 598.92 173.56 622.43 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n21 19 Pedestrian -1 -1 -1 195.40 154.51 213.98 196.06 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n22 2 Car -1 -1 -1 1094.88 185.23 1221.04 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n22 1 Car -1 -1 -1 954.86 183.82 1066.83 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n22 3 Car -1 -1 -1 1029.59 183.83 1156.11 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n22 5 Car -1 -1 -1 602.82 172.80 637.35 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n22 4 Pedestrian -1 -1 -1 533.30 163.89 565.51 258.25 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n22 14 Cyclist -1 -1 -1 179.17 156.74 244.41 237.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n22 13 Cyclist -1 -1 -1 397.50 155.04 486.23 311.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 6 Pedestrian -1 -1 -1 306.70 159.45 324.55 200.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n22 8 Pedestrian -1 -1 -1 338.61 161.80 354.25 201.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n22 19 Pedestrian -1 -1 -1 202.42 154.03 221.66 196.06 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n22 7 Pedestrian -1 -1 -1 374.32 159.93 394.42 220.36 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n23 2 Car -1 -1 -1 1094.83 185.30 1221.05 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n23 1 Car -1 -1 -1 954.74 183.81 1067.01 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n23 3 Car -1 -1 -1 1029.49 183.83 1156.28 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n23 4 Pedestrian -1 -1 -1 539.99 162.24 572.73 259.28 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n23 14 Cyclist -1 -1 -1 191.56 154.63 248.53 235.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n23 8 Pedestrian -1 -1 -1 338.21 162.00 354.45 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n23 13 Cyclist -1 -1 -1 410.21 155.94 490.44 303.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n23 5 Car -1 -1 -1 603.02 172.74 637.12 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n23 6 Pedestrian -1 -1 -1 306.55 159.36 323.90 201.27 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n23 20 Pedestrian -1 -1 -1 315.67 159.72 330.92 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n23 21 Car -1 -1 -1 598.90 173.61 622.28 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n24 2 Car -1 -1 -1 1094.84 185.19 1220.93 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n24 1 Car -1 -1 -1 954.91 183.84 1066.89 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n24 3 Car -1 -1 -1 1029.68 183.93 1156.12 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n24 14 Cyclist -1 -1 -1 201.55 155.25 260.48 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n24 4 Pedestrian -1 -1 -1 543.69 164.58 585.17 257.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 8 Pedestrian -1 -1 -1 338.06 162.41 354.06 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n24 13 Cyclist -1 -1 -1 420.45 156.76 502.04 293.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n24 5 Car -1 -1 -1 602.56 173.02 636.20 202.35 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n24 6 Pedestrian -1 -1 -1 305.87 159.47 323.38 201.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n24 20 Pedestrian -1 -1 -1 316.13 159.98 331.05 196.36 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n25 2 Car -1 -1 -1 1094.74 185.23 1221.14 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n25 1 Car -1 -1 -1 954.87 183.86 1066.73 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n25 3 Car -1 -1 -1 1029.36 183.86 1156.34 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n25 4 Pedestrian -1 -1 -1 545.13 165.81 592.15 259.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n25 14 Cyclist -1 -1 -1 212.48 156.78 265.19 232.70 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n25 13 Cyclist -1 -1 -1 430.29 156.80 506.33 286.75 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n25 8 Pedestrian -1 -1 -1 337.67 162.47 354.01 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n25 5 Car -1 -1 -1 602.47 173.10 636.28 202.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n25 6 Pedestrian -1 -1 -1 305.68 159.56 323.14 201.01 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n25 20 Pedestrian -1 -1 -1 316.14 160.21 331.50 195.97 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n25 22 Pedestrian -1 -1 -1 183.83 160.15 202.48 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n25 23 Pedestrian -1 -1 -1 377.58 161.58 392.27 204.19 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n26 2 Car -1 -1 -1 1095.15 185.30 1220.75 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n26 1 Car -1 -1 -1 954.86 183.87 1066.79 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n26 3 Car -1 -1 -1 1029.68 183.96 1156.12 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n26 4 Pedestrian -1 -1 -1 549.12 166.20 596.04 261.15 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n26 14 Cyclist -1 -1 -1 220.91 157.22 272.59 232.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n26 13 Cyclist -1 -1 -1 440.91 158.19 510.08 278.65 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n26 5 Car -1 -1 -1 601.94 173.00 636.65 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n26 8 Pedestrian -1 -1 -1 336.95 162.62 353.19 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n26 6 Pedestrian -1 -1 -1 303.92 159.07 321.11 201.76 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n26 20 Pedestrian -1 -1 -1 316.21 160.17 331.18 195.57 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n26 22 Pedestrian -1 -1 -1 183.54 159.34 202.44 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n26 23 Pedestrian -1 -1 -1 378.23 162.10 392.51 203.35 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n27 2 Car -1 -1 -1 1095.07 185.29 1220.70 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n27 1 Car -1 -1 -1 954.82 183.82 1066.81 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n27 3 Car -1 -1 -1 1029.64 183.98 1156.23 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n27 4 Pedestrian -1 -1 -1 560.34 164.47 597.22 262.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n27 14 Cyclist -1 -1 -1 227.74 157.51 281.96 230.89 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n27 13 Cyclist -1 -1 -1 447.06 158.45 515.30 277.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n27 5 Car -1 -1 -1 601.60 172.77 636.91 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n27 8 Pedestrian -1 -1 -1 337.00 162.59 352.83 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n27 6 Pedestrian -1 -1 -1 303.33 159.02 320.59 201.92 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n27 20 Pedestrian -1 -1 -1 316.79 159.17 330.70 194.80 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n27 22 Pedestrian -1 -1 -1 183.31 159.39 202.54 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n27 23 Pedestrian -1 -1 -1 378.30 162.13 392.68 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n28 2 Car -1 -1 -1 1094.96 185.33 1220.90 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n28 1 Car -1 -1 -1 954.83 183.76 1066.76 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n28 3 Car -1 -1 -1 1029.80 183.98 1156.10 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n28 14 Cyclist -1 -1 -1 236.59 158.97 286.94 229.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n28 4 Pedestrian -1 -1 -1 569.69 164.03 603.43 263.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n28 5 Car -1 -1 -1 601.66 172.69 636.78 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n28 13 Cyclist -1 -1 -1 456.59 158.97 520.37 271.15 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n28 8 Pedestrian -1 -1 -1 335.25 162.27 351.72 203.31 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n28 6 Pedestrian -1 -1 -1 302.50 161.01 320.22 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n28 22 Pedestrian -1 -1 -1 184.17 159.97 202.07 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n28 20 Pedestrian -1 -1 -1 316.73 159.01 331.03 194.95 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n28 23 Pedestrian -1 -1 -1 379.95 161.82 394.19 202.30 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n29 2 Car -1 -1 -1 1095.09 185.29 1220.89 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n29 1 Car -1 -1 -1 954.76 183.74 1066.94 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n29 3 Car -1 -1 -1 1030.05 183.96 1155.88 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 4 Pedestrian -1 -1 -1 572.60 164.63 616.01 264.27 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n29 13 Cyclist -1 -1 -1 463.17 161.38 526.18 268.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n29 14 Cyclist -1 -1 -1 244.78 158.55 293.61 228.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n29 5 Car -1 -1 -1 604.00 173.28 636.44 202.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n29 8 Pedestrian -1 -1 -1 335.18 162.69 351.25 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n29 6 Pedestrian -1 -1 -1 302.41 159.24 319.90 201.65 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n29 22 Pedestrian -1 -1 -1 183.98 160.06 202.01 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n29 20 Pedestrian -1 -1 -1 316.77 159.39 331.02 194.55 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n30 2 Car -1 -1 -1 1094.99 185.31 1220.87 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n30 1 Car -1 -1 -1 954.65 183.77 1067.00 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n30 3 Car -1 -1 -1 1029.91 183.98 1156.01 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n30 4 Pedestrian -1 -1 -1 575.19 164.65 622.32 265.11 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 13 Cyclist -1 -1 -1 468.90 164.41 530.53 264.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n30 5 Car -1 -1 -1 604.44 173.23 637.01 201.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n30 14 Cyclist -1 -1 -1 253.43 158.24 298.74 226.22 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 8 Pedestrian -1 -1 -1 335.07 162.84 351.12 203.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n30 6 Pedestrian -1 -1 -1 300.00 160.67 317.13 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n30 22 Pedestrian -1 -1 -1 183.76 160.32 201.95 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n30 20 Pedestrian -1 -1 -1 316.53 159.19 331.15 194.61 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n30 24 Pedestrian -1 -1 -1 380.90 163.05 394.58 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n31 2 Car -1 -1 -1 1095.14 185.28 1220.96 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n31 1 Car -1 -1 -1 954.89 183.75 1066.95 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n31 3 Car -1 -1 -1 1029.65 183.86 1156.16 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n31 4 Pedestrian -1 -1 -1 584.24 164.80 627.35 265.27 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n31 14 Cyclist -1 -1 -1 259.11 158.90 305.73 225.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n31 5 Car -1 -1 -1 603.42 173.02 637.61 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n31 8 Pedestrian -1 -1 -1 334.82 162.59 351.10 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n31 13 Cyclist -1 -1 -1 475.99 163.24 537.00 258.86 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n31 22 Pedestrian -1 -1 -1 184.22 160.97 201.31 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n31 6 Pedestrian -1 -1 -1 299.74 160.81 316.09 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n31 24 Pedestrian -1 -1 -1 381.41 163.41 394.99 196.98 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n31 20 Pedestrian -1 -1 -1 316.60 159.26 331.03 194.61 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n32 2 Car -1 -1 -1 1095.00 185.33 1221.06 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n32 1 Car -1 -1 -1 954.68 183.77 1067.04 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n32 3 Car -1 -1 -1 1029.65 183.91 1156.20 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n32 14 Cyclist -1 -1 -1 267.36 159.16 312.00 224.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n32 5 Car -1 -1 -1 601.43 173.10 636.71 201.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n32 4 Pedestrian -1 -1 -1 597.01 163.69 631.85 266.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n32 8 Pedestrian -1 -1 -1 334.17 162.45 350.86 204.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n32 13 Cyclist -1 -1 -1 482.34 163.64 539.17 255.52 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n32 22 Pedestrian -1 -1 -1 184.41 161.11 201.10 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n32 20 Pedestrian -1 -1 -1 317.07 160.44 330.48 195.27 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n32 24 Pedestrian -1 -1 -1 381.39 163.55 395.07 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n32 6 Pedestrian -1 -1 -1 298.92 161.08 315.19 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n33 2 Car -1 -1 -1 1095.08 185.27 1220.74 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n33 1 Car -1 -1 -1 954.73 183.84 1066.98 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n33 3 Car -1 -1 -1 1029.88 183.93 1155.97 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n33 14 Cyclist -1 -1 -1 273.48 158.68 318.35 223.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n33 13 Cyclist -1 -1 -1 490.62 162.86 538.44 250.65 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n33 4 Pedestrian -1 -1 -1 601.54 162.89 641.13 267.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n33 8 Pedestrian -1 -1 -1 334.07 162.41 350.44 204.09 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n33 5 Car -1 -1 -1 601.82 173.89 636.60 201.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n33 6 Pedestrian -1 -1 -1 296.45 160.11 312.79 205.31 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n33 22 Pedestrian -1 -1 -1 184.80 161.28 200.82 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n33 24 Pedestrian -1 -1 -1 381.64 163.04 395.17 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n33 20 Pedestrian -1 -1 -1 316.62 158.87 331.13 194.88 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n34 2 Car -1 -1 -1 1094.97 185.35 1220.77 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n34 1 Car -1 -1 -1 954.79 183.88 1066.90 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n34 3 Car -1 -1 -1 1029.61 183.92 1156.12 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n34 4 Pedestrian -1 -1 -1 604.36 162.34 654.90 267.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n34 14 Cyclist -1 -1 -1 279.92 159.05 323.11 222.20 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n34 13 Cyclist -1 -1 -1 494.32 164.14 543.49 248.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n34 5 Car -1 -1 -1 602.32 173.37 638.67 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n34 8 Pedestrian -1 -1 -1 333.76 162.62 350.05 203.96 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n34 22 Pedestrian -1 -1 -1 184.87 161.00 200.77 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n34 24 Pedestrian -1 -1 -1 381.78 162.26 395.64 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n34 20 Pedestrian -1 -1 -1 316.88 158.76 331.13 194.83 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n35 2 Car -1 -1 -1 1095.10 185.34 1220.78 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n35 1 Car -1 -1 -1 954.73 183.85 1066.97 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n35 3 Car -1 -1 -1 1029.66 183.94 1156.07 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n35 14 Cyclist -1 -1 -1 286.30 159.96 330.16 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n35 4 Pedestrian -1 -1 -1 606.34 161.28 661.09 268.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n35 5 Car -1 -1 -1 601.58 173.63 636.76 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n35 13 Cyclist -1 -1 -1 501.34 161.90 544.95 245.53 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n35 8 Pedestrian -1 -1 -1 333.37 162.69 349.68 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n35 22 Pedestrian -1 -1 -1 185.00 161.05 200.81 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n35 24 Pedestrian -1 -1 -1 382.39 162.77 395.61 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n35 20 Pedestrian -1 -1 -1 319.37 159.29 332.59 194.23 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n35 25 Pedestrian -1 -1 -1 190.80 154.49 209.19 196.41 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n35 26 Car -1 -1 -1 598.47 173.94 623.08 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n36 2 Car -1 -1 -1 1095.31 185.39 1220.46 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n36 1 Car -1 -1 -1 954.75 183.83 1066.98 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n36 3 Car -1 -1 -1 1029.73 183.97 1155.98 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n36 4 Pedestrian -1 -1 -1 614.49 163.45 666.31 270.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n36 5 Car -1 -1 -1 601.69 173.29 636.54 202.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n36 14 Cyclist -1 -1 -1 290.12 161.11 335.03 219.63 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n36 13 Cyclist -1 -1 -1 506.54 162.99 547.39 244.36 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n36 8 Pedestrian -1 -1 -1 331.40 162.79 347.72 203.42 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n36 25 Pedestrian -1 -1 -1 189.40 162.01 205.20 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n36 24 Pedestrian -1 -1 -1 382.43 163.10 396.15 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n36 20 Pedestrian -1 -1 -1 318.39 160.15 329.27 196.31 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n36 26 Car -1 -1 -1 598.32 173.88 623.07 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n37 2 Car -1 -1 -1 1095.24 185.40 1220.62 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n37 1 Car -1 -1 -1 954.60 183.81 1067.09 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n37 3 Car -1 -1 -1 1029.72 183.98 1156.01 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n37 4 Pedestrian -1 -1 -1 628.69 163.40 668.71 270.68 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n37 13 Cyclist -1 -1 -1 510.79 163.41 550.03 242.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n37 5 Car -1 -1 -1 601.57 173.17 636.83 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n37 14 Cyclist -1 -1 -1 297.64 159.80 339.89 219.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n37 8 Pedestrian -1 -1 -1 330.86 162.59 347.29 203.49 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n37 25 Pedestrian -1 -1 -1 189.80 162.59 205.11 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n37 24 Pedestrian -1 -1 -1 384.97 163.71 397.73 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n37 20 Pedestrian -1 -1 -1 319.22 159.98 328.65 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n37 26 Car -1 -1 -1 598.32 173.83 622.79 193.50 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n38 2 Car -1 -1 -1 1095.01 185.31 1220.72 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n38 3 Car -1 -1 -1 1029.65 183.98 1156.13 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n38 1 Car -1 -1 -1 954.70 183.81 1066.96 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n38 4 Pedestrian -1 -1 -1 640.39 162.95 678.68 270.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n38 13 Cyclist -1 -1 -1 515.28 163.53 552.01 240.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n38 5 Car -1 -1 -1 601.69 173.26 637.08 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n38 14 Cyclist -1 -1 -1 302.55 161.15 344.65 218.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n38 8 Pedestrian -1 -1 -1 330.66 162.73 347.37 204.01 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n38 25 Pedestrian -1 -1 -1 189.84 162.89 205.21 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n38 24 Pedestrian -1 -1 -1 385.29 163.34 398.66 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n38 20 Pedestrian -1 -1 -1 318.80 159.87 328.88 197.13 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n38 26 Car -1 -1 -1 598.28 173.79 622.79 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n38 27 Pedestrian -1 -1 -1 293.38 160.61 308.76 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n39 2 Car -1 -1 -1 1095.28 185.32 1220.71 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n39 1 Car -1 -1 -1 954.45 183.82 1067.04 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n39 3 Car -1 -1 -1 1029.61 184.01 1156.19 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n39 4 Pedestrian -1 -1 -1 643.49 162.86 691.80 271.45 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n39 13 Cyclist -1 -1 -1 519.98 162.83 552.50 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n39 5 Car -1 -1 -1 601.73 173.28 637.13 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n39 27 Pedestrian -1 -1 -1 292.14 160.40 308.93 203.77 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n39 14 Cyclist -1 -1 -1 309.52 160.61 350.30 219.44 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n39 8 Pedestrian -1 -1 -1 331.93 162.71 345.18 205.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n39 24 Pedestrian -1 -1 -1 386.34 163.36 399.47 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n39 25 Pedestrian -1 -1 -1 189.98 163.02 205.07 197.21 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n39 26 Car -1 -1 -1 598.26 173.85 622.58 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n39 20 Pedestrian -1 -1 -1 318.50 159.96 328.68 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n40 2 Car -1 -1 -1 1095.23 185.34 1220.70 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n40 1 Car -1 -1 -1 954.54 183.86 1066.99 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n40 3 Car -1 -1 -1 1029.76 183.97 1155.99 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n40 4 Pedestrian -1 -1 -1 647.43 163.29 702.05 272.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n40 13 Cyclist -1 -1 -1 522.46 163.13 555.15 235.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n40 14 Cyclist -1 -1 -1 315.47 160.52 352.77 218.79 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n40 5 Car -1 -1 -1 602.69 172.93 637.38 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n40 27 Pedestrian -1 -1 -1 291.53 160.20 308.39 203.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n40 24 Pedestrian -1 -1 -1 386.15 163.28 399.93 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n40 25 Pedestrian -1 -1 -1 189.87 162.88 205.34 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n40 8 Pedestrian -1 -1 -1 332.62 163.09 343.90 204.27 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n40 26 Car -1 -1 -1 598.63 173.68 622.65 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n40 20 Pedestrian -1 -1 -1 318.40 160.63 329.22 196.22 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n41 2 Car -1 -1 -1 1095.27 185.37 1220.62 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n41 3 Car -1 -1 -1 1029.68 183.98 1156.01 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n41 1 Car -1 -1 -1 954.44 183.89 1066.92 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n41 4 Pedestrian -1 -1 -1 660.28 163.18 704.26 272.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n41 13 Cyclist -1 -1 -1 525.11 164.17 556.92 232.25 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n41 5 Car -1 -1 -1 602.53 172.90 637.57 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n41 27 Pedestrian -1 -1 -1 291.40 160.02 308.36 204.58 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n41 14 Cyclist -1 -1 -1 322.43 158.95 353.76 215.89 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n41 25 Pedestrian -1 -1 -1 189.92 162.90 205.22 197.28 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n41 24 Pedestrian -1 -1 -1 386.25 163.05 400.26 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n41 20 Pedestrian -1 -1 -1 319.96 159.06 333.34 194.57 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n41 26 Car -1 -1 -1 598.77 173.73 622.62 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n42 2 Car -1 -1 -1 1095.40 185.32 1220.51 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n42 1 Car -1 -1 -1 954.46 183.87 1067.07 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n42 3 Car -1 -1 -1 1029.68 183.97 1156.01 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n42 4 Pedestrian -1 -1 -1 670.51 162.21 710.97 273.70 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n42 27 Pedestrian -1 -1 -1 291.37 160.00 308.55 205.18 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n42 5 Car -1 -1 -1 602.29 172.98 637.68 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n42 13 Cyclist -1 -1 -1 527.25 162.95 557.76 229.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n42 14 Cyclist -1 -1 -1 325.36 159.84 358.19 214.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n42 24 Pedestrian -1 -1 -1 387.83 163.42 401.85 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n42 25 Pedestrian -1 -1 -1 189.84 162.92 205.49 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n42 20 Pedestrian -1 -1 -1 320.45 159.16 333.62 194.45 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n42 26 Car -1 -1 -1 598.43 173.75 622.71 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n43 2 Car -1 -1 -1 1095.39 185.27 1220.39 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n43 1 Car -1 -1 -1 954.43 183.82 1067.17 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n43 3 Car -1 -1 -1 1029.60 183.96 1156.28 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n43 4 Pedestrian -1 -1 -1 679.78 160.83 723.04 275.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n43 13 Cyclist -1 -1 -1 530.00 164.19 559.64 227.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n43 27 Pedestrian -1 -1 -1 290.96 159.91 308.68 205.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n43 14 Cyclist -1 -1 -1 329.14 160.19 363.11 213.63 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n43 5 Car -1 -1 -1 602.49 172.88 637.46 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n43 24 Pedestrian -1 -1 -1 388.55 163.27 402.01 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n43 25 Pedestrian -1 -1 -1 191.76 161.97 207.85 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n43 26 Car -1 -1 -1 598.68 173.69 622.57 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n43 20 Pedestrian -1 -1 -1 320.80 159.20 333.76 194.60 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n44 2 Car -1 -1 -1 1095.04 185.32 1220.75 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n44 1 Car -1 -1 -1 954.46 183.83 1066.98 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n44 3 Car -1 -1 -1 1029.65 183.95 1156.10 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n44 4 Pedestrian -1 -1 -1 682.73 161.64 736.52 275.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n44 27 Pedestrian -1 -1 -1 290.72 159.60 308.89 204.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n44 13 Cyclist -1 -1 -1 532.75 164.17 559.98 226.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n44 14 Cyclist -1 -1 -1 335.01 160.20 366.30 212.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n44 5 Car -1 -1 -1 601.83 173.09 637.04 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n44 24 Pedestrian -1 -1 -1 388.93 163.22 402.65 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n44 20 Pedestrian -1 -1 -1 327.25 161.12 343.39 205.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n44 25 Pedestrian -1 -1 -1 189.93 162.77 205.59 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n44 26 Car -1 -1 -1 598.89 173.71 622.29 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n44 28 Pedestrian -1 -1 -1 320.81 159.85 333.97 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n45 2 Car -1 -1 -1 1095.24 185.31 1220.69 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n45 1 Car -1 -1 -1 954.61 183.79 1067.08 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n45 3 Car -1 -1 -1 1029.59 183.96 1156.22 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n45 4 Pedestrian -1 -1 -1 684.25 163.10 743.36 277.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n45 13 Cyclist -1 -1 -1 534.47 164.94 561.81 225.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n45 27 Pedestrian -1 -1 -1 290.37 159.81 308.87 205.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n45 5 Car -1 -1 -1 601.71 173.08 636.97 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n45 20 Pedestrian -1 -1 -1 325.96 161.24 343.87 206.20 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n45 24 Pedestrian -1 -1 -1 389.63 163.58 403.12 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n45 14 Cyclist -1 -1 -1 339.89 160.71 369.51 211.78 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n45 25 Pedestrian -1 -1 -1 191.86 162.01 207.75 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n46 2 Car -1 -1 -1 1095.17 185.33 1220.54 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n46 1 Car -1 -1 -1 954.67 183.81 1067.08 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n46 3 Car -1 -1 -1 1029.56 183.98 1156.36 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n46 4 Pedestrian -1 -1 -1 695.25 162.35 747.54 278.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n46 13 Cyclist -1 -1 -1 536.68 164.15 563.54 225.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n46 27 Pedestrian -1 -1 -1 290.58 160.13 308.53 205.19 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n46 5 Car -1 -1 -1 601.70 172.96 637.00 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n46 20 Pedestrian -1 -1 -1 325.56 161.49 343.82 206.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n46 25 Pedestrian -1 -1 -1 189.99 162.97 205.42 197.28 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n46 24 Pedestrian -1 -1 -1 390.61 164.12 403.40 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n46 14 Cyclist -1 -1 -1 346.05 160.33 370.46 211.36 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n47 2 Car -1 -1 -1 1095.16 185.34 1220.81 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n47 1 Car -1 -1 -1 954.69 183.83 1066.93 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n47 3 Car -1 -1 -1 1029.71 184.06 1156.26 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n47 4 Pedestrian -1 -1 -1 711.07 162.22 753.02 278.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n47 13 Cyclist -1 -1 -1 538.25 163.77 565.80 225.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n47 5 Car -1 -1 -1 601.54 172.89 636.99 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n47 27 Pedestrian -1 -1 -1 290.37 160.18 308.52 205.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n47 20 Pedestrian -1 -1 -1 324.82 161.37 342.63 205.89 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n47 14 Cyclist -1 -1 -1 349.63 159.76 372.91 211.32 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n47 25 Pedestrian -1 -1 -1 190.07 162.95 205.52 197.21 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n47 24 Pedestrian -1 -1 -1 392.66 165.00 404.60 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n48 2 Car -1 -1 -1 1095.38 185.39 1220.47 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n48 1 Car -1 -1 -1 954.79 183.87 1066.97 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n48 3 Car -1 -1 -1 1029.87 184.01 1156.05 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n48 4 Pedestrian -1 -1 -1 722.63 161.63 763.96 279.90 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n48 5 Car -1 -1 -1 601.56 172.80 636.96 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n48 27 Pedestrian -1 -1 -1 290.53 160.21 308.42 205.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n48 13 Cyclist -1 -1 -1 541.44 164.07 565.65 219.29 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n48 20 Pedestrian -1 -1 -1 323.19 161.14 340.59 206.54 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n48 24 Pedestrian -1 -1 -1 393.44 164.25 405.95 197.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n48 25 Pedestrian -1 -1 -1 192.03 162.09 207.57 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n48 14 Cyclist -1 -1 -1 353.88 160.15 375.94 211.32 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n48 29 Car -1 -1 -1 598.66 173.36 622.38 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n49 2 Car -1 -1 -1 1095.52 185.37 1220.56 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n49 1 Car -1 -1 -1 954.70 183.85 1067.12 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n49 3 Car -1 -1 -1 1029.64 183.96 1156.29 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n49 4 Pedestrian -1 -1 -1 723.10 163.97 779.75 278.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n49 5 Car -1 -1 -1 601.55 172.84 636.91 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n49 20 Pedestrian -1 -1 -1 322.67 160.64 340.90 206.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n49 27 Pedestrian -1 -1 -1 290.32 159.98 308.20 205.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n49 24 Pedestrian -1 -1 -1 394.05 163.35 406.75 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n49 13 Cyclist -1 -1 -1 542.84 163.98 565.30 217.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n49 25 Pedestrian -1 -1 -1 191.94 161.99 207.66 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n49 14 Cyclist -1 -1 -1 356.76 160.33 379.44 210.76 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n49 29 Car -1 -1 -1 598.56 173.44 622.37 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n50 2 Car -1 -1 -1 1095.58 185.38 1220.52 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n50 1 Car -1 -1 -1 954.76 183.90 1067.05 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n50 3 Car -1 -1 -1 1029.57 183.99 1156.33 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n50 4 Pedestrian -1 -1 -1 728.96 163.02 789.01 281.34 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n50 5 Car -1 -1 -1 601.67 172.85 636.90 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n50 13 Cyclist -1 -1 -1 543.25 163.63 567.29 218.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n50 27 Pedestrian -1 -1 -1 290.09 159.96 308.08 205.22 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n50 20 Pedestrian -1 -1 -1 322.80 160.89 340.88 206.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n50 14 Cyclist -1 -1 -1 360.89 160.47 382.76 208.11 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n50 24 Pedestrian -1 -1 -1 395.23 162.49 407.17 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n50 25 Pedestrian -1 -1 -1 189.99 162.55 205.53 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n50 29 Car -1 -1 -1 598.70 173.45 622.26 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n50 30 Pedestrian -1 -1 -1 312.68 161.84 326.50 194.30 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n51 2 Car -1 -1 -1 1095.50 185.39 1220.48 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n51 1 Car -1 -1 -1 954.78 183.86 1066.85 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n51 3 Car -1 -1 -1 1029.57 184.05 1156.34 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n51 4 Pedestrian -1 -1 -1 742.90 162.01 790.71 282.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n51 5 Car -1 -1 -1 601.64 172.88 636.86 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n51 27 Pedestrian -1 -1 -1 290.12 160.16 308.32 205.65 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n51 24 Pedestrian -1 -1 -1 396.44 163.13 408.66 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n51 13 Cyclist -1 -1 -1 544.50 162.85 566.81 217.09 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n51 20 Pedestrian -1 -1 -1 322.65 161.36 340.89 206.91 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n51 14 Cyclist -1 -1 -1 360.90 160.08 386.25 211.69 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n51 25 Pedestrian -1 -1 -1 191.85 161.75 207.82 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n51 30 Pedestrian -1 -1 -1 313.47 162.07 326.84 194.53 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n51 29 Car -1 -1 -1 598.78 173.45 622.18 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n52 2 Car -1 -1 -1 1095.49 185.53 1220.42 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n52 1 Car -1 -1 -1 954.78 183.82 1066.95 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n52 3 Car -1 -1 -1 1029.72 184.10 1156.19 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n52 4 Pedestrian -1 -1 -1 757.72 160.32 799.32 284.53 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n52 5 Car -1 -1 -1 601.69 172.91 636.78 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n52 24 Pedestrian -1 -1 -1 397.18 163.58 409.30 196.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n52 13 Cyclist -1 -1 -1 545.14 162.64 566.83 214.34 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n52 27 Pedestrian -1 -1 -1 290.24 160.32 307.84 205.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n52 20 Pedestrian -1 -1 -1 322.73 161.32 340.79 207.15 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n52 14 Cyclist -1 -1 -1 363.94 162.12 387.37 205.77 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n52 25 Pedestrian -1 -1 -1 191.76 161.78 207.90 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n52 30 Pedestrian -1 -1 -1 316.16 161.47 329.27 195.96 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n52 29 Car -1 -1 -1 598.79 173.38 622.22 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n53 2 Car -1 -1 -1 1095.42 185.35 1220.57 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n53 1 Car -1 -1 -1 954.84 183.80 1067.14 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n53 3 Car -1 -1 -1 1029.70 184.06 1156.28 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n53 4 Pedestrian -1 -1 -1 763.75 161.78 816.60 286.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n53 13 Cyclist -1 -1 -1 546.38 163.76 567.26 212.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n53 5 Car -1 -1 -1 601.78 172.89 636.72 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n53 14 Cyclist -1 -1 -1 367.13 162.34 387.66 204.58 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n53 27 Pedestrian -1 -1 -1 290.31 160.21 307.89 206.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n53 20 Pedestrian -1 -1 -1 322.16 161.10 341.20 206.94 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n53 24 Pedestrian -1 -1 -1 397.62 163.99 409.94 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n53 25 Pedestrian -1 -1 -1 191.87 161.64 207.82 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n53 30 Pedestrian -1 -1 -1 317.24 160.97 330.82 196.48 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n54 2 Car -1 -1 -1 1095.33 185.46 1220.79 235.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n54 1 Car -1 -1 -1 954.89 183.86 1067.04 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n54 3 Car -1 -1 -1 1032.46 183.84 1157.45 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n54 4 Pedestrian -1 -1 -1 768.96 162.96 833.27 286.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n54 5 Car -1 -1 -1 601.67 172.83 636.82 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n54 13 Cyclist -1 -1 -1 547.21 164.88 566.94 210.43 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n54 20 Pedestrian -1 -1 -1 321.76 161.40 340.58 206.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n54 27 Pedestrian -1 -1 -1 288.39 159.63 306.41 206.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n54 24 Pedestrian -1 -1 -1 398.14 163.53 410.05 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n54 25 Pedestrian -1 -1 -1 190.06 162.28 205.48 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n54 14 Cyclist -1 -1 -1 370.04 161.85 389.00 204.65 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n54 31 Car -1 -1 -1 598.68 173.33 622.27 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n55 2 Car -1 -1 -1 1095.32 185.49 1220.69 235.61 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n55 1 Car -1 -1 -1 955.07 183.94 1066.79 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n55 4 Pedestrian -1 -1 -1 772.45 163.15 838.78 286.78 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n55 3 Car -1 -1 -1 1032.59 183.81 1157.31 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n55 5 Car -1 -1 -1 601.76 172.88 636.78 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n55 27 Pedestrian -1 -1 -1 288.19 159.43 306.63 205.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n55 24 Pedestrian -1 -1 -1 398.22 163.40 410.57 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n55 13 Cyclist -1 -1 -1 547.02 164.20 567.67 210.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n55 20 Pedestrian -1 -1 -1 320.22 161.34 339.68 206.65 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n55 25 Pedestrian -1 -1 -1 189.67 161.91 205.76 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n55 14 Cyclist -1 -1 -1 371.68 161.32 389.77 204.59 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n55 31 Car -1 -1 -1 598.88 173.34 622.28 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n55 32 Pedestrian -1 -1 -1 330.46 160.27 347.37 196.61 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n56 2 Car -1 -1 -1 1095.16 185.44 1220.92 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n56 1 Car -1 -1 -1 955.07 183.91 1066.77 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n56 3 Car -1 -1 -1 1029.99 184.07 1156.01 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n56 4 Pedestrian -1 -1 -1 785.68 162.02 840.99 287.51 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n56 5 Car -1 -1 -1 601.65 172.91 636.76 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n56 27 Pedestrian -1 -1 -1 288.22 159.75 306.48 206.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n56 13 Cyclist -1 -1 -1 547.15 164.34 567.63 209.82 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n56 24 Pedestrian -1 -1 -1 398.75 163.58 411.12 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n56 20 Pedestrian -1 -1 -1 320.02 161.59 339.24 207.25 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n56 25 Pedestrian -1 -1 -1 189.50 162.12 205.67 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n56 32 Pedestrian -1 -1 -1 330.84 161.05 347.77 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n57 2 Car -1 -1 -1 1095.11 185.44 1220.77 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n57 1 Car -1 -1 -1 955.11 183.94 1066.65 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n57 3 Car -1 -1 -1 1029.93 184.14 1156.02 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n57 4 Pedestrian -1 -1 -1 802.64 160.41 846.83 289.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n57 5 Car -1 -1 -1 601.63 172.96 636.68 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n57 27 Pedestrian -1 -1 -1 288.14 160.19 306.51 206.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n57 32 Pedestrian -1 -1 -1 334.15 161.20 350.23 196.75 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n57 20 Pedestrian -1 -1 -1 318.06 161.52 337.50 206.88 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n57 13 Cyclist -1 -1 -1 547.69 164.23 567.61 208.05 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n57 24 Pedestrian -1 -1 -1 399.57 164.38 412.64 196.84 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n57 25 Pedestrian -1 -1 -1 189.24 162.16 205.70 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n58 2 Car -1 -1 -1 1094.99 185.50 1220.78 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n58 1 Car -1 -1 -1 955.02 183.93 1066.58 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n58 3 Car -1 -1 -1 1029.95 184.13 1155.94 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n58 4 Pedestrian -1 -1 -1 811.55 161.13 860.62 289.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n58 5 Car -1 -1 -1 601.67 172.90 636.72 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n58 27 Pedestrian -1 -1 -1 287.98 160.29 306.09 206.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n58 24 Pedestrian -1 -1 -1 400.12 164.30 412.79 196.73 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n58 20 Pedestrian -1 -1 -1 318.10 162.50 336.70 208.41 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n58 32 Pedestrian -1 -1 -1 337.77 161.86 352.56 196.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n58 13 Cyclist -1 -1 -1 548.38 163.25 566.96 206.01 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n58 25 Pedestrian -1 -1 -1 189.20 162.01 205.76 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n59 2 Car -1 -1 -1 1094.80 185.33 1220.75 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n59 1 Car -1 -1 -1 954.87 183.86 1066.80 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n59 3 Car -1 -1 -1 1029.85 184.15 1156.10 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n59 4 Pedestrian -1 -1 -1 813.17 161.90 874.77 290.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n59 5 Car -1 -1 -1 601.71 172.95 636.72 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n59 24 Pedestrian -1 -1 -1 400.97 164.01 413.82 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n59 27 Pedestrian -1 -1 -1 287.57 160.36 305.33 206.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n59 32 Pedestrian -1 -1 -1 339.85 161.69 354.01 196.59 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n59 20 Pedestrian -1 -1 -1 317.30 161.37 336.66 207.28 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n59 13 Cyclist -1 -1 -1 547.80 163.76 567.63 209.30 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n59 25 Pedestrian -1 -1 -1 189.14 161.88 205.84 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n59 33 Pedestrian -1 -1 -1 377.76 161.78 392.71 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n59 34 Car -1 -1 -1 598.63 173.45 622.14 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n60 2 Car -1 -1 -1 1094.83 185.31 1220.74 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n60 1 Car -1 -1 -1 954.62 183.79 1067.15 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n60 4 Pedestrian -1 -1 -1 818.94 162.90 883.91 295.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n60 3 Car -1 -1 -1 1029.97 184.10 1155.97 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n60 5 Car -1 -1 -1 601.54 172.92 636.82 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n60 24 Pedestrian -1 -1 -1 401.77 163.96 414.40 196.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n60 27 Pedestrian -1 -1 -1 287.62 160.32 305.34 207.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n60 20 Pedestrian -1 -1 -1 316.76 161.20 336.41 208.02 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n60 32 Pedestrian -1 -1 -1 340.45 162.09 354.07 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n60 25 Pedestrian -1 -1 -1 189.09 161.86 205.94 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n60 13 Cyclist -1 -1 -1 547.81 164.25 567.32 209.47 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n60 33 Pedestrian -1 -1 -1 379.82 162.03 394.75 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n60 34 Car -1 -1 -1 598.71 173.41 622.12 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n61 2 Car -1 -1 -1 1095.04 185.36 1220.52 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n61 1 Car -1 -1 -1 954.79 183.78 1066.93 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n61 3 Car -1 -1 -1 1030.06 184.05 1155.79 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n61 4 Pedestrian -1 -1 -1 837.87 162.32 886.85 296.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n61 5 Car -1 -1 -1 601.51 172.84 636.71 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n61 27 Pedestrian -1 -1 -1 287.36 160.47 305.48 207.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n61 24 Pedestrian -1 -1 -1 402.57 163.56 414.86 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n61 20 Pedestrian -1 -1 -1 317.01 161.59 335.61 209.28 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n61 32 Pedestrian -1 -1 -1 341.85 161.95 355.94 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n61 25 Pedestrian -1 -1 -1 189.07 161.81 205.99 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n61 13 Cyclist -1 -1 -1 549.19 164.27 565.70 204.20 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n61 33 Pedestrian -1 -1 -1 380.47 162.00 395.68 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n61 34 Car -1 -1 -1 598.63 173.35 622.10 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n62 2 Car -1 -1 -1 1094.78 185.34 1220.55 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n62 1 Car -1 -1 -1 954.81 183.75 1067.09 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n62 3 Car -1 -1 -1 1030.02 184.07 1155.85 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n62 4 Pedestrian -1 -1 -1 849.95 160.92 897.15 298.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n62 5 Car -1 -1 -1 601.55 172.92 636.83 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n62 27 Pedestrian -1 -1 -1 286.80 160.52 304.97 207.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n62 20 Pedestrian -1 -1 -1 313.71 161.93 333.27 209.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n62 24 Pedestrian -1 -1 -1 403.79 163.77 416.11 196.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n62 32 Pedestrian -1 -1 -1 342.42 162.18 357.01 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n62 13 Cyclist -1 -1 -1 549.23 164.83 564.59 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n62 25 Pedestrian -1 -1 -1 189.17 161.79 205.99 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n62 33 Pedestrian -1 -1 -1 381.18 162.03 395.98 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n62 35 Pedestrian -1 -1 -1 327.24 160.76 341.74 195.76 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n63 2 Car -1 -1 -1 1094.51 185.33 1221.22 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n63 1 Car -1 -1 -1 954.64 183.74 1067.29 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n63 3 Car -1 -1 -1 1029.90 184.03 1155.95 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n63 4 Pedestrian -1 -1 -1 857.10 160.14 920.83 299.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n63 5 Car -1 -1 -1 601.68 172.77 636.84 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n63 24 Pedestrian -1 -1 -1 404.41 164.03 417.08 195.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n63 20 Pedestrian -1 -1 -1 313.33 162.34 332.15 210.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n63 32 Pedestrian -1 -1 -1 343.97 162.34 357.46 197.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n63 27 Pedestrian -1 -1 -1 284.53 159.84 302.71 208.51 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n63 35 Pedestrian -1 -1 -1 328.40 161.24 342.06 195.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n63 25 Pedestrian -1 -1 -1 188.91 161.55 206.24 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n63 33 Pedestrian -1 -1 -1 381.66 162.31 396.30 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n63 13 Cyclist -1 -1 -1 548.23 164.98 563.73 203.21 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n64 2 Car -1 -1 -1 1094.62 185.37 1220.83 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n64 1 Car -1 -1 -1 954.66 183.79 1067.18 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n64 3 Car -1 -1 -1 1030.30 184.00 1155.44 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n64 4 Pedestrian -1 -1 -1 862.51 160.11 931.92 300.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n64 5 Car -1 -1 -1 601.72 172.88 636.73 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n64 24 Pedestrian -1 -1 -1 404.88 164.02 417.38 196.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n64 20 Pedestrian -1 -1 -1 313.62 161.65 332.23 210.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n64 27 Pedestrian -1 -1 -1 284.00 159.99 303.06 208.51 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n64 32 Pedestrian -1 -1 -1 346.18 162.68 359.23 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n64 35 Pedestrian -1 -1 -1 331.18 160.64 344.52 196.06 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n64 25 Pedestrian -1 -1 -1 188.88 161.43 206.28 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n64 33 Pedestrian -1 -1 -1 381.83 162.29 396.43 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n64 13 Cyclist -1 -1 -1 547.07 166.04 563.67 208.30 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n64 36 Car -1 -1 -1 598.78 173.38 621.93 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n65 2 Car -1 -1 -1 1094.36 185.33 1221.27 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n65 1 Car -1 -1 -1 954.51 183.73 1067.34 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n65 3 Car -1 -1 -1 1030.24 184.00 1155.53 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n65 4 Pedestrian -1 -1 -1 868.87 160.76 941.31 304.57 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n65 5 Car -1 -1 -1 601.77 172.79 636.80 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 24 Pedestrian -1 -1 -1 404.82 163.80 417.10 195.39 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 27 Pedestrian -1 -1 -1 283.43 159.41 302.68 208.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n65 20 Pedestrian -1 -1 -1 313.22 161.73 331.98 210.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n65 32 Pedestrian -1 -1 -1 347.06 162.04 360.14 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n65 35 Pedestrian -1 -1 -1 331.67 160.74 345.10 196.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n65 33 Pedestrian -1 -1 -1 381.90 162.20 396.52 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n65 25 Pedestrian -1 -1 -1 188.60 161.27 206.59 198.54 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n65 13 Cyclist -1 -1 -1 544.38 166.57 562.28 207.48 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n66 2 Car -1 -1 -1 1094.30 185.41 1221.28 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n66 1 Car -1 -1 -1 954.46 183.63 1067.45 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n66 3 Car -1 -1 -1 1030.33 184.02 1155.52 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n66 4 Pedestrian -1 -1 -1 881.40 159.98 943.99 305.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n66 5 Car -1 -1 -1 601.54 172.80 636.94 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n66 24 Pedestrian -1 -1 -1 406.10 163.14 417.87 194.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n66 27 Pedestrian -1 -1 -1 282.95 159.33 302.54 208.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n66 20 Pedestrian -1 -1 -1 312.65 161.42 331.98 210.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n66 32 Pedestrian -1 -1 -1 347.70 161.78 361.87 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n66 35 Pedestrian -1 -1 -1 331.35 161.18 345.59 196.16 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n66 33 Pedestrian -1 -1 -1 383.74 162.44 398.44 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n66 13 Cyclist -1 -1 -1 543.63 167.20 559.97 206.83 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n66 25 Pedestrian -1 -1 -1 191.46 160.70 208.49 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n67 2 Car -1 -1 -1 1094.63 185.41 1220.75 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n67 1 Car -1 -1 -1 954.31 183.69 1067.50 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n67 3 Car -1 -1 -1 1030.34 184.02 1155.57 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n67 4 Pedestrian -1 -1 -1 905.54 160.61 956.89 310.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n67 5 Car -1 -1 -1 601.79 172.87 636.83 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n67 27 Pedestrian -1 -1 -1 282.52 159.33 302.45 208.59 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n67 20 Pedestrian -1 -1 -1 312.46 161.52 331.63 210.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n67 24 Pedestrian -1 -1 -1 406.14 163.40 418.33 194.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n67 35 Pedestrian -1 -1 -1 332.04 161.31 346.82 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n67 32 Pedestrian -1 -1 -1 347.47 162.40 362.53 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n67 25 Pedestrian -1 -1 -1 191.53 160.74 208.46 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n67 33 Pedestrian -1 -1 -1 384.37 162.81 398.65 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n67 13 Cyclist -1 -1 -1 541.19 166.61 557.01 206.32 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n68 2 Car -1 -1 -1 1094.46 185.41 1221.10 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n68 3 Car -1 -1 -1 1030.28 184.14 1155.73 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n68 4 Pedestrian -1 -1 -1 911.31 161.23 981.59 311.24 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n68 1 Car -1 -1 -1 954.33 183.75 1067.60 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n68 5 Car -1 -1 -1 601.86 172.91 636.93 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n68 20 Pedestrian -1 -1 -1 312.42 161.88 331.60 211.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n68 27 Pedestrian -1 -1 -1 282.23 159.42 302.14 208.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n68 24 Pedestrian -1 -1 -1 407.68 163.59 419.88 194.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n68 32 Pedestrian -1 -1 -1 349.32 162.24 364.86 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n68 35 Pedestrian -1 -1 -1 334.11 161.49 348.55 197.21 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n68 25 Pedestrian -1 -1 -1 191.63 160.62 208.48 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n68 33 Pedestrian -1 -1 -1 384.51 162.78 398.83 198.58 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n68 13 Cyclist -1 -1 -1 539.82 166.76 555.17 205.41 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n69 2 Car -1 -1 -1 1094.51 185.45 1221.22 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n69 3 Car -1 -1 -1 1029.91 184.11 1155.98 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n69 4 Pedestrian -1 -1 -1 913.05 161.17 996.18 314.08 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n69 1 Car -1 -1 -1 954.61 184.11 1066.91 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n69 20 Pedestrian -1 -1 -1 310.67 162.05 330.00 211.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n69 24 Pedestrian -1 -1 -1 408.45 163.73 420.65 194.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n69 5 Car -1 -1 -1 601.93 172.92 636.85 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n69 27 Pedestrian -1 -1 -1 282.16 159.17 301.66 209.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n69 35 Pedestrian -1 -1 -1 334.03 161.37 349.56 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n69 33 Pedestrian -1 -1 -1 384.71 162.79 398.81 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n69 25 Pedestrian -1 -1 -1 191.75 160.64 208.56 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n69 13 Cyclist -1 -1 -1 535.45 167.72 552.38 205.91 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n69 32 Pedestrian -1 -1 -1 350.98 162.19 365.98 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n70 2 Car -1 -1 -1 1094.45 185.45 1221.44 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n70 4 Pedestrian -1 -1 -1 926.10 159.99 1005.48 315.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n70 3 Car -1 -1 -1 1029.92 184.10 1155.93 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n70 1 Car -1 -1 -1 951.13 182.99 1066.31 232.00 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n70 24 Pedestrian -1 -1 -1 409.18 163.54 421.12 193.94 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n70 20 Pedestrian -1 -1 -1 310.17 161.66 330.05 211.64 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n70 5 Car -1 -1 -1 601.87 172.93 636.86 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n70 32 Pedestrian -1 -1 -1 353.70 161.78 368.80 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n70 27 Pedestrian -1 -1 -1 281.56 158.97 301.70 209.21 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n70 35 Pedestrian -1 -1 -1 335.27 161.85 350.58 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n70 13 Cyclist -1 -1 -1 531.60 168.09 550.74 205.56 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n70 25 Pedestrian -1 -1 -1 191.56 160.49 208.89 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n70 33 Pedestrian -1 -1 -1 384.81 162.76 399.14 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n71 2 Car -1 -1 -1 1094.71 185.55 1221.39 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n71 3 Car -1 -1 -1 1030.21 184.09 1155.37 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n71 1 Car -1 -1 -1 953.19 182.97 1068.41 231.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n71 4 Pedestrian -1 -1 -1 950.91 160.93 1011.25 317.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n71 5 Car -1 -1 -1 601.80 172.90 636.84 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n71 20 Pedestrian -1 -1 -1 310.26 161.16 329.68 211.50 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n71 24 Pedestrian -1 -1 -1 410.06 163.46 421.78 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n71 27 Pedestrian -1 -1 -1 279.54 159.02 299.53 209.25 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n71 32 Pedestrian -1 -1 -1 355.33 161.76 369.61 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n71 35 Pedestrian -1 -1 -1 337.47 162.18 352.93 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n71 13 Cyclist -1 -1 -1 526.97 168.10 547.81 204.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n71 25 Pedestrian -1 -1 -1 191.61 160.30 208.81 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n71 33 Pedestrian -1 -1 -1 384.97 162.49 399.18 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n72 2 Car -1 -1 -1 1098.68 185.42 1220.99 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n72 3 Car -1 -1 -1 1029.96 184.15 1155.56 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n72 1 Car -1 -1 -1 953.06 182.99 1069.08 231.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n72 4 Pedestrian -1 -1 -1 965.77 163.00 1027.14 318.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n72 5 Car -1 -1 -1 601.62 172.83 636.96 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n72 20 Pedestrian -1 -1 -1 310.35 161.17 329.92 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n72 35 Pedestrian -1 -1 -1 338.81 162.26 354.29 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n72 27 Pedestrian -1 -1 -1 280.09 158.85 299.52 209.69 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n72 13 Cyclist -1 -1 -1 522.71 168.41 545.01 204.14 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n72 32 Pedestrian -1 -1 -1 355.55 161.99 369.56 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n72 33 Pedestrian -1 -1 -1 384.95 162.34 399.18 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n72 24 Pedestrian -1 -1 -1 411.72 163.57 422.99 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n72 25 Pedestrian -1 -1 -1 191.28 160.11 209.14 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n73 2 Car -1 -1 -1 1095.01 185.48 1221.17 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n73 3 Car -1 -1 -1 1029.88 184.18 1155.79 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n73 1 Car -1 -1 -1 954.22 183.23 1067.54 231.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n73 4 Pedestrian -1 -1 -1 977.52 162.82 1061.31 319.15 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n73 20 Pedestrian -1 -1 -1 310.28 161.20 329.56 212.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n73 5 Car -1 -1 -1 601.72 173.06 636.89 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n73 24 Pedestrian -1 -1 -1 411.78 163.89 423.20 193.75 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n73 27 Pedestrian -1 -1 -1 280.00 158.88 299.44 210.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n73 35 Pedestrian -1 -1 -1 339.89 162.26 354.58 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n73 13 Cyclist -1 -1 -1 518.46 168.12 539.36 204.02 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n73 33 Pedestrian -1 -1 -1 385.17 162.43 399.44 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n73 32 Pedestrian -1 -1 -1 357.20 161.54 371.15 199.17 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n73 25 Pedestrian -1 -1 -1 191.12 159.89 209.14 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n74 2 Car -1 -1 -1 1098.44 185.46 1221.22 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n74 4 Pedestrian -1 -1 -1 982.69 163.31 1078.81 324.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n74 3 Car -1 -1 -1 1029.12 184.20 1156.35 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n74 1 Car -1 -1 -1 954.44 183.74 1067.55 231.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n74 20 Pedestrian -1 -1 -1 309.83 161.49 329.73 212.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n74 5 Car -1 -1 -1 601.62 172.97 636.89 203.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n74 24 Pedestrian -1 -1 -1 411.94 163.72 423.36 193.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n74 35 Pedestrian -1 -1 -1 340.06 161.99 354.46 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n74 13 Cyclist -1 -1 -1 514.90 167.94 536.44 203.72 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n74 27 Pedestrian -1 -1 -1 279.38 159.67 299.96 211.17 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n74 32 Pedestrian -1 -1 -1 358.23 161.52 371.84 199.10 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n74 33 Pedestrian -1 -1 -1 385.25 162.31 399.75 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n74 25 Pedestrian -1 -1 -1 190.99 159.78 209.22 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n75 2 Car -1 -1 -1 1094.60 185.31 1221.53 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n75 3 Car -1 -1 -1 1033.33 184.34 1157.28 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n75 4 Pedestrian -1 -1 -1 999.92 162.75 1084.92 327.12 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n75 1 Car -1 -1 -1 955.00 183.84 1066.43 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n75 20 Pedestrian -1 -1 -1 310.15 161.28 329.63 212.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n75 24 Pedestrian -1 -1 -1 412.33 163.71 423.72 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n75 5 Car -1 -1 -1 601.71 172.90 636.83 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n75 35 Pedestrian -1 -1 -1 342.19 162.14 356.95 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n75 27 Pedestrian -1 -1 -1 279.23 159.69 299.80 211.15 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n75 13 Cyclist -1 -1 -1 510.58 168.03 532.29 203.53 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n75 33 Pedestrian -1 -1 -1 385.45 162.37 399.91 196.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n75 25 Pedestrian -1 -1 -1 190.92 159.60 209.33 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n75 32 Pedestrian -1 -1 -1 359.45 162.09 373.21 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n76 4 Pedestrian -1 -1 -1 1021.72 159.65 1093.50 329.73 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n76 2 Car -1 -1 -1 1094.66 184.95 1221.08 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n76 3 Car -1 -1 -1 1034.57 184.41 1156.79 233.70 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n76 1 Car -1 -1 -1 954.24 183.62 1063.45 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n76 20 Pedestrian -1 -1 -1 310.07 160.75 329.83 212.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n76 5 Car -1 -1 -1 601.56 173.00 636.80 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n76 24 Pedestrian -1 -1 -1 412.96 163.56 424.14 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n76 27 Pedestrian -1 -1 -1 279.68 158.73 299.31 210.54 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n76 33 Pedestrian -1 -1 -1 385.97 162.24 400.16 196.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n76 35 Pedestrian -1 -1 -1 343.74 161.82 358.33 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n76 13 Cyclist -1 -1 -1 506.31 168.84 528.80 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n76 32 Pedestrian -1 -1 -1 361.95 162.49 376.08 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n76 25 Pedestrian -1 -1 -1 190.76 159.33 209.52 199.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n77 2 Car -1 -1 -1 1094.66 184.98 1220.47 235.53 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n77 1 Car -1 -1 -1 955.02 183.46 1066.62 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n77 3 Car -1 -1 -1 1034.95 184.43 1156.11 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n77 4 Pedestrian -1 -1 -1 1050.51 163.19 1117.86 332.64 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n77 20 Pedestrian -1 -1 -1 309.50 160.42 330.09 212.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n77 5 Car -1 -1 -1 601.60 172.89 636.97 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n77 13 Cyclist -1 -1 -1 499.79 166.56 521.51 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n77 35 Pedestrian -1 -1 -1 346.44 161.97 360.16 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n77 24 Pedestrian -1 -1 -1 413.37 163.60 424.66 192.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n77 27 Pedestrian -1 -1 -1 278.71 159.49 299.81 211.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n77 33 Pedestrian -1 -1 -1 385.34 162.22 400.34 195.89 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n77 32 Pedestrian -1 -1 -1 362.02 162.97 377.62 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n77 25 Pedestrian -1 -1 -1 190.73 159.16 209.61 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n77 37 Pedestrian -1 -1 -1 1121.70 144.03 1223.03 367.08 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n77 38 Car -1 -1 -1 598.74 173.50 622.36 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n78 1 Car -1 -1 -1 955.23 183.63 1066.42 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n78 2 Car -1 -1 -1 1094.49 185.36 1220.23 234.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n78 3 Car -1 -1 -1 1034.31 184.39 1156.45 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n78 20 Pedestrian -1 -1 -1 309.48 160.53 330.40 212.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n78 27 Pedestrian -1 -1 -1 278.51 160.04 299.96 212.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n78 5 Car -1 -1 -1 601.70 172.97 637.03 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n78 4 Pedestrian -1 -1 -1 1056.52 164.14 1150.31 332.57 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n78 13 Cyclist -1 -1 -1 495.17 166.78 517.27 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n78 24 Pedestrian -1 -1 -1 413.56 163.52 425.05 192.74 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n78 35 Pedestrian -1 -1 -1 347.82 161.68 361.31 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n78 33 Pedestrian -1 -1 -1 385.30 162.23 400.67 195.81 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n78 32 Pedestrian -1 -1 -1 361.94 163.00 378.11 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n78 25 Pedestrian -1 -1 -1 190.78 159.16 209.63 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n78 37 Pedestrian -1 -1 -1 1108.21 152.62 1220.98 365.92 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n78 38 Car -1 -1 -1 598.62 173.63 622.21 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n79 2 Car -1 -1 -1 1092.93 185.26 1221.94 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n79 1 Car -1 -1 -1 955.34 183.65 1066.35 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n79 3 Car -1 -1 -1 1031.78 184.22 1153.49 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n79 20 Pedestrian -1 -1 -1 309.87 160.97 330.39 213.19 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n79 13 Cyclist -1 -1 -1 489.25 166.58 511.14 201.86 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n79 27 Pedestrian -1 -1 -1 278.62 160.10 299.37 212.15 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n79 5 Car -1 -1 -1 601.73 172.98 637.15 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n79 35 Pedestrian -1 -1 -1 349.05 161.38 364.30 199.44 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n79 24 Pedestrian -1 -1 -1 414.21 163.44 425.47 192.41 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n79 4 Pedestrian -1 -1 -1 1059.40 160.44 1186.17 343.72 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n79 32 Pedestrian -1 -1 -1 365.37 162.84 380.57 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n79 33 Pedestrian -1 -1 -1 385.51 162.36 400.60 195.52 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n79 37 Pedestrian -1 -1 -1 1077.92 158.17 1221.04 361.45 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n79 25 Pedestrian -1 -1 -1 190.94 159.22 209.56 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n79 38 Car -1 -1 -1 598.67 173.71 622.38 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n80 2 Car -1 -1 -1 1093.44 185.18 1221.59 235.20 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n80 1 Car -1 -1 -1 955.34 183.71 1066.23 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n80 3 Car -1 -1 -1 1031.85 184.13 1152.78 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n80 20 Pedestrian -1 -1 -1 309.57 161.57 330.57 213.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n80 13 Cyclist -1 -1 -1 484.70 166.85 506.24 202.09 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n80 27 Pedestrian -1 -1 -1 278.02 160.56 298.71 212.43 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n80 5 Car -1 -1 -1 601.89 173.10 637.04 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n80 37 Pedestrian -1 -1 -1 1064.20 154.92 1226.84 364.82 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n80 35 Pedestrian -1 -1 -1 350.00 161.46 366.41 199.61 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n80 33 Pedestrian -1 -1 -1 385.59 162.23 400.43 194.94 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n80 25 Pedestrian -1 -1 -1 191.08 159.31 209.61 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n80 24 Pedestrian -1 -1 -1 415.60 163.81 426.73 192.18 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n80 32 Pedestrian -1 -1 -1 366.51 162.86 381.16 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n80 38 Car -1 -1 -1 598.73 173.68 622.29 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n81 2 Car -1 -1 -1 1094.17 185.26 1220.93 235.13 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n81 1 Car -1 -1 -1 955.45 183.73 1066.13 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n81 3 Car -1 -1 -1 1031.55 184.28 1153.05 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n81 20 Pedestrian -1 -1 -1 309.37 161.64 330.69 213.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n81 13 Cyclist -1 -1 -1 477.16 167.70 500.79 200.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n81 5 Car -1 -1 -1 601.90 173.10 636.98 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n81 27 Pedestrian -1 -1 -1 277.83 160.68 298.27 212.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n81 37 Pedestrian -1 -1 -1 1051.13 159.72 1232.46 365.27 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n81 24 Pedestrian -1 -1 -1 415.85 163.74 427.13 192.02 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n81 35 Pedestrian -1 -1 -1 349.54 161.33 367.13 200.05 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n81 32 Pedestrian -1 -1 -1 370.87 163.11 384.24 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n81 33 Pedestrian -1 -1 -1 385.27 162.13 400.12 195.18 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n81 25 Pedestrian -1 -1 -1 191.18 159.52 209.28 199.29 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n81 38 Car -1 -1 -1 598.66 173.65 622.20 193.23 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n82 2 Car -1 -1 -1 1094.96 185.41 1220.27 235.54 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n82 1 Car -1 -1 -1 955.57 183.69 1066.03 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n82 3 Car -1 -1 -1 1031.93 184.23 1153.08 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n82 20 Pedestrian -1 -1 -1 309.07 161.33 330.30 213.76 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n82 13 Cyclist -1 -1 -1 473.30 166.69 495.48 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n82 27 Pedestrian -1 -1 -1 275.17 159.75 295.87 212.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n82 5 Car -1 -1 -1 601.81 173.15 637.04 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n82 37 Pedestrian -1 -1 -1 1046.49 158.84 1229.44 366.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n82 32 Pedestrian -1 -1 -1 373.68 163.25 387.21 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n82 33 Pedestrian -1 -1 -1 385.22 161.93 399.82 195.46 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n82 24 Pedestrian -1 -1 -1 416.03 163.51 427.73 192.28 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n82 25 Pedestrian -1 -1 -1 191.27 159.87 209.08 199.21 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n82 35 Pedestrian -1 -1 -1 352.48 161.39 368.92 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n82 38 Car -1 -1 -1 598.41 173.73 622.06 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n82 39 Pedestrian -1 -1 -1 1109.70 156.16 1219.61 362.91 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n83 1 Car -1 -1 -1 955.88 183.70 1065.80 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n83 2 Car -1 -1 -1 1095.51 185.49 1218.77 235.20 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n83 20 Pedestrian -1 -1 -1 309.02 160.86 330.04 213.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n83 3 Car -1 -1 -1 1031.66 184.04 1153.48 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n83 13 Cyclist -1 -1 -1 468.04 166.85 490.39 200.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n83 5 Car -1 -1 -1 601.85 173.00 636.91 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n83 27 Pedestrian -1 -1 -1 275.50 159.35 296.06 213.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n83 37 Pedestrian -1 -1 -1 1035.02 153.44 1217.95 366.33 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n83 33 Pedestrian -1 -1 -1 385.27 161.42 399.62 195.46 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n83 32 Pedestrian -1 -1 -1 374.59 163.84 388.20 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n83 25 Pedestrian -1 -1 -1 191.10 160.04 209.02 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n83 35 Pedestrian -1 -1 -1 353.58 160.66 369.61 200.58 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n83 24 Pedestrian -1 -1 -1 416.43 163.20 428.13 192.42 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n83 38 Car -1 -1 -1 598.32 173.60 621.98 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n83 39 Pedestrian -1 -1 -1 1166.30 164.65 1216.39 331.84 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n84 1 Car -1 -1 -1 956.00 183.65 1065.65 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n84 3 Car -1 -1 -1 1033.90 184.00 1150.74 230.83 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n84 2 Car -1 -1 -1 1096.22 185.07 1217.78 235.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n84 20 Pedestrian -1 -1 -1 309.03 160.63 329.96 214.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n84 5 Car -1 -1 -1 601.95 173.11 636.93 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n84 37 Pedestrian -1 -1 -1 1024.92 153.48 1213.07 366.43 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n84 27 Pedestrian -1 -1 -1 276.17 159.82 295.66 214.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n84 13 Cyclist -1 -1 -1 462.61 166.37 484.19 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n84 35 Pedestrian -1 -1 -1 353.39 160.44 371.21 200.36 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n84 33 Pedestrian -1 -1 -1 385.40 161.03 399.27 195.21 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n84 25 Pedestrian -1 -1 -1 190.70 160.07 209.29 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n84 38 Car -1 -1 -1 598.39 173.53 622.29 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n84 32 Pedestrian -1 -1 -1 374.93 163.71 388.98 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n84 24 Pedestrian -1 -1 -1 416.82 162.98 428.65 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n85 1 Car -1 -1 -1 955.87 183.67 1065.81 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n85 3 Car -1 -1 -1 1032.98 183.69 1151.73 231.25 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n85 20 Pedestrian -1 -1 -1 309.31 161.03 330.03 214.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n85 2 Car -1 -1 -1 1096.49 185.12 1217.44 235.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n85 5 Car -1 -1 -1 601.68 173.03 637.02 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n85 27 Pedestrian -1 -1 -1 276.23 160.03 295.55 214.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n85 37 Pedestrian -1 -1 -1 1015.66 152.72 1199.14 367.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n85 32 Pedestrian -1 -1 -1 376.75 162.20 392.55 199.14 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n85 25 Pedestrian -1 -1 -1 190.99 160.33 208.94 199.10 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n85 35 Pedestrian -1 -1 -1 355.72 160.64 373.12 200.29 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n85 38 Car -1 -1 -1 598.10 173.51 622.29 193.29 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n85 13 Cyclist -1 -1 -1 460.31 166.09 478.07 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n85 33 Pedestrian -1 -1 -1 385.45 161.12 399.35 194.60 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n85 24 Pedestrian -1 -1 -1 417.29 161.83 429.03 191.67 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n85 40 Pedestrian -1 -1 -1 1113.88 161.74 1215.38 357.76 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n86 1 Car -1 -1 -1 955.64 183.68 1066.34 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n86 3 Car -1 -1 -1 1033.78 183.71 1151.30 231.16 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n86 2 Car -1 -1 -1 1095.63 185.33 1218.24 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n86 20 Pedestrian -1 -1 -1 309.75 161.38 330.22 214.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n86 27 Pedestrian -1 -1 -1 276.04 160.48 295.49 214.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n86 5 Car -1 -1 -1 601.85 172.99 636.96 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n86 37 Pedestrian -1 -1 -1 997.99 152.23 1186.22 368.08 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n86 25 Pedestrian -1 -1 -1 188.39 160.90 206.77 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n86 38 Car -1 -1 -1 598.46 173.47 622.18 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n86 35 Pedestrian -1 -1 -1 358.39 160.93 373.16 200.21 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n86 33 Pedestrian -1 -1 -1 384.85 161.50 399.77 194.62 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n86 40 Pedestrian -1 -1 -1 1105.92 162.70 1207.95 349.39 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n87 1 Car -1 -1 -1 955.70 183.79 1066.03 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n87 3 Car -1 -1 -1 1034.80 183.80 1150.44 231.01 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n87 2 Car -1 -1 -1 1095.65 185.51 1218.20 235.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n87 27 Pedestrian -1 -1 -1 274.94 159.86 295.23 214.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n87 5 Car -1 -1 -1 601.77 172.94 636.96 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n87 20 Pedestrian -1 -1 -1 310.06 161.62 330.59 214.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n87 37 Pedestrian -1 -1 -1 996.80 158.55 1179.42 367.16 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n87 35 Pedestrian -1 -1 -1 361.24 163.59 375.64 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n87 40 Pedestrian -1 -1 -1 1115.15 171.15 1183.50 324.64 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n87 25 Pedestrian -1 -1 -1 188.19 161.14 206.64 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n87 38 Car -1 -1 -1 598.25 173.48 622.27 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n87 33 Pedestrian -1 -1 -1 384.57 161.27 398.89 195.46 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n88 3 Car -1 -1 -1 1034.70 183.85 1150.47 230.98 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n88 1 Car -1 -1 -1 955.80 183.81 1065.41 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n88 27 Pedestrian -1 -1 -1 274.03 159.75 295.31 214.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n88 20 Pedestrian -1 -1 -1 310.04 160.85 330.88 214.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n88 2 Car -1 -1 -1 1095.45 185.55 1218.03 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n88 5 Car -1 -1 -1 601.89 173.05 636.84 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n88 37 Pedestrian -1 -1 -1 993.92 154.63 1151.94 365.82 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n88 33 Pedestrian -1 -1 -1 383.68 162.01 399.29 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n88 35 Pedestrian -1 -1 -1 362.24 164.09 376.95 199.35 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n88 40 Pedestrian -1 -1 -1 1101.48 171.75 1174.43 323.74 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n88 38 Car -1 -1 -1 598.23 173.53 622.20 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n88 25 Pedestrian -1 -1 -1 188.09 161.20 206.66 198.59 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n89 3 Car -1 -1 -1 1033.33 184.47 1151.46 232.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n89 1 Car -1 -1 -1 955.36 183.93 1062.65 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n89 2 Car -1 -1 -1 1087.32 184.56 1219.44 236.61 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n89 27 Pedestrian -1 -1 -1 273.78 160.41 295.22 214.57 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n89 5 Car -1 -1 -1 601.90 173.16 636.86 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n89 33 Pedestrian -1 -1 -1 383.95 164.32 401.50 200.40 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n89 20 Pedestrian -1 -1 -1 310.32 161.17 330.91 215.14 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n89 37 Pedestrian -1 -1 -1 963.62 153.83 1144.03 365.73 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n89 35 Pedestrian -1 -1 -1 364.69 163.94 379.23 199.74 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n89 40 Pedestrian -1 -1 -1 1070.43 169.94 1174.92 325.45 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n89 25 Pedestrian -1 -1 -1 188.19 161.19 206.37 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n89 38 Car -1 -1 -1 598.37 173.53 622.37 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n90 2 Car -1 -1 -1 1092.86 184.74 1220.75 236.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n90 3 Car -1 -1 -1 1032.71 184.46 1152.30 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n90 1 Car -1 -1 -1 955.60 183.82 1062.32 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n90 5 Car -1 -1 -1 601.81 173.21 636.77 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n90 33 Pedestrian -1 -1 -1 386.77 165.00 403.20 201.02 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n90 27 Pedestrian -1 -1 -1 273.50 160.42 294.44 214.86 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n90 20 Pedestrian -1 -1 -1 311.91 162.27 332.19 217.03 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n90 37 Pedestrian -1 -1 -1 951.12 152.59 1133.60 366.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n90 35 Pedestrian -1 -1 -1 365.09 163.86 380.39 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n90 40 Pedestrian -1 -1 -1 1041.93 169.12 1149.92 327.47 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n90 38 Car -1 -1 -1 598.28 173.61 622.50 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n90 25 Pedestrian -1 -1 -1 188.46 161.37 206.12 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n90 41 Pedestrian -1 -1 -1 420.12 162.47 430.92 190.57 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n91 2 Car -1 -1 -1 1092.83 184.76 1221.29 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n91 1 Car -1 -1 -1 955.71 183.58 1065.81 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n91 3 Car -1 -1 -1 1032.82 184.39 1152.23 234.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n91 27 Pedestrian -1 -1 -1 270.88 159.96 292.89 214.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n91 20 Pedestrian -1 -1 -1 312.06 162.52 332.55 217.13 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n91 5 Car -1 -1 -1 601.94 173.22 636.73 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n91 37 Pedestrian -1 -1 -1 951.33 152.42 1125.71 366.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n91 33 Pedestrian -1 -1 -1 388.84 164.76 404.77 199.99 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n91 25 Pedestrian -1 -1 -1 188.60 161.31 206.21 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n91 35 Pedestrian -1 -1 -1 365.23 163.73 382.64 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n91 38 Car -1 -1 -1 598.17 173.51 622.34 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n91 40 Pedestrian -1 -1 -1 1034.79 164.77 1126.76 331.65 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n92 2 Car -1 -1 -1 1093.49 184.85 1220.76 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n92 3 Car -1 -1 -1 1031.70 184.22 1153.45 234.57 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n92 1 Car -1 -1 -1 955.75 183.57 1065.71 233.54 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n92 27 Pedestrian -1 -1 -1 270.92 159.58 292.35 214.87 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n92 5 Car -1 -1 -1 601.89 173.24 636.69 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n92 33 Pedestrian -1 -1 -1 392.86 164.78 407.39 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n92 37 Pedestrian -1 -1 -1 956.62 153.11 1112.60 367.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n92 20 Pedestrian -1 -1 -1 311.69 162.45 332.32 216.63 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n92 35 Pedestrian -1 -1 -1 367.35 163.37 384.55 200.44 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n92 25 Pedestrian -1 -1 -1 189.19 161.26 206.23 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n92 38 Car -1 -1 -1 598.29 173.54 622.05 193.08 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n92 40 Pedestrian -1 -1 -1 1032.99 167.26 1105.47 322.20 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n93 2 Car -1 -1 -1 1093.72 184.91 1221.25 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n93 3 Car -1 -1 -1 1030.71 184.11 1154.00 234.68 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n93 1 Car -1 -1 -1 956.04 183.42 1065.70 231.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n93 27 Pedestrian -1 -1 -1 271.06 159.34 292.42 215.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n93 5 Car -1 -1 -1 601.91 173.20 636.60 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n93 37 Pedestrian -1 -1 -1 954.21 158.49 1099.78 367.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n93 33 Pedestrian -1 -1 -1 395.79 164.70 409.18 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n93 20 Pedestrian -1 -1 -1 311.55 162.47 332.70 216.32 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n93 35 Pedestrian -1 -1 -1 368.91 163.83 384.96 200.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n93 25 Pedestrian -1 -1 -1 191.55 160.87 208.27 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n93 38 Car -1 -1 -1 598.39 173.58 622.14 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n93 40 Pedestrian -1 -1 -1 1016.08 166.72 1098.78 330.00 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n94 2 Car -1 -1 -1 1094.52 185.13 1220.95 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n94 1 Car -1 -1 -1 955.57 183.65 1065.99 231.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n94 3 Car -1 -1 -1 1029.54 184.03 1155.53 234.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n94 27 Pedestrian -1 -1 -1 270.80 159.52 292.06 215.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n94 33 Pedestrian -1 -1 -1 396.80 165.28 411.33 200.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n94 37 Pedestrian -1 -1 -1 944.55 160.58 1094.15 365.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n94 5 Car -1 -1 -1 601.98 173.11 636.66 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n94 20 Pedestrian -1 -1 -1 309.08 162.33 331.07 216.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n94 35 Pedestrian -1 -1 -1 371.58 163.39 387.61 200.55 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n94 25 Pedestrian -1 -1 -1 191.70 160.88 208.23 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n94 40 Pedestrian -1 -1 -1 997.69 164.56 1087.27 332.05 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n94 38 Car -1 -1 -1 598.23 173.49 622.15 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n94 42 Pedestrian -1 -1 -1 381.89 159.06 395.36 190.83 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n95 1 Car -1 -1 -1 955.90 183.63 1065.74 231.45 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n95 2 Car -1 -1 -1 1095.23 185.33 1220.38 235.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n95 3 Car -1 -1 -1 1029.27 183.95 1156.28 234.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n95 27 Pedestrian -1 -1 -1 270.77 159.84 291.26 215.77 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n95 37 Pedestrian -1 -1 -1 936.62 160.70 1086.77 366.00 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n95 5 Car -1 -1 -1 601.88 173.21 636.67 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n95 33 Pedestrian -1 -1 -1 396.91 165.47 412.07 200.46 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n95 20 Pedestrian -1 -1 -1 309.26 161.81 330.30 217.10 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n95 35 Pedestrian -1 -1 -1 373.10 164.03 388.66 200.57 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n95 25 Pedestrian -1 -1 -1 191.81 160.89 208.16 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n95 40 Pedestrian -1 -1 -1 978.45 159.34 1083.35 344.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n95 38 Car -1 -1 -1 598.13 173.57 622.24 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n96 2 Car -1 -1 -1 1095.20 185.54 1220.70 235.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n96 1 Car -1 -1 -1 955.83 183.98 1062.00 230.98 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n96 3 Car -1 -1 -1 1029.70 184.08 1155.94 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n96 20 Pedestrian -1 -1 -1 309.25 161.93 330.30 217.99 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n96 27 Pedestrian -1 -1 -1 271.05 160.08 291.31 216.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n96 37 Pedestrian -1 -1 -1 923.20 162.34 1069.94 364.49 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n96 33 Pedestrian -1 -1 -1 399.15 165.49 414.29 200.27 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n96 5 Car -1 -1 -1 601.94 173.05 636.73 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n96 35 Pedestrian -1 -1 -1 375.15 164.38 391.89 201.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n96 25 Pedestrian -1 -1 -1 191.90 160.86 208.37 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n96 38 Car -1 -1 -1 598.28 173.53 622.29 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n96 40 Pedestrian -1 -1 -1 989.42 164.74 1057.09 316.22 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n97 2 Car -1 -1 -1 1094.65 185.53 1221.12 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n97 3 Car -1 -1 -1 1029.98 184.13 1155.68 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n97 1 Car -1 -1 -1 956.17 184.01 1061.63 230.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n97 20 Pedestrian -1 -1 -1 309.26 162.16 330.94 219.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n97 35 Pedestrian -1 -1 -1 376.11 165.10 391.77 201.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n97 37 Pedestrian -1 -1 -1 915.18 161.80 1062.59 364.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n97 5 Car -1 -1 -1 602.00 173.19 636.72 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n97 33 Pedestrian -1 -1 -1 401.31 165.27 414.99 200.71 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n97 27 Pedestrian -1 -1 -1 270.77 160.06 291.22 216.47 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n97 25 Pedestrian -1 -1 -1 192.07 160.73 208.28 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n97 38 Car -1 -1 -1 598.30 173.61 622.21 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n97 40 Pedestrian -1 -1 -1 976.65 164.05 1046.99 317.13 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n98 2 Car -1 -1 -1 1094.74 185.61 1221.02 235.61 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n98 3 Car -1 -1 -1 1029.70 184.16 1155.69 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n98 1 Car -1 -1 -1 956.59 183.82 1065.55 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n98 20 Pedestrian -1 -1 -1 309.14 162.43 331.04 219.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n98 5 Car -1 -1 -1 601.91 173.10 636.57 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n98 37 Pedestrian -1 -1 -1 911.11 161.89 1051.34 364.23 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n98 33 Pedestrian -1 -1 -1 404.46 165.21 418.03 201.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n98 27 Pedestrian -1 -1 -1 270.36 159.74 291.78 216.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n98 35 Pedestrian -1 -1 -1 378.13 166.24 392.39 200.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n98 25 Pedestrian -1 -1 -1 192.02 160.73 208.29 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n98 38 Car -1 -1 -1 598.29 173.58 622.07 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n99 2 Car -1 -1 -1 1094.78 185.72 1221.08 235.54 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n99 3 Car -1 -1 -1 1029.73 184.25 1155.66 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n99 1 Car -1 -1 -1 957.56 183.01 1064.18 231.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n99 20 Pedestrian -1 -1 -1 309.22 162.24 331.34 219.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n99 27 Pedestrian -1 -1 -1 269.73 159.22 292.29 216.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n99 37 Pedestrian -1 -1 -1 903.72 158.45 1027.61 362.31 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n99 5 Car -1 -1 -1 602.05 173.19 636.51 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n99 35 Pedestrian -1 -1 -1 380.50 164.28 396.15 201.40 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n99 33 Pedestrian -1 -1 -1 404.53 165.46 420.05 200.80 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n99 25 Pedestrian -1 -1 -1 191.88 160.71 208.35 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n99 38 Car -1 -1 -1 598.47 173.62 622.05 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n99 43 Pedestrian -1 -1 -1 425.37 163.31 435.55 189.72 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n100 2 Car -1 -1 -1 1094.65 185.72 1221.26 235.54 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n100 3 Car -1 -1 -1 1029.99 184.26 1155.50 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n100 1 Car -1 -1 -1 954.03 183.12 1063.25 231.74 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n100 20 Pedestrian -1 -1 -1 309.48 162.13 330.95 219.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n100 33 Pedestrian -1 -1 -1 405.78 166.12 423.65 200.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n100 5 Car -1 -1 -1 601.84 173.12 636.68 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n100 37 Pedestrian -1 -1 -1 892.11 158.58 1009.29 361.68 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n100 27 Pedestrian -1 -1 -1 269.66 159.50 292.31 217.15 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n100 35 Pedestrian -1 -1 -1 383.76 165.00 398.50 199.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n100 25 Pedestrian -1 -1 -1 191.84 160.70 208.26 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n100 43 Pedestrian -1 -1 -1 425.34 163.29 436.09 189.76 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n100 38 Car -1 -1 -1 598.24 173.65 622.10 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n100 44 Pedestrian -1 -1 -1 400.43 162.82 412.53 189.90 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n101 2 Car -1 -1 -1 1094.67 185.71 1221.42 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n101 3 Car -1 -1 -1 1030.12 184.16 1155.53 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n101 1 Car -1 -1 -1 956.50 183.00 1065.06 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n101 20 Pedestrian -1 -1 -1 309.08 162.41 331.66 220.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n101 5 Car -1 -1 -1 601.84 173.08 636.62 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n101 37 Pedestrian -1 -1 -1 878.04 158.23 1007.67 361.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n101 27 Pedestrian -1 -1 -1 269.74 160.64 292.10 218.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n101 33 Pedestrian -1 -1 -1 407.30 165.62 425.17 200.95 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n101 35 Pedestrian -1 -1 -1 384.22 165.12 399.95 199.98 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n101 25 Pedestrian -1 -1 -1 191.77 160.67 208.17 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n101 38 Car -1 -1 -1 598.33 173.57 622.11 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n101 43 Pedestrian -1 -1 -1 425.55 162.97 436.33 189.82 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n102 2 Car -1 -1 -1 1094.41 185.66 1221.52 235.46 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n102 3 Car -1 -1 -1 1030.18 184.16 1155.41 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n102 1 Car -1 -1 -1 955.80 183.01 1066.03 232.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n102 5 Car -1 -1 -1 601.74 173.20 636.46 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n102 33 Pedestrian -1 -1 -1 409.86 165.42 427.77 201.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n102 37 Pedestrian -1 -1 -1 865.42 158.34 1004.96 362.25 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n102 27 Pedestrian -1 -1 -1 270.29 160.59 292.35 219.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n102 20 Pedestrian -1 -1 -1 310.84 162.53 333.37 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n102 35 Pedestrian -1 -1 -1 384.22 164.94 401.42 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n102 25 Pedestrian -1 -1 -1 191.99 160.69 208.04 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n102 43 Pedestrian -1 -1 -1 425.20 162.49 436.74 189.88 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n102 38 Car -1 -1 -1 598.26 173.55 621.89 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n103 2 Car -1 -1 -1 1094.27 185.60 1221.58 235.56 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n103 3 Car -1 -1 -1 1030.26 184.03 1155.24 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n103 1 Car -1 -1 -1 955.25 183.22 1066.27 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n103 5 Car -1 -1 -1 601.84 173.08 636.54 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n103 27 Pedestrian -1 -1 -1 269.98 160.47 292.24 219.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n103 37 Pedestrian -1 -1 -1 858.79 158.37 1003.81 362.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n103 20 Pedestrian -1 -1 -1 311.36 162.87 333.32 220.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n103 35 Pedestrian -1 -1 -1 386.95 165.06 403.66 201.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n103 33 Pedestrian -1 -1 -1 413.84 165.30 429.06 200.55 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n103 25 Pedestrian -1 -1 -1 191.99 160.82 208.07 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n103 38 Car -1 -1 -1 598.40 173.55 621.79 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n103 43 Pedestrian -1 -1 -1 425.45 162.30 436.91 189.50 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n104 2 Car -1 -1 -1 1094.09 185.60 1221.55 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n104 3 Car -1 -1 -1 1030.54 184.09 1154.98 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n104 1 Car -1 -1 -1 950.82 183.15 1066.85 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n104 37 Pedestrian -1 -1 -1 845.47 157.04 994.40 363.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n104 5 Car -1 -1 -1 601.75 173.05 636.64 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n104 20 Pedestrian -1 -1 -1 311.14 162.95 333.29 220.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n104 27 Pedestrian -1 -1 -1 271.07 160.51 292.94 218.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n104 33 Pedestrian -1 -1 -1 416.53 165.34 430.90 200.92 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n104 35 Pedestrian -1 -1 -1 388.68 165.38 404.30 201.56 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n104 25 Pedestrian -1 -1 -1 191.82 160.80 208.24 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n104 38 Car -1 -1 -1 598.37 173.45 621.81 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n105 2 Car -1 -1 -1 1094.20 185.59 1221.02 235.47 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n105 3 Car -1 -1 -1 1030.53 184.08 1155.01 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n105 1 Car -1 -1 -1 950.47 183.22 1067.06 231.84 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n105 37 Pedestrian -1 -1 -1 840.41 155.87 991.47 364.06 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n105 5 Car -1 -1 -1 601.69 173.11 636.67 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n105 20 Pedestrian -1 -1 -1 310.96 162.74 333.62 220.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n105 27 Pedestrian -1 -1 -1 273.49 160.57 294.61 219.29 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n105 33 Pedestrian -1 -1 -1 417.39 165.52 434.38 201.76 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 35 Pedestrian -1 -1 -1 392.52 165.50 406.96 201.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n105 25 Pedestrian -1 -1 -1 191.92 160.93 208.24 198.54 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n105 38 Car -1 -1 -1 598.32 173.49 621.93 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n106 2 Car -1 -1 -1 1094.08 185.46 1221.33 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n106 3 Car -1 -1 -1 1030.44 184.07 1155.07 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n106 1 Car -1 -1 -1 950.62 183.66 1066.83 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n106 37 Pedestrian -1 -1 -1 833.98 154.58 983.02 364.73 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n106 27 Pedestrian -1 -1 -1 273.81 160.75 294.85 219.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n106 20 Pedestrian -1 -1 -1 311.25 162.58 333.52 220.77 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n106 5 Car -1 -1 -1 601.72 173.14 636.81 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n106 35 Pedestrian -1 -1 -1 394.50 165.80 410.71 201.23 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n106 33 Pedestrian -1 -1 -1 417.35 164.58 436.67 203.59 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n106 25 Pedestrian -1 -1 -1 191.51 160.76 208.41 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n106 38 Car -1 -1 -1 598.44 173.63 622.09 193.30 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n107 2 Car -1 -1 -1 1094.13 185.45 1221.42 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n107 3 Car -1 -1 -1 1030.10 184.04 1155.54 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n107 1 Car -1 -1 -1 954.21 183.52 1067.09 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n107 37 Pedestrian -1 -1 -1 837.01 154.45 972.42 364.69 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n107 27 Pedestrian -1 -1 -1 274.49 160.81 295.23 219.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n107 5 Car -1 -1 -1 601.84 173.15 636.78 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n107 20 Pedestrian -1 -1 -1 311.03 162.75 333.41 221.03 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n107 35 Pedestrian -1 -1 -1 394.76 166.51 411.24 201.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n107 25 Pedestrian -1 -1 -1 191.88 161.05 208.04 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n107 38 Car -1 -1 -1 598.43 173.60 622.19 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n107 33 Pedestrian -1 -1 -1 420.27 164.56 438.32 203.96 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n108 2 Car -1 -1 -1 1093.92 185.43 1221.52 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n108 3 Car -1 -1 -1 1030.06 184.06 1155.62 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n108 1 Car -1 -1 -1 954.57 183.58 1066.87 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n108 27 Pedestrian -1 -1 -1 275.01 160.78 295.92 220.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n108 37 Pedestrian -1 -1 -1 841.30 154.14 967.80 365.62 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n108 5 Car -1 -1 -1 601.84 173.24 636.72 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n108 20 Pedestrian -1 -1 -1 311.12 162.96 333.31 221.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n108 35 Pedestrian -1 -1 -1 396.49 166.44 412.81 201.90 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n108 25 Pedestrian -1 -1 -1 191.94 161.07 208.27 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n108 38 Car -1 -1 -1 598.30 173.61 622.02 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n109 2 Car -1 -1 -1 1094.08 185.40 1221.38 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n109 3 Car -1 -1 -1 1030.13 184.03 1155.62 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n109 1 Car -1 -1 -1 955.03 183.64 1066.70 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n109 27 Pedestrian -1 -1 -1 274.71 160.62 296.55 220.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n109 5 Car -1 -1 -1 601.72 173.04 636.74 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n109 20 Pedestrian -1 -1 -1 311.17 163.53 334.10 222.76 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n109 35 Pedestrian -1 -1 -1 398.62 166.67 414.54 201.96 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n109 37 Pedestrian -1 -1 -1 828.44 156.99 957.83 364.05 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n109 25 Pedestrian -1 -1 -1 191.65 160.93 208.36 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n109 38 Car -1 -1 -1 598.46 173.64 622.17 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n109 45 Pedestrian -1 -1 -1 426.74 166.21 442.42 205.81 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n110 2 Car -1 -1 -1 1094.35 185.39 1221.45 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n110 3 Car -1 -1 -1 1030.05 183.97 1155.72 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n110 1 Car -1 -1 -1 955.31 183.71 1066.51 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n110 20 Pedestrian -1 -1 -1 311.02 162.29 335.21 221.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n110 27 Pedestrian -1 -1 -1 274.08 160.44 296.69 220.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n110 5 Car -1 -1 -1 601.93 173.10 636.57 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n110 37 Pedestrian -1 -1 -1 822.53 158.60 948.57 366.31 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n110 35 Pedestrian -1 -1 -1 402.92 166.05 417.05 202.18 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n110 45 Pedestrian -1 -1 -1 428.21 165.15 445.44 207.65 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n110 25 Pedestrian -1 -1 -1 191.58 160.79 208.36 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n110 38 Car -1 -1 -1 598.37 173.60 622.02 193.30 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n111 2 Car -1 -1 -1 1094.29 185.28 1221.24 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n111 3 Car -1 -1 -1 1029.98 183.95 1155.78 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n111 1 Car -1 -1 -1 955.52 183.73 1066.38 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n111 27 Pedestrian -1 -1 -1 273.86 160.43 296.64 221.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n111 37 Pedestrian -1 -1 -1 818.28 160.85 930.08 364.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n111 20 Pedestrian -1 -1 -1 311.44 162.20 335.66 221.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n111 5 Car -1 -1 -1 601.74 173.07 636.69 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n111 35 Pedestrian -1 -1 -1 405.04 166.45 419.16 201.96 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n111 25 Pedestrian -1 -1 -1 191.55 160.83 208.33 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n111 45 Pedestrian -1 -1 -1 429.28 165.19 445.73 206.51 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n111 38 Car -1 -1 -1 598.28 173.65 621.99 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n112 2 Car -1 -1 -1 1094.46 185.29 1221.17 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n112 1 Car -1 -1 -1 955.39 183.73 1066.38 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n112 3 Car -1 -1 -1 1029.88 183.95 1155.89 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n112 37 Pedestrian -1 -1 -1 814.00 158.93 918.80 361.79 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n112 27 Pedestrian -1 -1 -1 273.63 160.64 296.65 222.42 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n112 5 Car -1 -1 -1 601.73 173.00 636.75 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n112 20 Pedestrian -1 -1 -1 311.45 162.84 336.45 223.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n112 35 Pedestrian -1 -1 -1 406.58 166.90 422.25 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n112 25 Pedestrian -1 -1 -1 191.58 160.91 208.39 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n112 45 Pedestrian -1 -1 -1 433.28 166.97 449.31 206.83 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n112 38 Car -1 -1 -1 598.20 173.65 622.01 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n113 2 Car -1 -1 -1 1094.67 185.32 1221.12 236.19 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n113 1 Car -1 -1 -1 955.26 183.72 1066.52 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n113 3 Car -1 -1 -1 1029.93 183.94 1155.93 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n113 37 Pedestrian -1 -1 -1 808.44 160.27 902.01 360.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n113 5 Car -1 -1 -1 601.72 172.90 636.85 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n113 20 Pedestrian -1 -1 -1 311.39 163.07 336.99 224.30 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n113 27 Pedestrian -1 -1 -1 273.08 160.47 296.31 222.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n113 35 Pedestrian -1 -1 -1 407.61 166.97 424.11 202.13 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n113 25 Pedestrian -1 -1 -1 191.36 160.73 208.53 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n113 38 Car -1 -1 -1 598.23 173.55 621.97 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n113 45 Pedestrian -1 -1 -1 436.87 167.27 451.80 206.50 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n114 2 Car -1 -1 -1 1094.99 185.27 1220.84 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n114 1 Car -1 -1 -1 955.18 183.69 1066.88 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n114 3 Car -1 -1 -1 1032.46 183.72 1157.44 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n114 37 Pedestrian -1 -1 -1 803.38 161.60 890.76 363.35 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n114 20 Pedestrian -1 -1 -1 313.47 163.57 338.64 224.98 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n114 5 Car -1 -1 -1 601.74 173.06 636.85 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n114 27 Pedestrian -1 -1 -1 273.25 160.56 296.08 222.65 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n114 25 Pedestrian -1 -1 -1 191.48 160.71 208.49 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n114 35 Pedestrian -1 -1 -1 410.04 167.31 426.53 202.08 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n114 38 Car -1 -1 -1 598.33 173.71 622.09 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n114 45 Pedestrian -1 -1 -1 437.83 167.79 452.62 206.79 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n115 2 Car -1 -1 -1 1094.87 185.24 1220.99 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n115 1 Car -1 -1 -1 955.00 183.78 1067.12 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n115 3 Car -1 -1 -1 1032.69 183.83 1157.17 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n115 37 Pedestrian -1 -1 -1 791.34 161.72 887.24 363.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n115 20 Pedestrian -1 -1 -1 311.25 163.72 336.68 225.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n115 5 Car -1 -1 -1 601.80 173.09 636.83 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n115 27 Pedestrian -1 -1 -1 273.32 160.54 296.06 222.56 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n115 35 Pedestrian -1 -1 -1 411.28 167.94 427.62 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n115 25 Pedestrian -1 -1 -1 191.68 160.74 208.34 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n115 45 Pedestrian -1 -1 -1 438.77 167.43 454.50 207.21 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n115 38 Car -1 -1 -1 598.22 173.66 622.23 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n116 2 Car -1 -1 -1 1094.90 185.27 1221.03 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n116 1 Car -1 -1 -1 954.97 183.82 1067.07 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n116 3 Car -1 -1 -1 1029.89 184.09 1156.06 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n116 37 Pedestrian -1 -1 -1 780.15 162.66 883.91 362.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n116 20 Pedestrian -1 -1 -1 311.48 162.68 335.98 225.21 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n116 5 Car -1 -1 -1 601.76 173.12 636.83 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n116 27 Pedestrian -1 -1 -1 273.66 159.94 295.77 223.34 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n116 45 Pedestrian -1 -1 -1 441.59 168.27 458.31 206.13 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n116 25 Pedestrian -1 -1 -1 191.52 160.72 208.47 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n116 35 Pedestrian -1 -1 -1 414.71 166.91 428.43 202.38 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n116 38 Car -1 -1 -1 598.22 173.80 622.08 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n117 2 Car -1 -1 -1 1094.90 185.26 1221.11 236.16 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n117 1 Car -1 -1 -1 954.91 183.79 1067.10 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n117 3 Car -1 -1 -1 1032.29 183.79 1157.53 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n117 20 Pedestrian -1 -1 -1 310.99 162.65 336.22 226.36 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n117 37 Pedestrian -1 -1 -1 768.24 163.94 872.59 361.97 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n117 5 Car -1 -1 -1 601.67 173.08 636.93 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n117 27 Pedestrian -1 -1 -1 273.47 159.92 295.64 223.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n117 45 Pedestrian -1 -1 -1 444.32 168.13 460.68 206.66 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n117 25 Pedestrian -1 -1 -1 191.46 160.77 208.44 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n117 35 Pedestrian -1 -1 -1 415.40 167.82 431.04 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n117 38 Car -1 -1 -1 598.11 173.62 622.20 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n117 46 Pedestrian -1 -1 -1 815.79 164.64 864.15 278.25 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n118 2 Car -1 -1 -1 1094.65 185.17 1221.26 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n118 1 Car -1 -1 -1 955.01 183.83 1067.01 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n118 3 Car -1 -1 -1 1032.57 183.72 1157.27 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n118 37 Pedestrian -1 -1 -1 760.98 162.42 864.32 363.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n118 20 Pedestrian -1 -1 -1 311.36 163.16 336.87 226.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n118 5 Car -1 -1 -1 601.60 173.07 636.99 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n118 45 Pedestrian -1 -1 -1 445.24 168.34 462.66 206.37 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n118 27 Pedestrian -1 -1 -1 273.08 160.03 295.53 224.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n118 25 Pedestrian -1 -1 -1 191.41 160.81 208.32 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n118 38 Car -1 -1 -1 598.14 173.57 622.42 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n118 35 Pedestrian -1 -1 -1 416.70 167.60 433.81 203.35 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n118 46 Pedestrian -1 -1 -1 808.75 164.09 855.69 278.60 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n119 2 Car -1 -1 -1 1094.83 185.31 1221.13 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n119 1 Car -1 -1 -1 955.10 183.83 1066.85 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n119 3 Car -1 -1 -1 1032.44 183.76 1157.45 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n119 37 Pedestrian -1 -1 -1 753.39 162.34 848.98 363.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n119 20 Pedestrian -1 -1 -1 311.60 163.92 337.03 227.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n119 5 Car -1 -1 -1 601.66 173.11 636.92 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n119 45 Pedestrian -1 -1 -1 450.60 168.28 464.29 204.59 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n119 35 Pedestrian -1 -1 -1 416.90 167.85 436.84 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n119 27 Pedestrian -1 -1 -1 272.72 160.84 296.16 225.78 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n119 46 Pedestrian -1 -1 -1 801.89 164.25 846.62 278.52 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n119 25 Pedestrian -1 -1 -1 191.40 160.76 208.33 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n119 38 Car -1 -1 -1 598.00 173.67 622.23 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n120 2 Car -1 -1 -1 1095.08 185.46 1220.85 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n120 1 Car -1 -1 -1 955.18 183.88 1066.58 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n120 3 Car -1 -1 -1 1029.79 184.11 1156.04 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n120 20 Pedestrian -1 -1 -1 313.55 163.49 338.82 227.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n120 37 Pedestrian -1 -1 -1 750.32 159.63 829.10 361.43 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n120 5 Car -1 -1 -1 601.75 173.14 636.72 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n120 45 Pedestrian -1 -1 -1 453.47 168.17 466.85 204.29 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n120 27 Pedestrian -1 -1 -1 269.91 161.31 294.06 225.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n120 35 Pedestrian -1 -1 -1 418.55 167.57 440.13 206.26 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n120 46 Pedestrian -1 -1 -1 793.84 163.99 840.03 276.97 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n120 25 Pedestrian -1 -1 -1 191.44 160.87 208.18 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n120 38 Car -1 -1 -1 597.72 173.80 621.97 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n121 2 Car -1 -1 -1 1095.08 185.46 1220.90 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n121 1 Car -1 -1 -1 955.16 183.88 1066.64 232.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n121 3 Car -1 -1 -1 1029.89 184.16 1155.95 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n121 20 Pedestrian -1 -1 -1 313.63 162.77 339.81 227.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n121 37 Pedestrian -1 -1 -1 740.34 160.05 823.71 364.47 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n121 5 Car -1 -1 -1 601.64 173.08 636.73 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n121 27 Pedestrian -1 -1 -1 269.28 161.11 294.66 225.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n121 45 Pedestrian -1 -1 -1 453.75 168.76 468.81 204.07 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n121 46 Pedestrian -1 -1 -1 790.40 164.44 835.43 272.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n121 35 Pedestrian -1 -1 -1 422.12 168.06 439.68 206.30 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n121 38 Car -1 -1 -1 597.91 173.60 621.96 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n121 25 Pedestrian -1 -1 -1 191.53 160.94 208.08 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n122 2 Car -1 -1 -1 1095.05 185.53 1221.03 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n122 3 Car -1 -1 -1 1029.77 184.09 1156.01 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n122 1 Car -1 -1 -1 954.46 183.57 1067.28 231.60 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n122 37 Pedestrian -1 -1 -1 724.43 161.69 817.13 363.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n122 20 Pedestrian -1 -1 -1 313.62 162.34 340.15 227.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n122 5 Car -1 -1 -1 601.62 173.13 636.72 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n122 27 Pedestrian -1 -1 -1 266.94 161.11 297.07 227.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n122 46 Pedestrian -1 -1 -1 786.60 166.36 831.00 270.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n122 45 Pedestrian -1 -1 -1 455.22 168.66 472.09 204.21 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n122 35 Pedestrian -1 -1 -1 426.29 166.90 442.60 208.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n122 38 Car -1 -1 -1 597.69 173.61 621.98 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n122 25 Pedestrian -1 -1 -1 189.21 161.07 206.18 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n123 2 Car -1 -1 -1 1094.94 185.47 1220.94 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n123 3 Car -1 -1 -1 1029.71 184.06 1155.90 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n123 1 Car -1 -1 -1 954.48 183.64 1067.24 231.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n123 37 Pedestrian -1 -1 -1 710.89 162.79 815.67 363.55 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n123 20 Pedestrian -1 -1 -1 313.49 162.49 340.69 227.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n123 27 Pedestrian -1 -1 -1 267.49 161.20 296.03 228.40 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n123 5 Car -1 -1 -1 601.60 172.96 636.73 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 45 Pedestrian -1 -1 -1 457.06 168.65 474.29 204.13 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n123 46 Pedestrian -1 -1 -1 786.32 164.66 823.34 268.73 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n123 38 Car -1 -1 -1 597.75 173.65 621.95 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n123 25 Pedestrian -1 -1 -1 189.24 161.10 206.22 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n123 35 Pedestrian -1 -1 -1 428.17 166.60 445.41 209.23 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n124 2 Car -1 -1 -1 1095.18 185.50 1220.92 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n124 3 Car -1 -1 -1 1029.46 184.07 1156.29 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n124 1 Car -1 -1 -1 954.95 183.92 1067.11 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n124 37 Pedestrian -1 -1 -1 703.92 163.92 806.48 362.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n124 27 Pedestrian -1 -1 -1 268.45 160.86 295.30 228.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n124 20 Pedestrian -1 -1 -1 313.43 163.12 341.67 228.55 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n124 5 Car -1 -1 -1 601.76 173.14 636.60 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n124 45 Pedestrian -1 -1 -1 460.34 168.49 475.28 204.17 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n124 46 Pedestrian -1 -1 -1 780.64 165.89 815.37 263.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n124 35 Pedestrian -1 -1 -1 428.34 166.82 448.61 208.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n124 38 Car -1 -1 -1 597.92 173.63 621.89 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n124 25 Pedestrian -1 -1 -1 189.29 161.12 206.30 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n125 2 Car -1 -1 -1 1095.26 185.44 1220.76 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n125 3 Car -1 -1 -1 1029.55 184.06 1156.14 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n125 1 Car -1 -1 -1 955.04 183.98 1066.83 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n125 37 Pedestrian -1 -1 -1 690.88 162.10 796.94 363.97 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n125 20 Pedestrian -1 -1 -1 313.73 164.09 340.98 230.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n125 27 Pedestrian -1 -1 -1 271.44 160.25 297.12 228.53 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n125 5 Car -1 -1 -1 601.94 173.22 636.67 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n125 46 Pedestrian -1 -1 -1 778.29 166.52 815.65 263.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n125 45 Pedestrian -1 -1 -1 462.27 168.36 476.66 204.59 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n125 35 Pedestrian -1 -1 -1 430.53 166.56 451.82 207.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n125 38 Car -1 -1 -1 598.15 173.69 621.86 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n125 25 Pedestrian -1 -1 -1 191.66 161.43 208.04 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n126 2 Car -1 -1 -1 1095.25 185.35 1220.77 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n126 3 Car -1 -1 -1 1029.50 184.02 1156.25 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n126 1 Car -1 -1 -1 955.14 183.93 1066.85 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n126 37 Pedestrian -1 -1 -1 685.68 158.83 779.91 361.95 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n126 27 Pedestrian -1 -1 -1 272.74 160.41 297.59 228.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n126 45 Pedestrian -1 -1 -1 464.58 168.66 479.42 204.40 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n126 46 Pedestrian -1 -1 -1 773.81 167.13 812.66 262.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n126 20 Pedestrian -1 -1 -1 313.48 163.71 340.53 230.42 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n126 5 Car -1 -1 -1 602.05 173.20 636.83 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n126 35 Pedestrian -1 -1 -1 432.81 166.26 451.83 207.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n126 38 Car -1 -1 -1 598.48 173.75 622.10 193.68 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n126 25 Pedestrian -1 -1 -1 191.64 161.07 207.94 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n126 47 Cyclist -1 -1 -1 -15.25 146.85 217.80 365.35 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n127 2 Car -1 -1 -1 1095.30 185.43 1220.87 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n127 47 Cyclist -1 -1 -1 34.69 153.48 274.88 365.67 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n127 3 Car -1 -1 -1 1029.25 183.86 1156.31 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n127 1 Car -1 -1 -1 955.14 183.97 1066.61 232.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n127 37 Pedestrian -1 -1 -1 678.73 158.10 764.00 361.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n127 27 Pedestrian -1 -1 -1 273.43 160.71 297.89 229.43 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n127 20 Pedestrian -1 -1 -1 313.06 163.23 340.62 228.21 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n127 46 Pedestrian -1 -1 -1 770.93 166.72 808.41 262.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n127 5 Car -1 -1 -1 602.09 173.26 636.67 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n127 35 Pedestrian -1 -1 -1 437.18 166.86 453.94 207.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n127 45 Pedestrian -1 -1 -1 466.35 169.99 480.98 204.76 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n127 25 Pedestrian -1 -1 -1 192.51 161.63 207.73 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n127 38 Car -1 -1 -1 598.55 173.86 621.82 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n128 2 Car -1 -1 -1 1095.63 185.40 1220.44 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n128 47 Cyclist -1 -1 -1 105.31 153.74 311.31 366.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n128 3 Car -1 -1 -1 1029.33 183.89 1156.39 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n128 1 Car -1 -1 -1 955.13 183.97 1066.71 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n128 37 Pedestrian -1 -1 -1 667.13 157.30 758.89 362.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n128 46 Pedestrian -1 -1 -1 765.29 166.21 800.40 261.46 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n128 27 Pedestrian -1 -1 -1 275.12 159.96 300.94 230.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n128 45 Pedestrian -1 -1 -1 468.34 170.31 484.18 205.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n128 20 Pedestrian -1 -1 -1 312.60 163.20 341.06 230.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n128 5 Car -1 -1 -1 602.03 173.17 636.91 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n128 38 Car -1 -1 -1 598.61 173.78 621.88 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n128 35 Pedestrian -1 -1 -1 440.91 167.41 456.57 207.74 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n128 25 Pedestrian -1 -1 -1 191.96 160.51 208.69 199.24 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n128 48 Pedestrian -1 -1 -1 181.32 159.70 198.35 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n129 2 Car -1 -1 -1 1095.53 185.47 1220.56 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n129 3 Car -1 -1 -1 1029.33 183.81 1156.31 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n129 47 Cyclist -1 -1 -1 155.92 157.10 345.37 368.87 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n129 1 Car -1 -1 -1 955.16 183.88 1066.61 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n129 37 Pedestrian -1 -1 -1 648.92 156.65 747.31 363.08 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n129 20 Pedestrian -1 -1 -1 312.75 163.18 341.11 230.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n129 46 Pedestrian -1 -1 -1 761.26 163.45 795.06 258.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n129 27 Pedestrian -1 -1 -1 275.45 160.40 301.48 230.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n129 5 Car -1 -1 -1 601.92 173.10 637.08 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n129 45 Pedestrian -1 -1 -1 471.30 169.68 486.29 204.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n129 38 Car -1 -1 -1 598.52 173.67 622.28 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n129 25 Pedestrian -1 -1 -1 191.92 160.94 208.19 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n129 35 Pedestrian -1 -1 -1 440.83 167.00 458.15 207.37 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n129 48 Pedestrian -1 -1 -1 182.15 160.10 197.13 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n130 2 Car -1 -1 -1 1095.33 185.40 1220.84 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n130 1 Car -1 -1 -1 955.10 183.81 1066.85 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n130 3 Car -1 -1 -1 1029.46 183.79 1156.40 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n130 47 Cyclist -1 -1 -1 197.82 152.16 379.85 365.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n130 37 Pedestrian -1 -1 -1 628.36 157.52 744.57 363.26 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n130 46 Pedestrian -1 -1 -1 754.20 163.63 793.78 258.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n130 27 Pedestrian -1 -1 -1 277.13 160.41 300.48 230.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n130 45 Pedestrian -1 -1 -1 473.84 169.44 487.23 204.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n130 5 Car -1 -1 -1 601.84 173.16 637.13 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n130 20 Pedestrian -1 -1 -1 313.41 162.72 340.37 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n130 38 Car -1 -1 -1 598.16 173.65 622.57 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n130 48 Pedestrian -1 -1 -1 181.54 160.77 196.75 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n130 25 Pedestrian -1 -1 -1 192.19 161.46 207.45 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n130 35 Pedestrian -1 -1 -1 443.81 167.59 463.14 207.41 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n131 2 Car -1 -1 -1 1095.20 185.36 1220.97 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n131 46 Pedestrian -1 -1 -1 750.87 167.45 790.64 260.40 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n131 1 Car -1 -1 -1 955.27 183.81 1066.75 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n131 47 Cyclist -1 -1 -1 238.23 152.83 400.87 365.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n131 3 Car -1 -1 -1 1029.37 183.79 1156.59 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n131 37 Pedestrian -1 -1 -1 619.71 160.96 737.77 364.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n131 20 Pedestrian -1 -1 -1 313.71 163.26 341.17 232.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n131 5 Car -1 -1 -1 602.05 173.22 636.86 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n131 45 Pedestrian -1 -1 -1 476.07 168.92 489.41 204.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n131 27 Pedestrian -1 -1 -1 274.74 160.91 302.69 230.23 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n131 48 Pedestrian -1 -1 -1 181.17 160.51 196.95 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n131 38 Car -1 -1 -1 598.26 173.76 622.41 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n131 25 Pedestrian -1 -1 -1 192.27 161.15 207.50 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n131 35 Pedestrian -1 -1 -1 444.28 167.97 463.09 206.84 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n132 2 Car -1 -1 -1 1095.18 185.41 1220.94 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n132 1 Car -1 -1 -1 955.14 183.85 1066.88 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n132 3 Car -1 -1 -1 1029.28 183.74 1156.51 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n132 46 Pedestrian -1 -1 -1 747.63 168.57 786.30 259.60 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n132 37 Pedestrian -1 -1 -1 611.10 160.72 723.44 365.46 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n132 47 Cyclist -1 -1 -1 275.93 157.22 416.05 354.35 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 45 Pedestrian -1 -1 -1 476.15 169.36 492.48 204.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n132 27 Pedestrian -1 -1 -1 274.08 159.78 303.82 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n132 20 Pedestrian -1 -1 -1 312.69 161.33 341.91 234.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n132 5 Car -1 -1 -1 601.91 173.01 636.90 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n132 35 Pedestrian -1 -1 -1 445.72 167.90 461.90 206.71 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n132 38 Car -1 -1 -1 598.63 173.64 622.28 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n132 25 Pedestrian -1 -1 -1 192.03 161.20 207.56 199.02 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n132 48 Pedestrian -1 -1 -1 181.00 160.86 195.97 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n133 2 Car -1 -1 -1 1095.39 185.41 1220.83 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n133 1 Car -1 -1 -1 955.23 183.86 1066.87 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n133 3 Car -1 -1 -1 1029.52 183.76 1156.39 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n133 37 Pedestrian -1 -1 -1 603.28 162.25 708.30 364.01 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n133 47 Cyclist -1 -1 -1 306.89 155.45 431.75 334.15 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n133 5 Car -1 -1 -1 601.94 172.75 636.58 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n133 46 Pedestrian -1 -1 -1 745.74 168.68 779.32 257.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n133 27 Pedestrian -1 -1 -1 274.40 161.40 303.80 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n133 20 Pedestrian -1 -1 -1 312.59 161.61 341.70 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n133 35 Pedestrian -1 -1 -1 450.19 167.75 464.36 206.04 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n133 45 Pedestrian -1 -1 -1 477.88 168.68 494.62 204.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n133 25 Pedestrian -1 -1 -1 192.22 161.41 207.53 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n133 48 Pedestrian -1 -1 -1 178.07 160.36 194.74 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n133 38 Car -1 -1 -1 598.73 173.63 622.26 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n134 2 Car -1 -1 -1 1095.44 185.35 1220.71 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n134 1 Car -1 -1 -1 955.27 183.80 1066.94 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n134 3 Car -1 -1 -1 1029.47 183.78 1156.46 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n134 5 Car -1 -1 -1 602.31 173.00 635.34 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n134 45 Pedestrian -1 -1 -1 479.00 169.57 495.91 204.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n134 27 Pedestrian -1 -1 -1 275.27 161.20 302.85 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n134 20 Pedestrian -1 -1 -1 312.61 161.32 343.38 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n134 47 Cyclist -1 -1 -1 335.41 155.47 441.13 319.06 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n134 46 Pedestrian -1 -1 -1 740.18 167.32 771.87 257.45 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n134 37 Pedestrian -1 -1 -1 596.07 160.66 684.82 364.54 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n134 35 Pedestrian -1 -1 -1 453.28 167.71 468.72 205.61 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n134 25 Pedestrian -1 -1 -1 192.21 161.28 207.78 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n134 48 Pedestrian -1 -1 -1 180.81 160.29 195.81 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n134 38 Car -1 -1 -1 598.92 173.20 622.19 193.80 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n135 2 Car -1 -1 -1 1095.42 185.39 1220.62 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n135 1 Car -1 -1 -1 955.15 183.85 1066.99 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n135 3 Car -1 -1 -1 1029.50 183.79 1156.37 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n135 47 Cyclist -1 -1 -1 356.74 156.51 449.61 310.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n135 20 Pedestrian -1 -1 -1 315.29 161.13 345.69 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n135 27 Pedestrian -1 -1 -1 275.64 160.59 302.66 234.41 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n135 46 Pedestrian -1 -1 -1 735.86 168.04 768.69 254.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n135 5 Car -1 -1 -1 602.59 173.60 635.96 202.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n135 45 Pedestrian -1 -1 -1 480.83 169.30 496.10 204.68 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n135 35 Pedestrian -1 -1 -1 455.74 168.49 474.16 205.67 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n135 37 Pedestrian -1 -1 -1 585.13 160.32 673.04 364.10 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n135 25 Pedestrian -1 -1 -1 192.12 161.28 207.85 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n135 48 Pedestrian -1 -1 -1 178.29 160.71 194.40 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n135 38 Car -1 -1 -1 599.31 173.41 621.70 193.73 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n136 2 Car -1 -1 -1 1095.52 185.47 1220.50 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n136 1 Car -1 -1 -1 955.10 183.78 1067.07 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n136 3 Car -1 -1 -1 1029.67 183.83 1156.19 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n136 20 Pedestrian -1 -1 -1 315.56 161.77 345.67 234.37 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n136 47 Cyclist -1 -1 -1 378.36 157.84 458.08 301.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n136 37 Pedestrian -1 -1 -1 567.68 162.39 661.26 362.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n136 5 Car -1 -1 -1 602.43 173.60 636.09 201.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n136 45 Pedestrian -1 -1 -1 483.36 169.44 498.50 204.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n136 46 Pedestrian -1 -1 -1 733.81 168.57 767.16 254.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n136 27 Pedestrian -1 -1 -1 275.97 160.09 302.94 234.26 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n136 35 Pedestrian -1 -1 -1 453.85 168.95 475.59 205.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n136 25 Pedestrian -1 -1 -1 192.09 161.31 208.14 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n136 38 Car -1 -1 -1 598.31 173.31 622.61 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n136 48 Pedestrian -1 -1 -1 178.18 160.77 194.48 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n137 2 Car -1 -1 -1 1095.51 185.47 1220.69 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n137 1 Car -1 -1 -1 955.14 183.80 1067.08 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n137 3 Car -1 -1 -1 1029.78 183.88 1156.13 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n137 37 Pedestrian -1 -1 -1 563.68 163.53 664.06 363.17 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n137 20 Pedestrian -1 -1 -1 316.00 161.86 346.49 234.31 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n137 47 Cyclist -1 -1 -1 394.60 159.05 471.74 292.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n137 27 Pedestrian -1 -1 -1 278.95 160.15 304.91 234.52 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n137 5 Car -1 -1 -1 602.06 172.80 636.46 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n137 46 Pedestrian -1 -1 -1 730.17 168.58 764.30 256.12 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n137 45 Pedestrian -1 -1 -1 485.46 168.93 499.65 204.77 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n137 35 Pedestrian -1 -1 -1 455.06 168.48 475.23 205.94 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n137 25 Pedestrian -1 -1 -1 192.29 161.35 208.30 199.00 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n137 48 Pedestrian -1 -1 -1 178.10 160.70 194.43 199.04 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n138 2 Car -1 -1 -1 1095.35 185.50 1220.89 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n138 1 Car -1 -1 -1 955.13 183.84 1066.88 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n138 3 Car -1 -1 -1 1029.80 183.90 1156.06 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n138 20 Pedestrian -1 -1 -1 315.65 161.51 347.09 234.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n138 27 Pedestrian -1 -1 -1 279.56 160.56 305.83 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n138 37 Pedestrian -1 -1 -1 561.34 163.99 651.57 363.88 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n138 47 Cyclist -1 -1 -1 403.79 161.40 480.46 289.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n138 5 Car -1 -1 -1 601.15 172.75 637.17 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n138 46 Pedestrian -1 -1 -1 725.10 168.31 757.13 253.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n138 35 Pedestrian -1 -1 -1 460.05 168.13 477.47 206.73 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n138 45 Pedestrian -1 -1 -1 486.17 168.49 501.71 204.75 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n138 25 Pedestrian -1 -1 -1 192.22 161.25 208.44 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n138 38 Car -1 -1 -1 597.95 173.19 622.85 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n138 48 Pedestrian -1 -1 -1 178.12 160.64 194.39 199.09 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n139 2 Car -1 -1 -1 1095.61 185.51 1220.62 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n139 1 Car -1 -1 -1 955.12 183.86 1066.91 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n139 3 Car -1 -1 -1 1029.91 183.93 1155.89 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n139 46 Pedestrian -1 -1 -1 723.01 167.89 750.89 252.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n139 5 Car -1 -1 -1 600.91 172.82 636.82 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n139 37 Pedestrian -1 -1 -1 556.89 162.44 641.00 364.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n139 47 Cyclist -1 -1 -1 418.06 162.79 481.51 281.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n139 27 Pedestrian -1 -1 -1 279.65 160.97 307.00 236.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n139 20 Pedestrian -1 -1 -1 316.03 160.88 347.67 234.90 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n139 45 Pedestrian -1 -1 -1 487.46 168.88 503.17 205.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n139 38 Car -1 -1 -1 598.25 173.39 622.79 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n139 25 Pedestrian -1 -1 -1 191.98 161.20 208.53 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n139 35 Pedestrian -1 -1 -1 464.10 168.14 481.28 207.88 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n139 48 Pedestrian -1 -1 -1 178.25 160.73 194.22 199.17 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n140 2 Car -1 -1 -1 1095.63 185.48 1220.47 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n140 1 Car -1 -1 -1 955.19 183.88 1066.94 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n140 3 Car -1 -1 -1 1029.70 183.88 1156.09 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n140 46 Pedestrian -1 -1 -1 714.81 167.35 748.92 251.95 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n140 5 Car -1 -1 -1 601.45 172.87 636.65 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n140 20 Pedestrian -1 -1 -1 318.41 161.06 350.00 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n140 27 Pedestrian -1 -1 -1 283.07 161.33 308.75 237.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n140 37 Pedestrian -1 -1 -1 544.23 159.42 615.26 367.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n140 47 Cyclist -1 -1 -1 429.25 161.47 492.44 275.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n140 45 Pedestrian -1 -1 -1 490.79 168.94 504.87 205.28 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n140 25 Pedestrian -1 -1 -1 191.82 161.26 208.48 199.17 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n140 38 Car -1 -1 -1 598.45 173.54 622.17 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n140 35 Pedestrian -1 -1 -1 468.79 168.66 484.24 207.60 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n140 48 Pedestrian -1 -1 -1 178.29 160.73 193.77 199.21 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n141 2 Car -1 -1 -1 1095.70 185.48 1220.47 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n141 1 Car -1 -1 -1 955.13 183.85 1066.87 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n141 37 Pedestrian -1 -1 -1 519.81 161.29 600.74 365.95 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n141 3 Car -1 -1 -1 1029.90 183.94 1155.94 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n141 46 Pedestrian -1 -1 -1 709.51 167.60 747.01 251.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n141 20 Pedestrian -1 -1 -1 319.78 161.93 350.53 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n141 47 Cyclist -1 -1 -1 438.03 163.11 499.52 272.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n141 5 Car -1 -1 -1 601.49 173.06 636.66 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n141 27 Pedestrian -1 -1 -1 283.91 161.62 310.35 237.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n141 45 Pedestrian -1 -1 -1 493.26 169.40 506.62 205.91 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n141 25 Pedestrian -1 -1 -1 191.91 161.24 208.36 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n141 38 Car -1 -1 -1 598.34 173.50 622.13 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n141 35 Pedestrian -1 -1 -1 472.44 169.02 486.64 207.87 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n141 48 Pedestrian -1 -1 -1 178.02 160.43 193.56 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n142 2 Car -1 -1 -1 1095.78 185.50 1220.38 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n142 1 Car -1 -1 -1 955.29 183.84 1066.89 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n142 3 Car -1 -1 -1 1029.96 183.94 1155.93 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n142 37 Pedestrian -1 -1 -1 499.80 163.78 598.07 364.33 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n142 46 Pedestrian -1 -1 -1 707.70 168.18 743.29 251.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n142 5 Car -1 -1 -1 601.72 173.29 636.71 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n142 47 Cyclist -1 -1 -1 446.93 163.92 505.60 270.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n142 27 Pedestrian -1 -1 -1 286.25 160.93 312.25 237.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n142 20 Pedestrian -1 -1 -1 320.52 162.86 350.97 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n142 45 Pedestrian -1 -1 -1 495.22 169.86 509.09 206.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n142 25 Pedestrian -1 -1 -1 191.87 161.15 208.32 199.09 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n142 38 Car -1 -1 -1 598.61 173.65 622.38 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n143 2 Car -1 -1 -1 1095.38 185.40 1220.70 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n143 1 Car -1 -1 -1 955.17 183.82 1067.02 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n143 3 Car -1 -1 -1 1029.81 183.90 1156.12 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n143 37 Pedestrian -1 -1 -1 491.92 164.46 589.93 363.59 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n143 20 Pedestrian -1 -1 -1 323.92 163.57 353.34 238.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n143 27 Pedestrian -1 -1 -1 286.49 160.12 313.29 237.43 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n143 46 Pedestrian -1 -1 -1 705.64 168.33 737.36 249.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n143 5 Car -1 -1 -1 601.67 173.30 636.94 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n143 47 Cyclist -1 -1 -1 457.00 163.87 510.49 265.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n143 25 Pedestrian -1 -1 -1 191.87 161.08 208.36 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n143 45 Pedestrian -1 -1 -1 499.27 169.63 511.83 206.39 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n143 38 Car -1 -1 -1 598.58 173.74 622.43 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n144 2 Car -1 -1 -1 1095.65 185.45 1220.29 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n144 1 Car -1 -1 -1 955.21 183.87 1066.86 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n144 3 Car -1 -1 -1 1029.84 183.87 1155.97 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n144 37 Pedestrian -1 -1 -1 483.88 163.79 560.01 364.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n144 27 Pedestrian -1 -1 -1 285.97 159.91 313.72 237.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n144 46 Pedestrian -1 -1 -1 703.52 167.63 730.32 249.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n144 20 Pedestrian -1 -1 -1 324.34 163.61 354.78 238.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n144 5 Car -1 -1 -1 601.53 173.33 637.07 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n144 45 Pedestrian -1 -1 -1 500.67 170.28 512.07 206.05 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n144 25 Pedestrian -1 -1 -1 191.88 160.90 208.45 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n144 47 Cyclist -1 -1 -1 468.36 164.66 512.65 256.41 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n144 38 Car -1 -1 -1 598.34 173.75 622.55 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n144 49 Pedestrian -1 -1 -1 478.08 171.13 496.78 208.85 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n145 2 Car -1 -1 -1 1095.63 185.47 1220.48 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n145 1 Car -1 -1 -1 955.11 183.87 1066.91 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n145 3 Car -1 -1 -1 1029.68 183.82 1156.20 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n145 37 Pedestrian -1 -1 -1 469.88 161.05 551.62 365.44 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n145 20 Pedestrian -1 -1 -1 328.06 162.99 357.44 239.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n145 27 Pedestrian -1 -1 -1 285.25 160.39 315.12 238.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n145 46 Pedestrian -1 -1 -1 700.26 167.71 727.50 249.32 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n145 5 Car -1 -1 -1 601.60 173.33 637.01 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n145 45 Pedestrian -1 -1 -1 501.07 169.94 512.77 206.31 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n145 38 Car -1 -1 -1 598.44 173.70 622.53 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n145 25 Pedestrian -1 -1 -1 192.00 160.84 208.26 199.44 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n145 47 Cyclist -1 -1 -1 475.32 164.21 515.35 255.29 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n145 49 Pedestrian -1 -1 -1 479.24 171.14 496.29 208.80 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n146 2 Car -1 -1 -1 1095.41 185.43 1220.56 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n146 1 Car -1 -1 -1 955.04 183.89 1066.72 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n146 3 Car -1 -1 -1 1029.52 183.75 1156.17 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n146 37 Pedestrian -1 -1 -1 455.00 159.18 536.14 366.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n146 46 Pedestrian -1 -1 -1 694.38 168.59 725.50 249.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n146 20 Pedestrian -1 -1 -1 331.21 162.38 360.63 239.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n146 5 Car -1 -1 -1 601.66 173.26 637.06 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n146 27 Pedestrian -1 -1 -1 285.91 160.54 315.61 238.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n146 25 Pedestrian -1 -1 -1 192.40 161.21 207.91 199.04 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n146 38 Car -1 -1 -1 598.57 173.75 622.45 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n146 45 Pedestrian -1 -1 -1 503.83 169.08 515.90 206.58 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n146 49 Pedestrian -1 -1 -1 482.77 171.04 500.01 209.20 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n147 2 Car -1 -1 -1 1095.37 185.46 1220.72 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n147 1 Car -1 -1 -1 954.99 183.84 1066.78 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n147 3 Car -1 -1 -1 1029.50 183.83 1156.25 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n147 37 Pedestrian -1 -1 -1 442.26 161.03 532.24 364.10 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n147 46 Pedestrian -1 -1 -1 692.90 169.02 724.15 248.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n147 5 Car -1 -1 -1 601.63 173.15 637.13 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n147 20 Pedestrian -1 -1 -1 333.65 162.12 364.96 237.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n147 27 Pedestrian -1 -1 -1 288.86 159.90 318.20 239.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n147 25 Pedestrian -1 -1 -1 192.60 161.27 207.89 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n147 38 Car -1 -1 -1 598.63 173.62 622.49 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n147 45 Pedestrian -1 -1 -1 500.82 169.28 513.86 206.53 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n148 2 Car -1 -1 -1 1095.54 185.43 1220.34 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n148 1 Car -1 -1 -1 954.88 183.88 1066.84 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n148 3 Car -1 -1 -1 1029.55 183.83 1156.14 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n148 37 Pedestrian -1 -1 -1 424.40 160.14 520.86 364.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n148 20 Pedestrian -1 -1 -1 334.99 163.77 366.32 240.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n148 5 Car -1 -1 -1 601.49 173.05 637.19 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n148 46 Pedestrian -1 -1 -1 690.44 169.00 719.54 248.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n148 27 Pedestrian -1 -1 -1 290.29 160.74 319.04 241.02 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n148 25 Pedestrian -1 -1 -1 193.03 161.64 207.91 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n148 38 Car -1 -1 -1 598.39 173.68 622.55 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n148 50 Cyclist -1 -1 -1 491.14 163.33 536.12 239.95 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n149 2 Car -1 -1 -1 1095.38 185.39 1220.63 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n149 1 Car -1 -1 -1 955.01 183.90 1066.80 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n149 3 Car -1 -1 -1 1029.48 183.86 1156.18 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n149 37 Pedestrian -1 -1 -1 413.43 161.94 516.16 363.67 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n149 20 Pedestrian -1 -1 -1 338.17 164.52 369.40 240.64 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n149 27 Pedestrian -1 -1 -1 294.57 160.76 321.68 241.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n149 5 Car -1 -1 -1 601.40 173.07 637.29 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n149 46 Pedestrian -1 -1 -1 687.88 167.95 713.72 246.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n149 50 Cyclist -1 -1 -1 493.55 163.75 534.92 239.61 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n149 25 Pedestrian -1 -1 -1 193.33 161.79 207.80 197.85 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n149 38 Car -1 -1 -1 598.43 173.60 622.60 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n150 2 Car -1 -1 -1 1095.49 185.49 1220.64 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n150 1 Car -1 -1 -1 955.09 183.91 1066.77 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n150 3 Car -1 -1 -1 1029.85 183.94 1155.87 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n150 46 Pedestrian -1 -1 -1 680.54 168.05 708.99 246.31 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n150 37 Pedestrian -1 -1 -1 401.93 162.09 497.65 363.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n150 27 Pedestrian -1 -1 -1 296.94 160.67 325.06 242.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n150 50 Cyclist -1 -1 -1 499.45 164.85 537.18 239.37 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n150 5 Car -1 -1 -1 601.60 173.27 637.06 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n150 20 Pedestrian -1 -1 -1 340.48 164.93 368.99 240.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n150 25 Pedestrian -1 -1 -1 193.37 161.80 207.67 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n150 38 Car -1 -1 -1 598.37 173.83 622.74 193.50 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n150 51 Pedestrian -1 -1 -1 484.27 166.70 500.66 206.08 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n151 2 Car -1 -1 -1 1095.09 185.47 1220.89 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n151 1 Car -1 -1 -1 955.17 183.98 1066.55 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n151 3 Car -1 -1 -1 1029.56 183.90 1156.09 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n151 27 Pedestrian -1 -1 -1 297.63 160.98 326.08 242.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n151 46 Pedestrian -1 -1 -1 675.25 168.71 706.41 245.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n151 37 Pedestrian -1 -1 -1 393.42 157.20 482.54 363.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n151 20 Pedestrian -1 -1 -1 346.76 163.38 374.02 241.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n151 5 Car -1 -1 -1 601.70 173.31 636.95 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n151 50 Cyclist -1 -1 -1 503.17 165.38 538.91 239.02 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n151 51 Pedestrian -1 -1 -1 485.61 167.99 503.91 206.42 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n151 25 Pedestrian -1 -1 -1 193.28 161.83 207.73 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n151 38 Car -1 -1 -1 598.57 173.80 622.43 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n152 2 Car -1 -1 -1 1095.19 185.44 1220.93 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n152 1 Car -1 -1 -1 955.10 183.91 1066.59 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n152 3 Car -1 -1 -1 1029.74 183.94 1155.99 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n152 20 Pedestrian -1 -1 -1 349.83 163.46 379.43 241.57 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n152 37 Pedestrian -1 -1 -1 382.40 157.35 470.41 363.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n152 27 Pedestrian -1 -1 -1 300.04 160.70 329.05 244.44 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n152 5 Car -1 -1 -1 601.72 173.11 636.97 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n152 50 Cyclist -1 -1 -1 506.70 164.94 537.95 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n152 46 Pedestrian -1 -1 -1 674.48 169.18 704.39 245.48 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n152 51 Pedestrian -1 -1 -1 486.54 167.67 505.50 206.62 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n152 25 Pedestrian -1 -1 -1 193.32 161.85 207.81 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n152 38 Car -1 -1 -1 598.57 173.77 622.53 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n153 2 Car -1 -1 -1 1095.21 185.47 1220.76 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n153 1 Car -1 -1 -1 955.00 183.86 1066.81 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n153 3 Car -1 -1 -1 1029.77 183.94 1155.99 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n153 27 Pedestrian -1 -1 -1 301.31 160.52 331.70 244.50 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n153 37 Pedestrian -1 -1 -1 364.11 158.85 458.51 366.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n153 20 Pedestrian -1 -1 -1 350.84 164.17 385.80 241.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n153 46 Pedestrian -1 -1 -1 672.25 169.58 699.82 244.19 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n153 5 Car -1 -1 -1 601.78 173.08 636.91 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n153 50 Cyclist -1 -1 -1 508.78 165.60 537.96 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n153 51 Pedestrian -1 -1 -1 489.60 168.18 508.57 207.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n153 25 Pedestrian -1 -1 -1 193.52 161.88 207.81 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n153 38 Car -1 -1 -1 598.83 173.64 622.39 193.16 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n153 52 Pedestrian -1 -1 -1 181.05 160.13 196.71 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n154 2 Car -1 -1 -1 1095.26 185.40 1220.55 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n154 1 Car -1 -1 -1 955.01 183.86 1066.67 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n154 3 Car -1 -1 -1 1029.53 183.91 1156.06 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n154 27 Pedestrian -1 -1 -1 305.23 160.08 333.51 244.87 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n154 37 Pedestrian -1 -1 -1 347.42 159.07 452.00 366.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n154 46 Pedestrian -1 -1 -1 670.24 168.80 694.67 243.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n154 5 Car -1 -1 -1 601.50 173.09 637.16 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n154 20 Pedestrian -1 -1 -1 352.13 164.84 386.14 241.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n154 50 Cyclist -1 -1 -1 509.23 165.18 536.19 230.78 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n154 51 Pedestrian -1 -1 -1 493.18 166.58 510.58 208.34 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n154 25 Pedestrian -1 -1 -1 193.71 162.01 207.71 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n154 38 Car -1 -1 -1 598.64 173.70 622.35 193.16 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n155 2 Car -1 -1 -1 1095.17 185.42 1220.71 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n155 1 Car -1 -1 -1 955.04 183.88 1066.76 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n155 3 Car -1 -1 -1 1029.58 183.92 1156.06 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n155 27 Pedestrian -1 -1 -1 307.34 160.95 337.37 244.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n155 5 Car -1 -1 -1 601.44 173.06 637.19 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n155 46 Pedestrian -1 -1 -1 667.98 168.49 692.01 243.62 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n155 37 Pedestrian -1 -1 -1 334.55 161.21 442.21 364.06 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n155 20 Pedestrian -1 -1 -1 356.79 164.37 388.64 242.10 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n155 50 Cyclist -1 -1 -1 510.71 165.37 534.85 225.80 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n155 25 Pedestrian -1 -1 -1 193.76 162.37 207.76 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n155 51 Pedestrian -1 -1 -1 496.54 166.90 511.51 207.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n155 38 Car -1 -1 -1 598.50 173.68 622.40 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n155 53 Pedestrian -1 -1 -1 181.24 160.49 196.55 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n156 2 Car -1 -1 -1 1095.19 185.38 1220.81 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n156 1 Car -1 -1 -1 955.01 183.86 1066.75 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n156 3 Car -1 -1 -1 1029.64 183.94 1156.04 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n156 46 Pedestrian -1 -1 -1 665.60 169.01 691.40 242.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n156 37 Pedestrian -1 -1 -1 328.87 160.48 432.28 364.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n156 27 Pedestrian -1 -1 -1 308.06 161.55 339.04 245.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n156 20 Pedestrian -1 -1 -1 357.23 164.41 396.62 246.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n156 5 Car -1 -1 -1 601.48 173.08 637.17 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n156 51 Pedestrian -1 -1 -1 499.86 167.06 514.43 208.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n156 25 Pedestrian -1 -1 -1 193.95 162.20 208.02 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n156 50 Cyclist -1 -1 -1 509.39 167.35 535.09 228.73 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n156 38 Car -1 -1 -1 598.43 173.80 622.20 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n156 53 Pedestrian -1 -1 -1 180.97 160.40 196.78 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n157 2 Car -1 -1 -1 1095.23 185.40 1220.84 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n157 1 Car -1 -1 -1 954.92 183.87 1066.69 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n157 3 Car -1 -1 -1 1029.53 183.91 1156.14 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n157 46 Pedestrian -1 -1 -1 661.39 169.55 689.99 242.47 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n157 5 Car -1 -1 -1 601.27 172.96 637.23 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n157 27 Pedestrian -1 -1 -1 311.52 161.57 341.96 245.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n157 37 Pedestrian -1 -1 -1 318.77 161.19 412.70 364.69 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n157 50 Cyclist -1 -1 -1 508.05 166.04 535.32 225.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n157 25 Pedestrian -1 -1 -1 193.92 162.27 208.20 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n157 20 Pedestrian -1 -1 -1 360.50 164.87 401.13 246.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n157 38 Car -1 -1 -1 598.30 173.62 622.44 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n157 51 Pedestrian -1 -1 -1 501.15 168.28 514.62 207.89 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n157 53 Pedestrian -1 -1 -1 181.07 160.84 196.62 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n158 2 Car -1 -1 -1 1095.21 185.43 1220.85 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n158 1 Car -1 -1 -1 954.81 183.85 1066.78 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n158 3 Car -1 -1 -1 1029.69 183.89 1156.04 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n158 46 Pedestrian -1 -1 -1 660.24 169.32 688.78 242.54 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n158 5 Car -1 -1 -1 601.44 173.00 637.23 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n158 20 Pedestrian -1 -1 -1 367.12 165.42 402.60 245.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n158 27 Pedestrian -1 -1 -1 316.06 163.10 345.04 247.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n158 50 Cyclist -1 -1 -1 505.05 166.31 534.08 224.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n158 37 Pedestrian -1 -1 -1 307.58 160.80 393.95 365.49 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n158 25 Pedestrian -1 -1 -1 193.92 161.98 208.48 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n158 38 Car -1 -1 -1 598.30 173.65 622.17 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n158 53 Pedestrian -1 -1 -1 180.97 160.71 196.98 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n159 2 Car -1 -1 -1 1095.25 185.45 1220.95 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n159 1 Car -1 -1 -1 954.82 183.81 1066.74 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n159 3 Car -1 -1 -1 1029.88 183.95 1155.95 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n159 50 Cyclist -1 -1 -1 502.84 167.07 533.17 223.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n159 20 Pedestrian -1 -1 -1 374.42 165.68 408.83 246.41 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n159 46 Pedestrian -1 -1 -1 657.77 168.67 683.97 241.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n159 27 Pedestrian -1 -1 -1 317.63 163.00 351.55 247.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n159 5 Car -1 -1 -1 601.52 172.96 637.16 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n159 37 Pedestrian -1 -1 -1 295.72 159.65 390.12 365.87 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n159 25 Pedestrian -1 -1 -1 193.42 161.74 208.56 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n159 38 Car -1 -1 -1 598.41 173.58 622.19 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n159 53 Pedestrian -1 -1 -1 181.40 160.98 196.79 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n159 54 Cyclist -1 -1 -1 505.45 169.04 517.41 206.13 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n160 2 Car -1 -1 -1 1095.54 185.59 1220.68 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n160 1 Car -1 -1 -1 954.83 183.82 1066.66 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n160 3 Car -1 -1 -1 1029.67 183.93 1156.14 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n160 20 Pedestrian -1 -1 -1 378.55 166.03 412.67 245.87 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n160 46 Pedestrian -1 -1 -1 656.04 168.17 679.53 239.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n160 5 Car -1 -1 -1 601.57 173.07 637.10 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n160 37 Pedestrian -1 -1 -1 279.29 161.01 376.19 365.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n160 50 Cyclist -1 -1 -1 501.75 166.99 528.28 222.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n160 27 Pedestrian -1 -1 -1 318.03 163.74 352.69 247.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n160 25 Pedestrian -1 -1 -1 193.32 161.79 208.36 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n160 38 Car -1 -1 -1 598.24 173.59 622.12 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n160 53 Pedestrian -1 -1 -1 181.50 160.98 196.69 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n160 55 Pedestrian -1 -1 -1 525.66 168.21 541.14 208.33 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n161 2 Car -1 -1 -1 1095.20 185.47 1220.93 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n161 1 Car -1 -1 -1 954.85 183.80 1066.82 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n161 3 Car -1 -1 -1 1029.90 183.93 1155.92 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n161 46 Pedestrian -1 -1 -1 650.19 168.43 676.88 238.50 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n161 37 Pedestrian -1 -1 -1 259.72 161.69 379.48 364.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n161 20 Pedestrian -1 -1 -1 384.58 164.59 414.14 244.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n161 5 Car -1 -1 -1 601.58 172.88 637.11 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n161 50 Cyclist -1 -1 -1 501.35 166.92 526.81 221.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n161 27 Pedestrian -1 -1 -1 321.98 163.61 354.93 249.07 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n161 55 Pedestrian -1 -1 -1 529.31 168.50 543.01 207.55 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n161 25 Pedestrian -1 -1 -1 193.11 161.56 208.49 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n161 38 Car -1 -1 -1 598.30 173.51 621.87 193.30 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n161 53 Pedestrian -1 -1 -1 181.23 160.70 196.98 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n162 2 Car -1 -1 -1 1095.27 185.46 1220.90 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n162 1 Car -1 -1 -1 955.10 183.85 1066.59 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n162 3 Car -1 -1 -1 1029.88 183.94 1156.00 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n162 37 Pedestrian -1 -1 -1 244.71 162.96 370.76 362.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n162 27 Pedestrian -1 -1 -1 326.59 163.53 357.66 249.38 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n162 20 Pedestrian -1 -1 -1 390.27 164.64 417.71 244.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n162 46 Pedestrian -1 -1 -1 650.11 168.85 675.63 238.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n162 5 Car -1 -1 -1 601.66 172.89 637.13 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n162 55 Pedestrian -1 -1 -1 530.41 168.16 544.02 208.02 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n162 50 Cyclist -1 -1 -1 499.24 166.50 523.50 220.34 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n162 25 Pedestrian -1 -1 -1 192.87 161.38 208.50 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n162 38 Car -1 -1 -1 598.19 173.47 621.96 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n162 53 Pedestrian -1 -1 -1 181.10 160.67 196.92 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n162 56 Pedestrian -1 -1 -1 516.27 169.41 529.31 209.06 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n163 2 Car -1 -1 -1 1095.22 185.47 1220.76 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n163 1 Car -1 -1 -1 955.08 183.92 1066.47 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n163 3 Car -1 -1 -1 1029.74 183.85 1155.90 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n163 27 Pedestrian -1 -1 -1 325.94 163.18 359.57 249.28 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n163 20 Pedestrian -1 -1 -1 391.85 164.10 424.37 245.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n163 46 Pedestrian -1 -1 -1 648.12 169.03 672.69 237.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n163 37 Pedestrian -1 -1 -1 223.05 161.12 355.34 364.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n163 5 Car -1 -1 -1 601.81 172.82 637.04 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n163 25 Pedestrian -1 -1 -1 192.45 161.23 208.81 198.59 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n163 55 Pedestrian -1 -1 -1 533.12 168.51 546.81 207.81 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n163 56 Pedestrian -1 -1 -1 517.59 168.17 532.64 208.04 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n163 38 Car -1 -1 -1 598.42 173.53 621.82 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n163 50 Cyclist -1 -1 -1 499.23 165.28 520.20 217.85 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n163 53 Pedestrian -1 -1 -1 180.88 160.45 197.15 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n164 2 Car -1 -1 -1 1095.25 185.51 1220.91 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n164 1 Car -1 -1 -1 954.96 183.89 1066.53 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n164 3 Car -1 -1 -1 1029.71 183.88 1155.93 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n164 20 Pedestrian -1 -1 -1 394.46 163.95 434.39 246.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n164 27 Pedestrian -1 -1 -1 332.02 162.65 361.87 249.84 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n164 5 Car -1 -1 -1 601.90 172.71 636.92 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n164 46 Pedestrian -1 -1 -1 647.19 168.83 670.44 237.26 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n164 55 Pedestrian -1 -1 -1 533.67 168.92 548.20 207.37 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n164 37 Pedestrian -1 -1 -1 206.67 160.75 333.66 364.11 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n164 25 Pedestrian -1 -1 -1 192.53 161.25 208.60 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n164 38 Car -1 -1 -1 598.36 173.56 621.72 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n164 50 Cyclist -1 -1 -1 497.22 165.82 518.02 216.81 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n164 56 Pedestrian -1 -1 -1 511.26 168.18 526.34 207.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n164 53 Pedestrian -1 -1 -1 180.69 160.40 197.15 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n164 57 Pedestrian -1 -1 -1 520.00 169.10 534.07 207.59 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n165 2 Car -1 -1 -1 1095.21 185.47 1220.89 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n165 3 Car -1 -1 -1 1029.52 183.84 1155.94 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n165 1 Car -1 -1 -1 955.00 183.88 1066.45 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n165 46 Pedestrian -1 -1 -1 644.85 168.53 668.27 236.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n165 20 Pedestrian -1 -1 -1 398.30 163.95 437.61 246.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n165 27 Pedestrian -1 -1 -1 335.21 162.21 366.39 250.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n165 5 Car -1 -1 -1 602.22 172.81 636.67 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n165 55 Pedestrian -1 -1 -1 533.78 169.24 549.72 207.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n165 25 Pedestrian -1 -1 -1 192.44 161.19 208.58 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n165 38 Car -1 -1 -1 598.63 173.57 621.41 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n165 57 Pedestrian -1 -1 -1 525.86 168.02 540.21 208.19 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n165 50 Cyclist -1 -1 -1 496.52 166.03 515.80 216.80 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n165 37 Pedestrian -1 -1 -1 181.06 161.76 321.03 362.88 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n165 56 Pedestrian -1 -1 -1 513.82 170.31 528.86 208.41 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n165 53 Pedestrian -1 -1 -1 180.52 160.34 197.36 198.86 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n166 2 Car -1 -1 -1 1095.40 185.44 1220.79 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n166 1 Car -1 -1 -1 954.93 183.87 1066.64 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n166 3 Car -1 -1 -1 1029.92 183.93 1155.77 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n166 27 Pedestrian -1 -1 -1 339.71 163.27 374.09 250.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n166 46 Pedestrian -1 -1 -1 643.04 169.01 667.57 236.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n166 20 Pedestrian -1 -1 -1 400.14 162.89 439.16 246.68 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n166 5 Car -1 -1 -1 603.15 172.54 637.02 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n166 56 Pedestrian -1 -1 -1 516.33 168.10 530.00 207.86 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n166 55 Pedestrian -1 -1 -1 535.86 169.29 552.51 207.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n166 38 Car -1 -1 -1 598.81 173.49 621.08 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n166 25 Pedestrian -1 -1 -1 192.48 161.32 208.78 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n166 37 Pedestrian -1 -1 -1 171.65 160.59 307.25 364.22 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n166 57 Pedestrian -1 -1 -1 527.19 168.59 541.02 207.99 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n166 58 Pedestrian -1 -1 -1 495.41 165.83 515.54 216.03 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n167 2 Car -1 -1 -1 1095.29 185.47 1220.83 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n167 1 Car -1 -1 -1 955.02 183.94 1066.60 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n167 3 Car -1 -1 -1 1029.99 183.94 1155.70 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n167 20 Pedestrian -1 -1 -1 406.71 163.65 440.21 246.33 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n167 27 Pedestrian -1 -1 -1 342.35 162.89 378.64 251.83 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n167 5 Car -1 -1 -1 602.23 172.82 636.53 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n167 46 Pedestrian -1 -1 -1 640.61 169.86 665.94 236.43 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n167 57 Pedestrian -1 -1 -1 528.67 167.59 544.92 208.36 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n167 56 Pedestrian -1 -1 -1 518.85 167.58 531.64 207.71 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n167 55 Pedestrian -1 -1 -1 538.94 169.23 553.14 207.31 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n167 38 Car -1 -1 -1 598.95 173.53 620.93 192.88 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n167 25 Pedestrian -1 -1 -1 193.28 161.43 208.73 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n167 58 Pedestrian -1 -1 -1 492.53 166.24 514.14 215.73 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n167 37 Pedestrian -1 -1 -1 142.83 160.23 297.95 364.96 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n167 59 Cyclist -1 -1 -1 492.53 166.24 514.14 215.73 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n168 2 Car -1 -1 -1 1095.38 185.47 1220.62 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n168 1 Car -1 -1 -1 955.08 183.96 1066.49 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n168 3 Car -1 -1 -1 1030.03 183.94 1155.57 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n168 20 Pedestrian -1 -1 -1 415.39 163.02 444.90 247.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n168 27 Pedestrian -1 -1 -1 347.57 163.48 381.12 251.25 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n168 5 Car -1 -1 -1 602.31 172.81 636.29 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n168 46 Pedestrian -1 -1 -1 639.76 169.69 665.52 236.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n168 55 Pedestrian -1 -1 -1 541.56 168.97 556.30 207.30 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n168 56 Pedestrian -1 -1 -1 519.65 168.26 533.03 207.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n168 57 Pedestrian -1 -1 -1 529.23 167.39 547.76 208.70 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n168 38 Car -1 -1 -1 599.30 173.70 620.91 193.05 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n168 25 Pedestrian -1 -1 -1 193.54 161.83 208.68 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n168 58 Pedestrian -1 -1 -1 492.17 166.45 513.28 214.35 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n168 37 Pedestrian -1 -1 -1 118.96 158.25 291.11 367.08 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n168 60 Cyclist -1 -1 -1 137.60 158.80 300.90 365.81 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n168 61 Pedestrian -1 -1 -1 180.66 160.45 197.99 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n169 2 Car -1 -1 -1 1095.20 185.41 1220.89 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n169 1 Car -1 -1 -1 955.04 183.91 1066.53 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n169 3 Car -1 -1 -1 1030.05 183.91 1155.65 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n169 20 Pedestrian -1 -1 -1 419.11 163.06 449.36 247.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n169 27 Pedestrian -1 -1 -1 354.49 161.90 384.71 252.24 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n169 5 Car -1 -1 -1 602.24 172.57 636.32 202.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n169 46 Pedestrian -1 -1 -1 638.85 169.59 663.72 235.44 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n169 57 Pedestrian -1 -1 -1 533.34 167.69 549.94 208.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n169 55 Pedestrian -1 -1 -1 541.73 168.95 557.30 207.49 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n169 56 Pedestrian -1 -1 -1 520.59 168.36 533.83 207.45 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n169 38 Car -1 -1 -1 598.98 173.66 620.87 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n169 25 Pedestrian -1 -1 -1 192.70 161.50 209.54 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n169 37 Pedestrian -1 -1 -1 109.52 159.18 277.53 366.40 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n169 58 Pedestrian -1 -1 -1 491.67 166.56 513.13 213.70 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n169 61 Pedestrian -1 -1 -1 180.21 161.13 196.79 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n170 2 Car -1 -1 -1 1095.13 185.44 1220.87 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n170 1 Car -1 -1 -1 955.15 183.90 1066.51 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n170 3 Car -1 -1 -1 1029.98 183.91 1155.68 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n170 20 Pedestrian -1 -1 -1 421.77 164.54 454.94 248.27 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n170 27 Pedestrian -1 -1 -1 362.09 162.31 391.84 252.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n170 46 Pedestrian -1 -1 -1 637.10 168.61 660.83 234.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n170 5 Car -1 -1 -1 602.25 172.68 636.40 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n170 56 Pedestrian -1 -1 -1 522.33 168.74 535.76 207.13 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n170 55 Pedestrian -1 -1 -1 544.93 169.01 560.41 207.09 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n170 37 Pedestrian -1 -1 -1 105.61 158.73 258.61 366.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n170 38 Car -1 -1 -1 598.97 173.75 620.91 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n170 57 Pedestrian -1 -1 -1 537.39 168.91 552.64 208.01 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n170 25 Pedestrian -1 -1 -1 194.48 162.62 208.88 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n170 62 Cyclist -1 -1 -1 491.53 167.09 513.27 213.42 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n171 2 Car -1 -1 -1 1095.18 185.42 1220.93 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n171 1 Car -1 -1 -1 955.19 183.90 1066.55 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n171 3 Car -1 -1 -1 1030.02 183.92 1155.70 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n171 20 Pedestrian -1 -1 -1 425.26 164.53 458.94 249.43 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n171 27 Pedestrian -1 -1 -1 363.30 162.62 398.89 251.73 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n171 5 Car -1 -1 -1 602.21 172.69 636.38 202.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n171 46 Pedestrian -1 -1 -1 635.34 168.53 658.92 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n171 56 Pedestrian -1 -1 -1 523.20 168.36 537.24 207.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n171 55 Pedestrian -1 -1 -1 545.32 169.01 561.58 207.78 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n171 38 Car -1 -1 -1 598.99 173.69 621.01 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n171 37 Pedestrian -1 -1 -1 86.35 155.34 239.29 364.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n171 25 Pedestrian -1 -1 -1 194.94 163.52 208.27 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n171 62 Cyclist -1 -1 -1 491.42 167.27 513.03 212.35 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n171 63 Pedestrian -1 -1 -1 180.98 161.91 197.28 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n171 64 Pedestrian -1 -1 -1 288.13 157.49 305.13 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n172 2 Car -1 -1 -1 1095.09 185.40 1220.79 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n172 1 Car -1 -1 -1 955.14 183.96 1066.52 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n172 3 Car -1 -1 -1 1029.81 183.94 1155.87 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n172 27 Pedestrian -1 -1 -1 364.07 162.18 405.76 255.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n172 20 Pedestrian -1 -1 -1 429.73 164.24 462.88 249.87 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n172 5 Car -1 -1 -1 602.20 172.55 636.27 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n172 46 Pedestrian -1 -1 -1 632.65 168.63 657.32 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n172 55 Pedestrian -1 -1 -1 549.63 169.29 563.27 207.00 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n172 56 Pedestrian -1 -1 -1 525.54 167.67 539.14 208.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n172 37 Pedestrian -1 -1 -1 71.56 154.01 223.82 366.19 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n172 25 Pedestrian -1 -1 -1 194.43 162.98 208.32 197.08 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n172 38 Car -1 -1 -1 598.98 173.66 621.23 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n172 64 Pedestrian -1 -1 -1 287.77 157.60 304.39 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n172 63 Pedestrian -1 -1 -1 181.52 161.21 197.20 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n172 62 Cyclist -1 -1 -1 492.12 167.59 514.11 211.30 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n172 65 Pedestrian -1 -1 -1 541.97 170.12 556.56 208.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n173 2 Car -1 -1 -1 1095.19 185.45 1220.57 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n173 1 Car -1 -1 -1 955.09 183.93 1066.55 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n173 3 Car -1 -1 -1 1029.58 183.90 1155.93 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n173 27 Pedestrian -1 -1 -1 366.83 162.22 410.23 255.50 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n173 5 Car -1 -1 -1 602.45 172.63 636.03 202.04 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n173 20 Pedestrian -1 -1 -1 437.33 163.58 467.37 250.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n173 46 Pedestrian -1 -1 -1 632.93 168.67 655.43 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n173 56 Pedestrian -1 -1 -1 526.12 167.96 540.43 207.62 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n173 55 Pedestrian -1 -1 -1 549.68 168.62 565.25 207.37 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n173 25 Pedestrian -1 -1 -1 194.17 162.75 208.29 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n173 37 Pedestrian -1 -1 -1 60.81 158.31 211.64 366.92 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n173 64 Pedestrian -1 -1 -1 287.84 157.72 304.22 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n173 38 Car -1 -1 -1 598.99 173.58 621.31 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n173 63 Pedestrian -1 -1 -1 181.57 161.41 197.32 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n173 62 Cyclist -1 -1 -1 494.51 166.96 513.79 210.00 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n174 2 Car -1 -1 -1 1094.93 185.41 1221.05 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n174 1 Car -1 -1 -1 954.96 183.85 1066.65 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n174 3 Car -1 -1 -1 1029.47 183.88 1156.08 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n174 20 Pedestrian -1 -1 -1 441.16 163.51 472.13 249.23 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n174 27 Pedestrian -1 -1 -1 376.87 162.07 413.50 255.68 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n174 5 Car -1 -1 -1 602.59 172.50 635.84 202.11 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n174 46 Pedestrian -1 -1 -1 630.04 168.68 652.87 232.78 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n174 55 Pedestrian -1 -1 -1 545.04 170.83 562.38 208.73 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n174 56 Pedestrian -1 -1 -1 526.05 168.49 542.90 207.77 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n174 25 Pedestrian -1 -1 -1 194.09 162.53 208.15 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n174 64 Pedestrian -1 -1 -1 287.68 157.96 303.71 198.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n174 37 Pedestrian -1 -1 -1 34.21 160.55 207.62 366.31 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n174 38 Car -1 -1 -1 599.23 173.63 621.46 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n174 63 Pedestrian -1 -1 -1 181.55 161.37 197.39 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n174 62 Cyclist -1 -1 -1 496.97 167.62 514.81 208.98 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n175 2 Car -1 -1 -1 1095.18 185.47 1220.86 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n175 1 Car -1 -1 -1 955.03 183.96 1066.61 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n175 3 Car -1 -1 -1 1029.69 183.96 1156.03 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n175 20 Pedestrian -1 -1 -1 441.86 163.47 479.87 249.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n175 5 Car -1 -1 -1 602.33 172.39 635.66 202.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n175 27 Pedestrian -1 -1 -1 384.90 161.46 414.47 256.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n175 46 Pedestrian -1 -1 -1 629.54 169.06 651.47 232.31 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n175 55 Pedestrian -1 -1 -1 548.15 170.49 564.64 208.91 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n175 25 Pedestrian -1 -1 -1 194.11 162.49 208.08 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n175 56 Pedestrian -1 -1 -1 526.00 168.13 543.68 208.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n175 37 Pedestrian -1 -1 -1 20.57 161.68 190.54 365.27 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n175 38 Car -1 -1 -1 599.18 173.54 621.36 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n175 64 Pedestrian -1 -1 -1 287.17 157.66 303.68 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n175 63 Pedestrian -1 -1 -1 181.47 161.31 197.18 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n175 62 Cyclist -1 -1 -1 498.87 167.40 515.90 208.63 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n175 66 Pedestrian -1 -1 -1 554.68 170.10 572.83 206.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n175 67 Pedestrian -1 -1 -1 498.87 167.40 515.90 208.63 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n176 2 Car -1 -1 -1 1095.22 185.49 1220.85 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n176 1 Car -1 -1 -1 954.93 183.99 1066.68 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n176 3 Car -1 -1 -1 1029.60 183.94 1156.04 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n176 5 Car -1 -1 -1 602.13 172.25 635.55 202.23 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n176 20 Pedestrian -1 -1 -1 444.45 164.55 483.73 249.88 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n176 27 Pedestrian -1 -1 -1 388.04 161.23 419.52 256.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n176 56 Pedestrian -1 -1 -1 528.33 168.37 545.28 208.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n176 46 Pedestrian -1 -1 -1 628.91 168.27 650.97 231.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n176 66 Pedestrian -1 -1 -1 554.20 170.08 575.32 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n176 25 Pedestrian -1 -1 -1 193.65 162.24 208.05 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n176 38 Car -1 -1 -1 599.18 173.41 621.33 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n176 37 Pedestrian -1 -1 -1 4.19 169.69 183.47 362.73 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n176 63 Pedestrian -1 -1 -1 181.21 161.46 197.03 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n176 64 Pedestrian -1 -1 -1 286.67 157.59 304.38 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n176 62 Cyclist -1 -1 -1 501.05 167.42 517.88 208.05 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n177 2 Car -1 -1 -1 1095.12 185.37 1220.98 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n177 1 Car -1 -1 -1 954.96 183.89 1066.72 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n177 3 Car -1 -1 -1 1029.60 183.91 1156.04 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n177 5 Car -1 -1 -1 602.10 172.28 635.73 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n177 27 Pedestrian -1 -1 -1 389.95 163.01 426.57 255.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n177 20 Pedestrian -1 -1 -1 447.61 163.30 488.16 251.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n177 56 Pedestrian -1 -1 -1 530.65 167.63 546.34 208.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n177 66 Pedestrian -1 -1 -1 558.99 169.33 577.28 207.26 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n177 25 Pedestrian -1 -1 -1 193.46 162.14 207.92 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n177 46 Pedestrian -1 -1 -1 628.54 168.77 650.49 232.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n177 38 Car -1 -1 -1 599.26 173.41 621.15 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n177 62 Cyclist -1 -1 -1 502.17 167.42 520.37 207.48 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n177 63 Pedestrian -1 -1 -1 181.07 161.59 196.68 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n177 37 Pedestrian -1 -1 -1 -3.61 167.67 160.97 364.72 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n177 68 Pedestrian -1 -1 -1 552.63 168.84 568.02 208.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n177 69 Pedestrian -1 -1 -1 -4.21 160.44 146.48 366.35 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n178 2 Car -1 -1 -1 1095.28 185.46 1220.64 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n178 1 Car -1 -1 -1 954.92 183.94 1066.68 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n178 3 Car -1 -1 -1 1029.78 183.93 1155.88 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n178 27 Pedestrian -1 -1 -1 395.03 162.26 433.71 257.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n178 20 Pedestrian -1 -1 -1 453.74 163.76 491.01 250.69 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n178 5 Car -1 -1 -1 601.78 172.35 635.92 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n178 56 Pedestrian -1 -1 -1 533.69 167.58 548.36 208.26 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n178 46 Pedestrian -1 -1 -1 626.13 169.77 648.59 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n178 66 Pedestrian -1 -1 -1 563.35 169.45 579.63 207.13 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n178 25 Pedestrian -1 -1 -1 193.47 161.98 207.98 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n178 68 Pedestrian -1 -1 -1 551.69 169.77 570.84 208.64 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n178 62 Cyclist -1 -1 -1 504.94 166.96 523.36 207.38 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n178 38 Car -1 -1 -1 599.13 173.44 621.32 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n178 37 Pedestrian -1 -1 -1 -4.88 160.63 131.71 365.71 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n178 63 Pedestrian -1 -1 -1 181.05 161.37 196.94 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n178 70 Pedestrian -1 -1 -1 286.65 157.79 304.27 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n179 2 Car -1 -1 -1 1095.53 185.44 1220.53 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n179 1 Car -1 -1 -1 954.94 183.91 1066.62 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n179 3 Car -1 -1 -1 1029.69 183.93 1156.00 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n179 27 Pedestrian -1 -1 -1 401.52 162.22 436.17 258.15 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n179 5 Car -1 -1 -1 601.82 172.58 636.09 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n179 20 Pedestrian -1 -1 -1 460.27 163.38 493.95 251.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n179 46 Pedestrian -1 -1 -1 624.54 170.13 646.93 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n179 62 Cyclist -1 -1 -1 508.72 167.40 526.30 206.93 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n179 25 Pedestrian -1 -1 -1 193.24 161.98 208.22 197.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n179 66 Pedestrian -1 -1 -1 567.37 168.90 581.96 207.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n179 68 Pedestrian -1 -1 -1 554.25 169.67 572.93 208.78 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n179 56 Pedestrian -1 -1 -1 535.71 167.81 549.56 207.96 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n179 38 Car -1 -1 -1 599.28 173.42 621.26 193.71 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n179 37 Pedestrian -1 -1 -1 -6.42 159.44 117.83 367.11 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n179 63 Pedestrian -1 -1 -1 181.17 161.06 197.41 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n179 70 Pedestrian -1 -1 -1 286.95 157.97 304.15 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n180 2 Car -1 -1 -1 1095.33 185.42 1220.63 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n180 1 Car -1 -1 -1 955.08 183.86 1066.58 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n180 3 Car -1 -1 -1 1029.89 183.99 1155.82 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n180 27 Pedestrian -1 -1 -1 409.86 161.55 441.27 258.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n180 5 Car -1 -1 -1 601.61 172.48 636.62 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n180 20 Pedestrian -1 -1 -1 466.30 162.11 500.48 252.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n180 56 Pedestrian -1 -1 -1 537.35 168.14 551.87 207.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n180 46 Pedestrian -1 -1 -1 621.92 169.86 644.63 231.83 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n180 68 Pedestrian -1 -1 -1 555.79 170.04 573.93 208.63 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n180 25 Pedestrian -1 -1 -1 193.33 162.01 208.23 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n180 66 Pedestrian -1 -1 -1 568.71 169.99 584.18 206.95 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n180 62 Cyclist -1 -1 -1 510.85 167.77 528.04 205.83 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n180 38 Car -1 -1 -1 599.24 173.23 621.18 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n180 63 Pedestrian -1 -1 -1 181.23 161.01 197.25 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n180 37 Pedestrian -1 -1 -1 -6.07 165.74 94.27 368.18 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n180 70 Pedestrian -1 -1 -1 287.05 157.91 303.74 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n180 71 Pedestrian -1 -1 -1 512.46 167.59 528.94 206.04 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n181 2 Car -1 -1 -1 1095.34 185.53 1220.73 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n181 1 Car -1 -1 -1 955.15 183.92 1066.53 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n181 3 Car -1 -1 -1 1029.89 183.98 1155.84 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n181 27 Pedestrian -1 -1 -1 413.82 162.16 447.24 258.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n181 20 Pedestrian -1 -1 -1 469.75 163.73 507.19 254.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n181 5 Car -1 -1 -1 601.62 172.36 636.72 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n181 56 Pedestrian -1 -1 -1 538.34 168.74 552.69 207.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n181 46 Pedestrian -1 -1 -1 621.55 169.27 642.55 229.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n181 66 Pedestrian -1 -1 -1 570.11 171.20 589.19 207.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n181 68 Pedestrian -1 -1 -1 560.12 170.22 575.65 208.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n181 25 Pedestrian -1 -1 -1 193.37 161.99 208.16 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n181 38 Car -1 -1 -1 599.04 173.07 621.19 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n181 71 Pedestrian -1 -1 -1 515.23 167.94 529.92 205.07 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n181 62 Cyclist -1 -1 -1 515.23 167.94 529.92 205.07 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n181 70 Pedestrian -1 -1 -1 284.03 157.34 303.08 199.96 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n181 37 Pedestrian -1 -1 -1 -2.88 160.34 83.56 366.74 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n181 63 Pedestrian -1 -1 -1 181.27 160.55 197.53 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n182 2 Car -1 -1 -1 1095.28 185.50 1220.91 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n182 1 Car -1 -1 -1 955.07 183.90 1066.78 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n182 3 Car -1 -1 -1 1029.93 183.99 1155.90 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n182 27 Pedestrian -1 -1 -1 415.91 161.47 452.89 259.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n182 20 Pedestrian -1 -1 -1 472.64 163.98 511.75 254.17 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n182 5 Car -1 -1 -1 601.46 172.03 636.35 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n182 46 Pedestrian -1 -1 -1 618.45 169.28 639.99 228.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n182 25 Pedestrian -1 -1 -1 193.54 161.82 208.15 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n182 71 Pedestrian -1 -1 -1 517.93 167.61 531.85 204.78 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n182 66 Pedestrian -1 -1 -1 573.86 170.45 591.36 206.61 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n182 56 Pedestrian -1 -1 -1 539.18 168.35 553.13 208.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n182 68 Pedestrian -1 -1 -1 562.31 170.67 576.19 208.04 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n182 38 Car -1 -1 -1 598.50 172.88 621.44 194.12 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n182 70 Pedestrian -1 -1 -1 284.03 157.29 303.21 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n182 63 Pedestrian -1 -1 -1 181.46 160.59 197.51 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n183 2 Car -1 -1 -1 1094.93 185.50 1221.15 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n183 1 Car -1 -1 -1 955.16 183.92 1066.68 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n183 3 Car -1 -1 -1 1029.87 183.91 1155.88 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n183 5 Car -1 -1 -1 601.25 172.01 635.67 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n183 20 Pedestrian -1 -1 -1 477.48 163.99 514.46 254.86 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n183 27 Pedestrian -1 -1 -1 418.35 162.78 459.36 262.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n183 46 Pedestrian -1 -1 -1 614.85 168.71 638.03 227.91 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n183 68 Pedestrian -1 -1 -1 565.09 170.45 579.53 208.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n183 25 Pedestrian -1 -1 -1 193.28 161.69 207.97 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n183 71 Pedestrian -1 -1 -1 520.60 167.61 534.02 204.31 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n183 66 Pedestrian -1 -1 -1 576.72 170.18 591.81 206.40 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n183 56 Pedestrian -1 -1 -1 540.39 168.07 554.94 209.08 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n183 70 Pedestrian -1 -1 -1 287.15 157.22 303.64 200.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n183 38 Car -1 -1 -1 597.94 172.64 622.12 193.98 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n183 63 Pedestrian -1 -1 -1 181.55 160.53 197.46 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n184 2 Car -1 -1 -1 1095.29 185.51 1220.92 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n184 1 Car -1 -1 -1 955.14 183.88 1066.71 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n184 3 Car -1 -1 -1 1030.02 183.94 1155.76 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n184 27 Pedestrian -1 -1 -1 421.03 162.37 462.24 263.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n184 5 Car -1 -1 -1 600.97 172.20 634.20 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n184 20 Pedestrian -1 -1 -1 485.50 164.52 519.24 254.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n184 46 Pedestrian -1 -1 -1 613.68 169.02 636.69 228.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n184 71 Pedestrian -1 -1 -1 523.49 168.33 537.03 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n184 56 Pedestrian -1 -1 -1 541.22 168.25 556.76 208.82 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n184 25 Pedestrian -1 -1 -1 193.52 161.79 208.01 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n184 70 Pedestrian -1 -1 -1 286.99 157.52 303.88 200.79 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n184 66 Pedestrian -1 -1 -1 582.74 168.63 597.31 208.06 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n184 68 Pedestrian -1 -1 -1 565.38 170.34 581.05 208.25 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n184 63 Pedestrian -1 -1 -1 181.60 160.28 197.81 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n185 2 Car -1 -1 -1 1095.05 185.49 1221.05 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n185 1 Car -1 -1 -1 954.93 183.89 1066.82 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n185 3 Car -1 -1 -1 1030.38 184.04 1155.51 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n185 20 Pedestrian -1 -1 -1 490.91 163.41 522.44 255.44 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n185 27 Pedestrian -1 -1 -1 431.14 160.54 466.38 264.49 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n185 5 Car -1 -1 -1 600.70 172.33 633.87 202.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n185 56 Pedestrian -1 -1 -1 540.65 168.19 558.36 208.20 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n185 46 Pedestrian -1 -1 -1 613.55 168.52 635.12 227.98 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n185 70 Pedestrian -1 -1 -1 287.29 158.10 303.78 200.98 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n185 68 Pedestrian -1 -1 -1 566.92 170.71 583.23 208.07 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n185 25 Pedestrian -1 -1 -1 193.70 161.80 208.01 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n185 66 Pedestrian -1 -1 -1 584.44 168.39 599.60 207.53 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n185 71 Pedestrian -1 -1 -1 526.57 167.36 540.35 203.62 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n185 63 Pedestrian -1 -1 -1 181.71 160.34 197.79 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n186 2 Car -1 -1 -1 1095.16 185.42 1220.96 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n186 1 Car -1 -1 -1 955.06 183.82 1066.88 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n186 3 Car -1 -1 -1 1030.21 184.03 1155.66 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n186 20 Pedestrian -1 -1 -1 493.46 162.62 533.89 256.08 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n186 46 Pedestrian -1 -1 -1 610.87 168.87 633.63 227.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n186 27 Pedestrian -1 -1 -1 437.99 159.56 469.88 267.00 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n186 5 Car -1 -1 -1 600.23 172.45 634.84 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n186 25 Pedestrian -1 -1 -1 193.74 161.63 207.92 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n186 68 Pedestrian -1 -1 -1 569.00 170.56 584.79 208.66 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n186 66 Pedestrian -1 -1 -1 585.43 168.39 604.69 208.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n186 70 Pedestrian -1 -1 -1 286.97 158.24 303.97 201.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n186 56 Pedestrian -1 -1 -1 542.80 168.11 560.09 208.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n186 71 Pedestrian -1 -1 -1 529.37 167.40 543.37 203.47 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n186 63 Pedestrian -1 -1 -1 181.57 160.20 197.83 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n187 2 Car -1 -1 -1 1095.21 185.49 1220.97 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n187 1 Car -1 -1 -1 955.15 183.84 1066.75 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n187 3 Car -1 -1 -1 1030.05 184.04 1155.81 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n187 20 Pedestrian -1 -1 -1 496.83 163.65 538.66 256.16 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n187 27 Pedestrian -1 -1 -1 439.37 161.08 476.33 264.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n187 46 Pedestrian -1 -1 -1 610.20 169.00 632.05 227.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n187 68 Pedestrian -1 -1 -1 571.65 170.97 588.24 208.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n187 5 Car -1 -1 -1 599.64 172.17 635.15 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n187 56 Pedestrian -1 -1 -1 543.80 168.09 561.42 208.95 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n187 25 Pedestrian -1 -1 -1 193.52 161.43 207.96 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n187 66 Pedestrian -1 -1 -1 583.59 170.61 611.57 208.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n187 70 Pedestrian -1 -1 -1 284.68 157.68 302.82 201.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n187 72 Cyclist -1 -1 -1 531.62 166.77 545.16 202.23 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n188 2 Car -1 -1 -1 1095.15 185.43 1220.86 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n188 1 Car -1 -1 -1 955.02 183.80 1066.93 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n188 3 Car -1 -1 -1 1030.00 183.97 1155.78 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n188 20 Pedestrian -1 -1 -1 499.56 163.29 544.18 256.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n188 27 Pedestrian -1 -1 -1 443.67 159.90 484.67 267.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n188 5 Car -1 -1 -1 599.85 172.27 635.01 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n188 46 Pedestrian -1 -1 -1 607.47 170.47 630.27 226.51 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n188 68 Pedestrian -1 -1 -1 572.15 170.31 592.28 209.15 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n188 25 Pedestrian -1 -1 -1 193.35 161.32 208.04 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n188 70 Pedestrian -1 -1 -1 286.59 157.54 304.12 200.92 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n188 66 Pedestrian -1 -1 -1 589.18 169.58 609.78 209.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n188 56 Pedestrian -1 -1 -1 546.11 168.87 562.11 209.46 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n188 72 Cyclist -1 -1 -1 534.41 166.96 546.20 201.47 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n188 73 Pedestrian -1 -1 -1 534.41 166.96 546.20 201.47 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n189 2 Car -1 -1 -1 1095.33 185.43 1220.73 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n189 1 Car -1 -1 -1 955.09 183.83 1066.79 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n189 3 Car -1 -1 -1 1029.87 183.98 1155.83 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n189 20 Pedestrian -1 -1 -1 504.65 163.47 546.76 257.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n189 27 Pedestrian -1 -1 -1 447.85 160.98 488.33 268.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n189 46 Pedestrian -1 -1 -1 607.26 171.51 629.43 226.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n189 5 Car -1 -1 -1 600.27 172.07 635.42 202.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n189 56 Pedestrian -1 -1 -1 549.66 167.32 565.30 209.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n189 70 Pedestrian -1 -1 -1 284.22 157.13 302.85 201.12 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n189 68 Pedestrian -1 -1 -1 570.83 171.16 596.41 208.25 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n189 25 Pedestrian -1 -1 -1 193.17 161.18 208.04 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n189 66 Pedestrian -1 -1 -1 595.04 168.95 611.78 209.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n189 73 Pedestrian -1 -1 -1 535.23 166.85 548.07 201.46 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n189 72 Cyclist -1 -1 -1 535.23 166.85 548.07 201.46 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n189 74 Pedestrian -1 -1 -1 181.41 160.62 197.62 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n190 2 Car -1 -1 -1 1095.33 185.51 1220.94 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n190 1 Car -1 -1 -1 955.05 183.83 1066.85 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n190 3 Car -1 -1 -1 1029.98 183.99 1155.83 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n190 20 Pedestrian -1 -1 -1 512.97 162.94 548.00 257.68 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n190 27 Pedestrian -1 -1 -1 458.67 162.63 492.26 266.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n190 56 Pedestrian -1 -1 -1 552.34 167.11 567.72 208.24 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n190 5 Car -1 -1 -1 601.20 171.51 635.55 201.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n190 46 Pedestrian -1 -1 -1 605.65 172.22 627.75 226.44 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n190 70 Pedestrian -1 -1 -1 284.18 157.52 302.59 201.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n190 68 Pedestrian -1 -1 -1 572.84 170.30 599.99 209.09 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n190 25 Pedestrian -1 -1 -1 193.29 161.28 208.00 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n190 66 Pedestrian -1 -1 -1 597.91 166.59 615.06 209.91 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n190 74 Pedestrian -1 -1 -1 181.67 160.73 197.73 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n190 75 Pedestrian -1 -1 -1 340.31 158.66 351.69 184.40 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n191 2 Car -1 -1 -1 1095.21 185.41 1221.08 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n191 1 Car -1 -1 -1 955.12 183.85 1066.72 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n191 3 Car -1 -1 -1 1029.97 184.07 1155.93 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n191 20 Pedestrian -1 -1 -1 519.74 161.79 553.84 258.35 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n191 27 Pedestrian -1 -1 -1 462.97 161.34 497.20 267.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n191 46 Pedestrian -1 -1 -1 603.40 170.65 623.85 226.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n191 5 Car -1 -1 -1 600.73 171.40 635.90 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n191 70 Pedestrian -1 -1 -1 284.31 157.27 302.31 201.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n191 56 Pedestrian -1 -1 -1 553.17 167.36 569.34 208.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n191 68 Pedestrian -1 -1 -1 576.56 170.40 599.15 209.14 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n191 25 Pedestrian -1 -1 -1 193.33 161.35 208.12 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n191 75 Pedestrian -1 -1 -1 340.95 158.80 352.11 184.50 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n191 74 Pedestrian -1 -1 -1 181.80 160.93 197.80 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n191 66 Pedestrian -1 -1 -1 600.01 168.06 619.00 207.98 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n191 76 Cyclist -1 -1 -1 600.01 168.06 619.00 207.98 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n192 2 Car -1 -1 -1 1095.22 185.45 1220.94 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n192 1 Car -1 -1 -1 955.18 183.81 1066.82 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n192 3 Car -1 -1 -1 1029.93 184.04 1155.84 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n192 27 Pedestrian -1 -1 -1 467.48 161.73 507.41 267.24 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n192 20 Pedestrian -1 -1 -1 523.09 162.87 559.59 258.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n192 70 Pedestrian -1 -1 -1 284.04 157.33 301.78 201.44 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n192 5 Car -1 -1 -1 600.20 172.01 635.56 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n192 56 Pedestrian -1 -1 -1 555.45 167.35 571.62 209.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n192 46 Pedestrian -1 -1 -1 601.87 171.33 620.78 223.88 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n192 68 Pedestrian -1 -1 -1 582.79 170.13 599.62 209.34 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n192 25 Pedestrian -1 -1 -1 193.12 161.47 208.17 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n192 75 Pedestrian -1 -1 -1 341.57 158.13 352.67 184.27 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n192 74 Pedestrian -1 -1 -1 181.65 160.71 197.90 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n192 77 Pedestrian -1 -1 -1 537.71 165.71 552.03 206.67 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n193 2 Car -1 -1 -1 1095.03 185.40 1221.00 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n193 1 Car -1 -1 -1 955.07 183.81 1066.87 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n193 3 Car -1 -1 -1 1029.61 183.95 1156.09 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n193 27 Pedestrian -1 -1 -1 469.55 161.42 514.88 267.03 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n193 20 Pedestrian -1 -1 -1 525.08 162.98 566.38 262.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n193 70 Pedestrian -1 -1 -1 284.01 157.47 301.87 201.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n193 5 Car -1 -1 -1 601.28 172.40 635.50 201.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n193 25 Pedestrian -1 -1 -1 193.27 161.63 208.22 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n193 46 Pedestrian -1 -1 -1 600.07 168.54 620.63 223.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n193 68 Pedestrian -1 -1 -1 587.55 169.89 601.47 209.09 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n193 56 Pedestrian -1 -1 -1 556.26 166.46 573.21 212.76 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n193 74 Pedestrian -1 -1 -1 181.40 160.33 198.06 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n193 75 Pedestrian -1 -1 -1 341.61 157.87 352.85 184.63 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n194 2 Car -1 -1 -1 1095.00 185.46 1221.17 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n194 1 Car -1 -1 -1 955.05 183.82 1066.85 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n194 3 Car -1 -1 -1 1029.72 183.99 1155.98 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n194 27 Pedestrian -1 -1 -1 470.87 160.90 520.48 268.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n194 20 Pedestrian -1 -1 -1 531.88 162.97 571.92 262.21 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n194 5 Car -1 -1 -1 603.50 172.29 633.08 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n194 25 Pedestrian -1 -1 -1 193.16 161.63 208.08 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n194 70 Pedestrian -1 -1 -1 282.97 157.65 301.40 200.92 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n194 46 Pedestrian -1 -1 -1 598.62 171.86 619.46 222.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n194 56 Pedestrian -1 -1 -1 558.64 166.76 575.36 214.00 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n194 68 Pedestrian -1 -1 -1 589.15 170.17 602.97 209.23 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n194 74 Pedestrian -1 -1 -1 181.28 160.24 197.75 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n194 75 Pedestrian -1 -1 -1 341.84 157.88 352.93 185.03 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n195 2 Car -1 -1 -1 1094.80 185.44 1221.22 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n195 1 Car -1 -1 -1 955.15 183.81 1066.84 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n195 3 Car -1 -1 -1 1029.83 184.05 1155.95 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n195 20 Pedestrian -1 -1 -1 538.41 163.14 574.99 262.48 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n195 27 Pedestrian -1 -1 -1 480.72 162.00 524.12 267.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n195 46 Pedestrian -1 -1 -1 596.72 171.14 617.05 223.15 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n195 5 Car -1 -1 -1 603.56 172.28 633.66 203.73 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n195 70 Pedestrian -1 -1 -1 278.27 157.44 300.43 201.35 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n195 25 Pedestrian -1 -1 -1 192.87 161.47 208.03 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n195 68 Pedestrian -1 -1 -1 591.45 170.00 605.30 210.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n195 56 Pedestrian -1 -1 -1 559.38 167.20 576.43 213.57 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n195 74 Pedestrian -1 -1 -1 181.01 160.55 197.33 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n195 78 Cyclist -1 -1 -1 560.46 166.86 575.42 209.55 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n196 2 Car -1 -1 -1 1094.91 185.41 1221.16 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n196 1 Car -1 -1 -1 955.05 183.74 1066.89 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n196 3 Car -1 -1 -1 1029.76 184.04 1156.04 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n196 20 Pedestrian -1 -1 -1 543.08 162.28 579.06 263.19 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n196 27 Pedestrian -1 -1 -1 489.59 161.61 525.50 268.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n196 5 Car -1 -1 -1 602.90 172.15 633.48 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n196 25 Pedestrian -1 -1 -1 192.89 161.58 207.95 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n196 56 Pedestrian -1 -1 -1 559.67 167.34 577.06 213.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n196 46 Pedestrian -1 -1 -1 595.01 170.95 615.51 223.06 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n196 70 Pedestrian -1 -1 -1 278.70 157.41 299.91 201.82 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n196 74 Pedestrian -1 -1 -1 180.86 160.70 197.04 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n196 78 Cyclist -1 -1 -1 560.28 166.37 576.07 209.21 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n197 2 Car -1 -1 -1 1094.92 185.45 1221.27 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n197 1 Car -1 -1 -1 955.14 183.73 1066.92 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n197 3 Car -1 -1 -1 1030.01 184.05 1155.79 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n197 20 Pedestrian -1 -1 -1 546.10 162.92 588.92 262.39 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n197 27 Pedestrian -1 -1 -1 498.22 161.18 531.71 271.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n197 5 Car -1 -1 -1 602.75 172.89 633.12 201.71 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n197 46 Pedestrian -1 -1 -1 593.33 170.59 611.68 223.28 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n197 25 Pedestrian -1 -1 -1 192.63 161.42 208.01 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n197 56 Pedestrian -1 -1 -1 560.07 167.10 577.50 214.13 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n197 70 Pedestrian -1 -1 -1 278.57 157.46 299.75 201.48 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n197 74 Pedestrian -1 -1 -1 180.80 160.19 197.12 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n197 78 Cyclist -1 -1 -1 560.50 166.38 576.71 209.23 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n198 2 Car -1 -1 -1 1095.06 185.51 1221.19 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n198 1 Car -1 -1 -1 955.10 183.73 1067.02 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n198 3 Car -1 -1 -1 1030.03 184.07 1155.84 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n198 20 Pedestrian -1 -1 -1 548.21 163.17 596.12 263.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n198 27 Pedestrian -1 -1 -1 501.99 161.25 542.99 268.37 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n198 5 Car -1 -1 -1 603.53 173.25 633.51 201.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n198 46 Pedestrian -1 -1 -1 593.16 170.56 610.54 223.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n198 25 Pedestrian -1 -1 -1 192.62 161.42 208.01 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n198 56 Pedestrian -1 -1 -1 563.98 166.29 580.35 215.64 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n198 70 Pedestrian -1 -1 -1 278.23 157.30 299.20 201.45 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n198 74 Pedestrian -1 -1 -1 180.85 160.48 197.21 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n198 79 Pedestrian -1 -1 -1 619.55 169.24 637.18 210.16 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n199 2 Car -1 -1 -1 1095.05 185.46 1221.07 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n199 1 Car -1 -1 -1 955.20 183.74 1066.87 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n199 20 Pedestrian -1 -1 -1 552.38 163.46 598.51 263.87 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n199 3 Car -1 -1 -1 1030.17 184.10 1155.63 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n199 27 Pedestrian -1 -1 -1 503.76 160.44 549.45 272.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n199 5 Car -1 -1 -1 602.46 173.53 632.90 201.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n199 25 Pedestrian -1 -1 -1 192.76 161.57 207.94 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n199 46 Pedestrian -1 -1 -1 589.72 170.55 608.78 223.20 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n199 79 Pedestrian -1 -1 -1 621.52 168.75 637.47 209.83 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n199 70 Pedestrian -1 -1 -1 278.52 157.01 297.75 201.95 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n199 74 Pedestrian -1 -1 -1 180.81 160.87 196.71 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n200 2 Car -1 -1 -1 1095.07 185.53 1221.03 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n200 1 Car -1 -1 -1 954.99 183.75 1067.00 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n200 3 Car -1 -1 -1 1029.85 184.03 1155.95 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n200 20 Pedestrian -1 -1 -1 554.89 163.54 603.47 264.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n200 27 Pedestrian -1 -1 -1 511.74 160.27 555.10 272.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n200 5 Car -1 -1 -1 602.58 173.20 634.22 202.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n200 25 Pedestrian -1 -1 -1 193.07 161.60 208.08 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n200 70 Pedestrian -1 -1 -1 278.20 157.43 297.55 202.18 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n200 46 Pedestrian -1 -1 -1 587.95 170.72 607.93 223.12 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n200 79 Pedestrian -1 -1 -1 625.68 169.15 640.25 209.29 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n200 74 Pedestrian -1 -1 -1 181.05 160.83 196.73 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n201 2 Car -1 -1 -1 1095.05 185.43 1220.84 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n201 1 Car -1 -1 -1 954.99 183.71 1066.87 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n201 3 Car -1 -1 -1 1029.87 184.06 1155.99 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n201 20 Pedestrian -1 -1 -1 562.81 162.49 603.94 265.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n201 5 Car -1 -1 -1 601.77 172.83 635.40 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n201 27 Pedestrian -1 -1 -1 522.55 158.69 558.85 273.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n201 25 Pedestrian -1 -1 -1 193.12 161.58 208.06 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n201 46 Pedestrian -1 -1 -1 584.78 169.19 604.91 224.66 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n201 70 Pedestrian -1 -1 -1 275.90 157.14 296.36 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n201 74 Pedestrian -1 -1 -1 181.26 160.67 196.83 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n201 79 Pedestrian -1 -1 -1 629.07 170.12 642.09 208.30 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n201 80 Pedestrian -1 -1 -1 569.39 169.37 589.96 224.90 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n202 2 Car -1 -1 -1 1094.95 185.42 1221.13 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n202 1 Car -1 -1 -1 954.91 183.71 1066.89 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n202 3 Car -1 -1 -1 1029.67 184.02 1156.03 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n202 20 Pedestrian -1 -1 -1 571.38 161.51 610.36 267.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n202 27 Pedestrian -1 -1 -1 530.10 158.69 566.43 274.13 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n202 5 Car -1 -1 -1 605.70 172.80 635.23 201.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n202 70 Pedestrian -1 -1 -1 275.39 156.81 296.01 203.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n202 25 Pedestrian -1 -1 -1 193.07 161.49 208.07 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n202 79 Pedestrian -1 -1 -1 629.87 170.68 644.72 208.13 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n202 46 Pedestrian -1 -1 -1 584.57 169.34 604.75 221.41 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n202 74 Pedestrian -1 -1 -1 181.26 160.58 196.93 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n203 2 Car -1 -1 -1 1094.96 185.53 1221.18 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n203 1 Car -1 -1 -1 954.97 183.69 1066.90 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n203 3 Car -1 -1 -1 1029.61 184.01 1156.12 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n203 27 Pedestrian -1 -1 -1 533.71 159.51 577.85 274.14 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n203 20 Pedestrian -1 -1 -1 577.44 162.11 618.77 267.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n203 79 Pedestrian -1 -1 -1 631.14 171.67 647.92 208.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n203 5 Car -1 -1 -1 606.23 172.64 634.62 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n203 70 Pedestrian -1 -1 -1 274.86 157.25 295.43 203.68 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n203 25 Pedestrian -1 -1 -1 192.96 161.44 208.17 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n203 46 Pedestrian -1 -1 -1 584.44 170.56 604.48 220.28 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n203 74 Pedestrian -1 -1 -1 181.27 160.53 196.90 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n204 2 Car -1 -1 -1 1095.07 185.50 1221.07 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n204 1 Car -1 -1 -1 954.92 183.67 1066.96 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n204 3 Car -1 -1 -1 1029.62 184.04 1156.14 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n204 27 Pedestrian -1 -1 -1 535.79 159.32 583.57 274.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n204 20 Pedestrian -1 -1 -1 577.87 162.35 626.90 270.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n204 79 Pedestrian -1 -1 -1 633.09 170.82 649.56 208.72 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n204 5 Car -1 -1 -1 606.42 172.30 635.19 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n204 70 Pedestrian -1 -1 -1 274.98 157.33 293.55 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n204 25 Pedestrian -1 -1 -1 193.07 161.54 208.07 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n204 74 Pedestrian -1 -1 -1 181.25 160.53 196.78 197.68 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n204 46 Pedestrian -1 -1 -1 578.49 169.69 596.75 220.84 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n205 2 Car -1 -1 -1 1095.13 185.53 1220.88 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n205 1 Car -1 -1 -1 954.94 183.67 1067.05 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n205 3 Car -1 -1 -1 1029.75 184.05 1156.14 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n205 27 Pedestrian -1 -1 -1 536.16 160.70 585.65 275.79 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n205 20 Pedestrian -1 -1 -1 583.00 162.46 630.70 271.41 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n205 5 Car -1 -1 -1 605.98 171.85 635.47 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n205 79 Pedestrian -1 -1 -1 637.83 170.05 651.91 208.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n205 25 Pedestrian -1 -1 -1 193.05 161.59 207.80 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n205 70 Pedestrian -1 -1 -1 274.92 157.34 292.57 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n205 46 Pedestrian -1 -1 -1 577.01 169.75 595.64 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n205 74 Pedestrian -1 -1 -1 181.39 160.22 197.24 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n206 2 Car -1 -1 -1 1095.17 185.56 1220.90 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n206 1 Car -1 -1 -1 954.99 183.70 1067.09 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n206 3 Car -1 -1 -1 1029.64 183.94 1156.20 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n206 20 Pedestrian -1 -1 -1 592.65 162.76 635.49 271.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n206 5 Car -1 -1 -1 605.27 171.43 636.81 200.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n206 27 Pedestrian -1 -1 -1 544.69 160.75 590.28 276.05 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n206 79 Pedestrian -1 -1 -1 641.24 169.43 654.59 209.06 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n206 25 Pedestrian -1 -1 -1 193.26 161.50 207.84 198.54 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n206 70 Pedestrian -1 -1 -1 273.65 158.55 290.83 204.75 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n206 74 Pedestrian -1 -1 -1 181.70 160.35 197.37 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n206 46 Pedestrian -1 -1 -1 573.46 168.92 592.90 220.88 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n207 2 Car -1 -1 -1 1094.98 185.45 1221.14 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n207 1 Car -1 -1 -1 954.99 183.73 1067.09 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n207 3 Car -1 -1 -1 1029.66 183.94 1156.10 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n207 5 Car -1 -1 -1 601.27 171.64 635.12 202.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n207 20 Pedestrian -1 -1 -1 602.29 162.86 640.71 271.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n207 27 Pedestrian -1 -1 -1 555.36 160.29 595.18 280.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n207 25 Pedestrian -1 -1 -1 193.26 161.48 207.72 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n207 79 Pedestrian -1 -1 -1 641.73 169.89 657.42 208.97 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n207 70 Pedestrian -1 -1 -1 273.09 158.33 290.19 205.23 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n207 46 Pedestrian -1 -1 -1 570.37 167.91 589.78 221.00 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n207 74 Pedestrian -1 -1 -1 181.53 160.41 197.10 197.85 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n208 2 Car -1 -1 -1 1095.10 185.42 1221.07 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n208 1 Car -1 -1 -1 954.86 183.69 1067.12 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n208 3 Car -1 -1 -1 1029.84 183.99 1156.01 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n208 27 Pedestrian -1 -1 -1 560.00 161.04 599.26 280.01 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n208 5 Car -1 -1 -1 603.43 171.87 637.91 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n208 20 Pedestrian -1 -1 -1 606.96 162.40 650.37 271.61 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n208 79 Pedestrian -1 -1 -1 643.29 169.74 660.21 209.45 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n208 25 Pedestrian -1 -1 -1 193.41 161.56 207.79 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n208 70 Pedestrian -1 -1 -1 272.52 158.64 289.37 205.27 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n208 74 Pedestrian -1 -1 -1 181.73 160.33 197.51 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n208 46 Pedestrian -1 -1 -1 570.27 167.89 589.20 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n209 2 Car -1 -1 -1 1095.21 185.54 1220.96 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n209 1 Car -1 -1 -1 954.83 183.70 1067.08 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n209 3 Car -1 -1 -1 1029.69 183.97 1156.09 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n209 20 Pedestrian -1 -1 -1 609.15 161.63 663.85 272.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n209 5 Car -1 -1 -1 602.99 172.16 638.31 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n209 27 Pedestrian -1 -1 -1 562.62 159.50 610.53 281.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n209 25 Pedestrian -1 -1 -1 193.40 161.56 207.80 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n209 79 Pedestrian -1 -1 -1 645.29 169.14 661.50 210.28 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n209 70 Pedestrian -1 -1 -1 271.93 158.17 288.53 205.67 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n209 74 Pedestrian -1 -1 -1 181.84 160.32 197.45 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n210 2 Car -1 -1 -1 1095.28 185.42 1220.88 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n210 1 Car -1 -1 -1 954.69 183.67 1067.26 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n210 3 Car -1 -1 -1 1029.59 183.95 1156.22 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n210 27 Pedestrian -1 -1 -1 565.87 158.71 615.21 283.28 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n210 20 Pedestrian -1 -1 -1 612.44 161.51 668.42 273.00 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n210 5 Car -1 -1 -1 600.56 171.64 635.33 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n210 25 Pedestrian -1 -1 -1 193.80 161.78 208.02 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n210 79 Pedestrian -1 -1 -1 649.40 168.83 663.90 210.21 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n210 70 Pedestrian -1 -1 -1 271.67 158.50 288.15 205.89 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n210 74 Pedestrian -1 -1 -1 182.02 160.43 197.66 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n211 2 Car -1 -1 -1 1095.34 185.43 1220.85 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n211 1 Car -1 -1 -1 954.73 183.64 1067.25 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n211 3 Car -1 -1 -1 1029.71 183.93 1156.11 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n211 27 Pedestrian -1 -1 -1 571.44 159.43 618.12 284.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n211 20 Pedestrian -1 -1 -1 616.78 159.84 671.47 274.41 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n211 5 Car -1 -1 -1 600.50 171.61 635.38 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n211 25 Pedestrian -1 -1 -1 193.99 161.90 207.94 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n211 79 Pedestrian -1 -1 -1 648.89 168.72 664.56 211.41 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n211 70 Pedestrian -1 -1 -1 269.93 157.94 287.23 205.67 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n211 74 Pedestrian -1 -1 -1 182.18 160.37 197.81 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n211 81 Pedestrian -1 -1 -1 566.45 170.29 584.01 220.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n212 2 Car -1 -1 -1 1095.27 185.46 1220.85 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n212 1 Car -1 -1 -1 954.66 183.62 1067.20 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n212 3 Car -1 -1 -1 1029.61 183.92 1156.10 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n212 20 Pedestrian -1 -1 -1 628.94 161.16 674.14 273.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n212 27 Pedestrian -1 -1 -1 580.94 158.87 624.34 285.91 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n212 5 Car -1 -1 -1 601.61 171.70 635.60 203.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n212 81 Pedestrian -1 -1 -1 563.47 170.97 581.53 219.79 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n212 25 Pedestrian -1 -1 -1 193.81 161.74 207.78 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n212 70 Pedestrian -1 -1 -1 269.56 157.74 286.90 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n212 79 Pedestrian -1 -1 -1 651.98 169.75 667.42 211.39 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n212 74 Pedestrian -1 -1 -1 184.66 159.82 200.49 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n213 2 Car -1 -1 -1 1095.36 185.44 1220.70 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n213 1 Car -1 -1 -1 954.67 183.60 1067.12 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n213 3 Car -1 -1 -1 1029.69 183.97 1156.03 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n213 20 Pedestrian -1 -1 -1 639.78 160.00 679.76 276.16 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n213 27 Pedestrian -1 -1 -1 589.12 159.34 632.03 284.97 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n213 5 Car -1 -1 -1 600.97 171.44 636.02 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n213 81 Pedestrian -1 -1 -1 562.41 170.45 580.01 218.87 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n213 70 Pedestrian -1 -1 -1 268.91 156.05 286.64 205.35 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n213 25 Pedestrian -1 -1 -1 193.90 161.74 207.89 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n213 74 Pedestrian -1 -1 -1 184.68 159.79 200.46 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n214 2 Car -1 -1 -1 1095.31 185.54 1220.70 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n214 1 Car -1 -1 -1 954.64 183.64 1067.12 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n214 3 Car -1 -1 -1 1029.50 183.87 1156.19 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n214 20 Pedestrian -1 -1 -1 646.23 159.86 688.06 277.10 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n214 27 Pedestrian -1 -1 -1 594.87 158.44 646.81 285.89 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n214 5 Car -1 -1 -1 601.05 171.83 635.72 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n214 25 Pedestrian -1 -1 -1 194.19 161.82 207.89 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n214 70 Pedestrian -1 -1 -1 268.56 157.51 286.71 205.83 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n214 81 Pedestrian -1 -1 -1 559.44 169.87 578.52 218.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n214 74 Pedestrian -1 -1 -1 181.97 160.39 197.64 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n214 82 Pedestrian -1 -1 -1 629.03 168.02 645.65 213.65 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n215 2 Car -1 -1 -1 1095.31 185.42 1220.88 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n215 1 Car -1 -1 -1 954.57 183.61 1067.38 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n215 3 Car -1 -1 -1 1029.68 183.89 1156.17 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n215 27 Pedestrian -1 -1 -1 594.37 158.76 650.01 286.00 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n215 20 Pedestrian -1 -1 -1 650.63 160.83 698.48 276.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n215 5 Car -1 -1 -1 601.37 172.31 635.14 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n215 81 Pedestrian -1 -1 -1 556.47 169.57 578.20 218.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n215 25 Pedestrian -1 -1 -1 194.20 161.94 208.00 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n215 70 Pedestrian -1 -1 -1 267.99 157.88 286.54 205.72 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n215 74 Pedestrian -1 -1 -1 182.11 160.48 197.69 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n216 2 Car -1 -1 -1 1095.59 185.44 1220.47 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n216 1 Car -1 -1 -1 954.60 183.66 1067.21 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n216 3 Car -1 -1 -1 1032.60 183.63 1157.30 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n216 20 Pedestrian -1 -1 -1 654.50 159.46 704.31 277.98 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n216 27 Pedestrian -1 -1 -1 596.91 160.98 654.73 288.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n216 5 Car -1 -1 -1 601.30 172.18 634.29 202.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n216 81 Pedestrian -1 -1 -1 554.89 170.62 575.26 217.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n216 25 Pedestrian -1 -1 -1 194.23 161.74 208.15 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n216 70 Pedestrian -1 -1 -1 267.70 157.55 285.62 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n216 74 Pedestrian -1 -1 -1 181.86 160.51 197.65 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n216 83 Pedestrian -1 -1 -1 661.49 170.10 675.23 211.03 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n217 2 Car -1 -1 -1 1095.43 185.42 1220.53 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n217 1 Car -1 -1 -1 954.63 183.70 1067.01 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n217 3 Car -1 -1 -1 1029.74 183.88 1156.09 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n217 27 Pedestrian -1 -1 -1 608.97 160.99 657.56 288.91 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n217 5 Car -1 -1 -1 601.03 172.39 635.11 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n217 20 Pedestrian -1 -1 -1 663.37 159.24 708.56 278.35 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n217 81 Pedestrian -1 -1 -1 553.74 170.10 574.01 218.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n217 25 Pedestrian -1 -1 -1 193.88 161.59 208.07 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n217 70 Pedestrian -1 -1 -1 265.80 155.97 283.53 205.49 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n217 74 Pedestrian -1 -1 -1 181.66 160.55 197.49 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n217 83 Pedestrian -1 -1 -1 663.68 168.23 677.92 212.00 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n218 2 Car -1 -1 -1 1095.28 185.45 1220.77 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n218 1 Car -1 -1 -1 954.58 183.68 1067.14 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n218 3 Car -1 -1 -1 1029.83 183.92 1156.05 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n218 27 Pedestrian -1 -1 -1 618.77 159.79 662.24 290.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n218 5 Car -1 -1 -1 600.88 172.70 635.85 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n218 20 Pedestrian -1 -1 -1 675.38 159.56 712.74 280.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n218 81 Pedestrian -1 -1 -1 551.83 169.94 570.55 217.76 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n218 25 Pedestrian -1 -1 -1 194.02 161.63 208.11 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n218 70 Pedestrian -1 -1 -1 264.81 157.50 283.15 205.98 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n218 74 Pedestrian -1 -1 -1 181.63 160.66 197.14 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n219 2 Car -1 -1 -1 1095.43 185.45 1220.58 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n219 1 Car -1 -1 -1 954.66 183.79 1066.96 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n219 3 Car -1 -1 -1 1029.78 183.87 1156.05 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n219 27 Pedestrian -1 -1 -1 626.31 159.03 670.19 291.24 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n219 5 Car -1 -1 -1 600.77 172.76 636.12 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n219 81 Pedestrian -1 -1 -1 550.26 170.14 568.82 217.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n219 20 Pedestrian -1 -1 -1 681.07 160.56 721.57 280.21 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n219 25 Pedestrian -1 -1 -1 193.95 161.67 208.01 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n219 70 Pedestrian -1 -1 -1 264.04 156.93 283.00 206.62 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n219 74 Pedestrian -1 -1 -1 181.54 161.15 196.68 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n220 2 Car -1 -1 -1 1095.40 185.39 1220.64 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n220 1 Car -1 -1 -1 954.58 183.68 1067.05 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n220 3 Car -1 -1 -1 1029.85 183.83 1156.00 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n220 20 Pedestrian -1 -1 -1 681.17 160.02 737.44 281.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n220 5 Car -1 -1 -1 601.11 172.73 635.64 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n220 27 Pedestrian -1 -1 -1 630.05 159.30 682.76 291.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n220 81 Pedestrian -1 -1 -1 548.09 170.77 567.36 217.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n220 25 Pedestrian -1 -1 -1 194.31 161.95 208.02 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n220 70 Pedestrian -1 -1 -1 263.92 155.64 282.25 205.55 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n220 74 Pedestrian -1 -1 -1 181.51 161.01 196.66 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n221 2 Car -1 -1 -1 1095.33 185.33 1220.75 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n221 1 Car -1 -1 -1 954.64 183.62 1067.13 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n221 3 Car -1 -1 -1 1029.72 183.83 1156.18 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n221 5 Car -1 -1 -1 601.23 172.68 635.14 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n221 20 Pedestrian -1 -1 -1 683.31 160.45 743.89 282.47 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n221 27 Pedestrian -1 -1 -1 633.55 159.33 686.89 291.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n221 81 Pedestrian -1 -1 -1 546.47 171.30 566.12 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n221 25 Pedestrian -1 -1 -1 194.13 161.84 208.11 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n221 70 Pedestrian -1 -1 -1 264.30 156.71 282.26 206.61 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n221 74 Pedestrian -1 -1 -1 181.37 160.79 196.73 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n221 84 Pedestrian -1 -1 -1 620.63 166.88 636.39 212.51 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n221 85 Pedestrian -1 -1 -1 644.42 170.61 658.81 213.02 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n222 2 Car -1 -1 -1 1095.25 185.38 1220.74 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n222 1 Car -1 -1 -1 954.63 183.59 1067.21 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n222 3 Car -1 -1 -1 1029.53 183.77 1156.36 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n222 5 Car -1 -1 -1 601.41 172.96 634.95 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n222 27 Pedestrian -1 -1 -1 644.71 156.46 696.82 293.79 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n222 20 Pedestrian -1 -1 -1 687.77 159.61 747.47 283.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n222 81 Pedestrian -1 -1 -1 546.22 170.41 564.88 216.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n222 25 Pedestrian -1 -1 -1 194.10 161.80 208.13 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n222 70 Pedestrian -1 -1 -1 263.96 156.84 281.90 206.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n222 84 Pedestrian -1 -1 -1 621.38 168.03 636.89 211.54 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n222 85 Pedestrian -1 -1 -1 645.99 169.64 660.45 214.44 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n222 74 Pedestrian -1 -1 -1 181.30 161.03 196.64 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n223 2 Car -1 -1 -1 1095.47 185.41 1220.54 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n223 1 Car -1 -1 -1 954.61 183.61 1067.25 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n223 3 Car -1 -1 -1 1029.39 183.72 1156.41 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n223 5 Car -1 -1 -1 601.42 172.89 634.76 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n223 27 Pedestrian -1 -1 -1 656.64 156.55 701.16 294.10 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n223 20 Pedestrian -1 -1 -1 699.62 159.62 751.00 283.35 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n223 81 Pedestrian -1 -1 -1 544.56 169.82 562.49 216.58 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n223 25 Pedestrian -1 -1 -1 194.21 161.78 208.19 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n223 70 Pedestrian -1 -1 -1 263.86 155.08 282.37 206.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n223 85 Pedestrian -1 -1 -1 648.52 169.74 663.33 214.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n223 74 Pedestrian -1 -1 -1 181.55 161.20 196.53 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n223 84 Pedestrian -1 -1 -1 620.97 168.58 638.46 210.35 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n224 2 Car -1 -1 -1 1095.75 185.42 1220.30 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n224 1 Car -1 -1 -1 954.68 183.66 1067.08 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n224 3 Car -1 -1 -1 1029.52 183.78 1156.36 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n224 5 Car -1 -1 -1 601.55 172.67 635.11 203.21 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n224 27 Pedestrian -1 -1 -1 667.08 156.41 713.17 294.44 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n224 20 Pedestrian -1 -1 -1 716.27 158.69 756.26 285.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n224 81 Pedestrian -1 -1 -1 543.12 170.51 560.75 216.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n224 25 Pedestrian -1 -1 -1 194.38 161.80 208.00 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n224 70 Pedestrian -1 -1 -1 264.45 156.40 282.65 206.94 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n224 85 Pedestrian -1 -1 -1 648.70 170.89 665.16 213.61 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n224 84 Pedestrian -1 -1 -1 623.89 168.09 641.54 210.79 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n224 74 Pedestrian -1 -1 -1 178.69 161.51 192.94 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n225 2 Car -1 -1 -1 1095.97 185.50 1220.26 235.66 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n225 1 Car -1 -1 -1 954.72 183.71 1067.23 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n225 3 Car -1 -1 -1 1032.45 183.56 1157.40 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n225 5 Car -1 -1 -1 601.76 172.47 635.68 203.46 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n225 27 Pedestrian -1 -1 -1 672.56 156.60 724.01 294.40 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n225 20 Pedestrian -1 -1 -1 722.05 159.29 766.55 285.53 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n225 81 Pedestrian -1 -1 -1 540.26 170.40 559.68 216.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n225 25 Pedestrian -1 -1 -1 194.52 162.02 208.20 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n225 85 Pedestrian -1 -1 -1 650.11 170.57 668.37 214.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n225 70 Pedestrian -1 -1 -1 264.53 156.12 282.97 207.30 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n225 74 Pedestrian -1 -1 -1 178.73 161.93 192.99 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n225 84 Pedestrian -1 -1 -1 624.72 168.32 642.46 211.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n226 2 Car -1 -1 -1 1095.78 185.49 1220.36 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n226 1 Car -1 -1 -1 954.74 183.78 1066.98 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n226 3 Car -1 -1 -1 1032.37 183.63 1157.55 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n226 5 Car -1 -1 -1 601.60 172.19 635.92 203.55 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n226 20 Pedestrian -1 -1 -1 727.95 161.17 782.19 287.83 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n226 27 Pedestrian -1 -1 -1 677.13 157.29 734.62 295.15 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n226 81 Pedestrian -1 -1 -1 539.65 170.88 557.72 215.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n226 25 Pedestrian -1 -1 -1 194.32 161.92 208.13 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n226 70 Pedestrian -1 -1 -1 264.68 156.35 283.63 207.40 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n226 84 Pedestrian -1 -1 -1 627.16 167.70 645.57 211.99 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n226 85 Pedestrian -1 -1 -1 651.72 171.34 668.87 214.84 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n226 74 Pedestrian -1 -1 -1 178.81 161.96 192.91 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n226 86 Pedestrian -1 -1 -1 678.04 170.61 694.15 212.79 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n227 2 Car -1 -1 -1 1095.67 185.54 1220.51 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n227 1 Car -1 -1 -1 954.86 183.77 1066.89 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n227 3 Car -1 -1 -1 1029.44 183.85 1156.36 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n227 5 Car -1 -1 -1 601.49 172.09 636.61 203.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n227 20 Pedestrian -1 -1 -1 731.54 160.91 787.26 289.36 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n227 27 Pedestrian -1 -1 -1 679.11 158.48 740.43 298.74 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n227 25 Pedestrian -1 -1 -1 194.06 161.76 208.08 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n227 70 Pedestrian -1 -1 -1 264.57 156.65 283.80 207.44 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n227 84 Pedestrian -1 -1 -1 629.23 166.86 645.69 212.23 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n227 81 Pedestrian -1 -1 -1 538.94 170.35 556.53 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n227 85 Pedestrian -1 -1 -1 653.95 169.98 670.96 214.56 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n227 74 Pedestrian -1 -1 -1 178.74 161.80 193.09 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n227 86 Pedestrian -1 -1 -1 680.07 170.42 695.43 212.41 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n228 2 Car -1 -1 -1 1095.98 185.54 1220.17 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n228 1 Car -1 -1 -1 954.95 183.78 1066.81 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n228 3 Car -1 -1 -1 1029.25 183.84 1156.47 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n228 5 Car -1 -1 -1 601.42 172.30 636.69 203.41 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n228 20 Pedestrian -1 -1 -1 741.26 160.51 791.96 289.40 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n228 27 Pedestrian -1 -1 -1 692.11 156.94 749.01 300.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n228 81 Pedestrian -1 -1 -1 536.95 169.44 553.48 215.06 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n228 25 Pedestrian -1 -1 -1 194.00 161.61 208.18 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n228 86 Pedestrian -1 -1 -1 682.04 169.69 697.45 212.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n228 70 Pedestrian -1 -1 -1 264.51 156.67 284.30 207.77 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n228 85 Pedestrian -1 -1 -1 656.19 170.09 671.80 214.26 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n228 84 Pedestrian -1 -1 -1 629.75 166.96 646.16 212.73 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n228 74 Pedestrian -1 -1 -1 178.85 161.82 193.16 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n229 2 Car -1 -1 -1 1095.83 185.45 1220.24 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n229 1 Car -1 -1 -1 954.84 183.74 1067.08 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n229 3 Car -1 -1 -1 1029.15 183.90 1156.60 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n229 5 Car -1 -1 -1 601.20 172.41 636.89 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n229 27 Pedestrian -1 -1 -1 705.15 156.37 751.74 302.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n229 20 Pedestrian -1 -1 -1 748.40 157.57 793.83 291.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n229 84 Pedestrian -1 -1 -1 631.24 166.32 649.71 212.89 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n229 25 Pedestrian -1 -1 -1 193.75 161.56 208.18 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n229 86 Pedestrian -1 -1 -1 682.10 169.75 699.06 213.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n229 70 Pedestrian -1 -1 -1 265.25 156.56 284.11 207.73 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n229 81 Pedestrian -1 -1 -1 535.68 170.51 551.94 215.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n229 85 Pedestrian -1 -1 -1 657.32 169.82 675.59 216.54 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n229 74 Pedestrian -1 -1 -1 178.74 161.83 193.19 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n229 87 Car -1 -1 -1 598.40 173.57 622.34 193.80 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n230 2 Car -1 -1 -1 1095.81 185.43 1220.21 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n230 3 Car -1 -1 -1 1029.14 183.89 1156.58 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n230 1 Car -1 -1 -1 954.96 183.76 1066.94 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n230 5 Car -1 -1 -1 601.23 172.66 636.89 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n230 27 Pedestrian -1 -1 -1 715.86 155.31 763.65 303.28 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n230 20 Pedestrian -1 -1 -1 756.77 157.86 808.12 291.26 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n230 85 Pedestrian -1 -1 -1 658.52 170.33 677.23 217.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n230 84 Pedestrian -1 -1 -1 633.06 166.99 653.63 214.22 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n230 70 Pedestrian -1 -1 -1 265.09 156.67 284.24 208.11 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n230 25 Pedestrian -1 -1 -1 193.56 161.45 208.12 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n230 81 Pedestrian -1 -1 -1 534.42 170.98 550.59 215.15 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n230 86 Pedestrian -1 -1 -1 683.09 170.19 699.33 213.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n230 74 Pedestrian -1 -1 -1 178.73 161.89 193.17 197.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n230 87 Car -1 -1 -1 598.37 173.75 622.15 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n231 2 Car -1 -1 -1 1095.74 185.52 1220.37 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n231 3 Car -1 -1 -1 1029.01 183.90 1156.56 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n231 1 Car -1 -1 -1 954.91 183.83 1066.85 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n231 5 Car -1 -1 -1 601.35 172.77 636.73 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n231 20 Pedestrian -1 -1 -1 763.01 159.45 824.32 290.87 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n231 84 Pedestrian -1 -1 -1 633.89 167.54 654.35 215.04 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n231 27 Pedestrian -1 -1 -1 719.79 156.75 776.12 302.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n231 81 Pedestrian -1 -1 -1 533.68 171.17 549.85 215.13 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n231 85 Pedestrian -1 -1 -1 660.90 170.17 679.29 218.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n231 25 Pedestrian -1 -1 -1 193.74 161.46 208.14 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n231 70 Pedestrian -1 -1 -1 266.60 156.87 285.48 208.00 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n231 86 Pedestrian -1 -1 -1 685.12 170.18 701.41 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n231 74 Pedestrian -1 -1 -1 178.70 162.04 193.18 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n231 87 Car -1 -1 -1 598.41 173.74 621.98 193.73 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n232 2 Car -1 -1 -1 1095.34 185.49 1220.76 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n232 3 Car -1 -1 -1 1029.21 183.92 1156.40 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n232 1 Car -1 -1 -1 955.05 183.84 1066.74 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n232 5 Car -1 -1 -1 601.25 172.84 637.02 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n232 84 Pedestrian -1 -1 -1 635.50 167.96 654.67 215.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n232 85 Pedestrian -1 -1 -1 662.72 170.31 679.40 219.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n232 81 Pedestrian -1 -1 -1 533.08 171.39 548.94 215.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n232 20 Pedestrian -1 -1 -1 766.33 159.97 835.63 291.76 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n232 27 Pedestrian -1 -1 -1 725.66 158.13 785.29 301.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n232 70 Pedestrian -1 -1 -1 266.68 157.32 286.30 208.56 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n232 25 Pedestrian -1 -1 -1 193.89 161.52 208.17 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n232 86 Pedestrian -1 -1 -1 687.03 170.18 702.36 215.96 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n232 74 Pedestrian -1 -1 -1 178.84 161.93 193.21 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n232 87 Car -1 -1 -1 598.25 173.75 621.54 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n233 2 Car -1 -1 -1 1095.33 185.49 1220.67 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n233 3 Car -1 -1 -1 1029.55 183.94 1156.34 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n233 1 Car -1 -1 -1 954.95 183.79 1066.77 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n233 5 Car -1 -1 -1 601.53 172.73 636.89 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n233 70 Pedestrian -1 -1 -1 267.50 157.56 286.88 209.05 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n233 84 Pedestrian -1 -1 -1 638.45 167.23 656.59 214.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n233 20 Pedestrian -1 -1 -1 772.42 158.84 838.23 292.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n233 27 Pedestrian -1 -1 -1 735.11 157.13 791.20 307.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n233 25 Pedestrian -1 -1 -1 193.48 161.46 208.27 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n233 81 Pedestrian -1 -1 -1 531.77 171.26 548.19 215.59 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n233 85 Pedestrian -1 -1 -1 664.56 170.26 680.05 219.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n233 86 Pedestrian -1 -1 -1 688.90 170.02 704.56 216.37 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n233 87 Car -1 -1 -1 598.49 173.45 621.79 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n233 74 Pedestrian -1 -1 -1 178.68 161.86 193.31 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n234 2 Car -1 -1 -1 1095.29 185.46 1220.61 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n234 3 Car -1 -1 -1 1029.70 183.97 1156.03 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n234 1 Car -1 -1 -1 955.02 183.81 1066.69 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n234 5 Car -1 -1 -1 601.40 172.67 637.16 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n234 20 Pedestrian -1 -1 -1 782.43 157.99 842.97 293.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n234 70 Pedestrian -1 -1 -1 268.61 157.34 287.38 209.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n234 84 Pedestrian -1 -1 -1 640.28 166.88 658.62 214.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n234 27 Pedestrian -1 -1 -1 744.88 156.46 796.88 308.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n234 85 Pedestrian -1 -1 -1 666.87 169.84 682.09 219.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n234 86 Pedestrian -1 -1 -1 689.26 170.46 706.23 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n234 25 Pedestrian -1 -1 -1 193.65 161.58 208.08 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n234 81 Pedestrian -1 -1 -1 530.59 171.25 546.86 214.84 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n234 87 Car -1 -1 -1 598.30 173.49 621.90 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n234 74 Pedestrian -1 -1 -1 178.71 161.89 193.30 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n235 2 Car -1 -1 -1 1095.44 185.46 1220.54 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n235 1 Car -1 -1 -1 955.10 183.84 1066.57 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n235 3 Car -1 -1 -1 1029.75 184.01 1156.13 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n235 5 Car -1 -1 -1 601.25 172.58 637.39 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n235 84 Pedestrian -1 -1 -1 643.45 167.31 661.25 214.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n235 27 Pedestrian -1 -1 -1 757.92 156.11 813.86 308.62 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n235 20 Pedestrian -1 -1 -1 795.56 157.31 845.97 295.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n235 70 Pedestrian -1 -1 -1 269.09 157.20 287.73 209.19 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n235 86 Pedestrian -1 -1 -1 689.58 171.03 707.93 216.29 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n235 25 Pedestrian -1 -1 -1 193.87 161.57 208.19 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n235 85 Pedestrian -1 -1 -1 669.24 170.28 686.19 219.02 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n235 81 Pedestrian -1 -1 -1 529.92 170.92 545.74 213.74 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n235 87 Car -1 -1 -1 598.14 173.40 621.87 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n235 74 Pedestrian -1 -1 -1 178.79 161.93 193.24 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n236 2 Car -1 -1 -1 1095.18 185.41 1220.76 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n236 3 Car -1 -1 -1 1029.63 183.91 1156.13 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n236 1 Car -1 -1 -1 955.05 183.84 1066.60 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n236 5 Car -1 -1 -1 601.19 172.67 637.41 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n236 27 Pedestrian -1 -1 -1 762.81 155.92 825.04 310.16 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n236 85 Pedestrian -1 -1 -1 669.19 171.33 688.63 218.98 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n236 20 Pedestrian -1 -1 -1 810.34 157.88 860.71 298.41 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n236 84 Pedestrian -1 -1 -1 644.48 167.54 661.48 215.04 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n236 86 Pedestrian -1 -1 -1 691.30 171.07 710.28 216.92 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n236 70 Pedestrian -1 -1 -1 269.18 156.68 287.78 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n236 25 Pedestrian -1 -1 -1 193.89 161.77 208.20 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n236 81 Pedestrian -1 -1 -1 528.51 171.16 544.18 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n236 87 Car -1 -1 -1 597.81 173.28 621.81 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n236 74 Pedestrian -1 -1 -1 178.79 162.07 193.17 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n237 2 Car -1 -1 -1 1094.92 185.45 1220.96 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n237 1 Car -1 -1 -1 955.01 183.80 1066.91 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n237 3 Car -1 -1 -1 1029.81 183.95 1156.14 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n237 5 Car -1 -1 -1 601.15 172.61 637.57 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n237 27 Pedestrian -1 -1 -1 770.17 157.27 839.97 309.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n237 86 Pedestrian -1 -1 -1 693.67 170.91 711.07 216.66 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n237 20 Pedestrian -1 -1 -1 813.89 160.17 880.26 297.19 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n237 84 Pedestrian -1 -1 -1 646.63 167.33 663.62 215.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n237 70 Pedestrian -1 -1 -1 268.76 156.49 287.62 211.71 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n237 25 Pedestrian -1 -1 -1 193.81 161.80 208.22 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n237 85 Pedestrian -1 -1 -1 671.80 171.56 691.14 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n237 81 Pedestrian -1 -1 -1 526.37 170.79 542.64 213.87 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n237 87 Car -1 -1 -1 597.79 173.21 621.65 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n237 74 Pedestrian -1 -1 -1 178.78 162.12 193.18 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n238 2 Car -1 -1 -1 1094.93 185.30 1221.02 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n238 1 Car -1 -1 -1 955.22 183.76 1066.62 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n238 3 Car -1 -1 -1 1032.46 183.67 1157.37 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n238 5 Car -1 -1 -1 601.16 172.69 637.55 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n238 27 Pedestrian -1 -1 -1 776.84 157.09 848.68 315.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n238 84 Pedestrian -1 -1 -1 647.65 167.09 664.11 215.63 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n238 85 Pedestrian -1 -1 -1 672.62 171.41 691.58 219.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n238 70 Pedestrian -1 -1 -1 268.18 156.30 288.18 212.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n238 81 Pedestrian -1 -1 -1 525.52 170.49 541.38 213.97 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n238 20 Pedestrian -1 -1 -1 824.36 159.37 892.71 300.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n238 25 Pedestrian -1 -1 -1 193.61 161.80 208.22 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n238 86 Pedestrian -1 -1 -1 695.33 170.39 710.93 216.14 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n238 87 Car -1 -1 -1 597.84 173.28 621.78 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n238 74 Pedestrian -1 -1 -1 178.92 162.15 193.34 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n239 2 Car -1 -1 -1 1094.92 185.29 1220.85 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n239 1 Car -1 -1 -1 955.12 183.84 1066.62 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n239 3 Car -1 -1 -1 1032.44 183.77 1157.38 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n239 27 Pedestrian -1 -1 -1 783.48 156.51 850.08 316.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n239 5 Car -1 -1 -1 601.44 172.84 637.30 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n239 84 Pedestrian -1 -1 -1 647.82 166.71 665.07 215.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n239 20 Pedestrian -1 -1 -1 832.79 158.98 892.69 300.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n239 81 Pedestrian -1 -1 -1 524.51 169.91 540.38 213.65 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n239 25 Pedestrian -1 -1 -1 193.53 161.64 208.09 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n239 86 Pedestrian -1 -1 -1 696.80 170.16 713.33 216.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n239 85 Pedestrian -1 -1 -1 675.37 171.29 691.96 218.80 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n239 70 Pedestrian -1 -1 -1 269.98 156.17 289.63 212.83 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n239 87 Car -1 -1 -1 597.89 173.31 621.67 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n239 74 Pedestrian -1 -1 -1 178.90 162.19 193.21 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n240 2 Car -1 -1 -1 1095.09 185.36 1220.73 236.12 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n240 1 Car -1 -1 -1 954.97 183.79 1066.76 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n240 3 Car -1 -1 -1 1032.49 183.70 1157.39 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n240 27 Pedestrian -1 -1 -1 803.92 155.41 859.35 317.96 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n240 5 Car -1 -1 -1 601.43 172.92 637.21 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n240 84 Pedestrian -1 -1 -1 649.94 166.46 668.56 216.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n240 20 Pedestrian -1 -1 -1 848.74 158.51 899.07 300.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n240 86 Pedestrian -1 -1 -1 696.93 170.56 715.69 216.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n240 25 Pedestrian -1 -1 -1 193.67 161.81 208.22 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n240 81 Pedestrian -1 -1 -1 523.06 170.35 539.29 212.78 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n240 85 Pedestrian -1 -1 -1 678.07 171.32 694.27 218.53 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n240 87 Car -1 -1 -1 597.79 173.31 621.66 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n240 70 Pedestrian -1 -1 -1 268.62 156.49 288.35 212.71 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n240 74 Pedestrian -1 -1 -1 178.81 162.27 193.30 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n241 2 Car -1 -1 -1 1095.19 185.24 1220.45 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n241 1 Car -1 -1 -1 954.91 183.75 1067.03 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n241 3 Car -1 -1 -1 1032.47 183.68 1157.40 233.54 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n241 27 Pedestrian -1 -1 -1 814.16 153.91 872.85 320.47 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n241 5 Car -1 -1 -1 601.56 172.93 637.16 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n241 81 Pedestrian -1 -1 -1 523.11 170.98 538.64 212.80 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n241 20 Pedestrian -1 -1 -1 857.50 158.34 913.63 301.34 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n241 25 Pedestrian -1 -1 -1 193.61 161.84 208.17 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n241 84 Pedestrian -1 -1 -1 650.56 166.74 671.02 216.91 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n241 86 Pedestrian -1 -1 -1 697.68 170.66 716.26 216.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n241 85 Pedestrian -1 -1 -1 679.33 171.86 695.86 218.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n241 70 Pedestrian -1 -1 -1 268.76 157.36 288.44 213.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n241 87 Car -1 -1 -1 597.87 173.51 621.51 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n241 74 Pedestrian -1 -1 -1 178.87 162.20 193.21 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n242 2 Car -1 -1 -1 1095.09 185.32 1220.42 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n242 1 Car -1 -1 -1 954.85 183.72 1067.05 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n242 3 Car -1 -1 -1 1030.06 183.87 1155.70 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n242 5 Car -1 -1 -1 601.65 172.94 637.16 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n242 27 Pedestrian -1 -1 -1 824.07 156.13 893.22 318.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n242 85 Pedestrian -1 -1 -1 681.52 171.92 698.11 218.28 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n242 81 Pedestrian -1 -1 -1 522.26 171.42 538.24 213.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n242 20 Pedestrian -1 -1 -1 864.55 160.27 936.68 299.29 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n242 86 Pedestrian -1 -1 -1 700.15 170.45 717.48 216.87 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n242 84 Pedestrian -1 -1 -1 652.43 166.95 673.25 217.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n242 25 Pedestrian -1 -1 -1 193.65 161.94 208.03 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n242 70 Pedestrian -1 -1 -1 268.74 157.51 288.38 214.02 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n242 87 Car -1 -1 -1 597.65 173.30 621.66 193.68 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n242 74 Pedestrian -1 -1 -1 178.92 162.44 193.16 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n243 2 Car -1 -1 -1 1094.78 185.33 1220.91 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n243 3 Car -1 -1 -1 1029.95 183.83 1155.78 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n243 1 Car -1 -1 -1 954.83 183.67 1066.91 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n243 5 Car -1 -1 -1 601.50 172.91 637.16 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n243 27 Pedestrian -1 -1 -1 829.17 157.08 903.53 317.09 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n243 20 Pedestrian -1 -1 -1 866.70 159.93 949.87 305.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n243 85 Pedestrian -1 -1 -1 682.41 171.59 699.37 218.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n243 86 Pedestrian -1 -1 -1 702.18 170.25 718.02 216.80 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n243 84 Pedestrian -1 -1 -1 654.19 167.18 673.68 217.09 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n243 25 Pedestrian -1 -1 -1 193.52 161.97 208.15 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n243 81 Pedestrian -1 -1 -1 520.69 171.05 537.16 213.57 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n243 70 Pedestrian -1 -1 -1 270.02 157.08 289.65 214.09 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n243 87 Car -1 -1 -1 597.61 173.35 621.61 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n243 74 Pedestrian -1 -1 -1 178.98 162.38 193.19 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n244 2 Car -1 -1 -1 1094.65 185.36 1220.98 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n244 3 Car -1 -1 -1 1030.09 183.69 1155.50 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n244 1 Car -1 -1 -1 954.75 183.62 1067.15 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n244 27 Pedestrian -1 -1 -1 840.72 157.18 914.65 323.49 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n244 20 Pedestrian -1 -1 -1 882.63 157.51 957.16 308.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n244 5 Car -1 -1 -1 601.23 172.79 637.26 203.23 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n244 85 Pedestrian -1 -1 -1 685.56 171.90 701.12 218.41 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n244 70 Pedestrian -1 -1 -1 270.49 157.37 290.09 213.81 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n244 81 Pedestrian -1 -1 -1 520.65 171.07 536.45 212.68 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n244 25 Pedestrian -1 -1 -1 193.77 162.12 208.14 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n244 86 Pedestrian -1 -1 -1 704.30 169.96 720.22 217.47 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n244 84 Pedestrian -1 -1 -1 657.62 168.14 676.86 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n244 87 Car -1 -1 -1 597.64 173.25 621.74 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n244 74 Pedestrian -1 -1 -1 179.05 162.43 193.30 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n245 2 Car -1 -1 -1 1094.62 185.31 1220.81 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n245 3 Car -1 -1 -1 1030.06 183.67 1155.62 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n245 1 Car -1 -1 -1 954.55 183.59 1067.26 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n245 27 Pedestrian -1 -1 -1 854.16 157.59 916.98 324.29 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n245 5 Car -1 -1 -1 601.31 172.65 637.16 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n245 85 Pedestrian -1 -1 -1 687.29 171.06 702.12 218.69 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n245 86 Pedestrian -1 -1 -1 705.15 170.18 720.62 217.87 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n245 84 Pedestrian -1 -1 -1 661.32 167.43 680.42 218.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n245 25 Pedestrian -1 -1 -1 193.81 162.12 208.20 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n245 20 Pedestrian -1 -1 -1 894.52 156.96 960.56 309.31 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n245 70 Pedestrian -1 -1 -1 270.77 157.21 290.57 213.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n245 81 Pedestrian -1 -1 -1 519.95 171.17 534.79 211.73 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n245 87 Car -1 -1 -1 597.82 173.16 621.58 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n245 74 Pedestrian -1 -1 -1 179.14 162.36 193.40 197.62 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n246 2 Car -1 -1 -1 1094.81 185.47 1220.44 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n246 3 Car -1 -1 -1 1030.14 183.79 1155.51 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n246 1 Car -1 -1 -1 954.52 183.60 1067.39 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n246 27 Pedestrian -1 -1 -1 870.54 157.87 930.66 323.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n246 5 Car -1 -1 -1 601.31 172.91 637.37 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n246 81 Pedestrian -1 -1 -1 519.93 170.84 533.84 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n246 86 Pedestrian -1 -1 -1 705.32 170.62 721.98 218.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n246 85 Pedestrian -1 -1 -1 688.98 170.92 705.34 218.97 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n246 25 Pedestrian -1 -1 -1 193.95 162.08 208.27 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n246 70 Pedestrian -1 -1 -1 270.78 157.28 290.40 213.97 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n246 20 Pedestrian -1 -1 -1 913.70 157.05 971.75 309.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n246 87 Car -1 -1 -1 597.73 173.24 621.55 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n246 84 Pedestrian -1 -1 -1 663.97 166.29 681.09 218.31 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n246 74 Pedestrian -1 -1 -1 181.36 161.59 196.47 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n247 2 Car -1 -1 -1 1094.60 185.40 1220.98 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n247 3 Car -1 -1 -1 1030.03 183.87 1155.74 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n247 1 Car -1 -1 -1 954.45 183.66 1067.46 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n247 27 Pedestrian -1 -1 -1 875.87 154.42 948.93 326.64 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n247 81 Pedestrian -1 -1 -1 518.99 171.35 533.04 210.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n247 5 Car -1 -1 -1 601.34 172.90 637.28 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n247 86 Pedestrian -1 -1 -1 706.07 170.95 721.89 218.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n247 70 Pedestrian -1 -1 -1 271.25 157.02 290.83 214.65 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n247 85 Pedestrian -1 -1 -1 688.90 171.26 707.18 219.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n247 25 Pedestrian -1 -1 -1 194.02 162.19 208.16 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n247 20 Pedestrian -1 -1 -1 924.59 157.60 984.20 314.24 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n247 84 Pedestrian -1 -1 -1 665.41 166.21 683.36 218.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n247 87 Car -1 -1 -1 597.74 173.19 621.66 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n247 74 Pedestrian -1 -1 -1 181.37 161.64 196.42 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n248 2 Car -1 -1 -1 1094.32 185.39 1221.56 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n248 3 Car -1 -1 -1 1030.32 183.91 1155.42 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n248 1 Car -1 -1 -1 954.99 183.79 1066.91 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n248 27 Pedestrian -1 -1 -1 882.09 155.11 973.22 325.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n248 5 Car -1 -1 -1 601.41 172.92 637.18 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n248 70 Pedestrian -1 -1 -1 271.59 157.02 290.97 215.35 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n248 81 Pedestrian -1 -1 -1 518.10 171.69 532.38 211.25 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n248 86 Pedestrian -1 -1 -1 706.35 170.44 721.92 218.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n248 25 Pedestrian -1 -1 -1 193.76 162.03 208.26 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n248 84 Pedestrian -1 -1 -1 666.42 167.86 684.80 218.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n248 85 Pedestrian -1 -1 -1 689.84 171.09 708.00 219.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n248 20 Pedestrian -1 -1 -1 929.14 160.40 1009.97 313.63 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n248 87 Car -1 -1 -1 597.73 173.28 621.38 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n248 74 Pedestrian -1 -1 -1 181.32 161.40 196.63 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n249 2 Car -1 -1 -1 1094.13 185.36 1221.68 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n249 3 Car -1 -1 -1 1029.98 183.86 1155.60 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n249 1 Car -1 -1 -1 954.82 183.88 1067.09 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n249 5 Car -1 -1 -1 601.39 172.96 637.00 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n249 27 Pedestrian -1 -1 -1 892.36 154.66 985.47 327.34 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n249 70 Pedestrian -1 -1 -1 271.08 157.25 291.49 215.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n249 25 Pedestrian -1 -1 -1 193.73 162.04 208.20 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n249 81 Pedestrian -1 -1 -1 517.46 171.95 531.66 211.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n249 84 Pedestrian -1 -1 -1 668.38 167.58 687.05 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n249 86 Pedestrian -1 -1 -1 706.44 170.35 722.21 218.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n249 85 Pedestrian -1 -1 -1 692.40 171.01 710.27 220.03 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n249 87 Car -1 -1 -1 597.70 173.29 621.33 193.50 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n249 20 Pedestrian -1 -1 -1 939.23 159.46 1022.97 321.58 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n249 74 Pedestrian -1 -1 -1 181.38 161.44 196.54 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n250 2 Car -1 -1 -1 1094.17 185.34 1221.62 236.22 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n250 3 Car -1 -1 -1 1029.62 183.91 1155.98 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n250 1 Car -1 -1 -1 954.81 183.94 1067.07 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n250 27 Pedestrian -1 -1 -1 903.60 151.21 989.75 330.47 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n250 5 Car -1 -1 -1 601.32 172.88 637.17 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n250 84 Pedestrian -1 -1 -1 669.51 167.52 687.26 219.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n250 70 Pedestrian -1 -1 -1 271.78 157.30 292.21 215.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n250 25 Pedestrian -1 -1 -1 193.76 161.98 208.25 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n250 20 Pedestrian -1 -1 -1 962.21 161.38 1030.45 320.33 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n250 86 Pedestrian -1 -1 -1 708.64 170.52 723.69 218.33 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n250 85 Pedestrian -1 -1 -1 694.11 171.00 710.88 220.14 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n250 87 Car -1 -1 -1 597.60 173.22 621.41 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n250 81 Pedestrian -1 -1 -1 517.25 172.23 531.78 211.44 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n250 74 Pedestrian -1 -1 -1 181.47 161.07 196.90 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n251 2 Car -1 -1 -1 1094.55 185.31 1221.61 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n251 3 Car -1 -1 -1 1029.32 183.99 1156.24 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n251 1 Car -1 -1 -1 954.05 183.93 1068.20 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n251 27 Pedestrian -1 -1 -1 922.66 152.34 993.53 335.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n251 5 Car -1 -1 -1 601.44 172.99 637.10 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n251 70 Pedestrian -1 -1 -1 273.62 157.17 294.64 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n251 20 Pedestrian -1 -1 -1 972.88 158.42 1035.34 322.18 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n251 25 Pedestrian -1 -1 -1 193.89 162.04 208.14 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n251 81 Pedestrian -1 -1 -1 515.84 172.34 530.95 210.72 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n251 85 Pedestrian -1 -1 -1 697.10 170.51 713.45 220.26 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n251 84 Pedestrian -1 -1 -1 671.83 167.38 691.09 219.63 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n251 86 Pedestrian -1 -1 -1 708.92 170.54 724.02 218.40 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n251 87 Car -1 -1 -1 597.79 173.30 621.36 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n251 74 Pedestrian -1 -1 -1 181.48 161.14 196.89 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n252 2 Car -1 -1 -1 1094.72 185.23 1221.45 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n252 3 Car -1 -1 -1 1029.36 183.97 1156.38 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n252 1 Car -1 -1 -1 954.73 183.71 1067.55 231.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n252 27 Pedestrian -1 -1 -1 934.72 153.08 1011.73 336.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n252 5 Car -1 -1 -1 601.43 173.10 637.05 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n252 70 Pedestrian -1 -1 -1 273.96 157.63 294.88 215.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n252 84 Pedestrian -1 -1 -1 671.24 168.35 693.27 219.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n252 20 Pedestrian -1 -1 -1 986.03 158.55 1052.76 322.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n252 81 Pedestrian -1 -1 -1 515.31 172.65 530.49 210.57 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n252 25 Pedestrian -1 -1 -1 193.83 161.97 208.11 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n252 86 Pedestrian -1 -1 -1 709.24 170.76 724.63 218.37 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n252 85 Pedestrian -1 -1 -1 698.47 171.05 714.54 219.89 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n252 87 Car -1 -1 -1 597.73 173.35 621.26 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n252 74 Pedestrian -1 -1 -1 181.50 160.95 197.10 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n253 2 Car -1 -1 -1 1098.71 185.22 1220.95 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n253 3 Car -1 -1 -1 1029.09 184.03 1156.33 233.70 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n253 1 Car -1 -1 -1 957.67 183.89 1064.58 231.12 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n253 5 Car -1 -1 -1 601.43 173.14 637.00 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n253 70 Pedestrian -1 -1 -1 274.48 157.94 295.55 216.15 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n253 84 Pedestrian -1 -1 -1 672.80 169.02 694.42 219.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n253 27 Pedestrian -1 -1 -1 948.58 154.12 1036.53 335.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n253 86 Pedestrian -1 -1 -1 709.59 171.24 724.86 218.93 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n253 81 Pedestrian -1 -1 -1 515.11 172.34 530.40 211.49 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n253 25 Pedestrian -1 -1 -1 193.72 161.84 208.04 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n253 20 Pedestrian -1 -1 -1 997.43 161.48 1086.93 325.59 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n253 87 Car -1 -1 -1 597.74 173.56 621.32 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n253 85 Pedestrian -1 -1 -1 698.99 171.80 714.67 219.62 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n253 74 Pedestrian -1 -1 -1 181.40 160.91 197.01 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n254 2 Car -1 -1 -1 1094.50 185.34 1221.70 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n254 3 Car -1 -1 -1 1033.80 184.17 1156.45 234.09 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n254 1 Car -1 -1 -1 956.94 183.69 1064.99 231.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n254 5 Car -1 -1 -1 601.42 173.07 636.94 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n254 84 Pedestrian -1 -1 -1 674.49 169.25 696.96 220.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n254 70 Pedestrian -1 -1 -1 275.12 156.66 296.90 216.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n254 81 Pedestrian -1 -1 -1 514.78 172.34 530.10 211.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n254 20 Pedestrian -1 -1 -1 1001.19 158.78 1106.27 329.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n254 25 Pedestrian -1 -1 -1 193.71 161.81 208.10 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n254 27 Pedestrian -1 -1 -1 956.68 154.65 1051.38 335.21 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n254 86 Pedestrian -1 -1 -1 710.04 171.18 725.52 219.16 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n254 87 Car -1 -1 -1 597.97 173.46 621.27 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n254 85 Pedestrian -1 -1 -1 700.81 171.58 716.31 220.17 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n254 74 Pedestrian -1 -1 -1 181.34 160.76 196.98 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n255 2 Car -1 -1 -1 1094.19 185.22 1221.55 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n255 1 Car -1 -1 -1 956.58 183.54 1065.27 231.35 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n255 3 Car -1 -1 -1 1034.40 184.33 1155.77 233.93 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n255 5 Car -1 -1 -1 601.37 173.14 636.95 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n255 70 Pedestrian -1 -1 -1 277.08 157.39 298.67 217.08 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n255 20 Pedestrian -1 -1 -1 1023.19 158.96 1114.62 329.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n255 84 Pedestrian -1 -1 -1 676.74 169.48 697.72 220.04 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n255 27 Pedestrian -1 -1 -1 972.81 154.07 1065.74 342.08 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n255 25 Pedestrian -1 -1 -1 193.63 161.81 208.17 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n255 81 Pedestrian -1 -1 -1 514.67 172.39 529.30 210.76 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n255 85 Pedestrian -1 -1 -1 701.61 171.70 717.04 220.12 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n255 87 Car -1 -1 -1 598.02 173.56 621.29 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n255 86 Pedestrian -1 -1 -1 712.24 170.69 727.60 219.19 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n255 74 Pedestrian -1 -1 -1 181.12 160.67 197.00 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n256 2 Car -1 -1 -1 1093.71 185.13 1222.11 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n256 1 Car -1 -1 -1 956.29 183.57 1066.05 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n256 3 Car -1 -1 -1 1035.04 184.13 1155.38 234.31 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n256 5 Car -1 -1 -1 601.51 173.18 636.88 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n256 27 Pedestrian -1 -1 -1 986.13 154.32 1067.90 342.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n256 70 Pedestrian -1 -1 -1 277.82 157.47 298.52 217.67 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n256 81 Pedestrian -1 -1 -1 515.03 172.49 528.17 209.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n256 84 Pedestrian -1 -1 -1 681.56 168.58 700.38 220.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n256 20 Pedestrian -1 -1 -1 1049.63 160.85 1118.85 328.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n256 86 Pedestrian -1 -1 -1 712.81 170.52 728.70 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n256 25 Pedestrian -1 -1 -1 193.71 161.80 208.12 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n256 87 Car -1 -1 -1 598.16 173.51 621.23 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n256 85 Pedestrian -1 -1 -1 702.91 171.70 718.20 220.25 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n256 74 Pedestrian -1 -1 -1 181.08 160.57 197.03 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n257 2 Car -1 -1 -1 1093.85 185.23 1221.77 236.46 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n257 3 Car -1 -1 -1 1034.94 184.43 1155.63 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n257 1 Car -1 -1 -1 955.93 183.67 1061.59 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n257 27 Pedestrian -1 -1 -1 1008.11 153.68 1076.41 343.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n257 5 Car -1 -1 -1 601.55 173.12 636.93 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n257 20 Pedestrian -1 -1 -1 1060.89 158.41 1130.70 331.38 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n257 70 Pedestrian -1 -1 -1 278.29 157.38 298.90 218.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n257 84 Pedestrian -1 -1 -1 685.49 168.21 703.29 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n257 81 Pedestrian -1 -1 -1 514.59 172.24 527.52 209.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n257 86 Pedestrian -1 -1 -1 713.12 170.15 729.19 219.10 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n257 25 Pedestrian -1 -1 -1 193.62 161.77 208.12 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n257 87 Car -1 -1 -1 598.47 173.41 621.32 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n257 85 Pedestrian -1 -1 -1 705.13 171.35 720.63 220.13 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n257 74 Pedestrian -1 -1 -1 181.16 160.57 197.01 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n258 2 Car -1 -1 -1 1093.42 185.23 1221.63 236.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n258 3 Car -1 -1 -1 1034.13 184.44 1156.32 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n258 1 Car -1 -1 -1 955.66 183.37 1065.93 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n258 5 Car -1 -1 -1 601.34 173.08 637.09 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n258 27 Pedestrian -1 -1 -1 1022.44 152.78 1100.67 344.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n258 70 Pedestrian -1 -1 -1 278.29 157.79 299.50 218.35 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n258 20 Pedestrian -1 -1 -1 1073.17 158.12 1149.32 337.68 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n258 25 Pedestrian -1 -1 -1 193.45 161.69 207.98 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n258 84 Pedestrian -1 -1 -1 688.26 168.17 706.36 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n258 86 Pedestrian -1 -1 -1 712.90 169.88 729.38 218.99 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n258 81 Pedestrian -1 -1 -1 514.11 172.19 527.30 210.11 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n258 87 Car -1 -1 -1 598.13 173.59 621.30 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n258 85 Pedestrian -1 -1 -1 706.05 171.13 722.06 220.34 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n258 74 Pedestrian -1 -1 -1 181.09 160.57 196.83 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n259 2 Car -1 -1 -1 1093.35 185.11 1221.91 236.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n259 1 Car -1 -1 -1 955.23 183.36 1066.30 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n259 3 Car -1 -1 -1 1034.35 184.66 1156.02 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n259 5 Car -1 -1 -1 601.45 173.18 636.99 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n259 81 Pedestrian -1 -1 -1 512.50 171.85 525.93 209.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n259 70 Pedestrian -1 -1 -1 278.43 157.51 300.02 218.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n259 84 Pedestrian -1 -1 -1 688.68 168.51 706.26 222.22 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n259 27 Pedestrian -1 -1 -1 1032.57 152.52 1128.58 344.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n259 25 Pedestrian -1 -1 -1 193.33 161.70 208.13 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n259 87 Car -1 -1 -1 598.18 173.48 621.41 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n259 86 Pedestrian -1 -1 -1 712.46 170.28 729.12 220.09 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n259 20 Pedestrian -1 -1 -1 1085.81 160.20 1182.19 337.18 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n259 74 Pedestrian -1 -1 -1 181.08 160.46 197.07 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n260 1 Car -1 -1 -1 955.00 183.31 1066.49 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n260 2 Car -1 -1 -1 1093.72 185.21 1221.75 236.34 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n260 3 Car -1 -1 -1 1035.54 183.99 1155.19 234.26 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n260 81 Pedestrian -1 -1 -1 511.69 171.36 525.26 209.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n260 5 Car -1 -1 -1 601.67 173.24 636.83 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n260 84 Pedestrian -1 -1 -1 690.27 168.03 706.60 222.92 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n260 70 Pedestrian -1 -1 -1 278.21 157.99 300.09 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n260 25 Pedestrian -1 -1 -1 193.41 161.69 208.18 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n260 27 Pedestrian -1 -1 -1 1047.25 156.12 1151.92 348.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n260 86 Pedestrian -1 -1 -1 708.93 170.53 726.48 221.38 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n260 87 Car -1 -1 -1 598.40 173.59 621.47 193.71 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n260 74 Pedestrian -1 -1 -1 181.19 160.44 197.11 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n260 20 Pedestrian -1 -1 -1 1093.34 161.76 1197.78 343.03 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n261 2 Car -1 -1 -1 1093.73 185.38 1221.76 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n261 1 Car -1 -1 -1 954.99 183.42 1066.54 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n261 3 Car -1 -1 -1 1035.23 184.10 1155.12 234.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n261 81 Pedestrian -1 -1 -1 511.33 171.65 524.72 209.18 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n261 5 Car -1 -1 -1 601.64 173.23 636.96 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n261 27 Pedestrian -1 -1 -1 1049.35 152.06 1165.32 359.39 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n261 25 Pedestrian -1 -1 -1 193.33 161.66 208.23 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n261 70 Pedestrian -1 -1 -1 278.67 158.78 300.73 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n261 84 Pedestrian -1 -1 -1 692.49 167.59 709.28 223.97 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n261 86 Pedestrian -1 -1 -1 711.46 171.23 728.98 222.70 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n261 87 Car -1 -1 -1 598.35 173.67 621.42 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n261 74 Pedestrian -1 -1 -1 181.27 160.45 197.19 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n261 20 Pedestrian -1 -1 -1 1117.00 160.48 1204.86 350.85 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n262 1 Car -1 -1 -1 954.80 183.46 1066.71 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n262 2 Car -1 -1 -1 1094.38 185.41 1221.38 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n262 3 Car -1 -1 -1 1032.68 184.04 1152.67 233.74 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n262 5 Car -1 -1 -1 601.61 173.05 636.90 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n262 27 Pedestrian -1 -1 -1 1075.73 150.20 1169.32 361.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n262 81 Pedestrian -1 -1 -1 510.81 172.01 524.76 209.34 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n262 25 Pedestrian -1 -1 -1 193.30 161.50 208.19 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n262 70 Pedestrian -1 -1 -1 280.92 158.14 302.37 220.80 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n262 84 Pedestrian -1 -1 -1 692.90 167.77 709.52 224.00 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n262 86 Pedestrian -1 -1 -1 712.86 171.93 727.96 222.37 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n262 87 Car -1 -1 -1 598.33 173.50 621.58 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n262 20 Pedestrian -1 -1 -1 1134.22 160.32 1210.30 343.51 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n262 74 Pedestrian -1 -1 -1 181.35 160.57 197.05 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n263 1 Car -1 -1 -1 954.71 183.41 1066.76 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n263 2 Car -1 -1 -1 1093.62 185.40 1221.96 235.41 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n263 3 Car -1 -1 -1 1032.32 183.79 1152.64 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n263 27 Pedestrian -1 -1 -1 1099.82 152.27 1191.12 365.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n263 5 Car -1 -1 -1 601.55 173.11 636.94 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n263 81 Pedestrian -1 -1 -1 510.29 172.52 524.53 209.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n263 25 Pedestrian -1 -1 -1 193.29 161.55 208.18 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n263 70 Pedestrian -1 -1 -1 281.41 158.05 302.46 220.66 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n263 86 Pedestrian -1 -1 -1 712.73 171.72 728.17 222.37 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n263 84 Pedestrian -1 -1 -1 693.84 168.53 711.44 223.46 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n263 87 Car -1 -1 -1 598.36 173.49 621.72 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n263 20 Pedestrian -1 -1 -1 1173.46 159.36 1216.99 345.20 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n263 74 Pedestrian -1 -1 -1 181.32 160.73 197.00 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n264 1 Car -1 -1 -1 955.01 183.59 1066.38 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n264 2 Car -1 -1 -1 1094.60 185.12 1220.69 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n264 3 Car -1 -1 -1 1032.08 183.69 1153.05 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n264 27 Pedestrian -1 -1 -1 1111.16 156.07 1210.63 363.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n264 5 Car -1 -1 -1 601.61 173.17 636.94 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n264 70 Pedestrian -1 -1 -1 281.11 159.03 303.27 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n264 25 Pedestrian -1 -1 -1 193.59 161.74 208.18 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n264 81 Pedestrian -1 -1 -1 509.68 172.58 524.35 209.53 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n264 86 Pedestrian -1 -1 -1 712.75 171.55 728.78 222.44 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n264 84 Pedestrian -1 -1 -1 695.50 168.64 714.03 223.43 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n264 87 Car -1 -1 -1 598.38 173.51 621.67 193.68 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n264 74 Pedestrian -1 -1 -1 181.45 160.68 197.00 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n264 20 Pedestrian -1 -1 -1 1194.71 163.23 1218.66 340.72 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n265 1 Car -1 -1 -1 955.08 183.63 1066.43 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n265 3 Car -1 -1 -1 1031.70 183.69 1153.41 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n265 2 Car -1 -1 -1 1095.47 185.01 1220.30 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n265 70 Pedestrian -1 -1 -1 281.85 158.52 303.61 221.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n265 5 Car -1 -1 -1 601.38 173.09 637.08 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n265 27 Pedestrian -1 -1 -1 1120.52 153.66 1216.54 365.20 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n265 25 Pedestrian -1 -1 -1 193.44 161.67 208.31 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n265 81 Pedestrian -1 -1 -1 509.62 172.22 524.15 209.02 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n265 84 Pedestrian -1 -1 -1 695.68 170.19 715.33 224.12 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n265 87 Car -1 -1 -1 598.20 173.59 621.67 193.76 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n265 86 Pedestrian -1 -1 -1 713.27 171.12 729.54 222.76 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n265 74 Pedestrian -1 -1 -1 181.28 160.89 196.86 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n266 1 Car -1 -1 -1 955.18 183.71 1066.44 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n266 3 Car -1 -1 -1 1030.99 183.77 1154.31 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n266 2 Car -1 -1 -1 1097.28 185.12 1218.16 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n266 5 Car -1 -1 -1 601.39 173.06 637.03 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n266 70 Pedestrian -1 -1 -1 282.51 158.50 304.63 221.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n266 27 Pedestrian -1 -1 -1 1134.64 157.94 1217.87 361.19 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n266 25 Pedestrian -1 -1 -1 193.53 161.71 208.35 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n266 81 Pedestrian -1 -1 -1 509.15 172.01 522.47 208.22 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n266 87 Car -1 -1 -1 598.07 173.56 621.83 193.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n266 84 Pedestrian -1 -1 -1 696.93 170.41 716.34 223.93 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n266 86 Pedestrian -1 -1 -1 713.33 171.30 729.44 222.60 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n266 74 Pedestrian -1 -1 -1 181.35 160.60 197.08 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n267 1 Car -1 -1 -1 955.26 183.81 1066.32 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n267 2 Car -1 -1 -1 1097.67 185.26 1217.91 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n267 3 Car -1 -1 -1 1030.59 183.76 1154.95 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n267 81 Pedestrian -1 -1 -1 508.16 172.27 521.56 207.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n267 5 Car -1 -1 -1 601.50 173.31 637.05 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n267 70 Pedestrian -1 -1 -1 284.79 158.40 306.11 221.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n267 25 Pedestrian -1 -1 -1 193.52 161.64 208.43 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n267 84 Pedestrian -1 -1 -1 700.28 170.15 718.26 224.42 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n267 86 Pedestrian -1 -1 -1 717.02 171.56 732.71 223.65 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n267 87 Car -1 -1 -1 598.02 173.67 621.94 193.98 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n267 27 Pedestrian -1 -1 -1 1162.16 169.58 1220.68 357.14 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n267 74 Pedestrian -1 -1 -1 181.36 160.47 197.16 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n268 2 Car -1 -1 -1 1097.05 185.35 1218.79 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n268 1 Car -1 -1 -1 955.29 183.78 1066.31 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n268 3 Car -1 -1 -1 1030.11 183.73 1155.46 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n268 70 Pedestrian -1 -1 -1 285.81 158.69 307.05 222.20 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n268 5 Car -1 -1 -1 601.55 173.26 636.94 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n268 81 Pedestrian -1 -1 -1 506.95 172.78 520.31 207.65 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n268 25 Pedestrian -1 -1 -1 193.83 161.68 208.54 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n268 84 Pedestrian -1 -1 -1 704.12 170.00 721.60 224.72 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n268 87 Car -1 -1 -1 598.07 173.71 621.82 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n268 86 Pedestrian -1 -1 -1 720.81 171.66 735.75 224.97 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n268 74 Pedestrian -1 -1 -1 181.44 160.36 197.28 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n269 2 Car -1 -1 -1 1096.32 185.46 1219.57 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n269 1 Car -1 -1 -1 955.14 183.80 1066.45 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n269 3 Car -1 -1 -1 1030.12 183.73 1155.44 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n269 70 Pedestrian -1 -1 -1 286.68 158.02 307.28 222.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n269 5 Car -1 -1 -1 601.65 173.22 636.93 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n269 25 Pedestrian -1 -1 -1 193.71 161.52 208.39 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n269 84 Pedestrian -1 -1 -1 705.13 170.01 722.38 224.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n269 87 Car -1 -1 -1 598.20 173.54 621.83 193.80 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n269 81 Pedestrian -1 -1 -1 505.19 173.01 519.14 208.03 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n269 86 Pedestrian -1 -1 -1 720.42 171.53 737.58 224.68 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n269 74 Pedestrian -1 -1 -1 181.41 160.23 197.21 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n270 2 Car -1 -1 -1 1096.22 185.48 1219.81 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n270 1 Car -1 -1 -1 955.25 183.76 1066.35 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n270 3 Car -1 -1 -1 1029.96 183.76 1155.63 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n270 5 Car -1 -1 -1 601.43 173.15 637.02 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n270 70 Pedestrian -1 -1 -1 286.25 158.60 308.33 223.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n270 81 Pedestrian -1 -1 -1 504.92 172.58 518.60 207.76 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n270 25 Pedestrian -1 -1 -1 193.67 161.39 208.47 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n270 84 Pedestrian -1 -1 -1 708.26 170.41 725.23 224.13 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n270 87 Car -1 -1 -1 598.21 173.50 621.75 193.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n270 86 Pedestrian -1 -1 -1 722.38 170.34 741.75 225.28 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n270 74 Pedestrian -1 -1 -1 181.35 160.21 197.38 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n271 2 Car -1 -1 -1 1095.99 185.54 1220.06 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n271 1 Car -1 -1 -1 955.12 183.74 1066.49 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n271 3 Car -1 -1 -1 1030.20 183.81 1155.43 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n271 81 Pedestrian -1 -1 -1 504.35 172.28 517.80 208.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n271 5 Car -1 -1 -1 601.49 173.14 637.03 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n271 70 Pedestrian -1 -1 -1 287.81 158.39 310.38 223.86 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n271 25 Pedestrian -1 -1 -1 193.61 161.31 208.56 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n271 84 Pedestrian -1 -1 -1 708.85 170.07 726.06 224.89 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n271 87 Car -1 -1 -1 598.17 173.53 621.77 193.84 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n271 86 Pedestrian -1 -1 -1 722.61 169.28 742.48 225.67 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n271 74 Pedestrian -1 -1 -1 181.43 160.21 197.42 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n272 2 Car -1 -1 -1 1096.04 185.56 1220.10 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n272 1 Car -1 -1 -1 955.25 183.79 1066.49 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n272 3 Car -1 -1 -1 1030.22 183.82 1155.43 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n272 5 Car -1 -1 -1 601.49 173.12 637.04 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n272 70 Pedestrian -1 -1 -1 288.12 158.02 310.97 224.57 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n272 81 Pedestrian -1 -1 -1 503.63 172.43 517.22 207.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n272 25 Pedestrian -1 -1 -1 193.62 161.25 208.55 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n272 84 Pedestrian -1 -1 -1 711.57 169.53 728.96 225.81 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n272 87 Car -1 -1 -1 598.11 173.32 622.03 193.81 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n272 86 Pedestrian -1 -1 -1 725.49 168.95 745.43 226.31 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n272 74 Pedestrian -1 -1 -1 181.38 160.13 197.34 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n273 2 Car -1 -1 -1 1096.07 185.53 1220.14 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n273 1 Car -1 -1 -1 955.22 183.82 1066.56 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n273 3 Car -1 -1 -1 1030.18 183.81 1155.49 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n273 70 Pedestrian -1 -1 -1 288.93 158.00 311.71 224.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n273 5 Car -1 -1 -1 601.28 173.12 637.05 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n273 81 Pedestrian -1 -1 -1 502.99 172.69 516.97 207.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n273 25 Pedestrian -1 -1 -1 193.63 161.14 208.51 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n273 84 Pedestrian -1 -1 -1 712.58 169.46 730.15 226.42 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n273 87 Car -1 -1 -1 598.12 173.42 622.08 193.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n273 86 Pedestrian -1 -1 -1 726.17 169.30 746.24 226.69 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n273 74 Pedestrian -1 -1 -1 181.32 160.00 197.31 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n274 2 Car -1 -1 -1 1096.12 185.54 1220.14 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n274 1 Car -1 -1 -1 955.12 183.81 1066.54 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n274 3 Car -1 -1 -1 1029.80 183.82 1155.78 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n274 5 Car -1 -1 -1 601.35 173.07 637.17 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n274 70 Pedestrian -1 -1 -1 289.89 157.19 312.72 224.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n274 25 Pedestrian -1 -1 -1 193.73 161.18 208.60 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n274 81 Pedestrian -1 -1 -1 502.13 173.18 516.36 208.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n274 84 Pedestrian -1 -1 -1 715.20 169.38 734.35 226.78 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n274 87 Car -1 -1 -1 598.08 173.31 622.14 193.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n274 86 Pedestrian -1 -1 -1 726.53 169.03 746.10 226.46 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n274 74 Pedestrian -1 -1 -1 181.30 160.23 197.17 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n275 2 Car -1 -1 -1 1096.04 185.56 1220.21 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n275 1 Car -1 -1 -1 955.00 183.79 1066.74 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n275 3 Car -1 -1 -1 1030.03 183.84 1155.63 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n275 5 Car -1 -1 -1 601.55 173.26 636.96 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n275 70 Pedestrian -1 -1 -1 291.48 156.98 314.56 225.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n275 84 Pedestrian -1 -1 -1 715.02 170.01 735.40 227.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n275 25 Pedestrian -1 -1 -1 193.69 161.27 208.69 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n275 81 Pedestrian -1 -1 -1 500.88 172.34 515.44 207.98 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n275 87 Car -1 -1 -1 598.03 173.55 621.81 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n275 86 Pedestrian -1 -1 -1 727.04 169.96 746.44 226.58 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n275 74 Pedestrian -1 -1 -1 181.38 160.39 197.17 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n276 2 Car -1 -1 -1 1096.03 185.60 1220.05 235.56 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n276 1 Car -1 -1 -1 955.10 183.85 1066.69 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n276 3 Car -1 -1 -1 1030.12 183.97 1155.67 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n276 5 Car -1 -1 -1 601.29 173.09 637.02 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n276 70 Pedestrian -1 -1 -1 291.58 157.52 315.25 225.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n276 84 Pedestrian -1 -1 -1 714.93 170.51 735.63 227.27 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n276 81 Pedestrian -1 -1 -1 500.76 171.77 514.41 207.56 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n276 25 Pedestrian -1 -1 -1 193.50 161.12 208.71 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n276 87 Car -1 -1 -1 598.03 173.48 621.88 193.73 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n276 74 Pedestrian -1 -1 -1 181.32 160.48 196.94 198.69 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n276 86 Pedestrian -1 -1 -1 731.85 171.25 749.70 227.53 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n277 2 Car -1 -1 -1 1095.94 185.50 1220.20 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n277 1 Car -1 -1 -1 955.16 183.86 1066.53 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n277 3 Car -1 -1 -1 1030.20 183.91 1155.47 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n277 5 Car -1 -1 -1 601.38 173.08 636.86 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n277 70 Pedestrian -1 -1 -1 292.77 158.44 316.50 225.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n277 84 Pedestrian -1 -1 -1 714.99 169.91 736.02 226.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n277 81 Pedestrian -1 -1 -1 500.09 172.01 513.04 207.69 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n277 25 Pedestrian -1 -1 -1 193.47 161.18 208.84 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n277 87 Car -1 -1 -1 598.06 173.42 621.77 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n277 86 Pedestrian -1 -1 -1 736.25 173.54 751.54 228.12 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n277 74 Pedestrian -1 -1 -1 181.23 160.81 196.85 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n278 2 Car -1 -1 -1 1095.85 185.55 1220.32 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n278 1 Car -1 -1 -1 955.17 183.85 1066.65 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n278 3 Car -1 -1 -1 1030.01 183.90 1155.63 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n278 5 Car -1 -1 -1 601.38 173.07 636.84 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n278 81 Pedestrian -1 -1 -1 499.09 172.44 512.74 207.81 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n278 84 Pedestrian -1 -1 -1 719.13 169.24 738.42 226.99 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n278 70 Pedestrian -1 -1 -1 295.57 158.96 318.65 227.45 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n278 25 Pedestrian -1 -1 -1 193.52 161.18 208.88 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n278 87 Car -1 -1 -1 598.05 173.29 621.85 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n278 86 Pedestrian -1 -1 -1 735.60 172.38 752.58 227.35 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n278 74 Pedestrian -1 -1 -1 181.29 160.98 196.74 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n279 2 Car -1 -1 -1 1095.95 185.54 1220.14 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n279 1 Car -1 -1 -1 955.15 183.87 1066.66 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n279 3 Car -1 -1 -1 1030.09 183.88 1155.46 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n279 5 Car -1 -1 -1 601.38 173.08 636.87 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n279 70 Pedestrian -1 -1 -1 297.17 158.79 320.24 227.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n279 84 Pedestrian -1 -1 -1 722.61 168.87 741.82 227.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n279 25 Pedestrian -1 -1 -1 193.80 161.19 208.80 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n279 87 Car -1 -1 -1 598.00 173.37 621.90 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n279 81 Pedestrian -1 -1 -1 498.41 172.57 512.36 207.67 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n279 86 Pedestrian -1 -1 -1 739.42 172.45 754.79 229.01 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n279 74 Pedestrian -1 -1 -1 181.29 160.79 196.80 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n280 2 Car -1 -1 -1 1095.80 185.51 1220.26 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n280 1 Car -1 -1 -1 955.10 183.89 1066.63 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n280 3 Car -1 -1 -1 1029.81 183.91 1155.74 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n280 5 Car -1 -1 -1 601.53 173.22 636.78 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n280 81 Pedestrian -1 -1 -1 496.23 172.65 511.49 207.45 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n280 70 Pedestrian -1 -1 -1 297.87 158.46 323.94 228.40 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n280 84 Pedestrian -1 -1 -1 723.47 169.43 742.26 226.86 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n280 25 Pedestrian -1 -1 -1 193.85 161.26 208.73 198.59 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n280 87 Car -1 -1 -1 598.07 173.43 621.96 193.71 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n280 86 Pedestrian -1 -1 -1 738.44 171.58 756.65 228.17 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n280 74 Pedestrian -1 -1 -1 181.22 161.01 196.69 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n281 2 Car -1 -1 -1 1095.77 185.48 1220.25 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n281 1 Car -1 -1 -1 955.09 183.94 1066.60 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n281 3 Car -1 -1 -1 1029.81 183.89 1155.78 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n281 5 Car -1 -1 -1 601.51 173.22 636.83 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n281 81 Pedestrian -1 -1 -1 495.52 171.89 510.53 207.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n281 84 Pedestrian -1 -1 -1 723.25 170.41 742.26 226.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n281 25 Pedestrian -1 -1 -1 194.00 161.17 208.80 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n281 87 Car -1 -1 -1 597.91 173.39 621.99 193.73 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n281 86 Pedestrian -1 -1 -1 742.01 172.34 759.28 229.10 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n281 70 Pedestrian -1 -1 -1 297.11 158.65 325.47 228.14 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n281 74 Pedestrian -1 -1 -1 181.30 160.88 196.80 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n282 2 Car -1 -1 -1 1095.73 185.50 1220.39 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n282 1 Car -1 -1 -1 954.98 183.93 1066.70 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n282 3 Car -1 -1 -1 1030.01 183.90 1155.51 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n282 5 Car -1 -1 -1 601.40 173.13 636.88 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n282 84 Pedestrian -1 -1 -1 723.50 170.79 742.39 226.34 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n282 25 Pedestrian -1 -1 -1 193.90 161.14 208.82 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n282 86 Pedestrian -1 -1 -1 741.91 172.44 762.02 229.28 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n282 81 Pedestrian -1 -1 -1 494.82 171.95 509.12 207.20 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n282 87 Car -1 -1 -1 597.88 173.39 622.07 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n282 70 Pedestrian -1 -1 -1 298.82 159.38 326.03 229.72 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n282 74 Pedestrian -1 -1 -1 181.38 161.01 196.71 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n283 2 Car -1 -1 -1 1095.78 185.49 1220.40 235.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n283 1 Car -1 -1 -1 955.03 183.89 1066.77 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n283 3 Car -1 -1 -1 1029.91 183.88 1155.78 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n283 5 Car -1 -1 -1 601.46 173.04 636.84 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n283 86 Pedestrian -1 -1 -1 743.69 172.74 765.89 229.69 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n283 25 Pedestrian -1 -1 -1 193.69 161.16 208.78 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n283 84 Pedestrian -1 -1 -1 725.96 170.52 744.97 226.05 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n283 70 Pedestrian -1 -1 -1 301.81 160.06 327.60 230.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n283 87 Car -1 -1 -1 598.00 173.40 622.12 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n283 81 Pedestrian -1 -1 -1 492.95 171.93 508.11 207.38 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n283 74 Pedestrian -1 -1 -1 181.46 161.05 196.70 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n284 2 Car -1 -1 -1 1095.92 185.53 1220.14 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n284 1 Car -1 -1 -1 955.04 183.97 1066.64 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n284 3 Car -1 -1 -1 1030.08 183.93 1155.60 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n284 5 Car -1 -1 -1 601.43 173.10 636.99 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n284 84 Pedestrian -1 -1 -1 727.38 171.19 745.32 225.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n284 25 Pedestrian -1 -1 -1 193.47 161.21 208.82 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n284 86 Pedestrian -1 -1 -1 745.10 172.80 766.97 230.06 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n284 87 Car -1 -1 -1 597.88 173.34 622.07 193.81 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n284 81 Pedestrian -1 -1 -1 493.13 172.10 507.70 207.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n284 74 Pedestrian -1 -1 -1 181.38 161.26 196.69 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n284 70 Pedestrian -1 -1 -1 301.99 160.34 328.62 230.81 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n285 2 Car -1 -1 -1 1095.83 185.52 1220.36 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n285 1 Car -1 -1 -1 955.04 183.97 1066.70 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n285 3 Car -1 -1 -1 1030.19 183.98 1155.59 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n285 5 Car -1 -1 -1 601.35 173.16 637.01 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n285 84 Pedestrian -1 -1 -1 728.11 171.52 746.51 225.37 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n285 25 Pedestrian -1 -1 -1 193.66 161.32 208.65 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n285 81 Pedestrian -1 -1 -1 492.64 172.14 507.31 207.74 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n285 86 Pedestrian -1 -1 -1 748.25 172.48 770.19 230.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n285 87 Car -1 -1 -1 598.00 173.49 622.00 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n285 74 Pedestrian -1 -1 -1 181.40 161.29 196.58 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n285 88 Cyclist -1 -1 -1 302.22 159.31 330.34 232.06 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n286 2 Car -1 -1 -1 1095.82 185.52 1220.39 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n286 1 Car -1 -1 -1 955.08 183.97 1066.75 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n286 3 Car -1 -1 -1 1030.19 183.97 1155.63 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n286 5 Car -1 -1 -1 601.33 173.08 637.13 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n286 84 Pedestrian -1 -1 -1 729.61 171.91 748.48 226.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n286 25 Pedestrian -1 -1 -1 193.76 161.39 208.64 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n286 81 Pedestrian -1 -1 -1 492.58 172.47 506.20 207.26 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n286 87 Car -1 -1 -1 597.85 173.29 621.99 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n286 86 Pedestrian -1 -1 -1 743.96 171.38 765.98 230.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n286 74 Pedestrian -1 -1 -1 181.39 161.31 196.58 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n286 89 Pedestrian -1 -1 -1 306.83 160.35 330.97 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n287 2 Car -1 -1 -1 1095.68 185.56 1220.51 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n287 1 Car -1 -1 -1 955.17 184.00 1066.54 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n287 3 Car -1 -1 -1 1030.03 183.90 1155.74 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n287 5 Car -1 -1 -1 601.49 173.13 636.97 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n287 89 Pedestrian -1 -1 -1 306.99 160.44 333.10 231.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n287 84 Pedestrian -1 -1 -1 730.61 172.46 748.90 226.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n287 25 Pedestrian -1 -1 -1 193.74 161.59 208.52 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n287 81 Pedestrian -1 -1 -1 492.11 172.24 505.84 206.68 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n287 87 Car -1 -1 -1 597.89 173.33 622.05 193.71 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n287 86 Pedestrian -1 -1 -1 741.64 173.21 763.47 229.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n287 74 Pedestrian -1 -1 -1 181.45 161.13 196.80 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n287 90 Pedestrian -1 -1 -1 753.69 173.89 773.07 230.86 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n288 2 Car -1 -1 -1 1095.78 185.56 1220.49 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n288 1 Car -1 -1 -1 955.16 184.05 1066.68 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n288 3 Car -1 -1 -1 1030.08 183.95 1155.70 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n288 5 Car -1 -1 -1 601.44 173.09 636.85 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n288 89 Pedestrian -1 -1 -1 307.67 160.26 333.67 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n288 25 Pedestrian -1 -1 -1 193.77 161.49 208.55 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n288 84 Pedestrian -1 -1 -1 731.45 172.31 750.91 226.89 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n288 86 Pedestrian -1 -1 -1 744.24 173.47 768.07 229.67 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n288 81 Pedestrian -1 -1 -1 491.40 172.20 504.83 206.39 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n288 87 Car -1 -1 -1 597.79 173.39 621.98 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n288 90 Pedestrian -1 -1 -1 757.56 174.53 776.27 231.26 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n288 74 Pedestrian -1 -1 -1 181.46 160.95 196.85 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n289 2 Car -1 -1 -1 1095.68 185.52 1220.43 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n289 1 Car -1 -1 -1 955.18 184.08 1066.70 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n289 3 Car -1 -1 -1 1030.09 184.01 1155.71 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n289 5 Car -1 -1 -1 601.36 173.14 636.88 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n289 89 Pedestrian -1 -1 -1 309.75 160.35 336.13 233.83 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n289 84 Pedestrian -1 -1 -1 733.94 171.88 753.36 227.23 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n289 25 Pedestrian -1 -1 -1 193.76 161.50 208.55 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n289 87 Car -1 -1 -1 597.74 173.31 622.09 193.71 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n289 90 Pedestrian -1 -1 -1 761.52 174.95 780.31 231.26 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n289 81 Pedestrian -1 -1 -1 491.30 172.50 504.17 206.60 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n289 86 Pedestrian -1 -1 -1 748.50 173.27 770.17 229.56 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n289 74 Pedestrian -1 -1 -1 181.39 161.08 196.83 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n290 2 Car -1 -1 -1 1095.76 185.53 1220.39 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n290 1 Car -1 -1 -1 955.33 184.11 1066.63 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n290 3 Car -1 -1 -1 1030.07 184.02 1155.80 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n290 5 Car -1 -1 -1 601.29 173.17 636.94 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n290 89 Pedestrian -1 -1 -1 314.10 160.35 338.46 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n290 25 Pedestrian -1 -1 -1 193.42 161.45 208.60 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n290 81 Pedestrian -1 -1 -1 489.95 172.59 503.10 207.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n290 84 Pedestrian -1 -1 -1 736.65 171.34 756.44 227.74 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n290 87 Car -1 -1 -1 597.68 173.38 622.13 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n290 90 Pedestrian -1 -1 -1 763.03 175.19 784.63 231.40 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n290 86 Pedestrian -1 -1 -1 753.20 173.89 772.93 229.82 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n290 74 Pedestrian -1 -1 -1 181.37 160.74 197.14 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n291 2 Car -1 -1 -1 1095.80 185.57 1220.27 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n291 1 Car -1 -1 -1 955.31 184.06 1066.74 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n291 3 Car -1 -1 -1 1030.17 184.04 1155.68 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n291 5 Car -1 -1 -1 601.35 173.14 636.89 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n291 89 Pedestrian -1 -1 -1 314.92 160.10 339.88 234.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n291 84 Pedestrian -1 -1 -1 737.14 171.77 757.34 227.94 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n291 25 Pedestrian -1 -1 -1 193.14 161.25 208.55 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n291 81 Pedestrian -1 -1 -1 488.56 171.82 502.24 206.81 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n291 90 Pedestrian -1 -1 -1 763.11 173.97 787.43 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n291 87 Car -1 -1 -1 597.80 173.39 622.11 193.75 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n291 74 Pedestrian -1 -1 -1 181.26 160.92 196.96 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n292 2 Car -1 -1 -1 1095.96 185.62 1220.10 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n292 1 Car -1 -1 -1 955.45 184.16 1066.64 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n292 3 Car -1 -1 -1 1030.02 184.03 1155.79 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n292 5 Car -1 -1 -1 601.23 173.01 636.93 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n292 84 Pedestrian -1 -1 -1 736.82 172.57 758.79 229.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n292 25 Pedestrian -1 -1 -1 193.34 161.19 208.49 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n292 89 Pedestrian -1 -1 -1 318.19 160.60 342.12 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n292 90 Pedestrian -1 -1 -1 760.05 171.20 780.43 232.77 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n292 87 Car -1 -1 -1 597.56 173.41 622.00 193.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n292 81 Pedestrian -1 -1 -1 488.36 171.81 500.74 206.45 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n292 74 Pedestrian -1 -1 -1 181.29 160.75 197.02 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n292 91 Pedestrian -1 -1 -1 768.33 173.27 789.59 232.23 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n293 2 Car -1 -1 -1 1095.92 185.56 1220.20 235.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n293 1 Car -1 -1 -1 955.53 184.15 1066.50 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n293 3 Car -1 -1 -1 1029.90 184.03 1155.86 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n293 5 Car -1 -1 -1 601.24 173.12 636.69 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n293 89 Pedestrian -1 -1 -1 318.65 160.85 344.03 234.66 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n293 84 Pedestrian -1 -1 -1 737.13 172.97 760.26 229.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n293 25 Pedestrian -1 -1 -1 193.41 161.31 208.33 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n293 91 Pedestrian -1 -1 -1 772.53 174.12 793.14 232.20 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n293 87 Car -1 -1 -1 597.66 173.39 622.01 193.73 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n293 90 Pedestrian -1 -1 -1 760.12 172.06 782.77 232.43 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n293 81 Pedestrian -1 -1 -1 486.63 170.79 499.37 205.43 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n293 74 Pedestrian -1 -1 -1 181.37 160.82 196.93 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n294 2 Car -1 -1 -1 1096.06 185.61 1219.97 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n294 1 Car -1 -1 -1 955.43 184.12 1066.56 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n294 3 Car -1 -1 -1 1029.80 184.07 1155.96 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n294 5 Car -1 -1 -1 601.22 173.03 636.99 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n294 84 Pedestrian -1 -1 -1 740.56 172.57 762.17 229.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n294 89 Pedestrian -1 -1 -1 320.26 160.58 346.91 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n294 25 Pedestrian -1 -1 -1 193.46 161.33 208.16 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n294 91 Pedestrian -1 -1 -1 776.17 173.96 797.45 232.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n294 87 Car -1 -1 -1 597.63 173.32 622.09 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n294 90 Pedestrian -1 -1 -1 762.96 172.07 787.19 232.27 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n294 81 Pedestrian -1 -1 -1 485.39 171.75 499.68 206.70 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n294 74 Pedestrian -1 -1 -1 181.26 160.63 196.92 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n295 2 Car -1 -1 -1 1096.05 185.59 1220.00 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n295 1 Car -1 -1 -1 955.42 184.10 1066.68 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n295 3 Car -1 -1 -1 1029.89 184.06 1155.85 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n295 5 Car -1 -1 -1 601.43 173.12 636.76 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n295 84 Pedestrian -1 -1 -1 745.49 172.24 766.05 229.93 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n295 89 Pedestrian -1 -1 -1 321.28 160.33 347.94 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n295 25 Pedestrian -1 -1 -1 193.47 161.32 208.21 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n295 91 Pedestrian -1 -1 -1 779.27 174.38 806.34 232.53 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n295 87 Car -1 -1 -1 597.66 173.39 622.14 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n295 81 Pedestrian -1 -1 -1 485.11 170.92 499.31 205.53 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n295 90 Pedestrian -1 -1 -1 767.15 172.74 789.51 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n295 74 Pedestrian -1 -1 -1 181.14 160.36 197.11 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n296 2 Car -1 -1 -1 1096.01 185.52 1220.08 235.63 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n296 1 Car -1 -1 -1 955.39 184.09 1066.54 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n296 3 Car -1 -1 -1 1029.90 184.12 1155.89 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n296 5 Car -1 -1 -1 601.47 173.06 636.76 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n296 84 Pedestrian -1 -1 -1 748.60 172.76 768.09 229.11 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n296 25 Pedestrian -1 -1 -1 193.35 161.26 208.34 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n296 91 Pedestrian -1 -1 -1 780.63 175.52 808.48 233.70 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n296 87 Car -1 -1 -1 597.74 173.38 622.09 193.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n296 89 Pedestrian -1 -1 -1 322.12 159.92 349.21 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n296 81 Pedestrian -1 -1 -1 483.53 170.79 498.38 205.24 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n296 90 Pedestrian -1 -1 -1 772.69 173.24 793.10 232.68 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n296 74 Pedestrian -1 -1 -1 181.18 160.22 197.28 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n297 2 Car -1 -1 -1 1096.12 185.53 1220.20 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n297 1 Car -1 -1 -1 955.30 184.09 1066.68 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n297 3 Car -1 -1 -1 1029.82 184.08 1155.95 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n297 89 Pedestrian -1 -1 -1 324.77 159.60 352.38 236.84 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n297 5 Car -1 -1 -1 601.55 173.16 636.82 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n297 84 Pedestrian -1 -1 -1 748.61 173.11 771.35 229.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n297 25 Pedestrian -1 -1 -1 193.54 161.39 207.99 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n297 87 Car -1 -1 -1 597.77 173.47 622.12 193.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n297 90 Pedestrian -1 -1 -1 776.86 172.05 796.65 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n297 91 Pedestrian -1 -1 -1 789.25 174.21 811.63 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n297 81 Pedestrian -1 -1 -1 483.31 170.42 497.76 205.58 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n298 2 Car -1 -1 -1 1096.03 185.58 1220.24 235.66 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n298 1 Car -1 -1 -1 955.44 184.10 1066.51 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n298 3 Car -1 -1 -1 1029.68 184.07 1156.06 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n298 84 Pedestrian -1 -1 -1 750.18 173.43 776.10 230.97 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n298 89 Pedestrian -1 -1 -1 324.83 160.06 354.25 236.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n298 5 Car -1 -1 -1 601.69 173.18 636.91 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n298 25 Pedestrian -1 -1 -1 193.53 161.25 208.07 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n298 87 Car -1 -1 -1 598.05 173.49 622.05 193.73 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n298 90 Pedestrian -1 -1 -1 781.12 173.02 806.27 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n298 91 Pedestrian -1 -1 -1 790.21 174.07 813.45 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n298 81 Pedestrian -1 -1 -1 482.93 170.60 497.45 205.28 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n298 92 Pedestrian -1 -1 -1 181.15 160.75 196.75 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n299 2 Car -1 -1 -1 1095.90 185.61 1220.36 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n299 1 Car -1 -1 -1 955.39 184.07 1066.57 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n299 3 Car -1 -1 -1 1029.68 184.11 1156.10 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n299 89 Pedestrian -1 -1 -1 327.00 160.50 356.47 238.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n299 84 Pedestrian -1 -1 -1 753.99 173.03 778.53 230.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n299 5 Car -1 -1 -1 601.55 173.18 637.02 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n299 25 Pedestrian -1 -1 -1 193.56 161.40 208.23 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n299 90 Pedestrian -1 -1 -1 781.07 172.76 807.25 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n299 87 Car -1 -1 -1 597.87 173.39 622.27 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n299 81 Pedestrian -1 -1 -1 481.37 171.34 496.26 204.79 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n299 91 Pedestrian -1 -1 -1 795.05 175.09 815.99 234.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n299 92 Pedestrian -1 -1 -1 181.27 160.39 197.17 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n300 2 Car -1 -1 -1 1095.65 185.62 1220.52 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n300 1 Car -1 -1 -1 955.50 184.14 1066.31 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n300 3 Car -1 -1 -1 1029.78 184.08 1155.90 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n300 89 Pedestrian -1 -1 -1 327.23 160.48 358.24 238.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n300 5 Car -1 -1 -1 601.74 173.17 636.95 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n300 84 Pedestrian -1 -1 -1 761.24 172.19 780.18 231.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n300 25 Pedestrian -1 -1 -1 193.60 161.42 208.26 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n300 81 Pedestrian -1 -1 -1 480.79 171.65 495.38 205.28 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n300 90 Pedestrian -1 -1 -1 784.54 172.87 811.20 234.00 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n300 87 Car -1 -1 -1 597.98 173.52 622.35 193.79 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n300 91 Pedestrian -1 -1 -1 801.85 174.94 821.69 235.13 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n300 92 Pedestrian -1 -1 -1 181.27 160.63 197.13 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n301 2 Car -1 -1 -1 1095.56 185.64 1220.64 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n301 1 Car -1 -1 -1 955.64 184.08 1066.37 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n301 3 Car -1 -1 -1 1029.89 184.09 1155.89 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n301 89 Pedestrian -1 -1 -1 330.90 160.43 361.00 239.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n301 5 Car -1 -1 -1 601.69 173.26 637.01 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n301 84 Pedestrian -1 -1 -1 765.85 171.97 783.81 231.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n301 25 Pedestrian -1 -1 -1 193.62 161.48 208.24 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n301 91 Pedestrian -1 -1 -1 803.51 176.47 827.88 234.07 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n301 81 Pedestrian -1 -1 -1 480.50 172.78 494.22 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n301 87 Car -1 -1 -1 597.92 173.69 622.23 193.85 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n301 92 Pedestrian -1 -1 -1 181.22 160.82 197.01 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n301 90 Pedestrian -1 -1 -1 788.45 175.37 814.73 234.16 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n302 2 Car -1 -1 -1 1095.41 185.60 1220.77 235.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n302 1 Car -1 -1 -1 955.60 184.06 1066.34 232.82 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n302 3 Car -1 -1 -1 1030.00 184.15 1155.88 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n302 84 Pedestrian -1 -1 -1 766.99 171.83 789.11 231.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n302 5 Car -1 -1 -1 601.86 173.22 636.75 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n302 89 Pedestrian -1 -1 -1 336.23 159.98 364.43 239.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n302 91 Pedestrian -1 -1 -1 806.27 176.75 833.07 234.85 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n302 25 Pedestrian -1 -1 -1 193.85 161.61 208.12 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n302 81 Pedestrian -1 -1 -1 480.30 173.29 493.04 205.51 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n302 87 Car -1 -1 -1 598.01 173.70 622.06 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n302 90 Pedestrian -1 -1 -1 796.70 175.44 820.36 234.38 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n302 92 Pedestrian -1 -1 -1 181.30 160.69 197.09 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n303 2 Car -1 -1 -1 1095.48 185.65 1220.67 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n303 1 Car -1 -1 -1 955.67 184.03 1066.27 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n303 3 Car -1 -1 -1 1030.02 184.06 1155.65 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n303 84 Pedestrian -1 -1 -1 767.40 172.67 795.64 231.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n303 5 Car -1 -1 -1 601.91 173.10 636.63 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n303 89 Pedestrian -1 -1 -1 339.51 159.82 369.71 239.86 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n303 25 Pedestrian -1 -1 -1 193.86 161.63 208.14 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n303 91 Pedestrian -1 -1 -1 808.72 175.13 833.18 235.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n303 87 Car -1 -1 -1 598.23 173.71 621.89 193.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n303 81 Pedestrian -1 -1 -1 478.33 173.41 491.21 204.97 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n303 92 Pedestrian -1 -1 -1 181.40 160.80 197.11 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n304 2 Car -1 -1 -1 1095.30 185.61 1220.79 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n304 1 Car -1 -1 -1 955.61 184.04 1066.33 232.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n304 3 Car -1 -1 -1 1029.80 184.06 1155.87 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n304 89 Pedestrian -1 -1 -1 343.45 162.00 372.68 240.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n304 84 Pedestrian -1 -1 -1 767.32 173.82 798.18 231.30 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n304 5 Car -1 -1 -1 601.99 173.08 636.34 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n304 25 Pedestrian -1 -1 -1 193.86 161.65 208.19 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n304 81 Pedestrian -1 -1 -1 478.15 171.48 491.19 204.84 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n304 91 Pedestrian -1 -1 -1 813.20 175.04 835.81 235.01 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n304 87 Car -1 -1 -1 598.27 173.69 621.74 193.68 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n304 92 Pedestrian -1 -1 -1 181.39 160.97 197.00 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n305 2 Car -1 -1 -1 1095.10 185.57 1220.90 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n305 1 Car -1 -1 -1 955.62 184.07 1066.39 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n305 3 Car -1 -1 -1 1029.92 184.06 1155.77 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n305 89 Pedestrian -1 -1 -1 345.79 160.72 375.53 242.51 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n305 5 Car -1 -1 -1 601.66 172.99 636.69 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n305 84 Pedestrian -1 -1 -1 771.64 173.09 799.69 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n305 25 Pedestrian -1 -1 -1 194.14 161.79 208.24 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n305 91 Pedestrian -1 -1 -1 816.56 175.07 840.73 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n305 81 Pedestrian -1 -1 -1 477.71 171.33 490.94 205.00 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n305 87 Car -1 -1 -1 598.19 173.60 621.91 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n305 92 Pedestrian -1 -1 -1 181.54 161.21 197.06 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n305 93 Pedestrian -1 -1 -1 807.42 172.16 833.59 234.90 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n306 2 Car -1 -1 -1 1095.22 185.63 1220.93 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n306 1 Car -1 -1 -1 955.57 184.03 1066.39 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n306 3 Car -1 -1 -1 1029.74 183.98 1155.96 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n306 5 Car -1 -1 -1 601.79 172.98 636.64 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n306 89 Pedestrian -1 -1 -1 350.35 160.44 378.93 242.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n306 84 Pedestrian -1 -1 -1 779.55 172.48 800.55 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n306 91 Pedestrian -1 -1 -1 823.29 175.04 847.25 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n306 25 Pedestrian -1 -1 -1 194.35 161.82 208.19 197.85 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n306 87 Car -1 -1 -1 598.27 173.52 621.99 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n306 81 Pedestrian -1 -1 -1 476.64 171.25 489.51 204.74 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n306 92 Pedestrian -1 -1 -1 181.81 161.10 197.14 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n306 93 Pedestrian -1 -1 -1 811.64 172.42 837.38 234.65 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n307 2 Car -1 -1 -1 1095.06 185.61 1221.06 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n307 1 Car -1 -1 -1 955.52 184.05 1066.39 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n307 3 Car -1 -1 -1 1030.03 184.02 1155.68 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n307 5 Car -1 -1 -1 601.65 173.05 636.79 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n307 89 Pedestrian -1 -1 -1 355.46 159.86 382.51 243.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n307 84 Pedestrian -1 -1 -1 787.23 172.95 806.15 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n307 25 Pedestrian -1 -1 -1 194.54 161.80 208.11 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n307 91 Pedestrian -1 -1 -1 824.97 175.25 852.69 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n307 81 Pedestrian -1 -1 -1 474.49 170.51 488.03 204.62 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n307 87 Car -1 -1 -1 598.38 173.49 622.06 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n307 92 Pedestrian -1 -1 -1 181.93 161.37 196.91 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n307 93 Pedestrian -1 -1 -1 815.06 171.65 841.00 235.30 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n307 94 Car -1 -1 -1 546.44 170.38 563.74 183.57 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n308 2 Car -1 -1 -1 1095.04 185.58 1221.07 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n308 1 Car -1 -1 -1 955.58 184.05 1066.44 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n308 3 Car -1 -1 -1 1030.04 183.99 1155.69 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n308 89 Pedestrian -1 -1 -1 358.89 160.50 388.57 242.36 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n308 5 Car -1 -1 -1 601.69 173.10 636.84 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n308 84 Pedestrian -1 -1 -1 789.08 173.54 815.02 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n308 81 Pedestrian -1 -1 -1 474.96 170.37 487.64 203.76 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n308 25 Pedestrian -1 -1 -1 194.44 161.94 208.00 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n308 91 Pedestrian -1 -1 -1 825.80 175.14 854.78 236.58 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n308 87 Car -1 -1 -1 598.14 173.52 622.05 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n308 92 Pedestrian -1 -1 -1 181.47 161.96 196.04 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n308 94 Car -1 -1 -1 546.06 170.42 563.43 183.69 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n309 2 Car -1 -1 -1 1095.15 185.68 1220.97 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n309 1 Car -1 -1 -1 955.48 184.07 1066.47 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n309 3 Car -1 -1 -1 1029.98 183.96 1155.57 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n309 89 Pedestrian -1 -1 -1 361.28 160.74 397.70 243.42 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n309 5 Car -1 -1 -1 601.77 173.22 636.77 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n309 81 Pedestrian -1 -1 -1 474.36 170.16 487.22 203.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n309 91 Pedestrian -1 -1 -1 830.56 174.59 856.19 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n309 84 Pedestrian -1 -1 -1 788.82 175.54 820.65 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n309 25 Pedestrian -1 -1 -1 194.45 161.93 208.07 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n309 87 Car -1 -1 -1 598.14 173.58 622.17 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n309 94 Car -1 -1 -1 545.35 171.12 562.48 184.19 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n309 92 Pedestrian -1 -1 -1 181.62 162.20 195.72 196.90 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n310 2 Car -1 -1 -1 1095.33 185.63 1220.80 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n310 1 Car -1 -1 -1 955.54 184.02 1066.48 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n310 3 Car -1 -1 -1 1030.17 183.98 1155.48 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n310 89 Pedestrian -1 -1 -1 362.24 161.72 399.13 244.41 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n310 5 Car -1 -1 -1 601.74 173.23 636.88 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n310 81 Pedestrian -1 -1 -1 473.60 170.46 486.42 203.77 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n310 25 Pedestrian -1 -1 -1 194.38 161.90 207.85 197.45 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n310 91 Pedestrian -1 -1 -1 832.97 174.13 860.14 236.66 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n310 94 Car -1 -1 -1 545.08 171.47 562.14 184.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n310 84 Pedestrian -1 -1 -1 792.40 174.89 824.08 234.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n310 87 Car -1 -1 -1 598.40 173.49 622.15 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n310 92 Pedestrian -1 -1 -1 181.77 162.27 195.56 196.61 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n311 2 Car -1 -1 -1 1095.21 185.45 1221.01 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n311 1 Car -1 -1 -1 955.49 184.02 1066.56 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n311 3 Car -1 -1 -1 1030.25 183.92 1155.36 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n311 89 Pedestrian -1 -1 -1 365.94 164.65 401.96 245.08 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n311 5 Car -1 -1 -1 601.85 173.19 636.83 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n311 25 Pedestrian -1 -1 -1 194.12 161.96 207.83 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n311 91 Pedestrian -1 -1 -1 830.94 173.88 863.26 237.11 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n311 94 Car -1 -1 -1 544.67 171.51 561.71 184.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n311 84 Pedestrian -1 -1 -1 796.45 173.65 823.10 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n311 81 Pedestrian -1 -1 -1 472.12 170.74 485.58 204.37 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n311 87 Car -1 -1 -1 598.22 173.54 622.22 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n311 92 Pedestrian -1 -1 -1 181.76 161.81 196.22 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n312 2 Car -1 -1 -1 1095.28 185.57 1220.96 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n312 1 Car -1 -1 -1 955.57 184.08 1066.45 232.83 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n312 3 Car -1 -1 -1 1030.42 183.97 1155.15 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n312 89 Pedestrian -1 -1 -1 372.72 160.37 403.72 246.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n312 5 Car -1 -1 -1 601.87 173.28 636.83 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n312 91 Pedestrian -1 -1 -1 834.64 173.20 866.88 237.94 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n312 25 Pedestrian -1 -1 -1 193.90 161.98 207.62 197.22 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n312 84 Pedestrian -1 -1 -1 806.76 173.47 826.39 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n312 94 Car -1 -1 -1 544.39 171.69 561.07 183.93 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n312 81 Pedestrian -1 -1 -1 470.62 170.15 484.43 203.95 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n312 87 Car -1 -1 -1 598.09 173.62 622.20 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n312 92 Pedestrian -1 -1 -1 182.22 161.18 196.99 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n313 2 Car -1 -1 -1 1095.35 185.58 1220.92 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n313 1 Car -1 -1 -1 955.61 184.02 1066.52 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n313 3 Car -1 -1 -1 1030.57 183.96 1155.20 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n313 5 Car -1 -1 -1 601.57 173.11 636.89 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n313 81 Pedestrian -1 -1 -1 470.49 169.57 483.86 203.76 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n313 89 Pedestrian -1 -1 -1 379.77 162.01 406.03 247.95 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n313 91 Pedestrian -1 -1 -1 838.64 171.60 869.60 239.42 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n313 25 Pedestrian -1 -1 -1 193.62 161.83 207.84 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n313 84 Pedestrian -1 -1 -1 810.44 173.19 831.42 234.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n313 87 Car -1 -1 -1 597.99 173.69 622.08 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n313 94 Car -1 -1 -1 543.87 171.72 560.33 183.90 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n313 92 Pedestrian -1 -1 -1 185.20 161.79 199.89 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n314 2 Car -1 -1 -1 1095.08 185.56 1221.13 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n314 1 Car -1 -1 -1 955.60 183.96 1066.53 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n314 3 Car -1 -1 -1 1030.43 183.94 1155.32 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n314 89 Pedestrian -1 -1 -1 383.73 162.71 417.24 248.04 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n314 5 Car -1 -1 -1 601.81 173.12 636.80 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n314 81 Pedestrian -1 -1 -1 469.32 169.88 483.12 203.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n314 84 Pedestrian -1 -1 -1 809.41 175.01 840.32 234.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n314 91 Pedestrian -1 -1 -1 840.56 172.43 870.39 238.63 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n314 94 Car -1 -1 -1 543.45 171.41 559.72 184.38 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n314 25 Pedestrian -1 -1 -1 193.21 161.75 207.83 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n314 87 Car -1 -1 -1 598.14 173.55 622.23 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n314 92 Pedestrian -1 -1 -1 185.31 161.41 200.60 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n315 2 Car -1 -1 -1 1094.69 185.53 1221.48 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n315 1 Car -1 -1 -1 955.46 184.00 1066.53 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n315 3 Car -1 -1 -1 1030.29 183.94 1155.50 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n315 89 Pedestrian -1 -1 -1 385.32 163.82 423.38 247.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n315 5 Car -1 -1 -1 601.48 173.07 636.94 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n315 84 Pedestrian -1 -1 -1 811.85 175.48 844.17 234.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n315 81 Pedestrian -1 -1 -1 468.59 170.37 481.52 203.74 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n315 25 Pedestrian -1 -1 -1 189.53 161.84 204.82 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n315 87 Car -1 -1 -1 598.03 173.61 622.34 193.68 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n315 91 Pedestrian -1 -1 -1 844.20 171.86 874.56 238.63 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n315 94 Car -1 -1 -1 542.66 171.18 559.63 184.74 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n316 2 Car -1 -1 -1 1094.87 185.49 1221.36 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n316 1 Car -1 -1 -1 955.44 183.95 1066.53 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n316 3 Car -1 -1 -1 1030.56 183.99 1155.26 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n316 89 Pedestrian -1 -1 -1 392.24 162.34 428.44 248.61 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n316 5 Car -1 -1 -1 601.77 173.10 636.95 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n316 81 Pedestrian -1 -1 -1 465.66 170.34 480.76 203.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n316 84 Pedestrian -1 -1 -1 817.16 175.11 845.81 234.62 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n316 25 Pedestrian -1 -1 -1 189.32 160.97 205.42 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n316 87 Car -1 -1 -1 598.14 173.49 622.41 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n316 94 Car -1 -1 -1 542.02 171.18 558.71 185.27 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n316 91 Pedestrian -1 -1 -1 852.27 171.88 879.77 239.30 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n317 2 Car -1 -1 -1 1094.81 185.49 1221.30 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n317 1 Car -1 -1 -1 955.40 183.87 1066.49 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n317 3 Car -1 -1 -1 1030.66 184.00 1155.14 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n317 89 Pedestrian -1 -1 -1 399.55 158.85 431.02 248.30 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n317 5 Car -1 -1 -1 601.69 173.19 637.06 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n317 81 Pedestrian -1 -1 -1 465.42 170.19 479.80 203.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n317 94 Car -1 -1 -1 541.24 171.01 558.87 185.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n317 25 Pedestrian -1 -1 -1 192.17 160.83 208.05 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n317 84 Pedestrian -1 -1 -1 822.43 174.34 848.51 235.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n317 87 Car -1 -1 -1 598.26 173.54 622.57 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n317 91 Pedestrian -1 -1 -1 854.55 171.98 885.17 239.25 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n318 2 Car -1 -1 -1 1094.90 185.42 1221.21 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n318 1 Car -1 -1 -1 955.32 183.80 1066.48 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n318 3 Car -1 -1 -1 1030.56 184.01 1155.27 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n318 94 Car -1 -1 -1 540.91 171.09 558.71 186.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n318 89 Pedestrian -1 -1 -1 406.88 158.07 438.30 249.29 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n318 5 Car -1 -1 -1 601.72 173.24 637.02 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n318 25 Pedestrian -1 -1 -1 192.05 159.91 208.23 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n318 84 Pedestrian -1 -1 -1 828.84 173.76 851.26 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n318 81 Pedestrian -1 -1 -1 464.60 170.04 478.74 203.48 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n318 87 Car -1 -1 -1 598.27 173.63 622.62 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n319 2 Car -1 -1 -1 1095.19 185.52 1220.92 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n319 1 Car -1 -1 -1 955.30 183.77 1066.57 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n319 3 Car -1 -1 -1 1030.48 183.99 1155.20 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n319 94 Car -1 -1 -1 539.74 171.13 558.28 186.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n319 89 Pedestrian -1 -1 -1 411.75 157.07 447.56 249.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n319 5 Car -1 -1 -1 601.67 173.26 637.03 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n319 84 Pedestrian -1 -1 -1 832.36 173.75 855.78 236.69 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n319 25 Pedestrian -1 -1 -1 192.54 156.48 207.73 196.32 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n319 81 Pedestrian -1 -1 -1 464.59 169.79 477.43 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n319 87 Car -1 -1 -1 598.17 173.69 622.78 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n320 2 Car -1 -1 -1 1094.95 185.43 1221.19 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n320 1 Car -1 -1 -1 955.20 183.69 1066.65 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n320 3 Car -1 -1 -1 1030.56 183.99 1155.24 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n320 89 Pedestrian -1 -1 -1 413.36 157.92 454.52 248.57 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n320 94 Car -1 -1 -1 539.24 171.49 557.68 186.30 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n320 84 Pedestrian -1 -1 -1 836.38 174.81 865.43 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n320 5 Car -1 -1 -1 601.73 173.25 637.07 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n320 25 Pedestrian -1 -1 -1 190.08 155.35 205.83 196.54 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n320 81 Pedestrian -1 -1 -1 462.70 169.54 476.49 204.68 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n320 87 Car -1 -1 -1 598.34 173.62 622.83 193.75 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n321 2 Car -1 -1 -1 1095.04 185.46 1221.02 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n321 1 Car -1 -1 -1 955.00 183.73 1066.75 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n321 3 Car -1 -1 -1 1030.57 183.98 1155.21 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n321 89 Pedestrian -1 -1 -1 416.83 157.56 459.09 249.29 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n321 94 Car -1 -1 -1 538.47 170.97 556.94 186.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n321 5 Car -1 -1 -1 601.85 173.37 637.05 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n321 25 Pedestrian -1 -1 -1 190.21 153.79 205.64 196.76 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n321 81 Pedestrian -1 -1 -1 462.54 169.52 475.66 204.88 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n321 87 Car -1 -1 -1 598.42 173.59 622.94 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n321 84 Pedestrian -1 -1 -1 838.72 175.47 869.84 236.46 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n322 2 Car -1 -1 -1 1094.75 185.48 1221.32 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n322 3 Car -1 -1 -1 1030.26 183.89 1155.29 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n322 1 Car -1 -1 -1 955.04 183.74 1066.70 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n322 89 Pedestrian -1 -1 -1 421.62 159.05 463.07 251.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n322 5 Car -1 -1 -1 601.86 173.34 637.09 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n322 81 Pedestrian -1 -1 -1 462.44 169.37 475.15 204.59 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n322 25 Pedestrian -1 -1 -1 190.55 152.93 205.38 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n322 84 Pedestrian -1 -1 -1 841.04 174.35 869.53 237.85 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n322 94 Car -1 -1 -1 537.76 170.78 556.54 186.99 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n322 87 Car -1 -1 -1 598.42 173.62 622.88 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n323 2 Car -1 -1 -1 1094.84 185.55 1221.09 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n323 3 Car -1 -1 -1 1030.02 183.94 1155.58 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n323 1 Car -1 -1 -1 954.88 183.71 1066.81 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n323 89 Pedestrian -1 -1 -1 429.41 159.40 463.49 251.95 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n323 94 Car -1 -1 -1 536.54 170.85 556.31 187.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n323 25 Pedestrian -1 -1 -1 190.27 152.84 205.23 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n323 5 Car -1 -1 -1 602.66 172.99 637.44 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n323 84 Pedestrian -1 -1 -1 845.51 173.52 872.97 239.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n323 81 Pedestrian -1 -1 -1 462.85 169.71 474.75 203.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n323 87 Car -1 -1 -1 598.47 173.65 623.12 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n324 2 Car -1 -1 -1 1094.95 185.57 1220.88 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n324 1 Car -1 -1 -1 954.66 183.63 1067.15 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n324 3 Car -1 -1 -1 1030.22 183.95 1155.52 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n324 94 Car -1 -1 -1 536.01 170.76 556.02 188.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n324 89 Pedestrian -1 -1 -1 439.32 160.20 467.19 252.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n324 25 Pedestrian -1 -1 -1 190.30 152.97 204.98 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n324 5 Car -1 -1 -1 602.57 172.96 637.53 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n324 84 Pedestrian -1 -1 -1 849.35 174.48 876.82 238.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n324 87 Car -1 -1 -1 598.33 173.73 623.09 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n324 81 Pedestrian -1 -1 -1 462.13 169.77 474.06 203.63 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n325 2 Car -1 -1 -1 1094.90 185.59 1220.97 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n325 3 Car -1 -1 -1 1030.29 183.92 1155.31 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n325 1 Car -1 -1 -1 954.87 183.59 1066.92 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n325 89 Pedestrian -1 -1 -1 443.90 160.65 477.80 252.53 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n325 94 Car -1 -1 -1 534.43 171.06 554.96 188.40 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n325 25 Pedestrian -1 -1 -1 190.53 152.91 205.14 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n325 5 Car -1 -1 -1 602.58 173.02 637.44 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n325 84 Pedestrian -1 -1 -1 852.33 174.78 881.47 238.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n325 87 Car -1 -1 -1 598.36 173.75 623.08 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n325 81 Pedestrian -1 -1 -1 461.38 169.80 474.10 203.95 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n325 95 Pedestrian -1 -1 -1 351.04 160.96 363.95 187.23 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n326 2 Car -1 -1 -1 1094.83 185.55 1220.87 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n326 1 Car -1 -1 -1 954.86 183.63 1066.97 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n326 3 Car -1 -1 -1 1030.44 183.87 1155.19 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n326 89 Pedestrian -1 -1 -1 445.57 159.49 484.38 253.07 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n326 94 Car -1 -1 -1 534.20 171.45 554.15 188.64 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n326 25 Pedestrian -1 -1 -1 190.58 152.89 205.31 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n326 5 Car -1 -1 -1 601.68 173.29 637.26 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n326 87 Car -1 -1 -1 598.30 173.68 623.11 193.69 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n326 84 Pedestrian -1 -1 -1 855.85 175.53 885.70 237.51 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n326 95 Pedestrian -1 -1 -1 351.19 159.58 362.56 186.29 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n326 96 Pedestrian -1 -1 -1 360.93 159.94 371.41 186.13 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n327 2 Car -1 -1 -1 1094.70 185.51 1220.96 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n327 3 Car -1 -1 -1 1030.49 183.88 1155.06 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n327 1 Car -1 -1 -1 954.85 183.57 1066.91 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n327 89 Pedestrian -1 -1 -1 450.19 158.99 487.31 254.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n327 25 Pedestrian -1 -1 -1 190.21 152.99 205.31 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n327 5 Car -1 -1 -1 602.61 172.92 637.38 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n327 84 Pedestrian -1 -1 -1 862.47 176.03 891.72 237.07 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n327 87 Car -1 -1 -1 598.38 173.74 623.03 193.71 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n327 94 Car -1 -1 -1 532.69 173.35 552.53 189.71 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n327 96 Pedestrian -1 -1 -1 360.35 159.89 371.66 186.34 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n327 95 Pedestrian -1 -1 -1 351.40 159.59 363.17 186.20 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n328 2 Car -1 -1 -1 1094.66 185.46 1221.06 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n328 3 Car -1 -1 -1 1030.41 183.94 1155.10 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n328 1 Car -1 -1 -1 954.92 183.55 1066.92 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n328 25 Pedestrian -1 -1 -1 189.29 152.95 205.21 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n328 89 Pedestrian -1 -1 -1 456.62 159.40 490.11 255.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n328 5 Car -1 -1 -1 602.60 173.03 637.40 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n328 94 Car -1 -1 -1 531.34 173.27 552.02 189.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n328 87 Car -1 -1 -1 598.37 173.72 623.09 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n328 84 Pedestrian -1 -1 -1 868.04 175.37 894.15 237.98 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n328 96 Pedestrian -1 -1 -1 359.68 161.06 371.63 187.54 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n328 95 Pedestrian -1 -1 -1 350.67 158.93 364.53 187.26 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n328 97 Pedestrian -1 -1 -1 915.47 175.74 939.56 237.48 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n329 2 Car -1 -1 -1 1094.58 185.42 1221.23 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n329 3 Car -1 -1 -1 1030.47 183.91 1154.96 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n329 1 Car -1 -1 -1 954.97 183.56 1066.76 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n329 25 Pedestrian -1 -1 -1 189.52 153.01 205.64 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n329 94 Car -1 -1 -1 530.05 173.23 551.35 190.30 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n329 5 Car -1 -1 -1 601.74 173.39 637.20 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n329 89 Pedestrian -1 -1 -1 467.62 161.25 498.65 256.43 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n329 97 Pedestrian -1 -1 -1 920.89 177.45 949.37 240.80 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n329 87 Car -1 -1 -1 598.23 173.73 623.10 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n329 96 Pedestrian -1 -1 -1 359.39 161.02 371.62 187.39 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n329 95 Pedestrian -1 -1 -1 349.73 161.11 365.71 187.19 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n330 2 Car -1 -1 -1 1094.59 185.51 1221.01 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n330 3 Car -1 -1 -1 1030.45 183.93 1155.15 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n330 1 Car -1 -1 -1 954.80 183.51 1066.96 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n330 25 Pedestrian -1 -1 -1 189.62 152.97 206.20 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n330 89 Pedestrian -1 -1 -1 469.62 157.33 507.54 255.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n330 94 Car -1 -1 -1 528.96 173.38 551.14 190.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n330 5 Car -1 -1 -1 602.70 173.04 637.29 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n330 87 Car -1 -1 -1 598.31 173.85 622.99 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n330 95 Pedestrian -1 -1 -1 350.18 160.63 364.02 187.70 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n330 97 Pedestrian -1 -1 -1 928.95 178.71 955.60 240.57 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n330 96 Pedestrian -1 -1 -1 359.53 160.45 371.86 187.66 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n331 2 Car -1 -1 -1 1094.59 185.53 1221.01 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n331 3 Car -1 -1 -1 1030.33 184.01 1155.34 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n331 1 Car -1 -1 -1 954.62 183.52 1067.22 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n331 89 Pedestrian -1 -1 -1 470.24 158.92 518.88 253.84 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n331 25 Pedestrian -1 -1 -1 189.74 153.05 206.42 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n331 5 Car -1 -1 -1 601.81 173.36 637.17 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n331 94 Car -1 -1 -1 527.53 173.03 550.36 191.10 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n331 87 Car -1 -1 -1 598.27 173.84 623.15 193.75 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n331 97 Pedestrian -1 -1 -1 934.40 178.13 958.70 240.82 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n331 95 Pedestrian -1 -1 -1 350.77 160.99 363.46 187.19 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n331 96 Pedestrian -1 -1 -1 359.17 160.31 371.52 187.84 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n331 98 Pedestrian -1 -1 -1 455.73 169.13 468.00 205.60 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n331 99 Pedestrian -1 -1 -1 909.88 172.67 937.95 237.79 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n332 2 Car -1 -1 -1 1094.50 185.58 1221.05 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n332 3 Car -1 -1 -1 1030.40 184.05 1155.36 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n332 1 Car -1 -1 -1 954.60 183.58 1067.23 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n332 89 Pedestrian -1 -1 -1 477.05 159.36 521.96 254.23 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n332 94 Car -1 -1 -1 526.58 172.81 550.31 191.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n332 5 Car -1 -1 -1 601.89 173.42 637.06 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n332 25 Pedestrian -1 -1 -1 191.91 153.15 207.50 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n332 95 Pedestrian -1 -1 -1 350.65 159.56 363.24 186.19 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n332 87 Car -1 -1 -1 598.22 173.88 623.15 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n332 99 Pedestrian -1 -1 -1 914.18 172.42 940.04 238.07 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n332 96 Pedestrian -1 -1 -1 360.24 160.64 371.33 185.46 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n332 100 Pedestrian -1 -1 -1 889.49 174.74 912.25 230.57 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n333 2 Car -1 -1 -1 1094.22 185.49 1221.34 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n333 3 Car -1 -1 -1 1030.65 184.03 1155.13 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n333 1 Car -1 -1 -1 954.56 183.54 1066.95 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n333 89 Pedestrian -1 -1 -1 480.24 161.84 525.68 256.40 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n333 94 Car -1 -1 -1 525.25 172.27 549.29 191.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n333 5 Car -1 -1 -1 601.86 173.39 637.05 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n333 25 Pedestrian -1 -1 -1 191.92 153.20 207.42 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n333 99 Pedestrian -1 -1 -1 918.64 171.08 944.48 240.18 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n333 87 Car -1 -1 -1 598.23 173.93 623.09 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n333 95 Pedestrian -1 -1 -1 349.98 161.12 363.16 187.04 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n333 101 Pedestrian -1 -1 -1 455.16 169.12 467.51 205.90 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n334 2 Car -1 -1 -1 1094.14 185.57 1221.38 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n334 3 Car -1 -1 -1 1030.54 183.97 1155.34 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n334 1 Car -1 -1 -1 954.94 183.49 1067.04 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n334 89 Pedestrian -1 -1 -1 490.05 159.64 524.93 257.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n334 25 Pedestrian -1 -1 -1 191.95 153.04 207.63 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n334 5 Car -1 -1 -1 602.79 173.11 637.21 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n334 94 Car -1 -1 -1 523.82 172.37 548.37 192.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n334 99 Pedestrian -1 -1 -1 896.26 174.93 919.91 237.63 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n334 101 Pedestrian -1 -1 -1 454.77 168.24 467.37 205.95 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n334 87 Car -1 -1 -1 598.58 173.90 623.14 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n334 95 Pedestrian -1 -1 -1 350.40 161.33 363.08 187.25 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n334 102 Pedestrian -1 -1 -1 922.19 172.03 948.55 238.57 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n334 103 Pedestrian -1 -1 -1 950.20 179.13 975.06 242.57 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n335 2 Car -1 -1 -1 1094.49 185.58 1221.19 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n335 3 Car -1 -1 -1 1030.50 183.97 1155.23 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n335 1 Car -1 -1 -1 954.50 183.22 1067.29 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n335 89 Pedestrian -1 -1 -1 499.12 159.73 529.66 258.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n335 25 Pedestrian -1 -1 -1 191.70 153.04 207.77 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n335 5 Car -1 -1 -1 602.82 173.00 637.19 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n335 99 Pedestrian -1 -1 -1 903.24 174.14 927.94 238.76 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n335 102 Pedestrian -1 -1 -1 925.83 172.05 952.69 239.21 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n335 94 Car -1 -1 -1 522.33 173.13 547.76 192.87 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n335 87 Car -1 -1 -1 598.57 173.79 623.23 193.69 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n335 95 Pedestrian -1 -1 -1 350.29 161.33 362.68 187.53 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n335 103 Pedestrian -1 -1 -1 955.17 179.26 977.97 241.77 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n335 101 Pedestrian -1 -1 -1 454.53 168.08 466.76 205.38 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n336 2 Car -1 -1 -1 1094.28 185.54 1221.56 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n336 3 Car -1 -1 -1 1030.47 184.09 1155.43 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n336 1 Car -1 -1 -1 953.80 183.16 1067.90 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n336 89 Pedestrian -1 -1 -1 503.32 161.54 539.96 258.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n336 5 Car -1 -1 -1 601.74 173.33 636.96 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n336 25 Pedestrian -1 -1 -1 189.64 152.83 206.48 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n336 99 Pedestrian -1 -1 -1 905.56 174.07 934.92 239.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n336 94 Car -1 -1 -1 521.50 173.82 547.30 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n336 102 Pedestrian -1 -1 -1 929.02 172.05 957.38 240.08 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n336 87 Car -1 -1 -1 598.34 173.74 623.19 193.70 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n336 95 Pedestrian -1 -1 -1 348.55 161.59 361.10 187.22 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n336 101 Pedestrian -1 -1 -1 454.33 168.04 466.60 205.64 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n337 2 Car -1 -1 -1 1094.40 185.57 1221.33 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n337 3 Car -1 -1 -1 1030.83 184.10 1155.06 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n337 1 Car -1 -1 -1 953.52 183.08 1068.05 231.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n337 89 Pedestrian -1 -1 -1 505.33 157.90 546.26 260.04 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n337 25 Pedestrian -1 -1 -1 189.57 153.04 206.06 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n337 5 Car -1 -1 -1 601.73 173.27 637.09 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n337 94 Car -1 -1 -1 519.87 173.67 547.61 194.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n337 99 Pedestrian -1 -1 -1 907.45 174.37 941.10 240.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n337 95 Pedestrian -1 -1 -1 348.66 161.38 360.34 187.15 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n337 87 Car -1 -1 -1 598.51 173.72 622.93 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n337 102 Pedestrian -1 -1 -1 931.51 171.83 962.46 241.07 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n338 2 Car -1 -1 -1 1094.29 185.50 1221.20 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n338 3 Car -1 -1 -1 1030.53 184.06 1155.20 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n338 1 Car -1 -1 -1 953.81 183.03 1067.67 231.95 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n338 89 Pedestrian -1 -1 -1 509.56 157.84 548.87 261.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n338 25 Pedestrian -1 -1 -1 188.99 153.06 205.62 196.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n338 5 Car -1 -1 -1 601.82 173.24 636.98 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n338 94 Car -1 -1 -1 519.01 173.72 548.31 193.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n338 99 Pedestrian -1 -1 -1 911.70 174.82 944.44 239.69 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n338 87 Car -1 -1 -1 598.29 173.60 622.88 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n338 95 Pedestrian -1 -1 -1 349.06 161.60 360.67 186.61 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n338 102 Pedestrian -1 -1 -1 935.62 172.50 965.69 239.75 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n338 104 Pedestrian -1 -1 -1 358.48 160.37 371.36 188.43 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n339 2 Car -1 -1 -1 1094.32 185.55 1221.47 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n339 3 Car -1 -1 -1 1030.30 183.95 1155.46 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n339 89 Pedestrian -1 -1 -1 515.92 158.53 552.01 261.02 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n339 1 Car -1 -1 -1 953.69 183.04 1067.95 232.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n339 5 Car -1 -1 -1 602.00 173.16 636.80 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n339 25 Pedestrian -1 -1 -1 188.16 153.03 204.85 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n339 99 Pedestrian -1 -1 -1 919.47 174.82 949.85 242.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n339 94 Car -1 -1 -1 516.71 173.61 548.79 195.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n339 87 Car -1 -1 -1 598.62 173.68 622.70 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n339 95 Pedestrian -1 -1 -1 348.33 161.66 360.81 186.95 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n339 105 Pedestrian -1 -1 -1 453.63 168.41 466.25 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n340 2 Car -1 -1 -1 1094.50 185.57 1221.41 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n340 3 Car -1 -1 -1 1030.28 183.96 1155.51 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n340 1 Car -1 -1 -1 953.45 182.82 1068.31 232.16 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n340 89 Pedestrian -1 -1 -1 526.55 156.98 562.25 263.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n340 5 Car -1 -1 -1 602.05 173.27 636.76 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n340 25 Pedestrian -1 -1 -1 187.85 153.02 204.64 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n340 99 Pedestrian -1 -1 -1 925.77 174.30 952.96 243.35 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n340 94 Car -1 -1 -1 514.41 173.66 543.84 195.48 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n340 87 Car -1 -1 -1 598.58 173.54 622.76 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n340 95 Pedestrian -1 -1 -1 357.95 160.40 370.56 188.40 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n341 2 Car -1 -1 -1 1094.63 185.56 1221.42 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n341 3 Car -1 -1 -1 1030.40 183.99 1155.45 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n341 1 Car -1 -1 -1 953.76 182.66 1068.82 232.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n341 89 Pedestrian -1 -1 -1 529.94 155.95 569.13 262.86 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n341 99 Pedestrian -1 -1 -1 932.44 174.02 960.50 240.39 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n341 5 Car -1 -1 -1 602.07 173.30 636.67 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n341 25 Pedestrian -1 -1 -1 185.32 152.46 202.85 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n341 94 Car -1 -1 -1 514.13 173.68 543.52 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n341 87 Car -1 -1 -1 598.71 173.79 622.79 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n341 95 Pedestrian -1 -1 -1 348.39 161.21 361.09 187.13 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n342 2 Car -1 -1 -1 1094.58 185.61 1221.42 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n342 3 Car -1 -1 -1 1030.22 184.03 1155.63 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n342 1 Car -1 -1 -1 953.94 182.91 1068.95 232.06 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n342 89 Pedestrian -1 -1 -1 532.07 156.61 579.71 262.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n342 99 Pedestrian -1 -1 -1 934.97 174.57 965.77 244.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n342 25 Pedestrian -1 -1 -1 185.13 152.20 202.65 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n342 94 Car -1 -1 -1 511.23 174.05 541.41 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n342 5 Car -1 -1 -1 601.96 173.22 636.81 202.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n342 95 Pedestrian -1 -1 -1 348.21 161.38 360.87 187.25 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n342 87 Car -1 -1 -1 598.73 173.71 622.95 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n343 2 Car -1 -1 -1 1094.78 185.53 1221.26 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n343 89 Pedestrian -1 -1 -1 535.27 158.44 585.12 262.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n343 3 Car -1 -1 -1 1030.20 184.09 1155.71 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n343 1 Car -1 -1 -1 954.17 183.01 1068.53 231.96 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n343 94 Car -1 -1 -1 509.63 174.48 540.65 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n343 25 Pedestrian -1 -1 -1 185.09 152.14 202.86 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n343 5 Car -1 -1 -1 602.04 173.37 636.67 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n343 99 Pedestrian -1 -1 -1 939.85 175.28 970.12 244.11 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n343 87 Car -1 -1 -1 598.69 173.86 623.07 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n343 95 Pedestrian -1 -1 -1 348.08 161.33 360.35 187.06 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n344 2 Car -1 -1 -1 1094.67 185.55 1221.33 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n344 3 Car -1 -1 -1 1030.07 183.94 1155.58 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n344 89 Pedestrian -1 -1 -1 540.23 156.94 588.95 264.34 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n344 1 Car -1 -1 -1 956.51 184.14 1065.96 232.70 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n344 25 Pedestrian -1 -1 -1 185.02 152.08 202.53 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n344 5 Car -1 -1 -1 602.25 173.46 636.50 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n344 99 Pedestrian -1 -1 -1 948.43 175.84 976.18 244.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n344 94 Car -1 -1 -1 508.08 175.15 539.28 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n344 95 Pedestrian -1 -1 -1 347.57 161.19 359.93 187.02 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n344 87 Car -1 -1 -1 598.98 173.94 622.84 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n345 2 Car -1 -1 -1 1094.69 185.45 1221.51 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n345 3 Car -1 -1 -1 1030.09 184.04 1155.65 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n345 94 Car -1 -1 -1 505.32 174.91 538.65 199.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n345 1 Car -1 -1 -1 955.74 183.86 1066.88 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n345 89 Pedestrian -1 -1 -1 550.66 156.19 592.07 265.72 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n345 25 Pedestrian -1 -1 -1 184.83 152.01 202.09 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n345 5 Car -1 -1 -1 602.11 173.31 636.43 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n345 99 Pedestrian -1 -1 -1 953.57 175.82 979.49 243.65 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n345 95 Pedestrian -1 -1 -1 347.05 161.57 359.45 187.43 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n345 87 Car -1 -1 -1 599.27 173.92 622.65 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n346 2 Car -1 -1 -1 1098.41 185.46 1221.25 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n346 3 Car -1 -1 -1 1030.04 184.11 1155.86 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n346 1 Car -1 -1 -1 955.63 184.02 1067.07 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n346 25 Pedestrian -1 -1 -1 184.50 152.18 201.45 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n346 89 Pedestrian -1 -1 -1 562.19 159.26 595.66 266.75 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n346 5 Car -1 -1 -1 601.86 173.19 636.57 202.17 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n346 94 Car -1 -1 -1 502.41 175.12 536.70 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n346 99 Pedestrian -1 -1 -1 959.32 175.32 986.66 243.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n346 87 Car -1 -1 -1 598.48 173.70 623.43 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n346 95 Pedestrian -1 -1 -1 347.58 161.59 359.77 187.60 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n346 106 Pedestrian -1 -1 -1 1014.68 182.80 1040.95 245.95 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n347 2 Car -1 -1 -1 1098.31 185.54 1221.32 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n347 3 Car -1 -1 -1 1030.25 184.11 1155.70 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n347 1 Car -1 -1 -1 955.79 183.58 1066.96 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n347 94 Car -1 -1 -1 500.86 175.20 535.17 201.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n347 89 Pedestrian -1 -1 -1 565.07 155.31 608.46 266.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n347 25 Pedestrian -1 -1 -1 184.54 152.33 201.23 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n347 5 Car -1 -1 -1 601.70 173.20 636.87 202.23 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n347 99 Pedestrian -1 -1 -1 963.70 175.90 991.62 242.80 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n347 95 Pedestrian -1 -1 -1 348.08 161.52 359.86 187.52 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n347 106 Pedestrian -1 -1 -1 1021.57 180.67 1046.77 248.11 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n347 107 Pedestrian -1 -1 -1 196.23 154.26 211.64 195.82 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n348 2 Car -1 -1 -1 1098.37 185.54 1221.25 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n348 3 Car -1 -1 -1 1029.78 184.13 1155.87 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n348 1 Car -1 -1 -1 955.34 183.29 1067.39 234.07 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n348 89 Pedestrian -1 -1 -1 567.61 156.00 615.54 266.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n348 25 Pedestrian -1 -1 -1 184.46 152.47 200.93 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n348 5 Car -1 -1 -1 604.16 173.17 636.47 202.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n348 99 Pedestrian -1 -1 -1 970.49 176.25 997.97 242.64 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n348 94 Car -1 -1 -1 499.03 175.07 534.78 202.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n348 106 Pedestrian -1 -1 -1 1030.75 180.19 1053.30 248.17 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n348 95 Pedestrian -1 -1 -1 347.85 160.91 359.82 187.71 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n349 2 Car -1 -1 -1 1098.51 185.58 1221.14 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n349 3 Car -1 -1 -1 1029.64 184.03 1155.94 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n349 1 Car -1 -1 -1 955.83 182.97 1067.33 234.38 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n349 89 Pedestrian -1 -1 -1 574.70 157.82 621.15 268.79 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n349 25 Pedestrian -1 -1 -1 184.45 152.22 201.22 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n349 94 Car -1 -1 -1 496.37 175.60 533.57 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n349 5 Car -1 -1 -1 601.64 173.36 636.89 201.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n349 106 Pedestrian -1 -1 -1 1034.37 178.40 1056.43 247.76 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n349 95 Pedestrian -1 -1 -1 347.59 160.77 360.23 187.56 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n349 109 Pedestrian -1 -1 -1 357.80 160.27 370.18 189.12 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n350 2 Car -1 -1 -1 1098.50 185.73 1221.18 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n350 3 Car -1 -1 -1 1029.49 184.13 1155.82 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n350 89 Pedestrian -1 -1 -1 581.44 157.35 623.79 269.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n350 1 Car -1 -1 -1 957.27 182.38 1065.26 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n350 94 Car -1 -1 -1 494.50 175.66 532.54 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n350 25 Pedestrian -1 -1 -1 184.73 152.17 201.41 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n350 5 Car -1 -1 -1 603.92 173.23 636.92 201.24 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n350 106 Pedestrian -1 -1 -1 1037.62 179.17 1061.90 246.77 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n350 109 Pedestrian -1 -1 -1 358.05 160.26 370.01 188.97 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n350 95 Pedestrian -1 -1 -1 347.62 158.84 359.83 187.14 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n351 2 Car -1 -1 -1 1098.75 185.54 1221.05 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n351 94 Car -1 -1 -1 491.28 175.59 530.79 204.38 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n351 3 Car -1 -1 -1 1033.51 184.21 1156.75 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n351 1 Car -1 -1 -1 961.83 181.83 1060.09 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n351 89 Pedestrian -1 -1 -1 590.54 156.38 630.34 271.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n351 25 Pedestrian -1 -1 -1 185.06 152.26 201.56 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n351 5 Car -1 -1 -1 600.64 172.98 637.37 201.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n351 106 Pedestrian -1 -1 -1 1040.18 178.13 1068.82 248.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n351 95 Pedestrian -1 -1 -1 347.59 160.58 359.52 187.78 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n351 109 Pedestrian -1 -1 -1 357.82 159.95 370.09 189.14 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n352 2 Car -1 -1 -1 1098.93 185.57 1220.93 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n352 3 Car -1 -1 -1 1034.68 184.05 1156.20 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n352 89 Pedestrian -1 -1 -1 595.78 156.32 641.06 271.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n352 1 Car -1 -1 -1 967.46 180.31 1054.84 238.23 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n352 25 Pedestrian -1 -1 -1 185.26 152.42 201.68 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n352 5 Car -1 -1 -1 600.58 172.98 637.20 201.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n352 94 Car -1 -1 -1 489.46 175.62 528.60 205.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n352 106 Pedestrian -1 -1 -1 969.48 179.31 1047.23 240.06 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n352 109 Pedestrian -1 -1 -1 358.11 159.67 370.43 188.83 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n352 110 Pedestrian -1 -1 -1 1047.15 178.35 1076.42 248.00 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n352 111 Pedestrian -1 -1 -1 197.04 150.41 213.10 194.43 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n353 2 Car -1 -1 -1 1099.28 185.56 1220.44 235.42 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n353 1 Car -1 -1 -1 960.99 181.53 1061.45 237.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n353 94 Car -1 -1 -1 486.29 175.59 526.19 207.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n353 3 Car -1 -1 -1 1034.56 184.37 1155.95 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n353 89 Pedestrian -1 -1 -1 598.53 156.93 653.49 270.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n353 25 Pedestrian -1 -1 -1 184.81 152.35 201.80 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n353 5 Car -1 -1 -1 601.66 173.86 636.33 201.50 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n353 110 Pedestrian -1 -1 -1 1051.71 177.42 1085.30 249.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n353 111 Pedestrian -1 -1 -1 196.83 150.42 213.23 194.30 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n354 1 Car -1 -1 -1 958.06 181.88 1064.71 237.33 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n354 2 Car -1 -1 -1 1099.07 185.42 1220.83 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n354 89 Pedestrian -1 -1 -1 602.62 156.15 662.68 272.70 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n354 3 Car -1 -1 -1 1034.94 184.49 1156.07 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n354 94 Car -1 -1 -1 483.11 175.75 525.21 208.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n354 25 Pedestrian -1 -1 -1 184.76 152.42 201.48 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n354 5 Car -1 -1 -1 601.96 173.95 635.89 201.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n354 110 Pedestrian -1 -1 -1 1059.93 179.06 1086.77 248.60 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n354 112 Pedestrian -1 -1 -1 347.06 160.35 360.31 188.32 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n355 1 Car -1 -1 -1 957.34 182.26 1064.99 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n355 2 Car -1 -1 -1 1098.93 185.48 1220.75 235.45 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n355 3 Car -1 -1 -1 1034.92 184.32 1155.68 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n355 89 Pedestrian -1 -1 -1 607.78 155.11 666.63 274.53 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n355 25 Pedestrian -1 -1 -1 184.49 152.49 201.33 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n355 94 Car -1 -1 -1 479.90 175.46 523.44 208.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n355 5 Car -1 -1 -1 602.27 173.97 636.01 201.89 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n356 2 Car -1 -1 -1 1098.97 185.39 1220.72 235.49 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n356 1 Car -1 -1 -1 956.98 182.32 1064.66 235.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n356 3 Car -1 -1 -1 1034.85 183.96 1155.60 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n356 89 Pedestrian -1 -1 -1 620.91 154.86 667.38 274.95 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n356 94 Car -1 -1 -1 476.06 175.70 521.36 210.25 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n356 25 Pedestrian -1 -1 -1 184.74 152.26 201.53 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n356 5 Car -1 -1 -1 603.57 173.45 636.73 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n357 2 Car -1 -1 -1 1098.95 185.34 1220.77 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n357 94 Car -1 -1 -1 471.78 175.67 519.31 211.93 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n357 1 Car -1 -1 -1 956.97 182.56 1064.72 234.95 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n357 3 Car -1 -1 -1 1034.52 183.63 1155.55 234.37 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n357 89 Pedestrian -1 -1 -1 632.84 156.00 672.11 277.55 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n357 25 Pedestrian -1 -1 -1 185.49 152.27 201.79 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n357 5 Car -1 -1 -1 603.65 173.27 636.74 202.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n358 2 Car -1 -1 -1 1098.94 185.50 1220.68 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n358 94 Car -1 -1 -1 467.61 175.01 517.32 212.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n358 1 Car -1 -1 -1 955.47 182.40 1066.25 235.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n358 3 Car -1 -1 -1 1034.99 183.64 1155.45 235.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n358 89 Pedestrian -1 -1 -1 638.52 155.55 682.13 277.44 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n358 5 Car -1 -1 -1 603.35 173.29 637.20 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n358 25 Pedestrian -1 -1 -1 185.46 152.53 202.17 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n359 2 Car -1 -1 -1 1099.01 185.50 1220.74 235.59 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n359 1 Car -1 -1 -1 955.11 182.68 1066.50 234.86 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n359 3 Car -1 -1 -1 1034.94 183.69 1155.94 235.12 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n359 94 Car -1 -1 -1 463.60 175.42 514.83 214.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n359 89 Pedestrian -1 -1 -1 641.66 155.15 694.27 278.23 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n359 5 Car -1 -1 -1 603.24 173.36 637.35 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n359 25 Pedestrian -1 -1 -1 185.41 152.75 202.10 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n359 113 Pedestrian -1 -1 -1 358.13 159.83 370.25 188.71 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n359 114 Pedestrian -1 -1 -1 346.55 160.26 361.00 188.58 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n360 2 Car -1 -1 -1 1099.28 185.65 1220.35 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n360 1 Car -1 -1 -1 955.22 182.56 1066.18 234.71 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n360 89 Pedestrian -1 -1 -1 648.75 154.68 701.23 279.54 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n360 3 Car -1 -1 -1 1029.58 183.43 1155.72 234.83 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n360 94 Car -1 -1 -1 458.61 175.53 512.13 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n360 5 Car -1 -1 -1 603.22 173.37 637.26 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n360 25 Pedestrian -1 -1 -1 185.32 153.00 202.17 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n360 115 Pedestrian -1 -1 -1 1101.96 179.01 1126.71 246.79 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n361 2 Car -1 -1 -1 1099.37 185.56 1220.35 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n361 1 Car -1 -1 -1 954.14 182.84 1063.41 234.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n361 89 Pedestrian -1 -1 -1 656.51 153.98 702.54 280.60 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n361 3 Car -1 -1 -1 1030.75 183.44 1154.48 234.69 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n361 5 Car -1 -1 -1 603.18 173.43 637.18 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n361 25 Pedestrian -1 -1 -1 188.41 152.86 205.00 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n361 94 Car -1 -1 -1 453.14 175.47 510.06 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n361 115 Pedestrian -1 -1 -1 1107.08 179.42 1131.86 247.87 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n361 116 Pedestrian -1 -1 -1 346.35 160.18 360.01 188.64 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n362 2 Car -1 -1 -1 1099.29 185.41 1220.75 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n362 89 Pedestrian -1 -1 -1 668.61 154.65 711.90 280.61 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n362 1 Car -1 -1 -1 955.10 182.81 1066.11 234.12 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n362 94 Car -1 -1 -1 447.98 175.78 507.46 219.94 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n362 3 Car -1 -1 -1 1035.11 183.80 1156.06 234.21 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n362 5 Car -1 -1 -1 602.98 173.29 637.25 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n362 25 Pedestrian -1 -1 -1 188.59 152.99 204.96 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n362 115 Pedestrian -1 -1 -1 1113.97 180.65 1137.69 246.10 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n362 116 Pedestrian -1 -1 -1 346.24 160.66 360.16 188.94 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n362 117 Car -1 -1 -1 598.05 174.11 622.76 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n363 2 Car -1 -1 -1 1100.28 185.34 1220.09 235.61 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n363 94 Car -1 -1 -1 442.57 176.24 504.79 221.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n363 1 Car -1 -1 -1 955.22 182.93 1065.96 234.05 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n363 89 Pedestrian -1 -1 -1 675.67 152.68 720.90 283.11 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n363 3 Car -1 -1 -1 1035.97 183.88 1155.11 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n363 5 Car -1 -1 -1 602.97 173.30 637.26 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n363 25 Pedestrian -1 -1 -1 188.90 153.11 204.87 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n363 115 Pedestrian -1 -1 -1 1118.54 180.36 1142.97 247.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n363 116 Pedestrian -1 -1 -1 346.89 160.91 359.48 188.67 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n363 117 Car -1 -1 -1 598.39 174.15 622.58 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n363 118 Pedestrian -1 -1 -1 1055.48 174.02 1083.18 245.38 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n364 2 Car -1 -1 -1 1099.88 185.71 1220.70 235.31 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n364 1 Car -1 -1 -1 955.13 182.93 1066.46 234.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n364 94 Car -1 -1 -1 435.23 176.70 503.30 223.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n364 89 Pedestrian -1 -1 -1 680.57 152.83 737.46 283.22 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n364 3 Car -1 -1 -1 1035.83 184.21 1155.58 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n364 5 Car -1 -1 -1 603.11 173.27 637.21 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n364 25 Pedestrian -1 -1 -1 188.93 153.25 205.09 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n364 115 Pedestrian -1 -1 -1 1124.05 180.50 1151.64 248.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n364 118 Pedestrian -1 -1 -1 1060.73 172.48 1092.15 246.07 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n364 117 Car -1 -1 -1 598.47 174.20 622.65 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n364 116 Pedestrian -1 -1 -1 346.86 160.83 359.81 188.66 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n365 94 Car -1 -1 -1 429.15 176.94 499.78 225.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n365 1 Car -1 -1 -1 955.20 183.05 1066.41 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n365 2 Car -1 -1 -1 1100.04 185.93 1220.58 235.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n365 89 Pedestrian -1 -1 -1 681.87 156.30 745.34 284.76 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n365 3 Car -1 -1 -1 1036.37 183.98 1155.14 234.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n365 5 Car -1 -1 -1 603.25 173.24 637.03 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n365 25 Pedestrian -1 -1 -1 188.78 153.35 205.11 197.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n365 115 Pedestrian -1 -1 -1 1130.69 184.11 1159.56 249.30 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n365 117 Car -1 -1 -1 598.42 174.13 622.59 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n365 118 Pedestrian -1 -1 -1 1062.05 176.35 1098.85 242.91 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n366 1 Car -1 -1 -1 955.35 183.22 1066.37 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n366 94 Car -1 -1 -1 420.98 176.76 495.84 228.74 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n366 2 Car -1 -1 -1 1099.51 186.27 1221.47 234.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n366 89 Pedestrian -1 -1 -1 685.68 158.24 749.39 284.29 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n366 3 Car -1 -1 -1 1036.03 183.55 1155.52 234.49 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n366 25 Pedestrian -1 -1 -1 189.09 153.33 205.29 197.17 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n366 5 Car -1 -1 -1 603.17 173.27 636.96 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n366 117 Car -1 -1 -1 598.43 174.05 622.69 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n366 115 Pedestrian -1 -1 -1 1135.67 183.61 1163.91 249.32 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n367 1 Car -1 -1 -1 955.52 183.32 1066.42 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n367 94 Car -1 -1 -1 412.91 177.40 492.89 231.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n367 2 Car -1 -1 -1 1100.60 186.28 1220.15 234.36 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n367 89 Pedestrian -1 -1 -1 698.45 159.64 751.99 284.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n367 3 Car -1 -1 -1 1036.92 182.97 1154.72 234.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n367 5 Car -1 -1 -1 603.05 173.19 637.07 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n367 25 Pedestrian -1 -1 -1 189.26 153.45 205.46 197.02 -1 -1 -1 -1000 -1000 -1000 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193.29 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n387 25 Pedestrian -1 -1 -1 182.24 152.72 203.82 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n387 124 Pedestrian -1 -1 -1 359.88 160.46 372.49 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n387 117 Car -1 -1 -1 598.09 173.77 622.03 193.29 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n387 125 Pedestrian -1 -1 -1 192.23 160.05 208.50 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n388 2 Car -1 -1 -1 1095.67 185.30 1218.73 236.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n388 3 Car -1 -1 -1 1030.52 183.72 1155.30 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n388 1 Car -1 -1 -1 954.17 183.45 1067.33 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n388 89 Pedestrian -1 -1 -1 899.59 167.90 1001.69 312.79 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n388 5 Car -1 -1 -1 601.59 173.19 636.83 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n388 119 Pedestrian -1 -1 -1 346.44 159.43 360.40 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n388 125 Pedestrian -1 -1 -1 185.87 153.88 207.90 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n388 124 Pedestrian -1 -1 -1 359.74 160.74 372.34 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n388 117 Car -1 -1 -1 598.10 173.69 621.99 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n389 2 Car -1 -1 -1 1096.13 185.40 1218.93 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n389 3 Car -1 -1 -1 1030.30 183.86 1155.61 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n389 1 Car -1 -1 -1 949.12 182.66 1068.41 232.38 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n389 89 Pedestrian -1 -1 -1 913.32 165.02 1003.07 315.84 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n389 5 Car -1 -1 -1 601.51 173.11 637.01 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n389 119 Pedestrian -1 -1 -1 346.70 159.80 360.05 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n389 125 Pedestrian -1 -1 -1 185.90 153.72 207.35 197.19 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n389 124 Pedestrian -1 -1 -1 359.03 160.12 372.56 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n389 117 Car -1 -1 -1 598.18 173.71 621.99 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n390 2 Car -1 -1 -1 1096.40 185.59 1219.08 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n390 3 Car -1 -1 -1 1033.26 183.86 1156.56 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n390 1 Car -1 -1 -1 948.94 182.27 1068.13 232.57 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n390 89 Pedestrian -1 -1 -1 925.75 163.00 1006.18 317.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n390 5 Car -1 -1 -1 601.61 173.14 636.94 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n390 119 Pedestrian -1 -1 -1 346.55 159.68 359.97 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n390 125 Pedestrian -1 -1 -1 183.12 153.22 203.00 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n390 117 Car -1 -1 -1 598.17 173.55 622.17 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n390 124 Pedestrian -1 -1 -1 358.59 160.08 372.43 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n391 2 Car -1 -1 -1 1095.65 185.52 1220.04 236.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n391 3 Car -1 -1 -1 1030.14 183.84 1155.24 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n391 89 Pedestrian -1 -1 -1 949.84 165.03 1012.24 315.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n391 1 Car -1 -1 -1 952.82 182.38 1068.82 232.52 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n391 5 Car -1 -1 -1 601.47 173.06 637.24 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n391 119 Pedestrian -1 -1 -1 346.88 159.78 359.96 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n391 125 Pedestrian -1 -1 -1 183.93 157.98 202.12 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n391 117 Car -1 -1 -1 598.25 173.69 622.32 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n391 124 Pedestrian -1 -1 -1 358.64 160.16 372.30 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n392 2 Car -1 -1 -1 1095.39 185.66 1220.48 236.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n392 3 Car -1 -1 -1 1029.65 183.87 1155.53 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n392 1 Car -1 -1 -1 953.46 182.36 1068.46 232.46 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n392 89 Pedestrian -1 -1 -1 966.20 162.60 1027.27 319.13 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n392 5 Car -1 -1 -1 601.59 173.16 637.13 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n392 119 Pedestrian -1 -1 -1 346.76 160.05 359.67 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n392 125 Pedestrian -1 -1 -1 187.81 153.60 206.81 197.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n392 117 Car -1 -1 -1 598.03 173.59 622.40 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n392 124 Pedestrian -1 -1 -1 358.35 160.24 371.98 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n393 1 Car -1 -1 -1 954.33 182.30 1067.86 231.97 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n393 2 Car -1 -1 -1 1099.22 185.64 1220.54 236.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n393 3 Car -1 -1 -1 1029.49 183.87 1155.56 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n393 89 Pedestrian -1 -1 -1 981.80 161.96 1057.05 320.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n393 5 Car -1 -1 -1 601.51 173.12 637.16 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n393 119 Pedestrian -1 -1 -1 346.98 160.07 359.55 193.71 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n393 125 Pedestrian -1 -1 -1 183.79 158.97 201.32 198.96 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n393 117 Car -1 -1 -1 598.01 173.73 622.38 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n393 124 Pedestrian -1 -1 -1 354.22 161.06 369.76 195.19 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n393 126 Pedestrian -1 -1 -1 191.27 154.25 209.21 196.86 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n394 2 Car -1 -1 -1 1099.17 185.45 1220.69 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n394 1 Car -1 -1 -1 955.52 182.94 1066.91 231.43 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n394 3 Car -1 -1 -1 1028.82 183.90 1156.22 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n394 89 Pedestrian -1 -1 -1 983.03 159.27 1079.00 321.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n394 5 Car -1 -1 -1 601.43 173.12 637.06 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n394 119 Pedestrian -1 -1 -1 346.70 159.90 359.98 194.00 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n394 126 Pedestrian -1 -1 -1 192.47 160.43 207.58 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n394 117 Car -1 -1 -1 597.95 173.64 622.40 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n394 124 Pedestrian -1 -1 -1 354.10 161.58 370.12 194.96 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n395 2 Car -1 -1 -1 1099.40 185.39 1220.79 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n395 89 Pedestrian -1 -1 -1 992.81 155.57 1084.47 325.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n395 1 Car -1 -1 -1 955.46 183.34 1066.90 231.55 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n395 3 Car -1 -1 -1 1028.53 183.82 1157.03 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n395 5 Car -1 -1 -1 601.36 173.17 637.16 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n395 119 Pedestrian -1 -1 -1 346.38 161.18 360.64 194.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n395 126 Pedestrian -1 -1 -1 193.22 160.66 207.86 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n395 117 Car -1 -1 -1 598.07 173.55 622.31 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n395 124 Pedestrian -1 -1 -1 357.38 161.01 371.15 195.02 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n395 127 Pedestrian -1 -1 -1 181.62 158.48 198.88 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n396 2 Car -1 -1 -1 1099.49 185.64 1220.32 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n396 3 Car -1 -1 -1 1033.15 184.00 1157.45 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n396 89 Pedestrian -1 -1 -1 1014.21 156.64 1092.98 325.65 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n396 1 Car -1 -1 -1 955.31 183.55 1066.09 231.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n396 5 Car -1 -1 -1 601.57 173.25 636.94 202.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n396 126 Pedestrian -1 -1 -1 193.34 160.90 208.18 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n396 119 Pedestrian -1 -1 -1 346.99 161.16 361.04 194.71 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n396 127 Pedestrian -1 -1 -1 181.49 159.11 198.34 199.47 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n396 117 Car -1 -1 -1 598.16 173.63 622.39 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n396 124 Pedestrian -1 -1 -1 357.51 161.13 371.11 194.93 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n397 2 Car -1 -1 -1 1094.18 185.36 1220.78 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n397 3 Car -1 -1 -1 1033.79 183.48 1157.61 234.12 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n397 1 Car -1 -1 -1 954.62 183.43 1063.06 231.55 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n397 89 Pedestrian -1 -1 -1 1040.69 163.88 1105.04 323.64 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n397 5 Car -1 -1 -1 601.46 173.11 637.00 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n397 126 Pedestrian -1 -1 -1 193.42 161.10 208.17 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n397 119 Pedestrian -1 -1 -1 346.73 159.28 361.33 194.57 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n397 127 Pedestrian -1 -1 -1 180.80 159.85 197.07 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n397 117 Car -1 -1 -1 597.83 173.70 622.36 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n398 2 Car -1 -1 -1 1093.32 184.80 1221.53 236.58 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n398 1 Car -1 -1 -1 955.23 183.20 1066.35 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n398 3 Car -1 -1 -1 1033.95 183.75 1157.69 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n398 89 Pedestrian -1 -1 -1 1058.99 160.11 1125.40 328.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n398 5 Car -1 -1 -1 601.48 173.13 637.16 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n398 126 Pedestrian -1 -1 -1 193.27 161.14 208.22 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n398 119 Pedestrian -1 -1 -1 345.94 160.81 360.50 195.11 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n398 117 Car -1 -1 -1 598.09 173.57 622.57 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n398 127 Pedestrian -1 -1 -1 180.71 160.31 196.36 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n399 2 Car -1 -1 -1 1092.44 184.41 1221.94 236.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n399 1 Car -1 -1 -1 955.21 183.31 1066.59 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n399 3 Car -1 -1 -1 1034.03 184.27 1156.83 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n399 89 Pedestrian -1 -1 -1 1063.64 159.14 1166.46 329.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n399 5 Car -1 -1 -1 601.44 173.17 637.22 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n399 126 Pedestrian -1 -1 -1 192.87 161.09 208.21 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n399 119 Pedestrian -1 -1 -1 345.80 159.32 360.02 194.67 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n399 127 Pedestrian -1 -1 -1 178.21 159.96 194.30 199.06 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n399 117 Car -1 -1 -1 598.15 173.59 622.59 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n400 2 Car -1 -1 -1 1092.98 184.43 1221.53 236.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n400 1 Car -1 -1 -1 955.03 183.40 1066.65 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n400 89 Pedestrian -1 -1 -1 1068.67 165.75 1191.96 338.24 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n400 5 Car -1 -1 -1 601.37 173.19 637.11 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n400 3 Car -1 -1 -1 1031.17 183.72 1153.62 231.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n400 126 Pedestrian -1 -1 -1 192.66 161.23 208.00 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n400 119 Pedestrian -1 -1 -1 345.05 160.65 360.24 195.05 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n400 127 Pedestrian -1 -1 -1 177.79 159.69 193.99 199.27 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n400 117 Car -1 -1 -1 597.95 173.67 622.57 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n401 1 Car -1 -1 -1 954.69 183.43 1067.08 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n401 2 Car -1 -1 -1 1092.40 184.36 1222.90 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n401 3 Car -1 -1 -1 1031.75 183.90 1152.89 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n401 5 Car -1 -1 -1 601.48 173.11 637.10 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n401 89 Pedestrian -1 -1 -1 1086.47 166.10 1189.50 338.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n401 126 Pedestrian -1 -1 -1 192.50 161.14 208.01 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n401 119 Pedestrian -1 -1 -1 344.79 161.05 360.86 195.18 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n401 117 Car -1 -1 -1 597.95 173.58 622.64 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n401 127 Pedestrian -1 -1 -1 177.61 159.53 193.58 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n402 1 Car -1 -1 -1 954.76 183.43 1067.11 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n402 2 Car -1 -1 -1 1091.88 184.29 1223.26 236.15 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n402 3 Car -1 -1 -1 1032.11 183.65 1152.07 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n402 89 Pedestrian -1 -1 -1 1106.69 162.82 1200.11 340.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n402 5 Car -1 -1 -1 601.48 173.15 636.94 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n402 126 Pedestrian -1 -1 -1 192.13 161.11 207.82 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n402 119 Pedestrian -1 -1 -1 344.92 160.73 359.97 195.90 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n402 117 Car -1 -1 -1 597.99 173.67 622.50 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n402 127 Pedestrian -1 -1 -1 177.61 159.50 193.47 199.48 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n402 128 Pedestrian -1 -1 -1 354.29 161.22 370.48 196.23 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n403 1 Car -1 -1 -1 954.98 183.47 1066.95 233.70 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n403 2 Car -1 -1 -1 1093.90 184.47 1220.85 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n403 3 Car -1 -1 -1 1032.42 183.85 1152.71 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n403 89 Pedestrian -1 -1 -1 1137.49 154.83 1214.35 342.30 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n403 5 Car -1 -1 -1 601.51 173.25 636.95 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n403 119 Pedestrian -1 -1 -1 343.88 160.59 358.77 196.12 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n403 126 Pedestrian -1 -1 -1 191.99 161.10 208.03 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n403 117 Car -1 -1 -1 598.01 173.67 622.45 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n403 128 Pedestrian -1 -1 -1 357.42 160.89 370.90 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n403 127 Pedestrian -1 -1 -1 177.49 159.42 193.24 199.53 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n404 1 Car -1 -1 -1 955.10 183.52 1066.78 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n404 3 Car -1 -1 -1 1031.24 183.99 1154.05 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n404 2 Car -1 -1 -1 1094.85 184.43 1220.07 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n404 5 Car -1 -1 -1 601.41 173.13 637.06 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n404 119 Pedestrian -1 -1 -1 343.73 160.51 357.73 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n404 89 Pedestrian -1 -1 -1 1162.88 148.89 1220.00 346.90 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n404 126 Pedestrian -1 -1 -1 191.98 161.06 207.81 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n404 117 Car -1 -1 -1 597.92 173.64 622.46 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n404 128 Pedestrian -1 -1 -1 357.46 160.61 371.11 196.00 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n404 127 Pedestrian -1 -1 -1 177.68 159.62 193.23 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n405 1 Car -1 -1 -1 955.10 183.60 1066.84 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n405 3 Car -1 -1 -1 1030.78 184.00 1154.53 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n405 2 Car -1 -1 -1 1096.11 184.75 1218.55 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n405 5 Car -1 -1 -1 601.70 173.19 636.88 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n405 119 Pedestrian -1 -1 -1 343.43 160.74 357.44 196.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n405 126 Pedestrian -1 -1 -1 192.19 161.07 208.07 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n405 89 Pedestrian -1 -1 -1 1170.44 159.25 1219.92 337.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n405 117 Car -1 -1 -1 598.14 173.59 622.49 193.50 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n405 128 Pedestrian -1 -1 -1 354.79 160.86 370.06 196.36 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n406 1 Car -1 -1 -1 954.98 183.62 1067.02 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n406 2 Car -1 -1 -1 1096.89 185.05 1218.48 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n406 3 Car -1 -1 -1 1030.69 183.98 1154.93 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n406 5 Car -1 -1 -1 601.55 173.15 637.06 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n406 119 Pedestrian -1 -1 -1 342.41 160.49 357.22 196.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n406 126 Pedestrian -1 -1 -1 192.74 161.41 208.06 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n406 117 Car -1 -1 -1 598.13 173.66 622.52 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n406 89 Pedestrian -1 -1 -1 1184.85 163.71 1221.00 339.72 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n406 128 Pedestrian -1 -1 -1 354.91 161.11 369.85 195.47 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n407 1 Car -1 -1 -1 954.98 183.63 1067.08 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n407 2 Car -1 -1 -1 1095.89 185.42 1219.72 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n407 3 Car -1 -1 -1 1030.54 183.93 1154.97 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n407 5 Car -1 -1 -1 601.48 173.23 637.01 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n407 119 Pedestrian -1 -1 -1 342.08 160.25 356.27 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n407 126 Pedestrian -1 -1 -1 192.77 161.57 207.88 198.69 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n407 117 Car -1 -1 -1 598.03 173.76 622.38 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n407 129 Pedestrian -1 -1 -1 322.20 158.93 333.60 186.09 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n408 1 Car -1 -1 -1 954.87 183.69 1067.17 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n408 2 Car -1 -1 -1 1095.82 185.52 1219.91 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n408 3 Car -1 -1 -1 1030.18 183.89 1155.24 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n408 5 Car -1 -1 -1 601.52 173.09 636.92 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n408 119 Pedestrian -1 -1 -1 342.13 159.72 356.15 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n408 126 Pedestrian -1 -1 -1 193.05 161.77 207.65 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n408 117 Car -1 -1 -1 598.04 173.69 622.35 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n409 2 Car -1 -1 -1 1095.44 185.57 1220.44 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n409 1 Car -1 -1 -1 955.03 183.73 1067.08 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n409 3 Car -1 -1 -1 1030.18 183.96 1155.47 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n409 5 Car -1 -1 -1 601.48 173.17 637.02 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n409 119 Pedestrian -1 -1 -1 342.38 160.07 355.69 197.02 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n409 126 Pedestrian -1 -1 -1 193.11 161.83 207.55 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n409 117 Car -1 -1 -1 598.25 173.73 622.32 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n409 130 Pedestrian -1 -1 -1 354.91 160.87 370.25 196.20 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n410 1 Car -1 -1 -1 955.17 183.75 1066.92 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n410 2 Car -1 -1 -1 1095.46 185.55 1220.60 235.59 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n410 3 Car -1 -1 -1 1030.20 183.98 1155.48 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n410 5 Car -1 -1 -1 601.71 173.13 636.91 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n410 126 Pedestrian -1 -1 -1 192.82 161.67 207.56 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n410 119 Pedestrian -1 -1 -1 342.06 160.19 355.29 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n410 117 Car -1 -1 -1 598.17 173.70 622.41 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n410 130 Pedestrian -1 -1 -1 355.13 161.22 370.17 195.94 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n411 2 Car -1 -1 -1 1095.37 185.53 1220.60 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n411 1 Car -1 -1 -1 955.02 183.71 1066.92 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n411 3 Car -1 -1 -1 1029.96 183.93 1155.64 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n411 5 Car -1 -1 -1 601.55 173.12 637.05 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n411 119 Pedestrian -1 -1 -1 340.35 160.51 354.49 196.83 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n411 126 Pedestrian -1 -1 -1 192.62 161.48 207.52 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n411 130 Pedestrian -1 -1 -1 355.05 161.13 369.97 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n411 117 Car -1 -1 -1 598.17 173.64 622.50 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n412 2 Car -1 -1 -1 1095.28 185.54 1220.65 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n412 1 Car -1 -1 -1 955.06 183.71 1067.03 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n412 3 Car -1 -1 -1 1029.92 183.91 1155.67 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n412 5 Car -1 -1 -1 601.54 173.10 636.99 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n412 119 Pedestrian -1 -1 -1 339.78 159.76 354.61 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n412 126 Pedestrian -1 -1 -1 192.41 161.41 207.89 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n412 130 Pedestrian -1 -1 -1 354.57 160.35 370.11 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n412 117 Car -1 -1 -1 598.28 173.70 622.47 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n413 2 Car -1 -1 -1 1095.14 185.53 1220.81 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n413 1 Car -1 -1 -1 955.02 183.72 1067.06 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n413 3 Car -1 -1 -1 1030.23 183.96 1155.54 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n413 5 Car -1 -1 -1 601.66 173.09 636.86 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n413 119 Pedestrian -1 -1 -1 339.64 159.95 354.17 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n413 130 Pedestrian -1 -1 -1 354.77 159.71 370.02 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n413 126 Pedestrian -1 -1 -1 192.43 161.24 207.96 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n413 117 Car -1 -1 -1 598.37 173.74 622.43 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n414 2 Car -1 -1 -1 1095.15 185.49 1220.74 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n414 1 Car -1 -1 -1 954.97 183.70 1066.99 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n414 3 Car -1 -1 -1 1029.94 183.92 1155.57 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n414 5 Car -1 -1 -1 601.49 173.09 636.76 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n414 119 Pedestrian -1 -1 -1 339.00 159.80 353.52 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n414 130 Pedestrian -1 -1 -1 354.28 159.58 370.08 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n414 126 Pedestrian -1 -1 -1 192.49 161.24 207.97 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n414 117 Car -1 -1 -1 598.11 173.71 622.21 193.30 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n415 2 Car -1 -1 -1 1095.22 185.51 1220.71 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n415 1 Car -1 -1 -1 954.91 183.78 1067.12 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n415 3 Car -1 -1 -1 1029.99 184.03 1155.68 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n415 5 Car -1 -1 -1 601.51 173.04 636.89 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n415 119 Pedestrian -1 -1 -1 338.75 159.64 352.83 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n415 130 Pedestrian -1 -1 -1 353.97 159.45 369.84 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n415 126 Pedestrian -1 -1 -1 192.51 161.19 207.95 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n415 117 Car -1 -1 -1 598.24 173.58 622.35 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n416 2 Car -1 -1 -1 1094.96 185.47 1220.91 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n416 1 Car -1 -1 -1 955.07 183.80 1066.93 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n416 3 Car -1 -1 -1 1029.99 183.97 1155.56 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n416 5 Car -1 -1 -1 601.61 173.05 636.88 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n416 119 Pedestrian -1 -1 -1 338.62 160.24 352.55 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n416 130 Pedestrian -1 -1 -1 353.58 160.18 369.55 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n416 126 Pedestrian -1 -1 -1 192.69 161.14 208.01 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n416 117 Car -1 -1 -1 598.15 173.51 622.39 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n417 2 Car -1 -1 -1 1095.14 185.51 1220.81 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n417 1 Car -1 -1 -1 955.02 183.79 1066.99 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n417 3 Car -1 -1 -1 1029.85 183.95 1155.78 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n417 5 Car -1 -1 -1 601.61 173.09 636.95 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n417 119 Pedestrian -1 -1 -1 338.46 160.37 352.14 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n417 130 Pedestrian -1 -1 -1 353.61 160.65 368.92 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n417 126 Pedestrian -1 -1 -1 192.50 161.01 208.21 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n417 117 Car -1 -1 -1 598.27 173.62 622.61 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n418 2 Car -1 -1 -1 1095.18 185.50 1220.75 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n418 1 Car -1 -1 -1 955.03 183.81 1066.94 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n418 3 Car -1 -1 -1 1029.66 183.91 1155.90 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n418 5 Car -1 -1 -1 601.55 173.07 636.99 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n418 130 Pedestrian -1 -1 -1 354.15 160.41 369.05 200.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n418 119 Pedestrian -1 -1 -1 338.04 160.44 351.94 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n418 126 Pedestrian -1 -1 -1 192.64 160.99 208.10 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n418 117 Car -1 -1 -1 598.15 173.70 622.45 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n419 2 Car -1 -1 -1 1095.07 185.45 1220.89 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n419 1 Car -1 -1 -1 954.90 183.77 1067.15 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n419 3 Car -1 -1 -1 1029.78 183.91 1155.88 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n419 130 Pedestrian -1 -1 -1 354.50 160.00 369.01 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n419 5 Car -1 -1 -1 601.53 173.09 636.97 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n419 119 Pedestrian -1 -1 -1 337.83 159.85 352.37 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n419 126 Pedestrian -1 -1 -1 192.46 160.77 208.34 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n419 117 Car -1 -1 -1 598.30 173.58 622.67 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n420 2 Car -1 -1 -1 1095.14 185.48 1220.78 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n420 1 Car -1 -1 -1 955.00 183.81 1067.05 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n420 3 Car -1 -1 -1 1029.93 183.93 1155.74 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n420 5 Car -1 -1 -1 601.54 173.14 636.85 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n420 130 Pedestrian -1 -1 -1 354.57 160.08 369.33 199.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n420 119 Pedestrian -1 -1 -1 337.23 159.92 352.48 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n420 126 Pedestrian -1 -1 -1 192.45 160.72 208.17 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n420 117 Car -1 -1 -1 598.16 173.67 622.65 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n421 1 Car -1 -1 -1 954.94 183.83 1066.96 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n421 2 Car -1 -1 -1 1095.09 185.50 1221.02 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n421 3 Car -1 -1 -1 1029.85 183.90 1155.75 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n421 5 Car -1 -1 -1 601.59 173.15 636.87 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n421 130 Pedestrian -1 -1 -1 354.12 160.04 369.78 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n421 119 Pedestrian -1 -1 -1 336.45 159.70 350.78 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n421 126 Pedestrian -1 -1 -1 192.31 160.61 208.00 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n421 117 Car -1 -1 -1 598.11 173.73 622.44 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n422 2 Car -1 -1 -1 1095.13 185.48 1220.89 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n422 1 Car -1 -1 -1 954.86 183.81 1067.13 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n422 3 Car -1 -1 -1 1030.02 183.95 1155.68 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n422 5 Car -1 -1 -1 601.61 173.13 637.00 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n422 130 Pedestrian -1 -1 -1 353.77 159.94 369.91 200.39 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n422 119 Pedestrian -1 -1 -1 336.53 159.68 350.38 198.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n422 117 Car -1 -1 -1 598.08 173.64 622.30 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n422 126 Pedestrian -1 -1 -1 192.28 160.53 208.06 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n423 2 Car -1 -1 -1 1095.14 185.55 1220.87 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n423 1 Car -1 -1 -1 954.86 183.73 1067.02 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n423 3 Car -1 -1 -1 1030.13 183.97 1155.56 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n423 5 Car -1 -1 -1 601.57 173.08 636.95 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n423 130 Pedestrian -1 -1 -1 353.16 159.83 369.54 200.63 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n423 119 Pedestrian -1 -1 -1 336.65 159.91 349.94 199.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n423 126 Pedestrian -1 -1 -1 192.30 160.73 208.08 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n423 117 Car -1 -1 -1 598.00 173.64 622.44 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n424 2 Car -1 -1 -1 1095.26 185.47 1220.66 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n424 1 Car -1 -1 -1 954.94 183.72 1067.03 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n424 3 Car -1 -1 -1 1030.00 183.95 1155.63 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n424 119 Pedestrian -1 -1 -1 335.59 159.68 350.14 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n424 5 Car -1 -1 -1 601.56 173.09 636.91 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n424 130 Pedestrian -1 -1 -1 352.50 159.81 368.76 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n424 126 Pedestrian -1 -1 -1 192.25 160.78 208.02 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n424 117 Car -1 -1 -1 598.07 173.65 622.42 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n425 2 Car -1 -1 -1 1095.20 185.40 1220.85 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n425 1 Car -1 -1 -1 954.98 183.74 1067.13 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n425 3 Car -1 -1 -1 1030.09 183.94 1155.61 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n425 119 Pedestrian -1 -1 -1 334.45 159.41 349.72 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n425 5 Car -1 -1 -1 601.54 173.09 636.93 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n425 130 Pedestrian -1 -1 -1 351.86 159.67 369.06 200.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n425 126 Pedestrian -1 -1 -1 192.21 160.70 207.95 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n425 117 Car -1 -1 -1 598.15 173.70 622.44 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n426 2 Car -1 -1 -1 1095.26 185.39 1220.84 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n426 1 Car -1 -1 -1 954.92 183.73 1067.04 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n426 3 Car -1 -1 -1 1030.12 183.95 1155.59 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n426 5 Car -1 -1 -1 601.66 173.11 636.75 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n426 119 Pedestrian -1 -1 -1 334.26 159.16 348.99 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n426 130 Pedestrian -1 -1 -1 351.79 159.87 369.12 200.64 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n426 126 Pedestrian -1 -1 -1 192.39 160.73 207.80 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n426 117 Car -1 -1 -1 598.27 173.66 622.40 193.18 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n427 2 Car -1 -1 -1 1095.28 185.45 1220.73 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n427 1 Car -1 -1 -1 954.94 183.75 1067.03 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n427 3 Car -1 -1 -1 1030.10 183.96 1155.52 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n427 119 Pedestrian -1 -1 -1 334.26 159.18 348.92 199.45 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n427 5 Car -1 -1 -1 601.74 173.27 636.96 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n427 130 Pedestrian -1 -1 -1 350.57 159.99 366.95 200.82 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n427 126 Pedestrian -1 -1 -1 192.03 160.86 207.82 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n427 117 Car -1 -1 -1 598.34 173.80 622.33 193.29 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n428 2 Car -1 -1 -1 1095.33 185.47 1220.65 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n428 1 Car -1 -1 -1 954.96 183.71 1067.20 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n428 3 Car -1 -1 -1 1030.14 184.00 1155.67 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n428 5 Car -1 -1 -1 601.76 173.21 636.82 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n428 119 Pedestrian -1 -1 -1 334.12 159.49 349.07 199.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n428 130 Pedestrian -1 -1 -1 351.87 159.78 368.52 201.19 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n428 126 Pedestrian -1 -1 -1 192.16 161.07 207.91 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n428 117 Car -1 -1 -1 598.20 173.84 622.40 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n429 2 Car -1 -1 -1 1095.28 185.45 1220.70 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n429 1 Car -1 -1 -1 954.91 183.77 1067.09 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n429 3 Car -1 -1 -1 1030.04 183.97 1155.66 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n429 119 Pedestrian -1 -1 -1 334.14 159.37 348.93 200.43 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n429 5 Car -1 -1 -1 601.84 173.21 636.97 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n429 130 Pedestrian -1 -1 -1 352.06 159.57 368.60 201.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n429 126 Pedestrian -1 -1 -1 192.13 161.01 207.94 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n429 117 Car -1 -1 -1 598.42 173.85 622.34 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0024.txt",
    "content": "0 1 Car -1 -1 -1 1095.16 185.54 1220.62 235.63 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n0 2 Car -1 -1 -1 953.48 183.67 1068.61 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n0 3 Car -1 -1 -1 1029.58 183.96 1155.78 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n0 4 Pedestrian -1 -1 -1 825.20 165.91 877.01 285.36 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n0 5 Pedestrian -1 -1 -1 388.01 160.41 417.35 243.04 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n0 6 Pedestrian -1 -1 -1 345.23 158.66 376.20 246.68 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n0 7 Pedestrian -1 -1 -1 242.26 157.41 260.82 208.21 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n0 8 Car -1 -1 -1 602.28 173.06 636.30 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n0 9 Pedestrian -1 -1 -1 264.39 158.97 284.05 210.08 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n0 10 Pedestrian -1 -1 -1 363.36 158.89 377.05 190.71 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n0 11 Pedestrian -1 -1 -1 191.19 159.43 209.99 199.49 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n1 2 Car -1 -1 -1 953.99 183.52 1068.04 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 1 Car -1 -1 -1 1095.27 185.42 1220.64 235.46 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 3 Car -1 -1 -1 1029.46 183.85 1156.35 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1 5 Pedestrian -1 -1 -1 389.71 160.41 424.06 244.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1 6 Pedestrian -1 -1 -1 352.03 158.17 379.56 247.40 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n1 4 Pedestrian -1 -1 -1 818.53 165.56 869.34 283.35 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n1 7 Pedestrian -1 -1 -1 244.47 157.38 262.74 208.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n1 8 Car -1 -1 -1 602.85 172.84 637.06 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n1 9 Pedestrian -1 -1 -1 264.57 158.90 284.32 209.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n1 11 Pedestrian -1 -1 -1 192.03 159.60 209.30 199.44 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n1 10 Pedestrian -1 -1 -1 370.67 159.29 382.82 190.68 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n2 1 Car -1 -1 -1 1095.25 185.43 1220.98 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n2 2 Car -1 -1 -1 954.12 183.77 1067.78 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n2 3 Car -1 -1 -1 1029.50 183.86 1156.06 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n2 6 Pedestrian -1 -1 -1 355.11 158.90 388.92 246.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n2 4 Pedestrian -1 -1 -1 814.62 163.10 865.93 281.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n2 5 Pedestrian -1 -1 -1 390.35 160.75 426.17 245.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n2 7 Pedestrian -1 -1 -1 245.92 157.73 263.44 208.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n2 9 Pedestrian -1 -1 -1 266.24 158.84 286.02 208.14 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n2 8 Car -1 -1 -1 602.73 172.82 637.17 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n2 11 Pedestrian -1 -1 -1 191.49 159.84 209.82 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n3 1 Car -1 -1 -1 1099.14 185.54 1220.34 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n3 2 Car -1 -1 -1 954.14 183.66 1067.74 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n3 3 Car -1 -1 -1 1029.68 183.83 1155.98 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n3 4 Pedestrian -1 -1 -1 814.71 163.68 863.52 279.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n3 6 Pedestrian -1 -1 -1 355.90 160.51 391.64 245.98 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n3 5 Pedestrian -1 -1 -1 394.91 160.92 428.79 244.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n3 9 Pedestrian -1 -1 -1 266.86 159.64 286.40 208.31 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n3 8 Car -1 -1 -1 602.00 172.92 636.83 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n3 7 Pedestrian -1 -1 -1 246.85 158.19 264.09 208.35 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n3 11 Pedestrian -1 -1 -1 191.64 160.10 209.57 199.30 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n4 1 Car -1 -1 -1 1095.13 185.41 1220.90 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n4 2 Car -1 -1 -1 954.15 183.67 1067.74 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n4 3 Car -1 -1 -1 1029.39 183.82 1156.27 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n4 4 Pedestrian -1 -1 -1 811.19 164.06 860.96 279.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n4 6 Pedestrian -1 -1 -1 358.98 159.33 395.22 247.77 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n4 5 Pedestrian -1 -1 -1 403.51 159.21 433.69 246.79 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n4 7 Pedestrian -1 -1 -1 249.48 158.25 266.48 208.23 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n4 9 Pedestrian -1 -1 -1 267.95 159.65 287.30 208.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 8 Car -1 -1 -1 602.05 172.87 636.84 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n4 11 Pedestrian -1 -1 -1 191.86 160.42 209.66 199.14 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n5 1 Car -1 -1 -1 1095.03 185.44 1221.02 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n5 2 Car -1 -1 -1 954.03 183.59 1068.16 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n5 3 Car -1 -1 -1 1029.65 183.85 1156.00 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n5 5 Pedestrian -1 -1 -1 407.48 159.80 438.29 246.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n5 4 Pedestrian -1 -1 -1 810.71 165.53 859.58 278.79 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n5 6 Pedestrian -1 -1 -1 362.55 159.48 397.82 249.90 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n5 7 Pedestrian -1 -1 -1 250.22 158.38 267.91 207.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n5 9 Pedestrian -1 -1 -1 268.62 159.34 288.07 208.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n5 8 Car -1 -1 -1 602.80 172.85 637.09 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n5 11 Pedestrian -1 -1 -1 191.78 160.44 209.83 199.10 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n5 12 Cyclist -1 -1 -1 365.83 159.25 378.43 191.75 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n6 1 Car -1 -1 -1 1094.85 185.39 1221.13 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n6 2 Car -1 -1 -1 954.09 183.67 1068.08 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n6 3 Car -1 -1 -1 1029.63 183.88 1156.08 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n6 4 Pedestrian -1 -1 -1 808.71 164.70 856.68 278.55 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n6 5 Pedestrian -1 -1 -1 409.59 160.55 443.55 246.04 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n6 6 Pedestrian -1 -1 -1 368.38 158.09 399.42 249.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n6 9 Pedestrian -1 -1 -1 270.53 159.51 290.44 207.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n6 7 Pedestrian -1 -1 -1 251.60 158.28 270.62 206.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n6 8 Car -1 -1 -1 602.16 173.07 636.72 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n6 12 Cyclist -1 -1 -1 365.68 159.75 378.39 191.01 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n6 11 Pedestrian -1 -1 -1 191.51 160.26 210.10 199.10 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n7 1 Car -1 -1 -1 1095.31 185.39 1220.83 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n7 2 Car -1 -1 -1 954.24 183.69 1068.18 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n7 3 Car -1 -1 -1 1029.73 183.93 1156.16 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n7 4 Pedestrian -1 -1 -1 808.72 163.85 855.87 277.44 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n7 7 Pedestrian -1 -1 -1 251.78 158.45 272.06 206.42 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n7 5 Pedestrian -1 -1 -1 412.16 160.33 448.04 246.88 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n7 6 Pedestrian -1 -1 -1 372.47 158.05 403.60 251.26 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n7 9 Pedestrian -1 -1 -1 271.88 159.61 291.98 207.34 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n7 8 Car -1 -1 -1 602.64 172.94 637.28 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n7 11 Pedestrian -1 -1 -1 191.98 160.80 209.83 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n8 1 Car -1 -1 -1 1095.11 185.43 1221.13 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n8 2 Car -1 -1 -1 954.42 183.76 1067.91 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n8 3 Car -1 -1 -1 1029.80 183.93 1156.04 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n8 5 Pedestrian -1 -1 -1 417.02 159.80 451.65 246.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n8 6 Pedestrian -1 -1 -1 374.43 157.94 408.61 249.33 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n8 4 Pedestrian -1 -1 -1 806.73 164.85 850.81 271.99 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n8 7 Pedestrian -1 -1 -1 253.38 158.57 272.88 206.34 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n8 9 Pedestrian -1 -1 -1 274.07 159.77 293.36 207.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n8 8 Car -1 -1 -1 601.94 172.97 636.96 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n8 11 Pedestrian -1 -1 -1 192.31 160.92 209.50 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n8 13 Cyclist -1 -1 -1 365.44 159.97 378.56 191.04 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n9 1 Car -1 -1 -1 1095.16 185.35 1221.16 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n9 2 Car -1 -1 -1 954.45 183.73 1068.07 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n9 3 Car -1 -1 -1 1029.92 183.89 1155.96 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n9 4 Pedestrian -1 -1 -1 804.43 165.32 850.64 271.61 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n9 7 Pedestrian -1 -1 -1 256.08 159.15 274.72 206.07 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n9 6 Pedestrian -1 -1 -1 378.60 158.24 414.36 251.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n9 5 Pedestrian -1 -1 -1 424.19 158.12 453.79 247.26 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n9 9 Pedestrian -1 -1 -1 273.96 160.10 294.31 207.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n9 8 Car -1 -1 -1 602.00 172.97 636.89 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n9 11 Pedestrian -1 -1 -1 192.19 160.83 209.68 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n9 13 Cyclist -1 -1 -1 365.03 160.44 379.17 191.25 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n10 1 Car -1 -1 -1 1095.10 185.29 1221.13 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n10 2 Car -1 -1 -1 954.59 183.71 1067.79 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n10 3 Car -1 -1 -1 1029.99 183.87 1155.83 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n10 4 Pedestrian -1 -1 -1 801.98 166.82 846.80 270.46 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n10 6 Pedestrian -1 -1 -1 385.76 158.82 419.74 252.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n10 7 Pedestrian -1 -1 -1 257.63 158.93 275.46 206.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n10 9 Pedestrian -1 -1 -1 274.84 160.17 294.51 207.12 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n10 5 Pedestrian -1 -1 -1 428.76 158.66 460.74 248.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n10 8 Car -1 -1 -1 601.97 172.93 636.92 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n10 11 Pedestrian -1 -1 -1 192.30 160.80 209.55 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n10 13 Cyclist -1 -1 -1 362.56 160.32 377.89 191.50 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n11 1 Car -1 -1 -1 1094.98 185.25 1221.17 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n11 2 Car -1 -1 -1 954.54 183.73 1067.84 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n11 3 Car -1 -1 -1 1030.12 183.89 1155.78 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n11 6 Pedestrian -1 -1 -1 391.14 158.32 423.33 252.88 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n11 4 Pedestrian -1 -1 -1 799.42 166.61 842.42 269.71 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n11 9 Pedestrian -1 -1 -1 275.46 160.53 295.12 206.86 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n11 5 Pedestrian -1 -1 -1 430.42 158.81 466.86 248.37 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n11 8 Car -1 -1 -1 602.64 172.88 637.32 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n11 7 Pedestrian -1 -1 -1 258.84 158.66 275.61 205.67 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n11 11 Pedestrian -1 -1 -1 192.50 161.04 209.08 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n12 1 Car -1 -1 -1 1094.97 185.39 1221.31 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n12 2 Car -1 -1 -1 954.50 183.79 1067.69 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n12 3 Car -1 -1 -1 1030.12 183.91 1155.72 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n12 6 Pedestrian -1 -1 -1 396.45 157.34 427.47 253.87 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n12 4 Pedestrian -1 -1 -1 796.73 165.70 838.30 268.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n12 5 Pedestrian -1 -1 -1 433.06 159.67 471.43 250.84 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n12 9 Pedestrian -1 -1 -1 276.60 160.72 294.94 206.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n12 7 Pedestrian -1 -1 -1 260.58 158.78 277.85 205.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n12 8 Car -1 -1 -1 602.73 172.93 637.28 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n12 11 Pedestrian -1 -1 -1 192.03 160.84 209.35 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n13 1 Car -1 -1 -1 1099.00 185.51 1220.54 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n13 2 Car -1 -1 -1 954.58 183.80 1067.69 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n13 3 Car -1 -1 -1 1030.30 183.90 1155.59 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n13 6 Pedestrian -1 -1 -1 399.99 157.24 437.18 254.45 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n13 5 Pedestrian -1 -1 -1 435.05 159.62 472.93 251.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n13 4 Pedestrian -1 -1 -1 795.91 163.85 838.40 266.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n13 7 Pedestrian -1 -1 -1 261.32 159.23 278.33 205.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n13 8 Car -1 -1 -1 602.84 172.91 637.10 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n13 9 Pedestrian -1 -1 -1 277.09 160.78 295.15 206.59 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n13 11 Pedestrian -1 -1 -1 192.31 160.95 208.97 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n13 14 Pedestrian -1 -1 -1 363.31 159.97 376.99 191.82 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n14 1 Car -1 -1 -1 1094.88 185.32 1221.36 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n14 2 Car -1 -1 -1 954.58 183.78 1067.52 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n14 3 Car -1 -1 -1 1030.02 183.87 1155.81 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n14 6 Pedestrian -1 -1 -1 401.98 157.53 443.62 254.71 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n14 5 Pedestrian -1 -1 -1 440.17 159.02 475.54 251.87 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 4 Pedestrian -1 -1 -1 795.63 165.60 837.57 266.94 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 9 Pedestrian -1 -1 -1 279.30 161.20 296.58 205.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n14 8 Car -1 -1 -1 602.83 172.95 637.12 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n14 7 Pedestrian -1 -1 -1 262.65 159.46 279.39 205.47 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n14 11 Pedestrian -1 -1 -1 192.61 161.07 209.10 198.59 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n15 1 Car -1 -1 -1 1094.99 185.25 1221.31 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n15 2 Car -1 -1 -1 954.59 183.74 1067.64 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n15 6 Pedestrian -1 -1 -1 405.84 158.28 447.21 254.70 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 3 Car -1 -1 -1 1029.96 183.82 1155.94 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n15 4 Pedestrian -1 -1 -1 795.75 166.62 836.58 266.08 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n15 9 Pedestrian -1 -1 -1 279.70 161.14 298.27 205.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n15 5 Pedestrian -1 -1 -1 448.15 158.57 480.62 252.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n15 8 Car -1 -1 -1 602.77 172.96 637.23 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n15 7 Pedestrian -1 -1 -1 263.25 159.29 281.11 204.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n15 11 Pedestrian -1 -1 -1 192.71 161.35 208.85 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n16 1 Car -1 -1 -1 1095.04 185.29 1221.07 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n16 2 Car -1 -1 -1 954.76 183.79 1067.58 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n16 6 Pedestrian -1 -1 -1 410.04 157.17 450.97 255.87 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n16 3 Car -1 -1 -1 1030.23 183.85 1155.68 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n16 5 Pedestrian -1 -1 -1 451.82 158.87 486.37 252.67 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n16 4 Pedestrian -1 -1 -1 795.61 166.34 835.93 266.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n16 7 Pedestrian -1 -1 -1 264.38 158.69 281.59 204.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n16 9 Pedestrian -1 -1 -1 280.66 160.58 299.26 205.37 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 8 Car -1 -1 -1 602.65 172.90 637.23 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n16 11 Pedestrian -1 -1 -1 192.81 161.38 208.69 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n17 1 Car -1 -1 -1 1095.06 185.32 1221.06 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n17 2 Car -1 -1 -1 954.77 183.83 1067.52 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n17 3 Car -1 -1 -1 1030.06 183.91 1155.83 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n17 6 Pedestrian -1 -1 -1 417.62 156.57 452.18 256.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n17 4 Pedestrian -1 -1 -1 793.03 165.45 833.49 264.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n17 5 Pedestrian -1 -1 -1 453.81 159.25 492.33 252.68 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n17 9 Pedestrian -1 -1 -1 282.85 159.77 301.23 204.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n17 8 Car -1 -1 -1 602.83 172.99 637.23 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n17 7 Pedestrian -1 -1 -1 265.90 158.78 282.92 204.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n17 11 Pedestrian -1 -1 -1 192.71 161.53 208.86 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n18 1 Car -1 -1 -1 1095.16 185.37 1221.00 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n18 2 Car -1 -1 -1 954.96 183.87 1067.38 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n18 3 Car -1 -1 -1 1030.20 183.90 1155.63 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n18 4 Pedestrian -1 -1 -1 792.81 163.89 832.24 262.87 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n18 5 Pedestrian -1 -1 -1 456.74 158.54 497.13 254.66 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n18 9 Pedestrian -1 -1 -1 283.31 160.05 302.88 204.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n18 6 Pedestrian -1 -1 -1 426.44 157.30 457.01 257.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n18 8 Car -1 -1 -1 602.69 173.04 637.33 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 7 Pedestrian -1 -1 -1 267.16 158.98 284.87 204.67 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n18 11 Pedestrian -1 -1 -1 192.84 161.68 208.63 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n19 1 Car -1 -1 -1 1095.11 185.38 1221.19 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n19 2 Car -1 -1 -1 955.02 183.88 1067.31 232.95 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n19 3 Car -1 -1 -1 1030.09 183.86 1155.62 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n19 5 Pedestrian -1 -1 -1 466.18 158.82 500.65 254.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n19 4 Pedestrian -1 -1 -1 789.85 163.95 829.55 262.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n19 6 Pedestrian -1 -1 -1 429.56 158.11 463.21 256.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n19 9 Pedestrian -1 -1 -1 283.48 160.78 303.53 205.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n19 7 Pedestrian -1 -1 -1 268.00 159.45 285.30 204.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n19 8 Car -1 -1 -1 602.65 172.92 637.43 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n19 11 Pedestrian -1 -1 -1 192.74 161.72 208.59 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n20 1 Car -1 -1 -1 1095.07 185.28 1220.96 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n20 2 Car -1 -1 -1 954.96 183.89 1067.37 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n20 3 Car -1 -1 -1 1030.07 183.90 1155.74 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n20 4 Pedestrian -1 -1 -1 788.71 164.66 828.88 261.49 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n20 6 Pedestrian -1 -1 -1 435.17 158.16 470.36 256.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n20 5 Pedestrian -1 -1 -1 471.78 159.00 504.12 255.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n20 8 Car -1 -1 -1 602.65 172.93 637.43 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n20 7 Pedestrian -1 -1 -1 269.72 159.53 286.95 204.69 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n20 9 Pedestrian -1 -1 -1 285.79 160.59 304.43 205.98 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n20 11 Pedestrian -1 -1 -1 192.73 161.87 208.67 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n20 15 Pedestrian -1 -1 -1 362.94 160.65 377.17 195.09 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n21 1 Car -1 -1 -1 1095.16 185.32 1220.87 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n21 2 Car -1 -1 -1 954.93 183.91 1067.41 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n21 3 Car -1 -1 -1 1030.13 183.88 1155.66 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n21 4 Pedestrian -1 -1 -1 785.40 165.24 826.51 261.69 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n21 5 Pedestrian -1 -1 -1 474.18 158.36 510.49 254.87 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n21 6 Pedestrian -1 -1 -1 439.40 157.51 473.31 256.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 8 Car -1 -1 -1 602.54 172.98 637.55 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n21 9 Pedestrian -1 -1 -1 286.49 160.34 304.39 205.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n21 7 Pedestrian -1 -1 -1 271.25 159.17 288.31 204.71 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n21 11 Pedestrian -1 -1 -1 192.73 161.82 208.48 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n21 15 Pedestrian -1 -1 -1 362.55 160.83 376.61 194.95 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n22 1 Car -1 -1 -1 1095.21 185.30 1220.73 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n22 2 Car -1 -1 -1 955.00 183.96 1067.14 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n22 3 Car -1 -1 -1 1029.82 183.87 1155.82 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n22 4 Pedestrian -1 -1 -1 785.32 166.35 824.95 260.86 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n22 5 Pedestrian -1 -1 -1 477.64 157.92 519.23 256.14 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n22 9 Pedestrian -1 -1 -1 287.52 159.81 305.14 204.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n22 6 Pedestrian -1 -1 -1 444.19 155.85 476.84 261.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n22 8 Car -1 -1 -1 602.52 173.00 637.53 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n22 11 Pedestrian -1 -1 -1 192.69 161.75 208.54 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n22 7 Pedestrian -1 -1 -1 271.57 158.80 288.11 204.63 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n22 15 Pedestrian -1 -1 -1 362.20 160.11 376.16 193.97 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n23 1 Car -1 -1 -1 1095.27 185.43 1220.75 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n23 2 Car -1 -1 -1 954.89 183.95 1067.20 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n23 3 Car -1 -1 -1 1030.12 183.93 1155.75 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n23 5 Pedestrian -1 -1 -1 482.00 160.03 523.92 256.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n23 4 Pedestrian -1 -1 -1 782.12 165.95 821.74 259.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n23 6 Pedestrian -1 -1 -1 448.15 156.33 482.14 262.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n23 9 Pedestrian -1 -1 -1 288.56 160.63 306.23 204.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n23 7 Pedestrian -1 -1 -1 272.27 159.15 288.44 204.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n23 8 Car -1 -1 -1 602.47 172.98 637.52 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n23 15 Pedestrian -1 -1 -1 361.79 159.97 375.81 193.91 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n23 11 Pedestrian -1 -1 -1 192.61 161.75 208.53 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n24 1 Car -1 -1 -1 1095.20 185.31 1220.89 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n24 2 Car -1 -1 -1 954.93 183.92 1067.25 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n24 3 Car -1 -1 -1 1030.18 183.93 1155.68 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n24 4 Pedestrian -1 -1 -1 782.22 165.54 820.57 259.45 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 5 Pedestrian -1 -1 -1 487.10 158.70 526.60 258.52 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 6 Pedestrian -1 -1 -1 452.60 156.44 493.26 261.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n24 7 Pedestrian -1 -1 -1 272.75 159.43 288.82 205.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 8 Car -1 -1 -1 601.71 172.85 637.20 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n24 9 Pedestrian -1 -1 -1 290.73 161.65 307.25 205.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n24 11 Pedestrian -1 -1 -1 192.47 161.68 208.40 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n24 15 Pedestrian -1 -1 -1 361.15 160.11 376.04 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n24 16 Pedestrian -1 -1 -1 389.51 160.41 401.51 192.64 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n25 1 Car -1 -1 -1 1095.83 185.14 1219.97 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n25 2 Car -1 -1 -1 954.86 183.94 1067.32 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n25 3 Car -1 -1 -1 1029.87 183.91 1155.94 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n25 6 Pedestrian -1 -1 -1 456.92 157.30 501.73 260.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n25 5 Pedestrian -1 -1 -1 495.85 158.12 532.99 259.30 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n25 9 Pedestrian -1 -1 -1 291.92 161.50 308.58 204.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n25 7 Pedestrian -1 -1 -1 273.22 159.55 289.51 205.26 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n25 4 Pedestrian -1 -1 -1 782.18 165.90 819.93 258.97 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n25 8 Car -1 -1 -1 601.69 172.79 637.04 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n25 11 Pedestrian -1 -1 -1 192.50 161.65 208.35 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n25 17 Pedestrian -1 -1 -1 1157.91 158.80 1217.37 345.19 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n25 18 Cyclist -1 -1 -1 360.55 160.31 376.44 193.70 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n25 19 Car -1 -1 -1 598.61 173.71 622.42 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n26 2 Car -1 -1 -1 954.91 183.87 1067.22 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n26 1 Car -1 -1 -1 1096.35 185.23 1219.16 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n26 3 Car -1 -1 -1 1030.98 184.02 1154.85 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n26 6 Pedestrian -1 -1 -1 460.36 157.68 506.82 262.45 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 5 Pedestrian -1 -1 -1 499.01 158.46 538.42 260.40 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n26 9 Pedestrian -1 -1 -1 292.40 161.18 310.04 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n26 4 Pedestrian -1 -1 -1 783.00 165.98 819.13 258.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n26 7 Pedestrian -1 -1 -1 273.64 159.21 290.02 204.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n26 8 Car -1 -1 -1 601.74 172.86 637.10 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n26 17 Pedestrian -1 -1 -1 1142.65 162.85 1217.03 334.00 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n26 11 Pedestrian -1 -1 -1 192.22 161.56 208.45 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n26 18 Cyclist -1 -1 -1 360.03 161.02 377.02 194.76 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n27 2 Car -1 -1 -1 954.90 183.78 1067.28 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n27 3 Car -1 -1 -1 1031.05 183.73 1154.69 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n27 1 Car -1 -1 -1 1096.02 185.18 1219.53 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n27 5 Pedestrian -1 -1 -1 501.34 159.97 543.56 259.37 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n27 6 Pedestrian -1 -1 -1 464.82 157.45 510.70 264.76 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n27 4 Pedestrian -1 -1 -1 783.34 166.27 818.40 258.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n27 8 Car -1 -1 -1 601.66 172.86 637.19 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n27 17 Pedestrian -1 -1 -1 1125.81 160.09 1218.87 336.73 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n27 7 Pedestrian -1 -1 -1 273.30 158.74 290.94 204.61 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n27 9 Pedestrian -1 -1 -1 292.29 160.71 310.90 204.83 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n27 11 Pedestrian -1 -1 -1 192.03 161.45 208.50 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n27 18 Cyclist -1 -1 -1 360.15 161.10 377.01 195.05 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n28 1 Car -1 -1 -1 1095.41 184.97 1220.24 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n28 2 Car -1 -1 -1 955.08 183.76 1067.12 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n28 3 Car -1 -1 -1 1030.85 183.80 1155.11 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n28 5 Pedestrian -1 -1 -1 505.67 158.92 552.54 262.24 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n28 4 Pedestrian -1 -1 -1 783.42 166.77 818.12 258.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n28 17 Pedestrian -1 -1 -1 1121.29 156.49 1215.62 340.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 9 Pedestrian -1 -1 -1 293.30 161.01 312.36 204.82 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 6 Pedestrian -1 -1 -1 476.23 160.02 513.22 266.09 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n28 8 Car -1 -1 -1 601.74 172.94 636.89 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n28 11 Pedestrian -1 -1 -1 191.99 161.38 208.34 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n28 7 Pedestrian -1 -1 -1 273.59 158.59 291.35 204.49 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n28 20 Pedestrian -1 -1 -1 360.61 161.08 376.87 195.17 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n28 21 Car -1 -1 -1 598.95 173.75 622.09 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n29 1 Car -1 -1 -1 1094.89 184.98 1220.52 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n29 2 Car -1 -1 -1 955.12 183.74 1067.06 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n29 3 Car -1 -1 -1 1031.21 183.77 1154.42 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n29 5 Pedestrian -1 -1 -1 511.92 159.43 556.34 262.00 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n29 9 Pedestrian -1 -1 -1 293.76 161.37 313.44 205.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n29 6 Pedestrian -1 -1 -1 482.70 159.57 515.72 268.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n29 8 Car -1 -1 -1 601.83 172.88 636.90 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n29 17 Pedestrian -1 -1 -1 1100.38 158.49 1190.90 338.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n29 4 Pedestrian -1 -1 -1 783.27 165.43 818.20 257.00 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n29 7 Pedestrian -1 -1 -1 274.90 159.22 292.90 204.19 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n29 11 Pedestrian -1 -1 -1 192.06 161.43 208.31 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n30 2 Car -1 -1 -1 955.22 183.72 1067.13 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n30 1 Car -1 -1 -1 1095.29 184.69 1220.26 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n30 3 Car -1 -1 -1 1031.48 183.83 1153.61 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n30 5 Pedestrian -1 -1 -1 521.97 158.15 561.60 263.40 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n30 9 Pedestrian -1 -1 -1 294.65 161.46 313.63 205.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n30 6 Pedestrian -1 -1 -1 488.48 158.31 524.39 268.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n30 4 Pedestrian -1 -1 -1 780.80 166.13 815.32 256.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n30 8 Car -1 -1 -1 601.99 172.90 636.55 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n30 7 Pedestrian -1 -1 -1 276.22 159.42 293.59 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n30 17 Pedestrian -1 -1 -1 1096.06 157.62 1187.37 338.05 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n30 11 Pedestrian -1 -1 -1 192.32 161.59 208.18 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n30 22 Pedestrian -1 -1 -1 360.09 161.28 377.48 195.49 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n31 2 Car -1 -1 -1 955.42 183.80 1066.84 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n31 1 Car -1 -1 -1 1094.97 184.57 1219.89 236.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n31 3 Car -1 -1 -1 1031.79 183.86 1153.25 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n31 5 Pedestrian -1 -1 -1 525.76 157.18 565.38 263.60 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n31 9 Pedestrian -1 -1 -1 295.64 161.01 313.38 204.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n31 4 Pedestrian -1 -1 -1 780.99 166.31 813.90 255.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n31 8 Car -1 -1 -1 602.03 172.76 636.51 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n31 6 Pedestrian -1 -1 -1 491.25 155.46 531.37 266.06 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n31 17 Pedestrian -1 -1 -1 1072.81 159.40 1172.42 335.97 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n31 7 Pedestrian -1 -1 -1 277.58 159.49 294.11 203.53 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n31 11 Pedestrian -1 -1 -1 192.45 161.77 208.13 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n31 22 Pedestrian -1 -1 -1 360.60 161.37 376.82 195.95 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n32 1 Car -1 -1 -1 1094.50 184.65 1221.15 237.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n32 2 Car -1 -1 -1 955.38 183.69 1066.65 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n32 3 Car -1 -1 -1 1031.11 183.88 1154.09 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n32 5 Pedestrian -1 -1 -1 529.37 157.68 574.81 263.94 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n32 8 Car -1 -1 -1 601.59 172.57 636.94 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n32 4 Pedestrian -1 -1 -1 779.99 166.18 814.25 255.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n32 17 Pedestrian -1 -1 -1 1057.94 157.91 1149.05 330.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n32 9 Pedestrian -1 -1 -1 296.75 161.20 313.31 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n32 6 Pedestrian -1 -1 -1 497.34 155.90 537.89 265.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n32 7 Pedestrian -1 -1 -1 277.96 158.01 294.05 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n32 22 Pedestrian -1 -1 -1 360.68 161.53 376.27 195.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n32 11 Pedestrian -1 -1 -1 192.27 161.69 208.25 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n32 23 Pedestrian -1 -1 -1 388.58 161.39 401.44 194.72 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n33 1 Car -1 -1 -1 1094.10 184.70 1220.77 236.86 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n33 2 Car -1 -1 -1 955.40 183.55 1066.58 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n33 5 Pedestrian -1 -1 -1 530.85 158.56 581.77 267.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n33 3 Car -1 -1 -1 1031.31 184.04 1154.00 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n33 17 Pedestrian -1 -1 -1 1044.98 155.72 1123.55 331.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n33 4 Pedestrian -1 -1 -1 779.30 165.85 814.61 256.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n33 8 Car -1 -1 -1 601.84 172.73 636.74 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n33 6 Pedestrian -1 -1 -1 503.63 157.34 541.37 269.46 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n33 9 Pedestrian -1 -1 -1 296.65 161.75 313.90 203.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n33 7 Pedestrian -1 -1 -1 278.19 157.95 294.31 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n33 11 Pedestrian -1 -1 -1 192.14 161.46 208.17 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n33 22 Pedestrian -1 -1 -1 360.77 161.35 376.45 195.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n33 23 Pedestrian -1 -1 -1 388.90 161.46 402.01 194.55 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n34 1 Car -1 -1 -1 1094.05 184.85 1221.52 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n34 2 Car -1 -1 -1 955.11 183.47 1067.17 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n34 5 Pedestrian -1 -1 -1 535.48 158.01 585.27 268.40 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n34 3 Car -1 -1 -1 1034.74 183.93 1156.56 234.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n34 6 Pedestrian -1 -1 -1 509.87 155.88 549.73 271.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n34 17 Pedestrian -1 -1 -1 1032.72 155.54 1098.02 325.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n34 8 Car -1 -1 -1 601.77 172.77 636.68 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n34 4 Pedestrian -1 -1 -1 779.12 165.91 814.10 256.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n34 9 Pedestrian -1 -1 -1 299.11 162.21 314.92 203.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n34 7 Pedestrian -1 -1 -1 279.67 158.76 295.47 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n34 11 Pedestrian -1 -1 -1 192.27 161.41 208.06 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n34 23 Pedestrian -1 -1 -1 389.28 161.61 402.21 194.48 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n34 22 Pedestrian -1 -1 -1 360.24 161.26 376.59 196.08 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n35 1 Car -1 -1 -1 1094.64 185.25 1221.35 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n35 2 Car -1 -1 -1 954.83 183.39 1067.35 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n35 5 Pedestrian -1 -1 -1 546.32 157.16 589.27 269.94 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n35 3 Car -1 -1 -1 1035.08 184.09 1156.52 234.47 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n35 6 Pedestrian -1 -1 -1 515.08 154.19 560.11 273.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n35 4 Pedestrian -1 -1 -1 776.82 166.11 812.15 256.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n35 8 Car -1 -1 -1 601.68 172.68 636.71 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n35 9 Pedestrian -1 -1 -1 299.43 162.20 315.30 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n35 7 Pedestrian -1 -1 -1 280.17 158.82 295.69 202.28 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n35 17 Pedestrian -1 -1 -1 1024.52 155.75 1090.12 318.12 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n35 11 Pedestrian -1 -1 -1 192.22 161.49 208.04 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n35 22 Pedestrian -1 -1 -1 359.99 161.30 375.99 196.31 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n35 23 Pedestrian -1 -1 -1 389.69 161.46 402.52 194.43 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n36 1 Car -1 -1 -1 1099.26 185.46 1220.49 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n36 2 Car -1 -1 -1 955.35 183.34 1067.30 233.70 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n36 3 Car -1 -1 -1 1034.87 184.12 1156.05 234.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n36 6 Pedestrian -1 -1 -1 519.62 154.89 569.53 272.93 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n36 5 Pedestrian -1 -1 -1 554.94 156.75 597.58 271.22 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n36 4 Pedestrian -1 -1 -1 776.30 166.31 811.86 255.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n36 8 Car -1 -1 -1 601.51 172.43 636.87 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n36 17 Pedestrian -1 -1 -1 982.41 157.91 1079.18 323.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n36 9 Pedestrian -1 -1 -1 299.97 162.07 315.69 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n36 7 Pedestrian -1 -1 -1 280.60 158.79 296.39 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n36 22 Pedestrian -1 -1 -1 358.69 160.92 374.41 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n36 23 Pedestrian -1 -1 -1 389.80 161.41 402.57 194.45 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n36 11 Pedestrian -1 -1 -1 192.02 161.50 207.96 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n37 1 Car -1 -1 -1 1099.35 185.51 1220.50 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n37 2 Car -1 -1 -1 956.18 182.93 1065.69 231.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n37 3 Car -1 -1 -1 1034.12 184.01 1156.23 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n37 6 Pedestrian -1 -1 -1 523.84 154.56 573.73 274.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n37 5 Pedestrian -1 -1 -1 560.96 157.57 612.56 270.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n37 4 Pedestrian -1 -1 -1 776.45 166.26 810.94 255.55 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n37 8 Car -1 -1 -1 601.51 172.44 636.99 202.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n37 7 Pedestrian -1 -1 -1 280.93 158.53 297.57 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n37 17 Pedestrian -1 -1 -1 975.72 159.07 1062.89 321.38 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n37 9 Pedestrian -1 -1 -1 301.37 161.67 316.39 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n37 23 Pedestrian -1 -1 -1 389.90 161.44 402.64 194.53 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n37 11 Pedestrian -1 -1 -1 192.13 161.54 208.03 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n37 22 Pedestrian -1 -1 -1 359.02 160.86 374.10 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n38 1 Car -1 -1 -1 1099.50 185.56 1220.32 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n38 2 Car -1 -1 -1 956.07 182.90 1066.05 231.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n38 3 Car -1 -1 -1 1030.42 183.93 1155.39 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n38 4 Pedestrian -1 -1 -1 776.20 165.77 810.96 255.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n38 6 Pedestrian -1 -1 -1 527.40 153.99 578.08 274.50 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n38 5 Pedestrian -1 -1 -1 561.38 157.11 620.67 271.83 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n38 17 Pedestrian -1 -1 -1 961.19 159.04 1038.96 321.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n38 8 Car -1 -1 -1 603.67 172.77 636.81 201.59 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n38 7 Pedestrian -1 -1 -1 280.64 157.96 298.48 202.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n38 9 Pedestrian -1 -1 -1 303.15 160.74 318.10 203.23 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n38 23 Pedestrian -1 -1 -1 389.97 161.47 402.55 194.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n38 22 Pedestrian -1 -1 -1 359.03 161.03 373.95 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n38 11 Pedestrian -1 -1 -1 192.12 161.51 208.05 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n39 1 Car -1 -1 -1 1099.24 185.62 1220.63 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n39 3 Car -1 -1 -1 1030.05 183.99 1155.57 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n39 5 Pedestrian -1 -1 -1 571.17 155.74 626.48 272.78 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n39 4 Pedestrian -1 -1 -1 775.91 166.06 811.22 255.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n39 2 Car -1 -1 -1 956.29 183.04 1065.76 231.96 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n39 6 Pedestrian -1 -1 -1 535.21 155.55 578.27 277.41 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n39 17 Pedestrian -1 -1 -1 952.48 161.21 1017.15 318.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n39 8 Car -1 -1 -1 603.59 172.57 636.87 201.66 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n39 7 Pedestrian -1 -1 -1 282.55 158.27 300.40 202.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n39 9 Pedestrian -1 -1 -1 303.61 160.71 318.70 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n39 23 Pedestrian -1 -1 -1 389.57 161.34 402.75 195.22 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n39 22 Pedestrian -1 -1 -1 358.86 160.94 374.12 197.28 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n39 11 Pedestrian -1 -1 -1 192.14 161.54 208.03 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n40 1 Car -1 -1 -1 1099.15 185.63 1220.67 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n40 3 Car -1 -1 -1 1030.01 184.10 1155.70 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n40 4 Pedestrian -1 -1 -1 775.49 166.10 811.43 255.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n40 2 Car -1 -1 -1 956.33 183.08 1065.60 231.91 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n40 5 Pedestrian -1 -1 -1 583.75 155.36 629.69 274.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n40 6 Pedestrian -1 -1 -1 545.41 155.42 583.47 279.77 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n40 8 Car -1 -1 -1 603.76 172.31 637.32 201.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n40 7 Pedestrian -1 -1 -1 282.80 158.40 300.86 201.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n40 17 Pedestrian -1 -1 -1 935.58 161.36 996.14 317.89 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n40 23 Pedestrian -1 -1 -1 389.73 161.52 402.67 195.27 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n40 9 Pedestrian -1 -1 -1 303.85 160.62 319.32 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n40 22 Pedestrian -1 -1 -1 358.58 160.85 374.28 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n40 11 Pedestrian -1 -1 -1 192.28 161.54 207.90 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n40 25 Pedestrian -1 -1 -1 953.34 151.89 1016.06 306.60 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n41 1 Car -1 -1 -1 1098.94 185.80 1220.69 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n41 3 Car -1 -1 -1 1033.29 183.88 1156.79 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n41 5 Pedestrian -1 -1 -1 591.51 154.44 635.98 274.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n41 6 Pedestrian -1 -1 -1 551.47 153.13 592.59 282.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 4 Pedestrian -1 -1 -1 775.10 165.89 811.92 255.38 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n41 2 Car -1 -1 -1 956.48 184.10 1065.22 232.54 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n41 7 Pedestrian -1 -1 -1 284.29 158.81 301.58 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n41 8 Car -1 -1 -1 603.44 172.50 637.37 201.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n41 17 Pedestrian -1 -1 -1 919.12 161.39 989.51 312.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n41 23 Pedestrian -1 -1 -1 389.31 161.18 402.61 196.00 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n41 9 Pedestrian -1 -1 -1 304.21 160.76 320.55 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n41 22 Pedestrian -1 -1 -1 359.11 160.55 373.93 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n41 25 Pedestrian -1 -1 -1 932.78 151.94 1006.38 306.15 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n41 11 Pedestrian -1 -1 -1 192.44 161.63 207.94 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n42 1 Car -1 -1 -1 1094.93 185.62 1221.25 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n42 3 Car -1 -1 -1 1033.25 183.84 1156.81 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n42 5 Pedestrian -1 -1 -1 594.94 155.53 647.92 273.73 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n42 2 Car -1 -1 -1 955.99 182.82 1065.82 232.19 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n42 4 Pedestrian -1 -1 -1 775.78 165.25 811.73 255.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n42 6 Pedestrian -1 -1 -1 556.27 155.76 601.64 280.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n42 8 Car -1 -1 -1 600.89 172.72 636.23 201.33 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n42 7 Pedestrian -1 -1 -1 285.57 158.69 301.70 201.75 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n42 17 Pedestrian -1 -1 -1 904.46 160.87 981.28 312.59 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n42 23 Pedestrian -1 -1 -1 389.24 161.00 402.58 196.38 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n42 22 Pedestrian -1 -1 -1 359.54 160.98 373.60 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n42 25 Pedestrian -1 -1 -1 919.19 151.72 1004.68 306.14 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n42 11 Pedestrian -1 -1 -1 192.61 161.76 207.98 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n42 9 Pedestrian -1 -1 -1 304.49 160.63 321.51 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n43 1 Car -1 -1 -1 1094.61 185.54 1221.42 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n43 3 Car -1 -1 -1 1033.68 183.79 1156.52 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n43 2 Car -1 -1 -1 955.32 183.46 1067.06 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n43 5 Pedestrian -1 -1 -1 597.29 156.73 654.44 276.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n43 6 Pedestrian -1 -1 -1 558.91 156.10 607.19 284.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n43 4 Pedestrian -1 -1 -1 776.30 165.07 811.54 255.60 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n43 8 Car -1 -1 -1 601.03 173.03 636.94 201.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n43 23 Pedestrian -1 -1 -1 389.44 161.38 402.41 196.12 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n43 17 Pedestrian -1 -1 -1 897.29 159.36 965.23 313.36 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n43 7 Pedestrian -1 -1 -1 286.08 158.59 301.83 201.38 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n43 25 Pedestrian -1 -1 -1 907.83 149.65 993.03 307.77 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n43 22 Pedestrian -1 -1 -1 359.77 161.49 373.35 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n43 11 Pedestrian -1 -1 -1 192.67 161.82 208.20 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n43 9 Pedestrian -1 -1 -1 305.68 158.72 322.90 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n44 1 Car -1 -1 -1 1094.74 185.38 1221.06 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n44 3 Car -1 -1 -1 1033.75 183.76 1156.23 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n44 2 Car -1 -1 -1 954.78 183.40 1067.44 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n44 5 Pedestrian -1 -1 -1 605.52 155.32 661.22 278.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n44 4 Pedestrian -1 -1 -1 776.86 164.66 811.46 255.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n44 6 Pedestrian -1 -1 -1 565.53 154.44 609.58 287.06 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n44 8 Car -1 -1 -1 600.95 173.04 636.94 201.93 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n44 7 Pedestrian -1 -1 -1 287.65 158.80 303.33 201.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n44 23 Pedestrian -1 -1 -1 389.71 161.59 402.75 196.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n44 17 Pedestrian -1 -1 -1 888.15 156.41 943.93 310.59 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n44 22 Pedestrian -1 -1 -1 359.43 161.10 373.39 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n44 9 Pedestrian -1 -1 -1 305.91 160.38 323.87 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n44 11 Pedestrian -1 -1 -1 192.75 161.79 208.25 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n44 25 Pedestrian -1 -1 -1 898.75 151.01 963.89 299.62 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n45 1 Car -1 -1 -1 1094.09 185.20 1221.86 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n45 3 Car -1 -1 -1 1033.48 183.78 1156.29 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n45 2 Car -1 -1 -1 955.38 183.39 1066.69 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n45 5 Pedestrian -1 -1 -1 614.21 153.14 667.27 280.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n45 6 Pedestrian -1 -1 -1 573.77 154.77 616.16 287.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n45 4 Pedestrian -1 -1 -1 777.39 164.25 810.98 255.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n45 7 Pedestrian -1 -1 -1 288.54 158.90 303.56 201.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n45 8 Car -1 -1 -1 603.51 172.86 637.26 201.60 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n45 17 Pedestrian -1 -1 -1 872.41 157.77 928.89 308.10 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n45 23 Pedestrian -1 -1 -1 389.63 161.70 403.08 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n45 9 Pedestrian -1 -1 -1 307.19 160.89 325.54 202.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n45 11 Pedestrian -1 -1 -1 192.79 161.79 208.31 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n45 22 Pedestrian -1 -1 -1 359.69 160.63 373.59 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n45 25 Pedestrian -1 -1 -1 891.30 149.91 948.46 293.49 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n46 1 Car -1 -1 -1 1094.68 185.04 1220.91 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n46 3 Car -1 -1 -1 1033.34 183.74 1156.67 234.00 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n46 2 Car -1 -1 -1 955.32 183.28 1066.76 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n46 6 Pedestrian -1 -1 -1 581.49 152.49 630.61 289.51 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n46 5 Pedestrian -1 -1 -1 626.06 152.95 677.99 281.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n46 4 Pedestrian -1 -1 -1 777.62 164.37 810.95 255.75 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n46 8 Car -1 -1 -1 601.15 172.75 636.48 201.45 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n46 17 Pedestrian -1 -1 -1 855.76 157.81 922.18 307.19 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n46 7 Pedestrian -1 -1 -1 289.28 158.90 304.40 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n46 23 Pedestrian -1 -1 -1 389.93 161.31 403.32 196.22 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n46 9 Pedestrian -1 -1 -1 308.86 161.49 327.16 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n46 11 Pedestrian -1 -1 -1 192.92 161.80 208.16 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n46 22 Pedestrian -1 -1 -1 359.38 160.17 373.92 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n46 25 Pedestrian -1 -1 -1 886.42 150.65 945.71 291.40 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n46 26 Pedestrian -1 -1 -1 1165.51 157.94 1217.35 331.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n47 3 Car -1 -1 -1 1033.69 183.81 1156.14 234.10 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n47 2 Car -1 -1 -1 955.51 183.33 1066.36 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n47 1 Car -1 -1 -1 1098.38 185.50 1221.77 236.46 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n47 6 Pedestrian -1 -1 -1 585.14 152.67 641.76 289.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n47 5 Pedestrian -1 -1 -1 626.69 153.16 686.30 283.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n47 4 Pedestrian -1 -1 -1 780.34 164.35 813.56 256.27 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n47 17 Pedestrian -1 -1 -1 842.51 161.77 913.20 303.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n47 7 Pedestrian -1 -1 -1 289.54 158.54 304.54 202.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n47 8 Car -1 -1 -1 600.43 172.59 637.35 201.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n47 26 Pedestrian -1 -1 -1 1140.22 160.60 1219.66 328.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n47 9 Pedestrian -1 -1 -1 308.75 161.46 327.60 202.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n47 23 Pedestrian -1 -1 -1 390.12 161.02 403.96 196.49 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n47 11 Pedestrian -1 -1 -1 192.98 161.68 208.11 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n47 25 Pedestrian -1 -1 -1 862.74 150.47 930.87 292.03 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n47 22 Pedestrian -1 -1 -1 358.91 160.07 374.25 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n47 27 Cyclist -1 -1 -1 360.41 160.58 375.12 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n48 2 Car -1 -1 -1 955.12 183.12 1067.19 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n48 3 Car -1 -1 -1 1033.58 183.83 1156.43 234.26 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n48 1 Car -1 -1 -1 1094.28 184.84 1221.56 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n48 5 Pedestrian -1 -1 -1 632.38 153.19 695.28 287.83 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n48 6 Pedestrian -1 -1 -1 587.17 153.25 648.48 290.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n48 4 Pedestrian -1 -1 -1 780.91 163.91 813.89 256.51 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n48 8 Car -1 -1 -1 600.95 172.91 636.85 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n48 26 Pedestrian -1 -1 -1 1127.49 161.61 1217.07 327.36 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n48 17 Pedestrian -1 -1 -1 836.35 161.51 903.61 304.03 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n48 9 Pedestrian -1 -1 -1 309.34 161.66 328.06 202.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n48 7 Pedestrian -1 -1 -1 290.10 158.32 305.21 201.85 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n48 25 Pedestrian -1 -1 -1 848.45 152.40 922.17 290.49 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n48 11 Pedestrian -1 -1 -1 192.97 161.66 208.02 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n48 23 Pedestrian -1 -1 -1 390.34 161.16 403.99 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n48 22 Pedestrian -1 -1 -1 359.07 160.43 374.27 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n48 28 Car -1 -1 -1 1171.63 191.48 1218.43 244.19 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n49 1 Car -1 -1 -1 1093.20 184.66 1222.49 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n49 2 Car -1 -1 -1 955.33 183.18 1067.07 233.88 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n49 3 Car -1 -1 -1 1030.98 183.59 1154.59 234.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n49 5 Pedestrian -1 -1 -1 640.05 152.41 701.94 290.12 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n49 6 Pedestrian -1 -1 -1 591.82 152.88 652.19 290.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n49 4 Pedestrian -1 -1 -1 781.27 163.95 814.20 256.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n49 26 Pedestrian -1 -1 -1 1118.67 160.03 1203.14 327.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n49 8 Car -1 -1 -1 601.19 173.44 636.88 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n49 7 Pedestrian -1 -1 -1 291.14 158.11 307.01 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n49 9 Pedestrian -1 -1 -1 310.57 161.65 328.64 202.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n49 17 Pedestrian -1 -1 -1 830.53 161.28 887.16 303.00 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n49 11 Pedestrian -1 -1 -1 192.89 161.59 208.23 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n49 28 Car -1 -1 -1 1156.19 189.74 1219.50 251.92 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n49 23 Pedestrian -1 -1 -1 392.26 161.71 404.49 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n49 25 Pedestrian -1 -1 -1 839.98 153.62 907.54 289.09 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n49 22 Pedestrian -1 -1 -1 359.11 160.57 374.29 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n50 1 Car -1 -1 -1 1093.60 184.89 1221.90 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n50 2 Car -1 -1 -1 955.58 183.33 1066.63 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n50 3 Car -1 -1 -1 1030.79 183.85 1154.22 234.15 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n50 5 Pedestrian -1 -1 -1 653.14 151.32 703.79 290.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n50 6 Pedestrian -1 -1 -1 604.28 151.20 653.42 292.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n50 7 Pedestrian -1 -1 -1 291.35 158.76 308.29 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n50 4 Pedestrian -1 -1 -1 781.63 164.00 814.04 256.31 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n50 26 Pedestrian -1 -1 -1 1107.75 160.01 1168.44 321.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n50 8 Car -1 -1 -1 601.64 173.81 636.35 201.75 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n50 17 Pedestrian -1 -1 -1 821.76 159.43 872.71 299.71 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n50 9 Pedestrian -1 -1 -1 311.69 161.54 328.92 202.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n50 11 Pedestrian -1 -1 -1 192.85 161.64 208.22 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n50 23 Pedestrian -1 -1 -1 390.32 161.77 403.93 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n50 28 Car -1 -1 -1 1141.95 189.55 1218.12 252.20 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n50 22 Pedestrian -1 -1 -1 359.25 160.80 374.03 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n50 25 Pedestrian -1 -1 -1 830.65 154.33 894.28 288.49 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n51 1 Car -1 -1 -1 1094.20 184.67 1221.18 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n51 2 Car -1 -1 -1 955.15 183.28 1066.89 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n51 3 Car -1 -1 -1 1029.96 184.08 1155.50 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n51 26 Pedestrian -1 -1 -1 1087.20 159.27 1157.68 321.79 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n51 5 Pedestrian -1 -1 -1 657.45 151.82 701.15 290.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n51 7 Pedestrian -1 -1 -1 293.31 158.71 308.72 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n51 8 Car -1 -1 -1 601.52 173.59 636.13 202.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n51 6 Pedestrian -1 -1 -1 614.49 150.05 659.32 294.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n51 4 Pedestrian -1 -1 -1 781.72 163.98 814.02 256.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n51 17 Pedestrian -1 -1 -1 807.00 160.86 864.30 297.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n51 11 Pedestrian -1 -1 -1 192.75 161.73 208.20 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n51 9 Pedestrian -1 -1 -1 312.28 161.79 329.21 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n51 23 Pedestrian -1 -1 -1 390.24 161.71 403.79 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n51 25 Pedestrian -1 -1 -1 828.80 153.86 888.36 288.03 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n51 22 Pedestrian -1 -1 -1 359.02 160.59 374.34 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n51 29 Pedestrian -1 -1 -1 380.47 162.47 395.75 195.73 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n52 3 Car -1 -1 -1 1030.97 183.94 1154.34 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n52 1 Car -1 -1 -1 1094.61 185.01 1220.74 235.63 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n52 2 Car -1 -1 -1 955.18 183.22 1066.59 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n52 8 Car -1 -1 -1 601.16 173.31 636.52 202.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n52 5 Pedestrian -1 -1 -1 659.61 152.94 713.26 290.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n52 26 Pedestrian -1 -1 -1 1064.93 160.05 1149.17 320.38 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n52 6 Pedestrian -1 -1 -1 619.97 150.63 669.10 298.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n52 4 Pedestrian -1 -1 -1 781.39 163.98 814.54 256.56 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n52 17 Pedestrian -1 -1 -1 797.92 161.57 857.91 296.73 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n52 7 Pedestrian -1 -1 -1 295.54 159.17 309.83 200.83 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n52 11 Pedestrian -1 -1 -1 192.80 161.81 208.22 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n52 25 Pedestrian -1 -1 -1 832.15 155.77 884.87 286.17 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n52 23 Pedestrian -1 -1 -1 390.10 161.53 403.73 196.34 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n52 9 Pedestrian -1 -1 -1 312.57 161.72 329.12 201.33 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n52 22 Pedestrian -1 -1 -1 361.10 161.22 375.31 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n52 30 Car -1 -1 -1 1040.70 185.47 1219.61 248.90 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n53 3 Car -1 -1 -1 1030.43 184.35 1154.67 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n53 2 Car -1 -1 -1 954.81 183.11 1066.98 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n53 1 Car -1 -1 -1 1094.10 185.56 1221.23 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n53 8 Car -1 -1 -1 601.25 173.21 637.04 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n53 7 Pedestrian -1 -1 -1 296.35 159.01 310.15 200.66 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n53 6 Pedestrian -1 -1 -1 627.40 151.28 684.05 298.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n53 5 Pedestrian -1 -1 -1 660.95 153.25 720.40 291.18 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n53 30 Car -1 -1 -1 1018.53 184.95 1203.46 249.31 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n53 26 Pedestrian -1 -1 -1 1049.41 162.06 1127.16 312.27 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n53 4 Pedestrian -1 -1 -1 782.04 164.73 813.84 256.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n53 17 Pedestrian -1 -1 -1 789.14 162.28 844.13 295.77 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n53 11 Pedestrian -1 -1 -1 192.78 161.75 208.18 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n53 23 Pedestrian -1 -1 -1 390.00 161.16 403.84 196.74 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n53 25 Pedestrian -1 -1 -1 813.40 152.77 873.21 289.19 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n53 22 Pedestrian -1 -1 -1 361.20 161.55 375.03 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n53 9 Pedestrian -1 -1 -1 312.97 161.78 328.82 201.23 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n54 3 Car -1 -1 -1 1026.71 184.60 1158.20 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n54 2 Car -1 -1 -1 954.83 183.44 1066.87 234.08 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n54 7 Pedestrian -1 -1 -1 296.88 158.72 310.66 200.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n54 1 Car -1 -1 -1 1093.89 185.30 1220.10 236.60 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n54 6 Pedestrian -1 -1 -1 632.92 150.61 694.58 299.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n54 26 Pedestrian -1 -1 -1 1041.01 159.42 1112.48 313.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n54 5 Pedestrian -1 -1 -1 672.68 152.13 730.98 297.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n54 8 Car -1 -1 -1 601.30 173.11 637.31 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n54 17 Pedestrian -1 -1 -1 784.29 161.06 833.29 296.01 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n54 4 Pedestrian -1 -1 -1 784.60 163.87 817.17 257.69 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n54 11 Pedestrian -1 -1 -1 192.90 161.94 208.13 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n54 23 Pedestrian -1 -1 -1 392.36 161.41 405.13 197.08 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n54 25 Pedestrian -1 -1 -1 792.29 155.18 863.87 286.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n54 9 Pedestrian -1 -1 -1 314.67 162.13 329.13 201.13 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n54 22 Pedestrian -1 -1 -1 361.37 161.65 375.28 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n55 2 Car -1 -1 -1 954.93 184.26 1066.90 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n55 3 Car -1 -1 -1 1030.03 185.05 1154.42 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n55 1 Car -1 -1 -1 1087.05 184.71 1220.48 237.36 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n55 5 Pedestrian -1 -1 -1 675.11 152.39 736.39 297.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n55 7 Pedestrian -1 -1 -1 296.84 158.63 310.71 200.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n55 6 Pedestrian -1 -1 -1 643.09 150.27 699.60 300.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n55 26 Pedestrian -1 -1 -1 1029.07 157.56 1086.74 314.11 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n55 8 Car -1 -1 -1 601.44 173.18 637.32 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n55 9 Pedestrian -1 -1 -1 315.10 162.40 330.00 201.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n55 17 Pedestrian -1 -1 -1 775.06 158.89 819.42 297.48 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n55 23 Pedestrian -1 -1 -1 392.64 161.91 405.14 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n55 11 Pedestrian -1 -1 -1 192.65 161.70 208.07 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n55 25 Pedestrian -1 -1 -1 794.48 155.32 853.89 280.93 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n55 4 Pedestrian -1 -1 -1 784.30 164.54 818.56 256.76 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n55 22 Pedestrian -1 -1 -1 361.42 161.95 375.59 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n55 31 Car -1 -1 -1 980.05 183.56 1157.91 249.48 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n55 32 Cyclist -1 -1 -1 361.42 161.95 375.59 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n56 2 Car -1 -1 -1 958.16 184.49 1063.68 232.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n56 1 Car -1 -1 -1 1094.25 185.13 1219.04 236.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n56 3 Car -1 -1 -1 1030.13 184.16 1154.62 234.25 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n56 26 Pedestrian -1 -1 -1 1003.04 157.65 1074.00 309.05 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n56 7 Pedestrian -1 -1 -1 298.12 158.92 311.76 200.24 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n56 5 Pedestrian -1 -1 -1 691.53 151.75 742.99 298.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n56 8 Car -1 -1 -1 601.52 173.10 637.30 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n56 6 Pedestrian -1 -1 -1 657.91 148.81 707.36 303.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n56 17 Pedestrian -1 -1 -1 754.18 157.19 810.58 294.16 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n56 31 Car -1 -1 -1 954.07 181.73 1138.14 246.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n56 9 Pedestrian -1 -1 -1 315.58 162.39 330.40 201.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n56 23 Pedestrian -1 -1 -1 392.80 162.33 405.74 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n56 11 Pedestrian -1 -1 -1 192.65 161.60 208.13 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n56 25 Pedestrian -1 -1 -1 789.98 156.58 843.04 278.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n56 4 Pedestrian -1 -1 -1 780.00 159.89 822.57 266.65 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n56 32 Cyclist -1 -1 -1 361.37 161.57 375.79 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n56 22 Pedestrian -1 -1 -1 361.37 161.57 375.79 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n57 2 Car -1 -1 -1 954.16 185.06 1067.83 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n57 1 Car -1 -1 -1 1093.91 185.01 1220.35 236.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n57 3 Car -1 -1 -1 1030.28 183.87 1153.81 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n57 31 Car -1 -1 -1 932.85 182.31 1113.44 246.26 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n57 26 Pedestrian -1 -1 -1 984.66 157.17 1069.42 309.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n57 8 Car -1 -1 -1 602.53 172.85 637.74 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n57 23 Pedestrian -1 -1 -1 393.56 162.12 406.19 196.70 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n57 9 Pedestrian -1 -1 -1 315.70 162.21 331.39 200.98 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n57 6 Pedestrian -1 -1 -1 666.21 149.76 721.93 307.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n57 5 Pedestrian -1 -1 -1 701.96 150.62 762.72 300.99 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n57 7 Pedestrian -1 -1 -1 298.44 159.10 312.17 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n57 11 Pedestrian -1 -1 -1 192.61 161.62 208.10 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n57 22 Pedestrian -1 -1 -1 361.36 161.36 376.00 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n57 25 Pedestrian -1 -1 -1 783.87 157.24 833.73 277.67 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n57 17 Pedestrian -1 -1 -1 736.46 160.05 797.49 291.68 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n57 33 Pedestrian -1 -1 -1 382.24 161.89 396.08 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n57 34 Pedestrian -1 -1 -1 369.21 160.64 385.16 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n57 35 Car -1 -1 -1 598.69 173.63 621.86 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n58 2 Car -1 -1 -1 945.78 185.31 1072.11 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n58 1 Car -1 -1 -1 1093.89 185.02 1220.84 236.44 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n58 3 Car -1 -1 -1 1029.26 183.55 1154.44 235.28 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n58 8 Car -1 -1 -1 602.34 172.91 637.79 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n58 26 Pedestrian -1 -1 -1 976.22 161.80 1054.82 310.08 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n58 23 Pedestrian -1 -1 -1 393.57 161.86 406.58 196.86 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n58 5 Pedestrian -1 -1 -1 716.72 156.78 778.83 301.15 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n58 6 Pedestrian -1 -1 -1 673.73 149.21 737.56 307.83 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n58 7 Pedestrian -1 -1 -1 299.58 159.00 313.63 200.05 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n58 25 Pedestrian -1 -1 -1 762.73 151.49 816.71 283.37 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n58 11 Pedestrian -1 -1 -1 192.79 161.67 208.06 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n58 9 Pedestrian -1 -1 -1 316.47 161.26 331.84 200.53 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n58 33 Pedestrian -1 -1 -1 382.23 162.31 396.03 197.09 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n58 22 Pedestrian -1 -1 -1 361.05 161.30 376.00 199.14 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n58 35 Car -1 -1 -1 598.72 173.66 621.83 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n59 1 Car -1 -1 -1 1093.80 185.13 1221.28 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n59 3 Car -1 -1 -1 1030.23 183.54 1154.65 235.05 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n59 2 Car -1 -1 -1 947.86 184.25 1069.38 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n59 26 Pedestrian -1 -1 -1 967.88 159.16 1040.34 306.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n59 7 Pedestrian -1 -1 -1 299.98 158.95 314.55 199.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n59 8 Car -1 -1 -1 601.68 173.18 637.11 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n59 23 Pedestrian -1 -1 -1 393.81 161.75 406.87 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n59 6 Pedestrian -1 -1 -1 679.88 149.76 747.16 307.72 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n59 5 Pedestrian -1 -1 -1 727.36 154.98 783.43 303.57 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n59 11 Pedestrian -1 -1 -1 192.75 161.54 208.05 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n59 25 Pedestrian -1 -1 -1 743.49 152.25 805.73 282.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n59 9 Pedestrian -1 -1 -1 317.11 161.25 331.85 200.48 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n59 35 Car -1 -1 -1 598.63 173.62 621.75 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n59 33 Pedestrian -1 -1 -1 382.20 162.96 395.88 197.08 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n59 36 Car -1 -1 -1 895.89 183.92 1073.90 249.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n59 37 Cyclist -1 -1 -1 360.86 161.41 376.53 198.88 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n60 1 Car -1 -1 -1 1094.18 185.26 1221.18 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n60 3 Car -1 -1 -1 1030.54 183.62 1154.73 234.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n60 2 Car -1 -1 -1 955.61 183.67 1066.42 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n60 7 Pedestrian -1 -1 -1 301.01 158.56 315.82 199.27 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n60 8 Car -1 -1 -1 601.53 172.97 637.13 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n60 26 Pedestrian -1 -1 -1 962.62 157.70 1021.98 305.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n60 23 Pedestrian -1 -1 -1 393.55 161.61 407.05 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n60 36 Car -1 -1 -1 884.11 185.07 1047.54 243.03 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n60 11 Pedestrian -1 -1 -1 192.47 161.45 208.12 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n60 6 Pedestrian -1 -1 -1 688.87 149.98 761.05 307.70 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n60 9 Pedestrian -1 -1 -1 318.40 161.50 333.03 200.16 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n60 5 Pedestrian -1 -1 -1 735.35 153.84 790.77 304.28 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n60 25 Pedestrian -1 -1 -1 740.41 153.22 801.09 282.45 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n60 37 Cyclist -1 -1 -1 360.78 161.18 376.17 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n60 35 Car -1 -1 -1 598.66 173.47 621.99 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n61 1 Car -1 -1 -1 1094.19 185.47 1221.41 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n61 3 Car -1 -1 -1 1030.16 183.84 1155.10 234.48 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n61 2 Car -1 -1 -1 952.03 183.91 1064.90 233.95 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n61 26 Pedestrian -1 -1 -1 943.70 158.24 1003.19 300.30 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n61 8 Car -1 -1 -1 601.38 172.99 637.07 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n61 7 Pedestrian -1 -1 -1 301.18 159.18 316.43 199.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n61 36 Car -1 -1 -1 858.05 184.34 1027.26 243.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n61 23 Pedestrian -1 -1 -1 393.68 161.89 407.10 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n61 11 Pedestrian -1 -1 -1 192.73 161.47 208.20 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n61 5 Pedestrian -1 -1 -1 743.89 154.00 797.86 304.08 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n61 9 Pedestrian -1 -1 -1 318.66 161.77 333.06 199.75 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n61 6 Pedestrian -1 -1 -1 703.50 148.64 769.10 302.00 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n61 37 Cyclist -1 -1 -1 361.24 161.00 376.44 199.45 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n61 35 Car -1 -1 -1 598.41 173.45 621.85 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n62 1 Car -1 -1 -1 1094.20 185.51 1220.87 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n62 3 Car -1 -1 -1 1029.61 183.78 1155.66 234.44 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n62 2 Car -1 -1 -1 950.97 183.46 1065.69 234.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n62 26 Pedestrian -1 -1 -1 924.68 158.26 999.07 301.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n62 36 Car -1 -1 -1 842.39 183.52 1005.28 244.41 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n62 8 Car -1 -1 -1 601.39 172.87 637.19 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n62 7 Pedestrian -1 -1 -1 303.18 159.54 317.44 199.39 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n62 23 Pedestrian -1 -1 -1 393.84 162.19 407.11 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n62 5 Pedestrian -1 -1 -1 753.46 150.63 818.55 306.68 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n62 11 Pedestrian -1 -1 -1 192.68 161.48 208.18 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n62 9 Pedestrian -1 -1 -1 318.90 161.88 333.17 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n62 6 Pedestrian -1 -1 -1 695.00 157.46 755.20 286.66 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n62 37 Cyclist -1 -1 -1 361.43 160.98 376.84 199.48 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n62 35 Car -1 -1 -1 598.68 173.53 621.77 193.23 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n62 38 Pedestrian -1 -1 -1 734.53 150.24 799.30 291.95 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n63 1 Car -1 -1 -1 1094.64 185.43 1220.56 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n63 3 Car -1 -1 -1 1029.13 183.69 1156.27 234.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n63 2 Car -1 -1 -1 955.37 182.80 1066.80 234.45 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n63 7 Pedestrian -1 -1 -1 304.63 159.33 318.32 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n63 36 Car -1 -1 -1 828.61 182.58 988.06 244.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n63 26 Pedestrian -1 -1 -1 917.30 158.00 990.84 301.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n63 8 Car -1 -1 -1 601.39 172.81 637.37 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n63 38 Pedestrian -1 -1 -1 726.05 144.75 785.26 313.60 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n63 6 Pedestrian -1 -1 -1 688.43 161.47 738.32 287.16 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n63 9 Pedestrian -1 -1 -1 319.02 162.02 333.22 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n63 11 Pedestrian -1 -1 -1 192.76 161.54 208.13 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n63 5 Pedestrian -1 -1 -1 762.68 150.89 839.76 306.48 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n63 23 Pedestrian -1 -1 -1 393.88 162.35 407.34 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n63 35 Car -1 -1 -1 598.61 173.47 621.79 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n63 37 Cyclist -1 -1 -1 361.29 160.31 376.72 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n63 39 Pedestrian -1 -1 -1 721.94 152.54 773.69 283.02 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n63 40 Pedestrian -1 -1 -1 361.29 160.31 376.72 199.72 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n64 1 Car -1 -1 -1 1094.47 185.36 1220.62 236.27 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n64 3 Car -1 -1 -1 1029.43 183.68 1156.10 234.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n64 2 Car -1 -1 -1 954.89 183.02 1067.16 234.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n64 7 Pedestrian -1 -1 -1 304.78 158.90 318.65 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n64 26 Pedestrian -1 -1 -1 910.13 155.43 974.95 296.78 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n64 8 Car -1 -1 -1 601.40 172.92 637.14 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n64 6 Pedestrian -1 -1 -1 685.61 161.56 725.98 286.97 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n64 38 Pedestrian -1 -1 -1 732.37 148.77 801.46 316.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n64 36 Car -1 -1 -1 820.58 182.41 973.17 243.35 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n64 5 Pedestrian -1 -1 -1 774.95 151.39 850.48 306.92 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n64 9 Pedestrian -1 -1 -1 319.64 162.00 333.69 199.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n64 11 Pedestrian -1 -1 -1 192.90 161.49 208.04 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n64 23 Pedestrian -1 -1 -1 393.91 162.07 407.26 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n64 39 Pedestrian -1 -1 -1 717.11 154.32 771.00 280.38 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n64 40 Pedestrian -1 -1 -1 361.38 160.43 376.74 199.79 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n64 35 Car -1 -1 -1 598.60 173.52 621.66 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n65 1 Car -1 -1 -1 1094.51 185.40 1221.03 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n65 2 Car -1 -1 -1 954.98 183.18 1067.04 234.45 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n65 3 Car -1 -1 -1 1029.60 183.72 1156.09 234.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n65 7 Pedestrian -1 -1 -1 305.43 158.54 319.03 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n65 26 Pedestrian -1 -1 -1 900.65 152.29 961.29 297.67 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 38 Pedestrian -1 -1 -1 736.75 150.38 820.12 322.25 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 6 Pedestrian -1 -1 -1 673.57 162.30 714.87 282.76 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n65 8 Car -1 -1 -1 601.54 172.87 637.28 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n65 5 Pedestrian -1 -1 -1 791.59 151.28 856.69 307.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n65 11 Pedestrian -1 -1 -1 192.81 161.48 208.08 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n65 36 Car -1 -1 -1 811.51 183.53 951.74 237.98 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n65 23 Pedestrian -1 -1 -1 393.87 161.47 407.45 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n65 9 Pedestrian -1 -1 -1 320.11 162.26 334.16 199.53 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n65 40 Pedestrian -1 -1 -1 361.56 160.66 376.89 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n65 39 Pedestrian -1 -1 -1 707.28 155.20 757.70 273.83 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n65 35 Car -1 -1 -1 598.68 173.54 621.61 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n66 1 Car -1 -1 -1 1094.36 185.23 1221.07 236.43 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n66 2 Car -1 -1 -1 955.41 183.24 1066.68 234.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n66 3 Car -1 -1 -1 1029.82 183.76 1156.08 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n66 6 Pedestrian -1 -1 -1 661.20 162.53 710.91 281.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n66 7 Pedestrian -1 -1 -1 305.46 158.53 319.57 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n66 8 Car -1 -1 -1 602.51 172.64 637.73 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n66 38 Pedestrian -1 -1 -1 748.07 147.58 823.87 325.45 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n66 26 Pedestrian -1 -1 -1 886.29 153.78 945.69 295.14 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n66 5 Pedestrian -1 -1 -1 803.27 152.83 860.84 306.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n66 39 Pedestrian -1 -1 -1 700.65 156.65 748.95 272.46 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n66 11 Pedestrian -1 -1 -1 192.65 161.42 208.10 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n66 23 Pedestrian -1 -1 -1 395.95 162.35 408.82 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n66 9 Pedestrian -1 -1 -1 321.06 162.10 334.91 199.66 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n66 40 Pedestrian -1 -1 -1 361.35 161.03 376.51 199.77 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n66 36 Car -1 -1 -1 795.78 180.07 929.25 239.26 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n66 35 Car -1 -1 -1 598.79 173.44 621.91 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n67 1 Car -1 -1 -1 1094.38 185.22 1221.10 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n67 2 Car -1 -1 -1 955.49 183.43 1066.64 234.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n67 3 Car -1 -1 -1 1032.58 183.85 1157.25 234.00 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n67 38 Pedestrian -1 -1 -1 770.58 146.82 831.40 326.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n67 6 Pedestrian -1 -1 -1 650.78 162.87 706.09 281.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n67 8 Car -1 -1 -1 602.40 172.59 637.92 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n67 39 Pedestrian -1 -1 -1 692.74 154.53 734.17 273.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n67 26 Pedestrian -1 -1 -1 864.58 153.27 936.86 295.75 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n67 7 Pedestrian -1 -1 -1 306.08 158.43 319.97 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n67 5 Pedestrian -1 -1 -1 824.56 149.63 885.28 309.56 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n67 23 Pedestrian -1 -1 -1 396.14 162.08 409.07 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n67 40 Pedestrian -1 -1 -1 361.27 160.68 376.60 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n67 11 Pedestrian -1 -1 -1 192.70 161.37 208.12 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n67 36 Car -1 -1 -1 754.02 171.27 917.11 255.24 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n67 9 Pedestrian -1 -1 -1 321.68 162.27 335.32 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n67 35 Car -1 -1 -1 598.70 173.50 621.94 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n67 41 Pedestrian -1 -1 -1 373.30 165.30 388.07 199.25 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n68 1 Car -1 -1 -1 1094.31 185.12 1221.05 236.40 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n68 2 Car -1 -1 -1 955.14 183.53 1066.97 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n68 3 Car -1 -1 -1 1032.51 183.78 1157.25 233.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n68 6 Pedestrian -1 -1 -1 645.04 163.27 696.77 281.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n68 8 Car -1 -1 -1 602.34 172.71 637.98 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n68 39 Pedestrian -1 -1 -1 685.99 154.01 724.47 273.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n68 38 Pedestrian -1 -1 -1 784.90 147.05 848.72 326.75 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n68 7 Pedestrian -1 -1 -1 307.16 158.77 321.35 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n68 23 Pedestrian -1 -1 -1 396.93 162.17 409.44 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n68 11 Pedestrian -1 -1 -1 192.78 161.42 208.33 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n68 40 Pedestrian -1 -1 -1 360.82 160.48 376.43 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n68 36 Car -1 -1 -1 755.03 177.60 840.16 249.25 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n68 26 Pedestrian -1 -1 -1 845.22 151.90 925.51 297.85 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n68 5 Pedestrian -1 -1 -1 831.98 149.67 908.31 308.30 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n68 9 Pedestrian -1 -1 -1 322.45 162.23 336.52 199.59 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n68 41 Pedestrian -1 -1 -1 381.77 162.48 396.38 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n68 42 Car -1 -1 -1 767.01 175.21 896.78 244.70 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n69 1 Car -1 -1 -1 1094.41 185.09 1220.96 236.53 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n69 2 Car -1 -1 -1 955.04 183.55 1067.09 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n69 3 Car -1 -1 -1 1032.41 183.66 1157.45 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n69 6 Pedestrian -1 -1 -1 639.61 163.52 680.99 279.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n69 8 Car -1 -1 -1 602.36 172.70 637.88 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n69 39 Pedestrian -1 -1 -1 669.95 154.21 718.70 273.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n69 38 Pedestrian -1 -1 -1 792.14 147.48 871.59 325.96 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n69 7 Pedestrian -1 -1 -1 307.33 158.66 321.30 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n69 23 Pedestrian -1 -1 -1 397.09 162.10 409.79 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n69 11 Pedestrian -1 -1 -1 193.00 161.54 208.43 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n69 26 Pedestrian -1 -1 -1 834.66 154.33 920.44 296.12 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n69 40 Pedestrian -1 -1 -1 360.74 160.53 376.51 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n69 5 Pedestrian -1 -1 -1 777.32 169.20 817.48 250.56 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n69 36 Car -1 -1 -1 721.81 183.37 842.57 236.56 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n69 9 Pedestrian -1 -1 -1 322.23 162.15 336.76 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n69 41 Pedestrian -1 -1 -1 381.82 162.52 396.36 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n70 1 Car -1 -1 -1 1094.71 185.24 1220.91 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n70 2 Car -1 -1 -1 955.19 183.59 1067.03 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n70 3 Car -1 -1 -1 1032.36 183.63 1157.47 233.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n70 6 Pedestrian -1 -1 -1 631.93 162.88 672.29 279.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n70 7 Pedestrian -1 -1 -1 307.93 158.62 321.67 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n70 8 Car -1 -1 -1 602.30 172.80 637.73 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n70 39 Pedestrian -1 -1 -1 658.29 155.22 715.10 272.88 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n70 36 Car -1 -1 -1 701.12 182.18 840.79 236.74 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n70 23 Pedestrian -1 -1 -1 397.50 161.86 409.82 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n70 38 Pedestrian -1 -1 -1 803.34 149.35 891.27 324.22 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n70 11 Pedestrian -1 -1 -1 192.96 161.60 208.38 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n70 26 Pedestrian -1 -1 -1 849.00 153.07 929.73 313.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n70 5 Pedestrian -1 -1 -1 776.69 168.91 817.82 250.59 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n70 40 Pedestrian -1 -1 -1 360.91 160.52 376.20 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n70 9 Pedestrian -1 -1 -1 322.26 162.28 337.07 199.62 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n70 41 Pedestrian -1 -1 -1 381.60 162.75 396.05 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n71 1 Car -1 -1 -1 1094.44 185.19 1220.86 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n71 2 Car -1 -1 -1 955.24 183.53 1067.27 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n71 3 Car -1 -1 -1 1032.68 183.62 1157.14 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n71 6 Pedestrian -1 -1 -1 614.69 162.75 667.26 278.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n71 7 Pedestrian -1 -1 -1 308.04 158.76 322.43 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n71 8 Car -1 -1 -1 601.41 172.86 637.20 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n71 39 Pedestrian -1 -1 -1 655.93 156.60 709.08 272.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n71 26 Pedestrian -1 -1 -1 872.44 150.72 936.41 314.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n71 38 Pedestrian -1 -1 -1 810.50 150.33 899.62 329.99 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n71 36 Car -1 -1 -1 685.29 180.69 826.20 237.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n71 23 Pedestrian -1 -1 -1 397.79 162.23 409.91 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n71 5 Pedestrian -1 -1 -1 780.59 170.14 813.60 248.99 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n71 11 Pedestrian -1 -1 -1 192.90 161.57 208.28 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n71 9 Pedestrian -1 -1 -1 322.43 162.27 337.15 199.14 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n71 41 Pedestrian -1 -1 -1 382.04 162.97 395.43 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n71 43 Pedestrian -1 -1 -1 805.92 152.86 896.23 296.77 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n71 44 Cyclist -1 -1 -1 360.63 160.65 376.99 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n72 1 Car -1 -1 -1 1094.61 185.23 1220.66 236.30 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n72 2 Car -1 -1 -1 955.14 183.59 1067.24 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n72 3 Car -1 -1 -1 1030.07 183.71 1155.54 233.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n72 7 Pedestrian -1 -1 -1 308.76 159.17 322.65 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n72 6 Pedestrian -1 -1 -1 606.13 163.54 660.57 277.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n72 36 Car -1 -1 -1 674.21 180.83 812.86 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n72 8 Car -1 -1 -1 601.54 173.23 637.01 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n72 39 Pedestrian -1 -1 -1 651.23 156.80 698.30 271.63 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n72 26 Pedestrian -1 -1 -1 885.90 148.82 953.61 323.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n72 9 Pedestrian -1 -1 -1 322.93 162.35 337.29 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n72 38 Pedestrian -1 -1 -1 833.81 154.71 906.60 333.42 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n72 5 Pedestrian -1 -1 -1 774.88 170.40 805.98 248.85 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n72 11 Pedestrian -1 -1 -1 192.83 161.58 208.19 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n72 23 Pedestrian -1 -1 -1 397.66 161.88 410.28 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n72 41 Pedestrian -1 -1 -1 381.69 162.90 395.87 197.05 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n72 43 Pedestrian -1 -1 -1 812.61 154.54 881.95 288.84 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n72 44 Cyclist -1 -1 -1 360.57 160.80 377.32 199.70 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n73 1 Car -1 -1 -1 1094.50 185.26 1220.75 236.47 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n73 2 Car -1 -1 -1 954.97 183.75 1067.32 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n73 3 Car -1 -1 -1 1030.05 183.70 1155.68 234.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n73 36 Car -1 -1 -1 653.77 179.16 796.21 232.30 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n73 6 Pedestrian -1 -1 -1 601.44 164.26 650.38 277.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n73 43 Pedestrian -1 -1 -1 805.24 156.15 866.51 286.48 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n73 7 Pedestrian -1 -1 -1 309.48 159.17 323.66 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n73 9 Pedestrian -1 -1 -1 323.81 162.40 337.97 198.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n73 8 Car -1 -1 -1 601.91 173.67 636.72 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n73 39 Pedestrian -1 -1 -1 643.15 155.09 684.17 271.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n73 38 Pedestrian -1 -1 -1 854.40 147.59 924.35 340.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n73 26 Pedestrian -1 -1 -1 893.00 148.08 977.14 318.47 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n73 11 Pedestrian -1 -1 -1 192.88 161.65 208.21 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n73 5 Pedestrian -1 -1 -1 775.32 170.08 804.88 248.32 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n73 23 Pedestrian -1 -1 -1 397.98 162.20 410.13 199.09 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n73 41 Pedestrian -1 -1 -1 381.84 162.94 396.03 196.36 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n73 44 Cyclist -1 -1 -1 360.48 160.87 377.71 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n74 1 Car -1 -1 -1 1094.07 185.38 1221.17 236.46 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n74 2 Car -1 -1 -1 955.20 183.54 1066.94 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n74 3 Car -1 -1 -1 1030.19 183.84 1155.67 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n74 36 Car -1 -1 -1 640.67 179.42 779.50 231.10 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n74 6 Pedestrian -1 -1 -1 601.47 164.09 640.95 276.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n74 8 Car -1 -1 -1 601.30 173.63 636.85 202.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n74 43 Pedestrian -1 -1 -1 802.05 156.22 853.70 284.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n74 38 Pedestrian -1 -1 -1 872.12 139.98 945.03 341.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n74 9 Pedestrian -1 -1 -1 324.13 162.22 338.45 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n74 7 Pedestrian -1 -1 -1 309.78 159.09 323.99 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n74 26 Pedestrian -1 -1 -1 905.64 148.79 995.28 323.59 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n74 39 Pedestrian -1 -1 -1 636.35 156.37 683.18 269.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n74 11 Pedestrian -1 -1 -1 192.85 161.70 208.02 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n74 23 Pedestrian -1 -1 -1 398.05 162.07 410.56 199.33 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n74 5 Pedestrian -1 -1 -1 772.01 167.03 801.22 247.58 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n74 41 Pedestrian -1 -1 -1 382.11 162.69 395.62 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n74 45 Pedestrian -1 -1 -1 360.66 160.54 377.55 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n75 1 Car -1 -1 -1 1094.03 185.33 1221.38 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n75 2 Car -1 -1 -1 955.38 183.59 1066.80 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n75 3 Car -1 -1 -1 1033.26 183.73 1156.58 234.07 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n75 36 Car -1 -1 -1 628.59 177.99 766.99 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n75 6 Pedestrian -1 -1 -1 590.41 164.55 630.14 275.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n75 43 Pedestrian -1 -1 -1 793.13 155.01 840.34 282.02 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n75 8 Car -1 -1 -1 601.41 173.37 637.08 202.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n75 38 Pedestrian -1 -1 -1 882.38 144.52 973.12 337.00 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n75 7 Pedestrian -1 -1 -1 311.32 159.54 325.11 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n75 11 Pedestrian -1 -1 -1 192.83 161.74 208.09 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n75 9 Pedestrian -1 -1 -1 325.12 162.54 338.74 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n75 39 Pedestrian -1 -1 -1 631.98 158.64 679.75 268.75 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n75 26 Pedestrian -1 -1 -1 925.95 150.95 1013.04 322.75 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n75 45 Pedestrian -1 -1 -1 360.44 160.22 377.45 200.41 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n75 41 Pedestrian -1 -1 -1 382.31 162.63 395.37 196.58 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n75 23 Pedestrian -1 -1 -1 397.95 162.03 411.38 201.25 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n75 5 Pedestrian -1 -1 -1 775.12 166.19 804.65 248.37 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n76 1 Car -1 -1 -1 1094.15 185.23 1221.53 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n76 3 Car -1 -1 -1 1030.52 183.76 1155.32 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n76 2 Car -1 -1 -1 955.36 183.72 1067.02 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n76 6 Pedestrian -1 -1 -1 578.00 165.04 627.00 272.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n76 38 Pedestrian -1 -1 -1 893.53 145.00 992.32 344.20 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n76 43 Pedestrian -1 -1 -1 779.60 153.77 830.95 280.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n76 36 Car -1 -1 -1 615.18 176.83 749.24 229.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n76 7 Pedestrian -1 -1 -1 311.44 159.66 325.45 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n76 8 Car -1 -1 -1 600.89 173.51 637.57 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n76 11 Pedestrian -1 -1 -1 193.07 161.89 208.10 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n76 39 Pedestrian -1 -1 -1 624.82 159.25 671.75 267.36 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n76 9 Pedestrian -1 -1 -1 326.10 162.42 340.58 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n76 26 Pedestrian -1 -1 -1 948.22 150.31 1021.71 323.98 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n76 45 Pedestrian -1 -1 -1 361.13 159.95 377.50 200.86 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n76 23 Pedestrian -1 -1 -1 398.28 161.67 410.79 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n76 5 Pedestrian -1 -1 -1 775.02 164.46 811.65 253.46 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n76 41 Pedestrian -1 -1 -1 382.40 162.83 395.32 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n76 46 Pedestrian -1 -1 -1 615.61 158.57 665.63 267.54 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n77 1 Car -1 -1 -1 1094.63 185.44 1221.40 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n77 2 Car -1 -1 -1 954.95 183.73 1067.63 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n77 3 Car -1 -1 -1 1029.98 183.78 1155.88 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n77 43 Pedestrian -1 -1 -1 767.82 157.45 827.32 277.65 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n77 8 Car -1 -1 -1 600.89 173.69 637.11 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n77 7 Pedestrian -1 -1 -1 311.70 160.03 325.42 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n77 36 Car -1 -1 -1 601.90 177.16 733.03 228.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n77 38 Pedestrian -1 -1 -1 908.42 145.09 1007.66 350.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n77 6 Pedestrian -1 -1 -1 569.18 165.24 621.07 275.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n77 9 Pedestrian -1 -1 -1 326.81 162.34 341.07 198.69 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n77 26 Pedestrian -1 -1 -1 959.71 148.19 1033.15 332.73 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n77 11 Pedestrian -1 -1 -1 192.84 161.84 208.22 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n77 46 Pedestrian -1 -1 -1 604.40 158.59 654.15 267.08 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n77 39 Pedestrian -1 -1 -1 618.75 158.29 663.13 268.54 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n77 45 Pedestrian -1 -1 -1 361.25 159.98 377.66 201.06 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n77 5 Pedestrian -1 -1 -1 770.26 166.58 809.14 251.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n77 23 Pedestrian -1 -1 -1 398.14 161.71 411.51 201.43 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n77 41 Pedestrian -1 -1 -1 382.03 162.83 394.55 196.75 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n78 2 Car -1 -1 -1 953.94 183.27 1068.66 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n78 1 Car -1 -1 -1 1094.71 185.32 1221.36 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n78 3 Car -1 -1 -1 1029.40 183.67 1156.26 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n78 8 Car -1 -1 -1 600.96 173.70 636.47 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n78 7 Pedestrian -1 -1 -1 312.51 159.88 325.96 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n78 43 Pedestrian -1 -1 -1 763.22 156.69 823.64 277.77 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n78 6 Pedestrian -1 -1 -1 566.06 164.86 615.06 272.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n78 36 Car -1 -1 -1 591.04 176.10 721.56 228.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n78 9 Pedestrian -1 -1 -1 327.17 162.32 341.33 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n78 38 Pedestrian -1 -1 -1 934.52 144.15 1012.26 352.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n78 11 Pedestrian -1 -1 -1 192.82 161.82 208.10 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n78 26 Pedestrian -1 -1 -1 974.29 147.78 1064.55 334.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n78 39 Pedestrian -1 -1 -1 614.70 158.49 658.79 262.99 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n78 46 Pedestrian -1 -1 -1 603.22 157.97 647.38 267.51 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n78 23 Pedestrian -1 -1 -1 398.31 162.62 411.33 201.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n78 45 Pedestrian -1 -1 -1 361.42 160.03 377.63 201.10 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n78 41 Pedestrian -1 -1 -1 381.88 162.75 393.98 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n79 2 Car -1 -1 -1 953.71 183.11 1068.88 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n79 1 Car -1 -1 -1 1094.46 185.35 1221.55 236.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n79 3 Car -1 -1 -1 1033.32 183.83 1156.95 234.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n79 8 Car -1 -1 -1 601.42 173.41 636.37 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n79 6 Pedestrian -1 -1 -1 562.34 164.61 603.63 272.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n79 7 Pedestrian -1 -1 -1 313.00 159.80 326.40 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n79 43 Pedestrian -1 -1 -1 760.19 156.92 811.18 276.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n79 36 Car -1 -1 -1 587.86 175.74 708.78 227.75 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n79 9 Pedestrian -1 -1 -1 327.50 162.33 342.23 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n79 39 Pedestrian -1 -1 -1 606.38 160.16 651.97 266.09 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n79 38 Pedestrian -1 -1 -1 952.65 146.14 1032.72 356.55 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n79 11 Pedestrian -1 -1 -1 192.96 161.85 208.01 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n79 46 Pedestrian -1 -1 -1 593.94 157.24 634.36 264.25 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n79 23 Pedestrian -1 -1 -1 398.59 162.84 411.13 201.33 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n79 26 Pedestrian -1 -1 -1 987.92 146.92 1088.94 341.66 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n79 45 Pedestrian -1 -1 -1 361.53 160.15 377.45 201.18 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n79 41 Pedestrian -1 -1 -1 381.82 162.47 394.25 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n80 1 Car -1 -1 -1 1098.47 185.24 1221.24 236.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n80 3 Car -1 -1 -1 1033.85 184.01 1156.83 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n80 8 Car -1 -1 -1 601.30 173.08 636.55 201.98 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n80 6 Pedestrian -1 -1 -1 556.30 163.89 594.47 272.27 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n80 2 Car -1 -1 -1 954.71 183.47 1068.47 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n80 7 Pedestrian -1 -1 -1 313.20 159.34 326.74 197.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n80 9 Pedestrian -1 -1 -1 327.84 162.16 342.64 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n80 43 Pedestrian -1 -1 -1 752.49 156.38 795.96 272.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n80 36 Car -1 -1 -1 578.46 174.81 696.38 224.46 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n80 39 Pedestrian -1 -1 -1 602.04 160.99 648.32 265.28 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n80 11 Pedestrian -1 -1 -1 192.95 161.84 208.13 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n80 46 Pedestrian -1 -1 -1 586.18 156.93 626.82 263.38 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n80 45 Pedestrian -1 -1 -1 361.45 160.00 377.46 201.02 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n80 26 Pedestrian -1 -1 -1 975.85 140.19 1101.28 356.16 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n80 23 Pedestrian -1 -1 -1 398.59 163.27 411.29 200.97 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n80 41 Pedestrian -1 -1 -1 380.86 162.22 395.99 201.80 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n81 1 Car -1 -1 -1 1094.16 185.32 1221.42 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n81 2 Car -1 -1 -1 955.12 182.61 1067.92 234.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n81 8 Car -1 -1 -1 601.58 173.14 636.16 201.91 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n81 3 Car -1 -1 -1 1034.74 184.16 1155.87 234.12 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n81 6 Pedestrian -1 -1 -1 544.67 164.84 589.76 271.92 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n81 43 Pedestrian -1 -1 -1 741.92 154.98 784.79 273.17 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n81 36 Car -1 -1 -1 575.67 174.65 683.43 223.93 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n81 9 Pedestrian -1 -1 -1 328.19 161.99 342.50 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n81 7 Pedestrian -1 -1 -1 313.62 159.28 326.97 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n81 26 Pedestrian -1 -1 -1 983.83 141.90 1123.72 354.92 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n81 46 Pedestrian -1 -1 -1 574.94 158.14 622.50 262.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n81 11 Pedestrian -1 -1 -1 192.75 161.73 207.92 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n81 45 Pedestrian -1 -1 -1 361.27 159.82 377.74 201.42 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n81 39 Pedestrian -1 -1 -1 596.09 159.98 639.21 266.00 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n81 23 Pedestrian -1 -1 -1 398.65 163.52 411.32 200.75 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n81 41 Pedestrian -1 -1 -1 384.51 163.07 398.02 201.49 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n81 47 Pedestrian -1 -1 -1 1030.87 144.56 1122.21 351.09 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n81 48 Pedestrian -1 -1 -1 756.58 161.66 800.37 250.70 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n82 1 Car -1 -1 -1 1093.34 185.29 1221.71 236.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n82 8 Car -1 -1 -1 602.03 173.37 635.51 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n82 3 Car -1 -1 -1 1033.80 183.91 1157.06 234.21 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n82 2 Car -1 -1 -1 955.50 182.67 1066.61 234.46 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n82 6 Pedestrian -1 -1 -1 537.01 165.81 584.39 270.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n82 26 Pedestrian -1 -1 -1 995.72 142.87 1134.56 360.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n82 36 Car -1 -1 -1 568.28 173.94 667.84 223.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n82 43 Pedestrian -1 -1 -1 731.47 157.65 780.07 271.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n82 9 Pedestrian -1 -1 -1 328.54 161.98 342.74 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n82 46 Pedestrian -1 -1 -1 571.25 157.94 618.14 263.41 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n82 11 Pedestrian -1 -1 -1 192.67 161.75 208.07 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n82 7 Pedestrian -1 -1 -1 313.95 159.76 327.31 197.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n82 39 Pedestrian -1 -1 -1 593.73 160.61 633.76 260.00 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n82 45 Pedestrian -1 -1 -1 361.12 159.57 378.44 201.65 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n82 23 Pedestrian -1 -1 -1 399.72 162.89 412.29 200.71 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n82 41 Pedestrian -1 -1 -1 384.70 163.44 397.84 200.93 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n82 47 Pedestrian -1 -1 -1 1057.50 146.97 1133.62 349.28 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n82 48 Pedestrian -1 -1 -1 764.46 165.07 799.63 246.96 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n83 3 Car -1 -1 -1 1032.18 184.03 1158.56 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n83 1 Car -1 -1 -1 1093.35 185.15 1221.18 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n83 2 Car -1 -1 -1 955.80 182.89 1066.81 232.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n83 8 Car -1 -1 -1 601.42 173.34 635.91 201.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n83 6 Pedestrian -1 -1 -1 532.87 163.86 579.68 271.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n83 43 Pedestrian -1 -1 -1 729.11 159.57 774.17 273.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n83 9 Pedestrian -1 -1 -1 328.85 162.33 342.50 197.50 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n83 36 Car -1 -1 -1 560.25 173.61 658.92 222.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n83 39 Pedestrian -1 -1 -1 585.55 160.38 626.41 260.13 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n83 46 Pedestrian -1 -1 -1 567.33 158.71 607.09 262.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n83 26 Pedestrian -1 -1 -1 1021.72 146.16 1131.71 365.01 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n83 11 Pedestrian -1 -1 -1 192.63 161.72 207.96 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n83 23 Pedestrian -1 -1 -1 399.79 163.01 412.52 200.98 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n83 7 Pedestrian -1 -1 -1 315.29 160.18 328.26 196.97 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n83 48 Pedestrian -1 -1 -1 766.01 167.63 797.75 244.82 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n83 41 Pedestrian -1 -1 -1 384.98 163.53 397.47 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n83 47 Pedestrian -1 -1 -1 1077.69 145.23 1159.81 351.19 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n83 45 Pedestrian -1 -1 -1 361.13 159.54 378.84 201.43 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n83 49 Pedestrian -1 -1 -1 1052.23 143.79 1147.40 352.85 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n84 2 Car -1 -1 -1 955.56 182.97 1067.27 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n84 1 Car -1 -1 -1 1093.75 185.33 1221.13 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n84 6 Pedestrian -1 -1 -1 531.37 163.47 566.45 270.72 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n84 3 Car -1 -1 -1 1034.33 183.93 1155.82 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n84 43 Pedestrian -1 -1 -1 723.84 157.19 763.59 272.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n84 9 Pedestrian -1 -1 -1 328.62 162.13 342.82 197.41 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n84 8 Car -1 -1 -1 601.73 173.35 636.29 201.56 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n84 39 Pedestrian -1 -1 -1 577.70 161.80 620.03 258.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n84 7 Pedestrian -1 -1 -1 315.48 160.20 328.32 196.71 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n84 46 Pedestrian -1 -1 -1 565.17 157.62 600.85 263.64 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n84 23 Pedestrian -1 -1 -1 400.11 163.11 412.38 201.40 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n84 36 Car -1 -1 -1 548.92 172.49 649.14 223.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n84 26 Pedestrian -1 -1 -1 1049.79 147.27 1134.53 364.89 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n84 11 Pedestrian -1 -1 -1 192.37 161.57 207.95 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n84 48 Pedestrian -1 -1 -1 763.17 168.31 794.47 243.66 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n84 47 Pedestrian -1 -1 -1 1077.30 146.65 1190.88 356.80 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n84 41 Pedestrian -1 -1 -1 385.00 163.40 397.44 200.92 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n84 45 Pedestrian -1 -1 -1 361.51 159.79 378.39 201.34 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n84 49 Pedestrian -1 -1 -1 1065.09 143.06 1172.68 361.16 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n85 1 Car -1 -1 -1 1091.75 185.11 1223.11 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n85 2 Car -1 -1 -1 955.27 183.11 1066.96 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n85 3 Car -1 -1 -1 1030.92 183.67 1154.58 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n85 6 Pedestrian -1 -1 -1 524.07 163.16 558.82 269.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n85 43 Pedestrian -1 -1 -1 714.78 155.56 751.38 270.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n85 46 Pedestrian -1 -1 -1 556.98 157.02 594.31 262.75 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n85 7 Pedestrian -1 -1 -1 315.88 159.91 328.54 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n85 8 Car -1 -1 -1 604.31 173.05 636.47 202.10 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n85 39 Pedestrian -1 -1 -1 573.70 160.41 616.66 260.77 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n85 23 Pedestrian -1 -1 -1 400.16 162.94 412.24 201.57 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n85 9 Pedestrian -1 -1 -1 329.07 162.10 342.91 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n85 36 Car -1 -1 -1 556.15 174.11 633.49 217.30 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n85 48 Pedestrian -1 -1 -1 762.54 167.32 794.15 244.43 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n85 11 Pedestrian -1 -1 -1 192.33 161.55 207.83 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n85 26 Pedestrian -1 -1 -1 1075.90 141.16 1177.01 362.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n85 41 Pedestrian -1 -1 -1 381.81 162.43 395.26 201.56 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n85 45 Pedestrian -1 -1 -1 361.64 159.97 378.42 201.39 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n86 1 Car -1 -1 -1 1091.73 184.90 1223.15 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n86 2 Car -1 -1 -1 954.70 183.26 1067.46 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n86 3 Car -1 -1 -1 1031.40 183.44 1153.80 234.28 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n86 6 Pedestrian -1 -1 -1 515.69 163.17 558.22 266.79 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n86 43 Pedestrian -1 -1 -1 702.31 156.74 747.49 265.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n86 8 Car -1 -1 -1 602.33 173.19 635.89 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n86 46 Pedestrian -1 -1 -1 546.91 157.93 589.54 261.33 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n86 7 Pedestrian -1 -1 -1 316.39 160.09 328.76 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n86 26 Pedestrian -1 -1 -1 1088.00 139.21 1218.34 364.88 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n86 23 Pedestrian -1 -1 -1 399.74 163.24 412.64 201.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n86 39 Pedestrian -1 -1 -1 572.21 161.30 608.97 259.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n86 48 Pedestrian -1 -1 -1 759.51 167.48 790.93 242.87 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n86 9 Pedestrian -1 -1 -1 330.16 162.44 344.00 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n86 11 Pedestrian -1 -1 -1 192.39 161.53 207.90 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n86 36 Car -1 -1 -1 551.44 173.97 622.87 216.92 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n86 41 Pedestrian -1 -1 -1 381.61 162.25 395.31 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n86 45 Pedestrian -1 -1 -1 361.47 160.10 378.27 201.40 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n87 1 Car -1 -1 -1 1091.98 184.70 1222.83 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n87 2 Car -1 -1 -1 954.63 183.42 1067.58 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n87 3 Car -1 -1 -1 1031.63 183.44 1153.97 234.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n87 43 Pedestrian -1 -1 -1 695.69 158.19 746.38 267.28 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n87 6 Pedestrian -1 -1 -1 508.14 163.49 552.25 266.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n87 8 Car -1 -1 -1 601.24 172.95 636.50 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n87 7 Pedestrian -1 -1 -1 316.52 160.25 329.46 196.58 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n87 48 Pedestrian -1 -1 -1 757.63 167.93 791.39 242.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n87 9 Pedestrian -1 -1 -1 330.76 162.31 344.39 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n87 26 Pedestrian -1 -1 -1 1098.46 143.15 1215.42 361.51 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n87 46 Pedestrian -1 -1 -1 541.56 158.08 587.07 261.90 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n87 23 Pedestrian -1 -1 -1 399.38 162.64 412.80 202.00 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n87 11 Pedestrian -1 -1 -1 192.38 161.61 207.79 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n87 36 Car -1 -1 -1 548.01 174.21 610.76 215.93 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n87 39 Pedestrian -1 -1 -1 564.57 160.08 601.81 261.50 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n87 41 Pedestrian -1 -1 -1 381.00 162.05 394.95 198.59 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n87 45 Pedestrian -1 -1 -1 361.14 159.68 378.71 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n88 2 Car -1 -1 -1 954.80 183.58 1067.41 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n88 3 Car -1 -1 -1 1031.53 183.78 1154.18 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n88 1 Car -1 -1 -1 1094.62 184.84 1219.65 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n88 43 Pedestrian -1 -1 -1 692.73 158.14 741.08 267.52 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n88 6 Pedestrian -1 -1 -1 504.55 164.14 546.24 264.89 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n88 8 Car -1 -1 -1 601.77 172.97 636.54 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n88 48 Pedestrian -1 -1 -1 755.04 169.57 788.06 241.60 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n88 7 Pedestrian -1 -1 -1 316.74 160.51 329.71 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n88 9 Pedestrian -1 -1 -1 330.74 162.00 344.93 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n88 26 Pedestrian -1 -1 -1 1118.70 146.51 1218.14 364.68 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n88 46 Pedestrian -1 -1 -1 533.51 158.65 579.84 260.29 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n88 39 Pedestrian -1 -1 -1 558.13 160.79 600.63 261.04 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n88 11 Pedestrian -1 -1 -1 192.43 161.76 207.80 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n88 23 Pedestrian -1 -1 -1 399.35 162.23 412.61 201.94 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n88 45 Pedestrian -1 -1 -1 360.99 161.28 378.93 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n88 36 Car -1 -1 -1 545.07 172.23 598.77 217.19 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n88 41 Pedestrian -1 -1 -1 381.00 162.03 394.92 202.40 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n89 2 Car -1 -1 -1 954.87 183.68 1067.19 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n89 1 Car -1 -1 -1 1095.97 184.70 1218.78 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n89 3 Car -1 -1 -1 1030.44 183.81 1155.31 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n89 6 Pedestrian -1 -1 -1 501.47 163.83 536.35 264.37 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n89 48 Pedestrian -1 -1 -1 753.63 169.55 787.01 241.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n89 43 Pedestrian -1 -1 -1 690.10 158.73 730.27 263.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n89 8 Car -1 -1 -1 602.03 172.94 636.48 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n89 7 Pedestrian -1 -1 -1 316.95 160.39 330.22 196.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n89 46 Pedestrian -1 -1 -1 532.06 159.45 573.39 259.95 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n89 9 Pedestrian -1 -1 -1 330.81 162.13 345.40 196.59 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n89 39 Pedestrian -1 -1 -1 554.84 163.29 596.29 257.64 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n89 11 Pedestrian -1 -1 -1 192.21 161.59 207.98 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n89 23 Pedestrian -1 -1 -1 399.18 161.98 412.84 201.90 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n89 45 Pedestrian -1 -1 -1 361.22 161.36 378.58 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n89 26 Pedestrian -1 -1 -1 1147.09 148.82 1220.43 362.67 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n89 41 Pedestrian -1 -1 -1 380.61 161.80 394.92 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n89 36 Car -1 -1 -1 541.71 172.82 586.90 216.05 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n90 2 Car -1 -1 -1 954.73 183.71 1067.17 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n90 1 Car -1 -1 -1 1096.44 184.95 1218.87 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n90 3 Car -1 -1 -1 1030.28 183.77 1155.64 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n90 6 Pedestrian -1 -1 -1 491.75 163.89 530.29 264.96 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n90 43 Pedestrian -1 -1 -1 686.14 157.36 724.57 264.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n90 8 Car -1 -1 -1 601.86 173.16 636.59 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n90 48 Pedestrian -1 -1 -1 750.73 168.75 783.04 240.86 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n90 39 Pedestrian -1 -1 -1 548.15 162.68 588.36 257.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n90 9 Pedestrian -1 -1 -1 330.91 162.27 345.89 196.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n90 7 Pedestrian -1 -1 -1 317.46 160.12 330.59 196.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n90 46 Pedestrian -1 -1 -1 526.15 159.10 564.15 259.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n90 11 Pedestrian -1 -1 -1 192.22 161.47 207.91 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n90 45 Pedestrian -1 -1 -1 361.15 161.39 378.53 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n90 23 Pedestrian -1 -1 -1 397.93 162.65 411.88 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n90 41 Pedestrian -1 -1 -1 380.34 161.37 395.63 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n90 26 Pedestrian -1 -1 -1 1184.48 150.56 1221.41 361.09 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n91 1 Car -1 -1 -1 1095.79 185.16 1219.60 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n91 2 Car -1 -1 -1 954.62 183.79 1067.35 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n91 3 Car -1 -1 -1 1029.85 183.83 1155.98 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n91 6 Pedestrian -1 -1 -1 481.56 165.55 525.50 263.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n91 43 Pedestrian -1 -1 -1 676.86 157.62 718.46 261.91 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n91 48 Pedestrian -1 -1 -1 750.98 169.29 781.57 240.47 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n91 9 Pedestrian -1 -1 -1 331.02 162.22 346.24 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n91 39 Pedestrian -1 -1 -1 543.14 162.43 578.00 255.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n91 8 Car -1 -1 -1 601.86 173.30 636.69 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n91 46 Pedestrian -1 -1 -1 523.22 159.99 558.63 257.78 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n91 7 Pedestrian -1 -1 -1 317.85 159.83 330.99 196.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n91 11 Pedestrian -1 -1 -1 192.14 161.41 208.13 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n91 23 Pedestrian -1 -1 -1 399.67 163.06 412.29 202.40 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n91 45 Pedestrian -1 -1 -1 360.90 161.26 378.65 203.25 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n91 41 Pedestrian -1 -1 -1 380.59 161.37 395.73 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n92 1 Car -1 -1 -1 1095.56 185.38 1220.17 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n92 2 Car -1 -1 -1 954.75 183.74 1067.28 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n92 3 Car -1 -1 -1 1029.78 183.89 1156.12 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n92 43 Pedestrian -1 -1 -1 668.79 159.31 712.20 260.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n92 6 Pedestrian -1 -1 -1 475.51 164.92 521.78 263.92 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n92 9 Pedestrian -1 -1 -1 331.38 161.98 346.09 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n92 8 Car -1 -1 -1 602.13 173.01 636.46 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n92 48 Pedestrian -1 -1 -1 751.62 170.43 780.02 240.12 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n92 39 Pedestrian -1 -1 -1 533.91 161.31 572.13 256.14 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n92 46 Pedestrian -1 -1 -1 515.42 158.82 552.72 255.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n92 11 Pedestrian -1 -1 -1 192.00 161.19 208.15 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n92 45 Pedestrian -1 -1 -1 360.68 160.86 378.94 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n92 23 Pedestrian -1 -1 -1 399.42 162.77 412.63 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n92 7 Pedestrian -1 -1 -1 317.62 159.84 331.58 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n92 41 Pedestrian -1 -1 -1 380.28 161.29 395.94 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n93 1 Car -1 -1 -1 1095.24 185.53 1220.65 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n93 2 Car -1 -1 -1 954.52 183.82 1067.47 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n93 3 Car -1 -1 -1 1029.93 183.93 1156.00 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n93 43 Pedestrian -1 -1 -1 667.37 160.51 705.44 259.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n93 6 Pedestrian -1 -1 -1 471.24 165.16 512.63 262.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n93 48 Pedestrian -1 -1 -1 749.89 170.73 777.92 240.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n93 9 Pedestrian -1 -1 -1 331.61 161.79 346.49 195.91 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n93 39 Pedestrian -1 -1 -1 528.08 161.85 569.80 256.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n93 8 Car -1 -1 -1 601.82 173.00 636.93 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n93 46 Pedestrian -1 -1 -1 510.60 159.21 549.13 255.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n93 23 Pedestrian -1 -1 -1 399.15 162.63 413.26 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n93 45 Pedestrian -1 -1 -1 361.11 160.79 378.62 203.43 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n93 11 Pedestrian -1 -1 -1 191.86 161.09 208.22 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n93 7 Pedestrian -1 -1 -1 318.89 160.16 332.32 195.42 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n93 41 Pedestrian -1 -1 -1 380.34 161.48 396.04 203.26 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n93 50 Car -1 -1 -1 533.68 174.23 577.59 213.89 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n94 1 Car -1 -1 -1 1095.38 185.52 1220.53 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n94 2 Car -1 -1 -1 954.56 183.77 1067.43 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n94 3 Car -1 -1 -1 1032.56 183.68 1157.25 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n94 6 Pedestrian -1 -1 -1 469.83 164.32 503.82 262.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n94 43 Pedestrian -1 -1 -1 660.82 159.55 697.62 259.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n94 48 Pedestrian -1 -1 -1 748.84 170.82 776.64 239.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n94 8 Car -1 -1 -1 601.40 172.98 637.23 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n94 9 Pedestrian -1 -1 -1 331.52 161.63 346.66 195.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n94 39 Pedestrian -1 -1 -1 519.97 162.70 562.82 254.96 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n94 46 Pedestrian -1 -1 -1 504.20 160.11 540.37 257.44 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n94 50 Car -1 -1 -1 528.98 174.58 575.38 212.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n94 45 Pedestrian -1 -1 -1 361.07 160.86 378.68 203.70 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n94 23 Pedestrian -1 -1 -1 399.09 161.88 413.37 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n94 11 Pedestrian -1 -1 -1 191.90 160.99 208.27 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n94 7 Pedestrian -1 -1 -1 319.93 158.71 332.84 195.49 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n94 41 Pedestrian -1 -1 -1 380.06 161.47 396.64 203.19 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n95 1 Car -1 -1 -1 1095.32 185.49 1220.57 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n95 2 Car -1 -1 -1 954.47 183.75 1067.54 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n95 3 Car -1 -1 -1 1032.60 183.66 1157.22 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n95 6 Pedestrian -1 -1 -1 460.70 163.79 499.24 262.51 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n95 43 Pedestrian -1 -1 -1 656.02 158.79 693.73 256.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n95 8 Car -1 -1 -1 601.54 173.20 637.14 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n95 9 Pedestrian -1 -1 -1 332.18 161.61 346.67 195.88 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n95 48 Pedestrian -1 -1 -1 748.93 170.48 775.32 239.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n95 50 Car -1 -1 -1 525.37 174.42 574.20 212.21 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n95 39 Pedestrian -1 -1 -1 518.64 162.32 556.61 252.42 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n95 46 Pedestrian -1 -1 -1 502.70 160.18 533.86 257.47 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n95 23 Pedestrian -1 -1 -1 399.20 162.22 413.78 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n95 7 Pedestrian -1 -1 -1 320.16 158.68 333.00 195.39 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n95 45 Pedestrian -1 -1 -1 363.68 160.70 380.56 204.00 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n95 11 Pedestrian -1 -1 -1 191.71 160.95 208.40 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n95 41 Pedestrian -1 -1 -1 380.25 161.72 396.53 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n96 1 Car -1 -1 -1 1095.42 185.48 1220.54 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n96 2 Car -1 -1 -1 954.40 183.74 1067.57 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n96 3 Car -1 -1 -1 1032.73 183.77 1157.10 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n96 43 Pedestrian -1 -1 -1 646.10 159.10 689.07 255.63 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n96 6 Pedestrian -1 -1 -1 454.98 164.90 496.27 261.28 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n96 8 Car -1 -1 -1 601.53 173.15 637.21 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n96 46 Pedestrian -1 -1 -1 498.78 159.76 529.64 254.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n96 50 Car -1 -1 -1 527.36 175.76 572.30 210.53 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n96 48 Pedestrian -1 -1 -1 746.97 170.50 772.89 238.89 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n96 39 Pedestrian -1 -1 -1 517.82 163.14 548.41 250.85 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n96 45 Pedestrian -1 -1 -1 363.89 160.45 380.77 203.94 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n96 7 Pedestrian -1 -1 -1 321.44 158.59 333.70 195.19 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n96 23 Pedestrian -1 -1 -1 399.33 162.22 413.73 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n96 11 Pedestrian -1 -1 -1 191.76 160.95 208.32 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n96 9 Pedestrian -1 -1 -1 332.67 161.39 347.16 195.72 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n96 41 Pedestrian -1 -1 -1 380.08 161.31 396.56 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n97 1 Car -1 -1 -1 1095.25 185.54 1220.72 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n97 2 Car -1 -1 -1 954.34 183.80 1067.73 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n97 3 Car -1 -1 -1 1029.91 183.98 1156.00 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n97 6 Pedestrian -1 -1 -1 448.31 165.79 489.69 260.25 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n97 43 Pedestrian -1 -1 -1 641.64 161.85 685.41 256.26 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n97 50 Car -1 -1 -1 528.99 175.99 569.33 208.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n97 46 Pedestrian -1 -1 -1 487.83 157.90 526.56 254.77 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n97 8 Car -1 -1 -1 601.66 173.17 637.04 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n97 39 Pedestrian -1 -1 -1 507.65 161.49 544.57 252.58 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n97 48 Pedestrian -1 -1 -1 746.12 171.02 771.95 238.44 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n97 45 Pedestrian -1 -1 -1 364.00 160.17 380.83 204.05 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n97 11 Pedestrian -1 -1 -1 191.69 160.82 208.43 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n97 23 Pedestrian -1 -1 -1 399.35 162.21 413.48 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n97 9 Pedestrian -1 -1 -1 333.93 161.65 347.84 195.21 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n97 7 Pedestrian -1 -1 -1 322.04 158.81 334.36 195.29 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n97 41 Pedestrian -1 -1 -1 380.22 161.07 396.37 203.70 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n98 1 Car -1 -1 -1 1095.38 185.55 1220.55 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n98 2 Car -1 -1 -1 954.40 183.86 1067.65 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n98 3 Car -1 -1 -1 1030.04 183.95 1155.76 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n98 6 Pedestrian -1 -1 -1 445.50 165.38 483.64 260.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n98 50 Car -1 -1 -1 528.92 175.69 569.37 208.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n98 43 Pedestrian -1 -1 -1 638.34 162.33 680.84 256.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n98 46 Pedestrian -1 -1 -1 482.82 159.42 523.06 253.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n98 8 Car -1 -1 -1 601.78 173.17 636.85 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n98 39 Pedestrian -1 -1 -1 503.39 163.34 541.51 251.08 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n98 48 Pedestrian -1 -1 -1 743.73 171.83 769.13 237.59 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n98 45 Pedestrian -1 -1 -1 363.93 160.02 381.03 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n98 11 Pedestrian -1 -1 -1 191.89 160.96 208.51 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n98 23 Pedestrian -1 -1 -1 399.25 162.50 413.43 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n98 9 Pedestrian -1 -1 -1 334.04 161.48 347.96 195.01 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n98 41 Pedestrian -1 -1 -1 380.49 161.02 396.20 203.79 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n98 7 Pedestrian -1 -1 -1 322.20 158.85 334.46 195.27 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n99 1 Car -1 -1 -1 1095.54 185.51 1220.40 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n99 2 Car -1 -1 -1 954.42 183.81 1067.73 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n99 3 Car -1 -1 -1 1032.69 183.68 1157.20 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n99 50 Car -1 -1 -1 528.15 175.77 569.52 207.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n99 8 Car -1 -1 -1 601.66 173.15 636.83 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n99 46 Pedestrian -1 -1 -1 475.77 159.80 515.91 252.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n99 48 Pedestrian -1 -1 -1 742.76 172.17 767.61 237.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n99 43 Pedestrian -1 -1 -1 636.70 161.38 674.02 253.36 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n99 39 Pedestrian -1 -1 -1 499.22 162.85 537.73 251.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n99 45 Pedestrian -1 -1 -1 364.12 160.29 381.00 204.47 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n99 6 Pedestrian -1 -1 -1 443.38 164.91 477.04 257.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n99 11 Pedestrian -1 -1 -1 191.94 160.91 208.53 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n99 9 Pedestrian -1 -1 -1 334.06 161.33 348.09 194.91 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n99 23 Pedestrian -1 -1 -1 399.20 162.39 413.07 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n99 7 Pedestrian -1 -1 -1 322.96 159.34 336.12 194.74 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n99 41 Pedestrian -1 -1 -1 381.00 161.48 396.24 203.22 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n100 1 Car -1 -1 -1 1095.55 185.58 1220.39 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n100 2 Car -1 -1 -1 954.39 183.88 1067.66 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n100 50 Car -1 -1 -1 528.99 175.36 569.98 207.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n100 3 Car -1 -1 -1 1032.89 183.78 1156.99 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n100 6 Pedestrian -1 -1 -1 434.39 165.43 471.27 257.34 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n100 43 Pedestrian -1 -1 -1 631.71 159.75 671.57 253.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n100 8 Car -1 -1 -1 601.81 173.08 636.56 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n100 48 Pedestrian -1 -1 -1 740.26 171.43 764.88 235.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n100 45 Pedestrian -1 -1 -1 364.74 160.38 381.20 204.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n100 46 Pedestrian -1 -1 -1 473.55 161.23 510.07 252.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n100 39 Pedestrian -1 -1 -1 492.78 162.69 528.99 251.19 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n100 11 Pedestrian -1 -1 -1 191.96 160.91 208.65 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n100 9 Pedestrian -1 -1 -1 334.54 161.33 348.54 194.84 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n100 7 Pedestrian -1 -1 -1 323.10 159.27 336.27 194.34 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n100 23 Pedestrian -1 -1 -1 397.19 161.52 412.15 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n100 41 Pedestrian -1 -1 -1 380.92 161.48 396.32 203.31 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n101 2 Car -1 -1 -1 954.49 183.87 1067.63 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n101 1 Car -1 -1 -1 1095.67 185.55 1220.40 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n101 3 Car -1 -1 -1 1032.88 183.83 1157.01 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n101 50 Car -1 -1 -1 530.36 175.34 570.03 206.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n101 48 Pedestrian -1 -1 -1 739.07 171.53 763.58 234.43 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n101 43 Pedestrian -1 -1 -1 626.15 161.02 664.23 252.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n101 8 Car -1 -1 -1 601.98 173.23 636.24 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n101 6 Pedestrian -1 -1 -1 428.34 164.97 468.76 257.66 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n101 39 Pedestrian -1 -1 -1 491.34 161.20 522.33 251.18 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n101 46 Pedestrian -1 -1 -1 469.07 158.57 499.29 252.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n101 45 Pedestrian -1 -1 -1 365.09 160.39 381.49 204.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n101 7 Pedestrian -1 -1 -1 323.58 159.39 336.47 194.16 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n101 11 Pedestrian -1 -1 -1 192.02 160.95 208.64 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n101 9 Pedestrian -1 -1 -1 334.51 161.23 348.85 194.86 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n101 23 Pedestrian -1 -1 -1 396.95 161.58 412.04 203.72 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n101 41 Pedestrian -1 -1 -1 380.66 161.32 396.33 203.35 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n102 1 Car -1 -1 -1 1095.82 185.55 1220.26 235.47 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n102 2 Car -1 -1 -1 954.56 183.87 1067.56 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n102 3 Car -1 -1 -1 1030.26 184.07 1155.66 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n102 50 Car -1 -1 -1 532.59 174.91 570.16 205.63 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n102 43 Pedestrian -1 -1 -1 622.35 162.56 659.84 251.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n102 39 Pedestrian -1 -1 -1 484.21 160.48 521.06 250.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n102 8 Car -1 -1 -1 602.06 173.46 636.19 202.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n102 6 Pedestrian -1 -1 -1 422.62 166.18 462.40 256.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n102 48 Pedestrian -1 -1 -1 738.35 171.82 762.57 234.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n102 46 Pedestrian -1 -1 -1 464.62 158.79 495.34 252.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n102 45 Pedestrian -1 -1 -1 364.75 160.80 382.04 203.95 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n102 7 Pedestrian -1 -1 -1 323.82 159.60 336.97 193.89 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n102 11 Pedestrian -1 -1 -1 192.33 161.17 208.70 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n102 23 Pedestrian -1 -1 -1 397.05 162.08 411.84 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n102 9 Pedestrian -1 -1 -1 334.91 161.36 348.53 194.35 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n102 41 Pedestrian -1 -1 -1 380.43 161.34 396.43 203.58 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n103 1 Car -1 -1 -1 1095.71 185.58 1220.39 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n103 2 Car -1 -1 -1 954.60 183.93 1067.54 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n103 3 Car -1 -1 -1 1030.21 184.01 1155.59 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n103 50 Car -1 -1 -1 534.07 174.93 570.89 204.97 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n103 43 Pedestrian -1 -1 -1 619.64 163.05 655.03 251.10 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n103 48 Pedestrian -1 -1 -1 736.15 172.07 761.03 234.49 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n103 6 Pedestrian -1 -1 -1 422.52 166.13 459.72 255.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n103 39 Pedestrian -1 -1 -1 477.03 161.50 514.26 249.63 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n103 8 Car -1 -1 -1 602.13 173.81 636.03 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n103 46 Pedestrian -1 -1 -1 458.86 159.07 493.76 251.76 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n103 7 Pedestrian -1 -1 -1 324.48 159.80 337.65 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n103 11 Pedestrian -1 -1 -1 192.37 161.30 208.61 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n103 45 Pedestrian -1 -1 -1 365.05 160.60 382.33 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n103 9 Pedestrian -1 -1 -1 335.36 161.39 348.72 194.38 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n103 23 Pedestrian -1 -1 -1 397.01 162.51 411.60 204.38 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n104 1 Car -1 -1 -1 1095.94 185.61 1220.10 235.44 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n104 2 Car -1 -1 -1 954.64 183.85 1067.58 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n104 3 Car -1 -1 -1 1032.91 183.82 1157.01 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n104 6 Pedestrian -1 -1 -1 418.56 165.93 451.27 254.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n104 43 Pedestrian -1 -1 -1 615.99 161.63 650.67 250.44 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n104 46 Pedestrian -1 -1 -1 453.32 159.92 491.81 251.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n104 50 Car -1 -1 -1 535.65 174.87 571.64 204.39 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n104 48 Pedestrian -1 -1 -1 735.17 171.58 760.10 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n104 39 Pedestrian -1 -1 -1 473.04 162.22 510.33 248.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n104 8 Car -1 -1 -1 602.39 174.07 635.34 202.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n104 7 Pedestrian -1 -1 -1 325.38 159.69 338.02 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n104 11 Pedestrian -1 -1 -1 192.35 161.35 208.53 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n104 45 Pedestrian -1 -1 -1 365.35 160.51 382.19 203.74 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n104 9 Pedestrian -1 -1 -1 336.07 161.43 349.15 194.14 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n104 23 Pedestrian -1 -1 -1 396.93 162.33 411.84 204.34 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n105 1 Car -1 -1 -1 1095.70 185.65 1220.24 235.49 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n105 2 Car -1 -1 -1 954.57 183.91 1067.51 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n105 3 Car -1 -1 -1 1030.05 184.03 1155.79 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n105 6 Pedestrian -1 -1 -1 413.48 164.40 448.02 255.00 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n105 43 Pedestrian -1 -1 -1 610.06 161.17 647.55 249.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n105 46 Pedestrian -1 -1 -1 449.66 160.65 486.79 251.39 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n105 39 Pedestrian -1 -1 -1 471.49 161.38 504.14 249.12 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n105 8 Car -1 -1 -1 602.60 174.15 635.36 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n105 48 Pedestrian -1 -1 -1 734.57 171.26 759.08 234.15 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 45 Pedestrian -1 -1 -1 367.94 160.45 384.34 204.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n105 11 Pedestrian -1 -1 -1 192.48 161.39 208.62 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n105 50 Car -1 -1 -1 538.07 174.50 571.92 202.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n105 7 Pedestrian -1 -1 -1 325.94 159.41 338.52 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n105 23 Pedestrian -1 -1 -1 397.19 162.25 411.94 204.31 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n105 9 Pedestrian -1 -1 -1 336.56 159.72 349.29 194.19 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n106 1 Car -1 -1 -1 1095.49 185.55 1220.37 235.44 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n106 2 Car -1 -1 -1 954.47 183.83 1067.74 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n106 3 Car -1 -1 -1 1032.91 183.83 1156.96 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n106 43 Pedestrian -1 -1 -1 605.95 163.01 644.41 248.22 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n106 6 Pedestrian -1 -1 -1 408.22 165.62 446.53 254.03 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n106 50 Car -1 -1 -1 538.76 174.60 572.41 201.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n106 8 Car -1 -1 -1 602.64 174.12 635.20 201.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n106 46 Pedestrian -1 -1 -1 445.39 160.68 477.29 249.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n106 39 Pedestrian -1 -1 -1 467.96 161.32 500.00 248.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n106 48 Pedestrian -1 -1 -1 733.80 171.49 756.60 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n106 45 Pedestrian -1 -1 -1 368.80 160.72 384.91 204.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n106 7 Pedestrian -1 -1 -1 327.11 159.52 340.40 193.00 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n106 11 Pedestrian -1 -1 -1 192.54 161.44 208.50 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n106 23 Pedestrian -1 -1 -1 397.05 161.45 411.90 204.19 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n106 9 Pedestrian -1 -1 -1 336.63 159.42 349.24 194.12 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n107 1 Car -1 -1 -1 1095.50 185.54 1220.30 235.46 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n107 2 Car -1 -1 -1 954.48 183.84 1067.73 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n107 3 Car -1 -1 -1 1030.17 184.04 1155.73 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n107 43 Pedestrian -1 -1 -1 602.71 163.68 640.08 247.65 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n107 6 Pedestrian -1 -1 -1 404.83 166.01 441.09 253.00 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n107 50 Car -1 -1 -1 540.13 174.75 573.33 201.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n107 48 Pedestrian -1 -1 -1 732.37 171.60 755.95 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n107 8 Car -1 -1 -1 602.36 174.05 635.44 201.39 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n107 46 Pedestrian -1 -1 -1 441.81 159.08 472.86 250.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n107 7 Pedestrian -1 -1 -1 328.10 159.96 340.88 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n107 39 Pedestrian -1 -1 -1 462.24 162.01 497.62 247.61 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n107 45 Pedestrian -1 -1 -1 369.46 161.10 385.18 204.91 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n107 11 Pedestrian -1 -1 -1 192.59 161.65 208.44 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n107 23 Pedestrian -1 -1 -1 396.87 161.51 412.35 205.17 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n107 9 Pedestrian -1 -1 -1 338.58 161.66 351.64 194.18 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n108 1 Car -1 -1 -1 1095.62 185.54 1220.21 235.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n108 2 Car -1 -1 -1 954.56 183.82 1067.69 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n108 3 Car -1 -1 -1 1030.08 184.06 1155.82 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n108 50 Car -1 -1 -1 541.33 174.35 574.03 200.82 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n108 8 Car -1 -1 -1 601.93 173.92 635.95 201.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n108 6 Pedestrian -1 -1 -1 402.26 165.77 435.97 252.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n108 48 Pedestrian -1 -1 -1 731.21 172.22 755.06 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n108 43 Pedestrian -1 -1 -1 600.71 162.28 635.47 247.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n108 46 Pedestrian -1 -1 -1 435.18 157.91 470.70 249.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n108 7 Pedestrian -1 -1 -1 328.43 160.13 340.74 192.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n108 39 Pedestrian -1 -1 -1 454.24 163.18 491.42 246.83 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n108 11 Pedestrian -1 -1 -1 192.47 161.61 208.35 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n108 45 Pedestrian -1 -1 -1 369.88 160.98 385.30 204.90 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n108 9 Pedestrian -1 -1 -1 339.36 161.91 351.88 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n108 23 Pedestrian -1 -1 -1 396.80 161.43 412.39 206.34 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n108 51 Cyclist -1 -1 -1 -12.39 150.97 238.03 361.47 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n109 1 Car -1 -1 -1 1095.56 185.55 1220.33 235.53 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n109 2 Car -1 -1 -1 954.66 183.89 1067.62 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n109 3 Car -1 -1 -1 1030.25 184.06 1155.60 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n109 43 Pedestrian -1 -1 -1 598.87 161.31 629.24 244.44 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n109 48 Pedestrian -1 -1 -1 728.56 172.00 752.40 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n109 6 Pedestrian -1 -1 -1 399.27 164.66 431.20 252.29 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n109 8 Car -1 -1 -1 601.91 173.55 636.10 201.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n109 39 Pedestrian -1 -1 -1 450.48 162.25 486.95 247.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n109 50 Car -1 -1 -1 541.94 174.43 574.23 200.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n109 7 Pedestrian -1 -1 -1 328.74 159.84 341.11 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n109 46 Pedestrian -1 -1 -1 428.68 159.27 468.99 250.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n109 51 Cyclist -1 -1 -1 -1.74 148.11 295.59 364.11 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n109 45 Pedestrian -1 -1 -1 371.97 160.88 387.76 205.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n109 11 Pedestrian -1 -1 -1 192.45 161.51 208.38 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n109 9 Pedestrian -1 -1 -1 338.88 159.61 351.78 194.13 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n109 23 Pedestrian -1 -1 -1 396.44 161.42 412.48 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n110 1 Car -1 -1 -1 1095.72 185.62 1220.27 235.41 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n110 2 Car -1 -1 -1 954.71 183.89 1067.48 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n110 3 Car -1 -1 -1 1030.37 184.07 1155.60 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n110 50 Car -1 -1 -1 543.63 174.55 575.19 200.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n110 43 Pedestrian -1 -1 -1 595.46 160.74 625.66 243.30 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n110 51 Cyclist -1 -1 -1 32.73 144.24 353.42 368.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n110 7 Pedestrian -1 -1 -1 329.06 159.06 341.24 193.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n110 48 Pedestrian -1 -1 -1 725.38 171.49 749.35 232.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n110 8 Car -1 -1 -1 602.90 172.90 637.83 202.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n110 46 Pedestrian -1 -1 -1 423.44 159.67 461.39 247.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n110 6 Pedestrian -1 -1 -1 393.28 165.16 427.76 251.76 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n110 39 Pedestrian -1 -1 -1 444.36 161.96 478.46 247.93 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n110 45 Pedestrian -1 -1 -1 372.13 160.66 388.08 205.47 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n110 11 Pedestrian -1 -1 -1 192.44 160.99 209.35 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n110 9 Pedestrian -1 -1 -1 339.80 158.82 352.41 194.31 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n110 23 Pedestrian -1 -1 -1 396.36 161.62 412.31 205.77 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n111 1 Car -1 -1 -1 1095.76 185.56 1220.18 235.41 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n111 2 Car -1 -1 -1 954.68 183.91 1067.45 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n111 3 Car -1 -1 -1 1030.39 184.09 1155.43 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n111 50 Car -1 -1 -1 544.91 174.71 575.49 199.67 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n111 51 Cyclist -1 -1 -1 103.47 153.85 382.27 365.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n111 43 Pedestrian -1 -1 -1 591.15 162.81 622.35 243.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n111 48 Pedestrian -1 -1 -1 724.49 170.87 748.12 232.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n111 6 Pedestrian -1 -1 -1 384.82 164.66 423.62 252.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n111 8 Car -1 -1 -1 600.81 173.44 637.06 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n111 7 Pedestrian -1 -1 -1 329.62 159.50 341.38 192.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n111 46 Pedestrian -1 -1 -1 421.04 160.04 455.48 247.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n111 39 Pedestrian -1 -1 -1 440.42 161.87 474.54 247.61 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n111 45 Pedestrian -1 -1 -1 372.85 160.73 388.08 206.46 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n111 11 Pedestrian -1 -1 -1 192.44 161.51 208.99 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n111 9 Pedestrian -1 -1 -1 340.03 158.61 353.17 194.15 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n111 23 Pedestrian -1 -1 -1 396.23 161.66 412.04 205.79 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n112 1 Car -1 -1 -1 1095.66 185.62 1220.29 235.49 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n112 2 Car -1 -1 -1 954.59 183.91 1067.42 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n112 51 Cyclist -1 -1 -1 160.03 153.26 410.02 366.53 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n112 3 Car -1 -1 -1 1030.38 184.09 1155.53 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n112 50 Car -1 -1 -1 546.41 174.58 575.57 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n112 43 Pedestrian -1 -1 -1 587.72 163.75 618.49 242.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n112 8 Car -1 -1 -1 600.75 173.16 637.17 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n112 46 Pedestrian -1 -1 -1 418.58 159.13 449.14 247.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n112 48 Pedestrian -1 -1 -1 724.15 171.26 747.13 231.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n112 6 Pedestrian -1 -1 -1 383.95 165.01 421.70 252.29 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n112 39 Pedestrian -1 -1 -1 436.14 162.05 471.07 244.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n112 7 Pedestrian -1 -1 -1 329.82 159.47 341.88 192.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n112 11 Pedestrian -1 -1 -1 191.87 161.08 208.69 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n112 45 Pedestrian -1 -1 -1 372.73 161.38 387.88 206.21 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n112 9 Pedestrian -1 -1 -1 340.10 158.55 353.22 194.01 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n112 23 Pedestrian -1 -1 -1 396.06 161.85 411.34 205.60 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n112 52 Pedestrian -1 -1 -1 364.30 160.28 381.17 204.96 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n112 53 Pedestrian -1 -1 -1 380.79 164.25 403.45 224.80 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n113 1 Car -1 -1 -1 1095.71 185.65 1220.39 235.54 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n113 2 Car -1 -1 -1 954.62 183.92 1067.50 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n113 3 Car -1 -1 -1 1030.43 184.10 1155.42 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n113 51 Cyclist -1 -1 -1 207.82 159.98 430.47 365.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n113 50 Car -1 -1 -1 547.41 174.46 575.80 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n113 39 Pedestrian -1 -1 -1 431.67 162.43 467.81 244.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n113 43 Pedestrian -1 -1 -1 584.22 162.65 614.23 243.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n113 8 Car -1 -1 -1 603.65 173.30 637.04 201.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n113 46 Pedestrian -1 -1 -1 411.97 159.38 442.21 246.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n113 6 Pedestrian -1 -1 -1 379.62 165.93 413.20 249.13 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n113 48 Pedestrian -1 -1 -1 721.92 172.23 745.31 231.87 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n113 11 Pedestrian -1 -1 -1 191.79 161.01 208.50 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n113 7 Pedestrian -1 -1 -1 329.66 159.78 341.98 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n113 9 Pedestrian -1 -1 -1 340.25 159.16 353.69 194.04 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n113 45 Pedestrian -1 -1 -1 372.66 161.58 388.29 205.82 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n113 52 Pedestrian -1 -1 -1 364.01 160.29 381.41 204.84 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n113 23 Pedestrian -1 -1 -1 395.96 161.79 411.39 205.39 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n114 51 Cyclist -1 -1 -1 252.94 161.16 439.26 366.61 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n114 1 Car -1 -1 -1 1095.60 185.65 1220.48 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n114 2 Car -1 -1 -1 954.71 183.91 1067.46 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n114 3 Car -1 -1 -1 1030.01 184.03 1155.74 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n114 46 Pedestrian -1 -1 -1 411.40 159.71 441.18 246.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n114 8 Car -1 -1 -1 603.98 173.24 636.91 201.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n114 48 Pedestrian -1 -1 -1 721.79 172.36 744.16 231.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n114 6 Pedestrian -1 -1 -1 376.56 164.81 407.43 252.51 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n114 43 Pedestrian -1 -1 -1 580.19 161.59 610.71 242.16 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n114 39 Pedestrian -1 -1 -1 429.77 162.37 461.99 244.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n114 50 Car -1 -1 -1 548.28 174.37 577.29 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n114 11 Pedestrian -1 -1 -1 192.21 161.13 208.26 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n114 7 Pedestrian -1 -1 -1 329.93 159.43 344.88 193.30 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n114 52 Pedestrian -1 -1 -1 363.88 160.22 381.45 205.91 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n114 45 Pedestrian -1 -1 -1 372.83 161.36 388.59 206.08 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n114 23 Pedestrian -1 -1 -1 396.37 162.13 410.87 205.44 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n115 1 Car -1 -1 -1 1095.70 185.64 1220.31 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n115 2 Car -1 -1 -1 954.62 183.94 1067.56 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n115 51 Cyclist -1 -1 -1 289.23 156.37 456.36 370.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n115 3 Car -1 -1 -1 1030.12 184.02 1155.69 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n115 43 Pedestrian -1 -1 -1 574.76 161.15 608.37 241.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n115 48 Pedestrian -1 -1 -1 720.86 171.76 743.74 231.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n115 6 Pedestrian -1 -1 -1 371.01 164.40 405.84 253.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n115 8 Car -1 -1 -1 601.35 173.16 636.90 202.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n115 50 Car -1 -1 -1 549.32 174.24 577.42 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n115 46 Pedestrian -1 -1 -1 406.63 160.77 438.71 246.28 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n115 39 Pedestrian -1 -1 -1 426.76 162.42 455.92 244.20 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n115 7 Pedestrian -1 -1 -1 331.50 160.16 343.97 192.11 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n115 11 Pedestrian -1 -1 -1 192.17 161.09 208.10 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n115 52 Pedestrian -1 -1 -1 364.16 160.14 381.44 206.33 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n115 45 Pedestrian -1 -1 -1 373.89 160.84 393.79 212.84 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n115 23 Pedestrian -1 -1 -1 396.52 162.62 410.81 204.78 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n115 54 Pedestrian -1 -1 -1 342.81 162.00 355.18 193.87 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n116 1 Car -1 -1 -1 1095.54 185.56 1220.43 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n116 2 Car -1 -1 -1 954.55 183.96 1067.60 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n116 3 Car -1 -1 -1 1030.07 184.05 1155.78 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n116 51 Cyclist -1 -1 -1 315.87 160.53 460.84 365.80 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n116 43 Pedestrian -1 -1 -1 570.84 163.67 605.04 240.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n116 39 Pedestrian -1 -1 -1 422.37 162.51 453.32 244.41 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n116 8 Car -1 -1 -1 601.32 173.15 637.07 202.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n116 46 Pedestrian -1 -1 -1 404.03 162.13 433.61 244.46 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n116 7 Pedestrian -1 -1 -1 332.08 160.15 344.09 191.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n116 48 Pedestrian -1 -1 -1 720.69 171.53 742.38 230.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n116 50 Car -1 -1 -1 550.60 174.19 577.45 196.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n116 6 Pedestrian -1 -1 -1 369.49 167.05 399.62 251.67 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n116 11 Pedestrian -1 -1 -1 192.31 161.05 208.12 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n116 54 Pedestrian -1 -1 -1 343.89 162.56 355.21 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n116 52 Pedestrian -1 -1 -1 364.51 160.90 381.26 204.90 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n116 23 Pedestrian -1 -1 -1 396.31 162.86 410.66 204.59 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n117 1 Car -1 -1 -1 1095.74 185.53 1220.18 235.47 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n117 2 Car -1 -1 -1 954.56 183.96 1067.54 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n117 3 Car -1 -1 -1 1030.06 184.06 1155.72 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n117 51 Cyclist -1 -1 -1 342.32 159.25 479.70 368.01 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n117 43 Pedestrian -1 -1 -1 567.65 163.99 600.57 240.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n117 39 Pedestrian -1 -1 -1 416.94 163.66 452.08 242.96 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n117 8 Car -1 -1 -1 601.14 173.09 637.19 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n117 48 Pedestrian -1 -1 -1 717.73 172.35 740.07 230.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n117 7 Pedestrian -1 -1 -1 332.88 159.90 345.13 191.61 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n117 54 Pedestrian -1 -1 -1 344.23 162.82 355.76 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n117 46 Pedestrian -1 -1 -1 399.35 162.30 431.61 243.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n117 50 Car -1 -1 -1 551.70 173.59 578.16 195.84 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n117 11 Pedestrian -1 -1 -1 192.20 161.06 208.05 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n117 6 Pedestrian -1 -1 -1 364.47 166.39 396.66 251.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n117 52 Pedestrian -1 -1 -1 368.12 161.82 385.30 205.12 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n117 23 Pedestrian -1 -1 -1 396.83 163.26 410.94 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n118 1 Car -1 -1 -1 1095.35 185.47 1220.66 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n118 2 Car -1 -1 -1 954.60 183.93 1067.56 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n118 3 Car -1 -1 -1 1029.92 184.01 1155.84 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n118 51 Cyclist -1 -1 -1 359.00 160.80 485.86 366.29 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n118 8 Car -1 -1 -1 601.03 172.91 637.27 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n118 48 Pedestrian -1 -1 -1 717.15 172.79 739.28 230.04 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n118 43 Pedestrian -1 -1 -1 567.55 162.86 597.94 239.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n118 39 Pedestrian -1 -1 -1 416.89 161.90 450.33 243.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n118 7 Pedestrian -1 -1 -1 332.87 160.06 345.21 191.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n118 6 Pedestrian -1 -1 -1 358.45 166.36 388.48 247.33 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n118 50 Car -1 -1 -1 552.06 173.32 578.31 195.40 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n118 46 Pedestrian -1 -1 -1 394.62 160.00 427.49 244.20 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n118 11 Pedestrian -1 -1 -1 192.13 161.18 208.12 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n118 54 Pedestrian -1 -1 -1 344.60 162.54 355.95 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n118 23 Pedestrian -1 -1 -1 384.08 163.58 415.34 241.81 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n118 52 Pedestrian -1 -1 -1 364.40 161.25 381.65 205.57 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n119 1 Car -1 -1 -1 1095.70 185.61 1220.38 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n119 2 Car -1 -1 -1 954.51 183.96 1067.61 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n119 3 Car -1 -1 -1 1029.94 184.04 1155.89 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n119 51 Cyclist -1 -1 -1 376.63 160.71 491.48 358.62 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n119 8 Car -1 -1 -1 601.24 172.92 637.21 202.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n119 6 Pedestrian -1 -1 -1 355.09 165.66 384.54 246.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n119 48 Pedestrian -1 -1 -1 715.13 173.00 736.22 229.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n119 46 Pedestrian -1 -1 -1 388.29 159.15 419.75 243.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n119 43 Pedestrian -1 -1 -1 564.01 162.46 593.92 239.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n119 7 Pedestrian -1 -1 -1 333.44 160.16 345.16 191.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n119 50 Car -1 -1 -1 552.76 173.41 581.36 194.64 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n119 11 Pedestrian -1 -1 -1 192.11 161.28 208.12 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n119 39 Pedestrian -1 -1 -1 405.98 161.63 439.65 242.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n119 54 Pedestrian -1 -1 -1 345.22 161.85 356.69 193.73 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n120 1 Car -1 -1 -1 1095.54 185.56 1220.43 235.57 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n120 2 Car -1 -1 -1 954.56 183.97 1067.58 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n120 3 Car -1 -1 -1 1029.91 183.98 1155.82 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n120 6 Pedestrian -1 -1 -1 348.05 166.01 381.19 247.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n120 46 Pedestrian -1 -1 -1 381.68 160.01 418.96 244.50 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n120 8 Car -1 -1 -1 601.83 173.24 636.62 202.21 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n120 51 Cyclist -1 -1 -1 399.54 157.89 505.48 346.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n120 43 Pedestrian -1 -1 -1 560.95 162.63 589.06 236.86 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n120 50 Car -1 -1 -1 553.47 173.87 581.61 194.18 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n120 48 Pedestrian -1 -1 -1 714.04 171.53 734.80 228.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n120 11 Pedestrian -1 -1 -1 192.16 161.25 208.18 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n120 39 Pedestrian -1 -1 -1 403.38 162.77 433.86 240.56 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n120 7 Pedestrian -1 -1 -1 333.83 159.97 345.73 191.26 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n120 54 Pedestrian -1 -1 -1 345.42 160.78 357.28 192.93 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n120 55 Pedestrian -1 -1 -1 368.63 161.39 384.70 204.87 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n121 1 Car -1 -1 -1 1095.56 185.58 1220.36 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n121 2 Car -1 -1 -1 954.48 183.94 1067.52 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n121 3 Car -1 -1 -1 1029.82 184.01 1155.94 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n121 6 Pedestrian -1 -1 -1 342.89 167.03 378.05 247.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n121 46 Pedestrian -1 -1 -1 378.04 161.23 414.30 243.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n121 8 Car -1 -1 -1 601.86 173.37 636.44 202.32 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n121 51 Cyclist -1 -1 -1 408.35 159.82 512.91 336.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n121 43 Pedestrian -1 -1 -1 556.58 163.52 585.58 238.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n121 50 Car -1 -1 -1 553.21 173.23 581.83 193.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n121 39 Pedestrian -1 -1 -1 394.41 162.77 428.58 242.18 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n121 48 Pedestrian -1 -1 -1 713.56 171.45 734.18 228.43 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n121 11 Pedestrian -1 -1 -1 192.01 161.00 208.33 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n121 7 Pedestrian -1 -1 -1 333.65 159.78 346.00 191.43 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n121 55 Pedestrian -1 -1 -1 364.33 161.05 380.76 205.65 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n121 54 Pedestrian -1 -1 -1 347.22 162.30 358.64 193.23 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n122 1 Car -1 -1 -1 1095.47 185.58 1220.58 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n122 2 Car -1 -1 -1 954.57 183.96 1067.50 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n122 3 Car -1 -1 -1 1029.84 184.03 1155.82 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n122 6 Pedestrian -1 -1 -1 339.81 167.12 374.03 246.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n122 8 Car -1 -1 -1 601.92 173.39 636.37 202.40 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n122 43 Pedestrian -1 -1 -1 553.34 163.15 582.04 238.80 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n122 46 Pedestrian -1 -1 -1 376.43 161.72 408.15 242.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n122 39 Pedestrian -1 -1 -1 390.46 163.75 424.94 242.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n122 50 Car -1 -1 -1 553.63 173.03 582.24 194.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n122 51 Cyclist -1 -1 -1 420.17 156.41 517.78 325.73 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n122 55 Pedestrian -1 -1 -1 364.36 161.34 380.35 205.85 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n122 11 Pedestrian -1 -1 -1 192.14 161.01 208.29 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n122 48 Pedestrian -1 -1 -1 712.02 171.55 731.84 228.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n122 7 Pedestrian -1 -1 -1 333.90 160.05 345.84 191.32 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n122 54 Pedestrian -1 -1 -1 347.18 161.80 360.23 194.15 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n122 56 Car -1 -1 -1 599.56 173.82 621.80 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n123 1 Car -1 -1 -1 1095.49 185.56 1220.52 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n123 2 Car -1 -1 -1 954.52 183.99 1067.42 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n123 3 Car -1 -1 -1 1029.72 184.02 1156.07 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n123 6 Pedestrian -1 -1 -1 337.42 165.74 369.65 244.75 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n123 8 Car -1 -1 -1 601.66 173.30 636.51 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n123 39 Pedestrian -1 -1 -1 387.33 163.28 420.44 240.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n123 43 Pedestrian -1 -1 -1 550.42 162.60 576.54 236.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n123 46 Pedestrian -1 -1 -1 373.76 159.90 403.59 243.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n123 50 Car -1 -1 -1 554.32 172.99 582.07 194.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n123 51 Cyclist -1 -1 -1 437.02 158.70 523.55 308.70 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n123 48 Pedestrian -1 -1 -1 711.95 171.64 731.71 227.92 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n123 11 Pedestrian -1 -1 -1 192.06 160.88 208.24 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n123 7 Pedestrian -1 -1 -1 335.24 160.36 347.40 191.45 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n123 55 Pedestrian -1 -1 -1 365.27 161.72 380.44 205.53 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n123 54 Pedestrian -1 -1 -1 346.35 160.13 361.02 196.12 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n124 1 Car -1 -1 -1 1095.67 185.58 1220.42 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n124 2 Car -1 -1 -1 954.53 184.00 1067.32 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n124 3 Car -1 -1 -1 1029.79 184.02 1155.98 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n124 51 Cyclist -1 -1 -1 446.05 157.54 529.12 309.61 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n124 8 Car -1 -1 -1 601.48 173.42 636.64 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n124 39 Pedestrian -1 -1 -1 385.95 162.84 414.48 240.80 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n124 6 Pedestrian -1 -1 -1 335.60 164.86 364.08 242.46 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n124 46 Pedestrian -1 -1 -1 370.76 160.53 397.89 242.50 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n124 48 Pedestrian -1 -1 -1 710.93 171.26 730.79 227.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n124 43 Pedestrian -1 -1 -1 544.40 162.29 570.90 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n124 50 Car -1 -1 -1 555.35 173.47 581.98 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n124 11 Pedestrian -1 -1 -1 191.85 160.88 208.35 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n124 55 Pedestrian -1 -1 -1 365.11 161.30 380.88 205.92 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n124 7 Pedestrian -1 -1 -1 335.42 160.19 348.71 192.20 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n124 54 Pedestrian -1 -1 -1 346.23 159.08 362.21 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n124 57 Car -1 -1 -1 599.16 173.77 621.65 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n125 1 Car -1 -1 -1 1095.52 185.56 1220.35 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n125 2 Car -1 -1 -1 954.51 183.98 1067.29 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n125 3 Car -1 -1 -1 1029.63 183.96 1155.97 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n125 43 Pedestrian -1 -1 -1 542.27 163.30 571.17 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n125 51 Cyclist -1 -1 -1 453.31 161.91 536.58 303.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n125 6 Pedestrian -1 -1 -1 329.03 164.57 362.06 242.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n125 8 Car -1 -1 -1 601.77 173.49 636.63 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n125 39 Pedestrian -1 -1 -1 379.42 162.22 412.81 241.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n125 46 Pedestrian -1 -1 -1 364.64 160.74 397.16 242.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n125 48 Pedestrian -1 -1 -1 710.76 170.82 729.98 226.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n125 50 Car -1 -1 -1 556.20 173.32 582.39 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n125 11 Pedestrian -1 -1 -1 191.50 160.83 208.67 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n125 55 Pedestrian -1 -1 -1 364.99 161.59 380.98 205.48 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n125 7 Pedestrian -1 -1 -1 335.15 159.72 349.62 192.57 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n125 57 Car -1 -1 -1 599.11 173.83 621.92 193.88 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n125 54 Pedestrian -1 -1 -1 346.17 159.25 362.46 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n126 1 Car -1 -1 -1 1095.46 185.61 1220.54 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n126 2 Car -1 -1 -1 954.54 183.98 1067.51 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n126 3 Car -1 -1 -1 1029.93 184.04 1155.86 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n126 8 Car -1 -1 -1 601.78 173.54 636.56 202.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n126 51 Cyclist -1 -1 -1 465.25 162.44 534.83 297.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n126 46 Pedestrian -1 -1 -1 360.57 162.21 393.29 241.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n126 6 Pedestrian -1 -1 -1 324.23 163.85 358.51 243.17 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n126 43 Pedestrian -1 -1 -1 539.07 164.30 568.10 235.50 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n126 39 Pedestrian -1 -1 -1 379.24 163.91 411.71 240.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n126 48 Pedestrian -1 -1 -1 710.78 170.70 729.21 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n126 50 Car -1 -1 -1 558.62 173.48 582.22 191.92 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n126 11 Pedestrian -1 -1 -1 191.51 160.82 208.71 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n126 57 Car -1 -1 -1 598.85 173.87 622.19 193.82 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n126 55 Pedestrian -1 -1 -1 364.92 161.49 381.40 205.57 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n126 7 Pedestrian -1 -1 -1 334.57 159.24 350.05 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n127 1 Car -1 -1 -1 1095.66 185.64 1220.28 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n127 2 Car -1 -1 -1 954.47 184.01 1067.44 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n127 3 Car -1 -1 -1 1029.83 183.97 1155.89 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n127 8 Car -1 -1 -1 601.70 173.59 636.69 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n127 51 Cyclist -1 -1 -1 473.34 160.73 540.40 291.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n127 6 Pedestrian -1 -1 -1 320.81 162.54 354.62 242.45 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n127 46 Pedestrian -1 -1 -1 357.34 162.28 388.32 240.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n127 43 Pedestrian -1 -1 -1 535.59 164.52 564.15 234.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n127 39 Pedestrian -1 -1 -1 375.00 164.13 408.14 240.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n127 50 Car -1 -1 -1 559.24 173.30 582.09 191.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n127 48 Pedestrian -1 -1 -1 708.74 171.16 728.13 224.67 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n127 11 Pedestrian -1 -1 -1 191.52 160.86 208.62 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n127 57 Car -1 -1 -1 598.86 173.90 622.23 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n127 55 Pedestrian -1 -1 -1 368.17 162.44 385.45 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n128 1 Car -1 -1 -1 1095.53 185.60 1220.46 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n128 2 Car -1 -1 -1 954.51 183.97 1067.38 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n128 3 Car -1 -1 -1 1029.93 184.00 1155.86 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n128 8 Car -1 -1 -1 601.77 173.62 636.60 202.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n128 51 Cyclist -1 -1 -1 478.26 162.01 541.56 288.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n128 46 Pedestrian -1 -1 -1 355.53 160.33 382.17 238.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n128 48 Pedestrian -1 -1 -1 707.93 171.20 727.78 224.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n128 6 Pedestrian -1 -1 -1 318.47 162.25 349.99 241.29 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n128 50 Car -1 -1 -1 559.53 172.94 582.28 191.86 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n128 39 Pedestrian -1 -1 -1 372.82 162.83 403.42 239.35 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n128 43 Pedestrian -1 -1 -1 532.81 163.63 558.88 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n128 11 Pedestrian -1 -1 -1 191.45 160.82 208.65 198.54 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n128 57 Car -1 -1 -1 598.67 173.86 622.24 193.79 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n128 55 Pedestrian -1 -1 -1 367.68 162.61 384.96 204.80 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n128 58 Pedestrian -1 -1 -1 397.39 162.78 418.03 209.72 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n129 1 Car -1 -1 -1 1095.40 185.61 1220.53 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n129 2 Car -1 -1 -1 954.46 183.95 1067.47 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n129 3 Car -1 -1 -1 1029.88 183.95 1155.87 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n129 51 Cyclist -1 -1 -1 485.20 159.78 544.07 284.08 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n129 8 Car -1 -1 -1 601.64 173.60 636.77 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n129 46 Pedestrian -1 -1 -1 351.29 159.55 377.95 238.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n129 50 Car -1 -1 -1 560.14 173.00 582.61 191.52 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n129 48 Pedestrian -1 -1 -1 707.00 171.02 726.19 224.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n129 6 Pedestrian -1 -1 -1 317.01 162.29 346.62 240.34 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n129 39 Pedestrian -1 -1 -1 370.31 163.51 398.11 238.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n129 11 Pedestrian -1 -1 -1 191.28 160.91 208.76 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n129 43 Pedestrian -1 -1 -1 527.56 163.26 556.35 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n129 58 Pedestrian -1 -1 -1 397.31 161.79 418.52 210.44 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n129 57 Car -1 -1 -1 598.72 173.88 622.33 193.84 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n129 55 Pedestrian -1 -1 -1 367.44 162.43 384.73 204.94 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n130 1 Car -1 -1 -1 1095.41 185.57 1220.58 235.57 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n130 2 Car -1 -1 -1 954.50 183.96 1067.31 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n130 3 Car -1 -1 -1 1030.10 184.05 1155.73 232.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n130 6 Pedestrian -1 -1 -1 312.61 163.31 343.23 240.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n130 8 Car -1 -1 -1 601.71 173.58 636.67 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n130 39 Pedestrian -1 -1 -1 364.71 163.24 396.02 238.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n130 46 Pedestrian -1 -1 -1 345.07 160.22 376.61 238.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n130 50 Car -1 -1 -1 561.37 172.89 582.95 191.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n130 51 Cyclist -1 -1 -1 488.28 162.71 549.82 279.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n130 48 Pedestrian -1 -1 -1 704.54 170.31 724.51 223.57 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n130 11 Pedestrian -1 -1 -1 191.49 160.92 208.60 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n130 43 Pedestrian -1 -1 -1 527.17 163.74 555.15 232.48 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n130 58 Pedestrian -1 -1 -1 402.13 163.12 420.58 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n130 55 Pedestrian -1 -1 -1 382.81 161.23 401.69 212.88 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n130 57 Car -1 -1 -1 598.59 173.82 622.55 193.91 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n131 1 Car -1 -1 -1 1095.59 185.61 1220.30 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n131 2 Car -1 -1 -1 954.57 183.95 1067.19 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n131 3 Car -1 -1 -1 1029.76 183.98 1155.93 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n131 46 Pedestrian -1 -1 -1 340.64 160.00 374.41 237.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n131 8 Car -1 -1 -1 601.56 173.58 636.82 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n131 39 Pedestrian -1 -1 -1 361.23 163.00 392.82 238.98 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n131 6 Pedestrian -1 -1 -1 307.96 164.77 340.25 240.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n131 51 Cyclist -1 -1 -1 495.92 163.69 554.20 273.74 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n131 48 Pedestrian -1 -1 -1 704.33 169.53 723.63 222.44 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n131 50 Car -1 -1 -1 562.22 172.53 583.64 190.95 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n131 11 Pedestrian -1 -1 -1 191.59 161.02 208.42 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n131 55 Pedestrian -1 -1 -1 383.39 160.85 401.82 213.93 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n131 58 Pedestrian -1 -1 -1 402.53 162.91 420.86 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n131 43 Pedestrian -1 -1 -1 522.62 163.53 553.05 232.42 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n131 57 Car -1 -1 -1 598.55 173.82 622.64 194.03 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n132 1 Car -1 -1 -1 1095.45 185.57 1220.40 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n132 2 Car -1 -1 -1 954.52 183.94 1067.24 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n132 3 Car -1 -1 -1 1029.79 183.98 1155.96 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n132 46 Pedestrian -1 -1 -1 337.29 160.61 371.32 237.89 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n132 6 Pedestrian -1 -1 -1 306.65 165.13 338.31 240.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n132 8 Car -1 -1 -1 601.65 173.62 636.87 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n132 39 Pedestrian -1 -1 -1 358.31 163.00 389.00 238.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n132 51 Cyclist -1 -1 -1 501.06 165.44 553.37 270.06 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n132 48 Pedestrian -1 -1 -1 703.99 169.68 723.67 222.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n132 55 Pedestrian -1 -1 -1 386.52 161.40 404.43 212.64 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n132 11 Pedestrian -1 -1 -1 191.74 161.07 208.37 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n132 50 Car -1 -1 -1 562.80 172.78 583.39 190.39 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n132 43 Pedestrian -1 -1 -1 519.33 163.84 548.79 231.65 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n132 57 Car -1 -1 -1 598.74 173.70 622.81 194.01 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n132 58 Pedestrian -1 -1 -1 405.15 165.09 424.49 211.13 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n133 1 Car -1 -1 -1 1095.43 185.53 1220.51 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n133 2 Car -1 -1 -1 954.58 183.98 1067.13 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n133 3 Car -1 -1 -1 1029.88 184.04 1155.92 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n133 6 Pedestrian -1 -1 -1 305.47 164.69 333.83 238.44 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n133 46 Pedestrian -1 -1 -1 335.01 159.99 366.60 237.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n133 51 Cyclist -1 -1 -1 505.77 163.07 555.63 266.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n133 8 Car -1 -1 -1 601.78 173.71 636.82 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n133 48 Pedestrian -1 -1 -1 703.70 170.67 723.18 223.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n133 39 Pedestrian -1 -1 -1 355.56 163.07 383.96 236.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n133 55 Pedestrian -1 -1 -1 386.36 161.45 404.71 212.68 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n133 58 Pedestrian -1 -1 -1 406.90 167.25 425.10 212.54 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n133 11 Pedestrian -1 -1 -1 191.75 161.13 208.31 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n133 43 Pedestrian -1 -1 -1 519.61 163.89 547.73 231.44 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n133 50 Car -1 -1 -1 563.53 172.15 582.94 189.66 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n133 57 Car -1 -1 -1 598.97 173.78 622.61 193.89 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n134 1 Car -1 -1 -1 1095.35 185.50 1220.37 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n134 2 Car -1 -1 -1 954.44 184.02 1067.35 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n134 3 Car -1 -1 -1 1030.28 184.10 1155.60 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n134 46 Pedestrian -1 -1 -1 334.02 159.17 360.15 236.85 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n134 8 Car -1 -1 -1 601.84 173.67 636.73 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n134 39 Pedestrian -1 -1 -1 356.25 163.30 380.71 235.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n134 51 Cyclist -1 -1 -1 511.81 164.61 555.29 262.32 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n134 48 Pedestrian -1 -1 -1 703.42 170.67 722.18 223.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n134 6 Pedestrian -1 -1 -1 303.51 163.37 329.20 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n134 55 Pedestrian -1 -1 -1 386.29 161.25 406.38 213.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n134 58 Pedestrian -1 -1 -1 408.79 167.55 427.48 212.81 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n134 11 Pedestrian -1 -1 -1 191.67 160.91 208.41 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n134 43 Pedestrian -1 -1 -1 522.05 167.56 544.80 222.42 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n134 50 Car -1 -1 -1 563.85 172.21 583.16 189.34 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n134 57 Car -1 -1 -1 598.75 173.70 622.61 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n134 59 Pedestrian -1 -1 -1 178.62 158.05 200.36 199.82 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n135 1 Car -1 -1 -1 1095.42 185.49 1220.38 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n135 2 Car -1 -1 -1 954.50 184.03 1067.21 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n135 3 Car -1 -1 -1 1030.00 184.07 1155.74 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n135 39 Pedestrian -1 -1 -1 352.00 162.75 379.10 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n135 51 Cyclist -1 -1 -1 515.00 164.25 558.58 256.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n135 8 Car -1 -1 -1 601.90 173.57 636.60 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n135 46 Pedestrian -1 -1 -1 332.89 159.24 357.89 236.69 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n135 6 Pedestrian -1 -1 -1 299.66 162.47 329.18 236.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n135 58 Pedestrian -1 -1 -1 409.35 167.48 428.84 213.13 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n135 48 Pedestrian -1 -1 -1 702.68 169.60 721.75 222.29 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n135 50 Car -1 -1 -1 564.52 172.69 583.80 188.59 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n135 55 Pedestrian -1 -1 -1 386.67 161.93 406.29 213.76 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n135 11 Pedestrian -1 -1 -1 191.69 160.87 208.61 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n135 57 Car -1 -1 -1 598.86 173.65 622.29 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n135 59 Pedestrian -1 -1 -1 178.54 157.58 200.80 200.16 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n136 1 Car -1 -1 -1 1095.60 185.52 1220.15 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n136 2 Car -1 -1 -1 954.48 183.99 1067.13 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n136 3 Car -1 -1 -1 1029.94 184.10 1155.83 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n136 46 Pedestrian -1 -1 -1 328.82 159.76 355.52 236.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n136 39 Pedestrian -1 -1 -1 347.67 163.11 375.81 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n136 51 Cyclist -1 -1 -1 518.10 164.77 558.70 254.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n136 8 Car -1 -1 -1 602.07 173.48 636.52 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n136 6 Pedestrian -1 -1 -1 296.18 163.01 326.92 236.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n136 50 Car -1 -1 -1 565.03 172.46 584.02 188.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n136 48 Pedestrian -1 -1 -1 701.77 169.76 719.77 221.51 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n136 58 Pedestrian -1 -1 -1 410.48 167.62 429.03 213.60 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n136 55 Pedestrian -1 -1 -1 386.48 162.47 406.65 212.97 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n136 11 Pedestrian -1 -1 -1 191.88 160.90 208.60 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n136 57 Car -1 -1 -1 598.97 173.58 622.38 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n136 59 Pedestrian -1 -1 -1 178.74 157.56 200.79 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n136 60 Pedestrian -1 -1 -1 363.99 160.53 383.23 212.00 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n137 1 Car -1 -1 -1 1095.55 185.52 1220.18 235.53 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n137 2 Car -1 -1 -1 954.50 184.03 1067.13 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n137 3 Car -1 -1 -1 1030.00 184.03 1155.65 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n137 39 Pedestrian -1 -1 -1 347.05 163.29 374.88 234.76 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n137 46 Pedestrian -1 -1 -1 324.70 160.59 353.54 235.36 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n137 8 Car -1 -1 -1 602.04 173.41 636.49 202.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n137 48 Pedestrian -1 -1 -1 701.15 169.96 719.15 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n137 50 Car -1 -1 -1 565.87 172.42 584.16 188.10 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n137 58 Pedestrian -1 -1 -1 413.46 168.03 430.65 213.87 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n137 55 Pedestrian -1 -1 -1 389.30 162.05 409.68 213.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n137 6 Pedestrian -1 -1 -1 295.83 163.44 325.54 236.34 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n137 51 Cyclist -1 -1 -1 519.97 166.51 561.31 252.32 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n137 60 Pedestrian -1 -1 -1 364.56 160.19 382.98 212.21 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n137 11 Pedestrian -1 -1 -1 191.71 160.82 208.93 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n137 57 Car -1 -1 -1 599.12 173.60 622.20 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n137 59 Pedestrian -1 -1 -1 178.68 157.67 201.00 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n137 61 Pedestrian -1 -1 -1 402.29 162.14 419.92 210.81 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n138 1 Car -1 -1 -1 1095.34 185.51 1220.46 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n138 2 Car -1 -1 -1 954.49 183.97 1067.07 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n138 3 Car -1 -1 -1 1029.77 183.99 1155.88 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n138 39 Pedestrian -1 -1 -1 344.74 163.35 371.17 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n138 8 Car -1 -1 -1 601.97 173.34 636.49 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n138 6 Pedestrian -1 -1 -1 293.95 163.99 323.10 235.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n138 48 Pedestrian -1 -1 -1 700.79 170.32 718.71 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n138 50 Car -1 -1 -1 566.72 172.65 584.20 187.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n138 46 Pedestrian -1 -1 -1 324.87 160.88 350.41 234.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n138 55 Pedestrian -1 -1 -1 390.02 161.90 409.60 213.09 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n138 58 Pedestrian -1 -1 -1 414.87 167.85 431.51 213.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n138 51 Cyclist -1 -1 -1 521.57 169.42 560.22 249.92 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n138 60 Pedestrian -1 -1 -1 367.87 161.15 385.36 211.60 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n138 11 Pedestrian -1 -1 -1 187.04 159.52 207.84 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n138 57 Car -1 -1 -1 598.86 173.62 621.94 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n138 59 Pedestrian -1 -1 -1 178.74 157.42 200.76 200.07 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n138 61 Pedestrian -1 -1 -1 402.66 163.84 420.33 210.95 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n138 62 Cyclist -1 -1 -1 536.98 167.49 561.62 228.60 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n139 1 Car -1 -1 -1 1095.16 185.51 1220.69 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n139 2 Car -1 -1 -1 954.52 183.96 1067.13 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n139 3 Car -1 -1 -1 1029.88 184.01 1155.73 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n139 6 Pedestrian -1 -1 -1 294.52 163.20 320.30 234.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n139 8 Car -1 -1 -1 601.62 173.27 636.92 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n139 50 Car -1 -1 -1 567.44 172.53 584.34 187.37 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n139 39 Pedestrian -1 -1 -1 341.40 162.54 368.14 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n139 48 Pedestrian -1 -1 -1 699.88 170.49 717.76 221.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n139 46 Pedestrian -1 -1 -1 321.36 160.22 347.46 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n139 55 Pedestrian -1 -1 -1 390.70 161.94 410.03 213.44 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n139 58 Pedestrian -1 -1 -1 414.05 168.17 432.18 213.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n139 60 Pedestrian -1 -1 -1 368.25 160.85 386.48 212.07 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n139 57 Car -1 -1 -1 598.64 173.56 622.11 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n139 11 Pedestrian -1 -1 -1 186.65 159.29 208.15 199.80 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n139 62 Cyclist -1 -1 -1 540.13 167.35 564.15 227.73 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n139 59 Pedestrian -1 -1 -1 178.16 157.22 201.21 200.14 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n139 61 Pedestrian -1 -1 -1 402.97 163.55 420.70 211.54 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n139 51 Cyclist -1 -1 -1 523.15 166.06 561.21 247.51 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n139 63 Pedestrian -1 -1 -1 515.19 164.21 536.60 227.10 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n140 1 Car -1 -1 -1 1095.23 185.49 1220.61 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n140 2 Car -1 -1 -1 954.46 183.99 1067.05 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n140 3 Car -1 -1 -1 1029.77 183.99 1155.75 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n140 6 Pedestrian -1 -1 -1 291.53 163.39 317.93 234.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n140 8 Car -1 -1 -1 601.60 173.29 636.91 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n140 46 Pedestrian -1 -1 -1 318.60 159.22 344.17 232.24 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n140 39 Pedestrian -1 -1 -1 339.93 162.78 366.44 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n140 50 Car -1 -1 -1 567.91 172.43 584.54 187.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n140 48 Pedestrian -1 -1 -1 697.52 170.27 715.77 220.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n140 55 Pedestrian -1 -1 -1 393.32 162.44 412.69 213.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n140 63 Pedestrian -1 -1 -1 515.36 166.10 535.76 228.53 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n140 58 Pedestrian -1 -1 -1 414.34 168.15 432.77 213.25 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n140 51 Cyclist -1 -1 -1 530.06 167.00 566.26 245.98 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n140 62 Cyclist -1 -1 -1 540.18 167.39 566.23 228.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n140 60 Pedestrian -1 -1 -1 370.50 160.39 390.02 213.18 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n140 57 Car -1 -1 -1 598.47 173.69 622.01 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n140 61 Pedestrian -1 -1 -1 406.81 163.37 423.41 211.39 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n140 11 Pedestrian -1 -1 -1 186.62 159.20 208.13 199.89 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n140 59 Pedestrian -1 -1 -1 178.24 157.20 201.25 200.14 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n141 1 Car -1 -1 -1 1095.19 185.57 1220.67 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n141 2 Car -1 -1 -1 954.50 184.04 1067.19 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n141 3 Car -1 -1 -1 1029.66 184.03 1155.87 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n141 39 Pedestrian -1 -1 -1 337.15 162.84 363.84 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n141 48 Pedestrian -1 -1 -1 695.31 169.82 715.28 219.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n141 8 Car -1 -1 -1 601.59 173.33 637.04 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n141 6 Pedestrian -1 -1 -1 289.45 164.36 316.51 234.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n141 50 Car -1 -1 -1 567.90 172.15 584.67 187.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n141 46 Pedestrian -1 -1 -1 314.15 159.76 342.04 232.39 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n141 55 Pedestrian -1 -1 -1 393.86 162.79 413.61 213.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n141 58 Pedestrian -1 -1 -1 415.59 168.28 435.52 213.19 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n141 63 Pedestrian -1 -1 -1 514.10 165.86 535.83 228.61 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n141 51 Cyclist -1 -1 -1 531.94 167.07 571.48 243.95 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n141 62 Cyclist -1 -1 -1 539.62 166.42 567.86 230.51 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n141 57 Car -1 -1 -1 598.50 173.50 622.32 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n141 60 Pedestrian -1 -1 -1 370.16 160.15 390.63 213.73 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n141 11 Pedestrian -1 -1 -1 186.71 159.11 208.15 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n141 59 Pedestrian -1 -1 -1 178.29 157.56 200.88 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n142 1 Car -1 -1 -1 1095.08 185.50 1220.73 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n142 2 Car -1 -1 -1 954.55 184.02 1067.10 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n142 3 Car -1 -1 -1 1029.77 184.01 1155.79 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n142 6 Pedestrian -1 -1 -1 287.75 165.15 313.79 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n142 39 Pedestrian -1 -1 -1 336.43 163.40 361.55 232.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n142 8 Car -1 -1 -1 601.45 173.26 637.14 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n142 46 Pedestrian -1 -1 -1 312.99 161.26 340.92 232.56 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n142 50 Car -1 -1 -1 567.72 171.98 584.69 186.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n142 51 Cyclist -1 -1 -1 537.42 166.13 573.49 239.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n142 48 Pedestrian -1 -1 -1 694.60 169.74 715.10 219.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n142 55 Pedestrian -1 -1 -1 393.38 162.47 413.89 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n142 58 Pedestrian -1 -1 -1 415.77 167.91 436.13 212.84 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n142 63 Pedestrian -1 -1 -1 511.37 165.20 533.89 228.77 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n142 57 Car -1 -1 -1 598.60 173.52 622.23 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n142 60 Pedestrian -1 -1 -1 369.92 160.06 390.58 213.32 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n142 11 Pedestrian -1 -1 -1 187.01 159.36 208.02 200.00 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n142 59 Pedestrian -1 -1 -1 178.48 157.97 200.43 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n143 1 Car -1 -1 -1 1094.99 185.51 1220.89 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n143 2 Car -1 -1 -1 954.53 184.04 1066.91 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n143 3 Car -1 -1 -1 1029.65 184.01 1155.90 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n143 51 Cyclist -1 -1 -1 538.26 166.08 575.78 239.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n143 6 Pedestrian -1 -1 -1 286.43 164.45 311.57 232.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n143 8 Car -1 -1 -1 601.76 173.20 636.75 202.38 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n143 39 Pedestrian -1 -1 -1 334.05 162.31 359.39 231.77 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n143 46 Pedestrian -1 -1 -1 309.21 160.60 338.16 231.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n143 48 Pedestrian -1 -1 -1 692.82 170.05 712.87 219.89 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n143 58 Pedestrian -1 -1 -1 416.75 167.88 437.40 214.18 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n143 63 Pedestrian -1 -1 -1 509.54 164.50 532.69 226.68 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n143 55 Pedestrian -1 -1 -1 393.95 162.06 414.13 213.51 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n143 50 Car -1 -1 -1 567.67 171.47 585.18 186.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n143 60 Pedestrian -1 -1 -1 369.74 159.39 392.51 214.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n143 57 Car -1 -1 -1 598.73 173.61 622.00 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n143 11 Pedestrian -1 -1 -1 186.73 159.21 208.18 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n143 59 Pedestrian -1 -1 -1 178.14 157.70 200.75 200.15 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n144 1 Car -1 -1 -1 1095.25 185.58 1220.54 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n144 2 Car -1 -1 -1 954.52 184.07 1067.07 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n144 3 Car -1 -1 -1 1029.69 184.05 1155.93 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n144 8 Car -1 -1 -1 601.58 173.13 636.81 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n144 6 Pedestrian -1 -1 -1 284.73 162.88 309.40 231.67 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n144 63 Pedestrian -1 -1 -1 507.13 164.41 530.78 226.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n144 48 Pedestrian -1 -1 -1 693.05 170.10 711.83 219.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n144 46 Pedestrian -1 -1 -1 308.67 160.28 336.18 231.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n144 39 Pedestrian -1 -1 -1 333.38 161.87 359.05 230.37 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n144 58 Pedestrian -1 -1 -1 420.21 167.64 439.26 215.11 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n144 51 Cyclist -1 -1 -1 540.77 166.46 574.26 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n144 55 Pedestrian -1 -1 -1 396.84 162.12 417.02 213.54 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n144 60 Pedestrian -1 -1 -1 370.52 159.23 392.71 215.32 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n144 57 Car -1 -1 -1 598.67 173.60 621.94 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n144 50 Car -1 -1 -1 567.84 171.10 585.51 186.03 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n144 11 Pedestrian -1 -1 -1 190.73 159.75 209.76 199.84 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n145 1 Car -1 -1 -1 1095.24 185.55 1220.49 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n145 2 Car -1 -1 -1 954.59 184.00 1067.02 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n145 3 Car -1 -1 -1 1029.77 184.06 1155.89 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n145 46 Pedestrian -1 -1 -1 307.13 159.62 331.82 230.28 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n145 8 Car -1 -1 -1 601.37 173.07 636.86 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n145 51 Cyclist -1 -1 -1 545.55 167.36 576.22 231.28 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n145 6 Pedestrian -1 -1 -1 283.81 163.53 308.21 231.42 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n145 48 Pedestrian -1 -1 -1 692.45 169.69 710.84 219.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n145 63 Pedestrian -1 -1 -1 505.70 165.54 529.97 226.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n145 58 Pedestrian -1 -1 -1 421.42 167.89 440.42 215.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n145 39 Pedestrian -1 -1 -1 332.64 162.43 357.91 229.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n145 55 Pedestrian -1 -1 -1 393.84 162.20 414.29 212.92 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n145 57 Car -1 -1 -1 598.55 173.66 622.00 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n145 60 Pedestrian -1 -1 -1 371.96 159.16 396.06 216.27 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n145 11 Pedestrian -1 -1 -1 190.85 159.61 209.49 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n145 50 Car -1 -1 -1 567.62 171.33 585.16 185.85 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n146 1 Car -1 -1 -1 1095.03 185.43 1220.55 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n146 2 Car -1 -1 -1 954.55 184.06 1067.07 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n146 3 Car -1 -1 -1 1030.00 184.09 1155.78 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n146 8 Car -1 -1 -1 601.54 173.13 636.65 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n146 46 Pedestrian -1 -1 -1 303.16 159.28 329.31 230.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n146 6 Pedestrian -1 -1 -1 282.83 164.13 307.50 231.13 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n146 63 Pedestrian -1 -1 -1 503.74 166.11 526.94 225.61 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n146 48 Pedestrian -1 -1 -1 691.80 169.66 710.23 218.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n146 51 Cyclist -1 -1 -1 546.62 167.39 576.27 230.78 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n146 39 Pedestrian -1 -1 -1 331.93 162.72 358.34 229.62 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n146 58 Pedestrian -1 -1 -1 421.28 168.35 440.77 215.25 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n146 60 Pedestrian -1 -1 -1 372.74 159.71 396.23 221.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n146 55 Pedestrian -1 -1 -1 399.78 162.35 423.07 216.83 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n146 57 Car -1 -1 -1 598.63 173.66 621.93 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n146 11 Pedestrian -1 -1 -1 187.18 158.71 207.96 200.34 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n147 1 Car -1 -1 -1 1095.16 185.51 1220.47 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n147 2 Car -1 -1 -1 954.56 184.07 1067.05 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n147 3 Car -1 -1 -1 1029.97 184.12 1155.84 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n147 8 Car -1 -1 -1 601.48 173.09 636.56 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n147 63 Pedestrian -1 -1 -1 502.78 165.60 525.70 224.65 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n147 39 Pedestrian -1 -1 -1 329.12 162.82 356.95 229.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n147 46 Pedestrian -1 -1 -1 302.81 159.46 329.05 229.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n147 48 Pedestrian -1 -1 -1 689.95 169.46 708.04 218.75 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n147 6 Pedestrian -1 -1 -1 280.81 164.07 305.05 230.36 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n147 58 Pedestrian -1 -1 -1 420.98 168.49 440.89 215.16 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n147 51 Cyclist -1 -1 -1 552.32 168.90 575.98 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n147 55 Pedestrian -1 -1 -1 404.74 162.74 424.96 217.45 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n147 60 Pedestrian -1 -1 -1 374.03 159.31 396.76 221.58 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n147 57 Car -1 -1 -1 598.59 173.63 621.70 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n147 11 Pedestrian -1 -1 -1 187.06 158.62 207.90 200.20 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n148 1 Car -1 -1 -1 1095.16 185.50 1220.58 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n148 2 Car -1 -1 -1 954.50 184.03 1067.09 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n148 3 Car -1 -1 -1 1030.11 184.12 1155.82 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n148 39 Pedestrian -1 -1 -1 329.64 162.15 355.37 227.69 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n148 8 Car -1 -1 -1 601.40 173.02 636.73 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n148 63 Pedestrian -1 -1 -1 500.09 164.50 522.68 224.12 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n148 48 Pedestrian -1 -1 -1 689.01 169.95 707.50 218.85 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n148 51 Cyclist -1 -1 -1 549.46 167.86 578.67 229.63 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n148 46 Pedestrian -1 -1 -1 302.44 160.27 329.53 229.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n148 6 Pedestrian -1 -1 -1 280.98 163.68 304.97 230.18 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n148 58 Pedestrian -1 -1 -1 424.11 168.37 443.67 216.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n148 55 Pedestrian -1 -1 -1 406.00 163.33 425.11 217.53 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n148 60 Pedestrian -1 -1 -1 377.32 159.43 398.84 221.23 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n148 11 Pedestrian -1 -1 -1 187.64 159.02 207.69 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n148 57 Car -1 -1 -1 598.64 173.63 621.72 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n148 64 Pedestrian -1 -1 -1 394.51 162.00 413.30 212.75 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n149 1 Car -1 -1 -1 1095.13 185.43 1220.58 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n149 2 Car -1 -1 -1 954.53 184.02 1067.01 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n149 3 Car -1 -1 -1 1030.11 184.09 1155.71 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n149 39 Pedestrian -1 -1 -1 329.94 161.57 354.05 227.71 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n149 63 Pedestrian -1 -1 -1 498.97 164.98 521.70 223.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n149 51 Cyclist -1 -1 -1 549.11 167.63 579.68 228.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n149 8 Car -1 -1 -1 601.62 173.22 636.61 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n149 48 Pedestrian -1 -1 -1 688.27 170.35 706.68 218.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n149 46 Pedestrian -1 -1 -1 302.61 160.14 328.84 229.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n149 6 Pedestrian -1 -1 -1 280.69 163.14 304.97 228.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n149 58 Pedestrian -1 -1 -1 425.17 168.37 443.25 215.50 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n149 64 Pedestrian -1 -1 -1 395.52 162.17 412.63 212.64 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n149 60 Pedestrian -1 -1 -1 377.84 159.87 399.83 221.33 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n149 55 Pedestrian -1 -1 -1 408.36 163.12 428.05 217.53 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n149 11 Pedestrian -1 -1 -1 190.97 160.16 208.88 199.14 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n149 57 Car -1 -1 -1 598.79 173.72 621.63 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n150 1 Car -1 -1 -1 1095.07 185.42 1220.70 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n150 2 Car -1 -1 -1 954.55 184.00 1067.01 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n150 3 Car -1 -1 -1 1029.72 184.06 1155.80 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n150 39 Pedestrian -1 -1 -1 329.64 161.86 353.17 227.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n150 8 Car -1 -1 -1 601.66 173.06 636.64 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n150 63 Pedestrian -1 -1 -1 499.29 165.67 520.67 223.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n150 6 Pedestrian -1 -1 -1 280.00 162.86 303.41 228.46 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n150 46 Pedestrian -1 -1 -1 302.38 159.61 327.82 228.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n150 51 Cyclist -1 -1 -1 548.41 167.64 579.07 227.63 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n150 48 Pedestrian -1 -1 -1 686.04 169.83 704.46 217.95 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n150 55 Pedestrian -1 -1 -1 409.70 163.41 429.03 217.31 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n150 64 Pedestrian -1 -1 -1 395.82 162.45 412.89 212.63 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n150 60 Pedestrian -1 -1 -1 380.85 161.71 401.11 219.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n150 58 Pedestrian -1 -1 -1 425.66 168.53 443.91 215.47 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n150 11 Pedestrian -1 -1 -1 187.75 159.69 207.49 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n150 57 Car -1 -1 -1 598.85 173.63 621.83 193.82 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n150 65 Pedestrian -1 -1 -1 369.55 161.53 381.98 195.00 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n151 1 Car -1 -1 -1 1095.32 185.42 1220.39 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n151 2 Car -1 -1 -1 954.49 184.00 1067.11 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n151 3 Car -1 -1 -1 1029.84 184.09 1155.74 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n151 8 Car -1 -1 -1 601.60 173.03 636.67 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n151 39 Pedestrian -1 -1 -1 327.56 161.79 351.15 227.49 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n151 51 Cyclist -1 -1 -1 550.54 167.40 578.78 224.00 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n151 63 Pedestrian -1 -1 -1 496.48 165.50 518.51 223.93 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n151 48 Pedestrian -1 -1 -1 685.71 169.49 703.80 217.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n151 6 Pedestrian -1 -1 -1 277.85 163.60 301.96 228.16 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n151 46 Pedestrian -1 -1 -1 299.53 158.73 325.58 227.94 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n151 55 Pedestrian -1 -1 -1 410.31 163.75 429.00 217.59 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n151 58 Pedestrian -1 -1 -1 429.14 169.79 446.32 216.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n151 64 Pedestrian -1 -1 -1 397.60 162.70 414.84 212.83 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n151 65 Pedestrian -1 -1 -1 369.68 161.86 381.88 194.94 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n151 60 Pedestrian -1 -1 -1 381.41 161.83 402.13 218.92 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n151 11 Pedestrian -1 -1 -1 187.99 159.76 207.22 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n152 1 Car -1 -1 -1 1095.30 185.51 1220.56 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n152 2 Car -1 -1 -1 954.43 183.97 1067.09 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n152 3 Car -1 -1 -1 1029.75 184.06 1155.98 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n152 39 Pedestrian -1 -1 -1 327.37 161.79 350.97 226.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n152 8 Car -1 -1 -1 601.82 173.13 636.53 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n152 6 Pedestrian -1 -1 -1 277.70 164.19 301.06 227.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n152 63 Pedestrian -1 -1 -1 494.60 165.49 517.76 223.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n152 51 Cyclist -1 -1 -1 551.69 167.24 578.07 221.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n152 48 Pedestrian -1 -1 -1 685.05 169.64 703.30 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n152 58 Pedestrian -1 -1 -1 429.17 169.91 447.67 217.35 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n152 46 Pedestrian -1 -1 -1 299.53 158.81 325.42 227.43 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n152 64 Pedestrian -1 -1 -1 398.03 162.67 415.39 212.70 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n152 55 Pedestrian -1 -1 -1 410.54 164.22 429.07 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n152 65 Pedestrian -1 -1 -1 370.08 162.08 382.48 195.06 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n152 60 Pedestrian -1 -1 -1 383.06 162.12 401.91 213.92 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n152 11 Pedestrian -1 -1 -1 182.95 158.96 202.38 199.36 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n152 66 Car -1 -1 -1 598.88 173.55 621.54 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n153 1 Car -1 -1 -1 1095.27 185.44 1220.59 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n153 2 Car -1 -1 -1 954.46 184.00 1067.10 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n153 3 Car -1 -1 -1 1029.96 184.07 1155.76 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n153 6 Pedestrian -1 -1 -1 276.95 163.33 300.03 226.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n153 51 Cyclist -1 -1 -1 552.71 166.88 577.59 222.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n153 8 Car -1 -1 -1 601.97 173.26 636.40 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n153 63 Pedestrian -1 -1 -1 492.43 164.82 515.09 222.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n153 39 Pedestrian -1 -1 -1 326.86 160.98 351.30 225.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n153 48 Pedestrian -1 -1 -1 684.97 170.06 703.10 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n153 55 Pedestrian -1 -1 -1 412.34 164.27 431.85 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n153 46 Pedestrian -1 -1 -1 302.24 159.13 326.88 227.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n153 58 Pedestrian -1 -1 -1 428.65 170.53 448.95 217.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n153 64 Pedestrian -1 -1 -1 398.41 162.38 415.84 212.62 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n153 60 Pedestrian -1 -1 -1 382.00 161.52 403.29 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n153 11 Pedestrian -1 -1 -1 182.95 159.13 201.84 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n153 65 Pedestrian -1 -1 -1 369.66 162.06 382.75 195.41 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n153 66 Car -1 -1 -1 598.92 173.66 621.68 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n154 1 Car -1 -1 -1 1095.35 185.48 1220.53 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n154 2 Car -1 -1 -1 954.48 184.04 1066.96 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n154 3 Car -1 -1 -1 1029.95 184.06 1155.68 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n154 63 Pedestrian -1 -1 -1 491.03 165.60 514.14 222.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n154 8 Car -1 -1 -1 602.20 173.23 636.23 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n154 6 Pedestrian -1 -1 -1 276.25 162.14 299.28 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n154 48 Pedestrian -1 -1 -1 685.15 170.34 702.95 217.74 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n154 58 Pedestrian -1 -1 -1 430.50 170.93 451.74 218.09 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n154 55 Pedestrian -1 -1 -1 412.40 164.01 432.43 218.45 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n154 39 Pedestrian -1 -1 -1 326.86 161.05 351.49 225.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n154 46 Pedestrian -1 -1 -1 302.37 159.56 326.76 227.37 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n154 51 Cyclist -1 -1 -1 553.50 167.18 577.23 220.68 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n154 11 Pedestrian -1 -1 -1 183.25 159.31 201.79 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n154 60 Pedestrian -1 -1 -1 382.51 161.70 403.21 214.32 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n154 64 Pedestrian -1 -1 -1 398.88 162.59 416.63 212.53 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n154 65 Pedestrian -1 -1 -1 369.82 161.67 382.96 195.97 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n154 66 Car -1 -1 -1 599.21 173.69 621.33 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n155 1 Car -1 -1 -1 1095.33 185.49 1220.40 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n155 2 Car -1 -1 -1 954.50 183.98 1067.04 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n155 3 Car -1 -1 -1 1029.93 184.07 1155.66 233.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n155 58 Pedestrian -1 -1 -1 431.54 171.59 452.74 218.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n155 8 Car -1 -1 -1 601.93 173.23 636.51 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n155 6 Pedestrian -1 -1 -1 273.96 162.61 297.96 225.00 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n155 63 Pedestrian -1 -1 -1 490.52 166.55 513.20 222.39 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n155 48 Pedestrian -1 -1 -1 684.61 170.25 702.20 217.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n155 46 Pedestrian -1 -1 -1 299.71 159.97 325.18 226.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n155 55 Pedestrian -1 -1 -1 411.98 164.64 432.07 218.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n155 51 Cyclist -1 -1 -1 555.36 167.91 575.59 215.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n155 39 Pedestrian -1 -1 -1 327.04 160.74 351.59 224.04 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n155 60 Pedestrian -1 -1 -1 385.83 162.07 405.48 213.70 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n155 11 Pedestrian -1 -1 -1 183.96 159.40 200.74 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n155 65 Pedestrian -1 -1 -1 369.91 162.01 383.11 196.12 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n155 64 Pedestrian -1 -1 -1 399.10 162.78 416.56 212.03 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n155 66 Car -1 -1 -1 599.14 173.78 621.49 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n156 1 Car -1 -1 -1 1095.28 185.51 1220.57 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n156 2 Car -1 -1 -1 954.44 183.97 1067.09 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n156 3 Car -1 -1 -1 1030.09 184.09 1155.58 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n156 63 Pedestrian -1 -1 -1 487.89 166.65 510.78 222.08 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n156 8 Car -1 -1 -1 601.97 173.12 636.45 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n156 6 Pedestrian -1 -1 -1 274.08 163.28 297.51 224.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n156 46 Pedestrian -1 -1 -1 299.73 159.10 324.67 224.45 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n156 55 Pedestrian -1 -1 -1 411.86 165.02 432.31 218.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n156 39 Pedestrian -1 -1 -1 326.73 161.20 351.96 223.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n156 58 Pedestrian -1 -1 -1 433.98 170.55 454.59 219.16 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n156 51 Cyclist -1 -1 -1 556.21 167.53 577.06 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n156 48 Pedestrian -1 -1 -1 684.47 169.89 701.81 216.78 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n156 60 Pedestrian -1 -1 -1 387.37 162.69 406.02 212.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n156 65 Pedestrian -1 -1 -1 370.28 162.00 383.36 196.33 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n156 11 Pedestrian -1 -1 -1 183.70 159.17 200.91 199.00 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n156 64 Pedestrian -1 -1 -1 399.45 163.24 416.51 211.73 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n156 66 Car -1 -1 -1 599.33 173.78 621.47 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n157 1 Car -1 -1 -1 1095.20 185.53 1220.66 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n157 2 Car -1 -1 -1 954.45 184.01 1067.11 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n157 3 Car -1 -1 -1 1029.99 184.09 1155.68 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n157 58 Pedestrian -1 -1 -1 435.27 170.62 455.54 218.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n157 8 Car -1 -1 -1 602.00 173.26 636.57 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n157 46 Pedestrian -1 -1 -1 300.14 157.99 324.03 224.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n157 63 Pedestrian -1 -1 -1 484.38 165.54 508.49 221.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n157 51 Cyclist -1 -1 -1 556.31 167.81 577.32 214.62 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n157 6 Pedestrian -1 -1 -1 274.81 163.16 297.28 224.36 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n157 39 Pedestrian -1 -1 -1 327.85 161.03 351.19 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n157 55 Pedestrian -1 -1 -1 411.93 165.48 432.03 218.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n157 48 Pedestrian -1 -1 -1 682.51 169.69 700.08 216.61 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n157 65 Pedestrian -1 -1 -1 370.09 161.95 383.39 196.33 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n157 60 Pedestrian -1 -1 -1 389.59 162.83 408.21 212.93 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n157 11 Pedestrian -1 -1 -1 183.56 159.08 201.79 199.06 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n157 64 Pedestrian -1 -1 -1 399.89 163.51 416.32 211.77 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n157 66 Car -1 -1 -1 599.30 173.74 621.60 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n158 1 Car -1 -1 -1 1095.22 185.51 1220.67 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n158 2 Car -1 -1 -1 954.45 184.03 1067.12 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n158 3 Car -1 -1 -1 1030.01 184.06 1155.70 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n158 8 Car -1 -1 -1 601.86 173.21 636.74 202.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n158 39 Pedestrian -1 -1 -1 330.79 160.24 352.36 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n158 63 Pedestrian -1 -1 -1 483.52 164.95 507.65 221.57 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n158 58 Pedestrian -1 -1 -1 436.62 169.66 456.24 218.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n158 46 Pedestrian -1 -1 -1 300.40 157.81 324.69 223.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n158 48 Pedestrian -1 -1 -1 682.28 169.99 699.78 216.67 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n158 55 Pedestrian -1 -1 -1 411.28 164.54 432.51 219.53 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n158 65 Pedestrian -1 -1 -1 369.73 161.41 383.29 196.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n158 60 Pedestrian -1 -1 -1 390.11 162.98 408.90 213.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n158 51 Cyclist -1 -1 -1 556.03 167.79 577.34 215.39 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n158 6 Pedestrian -1 -1 -1 276.79 162.19 298.22 223.77 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n158 11 Pedestrian -1 -1 -1 183.55 158.78 202.33 199.16 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n158 64 Pedestrian -1 -1 -1 403.23 163.37 419.50 210.82 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n158 66 Car -1 -1 -1 599.23 173.77 621.49 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n158 67 Pedestrian -1 -1 -1 428.87 165.49 446.67 215.50 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n159 1 Car -1 -1 -1 1095.21 185.53 1220.64 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n159 2 Car -1 -1 -1 954.54 184.05 1067.06 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n159 3 Car -1 -1 -1 1030.10 184.06 1155.61 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n159 8 Car -1 -1 -1 601.99 173.34 636.52 202.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n159 39 Pedestrian -1 -1 -1 330.89 160.36 352.55 222.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n159 6 Pedestrian -1 -1 -1 276.94 160.96 298.81 222.60 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n159 48 Pedestrian -1 -1 -1 681.93 170.36 699.44 216.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n159 63 Pedestrian -1 -1 -1 481.64 165.59 507.01 221.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n159 46 Pedestrian -1 -1 -1 302.21 158.75 326.64 223.51 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n159 55 Pedestrian -1 -1 -1 411.21 164.32 432.98 219.82 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n159 65 Pedestrian -1 -1 -1 369.70 160.70 383.46 196.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n159 58 Pedestrian -1 -1 -1 436.90 170.09 456.07 217.66 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n159 67 Pedestrian -1 -1 -1 429.35 164.43 447.86 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n159 60 Pedestrian -1 -1 -1 390.75 163.66 409.70 215.24 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n159 51 Cyclist -1 -1 -1 555.48 167.52 577.83 216.46 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n159 11 Pedestrian -1 -1 -1 188.02 159.50 206.17 198.92 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n159 66 Car -1 -1 -1 599.18 173.73 621.53 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n160 1 Car -1 -1 -1 1095.36 185.57 1220.52 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n160 2 Car -1 -1 -1 954.60 184.07 1067.10 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n160 3 Car -1 -1 -1 1030.12 184.11 1155.60 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n160 63 Pedestrian -1 -1 -1 479.92 166.01 503.31 220.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n160 8 Car -1 -1 -1 602.02 173.28 636.51 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n160 6 Pedestrian -1 -1 -1 277.19 161.24 298.98 222.61 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n160 65 Pedestrian -1 -1 -1 369.42 161.67 383.81 196.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n160 55 Pedestrian -1 -1 -1 411.08 163.97 433.46 219.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n160 46 Pedestrian -1 -1 -1 300.37 159.37 324.60 223.12 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n160 39 Pedestrian -1 -1 -1 327.53 160.60 351.15 222.63 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n160 51 Cyclist -1 -1 -1 555.74 168.05 577.60 215.11 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n160 60 Pedestrian -1 -1 -1 391.25 164.01 409.51 215.03 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n160 48 Pedestrian -1 -1 -1 681.47 170.31 698.09 216.07 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n160 58 Pedestrian -1 -1 -1 436.26 170.30 456.88 218.79 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n160 11 Pedestrian -1 -1 -1 188.22 159.01 206.40 199.38 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n160 66 Car -1 -1 -1 599.37 173.77 621.62 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n161 1 Car -1 -1 -1 1095.21 185.49 1220.70 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n161 2 Car -1 -1 -1 954.72 184.12 1066.87 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n161 3 Car -1 -1 -1 1029.95 184.09 1155.74 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n161 8 Car -1 -1 -1 601.95 173.35 636.48 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n161 58 Pedestrian -1 -1 -1 439.03 169.71 459.14 219.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n161 39 Pedestrian -1 -1 -1 327.57 160.66 351.13 222.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n161 63 Pedestrian -1 -1 -1 477.23 166.09 500.01 221.26 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n161 51 Cyclist -1 -1 -1 555.68 168.10 578.32 213.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n161 46 Pedestrian -1 -1 -1 303.33 159.03 326.11 222.51 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n161 55 Pedestrian -1 -1 -1 411.51 163.98 433.66 219.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n161 6 Pedestrian -1 -1 -1 277.40 161.73 298.47 222.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n161 60 Pedestrian -1 -1 -1 391.46 164.07 409.71 215.78 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n161 65 Pedestrian -1 -1 -1 369.19 161.72 383.14 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n161 48 Pedestrian -1 -1 -1 681.19 169.36 697.76 215.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n161 11 Pedestrian -1 -1 -1 188.16 158.70 206.83 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n161 66 Car -1 -1 -1 599.43 173.87 621.58 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n162 1 Car -1 -1 -1 1095.13 185.40 1220.73 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n162 2 Car -1 -1 -1 954.59 184.08 1067.05 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n162 3 Car -1 -1 -1 1029.94 184.06 1155.76 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n162 63 Pedestrian -1 -1 -1 476.50 165.94 498.90 220.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n162 8 Car -1 -1 -1 602.04 173.39 636.54 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n162 39 Pedestrian -1 -1 -1 330.23 160.43 352.07 221.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n162 55 Pedestrian -1 -1 -1 407.94 162.98 430.29 220.19 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n162 58 Pedestrian -1 -1 -1 440.14 170.22 460.17 219.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n162 46 Pedestrian -1 -1 -1 303.52 156.94 326.49 222.02 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n162 6 Pedestrian -1 -1 -1 277.50 161.75 297.73 222.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n162 60 Pedestrian -1 -1 -1 393.60 163.94 411.32 215.72 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n162 51 Cyclist -1 -1 -1 556.99 167.60 576.64 212.18 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n162 48 Pedestrian -1 -1 -1 681.35 169.51 697.87 215.18 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n162 65 Pedestrian -1 -1 -1 368.73 162.05 382.70 196.73 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n162 11 Pedestrian -1 -1 -1 187.73 158.24 207.21 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n162 68 Pedestrian -1 -1 -1 432.83 165.16 451.35 215.68 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n163 1 Car -1 -1 -1 1095.16 185.41 1220.79 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n163 2 Car -1 -1 -1 954.65 184.05 1067.04 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n163 3 Car -1 -1 -1 1029.98 184.09 1155.78 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n163 8 Car -1 -1 -1 601.92 173.35 636.55 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n163 39 Pedestrian -1 -1 -1 330.58 159.77 352.68 221.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n163 58 Pedestrian -1 -1 -1 442.43 169.38 463.08 219.52 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n163 6 Pedestrian -1 -1 -1 276.96 161.48 298.18 221.42 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n163 51 Cyclist -1 -1 -1 557.23 167.63 578.29 213.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n163 46 Pedestrian -1 -1 -1 304.11 156.26 326.75 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n163 63 Pedestrian -1 -1 -1 475.65 165.82 497.69 220.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n163 55 Pedestrian -1 -1 -1 408.18 162.92 430.50 219.77 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n163 60 Pedestrian -1 -1 -1 393.14 163.03 412.08 216.08 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n163 48 Pedestrian -1 -1 -1 681.39 169.46 697.72 215.26 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n163 65 Pedestrian -1 -1 -1 368.64 161.64 383.07 196.74 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n163 68 Pedestrian -1 -1 -1 433.40 164.78 452.00 215.24 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n163 11 Pedestrian -1 -1 -1 190.18 158.01 209.79 199.71 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n163 69 Car -1 -1 -1 599.33 173.87 621.53 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n164 1 Car -1 -1 -1 1095.00 185.50 1221.01 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n164 2 Car -1 -1 -1 954.65 184.03 1066.95 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n164 3 Car -1 -1 -1 1029.99 184.05 1155.65 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n164 39 Pedestrian -1 -1 -1 331.18 160.36 353.24 220.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n164 63 Pedestrian -1 -1 -1 471.78 166.49 495.85 220.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n164 8 Car -1 -1 -1 601.91 173.37 636.67 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n164 58 Pedestrian -1 -1 -1 444.24 168.95 463.74 219.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n164 6 Pedestrian -1 -1 -1 276.65 160.96 298.32 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n164 55 Pedestrian -1 -1 -1 411.24 163.53 433.74 219.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n164 51 Cyclist -1 -1 -1 558.51 168.58 578.35 212.91 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n164 46 Pedestrian -1 -1 -1 304.20 157.56 327.81 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n164 60 Pedestrian -1 -1 -1 393.93 162.91 412.84 215.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n164 68 Pedestrian -1 -1 -1 436.25 164.06 454.88 216.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n164 48 Pedestrian -1 -1 -1 681.60 169.67 697.41 214.94 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n164 69 Car -1 -1 -1 599.28 173.81 621.49 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n164 11 Pedestrian -1 -1 -1 190.33 158.36 209.58 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n165 1 Car -1 -1 -1 1095.21 185.50 1220.84 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n165 2 Car -1 -1 -1 954.56 183.98 1067.07 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n165 3 Car -1 -1 -1 1029.96 184.05 1155.70 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n165 39 Pedestrian -1 -1 -1 331.24 161.18 353.94 220.56 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n165 8 Car -1 -1 -1 601.87 173.36 636.73 202.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n165 55 Pedestrian -1 -1 -1 411.87 162.63 434.55 219.81 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n165 6 Pedestrian -1 -1 -1 276.73 160.86 298.43 219.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n165 63 Pedestrian -1 -1 -1 471.19 166.79 494.32 219.88 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n165 60 Pedestrian -1 -1 -1 394.37 162.72 414.13 216.81 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n165 46 Pedestrian -1 -1 -1 304.61 157.84 327.86 221.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n165 58 Pedestrian -1 -1 -1 444.48 169.76 464.67 219.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n165 51 Cyclist -1 -1 -1 559.18 168.29 578.66 212.02 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n165 68 Pedestrian -1 -1 -1 437.09 163.05 456.09 216.00 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n165 48 Pedestrian -1 -1 -1 679.78 168.92 695.50 215.28 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n165 69 Car -1 -1 -1 599.32 173.78 621.40 193.10 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n165 11 Pedestrian -1 -1 -1 190.76 158.38 209.61 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n166 1 Car -1 -1 -1 1095.43 185.52 1220.62 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n166 2 Car -1 -1 -1 954.69 183.96 1066.99 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n166 3 Car -1 -1 -1 1029.72 183.96 1155.82 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n166 39 Pedestrian -1 -1 -1 331.28 161.28 353.79 220.52 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n166 6 Pedestrian -1 -1 -1 277.09 161.79 298.37 219.66 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n166 8 Car -1 -1 -1 601.87 173.40 636.79 202.28 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n166 63 Pedestrian -1 -1 -1 468.86 167.02 490.36 219.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n166 55 Pedestrian -1 -1 -1 414.65 163.75 438.23 219.92 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n166 60 Pedestrian -1 -1 -1 393.76 162.53 415.17 218.34 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n166 58 Pedestrian -1 -1 -1 445.68 169.61 465.95 220.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n166 46 Pedestrian -1 -1 -1 304.69 158.77 327.80 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n166 48 Pedestrian -1 -1 -1 679.28 168.93 695.74 214.81 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n166 51 Cyclist -1 -1 -1 560.21 167.87 578.52 208.85 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n166 69 Car -1 -1 -1 599.36 173.88 621.47 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n167 1 Car -1 -1 -1 1095.36 185.48 1220.71 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n167 2 Car -1 -1 -1 954.59 183.98 1067.18 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n167 3 Car -1 -1 -1 1029.73 184.00 1155.87 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n167 6 Pedestrian -1 -1 -1 277.68 162.21 298.46 219.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n167 39 Pedestrian -1 -1 -1 331.98 161.34 354.19 219.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n167 8 Car -1 -1 -1 601.89 173.34 636.72 202.39 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n167 60 Pedestrian -1 -1 -1 396.02 162.96 418.16 219.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n167 58 Pedestrian -1 -1 -1 445.87 169.35 466.10 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n167 55 Pedestrian -1 -1 -1 418.86 165.17 440.62 222.31 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n167 63 Pedestrian -1 -1 -1 465.97 165.19 487.37 219.06 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n167 46 Pedestrian -1 -1 -1 306.91 158.74 329.78 220.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n167 48 Pedestrian -1 -1 -1 679.17 168.96 695.52 214.62 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n167 51 Cyclist -1 -1 -1 561.06 167.58 580.23 208.60 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n167 69 Car -1 -1 -1 599.44 173.91 621.39 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n168 1 Car -1 -1 -1 1095.33 185.49 1220.70 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n168 2 Car -1 -1 -1 954.54 183.97 1067.13 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n168 3 Car -1 -1 -1 1029.74 183.98 1155.88 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n168 6 Pedestrian -1 -1 -1 278.21 161.77 298.90 218.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n168 58 Pedestrian -1 -1 -1 446.71 168.88 466.52 221.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n168 8 Car -1 -1 -1 601.82 173.43 636.75 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n168 55 Pedestrian -1 -1 -1 420.23 165.23 442.05 223.35 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n168 39 Pedestrian -1 -1 -1 332.32 161.02 353.86 219.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n168 60 Pedestrian -1 -1 -1 397.13 163.01 418.52 220.27 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n168 46 Pedestrian -1 -1 -1 307.24 157.72 329.78 219.03 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n168 63 Pedestrian -1 -1 -1 461.06 165.03 485.06 218.81 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n168 48 Pedestrian -1 -1 -1 678.93 168.94 695.19 214.49 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n168 51 Cyclist -1 -1 -1 561.51 167.38 580.48 208.59 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n168 69 Car -1 -1 -1 599.40 173.94 621.45 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n169 1 Car -1 -1 -1 1095.27 185.53 1220.68 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n169 2 Car -1 -1 -1 954.51 184.02 1067.18 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n169 3 Car -1 -1 -1 1029.82 184.01 1155.91 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n169 58 Pedestrian -1 -1 -1 447.59 169.50 466.64 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n169 8 Car -1 -1 -1 601.81 173.38 636.77 202.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n169 60 Pedestrian -1 -1 -1 401.46 162.87 421.15 219.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n169 46 Pedestrian -1 -1 -1 307.76 157.95 330.21 218.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n169 55 Pedestrian -1 -1 -1 422.52 164.94 444.77 223.91 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n169 6 Pedestrian -1 -1 -1 278.81 160.93 300.51 218.43 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n169 39 Pedestrian -1 -1 -1 332.75 160.98 353.91 219.57 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n169 51 Cyclist -1 -1 -1 561.61 167.13 581.27 208.64 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n169 63 Pedestrian -1 -1 -1 461.17 165.74 484.05 218.10 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n169 48 Pedestrian -1 -1 -1 678.63 169.40 694.64 214.30 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n169 69 Car -1 -1 -1 599.39 174.00 621.46 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n169 70 Pedestrian -1 -1 -1 417.17 164.00 436.65 217.43 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n169 71 Pedestrian -1 -1 -1 440.51 163.95 459.50 217.44 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n170 1 Car -1 -1 -1 1095.23 185.50 1220.60 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n170 2 Car -1 -1 -1 954.60 184.02 1067.08 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n170 3 Car -1 -1 -1 1029.79 183.97 1155.71 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n170 55 Pedestrian -1 -1 -1 422.46 165.57 446.03 223.34 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n170 8 Car -1 -1 -1 601.88 173.26 636.83 202.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n170 46 Pedestrian -1 -1 -1 308.48 158.48 330.88 217.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n170 58 Pedestrian -1 -1 -1 447.94 170.11 466.35 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n170 39 Pedestrian -1 -1 -1 332.67 161.02 353.97 220.43 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n170 51 Cyclist -1 -1 -1 562.46 167.51 581.80 208.30 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n170 6 Pedestrian -1 -1 -1 279.02 160.86 300.53 218.15 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n170 60 Pedestrian -1 -1 -1 404.08 163.54 423.69 219.70 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n170 63 Pedestrian -1 -1 -1 458.11 166.15 480.38 218.47 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n170 48 Pedestrian -1 -1 -1 677.88 169.49 694.14 214.20 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n170 69 Car -1 -1 -1 599.54 173.95 621.36 193.05 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n171 1 Car -1 -1 -1 1095.43 185.52 1220.63 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n171 2 Car -1 -1 -1 954.52 183.97 1067.20 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n171 3 Car -1 -1 -1 1029.99 184.03 1155.72 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n171 8 Car -1 -1 -1 601.69 173.23 636.95 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n171 39 Pedestrian -1 -1 -1 332.87 161.51 353.81 220.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n171 60 Pedestrian -1 -1 -1 404.94 164.58 424.35 219.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n171 58 Pedestrian -1 -1 -1 448.39 169.60 465.50 220.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n171 55 Pedestrian -1 -1 -1 423.95 164.18 446.23 223.29 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n171 51 Cyclist -1 -1 -1 562.86 167.82 581.83 206.62 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n171 6 Pedestrian -1 -1 -1 281.05 161.75 301.89 218.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n171 46 Pedestrian -1 -1 -1 309.22 158.70 331.41 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n171 63 Pedestrian -1 -1 -1 459.08 165.99 479.47 218.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n171 48 Pedestrian -1 -1 -1 677.62 169.67 693.77 213.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n172 1 Car -1 -1 -1 1095.48 185.54 1220.34 235.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n172 2 Car -1 -1 -1 954.48 183.98 1067.14 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n172 3 Car -1 -1 -1 1030.01 184.00 1155.56 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n172 55 Pedestrian -1 -1 -1 425.94 164.50 449.36 224.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n172 8 Car -1 -1 -1 601.67 173.30 636.94 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n172 51 Cyclist -1 -1 -1 562.84 167.97 582.23 206.66 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n172 58 Pedestrian -1 -1 -1 448.24 168.84 466.96 219.96 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n172 6 Pedestrian -1 -1 -1 281.19 161.87 302.06 218.57 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n172 39 Pedestrian -1 -1 -1 332.90 161.54 354.03 219.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n172 60 Pedestrian -1 -1 -1 405.06 164.34 426.21 219.77 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n172 63 Pedestrian -1 -1 -1 458.09 165.43 478.83 218.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n172 46 Pedestrian -1 -1 -1 311.31 159.00 332.66 217.96 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n172 48 Pedestrian -1 -1 -1 675.73 169.23 691.77 213.70 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n173 1 Car -1 -1 -1 1095.26 185.47 1220.48 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n173 2 Car -1 -1 -1 954.44 183.99 1067.17 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n173 3 Car -1 -1 -1 1030.06 184.01 1155.43 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n173 55 Pedestrian -1 -1 -1 427.11 165.09 450.01 224.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n173 8 Car -1 -1 -1 601.51 173.28 637.07 202.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n173 58 Pedestrian -1 -1 -1 448.00 168.51 467.62 221.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n173 46 Pedestrian -1 -1 -1 311.28 158.31 332.95 217.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n173 6 Pedestrian -1 -1 -1 281.41 161.62 301.82 217.70 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n173 39 Pedestrian -1 -1 -1 334.93 161.12 354.99 218.17 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n173 60 Pedestrian -1 -1 -1 405.05 165.09 427.04 221.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n173 51 Cyclist -1 -1 -1 563.76 168.04 582.10 206.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n173 63 Pedestrian -1 -1 -1 458.03 165.24 478.88 218.34 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n173 48 Pedestrian -1 -1 -1 677.59 169.69 693.75 213.55 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n174 1 Car -1 -1 -1 1095.29 185.51 1220.55 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n174 2 Car -1 -1 -1 954.44 183.96 1067.11 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n174 3 Car -1 -1 -1 1030.07 184.01 1155.47 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n174 60 Pedestrian -1 -1 -1 408.44 164.19 428.44 218.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n174 8 Car -1 -1 -1 601.50 173.31 637.06 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n174 46 Pedestrian -1 -1 -1 312.51 157.65 332.98 216.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n174 58 Pedestrian -1 -1 -1 449.62 167.92 470.97 222.11 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n174 55 Pedestrian -1 -1 -1 427.40 164.65 450.52 224.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n174 39 Pedestrian -1 -1 -1 335.78 161.59 355.08 217.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n174 6 Pedestrian -1 -1 -1 281.30 160.40 303.20 216.64 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n174 51 Cyclist -1 -1 -1 564.55 168.32 581.83 205.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n174 48 Pedestrian -1 -1 -1 675.46 169.30 691.97 213.57 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n174 72 Pedestrian -1 -1 -1 366.40 160.34 379.49 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n175 1 Car -1 -1 -1 1095.34 185.53 1220.58 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n175 2 Car -1 -1 -1 954.35 183.96 1067.25 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n175 3 Car -1 -1 -1 1029.91 183.98 1155.63 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n175 58 Pedestrian -1 -1 -1 450.38 167.72 472.77 221.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n175 39 Pedestrian -1 -1 -1 335.93 161.93 355.58 217.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n175 55 Pedestrian -1 -1 -1 429.44 164.85 453.22 224.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n175 8 Car -1 -1 -1 601.60 173.33 636.91 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n175 46 Pedestrian -1 -1 -1 312.43 158.03 333.22 216.50 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n175 6 Pedestrian -1 -1 -1 282.80 161.01 303.95 215.89 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n175 60 Pedestrian -1 -1 -1 410.36 164.18 429.62 219.15 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n175 51 Cyclist -1 -1 -1 564.68 168.25 581.56 205.14 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n175 48 Pedestrian -1 -1 -1 675.50 169.42 691.68 213.36 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n175 73 Car -1 -1 -1 598.68 173.71 621.54 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n176 1 Car -1 -1 -1 1095.38 185.64 1220.57 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n176 2 Car -1 -1 -1 954.45 183.98 1067.27 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n176 3 Car -1 -1 -1 1030.17 184.05 1155.46 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n176 55 Pedestrian -1 -1 -1 429.78 163.66 454.00 224.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n176 46 Pedestrian -1 -1 -1 312.23 158.67 333.99 216.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n176 8 Car -1 -1 -1 601.50 173.17 637.10 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n176 39 Pedestrian -1 -1 -1 336.50 161.89 355.96 217.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n176 58 Pedestrian -1 -1 -1 453.45 167.60 474.47 222.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n176 60 Pedestrian -1 -1 -1 412.08 163.70 433.10 220.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n176 6 Pedestrian -1 -1 -1 285.34 161.76 305.86 215.32 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n176 51 Cyclist -1 -1 -1 566.13 167.93 582.52 204.23 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n176 48 Pedestrian -1 -1 -1 675.15 169.40 691.71 212.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n176 73 Car -1 -1 -1 598.34 173.61 621.67 192.90 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n177 1 Car -1 -1 -1 1095.44 185.54 1220.40 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n177 2 Car -1 -1 -1 954.40 183.99 1067.22 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n177 3 Car -1 -1 -1 1030.15 184.09 1155.50 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n177 58 Pedestrian -1 -1 -1 454.11 166.57 475.95 223.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n177 55 Pedestrian -1 -1 -1 430.76 163.74 453.93 224.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n177 8 Car -1 -1 -1 601.58 173.22 637.01 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n177 39 Pedestrian -1 -1 -1 336.44 162.10 356.53 216.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n177 46 Pedestrian -1 -1 -1 312.87 158.91 333.99 216.79 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n177 6 Pedestrian -1 -1 -1 286.83 162.61 306.52 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n177 60 Pedestrian -1 -1 -1 415.02 164.15 435.80 219.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n177 51 Cyclist -1 -1 -1 566.36 168.11 582.24 204.36 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n177 48 Pedestrian -1 -1 -1 674.78 169.45 691.44 212.26 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n177 73 Car -1 -1 -1 598.59 173.67 621.69 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n178 1 Car -1 -1 -1 1095.26 185.51 1220.62 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n178 2 Car -1 -1 -1 954.38 183.93 1067.29 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n178 3 Car -1 -1 -1 1030.05 184.03 1155.56 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n178 8 Car -1 -1 -1 601.43 173.15 637.20 202.56 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n178 58 Pedestrian -1 -1 -1 454.29 165.91 476.66 224.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n178 46 Pedestrian -1 -1 -1 313.52 159.26 334.34 216.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n178 55 Pedestrian -1 -1 -1 431.33 165.13 454.02 224.21 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n178 60 Pedestrian -1 -1 -1 415.44 165.54 437.97 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n178 39 Pedestrian -1 -1 -1 336.89 160.90 357.21 216.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n178 6 Pedestrian -1 -1 -1 287.65 162.26 307.35 214.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n178 51 Cyclist -1 -1 -1 566.71 167.94 582.25 204.43 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n178 48 Pedestrian -1 -1 -1 674.55 169.86 690.60 211.83 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n178 73 Car -1 -1 -1 598.68 173.69 621.63 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n178 74 Pedestrian -1 -1 -1 433.03 165.10 450.87 209.83 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n179 1 Car -1 -1 -1 1095.35 185.50 1220.48 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n179 2 Car -1 -1 -1 954.51 183.96 1067.09 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n179 3 Car -1 -1 -1 1030.00 183.99 1155.50 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n179 8 Car -1 -1 -1 601.48 173.12 637.14 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n179 6 Pedestrian -1 -1 -1 289.80 161.28 308.91 214.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n179 58 Pedestrian -1 -1 -1 457.20 165.81 478.91 224.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n179 55 Pedestrian -1 -1 -1 433.70 164.85 456.57 225.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n179 46 Pedestrian -1 -1 -1 315.75 158.61 336.11 216.12 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n179 39 Pedestrian -1 -1 -1 337.40 160.90 357.05 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n179 60 Pedestrian -1 -1 -1 417.70 163.85 437.60 220.24 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n179 51 Cyclist -1 -1 -1 566.96 168.21 582.13 204.30 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n179 48 Pedestrian -1 -1 -1 674.13 170.25 690.07 211.75 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n179 73 Car -1 -1 -1 598.63 173.71 621.72 193.17 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n180 1 Car -1 -1 -1 1095.21 185.50 1220.74 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n180 2 Car -1 -1 -1 954.58 183.96 1067.10 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n180 3 Car -1 -1 -1 1029.86 183.93 1155.65 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n180 6 Pedestrian -1 -1 -1 289.95 161.46 309.86 214.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n180 8 Car -1 -1 -1 601.56 173.11 636.96 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n180 58 Pedestrian -1 -1 -1 457.27 165.43 480.00 225.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n180 46 Pedestrian -1 -1 -1 316.90 158.14 336.50 214.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n180 55 Pedestrian -1 -1 -1 434.59 165.38 456.93 225.77 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n180 51 Cyclist -1 -1 -1 567.09 168.70 582.56 203.98 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n180 60 Pedestrian -1 -1 -1 419.04 163.62 440.35 220.65 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n180 39 Pedestrian -1 -1 -1 337.14 161.81 357.54 216.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n180 73 Car -1 -1 -1 598.84 173.73 621.53 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n180 48 Pedestrian -1 -1 -1 673.94 170.49 689.35 211.10 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n180 75 Pedestrian -1 -1 -1 443.86 165.41 463.12 215.73 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n181 1 Car -1 -1 -1 1095.22 185.42 1220.69 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n181 2 Car -1 -1 -1 954.59 183.88 1067.02 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n181 3 Car -1 -1 -1 1029.96 183.95 1155.55 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n181 46 Pedestrian -1 -1 -1 317.20 159.01 337.50 214.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n181 8 Car -1 -1 -1 601.46 173.15 637.25 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n181 55 Pedestrian -1 -1 -1 434.90 165.32 457.22 226.01 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n181 6 Pedestrian -1 -1 -1 289.90 162.01 310.63 215.04 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n181 58 Pedestrian -1 -1 -1 457.33 165.11 481.33 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n181 60 Pedestrian -1 -1 -1 421.24 164.90 440.95 222.28 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n181 51 Cyclist -1 -1 -1 567.92 168.19 583.22 200.62 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n181 39 Pedestrian -1 -1 -1 337.30 161.93 357.44 216.49 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n181 48 Pedestrian -1 -1 -1 671.59 170.03 687.08 211.61 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n181 76 Pedestrian -1 -1 -1 365.16 160.74 378.54 198.86 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n182 1 Car -1 -1 -1 1095.48 185.50 1220.48 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n182 2 Car -1 -1 -1 954.43 183.85 1067.17 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n182 3 Car -1 -1 -1 1029.82 183.96 1155.65 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n182 8 Car -1 -1 -1 601.48 173.20 637.18 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n182 55 Pedestrian -1 -1 -1 435.31 163.74 457.70 226.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n182 58 Pedestrian -1 -1 -1 461.17 165.73 483.92 225.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n182 46 Pedestrian -1 -1 -1 317.95 159.56 338.22 215.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n182 6 Pedestrian -1 -1 -1 290.52 162.16 310.70 214.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n182 60 Pedestrian -1 -1 -1 423.58 165.09 442.70 223.53 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n182 39 Pedestrian -1 -1 -1 339.23 160.80 358.39 215.98 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n182 51 Cyclist -1 -1 -1 569.10 168.57 583.20 200.02 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n183 1 Car -1 -1 -1 1095.38 185.53 1220.50 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n183 2 Car -1 -1 -1 954.57 183.87 1067.04 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n183 3 Car -1 -1 -1 1030.04 184.03 1155.64 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n183 46 Pedestrian -1 -1 -1 320.06 159.28 340.46 214.19 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n183 55 Pedestrian -1 -1 -1 434.78 163.24 458.31 226.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n183 8 Car -1 -1 -1 601.65 173.26 637.10 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n183 6 Pedestrian -1 -1 -1 291.38 161.73 310.86 214.52 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n183 39 Pedestrian -1 -1 -1 339.76 160.95 358.38 215.03 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n183 58 Pedestrian -1 -1 -1 461.51 166.88 485.08 224.85 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n183 60 Pedestrian -1 -1 -1 424.30 164.93 442.93 223.83 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n183 51 Cyclist -1 -1 -1 568.52 168.88 583.27 199.76 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n183 77 Pedestrian -1 -1 -1 444.03 164.35 463.22 211.21 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n184 1 Car -1 -1 -1 1095.28 185.53 1220.45 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n184 2 Car -1 -1 -1 954.46 183.92 1067.11 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n184 3 Car -1 -1 -1 1030.01 184.04 1155.57 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n184 46 Pedestrian -1 -1 -1 320.70 159.10 341.56 213.47 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n184 8 Car -1 -1 -1 601.49 173.04 637.20 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n184 39 Pedestrian -1 -1 -1 339.77 160.99 358.94 214.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n184 58 Pedestrian -1 -1 -1 464.64 166.80 487.41 224.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n184 6 Pedestrian -1 -1 -1 293.27 161.67 312.24 213.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n184 55 Pedestrian -1 -1 -1 437.39 164.13 460.19 226.92 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n184 60 Pedestrian -1 -1 -1 425.07 164.70 443.96 224.40 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n184 51 Cyclist -1 -1 -1 568.73 169.34 583.74 198.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n184 78 Pedestrian -1 -1 -1 362.62 160.68 376.53 199.24 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n185 1 Car -1 -1 -1 1095.37 185.50 1220.37 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n185 2 Car -1 -1 -1 954.43 183.89 1067.16 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n185 3 Car -1 -1 -1 1030.12 184.01 1155.54 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n185 6 Pedestrian -1 -1 -1 294.19 161.67 312.99 213.53 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n185 8 Car -1 -1 -1 601.61 173.08 637.11 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n185 39 Pedestrian -1 -1 -1 341.00 160.64 359.58 215.00 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n185 55 Pedestrian -1 -1 -1 437.03 164.77 460.97 226.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n185 58 Pedestrian -1 -1 -1 464.78 169.65 489.63 225.20 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n185 46 Pedestrian -1 -1 -1 321.45 158.18 341.71 213.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n185 51 Cyclist -1 -1 -1 569.83 169.42 583.67 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n185 60 Pedestrian -1 -1 -1 425.52 164.40 444.77 224.11 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n185 78 Pedestrian -1 -1 -1 362.30 160.70 376.27 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n185 79 Pedestrian -1 -1 -1 191.22 153.14 209.68 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n186 1 Car -1 -1 -1 1095.15 185.53 1220.72 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n186 2 Car -1 -1 -1 954.35 183.82 1067.19 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n186 3 Car -1 -1 -1 1030.04 183.99 1155.66 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n186 6 Pedestrian -1 -1 -1 294.79 162.66 314.08 213.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n186 8 Car -1 -1 -1 601.28 172.90 637.31 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n186 55 Pedestrian -1 -1 -1 437.70 164.31 462.08 227.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n186 39 Pedestrian -1 -1 -1 341.85 160.55 359.73 214.08 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n186 58 Pedestrian -1 -1 -1 467.39 169.13 491.44 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n186 46 Pedestrian -1 -1 -1 321.74 158.08 342.09 213.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n186 51 Cyclist -1 -1 -1 569.41 169.55 584.16 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n186 78 Pedestrian -1 -1 -1 361.71 160.81 376.34 198.96 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n186 60 Pedestrian -1 -1 -1 427.30 165.49 447.54 223.18 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n187 1 Car -1 -1 -1 1095.12 185.51 1220.78 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n187 2 Car -1 -1 -1 954.35 183.83 1067.25 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n187 3 Car -1 -1 -1 1029.98 183.93 1155.69 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n187 8 Car -1 -1 -1 601.46 172.90 637.06 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n187 55 Pedestrian -1 -1 -1 441.06 163.67 464.18 227.33 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n187 6 Pedestrian -1 -1 -1 296.26 162.83 314.07 212.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n187 60 Pedestrian -1 -1 -1 427.42 164.61 448.81 224.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n187 58 Pedestrian -1 -1 -1 469.23 166.94 492.65 224.50 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n187 39 Pedestrian -1 -1 -1 342.20 160.40 359.85 213.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n187 46 Pedestrian -1 -1 -1 323.87 158.46 343.39 213.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n187 51 Cyclist -1 -1 -1 569.93 169.72 583.78 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n187 78 Pedestrian -1 -1 -1 361.67 160.96 375.99 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n187 80 Pedestrian -1 -1 -1 190.77 154.00 209.59 196.26 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n188 1 Car -1 -1 -1 1095.07 185.47 1220.78 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n188 2 Car -1 -1 -1 954.48 183.87 1067.16 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n188 3 Car -1 -1 -1 1030.01 184.01 1155.71 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n188 6 Pedestrian -1 -1 -1 298.10 162.37 315.82 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n188 8 Car -1 -1 -1 601.44 172.92 637.16 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n188 55 Pedestrian -1 -1 -1 441.31 163.42 465.13 227.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n188 58 Pedestrian -1 -1 -1 471.59 165.66 495.04 225.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n188 60 Pedestrian -1 -1 -1 428.70 164.44 448.66 224.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n188 46 Pedestrian -1 -1 -1 324.14 158.44 344.02 213.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n188 39 Pedestrian -1 -1 -1 343.70 160.04 361.39 213.16 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n188 51 Cyclist -1 -1 -1 569.92 171.98 583.71 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n188 80 Pedestrian -1 -1 -1 192.03 160.09 208.11 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n188 78 Pedestrian -1 -1 -1 361.77 160.87 376.12 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n188 81 Car -1 -1 -1 598.67 173.75 621.30 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n189 1 Car -1 -1 -1 1095.19 185.47 1220.75 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n189 2 Car -1 -1 -1 954.57 183.87 1067.22 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n189 3 Car -1 -1 -1 1030.10 183.98 1155.59 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n189 6 Pedestrian -1 -1 -1 298.90 161.86 317.33 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n189 8 Car -1 -1 -1 601.37 172.90 637.18 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n189 58 Pedestrian -1 -1 -1 473.14 164.81 495.75 225.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n189 46 Pedestrian -1 -1 -1 324.30 158.66 344.41 213.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n189 60 Pedestrian -1 -1 -1 430.04 164.57 451.26 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n189 55 Pedestrian -1 -1 -1 442.37 163.37 465.61 227.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n189 39 Pedestrian -1 -1 -1 343.60 160.39 361.84 213.00 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n189 51 Cyclist -1 -1 -1 569.99 172.69 584.00 202.55 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n189 78 Pedestrian -1 -1 -1 361.85 160.83 375.88 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n189 81 Car -1 -1 -1 598.83 173.84 621.10 193.27 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n190 1 Car -1 -1 -1 1095.29 185.48 1220.70 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n190 2 Car -1 -1 -1 954.53 183.89 1067.21 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n190 3 Car -1 -1 -1 1030.11 184.01 1155.49 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n190 8 Car -1 -1 -1 601.34 172.84 637.27 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n190 6 Pedestrian -1 -1 -1 299.81 161.98 317.93 211.54 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n190 60 Pedestrian -1 -1 -1 429.87 163.93 451.82 225.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n190 58 Pedestrian -1 -1 -1 473.38 164.55 496.55 226.31 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n190 39 Pedestrian -1 -1 -1 343.52 160.40 362.40 212.97 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n190 46 Pedestrian -1 -1 -1 324.61 158.67 345.14 212.34 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n190 55 Pedestrian -1 -1 -1 445.12 164.49 468.28 227.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n190 51 Cyclist -1 -1 -1 570.90 172.56 585.79 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n190 82 Pedestrian -1 -1 -1 192.14 160.42 208.01 197.85 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n191 1 Car -1 -1 -1 1094.97 185.42 1221.01 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n191 2 Car -1 -1 -1 954.67 183.93 1067.16 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n191 3 Car -1 -1 -1 1030.07 184.02 1155.57 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n191 8 Car -1 -1 -1 601.40 172.81 637.33 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n191 60 Pedestrian -1 -1 -1 430.14 164.17 452.51 224.77 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n191 58 Pedestrian -1 -1 -1 474.74 164.24 498.80 226.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n191 46 Pedestrian -1 -1 -1 324.98 157.47 345.64 211.59 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n191 39 Pedestrian -1 -1 -1 344.02 160.64 362.44 212.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n191 6 Pedestrian -1 -1 -1 301.78 162.40 319.09 211.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n191 55 Pedestrian -1 -1 -1 445.53 164.72 468.51 230.06 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n191 82 Pedestrian -1 -1 -1 192.51 160.86 207.83 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n191 83 Pedestrian -1 -1 -1 438.14 164.14 454.04 209.22 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n191 84 Car -1 -1 -1 598.87 173.63 621.28 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n192 1 Car -1 -1 -1 1095.32 185.55 1220.54 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n192 2 Car -1 -1 -1 954.68 183.95 1066.98 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n192 3 Car -1 -1 -1 1030.04 184.01 1155.63 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n192 60 Pedestrian -1 -1 -1 430.31 165.00 453.00 224.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n192 58 Pedestrian -1 -1 -1 475.33 163.76 500.52 227.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n192 8 Car -1 -1 -1 601.37 172.73 637.33 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n192 6 Pedestrian -1 -1 -1 301.79 162.53 319.54 211.49 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n192 39 Pedestrian -1 -1 -1 344.93 160.48 362.60 212.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n192 46 Pedestrian -1 -1 -1 324.96 157.83 346.17 211.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n192 55 Pedestrian -1 -1 -1 448.45 165.42 472.52 229.93 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n192 82 Pedestrian -1 -1 -1 192.55 161.20 208.02 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n192 83 Pedestrian -1 -1 -1 437.54 164.07 454.73 208.85 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n192 84 Car -1 -1 -1 598.62 173.64 621.30 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n192 85 Pedestrian -1 -1 -1 185.49 160.52 200.10 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n192 86 Cyclist -1 -1 -1 571.20 169.68 586.01 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n193 1 Car -1 -1 -1 1095.31 185.49 1220.72 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n193 2 Car -1 -1 -1 954.73 183.97 1066.96 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n193 3 Car -1 -1 -1 1030.07 184.01 1155.57 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n193 60 Pedestrian -1 -1 -1 430.10 165.29 453.70 225.36 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n193 8 Car -1 -1 -1 601.43 172.58 637.23 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n193 6 Pedestrian -1 -1 -1 301.92 161.99 320.10 211.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n193 39 Pedestrian -1 -1 -1 345.84 160.64 363.37 211.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n193 58 Pedestrian -1 -1 -1 476.05 166.22 501.41 228.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n193 55 Pedestrian -1 -1 -1 447.78 165.13 473.14 229.84 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n193 46 Pedestrian -1 -1 -1 327.26 159.62 347.23 211.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n193 86 Cyclist -1 -1 -1 571.39 169.24 587.27 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n193 82 Pedestrian -1 -1 -1 191.97 160.80 208.41 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n193 85 Pedestrian -1 -1 -1 185.03 160.48 200.64 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n194 1 Car -1 -1 -1 1095.47 185.55 1220.68 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n194 2 Car -1 -1 -1 954.58 183.91 1067.10 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n194 3 Car -1 -1 -1 1029.93 183.95 1155.72 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n194 6 Pedestrian -1 -1 -1 302.40 161.72 321.19 210.76 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n194 8 Car -1 -1 -1 601.29 172.54 637.32 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n194 60 Pedestrian -1 -1 -1 430.66 165.85 454.10 224.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n194 58 Pedestrian -1 -1 -1 478.03 166.05 503.02 229.37 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n194 55 Pedestrian -1 -1 -1 448.59 164.28 472.15 230.71 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n194 46 Pedestrian -1 -1 -1 327.86 158.09 347.74 211.35 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n194 39 Pedestrian -1 -1 -1 347.55 160.87 365.28 211.84 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n194 85 Pedestrian -1 -1 -1 185.28 160.75 200.55 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n194 82 Pedestrian -1 -1 -1 192.17 161.24 208.45 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n194 86 Cyclist -1 -1 -1 572.77 169.48 586.36 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n194 87 Pedestrian -1 -1 -1 429.75 165.13 447.08 210.35 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n195 1 Car -1 -1 -1 1095.45 185.46 1220.41 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n195 2 Car -1 -1 -1 954.51 183.86 1067.26 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n195 3 Car -1 -1 -1 1030.27 184.01 1155.42 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n195 55 Pedestrian -1 -1 -1 448.62 163.46 474.42 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n195 8 Car -1 -1 -1 601.12 172.39 637.56 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n195 58 Pedestrian -1 -1 -1 477.81 167.17 504.53 229.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n195 6 Pedestrian -1 -1 -1 303.85 161.93 321.90 210.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n195 60 Pedestrian -1 -1 -1 433.08 164.96 456.05 225.22 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n195 39 Pedestrian -1 -1 -1 348.23 160.65 366.65 211.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n195 46 Pedestrian -1 -1 -1 327.97 159.66 348.76 211.15 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n195 87 Pedestrian -1 -1 -1 429.36 165.02 446.99 210.36 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n195 85 Pedestrian -1 -1 -1 185.02 160.64 200.99 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n195 86 Cyclist -1 -1 -1 572.40 169.59 586.87 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n195 88 Pedestrian -1 -1 -1 666.54 169.15 683.56 220.25 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n196 1 Car -1 -1 -1 1095.53 185.49 1220.40 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n196 2 Car -1 -1 -1 954.61 183.85 1067.16 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n196 3 Car -1 -1 -1 1030.09 183.99 1155.54 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n196 60 Pedestrian -1 -1 -1 433.57 164.91 457.43 225.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n196 8 Car -1 -1 -1 601.17 172.56 637.58 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n196 55 Pedestrian -1 -1 -1 450.34 164.36 477.46 231.75 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n196 58 Pedestrian -1 -1 -1 479.35 167.91 505.31 230.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n196 6 Pedestrian -1 -1 -1 305.68 162.44 323.18 210.13 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n196 39 Pedestrian -1 -1 -1 348.91 160.88 367.54 211.38 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n196 46 Pedestrian -1 -1 -1 328.51 158.43 348.45 211.03 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n196 87 Pedestrian -1 -1 -1 428.42 164.33 446.35 210.12 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n196 85 Pedestrian -1 -1 -1 184.82 160.60 201.08 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n197 1 Car -1 -1 -1 1095.41 185.54 1220.50 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n197 2 Car -1 -1 -1 954.68 183.88 1067.11 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n197 3 Car -1 -1 -1 1030.26 184.02 1155.41 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n197 6 Pedestrian -1 -1 -1 306.29 162.49 324.09 210.06 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n197 55 Pedestrian -1 -1 -1 451.63 163.35 479.15 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n197 8 Car -1 -1 -1 600.85 172.58 637.85 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n197 60 Pedestrian -1 -1 -1 434.05 164.34 457.89 226.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n197 58 Pedestrian -1 -1 -1 481.04 168.10 508.45 230.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n197 39 Pedestrian -1 -1 -1 349.44 160.71 367.84 211.22 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n197 46 Pedestrian -1 -1 -1 329.48 158.38 348.46 210.97 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n197 85 Pedestrian -1 -1 -1 185.01 160.70 201.08 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n197 87 Pedestrian -1 -1 -1 428.01 163.85 446.08 210.94 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n197 89 Pedestrian -1 -1 -1 190.48 154.26 210.60 196.17 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n197 90 Pedestrian -1 -1 -1 360.90 161.66 375.99 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n198 1 Car -1 -1 -1 1095.42 185.49 1220.55 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n198 2 Car -1 -1 -1 954.76 183.93 1066.98 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n198 3 Car -1 -1 -1 1030.13 183.98 1155.48 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n198 55 Pedestrian -1 -1 -1 454.44 163.36 482.16 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n198 8 Car -1 -1 -1 601.14 172.59 637.58 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n198 58 Pedestrian -1 -1 -1 483.03 167.92 509.66 231.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n198 60 Pedestrian -1 -1 -1 434.22 164.13 458.43 227.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n198 6 Pedestrian -1 -1 -1 308.03 162.75 325.17 209.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n198 39 Pedestrian -1 -1 -1 349.50 160.79 368.11 211.14 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n198 87 Pedestrian -1 -1 -1 425.77 164.12 443.26 210.47 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n198 46 Pedestrian -1 -1 -1 329.45 159.67 349.34 211.24 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n198 85 Pedestrian -1 -1 -1 184.64 160.49 201.21 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n198 89 Pedestrian -1 -1 -1 190.21 153.92 210.63 196.33 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n198 90 Pedestrian -1 -1 -1 361.15 160.87 376.66 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n199 1 Car -1 -1 -1 1095.42 185.57 1220.66 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n199 2 Car -1 -1 -1 954.71 183.94 1067.00 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n199 3 Car -1 -1 -1 1030.15 184.01 1155.41 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n199 8 Car -1 -1 -1 601.30 172.59 637.33 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n199 55 Pedestrian -1 -1 -1 455.56 162.71 483.20 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n199 58 Pedestrian -1 -1 -1 486.66 167.03 511.53 231.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n199 60 Pedestrian -1 -1 -1 434.31 164.03 458.75 227.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n199 87 Pedestrian -1 -1 -1 424.81 164.59 442.70 210.97 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n199 6 Pedestrian -1 -1 -1 309.72 162.73 326.46 208.93 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n199 39 Pedestrian -1 -1 -1 350.84 161.14 369.69 211.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n199 85 Pedestrian -1 -1 -1 184.66 160.30 201.15 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n199 46 Pedestrian -1 -1 -1 329.83 159.84 348.88 211.09 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n199 89 Pedestrian -1 -1 -1 190.33 153.77 210.55 196.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n199 90 Pedestrian -1 -1 -1 361.73 161.14 376.90 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n200 1 Car -1 -1 -1 1095.46 185.53 1220.57 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n200 2 Car -1 -1 -1 954.66 183.94 1067.07 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n200 3 Car -1 -1 -1 1030.27 184.05 1155.35 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n200 8 Car -1 -1 -1 601.26 172.54 637.31 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n200 55 Pedestrian -1 -1 -1 459.24 162.93 484.99 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n200 6 Pedestrian -1 -1 -1 310.27 162.73 327.59 209.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n200 60 Pedestrian -1 -1 -1 437.22 163.33 461.98 228.15 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n200 58 Pedestrian -1 -1 -1 487.24 166.74 512.10 232.25 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n200 39 Pedestrian -1 -1 -1 350.47 161.20 370.10 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n200 85 Pedestrian -1 -1 -1 184.42 160.17 201.14 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n200 87 Pedestrian -1 -1 -1 424.35 164.80 441.83 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n200 46 Pedestrian -1 -1 -1 330.07 159.81 348.81 211.10 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n200 89 Pedestrian -1 -1 -1 189.79 153.69 210.84 196.46 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n200 90 Pedestrian -1 -1 -1 361.83 161.34 376.84 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n200 91 Pedestrian -1 -1 -1 494.54 161.80 517.94 227.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n200 92 Car -1 -1 -1 598.96 173.56 620.95 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n201 1 Car -1 -1 -1 1095.23 185.52 1220.78 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n201 2 Car -1 -1 -1 954.61 183.92 1066.97 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n201 3 Car -1 -1 -1 1030.12 184.00 1155.43 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n201 8 Car -1 -1 -1 601.48 172.54 637.23 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n201 55 Pedestrian -1 -1 -1 459.96 162.93 486.55 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n201 6 Pedestrian -1 -1 -1 311.53 162.91 328.40 209.08 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n201 60 Pedestrian -1 -1 -1 437.98 162.59 462.68 228.55 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n201 87 Pedestrian -1 -1 -1 421.55 163.80 440.44 211.29 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n201 58 Pedestrian -1 -1 -1 489.26 165.49 515.83 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n201 39 Pedestrian -1 -1 -1 350.77 161.19 369.82 211.09 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n201 85 Pedestrian -1 -1 -1 184.71 160.15 201.26 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n201 46 Pedestrian -1 -1 -1 332.36 158.57 350.44 210.50 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n201 90 Pedestrian -1 -1 -1 361.64 161.25 377.45 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n201 89 Pedestrian -1 -1 -1 189.76 153.80 211.01 196.34 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n201 92 Car -1 -1 -1 599.11 173.61 621.09 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n202 1 Car -1 -1 -1 1095.12 185.48 1220.83 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n202 2 Car -1 -1 -1 954.59 183.93 1067.00 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n202 3 Car -1 -1 -1 1030.16 183.98 1155.43 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n202 8 Car -1 -1 -1 601.24 172.64 637.33 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n202 87 Pedestrian -1 -1 -1 420.14 163.44 439.71 209.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n202 55 Pedestrian -1 -1 -1 462.31 163.60 489.64 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n202 60 Pedestrian -1 -1 -1 440.95 162.56 465.20 229.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n202 58 Pedestrian -1 -1 -1 498.90 161.15 521.92 226.36 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n202 46 Pedestrian -1 -1 -1 332.56 158.26 350.85 210.06 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n202 6 Pedestrian -1 -1 -1 311.80 162.92 328.98 209.10 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n202 85 Pedestrian -1 -1 -1 184.54 160.05 201.16 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n202 39 Pedestrian -1 -1 -1 351.25 161.17 369.34 210.60 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n202 90 Pedestrian -1 -1 -1 361.80 160.92 377.61 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n202 89 Pedestrian -1 -1 -1 189.74 153.84 211.08 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n202 92 Car -1 -1 -1 598.77 173.50 621.19 193.21 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n202 93 Pedestrian -1 -1 -1 490.73 168.31 515.01 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n203 1 Car -1 -1 -1 1095.46 185.47 1220.40 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n203 2 Car -1 -1 -1 954.64 183.91 1067.04 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n203 3 Car -1 -1 -1 1030.07 183.95 1155.52 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n203 55 Pedestrian -1 -1 -1 462.64 163.64 490.91 233.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n203 8 Car -1 -1 -1 601.43 172.81 637.37 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n203 46 Pedestrian -1 -1 -1 332.67 158.22 351.40 209.78 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n203 87 Pedestrian -1 -1 -1 419.08 163.90 439.28 210.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n203 60 Pedestrian -1 -1 -1 442.09 163.66 465.47 227.70 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n203 58 Pedestrian -1 -1 -1 499.64 162.03 523.03 226.23 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n203 6 Pedestrian -1 -1 -1 311.98 162.76 329.29 208.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n203 93 Pedestrian -1 -1 -1 490.64 168.29 516.50 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n203 39 Pedestrian -1 -1 -1 351.39 161.36 369.55 210.40 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n203 85 Pedestrian -1 -1 -1 184.51 159.90 200.90 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n203 90 Pedestrian -1 -1 -1 361.67 161.09 377.54 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n203 89 Pedestrian -1 -1 -1 190.00 153.76 210.78 196.37 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n203 92 Car -1 -1 -1 598.84 173.62 621.14 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n203 94 Pedestrian -1 -1 -1 438.24 163.97 454.64 207.14 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n204 1 Car -1 -1 -1 1095.50 185.50 1220.46 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n204 2 Car -1 -1 -1 954.61 183.86 1067.10 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n204 3 Car -1 -1 -1 1030.05 183.97 1155.53 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n204 8 Car -1 -1 -1 601.62 172.85 637.14 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n204 87 Pedestrian -1 -1 -1 417.13 164.06 437.25 211.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n204 55 Pedestrian -1 -1 -1 465.05 163.89 494.35 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n204 46 Pedestrian -1 -1 -1 332.95 158.67 351.87 209.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n204 93 Pedestrian -1 -1 -1 494.10 168.27 518.34 234.86 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n204 58 Pedestrian -1 -1 -1 502.38 162.26 525.00 227.16 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n204 60 Pedestrian -1 -1 -1 444.92 164.30 468.26 229.83 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n204 39 Pedestrian -1 -1 -1 351.29 161.72 369.64 210.23 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n204 85 Pedestrian -1 -1 -1 184.48 159.67 200.92 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n204 6 Pedestrian -1 -1 -1 313.61 162.45 330.28 208.26 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n204 90 Pedestrian -1 -1 -1 361.58 160.62 378.05 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n204 89 Pedestrian -1 -1 -1 189.93 153.63 210.69 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n204 92 Car -1 -1 -1 598.85 173.58 621.33 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n205 1 Car -1 -1 -1 1095.56 185.59 1220.42 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n205 2 Car -1 -1 -1 954.69 183.91 1067.02 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n205 3 Car -1 -1 -1 1030.09 184.00 1155.46 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n205 55 Pedestrian -1 -1 -1 464.63 163.47 494.88 234.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n205 46 Pedestrian -1 -1 -1 332.96 158.88 352.19 209.47 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n205 8 Car -1 -1 -1 601.64 172.91 637.18 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n205 87 Pedestrian -1 -1 -1 415.97 164.06 436.21 211.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n205 60 Pedestrian -1 -1 -1 445.56 164.05 469.56 230.59 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n205 93 Pedestrian -1 -1 -1 494.28 167.83 520.94 234.98 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n205 58 Pedestrian -1 -1 -1 501.94 162.65 526.95 228.18 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n205 6 Pedestrian -1 -1 -1 313.63 161.99 330.93 207.33 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n205 39 Pedestrian -1 -1 -1 351.30 161.80 369.78 209.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n205 85 Pedestrian -1 -1 -1 184.28 159.74 201.56 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n205 90 Pedestrian -1 -1 -1 361.21 160.53 378.02 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n205 89 Pedestrian -1 -1 -1 189.37 153.42 210.95 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n205 92 Car -1 -1 -1 599.14 173.56 621.39 193.24 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n206 1 Car -1 -1 -1 1095.34 185.54 1220.70 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n206 2 Car -1 -1 -1 954.64 183.90 1067.12 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n206 3 Car -1 -1 -1 1030.02 183.99 1155.58 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n206 93 Pedestrian -1 -1 -1 496.00 168.33 525.19 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n206 55 Pedestrian -1 -1 -1 465.44 163.49 493.85 235.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n206 8 Car -1 -1 -1 601.58 172.81 637.23 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n206 46 Pedestrian -1 -1 -1 333.41 158.69 352.38 208.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n206 87 Pedestrian -1 -1 -1 415.52 163.53 435.77 211.54 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n206 60 Pedestrian -1 -1 -1 448.14 164.25 473.15 227.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n206 6 Pedestrian -1 -1 -1 314.58 162.88 331.36 208.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n206 58 Pedestrian -1 -1 -1 501.35 163.49 528.36 227.44 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n206 39 Pedestrian -1 -1 -1 351.31 161.85 369.75 209.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n206 85 Pedestrian -1 -1 -1 184.14 159.84 201.88 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n206 90 Pedestrian -1 -1 -1 361.21 160.35 377.63 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n206 92 Car -1 -1 -1 598.99 173.67 621.38 193.17 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n207 1 Car -1 -1 -1 1095.42 185.57 1220.62 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n207 2 Car -1 -1 -1 954.70 183.88 1067.09 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n207 3 Car -1 -1 -1 1030.20 183.97 1155.49 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n207 8 Car -1 -1 -1 601.32 172.80 637.39 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n207 46 Pedestrian -1 -1 -1 333.75 158.97 352.16 208.63 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n207 55 Pedestrian -1 -1 -1 465.78 163.82 495.09 235.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n207 93 Pedestrian -1 -1 -1 496.06 166.50 526.73 236.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n207 87 Pedestrian -1 -1 -1 415.26 163.33 435.36 211.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n207 60 Pedestrian -1 -1 -1 450.14 163.97 473.57 227.40 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n207 6 Pedestrian -1 -1 -1 315.82 162.96 331.76 207.99 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n207 85 Pedestrian -1 -1 -1 183.83 159.50 202.03 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n207 39 Pedestrian -1 -1 -1 351.66 161.97 369.18 209.15 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n207 90 Pedestrian -1 -1 -1 361.13 160.62 377.61 203.38 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n207 92 Car -1 -1 -1 598.72 173.65 621.52 193.11 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n208 1 Car -1 -1 -1 1095.31 185.58 1220.72 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n208 2 Car -1 -1 -1 954.62 183.91 1067.06 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n208 3 Car -1 -1 -1 1030.16 183.99 1155.48 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n208 55 Pedestrian -1 -1 -1 468.24 164.27 498.79 238.52 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n208 8 Car -1 -1 -1 601.46 172.82 637.37 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n208 46 Pedestrian -1 -1 -1 333.94 158.97 352.14 208.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n208 87 Pedestrian -1 -1 -1 413.94 163.29 432.92 211.69 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n208 93 Pedestrian -1 -1 -1 499.28 166.94 529.01 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n208 60 Pedestrian -1 -1 -1 451.61 164.47 478.04 230.07 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n208 6 Pedestrian -1 -1 -1 316.51 162.03 332.12 207.33 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n208 39 Pedestrian -1 -1 -1 351.79 161.83 369.21 209.19 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n208 85 Pedestrian -1 -1 -1 183.78 159.32 202.09 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n208 90 Pedestrian -1 -1 -1 361.08 160.55 377.68 203.69 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n208 92 Car -1 -1 -1 598.60 173.64 621.57 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n208 95 Pedestrian -1 -1 -1 505.02 162.26 532.73 227.84 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n209 1 Car -1 -1 -1 1095.28 185.52 1220.75 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n209 2 Car -1 -1 -1 954.61 183.92 1067.11 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n209 3 Car -1 -1 -1 1030.25 183.99 1155.41 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n209 55 Pedestrian -1 -1 -1 469.41 164.82 498.81 238.70 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n209 8 Car -1 -1 -1 601.58 172.81 637.30 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n209 93 Pedestrian -1 -1 -1 504.16 166.33 531.58 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n209 46 Pedestrian -1 -1 -1 333.89 159.06 352.43 208.22 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n209 60 Pedestrian -1 -1 -1 452.26 163.76 478.70 231.62 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n209 87 Pedestrian -1 -1 -1 412.43 163.62 432.34 212.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n209 6 Pedestrian -1 -1 -1 317.83 161.82 333.90 207.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n209 85 Pedestrian -1 -1 -1 183.79 159.32 201.65 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n209 39 Pedestrian -1 -1 -1 351.93 162.28 368.86 208.68 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n209 90 Pedestrian -1 -1 -1 361.06 160.14 377.74 203.40 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n209 92 Car -1 -1 -1 598.70 173.71 621.59 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n209 96 Pedestrian -1 -1 -1 190.03 153.82 210.32 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n210 1 Car -1 -1 -1 1095.41 185.45 1220.64 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n210 2 Car -1 -1 -1 954.78 183.92 1067.06 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n210 3 Car -1 -1 -1 1030.16 183.97 1155.48 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n210 55 Pedestrian -1 -1 -1 472.80 163.78 501.75 239.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n210 60 Pedestrian -1 -1 -1 454.53 163.23 482.58 232.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n210 8 Car -1 -1 -1 601.88 172.88 636.97 202.43 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n210 46 Pedestrian -1 -1 -1 333.82 159.50 352.54 208.06 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n210 6 Pedestrian -1 -1 -1 318.19 162.11 334.98 206.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n210 93 Pedestrian -1 -1 -1 506.88 164.18 536.34 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n210 87 Pedestrian -1 -1 -1 412.07 163.71 431.41 211.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n210 85 Pedestrian -1 -1 -1 184.22 159.36 201.33 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n210 39 Pedestrian -1 -1 -1 350.90 161.59 366.62 207.47 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n210 96 Pedestrian -1 -1 -1 190.54 154.15 210.22 196.46 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n210 90 Pedestrian -1 -1 -1 362.01 160.44 378.24 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n210 92 Car -1 -1 -1 599.04 173.69 621.43 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n211 1 Car -1 -1 -1 1095.35 185.51 1220.71 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n211 2 Car -1 -1 -1 954.68 183.90 1066.98 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n211 3 Car -1 -1 -1 1030.10 183.96 1155.55 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n211 8 Car -1 -1 -1 601.70 172.87 637.20 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n211 60 Pedestrian -1 -1 -1 457.71 162.21 485.61 232.69 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n211 55 Pedestrian -1 -1 -1 477.98 163.56 503.89 239.56 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n211 6 Pedestrian -1 -1 -1 318.58 162.14 335.71 206.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n211 46 Pedestrian -1 -1 -1 333.99 159.40 352.46 208.14 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n211 93 Pedestrian -1 -1 -1 508.70 167.54 534.98 236.64 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n211 87 Pedestrian -1 -1 -1 411.82 163.44 431.17 212.09 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n211 39 Pedestrian -1 -1 -1 350.61 161.28 366.40 207.58 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n211 85 Pedestrian -1 -1 -1 184.24 159.54 200.95 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n211 96 Pedestrian -1 -1 -1 190.67 154.72 210.20 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n211 92 Car -1 -1 -1 598.98 173.72 621.37 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n211 90 Pedestrian -1 -1 -1 362.38 160.69 378.47 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n211 97 Pedestrian -1 -1 -1 516.85 161.86 542.44 228.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n212 1 Car -1 -1 -1 1095.26 185.50 1220.81 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n212 2 Car -1 -1 -1 954.61 183.87 1067.12 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n212 3 Car -1 -1 -1 1030.33 183.97 1155.39 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n212 8 Car -1 -1 -1 601.65 172.86 637.20 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n212 60 Pedestrian -1 -1 -1 460.08 162.25 485.76 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n212 55 Pedestrian -1 -1 -1 478.65 164.02 505.98 239.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n212 46 Pedestrian -1 -1 -1 334.11 159.64 352.39 207.65 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n212 93 Pedestrian -1 -1 -1 512.09 169.05 538.86 236.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n212 6 Pedestrian -1 -1 -1 319.04 162.26 335.87 206.80 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n212 39 Pedestrian -1 -1 -1 350.48 160.98 366.59 207.47 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n212 87 Pedestrian -1 -1 -1 411.27 162.87 428.09 206.07 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n212 85 Pedestrian -1 -1 -1 184.15 159.44 200.93 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n212 90 Pedestrian -1 -1 -1 364.26 161.40 379.28 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n212 96 Pedestrian -1 -1 -1 190.88 154.80 210.27 196.26 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n212 97 Pedestrian -1 -1 -1 519.96 164.61 546.58 230.16 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n212 92 Car -1 -1 -1 598.73 173.80 621.36 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n213 1 Car -1 -1 -1 1095.53 185.51 1220.73 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n213 2 Car -1 -1 -1 954.70 183.90 1067.06 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n213 3 Car -1 -1 -1 1030.13 183.93 1155.58 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n213 60 Pedestrian -1 -1 -1 463.36 162.96 488.87 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n213 8 Car -1 -1 -1 601.89 172.78 636.85 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n213 55 Pedestrian -1 -1 -1 481.09 163.63 508.82 241.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n213 93 Pedestrian -1 -1 -1 512.34 168.85 540.36 236.87 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n213 87 Pedestrian -1 -1 -1 410.81 163.18 427.60 205.27 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n213 6 Pedestrian -1 -1 -1 319.65 161.96 336.06 206.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n213 46 Pedestrian -1 -1 -1 334.33 159.51 352.42 207.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n213 39 Pedestrian -1 -1 -1 350.57 160.88 366.92 207.78 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n213 85 Pedestrian -1 -1 -1 184.10 159.35 201.00 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n213 90 Pedestrian -1 -1 -1 364.26 161.38 379.55 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n213 92 Car -1 -1 -1 598.74 173.76 621.13 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n213 96 Pedestrian -1 -1 -1 190.75 154.84 210.35 196.38 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n214 1 Car -1 -1 -1 1095.44 185.61 1220.64 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n214 2 Car -1 -1 -1 954.75 183.91 1067.16 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n214 3 Car -1 -1 -1 1030.27 183.96 1155.48 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n214 8 Car -1 -1 -1 602.02 172.83 636.80 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n214 55 Pedestrian -1 -1 -1 481.73 163.98 510.40 241.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n214 60 Pedestrian -1 -1 -1 465.08 163.35 488.81 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n214 87 Pedestrian -1 -1 -1 410.06 163.65 427.03 205.05 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n214 46 Pedestrian -1 -1 -1 335.98 159.26 353.90 207.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n214 93 Pedestrian -1 -1 -1 512.83 168.41 541.01 237.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n214 6 Pedestrian -1 -1 -1 320.11 161.54 336.39 206.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n214 90 Pedestrian -1 -1 -1 364.58 161.04 380.16 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n214 92 Car -1 -1 -1 599.06 173.71 620.99 192.81 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n214 39 Pedestrian -1 -1 -1 350.75 161.16 366.80 207.41 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n214 85 Pedestrian -1 -1 -1 184.21 159.55 201.08 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n214 96 Pedestrian -1 -1 -1 190.66 154.91 210.51 196.37 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n215 1 Car -1 -1 -1 1095.40 185.58 1220.73 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n215 2 Car -1 -1 -1 954.69 183.93 1067.18 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n215 3 Car -1 -1 -1 1030.40 183.98 1155.39 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n215 93 Pedestrian -1 -1 -1 515.34 168.32 544.01 238.01 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n215 55 Pedestrian -1 -1 -1 485.51 163.68 513.95 241.68 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n215 60 Pedestrian -1 -1 -1 466.88 163.33 492.53 234.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n215 8 Car -1 -1 -1 601.90 172.79 636.93 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n215 46 Pedestrian -1 -1 -1 336.34 159.30 353.88 206.64 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n215 6 Pedestrian -1 -1 -1 321.65 161.69 337.46 205.61 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n215 87 Pedestrian -1 -1 -1 409.89 164.07 426.44 204.71 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n215 90 Pedestrian -1 -1 -1 365.26 161.19 381.41 203.28 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n215 92 Car -1 -1 -1 598.83 173.73 620.98 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n215 39 Pedestrian -1 -1 -1 350.78 161.45 366.72 207.03 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n215 85 Pedestrian -1 -1 -1 184.22 159.78 200.78 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n215 96 Pedestrian -1 -1 -1 191.00 155.18 210.28 196.28 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n216 1 Car -1 -1 -1 1095.22 185.47 1220.86 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n216 2 Car -1 -1 -1 954.71 183.92 1067.00 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n216 3 Car -1 -1 -1 1030.37 183.98 1155.36 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n216 55 Pedestrian -1 -1 -1 488.90 163.55 516.93 241.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n216 8 Car -1 -1 -1 601.89 172.65 636.92 202.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n216 46 Pedestrian -1 -1 -1 337.00 159.80 354.17 206.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n216 60 Pedestrian -1 -1 -1 470.48 163.51 496.01 234.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n216 93 Pedestrian -1 -1 -1 517.91 167.23 547.52 239.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n216 87 Pedestrian -1 -1 -1 409.95 163.95 426.36 204.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n216 6 Pedestrian -1 -1 -1 321.63 161.80 337.53 205.80 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n216 90 Pedestrian -1 -1 -1 365.79 161.30 381.83 203.20 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n216 39 Pedestrian -1 -1 -1 351.08 161.36 366.62 206.80 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n216 92 Car -1 -1 -1 598.79 173.66 621.20 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n216 85 Pedestrian -1 -1 -1 184.26 159.95 200.88 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n216 96 Pedestrian -1 -1 -1 191.02 155.32 210.30 196.21 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n217 1 Car -1 -1 -1 1095.13 185.49 1221.00 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n217 2 Car -1 -1 -1 954.69 183.90 1067.19 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n217 3 Car -1 -1 -1 1030.29 183.95 1155.52 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n217 55 Pedestrian -1 -1 -1 492.04 162.98 520.39 242.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n217 93 Pedestrian -1 -1 -1 518.95 166.93 549.07 239.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n217 46 Pedestrian -1 -1 -1 337.42 160.12 354.41 206.31 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n217 8 Car -1 -1 -1 601.94 172.72 636.90 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n217 6 Pedestrian -1 -1 -1 322.37 162.24 337.73 206.04 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n217 87 Pedestrian -1 -1 -1 410.03 163.81 426.18 204.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n217 60 Pedestrian -1 -1 -1 471.97 163.07 497.52 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n217 39 Pedestrian -1 -1 -1 351.38 160.99 366.45 206.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n217 92 Car -1 -1 -1 598.84 173.75 621.18 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n217 90 Pedestrian -1 -1 -1 365.79 160.67 382.07 203.34 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n217 85 Pedestrian -1 -1 -1 184.38 160.15 200.80 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n217 96 Pedestrian -1 -1 -1 190.90 155.43 210.46 196.12 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n218 1 Car -1 -1 -1 1095.37 185.49 1220.83 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n218 2 Car -1 -1 -1 954.60 183.88 1067.05 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n218 3 Car -1 -1 -1 1030.36 184.06 1155.51 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n218 55 Pedestrian -1 -1 -1 490.92 162.24 524.31 242.74 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n218 46 Pedestrian -1 -1 -1 337.39 159.96 354.52 206.08 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n218 6 Pedestrian -1 -1 -1 323.76 162.13 338.36 206.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n218 8 Car -1 -1 -1 602.89 172.48 637.24 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n218 93 Pedestrian -1 -1 -1 522.19 162.09 552.05 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n218 60 Pedestrian -1 -1 -1 473.73 162.39 500.74 235.07 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n218 87 Pedestrian -1 -1 -1 409.97 163.72 425.99 204.64 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n218 92 Car -1 -1 -1 598.86 173.63 621.10 193.01 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n218 39 Pedestrian -1 -1 -1 352.35 161.16 368.36 206.29 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n218 85 Pedestrian -1 -1 -1 184.43 160.21 200.63 198.74 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n218 90 Pedestrian -1 -1 -1 365.46 160.78 382.39 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n218 96 Pedestrian -1 -1 -1 191.15 155.58 210.25 195.96 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n218 98 Pedestrian -1 -1 -1 288.30 157.74 302.78 191.59 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n219 1 Car -1 -1 -1 1095.42 185.39 1220.53 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n219 2 Car -1 -1 -1 954.71 183.87 1067.10 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n219 3 Car -1 -1 -1 1030.34 184.03 1155.42 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n219 55 Pedestrian -1 -1 -1 493.56 163.12 528.81 243.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n219 46 Pedestrian -1 -1 -1 337.47 159.62 354.75 205.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n219 8 Car -1 -1 -1 602.99 172.61 637.12 202.52 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n219 60 Pedestrian -1 -1 -1 474.29 162.42 502.33 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n219 6 Pedestrian -1 -1 -1 323.97 161.68 339.52 205.78 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n219 93 Pedestrian -1 -1 -1 524.18 164.64 552.57 239.65 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n219 87 Pedestrian -1 -1 -1 409.81 163.72 425.73 204.63 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n219 39 Pedestrian -1 -1 -1 350.98 161.12 366.67 206.42 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n219 85 Pedestrian -1 -1 -1 184.35 160.09 200.97 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n219 92 Car -1 -1 -1 599.08 173.65 620.93 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n219 90 Pedestrian -1 -1 -1 365.70 160.16 382.52 203.54 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n220 1 Car -1 -1 -1 1095.40 185.52 1220.59 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n220 2 Car -1 -1 -1 954.68 183.87 1067.22 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n220 3 Car -1 -1 -1 1030.24 184.01 1155.46 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n220 46 Pedestrian -1 -1 -1 337.75 159.30 354.81 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n220 55 Pedestrian -1 -1 -1 497.92 163.91 531.60 243.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n220 60 Pedestrian -1 -1 -1 476.82 163.10 506.07 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n220 8 Car -1 -1 -1 603.08 172.64 636.98 202.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n220 6 Pedestrian -1 -1 -1 323.54 161.23 339.91 205.59 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n220 93 Pedestrian -1 -1 -1 530.37 159.93 559.73 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n220 39 Pedestrian -1 -1 -1 350.74 160.74 366.59 206.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n220 85 Pedestrian -1 -1 -1 184.16 160.23 200.89 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n220 87 Pedestrian -1 -1 -1 409.61 163.76 425.71 204.72 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n220 92 Car -1 -1 -1 599.03 173.76 621.01 193.23 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n220 90 Pedestrian -1 -1 -1 365.69 160.10 382.39 203.54 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n221 1 Car -1 -1 -1 1095.40 185.48 1220.67 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n221 2 Car -1 -1 -1 954.74 183.90 1066.98 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n221 3 Car -1 -1 -1 1030.36 183.98 1155.45 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n221 55 Pedestrian -1 -1 -1 501.20 162.41 535.24 243.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n221 8 Car -1 -1 -1 602.90 172.54 637.21 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n221 46 Pedestrian -1 -1 -1 337.93 159.34 354.61 205.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n221 60 Pedestrian -1 -1 -1 476.99 161.98 507.45 236.84 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n221 93 Pedestrian -1 -1 -1 530.24 165.67 558.90 239.84 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n221 6 Pedestrian -1 -1 -1 323.58 160.80 339.97 205.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n221 87 Pedestrian -1 -1 -1 409.65 163.65 426.28 204.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n221 39 Pedestrian -1 -1 -1 350.84 160.17 366.86 206.35 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n221 92 Car -1 -1 -1 598.97 173.36 621.21 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n221 90 Pedestrian -1 -1 -1 367.83 160.22 383.57 203.57 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n221 85 Pedestrian -1 -1 -1 184.02 160.11 201.17 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n222 1 Car -1 -1 -1 1095.40 185.51 1220.63 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n222 2 Car -1 -1 -1 954.73 183.91 1067.18 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n222 3 Car -1 -1 -1 1030.35 183.96 1155.42 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n222 8 Car -1 -1 -1 602.09 172.80 636.70 202.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n222 46 Pedestrian -1 -1 -1 338.27 159.52 354.74 205.55 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n222 60 Pedestrian -1 -1 -1 482.54 161.50 509.42 236.34 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n222 55 Pedestrian -1 -1 -1 506.31 161.79 537.73 244.33 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n222 6 Pedestrian -1 -1 -1 323.28 160.67 340.36 205.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n222 87 Pedestrian -1 -1 -1 409.94 163.51 425.69 204.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n222 93 Pedestrian -1 -1 -1 538.17 161.11 566.36 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n222 90 Pedestrian -1 -1 -1 368.32 160.22 384.15 203.59 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n222 39 Pedestrian -1 -1 -1 351.98 160.27 368.51 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n222 92 Car -1 -1 -1 599.24 173.28 621.26 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n222 85 Pedestrian -1 -1 -1 184.12 159.92 200.96 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n223 1 Car -1 -1 -1 1095.37 185.50 1220.76 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n223 2 Car -1 -1 -1 954.78 183.90 1067.02 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n223 3 Car -1 -1 -1 1030.31 183.99 1155.48 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n223 8 Car -1 -1 -1 601.71 172.55 637.00 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n223 55 Pedestrian -1 -1 -1 507.01 162.35 538.08 243.43 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n223 60 Pedestrian -1 -1 -1 485.56 162.76 513.39 235.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n223 6 Pedestrian -1 -1 -1 322.72 160.74 340.46 205.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n223 46 Pedestrian -1 -1 -1 337.81 159.44 354.22 204.86 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n223 87 Pedestrian -1 -1 -1 409.54 163.70 425.96 204.00 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n223 93 Pedestrian -1 -1 -1 533.36 168.75 563.64 241.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n223 90 Pedestrian -1 -1 -1 369.01 160.51 384.73 203.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n223 39 Pedestrian -1 -1 -1 350.61 160.33 366.95 206.03 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n223 85 Pedestrian -1 -1 -1 184.02 159.71 200.93 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n223 92 Car -1 -1 -1 599.10 173.29 621.32 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n223 99 Pedestrian -1 -1 -1 539.31 162.89 567.66 232.33 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n224 1 Car -1 -1 -1 1095.23 185.47 1220.77 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n224 2 Car -1 -1 -1 954.70 183.97 1067.07 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n224 3 Car -1 -1 -1 1030.04 183.98 1155.69 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n224 55 Pedestrian -1 -1 -1 510.78 162.41 541.62 243.83 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n224 8 Car -1 -1 -1 601.89 172.61 636.87 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n224 93 Pedestrian -1 -1 -1 538.05 168.79 566.72 242.20 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n224 46 Pedestrian -1 -1 -1 337.61 159.28 354.18 204.79 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n224 60 Pedestrian -1 -1 -1 488.31 162.93 516.24 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n224 6 Pedestrian -1 -1 -1 323.05 160.10 340.62 204.63 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n224 87 Pedestrian -1 -1 -1 409.84 163.74 425.72 204.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n224 90 Pedestrian -1 -1 -1 369.28 160.79 384.99 203.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n224 39 Pedestrian -1 -1 -1 352.18 160.64 368.50 206.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n224 85 Pedestrian -1 -1 -1 184.22 159.91 200.96 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n224 92 Car -1 -1 -1 599.48 173.30 621.39 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n224 99 Pedestrian -1 -1 -1 548.08 163.26 571.38 227.11 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n224 100 Pedestrian -1 -1 -1 293.45 157.77 306.76 191.01 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n225 1 Car -1 -1 -1 1095.17 185.47 1220.83 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n225 2 Car -1 -1 -1 954.64 183.97 1067.12 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n225 3 Car -1 -1 -1 1030.03 183.98 1155.60 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n225 60 Pedestrian -1 -1 -1 489.36 164.25 518.03 238.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n225 8 Car -1 -1 -1 602.00 172.48 636.76 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n225 90 Pedestrian -1 -1 -1 369.43 161.10 385.11 204.19 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n225 93 Pedestrian -1 -1 -1 539.68 168.22 567.28 243.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n225 55 Pedestrian -1 -1 -1 515.29 163.10 545.33 247.41 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n225 87 Pedestrian -1 -1 -1 409.69 164.09 425.70 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n225 46 Pedestrian -1 -1 -1 337.78 159.22 354.39 204.28 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n225 39 Pedestrian -1 -1 -1 352.56 160.25 368.82 205.98 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n225 6 Pedestrian -1 -1 -1 323.29 160.17 340.29 204.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n225 85 Pedestrian -1 -1 -1 184.44 160.14 200.77 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n225 99 Pedestrian -1 -1 -1 546.26 164.46 573.73 230.32 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n225 100 Pedestrian -1 -1 -1 294.29 157.82 307.29 191.57 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n225 92 Car -1 -1 -1 599.75 173.22 621.28 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n225 101 Cyclist -1 -1 -1 547.34 163.92 573.20 227.22 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n225 102 Pedestrian -1 -1 -1 384.28 164.65 400.58 207.24 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n226 1 Car -1 -1 -1 1095.28 185.46 1220.77 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n226 2 Car -1 -1 -1 954.86 184.00 1066.91 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n226 3 Car -1 -1 -1 1029.97 183.93 1155.60 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n226 60 Pedestrian -1 -1 -1 492.78 163.42 521.26 239.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n226 93 Pedestrian -1 -1 -1 542.82 168.33 571.07 243.20 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n226 55 Pedestrian -1 -1 -1 518.38 162.95 548.76 248.16 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n226 87 Pedestrian -1 -1 -1 409.56 164.15 426.16 203.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n226 46 Pedestrian -1 -1 -1 338.07 159.51 355.08 204.30 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n226 8 Car -1 -1 -1 603.26 172.48 636.88 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n226 90 Pedestrian -1 -1 -1 369.72 160.91 385.36 204.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n226 39 Pedestrian -1 -1 -1 353.07 159.76 369.20 205.62 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n226 6 Pedestrian -1 -1 -1 323.71 160.18 340.56 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n226 99 Pedestrian -1 -1 -1 547.17 163.94 574.49 231.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n226 85 Pedestrian -1 -1 -1 184.33 160.18 200.95 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n226 100 Pedestrian -1 -1 -1 294.87 157.35 308.00 191.30 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n226 92 Car -1 -1 -1 599.38 172.88 621.98 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n226 102 Pedestrian -1 -1 -1 384.38 164.58 401.12 208.03 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n227 1 Car -1 -1 -1 1095.48 185.40 1220.61 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n227 2 Car -1 -1 -1 954.73 183.94 1066.95 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n227 3 Car -1 -1 -1 1029.95 183.97 1155.79 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n227 87 Pedestrian -1 -1 -1 409.97 163.72 426.67 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n227 60 Pedestrian -1 -1 -1 496.69 162.85 524.69 239.10 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n227 8 Car -1 -1 -1 601.83 172.55 636.80 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n227 46 Pedestrian -1 -1 -1 338.85 160.03 355.35 204.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n227 55 Pedestrian -1 -1 -1 521.59 162.49 552.82 248.10 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n227 90 Pedestrian -1 -1 -1 369.24 160.21 385.14 204.63 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n227 39 Pedestrian -1 -1 -1 353.16 159.88 369.50 205.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n227 93 Pedestrian -1 -1 -1 546.12 166.85 574.80 244.36 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n227 85 Pedestrian -1 -1 -1 184.35 160.09 200.93 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n227 6 Pedestrian -1 -1 -1 324.91 160.96 341.70 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n227 100 Pedestrian -1 -1 -1 296.67 157.97 309.30 191.61 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n227 102 Pedestrian -1 -1 -1 384.45 164.49 401.35 208.37 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n227 92 Car -1 -1 -1 600.08 173.32 621.07 193.29 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n228 1 Car -1 -1 -1 1095.45 185.56 1220.70 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n228 2 Car -1 -1 -1 954.67 183.95 1067.07 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n228 3 Car -1 -1 -1 1030.31 184.02 1155.49 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n228 87 Pedestrian -1 -1 -1 410.19 163.84 427.01 203.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n228 60 Pedestrian -1 -1 -1 500.63 162.95 528.93 239.29 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n228 8 Car -1 -1 -1 601.66 172.55 636.83 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n228 55 Pedestrian -1 -1 -1 525.83 159.60 556.45 246.79 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n228 90 Pedestrian -1 -1 -1 369.36 159.88 385.16 204.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n228 39 Pedestrian -1 -1 -1 353.51 160.16 369.92 205.93 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n228 6 Pedestrian -1 -1 -1 325.53 161.03 341.54 203.53 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n228 100 Pedestrian -1 -1 -1 296.86 158.17 309.90 191.42 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n228 46 Pedestrian -1 -1 -1 339.34 160.00 355.37 203.93 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n228 85 Pedestrian -1 -1 -1 184.52 160.11 200.66 199.06 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n228 93 Pedestrian -1 -1 -1 548.93 166.61 579.62 245.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n228 102 Pedestrian -1 -1 -1 384.29 164.02 401.49 209.65 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n228 92 Car -1 -1 -1 599.96 173.30 621.19 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n228 103 Cyclist -1 -1 -1 552.75 160.49 581.83 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n229 1 Car -1 -1 -1 1095.32 185.45 1220.75 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n229 2 Car -1 -1 -1 954.61 183.90 1067.09 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n229 3 Car -1 -1 -1 1030.26 184.04 1155.55 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n229 87 Pedestrian -1 -1 -1 410.92 164.51 427.38 203.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n229 8 Car -1 -1 -1 602.02 172.71 636.59 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n229 100 Pedestrian -1 -1 -1 297.55 158.61 310.55 191.87 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n229 55 Pedestrian -1 -1 -1 527.88 158.70 561.82 247.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n229 6 Pedestrian -1 -1 -1 326.07 161.23 342.59 203.26 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n229 60 Pedestrian -1 -1 -1 500.08 162.91 530.04 239.44 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n229 90 Pedestrian -1 -1 -1 369.23 160.28 385.25 204.53 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n229 93 Pedestrian -1 -1 -1 553.99 166.03 583.68 245.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n229 39 Pedestrian -1 -1 -1 353.57 160.36 370.41 205.94 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n229 85 Pedestrian -1 -1 -1 184.69 160.18 200.48 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n229 46 Pedestrian -1 -1 -1 340.75 160.34 356.93 203.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n229 103 Cyclist -1 -1 -1 554.91 159.72 582.14 237.03 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n229 104 Pedestrian -1 -1 -1 191.47 161.01 209.10 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n230 1 Car -1 -1 -1 1095.23 185.47 1220.78 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n230 2 Car -1 -1 -1 954.60 183.93 1067.25 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n230 3 Car -1 -1 -1 1030.22 184.03 1155.59 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n230 60 Pedestrian -1 -1 -1 502.41 162.46 533.47 240.22 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n230 55 Pedestrian -1 -1 -1 531.28 161.03 566.70 248.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n230 8 Car -1 -1 -1 601.72 172.44 636.89 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n230 87 Pedestrian -1 -1 -1 411.71 164.54 427.50 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n230 90 Pedestrian -1 -1 -1 369.09 160.08 385.45 204.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n230 6 Pedestrian -1 -1 -1 325.92 160.33 343.30 203.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n230 93 Pedestrian -1 -1 -1 561.47 164.63 588.87 246.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n230 100 Pedestrian -1 -1 -1 298.36 158.30 311.29 191.86 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n230 46 Pedestrian -1 -1 -1 340.98 159.88 357.35 203.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n230 85 Pedestrian -1 -1 -1 184.41 160.08 200.55 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n230 39 Pedestrian -1 -1 -1 353.86 160.37 370.15 205.69 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n230 104 Pedestrian -1 -1 -1 191.43 160.74 208.91 198.48 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n231 1 Car -1 -1 -1 1095.38 185.54 1220.65 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n231 2 Car -1 -1 -1 954.72 183.90 1067.07 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n231 3 Car -1 -1 -1 1030.30 184.03 1155.42 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n231 55 Pedestrian -1 -1 -1 534.94 161.36 570.72 249.46 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n231 60 Pedestrian -1 -1 -1 505.00 162.00 538.03 241.27 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n231 93 Pedestrian -1 -1 -1 563.60 165.45 594.37 247.21 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n231 8 Car -1 -1 -1 601.67 172.48 637.01 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n231 90 Pedestrian -1 -1 -1 369.12 160.03 385.81 205.00 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n231 6 Pedestrian -1 -1 -1 326.86 161.27 343.82 203.36 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n231 46 Pedestrian -1 -1 -1 341.76 159.72 357.77 203.92 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n231 87 Pedestrian -1 -1 -1 412.21 164.52 428.00 203.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n231 100 Pedestrian -1 -1 -1 299.80 158.12 313.44 191.78 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n231 85 Pedestrian -1 -1 -1 184.32 159.99 200.38 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n231 39 Pedestrian -1 -1 -1 353.74 160.10 370.58 205.84 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n231 104 Pedestrian -1 -1 -1 191.64 160.73 208.94 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n231 105 Pedestrian -1 -1 -1 530.91 165.82 551.96 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n232 1 Car -1 -1 -1 1095.27 185.47 1220.69 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n232 2 Car -1 -1 -1 954.61 183.87 1067.13 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n232 3 Car -1 -1 -1 1030.11 184.00 1155.61 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n232 55 Pedestrian -1 -1 -1 538.97 161.54 574.30 250.45 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n232 60 Pedestrian -1 -1 -1 507.43 161.66 538.00 240.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n232 8 Car -1 -1 -1 601.42 172.39 637.20 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n232 100 Pedestrian -1 -1 -1 300.24 158.15 313.86 191.53 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n232 93 Pedestrian -1 -1 -1 564.57 166.45 595.55 246.48 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n232 90 Pedestrian -1 -1 -1 369.18 160.23 386.03 205.10 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n232 46 Pedestrian -1 -1 -1 342.09 159.49 358.49 204.27 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n232 87 Pedestrian -1 -1 -1 412.43 164.48 427.68 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n232 85 Pedestrian -1 -1 -1 184.61 159.79 200.34 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n232 6 Pedestrian -1 -1 -1 327.24 161.44 344.21 203.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n232 39 Pedestrian -1 -1 -1 353.72 160.00 371.04 205.93 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n232 105 Pedestrian -1 -1 -1 534.26 165.99 555.02 225.03 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n232 104 Pedestrian -1 -1 -1 192.07 160.74 208.67 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n233 1 Car -1 -1 -1 1095.27 185.41 1220.57 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n233 2 Car -1 -1 -1 954.64 183.87 1067.14 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n233 3 Car -1 -1 -1 1029.98 183.95 1155.64 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n233 8 Car -1 -1 -1 601.55 172.48 636.78 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n233 55 Pedestrian -1 -1 -1 543.39 162.12 576.48 250.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n233 60 Pedestrian -1 -1 -1 511.59 161.51 539.70 240.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n233 100 Pedestrian -1 -1 -1 301.06 158.28 315.15 191.90 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n233 93 Pedestrian -1 -1 -1 567.05 168.13 600.47 249.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n233 85 Pedestrian -1 -1 -1 184.70 159.67 200.28 199.03 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n233 90 Pedestrian -1 -1 -1 369.15 160.43 386.48 205.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n233 46 Pedestrian -1 -1 -1 342.49 159.68 359.07 204.08 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n233 6 Pedestrian -1 -1 -1 329.02 161.52 345.83 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n233 87 Pedestrian -1 -1 -1 413.07 164.50 429.44 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n233 39 Pedestrian -1 -1 -1 356.51 160.26 373.51 205.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n233 105 Pedestrian -1 -1 -1 533.30 163.03 557.08 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n233 106 Car -1 -1 -1 596.93 172.22 624.33 194.83 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n234 1 Car -1 -1 -1 1095.06 185.41 1220.74 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n234 2 Car -1 -1 -1 954.69 183.82 1067.14 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n234 3 Car -1 -1 -1 1030.52 184.11 1155.42 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n234 8 Car -1 -1 -1 601.70 172.55 636.61 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n234 55 Pedestrian -1 -1 -1 548.59 161.72 579.18 251.56 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n234 100 Pedestrian -1 -1 -1 301.97 158.29 315.55 192.42 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n234 60 Pedestrian -1 -1 -1 513.56 162.47 539.91 240.51 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n234 6 Pedestrian -1 -1 -1 328.93 162.08 346.02 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n234 93 Pedestrian -1 -1 -1 571.78 168.41 603.76 248.95 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n234 85 Pedestrian -1 -1 -1 184.50 159.76 200.22 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n234 87 Pedestrian -1 -1 -1 413.20 165.04 429.52 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n234 46 Pedestrian -1 -1 -1 344.80 159.78 360.92 203.85 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n234 105 Pedestrian -1 -1 -1 536.57 163.11 560.13 233.91 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n234 90 Pedestrian -1 -1 -1 369.13 160.82 386.96 205.40 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n234 39 Pedestrian -1 -1 -1 357.01 160.33 374.00 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n234 107 Pedestrian -1 -1 -1 568.53 161.88 598.08 234.75 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n234 108 Pedestrian -1 -1 -1 325.40 158.68 338.47 190.89 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n235 1 Car -1 -1 -1 1095.17 185.45 1220.78 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n235 2 Car -1 -1 -1 954.65 183.82 1067.21 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n235 3 Car -1 -1 -1 1030.47 184.05 1155.44 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n235 60 Pedestrian -1 -1 -1 514.21 163.28 544.13 240.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n235 6 Pedestrian -1 -1 -1 329.53 161.32 346.63 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n235 8 Car -1 -1 -1 601.98 172.40 636.61 202.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n235 55 Pedestrian -1 -1 -1 552.25 161.69 583.30 252.07 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n235 100 Pedestrian -1 -1 -1 303.79 158.31 316.96 192.60 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n235 93 Pedestrian -1 -1 -1 579.90 166.59 609.03 248.10 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n235 85 Pedestrian -1 -1 -1 184.61 160.11 200.89 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n235 87 Pedestrian -1 -1 -1 413.41 164.92 429.62 203.29 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n235 105 Pedestrian -1 -1 -1 536.10 164.34 561.80 239.81 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n235 46 Pedestrian -1 -1 -1 344.75 158.04 360.97 203.43 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n235 108 Pedestrian -1 -1 -1 325.56 158.73 338.67 191.28 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n235 39 Pedestrian -1 -1 -1 360.12 160.52 376.93 205.53 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n235 90 Pedestrian -1 -1 -1 369.32 160.95 386.79 205.48 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n236 1 Car -1 -1 -1 1095.24 185.50 1220.92 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n236 2 Car -1 -1 -1 954.74 183.81 1066.93 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n236 3 Car -1 -1 -1 1030.34 184.03 1155.44 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n236 8 Car -1 -1 -1 603.67 172.47 637.22 202.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n236 60 Pedestrian -1 -1 -1 515.45 163.44 544.87 242.29 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n236 55 Pedestrian -1 -1 -1 554.82 161.49 588.78 252.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n236 100 Pedestrian -1 -1 -1 304.33 158.20 317.18 192.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n236 46 Pedestrian -1 -1 -1 345.27 157.92 361.81 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n236 6 Pedestrian -1 -1 -1 330.83 161.03 347.63 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n236 93 Pedestrian -1 -1 -1 583.59 167.31 614.18 249.94 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n236 87 Pedestrian -1 -1 -1 413.57 164.59 429.83 203.31 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n236 85 Pedestrian -1 -1 -1 184.36 160.23 201.29 198.86 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n236 105 Pedestrian -1 -1 -1 539.06 163.10 566.09 240.90 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n236 39 Pedestrian -1 -1 -1 360.57 160.71 377.04 205.28 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n236 108 Pedestrian -1 -1 -1 325.63 158.76 338.29 190.90 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n237 1 Car -1 -1 -1 1095.31 185.51 1220.67 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n237 2 Car -1 -1 -1 954.51 183.80 1067.21 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n237 3 Car -1 -1 -1 1030.29 184.04 1155.56 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n237 8 Car -1 -1 -1 604.02 172.24 637.29 201.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n237 55 Pedestrian -1 -1 -1 557.17 163.02 594.39 254.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n237 60 Pedestrian -1 -1 -1 517.60 163.65 548.96 242.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n237 100 Pedestrian -1 -1 -1 304.83 157.58 317.91 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n237 87 Pedestrian -1 -1 -1 413.92 164.31 430.16 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n237 93 Pedestrian -1 -1 -1 587.69 167.88 623.47 250.21 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n237 46 Pedestrian -1 -1 -1 346.06 158.37 362.38 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n237 6 Pedestrian -1 -1 -1 331.44 161.28 347.98 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n237 85 Pedestrian -1 -1 -1 184.32 160.31 201.30 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n237 105 Pedestrian -1 -1 -1 544.83 163.35 568.35 232.33 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n237 108 Pedestrian -1 -1 -1 327.22 158.41 340.44 192.34 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n237 39 Pedestrian -1 -1 -1 360.84 161.10 376.57 205.14 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n238 1 Car -1 -1 -1 1095.39 185.44 1220.70 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n238 2 Car -1 -1 -1 954.61 183.75 1067.22 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n238 3 Car -1 -1 -1 1030.23 184.01 1155.55 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n238 8 Car -1 -1 -1 604.54 172.21 637.30 201.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n238 100 Pedestrian -1 -1 -1 305.77 157.91 319.06 192.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n238 60 Pedestrian -1 -1 -1 519.38 163.52 548.82 241.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n238 93 Pedestrian -1 -1 -1 592.89 168.08 626.51 250.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n238 55 Pedestrian -1 -1 -1 561.50 162.77 597.91 254.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n238 87 Pedestrian -1 -1 -1 414.08 164.28 430.57 203.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n238 6 Pedestrian -1 -1 -1 333.57 160.93 349.11 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n238 105 Pedestrian -1 -1 -1 543.90 161.96 570.12 242.40 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n238 85 Pedestrian -1 -1 -1 184.45 160.31 200.91 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n238 46 Pedestrian -1 -1 -1 346.70 158.33 362.50 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n238 108 Pedestrian -1 -1 -1 327.55 158.24 340.43 192.17 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n238 39 Pedestrian -1 -1 -1 361.27 161.43 376.48 205.00 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n239 1 Car -1 -1 -1 1095.50 185.52 1220.48 235.66 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n239 2 Car -1 -1 -1 954.59 183.78 1067.18 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n239 3 Car -1 -1 -1 1030.10 184.04 1155.70 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n239 93 Pedestrian -1 -1 -1 597.43 169.15 629.55 250.97 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n239 8 Car -1 -1 -1 604.33 172.48 637.58 201.89 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n239 60 Pedestrian -1 -1 -1 521.79 161.90 551.45 244.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n239 100 Pedestrian -1 -1 -1 306.44 158.18 319.31 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n239 87 Pedestrian -1 -1 -1 414.17 164.38 430.62 203.89 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n239 55 Pedestrian -1 -1 -1 569.24 162.17 604.16 255.21 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n239 6 Pedestrian -1 -1 -1 334.23 161.39 349.71 202.30 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n239 105 Pedestrian -1 -1 -1 544.95 162.02 575.36 242.80 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n239 85 Pedestrian -1 -1 -1 184.53 160.47 200.46 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n239 108 Pedestrian -1 -1 -1 327.33 158.15 340.23 192.02 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n239 46 Pedestrian -1 -1 -1 349.10 158.41 365.25 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n239 39 Pedestrian -1 -1 -1 361.10 161.17 376.78 204.68 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n239 109 Pedestrian -1 -1 -1 579.70 160.63 610.01 236.55 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n240 1 Car -1 -1 -1 1095.38 185.52 1220.67 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n240 2 Car -1 -1 -1 954.65 183.77 1067.23 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n240 3 Car -1 -1 -1 1030.18 183.96 1155.53 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n240 8 Car -1 -1 -1 603.87 172.55 637.82 201.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n240 60 Pedestrian -1 -1 -1 523.07 161.61 552.88 244.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n240 55 Pedestrian -1 -1 -1 573.49 161.04 609.04 256.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n240 93 Pedestrian -1 -1 -1 604.92 169.27 636.80 251.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n240 100 Pedestrian -1 -1 -1 306.46 158.10 319.24 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n240 6 Pedestrian -1 -1 -1 335.26 161.40 350.81 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n240 87 Pedestrian -1 -1 -1 414.69 164.39 430.93 204.03 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n240 105 Pedestrian -1 -1 -1 545.16 162.45 576.77 243.26 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n240 46 Pedestrian -1 -1 -1 350.66 160.46 365.81 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n240 85 Pedestrian -1 -1 -1 185.02 160.38 200.48 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n240 39 Pedestrian -1 -1 -1 361.81 161.92 377.00 203.84 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n240 108 Pedestrian -1 -1 -1 326.90 157.97 340.07 191.79 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n240 110 Cyclist -1 -1 -1 371.20 162.36 405.30 219.55 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n241 1 Car -1 -1 -1 1095.33 185.49 1220.66 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n241 2 Car -1 -1 -1 954.61 183.78 1067.25 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n241 3 Car -1 -1 -1 1030.11 183.95 1155.61 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n241 55 Pedestrian -1 -1 -1 575.20 161.12 614.94 257.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n241 8 Car -1 -1 -1 603.49 172.97 637.71 201.23 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n241 60 Pedestrian -1 -1 -1 526.21 161.20 555.57 248.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n241 87 Pedestrian -1 -1 -1 414.94 164.30 431.05 204.01 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n241 100 Pedestrian -1 -1 -1 306.51 157.64 319.42 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n241 93 Pedestrian -1 -1 -1 611.30 168.91 646.22 251.55 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n241 105 Pedestrian -1 -1 -1 550.98 163.91 583.83 242.50 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n241 108 Pedestrian -1 -1 -1 325.53 158.54 338.50 191.59 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n241 6 Pedestrian -1 -1 -1 337.51 161.42 352.76 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n241 85 Pedestrian -1 -1 -1 184.91 160.38 200.61 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n241 46 Pedestrian -1 -1 -1 351.19 160.43 366.48 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n241 39 Pedestrian -1 -1 -1 361.96 161.87 377.43 203.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n241 110 Cyclist -1 -1 -1 371.15 162.36 405.14 219.22 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n242 1 Car -1 -1 -1 1095.51 185.53 1220.49 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n242 2 Car -1 -1 -1 954.61 183.73 1067.27 233.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n242 3 Car -1 -1 -1 1030.33 184.07 1155.59 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n242 55 Pedestrian -1 -1 -1 577.88 161.00 620.41 257.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n242 60 Pedestrian -1 -1 -1 526.42 161.49 556.67 248.20 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n242 87 Pedestrian -1 -1 -1 414.79 164.21 430.81 203.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n242 8 Car -1 -1 -1 604.49 173.00 636.75 200.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n242 93 Pedestrian -1 -1 -1 613.07 167.31 652.61 253.90 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n242 6 Pedestrian -1 -1 -1 337.96 161.06 353.07 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n242 108 Pedestrian -1 -1 -1 325.43 158.61 338.26 191.90 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n242 105 Pedestrian -1 -1 -1 552.77 164.35 584.39 246.04 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n242 100 Pedestrian -1 -1 -1 306.38 157.71 319.81 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n242 85 Pedestrian -1 -1 -1 184.61 160.32 200.62 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n242 46 Pedestrian -1 -1 -1 351.37 160.28 366.52 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n242 39 Pedestrian -1 -1 -1 362.73 161.84 377.60 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n242 111 Pedestrian -1 -1 -1 191.39 160.92 208.82 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n242 112 Pedestrian -1 -1 -1 373.99 160.33 393.79 212.35 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n243 1 Car -1 -1 -1 1095.47 185.55 1220.69 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n243 2 Car -1 -1 -1 954.53 183.72 1067.46 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n243 3 Car -1 -1 -1 1030.31 184.05 1155.62 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n243 55 Pedestrian -1 -1 -1 581.13 161.65 624.55 257.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n243 60 Pedestrian -1 -1 -1 530.99 160.98 559.48 248.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n243 8 Car -1 -1 -1 605.58 173.08 636.20 200.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n243 87 Pedestrian -1 -1 -1 414.25 164.40 430.11 203.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n243 108 Pedestrian -1 -1 -1 325.04 158.41 337.92 191.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n243 100 Pedestrian -1 -1 -1 307.14 158.23 321.52 193.90 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n243 6 Pedestrian -1 -1 -1 338.32 160.85 353.64 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n243 93 Pedestrian -1 -1 -1 614.09 168.01 652.87 253.60 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n243 105 Pedestrian -1 -1 -1 560.37 162.50 590.27 247.69 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n243 39 Pedestrian -1 -1 -1 364.68 161.98 379.82 204.06 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n243 85 Pedestrian -1 -1 -1 184.54 160.18 200.92 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n243 111 Pedestrian -1 -1 -1 191.60 161.03 208.70 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n243 46 Pedestrian -1 -1 -1 352.90 160.79 368.15 203.32 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n243 112 Pedestrian -1 -1 -1 386.81 164.80 404.18 209.54 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n243 113 Pedestrian -1 -1 -1 374.73 159.79 393.59 213.91 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n244 1 Car -1 -1 -1 1095.64 185.57 1220.57 235.57 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n244 2 Car -1 -1 -1 954.52 183.77 1067.46 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n244 3 Car -1 -1 -1 1030.19 184.03 1155.77 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n244 55 Pedestrian -1 -1 -1 584.51 159.89 628.63 258.86 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n244 60 Pedestrian -1 -1 -1 535.16 160.14 563.56 250.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n244 8 Car -1 -1 -1 605.15 173.10 636.28 201.23 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n244 108 Pedestrian -1 -1 -1 324.35 158.12 337.75 191.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n244 93 Pedestrian -1 -1 -1 621.04 169.12 659.70 255.79 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n244 87 Pedestrian -1 -1 -1 413.85 164.59 429.23 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n244 100 Pedestrian -1 -1 -1 307.33 158.81 321.36 194.05 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n244 6 Pedestrian -1 -1 -1 338.58 161.28 354.16 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n244 105 Pedestrian -1 -1 -1 563.82 162.96 593.87 247.13 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n244 39 Pedestrian -1 -1 -1 365.22 162.20 380.58 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n244 46 Pedestrian -1 -1 -1 353.57 160.83 368.70 203.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n244 111 Pedestrian -1 -1 -1 192.33 161.36 208.28 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n244 112 Pedestrian -1 -1 -1 386.90 165.40 404.65 209.46 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n244 85 Pedestrian -1 -1 -1 184.56 160.05 200.61 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n244 113 Pedestrian -1 -1 -1 375.46 161.02 393.92 213.21 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n244 114 Pedestrian -1 -1 -1 565.95 162.43 592.40 232.39 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n245 1 Car -1 -1 -1 1095.54 185.50 1220.52 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n245 2 Car -1 -1 -1 954.49 183.72 1067.33 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n245 3 Car -1 -1 -1 1030.17 184.04 1155.78 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n245 8 Car -1 -1 -1 604.67 173.04 636.51 201.26 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n245 60 Pedestrian -1 -1 -1 539.06 161.35 567.18 249.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n245 55 Pedestrian -1 -1 -1 591.88 158.95 629.69 259.66 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n245 108 Pedestrian -1 -1 -1 324.05 158.17 337.34 192.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n245 93 Pedestrian -1 -1 -1 629.18 168.51 661.09 253.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n245 6 Pedestrian -1 -1 -1 338.96 161.66 354.82 202.14 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n245 87 Pedestrian -1 -1 -1 413.84 164.63 429.07 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n245 105 Pedestrian -1 -1 -1 567.51 163.10 597.63 247.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n245 100 Pedestrian -1 -1 -1 307.28 158.86 321.36 194.21 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n245 111 Pedestrian -1 -1 -1 192.48 161.23 208.49 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n245 85 Pedestrian -1 -1 -1 184.59 160.21 200.46 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n245 39 Pedestrian -1 -1 -1 365.09 161.66 381.54 203.56 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n245 46 Pedestrian -1 -1 -1 353.75 160.86 369.27 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n245 112 Pedestrian -1 -1 -1 386.79 165.26 405.20 209.95 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n246 1 Car -1 -1 -1 1095.44 185.48 1220.78 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n246 2 Car -1 -1 -1 954.38 183.73 1067.43 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n246 3 Car -1 -1 -1 1030.08 184.00 1155.84 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n246 60 Pedestrian -1 -1 -1 540.45 161.00 573.09 249.52 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n246 8 Car -1 -1 -1 604.43 173.11 636.66 201.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n246 55 Pedestrian -1 -1 -1 593.97 158.28 635.06 253.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n246 87 Pedestrian -1 -1 -1 413.60 164.41 429.65 202.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n246 105 Pedestrian -1 -1 -1 570.04 162.82 603.07 247.86 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n246 108 Pedestrian -1 -1 -1 323.71 158.30 336.67 191.58 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n246 93 Pedestrian -1 -1 -1 634.78 167.10 663.46 255.16 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n246 100 Pedestrian -1 -1 -1 307.26 158.64 321.15 194.29 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n246 111 Pedestrian -1 -1 -1 192.11 161.19 208.55 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n246 85 Pedestrian -1 -1 -1 184.68 159.92 200.53 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n246 6 Pedestrian -1 -1 -1 339.71 161.47 355.10 201.79 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n246 39 Pedestrian -1 -1 -1 368.12 161.41 384.15 203.44 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n246 112 Pedestrian -1 -1 -1 386.95 164.97 405.49 210.02 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n246 46 Pedestrian -1 -1 -1 353.90 160.49 369.31 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n247 1 Car -1 -1 -1 1095.49 185.53 1220.72 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n247 2 Car -1 -1 -1 954.29 183.65 1067.49 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n247 3 Car -1 -1 -1 1030.19 183.92 1155.74 233.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n247 8 Car -1 -1 -1 601.29 172.60 635.27 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n247 60 Pedestrian -1 -1 -1 540.52 161.05 574.18 250.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n247 105 Pedestrian -1 -1 -1 571.40 163.18 603.85 247.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n247 55 Pedestrian -1 -1 -1 595.29 158.16 640.79 255.11 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n247 93 Pedestrian -1 -1 -1 636.99 167.69 675.02 254.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n247 87 Pedestrian -1 -1 -1 413.52 164.22 429.37 202.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n247 108 Pedestrian -1 -1 -1 323.61 158.52 336.98 191.61 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n247 6 Pedestrian -1 -1 -1 341.31 160.32 357.61 201.12 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n247 100 Pedestrian -1 -1 -1 307.24 158.28 321.32 194.46 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n247 112 Pedestrian -1 -1 -1 387.33 164.96 406.04 209.79 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n247 85 Pedestrian -1 -1 -1 184.49 160.48 200.60 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n247 111 Pedestrian -1 -1 -1 191.88 161.13 208.74 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n247 39 Pedestrian -1 -1 -1 368.95 161.77 385.67 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n247 115 Pedestrian -1 -1 -1 373.18 160.95 397.12 221.27 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n248 1 Car -1 -1 -1 1095.51 185.52 1220.79 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n248 2 Car -1 -1 -1 954.24 183.66 1067.70 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n248 3 Car -1 -1 -1 1033.17 183.70 1156.69 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n248 60 Pedestrian -1 -1 -1 545.47 160.92 575.83 251.90 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n248 55 Pedestrian -1 -1 -1 603.10 159.86 648.35 259.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n248 8 Car -1 -1 -1 601.32 172.37 635.94 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n248 93 Pedestrian -1 -1 -1 637.66 166.55 681.65 258.55 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n248 105 Pedestrian -1 -1 -1 576.57 163.46 606.59 248.66 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n248 100 Pedestrian -1 -1 -1 307.55 158.47 320.97 194.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n248 112 Pedestrian -1 -1 -1 389.97 165.31 407.09 209.87 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n248 108 Pedestrian -1 -1 -1 323.38 158.67 336.12 191.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n248 85 Pedestrian -1 -1 -1 185.08 160.31 200.47 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n248 87 Pedestrian -1 -1 -1 413.75 164.42 428.85 201.89 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n248 6 Pedestrian -1 -1 -1 342.68 160.81 357.51 200.93 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n248 111 Pedestrian -1 -1 -1 192.60 161.09 208.37 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n248 39 Pedestrian -1 -1 -1 371.55 162.31 388.26 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n248 115 Pedestrian -1 -1 -1 373.23 161.83 395.85 220.31 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n248 116 Pedestrian -1 -1 -1 353.87 159.13 369.65 202.01 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n248 117 Pedestrian -1 -1 -1 574.53 168.15 599.37 222.27 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n249 1 Car -1 -1 -1 1095.53 185.55 1220.66 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n249 2 Car -1 -1 -1 954.19 183.69 1067.63 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n249 3 Car -1 -1 -1 1030.09 183.96 1155.83 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n249 93 Pedestrian -1 -1 -1 641.43 166.51 685.72 259.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n249 55 Pedestrian -1 -1 -1 609.95 158.52 655.14 261.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n249 60 Pedestrian -1 -1 -1 550.46 160.47 578.01 252.07 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n249 8 Car -1 -1 -1 602.65 171.79 639.59 203.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n249 105 Pedestrian -1 -1 -1 580.45 162.29 610.38 250.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n249 112 Pedestrian -1 -1 -1 390.69 165.92 407.43 210.21 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n249 87 Pedestrian -1 -1 -1 411.90 164.41 428.12 202.32 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n249 85 Pedestrian -1 -1 -1 185.23 160.25 200.59 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n249 100 Pedestrian -1 -1 -1 307.37 158.44 321.26 195.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n249 108 Pedestrian -1 -1 -1 321.37 158.02 335.00 192.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n249 111 Pedestrian -1 -1 -1 192.38 160.87 208.52 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n249 39 Pedestrian -1 -1 -1 372.40 162.64 388.66 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n249 6 Pedestrian -1 -1 -1 343.76 161.92 358.53 201.21 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n249 115 Pedestrian -1 -1 -1 373.22 161.95 395.41 220.05 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n249 116 Pedestrian -1 -1 -1 353.75 158.99 369.42 201.69 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n250 1 Car -1 -1 -1 1095.63 185.56 1220.54 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n250 2 Car -1 -1 -1 954.15 183.65 1067.69 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n250 3 Car -1 -1 -1 1030.09 183.97 1155.88 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n250 55 Pedestrian -1 -1 -1 615.52 158.62 658.04 261.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n250 60 Pedestrian -1 -1 -1 552.57 160.53 582.75 252.40 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n250 105 Pedestrian -1 -1 -1 581.88 162.29 616.07 249.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n250 8 Car -1 -1 -1 601.44 172.29 635.69 201.54 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n250 93 Pedestrian -1 -1 -1 649.03 166.05 687.24 258.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n250 87 Pedestrian -1 -1 -1 411.73 164.42 427.99 202.39 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n250 112 Pedestrian -1 -1 -1 391.72 166.18 408.51 210.21 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n250 100 Pedestrian -1 -1 -1 307.60 158.33 321.62 195.47 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n250 108 Pedestrian -1 -1 -1 321.27 157.85 334.92 192.19 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n250 85 Pedestrian -1 -1 -1 185.26 160.24 200.73 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n250 6 Pedestrian -1 -1 -1 345.98 161.89 360.78 201.36 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n250 111 Pedestrian -1 -1 -1 192.19 160.75 208.63 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n250 115 Pedestrian -1 -1 -1 372.49 161.13 395.28 221.36 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n250 39 Pedestrian -1 -1 -1 373.02 162.46 388.74 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n250 116 Pedestrian -1 -1 -1 358.32 159.61 373.82 201.71 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n251 1 Car -1 -1 -1 1095.44 185.40 1220.65 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n251 2 Car -1 -1 -1 954.13 183.63 1067.71 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n251 3 Car -1 -1 -1 1033.15 183.73 1156.74 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n251 105 Pedestrian -1 -1 -1 583.99 162.02 621.14 251.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n251 60 Pedestrian -1 -1 -1 554.35 161.12 589.53 253.32 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n251 55 Pedestrian -1 -1 -1 624.14 157.22 663.72 263.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n251 8 Car -1 -1 -1 601.63 172.41 635.90 202.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n251 87 Pedestrian -1 -1 -1 411.46 163.83 427.62 201.90 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n251 108 Pedestrian -1 -1 -1 321.08 157.87 334.75 192.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n251 100 Pedestrian -1 -1 -1 307.86 158.19 321.84 195.33 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n251 93 Pedestrian -1 -1 -1 657.82 165.29 692.08 259.68 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n251 115 Pedestrian -1 -1 -1 373.46 161.25 395.80 221.36 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n251 85 Pedestrian -1 -1 -1 185.34 160.40 201.50 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n251 6 Pedestrian -1 -1 -1 346.89 162.08 361.65 201.13 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n251 111 Pedestrian -1 -1 -1 192.21 161.27 208.62 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n251 112 Pedestrian -1 -1 -1 394.16 166.07 410.42 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n251 116 Pedestrian -1 -1 -1 361.48 161.19 376.01 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n251 39 Pedestrian -1 -1 -1 373.60 162.26 388.92 203.19 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n251 118 Pedestrian -1 -1 -1 336.91 158.69 349.61 193.19 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n252 1 Car -1 -1 -1 1095.26 185.40 1220.75 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n252 2 Car -1 -1 -1 954.17 183.66 1067.68 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n252 3 Car -1 -1 -1 1030.27 183.92 1155.62 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n252 60 Pedestrian -1 -1 -1 555.19 161.83 596.06 255.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n252 105 Pedestrian -1 -1 -1 586.51 162.22 624.76 250.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n252 8 Car -1 -1 -1 603.82 172.45 639.11 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n252 100 Pedestrian -1 -1 -1 308.33 157.84 322.21 195.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n252 55 Pedestrian -1 -1 -1 627.48 157.67 669.35 263.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n252 87 Pedestrian -1 -1 -1 411.80 163.59 426.91 200.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n252 93 Pedestrian -1 -1 -1 619.53 156.08 661.94 254.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n252 108 Pedestrian -1 -1 -1 320.97 158.02 334.78 192.52 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n252 112 Pedestrian -1 -1 -1 394.56 165.84 411.65 211.06 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n252 85 Pedestrian -1 -1 -1 185.41 160.88 201.27 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n252 115 Pedestrian -1 -1 -1 373.93 161.54 395.79 220.84 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n252 111 Pedestrian -1 -1 -1 192.30 161.46 208.55 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n252 6 Pedestrian -1 -1 -1 347.88 160.02 362.00 201.15 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n252 118 Pedestrian -1 -1 -1 336.53 158.60 349.66 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n252 116 Pedestrian -1 -1 -1 361.60 159.83 376.09 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n252 119 Pedestrian -1 -1 -1 665.60 162.77 707.13 258.66 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n253 1 Car -1 -1 -1 1095.33 185.48 1220.80 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n253 2 Car -1 -1 -1 953.99 183.66 1067.90 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n253 3 Car -1 -1 -1 1033.23 183.73 1156.68 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n253 60 Pedestrian -1 -1 -1 560.47 161.71 598.01 256.10 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n253 8 Car -1 -1 -1 603.56 172.26 638.62 202.23 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n253 105 Pedestrian -1 -1 -1 590.75 161.53 628.03 252.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n253 55 Pedestrian -1 -1 -1 630.88 158.29 674.37 262.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n253 108 Pedestrian -1 -1 -1 320.41 157.95 334.36 192.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n253 119 Pedestrian -1 -1 -1 667.89 162.78 713.18 258.70 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n253 87 Pedestrian -1 -1 -1 411.26 164.13 426.46 200.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n253 112 Pedestrian -1 -1 -1 395.75 165.81 412.47 210.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n253 93 Pedestrian -1 -1 -1 623.26 157.22 665.77 254.54 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n253 100 Pedestrian -1 -1 -1 308.50 157.94 322.42 195.65 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n253 85 Pedestrian -1 -1 -1 185.22 160.60 201.36 199.00 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n253 118 Pedestrian -1 -1 -1 336.74 158.33 349.77 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n253 115 Pedestrian -1 -1 -1 373.63 161.45 395.81 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n253 111 Pedestrian -1 -1 -1 192.17 161.04 208.73 198.88 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n253 6 Pedestrian -1 -1 -1 349.38 159.76 364.55 201.01 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n253 116 Pedestrian -1 -1 -1 361.90 159.99 375.87 200.91 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n254 1 Car -1 -1 -1 1095.21 185.39 1220.75 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n254 2 Car -1 -1 -1 954.06 183.69 1067.88 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n254 3 Car -1 -1 -1 1033.19 183.75 1156.70 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n254 8 Car -1 -1 -1 603.30 172.12 638.57 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n254 105 Pedestrian -1 -1 -1 596.47 160.55 631.66 252.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n254 55 Pedestrian -1 -1 -1 637.15 158.72 681.72 266.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n254 60 Pedestrian -1 -1 -1 566.66 159.64 600.04 254.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n254 108 Pedestrian -1 -1 -1 320.57 158.23 333.99 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n254 119 Pedestrian -1 -1 -1 671.39 164.53 716.50 261.94 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n254 87 Pedestrian -1 -1 -1 410.92 164.15 426.04 200.30 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n254 6 Pedestrian -1 -1 -1 350.37 160.13 365.50 200.68 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n254 116 Pedestrian -1 -1 -1 361.86 160.19 375.70 200.81 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n254 118 Pedestrian -1 -1 -1 336.58 158.66 349.77 193.10 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n254 115 Pedestrian -1 -1 -1 373.40 161.34 395.89 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n254 111 Pedestrian -1 -1 -1 192.53 161.08 208.93 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n254 100 Pedestrian -1 -1 -1 309.00 157.91 322.97 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n254 85 Pedestrian -1 -1 -1 185.53 160.54 201.63 198.95 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n254 112 Pedestrian -1 -1 -1 396.61 165.83 412.79 210.76 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n255 1 Car -1 -1 -1 1095.59 185.41 1220.29 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n255 2 Car -1 -1 -1 954.24 183.68 1067.56 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n255 3 Car -1 -1 -1 1030.27 183.93 1155.65 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n255 119 Pedestrian -1 -1 -1 676.72 164.79 719.43 262.50 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n255 8 Car -1 -1 -1 602.63 172.24 638.80 202.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n255 105 Pedestrian -1 -1 -1 600.91 159.59 635.31 254.19 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n255 108 Pedestrian -1 -1 -1 320.56 158.12 334.03 192.71 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n255 55 Pedestrian -1 -1 -1 641.69 159.25 685.19 266.31 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n255 60 Pedestrian -1 -1 -1 573.54 160.10 607.79 257.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n255 6 Pedestrian -1 -1 -1 351.01 160.22 365.59 200.80 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n255 100 Pedestrian -1 -1 -1 308.53 157.71 323.16 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n255 111 Pedestrian -1 -1 -1 192.77 161.19 208.81 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n255 115 Pedestrian -1 -1 -1 374.32 160.71 396.02 221.23 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n255 87 Pedestrian -1 -1 -1 410.25 163.95 425.68 200.24 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n255 85 Pedestrian -1 -1 -1 184.92 160.78 201.14 198.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n255 112 Pedestrian -1 -1 -1 397.79 165.32 414.92 211.29 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n255 116 Pedestrian -1 -1 -1 362.77 160.44 376.26 200.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n255 118 Pedestrian -1 -1 -1 336.08 158.48 349.89 193.17 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n255 120 Pedestrian -1 -1 -1 633.09 157.48 671.73 255.16 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n256 1 Car -1 -1 -1 1095.36 185.43 1220.52 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n256 2 Car -1 -1 -1 954.29 183.67 1067.59 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n256 3 Car -1 -1 -1 1030.25 183.89 1155.66 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n256 60 Pedestrian -1 -1 -1 577.57 160.32 612.55 257.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n256 119 Pedestrian -1 -1 -1 684.12 164.38 719.68 263.01 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n256 120 Pedestrian -1 -1 -1 635.31 157.10 677.14 254.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n256 8 Car -1 -1 -1 601.15 172.50 636.08 201.72 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n256 105 Pedestrian -1 -1 -1 604.44 159.47 639.17 254.80 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n256 55 Pedestrian -1 -1 -1 645.24 159.03 688.52 268.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n256 108 Pedestrian -1 -1 -1 319.92 157.94 333.60 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n256 6 Pedestrian -1 -1 -1 351.26 160.46 365.75 200.70 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n256 100 Pedestrian -1 -1 -1 308.29 157.55 322.64 196.40 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n256 118 Pedestrian -1 -1 -1 335.99 158.55 349.64 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n256 112 Pedestrian -1 -1 -1 397.71 165.28 415.97 211.30 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n256 111 Pedestrian -1 -1 -1 193.22 161.28 208.92 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n256 85 Pedestrian -1 -1 -1 184.95 160.68 200.51 198.90 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n256 115 Pedestrian -1 -1 -1 377.20 161.30 397.85 220.59 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n256 116 Pedestrian -1 -1 -1 363.01 161.01 376.83 200.51 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n256 87 Pedestrian -1 -1 -1 408.23 163.26 423.93 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n257 1 Car -1 -1 -1 1095.27 185.44 1220.66 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n257 2 Car -1 -1 -1 954.24 183.67 1067.50 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n257 3 Car -1 -1 -1 1030.14 183.82 1155.77 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n257 120 Pedestrian -1 -1 -1 637.96 156.66 681.95 255.44 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n257 60 Pedestrian -1 -1 -1 579.49 160.26 617.91 259.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n257 119 Pedestrian -1 -1 -1 689.60 165.16 722.83 262.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n257 8 Car -1 -1 -1 602.89 172.28 638.68 201.99 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n257 105 Pedestrian -1 -1 -1 607.36 159.84 642.65 254.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n257 108 Pedestrian -1 -1 -1 320.14 158.09 333.48 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n257 55 Pedestrian -1 -1 -1 649.96 158.46 691.34 269.25 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n257 112 Pedestrian -1 -1 -1 398.65 164.50 417.30 211.86 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n257 111 Pedestrian -1 -1 -1 193.18 161.38 208.89 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n257 115 Pedestrian -1 -1 -1 377.44 160.76 398.30 220.99 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n257 6 Pedestrian -1 -1 -1 351.44 159.61 366.12 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n257 100 Pedestrian -1 -1 -1 308.81 157.20 323.26 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n257 118 Pedestrian -1 -1 -1 335.11 157.96 349.25 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n257 85 Pedestrian -1 -1 -1 184.53 160.30 200.41 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n257 87 Pedestrian -1 -1 -1 408.22 162.96 423.83 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n257 116 Pedestrian -1 -1 -1 363.35 160.87 377.02 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n258 1 Car -1 -1 -1 1095.34 185.39 1220.58 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n258 2 Car -1 -1 -1 954.17 183.60 1067.71 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n258 3 Car -1 -1 -1 1032.90 183.60 1157.02 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n258 119 Pedestrian -1 -1 -1 692.36 165.22 734.12 263.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n258 120 Pedestrian -1 -1 -1 639.35 155.25 688.32 257.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n258 60 Pedestrian -1 -1 -1 583.61 159.98 620.58 260.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n258 8 Car -1 -1 -1 602.94 172.17 638.67 201.74 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n258 105 Pedestrian -1 -1 -1 611.09 159.95 646.09 254.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n258 108 Pedestrian -1 -1 -1 319.85 158.04 333.14 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n258 6 Pedestrian -1 -1 -1 351.09 158.90 366.45 200.89 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n258 112 Pedestrian -1 -1 -1 399.65 163.59 420.71 212.87 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n258 116 Pedestrian -1 -1 -1 365.01 161.04 379.46 199.51 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n258 115 Pedestrian -1 -1 -1 377.77 160.41 398.93 221.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n258 55 Pedestrian -1 -1 -1 653.90 157.81 695.51 270.35 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n258 111 Pedestrian -1 -1 -1 193.15 161.18 209.09 198.67 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n258 118 Pedestrian -1 -1 -1 334.87 157.31 348.92 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n258 100 Pedestrian -1 -1 -1 309.40 157.00 323.40 197.19 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n258 85 Pedestrian -1 -1 -1 184.40 160.45 200.39 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n259 1 Car -1 -1 -1 1095.36 185.42 1220.65 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n259 2 Car -1 -1 -1 954.14 183.57 1067.78 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n259 3 Car -1 -1 -1 1032.92 183.60 1156.92 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n259 119 Pedestrian -1 -1 -1 694.95 164.90 739.46 263.86 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n259 8 Car -1 -1 -1 603.14 172.55 638.34 201.74 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n259 60 Pedestrian -1 -1 -1 590.72 159.57 622.28 260.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n259 105 Pedestrian -1 -1 -1 615.80 160.87 649.28 256.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n259 112 Pedestrian -1 -1 -1 401.12 162.72 421.50 212.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n259 120 Pedestrian -1 -1 -1 644.57 154.55 691.21 258.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n259 108 Pedestrian -1 -1 -1 319.77 158.22 332.95 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n259 116 Pedestrian -1 -1 -1 365.70 160.84 380.55 199.57 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n259 55 Pedestrian -1 -1 -1 656.99 157.83 701.11 270.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n259 111 Pedestrian -1 -1 -1 193.00 161.02 209.09 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n259 115 Pedestrian -1 -1 -1 378.11 160.19 399.40 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n259 6 Pedestrian -1 -1 -1 352.88 159.64 367.93 200.65 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n259 85 Pedestrian -1 -1 -1 182.04 160.35 198.10 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n259 118 Pedestrian -1 -1 -1 334.61 156.97 348.28 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n259 100 Pedestrian -1 -1 -1 309.47 156.89 323.43 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n260 1 Car -1 -1 -1 1095.34 185.38 1220.49 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n260 2 Car -1 -1 -1 954.10 183.53 1067.78 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n260 3 Car -1 -1 -1 1032.99 183.59 1156.95 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n260 8 Car -1 -1 -1 603.13 172.44 638.48 201.78 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n260 60 Pedestrian -1 -1 -1 593.94 159.29 626.48 260.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n260 119 Pedestrian -1 -1 -1 699.14 165.07 743.67 264.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n260 108 Pedestrian -1 -1 -1 319.43 158.08 333.22 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n260 55 Pedestrian -1 -1 -1 663.19 157.50 710.31 270.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n260 120 Pedestrian -1 -1 -1 652.84 157.01 697.17 255.71 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n260 112 Pedestrian -1 -1 -1 401.56 163.19 422.20 212.97 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n260 105 Pedestrian -1 -1 -1 619.88 160.22 652.65 257.95 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n260 116 Pedestrian -1 -1 -1 365.76 160.96 381.45 199.48 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n260 6 Pedestrian -1 -1 -1 353.18 160.25 368.29 200.09 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n260 85 Pedestrian -1 -1 -1 182.41 160.64 197.75 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n260 111 Pedestrian -1 -1 -1 193.38 161.22 208.70 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n260 115 Pedestrian -1 -1 -1 378.07 160.48 399.24 220.82 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n260 100 Pedestrian -1 -1 -1 309.67 158.52 324.23 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n260 118 Pedestrian -1 -1 -1 334.23 156.95 348.36 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n261 1 Car -1 -1 -1 1095.40 185.38 1220.54 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n261 2 Car -1 -1 -1 954.13 183.50 1067.71 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n261 3 Car -1 -1 -1 1030.15 183.74 1155.70 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n261 119 Pedestrian -1 -1 -1 708.53 164.96 748.62 264.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n261 60 Pedestrian -1 -1 -1 597.06 158.74 637.75 261.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n261 8 Car -1 -1 -1 600.99 172.77 635.67 201.25 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n261 120 Pedestrian -1 -1 -1 661.10 155.57 704.41 256.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n261 55 Pedestrian -1 -1 -1 667.38 156.38 713.42 272.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n261 108 Pedestrian -1 -1 -1 319.83 157.88 333.57 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n261 105 Pedestrian -1 -1 -1 619.26 160.01 655.26 258.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n261 112 Pedestrian -1 -1 -1 403.45 162.62 424.42 212.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n261 115 Pedestrian -1 -1 -1 377.51 160.29 399.76 221.32 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n261 116 Pedestrian -1 -1 -1 368.40 161.41 383.75 199.26 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n261 111 Pedestrian -1 -1 -1 193.26 160.85 208.69 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n261 6 Pedestrian -1 -1 -1 354.03 159.54 369.68 200.30 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n261 85 Pedestrian -1 -1 -1 182.69 160.51 197.72 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n261 100 Pedestrian -1 -1 -1 309.62 158.59 324.28 197.13 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n261 118 Pedestrian -1 -1 -1 334.28 157.24 348.32 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n262 1 Car -1 -1 -1 1095.49 185.41 1220.43 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n262 2 Car -1 -1 -1 954.23 183.51 1067.68 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n262 3 Car -1 -1 -1 1030.27 183.76 1155.60 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n262 60 Pedestrian -1 -1 -1 599.51 158.11 642.71 263.39 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n262 120 Pedestrian -1 -1 -1 667.48 153.94 705.97 258.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n262 55 Pedestrian -1 -1 -1 676.46 156.17 719.01 272.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n262 8 Car -1 -1 -1 601.34 173.29 635.85 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n262 108 Pedestrian -1 -1 -1 320.34 158.17 333.72 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n262 105 Pedestrian -1 -1 -1 622.28 160.82 660.04 259.72 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n262 119 Pedestrian -1 -1 -1 716.04 163.38 756.80 266.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n262 112 Pedestrian -1 -1 -1 403.89 165.87 424.97 213.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n262 116 Pedestrian -1 -1 -1 370.06 161.10 384.65 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n262 115 Pedestrian -1 -1 -1 380.45 160.81 402.43 220.03 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n262 111 Pedestrian -1 -1 -1 193.80 161.12 208.71 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n262 6 Pedestrian -1 -1 -1 357.17 160.80 372.61 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n262 85 Pedestrian -1 -1 -1 185.12 160.41 199.76 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n262 100 Pedestrian -1 -1 -1 309.61 158.24 324.44 197.28 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n262 118 Pedestrian -1 -1 -1 334.35 157.69 348.18 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n263 1 Car -1 -1 -1 1095.50 185.37 1220.45 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n263 2 Car -1 -1 -1 954.21 183.50 1067.69 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n263 3 Car -1 -1 -1 1030.22 183.71 1155.66 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n263 120 Pedestrian -1 -1 -1 671.85 154.79 709.26 257.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n263 8 Car -1 -1 -1 601.28 173.56 635.83 201.94 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n263 119 Pedestrian -1 -1 -1 722.97 165.76 764.88 267.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n263 60 Pedestrian -1 -1 -1 599.10 158.44 644.49 263.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n263 108 Pedestrian -1 -1 -1 320.20 158.13 333.53 193.82 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n263 55 Pedestrian -1 -1 -1 686.24 154.00 724.66 274.25 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n263 105 Pedestrian -1 -1 -1 626.21 161.76 663.49 259.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n263 112 Pedestrian -1 -1 -1 404.68 166.26 425.93 212.99 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n263 116 Pedestrian -1 -1 -1 370.66 160.68 384.83 199.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n263 111 Pedestrian -1 -1 -1 193.62 161.15 208.81 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n263 85 Pedestrian -1 -1 -1 185.21 160.16 200.08 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n263 115 Pedestrian -1 -1 -1 380.08 160.74 403.18 218.85 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n263 100 Pedestrian -1 -1 -1 309.52 158.39 324.46 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n263 118 Pedestrian -1 -1 -1 332.50 157.16 346.26 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n263 6 Pedestrian -1 -1 -1 357.53 161.11 372.86 196.87 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n264 1 Car -1 -1 -1 1095.55 185.37 1220.44 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n264 2 Car -1 -1 -1 954.26 183.57 1067.67 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n264 3 Car -1 -1 -1 1030.30 183.74 1155.58 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n264 119 Pedestrian -1 -1 -1 725.85 166.01 769.68 268.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n264 55 Pedestrian -1 -1 -1 689.90 153.48 729.84 274.50 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n264 8 Car -1 -1 -1 601.69 173.52 635.30 201.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n264 60 Pedestrian -1 -1 -1 605.81 158.74 645.78 263.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n264 108 Pedestrian -1 -1 -1 319.99 158.41 333.40 193.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n264 120 Pedestrian -1 -1 -1 675.00 155.29 712.61 258.19 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n264 116 Pedestrian -1 -1 -1 373.16 161.46 387.18 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n264 105 Pedestrian -1 -1 -1 630.75 161.57 666.84 259.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n264 112 Pedestrian -1 -1 -1 408.30 166.33 427.44 213.07 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n264 111 Pedestrian -1 -1 -1 193.92 160.92 208.70 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n264 85 Pedestrian -1 -1 -1 185.09 159.95 200.02 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n264 100 Pedestrian -1 -1 -1 310.62 159.32 325.26 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n264 115 Pedestrian -1 -1 -1 378.98 161.09 404.91 218.57 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n264 118 Pedestrian -1 -1 -1 332.39 157.49 346.30 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n264 6 Pedestrian -1 -1 -1 357.62 160.60 373.11 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n264 121 Pedestrian -1 -1 -1 403.86 163.39 416.68 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n265 1 Car -1 -1 -1 1095.62 185.41 1220.38 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n265 2 Car -1 -1 -1 954.31 183.54 1067.65 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n265 3 Car -1 -1 -1 1030.04 183.68 1155.88 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n265 119 Pedestrian -1 -1 -1 732.40 166.84 777.98 269.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n265 55 Pedestrian -1 -1 -1 694.59 154.14 739.50 273.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n265 8 Car -1 -1 -1 601.50 173.41 635.70 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n265 108 Pedestrian -1 -1 -1 320.07 158.60 333.21 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n265 60 Pedestrian -1 -1 -1 616.10 159.47 650.14 262.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n265 112 Pedestrian -1 -1 -1 409.78 166.49 429.39 213.35 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n265 105 Pedestrian -1 -1 -1 638.16 160.31 672.92 260.57 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n265 116 Pedestrian -1 -1 -1 373.06 161.87 387.22 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n265 120 Pedestrian -1 -1 -1 675.96 155.51 712.59 258.87 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n265 111 Pedestrian -1 -1 -1 193.67 160.61 208.94 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n265 100 Pedestrian -1 -1 -1 310.51 159.28 325.57 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n265 121 Pedestrian -1 -1 -1 403.91 163.11 416.81 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n265 115 Pedestrian -1 -1 -1 378.89 161.45 405.62 218.57 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n265 118 Pedestrian -1 -1 -1 332.44 157.56 346.39 194.01 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n265 85 Pedestrian -1 -1 -1 184.76 159.82 200.19 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n265 6 Pedestrian -1 -1 -1 358.83 161.21 372.62 196.20 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n265 122 Pedestrian -1 -1 -1 348.63 158.00 361.01 191.80 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n266 1 Car -1 -1 -1 1095.70 185.32 1220.10 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n266 2 Car -1 -1 -1 954.36 183.60 1067.65 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n266 3 Car -1 -1 -1 1030.01 183.67 1155.82 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n266 8 Car -1 -1 -1 601.30 173.27 636.22 202.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n266 119 Pedestrian -1 -1 -1 737.23 166.53 782.42 269.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n266 55 Pedestrian -1 -1 -1 697.15 154.88 745.29 273.52 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n266 60 Pedestrian -1 -1 -1 623.21 160.69 658.29 264.42 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n266 108 Pedestrian -1 -1 -1 319.47 158.34 332.83 193.88 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n266 115 Pedestrian -1 -1 -1 382.63 161.97 408.35 218.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n266 105 Pedestrian -1 -1 -1 643.40 160.25 675.99 260.46 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n266 116 Pedestrian -1 -1 -1 374.12 162.00 387.53 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n266 112 Pedestrian -1 -1 -1 412.88 166.49 430.20 213.48 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n266 120 Pedestrian -1 -1 -1 681.02 155.12 715.50 258.76 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n266 6 Pedestrian -1 -1 -1 358.84 160.59 373.09 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n266 121 Pedestrian -1 -1 -1 403.35 162.68 416.39 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n266 100 Pedestrian -1 -1 -1 309.46 158.77 324.46 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n266 118 Pedestrian -1 -1 -1 332.27 157.67 346.35 193.97 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n266 111 Pedestrian -1 -1 -1 193.44 160.40 209.29 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n266 85 Pedestrian -1 -1 -1 184.43 160.06 200.15 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n266 122 Pedestrian -1 -1 -1 347.86 157.83 360.72 192.50 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n267 1 Car -1 -1 -1 1095.68 185.34 1220.10 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n267 2 Car -1 -1 -1 954.51 183.59 1067.61 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n267 3 Car -1 -1 -1 1030.01 183.68 1155.88 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n267 119 Pedestrian -1 -1 -1 747.90 166.28 785.48 270.34 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n267 8 Car -1 -1 -1 601.46 173.14 636.45 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n267 112 Pedestrian -1 -1 -1 412.99 166.69 431.44 213.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n267 55 Pedestrian -1 -1 -1 700.59 153.70 749.93 275.02 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n267 60 Pedestrian -1 -1 -1 627.70 161.84 669.01 263.90 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n267 121 Pedestrian -1 -1 -1 400.98 162.58 415.41 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n267 6 Pedestrian -1 -1 -1 359.11 160.22 373.18 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n267 105 Pedestrian -1 -1 -1 645.81 160.93 681.45 260.52 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n267 108 Pedestrian -1 -1 -1 319.32 158.31 332.59 194.24 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n267 115 Pedestrian -1 -1 -1 387.67 162.51 410.19 216.87 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n267 100 Pedestrian -1 -1 -1 310.51 159.23 325.53 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n267 120 Pedestrian -1 -1 -1 684.74 154.11 719.41 259.92 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n267 116 Pedestrian -1 -1 -1 374.45 161.86 388.30 198.87 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n267 85 Pedestrian -1 -1 -1 184.37 160.18 200.43 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n267 118 Pedestrian -1 -1 -1 332.30 157.70 345.94 194.12 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n267 111 Pedestrian -1 -1 -1 193.20 160.28 209.42 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n267 122 Pedestrian -1 -1 -1 346.80 157.89 359.71 192.47 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n268 1 Car -1 -1 -1 1095.76 185.34 1220.08 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n268 2 Car -1 -1 -1 954.59 183.61 1067.38 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n268 3 Car -1 -1 -1 1032.87 183.52 1156.94 233.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n268 8 Car -1 -1 -1 601.29 173.00 636.62 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n268 55 Pedestrian -1 -1 -1 708.69 154.09 756.43 279.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n268 119 Pedestrian -1 -1 -1 752.46 164.93 795.56 272.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n268 112 Pedestrian -1 -1 -1 414.44 167.29 432.15 213.61 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n268 105 Pedestrian -1 -1 -1 646.57 161.59 688.10 264.81 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n268 121 Pedestrian -1 -1 -1 400.66 162.73 414.74 196.71 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n268 60 Pedestrian -1 -1 -1 630.93 161.22 673.54 265.72 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n268 115 Pedestrian -1 -1 -1 390.18 161.91 410.95 214.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n268 100 Pedestrian -1 -1 -1 310.67 159.57 325.69 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n268 108 Pedestrian -1 -1 -1 319.03 158.32 332.72 194.48 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n268 120 Pedestrian -1 -1 -1 689.83 153.89 728.80 260.64 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n268 6 Pedestrian -1 -1 -1 359.45 161.08 373.21 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n268 116 Pedestrian -1 -1 -1 374.24 161.90 388.79 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n268 85 Pedestrian -1 -1 -1 184.50 160.45 200.26 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n268 118 Pedestrian -1 -1 -1 332.21 157.33 346.11 194.37 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n268 122 Pedestrian -1 -1 -1 346.29 158.13 359.23 192.95 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n268 111 Pedestrian -1 -1 -1 193.17 160.23 209.56 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n268 123 Car -1 -1 -1 598.62 173.62 621.98 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n269 1 Car -1 -1 -1 1095.91 185.35 1219.88 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n269 2 Car -1 -1 -1 954.50 183.54 1067.42 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n269 3 Car -1 -1 -1 1032.81 183.53 1156.99 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n269 8 Car -1 -1 -1 601.24 173.00 637.04 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n269 119 Pedestrian -1 -1 -1 754.06 164.03 803.18 272.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n269 55 Pedestrian -1 -1 -1 718.61 153.61 761.20 279.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n269 115 Pedestrian -1 -1 -1 392.32 161.47 413.97 214.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n269 100 Pedestrian -1 -1 -1 311.31 159.92 326.55 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n269 60 Pedestrian -1 -1 -1 635.15 158.74 676.81 268.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n269 105 Pedestrian -1 -1 -1 650.42 160.19 692.18 265.68 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n269 112 Pedestrian -1 -1 -1 416.74 167.50 434.06 213.81 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n269 120 Pedestrian -1 -1 -1 692.84 155.05 734.11 262.66 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n269 121 Pedestrian -1 -1 -1 400.35 163.05 413.86 195.89 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n269 6 Pedestrian -1 -1 -1 361.53 161.17 375.11 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n269 122 Pedestrian -1 -1 -1 346.80 159.26 359.32 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n269 123 Car -1 -1 -1 598.33 173.62 622.05 193.30 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n269 118 Pedestrian -1 -1 -1 332.19 156.88 346.27 194.59 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n269 85 Pedestrian -1 -1 -1 181.89 160.62 197.67 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n269 116 Pedestrian -1 -1 -1 373.73 161.72 388.18 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n269 111 Pedestrian -1 -1 -1 192.91 159.95 209.76 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n270 1 Car -1 -1 -1 1095.78 185.38 1219.95 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n270 2 Car -1 -1 -1 954.66 183.58 1067.31 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n270 3 Car -1 -1 -1 1029.89 183.71 1155.89 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n270 8 Car -1 -1 -1 601.29 172.94 637.20 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n270 112 Pedestrian -1 -1 -1 417.25 167.39 435.88 214.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n270 119 Pedestrian -1 -1 -1 757.28 166.70 808.02 273.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n270 55 Pedestrian -1 -1 -1 722.69 154.22 766.24 280.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n270 60 Pedestrian -1 -1 -1 642.27 159.60 677.96 268.22 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n270 100 Pedestrian -1 -1 -1 312.64 159.77 327.53 198.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n270 115 Pedestrian -1 -1 -1 393.43 161.37 415.33 215.18 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n270 120 Pedestrian -1 -1 -1 697.53 154.74 743.87 263.05 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n270 6 Pedestrian -1 -1 -1 361.98 161.65 375.21 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n270 122 Pedestrian -1 -1 -1 347.07 159.21 359.42 192.67 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n270 105 Pedestrian -1 -1 -1 656.71 158.65 693.96 262.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n270 123 Car -1 -1 -1 598.05 173.45 622.01 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n270 118 Pedestrian -1 -1 -1 331.83 157.11 345.99 194.94 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n270 121 Pedestrian -1 -1 -1 399.99 162.62 413.32 195.87 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n270 116 Pedestrian -1 -1 -1 375.93 161.69 391.15 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n270 85 Pedestrian -1 -1 -1 181.01 160.33 198.11 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n271 1 Car -1 -1 -1 1095.79 185.33 1220.21 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n271 2 Car -1 -1 -1 954.67 183.57 1067.31 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n271 3 Car -1 -1 -1 1029.84 183.71 1155.96 233.52 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n271 55 Pedestrian -1 -1 -1 727.30 153.78 776.47 281.57 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n271 8 Car -1 -1 -1 601.38 172.91 637.24 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n271 119 Pedestrian -1 -1 -1 762.92 167.19 810.24 273.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n271 112 Pedestrian -1 -1 -1 417.40 167.97 437.47 215.13 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n271 60 Pedestrian -1 -1 -1 646.82 159.00 688.03 269.53 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n271 120 Pedestrian -1 -1 -1 700.31 154.66 749.57 263.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n271 6 Pedestrian -1 -1 -1 362.64 162.15 376.02 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n271 115 Pedestrian -1 -1 -1 396.39 163.45 416.73 215.52 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n271 100 Pedestrian -1 -1 -1 314.66 158.97 330.58 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n271 122 Pedestrian -1 -1 -1 347.12 158.90 359.03 192.46 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n271 105 Pedestrian -1 -1 -1 662.61 158.38 702.65 263.02 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n271 123 Car -1 -1 -1 597.97 173.53 622.08 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n271 118 Pedestrian -1 -1 -1 331.76 157.37 345.51 194.61 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n271 121 Pedestrian -1 -1 -1 393.67 163.51 406.93 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n271 116 Pedestrian -1 -1 -1 377.28 161.84 391.93 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n271 85 Pedestrian -1 -1 -1 180.68 160.37 198.13 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n271 124 Pedestrian -1 -1 -1 305.97 157.02 319.67 192.73 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n272 1 Car -1 -1 -1 1095.72 185.29 1220.17 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n272 2 Car -1 -1 -1 954.68 183.56 1067.35 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n272 3 Car -1 -1 -1 1029.74 183.73 1156.07 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n272 55 Pedestrian -1 -1 -1 732.98 154.65 785.07 281.77 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n272 8 Car -1 -1 -1 602.22 172.58 638.06 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n272 60 Pedestrian -1 -1 -1 649.17 158.93 693.49 270.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n272 115 Pedestrian -1 -1 -1 397.98 163.93 417.95 216.44 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n272 119 Pedestrian -1 -1 -1 772.84 165.00 815.44 276.21 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n272 112 Pedestrian -1 -1 -1 419.52 168.55 438.69 215.05 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n272 120 Pedestrian -1 -1 -1 705.94 153.74 751.57 260.69 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n272 100 Pedestrian -1 -1 -1 314.13 158.93 330.66 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n272 6 Pedestrian -1 -1 -1 362.85 161.71 377.23 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n272 122 Pedestrian -1 -1 -1 346.55 158.68 359.15 193.14 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n272 105 Pedestrian -1 -1 -1 666.68 159.19 707.04 267.89 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n272 123 Car -1 -1 -1 597.89 173.64 622.02 193.50 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n272 124 Pedestrian -1 -1 -1 305.77 157.32 319.13 192.22 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n272 118 Pedestrian -1 -1 -1 331.41 157.36 345.24 194.77 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n272 121 Pedestrian -1 -1 -1 396.16 162.93 410.06 197.00 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n272 85 Pedestrian -1 -1 -1 184.16 159.38 201.49 199.21 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n273 1 Car -1 -1 -1 1095.57 185.35 1220.31 236.00 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n273 2 Car -1 -1 -1 954.69 183.58 1067.35 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n273 3 Car -1 -1 -1 1029.82 183.77 1155.97 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n273 55 Pedestrian -1 -1 -1 738.07 154.11 788.22 282.60 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n273 8 Car -1 -1 -1 602.32 172.66 637.99 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n273 115 Pedestrian -1 -1 -1 401.30 164.73 420.50 216.19 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n273 112 Pedestrian -1 -1 -1 421.41 168.96 440.18 215.18 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n273 60 Pedestrian -1 -1 -1 651.34 159.84 698.88 273.16 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n273 120 Pedestrian -1 -1 -1 717.13 153.65 754.89 264.29 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n273 119 Pedestrian -1 -1 -1 781.67 164.58 828.95 276.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n273 6 Pedestrian -1 -1 -1 365.22 161.14 380.26 198.45 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n273 100 Pedestrian -1 -1 -1 314.53 159.03 330.77 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n273 123 Car -1 -1 -1 597.92 173.40 622.06 193.26 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n273 124 Pedestrian -1 -1 -1 305.69 157.78 318.52 191.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n273 121 Pedestrian -1 -1 -1 395.94 162.85 409.39 197.55 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n273 105 Pedestrian -1 -1 -1 674.70 159.80 714.19 268.05 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n273 118 Pedestrian -1 -1 -1 331.17 157.60 345.04 194.99 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n273 122 Pedestrian -1 -1 -1 344.52 158.57 357.82 194.01 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n273 85 Pedestrian -1 -1 -1 184.49 159.12 201.18 199.20 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n273 125 Pedestrian -1 -1 -1 381.83 162.17 394.90 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n273 126 Pedestrian -1 -1 -1 193.04 159.11 208.90 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n274 1 Car -1 -1 -1 1095.76 185.30 1220.06 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n274 2 Car -1 -1 -1 954.65 183.62 1067.30 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n274 3 Car -1 -1 -1 1029.92 183.84 1155.88 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n274 8 Car -1 -1 -1 602.27 172.59 638.04 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n274 55 Pedestrian -1 -1 -1 746.81 153.62 794.67 283.41 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n274 6 Pedestrian -1 -1 -1 365.80 161.13 381.11 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n274 60 Pedestrian -1 -1 -1 654.85 158.41 703.31 274.59 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n274 119 Pedestrian -1 -1 -1 786.76 164.07 839.24 278.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n274 120 Pedestrian -1 -1 -1 722.89 153.27 757.95 265.18 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n274 115 Pedestrian -1 -1 -1 404.49 164.93 423.96 215.48 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n274 100 Pedestrian -1 -1 -1 314.62 159.32 330.78 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n274 112 Pedestrian -1 -1 -1 423.78 168.87 442.22 215.29 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n274 105 Pedestrian -1 -1 -1 681.60 159.13 721.87 269.28 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n274 123 Car -1 -1 -1 597.93 173.34 622.12 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n274 121 Pedestrian -1 -1 -1 396.29 162.91 409.05 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n274 124 Pedestrian -1 -1 -1 305.04 157.49 318.21 191.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n274 125 Pedestrian -1 -1 -1 382.07 161.54 395.58 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n274 118 Pedestrian -1 -1 -1 331.68 157.19 345.77 195.50 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n274 122 Pedestrian -1 -1 -1 344.78 158.85 357.73 194.26 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n274 126 Pedestrian -1 -1 -1 193.19 154.86 209.04 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n274 85 Pedestrian -1 -1 -1 184.58 158.77 201.13 199.40 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n275 1 Car -1 -1 -1 1095.57 185.34 1220.12 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n275 2 Car -1 -1 -1 954.67 183.63 1067.23 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n275 3 Car -1 -1 -1 1029.66 183.78 1156.12 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n275 8 Car -1 -1 -1 602.22 172.61 638.13 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n275 55 Pedestrian -1 -1 -1 753.06 150.42 804.53 285.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n275 119 Pedestrian -1 -1 -1 790.35 164.08 843.55 279.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n275 60 Pedestrian -1 -1 -1 660.78 156.09 704.92 273.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n275 115 Pedestrian -1 -1 -1 404.99 165.14 426.06 215.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n275 112 Pedestrian -1 -1 -1 426.03 168.49 442.98 215.07 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n275 100 Pedestrian -1 -1 -1 314.95 159.51 330.53 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n275 120 Pedestrian -1 -1 -1 728.12 151.52 767.83 266.50 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n275 6 Pedestrian -1 -1 -1 366.81 161.45 380.66 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n275 105 Pedestrian -1 -1 -1 685.46 158.93 725.96 269.94 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n275 121 Pedestrian -1 -1 -1 397.21 162.86 408.91 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n275 123 Car -1 -1 -1 597.91 173.33 622.16 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n275 124 Pedestrian -1 -1 -1 304.79 157.66 318.06 191.62 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n275 125 Pedestrian -1 -1 -1 382.09 161.44 395.80 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n275 122 Pedestrian -1 -1 -1 344.99 159.12 357.24 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n275 118 Pedestrian -1 -1 -1 330.66 157.99 345.16 195.33 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n275 85 Pedestrian -1 -1 -1 184.72 158.84 200.78 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n275 126 Pedestrian -1 -1 -1 193.05 159.29 208.44 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n276 1 Car -1 -1 -1 1095.35 185.32 1220.47 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n276 2 Car -1 -1 -1 954.68 183.65 1067.25 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n276 3 Car -1 -1 -1 1029.71 183.78 1156.02 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n276 8 Car -1 -1 -1 602.29 172.54 638.01 203.23 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n276 100 Pedestrian -1 -1 -1 315.06 159.36 330.24 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n276 120 Pedestrian -1 -1 -1 729.37 151.73 774.22 266.88 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n276 119 Pedestrian -1 -1 -1 798.66 163.74 850.24 284.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n276 60 Pedestrian -1 -1 -1 671.64 157.06 709.26 275.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n276 121 Pedestrian -1 -1 -1 397.94 162.73 409.99 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n276 115 Pedestrian -1 -1 -1 407.26 165.18 428.37 216.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n276 6 Pedestrian -1 -1 -1 367.28 161.00 380.95 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n276 55 Pedestrian -1 -1 -1 761.73 149.86 817.74 285.44 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n276 123 Car -1 -1 -1 598.18 173.30 622.15 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n276 112 Pedestrian -1 -1 -1 428.39 168.77 445.43 215.70 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n276 125 Pedestrian -1 -1 -1 382.26 161.57 395.74 197.50 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n276 124 Pedestrian -1 -1 -1 305.04 157.87 317.88 191.67 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n276 105 Pedestrian -1 -1 -1 695.02 159.41 731.00 269.63 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n276 122 Pedestrian -1 -1 -1 346.46 158.59 358.86 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n276 126 Pedestrian -1 -1 -1 193.07 155.16 208.97 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n276 85 Pedestrian -1 -1 -1 184.77 159.14 200.79 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n276 118 Pedestrian -1 -1 -1 328.01 158.23 343.25 195.61 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n277 1 Car -1 -1 -1 1095.34 185.30 1220.37 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n277 2 Car -1 -1 -1 954.88 183.62 1067.06 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n277 3 Car -1 -1 -1 1029.79 183.80 1156.02 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n277 100 Pedestrian -1 -1 -1 315.15 159.56 330.17 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n277 8 Car -1 -1 -1 602.50 172.50 637.87 203.19 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n277 60 Pedestrian -1 -1 -1 682.10 158.04 721.51 275.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n277 120 Pedestrian -1 -1 -1 733.84 152.53 777.43 266.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n277 115 Pedestrian -1 -1 -1 407.99 164.74 429.06 217.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n277 121 Pedestrian -1 -1 -1 397.98 162.56 409.97 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n277 119 Pedestrian -1 -1 -1 808.98 163.51 854.74 281.06 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n277 6 Pedestrian -1 -1 -1 369.13 162.17 382.37 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n277 55 Pedestrian -1 -1 -1 764.74 153.92 823.06 281.91 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n277 125 Pedestrian -1 -1 -1 382.93 161.73 395.90 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n277 105 Pedestrian -1 -1 -1 699.13 159.84 735.14 268.99 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n277 123 Car -1 -1 -1 598.23 173.28 622.13 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n277 124 Pedestrian -1 -1 -1 304.86 158.08 317.67 192.05 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n277 112 Pedestrian -1 -1 -1 429.45 169.65 446.29 216.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n277 122 Pedestrian -1 -1 -1 346.60 158.17 359.15 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n277 126 Pedestrian -1 -1 -1 193.12 155.21 208.85 196.67 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n277 85 Pedestrian -1 -1 -1 184.71 159.16 200.76 198.99 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n278 1 Car -1 -1 -1 1095.30 185.30 1220.51 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n278 2 Car -1 -1 -1 954.86 183.64 1067.06 233.33 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n278 3 Car -1 -1 -1 1030.03 183.84 1155.87 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n278 100 Pedestrian -1 -1 -1 315.22 159.56 330.79 199.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n278 8 Car -1 -1 -1 602.40 172.53 637.89 203.23 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n278 60 Pedestrian -1 -1 -1 683.97 156.99 727.90 276.55 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n278 120 Pedestrian -1 -1 -1 743.49 154.02 783.04 265.93 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n278 55 Pedestrian -1 -1 -1 770.73 155.89 832.04 286.20 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n278 121 Pedestrian -1 -1 -1 398.09 162.36 409.69 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n278 112 Pedestrian -1 -1 -1 429.88 169.80 447.53 216.83 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n278 119 Pedestrian -1 -1 -1 822.52 166.43 863.58 281.79 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n278 6 Pedestrian -1 -1 -1 369.48 162.11 382.59 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n278 123 Car -1 -1 -1 598.18 173.28 622.24 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n278 124 Pedestrian -1 -1 -1 304.87 158.26 317.65 192.26 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n278 115 Pedestrian -1 -1 -1 410.52 165.21 429.32 217.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n278 125 Pedestrian -1 -1 -1 385.00 162.29 397.38 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n278 105 Pedestrian -1 -1 -1 701.83 159.51 740.70 268.95 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n278 122 Pedestrian -1 -1 -1 346.46 158.72 358.83 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n278 126 Pedestrian -1 -1 -1 192.94 155.12 208.94 196.74 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n278 85 Pedestrian -1 -1 -1 184.81 159.22 200.69 198.97 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n278 127 Pedestrian -1 -1 -1 330.48 160.39 344.08 195.82 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n279 1 Car -1 -1 -1 1095.37 185.23 1220.28 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n279 2 Car -1 -1 -1 954.98 183.58 1067.00 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n279 3 Car -1 -1 -1 1032.75 183.68 1157.08 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n279 100 Pedestrian -1 -1 -1 315.05 159.75 331.42 200.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n279 8 Car -1 -1 -1 602.58 172.55 637.87 203.21 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n279 55 Pedestrian -1 -1 -1 778.82 155.49 839.08 288.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n279 120 Pedestrian -1 -1 -1 755.89 150.34 793.10 270.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n279 60 Pedestrian -1 -1 -1 686.90 157.17 732.50 277.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n279 115 Pedestrian -1 -1 -1 414.49 166.01 432.01 216.79 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n279 123 Car -1 -1 -1 598.30 173.29 622.14 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n279 6 Pedestrian -1 -1 -1 370.91 162.23 383.61 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n279 125 Pedestrian -1 -1 -1 385.39 162.04 398.38 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n279 112 Pedestrian -1 -1 -1 431.89 169.89 449.02 216.85 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n279 121 Pedestrian -1 -1 -1 398.09 162.95 409.76 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n279 119 Pedestrian -1 -1 -1 825.51 165.67 875.92 284.05 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n279 105 Pedestrian -1 -1 -1 709.28 158.50 747.57 269.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n279 124 Pedestrian -1 -1 -1 305.37 158.37 318.46 192.01 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n279 127 Pedestrian -1 -1 -1 330.63 160.70 344.01 196.09 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n279 126 Pedestrian -1 -1 -1 193.02 155.46 208.84 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n279 122 Pedestrian -1 -1 -1 344.76 159.09 357.62 194.24 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n279 85 Pedestrian -1 -1 -1 184.87 159.37 200.74 198.84 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n280 1 Car -1 -1 -1 1095.37 185.22 1220.26 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n280 2 Car -1 -1 -1 955.14 183.53 1067.05 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n280 3 Car -1 -1 -1 1032.69 183.73 1157.15 233.70 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n280 100 Pedestrian -1 -1 -1 315.13 159.49 331.41 199.92 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n280 120 Pedestrian -1 -1 -1 759.28 149.80 804.97 270.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n280 55 Pedestrian -1 -1 -1 788.32 155.46 845.63 288.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n280 8 Car -1 -1 -1 602.27 172.69 637.90 203.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n280 121 Pedestrian -1 -1 -1 397.85 162.76 409.69 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n280 60 Pedestrian -1 -1 -1 692.07 157.00 734.21 278.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n280 119 Pedestrian -1 -1 -1 827.71 164.65 882.56 285.80 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n280 112 Pedestrian -1 -1 -1 432.89 169.54 449.70 216.86 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n280 123 Car -1 -1 -1 598.21 173.33 622.08 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n280 125 Pedestrian -1 -1 -1 385.63 161.61 398.85 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n280 115 Pedestrian -1 -1 -1 416.46 165.96 434.18 216.75 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n280 105 Pedestrian -1 -1 -1 712.61 158.50 752.43 270.29 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n280 124 Pedestrian -1 -1 -1 305.32 158.10 318.27 191.70 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n280 127 Pedestrian -1 -1 -1 330.99 159.85 344.64 196.38 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n280 6 Pedestrian -1 -1 -1 370.96 162.22 384.15 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n280 126 Pedestrian -1 -1 -1 192.81 159.49 208.26 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n280 85 Pedestrian -1 -1 -1 184.82 159.60 200.66 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n280 122 Pedestrian -1 -1 -1 344.61 159.37 357.63 194.11 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n281 1 Car -1 -1 -1 1095.69 185.25 1219.81 236.11 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n281 2 Car -1 -1 -1 955.38 183.45 1066.89 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n281 3 Car -1 -1 -1 1029.90 183.82 1156.06 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n281 100 Pedestrian -1 -1 -1 315.04 159.15 331.28 199.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n281 120 Pedestrian -1 -1 -1 761.49 149.87 818.41 270.27 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n281 121 Pedestrian -1 -1 -1 397.58 162.19 409.93 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n281 8 Car -1 -1 -1 602.17 172.49 637.98 203.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n281 115 Pedestrian -1 -1 -1 416.52 165.52 436.00 217.40 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n281 119 Pedestrian -1 -1 -1 835.10 163.21 889.56 287.93 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n281 60 Pedestrian -1 -1 -1 697.64 156.87 737.52 279.61 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n281 112 Pedestrian -1 -1 -1 432.66 169.66 451.20 216.54 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n281 55 Pedestrian -1 -1 -1 805.21 155.11 858.56 294.59 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n281 125 Pedestrian -1 -1 -1 386.11 161.35 399.09 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n281 123 Car -1 -1 -1 598.19 173.44 621.84 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n281 124 Pedestrian -1 -1 -1 304.78 157.69 318.23 191.55 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n281 127 Pedestrian -1 -1 -1 330.99 159.35 344.72 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n281 6 Pedestrian -1 -1 -1 373.97 162.66 387.50 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n281 105 Pedestrian -1 -1 -1 720.19 157.29 759.39 271.56 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n281 126 Pedestrian -1 -1 -1 192.75 155.34 208.85 196.67 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n281 85 Pedestrian -1 -1 -1 184.80 159.53 200.64 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n281 122 Pedestrian -1 -1 -1 346.36 159.01 358.96 194.48 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n282 1 Car -1 -1 -1 1095.86 185.20 1219.69 236.18 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n282 2 Car -1 -1 -1 955.39 183.36 1066.99 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n282 3 Car -1 -1 -1 1032.49 183.67 1157.43 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n282 100 Pedestrian -1 -1 -1 314.35 159.30 331.34 199.90 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n282 120 Pedestrian -1 -1 -1 765.23 152.32 822.21 268.73 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n282 121 Pedestrian -1 -1 -1 397.83 162.38 409.84 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n282 8 Car -1 -1 -1 602.30 172.46 637.86 203.37 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n282 55 Pedestrian -1 -1 -1 813.65 155.34 873.01 294.76 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n282 60 Pedestrian -1 -1 -1 703.57 156.94 746.31 279.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n282 115 Pedestrian -1 -1 -1 417.24 165.16 437.59 217.93 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n282 124 Pedestrian -1 -1 -1 304.17 157.92 318.38 192.24 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n282 125 Pedestrian -1 -1 -1 386.37 161.11 399.42 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n282 119 Pedestrian -1 -1 -1 846.90 163.84 893.77 287.35 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n282 123 Car -1 -1 -1 598.17 173.42 622.01 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n282 112 Pedestrian -1 -1 -1 433.06 169.60 453.18 216.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n282 127 Pedestrian -1 -1 -1 330.97 159.90 344.57 196.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n282 105 Pedestrian -1 -1 -1 724.11 158.89 764.04 274.94 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n282 126 Pedestrian -1 -1 -1 192.97 155.25 208.81 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n282 6 Pedestrian -1 -1 -1 375.15 162.87 387.92 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n282 85 Pedestrian -1 -1 -1 184.92 159.44 200.49 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n282 122 Pedestrian -1 -1 -1 344.40 159.04 357.74 194.41 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n283 1 Car -1 -1 -1 1095.63 185.16 1219.79 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n283 2 Car -1 -1 -1 955.48 183.36 1066.83 233.54 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n283 3 Car -1 -1 -1 1032.70 183.74 1157.24 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n283 112 Pedestrian -1 -1 -1 434.95 169.81 456.05 217.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n283 8 Car -1 -1 -1 602.40 172.49 637.79 203.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n283 55 Pedestrian -1 -1 -1 816.57 155.26 885.57 295.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n283 121 Pedestrian -1 -1 -1 397.80 162.57 409.73 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n283 100 Pedestrian -1 -1 -1 313.84 159.43 331.17 200.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n283 120 Pedestrian -1 -1 -1 773.26 151.62 828.79 269.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n283 60 Pedestrian -1 -1 -1 709.28 157.50 756.24 283.44 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n283 115 Pedestrian -1 -1 -1 420.26 165.47 439.97 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n283 123 Car -1 -1 -1 598.45 173.31 622.01 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n283 124 Pedestrian -1 -1 -1 303.84 158.34 317.82 192.86 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n283 127 Pedestrian -1 -1 -1 330.46 160.39 344.30 195.93 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n283 125 Pedestrian -1 -1 -1 389.03 161.85 401.56 195.95 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n283 119 Pedestrian -1 -1 -1 851.64 163.53 904.14 288.17 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n283 105 Pedestrian -1 -1 -1 730.24 157.44 772.97 277.23 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n283 6 Pedestrian -1 -1 -1 363.46 159.21 376.48 194.42 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n283 126 Pedestrian -1 -1 -1 193.31 155.49 208.73 196.67 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n283 122 Pedestrian -1 -1 -1 344.32 159.57 357.72 194.32 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n283 85 Pedestrian -1 -1 -1 181.39 160.34 198.33 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n284 1 Car -1 -1 -1 1095.61 185.15 1219.90 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n284 2 Car -1 -1 -1 955.29 183.28 1067.21 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n284 3 Car -1 -1 -1 1032.66 183.78 1157.23 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n284 120 Pedestrian -1 -1 -1 779.41 150.74 831.15 270.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n284 55 Pedestrian -1 -1 -1 822.43 155.43 895.21 295.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n284 121 Pedestrian -1 -1 -1 397.91 162.15 409.99 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n284 8 Car -1 -1 -1 602.37 172.59 637.71 203.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n284 60 Pedestrian -1 -1 -1 710.34 157.49 762.71 284.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n284 100 Pedestrian -1 -1 -1 313.80 159.71 330.92 200.57 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n284 112 Pedestrian -1 -1 -1 436.35 170.16 457.04 217.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n284 123 Car -1 -1 -1 598.38 173.40 622.03 193.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n284 124 Pedestrian -1 -1 -1 303.73 158.41 317.62 193.22 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n284 127 Pedestrian -1 -1 -1 330.62 160.35 344.50 196.01 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n284 115 Pedestrian -1 -1 -1 423.77 165.80 442.47 218.53 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n284 125 Pedestrian -1 -1 -1 389.50 161.92 402.15 195.91 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n284 105 Pedestrian -1 -1 -1 736.66 158.16 782.01 277.40 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n284 119 Pedestrian -1 -1 -1 863.49 165.18 914.66 291.65 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n284 126 Pedestrian -1 -1 -1 193.24 155.52 208.76 196.66 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n284 6 Pedestrian -1 -1 -1 363.15 159.31 376.55 194.62 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n284 122 Pedestrian -1 -1 -1 344.35 159.75 357.43 194.38 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n284 85 Pedestrian -1 -1 -1 184.74 159.37 200.54 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n285 1 Car -1 -1 -1 1095.50 185.16 1220.06 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n285 2 Car -1 -1 -1 955.27 183.27 1067.27 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n285 3 Car -1 -1 -1 1032.79 183.78 1157.08 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n285 112 Pedestrian -1 -1 -1 439.97 170.40 460.07 218.38 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n285 60 Pedestrian -1 -1 -1 713.80 156.70 767.16 285.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n285 120 Pedestrian -1 -1 -1 789.61 148.33 835.63 273.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n285 55 Pedestrian -1 -1 -1 834.64 156.19 898.62 295.13 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n285 100 Pedestrian -1 -1 -1 313.90 159.87 330.91 199.95 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n285 8 Car -1 -1 -1 601.62 172.89 637.16 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n285 121 Pedestrian -1 -1 -1 398.20 161.72 410.03 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n285 124 Pedestrian -1 -1 -1 303.77 157.97 317.52 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n285 123 Car -1 -1 -1 598.24 173.50 622.21 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n285 115 Pedestrian -1 -1 -1 425.28 165.33 444.76 218.85 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n285 119 Pedestrian -1 -1 -1 867.24 164.43 926.67 293.07 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n285 127 Pedestrian -1 -1 -1 330.50 160.24 344.16 196.00 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n285 6 Pedestrian -1 -1 -1 362.61 159.03 375.99 194.64 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n285 105 Pedestrian -1 -1 -1 743.32 158.75 790.66 277.22 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n285 126 Pedestrian -1 -1 -1 192.98 159.52 208.19 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n285 122 Pedestrian -1 -1 -1 344.36 159.71 357.08 194.43 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n285 85 Pedestrian -1 -1 -1 184.77 159.43 200.51 198.59 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n286 1 Car -1 -1 -1 1095.31 185.21 1220.38 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n286 2 Car -1 -1 -1 955.30 183.24 1067.53 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n286 3 Car -1 -1 -1 1032.78 183.76 1157.10 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n286 60 Pedestrian -1 -1 -1 720.85 156.75 767.55 285.59 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n286 55 Pedestrian -1 -1 -1 847.36 152.84 908.40 298.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n286 120 Pedestrian -1 -1 -1 799.00 149.31 841.59 276.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n286 112 Pedestrian -1 -1 -1 442.60 169.11 462.71 218.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n286 100 Pedestrian -1 -1 -1 314.08 159.50 331.63 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n286 8 Car -1 -1 -1 601.51 172.78 637.28 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n286 121 Pedestrian -1 -1 -1 398.29 162.05 410.00 197.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n286 123 Car -1 -1 -1 598.38 173.30 622.18 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n286 124 Pedestrian -1 -1 -1 303.78 157.56 317.57 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n286 105 Pedestrian -1 -1 -1 750.33 157.30 798.65 279.38 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n286 115 Pedestrian -1 -1 -1 426.96 167.55 447.19 218.74 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n286 119 Pedestrian -1 -1 -1 873.17 163.38 935.89 293.86 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n286 6 Pedestrian -1 -1 -1 362.51 158.79 375.68 194.85 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n286 127 Pedestrian -1 -1 -1 328.62 159.32 343.22 196.47 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n286 122 Pedestrian -1 -1 -1 344.05 159.35 356.58 194.09 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n286 126 Pedestrian -1 -1 -1 193.11 159.53 208.20 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n286 85 Pedestrian -1 -1 -1 184.78 159.44 200.52 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n286 128 Pedestrian -1 -1 -1 390.10 160.42 403.50 196.25 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n287 1 Car -1 -1 -1 1095.38 185.19 1220.15 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n287 2 Car -1 -1 -1 955.10 183.23 1067.40 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n287 3 Car -1 -1 -1 1032.70 183.70 1157.08 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n287 120 Pedestrian -1 -1 -1 804.35 149.88 851.76 278.11 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n287 55 Pedestrian -1 -1 -1 859.83 152.27 919.22 298.79 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n287 8 Car -1 -1 -1 601.31 172.66 637.34 203.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n287 60 Pedestrian -1 -1 -1 735.42 155.43 775.09 287.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n287 112 Pedestrian -1 -1 -1 443.81 169.00 462.14 218.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n287 100 Pedestrian -1 -1 -1 313.51 159.57 331.43 200.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n287 121 Pedestrian -1 -1 -1 397.99 161.59 410.51 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n287 123 Car -1 -1 -1 598.03 173.36 622.13 193.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n287 115 Pedestrian -1 -1 -1 428.45 167.31 447.67 218.88 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n287 127 Pedestrian -1 -1 -1 328.42 159.88 342.94 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n287 124 Pedestrian -1 -1 -1 302.14 157.56 316.18 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n287 122 Pedestrian -1 -1 -1 343.48 158.88 356.27 194.18 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n287 105 Pedestrian -1 -1 -1 755.39 158.27 801.36 278.44 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n287 6 Pedestrian -1 -1 -1 362.97 160.41 376.34 195.35 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n287 126 Pedestrian -1 -1 -1 193.10 159.60 208.16 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n287 119 Pedestrian -1 -1 -1 895.62 165.24 943.98 292.04 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n287 85 Pedestrian -1 -1 -1 184.76 159.22 200.50 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n288 1 Car -1 -1 -1 1095.07 185.17 1220.34 236.27 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n288 2 Car -1 -1 -1 955.10 183.22 1067.46 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n288 3 Car -1 -1 -1 1033.03 183.63 1156.80 233.93 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n288 8 Car -1 -1 -1 601.33 172.72 637.21 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n288 120 Pedestrian -1 -1 -1 806.73 150.95 857.69 278.43 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n288 112 Pedestrian -1 -1 -1 443.92 169.11 463.13 218.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n288 55 Pedestrian -1 -1 -1 867.81 152.45 934.00 298.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n288 60 Pedestrian -1 -1 -1 741.08 156.11 784.65 287.96 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n288 100 Pedestrian -1 -1 -1 313.50 159.47 331.58 201.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n288 121 Pedestrian -1 -1 -1 397.78 161.20 410.86 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n288 123 Car -1 -1 -1 598.09 173.41 622.14 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n288 124 Pedestrian -1 -1 -1 301.73 157.65 316.09 193.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n288 115 Pedestrian -1 -1 -1 429.09 165.55 447.78 218.98 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n288 127 Pedestrian -1 -1 -1 328.33 160.07 342.78 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n288 105 Pedestrian -1 -1 -1 762.58 158.78 809.45 283.62 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n288 6 Pedestrian -1 -1 -1 362.50 160.14 376.27 195.50 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n288 122 Pedestrian -1 -1 -1 343.26 158.85 356.55 194.37 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n288 126 Pedestrian -1 -1 -1 193.00 159.49 208.29 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n288 119 Pedestrian -1 -1 -1 902.54 164.66 952.66 293.26 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n288 85 Pedestrian -1 -1 -1 181.54 159.85 198.04 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n289 1 Car -1 -1 -1 1094.58 185.05 1220.68 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n289 2 Car -1 -1 -1 955.22 183.26 1067.66 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n289 3 Car -1 -1 -1 1033.35 183.61 1156.58 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n289 8 Car -1 -1 -1 601.38 172.78 637.22 203.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n289 120 Pedestrian -1 -1 -1 813.36 150.75 865.74 282.82 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n289 55 Pedestrian -1 -1 -1 875.93 155.15 948.81 302.06 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n289 60 Pedestrian -1 -1 -1 741.96 156.34 792.22 288.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n289 112 Pedestrian -1 -1 -1 446.62 168.91 465.90 219.65 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n289 100 Pedestrian -1 -1 -1 314.37 159.74 332.03 201.20 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n289 121 Pedestrian -1 -1 -1 400.67 161.57 412.99 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n289 115 Pedestrian -1 -1 -1 431.66 167.13 450.02 219.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n289 123 Car -1 -1 -1 598.07 173.48 622.17 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n289 127 Pedestrian -1 -1 -1 328.20 160.19 343.01 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n289 6 Pedestrian -1 -1 -1 363.00 160.53 376.55 195.28 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n289 105 Pedestrian -1 -1 -1 773.01 159.71 814.26 282.51 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n289 124 Pedestrian -1 -1 -1 301.88 157.80 316.29 194.02 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n289 122 Pedestrian -1 -1 -1 343.01 159.03 356.17 194.28 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n289 126 Pedestrian -1 -1 -1 193.30 155.55 208.83 196.70 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n289 119 Pedestrian -1 -1 -1 912.45 163.86 965.42 293.96 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n289 85 Pedestrian -1 -1 -1 181.55 159.88 198.12 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n289 129 Pedestrian -1 -1 -1 392.44 160.91 405.71 196.65 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n290 1 Car -1 -1 -1 1094.55 185.07 1220.97 236.33 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n290 2 Car -1 -1 -1 955.46 183.43 1067.26 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n290 3 Car -1 -1 -1 1033.38 183.64 1156.55 233.91 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n290 8 Car -1 -1 -1 601.71 172.85 637.08 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n290 120 Pedestrian -1 -1 -1 819.06 151.58 868.09 282.37 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n290 100 Pedestrian -1 -1 -1 314.64 159.69 332.12 200.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n290 112 Pedestrian -1 -1 -1 448.63 169.04 467.25 219.99 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n290 121 Pedestrian -1 -1 -1 400.86 161.80 413.65 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n290 55 Pedestrian -1 -1 -1 887.03 157.88 960.57 299.77 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n290 115 Pedestrian -1 -1 -1 432.06 166.93 451.69 219.93 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n290 60 Pedestrian -1 -1 -1 744.51 154.24 797.20 290.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n290 123 Car -1 -1 -1 598.28 173.42 622.07 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n290 6 Pedestrian -1 -1 -1 362.53 160.59 376.45 195.84 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n290 105 Pedestrian -1 -1 -1 779.45 156.22 823.02 285.23 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n290 129 Pedestrian -1 -1 -1 392.73 161.08 405.64 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n290 124 Pedestrian -1 -1 -1 301.84 157.75 316.20 194.08 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n290 127 Pedestrian -1 -1 -1 328.28 160.09 343.44 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n290 126 Pedestrian -1 -1 -1 192.98 159.43 208.31 198.04 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n290 85 Pedestrian -1 -1 -1 184.63 159.06 200.75 198.78 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n291 1 Car -1 -1 -1 1094.87 185.14 1220.72 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n291 2 Car -1 -1 -1 955.17 183.30 1067.88 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n291 3 Car -1 -1 -1 1033.40 183.69 1156.72 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n291 112 Pedestrian -1 -1 -1 451.37 169.25 468.87 219.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n291 120 Pedestrian -1 -1 -1 829.47 149.40 880.29 284.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n291 8 Car -1 -1 -1 601.71 172.86 637.00 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n291 100 Pedestrian -1 -1 -1 314.28 159.24 332.20 200.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n291 60 Pedestrian -1 -1 -1 755.38 155.59 801.75 293.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n291 55 Pedestrian -1 -1 -1 895.93 155.15 966.86 302.57 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n291 121 Pedestrian -1 -1 -1 401.77 162.15 414.21 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n291 124 Pedestrian -1 -1 -1 301.08 157.62 316.04 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n291 6 Pedestrian -1 -1 -1 362.74 160.41 376.49 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n291 123 Car -1 -1 -1 598.24 173.38 621.99 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n291 115 Pedestrian -1 -1 -1 432.97 166.86 452.68 220.07 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n291 105 Pedestrian -1 -1 -1 783.77 155.53 833.67 286.85 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n291 129 Pedestrian -1 -1 -1 392.53 160.83 405.62 196.38 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n291 127 Pedestrian -1 -1 -1 330.23 159.68 344.43 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n291 126 Pedestrian -1 -1 -1 193.02 159.55 208.29 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n291 85 Pedestrian -1 -1 -1 181.48 159.80 198.09 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n291 130 Pedestrian -1 -1 -1 934.94 167.38 988.88 297.58 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n291 131 Pedestrian -1 -1 -1 343.07 159.01 356.55 194.84 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n292 1 Car -1 -1 -1 1094.79 185.07 1220.91 236.44 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n292 3 Car -1 -1 -1 1033.40 183.72 1157.01 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n292 2 Car -1 -1 -1 955.72 183.55 1067.18 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n292 112 Pedestrian -1 -1 -1 451.73 169.70 470.32 219.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n292 120 Pedestrian -1 -1 -1 837.08 148.74 895.35 286.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n292 8 Car -1 -1 -1 601.84 172.84 636.83 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n292 55 Pedestrian -1 -1 -1 911.58 150.71 974.39 307.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n292 121 Pedestrian -1 -1 -1 401.80 162.06 414.58 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n292 115 Pedestrian -1 -1 -1 434.61 167.01 455.11 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n292 100 Pedestrian -1 -1 -1 313.83 159.29 331.88 201.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n292 124 Pedestrian -1 -1 -1 301.25 157.51 315.82 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n292 6 Pedestrian -1 -1 -1 362.72 160.15 377.09 196.52 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n292 123 Car -1 -1 -1 598.30 173.40 622.01 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n292 60 Pedestrian -1 -1 -1 766.38 156.00 813.76 294.69 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n292 105 Pedestrian -1 -1 -1 787.64 156.49 837.86 287.24 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n292 127 Pedestrian -1 -1 -1 328.12 159.73 343.51 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n292 129 Pedestrian -1 -1 -1 390.46 160.25 403.70 196.38 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n292 126 Pedestrian -1 -1 -1 192.72 159.27 208.40 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n293 1 Car -1 -1 -1 1094.93 185.16 1220.85 236.40 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n293 3 Car -1 -1 -1 1033.62 183.64 1156.88 234.12 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n293 2 Car -1 -1 -1 955.22 183.55 1068.21 233.59 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n293 55 Pedestrian -1 -1 -1 924.79 151.04 991.94 307.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n293 8 Car -1 -1 -1 601.74 172.93 637.00 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n293 121 Pedestrian -1 -1 -1 401.23 161.89 414.14 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n293 115 Pedestrian -1 -1 -1 435.88 167.32 456.01 221.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n293 120 Pedestrian -1 -1 -1 840.07 149.32 908.03 285.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n293 60 Pedestrian -1 -1 -1 772.03 156.60 823.33 294.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n293 112 Pedestrian -1 -1 -1 452.54 170.18 471.46 220.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n293 124 Pedestrian -1 -1 -1 300.65 157.70 315.91 193.79 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n293 6 Pedestrian -1 -1 -1 362.42 159.88 377.76 196.92 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n293 100 Pedestrian -1 -1 -1 313.67 159.22 332.02 201.69 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n293 123 Car -1 -1 -1 598.21 173.31 621.93 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n293 127 Pedestrian -1 -1 -1 327.99 159.51 343.14 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n293 129 Pedestrian -1 -1 -1 390.40 160.30 403.70 195.99 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n293 105 Pedestrian -1 -1 -1 797.44 158.02 843.20 285.62 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n293 126 Pedestrian -1 -1 -1 192.62 159.34 208.12 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n294 1 Car -1 -1 -1 1095.09 185.26 1220.89 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n294 3 Car -1 -1 -1 1033.80 183.57 1156.67 234.34 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n294 2 Car -1 -1 -1 954.48 183.76 1068.52 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n294 112 Pedestrian -1 -1 -1 454.65 170.63 473.09 220.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n294 8 Car -1 -1 -1 601.92 172.97 636.89 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n294 55 Pedestrian -1 -1 -1 932.05 150.59 1007.38 308.21 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n294 60 Pedestrian -1 -1 -1 781.52 155.79 843.94 300.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n294 120 Pedestrian -1 -1 -1 846.23 149.83 916.96 286.31 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n294 121 Pedestrian -1 -1 -1 401.60 161.82 414.16 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n294 124 Pedestrian -1 -1 -1 300.48 158.05 315.88 193.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n294 115 Pedestrian -1 -1 -1 439.05 168.59 457.60 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n294 6 Pedestrian -1 -1 -1 362.29 160.36 377.82 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n294 100 Pedestrian -1 -1 -1 313.46 159.45 332.07 201.78 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n294 123 Car -1 -1 -1 598.37 173.44 621.77 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n294 127 Pedestrian -1 -1 -1 328.08 159.73 343.30 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n294 129 Pedestrian -1 -1 -1 392.45 160.78 405.59 195.99 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n294 126 Pedestrian -1 -1 -1 192.62 159.32 208.02 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n294 105 Pedestrian -1 -1 -1 796.03 155.20 852.95 287.85 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n295 1 Car -1 -1 -1 1095.24 185.19 1220.92 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n295 3 Car -1 -1 -1 1033.92 183.66 1156.47 234.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n295 2 Car -1 -1 -1 956.86 182.96 1066.12 232.08 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n295 112 Pedestrian -1 -1 -1 455.12 170.40 474.23 220.24 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n295 8 Car -1 -1 -1 602.03 172.98 636.74 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n295 55 Pedestrian -1 -1 -1 943.85 153.69 1025.89 311.43 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n295 115 Pedestrian -1 -1 -1 439.95 168.01 459.22 221.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n295 121 Pedestrian -1 -1 -1 401.05 161.71 414.28 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n295 60 Pedestrian -1 -1 -1 787.86 155.57 853.26 301.08 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n295 120 Pedestrian -1 -1 -1 853.59 150.42 917.37 285.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n295 100 Pedestrian -1 -1 -1 313.48 159.29 331.72 201.89 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n295 127 Pedestrian -1 -1 -1 327.57 159.40 343.34 199.08 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n295 124 Pedestrian -1 -1 -1 299.75 158.44 315.08 193.96 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n295 123 Car -1 -1 -1 598.42 173.40 621.74 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n295 6 Pedestrian -1 -1 -1 361.79 160.20 377.96 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n295 129 Pedestrian -1 -1 -1 392.57 160.97 405.88 196.18 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n295 126 Pedestrian -1 -1 -1 192.84 159.64 208.20 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n295 105 Pedestrian -1 -1 -1 808.00 155.52 863.89 287.43 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n296 2 Car -1 -1 -1 954.36 182.94 1068.68 232.25 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n296 1 Car -1 -1 -1 1099.08 184.90 1220.64 236.79 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n296 3 Car -1 -1 -1 1033.64 183.73 1156.54 234.40 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n296 8 Car -1 -1 -1 602.04 172.98 636.80 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n296 112 Pedestrian -1 -1 -1 456.10 170.26 475.17 220.41 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n296 120 Pedestrian -1 -1 -1 864.48 148.91 921.84 287.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n296 55 Pedestrian -1 -1 -1 956.51 154.12 1036.19 311.15 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n296 60 Pedestrian -1 -1 -1 795.08 156.81 853.07 300.29 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n296 100 Pedestrian -1 -1 -1 313.55 159.39 331.32 201.46 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n296 127 Pedestrian -1 -1 -1 326.71 159.65 342.90 199.06 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n296 115 Pedestrian -1 -1 -1 442.35 168.03 461.98 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n296 121 Pedestrian -1 -1 -1 402.18 162.15 414.41 196.99 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n296 124 Pedestrian -1 -1 -1 297.15 158.10 312.99 194.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n296 123 Car -1 -1 -1 598.47 173.50 621.84 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n296 129 Pedestrian -1 -1 -1 392.71 161.05 405.24 196.14 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n296 6 Pedestrian -1 -1 -1 361.39 160.66 377.69 196.72 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n296 126 Pedestrian -1 -1 -1 192.58 159.62 208.18 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n296 105 Pedestrian -1 -1 -1 815.14 155.08 871.80 288.30 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n297 2 Car -1 -1 -1 953.63 182.72 1069.15 232.23 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n297 1 Car -1 -1 -1 1099.51 184.86 1220.40 236.66 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n297 3 Car -1 -1 -1 1033.74 183.71 1156.79 234.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n297 112 Pedestrian -1 -1 -1 458.93 169.53 478.65 220.86 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n297 115 Pedestrian -1 -1 -1 441.98 166.71 464.12 221.64 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n297 55 Pedestrian -1 -1 -1 969.13 151.37 1046.33 314.49 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n297 8 Car -1 -1 -1 602.25 173.18 636.65 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n297 60 Pedestrian -1 -1 -1 804.91 156.06 858.93 300.49 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n297 120 Pedestrian -1 -1 -1 879.26 148.12 929.68 287.94 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n297 124 Pedestrian -1 -1 -1 297.08 157.83 312.75 194.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n297 121 Pedestrian -1 -1 -1 402.05 162.08 414.40 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n297 127 Pedestrian -1 -1 -1 326.44 159.83 342.29 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n297 129 Pedestrian -1 -1 -1 392.47 160.85 405.34 195.89 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n297 123 Car -1 -1 -1 598.41 173.43 621.84 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n297 100 Pedestrian -1 -1 -1 313.54 159.08 331.88 202.41 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n297 126 Pedestrian -1 -1 -1 192.52 159.47 208.43 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n297 6 Pedestrian -1 -1 -1 361.33 160.57 378.38 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n297 105 Pedestrian -1 -1 -1 824.80 154.59 884.77 288.81 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n298 1 Car -1 -1 -1 1099.58 184.97 1220.34 236.55 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n298 3 Car -1 -1 -1 1034.41 183.99 1156.21 234.39 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n298 2 Car -1 -1 -1 956.04 182.73 1066.83 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n298 60 Pedestrian -1 -1 -1 818.36 155.89 868.27 302.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n298 115 Pedestrian -1 -1 -1 442.73 167.08 464.99 221.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n298 55 Pedestrian -1 -1 -1 985.28 152.08 1053.42 313.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n298 8 Car -1 -1 -1 602.05 173.04 636.80 202.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n298 112 Pedestrian -1 -1 -1 459.75 169.75 479.25 220.73 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n298 121 Pedestrian -1 -1 -1 401.82 161.90 414.29 196.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n298 120 Pedestrian -1 -1 -1 892.21 147.40 947.10 293.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n298 127 Pedestrian -1 -1 -1 326.57 160.09 342.41 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n298 100 Pedestrian -1 -1 -1 313.80 159.83 332.35 204.08 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n298 124 Pedestrian -1 -1 -1 297.44 157.83 312.24 194.71 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n298 123 Car -1 -1 -1 598.58 173.50 621.99 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n298 129 Pedestrian -1 -1 -1 392.41 160.83 404.92 195.45 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n298 6 Pedestrian -1 -1 -1 361.26 160.23 378.98 196.83 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n298 126 Pedestrian -1 -1 -1 192.48 159.61 208.48 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n298 132 Pedestrian -1 -1 -1 1013.85 166.73 1071.17 312.94 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n298 133 Pedestrian -1 -1 -1 340.12 158.85 354.41 194.79 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n299 1 Car -1 -1 -1 1099.46 185.00 1220.31 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n299 3 Car -1 -1 -1 1034.61 184.09 1156.30 234.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n299 2 Car -1 -1 -1 956.62 182.29 1066.90 232.50 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n299 60 Pedestrian -1 -1 -1 828.09 156.14 889.67 301.62 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n299 8 Car -1 -1 -1 602.11 173.04 636.64 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n299 55 Pedestrian -1 -1 -1 997.24 150.92 1072.24 321.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n299 112 Pedestrian -1 -1 -1 460.77 169.67 482.14 221.22 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n299 124 Pedestrian -1 -1 -1 297.01 158.22 311.21 194.95 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n299 115 Pedestrian -1 -1 -1 445.77 168.49 467.12 222.53 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n299 100 Pedestrian -1 -1 -1 314.47 160.37 332.61 203.32 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n299 121 Pedestrian -1 -1 -1 402.33 161.96 414.57 195.99 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n299 120 Pedestrian -1 -1 -1 895.84 147.70 959.40 293.23 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n299 127 Pedestrian -1 -1 -1 326.60 160.33 342.09 198.96 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n299 129 Pedestrian -1 -1 -1 392.36 161.04 405.19 195.43 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n299 123 Car -1 -1 -1 598.36 173.49 622.18 193.59 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n299 6 Pedestrian -1 -1 -1 362.18 160.61 378.51 197.41 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n299 126 Pedestrian -1 -1 -1 192.54 159.71 208.22 198.19 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n299 133 Pedestrian -1 -1 -1 342.16 160.72 355.65 195.19 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n300 1 Car -1 -1 -1 1099.31 185.15 1220.40 236.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n300 3 Car -1 -1 -1 1035.22 183.97 1155.81 234.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n300 2 Car -1 -1 -1 956.70 182.71 1065.99 232.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n300 60 Pedestrian -1 -1 -1 832.22 155.88 900.72 303.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n300 8 Car -1 -1 -1 602.24 173.14 636.59 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n300 112 Pedestrian -1 -1 -1 462.23 170.04 483.34 221.46 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n300 120 Pedestrian -1 -1 -1 898.43 147.77 971.84 294.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n300 55 Pedestrian -1 -1 -1 1004.45 152.97 1088.06 319.77 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n300 127 Pedestrian -1 -1 -1 327.08 160.00 342.93 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n300 129 Pedestrian -1 -1 -1 392.49 161.30 405.21 195.44 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n300 124 Pedestrian -1 -1 -1 296.39 158.50 310.79 195.03 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n300 100 Pedestrian -1 -1 -1 315.37 159.79 332.13 201.23 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n300 121 Pedestrian -1 -1 -1 402.13 162.09 414.96 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n300 123 Car -1 -1 -1 598.57 173.51 622.07 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n300 115 Pedestrian -1 -1 -1 450.09 169.78 469.56 222.29 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n300 6 Pedestrian -1 -1 -1 362.95 160.64 377.74 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n300 133 Pedestrian -1 -1 -1 342.13 161.15 355.67 195.98 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n300 126 Pedestrian -1 -1 -1 192.75 159.95 208.44 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n300 134 Pedestrian -1 -1 -1 184.21 154.03 202.16 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n301 1 Car -1 -1 -1 1093.97 185.09 1221.64 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n301 3 Car -1 -1 -1 1034.65 183.83 1156.06 234.54 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n301 2 Car -1 -1 -1 956.07 182.80 1066.18 234.16 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n301 120 Pedestrian -1 -1 -1 907.02 146.71 978.25 296.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n301 60 Pedestrian -1 -1 -1 840.23 156.34 908.00 307.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n301 8 Car -1 -1 -1 602.07 173.11 636.74 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n301 112 Pedestrian -1 -1 -1 465.02 170.06 485.48 221.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n301 55 Pedestrian -1 -1 -1 1016.18 154.44 1106.89 319.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n301 124 Pedestrian -1 -1 -1 296.53 158.26 310.84 194.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n301 115 Pedestrian -1 -1 -1 450.26 169.70 470.29 222.26 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n301 129 Pedestrian -1 -1 -1 392.68 161.10 405.37 195.40 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n301 6 Pedestrian -1 -1 -1 362.79 160.67 377.41 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n301 127 Pedestrian -1 -1 -1 327.49 159.96 343.15 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n301 123 Car -1 -1 -1 598.60 173.51 621.97 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n301 121 Pedestrian -1 -1 -1 402.44 162.05 414.66 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n301 100 Pedestrian -1 -1 -1 315.22 160.23 333.41 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n301 126 Pedestrian -1 -1 -1 192.70 160.35 208.27 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n301 133 Pedestrian -1 -1 -1 342.33 161.44 355.62 196.21 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n301 134 Pedestrian -1 -1 -1 184.43 154.38 202.02 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n302 1 Car -1 -1 -1 1093.47 185.01 1222.07 236.61 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n302 3 Car -1 -1 -1 1032.87 183.52 1158.30 234.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n302 2 Car -1 -1 -1 957.53 183.01 1064.64 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n302 120 Pedestrian -1 -1 -1 920.64 145.96 980.18 297.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n302 112 Pedestrian -1 -1 -1 465.59 169.63 486.77 222.14 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n302 8 Car -1 -1 -1 602.26 173.03 636.60 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n302 60 Pedestrian -1 -1 -1 851.20 153.54 911.99 304.65 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n302 55 Pedestrian -1 -1 -1 1037.44 158.38 1116.16 322.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n302 115 Pedestrian -1 -1 -1 451.61 168.09 472.06 222.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n302 124 Pedestrian -1 -1 -1 296.35 158.16 310.62 194.80 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n302 100 Pedestrian -1 -1 -1 310.80 158.23 326.97 198.94 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n302 123 Car -1 -1 -1 598.57 173.48 621.76 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n302 129 Pedestrian -1 -1 -1 392.53 161.10 405.36 195.24 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n302 127 Pedestrian -1 -1 -1 327.46 160.05 342.71 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n302 121 Pedestrian -1 -1 -1 404.27 163.11 415.59 195.75 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n302 6 Pedestrian -1 -1 -1 362.70 160.46 377.52 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n302 126 Pedestrian -1 -1 -1 192.41 159.98 208.29 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n302 133 Pedestrian -1 -1 -1 341.98 160.71 356.08 196.43 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n303 1 Car -1 -1 -1 1093.47 185.02 1222.04 236.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n303 2 Car -1 -1 -1 956.82 182.61 1066.30 234.49 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n303 3 Car -1 -1 -1 1032.56 183.79 1158.29 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n303 120 Pedestrian -1 -1 -1 939.82 146.87 991.92 297.22 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n303 8 Car -1 -1 -1 602.20 173.18 636.64 202.69 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n303 115 Pedestrian -1 -1 -1 454.28 168.02 473.84 222.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n303 124 Pedestrian -1 -1 -1 296.13 157.70 310.95 195.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n303 55 Pedestrian -1 -1 -1 1049.24 157.44 1127.04 323.79 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n303 60 Pedestrian -1 -1 -1 870.94 155.60 930.83 302.86 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n303 100 Pedestrian -1 -1 -1 310.84 157.79 327.23 199.31 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n303 112 Pedestrian -1 -1 -1 467.13 169.55 487.40 222.48 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n303 127 Pedestrian -1 -1 -1 327.18 159.86 343.06 198.90 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n303 123 Car -1 -1 -1 598.73 173.66 622.16 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n303 121 Pedestrian -1 -1 -1 404.31 162.75 415.65 195.38 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n303 129 Pedestrian -1 -1 -1 392.87 160.62 405.67 195.16 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n303 6 Pedestrian -1 -1 -1 362.72 159.90 377.78 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n303 126 Pedestrian -1 -1 -1 192.50 160.26 208.38 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n303 133 Pedestrian -1 -1 -1 341.62 159.55 355.92 196.72 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n304 1 Car -1 -1 -1 1093.97 185.07 1221.46 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n304 3 Car -1 -1 -1 1034.53 183.54 1155.86 234.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n304 2 Car -1 -1 -1 955.73 182.50 1067.54 234.57 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n304 60 Pedestrian -1 -1 -1 877.02 154.62 947.59 309.77 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n304 120 Pedestrian -1 -1 -1 948.41 144.91 1013.72 299.64 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n304 8 Car -1 -1 -1 602.15 173.21 636.77 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n304 112 Pedestrian -1 -1 -1 469.44 169.79 488.94 221.80 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n304 55 Pedestrian -1 -1 -1 1063.38 150.80 1143.90 330.06 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n304 115 Pedestrian -1 -1 -1 455.87 167.96 475.58 223.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n304 124 Pedestrian -1 -1 -1 295.58 157.79 310.55 195.48 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n304 100 Pedestrian -1 -1 -1 310.23 157.88 326.86 199.42 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n304 127 Pedestrian -1 -1 -1 326.88 159.52 343.17 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n304 123 Car -1 -1 -1 598.77 173.65 622.07 193.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n304 6 Pedestrian -1 -1 -1 362.50 159.49 377.97 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n304 121 Pedestrian -1 -1 -1 402.26 162.20 414.69 195.60 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n304 129 Pedestrian -1 -1 -1 393.14 160.68 405.80 194.91 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n304 126 Pedestrian -1 -1 -1 192.49 160.32 208.35 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n304 133 Pedestrian -1 -1 -1 341.87 159.44 355.79 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n305 1 Car -1 -1 -1 1093.41 184.99 1221.89 236.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n305 2 Car -1 -1 -1 956.57 183.29 1066.32 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n305 3 Car -1 -1 -1 1031.17 183.08 1154.17 234.47 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n305 60 Pedestrian -1 -1 -1 884.54 156.15 962.75 308.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n305 120 Pedestrian -1 -1 -1 951.69 149.06 1033.50 300.30 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n305 8 Car -1 -1 -1 602.96 172.70 637.08 202.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n305 55 Pedestrian -1 -1 -1 1081.60 152.64 1163.63 328.78 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n305 112 Pedestrian -1 -1 -1 470.23 169.97 491.45 222.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n305 115 Pedestrian -1 -1 -1 458.32 171.64 478.50 223.41 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n305 100 Pedestrian -1 -1 -1 308.97 158.47 324.59 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n305 127 Pedestrian -1 -1 -1 327.44 159.32 343.21 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n305 123 Car -1 -1 -1 598.78 173.65 622.06 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n305 121 Pedestrian -1 -1 -1 404.62 162.57 415.93 195.33 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n305 124 Pedestrian -1 -1 -1 294.16 158.21 308.83 195.55 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n305 129 Pedestrian -1 -1 -1 393.44 160.09 405.48 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n305 6 Pedestrian -1 -1 -1 362.34 159.44 378.08 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n305 126 Pedestrian -1 -1 -1 192.64 160.40 208.52 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n305 133 Pedestrian -1 -1 -1 342.01 160.27 356.36 196.85 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n306 1 Car -1 -1 -1 1091.95 184.74 1223.46 236.22 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n306 2 Car -1 -1 -1 956.27 183.00 1066.48 231.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n306 3 Car -1 -1 -1 1030.60 183.13 1154.34 234.35 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n306 60 Pedestrian -1 -1 -1 891.15 151.48 971.64 313.99 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n306 8 Car -1 -1 -1 602.10 173.09 636.69 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n306 112 Pedestrian -1 -1 -1 472.19 169.75 493.11 222.10 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n306 120 Pedestrian -1 -1 -1 955.31 150.10 1045.40 301.70 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n306 124 Pedestrian -1 -1 -1 293.48 158.39 308.65 195.36 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n306 100 Pedestrian -1 -1 -1 309.02 158.70 323.72 198.59 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n306 55 Pedestrian -1 -1 -1 1090.58 157.62 1193.06 330.71 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n306 115 Pedestrian -1 -1 -1 461.52 168.73 480.98 222.60 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n306 123 Car -1 -1 -1 598.69 173.66 621.91 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n306 127 Pedestrian -1 -1 -1 327.05 159.19 343.16 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n306 129 Pedestrian -1 -1 -1 393.72 160.27 405.60 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n306 121 Pedestrian -1 -1 -1 404.57 162.37 416.35 195.05 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n306 133 Pedestrian -1 -1 -1 342.37 160.63 356.37 196.85 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n306 126 Pedestrian -1 -1 -1 192.79 160.38 208.70 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n306 6 Pedestrian -1 -1 -1 362.14 159.48 378.20 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n306 135 Pedestrian -1 -1 -1 316.23 159.99 335.71 203.81 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n307 1 Car -1 -1 -1 1091.77 184.49 1223.63 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n307 3 Car -1 -1 -1 1031.76 183.24 1153.24 234.39 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n307 2 Car -1 -1 -1 957.22 182.79 1065.62 231.55 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n307 60 Pedestrian -1 -1 -1 901.61 150.69 976.70 314.87 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n307 8 Car -1 -1 -1 603.02 172.80 637.10 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n307 120 Pedestrian -1 -1 -1 971.48 148.74 1051.81 308.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n307 115 Pedestrian -1 -1 -1 462.08 168.65 481.68 222.53 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n307 55 Pedestrian -1 -1 -1 1095.25 153.03 1210.91 343.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n307 112 Pedestrian -1 -1 -1 474.15 169.73 494.96 222.48 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n307 124 Pedestrian -1 -1 -1 292.62 158.25 307.35 195.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n307 100 Pedestrian -1 -1 -1 308.24 158.43 323.50 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n307 135 Pedestrian -1 -1 -1 315.85 159.84 336.78 204.70 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n307 127 Pedestrian -1 -1 -1 327.22 159.15 342.62 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n307 123 Car -1 -1 -1 598.64 173.72 621.95 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n307 129 Pedestrian -1 -1 -1 393.48 160.36 405.95 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n307 133 Pedestrian -1 -1 -1 342.63 160.50 356.53 197.13 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n307 121 Pedestrian -1 -1 -1 404.78 162.15 416.74 194.64 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n307 126 Pedestrian -1 -1 -1 192.67 160.40 208.72 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n307 6 Pedestrian -1 -1 -1 361.94 159.68 378.50 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n308 1 Car -1 -1 -1 1092.43 184.43 1222.84 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n308 2 Car -1 -1 -1 956.50 183.15 1065.89 231.10 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n308 3 Car -1 -1 -1 1031.49 183.55 1153.80 234.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n308 60 Pedestrian -1 -1 -1 912.02 150.95 989.00 314.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n308 120 Pedestrian -1 -1 -1 984.85 147.59 1054.24 304.23 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n308 8 Car -1 -1 -1 603.01 173.00 637.06 202.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n308 115 Pedestrian -1 -1 -1 463.25 168.49 483.11 222.69 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n308 135 Pedestrian -1 -1 -1 315.83 160.43 336.80 204.91 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n308 100 Pedestrian -1 -1 -1 307.62 158.15 322.99 198.86 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n308 112 Pedestrian -1 -1 -1 477.24 169.97 497.67 222.41 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n308 55 Pedestrian -1 -1 -1 1115.56 154.39 1213.66 341.38 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n308 124 Pedestrian -1 -1 -1 291.92 157.61 306.19 195.54 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n308 127 Pedestrian -1 -1 -1 327.13 159.82 342.44 199.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n308 129 Pedestrian -1 -1 -1 393.43 160.41 406.16 192.80 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n308 121 Pedestrian -1 -1 -1 405.38 162.46 417.22 194.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n308 123 Car -1 -1 -1 598.83 173.79 622.03 193.55 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n308 126 Pedestrian -1 -1 -1 192.55 160.37 208.74 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n308 133 Pedestrian -1 -1 -1 342.13 160.33 356.02 196.82 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n308 6 Pedestrian -1 -1 -1 362.13 159.48 378.33 198.33 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n309 1 Car -1 -1 -1 1094.90 184.76 1220.44 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n309 3 Car -1 -1 -1 1031.08 183.44 1154.15 234.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n309 2 Car -1 -1 -1 957.58 182.78 1064.82 231.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n309 60 Pedestrian -1 -1 -1 928.55 153.69 1002.88 312.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n309 120 Pedestrian -1 -1 -1 1003.93 146.67 1064.97 310.27 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n309 8 Car -1 -1 -1 603.16 172.86 637.02 202.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n309 135 Pedestrian -1 -1 -1 316.67 160.21 336.73 205.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n309 112 Pedestrian -1 -1 -1 481.53 169.83 500.07 221.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n309 115 Pedestrian -1 -1 -1 465.19 168.73 485.42 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n309 55 Pedestrian -1 -1 -1 1137.23 148.15 1214.85 340.47 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n309 124 Pedestrian -1 -1 -1 290.16 157.48 304.58 195.88 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n309 121 Pedestrian -1 -1 -1 405.77 162.61 417.85 194.40 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n309 129 Pedestrian -1 -1 -1 393.11 160.46 405.78 192.58 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n309 127 Pedestrian -1 -1 -1 327.00 159.86 342.81 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n309 100 Pedestrian -1 -1 -1 306.70 158.22 322.60 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n309 123 Car -1 -1 -1 598.79 173.77 622.04 193.58 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n309 133 Pedestrian -1 -1 -1 342.31 160.40 355.84 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n309 126 Pedestrian -1 -1 -1 192.59 160.41 208.72 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n309 6 Pedestrian -1 -1 -1 362.25 159.67 378.35 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n309 136 Car -1 -1 -1 1139.12 181.74 1221.03 238.52 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n309 137 Pedestrian -1 -1 -1 183.88 160.26 201.46 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n310 1 Car -1 -1 -1 1096.06 184.68 1219.63 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n310 3 Car -1 -1 -1 1031.05 183.67 1154.49 234.68 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n310 120 Pedestrian -1 -1 -1 1014.96 146.36 1085.19 311.68 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n310 60 Pedestrian -1 -1 -1 937.38 154.84 1024.89 318.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n310 2 Car -1 -1 -1 957.59 183.13 1064.86 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n310 8 Car -1 -1 -1 603.28 172.73 636.90 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n310 135 Pedestrian -1 -1 -1 317.00 159.99 337.03 205.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n310 115 Pedestrian -1 -1 -1 466.00 168.35 486.21 223.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n310 124 Pedestrian -1 -1 -1 289.22 157.38 303.75 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n310 127 Pedestrian -1 -1 -1 326.96 159.56 343.21 199.85 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n310 112 Pedestrian -1 -1 -1 482.11 170.23 502.41 222.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n310 121 Pedestrian -1 -1 -1 406.17 162.79 417.43 194.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n310 100 Pedestrian -1 -1 -1 304.75 158.36 320.94 199.41 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n310 129 Pedestrian -1 -1 -1 393.85 160.79 406.05 192.37 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n310 6 Pedestrian -1 -1 -1 364.65 159.86 380.00 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n310 123 Car -1 -1 -1 598.74 173.80 621.86 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n310 55 Pedestrian -1 -1 -1 1162.25 147.42 1220.69 348.77 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n310 133 Pedestrian -1 -1 -1 342.25 160.44 355.76 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n310 136 Car -1 -1 -1 1155.24 183.56 1221.18 236.97 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n310 126 Pedestrian -1 -1 -1 192.43 160.49 208.53 197.58 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n310 137 Pedestrian -1 -1 -1 183.94 160.33 201.38 197.87 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n311 1 Car -1 -1 -1 1102.25 184.69 1217.74 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n311 3 Car -1 -1 -1 1034.53 184.11 1156.36 234.79 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n311 2 Car -1 -1 -1 957.54 182.96 1065.08 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n311 60 Pedestrian -1 -1 -1 949.46 152.94 1043.00 327.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n311 120 Pedestrian -1 -1 -1 1020.05 146.56 1102.96 311.40 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n311 8 Car -1 -1 -1 602.27 173.26 636.57 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n311 135 Pedestrian -1 -1 -1 317.70 159.35 337.53 205.67 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n311 112 Pedestrian -1 -1 -1 484.69 170.04 504.27 222.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n311 127 Pedestrian -1 -1 -1 326.76 159.18 343.47 200.25 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n311 124 Pedestrian -1 -1 -1 289.42 157.81 303.79 196.13 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n311 100 Pedestrian -1 -1 -1 305.01 158.57 320.61 199.07 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n311 115 Pedestrian -1 -1 -1 467.73 170.71 486.34 224.20 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n311 6 Pedestrian -1 -1 -1 364.57 160.25 380.20 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n311 129 Pedestrian -1 -1 -1 394.16 160.67 406.58 192.26 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n311 123 Car -1 -1 -1 598.70 173.79 621.81 193.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n311 133 Pedestrian -1 -1 -1 342.45 160.90 356.02 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n311 126 Pedestrian -1 -1 -1 192.62 160.50 208.56 197.75 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n311 121 Pedestrian -1 -1 -1 406.16 163.06 418.10 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n311 55 Pedestrian -1 -1 -1 1175.82 155.49 1222.02 348.10 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n311 137 Pedestrian -1 -1 -1 183.98 160.03 201.48 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n311 136 Car -1 -1 -1 1175.38 183.97 1222.66 243.22 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n311 138 Pedestrian -1 -1 -1 386.29 160.48 399.12 192.37 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n312 1 Car -1 -1 -1 1097.37 184.72 1218.19 235.84 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n312 3 Car -1 -1 -1 1034.09 184.10 1156.61 234.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n312 2 Car -1 -1 -1 957.25 182.90 1065.76 231.47 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n312 60 Pedestrian -1 -1 -1 965.78 151.32 1049.65 322.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n312 120 Pedestrian -1 -1 -1 1032.50 148.82 1120.64 316.17 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n312 8 Car -1 -1 -1 602.35 173.22 636.46 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n312 135 Pedestrian -1 -1 -1 317.45 159.50 337.34 206.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n312 127 Pedestrian -1 -1 -1 326.08 159.18 343.52 200.76 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n312 6 Pedestrian -1 -1 -1 364.83 160.41 379.93 199.23 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n312 112 Pedestrian -1 -1 -1 485.85 170.39 506.40 223.34 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n312 124 Pedestrian -1 -1 -1 288.36 157.68 303.42 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n312 115 Pedestrian -1 -1 -1 469.63 170.97 488.66 224.42 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n312 100 Pedestrian -1 -1 -1 304.94 158.40 320.36 199.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n312 133 Pedestrian -1 -1 -1 343.07 160.92 356.96 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n312 123 Car -1 -1 -1 598.71 173.73 621.87 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n312 126 Pedestrian -1 -1 -1 192.80 160.73 208.65 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n312 121 Pedestrian -1 -1 -1 408.18 163.32 419.67 193.97 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n312 138 Pedestrian -1 -1 -1 386.33 160.74 399.23 191.98 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n312 129 Pedestrian -1 -1 -1 394.18 160.94 406.58 191.67 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n312 55 Pedestrian -1 -1 -1 1185.89 160.20 1220.19 343.68 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n312 137 Pedestrian -1 -1 -1 184.01 159.86 201.48 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n313 1 Car -1 -1 -1 1096.26 184.96 1219.42 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n313 2 Car -1 -1 -1 956.53 182.64 1065.88 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n313 3 Car -1 -1 -1 1033.96 183.86 1156.41 234.24 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n313 60 Pedestrian -1 -1 -1 978.03 150.44 1053.06 329.71 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n313 120 Pedestrian -1 -1 -1 1054.55 150.95 1129.40 316.09 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n313 112 Pedestrian -1 -1 -1 488.36 169.44 509.76 224.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n313 8 Car -1 -1 -1 602.33 173.28 636.55 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n313 6 Pedestrian -1 -1 -1 365.46 160.61 380.32 199.18 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n313 127 Pedestrian -1 -1 -1 326.03 159.67 343.48 200.62 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n313 135 Pedestrian -1 -1 -1 317.34 159.71 337.60 206.95 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n313 100 Pedestrian -1 -1 -1 304.91 158.35 320.18 199.11 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n313 124 Pedestrian -1 -1 -1 288.43 157.53 302.98 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n313 115 Pedestrian -1 -1 -1 470.99 169.08 490.62 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n313 133 Pedestrian -1 -1 -1 342.96 161.02 357.10 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n313 121 Pedestrian -1 -1 -1 408.59 163.38 419.93 193.68 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n313 123 Car -1 -1 -1 598.86 173.74 621.92 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n313 126 Pedestrian -1 -1 -1 192.63 160.70 208.86 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n313 138 Pedestrian -1 -1 -1 386.43 161.06 399.14 191.57 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n313 129 Pedestrian -1 -1 -1 396.54 161.01 409.38 191.52 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n313 137 Pedestrian -1 -1 -1 183.82 159.88 201.69 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n314 1 Car -1 -1 -1 1094.98 185.14 1220.67 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n314 2 Car -1 -1 -1 957.61 183.24 1064.55 231.52 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n314 3 Car -1 -1 -1 1032.83 184.05 1157.45 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n314 8 Car -1 -1 -1 602.27 173.29 636.58 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n314 120 Pedestrian -1 -1 -1 1066.41 150.18 1133.03 317.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n314 127 Pedestrian -1 -1 -1 325.91 159.61 343.46 200.47 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n314 6 Pedestrian -1 -1 -1 365.91 160.59 381.24 199.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n314 60 Pedestrian -1 -1 -1 991.74 148.44 1085.33 331.59 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n314 124 Pedestrian -1 -1 -1 288.30 157.17 302.71 196.48 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n314 135 Pedestrian -1 -1 -1 317.18 159.66 337.70 207.13 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n314 115 Pedestrian -1 -1 -1 472.71 166.35 493.82 225.35 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n314 112 Pedestrian -1 -1 -1 491.30 168.85 512.71 223.68 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n314 133 Pedestrian -1 -1 -1 342.70 160.90 356.99 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n314 100 Pedestrian -1 -1 -1 304.74 157.77 320.60 199.50 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n314 123 Car -1 -1 -1 598.74 173.74 621.90 193.51 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n314 138 Pedestrian -1 -1 -1 386.39 161.13 399.12 191.09 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n314 126 Pedestrian -1 -1 -1 192.71 160.75 209.03 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n314 137 Pedestrian -1 -1 -1 183.91 159.85 201.57 197.97 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n314 121 Pedestrian -1 -1 -1 408.87 163.14 420.29 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n314 129 Pedestrian -1 -1 -1 396.07 160.84 409.69 191.45 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n315 1 Car -1 -1 -1 1094.48 185.32 1220.17 235.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n315 3 Car -1 -1 -1 1033.21 184.16 1157.18 233.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n315 2 Car -1 -1 -1 957.22 182.88 1064.66 234.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n315 112 Pedestrian -1 -1 -1 492.72 168.77 514.21 223.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n315 8 Car -1 -1 -1 602.20 173.32 636.68 202.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n315 127 Pedestrian -1 -1 -1 325.91 159.42 343.23 200.21 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n315 115 Pedestrian -1 -1 -1 473.75 166.23 494.13 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n315 135 Pedestrian -1 -1 -1 317.17 159.82 337.95 207.08 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n315 120 Pedestrian -1 -1 -1 1085.94 149.52 1151.23 321.75 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n315 60 Pedestrian -1 -1 -1 1001.71 150.63 1105.79 331.00 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n315 124 Pedestrian -1 -1 -1 287.79 157.27 302.83 196.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n315 6 Pedestrian -1 -1 -1 366.15 160.56 381.56 199.43 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n315 133 Pedestrian -1 -1 -1 342.86 160.86 357.10 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n315 100 Pedestrian -1 -1 -1 306.36 158.06 322.48 200.59 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n315 123 Car -1 -1 -1 598.70 173.79 621.87 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n315 126 Pedestrian -1 -1 -1 192.63 160.79 209.15 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n315 138 Pedestrian -1 -1 -1 386.20 160.69 399.28 191.21 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n315 137 Pedestrian -1 -1 -1 183.79 159.91 201.66 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n315 121 Pedestrian -1 -1 -1 408.79 162.98 420.70 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n315 129 Pedestrian -1 -1 -1 396.06 160.61 409.54 191.41 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0025.txt",
    "content": "0 1 Car -1 -1 -1 955.03 183.02 1067.18 234.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n0 2 Pedestrian -1 -1 -1 144.54 150.71 174.30 222.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n0 3 Car -1 -1 -1 1092.78 185.31 1221.90 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n0 4 Pedestrian -1 -1 -1 408.03 163.81 430.41 220.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n0 5 Pedestrian -1 -1 -1 1053.31 154.61 1123.02 319.59 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n0 6 Car -1 -1 -1 1043.85 184.50 1154.84 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n0 7 Pedestrian -1 -1 -1 435.64 164.06 455.94 220.12 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n0 8 Car -1 -1 -1 602.77 172.54 637.20 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n0 9 Pedestrian -1 -1 -1 305.84 156.89 319.81 193.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n0 10 Pedestrian -1 -1 -1 181.48 152.95 198.40 196.23 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n0 11 Pedestrian -1 -1 -1 336.49 161.96 348.95 195.72 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n0 12 Pedestrian -1 -1 -1 322.35 160.13 334.23 194.14 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n0 13 Pedestrian -1 -1 -1 359.97 161.16 372.05 188.84 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n1 2 Pedestrian -1 -1 -1 138.30 150.95 171.12 222.52 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n1 3 Car -1 -1 -1 1091.96 185.43 1222.70 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n1 1 Car -1 -1 -1 953.90 182.95 1063.56 234.03 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n1 5 Pedestrian -1 -1 -1 1035.66 153.37 1102.10 318.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n1 6 Car -1 -1 -1 1042.57 184.50 1155.92 234.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n1 8 Car -1 -1 -1 602.61 172.55 637.34 203.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n1 4 Pedestrian -1 -1 -1 409.05 163.37 430.90 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n1 7 Pedestrian -1 -1 -1 438.28 164.47 458.37 220.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n1 10 Pedestrian -1 -1 -1 181.29 153.23 198.98 196.24 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n1 9 Pedestrian -1 -1 -1 307.51 156.88 321.04 193.94 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n1 12 Pedestrian -1 -1 -1 323.63 159.88 336.04 194.26 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n1 11 Pedestrian -1 -1 -1 336.58 161.36 349.08 194.76 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n2 2 Pedestrian -1 -1 -1 133.85 151.60 167.58 223.72 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n2 3 Car -1 -1 -1 1092.74 185.74 1223.07 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n2 6 Car -1 -1 -1 1034.88 184.31 1157.57 234.67 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n2 1 Car -1 -1 -1 954.38 183.50 1062.08 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n2 5 Pedestrian -1 -1 -1 1008.13 152.19 1091.83 319.19 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n2 4 Pedestrian -1 -1 -1 410.56 163.23 433.00 220.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n2 7 Pedestrian -1 -1 -1 438.62 164.93 461.47 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n2 9 Pedestrian -1 -1 -1 307.80 157.08 321.53 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n2 8 Car -1 -1 -1 601.71 172.68 637.06 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n2 10 Pedestrian -1 -1 -1 180.60 153.47 199.28 196.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n2 12 Pedestrian -1 -1 -1 323.80 160.16 336.59 193.92 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n2 11 Pedestrian -1 -1 -1 336.92 161.49 349.75 194.57 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n3 3 Car -1 -1 -1 1099.52 186.00 1220.72 235.40 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n3 2 Pedestrian -1 -1 -1 130.04 152.14 163.56 223.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n3 6 Car -1 -1 -1 1033.90 184.12 1157.29 234.53 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n3 4 Pedestrian -1 -1 -1 411.46 163.01 433.24 220.25 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n3 1 Car -1 -1 -1 956.65 183.63 1060.34 231.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n3 5 Pedestrian -1 -1 -1 994.05 153.03 1083.06 318.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n3 9 Pedestrian -1 -1 -1 308.50 156.92 321.93 193.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n3 7 Pedestrian -1 -1 -1 440.45 165.52 463.53 221.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n3 10 Pedestrian -1 -1 -1 180.04 153.60 199.57 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n3 8 Car -1 -1 -1 601.71 172.60 637.11 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n3 11 Pedestrian -1 -1 -1 336.96 161.51 349.78 194.47 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n3 12 Pedestrian -1 -1 -1 323.82 160.51 337.12 193.80 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n4 3 Car -1 -1 -1 1098.91 185.86 1220.83 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n4 2 Pedestrian -1 -1 -1 127.80 152.20 158.36 223.37 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n4 6 Car -1 -1 -1 1029.59 184.10 1155.91 233.93 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n4 7 Pedestrian -1 -1 -1 442.79 165.60 464.30 221.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 1 Car -1 -1 -1 956.42 183.42 1065.08 230.98 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n4 9 Pedestrian -1 -1 -1 309.01 157.02 322.78 193.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 5 Pedestrian -1 -1 -1 987.16 155.67 1066.95 311.33 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 10 Pedestrian -1 -1 -1 180.33 153.50 199.44 196.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n4 8 Car -1 -1 -1 602.69 172.61 637.22 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n4 4 Pedestrian -1 -1 -1 413.07 163.87 434.32 220.56 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n4 11 Pedestrian -1 -1 -1 336.93 161.78 349.60 194.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n4 12 Pedestrian -1 -1 -1 323.57 160.35 337.11 193.91 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n5 3 Car -1 -1 -1 1098.81 185.87 1221.01 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n5 2 Pedestrian -1 -1 -1 125.63 151.09 153.43 223.07 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n5 6 Car -1 -1 -1 1029.69 183.98 1155.61 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n5 1 Car -1 -1 -1 956.16 183.11 1065.28 230.84 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n5 5 Pedestrian -1 -1 -1 981.24 155.06 1049.42 309.60 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n5 7 Pedestrian -1 -1 -1 447.32 164.40 467.27 222.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n5 10 Pedestrian -1 -1 -1 180.13 153.45 199.48 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n5 9 Pedestrian -1 -1 -1 309.71 157.03 323.11 193.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n5 8 Car -1 -1 -1 602.58 172.70 637.42 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n5 4 Pedestrian -1 -1 -1 415.35 164.17 436.01 221.72 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n5 11 Pedestrian -1 -1 -1 337.26 162.13 349.90 194.31 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n5 12 Pedestrian -1 -1 -1 323.80 160.50 337.49 193.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n6 3 Car -1 -1 -1 1098.94 185.78 1221.04 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n6 6 Car -1 -1 -1 1029.09 183.99 1155.63 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n6 2 Pedestrian -1 -1 -1 118.84 150.24 150.73 223.39 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n6 1 Car -1 -1 -1 955.66 182.73 1065.85 231.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n6 7 Pedestrian -1 -1 -1 450.51 164.83 470.77 222.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n6 5 Pedestrian -1 -1 -1 966.18 154.40 1026.11 309.39 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n6 10 Pedestrian -1 -1 -1 180.35 153.11 199.11 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n6 4 Pedestrian -1 -1 -1 416.21 163.98 437.15 222.26 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n6 9 Pedestrian -1 -1 -1 309.51 157.17 323.31 193.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n6 8 Car -1 -1 -1 602.53 172.67 637.42 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n6 12 Pedestrian -1 -1 -1 324.50 160.46 337.82 193.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n6 11 Pedestrian -1 -1 -1 337.20 161.86 350.16 194.39 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n7 3 Car -1 -1 -1 1099.01 185.75 1220.97 236.12 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n7 6 Car -1 -1 -1 1028.95 184.09 1155.75 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n7 2 Pedestrian -1 -1 -1 111.96 150.67 149.76 224.84 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n7 5 Pedestrian -1 -1 -1 945.10 154.98 1023.84 308.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n7 1 Car -1 -1 -1 955.81 182.97 1066.32 231.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n7 10 Pedestrian -1 -1 -1 180.96 152.36 198.80 196.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n7 7 Pedestrian -1 -1 -1 452.40 165.54 474.86 222.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n7 8 Car -1 -1 -1 602.73 172.69 637.40 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n7 4 Pedestrian -1 -1 -1 416.14 162.97 437.96 221.56 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n7 12 Pedestrian -1 -1 -1 324.32 160.40 338.04 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n7 9 Pedestrian -1 -1 -1 309.70 157.05 323.61 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n7 11 Pedestrian -1 -1 -1 336.77 161.71 350.55 194.07 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n8 3 Car -1 -1 -1 1093.87 185.59 1222.23 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n8 6 Car -1 -1 -1 1029.67 184.10 1155.73 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n8 2 Pedestrian -1 -1 -1 107.20 150.38 146.61 225.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n8 7 Pedestrian -1 -1 -1 453.13 166.01 477.02 222.51 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n8 5 Pedestrian -1 -1 -1 930.49 154.90 1016.56 308.97 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n8 1 Car -1 -1 -1 955.71 183.40 1066.32 231.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n8 8 Car -1 -1 -1 602.73 172.76 637.32 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n8 4 Pedestrian -1 -1 -1 418.94 163.17 440.69 223.17 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n8 12 Pedestrian -1 -1 -1 324.49 160.45 338.51 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n8 10 Pedestrian -1 -1 -1 181.76 151.74 198.94 196.36 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n8 9 Pedestrian -1 -1 -1 310.09 156.99 323.53 192.85 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n8 11 Pedestrian -1 -1 -1 338.74 161.91 351.53 193.97 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n9 3 Car -1 -1 -1 1094.06 185.39 1221.54 236.43 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n9 6 Car -1 -1 -1 1029.58 184.03 1156.09 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n9 2 Pedestrian -1 -1 -1 104.13 150.28 142.66 225.80 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n9 5 Pedestrian -1 -1 -1 923.68 153.92 1007.18 304.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n9 7 Pedestrian -1 -1 -1 456.86 166.38 478.73 222.96 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n9 1 Car -1 -1 -1 950.65 183.20 1066.20 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n9 8 Car -1 -1 -1 602.62 172.69 637.45 203.04 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n9 4 Pedestrian -1 -1 -1 420.01 163.29 441.96 223.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n9 9 Pedestrian -1 -1 -1 309.84 156.96 323.86 192.71 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n9 12 Pedestrian -1 -1 -1 324.94 160.69 338.71 193.46 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n9 10 Pedestrian -1 -1 -1 182.05 151.29 198.98 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n9 11 Pedestrian -1 -1 -1 339.27 162.08 351.79 193.81 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n10 2 Pedestrian -1 -1 -1 101.11 149.32 132.57 225.58 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n10 6 Car -1 -1 -1 1030.31 183.91 1155.59 233.60 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 3 Car -1 -1 -1 1098.80 185.32 1221.61 236.67 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n10 5 Pedestrian -1 -1 -1 919.93 153.24 988.71 298.85 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n10 1 Car -1 -1 -1 954.54 183.35 1067.46 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n10 7 Pedestrian -1 -1 -1 458.99 165.38 479.86 223.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n10 8 Car -1 -1 -1 602.65 172.73 637.43 203.07 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n10 4 Pedestrian -1 -1 -1 422.17 163.48 444.29 223.51 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n10 9 Pedestrian -1 -1 -1 310.94 157.32 325.02 192.50 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n10 10 Pedestrian -1 -1 -1 182.08 150.20 198.88 196.14 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n10 12 Pedestrian -1 -1 -1 327.02 160.78 339.94 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n10 11 Pedestrian -1 -1 -1 338.91 162.08 352.22 193.78 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n10 14 Car -1 -1 -1 598.84 173.61 622.13 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n11 6 Car -1 -1 -1 1030.40 183.78 1155.26 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n11 2 Pedestrian -1 -1 -1 95.84 149.82 127.81 225.98 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n11 1 Car -1 -1 -1 954.59 183.46 1067.34 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n11 3 Car -1 -1 -1 1094.48 185.20 1221.02 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n11 5 Pedestrian -1 -1 -1 906.50 151.59 971.51 299.06 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n11 7 Pedestrian -1 -1 -1 460.53 164.66 482.77 223.09 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n11 4 Pedestrian -1 -1 -1 423.05 163.94 446.09 223.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n11 8 Car -1 -1 -1 602.50 172.76 637.61 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n11 9 Pedestrian -1 -1 -1 311.10 157.51 325.13 192.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n11 10 Pedestrian -1 -1 -1 181.89 149.81 198.70 196.35 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n11 12 Pedestrian -1 -1 -1 327.41 160.91 340.44 193.06 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n11 11 Pedestrian -1 -1 -1 339.05 160.77 352.74 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n11 14 Car -1 -1 -1 598.99 173.65 621.93 193.43 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n12 6 Car -1 -1 -1 1030.55 183.75 1154.59 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n12 1 Car -1 -1 -1 954.96 183.55 1067.06 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n12 5 Pedestrian -1 -1 -1 889.86 151.34 956.88 299.20 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n12 3 Car -1 -1 -1 1093.61 184.43 1221.51 236.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n12 4 Pedestrian -1 -1 -1 426.43 163.48 449.20 223.57 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n12 7 Pedestrian -1 -1 -1 460.09 165.04 486.86 223.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 9 Pedestrian -1 -1 -1 311.55 157.72 325.31 192.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n12 2 Pedestrian -1 -1 -1 90.89 150.56 124.86 227.79 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n12 8 Car -1 -1 -1 602.42 172.85 637.58 203.22 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n12 10 Pedestrian -1 -1 -1 182.05 149.66 198.66 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n12 12 Pedestrian -1 -1 -1 327.59 160.66 341.32 193.15 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n12 11 Pedestrian -1 -1 -1 340.05 160.73 352.89 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n12 14 Car -1 -1 -1 599.05 173.58 621.98 193.40 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n13 1 Car -1 -1 -1 955.07 183.49 1067.01 233.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n13 7 Pedestrian -1 -1 -1 462.61 164.81 489.78 224.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n13 5 Pedestrian -1 -1 -1 878.98 150.55 952.94 299.23 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n13 3 Car -1 -1 -1 1094.50 184.65 1220.06 236.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n13 6 Car -1 -1 -1 1030.51 183.83 1154.85 233.70 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n13 9 Pedestrian -1 -1 -1 312.40 157.64 325.78 192.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n13 2 Pedestrian -1 -1 -1 85.50 150.44 122.49 228.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n13 4 Pedestrian -1 -1 -1 428.68 163.31 449.89 223.74 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n13 8 Car -1 -1 -1 602.36 172.77 637.59 203.22 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n13 12 Pedestrian -1 -1 -1 327.83 160.74 341.43 193.09 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n13 10 Pedestrian -1 -1 -1 181.96 149.72 198.86 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n13 11 Pedestrian -1 -1 -1 340.46 160.77 352.66 193.02 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n14 3 Car -1 -1 -1 1094.29 184.71 1220.43 236.47 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n14 1 Car -1 -1 -1 955.07 183.56 1067.09 233.73 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n14 6 Car -1 -1 -1 1029.86 183.40 1155.51 234.17 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n14 5 Pedestrian -1 -1 -1 867.56 152.93 941.61 296.05 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 4 Pedestrian -1 -1 -1 431.15 163.43 454.17 223.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n14 2 Pedestrian -1 -1 -1 80.08 149.45 117.59 227.23 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n14 9 Pedestrian -1 -1 -1 312.55 157.40 325.96 192.31 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n14 7 Pedestrian -1 -1 -1 464.05 164.56 490.77 225.11 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n14 8 Car -1 -1 -1 602.51 172.76 637.57 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n14 10 Pedestrian -1 -1 -1 181.85 149.26 198.59 196.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n14 12 Pedestrian -1 -1 -1 328.18 160.75 341.56 193.12 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n14 11 Pedestrian -1 -1 -1 340.60 160.95 352.94 192.89 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n15 3 Car -1 -1 -1 1092.74 185.07 1221.04 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n15 1 Car -1 -1 -1 954.97 183.60 1066.81 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n15 6 Car -1 -1 -1 1028.12 183.40 1155.86 234.01 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n15 4 Pedestrian -1 -1 -1 431.77 163.25 458.11 224.75 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n15 2 Pedestrian -1 -1 -1 78.35 149.14 111.25 227.38 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n15 5 Pedestrian -1 -1 -1 861.41 152.84 932.26 291.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n15 9 Pedestrian -1 -1 -1 313.18 157.17 326.66 192.56 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n15 7 Pedestrian -1 -1 -1 469.23 164.04 493.16 224.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n15 8 Car -1 -1 -1 602.59 172.77 637.57 203.20 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n15 10 Pedestrian -1 -1 -1 181.93 148.86 198.40 196.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n15 12 Pedestrian -1 -1 -1 328.58 160.55 341.79 193.36 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n15 11 Pedestrian -1 -1 -1 340.93 160.98 353.31 193.07 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n15 15 Car -1 -1 -1 598.82 173.81 621.92 193.45 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n16 3 Car -1 -1 -1 1092.96 185.16 1221.12 236.08 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n16 1 Car -1 -1 -1 954.75 183.31 1066.89 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n16 6 Car -1 -1 -1 1027.83 183.02 1157.71 234.45 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n16 2 Pedestrian -1 -1 -1 75.45 149.48 105.90 226.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n16 4 Pedestrian -1 -1 -1 433.16 163.10 459.76 225.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n16 7 Pedestrian -1 -1 -1 473.27 163.67 495.82 224.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n16 5 Pedestrian -1 -1 -1 849.23 152.75 906.74 291.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n16 10 Pedestrian -1 -1 -1 181.76 148.64 198.61 196.77 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n16 9 Pedestrian -1 -1 -1 313.73 157.33 326.84 192.31 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n16 8 Car -1 -1 -1 602.66 172.80 637.38 203.24 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 12 Pedestrian -1 -1 -1 328.36 160.65 342.16 192.99 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n16 11 Pedestrian -1 -1 -1 341.50 160.88 353.38 192.94 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n16 15 Car -1 -1 -1 598.97 173.74 621.96 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n16 16 Pedestrian -1 -1 -1 429.56 162.38 439.30 186.65 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n17 3 Car -1 -1 -1 1093.55 185.18 1221.67 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n17 1 Car -1 -1 -1 953.10 183.18 1063.99 233.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n17 4 Pedestrian -1 -1 -1 436.27 162.52 462.21 225.43 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n17 6 Car -1 -1 -1 1033.05 183.35 1157.07 234.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n17 5 Pedestrian -1 -1 -1 832.93 150.97 899.69 292.78 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n17 7 Pedestrian -1 -1 -1 475.73 164.03 499.28 225.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n17 2 Pedestrian -1 -1 -1 65.49 149.25 104.74 227.60 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n17 10 Pedestrian -1 -1 -1 181.72 148.65 198.73 196.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n17 8 Car -1 -1 -1 602.66 172.75 637.41 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n17 12 Pedestrian -1 -1 -1 328.63 160.54 342.40 192.68 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n17 9 Pedestrian -1 -1 -1 313.70 157.57 327.33 192.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n17 11 Pedestrian -1 -1 -1 343.07 160.77 355.60 192.35 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n17 15 Car -1 -1 -1 599.04 173.80 622.05 193.39 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n18 3 Car -1 -1 -1 1094.05 185.27 1221.48 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n18 6 Car -1 -1 -1 1033.56 184.00 1157.20 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n18 4 Pedestrian -1 -1 -1 442.41 162.10 463.78 226.12 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n18 1 Car -1 -1 -1 953.40 183.37 1068.31 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n18 5 Pedestrian -1 -1 -1 826.19 152.99 890.79 291.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n18 7 Pedestrian -1 -1 -1 478.74 165.23 503.05 225.13 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n18 10 Pedestrian -1 -1 -1 181.33 148.66 198.90 196.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n18 2 Pedestrian -1 -1 -1 59.49 148.73 102.71 228.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n18 8 Car -1 -1 -1 602.55 172.77 637.48 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n18 12 Pedestrian -1 -1 -1 329.02 160.37 342.65 192.71 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n18 11 Pedestrian -1 -1 -1 343.30 160.41 356.29 192.38 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n18 9 Pedestrian -1 -1 -1 314.16 157.74 327.22 192.51 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n19 3 Car -1 -1 -1 1093.95 185.29 1221.90 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n19 6 Car -1 -1 -1 1029.14 183.94 1156.17 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n19 1 Car -1 -1 -1 953.39 183.10 1068.38 231.51 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n19 2 Pedestrian -1 -1 -1 57.00 147.46 98.55 229.00 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n19 5 Pedestrian -1 -1 -1 819.19 155.38 882.26 288.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n19 10 Pedestrian -1 -1 -1 181.26 148.73 198.64 196.51 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n19 4 Pedestrian -1 -1 -1 445.70 163.23 466.45 226.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n19 8 Car -1 -1 -1 602.50 172.73 637.54 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n19 7 Pedestrian -1 -1 -1 480.61 165.57 504.32 225.42 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n19 12 Pedestrian -1 -1 -1 328.74 160.36 342.56 192.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n19 9 Pedestrian -1 -1 -1 314.30 157.45 327.05 192.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n19 11 Pedestrian -1 -1 -1 344.07 160.72 356.90 192.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n20 3 Car -1 -1 -1 1094.49 185.55 1221.37 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n20 6 Car -1 -1 -1 1029.40 184.00 1156.29 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n20 1 Car -1 -1 -1 953.08 182.30 1068.41 232.09 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n20 2 Pedestrian -1 -1 -1 54.22 147.52 93.60 228.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n20 10 Pedestrian -1 -1 -1 181.06 148.52 198.79 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n20 7 Pedestrian -1 -1 -1 482.90 165.29 506.62 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n20 4 Pedestrian -1 -1 -1 447.01 163.52 469.12 225.43 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n20 8 Car -1 -1 -1 602.45 172.69 637.62 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n20 5 Pedestrian -1 -1 -1 809.00 153.12 870.31 289.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 12 Pedestrian -1 -1 -1 328.78 160.53 342.61 192.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n20 9 Pedestrian -1 -1 -1 314.17 157.20 327.11 192.28 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n20 11 Pedestrian -1 -1 -1 343.89 160.82 357.19 192.49 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n21 3 Car -1 -1 -1 1094.60 185.61 1221.18 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n21 6 Car -1 -1 -1 1029.32 184.09 1156.38 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n21 1 Car -1 -1 -1 949.88 182.02 1066.79 232.50 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n21 4 Pedestrian -1 -1 -1 449.38 164.37 472.67 225.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n21 10 Pedestrian -1 -1 -1 180.64 148.62 198.79 196.62 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n21 7 Pedestrian -1 -1 -1 484.62 164.40 507.77 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n21 2 Pedestrian -1 -1 -1 47.33 146.72 80.68 229.66 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n21 5 Pedestrian -1 -1 -1 795.55 152.32 845.69 290.11 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n21 8 Car -1 -1 -1 602.53 172.64 637.58 203.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 9 Pedestrian -1 -1 -1 313.90 157.32 327.27 192.11 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n21 12 Pedestrian -1 -1 -1 328.89 160.56 342.74 192.39 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n21 11 Pedestrian -1 -1 -1 344.26 160.94 357.31 192.40 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n21 17 Pedestrian -1 -1 -1 801.80 150.43 861.74 285.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n22 3 Car -1 -1 -1 1094.52 185.61 1221.48 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n22 6 Car -1 -1 -1 1029.36 184.04 1156.39 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n22 1 Car -1 -1 -1 948.34 182.30 1067.42 232.51 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n22 2 Pedestrian -1 -1 -1 41.09 146.93 77.35 229.73 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n22 5 Pedestrian -1 -1 -1 778.02 152.06 840.50 289.56 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n22 10 Pedestrian -1 -1 -1 180.49 148.41 199.09 196.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n22 7 Pedestrian -1 -1 -1 486.78 164.99 512.37 226.26 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n22 8 Car -1 -1 -1 602.51 172.70 637.68 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n22 4 Pedestrian -1 -1 -1 451.94 164.25 475.20 226.93 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n22 12 Pedestrian -1 -1 -1 330.96 160.68 343.98 191.92 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n22 9 Pedestrian -1 -1 -1 314.11 157.55 327.36 192.17 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n22 11 Pedestrian -1 -1 -1 344.82 161.13 357.34 192.19 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n22 18 Pedestrian -1 -1 -1 197.93 148.76 217.60 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n23 3 Car -1 -1 -1 1094.13 185.52 1221.56 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n23 1 Car -1 -1 -1 947.16 182.29 1068.71 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n23 6 Car -1 -1 -1 1029.52 184.05 1156.17 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n23 2 Pedestrian -1 -1 -1 35.73 147.39 74.35 231.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n23 5 Pedestrian -1 -1 -1 773.64 151.05 835.70 290.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n23 10 Pedestrian -1 -1 -1 180.69 148.43 199.20 196.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n23 7 Pedestrian -1 -1 -1 489.16 164.81 515.12 226.36 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n23 4 Pedestrian -1 -1 -1 454.64 163.03 476.49 227.39 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n23 8 Car -1 -1 -1 602.43 172.67 637.73 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n23 12 Pedestrian -1 -1 -1 330.96 160.75 344.37 191.63 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n23 9 Pedestrian -1 -1 -1 315.11 158.09 328.65 191.96 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n23 11 Pedestrian -1 -1 -1 345.53 161.31 357.00 192.16 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n23 18 Pedestrian -1 -1 -1 199.05 149.79 216.70 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n23 19 Cyclist -1 -1 -1 929.82 175.15 963.97 222.63 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n24 3 Car -1 -1 -1 1094.14 185.52 1221.48 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n24 1 Car -1 -1 -1 947.95 183.24 1069.09 233.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n24 6 Car -1 -1 -1 1029.74 184.12 1156.13 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n24 2 Pedestrian -1 -1 -1 29.79 147.59 72.14 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n24 4 Pedestrian -1 -1 -1 457.55 162.20 479.68 227.18 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n24 5 Pedestrian -1 -1 -1 770.60 151.57 823.82 284.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n24 10 Pedestrian -1 -1 -1 180.93 148.48 199.41 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n24 7 Pedestrian -1 -1 -1 491.33 164.68 516.25 227.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n24 8 Car -1 -1 -1 602.67 172.54 637.52 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n24 9 Pedestrian -1 -1 -1 315.46 157.99 328.77 191.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n24 12 Pedestrian -1 -1 -1 331.69 160.95 344.45 191.49 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n24 19 Cyclist -1 -1 -1 907.34 169.91 948.52 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n24 11 Pedestrian -1 -1 -1 346.77 161.21 358.24 192.12 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n24 18 Pedestrian -1 -1 -1 200.08 150.80 215.96 193.00 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n25 3 Car -1 -1 -1 1094.56 185.49 1221.10 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n25 6 Car -1 -1 -1 1029.74 184.18 1156.16 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n25 1 Car -1 -1 -1 952.87 183.61 1068.83 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n25 2 Pedestrian -1 -1 -1 26.70 146.54 67.83 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n25 7 Pedestrian -1 -1 -1 495.11 163.80 519.71 227.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n25 4 Pedestrian -1 -1 -1 457.67 161.73 484.75 227.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n25 9 Pedestrian -1 -1 -1 316.26 157.82 329.43 191.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n25 8 Car -1 -1 -1 602.67 172.61 637.56 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n25 10 Pedestrian -1 -1 -1 180.95 148.29 199.46 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n25 5 Pedestrian -1 -1 -1 761.29 151.96 818.59 282.10 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n25 12 Pedestrian -1 -1 -1 332.05 161.07 344.77 191.51 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n25 11 Pedestrian -1 -1 -1 346.81 161.19 359.02 192.11 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n25 18 Pedestrian -1 -1 -1 199.40 153.23 215.49 195.47 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n25 20 Car -1 -1 -1 598.81 173.81 622.20 193.79 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n26 3 Car -1 -1 -1 1094.63 185.49 1221.30 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n26 6 Car -1 -1 -1 1029.78 184.04 1156.05 233.16 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n26 4 Pedestrian -1 -1 -1 459.17 162.59 486.67 227.21 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 1 Car -1 -1 -1 953.62 183.66 1068.14 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n26 7 Pedestrian -1 -1 -1 498.58 162.77 522.83 227.53 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n26 2 Pedestrian -1 -1 -1 23.07 145.18 58.98 234.23 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n26 8 Car -1 -1 -1 602.54 172.39 637.61 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n26 10 Pedestrian -1 -1 -1 180.98 148.24 199.55 197.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n26 5 Pedestrian -1 -1 -1 748.83 152.90 799.95 282.46 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n26 9 Pedestrian -1 -1 -1 316.32 157.72 329.69 191.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n26 12 Pedestrian -1 -1 -1 331.67 160.91 344.97 191.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n26 11 Pedestrian -1 -1 -1 347.02 161.16 359.02 191.91 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n26 18 Pedestrian -1 -1 -1 199.05 149.73 216.82 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n26 20 Car -1 -1 -1 598.52 173.75 622.28 193.67 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n26 21 Cyclist -1 -1 -1 868.62 170.56 918.09 225.53 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n27 3 Car -1 -1 -1 1094.88 185.59 1221.16 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n27 6 Car -1 -1 -1 1029.68 184.01 1156.15 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n27 1 Car -1 -1 -1 953.82 183.66 1068.17 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n27 4 Pedestrian -1 -1 -1 461.66 162.93 489.74 227.08 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n27 7 Pedestrian -1 -1 -1 501.18 162.97 527.02 227.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n27 2 Pedestrian -1 -1 -1 18.00 145.49 54.66 234.25 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n27 5 Pedestrian -1 -1 -1 733.60 152.94 793.09 281.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n27 8 Car -1 -1 -1 602.54 172.33 637.54 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n27 10 Pedestrian -1 -1 -1 181.17 148.28 199.76 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n27 12 Pedestrian -1 -1 -1 331.98 160.50 345.24 191.39 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n27 9 Pedestrian -1 -1 -1 316.43 157.80 330.57 190.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n27 21 Cyclist -1 -1 -1 841.26 170.48 899.52 226.65 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n27 11 Pedestrian -1 -1 -1 347.12 161.30 359.39 191.35 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n27 18 Pedestrian -1 -1 -1 199.15 149.77 216.36 193.60 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n27 20 Car -1 -1 -1 598.41 173.75 622.22 193.76 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n28 3 Car -1 -1 -1 1094.66 185.51 1221.37 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n28 1 Car -1 -1 -1 953.90 183.79 1068.05 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n28 6 Car -1 -1 -1 1029.42 184.09 1156.37 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n28 4 Pedestrian -1 -1 -1 467.08 163.14 492.42 227.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n28 2 Pedestrian -1 -1 -1 9.20 146.58 54.63 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n28 5 Pedestrian -1 -1 -1 726.39 154.22 784.29 281.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n28 7 Pedestrian -1 -1 -1 502.03 164.02 528.37 227.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n28 12 Pedestrian -1 -1 -1 332.61 159.92 345.57 191.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n28 8 Car -1 -1 -1 601.53 172.51 637.31 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n28 9 Pedestrian -1 -1 -1 316.76 157.52 330.91 191.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n28 10 Pedestrian -1 -1 -1 181.29 148.36 199.64 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n28 21 Cyclist -1 -1 -1 824.48 169.53 878.40 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n28 11 Pedestrian -1 -1 -1 347.42 160.85 360.18 191.07 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n28 18 Pedestrian -1 -1 -1 197.45 153.11 214.30 195.76 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n28 20 Car -1 -1 -1 598.54 173.68 622.29 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n29 3 Car -1 -1 -1 1094.82 185.59 1221.38 235.70 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n29 6 Car -1 -1 -1 1029.29 184.00 1156.47 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 1 Car -1 -1 -1 954.07 183.90 1067.74 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 2 Pedestrian -1 -1 -1 3.69 147.17 52.75 234.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n29 4 Pedestrian -1 -1 -1 471.73 162.09 495.17 227.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n29 7 Pedestrian -1 -1 -1 505.21 163.79 531.64 228.07 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n29 8 Car -1 -1 -1 602.37 172.35 637.80 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n29 5 Pedestrian -1 -1 -1 722.88 156.18 765.82 280.32 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n29 12 Pedestrian -1 -1 -1 332.62 160.01 345.61 191.20 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n29 21 Cyclist -1 -1 -1 809.39 166.73 861.58 231.73 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n29 9 Pedestrian -1 -1 -1 317.11 157.66 331.20 191.54 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n29 10 Pedestrian -1 -1 -1 183.29 148.78 200.72 196.92 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n29 11 Pedestrian -1 -1 -1 347.51 161.09 360.34 190.69 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n29 18 Pedestrian -1 -1 -1 197.10 153.06 214.30 195.93 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n29 20 Car -1 -1 -1 598.68 173.68 622.31 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n30 3 Car -1 -1 -1 1095.01 185.69 1221.18 235.53 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n30 1 Car -1 -1 -1 954.03 183.84 1067.87 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n30 6 Car -1 -1 -1 1029.46 184.02 1156.36 233.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n30 4 Pedestrian -1 -1 -1 473.77 163.23 500.78 228.05 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n30 2 Pedestrian -1 -1 -1 1.88 146.72 47.73 234.44 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n30 12 Pedestrian -1 -1 -1 332.88 160.20 345.50 190.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n30 8 Car -1 -1 -1 602.50 172.38 637.59 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 7 Pedestrian -1 -1 -1 507.31 163.74 530.97 227.84 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n30 18 Pedestrian -1 -1 -1 196.57 153.01 213.84 196.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n30 10 Pedestrian -1 -1 -1 183.35 148.86 200.68 196.90 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n30 5 Pedestrian -1 -1 -1 712.01 155.92 753.50 279.32 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n30 9 Pedestrian -1 -1 -1 316.95 157.43 330.55 191.30 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n30 11 Pedestrian -1 -1 -1 347.81 161.17 360.06 190.62 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n30 21 Cyclist -1 -1 -1 787.51 163.51 845.24 234.28 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n30 20 Car -1 -1 -1 598.92 173.83 622.02 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n30 22 Pedestrian -1 -1 -1 722.20 151.87 773.17 274.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 23 Pedestrian -1 -1 -1 366.38 159.53 377.26 186.07 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n31 3 Car -1 -1 -1 1095.10 185.67 1221.07 235.39 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n31 1 Car -1 -1 -1 954.07 183.85 1068.00 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n31 6 Car -1 -1 -1 1029.57 184.04 1156.32 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n31 2 Pedestrian -1 -1 -1 1.08 147.41 40.35 234.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n31 8 Car -1 -1 -1 602.61 172.40 637.66 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n31 12 Pedestrian -1 -1 -1 332.96 160.14 345.70 190.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n31 7 Pedestrian -1 -1 -1 509.64 163.16 533.29 228.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 4 Pedestrian -1 -1 -1 476.73 163.37 504.39 228.57 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 5 Pedestrian -1 -1 -1 701.66 155.14 747.52 279.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n31 18 Pedestrian -1 -1 -1 195.98 152.46 213.73 196.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n31 10 Pedestrian -1 -1 -1 183.67 148.91 200.50 196.94 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n31 9 Pedestrian -1 -1 -1 317.25 157.25 330.30 191.15 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n31 22 Pedestrian -1 -1 -1 714.52 153.57 765.97 272.94 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n31 11 Pedestrian -1 -1 -1 347.61 160.97 360.60 190.63 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n31 21 Cyclist -1 -1 -1 770.77 164.55 824.77 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n31 23 Pedestrian -1 -1 -1 366.44 159.50 377.56 186.10 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n31 20 Car -1 -1 -1 598.81 173.82 622.17 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n32 3 Car -1 -1 -1 1099.09 185.64 1220.49 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n32 1 Car -1 -1 -1 954.17 183.99 1067.82 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n32 6 Car -1 -1 -1 1029.48 184.05 1156.44 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n32 8 Car -1 -1 -1 602.76 172.47 637.60 203.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n32 5 Pedestrian -1 -1 -1 692.58 156.07 741.95 278.25 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n32 12 Pedestrian -1 -1 -1 333.21 159.75 345.87 190.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n32 4 Pedestrian -1 -1 -1 480.09 163.78 508.80 230.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n32 10 Pedestrian -1 -1 -1 183.67 148.74 200.72 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n32 2 Pedestrian -1 -1 -1 0.02 147.62 26.49 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n32 18 Pedestrian -1 -1 -1 195.72 152.58 213.14 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n32 7 Pedestrian -1 -1 -1 510.42 163.45 535.88 228.77 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n32 9 Pedestrian -1 -1 -1 317.28 157.12 330.80 191.18 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n32 22 Pedestrian -1 -1 -1 708.36 155.47 756.37 271.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n32 11 Pedestrian -1 -1 -1 347.44 160.86 360.90 190.69 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n32 23 Pedestrian -1 -1 -1 366.61 159.32 377.91 185.96 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n32 20 Car -1 -1 -1 599.02 173.81 622.03 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n33 3 Car -1 -1 -1 1094.96 185.56 1221.26 235.54 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n33 1 Car -1 -1 -1 954.01 183.93 1067.71 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n33 6 Car -1 -1 -1 1029.13 184.01 1156.71 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n33 8 Car -1 -1 -1 602.66 172.36 637.69 203.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n33 5 Pedestrian -1 -1 -1 685.37 155.30 734.65 278.94 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n33 7 Pedestrian -1 -1 -1 512.80 162.97 539.83 230.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n33 4 Pedestrian -1 -1 -1 483.35 163.45 508.86 230.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n33 18 Pedestrian -1 -1 -1 195.18 152.86 213.00 196.71 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n33 12 Pedestrian -1 -1 -1 333.59 159.57 345.99 190.43 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n33 10 Pedestrian -1 -1 -1 183.58 148.87 200.97 197.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n33 2 Pedestrian -1 -1 -1 0.24 147.47 18.30 235.16 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n33 22 Pedestrian -1 -1 -1 702.19 152.21 747.03 269.12 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n33 9 Pedestrian -1 -1 -1 317.73 157.35 330.87 191.07 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n33 23 Pedestrian -1 -1 -1 367.17 159.51 378.30 185.58 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n33 11 Pedestrian -1 -1 -1 347.27 160.84 359.97 190.69 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n33 20 Car -1 -1 -1 599.10 173.74 622.25 193.75 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n34 3 Car -1 -1 -1 1095.17 185.59 1220.96 235.53 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n34 1 Car -1 -1 -1 953.96 183.97 1067.66 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n34 6 Car -1 -1 -1 1029.31 184.02 1156.46 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n34 8 Car -1 -1 -1 602.57 172.39 637.78 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n34 5 Pedestrian -1 -1 -1 680.99 155.64 729.75 278.08 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n34 7 Pedestrian -1 -1 -1 516.67 163.20 541.29 229.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n34 18 Pedestrian -1 -1 -1 194.59 153.00 212.84 196.88 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n34 4 Pedestrian -1 -1 -1 488.54 162.42 512.02 231.32 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n34 12 Pedestrian -1 -1 -1 333.64 159.83 346.10 190.42 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n34 9 Pedestrian -1 -1 -1 317.88 157.44 330.83 190.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n34 10 Pedestrian -1 -1 -1 183.54 148.91 200.88 197.31 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n34 11 Pedestrian -1 -1 -1 347.67 161.03 360.04 190.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n34 23 Pedestrian -1 -1 -1 367.41 159.90 378.24 185.40 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n34 2 Pedestrian -1 -1 -1 -0.64 151.95 18.05 235.48 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n34 20 Car -1 -1 -1 599.12 173.80 621.96 193.68 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n34 24 Cyclist -1 -1 -1 723.53 168.28 765.44 227.46 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n35 3 Car -1 -1 -1 1095.20 185.61 1220.96 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n35 1 Car -1 -1 -1 954.17 183.90 1067.60 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n35 6 Car -1 -1 -1 1029.40 184.02 1156.40 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n35 8 Car -1 -1 -1 602.84 172.44 637.36 203.15 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n35 7 Pedestrian -1 -1 -1 517.82 162.87 543.69 231.09 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n35 4 Pedestrian -1 -1 -1 490.57 162.71 516.97 229.36 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n35 5 Pedestrian -1 -1 -1 670.61 155.34 717.93 273.63 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n35 18 Pedestrian -1 -1 -1 192.09 152.58 211.38 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n35 9 Pedestrian -1 -1 -1 318.06 157.46 330.77 191.14 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n35 11 Pedestrian -1 -1 -1 347.99 161.54 359.56 190.42 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n35 12 Pedestrian -1 -1 -1 333.99 160.10 345.89 190.65 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n35 10 Pedestrian -1 -1 -1 183.49 148.95 200.78 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n35 23 Pedestrian -1 -1 -1 367.65 160.01 378.43 185.55 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n35 2 Pedestrian -1 -1 -1 0.99 151.14 11.78 236.91 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n35 20 Car -1 -1 -1 599.02 173.87 621.64 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n35 25 Pedestrian -1 -1 -1 683.75 153.75 734.75 272.88 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n36 1 Car -1 -1 -1 954.20 183.87 1067.65 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n36 3 Car -1 -1 -1 1099.47 185.57 1220.46 235.44 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n36 6 Car -1 -1 -1 1029.26 184.00 1156.60 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n36 4 Pedestrian -1 -1 -1 492.78 162.81 522.28 231.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n36 8 Car -1 -1 -1 602.74 172.25 637.60 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n36 7 Pedestrian -1 -1 -1 523.45 162.51 549.71 229.86 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n36 5 Pedestrian -1 -1 -1 660.79 155.54 712.34 272.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n36 18 Pedestrian -1 -1 -1 192.25 152.64 211.53 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n36 9 Pedestrian -1 -1 -1 319.65 157.61 331.91 191.00 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n36 25 Pedestrian -1 -1 -1 678.99 153.45 731.99 272.70 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n36 11 Pedestrian -1 -1 -1 348.05 161.84 359.39 190.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n36 23 Pedestrian -1 -1 -1 367.89 159.86 379.05 185.63 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n36 12 Pedestrian -1 -1 -1 335.48 160.64 346.35 190.29 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n36 10 Pedestrian -1 -1 -1 183.50 149.21 200.67 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n36 20 Car -1 -1 -1 599.19 173.80 621.76 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n36 2 Pedestrian -1 -1 -1 0.65 152.08 9.67 236.43 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n37 1 Car -1 -1 -1 954.15 183.78 1067.63 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n37 3 Car -1 -1 -1 1100.89 185.24 1219.23 234.96 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n37 6 Car -1 -1 -1 1029.86 183.92 1155.93 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n37 8 Car -1 -1 -1 602.78 172.11 637.56 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n37 4 Pedestrian -1 -1 -1 494.61 161.63 525.91 232.32 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n37 5 Pedestrian -1 -1 -1 648.11 155.47 702.28 272.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n37 7 Pedestrian -1 -1 -1 524.56 162.48 552.17 231.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n37 9 Pedestrian -1 -1 -1 320.13 157.52 331.89 190.82 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n37 18 Pedestrian -1 -1 -1 194.55 153.32 212.71 197.41 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n37 12 Pedestrian -1 -1 -1 334.13 160.12 345.68 190.70 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n37 10 Pedestrian -1 -1 -1 183.16 149.76 201.05 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n37 11 Pedestrian -1 -1 -1 347.85 161.61 359.25 190.29 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n37 23 Pedestrian -1 -1 -1 368.00 159.69 378.97 185.44 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n37 25 Pedestrian -1 -1 -1 668.73 153.90 719.89 271.95 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n37 20 Car -1 -1 -1 599.30 173.61 621.65 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n37 26 Pedestrian -1 -1 -1 1142.97 134.62 1217.16 355.24 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n38 1 Car -1 -1 -1 953.99 183.71 1067.62 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n38 3 Car -1 -1 -1 1097.31 185.21 1218.30 234.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n38 6 Car -1 -1 -1 1031.97 183.76 1158.57 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n38 7 Pedestrian -1 -1 -1 528.19 162.99 555.48 232.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n38 8 Car -1 -1 -1 602.87 172.23 637.45 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n38 4 Pedestrian -1 -1 -1 500.41 161.85 527.68 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n38 5 Pedestrian -1 -1 -1 642.98 155.24 692.36 273.43 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n38 18 Pedestrian -1 -1 -1 192.62 152.87 211.02 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n38 9 Pedestrian -1 -1 -1 320.18 157.69 332.43 190.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n38 12 Pedestrian -1 -1 -1 335.17 159.94 346.68 190.39 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n38 10 Pedestrian -1 -1 -1 181.44 150.12 199.63 197.96 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n38 23 Pedestrian -1 -1 -1 368.06 159.29 379.39 186.38 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n38 26 Pedestrian -1 -1 -1 1139.06 136.69 1220.44 352.51 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n38 11 Pedestrian -1 -1 -1 347.95 161.24 359.96 190.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n38 25 Pedestrian -1 -1 -1 668.90 152.06 711.70 268.04 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n38 20 Car -1 -1 -1 599.62 173.57 621.56 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n39 3 Car -1 -1 -1 1095.22 185.34 1220.44 234.17 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n39 1 Car -1 -1 -1 953.79 183.64 1067.92 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n39 6 Car -1 -1 -1 1031.96 183.90 1158.12 233.79 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n39 7 Pedestrian -1 -1 -1 530.90 163.83 558.61 231.93 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n39 8 Car -1 -1 -1 602.77 172.25 637.44 202.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n39 4 Pedestrian -1 -1 -1 505.33 162.43 530.23 231.72 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n39 5 Pedestrian -1 -1 -1 641.37 155.39 685.05 272.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n39 18 Pedestrian -1 -1 -1 192.81 152.78 210.81 197.78 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n39 9 Pedestrian -1 -1 -1 320.06 158.11 332.45 190.37 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n39 10 Pedestrian -1 -1 -1 181.63 150.09 199.62 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n39 12 Pedestrian -1 -1 -1 334.99 159.73 346.75 190.42 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n39 25 Pedestrian -1 -1 -1 661.92 151.59 703.63 266.94 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n39 26 Pedestrian -1 -1 -1 1117.73 138.61 1218.90 356.26 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n39 23 Pedestrian -1 -1 -1 368.50 159.21 379.75 187.05 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n39 11 Pedestrian -1 -1 -1 347.61 160.82 360.30 190.25 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n39 20 Car -1 -1 -1 599.39 173.54 621.51 193.18 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n40 3 Car -1 -1 -1 1098.10 184.75 1222.00 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n40 1 Car -1 -1 -1 954.00 183.63 1067.55 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n40 6 Car -1 -1 -1 1029.17 183.85 1156.21 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n40 7 Pedestrian -1 -1 -1 532.98 163.56 558.74 233.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n40 4 Pedestrian -1 -1 -1 506.41 162.82 531.97 232.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n40 5 Pedestrian -1 -1 -1 634.90 155.41 676.20 271.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n40 8 Car -1 -1 -1 602.54 172.46 637.54 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n40 26 Pedestrian -1 -1 -1 1098.35 142.94 1208.15 345.82 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n40 9 Pedestrian -1 -1 -1 319.78 158.02 332.32 190.93 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n40 18 Pedestrian -1 -1 -1 192.54 152.68 211.11 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n40 25 Pedestrian -1 -1 -1 651.25 150.44 698.83 267.06 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n40 12 Pedestrian -1 -1 -1 335.34 160.19 346.52 190.34 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n40 10 Pedestrian -1 -1 -1 181.86 149.90 199.54 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n40 11 Pedestrian -1 -1 -1 347.47 160.90 360.54 190.18 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n40 23 Pedestrian -1 -1 -1 368.80 159.88 379.63 187.92 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n41 3 Car -1 -1 -1 1095.73 184.79 1219.80 234.42 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n41 1 Car -1 -1 -1 954.10 183.49 1067.64 233.74 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n41 6 Car -1 -1 -1 1029.51 183.88 1155.29 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n41 7 Pedestrian -1 -1 -1 536.36 162.21 561.07 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 4 Pedestrian -1 -1 -1 508.11 163.10 536.16 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 8 Car -1 -1 -1 601.79 172.64 636.82 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n41 9 Pedestrian -1 -1 -1 320.00 158.04 332.07 190.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n41 5 Pedestrian -1 -1 -1 622.57 158.27 667.07 268.99 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n41 25 Pedestrian -1 -1 -1 642.39 153.47 692.80 265.07 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n41 26 Pedestrian -1 -1 -1 1074.93 144.59 1193.17 344.44 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n41 18 Pedestrian -1 -1 -1 192.94 152.56 210.94 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n41 12 Pedestrian -1 -1 -1 335.48 160.44 346.33 190.19 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n41 10 Pedestrian -1 -1 -1 181.88 149.74 199.42 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n41 11 Pedestrian -1 -1 -1 347.87 161.21 360.31 190.07 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n41 23 Pedestrian -1 -1 -1 368.59 160.08 379.98 187.82 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n42 1 Car -1 -1 -1 954.40 183.29 1067.38 233.95 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n42 3 Car -1 -1 -1 1095.52 184.63 1220.06 234.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n42 7 Pedestrian -1 -1 -1 538.09 162.46 566.70 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n42 4 Pedestrian -1 -1 -1 511.82 162.10 540.40 234.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n42 6 Car -1 -1 -1 1029.99 183.27 1155.01 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n42 5 Pedestrian -1 -1 -1 618.81 157.48 662.59 270.51 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n42 8 Car -1 -1 -1 601.98 172.42 638.29 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n42 26 Pedestrian -1 -1 -1 1058.00 146.94 1187.05 341.27 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n42 9 Pedestrian -1 -1 -1 319.96 157.88 332.43 190.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n42 25 Pedestrian -1 -1 -1 637.32 155.43 689.46 264.59 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n42 18 Pedestrian -1 -1 -1 194.68 152.71 212.58 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n42 12 Pedestrian -1 -1 -1 335.46 160.42 346.77 190.24 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n42 10 Pedestrian -1 -1 -1 181.69 149.46 199.55 198.18 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n42 11 Pedestrian -1 -1 -1 347.69 161.00 359.79 190.35 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n43 1 Car -1 -1 -1 953.83 183.10 1069.02 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n43 6 Car -1 -1 -1 1031.30 183.14 1153.16 231.73 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n43 3 Car -1 -1 -1 1094.55 184.67 1220.48 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n43 4 Pedestrian -1 -1 -1 515.04 161.83 543.89 234.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n43 7 Pedestrian -1 -1 -1 540.66 162.82 571.10 233.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n43 5 Pedestrian -1 -1 -1 611.25 159.40 654.23 269.47 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n43 18 Pedestrian -1 -1 -1 195.76 152.58 212.79 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n43 8 Car -1 -1 -1 601.35 172.70 636.95 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n43 25 Pedestrian -1 -1 -1 636.90 155.76 682.35 262.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n43 9 Pedestrian -1 -1 -1 320.13 157.95 332.81 190.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n43 12 Pedestrian -1 -1 -1 335.35 160.27 347.38 189.85 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n43 26 Pedestrian -1 -1 -1 1055.62 149.92 1143.71 331.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n43 10 Pedestrian -1 -1 -1 181.27 148.58 199.48 197.84 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n43 11 Pedestrian -1 -1 -1 347.52 161.20 359.14 190.38 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n43 27 Pedestrian -1 -1 -1 1075.68 147.97 1177.21 317.98 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n44 6 Car -1 -1 -1 1030.80 182.78 1153.45 232.03 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n44 1 Car -1 -1 -1 954.02 183.06 1067.41 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n44 4 Pedestrian -1 -1 -1 519.66 161.14 546.96 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n44 3 Car -1 -1 -1 1094.04 184.62 1220.35 236.38 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n44 7 Pedestrian -1 -1 -1 543.77 163.80 575.00 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n44 5 Pedestrian -1 -1 -1 605.21 158.51 645.58 268.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n44 18 Pedestrian -1 -1 -1 195.56 152.59 213.35 198.51 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n44 8 Car -1 -1 -1 600.87 172.80 637.22 202.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n44 25 Pedestrian -1 -1 -1 633.18 154.03 671.68 264.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n44 26 Pedestrian -1 -1 -1 1037.22 143.28 1116.15 329.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n44 12 Pedestrian -1 -1 -1 335.98 160.10 347.86 189.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n44 9 Pedestrian -1 -1 -1 320.40 158.14 332.50 190.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n44 10 Pedestrian -1 -1 -1 181.32 148.60 199.54 197.86 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n44 27 Pedestrian -1 -1 -1 1049.35 149.13 1127.01 330.85 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n44 11 Pedestrian -1 -1 -1 347.46 161.32 359.34 190.15 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n44 28 Pedestrian -1 -1 -1 370.63 160.19 382.11 187.90 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n45 1 Car -1 -1 -1 954.03 182.86 1068.78 234.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n45 6 Car -1 -1 -1 1031.10 182.36 1153.16 232.27 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n45 4 Pedestrian -1 -1 -1 522.12 161.06 551.49 234.78 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n45 7 Pedestrian -1 -1 -1 546.69 162.76 575.20 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 18 Pedestrian -1 -1 -1 195.07 152.41 214.01 198.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n45 8 Car -1 -1 -1 601.08 173.02 636.79 202.08 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n45 3 Car -1 -1 -1 1085.31 183.81 1222.32 238.30 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n45 5 Pedestrian -1 -1 -1 594.28 156.55 642.05 265.57 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n45 25 Pedestrian -1 -1 -1 625.15 152.70 664.64 265.32 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n45 9 Pedestrian -1 -1 -1 320.70 158.36 332.52 190.25 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n45 12 Pedestrian -1 -1 -1 336.65 160.15 348.40 189.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n45 26 Pedestrian -1 -1 -1 1011.85 142.34 1103.09 330.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n45 10 Pedestrian -1 -1 -1 181.24 148.76 199.44 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n45 11 Pedestrian -1 -1 -1 347.26 161.50 359.21 189.94 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n45 28 Pedestrian -1 -1 -1 370.78 160.30 382.09 188.20 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n46 8 Car -1 -1 -1 601.13 172.69 637.04 202.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n46 1 Car -1 -1 -1 953.91 182.99 1069.63 234.64 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n46 6 Car -1 -1 -1 1028.84 183.69 1155.46 233.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n46 18 Pedestrian -1 -1 -1 195.08 152.49 214.21 199.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n46 7 Pedestrian -1 -1 -1 554.01 161.94 581.10 234.48 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n46 3 Car -1 -1 -1 1085.69 184.29 1221.03 237.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n46 4 Pedestrian -1 -1 -1 524.91 161.50 555.47 234.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n46 5 Pedestrian -1 -1 -1 589.95 156.53 637.53 265.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n46 25 Pedestrian -1 -1 -1 617.33 153.40 664.60 266.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n46 12 Pedestrian -1 -1 -1 337.17 160.55 348.79 189.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n46 9 Pedestrian -1 -1 -1 320.90 158.61 332.76 189.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n46 26 Pedestrian -1 -1 -1 982.23 147.01 1094.74 327.01 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n46 10 Pedestrian -1 -1 -1 181.33 148.73 199.39 197.72 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n46 11 Pedestrian -1 -1 -1 347.44 161.67 359.39 189.78 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n46 28 Pedestrian -1 -1 -1 371.49 160.27 382.26 187.70 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n46 29 Pedestrian -1 -1 -1 1057.38 151.48 1118.71 307.19 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n47 8 Car -1 -1 -1 601.11 172.53 636.88 202.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n47 1 Car -1 -1 -1 952.64 183.04 1070.54 234.60 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n47 4 Pedestrian -1 -1 -1 527.17 161.80 556.38 235.36 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n47 6 Car -1 -1 -1 1029.44 183.34 1155.25 234.25 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n47 18 Pedestrian -1 -1 -1 195.02 152.52 214.32 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n47 7 Pedestrian -1 -1 -1 558.18 162.08 585.30 235.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n47 3 Car -1 -1 -1 1091.84 184.71 1222.32 237.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n47 25 Pedestrian -1 -1 -1 612.28 156.09 660.91 264.10 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n47 5 Pedestrian -1 -1 -1 584.47 158.31 628.35 264.02 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n47 12 Pedestrian -1 -1 -1 337.60 160.68 348.99 189.44 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n47 26 Pedestrian -1 -1 -1 972.02 150.68 1081.83 323.60 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n47 9 Pedestrian -1 -1 -1 321.30 158.60 333.22 189.95 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n47 10 Pedestrian -1 -1 -1 181.54 149.38 199.23 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n47 11 Pedestrian -1 -1 -1 347.50 161.76 359.15 189.75 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n47 29 Pedestrian -1 -1 -1 1039.81 150.57 1105.78 306.83 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n47 28 Pedestrian -1 -1 -1 372.08 160.20 383.43 187.83 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n48 8 Car -1 -1 -1 601.27 172.53 636.69 202.10 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n48 1 Car -1 -1 -1 953.29 182.17 1069.25 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n48 4 Pedestrian -1 -1 -1 529.69 160.63 559.55 235.41 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n48 18 Pedestrian -1 -1 -1 195.14 152.62 214.31 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n48 3 Car -1 -1 -1 1092.51 184.70 1222.04 237.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n48 6 Car -1 -1 -1 1032.21 182.96 1158.64 234.93 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n48 5 Pedestrian -1 -1 -1 582.49 157.73 621.85 267.34 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n48 25 Pedestrian -1 -1 -1 607.91 156.70 649.80 262.48 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n48 7 Pedestrian -1 -1 -1 559.50 162.34 585.56 235.28 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n48 26 Pedestrian -1 -1 -1 960.47 150.50 1055.66 323.30 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n48 12 Pedestrian -1 -1 -1 337.56 160.29 349.20 189.58 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n48 9 Pedestrian -1 -1 -1 321.68 158.70 334.03 190.11 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n48 10 Pedestrian -1 -1 -1 181.43 148.63 199.26 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n48 11 Pedestrian -1 -1 -1 347.54 161.45 359.27 189.56 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n48 29 Pedestrian -1 -1 -1 1025.36 147.92 1097.01 303.19 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n48 28 Pedestrian -1 -1 -1 373.36 160.27 385.42 188.15 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n49 1 Car -1 -1 -1 953.20 181.77 1069.28 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n49 8 Car -1 -1 -1 601.27 172.46 636.44 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n49 4 Pedestrian -1 -1 -1 535.82 159.25 562.36 237.09 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n49 6 Car -1 -1 -1 1033.12 183.15 1158.09 235.17 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n49 3 Car -1 -1 -1 1092.50 184.18 1222.07 237.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n49 18 Pedestrian -1 -1 -1 195.85 152.73 214.10 198.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n49 12 Pedestrian -1 -1 -1 337.27 160.36 348.95 189.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n49 7 Pedestrian -1 -1 -1 562.79 162.19 588.07 236.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n49 5 Pedestrian -1 -1 -1 576.93 157.45 613.67 264.91 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n49 25 Pedestrian -1 -1 -1 598.54 154.16 637.41 264.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n49 10 Pedestrian -1 -1 -1 181.51 149.31 199.09 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n49 26 Pedestrian -1 -1 -1 953.08 143.72 1024.36 323.07 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n49 9 Pedestrian -1 -1 -1 322.08 158.58 333.90 189.94 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n49 11 Pedestrian -1 -1 -1 347.78 161.41 359.68 189.60 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n49 29 Pedestrian -1 -1 -1 995.65 147.88 1088.92 303.18 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n49 28 Pedestrian -1 -1 -1 373.64 160.58 386.47 187.97 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n50 1 Car -1 -1 -1 954.38 181.86 1067.93 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n50 8 Car -1 -1 -1 601.38 172.65 636.16 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n50 4 Pedestrian -1 -1 -1 539.15 159.80 566.76 237.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n50 3 Car -1 -1 -1 1093.99 183.42 1220.82 238.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n50 18 Pedestrian -1 -1 -1 196.01 152.40 214.24 198.49 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n50 6 Car -1 -1 -1 1030.35 183.56 1161.43 236.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n50 5 Pedestrian -1 -1 -1 570.83 159.85 610.54 262.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n50 7 Pedestrian -1 -1 -1 565.35 162.41 592.62 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n50 12 Pedestrian -1 -1 -1 337.39 160.47 349.17 189.54 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n50 10 Pedestrian -1 -1 -1 181.36 148.49 199.38 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n50 26 Pedestrian -1 -1 -1 931.11 144.38 1000.36 327.64 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n50 25 Pedestrian -1 -1 -1 595.15 154.25 631.76 259.74 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n50 9 Pedestrian -1 -1 -1 322.22 158.80 334.14 189.78 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n50 29 Pedestrian -1 -1 -1 996.23 150.90 1072.79 299.97 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n50 11 Pedestrian -1 -1 -1 347.64 161.13 360.12 189.52 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n50 30 Pedestrian -1 -1 -1 946.58 150.57 1030.95 315.59 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n51 1 Car -1 -1 -1 952.18 182.61 1070.28 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n51 4 Pedestrian -1 -1 -1 542.63 159.37 571.03 237.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n51 6 Car -1 -1 -1 1030.47 183.51 1161.30 236.47 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n51 3 Car -1 -1 -1 1094.76 184.47 1220.38 237.41 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n51 8 Car -1 -1 -1 601.82 172.75 636.03 201.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n51 18 Pedestrian -1 -1 -1 195.76 152.43 213.82 198.34 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n51 5 Pedestrian -1 -1 -1 563.36 162.49 603.19 259.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n51 12 Pedestrian -1 -1 -1 337.56 160.57 349.29 189.47 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n51 10 Pedestrian -1 -1 -1 181.50 148.43 199.44 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n51 7 Pedestrian -1 -1 -1 567.35 162.65 597.37 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n51 25 Pedestrian -1 -1 -1 585.71 156.22 626.96 258.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n51 9 Pedestrian -1 -1 -1 321.86 158.86 334.23 189.66 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n51 26 Pedestrian -1 -1 -1 905.22 147.82 995.55 317.84 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n51 29 Pedestrian -1 -1 -1 990.93 153.34 1047.60 296.46 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n51 30 Pedestrian -1 -1 -1 932.18 152.33 1014.55 313.57 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n51 11 Pedestrian -1 -1 -1 347.83 161.39 360.03 189.43 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n51 31 Pedestrian -1 -1 -1 373.43 160.51 387.00 187.97 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n52 3 Car -1 -1 -1 1093.94 184.57 1221.09 237.35 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n52 1 Car -1 -1 -1 952.55 182.31 1070.08 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n52 6 Car -1 -1 -1 1029.25 183.30 1156.08 234.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n52 4 Pedestrian -1 -1 -1 545.34 159.95 575.01 237.75 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n52 8 Car -1 -1 -1 604.34 172.70 636.80 202.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n52 18 Pedestrian -1 -1 -1 195.36 152.56 213.62 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n52 5 Pedestrian -1 -1 -1 557.43 162.15 594.10 259.93 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n52 25 Pedestrian -1 -1 -1 574.56 156.94 622.20 257.08 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n52 12 Pedestrian -1 -1 -1 337.83 160.72 349.49 189.69 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n52 7 Pedestrian -1 -1 -1 567.63 163.92 598.28 234.34 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n52 10 Pedestrian -1 -1 -1 181.70 148.64 199.49 197.68 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n52 9 Pedestrian -1 -1 -1 322.09 158.86 334.44 189.63 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n52 26 Pedestrian -1 -1 -1 888.72 146.84 981.51 318.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n52 29 Pedestrian -1 -1 -1 974.32 151.17 1033.86 298.10 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n52 11 Pedestrian -1 -1 -1 348.14 161.59 360.06 189.70 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n52 31 Pedestrian -1 -1 -1 374.87 160.00 387.00 188.34 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n52 30 Pedestrian -1 -1 -1 927.63 154.66 1003.91 304.50 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n53 3 Car -1 -1 -1 1092.97 184.82 1221.92 237.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n53 6 Car -1 -1 -1 1026.69 183.43 1157.64 234.86 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n53 8 Car -1 -1 -1 604.54 172.86 636.64 201.76 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n53 1 Car -1 -1 -1 955.20 181.21 1066.94 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n53 18 Pedestrian -1 -1 -1 195.66 152.33 213.87 198.64 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n53 4 Pedestrian -1 -1 -1 549.12 161.41 578.67 237.56 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n53 25 Pedestrian -1 -1 -1 571.50 157.49 618.00 256.99 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n53 5 Pedestrian -1 -1 -1 554.16 161.91 590.27 259.99 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n53 12 Pedestrian -1 -1 -1 337.86 160.55 349.32 189.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n53 26 Pedestrian -1 -1 -1 877.95 149.60 969.45 315.74 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n53 10 Pedestrian -1 -1 -1 181.93 148.78 199.31 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n53 9 Pedestrian -1 -1 -1 322.08 158.80 334.42 189.45 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n53 29 Pedestrian -1 -1 -1 955.69 149.71 1021.66 294.06 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n53 30 Pedestrian -1 -1 -1 925.81 156.97 990.29 301.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n53 11 Pedestrian -1 -1 -1 348.22 161.53 359.64 189.42 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n53 31 Pedestrian -1 -1 -1 375.24 160.15 387.30 188.53 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n54 3 Car -1 -1 -1 1093.78 184.76 1221.89 236.58 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n54 6 Car -1 -1 -1 1030.53 181.94 1160.54 236.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n54 1 Car -1 -1 -1 954.95 180.89 1067.46 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n54 8 Car -1 -1 -1 601.83 172.79 636.52 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n54 18 Pedestrian -1 -1 -1 195.80 152.16 213.37 198.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n54 26 Pedestrian -1 -1 -1 875.12 145.72 949.75 318.69 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n54 5 Pedestrian -1 -1 -1 549.71 161.14 585.99 257.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n54 25 Pedestrian -1 -1 -1 566.23 158.31 608.42 255.88 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n54 4 Pedestrian -1 -1 -1 553.10 162.64 581.51 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n54 12 Pedestrian -1 -1 -1 337.85 160.31 349.28 189.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n54 29 Pedestrian -1 -1 -1 944.42 149.69 1017.72 300.07 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n54 10 Pedestrian -1 -1 -1 182.16 148.97 199.06 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n54 11 Pedestrian -1 -1 -1 347.94 161.38 359.44 189.44 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n54 30 Pedestrian -1 -1 -1 912.44 154.66 973.14 302.56 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n54 9 Pedestrian -1 -1 -1 322.05 158.88 334.62 189.44 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n54 31 Pedestrian -1 -1 -1 375.28 160.16 387.42 188.83 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n55 6 Car -1 -1 -1 1033.37 183.53 1157.89 235.41 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n55 3 Car -1 -1 -1 1093.88 184.96 1221.96 236.25 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n55 1 Car -1 -1 -1 956.54 181.68 1066.57 235.54 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n55 8 Car -1 -1 -1 602.62 172.40 635.73 202.08 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n55 5 Pedestrian -1 -1 -1 539.43 160.68 581.86 257.21 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n55 18 Pedestrian -1 -1 -1 194.80 152.25 213.17 198.17 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n55 25 Pedestrian -1 -1 -1 560.23 156.43 598.92 257.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n55 26 Pedestrian -1 -1 -1 861.43 143.18 917.31 314.66 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n55 30 Pedestrian -1 -1 -1 897.86 152.25 964.73 304.71 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n55 10 Pedestrian -1 -1 -1 181.82 149.16 199.05 197.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n55 29 Pedestrian -1 -1 -1 935.43 148.69 1003.54 294.70 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n55 4 Pedestrian -1 -1 -1 554.38 162.76 581.43 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n55 11 Pedestrian -1 -1 -1 348.21 161.01 359.53 189.47 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n55 12 Pedestrian -1 -1 -1 339.52 159.79 350.05 189.34 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n55 31 Pedestrian -1 -1 -1 375.47 159.83 387.18 188.93 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n55 9 Pedestrian -1 -1 -1 323.41 158.73 335.62 189.30 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n55 32 Pedestrian -1 -1 -1 573.93 158.59 608.79 244.50 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n56 3 Car -1 -1 -1 1093.94 185.12 1221.56 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n56 6 Car -1 -1 -1 1027.47 183.55 1157.00 235.09 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n56 1 Car -1 -1 -1 953.61 182.18 1069.25 235.45 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n56 8 Car -1 -1 -1 605.26 172.81 635.69 201.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n56 5 Pedestrian -1 -1 -1 535.90 160.77 577.14 257.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n56 26 Pedestrian -1 -1 -1 840.22 144.96 907.26 312.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n56 25 Pedestrian -1 -1 -1 560.75 156.08 597.29 258.03 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n56 18 Pedestrian -1 -1 -1 192.81 152.07 210.98 198.50 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n56 29 Pedestrian -1 -1 -1 933.76 147.34 990.27 294.61 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n56 32 Pedestrian -1 -1 -1 585.24 161.17 611.44 237.07 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n56 10 Pedestrian -1 -1 -1 181.90 149.10 198.78 197.10 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n56 30 Pedestrian -1 -1 -1 887.46 154.73 952.30 296.72 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n56 11 Pedestrian -1 -1 -1 348.11 161.13 359.51 189.32 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n56 9 Pedestrian -1 -1 -1 321.80 158.42 334.56 189.55 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n56 12 Pedestrian -1 -1 -1 339.19 160.05 350.68 189.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n56 31 Pedestrian -1 -1 -1 375.92 159.75 387.50 188.87 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n57 3 Car -1 -1 -1 1093.49 185.23 1221.87 235.80 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n57 6 Car -1 -1 -1 1027.26 183.27 1157.02 234.90 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n57 1 Car -1 -1 -1 953.21 182.40 1069.30 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n57 5 Pedestrian -1 -1 -1 530.30 160.51 568.76 258.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n57 25 Pedestrian -1 -1 -1 556.61 158.50 594.98 255.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n57 8 Car -1 -1 -1 605.44 172.80 635.39 201.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n57 18 Pedestrian -1 -1 -1 192.61 152.10 210.66 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n57 29 Pedestrian -1 -1 -1 921.54 149.67 972.18 292.86 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n57 26 Pedestrian -1 -1 -1 816.30 148.67 908.31 310.00 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n57 32 Pedestrian -1 -1 -1 588.91 161.62 615.75 237.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n57 10 Pedestrian -1 -1 -1 182.14 149.26 198.66 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n57 30 Pedestrian -1 -1 -1 875.95 155.20 940.79 296.35 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n57 9 Pedestrian -1 -1 -1 323.12 159.01 336.04 189.24 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n57 31 Pedestrian -1 -1 -1 375.98 159.87 387.54 188.82 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n57 12 Pedestrian -1 -1 -1 339.01 160.30 351.03 189.33 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n57 11 Pedestrian -1 -1 -1 347.69 161.02 359.84 189.61 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n58 3 Car -1 -1 -1 1093.45 185.14 1221.85 236.05 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n58 6 Car -1 -1 -1 1028.69 183.34 1156.55 235.01 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n58 1 Car -1 -1 -1 951.58 182.35 1070.79 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n58 8 Car -1 -1 -1 605.63 172.46 635.68 201.57 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n58 26 Pedestrian -1 -1 -1 809.28 148.49 893.09 309.32 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n58 18 Pedestrian -1 -1 -1 192.46 152.26 210.67 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n58 25 Pedestrian -1 -1 -1 552.00 159.46 590.62 254.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n58 29 Pedestrian -1 -1 -1 908.57 149.32 961.88 292.63 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n58 5 Pedestrian -1 -1 -1 526.17 158.62 562.88 258.62 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n58 9 Pedestrian -1 -1 -1 323.76 159.25 336.18 189.13 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n58 10 Pedestrian -1 -1 -1 182.12 149.33 198.75 197.03 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n58 32 Pedestrian -1 -1 -1 589.63 163.47 621.99 238.39 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n58 30 Pedestrian -1 -1 -1 869.26 154.06 924.49 296.98 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n58 12 Pedestrian -1 -1 -1 339.40 160.58 350.46 188.93 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n58 31 Pedestrian -1 -1 -1 377.09 159.69 389.31 188.91 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n59 3 Car -1 -1 -1 1093.63 185.18 1221.82 236.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n59 6 Car -1 -1 -1 1029.37 183.54 1156.19 234.75 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n59 1 Car -1 -1 -1 953.34 181.78 1068.56 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n59 8 Car -1 -1 -1 605.51 172.57 636.15 201.77 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n59 25 Pedestrian -1 -1 -1 547.69 158.34 581.32 253.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n59 18 Pedestrian -1 -1 -1 192.36 152.23 210.77 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n59 5 Pedestrian -1 -1 -1 519.11 158.56 555.39 256.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n59 29 Pedestrian -1 -1 -1 896.32 149.43 958.56 293.26 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n59 30 Pedestrian -1 -1 -1 856.10 153.60 906.90 297.02 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n59 32 Pedestrian -1 -1 -1 590.80 163.52 621.97 239.83 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n59 26 Pedestrian -1 -1 -1 804.77 147.37 874.53 309.40 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n59 9 Pedestrian -1 -1 -1 323.94 159.20 336.56 189.14 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n59 10 Pedestrian -1 -1 -1 182.01 149.43 198.66 196.88 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n59 31 Pedestrian -1 -1 -1 377.35 159.45 389.88 189.17 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n59 12 Pedestrian -1 -1 -1 337.43 159.98 349.91 189.53 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n59 33 Pedestrian -1 -1 -1 558.82 160.35 593.16 243.24 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n60 3 Car -1 -1 -1 1093.74 185.17 1221.42 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n60 6 Car -1 -1 -1 1029.54 183.77 1156.07 234.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n60 1 Car -1 -1 -1 954.50 181.47 1068.01 235.94 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n60 18 Pedestrian -1 -1 -1 191.90 152.34 210.77 197.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n60 5 Pedestrian -1 -1 -1 514.66 159.55 552.02 255.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n60 8 Car -1 -1 -1 605.15 172.72 636.40 201.68 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n60 32 Pedestrian -1 -1 -1 594.71 162.19 626.51 241.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n60 25 Pedestrian -1 -1 -1 544.56 156.25 575.39 253.83 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n60 29 Pedestrian -1 -1 -1 884.96 149.23 954.94 292.72 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n60 26 Pedestrian -1 -1 -1 798.71 145.48 849.72 305.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n60 30 Pedestrian -1 -1 -1 833.58 152.10 899.31 297.67 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n60 10 Pedestrian -1 -1 -1 181.92 149.30 198.73 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n60 9 Pedestrian -1 -1 -1 323.99 159.21 336.54 189.01 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n60 31 Pedestrian -1 -1 -1 376.97 159.42 389.91 189.03 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n60 33 Pedestrian -1 -1 -1 573.08 160.53 600.85 241.95 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n60 12 Pedestrian -1 -1 -1 337.16 159.60 349.72 189.29 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n61 3 Car -1 -1 -1 1093.68 185.04 1221.79 236.20 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n61 6 Car -1 -1 -1 1029.22 183.86 1156.50 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n61 26 Pedestrian -1 -1 -1 777.94 145.01 839.79 305.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n61 1 Car -1 -1 -1 950.44 181.93 1065.87 235.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n61 18 Pedestrian -1 -1 -1 192.14 152.42 210.70 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n61 8 Car -1 -1 -1 604.83 172.59 636.59 202.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n61 25 Pedestrian -1 -1 -1 535.13 155.90 570.19 251.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n61 5 Pedestrian -1 -1 -1 507.60 160.75 545.68 253.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n61 33 Pedestrian -1 -1 -1 574.49 161.15 606.75 242.60 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n61 32 Pedestrian -1 -1 -1 599.39 160.47 628.49 241.45 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n61 9 Pedestrian -1 -1 -1 324.14 159.06 336.82 189.32 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n61 29 Pedestrian -1 -1 -1 884.08 150.68 940.09 284.91 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n61 30 Pedestrian -1 -1 -1 824.95 156.66 892.08 292.68 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n61 10 Pedestrian -1 -1 -1 181.59 149.99 198.70 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n61 12 Pedestrian -1 -1 -1 337.27 160.03 349.97 189.49 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n61 31 Pedestrian -1 -1 -1 377.40 159.33 390.46 189.10 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n62 3 Car -1 -1 -1 1094.19 185.17 1221.32 236.03 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n62 6 Car -1 -1 -1 1032.00 183.70 1158.08 234.05 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n62 1 Car -1 -1 -1 949.82 181.73 1065.95 235.56 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n62 25 Pedestrian -1 -1 -1 528.14 156.88 569.26 253.25 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n62 8 Car -1 -1 -1 604.35 172.37 637.05 201.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n62 18 Pedestrian -1 -1 -1 191.93 152.34 210.62 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n62 33 Pedestrian -1 -1 -1 576.86 162.19 612.47 242.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n62 5 Pedestrian -1 -1 -1 504.65 161.33 539.66 253.39 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n62 26 Pedestrian -1 -1 -1 765.50 144.33 836.76 305.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n62 30 Pedestrian -1 -1 -1 817.77 155.62 891.69 294.82 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n62 29 Pedestrian -1 -1 -1 874.03 148.05 927.34 285.76 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n62 32 Pedestrian -1 -1 -1 599.09 161.58 630.16 240.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n62 10 Pedestrian -1 -1 -1 181.45 149.81 198.82 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n62 9 Pedestrian -1 -1 -1 324.83 159.10 337.23 189.38 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n62 31 Pedestrian -1 -1 -1 377.60 159.32 390.63 189.16 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n62 12 Pedestrian -1 -1 -1 337.77 160.42 349.53 189.43 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n62 34 Cyclist -1 -1 -1 949.98 173.64 988.28 237.66 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n63 3 Car -1 -1 -1 1094.46 185.44 1221.00 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n63 6 Car -1 -1 -1 1032.27 183.78 1157.67 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n63 1 Car -1 -1 -1 954.63 182.07 1068.05 235.18 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n63 33 Pedestrian -1 -1 -1 579.67 160.48 617.36 243.91 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n63 25 Pedestrian -1 -1 -1 523.91 158.14 566.29 252.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n63 8 Car -1 -1 -1 604.41 172.48 637.01 201.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n63 26 Pedestrian -1 -1 -1 756.08 147.28 823.77 303.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n63 18 Pedestrian -1 -1 -1 191.90 152.38 210.46 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n63 29 Pedestrian -1 -1 -1 862.00 146.92 909.35 287.01 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n63 5 Pedestrian -1 -1 -1 503.79 161.79 533.14 255.44 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n63 32 Pedestrian -1 -1 -1 602.81 160.85 638.94 241.34 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n63 30 Pedestrian -1 -1 -1 811.69 154.52 868.13 293.93 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n63 10 Pedestrian -1 -1 -1 181.55 149.80 198.89 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n63 9 Pedestrian -1 -1 -1 325.14 159.04 337.75 189.56 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n63 31 Pedestrian -1 -1 -1 378.07 159.18 391.03 189.16 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n63 12 Pedestrian -1 -1 -1 337.45 160.69 349.81 189.45 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n63 34 Cyclist -1 -1 -1 928.72 178.11 980.50 226.77 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n64 3 Car -1 -1 -1 1094.39 185.20 1220.94 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n64 6 Car -1 -1 -1 1031.98 183.71 1157.94 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n64 1 Car -1 -1 -1 954.48 182.70 1067.18 234.50 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n64 33 Pedestrian -1 -1 -1 584.51 159.31 620.11 244.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n64 8 Car -1 -1 -1 604.18 172.33 637.36 201.60 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n64 5 Pedestrian -1 -1 -1 496.37 159.98 531.14 253.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n64 18 Pedestrian -1 -1 -1 191.77 152.44 210.51 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n64 32 Pedestrian -1 -1 -1 607.08 161.42 642.15 242.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n64 26 Pedestrian -1 -1 -1 752.11 147.70 812.56 302.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n64 29 Pedestrian -1 -1 -1 842.01 148.31 905.42 286.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n64 25 Pedestrian -1 -1 -1 521.11 158.06 560.75 251.49 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n64 31 Pedestrian -1 -1 -1 377.92 158.88 390.92 189.41 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n64 10 Pedestrian -1 -1 -1 181.67 149.78 198.92 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n64 34 Cyclist -1 -1 -1 918.13 171.42 960.34 234.62 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n64 30 Pedestrian -1 -1 -1 799.84 152.99 848.78 290.43 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n64 9 Pedestrian -1 -1 -1 325.31 158.85 338.03 189.36 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n64 12 Pedestrian -1 -1 -1 337.22 160.40 349.80 189.39 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n65 3 Car -1 -1 -1 1094.08 185.07 1221.59 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n65 6 Car -1 -1 -1 1029.02 183.67 1156.61 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n65 1 Car -1 -1 -1 954.09 182.59 1067.96 234.57 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n65 5 Pedestrian -1 -1 -1 489.00 159.58 526.19 252.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n65 8 Car -1 -1 -1 604.09 172.53 637.53 201.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n65 33 Pedestrian -1 -1 -1 589.98 159.54 621.91 244.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 18 Pedestrian -1 -1 -1 191.77 152.54 210.32 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n65 32 Pedestrian -1 -1 -1 609.00 161.46 643.04 242.64 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n65 25 Pedestrian -1 -1 -1 517.60 157.37 550.64 248.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n65 30 Pedestrian -1 -1 -1 788.15 151.00 845.29 292.09 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n65 29 Pedestrian -1 -1 -1 833.54 149.50 899.08 285.19 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n65 26 Pedestrian -1 -1 -1 739.95 147.14 794.01 296.71 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n65 10 Pedestrian -1 -1 -1 181.58 149.82 199.11 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n65 31 Pedestrian -1 -1 -1 377.19 158.87 390.82 189.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n65 12 Pedestrian -1 -1 -1 337.62 160.32 349.75 188.84 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n65 34 Cyclist -1 -1 -1 907.61 170.74 946.60 235.29 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n65 9 Pedestrian -1 -1 -1 325.79 159.21 337.63 189.14 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n66 3 Car -1 -1 -1 1094.17 185.13 1221.62 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n66 6 Car -1 -1 -1 1029.11 183.82 1156.71 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n66 1 Car -1 -1 -1 954.26 182.78 1067.62 234.51 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n66 8 Car -1 -1 -1 603.81 172.34 638.01 201.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n66 18 Pedestrian -1 -1 -1 191.57 152.61 210.22 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n66 33 Pedestrian -1 -1 -1 593.71 159.42 626.22 243.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n66 25 Pedestrian -1 -1 -1 512.91 156.76 547.83 249.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n66 5 Pedestrian -1 -1 -1 485.15 160.40 521.79 251.89 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n66 32 Pedestrian -1 -1 -1 613.03 159.98 646.90 243.56 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n66 26 Pedestrian -1 -1 -1 725.49 145.12 777.88 298.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n66 30 Pedestrian -1 -1 -1 776.63 152.82 841.02 289.69 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n66 29 Pedestrian -1 -1 -1 826.20 149.21 891.62 285.11 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n66 10 Pedestrian -1 -1 -1 181.70 149.84 198.94 197.88 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n66 31 Pedestrian -1 -1 -1 377.16 158.85 390.46 189.24 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n66 12 Pedestrian -1 -1 -1 338.92 160.56 351.02 188.64 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n66 9 Pedestrian -1 -1 -1 325.56 158.90 337.76 189.66 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n66 35 Pedestrian -1 -1 -1 1164.58 158.40 1217.97 345.80 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n66 36 Cyclist -1 -1 -1 569.08 165.89 584.05 202.05 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n67 1 Car -1 -1 -1 954.23 182.89 1067.53 234.37 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n67 3 Car -1 -1 -1 1094.54 185.22 1220.89 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n67 6 Car -1 -1 -1 1029.03 183.71 1156.79 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n67 8 Car -1 -1 -1 604.48 172.46 637.49 201.61 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n67 5 Pedestrian -1 -1 -1 480.95 160.38 517.25 251.14 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n67 33 Pedestrian -1 -1 -1 595.44 159.15 631.54 245.86 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n67 25 Pedestrian -1 -1 -1 508.03 157.21 544.51 248.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n67 18 Pedestrian -1 -1 -1 191.31 152.72 210.10 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n67 26 Pedestrian -1 -1 -1 709.43 145.73 770.86 297.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n67 30 Pedestrian -1 -1 -1 770.75 154.54 832.37 288.79 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n67 32 Pedestrian -1 -1 -1 623.58 159.53 657.01 244.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n67 29 Pedestrian -1 -1 -1 823.91 150.27 878.41 283.31 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n67 35 Pedestrian -1 -1 -1 1157.84 156.19 1217.71 346.44 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n67 10 Pedestrian -1 -1 -1 181.65 150.01 198.97 197.70 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n67 31 Pedestrian -1 -1 -1 377.15 158.90 389.93 189.15 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n67 9 Pedestrian -1 -1 -1 325.44 159.50 338.32 189.06 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n67 36 Cyclist -1 -1 -1 571.30 166.22 585.64 201.73 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n67 12 Pedestrian -1 -1 -1 339.19 161.32 350.90 188.73 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n68 1 Car -1 -1 -1 954.47 182.89 1067.68 234.36 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n68 6 Car -1 -1 -1 1029.33 183.69 1156.58 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n68 3 Car -1 -1 -1 1098.91 185.14 1221.24 236.16 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n68 8 Car -1 -1 -1 604.91 172.77 636.89 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n68 25 Pedestrian -1 -1 -1 503.23 157.43 541.70 248.41 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n68 5 Pedestrian -1 -1 -1 474.35 159.93 510.04 251.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n68 26 Pedestrian -1 -1 -1 698.28 147.44 766.62 296.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n68 18 Pedestrian -1 -1 -1 191.75 152.49 210.27 197.16 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n68 33 Pedestrian -1 -1 -1 599.12 159.42 634.95 247.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n68 32 Pedestrian -1 -1 -1 625.48 159.41 664.40 246.81 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n68 35 Pedestrian -1 -1 -1 1145.29 156.54 1214.83 340.12 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n68 30 Pedestrian -1 -1 -1 764.64 154.00 815.34 288.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n68 29 Pedestrian -1 -1 -1 814.79 149.85 856.97 284.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n68 31 Pedestrian -1 -1 -1 377.14 158.90 390.22 189.31 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n68 10 Pedestrian -1 -1 -1 183.49 149.62 200.41 196.55 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n68 12 Pedestrian -1 -1 -1 339.60 161.55 350.89 188.55 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n68 9 Pedestrian -1 -1 -1 327.05 160.16 339.86 188.28 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n68 36 Cyclist -1 -1 -1 571.14 166.39 585.79 201.41 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n68 37 Cyclist -1 -1 -1 856.09 169.70 907.31 228.12 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n69 1 Car -1 -1 -1 954.20 183.03 1067.63 234.17 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n69 6 Car -1 -1 -1 1029.39 183.63 1156.46 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n69 3 Car -1 -1 -1 1094.19 184.88 1221.31 235.54 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n69 26 Pedestrian -1 -1 -1 692.21 147.75 757.35 296.44 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n69 25 Pedestrian -1 -1 -1 499.43 157.89 537.65 248.95 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n69 5 Pedestrian -1 -1 -1 471.17 159.24 504.75 251.21 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n69 18 Pedestrian -1 -1 -1 192.07 152.61 210.30 196.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n69 29 Pedestrian -1 -1 -1 797.17 149.23 852.06 284.85 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n69 8 Car -1 -1 -1 604.81 173.02 636.43 201.45 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n69 35 Pedestrian -1 -1 -1 1121.84 157.68 1207.33 337.76 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n69 30 Pedestrian -1 -1 -1 755.61 152.28 801.16 289.54 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n69 33 Pedestrian -1 -1 -1 600.03 159.76 636.52 246.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n69 32 Pedestrian -1 -1 -1 625.67 160.21 671.05 249.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n69 31 Pedestrian -1 -1 -1 376.82 158.92 390.74 189.99 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n69 10 Pedestrian -1 -1 -1 183.40 149.61 200.49 196.47 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n69 12 Pedestrian -1 -1 -1 340.26 161.34 351.00 187.92 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n69 9 Pedestrian -1 -1 -1 327.28 160.29 340.31 188.12 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n69 37 Cyclist -1 -1 -1 842.42 172.61 890.65 223.85 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n69 36 Cyclist -1 -1 -1 570.94 166.90 586.16 200.31 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n69 38 Pedestrian -1 -1 -1 1179.58 162.16 1218.80 333.45 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n70 1 Car -1 -1 -1 954.30 183.17 1067.40 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n70 3 Car -1 -1 -1 1093.10 184.99 1222.45 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n70 6 Car -1 -1 -1 1029.25 183.53 1156.36 234.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n70 25 Pedestrian -1 -1 -1 496.45 158.29 531.54 248.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n70 30 Pedestrian -1 -1 -1 736.71 151.81 797.31 289.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n70 18 Pedestrian -1 -1 -1 192.19 152.67 210.59 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n70 26 Pedestrian -1 -1 -1 689.14 147.84 738.41 295.46 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n70 5 Pedestrian -1 -1 -1 467.75 159.76 500.60 251.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n70 33 Pedestrian -1 -1 -1 605.06 158.81 638.73 247.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n70 8 Car -1 -1 -1 603.99 172.91 637.06 201.62 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n70 29 Pedestrian -1 -1 -1 783.90 148.57 849.37 285.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n70 32 Pedestrian -1 -1 -1 631.90 160.28 672.53 246.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n70 35 Pedestrian -1 -1 -1 1105.15 158.34 1200.96 337.71 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n70 31 Pedestrian -1 -1 -1 376.93 159.13 390.46 189.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n70 10 Pedestrian -1 -1 -1 183.44 149.58 200.62 196.50 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n70 38 Pedestrian -1 -1 -1 1167.34 164.61 1215.60 330.99 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n70 9 Pedestrian -1 -1 -1 328.03 159.91 341.00 188.45 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n70 12 Pedestrian -1 -1 -1 339.93 161.17 351.26 187.98 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n71 3 Car -1 -1 -1 1093.07 184.92 1222.46 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n71 1 Car -1 -1 -1 953.96 183.33 1067.86 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n71 6 Car -1 -1 -1 1029.00 183.60 1155.82 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n71 25 Pedestrian -1 -1 -1 490.91 156.95 523.16 247.28 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n71 5 Pedestrian -1 -1 -1 462.93 161.21 496.87 250.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n71 18 Pedestrian -1 -1 -1 192.04 152.66 210.56 196.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n71 33 Pedestrian -1 -1 -1 612.97 157.24 645.80 248.37 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n71 30 Pedestrian -1 -1 -1 729.18 153.52 789.15 287.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n71 35 Pedestrian -1 -1 -1 1094.11 157.91 1204.65 337.40 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n71 29 Pedestrian -1 -1 -1 776.62 148.77 841.40 284.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n71 8 Car -1 -1 -1 603.17 173.72 634.05 201.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n71 32 Pedestrian -1 -1 -1 640.12 159.25 672.77 246.98 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n71 26 Pedestrian -1 -1 -1 680.80 149.04 722.27 294.75 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n71 10 Pedestrian -1 -1 -1 183.39 149.57 200.58 196.41 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n71 31 Pedestrian -1 -1 -1 376.97 158.86 390.36 189.64 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n71 12 Pedestrian -1 -1 -1 340.10 161.24 351.53 188.37 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n71 9 Pedestrian -1 -1 -1 328.68 160.03 341.49 188.26 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n72 3 Car -1 -1 -1 1093.58 185.08 1221.75 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n72 1 Car -1 -1 -1 954.25 183.40 1067.61 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n72 6 Car -1 -1 -1 1030.03 183.80 1154.42 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n72 25 Pedestrian -1 -1 -1 484.06 156.54 521.60 247.30 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n72 5 Pedestrian -1 -1 -1 455.52 161.72 489.81 249.68 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n72 26 Pedestrian -1 -1 -1 662.16 149.11 717.88 293.17 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n72 30 Pedestrian -1 -1 -1 723.98 155.06 779.14 287.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n72 18 Pedestrian -1 -1 -1 191.89 152.69 210.57 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n72 33 Pedestrian -1 -1 -1 616.54 157.44 650.19 249.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n72 8 Car -1 -1 -1 603.61 172.84 637.70 203.51 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n72 35 Pedestrian -1 -1 -1 1083.67 157.60 1176.78 329.76 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n72 32 Pedestrian -1 -1 -1 648.28 158.90 679.01 247.73 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n72 29 Pedestrian -1 -1 -1 768.52 147.84 826.92 285.67 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n72 31 Pedestrian -1 -1 -1 377.13 158.74 390.43 189.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n72 12 Pedestrian -1 -1 -1 340.57 161.16 351.82 187.95 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n72 10 Pedestrian -1 -1 -1 183.42 149.63 200.51 196.51 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n72 9 Pedestrian -1 -1 -1 329.56 159.91 341.50 188.23 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n72 39 Pedestrian -1 -1 -1 1118.33 165.40 1203.42 323.88 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n73 1 Car -1 -1 -1 954.44 183.22 1067.81 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n73 3 Car -1 -1 -1 1093.70 184.60 1221.82 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n73 25 Pedestrian -1 -1 -1 480.23 157.41 518.27 246.92 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n73 6 Car -1 -1 -1 1030.73 183.53 1153.53 234.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n73 18 Pedestrian -1 -1 -1 192.21 152.73 210.49 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n73 5 Pedestrian -1 -1 -1 453.19 160.59 485.21 249.59 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n73 30 Pedestrian -1 -1 -1 723.80 155.76 771.39 285.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n73 26 Pedestrian -1 -1 -1 648.43 151.82 709.80 291.64 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n73 8 Car -1 -1 -1 603.13 172.39 637.87 203.78 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n73 33 Pedestrian -1 -1 -1 619.44 158.41 654.93 248.57 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n73 32 Pedestrian -1 -1 -1 651.63 159.29 690.37 250.61 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n73 39 Pedestrian -1 -1 -1 1115.56 165.44 1183.35 323.05 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n73 29 Pedestrian -1 -1 -1 760.49 150.39 804.18 277.42 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n73 35 Pedestrian -1 -1 -1 1079.20 156.70 1142.92 325.76 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n73 31 Pedestrian -1 -1 -1 377.39 159.15 390.56 189.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n73 12 Pedestrian -1 -1 -1 340.95 161.04 352.16 187.62 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n73 10 Pedestrian -1 -1 -1 183.53 149.70 200.44 196.53 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n73 40 Cyclist -1 -1 -1 571.39 167.44 585.89 199.53 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n74 1 Car -1 -1 -1 954.65 183.16 1067.56 234.00 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n74 3 Car -1 -1 -1 1094.41 184.42 1220.42 235.98 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n74 25 Pedestrian -1 -1 -1 477.37 158.00 514.10 246.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n74 6 Car -1 -1 -1 1030.43 183.59 1153.81 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n74 26 Pedestrian -1 -1 -1 639.20 155.28 703.81 293.83 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n74 33 Pedestrian -1 -1 -1 623.12 159.11 658.55 251.34 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n74 18 Pedestrian -1 -1 -1 191.90 152.50 210.55 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n74 5 Pedestrian -1 -1 -1 449.57 158.49 485.98 248.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n74 8 Car -1 -1 -1 601.79 172.66 636.31 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n74 29 Pedestrian -1 -1 -1 748.73 148.01 799.81 279.54 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n74 30 Pedestrian -1 -1 -1 712.06 156.16 753.52 281.03 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n74 32 Pedestrian -1 -1 -1 649.17 160.00 694.39 250.67 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n74 35 Pedestrian -1 -1 -1 1059.94 158.38 1123.55 322.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n74 39 Pedestrian -1 -1 -1 1100.23 163.68 1160.42 323.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n74 31 Pedestrian -1 -1 -1 377.15 159.41 390.22 189.83 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n74 12 Pedestrian -1 -1 -1 340.98 160.84 352.02 187.64 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n74 10 Pedestrian -1 -1 -1 183.53 149.57 200.50 196.71 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n74 40 Cyclist -1 -1 -1 571.48 167.49 586.02 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n75 1 Car -1 -1 -1 954.36 183.06 1068.04 234.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n75 3 Car -1 -1 -1 1093.80 184.60 1220.41 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n75 6 Car -1 -1 -1 1029.27 183.47 1155.00 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n75 30 Pedestrian -1 -1 -1 701.47 153.44 747.95 282.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n75 8 Car -1 -1 -1 601.39 171.99 636.81 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n75 18 Pedestrian -1 -1 -1 192.02 152.45 210.56 197.25 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n75 25 Pedestrian -1 -1 -1 475.11 158.24 509.06 245.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n75 26 Pedestrian -1 -1 -1 637.06 153.86 690.58 290.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n75 5 Pedestrian -1 -1 -1 442.96 160.50 480.45 249.04 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n75 33 Pedestrian -1 -1 -1 630.31 157.82 665.10 254.25 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n75 29 Pedestrian -1 -1 -1 732.86 147.71 793.57 278.52 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n75 35 Pedestrian -1 -1 -1 1031.04 157.69 1114.70 323.73 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n75 32 Pedestrian -1 -1 -1 658.53 160.02 698.02 251.85 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n75 39 Pedestrian -1 -1 -1 1079.57 162.39 1150.32 324.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n75 31 Pedestrian -1 -1 -1 376.97 159.52 390.01 189.90 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n75 10 Pedestrian -1 -1 -1 183.40 149.34 200.65 196.89 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n75 40 Cyclist -1 -1 -1 571.46 167.72 586.15 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n75 12 Pedestrian -1 -1 -1 341.01 160.56 352.68 187.81 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n75 41 Car -1 -1 -1 599.27 173.25 621.97 193.28 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n76 3 Car -1 -1 -1 1093.40 184.66 1221.80 236.85 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n76 1 Car -1 -1 -1 954.33 183.07 1067.62 234.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n76 6 Car -1 -1 -1 1029.93 184.07 1154.74 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n76 8 Car -1 -1 -1 601.56 172.09 636.74 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n76 30 Pedestrian -1 -1 -1 691.57 154.16 742.88 281.38 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n76 5 Pedestrian -1 -1 -1 443.21 160.30 476.84 246.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n76 33 Pedestrian -1 -1 -1 631.27 157.45 666.68 255.04 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n76 18 Pedestrian -1 -1 -1 192.51 152.64 210.67 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n76 25 Pedestrian -1 -1 -1 472.71 157.47 502.88 242.28 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n76 26 Pedestrian -1 -1 -1 633.00 150.81 672.20 291.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n76 35 Pedestrian -1 -1 -1 1014.68 159.27 1107.99 321.76 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n76 32 Pedestrian -1 -1 -1 664.64 159.32 699.58 251.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n76 29 Pedestrian -1 -1 -1 725.71 151.32 785.47 275.11 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n76 10 Pedestrian -1 -1 -1 183.57 149.36 200.80 196.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n76 31 Pedestrian -1 -1 -1 376.88 159.45 389.89 189.95 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n76 39 Pedestrian -1 -1 -1 1064.47 163.82 1142.33 318.65 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n76 40 Cyclist -1 -1 -1 571.51 167.94 586.42 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n76 41 Car -1 -1 -1 599.40 173.28 621.86 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n77 3 Car -1 -1 -1 1093.31 184.87 1221.71 236.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n77 1 Car -1 -1 -1 954.29 183.01 1067.52 234.16 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n77 5 Pedestrian -1 -1 -1 436.68 159.41 469.95 246.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n77 6 Car -1 -1 -1 1030.32 184.28 1154.93 234.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n77 8 Car -1 -1 -1 601.73 172.23 636.40 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n77 26 Pedestrian -1 -1 -1 617.18 149.52 665.22 287.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n77 30 Pedestrian -1 -1 -1 685.90 155.53 733.48 281.24 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n77 18 Pedestrian -1 -1 -1 192.39 152.72 210.57 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n77 25 Pedestrian -1 -1 -1 467.89 156.85 500.35 242.73 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n77 33 Pedestrian -1 -1 -1 632.50 156.48 671.89 255.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n77 35 Pedestrian -1 -1 -1 1005.39 157.46 1102.03 322.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n77 29 Pedestrian -1 -1 -1 720.16 150.60 775.67 275.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n77 32 Pedestrian -1 -1 -1 673.27 157.52 707.41 253.48 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n77 39 Pedestrian -1 -1 -1 1058.00 163.69 1133.42 316.99 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n77 10 Pedestrian -1 -1 -1 183.59 149.27 200.80 197.06 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n77 31 Pedestrian -1 -1 -1 375.15 159.40 387.94 190.39 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n77 40 Cyclist -1 -1 -1 571.70 167.84 586.28 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n77 41 Car -1 -1 -1 599.40 173.37 621.73 193.65 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n78 3 Car -1 -1 -1 1093.42 185.01 1221.78 236.33 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n78 1 Car -1 -1 -1 954.41 182.76 1067.91 234.57 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n78 6 Car -1 -1 -1 1030.36 183.99 1154.84 234.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n78 5 Pedestrian -1 -1 -1 432.41 159.04 466.66 246.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n78 26 Pedestrian -1 -1 -1 603.24 149.87 663.02 287.08 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n78 30 Pedestrian -1 -1 -1 682.92 155.94 721.30 281.08 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n78 18 Pedestrian -1 -1 -1 191.96 152.70 210.59 197.20 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n78 8 Car -1 -1 -1 602.38 172.85 635.33 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n78 25 Pedestrian -1 -1 -1 463.21 156.35 498.03 243.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n78 32 Pedestrian -1 -1 -1 674.56 158.07 714.56 253.86 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n78 39 Pedestrian -1 -1 -1 1038.37 162.94 1107.38 310.74 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n78 33 Pedestrian -1 -1 -1 636.36 157.73 675.19 255.38 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n78 29 Pedestrian -1 -1 -1 714.91 149.50 758.20 276.08 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n78 35 Pedestrian -1 -1 -1 1003.33 156.27 1081.11 318.50 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n78 10 Pedestrian -1 -1 -1 183.66 149.29 200.73 196.95 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n78 31 Pedestrian -1 -1 -1 374.61 159.57 388.18 190.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n78 40 Cyclist -1 -1 -1 571.45 167.94 586.10 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n79 3 Car -1 -1 -1 1093.66 185.14 1222.06 236.06 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n79 1 Car -1 -1 -1 954.55 182.99 1067.72 234.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n79 6 Car -1 -1 -1 1034.74 184.15 1155.66 234.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n79 26 Pedestrian -1 -1 -1 597.34 149.11 660.15 287.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n79 5 Pedestrian -1 -1 -1 428.49 158.61 463.80 245.93 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n79 18 Pedestrian -1 -1 -1 191.94 152.69 210.76 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n79 30 Pedestrian -1 -1 -1 673.48 156.25 715.04 279.78 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n79 25 Pedestrian -1 -1 -1 459.11 157.56 494.28 244.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n79 35 Pedestrian -1 -1 -1 991.62 157.22 1047.50 315.55 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n79 8 Car -1 -1 -1 602.11 172.87 635.42 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n79 33 Pedestrian -1 -1 -1 638.91 157.55 681.21 255.75 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n79 39 Pedestrian -1 -1 -1 1023.98 160.26 1083.65 312.21 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n79 29 Pedestrian -1 -1 -1 704.38 148.63 745.88 272.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n79 10 Pedestrian -1 -1 -1 183.84 149.34 200.84 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n79 32 Pedestrian -1 -1 -1 678.71 156.87 724.77 255.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n79 31 Pedestrian -1 -1 -1 374.44 159.68 388.01 190.88 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n79 40 Cyclist -1 -1 -1 571.37 167.66 585.93 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n80 3 Car -1 -1 -1 1099.33 185.29 1220.73 236.13 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n80 1 Car -1 -1 -1 955.33 183.28 1066.97 231.66 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n80 6 Car -1 -1 -1 1034.03 184.21 1156.20 234.54 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n80 26 Pedestrian -1 -1 -1 595.48 148.32 654.29 288.61 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n80 5 Pedestrian -1 -1 -1 426.75 159.99 462.98 246.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n80 18 Pedestrian -1 -1 -1 191.48 152.56 210.92 197.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n80 8 Car -1 -1 -1 601.60 172.96 635.57 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n80 30 Pedestrian -1 -1 -1 660.03 156.24 705.95 279.11 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n80 35 Pedestrian -1 -1 -1 968.55 156.89 1039.32 315.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n80 25 Pedestrian -1 -1 -1 459.11 158.10 492.30 241.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n80 39 Pedestrian -1 -1 -1 1003.54 159.96 1073.54 313.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n80 29 Pedestrian -1 -1 -1 691.09 151.02 743.53 269.00 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n80 33 Pedestrian -1 -1 -1 644.92 156.60 689.91 257.08 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n80 10 Pedestrian -1 -1 -1 183.95 149.48 200.93 196.75 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n80 31 Pedestrian -1 -1 -1 374.45 159.84 387.93 190.64 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n80 40 Cyclist -1 -1 -1 572.13 167.65 585.41 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n81 1 Car -1 -1 -1 953.96 183.23 1068.18 231.79 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n81 3 Car -1 -1 -1 1099.29 185.55 1220.63 236.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n81 6 Car -1 -1 -1 1033.19 184.23 1157.00 234.46 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n81 26 Pedestrian -1 -1 -1 589.88 150.11 638.12 284.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n81 30 Pedestrian -1 -1 -1 648.94 157.96 701.83 277.24 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n81 5 Pedestrian -1 -1 -1 423.87 161.63 459.75 244.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n81 18 Pedestrian -1 -1 -1 191.50 152.67 210.98 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n81 8 Car -1 -1 -1 600.90 172.84 636.63 202.35 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n81 25 Pedestrian -1 -1 -1 456.37 158.15 486.81 240.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n81 29 Pedestrian -1 -1 -1 687.14 151.51 739.86 268.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n81 35 Pedestrian -1 -1 -1 946.03 155.11 1038.83 311.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n81 39 Pedestrian -1 -1 -1 992.31 161.76 1068.88 311.52 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n81 33 Pedestrian -1 -1 -1 646.39 157.38 696.70 260.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n81 10 Pedestrian -1 -1 -1 183.93 149.58 201.14 196.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n81 31 Pedestrian -1 -1 -1 374.15 159.64 388.36 190.70 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n81 40 Cyclist -1 -1 -1 572.32 167.63 585.20 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n82 3 Car -1 -1 -1 1099.41 185.43 1220.65 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n82 1 Car -1 -1 -1 955.41 183.37 1067.12 231.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n82 6 Car -1 -1 -1 1033.18 184.08 1156.99 234.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n82 30 Pedestrian -1 -1 -1 645.73 157.82 697.03 276.74 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n82 5 Pedestrian -1 -1 -1 424.49 160.53 457.31 243.36 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n82 26 Pedestrian -1 -1 -1 580.68 149.18 625.27 284.77 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n82 25 Pedestrian -1 -1 -1 452.90 157.79 482.59 241.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n82 18 Pedestrian -1 -1 -1 191.57 152.47 210.91 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n82 8 Car -1 -1 -1 603.50 172.55 637.55 202.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n82 35 Pedestrian -1 -1 -1 933.68 155.32 1036.10 312.16 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n82 39 Pedestrian -1 -1 -1 976.21 159.94 1062.27 312.49 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n82 10 Pedestrian -1 -1 -1 183.94 149.42 201.01 197.01 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n82 31 Pedestrian -1 -1 -1 374.35 159.66 388.36 190.60 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n82 29 Pedestrian -1 -1 -1 683.99 152.26 727.65 268.20 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n82 33 Pedestrian -1 -1 -1 692.26 154.36 734.09 258.08 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n82 40 Cyclist -1 -1 -1 573.61 168.29 584.84 197.65 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n83 3 Car -1 -1 -1 1099.10 185.65 1220.73 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n83 6 Car -1 -1 -1 1032.91 184.09 1157.01 234.14 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n83 26 Pedestrian -1 -1 -1 568.80 148.95 620.53 284.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n83 1 Car -1 -1 -1 953.74 182.92 1068.76 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n83 5 Pedestrian -1 -1 -1 422.07 159.73 453.83 243.06 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n83 25 Pedestrian -1 -1 -1 446.67 157.53 482.03 242.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n83 8 Car -1 -1 -1 604.01 172.54 637.13 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n83 18 Pedestrian -1 -1 -1 191.65 152.55 210.82 198.23 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n83 30 Pedestrian -1 -1 -1 639.58 157.43 687.45 276.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n83 35 Pedestrian -1 -1 -1 932.59 154.00 1006.68 311.97 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n83 10 Pedestrian -1 -1 -1 184.11 149.37 201.03 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n83 39 Pedestrian -1 -1 -1 971.90 160.93 1036.18 305.64 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n83 29 Pedestrian -1 -1 -1 667.74 153.66 712.63 267.07 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n83 33 Pedestrian -1 -1 -1 696.44 156.79 738.30 255.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n83 31 Pedestrian -1 -1 -1 374.19 159.71 388.05 190.84 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n83 40 Cyclist -1 -1 -1 573.59 169.30 586.16 197.26 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n84 3 Car -1 -1 -1 1099.09 185.60 1220.67 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n84 6 Car -1 -1 -1 1032.67 184.02 1157.35 234.12 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n84 26 Pedestrian -1 -1 -1 558.82 150.67 615.59 278.89 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n84 5 Pedestrian -1 -1 -1 418.03 160.45 451.36 242.70 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n84 25 Pedestrian -1 -1 -1 443.47 158.63 478.65 240.91 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n84 1 Car -1 -1 -1 957.24 182.92 1064.50 231.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n84 18 Pedestrian -1 -1 -1 191.59 152.65 210.96 198.32 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n84 35 Pedestrian -1 -1 -1 927.20 154.47 989.13 310.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n84 8 Car -1 -1 -1 601.12 172.43 637.15 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n84 30 Pedestrian -1 -1 -1 630.46 156.59 674.70 273.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n84 39 Pedestrian -1 -1 -1 966.29 160.47 1018.00 304.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n84 33 Pedestrian -1 -1 -1 702.53 158.07 747.50 255.64 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n84 29 Pedestrian -1 -1 -1 664.31 153.10 708.78 266.80 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n84 10 Pedestrian -1 -1 -1 184.03 149.15 200.84 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n84 31 Pedestrian -1 -1 -1 373.59 159.77 387.43 190.84 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n85 3 Car -1 -1 -1 1094.67 185.46 1221.17 235.88 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n85 6 Car -1 -1 -1 1032.53 183.90 1157.47 234.01 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n85 26 Pedestrian -1 -1 -1 550.14 152.46 609.43 282.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n85 5 Pedestrian -1 -1 -1 414.53 161.71 447.32 242.51 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n85 8 Car -1 -1 -1 600.95 172.28 637.10 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n85 1 Car -1 -1 -1 953.23 182.66 1063.88 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n85 25 Pedestrian -1 -1 -1 441.01 158.70 474.58 240.55 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n85 30 Pedestrian -1 -1 -1 627.94 156.25 668.40 272.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n85 39 Pedestrian -1 -1 -1 946.26 159.21 1008.02 305.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n85 18 Pedestrian -1 -1 -1 192.16 152.46 211.04 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n85 33 Pedestrian -1 -1 -1 708.97 158.55 755.07 258.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n85 35 Pedestrian -1 -1 -1 914.55 155.36 970.87 308.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n85 10 Pedestrian -1 -1 -1 184.04 149.00 201.13 197.42 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n85 29 Pedestrian -1 -1 -1 668.07 152.88 712.61 259.17 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n85 31 Pedestrian -1 -1 -1 373.07 159.98 386.12 191.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n85 42 Pedestrian -1 -1 -1 658.26 152.72 706.76 267.00 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n86 3 Car -1 -1 -1 1094.44 185.48 1221.24 235.74 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n86 6 Car -1 -1 -1 1032.85 183.87 1157.48 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n86 1 Car -1 -1 -1 956.02 183.44 1065.67 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n86 26 Pedestrian -1 -1 -1 553.94 151.49 597.35 281.82 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n86 8 Car -1 -1 -1 602.18 172.35 638.50 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n86 5 Pedestrian -1 -1 -1 411.07 162.17 443.52 241.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n86 25 Pedestrian -1 -1 -1 438.89 158.38 469.07 238.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n86 30 Pedestrian -1 -1 -1 617.78 157.03 662.96 271.37 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n86 33 Pedestrian -1 -1 -1 715.08 157.89 756.63 259.32 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n86 35 Pedestrian -1 -1 -1 889.96 154.86 965.18 309.25 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n86 18 Pedestrian -1 -1 -1 192.48 152.19 211.19 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n86 39 Pedestrian -1 -1 -1 931.07 160.43 1000.28 304.73 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n86 10 Pedestrian -1 -1 -1 183.92 149.06 201.13 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n86 42 Pedestrian -1 -1 -1 643.51 152.91 691.33 267.11 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n86 31 Pedestrian -1 -1 -1 371.31 159.59 384.57 192.07 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n86 29 Pedestrian -1 -1 -1 677.05 152.53 718.34 260.63 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n87 3 Car -1 -1 -1 1093.96 185.39 1221.48 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n87 6 Car -1 -1 -1 1032.83 183.87 1157.41 233.82 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n87 1 Car -1 -1 -1 951.38 182.83 1065.99 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n87 33 Pedestrian -1 -1 -1 724.58 156.08 764.55 257.43 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n87 25 Pedestrian -1 -1 -1 438.44 158.38 467.38 238.76 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n87 30 Pedestrian -1 -1 -1 611.19 159.25 655.77 269.66 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n87 8 Car -1 -1 -1 602.34 172.59 638.53 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n87 26 Pedestrian -1 -1 -1 543.98 150.38 585.82 277.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n87 5 Pedestrian -1 -1 -1 409.91 160.39 441.52 239.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n87 35 Pedestrian -1 -1 -1 875.40 155.87 956.68 308.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n87 29 Pedestrian -1 -1 -1 683.12 153.15 720.15 260.78 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n87 42 Pedestrian -1 -1 -1 635.15 153.40 684.44 266.64 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n87 18 Pedestrian -1 -1 -1 194.67 152.46 212.57 198.20 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n87 10 Pedestrian -1 -1 -1 184.05 149.13 200.83 197.38 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n87 39 Pedestrian -1 -1 -1 914.96 160.07 993.43 304.46 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n87 31 Pedestrian -1 -1 -1 372.96 159.97 385.61 192.38 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n88 3 Car -1 -1 -1 1093.87 185.55 1221.30 235.69 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n88 6 Car -1 -1 -1 1032.62 183.82 1157.68 233.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n88 1 Car -1 -1 -1 954.48 183.05 1067.46 234.17 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n88 26 Pedestrian -1 -1 -1 531.39 148.70 582.91 277.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n88 25 Pedestrian -1 -1 -1 437.15 158.47 467.98 239.01 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n88 30 Pedestrian -1 -1 -1 605.28 158.31 646.21 270.55 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n88 8 Car -1 -1 -1 601.32 172.66 636.18 202.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n88 33 Pedestrian -1 -1 -1 730.69 156.67 772.89 257.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n88 29 Pedestrian -1 -1 -1 686.65 153.90 732.00 263.47 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n88 5 Pedestrian -1 -1 -1 407.10 160.13 439.60 239.59 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n88 42 Pedestrian -1 -1 -1 634.36 152.41 677.55 268.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n88 18 Pedestrian -1 -1 -1 194.82 152.42 212.86 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n88 35 Pedestrian -1 -1 -1 868.81 154.66 956.11 309.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n88 10 Pedestrian -1 -1 -1 183.86 149.17 200.78 197.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n88 39 Pedestrian -1 -1 -1 909.28 160.49 976.15 297.82 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n88 31 Pedestrian -1 -1 -1 372.88 160.24 385.66 192.96 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n89 3 Car -1 -1 -1 1094.25 185.45 1221.04 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n89 6 Car -1 -1 -1 1032.78 183.76 1157.29 233.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n89 1 Car -1 -1 -1 954.62 183.03 1067.23 234.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n89 26 Pedestrian -1 -1 -1 523.79 150.41 581.03 275.45 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n89 25 Pedestrian -1 -1 -1 433.35 158.59 466.19 239.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n89 29 Pedestrian -1 -1 -1 689.30 154.34 736.63 263.34 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n89 5 Pedestrian -1 -1 -1 406.34 161.81 437.57 241.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n89 30 Pedestrian -1 -1 -1 598.00 157.53 637.88 270.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n89 33 Pedestrian -1 -1 -1 733.01 156.95 778.89 260.91 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n89 18 Pedestrian -1 -1 -1 195.40 152.41 213.51 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n89 8 Car -1 -1 -1 601.04 172.91 636.14 201.78 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n89 35 Pedestrian -1 -1 -1 869.99 156.57 931.87 302.98 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n89 39 Pedestrian -1 -1 -1 900.00 158.00 954.90 298.71 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n89 10 Pedestrian -1 -1 -1 184.06 149.17 200.73 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n89 42 Pedestrian -1 -1 -1 626.05 153.63 663.09 267.85 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n89 31 Pedestrian -1 -1 -1 371.91 160.07 384.15 193.38 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n90 3 Car -1 -1 -1 1094.20 185.41 1221.06 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n90 6 Car -1 -1 -1 1032.82 183.74 1157.26 234.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n90 1 Car -1 -1 -1 954.74 182.95 1067.05 234.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n90 26 Pedestrian -1 -1 -1 516.71 151.25 574.18 274.29 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n90 8 Car -1 -1 -1 600.86 172.57 636.83 201.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n90 5 Pedestrian -1 -1 -1 403.20 163.33 434.72 241.14 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n90 25 Pedestrian -1 -1 -1 432.20 158.79 465.87 238.53 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n90 30 Pedestrian -1 -1 -1 586.58 157.28 633.77 269.23 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n90 29 Pedestrian -1 -1 -1 692.94 155.00 740.33 263.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n90 33 Pedestrian -1 -1 -1 735.18 157.09 783.51 261.66 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n90 18 Pedestrian -1 -1 -1 196.51 152.77 214.24 198.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n90 35 Pedestrian -1 -1 -1 861.59 153.49 908.56 302.77 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n90 42 Pedestrian -1 -1 -1 614.66 152.16 659.48 268.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n90 39 Pedestrian -1 -1 -1 883.49 158.24 940.88 293.86 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n90 10 Pedestrian -1 -1 -1 184.01 149.22 200.61 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n90 31 Pedestrian -1 -1 -1 371.44 159.74 384.41 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n91 3 Car -1 -1 -1 1094.43 185.31 1221.11 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n91 1 Car -1 -1 -1 954.73 182.91 1067.32 234.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n91 6 Car -1 -1 -1 1032.65 183.69 1157.52 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n91 8 Car -1 -1 -1 601.20 172.83 636.51 201.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n91 35 Pedestrian -1 -1 -1 836.58 152.97 896.37 303.60 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n91 25 Pedestrian -1 -1 -1 429.60 158.72 461.04 237.54 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n91 30 Pedestrian -1 -1 -1 575.89 157.96 629.09 269.15 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n91 29 Pedestrian -1 -1 -1 694.36 153.75 741.16 264.43 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n91 26 Pedestrian -1 -1 -1 514.85 152.42 567.71 273.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n91 33 Pedestrian -1 -1 -1 741.48 156.28 785.41 262.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n91 5 Pedestrian -1 -1 -1 404.31 162.93 432.95 239.50 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n91 42 Pedestrian -1 -1 -1 608.19 152.15 657.54 268.57 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n91 18 Pedestrian -1 -1 -1 199.02 153.03 215.51 198.28 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n91 10 Pedestrian -1 -1 -1 183.92 149.24 200.45 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n91 39 Pedestrian -1 -1 -1 872.28 158.85 937.11 293.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n91 31 Pedestrian -1 -1 -1 371.69 159.51 384.12 193.89 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n92 3 Car -1 -1 -1 1094.64 185.44 1220.72 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n92 1 Car -1 -1 -1 954.81 182.94 1067.30 234.27 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n92 6 Car -1 -1 -1 1032.67 183.74 1157.37 233.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n92 8 Car -1 -1 -1 601.38 172.86 636.23 201.86 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n92 25 Pedestrian -1 -1 -1 428.22 158.08 456.99 238.41 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n92 30 Pedestrian -1 -1 -1 569.20 159.65 621.23 267.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n92 26 Pedestrian -1 -1 -1 507.61 152.01 552.89 273.23 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n92 5 Pedestrian -1 -1 -1 403.57 160.79 432.50 237.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n92 29 Pedestrian -1 -1 -1 706.68 150.85 750.07 266.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n92 33 Pedestrian -1 -1 -1 751.47 155.70 788.85 263.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n92 42 Pedestrian -1 -1 -1 600.46 153.90 650.59 266.69 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n92 35 Pedestrian -1 -1 -1 818.60 153.92 891.37 297.88 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n92 18 Pedestrian -1 -1 -1 199.27 153.24 215.54 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n92 39 Pedestrian -1 -1 -1 859.40 159.35 934.71 297.99 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n92 10 Pedestrian -1 -1 -1 183.88 149.25 200.36 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n92 31 Pedestrian -1 -1 -1 371.85 159.69 384.05 193.92 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n93 3 Car -1 -1 -1 1094.79 185.38 1220.67 235.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n93 1 Car -1 -1 -1 954.71 182.94 1067.65 234.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n93 6 Car -1 -1 -1 1032.45 183.70 1157.62 233.88 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n93 5 Pedestrian -1 -1 -1 400.68 160.82 430.76 237.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n93 8 Car -1 -1 -1 600.85 172.75 636.59 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n93 25 Pedestrian -1 -1 -1 427.06 158.63 454.86 237.64 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n93 30 Pedestrian -1 -1 -1 568.06 158.58 614.51 268.57 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n93 33 Pedestrian -1 -1 -1 753.24 156.06 795.65 263.31 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n93 26 Pedestrian -1 -1 -1 498.78 152.47 546.53 272.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n93 35 Pedestrian -1 -1 -1 806.60 154.66 887.82 297.18 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n93 29 Pedestrian -1 -1 -1 711.31 151.28 754.36 266.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n93 18 Pedestrian -1 -1 -1 198.97 153.58 216.20 198.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n93 42 Pedestrian -1 -1 -1 598.79 152.17 643.94 266.59 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n93 10 Pedestrian -1 -1 -1 183.91 149.31 200.39 197.18 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n93 39 Pedestrian -1 -1 -1 856.92 160.61 921.62 295.90 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n93 31 Pedestrian -1 -1 -1 371.64 159.70 384.30 194.06 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n94 3 Car -1 -1 -1 1094.60 185.30 1220.80 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n94 1 Car -1 -1 -1 954.66 182.96 1067.53 234.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n94 6 Car -1 -1 -1 1032.19 183.66 1158.06 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n94 25 Pedestrian -1 -1 -1 422.99 159.18 452.57 237.07 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n94 8 Car -1 -1 -1 600.64 172.35 636.46 202.38 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n94 29 Pedestrian -1 -1 -1 717.18 151.84 762.51 266.84 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n94 26 Pedestrian -1 -1 -1 496.53 154.15 540.76 272.06 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n94 5 Pedestrian -1 -1 -1 398.82 161.40 429.38 237.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n94 33 Pedestrian -1 -1 -1 756.67 155.24 806.85 264.09 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n94 30 Pedestrian -1 -1 -1 563.58 158.55 603.91 268.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n94 35 Pedestrian -1 -1 -1 805.14 154.38 874.10 297.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n94 18 Pedestrian -1 -1 -1 199.19 153.42 216.73 198.41 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n94 39 Pedestrian -1 -1 -1 855.13 159.63 900.15 292.67 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n94 42 Pedestrian -1 -1 -1 593.74 153.28 634.00 265.19 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n94 10 Pedestrian -1 -1 -1 183.88 149.27 200.36 197.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n94 31 Pedestrian -1 -1 -1 371.39 160.72 384.60 195.13 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n95 3 Car -1 -1 -1 1094.62 185.40 1221.03 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n95 1 Car -1 -1 -1 954.58 183.06 1067.53 234.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n95 6 Car -1 -1 -1 1032.05 183.74 1158.12 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n95 5 Pedestrian -1 -1 -1 395.72 161.26 426.60 236.93 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n95 8 Car -1 -1 -1 600.68 172.36 636.58 202.32 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n95 26 Pedestrian -1 -1 -1 493.31 155.35 536.19 270.31 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n95 25 Pedestrian -1 -1 -1 423.35 160.33 450.36 236.43 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n95 29 Pedestrian -1 -1 -1 721.07 153.07 767.93 267.04 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n95 35 Pedestrian -1 -1 -1 802.30 155.26 854.46 296.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n95 39 Pedestrian -1 -1 -1 839.02 159.72 886.05 291.61 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n95 33 Pedestrian -1 -1 -1 760.70 155.84 810.81 264.53 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n95 18 Pedestrian -1 -1 -1 199.48 153.47 216.92 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n95 30 Pedestrian -1 -1 -1 559.32 157.79 598.59 264.22 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n95 42 Pedestrian -1 -1 -1 586.28 152.93 626.61 261.60 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n95 10 Pedestrian -1 -1 -1 183.73 149.19 200.40 197.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n95 31 Pedestrian -1 -1 -1 371.35 159.53 384.27 194.53 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n96 3 Car -1 -1 -1 1094.57 185.28 1221.25 235.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n96 1 Car -1 -1 -1 954.37 183.07 1067.74 234.04 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n96 6 Car -1 -1 -1 1032.06 183.74 1157.96 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n96 5 Pedestrian -1 -1 -1 392.54 161.11 422.19 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n96 25 Pedestrian -1 -1 -1 420.28 160.39 448.24 235.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n96 26 Pedestrian -1 -1 -1 489.39 155.01 531.84 270.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n96 29 Pedestrian -1 -1 -1 724.37 152.39 771.47 268.43 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n96 18 Pedestrian -1 -1 -1 199.71 153.57 216.88 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n96 8 Car -1 -1 -1 601.13 172.19 636.85 201.98 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n96 30 Pedestrian -1 -1 -1 551.39 156.67 591.61 265.20 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n96 39 Pedestrian -1 -1 -1 822.17 160.41 880.08 291.03 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n96 33 Pedestrian -1 -1 -1 763.50 155.27 816.18 265.91 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n96 42 Pedestrian -1 -1 -1 576.38 153.78 621.07 260.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n96 10 Pedestrian -1 -1 -1 183.78 149.08 200.57 197.36 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n96 35 Pedestrian -1 -1 -1 789.25 154.94 843.65 295.14 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n96 31 Pedestrian -1 -1 -1 370.95 159.39 384.67 194.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n97 3 Car -1 -1 -1 1094.90 185.29 1220.91 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n97 1 Car -1 -1 -1 954.23 183.12 1067.86 233.95 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n97 6 Car -1 -1 -1 1031.99 183.69 1158.11 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n97 5 Pedestrian -1 -1 -1 389.42 161.05 418.72 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n97 8 Car -1 -1 -1 604.43 172.27 636.72 201.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n97 25 Pedestrian -1 -1 -1 417.20 160.54 445.24 234.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n97 18 Pedestrian -1 -1 -1 199.69 153.55 216.98 198.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n97 26 Pedestrian -1 -1 -1 485.30 155.44 526.63 266.70 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n97 29 Pedestrian -1 -1 -1 733.55 150.07 776.33 271.14 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n97 30 Pedestrian -1 -1 -1 548.16 157.63 587.13 263.78 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n97 35 Pedestrian -1 -1 -1 767.91 151.98 834.58 297.33 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n97 42 Pedestrian -1 -1 -1 571.87 154.98 617.72 259.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n97 10 Pedestrian -1 -1 -1 183.98 149.13 200.52 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n97 39 Pedestrian -1 -1 -1 807.00 160.69 872.27 290.59 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n97 33 Pedestrian -1 -1 -1 765.77 154.30 821.89 267.25 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n97 31 Pedestrian -1 -1 -1 371.64 160.62 384.55 195.32 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n98 3 Car -1 -1 -1 1094.79 185.33 1221.07 235.68 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n98 1 Car -1 -1 -1 954.11 183.20 1067.99 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n98 6 Car -1 -1 -1 1032.28 183.80 1157.96 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n98 5 Pedestrian -1 -1 -1 389.20 161.42 416.62 235.48 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n98 26 Pedestrian -1 -1 -1 480.34 155.07 519.45 266.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n98 8 Car -1 -1 -1 601.29 172.17 636.72 202.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n98 25 Pedestrian -1 -1 -1 416.35 160.14 443.90 234.22 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n98 18 Pedestrian -1 -1 -1 199.39 153.71 216.92 198.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n98 30 Pedestrian -1 -1 -1 541.50 158.76 580.12 262.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n98 42 Pedestrian -1 -1 -1 569.34 154.38 612.31 259.95 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n98 29 Pedestrian -1 -1 -1 738.62 149.80 779.16 271.49 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n98 10 Pedestrian -1 -1 -1 184.04 149.11 200.63 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n98 35 Pedestrian -1 -1 -1 760.63 154.93 826.68 295.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n98 31 Pedestrian -1 -1 -1 372.01 160.86 384.28 195.57 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n98 39 Pedestrian -1 -1 -1 798.87 159.99 857.48 291.50 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n98 33 Pedestrian -1 -1 -1 770.51 153.62 831.74 267.63 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n99 3 Car -1 -1 -1 1094.86 185.47 1221.09 235.57 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n99 1 Car -1 -1 -1 954.22 183.26 1067.75 233.74 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n99 6 Car -1 -1 -1 1032.31 183.90 1157.86 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n99 26 Pedestrian -1 -1 -1 474.05 153.68 516.24 266.15 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n99 8 Car -1 -1 -1 600.34 171.93 637.32 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n99 5 Pedestrian -1 -1 -1 385.77 162.45 414.21 235.35 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n99 25 Pedestrian -1 -1 -1 416.10 160.61 442.84 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n99 18 Pedestrian -1 -1 -1 199.07 153.48 216.78 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n99 30 Pedestrian -1 -1 -1 537.78 158.03 574.74 262.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n99 29 Pedestrian -1 -1 -1 745.71 151.34 787.67 270.58 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n99 35 Pedestrian -1 -1 -1 755.06 155.69 816.90 295.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n99 42 Pedestrian -1 -1 -1 564.92 155.11 601.81 259.00 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n99 10 Pedestrian -1 -1 -1 184.10 149.18 200.53 197.37 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n99 31 Pedestrian -1 -1 -1 371.99 161.27 384.35 195.45 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n99 39 Pedestrian -1 -1 -1 789.94 159.93 842.99 290.27 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n99 33 Pedestrian -1 -1 -1 773.64 155.23 836.37 272.27 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n100 3 Car -1 -1 -1 1094.88 185.53 1221.21 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n100 1 Car -1 -1 -1 954.21 183.36 1067.71 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n100 6 Car -1 -1 -1 1031.95 183.89 1158.25 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n100 26 Pedestrian -1 -1 -1 470.38 154.62 513.31 265.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n100 30 Pedestrian -1 -1 -1 529.14 158.28 568.69 261.12 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n100 8 Car -1 -1 -1 600.88 172.16 637.23 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n100 5 Pedestrian -1 -1 -1 385.50 163.27 412.82 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n100 25 Pedestrian -1 -1 -1 415.03 161.10 443.52 233.41 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n100 35 Pedestrian -1 -1 -1 752.42 156.46 804.25 293.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n100 29 Pedestrian -1 -1 -1 748.30 154.47 792.87 271.48 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n100 18 Pedestrian -1 -1 -1 198.59 153.57 216.17 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n100 10 Pedestrian -1 -1 -1 184.13 149.12 200.47 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n100 42 Pedestrian -1 -1 -1 559.19 156.44 592.79 257.06 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n100 39 Pedestrian -1 -1 -1 783.20 159.65 834.45 284.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n100 31 Pedestrian -1 -1 -1 371.65 161.00 384.48 195.01 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n101 3 Car -1 -1 -1 1099.07 185.61 1220.67 235.71 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n101 1 Car -1 -1 -1 953.96 183.34 1067.97 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n101 6 Car -1 -1 -1 1032.16 183.89 1157.99 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n101 26 Pedestrian -1 -1 -1 465.19 156.12 511.02 264.94 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n101 30 Pedestrian -1 -1 -1 523.19 158.60 566.46 260.40 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n101 25 Pedestrian -1 -1 -1 412.89 161.20 440.56 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n101 5 Pedestrian -1 -1 -1 382.83 162.68 410.11 232.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n101 8 Car -1 -1 -1 602.69 172.63 637.61 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n101 42 Pedestrian -1 -1 -1 552.57 154.45 591.33 259.00 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n101 35 Pedestrian -1 -1 -1 736.06 154.95 783.16 293.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n101 18 Pedestrian -1 -1 -1 196.85 153.72 214.71 197.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n101 10 Pedestrian -1 -1 -1 184.00 149.19 200.49 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n101 29 Pedestrian -1 -1 -1 748.92 153.32 800.56 273.00 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n101 31 Pedestrian -1 -1 -1 371.37 160.80 384.63 195.06 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n101 39 Pedestrian -1 -1 -1 772.55 160.97 822.46 282.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n101 43 Pedestrian -1 -1 -1 784.00 157.38 841.73 270.51 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n102 3 Car -1 -1 -1 1094.88 185.50 1221.23 235.72 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n102 1 Car -1 -1 -1 954.15 183.46 1067.88 233.58 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n102 6 Car -1 -1 -1 1032.26 183.90 1157.85 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n102 30 Pedestrian -1 -1 -1 517.19 159.91 558.67 259.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n102 26 Pedestrian -1 -1 -1 462.76 155.08 505.72 263.42 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n102 35 Pedestrian -1 -1 -1 721.18 154.11 781.65 290.34 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n102 8 Car -1 -1 -1 602.66 172.39 637.60 203.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n102 5 Pedestrian -1 -1 -1 382.60 162.43 407.71 232.55 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n102 42 Pedestrian -1 -1 -1 548.60 154.58 587.71 257.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n102 18 Pedestrian -1 -1 -1 196.81 153.61 214.32 197.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n102 25 Pedestrian -1 -1 -1 412.77 160.80 438.53 231.48 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n102 10 Pedestrian -1 -1 -1 184.01 149.68 200.45 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n102 43 Pedestrian -1 -1 -1 796.55 153.69 844.49 272.80 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n102 29 Pedestrian -1 -1 -1 751.24 153.13 812.89 273.65 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n102 31 Pedestrian -1 -1 -1 371.35 161.23 384.87 195.25 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n102 44 Pedestrian -1 -1 -1 202.80 148.92 221.36 192.93 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n103 3 Car -1 -1 -1 1094.98 185.47 1221.03 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n103 1 Car -1 -1 -1 954.08 183.55 1067.69 233.51 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n103 6 Car -1 -1 -1 1031.87 183.86 1158.13 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n103 30 Pedestrian -1 -1 -1 513.93 159.27 553.67 259.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n103 26 Pedestrian -1 -1 -1 460.01 153.75 500.36 264.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n103 8 Car -1 -1 -1 602.87 172.23 637.67 203.25 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n103 25 Pedestrian -1 -1 -1 410.35 160.08 435.70 231.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n103 5 Pedestrian -1 -1 -1 377.49 161.39 406.53 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n103 18 Pedestrian -1 -1 -1 195.91 153.33 214.34 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n103 35 Pedestrian -1 -1 -1 709.50 155.52 771.13 288.50 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n103 42 Pedestrian -1 -1 -1 545.34 154.58 582.98 257.48 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n103 43 Pedestrian -1 -1 -1 805.27 152.90 849.63 273.55 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n103 10 Pedestrian -1 -1 -1 183.93 149.69 200.92 198.07 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n103 31 Pedestrian -1 -1 -1 371.28 161.66 384.84 195.51 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n103 29 Pedestrian -1 -1 -1 759.99 150.02 811.85 277.01 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n103 44 Pedestrian -1 -1 -1 202.62 149.37 221.65 192.77 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n104 3 Car -1 -1 -1 1095.08 185.35 1220.95 235.83 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n104 1 Car -1 -1 -1 954.06 183.50 1067.85 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n104 6 Car -1 -1 -1 1031.88 183.81 1158.15 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n104 8 Car -1 -1 -1 602.99 172.35 637.49 203.08 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n104 35 Pedestrian -1 -1 -1 703.25 156.11 768.70 288.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n104 26 Pedestrian -1 -1 -1 454.94 156.15 496.48 258.52 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n104 18 Pedestrian -1 -1 -1 195.24 153.26 213.93 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n104 25 Pedestrian -1 -1 -1 409.05 160.28 434.40 230.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n104 43 Pedestrian -1 -1 -1 810.16 154.42 860.73 273.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n104 5 Pedestrian -1 -1 -1 374.49 161.99 403.22 232.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n104 30 Pedestrian -1 -1 -1 507.18 159.18 545.68 259.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n104 42 Pedestrian -1 -1 -1 542.39 155.90 577.74 256.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n104 10 Pedestrian -1 -1 -1 183.86 149.75 201.02 198.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n104 29 Pedestrian -1 -1 -1 767.09 150.39 812.64 276.94 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n104 31 Pedestrian -1 -1 -1 371.01 161.56 384.59 195.29 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n104 44 Pedestrian -1 -1 -1 202.98 149.53 221.15 192.79 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n104 45 Pedestrian -1 -1 -1 750.13 160.82 791.27 282.64 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n105 3 Car -1 -1 -1 1095.15 185.39 1220.81 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n105 1 Car -1 -1 -1 954.10 183.43 1067.69 233.64 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n105 6 Car -1 -1 -1 1031.79 183.72 1158.17 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n105 8 Car -1 -1 -1 602.82 172.00 637.73 203.03 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n105 26 Pedestrian -1 -1 -1 452.85 156.67 491.85 257.97 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n105 35 Pedestrian -1 -1 -1 701.74 157.28 755.10 286.88 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n105 5 Pedestrian -1 -1 -1 373.92 162.30 400.78 229.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n105 25 Pedestrian -1 -1 -1 406.49 160.86 432.78 230.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n105 18 Pedestrian -1 -1 -1 195.06 153.12 213.50 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n105 42 Pedestrian -1 -1 -1 535.41 155.44 569.92 255.35 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n105 29 Pedestrian -1 -1 -1 776.72 149.82 825.35 278.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n105 30 Pedestrian -1 -1 -1 503.96 159.62 540.93 258.00 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n105 43 Pedestrian -1 -1 -1 811.65 155.46 867.75 277.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n105 10 Pedestrian -1 -1 -1 183.78 149.42 200.85 196.97 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n105 45 Pedestrian -1 -1 -1 735.56 161.03 775.36 282.21 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n105 44 Pedestrian -1 -1 -1 202.73 149.48 221.05 193.01 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n105 31 Pedestrian -1 -1 -1 371.09 161.28 384.51 195.24 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n106 3 Car -1 -1 -1 1094.98 185.34 1221.03 235.75 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n106 1 Car -1 -1 -1 954.08 183.33 1067.51 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n106 6 Car -1 -1 -1 1031.71 183.60 1158.55 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n106 8 Car -1 -1 -1 602.78 171.87 637.65 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n106 26 Pedestrian -1 -1 -1 449.02 156.92 488.29 260.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n106 5 Pedestrian -1 -1 -1 371.02 161.78 397.10 230.39 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n106 25 Pedestrian -1 -1 -1 406.22 161.28 431.42 230.85 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n106 18 Pedestrian -1 -1 -1 195.01 153.02 212.89 197.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n106 30 Pedestrian -1 -1 -1 499.85 160.16 536.73 257.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n106 29 Pedestrian -1 -1 -1 778.47 148.05 831.73 280.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n106 42 Pedestrian -1 -1 -1 526.13 154.08 564.79 255.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n106 35 Pedestrian -1 -1 -1 698.58 155.68 742.80 288.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n106 45 Pedestrian -1 -1 -1 722.78 159.82 772.76 284.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n106 43 Pedestrian -1 -1 -1 817.02 154.97 870.10 274.25 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n106 10 Pedestrian -1 -1 -1 184.12 149.48 201.13 196.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n106 44 Pedestrian -1 -1 -1 202.76 149.52 220.96 193.25 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n106 31 Pedestrian -1 -1 -1 370.51 161.38 384.26 195.79 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n106 46 Car -1 -1 -1 599.25 173.18 621.46 193.47 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n107 3 Car -1 -1 -1 1094.99 185.25 1221.09 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n107 1 Car -1 -1 -1 954.33 183.19 1067.49 233.81 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n107 6 Car -1 -1 -1 1031.75 183.72 1158.37 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n107 8 Car -1 -1 -1 602.87 171.90 637.63 203.13 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n107 5 Pedestrian -1 -1 -1 366.88 160.67 395.30 230.93 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n107 26 Pedestrian -1 -1 -1 444.64 156.84 484.50 257.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n107 43 Pedestrian -1 -1 -1 824.21 153.08 878.23 280.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n107 25 Pedestrian -1 -1 -1 406.23 160.27 430.11 229.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n107 42 Pedestrian -1 -1 -1 520.48 155.00 562.56 255.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n107 29 Pedestrian -1 -1 -1 783.93 148.69 834.65 284.82 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n107 18 Pedestrian -1 -1 -1 194.69 152.98 213.01 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n107 35 Pedestrian -1 -1 -1 681.67 155.91 730.28 286.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n107 45 Pedestrian -1 -1 -1 717.04 160.35 770.91 288.32 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n107 30 Pedestrian -1 -1 -1 495.50 160.41 532.94 257.61 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n107 10 Pedestrian -1 -1 -1 183.78 149.56 201.23 196.96 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n107 44 Pedestrian -1 -1 -1 203.13 149.80 220.66 193.00 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n107 31 Pedestrian -1 -1 -1 369.90 161.78 384.44 195.58 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n107 46 Car -1 -1 -1 599.21 173.26 621.68 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n108 3 Car -1 -1 -1 1095.00 185.25 1221.06 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n108 1 Car -1 -1 -1 954.45 183.10 1067.50 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n108 6 Car -1 -1 -1 1031.67 183.69 1158.59 233.74 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n108 8 Car -1 -1 -1 602.99 172.00 637.71 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n108 26 Pedestrian -1 -1 -1 442.02 157.02 479.30 256.55 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n108 29 Pedestrian -1 -1 -1 794.98 148.91 838.88 285.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n108 25 Pedestrian -1 -1 -1 403.59 159.81 427.76 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n108 42 Pedestrian -1 -1 -1 516.85 155.40 558.27 254.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n108 43 Pedestrian -1 -1 -1 833.80 152.69 884.24 280.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n108 5 Pedestrian -1 -1 -1 366.81 161.58 393.91 230.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n108 30 Pedestrian -1 -1 -1 489.90 161.51 524.77 256.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n108 35 Pedestrian -1 -1 -1 668.15 156.63 728.44 284.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n108 18 Pedestrian -1 -1 -1 192.21 152.33 211.41 198.13 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n108 45 Pedestrian -1 -1 -1 712.83 161.16 760.37 283.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n108 10 Pedestrian -1 -1 -1 183.66 149.75 200.62 196.70 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n108 46 Car -1 -1 -1 599.15 173.25 621.87 193.54 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n109 1 Car -1 -1 -1 954.46 183.13 1067.61 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n109 3 Car -1 -1 -1 1098.62 185.38 1221.23 235.95 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n109 6 Car -1 -1 -1 1031.73 183.72 1158.59 233.77 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n109 26 Pedestrian -1 -1 -1 438.23 155.61 474.60 255.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n109 8 Car -1 -1 -1 602.97 172.06 637.68 203.17 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n109 29 Pedestrian -1 -1 -1 797.80 149.70 843.98 285.33 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n109 35 Pedestrian -1 -1 -1 663.53 157.03 724.97 284.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n109 43 Pedestrian -1 -1 -1 837.90 151.58 895.34 282.40 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n109 25 Pedestrian -1 -1 -1 401.66 160.03 427.35 228.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n109 30 Pedestrian -1 -1 -1 486.50 161.10 519.67 256.95 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n109 42 Pedestrian -1 -1 -1 515.51 155.68 551.83 254.26 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n109 18 Pedestrian -1 -1 -1 191.95 152.31 211.57 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n109 5 Pedestrian -1 -1 -1 365.15 161.79 390.23 229.90 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n109 45 Pedestrian -1 -1 -1 709.50 160.36 747.04 282.60 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n109 10 Pedestrian -1 -1 -1 181.73 150.40 198.98 197.35 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n109 46 Car -1 -1 -1 599.73 173.17 621.82 193.48 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n110 1 Car -1 -1 -1 954.42 183.09 1067.71 234.10 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n110 6 Car -1 -1 -1 1031.62 183.79 1158.53 233.80 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n110 3 Car -1 -1 -1 1095.14 185.36 1220.74 235.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n110 26 Pedestrian -1 -1 -1 429.55 155.85 470.76 256.35 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n110 8 Car -1 -1 -1 602.90 172.14 637.61 203.14 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n110 25 Pedestrian -1 -1 -1 398.39 159.89 425.27 228.58 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n110 5 Pedestrian -1 -1 -1 364.02 161.65 389.65 229.21 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n110 43 Pedestrian -1 -1 -1 842.95 152.78 905.09 282.85 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n110 29 Pedestrian -1 -1 -1 804.23 150.34 859.31 286.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n110 10 Pedestrian -1 -1 -1 181.00 151.11 198.97 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n110 18 Pedestrian -1 -1 -1 192.27 152.30 211.31 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n110 42 Pedestrian -1 -1 -1 513.53 156.57 546.18 250.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n110 35 Pedestrian -1 -1 -1 660.02 157.63 713.00 283.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n110 30 Pedestrian -1 -1 -1 477.97 160.23 513.92 254.84 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n110 45 Pedestrian -1 -1 -1 695.43 159.48 738.30 281.56 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n110 46 Car -1 -1 -1 599.34 173.33 621.70 193.34 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n110 47 Pedestrian -1 -1 -1 1159.33 160.88 1216.27 343.36 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n110 48 Pedestrian -1 -1 -1 1.08 149.29 18.45 246.16 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n111 1 Car -1 -1 -1 954.57 183.12 1067.65 234.02 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n111 6 Car -1 -1 -1 1031.78 183.89 1158.37 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n111 3 Car -1 -1 -1 1095.76 185.42 1220.04 235.43 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n111 26 Pedestrian -1 -1 -1 425.44 156.56 467.17 255.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n111 8 Car -1 -1 -1 602.83 172.15 637.78 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n111 30 Pedestrian -1 -1 -1 471.91 160.23 511.65 254.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n111 25 Pedestrian -1 -1 -1 398.01 160.47 423.87 228.32 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n111 5 Pedestrian -1 -1 -1 363.96 161.63 388.39 228.20 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n111 29 Pedestrian -1 -1 -1 805.05 151.48 866.65 289.87 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n111 10 Pedestrian -1 -1 -1 180.87 151.79 198.45 197.50 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n111 18 Pedestrian -1 -1 -1 192.40 152.36 211.12 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n111 42 Pedestrian -1 -1 -1 508.78 156.12 542.79 250.07 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n111 47 Pedestrian -1 -1 -1 1150.26 163.12 1216.92 339.86 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n111 43 Pedestrian -1 -1 -1 848.48 153.95 907.10 287.48 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n111 45 Pedestrian -1 -1 -1 686.01 158.78 732.35 277.47 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n111 35 Pedestrian -1 -1 -1 654.62 155.20 695.50 282.01 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n111 48 Pedestrian -1 -1 -1 0.37 150.36 27.13 246.28 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n112 1 Car -1 -1 -1 954.63 183.06 1067.57 234.16 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n112 6 Car -1 -1 -1 1032.01 183.99 1158.61 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n112 3 Car -1 -1 -1 1096.09 185.42 1219.67 235.23 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n112 26 Pedestrian -1 -1 -1 424.26 156.19 466.72 254.89 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n112 8 Car -1 -1 -1 602.92 172.01 637.73 203.00 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n112 30 Pedestrian -1 -1 -1 467.79 160.58 508.01 253.95 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n112 47 Pedestrian -1 -1 -1 1139.03 164.37 1213.37 339.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n112 42 Pedestrian -1 -1 -1 503.38 156.45 540.51 250.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n112 35 Pedestrian -1 -1 -1 638.95 155.37 687.65 280.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n112 10 Pedestrian -1 -1 -1 180.69 152.39 198.28 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n112 29 Pedestrian -1 -1 -1 811.62 150.59 874.68 291.58 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n112 5 Pedestrian -1 -1 -1 361.05 161.49 386.12 228.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n112 25 Pedestrian -1 -1 -1 395.07 160.50 421.25 228.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n112 18 Pedestrian -1 -1 -1 192.61 152.54 211.09 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n112 43 Pedestrian -1 -1 -1 855.27 155.21 908.14 287.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n112 45 Pedestrian -1 -1 -1 676.90 160.79 726.26 276.08 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n112 48 Pedestrian -1 -1 -1 1.76 148.99 33.64 247.37 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n113 3 Car -1 -1 -1 1097.80 185.14 1222.32 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n113 1 Car -1 -1 -1 954.46 182.91 1067.65 234.24 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n113 6 Car -1 -1 -1 1032.46 183.87 1157.98 233.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n113 30 Pedestrian -1 -1 -1 464.23 160.50 504.20 253.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n113 35 Pedestrian -1 -1 -1 622.71 155.60 681.98 279.64 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n113 26 Pedestrian -1 -1 -1 422.21 154.75 461.35 255.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n113 47 Pedestrian -1 -1 -1 1122.17 162.39 1199.66 340.17 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n113 8 Car -1 -1 -1 602.59 172.00 637.85 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n113 5 Pedestrian -1 -1 -1 359.87 161.15 385.34 228.88 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n113 10 Pedestrian -1 -1 -1 180.48 152.63 197.96 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n113 42 Pedestrian -1 -1 -1 499.46 156.56 536.64 249.57 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n113 25 Pedestrian -1 -1 -1 395.64 160.01 419.70 228.40 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n113 29 Pedestrian -1 -1 -1 818.08 149.76 877.12 292.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n113 18 Pedestrian -1 -1 -1 192.65 152.56 211.12 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n113 45 Pedestrian -1 -1 -1 671.51 160.88 717.47 275.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n113 43 Pedestrian -1 -1 -1 863.32 152.65 914.58 288.94 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n113 48 Pedestrian -1 -1 -1 2.88 149.27 38.90 246.92 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n114 1 Car -1 -1 -1 954.30 182.89 1067.81 234.26 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n114 3 Car -1 -1 -1 1094.71 185.31 1220.88 235.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n114 6 Car -1 -1 -1 1030.84 183.99 1154.72 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n114 30 Pedestrian -1 -1 -1 461.14 160.37 499.28 253.11 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n114 47 Pedestrian -1 -1 -1 1095.86 161.43 1180.24 335.13 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n114 42 Pedestrian -1 -1 -1 497.88 156.12 530.31 249.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n114 8 Car -1 -1 -1 602.42 171.97 637.97 202.86 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n114 10 Pedestrian -1 -1 -1 180.00 152.61 197.67 197.94 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n114 35 Pedestrian -1 -1 -1 617.13 156.67 679.16 278.99 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n114 5 Pedestrian -1 -1 -1 356.51 162.01 383.01 228.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n114 26 Pedestrian -1 -1 -1 416.23 155.08 453.21 254.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n114 25 Pedestrian -1 -1 -1 394.90 160.26 418.02 227.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n114 29 Pedestrian -1 -1 -1 833.02 146.57 884.44 294.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n114 18 Pedestrian -1 -1 -1 192.37 152.36 211.20 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n114 43 Pedestrian -1 -1 -1 865.60 152.75 920.70 288.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n114 45 Pedestrian -1 -1 -1 667.97 160.43 704.65 275.31 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n114 48 Pedestrian -1 -1 -1 4.68 146.92 44.45 244.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n114 49 Pedestrian -1 -1 -1 209.02 164.11 222.98 195.86 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n114 50 Car -1 -1 -1 599.18 172.94 621.60 193.32 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n115 1 Car -1 -1 -1 954.42 182.83 1067.77 234.24 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n115 3 Car -1 -1 -1 1094.55 185.66 1220.02 235.86 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n115 6 Car -1 -1 -1 1031.99 184.03 1152.98 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n115 47 Pedestrian -1 -1 -1 1065.98 160.68 1171.59 335.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n115 8 Car -1 -1 -1 602.37 172.08 638.11 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n115 25 Pedestrian -1 -1 -1 391.30 160.37 416.22 227.69 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n115 42 Pedestrian -1 -1 -1 495.15 155.87 525.98 248.65 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n115 30 Pedestrian -1 -1 -1 457.82 159.20 493.52 253.08 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n115 5 Pedestrian -1 -1 -1 354.73 161.08 382.14 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n115 26 Pedestrian -1 -1 -1 415.47 156.29 451.03 254.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n115 29 Pedestrian -1 -1 -1 841.45 146.87 898.32 295.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n115 10 Pedestrian -1 -1 -1 180.07 152.57 197.23 197.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n115 48 Pedestrian -1 -1 -1 3.36 147.79 47.67 243.25 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n115 18 Pedestrian -1 -1 -1 192.30 152.31 211.33 198.06 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n115 45 Pedestrian -1 -1 -1 653.87 159.76 696.57 276.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n115 35 Pedestrian -1 -1 -1 610.56 157.98 670.48 278.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n115 43 Pedestrian -1 -1 -1 868.45 153.57 932.93 288.86 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n115 49 Pedestrian -1 -1 -1 208.75 164.01 222.62 196.02 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n116 1 Car -1 -1 -1 954.51 182.67 1068.11 234.38 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n116 6 Car -1 -1 -1 1031.33 183.82 1154.30 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n116 5 Pedestrian -1 -1 -1 351.12 159.89 378.98 228.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n116 48 Pedestrian -1 -1 -1 5.85 149.12 58.31 242.40 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n116 30 Pedestrian -1 -1 -1 452.44 159.52 486.33 252.23 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n116 8 Car -1 -1 -1 602.45 172.39 638.21 203.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n116 25 Pedestrian -1 -1 -1 390.85 160.71 415.27 228.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n116 26 Pedestrian -1 -1 -1 413.02 157.64 446.42 252.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n116 47 Pedestrian -1 -1 -1 1056.14 159.13 1165.89 335.48 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n116 3 Car -1 -1 -1 1095.95 186.21 1217.86 235.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n116 42 Pedestrian -1 -1 -1 489.62 156.29 523.14 247.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n116 35 Pedestrian -1 -1 -1 607.12 158.52 658.78 278.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n116 29 Pedestrian -1 -1 -1 846.22 146.72 909.27 296.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n116 10 Pedestrian -1 -1 -1 180.10 152.49 196.99 197.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n116 45 Pedestrian -1 -1 -1 647.62 161.02 694.23 274.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n116 18 Pedestrian -1 -1 -1 192.68 152.34 210.99 197.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n116 43 Pedestrian -1 -1 -1 876.55 152.20 947.33 291.55 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n116 49 Pedestrian -1 -1 -1 208.33 163.80 222.62 196.08 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n117 1 Car -1 -1 -1 954.32 182.79 1068.38 234.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n117 48 Pedestrian -1 -1 -1 9.06 149.07 62.60 241.51 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n117 6 Car -1 -1 -1 1031.50 184.43 1153.56 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n117 30 Pedestrian -1 -1 -1 446.52 159.96 483.45 252.74 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n117 26 Pedestrian -1 -1 -1 409.53 157.82 442.97 253.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n117 8 Car -1 -1 -1 602.72 172.70 638.31 203.25 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n117 42 Pedestrian -1 -1 -1 483.94 155.77 520.94 247.95 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n117 47 Pedestrian -1 -1 -1 1055.41 160.34 1135.96 329.41 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n117 3 Car -1 -1 -1 1094.22 185.64 1219.51 236.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n117 45 Pedestrian -1 -1 -1 638.54 161.03 688.36 274.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n117 10 Pedestrian -1 -1 -1 180.02 152.55 196.91 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n117 5 Pedestrian -1 -1 -1 347.59 160.28 377.05 227.78 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n117 25 Pedestrian -1 -1 -1 387.62 160.77 412.42 226.88 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n117 18 Pedestrian -1 -1 -1 192.57 152.38 210.92 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n117 35 Pedestrian -1 -1 -1 603.82 158.37 647.45 276.43 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n117 29 Pedestrian -1 -1 -1 851.86 147.70 918.97 296.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n117 43 Pedestrian -1 -1 -1 886.97 150.91 952.53 292.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n117 49 Pedestrian -1 -1 -1 208.30 163.87 222.56 195.99 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n117 51 Pedestrian -1 -1 -1 366.56 160.85 380.53 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n118 1 Car -1 -1 -1 954.02 182.79 1068.75 234.26 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n118 6 Car -1 -1 -1 1033.30 184.13 1157.65 234.10 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n118 48 Pedestrian -1 -1 -1 15.43 149.84 64.11 241.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n118 30 Pedestrian -1 -1 -1 442.65 160.47 480.11 252.29 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n118 42 Pedestrian -1 -1 -1 480.70 156.06 516.70 247.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n118 47 Pedestrian -1 -1 -1 1043.77 159.62 1109.27 328.31 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n118 3 Car -1 -1 -1 1094.65 185.03 1219.71 236.68 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n118 45 Pedestrian -1 -1 -1 634.15 162.46 677.84 272.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n118 10 Pedestrian -1 -1 -1 180.04 152.35 197.10 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n118 35 Pedestrian -1 -1 -1 590.71 157.69 645.09 276.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n118 8 Car -1 -1 -1 602.57 172.97 638.03 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n118 5 Pedestrian -1 -1 -1 347.10 160.75 375.58 228.12 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n118 26 Pedestrian -1 -1 -1 405.09 157.12 440.87 252.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n118 18 Pedestrian -1 -1 -1 192.74 152.21 210.82 197.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n118 29 Pedestrian -1 -1 -1 860.55 148.01 925.57 300.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n118 43 Pedestrian -1 -1 -1 901.80 150.42 960.41 292.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n118 25 Pedestrian -1 -1 -1 384.58 160.27 408.54 225.95 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n118 51 Pedestrian -1 -1 -1 366.78 160.92 380.47 197.91 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n118 49 Pedestrian -1 -1 -1 208.05 163.57 222.65 196.10 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n119 1 Car -1 -1 -1 953.76 183.23 1069.54 233.78 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n119 48 Pedestrian -1 -1 -1 23.23 147.93 64.19 241.17 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n119 42 Pedestrian -1 -1 -1 477.53 156.64 512.67 246.94 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n119 30 Pedestrian -1 -1 -1 439.24 161.06 474.50 251.35 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n119 6 Car -1 -1 -1 1030.07 184.35 1154.91 234.18 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n119 3 Car -1 -1 -1 1093.98 184.62 1220.19 236.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n119 47 Pedestrian -1 -1 -1 1020.92 158.60 1093.83 328.19 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n119 26 Pedestrian -1 -1 -1 401.61 155.61 437.08 250.69 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n119 35 Pedestrian -1 -1 -1 581.86 159.48 638.87 274.18 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n119 10 Pedestrian -1 -1 -1 180.28 152.32 197.38 198.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n119 45 Pedestrian -1 -1 -1 629.07 162.10 666.91 272.33 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n119 8 Car -1 -1 -1 602.51 172.77 638.26 202.43 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n119 5 Pedestrian -1 -1 -1 342.67 161.00 374.15 228.84 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n119 25 Pedestrian -1 -1 -1 383.30 159.16 407.15 225.14 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n119 29 Pedestrian -1 -1 -1 877.09 147.29 931.95 302.05 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n119 18 Pedestrian -1 -1 -1 195.55 152.00 211.80 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n119 43 Pedestrian -1 -1 -1 911.97 150.07 973.95 294.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n119 51 Pedestrian -1 -1 -1 366.73 159.95 381.01 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n120 1 Car -1 -1 -1 953.55 183.61 1069.11 233.27 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n120 48 Pedestrian -1 -1 -1 28.97 146.51 67.17 241.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n120 42 Pedestrian -1 -1 -1 475.32 156.48 507.39 246.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n120 30 Pedestrian -1 -1 -1 436.60 160.67 468.90 251.74 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n120 26 Pedestrian -1 -1 -1 397.58 154.70 433.62 250.01 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n120 47 Pedestrian -1 -1 -1 998.58 158.08 1085.38 323.86 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n120 3 Car -1 -1 -1 1093.76 184.23 1221.16 237.19 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n120 35 Pedestrian -1 -1 -1 581.07 160.01 631.68 273.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n120 8 Car -1 -1 -1 602.62 172.50 638.34 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n120 6 Car -1 -1 -1 1030.51 184.75 1160.55 235.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n120 10 Pedestrian -1 -1 -1 180.20 152.50 197.28 197.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n120 18 Pedestrian -1 -1 -1 195.97 152.40 212.73 197.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n120 45 Pedestrian -1 -1 -1 615.31 160.93 659.46 272.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n120 5 Pedestrian -1 -1 -1 346.27 160.59 374.91 229.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n120 25 Pedestrian -1 -1 -1 379.93 159.27 403.69 225.18 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n120 29 Pedestrian -1 -1 -1 882.72 147.94 941.79 301.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n120 43 Pedestrian -1 -1 -1 919.92 151.54 988.73 298.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n120 51 Pedestrian -1 -1 -1 366.81 160.28 380.98 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n120 52 Pedestrian -1 -1 -1 207.68 163.63 222.95 196.22 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n121 1 Car -1 -1 -1 952.67 183.53 1069.51 233.85 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n121 26 Pedestrian -1 -1 -1 392.21 155.65 430.87 248.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n121 30 Pedestrian -1 -1 -1 428.68 160.13 463.51 250.99 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n121 3 Car -1 -1 -1 1094.42 184.65 1220.66 237.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n121 10 Pedestrian -1 -1 -1 180.09 152.12 198.06 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n121 48 Pedestrian -1 -1 -1 33.06 147.84 70.26 240.38 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n121 18 Pedestrian -1 -1 -1 196.36 152.57 213.48 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n121 47 Pedestrian -1 -1 -1 985.77 160.26 1075.27 321.68 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n121 6 Car -1 -1 -1 1031.72 184.65 1158.91 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n121 8 Car -1 -1 -1 602.75 172.76 638.29 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n121 42 Pedestrian -1 -1 -1 472.08 156.19 503.41 246.00 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n121 45 Pedestrian -1 -1 -1 608.40 162.34 657.38 270.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n121 35 Pedestrian -1 -1 -1 579.84 158.42 624.46 274.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n121 5 Pedestrian -1 -1 -1 345.44 160.47 371.61 227.33 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n121 25 Pedestrian -1 -1 -1 376.69 159.49 400.85 225.26 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n121 29 Pedestrian -1 -1 -1 892.55 148.46 962.52 302.62 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n121 51 Pedestrian -1 -1 -1 367.06 159.94 380.98 198.05 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n121 43 Pedestrian -1 -1 -1 931.56 153.58 1000.06 303.93 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n122 1 Car -1 -1 -1 954.84 183.39 1067.34 231.60 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n122 3 Car -1 -1 -1 1094.77 185.15 1220.74 236.59 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n122 30 Pedestrian -1 -1 -1 424.05 160.63 460.65 249.78 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n122 6 Car -1 -1 -1 1028.39 184.13 1156.78 235.31 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n122 42 Pedestrian -1 -1 -1 468.23 156.27 500.72 246.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n122 26 Pedestrian -1 -1 -1 391.26 156.06 430.11 249.83 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n122 10 Pedestrian -1 -1 -1 180.57 151.73 198.36 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n122 45 Pedestrian -1 -1 -1 600.50 163.62 651.15 269.73 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n122 18 Pedestrian -1 -1 -1 196.62 152.51 213.88 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n122 48 Pedestrian -1 -1 -1 40.54 148.56 76.55 239.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n122 35 Pedestrian -1 -1 -1 570.35 158.23 611.71 271.55 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n122 8 Car -1 -1 -1 601.57 173.30 636.03 201.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n122 5 Pedestrian -1 -1 -1 345.51 158.42 371.00 224.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n122 25 Pedestrian -1 -1 -1 376.90 159.93 399.93 226.54 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n122 47 Pedestrian -1 -1 -1 977.33 159.50 1053.70 322.03 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n122 29 Pedestrian -1 -1 -1 894.75 149.08 975.92 307.58 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n122 51 Pedestrian -1 -1 -1 367.05 160.67 380.69 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n122 43 Pedestrian -1 -1 -1 946.44 155.05 1008.00 303.77 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n123 3 Car -1 -1 -1 1094.28 185.23 1221.14 236.12 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n123 1 Car -1 -1 -1 952.93 183.01 1069.66 234.05 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n123 35 Pedestrian -1 -1 -1 559.66 159.82 606.88 269.55 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n123 6 Car -1 -1 -1 1028.13 183.31 1157.22 235.47 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n123 26 Pedestrian -1 -1 -1 388.13 157.74 425.84 247.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n123 30 Pedestrian -1 -1 -1 420.29 161.37 457.18 249.34 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n123 10 Pedestrian -1 -1 -1 180.92 151.81 199.08 198.46 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n123 42 Pedestrian -1 -1 -1 463.84 156.55 497.61 245.40 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n123 48 Pedestrian -1 -1 -1 41.38 149.20 78.07 238.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 8 Car -1 -1 -1 602.67 172.90 638.22 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 18 Pedestrian -1 -1 -1 196.48 152.72 214.40 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 5 Pedestrian -1 -1 -1 343.74 158.56 370.82 225.21 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n123 45 Pedestrian -1 -1 -1 600.31 163.96 642.62 269.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n123 25 Pedestrian -1 -1 -1 375.99 160.10 399.76 227.07 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n123 47 Pedestrian -1 -1 -1 965.80 157.08 1027.16 322.53 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n123 51 Pedestrian -1 -1 -1 366.73 160.90 380.23 198.26 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n123 29 Pedestrian -1 -1 -1 907.37 150.24 985.88 307.90 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n124 3 Car -1 -1 -1 1094.69 185.37 1221.06 235.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n124 1 Car -1 -1 -1 953.30 182.74 1069.27 234.31 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n124 6 Car -1 -1 -1 1028.43 183.52 1156.85 235.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n124 35 Pedestrian -1 -1 -1 549.79 160.98 601.89 267.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n124 8 Car -1 -1 -1 603.40 172.51 637.67 202.34 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n124 26 Pedestrian -1 -1 -1 386.17 157.71 420.01 247.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n124 30 Pedestrian -1 -1 -1 419.62 161.53 454.64 248.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n124 48 Pedestrian -1 -1 -1 48.44 148.13 84.37 238.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n124 10 Pedestrian -1 -1 -1 182.60 151.68 201.38 199.12 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n124 42 Pedestrian -1 -1 -1 463.16 157.32 495.35 244.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n124 18 Pedestrian -1 -1 -1 197.06 153.10 214.45 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n124 45 Pedestrian -1 -1 -1 593.96 163.74 626.59 265.69 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n124 25 Pedestrian -1 -1 -1 373.52 160.22 396.50 226.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n124 29 Pedestrian -1 -1 -1 923.76 148.25 1007.66 310.02 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n124 47 Pedestrian -1 -1 -1 935.06 154.77 1004.34 319.18 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n124 5 Pedestrian -1 -1 -1 343.29 158.52 370.06 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n124 51 Pedestrian -1 -1 -1 366.48 161.23 380.06 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n125 3 Car -1 -1 -1 1094.42 185.46 1221.25 235.96 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n125 1 Car -1 -1 -1 951.30 182.70 1071.53 234.42 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n125 6 Car -1 -1 -1 1028.28 183.62 1156.39 234.93 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n125 35 Pedestrian -1 -1 -1 539.97 160.10 596.26 267.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n125 10 Pedestrian -1 -1 -1 183.18 151.76 201.62 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n125 48 Pedestrian -1 -1 -1 52.87 147.79 88.91 236.56 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n125 30 Pedestrian -1 -1 -1 417.53 160.09 449.57 246.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n125 8 Car -1 -1 -1 603.61 172.44 637.28 202.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n125 47 Pedestrian -1 -1 -1 923.28 159.74 1000.93 314.60 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n125 45 Pedestrian -1 -1 -1 583.56 162.92 620.86 266.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n125 5 Pedestrian -1 -1 -1 340.18 159.15 367.64 224.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n125 26 Pedestrian -1 -1 -1 380.33 157.14 412.42 246.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n125 18 Pedestrian -1 -1 -1 197.13 153.55 214.58 197.68 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n125 42 Pedestrian -1 -1 -1 458.40 156.98 488.03 244.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n125 51 Pedestrian -1 -1 -1 366.27 161.33 379.73 198.39 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n125 25 Pedestrian -1 -1 -1 373.10 160.29 394.63 224.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n125 29 Pedestrian -1 -1 -1 971.93 156.43 1036.30 301.63 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n126 3 Car -1 -1 -1 1099.02 185.46 1220.84 236.07 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n126 1 Car -1 -1 -1 951.53 182.73 1071.44 234.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n126 6 Car -1 -1 -1 1031.86 183.59 1158.64 234.90 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n126 48 Pedestrian -1 -1 -1 56.66 148.87 93.63 234.90 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n126 10 Pedestrian -1 -1 -1 183.93 152.09 201.88 198.79 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n126 26 Pedestrian -1 -1 -1 375.78 157.53 409.20 246.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n126 30 Pedestrian -1 -1 -1 413.20 160.01 446.43 245.54 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n126 35 Pedestrian -1 -1 -1 540.05 159.90 587.76 267.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n126 45 Pedestrian -1 -1 -1 573.25 163.38 616.39 266.34 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n126 8 Car -1 -1 -1 601.29 172.32 636.90 202.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n126 42 Pedestrian -1 -1 -1 454.30 156.67 484.14 242.16 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n126 5 Pedestrian -1 -1 -1 339.29 159.63 366.46 222.91 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n126 47 Pedestrian -1 -1 -1 916.30 159.43 992.41 315.21 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n126 18 Pedestrian -1 -1 -1 197.12 153.73 214.58 197.92 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n126 25 Pedestrian -1 -1 -1 370.51 160.90 391.83 223.09 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n126 51 Pedestrian -1 -1 -1 366.00 161.08 379.84 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n126 29 Pedestrian -1 -1 -1 943.90 148.63 1018.05 310.14 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n126 53 Pedestrian -1 -1 -1 955.31 150.44 1045.19 308.23 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n127 3 Car -1 -1 -1 1099.21 185.51 1221.08 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n127 1 Car -1 -1 -1 952.94 182.24 1069.26 232.33 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n127 6 Car -1 -1 -1 1032.25 183.82 1158.01 234.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n127 26 Pedestrian -1 -1 -1 370.70 158.40 406.09 245.55 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n127 10 Pedestrian -1 -1 -1 183.81 152.15 202.10 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n127 42 Pedestrian -1 -1 -1 448.21 156.16 482.16 242.87 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n127 48 Pedestrian -1 -1 -1 58.61 149.13 99.73 235.06 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n127 8 Car -1 -1 -1 600.78 172.47 637.32 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n127 5 Pedestrian -1 -1 -1 336.97 159.75 363.54 221.55 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n127 30 Pedestrian -1 -1 -1 410.11 160.48 444.15 246.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n127 45 Pedestrian -1 -1 -1 565.77 163.45 608.87 265.74 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n127 35 Pedestrian -1 -1 -1 540.52 158.57 580.06 269.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n127 47 Pedestrian -1 -1 -1 906.17 159.45 979.00 314.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n127 18 Pedestrian -1 -1 -1 196.66 153.74 214.71 197.99 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n127 29 Pedestrian -1 -1 -1 954.33 150.26 1038.58 315.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n127 25 Pedestrian -1 -1 -1 369.43 161.66 391.39 222.36 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n127 51 Pedestrian -1 -1 -1 366.00 161.79 379.53 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n127 53 Pedestrian -1 -1 -1 992.72 153.10 1068.90 311.46 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n128 3 Car -1 -1 -1 1098.93 185.47 1221.23 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n128 1 Car -1 -1 -1 953.20 182.25 1069.11 232.45 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n128 6 Car -1 -1 -1 1032.44 184.03 1157.85 234.78 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n128 48 Pedestrian -1 -1 -1 62.59 149.29 103.83 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n128 10 Pedestrian -1 -1 -1 183.60 152.05 202.08 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n128 42 Pedestrian -1 -1 -1 443.56 156.60 479.12 242.67 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n128 26 Pedestrian -1 -1 -1 366.86 158.75 401.81 245.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n128 8 Car -1 -1 -1 600.63 172.20 637.61 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n128 47 Pedestrian -1 -1 -1 896.23 160.41 952.12 312.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n128 35 Pedestrian -1 -1 -1 532.22 158.72 573.08 267.64 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n128 30 Pedestrian -1 -1 -1 409.60 161.97 442.59 245.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n128 5 Pedestrian -1 -1 -1 335.80 159.73 362.03 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n128 18 Pedestrian -1 -1 -1 196.55 153.46 214.77 198.16 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n128 29 Pedestrian -1 -1 -1 966.89 150.36 1056.38 322.21 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n128 45 Pedestrian -1 -1 -1 561.73 164.30 598.04 264.64 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n128 25 Pedestrian -1 -1 -1 366.37 161.61 388.40 221.87 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n128 53 Pedestrian -1 -1 -1 1014.14 153.00 1078.07 312.42 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n128 51 Pedestrian -1 -1 -1 365.83 161.87 379.71 198.21 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n129 3 Car -1 -1 -1 1099.48 185.26 1220.69 235.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n129 6 Car -1 -1 -1 1032.84 183.99 1158.47 234.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n129 1 Car -1 -1 -1 954.39 182.53 1067.92 231.90 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n129 48 Pedestrian -1 -1 -1 66.29 149.15 107.30 234.34 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n129 26 Pedestrian -1 -1 -1 364.47 159.17 397.58 244.84 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n129 47 Pedestrian -1 -1 -1 877.44 157.40 939.74 310.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n129 10 Pedestrian -1 -1 -1 183.53 152.10 201.78 198.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n129 5 Pedestrian -1 -1 -1 332.76 159.55 360.24 221.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n129 30 Pedestrian -1 -1 -1 406.26 162.32 440.38 244.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n129 8 Car -1 -1 -1 601.15 172.35 637.15 203.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n129 35 Pedestrian -1 -1 -1 520.15 158.60 570.63 267.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n129 45 Pedestrian -1 -1 -1 558.68 162.73 591.85 264.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n129 42 Pedestrian -1 -1 -1 440.00 157.40 475.33 242.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n129 18 Pedestrian -1 -1 -1 196.48 153.64 214.76 198.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n129 29 Pedestrian -1 -1 -1 984.06 150.46 1062.30 316.12 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n129 25 Pedestrian -1 -1 -1 364.90 161.24 388.76 222.41 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n129 53 Pedestrian -1 -1 -1 1032.46 150.74 1090.08 314.54 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n129 51 Pedestrian -1 -1 -1 365.96 162.00 379.70 197.95 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n130 3 Car -1 -1 -1 1093.56 185.43 1221.65 235.63 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n130 48 Pedestrian -1 -1 -1 75.67 149.19 111.09 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n130 1 Car -1 -1 -1 955.08 182.28 1068.38 235.20 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n130 6 Car -1 -1 -1 1032.38 184.31 1159.21 234.51 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n130 47 Pedestrian -1 -1 -1 862.74 158.25 931.75 308.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n130 35 Pedestrian -1 -1 -1 512.17 160.13 563.12 265.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n130 10 Pedestrian -1 -1 -1 183.88 152.01 201.81 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n130 5 Pedestrian -1 -1 -1 332.74 160.42 358.89 221.74 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n130 30 Pedestrian -1 -1 -1 405.78 162.21 438.56 244.62 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n130 45 Pedestrian -1 -1 -1 549.62 163.15 587.63 263.84 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n130 8 Car -1 -1 -1 602.74 172.90 637.48 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n130 26 Pedestrian -1 -1 -1 361.88 158.08 393.13 244.20 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n130 42 Pedestrian -1 -1 -1 439.67 158.12 473.43 243.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n130 18 Pedestrian -1 -1 -1 196.58 154.06 214.76 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n130 29 Pedestrian -1 -1 -1 1008.88 148.68 1075.89 317.96 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n130 25 Pedestrian -1 -1 -1 363.94 160.71 389.75 222.85 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n130 53 Pedestrian -1 -1 -1 1045.04 151.81 1115.82 315.19 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n131 3 Car -1 -1 -1 1092.84 185.45 1222.11 235.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n131 48 Pedestrian -1 -1 -1 81.74 148.98 114.86 233.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n131 47 Pedestrian -1 -1 -1 853.97 159.07 924.75 308.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n131 30 Pedestrian -1 -1 -1 402.61 161.97 434.22 244.02 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n131 6 Car -1 -1 -1 1031.97 184.62 1159.46 234.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n131 1 Car -1 -1 -1 954.02 182.22 1067.78 235.13 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n131 10 Pedestrian -1 -1 -1 183.70 152.09 201.99 198.57 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n131 45 Pedestrian -1 -1 -1 541.20 163.65 586.70 264.19 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n131 5 Pedestrian -1 -1 -1 333.97 160.43 358.06 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n131 8 Car -1 -1 -1 601.85 173.07 636.74 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n131 26 Pedestrian -1 -1 -1 359.32 156.75 392.64 242.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n131 42 Pedestrian -1 -1 -1 435.74 157.41 465.04 241.10 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n131 35 Pedestrian -1 -1 -1 511.00 160.16 555.87 265.32 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n131 18 Pedestrian -1 -1 -1 196.58 154.16 214.82 197.69 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n131 29 Pedestrian -1 -1 -1 1019.22 146.68 1095.94 325.22 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n131 25 Pedestrian -1 -1 -1 363.26 160.07 389.80 222.82 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n131 53 Pedestrian -1 -1 -1 1058.28 155.78 1133.22 317.73 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n131 54 Pedestrian -1 -1 -1 452.15 160.91 469.70 204.89 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n132 1 Car -1 -1 -1 955.09 182.02 1067.45 235.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n132 30 Pedestrian -1 -1 -1 398.78 161.49 430.90 243.74 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n132 6 Car -1 -1 -1 1030.50 184.39 1160.92 234.29 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n132 3 Car -1 -1 -1 1091.88 185.12 1222.40 235.99 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n132 48 Pedestrian -1 -1 -1 84.47 150.35 120.07 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n132 47 Pedestrian -1 -1 -1 848.35 160.67 906.62 306.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 10 Pedestrian -1 -1 -1 183.41 152.15 201.80 198.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 45 Pedestrian -1 -1 -1 535.35 163.96 577.99 262.74 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n132 26 Pedestrian -1 -1 -1 354.72 158.54 390.23 243.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n132 8 Car -1 -1 -1 601.79 173.12 636.82 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n132 35 Pedestrian -1 -1 -1 506.65 159.70 545.42 265.43 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n132 42 Pedestrian -1 -1 -1 431.72 157.21 461.01 240.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n132 5 Pedestrian -1 -1 -1 333.96 159.99 357.34 220.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n132 18 Pedestrian -1 -1 -1 196.55 154.20 214.78 197.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n132 29 Pedestrian -1 -1 -1 1032.35 149.16 1121.11 323.22 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n132 25 Pedestrian -1 -1 -1 359.75 160.24 386.72 222.67 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n132 54 Pedestrian -1 -1 -1 448.97 161.43 465.90 204.29 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n133 1 Car -1 -1 -1 955.14 182.43 1067.25 234.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n133 30 Pedestrian -1 -1 -1 394.62 162.82 428.70 243.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n133 3 Car -1 -1 -1 1091.95 184.85 1222.95 236.04 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n133 6 Car -1 -1 -1 1030.59 183.93 1160.42 234.21 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n133 48 Pedestrian -1 -1 -1 87.36 151.30 123.55 232.44 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n133 10 Pedestrian -1 -1 -1 183.32 152.15 202.07 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n133 47 Pedestrian -1 -1 -1 836.76 159.90 888.38 305.31 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n133 8 Car -1 -1 -1 602.68 172.67 637.60 202.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n133 45 Pedestrian -1 -1 -1 531.05 162.66 566.90 263.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n133 42 Pedestrian -1 -1 -1 429.74 157.97 460.52 240.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n133 26 Pedestrian -1 -1 -1 350.66 158.94 387.03 243.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n133 35 Pedestrian -1 -1 -1 499.44 160.16 536.65 264.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n133 5 Pedestrian -1 -1 -1 333.26 160.13 357.03 219.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n133 29 Pedestrian -1 -1 -1 1041.01 149.06 1135.29 330.68 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n133 18 Pedestrian -1 -1 -1 196.70 154.44 214.82 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n133 25 Pedestrian -1 -1 -1 359.47 161.44 386.24 221.96 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n133 55 Pedestrian -1 -1 -1 1094.07 155.88 1158.82 325.00 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n133 56 Car -1 -1 -1 599.26 172.92 622.41 192.93 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n134 1 Car -1 -1 -1 955.66 182.90 1066.44 234.35 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n134 3 Car -1 -1 -1 1093.16 185.30 1221.63 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n134 48 Pedestrian -1 -1 -1 92.73 151.83 125.62 232.30 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n134 47 Pedestrian -1 -1 -1 817.55 159.71 877.27 303.84 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n134 10 Pedestrian -1 -1 -1 183.26 152.23 202.13 198.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n134 30 Pedestrian -1 -1 -1 391.56 163.73 424.93 242.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n134 6 Car -1 -1 -1 1032.72 183.69 1157.58 234.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n134 35 Pedestrian -1 -1 -1 489.81 159.83 531.24 262.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n134 42 Pedestrian -1 -1 -1 426.26 157.97 457.06 241.20 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n134 8 Car -1 -1 -1 602.66 172.66 637.58 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n134 26 Pedestrian -1 -1 -1 348.73 158.49 383.04 240.79 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n134 5 Pedestrian -1 -1 -1 331.20 160.83 355.07 219.17 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n134 29 Pedestrian -1 -1 -1 1061.58 145.38 1153.11 334.88 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n134 18 Pedestrian -1 -1 -1 196.67 154.43 214.92 197.59 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n134 45 Pedestrian -1 -1 -1 526.00 163.24 556.49 259.34 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n134 56 Car -1 -1 -1 599.13 173.12 622.48 193.31 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n135 1 Car -1 -1 -1 955.32 183.07 1066.73 234.19 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n135 48 Pedestrian -1 -1 -1 97.27 150.93 128.21 230.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n135 3 Car -1 -1 -1 1091.75 185.33 1223.15 235.37 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n135 6 Car -1 -1 -1 1031.35 183.41 1153.86 234.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n135 47 Pedestrian -1 -1 -1 801.42 160.69 870.70 299.56 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n135 26 Pedestrian -1 -1 -1 348.76 157.56 380.48 240.44 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n135 30 Pedestrian -1 -1 -1 388.81 162.71 420.25 241.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n135 10 Pedestrian -1 -1 -1 183.08 152.24 201.97 198.81 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n135 42 Pedestrian -1 -1 -1 423.62 158.61 452.86 240.26 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n135 8 Car -1 -1 -1 602.55 172.68 637.68 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n135 35 Pedestrian -1 -1 -1 485.74 159.55 527.11 261.39 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n135 29 Pedestrian -1 -1 -1 1078.85 145.15 1158.63 336.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n135 18 Pedestrian -1 -1 -1 198.11 154.33 216.63 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n135 5 Pedestrian -1 -1 -1 330.57 160.34 353.23 218.60 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n135 45 Pedestrian -1 -1 -1 517.34 162.77 551.06 259.73 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n135 56 Car -1 -1 -1 598.97 173.22 622.54 193.42 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n135 57 Cyclist -1 -1 -1 846.87 171.19 879.59 226.74 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n135 58 Pedestrian -1 -1 -1 1119.42 155.35 1194.65 332.29 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n136 1 Car -1 -1 -1 954.77 183.10 1067.12 234.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n136 3 Car -1 -1 -1 1090.88 185.08 1224.16 235.54 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n136 48 Pedestrian -1 -1 -1 101.97 150.56 132.16 230.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n136 6 Car -1 -1 -1 1031.25 183.59 1153.82 234.93 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n136 47 Pedestrian -1 -1 -1 793.11 162.40 862.43 301.35 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n136 30 Pedestrian -1 -1 -1 387.95 162.71 418.35 241.50 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n136 26 Pedestrian -1 -1 -1 345.99 156.49 376.22 240.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n136 10 Pedestrian -1 -1 -1 183.11 152.17 202.05 198.91 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n136 42 Pedestrian -1 -1 -1 419.56 157.98 449.80 240.04 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n136 8 Car -1 -1 -1 602.58 172.75 637.72 202.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n136 35 Pedestrian -1 -1 -1 478.03 161.09 520.46 260.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n136 29 Pedestrian -1 -1 -1 1102.38 146.73 1196.30 341.40 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n136 18 Pedestrian -1 -1 -1 196.67 154.31 215.05 197.67 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n136 45 Pedestrian -1 -1 -1 512.07 163.35 547.76 261.77 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n136 5 Pedestrian -1 -1 -1 328.84 158.94 350.22 217.46 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n136 56 Car -1 -1 -1 599.06 173.43 622.60 193.56 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n136 57 Cyclist -1 -1 -1 826.91 170.21 860.97 226.53 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n136 59 Pedestrian -1 -1 -1 351.65 159.37 379.13 222.72 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n137 1 Car -1 -1 -1 954.79 183.34 1067.02 233.95 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n137 3 Car -1 -1 -1 1090.68 184.89 1224.12 235.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n137 6 Car -1 -1 -1 1030.99 183.44 1154.15 234.48 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n137 48 Pedestrian -1 -1 -1 104.48 151.13 138.07 230.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n137 10 Pedestrian -1 -1 -1 183.59 152.35 201.98 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n137 47 Pedestrian -1 -1 -1 787.41 162.95 845.91 297.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n137 42 Pedestrian -1 -1 -1 416.54 157.46 445.55 239.24 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n137 26 Pedestrian -1 -1 -1 341.64 157.12 373.03 239.66 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n137 30 Pedestrian -1 -1 -1 384.94 161.99 414.56 241.23 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n137 8 Car -1 -1 -1 602.49 172.90 637.59 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n137 35 Pedestrian -1 -1 -1 470.62 162.32 513.40 262.64 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n137 29 Pedestrian -1 -1 -1 1109.23 149.85 1212.64 339.10 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n137 5 Pedestrian -1 -1 -1 327.98 158.33 350.33 216.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n137 45 Pedestrian -1 -1 -1 507.45 163.42 544.07 259.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n137 18 Pedestrian -1 -1 -1 196.56 154.34 215.14 197.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n137 59 Pedestrian -1 -1 -1 352.28 159.38 378.82 222.37 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n137 56 Car -1 -1 -1 598.66 173.62 622.39 193.90 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n138 1 Car -1 -1 -1 954.86 183.51 1067.10 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n138 48 Pedestrian -1 -1 -1 106.92 151.72 142.80 230.43 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n138 6 Car -1 -1 -1 1031.13 183.80 1154.39 233.98 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n138 3 Car -1 -1 -1 1093.53 184.64 1221.23 235.79 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n138 10 Pedestrian -1 -1 -1 183.39 152.43 202.05 198.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n138 42 Pedestrian -1 -1 -1 411.84 157.47 442.59 239.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n138 26 Pedestrian -1 -1 -1 338.97 157.53 368.49 239.31 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n138 30 Pedestrian -1 -1 -1 382.02 162.02 410.86 241.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n138 29 Pedestrian -1 -1 -1 1117.08 150.28 1219.61 345.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n138 35 Pedestrian -1 -1 -1 470.02 162.00 506.29 260.23 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n138 8 Car -1 -1 -1 602.38 172.84 637.71 202.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n138 5 Pedestrian -1 -1 -1 327.21 159.33 350.83 216.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n138 47 Pedestrian -1 -1 -1 775.61 161.95 827.69 295.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n138 45 Pedestrian -1 -1 -1 504.44 163.56 538.66 258.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n138 18 Pedestrian -1 -1 -1 196.35 154.24 215.28 197.83 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n138 56 Car -1 -1 -1 598.36 173.53 622.80 193.95 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n138 59 Pedestrian -1 -1 -1 353.48 158.92 378.09 220.75 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n138 60 Pedestrian -1 -1 -1 436.52 164.01 455.03 209.12 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n139 1 Car -1 -1 -1 954.69 183.60 1067.22 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n139 48 Pedestrian -1 -1 -1 109.32 151.75 146.71 229.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n139 47 Pedestrian -1 -1 -1 760.55 160.70 819.09 296.47 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n139 6 Car -1 -1 -1 1032.36 183.76 1157.47 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n139 42 Pedestrian -1 -1 -1 410.36 158.21 441.80 239.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n139 35 Pedestrian -1 -1 -1 462.61 160.54 504.12 260.43 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n139 10 Pedestrian -1 -1 -1 183.51 152.42 202.02 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n139 3 Car -1 -1 -1 1100.37 184.93 1220.14 236.41 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n139 45 Pedestrian -1 -1 -1 497.95 162.97 531.91 258.11 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n139 30 Pedestrian -1 -1 -1 380.70 162.85 410.03 240.06 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n139 26 Pedestrian -1 -1 -1 334.63 158.97 365.46 238.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n139 8 Car -1 -1 -1 602.37 172.87 637.68 202.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n139 18 Pedestrian -1 -1 -1 196.63 154.27 214.99 197.81 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n139 5 Pedestrian -1 -1 -1 327.54 159.66 350.09 216.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n139 29 Pedestrian -1 -1 -1 1135.18 148.93 1217.42 346.93 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n139 59 Pedestrian -1 -1 -1 358.27 159.08 378.26 216.24 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n139 56 Car -1 -1 -1 598.42 173.61 622.75 193.89 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n140 1 Car -1 -1 -1 954.65 183.77 1067.11 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n140 6 Car -1 -1 -1 1029.58 184.11 1156.13 233.44 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n140 35 Pedestrian -1 -1 -1 452.65 160.36 499.96 259.36 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n140 3 Car -1 -1 -1 1096.57 185.27 1219.03 235.41 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n140 42 Pedestrian -1 -1 -1 407.91 158.45 438.33 238.28 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n140 10 Pedestrian -1 -1 -1 183.39 152.47 201.99 198.55 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n140 48 Pedestrian -1 -1 -1 114.11 151.02 148.23 229.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n140 47 Pedestrian -1 -1 -1 744.47 159.26 812.69 296.71 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n140 45 Pedestrian -1 -1 -1 491.16 162.82 529.60 257.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n140 26 Pedestrian -1 -1 -1 330.47 159.33 363.05 237.36 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n140 8 Car -1 -1 -1 601.48 173.05 637.21 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n140 18 Pedestrian -1 -1 -1 196.89 154.39 214.71 197.73 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n140 30 Pedestrian -1 -1 -1 377.84 162.43 404.99 237.27 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n140 5 Pedestrian -1 -1 -1 327.89 160.08 349.29 216.18 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n140 29 Pedestrian -1 -1 -1 1164.15 157.43 1219.12 346.27 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n140 56 Car -1 -1 -1 598.27 173.53 622.48 193.91 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n140 59 Pedestrian -1 -1 -1 354.63 159.76 377.07 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n141 1 Car -1 -1 -1 954.64 183.80 1067.11 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n141 6 Car -1 -1 -1 1031.99 183.95 1157.82 233.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n141 3 Car -1 -1 -1 1095.38 185.41 1220.38 235.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n141 47 Pedestrian -1 -1 -1 741.64 162.01 806.97 295.19 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n141 48 Pedestrian -1 -1 -1 120.19 151.32 150.82 228.37 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n141 35 Pedestrian -1 -1 -1 450.49 161.45 494.40 259.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n141 10 Pedestrian -1 -1 -1 183.62 152.55 202.38 198.61 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n141 26 Pedestrian -1 -1 -1 329.71 157.52 361.85 237.09 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n141 45 Pedestrian -1 -1 -1 487.25 163.02 526.25 257.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n141 42 Pedestrian -1 -1 -1 405.27 157.92 434.45 237.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n141 8 Car -1 -1 -1 601.63 172.89 637.21 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n141 18 Pedestrian -1 -1 -1 197.06 154.54 214.65 197.66 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n141 30 Pedestrian -1 -1 -1 370.44 161.57 400.04 239.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n141 59 Pedestrian -1 -1 -1 354.91 159.20 376.55 216.07 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n141 5 Pedestrian -1 -1 -1 327.75 159.79 349.44 216.14 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n141 56 Car -1 -1 -1 598.46 173.33 622.31 193.70 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n141 29 Pedestrian -1 -1 -1 1192.69 156.03 1220.90 348.03 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n141 61 Cyclist -1 -1 -1 728.12 169.75 768.05 227.68 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n142 1 Car -1 -1 -1 954.66 183.71 1067.12 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n142 3 Car -1 -1 -1 1099.20 185.77 1220.57 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n142 6 Car -1 -1 -1 1032.23 183.95 1157.77 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n142 35 Pedestrian -1 -1 -1 448.71 162.19 488.67 258.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n142 10 Pedestrian -1 -1 -1 184.06 152.74 202.68 198.56 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n142 26 Pedestrian -1 -1 -1 326.62 157.26 358.46 237.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n142 47 Pedestrian -1 -1 -1 742.11 162.88 790.55 295.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n142 45 Pedestrian -1 -1 -1 484.17 164.83 521.39 255.60 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n142 48 Pedestrian -1 -1 -1 124.76 151.10 153.06 228.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n142 30 Pedestrian -1 -1 -1 368.42 161.58 398.91 237.49 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n142 8 Car -1 -1 -1 602.43 172.59 637.67 203.30 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n142 42 Pedestrian -1 -1 -1 404.83 157.94 432.03 236.16 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n142 18 Pedestrian -1 -1 -1 198.36 154.53 216.12 197.39 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n142 59 Pedestrian -1 -1 -1 355.26 159.21 375.89 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n142 61 Cyclist -1 -1 -1 700.19 168.24 758.66 228.79 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n142 56 Car -1 -1 -1 598.65 173.31 622.30 193.86 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n143 1 Car -1 -1 -1 954.33 183.65 1067.33 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n143 3 Car -1 -1 -1 1099.27 185.80 1220.44 235.46 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n143 6 Car -1 -1 -1 1032.28 183.89 1157.72 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n143 47 Pedestrian -1 -1 -1 728.73 163.97 773.57 294.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n143 10 Pedestrian -1 -1 -1 184.57 152.84 202.91 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n143 35 Pedestrian -1 -1 -1 443.49 161.49 477.72 258.74 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n143 30 Pedestrian -1 -1 -1 364.43 161.73 396.11 237.08 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n143 48 Pedestrian -1 -1 -1 125.26 151.42 155.20 228.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n143 26 Pedestrian -1 -1 -1 322.20 156.62 356.09 237.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n143 42 Pedestrian -1 -1 -1 400.83 157.98 429.92 236.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n143 45 Pedestrian -1 -1 -1 479.31 163.22 510.49 256.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n143 8 Car -1 -1 -1 602.50 172.62 637.62 203.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n143 18 Pedestrian -1 -1 -1 197.32 154.41 214.48 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n143 61 Cyclist -1 -1 -1 683.12 165.50 743.80 230.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n143 59 Pedestrian -1 -1 -1 354.85 159.48 376.51 216.22 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n143 56 Car -1 -1 -1 598.75 173.37 622.24 193.99 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n144 1 Car -1 -1 -1 954.20 183.63 1067.34 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n144 3 Car -1 -1 -1 1095.31 185.68 1220.66 235.45 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n144 6 Car -1 -1 -1 1032.02 183.91 1157.74 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n144 47 Pedestrian -1 -1 -1 714.47 163.30 765.31 292.66 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n144 30 Pedestrian -1 -1 -1 361.55 162.19 392.30 236.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n144 35 Pedestrian -1 -1 -1 436.04 161.74 471.85 257.49 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n144 48 Pedestrian -1 -1 -1 129.11 151.71 158.27 228.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n144 42 Pedestrian -1 -1 -1 399.45 159.04 428.89 235.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n144 26 Pedestrian -1 -1 -1 322.09 157.28 354.19 234.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n144 45 Pedestrian -1 -1 -1 472.17 163.63 503.34 254.68 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n144 10 Pedestrian -1 -1 -1 185.51 153.20 203.05 199.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n144 8 Car -1 -1 -1 602.47 172.72 637.78 203.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n144 18 Pedestrian -1 -1 -1 198.52 154.52 216.17 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n144 61 Cyclist -1 -1 -1 667.94 163.69 729.38 227.05 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n144 56 Car -1 -1 -1 598.86 173.40 622.16 193.83 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n144 59 Pedestrian -1 -1 -1 355.02 159.53 376.41 216.45 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n145 1 Car -1 -1 -1 954.22 183.66 1067.50 233.50 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n145 3 Car -1 -1 -1 1095.19 185.66 1220.83 235.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n145 6 Car -1 -1 -1 1031.87 183.84 1157.99 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n145 47 Pedestrian -1 -1 -1 699.47 163.37 758.72 292.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n145 42 Pedestrian -1 -1 -1 396.75 158.74 425.92 235.90 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n145 35 Pedestrian -1 -1 -1 429.71 161.36 469.47 256.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n145 48 Pedestrian -1 -1 -1 132.95 151.60 161.16 227.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n145 30 Pedestrian -1 -1 -1 359.58 161.78 387.82 236.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n145 26 Pedestrian -1 -1 -1 319.59 157.39 351.40 234.41 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n145 8 Car -1 -1 -1 602.42 172.67 637.74 203.30 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n145 10 Pedestrian -1 -1 -1 187.15 153.25 204.79 199.37 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n145 18 Pedestrian -1 -1 -1 198.52 154.67 216.36 197.27 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n145 45 Pedestrian -1 -1 -1 466.03 162.91 500.69 254.67 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n145 61 Cyclist -1 -1 -1 656.82 163.42 715.08 226.34 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n145 56 Car -1 -1 -1 598.99 173.37 621.78 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n145 59 Pedestrian -1 -1 -1 354.47 159.79 376.82 216.08 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n146 1 Car -1 -1 -1 954.41 183.60 1067.57 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n146 3 Car -1 -1 -1 1099.08 185.82 1220.64 235.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n146 6 Car -1 -1 -1 1031.77 183.73 1158.07 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n146 35 Pedestrian -1 -1 -1 427.48 161.65 463.52 256.13 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n146 47 Pedestrian -1 -1 -1 695.12 161.58 753.85 291.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n146 26 Pedestrian -1 -1 -1 318.22 156.90 349.31 233.68 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n146 61 Cyclist -1 -1 -1 643.75 165.56 699.15 224.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n146 30 Pedestrian -1 -1 -1 356.54 160.56 383.17 236.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n146 10 Pedestrian -1 -1 -1 187.48 153.41 204.81 199.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n146 48 Pedestrian -1 -1 -1 133.98 151.27 162.23 225.20 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n146 45 Pedestrian -1 -1 -1 458.60 163.69 494.56 254.08 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n146 8 Car -1 -1 -1 602.51 172.77 637.55 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n146 18 Pedestrian -1 -1 -1 198.28 154.56 216.63 197.50 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n146 42 Pedestrian -1 -1 -1 395.12 159.11 421.64 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n146 56 Car -1 -1 -1 598.90 173.49 621.78 193.85 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n146 59 Pedestrian -1 -1 -1 354.80 159.65 376.44 219.79 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n146 62 Pedestrian -1 -1 -1 329.27 158.63 355.38 216.55 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n147 1 Car -1 -1 -1 954.23 183.66 1067.51 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n147 3 Car -1 -1 -1 1095.07 185.69 1221.04 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n147 6 Car -1 -1 -1 1029.26 183.95 1156.65 233.32 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n147 48 Pedestrian -1 -1 -1 136.64 150.75 166.65 225.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n147 35 Pedestrian -1 -1 -1 418.38 162.40 458.65 255.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n147 47 Pedestrian -1 -1 -1 692.60 162.90 741.17 289.98 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n147 26 Pedestrian -1 -1 -1 315.65 155.58 345.02 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n147 42 Pedestrian -1 -1 -1 394.27 159.14 420.08 232.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n147 30 Pedestrian -1 -1 -1 356.06 161.59 381.39 235.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n147 10 Pedestrian -1 -1 -1 187.83 153.53 204.81 199.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n147 45 Pedestrian -1 -1 -1 455.98 164.67 489.12 253.54 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n147 8 Car -1 -1 -1 602.88 172.97 637.29 203.09 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n147 61 Cyclist -1 -1 -1 633.37 166.32 685.17 223.99 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n147 18 Pedestrian -1 -1 -1 198.28 154.38 216.89 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n147 56 Car -1 -1 -1 598.89 173.57 622.14 193.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n147 59 Pedestrian -1 -1 -1 353.32 159.84 377.25 215.67 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n147 62 Pedestrian -1 -1 -1 334.97 158.62 356.22 214.31 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n148 1 Car -1 -1 -1 954.40 183.65 1067.48 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n148 3 Car -1 -1 -1 1098.97 185.87 1220.65 235.49 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n148 6 Car -1 -1 -1 1029.29 183.96 1156.59 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n148 48 Pedestrian -1 -1 -1 138.77 151.03 170.99 224.78 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n148 42 Pedestrian -1 -1 -1 389.30 158.50 417.90 233.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n148 30 Pedestrian -1 -1 -1 351.86 161.84 379.22 235.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n148 47 Pedestrian -1 -1 -1 687.35 163.35 723.62 289.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n148 26 Pedestrian -1 -1 -1 313.77 155.95 341.72 233.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n148 10 Pedestrian -1 -1 -1 187.98 153.77 204.74 199.01 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n148 8 Car -1 -1 -1 602.45 173.41 636.15 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n148 61 Cyclist -1 -1 -1 620.36 167.02 670.93 221.44 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n148 45 Pedestrian -1 -1 -1 452.76 164.32 483.92 253.09 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n148 35 Pedestrian -1 -1 -1 419.46 161.24 455.14 256.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n148 18 Pedestrian -1 -1 -1 198.27 154.46 216.95 197.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n148 56 Car -1 -1 -1 599.00 173.71 622.08 193.93 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n148 62 Pedestrian -1 -1 -1 335.58 158.67 356.37 213.69 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n148 59 Pedestrian -1 -1 -1 352.77 159.61 377.79 215.71 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n149 1 Car -1 -1 -1 954.33 183.65 1067.65 233.43 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n149 3 Car -1 -1 -1 1095.16 185.78 1221.02 235.48 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n149 6 Car -1 -1 -1 1032.00 183.82 1157.91 233.55 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n149 47 Pedestrian -1 -1 -1 672.80 162.87 715.89 287.42 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n149 35 Pedestrian -1 -1 -1 414.06 161.33 448.23 253.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n149 48 Pedestrian -1 -1 -1 140.15 152.53 175.50 224.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n149 42 Pedestrian -1 -1 -1 386.22 158.57 414.66 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n149 30 Pedestrian -1 -1 -1 347.67 161.70 376.39 236.42 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n149 26 Pedestrian -1 -1 -1 312.53 156.30 340.22 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n149 45 Pedestrian -1 -1 -1 449.34 164.71 479.40 250.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n149 10 Pedestrian -1 -1 -1 188.01 153.84 204.75 199.02 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n149 8 Car -1 -1 -1 602.19 173.20 636.40 202.61 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n149 18 Pedestrian -1 -1 -1 198.18 154.40 217.05 197.48 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n149 62 Pedestrian -1 -1 -1 336.41 159.26 356.41 214.95 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n149 56 Car -1 -1 -1 598.98 173.49 622.27 193.84 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n149 61 Cyclist -1 -1 -1 611.77 170.36 656.46 217.98 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n149 59 Pedestrian -1 -1 -1 348.95 158.12 374.77 216.68 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n149 63 Pedestrian -1 -1 -1 174.45 153.37 190.44 196.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n150 1 Car -1 -1 -1 954.25 183.66 1067.72 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n150 3 Car -1 -1 -1 1099.00 185.80 1220.76 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n150 6 Car -1 -1 -1 1032.18 183.77 1157.76 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n150 47 Pedestrian -1 -1 -1 660.96 163.92 712.05 285.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n150 42 Pedestrian -1 -1 -1 385.34 159.09 413.03 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n150 26 Pedestrian -1 -1 -1 309.83 156.83 337.10 232.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n150 30 Pedestrian -1 -1 -1 347.53 161.65 375.09 234.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n150 35 Pedestrian -1 -1 -1 407.47 161.54 446.23 253.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n150 48 Pedestrian -1 -1 -1 142.24 153.20 176.41 223.53 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n150 10 Pedestrian -1 -1 -1 188.15 153.85 204.96 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n150 8 Car -1 -1 -1 602.54 173.87 636.24 201.61 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n150 18 Pedestrian -1 -1 -1 198.20 154.21 217.14 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n150 45 Pedestrian -1 -1 -1 440.09 164.06 475.27 250.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n150 62 Pedestrian -1 -1 -1 337.53 159.79 356.64 214.74 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n150 56 Car -1 -1 -1 599.33 173.37 622.47 193.49 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n150 63 Pedestrian -1 -1 -1 174.15 153.19 190.48 196.78 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n150 61 Cyclist -1 -1 -1 605.36 170.89 646.48 217.54 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n150 59 Pedestrian -1 -1 -1 348.16 159.74 374.99 215.43 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n151 1 Car -1 -1 -1 954.19 183.67 1067.73 233.36 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n151 3 Car -1 -1 -1 1098.91 185.83 1220.75 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n151 6 Car -1 -1 -1 1032.36 183.89 1157.65 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n151 26 Pedestrian -1 -1 -1 308.58 156.45 337.18 232.18 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n151 47 Pedestrian -1 -1 -1 659.50 163.10 705.20 286.00 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n151 42 Pedestrian -1 -1 -1 382.66 158.75 409.80 231.95 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n151 30 Pedestrian -1 -1 -1 343.85 160.66 372.67 234.98 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n151 8 Car -1 -1 -1 600.19 173.07 636.91 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n151 35 Pedestrian -1 -1 -1 402.84 161.08 442.99 253.47 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n151 48 Pedestrian -1 -1 -1 146.78 153.32 178.85 223.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n151 10 Pedestrian -1 -1 -1 188.25 153.93 204.90 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n151 45 Pedestrian -1 -1 -1 438.87 165.89 474.33 251.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n151 18 Pedestrian -1 -1 -1 198.17 154.22 217.18 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n151 61 Cyclist -1 -1 -1 595.79 170.28 638.99 217.55 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n151 62 Pedestrian -1 -1 -1 339.40 160.33 359.49 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n151 63 Pedestrian -1 -1 -1 173.32 153.33 190.24 196.73 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n151 59 Pedestrian -1 -1 -1 347.00 159.37 376.54 214.97 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n152 3 Car -1 -1 -1 1098.81 185.85 1220.92 235.55 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n152 1 Car -1 -1 -1 954.23 183.60 1067.73 233.42 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n152 6 Car -1 -1 -1 1032.41 183.89 1157.54 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n152 8 Car -1 -1 -1 600.35 172.40 636.94 201.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n152 30 Pedestrian -1 -1 -1 342.53 159.73 371.91 234.92 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n152 47 Pedestrian -1 -1 -1 652.35 164.55 691.44 285.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n152 48 Pedestrian -1 -1 -1 151.48 152.81 182.11 223.28 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n152 35 Pedestrian -1 -1 -1 400.15 160.80 437.97 253.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n152 45 Pedestrian -1 -1 -1 436.05 165.19 469.52 249.53 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n152 42 Pedestrian -1 -1 -1 382.75 158.30 407.69 230.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n152 10 Pedestrian -1 -1 -1 188.18 154.02 205.03 198.68 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n152 26 Pedestrian -1 -1 -1 309.14 155.80 335.89 231.78 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n152 18 Pedestrian -1 -1 -1 198.19 154.29 217.17 197.52 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n152 61 Cyclist -1 -1 -1 590.68 167.78 630.52 216.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n152 62 Pedestrian -1 -1 -1 338.58 159.57 360.97 213.70 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n152 59 Pedestrian -1 -1 -1 351.72 157.49 378.74 215.34 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n152 63 Pedestrian -1 -1 -1 172.86 153.49 190.23 196.80 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n152 64 Car -1 -1 -1 597.37 173.51 623.45 192.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n153 3 Car -1 -1 -1 1098.80 185.81 1220.93 235.57 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n153 1 Car -1 -1 -1 954.40 183.63 1067.70 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n153 6 Car -1 -1 -1 1032.57 183.94 1157.45 233.29 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n153 48 Pedestrian -1 -1 -1 155.41 152.08 184.74 223.34 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n153 35 Pedestrian -1 -1 -1 394.73 161.03 428.72 251.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n153 47 Pedestrian -1 -1 -1 643.56 164.28 682.47 285.10 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n153 8 Car -1 -1 -1 600.18 172.29 637.24 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n153 26 Pedestrian -1 -1 -1 306.02 156.04 333.61 231.88 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n153 45 Pedestrian -1 -1 -1 429.85 163.91 460.61 250.34 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n153 42 Pedestrian -1 -1 -1 379.61 157.86 404.50 230.85 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n153 30 Pedestrian -1 -1 -1 339.42 160.04 368.98 234.13 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n153 10 Pedestrian -1 -1 -1 188.08 153.94 205.38 198.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n153 18 Pedestrian -1 -1 -1 198.13 154.22 217.16 197.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n153 61 Cyclist -1 -1 -1 584.06 166.55 621.95 216.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n153 64 Car -1 -1 -1 596.64 173.07 624.06 193.86 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n153 62 Pedestrian -1 -1 -1 338.34 159.37 361.69 213.73 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n153 59 Pedestrian -1 -1 -1 357.82 159.17 379.70 212.70 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n154 3 Car -1 -1 -1 1098.72 185.76 1221.13 235.64 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n154 1 Car -1 -1 -1 954.40 183.67 1067.72 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n154 6 Car -1 -1 -1 1032.67 183.93 1157.36 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n154 45 Pedestrian -1 -1 -1 422.25 163.90 454.87 249.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n154 47 Pedestrian -1 -1 -1 628.22 163.37 675.81 284.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n154 35 Pedestrian -1 -1 -1 389.96 161.67 424.62 251.43 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n154 48 Pedestrian -1 -1 -1 159.39 152.71 187.45 223.24 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n154 26 Pedestrian -1 -1 -1 304.91 156.42 332.23 232.11 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n154 8 Car -1 -1 -1 600.99 172.46 637.19 201.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n154 10 Pedestrian -1 -1 -1 188.12 153.82 205.30 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n154 42 Pedestrian -1 -1 -1 376.95 158.36 401.36 231.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n154 61 Cyclist -1 -1 -1 579.83 166.62 616.06 215.76 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n154 18 Pedestrian -1 -1 -1 198.38 154.23 217.07 197.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n154 30 Pedestrian -1 -1 -1 336.62 160.60 365.01 233.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n154 59 Pedestrian -1 -1 -1 359.17 159.54 379.45 213.39 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n154 62 Pedestrian -1 -1 -1 345.19 159.64 370.61 214.02 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n154 65 Pedestrian -1 -1 -1 172.45 153.78 189.67 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n155 1 Car -1 -1 -1 954.45 183.67 1067.38 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n155 3 Car -1 -1 -1 1098.84 185.89 1220.75 235.49 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n155 6 Car -1 -1 -1 1029.91 184.09 1155.97 233.07 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n155 47 Pedestrian -1 -1 -1 617.08 163.57 671.92 281.41 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n155 8 Car -1 -1 -1 600.17 172.16 637.17 202.68 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n155 26 Pedestrian -1 -1 -1 301.50 156.74 330.38 233.08 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n155 30 Pedestrian -1 -1 -1 336.42 159.89 362.86 230.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n155 35 Pedestrian -1 -1 -1 386.57 162.38 419.89 251.52 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n155 45 Pedestrian -1 -1 -1 422.90 164.86 451.71 249.13 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n155 42 Pedestrian -1 -1 -1 375.21 158.88 400.19 231.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n155 10 Pedestrian -1 -1 -1 188.42 153.94 205.18 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n155 48 Pedestrian -1 -1 -1 161.04 153.25 188.73 222.93 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n155 18 Pedestrian -1 -1 -1 198.52 154.37 216.98 197.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n155 61 Cyclist -1 -1 -1 575.62 167.81 607.91 214.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n155 65 Pedestrian -1 -1 -1 172.98 154.02 189.62 196.60 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n155 59 Pedestrian -1 -1 -1 359.55 159.26 379.95 213.69 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n155 62 Pedestrian -1 -1 -1 346.02 159.03 369.84 213.65 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n155 66 Pedestrian -1 -1 -1 336.89 158.95 362.19 214.12 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n156 1 Car -1 -1 -1 954.27 183.70 1067.70 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n156 3 Car -1 -1 -1 1098.65 185.90 1220.93 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n156 6 Car -1 -1 -1 1029.80 184.14 1156.07 233.12 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n156 47 Pedestrian -1 -1 -1 614.16 163.36 666.68 281.34 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n156 35 Pedestrian -1 -1 -1 383.48 163.27 415.49 250.67 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n156 8 Car -1 -1 -1 600.44 172.97 637.41 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n156 30 Pedestrian -1 -1 -1 333.32 159.15 360.16 230.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n156 45 Pedestrian -1 -1 -1 414.04 165.80 447.47 248.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n156 48 Pedestrian -1 -1 -1 163.44 153.06 190.86 222.33 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n156 42 Pedestrian -1 -1 -1 371.36 159.11 397.44 231.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n156 10 Pedestrian -1 -1 -1 188.26 153.93 205.05 198.72 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n156 26 Pedestrian -1 -1 -1 298.67 156.61 326.42 230.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n156 18 Pedestrian -1 -1 -1 198.33 154.33 217.12 197.63 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n156 61 Cyclist -1 -1 -1 566.54 168.58 602.22 213.61 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n156 64 Car -1 -1 -1 598.35 173.81 622.87 193.77 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n156 59 Pedestrian -1 -1 -1 363.35 159.86 382.66 212.98 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n156 65 Pedestrian -1 -1 -1 173.18 154.15 189.75 197.12 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n156 62 Pedestrian -1 -1 -1 345.76 158.68 369.27 213.69 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n157 1 Car -1 -1 -1 954.46 183.76 1067.60 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n157 3 Car -1 -1 -1 1098.67 185.80 1220.91 235.62 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n157 6 Car -1 -1 -1 1032.71 183.94 1157.26 233.37 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n157 45 Pedestrian -1 -1 -1 410.86 166.66 442.28 247.57 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n157 47 Pedestrian -1 -1 -1 612.07 162.94 653.46 280.79 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n157 35 Pedestrian -1 -1 -1 378.98 163.44 412.46 250.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n157 48 Pedestrian -1 -1 -1 163.83 152.48 192.11 221.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n157 26 Pedestrian -1 -1 -1 299.11 155.63 324.94 227.95 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n157 30 Pedestrian -1 -1 -1 332.07 159.59 358.63 229.61 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n157 8 Car -1 -1 -1 601.29 173.37 636.49 202.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n157 10 Pedestrian -1 -1 -1 188.47 153.94 205.22 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n157 42 Pedestrian -1 -1 -1 368.28 159.74 394.50 230.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n157 61 Cyclist -1 -1 -1 564.54 169.59 601.27 213.36 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n157 18 Pedestrian -1 -1 -1 198.48 154.41 217.21 197.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n157 62 Pedestrian -1 -1 -1 346.95 160.04 367.71 211.53 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n157 59 Pedestrian -1 -1 -1 365.91 159.69 386.28 213.53 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n157 65 Pedestrian -1 -1 -1 173.44 154.28 190.71 197.62 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n158 1 Car -1 -1 -1 954.53 183.76 1067.42 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n158 3 Car -1 -1 -1 1094.98 185.67 1221.19 235.47 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n158 6 Car -1 -1 -1 1032.60 183.95 1157.24 233.35 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n158 45 Pedestrian -1 -1 -1 408.30 165.85 438.03 246.62 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n158 35 Pedestrian -1 -1 -1 374.91 162.78 408.35 249.57 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n158 48 Pedestrian -1 -1 -1 166.94 151.91 195.93 221.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n158 30 Pedestrian -1 -1 -1 328.85 159.05 357.10 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n158 8 Car -1 -1 -1 600.53 173.40 636.83 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n158 47 Pedestrian -1 -1 -1 605.02 163.36 639.49 278.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n158 26 Pedestrian -1 -1 -1 298.76 155.01 323.90 227.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n158 61 Cyclist -1 -1 -1 564.73 170.63 595.25 212.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n158 42 Pedestrian -1 -1 -1 367.42 159.46 393.58 229.44 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n158 10 Pedestrian -1 -1 -1 188.78 154.17 205.49 198.43 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n158 18 Pedestrian -1 -1 -1 198.81 154.58 216.97 197.33 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n158 62 Pedestrian -1 -1 -1 347.12 158.16 368.01 210.44 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n159 1 Car -1 -1 -1 954.74 183.82 1067.31 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n159 3 Car -1 -1 -1 1094.87 185.65 1221.23 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n159 6 Car -1 -1 -1 1032.70 183.98 1157.12 233.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n159 48 Pedestrian -1 -1 -1 169.45 152.64 199.78 220.27 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n159 35 Pedestrian -1 -1 -1 370.66 162.37 405.29 249.17 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n159 30 Pedestrian -1 -1 -1 328.32 160.03 355.91 230.22 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n159 45 Pedestrian -1 -1 -1 404.21 165.05 434.20 246.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n159 8 Car -1 -1 -1 600.34 172.79 637.37 202.27 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n159 47 Pedestrian -1 -1 -1 595.21 163.05 633.86 277.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n159 10 Pedestrian -1 -1 -1 188.55 154.24 205.43 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n159 26 Pedestrian -1 -1 -1 294.35 155.57 322.67 227.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n159 18 Pedestrian -1 -1 -1 199.19 154.45 216.75 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n159 61 Cyclist -1 -1 -1 563.52 169.56 590.20 212.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n159 42 Pedestrian -1 -1 -1 364.39 159.19 390.17 229.75 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n159 62 Pedestrian -1 -1 -1 347.71 158.52 366.73 210.25 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n160 1 Car -1 -1 -1 954.62 183.79 1067.43 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n160 3 Car -1 -1 -1 1094.79 185.62 1221.42 235.56 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n160 6 Car -1 -1 -1 1030.04 184.18 1155.86 232.88 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n160 48 Pedestrian -1 -1 -1 170.50 153.08 202.28 220.13 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n160 35 Pedestrian -1 -1 -1 366.23 161.81 402.32 249.19 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n160 47 Pedestrian -1 -1 -1 587.86 163.45 631.53 274.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n160 45 Pedestrian -1 -1 -1 397.73 165.21 431.37 245.30 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n160 30 Pedestrian -1 -1 -1 325.54 159.75 352.58 230.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n160 8 Car -1 -1 -1 600.28 172.79 637.36 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n160 42 Pedestrian -1 -1 -1 360.77 158.91 386.05 229.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n160 10 Pedestrian -1 -1 -1 188.40 154.31 205.42 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n160 26 Pedestrian -1 -1 -1 294.85 155.86 321.65 227.65 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n160 18 Pedestrian -1 -1 -1 199.27 154.37 216.38 197.38 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n160 61 Cyclist -1 -1 -1 561.78 168.58 588.41 211.08 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n160 62 Pedestrian -1 -1 -1 347.89 160.35 366.41 211.35 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n161 3 Car -1 -1 -1 1095.08 185.69 1221.07 235.46 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n161 1 Car -1 -1 -1 954.66 183.84 1067.16 233.15 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n161 6 Car -1 -1 -1 1029.79 184.20 1156.13 232.93 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n161 48 Pedestrian -1 -1 -1 173.53 152.99 204.57 220.33 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n161 47 Pedestrian -1 -1 -1 584.57 164.28 626.92 277.40 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n161 35 Pedestrian -1 -1 -1 361.85 162.44 399.43 248.54 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n161 30 Pedestrian -1 -1 -1 325.62 159.10 350.80 229.37 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n161 45 Pedestrian -1 -1 -1 393.76 165.97 427.52 245.12 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n161 42 Pedestrian -1 -1 -1 360.26 159.23 385.74 229.69 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n161 8 Car -1 -1 -1 600.78 172.82 637.32 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n161 26 Pedestrian -1 -1 -1 294.88 155.67 321.44 227.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n161 10 Pedestrian -1 -1 -1 188.25 154.75 205.20 197.64 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n161 18 Pedestrian -1 -1 -1 199.35 154.33 215.79 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n161 61 Cyclist -1 -1 -1 563.62 167.97 585.45 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n161 62 Pedestrian -1 -1 -1 347.52 159.88 367.09 211.99 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n162 1 Car -1 -1 -1 954.57 183.79 1067.38 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n162 3 Car -1 -1 -1 1095.05 185.74 1221.18 235.57 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n162 6 Car -1 -1 -1 1029.82 184.16 1155.99 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n162 45 Pedestrian -1 -1 -1 390.45 166.18 423.56 244.89 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n162 35 Pedestrian -1 -1 -1 361.55 162.81 398.61 248.13 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n162 48 Pedestrian -1 -1 -1 178.31 153.16 206.11 219.91 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n162 8 Car -1 -1 -1 600.66 172.82 637.25 202.66 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n162 30 Pedestrian -1 -1 -1 324.32 159.82 350.37 229.51 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n162 26 Pedestrian -1 -1 -1 294.94 154.97 320.86 226.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n162 47 Pedestrian -1 -1 -1 579.78 164.54 616.26 277.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n162 10 Pedestrian -1 -1 -1 188.58 154.45 205.84 198.00 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n162 42 Pedestrian -1 -1 -1 359.00 159.71 386.06 229.58 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n162 61 Cyclist -1 -1 -1 562.77 166.52 582.44 209.93 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n162 18 Pedestrian -1 -1 -1 199.17 154.22 215.77 197.44 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n162 62 Pedestrian -1 -1 -1 347.51 160.17 367.24 211.35 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n163 3 Car -1 -1 -1 1098.61 185.80 1220.93 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n163 1 Car -1 -1 -1 954.62 183.82 1067.13 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n163 6 Car -1 -1 -1 1029.66 184.13 1156.09 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n163 30 Pedestrian -1 -1 -1 321.92 160.59 348.07 229.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n163 8 Car -1 -1 -1 599.90 172.74 637.17 202.89 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n163 45 Pedestrian -1 -1 -1 385.22 165.35 415.12 245.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n163 26 Pedestrian -1 -1 -1 294.97 155.43 320.75 226.11 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n163 35 Pedestrian -1 -1 -1 359.86 163.24 392.81 247.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n163 48 Pedestrian -1 -1 -1 180.87 152.26 207.42 219.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n163 47 Pedestrian -1 -1 -1 569.28 164.33 605.86 276.09 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n163 18 Pedestrian -1 -1 -1 196.83 154.70 213.98 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n163 42 Pedestrian -1 -1 -1 360.00 159.94 385.12 229.16 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n163 61 Cyclist -1 -1 -1 562.91 167.44 580.69 208.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n163 10 Pedestrian -1 -1 -1 188.66 154.18 206.20 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n163 62 Pedestrian -1 -1 -1 348.19 161.12 367.11 211.01 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n164 3 Car -1 -1 -1 1098.77 185.77 1220.77 235.58 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n164 1 Car -1 -1 -1 954.66 183.90 1067.02 233.17 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n164 6 Car -1 -1 -1 1029.52 184.11 1156.22 232.97 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n164 47 Pedestrian -1 -1 -1 557.03 164.30 601.36 272.22 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n164 8 Car -1 -1 -1 600.45 172.63 637.43 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n164 26 Pedestrian -1 -1 -1 295.22 156.09 320.12 225.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n164 30 Pedestrian -1 -1 -1 320.88 160.13 346.70 228.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n164 45 Pedestrian -1 -1 -1 382.18 165.54 410.31 244.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n164 48 Pedestrian -1 -1 -1 185.18 152.20 209.40 218.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n164 35 Pedestrian -1 -1 -1 353.99 162.26 385.57 247.27 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n164 18 Pedestrian -1 -1 -1 196.67 154.81 213.62 196.88 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n164 42 Pedestrian -1 -1 -1 356.00 160.04 382.52 228.62 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n164 10 Pedestrian -1 -1 -1 188.28 154.19 205.98 198.08 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n164 61 Cyclist -1 -1 -1 563.46 167.86 579.21 208.03 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n164 62 Pedestrian -1 -1 -1 350.83 161.21 370.59 211.70 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n165 3 Car -1 -1 -1 1098.79 185.82 1220.81 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n165 1 Car -1 -1 -1 954.53 183.91 1067.10 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n165 6 Car -1 -1 -1 1029.46 184.12 1156.27 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n165 47 Pedestrian -1 -1 -1 549.85 163.00 600.24 271.92 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n165 8 Car -1 -1 -1 600.82 172.84 637.31 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n165 26 Pedestrian -1 -1 -1 295.14 156.65 319.58 225.32 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n165 45 Pedestrian -1 -1 -1 378.69 166.36 406.50 244.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n165 30 Pedestrian -1 -1 -1 318.79 160.09 344.72 227.23 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n165 48 Pedestrian -1 -1 -1 187.10 152.96 213.03 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n165 18 Pedestrian -1 -1 -1 196.48 154.32 213.28 197.29 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n165 35 Pedestrian -1 -1 -1 350.13 162.20 381.60 245.15 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n165 10 Pedestrian -1 -1 -1 188.41 153.91 205.50 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n165 42 Pedestrian -1 -1 -1 351.55 160.75 379.41 227.44 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n165 61 Cyclist -1 -1 -1 563.45 168.08 580.11 207.22 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n165 62 Pedestrian -1 -1 -1 350.88 160.86 371.33 212.41 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n165 68 Pedestrian -1 -1 -1 341.31 159.23 360.17 205.06 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n166 3 Car -1 -1 -1 1098.77 185.76 1220.79 235.52 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n166 1 Car -1 -1 -1 954.65 183.87 1067.07 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n166 6 Car -1 -1 -1 1029.49 184.17 1156.36 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n166 47 Pedestrian -1 -1 -1 546.82 162.84 595.71 272.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n166 8 Car -1 -1 -1 601.12 173.03 637.20 202.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n166 45 Pedestrian -1 -1 -1 374.49 166.24 403.05 244.12 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n166 48 Pedestrian -1 -1 -1 188.99 153.97 214.54 217.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n166 30 Pedestrian -1 -1 -1 318.77 160.43 344.07 225.99 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n166 18 Pedestrian -1 -1 -1 196.34 154.84 213.17 197.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n166 26 Pedestrian -1 -1 -1 292.55 156.23 317.45 224.74 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n166 10 Pedestrian -1 -1 -1 188.74 153.96 205.49 198.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n166 35 Pedestrian -1 -1 -1 350.29 163.53 378.55 246.13 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n166 42 Pedestrian -1 -1 -1 351.96 161.23 378.71 227.08 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n166 68 Pedestrian -1 -1 -1 341.33 159.84 359.50 203.92 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n166 62 Pedestrian -1 -1 -1 353.96 160.52 376.34 212.92 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n166 61 Cyclist -1 -1 -1 562.34 167.95 580.32 207.25 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n166 69 Car -1 -1 -1 598.53 173.44 622.56 193.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n166 70 Pedestrian -1 -1 -1 176.72 154.06 193.19 196.29 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n166 71 Pedestrian -1 -1 -1 368.60 158.88 391.66 214.47 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n167 3 Car -1 -1 -1 1098.69 185.80 1220.88 235.56 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n167 1 Car -1 -1 -1 954.59 183.90 1067.14 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n167 6 Car -1 -1 -1 1029.51 184.21 1156.40 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n167 47 Pedestrian -1 -1 -1 543.26 163.85 585.19 271.76 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n167 48 Pedestrian -1 -1 -1 191.11 154.13 217.31 217.90 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n167 8 Car -1 -1 -1 601.32 173.12 637.05 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n167 30 Pedestrian -1 -1 -1 318.76 160.83 343.63 225.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n167 45 Pedestrian -1 -1 -1 370.67 166.40 399.84 243.76 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n167 26 Pedestrian -1 -1 -1 292.44 155.57 317.07 224.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n167 10 Pedestrian -1 -1 -1 188.57 154.15 205.12 198.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n167 35 Pedestrian -1 -1 -1 346.30 162.09 376.55 244.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n167 18 Pedestrian -1 -1 -1 196.20 155.10 213.15 197.15 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n167 68 Pedestrian -1 -1 -1 341.42 160.67 358.48 203.59 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n167 69 Car -1 -1 -1 598.50 173.25 622.88 194.18 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n167 62 Pedestrian -1 -1 -1 355.05 161.78 376.48 211.98 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n167 70 Pedestrian -1 -1 -1 173.89 153.94 190.91 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n167 71 Pedestrian -1 -1 -1 373.03 160.04 395.18 213.15 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n168 3 Car -1 -1 -1 1099.00 185.83 1220.57 235.49 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n168 1 Car -1 -1 -1 954.52 183.87 1067.09 233.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n168 6 Car -1 -1 -1 1029.19 184.12 1156.43 233.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n168 8 Car -1 -1 -1 601.20 173.16 637.05 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n168 47 Pedestrian -1 -1 -1 539.59 163.68 573.03 269.94 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n168 48 Pedestrian -1 -1 -1 191.59 153.99 218.35 217.21 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n168 45 Pedestrian -1 -1 -1 370.08 166.50 397.38 243.24 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n168 35 Pedestrian -1 -1 -1 343.18 161.64 373.57 243.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n168 26 Pedestrian -1 -1 -1 291.34 155.36 317.04 223.49 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n168 30 Pedestrian -1 -1 -1 318.39 160.95 343.17 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n168 10 Pedestrian -1 -1 -1 188.47 154.18 204.70 198.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n168 68 Pedestrian -1 -1 -1 341.81 160.45 358.51 203.55 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n168 69 Car -1 -1 -1 598.80 173.44 622.18 193.71 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n168 62 Pedestrian -1 -1 -1 355.81 162.13 375.90 211.31 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n168 71 Pedestrian -1 -1 -1 348.23 161.95 375.08 226.40 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n168 70 Pedestrian -1 -1 -1 173.98 153.50 191.23 196.66 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n168 72 Cyclist -1 -1 -1 562.78 168.64 579.11 205.15 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n169 1 Car -1 -1 -1 954.40 183.89 1067.26 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n169 3 Car -1 -1 -1 1098.78 185.82 1220.75 235.57 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n169 6 Car -1 -1 -1 1029.27 184.11 1156.40 233.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n169 47 Pedestrian -1 -1 -1 531.37 162.22 566.13 267.37 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n169 8 Car -1 -1 -1 601.28 173.19 637.05 202.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n169 35 Pedestrian -1 -1 -1 342.71 162.32 371.82 242.80 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n169 26 Pedestrian -1 -1 -1 291.11 154.38 315.76 221.72 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n169 10 Pedestrian -1 -1 -1 187.95 154.24 205.03 198.03 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n169 45 Pedestrian -1 -1 -1 365.81 165.60 394.71 241.79 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n169 30 Pedestrian -1 -1 -1 318.17 161.27 342.76 225.11 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n169 48 Pedestrian -1 -1 -1 192.80 154.79 218.30 214.61 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n169 62 Pedestrian -1 -1 -1 359.48 162.03 378.46 211.23 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n169 69 Car -1 -1 -1 598.89 173.36 622.04 193.52 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n169 72 Cyclist -1 -1 -1 563.81 168.84 579.58 204.00 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n169 68 Pedestrian -1 -1 -1 342.55 159.75 357.72 200.83 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n169 70 Pedestrian -1 -1 -1 173.79 153.84 191.33 196.15 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n170 3 Car -1 -1 -1 1098.86 185.87 1220.69 235.56 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n170 1 Car -1 -1 -1 954.42 183.87 1067.23 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n170 6 Car -1 -1 -1 1029.46 184.15 1156.22 232.99 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n170 8 Car -1 -1 -1 601.46 173.06 636.85 202.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n170 47 Pedestrian -1 -1 -1 520.73 162.82 562.60 266.60 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n170 35 Pedestrian -1 -1 -1 339.11 162.20 367.97 241.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n170 45 Pedestrian -1 -1 -1 361.42 164.60 391.33 242.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n170 10 Pedestrian -1 -1 -1 187.92 154.49 205.30 198.02 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n170 48 Pedestrian -1 -1 -1 194.87 155.20 222.60 216.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n170 30 Pedestrian -1 -1 -1 317.63 160.26 343.22 223.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n170 26 Pedestrian -1 -1 -1 291.26 154.84 315.73 221.78 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n170 62 Pedestrian -1 -1 -1 349.29 160.95 373.57 221.09 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n170 69 Car -1 -1 -1 598.97 173.35 622.05 193.61 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n170 72 Cyclist -1 -1 -1 564.99 168.47 579.30 203.88 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n170 68 Pedestrian -1 -1 -1 341.51 159.30 358.42 204.65 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n170 70 Pedestrian -1 -1 -1 173.21 153.48 191.40 196.42 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n171 1 Car -1 -1 -1 954.58 183.84 1067.11 233.26 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n171 3 Car -1 -1 -1 1094.83 185.66 1221.38 235.54 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n171 6 Car -1 -1 -1 1029.43 184.14 1156.26 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n171 48 Pedestrian -1 -1 -1 197.55 156.80 225.72 215.61 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n171 35 Pedestrian -1 -1 -1 333.01 161.79 365.96 242.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n171 8 Car -1 -1 -1 601.47 173.03 636.84 202.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n171 47 Pedestrian -1 -1 -1 517.56 163.09 556.84 266.79 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n171 10 Pedestrian -1 -1 -1 187.70 154.59 205.64 198.31 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n171 45 Pedestrian -1 -1 -1 360.97 164.89 390.56 241.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n171 26 Pedestrian -1 -1 -1 291.79 156.23 316.35 222.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n171 30 Pedestrian -1 -1 -1 318.76 159.23 342.58 222.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n171 72 Cyclist -1 -1 -1 565.04 166.34 579.80 202.27 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n171 69 Car -1 -1 -1 598.95 173.53 622.11 193.68 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n171 68 Pedestrian -1 -1 -1 341.34 158.54 358.06 205.81 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n171 62 Pedestrian -1 -1 -1 358.51 161.41 379.70 212.00 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n171 73 Pedestrian -1 -1 -1 348.93 160.93 373.60 220.48 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n172 3 Car -1 -1 -1 1098.74 185.80 1220.82 235.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n172 1 Car -1 -1 -1 954.53 183.83 1067.09 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n172 6 Car -1 -1 -1 1029.45 184.16 1156.39 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n172 35 Pedestrian -1 -1 -1 329.18 161.83 363.67 242.22 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n172 48 Pedestrian -1 -1 -1 199.25 156.50 226.97 215.86 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n172 8 Car -1 -1 -1 601.40 173.19 637.02 202.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n172 47 Pedestrian -1 -1 -1 512.77 163.83 547.26 269.01 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n172 10 Pedestrian -1 -1 -1 187.63 155.16 205.32 198.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n172 26 Pedestrian -1 -1 -1 292.22 156.31 316.92 222.30 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n172 45 Pedestrian -1 -1 -1 355.05 164.71 384.91 242.06 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n172 30 Pedestrian -1 -1 -1 318.90 158.91 342.44 222.11 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n172 72 Cyclist -1 -1 -1 565.33 166.74 580.04 201.92 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n172 73 Pedestrian -1 -1 -1 347.76 161.41 367.74 213.61 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n172 69 Car -1 -1 -1 598.73 173.42 621.98 193.73 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n172 62 Pedestrian -1 -1 -1 358.58 160.77 379.69 212.69 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n173 3 Car -1 -1 -1 1094.88 185.66 1221.24 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n173 1 Car -1 -1 -1 954.47 183.87 1067.12 233.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n173 6 Car -1 -1 -1 1029.46 184.15 1156.27 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n173 48 Pedestrian -1 -1 -1 203.31 155.72 229.01 216.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n173 8 Car -1 -1 -1 601.74 173.25 636.84 202.65 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n173 35 Pedestrian -1 -1 -1 326.78 161.49 359.43 242.41 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n173 45 Pedestrian -1 -1 -1 354.86 166.02 381.37 240.50 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n173 47 Pedestrian -1 -1 -1 506.08 164.01 539.10 266.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n173 10 Pedestrian -1 -1 -1 187.66 154.95 205.51 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n173 30 Pedestrian -1 -1 -1 319.28 160.14 342.42 222.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n173 26 Pedestrian -1 -1 -1 292.78 155.62 317.35 220.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n173 72 Cyclist -1 -1 -1 565.92 167.61 579.76 200.70 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n173 73 Pedestrian -1 -1 -1 341.66 159.04 366.88 221.55 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n173 69 Car -1 -1 -1 598.78 173.63 621.75 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n174 3 Car -1 -1 -1 1094.79 185.66 1221.35 235.51 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n174 1 Car -1 -1 -1 954.43 183.87 1067.12 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n174 6 Car -1 -1 -1 1029.39 184.14 1156.31 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n174 47 Pedestrian -1 -1 -1 495.51 164.25 533.99 264.77 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n174 35 Pedestrian -1 -1 -1 327.42 161.75 355.94 240.21 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n174 8 Car -1 -1 -1 601.75 173.35 636.90 202.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n174 26 Pedestrian -1 -1 -1 294.99 156.00 318.79 220.48 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n174 10 Pedestrian -1 -1 -1 187.84 155.02 205.38 198.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n174 48 Pedestrian -1 -1 -1 207.62 154.89 230.04 216.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n174 45 Pedestrian -1 -1 -1 351.55 166.54 377.29 239.50 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n174 30 Pedestrian -1 -1 -1 319.00 160.11 341.36 221.61 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n174 72 Cyclist -1 -1 -1 565.95 167.94 579.96 200.06 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n174 73 Pedestrian -1 -1 -1 336.30 157.80 364.57 222.06 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n174 69 Car -1 -1 -1 598.85 173.74 621.52 193.74 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n174 74 Pedestrian -1 -1 -1 351.72 161.15 379.44 220.39 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n175 3 Car -1 -1 -1 1094.92 185.68 1221.19 235.53 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n175 1 Car -1 -1 -1 954.60 183.87 1067.07 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n175 6 Car -1 -1 -1 1029.51 184.14 1156.27 232.98 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n175 47 Pedestrian -1 -1 -1 488.76 164.51 531.85 264.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n175 45 Pedestrian -1 -1 -1 348.76 166.43 374.76 238.40 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n175 8 Car -1 -1 -1 601.71 173.34 636.96 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n175 26 Pedestrian -1 -1 -1 295.78 156.09 319.02 220.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n175 10 Pedestrian -1 -1 -1 187.84 155.33 205.74 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n175 48 Pedestrian -1 -1 -1 208.74 154.98 231.46 215.99 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n175 35 Pedestrian -1 -1 -1 323.89 162.06 351.91 239.71 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n175 73 Pedestrian -1 -1 -1 339.79 157.31 366.96 222.32 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n175 30 Pedestrian -1 -1 -1 318.06 160.53 342.15 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n175 72 Cyclist -1 -1 -1 566.66 168.16 579.85 199.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n175 69 Car -1 -1 -1 598.62 173.70 621.56 193.75 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n175 74 Pedestrian -1 -1 -1 374.22 157.94 394.86 216.87 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0026.txt",
    "content": "0 1 Pedestrian -1 -1 -1 729.08 163.15 752.05 226.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n0 2 Pedestrian -1 -1 -1 569.54 166.11 583.11 205.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n0 3 Pedestrian -1 -1 -1 541.27 168.36 557.80 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n0 4 Pedestrian -1 -1 -1 626.82 167.14 640.96 208.98 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n0 5 Pedestrian -1 -1 -1 505.71 170.21 522.02 216.22 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n0 6 Pedestrian -1 -1 -1 615.21 166.91 629.75 208.05 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n0 7 Pedestrian -1 -1 -1 553.13 164.67 566.69 207.51 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n0 8 Pedestrian -1 -1 -1 483.12 165.13 501.08 215.06 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n1 1 Pedestrian -1 -1 -1 730.74 163.00 755.80 227.61 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n1 3 Pedestrian -1 -1 -1 541.14 167.58 558.33 213.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n1 8 Pedestrian -1 -1 -1 482.03 164.86 501.02 216.30 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n1 5 Pedestrian -1 -1 -1 503.32 170.27 519.99 217.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n1 2 Pedestrian -1 -1 -1 566.16 166.14 579.20 205.61 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n1 4 Pedestrian -1 -1 -1 629.46 168.28 643.32 210.68 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n1 6 Pedestrian -1 -1 -1 614.94 166.46 630.01 210.00 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n1 7 Pedestrian -1 -1 -1 532.99 164.33 547.43 206.74 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n2 5 Pedestrian -1 -1 -1 502.69 171.12 519.78 218.40 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n2 1 Pedestrian -1 -1 -1 732.25 163.37 757.40 228.66 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n2 4 Pedestrian -1 -1 -1 630.42 169.47 645.05 211.61 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n2 2 Pedestrian -1 -1 -1 564.86 167.19 580.26 207.30 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n2 6 Pedestrian -1 -1 -1 617.25 167.23 632.69 211.79 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n2 3 Pedestrian -1 -1 -1 543.20 168.31 561.21 214.69 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n2 8 Pedestrian -1 -1 -1 480.96 165.73 501.58 218.41 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n2 7 Pedestrian -1 -1 -1 529.73 166.13 545.37 206.86 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n3 1 Pedestrian -1 -1 -1 736.44 164.26 761.22 230.22 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n3 5 Pedestrian -1 -1 -1 501.71 171.48 519.62 218.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n3 8 Pedestrian -1 -1 -1 479.24 168.13 502.84 220.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n3 4 Pedestrian -1 -1 -1 632.05 169.79 648.15 212.28 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n3 6 Pedestrian -1 -1 -1 618.62 167.62 634.28 212.15 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n3 2 Pedestrian -1 -1 -1 564.86 167.32 579.84 207.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n3 3 Pedestrian -1 -1 -1 544.72 168.41 561.80 215.31 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n3 7 Pedestrian -1 -1 -1 529.18 166.15 544.79 207.98 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n4 1 Pedestrian -1 -1 -1 739.40 164.91 764.88 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n4 3 Pedestrian -1 -1 -1 546.64 169.71 566.22 217.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n4 6 Pedestrian -1 -1 -1 620.20 168.31 637.23 213.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n4 5 Pedestrian -1 -1 -1 500.71 172.29 519.26 219.76 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n4 8 Pedestrian -1 -1 -1 479.26 169.36 502.61 222.26 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n4 2 Pedestrian -1 -1 -1 564.35 166.96 579.56 208.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n4 4 Pedestrian -1 -1 -1 633.24 170.27 649.49 213.80 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n4 7 Pedestrian -1 -1 -1 526.53 166.10 542.06 209.29 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n5 3 Pedestrian -1 -1 -1 547.99 169.78 571.08 218.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n5 1 Pedestrian -1 -1 -1 743.43 165.50 769.23 236.12 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n5 6 Pedestrian -1 -1 -1 621.07 168.80 638.24 214.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n5 5 Pedestrian -1 -1 -1 500.77 172.90 518.65 221.77 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n5 8 Pedestrian -1 -1 -1 480.36 171.24 502.37 223.86 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n5 4 Pedestrian -1 -1 -1 635.85 171.13 652.74 215.56 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n5 2 Pedestrian -1 -1 -1 563.81 167.01 579.64 209.27 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n5 7 Pedestrian -1 -1 -1 525.89 166.03 541.68 209.98 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n6 1 Pedestrian -1 -1 -1 746.02 163.35 773.31 235.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n6 3 Pedestrian -1 -1 -1 548.80 169.78 573.45 218.28 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n6 6 Pedestrian -1 -1 -1 623.66 167.28 641.02 215.10 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n6 8 Pedestrian -1 -1 -1 481.69 167.20 501.64 223.72 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n6 5 Pedestrian -1 -1 -1 498.23 171.42 516.25 220.29 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n6 7 Pedestrian -1 -1 -1 525.17 164.99 540.99 209.82 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n6 4 Pedestrian -1 -1 -1 636.14 170.48 654.38 215.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n7 6 Pedestrian -1 -1 -1 624.47 166.27 642.37 215.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n7 3 Pedestrian -1 -1 -1 552.04 169.58 576.97 218.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n7 1 Pedestrian -1 -1 -1 750.03 162.18 777.63 236.85 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n7 4 Pedestrian -1 -1 -1 639.94 168.47 657.33 214.82 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n7 5 Pedestrian -1 -1 -1 497.08 170.30 515.32 221.26 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n7 8 Pedestrian -1 -1 -1 481.15 167.26 501.75 224.10 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n7 7 Pedestrian -1 -1 -1 522.97 164.43 538.37 209.59 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n7 9 Pedestrian -1 -1 -1 546.16 164.60 566.79 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n8 1 Pedestrian -1 -1 -1 753.22 162.32 782.72 242.52 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n8 3 Pedestrian -1 -1 -1 555.20 169.78 579.04 219.93 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n8 6 Pedestrian -1 -1 -1 627.31 166.23 645.67 218.13 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n8 8 Pedestrian -1 -1 -1 480.46 168.14 501.86 226.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n8 5 Pedestrian -1 -1 -1 494.57 170.77 513.22 223.86 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n8 7 Pedestrian -1 -1 -1 522.18 164.91 537.92 210.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n8 9 Pedestrian -1 -1 -1 544.01 163.36 562.22 213.06 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n8 4 Pedestrian -1 -1 -1 643.51 168.95 659.73 217.35 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n9 1 Pedestrian -1 -1 -1 758.29 162.52 790.40 242.62 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n9 6 Pedestrian -1 -1 -1 630.41 167.85 650.38 219.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n9 5 Pedestrian -1 -1 -1 493.67 171.99 512.47 224.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n9 3 Pedestrian -1 -1 -1 559.91 169.22 582.50 221.82 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n9 8 Pedestrian -1 -1 -1 480.14 169.10 501.30 227.74 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n9 4 Pedestrian -1 -1 -1 644.43 169.17 661.67 219.82 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n9 9 Pedestrian -1 -1 -1 544.34 163.22 561.23 213.47 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n9 7 Pedestrian -1 -1 -1 521.88 165.08 537.43 211.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n10 1 Pedestrian -1 -1 -1 763.53 163.68 792.76 242.82 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n10 3 Pedestrian -1 -1 -1 563.62 169.28 587.42 222.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n10 6 Pedestrian -1 -1 -1 631.30 168.05 652.20 221.39 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n10 8 Pedestrian -1 -1 -1 476.29 168.11 500.10 230.56 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n10 4 Pedestrian -1 -1 -1 646.94 169.97 665.23 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n10 5 Pedestrian -1 -1 -1 492.79 172.04 512.12 225.89 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n10 9 Pedestrian -1 -1 -1 543.54 164.71 560.72 214.19 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n10 7 Pedestrian -1 -1 -1 518.76 165.87 535.03 212.71 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n10 10 Pedestrian -1 -1 -1 555.74 165.96 574.39 213.04 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n11 1 Pedestrian -1 -1 -1 764.26 163.59 794.06 248.10 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n11 3 Pedestrian -1 -1 -1 564.59 170.41 594.56 224.51 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n11 6 Pedestrian -1 -1 -1 634.04 168.23 656.32 223.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n11 8 Pedestrian -1 -1 -1 476.36 168.26 499.98 231.24 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n11 10 Pedestrian -1 -1 -1 555.65 166.52 573.52 213.39 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n11 7 Pedestrian -1 -1 -1 518.20 166.41 535.06 213.12 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n11 9 Pedestrian -1 -1 -1 540.41 165.40 558.22 215.09 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n11 4 Pedestrian -1 -1 -1 647.66 171.64 666.03 223.04 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n11 5 Pedestrian -1 -1 -1 489.47 173.22 509.62 228.62 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n12 1 Pedestrian -1 -1 -1 765.92 163.26 799.80 248.64 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n12 3 Pedestrian -1 -1 -1 567.31 169.44 599.18 225.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n12 6 Pedestrian -1 -1 -1 637.07 168.75 660.43 225.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n12 8 Pedestrian -1 -1 -1 475.11 168.43 498.72 230.82 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n12 5 Pedestrian -1 -1 -1 488.97 171.45 508.93 227.89 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n12 4 Pedestrian -1 -1 -1 653.22 170.89 672.94 223.59 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n12 7 Pedestrian -1 -1 -1 517.26 166.43 534.18 213.39 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n12 9 Pedestrian -1 -1 -1 539.51 164.45 558.36 215.24 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n12 10 Pedestrian -1 -1 -1 553.97 165.86 573.39 213.96 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n13 1 Pedestrian -1 -1 -1 770.95 162.12 808.34 249.84 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n13 3 Pedestrian -1 -1 -1 572.17 166.36 602.17 224.78 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n13 8 Pedestrian -1 -1 -1 471.37 165.68 495.98 231.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n13 6 Pedestrian -1 -1 -1 641.84 164.40 664.23 224.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n13 5 Pedestrian -1 -1 -1 485.22 168.82 506.94 228.27 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n13 4 Pedestrian -1 -1 -1 657.11 166.64 678.27 223.20 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n13 9 Pedestrian -1 -1 -1 538.84 162.74 558.16 213.67 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n13 10 Pedestrian -1 -1 -1 550.22 164.03 569.88 211.80 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n13 7 Pedestrian -1 -1 -1 516.91 163.53 533.39 212.46 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n14 1 Pedestrian -1 -1 -1 777.06 160.85 810.93 249.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n14 3 Pedestrian -1 -1 -1 579.95 164.47 603.32 224.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n14 8 Pedestrian -1 -1 -1 467.74 163.82 492.83 230.98 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n14 6 Pedestrian -1 -1 -1 647.50 160.63 671.50 223.48 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n14 9 Pedestrian -1 -1 -1 535.23 160.48 555.38 213.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n14 5 Pedestrian -1 -1 -1 484.35 167.87 505.24 227.06 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n14 7 Pedestrian -1 -1 -1 514.22 161.23 530.93 211.08 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n14 10 Pedestrian -1 -1 -1 547.46 161.81 566.87 210.78 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n14 4 Pedestrian -1 -1 -1 660.73 162.57 682.30 221.71 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n15 1 Pedestrian -1 -1 -1 778.40 158.43 817.29 251.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n15 3 Pedestrian -1 -1 -1 584.89 162.13 612.92 221.68 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n15 5 Pedestrian -1 -1 -1 480.77 165.47 502.32 225.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n15 8 Pedestrian -1 -1 -1 464.10 162.10 489.03 229.76 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n15 6 Pedestrian -1 -1 -1 651.25 160.28 674.59 223.46 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n15 10 Pedestrian -1 -1 -1 548.05 159.47 565.45 209.23 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n15 7 Pedestrian -1 -1 -1 512.86 158.40 530.35 210.12 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n15 9 Pedestrian -1 -1 -1 531.39 158.70 552.02 212.85 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n15 4 Pedestrian -1 -1 -1 664.27 162.86 686.35 220.93 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n16 1 Pedestrian -1 -1 -1 784.65 156.29 824.91 254.22 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n16 3 Pedestrian -1 -1 -1 587.66 162.48 618.26 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n16 6 Pedestrian -1 -1 -1 654.80 160.78 678.92 226.05 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n16 8 Pedestrian -1 -1 -1 458.36 160.80 484.75 231.07 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n16 5 Pedestrian -1 -1 -1 477.17 164.99 499.77 226.24 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n16 7 Pedestrian -1 -1 -1 509.49 157.82 527.43 210.82 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n16 10 Pedestrian -1 -1 -1 546.98 159.11 564.76 209.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n16 9 Pedestrian -1 -1 -1 527.30 157.84 548.86 213.52 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n16 4 Pedestrian -1 -1 -1 663.74 163.31 686.93 224.17 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n16 11 Pedestrian -1 -1 -1 581.12 159.76 593.48 191.31 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n17 1 Pedestrian -1 -1 -1 792.13 157.28 832.43 255.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n17 5 Pedestrian -1 -1 -1 475.71 166.32 499.16 228.20 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n17 8 Pedestrian -1 -1 -1 452.07 161.45 478.62 234.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n17 9 Pedestrian -1 -1 -1 526.03 159.38 547.68 214.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n17 10 Pedestrian -1 -1 -1 543.02 160.79 562.96 211.89 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n17 7 Pedestrian -1 -1 -1 504.85 159.91 525.06 213.28 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n17 3 Pedestrian -1 -1 -1 590.04 161.99 628.99 225.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n17 6 Pedestrian -1 -1 -1 657.33 162.14 683.83 228.39 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n17 4 Pedestrian -1 -1 -1 666.78 164.01 691.64 227.27 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n17 11 Pedestrian -1 -1 -1 581.61 160.64 594.50 192.76 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n18 1 Pedestrian -1 -1 -1 795.85 160.19 838.55 260.74 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n18 5 Pedestrian -1 -1 -1 472.00 169.58 495.49 232.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n18 8 Pedestrian -1 -1 -1 448.57 163.54 474.45 238.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n18 6 Pedestrian -1 -1 -1 662.04 163.73 687.05 231.58 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 9 Pedestrian -1 -1 -1 524.15 161.70 544.41 218.77 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n18 3 Pedestrian -1 -1 -1 595.95 165.05 631.19 230.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n18 7 Pedestrian -1 -1 -1 503.92 161.98 524.34 217.04 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 4 Pedestrian -1 -1 -1 674.67 166.32 698.68 230.53 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 10 Pedestrian -1 -1 -1 543.03 161.62 562.50 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n18 11 Pedestrian -1 -1 -1 584.09 162.40 597.24 194.84 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n19 1 Pedestrian -1 -1 -1 805.00 162.67 844.58 265.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n19 3 Pedestrian -1 -1 -1 601.63 165.68 632.70 233.45 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n19 8 Pedestrian -1 -1 -1 443.22 165.36 470.23 241.62 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n19 5 Pedestrian -1 -1 -1 467.32 171.82 491.72 235.18 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n19 4 Pedestrian -1 -1 -1 678.56 168.59 702.32 235.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n19 10 Pedestrian -1 -1 -1 538.94 163.89 560.18 218.21 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n19 7 Pedestrian -1 -1 -1 500.36 162.82 522.31 220.47 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n19 9 Pedestrian -1 -1 -1 523.21 165.59 542.96 221.95 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n19 6 Pedestrian -1 -1 -1 664.95 165.44 691.84 236.86 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n19 11 Pedestrian -1 -1 -1 584.86 164.26 598.05 196.67 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n20 1 Pedestrian -1 -1 -1 812.13 160.62 852.67 272.10 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n20 3 Pedestrian -1 -1 -1 611.41 166.19 638.36 237.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n20 5 Pedestrian -1 -1 -1 464.12 171.90 487.77 238.53 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n20 6 Pedestrian -1 -1 -1 668.48 165.54 696.72 240.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n20 8 Pedestrian -1 -1 -1 438.72 166.44 465.98 243.70 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n20 7 Pedestrian -1 -1 -1 500.18 163.49 521.81 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n20 9 Pedestrian -1 -1 -1 519.97 164.02 539.37 223.30 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n20 10 Pedestrian -1 -1 -1 538.35 163.91 560.01 220.28 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n20 4 Pedestrian -1 -1 -1 680.69 170.14 708.72 239.80 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n20 11 Pedestrian -1 -1 -1 584.89 165.88 598.95 199.13 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n20 12 Pedestrian -1 -1 -1 569.15 167.51 580.78 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n21 1 Pedestrian -1 -1 -1 817.08 160.98 861.58 273.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n21 5 Pedestrian -1 -1 -1 459.78 170.93 485.13 238.88 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n21 3 Pedestrian -1 -1 -1 615.84 166.40 650.66 238.01 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n21 6 Pedestrian -1 -1 -1 672.03 165.24 701.22 244.76 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 8 Pedestrian -1 -1 -1 432.85 166.57 459.63 244.81 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n21 7 Pedestrian -1 -1 -1 499.46 162.94 522.22 223.55 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n21 10 Pedestrian -1 -1 -1 537.62 163.50 559.60 220.64 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n21 4 Pedestrian -1 -1 -1 687.10 169.95 715.85 243.96 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n21 9 Pedestrian -1 -1 -1 515.92 162.88 537.15 224.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n21 12 Pedestrian -1 -1 -1 569.81 166.88 581.86 198.29 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n21 11 Pedestrian -1 -1 -1 586.77 165.87 602.34 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n22 1 Pedestrian -1 -1 -1 820.97 160.57 873.09 276.48 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n22 3 Pedestrian -1 -1 -1 620.41 165.33 661.25 238.58 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n22 8 Pedestrian -1 -1 -1 428.57 165.48 455.22 247.01 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n22 6 Pedestrian -1 -1 -1 674.26 164.06 706.78 247.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n22 5 Pedestrian -1 -1 -1 456.71 170.34 481.81 239.18 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n22 10 Pedestrian -1 -1 -1 537.52 162.19 559.59 220.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n22 4 Pedestrian -1 -1 -1 693.06 168.91 724.98 250.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n22 7 Pedestrian -1 -1 -1 498.73 161.58 521.44 222.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n22 9 Pedestrian -1 -1 -1 511.08 160.65 534.34 223.83 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n22 12 Pedestrian -1 -1 -1 570.34 166.00 583.09 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n22 11 Pedestrian -1 -1 -1 587.95 163.54 602.72 197.62 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n23 1 Pedestrian -1 -1 -1 828.30 161.41 881.00 281.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n23 3 Pedestrian -1 -1 -1 624.44 164.04 664.78 240.31 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n23 8 Pedestrian -1 -1 -1 423.30 164.69 451.62 248.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n23 7 Pedestrian -1 -1 -1 495.09 160.03 519.13 223.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n23 6 Pedestrian -1 -1 -1 678.07 162.86 711.25 250.73 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n23 5 Pedestrian -1 -1 -1 452.55 170.57 478.95 240.67 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n23 10 Pedestrian -1 -1 -1 536.98 161.90 559.51 221.17 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n23 4 Pedestrian -1 -1 -1 696.40 168.58 730.41 252.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n23 9 Pedestrian -1 -1 -1 511.00 161.23 533.02 225.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n23 12 Pedestrian -1 -1 -1 572.76 165.91 585.65 198.12 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n23 13 Pedestrian -1 -1 -1 557.35 163.57 570.17 196.75 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n24 1 Pedestrian -1 -1 -1 834.41 161.67 890.31 287.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 5 Pedestrian -1 -1 -1 450.94 171.06 477.95 242.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n24 3 Pedestrian -1 -1 -1 635.40 164.16 668.13 242.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 9 Pedestrian -1 -1 -1 510.59 160.82 534.07 227.34 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 8 Pedestrian -1 -1 -1 415.61 163.74 446.65 250.75 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n24 7 Pedestrian -1 -1 -1 490.47 161.02 516.63 225.71 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n24 4 Pedestrian -1 -1 -1 709.11 168.95 739.09 252.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n24 6 Pedestrian -1 -1 -1 684.02 162.64 720.59 254.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n24 10 Pedestrian -1 -1 -1 536.02 162.38 560.29 224.18 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n24 12 Pedestrian -1 -1 -1 574.31 166.19 586.63 198.27 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n24 13 Pedestrian -1 -1 -1 558.15 163.47 571.29 197.30 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n24 14 Cyclist -1 -1 -1 591.17 163.91 611.81 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n25 8 Pedestrian -1 -1 -1 409.91 163.07 442.67 254.65 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n25 1 Pedestrian -1 -1 -1 847.03 160.95 900.99 294.93 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n25 3 Pedestrian -1 -1 -1 648.42 165.41 678.16 246.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n25 7 Pedestrian -1 -1 -1 490.11 161.81 516.08 227.52 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n25 6 Pedestrian -1 -1 -1 691.45 162.65 727.84 259.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n25 5 Pedestrian -1 -1 -1 447.78 172.21 473.94 245.01 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n25 10 Pedestrian -1 -1 -1 533.89 162.52 557.48 226.37 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n25 9 Pedestrian -1 -1 -1 506.73 160.78 530.93 229.25 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n25 4 Pedestrian -1 -1 -1 715.05 170.05 748.70 258.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n25 12 Pedestrian -1 -1 -1 576.87 167.54 588.78 198.97 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n25 13 Pedestrian -1 -1 -1 558.62 165.22 572.11 199.14 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n25 14 Cyclist -1 -1 -1 592.32 165.52 611.87 199.47 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n26 1 Pedestrian -1 -1 -1 860.14 161.32 910.87 297.90 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n26 6 Pedestrian -1 -1 -1 698.48 163.07 735.46 264.77 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n26 8 Pedestrian -1 -1 -1 404.07 163.70 439.44 258.04 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n26 5 Pedestrian -1 -1 -1 446.08 171.86 473.75 247.51 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n26 3 Pedestrian -1 -1 -1 656.49 167.31 692.72 247.22 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n26 10 Pedestrian -1 -1 -1 532.92 162.83 558.00 227.81 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n26 7 Pedestrian -1 -1 -1 489.75 162.39 515.84 229.36 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n26 4 Pedestrian -1 -1 -1 722.91 171.46 755.92 262.72 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n26 9 Pedestrian -1 -1 -1 506.42 162.64 529.95 231.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n26 12 Pedestrian -1 -1 -1 578.05 168.00 590.30 200.26 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n26 13 Pedestrian -1 -1 -1 560.62 165.64 573.78 199.97 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n26 14 Cyclist -1 -1 -1 592.69 166.06 614.12 200.54 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n27 8 Pedestrian -1 -1 -1 396.61 163.82 432.34 262.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n27 1 Pedestrian -1 -1 -1 868.00 161.08 919.16 302.58 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n27 5 Pedestrian -1 -1 -1 442.76 171.84 470.78 249.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n27 3 Pedestrian -1 -1 -1 662.03 166.06 703.54 252.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n27 4 Pedestrian -1 -1 -1 729.19 170.83 765.68 265.46 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n27 6 Pedestrian -1 -1 -1 705.86 163.28 743.32 266.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n27 10 Pedestrian -1 -1 -1 532.94 163.64 558.10 230.73 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n27 7 Pedestrian -1 -1 -1 486.55 162.60 512.93 231.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n27 9 Pedestrian -1 -1 -1 505.83 162.36 529.60 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n27 13 Pedestrian -1 -1 -1 562.06 165.90 575.08 200.07 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n27 12 Pedestrian -1 -1 -1 580.08 168.30 592.45 200.28 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n27 14 Cyclist -1 -1 -1 594.33 166.35 617.57 200.83 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n28 8 Pedestrian -1 -1 -1 387.42 163.42 425.85 265.85 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n28 1 Pedestrian -1 -1 -1 878.93 159.37 930.74 306.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n28 5 Pedestrian -1 -1 -1 439.44 173.26 466.92 253.40 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n28 3 Pedestrian -1 -1 -1 668.05 166.42 711.91 254.39 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 10 Pedestrian -1 -1 -1 532.66 163.59 556.96 231.66 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 7 Pedestrian -1 -1 -1 485.69 163.17 512.25 233.22 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n28 4 Pedestrian -1 -1 -1 735.74 170.87 775.77 271.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n28 6 Pedestrian -1 -1 -1 711.96 163.50 752.49 272.68 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n28 9 Pedestrian -1 -1 -1 501.94 162.57 527.30 234.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n28 13 Pedestrian -1 -1 -1 563.96 166.25 577.52 200.89 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n28 12 Pedestrian -1 -1 -1 580.65 168.14 593.54 200.67 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n28 14 Cyclist -1 -1 -1 596.16 166.45 618.22 201.23 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n29 1 Pedestrian -1 -1 -1 887.97 157.40 944.87 314.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n29 5 Pedestrian -1 -1 -1 435.14 172.25 464.69 255.89 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n29 10 Pedestrian -1 -1 -1 531.02 162.71 557.68 233.75 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n29 3 Pedestrian -1 -1 -1 679.09 165.29 716.15 256.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n29 4 Pedestrian -1 -1 -1 747.15 169.46 785.42 274.92 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n29 8 Pedestrian -1 -1 -1 379.55 161.84 418.09 268.42 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n29 6 Pedestrian -1 -1 -1 719.63 162.39 759.88 279.14 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n29 7 Pedestrian -1 -1 -1 485.17 162.94 511.79 234.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n29 9 Pedestrian -1 -1 -1 501.17 161.99 526.44 236.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n29 13 Pedestrian -1 -1 -1 564.03 165.79 578.89 201.28 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n29 14 Cyclist -1 -1 -1 601.30 166.40 621.09 201.37 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n29 12 Pedestrian -1 -1 -1 583.18 167.06 596.37 201.71 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n30 1 Pedestrian -1 -1 -1 894.37 156.98 953.99 315.31 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n30 5 Pedestrian -1 -1 -1 431.92 171.34 460.96 256.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n30 6 Pedestrian -1 -1 -1 726.39 161.23 768.96 282.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n30 10 Pedestrian -1 -1 -1 530.70 162.45 558.72 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n30 4 Pedestrian -1 -1 -1 758.13 168.24 797.27 279.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 8 Pedestrian -1 -1 -1 369.99 159.90 407.75 270.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 7 Pedestrian -1 -1 -1 481.18 161.74 510.22 235.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n30 3 Pedestrian -1 -1 -1 688.64 164.03 723.48 258.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n30 9 Pedestrian -1 -1 -1 496.98 161.03 525.16 237.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n30 12 Pedestrian -1 -1 -1 585.60 166.24 598.14 201.11 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 13 Pedestrian -1 -1 -1 564.50 165.39 579.82 200.77 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n30 14 Cyclist -1 -1 -1 605.02 166.40 624.81 200.86 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n31 1 Pedestrian -1 -1 -1 902.06 156.57 968.28 322.51 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n31 5 Pedestrian -1 -1 -1 430.55 170.66 459.62 256.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n31 3 Pedestrian -1 -1 -1 699.01 163.49 735.65 258.06 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n31 8 Pedestrian -1 -1 -1 362.26 160.03 399.55 272.41 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n31 10 Pedestrian -1 -1 -1 527.23 160.75 556.82 235.67 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 4 Pedestrian -1 -1 -1 769.83 168.92 809.36 282.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 6 Pedestrian -1 -1 -1 736.17 160.53 781.74 290.00 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n31 7 Pedestrian -1 -1 -1 480.12 160.74 510.55 235.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n31 12 Pedestrian -1 -1 -1 587.57 165.52 600.90 200.82 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 14 Cyclist -1 -1 -1 607.25 165.45 628.61 200.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 9 Pedestrian -1 -1 -1 495.87 159.64 524.87 238.68 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n31 13 Pedestrian -1 -1 -1 567.24 164.37 583.06 200.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n32 1 Pedestrian -1 -1 -1 909.94 157.29 982.71 325.23 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n32 8 Pedestrian -1 -1 -1 354.71 159.71 392.09 274.52 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n32 5 Pedestrian -1 -1 -1 425.12 170.88 458.06 257.70 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n32 10 Pedestrian -1 -1 -1 526.39 160.72 556.67 236.65 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n32 6 Pedestrian -1 -1 -1 743.08 158.69 791.21 298.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n32 3 Pedestrian -1 -1 -1 704.73 162.61 745.73 262.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n32 4 Pedestrian -1 -1 -1 778.13 171.35 824.08 287.66 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n32 7 Pedestrian -1 -1 -1 479.20 160.13 510.75 237.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n32 9 Pedestrian -1 -1 -1 492.37 158.47 522.09 240.27 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n32 12 Pedestrian -1 -1 -1 588.59 165.83 603.07 200.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n32 13 Pedestrian -1 -1 -1 571.05 163.83 586.25 200.29 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n32 14 Cyclist -1 -1 -1 609.09 165.40 632.22 200.35 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n33 1 Pedestrian -1 -1 -1 921.70 156.99 994.48 338.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n33 6 Pedestrian -1 -1 -1 752.02 162.19 804.91 303.30 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n33 3 Pedestrian -1 -1 -1 713.00 164.94 753.19 264.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n33 5 Pedestrian -1 -1 -1 422.01 173.34 455.91 262.51 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n33 10 Pedestrian -1 -1 -1 526.12 160.90 556.79 238.16 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n33 8 Pedestrian -1 -1 -1 346.49 161.55 383.96 278.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n33 4 Pedestrian -1 -1 -1 790.10 173.40 834.54 293.28 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n33 9 Pedestrian -1 -1 -1 489.47 159.98 517.65 242.76 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n33 7 Pedestrian -1 -1 -1 475.75 160.14 507.58 238.91 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n33 12 Pedestrian -1 -1 -1 591.09 166.45 605.90 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n33 14 Cyclist -1 -1 -1 611.94 166.10 632.84 201.49 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n33 13 Pedestrian -1 -1 -1 572.69 164.66 588.17 201.08 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n33 15 Pedestrian -1 -1 -1 556.42 165.07 579.24 221.95 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n34 8 Pedestrian -1 -1 -1 340.47 160.71 374.73 283.08 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n34 1 Pedestrian -1 -1 -1 930.47 157.06 1001.82 340.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n34 3 Pedestrian -1 -1 -1 721.00 164.70 760.53 269.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n34 6 Pedestrian -1 -1 -1 761.62 161.53 818.56 311.95 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n34 10 Pedestrian -1 -1 -1 525.73 161.53 556.75 241.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n34 5 Pedestrian -1 -1 -1 419.87 172.95 454.97 264.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n34 9 Pedestrian -1 -1 -1 488.43 160.19 518.22 245.67 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n34 4 Pedestrian -1 -1 -1 799.44 172.23 848.73 301.18 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n34 7 Pedestrian -1 -1 -1 474.80 160.88 507.89 244.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n34 14 Cyclist -1 -1 -1 614.54 166.00 636.24 202.25 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n34 13 Pedestrian -1 -1 -1 576.66 165.11 590.88 201.59 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n34 15 Pedestrian -1 -1 -1 564.89 165.22 585.82 222.81 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n34 12 Pedestrian -1 -1 -1 594.34 166.69 608.11 202.47 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n35 5 Pedestrian -1 -1 -1 417.73 171.33 452.15 264.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n35 3 Pedestrian -1 -1 -1 728.60 163.80 765.85 265.98 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n35 1 Pedestrian -1 -1 -1 949.25 152.09 1020.47 344.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n35 6 Pedestrian -1 -1 -1 775.82 162.23 834.50 317.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n35 10 Pedestrian -1 -1 -1 525.05 160.87 556.65 242.55 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n35 9 Pedestrian -1 -1 -1 486.30 158.69 518.28 247.09 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n35 4 Pedestrian -1 -1 -1 819.37 168.67 866.68 310.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n35 7 Pedestrian -1 -1 -1 473.70 159.18 508.71 246.36 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n35 8 Pedestrian -1 -1 -1 332.41 159.66 366.97 288.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n35 15 Pedestrian -1 -1 -1 566.60 165.50 593.29 222.26 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n35 12 Pedestrian -1 -1 -1 596.20 165.89 609.82 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n35 14 Cyclist -1 -1 -1 616.59 165.14 639.76 202.31 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n35 13 Pedestrian -1 -1 -1 579.73 165.12 593.24 201.45 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n36 5 Pedestrian -1 -1 -1 412.91 170.77 448.20 265.80 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n36 4 Pedestrian -1 -1 -1 832.87 166.28 884.50 314.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n36 8 Pedestrian -1 -1 -1 323.36 158.94 360.16 291.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n36 3 Pedestrian -1 -1 -1 733.51 163.99 770.60 265.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n36 6 Pedestrian -1 -1 -1 784.62 156.26 848.76 325.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n36 1 Pedestrian -1 -1 -1 966.57 153.89 1033.63 343.51 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n36 10 Pedestrian -1 -1 -1 521.49 159.97 554.64 243.57 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n36 7 Pedestrian -1 -1 -1 472.42 157.97 510.04 248.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n36 15 Pedestrian -1 -1 -1 569.35 165.94 598.16 221.68 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n36 12 Pedestrian -1 -1 -1 598.62 165.04 613.33 202.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n36 14 Cyclist -1 -1 -1 620.00 164.55 639.63 201.19 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n36 13 Pedestrian -1 -1 -1 580.62 164.73 594.12 201.64 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n37 6 Pedestrian -1 -1 -1 799.54 152.01 864.72 336.44 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n37 3 Pedestrian -1 -1 -1 736.78 163.05 780.13 270.62 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n37 10 Pedestrian -1 -1 -1 520.68 159.73 554.18 245.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n37 15 Pedestrian -1 -1 -1 571.84 165.10 602.90 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n37 1 Pedestrian -1 -1 -1 972.75 153.27 1050.87 357.55 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n37 7 Pedestrian -1 -1 -1 468.78 158.52 506.65 251.71 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n37 8 Pedestrian -1 -1 -1 310.59 156.80 350.45 298.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n37 5 Pedestrian -1 -1 -1 406.41 169.71 444.82 268.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n37 4 Pedestrian -1 -1 -1 847.90 166.08 900.51 322.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n37 12 Pedestrian -1 -1 -1 599.55 164.87 614.45 201.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n37 14 Cyclist -1 -1 -1 620.94 164.41 638.85 200.99 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n37 13 Pedestrian -1 -1 -1 579.84 164.21 594.42 200.92 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n38 3 Pedestrian -1 -1 -1 740.39 163.45 785.64 272.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n38 8 Pedestrian -1 -1 -1 298.19 155.39 340.36 301.45 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n38 5 Pedestrian -1 -1 -1 398.60 171.40 437.99 270.85 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n38 7 Pedestrian -1 -1 -1 468.31 157.79 507.18 253.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n38 10 Pedestrian -1 -1 -1 516.40 158.52 552.10 247.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n38 15 Pedestrian -1 -1 -1 580.23 163.49 607.54 224.42 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n38 6 Pedestrian -1 -1 -1 822.33 153.44 887.46 350.39 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n38 1 Pedestrian -1 -1 -1 983.39 155.02 1063.40 363.84 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n38 12 Pedestrian -1 -1 -1 601.01 165.24 617.24 201.53 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n38 14 Cyclist -1 -1 -1 621.75 164.43 642.90 200.81 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n38 4 Pedestrian -1 -1 -1 864.44 170.69 921.38 333.82 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n38 13 Pedestrian -1 -1 -1 580.70 163.45 595.20 200.37 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n39 5 Pedestrian -1 -1 -1 391.32 170.30 430.55 272.67 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n39 8 Pedestrian -1 -1 -1 285.48 153.42 330.32 305.24 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n39 3 Pedestrian -1 -1 -1 744.32 162.26 790.45 274.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n39 7 Pedestrian -1 -1 -1 459.88 155.51 501.27 255.06 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n39 10 Pedestrian -1 -1 -1 514.79 157.75 550.67 249.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n39 15 Pedestrian -1 -1 -1 586.59 162.94 610.99 224.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n39 1 Pedestrian -1 -1 -1 995.67 154.69 1073.93 363.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n39 6 Pedestrian -1 -1 -1 839.78 153.45 908.53 364.59 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n39 12 Pedestrian -1 -1 -1 602.95 165.02 618.02 200.63 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n39 13 Pedestrian -1 -1 -1 583.81 163.26 597.99 200.62 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n39 14 Cyclist -1 -1 -1 623.83 164.52 642.38 200.73 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n39 16 Pedestrian -1 -1 -1 623.83 164.52 642.38 200.73 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n40 5 Pedestrian -1 -1 -1 384.08 168.67 422.86 273.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n40 7 Pedestrian -1 -1 -1 456.85 154.51 496.46 256.70 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n40 8 Pedestrian -1 -1 -1 272.50 151.64 320.09 311.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n40 10 Pedestrian -1 -1 -1 511.36 156.89 548.69 250.08 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n40 1 Pedestrian -1 -1 -1 1010.52 149.10 1089.46 364.08 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n40 3 Pedestrian -1 -1 -1 748.75 162.32 799.36 279.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n40 15 Pedestrian -1 -1 -1 590.97 162.52 620.40 224.63 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n40 6 Pedestrian -1 -1 -1 858.87 156.34 935.32 362.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n40 12 Pedestrian -1 -1 -1 602.92 164.42 619.27 200.72 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n40 16 Pedestrian -1 -1 -1 625.03 164.18 642.67 200.80 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n40 13 Pedestrian -1 -1 -1 584.37 163.51 599.06 200.10 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n40 17 Pedestrian -1 -1 -1 896.29 166.66 974.21 360.72 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n41 7 Pedestrian -1 -1 -1 453.06 153.72 491.97 258.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n41 5 Pedestrian -1 -1 -1 377.37 168.06 414.78 273.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 3 Pedestrian -1 -1 -1 752.46 161.73 804.51 281.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 8 Pedestrian -1 -1 -1 260.41 152.24 309.08 315.06 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n41 1 Pedestrian -1 -1 -1 1015.55 149.07 1115.28 364.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n41 10 Pedestrian -1 -1 -1 511.04 158.42 547.52 252.04 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n41 15 Pedestrian -1 -1 -1 593.66 162.45 626.98 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n41 6 Pedestrian -1 -1 -1 883.01 157.34 979.78 360.84 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n41 12 Pedestrian -1 -1 -1 605.68 163.96 621.79 201.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n41 13 Pedestrian -1 -1 -1 585.22 162.43 602.68 200.94 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n41 17 Pedestrian -1 -1 -1 911.90 166.10 997.14 361.33 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n41 16 Pedestrian -1 -1 -1 628.04 163.05 646.88 200.80 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n41 18 Cyclist -1 -1 -1 628.04 163.05 646.88 200.80 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n42 5 Pedestrian -1 -1 -1 367.66 167.34 408.07 275.65 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n42 7 Pedestrian -1 -1 -1 448.88 152.56 488.69 261.16 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n42 8 Pedestrian -1 -1 -1 245.21 152.47 294.46 322.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n42 3 Pedestrian -1 -1 -1 756.98 162.04 808.60 286.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n42 15 Pedestrian -1 -1 -1 597.72 162.06 631.01 226.94 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n42 10 Pedestrian -1 -1 -1 506.95 157.27 543.82 254.26 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n42 1 Pedestrian -1 -1 -1 1027.33 149.91 1141.44 363.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n42 6 Pedestrian -1 -1 -1 909.84 152.91 998.98 359.96 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n42 13 Pedestrian -1 -1 -1 585.62 162.19 602.82 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n42 17 Pedestrian -1 -1 -1 953.18 170.87 1031.82 362.42 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n42 12 Pedestrian -1 -1 -1 606.71 163.76 622.13 201.62 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n42 16 Pedestrian -1 -1 -1 629.12 163.04 646.69 200.51 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n42 19 Pedestrian -1 -1 -1 532.32 161.78 551.14 202.77 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n43 7 Pedestrian -1 -1 -1 445.21 152.48 484.27 265.53 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n43 3 Pedestrian -1 -1 -1 764.20 163.01 816.83 288.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n43 10 Pedestrian -1 -1 -1 502.22 156.63 540.94 256.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n43 8 Pedestrian -1 -1 -1 228.20 151.77 280.36 335.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n43 5 Pedestrian -1 -1 -1 356.21 168.78 398.97 279.12 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n43 15 Pedestrian -1 -1 -1 604.58 163.53 632.05 226.42 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n43 6 Pedestrian -1 -1 -1 938.25 147.77 1047.06 364.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n43 1 Pedestrian -1 -1 -1 1046.54 154.06 1160.13 363.66 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n43 19 Pedestrian -1 -1 -1 533.36 162.10 549.92 202.91 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n43 12 Pedestrian -1 -1 -1 609.18 163.90 625.96 201.87 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n43 13 Pedestrian -1 -1 -1 585.64 162.41 602.67 201.10 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n43 17 Pedestrian -1 -1 -1 972.50 163.31 1066.46 364.11 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n43 20 Cyclist -1 -1 -1 629.88 162.99 651.35 200.93 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n44 3 Pedestrian -1 -1 -1 770.09 164.30 824.68 294.30 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n44 7 Pedestrian -1 -1 -1 440.74 152.49 479.83 268.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n44 5 Pedestrian -1 -1 -1 347.42 168.95 391.55 282.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n44 8 Pedestrian -1 -1 -1 208.29 153.17 261.95 342.43 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n44 15 Pedestrian -1 -1 -1 612.15 162.37 639.95 228.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n44 10 Pedestrian -1 -1 -1 495.75 156.66 534.12 261.08 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n44 12 Pedestrian -1 -1 -1 609.28 163.80 625.73 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n44 6 Pedestrian -1 -1 -1 983.50 148.86 1100.84 362.24 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n44 13 Pedestrian -1 -1 -1 586.94 161.76 603.41 202.11 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n44 1 Pedestrian -1 -1 -1 1065.69 153.28 1171.81 365.29 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n44 19 Pedestrian -1 -1 -1 532.69 162.42 550.35 203.56 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n44 17 Pedestrian -1 -1 -1 1004.88 172.92 1118.12 361.41 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n44 20 Cyclist -1 -1 -1 630.37 163.42 650.62 200.83 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n44 21 Pedestrian -1 -1 -1 1000.35 166.36 1107.22 360.78 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n45 7 Pedestrian -1 -1 -1 432.99 152.16 473.60 270.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n45 8 Pedestrian -1 -1 -1 183.63 151.60 241.19 351.49 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 3 Pedestrian -1 -1 -1 773.95 165.79 829.82 300.34 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 5 Pedestrian -1 -1 -1 339.25 168.12 384.31 284.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n45 10 Pedestrian -1 -1 -1 490.34 156.42 530.89 263.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n45 15 Pedestrian -1 -1 -1 615.73 163.09 649.17 228.83 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n45 13 Pedestrian -1 -1 -1 586.17 162.49 603.75 202.22 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n45 1 Pedestrian -1 -1 -1 1056.84 149.83 1196.14 368.57 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n45 12 Pedestrian -1 -1 -1 610.00 164.25 625.15 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n45 19 Pedestrian -1 -1 -1 532.97 163.14 549.64 204.24 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n45 6 Pedestrian -1 -1 -1 1011.91 139.39 1195.28 371.35 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n45 20 Cyclist -1 -1 -1 630.02 163.87 651.17 200.63 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n46 7 Pedestrian -1 -1 -1 424.05 152.78 467.55 273.45 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n46 3 Pedestrian -1 -1 -1 780.62 165.30 837.94 307.35 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n46 5 Pedestrian -1 -1 -1 331.50 168.03 375.11 283.80 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n46 10 Pedestrian -1 -1 -1 482.19 155.85 523.63 265.74 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n46 8 Pedestrian -1 -1 -1 157.82 148.16 221.13 362.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n46 15 Pedestrian -1 -1 -1 614.63 164.80 652.21 230.36 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n46 13 Pedestrian -1 -1 -1 585.31 162.39 603.07 202.97 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n46 12 Pedestrian -1 -1 -1 609.66 164.80 624.09 202.72 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n46 1 Pedestrian -1 -1 -1 1095.03 158.19 1203.70 360.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n46 19 Pedestrian -1 -1 -1 532.41 163.88 548.75 204.12 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n46 22 Pedestrian -1 -1 -1 524.11 163.39 543.10 204.05 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n46 23 Pedestrian -1 -1 -1 742.87 157.86 768.55 224.03 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n47 7 Pedestrian -1 -1 -1 415.00 152.10 460.55 275.80 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n47 5 Pedestrian -1 -1 -1 320.75 166.03 364.05 287.03 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n47 3 Pedestrian -1 -1 -1 787.33 166.00 844.85 308.49 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n47 8 Pedestrian -1 -1 -1 128.77 147.38 196.41 364.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n47 10 Pedestrian -1 -1 -1 470.02 155.12 514.38 267.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n47 15 Pedestrian -1 -1 -1 619.26 163.54 654.11 231.96 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n47 12 Pedestrian -1 -1 -1 609.87 163.02 623.87 202.63 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n47 13 Pedestrian -1 -1 -1 582.76 162.00 600.30 202.81 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n47 19 Pedestrian -1 -1 -1 523.61 162.67 544.10 204.13 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n47 1 Pedestrian -1 -1 -1 1112.96 156.22 1216.59 362.77 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n48 5 Pedestrian -1 -1 -1 308.10 166.78 354.02 290.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n48 10 Pedestrian -1 -1 -1 461.97 155.17 507.12 270.78 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n48 8 Pedestrian -1 -1 -1 94.72 145.72 169.30 364.30 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n48 3 Pedestrian -1 -1 -1 790.81 169.48 850.12 312.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n48 7 Pedestrian -1 -1 -1 407.18 152.45 452.68 277.30 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n48 15 Pedestrian -1 -1 -1 625.55 161.65 654.24 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n48 13 Pedestrian -1 -1 -1 582.11 161.85 598.95 202.24 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n48 12 Pedestrian -1 -1 -1 608.37 164.14 624.80 202.13 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n48 19 Pedestrian -1 -1 -1 524.44 162.59 543.59 204.05 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n48 1 Pedestrian -1 -1 -1 1127.79 154.72 1217.05 364.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n48 24 Pedestrian -1 -1 -1 628.48 162.40 644.17 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n48 25 Pedestrian -1 -1 -1 505.09 160.98 524.19 204.41 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n49 10 Pedestrian -1 -1 -1 456.14 154.50 503.00 273.35 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n49 5 Pedestrian -1 -1 -1 296.54 166.09 343.01 297.29 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n49 12 Pedestrian -1 -1 -1 604.96 164.58 622.52 201.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n49 8 Pedestrian -1 -1 -1 55.17 144.02 139.83 367.41 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n49 7 Pedestrian -1 -1 -1 389.45 151.49 440.97 282.60 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n49 3 Pedestrian -1 -1 -1 794.27 174.48 854.46 321.13 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n49 15 Pedestrian -1 -1 -1 631.11 161.06 657.11 233.28 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n49 13 Pedestrian -1 -1 -1 578.64 161.63 596.20 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n49 24 Pedestrian -1 -1 -1 625.67 162.38 640.68 202.95 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n49 1 Pedestrian -1 -1 -1 1148.09 149.87 1219.62 361.83 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n49 19 Pedestrian -1 -1 -1 525.29 162.54 542.55 204.02 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n49 25 Pedestrian -1 -1 -1 505.56 160.66 522.86 205.05 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n49 26 Pedestrian -1 -1 -1 723.42 157.23 749.66 224.49 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n50 5 Pedestrian -1 -1 -1 284.02 164.46 332.27 300.20 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n50 10 Pedestrian -1 -1 -1 445.63 153.89 492.62 275.73 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n50 15 Pedestrian -1 -1 -1 631.13 160.58 665.05 234.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n50 3 Pedestrian -1 -1 -1 797.90 176.34 858.86 326.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n50 26 Pedestrian -1 -1 -1 716.61 156.09 741.75 226.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n50 7 Pedestrian -1 -1 -1 373.93 150.42 425.83 286.44 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n50 8 Pedestrian -1 -1 -1 15.90 143.49 110.19 366.44 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n50 13 Pedestrian -1 -1 -1 578.51 161.69 594.33 202.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n50 12 Pedestrian -1 -1 -1 602.35 164.72 620.03 201.84 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n50 24 Pedestrian -1 -1 -1 622.56 161.91 641.65 203.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n50 19 Pedestrian -1 -1 -1 525.18 162.04 541.44 204.35 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n50 25 Pedestrian -1 -1 -1 501.78 160.20 519.19 205.52 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n50 1 Pedestrian -1 -1 -1 1154.04 150.02 1221.21 361.83 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n50 27 Pedestrian -1 -1 -1 514.87 162.00 530.52 204.76 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n50 28 Pedestrian -1 -1 -1 392.75 151.80 436.24 277.35 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n51 5 Pedestrian -1 -1 -1 270.81 163.66 321.38 301.56 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n51 10 Pedestrian -1 -1 -1 437.42 153.25 484.98 280.23 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n51 26 Pedestrian -1 -1 -1 706.66 155.80 736.71 226.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n51 15 Pedestrian -1 -1 -1 632.81 160.48 670.27 235.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n51 3 Pedestrian -1 -1 -1 807.41 176.40 871.57 335.62 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n51 12 Pedestrian -1 -1 -1 601.08 163.86 619.18 202.29 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n51 7 Pedestrian -1 -1 -1 366.61 150.79 417.08 290.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n51 13 Pedestrian -1 -1 -1 575.54 161.54 593.33 202.36 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n51 28 Pedestrian -1 -1 -1 379.46 150.71 427.69 283.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n51 19 Pedestrian -1 -1 -1 521.19 161.82 539.57 204.85 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n51 24 Pedestrian -1 -1 -1 620.20 161.45 639.60 203.41 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n51 25 Pedestrian -1 -1 -1 499.02 159.41 515.94 206.02 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n51 27 Pedestrian -1 -1 -1 513.34 161.97 529.70 204.49 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n51 8 Pedestrian -1 -1 -1 -1.31 144.38 66.63 366.20 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n51 1 Pedestrian -1 -1 -1 1174.72 157.01 1222.89 361.80 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n52 10 Pedestrian -1 -1 -1 428.54 151.61 477.50 283.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n52 5 Pedestrian -1 -1 -1 258.49 163.31 310.13 303.56 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n52 26 Pedestrian -1 -1 -1 698.11 154.53 735.23 229.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n52 15 Pedestrian -1 -1 -1 634.94 160.11 670.04 235.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n52 7 Pedestrian -1 -1 -1 354.95 150.21 405.59 293.08 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n52 3 Pedestrian -1 -1 -1 815.99 177.28 878.59 341.68 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n52 12 Pedestrian -1 -1 -1 601.30 163.11 617.13 201.44 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n52 19 Pedestrian -1 -1 -1 519.21 161.81 538.63 204.35 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n52 28 Pedestrian -1 -1 -1 372.05 150.80 419.09 283.86 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n52 13 Pedestrian -1 -1 -1 574.24 161.48 591.71 202.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n52 24 Pedestrian -1 -1 -1 619.91 160.68 639.43 203.32 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n52 25 Pedestrian -1 -1 -1 497.85 159.84 514.91 205.10 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n52 27 Pedestrian -1 -1 -1 509.56 161.08 527.46 204.03 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n52 1 Pedestrian -1 -1 -1 1179.92 160.11 1225.81 358.92 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n53 10 Pedestrian -1 -1 -1 417.04 149.45 467.54 286.36 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n53 26 Pedestrian -1 -1 -1 690.34 153.40 727.88 228.95 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n53 5 Pedestrian -1 -1 -1 242.18 161.93 296.57 306.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n53 15 Pedestrian -1 -1 -1 640.04 158.50 672.83 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n53 3 Pedestrian -1 -1 -1 821.21 177.11 881.52 348.79 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n53 7 Pedestrian -1 -1 -1 339.64 148.54 390.38 299.80 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n53 13 Pedestrian -1 -1 -1 569.55 159.97 588.77 201.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n53 28 Pedestrian -1 -1 -1 360.54 147.89 407.75 288.02 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n53 24 Pedestrian -1 -1 -1 619.67 159.29 639.24 201.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n53 19 Pedestrian -1 -1 -1 511.36 160.27 534.20 204.36 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n53 12 Pedestrian -1 -1 -1 598.70 163.17 614.36 200.21 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n53 25 Pedestrian -1 -1 -1 494.01 159.76 511.13 204.77 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n53 1 Pedestrian -1 -1 -1 1198.76 158.41 1222.20 360.83 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n53 29 Pedestrian -1 -1 -1 469.04 153.49 490.54 202.49 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n54 10 Pedestrian -1 -1 -1 407.81 151.08 460.40 291.29 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n54 5 Pedestrian -1 -1 -1 226.31 163.25 281.90 316.61 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n54 15 Pedestrian -1 -1 -1 645.85 158.20 674.97 237.61 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n54 26 Pedestrian -1 -1 -1 687.27 154.13 716.05 228.75 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n54 7 Pedestrian -1 -1 -1 324.16 147.98 376.18 303.29 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n54 12 Pedestrian -1 -1 -1 594.24 163.33 611.97 201.14 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n54 13 Pedestrian -1 -1 -1 564.91 159.02 585.38 202.20 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n54 3 Pedestrian -1 -1 -1 828.00 178.35 889.93 356.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n54 19 Pedestrian -1 -1 -1 511.56 160.09 531.36 205.03 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n54 28 Pedestrian -1 -1 -1 347.82 148.66 397.61 293.30 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n54 25 Pedestrian -1 -1 -1 489.89 159.90 507.22 205.19 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n54 24 Pedestrian -1 -1 -1 615.49 160.49 636.88 202.96 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n54 1 Pedestrian -1 -1 -1 1199.95 160.47 1221.41 358.76 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n54 30 Pedestrian -1 -1 -1 837.80 179.57 910.40 356.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n54 31 Pedestrian -1 -1 -1 501.60 160.25 519.67 203.75 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n55 10 Pedestrian -1 -1 -1 396.81 153.18 448.77 296.43 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n55 5 Pedestrian -1 -1 -1 209.59 163.44 267.81 323.38 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n55 15 Pedestrian -1 -1 -1 648.92 159.14 685.66 239.77 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n55 30 Pedestrian -1 -1 -1 841.64 181.34 929.63 361.54 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n55 7 Pedestrian -1 -1 -1 308.06 148.49 361.72 310.62 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n55 26 Pedestrian -1 -1 -1 676.38 155.45 705.12 231.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n55 13 Pedestrian -1 -1 -1 563.03 160.69 581.73 203.02 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n55 25 Pedestrian -1 -1 -1 486.26 160.15 503.83 206.44 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n55 31 Pedestrian -1 -1 -1 498.52 161.33 515.78 205.56 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n55 12 Pedestrian -1 -1 -1 592.23 164.11 610.70 203.57 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n55 19 Pedestrian -1 -1 -1 509.82 160.70 527.65 206.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n55 28 Pedestrian -1 -1 -1 332.34 148.98 382.97 300.34 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n55 24 Pedestrian -1 -1 -1 612.25 161.42 632.96 203.47 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n55 1 Pedestrian -1 -1 -1 1208.36 167.25 1220.01 359.81 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n55 32 Pedestrian -1 -1 -1 456.94 153.27 481.33 203.41 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n56 10 Pedestrian -1 -1 -1 382.94 152.99 439.67 303.49 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n56 5 Pedestrian -1 -1 -1 195.40 163.90 251.03 322.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n56 26 Pedestrian -1 -1 -1 666.19 156.29 698.53 232.59 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n56 15 Pedestrian -1 -1 -1 650.46 161.71 691.52 243.83 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n56 13 Pedestrian -1 -1 -1 562.66 161.51 580.36 204.14 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n56 30 Pedestrian -1 -1 -1 846.13 183.36 924.84 360.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n56 7 Pedestrian -1 -1 -1 292.40 147.70 346.18 317.55 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n56 12 Pedestrian -1 -1 -1 590.02 164.50 607.00 204.31 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n56 25 Pedestrian -1 -1 -1 481.97 161.21 500.32 207.01 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n56 28 Pedestrian -1 -1 -1 320.60 147.62 371.66 303.92 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n56 19 Pedestrian -1 -1 -1 508.43 161.40 526.80 207.21 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n56 31 Pedestrian -1 -1 -1 493.48 162.33 512.76 206.37 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n56 24 Pedestrian -1 -1 -1 610.48 161.97 633.67 204.88 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n56 33 Pedestrian -1 -1 -1 851.75 187.88 949.94 360.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n56 34 Cyclist -1 -1 -1 454.69 153.98 481.61 204.62 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n57 5 Pedestrian -1 -1 -1 173.23 165.51 229.54 325.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n57 10 Pedestrian -1 -1 -1 370.21 150.13 429.84 309.65 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n57 33 Pedestrian -1 -1 -1 859.22 186.59 965.61 363.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n57 7 Pedestrian -1 -1 -1 272.10 147.11 328.75 325.41 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n57 26 Pedestrian -1 -1 -1 654.92 161.20 688.42 234.68 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n57 31 Pedestrian -1 -1 -1 490.47 163.94 508.71 208.47 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n57 12 Pedestrian -1 -1 -1 587.66 166.76 604.06 206.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n57 13 Pedestrian -1 -1 -1 559.47 162.24 578.63 206.33 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n57 19 Pedestrian -1 -1 -1 504.02 163.28 523.44 208.47 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n57 25 Pedestrian -1 -1 -1 477.86 162.37 496.57 209.57 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n57 28 Pedestrian -1 -1 -1 308.97 151.54 360.02 313.19 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n57 34 Cyclist -1 -1 -1 450.35 155.52 478.16 205.27 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n57 24 Pedestrian -1 -1 -1 610.98 162.71 631.37 205.71 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n58 5 Pedestrian -1 -1 -1 154.27 167.30 208.54 336.58 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n58 10 Pedestrian -1 -1 -1 355.11 149.69 421.44 317.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n58 13 Pedestrian -1 -1 -1 556.44 164.43 578.14 208.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n58 12 Pedestrian -1 -1 -1 586.58 166.13 602.61 207.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n58 26 Pedestrian -1 -1 -1 662.28 164.55 694.36 248.31 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n58 7 Pedestrian -1 -1 -1 252.45 147.04 310.10 333.62 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n58 25 Pedestrian -1 -1 -1 472.83 163.05 494.02 211.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n58 33 Pedestrian -1 -1 -1 866.85 186.74 1003.62 364.11 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n58 19 Pedestrian -1 -1 -1 499.60 164.42 520.66 210.03 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n58 28 Pedestrian -1 -1 -1 288.38 149.66 343.08 322.58 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n58 31 Pedestrian -1 -1 -1 489.96 164.47 507.62 209.37 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n58 34 Cyclist -1 -1 -1 444.49 157.19 470.12 207.38 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n58 24 Pedestrian -1 -1 -1 608.39 164.20 628.17 207.09 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n58 35 Pedestrian -1 -1 -1 648.10 160.02 680.10 237.46 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n59 10 Pedestrian -1 -1 -1 341.87 150.17 411.23 324.23 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n59 13 Pedestrian -1 -1 -1 552.72 164.41 575.88 209.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n59 26 Pedestrian -1 -1 -1 671.54 164.96 700.25 248.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n59 5 Pedestrian -1 -1 -1 130.66 167.38 186.08 345.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n59 7 Pedestrian -1 -1 -1 232.36 146.46 292.04 341.98 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n59 35 Pedestrian -1 -1 -1 644.12 159.59 674.68 238.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n59 25 Pedestrian -1 -1 -1 468.92 163.94 490.70 211.79 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n59 12 Pedestrian -1 -1 -1 583.34 166.31 600.81 208.43 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n59 33 Pedestrian -1 -1 -1 886.30 190.74 1037.73 366.05 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n59 28 Pedestrian -1 -1 -1 272.22 150.23 328.08 330.02 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n59 31 Pedestrian -1 -1 -1 486.66 165.11 504.57 209.39 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n59 19 Pedestrian -1 -1 -1 495.99 164.70 517.75 211.27 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n59 24 Pedestrian -1 -1 -1 607.95 163.48 627.24 208.67 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n59 34 Cyclist -1 -1 -1 440.50 158.28 466.67 207.94 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n60 10 Pedestrian -1 -1 -1 325.94 150.94 396.89 330.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n60 26 Pedestrian -1 -1 -1 669.92 163.89 712.19 250.56 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n60 5 Pedestrian -1 -1 -1 103.80 165.49 160.44 355.03 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n60 13 Pedestrian -1 -1 -1 549.67 164.49 571.93 209.30 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n60 35 Pedestrian -1 -1 -1 631.40 157.60 664.88 240.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n60 7 Pedestrian -1 -1 -1 206.23 144.71 272.27 351.14 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n60 12 Pedestrian -1 -1 -1 580.52 167.00 599.92 208.30 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n60 33 Pedestrian -1 -1 -1 894.79 191.35 1067.43 366.43 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n60 25 Pedestrian -1 -1 -1 464.98 164.13 486.75 212.36 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n60 31 Pedestrian -1 -1 -1 482.14 165.56 500.95 209.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n60 28 Pedestrian -1 -1 -1 252.41 148.76 309.91 339.01 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n60 19 Pedestrian -1 -1 -1 494.01 165.00 512.66 210.69 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n60 24 Pedestrian -1 -1 -1 605.01 164.13 623.97 208.95 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n60 34 Cyclist -1 -1 -1 437.47 158.27 461.86 208.40 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n61 26 Pedestrian -1 -1 -1 671.68 164.47 717.29 253.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n61 35 Pedestrian -1 -1 -1 616.93 156.89 656.21 241.45 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n61 10 Pedestrian -1 -1 -1 309.72 148.73 383.06 340.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n61 12 Pedestrian -1 -1 -1 576.85 166.47 597.49 208.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n61 5 Pedestrian -1 -1 -1 76.86 163.25 140.71 362.06 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n61 7 Pedestrian -1 -1 -1 185.31 144.38 254.35 359.71 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n61 13 Pedestrian -1 -1 -1 550.13 164.48 569.78 209.47 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n61 31 Pedestrian -1 -1 -1 478.74 165.90 496.92 210.12 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n61 25 Pedestrian -1 -1 -1 462.17 164.06 482.60 212.21 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n61 24 Pedestrian -1 -1 -1 603.08 163.86 624.36 210.27 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n61 28 Pedestrian -1 -1 -1 229.63 148.25 294.16 347.19 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n61 19 Pedestrian -1 -1 -1 493.62 164.83 511.30 211.82 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n61 33 Pedestrian -1 -1 -1 894.35 198.31 1136.78 365.10 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n61 36 Pedestrian -1 -1 -1 433.89 158.11 457.87 209.59 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n61 37 Pedestrian -1 -1 -1 887.69 190.99 1120.70 366.87 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n62 26 Pedestrian -1 -1 -1 682.28 165.29 720.96 255.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n62 35 Pedestrian -1 -1 -1 602.69 158.02 649.05 244.49 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n62 10 Pedestrian -1 -1 -1 294.23 149.19 367.81 347.31 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n62 5 Pedestrian -1 -1 -1 50.15 164.77 114.71 363.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n62 7 Pedestrian -1 -1 -1 156.21 145.73 230.41 364.44 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n62 13 Pedestrian -1 -1 -1 546.90 165.26 566.80 210.25 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n62 25 Pedestrian -1 -1 -1 459.10 164.84 478.66 214.17 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n62 28 Pedestrian -1 -1 -1 203.45 146.18 274.87 356.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n62 12 Pedestrian -1 -1 -1 575.25 166.42 593.37 209.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n62 31 Pedestrian -1 -1 -1 475.94 167.20 492.79 212.02 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n62 19 Pedestrian -1 -1 -1 490.29 166.30 509.34 212.68 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n62 33 Pedestrian -1 -1 -1 929.97 196.79 1170.15 367.52 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n62 36 Pedestrian -1 -1 -1 428.77 158.33 455.90 210.02 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n62 24 Pedestrian -1 -1 -1 600.72 165.04 620.78 210.96 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n63 5 Pedestrian -1 -1 -1 21.22 165.72 96.53 367.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n63 10 Pedestrian -1 -1 -1 278.07 148.08 353.16 356.21 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n63 35 Pedestrian -1 -1 -1 596.95 159.29 638.69 246.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n63 7 Pedestrian -1 -1 -1 133.34 146.22 206.65 364.40 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n63 26 Pedestrian -1 -1 -1 694.98 167.74 725.26 257.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n63 19 Pedestrian -1 -1 -1 486.95 167.41 510.51 213.28 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n63 25 Pedestrian -1 -1 -1 458.59 165.53 477.52 215.06 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n63 28 Pedestrian -1 -1 -1 183.88 147.77 255.78 363.38 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n63 13 Pedestrian -1 -1 -1 546.36 165.45 565.09 211.20 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n63 31 Pedestrian -1 -1 -1 475.18 167.60 492.40 213.20 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n63 12 Pedestrian -1 -1 -1 578.07 168.67 594.41 210.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n63 36 Pedestrian -1 -1 -1 427.57 159.99 455.56 211.08 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n64 10 Pedestrian -1 -1 -1 258.69 149.07 341.31 362.80 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n64 26 Pedestrian -1 -1 -1 703.67 167.16 737.47 260.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n64 5 Pedestrian -1 -1 -1 2.21 167.23 78.19 367.70 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n64 35 Pedestrian -1 -1 -1 592.49 160.68 628.48 250.17 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n64 7 Pedestrian -1 -1 -1 99.07 140.62 180.12 364.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n64 36 Pedestrian -1 -1 -1 423.38 160.81 453.06 211.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n64 28 Pedestrian -1 -1 -1 158.64 144.99 235.01 367.20 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n64 13 Pedestrian -1 -1 -1 545.99 167.59 565.72 211.95 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n64 19 Pedestrian -1 -1 -1 483.47 167.22 508.60 215.15 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n64 12 Pedestrian -1 -1 -1 577.89 169.42 594.59 211.76 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n64 25 Pedestrian -1 -1 -1 455.54 166.20 474.45 214.70 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n64 31 Pedestrian -1 -1 -1 471.51 167.44 489.97 213.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n65 10 Pedestrian -1 -1 -1 235.10 145.41 327.27 366.64 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n65 26 Pedestrian -1 -1 -1 706.76 166.77 751.37 266.55 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n65 35 Pedestrian -1 -1 -1 580.44 160.14 617.60 254.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n65 19 Pedestrian -1 -1 -1 484.02 166.84 507.50 215.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n65 7 Pedestrian -1 -1 -1 64.14 137.35 154.07 365.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n65 28 Pedestrian -1 -1 -1 126.76 145.25 213.40 365.56 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n65 13 Pedestrian -1 -1 -1 543.13 166.87 564.20 213.37 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n65 36 Pedestrian -1 -1 -1 422.65 160.46 452.88 213.44 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n65 5 Pedestrian -1 -1 -1 0.59 172.78 57.06 362.99 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n65 31 Pedestrian -1 -1 -1 471.20 166.63 489.61 214.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n65 12 Pedestrian -1 -1 -1 577.95 169.80 594.11 211.85 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n65 25 Pedestrian -1 -1 -1 455.02 166.11 473.16 216.20 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n66 10 Pedestrian -1 -1 -1 219.50 144.75 312.03 366.49 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n66 26 Pedestrian -1 -1 -1 713.36 167.40 760.07 267.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n66 35 Pedestrian -1 -1 -1 567.94 159.43 612.48 253.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n66 28 Pedestrian -1 -1 -1 97.63 140.62 189.15 364.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n66 19 Pedestrian -1 -1 -1 484.24 165.87 507.13 216.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n66 31 Pedestrian -1 -1 -1 469.80 167.00 490.91 214.73 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n66 7 Pedestrian -1 -1 -1 27.54 136.75 129.18 365.33 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n66 36 Pedestrian -1 -1 -1 424.22 160.02 451.94 214.22 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n66 12 Pedestrian -1 -1 -1 575.37 170.20 592.56 211.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n66 13 Pedestrian -1 -1 -1 544.42 166.46 562.64 212.76 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n66 25 Pedestrian -1 -1 -1 452.37 165.83 470.67 216.52 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n67 10 Pedestrian -1 -1 -1 197.77 145.77 295.35 364.86 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n67 26 Pedestrian -1 -1 -1 731.17 165.25 772.08 269.06 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n67 35 Pedestrian -1 -1 -1 557.49 156.28 609.24 256.71 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n67 28 Pedestrian -1 -1 -1 74.00 140.24 166.35 364.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n67 7 Pedestrian -1 -1 -1 4.53 131.65 106.38 364.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n67 19 Pedestrian -1 -1 -1 488.44 165.40 509.58 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n67 12 Pedestrian -1 -1 -1 573.77 169.65 593.53 212.18 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n67 31 Pedestrian -1 -1 -1 469.57 166.53 491.44 214.43 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n67 13 Pedestrian -1 -1 -1 548.26 164.29 564.82 212.07 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n67 25 Pedestrian -1 -1 -1 451.76 165.40 471.11 216.47 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n67 36 Pedestrian -1 -1 -1 429.93 159.20 453.51 214.73 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n67 38 Pedestrian -1 -1 -1 600.90 161.84 627.45 221.36 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n68 10 Pedestrian -1 -1 -1 166.57 142.20 280.50 363.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n68 26 Pedestrian -1 -1 -1 745.49 162.50 788.18 271.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n68 35 Pedestrian -1 -1 -1 555.17 157.70 603.87 256.72 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n68 28 Pedestrian -1 -1 -1 38.22 139.83 141.26 364.53 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n68 19 Pedestrian -1 -1 -1 492.19 165.99 512.98 215.14 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n68 36 Pedestrian -1 -1 -1 433.73 159.83 457.81 214.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n68 13 Pedestrian -1 -1 -1 549.37 164.76 565.65 211.44 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n68 12 Pedestrian -1 -1 -1 573.03 168.91 593.86 212.30 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n68 31 Pedestrian -1 -1 -1 472.88 166.46 494.05 214.25 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n68 25 Pedestrian -1 -1 -1 454.81 164.60 473.89 216.13 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n68 7 Pedestrian -1 -1 -1 1.65 132.90 86.28 363.45 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n69 10 Pedestrian -1 -1 -1 141.38 143.01 259.57 362.60 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n69 28 Pedestrian -1 -1 -1 3.26 137.89 115.63 365.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n69 35 Pedestrian -1 -1 -1 550.75 157.07 593.92 261.41 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n69 26 Pedestrian -1 -1 -1 761.47 164.12 802.40 272.78 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n69 19 Pedestrian -1 -1 -1 492.26 166.55 514.40 216.22 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n69 13 Pedestrian -1 -1 -1 551.57 164.97 568.19 211.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n69 36 Pedestrian -1 -1 -1 433.29 160.73 458.54 214.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n69 25 Pedestrian -1 -1 -1 453.85 164.81 475.48 217.51 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n69 31 Pedestrian -1 -1 -1 474.68 166.67 493.85 214.60 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n69 12 Pedestrian -1 -1 -1 586.25 167.96 602.40 210.88 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n69 39 Pedestrian -1 -1 -1 607.15 162.81 636.56 224.85 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n70 10 Pedestrian -1 -1 -1 107.49 144.85 232.90 365.09 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n70 35 Pedestrian -1 -1 -1 541.43 157.67 587.26 262.97 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n70 26 Pedestrian -1 -1 -1 767.18 166.62 813.28 276.23 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n70 28 Pedestrian -1 -1 -1 -3.07 137.87 90.79 366.22 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n70 31 Pedestrian -1 -1 -1 479.35 167.65 496.74 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n70 13 Pedestrian -1 -1 -1 551.80 164.33 568.88 211.81 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n70 19 Pedestrian -1 -1 -1 495.61 167.08 517.36 217.12 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n70 36 Pedestrian -1 -1 -1 436.43 163.26 462.00 216.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n70 25 Pedestrian -1 -1 -1 453.48 165.81 475.65 217.64 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n70 39 Pedestrian -1 -1 -1 610.64 163.39 639.98 225.31 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n70 12 Pedestrian -1 -1 -1 589.66 167.02 606.36 212.39 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n71 35 Pedestrian -1 -1 -1 524.88 157.29 580.36 264.14 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n71 10 Pedestrian -1 -1 -1 67.34 139.54 211.92 365.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n71 31 Pedestrian -1 -1 -1 482.40 168.31 499.76 215.21 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n71 26 Pedestrian -1 -1 -1 788.21 166.35 837.61 284.21 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n71 13 Pedestrian -1 -1 -1 557.66 166.12 577.84 215.46 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n71 12 Pedestrian -1 -1 -1 593.03 166.87 610.48 213.64 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n71 39 Pedestrian -1 -1 -1 614.45 163.82 643.13 226.08 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n71 36 Pedestrian -1 -1 -1 436.30 163.32 463.07 216.68 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n71 25 Pedestrian -1 -1 -1 456.50 166.70 480.50 220.12 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n71 19 Pedestrian -1 -1 -1 500.04 167.49 520.50 216.71 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n72 35 Pedestrian -1 -1 -1 515.00 156.02 575.65 269.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n72 10 Pedestrian -1 -1 -1 19.95 137.95 198.15 365.45 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n72 31 Pedestrian -1 -1 -1 485.61 167.56 503.89 215.50 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n72 26 Pedestrian -1 -1 -1 813.79 163.58 857.35 293.53 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n72 19 Pedestrian -1 -1 -1 504.44 166.55 523.70 216.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n72 25 Pedestrian -1 -1 -1 458.17 164.86 480.18 219.31 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n72 36 Pedestrian -1 -1 -1 441.18 160.92 465.47 214.09 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n72 13 Pedestrian -1 -1 -1 561.39 164.82 581.14 215.14 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n72 12 Pedestrian -1 -1 -1 596.15 165.62 614.36 213.26 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n72 39 Pedestrian -1 -1 -1 618.06 164.03 641.94 225.82 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n72 40 Pedestrian -1 -1 -1 42.92 136.17 105.74 259.83 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n73 35 Pedestrian -1 -1 -1 513.21 155.87 569.98 271.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n73 31 Pedestrian -1 -1 -1 490.06 166.35 507.59 215.15 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n73 19 Pedestrian -1 -1 -1 509.52 165.70 527.28 216.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n73 25 Pedestrian -1 -1 -1 465.92 164.19 485.96 218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n73 26 Pedestrian -1 -1 -1 835.69 160.98 881.79 296.77 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n73 10 Pedestrian -1 -1 -1 -0.70 135.36 172.97 368.14 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n73 12 Pedestrian -1 -1 -1 600.39 164.24 618.83 212.63 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n73 13 Pedestrian -1 -1 -1 566.66 163.40 585.03 212.72 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n73 36 Pedestrian -1 -1 -1 446.50 160.01 468.14 214.48 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n74 35 Pedestrian -1 -1 -1 513.28 153.94 561.57 275.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n74 31 Pedestrian -1 -1 -1 494.48 165.67 511.95 215.55 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n74 25 Pedestrian -1 -1 -1 470.48 163.58 489.91 218.96 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n74 26 Pedestrian -1 -1 -1 854.32 163.31 901.71 295.46 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n74 19 Pedestrian -1 -1 -1 512.59 164.55 533.23 216.47 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n74 13 Pedestrian -1 -1 -1 573.17 162.65 592.51 212.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n74 36 Pedestrian -1 -1 -1 452.74 159.39 476.71 214.00 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n74 10 Pedestrian -1 -1 -1 -1.31 138.04 135.15 365.90 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n74 12 Pedestrian -1 -1 -1 605.10 165.80 623.66 213.29 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n74 41 Pedestrian -1 -1 -1 628.17 162.49 652.23 227.29 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n75 35 Pedestrian -1 -1 -1 501.55 153.80 550.55 279.09 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n75 31 Pedestrian -1 -1 -1 498.65 165.68 515.92 214.95 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n75 25 Pedestrian -1 -1 -1 473.94 164.40 493.33 218.60 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n75 36 Pedestrian -1 -1 -1 454.92 160.13 482.10 215.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n75 13 Pedestrian -1 -1 -1 576.40 163.29 596.56 213.35 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n75 19 Pedestrian -1 -1 -1 517.21 165.01 541.42 215.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n75 12 Pedestrian -1 -1 -1 611.82 165.41 630.19 214.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n75 10 Pedestrian -1 -1 -1 52.40 153.64 103.99 244.89 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n75 41 Pedestrian -1 -1 -1 631.41 162.70 656.64 227.19 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n76 35 Pedestrian -1 -1 -1 482.20 152.95 546.76 282.02 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n76 13 Pedestrian -1 -1 -1 579.58 164.83 601.73 215.01 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n76 31 Pedestrian -1 -1 -1 498.89 166.73 523.47 215.31 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n76 12 Pedestrian -1 -1 -1 617.02 166.22 634.73 215.42 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n76 25 Pedestrian -1 -1 -1 477.41 165.94 497.10 221.31 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n76 36 Pedestrian -1 -1 -1 457.98 160.66 486.74 215.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n76 19 Pedestrian -1 -1 -1 522.57 165.42 544.52 217.61 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n76 10 Pedestrian -1 -1 -1 67.21 156.65 105.02 248.35 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n76 41 Pedestrian -1 -1 -1 637.32 163.94 658.99 217.09 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n77 35 Pedestrian -1 -1 -1 472.71 153.49 540.80 287.69 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n77 13 Pedestrian -1 -1 -1 583.37 165.00 606.88 215.67 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n77 25 Pedestrian -1 -1 -1 480.59 166.51 502.77 221.30 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n77 19 Pedestrian -1 -1 -1 522.50 167.27 545.04 219.15 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n77 36 Pedestrian -1 -1 -1 461.77 161.56 491.19 218.25 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n77 12 Pedestrian -1 -1 -1 621.80 166.62 638.52 215.38 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n77 10 Pedestrian -1 -1 -1 60.62 157.41 103.75 254.42 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n77 31 Pedestrian -1 -1 -1 503.92 165.57 526.32 217.03 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n77 41 Pedestrian -1 -1 -1 643.22 165.21 662.22 215.04 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n77 42 Pedestrian -1 -1 -1 70.59 156.68 116.99 248.98 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n78 35 Pedestrian -1 -1 -1 468.41 154.22 530.35 288.89 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n78 10 Pedestrian -1 -1 -1 64.33 157.47 108.29 253.74 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n78 31 Pedestrian -1 -1 -1 509.70 166.15 533.97 217.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n78 13 Pedestrian -1 -1 -1 586.96 164.46 610.55 215.11 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n78 25 Pedestrian -1 -1 -1 479.57 169.06 510.26 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n78 12 Pedestrian -1 -1 -1 625.47 166.42 642.80 215.52 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n78 19 Pedestrian -1 -1 -1 531.13 165.87 550.46 216.97 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n78 36 Pedestrian -1 -1 -1 469.46 163.07 498.08 217.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n79 35 Pedestrian -1 -1 -1 464.03 151.10 519.61 297.33 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n79 10 Pedestrian -1 -1 -1 65.84 156.16 113.62 255.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n79 25 Pedestrian -1 -1 -1 486.54 164.06 510.56 224.13 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n79 31 Pedestrian -1 -1 -1 511.63 165.99 533.65 216.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n79 19 Pedestrian -1 -1 -1 535.54 165.25 553.94 216.61 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n79 13 Pedestrian -1 -1 -1 595.58 162.45 616.43 213.76 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n79 12 Pedestrian -1 -1 -1 628.79 165.32 646.79 216.73 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n79 43 Pedestrian -1 -1 -1 651.12 163.42 669.80 212.86 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n80 35 Pedestrian -1 -1 -1 442.05 149.32 503.01 300.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n80 10 Pedestrian -1 -1 -1 59.12 154.15 112.95 256.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n80 13 Pedestrian -1 -1 -1 599.31 161.23 621.82 214.19 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n80 19 Pedestrian -1 -1 -1 537.39 165.16 559.46 216.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n80 31 Pedestrian -1 -1 -1 520.34 164.97 538.55 215.40 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n80 25 Pedestrian -1 -1 -1 488.81 161.70 510.25 219.83 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n80 12 Pedestrian -1 -1 -1 631.88 163.93 650.84 217.88 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n80 43 Pedestrian -1 -1 -1 653.88 162.36 674.64 212.61 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n81 35 Pedestrian -1 -1 -1 417.75 147.66 496.68 301.25 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n81 13 Pedestrian -1 -1 -1 603.35 161.90 624.89 213.64 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n81 19 Pedestrian -1 -1 -1 540.52 164.85 563.53 216.53 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n81 10 Pedestrian -1 -1 -1 56.61 153.87 108.12 257.34 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n81 12 Pedestrian -1 -1 -1 634.71 162.21 655.50 220.50 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n81 25 Pedestrian -1 -1 -1 491.70 161.30 514.48 218.29 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n81 31 Pedestrian -1 -1 -1 524.03 162.92 542.59 213.84 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n81 43 Pedestrian -1 -1 -1 657.43 160.98 678.01 213.08 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n82 35 Pedestrian -1 -1 -1 403.10 148.30 488.09 302.96 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n82 10 Pedestrian -1 -1 -1 58.50 152.52 106.25 259.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n82 13 Pedestrian -1 -1 -1 607.15 161.96 628.77 213.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n82 19 Pedestrian -1 -1 -1 542.67 164.33 564.03 216.11 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n82 25 Pedestrian -1 -1 -1 490.20 161.91 517.07 217.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n82 43 Pedestrian -1 -1 -1 661.46 160.62 682.33 212.47 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n82 31 Pedestrian -1 -1 -1 527.34 165.27 546.87 213.82 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n82 12 Pedestrian -1 -1 -1 638.73 161.80 658.89 219.27 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n82 44 Pedestrian -1 -1 -1 480.39 158.54 510.08 216.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n82 45 Pedestrian -1 -1 -1 75.23 152.30 119.95 252.28 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n83 35 Pedestrian -1 -1 -1 396.99 148.43 471.31 310.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n83 25 Pedestrian -1 -1 -1 494.02 161.31 519.65 218.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n83 13 Pedestrian -1 -1 -1 609.17 161.64 634.03 213.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n83 43 Pedestrian -1 -1 -1 664.45 161.03 685.72 212.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n83 19 Pedestrian -1 -1 -1 546.79 163.60 567.31 215.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n83 10 Pedestrian -1 -1 -1 61.28 150.77 103.19 260.03 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n83 44 Pedestrian -1 -1 -1 485.06 158.20 512.73 217.18 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n83 12 Pedestrian -1 -1 -1 642.56 163.14 660.64 212.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n83 31 Pedestrian -1 -1 -1 530.28 164.81 550.48 214.06 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n83 45 Pedestrian -1 -1 -1 70.71 150.84 116.93 254.69 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n84 35 Pedestrian -1 -1 -1 391.94 147.73 452.60 317.13 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n84 10 Pedestrian -1 -1 -1 61.11 149.70 110.98 261.53 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n84 25 Pedestrian -1 -1 -1 497.03 160.58 522.91 219.28 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n84 13 Pedestrian -1 -1 -1 612.67 161.72 637.40 212.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n84 43 Pedestrian -1 -1 -1 667.40 160.80 689.53 213.43 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n84 19 Pedestrian -1 -1 -1 550.96 163.10 570.90 216.17 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n84 12 Pedestrian -1 -1 -1 645.42 163.00 664.57 212.70 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n84 31 Pedestrian -1 -1 -1 531.80 164.47 550.74 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n84 45 Pedestrian -1 -1 -1 74.27 150.83 120.98 253.85 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n85 35 Pedestrian -1 -1 -1 366.66 145.15 432.34 327.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n85 19 Pedestrian -1 -1 -1 553.76 163.15 576.06 217.37 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n85 10 Pedestrian -1 -1 -1 59.70 150.66 112.14 261.59 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n85 25 Pedestrian -1 -1 -1 499.68 160.44 522.03 219.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n85 13 Pedestrian -1 -1 -1 614.08 161.93 636.31 212.79 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n85 31 Pedestrian -1 -1 -1 535.28 163.98 554.46 215.87 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n85 12 Pedestrian -1 -1 -1 646.93 164.01 665.02 212.60 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n85 43 Pedestrian -1 -1 -1 670.55 160.88 693.19 213.41 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n85 46 Pedestrian -1 -1 -1 491.02 157.17 514.31 216.52 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n86 35 Pedestrian -1 -1 -1 327.07 143.79 418.19 329.12 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n86 19 Pedestrian -1 -1 -1 554.79 163.60 580.97 217.89 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n86 10 Pedestrian -1 -1 -1 43.85 150.50 113.13 269.21 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n86 13 Pedestrian -1 -1 -1 615.65 162.00 636.15 212.87 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n86 25 Pedestrian -1 -1 -1 502.98 160.56 524.97 219.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n86 31 Pedestrian -1 -1 -1 537.95 164.17 558.70 216.11 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n86 43 Pedestrian -1 -1 -1 672.83 160.86 694.15 213.94 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n86 46 Pedestrian -1 -1 -1 490.74 157.32 515.99 216.60 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n86 12 Pedestrian -1 -1 -1 649.87 163.84 667.92 212.66 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n87 35 Pedestrian -1 -1 -1 303.97 146.53 403.51 333.54 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n87 19 Pedestrian -1 -1 -1 555.14 164.07 581.55 217.33 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n87 13 Pedestrian -1 -1 -1 618.72 161.27 639.85 214.00 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n87 43 Pedestrian -1 -1 -1 676.33 160.15 697.46 213.92 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n87 31 Pedestrian -1 -1 -1 539.34 164.15 559.22 215.70 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n87 25 Pedestrian -1 -1 -1 503.63 160.91 525.38 219.35 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n87 46 Pedestrian -1 -1 -1 493.09 157.33 519.68 217.69 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n87 10 Pedestrian -1 -1 -1 42.65 154.94 106.89 270.90 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n87 12 Pedestrian -1 -1 -1 650.89 163.89 668.67 212.16 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n87 47 Pedestrian -1 -1 -1 79.73 153.80 122.86 257.88 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n88 35 Pedestrian -1 -1 -1 287.77 145.57 381.29 344.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n88 31 Pedestrian -1 -1 -1 540.05 164.65 564.00 216.96 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n88 43 Pedestrian -1 -1 -1 678.58 160.15 701.04 214.63 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n88 19 Pedestrian -1 -1 -1 555.61 164.06 581.28 217.97 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n88 13 Pedestrian -1 -1 -1 622.99 161.28 643.46 214.84 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n88 46 Pedestrian -1 -1 -1 493.17 157.64 521.37 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n88 10 Pedestrian -1 -1 -1 46.70 154.02 102.51 273.99 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n88 25 Pedestrian -1 -1 -1 502.49 160.85 527.76 219.78 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n88 12 Pedestrian -1 -1 -1 653.18 163.81 672.18 215.09 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n88 47 Pedestrian -1 -1 -1 72.28 154.83 122.71 258.70 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n89 35 Pedestrian -1 -1 -1 266.40 140.47 349.44 356.31 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n89 31 Pedestrian -1 -1 -1 541.10 165.20 565.34 217.76 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n89 19 Pedestrian -1 -1 -1 559.73 163.78 583.58 219.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n89 25 Pedestrian -1 -1 -1 506.15 161.51 530.53 220.21 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n89 13 Pedestrian -1 -1 -1 625.57 162.95 647.60 216.50 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n89 10 Pedestrian -1 -1 -1 50.16 150.99 98.70 275.99 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n89 12 Pedestrian -1 -1 -1 653.79 164.72 673.50 214.60 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n89 46 Pedestrian -1 -1 -1 495.84 157.86 524.96 218.51 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n89 43 Pedestrian -1 -1 -1 680.96 160.59 701.51 215.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n89 47 Pedestrian -1 -1 -1 68.86 155.25 118.56 264.14 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n90 35 Pedestrian -1 -1 -1 223.73 136.60 315.49 360.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n90 10 Pedestrian -1 -1 -1 47.38 150.21 101.24 277.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n90 12 Pedestrian -1 -1 -1 654.68 165.08 673.94 216.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n90 13 Pedestrian -1 -1 -1 629.46 162.18 650.48 218.10 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n90 43 Pedestrian -1 -1 -1 684.04 162.06 705.57 217.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n90 31 Pedestrian -1 -1 -1 543.71 165.56 567.93 218.32 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n90 19 Pedestrian -1 -1 -1 563.38 163.72 587.21 220.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n90 25 Pedestrian -1 -1 -1 509.64 161.67 533.13 220.92 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n90 47 Pedestrian -1 -1 -1 65.66 153.66 114.09 265.64 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n91 35 Pedestrian -1 -1 -1 171.18 137.16 299.38 365.55 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n91 10 Pedestrian -1 -1 -1 37.45 150.40 104.34 278.46 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n91 25 Pedestrian -1 -1 -1 509.16 160.94 534.82 221.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n91 43 Pedestrian -1 -1 -1 686.38 162.09 708.65 218.55 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n91 13 Pedestrian -1 -1 -1 631.06 162.58 651.76 218.01 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n91 12 Pedestrian -1 -1 -1 656.55 164.86 676.84 218.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n91 31 Pedestrian -1 -1 -1 546.42 165.16 568.05 218.43 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n91 19 Pedestrian -1 -1 -1 566.50 164.56 591.45 219.62 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n91 47 Pedestrian -1 -1 -1 59.24 153.05 113.11 266.52 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n92 35 Pedestrian -1 -1 -1 141.71 138.47 275.39 365.95 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n92 25 Pedestrian -1 -1 -1 508.44 160.49 536.30 222.11 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n92 13 Pedestrian -1 -1 -1 634.92 162.16 655.76 219.28 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n92 10 Pedestrian -1 -1 -1 26.90 152.06 99.51 281.26 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n92 12 Pedestrian -1 -1 -1 658.55 165.04 677.54 218.91 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n92 43 Pedestrian -1 -1 -1 688.17 161.84 709.52 219.00 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n92 19 Pedestrian -1 -1 -1 569.54 165.74 595.71 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n92 31 Pedestrian -1 -1 -1 553.26 165.34 574.23 218.87 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n92 47 Pedestrian -1 -1 -1 64.86 153.30 114.78 266.41 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n93 35 Pedestrian -1 -1 -1 106.17 137.23 234.53 366.41 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n93 13 Pedestrian -1 -1 -1 638.26 162.16 659.19 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n93 19 Pedestrian -1 -1 -1 570.55 165.76 596.81 222.10 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n93 43 Pedestrian -1 -1 -1 690.75 161.57 712.85 219.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n93 25 Pedestrian -1 -1 -1 507.09 160.24 536.72 221.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n93 31 Pedestrian -1 -1 -1 553.43 165.00 576.32 219.21 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n93 12 Pedestrian -1 -1 -1 661.20 164.75 680.04 219.55 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n93 10 Pedestrian -1 -1 -1 41.29 151.64 100.13 282.31 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n93 47 Pedestrian -1 -1 -1 63.58 153.33 116.16 266.82 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n94 35 Pedestrian -1 -1 -1 71.67 131.78 184.50 364.51 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n94 19 Pedestrian -1 -1 -1 574.81 164.43 599.55 222.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n94 13 Pedestrian -1 -1 -1 641.68 161.50 662.59 221.59 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n94 25 Pedestrian -1 -1 -1 515.25 161.77 543.99 221.87 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n94 31 Pedestrian -1 -1 -1 556.67 164.85 579.95 219.35 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n94 43 Pedestrian -1 -1 -1 694.20 161.83 715.91 218.92 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n94 12 Pedestrian -1 -1 -1 663.02 165.05 680.66 218.35 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n94 10 Pedestrian -1 -1 -1 34.40 150.33 92.02 283.41 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n94 47 Pedestrian -1 -1 -1 61.92 152.91 117.69 267.88 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n94 48 Pedestrian -1 -1 -1 507.67 159.65 536.28 220.73 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n95 35 Pedestrian -1 -1 -1 20.89 126.12 143.31 363.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n95 13 Pedestrian -1 -1 -1 645.14 161.33 666.02 220.40 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n95 31 Pedestrian -1 -1 -1 560.48 164.66 582.57 218.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n95 12 Pedestrian -1 -1 -1 665.19 163.35 684.08 218.00 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n95 19 Pedestrian -1 -1 -1 579.20 163.26 602.77 220.28 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n95 25 Pedestrian -1 -1 -1 515.35 160.96 545.29 222.14 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n95 10 Pedestrian -1 -1 -1 32.78 147.30 93.47 286.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n95 43 Pedestrian -1 -1 -1 696.21 161.23 716.52 217.98 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n96 35 Pedestrian -1 -1 -1 -1.39 132.87 104.94 362.01 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n96 25 Pedestrian -1 -1 -1 519.23 160.19 547.78 222.37 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n96 31 Pedestrian -1 -1 -1 563.80 163.46 586.70 218.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n96 19 Pedestrian -1 -1 -1 583.31 162.01 606.93 220.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n96 13 Pedestrian -1 -1 -1 647.96 159.76 671.36 220.25 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n96 43 Pedestrian -1 -1 -1 699.69 160.10 719.97 216.80 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n96 12 Pedestrian -1 -1 -1 668.50 162.27 687.65 216.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n96 10 Pedestrian -1 -1 -1 31.45 147.62 94.50 286.92 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n96 49 Pedestrian -1 -1 -1 67.51 148.17 119.59 272.14 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n97 31 Pedestrian -1 -1 -1 567.57 162.71 590.70 218.94 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n97 25 Pedestrian -1 -1 -1 524.67 159.92 549.95 222.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n97 19 Pedestrian -1 -1 -1 584.90 162.85 611.66 219.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n97 12 Pedestrian -1 -1 -1 669.64 162.85 688.46 216.86 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n97 43 Pedestrian -1 -1 -1 702.55 160.13 723.72 216.69 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n97 13 Pedestrian -1 -1 -1 651.31 159.53 674.86 220.72 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n97 10 Pedestrian -1 -1 -1 22.55 148.90 96.17 293.65 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n97 49 Pedestrian -1 -1 -1 58.83 148.92 113.35 278.67 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n97 50 Pedestrian -1 -1 -1 513.23 157.65 539.24 217.57 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n98 31 Pedestrian -1 -1 -1 570.82 163.18 594.50 218.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n98 12 Pedestrian -1 -1 -1 673.19 162.88 692.25 217.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n98 25 Pedestrian -1 -1 -1 528.95 160.27 553.96 222.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n98 19 Pedestrian -1 -1 -1 586.01 162.94 612.43 220.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n98 13 Pedestrian -1 -1 -1 656.61 160.97 677.69 219.99 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n98 10 Pedestrian -1 -1 -1 16.28 149.49 94.70 294.13 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n98 49 Pedestrian -1 -1 -1 61.98 149.60 117.77 277.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n98 43 Pedestrian -1 -1 -1 706.33 159.63 727.82 217.35 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n99 31 Pedestrian -1 -1 -1 571.78 163.35 596.15 218.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n99 19 Pedestrian -1 -1 -1 589.26 163.35 615.21 220.39 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n99 25 Pedestrian -1 -1 -1 531.83 160.49 558.15 222.54 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n99 12 Pedestrian -1 -1 -1 676.17 162.16 695.82 218.00 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n99 43 Pedestrian -1 -1 -1 708.70 158.73 731.63 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n99 13 Pedestrian -1 -1 -1 657.29 159.96 678.79 220.81 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n99 49 Pedestrian -1 -1 -1 59.59 149.76 120.20 277.82 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n99 10 Pedestrian -1 -1 -1 22.21 148.91 88.78 293.21 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n99 51 Pedestrian -1 -1 -1 519.46 157.50 548.84 217.78 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n99 52 Pedestrian -1 -1 -1 649.84 159.48 670.64 219.57 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n100 31 Pedestrian -1 -1 -1 574.27 163.24 599.89 219.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n100 25 Pedestrian -1 -1 -1 535.90 160.98 561.73 222.10 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n100 19 Pedestrian -1 -1 -1 592.35 161.75 619.25 222.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n100 43 Pedestrian -1 -1 -1 710.89 159.61 732.40 219.41 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n100 12 Pedestrian -1 -1 -1 677.49 162.20 696.55 217.63 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n100 52 Pedestrian -1 -1 -1 652.86 159.22 673.73 220.37 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n100 13 Pedestrian -1 -1 -1 661.11 159.28 682.30 221.45 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n100 51 Pedestrian -1 -1 -1 521.61 157.23 553.55 218.99 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n100 49 Pedestrian -1 -1 -1 55.96 149.87 116.15 278.78 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n100 10 Pedestrian -1 -1 -1 19.54 148.53 83.72 300.88 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n101 19 Pedestrian -1 -1 -1 597.34 161.98 622.59 221.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n101 10 Pedestrian -1 -1 -1 16.11 146.79 79.30 303.33 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n101 12 Pedestrian -1 -1 -1 680.96 161.90 700.20 218.39 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n101 31 Pedestrian -1 -1 -1 577.69 162.84 602.65 219.74 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n101 52 Pedestrian -1 -1 -1 654.03 160.54 675.15 219.91 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n101 13 Pedestrian -1 -1 -1 665.00 160.38 685.65 221.07 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n101 43 Pedestrian -1 -1 -1 713.82 159.32 736.86 220.01 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n101 25 Pedestrian -1 -1 -1 535.44 160.79 563.30 221.80 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n101 51 Pedestrian -1 -1 -1 524.72 157.49 557.27 221.67 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n101 49 Pedestrian -1 -1 -1 56.90 149.25 114.93 284.31 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n102 31 Pedestrian -1 -1 -1 583.29 162.27 606.12 219.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n102 10 Pedestrian -1 -1 -1 10.92 146.14 77.78 305.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n102 25 Pedestrian -1 -1 -1 539.41 160.05 566.81 222.39 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n102 52 Pedestrian -1 -1 -1 656.74 160.44 677.67 220.00 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n102 43 Pedestrian -1 -1 -1 717.39 159.62 740.06 220.07 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n102 12 Pedestrian -1 -1 -1 684.50 162.14 702.79 218.68 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n102 19 Pedestrian -1 -1 -1 601.53 161.92 624.95 221.93 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n102 13 Pedestrian -1 -1 -1 668.23 160.69 688.86 220.88 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n102 51 Pedestrian -1 -1 -1 528.88 155.91 560.34 220.24 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n102 49 Pedestrian -1 -1 -1 57.88 147.33 114.28 286.61 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n103 31 Pedestrian -1 -1 -1 586.47 162.87 610.68 219.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n103 25 Pedestrian -1 -1 -1 542.36 159.42 570.24 223.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n103 43 Pedestrian -1 -1 -1 720.02 159.72 743.99 220.74 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n103 10 Pedestrian -1 -1 -1 9.88 148.79 78.30 309.13 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n103 12 Pedestrian -1 -1 -1 685.57 161.79 704.32 219.61 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n103 52 Pedestrian -1 -1 -1 658.04 160.93 678.37 219.60 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n103 19 Pedestrian -1 -1 -1 602.10 162.05 626.93 222.07 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n103 49 Pedestrian -1 -1 -1 60.85 148.11 118.80 287.21 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n103 13 Pedestrian -1 -1 -1 671.60 160.57 693.10 221.36 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n104 43 Pedestrian -1 -1 -1 722.65 160.02 748.84 222.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n104 19 Pedestrian -1 -1 -1 605.63 164.05 630.43 223.67 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n104 31 Pedestrian -1 -1 -1 589.55 164.96 614.60 221.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n104 52 Pedestrian -1 -1 -1 661.16 160.63 681.85 220.74 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n104 25 Pedestrian -1 -1 -1 541.43 158.83 571.97 223.57 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n104 12 Pedestrian -1 -1 -1 688.59 162.21 707.81 220.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n104 10 Pedestrian -1 -1 -1 6.19 152.97 74.62 311.43 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n104 49 Pedestrian -1 -1 -1 59.77 148.80 127.42 287.61 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n104 13 Pedestrian -1 -1 -1 675.11 161.03 696.82 222.12 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n105 31 Pedestrian -1 -1 -1 592.57 165.78 618.93 223.64 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 25 Pedestrian -1 -1 -1 548.27 161.87 579.30 225.97 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 19 Pedestrian -1 -1 -1 609.20 164.75 634.97 225.56 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n105 43 Pedestrian -1 -1 -1 724.82 160.37 749.30 223.10 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n105 52 Pedestrian -1 -1 -1 663.65 160.50 685.60 222.82 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n105 12 Pedestrian -1 -1 -1 691.26 163.53 711.50 223.08 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n105 10 Pedestrian -1 -1 -1 10.79 153.41 76.91 312.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n105 13 Pedestrian -1 -1 -1 678.20 161.72 701.42 224.78 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n105 49 Pedestrian -1 -1 -1 47.73 150.18 124.33 293.35 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n105 53 Pedestrian -1 -1 -1 541.32 158.47 571.46 223.26 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n106 31 Pedestrian -1 -1 -1 595.23 165.45 618.86 225.55 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n106 25 Pedestrian -1 -1 -1 553.15 163.45 582.46 227.34 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n106 43 Pedestrian -1 -1 -1 728.63 160.73 752.24 223.30 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n106 12 Pedestrian -1 -1 -1 695.29 163.93 714.60 222.92 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n106 53 Pedestrian -1 -1 -1 542.33 159.75 571.32 223.27 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n106 13 Pedestrian -1 -1 -1 679.16 161.92 702.80 224.97 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n106 52 Pedestrian -1 -1 -1 665.18 161.73 685.85 222.18 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n106 19 Pedestrian -1 -1 -1 613.37 165.06 638.00 226.62 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n106 49 Pedestrian -1 -1 -1 47.88 150.64 124.31 299.92 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n106 10 Pedestrian -1 -1 -1 8.41 152.95 71.93 320.67 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n107 25 Pedestrian -1 -1 -1 556.70 164.01 586.60 230.02 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n107 43 Pedestrian -1 -1 -1 731.13 161.33 756.59 225.64 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n107 31 Pedestrian -1 -1 -1 601.38 165.21 625.39 226.64 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n107 13 Pedestrian -1 -1 -1 683.32 162.88 705.43 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n107 19 Pedestrian -1 -1 -1 617.71 165.20 641.17 228.68 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n107 12 Pedestrian -1 -1 -1 699.26 164.76 718.30 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n107 52 Pedestrian -1 -1 -1 668.47 163.41 688.53 223.32 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n107 53 Pedestrian -1 -1 -1 545.95 161.16 574.92 222.47 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n107 10 Pedestrian -1 -1 -1 0.25 153.95 57.16 327.30 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n107 49 Pedestrian -1 -1 -1 39.64 152.39 117.09 306.10 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n108 43 Pedestrian -1 -1 -1 734.85 160.76 760.70 226.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n108 25 Pedestrian -1 -1 -1 556.57 163.69 587.98 227.95 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n108 31 Pedestrian -1 -1 -1 603.38 164.67 625.59 225.29 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n108 13 Pedestrian -1 -1 -1 687.03 162.23 709.03 225.48 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n108 52 Pedestrian -1 -1 -1 669.34 162.41 690.02 224.30 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n108 10 Pedestrian -1 -1 -1 -0.67 151.83 58.32 336.45 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n108 49 Pedestrian -1 -1 -1 42.92 151.11 114.08 308.05 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n108 19 Pedestrian -1 -1 -1 621.53 165.11 645.79 226.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n108 12 Pedestrian -1 -1 -1 703.04 164.22 722.00 222.64 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n109 25 Pedestrian -1 -1 -1 565.74 162.03 592.79 228.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n109 31 Pedestrian -1 -1 -1 608.18 164.68 633.31 225.17 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n109 19 Pedestrian -1 -1 -1 624.18 164.66 650.49 227.09 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n109 52 Pedestrian -1 -1 -1 672.06 161.68 692.38 222.37 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n109 13 Pedestrian -1 -1 -1 690.99 161.73 713.21 225.52 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n109 12 Pedestrian -1 -1 -1 704.07 164.10 723.63 222.96 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n109 49 Pedestrian -1 -1 -1 43.84 150.67 113.10 313.76 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n109 43 Pedestrian -1 -1 -1 738.86 161.32 763.20 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n109 10 Pedestrian -1 -1 -1 4.41 148.94 68.28 339.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n109 54 Pedestrian -1 -1 -1 551.16 159.63 578.02 222.50 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n110 19 Pedestrian -1 -1 -1 626.34 165.41 655.33 229.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n110 52 Pedestrian -1 -1 -1 672.73 162.65 693.87 224.32 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n110 49 Pedestrian -1 -1 -1 32.64 149.02 124.22 317.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n110 12 Pedestrian -1 -1 -1 707.09 164.09 727.84 224.73 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n110 25 Pedestrian -1 -1 -1 571.59 163.05 596.13 228.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n110 43 Pedestrian -1 -1 -1 741.79 161.81 768.26 225.90 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n110 31 Pedestrian -1 -1 -1 609.58 165.44 635.10 226.35 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n110 13 Pedestrian -1 -1 -1 694.80 162.00 717.67 226.07 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n110 54 Pedestrian -1 -1 -1 557.58 159.00 585.16 224.86 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n110 10 Pedestrian -1 -1 -1 4.69 150.19 60.76 346.01 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n111 19 Pedestrian -1 -1 -1 630.42 166.52 658.62 231.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n111 31 Pedestrian -1 -1 -1 612.49 168.16 639.03 229.90 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n111 54 Pedestrian -1 -1 -1 557.51 161.63 587.04 228.38 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n111 49 Pedestrian -1 -1 -1 27.96 150.91 121.33 322.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n111 25 Pedestrian -1 -1 -1 577.32 165.95 603.57 231.31 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n111 43 Pedestrian -1 -1 -1 744.09 162.35 768.36 227.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n111 52 Pedestrian -1 -1 -1 674.06 163.85 697.78 226.35 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n111 13 Pedestrian -1 -1 -1 698.09 163.06 721.46 228.01 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n111 12 Pedestrian -1 -1 -1 710.21 164.62 730.98 226.98 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n111 10 Pedestrian -1 -1 -1 5.41 155.09 51.80 349.06 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n112 19 Pedestrian -1 -1 -1 636.16 167.66 661.03 231.55 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n112 12 Pedestrian -1 -1 -1 709.00 165.43 733.77 229.86 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n112 25 Pedestrian -1 -1 -1 578.72 167.21 604.39 234.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n112 43 Pedestrian -1 -1 -1 748.64 163.67 770.86 227.43 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n112 31 Pedestrian -1 -1 -1 616.15 168.08 641.19 231.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n112 52 Pedestrian -1 -1 -1 674.33 165.94 698.34 228.90 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n112 49 Pedestrian -1 -1 -1 26.58 154.98 107.41 325.92 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n112 13 Pedestrian -1 -1 -1 701.18 164.08 725.99 230.37 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n112 54 Pedestrian -1 -1 -1 560.94 164.26 589.41 230.65 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n112 10 Pedestrian -1 -1 -1 1.44 160.37 33.30 351.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n113 19 Pedestrian -1 -1 -1 640.60 166.90 664.50 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n113 31 Pedestrian -1 -1 -1 621.33 167.21 644.29 230.50 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n113 49 Pedestrian -1 -1 -1 19.69 150.49 91.73 331.10 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n113 12 Pedestrian -1 -1 -1 712.85 165.30 737.38 229.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n113 43 Pedestrian -1 -1 -1 752.79 163.58 774.41 227.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n113 25 Pedestrian -1 -1 -1 582.43 165.63 608.37 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n113 52 Pedestrian -1 -1 -1 675.44 166.10 698.06 228.34 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n113 13 Pedestrian -1 -1 -1 704.78 164.19 730.01 229.93 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n113 54 Pedestrian -1 -1 -1 562.13 162.95 590.41 231.85 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n114 19 Pedestrian -1 -1 -1 643.33 165.25 669.42 231.33 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n114 49 Pedestrian -1 -1 -1 18.07 150.08 85.31 331.78 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n114 25 Pedestrian -1 -1 -1 585.38 162.41 612.14 233.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n114 31 Pedestrian -1 -1 -1 625.15 165.07 648.64 229.74 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n114 12 Pedestrian -1 -1 -1 716.50 162.30 740.27 228.64 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n114 13 Pedestrian -1 -1 -1 709.38 160.97 733.19 229.03 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n114 54 Pedestrian -1 -1 -1 565.53 158.41 593.72 230.68 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n114 43 Pedestrian -1 -1 -1 756.07 162.56 778.02 226.38 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n114 52 Pedestrian -1 -1 -1 676.22 163.09 698.06 226.55 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n115 49 Pedestrian -1 -1 -1 8.78 149.28 94.34 338.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n115 19 Pedestrian -1 -1 -1 646.47 163.46 673.99 231.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n115 54 Pedestrian -1 -1 -1 569.08 154.43 597.69 229.17 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n115 13 Pedestrian -1 -1 -1 712.54 158.43 737.71 229.71 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n115 25 Pedestrian -1 -1 -1 586.10 159.61 611.53 230.74 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n115 31 Pedestrian -1 -1 -1 629.18 164.80 651.43 226.95 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n115 43 Pedestrian -1 -1 -1 759.61 159.72 782.26 224.46 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n115 52 Pedestrian -1 -1 -1 679.19 162.12 700.99 227.35 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n116 43 Pedestrian -1 -1 -1 762.20 158.43 786.67 224.75 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n116 49 Pedestrian -1 -1 -1 4.88 149.79 91.24 339.11 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n116 25 Pedestrian -1 -1 -1 588.53 158.87 617.29 231.41 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n116 54 Pedestrian -1 -1 -1 573.50 156.12 601.05 230.71 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n116 13 Pedestrian -1 -1 -1 714.82 157.90 742.18 229.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n116 19 Pedestrian -1 -1 -1 653.05 161.44 680.42 230.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n116 52 Pedestrian -1 -1 -1 679.66 160.35 701.47 223.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n116 31 Pedestrian -1 -1 -1 630.36 163.97 652.43 228.08 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n117 31 Pedestrian -1 -1 -1 632.49 164.61 657.51 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n117 54 Pedestrian -1 -1 -1 576.04 158.19 605.41 232.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n117 19 Pedestrian -1 -1 -1 657.62 162.30 684.40 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n117 13 Pedestrian -1 -1 -1 718.29 159.38 746.06 231.30 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n117 43 Pedestrian -1 -1 -1 764.76 159.87 792.27 226.65 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n117 25 Pedestrian -1 -1 -1 591.16 161.64 622.08 234.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n117 52 Pedestrian -1 -1 -1 680.05 163.11 701.82 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n117 49 Pedestrian -1 -1 -1 3.92 155.19 76.76 348.90 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n118 13 Pedestrian -1 -1 -1 720.60 161.83 745.59 233.86 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n118 19 Pedestrian -1 -1 -1 661.41 165.41 688.45 237.49 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n118 31 Pedestrian -1 -1 -1 635.22 167.07 662.74 235.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n118 43 Pedestrian -1 -1 -1 767.08 161.26 797.23 229.25 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n118 25 Pedestrian -1 -1 -1 593.95 165.15 626.34 238.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n118 49 Pedestrian -1 -1 -1 3.37 160.74 54.05 359.16 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n118 54 Pedestrian -1 -1 -1 576.73 162.07 605.89 236.67 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n118 52 Pedestrian -1 -1 -1 683.56 165.48 704.55 229.11 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n119 31 Pedestrian -1 -1 -1 638.98 167.92 664.40 236.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n119 19 Pedestrian -1 -1 -1 665.46 167.08 692.72 238.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n119 43 Pedestrian -1 -1 -1 770.46 162.42 801.45 232.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n119 13 Pedestrian -1 -1 -1 724.24 163.26 749.20 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n119 25 Pedestrian -1 -1 -1 594.30 166.34 626.79 239.79 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n119 54 Pedestrian -1 -1 -1 580.61 162.99 608.47 239.44 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n119 52 Pedestrian -1 -1 -1 683.30 167.25 706.29 231.11 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n119 49 Pedestrian -1 -1 -1 0.41 164.37 19.42 361.76 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n120 31 Pedestrian -1 -1 -1 642.08 167.21 669.41 236.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n120 19 Pedestrian -1 -1 -1 668.70 166.19 697.94 238.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n120 52 Pedestrian -1 -1 -1 686.92 165.15 709.79 232.15 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n120 13 Pedestrian -1 -1 -1 729.92 162.21 756.70 236.54 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n120 25 Pedestrian -1 -1 -1 598.56 164.18 629.94 241.81 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n120 43 Pedestrian -1 -1 -1 774.14 161.31 804.85 232.80 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n120 54 Pedestrian -1 -1 -1 584.18 160.13 613.32 238.68 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n121 31 Pedestrian -1 -1 -1 645.23 166.81 674.39 235.78 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n121 19 Pedestrian -1 -1 -1 671.84 164.53 702.98 239.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n121 52 Pedestrian -1 -1 -1 689.46 162.38 714.77 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n121 13 Pedestrian -1 -1 -1 732.11 160.55 757.18 236.01 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n121 25 Pedestrian -1 -1 -1 607.76 161.06 635.38 242.48 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n121 43 Pedestrian -1 -1 -1 779.40 157.71 806.82 231.98 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n121 54 Pedestrian -1 -1 -1 588.15 158.05 617.13 237.60 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n122 52 Pedestrian -1 -1 -1 693.12 162.89 718.55 234.71 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n122 31 Pedestrian -1 -1 -1 647.71 166.55 678.81 238.10 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n122 19 Pedestrian -1 -1 -1 677.96 165.19 709.98 239.88 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n122 54 Pedestrian -1 -1 -1 590.59 158.88 622.64 239.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n122 25 Pedestrian -1 -1 -1 611.47 162.51 639.71 243.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n122 13 Pedestrian -1 -1 -1 737.18 159.45 764.57 239.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n122 43 Pedestrian -1 -1 -1 782.75 159.40 812.51 235.24 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n123 31 Pedestrian -1 -1 -1 652.19 168.55 682.59 242.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n123 54 Pedestrian -1 -1 -1 593.30 161.95 626.96 243.93 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n123 19 Pedestrian -1 -1 -1 682.17 166.54 714.95 245.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n123 52 Pedestrian -1 -1 -1 696.53 166.15 722.69 237.93 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n123 13 Pedestrian -1 -1 -1 738.11 162.78 766.15 242.34 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n123 25 Pedestrian -1 -1 -1 616.98 166.67 648.89 246.81 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n123 43 Pedestrian -1 -1 -1 786.64 162.12 816.22 236.70 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n124 19 Pedestrian -1 -1 -1 691.07 166.73 721.60 246.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n124 31 Pedestrian -1 -1 -1 658.07 168.70 686.02 242.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n124 54 Pedestrian -1 -1 -1 596.08 163.46 630.69 246.55 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n124 13 Pedestrian -1 -1 -1 742.19 164.73 769.48 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-1 -1 644.64 167.79 683.38 259.99 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n128 54 Pedestrian -1 -1 -1 616.92 163.33 655.98 255.56 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n128 13 Pedestrian -1 -1 -1 760.99 164.62 794.87 253.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n128 31 Pedestrian -1 -1 -1 678.59 170.41 710.48 251.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n128 43 Pedestrian -1 -1 -1 815.90 165.53 847.38 246.19 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n129 54 Pedestrian -1 -1 -1 622.29 161.82 660.39 257.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n129 19 Pedestrian -1 -1 -1 726.40 164.10 760.94 254.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n129 13 Pedestrian -1 -1 -1 766.60 163.04 799.29 255.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n129 31 Pedestrian -1 -1 -1 685.79 167.14 718.25 253.97 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n129 25 Pedestrian -1 -1 -1 652.34 167.09 691.12 261.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n129 43 Pedestrian -1 -1 -1 824.12 163.56 855.27 249.33 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n130 54 Pedestrian -1 -1 -1 628.18 159.40 667.91 258.81 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n130 25 Pedestrian -1 -1 -1 660.80 164.28 704.78 262.82 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n130 19 Pedestrian -1 -1 -1 734.55 162.95 769.10 254.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n130 13 Pedestrian -1 -1 -1 774.58 159.81 811.70 258.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n130 31 Pedestrian -1 -1 -1 694.39 165.97 724.88 251.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n130 43 Pedestrian -1 -1 -1 830.19 158.58 865.09 247.04 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n131 54 Pedestrian -1 -1 -1 634.22 157.46 670.36 260.23 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n131 13 Pedestrian -1 -1 -1 782.90 157.87 819.35 260.03 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n131 25 Pedestrian -1 -1 -1 668.28 162.55 712.88 263.62 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n131 31 Pedestrian -1 -1 -1 701.00 164.34 732.97 249.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n131 43 Pedestrian -1 -1 -1 842.58 156.80 882.82 248.29 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n131 19 Pedestrian -1 -1 -1 744.07 160.69 781.00 256.98 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n132 19 Pedestrian -1 -1 -1 752.17 161.57 788.80 257.58 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n132 13 Pedestrian -1 -1 -1 791.93 157.86 826.55 261.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n132 25 Pedestrian -1 -1 -1 676.66 161.90 720.20 265.16 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n132 54 Pedestrian -1 -1 -1 640.60 155.89 678.35 261.32 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n132 31 Pedestrian -1 -1 -1 711.17 165.50 745.37 252.10 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n132 43 Pedestrian -1 -1 -1 853.02 158.35 894.94 252.04 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n133 13 Pedestrian -1 -1 -1 803.55 156.15 837.09 264.79 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n133 19 Pedestrian -1 -1 -1 761.10 162.45 797.07 259.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n133 54 Pedestrian -1 -1 -1 644.46 156.30 683.61 261.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n133 43 Pedestrian -1 -1 -1 864.63 158.60 906.47 253.99 -1 -1 -1 -1000 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-1 -1 -1000 -1000 -1000 -10 0.81\n135 54 Pedestrian -1 -1 -1 659.19 158.79 706.13 267.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n135 43 Pedestrian -1 -1 -1 888.49 158.00 935.92 263.45 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n135 31 Pedestrian -1 -1 -1 739.29 164.93 779.61 262.81 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n136 54 Pedestrian -1 -1 -1 664.63 158.10 709.05 270.38 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n136 13 Pedestrian -1 -1 -1 837.38 155.52 879.58 272.13 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n136 19 Pedestrian -1 -1 -1 790.97 159.19 834.62 268.23 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n136 25 Pedestrian -1 -1 -1 732.70 161.99 777.72 281.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n136 43 Pedestrian -1 -1 -1 902.79 158.08 952.18 267.02 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n136 31 Pedestrian -1 -1 -1 750.58 163.52 791.13 264.72 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n137 25 Pedestrian -1 -1 -1 744.52 159.29 789.76 290.29 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n137 54 Pedestrian -1 -1 -1 676.12 160.54 719.32 272.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n137 19 Pedestrian -1 -1 -1 802.20 159.71 846.42 268.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n137 13 Pedestrian -1 -1 -1 850.69 155.03 896.70 280.28 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n137 43 Pedestrian -1 -1 -1 916.69 155.03 968.77 270.40 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n137 31 Pedestrian -1 -1 -1 763.60 163.89 807.92 266.05 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n138 54 Pedestrian -1 -1 -1 688.34 156.15 730.74 277.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n138 25 Pedestrian -1 -1 -1 761.55 158.47 809.65 292.47 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n138 13 Pedestrian -1 -1 -1 868.51 152.29 917.15 283.24 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n138 19 Pedestrian -1 -1 -1 817.19 160.75 861.50 275.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n138 43 Pedestrian -1 -1 -1 931.28 152.88 985.78 272.69 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n138 31 Pedestrian -1 -1 -1 770.82 162.23 817.23 272.30 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n139 54 Pedestrian -1 -1 -1 701.05 153.62 748.95 281.59 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n139 25 Pedestrian -1 -1 -1 774.34 157.61 821.73 292.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n139 13 Pedestrian -1 -1 -1 886.87 149.04 937.12 293.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n139 43 Pedestrian -1 -1 -1 949.31 153.07 1005.26 275.56 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n139 19 Pedestrian -1 -1 -1 827.96 159.58 874.30 282.96 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n139 55 Car -1 -1 -1 617.96 175.41 663.71 191.20 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n140 54 Pedestrian -1 -1 -1 709.46 155.55 763.43 288.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n140 25 Pedestrian -1 -1 -1 791.27 159.57 841.85 299.24 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n140 13 Pedestrian -1 -1 -1 905.80 151.71 956.39 298.46 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n140 43 Pedestrian -1 -1 -1 968.14 156.14 1024.63 280.66 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n140 19 Pedestrian -1 -1 -1 843.23 161.03 889.37 282.45 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n141 25 Pedestrian -1 -1 -1 806.81 161.53 857.54 305.41 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n141 54 Pedestrian -1 -1 -1 724.36 159.21 778.53 298.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n141 43 Pedestrian -1 -1 -1 987.45 157.91 1043.65 291.00 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n141 19 Pedestrian -1 -1 -1 852.64 162.68 903.31 288.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n141 13 Pedestrian -1 -1 -1 925.05 158.23 975.57 301.04 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n141 56 Car -1 -1 -1 625.74 179.43 670.25 195.05 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n142 25 Pedestrian -1 -1 -1 825.76 161.26 876.97 319.05 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n142 54 Pedestrian -1 -1 -1 736.50 158.16 789.98 306.73 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n142 43 Pedestrian -1 -1 -1 1005.56 156.69 1064.13 299.41 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n142 13 Pedestrian -1 -1 -1 947.55 159.07 1000.02 313.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n142 19 Pedestrian -1 -1 -1 867.05 166.48 912.35 284.19 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n142 56 Car 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Pedestrian -1 -1 -1 1054.52 149.85 1114.34 301.66 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n144 56 Car -1 -1 -1 629.88 175.34 675.38 192.08 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n144 57 Pedestrian -1 -1 -1 915.19 160.54 970.61 306.04 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n145 54 Pedestrian -1 -1 -1 778.75 147.90 838.87 316.31 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n145 25 Pedestrian -1 -1 -1 891.93 151.82 955.86 336.41 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n145 13 Pedestrian -1 -1 -1 1033.57 146.60 1097.00 332.97 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n145 56 Car -1 -1 -1 629.21 174.28 677.00 191.55 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n145 43 Pedestrian -1 -1 -1 1081.41 148.25 1148.62 310.30 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n145 57 Pedestrian -1 -1 -1 942.87 161.16 996.10 312.35 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n146 54 Pedestrian -1 -1 -1 792.37 146.42 856.27 326.18 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n146 25 Pedestrian -1 -1 -1 922.67 152.36 993.99 352.21 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n146 57 Pedestrian -1 -1 -1 970.12 161.48 1029.80 319.84 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n146 13 Pedestrian -1 -1 -1 1067.56 149.60 1139.74 339.74 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n146 56 Car -1 -1 -1 632.25 174.32 677.88 192.15 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n146 43 Pedestrian -1 -1 -1 1100.00 151.39 1183.27 315.26 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n146 58 Pedestrian -1 -1 -1 902.64 159.62 960.46 313.50 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n147 54 Pedestrian -1 -1 -1 810.32 147.43 876.79 334.39 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n147 25 Pedestrian -1 -1 -1 958.47 156.46 1034.37 363.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n147 58 Pedestrian -1 -1 -1 923.31 161.36 985.36 320.82 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n147 57 Pedestrian -1 -1 -1 999.38 161.08 1069.79 327.65 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n147 56 Car -1 -1 -1 633.25 176.70 677.88 194.30 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n147 13 Pedestrian -1 -1 -1 1114.25 151.71 1192.18 352.54 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n148 25 Pedestrian -1 -1 -1 999.11 164.74 1077.63 360.55 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n148 58 Pedestrian -1 -1 -1 947.93 163.85 1014.33 332.61 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n148 54 Pedestrian -1 -1 -1 830.81 145.76 901.82 358.28 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n148 56 Car -1 -1 -1 633.02 179.03 680.01 196.03 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n148 13 Pedestrian -1 -1 -1 1154.78 149.31 1220.41 361.79 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n148 57 Pedestrian -1 -1 -1 1018.23 159.52 1104.82 344.70 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n148 59 Pedestrian -1 -1 -1 1045.26 163.86 1115.33 332.09 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n149 58 Pedestrian -1 -1 -1 970.67 160.30 1045.41 351.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n149 54 Pedestrian -1 -1 -1 848.94 150.66 929.79 361.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n149 25 Pedestrian -1 -1 -1 1044.92 158.62 1131.69 360.99 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n149 56 Car -1 -1 -1 632.70 179.76 679.22 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n149 59 Pedestrian -1 -1 -1 1057.46 161.24 1157.08 335.39 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n150 58 Pedestrian -1 -1 -1 995.81 156.96 1081.01 361.44 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n150 54 Pedestrian -1 -1 -1 872.77 140.22 967.18 363.59 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n150 25 Pedestrian -1 -1 -1 1078.73 148.88 1205.27 362.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n150 56 Car -1 -1 -1 629.72 179.26 676.48 195.11 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n150 59 Pedestrian -1 -1 -1 1009.15 151.07 1090.40 337.25 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n151 58 Pedestrian -1 -1 -1 1021.18 153.60 1124.92 365.65 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n151 54 Pedestrian -1 -1 -1 900.83 132.94 1007.86 363.50 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n151 59 Pedestrian -1 -1 -1 1027.33 147.55 1126.05 341.18 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n151 25 Pedestrian -1 -1 -1 1127.93 147.21 1216.86 357.13 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n151 56 Car -1 -1 -1 628.12 178.80 674.81 194.81 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n152 54 Pedestrian -1 -1 -1 928.77 131.43 1048.74 365.72 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n152 58 Pedestrian -1 -1 -1 1045.00 152.90 1162.30 365.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n152 56 Car -1 -1 -1 624.21 178.58 671.00 195.20 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n152 60 Cyclist -1 -1 -1 843.25 158.28 882.38 238.37 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n153 54 Pedestrian -1 -1 -1 970.09 128.08 1099.75 368.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n153 58 Pedestrian -1 -1 -1 1078.53 153.76 1212.86 365.26 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n153 60 Cyclist -1 -1 -1 849.37 156.19 884.38 240.91 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n153 56 Car -1 -1 -1 618.47 178.19 663.67 195.74 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n154 54 Pedestrian -1 -1 -1 1011.59 124.19 1165.11 365.02 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n154 56 Car -1 -1 -1 612.30 178.14 660.35 195.91 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n154 60 Cyclist -1 -1 -1 848.80 156.87 891.00 246.11 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n154 58 Pedestrian -1 -1 -1 1145.07 156.82 1214.82 362.59 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n155 56 Car -1 -1 -1 605.40 178.39 653.20 196.01 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n155 54 Pedestrian -1 -1 -1 1060.62 122.47 1223.00 366.67 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n155 60 Cyclist -1 -1 -1 844.20 157.31 889.21 254.80 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n156 54 Pedestrian -1 -1 -1 1130.91 116.94 1221.53 363.54 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n156 60 Cyclist -1 -1 -1 842.00 156.65 891.19 254.77 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n156 56 Car -1 -1 -1 597.43 177.05 644.61 195.66 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n157 60 Cyclist -1 -1 -1 843.20 155.38 896.26 256.03 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n157 56 Car -1 -1 -1 586.82 176.59 634.93 195.54 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n158 60 Cyclist -1 -1 -1 838.83 153.57 902.66 259.98 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n158 56 Car -1 -1 -1 578.28 176.89 627.45 197.05 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n159 60 Cyclist -1 -1 -1 835.77 154.64 905.76 263.48 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n159 56 Car -1 -1 -1 569.48 176.29 620.12 196.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n160 60 Cyclist -1 -1 -1 843.08 152.53 911.96 268.33 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n160 56 Car -1 -1 -1 561.25 176.15 613.35 195.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n161 60 Cyclist -1 -1 -1 844.64 150.55 919.57 271.35 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n161 56 Car -1 -1 -1 553.23 176.05 606.14 195.45 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n161 61 Cyclist -1 -1 -1 805.15 166.68 859.01 266.83 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n161 62 Pedestrian -1 -1 -1 1157.52 175.94 1179.07 226.63 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n161 63 Pedestrian -1 -1 -1 522.19 170.94 535.83 204.18 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n162 60 Cyclist -1 -1 -1 858.19 147.85 927.71 277.55 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n162 56 Car -1 -1 -1 544.31 174.50 600.35 194.06 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n162 62 Pedestrian -1 -1 -1 1162.14 172.84 1182.98 222.64 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n162 63 Pedestrian -1 -1 -1 511.59 169.15 526.85 206.05 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n162 61 Cyclist -1 -1 -1 805.76 164.77 858.98 269.86 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n163 60 Cyclist -1 -1 -1 863.30 145.30 939.08 280.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n163 56 Car -1 -1 -1 537.07 173.64 592.85 194.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n163 63 Pedestrian -1 -1 -1 501.68 167.57 518.93 208.69 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n163 62 Pedestrian -1 -1 -1 1167.40 178.09 1191.98 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n164 60 Cyclist -1 -1 -1 878.30 144.30 953.53 284.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n164 56 Car -1 -1 -1 531.74 174.02 587.74 194.70 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n164 63 Pedestrian -1 -1 -1 493.25 168.81 511.01 211.55 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n164 62 Pedestrian -1 -1 -1 1173.89 168.19 1201.10 228.76 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n164 64 Cyclist -1 -1 -1 814.12 161.64 872.29 280.71 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n165 60 Cyclist -1 -1 -1 876.25 145.71 979.22 296.54 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n165 56 Car -1 -1 -1 524.84 174.92 582.04 197.32 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n165 62 Pedestrian -1 -1 -1 1187.02 173.26 1211.06 230.31 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n165 63 Pedestrian -1 -1 -1 484.90 170.25 503.65 213.10 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n165 65 Pedestrian -1 -1 -1 822.58 163.48 886.53 292.88 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n166 60 Cyclist -1 -1 -1 905.77 143.52 995.58 306.98 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n166 56 Car -1 -1 -1 520.59 176.22 577.92 198.42 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n166 63 Pedestrian -1 -1 -1 474.65 172.07 494.99 217.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n166 62 Pedestrian -1 -1 -1 1197.18 170.85 1217.32 232.11 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n166 66 Cyclist -1 -1 -1 836.20 164.08 903.03 300.92 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n167 56 Car -1 -1 -1 516.84 178.05 574.18 201.09 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n167 60 Cyclist -1 -1 -1 936.64 142.08 1010.51 315.21 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n167 63 Pedestrian -1 -1 -1 469.30 173.96 490.15 223.28 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n167 66 Cyclist -1 -1 -1 850.62 162.91 920.39 316.80 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n168 56 Car -1 -1 -1 515.29 178.68 574.08 201.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n168 66 Cyclist -1 -1 -1 867.81 163.09 949.07 325.46 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n168 63 Pedestrian -1 -1 -1 464.50 174.60 485.89 228.48 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n168 60 Cyclist -1 -1 -1 961.10 140.45 1046.88 316.87 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n169 56 Car -1 -1 -1 514.76 178.92 574.47 201.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n169 66 Cyclist -1 -1 -1 890.04 159.44 987.31 343.39 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n169 63 Pedestrian -1 -1 -1 460.76 174.14 481.76 229.49 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n169 67 Pedestrian -1 -1 -1 995.45 141.77 1066.53 317.39 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0027.txt",
    "content": "0 1 Car -1 -1 -1 555.45 194.16 680.29 301.62 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n0 2 Van -1 -1 -1 998.96 119.68 1220.17 232.41 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n0 3 Pedestrian -1 -1 -1 481.67 178.65 511.36 262.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n0 4 Pedestrian -1 -1 -1 178.17 166.82 285.40 368.80 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n0 5 Pedestrian -1 -1 -1 940.12 155.60 971.63 231.53 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n0 6 Pedestrian -1 -1 -1 389.21 174.43 450.08 337.02 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n0 7 Car -1 -1 -1 557.60 186.73 595.12 216.50 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n0 8 Pedestrian -1 -1 -1 464.48 172.58 489.27 254.81 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n0 9 Car -1 -1 -1 571.93 181.89 640.65 244.33 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n0 10 Pedestrian -1 -1 -1 421.51 170.44 463.01 287.42 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n0 11 Pedestrian -1 -1 -1 498.26 172.18 524.01 239.69 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n0 12 Pedestrian -1 -1 -1 184.19 177.01 217.86 259.19 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n1 1 Car -1 -1 -1 554.89 194.12 676.58 301.29 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n1 2 Van -1 -1 -1 1013.69 119.60 1218.04 232.72 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n1 3 Pedestrian -1 -1 -1 477.53 178.70 508.30 265.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n1 6 Pedestrian -1 -1 -1 374.74 175.77 441.86 350.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1 4 Pedestrian -1 -1 -1 135.29 171.52 244.14 363.07 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n1 5 Pedestrian -1 -1 -1 953.41 153.12 987.13 234.54 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n1 7 Car -1 -1 -1 553.29 185.51 593.91 218.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n1 8 Pedestrian -1 -1 -1 460.46 171.80 486.41 257.76 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n1 11 Pedestrian -1 -1 -1 499.17 172.34 523.74 240.86 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n1 10 Pedestrian -1 -1 -1 405.22 171.29 449.68 293.69 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n1 9 Car -1 -1 -1 573.47 182.98 638.81 243.94 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n1 12 Pedestrian -1 -1 -1 136.31 188.76 165.85 269.58 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n1 13 Pedestrian -1 -1 -1 376.27 172.62 424.89 293.53 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n1 14 Cyclist -1 -1 -1 263.79 185.50 285.84 220.32 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n2 1 Car -1 -1 -1 555.44 195.30 674.82 301.35 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n2 2 Van -1 -1 -1 1022.28 118.82 1217.94 233.76 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n2 3 Pedestrian -1 -1 -1 472.95 179.84 505.45 270.02 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n2 6 Pedestrian -1 -1 -1 357.83 175.04 427.65 360.81 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n2 5 Pedestrian -1 -1 -1 966.95 155.61 999.73 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n2 8 Pedestrian -1 -1 -1 455.88 173.08 482.94 261.35 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n2 4 Pedestrian -1 -1 -1 69.30 161.79 187.91 366.12 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n2 10 Pedestrian -1 -1 -1 401.32 173.72 445.27 298.55 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n2 7 Car -1 -1 -1 552.71 187.51 592.43 218.96 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n2 14 Cyclist -1 -1 -1 258.14 185.02 280.02 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n2 9 Car -1 -1 -1 573.59 184.59 634.85 242.14 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n2 13 Pedestrian -1 -1 -1 360.08 172.57 417.78 300.84 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n2 11 Pedestrian -1 -1 -1 498.41 172.21 524.65 241.27 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n2 15 Pedestrian -1 -1 -1 137.81 186.21 233.69 363.51 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n2 16 Car -1 -1 -1 408.56 185.87 445.96 210.70 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n3 1 Car -1 -1 -1 555.67 194.98 675.07 300.50 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n3 3 Pedestrian -1 -1 -1 468.20 179.11 501.86 272.94 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n3 5 Pedestrian -1 -1 -1 979.44 154.33 1016.77 237.01 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n3 2 Van -1 -1 -1 1033.41 120.85 1217.18 237.17 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n3 6 Pedestrian -1 -1 -1 333.93 176.30 413.14 364.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n3 8 Pedestrian -1 -1 -1 450.50 172.25 479.82 264.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n3 15 Pedestrian -1 -1 -1 85.02 178.43 194.79 364.19 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n3 14 Cyclist -1 -1 -1 250.24 184.45 273.98 221.48 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n3 10 Pedestrian -1 -1 -1 389.48 173.08 433.95 302.58 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n3 11 Pedestrian -1 -1 -1 489.27 170.07 518.98 248.50 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n3 7 Car -1 -1 -1 550.43 187.84 592.88 218.66 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n3 4 Pedestrian -1 -1 -1 8.06 162.68 141.43 363.74 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n3 9 Car -1 -1 -1 573.45 184.51 635.23 241.98 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n3 16 Car -1 -1 -1 402.39 185.06 443.81 210.44 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n3 17 Car -1 -1 -1 794.26 173.66 856.01 198.60 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n3 18 Pedestrian -1 -1 -1 148.59 181.79 177.86 262.44 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n3 19 Pedestrian -1 -1 -1 711.85 183.05 733.79 244.42 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n4 1 Car -1 -1 -1 555.14 194.45 674.78 299.43 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n4 5 Pedestrian -1 -1 -1 995.93 152.91 1037.21 243.03 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n4 6 Pedestrian -1 -1 -1 308.37 175.21 392.96 365.53 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n4 3 Pedestrian -1 -1 -1 462.86 177.41 498.11 275.20 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n4 2 Van -1 -1 -1 1046.62 118.27 1216.90 240.47 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n4 14 Cyclist -1 -1 -1 239.02 184.35 270.67 220.76 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n4 15 Pedestrian -1 -1 -1 22.77 177.81 157.63 364.65 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n4 18 Pedestrian -1 -1 -1 136.44 182.01 164.21 259.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n4 7 Car -1 -1 -1 549.04 187.23 589.54 219.03 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n4 10 Pedestrian -1 -1 -1 376.64 172.54 424.16 310.19 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n4 8 Pedestrian -1 -1 -1 443.32 170.07 473.19 267.39 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n4 9 Car -1 -1 -1 573.97 184.52 633.31 236.78 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n4 17 Car -1 -1 -1 794.46 173.60 856.83 197.93 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n4 11 Pedestrian -1 -1 -1 485.31 169.63 514.96 248.49 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n4 16 Car -1 -1 -1 393.89 184.55 437.72 210.04 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n4 4 Pedestrian -1 -1 -1 87.97 189.75 122.23 283.40 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n4 19 Pedestrian -1 -1 -1 714.68 182.93 737.38 244.97 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n4 20 Pedestrian -1 -1 -1 200.60 185.57 215.66 226.55 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n5 1 Car -1 -1 -1 556.33 194.49 672.24 296.53 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n5 5 Pedestrian -1 -1 -1 1017.93 151.43 1054.92 243.96 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n5 18 Pedestrian -1 -1 -1 117.16 179.74 147.53 261.76 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n5 2 Van -1 -1 -1 1059.79 109.86 1219.44 242.13 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n5 6 Pedestrian -1 -1 -1 280.59 171.67 373.77 369.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n5 3 Pedestrian -1 -1 -1 455.82 176.93 491.29 279.32 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n5 9 Car -1 -1 -1 573.97 183.46 633.63 237.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n5 14 Cyclist -1 -1 -1 233.51 182.12 262.27 221.57 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n5 7 Car -1 -1 -1 547.82 187.12 588.17 218.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n5 8 Pedestrian -1 -1 -1 438.01 168.74 469.63 269.02 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n5 10 Pedestrian -1 -1 -1 360.96 169.61 409.50 319.41 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n5 11 Pedestrian -1 -1 -1 481.25 169.52 510.95 249.66 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n5 16 Car -1 -1 -1 386.58 184.04 430.65 210.38 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n5 20 Pedestrian -1 -1 -1 193.62 185.84 209.19 225.49 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n5 17 Car -1 -1 -1 797.25 172.83 862.55 196.34 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n5 15 Pedestrian -1 -1 -1 4.28 179.22 129.69 362.98 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n5 21 Pedestrian -1 -1 -1 320.38 168.38 380.86 320.58 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n6 1 Car -1 -1 -1 557.19 193.62 671.03 295.28 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n6 18 Pedestrian -1 -1 -1 100.55 178.86 134.15 264.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n6 3 Pedestrian -1 -1 -1 449.54 176.22 488.12 282.60 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n6 5 Pedestrian -1 -1 -1 1040.97 157.32 1070.19 239.91 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n6 6 Pedestrian -1 -1 -1 240.75 169.32 345.17 366.72 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n6 9 Car -1 -1 -1 574.58 182.60 633.63 237.04 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n6 8 Pedestrian -1 -1 -1 430.51 167.59 463.31 273.85 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n6 7 Car -1 -1 -1 546.49 186.64 585.95 218.33 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n6 2 Van -1 -1 -1 1073.16 108.52 1221.83 242.40 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n6 10 Pedestrian -1 -1 -1 348.88 168.31 398.23 329.08 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n6 14 Cyclist -1 -1 -1 228.59 181.15 256.72 220.85 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n6 11 Pedestrian -1 -1 -1 477.24 168.84 507.90 251.45 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n6 17 Car -1 -1 -1 800.00 171.24 865.98 195.26 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n6 16 Car -1 -1 -1 383.15 184.46 424.81 209.58 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n6 15 Pedestrian -1 -1 -1 50.28 188.45 83.15 277.06 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n6 21 Pedestrian -1 -1 -1 291.59 165.39 363.82 346.56 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n6 20 Pedestrian -1 -1 -1 185.31 185.36 203.02 225.30 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n6 22 Pedestrian -1 -1 -1 495.39 169.99 518.87 233.94 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n7 1 Car -1 -1 -1 558.44 189.64 669.89 291.38 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n7 3 Pedestrian -1 -1 -1 442.75 173.86 481.28 285.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n7 18 Pedestrian -1 -1 -1 84.93 177.08 119.46 266.32 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n7 6 Pedestrian -1 -1 -1 191.09 167.65 310.96 367.29 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n7 9 Car -1 -1 -1 575.35 180.59 633.91 232.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n7 10 Pedestrian -1 -1 -1 332.83 166.74 383.98 337.85 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n7 7 Car -1 -1 -1 546.22 182.18 584.41 216.17 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n7 8 Pedestrian -1 -1 -1 424.49 165.30 460.04 276.16 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n7 17 Car -1 -1 -1 801.36 167.13 873.53 193.16 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n7 21 Pedestrian -1 -1 -1 273.99 163.79 342.77 356.14 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n7 16 Car -1 -1 -1 376.32 181.15 417.73 209.59 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n7 2 Van -1 -1 -1 1096.36 106.77 1221.25 236.34 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n7 11 Pedestrian -1 -1 -1 476.18 165.17 508.30 247.75 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n7 15 Pedestrian -1 -1 -1 28.87 192.47 59.41 278.83 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n7 20 Pedestrian -1 -1 -1 181.69 184.45 198.11 225.44 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n7 23 Car -1 -1 -1 573.03 174.00 611.57 208.85 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n7 24 Pedestrian -1 -1 -1 724.40 178.89 750.77 249.10 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n8 1 Car -1 -1 -1 559.39 186.78 670.83 287.70 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n8 3 Pedestrian -1 -1 -1 434.21 170.42 475.06 289.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 21 Pedestrian -1 -1 -1 246.48 160.34 324.58 367.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 9 Car -1 -1 -1 574.97 180.13 633.90 232.42 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n8 18 Pedestrian -1 -1 -1 68.09 176.94 104.08 268.19 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n8 8 Pedestrian -1 -1 -1 416.15 161.55 453.84 280.43 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n8 6 Pedestrian -1 -1 -1 127.84 167.30 267.16 367.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n8 23 Car -1 -1 -1 572.48 172.81 611.07 206.41 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n8 10 Pedestrian -1 -1 -1 310.37 163.91 367.90 348.69 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n8 15 Pedestrian -1 -1 -1 9.16 191.76 40.63 281.44 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n8 17 Car -1 -1 -1 806.65 165.11 875.41 192.25 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n8 11 Pedestrian -1 -1 -1 472.86 164.86 504.59 247.96 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n8 16 Car -1 -1 -1 369.17 179.86 414.73 209.63 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n9 1 Car -1 -1 -1 560.53 188.22 670.30 285.07 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n9 18 Pedestrian -1 -1 -1 46.67 177.99 87.43 270.72 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n9 16 Car -1 -1 -1 355.38 179.89 406.79 209.42 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n9 9 Car -1 -1 -1 575.48 177.67 637.17 233.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n9 3 Pedestrian -1 -1 -1 424.30 170.20 469.53 295.14 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n9 21 Pedestrian -1 -1 -1 209.99 157.98 299.84 368.43 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n9 8 Pedestrian -1 -1 -1 407.12 159.13 447.92 285.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n9 23 Car -1 -1 -1 570.36 173.09 605.78 201.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n9 17 Car -1 -1 -1 813.31 164.56 876.18 192.04 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n9 10 Pedestrian -1 -1 -1 280.08 160.42 352.34 365.65 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n9 6 Pedestrian -1 -1 -1 26.66 166.96 222.84 367.05 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n9 11 Pedestrian -1 -1 -1 472.91 164.74 504.18 248.16 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n9 15 Pedestrian -1 -1 -1 1.41 189.31 16.96 290.81 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n9 25 Pedestrian -1 -1 -1 487.85 164.69 512.70 232.05 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n10 1 Car -1 -1 -1 560.51 186.89 671.67 284.36 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n10 16 Car -1 -1 -1 345.61 179.73 401.84 209.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n10 9 Car -1 -1 -1 575.23 177.31 638.19 234.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n10 18 Pedestrian -1 -1 -1 30.62 175.06 65.42 274.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n10 21 Pedestrian -1 -1 -1 165.54 158.04 267.73 368.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n10 3 Pedestrian -1 -1 -1 415.00 168.61 463.50 302.77 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n10 8 Pedestrian -1 -1 -1 398.71 158.40 440.75 291.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n10 10 Pedestrian -1 -1 -1 246.52 158.57 332.13 368.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n10 23 Car -1 -1 -1 568.57 173.20 602.52 199.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n10 17 Car -1 -1 -1 814.82 164.01 881.70 192.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n10 11 Pedestrian -1 -1 -1 469.12 164.29 500.39 248.89 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n10 6 Pedestrian -1 -1 -1 -9.24 168.43 197.05 366.35 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n10 25 Pedestrian -1 -1 -1 488.06 164.67 511.70 232.31 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n11 1 Car -1 -1 -1 562.59 186.66 672.43 281.80 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n11 16 Car -1 -1 -1 337.70 179.45 394.33 209.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n11 9 Car -1 -1 -1 575.55 175.59 638.31 230.14 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n11 3 Pedestrian -1 -1 -1 405.15 166.54 457.35 308.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n11 21 Pedestrian -1 -1 -1 112.31 158.48 229.33 369.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n11 18 Pedestrian -1 -1 -1 3.55 172.87 46.69 278.29 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n11 8 Pedestrian -1 -1 -1 389.23 155.18 433.84 296.67 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n11 23 Car -1 -1 -1 566.99 172.62 600.98 198.09 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n11 17 Car -1 -1 -1 816.40 163.44 889.00 192.77 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n11 10 Pedestrian -1 -1 -1 204.83 158.09 304.90 368.68 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n11 11 Pedestrian -1 -1 -1 462.99 162.77 498.89 257.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n11 25 Pedestrian -1 -1 -1 482.98 163.10 509.88 233.56 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n11 26 Pedestrian -1 -1 -1 696.26 166.85 718.07 228.94 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n12 1 Car -1 -1 -1 562.69 186.72 672.67 280.90 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n12 9 Car -1 -1 -1 576.33 174.26 638.80 229.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n12 16 Car -1 -1 -1 328.61 178.77 389.13 211.11 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n12 3 Pedestrian -1 -1 -1 393.80 167.60 445.87 319.74 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n12 21 Pedestrian -1 -1 -1 39.90 153.02 186.04 367.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n12 17 Car -1 -1 -1 823.76 163.56 889.23 193.20 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n12 11 Pedestrian -1 -1 -1 458.57 162.12 495.48 258.59 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n12 10 Pedestrian -1 -1 -1 151.50 159.34 273.75 367.07 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n12 23 Car -1 -1 -1 567.38 172.29 600.56 197.05 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n12 8 Pedestrian -1 -1 -1 373.50 151.68 420.59 305.52 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n12 26 Pedestrian -1 -1 -1 699.38 166.58 722.12 229.82 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n12 18 Pedestrian -1 -1 -1 1.27 174.89 26.25 282.68 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n12 25 Pedestrian -1 -1 -1 477.85 162.89 507.41 234.44 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n13 1 Car -1 -1 -1 563.25 186.10 671.72 279.27 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n13 16 Car -1 -1 -1 318.32 178.54 383.43 212.85 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n13 9 Car -1 -1 -1 579.05 173.96 635.38 224.36 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n13 3 Pedestrian -1 -1 -1 376.42 168.24 432.68 329.60 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n13 11 Pedestrian -1 -1 -1 448.41 159.50 490.87 268.51 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n13 26 Pedestrian -1 -1 -1 702.32 165.85 725.55 230.54 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n13 10 Pedestrian -1 -1 -1 105.29 155.90 235.61 363.48 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n13 17 Car -1 -1 -1 829.77 163.55 890.17 192.58 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n13 23 Car -1 -1 -1 565.29 172.32 598.15 196.73 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n13 21 Pedestrian -1 -1 -1 6.57 152.93 165.78 366.63 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n13 8 Pedestrian -1 -1 -1 356.46 147.28 414.06 311.11 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n13 25 Pedestrian -1 -1 -1 477.98 163.48 506.56 234.14 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n13 18 Pedestrian -1 -1 -1 -0.20 173.06 11.34 284.93 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n13 27 Car -1 -1 -1 419.22 173.94 458.13 200.49 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n14 1 Car -1 -1 -1 563.87 185.37 671.76 278.79 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n14 9 Car -1 -1 -1 578.62 174.86 637.44 228.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n14 16 Car -1 -1 -1 307.76 179.75 376.47 215.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n14 3 Pedestrian -1 -1 -1 357.13 167.97 420.91 345.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n14 23 Car -1 -1 -1 563.46 172.53 598.37 198.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n14 17 Car -1 -1 -1 833.32 163.52 893.82 192.70 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n14 26 Pedestrian -1 -1 -1 706.35 166.65 730.02 231.75 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n14 10 Pedestrian -1 -1 -1 31.12 161.06 194.89 365.97 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n14 11 Pedestrian -1 -1 -1 442.29 158.79 488.79 276.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n14 27 Car -1 -1 -1 413.00 174.67 456.56 201.87 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n14 25 Pedestrian -1 -1 -1 473.09 162.54 504.19 240.60 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n14 28 Cyclist -1 -1 -1 336.79 151.84 395.33 291.75 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n15 1 Car -1 -1 -1 565.94 184.24 670.24 276.58 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n15 16 Car -1 -1 -1 296.07 180.25 366.81 217.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n15 3 Pedestrian -1 -1 -1 335.35 168.85 404.70 359.61 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n15 9 Car -1 -1 -1 578.80 174.57 638.19 228.92 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n15 17 Car -1 -1 -1 833.34 160.85 895.17 191.69 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n15 26 Pedestrian -1 -1 -1 710.69 166.29 734.58 232.66 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n15 23 Car -1 -1 -1 562.43 172.10 597.88 198.71 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n15 11 Pedestrian -1 -1 -1 437.28 159.94 485.98 281.77 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n15 10 Pedestrian -1 -1 -1 8.14 162.69 149.32 364.28 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n15 27 Car -1 -1 -1 406.59 175.05 453.91 204.17 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n15 25 Pedestrian -1 -1 -1 473.69 161.99 503.51 241.11 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n15 28 Cyclist -1 -1 -1 321.07 149.65 380.32 300.45 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n15 29 Cyclist -1 -1 -1 154.29 177.44 195.02 221.10 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n15 30 Pedestrian -1 -1 -1 121.69 183.39 143.13 227.75 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n15 31 Pedestrian -1 -1 -1 450.15 161.85 488.25 264.63 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n16 1 Car -1 -1 -1 567.04 185.02 669.35 274.99 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n16 3 Pedestrian -1 -1 -1 311.26 172.20 390.01 364.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n16 9 Car -1 -1 -1 581.20 174.04 635.35 223.87 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n16 16 Car -1 -1 -1 285.66 180.74 354.47 218.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n16 11 Pedestrian -1 -1 -1 423.44 157.71 477.03 287.02 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 27 Car -1 -1 -1 400.22 174.98 446.11 204.29 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n16 26 Pedestrian -1 -1 -1 713.89 166.11 737.65 235.61 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n16 23 Car -1 -1 -1 560.38 172.21 593.96 196.04 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n16 17 Car -1 -1 -1 838.99 160.81 897.00 191.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n16 30 Pedestrian -1 -1 -1 115.14 182.05 133.85 228.65 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n16 25 Pedestrian -1 -1 -1 472.69 161.67 504.34 241.30 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n16 31 Pedestrian -1 -1 -1 445.37 162.28 485.14 264.10 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n16 29 Cyclist -1 -1 -1 148.06 177.52 185.56 221.44 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n16 28 Cyclist -1 -1 -1 300.12 150.29 362.52 307.01 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n17 1 Car -1 -1 -1 567.20 184.74 669.39 274.14 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n17 3 Pedestrian -1 -1 -1 277.81 170.33 369.14 364.95 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n17 9 Car -1 -1 -1 581.71 173.76 633.78 223.67 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n17 26 Pedestrian -1 -1 -1 717.70 165.93 742.42 236.71 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n17 29 Cyclist -1 -1 -1 140.67 175.75 177.57 222.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n17 11 Pedestrian -1 -1 -1 415.07 157.62 470.26 293.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n17 27 Car -1 -1 -1 392.07 175.12 439.07 203.96 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n17 23 Car -1 -1 -1 557.88 171.87 593.56 195.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n17 16 Car -1 -1 -1 277.10 181.04 346.61 221.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n17 25 Pedestrian -1 -1 -1 469.42 161.57 500.00 243.24 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n17 17 Car -1 -1 -1 842.74 161.11 899.77 191.84 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n17 31 Pedestrian -1 -1 -1 441.45 163.17 481.66 264.87 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n17 30 Pedestrian -1 -1 -1 105.65 181.26 127.03 228.82 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n18 1 Car -1 -1 -1 567.94 185.15 669.32 274.01 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n18 9 Car -1 -1 -1 580.69 173.50 634.64 224.34 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n18 26 Pedestrian -1 -1 -1 720.76 165.74 747.38 238.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n18 27 Car -1 -1 -1 384.61 176.61 430.79 205.60 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n18 23 Car -1 -1 -1 555.44 172.73 590.14 195.67 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n18 29 Cyclist -1 -1 -1 132.06 179.29 169.93 226.05 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n18 16 Car -1 -1 -1 266.30 181.72 334.49 223.04 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n18 3 Pedestrian -1 -1 -1 240.43 165.27 344.58 370.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n18 11 Pedestrian -1 -1 -1 403.47 157.81 465.76 301.41 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n18 25 Pedestrian -1 -1 -1 464.50 162.10 497.08 248.70 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n18 30 Pedestrian -1 -1 -1 96.30 181.80 115.19 232.19 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n18 17 Car -1 -1 -1 844.92 160.75 906.33 192.03 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n18 31 Pedestrian -1 -1 -1 437.83 164.03 477.89 270.19 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n19 1 Car -1 -1 -1 569.12 185.20 667.69 272.74 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n19 9 Car -1 -1 -1 580.17 173.29 634.68 224.91 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n19 16 Car -1 -1 -1 240.33 185.88 323.30 227.39 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n19 26 Pedestrian -1 -1 -1 724.24 163.91 751.73 241.80 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n19 25 Pedestrian -1 -1 -1 460.83 162.27 493.03 251.77 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n19 11 Pedestrian -1 -1 -1 391.64 156.17 455.25 310.69 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n19 3 Pedestrian -1 -1 -1 191.13 167.83 310.72 367.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n19 27 Car -1 -1 -1 375.74 178.76 424.83 208.15 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n19 30 Pedestrian -1 -1 -1 87.44 185.77 107.41 234.75 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n19 29 Cyclist -1 -1 -1 124.80 181.95 161.26 228.52 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n19 17 Car -1 -1 -1 839.01 159.90 919.82 192.25 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n19 23 Car -1 -1 -1 555.14 173.41 589.55 195.85 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n19 31 Pedestrian -1 -1 -1 433.37 164.90 474.58 271.44 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n19 32 Pedestrian -1 -1 -1 510.00 167.37 528.25 228.57 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n20 1 Car -1 -1 -1 568.80 184.16 667.96 271.65 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n20 16 Car -1 -1 -1 215.17 186.85 309.40 231.68 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n20 9 Car -1 -1 -1 579.61 173.14 634.25 224.43 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n20 11 Pedestrian -1 -1 -1 380.32 158.23 443.23 322.34 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n20 26 Pedestrian -1 -1 -1 728.18 162.89 755.68 243.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n20 27 Car -1 -1 -1 365.12 179.40 413.12 208.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n20 25 Pedestrian -1 -1 -1 452.78 161.95 486.81 256.19 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n20 31 Pedestrian -1 -1 -1 425.85 165.03 467.43 278.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n20 30 Pedestrian -1 -1 -1 76.31 185.40 96.62 235.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n20 23 Car -1 -1 -1 551.72 173.35 586.75 195.18 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n20 17 Car -1 -1 -1 845.72 159.44 935.49 192.22 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n20 29 Cyclist -1 -1 -1 115.95 182.55 148.77 229.83 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n20 32 Pedestrian -1 -1 -1 509.07 168.09 528.06 230.77 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n20 33 Cyclist -1 -1 -1 93.92 145.07 300.79 367.15 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n21 1 Car -1 -1 -1 570.30 182.18 666.43 268.12 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n21 16 Car -1 -1 -1 194.47 186.98 293.26 231.30 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n21 9 Car -1 -1 -1 578.50 171.84 634.29 224.86 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n21 26 Pedestrian -1 -1 -1 732.83 160.48 763.23 246.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n21 27 Car -1 -1 -1 352.75 180.15 402.42 207.94 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n21 31 Pedestrian -1 -1 -1 420.63 162.67 463.97 281.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n21 25 Pedestrian -1 -1 -1 448.70 159.40 483.47 258.23 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n21 17 Car -1 -1 -1 848.17 158.95 940.92 192.14 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n21 30 Pedestrian -1 -1 -1 62.53 182.52 86.91 237.61 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n21 32 Pedestrian -1 -1 -1 504.78 165.88 526.05 232.74 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n22 1 Car -1 -1 -1 571.55 179.95 665.80 264.05 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n22 16 Car -1 -1 -1 171.26 185.28 277.43 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n22 9 Car -1 -1 -1 577.46 169.17 636.18 226.97 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n22 26 Pedestrian -1 -1 -1 736.95 158.95 767.79 248.39 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 25 Pedestrian -1 -1 -1 445.21 157.69 478.73 256.43 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n22 31 Pedestrian -1 -1 -1 408.24 161.21 454.68 288.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n22 17 Car -1 -1 -1 854.41 158.15 950.12 192.03 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n22 27 Car -1 -1 -1 340.34 178.72 399.39 209.58 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n22 30 Pedestrian -1 -1 -1 46.53 180.77 80.58 239.96 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n22 32 Pedestrian -1 -1 -1 500.73 163.81 523.05 233.21 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n22 34 Pedestrian -1 -1 -1 348.27 151.59 414.13 338.69 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n22 35 Pedestrian -1 -1 -1 2.80 142.46 192.67 369.23 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n23 1 Car -1 -1 -1 571.75 178.15 666.67 263.42 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n23 16 Car -1 -1 -1 142.75 185.39 261.10 237.07 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n23 9 Car -1 -1 -1 576.75 168.16 637.39 228.01 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n23 26 Pedestrian -1 -1 -1 740.67 158.40 772.71 251.09 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n23 34 Pedestrian -1 -1 -1 329.54 149.22 402.89 355.41 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n23 25 Pedestrian -1 -1 -1 439.02 157.34 476.19 260.63 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n23 31 Pedestrian -1 -1 -1 399.04 159.99 447.72 296.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n23 32 Pedestrian -1 -1 -1 500.02 163.26 522.57 233.93 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n23 17 Car -1 -1 -1 861.35 156.93 951.29 188.72 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n23 27 Car -1 -1 -1 336.38 175.83 395.35 206.92 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n23 35 Pedestrian -1 -1 -1 -0.92 135.17 135.03 361.55 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n24 1 Car -1 -1 -1 572.09 178.59 666.04 261.80 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n24 16 Car -1 -1 -1 113.07 187.34 242.31 242.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n24 9 Car -1 -1 -1 576.98 167.37 635.87 223.15 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n24 26 Pedestrian -1 -1 -1 744.13 157.80 778.31 252.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n24 34 Pedestrian -1 -1 -1 307.35 154.20 385.84 365.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n24 25 Pedestrian -1 -1 -1 430.21 156.45 470.16 264.90 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 31 Pedestrian -1 -1 -1 386.80 158.41 437.37 300.76 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n24 27 Car -1 -1 -1 321.61 174.69 387.66 208.63 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n24 17 Car -1 -1 -1 866.56 156.87 960.43 188.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n24 32 Pedestrian -1 -1 -1 494.64 163.10 520.66 239.74 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n24 36 Pedestrian -1 -1 -1 515.37 164.68 538.75 231.98 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n24 37 Pedestrian -1 -1 -1 803.75 150.90 847.30 269.39 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n25 1 Car -1 -1 -1 572.30 179.52 665.28 260.80 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n25 16 Car -1 -1 -1 78.96 190.92 217.44 251.36 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n25 26 Pedestrian -1 -1 -1 750.47 156.96 785.37 255.60 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n25 9 Car -1 -1 -1 577.28 167.65 635.21 222.67 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n25 34 Pedestrian -1 -1 -1 276.30 151.51 370.68 368.77 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n25 31 Pedestrian -1 -1 -1 374.07 157.54 427.31 309.21 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n25 27 Car -1 -1 -1 312.93 177.21 379.32 211.47 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n25 25 Pedestrian -1 -1 -1 421.60 156.14 464.45 272.20 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n25 17 Car -1 -1 -1 869.04 156.21 958.92 187.85 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n25 37 Pedestrian -1 -1 -1 814.94 152.30 859.47 275.09 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n25 36 Pedestrian -1 -1 -1 514.91 168.10 539.14 235.12 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n25 32 Pedestrian -1 -1 -1 491.02 163.74 517.69 240.10 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n25 38 Pedestrian -1 -1 -1 10.62 184.60 32.46 243.69 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n25 39 Truck -1 -1 -1 456.78 165.55 512.28 205.93 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n25 40 Van -1 -1 -1 456.78 165.55 512.28 205.93 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n26 1 Car -1 -1 -1 572.07 179.94 664.06 260.35 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n26 16 Car -1 -1 -1 41.93 193.84 191.56 257.52 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n26 26 Pedestrian -1 -1 -1 755.55 157.59 795.02 261.55 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n26 9 Car -1 -1 -1 575.12 169.12 633.44 221.72 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n26 34 Pedestrian -1 -1 -1 243.50 149.38 350.01 370.99 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n26 25 Pedestrian -1 -1 -1 413.35 157.10 457.29 277.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n26 31 Pedestrian -1 -1 -1 360.37 156.66 416.98 318.21 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n26 27 Car -1 -1 -1 301.64 179.63 369.54 214.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n26 36 Pedestrian -1 -1 -1 513.90 167.35 539.25 237.84 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n26 37 Pedestrian -1 -1 -1 824.07 153.51 873.21 281.50 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n26 32 Pedestrian -1 -1 -1 489.39 163.21 517.16 241.94 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n26 38 Pedestrian -1 -1 -1 0.71 186.46 18.33 247.17 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n26 40 Van -1 -1 -1 454.51 165.82 514.01 207.94 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n26 39 Truck -1 -1 -1 453.89 166.01 510.37 207.61 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n27 1 Car -1 -1 -1 571.80 180.21 662.90 260.41 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n27 16 Car -1 -1 -1 -1.32 197.24 165.22 266.13 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n27 9 Car -1 -1 -1 573.80 169.91 632.79 226.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n27 26 Pedestrian -1 -1 -1 762.98 158.27 802.76 267.74 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n27 27 Car -1 -1 -1 293.83 179.87 360.41 217.93 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n27 31 Pedestrian -1 -1 -1 341.48 156.24 405.45 332.66 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n27 25 Pedestrian -1 -1 -1 405.07 157.87 450.63 284.28 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n27 34 Pedestrian -1 -1 -1 200.37 149.33 317.43 369.32 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n27 37 Pedestrian -1 -1 -1 835.10 154.67 884.92 287.90 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n27 36 Pedestrian -1 -1 -1 506.07 168.82 532.68 241.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n27 32 Pedestrian -1 -1 -1 486.11 166.98 513.51 244.47 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n27 39 Truck -1 -1 -1 451.25 167.49 504.59 210.98 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n28 1 Car -1 -1 -1 571.87 180.84 660.56 259.20 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n28 16 Car -1 -1 -1 -0.68 198.68 133.16 273.11 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n28 26 Pedestrian -1 -1 -1 770.05 160.00 810.96 272.73 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n28 9 Car -1 -1 -1 573.38 170.45 631.84 226.29 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n28 31 Pedestrian -1 -1 -1 321.94 158.79 387.38 345.40 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n28 25 Pedestrian -1 -1 -1 396.63 158.47 442.99 290.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n28 27 Car -1 -1 -1 264.86 181.61 344.05 221.39 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n28 34 Pedestrian -1 -1 -1 143.02 139.92 282.56 371.13 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n28 36 Pedestrian -1 -1 -1 506.06 168.07 532.12 244.46 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n28 32 Pedestrian -1 -1 -1 481.79 167.09 510.52 244.85 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n28 37 Pedestrian -1 -1 -1 845.38 155.08 897.76 295.04 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n28 41 Car -1 -1 -1 449.03 166.77 506.95 212.03 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n29 1 Car -1 -1 -1 572.28 179.46 659.33 256.31 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n29 9 Car -1 -1 -1 571.18 169.60 627.82 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n29 26 Pedestrian -1 -1 -1 777.50 158.06 818.88 277.57 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n29 27 Car -1 -1 -1 247.33 181.42 324.82 222.50 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n29 25 Pedestrian -1 -1 -1 384.84 154.18 438.41 296.89 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n29 31 Pedestrian -1 -1 -1 300.24 157.24 370.35 361.13 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n29 16 Car -1 -1 -1 2.86 199.12 93.74 281.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n29 34 Pedestrian -1 -1 -1 47.81 134.45 254.89 369.61 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n29 36 Pedestrian -1 -1 -1 501.34 167.08 529.18 245.90 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n29 32 Pedestrian -1 -1 -1 477.85 161.61 506.93 242.69 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n29 37 Pedestrian -1 -1 -1 856.53 154.23 909.92 302.50 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n30 1 Car -1 -1 -1 571.22 177.69 658.52 254.71 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n30 9 Car -1 -1 -1 569.03 171.80 622.91 217.35 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n30 26 Pedestrian -1 -1 -1 784.09 159.07 828.71 281.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n30 31 Pedestrian -1 -1 -1 274.49 159.36 350.32 367.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n30 27 Car -1 -1 -1 226.01 180.44 312.87 225.37 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n30 25 Pedestrian -1 -1 -1 369.27 151.04 424.85 306.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n30 32 Pedestrian -1 -1 -1 474.06 159.95 503.45 243.94 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n30 36 Pedestrian -1 -1 -1 497.21 165.66 526.65 248.53 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 16 Car -1 -1 -1 1.41 194.77 64.01 286.58 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n30 37 Pedestrian -1 -1 -1 871.78 153.13 925.41 311.95 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n30 42 Car -1 -1 -1 541.30 169.42 568.23 191.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n30 43 Car -1 -1 -1 443.76 165.37 501.97 211.13 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n30 44 Pedestrian -1 -1 -1 921.26 177.49 990.79 349.32 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n31 1 Car -1 -1 -1 570.70 177.79 654.27 251.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n31 31 Pedestrian -1 -1 -1 241.02 159.97 330.01 366.55 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n31 26 Pedestrian -1 -1 -1 792.59 155.71 842.64 288.98 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n31 25 Pedestrian -1 -1 -1 356.96 152.34 414.13 313.17 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n31 27 Car -1 -1 -1 204.01 184.57 290.53 228.70 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n31 9 Car -1 -1 -1 565.95 172.63 618.55 215.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n31 37 Pedestrian -1 -1 -1 884.18 150.72 943.57 322.00 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n31 36 Pedestrian -1 -1 -1 492.95 166.77 522.76 252.15 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n31 32 Pedestrian -1 -1 -1 468.78 159.37 500.50 245.82 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n31 42 Car -1 -1 -1 540.22 169.34 568.46 191.74 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n31 43 Car -1 -1 -1 439.62 165.55 499.51 213.50 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n31 44 Pedestrian -1 -1 -1 935.83 179.88 1021.76 361.39 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n31 45 Car -1 -1 -1 256.02 187.86 306.91 216.35 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n31 46 Car -1 -1 -1 614.85 162.41 661.63 190.27 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n31 47 Car -1 -1 -1 401.37 176.92 444.47 203.66 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n32 1 Car -1 -1 -1 570.39 178.05 653.71 250.56 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n32 26 Pedestrian -1 -1 -1 802.19 155.91 856.41 295.60 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n32 31 Pedestrian -1 -1 -1 207.36 158.26 302.09 368.46 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n32 9 Car -1 -1 -1 563.90 174.49 614.03 213.62 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n32 27 Car -1 -1 -1 180.25 186.93 274.80 233.72 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n32 25 Pedestrian -1 -1 -1 339.59 151.81 401.15 323.04 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n32 37 Pedestrian -1 -1 -1 904.65 152.26 968.69 329.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n32 47 Car -1 -1 -1 389.90 177.73 440.71 204.18 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n32 36 Pedestrian -1 -1 -1 488.66 166.81 519.14 254.52 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n32 42 Car -1 -1 -1 539.40 169.64 566.68 191.82 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n32 32 Pedestrian -1 -1 -1 461.57 159.78 493.46 246.99 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n32 44 Pedestrian -1 -1 -1 957.45 177.68 1053.93 364.52 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n32 43 Car -1 -1 -1 436.31 166.16 494.46 214.63 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n32 48 Cyclist -1 -1 -1 -0.24 184.94 25.99 250.17 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n33 1 Car -1 -1 -1 571.03 178.65 650.98 248.81 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n33 9 Car -1 -1 -1 561.37 173.52 613.67 214.52 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n33 27 Car -1 -1 -1 154.48 186.09 271.12 241.63 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n33 25 Pedestrian -1 -1 -1 326.03 149.97 390.50 339.30 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n33 26 Pedestrian -1 -1 -1 812.88 156.64 869.47 303.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n33 47 Car -1 -1 -1 381.16 179.88 434.75 207.01 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n33 31 Pedestrian -1 -1 -1 159.34 157.13 266.04 370.19 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n33 32 Pedestrian -1 -1 -1 456.70 160.33 490.35 252.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n33 36 Pedestrian -1 -1 -1 483.99 166.89 516.14 259.40 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n33 37 Pedestrian -1 -1 -1 922.16 150.36 989.89 339.16 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n33 42 Car -1 -1 -1 539.06 171.79 565.98 192.70 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n33 43 Car -1 -1 -1 434.03 167.56 488.83 214.68 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n33 44 Pedestrian -1 -1 -1 977.66 183.59 1087.61 366.59 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n33 49 Pedestrian -1 -1 -1 99.53 187.80 117.58 222.23 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n33 50 Car -1 -1 -1 615.08 163.58 661.01 192.77 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n33 51 Pedestrian -1 -1 -1 1033.11 182.29 1138.96 367.51 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n34 1 Car -1 -1 -1 570.42 180.24 649.14 249.35 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n34 9 Car -1 -1 -1 557.37 175.41 610.86 215.57 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n34 26 Pedestrian -1 -1 -1 824.44 158.03 888.38 315.41 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n34 47 Car -1 -1 -1 372.52 181.54 429.08 210.41 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n34 25 Pedestrian -1 -1 -1 306.93 151.85 372.09 351.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n34 27 Car -1 -1 -1 129.98 191.04 248.92 244.41 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n34 42 Car -1 -1 -1 536.93 172.97 563.84 194.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n34 32 Pedestrian -1 -1 -1 446.28 161.13 485.47 258.45 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n34 36 Pedestrian -1 -1 -1 475.30 169.20 509.78 265.36 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n34 31 Pedestrian -1 -1 -1 88.86 154.68 236.55 372.02 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n34 37 Pedestrian -1 -1 -1 942.57 149.70 1022.52 354.38 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n34 49 Pedestrian -1 -1 -1 86.20 189.91 108.13 224.22 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n34 43 Car -1 -1 -1 430.63 170.02 478.00 212.72 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n34 44 Pedestrian -1 -1 -1 1000.05 180.66 1157.06 370.05 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n35 1 Car -1 -1 -1 569.25 180.74 646.58 249.22 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n35 25 Pedestrian -1 -1 -1 280.90 149.64 359.41 362.18 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n35 47 Car -1 -1 -1 364.60 183.66 421.78 212.62 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n35 27 Car -1 -1 -1 90.38 192.21 227.45 252.07 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n35 9 Car -1 -1 -1 555.29 177.24 607.05 217.62 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n35 26 Pedestrian -1 -1 -1 840.39 162.31 903.28 326.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n35 36 Pedestrian -1 -1 -1 470.34 171.26 507.09 271.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n35 37 Pedestrian -1 -1 -1 968.67 152.08 1050.01 360.21 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n35 32 Pedestrian -1 -1 -1 439.47 161.13 483.57 264.76 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n35 42 Car -1 -1 -1 536.95 175.18 562.98 196.19 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n35 43 Car -1 -1 -1 427.99 172.13 480.05 216.21 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n35 44 Pedestrian -1 -1 -1 1027.57 184.98 1205.95 365.48 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n35 31 Pedestrian -1 -1 -1 3.22 152.31 207.47 367.67 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n36 1 Car -1 -1 -1 568.57 180.18 644.32 247.63 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n36 47 Car -1 -1 -1 356.37 183.52 413.26 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n36 27 Car -1 -1 -1 43.98 192.68 205.54 259.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n36 9 Car -1 -1 -1 552.63 177.07 606.57 217.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n36 25 Pedestrian -1 -1 -1 251.31 150.26 342.58 367.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n36 36 Pedestrian -1 -1 -1 461.96 171.05 500.41 273.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n36 26 Pedestrian -1 -1 -1 851.66 161.06 922.61 341.98 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n36 37 Pedestrian -1 -1 -1 996.17 159.24 1076.31 360.25 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n36 32 Pedestrian -1 -1 -1 426.66 159.12 473.65 269.36 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n36 43 Car -1 -1 -1 425.25 174.10 475.09 215.05 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n36 42 Car -1 -1 -1 536.22 174.75 561.92 196.34 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n37 1 Car -1 -1 -1 566.82 179.64 641.92 245.29 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n37 27 Car -1 -1 -1 2.06 195.93 177.11 269.14 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n37 9 Car -1 -1 -1 549.51 175.29 602.21 214.64 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n37 47 Car -1 -1 -1 345.46 183.98 407.26 214.19 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n37 25 Pedestrian -1 -1 -1 215.78 147.36 309.43 365.68 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n37 36 Pedestrian -1 -1 -1 457.95 170.70 496.50 278.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n37 26 Pedestrian -1 -1 -1 871.18 157.05 949.05 353.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n37 32 Pedestrian -1 -1 -1 424.57 158.99 468.32 269.80 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n37 37 Pedestrian -1 -1 -1 1024.24 151.05 1109.97 353.17 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n38 1 Car -1 -1 -1 565.58 178.11 639.30 242.58 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n38 9 Car -1 -1 -1 547.21 174.70 599.40 213.52 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n38 47 Car -1 -1 -1 332.74 183.66 398.05 215.38 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n38 27 Car -1 -1 -1 -0.85 198.53 144.31 275.72 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n38 25 Pedestrian -1 -1 -1 173.74 147.64 267.19 365.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n38 26 Pedestrian -1 -1 -1 890.04 154.48 983.75 364.47 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n38 36 Pedestrian -1 -1 -1 448.49 168.17 490.17 283.71 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n38 32 Pedestrian -1 -1 -1 415.89 157.76 461.82 275.62 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n38 37 Pedestrian -1 -1 -1 1071.88 144.12 1169.38 360.09 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n38 52 Pedestrian -1 -1 -1 200.76 146.57 301.23 366.36 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n38 53 Car -1 -1 -1 533.62 172.55 559.59 194.52 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n39 1 Car -1 -1 -1 563.94 177.51 636.11 240.53 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n39 9 Car -1 -1 -1 545.29 175.19 593.84 212.21 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n39 47 Car -1 -1 -1 320.24 183.11 391.18 216.15 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n39 27 Car -1 -1 -1 2.80 200.36 108.06 287.11 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n39 25 Pedestrian -1 -1 -1 119.11 145.45 222.24 367.31 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n39 36 Pedestrian -1 -1 -1 441.91 168.60 482.73 288.28 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n39 52 Pedestrian -1 -1 -1 166.76 149.33 273.61 369.01 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n39 26 Pedestrian -1 -1 -1 913.53 155.19 1021.45 364.25 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n39 32 Pedestrian -1 -1 -1 411.20 160.09 458.22 274.60 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n39 53 Car -1 -1 -1 533.44 171.70 559.81 193.76 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n40 1 Car -1 -1 -1 562.00 176.55 632.26 238.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n40 47 Car -1 -1 -1 306.10 183.01 381.10 217.00 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n40 9 Car -1 -1 -1 542.71 176.18 589.43 211.41 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n40 36 Pedestrian -1 -1 -1 432.56 170.23 475.74 295.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n40 25 Pedestrian -1 -1 -1 36.17 137.77 189.88 366.84 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n40 26 Pedestrian -1 -1 -1 947.71 153.70 1056.10 365.39 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n40 52 Pedestrian -1 -1 -1 93.33 144.36 232.23 367.39 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n40 27 Car -1 -1 -1 -3.66 197.75 75.92 297.89 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n40 32 Pedestrian -1 -1 -1 423.97 166.13 468.87 276.45 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n40 54 Car -1 -1 -1 417.71 171.53 490.23 226.04 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n40 55 Pedestrian -1 -1 -1 405.31 158.90 449.06 276.84 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n41 1 Car -1 -1 -1 559.13 176.10 630.13 237.91 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n41 47 Car -1 -1 -1 291.90 183.92 371.93 219.28 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n41 9 Car -1 -1 -1 539.75 176.22 588.34 211.72 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n41 36 Pedestrian -1 -1 -1 422.66 169.06 469.56 303.65 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n41 55 Pedestrian -1 -1 -1 394.01 157.69 437.18 285.85 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n41 26 Pedestrian -1 -1 -1 984.99 149.70 1103.16 362.46 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n41 52 Pedestrian -1 -1 -1 24.84 139.93 185.81 364.96 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n41 54 Car -1 -1 -1 426.24 173.00 489.38 224.51 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n42 1 Car -1 -1 -1 556.59 177.25 627.34 237.48 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n42 47 Car -1 -1 -1 276.42 184.35 362.87 221.87 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n42 9 Car -1 -1 -1 537.85 176.84 582.34 210.85 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n42 54 Car -1 -1 -1 416.08 174.41 484.82 227.72 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n42 36 Pedestrian -1 -1 -1 406.83 167.68 455.88 312.25 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n42 55 Pedestrian -1 -1 -1 383.13 156.47 425.52 293.40 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n42 26 Pedestrian -1 -1 -1 1026.59 146.29 1176.42 365.25 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n42 52 Pedestrian -1 -1 -1 -1.42 140.61 143.12 363.71 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n42 56 Pedestrian -1 -1 -1 400.29 169.25 446.55 289.23 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n43 47 Car -1 -1 -1 259.91 186.22 349.59 225.35 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n43 1 Car -1 -1 -1 555.01 177.78 623.49 237.09 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n43 9 Car -1 -1 -1 535.86 177.54 577.33 210.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n43 54 Car -1 -1 -1 411.34 175.17 480.99 229.19 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n43 36 Pedestrian -1 -1 -1 393.16 165.67 446.49 323.77 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n43 55 Pedestrian -1 -1 -1 368.97 155.38 416.52 301.46 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n43 26 Pedestrian -1 -1 -1 1070.95 138.59 1216.33 365.79 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n43 57 Cyclist -1 -1 -1 674.98 164.97 706.43 205.82 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n43 58 Cyclist -1 -1 -1 230.94 182.49 249.37 214.83 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n44 1 Car -1 -1 -1 553.79 178.47 619.53 236.50 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n44 9 Car -1 -1 -1 533.32 177.90 575.32 210.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n44 47 Car -1 -1 -1 240.25 187.89 336.92 227.34 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n44 54 Car -1 -1 -1 400.61 175.19 476.21 230.06 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n44 36 Pedestrian -1 -1 -1 376.93 165.16 431.80 338.52 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n44 55 Pedestrian -1 -1 -1 357.36 156.86 412.83 308.82 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n44 59 Pedestrian -1 -1 -1 351.89 152.50 403.34 305.22 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n45 1 Car -1 -1 -1 550.85 178.89 615.50 234.46 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n45 47 Car -1 -1 -1 219.43 188.27 321.41 230.50 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n45 54 Car -1 -1 -1 388.16 174.90 473.23 229.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n45 9 Car -1 -1 -1 530.84 177.83 570.78 209.91 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n45 36 Pedestrian -1 -1 -1 357.90 161.85 419.77 350.76 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n45 55 Pedestrian -1 -1 -1 341.75 153.69 398.26 320.14 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n45 60 Pedestrian -1 -1 -1 961.47 160.91 981.39 213.64 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n46 1 Car -1 -1 -1 548.22 178.39 612.35 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n46 47 Car -1 -1 -1 196.63 188.34 310.35 233.00 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n46 54 Car -1 -1 -1 376.91 173.86 468.55 229.15 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n46 9 Car -1 -1 -1 529.89 175.68 567.98 208.35 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n46 36 Pedestrian -1 -1 -1 334.68 164.18 404.41 362.84 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n46 55 Pedestrian -1 -1 -1 324.18 157.62 385.17 331.65 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n46 61 Cyclist -1 -1 -1 316.41 156.81 360.99 278.37 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n46 62 Car -1 -1 -1 941.38 152.50 1070.12 199.54 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n47 1 Car -1 -1 -1 544.18 178.16 609.34 232.57 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n47 47 Car -1 -1 -1 169.79 189.76 292.05 236.52 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n47 54 Car -1 -1 -1 369.57 173.75 463.31 232.39 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n47 9 Car -1 -1 -1 526.76 177.30 566.12 209.12 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n47 36 Pedestrian -1 -1 -1 296.89 162.07 381.38 365.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n47 62 Car -1 -1 -1 963.02 152.19 1102.24 200.23 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n47 61 Cyclist -1 -1 -1 292.30 160.68 340.39 282.06 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n48 1 Car -1 -1 -1 541.51 179.36 604.86 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n48 54 Car -1 -1 -1 364.89 175.65 464.44 235.77 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n48 47 Car -1 -1 -1 138.88 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0.90\n79 71 Car -1 -1 -1 656.94 175.28 692.90 204.83 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n79 64 Truck -1 -1 -1 541.40 153.00 579.33 190.92 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n79 73 Car -1 -1 -1 436.32 176.94 478.63 201.57 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n79 83 Car -1 -1 -1 722.91 161.81 882.63 234.75 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n79 77 Car -1 -1 -1 396.91 178.48 451.85 207.54 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n79 78 Car -1 -1 -1 -4.59 167.18 77.03 359.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n79 1 Car -1 -1 -1 593.67 176.32 633.07 208.13 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n79 89 Cyclist -1 -1 -1 751.98 170.89 814.84 281.24 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n79 82 Car -1 -1 -1 482.83 172.71 514.53 194.50 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n79 90 Car -1 -1 -1 466.84 172.60 500.95 196.81 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n80 81 Car -1 -1 -1 355.92 178.83 427.75 213.30 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n80 71 Car -1 -1 -1 660.06 175.12 697.34 204.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n80 77 Car -1 -1 -1 395.25 178.27 451.51 208.00 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n80 73 Car -1 -1 -1 436.92 176.74 479.52 201.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n80 82 Car -1 -1 -1 483.52 172.80 516.57 194.45 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n80 1 Car -1 -1 -1 597.45 176.21 635.45 207.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n80 83 Car -1 -1 -1 729.39 162.89 899.66 234.77 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n80 64 Truck -1 -1 -1 542.67 152.91 581.73 190.93 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n80 89 Cyclist -1 -1 -1 759.78 170.63 829.55 289.13 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n80 90 Car -1 -1 -1 466.79 172.42 501.88 196.88 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n81 71 Car -1 -1 -1 664.00 174.58 701.65 205.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n81 77 Car -1 -1 -1 394.95 178.20 452.39 208.35 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n81 73 Car -1 -1 -1 436.14 176.59 480.47 202.33 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n81 1 Car -1 -1 -1 602.47 175.79 639.78 207.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n81 81 Car -1 -1 -1 353.73 179.31 426.78 214.51 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n81 82 Car -1 -1 -1 483.87 173.03 517.90 195.22 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n81 83 Car -1 -1 -1 737.11 163.06 914.07 240.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n81 64 Truck -1 -1 -1 544.72 152.92 584.10 190.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n81 89 Cyclist -1 -1 -1 763.15 167.19 864.70 300.20 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n81 90 Car -1 -1 -1 467.80 173.43 502.87 197.40 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n82 81 Car -1 -1 -1 350.51 180.87 426.01 217.50 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n82 77 Car -1 -1 -1 393.02 179.65 452.90 211.02 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n82 73 Car -1 -1 -1 434.28 178.26 481.54 204.82 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n82 71 Car -1 -1 -1 668.14 175.87 706.64 207.97 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n82 90 Car -1 -1 -1 466.12 175.03 502.72 200.03 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n82 1 Car -1 -1 -1 606.07 177.81 643.96 208.46 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n82 82 Car -1 -1 -1 486.00 174.51 518.84 197.49 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n82 83 Car -1 -1 -1 746.26 165.33 920.63 240.26 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n82 89 Cyclist -1 -1 -1 792.85 169.14 873.55 313.08 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n82 64 Truck -1 -1 -1 548.29 153.54 587.59 191.93 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n83 81 Car -1 -1 -1 346.94 183.77 424.46 220.97 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n83 71 Car -1 -1 -1 671.86 179.34 710.51 210.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n83 73 Car -1 -1 -1 433.51 180.60 481.60 208.27 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n83 77 Car -1 -1 -1 392.89 182.43 452.76 213.84 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n83 1 Car -1 -1 -1 610.33 180.45 647.80 210.19 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n83 83 Car -1 -1 -1 758.99 166.36 962.12 246.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n83 89 Cyclist -1 -1 -1 805.40 165.13 914.90 331.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n83 90 Car -1 -1 -1 466.65 177.10 503.67 203.10 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n83 82 Car -1 -1 -1 486.34 176.71 520.26 200.25 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n83 64 Truck -1 -1 -1 548.80 157.49 588.94 194.54 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n84 81 Car -1 -1 -1 343.32 186.32 421.41 224.27 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n84 82 Car -1 -1 -1 487.24 178.65 521.13 202.84 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n84 71 Car -1 -1 -1 675.50 180.75 714.64 213.85 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n84 83 Car -1 -1 -1 769.83 165.52 1003.79 255.54 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n84 73 Car -1 -1 -1 433.52 182.33 481.40 210.36 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n84 1 Car -1 -1 -1 614.31 181.55 651.65 212.53 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n84 90 Car -1 -1 -1 465.69 178.71 504.85 205.79 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n84 77 Car -1 -1 -1 389.05 184.73 452.14 216.89 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n84 89 Cyclist -1 -1 -1 828.09 149.99 953.59 354.19 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n84 64 Truck -1 -1 -1 550.11 159.09 589.39 197.57 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n84 91 Car -1 -1 -1 742.42 170.61 885.72 234.25 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n"
  },
  {
    "path": "exps/permatrack_kitti_test/0028.txt",
    "content": "0 1 Car -1 -1 -1 560.18 164.07 599.56 201.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n0 2 Pedestrian -1 -1 -1 665.64 162.72 694.99 236.48 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n0 3 Car -1 -1 -1 889.65 178.96 962.18 215.00 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n0 4 Pedestrian -1 -1 -1 601.63 173.08 629.77 232.73 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n0 5 Pedestrian -1 -1 -1 579.54 172.26 605.62 233.63 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n0 6 Pedestrian -1 -1 -1 798.06 175.06 824.06 254.23 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n0 7 Car -1 -1 -1 1053.79 177.71 1203.06 228.86 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n0 8 Pedestrian -1 -1 -1 488.96 168.14 508.54 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n0 9 Pedestrian -1 -1 -1 553.33 169.03 571.06 217.61 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n0 10 Pedestrian -1 -1 -1 440.50 169.54 457.51 212.79 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n0 11 Pedestrian -1 -1 -1 448.26 175.26 465.36 214.88 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n0 12 Car -1 -1 -1 618.54 170.63 643.44 194.64 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n0 13 Car -1 -1 -1 1010.10 183.72 1108.84 220.13 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n0 14 Car -1 -1 -1 1200.67 191.53 1223.74 221.29 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n0 15 Car -1 -1 -1 941.73 182.50 1031.13 216.86 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n1 2 Pedestrian -1 -1 -1 668.45 163.57 699.23 238.39 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n1 1 Car -1 -1 -1 562.38 164.19 599.93 202.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n1 6 Pedestrian -1 -1 -1 807.31 171.82 843.06 258.17 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n1 3 Car -1 -1 -1 893.21 179.31 966.69 214.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n1 8 Pedestrian -1 -1 -1 487.31 169.72 510.22 218.18 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n1 5 Pedestrian -1 -1 -1 586.41 173.15 612.20 234.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n1 9 Pedestrian -1 -1 -1 555.53 169.02 572.37 218.45 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n1 7 Car -1 -1 -1 1058.11 179.71 1206.02 230.40 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n1 4 Pedestrian -1 -1 -1 607.60 174.36 636.86 232.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n1 10 Pedestrian -1 -1 -1 439.64 169.13 458.60 215.05 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n1 12 Car -1 -1 -1 618.55 170.89 643.75 194.56 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n1 15 Car -1 -1 -1 942.98 183.60 1030.24 218.60 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n1 13 Car -1 -1 -1 1015.00 185.00 1111.57 220.53 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n1 16 Pedestrian -1 -1 -1 400.60 161.74 417.07 205.23 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n2 2 Pedestrian -1 -1 -1 671.95 163.85 704.23 240.51 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n2 3 Car -1 -1 -1 897.81 179.77 975.35 215.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n2 8 Pedestrian -1 -1 -1 488.32 169.98 513.05 219.82 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n2 6 Pedestrian -1 -1 -1 812.66 174.85 855.58 260.56 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n2 5 Pedestrian -1 -1 -1 592.06 173.58 615.66 236.60 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n2 4 Pedestrian -1 -1 -1 610.23 175.17 641.27 235.45 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n2 9 Pedestrian -1 -1 -1 555.37 168.90 572.48 219.37 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n2 7 Car -1 -1 -1 1062.21 179.53 1217.44 232.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n2 10 Pedestrian -1 -1 -1 440.06 169.15 458.88 215.48 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n2 15 Car -1 -1 -1 950.84 183.46 1037.35 219.63 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n2 1 Car -1 -1 -1 564.43 163.94 602.90 204.05 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n2 16 Pedestrian -1 -1 -1 399.40 162.83 417.22 204.64 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n2 13 Car -1 -1 -1 1017.30 185.59 1109.12 220.50 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n2 12 Car -1 -1 -1 620.38 170.80 645.74 195.53 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n2 17 Pedestrian -1 -1 -1 448.77 174.25 465.45 215.92 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n2 18 Cyclist -1 -1 -1 535.80 171.20 554.61 209.56 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n2 19 Van -1 -1 -1 564.43 163.94 602.90 204.05 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n3 5 Pedestrian -1 -1 -1 594.40 173.21 620.01 238.35 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n3 7 Car -1 -1 -1 1070.90 179.69 1223.95 232.59 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n3 6 Pedestrian -1 -1 -1 823.38 174.03 866.95 263.09 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n3 2 Pedestrian -1 -1 -1 677.08 163.97 707.06 242.32 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n3 4 Pedestrian -1 -1 -1 618.07 175.20 644.16 237.01 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n3 3 Car -1 -1 -1 901.72 180.24 980.15 216.20 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n3 8 Pedestrian -1 -1 -1 491.62 169.89 513.71 220.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n3 9 Pedestrian -1 -1 -1 553.64 169.11 571.32 218.93 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n3 15 Car -1 -1 -1 951.75 183.81 1043.72 219.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n3 1 Car -1 -1 -1 566.53 164.02 607.56 204.84 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n3 12 Car -1 -1 -1 619.90 170.72 646.19 195.03 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n3 10 Pedestrian -1 -1 -1 439.10 170.02 458.88 216.54 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n3 18 Cyclist -1 -1 -1 536.53 170.59 554.90 211.36 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n3 13 Car -1 -1 -1 1020.78 186.04 1113.34 220.87 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n4 7 Car -1 -1 -1 1078.74 180.55 1224.18 231.89 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n4 5 Pedestrian -1 -1 -1 596.87 173.20 626.16 240.45 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n4 6 Pedestrian -1 -1 -1 844.08 175.80 877.12 267.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n4 2 Pedestrian -1 -1 -1 685.03 163.36 712.95 243.50 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n4 3 Car -1 -1 -1 904.28 179.92 985.27 216.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n4 8 Pedestrian -1 -1 -1 493.65 169.81 514.16 222.00 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n4 4 Pedestrian -1 -1 -1 624.85 175.01 651.98 239.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n4 9 Pedestrian -1 -1 -1 553.28 168.79 571.23 219.67 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n4 15 Car -1 -1 -1 955.81 183.99 1047.33 219.35 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n4 10 Pedestrian -1 -1 -1 439.65 170.88 458.82 217.62 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n4 12 Car -1 -1 -1 618.34 171.08 643.61 194.70 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n4 1 Car -1 -1 -1 566.32 164.00 610.26 205.32 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n4 18 Cyclist -1 -1 -1 537.29 170.47 556.29 211.61 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n4 13 Car -1 -1 -1 1030.72 185.21 1126.63 221.06 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n4 20 Pedestrian -1 -1 -1 396.08 164.49 419.40 209.67 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n5 7 Car -1 -1 -1 1085.97 180.37 1224.11 231.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n5 3 Car -1 -1 -1 907.15 179.46 988.77 216.67 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n5 5 Pedestrian -1 -1 -1 599.37 175.37 630.82 242.70 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n5 6 Pedestrian -1 -1 -1 859.89 177.16 890.50 271.44 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n5 15 Car -1 -1 -1 958.80 183.39 1052.61 219.61 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n5 9 Pedestrian -1 -1 -1 553.08 168.51 571.31 220.21 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n5 10 Pedestrian -1 -1 -1 436.48 170.80 457.31 218.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n5 4 Pedestrian -1 -1 -1 627.65 176.49 662.01 240.79 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n5 8 Pedestrian -1 -1 -1 496.21 170.03 516.57 222.22 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n5 12 Car -1 -1 -1 619.92 171.05 646.73 195.36 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n5 2 Pedestrian -1 -1 -1 690.98 162.83 722.43 246.66 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n5 13 Car -1 -1 -1 1032.22 185.39 1124.89 221.12 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n5 18 Cyclist -1 -1 -1 539.26 171.08 558.86 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n5 20 Pedestrian -1 -1 -1 416.07 165.25 428.03 194.51 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n5 21 Pedestrian -1 -1 -1 449.20 175.91 467.71 219.81 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n6 7 Car -1 -1 -1 1094.94 180.74 1223.85 232.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n6 6 Pedestrian -1 -1 -1 871.11 176.73 909.05 273.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n6 5 Pedestrian -1 -1 -1 605.34 174.92 633.36 245.53 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n6 15 Car -1 -1 -1 963.79 183.38 1055.33 220.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n6 4 Pedestrian -1 -1 -1 631.27 177.21 666.39 242.62 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n6 8 Pedestrian -1 -1 -1 495.59 170.31 519.01 223.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n6 3 Car -1 -1 -1 909.77 179.63 993.25 217.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n6 9 Pedestrian -1 -1 -1 552.19 168.52 571.70 221.61 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n6 13 Car -1 -1 -1 1041.52 184.76 1130.42 222.00 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n6 10 Pedestrian -1 -1 -1 438.28 170.38 460.06 219.85 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n6 18 Cyclist -1 -1 -1 540.26 169.85 559.60 211.60 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n6 2 Pedestrian -1 -1 -1 695.79 163.66 726.22 247.41 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n6 12 Car -1 -1 -1 620.20 171.33 647.19 195.71 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n6 21 Pedestrian -1 -1 -1 448.15 175.01 468.90 220.90 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n6 20 Pedestrian -1 -1 -1 415.77 165.66 428.03 195.26 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n6 22 Cyclist -1 -1 -1 389.57 168.16 419.55 213.31 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n7 6 Pedestrian -1 -1 -1 881.55 177.99 923.07 279.54 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n7 8 Pedestrian -1 -1 -1 496.45 171.07 518.91 224.94 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n7 7 Car -1 -1 -1 1108.18 183.28 1224.33 234.40 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n7 5 Pedestrian -1 -1 -1 613.13 174.01 638.33 247.68 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n7 15 Car -1 -1 -1 965.13 184.16 1060.93 221.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n7 2 Pedestrian -1 -1 -1 701.67 163.48 733.16 249.29 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n7 9 Pedestrian -1 -1 -1 551.45 168.19 572.08 223.05 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n7 3 Car -1 -1 -1 909.28 180.02 993.96 218.25 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n7 22 Cyclist -1 -1 -1 387.31 168.16 421.20 213.83 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n7 4 Pedestrian -1 -1 -1 638.77 177.08 668.82 243.98 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n7 10 Pedestrian -1 -1 -1 438.36 170.30 459.80 221.50 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n7 13 Car -1 -1 -1 1045.43 185.45 1141.68 224.23 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n7 21 Pedestrian -1 -1 -1 448.52 174.88 468.60 222.38 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n7 18 Cyclist -1 -1 -1 543.93 168.57 562.96 213.73 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n7 23 Van -1 -1 -1 567.44 164.42 623.64 208.31 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n8 6 Pedestrian -1 -1 -1 900.69 178.17 935.78 282.25 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n8 7 Car -1 -1 -1 1116.09 183.49 1223.33 235.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n8 8 Pedestrian -1 -1 -1 499.30 170.80 522.38 226.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n8 15 Car -1 -1 -1 968.31 184.40 1064.92 222.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n8 5 Pedestrian -1 -1 -1 617.35 174.22 643.85 248.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n8 2 Pedestrian -1 -1 -1 704.78 163.08 738.70 254.69 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n8 9 Pedestrian -1 -1 -1 550.92 168.31 571.57 223.56 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n8 10 Pedestrian -1 -1 -1 436.37 170.72 457.88 221.14 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n8 13 Car -1 -1 -1 1046.45 185.98 1141.60 224.83 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n8 3 Car -1 -1 -1 910.06 179.76 993.99 219.46 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n8 23 Van -1 -1 -1 565.43 164.71 627.52 210.28 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n8 4 Pedestrian -1 -1 -1 643.60 178.20 672.10 246.45 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n8 21 Pedestrian -1 -1 -1 449.25 176.27 468.64 222.91 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n8 24 Pedestrian -1 -1 -1 389.71 166.58 417.92 215.72 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n9 6 Pedestrian -1 -1 -1 917.14 178.62 957.33 287.82 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n9 15 Car -1 -1 -1 966.07 184.47 1068.55 222.45 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n9 5 Pedestrian -1 -1 -1 620.78 175.94 653.73 252.29 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n9 8 Pedestrian -1 -1 -1 502.95 171.23 526.34 227.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n9 10 Pedestrian -1 -1 -1 434.90 172.90 459.76 223.25 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n9 7 Car -1 -1 -1 1124.45 184.82 1223.49 236.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n9 9 Pedestrian -1 -1 -1 550.97 168.42 572.09 225.65 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n9 3 Car -1 -1 -1 917.80 180.39 1001.17 221.37 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n9 4 Pedestrian -1 -1 -1 649.07 176.84 681.57 248.52 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n9 13 Car -1 -1 -1 1048.29 185.97 1147.11 225.45 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n9 21 Pedestrian -1 -1 -1 451.39 178.95 470.43 225.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n9 23 Van -1 -1 -1 566.52 164.31 632.36 211.30 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n9 2 Pedestrian -1 -1 -1 711.55 163.10 747.60 258.74 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n9 24 Pedestrian -1 -1 -1 385.52 171.75 417.28 216.29 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n10 6 Pedestrian -1 -1 -1 929.07 178.44 988.86 293.09 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n10 15 Car -1 -1 -1 969.99 184.30 1072.56 222.40 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n10 2 Pedestrian -1 -1 -1 719.48 163.17 754.64 257.21 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n10 8 Pedestrian -1 -1 -1 501.96 171.43 529.43 227.89 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n10 4 Pedestrian -1 -1 -1 654.78 176.97 689.65 250.80 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n10 10 Pedestrian -1 -1 -1 434.17 172.83 459.69 223.49 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n10 23 Van -1 -1 -1 566.56 164.01 638.96 212.28 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n10 7 Car -1 -1 -1 1123.48 183.21 1224.97 235.89 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n10 24 Pedestrian -1 -1 -1 388.04 170.68 414.00 218.46 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n10 5 Pedestrian -1 -1 -1 625.95 177.36 658.28 255.02 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n10 13 Car -1 -1 -1 1052.23 185.21 1150.46 226.02 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n10 3 Car -1 -1 -1 920.11 180.89 1006.99 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n10 21 Pedestrian -1 -1 -1 450.43 178.77 473.10 226.67 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n10 9 Pedestrian -1 -1 -1 549.14 169.24 572.57 225.74 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n10 25 Pedestrian -1 -1 -1 650.66 175.67 679.12 237.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n11 6 Pedestrian -1 -1 -1 943.25 178.98 1007.67 296.21 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n11 7 Car -1 -1 -1 1123.78 182.24 1224.97 236.23 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n11 4 Pedestrian -1 -1 -1 660.28 176.73 692.65 252.05 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n11 2 Pedestrian -1 -1 -1 723.74 162.53 765.39 259.22 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n11 23 Van -1 -1 -1 569.52 163.61 643.63 212.84 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n11 10 Pedestrian -1 -1 -1 436.09 172.36 457.85 224.93 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n11 5 Pedestrian -1 -1 -1 635.37 177.01 663.64 256.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n11 15 Car -1 -1 -1 979.92 184.27 1076.71 221.33 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n11 24 Pedestrian -1 -1 -1 387.17 170.97 413.17 218.93 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n11 8 Pedestrian -1 -1 -1 502.76 170.50 533.66 231.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n11 3 Car -1 -1 -1 922.81 180.11 1004.70 218.95 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n11 21 Pedestrian -1 -1 -1 451.93 178.41 470.74 226.85 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n11 13 Car -1 -1 -1 1055.24 184.31 1155.10 225.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n11 25 Pedestrian -1 -1 -1 655.07 176.15 681.26 238.16 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n11 9 Pedestrian -1 -1 -1 549.70 169.51 571.99 225.70 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n12 6 Pedestrian -1 -1 -1 958.43 178.04 1029.75 303.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n12 7 Car -1 -1 -1 1130.24 181.14 1225.09 236.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n12 5 Pedestrian -1 -1 -1 638.91 174.94 668.56 258.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n12 8 Pedestrian -1 -1 -1 506.27 170.97 533.58 232.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n12 2 Pedestrian -1 -1 -1 727.52 162.04 771.44 264.09 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n12 15 Car -1 -1 -1 981.01 183.38 1083.63 220.93 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n12 13 Car -1 -1 -1 1059.01 183.83 1160.35 225.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n12 21 Pedestrian -1 -1 -1 451.80 177.77 472.75 227.21 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n12 3 Car -1 -1 -1 927.72 179.39 1015.21 220.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n12 23 Van -1 -1 -1 568.92 163.16 648.02 213.52 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n12 10 Pedestrian -1 -1 -1 438.11 172.00 460.32 225.75 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n12 4 Pedestrian -1 -1 -1 670.38 177.27 696.87 255.15 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n12 24 Pedestrian -1 -1 -1 384.68 170.71 415.23 218.47 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n12 25 Pedestrian -1 -1 -1 658.05 174.99 685.53 242.47 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n12 9 Pedestrian -1 -1 -1 549.66 169.65 572.09 226.76 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n12 26 Cyclist -1 -1 -1 549.66 169.65 572.09 226.76 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n12 27 Cyclist -1 -1 -1 555.98 168.01 581.44 220.23 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n13 6 Pedestrian -1 -1 -1 986.77 179.22 1047.62 308.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n13 2 Pedestrian -1 -1 -1 733.86 161.57 779.20 265.83 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n13 5 Pedestrian -1 -1 -1 642.43 173.97 679.88 262.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n13 7 Car -1 -1 -1 1138.74 181.44 1225.18 237.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n13 13 Car -1 -1 -1 1063.88 184.20 1169.37 225.46 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n13 8 Pedestrian -1 -1 -1 513.17 170.62 533.85 233.14 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n13 15 Car -1 -1 -1 983.68 183.01 1089.20 222.00 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n13 26 Cyclist -1 -1 -1 550.28 169.40 572.29 227.26 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n13 4 Pedestrian -1 -1 -1 678.13 175.94 703.99 257.74 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n13 21 Pedestrian -1 -1 -1 450.45 178.53 474.12 227.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n13 25 Pedestrian -1 -1 -1 661.16 174.99 690.30 243.41 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n13 23 Van -1 -1 -1 572.19 163.16 656.01 213.67 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n13 3 Car -1 -1 -1 929.14 180.01 1020.86 221.73 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n13 10 Pedestrian -1 -1 -1 437.58 172.66 461.00 226.55 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n13 27 Cyclist -1 -1 -1 558.07 167.45 586.26 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n13 24 Pedestrian -1 -1 -1 385.76 171.04 414.35 219.95 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n14 6 Pedestrian -1 -1 -1 1025.29 175.55 1068.97 314.84 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n14 13 Car -1 -1 -1 1067.23 184.68 1174.98 226.70 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n14 5 Pedestrian -1 -1 -1 645.76 176.01 690.27 265.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n14 2 Pedestrian -1 -1 -1 742.13 161.25 785.21 268.23 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n14 7 Car -1 -1 -1 1145.34 182.34 1225.22 237.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n14 8 Pedestrian -1 -1 -1 514.05 171.27 537.43 234.27 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n14 21 Pedestrian -1 -1 -1 450.85 178.97 474.38 231.42 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n14 4 Pedestrian -1 -1 -1 683.49 177.23 713.79 258.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n14 10 Pedestrian -1 -1 -1 437.11 174.63 461.87 230.04 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n14 3 Car -1 -1 -1 931.92 180.11 1026.35 222.20 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n14 26 Cyclist -1 -1 -1 551.00 169.48 570.92 228.20 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n14 27 Cyclist -1 -1 -1 561.57 167.20 590.78 221.19 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n14 25 Pedestrian -1 -1 -1 663.65 176.08 695.97 244.33 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n14 15 Car -1 -1 -1 995.96 183.60 1099.48 222.67 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n14 24 Pedestrian -1 -1 -1 388.58 172.74 412.73 221.56 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n14 23 Van -1 -1 -1 573.72 163.92 664.07 215.77 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n14 28 Pedestrian -1 -1 -1 379.29 172.13 397.15 215.05 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n14 29 Car -1 -1 -1 573.72 163.92 664.07 215.77 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n15 6 Pedestrian -1 -1 -1 1049.27 179.05 1107.42 324.75 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n15 13 Car -1 -1 -1 1075.53 186.09 1181.29 227.18 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n15 3 Car -1 -1 -1 934.26 180.64 1023.36 222.74 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n15 27 Cyclist -1 -1 -1 562.82 167.18 597.21 222.44 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n15 2 Pedestrian -1 -1 -1 747.28 162.07 789.56 271.60 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n15 5 Pedestrian -1 -1 -1 652.26 175.66 692.87 267.88 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n15 8 Pedestrian -1 -1 -1 514.34 171.68 539.95 235.13 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n15 10 Pedestrian -1 -1 -1 436.60 174.17 461.75 231.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n15 7 Car -1 -1 -1 1153.52 184.03 1224.16 241.52 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n15 21 Pedestrian -1 -1 -1 451.35 178.72 473.66 232.96 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n15 24 Pedestrian -1 -1 -1 388.22 173.40 412.01 222.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n15 15 Car -1 -1 -1 1000.83 185.33 1102.44 224.87 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n15 4 Pedestrian -1 -1 -1 685.48 178.46 720.51 262.58 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n15 25 Pedestrian -1 -1 -1 667.49 175.80 700.26 246.30 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n15 28 Pedestrian -1 -1 -1 376.38 172.75 392.95 214.65 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n15 23 Van -1 -1 -1 574.96 164.76 672.04 216.89 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n15 26 Cyclist -1 -1 -1 550.44 169.97 569.89 229.87 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n15 29 Car -1 -1 -1 574.96 164.76 672.04 216.89 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n15 30 Pedestrian -1 -1 -1 550.07 170.61 570.47 230.73 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n16 3 Car -1 -1 -1 934.44 181.10 1024.68 223.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n16 6 Pedestrian -1 -1 -1 1072.03 185.01 1146.97 333.16 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n16 30 Pedestrian -1 -1 -1 547.72 169.98 568.38 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n16 2 Pedestrian -1 -1 -1 760.28 162.05 798.91 273.57 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n16 27 Cyclist -1 -1 -1 568.59 167.34 600.41 222.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n16 8 Pedestrian -1 -1 -1 517.74 171.76 542.88 238.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n16 21 Pedestrian -1 -1 -1 453.50 178.52 478.63 233.90 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n16 15 Car -1 -1 -1 1000.55 185.29 1110.15 225.80 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n16 13 Car -1 -1 -1 1080.00 186.93 1185.34 227.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n16 7 Car -1 -1 -1 1161.84 185.27 1223.71 240.28 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n16 10 Pedestrian -1 -1 -1 435.08 173.57 459.35 231.47 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n16 24 Pedestrian -1 -1 -1 386.18 174.50 408.62 223.19 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n16 28 Pedestrian -1 -1 -1 371.96 173.18 389.57 214.67 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n16 5 Pedestrian -1 -1 -1 663.05 176.11 697.69 271.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n16 4 Pedestrian -1 -1 -1 694.15 177.72 727.63 266.53 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n16 25 Pedestrian -1 -1 -1 671.53 176.17 704.34 250.74 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n16 23 Van -1 -1 -1 580.92 164.52 678.44 218.39 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n16 29 Car -1 -1 -1 580.92 164.52 678.44 218.39 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n16 31 Pedestrian -1 -1 -1 393.24 175.46 415.12 223.57 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n16 32 Pedestrian -1 -1 -1 357.00 174.26 373.65 212.43 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n17 3 Car -1 -1 -1 936.67 181.01 1029.96 224.53 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n17 6 Pedestrian -1 -1 -1 1096.41 180.58 1182.87 346.19 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n17 5 Pedestrian -1 -1 -1 670.27 175.96 705.73 274.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n17 15 Car -1 -1 -1 999.50 185.31 1111.98 226.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n17 2 Pedestrian -1 -1 -1 765.99 161.20 807.53 275.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n17 27 Cyclist -1 -1 -1 571.73 167.62 606.15 223.37 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n17 10 Pedestrian -1 -1 -1 435.08 172.24 459.38 232.94 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n17 8 Pedestrian -1 -1 -1 519.00 171.38 543.23 239.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n17 24 Pedestrian -1 -1 -1 383.86 173.06 408.60 225.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n17 28 Pedestrian -1 -1 -1 369.13 172.79 387.06 215.24 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n17 30 Pedestrian -1 -1 -1 543.50 169.85 565.93 233.39 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n17 21 Pedestrian -1 -1 -1 454.24 178.41 476.27 235.00 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n17 13 Car -1 -1 -1 1078.65 186.82 1186.06 227.49 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n17 25 Pedestrian -1 -1 -1 674.76 176.74 708.94 251.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n17 23 Van -1 -1 -1 581.96 164.12 685.41 219.29 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n17 4 Pedestrian -1 -1 -1 703.32 179.69 734.19 268.90 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n17 7 Car -1 -1 -1 1169.45 186.43 1223.65 239.70 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n17 32 Pedestrian -1 -1 -1 350.58 171.92 367.99 212.20 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n17 29 Car -1 -1 -1 581.96 164.12 685.41 219.29 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n18 3 Car -1 -1 -1 938.43 180.46 1033.80 224.43 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n18 15 Car -1 -1 -1 997.65 184.57 1114.40 226.93 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n18 2 Pedestrian -1 -1 -1 769.08 160.46 813.87 280.35 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n18 30 Pedestrian -1 -1 -1 542.55 170.36 564.64 233.46 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n18 27 Cyclist -1 -1 -1 576.44 167.23 614.15 223.30 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n18 10 Pedestrian -1 -1 -1 433.97 172.57 460.53 233.48 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n18 8 Pedestrian -1 -1 -1 522.62 169.97 547.97 241.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n18 5 Pedestrian -1 -1 -1 675.81 176.18 714.08 276.15 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n18 6 Pedestrian -1 -1 -1 1136.23 181.19 1212.33 352.61 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n18 4 Pedestrian -1 -1 -1 710.75 177.58 748.76 271.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n18 21 Pedestrian -1 -1 -1 453.63 180.32 477.13 236.68 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n18 13 Car -1 -1 -1 1083.33 187.15 1196.12 230.40 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 28 Pedestrian -1 -1 -1 366.89 173.09 385.16 217.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 24 Pedestrian -1 -1 -1 381.61 173.94 405.86 225.01 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n18 32 Pedestrian -1 -1 -1 345.75 171.71 362.69 212.60 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n18 29 Car -1 -1 -1 583.78 164.42 693.72 219.82 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n18 7 Car -1 -1 -1 1177.63 187.22 1223.70 239.88 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n18 25 Pedestrian -1 -1 -1 683.14 176.88 715.23 251.53 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n18 23 Van -1 -1 -1 586.89 164.60 694.56 219.54 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n18 33 Car -1 -1 -1 557.48 173.53 580.44 193.64 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n19 3 Car -1 -1 -1 937.94 179.81 1036.99 224.48 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n19 2 Pedestrian -1 -1 -1 775.86 158.85 821.86 282.71 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n19 27 Cyclist -1 -1 -1 581.39 166.31 618.63 224.57 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n19 13 Car -1 -1 -1 1086.60 185.57 1201.17 228.50 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n19 5 Pedestrian -1 -1 -1 680.93 176.90 724.96 280.49 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n19 15 Car -1 -1 -1 999.31 183.72 1120.36 226.96 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n19 30 Pedestrian -1 -1 -1 541.34 170.18 564.70 234.70 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n19 10 Pedestrian -1 -1 -1 433.04 172.20 461.23 233.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n19 8 Pedestrian -1 -1 -1 523.04 171.57 554.56 241.64 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n19 21 Pedestrian -1 -1 -1 454.06 179.91 476.47 238.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n19 33 Car -1 -1 -1 556.53 173.72 579.29 193.29 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n19 24 Pedestrian -1 -1 -1 381.50 174.61 405.25 227.28 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n19 28 Pedestrian -1 -1 -1 363.76 173.25 381.35 218.59 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n19 4 Pedestrian -1 -1 -1 712.98 178.20 754.02 273.73 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n19 6 Pedestrian -1 -1 -1 1174.83 175.56 1219.75 360.03 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n19 23 Van -1 -1 -1 588.04 164.49 703.53 219.84 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n19 32 Pedestrian -1 -1 -1 341.75 171.87 357.53 214.67 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n19 7 Car -1 -1 -1 1174.90 183.94 1226.98 236.84 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n19 25 Pedestrian -1 -1 -1 686.55 176.87 719.86 257.88 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n20 3 Car -1 -1 -1 940.19 178.68 1041.69 224.41 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n20 2 Pedestrian -1 -1 -1 781.20 159.17 831.77 285.62 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n20 13 Car -1 -1 -1 1088.92 184.40 1207.51 228.38 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n20 15 Car -1 -1 -1 1006.39 182.73 1126.69 226.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n20 27 Cyclist -1 -1 -1 587.75 165.79 625.36 225.63 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n20 21 Pedestrian -1 -1 -1 454.24 179.62 477.72 238.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n20 30 Pedestrian -1 -1 -1 541.96 170.18 564.51 234.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n20 10 Pedestrian -1 -1 -1 431.77 173.01 461.70 236.70 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n20 24 Pedestrian -1 -1 -1 383.97 174.81 408.62 227.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n20 8 Pedestrian -1 -1 -1 524.19 171.21 558.72 243.08 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n20 4 Pedestrian -1 -1 -1 721.78 178.89 760.38 277.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n20 5 Pedestrian -1 -1 -1 690.72 177.59 731.32 282.86 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n20 28 Pedestrian -1 -1 -1 360.78 174.74 378.33 219.14 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n20 33 Car -1 -1 -1 554.33 173.57 578.32 193.63 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n20 32 Pedestrian -1 -1 -1 333.50 167.14 354.20 216.48 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n20 25 Pedestrian -1 -1 -1 693.06 177.42 728.02 257.85 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n20 7 Car -1 -1 -1 1184.68 182.79 1225.08 237.95 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n20 23 Van -1 -1 -1 594.39 164.20 711.48 220.26 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n20 34 Car -1 -1 -1 593.01 164.47 712.78 221.59 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n21 3 Car -1 -1 -1 943.55 178.81 1046.08 225.10 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n21 13 Car -1 -1 -1 1094.68 184.31 1215.35 229.03 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n21 2 Pedestrian -1 -1 -1 788.83 158.30 840.03 287.09 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n21 15 Car -1 -1 -1 1010.32 182.81 1131.08 226.88 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n21 24 Pedestrian -1 -1 -1 380.98 173.72 405.25 229.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n21 10 Pedestrian -1 -1 -1 433.09 172.58 460.45 237.41 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n21 27 Cyclist -1 -1 -1 588.61 167.14 635.02 228.33 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n21 5 Pedestrian -1 -1 -1 705.47 178.61 739.44 286.12 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n21 30 Pedestrian -1 -1 -1 541.21 170.48 565.54 235.48 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n21 8 Pedestrian -1 -1 -1 526.75 172.84 558.87 244.73 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n21 21 Pedestrian -1 -1 -1 455.97 178.85 477.35 238.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n21 4 Pedestrian -1 -1 -1 726.60 178.80 764.38 278.94 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n21 33 Car -1 -1 -1 555.08 174.22 577.63 193.66 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n21 32 Pedestrian -1 -1 -1 329.27 167.92 349.80 216.11 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n21 28 Pedestrian -1 -1 -1 358.59 173.18 375.84 218.92 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n21 23 Van -1 -1 -1 593.94 164.07 720.40 222.47 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n21 25 Pedestrian -1 -1 -1 698.10 176.39 732.13 260.09 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n22 2 Pedestrian -1 -1 -1 798.64 158.52 851.32 292.47 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n22 3 Car -1 -1 -1 949.73 179.31 1052.22 224.98 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n22 13 Car -1 -1 -1 1098.69 185.63 1218.97 229.49 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n22 15 Car -1 -1 -1 1007.36 183.33 1135.63 228.49 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n22 27 Cyclist -1 -1 -1 593.69 166.00 645.19 230.31 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n22 5 Pedestrian -1 -1 -1 714.89 177.48 752.83 289.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n22 10 Pedestrian -1 -1 -1 433.82 171.70 460.28 238.85 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n22 24 Pedestrian -1 -1 -1 380.43 173.62 404.79 229.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n22 30 Pedestrian -1 -1 -1 541.60 169.54 565.18 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n22 32 Pedestrian -1 -1 -1 325.12 169.79 345.04 217.21 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n22 21 Pedestrian -1 -1 -1 456.63 177.82 480.57 239.71 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n22 8 Pedestrian -1 -1 -1 536.08 170.80 562.93 246.89 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n22 33 Car -1 -1 -1 554.65 174.23 577.40 193.91 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n22 28 Pedestrian -1 -1 -1 356.96 173.94 374.19 219.82 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n22 25 Pedestrian -1 -1 -1 705.68 174.93 739.69 261.31 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n22 4 Pedestrian -1 -1 -1 738.45 181.19 774.34 282.55 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n22 23 Van -1 -1 -1 598.91 163.79 730.76 223.55 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n22 35 Car -1 -1 -1 598.91 163.79 730.76 223.55 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n23 3 Car -1 -1 -1 952.76 179.22 1058.77 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n23 2 Pedestrian -1 -1 -1 806.01 157.88 861.61 297.92 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n23 15 Car -1 -1 -1 1011.12 183.70 1139.46 228.88 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n23 13 Car -1 -1 -1 1098.36 186.20 1221.20 231.19 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n23 27 Cyclist -1 -1 -1 599.68 163.76 653.92 232.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n23 10 Pedestrian -1 -1 -1 435.86 171.90 463.89 239.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n23 8 Pedestrian -1 -1 -1 536.26 170.72 569.67 247.71 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n23 24 Pedestrian -1 -1 -1 380.61 173.11 404.58 229.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n23 5 Pedestrian -1 -1 -1 721.90 176.41 768.50 295.67 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n23 32 Pedestrian -1 -1 -1 321.03 170.29 339.33 218.16 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n23 25 Pedestrian -1 -1 -1 713.11 174.65 747.15 266.25 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n23 21 Pedestrian -1 -1 -1 456.21 178.12 480.72 240.18 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n23 4 Pedestrian -1 -1 -1 742.03 178.09 786.10 288.37 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n23 28 Pedestrian -1 -1 -1 353.87 173.41 371.68 221.01 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n23 35 Car -1 -1 -1 603.56 163.30 742.64 223.89 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n23 33 Car -1 -1 -1 554.07 174.53 576.88 193.57 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n24 2 Pedestrian -1 -1 -1 813.06 155.59 870.02 300.41 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n24 3 Car -1 -1 -1 954.77 177.11 1063.85 225.47 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n24 13 Car -1 -1 -1 1105.30 184.56 1221.55 229.19 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n24 15 Car -1 -1 -1 1014.31 182.38 1143.64 227.46 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n24 8 Pedestrian -1 -1 -1 538.89 169.43 575.73 249.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n24 27 Cyclist -1 -1 -1 606.46 162.10 661.96 234.15 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n24 21 Pedestrian -1 -1 -1 452.33 178.06 479.39 240.35 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n24 25 Pedestrian -1 -1 -1 720.37 172.94 754.14 268.10 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 10 Pedestrian -1 -1 -1 436.08 172.64 463.91 240.20 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 5 Pedestrian -1 -1 -1 727.83 174.83 778.31 299.27 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n24 24 Pedestrian -1 -1 -1 380.32 172.40 404.78 229.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n24 32 Pedestrian -1 -1 -1 315.47 169.25 334.03 217.84 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n24 4 Pedestrian -1 -1 -1 751.90 176.74 799.24 290.46 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n24 28 Pedestrian -1 -1 -1 351.99 171.96 369.72 220.21 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n24 33 Car -1 -1 -1 551.74 173.43 576.91 193.18 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n24 35 Car -1 -1 -1 607.44 161.72 753.30 224.21 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n24 36 Van -1 -1 -1 612.61 160.76 754.09 223.20 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n25 3 Car -1 -1 -1 959.28 174.71 1065.81 223.48 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n25 2 Pedestrian -1 -1 -1 820.24 151.46 878.37 301.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n25 13 Car -1 -1 -1 1111.00 182.55 1221.73 228.30 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n25 15 Car -1 -1 -1 1022.90 180.39 1149.33 225.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n25 25 Pedestrian -1 -1 -1 723.71 171.69 765.83 270.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n25 27 Cyclist -1 -1 -1 622.37 161.15 666.62 228.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n25 28 Pedestrian -1 -1 -1 349.34 170.90 367.70 220.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n25 8 Pedestrian -1 -1 -1 540.03 167.24 575.95 247.23 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n25 21 Pedestrian -1 -1 -1 453.67 177.14 478.91 240.97 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n25 10 Pedestrian -1 -1 -1 436.17 172.00 463.13 241.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n25 5 Pedestrian -1 -1 -1 736.41 174.86 791.95 304.27 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n25 24 Pedestrian -1 -1 -1 379.42 170.67 404.51 229.02 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n25 32 Pedestrian -1 -1 -1 313.66 166.26 331.39 217.48 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n25 36 Van -1 -1 -1 617.11 158.96 765.04 223.34 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n25 4 Pedestrian -1 -1 -1 759.84 174.84 806.96 297.02 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n25 33 Car -1 -1 -1 547.91 171.83 574.32 194.40 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n25 37 Pedestrian -1 -1 -1 378.23 161.19 391.18 195.82 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n26 3 Car -1 -1 -1 962.49 173.93 1070.07 223.36 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n26 2 Pedestrian -1 -1 -1 828.99 149.40 890.72 306.73 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n26 15 Car -1 -1 -1 1026.61 179.30 1153.88 225.08 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n26 25 Pedestrian -1 -1 -1 729.69 170.47 774.92 271.77 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n26 13 Car -1 -1 -1 1117.45 181.55 1221.38 228.23 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n26 27 Cyclist -1 -1 -1 629.66 159.19 676.21 230.98 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n26 10 Pedestrian -1 -1 -1 435.66 169.71 464.59 241.93 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n26 5 Pedestrian -1 -1 -1 748.42 174.19 795.77 306.33 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n26 28 Pedestrian -1 -1 -1 347.90 169.29 366.56 218.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n26 8 Pedestrian -1 -1 -1 546.52 165.32 582.48 249.49 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n26 24 Pedestrian -1 -1 -1 376.13 169.90 403.69 229.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n26 21 Pedestrian -1 -1 -1 457.18 176.06 480.78 241.26 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n26 4 Pedestrian -1 -1 -1 773.17 177.00 816.76 295.25 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n26 32 Pedestrian -1 -1 -1 306.35 165.67 326.67 218.55 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n26 36 Van -1 -1 -1 621.82 157.31 775.74 222.71 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n26 37 Pedestrian -1 -1 -1 377.28 161.48 390.29 195.92 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n26 33 Car -1 -1 -1 543.40 169.77 571.76 194.00 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n26 38 Car -1 -1 -1 619.27 157.70 772.58 222.15 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n27 3 Car -1 -1 -1 963.23 174.10 1071.68 223.65 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n27 2 Pedestrian -1 -1 -1 836.73 150.09 898.81 308.21 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n27 13 Car -1 -1 -1 1120.18 182.07 1220.42 228.34 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n27 25 Pedestrian -1 -1 -1 736.31 170.25 778.27 273.78 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n27 15 Car -1 -1 -1 1029.63 178.93 1158.80 224.98 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n27 27 Cyclist -1 -1 -1 637.38 159.17 690.57 237.50 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n27 10 Pedestrian -1 -1 -1 439.30 168.57 467.70 243.00 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n27 21 Pedestrian -1 -1 -1 457.22 175.87 481.53 242.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n27 8 Pedestrian -1 -1 -1 551.07 165.53 578.84 246.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n27 24 Pedestrian -1 -1 -1 375.39 169.88 403.92 229.83 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n27 28 Pedestrian -1 -1 -1 346.68 170.05 367.02 220.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n27 5 Pedestrian -1 -1 -1 766.41 173.55 815.91 313.85 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n27 4 Pedestrian -1 -1 -1 782.83 175.95 829.77 297.88 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n27 37 Pedestrian -1 -1 -1 375.08 160.88 388.77 196.40 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n27 36 Van -1 -1 -1 626.29 156.55 787.44 224.54 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n27 32 Pedestrian -1 -1 -1 305.21 168.15 325.12 219.50 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n27 33 Car -1 -1 -1 543.39 169.69 569.84 194.07 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n27 38 Car -1 -1 -1 623.14 157.14 784.43 223.98 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n28 3 Car -1 -1 -1 964.76 173.61 1075.49 224.34 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n28 2 Pedestrian -1 -1 -1 844.47 149.06 906.42 310.78 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n28 15 Car -1 -1 -1 1034.06 178.69 1161.99 225.40 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n28 25 Pedestrian -1 -1 -1 749.56 170.60 786.98 277.74 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n28 10 Pedestrian -1 -1 -1 436.57 167.40 471.91 244.19 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n28 27 Cyclist -1 -1 -1 641.09 160.55 705.40 243.19 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 13 Car -1 -1 -1 1127.19 181.86 1219.87 228.51 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 5 Pedestrian -1 -1 -1 776.20 172.58 829.17 317.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n28 24 Pedestrian -1 -1 -1 378.37 170.87 405.95 232.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n28 8 Pedestrian -1 -1 -1 555.41 164.43 591.13 250.17 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n28 21 Pedestrian -1 -1 -1 457.47 176.53 482.10 243.67 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n28 37 Pedestrian -1 -1 -1 373.70 161.39 387.32 196.56 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n28 4 Pedestrian -1 -1 -1 787.92 175.07 840.73 306.10 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n28 28 Pedestrian -1 -1 -1 344.98 171.43 366.60 222.59 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n28 36 Van -1 -1 -1 634.71 156.69 801.11 224.17 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n28 32 Pedestrian -1 -1 -1 303.20 164.67 323.47 219.52 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n28 33 Car -1 -1 -1 540.97 170.29 568.14 193.17 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n28 38 Car -1 -1 -1 631.96 157.13 797.89 223.72 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n28 39 Car -1 -1 -1 616.40 165.12 649.76 194.44 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n29 3 Car -1 -1 -1 966.38 173.36 1077.62 224.01 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n29 25 Pedestrian -1 -1 -1 756.91 171.21 794.44 279.79 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 2 Pedestrian -1 -1 -1 852.43 148.90 914.06 314.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n29 15 Car -1 -1 -1 1037.29 178.61 1166.39 225.26 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n29 8 Pedestrian -1 -1 -1 560.06 164.91 600.70 253.96 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n29 10 Pedestrian -1 -1 -1 436.65 166.96 472.55 246.00 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n29 28 Pedestrian -1 -1 -1 344.68 171.33 365.75 223.08 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n29 27 Cyclist -1 -1 -1 658.21 158.34 710.63 239.64 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n29 33 Car -1 -1 -1 539.25 170.04 567.94 193.98 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n29 24 Pedestrian -1 -1 -1 380.35 170.49 406.06 233.66 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n29 32 Pedestrian -1 -1 -1 302.82 166.38 323.89 222.69 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n29 5 Pedestrian -1 -1 -1 782.40 171.87 846.25 323.87 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n29 13 Car -1 -1 -1 1127.13 181.60 1220.70 228.21 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n29 21 Pedestrian -1 -1 -1 461.95 175.87 490.83 245.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n29 39 Car -1 -1 -1 616.10 164.60 647.25 193.72 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n29 37 Pedestrian -1 -1 -1 371.76 160.62 385.50 196.47 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n29 38 Car -1 -1 -1 641.36 157.12 811.99 224.72 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n29 36 Van -1 -1 -1 644.38 156.78 814.72 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n29 40 Pedestrian -1 -1 -1 552.50 164.75 585.11 245.56 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n30 3 Car -1 -1 -1 969.60 173.53 1080.70 223.99 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n30 2 Pedestrian -1 -1 -1 858.98 149.83 921.62 316.76 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n30 15 Car -1 -1 -1 1039.80 178.35 1170.77 225.94 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n30 10 Pedestrian -1 -1 -1 438.88 165.78 476.42 248.23 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n30 25 Pedestrian -1 -1 -1 761.43 172.48 799.32 284.85 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n30 27 Cyclist -1 -1 -1 671.04 157.19 720.64 240.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n30 8 Pedestrian -1 -1 -1 563.82 165.91 604.38 255.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n30 32 Pedestrian -1 -1 -1 301.55 167.06 324.27 224.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n30 28 Pedestrian -1 -1 -1 345.16 170.65 365.45 224.50 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 24 Pedestrian -1 -1 -1 379.71 170.60 407.49 234.74 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n30 39 Car -1 -1 -1 615.47 165.06 647.54 194.49 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n30 5 Pedestrian -1 -1 -1 792.69 173.33 858.14 329.95 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n30 33 Car -1 -1 -1 537.09 170.74 567.24 195.71 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n30 40 Pedestrian -1 -1 -1 552.47 164.26 585.47 246.21 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n30 37 Pedestrian -1 -1 -1 370.18 160.69 383.84 196.71 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 21 Pedestrian -1 -1 -1 460.39 176.93 485.95 248.70 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n30 13 Car -1 -1 -1 1127.23 181.89 1221.60 227.90 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n30 38 Car -1 -1 -1 656.23 158.31 819.47 223.37 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n31 3 Car -1 -1 -1 972.58 173.97 1084.82 224.79 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n31 2 Pedestrian -1 -1 -1 863.78 150.15 925.70 321.77 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n31 15 Car -1 -1 -1 1042.88 179.07 1175.49 227.15 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n31 10 Pedestrian -1 -1 -1 444.78 166.86 478.39 251.66 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n31 27 Cyclist -1 -1 -1 682.00 156.05 738.37 248.11 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n31 25 Pedestrian -1 -1 -1 767.79 173.00 807.96 287.44 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n31 28 Pedestrian -1 -1 -1 343.25 171.28 366.48 226.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n31 8 Pedestrian -1 -1 -1 570.78 166.58 606.19 259.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n31 21 Pedestrian -1 -1 -1 463.14 176.35 490.93 250.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n31 33 Car -1 -1 -1 535.06 171.49 566.41 196.14 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n31 32 Pedestrian -1 -1 -1 300.35 165.98 323.29 225.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n31 39 Car -1 -1 -1 615.51 165.66 647.45 194.32 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n31 40 Pedestrian -1 -1 -1 556.70 163.43 587.68 248.78 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n31 5 Pedestrian -1 -1 -1 800.13 174.51 866.33 336.24 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n31 24 Pedestrian -1 -1 -1 381.44 169.56 411.13 236.93 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n31 13 Car -1 -1 -1 1134.63 182.18 1221.18 228.65 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n31 38 Car -1 -1 -1 666.06 157.84 839.25 226.08 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n31 37 Pedestrian -1 -1 -1 368.11 160.32 381.88 197.07 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n32 3 Car -1 -1 -1 974.52 175.16 1089.03 226.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n32 39 Car -1 -1 -1 613.23 165.37 647.55 194.00 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n32 2 Pedestrian -1 -1 -1 866.93 150.20 930.69 323.78 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n32 15 Car -1 -1 -1 1045.54 179.22 1179.04 227.88 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n32 40 Pedestrian -1 -1 -1 558.55 162.54 587.66 250.27 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n32 33 Car -1 -1 -1 533.68 171.84 566.20 196.45 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n32 25 Pedestrian -1 -1 -1 774.96 175.30 820.87 290.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n32 8 Pedestrian -1 -1 -1 580.28 164.53 612.26 262.64 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n32 27 Cyclist -1 -1 -1 695.20 155.33 748.94 249.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n32 10 Pedestrian -1 -1 -1 450.18 167.08 482.04 253.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n32 32 Pedestrian -1 -1 -1 298.93 165.44 324.98 226.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n32 38 Car -1 -1 -1 670.62 158.67 857.65 230.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n32 21 Pedestrian -1 -1 -1 465.34 176.78 495.56 250.11 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n32 28 Pedestrian -1 -1 -1 345.73 171.89 369.20 227.42 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n32 5 Pedestrian -1 -1 -1 813.71 176.36 875.81 342.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n32 37 Pedestrian -1 -1 -1 367.46 161.07 380.90 197.22 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n32 13 Car -1 -1 -1 1134.25 182.91 1221.70 229.72 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n32 24 Pedestrian -1 -1 -1 381.90 172.12 409.98 237.76 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n32 41 Pedestrian -1 -1 -1 393.36 172.43 422.50 238.31 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n33 3 Car -1 -1 -1 975.62 175.28 1090.73 227.47 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n33 39 Car -1 -1 -1 613.39 165.38 647.71 194.15 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n33 8 Pedestrian -1 -1 -1 585.94 164.94 626.78 264.36 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n33 2 Pedestrian -1 -1 -1 875.65 151.95 936.82 327.14 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n33 25 Pedestrian -1 -1 -1 782.39 175.09 829.33 293.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n33 40 Pedestrian -1 -1 -1 561.59 161.76 592.83 251.82 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n33 15 Car -1 -1 -1 1044.52 180.16 1182.60 229.19 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n33 10 Pedestrian -1 -1 -1 454.75 168.21 491.19 252.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n33 33 Car -1 -1 -1 531.82 171.52 565.57 196.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n33 32 Pedestrian -1 -1 -1 299.99 167.79 326.42 229.40 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n33 27 Cyclist -1 -1 -1 710.99 157.62 762.74 246.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n33 28 Pedestrian -1 -1 -1 346.02 171.80 370.73 230.03 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n33 5 Pedestrian -1 -1 -1 831.62 174.28 896.08 345.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n33 21 Pedestrian -1 -1 -1 463.33 176.40 499.36 252.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n33 38 Car -1 -1 -1 680.23 159.52 871.82 232.00 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n33 41 Pedestrian -1 -1 -1 398.70 173.45 423.90 239.10 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n33 37 Pedestrian -1 -1 -1 366.47 162.17 379.64 197.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n33 24 Pedestrian -1 -1 -1 386.86 170.94 412.60 239.75 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n33 13 Car -1 -1 -1 1134.67 183.36 1221.44 229.94 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n34 3 Car -1 -1 -1 978.21 175.76 1092.95 228.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n34 15 Car -1 -1 -1 1047.51 179.99 1185.88 230.31 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n34 8 Pedestrian -1 -1 -1 588.50 167.65 634.24 266.39 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n34 39 Car -1 -1 -1 613.41 166.40 647.28 194.57 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n34 25 Pedestrian -1 -1 -1 786.77 174.54 835.01 297.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n34 32 Pedestrian -1 -1 -1 304.17 166.43 327.02 230.86 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n34 27 Cyclist -1 -1 -1 717.27 156.44 787.51 254.86 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n34 40 Pedestrian -1 -1 -1 562.58 162.29 598.58 252.52 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n34 24 Pedestrian -1 -1 -1 386.60 170.65 414.14 241.49 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n34 38 Car -1 -1 -1 686.17 159.68 896.75 236.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n34 5 Pedestrian -1 -1 -1 843.24 173.87 915.25 352.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n34 2 Pedestrian -1 -1 -1 877.74 151.80 942.53 328.03 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n34 10 Pedestrian -1 -1 -1 457.25 167.28 497.06 254.56 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n34 33 Car -1 -1 -1 529.62 171.76 564.18 197.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n34 28 Pedestrian -1 -1 -1 349.56 170.81 372.48 229.02 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n34 41 Pedestrian -1 -1 -1 399.49 173.04 425.08 240.34 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n34 13 Car -1 -1 -1 1127.80 183.71 1221.07 229.89 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n34 37 Pedestrian -1 -1 -1 365.62 162.60 378.89 198.11 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n35 3 Car -1 -1 -1 978.85 176.57 1095.00 228.81 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n35 15 Car -1 -1 -1 1049.59 180.78 1190.47 230.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n35 28 Pedestrian -1 -1 -1 349.65 172.61 375.58 232.79 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n35 33 Car -1 -1 -1 527.80 172.87 562.73 199.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n35 40 Pedestrian -1 -1 -1 563.83 163.15 603.72 255.66 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n35 25 Pedestrian -1 -1 -1 792.29 175.67 842.62 303.14 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n35 2 Pedestrian -1 -1 -1 882.59 147.18 952.43 334.63 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n35 8 Pedestrian -1 -1 -1 591.69 167.57 638.85 269.99 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n35 39 Car -1 -1 -1 613.61 166.75 647.48 194.70 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n35 27 Cyclist -1 -1 -1 729.19 160.19 806.44 257.98 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n35 5 Pedestrian -1 -1 -1 852.50 175.76 936.63 352.03 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n35 32 Pedestrian -1 -1 -1 305.20 167.36 328.09 232.26 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n35 10 Pedestrian -1 -1 -1 466.17 171.91 503.41 255.93 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n35 24 Pedestrian -1 -1 -1 388.32 171.54 418.55 243.23 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n35 38 Car -1 -1 -1 701.40 160.42 912.19 238.50 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n35 41 Pedestrian -1 -1 -1 403.38 176.68 428.54 241.87 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n35 13 Car -1 -1 -1 1142.44 185.81 1221.22 232.41 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n35 37 Pedestrian -1 -1 -1 363.35 162.04 377.41 198.93 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n35 42 Car -1 -1 -1 1112.58 183.27 1220.98 230.53 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n36 25 Pedestrian -1 -1 -1 796.36 177.52 847.41 305.17 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n36 3 Car -1 -1 -1 980.89 177.69 1098.52 229.77 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n36 28 Pedestrian -1 -1 -1 350.27 175.11 380.19 235.82 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n36 15 Car -1 -1 -1 1049.33 182.46 1192.80 232.38 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n36 8 Pedestrian -1 -1 -1 601.65 168.77 642.27 274.39 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n36 40 Pedestrian -1 -1 -1 564.62 164.11 604.58 257.28 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n36 27 Cyclist -1 -1 -1 746.41 161.20 820.36 259.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n36 24 Pedestrian -1 -1 -1 389.09 174.38 420.19 245.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n36 10 Pedestrian -1 -1 -1 476.89 178.12 515.25 265.33 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n36 33 Car -1 -1 -1 526.32 173.79 562.56 200.26 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n36 38 Car -1 -1 -1 716.52 162.27 920.16 240.62 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n36 41 Pedestrian -1 -1 -1 406.90 180.07 431.58 245.78 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n36 5 Pedestrian -1 -1 -1 861.48 180.30 958.71 362.25 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n36 32 Pedestrian -1 -1 -1 307.74 170.38 330.57 237.00 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n36 2 Pedestrian -1 -1 -1 878.29 149.52 964.22 332.71 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n36 39 Car -1 -1 -1 613.07 168.08 647.13 195.97 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n36 13 Car -1 -1 -1 1142.74 186.71 1221.16 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n36 37 Pedestrian -1 -1 -1 361.55 162.44 377.87 202.82 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n36 43 Pedestrian -1 -1 -1 473.11 171.43 511.91 256.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n37 3 Car -1 -1 -1 981.86 179.05 1100.43 232.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n37 15 Car -1 -1 -1 1054.63 183.80 1194.07 233.69 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n37 33 Car -1 -1 -1 522.63 174.19 561.04 202.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n37 25 Pedestrian -1 -1 -1 803.63 180.73 854.55 307.67 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n37 40 Pedestrian -1 -1 -1 569.02 165.05 606.40 259.81 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n37 28 Pedestrian -1 -1 -1 355.12 175.37 381.71 237.38 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n37 38 Car -1 -1 -1 726.33 162.70 939.88 240.75 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n37 43 Pedestrian -1 -1 -1 479.37 168.45 519.55 259.17 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n37 8 Pedestrian -1 -1 -1 610.27 169.12 644.12 275.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n37 2 Pedestrian -1 -1 -1 882.21 155.94 967.99 340.68 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n37 41 Pedestrian -1 -1 -1 408.09 179.72 432.27 246.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n37 32 Pedestrian -1 -1 -1 307.24 169.25 332.77 238.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n37 27 Cyclist -1 -1 -1 748.75 161.66 849.05 264.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n37 5 Pedestrian -1 -1 -1 895.52 186.99 978.02 363.79 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n37 24 Pedestrian -1 -1 -1 389.22 173.65 420.81 245.93 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n37 39 Car -1 -1 -1 613.60 168.39 645.42 196.20 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n37 10 Pedestrian -1 -1 -1 480.94 179.69 518.94 265.26 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n37 13 Car -1 -1 -1 1141.90 187.31 1221.67 234.22 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n38 3 Car -1 -1 -1 984.94 178.99 1101.96 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n38 15 Car -1 -1 -1 1053.82 184.11 1195.63 233.87 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n38 40 Pedestrian -1 -1 -1 574.59 164.10 608.92 261.38 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n38 25 Pedestrian -1 -1 -1 805.06 178.73 861.33 311.13 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n38 39 Car -1 -1 -1 613.91 168.71 647.04 197.46 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n38 10 Pedestrian -1 -1 -1 480.58 169.62 525.87 264.95 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n38 38 Car -1 -1 -1 738.63 162.41 958.16 242.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n38 8 Pedestrian -1 -1 -1 615.80 169.84 659.01 275.59 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n38 28 Pedestrian -1 -1 -1 361.40 173.29 383.31 238.67 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n38 2 Pedestrian -1 -1 -1 890.64 153.69 975.48 350.36 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n38 41 Pedestrian -1 -1 -1 411.66 178.61 436.78 248.48 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n38 32 Pedestrian -1 -1 -1 309.11 170.55 336.40 239.94 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n38 27 Cyclist -1 -1 -1 766.08 161.40 869.80 266.25 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n38 33 Car -1 -1 -1 521.60 174.34 559.71 202.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n38 5 Pedestrian -1 -1 -1 915.27 187.69 996.78 368.89 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n38 24 Pedestrian -1 -1 -1 395.51 176.37 421.46 249.04 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n38 13 Car -1 -1 -1 1134.64 187.38 1221.52 233.97 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n39 3 Car -1 -1 -1 983.83 179.11 1103.55 232.85 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n39 15 Car -1 -1 -1 1050.28 183.97 1199.75 234.51 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n39 8 Pedestrian -1 -1 -1 616.09 170.41 667.75 278.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n39 10 Pedestrian -1 -1 -1 484.96 172.62 529.58 270.19 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n39 39 Car -1 -1 -1 614.53 169.31 647.38 198.36 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n39 40 Pedestrian -1 -1 -1 579.48 165.09 612.69 262.25 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n39 41 Pedestrian -1 -1 -1 413.47 178.53 442.68 248.71 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n39 28 Pedestrian -1 -1 -1 362.03 174.22 385.88 240.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n39 5 Pedestrian -1 -1 -1 922.61 183.32 1004.80 367.38 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n39 25 Pedestrian -1 -1 -1 806.39 175.86 868.72 313.07 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n39 33 Car -1 -1 -1 518.48 174.64 558.81 203.68 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n39 32 Pedestrian -1 -1 -1 311.53 172.98 337.08 241.69 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n39 2 Pedestrian -1 -1 -1 902.17 158.03 979.31 346.84 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n39 38 Car -1 -1 -1 753.53 162.70 974.38 248.21 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n39 27 Cyclist -1 -1 -1 786.99 155.48 887.86 286.78 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n39 13 Car -1 -1 -1 1141.75 188.35 1222.00 237.05 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n39 24 Pedestrian -1 -1 -1 399.23 175.81 422.88 249.73 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n40 15 Car -1 -1 -1 1053.12 184.53 1204.18 235.09 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n40 8 Pedestrian -1 -1 -1 620.82 169.02 671.35 280.17 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n40 39 Car -1 -1 -1 614.46 169.58 646.28 198.53 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n40 3 Car -1 -1 -1 982.85 179.51 1105.94 233.40 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n40 41 Pedestrian -1 -1 -1 414.58 178.65 447.49 250.24 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n40 28 Pedestrian -1 -1 -1 364.50 176.97 389.68 242.82 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n40 40 Pedestrian -1 -1 -1 581.57 166.51 618.34 262.94 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n40 33 Car -1 -1 -1 517.86 175.28 557.36 204.45 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n40 10 Pedestrian -1 -1 -1 492.78 173.78 530.38 271.06 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n40 2 Pedestrian -1 -1 -1 904.04 153.54 984.70 351.87 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n40 5 Pedestrian -1 -1 -1 952.71 183.63 1035.61 367.06 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n40 25 Pedestrian -1 -1 -1 811.12 170.01 879.26 320.63 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n40 24 Pedestrian -1 -1 -1 399.62 177.18 425.26 250.36 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n40 38 Car -1 -1 -1 772.15 161.68 987.12 249.84 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n40 32 Pedestrian -1 -1 -1 319.63 173.03 341.18 243.98 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n40 13 Car -1 -1 -1 1141.76 188.28 1222.11 237.30 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n40 44 Pedestrian -1 -1 -1 363.27 164.71 377.77 202.45 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n41 15 Car -1 -1 -1 1057.66 184.78 1206.71 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n41 8 Pedestrian -1 -1 -1 630.76 168.51 676.58 282.78 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n41 25 Pedestrian -1 -1 -1 820.35 180.25 876.89 324.56 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n41 39 Car -1 -1 -1 614.70 169.57 646.51 198.47 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n41 3 Car -1 -1 -1 984.03 179.18 1111.01 234.01 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n41 28 Pedestrian -1 -1 -1 368.90 176.93 393.74 243.65 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n41 40 Pedestrian -1 -1 -1 583.74 167.01 622.82 263.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n41 2 Pedestrian -1 -1 -1 903.69 153.54 985.46 352.42 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n41 33 Car -1 -1 -1 518.08 175.61 556.84 205.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n41 41 Pedestrian -1 -1 -1 420.14 179.13 448.47 250.73 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n41 5 Pedestrian -1 -1 -1 968.25 183.82 1066.68 365.66 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n41 38 Car -1 -1 -1 786.32 160.90 1010.56 252.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n41 32 Pedestrian -1 -1 -1 321.09 172.45 343.46 246.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n41 24 Pedestrian -1 -1 -1 401.62 176.91 428.35 251.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n41 10 Pedestrian -1 -1 -1 503.64 171.53 541.55 272.44 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n41 44 Pedestrian -1 -1 -1 363.51 164.72 378.14 202.10 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n41 13 Car -1 -1 -1 1140.81 188.11 1223.02 237.86 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n41 45 Cyclist -1 -1 -1 827.53 158.61 930.67 291.44 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n42 25 Pedestrian -1 -1 -1 823.40 179.55 882.43 330.70 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n42 39 Car -1 -1 -1 614.87 169.45 646.54 198.63 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n42 15 Car -1 -1 -1 1055.96 184.91 1209.59 236.09 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n42 28 Pedestrian -1 -1 -1 372.43 175.38 398.15 244.34 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n42 3 Car -1 -1 -1 983.24 179.38 1112.90 234.06 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n42 10 Pedestrian -1 -1 -1 508.06 170.18 552.91 274.05 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n42 2 Pedestrian -1 -1 -1 914.24 153.57 997.46 357.85 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n42 40 Pedestrian -1 -1 -1 584.94 167.83 623.40 265.71 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n42 32 Pedestrian -1 -1 -1 325.03 172.85 346.56 248.30 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n42 8 Pedestrian -1 -1 -1 649.00 168.11 687.20 287.83 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n42 33 Car -1 -1 -1 513.88 176.20 555.70 206.78 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n42 41 Pedestrian -1 -1 -1 425.07 179.14 451.19 251.02 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n42 5 Pedestrian -1 -1 -1 987.66 185.53 1093.11 364.43 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n42 24 Pedestrian -1 -1 -1 405.27 175.73 432.10 253.30 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n42 13 Car -1 -1 -1 1134.02 187.84 1222.17 238.07 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n42 44 Pedestrian -1 -1 -1 363.63 163.98 378.01 202.40 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n42 38 Car -1 -1 -1 803.97 160.06 1015.37 259.98 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n43 25 Pedestrian -1 -1 -1 829.37 178.92 891.20 333.78 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n43 8 Pedestrian -1 -1 -1 649.99 169.62 701.84 289.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n43 40 Pedestrian -1 -1 -1 588.94 167.40 625.89 266.60 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n43 39 Car -1 -1 -1 614.94 169.69 647.09 198.73 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n43 2 Pedestrian -1 -1 -1 913.54 151.59 998.60 360.05 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n43 10 Pedestrian -1 -1 -1 509.22 175.12 560.72 275.68 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n43 28 Pedestrian -1 -1 -1 372.49 175.38 404.77 246.34 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n43 32 Pedestrian -1 -1 -1 328.44 173.38 351.39 248.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n43 15 Car -1 -1 -1 1058.74 185.56 1212.74 236.16 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n43 24 Pedestrian -1 -1 -1 407.46 176.02 438.18 254.05 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n43 33 Car -1 -1 -1 510.12 175.75 553.70 207.35 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n43 41 Pedestrian -1 -1 -1 428.07 180.90 455.76 252.54 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n43 3 Car -1 -1 -1 982.59 180.38 1120.02 237.63 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n43 5 Pedestrian -1 -1 -1 1014.62 188.86 1119.39 361.62 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n43 38 Car -1 -1 -1 824.40 167.44 1011.76 245.68 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n43 44 Pedestrian -1 -1 -1 363.28 164.13 378.18 203.34 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n43 13 Car -1 -1 -1 1141.93 188.77 1221.80 237.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n44 25 Pedestrian -1 -1 -1 832.25 180.23 896.56 338.88 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n44 10 Pedestrian -1 -1 -1 515.50 177.06 569.02 279.30 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n44 8 Pedestrian -1 -1 -1 651.93 170.03 716.12 294.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n44 39 Car -1 -1 -1 614.28 169.95 647.21 198.77 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n44 2 Pedestrian -1 -1 -1 915.24 153.24 1004.45 364.69 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n44 33 Car -1 -1 -1 509.18 177.06 551.51 209.64 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n44 28 Pedestrian -1 -1 -1 375.01 177.85 408.73 248.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n44 40 Pedestrian -1 -1 -1 592.49 168.21 628.58 268.37 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n44 24 Pedestrian -1 -1 -1 410.23 178.54 443.59 255.47 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n44 32 Pedestrian -1 -1 -1 327.83 172.82 358.65 249.96 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n44 15 Car -1 -1 -1 1057.16 185.30 1215.18 236.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n44 5 Pedestrian -1 -1 -1 1051.48 185.86 1151.47 364.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n44 41 Pedestrian -1 -1 -1 431.08 181.86 459.92 254.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n44 38 Car -1 -1 -1 838.91 166.87 1050.65 253.42 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n44 44 Pedestrian -1 -1 -1 363.63 164.52 377.98 203.16 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n44 13 Car -1 -1 -1 1150.79 189.18 1220.37 237.78 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n44 3 Car -1 -1 -1 980.85 180.87 1122.66 233.84 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n45 8 Pedestrian -1 -1 -1 661.21 171.19 722.03 296.44 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n45 10 Pedestrian -1 -1 -1 528.30 178.32 571.12 279.45 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n45 2 Pedestrian -1 -1 -1 922.38 154.27 1005.00 364.49 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n45 32 Pedestrian -1 -1 -1 327.92 174.19 359.11 252.61 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n45 25 Pedestrian -1 -1 -1 839.94 181.20 902.70 346.21 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n45 33 Car -1 -1 -1 505.40 176.53 551.03 211.10 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n45 39 Car -1 -1 -1 614.21 170.32 646.85 198.69 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n45 24 Pedestrian -1 -1 -1 410.42 177.59 444.90 257.85 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n45 40 Pedestrian -1 -1 -1 597.48 168.26 630.78 268.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n45 28 Pedestrian -1 -1 -1 381.30 177.05 409.76 249.83 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n45 41 Pedestrian -1 -1 -1 432.88 181.59 460.26 255.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n45 15 Car -1 -1 -1 1068.07 186.09 1219.19 239.09 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n45 5 Pedestrian -1 -1 -1 1083.18 184.82 1196.34 365.18 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n45 38 Car -1 -1 -1 864.03 168.35 1071.09 251.89 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n45 44 Pedestrian -1 -1 -1 363.66 164.85 377.66 203.22 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n45 3 Car -1 -1 -1 980.07 180.65 1130.52 237.77 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n45 13 Car -1 -1 -1 1143.25 189.54 1220.76 237.67 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n46 8 Pedestrian -1 -1 -1 671.17 168.14 720.68 298.61 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n46 33 Car -1 -1 -1 502.45 176.38 550.07 212.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n46 25 Pedestrian -1 -1 -1 848.32 184.28 909.92 349.80 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n46 2 Pedestrian -1 -1 -1 929.72 154.81 1012.70 365.20 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n46 10 Pedestrian -1 -1 -1 540.31 175.94 581.60 281.02 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n46 40 Pedestrian -1 -1 -1 599.78 167.43 631.37 268.97 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n46 39 Car -1 -1 -1 614.25 170.14 647.06 198.83 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n46 41 Pedestrian -1 -1 -1 436.71 180.59 464.13 256.33 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n46 32 Pedestrian -1 -1 -1 333.63 172.94 360.56 254.12 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n46 28 Pedestrian -1 -1 -1 382.94 177.19 411.84 250.56 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n46 24 Pedestrian -1 -1 -1 416.59 177.99 445.73 259.04 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n46 15 Car -1 -1 -1 1074.23 186.69 1220.32 239.81 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n46 38 Car -1 -1 -1 871.14 167.80 1102.19 253.00 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n46 3 Car -1 -1 -1 985.38 180.56 1125.24 237.62 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n46 44 Pedestrian -1 -1 -1 363.72 165.03 377.39 203.53 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n46 5 Pedestrian -1 -1 -1 1098.81 187.08 1219.06 363.16 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n46 13 Car -1 -1 -1 1142.62 190.06 1221.50 237.76 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n46 46 Cyclist -1 -1 -1 945.94 162.37 1111.15 310.48 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n47 33 Car -1 -1 -1 499.72 176.13 547.97 212.41 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n47 25 Pedestrian -1 -1 -1 851.83 182.01 921.01 352.32 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n47 2 Pedestrian -1 -1 -1 937.78 153.90 1019.90 366.37 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n47 8 Pedestrian -1 -1 -1 679.21 168.03 727.44 298.90 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n47 40 Pedestrian -1 -1 -1 602.73 166.19 634.82 269.59 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n47 39 Car -1 -1 -1 614.60 170.07 647.19 198.75 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n47 10 Pedestrian -1 -1 -1 546.09 178.67 583.98 279.88 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n47 41 Pedestrian -1 -1 -1 439.01 179.88 468.31 257.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n47 32 Pedestrian -1 -1 -1 337.50 173.40 364.96 255.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n47 28 Pedestrian -1 -1 -1 386.79 177.81 414.70 252.11 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n47 24 Pedestrian -1 -1 -1 420.78 177.51 449.55 259.42 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n47 46 Cyclist -1 -1 -1 978.30 161.95 1140.16 311.27 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n47 3 Car -1 -1 -1 981.36 180.17 1129.99 238.28 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n47 15 Car -1 -1 -1 1070.09 185.81 1217.04 239.54 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n47 38 Car -1 -1 -1 882.84 168.18 1113.71 258.18 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n47 13 Car -1 -1 -1 1149.47 189.43 1221.97 237.95 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n47 44 Pedestrian -1 -1 -1 364.27 165.17 377.35 203.72 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n47 5 Pedestrian -1 -1 -1 1149.05 194.95 1222.35 362.41 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n48 25 Pedestrian -1 -1 -1 854.83 178.58 927.14 355.23 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n48 33 Car -1 -1 -1 497.34 176.44 546.63 212.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n48 8 Pedestrian -1 -1 -1 684.87 167.33 744.80 300.85 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n48 2 Pedestrian -1 -1 -1 941.75 151.69 1023.56 368.32 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n48 46 Cyclist -1 -1 -1 1006.23 154.02 1181.41 319.99 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n48 10 Pedestrian -1 -1 -1 552.05 174.91 601.09 284.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n48 32 Pedestrian -1 -1 -1 339.73 175.31 368.87 258.33 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n48 40 Pedestrian -1 -1 -1 604.17 166.22 640.42 270.17 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n48 39 Car -1 -1 -1 614.51 170.05 647.32 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n48 28 Pedestrian -1 -1 -1 389.52 180.13 418.20 254.88 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n48 41 Pedestrian -1 -1 -1 440.24 182.05 473.97 260.54 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n48 15 Car -1 -1 -1 1066.88 185.69 1219.90 239.60 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n48 24 Pedestrian -1 -1 -1 424.61 177.80 452.29 260.06 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n48 3 Car -1 -1 -1 987.21 179.30 1124.08 234.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n48 13 Car -1 -1 -1 1156.64 189.60 1222.24 238.11 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n48 38 Car -1 -1 -1 899.95 168.34 1111.26 252.12 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n48 44 Pedestrian -1 -1 -1 364.18 164.83 377.55 204.16 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n48 47 Pedestrian -1 -1 -1 384.87 165.33 400.74 208.75 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n49 33 Car -1 -1 -1 493.71 176.71 546.08 213.81 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n49 8 Pedestrian -1 -1 -1 690.37 168.61 754.30 305.53 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n49 2 Pedestrian -1 -1 -1 943.70 153.03 1029.77 366.24 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n49 25 Pedestrian -1 -1 -1 856.86 178.45 932.96 357.71 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n49 32 Pedestrian -1 -1 -1 343.47 174.53 372.81 260.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n49 46 Cyclist -1 -1 -1 1035.77 152.76 1220.75 328.08 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n49 39 Car -1 -1 -1 613.58 170.02 647.33 198.69 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n49 41 Pedestrian -1 -1 -1 441.99 182.97 473.55 260.73 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n49 28 Pedestrian -1 -1 -1 390.11 177.96 419.19 256.17 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n49 15 Car -1 -1 -1 1066.93 185.11 1220.28 236.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n49 40 Pedestrian -1 -1 -1 606.65 165.86 644.52 271.59 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n49 10 Pedestrian -1 -1 -1 561.12 173.61 607.10 286.59 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n49 24 Pedestrian -1 -1 -1 427.39 178.61 457.03 263.10 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n49 3 Car -1 -1 -1 973.35 176.79 1137.69 241.45 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n49 13 Car -1 -1 -1 1156.36 189.03 1222.56 238.42 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n49 47 Pedestrian -1 -1 -1 380.50 164.10 397.64 210.71 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n49 44 Pedestrian -1 -1 -1 362.70 165.80 378.34 206.17 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n49 48 Pedestrian -1 -1 -1 551.96 181.93 593.64 284.27 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n50 25 Pedestrian -1 -1 -1 864.46 179.86 939.66 363.72 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n50 2 Pedestrian -1 -1 -1 948.83 152.29 1032.34 367.63 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n50 33 Car -1 -1 -1 491.64 176.80 544.11 214.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n50 8 Pedestrian -1 -1 -1 695.67 168.14 763.47 311.02 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n50 28 Pedestrian -1 -1 -1 394.58 177.47 423.11 257.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n50 39 Car -1 -1 -1 613.41 169.93 648.13 198.98 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n50 32 Pedestrian -1 -1 -1 346.02 175.04 379.46 262.56 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n50 3 Car -1 -1 -1 967.08 172.68 1159.11 246.28 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n50 15 Car -1 -1 -1 1066.89 185.39 1220.63 236.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n50 10 Pedestrian -1 -1 -1 565.08 174.53 611.28 288.92 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n50 40 Pedestrian -1 -1 -1 606.78 165.68 646.04 271.92 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n50 41 Pedestrian -1 -1 -1 446.58 181.54 476.78 263.18 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n50 24 Pedestrian -1 -1 -1 430.90 178.11 460.88 265.63 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n50 46 Cyclist -1 -1 -1 1078.27 149.46 1216.42 339.37 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n50 47 Pedestrian -1 -1 -1 380.45 163.90 396.93 211.34 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n50 48 Pedestrian -1 -1 -1 558.17 181.28 595.71 284.27 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n50 13 Car -1 -1 -1 1155.69 188.87 1223.01 238.78 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n50 44 Pedestrian -1 -1 -1 364.79 165.67 382.07 207.13 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n51 25 Pedestrian -1 -1 -1 868.45 179.62 944.78 364.46 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n51 2 Pedestrian -1 -1 -1 949.62 152.90 1039.41 366.79 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n51 33 Car -1 -1 -1 487.51 176.60 542.15 215.47 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n51 8 Pedestrian -1 -1 -1 709.06 168.00 765.99 311.79 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n51 39 Car -1 -1 -1 613.59 169.99 648.09 198.90 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n51 28 Pedestrian -1 -1 -1 396.00 178.56 429.08 258.57 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n51 15 Car -1 -1 -1 1070.97 186.14 1223.54 239.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n51 32 Pedestrian -1 -1 -1 348.70 175.10 388.83 265.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n51 41 Pedestrian -1 -1 -1 449.85 183.17 481.35 265.06 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n51 3 Car -1 -1 -1 967.91 168.94 1189.26 252.79 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n51 10 Pedestrian -1 -1 -1 575.13 170.97 616.30 289.34 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n51 40 Pedestrian -1 -1 -1 606.85 166.52 646.13 274.27 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n51 48 Pedestrian -1 -1 -1 564.80 179.76 603.85 285.63 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n51 24 Pedestrian -1 -1 -1 433.60 181.29 466.15 267.01 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n51 13 Car -1 -1 -1 1155.83 188.86 1222.95 238.58 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n51 47 Pedestrian -1 -1 -1 376.48 164.13 394.90 211.61 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n52 8 Pedestrian -1 -1 -1 718.18 166.47 780.90 315.14 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n52 25 Pedestrian -1 -1 -1 869.58 179.28 951.28 365.14 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n52 2 Pedestrian -1 -1 -1 956.92 154.07 1046.74 365.20 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n52 33 Car -1 -1 -1 483.45 177.32 540.89 216.91 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n52 32 Pedestrian -1 -1 -1 349.54 174.27 390.72 267.35 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n52 28 Pedestrian -1 -1 -1 398.83 181.28 434.32 260.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n52 39 Car -1 -1 -1 613.58 170.08 648.09 198.80 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n52 41 Pedestrian -1 -1 -1 452.25 184.06 486.30 267.29 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n52 24 Pedestrian -1 -1 -1 435.60 181.39 471.98 267.65 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n52 10 Pedestrian -1 -1 -1 583.17 170.04 630.93 289.68 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n52 48 Pedestrian -1 -1 -1 565.51 181.59 603.50 285.26 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n52 15 Car -1 -1 -1 1075.56 186.57 1219.54 239.32 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n52 47 Pedestrian -1 -1 -1 377.06 164.97 393.60 211.15 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n52 40 Pedestrian -1 -1 -1 605.51 166.64 647.60 274.91 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n52 3 Car -1 -1 -1 976.83 167.15 1211.14 252.76 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n52 13 Car -1 -1 -1 1162.86 189.42 1223.77 238.19 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n53 25 Pedestrian -1 -1 -1 877.70 184.43 957.57 365.60 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n53 33 Car -1 -1 -1 481.53 177.36 536.03 218.12 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n53 2 Pedestrian -1 -1 -1 960.31 154.02 1050.89 365.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n53 8 Pedestrian -1 -1 -1 721.86 164.93 782.89 322.66 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n53 32 Pedestrian -1 -1 -1 356.05 172.97 391.90 269.60 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n53 28 Pedestrian -1 -1 -1 403.23 180.87 437.05 261.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n53 24 Pedestrian -1 -1 -1 435.09 180.03 473.59 270.43 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n53 39 Car -1 -1 -1 613.82 170.16 647.81 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n53 41 Pedestrian -1 -1 -1 457.42 184.02 488.81 268.32 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n53 48 Pedestrian -1 -1 -1 570.32 184.00 606.27 287.31 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n53 10 Pedestrian -1 -1 -1 588.45 169.45 641.92 290.37 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n53 40 Pedestrian -1 -1 -1 604.22 166.07 648.83 275.51 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n53 47 Pedestrian -1 -1 -1 375.81 165.47 393.09 210.60 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n53 3 Car -1 -1 -1 985.11 165.34 1226.07 255.53 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n53 13 Car -1 -1 -1 1162.68 189.74 1223.89 238.30 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n54 33 Car -1 -1 -1 478.76 177.05 536.12 219.28 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n54 2 Pedestrian -1 -1 -1 964.77 153.62 1054.12 365.78 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n54 25 Pedestrian -1 -1 -1 885.38 184.17 964.85 365.39 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n54 8 Pedestrian -1 -1 -1 719.21 167.96 786.50 326.73 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n54 32 Pedestrian -1 -1 -1 364.64 172.04 397.11 269.92 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n54 48 Pedestrian -1 -1 -1 570.75 183.27 611.86 290.87 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n54 24 Pedestrian -1 -1 -1 440.72 176.76 474.45 274.81 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n54 41 Pedestrian -1 -1 -1 466.59 184.21 494.17 267.58 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n54 39 Car -1 -1 -1 613.92 170.09 647.66 198.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n54 10 Pedestrian -1 -1 -1 597.74 171.54 647.57 293.01 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n54 28 Pedestrian -1 -1 -1 408.81 179.74 439.32 263.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n54 40 Pedestrian -1 -1 -1 603.94 166.61 649.16 274.45 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n54 3 Car -1 -1 -1 1021.18 165.92 1219.66 255.93 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n54 13 Car -1 -1 -1 1162.03 190.18 1224.65 238.02 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n54 49 Car -1 -1 -1 1033.67 170.66 1221.59 255.74 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n55 33 Car -1 -1 -1 475.14 177.32 534.30 220.61 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n55 8 Pedestrian -1 -1 -1 727.54 168.31 792.60 326.97 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n55 25 Pedestrian -1 -1 -1 892.16 184.60 973.52 365.66 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n55 48 Pedestrian -1 -1 -1 571.30 184.10 612.95 290.63 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n55 2 Pedestrian -1 -1 -1 964.75 152.30 1054.82 366.63 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n55 32 Pedestrian -1 -1 -1 366.85 173.38 404.72 270.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n55 28 Pedestrian -1 -1 -1 410.70 178.94 444.49 264.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n55 10 Pedestrian -1 -1 -1 607.38 172.97 652.62 291.91 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n55 39 Car -1 -1 -1 613.57 171.89 647.57 200.81 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n55 40 Pedestrian -1 -1 -1 608.85 167.16 651.38 274.52 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n55 41 Pedestrian -1 -1 -1 467.94 184.42 502.50 268.79 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n55 24 Pedestrian -1 -1 -1 447.03 177.69 477.21 275.33 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n55 49 Car -1 -1 -1 1042.10 172.11 1221.21 254.64 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n56 8 Pedestrian -1 -1 -1 737.41 169.39 806.33 326.76 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n56 33 Car -1 -1 -1 473.08 177.62 533.15 221.48 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n56 10 Pedestrian -1 -1 -1 610.51 173.20 657.03 291.90 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n56 25 Pedestrian -1 -1 -1 901.04 185.16 986.96 364.21 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n56 2 Pedestrian -1 -1 -1 966.65 151.30 1060.51 367.16 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n56 48 Pedestrian -1 -1 -1 573.41 184.81 617.73 294.13 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n56 41 Pedestrian -1 -1 -1 469.68 185.89 507.79 271.77 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n56 39 Car -1 -1 -1 613.68 171.97 647.62 200.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n56 28 Pedestrian -1 -1 -1 413.97 179.04 448.85 266.39 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n56 32 Pedestrian -1 -1 -1 374.92 176.74 409.61 273.27 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n56 40 Pedestrian -1 -1 -1 608.89 168.15 652.29 274.03 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n56 49 Car -1 -1 -1 1048.70 169.89 1222.33 256.93 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n56 24 Pedestrian -1 -1 -1 452.85 181.17 485.58 275.96 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n56 50 Pedestrian -1 -1 -1 362.52 162.18 384.86 213.53 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n57 8 Pedestrian -1 -1 -1 746.27 168.54 813.91 333.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n57 33 Car -1 -1 -1 467.41 177.42 532.13 224.34 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n57 10 Pedestrian -1 -1 -1 617.55 172.98 665.44 292.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n57 48 Pedestrian -1 -1 -1 577.47 185.90 620.20 295.51 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n57 28 Pedestrian -1 -1 -1 418.23 181.35 451.18 267.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n57 25 Pedestrian -1 -1 -1 905.66 185.91 998.51 364.55 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n57 2 Pedestrian -1 -1 -1 969.84 151.12 1064.94 367.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n57 39 Car -1 -1 -1 614.25 172.18 647.10 200.96 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n57 41 Pedestrian -1 -1 -1 474.32 186.18 510.22 272.91 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n57 32 Pedestrian -1 -1 -1 379.92 174.74 414.08 275.78 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n57 24 Pedestrian -1 -1 -1 455.86 181.00 490.61 277.37 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n57 15 Car -1 -1 -1 1088.82 176.57 1221.87 250.05 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n57 49 Car -1 -1 -1 1052.61 173.10 1219.06 254.10 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n57 40 Pedestrian -1 -1 -1 610.89 169.03 649.88 272.93 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n57 50 Pedestrian -1 -1 -1 361.82 160.66 384.69 214.29 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n58 8 Pedestrian -1 -1 -1 753.35 169.06 820.22 334.68 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n58 33 Car -1 -1 -1 461.05 177.90 531.05 223.90 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n58 32 Pedestrian -1 -1 -1 386.56 175.90 429.52 275.97 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n58 25 Pedestrian -1 -1 -1 911.13 184.93 1008.72 365.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n58 2 Pedestrian -1 -1 -1 975.54 152.21 1066.46 366.24 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n58 39 Car -1 -1 -1 614.43 172.18 646.86 201.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n58 24 Pedestrian -1 -1 -1 459.68 180.97 494.77 278.31 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n58 28 Pedestrian -1 -1 -1 424.06 181.66 453.66 267.92 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n58 10 Pedestrian -1 -1 -1 617.30 173.75 666.88 291.37 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n58 48 Pedestrian -1 -1 -1 582.05 188.07 625.28 299.62 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n58 15 Car -1 -1 -1 1107.67 173.57 1219.20 253.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n58 49 Car -1 -1 -1 1055.67 173.24 1223.78 252.51 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n58 41 Pedestrian -1 -1 -1 479.75 184.84 513.02 273.53 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n58 50 Pedestrian -1 -1 -1 362.38 161.79 384.44 213.62 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n58 40 Pedestrian -1 -1 -1 610.22 168.59 650.23 273.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n59 8 Pedestrian -1 -1 -1 759.76 168.55 822.16 334.67 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n59 33 Car -1 -1 -1 458.04 177.81 528.01 225.02 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n59 25 Pedestrian -1 -1 -1 921.52 185.25 1020.90 365.51 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n59 48 Pedestrian -1 -1 -1 588.01 186.66 626.71 302.07 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n59 32 Pedestrian -1 -1 -1 392.07 175.81 438.11 276.87 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n59 39 Car -1 -1 -1 614.87 172.38 646.44 200.81 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n59 24 Pedestrian -1 -1 -1 466.17 180.28 502.54 279.48 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n59 49 Car -1 -1 -1 1071.33 174.34 1223.49 251.91 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n59 2 Pedestrian -1 -1 -1 962.61 151.54 1064.24 366.82 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n59 41 Pedestrian -1 -1 -1 483.73 183.97 516.72 273.68 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n59 28 Pedestrian -1 -1 -1 428.27 180.63 463.14 268.15 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n59 10 Pedestrian -1 -1 -1 613.53 176.27 663.11 289.66 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n59 15 Car -1 -1 -1 1119.34 173.08 1221.47 254.43 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n59 50 Pedestrian -1 -1 -1 361.74 162.56 383.81 213.45 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n59 40 Pedestrian -1 -1 -1 608.76 168.25 651.13 273.85 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n60 33 Car -1 -1 -1 455.81 178.07 527.62 227.02 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n60 8 Pedestrian -1 -1 -1 772.25 170.05 825.91 335.21 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n60 24 Pedestrian -1 -1 -1 468.17 179.45 509.26 281.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n60 32 Pedestrian -1 -1 -1 397.80 177.44 440.26 278.96 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n60 28 Pedestrian -1 -1 -1 429.39 180.54 470.12 269.13 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n60 39 Car -1 -1 -1 614.92 172.70 646.41 200.70 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n60 25 Pedestrian -1 -1 -1 924.22 185.55 1033.34 365.38 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n60 48 Pedestrian -1 -1 -1 596.11 186.57 632.28 301.61 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n60 2 Pedestrian -1 -1 -1 959.94 151.34 1066.89 366.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n60 10 Pedestrian -1 -1 -1 615.62 177.43 660.23 289.71 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n60 49 Car -1 -1 -1 1081.91 175.58 1221.04 250.31 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n60 41 Pedestrian -1 -1 -1 490.74 184.89 523.41 274.92 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n60 50 Pedestrian -1 -1 -1 361.91 163.71 383.73 216.01 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n60 15 Car -1 -1 -1 1138.42 182.86 1217.70 251.41 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n60 40 Pedestrian -1 -1 -1 607.38 168.15 646.50 267.52 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n60 51 Pedestrian -1 -1 -1 354.79 164.01 375.38 216.57 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n61 33 Car -1 -1 -1 452.76 177.75 525.80 227.94 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n61 8 Pedestrian -1 -1 -1 781.19 167.11 831.46 337.90 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n61 48 Pedestrian -1 -1 -1 598.17 186.67 640.08 301.82 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n61 28 Pedestrian -1 -1 -1 431.91 180.11 475.25 271.54 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n61 24 Pedestrian -1 -1 -1 474.02 179.78 518.20 284.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n61 39 Car -1 -1 -1 615.14 172.91 646.07 200.53 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n61 25 Pedestrian -1 -1 -1 921.46 185.97 1029.02 365.42 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n61 2 Pedestrian -1 -1 -1 964.85 151.31 1069.61 366.90 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n61 32 Pedestrian -1 -1 -1 407.28 178.25 440.33 278.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n61 49 Car -1 -1 -1 1098.48 173.71 1220.54 252.12 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n61 10 Pedestrian -1 -1 -1 612.86 179.23 655.51 293.56 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n61 41 Pedestrian -1 -1 -1 493.93 186.03 528.73 279.12 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n61 51 Pedestrian -1 -1 -1 352.90 165.74 372.84 216.21 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n61 40 Pedestrian -1 -1 -1 607.42 167.59 646.42 268.28 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n61 50 Pedestrian -1 -1 -1 361.86 164.13 383.25 215.80 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n61 15 Car -1 -1 -1 1155.87 185.37 1223.12 249.12 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n61 52 Pedestrian -1 -1 -1 381.33 166.35 398.11 207.52 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n62 33 Car -1 -1 -1 449.11 177.35 521.34 229.29 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n62 8 Pedestrian -1 -1 -1 789.73 165.50 845.79 338.02 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n62 25 Pedestrian -1 -1 -1 923.34 186.40 1034.74 365.23 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n62 24 Pedestrian -1 -1 -1 477.40 180.26 522.22 284.02 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n62 48 Pedestrian -1 -1 -1 603.57 185.96 648.49 303.67 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n62 28 Pedestrian -1 -1 -1 438.45 179.15 477.45 273.75 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n62 2 Pedestrian -1 -1 -1 974.41 151.52 1075.20 366.87 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n62 49 Car -1 -1 -1 1119.26 173.15 1222.76 252.72 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n62 39 Car -1 -1 -1 615.24 173.16 646.04 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n62 32 Pedestrian -1 -1 -1 417.55 179.18 451.79 278.82 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n62 10 Pedestrian -1 -1 -1 612.11 179.87 656.03 294.32 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n62 50 Pedestrian -1 -1 -1 358.80 163.82 381.90 216.76 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n62 41 Pedestrian -1 -1 -1 502.53 186.26 533.96 278.18 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n62 51 Pedestrian -1 -1 -1 352.97 166.41 372.31 216.25 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n62 40 Pedestrian -1 -1 -1 606.52 167.66 647.09 268.05 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n62 52 Pedestrian -1 -1 -1 383.04 166.50 400.58 206.85 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n63 33 Car -1 -1 -1 447.54 177.51 521.45 229.42 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n63 8 Pedestrian -1 -1 -1 791.34 165.45 859.99 338.33 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n63 24 Pedestrian -1 -1 -1 483.71 180.15 523.29 285.64 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n63 25 Pedestrian -1 -1 -1 924.80 185.28 1040.94 366.00 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n63 32 Pedestrian -1 -1 -1 422.66 178.86 461.73 279.62 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n63 2 Pedestrian -1 -1 -1 974.20 151.20 1075.56 367.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n63 48 Pedestrian -1 -1 -1 607.02 187.42 652.69 302.71 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n63 41 Pedestrian -1 -1 -1 505.98 186.26 540.07 279.33 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n63 39 Car -1 -1 -1 615.46 173.23 646.00 200.44 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n63 51 Pedestrian -1 -1 -1 352.44 165.14 372.48 216.99 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n63 49 Car -1 -1 -1 1140.97 173.71 1223.29 252.53 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n63 28 Pedestrian -1 -1 -1 448.70 178.90 481.81 274.55 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n63 15 Car -1 -1 -1 1104.14 185.51 1221.56 241.17 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n63 40 Pedestrian -1 -1 -1 610.08 167.36 649.08 268.24 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n63 52 Pedestrian -1 -1 -1 383.82 166.59 400.33 207.36 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n64 33 Car -1 -1 -1 445.81 178.21 521.67 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n64 8 Pedestrian -1 -1 -1 789.49 168.23 862.59 337.00 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n64 25 Pedestrian -1 -1 -1 931.98 189.29 1041.42 366.65 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n64 2 Pedestrian -1 -1 -1 969.66 152.64 1072.52 366.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n64 32 Pedestrian -1 -1 -1 427.03 178.73 465.61 281.43 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n64 28 Pedestrian -1 -1 -1 450.67 181.44 488.66 275.30 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n64 39 Car -1 -1 -1 615.63 173.38 645.79 200.26 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n64 49 Car -1 -1 -1 1158.29 176.08 1221.21 251.51 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n64 41 Pedestrian -1 -1 -1 506.82 185.61 546.78 280.60 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n64 24 Pedestrian -1 -1 -1 492.59 181.63 530.02 284.96 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n64 48 Pedestrian -1 -1 -1 616.99 185.57 658.50 303.59 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n64 51 Pedestrian -1 -1 -1 357.84 164.26 381.63 217.47 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n64 15 Car -1 -1 -1 1085.89 187.56 1216.53 240.21 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n64 40 Pedestrian -1 -1 -1 607.80 163.26 652.72 287.09 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n64 53 Pedestrian -1 -1 -1 352.00 165.12 372.90 217.21 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n64 54 Car -1 -1 -1 993.85 181.75 1132.49 236.60 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n65 33 Car -1 -1 -1 444.00 179.17 518.31 232.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n65 8 Pedestrian -1 -1 -1 797.85 169.12 867.90 336.63 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n65 48 Pedestrian -1 -1 -1 619.09 185.09 664.79 305.71 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n65 25 Pedestrian -1 -1 -1 939.94 191.38 1048.30 364.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n65 28 Pedestrian -1 -1 -1 455.30 181.89 498.52 276.38 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n65 24 Pedestrian -1 -1 -1 495.18 181.05 536.07 285.69 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n65 2 Pedestrian -1 -1 -1 979.11 151.30 1078.10 367.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n65 15 Car -1 -1 -1 1077.65 187.00 1217.32 240.06 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n65 39 Car -1 -1 -1 615.53 173.45 645.90 200.17 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n65 32 Pedestrian -1 -1 -1 435.60 181.08 472.21 282.43 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n65 49 Car -1 -1 -1 1173.14 176.23 1221.70 258.00 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n65 41 Pedestrian -1 -1 -1 510.84 185.93 550.61 280.31 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n65 53 Pedestrian -1 -1 -1 352.05 165.75 372.49 216.97 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n65 54 Car -1 -1 -1 988.54 181.54 1130.31 237.06 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n65 51 Pedestrian -1 -1 -1 358.36 164.68 380.90 217.32 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n65 40 Pedestrian -1 -1 -1 609.77 163.89 650.68 284.86 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n65 55 Pedestrian -1 -1 -1 384.15 166.92 399.87 207.63 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n66 33 Car -1 -1 -1 442.83 179.32 518.08 231.99 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n66 8 Pedestrian -1 -1 -1 805.86 169.04 868.40 337.14 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n66 48 Pedestrian -1 -1 -1 625.91 187.47 671.97 310.16 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n66 15 Car -1 -1 -1 1084.04 187.01 1219.40 240.29 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n66 2 Pedestrian -1 -1 -1 983.37 151.70 1081.47 367.18 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n66 25 Pedestrian -1 -1 -1 950.25 191.68 1053.29 364.57 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n66 39 Car -1 -1 -1 615.76 173.39 645.69 200.11 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n66 28 Pedestrian -1 -1 -1 460.39 180.30 501.60 280.38 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n66 32 Pedestrian -1 -1 -1 441.55 180.99 481.91 283.25 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n66 24 Pedestrian -1 -1 -1 502.02 181.67 543.84 285.86 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n66 41 Pedestrian -1 -1 -1 520.56 185.99 554.88 285.24 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n66 54 Car -1 -1 -1 993.57 181.61 1132.75 237.51 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n66 51 Pedestrian -1 -1 -1 358.20 164.21 381.45 218.31 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n66 53 Pedestrian -1 -1 -1 351.35 165.76 372.25 217.21 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n66 49 Car -1 -1 -1 1186.77 176.26 1224.37 258.30 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n66 40 Pedestrian -1 -1 -1 610.21 163.63 649.98 284.56 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n67 15 Car -1 -1 -1 1082.71 186.16 1220.68 240.90 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n67 33 Car -1 -1 -1 437.44 180.21 518.07 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n67 48 Pedestrian -1 -1 -1 630.82 188.48 682.84 309.73 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n67 32 Pedestrian -1 -1 -1 447.40 180.39 491.49 284.35 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n67 8 Pedestrian -1 -1 -1 814.94 169.09 874.26 341.22 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n67 2 Pedestrian -1 -1 -1 981.07 152.89 1083.83 365.47 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n67 25 Pedestrian -1 -1 -1 958.32 188.53 1060.76 362.41 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n67 24 Pedestrian -1 -1 -1 510.87 182.66 550.16 288.85 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n67 39 Car -1 -1 -1 615.47 173.39 646.09 200.30 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n67 28 Pedestrian -1 -1 -1 470.51 180.44 506.85 280.26 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n67 54 Car -1 -1 -1 1001.72 181.88 1132.51 237.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n67 40 Pedestrian -1 -1 -1 611.32 164.19 648.69 279.09 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n67 53 Pedestrian -1 -1 -1 350.03 164.41 373.32 217.92 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n67 51 Pedestrian -1 -1 -1 358.11 164.12 381.27 218.70 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n67 49 Car -1 -1 -1 1193.20 183.49 1223.88 244.72 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n68 15 Car -1 -1 -1 1080.97 185.85 1222.64 241.42 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n68 48 Pedestrian -1 -1 -1 637.43 190.17 691.33 314.75 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n68 8 Pedestrian -1 -1 -1 820.35 167.50 884.55 343.54 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n68 33 Car -1 -1 -1 429.46 180.07 515.84 238.55 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n68 32 Pedestrian -1 -1 -1 449.52 179.32 497.14 287.26 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n68 24 Pedestrian -1 -1 -1 518.71 181.39 558.00 290.84 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n68 2 Pedestrian -1 -1 -1 968.22 149.80 1089.31 362.27 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n68 39 Car -1 -1 -1 615.61 173.33 645.94 200.33 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n68 28 Pedestrian -1 -1 -1 479.36 180.83 519.99 278.65 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n68 54 Car -1 -1 -1 1003.00 181.74 1131.27 237.20 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n68 25 Pedestrian -1 -1 -1 977.35 196.09 1072.36 360.58 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n68 40 Pedestrian -1 -1 -1 610.95 164.27 649.39 278.79 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n68 53 Pedestrian -1 -1 -1 347.93 164.87 369.33 217.49 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n68 49 Car -1 -1 -1 1179.07 189.25 1222.71 238.66 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n68 56 Pedestrian -1 -1 -1 619.00 186.21 657.10 295.02 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n69 33 Car -1 -1 -1 425.35 180.06 507.35 239.75 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n69 15 Car -1 -1 -1 1087.52 186.10 1221.45 241.06 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n69 8 Pedestrian -1 -1 -1 818.33 167.80 886.69 344.37 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n69 32 Pedestrian -1 -1 -1 460.07 179.11 501.64 288.53 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n69 48 Pedestrian -1 -1 -1 645.07 191.67 691.68 317.80 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n69 2 Pedestrian -1 -1 -1 975.74 148.92 1096.81 363.02 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n69 24 Pedestrian -1 -1 -1 521.71 182.14 562.83 290.16 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n69 39 Car -1 -1 -1 615.61 173.41 646.03 200.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n69 40 Pedestrian -1 -1 -1 610.62 163.80 650.17 278.83 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n69 53 Pedestrian -1 -1 -1 354.97 163.98 376.72 218.01 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n69 28 Pedestrian -1 -1 -1 481.75 180.78 525.83 279.18 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n69 54 Car -1 -1 -1 1003.03 182.09 1131.35 237.22 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n69 49 Car -1 -1 -1 1178.96 189.04 1222.82 239.07 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n69 25 Pedestrian -1 -1 -1 991.09 195.99 1089.20 361.54 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n69 56 Pedestrian -1 -1 -1 618.14 186.24 657.65 294.91 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n69 57 Pedestrian -1 -1 -1 347.79 164.92 368.54 217.60 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n70 33 Car -1 -1 -1 420.45 180.09 504.62 240.56 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n70 8 Pedestrian -1 -1 -1 820.90 167.02 884.82 345.19 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n70 15 Car -1 -1 -1 1080.35 185.84 1223.09 241.07 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n70 24 Pedestrian -1 -1 -1 523.40 181.74 569.48 292.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n70 48 Pedestrian -1 -1 -1 654.72 189.70 698.00 316.35 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n70 2 Pedestrian -1 -1 -1 975.17 149.37 1105.32 362.47 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n70 32 Pedestrian -1 -1 -1 470.22 179.98 506.56 287.97 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n70 39 Car -1 -1 -1 616.19 173.50 645.22 200.18 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n70 40 Pedestrian -1 -1 -1 610.51 163.41 650.04 279.14 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n70 28 Pedestrian -1 -1 -1 484.86 179.71 530.07 280.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n70 53 Pedestrian -1 -1 -1 358.82 163.54 380.66 219.26 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n70 54 Car -1 -1 -1 1003.17 182.29 1130.66 237.33 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n70 57 Pedestrian -1 -1 -1 347.52 164.73 367.89 217.90 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n70 49 Car -1 -1 -1 1179.45 189.58 1222.59 238.71 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n70 25 Pedestrian -1 -1 -1 998.54 195.97 1104.95 361.65 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n70 58 Pedestrian -1 -1 -1 388.19 167.82 403.85 207.39 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n71 33 Car -1 -1 -1 415.07 179.79 507.72 242.70 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n71 8 Pedestrian -1 -1 -1 827.81 168.45 891.68 343.90 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n71 24 Pedestrian -1 -1 -1 531.83 180.85 575.22 293.07 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n71 48 Pedestrian -1 -1 -1 664.58 188.05 710.70 318.14 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n71 15 Car -1 -1 -1 1080.16 185.73 1223.34 240.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n71 2 Pedestrian -1 -1 -1 976.22 150.03 1126.89 361.72 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n71 28 Pedestrian -1 -1 -1 495.34 180.00 535.08 280.18 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n71 32 Pedestrian -1 -1 -1 474.25 179.99 511.39 287.84 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n71 39 Car -1 -1 -1 615.98 173.50 645.31 200.14 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n71 40 Pedestrian -1 -1 -1 610.24 164.06 649.62 278.47 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n71 54 Car -1 -1 -1 1002.61 182.65 1131.46 237.88 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n71 49 Car -1 -1 -1 1179.22 189.70 1222.92 238.17 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n71 25 Pedestrian -1 -1 -1 1003.54 196.17 1115.31 361.12 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n71 53 Pedestrian -1 -1 -1 355.51 164.47 376.11 218.79 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n71 57 Pedestrian -1 -1 -1 345.51 165.13 364.29 218.63 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n71 58 Pedestrian -1 -1 -1 388.84 168.00 403.66 207.09 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n71 59 Pedestrian -1 -1 -1 614.04 178.22 654.38 295.72 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n72 33 Car -1 -1 -1 409.84 180.44 503.82 246.22 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n72 8 Pedestrian -1 -1 -1 830.78 169.00 898.23 343.58 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n72 48 Pedestrian -1 -1 -1 669.35 188.94 728.69 321.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n72 24 Pedestrian -1 -1 -1 540.47 179.29 581.21 294.38 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n72 15 Car -1 -1 -1 1080.64 185.93 1222.52 240.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n72 32 Pedestrian -1 -1 -1 480.21 181.64 519.48 289.71 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n72 2 Pedestrian -1 -1 -1 980.07 151.03 1146.00 360.23 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n72 39 Car -1 -1 -1 615.96 173.16 645.40 200.16 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n72 53 Pedestrian -1 -1 -1 351.71 164.73 372.64 218.84 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n72 40 Pedestrian -1 -1 -1 610.44 165.46 649.55 277.49 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n72 59 Pedestrian -1 -1 -1 614.56 178.05 654.09 295.43 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n72 25 Pedestrian -1 -1 -1 996.61 189.30 1130.09 360.88 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n72 54 Car -1 -1 -1 1001.33 181.67 1133.01 237.26 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n72 49 Car -1 -1 -1 1179.16 189.81 1222.97 237.72 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n72 28 Pedestrian -1 -1 -1 506.37 179.87 539.62 281.04 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n72 58 Pedestrian -1 -1 -1 368.32 167.38 386.06 214.38 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n72 60 Pedestrian -1 -1 -1 389.22 168.19 403.69 206.65 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n73 33 Car -1 -1 -1 402.71 181.66 499.89 247.94 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n73 8 Pedestrian -1 -1 -1 833.27 166.79 902.54 345.49 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n73 48 Pedestrian -1 -1 -1 671.75 191.11 733.77 321.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n73 24 Pedestrian -1 -1 -1 545.32 179.16 585.96 294.99 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n73 32 Pedestrian -1 -1 -1 485.42 182.51 522.36 288.96 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n73 15 Car -1 -1 -1 1088.01 186.21 1221.67 240.90 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n73 25 Pedestrian -1 -1 -1 993.46 187.19 1140.89 363.47 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n73 59 Pedestrian -1 -1 -1 614.09 177.26 654.32 296.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n73 39 Car -1 -1 -1 616.01 173.30 645.06 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n73 53 Pedestrian -1 -1 -1 347.45 163.86 368.22 218.86 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n73 40 Pedestrian -1 -1 -1 610.46 166.35 650.10 276.75 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n73 54 Car -1 -1 -1 1002.93 181.28 1131.36 236.97 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n73 28 Pedestrian -1 -1 -1 512.91 179.51 548.02 281.32 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n73 49 Car -1 -1 -1 1179.50 190.11 1222.50 237.66 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n73 2 Pedestrian -1 -1 -1 996.88 152.22 1152.46 358.68 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n74 33 Car -1 -1 -1 395.70 181.40 502.23 251.47 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n74 48 Pedestrian -1 -1 -1 677.72 190.30 736.28 323.04 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n74 24 Pedestrian -1 -1 -1 550.31 178.82 595.62 296.04 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n74 8 Pedestrian -1 -1 -1 836.03 166.74 907.65 345.51 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n74 15 Car -1 -1 -1 1079.85 186.09 1223.15 241.39 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n74 32 Pedestrian -1 -1 -1 493.59 181.02 528.92 291.03 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n74 59 Pedestrian -1 -1 -1 614.16 177.78 653.95 296.07 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n74 25 Pedestrian -1 -1 -1 993.71 187.08 1148.18 363.56 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n74 28 Pedestrian -1 -1 -1 514.91 181.06 554.28 283.45 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n74 54 Car -1 -1 -1 1006.43 180.71 1127.55 233.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n74 2 Pedestrian -1 -1 -1 1013.15 151.24 1159.10 360.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n74 40 Pedestrian -1 -1 -1 610.71 167.57 649.70 275.76 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n74 39 Car -1 -1 -1 615.76 172.96 645.23 200.13 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n74 53 Pedestrian -1 -1 -1 351.38 164.30 373.74 219.18 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n74 49 Car -1 -1 -1 1180.18 189.93 1221.65 238.07 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n74 61 Pedestrian -1 -1 -1 344.64 163.58 365.44 219.28 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n74 62 Pedestrian -1 -1 -1 389.29 167.70 404.21 207.94 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n75 33 Car -1 -1 -1 388.70 182.40 497.91 253.88 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n75 48 Pedestrian -1 -1 -1 687.51 188.66 741.97 323.92 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n75 8 Pedestrian -1 -1 -1 836.03 167.30 914.62 345.20 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n75 24 Pedestrian -1 -1 -1 556.46 179.24 603.35 301.37 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n75 32 Pedestrian -1 -1 -1 499.74 180.58 538.21 291.05 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n75 15 Car -1 -1 -1 1078.81 185.92 1223.94 241.65 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n75 25 Pedestrian -1 -1 -1 996.21 187.80 1160.41 363.08 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n75 28 Pedestrian -1 -1 -1 521.13 180.82 555.89 284.12 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n75 53 Pedestrian -1 -1 -1 349.43 164.37 374.82 219.14 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n75 54 Car -1 -1 -1 1006.23 180.97 1127.28 232.31 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n75 40 Pedestrian -1 -1 -1 609.83 165.91 651.17 283.44 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n75 59 Pedestrian -1 -1 -1 613.39 177.31 655.18 296.37 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n75 61 Pedestrian -1 -1 -1 343.21 163.67 366.63 218.65 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n75 2 Pedestrian -1 -1 -1 1025.07 151.34 1162.71 360.20 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n75 39 Car -1 -1 -1 615.55 172.79 644.96 199.87 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n75 49 Car -1 -1 -1 1180.41 189.54 1221.66 238.05 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n75 62 Pedestrian -1 -1 -1 388.49 167.12 405.30 214.03 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n76 33 Car -1 -1 -1 381.57 182.16 496.47 255.64 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n76 8 Pedestrian -1 -1 -1 836.20 167.85 914.80 344.63 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n76 48 Pedestrian -1 -1 -1 697.10 187.06 755.69 325.61 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n76 32 Pedestrian -1 -1 -1 504.67 181.39 549.14 291.21 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n76 24 Pedestrian -1 -1 -1 561.97 180.20 606.69 301.17 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n76 15 Car -1 -1 -1 1079.03 186.08 1223.93 241.72 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n76 61 Pedestrian -1 -1 -1 341.96 162.68 366.26 219.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n76 28 Pedestrian -1 -1 -1 528.29 181.05 563.12 285.40 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n76 54 Car -1 -1 -1 1004.64 181.11 1121.42 231.86 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n76 25 Pedestrian -1 -1 -1 1003.03 185.97 1161.11 364.90 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n76 40 Pedestrian -1 -1 -1 610.53 166.75 650.57 282.84 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n76 53 Pedestrian -1 -1 -1 349.57 164.21 373.86 219.44 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n76 39 Car -1 -1 -1 615.42 173.04 644.34 199.53 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n76 62 Pedestrian -1 -1 -1 372.93 168.73 389.40 212.01 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n76 59 Pedestrian -1 -1 -1 614.26 178.00 654.67 295.82 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n76 49 Car -1 -1 -1 1180.58 189.70 1221.60 238.20 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n76 2 Pedestrian -1 -1 -1 1030.40 148.26 1172.73 363.49 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n76 63 Pedestrian -1 -1 -1 388.90 167.80 405.01 213.01 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n77 33 Car -1 -1 -1 374.68 182.67 493.79 258.65 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n77 48 Pedestrian -1 -1 -1 706.59 187.91 767.78 331.18 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n77 8 Pedestrian -1 -1 -1 839.48 167.85 911.18 344.41 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n77 32 Pedestrian -1 -1 -1 506.60 180.08 554.76 294.09 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n77 25 Pedestrian -1 -1 -1 1015.44 194.05 1157.11 363.29 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n77 40 Pedestrian -1 -1 -1 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213.20 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n78 33 Car -1 -1 -1 366.88 181.97 492.47 261.42 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n78 48 Pedestrian -1 -1 -1 712.00 189.56 777.69 331.63 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n78 8 Pedestrian -1 -1 -1 844.32 166.90 914.62 345.49 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n78 32 Pedestrian -1 -1 -1 512.13 180.23 556.59 294.26 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n78 25 Pedestrian -1 -1 -1 1022.07 194.91 1165.50 362.60 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n78 53 Pedestrian -1 -1 -1 346.16 163.74 370.51 219.70 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n78 15 Car -1 -1 -1 1078.40 186.25 1224.40 241.57 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n78 54 Car -1 -1 -1 1003.70 181.24 1122.65 231.72 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n78 62 Pedestrian -1 -1 -1 373.39 169.10 389.90 213.24 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n78 40 Pedestrian -1 -1 -1 609.14 167.17 652.83 282.98 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n78 24 Pedestrian -1 -1 -1 577.58 180.39 621.71 301.97 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n78 39 Car -1 -1 -1 611.32 173.31 642.20 199.58 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n78 28 Pedestrian -1 -1 -1 534.37 181.18 573.25 286.63 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n78 49 Car -1 -1 -1 1180.30 189.44 1221.69 238.05 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n78 59 Pedestrian -1 -1 -1 567.42 181.80 608.74 298.09 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n78 2 Pedestrian -1 -1 -1 1056.48 152.86 1184.68 359.46 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n78 63 Pedestrian -1 -1 -1 395.70 169.01 410.79 213.41 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n78 64 Pedestrian -1 -1 -1 338.74 163.41 361.85 219.71 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n78 65 Pedestrian -1 -1 -1 613.64 177.92 654.68 295.77 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n79 33 Car -1 -1 -1 358.29 182.59 487.84 265.43 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n79 48 Pedestrian -1 -1 -1 718.93 190.29 780.06 335.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n79 8 Pedestrian -1 -1 -1 853.83 166.83 920.34 345.15 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n79 25 Pedestrian -1 -1 -1 1023.80 194.21 1179.02 364.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n79 53 Pedestrian -1 -1 -1 346.35 163.63 370.22 220.07 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n79 65 Pedestrian -1 -1 -1 608.70 166.41 658.46 291.72 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n79 15 Car -1 -1 -1 1078.01 185.73 1224.67 241.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n79 54 Car -1 -1 -1 1003.54 181.17 1123.14 231.84 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n79 32 Pedestrian -1 -1 -1 522.44 181.87 561.74 293.57 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n79 39 Car -1 -1 -1 611.12 172.98 642.90 199.83 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n79 24 Pedestrian -1 -1 -1 585.35 179.87 636.61 307.18 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n79 62 Pedestrian -1 -1 -1 372.80 168.26 390.36 215.17 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n79 28 Pedestrian -1 -1 -1 537.52 182.92 577.50 290.75 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n79 64 Pedestrian -1 -1 -1 336.99 164.61 357.57 218.96 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n79 63 Pedestrian -1 -1 -1 396.44 168.66 411.73 212.19 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n79 59 Pedestrian -1 -1 -1 565.54 183.43 602.57 297.41 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n79 2 Pedestrian -1 -1 -1 1055.59 153.91 1193.31 365.29 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n79 49 Car -1 -1 -1 1179.73 189.62 1221.96 237.75 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n79 66 Car -1 -1 -1 443.31 174.31 478.99 200.04 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n80 33 Car -1 -1 -1 348.77 183.18 483.70 268.76 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n80 8 Pedestrian -1 -1 -1 859.89 166.27 929.39 346.12 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n80 48 Pedestrian -1 -1 -1 732.51 190.96 780.39 335.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n80 65 Pedestrian -1 -1 -1 605.58 168.01 655.67 289.84 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n80 54 Car -1 -1 -1 1004.87 180.70 1128.63 232.92 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n80 32 Pedestrian -1 -1 -1 528.90 180.92 570.81 294.25 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n80 15 Car -1 -1 -1 1077.87 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211.72 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n80 67 Pedestrian -1 -1 -1 1039.89 152.18 1209.12 367.40 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n80 68 Cyclist -1 -1 -1 514.04 169.80 530.64 209.45 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n81 33 Car -1 -1 -1 338.22 183.76 479.97 272.84 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n81 8 Pedestrian -1 -1 -1 862.29 167.32 934.32 345.21 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n81 48 Pedestrian -1 -1 -1 746.16 190.43 789.74 336.63 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n81 54 Car -1 -1 -1 1004.59 180.87 1129.44 233.25 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n81 32 Pedestrian -1 -1 -1 531.85 180.75 575.66 294.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n81 15 Car -1 -1 -1 1084.93 185.52 1225.16 242.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n81 24 Pedestrian -1 -1 -1 602.82 182.62 648.93 306.98 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n81 65 Pedestrian -1 -1 -1 608.63 168.18 658.91 283.52 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n81 53 Pedestrian -1 -1 -1 346.29 163.26 368.86 220.80 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n81 39 Car -1 -1 -1 611.93 172.98 641.99 199.19 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n81 59 Pedestrian -1 -1 -1 568.55 186.82 607.61 301.28 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n81 68 Cyclist -1 -1 -1 513.24 170.11 530.51 209.96 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n81 2 Pedestrian -1 -1 -1 1058.31 145.30 1221.33 366.97 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n81 62 Pedestrian -1 -1 -1 372.51 167.70 391.34 219.48 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n81 63 Pedestrian -1 -1 -1 397.07 169.19 412.49 212.53 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n81 49 Car -1 -1 -1 1172.69 189.51 1221.67 238.04 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n81 25 Pedestrian -1 -1 -1 1035.94 193.98 1205.47 364.42 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n81 66 Car -1 -1 -1 438.40 174.05 477.97 201.31 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n81 69 Pedestrian -1 -1 -1 336.86 166.63 356.65 220.77 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n82 33 Car -1 -1 -1 327.77 183.40 478.26 276.62 -1 -1 -1 -1000 -1000 -1000 -10 0.97\n82 8 Pedestrian -1 -1 -1 861.74 166.19 936.14 346.74 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n82 54 Car -1 -1 -1 1003.82 181.01 1131.23 233.53 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n82 48 Pedestrian -1 -1 -1 753.00 191.22 806.81 337.15 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n82 32 Pedestrian -1 -1 -1 538.38 182.11 583.57 298.75 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n82 15 Car -1 -1 -1 1076.58 185.47 1226.24 242.17 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n82 65 Pedestrian -1 -1 -1 610.10 168.27 656.85 289.97 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n82 68 Cyclist -1 -1 -1 509.99 170.39 528.99 210.19 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n82 24 Pedestrian -1 -1 -1 612.23 182.45 656.10 312.32 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n82 53 Pedestrian -1 -1 -1 343.55 163.65 365.70 220.23 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n82 59 Pedestrian -1 -1 -1 571.85 187.32 611.26 302.29 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n82 39 Car -1 -1 -1 612.15 172.70 641.79 198.96 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n82 69 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1080.12 186.11 1222.90 241.65 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n83 65 Pedestrian -1 -1 -1 611.36 168.70 655.86 289.81 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n83 68 Cyclist -1 -1 -1 509.17 170.22 528.41 211.13 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n83 39 Car -1 -1 -1 612.08 172.59 642.28 199.81 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n83 53 Pedestrian -1 -1 -1 342.11 164.22 365.65 219.20 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n83 59 Pedestrian -1 -1 -1 577.73 187.42 613.78 302.95 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n83 69 Pedestrian -1 -1 -1 333.62 166.84 353.21 220.42 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n83 49 Car -1 -1 -1 1173.55 190.11 1220.94 237.83 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n83 62 Pedestrian -1 -1 -1 373.59 167.77 391.34 214.38 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n83 2 Pedestrian -1 -1 -1 1068.46 143.90 1226.12 367.98 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n83 63 Pedestrian -1 -1 -1 397.72 170.03 411.79 211.02 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n84 33 Car -1 -1 -1 303.91 183.90 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-1000 -1000 -1000 -10 0.73\n97 62 Pedestrian -1 -1 -1 386.51 168.69 407.08 221.88 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n97 65 Pedestrian -1 -1 -1 602.27 169.61 658.21 289.56 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n97 71 Pedestrian -1 -1 -1 658.79 175.49 716.54 291.84 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n97 68 Cyclist -1 -1 -1 476.50 171.69 501.77 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n97 39 Car -1 -1 -1 613.66 173.05 640.70 200.45 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n97 59 Pedestrian -1 -1 -1 659.92 180.28 715.82 331.02 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n97 72 Pedestrian -1 -1 -1 649.53 187.17 695.25 294.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n97 63 Pedestrian -1 -1 -1 402.01 169.96 420.11 214.75 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n97 70 Car -1 -1 -1 1171.76 189.17 1222.95 239.68 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n97 73 Pedestrian -1 -1 -1 327.61 165.97 349.69 224.14 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n98 33 Car -1 -1 -1 -1.52 194.43 359.04 363.28 -1 -1 -1 -1000 -1000 -1000 -10 0.99\n98 54 Car -1 -1 -1 1002.42 181.70 1133.37 237.48 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n98 15 Car -1 -1 -1 1080.67 186.23 1221.41 242.30 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n98 24 Pedestrian -1 -1 -1 747.45 184.35 812.26 343.52 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n98 48 Pedestrian -1 -1 -1 879.02 193.69 964.49 365.46 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n98 8 Pedestrian -1 -1 -1 905.14 169.05 975.56 357.50 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n98 63 Pedestrian -1 -1 -1 401.64 170.13 422.16 216.76 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n98 65 Pedestrian -1 -1 -1 603.34 169.69 657.83 296.20 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n98 68 Cyclist -1 -1 -1 470.61 173.50 500.51 222.47 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n98 32 Pedestrian -1 -1 -1 612.28 183.83 664.00 318.47 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n98 73 Pedestrian -1 -1 -1 327.23 164.72 350.12 225.15 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n98 62 Pedestrian -1 -1 -1 387.32 168.73 407.49 222.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n98 39 Car -1 -1 -1 613.79 173.41 640.70 200.82 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n98 71 Pedestrian -1 -1 -1 662.41 173.40 728.14 299.96 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n98 72 Pedestrian -1 -1 -1 651.29 185.66 701.66 302.55 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n98 70 Car -1 -1 -1 1179.25 190.17 1222.64 243.22 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n98 59 Pedestrian -1 -1 -1 668.90 179.01 729.15 332.97 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n98 74 Pedestrian -1 -1 -1 336.03 166.24 357.83 224.05 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n99 33 Car -1 -1 -1 -1.13 194.73 343.46 364.04 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n99 54 Car -1 -1 -1 1002.74 181.47 1133.30 237.50 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n99 15 Car -1 -1 -1 1080.74 186.01 1222.25 242.43 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n99 48 Pedestrian -1 -1 -1 889.85 196.12 983.30 363.29 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n99 24 Pedestrian -1 -1 -1 753.68 185.82 820.75 347.43 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n99 32 Pedestrian -1 -1 -1 622.58 183.04 669.26 319.61 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n99 65 Pedestrian -1 -1 -1 609.21 170.27 658.98 295.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n99 63 Pedestrian -1 -1 -1 401.75 170.30 423.17 217.06 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n99 8 Pedestrian -1 -1 -1 901.32 168.21 980.14 358.19 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n99 73 Pedestrian -1 -1 -1 327.28 164.92 349.74 224.78 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n99 68 Cyclist -1 -1 -1 467.35 173.42 499.46 222.14 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n99 72 Pedestrian -1 -1 -1 660.46 182.21 707.40 308.05 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n99 71 Pedestrian -1 -1 -1 680.05 172.25 733.03 301.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n99 62 Pedestrian -1 -1 -1 387.05 168.79 407.82 222.95 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n99 39 Car -1 -1 -1 614.67 171.86 644.95 203.11 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n99 70 Car -1 -1 -1 1171.61 188.82 1223.15 239.76 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n99 74 Pedestrian -1 -1 -1 336.30 166.60 357.89 223.68 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n99 59 Pedestrian -1 -1 -1 682.82 195.32 731.14 331.54 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n99 75 Cyclist -1 -1 -1 493.50 172.90 508.57 208.77 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n100 33 Car -1 -1 -1 -0.04 200.85 323.99 363.11 -1 -1 -1 -1000 -1000 -1000 -10 0.98\n100 54 Car -1 -1 -1 1003.94 180.90 1136.17 238.02 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n100 15 Car -1 -1 -1 1080.67 186.10 1222.23 242.33 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n100 48 Pedestrian -1 -1 -1 910.15 194.85 993.77 364.87 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n100 24 Pedestrian -1 -1 -1 763.90 185.73 825.63 348.75 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n100 65 Pedestrian -1 -1 -1 613.52 170.14 662.44 295.78 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n100 63 Pedestrian -1 -1 -1 402.30 170.88 423.66 217.34 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n100 68 Cyclist -1 -1 -1 463.65 172.92 495.66 223.45 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n100 71 Pedestrian -1 -1 -1 688.85 170.39 739.81 296.12 -1 -1 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-1000 -1000 -10 0.97\n101 54 Car -1 -1 -1 1004.32 181.17 1137.20 238.07 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n101 24 Pedestrian -1 -1 -1 778.10 182.50 842.42 359.66 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n101 15 Car -1 -1 -1 1081.21 186.23 1222.00 242.37 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n101 48 Pedestrian -1 -1 -1 931.53 199.18 1010.72 365.57 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n101 63 Pedestrian -1 -1 -1 404.37 170.55 425.21 216.87 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n101 65 Pedestrian -1 -1 -1 618.68 171.48 671.63 296.02 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n101 32 Pedestrian -1 -1 -1 640.37 181.69 688.70 323.42 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n101 68 Cyclist -1 -1 -1 460.43 172.14 492.13 225.11 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n101 39 Car -1 -1 -1 614.67 171.47 646.82 203.27 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n101 73 Pedestrian -1 -1 -1 327.39 164.76 350.57 225.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n101 72 Pedestrian -1 -1 -1 675.90 181.53 729.76 315.54 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n101 71 Pedestrian -1 -1 -1 694.48 169.32 749.39 297.37 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n101 62 Pedestrian -1 -1 -1 386.25 168.77 408.56 225.49 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n101 8 Pedestrian -1 -1 -1 920.49 164.84 998.95 361.93 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n101 70 Car -1 -1 -1 1178.91 189.12 1222.91 239.54 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n101 59 Pedestrian -1 -1 -1 704.42 196.65 746.90 337.21 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n102 33 Car -1 -1 -1 1.57 209.85 269.18 363.44 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n102 54 Car -1 -1 -1 1004.39 181.46 1138.24 237.89 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n102 15 Car -1 -1 -1 1081.22 186.15 1221.73 242.69 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n102 24 Pedestrian -1 -1 -1 789.16 181.60 861.80 368.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n102 39 Car -1 -1 -1 615.21 171.78 647.37 203.56 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n102 63 Pedestrian -1 -1 -1 405.19 170.33 425.06 217.16 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n102 65 Pedestrian -1 -1 -1 619.03 172.22 672.98 300.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n102 32 Pedestrian -1 -1 -1 646.19 183.56 698.21 322.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n102 48 Pedestrian -1 -1 -1 949.10 199.79 1031.86 365.22 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n102 8 Pedestrian -1 -1 -1 928.55 165.87 998.84 361.16 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n102 73 Pedestrian -1 -1 -1 328.53 164.48 351.02 225.54 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n102 72 Pedestrian -1 -1 -1 683.46 183.78 738.13 320.31 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n102 68 Cyclist -1 -1 -1 458.91 172.35 488.19 226.81 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n102 62 Pedestrian -1 -1 -1 386.27 168.27 408.76 226.09 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n102 71 Pedestrian -1 -1 -1 696.46 169.36 762.83 305.20 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n102 70 Car -1 -1 -1 1178.81 188.90 1223.15 239.60 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n102 59 Pedestrian -1 -1 -1 710.87 196.88 756.28 338.38 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n103 54 Car -1 -1 -1 1002.86 181.71 1140.01 237.96 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n103 33 Car -1 -1 -1 -1.62 216.41 234.51 363.85 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n103 15 Car -1 -1 -1 1081.35 186.40 1222.32 242.88 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n103 24 Pedestrian -1 -1 -1 794.29 184.45 872.42 367.11 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n103 63 Pedestrian -1 -1 -1 406.78 169.78 425.63 217.35 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n103 65 Pedestrian -1 -1 -1 622.54 171.82 677.03 301.89 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n103 32 Pedestrian -1 -1 -1 650.46 183.78 702.20 327.35 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n103 68 Cyclist -1 -1 -1 454.33 171.14 485.85 228.59 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n103 73 Pedestrian -1 -1 -1 331.64 164.42 353.88 225.17 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n103 48 Pedestrian -1 -1 -1 953.06 197.34 1050.95 367.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n103 39 Car -1 -1 -1 615.85 172.51 647.50 203.50 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n103 8 Pedestrian -1 -1 -1 929.37 169.90 998.09 357.41 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n103 62 Pedestrian -1 -1 -1 388.32 168.07 410.53 226.48 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n103 72 Pedestrian -1 -1 -1 697.04 183.11 747.78 322.19 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n103 71 Pedestrian -1 -1 -1 702.83 170.48 772.36 303.90 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n103 70 Car -1 -1 -1 1178.96 189.06 1223.01 239.72 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n103 59 Pedestrian -1 -1 -1 715.80 198.59 766.70 344.19 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n104 54 Car -1 -1 -1 1007.09 182.15 1141.77 238.72 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n104 24 Pedestrian -1 -1 -1 803.61 185.86 886.17 371.15 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n104 15 Car -1 -1 -1 1087.91 186.80 1221.34 242.81 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n104 8 Pedestrian -1 -1 -1 925.25 169.61 994.24 358.07 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n104 39 Car -1 -1 -1 616.88 172.48 649.23 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n104 65 Pedestrian -1 -1 -1 628.35 171.76 678.23 303.13 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n104 33 Car -1 -1 -1 -0.07 210.20 179.13 363.43 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n104 32 Pedestrian -1 -1 -1 664.78 182.24 710.47 330.15 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n104 48 Pedestrian -1 -1 -1 976.78 198.70 1065.08 366.23 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n104 68 Cyclist -1 -1 -1 447.11 173.61 483.90 230.31 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n104 73 Pedestrian -1 -1 -1 332.13 165.25 353.56 224.97 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n104 63 Pedestrian -1 -1 -1 407.33 169.52 426.13 217.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n104 72 Pedestrian -1 -1 -1 709.00 183.17 758.67 322.50 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n104 62 Pedestrian -1 -1 -1 388.11 167.90 410.72 226.98 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n104 71 Pedestrian -1 -1 -1 717.89 173.08 779.64 316.16 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n104 70 Car -1 -1 -1 1179.66 190.28 1222.50 243.72 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n104 59 Pedestrian -1 -1 -1 724.47 200.91 773.28 349.18 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n105 8 Pedestrian -1 -1 -1 920.83 169.29 998.48 358.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n105 15 Car -1 -1 -1 1088.11 187.08 1221.76 242.76 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n105 54 Car -1 -1 -1 1007.92 182.86 1141.87 238.01 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n105 24 Pedestrian -1 -1 -1 820.60 187.11 899.21 369.64 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n105 39 Car -1 -1 -1 617.01 172.50 650.44 204.03 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n105 65 Pedestrian -1 -1 -1 638.48 170.38 683.43 305.33 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n105 62 Pedestrian -1 -1 -1 388.82 168.10 411.30 227.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n105 48 Pedestrian -1 -1 -1 995.38 201.65 1092.80 364.69 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n105 68 Cyclist -1 -1 -1 443.00 172.24 481.14 231.21 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n105 63 Pedestrian -1 -1 -1 409.84 169.57 427.78 218.20 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 73 Pedestrian -1 -1 -1 332.03 164.93 354.04 225.94 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n105 32 Pedestrian -1 -1 -1 676.12 185.55 730.16 332.23 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n105 71 Pedestrian -1 -1 -1 725.06 174.48 788.36 321.55 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n105 72 Pedestrian -1 -1 -1 715.83 185.07 766.58 325.02 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n105 70 Car -1 -1 -1 1179.78 190.06 1222.58 244.09 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n105 59 Pedestrian -1 -1 -1 732.93 199.47 780.77 350.97 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n105 76 Pedestrian -1 -1 -1 490.32 173.02 504.93 214.10 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n106 8 Pedestrian -1 -1 -1 920.91 169.21 999.35 358.73 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n106 24 Pedestrian -1 -1 -1 839.25 188.42 910.76 367.74 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n106 68 Cyclist -1 -1 -1 440.37 171.97 475.19 233.18 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n106 39 Car -1 -1 -1 617.17 172.50 651.07 203.99 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n106 15 Car -1 -1 -1 1088.32 187.20 1222.30 242.85 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n106 54 Car -1 -1 -1 1013.43 183.00 1143.66 237.93 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n106 65 Pedestrian -1 -1 -1 643.25 171.05 694.19 304.56 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n106 63 Pedestrian -1 -1 -1 410.66 169.93 428.36 219.15 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n106 48 Pedestrian -1 -1 -1 1020.20 205.17 1121.54 366.59 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n106 62 Pedestrian -1 -1 -1 389.86 168.49 412.22 228.86 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n106 73 Pedestrian -1 -1 -1 331.73 165.06 353.93 225.66 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n106 71 Pedestrian -1 -1 -1 732.50 170.48 795.75 319.12 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n106 32 Pedestrian -1 -1 -1 682.50 183.30 746.34 335.09 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n106 72 Pedestrian -1 -1 -1 720.52 185.93 777.74 326.41 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n106 70 Car -1 -1 -1 1179.50 190.54 1222.83 243.88 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n106 76 Pedestrian -1 -1 -1 489.90 173.28 505.11 214.50 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n106 59 Pedestrian -1 -1 -1 746.86 198.04 797.05 359.39 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n107 8 Pedestrian -1 -1 -1 930.33 169.38 1003.98 359.21 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n107 24 Pedestrian -1 -1 -1 850.04 188.59 931.10 368.82 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n107 68 Cyclist -1 -1 -1 436.69 171.37 471.99 235.33 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n107 39 Car -1 -1 -1 618.11 172.47 651.92 203.91 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n107 54 Car -1 -1 -1 1015.58 182.96 1148.09 237.69 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n107 15 Car -1 -1 -1 1094.64 187.91 1222.21 244.95 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n107 48 Pedestrian -1 -1 -1 1044.08 205.96 1143.65 365.63 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n107 62 Pedestrian -1 -1 -1 392.78 169.50 414.10 227.43 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n107 32 Pedestrian -1 -1 -1 683.98 183.41 752.59 336.89 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n107 65 Pedestrian -1 -1 -1 651.45 171.98 715.95 303.42 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n107 63 Pedestrian -1 -1 -1 411.28 170.48 429.22 219.08 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n107 71 Pedestrian -1 -1 -1 736.55 169.30 807.78 320.24 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n107 73 Pedestrian -1 -1 -1 333.87 169.57 353.94 226.68 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n107 59 Pedestrian -1 -1 -1 750.07 197.23 809.24 361.35 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n107 72 Pedestrian -1 -1 -1 734.49 188.28 794.23 330.69 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n107 70 Car -1 -1 -1 1178.91 190.99 1223.44 243.59 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n107 76 Pedestrian -1 -1 -1 489.65 173.78 504.83 215.23 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n107 77 Pedestrian -1 -1 -1 343.25 170.81 365.01 225.22 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n108 8 Pedestrian -1 -1 -1 931.64 169.61 1003.49 358.66 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n108 15 Car -1 -1 -1 1096.13 188.18 1221.10 245.45 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n108 54 Car -1 -1 -1 1020.04 183.64 1151.40 238.47 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n108 24 Pedestrian -1 -1 -1 860.84 189.71 944.02 367.73 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n108 62 Pedestrian -1 -1 -1 394.00 168.88 415.61 228.42 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n108 65 Pedestrian -1 -1 -1 656.06 171.77 727.01 308.95 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n108 63 Pedestrian -1 -1 -1 413.88 170.81 431.57 219.20 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n108 39 Car -1 -1 -1 618.36 172.91 652.67 203.85 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n108 48 Pedestrian -1 -1 -1 1056.14 201.56 1177.35 364.52 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n108 68 Cyclist -1 -1 -1 433.93 170.74 468.07 236.48 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n108 59 Pedestrian -1 -1 -1 759.56 199.68 822.83 365.24 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n108 73 Pedestrian -1 -1 -1 335.75 170.35 355.91 226.82 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n108 32 Pedestrian -1 -1 -1 694.61 183.83 765.03 342.70 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n108 72 Pedestrian -1 -1 -1 748.28 187.60 811.03 332.48 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n108 71 Pedestrian -1 -1 -1 751.50 166.57 823.03 314.92 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n108 76 Pedestrian -1 -1 -1 489.68 173.72 504.90 213.90 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n108 70 Car -1 -1 -1 1178.64 191.53 1223.71 243.18 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n108 77 Pedestrian -1 -1 -1 342.66 171.20 366.87 225.39 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n109 54 Car -1 -1 -1 1020.59 183.87 1158.30 241.44 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n109 8 Pedestrian -1 -1 -1 931.76 168.12 1003.55 359.56 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n109 65 Pedestrian -1 -1 -1 665.77 167.81 732.31 313.39 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n109 15 Car -1 -1 -1 1094.81 188.76 1222.91 245.53 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n109 68 Cyclist -1 -1 -1 429.62 173.05 463.55 238.20 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n109 39 Car -1 -1 -1 619.73 172.91 654.14 204.10 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n109 24 Pedestrian -1 -1 -1 879.68 193.02 963.03 364.77 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n109 62 Pedestrian -1 -1 -1 394.31 169.39 415.87 228.43 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n109 48 Pedestrian -1 -1 -1 1069.17 202.24 1210.46 363.84 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n109 63 Pedestrian -1 -1 -1 415.52 170.68 432.61 219.45 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n109 32 Pedestrian -1 -1 -1 714.87 183.52 775.15 344.19 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n109 72 Pedestrian -1 -1 -1 762.48 181.85 826.98 337.83 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n109 59 Pedestrian -1 -1 -1 767.72 200.01 830.30 365.20 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n109 73 Pedestrian -1 -1 -1 336.11 170.27 355.83 227.04 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n109 77 Pedestrian -1 -1 -1 346.33 171.24 368.43 226.33 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n109 70 Car -1 -1 -1 1178.91 191.84 1223.45 243.02 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n110 54 Car -1 -1 -1 1022.50 183.62 1158.28 241.51 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n110 65 Pedestrian -1 -1 -1 676.93 166.79 736.51 315.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n110 68 Cyclist -1 -1 -1 424.54 173.03 459.55 240.22 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n110 24 Pedestrian -1 -1 -1 906.55 192.20 989.73 365.62 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n110 39 Car -1 -1 -1 620.98 172.85 655.15 204.04 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n110 8 Pedestrian -1 -1 -1 934.71 168.09 1007.86 359.25 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n110 62 Pedestrian -1 -1 -1 397.48 170.19 417.92 228.05 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n110 72 Pedestrian -1 -1 -1 770.90 175.83 849.83 336.81 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n110 15 Car -1 -1 -1 1094.18 189.31 1223.44 245.83 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n110 73 Pedestrian -1 -1 -1 335.61 170.74 356.58 228.17 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n110 32 Pedestrian -1 -1 -1 730.73 182.53 790.11 351.25 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n110 63 Pedestrian -1 -1 -1 415.65 171.43 432.95 220.13 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n110 48 Pedestrian -1 -1 -1 1094.74 201.36 1223.16 364.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n110 77 Pedestrian -1 -1 -1 347.22 171.58 368.37 227.05 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n110 59 Pedestrian -1 -1 -1 779.00 200.78 841.55 365.10 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n110 70 Car -1 -1 -1 1178.79 192.41 1223.44 242.87 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n110 78 Pedestrian -1 -1 -1 490.12 173.63 504.73 213.77 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n111 54 Car -1 -1 -1 1026.04 183.44 1162.25 242.29 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n111 24 Pedestrian -1 -1 -1 932.95 190.80 1017.00 365.51 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n111 65 Pedestrian -1 -1 -1 691.30 166.96 745.92 316.41 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n111 72 Pedestrian -1 -1 -1 778.66 171.04 865.09 340.81 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n111 62 Pedestrian -1 -1 -1 397.85 170.53 418.32 227.86 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n111 39 Car -1 -1 -1 622.02 173.91 656.08 204.42 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n111 8 Pedestrian -1 -1 -1 936.38 164.39 1020.74 355.54 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n111 32 Pedestrian -1 -1 -1 742.85 184.17 808.87 351.43 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n111 48 Pedestrian -1 -1 -1 1134.27 209.88 1221.69 363.56 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n111 68 Cyclist -1 -1 -1 416.78 175.00 459.46 243.25 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n111 73 Pedestrian -1 -1 -1 333.03 170.31 354.74 227.48 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n111 15 Car -1 -1 -1 1103.63 190.13 1221.99 245.78 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n111 77 Pedestrian -1 -1 -1 348.27 171.74 369.20 227.37 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n111 63 Pedestrian -1 -1 -1 417.45 173.14 435.79 221.25 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n111 59 Pedestrian -1 -1 -1 789.04 202.25 854.62 363.56 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n111 78 Pedestrian -1 -1 -1 489.52 173.78 505.45 215.67 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n112 54 Car -1 -1 -1 1030.57 184.07 1165.02 242.18 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n112 24 Pedestrian -1 -1 -1 952.85 194.15 1043.30 363.37 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n112 65 Pedestrian -1 -1 -1 705.44 166.49 762.31 320.92 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n112 39 Car -1 -1 -1 623.79 173.86 657.44 204.83 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n112 62 Pedestrian -1 -1 -1 398.62 170.42 418.51 228.34 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n112 15 Car -1 -1 -1 1111.42 189.99 1221.82 245.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n112 72 Pedestrian -1 -1 -1 792.80 165.26 874.16 346.88 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n112 68 Cyclist -1 -1 -1 409.90 173.17 453.75 246.41 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n112 32 Pedestrian -1 -1 -1 753.70 185.58 828.69 356.80 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n112 73 Pedestrian -1 -1 -1 334.78 169.66 356.79 228.55 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n112 8 Pedestrian -1 -1 -1 941.19 165.59 1032.44 353.97 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n112 59 Pedestrian -1 -1 -1 805.64 201.46 876.09 364.86 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n112 63 Pedestrian -1 -1 -1 417.93 172.94 437.62 221.60 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n112 77 Pedestrian -1 -1 -1 348.43 170.56 369.84 227.86 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n112 48 Pedestrian -1 -1 -1 1162.44 217.38 1224.47 363.10 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n113 54 Car -1 -1 -1 1032.63 184.41 1169.20 242.46 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n113 39 Car -1 -1 -1 625.12 174.53 659.07 204.99 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n113 24 Pedestrian -1 -1 -1 976.22 195.52 1066.28 363.60 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n113 68 Cyclist -1 -1 -1 404.79 172.75 449.07 248.33 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n113 65 Pedestrian -1 -1 -1 719.74 168.12 785.83 320.95 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n113 8 Pedestrian -1 -1 -1 963.66 165.63 1039.83 353.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n113 77 Pedestrian -1 -1 -1 351.61 169.56 373.18 227.80 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n113 59 Pedestrian -1 -1 -1 821.14 207.10 891.40 365.66 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n113 73 Pedestrian -1 -1 -1 332.13 169.71 355.16 228.19 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n113 62 Pedestrian -1 -1 -1 398.68 170.58 418.65 228.70 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n113 72 Pedestrian -1 -1 -1 805.22 164.41 891.96 347.34 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n113 15 Car -1 -1 -1 1110.80 191.32 1222.53 249.53 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n113 63 Pedestrian -1 -1 -1 420.67 171.61 442.03 224.19 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n113 32 Pedestrian -1 -1 -1 765.00 182.07 839.99 361.78 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n113 79 Pedestrian -1 -1 -1 811.15 172.17 878.85 316.72 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n113 80 Car -1 -1 -1 1157.48 192.21 1221.29 250.34 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n114 54 Car -1 -1 -1 1037.22 184.69 1173.06 243.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n114 39 Car -1 -1 -1 626.11 174.95 659.85 205.35 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n114 8 Pedestrian -1 -1 -1 977.76 172.22 1041.28 355.10 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n114 24 Pedestrian -1 -1 -1 1015.44 200.53 1103.65 364.34 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n114 65 Pedestrian -1 -1 -1 727.86 168.62 800.38 321.68 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n114 73 Pedestrian -1 -1 -1 332.11 169.46 354.64 229.09 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n114 77 Pedestrian -1 -1 -1 354.93 169.12 375.56 228.70 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n114 68 Cyclist -1 -1 -1 398.20 171.49 441.88 251.32 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n114 32 Pedestrian -1 -1 -1 784.66 182.46 858.81 368.28 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n114 15 Car -1 -1 -1 1118.72 192.13 1221.99 248.80 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n114 62 Pedestrian -1 -1 -1 400.47 171.49 422.48 230.74 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n114 72 Pedestrian -1 -1 -1 820.40 167.42 892.68 337.14 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n114 59 Pedestrian -1 -1 -1 833.16 190.80 902.39 359.63 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n114 63 Pedestrian -1 -1 -1 421.44 171.85 442.53 224.63 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n114 79 Pedestrian -1 -1 -1 839.33 200.30 911.94 365.88 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n114 80 Car -1 -1 -1 1164.94 193.25 1221.52 249.38 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n114 81 Cyclist -1 -1 -1 488.56 173.79 505.53 216.44 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n115 65 Pedestrian -1 -1 -1 734.25 168.51 809.47 328.71 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n115 39 Car -1 -1 -1 627.75 175.36 660.92 205.66 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n115 68 Cyclist -1 -1 -1 384.75 172.87 440.03 256.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n115 54 Car -1 -1 -1 1040.02 185.50 1178.54 242.27 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n115 8 Pedestrian -1 -1 -1 990.07 175.43 1052.61 357.70 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n115 32 Pedestrian -1 -1 -1 810.24 189.83 879.38 366.76 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n115 73 Pedestrian -1 -1 -1 330.60 168.72 354.78 230.16 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n115 24 Pedestrian -1 -1 -1 1043.68 200.57 1159.56 364.77 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n115 77 Pedestrian -1 -1 -1 355.84 167.02 377.74 229.38 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n115 15 Car -1 -1 -1 1126.64 192.89 1221.26 248.04 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n115 79 Pedestrian -1 -1 -1 858.75 208.32 938.29 364.57 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n115 62 Pedestrian -1 -1 -1 401.33 172.23 422.74 231.42 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n115 63 Pedestrian -1 -1 -1 424.12 172.62 443.87 223.63 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n115 59 Pedestrian -1 -1 -1 848.79 195.89 924.98 362.40 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n115 81 Cyclist -1 -1 -1 487.31 174.35 505.71 216.74 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n115 72 Pedestrian -1 -1 -1 834.99 174.19 907.98 337.97 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n116 39 Car -1 -1 -1 628.34 175.02 661.71 205.32 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n116 8 Pedestrian -1 -1 -1 1001.60 170.20 1064.10 358.51 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n116 65 Pedestrian -1 -1 -1 750.19 168.57 816.28 334.40 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n116 54 Car -1 -1 -1 1032.52 185.07 1185.72 243.84 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n116 73 Pedestrian -1 -1 -1 331.36 168.67 354.57 230.27 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n116 32 Pedestrian -1 -1 -1 821.84 188.67 898.68 368.47 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n116 77 Pedestrian -1 -1 -1 359.47 165.83 381.16 229.93 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n116 24 Pedestrian -1 -1 -1 1075.76 200.10 1196.27 365.37 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n116 68 Cyclist -1 -1 -1 378.75 170.41 432.00 259.94 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n116 63 Pedestrian -1 -1 -1 421.29 172.21 441.98 223.77 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n116 62 Pedestrian -1 -1 -1 401.59 171.98 423.59 232.04 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n116 15 Car -1 -1 -1 1128.05 192.73 1220.11 248.37 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n116 79 Pedestrian -1 -1 -1 879.41 209.08 963.62 364.67 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n116 72 Pedestrian -1 -1 -1 845.32 174.45 913.33 337.79 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n116 81 Cyclist -1 -1 -1 484.80 174.63 506.64 221.81 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n116 59 Pedestrian -1 -1 -1 868.86 195.08 958.66 362.85 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n116 82 Pedestrian -1 -1 -1 861.21 195.36 943.58 362.82 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n117 8 Pedestrian -1 -1 -1 1014.74 167.58 1088.82 361.06 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n117 39 Car -1 -1 -1 629.69 174.41 663.31 204.97 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n117 54 Car -1 -1 -1 1042.00 184.98 1191.01 243.58 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n117 65 Pedestrian -1 -1 -1 768.66 167.56 828.80 336.63 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n117 63 Pedestrian -1 -1 -1 421.76 171.97 441.85 224.11 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n117 68 Cyclist -1 -1 -1 368.30 171.86 427.06 263.44 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n117 32 Pedestrian -1 -1 -1 833.18 189.23 917.94 367.98 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n117 81 Cyclist -1 -1 -1 483.93 173.73 507.70 222.90 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n117 73 Pedestrian -1 -1 -1 332.72 168.69 354.93 230.49 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n117 77 Pedestrian -1 -1 -1 359.45 165.84 381.42 229.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n117 72 Pedestrian -1 -1 -1 849.05 173.28 924.79 338.16 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n117 59 Pedestrian -1 -1 -1 889.44 193.85 976.51 363.39 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n117 24 Pedestrian -1 -1 -1 1107.93 201.43 1217.79 363.40 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n117 79 Pedestrian -1 -1 -1 910.67 211.70 993.85 362.01 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n117 62 Pedestrian -1 -1 -1 400.40 170.95 423.60 232.14 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n117 15 Car -1 -1 -1 1128.02 193.39 1220.62 248.44 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n117 83 Car -1 -1 -1 1169.33 194.09 1224.96 249.18 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n118 8 Pedestrian -1 -1 -1 1030.40 166.53 1111.82 362.05 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n118 65 Pedestrian -1 -1 -1 785.62 167.92 858.08 342.79 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n118 54 Car -1 -1 -1 1041.56 184.26 1200.09 243.90 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n118 63 Pedestrian -1 -1 -1 422.55 172.14 441.68 223.86 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n118 32 Pedestrian -1 -1 -1 855.42 190.96 941.55 365.74 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n118 68 Cyclist -1 -1 -1 358.67 171.54 420.34 266.38 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n118 81 Cyclist -1 -1 -1 483.57 173.89 506.94 222.92 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n118 39 Car -1 -1 -1 629.81 173.72 663.98 204.73 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n118 72 Pedestrian -1 -1 -1 856.79 172.02 940.21 339.65 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n118 73 Pedestrian -1 -1 -1 336.16 170.05 356.16 231.70 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n118 77 Pedestrian -1 -1 -1 362.55 166.47 383.31 229.87 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n118 79 Pedestrian -1 -1 -1 947.72 211.16 1032.84 362.27 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n118 62 Pedestrian -1 -1 -1 401.28 171.38 423.40 230.71 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n118 15 Car -1 -1 -1 1134.75 192.08 1220.89 243.49 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n118 59 Pedestrian -1 -1 -1 922.95 195.80 1011.75 362.21 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n119 8 Pedestrian -1 -1 -1 1046.90 167.82 1133.77 360.94 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n119 63 Pedestrian -1 -1 -1 421.77 171.94 441.83 224.03 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n119 39 Car -1 -1 -1 631.65 173.93 665.44 204.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n119 65 Pedestrian -1 -1 -1 797.00 161.45 885.00 350.33 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n119 73 Pedestrian -1 -1 -1 336.32 170.38 356.01 232.58 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n119 32 Pedestrian -1 -1 -1 881.13 185.81 961.92 364.22 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n119 68 Cyclist -1 -1 -1 350.06 175.08 412.43 269.34 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n119 54 Car -1 -1 -1 1052.23 184.07 1211.65 243.48 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n119 62 Pedestrian -1 -1 -1 402.30 171.34 423.29 227.71 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n119 81 Cyclist -1 -1 -1 484.26 173.51 505.15 222.83 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n119 77 Pedestrian -1 -1 -1 364.30 166.58 390.08 230.86 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n119 79 Pedestrian -1 -1 -1 981.53 218.12 1068.27 361.96 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n119 59 Pedestrian -1 -1 -1 946.45 194.64 1034.42 362.73 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n119 72 Pedestrian -1 -1 -1 876.55 172.58 958.78 339.15 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n119 15 Car -1 -1 -1 1127.43 192.44 1221.27 242.66 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n120 39 Car -1 -1 -1 632.68 174.46 667.06 205.20 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n120 8 Pedestrian -1 -1 -1 1063.88 167.30 1170.02 361.46 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n120 65 Pedestrian -1 -1 -1 810.12 168.49 902.44 351.07 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n120 63 Pedestrian -1 -1 -1 421.87 171.38 441.97 224.37 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n120 68 Cyclist -1 -1 -1 335.48 170.94 404.33 274.04 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n120 32 Pedestrian -1 -1 -1 923.17 193.24 996.35 362.97 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n120 73 Pedestrian -1 -1 -1 337.01 170.13 357.04 232.51 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n120 54 Car -1 -1 -1 1077.32 184.67 1217.40 243.52 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n120 72 Pedestrian -1 -1 -1 902.11 177.52 963.88 325.99 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n120 62 Pedestrian -1 -1 -1 401.95 170.82 423.72 228.91 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n120 59 Pedestrian -1 -1 -1 967.42 198.32 1059.70 365.23 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n120 81 Cyclist -1 -1 -1 483.26 173.29 503.98 223.63 -1 -1 -1 -1000 -1000 -1000 -10 0.55\n120 77 Pedestrian -1 -1 -1 364.57 167.66 389.39 229.82 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n120 79 Pedestrian -1 -1 -1 1023.91 228.13 1133.17 359.66 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n120 15 Car -1 -1 -1 1135.81 194.20 1220.06 247.58 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n121 39 Car -1 -1 -1 633.40 174.74 667.88 205.39 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n121 68 Cyclist -1 -1 -1 314.13 172.00 394.49 280.38 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n121 63 Pedestrian -1 -1 -1 421.53 171.37 442.34 225.57 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n121 65 Pedestrian -1 -1 -1 822.50 165.69 913.15 354.27 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n121 32 Pedestrian -1 -1 -1 938.40 192.40 1027.36 364.68 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n121 8 Pedestrian -1 -1 -1 1085.32 172.29 1194.70 355.79 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n121 73 Pedestrian -1 -1 -1 336.80 171.01 357.33 231.94 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n121 54 Car -1 -1 -1 1060.80 185.56 1225.83 243.28 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n121 62 Pedestrian -1 -1 -1 402.17 170.72 423.62 228.25 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n121 59 Pedestrian -1 -1 -1 995.26 202.37 1093.08 362.60 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n121 72 Pedestrian -1 -1 -1 912.86 173.49 983.66 332.04 -1 -1 -1 -1000 -1000 -1000 -10 0.59\n121 81 Cyclist -1 -1 -1 484.24 173.59 502.91 221.33 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n121 15 Car -1 -1 -1 1134.80 193.75 1221.57 247.88 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n121 79 Pedestrian -1 -1 -1 1066.38 191.82 1205.34 357.73 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n121 77 Pedestrian -1 -1 -1 362.05 167.63 384.20 229.84 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n121 84 Pedestrian -1 -1 -1 1053.86 230.68 1202.65 357.40 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n122 39 Car -1 -1 -1 634.96 174.77 669.08 206.01 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n122 65 Pedestrian -1 -1 -1 844.14 163.74 929.24 356.67 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n122 63 Pedestrian -1 -1 -1 421.09 171.82 442.70 226.82 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n122 68 Cyclist -1 -1 -1 305.80 174.70 381.11 291.55 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n122 54 Car -1 -1 -1 1061.33 184.36 1226.42 244.75 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n122 73 Pedestrian -1 -1 -1 337.53 169.93 363.38 234.17 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n122 59 Pedestrian -1 -1 -1 1033.90 203.77 1138.28 361.64 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n122 32 Pedestrian -1 -1 -1 964.46 192.53 1062.47 365.51 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n122 62 Pedestrian -1 -1 -1 402.35 171.46 423.75 227.98 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n122 8 Pedestrian -1 -1 -1 1110.50 169.93 1222.39 357.91 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n122 72 Pedestrian -1 -1 -1 928.39 174.41 1006.29 343.69 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n122 15 Car -1 -1 -1 1141.96 193.61 1221.61 248.86 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n122 81 Cyclist -1 -1 -1 483.72 174.01 501.61 220.80 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n123 39 Car -1 -1 -1 635.26 174.44 670.39 206.09 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n123 65 Pedestrian -1 -1 -1 871.46 165.25 948.34 363.38 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n123 68 Cyclist -1 -1 -1 279.58 171.32 368.77 302.39 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n123 63 Pedestrian -1 -1 -1 421.25 171.96 442.74 226.84 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n123 72 Pedestrian -1 -1 -1 937.99 170.11 1004.50 350.77 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n123 32 Pedestrian -1 -1 -1 1002.64 195.21 1108.42 363.52 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n123 62 Pedestrian -1 -1 -1 402.67 171.98 423.11 227.45 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n123 8 Pedestrian -1 -1 -1 1137.02 172.07 1219.28 355.93 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n123 59 Pedestrian -1 -1 -1 1078.24 202.28 1185.90 363.66 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n123 54 Car -1 -1 -1 1071.39 186.00 1223.83 248.57 -1 -1 -1 -1000 -1000 -1000 -10 0.63\n123 73 Pedestrian -1 -1 -1 335.28 170.58 358.35 233.04 -1 -1 -1 -1000 -1000 -1000 -10 0.60\n123 81 Cyclist -1 -1 -1 481.67 173.48 500.90 224.08 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n123 15 Car -1 -1 -1 1134.70 194.78 1221.60 247.86 -1 -1 -1 -1000 -1000 -1000 -10 0.50\n123 85 Pedestrian -1 -1 -1 357.12 165.64 382.06 232.45 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n124 39 Car -1 -1 -1 636.10 173.61 671.20 205.31 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n124 68 Cyclist -1 -1 -1 252.62 169.56 356.42 311.72 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n124 63 Pedestrian -1 -1 -1 422.82 171.89 444.81 227.19 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n124 62 Pedestrian -1 -1 -1 402.31 171.35 423.12 227.53 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n124 65 Pedestrian -1 -1 -1 893.27 169.92 980.12 364.14 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n124 73 Pedestrian -1 -1 -1 335.82 170.33 358.09 233.65 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n124 72 Pedestrian -1 -1 -1 961.94 172.11 1034.43 354.70 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n124 85 Pedestrian -1 -1 -1 358.10 165.40 381.71 232.89 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n124 54 Car -1 -1 -1 1085.31 186.45 1224.76 248.39 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n124 32 Pedestrian -1 -1 -1 1034.17 193.41 1168.86 365.34 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n124 59 Pedestrian -1 -1 -1 1127.85 203.32 1220.67 361.76 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n124 8 Pedestrian -1 -1 -1 1177.68 172.37 1223.95 362.00 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n125 39 Car -1 -1 -1 636.47 171.64 672.14 203.92 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n125 62 Pedestrian -1 -1 -1 401.19 169.97 423.34 227.33 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n125 68 Cyclist -1 -1 -1 219.69 163.56 341.89 325.81 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n125 63 Pedestrian -1 -1 -1 423.61 171.02 444.99 226.40 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n125 65 Pedestrian -1 -1 -1 918.72 164.58 1008.62 363.79 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n125 72 Pedestrian -1 -1 -1 982.00 168.66 1067.55 359.56 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n125 54 Car -1 -1 -1 1077.17 186.35 1226.35 247.64 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n125 85 Pedestrian -1 -1 -1 358.03 166.47 381.59 230.79 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n125 73 Pedestrian -1 -1 -1 335.37 169.06 358.32 233.34 -1 -1 -1 -1000 -1000 -1000 -10 0.67\n125 32 Pedestrian -1 -1 -1 1083.43 194.39 1219.14 364.13 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n125 8 Pedestrian -1 -1 -1 1195.19 174.35 1222.29 360.26 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n125 86 Cyclist -1 -1 -1 476.64 172.77 497.97 226.15 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n126 39 Car -1 -1 -1 638.35 170.89 674.05 203.31 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n126 68 Cyclist -1 -1 -1 180.10 159.52 322.38 337.46 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n126 63 Pedestrian -1 -1 -1 423.69 169.76 445.20 226.27 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n126 54 Car -1 -1 -1 1086.83 185.22 1222.72 248.26 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n126 65 Pedestrian -1 -1 -1 936.35 168.72 1036.72 364.65 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n126 62 Pedestrian -1 -1 -1 400.18 168.30 422.45 226.96 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n126 85 Pedestrian -1 -1 -1 356.62 167.00 382.48 230.82 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n126 86 Cyclist -1 -1 -1 471.18 170.66 498.50 228.96 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n126 73 Pedestrian -1 -1 -1 333.36 165.71 359.80 233.61 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n126 72 Pedestrian -1 -1 -1 987.18 163.58 1100.93 364.90 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n126 15 Car -1 -1 -1 1149.19 192.32 1222.51 249.80 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n127 39 Car -1 -1 -1 639.23 170.58 675.40 203.06 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n127 63 Pedestrian -1 -1 -1 424.24 169.15 445.88 226.00 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n127 68 Cyclist -1 -1 -1 139.83 163.95 300.78 354.39 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n127 54 Car -1 -1 -1 1086.96 184.51 1222.51 249.22 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n127 72 Pedestrian -1 -1 -1 1020.44 169.00 1121.00 365.16 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n127 65 Pedestrian -1 -1 -1 954.31 169.75 1057.18 363.49 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n127 86 Cyclist -1 -1 -1 466.08 170.69 497.21 232.36 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n127 85 Pedestrian -1 -1 -1 357.23 166.76 382.63 231.01 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n127 62 Pedestrian -1 -1 -1 397.63 167.40 420.31 227.34 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n127 73 Pedestrian -1 -1 -1 334.82 165.51 358.78 233.38 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n128 39 Car -1 -1 -1 639.27 169.95 675.94 202.67 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n128 68 Cyclist -1 -1 -1 86.54 159.13 277.21 367.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n128 72 Pedestrian -1 -1 -1 1041.97 168.78 1161.29 365.61 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n128 86 Cyclist -1 -1 -1 464.18 170.46 495.92 233.47 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n128 54 Car -1 -1 -1 1095.67 183.60 1222.08 250.68 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n128 63 Pedestrian -1 -1 -1 423.63 168.62 445.57 225.70 -1 -1 -1 -1000 -1000 -1000 -10 0.78\n128 65 Pedestrian -1 -1 -1 971.61 164.81 1070.55 363.53 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n128 62 Pedestrian -1 -1 -1 397.19 167.04 419.81 227.44 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n128 85 Pedestrian -1 -1 -1 361.31 165.61 383.71 231.99 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n128 73 Pedestrian -1 -1 -1 334.98 165.40 358.96 233.62 -1 -1 -1 -1000 -1000 -1000 -10 0.70\n129 39 Car -1 -1 -1 639.05 169.90 675.94 202.60 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n129 68 Cyclist -1 -1 -1 16.54 153.61 240.15 367.07 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n129 85 Pedestrian -1 -1 -1 358.28 165.00 382.30 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0.70\n138 62 Pedestrian -1 -1 -1 401.15 170.35 428.81 241.80 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n138 86 Cyclist -1 -1 -1 371.46 172.21 419.92 262.35 -1 -1 -1 -1000 -1000 -1000 -10 0.56\n138 88 Pedestrian -1 -1 -1 369.80 174.22 395.07 240.55 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n139 54 Car -1 -1 -1 1136.25 185.77 1221.37 256.68 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n139 39 Car -1 -1 -1 627.34 172.13 665.00 206.55 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n139 86 Cyclist -1 -1 -1 351.38 171.22 410.46 265.84 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n139 62 Pedestrian -1 -1 -1 394.16 170.80 422.95 240.00 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n139 73 Pedestrian -1 -1 -1 293.72 166.61 324.44 247.49 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n139 85 Pedestrian -1 -1 -1 318.06 168.67 345.58 244.94 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n140 54 Car -1 -1 -1 1141.98 185.54 1221.15 256.98 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n140 39 Car -1 -1 -1 625.74 172.21 663.43 206.67 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n140 62 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210.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n148 62 Pedestrian -1 -1 -1 352.11 171.94 388.43 257.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n148 86 Cyclist -1 -1 -1 74.64 169.26 243.49 365.26 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n148 89 Pedestrian -1 -1 -1 315.38 172.51 348.82 257.52 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n148 85 Pedestrian -1 -1 -1 270.66 163.95 308.20 263.35 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n148 73 Pedestrian -1 -1 -1 238.57 169.43 271.84 267.62 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n149 86 Cyclist -1 -1 -1 11.02 165.09 214.35 369.54 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n149 89 Pedestrian -1 -1 -1 310.31 173.64 344.33 262.69 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n149 62 Pedestrian -1 -1 -1 348.40 171.40 385.21 258.63 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n149 39 Car -1 -1 -1 624.41 174.22 664.85 211.96 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n149 85 Pedestrian -1 -1 -1 267.05 164.97 304.65 265.05 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n149 73 Pedestrian -1 -1 -1 234.22 171.05 267.17 270.35 -1 -1 -1 -1000 -1000 -1000 -10 0.71\n150 62 Pedestrian -1 -1 -1 342.74 172.62 382.99 264.08 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n150 39 Car -1 -1 -1 626.32 175.79 667.33 213.43 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n150 85 Pedestrian -1 -1 -1 261.28 165.79 301.00 270.36 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n150 86 Cyclist -1 -1 -1 -12.91 164.06 177.00 370.74 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n150 89 Pedestrian -1 -1 -1 302.68 174.60 339.00 267.37 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n150 73 Pedestrian -1 -1 -1 226.99 173.09 258.75 275.05 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n150 91 Car -1 -1 -1 616.15 179.34 644.72 203.18 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n151 39 Car -1 -1 -1 628.69 176.02 670.02 214.05 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n151 62 Pedestrian -1 -1 -1 341.28 173.93 381.48 268.83 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n151 85 Pedestrian -1 -1 -1 253.72 166.76 294.76 275.65 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n151 89 Pedestrian -1 -1 -1 298.29 175.21 334.36 269.85 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n151 73 Pedestrian -1 -1 -1 217.29 172.35 252.85 280.24 -1 -1 -1 -1000 -1000 -1000 -10 0.75\n151 86 Cyclist -1 -1 -1 -3.14 161.36 98.90 366.17 -1 -1 -1 -1000 -1000 -1000 -10 0.41\n152 39 Car -1 -1 -1 631.23 175.69 672.47 214.18 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n152 62 Pedestrian -1 -1 -1 334.14 176.19 376.13 271.84 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n152 85 Pedestrian -1 -1 -1 249.53 167.58 290.50 277.43 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n152 89 Pedestrian -1 -1 -1 293.41 176.55 329.36 273.59 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n152 73 Pedestrian -1 -1 -1 210.03 173.60 245.41 283.65 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n153 39 Car -1 -1 -1 632.01 175.19 674.65 214.19 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n153 62 Pedestrian -1 -1 -1 327.97 175.54 373.82 274.83 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n153 85 Pedestrian -1 -1 -1 240.64 167.59 283.83 282.25 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n153 73 Pedestrian -1 -1 -1 197.85 172.43 235.10 287.43 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n153 89 Pedestrian -1 -1 -1 285.31 177.65 323.63 278.05 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n154 89 Pedestrian -1 -1 -1 277.71 177.04 315.60 281.17 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n154 85 Pedestrian -1 -1 -1 233.15 165.94 276.41 285.12 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n154 62 Pedestrian -1 -1 -1 323.31 174.70 369.04 278.21 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n154 39 Car -1 -1 -1 633.09 175.99 676.19 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n154 73 Pedestrian -1 -1 -1 189.86 170.12 226.42 290.66 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n155 39 Car -1 -1 -1 634.34 175.40 678.34 215.25 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n155 85 Pedestrian -1 -1 -1 220.78 163.90 265.48 287.61 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n155 89 Pedestrian -1 -1 -1 269.12 175.32 308.03 283.04 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n155 62 Pedestrian -1 -1 -1 311.75 173.16 358.84 283.03 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n155 73 Pedestrian -1 -1 -1 171.82 168.04 215.12 292.60 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n155 92 Pedestrian -1 -1 -1 1151.76 186.40 1174.06 238.72 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n156 39 Car -1 -1 -1 635.71 175.70 680.45 215.51 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n156 85 Pedestrian -1 -1 -1 208.11 162.94 254.78 293.66 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n156 62 Pedestrian -1 -1 -1 304.20 172.29 350.61 284.04 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n156 89 Pedestrian -1 -1 -1 257.36 173.50 299.23 285.06 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n156 73 Pedestrian -1 -1 -1 159.34 169.47 203.82 297.88 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n156 92 Pedestrian -1 -1 -1 1156.30 185.75 1184.77 240.95 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n157 62 Pedestrian -1 -1 -1 292.73 170.79 339.57 288.48 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n157 39 Car -1 -1 -1 636.43 174.45 682.36 215.90 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n157 85 Pedestrian -1 -1 -1 190.54 163.14 235.28 296.35 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n157 89 Pedestrian -1 -1 -1 243.97 171.08 289.65 288.97 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n157 73 Pedestrian -1 -1 -1 145.27 169.32 187.49 302.95 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n157 92 Pedestrian -1 -1 -1 1156.94 186.96 1192.79 239.98 -1 -1 -1 -1000 -1000 -1000 -10 0.64\n158 39 Car -1 -1 -1 637.79 172.83 683.13 214.73 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n158 85 Pedestrian -1 -1 -1 172.65 162.56 220.74 302.78 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n158 62 Pedestrian -1 -1 -1 280.77 168.46 328.91 295.70 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n158 89 Pedestrian -1 -1 -1 231.71 170.13 278.36 295.70 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n158 73 Pedestrian -1 -1 -1 126.36 174.07 168.57 306.83 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n158 92 Pedestrian -1 -1 -1 1165.30 183.15 1199.23 238.33 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n158 93 Car -1 -1 -1 626.99 178.10 656.67 202.99 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n159 39 Car -1 -1 -1 638.31 172.34 685.36 214.64 -1 -1 -1 -1000 -1000 -1000 -10 0.89\n159 85 Pedestrian -1 -1 -1 150.69 161.38 205.89 312.08 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n159 62 Pedestrian -1 -1 -1 267.53 168.06 319.56 304.64 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n159 89 Pedestrian -1 -1 -1 214.83 170.37 264.47 303.52 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n159 92 Pedestrian -1 -1 -1 1173.39 179.64 1205.40 238.89 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n159 73 Pedestrian -1 -1 -1 107.65 175.29 149.08 314.00 -1 -1 -1 -1000 -1000 -1000 -10 0.58\n159 93 Car -1 -1 -1 627.43 178.09 656.97 202.48 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n160 62 Pedestrian -1 -1 -1 249.53 169.36 306.84 312.53 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n160 39 Car -1 -1 -1 638.52 172.71 686.15 215.00 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n160 85 Pedestrian -1 -1 -1 129.82 163.63 187.37 318.02 -1 -1 -1 -1000 -1000 -1000 -10 0.82\n160 89 Pedestrian -1 -1 -1 198.73 172.38 248.95 310.31 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n160 73 Pedestrian -1 -1 -1 77.40 177.59 125.92 319.30 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n161 62 Pedestrian -1 -1 -1 237.09 171.49 295.51 318.52 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n161 39 Car -1 -1 -1 640.39 173.99 687.69 217.19 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n161 85 Pedestrian -1 -1 -1 104.58 164.48 167.23 324.53 -1 -1 -1 -1000 -1000 -1000 -10 0.80\n161 89 Pedestrian -1 -1 -1 177.96 173.56 231.76 323.91 -1 -1 -1 -1000 -1000 -1000 -10 0.77\n161 73 Pedestrian -1 -1 -1 52.23 174.17 104.49 330.37 -1 -1 -1 -1000 -1000 -1000 -10 0.57\n162 39 Car -1 -1 -1 640.59 175.79 688.90 219.41 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n162 62 Pedestrian -1 -1 -1 219.97 171.99 281.91 324.69 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n162 89 Pedestrian -1 -1 -1 153.49 168.65 210.62 335.67 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n162 85 Pedestrian -1 -1 -1 70.88 160.81 139.90 333.81 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n162 73 Pedestrian -1 -1 -1 15.52 170.48 80.19 341.09 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n162 94 Car -1 -1 -1 629.36 181.46 659.37 206.41 -1 -1 -1 -1000 -1000 -1000 -10 0.40\n163 39 Car -1 -1 -1 641.34 175.38 690.28 219.62 -1 -1 -1 -1000 -1000 -1000 -10 0.91\n163 62 Pedestrian -1 -1 -1 196.80 168.41 266.35 333.93 -1 -1 -1 -1000 -1000 -1000 -10 0.84\n163 89 Pedestrian -1 -1 -1 123.61 164.35 187.40 346.09 -1 -1 -1 -1000 -1000 -1000 -10 0.83\n163 85 Pedestrian -1 -1 -1 41.18 155.45 116.04 341.34 -1 -1 -1 -1000 -1000 -1000 -10 0.76\n163 73 Pedestrian -1 -1 -1 1.45 169.90 48.82 349.10 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n163 94 Car -1 -1 -1 629.46 180.58 660.22 206.27 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n164 39 Car -1 -1 -1 642.31 174.44 691.86 220.32 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n164 89 Pedestrian -1 -1 -1 93.20 164.54 163.22 354.62 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n164 62 Pedestrian -1 -1 -1 163.75 166.43 246.52 344.27 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n164 85 Pedestrian -1 -1 -1 7.00 149.97 89.03 354.04 -1 -1 -1 -1000 -1000 -1000 -10 0.69\n164 94 Car -1 -1 -1 629.11 180.48 660.08 206.24 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n164 95 Pedestrian -1 -1 -1 416.11 169.64 430.99 210.80 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n165 89 Pedestrian -1 -1 -1 53.93 166.50 126.52 366.81 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n165 39 Car -1 -1 -1 642.74 172.90 693.26 218.99 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n165 62 Pedestrian -1 -1 -1 133.70 166.99 222.48 358.78 -1 -1 -1 -1000 -1000 -1000 -10 0.81\n165 85 Pedestrian -1 -1 -1 -1.65 152.90 52.04 358.72 -1 -1 -1 -1000 -1000 -1000 -10 0.54\n165 95 Pedestrian -1 -1 -1 413.44 169.48 428.25 211.97 -1 -1 -1 -1000 -1000 -1000 -10 0.52\n166 39 Car -1 -1 -1 643.10 171.59 695.08 219.03 -1 -1 -1 -1000 -1000 -1000 -10 0.93\n166 89 Pedestrian -1 -1 -1 8.60 168.28 87.64 366.00 -1 -1 -1 -1000 -1000 -1000 -10 0.87\n166 62 Pedestrian -1 -1 -1 97.49 161.22 189.61 366.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n166 95 Pedestrian -1 -1 -1 407.65 167.85 425.79 213.99 -1 -1 -1 -1000 -1000 -1000 -10 0.44\n166 96 Car -1 -1 -1 629.85 178.24 660.15 203.47 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n167 39 Car -1 -1 -1 642.99 169.82 696.27 217.88 -1 -1 -1 -1000 -1000 -1000 -10 0.94\n167 62 Pedestrian -1 -1 -1 49.49 155.94 153.54 364.58 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n167 95 Pedestrian -1 -1 -1 403.54 166.80 422.70 214.53 -1 -1 -1 -1000 -1000 -1000 -10 0.62\n167 96 Car -1 -1 -1 629.37 176.30 660.60 202.42 -1 -1 -1 -1000 -1000 -1000 -10 0.46\n168 39 Car -1 -1 -1 642.84 169.11 697.13 217.55 -1 -1 -1 -1000 -1000 -1000 -10 0.88\n168 62 Pedestrian -1 -1 -1 8.47 155.24 117.88 364.74 -1 -1 -1 -1000 -1000 -1000 -10 0.85\n168 95 Pedestrian -1 -1 -1 401.63 165.58 419.86 213.88 -1 -1 -1 -1000 -1000 -1000 -10 0.66\n168 96 Car -1 -1 -1 629.03 175.22 659.99 200.41 -1 -1 -1 -1000 -1000 -1000 -10 0.45\n169 39 Car -1 -1 -1 643.55 168.38 698.57 218.04 -1 -1 -1 -1000 -1000 -1000 -10 0.86\n169 62 Pedestrian -1 -1 -1 -1.87 162.48 74.85 363.89 -1 -1 -1 -1000 -1000 -1000 -10 0.68\n169 95 Pedestrian -1 -1 -1 397.81 164.70 417.52 213.98 -1 -1 -1 -1000 -1000 -1000 -10 0.61\n169 96 Car -1 -1 -1 629.30 174.62 660.54 200.08 -1 -1 -1 -1000 -1000 -1000 -10 0.51\n170 39 Car -1 -1 -1 643.94 167.80 700.61 218.72 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n170 96 Car -1 -1 -1 638.75 174.18 681.63 208.07 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n170 95 Pedestrian -1 -1 -1 394.33 162.11 414.44 214.76 -1 -1 -1 -1000 -1000 -1000 -10 0.43\n170 97 Car -1 -1 -1 629.44 174.64 661.27 200.27 -1 -1 -1 -1000 -1000 -1000 -10 0.48\n171 39 Car -1 -1 -1 644.40 168.74 702.19 220.55 -1 -1 -1 -1000 -1000 -1000 -10 0.95\n171 96 Car -1 -1 -1 639.29 175.60 681.63 211.08 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n171 95 Pedestrian -1 -1 -1 389.35 162.77 411.18 219.50 -1 -1 -1 -1000 -1000 -1000 -10 0.49\n171 97 Car -1 -1 -1 630.03 176.35 661.82 202.19 -1 -1 -1 -1000 -1000 -1000 -10 0.42\n171 98 Cyclist -1 -1 -1 389.35 162.77 411.18 219.50 -1 -1 -1 -1000 -1000 -1000 -10 0.53\n172 39 Car -1 -1 -1 645.57 170.74 704.47 223.92 -1 -1 -1 -1000 -1000 -1000 -10 0.92\n172 95 Pedestrian -1 -1 -1 384.61 164.90 406.87 223.80 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n172 96 Car -1 -1 -1 638.77 177.28 682.11 212.40 -1 -1 -1 -1000 -1000 -1000 -10 0.72\n172 97 Car -1 -1 -1 629.55 177.75 661.61 203.67 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n173 39 Car -1 -1 -1 646.43 171.84 706.94 225.71 -1 -1 -1 -1000 -1000 -1000 -10 0.96\n173 96 Car -1 -1 -1 639.46 178.40 682.76 213.02 -1 -1 -1 -1000 -1000 -1000 -10 0.74\n173 95 Pedestrian -1 -1 -1 378.45 167.32 400.72 226.99 -1 -1 -1 -1000 -1000 -1000 -10 0.73\n173 97 Car -1 -1 -1 630.10 178.53 662.64 204.63 -1 -1 -1 -1000 -1000 -1000 -10 0.47\n174 39 Car -1 -1 -1 647.48 171.65 710.04 227.59 -1 -1 -1 -1000 -1000 -1000 -10 0.90\n174 95 Pedestrian -1 -1 -1 372.89 167.74 395.35 229.91 -1 -1 -1 -1000 -1000 -1000 -10 0.79\n174 96 Car -1 -1 -1 640.27 179.68 682.91 214.69 -1 -1 -1 -1000 -1000 -1000 -10 0.65\n"
  },
  {
    "path": "fast_reid/CHANGELOG.md",
    "content": "# Changelog\n\n### v1.3\n\n#### New Features\n- Vision Transformer backbone, see config in `configs/Market1501/bagtricks_vit.yml`\n- Self-Distillation with EMA update\n- Gradient Clip\n\n#### Improvements\n- Faster dataloader with pre-fetch thread and cuda stream\n- Optimize DDP training speed by removing `find_unused_parameters` in DDP\n\n\n### v1.2 (06/04/2021)\n\n#### New Features\n\n- Multiple machine training support\n- [RepVGG](https://github.com/DingXiaoH/RepVGG) backbone \n- [Partial FC](projects/FastFace)\n\n#### Improvements\n\n- Torch2trt pipeline \n- Decouple linear transforms and softmax\n- config decorator\n\n### v1.1 (29/01/2021)\n\n#### New Features\n\n- NAIC20(reid track) [1-st solution](projects/NAIC20) \n- Multi-teacher Knowledge Distillation\n- TRT network definition APIs in [FastRT](projects/FastRT)\n\n#### Bug Fixes\n\n#### Improvements"
  },
  {
    "path": "fast_reid/GETTING_STARTED.md",
    "content": "# Getting Started with Fastreid\n\n## Prepare pretrained model\n\nIf you use backbones supported by fastreid, you do not need to do anything. It will automatically download the pre-train models.\nBut if your network is not connected, you can download pre-train models manually and put it in `~/.cache/torch/checkpoints`.\n\nIf you want to use other pre-train models, such as MoCo pre-train, you can download by yourself and set the pre-train model path in `configs/Base-bagtricks.yml`.\n\n## Compile with cython to accelerate evalution\n\n```bash\ncd fastreid/evaluation/rank_cylib; make all\n```\n\n## Training & Evaluation in Command Line\n\nWe provide a script in \"tools/train_net.py\", that is made to train all the configs provided in fastreid.\nYou may want to use it as a reference to write your own training script.\n\nTo train a model with \"train_net.py\", first setup up the corresponding datasets following [datasets/README.md](https://github.com/JDAI-CV/fast-reid/tree/master/datasets), then run:\n\n```bash\npython3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml MODEL.DEVICE \"cuda:0\"\n```\n\nThe configs are made for 1-GPU training.\n\nIf you want to train model with 4 GPUs, you can run:\n\n```bash\npython3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml --num-gpus 4\n```\n\nIf you want to train model with multiple machines, you can run:\n\n```\n# machine 1\nexport GLOO_SOCKET_IFNAME=eth0\nexport NCCL_SOCKET_IFNAME=eth0\n\npython3 tools/train_net.py --config-file configs/Market1501/bagtricks_R50.yml \\\n--num-gpus 4 --num-machines 2 --machine-rank 0 --dist-url tcp://ip:port \n\n# machine 2\nexport GLOO_SOCKET_IFNAME=eth0\nexport NCCL_SOCKET_IFNAME=eth0\n\npython3 tools/train_net.py --config-file configs/Market1501/bagtricks_R50.yml \\\n--num-gpus 4 --num-machines 2 --machine-rank 1 --dist-url tcp://ip:port \n```\n\nMake sure the dataset path and code are the same in different machines, and machines can communicate with each other. \n\nTo evaluate a model's performance, use\n\n```bash\npython3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml --eval-only \\\nMODEL.WEIGHTS /path/to/checkpoint_file MODEL.DEVICE \"cuda:0\"\n```\n\nFor more options, see `python3 tools/train_net.py -h`.\n"
  },
  {
    "path": "fast_reid/INSTALL.md",
    "content": "# Installation\n\n## Requirements\n\n- Linux or macOS with python ≥ 3.6\n- PyTorch ≥ 1.6\n- torchvision that matches the Pytorch installation. You can install them together at [pytorch.org](https://pytorch.org/) to make sure of this.\n- [yacs](https://github.com/rbgirshick/yacs)\n- Cython (optional to compile evaluation code)\n- tensorboard (needed for visualization): `pip install tensorboard`\n- gdown (for automatically downloading pre-train model)\n- sklearn\n- termcolor\n- tabulate\n- [faiss](https://github.com/facebookresearch/faiss) `pip install faiss-cpu`\n\n\n\n# Set up with Conda\n```shell script\nconda create -n fastreid python=3.7\nconda activate fastreid\nconda install pytorch==1.6.0 torchvision tensorboard -c pytorch\npip install -r docs/requirements.txt\n```\n\n# Set up with Dockder\ncomming soon\n"
  },
  {
    "path": "fast_reid/LICENSE",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. 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  },
  {
    "path": "fast_reid/MODEL_ZOO.md",
    "content": "# FastReID Model Zoo and Baselines\n\n## Introduction\n\nThis file documents collection of baselines trained with fastreid. All numbers were obtained with 1 NVIDIA V100 GPU.\nThe software in use were PyTorch 1.6, CUDA 10.1.\n\nIn addition to these official baseline models, you can find more models in [projects/](https://github.com/JDAI-CV/fast-reid/tree/master/projects).\n\n### How to Read the Tables\n\n- The \"Name\" column contains a link to the config file.\nRunning `tools/train_net.py` with this config file and 1 GPU will reproduce the model.\n\n### Common Settings for all Person reid models\n\n**BoT**:\n\n[Bag of Tricks and A Strong Baseline for Deep Person Re-identification](http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Luo_Bag_of_Tricks_and_a_Strong_Baseline_for_Deep_Person_CVPRW_2019_paper.pdf). CVPRW2019, Oral.\n\n**AGW**:\n\n[ReID-Survey with a Powerful AGW Baseline](https://github.com/mangye16/ReID-Survey).\n\n**MGN**:\n\n[Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://arxiv.org/abs/1804.01438v1)\n\n**SBS**:\n\nstronger baseline on top of BoT:\n\nBag of Freebies(BoF):\n\n1. Circle loss\n2. Freeze backbone training\n3. Cutout data augmentation & Auto Augmentation\n4. Cosine annealing learning rate decay\n5. Soft margin triplet loss\n\nBag of Specials(BoS):\n\n1. Non-local block\n2. GeM pooling\n\n### Market1501 Baselines\n\n**BoT**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---: |\n| [BoT(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_R50.yml) | ImageNet | 94.4% | 86.1% | 59.4% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_R50.pth) |\n| [BoT(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_R50-ibn.yml) | ImageNet | 94.9% | 87.6% | 64.1% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_R50-ibn.pth) |\n| [BoT(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_S50.yml) | ImageNet | 95.2% | 88.7% | 66.9% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_S50.pth) |\n| [BoT(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_R101-ibn.yml) | ImageNet| 95.4% | 88.9% | 67.4% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_R101-ibn.pth) |\n\n**AGW**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: |:---: |\n| [AGW(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R50.yml) | ImageNet | 95.3% | 88.2% | 66.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R50.pth) |\n| [AGW(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R50-ibn.yml) | ImageNet | 95.1% | 88.7% | 67.1% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R50-ibn.pth) |\n| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_S50.yml) | ImageNet | 95.3% | 89.3% | 68.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_S50.pth) |\n| [AGW(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R101-ibn.yml) | ImageNet | 95.5% | 89.5% | 69.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R101-ibn.pth) |\n\n**SBS**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: |:---:|\n| [SBS(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_R50.yml) | ImageNet | 95.4% | 88.2% | 64.8% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_R50.pth) |\n| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_R50-ibn.yml) | ImageNet | 95.7% | 89.3% | 67.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_R50-ibn.pth) |\n| [SBS(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_S50.yml) | ImageNet | 95.8% | 89.4% | 67.6% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_S50.pth) |\n| [SBS(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_R101-ibn.yml) | ImageNet | 96.3% | 90.3% | 70.0% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_R101-ibn.pth) |\n\n**MGN**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---:|\n| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/mgn_R50-ibn.yml) | ImageNet | 95.8% | 89.8% | 67.7% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_mgn_R50-ibn.pth) |\n\n### DukeMTMC Baseline\n\n**BoT**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---: |\n| [BoT(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R50.yml) | ImageNet | 87.2% | 77.0% | 42.1% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_R50.pth) |\n| [BoT(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R50-ibn.yml) | ImageNet | 89.3% | 79.6% | 45.2% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_R50-ibn.pth) |\n| [BoT(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_S50.yml) | ImageNet | 90.0% | 80.13% | 45.8% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_S50.pth) |\n| [BoT(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R101-ibn.yml) | ImageNet| 91.2% | 81.2% | 47.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_R101-ibn.pth) |\n\n**AGW**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---:|\n| [AGW(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R50.yml) | ImageNet | 89.0% | 79.9% | 46.1% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_R50.pth) |\n| [AGW(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R50-ibn.yml) | ImageNet | 90.5% | 80.8% | 47.6% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_R50-ibn.pth) |\n| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_S50.yml) | ImageNet | 90.9% | 82.4% | 49.2% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_S50.pth) |\n| [AGW(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R101-ibn.yml) | ImageNet | 91.7% | 82.3% | 50.0% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_R101-ibn.pth) |\n\n**SBS**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---:|\n| [SBS(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R50.yml) | ImageNet | 90.3% | 80.3% | 46.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_R50.pth) |\n| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R50-ibn.yml) | ImageNet | 90.8% | 81.2% | 47.0% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_R50-ibn.pth) |\n| [SBS(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_S50.yml) | ImageNet | 91.0% | 81.4% | 47.6% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_S50.pth) |\n| [SBS(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R101-ibn.yml) | ImageNet | 91.9% | 83.6% | 51.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_R101-ibn.pth) |\n\n**MGN**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---:|\n| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/mgn_R50-ibn.yml) | ImageNet | 91.1% | 82.0% | 46.8% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_mgn_R50-ibn.pth) |\n\n### MSMT17 Baseline\n\n**BoT**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---:|\n| [BoT(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R50.yml) | ImageNet | 74.1%  | 50.2% | 10.4% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_R50.pth) |\n| [BoT(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R50-ibn.yml) | ImageNet | 77.0% | 54.4% | 12.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_R50-ibn.pth) |\n| [BoT(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_S50.yml) | ImageNet | 80.8% | 59.9% | 16.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_S50.pth) |\n| [BoT(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R101-ibn.yml) | ImageNet| 81.0% | 59.4% | 15.6% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_R101-ibn.pth) |\n\n**AGW**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---:|\n| [AGW(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R50.yml) | ImageNet | 78.3% | 55.6% | 12.9% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_R50.pth) |\n| [AGW(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R50-ibn.yml) | ImageNet | 81.2% | 59.7% | 15.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_R50-ibn.pth) |\n| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_S50.yml) | ImageNet | 82.6% | 62.6% | 17.7% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_S50.pth) |\n| [AGW(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R101-ibn.yml) | ImageNet | 82.0% | 61.4% | 17.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_R101-ibn.pth) |\n\n**SBS**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---:|\n| [SBS(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R50.yml) | ImageNet | 81.8% | 58.4% | 13.9% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_R50.pth) |\n| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R50-ibn.yml) | ImageNet | 83.9% | 60.6% | 15.2% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_R50-ibn.pth) |\n| [SBS(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_S50.yml) | ImageNet | 84.1% | 61.7% | 15.2% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_S50.pth) |\n| [SBS(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R101-ibn.yml) | ImageNet | 84.8% | 62.8% | 16.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_R101-ibn.pth) |\n\n**MGN**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---:|\n| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/mgn_R50-ibn.yml) | ImageNet | 85.1% | 65.4% | 18.4% | - |\n\n### VeRi Baseline\n\n**SBS**:\n\n| Method | Pretrained | Rank@1 | mAP | mINP | download |\n| :---: | :---: | :---: |:---: | :---: | :---:| \n| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/VeRi/sbs_R50-ibn.yml) | ImageNet | 97.0%  | 81.9% | 46.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/veri_sbs_R50-ibn.pth) |\n\n### VehicleID Baseline\n\n**BoT**:  \nTest protocol: 10-fold cross-validation; trained on 4 NVIDIA P40 GPU.\n\n<table>\n<thead>\n  <tr>\n    <th rowspan=\"3\" align=\"center\">Method</th>\n    <th rowspan=\"3\" align=\"center\">Pretrained</th>\n    <th colspan=\"6\" align=\"center\">Testset size</th>\n    <th rowspan=\"3\" align=\"center\">download</th>\n  </tr>\n  <tr>\n    <td colspan=\"2\" align=\"center\">Small</td>\n    <td colspan=\"2\" align=\"center\">Medium</td>\n    <td colspan=\"2\" align=\"center\">Large</td>\n  </tr>\n  <tr>\n    <td align=\"center\">Rank@1</td>\n    <td align=\"center\">Rank@5</td>\n    <td align=\"center\">Rank@1</td>\n    <td align=\"center\">Rank@5</td>\n    <td align=\"center\">Rank@1</td>\n    <td align=\"center\">Rank@5</td>\n  </tr>\n</thead>\n<tbody>\n  <tr>\n    <td nowrap align=\"center\"><a href=\"https://github.com/JDAI-CV/fast-reid/blob/master/configs/VehicleID/bagtricks_R50-ibn.yml\">BoT(R50-ibn)</a></td>\n    <td align=\"center\">ImageNet</td>\n    <td align=\"center\">86.6%</td>\n    <td align=\"center\">97.9%</td>\n    <td align=\"center\">82.9%</td>\n    <td align=\"center\">96.0%</td>\n    <td align=\"center\">80.6%</td>\n    <td align=\"center\">93.9%</td>\n    <td align=\"center\"><a href=\"https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/vehicleid_bot_R50-ibn.pth\">model</a></td>\n  </tr>\n</tbody>\n</table>\n\n### VERI-Wild Baseline\n\n**BoT**:  \nTest protocol: Trained on 4 NVIDIA P40 GPU.\n\n<table>\n<thead>\n  <tr>\n    <th rowspan=\"3\" align=\"center\"> Method</th>\n    <th rowspan=\"3\" align=\"center\">Pretrained</th>\n    <th colspan=\"9\" align=\"center\">Testset size</th>\n    <th rowspan=\"3\" align=\"center\">download</th>\n  </tr>\n  <tr>\n    <td colspan=\"3\" align=\"center\">Small</td>\n    <td colspan=\"3\" align=\"center\">Medium</td>\n    <td colspan=\"3\" align=\"center\">Large</td>\n  </tr>\n  <tr>\n    <td align=\"center\">Rank@1</td>\n    <td align=\"center\">mAP</td>\n    <td align=\"center\">mINP</td>\n    <td align=\"center\">Rank@1</td>\n    <td align=\"center\">mAP</td>\n    <td align=\"center\">mINP</td>\n    <td align=\"center\">Rank@1</td>\n    <td align=\"center\">mAP</td>\n    <td align=\"center\">mINP</td>\n  </tr>\n</thead>\n<tbody>\n  <tr>\n    <td nowrap align=\"center\"><a href=\"https://github.com/JDAI-CV/fast-reid/blob/master/configs/VERIWild/bagtricks_R50-ibn.yml\">BoT(R50-ibn)</a></td>\n    <td align=\"center\">ImageNet</td>\n    <td align=\"center\">96.4%</td>\n    <td align=\"center\">87.7%</td>\n    <td align=\"center\">69.2%</td>\n    <td align=\"center\">95.1%</td>\n    <td align=\"center\">83.5%</td>\n    <td align=\"center\">61.2%</td>\n    <td align=\"center\">92.5%</td>\n    <td align=\"center\">77.3%</td>\n    <td align=\"center\">49.8%</td>\n    <td align=\"center\"><a href=\"https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/veriwild_bot_R50-ibn.pth\">model</a></td>\n  </tr>\n</tbody>\n</table>\n"
  },
  {
    "path": "fast_reid/README.md",
    "content": "<img src=\".github/FastReID-Logo.png\" width=\"300\" >\n\n[![Gitter](https://badges.gitter.im/fast-reid/community.svg)](https://gitter.im/fast-reid/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)\n\nGitter: [fast-reid/community](https://gitter.im/fast-reid/community?utm_source=share-link&utm_medium=link&utm_campaign=share-link)\n\nWechat: \n\n<img src=\".github/wechat_group.png\" width=\"150\" >\n\n\nFastReID is a research platform that implements state-of-the-art re-identification algorithms. It is a ground-up rewrite of the previous version, [reid strong baseline](https://github.com/michuanhaohao/reid-strong-baseline).\n\n## What's New\n\n- [Sep 2021] [DG-ReID](https://github.com/xiaomingzhid/sskd) is updated, you can check the [paper](https://arxiv.org/pdf/2108.05045.pdf).\n- [June 2021] [Contiguous parameters](https://github.com/PhilJd/contiguous_pytorch_params) is supported, now it can\n  accelerate ~20%.\n- [May 2021] Vision Transformer backbone supported, see `configs/Market1501/bagtricks_vit.yml`.\n- [Apr 2021] Partial FC supported in [FastFace](projects/FastFace)!\n- [Jan 2021] TRT network definition APIs in [FastRT](projects/FastRT) has been released! \nThanks for [Darren](https://github.com/TCHeish)'s contribution.\n- [Jan 2021] NAIC20(reid track) [1-st solution](projects/NAIC20) based on fastreid has been released！\n- [Jan 2021] FastReID V1.0 has been released！🎉\n  Support many tasks beyond reid, such image retrieval and face recognition. See [release notes](https://github.com/JDAI-CV/fast-reid/releases/tag/v1.0.0).\n- [Oct 2020] Added the [Hyper-Parameter Optimization](projects/FastTune) based on fastreid. See `projects/FastTune`.\n- [Sep 2020] Added the [person attribute recognition](projects/FastAttr) based on fastreid. See `projects/FastAttr`.\n- [Sep 2020] Automatic Mixed Precision training is supported with `apex`. Set `cfg.SOLVER.FP16_ENABLED=True` to switch it on.\n- [Aug 2020] [Model Distillation](projects/FastDistill) is supported, thanks for [guan'an wang](https://github.com/wangguanan)'s contribution.\n- [Aug 2020] ONNX/TensorRT converter is supported.\n- [Jul 2020] Distributed training with multiple GPUs, it trains much faster.\n- Includes more features such as circle loss, abundant visualization methods and evaluation metrics, SoTA results on conventional, cross-domain, partial and vehicle re-id, testing on multi-datasets simultaneously, etc.\n- Can be used as a library to support [different projects](projects) on top of it. We'll open source more research projects in this way.\n- Remove [ignite](https://github.com/pytorch/ignite)(a high-level library) dependency and powered by [PyTorch](https://pytorch.org/).\n\nWe write a [fastreid intro](https://l1aoxingyu.github.io/blogpages/reid/fastreid/2020/05/29/fastreid.html) \nand [fastreid v1.0](https://l1aoxingyu.github.io/blogpages/reid/fastreid/2021/04/28/fastreid-v1.html) about this toolbox.\n\n## Changelog\n\nPlease refer to [changelog.md](CHANGELOG.md) for details and release history.\n\n## Installation\n\nSee [INSTALL.md](INSTALL.md).\n\n## Quick Start\n\nThe designed architecture follows this guide [PyTorch-Project-Template](https://github.com/L1aoXingyu/PyTorch-Project-Template), you can check each folder's purpose by yourself.\n\nSee [GETTING_STARTED.md](GETTING_STARTED.md).\n\nLearn more at out [documentation](https://fast-reid.readthedocs.io/). And see [projects/](projects) for some projects that are build on top of fastreid.\n\n## Model Zoo and Baselines\n\nWe provide a large set of baseline results and trained models available for download in the [Fastreid Model Zoo](MODEL_ZOO.md).\n\n## Deployment\n\nWe provide some examples and scripts to convert fastreid model to Caffe, ONNX and TensorRT format in [Fastreid deploy](tools/deploy).\n\n## License\n\nFastreid is released under the [Apache 2.0 license](LICENSE).\n\n## Citing FastReID\n\nIf you use FastReID in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.\n\n```BibTeX\n@article{he2020fastreid,\n  title={FastReID: A Pytorch Toolbox for General Instance Re-identification},\n  author={He, Lingxiao and Liao, Xingyu and Liu, Wu and Liu, Xinchen and Cheng, Peng and Mei, Tao},\n  journal={arXiv preprint arXiv:2006.02631},\n  year={2020}\n}\n```\n"
  },
  {
    "path": "fast_reid/__init__.py",
    "content": "# hgx0914\n"
  },
  {
    "path": "fast_reid/configs/Base-AGW.yml",
    "content": "_BASE_: Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    WITH_NL: True\n\n  HEADS:\n    POOL_LAYER: GeneralizedMeanPooling\n\n  LOSSES:\n    NAME: (\"CrossEntropyLoss\", \"TripletLoss\")\n    CE:\n      EPSILON: 0.1\n      SCALE: 1.0\n\n    TRI:\n      MARGIN: 0.0\n      HARD_MINING: False\n      SCALE: 1.0\n"
  },
  {
    "path": "fast_reid/configs/Base-MGN.yml",
    "content": "_BASE_: Base-SBS.yml\n\nMODEL:\n  META_ARCHITECTURE: MGN\n\n  FREEZE_LAYERS: [backbone, b1, b2, b3,]\n\n  BACKBONE:\n    WITH_NL: False\n\n  HEADS:\n    EMBEDDING_DIM: 256\n"
  },
  {
    "path": "fast_reid/configs/Base-SBS.yml",
    "content": "_BASE_: Base-bagtricks.yml\n\nMODEL:\n  FREEZE_LAYERS: [ backbone ]\n\n  BACKBONE:\n    WITH_NL: True\n\n  HEADS:\n    NECK_FEAT: after\n    POOL_LAYER: GeneralizedMeanPoolingP\n    CLS_LAYER: CircleSoftmax\n    SCALE: 64\n    MARGIN: 0.35\n\n  LOSSES:\n    NAME: (\"CrossEntropyLoss\", \"TripletLoss\",)\n    CE:\n      EPSILON: 0.1\n      SCALE: 1.0\n\n    TRI:\n      MARGIN: 0.0\n      HARD_MINING: True\n      NORM_FEAT: False\n      SCALE: 1.0\n\nINPUT:\n  SIZE_TRAIN: [ 384, 128 ]\n  SIZE_TEST: [ 384, 128 ]\n\n  AUTOAUG:\n    ENABLED: True\n    PROB: 0.1\n\nDATALOADER:\n  NUM_INSTANCE: 16\n\nSOLVER:\n  AMP:\n    ENABLED: True\n  OPT: Adam\n  MAX_EPOCH: 60\n  BASE_LR: 0.00035\n  WEIGHT_DECAY: 0.0005\n  IMS_PER_BATCH: 64\n\n  SCHED: CosineAnnealingLR\n  DELAY_EPOCHS: 30\n  ETA_MIN_LR: 0.0000007\n\n  WARMUP_FACTOR: 0.1\n  WARMUP_ITERS: 2000\n\n  FREEZE_ITERS: 1000\n\n  CHECKPOINT_PERIOD: 5    # [hgx0916] 1 --> 5\n\nTEST:\n  EVAL_PERIOD: 1000\n  IMS_PER_BATCH: 128\n\nCUDNN_BENCHMARK: False # True\n"
  },
  {
    "path": "fast_reid/configs/Base-bagtricks.yml",
    "content": "MODEL:\n  META_ARCHITECTURE: Baseline\n\n  BACKBONE:\n    NAME: build_resnet_backbone\n    NORM: BN\n    DEPTH: 50x\n    LAST_STRIDE: 1\n    FEAT_DIM: 2048\n    WITH_IBN: False\n    PRETRAIN: True\n\n  HEADS:\n    NAME: EmbeddingHead\n    NORM: BN\n    WITH_BNNECK: True\n    POOL_LAYER: GlobalAvgPool\n    NECK_FEAT: before\n    CLS_LAYER: Linear\n\n  LOSSES:\n    NAME: (\"CrossEntropyLoss\", \"TripletLoss\",)\n\n    CE:\n      EPSILON: 0.1\n      SCALE: 1.\n\n    TRI:\n      MARGIN: 0.3\n      HARD_MINING: True\n      NORM_FEAT: False\n      SCALE: 1.\n\nINPUT:\n  SIZE_TRAIN: [ 256, 128 ]\n  SIZE_TEST: [ 256, 128 ]\n\n  REA:\n    ENABLED: True\n    PROB: 0.5\n\n  FLIP:\n    ENABLED: True\n\n  PADDING:\n    ENABLED: True\n\nDATALOADER:\n  SAMPLER_TRAIN: NaiveIdentitySampler\n  NUM_INSTANCE: 4\n  NUM_WORKERS: 8\n\nSOLVER:\n  AMP:\n    ENABLED: True\n  OPT: Adam\n  MAX_EPOCH: 120\n  BASE_LR: 0.00035\n  WEIGHT_DECAY: 0.0005\n  WEIGHT_DECAY_NORM: 0.0005\n  IMS_PER_BATCH: 64\n\n  SCHED: MultiStepLR\n  STEPS: [ 40, 90 ]\n  GAMMA: 0.1\n\n  WARMUP_FACTOR: 0.1\n  WARMUP_ITERS: 2000\n\n  CHECKPOINT_PERIOD: 30\n\nTEST:\n  EVAL_PERIOD: 30\n  IMS_PER_BATCH: 128\n\nCUDNN_BENCHMARK: True\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/AGW_R101-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/agw_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/AGW_R50-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/agw_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/AGW_R50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/agw_R50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/AGW_S50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/agw_S50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/bagtricks_R101-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/bagtricks_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/bagtricks_R50-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/bagtricks_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/bagtricks_R50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/bagtricks_R50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/bagtricks_S50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/bagtricks_S50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/mgn_R50-ibn.yml",
    "content": "_BASE_: ../Base-MGN.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/mgn_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/sbs_R101-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/sbs_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/sbs_R50-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/sbs_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/sbs_R50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/sbs_R50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU/sbs_S50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n#    NORM: syncBN\n    NAME: build_resnest_backbone\n#  HEADS:\n#    NORM: syncBN\n\nDATASETS:\n  NAMES: (\"CUHKSYSU\",)\n  TESTS: (\"CUHKSYSU\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU/sbs_S50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/AGW_R101-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/agw_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/AGW_R50-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/agw_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/AGW_R50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/agw_R50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/AGW_S50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/agw_S50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/bagtricks_R101-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/bagtricks_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/bagtricks_R50-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/bagtricks_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/bagtricks_R50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/bagtricks_R50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/bagtricks_S50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/bagtricks_S50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/mgn_R50-ibn.yml",
    "content": "_BASE_: ../Base-MGN.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/mgn_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/mgn_R50-ibn_64d.yml",
    "content": "_BASE_: ../Base-MGN.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n  HEADS:\n    EMBEDDING_DIM: 64     # 256 to 64\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/mgn_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/sbs_R101-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/sbs_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/sbs_R50-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/sbs_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/sbs_R50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/sbs_R50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_DanceTrack/sbs_S50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n#    NORM: syncBN\n    NAME: build_resnest_backbone\n#  HEADS:\n#    NORM: syncBN\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_DanceTrack\",)\n  TESTS: (\"CUHKSYSU_DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/CUHKSYSU_DanceTrack/sbs_S50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_MOT17/sbs_S50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_MOT17\",)\n  TESTS: (\"CUHKSYSU_MOT17\",)\n\nOUTPUT_DIR: logs/CUHKSYSU_MOT17/sbs_S50\n"
  },
  {
    "path": "fast_reid/configs/CUHKSYSU_MOT20/sbs_S50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"CUHKSYSU_MOT20\",)\n  TESTS: (\"CUHKSYSU_MOT20\",)\n\nOUTPUT_DIR: logs/CUHKSYSU_MOT20/sbs_S50\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/AGW_R101-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/agw_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/AGW_R50-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/agw_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/AGW_R50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/agw_R50\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/AGW_S50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/agw_S50\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/bagtricks_R101-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/bagtricks_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/bagtricks_R50-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/bagtricks_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/bagtricks_R50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/bagtricks_R50\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/bagtricks_S50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/bagtricks_S50\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/mgn_R50-ibn.yml",
    "content": "_BASE_: ../Base-MGN.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/mgn_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/sbs_R101-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/sbs_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/sbs_R50-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/sbs_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/sbs_R50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/sbs_R50\n"
  },
  {
    "path": "fast_reid/configs/DanceTrack/sbs_S50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"DanceTrack\",)\n  TESTS: (\"DanceTrack\",)\n\nOUTPUT_DIR: fast_reid/logs/dancetrack/sbs_S50\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/AGW_R101-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/agw_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/AGW_R50-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/agw_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/AGW_R50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/agw_R50\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/AGW_S50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/agw_S50\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/bagtricks_R101-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/bagtricks_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/bagtricks_R50-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/bagtricks_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/bagtricks_R50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/bagtricks_R50\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/bagtricks_S50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/bagtricks_S50\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/mgn_R50-ibn.yml",
    "content": "_BASE_: ../Base-MGN.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/mgn_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/sbs_R101-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/sbs_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/sbs_R50-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/sbs_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/sbs_R50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/sbs_R50\n"
  },
  {
    "path": "fast_reid/configs/DukeMTMC/sbs_S50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: logs/dukemtmc/sbs_S50\n"
  },
  {
    "path": "fast_reid/configs/MOT17/AGW_R101-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/agw_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT17/AGW_R50-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/agw_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT17/AGW_R50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/agw_R50\n"
  },
  {
    "path": "fast_reid/configs/MOT17/AGW_S50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/agw_S50\n"
  },
  {
    "path": "fast_reid/configs/MOT17/bagtricks_R101-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/bagtricks_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT17/bagtricks_R50-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/bagtricks_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT17/bagtricks_R50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/bagtricks_R50\n"
  },
  {
    "path": "fast_reid/configs/MOT17/bagtricks_S50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/bagtricks_S50\n"
  },
  {
    "path": "fast_reid/configs/MOT17/mgn_R50-ibn.yml",
    "content": "_BASE_: ../Base-MGN.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/mgn_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT17/sbs_R101-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/sbs_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT17/sbs_R50-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/sbs_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT17/sbs_R50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/mot17/sbs_R50\n"
  },
  {
    "path": "fast_reid/configs/MOT17/sbs_S50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"MOT17\",)\n  TESTS: (\"MOT17\",)\n\nOUTPUT_DIR: logs/MOT17/sbs_S50\n"
  },
  {
    "path": "fast_reid/configs/MOT20/AGW_R101-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/agw_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT20/AGW_R50-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/agw_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT20/AGW_R50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/agw_R50\n"
  },
  {
    "path": "fast_reid/configs/MOT20/AGW_S50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/agw_S50\n"
  },
  {
    "path": "fast_reid/configs/MOT20/bagtricks_R101-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/bagtricks_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT20/bagtricks_R50-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/bagtricks_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT20/bagtricks_R50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/bagtricks_R50\n"
  },
  {
    "path": "fast_reid/configs/MOT20/bagtricks_S50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/bagtricks_S50\n"
  },
  {
    "path": "fast_reid/configs/MOT20/mgn_R50-ibn.yml",
    "content": "_BASE_: ../Base-MGN.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/mgn_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT20/sbs_R101-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/sbs_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT20/sbs_R50-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/sbs_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MOT20/sbs_R50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/mot20/sbs_R50\n"
  },
  {
    "path": "fast_reid/configs/MOT20/sbs_S50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"MOT20\",)\n  TESTS: (\"MOT20\",)\n\nOUTPUT_DIR: logs/MOT20/sbs_S50\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/AGW_R101-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/agw_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/AGW_R50-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/agw_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/AGW_R50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/agw_R50\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/AGW_S50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/agw_S50\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/bagtricks_R101-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/bagtricks_R101-ibn\n\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/bagtricks_R50-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/bagtricks_R50-ibn\n\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/bagtricks_R50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/bagtricks_R50\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/bagtricks_S50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/bagtricks_S50\n\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/mgn_R50-ibn.yml",
    "content": "_BASE_: ../Base-MGN.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/mgn_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/sbs_R101-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/sbs_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/sbs_R50-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/sbs_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/sbs_R50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/sbs_R50\n"
  },
  {
    "path": "fast_reid/configs/MSMT17/sbs_S50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"MSMT17\",)\n  TESTS: (\"MSMT17\",)\n\nOUTPUT_DIR: logs/msmt17/sbs_S50\n"
  },
  {
    "path": "fast_reid/configs/Market1501/AGW_R101-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/agw_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/Market1501/AGW_R50-ibn.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/agw_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/Market1501/AGW_R50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/agw_R50\n"
  },
  {
    "path": "fast_reid/configs/Market1501/AGW_S50.yml",
    "content": "_BASE_: ../Base-AGW.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/agw_S50\n"
  },
  {
    "path": "fast_reid/configs/Market1501/bagtricks_R101-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/bagtricks_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/Market1501/bagtricks_R50-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/bagtricks_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/Market1501/bagtricks_R50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/bagtricks_R50\n"
  },
  {
    "path": "fast_reid/configs/Market1501/bagtricks_S50.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/bagtricks_S50\n"
  },
  {
    "path": "fast_reid/configs/Market1501/bagtricks_vit.yml",
    "content": "\nMODEL:\n  META_ARCHITECTURE: Baseline\n  PIXEL_MEAN: [127.5, 127.5, 127.5]\n  PIXEL_STD: [127.5, 127.5, 127.5]\n\n  BACKBONE:\n    NAME: build_vit_backbone\n    DEPTH: base\n    FEAT_DIM: 768\n    PRETRAIN: True\n    PRETRAIN_PATH: /home/nir/.cache/torch/checkpoints/jx_vit_base_p16_224-80ecf9dd.pth\n    STRIDE_SIZE: (16, 16)\n    DROP_PATH_RATIO: 0.1\n    DROP_RATIO: 0.0\n    ATT_DROP_RATE: 0.0\n\n  HEADS:\n    NAME: EmbeddingHead\n    NORM: BN\n    WITH_BNNECK: True\n    POOL_LAYER: Identity\n    NECK_FEAT: before\n    CLS_LAYER: Linear\n\n  LOSSES:\n    NAME: (\"CrossEntropyLoss\", \"TripletLoss\",)\n\n    CE:\n      EPSILON: 0. # no smooth\n      SCALE: 1.\n\n    TRI:\n      MARGIN: 0.0\n      HARD_MINING: True\n      NORM_FEAT: False\n      SCALE: 1.\n\nINPUT:\n  SIZE_TRAIN: [ 256, 128 ]\n  SIZE_TEST: [ 256, 128 ]\n\n  REA:\n    ENABLED: True\n    PROB: 0.5\n\n  FLIP:\n    ENABLED: True\n\n  PADDING:\n    ENABLED: True\n\nDATALOADER:\n  SAMPLER_TRAIN: NaiveIdentitySampler\n  NUM_INSTANCE: 4\n  NUM_WORKERS: 8\n\nSOLVER:\n  AMP:\n    ENABLED: False\n  OPT: SGD\n  MAX_EPOCH: 120\n  BASE_LR: 0.008\n  WEIGHT_DECAY: 0.0001\n  IMS_PER_BATCH: 64\n\n#  SCHED: CosineAnnealingLR\n#  ETA_MIN_LR: 0.000016\n  SCHED: MultiStepLR\n  STEPS: [ 40, 90 ]\n  GAMMA: 0.1\n\n  WARMUP_FACTOR: 0.01\n  WARMUP_ITERS: 1000\n\n  CLIP_GRADIENTS:\n    ENABLED: True\n\n  CHECKPOINT_PERIOD: 1\n\nTEST:\n  EVAL_PERIOD: 5000000\n  IMS_PER_BATCH: 128\n\n# CUDNN_BENCHMARK: True\nCUDNN_BENCHMARK: False\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/sbs_vit_base\n"
  },
  {
    "path": "fast_reid/configs/Market1501/mgn_R50-ibn.yml",
    "content": "_BASE_: ../Base-MGN.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/mgn_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/Market1501/sbs_R101-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 101x\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/sbs_R101-ibn\n"
  },
  {
    "path": "fast_reid/configs/Market1501/sbs_R50-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/sbs_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/Market1501/sbs_R50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/sbs_R50\n"
  },
  {
    "path": "fast_reid/configs/Market1501/sbs_S50.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n#    NORM: syncBN\n#  HEADS:\n#    NORM: syncBN\n#SOLVER:\n#  IMS_PER_BATCH: 256\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"Market1501\",)\n\nOUTPUT_DIR: logs/market1501/sbs_S50\n"
  },
  {
    "path": "fast_reid/configs/VERIWild/bagtricks_R50-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nINPUT:\n  SIZE_TRAIN: [256, 256]\n  SIZE_TEST: [256, 256]\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n\n  HEADS:\n    POOL_LAYER: GeneralizedMeanPooling\n\n  LOSSES:\n    TRI:\n      HARD_MINING: False\n      MARGIN: 0.0\n\nDATASETS:\n  NAMES: (\"VeRiWild\",)\n  TESTS: (\"SmallVeRiWild\", \"MediumVeRiWild\", \"LargeVeRiWild\",)\n\nSOLVER:\n  IMS_PER_BATCH: 512 # 512 For 4 GPUs\n  MAX_EPOCH: 120\n  STEPS: [30, 70, 90]\n  WARMUP_ITERS: 5000\n\n  CHECKPOINT_PERIOD: 20\n\nTEST:\n  EVAL_PERIOD: 10\n  IMS_PER_BATCH: 128\n\nOUTPUT_DIR: logs/veriwild/bagtricks_R50-ibn_4gpu\n"
  },
  {
    "path": "fast_reid/configs/VeRi/sbs_R50-ibn.yml",
    "content": "_BASE_: ../Base-SBS.yml\n\nINPUT:\n  SIZE_TRAIN: [256, 256]\n  SIZE_TEST: [256, 256]\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n    WITH_NL: True\n\nSOLVER:\n  OPT: SGD\n  BASE_LR: 0.01\n  ETA_MIN_LR: 7.7e-5\n\n  IMS_PER_BATCH: 64\n  MAX_EPOCH: 60\n  WARMUP_ITERS: 3000\n  FREEZE_ITERS: 3000\n\n  CHECKPOINT_PERIOD: 10\n\nDATASETS:\n  NAMES: (\"VeRi\",)\n  TESTS: (\"VeRi\",)\n\nDATALOADER:\n  SAMPLER_TRAIN: BalancedIdentitySampler\n\nTEST:\n  EVAL_PERIOD: 10\n  IMS_PER_BATCH: 256\n\nOUTPUT_DIR: logs/veri/sbs_R50-ibn\n"
  },
  {
    "path": "fast_reid/configs/VehicleID/bagtricks_R50-ibn.yml",
    "content": "_BASE_: ../Base-bagtricks.yml\n\nINPUT:\n  SIZE_TRAIN: [256, 256]\n  SIZE_TEST: [256, 256]\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n  HEADS:\n    POOL_LAYER: GeneralizedMeanPooling\n\n  LOSSES:\n    TRI:\n      HARD_MINING: False\n      MARGIN: 0.0\n\nDATASETS:\n  NAMES: (\"VehicleID\",)\n  TESTS: (\"SmallVehicleID\", \"MediumVehicleID\", \"LargeVehicleID\",)\n\nSOLVER:\n  BIAS_LR_FACTOR: 1.\n\n  IMS_PER_BATCH: 512\n  MAX_EPOCH: 60\n  STEPS: [30, 50]\n  WARMUP_ITERS: 2000\n\n  CHECKPOINT_PERIOD: 20\n\nTEST:\n  EVAL_PERIOD: 20\n  IMS_PER_BATCH: 128\n\nOUTPUT_DIR: logs/vehicleid/bagtricks_R50-ibn_4gpu\n"
  },
  {
    "path": "fast_reid/datasets/generate_cuhksysu_dance_patches.py",
    "content": "import os\nimport argparse\nimport math\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom tqdm import tqdm\nimport json\n\ndef make_parser():\n    parser = argparse.ArgumentParser(\"dancetrack reid dataset\")\n\n    parser.add_argument(\"--data_path\", default=\"datasets\", help=\"path to dancetrack data\")\n    parser.add_argument(\"--save_path\", default=\"fast_reid/datasets\", help=\"Path to save the dancetrack-reid dataset\")\n\n    return parser\n\n# ============================ for dancetrack ============================\ndef generate_trajectories(file_path, GroundTrues):\n    f = open(file_path, 'r')\n\n    lines = f.read().split('\\n')        # list of [n_lines] or [n_objs]\n    values = []\n    for l in lines:\n        split = l.split(',')    # <frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <active>, <category>, <visible_ratio>\n        if len(split) < 2:\n            break\n        numbers = [float(i) for i in split]     # int to float\n        values.append(numbers)\n\n    values = np.array(values, np.float_)\n\n    if GroundTrues:     # filter objects\n        # values = values[values[:, 6] == 1, :]  # Remove ignore objects, only active objects\n        # values = values[values[:, 7] == 1, :]  # Pedestrian only\n        values = values[values[:, 8] > 0.4, :]  # visibility only\n\n    values = np.array(values)\n    values[:, 4] += values[:, 2]        # tlwh to tlbr\n    values[:, 5] += values[:, 3]\n\n    return values\n\ndef main_dancetrack(args):\n    # NOTE: id starts from 0.\n    # Create folder for outputs\n    save_path = os.path.join(args.save_path, 'cuhksysu-dancetrack-reid')\n    os.makedirs(save_path, exist_ok=True)\n    train_save_path = os.path.join(save_path, 'bounding_box_train')\n    os.makedirs(train_save_path, exist_ok=True)\n    test_save_path = os.path.join(save_path, 'bounding_box_test')\n    os.makedirs(test_save_path, exist_ok=True)\n\n    # Get gt data\n    data_path = os.path.join(args.data_path, 'dancetrack', 'train')\n\n    seqs = os.listdir(data_path)\n\n    seqs.sort()\n\n    id_offset = 0\n\n    for seq in seqs:        # iteration over seqs\n        print(\"current seq\", seq)\n        print(\"current id_offset\", id_offset)\n\n        ground_truth_path = os.path.join(data_path, seq, 'gt/gt.txt')\n        gt = generate_trajectories(ground_truth_path, GroundTrues=False)  # frame, id, x_tl, y_tl, x_br, y_br, active, category, visible_ratio\n\n        images_path = os.path.join(data_path, seq, 'img1')\n        img_files = os.listdir(images_path)\n        img_files.sort()\n\n        num_frames = len(img_files)\n        max_id_per_seq = 0\n        for f in tqdm(range(num_frames)):     # iteration over frames\n            img = cv2.imread(os.path.join(images_path, img_files[f]))\n            if img is None:\n                print(\"ERROR: Receive empty frame\")\n                continue\n            H, W, _ = np.shape(img)\n            det = gt[f + 1 == gt[:, 0], 1:].astype(np.int_)     # dets in current frame. [id, x_tl, y_tl, x_br, y_br, active, category, visible_ratio]\n            for d in range(np.size(det, 0)):\n                id_ = det[d, 0] + 1     # 0-index to 1-index\n                x1 = det[d, 1]\n                y1 = det[d, 2]\n                x2 = det[d, 3]\n                y2 = det[d, 4]\n                # clamp\n                x1 = max(0, x1)\n                y1 = max(0, y1)\n                x2 = min(x2, W)\n                y2 = min(y2, H)\n\n                # patch = cv2.cvtColor(img[y1:y2, x1:x2, :], cv2.COLOR_BGR2RGB)\n                patch = img[y1:y2, x1:x2, :]        # crop image\n\n                max_id_per_seq = max(max_id_per_seq, id_)       # update 'max_id_per_seq'\n\n                # plt.figure()\n                # plt.imshow(patch)\n                # plt.show()\n\n                fileName = (str(id_+id_offset)).zfill(7) + '_' + seq[-4:] + '_' + (str(f+1)).zfill(7) + '_acc_data.bmp'\n\n\n                cv2.imwrite(os.path.join(train_save_path, fileName), patch)\n\n        id_offset += max_id_per_seq\n    return id_offset        # just add as above\n\n# ============================ for cuhksysu ============================\ndef tlwh2xyxy(det, H, W):\n    x1 = det[0]\n    y1 = det[1]\n    x2 = det[0] + det[2]        # tlwh2xyxy\n    y2 = det[1] + det[3]\n    # clamp\n    x1 = int(max(0, x1))\n    y1 = int(max(0, y1))\n    x2 = int(min(x2, W))\n    y2 = int(min(y2, H))\n    return [x1, y1, x2, y2]\n\ndef save_patch(img, det, id, save_path, seq=1, frame=1,):\n    x1, y1, x2, y2 = det\n\n    # patch = cv2.cvtColor(img[y1:y2, x1:x2, :], cv2.COLOR_BGR2RGB)\n    patch = img[y1:y2, x1:x2, :]  # crop image\n\n    # plt.figure()\n    # plt.imshow(patch)\n    # plt.show()\n\n    # -000001_5_0000002_acc_data.bmp\n    fileName = (str(id)).zfill(7) + '_' + str(seq) + '_' + (str(frame + 1)).zfill(7) + '_acc_data.bmp'\n\n    try:\n        cv2.imwrite(os.path.join(save_path, fileName), patch)\n    except:\n        print('skip box which is too small...')\n\ndef main_cuhksysu(args, id_offset, seq_offset=1000):\n\n    # Create folder for outputs\n    save_path = os.path.join(args.save_path, 'cuhksysu-dancetrack-reid')\n    os.makedirs(save_path, exist_ok=True)\n    train_save_path = os.path.join(save_path, 'bounding_box_train')\n    os.makedirs(train_save_path, exist_ok=True)\n    test_save_path = os.path.join(save_path, 'bounding_box_test')\n    os.makedirs(test_save_path, exist_ok=True)\n\n    # Get gt data\n    data_path = os.path.join(args.data_path, 'CUHKSYSU')\n    anno_path = os.path.join(data_path, 'annotations/train.json')\n    img_dir = os.path.join(data_path, 'images')\n\n    with open(anno_path) as f:\n        annos = json.load(f)\n\n    for anno in tqdm(annos['annotations']):\n        img_file_name = annos['images'][anno['image_id']-1]['file_name'].split('/')[-1]\n        W, H = annos['images'][anno['image_id']-1]['width'], annos['images'][anno['image_id']-1]['height']\n        seq = int(os.path.basename(img_file_name)[1:-4]) + seq_offset        # + seq_offset in case\n        img = cv2.imread(os.path.join(img_dir, img_file_name))\n        id = int(anno['track_id']) + id_offset + 1      # + 1 in case 0-index\n        det = tlwh2xyxy(anno['bbox'], H, W)\n\n        save_patch(img, det, id, train_save_path, seq=str(seq))\n        # cv2.imwrite('img.png', img)\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    id_offset = main_dancetrack(args)                   # dancetrack\n    main_cuhksysu(args, id_offset, seq_offset=1000)     # cuhksysu\n"
  },
  {
    "path": "fast_reid/datasets/generate_mot_patches.py",
    "content": "import os\nimport argparse\nimport math\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom tqdm import tqdm\n\ndef generate_trajectories(file_path, GroundTrues):\n    f = open(file_path, 'r')\n\n    lines = f.read().split('\\n')        # list of [n_lines] or [n_objs]\n    values = []\n    for l in lines:\n        split = l.split(',')    # <frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <active>, <category>, <visible_ratio>\n        if len(split) < 2:\n            break\n        numbers = [float(i) for i in split]     # int to float\n        values.append(numbers)\n\n    values = np.array(values, np.float_)\n\n    if GroundTrues:     # filter objects\n        # values = values[values[:, 6] == 1, :]  # Remove ignore objects, only active objects\n        # values = values[values[:, 7] == 1, :]  # Pedestrian only\n        values = values[values[:, 8] > 0.4, :]  # visibility only\n\n    values = np.array(values)\n    values[:, 4] += values[:, 2]        # tlwh to tlbr\n    values[:, 5] += values[:, 3]\n\n    return values\n\ndef make_parser():\n    parser = argparse.ArgumentParser(\"MOTChallenge ReID dataset\")\n\n    parser.add_argument(\"--data_path\", default=\"\", help=\"path to MOT data\")\n    parser.add_argument(\"--save_path\", default=\"fast_reid/datasets\", help=\"Path to save the MOT-ReID dataset\")\n    parser.add_argument(\"--mot\", default=17, help=\"MOTChallenge dataset number e.g. 17, 20\")\n\n    return parser\n\n\ndef main(args):\n\n    # Create folder for outputs\n    save_path = os.path.join(args.save_path, 'MOT' + str(args.mot) + '-ReID')\n    os.makedirs(save_path, exist_ok=True)\n    train_save_path = os.path.join(save_path, 'bounding_box_train')\n    os.makedirs(train_save_path, exist_ok=True)\n    test_save_path = os.path.join(save_path, 'bounding_box_test')\n    os.makedirs(test_save_path, exist_ok=True)\n\n    # Get gt data\n    data_path = os.path.join(args.data_path, 'MOT' + str(args.mot), 'train')\n\n    if args.mot == '17':\n        seqs = [f for f in os.listdir(data_path) if 'FRCNN' in f]\n    else:\n        seqs = os.listdir(data_path)\n\n    seqs.sort()\n\n    id_offset = 0\n\n    for seq in seqs:        # iteration over seqs\n        print(\"current seq\", seq)\n        print(\"current id_offset\", id_offset)\n\n        ground_truth_path = os.path.join(data_path, seq, 'gt/gt.txt')\n        gt = generate_trajectories(ground_truth_path, GroundTrues=True)  # [do filter] frame, id, x_tl, y_tl, x_br, y_br, active, category, visible_ratio\n\n        images_path = os.path.join(data_path, seq, 'img1')\n        img_files = os.listdir(images_path)\n        img_files.sort()\n\n        num_frames = len(img_files)\n        max_id_per_seq = 0\n        for f in tqdm(range(num_frames)):     # iteration over frames\n            img = cv2.imread(os.path.join(images_path, img_files[f]))\n            if img is None:\n                print(\"ERROR: Receive empty frame\")\n                continue\n            H, W, _ = np.shape(img)\n            det = gt[f + 1 == gt[:, 0], 1:].astype(np.int_)     # dets in current frame. [id, x_tl, y_tl, x_br, y_br, active, category, visible_ratio]\n            for d in range(np.size(det, 0)):\n                id_ = det[d, 0]\n                x1 = det[d, 1]\n                y1 = det[d, 2]\n                x2 = det[d, 3]\n                y2 = det[d, 4]\n                # clamp\n                x1 = max(0, x1)\n                y1 = max(0, y1)\n                x2 = min(x2, W)\n                y2 = min(y2, H)\n\n                # patch = cv2.cvtColor(img[y1:y2, x1:x2, :], cv2.COLOR_BGR2RGB)\n                patch = img[y1:y2, x1:x2, :]        # crop image\n\n                max_id_per_seq = max(max_id_per_seq, id_)       # update 'max_id_per_seq'\n\n                # plt.figure()\n                # plt.imshow(patch)\n                # plt.show()\n\n                fileName = (str(id_+id_offset)).zfill(7) + '_' + seq + '_' + (str(f+1)).zfill(7) + '_acc_data.bmp'\n\n                if f < num_frames // 2:\n                    cv2.imwrite(os.path.join(train_save_path, fileName), patch)\n                else:\n                    cv2.imwrite(os.path.join(test_save_path, fileName), patch)\n\n        id_offset += max_id_per_seq\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    main(args)\n"
  },
  {
    "path": "fast_reid/demo/README.md",
    "content": "# FastReID Demo\n\nWe provide a command line tool to run a simple demo of builtin models.\n\nYou can run this command to get cosine similarites between different images\n\n```bash\npython demo/visualize_result.py --config-file logs/dukemtmc/mgn_R50-ibn/config.yaml \\\n--parallel --vis-label --dataset-name DukeMTMC --output logs/mgn_duke_vis \\\n--opts MODEL.WEIGHTS logs/dukemtmc/mgn_R50-ibn/model_final.pth\n```\n"
  },
  {
    "path": "fast_reid/demo/demo.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport argparse\nimport glob\nimport os\nimport sys\n\nimport torch.nn.functional as F\nimport cv2\nimport numpy as np\nimport tqdm\nfrom torch.backends import cudnn\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.utils.logger import setup_logger\nfrom fast_reid.fastreid.utils.file_io import PathManager\n\nfrom predictor import FeatureExtractionDemo\n\n# import some modules added in project like this below\n# sys.path.append(\"projects/PartialReID\")\n# from partialreid import *\n\ncudnn.benchmark = True\nsetup_logger(name=\"fastreid\")\n\n\ndef setup_cfg(args):\n    # load config from file and command-line arguments\n    cfg = get_cfg()\n    # add_partialreid_config(cfg)\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    return cfg\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(description=\"Feature extraction with reid models\")\n    parser.add_argument(\n        \"--config-file\",\n        metavar=\"FILE\",\n        help=\"path to config file\",\n    )\n    parser.add_argument(\n        \"--parallel\",\n        action='store_true',\n        help='If use multiprocess for feature extraction.'\n    )\n    parser.add_argument(\n        \"--input\",\n        nargs=\"+\",\n        help=\"A list of space separated input images; \"\n             \"or a single glob pattern such as 'directory/*.jpg'\",\n    )\n    parser.add_argument(\n        \"--output\",\n        default='demo_output',\n        help='path to save features'\n    )\n    parser.add_argument(\n        \"--opts\",\n        help=\"Modify config options using the command-line 'KEY VALUE' pairs\",\n        default=[],\n        nargs=argparse.REMAINDER,\n    )\n    return parser\n\n\ndef postprocess(features):\n    # Normalize feature to compute cosine distance\n    features = F.normalize(features)\n    features = features.cpu().data.numpy()\n    return features\n\n\nif __name__ == '__main__':\n    args = get_parser().parse_args()\n\n    # ------------------------------------------------------------------------------------------------------------------\n    train_data = 'DukeMTMC'\n    method = 'sbs_S50'  # bagtricks_S50 | sbs_S50\n    seq = 'MOT20-02'\n\n    args.config_file = r'../configs/' + train_data + '/' + method + '.yml'\n    args.input = [r'/home/nir/Datasets/MOT20/train/' + seq + '/img1', '*.jpg']\n    args.output = seq + '_' + method + '_' + train_data\n    args.opts = ['MODEL.WEIGHTS', '../pretrained/duke_bot_S50.pth']\n    # ------------------------------------------------------------------------------------------------------------------\n\n    cfg = setup_cfg(args)\n    demo = FeatureExtractionDemo(cfg, parallel=args.parallel)\n\n    PathManager.mkdirs(args.output)\n    if args.input:\n        if PathManager.isdir(args.input[0]):\n            # args.input = glob.glob(os.path.expanduser(args.input[0]))\n            args.input = glob.glob(os.path.expanduser(os.path.join(args.input[0], args.input[1])))\n            args.input = sorted(args.input)\n            assert args.input, \"The input path(s) was not found\"\n        for path in tqdm.tqdm(args.input):\n            img = cv2.imread(path)\n            feat = demo.run_on_image(img)\n            feat = postprocess(feat)\n            np.save(os.path.join(args.output, os.path.basename(path).split('.')[0] + '.npy'), feat)\n"
  },
  {
    "path": "fast_reid/demo/plot_roc_with_pickle.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport sys\n\nsys.path.append('.')\nfrom fast_reid.fastreid.utils.visualizer import Visualizer\n\nif __name__ == \"__main__\":\n    baseline_res = Visualizer.load_roc_info(\"logs/duke_vis/roc_info.pickle\")\n    mgn_res = Visualizer.load_roc_info(\"logs/mgn_duke_vis/roc_info.pickle\")\n\n    fig = Visualizer.plot_roc_curve(baseline_res['fpr'], baseline_res['tpr'], name='baseline')\n    Visualizer.plot_roc_curve(mgn_res['fpr'], mgn_res['tpr'], name='mgn', fig=fig)\n    plt.savefig('roc.jpg')\n\n    fig = Visualizer.plot_distribution(baseline_res['pos'], baseline_res['neg'], name='baseline')\n    Visualizer.plot_distribution(mgn_res['pos'], mgn_res['neg'], name='mgn', fig=fig)\n    plt.savefig('dist.jpg')\n"
  },
  {
    "path": "fast_reid/demo/predictor.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport atexit\nimport bisect\nfrom collections import deque\n\nimport cv2\nimport torch\nimport torch.multiprocessing as mp\n\nfrom fast_reid.fastreid.engine import DefaultPredictor\n\ntry:\n    mp.set_start_method('spawn')\nexcept RuntimeError:\n    pass\n\n\nclass FeatureExtractionDemo(object):\n    def __init__(self, cfg, parallel=False):\n        \"\"\"\n        Args:\n            cfg (CfgNode):\n            parallel (bool) whether to run the model in different processes from visualization.:\n                Useful since the visualization logic can be slow.\n        \"\"\"\n        self.cfg = cfg\n        self.parallel = parallel\n\n        if parallel:\n            self.num_gpus = torch.cuda.device_count()\n            self.predictor = AsyncPredictor(cfg, self.num_gpus)\n        else:\n            self.predictor = DefaultPredictor(cfg)\n\n    def run_on_image(self, original_image):\n        \"\"\"\n\n        Args:\n            original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).\n                This is the format used by OpenCV.\n\n        Returns:\n            predictions (np.ndarray): normalized feature of the model.\n        \"\"\"\n        # the model expects RGB inputs\n        original_image = original_image[:, :, ::-1]\n        # Apply pre-processing to image.\n        image = cv2.resize(original_image, tuple(self.cfg.INPUT.SIZE_TEST[::-1]), interpolation=cv2.INTER_CUBIC)\n        # Make shape with a new batch dimension which is adapted for\n        # network input\n        image = torch.as_tensor(image.astype(\"float32\").transpose(2, 0, 1))[None]\n        predictions = self.predictor(image)\n        return predictions\n\n    def run_on_loader(self, data_loader):\n        if self.parallel:\n            buffer_size = self.predictor.default_buffer_size\n\n            batch_data = deque()\n\n            for cnt, batch in enumerate(data_loader):\n                batch_data.append(batch)\n                self.predictor.put(batch[\"images\"])\n\n                if cnt >= buffer_size:\n                    batch = batch_data.popleft()\n                    predictions = self.predictor.get()\n                    yield predictions, batch[\"targets\"].cpu().numpy(), batch[\"camids\"].cpu().numpy()\n\n            while len(batch_data):\n                batch = batch_data.popleft()\n                predictions = self.predictor.get()\n                yield predictions, batch[\"targets\"].cpu().numpy(), batch[\"camids\"].cpu().numpy()\n        else:\n            for batch in data_loader:\n                predictions = self.predictor(batch[\"images\"])\n                yield predictions, batch[\"targets\"].cpu().numpy(), batch[\"camids\"].cpu().numpy()\n\n\nclass AsyncPredictor:\n    \"\"\"\n    A predictor that runs the model asynchronously, possibly on >1 GPUs.\n    Because when the amount of data is large.\n    \"\"\"\n\n    class _StopToken:\n        pass\n\n    class _PredictWorker(mp.Process):\n        def __init__(self, cfg, task_queue, result_queue):\n            self.cfg = cfg\n            self.task_queue = task_queue\n            self.result_queue = result_queue\n            super().__init__()\n\n        def run(self):\n            predictor = DefaultPredictor(self.cfg)\n\n            while True:\n                task = self.task_queue.get()\n                if isinstance(task, AsyncPredictor._StopToken):\n                    break\n                idx, data = task\n                result = predictor(data)\n                self.result_queue.put((idx, result))\n\n    def __init__(self, cfg, num_gpus: int = 1):\n        \"\"\"\n\n        Args:\n            cfg (CfgNode):\n            num_gpus (int): if 0, will run on CPU\n        \"\"\"\n        num_workers = max(num_gpus, 1)\n        self.task_queue = mp.Queue(maxsize=num_workers * 3)\n        self.result_queue = mp.Queue(maxsize=num_workers * 3)\n        self.procs = []\n        for gpuid in range(max(num_gpus, 1)):\n            cfg = cfg.clone()\n            cfg.defrost()\n            cfg.MODEL.DEVICE = \"cuda:{}\".format(gpuid) if num_gpus > 0 else \"cpu\"\n            self.procs.append(\n                AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)\n            )\n\n        self.put_idx = 0\n        self.get_idx = 0\n        self.result_rank = []\n        self.result_data = []\n\n        for p in self.procs:\n            p.start()\n\n        atexit.register(self.shutdown)\n\n    def put(self, image):\n        self.put_idx += 1\n        self.task_queue.put((self.put_idx, image))\n\n    def get(self):\n        self.get_idx += 1\n        if len(self.result_rank) and self.result_rank[0] == self.get_idx:\n            res = self.result_data[0]\n            del self.result_data[0], self.result_rank[0]\n            return res\n\n        while True:\n            # Make sure the results are returned in the correct order\n            idx, res = self.result_queue.get()\n            if idx == self.get_idx:\n                return res\n            insert = bisect.bisect(self.result_rank, idx)\n            self.result_rank.insert(insert, idx)\n            self.result_data.insert(insert, res)\n\n    def __len__(self):\n        return self.put_idx - self.get_idx\n\n    def __call__(self, image):\n        self.put(image)\n        return self.get()\n\n    def shutdown(self):\n        for _ in self.procs:\n            self.task_queue.put(AsyncPredictor._StopToken())\n\n    @property\n    def default_buffer_size(self):\n        return len(self.procs) * 5\n"
  },
  {
    "path": "fast_reid/demo/visualize_result.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport argparse\nimport logging\nimport sys\n\nimport numpy as np\nimport torch\nimport tqdm\nfrom torch.backends import cudnn\n\nsys.path.append('.')\n\n# from fast_reid.fastreid.evaluation import evaluate_rank\nfrom fast_reid.fastreid.evaluation.rank import evaluate_rank\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.utils.logger import setup_logger\nfrom fast_reid.fastreid.data import build_reid_test_loader\nfrom predictor import FeatureExtractionDemo\nfrom fast_reid.fastreid.utils.visualizer import Visualizer\n\n# import some modules added in project\n# for example, add partial reid like this below\n# sys.path.append(\"projects/PartialReID\")\n# from partialreid import *\n\ncudnn.benchmark = True\nsetup_logger(name=\"fastreid\")\n\nlogger = logging.getLogger('fastreid.visualize_result')\n\n\ndef setup_cfg(args):\n    # load config from file and command-line arguments\n    cfg = get_cfg()\n    # add_partialreid_config(cfg)\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    return cfg\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(description=\"Feature extraction with reid models\")\n    parser.add_argument(\n        \"--config-file\",\n        metavar=\"FILE\",\n        help=\"path to config file\",\n    )\n    parser.add_argument(\n        '--parallel',\n        action='store_true',\n        help='if use multiprocess for feature extraction.'\n    )\n    parser.add_argument(\n        \"--dataset-name\",\n        help=\"a test dataset name for visualizing ranking list.\"\n    )\n    parser.add_argument(\n        \"--output\",\n        default=\"./vis_rank_list\",\n        help=\"a file or directory to save rankling list result.\",\n\n    )\n    parser.add_argument(\n        \"--vis-label\",\n        action='store_true',\n        help=\"if visualize label of query instance\"\n    )\n    parser.add_argument(\n        \"--num-vis\",\n        default=100,\n        help=\"number of query images to be visualized\",\n    )\n    parser.add_argument(\n        \"--rank-sort\",\n        default=\"ascending\",\n        help=\"rank order of visualization images by AP metric\",\n    )\n    parser.add_argument(\n        \"--label-sort\",\n        default=\"ascending\",\n        help=\"label order of visualization images by cosine similarity metric\",\n    )\n    parser.add_argument(\n        \"--max-rank\",\n        default=10,\n        help=\"maximum number of rank list to be visualized\",\n    )\n    parser.add_argument(\n        \"--opts\",\n        help=\"Modify config options using the command-line 'KEY VALUE' pairs\",\n        default=[],\n        nargs=argparse.REMAINDER,\n    )\n    return parser\n\n\nif __name__ == '__main__':\n    args = get_parser().parse_args()\n\n    # ------------------------------------------------------------------------------------------------------------------\n    # train_data = 'DukeMTMC'\n    # method = 'sbs_S50'  # bagtricks_S50 | sbs_S50\n    # seq = 'MOT20-02'\n    # args.dataset_name = seq\n    # args.config_file = r'../configs/' + train_data + '/' + method + '.yml'\n    # args.input = [r'/home/nir/Datasets/MOT20/train/' + seq + '/img1', '*.jpg']\n    # args.output = seq + '_' + method + '_' + train_data\n    # args.opts = ['MODEL.WEIGHTS', '../pretrained/duke_bot_S50.pth']\n    # ------------------------------------------------------------------------------------------------------------------\n\n    cfg = setup_cfg(args)\n    test_loader, num_query = build_reid_test_loader(cfg, dataset_name=args.dataset_name)\n    demo = FeatureExtractionDemo(cfg, parallel=args.parallel)\n\n    logger.info(\"Start extracting image features\")\n    feats = []\n    pids = []\n    camids = []\n    for (feat, pid, camid) in tqdm.tqdm(demo.run_on_loader(test_loader), total=len(test_loader)):\n        feats.append(feat)\n        pids.extend(pid)\n        camids.extend(camid)\n\n    feats = torch.cat(feats, dim=0)\n    q_feat = feats[:num_query]\n    g_feat = feats[num_query:]\n    q_pids = np.asarray(pids[:num_query])\n    g_pids = np.asarray(pids[num_query:])\n    q_camids = np.asarray(camids[:num_query])\n    g_camids = np.asarray(camids[num_query:])\n\n    # compute cosine distance\n    distmat = 1 - torch.mm(q_feat, g_feat.t())\n    distmat = distmat.numpy()\n\n    logger.info(\"Computing APs for all query images ...\")\n    cmc, all_ap, all_inp = evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids)\n    logger.info(\"Finish computing APs for all query images!\")\n\n    visualizer = Visualizer(test_loader.dataset)\n    visualizer.get_model_output(all_ap, distmat, q_pids, g_pids, q_camids, g_camids)\n\n    logger.info(\"Start saving ROC curve ...\")\n    fpr, tpr, pos, neg = visualizer.vis_roc_curve(args.output)\n    visualizer.save_roc_info(args.output, fpr, tpr, pos, neg)\n    logger.info(\"Finish saving ROC curve!\")\n\n    logger.info(\"Saving rank list result ...\")\n    query_indices = visualizer.vis_rank_list(args.output, args.vis_label, args.num_vis,\n                                             args.rank_sort, args.label_sort, args.max_rank)\n    logger.info(\"Finish saving rank list results!\")\n"
  },
  {
    "path": "fast_reid/docker/Dockerfile",
    "content": "FROM nvidia/cuda:10.1-cudnn7-devel\n\nENV DEBIAN_FRONTEND noninteractive\nRUN apt-get update && apt-get install -y \\\n\tpython3-opencv ca-certificates python3-dev git wget sudo ninja-build\nRUN ln -sv /usr/bin/python3 /usr/bin/python\n\n# create a non-root user\nARG USER_ID=1000\nRUN useradd -m --no-log-init --system  --uid ${USER_ID} appuser -g sudo\nRUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers\nUSER appuser\nWORKDIR /home/appuser\n\nENV PATH=\"/home/appuser/.local/bin:${PATH}\"\nRUN wget https://bootstrap.pypa.io/get-pip.py && \\\n\tpython3 get-pip.py --user && \\\n\trm get-pip.py\n\n# install dependencies\n# See https://pytorch.org/ for other options if you use a different version of CUDA\nRUN pip install --user tensorboard cmake   # cmake from apt-get is too old\nRUN pip install --user torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/cu101/torch_stable.html\nRUN pip install --user -i https://pypi.tuna.tsinghua.edu.cn/simple tensorboard opencv-python cython yacs termcolor scikit-learn tabulate gdown gpustat faiss-gpu ipdb h5py\n"
  },
  {
    "path": "fast_reid/docker/README.md",
    "content": "# Use the container\n\n```shell script\ncd docker/\n# Build:\ndocker build -t=fastreid:v0 .\n# Launch (requires GPUs)\nnvidia-docker run -v server_path:docker_path --name=fastreid --net=host --ipc=host -it fastreid:v0 /bin/sh\n```\n\n## Install new dependencies\n\nAdd the following to `Dockerfile` to make persist changes.\n```shell script\nRUN sudo apt-get update && sudo apt-get install -y vim\n```\n\nOr run them in the container to make temporary changes.\n\n## A more complete docker container\n\nIf you want to use a complete docker container which contains many useful tools, you can check my development environment [Dockerfile](https://github.com/L1aoXingyu/fastreid_docker)"
  },
  {
    "path": "fast_reid/docs/.gitignore",
    "content": "_build"
  },
  {
    "path": "fast_reid/docs/Makefile",
    "content": "# Minimal makefile for Sphinx documentation\n# Copyright (c) Facebook, Inc. and its affiliates.\n\n# You can set these variables from the command line.\nSPHINXOPTS    =\nSPHINXBUILD   = sphinx-build\nSOURCEDIR     = .\nBUILDDIR      = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n"
  },
  {
    "path": "fast_reid/docs/README.md",
    "content": "# Read the docs:\n\nThe latest documentation built from this directory is available at [detectron2.readthedocs.io](https://detectron2.readthedocs.io/).\nDocuments in this directory are not meant to be read on github.\n\n# Build the docs:\n\n1. Install detectron2 according to [INSTALL.md](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md).\n2. Install additional libraries required to build docs:\n  - docutils==0.16\n  - Sphinx==3.0.0\n  - recommonmark==0.6.0\n  - sphinx_rtd_theme\n  - mock\n\n3. Run `make html` from this directory.\n"
  },
  {
    "path": "fast_reid/docs/_static/css/custom.css",
    "content": "/*\n * Copyright (c) Facebook, Inc. and its affiliates.\n * some extra css to make markdown look similar between github/sphinx\n */\n\n/*\n * Below is for install.md:\n */\n.rst-content code {\n  white-space: pre;\n  border: 0px;\n}\n\n.rst-content th {\n  border: 1px solid #e1e4e5;\n}\n\n.rst-content th p {\n  /* otherwise will be default 24px for regular paragraph */\n  margin-bottom: 0px;\n}\n\ndiv.section > details {\n  padding-bottom: 1em;\n}\n"
  },
  {
    "path": "fast_reid/docs/conf.py",
    "content": "# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates.\n\n# flake8: noqa\n\n# Configuration file for the Sphinx documentation builder.\n#\n# This file does only contain a selection of the most common options. For a\n# full list see the documentation:\n# http://www.sphinx-doc.org/en/master/config\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nfrom unittest import mock\nfrom sphinx.domains import Domain\nfrom typing import Dict, List, Tuple\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n#\nimport sphinx_rtd_theme\n\n\nclass GithubURLDomain(Domain):\n    \"\"\"\n    Resolve certain links in markdown files to github source.\n    \"\"\"\n\n    name = \"githuburl\"\n    ROOT = \"https://github.com/JDAI-CV/fast-reid/tree/master\"\n    LINKED_DOC = [\"tutorials/install\", \"tutorials/getting_started\"]\n\n    def resolve_any_xref(self, env, fromdocname, builder, target, node, contnode):\n        github_url = None\n        if not target.endswith(\"html\") and target.startswith(\"../../\"):\n            url = target.replace(\"../\", \"\")\n            github_url = url\n        if fromdocname in self.LINKED_DOC:\n            # unresolved links in these docs are all github links\n            github_url = target\n\n        if github_url is not None:\n            if github_url.endswith(\"MODEL_ZOO\") or github_url.endswith(\"README\"):\n                # bug of recommonmark.\n                # https://github.com/readthedocs/recommonmark/blob/ddd56e7717e9745f11300059e4268e204138a6b1/recommonmark/parser.py#L152-L155\n                github_url += \".md\"\n            print(\"Ref {} resolved to github:{}\".format(target, github_url))\n            contnode[\"refuri\"] = self.ROOT + github_url\n            return [(\"githuburl:any\", contnode)]\n        else:\n            return []\n\n\n# to support markdown\nfrom recommonmark.parser import CommonMarkParser\n\nsys.path.insert(0, os.path.abspath(\"../\"))\nos.environ[\"DOC_BUILDING\"] = \"True\"\nDEPLOY = os.environ.get(\"READTHEDOCS\") == \"True\"\n\n\n# -- Project information -----------------------------------------------------\n\n# fmt: off\ntry:\n    import torch  # noqa\nexcept ImportError:\n    for m in [\n        \"torch\", \"torchvision\", \"torch.nn\", \"torch.nn.parallel\", \"torch.distributed\", \"torch.multiprocessing\", \"torch.autograd\",\n        \"torch.autograd.function\", \"torch.nn.modules\", \"torch.nn.modules.utils\", \"torch.utils\", \"torch.utils.data\", \"torch.onnx\",\n        \"torchvision\", \"torchvision.ops\",\n    ]:\n        sys.modules[m] = mock.Mock(name=m)\n    sys.modules['torch'].__version__ = \"1.5\"  # fake version\n\nfor m in [\n    \"cv2\", \"scipy\", \"portalocker\", \n    \"google\", \"google.protobuf\", \"google.protobuf.internal\", \"onnx\",\n]:\n    sys.modules[m] = mock.Mock(name=m)\n# fmt: on\nsys.modules[\"cv2\"].__version__ = \"3.4\"\n\nimport fastreid  # isort: skip\n\n\nproject = \"fastreid\"\ncopyright = \"2019-2020, fastreid contributors\"\nauthor = \"fastreid contributors\"\n\n# The short X.Y version\nversion = fastreid.__version__\n# The full version, including alpha/beta/rc tags\nrelease = version\n\n\n# -- General configuration ---------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\nneeds_sphinx = \"3.0\"\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n    \"recommonmark\",\n    \"sphinx.ext.autodoc\",\n    \"sphinx.ext.napoleon\",\n    \"sphinx.ext.intersphinx\",\n    \"sphinx.ext.todo\",\n    \"sphinx.ext.coverage\",\n    \"sphinx.ext.mathjax\",\n    \"sphinx.ext.viewcode\",\n    \"sphinx.ext.githubpages\",\n]\n\n# -- Configurations for plugins ------------\nnapoleon_google_docstring = True\nnapoleon_include_init_with_doc = True\nnapoleon_include_special_with_doc = True\nnapoleon_numpy_docstring = False\nnapoleon_use_rtype = False\nautodoc_inherit_docstrings = False\nautodoc_member_order = \"bysource\"\n\nif DEPLOY:\n    intersphinx_timeout = 10\nelse:\n    # skip this when building locally\n    intersphinx_timeout = 0.1\nintersphinx_mapping = {\n    \"python\": (\"https://docs.python.org/3.6\", None),\n    \"numpy\": (\"https://docs.scipy.org/doc/numpy/\", None),\n    \"torch\": (\"https://pytorch.org/docs/master/\", None),\n}\n# -------------------------\n\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\nsource_suffix = [\".rst\", \".md\"]\n\n# The master toctree document.\nmaster_doc = \"index\"\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = [\"_build\", \"Thumbs.db\", \".DS_Store\", \"build\", \"README.md\", \"tutorials/README.md\"]\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = \"sphinx\"\n\n\n# -- Options for HTML output -------------------------------------------------\n\nhtml_theme = \"sphinx_rtd_theme\"\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further.  For a list of options available for each theme, see the\n# documentation.\n#\n# html_theme_options = {}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\nhtml_css_files = [\"css/custom.css\"]\n\n# Custom sidebar templates, must be a dictionary that maps document names\n# to template names.\n#\n# The default sidebars (for documents that don't match any pattern) are\n# defined by theme itself.  Builtin themes are using these templates by\n# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',\n# 'searchbox.html']``.\n#\n# html_sidebars = {}\n\n\n# -- Options for HTMLHelp output ---------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = \"fastreiddoc\"\n\n\n# -- Options for LaTeX output ------------------------------------------------\n\nlatex_elements = {\n    # The paper size ('letterpaper' or 'a4paper').\n    #\n    # 'papersize': 'letterpaper',\n    # The font size ('10pt', '11pt' or '12pt').\n    #\n    # 'pointsize': '10pt',\n    # Additional stuff for the LaTeX preamble.\n    #\n    # 'preamble': '',\n    # Latex figure (float) alignment\n    #\n    # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n#  author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n    (master_doc, \"fastreid.tex\", \"fastreid Documentation\", \"fastreid contributors\", \"manual\")\n]\n\n\n# -- Options for manual page output ------------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(master_doc, \"fastreid\", \"fastreid Documentation\", [author], 1)]\n\n\n# -- Options for Texinfo output ----------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n#  dir menu entry, description, category)\ntexinfo_documents = [\n    (\n        master_doc,\n        \"fastreid\",\n        \"fastreid Documentation\",\n        author,\n        \"fastreid\",\n        \"One line description of project.\",\n        \"Miscellaneous\",\n    )\n]\n\n\n# -- Options for todo extension ----------------------------------------------\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = True\n\n\ndef autodoc_skip_member(app, what, name, obj, skip, options):\n    # we hide something deliberately\n    if getattr(obj, \"__HIDE_SPHINX_DOC__\", False):\n        return True\n\n    # Hide some that are deprecated or not intended to be used\n    HIDDEN = {\n        # \"ResNetBlockBase\",\n        \"GroupedBatchSampler\",\n        # \"build_transform_gen\",\n        # \"export_caffe2_model\",\n        # \"export_onnx_model\",\n        # \"apply_transform_gens\",\n        # \"TransformGen\",\n        # \"apply_augmentations\",\n        # \"StandardAugInput\",\n        # \"build_batch_data_loader\",\n        # \"draw_panoptic_seg_predictions\",\n    }\n    try:\n        if name in HIDDEN or (\n            hasattr(obj, \"__doc__\") and obj.__doc__.lower().strip().startswith(\"deprecated\")\n        ):\n            print(\"Skipping deprecated object: {}\".format(name))\n            return True\n    except:\n        pass\n    return skip\n\n\n_PAPER_DATA = {\n    \"resnet\": (\"1512.03385\", \"Deep Residual Learning for Image Recognition\"),\n    \"fpn\": (\"1612.03144\", \"Feature Pyramid Networks for Object Detection\"),\n    \"mask r-cnn\": (\"1703.06870\", \"Mask R-CNN\"),\n    \"faster r-cnn\": (\n        \"1506.01497\",\n        \"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks\",\n    ),\n    \"deformconv\": (\"1703.06211\", \"Deformable Convolutional Networks\"),\n    \"deformconv2\": (\"1811.11168\", \"Deformable ConvNets v2: More Deformable, Better Results\"),\n    \"panopticfpn\": (\"1901.02446\", \"Panoptic Feature Pyramid Networks\"),\n    \"retinanet\": (\"1708.02002\", \"Focal Loss for Dense Object Detection\"),\n    \"cascade r-cnn\": (\"1712.00726\", \"Cascade R-CNN: Delving into High Quality Object Detection\"),\n    \"lvis\": (\"1908.03195\", \"LVIS: A Dataset for Large Vocabulary Instance Segmentation\"),\n    \"rrpn\": (\"1703.01086\", \"Arbitrary-Oriented Scene Text Detection via Rotation Proposals\"),\n    \"imagenet in 1h\": (\"1706.02677\", \"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour\"),\n    \"xception\": (\"1610.02357\", \"Xception: Deep Learning with Depthwise Separable Convolutions\"),\n    \"mobilenet\": (\n        \"1704.04861\",\n        \"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications\",\n    ),\n}\n\n\ndef paper_ref_role(\n    typ: str,\n    rawtext: str,\n    text: str,\n    lineno: int,\n    inliner,\n    options: Dict = {},\n    content: List[str] = [],\n):\n    \"\"\"\n    Parse :paper:`xxx`. Similar to the \"extlinks\" sphinx extension.\n    \"\"\"\n    from docutils import nodes, utils\n    from sphinx.util.nodes import split_explicit_title\n\n    text = utils.unescape(text)\n    has_explicit_title, title, link = split_explicit_title(text)\n    link = link.lower()\n    if link not in _PAPER_DATA:\n        inliner.reporter.warning(\"Cannot find paper \" + link)\n        paper_url, paper_title = \"#\", link\n    else:\n        paper_url, paper_title = _PAPER_DATA[link]\n        if \"/\" not in paper_url:\n            paper_url = \"https://arxiv.org/abs/\" + paper_url\n    if not has_explicit_title:\n        title = paper_title\n    pnode = nodes.reference(title, title, internal=False, refuri=paper_url)\n    return [pnode], []\n\n\ndef setup(app):\n    from recommonmark.transform import AutoStructify\n\n    app.add_domain(GithubURLDomain)\n    app.connect(\"autodoc-skip-member\", autodoc_skip_member)\n    app.add_role(\"paper\", paper_ref_role)\n    app.add_config_value(\n        \"recommonmark_config\",\n        {\"enable_math\": True, \"enable_inline_math\": True, \"enable_eval_rst\": True},\n        True,\n    )\n    app.add_transform(AutoStructify)\n"
  },
  {
    "path": "fast_reid/docs/index.rst",
    "content": ".. fastreid documentation master file, created by\n   sphinx-quickstart on Sat Sep 21 13:46:45 2019.\n   You can adapt this file completely to your liking, but it should at least\n   contain the root `toctree` directive.\n\nWelcome to fastreid's documentation!\n======================================\n\n.. toctree::\n   :maxdepth: 2\n\n   tutorials/index\n   notes/index\n   modules/index\n"
  },
  {
    "path": "fast_reid/docs/modules/checkpoint.rst",
    "content": "fastreid.checkpoint\n=============================\n\n.. automodule:: fastreid.utils.checkpoint\n    :members:\n    :undoc-members:\n    :show-inheritance:\n"
  },
  {
    "path": "fast_reid/docs/modules/config.rst",
    "content": "fastreid.config \n=========================\n\nRelated tutorials: :doc:`../tutorials/configs`, :doc:`../tutorials/extend`.\n\n.. automodule:: fastreid.config\n    :members:\n    :undoc-members:\n    :show-inheritance:\n    :inherited-members:\n\n\nConfig References\n-----------------\n\n.. literalinclude:: ../../fastreid/config/defaults.py\n  :language: python\n  :linenos:\n  :lines: 4-\n"
  },
  {
    "path": "fast_reid/docs/modules/data.rst",
    "content": "fastreid.data\n=======================\n\n.. automodule:: fastreid.data.build\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n\nfastreid.data.data\\_utils module\n---------------------------------------\n\n.. automodule:: fastreid.data.data_utils\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n\nfastreid.data.datasets module\n---------------------------------------\n\n.. automodule:: fastreid.data.datasets.market1501\n    :members:\n\n.. automodule:: fastreid.data.datasets.cuhk03\n    :members:\n\n.. automodule:: fastreid.data.datasets.dukemtmcreid\n    :members:\n\n.. automodule:: fastreid.data.datasets.msmt17\n    :members:\n\n.. automodule:: fastreid.data.datasets.AirportALERT\n    :members:\n\n.. automodule:: fastreid.data.datasets.iLIDS\n    :members:\n\n.. automodule:: fastreid.data.datasets.pku\n    :members:\n\n.. automodule:: fastreid.data.datasets.prai\n    :members:\n\n.. automodule:: fastreid.data.datasets.saivt\n    :members:\n\n.. automodule:: fastreid.data.datasets.sensereid\n    :members:\n\n.. automodule:: fastreid.data.datasets.sysu_mm\n    :members:\n\n.. automodule:: fastreid.data.datasets.thermalworld\n    :members:\n\n.. automodule:: fastreid.data.datasets.pes3d\n    :members:\n\n.. automodule:: fastreid.data.datasets.caviara\n    :members:\n\n.. automodule:: fastreid.data.datasets.viper\n    :members:\n\n.. automodule:: fastreid.data.datasets.lpw\n    :members:\n\n.. automodule:: fastreid.data.datasets.shinpuhkan\n    :members:\n\n.. automodule:: fastreid.data.datasets.wildtracker\n    :members:\n\n.. automodule:: fastreid.data.datasets.cuhk_sysu\n    :members:\n\n\nfastreid.data.samplers module\n---------------------------------------\n\n.. automodule:: fastreid.data.samplers\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n\nfastreid.data.transforms module\n---------------------------------------\n\n.. automodule:: fastreid.data.transforms\n    :members:\n    :undoc-members:\n    :show-inheritance:\n    :imported-members:\n"
  },
  {
    "path": "fast_reid/docs/modules/data_transforms.rst",
    "content": "fastreid.data.transforms\n====================================\n\n\n.. automodule:: fastreid.data.transforms\n    :members:\n    :undoc-members:\n    :show-inheritance:\n    :imported-members:\n"
  },
  {
    "path": "fast_reid/docs/modules/engine.rst",
    "content": "fastreid.engine\n=========================\n\n.. automodule:: fastreid.engine\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n\nfastreid.engine.defaults module\n---------------------------------\n\n.. automodule:: fastreid.engine.defaults\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\nfastreid.engine.hooks module\n---------------------------------\n\n.. automodule:: fastreid.engine.hooks\n    :members:\n    :undoc-members:\n    :show-inheritance:\n"
  },
  {
    "path": "fast_reid/docs/modules/evaluation.rst",
    "content": "fastreid.evaluation\n=============================\n\n.. automodule:: fastreid.evaluation\n    :members:\n    :undoc-members:\n    :show-inheritance:\n"
  },
  {
    "path": "fast_reid/docs/modules/index.rst",
    "content": "API Documentation\n==================\n\n.. toctree::\n\n    checkpoint\n    config\n    data\n    data_transforms\n    engine\n    evaluation\n    layers\n    model_zoo\n    modeling\n    solver\n    utils\n    export\n"
  },
  {
    "path": "fast_reid/docs/modules/layers.rst",
    "content": "fastreid.layers\n=========================\n\n.. automodule:: fastreid.layers\n    :members:\n    :undoc-members:\n    :show-inheritance:\n"
  },
  {
    "path": "fast_reid/docs/modules/modeling.rst",
    "content": "fastreid.modeling\n===========================\n\n.. automodule:: fastreid.modeling\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\nModel Registries\n-----------------\n\nThese are different registries provided in modeling.\nEach registry provide you the ability to replace it with your customized component,\nwithout having to modify fastreid's code.\n\nNote that it is impossible to allow users to customize any line of code directly.\nEven just to add one line at some place,\nyou'll likely need to find out the smallest registry which contains that line,\nand register your component to that registry.\n\n\n.. autodata:: fastreid.modeling.BACKBONE_REGISTRY\n.. autodata:: fastreid.modeling.META_ARCH_REGISTRY\n.. autodata:: fastreid.modeling.REID_HEADS_REGISTRY\n"
  },
  {
    "path": "fast_reid/docs/modules/solver.rst",
    "content": "fastreid.solver\n=========================\n\n.. automodule:: fastreid.solver\n    :members:\n    :undoc-members:\n    :show-inheritance:\n"
  },
  {
    "path": "fast_reid/docs/modules/utils.rst",
    "content": "fastreid.utils\n========================\n\nfastreid.utils.colormap module\n--------------------------------\n\n.. automodule:: fastreid.utils.colormap\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\nfastreid.utils.comm module\n----------------------------\n\n.. automodule:: fastreid.utils.comm\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n\nfastreid.utils.events module\n------------------------------\n\n.. automodule:: fastreid.utils.events\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n\nfastreid.utils.logger module\n------------------------------\n\n.. automodule:: fastreid.utils.logger\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n\nfastreid.utils.registry module\n--------------------------------\n\n.. automodule:: fastreid.utils.registry\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\nfastreid.utils.memory module\n----------------------------------\n\n.. automodule:: fastreid.utils.memory\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n\nfastreid.utils.analysis module\n----------------------------------\n\n.. automodule:: fastreid.utils.analysis\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n\nfastreid.utils.visualizer module\n----------------------------------\n\n.. automodule:: fastreid.utils.visualizer\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\nfastreid.utils.video\\_visualizer module\n-----------------------------------------\n\n.. automodule:: fastreid.utils.video_visualizer\n    :members:\n    :undoc-members:\n    :show-inheritance:\n\n"
  },
  {
    "path": "fast_reid/docs/requirements.txt",
    "content": "matplotlib\nscipy\nPillow\nnumpy\nprettytable\neasydict\nscikit-learn\npyyaml\nyacs\ntermcolor\ntabulate\ntensorboard\nopencv-python\npyyaml\nyacs\ntermcolor\nscikit-learn\ntabulate\ngdown\nfaiss-gpu"
  },
  {
    "path": "fast_reid/fast_reid_interfece.py",
    "content": "import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.nn.functional as F\n# from torch.backends import cudnn\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.modeling.meta_arch import build_model\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.engine import DefaultTrainer, default_argument_parser, default_setup, launch\n\n# cudnn.benchmark = True\n\n\ndef setup_cfg(config_file, opts):\n    # load config from file and command-line arguments\n    cfg = get_cfg()\n    cfg.merge_from_file(config_file)\n    cfg.merge_from_list(opts)\n    cfg.MODEL.BACKBONE.PRETRAIN = False\n\n    cfg.freeze()\n\n    return cfg\n\n\ndef postprocess(features):\n    # Normalize feature to compute cosine distance\n    features = F.normalize(features)\n    features = features.cpu().data.numpy()\n    return features\n\n\ndef preprocess(image, input_size):\n    if len(image.shape) == 3:\n        padded_img = np.ones((input_size[1], input_size[0], 3), dtype=np.uint8) * 114\n    else:\n        padded_img = np.ones(input_size) * 114\n    img = np.array(image)\n    r = min(input_size[1] / img.shape[0], input_size[0] / img.shape[1])\n    resized_img = cv2.resize(\n        img,\n        (int(img.shape[1] * r), int(img.shape[0] * r)),\n        interpolation=cv2.INTER_LINEAR,\n    )\n    padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img\n\n    return padded_img, r\n\n\nclass FastReIDInterface:\n    def __init__(self, config_file, weights_path, device, batch_size=16):\n        super(FastReIDInterface, self).__init__()\n        if device != 'cpu':\n            self.device = 'cuda'\n        else:\n            self.device = 'cpu'\n\n        self.batch_size = batch_size    # 8\n\n        self.cfg = setup_cfg(config_file, ['MODEL.WEIGHTS', weights_path])\n\n        self.model = build_model(self.cfg)\n        self.model.eval()\n\n        Checkpointer(self.model).load(weights_path)\n\n        if self.device != 'cpu':\n            self.model = self.model.eval().to(device='cuda').half()\n        else:\n            self.model = self.model.eval()\n\n        self.pH, self.pW = self.cfg.INPUT.SIZE_TEST     # [384, 128]\n\n    def inference(self, image, detections):\n\n        if detections is None or np.size(detections) == 0:\n            return []\n\n        H, W, _ = np.shape(image)       # original size, [1080, 1920] for MOT17\n\n        batch_patches = []\n        patches = []\n        for d in range(np.size(detections, 0)):     # iteration over detections\n            tlbr = detections[d, :4].astype(np.int_)\n            tlbr[0] = max(0, tlbr[0])           # clamp\n            tlbr[1] = max(0, tlbr[1])           # clamp\n            tlbr[2] = min(W - 1, tlbr[2])       # clamp\n            tlbr[3] = min(H - 1, tlbr[3])       # clamp\n            patch = image[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2], :]      # crop image, BGR\n\n            # the model expects RGB inputs\n            patch = patch[:, :, ::-1]\n\n            # Apply pre-processing to image.\n            patch = cv2.resize(patch, tuple(self.cfg.INPUT.SIZE_TEST[::-1]), interpolation=cv2.INTER_LINEAR)    # [384, 128, 3]\n            # patch, scale = preprocess(patch, self.cfg.INPUT.SIZE_TEST[::-1])\n\n            # plt.figure()\n            # plt.imshow(patch)\n            # plt.show()\n\n            # Make shape with a new batch dimension which is adapted for network input\n            patch = torch.as_tensor(patch.astype(\"float32\").transpose(2, 0, 1))     # [3, 384, 128]\n            patch = patch.to(device=self.device).half()\n\n            patches.append(patch)\n\n            if (d + 1) % self.batch_size == 0:      # if already get a batch\n                patches = torch.stack(patches, dim=0)\n                batch_patches.append(patches)\n                patches = []\n\n        if len(patches):        # stack each batch\n            patches = torch.stack(patches, dim=0)\n            batch_patches.append(patches)\n\n        features = np.zeros((0, 2048))          # TODO: [hgx1001] need to be set by hand\n        # features = np.zeros((0, 768))\n\n        for patches in batch_patches:       # iteration over batch\n            # Run model\n            patches_ = torch.clone(patches)     # [8, 3, 384, 128]\n            pred = self.model(patches)          # [8, 2048]\n            pred[torch.isinf(pred)] = 1.0\n\n            feat = postprocess(pred)            # normalization() and numpy()\n\n            nans = np.isnan(np.sum(feat, axis=1))\n            if np.isnan(feat).any():        # handle nans, pass for now\n                for n in range(np.size(nans)):\n                    if nans[n]:\n                        # patch_np = patches[n, ...].squeeze().transpose(1, 2, 0).cpu().numpy()\n                        patch_np = patches_[n, ...]\n                        patch_np_ = torch.unsqueeze(patch_np, 0)\n                        pred_ = self.model(patch_np_)\n\n                        patch_np = torch.squeeze(patch_np).cpu()\n                        patch_np = torch.permute(patch_np, (1, 2, 0)).int()\n                        patch_np = patch_np.numpy()\n\n                        plt.figure()\n                        plt.imshow(patch_np)\n                        plt.show()\n\n            features = np.vstack((features, feat))\n\n        return features     # [n_det, 2048]\n\n"
  },
  {
    "path": "fast_reid/fastreid/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\n__version__ = \"1.3\"\n"
  },
  {
    "path": "fast_reid/fastreid/config/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .config import CfgNode, get_cfg, global_cfg, set_global_cfg, configurable\n\n__all__ = [\n    'CfgNode',\n    'get_cfg',\n    'global_cfg',\n    'set_global_cfg',\n    'configurable'\n]\n"
  },
  {
    "path": "fast_reid/fastreid/config/config.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport functools\nimport inspect\nimport logging\nimport os\nfrom typing import Any\n\nimport yaml\nfrom yacs.config import CfgNode as _CfgNode\n\nfrom ..utils.file_io import PathManager\n\nBASE_KEY = \"_BASE_\"\n\n\nclass CfgNode(_CfgNode):\n    \"\"\"\n    Our own extended version of :class:`yacs.config.CfgNode`.\n    It contains the following extra features:\n    1. The :meth:`merge_from_file` method supports the \"_BASE_\" key,\n       which allows the new CfgNode to inherit all the attributes from the\n       base configuration file.\n    2. Keys that start with \"COMPUTED_\" are treated as insertion-only\n       \"computed\" attributes. They can be inserted regardless of whether\n       the CfgNode is frozen or not.\n    3. With \"allow_unsafe=True\", it supports pyyaml tags that evaluate\n       expressions in config. See examples in\n       https://pyyaml.org/wiki/PyYAMLDocumentation#yaml-tags-and-python-types\n       Note that this may lead to arbitrary code execution: you must not\n       load a config file from untrusted sources before manually inspecting\n       the content of the file.\n    \"\"\"\n\n    @staticmethod\n    def load_yaml_with_base(filename: str, allow_unsafe: bool = False):\n        \"\"\"\n        Just like `yaml.load(open(filename))`, but inherit attributes from its\n            `_BASE_`.\n        Args:\n            filename (str): the file name of the current config. Will be used to\n                find the base config file.\n            allow_unsafe (bool): whether to allow loading the config file with\n                `yaml.unsafe_load`.\n        Returns:\n            (dict): the loaded yaml\n        \"\"\"\n        with PathManager.open(filename, \"r\") as f:\n            try:\n                cfg = yaml.safe_load(f)\n            except yaml.constructor.ConstructorError:\n                if not allow_unsafe:\n                    raise\n                logger = logging.getLogger(__name__)\n                logger.warning(\n                    \"Loading config {} with yaml.unsafe_load. Your machine may \"\n                    \"be at risk if the file contains malicious content.\".format(\n                        filename\n                    )\n                )\n                f.close()\n                with open(filename, \"r\") as f:\n                    cfg = yaml.unsafe_load(f)\n\n        def merge_a_into_b(a, b):\n            # merge dict a into dict b. values in a will overwrite b.\n            for k, v in a.items():\n                if isinstance(v, dict) and k in b:\n                    assert isinstance(\n                        b[k], dict\n                    ), \"Cannot inherit key '{}' from base!\".format(k)\n                    merge_a_into_b(v, b[k])\n                else:\n                    b[k] = v\n\n        if BASE_KEY in cfg:\n            base_cfg_file = cfg[BASE_KEY]\n            if base_cfg_file.startswith(\"~\"):\n                base_cfg_file = os.path.expanduser(base_cfg_file)\n            if not any(\n                    map(base_cfg_file.startswith, [\"/\", \"https://\", \"http://\"])\n            ):\n                # the path to base cfg is relative to the config file itself.\n                base_cfg_file = os.path.join(\n                    os.path.dirname(filename), base_cfg_file\n                )\n            base_cfg = CfgNode.load_yaml_with_base(\n                base_cfg_file, allow_unsafe=allow_unsafe\n            )\n            del cfg[BASE_KEY]\n\n            merge_a_into_b(cfg, base_cfg)\n            return base_cfg\n        return cfg\n\n    def merge_from_file(self, cfg_filename: str, allow_unsafe: bool = False):\n        \"\"\"\n        Merge configs from a given yaml file.\n        Args:\n            cfg_filename: the file name of the yaml config.\n            allow_unsafe: whether to allow loading the config file with\n                `yaml.unsafe_load`.\n        \"\"\"\n        loaded_cfg = CfgNode.load_yaml_with_base(\n            cfg_filename, allow_unsafe=allow_unsafe\n        )\n        loaded_cfg = type(self)(loaded_cfg)\n        self.merge_from_other_cfg(loaded_cfg)\n\n    # Forward the following calls to base, but with a check on the BASE_KEY.\n    def merge_from_other_cfg(self, cfg_other):\n        \"\"\"\n        Args:\n            cfg_other (CfgNode): configs to merge from.\n        \"\"\"\n        assert (\n                BASE_KEY not in cfg_other\n        ), \"The reserved key '{}' can only be used in files!\".format(BASE_KEY)\n        return super().merge_from_other_cfg(cfg_other)\n\n    def merge_from_list(self, cfg_list: list):\n        \"\"\"\n        Args:\n            cfg_list (list): list of configs to merge from.\n        \"\"\"\n        keys = set(cfg_list[0::2])\n        assert (\n                BASE_KEY not in keys\n        ), \"The reserved key '{}' can only be used in files!\".format(BASE_KEY)\n        return super().merge_from_list(cfg_list)\n\n    def __setattr__(self, name: str, val: Any):\n        if name.startswith(\"COMPUTED_\"):\n            if name in self:\n                old_val = self[name]\n                if old_val == val:\n                    return\n                raise KeyError(\n                    \"Computed attributed '{}' already exists \"\n                    \"with a different value! old={}, new={}.\".format(\n                        name, old_val, val\n                    )\n                )\n            self[name] = val\n        else:\n            super().__setattr__(name, val)\n\n\nglobal_cfg = CfgNode()\n\n\ndef get_cfg() -> CfgNode:\n    \"\"\"\n    Get a copy of the default config.\n    Returns:\n        a fastreid CfgNode instance.\n    \"\"\"\n    from .defaults import _C\n\n    return _C.clone()\n\n\ndef set_global_cfg(cfg: CfgNode) -> None:\n    \"\"\"\n    Let the global config point to the given cfg.\n    Assume that the given \"cfg\" has the key \"KEY\", after calling\n    `set_global_cfg(cfg)`, the key can be accessed by:\n    ::\n        from detectron2.config import global_cfg\n        print(global_cfg.KEY)\n    By using a hacky global config, you can access these configs anywhere,\n    without having to pass the config object or the values deep into the code.\n    This is a hacky feature introduced for quick prototyping / research exploration.\n    \"\"\"\n    global global_cfg\n    global_cfg.clear()\n    global_cfg.update(cfg)\n\n\ndef configurable(init_func=None, *, from_config=None):\n    \"\"\"\n    Decorate a function or a class's __init__ method so that it can be called\n    with a :class:`CfgNode` object using a :func:`from_config` function that translates\n    :class:`CfgNode` to arguments.\n    Examples:\n    ::\n        # Usage 1: Decorator on __init__:\n        class A:\n            @configurable\n            def __init__(self, a, b=2, c=3):\n                pass\n            @classmethod\n            def from_config(cls, cfg):   # 'cfg' must be the first argument\n                # Returns kwargs to be passed to __init__\n                return {\"a\": cfg.A, \"b\": cfg.B}\n        a1 = A(a=1, b=2)  # regular construction\n        a2 = A(cfg)       # construct with a cfg\n        a3 = A(cfg, b=3, c=4)  # construct with extra overwrite\n        # Usage 2: Decorator on any function. Needs an extra from_config argument:\n        @configurable(from_config=lambda cfg: {\"a: cfg.A, \"b\": cfg.B})\n        def a_func(a, b=2, c=3):\n            pass\n        a1 = a_func(a=1, b=2)  # regular call\n        a2 = a_func(cfg)       # call with a cfg\n        a3 = a_func(cfg, b=3, c=4)  # call with extra overwrite\n    Args:\n        init_func (callable): a class's ``__init__`` method in usage 1. The\n            class must have a ``from_config`` classmethod which takes `cfg` as\n            the first argument.\n        from_config (callable): the from_config function in usage 2. It must take `cfg`\n            as its first argument.\n    \"\"\"\n\n    def check_docstring(func):\n        if func.__module__.startswith(\"fastreid.\"):\n            assert (\n                    func.__doc__ is not None and \"experimental\" in func.__doc__.lower()\n            ), f\"configurable {func} should be marked experimental\"\n\n    if init_func is not None:\n        assert (\n                inspect.isfunction(init_func)\n                and from_config is None\n                and init_func.__name__ == \"__init__\"\n        ), \"Incorrect use of @configurable. Check API documentation for examples.\"\n        check_docstring(init_func)\n\n        @functools.wraps(init_func)\n        def wrapped(self, *args, **kwargs):\n            try:\n                from_config_func = type(self).from_config\n            except AttributeError as e:\n                raise AttributeError(\n                    \"Class with @configurable must have a 'from_config' classmethod.\"\n                ) from e\n            if not inspect.ismethod(from_config_func):\n                raise TypeError(\"Class with @configurable must have a 'from_config' classmethod.\")\n\n            if _called_with_cfg(*args, **kwargs):\n                explicit_args = _get_args_from_config(from_config_func, *args, **kwargs)\n                init_func(self, **explicit_args)\n            else:\n                init_func(self, *args, **kwargs)\n\n        return wrapped\n\n    else:\n        if from_config is None:\n            return configurable  # @configurable() is made equivalent to @configurable\n        assert inspect.isfunction(\n            from_config\n        ), \"from_config argument of configurable must be a function!\"\n\n        def wrapper(orig_func):\n            check_docstring(orig_func)\n\n            @functools.wraps(orig_func)\n            def wrapped(*args, **kwargs):\n                if _called_with_cfg(*args, **kwargs):\n                    explicit_args = _get_args_from_config(from_config, *args, **kwargs)\n                    return orig_func(**explicit_args)\n                else:\n                    return orig_func(*args, **kwargs)\n\n            return wrapped\n\n        return wrapper\n\n\ndef _get_args_from_config(from_config_func, *args, **kwargs):\n    \"\"\"\n    Use `from_config` to obtain explicit arguments.\n    Returns:\n        dict: arguments to be used for cls.__init__\n    \"\"\"\n    signature = inspect.signature(from_config_func)\n    if list(signature.parameters.keys())[0] != \"cfg\":\n        if inspect.isfunction(from_config_func):\n            name = from_config_func.__name__\n        else:\n            name = f\"{from_config_func.__self__}.from_config\"\n        raise TypeError(f\"{name} must take 'cfg' as the first argument!\")\n    support_var_arg = any(\n        param.kind in [param.VAR_POSITIONAL, param.VAR_KEYWORD]\n        for param in signature.parameters.values()\n    )\n\n    if support_var_arg:  # forward all arguments to from_config, if from_config accepts them\n        ret = from_config_func(*args, **kwargs)\n    else:\n        # forward supported arguments to from_config\n        supported_arg_names = set(signature.parameters.keys())\n        extra_kwargs = {}\n        for name in list(kwargs.keys()):\n            if name not in supported_arg_names:\n                extra_kwargs[name] = kwargs.pop(name)\n        ret = from_config_func(*args, **kwargs)\n        # forward the other arguments to __init__\n        ret.update(extra_kwargs)\n    return ret\n\n\ndef _called_with_cfg(*args, **kwargs):\n    \"\"\"\n    Returns:\n        bool: whether the arguments contain CfgNode and should be considered\n            forwarded to from_config.\n    \"\"\"\n\n    if len(args) and isinstance(args[0], _CfgNode):\n        return True\n    if isinstance(kwargs.pop(\"cfg\", None), _CfgNode):\n        return True\n    # `from_config`'s first argument is forced to be \"cfg\".\n    # So the above check covers all cases.\n    return False\n"
  },
  {
    "path": "fast_reid/fastreid/config/defaults.py",
    "content": "from .config import CfgNode as CN\n\n# -----------------------------------------------------------------------------\n# Convention about Training / Test specific parameters\n# -----------------------------------------------------------------------------\n# Whenever an argument can be either used for training or for testing, the\n# corresponding name will be post-fixed by a _TRAIN for a training parameter,\n# or _TEST for a test-specific parameter.\n# For example, the number of images during training will be\n# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be\n# IMAGES_PER_BATCH_TEST\n\n# -----------------------------------------------------------------------------\n# Config definition\n# -----------------------------------------------------------------------------\n\n_C = CN()\n\n# -----------------------------------------------------------------------------\n# MODEL\n# -----------------------------------------------------------------------------\n_C.MODEL = CN()\n_C.MODEL.DEVICE = \"cuda\"\n_C.MODEL.META_ARCHITECTURE = \"Baseline\"\n\n_C.MODEL.FREEZE_LAYERS = []\n\n# MoCo memory size\n_C.MODEL.QUEUE_SIZE = 8192\n\n# ---------------------------------------------------------------------------- #\n# Backbone options\n# ---------------------------------------------------------------------------- #\n_C.MODEL.BACKBONE = CN()\n\n_C.MODEL.BACKBONE.NAME = \"build_resnet_backbone\"\n_C.MODEL.BACKBONE.DEPTH = \"50x\"\n_C.MODEL.BACKBONE.LAST_STRIDE = 1\n# Backbone feature dimension\n_C.MODEL.BACKBONE.FEAT_DIM = 2048\n# Normalization method for the convolution layers.\n_C.MODEL.BACKBONE.NORM = \"BN\"\n# If use IBN block in backbone\n_C.MODEL.BACKBONE.WITH_IBN = False\n# If use SE block in backbone\n_C.MODEL.BACKBONE.WITH_SE = False\n# If use Non-local block in backbone\n_C.MODEL.BACKBONE.WITH_NL = False\n# Vision Transformer options\n_C.MODEL.BACKBONE.SIE_COE = 3.0\n_C.MODEL.BACKBONE.STRIDE_SIZE = (16, 16)\n_C.MODEL.BACKBONE.DROP_PATH_RATIO = 0.1\n_C.MODEL.BACKBONE.DROP_RATIO = 0.0\n_C.MODEL.BACKBONE.ATT_DROP_RATE = 0.0\n# If use ImageNet pretrain model\n_C.MODEL.BACKBONE.PRETRAIN = False\n# Pretrain model path\n_C.MODEL.BACKBONE.PRETRAIN_PATH = ''\n\n# ---------------------------------------------------------------------------- #\n# REID HEADS options\n# ---------------------------------------------------------------------------- #\n_C.MODEL.HEADS = CN()\n_C.MODEL.HEADS.NAME = \"EmbeddingHead\"\n# Normalization method for the convolution layers.\n_C.MODEL.HEADS.NORM = \"BN\"\n# Number of identity\n_C.MODEL.HEADS.NUM_CLASSES = 0\n# Embedding dimension in head\n_C.MODEL.HEADS.EMBEDDING_DIM = 0\n# If use BNneck in embedding\n_C.MODEL.HEADS.WITH_BNNECK = False\n# Triplet feature using feature before(after) bnneck\n_C.MODEL.HEADS.NECK_FEAT = \"before\"  # options: before, after\n# Pooling layer type\n_C.MODEL.HEADS.POOL_LAYER = \"GlobalAvgPool\"\n\n# Classification layer type\n_C.MODEL.HEADS.CLS_LAYER = \"Linear\"  # ArcSoftmax\" or \"CircleSoftmax\"\n\n# Margin and Scale for margin-based classification layer\n_C.MODEL.HEADS.MARGIN = 0.\n_C.MODEL.HEADS.SCALE = 1\n\n# ---------------------------------------------------------------------------- #\n# REID LOSSES options\n# ---------------------------------------------------------------------------- #\n_C.MODEL.LOSSES = CN()\n_C.MODEL.LOSSES.NAME = (\"CrossEntropyLoss\",)\n\n# Cross Entropy Loss options\n_C.MODEL.LOSSES.CE = CN()\n# if epsilon == 0, it means no label smooth regularization,\n# if epsilon == -1, it means adaptive label smooth regularization\n_C.MODEL.LOSSES.CE.EPSILON = 0.0\n_C.MODEL.LOSSES.CE.ALPHA = 0.2\n_C.MODEL.LOSSES.CE.SCALE = 1.0\n\n# Focal Loss options\n_C.MODEL.LOSSES.FL = CN()\n_C.MODEL.LOSSES.FL.ALPHA = 0.25\n_C.MODEL.LOSSES.FL.GAMMA = 2\n_C.MODEL.LOSSES.FL.SCALE = 1.0\n\n# Triplet Loss options\n_C.MODEL.LOSSES.TRI = CN()\n_C.MODEL.LOSSES.TRI.MARGIN = 0.3\n_C.MODEL.LOSSES.TRI.NORM_FEAT = False\n_C.MODEL.LOSSES.TRI.HARD_MINING = False\n_C.MODEL.LOSSES.TRI.SCALE = 1.0\n\n# Circle Loss options\n_C.MODEL.LOSSES.CIRCLE = CN()\n_C.MODEL.LOSSES.CIRCLE.MARGIN = 0.25\n_C.MODEL.LOSSES.CIRCLE.GAMMA = 128\n_C.MODEL.LOSSES.CIRCLE.SCALE = 1.0\n\n# Cosface Loss options\n_C.MODEL.LOSSES.COSFACE = CN()\n_C.MODEL.LOSSES.COSFACE.MARGIN = 0.25\n_C.MODEL.LOSSES.COSFACE.GAMMA = 128\n_C.MODEL.LOSSES.COSFACE.SCALE = 1.0\n\n# Path to a checkpoint file to be loaded to the model. You can find available models in the model zoo.\n_C.MODEL.WEIGHTS = \"\"\n\n# Values to be used for image normalization\n_C.MODEL.PIXEL_MEAN = [0.485*255, 0.456*255, 0.406*255]\n# Values to be used for image normalization\n_C.MODEL.PIXEL_STD = [0.229*255, 0.224*255, 0.225*255]\n\n# -----------------------------------------------------------------------------\n# KNOWLEDGE DISTILLATION\n# -----------------------------------------------------------------------------\n\n_C.KD = CN()\n_C.KD.MODEL_CONFIG = []\n_C.KD.MODEL_WEIGHTS = []\n_C.KD.EMA = CN({\"ENABLED\": False})\n_C.KD.EMA.MOMENTUM = 0.999\n\n# -----------------------------------------------------------------------------\n# INPUT\n# -----------------------------------------------------------------------------\n_C.INPUT = CN()\n# Size of the image during training\n_C.INPUT.SIZE_TRAIN = [256, 128]\n# Size of the image during test\n_C.INPUT.SIZE_TEST = [256, 128]\n\n# `True` if cropping is used for data augmentation during training\n_C.INPUT.CROP = CN({\"ENABLED\": False})\n# Size of the image cropped\n_C.INPUT.CROP.SIZE = [224, 224]\n# Size of the origin size cropped\n_C.INPUT.CROP.SCALE = [0.16, 1]\n# Aspect ratio of the origin aspect ratio cropped\n_C.INPUT.CROP.RATIO = [3./4., 4./3.]\n\n# Random probability for image horizontal flip\n_C.INPUT.FLIP = CN({\"ENABLED\": False})\n_C.INPUT.FLIP.PROB = 0.5\n\n# Value of padding size\n_C.INPUT.PADDING = CN({\"ENABLED\": False})\n_C.INPUT.PADDING.MODE = 'constant'\n_C.INPUT.PADDING.SIZE = 10\n\n# Random color jitter\n_C.INPUT.CJ = CN({\"ENABLED\": False})\n_C.INPUT.CJ.PROB = 0.5\n_C.INPUT.CJ.BRIGHTNESS = 0.15\n_C.INPUT.CJ.CONTRAST = 0.15\n_C.INPUT.CJ.SATURATION = 0.1\n_C.INPUT.CJ.HUE = 0.1\n\n# Random Affine\n_C.INPUT.AFFINE = CN({\"ENABLED\": False})\n\n# Auto augmentation\n_C.INPUT.AUTOAUG = CN({\"ENABLED\": False})\n_C.INPUT.AUTOAUG.PROB = 0.0\n\n# Augmix augmentation\n_C.INPUT.AUGMIX = CN({\"ENABLED\": False})\n_C.INPUT.AUGMIX.PROB = 0.0\n\n# Random Erasing\n_C.INPUT.REA = CN({\"ENABLED\": False})\n_C.INPUT.REA.PROB = 0.5\n_C.INPUT.REA.VALUE = [0.485*255, 0.456*255, 0.406*255]\n# Random Patch\n_C.INPUT.RPT = CN({\"ENABLED\": False})\n_C.INPUT.RPT.PROB = 0.5\n\n# -----------------------------------------------------------------------------\n# Dataset\n# -----------------------------------------------------------------------------\n_C.DATASETS = CN()\n# List of the dataset names for training\n_C.DATASETS.NAMES = (\"Market1501\",)\n# List of the dataset names for testing\n_C.DATASETS.TESTS = (\"Market1501\",)\n# Combine trainset and testset joint training\n_C.DATASETS.COMBINEALL = False\n\n# -----------------------------------------------------------------------------\n# DataLoader\n# -----------------------------------------------------------------------------\n_C.DATALOADER = CN()\n# Options: TrainingSampler, NaiveIdentitySampler, BalancedIdentitySampler\n_C.DATALOADER.SAMPLER_TRAIN = \"TrainingSampler\"\n# Number of instance for each person\n_C.DATALOADER.NUM_INSTANCE = 4\n_C.DATALOADER.NUM_WORKERS = 8\n\n# For set re-weight\n_C.DATALOADER.SET_WEIGHT = []\n\n# ---------------------------------------------------------------------------- #\n# Solver\n# ---------------------------------------------------------------------------- #\n_C.SOLVER = CN()\n\n# AUTOMATIC MIXED PRECISION\n_C.SOLVER.AMP = CN({\"ENABLED\": False})\n\n# Optimizer\n_C.SOLVER.OPT = \"Adam\"\n\n_C.SOLVER.MAX_EPOCH = 120\n\n_C.SOLVER.BASE_LR = 3e-4\n\n# This LR is applied to the last classification layer if\n# you want to 10x higher than BASE_LR.\n_C.SOLVER.HEADS_LR_FACTOR = 1.\n\n_C.SOLVER.MOMENTUM = 0.9\n_C.SOLVER.NESTEROV = False\n\n_C.SOLVER.WEIGHT_DECAY = 0.0005\n# The weight decay that's applied to parameters of normalization layers\n# (typically the affine transformation)\n_C.SOLVER.WEIGHT_DECAY_NORM = 0.0005\n\n# The previous detection code used a 2x higher LR and 0 WD for bias.\n# This is not useful (at least for recent models). You should avoid\n# changing these and they exists only to reproduce previous model\n# training if desired.\n_C.SOLVER.BIAS_LR_FACTOR = 1.0\n_C.SOLVER.WEIGHT_DECAY_BIAS = _C.SOLVER.WEIGHT_DECAY\n\n# Multi-step learning rate options\n_C.SOLVER.SCHED = \"MultiStepLR\"\n\n_C.SOLVER.DELAY_EPOCHS = 0\n\n_C.SOLVER.GAMMA = 0.1\n_C.SOLVER.STEPS = [30, 55]\n\n# Cosine annealing learning rate options\n_C.SOLVER.ETA_MIN_LR = 1e-7\n\n# Warmup options\n_C.SOLVER.WARMUP_FACTOR = 0.1\n_C.SOLVER.WARMUP_ITERS = 1000\n_C.SOLVER.WARMUP_METHOD = \"linear\"\n\n# Backbone freeze iters\n_C.SOLVER.FREEZE_ITERS = 0\n\n_C.SOLVER.CHECKPOINT_PERIOD = 20\n\n# Number of images per batch across all machines.\n# This is global, so if we have 8 GPUs and IMS_PER_BATCH = 256, each GPU will\n# see 32 images per batch\n_C.SOLVER.IMS_PER_BATCH = 64\n\n# Gradient clipping\n_C.SOLVER.CLIP_GRADIENTS = CN({\"ENABLED\": False})\n# Type of gradient clipping, currently 2 values are supported:\n# - \"value\": the absolute values of elements of each gradients are clipped\n# - \"norm\": the norm of the gradient for each parameter is clipped thus\n#   affecting all elements in the parameter\n_C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = \"norm\"\n# Maximum absolute value used for clipping gradients\n_C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 5.0\n# Floating point number p for L-p norm to be used with the \"norm\"\n# gradient clipping type; for L-inf, please specify .inf\n_C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0\n\n_C.TEST = CN()\n\n_C.TEST.EVAL_PERIOD = 20\n\n# Number of images per batch across all machines.\n_C.TEST.IMS_PER_BATCH = 64\n_C.TEST.METRIC = \"cosine\"\n_C.TEST.ROC = CN({\"ENABLED\": False})\n_C.TEST.FLIP = CN({\"ENABLED\": False})\n\n# Average query expansion\n_C.TEST.AQE = CN({\"ENABLED\": False})\n_C.TEST.AQE.ALPHA = 3.0\n_C.TEST.AQE.QE_TIME = 1\n_C.TEST.AQE.QE_K = 5\n\n# Re-rank\n_C.TEST.RERANK = CN({\"ENABLED\": False})\n_C.TEST.RERANK.K1 = 20\n_C.TEST.RERANK.K2 = 6\n_C.TEST.RERANK.LAMBDA = 0.3\n\n# Precise batchnorm\n_C.TEST.PRECISE_BN = CN({\"ENABLED\": False})\n_C.TEST.PRECISE_BN.DATASET = 'Market1501'\n_C.TEST.PRECISE_BN.NUM_ITER = 300\n\n# ---------------------------------------------------------------------------- #\n# Misc options\n# ---------------------------------------------------------------------------- #\n_C.OUTPUT_DIR = \"logs/\"\n\n# Benchmark different cudnn algorithms.\n# If input images have very different sizes, this option will have large overhead\n# for about 10k iterations. It usually hurts total time, but can benefit for certain models.\n# If input images have the same or similar sizes, benchmark is often helpful.\n_C.CUDNN_BENCHMARK = False\n"
  },
  {
    "path": "fast_reid/fastreid/data/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom . import transforms  # isort:skip\nfrom .build import (\n    build_reid_train_loader,\n    build_reid_test_loader\n)\nfrom .common import CommDataset\n\n# ensure the builtin datasets are registered\nfrom . import datasets, samplers  # isort:skip\n\n__all__ = [k for k in globals().keys() if not k.startswith(\"_\")]\n"
  },
  {
    "path": "fast_reid/fastreid/data/build.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\nimport os\n\nimport torch\nfrom torch._six import string_classes\nfrom collections import Mapping\n\nfrom fast_reid.fastreid.config import configurable\nfrom fast_reid.fastreid.utils import comm\nfrom . import samplers\nfrom .common import CommDataset\nfrom .data_utils import DataLoaderX\nfrom .datasets import DATASET_REGISTRY\nfrom .transforms import build_transforms\n\n\n__all__ = [\n    \"build_reid_train_loader\",\n    \"build_reid_test_loader\"\n]\n\n# _root = os.getenv(\"FASTREID_DATASETS\", \"datasets\")\n_root = os.getenv(\"FASTREID_DATASETS\", \"fast_reid/datasets\")\n\n\ndef _train_loader_from_config(cfg, *, train_set=None, transforms=None, sampler=None, **kwargs):\n\n    if transforms is None:\n        transforms = build_transforms(cfg, is_train=True)   # Crop, HFlip, Erasing\n\n    if train_set is None:\n        train_items = list()\n        for d in cfg.DATASETS.NAMES:\n            data = DATASET_REGISTRY.get(d)(root=_root, **kwargs)\n            if comm.is_main_process():\n                data.show_train()\n            train_items.extend(data.train)\n\n        train_set = CommDataset(train_items, transforms, relabel=True)\n\n    if sampler is None:\n        sampler_name = cfg.DATALOADER.SAMPLER_TRAIN\n        num_instance = cfg.DATALOADER.NUM_INSTANCE\n        mini_batch_size = cfg.SOLVER.IMS_PER_BATCH // comm.get_world_size()\n\n        logger = logging.getLogger(__name__)\n        logger.info(\"Using training sampler {}\".format(sampler_name))\n        if sampler_name == \"TrainingSampler\":\n            sampler = samplers.TrainingSampler(len(train_set))\n        elif sampler_name == \"NaiveIdentitySampler\":\n            sampler = samplers.NaiveIdentitySampler(train_set.img_items, mini_batch_size, num_instance)\n        elif sampler_name == \"BalancedIdentitySampler\":\n            sampler = samplers.BalancedIdentitySampler(train_set.img_items, mini_batch_size, num_instance)\n        elif sampler_name == \"SetReWeightSampler\":\n            set_weight = cfg.DATALOADER.SET_WEIGHT\n            sampler = samplers.SetReWeightSampler(train_set.img_items, mini_batch_size, num_instance, set_weight)\n        elif sampler_name == \"ImbalancedDatasetSampler\":\n            sampler = samplers.ImbalancedDatasetSampler(train_set.img_items)\n        else:\n            raise ValueError(\"Unknown training sampler: {}\".format(sampler_name))\n\n    return {\n        \"train_set\": train_set,\n        \"sampler\": sampler,\n        \"total_batch_size\": cfg.SOLVER.IMS_PER_BATCH,\n        \"num_workers\": cfg.DATALOADER.NUM_WORKERS,\n    }\n\n\n@configurable(from_config=_train_loader_from_config)\ndef build_reid_train_loader(\n        train_set, *, sampler=None, total_batch_size, num_workers=0,\n):\n    \"\"\"\n    Build a dataloader for object re-identification with some default features.\n    This interface is experimental.\n\n    Returns:\n        torch.utils.data.DataLoader: a dataloader.\n    \"\"\"\n\n    mini_batch_size = total_batch_size // comm.get_world_size()\n\n    batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, mini_batch_size, True)\n\n    train_loader = DataLoaderX(\n        comm.get_local_rank(),\n        dataset=train_set,\n        num_workers=num_workers,\n        batch_sampler=batch_sampler,\n        collate_fn=fast_batch_collator,\n        pin_memory=True,\n    )\n\n    return train_loader\n\n\ndef _test_loader_from_config(cfg, *, dataset_name=None, test_set=None, num_query=0, transforms=None, **kwargs):\n    if transforms is None:\n        transforms = build_transforms(cfg, is_train=False)\n\n    if test_set is None:\n        assert dataset_name is not None, \"dataset_name must be explicitly passed in when test_set is not provided\"\n        data = DATASET_REGISTRY.get(dataset_name)(root=_root, **kwargs)\n        if comm.is_main_process():\n            data.show_test()\n        test_items = data.query + data.gallery\n        test_set = CommDataset(test_items, transforms, relabel=False)\n\n        # Update query number\n        num_query = len(data.query)\n\n    return {\n        \"test_set\": test_set,\n        \"test_batch_size\": cfg.TEST.IMS_PER_BATCH,\n        \"num_query\": num_query,\n    }\n\n\n@configurable(from_config=_test_loader_from_config)\ndef build_reid_test_loader(test_set, test_batch_size, num_query, num_workers=4):\n    \"\"\"\n    Similar to `build_reid_train_loader`. This sampler coordinates all workers to produce\n    the exact set of all samples\n    This interface is experimental.\n\n    Args:\n        test_set:\n        test_batch_size:\n        num_query:\n        num_workers:\n\n    Returns:\n        DataLoader: a torch DataLoader, that loads the given reid dataset, with\n        the test-time transformation.\n\n    Examples:\n    ::\n        data_loader = build_reid_test_loader(test_set, test_batch_size, num_query)\n        # or, instantiate with a CfgNode:\n        data_loader = build_reid_test_loader(cfg, \"my_test\")\n    \"\"\"\n\n    mini_batch_size = test_batch_size // comm.get_world_size()\n    data_sampler = samplers.InferenceSampler(len(test_set))\n    batch_sampler = torch.utils.data.BatchSampler(data_sampler, mini_batch_size, False)\n    test_loader = DataLoaderX(\n        comm.get_local_rank(),\n        dataset=test_set,\n        batch_sampler=batch_sampler,\n        num_workers=num_workers,  # save some memory\n        collate_fn=fast_batch_collator,\n        pin_memory=True,\n    )\n    return test_loader, num_query\n\n\ndef trivial_batch_collator(batch):\n    \"\"\"\n    A batch collator that does nothing.\n    \"\"\"\n    return batch\n\n\ndef fast_batch_collator(batched_inputs):\n    \"\"\"\n    A simple batch collator for most common reid tasks\n    \"\"\"\n    elem = batched_inputs[0]\n    if isinstance(elem, torch.Tensor):\n        out = torch.zeros((len(batched_inputs), *elem.size()), dtype=elem.dtype)\n        for i, tensor in enumerate(batched_inputs):\n            out[i] += tensor\n        return out\n\n    elif isinstance(elem, Mapping):\n        return {key: fast_batch_collator([d[key] for d in batched_inputs]) for key in elem}\n\n    elif isinstance(elem, float):\n        return torch.tensor(batched_inputs, dtype=torch.float64)\n    elif isinstance(elem, int):\n        return torch.tensor(batched_inputs)\n    elif isinstance(elem, string_classes):\n        return batched_inputs\n"
  },
  {
    "path": "fast_reid/fastreid/data/common.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom torch.utils.data import Dataset\n\nfrom .data_utils import read_image\n\n\nclass CommDataset(Dataset):\n    \"\"\"Image Person ReID Dataset\"\"\"\n\n    def __init__(self, img_items, transform=None, relabel=True):\n        self.img_items = img_items\n        self.transform = transform\n        self.relabel = relabel\n\n        pid_set = set()\n        cam_set = set()\n        for i in img_items:\n            pid_set.add(i[1])\n            cam_set.add(i[2])\n\n        self.pids = sorted(list(pid_set))\n        self.cams = sorted(list(cam_set))\n        if relabel:\n            self.pid_dict = dict([(p, i) for i, p in enumerate(self.pids)])\n            self.cam_dict = dict([(p, i) for i, p in enumerate(self.cams)])\n\n    def __len__(self):\n        return len(self.img_items)\n\n    def __getitem__(self, index):\n        img_item = self.img_items[index]\n        img_path = img_item[0]\n        pid = img_item[1]\n        camid = img_item[2]\n        img = read_image(img_path)\n        if self.transform is not None: img = self.transform(img)\n        if self.relabel:\n            pid = self.pid_dict[pid]\n            camid = self.cam_dict[camid]\n        return {\n            \"images\": img,\n            \"targets\": pid,\n            \"camids\": camid,\n            \"img_paths\": img_path,\n        }\n\n    @property\n    def num_classes(self):\n        return len(self.pids)\n\n    @property\n    def num_cameras(self):\n        return len(self.cams)\n"
  },
  {
    "path": "fast_reid/fastreid/data/data_utils.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport torch\nimport numpy as np\nfrom PIL import Image, ImageOps\nimport threading\n\nimport queue\nfrom torch.utils.data import DataLoader\n\nfrom fast_reid.fastreid.utils.file_io import PathManager\n\n\ndef read_image(file_name, format=None):\n    \"\"\"\n    Read an image into the given format.\n    Will apply rotation and flipping if the image has such exif information.\n\n    Args:\n        file_name (str): image file path\n        format (str): one of the supported image modes in PIL, or \"BGR\"\n    Returns:\n        image (np.ndarray): an HWC image\n    \"\"\"\n    with PathManager.open(file_name, \"rb\") as f:\n        image = Image.open(f)\n\n        # work around this bug: https://github.com/python-pillow/Pillow/issues/3973\n        try:\n            image = ImageOps.exif_transpose(image)\n        except Exception:\n            pass\n\n        if format is not None:\n            # PIL only supports RGB, so convert to RGB and flip channels over below\n            conversion_format = format\n            if format == \"BGR\":\n                conversion_format = \"RGB\"\n            image = image.convert(conversion_format)\n        image = np.asarray(image)\n\n        # PIL squeezes out the channel dimension for \"L\", so make it HWC\n        if format == \"L\":\n            image = np.expand_dims(image, -1)\n\n        # handle formats not supported by PIL\n        elif format == \"BGR\":\n            # flip channels if needed\n            image = image[:, :, ::-1]\n\n        # handle grayscale mixed in RGB images\n        elif len(image.shape) == 2:\n            image = np.repeat(image[..., np.newaxis], 3, axis=-1)\n\n        image = Image.fromarray(image)\n\n        return image\n\n\n\"\"\"\n#based on http://stackoverflow.com/questions/7323664/python-generator-pre-fetch\nThis is a single-function package that transforms arbitrary generator into a background-thead generator that \nprefetches several batches of data in a parallel background thead.\n\nThis is useful if you have a computationally heavy process (CPU or GPU) that \niteratively processes minibatches from the generator while the generator \nconsumes some other resource (disk IO / loading from database / more CPU if you have unused cores). \n\nBy default these two processes will constantly wait for one another to finish. If you make generator work in \nprefetch mode (see examples below), they will work in parallel, potentially saving you your GPU time.\nWe personally use the prefetch generator when iterating minibatches of data for deep learning with PyTorch etc.\n\nQuick usage example (ipython notebook) - https://github.com/justheuristic/prefetch_generator/blob/master/example.ipynb\nThis package contains this object\n - BackgroundGenerator(any_other_generator[,max_prefetch = something])\n\"\"\"\n\n\nclass BackgroundGenerator(threading.Thread):\n    \"\"\"\n    the usage is below\n    >> for batch in BackgroundGenerator(my_minibatch_iterator):\n    >>    doit()\n    More details are written in the BackgroundGenerator doc\n    >> help(BackgroundGenerator)\n    \"\"\"\n\n    def __init__(self, generator, local_rank, max_prefetch=10):\n        \"\"\"\n        This function transforms generator into a background-thead generator.\n        :param generator: generator or genexp or any\n        It can be used with any minibatch generator.\n\n        It is quite lightweight, but not entirely weightless.\n        Using global variables inside generator is not recommended (may raise GIL and zero-out the\n        benefit of having a background thread.)\n        The ideal use case is when everything it requires is store inside it and everything it\n        outputs is passed through queue.\n\n        There's no restriction on doing weird stuff, reading/writing files, retrieving\n        URLs [or whatever] wlilst iterating.\n\n        :param max_prefetch: defines, how many iterations (at most) can background generator keep\n        stored at any moment of time.\n        Whenever there's already max_prefetch batches stored in queue, the background process will halt until\n        one of these batches is dequeued.\n\n        !Default max_prefetch=1 is okay unless you deal with some weird file IO in your generator!\n\n        Setting max_prefetch to -1 lets it store as many batches as it can, which will work\n        slightly (if any) faster, but will require storing\n        all batches in memory. If you use infinite generator with max_prefetch=-1, it will exceed the RAM size\n        unless dequeued quickly enough.\n        \"\"\"\n        super().__init__()\n        self.queue = queue.Queue(max_prefetch)\n        self.generator = generator\n        self.local_rank = local_rank\n        self.daemon = True\n        self.exit_event = threading.Event()\n        self.start()\n\n    def run(self):\n        torch.cuda.set_device(self.local_rank)\n        for item in self.generator:\n            if self.exit_event.is_set():\n                break\n            self.queue.put(item)\n        self.queue.put(None)\n\n    def next(self):\n        next_item = self.queue.get()\n        if next_item is None:\n            raise StopIteration\n        return next_item\n\n    # Python 3 compatibility\n    def __next__(self):\n        return self.next()\n\n    def __iter__(self):\n        return self\n\n\nclass DataLoaderX(DataLoader):\n    def __init__(self, local_rank, **kwargs):\n        super().__init__(**kwargs)\n        self.stream = torch.cuda.Stream(\n            local_rank\n        )  # create a new cuda stream in each process\n        self.local_rank = local_rank\n\n    def __iter__(self):\n        self.iter = super().__iter__()\n        self.iter = BackgroundGenerator(self.iter, self.local_rank)\n        self.preload()\n        return self\n\n    def _shutdown_background_thread(self):\n        if not self.iter.is_alive():\n            # avoid re-entrance or ill-conditioned thread state\n            return\n\n        # Set exit event to True for background threading stopping\n        self.iter.exit_event.set()\n\n        # Exhaust all remaining elements, so that the queue becomes empty,\n        # and the thread should quit\n        for _ in self.iter:\n            pass\n\n        # Waiting for background thread to quit\n        self.iter.join()\n\n    def preload(self):\n        self.batch = next(self.iter, None)\n        if self.batch is None:\n            return None\n        with torch.cuda.stream(self.stream):\n            for k in self.batch:\n                if isinstance(self.batch[k], torch.Tensor):\n                    self.batch[k] = self.batch[k].to(\n                        device=self.local_rank, non_blocking=True\n                    )\n\n    def __next__(self):\n        torch.cuda.current_stream().wait_stream(\n            self.stream\n        )  # wait tensor to put on GPU\n        batch = self.batch\n        if batch is None:\n            raise StopIteration\n        self.preload()\n        return batch\n\n    # Signal for shutting down background thread\n    def shutdown(self):\n        # If the dataloader is to be freed, shutdown its BackgroundGenerator\n        self._shutdown_background_thread()\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/AirportALERT.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['AirportALERT', ]\n\n\n@DATASET_REGISTRY.register()\nclass AirportALERT(ImageDataset):\n    \"\"\"Airport \n\n    \"\"\"\n    dataset_dir = \"AirportALERT\"\n    dataset_name = \"airport\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n        self.train_file = os.path.join(self.root, self.dataset_dir, 'filepath.txt')\n\n        required_files = [self.train_file, self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path, self.train_file)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, dir_path, train_file):\n        data = []\n        with open(train_file, \"r\") as f:\n            img_paths = [line.strip('\\n') for line in f.readlines()]\n\n        for path in img_paths:\n            split_path = path.split('\\\\')\n            img_path = '/'.join(split_path)\n            camid = self.dataset_name + \"_\" + split_path[0]\n            pid = self.dataset_name + \"_\" + split_path[1]\n            img_path = os.path.join(dir_path, img_path)\n            # if 11001 <= int(split_path[1]) <= 401999:\n            if 11001 <= int(split_path[1]):\n                data.append([img_path, pid, camid])\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom ...utils.registry import Registry\n\nDATASET_REGISTRY = Registry(\"DATASET\")\nDATASET_REGISTRY.__doc__ = \"\"\"\nRegistry for datasets\nIt must returns an instance of :class:`Backbone`.\n\"\"\"\n\n# Person re-id datasets\nfrom .mot17 import MOT17\nfrom .mot20 import MOT20\nfrom .cuhk03 import CUHK03              # 1367 ids, 26264 images\nfrom .dukemtmcreid import DukeMTMC      # 702 ids, 16522 images\nfrom .market1501 import Market1501      # 751 ids, 12936 images\nfrom .msmt17 import MSMT17              # --- ids, 32621 images\nfrom .AirportALERT import AirportALERT\nfrom .iLIDS import iLIDS\nfrom .pku import PKU\nfrom .prai import PRAI\nfrom .prid import PRID\nfrom .grid import GRID\nfrom .saivt import SAIVT\nfrom .sensereid import SenseReID\nfrom .sysu_mm import SYSU_mm\nfrom .thermalworld import Thermalworld\nfrom .pes3d import PeS3D\nfrom .caviara import CAVIARa\nfrom .viper import VIPeR\nfrom .lpw import LPW\nfrom .shinpuhkan import Shinpuhkan\nfrom .wildtracker import WildTrackCrop\nfrom .cuhksysu import CUHKSYSU          # [hgx0913]\nfrom .dancetrack import DanceTrack      # [hgx0911]\nfrom .cuhksysu_dancetrack import CUHKSYSU_DanceTrack        # [hgx0914]\nfrom .cuhksysu_mot17 import CUHKSYSU_MOT17\nfrom .cuhksysu_mot20 import CUHKSYSU_MOT20\n\n\n# Vehicle re-id datasets\nfrom .veri import VeRi\nfrom .vehicleid import VehicleID, SmallVehicleID, MediumVehicleID, LargeVehicleID\nfrom .veriwild import VeRiWild, SmallVeRiWild, MediumVeRiWild, LargeVeRiWild\n\n__all__ = [k for k in globals().keys() if \"builtin\" not in k and not k.startswith(\"_\")]\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/bases.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport copy\nimport logging\nimport os\n\nfrom tabulate import tabulate\nfrom termcolor import colored\n\nlogger = logging.getLogger(__name__)\n\n\nclass Dataset(object):\n    \"\"\"An abstract class representing a Dataset.\n    This is the base class for ``ImageDataset`` and ``VideoDataset``.\n\n    Args:\n        train (list or Callable): contains tuples of (img_path(s), pid, camid).\n        query (list or Callable): contains tuples of (img_path(s), pid, camid).\n        gallery (list or Callable): contains tuples of (img_path(s), pid, camid).\n        transform: transform function.\n        mode (str): 'train', 'query' or 'gallery'.\n        combineall (bool): combines train, query and gallery in a\n            dataset for training.\n        verbose (bool): show information.\n    \"\"\"\n    _junk_pids = []  # contains useless person IDs, e.g. background, false detections\n\n    def __init__(self, train, query, gallery, transform=None, mode='train',\n                 combineall=False, verbose=True, **kwargs):\n        self._train = train\n        self._query = query\n        self._gallery = gallery\n        self.transform = transform\n        self.mode = mode\n        self.combineall = combineall\n        self.verbose = verbose\n\n        if self.combineall:\n            self.combine_all()\n\n        if self.mode == 'train':\n            self.data = self.train\n        elif self.mode == 'query':\n            self.data = self.query\n        elif self.mode == 'gallery':\n            self.data = self.gallery\n        else:\n            raise ValueError('Invalid mode. Got {}, but expected to be '\n                             'one of [train | query | gallery]'.format(self.mode))\n\n    @property\n    def train(self):\n        if callable(self._train):\n            self._train = self._train()\n        return self._train\n\n    @property\n    def query(self):\n        if callable(self._query):\n            self._query = self._query()\n        return self._query\n\n    @property\n    def gallery(self):\n        if callable(self._gallery):\n            self._gallery = self._gallery()\n        return self._gallery\n\n    def __getitem__(self, index):\n        raise NotImplementedError\n\n    def __len__(self):\n        return len(self.data)\n\n    def __radd__(self, other):\n        \"\"\"Supports sum([dataset1, dataset2, dataset3]).\"\"\"\n        if other == 0:\n            return self\n        else:\n            return self.__add__(other)\n\n    def parse_data(self, data):\n        \"\"\"Parses data list and returns the number of person IDs\n        and the number of camera views.\n        Args:\n            data (list): contains tuples of (img_path(s), pid, camid)\n        \"\"\"\n        pids = set()\n        cams = set()\n        for info in data:\n            pids.add(info[1])\n            cams.add(info[2])\n        return len(pids), len(cams)\n\n    def get_num_pids(self, data):\n        \"\"\"Returns the number of training person identities.\"\"\"\n        return self.parse_data(data)[0]\n\n    def get_num_cams(self, data):\n        \"\"\"Returns the number of training cameras.\"\"\"\n        return self.parse_data(data)[1]\n\n    def show_summary(self):\n        \"\"\"Shows dataset statistics.\"\"\"\n        pass\n\n    def combine_all(self):\n        \"\"\"Combines train, query and gallery in a dataset for training.\"\"\"\n        combined = copy.deepcopy(self.train)\n\n        def _combine_data(data):\n            for img_path, pid, camid in data:\n                if pid in self._junk_pids:\n                    continue\n                pid = getattr(self, \"dataset_name\", \"Unknown\") + \"_test_\" + str(pid)\n                camid = getattr(self, \"dataset_name\", \"Unknown\") + \"_test_\" + str(camid)\n                combined.append((img_path, pid, camid))\n\n        _combine_data(self.query)\n        _combine_data(self.gallery)\n\n        self._train = combined\n\n    def check_before_run(self, required_files):\n        \"\"\"Checks if required files exist before going deeper.\n        Args:\n            required_files (str or list): string file name(s).\n        \"\"\"\n        if isinstance(required_files, str):\n            required_files = [required_files]\n\n        for fpath in required_files:\n            if not os.path.exists(fpath):\n                raise RuntimeError('\"{}\" is not found'.format(fpath))\n\n\nclass ImageDataset(Dataset):\n    \"\"\"A base class representing ImageDataset.\n    All other image datasets should subclass it.\n    ``__getitem__`` returns an image given index.\n    It will return ``img``, ``pid``, ``camid`` and ``img_path``\n    where ``img`` has shape (channel, height, width). As a result,\n    data in each batch has shape (batch_size, channel, height, width).\n    \"\"\"\n\n    def show_train(self):\n        num_train_pids, num_train_cams = self.parse_data(self.train)\n\n        headers = ['subset', '# ids', '# images', '# cameras']\n        csv_results = [['train', num_train_pids, len(self.train), num_train_cams]]\n\n        # tabulate it\n        table = tabulate(\n            csv_results,\n            tablefmt=\"pipe\",\n            headers=headers,\n            numalign=\"left\",\n        )\n        logger.info(f\"=> Loaded {self.__class__.__name__} in csv format: \\n\" + colored(table, \"cyan\"))\n\n    def show_test(self):\n        num_query_pids, num_query_cams = self.parse_data(self.query)\n        num_gallery_pids, num_gallery_cams = self.parse_data(self.gallery)\n\n        headers = ['subset', '# ids', '# images', '# cameras']\n        csv_results = [\n            ['query', num_query_pids, len(self.query), num_query_cams],\n            ['gallery', num_gallery_pids, len(self.gallery), num_gallery_cams],\n        ]\n\n        # tabulate it\n        table = tabulate(\n            csv_results,\n            tablefmt=\"pipe\",\n            headers=headers,\n            numalign=\"left\",\n        )\n        logger.info(f\"=> Loaded {self.__class__.__name__} in csv format: \\n\" + colored(table, \"cyan\"))\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/caviara.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['CAVIARa', ]\n\n\n@DATASET_REGISTRY.register()\nclass CAVIARa(ImageDataset):\n    \"\"\"CAVIARa\n    \"\"\"\n    dataset_dir = \"CAVIARa\"\n    dataset_name = \"caviara\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n\n        img_list = glob(os.path.join(train_path, \"*.jpg\"))\n        for img_path in img_list:\n            img_name = img_path.split('/')[-1]\n            pid = self.dataset_name + \"_\" + img_name[:4]\n            camid = self.dataset_name + \"_cam0\"\n            data.append([img_path, pid, camid])\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/cuhk03.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: liaoxingyu2@jd.com\n\"\"\"\n\nimport json\nimport os.path as osp\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.utils.file_io import PathManager\nfrom .bases import ImageDataset\n\n\n@DATASET_REGISTRY.register()\nclass CUHK03(ImageDataset):\n    \"\"\"CUHK03.\n\n    Reference:\n        Li et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. CVPR 2014.\n\n    URL: `<http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html#!>`_\n\n    Dataset statistics:\n        - identities: 1360.\n        - images: 13164.\n        - cameras: 6.\n        - splits: 20 (classic).\n    \"\"\"\n    dataset_dir = 'cuhk03'\n    dataset_url = None\n    dataset_name = \"cuhk03\"\n\n    def __init__(self, root='datasets', split_id=0, cuhk03_labeled=True, cuhk03_classic_split=False, **kwargs):\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release')\n        self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat')\n\n        self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected')\n        self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled')\n\n        self.split_classic_det_json_path = osp.join(self.dataset_dir, 'splits_classic_detected.json')\n        self.split_classic_lab_json_path = osp.join(self.dataset_dir, 'splits_classic_labeled.json')\n\n        self.split_new_det_json_path = osp.join(self.dataset_dir, 'splits_new_detected.json')\n        self.split_new_lab_json_path = osp.join(self.dataset_dir, 'splits_new_labeled.json')\n\n        self.split_new_det_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat')\n        self.split_new_lab_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat')\n\n        required_files = [\n            self.dataset_dir,\n            self.data_dir,\n            self.raw_mat_path,\n            self.split_new_det_mat_path,\n            self.split_new_lab_mat_path\n        ]\n        self.check_before_run(required_files)\n\n        self.preprocess_split()\n\n        if cuhk03_labeled:\n            split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path\n        else:\n            split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path\n\n        with PathManager.open(split_path) as f:\n            splits = json.load(f)\n        assert split_id < len(splits), 'Condition split_id ({}) < len(splits) ({}) is false'.format(split_id,\n                                                                                                    len(splits))\n        split = splits[split_id]\n\n        train = split['train']\n        tmp_train = []\n        for img_path, pid, camid in train:\n            new_pid = self.dataset_name + \"_\" + str(pid)\n            new_camid = self.dataset_name + \"_\" + str(camid)\n            tmp_train.append((img_path, new_pid, new_camid))\n        train = tmp_train\n        del tmp_train\n        query = split['query']\n        gallery = split['gallery']\n\n        super(CUHK03, self).__init__(train, query, gallery, **kwargs)\n\n    def preprocess_split(self):\n        # This function is a bit complex and ugly, what it does is\n        # 1. extract data from cuhk-03.mat and save as png images\n        # 2. create 20 classic splits (Li et al. CVPR'14)\n        # 3. create new split (Zhong et al. CVPR'17)\n        if osp.exists(self.imgs_labeled_dir) \\\n                and osp.exists(self.imgs_detected_dir) \\\n                and osp.exists(self.split_classic_det_json_path) \\\n                and osp.exists(self.split_classic_lab_json_path) \\\n                and osp.exists(self.split_new_det_json_path) \\\n                and osp.exists(self.split_new_lab_json_path):\n            return\n\n        import h5py\n        from imageio import imwrite\n        from scipy import io\n\n        PathManager.mkdirs(self.imgs_detected_dir)\n        PathManager.mkdirs(self.imgs_labeled_dir)\n\n        print('Extract image data from \"{}\" and save as png'.format(self.raw_mat_path))\n        mat = h5py.File(self.raw_mat_path, 'r')\n\n        def _deref(ref):\n            return mat[ref][:].T\n\n        def _process_images(img_refs, campid, pid, save_dir):\n            img_paths = []  # Note: some persons only have images for one view\n            for imgid, img_ref in enumerate(img_refs):\n                img = _deref(img_ref)\n                if img.size == 0 or img.ndim < 3:\n                    continue  # skip empty cell\n                # images are saved with the following format, index-1 (ensure uniqueness)\n                # campid: index of camera pair (1-5)\n                # pid: index of person in 'campid'-th camera pair\n                # viewid: index of view, {1, 2}\n                # imgid: index of image, (1-10)\n                viewid = 1 if imgid < 5 else 2\n                img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid + 1, pid + 1, viewid, imgid + 1)\n                img_path = osp.join(save_dir, img_name)\n                if not osp.isfile(img_path):\n                    imwrite(img_path, img)\n                img_paths.append(img_path)\n            return img_paths\n\n        def _extract_img(image_type):\n            print('Processing {} images ...'.format(image_type))\n            meta_data = []\n            imgs_dir = self.imgs_detected_dir if image_type == 'detected' else self.imgs_labeled_dir\n            for campid, camp_ref in enumerate(mat[image_type][0]):\n                camp = _deref(camp_ref)\n                num_pids = camp.shape[0]\n                for pid in range(num_pids):\n                    img_paths = _process_images(camp[pid, :], campid, pid, imgs_dir)\n                    assert len(img_paths) > 0, 'campid{}-pid{} has no images'.format(campid, pid)\n                    meta_data.append((campid + 1, pid + 1, img_paths))\n                print('- done camera pair {} with {} identities'.format(campid + 1, num_pids))\n            return meta_data\n\n        meta_detected = _extract_img('detected')\n        meta_labeled = _extract_img('labeled')\n\n        def _extract_classic_split(meta_data, test_split):\n            train, test = [], []\n            num_train_pids, num_test_pids = 0, 0\n            num_train_imgs, num_test_imgs = 0, 0\n            for i, (campid, pid, img_paths) in enumerate(meta_data):\n\n                if [campid, pid] in test_split:\n                    for img_path in img_paths:\n                        camid = int(osp.basename(img_path).split('_')[2]) - 1  # make it 0-based\n                        test.append((img_path, num_test_pids, camid))\n                    num_test_pids += 1\n                    num_test_imgs += len(img_paths)\n                else:\n                    for img_path in img_paths:\n                        camid = int(osp.basename(img_path).split('_')[2]) - 1  # make it 0-based\n                        train.append((img_path, num_train_pids, camid))\n                    num_train_pids += 1\n                    num_train_imgs += len(img_paths)\n            return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs\n\n        print('Creating classic splits (# = 20) ...')\n        splits_classic_det, splits_classic_lab = [], []\n        for split_ref in mat['testsets'][0]:\n            test_split = _deref(split_ref).tolist()\n\n            # create split for detected images\n            train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \\\n                _extract_classic_split(meta_detected, test_split)\n            splits_classic_det.append({\n                'train': train,\n                'query': test,\n                'gallery': test,\n                'num_train_pids': num_train_pids,\n                'num_train_imgs': num_train_imgs,\n                'num_query_pids': num_test_pids,\n                'num_query_imgs': num_test_imgs,\n                'num_gallery_pids': num_test_pids,\n                'num_gallery_imgs': num_test_imgs\n            })\n\n            # create split for labeled images\n            train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \\\n                _extract_classic_split(meta_labeled, test_split)\n            splits_classic_lab.append({\n                'train': train,\n                'query': test,\n                'gallery': test,\n                'num_train_pids': num_train_pids,\n                'num_train_imgs': num_train_imgs,\n                'num_query_pids': num_test_pids,\n                'num_query_imgs': num_test_imgs,\n                'num_gallery_pids': num_test_pids,\n                'num_gallery_imgs': num_test_imgs\n            })\n\n        with PathManager.open(self.split_classic_det_json_path, 'w') as f:\n            json.dump(splits_classic_det, f, indent=4, separators=(',', ': '))\n        with PathManager.open(self.split_classic_lab_json_path, 'w') as f:\n            json.dump(splits_classic_lab, f, indent=4, separators=(',', ': '))\n\n        def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):\n            tmp_set = []\n            unique_pids = set()\n            for idx in idxs:\n                img_name = filelist[idx][0]\n                camid = int(img_name.split('_')[2]) - 1  # make it 0-based\n                pid = pids[idx]\n                if relabel:\n                    pid = pid2label[pid]\n                img_path = osp.join(img_dir, img_name)\n                tmp_set.append((img_path, int(pid), camid))\n                unique_pids.add(pid)\n            return tmp_set, len(unique_pids), len(idxs)\n\n        def _extract_new_split(split_dict, img_dir):\n            train_idxs = split_dict['train_idx'].flatten() - 1  # index-0\n            pids = split_dict['labels'].flatten()\n            train_pids = set(pids[train_idxs])\n            pid2label = {pid: label for label, pid in enumerate(train_pids)}\n            query_idxs = split_dict['query_idx'].flatten() - 1\n            gallery_idxs = split_dict['gallery_idx'].flatten() - 1\n            filelist = split_dict['filelist'].flatten()\n            train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True)\n            query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False)\n            gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False)\n            return train_info, query_info, gallery_info\n\n        print('Creating new split for detected images (767/700) ...')\n        train_info, query_info, gallery_info = _extract_new_split(\n            io.loadmat(self.split_new_det_mat_path),\n            self.imgs_detected_dir\n        )\n        split = [{\n            'train': train_info[0],\n            'query': query_info[0],\n            'gallery': gallery_info[0],\n            'num_train_pids': train_info[1],\n            'num_train_imgs': train_info[2],\n            'num_query_pids': query_info[1],\n            'num_query_imgs': query_info[2],\n            'num_gallery_pids': gallery_info[1],\n            'num_gallery_imgs': gallery_info[2]\n        }]\n\n        with PathManager.open(self.split_new_det_json_path, 'w') as f:\n            json.dump(split, f, indent=4, separators=(',', ': '))\n\n        print('Creating new split for labeled images (767/700) ...')\n        train_info, query_info, gallery_info = _extract_new_split(\n            io.loadmat(self.split_new_lab_mat_path),\n            self.imgs_labeled_dir\n        )\n        split = [{\n            'train': train_info[0],\n            'query': query_info[0],\n            'gallery': gallery_info[0],\n            'num_train_pids': train_info[1],\n            'num_train_imgs': train_info[2],\n            'num_query_pids': query_info[1],\n            'num_query_imgs': query_info[2],\n            'num_gallery_pids': gallery_info[1],\n            'num_gallery_imgs': gallery_info[2]\n        }]\n        with PathManager.open(self.split_new_lab_json_path, 'w') as f:\n            json.dump(split, f, indent=4, separators=(',', ': '))\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/cuhksysu.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock (changed by Nir)\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\nimport glob\nimport os.path as osp\nimport re\nimport warnings\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass CUHKSYSU(ImageDataset):\n    \"\"\"DanceTrack.\n\n    Dataset statistics:\n        - identities: ?\n        - images: ?\n    \"\"\"\n    _junk_pids = [0, -1]\n    dataset_dir = ''\n    dataset_url = ''\n    dataset_name = \"CUHKSYSU\"\n\n    def __init__(self, root='datasets', **kwargs):\n        # self.root = osp.abspath(osp.expanduser(root))\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        # allow alternative directory structure\n        self.data_dir = self.dataset_dir        # 'fast_reid/datasets/'\n        data_dir = osp.join(self.data_dir, 'cuhksysu-reid')    # 'fast_reid/datasets/cuhksysu-reid'\n        if osp.isdir(data_dir):\n            self.data_dir = data_dir        # 'fast_reid/datasets/cuhksysu-reid'\n        else:\n            warnings.warn('The current data structure is deprecated. Please '\n                          'put data folders such as \"bounding_box_train\" under '\n                          '\"dancetrack-reid\".')\n\n        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')\n        self.query_dir = osp.join(self.data_dir, 'query')\n        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')\n        self.extra_gallery_dir = osp.join(self.data_dir, 'images')\n        self.extra_gallery = False\n\n        required_files = [\n            self.data_dir,      # fast_reid/datasets/dancetrack-reid'\n            self.train_dir,     # 'fast_reid/datasets/dancetrack-reid/bounding_box_train'\n            # self.query_dir,\n            # self.gallery_dir,\n        ]\n\n        self.check_before_run(required_files)\n\n        train = lambda: self.process_dir(self.train_dir)\n        query = lambda: self.process_dir(self.query_dir, is_train=False)\n        gallery = lambda: self.process_dir(self.gallery_dir, is_train=False) + \\\n                          (self.process_dir(self.extra_gallery_dir, is_train=False) if self.extra_gallery else [])\n\n        super(CUHKSYSU, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n\n        img_paths = glob.glob(osp.join(dir_path, '*.bmp'))\n        pattern = re.compile(r'([-\\d]+)_(\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            if pid == -1:\n                # print('skip -1 id')\n                continue  # junk images are just ignored\n            # assert 0 <= pid   # pid == 0 means background\n            # assert 1 <= camid <= 5\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/cuhksysu_dancetrack.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock (changed by Nir)\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\nimport glob\nimport os.path as osp\nimport re\nimport warnings\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass CUHKSYSU_DanceTrack(ImageDataset):\n    \"\"\"CUHKSYSU & DanceTrack.\n\n    Dataset statistics:\n        - identities: ?\n        - images: ?\n    \"\"\"\n    _junk_pids = [0, -1]\n    dataset_dir = ''\n    dataset_url = ''\n    dataset_name = \"CUHKSYSU_DanceTrack\"\n\n    def __init__(self, root='datasets', **kwargs):\n        # self.root = osp.abspath(osp.expanduser(root))\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        # allow alternative directory structure\n        self.data_dir = self.dataset_dir        # 'fast_reid/datasets/'\n        data_dir = osp.join(self.data_dir, 'cuhksysu-dancetrack-reid')    # 'fast_reid/datasets/cuhksysu-reid'\n        if osp.isdir(data_dir):\n            self.data_dir = data_dir        # 'fast_reid/datasets/cuhksysu-reid'\n        else:\n            warnings.warn('The current data structure is deprecated. Please '\n                          'put data folders such as \"bounding_box_train\" under '\n                          '\"dancetrack-reid\".')\n\n        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')\n        self.query_dir = osp.join(self.data_dir, 'query')\n        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')\n        self.extra_gallery_dir = osp.join(self.data_dir, 'images')\n        self.extra_gallery = False\n\n        required_files = [\n            self.data_dir,      # fast_reid/datasets/dancetrack-reid'\n            self.train_dir,     # 'fast_reid/datasets/dancetrack-reid/bounding_box_train'\n            # self.query_dir,\n            # self.gallery_dir,\n        ]\n\n        self.check_before_run(required_files)\n\n        train = lambda: self.process_dir(self.train_dir)\n        query = lambda: self.process_dir(self.query_dir, is_train=False)\n        gallery = lambda: self.process_dir(self.gallery_dir, is_train=False) + \\\n                          (self.process_dir(self.extra_gallery_dir, is_train=False) if self.extra_gallery else [])\n\n        super(CUHKSYSU_DanceTrack, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n\n        img_paths = glob.glob(osp.join(dir_path, '*.bmp'))\n        pattern = re.compile(r'([-\\d]+)_(\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            if pid == -1:\n                # print('skip -1 id')\n                continue  # junk images are just ignored\n            # assert 0 <= pid   # pid == 0 means background\n            # assert 1 <= camid <= 5\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/cuhksysu_mot17.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock (changed by Nir)\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\nimport glob\nimport os.path as osp\nimport re\nimport warnings\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass CUHKSYSU_MOT17(ImageDataset):\n    \"\"\"CUHKSYSU & MOT17.\n\n    Dataset statistics:\n        - identities: ?\n        - images: ?\n    \"\"\"\n    _junk_pids = [0, -1]\n    dataset_dir = ''\n    dataset_url = ''\n    dataset_name = \"CUHKSYSU_MOT17\"\n\n    def __init__(self, root='datasets', **kwargs):\n        # self.root = osp.abspath(osp.expanduser(root))\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        # allow alternative directory structure\n        self.data_dir = self.dataset_dir        # 'fast_reid/datasets/'\n        data_dir = osp.join(self.data_dir, 'cuhksysu-mot17-reid')    # 'fast_reid/datasets/cuhksysu-reid'\n        if osp.isdir(data_dir):\n            self.data_dir = data_dir        # 'fast_reid/datasets/cuhksysu-reid'\n        else:\n            warnings.warn('The current data structure is deprecated. Please '\n                          'put data folders such as \"bounding_box_train\" under '\n                          '\"mot17-reid\".')\n\n        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')\n        self.query_dir = osp.join(self.data_dir, 'query')\n        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')\n        self.extra_gallery_dir = osp.join(self.data_dir, 'images')\n        self.extra_gallery = False\n\n        required_files = [\n            self.data_dir,      # fast_reid/datasets/dancetrack-reid'\n            self.train_dir,     # 'fast_reid/datasets/dancetrack-reid/bounding_box_train'\n            # self.query_dir,\n            # self.gallery_dir,\n        ]\n\n        self.check_before_run(required_files)\n\n        train = lambda: self.process_dir(self.train_dir)\n        query = lambda: self.process_dir(self.query_dir, is_train=False)\n        gallery = lambda: self.process_dir(self.gallery_dir, is_train=False) + \\\n                          (self.process_dir(self.extra_gallery_dir, is_train=False) if self.extra_gallery else [])\n\n        super(CUHKSYSU_MOT17, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n\n        img_paths = glob.glob(osp.join(dir_path, '*.bmp'))\n        pattern = re.compile(r'([-\\d]+)_(\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            if pid == -1:\n                # print('skip -1 id')\n                continue  # junk images are just ignored\n            # assert 0 <= pid   # pid == 0 means background\n            # assert 1 <= camid <= 5\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/cuhksysu_mot20.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock (changed by Nir)\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\nimport glob\nimport os.path as osp\nimport re\nimport warnings\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass CUHKSYSU_MOT20(ImageDataset):\n    \"\"\"CUHKSYSU & MOT20.\n\n    Dataset statistics:\n        - identities: ?\n        - images: ?\n    \"\"\"\n    _junk_pids = [0, -1]\n    dataset_dir = ''\n    dataset_url = ''\n    dataset_name = \"CUHKSYSU_MOT20\"\n\n    def __init__(self, root='datasets', **kwargs):\n        # self.root = osp.abspath(osp.expanduser(root))\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        # allow alternative directory structure\n        self.data_dir = self.dataset_dir        # 'fast_reid/datasets/'\n        data_dir = osp.join(self.data_dir, 'cuhksysu-mot20-reid')    # 'fast_reid/datasets/cuhksysu-reid'\n        if osp.isdir(data_dir):\n            self.data_dir = data_dir        # 'fast_reid/datasets/cuhksysu-reid'\n        else:\n            warnings.warn('The current data structure is deprecated. Please '\n                          'put data folders such as \"bounding_box_train\" under '\n                          '\"mot20-reid\".')\n\n        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')\n        self.query_dir = osp.join(self.data_dir, 'query')\n        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')\n        self.extra_gallery_dir = osp.join(self.data_dir, 'images')\n        self.extra_gallery = False\n\n        required_files = [\n            self.data_dir,      # fast_reid/datasets/dancetrack-reid'\n            self.train_dir,     # 'fast_reid/datasets/dancetrack-reid/bounding_box_train'\n            # self.query_dir,\n            # self.gallery_dir,\n        ]\n\n        self.check_before_run(required_files)\n\n        train = lambda: self.process_dir(self.train_dir)\n        query = lambda: self.process_dir(self.query_dir, is_train=False)\n        gallery = lambda: self.process_dir(self.gallery_dir, is_train=False) + \\\n                          (self.process_dir(self.extra_gallery_dir, is_train=False) if self.extra_gallery else [])\n\n        super(CUHKSYSU_MOT20, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n\n        img_paths = glob.glob(osp.join(dir_path, '*.bmp'))\n        pattern = re.compile(r'([-\\d]+)_(\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            if pid == -1:\n                # print('skip -1 id')\n                continue  # junk images are just ignored\n            # assert 0 <= pid   # pid == 0 means background\n            # assert 1 <= camid <= 5\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/dancetrack.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock (changed by Nir)\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\nimport glob\nimport os.path as osp\nimport re\nimport warnings\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass DanceTrack(ImageDataset):\n    \"\"\"DanceTrack.\n\n    Dataset statistics:\n        - identities: ?\n        - images: ?\n    \"\"\"\n    _junk_pids = [0, -1]\n    dataset_dir = ''\n    dataset_url = ''\n    dataset_name = \"DanceTrack\"\n\n    def __init__(self, root='datasets', **kwargs):\n        # self.root = osp.abspath(osp.expanduser(root))\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        # allow alternative directory structure\n        self.data_dir = self.dataset_dir        # 'fast_reid/datasets/'\n        data_dir = osp.join(self.data_dir, 'dancetrack-reid')    # 'fast_reid/datasets/dancetrack-reid'\n        if osp.isdir(data_dir):\n            self.data_dir = data_dir        # 'fast_reid/datasets/dancetrack-reid'\n        else:\n            warnings.warn('The current data structure is deprecated. Please '\n                          'put data folders such as \"bounding_box_train\" under '\n                          '\"dancetrack-reid\".')\n\n        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')\n        self.query_dir = osp.join(self.data_dir, 'query')\n        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')\n        self.extra_gallery_dir = osp.join(self.data_dir, 'images')\n        self.extra_gallery = False\n\n        required_files = [\n            self.data_dir,      # fast_reid/datasets/dancetrack-reid'\n            self.train_dir,     # 'fast_reid/datasets/dancetrack-reid/bounding_box_train'\n            # self.query_dir,\n            # self.gallery_dir,\n        ]\n\n        self.check_before_run(required_files)\n\n        train = lambda: self.process_dir(self.train_dir)\n        query = lambda: self.process_dir(self.query_dir, is_train=False)\n        gallery = lambda: self.process_dir(self.gallery_dir, is_train=False) + \\\n                          (self.process_dir(self.extra_gallery_dir, is_train=False) if self.extra_gallery else [])\n\n        super(DanceTrack, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n\n        img_paths = glob.glob(osp.join(dir_path, '*.bmp'))\n        pattern = re.compile(r'([-\\d]+)_([-\\d]+)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            # import pdb\n            # pdb.set_trace()\n            if pid == -1:\n                continue  # junk images are just ignored\n            # assert 0 <= pid   # pid == 0 means background\n            # assert 1 <= camid <= 5\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/dukemtmcreid.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: liaoxingyu2@jd.com\n\"\"\"\n\nimport glob\nimport os.path as osp\nimport re\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass DukeMTMC(ImageDataset):\n    \"\"\"DukeMTMC-reID.\n\n    Reference:\n        - Ristani et al. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. ECCVW 2016.\n        - Zheng et al. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro. ICCV 2017.\n\n    URL: `<https://github.com/layumi/DukeMTMC-reID_evaluation>`_\n\n    Dataset statistics:\n        - identities: 1404 (train + query).\n        - images:16522 (train) + 2228 (query) + 17661 (gallery).\n        - cameras: 8.\n    \"\"\"\n    dataset_dir = 'DukeMTMC-reID'\n    dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-reID.zip'\n    dataset_name = \"dukemtmc\"\n\n    def __init__(self, root='datasets', **kwargs):\n        # self.root = osp.abspath(osp.expanduser(root))\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n        self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train')\n        self.query_dir = osp.join(self.dataset_dir, 'query')\n        self.gallery_dir = osp.join(self.dataset_dir, 'bounding_box_test')\n\n        required_files = [\n            self.dataset_dir,\n            self.train_dir,\n            self.query_dir,\n            self.gallery_dir,\n        ]\n        self.check_before_run(required_files)\n\n        train = self.process_dir(self.train_dir)\n        query = self.process_dir(self.query_dir, is_train=False)\n        gallery = self.process_dir(self.gallery_dir, is_train=False)\n\n        super(DukeMTMC, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))\n        pattern = re.compile(r'([-\\d]+)_c(\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            assert 1 <= camid <= 8\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/grid.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['GRID', ]\n\n\n@DATASET_REGISTRY.register()\nclass GRID(ImageDataset):\n    \"\"\"GRID\n    \"\"\"\n    dataset_dir = \"underground_reid\"\n    dataset_name = 'grid'\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir, 'images')\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n        img_paths = glob(os.path.join(train_path, \"*.jpeg\"))\n\n        for img_path in img_paths:\n            img_name = os.path.basename(img_path)\n            img_info = img_name.split('_')\n            pid = self.dataset_name + \"_\" + img_info[0]\n            camid = self.dataset_name + \"_\" + img_info[1]\n            data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/iLIDS.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['iLIDS', ]\n\n\n@DATASET_REGISTRY.register()\nclass iLIDS(ImageDataset):\n    \"\"\"iLIDS\n    \"\"\"\n    dataset_dir = \"iLIDS\"\n    dataset_name = \"ilids\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n        file_path = os.listdir(train_path)\n        for pid_dir in file_path:\n            img_file = os.path.join(train_path, pid_dir)\n            img_paths = glob(os.path.join(img_file, \"*.png\"))\n            for img_path in img_paths:\n                split_path = img_path.split('/')\n                pid = self.dataset_name + \"_\" + split_path[-2]\n                camid = self.dataset_name + \"_\" + split_path[-1].split('_')[0]\n                data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/lpw.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['LPW', ]\n\n\n@DATASET_REGISTRY.register()\nclass LPW(ImageDataset):\n    \"\"\"LPW\n    \"\"\"\n    dataset_dir = \"pep_256x128/data_slim\"\n    dataset_name = \"lpw\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n\n        file_path_list = ['scen1', 'scen2', 'scen3']\n\n        for scene in file_path_list:\n            cam_list = os.listdir(os.path.join(train_path, scene))\n            for cam in cam_list:\n                camid = self.dataset_name + \"_\" + cam\n                pid_list = os.listdir(os.path.join(train_path, scene, cam))\n                for pid_dir in pid_list:\n                    img_paths = glob(os.path.join(train_path, scene, cam, pid_dir, \"*.jpg\"))\n                    for img_path in img_paths:\n                        pid = self.dataset_name + \"_\" + scene + \"-\" + pid_dir\n                        data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/market1501.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport glob\nimport os.path as osp\nimport re\nimport warnings\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass Market1501(ImageDataset):\n    \"\"\"Market1501.\n\n    Reference:\n        Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.\n\n    URL: `<http://www.liangzheng.org/Project/project_reid.html>`_\n\n    Dataset statistics:\n        - identities: 1501 (+1 for background).\n        - images: 12936 (train) + 3368 (query) + 15913 (gallery).\n    \"\"\"\n    _junk_pids = [0, -1]\n    dataset_dir = ''\n    dataset_url = 'http://188.138.127.15:81/Datasets/Market-1501-v15.09.15.zip'\n    dataset_name = \"market1501\"\n\n    def __init__(self, root='datasets', market1501_500k=False, **kwargs):\n        # self.root = osp.abspath(osp.expanduser(root))\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        # allow alternative directory structure\n        self.data_dir = self.dataset_dir\n        data_dir = osp.join(self.data_dir, 'Market-1501-v15.09.15')\n        if osp.isdir(data_dir):\n            self.data_dir = data_dir\n        else:\n            warnings.warn('The current data structure is deprecated. Please '\n                          'put data folders such as \"bounding_box_train\" under '\n                          '\"Market-1501-v15.09.15\".')\n\n        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')\n        self.query_dir = osp.join(self.data_dir, 'query')\n        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')\n        self.extra_gallery_dir = osp.join(self.data_dir, 'images')\n        self.market1501_500k = market1501_500k\n\n        required_files = [\n            self.data_dir,\n            self.train_dir,\n            self.query_dir,\n            self.gallery_dir,\n        ]\n        if self.market1501_500k:\n            required_files.append(self.extra_gallery_dir)\n        self.check_before_run(required_files)\n\n        train = lambda: self.process_dir(self.train_dir)\n        query = lambda: self.process_dir(self.query_dir, is_train=False)\n        gallery = lambda: self.process_dir(self.gallery_dir, is_train=False) + \\\n                          (self.process_dir(self.extra_gallery_dir, is_train=False) if self.market1501_500k else [])\n\n        super(Market1501, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))\n        pattern = re.compile(r'([-\\d]+)_c(\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            if pid == -1:\n                continue  # junk images are just ignored\n            assert 0 <= pid <= 1501  # pid == 0 means background\n            assert 1 <= camid <= 6\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/mot17.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock (changed by Nir)\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\nimport glob\nimport os.path as osp\nimport re\nimport warnings\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass MOT17(ImageDataset):\n    \"\"\"MOT17.\n\n    Reference:\n        Milan, A., Leal-Taixé, L., Reid, I., Roth, S. & Schindler, K. MOT16: A Benchmark for Multi-Object Tracking. arXiv:1603.00831 [cs], 2016., (arXiv: 1603.00831)\n\n    URL: `<https://motchallenge.net/data/MOT17/>`_\n\n    Dataset statistics:\n        - identities: ?\n        - images: ?\n    \"\"\"\n    _junk_pids = [0, -1]\n    dataset_dir = ''\n    dataset_url = ''  # 'https://motchallenge.net/data/MOT17.zip'\n    dataset_name = \"MOT17\"\n\n    def __init__(self, root='datasets', **kwargs):\n        # self.root = osp.abspath(osp.expanduser(root))\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        # allow alternative directory structure\n        self.data_dir = self.dataset_dir        # 'fast_reid/datasets/'\n        data_dir = osp.join(self.data_dir, 'MOT17-ReID')    # 'fast_reid/datasets/MOT17-ReID'\n        if osp.isdir(data_dir):\n            self.data_dir = data_dir        # 'fast_reid/datasets/MOT17-ReID'\n        else:\n            warnings.warn('The current data structure is deprecated. Please '\n                          'put data folders such as \"bounding_box_train\" under '\n                          '\"MOT17-ReID\".')\n\n        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')\n        self.query_dir = osp.join(self.data_dir, 'query')\n        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')\n        self.extra_gallery_dir = osp.join(self.data_dir, 'images')\n        self.extra_gallery = False\n\n        required_files = [\n            self.data_dir,      # fast_reid/datasets/MOT17-ReID'\n            self.train_dir,     # 'fast_reid/datasets/MOT17-ReID/bounding_box_train'\n            # self.query_dir,\n            # self.gallery_dir,\n        ]\n\n        self.check_before_run(required_files)\n\n        train = lambda: self.process_dir(self.train_dir)\n        query = lambda: self.process_dir(self.query_dir, is_train=False)\n        gallery = lambda: self.process_dir(self.gallery_dir, is_train=False) + \\\n                          (self.process_dir(self.extra_gallery_dir, is_train=False) if self.extra_gallery else [])\n\n        super(MOT17, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n\n        img_paths = glob.glob(osp.join(dir_path, '*.bmp'))\n        pattern = re.compile(r'([-\\d]+)_MOT17-([-\\d]+)-FRCNN')      # 0000142_MOT17-04-FRCNN_0000477_acc_data.bm\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            if pid == -1:\n                # print('skip -1 id')\n                continue  # junk images are just ignored\n            # assert 0 <= pid   # pid == 0 means background\n            # assert 1 <= camid <= 5\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/mot20.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport glob\nimport os.path as osp\nimport re\nimport warnings\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass MOT20(ImageDataset):\n    \"\"\"MOT20.\n\n    Reference:\n        Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., Roth, S., Schindler, K. & Leal-Taixé, L. MOT20: A benchmark for multi object tracking in crowded scenes. arXiv:2003.09003[cs], 2020., (arXiv: 2003.09003).\n\n    URL: `<https://motchallenge.net/data/MOT20/>`_\n\n    Dataset statistics:\n        - identities: ?\n        - images: ?\n    \"\"\"\n    _junk_pids = [0, -1]\n    dataset_dir = 'MOT20'\n    dataset_url = ''  # 'https://motchallenge.net/data/MOT20.zip'\n    dataset_name = \"MOT20\"\n\n    def __init__(self, root='datasets', **kwargs):\n        # self.root = osp.abspath(osp.expanduser(root))\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        # allow alternative directory structure\n        self.data_dir = self.dataset_dir\n        data_dir = osp.join(self.data_dir, 'MOT20-ReID')\n        if osp.isdir(data_dir):\n            self.data_dir = data_dir\n        else:\n            warnings.warn('The current data structure is deprecated. Please '\n                          'put data folders such as \"bounding_box_train\" under '\n                          '\"MOT20-ReID\".')\n\n        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')\n        self.query_dir = osp.join(self.data_dir, 'query')\n        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')\n        self.extra_gallery_dir = osp.join(self.data_dir, 'images')\n        self.extra_gallery = False\n\n        required_files = [\n            self.data_dir,\n            self.train_dir,\n            # self.query_dir,\n            # self.gallery_dir,\n        ]\n\n        self.check_before_run(required_files)\n\n        train = lambda: self.process_dir(self.train_dir)\n        query = lambda: self.process_dir(self.query_dir, is_train=False)\n        gallery = lambda: self.process_dir(self.gallery_dir, is_train=False) + \\\n                          (self.process_dir(self.extra_gallery_dir, is_train=False) if self.extra_gallery else [])\n\n        super(MOT20, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n\n        img_paths = glob.glob(osp.join(dir_path, '*.bmp'))\n        pattern = re.compile(r'([-\\d]+)_MOT20-0(\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            if pid == -1:\n                continue  # junk images are just ignored\n            # assert 0 <= pid   # pid == 0 means background\n            # assert 1 <= camid <= 5\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/mot20_.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport glob\nimport os.path as osp\nimport re\nimport warnings\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass Market1501(ImageDataset):\n    \"\"\"Market1501.\n\n    Reference:\n        Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.\n\n    URL: `<http://www.liangzheng.org/Project/project_reid.html>`_\n\n    Dataset statistics:\n        - identities: 1501 (+1 for background).\n        - images: 12936 (train) + 3368 (query) + 15913 (gallery).\n    \"\"\"\n    _junk_pids = [0, -1]\n    dataset_dir = ''\n    dataset_url = 'http://188.138.127.15:81/Datasets/Market-1501-v15.09.15.zip'\n    dataset_name = \"market1501\"\n\n    def __init__(self, root='datasets', market1501_500k=False, **kwargs):\n        # self.root = osp.abspath(osp.expanduser(root))\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        # allow alternative directory structure\n        self.data_dir = self.dataset_dir\n        data_dir = osp.join(self.data_dir, 'Market-1501-v15.09.15')\n        if osp.isdir(data_dir):\n            self.data_dir = data_dir\n        else:\n            warnings.warn('The current data structure is deprecated. Please '\n                          'put data folders such as \"bounding_box_train\" under '\n                          '\"Market-1501-v15.09.15\".')\n\n        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')\n        self.query_dir = osp.join(self.data_dir, 'query')\n        self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')\n        self.extra_gallery_dir = osp.join(self.data_dir, 'images')\n        self.market1501_500k = market1501_500k\n\n        required_files = [\n            self.data_dir,\n            self.train_dir,\n            self.query_dir,\n            self.gallery_dir,\n        ]\n        if self.market1501_500k:\n            required_files.append(self.extra_gallery_dir)\n        self.check_before_run(required_files)\n\n        train = lambda: self.process_dir(self.train_dir)\n        query = lambda: self.process_dir(self.query_dir, is_train=False)\n        gallery = lambda: self.process_dir(self.gallery_dir, is_train=False) + \\\n                          (self.process_dir(self.extra_gallery_dir, is_train=False) if self.market1501_500k else [])\n\n        super(Market1501, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n        img_paths_ = glob.glob(osp.join(dir_path, '*.jpg'))  # TODO: Concat\n        img_paths = glob.glob(osp.join(dir_path, '*.bmp'))\n\n        pattern = re.compile(r'([-\\d]+)_MOT20-0(\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            if pid == -1:\n                continue  # junk images are just ignored\n            assert 0 <= pid   # pid == 0 means background\n            assert 1 <= camid <= 5\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/msmt17.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport sys\nimport os\nimport os.path as osp\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n##### Log #####\n# 22.01.2019\n# - add v2\n# - v1 and v2 differ in dir names\n# - note that faces in v2 are blurred\nTRAIN_DIR_KEY = 'train_dir'\nTEST_DIR_KEY = 'test_dir'\nVERSION_DICT = {\n    'MSMT17_V1': {\n        TRAIN_DIR_KEY: 'train',\n        TEST_DIR_KEY: 'test',\n    },\n    'MSMT17_V2': {\n        TRAIN_DIR_KEY: 'mask_train_v2',\n        TEST_DIR_KEY: 'mask_test_v2',\n    }\n}\n\n\n@DATASET_REGISTRY.register()\nclass MSMT17(ImageDataset):\n    \"\"\"MSMT17.\n    Reference:\n        Wei et al. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. CVPR 2018.\n    URL: `<http://www.pkuvmc.com/publications/msmt17.html>`_\n\n    Dataset statistics:\n        - identities: 4101.\n        - images: 32621 (train) + 11659 (query) + 82161 (gallery).\n        - cameras: 15.\n    \"\"\"\n    # dataset_dir = 'MSMT17_V2'\n    dataset_url = None\n    dataset_name = 'msmt17'\n\n    def __init__(self, root='datasets', **kwargs):\n        self.dataset_dir = root\n\n        has_main_dir = False\n        for main_dir in VERSION_DICT:\n            if osp.exists(osp.join(self.dataset_dir, main_dir)):\n                train_dir = VERSION_DICT[main_dir][TRAIN_DIR_KEY]\n                test_dir = VERSION_DICT[main_dir][TEST_DIR_KEY]\n                has_main_dir = True\n                break\n        assert has_main_dir, 'Dataset folder not found'\n\n        self.train_dir = osp.join(self.dataset_dir, main_dir, train_dir)\n        self.test_dir = osp.join(self.dataset_dir, main_dir, test_dir)\n        self.list_train_path = osp.join(self.dataset_dir, main_dir, 'list_train.txt')\n        self.list_val_path = osp.join(self.dataset_dir, main_dir, 'list_val.txt')\n        self.list_query_path = osp.join(self.dataset_dir, main_dir, 'list_query.txt')\n        self.list_gallery_path = osp.join(self.dataset_dir, main_dir, 'list_gallery.txt')\n\n        required_files = [\n            self.dataset_dir,\n            self.train_dir,\n            self.test_dir\n        ]\n        self.check_before_run(required_files)\n\n        train = self.process_dir(self.train_dir, self.list_train_path)\n        val = self.process_dir(self.train_dir, self.list_val_path)\n        query = self.process_dir(self.test_dir, self.list_query_path, is_train=False)\n        gallery = self.process_dir(self.test_dir, self.list_gallery_path, is_train=False)\n\n        num_train_pids = self.get_num_pids(train)\n        query_tmp = []\n        for img_path, pid, camid in query:\n            query_tmp.append((img_path, pid+num_train_pids, camid))\n        del query\n        query = query_tmp\n\n        gallery_temp = []\n        for img_path, pid, camid in gallery:\n            gallery_temp.append((img_path, pid+num_train_pids, camid))\n        del gallery\n        gallery = gallery_temp\n\n        # Note: to fairly compare with published methods on the conventional ReID setting,\n        #       do not add val images to the training set.\n        if 'combineall' in kwargs and kwargs['combineall']:\n            train += val\n        super(MSMT17, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, list_path, is_train=True):\n        with open(list_path, 'r') as txt:\n            lines = txt.readlines()\n\n        data = []\n\n        for img_idx, img_info in enumerate(lines):\n            img_path, pid = img_info.split(' ')\n            pid = int(pid)  # no need to relabel\n            camid = int(img_path.split('_')[2]) - 1  # index starts from 0\n            img_path = osp.join(dir_path, img_path)\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/pes3d.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['PeS3D',]\n\n\n@DATASET_REGISTRY.register()\nclass PeS3D(ImageDataset):\n    \"\"\"3Dpes\n    \"\"\"\n    dataset_dir = \"3DPeS\"\n    dataset_name = \"pes3d\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n\n        pid_list = os.listdir(train_path)\n        for pid_dir in pid_list:\n            pid = self.dataset_name + \"_\" + pid_dir\n            img_list = glob(os.path.join(train_path, pid_dir,  \"*.bmp\"))\n            for img_path in img_list:\n                camid = self.dataset_name + \"_cam0\"\n                data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/pku.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['PKU', ]\n\n\n@DATASET_REGISTRY.register()\nclass PKU(ImageDataset):\n    \"\"\"PKU\n    \"\"\"\n    dataset_dir = \"PKUv1a_128x48\"\n    dataset_name = 'pku'\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n        img_paths = glob(os.path.join(train_path, \"*.png\"))\n\n        for img_path in img_paths:\n            split_path = img_path.split('/')\n            img_info = split_path[-1].split('_')\n            pid = self.dataset_name + \"_\" + img_info[0]\n            camid = self.dataset_name + \"_\" + img_info[1]\n            data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/prai.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['PRAI', ]\n\n\n@DATASET_REGISTRY.register()\nclass PRAI(ImageDataset):\n    \"\"\"PRAI\n    \"\"\"\n    dataset_dir = \"PRAI-1581\"\n    dataset_name = 'prai'\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir, 'images')\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n        img_paths = glob(os.path.join(train_path, \"*.jpg\"))\n        for img_path in img_paths:\n            split_path = img_path.split('/')\n            img_info = split_path[-1].split('_')\n            pid = self.dataset_name + \"_\" + img_info[0]\n            camid = self.dataset_name + \"_\" + img_info[1]\n            data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/prid.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['PRID', ]\n\n\n@DATASET_REGISTRY.register()\nclass PRID(ImageDataset):\n    \"\"\"PRID\n    \"\"\"\n    dataset_dir = \"prid_2011\"\n    dataset_name = 'prid'\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir, 'slim_train')\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n        for root, dirs, files in os.walk(train_path):\n            for img_name in filter(lambda x: x.endswith('.png'), files):\n                img_path = os.path.join(root, img_name)\n                pid = self.dataset_name + '_' + root.split('/')[-1].split('_')[1]\n                camid = self.dataset_name + '_' + img_name.split('_')[0]\n                data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/saivt.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['SAIVT', ]\n\n\n@DATASET_REGISTRY.register()\nclass SAIVT(ImageDataset):\n    \"\"\"SAIVT\n    \"\"\"\n    dataset_dir = \"SAIVT-SoftBio\"\n    dataset_name = \"saivt\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n\n        pid_path = os.path.join(train_path, \"cropped_images\")\n        pid_list = os.listdir(pid_path)\n\n        for pid_name in pid_list:\n            pid = self.dataset_name + '_' + pid_name\n            img_list = glob(os.path.join(pid_path, pid_name, \"*.jpeg\"))\n            for img_path in img_list:\n                img_name = os.path.basename(img_path)\n                camid = self.dataset_name + '_' + img_name.split('-')[2]\n                data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/sensereid.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['SenseReID', ]\n\n\n@DATASET_REGISTRY.register()\nclass SenseReID(ImageDataset):\n    \"\"\"Sense reid\n    \"\"\"\n    dataset_dir = \"SenseReID\"\n    dataset_name = \"senseid\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n        file_path_list = ['test_gallery', 'test_prob']\n\n        for file_path in file_path_list:\n            sub_file = os.path.join(train_path, file_path)\n            img_name = glob(os.path.join(sub_file, \"*.jpg\"))\n            for img_path in img_name:\n                img_name = img_path.split('/')[-1]\n                img_info = img_name.split('_')\n                pid = self.dataset_name + \"_\" + img_info[0]\n                camid = self.dataset_name + \"_\" + img_info[1].split('.')[0]\n                data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/shinpuhkan.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['Shinpuhkan', ]\n\n\n@DATASET_REGISTRY.register()\nclass Shinpuhkan(ImageDataset):\n    \"\"\"shinpuhkan\n    \"\"\"\n    dataset_dir = \"shinpuhkan\"\n    dataset_name = 'shinpuhkan'\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n\n        for root, dirs, files in os.walk(train_path):\n            img_names = list(filter(lambda x: x.endswith(\".jpg\"), files))\n            # fmt: off\n            if len(img_names) == 0: continue\n            # fmt: on\n            for img_name in img_names:\n                img_path = os.path.join(root, img_name)\n                split_path = img_name.split('_')\n                pid = self.dataset_name + \"_\" + split_path[0]\n                camid = self.dataset_name + \"_\" + split_path[2]\n                data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/sysu_mm.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['SYSU_mm', ]\n\n\n@DATASET_REGISTRY.register()\nclass SYSU_mm(ImageDataset):\n    \"\"\"sysu mm\n    \"\"\"\n    dataset_dir = \"SYSU-MM01\"\n    dataset_name = \"sysumm01\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n\n        file_path_list = ['cam1', 'cam2', 'cam4', 'cam5']\n\n        for file_path in file_path_list:\n            camid = self.dataset_name + \"_\" + file_path\n            pid_list = os.listdir(os.path.join(train_path, file_path))\n            for pid_dir in pid_list:\n                pid = self.dataset_name + \"_\" + pid_dir\n                img_list = glob(os.path.join(train_path, file_path, pid_dir, \"*.jpg\"))\n                for img_path in img_list:\n                    data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/thermalworld.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['Thermalworld', ]\n\n\n@DATASET_REGISTRY.register()\nclass Thermalworld(ImageDataset):\n    \"\"\"thermal world\n    \"\"\"\n    dataset_dir = \"thermalworld_rgb\"\n    dataset_name = \"thermalworld\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n        pid_list = os.listdir(train_path)\n        for pid_dir in pid_list:\n            pid = self.dataset_name + \"_\" + pid_dir\n            img_list = glob(os.path.join(train_path, pid_dir, \"*.jpg\"))\n            for img_path in img_list:\n                camid = self.dataset_name + \"_cam0\"\n                data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/vehicleid.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  Jinkai Zheng\n@contact: 1315673509@qq.com\n\"\"\"\n\nimport os.path as osp\nimport random\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass VehicleID(ImageDataset):\n    \"\"\"VehicleID.\n\n    Reference:\n        Liu et al. Deep relative distance learning: Tell the difference between similar vehicles. CVPR 2016.\n\n    URL: `<https://pkuml.org/resources/pku-vehicleid.html>`_\n\n    Train dataset statistics:\n        - identities: 13164.\n        - images: 113346.\n    \"\"\"\n    dataset_dir = \"vehicleid\"\n    dataset_name = \"vehicleid\"\n\n    def __init__(self, root='datasets', test_list='', **kwargs):\n        self.dataset_dir = osp.join(root, self.dataset_dir)\n\n        self.image_dir = osp.join(self.dataset_dir, 'image')\n        self.train_list = osp.join(self.dataset_dir, 'train_test_split/train_list.txt')\n        if test_list:\n            self.test_list = test_list\n        else:\n            self.test_list = osp.join(self.dataset_dir, 'train_test_split/test_list_13164.txt')\n\n        required_files = [\n            self.dataset_dir,\n            self.image_dir,\n            self.train_list,\n            self.test_list,\n        ]\n        self.check_before_run(required_files)\n\n        train = self.process_dir(self.train_list, is_train=True)\n        query, gallery = self.process_dir(self.test_list, is_train=False)\n\n        super(VehicleID, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, list_file, is_train=True):\n        img_list_lines = open(list_file, 'r').readlines()\n\n        dataset = []\n        for idx, line in enumerate(img_list_lines):\n            line = line.strip()\n            vid = int(line.split(' ')[1])\n            imgid = line.split(' ')[0]\n            img_path = osp.join(self.image_dir, f\"{imgid}.jpg\")\n            imgid = int(imgid)\n            if is_train:\n                vid = f\"{self.dataset_name}_{vid}\"\n                imgid = f\"{self.dataset_name}_{imgid}\"\n            dataset.append((img_path, vid, imgid))\n\n        if is_train: return dataset\n        else:\n            random.shuffle(dataset)\n            vid_container = set()\n            query = []\n            gallery = []\n            for sample in dataset:\n                if sample[1] not in vid_container:\n                    vid_container.add(sample[1])\n                    gallery.append(sample)\n                else:\n                    query.append(sample)\n\n            return query, gallery\n\n\n@DATASET_REGISTRY.register()\nclass SmallVehicleID(VehicleID):\n    \"\"\"VehicleID.\n    Small test dataset statistics:\n        - identities: 800.\n        - images: 6493.\n    \"\"\"\n\n    def __init__(self, root='datasets', **kwargs):\n        dataset_dir = osp.join(root, self.dataset_dir)\n        self.test_list = osp.join(dataset_dir, 'train_test_split/test_list_800.txt')\n\n        super(SmallVehicleID, self).__init__(root, self.test_list, **kwargs)\n\n\n@DATASET_REGISTRY.register()\nclass MediumVehicleID(VehicleID):\n    \"\"\"VehicleID.\n    Medium test dataset statistics:\n        - identities: 1600.\n        - images: 13377.\n    \"\"\"\n\n    def __init__(self, root='datasets', **kwargs):\n        dataset_dir = osp.join(root, self.dataset_dir)\n        self.test_list = osp.join(dataset_dir, 'train_test_split/test_list_1600.txt')\n\n        super(MediumVehicleID, self).__init__(root, self.test_list, **kwargs)\n\n\n@DATASET_REGISTRY.register()\nclass LargeVehicleID(VehicleID):\n    \"\"\"VehicleID.\n    Large test dataset statistics:\n        - identities: 2400.\n        - images: 19777.\n    \"\"\"\n\n    def __init__(self, root='datasets', **kwargs):\n        dataset_dir = osp.join(root, self.dataset_dir)\n        self.test_list = osp.join(dataset_dir, 'train_test_split/test_list_2400.txt')\n\n        super(LargeVehicleID, self).__init__(root, self.test_list, **kwargs)\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/veri.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  Jinkai Zheng\n@contact: 1315673509@qq.com\n\"\"\"\n\nimport glob\nimport os.path as osp\nimport re\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass VeRi(ImageDataset):\n    \"\"\"VeRi.\n\n    Reference:\n        Xinchen Liu et al. A Deep Learning based Approach for Progressive Vehicle Re-Identification. ECCV 2016.\n        Xinchen Liu et al. PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance. IEEE TMM 2018.\n\n    URL: `<https://vehiclereid.github.io/VeRi/>`_\n\n    Dataset statistics:\n        - identities: 775.\n        - images: 37778 (train) + 1678 (query) + 11579 (gallery).\n    \"\"\"\n    dataset_dir = \"veri\"\n    dataset_name = \"veri\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.dataset_dir = osp.join(root, self.dataset_dir)\n\n        self.train_dir = osp.join(self.dataset_dir, 'image_train')\n        self.query_dir = osp.join(self.dataset_dir, 'image_query')\n        self.gallery_dir = osp.join(self.dataset_dir, 'image_test')\n\n        required_files = [\n            self.dataset_dir,\n            self.train_dir,\n            self.query_dir,\n            self.gallery_dir,\n        ]\n        self.check_before_run(required_files)\n\n        train = self.process_dir(self.train_dir)\n        query = self.process_dir(self.query_dir, is_train=False)\n        gallery = self.process_dir(self.gallery_dir, is_train=False)\n\n        super(VeRi, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path, is_train=True):\n        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))\n        pattern = re.compile(r'([\\d]+)_c(\\d\\d\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            if pid == -1: continue  # junk images are just ignored\n            assert 0 <= pid <= 776\n            assert 1 <= camid <= 20\n            camid -= 1  # index starts from 0\n            if is_train:\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n            data.append((img_path, pid, camid))\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/veriwild.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  Jinkai Zheng\n@contact: 1315673509@qq.com\n\"\"\"\n\nimport os.path as osp\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass VeRiWild(ImageDataset):\n    \"\"\"VeRi-Wild.\n\n    Reference:\n        Lou et al. A Large-Scale Dataset for Vehicle Re-Identification in the Wild. CVPR 2019.\n\n    URL: `<https://github.com/PKU-IMRE/VERI-Wild>`_\n\n    Train dataset statistics:\n        - identities: 30671.\n        - images: 277797.\n    \"\"\"\n    dataset_dir = \"VERI-Wild\"\n    dataset_name = \"veriwild\"\n\n    def __init__(self, root='datasets', query_list='', gallery_list='', **kwargs):\n        self.dataset_dir = osp.join(root, self.dataset_dir)\n\n        self.image_dir = osp.join(self.dataset_dir, 'images')\n        self.train_list = osp.join(self.dataset_dir, 'train_test_split/train_list.txt')\n        self.vehicle_info = osp.join(self.dataset_dir, 'train_test_split/vehicle_info.txt')\n        if query_list and gallery_list:\n            self.query_list = query_list\n            self.gallery_list = gallery_list\n        else:\n            self.query_list = osp.join(self.dataset_dir, 'train_test_split/test_10000_query.txt')\n            self.gallery_list = osp.join(self.dataset_dir, 'train_test_split/test_10000.txt')\n\n        required_files = [\n            self.image_dir,\n            self.train_list,\n            self.query_list,\n            self.gallery_list,\n            self.vehicle_info,\n        ]\n        self.check_before_run(required_files)\n\n        self.imgid2vid, self.imgid2camid, self.imgid2imgpath = self.process_vehicle(self.vehicle_info)\n\n        train = self.process_dir(self.train_list)\n        query = self.process_dir(self.query_list, is_train=False)\n        gallery = self.process_dir(self.gallery_list, is_train=False)\n\n        super(VeRiWild, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, img_list, is_train=True):\n        img_list_lines = open(img_list, 'r').readlines()\n\n        dataset = []\n        for idx, line in enumerate(img_list_lines):\n            line = line.strip()\n            vid = int(line.split('/')[0])\n            imgid = line.split('/')[1].split('.')[0]\n            camid = int(self.imgid2camid[imgid])\n            if is_train:\n                vid = f\"{self.dataset_name}_{vid}\"\n                camid = f\"{self.dataset_name}_{camid}\"\n            dataset.append((self.imgid2imgpath[imgid], vid, camid))\n\n        assert len(dataset) == len(img_list_lines)\n        return dataset\n\n    def process_vehicle(self, vehicle_info):\n        imgid2vid = {}\n        imgid2camid = {}\n        imgid2imgpath = {}\n        vehicle_info_lines = open(vehicle_info, 'r').readlines()\n\n        for idx, line in enumerate(vehicle_info_lines[1:]):\n            vid = line.strip().split('/')[0]\n            imgid = line.strip().split(';')[0].split('/')[1]\n            camid = line.strip().split(';')[1]\n            img_path = osp.join(self.image_dir, vid, imgid + '.jpg')\n            imgid2vid[imgid] = vid\n            imgid2camid[imgid] = camid\n            imgid2imgpath[imgid] = img_path\n\n        assert len(imgid2vid) == len(vehicle_info_lines) - 1\n        return imgid2vid, imgid2camid, imgid2imgpath\n\n\n@DATASET_REGISTRY.register()\nclass SmallVeRiWild(VeRiWild):\n    \"\"\"VeRi-Wild.\n    Small test dataset statistics:\n        - identities: 3000.\n        - images: 41861.\n    \"\"\"\n\n    def __init__(self, root='datasets', **kwargs):\n        dataset_dir = osp.join(root, self.dataset_dir)\n        self.query_list = osp.join(dataset_dir, 'train_test_split/test_3000_query.txt')\n        self.gallery_list = osp.join(dataset_dir, 'train_test_split/test_3000.txt')\n\n        super(SmallVeRiWild, self).__init__(root, self.query_list, self.gallery_list, **kwargs)\n\n\n@DATASET_REGISTRY.register()\nclass MediumVeRiWild(VeRiWild):\n    \"\"\"VeRi-Wild.\n    Medium test dataset statistics:\n        - identities: 5000.\n        - images: 69389.\n    \"\"\"\n\n    def __init__(self, root='datasets', **kwargs):\n        dataset_dir = osp.join(root, self.dataset_dir)\n        self.query_list = osp.join(dataset_dir, 'train_test_split/test_5000_query.txt')\n        self.gallery_list = osp.join(dataset_dir, 'train_test_split/test_5000.txt')\n\n        super(MediumVeRiWild, self).__init__(root, self.query_list, self.gallery_list, **kwargs)\n\n\n@DATASET_REGISTRY.register()\nclass LargeVeRiWild(VeRiWild):\n    \"\"\"VeRi-Wild.\n    Large test dataset statistics:\n        - identities: 10000.\n        - images: 138517.\n    \"\"\"\n\n    def __init__(self, root='datasets', **kwargs):\n        dataset_dir = osp.join(root, self.dataset_dir)\n        self.query_list = osp.join(dataset_dir, 'train_test_split/test_10000_query.txt')\n        self.gallery_list = osp.join(dataset_dir, 'train_test_split/test_10000.txt')\n\n        super(LargeVeRiWild, self).__init__(root, self.query_list, self.gallery_list, **kwargs)\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/viper.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom glob import glob\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['VIPeR', ]\n\n\n@DATASET_REGISTRY.register()\nclass VIPeR(ImageDataset):\n    dataset_dir = \"VIPeR\"\n    dataset_name = \"viper\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.train_path = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.train_path]\n        self.check_before_run(required_files)\n\n        train = self.process_train(self.train_path)\n\n        super().__init__(train, [], [], **kwargs)\n\n    def process_train(self, train_path):\n        data = []\n\n        file_path_list = ['cam_a', 'cam_b']\n\n        for file_path in file_path_list:\n            camid = self.dataset_name + \"_\" + file_path\n            img_list = glob(os.path.join(train_path, file_path, \"*.bmp\"))\n            for img_path in img_list:\n                img_name = img_path.split('/')[-1]\n                pid = self.dataset_name + \"_\" + img_name.split('_')[0]\n                data.append([img_path, pid, camid])\n\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/datasets/wildtracker.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  wangguanan\n@contact: guan.wang0706@gmail.com\n\"\"\"\n\nimport glob\nimport os\n\nfrom .bases import ImageDataset\nfrom ..datasets import DATASET_REGISTRY\n\n\n@DATASET_REGISTRY.register()\nclass WildTrackCrop(ImageDataset):\n    \"\"\"WildTrack.\n    Reference:\n        WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection\n            T. Chavdarova; P. Baqué; A. Maksai; S. Bouquet; C. Jose et al.\n    URL: `<https://www.epfl.ch/labs/cvlab/data/data-wildtrack/>`_\n    Dataset statistics:\n        - identities: 313\n        - images: 33979 (train only)\n        - cameras: 7\n    Args:\n        data_path(str): path to WildTrackCrop dataset\n        combineall(bool): combine train and test sets as train set if True\n    \"\"\"\n    dataset_url = None\n    dataset_dir = 'Wildtrack_crop_dataset'\n    dataset_name = 'wildtrack'\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.dataset_dir = os.path.join(self.root, self.dataset_dir)\n\n        self.train_dir = os.path.join(self.dataset_dir, \"crop\")\n\n        train = self.process_dir(self.train_dir)\n        query = []\n        gallery = []\n\n        super(WildTrackCrop, self).__init__(train, query, gallery, **kwargs)\n\n    def process_dir(self, dir_path):\n        r\"\"\"\n        :param dir_path: directory path saving images\n        Returns\n            data(list) = [img_path, pid, camid]\n        \"\"\"\n        data = []\n        for dir_name in os.listdir(dir_path):\n            img_lists = glob.glob(os.path.join(dir_path, dir_name, \"*.png\"))\n            for img_path in img_lists:\n                pid = self.dataset_name + \"_\" + dir_name\n                camid = img_path.split('/')[-1].split('_')[0]\n                camid = self.dataset_name + \"_\" + camid\n                data.append([img_path, pid, camid])\n        return data\n"
  },
  {
    "path": "fast_reid/fastreid/data/samplers/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .triplet_sampler import BalancedIdentitySampler, NaiveIdentitySampler, SetReWeightSampler\nfrom .data_sampler import TrainingSampler, InferenceSampler\nfrom .imbalance_sampler import ImbalancedDatasetSampler\n\n__all__ = [\n    \"BalancedIdentitySampler\",\n    \"NaiveIdentitySampler\",\n    \"SetReWeightSampler\",\n    \"TrainingSampler\",\n    \"InferenceSampler\",\n    \"ImbalancedDatasetSampler\",\n]\n"
  },
  {
    "path": "fast_reid/fastreid/data/samplers/data_sampler.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport itertools\nfrom typing import Optional\n\nimport numpy as np\nfrom torch.utils.data import Sampler\n\nfrom fast_reid.fastreid.utils import comm\n\n\nclass TrainingSampler(Sampler):\n    \"\"\"\n    In training, we only care about the \"infinite stream\" of training data.\n    So this sampler produces an infinite stream of indices and\n    all workers cooperate to correctly shuffle the indices and sample different indices.\n    The samplers in each worker effectively produces `indices[worker_id::num_workers]`\n    where `indices` is an infinite stream of indices consisting of\n    `shuffle(range(size)) + shuffle(range(size)) + ...` (if shuffle is True)\n    or `range(size) + range(size) + ...` (if shuffle is False)\n    \"\"\"\n\n    def __init__(self, size: int, shuffle: bool = True, seed: Optional[int] = None):\n        \"\"\"\n        Args:\n            size (int): the total number of data of the underlying dataset to sample from\n            shuffle (bool): whether to shuffle the indices or not\n            seed (int): the initial seed of the shuffle. Must be the same\n                across all workers. If None, will use a random seed shared\n                among workers (require synchronization among all workers).\n        \"\"\"\n        self._size = size\n        assert size > 0\n        self._shuffle = shuffle\n        if seed is None:\n            seed = comm.shared_random_seed()\n        self._seed = int(seed)\n\n        self._rank = comm.get_rank()\n        self._world_size = comm.get_world_size()\n\n    def __iter__(self):\n        start = self._rank\n        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)\n\n    def _infinite_indices(self):\n        np.random.seed(self._seed)\n        while True:\n            if self._shuffle:\n                yield from np.random.permutation(self._size)\n            else:\n                yield from np.arange(self._size)\n\n\nclass InferenceSampler(Sampler):\n    \"\"\"\n    Produce indices for inference.\n    Inference needs to run on the __exact__ set of samples,\n    therefore when the total number of samples is not divisible by the number of workers,\n    this sampler produces different number of samples on different workers.\n    \"\"\"\n\n    def __init__(self, size: int):\n        \"\"\"\n        Args:\n            size (int): the total number of data of the underlying dataset to sample from\n        \"\"\"\n        self._size = size\n        assert size > 0\n        self._rank = comm.get_rank()\n        self._world_size = comm.get_world_size()\n\n        shard_size = (self._size - 1) // self._world_size + 1\n        begin = shard_size * self._rank\n        end = min(shard_size * (self._rank + 1), self._size)\n        self._local_indices = range(begin, end)\n\n    def __iter__(self):\n        yield from self._local_indices\n\n    def __len__(self):\n        return len(self._local_indices)\n"
  },
  {
    "path": "fast_reid/fastreid/data/samplers/imbalance_sampler.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# based on:\n# https://github.com/ufoym/imbalanced-dataset-sampler/blob/master/torchsampler/imbalanced.py\n\n\nimport itertools\nfrom typing import Optional, List, Callable\n\nimport numpy as np\nimport torch\nfrom torch.utils.data.sampler import Sampler\n\nfrom fast_reid.fastreid.utils import comm\n\n\nclass ImbalancedDatasetSampler(Sampler):\n    \"\"\"Samples elements randomly from a given list of indices for imbalanced dataset\n    Arguments:\n        data_source: a list of data items\n        size: number of samples to draw\n    \"\"\"\n\n    def __init__(self, data_source: List, size: int = None, seed: Optional[int] = None,\n                 callback_get_label: Callable = None):\n        self.data_source = data_source\n        # consider all elements in the dataset\n        self.indices = list(range(len(data_source)))\n        # if num_samples is not provided, draw `len(indices)` samples in each iteration\n        self._size = len(self.indices) if size is None else size\n        self.callback_get_label = callback_get_label\n\n        # distribution of classes in the dataset\n        label_to_count = {}\n        for idx in self.indices:\n            label = self._get_label(data_source, idx)\n            label_to_count[label] = label_to_count.get(label, 0) + 1\n\n        # weight for each sample\n        weights = [1.0 / label_to_count[self._get_label(data_source, idx)] for idx in self.indices]\n        self.weights = torch.DoubleTensor(weights)\n\n        if seed is None:\n            seed = comm.shared_random_seed()\n        self._seed = int(seed)\n        self._rank = comm.get_rank()\n        self._world_size = comm.get_world_size()\n\n    def _get_label(self, dataset, idx):\n        if self.callback_get_label:\n            return self.callback_get_label(dataset, idx)\n        else:\n            return dataset[idx][1]\n\n    def __iter__(self):\n        start = self._rank\n        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)\n\n    def _infinite_indices(self):\n        np.random.seed(self._seed)\n        while True:\n            for i in torch.multinomial(self.weights, self._size, replacement=True):\n                yield self.indices[i]\n"
  },
  {
    "path": "fast_reid/fastreid/data/samplers/triplet_sampler.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: liaoxingyu2@jd.com\n\"\"\"\n\nimport copy\nimport itertools\nfrom collections import defaultdict\nfrom typing import Optional, List\n\nimport numpy as np\nfrom torch.utils.data.sampler import Sampler\n\nfrom fast_reid.fastreid.utils import comm\n\n\ndef no_index(a, b):\n    assert isinstance(a, list)\n    return [i for i, j in enumerate(a) if j != b]\n\n\ndef reorder_index(batch_indices, world_size):\n    r\"\"\"Reorder indices of samples to align with DataParallel training.\n    In this order, each process will contain all images for one ID, triplet loss\n    can be computed within each process, and BatchNorm will get a stable result.\n    Args:\n        batch_indices: A batched indices generated by sampler\n        world_size: number of process\n    Returns:\n\n    \"\"\"\n    mini_batchsize = len(batch_indices) // world_size\n    reorder_indices = []\n    for i in range(0, mini_batchsize):\n        for j in range(0, world_size):\n            reorder_indices.append(batch_indices[i + j * mini_batchsize])\n    return reorder_indices\n\n\nclass BalancedIdentitySampler(Sampler):\n    def __init__(self, data_source: List, mini_batch_size: int, num_instances: int, seed: Optional[int] = None):\n        self.data_source = data_source\n        self.num_instances = num_instances\n        self.num_pids_per_batch = mini_batch_size // self.num_instances\n\n        self._rank = comm.get_rank()\n        self._world_size = comm.get_world_size()\n        self.batch_size = mini_batch_size * self._world_size\n\n        self.index_pid = dict()\n        self.pid_cam = defaultdict(list)\n        self.pid_index = defaultdict(list)\n\n        for index, info in enumerate(data_source):\n            pid = info[1]\n            camid = info[2]\n            self.index_pid[index] = pid\n            self.pid_cam[pid].append(camid)\n            self.pid_index[pid].append(index)\n\n        self.pids = sorted(list(self.pid_index.keys()))\n        self.num_identities = len(self.pids)\n\n        if seed is None:\n            seed = comm.shared_random_seed()\n        self._seed = int(seed)\n\n        self._rank = comm.get_rank()\n        self._world_size = comm.get_world_size()\n\n    def __iter__(self):\n        start = self._rank\n        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)\n\n    def _infinite_indices(self):\n        np.random.seed(self._seed)\n        while True:\n            # Shuffle identity list\n            identities = np.random.permutation(self.num_identities)\n\n            # If remaining identities cannot be enough for a batch,\n            # just drop the remaining parts\n            drop_indices = self.num_identities % (self.num_pids_per_batch * self._world_size)\n            if drop_indices: identities = identities[:-drop_indices]\n\n            batch_indices = []\n            for kid in identities:\n                i = np.random.choice(self.pid_index[self.pids[kid]])\n                _, i_pid, i_cam = self.data_source[i]\n                batch_indices.append(i)\n                pid_i = self.index_pid[i]\n                cams = self.pid_cam[pid_i]\n                index = self.pid_index[pid_i]\n                select_cams = no_index(cams, i_cam)\n\n                if select_cams:\n                    if len(select_cams) >= self.num_instances:\n                        cam_indexes = np.random.choice(select_cams, size=self.num_instances - 1, replace=False)\n                    else:\n                        cam_indexes = np.random.choice(select_cams, size=self.num_instances - 1, replace=True)\n                    for kk in cam_indexes:\n                        batch_indices.append(index[kk])\n                else:\n                    select_indexes = no_index(index, i)\n                    if not select_indexes:\n                        # Only one image for this identity\n                        ind_indexes = [0] * (self.num_instances - 1)\n                    elif len(select_indexes) >= self.num_instances:\n                        ind_indexes = np.random.choice(select_indexes, size=self.num_instances - 1, replace=False)\n                    else:\n                        ind_indexes = np.random.choice(select_indexes, size=self.num_instances - 1, replace=True)\n\n                    for kk in ind_indexes:\n                        batch_indices.append(index[kk])\n\n                if len(batch_indices) == self.batch_size:\n                    yield from reorder_index(batch_indices, self._world_size)\n                    batch_indices = []\n\n\nclass SetReWeightSampler(Sampler):\n    def __init__(self, data_source: str, mini_batch_size: int, num_instances: int, set_weight: list,\n                 seed: Optional[int] = None):\n        self.data_source = data_source\n        self.num_instances = num_instances\n        self.num_pids_per_batch = mini_batch_size // self.num_instances\n\n        self.set_weight = set_weight\n\n        self._rank = comm.get_rank()\n        self._world_size = comm.get_world_size()\n        self.batch_size = mini_batch_size * self._world_size\n\n        assert self.batch_size % (sum(self.set_weight) * self.num_instances) == 0 and \\\n               self.batch_size > sum(\n            self.set_weight) * self.num_instances, \"Batch size must be divisible by the sum set weight\"\n\n        self.index_pid = dict()\n        self.pid_cam = defaultdict(list)\n        self.pid_index = defaultdict(list)\n\n        self.cam_pid = defaultdict(list)\n\n        for index, info in enumerate(data_source):\n            pid = info[1]\n            camid = info[2]\n            self.index_pid[index] = pid\n            self.pid_cam[pid].append(camid)\n            self.pid_index[pid].append(index)\n            self.cam_pid[camid].append(pid)\n\n        # Get sampler prob for each cam\n        self.set_pid_prob = defaultdict(list)\n        for camid, pid_list in self.cam_pid.items():\n            index_per_pid = []\n            for pid in pid_list:\n                index_per_pid.append(len(self.pid_index[pid]))\n            cam_image_number = sum(index_per_pid)\n            prob = [i / cam_image_number for i in index_per_pid]\n            self.set_pid_prob[camid] = prob\n\n        self.pids = sorted(list(self.pid_index.keys()))\n        self.num_identities = len(self.pids)\n\n        if seed is None:\n            seed = comm.shared_random_seed()\n        self._seed = int(seed)\n\n        self._rank = comm.get_rank()\n        self._world_size = comm.get_world_size()\n\n    def __iter__(self):\n        start = self._rank\n        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)\n\n    def _infinite_indices(self):\n        np.random.seed(self._seed)\n        while True:\n            batch_indices = []\n            for camid in range(len(self.cam_pid.keys())):\n                select_pids = np.random.choice(self.cam_pid[camid], size=self.set_weight[camid], replace=False,\n                                               p=self.set_pid_prob[camid])\n                for pid in select_pids:\n                    index_list = self.pid_index[pid]\n                    if len(index_list) > self.num_instances:\n                        select_indexs = np.random.choice(index_list, size=self.num_instances, replace=False)\n                    else:\n                        select_indexs = np.random.choice(index_list, size=self.num_instances, replace=True)\n\n                    batch_indices += select_indexs\n            np.random.shuffle(batch_indices)\n\n            if len(batch_indices) == self.batch_size:\n                yield from reorder_index(batch_indices, self._world_size)\n\n\nclass NaiveIdentitySampler(Sampler):\n    \"\"\"\n    Randomly sample N identities, then for each identity,\n    randomly sample K instances, therefore batch size is N*K.\n    Args:\n    - data_source (list): list of (img_path, pid, camid).\n    - num_instances (int): number of instances per identity in a batch.\n    - batch_size (int): number of examples in a batch.\n    \"\"\"\n\n    def __init__(self, data_source: str, mini_batch_size: int, num_instances: int, seed: Optional[int] = None):\n        self.data_source = data_source\n        self.num_instances = num_instances\n        self.num_pids_per_batch = mini_batch_size // self.num_instances\n\n        self._rank = comm.get_rank()\n        self._world_size = comm.get_world_size()\n        self.batch_size = mini_batch_size * self._world_size\n\n        self.pid_index = defaultdict(list)\n\n        for index, info in enumerate(data_source):\n            pid = info[1]\n            self.pid_index[pid].append(index)\n\n        self.pids = sorted(list(self.pid_index.keys()))\n        self.num_identities = len(self.pids)\n\n        if seed is None:\n            seed = comm.shared_random_seed()\n        self._seed = int(seed)\n\n    def __iter__(self):\n        start = self._rank\n        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)\n\n    def _infinite_indices(self):\n        np.random.seed(self._seed)\n        while True:\n            avl_pids = copy.deepcopy(self.pids)\n            batch_idxs_dict = {}\n\n            batch_indices = []\n            while len(avl_pids) >= self.num_pids_per_batch:\n                selected_pids = np.random.choice(avl_pids, self.num_pids_per_batch, replace=False).tolist()\n                for pid in selected_pids:\n                    # Register pid in batch_idxs_dict if not\n                    if pid not in batch_idxs_dict:\n                        idxs = copy.deepcopy(self.pid_index[pid])\n                        if len(idxs) < self.num_instances:\n                            idxs = np.random.choice(idxs, size=self.num_instances, replace=True).tolist()\n                        np.random.shuffle(idxs)\n                        batch_idxs_dict[pid] = idxs\n\n                    avl_idxs = batch_idxs_dict[pid]\n                    for _ in range(self.num_instances):\n                        batch_indices.append(avl_idxs.pop(0))\n\n                    if len(avl_idxs) < self.num_instances: avl_pids.remove(pid)\n\n                if len(batch_indices) == self.batch_size:\n                    yield from reorder_index(batch_indices, self._world_size)\n                    batch_indices = []\n"
  },
  {
    "path": "fast_reid/fastreid/data/transforms/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .autoaugment import AutoAugment\nfrom .build import build_transforms\nfrom .transforms import *\n\n__all__ = [k for k in globals().keys() if not k.startswith(\"_\")]\n"
  },
  {
    "path": "fast_reid/fastreid/data/transforms/autoaugment.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\"\"\" AutoAugment, RandAugment, and AugMix for PyTorch\nThis code implements the searched ImageNet policies with various tweaks and improvements and\ndoes not include any of the search code.\nAA and RA Implementation adapted from:\n    https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py\nAugMix adapted from:\n    https://github.com/google-research/augmix\nPapers:\n    AutoAugment: Learning Augmentation Policies from Data - https://arxiv.org/abs/1805.09501\n    Learning Data Augmentation Strategies for Object Detection - https://arxiv.org/abs/1906.11172\n    RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719\n    AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781\nHacked together by Ross Wightman\n\"\"\"\nimport math\nimport random\nimport re\n\nimport PIL\nimport numpy as np\nfrom PIL import Image, ImageOps, ImageEnhance\n\n_PIL_VER = tuple([int(x) for x in PIL.__version__.split('.')[:2]])\n\n_FILL = (128, 128, 128)\n\n# This signifies the max integer that the controller RNN could predict for the\n# augmentation scheme.\n_MAX_LEVEL = 10.\n\n_HPARAMS_DEFAULT = dict(\n    translate_const=57,\n    img_mean=_FILL,\n)\n\n_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)\n\n\ndef _interpolation(kwargs):\n    interpolation = kwargs.pop('resample', Image.BILINEAR)\n    if isinstance(interpolation, (list, tuple)):\n        return random.choice(interpolation)\n    else:\n        return interpolation\n\n\ndef _check_args_tf(kwargs):\n    if 'fillcolor' in kwargs and _PIL_VER < (5, 0):\n        kwargs.pop('fillcolor')\n    kwargs['resample'] = _interpolation(kwargs)\n\n\ndef shear_x(img, factor, **kwargs):\n    _check_args_tf(kwargs)\n    return img.transform(img.size, Image.AFFINE, (1, factor, 0, 0, 1, 0), **kwargs)\n\n\ndef shear_y(img, factor, **kwargs):\n    _check_args_tf(kwargs)\n    return img.transform(img.size, Image.AFFINE, (1, 0, 0, factor, 1, 0), **kwargs)\n\n\ndef translate_x_rel(img, pct, **kwargs):\n    pixels = pct * img.size[0]\n    _check_args_tf(kwargs)\n    return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)\n\n\ndef translate_y_rel(img, pct, **kwargs):\n    pixels = pct * img.size[1]\n    _check_args_tf(kwargs)\n    return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)\n\n\ndef translate_x_abs(img, pixels, **kwargs):\n    _check_args_tf(kwargs)\n    return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)\n\n\ndef translate_y_abs(img, pixels, **kwargs):\n    _check_args_tf(kwargs)\n    return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)\n\n\ndef rotate(img, degrees, **kwargs):\n    _check_args_tf(kwargs)\n    if _PIL_VER >= (5, 2):\n        return img.rotate(degrees, **kwargs)\n    elif _PIL_VER >= (5, 0):\n        w, h = img.size\n        post_trans = (0, 0)\n        rotn_center = (w / 2.0, h / 2.0)\n        angle = -math.radians(degrees)\n        matrix = [\n            round(math.cos(angle), 15),\n            round(math.sin(angle), 15),\n            0.0,\n            round(-math.sin(angle), 15),\n            round(math.cos(angle), 15),\n            0.0,\n        ]\n\n        def transform(x, y, matrix):\n            (a, b, c, d, e, f) = matrix\n            return a * x + b * y + c, d * x + e * y + f\n\n        matrix[2], matrix[5] = transform(\n            -rotn_center[0] - post_trans[0], -rotn_center[1] - post_trans[1], matrix\n        )\n        matrix[2] += rotn_center[0]\n        matrix[5] += rotn_center[1]\n        return img.transform(img.size, Image.AFFINE, matrix, **kwargs)\n    else:\n        return img.rotate(degrees, resample=kwargs['resample'])\n\n\ndef auto_contrast(img, **__):\n    return ImageOps.autocontrast(img)\n\n\ndef invert(img, **__):\n    return ImageOps.invert(img)\n\n\ndef equalize(img, **__):\n    return ImageOps.equalize(img)\n\n\ndef solarize(img, thresh, **__):\n    return ImageOps.solarize(img, thresh)\n\n\ndef solarize_add(img, add, thresh=128, **__):\n    lut = []\n    for i in range(256):\n        if i < thresh:\n            lut.append(min(255, i + add))\n        else:\n            lut.append(i)\n    if img.mode in (\"L\", \"RGB\"):\n        if img.mode == \"RGB\" and len(lut) == 256:\n            lut = lut + lut + lut\n        return img.point(lut)\n    else:\n        return img\n\n\ndef posterize(img, bits_to_keep, **__):\n    if bits_to_keep >= 8:\n        return img\n    return ImageOps.posterize(img, bits_to_keep)\n\n\ndef contrast(img, factor, **__):\n    return ImageEnhance.Contrast(img).enhance(factor)\n\n\ndef color(img, factor, **__):\n    return ImageEnhance.Color(img).enhance(factor)\n\n\ndef brightness(img, factor, **__):\n    return ImageEnhance.Brightness(img).enhance(factor)\n\n\ndef sharpness(img, factor, **__):\n    return ImageEnhance.Sharpness(img).enhance(factor)\n\n\ndef _randomly_negate(v):\n    \"\"\"With 50% prob, negate the value\"\"\"\n    return -v if random.random() > 0.5 else v\n\n\ndef _rotate_level_to_arg(level, _hparams):\n    # range [-30, 30]\n    level = (level / _MAX_LEVEL) * 30.\n    level = _randomly_negate(level)\n    return level,\n\n\ndef _enhance_level_to_arg(level, _hparams):\n    # range [0.1, 1.9]\n    return (level / _MAX_LEVEL) * 1.8 + 0.1,\n\n\ndef _enhance_increasing_level_to_arg(level, _hparams):\n    # the 'no change' level is 1.0, moving away from that towards 0. or 2.0 increases the enhancement blend\n    # range [0.1, 1.9]\n    level = (level / _MAX_LEVEL) * .9\n    level = 1.0 + _randomly_negate(level)\n    return level,\n\n\ndef _shear_level_to_arg(level, _hparams):\n    # range [-0.3, 0.3]\n    level = (level / _MAX_LEVEL) * 0.3\n    level = _randomly_negate(level)\n    return level,\n\n\ndef _translate_abs_level_to_arg(level, hparams):\n    translate_const = hparams['translate_const']\n    level = (level / _MAX_LEVEL) * float(translate_const)\n    level = _randomly_negate(level)\n    return level,\n\n\ndef _translate_rel_level_to_arg(level, hparams):\n    # default range [-0.45, 0.45]\n    translate_pct = hparams.get('translate_pct', 0.45)\n    level = (level / _MAX_LEVEL) * translate_pct\n    level = _randomly_negate(level)\n    return level,\n\n\ndef _posterize_level_to_arg(level, _hparams):\n    # As per Tensorflow TPU EfficientNet impl\n    # range [0, 4], 'keep 0 up to 4 MSB of original image'\n    # intensity/severity of augmentation decreases with level\n    return int((level / _MAX_LEVEL) * 4),\n\n\ndef _posterize_increasing_level_to_arg(level, hparams):\n    # As per Tensorflow models research and UDA impl\n    # range [4, 0], 'keep 4 down to 0 MSB of original image',\n    # intensity/severity of augmentation increases with level\n    return 4 - _posterize_level_to_arg(level, hparams)[0],\n\n\ndef _posterize_original_level_to_arg(level, _hparams):\n    # As per original AutoAugment paper description\n    # range [4, 8], 'keep 4 up to 8 MSB of image'\n    # intensity/severity of augmentation decreases with level\n    return int((level / _MAX_LEVEL) * 4) + 4,\n\n\ndef _solarize_level_to_arg(level, _hparams):\n    # range [0, 256]\n    # intensity/severity of augmentation decreases with level\n    return int((level / _MAX_LEVEL) * 256),\n\n\ndef _solarize_increasing_level_to_arg(level, _hparams):\n    # range [0, 256]\n    # intensity/severity of augmentation increases with level\n    return 256 - _solarize_level_to_arg(level, _hparams)[0],\n\n\ndef _solarize_add_level_to_arg(level, _hparams):\n    # range [0, 110]\n    return int((level / _MAX_LEVEL) * 110),\n\n\nLEVEL_TO_ARG = {\n    'AutoContrast': None,\n    'Equalize': None,\n    'Invert': None,\n    'Rotate': _rotate_level_to_arg,\n    # There are several variations of the posterize level scaling in various Tensorflow/Google repositories/papers\n    'Posterize': _posterize_level_to_arg,\n    'PosterizeIncreasing': _posterize_increasing_level_to_arg,\n    'PosterizeOriginal': _posterize_original_level_to_arg,\n    'Solarize': _solarize_level_to_arg,\n    'SolarizeIncreasing': _solarize_increasing_level_to_arg,\n    'SolarizeAdd': _solarize_add_level_to_arg,\n    'Color': _enhance_level_to_arg,\n    'ColorIncreasing': _enhance_increasing_level_to_arg,\n    'Contrast': _enhance_level_to_arg,\n    'ContrastIncreasing': _enhance_increasing_level_to_arg,\n    'Brightness': _enhance_level_to_arg,\n    'BrightnessIncreasing': _enhance_increasing_level_to_arg,\n    'Sharpness': _enhance_level_to_arg,\n    'SharpnessIncreasing': _enhance_increasing_level_to_arg,\n    'ShearX': _shear_level_to_arg,\n    'ShearY': _shear_level_to_arg,\n    'TranslateX': _translate_abs_level_to_arg,\n    'TranslateY': _translate_abs_level_to_arg,\n    'TranslateXRel': _translate_rel_level_to_arg,\n    'TranslateYRel': _translate_rel_level_to_arg,\n}\n\nNAME_TO_OP = {\n    'AutoContrast': auto_contrast,\n    'Equalize': equalize,\n    'Invert': invert,\n    'Rotate': rotate,\n    'Posterize': posterize,\n    'PosterizeIncreasing': posterize,\n    'PosterizeOriginal': posterize,\n    'Solarize': solarize,\n    'SolarizeIncreasing': solarize,\n    'SolarizeAdd': solarize_add,\n    'Color': color,\n    'ColorIncreasing': color,\n    'Contrast': contrast,\n    'ContrastIncreasing': contrast,\n    'Brightness': brightness,\n    'BrightnessIncreasing': brightness,\n    'Sharpness': sharpness,\n    'SharpnessIncreasing': sharpness,\n    'ShearX': shear_x,\n    'ShearY': shear_y,\n    'TranslateX': translate_x_abs,\n    'TranslateY': translate_y_abs,\n    'TranslateXRel': translate_x_rel,\n    'TranslateYRel': translate_y_rel,\n}\n\n\nclass AugmentOp:\n\n    def __init__(self, name, prob=0.5, magnitude=10, hparams=None):\n        hparams = hparams or _HPARAMS_DEFAULT\n        self.aug_fn = NAME_TO_OP[name]\n        self.level_fn = LEVEL_TO_ARG[name]\n        self.prob = prob\n        self.magnitude = magnitude\n        self.hparams = hparams.copy()\n        self.kwargs = dict(\n            fillcolor=hparams['img_mean'] if 'img_mean' in hparams else _FILL,\n            resample=hparams['interpolation'] if 'interpolation' in hparams else _RANDOM_INTERPOLATION,\n        )\n\n        # If magnitude_std is > 0, we introduce some randomness\n        # in the usually fixed policy and sample magnitude from a normal distribution\n        # with mean `magnitude` and std-dev of `magnitude_std`.\n        # NOTE This is my own hack, being tested, not in papers or reference impls.\n        self.magnitude_std = self.hparams.get('magnitude_std', 0)\n\n    def __call__(self, img):\n        if self.prob < 1.0 and random.random() > self.prob:\n            return img\n        magnitude = self.magnitude\n        if self.magnitude_std and self.magnitude_std > 0:\n            magnitude = random.gauss(magnitude, self.magnitude_std)\n        magnitude = min(_MAX_LEVEL, max(0, magnitude))  # clip to valid range\n        level_args = self.level_fn(magnitude, self.hparams) if self.level_fn is not None else tuple()\n        return self.aug_fn(img, *level_args, **self.kwargs)\n\n\ndef auto_augment_policy_v0(hparams):\n    # ImageNet v0 policy from TPU EfficientNet impl, cannot find a paper reference.\n    policy = [\n        [('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],\n        [('Color', 0.4, 9), ('Equalize', 0.6, 3)],\n        [('Color', 0.4, 1), ('Rotate', 0.6, 8)],\n        [('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],\n        [('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],\n        [('Color', 0.2, 0), ('Equalize', 0.8, 8)],\n        [('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],\n        [('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],\n        [('Color', 0.6, 1), ('Equalize', 1.0, 2)],\n        [('Invert', 0.4, 9), ('Rotate', 0.6, 0)],\n        [('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],\n        [('Color', 0.4, 7), ('Equalize', 0.6, 0)],\n        [('Posterize', 0.4, 6), ('AutoContrast', 0.4, 7)],\n        [('Solarize', 0.6, 8), ('Color', 0.6, 9)],\n        [('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],\n        [('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],\n        [('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],\n        [('ShearY', 0.8, 0), ('Color', 0.6, 4)],\n        [('Color', 1.0, 0), ('Rotate', 0.6, 2)],\n        [('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],\n        [('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],\n        [('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],\n        [('Posterize', 0.8, 2), ('Solarize', 0.6, 10)],  # This results in black image with Tpu posterize\n        [('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],\n        [('Color', 0.8, 6), ('Rotate', 0.4, 5)],\n    ]\n    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]\n    return pc\n\n\ndef auto_augment_policy_v0r(hparams):\n    # ImageNet v0 policy from TPU EfficientNet impl, with variation of Posterize used\n    # in Google research implementation (number of bits discarded increases with magnitude)\n    policy = [\n        [('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],\n        [('Color', 0.4, 9), ('Equalize', 0.6, 3)],\n        [('Color', 0.4, 1), ('Rotate', 0.6, 8)],\n        [('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],\n        [('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],\n        [('Color', 0.2, 0), ('Equalize', 0.8, 8)],\n        [('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],\n        [('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],\n        [('Color', 0.6, 1), ('Equalize', 1.0, 2)],\n        [('Invert', 0.4, 9), ('Rotate', 0.6, 0)],\n        [('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],\n        [('Color', 0.4, 7), ('Equalize', 0.6, 0)],\n        [('PosterizeIncreasing', 0.4, 6), ('AutoContrast', 0.4, 7)],\n        [('Solarize', 0.6, 8), ('Color', 0.6, 9)],\n        [('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],\n        [('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],\n        [('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],\n        [('ShearY', 0.8, 0), ('Color', 0.6, 4)],\n        [('Color', 1.0, 0), ('Rotate', 0.6, 2)],\n        [('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],\n        [('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],\n        [('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],\n        [('PosterizeIncreasing', 0.8, 2), ('Solarize', 0.6, 10)],\n        [('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],\n        [('Color', 0.8, 6), ('Rotate', 0.4, 5)],\n    ]\n    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]\n    return pc\n\n\ndef auto_augment_policy_original(hparams):\n    # ImageNet policy from https://arxiv.org/abs/1805.09501\n    policy = [\n        [('PosterizeOriginal', 0.4, 8), ('Rotate', 0.6, 9)],\n        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],\n        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],\n        [('PosterizeOriginal', 0.6, 7), ('PosterizeOriginal', 0.6, 6)],\n        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],\n        [('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],\n        [('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],\n        [('PosterizeOriginal', 0.8, 5), ('Equalize', 1.0, 2)],\n        [('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],\n        [('Equalize', 0.6, 8), ('PosterizeOriginal', 0.4, 6)],\n        [('Rotate', 0.8, 8), ('Color', 0.4, 0)],\n        [('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],\n        [('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],\n        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],\n        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],\n        [('Rotate', 0.8, 8), ('Color', 1.0, 2)],\n        [('Color', 0.8, 8), ('Solarize', 0.8, 7)],\n        [('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],\n        [('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],\n        [('Color', 0.4, 0), ('Equalize', 0.6, 3)],\n        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],\n        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],\n        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],\n        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],\n        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],\n    ]\n    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]\n    return pc\n\n\ndef auto_augment_policy_originalr(hparams):\n    # ImageNet policy from https://arxiv.org/abs/1805.09501 with research posterize variation\n    policy = [\n        [('PosterizeIncreasing', 0.4, 8), ('Rotate', 0.6, 9)],\n        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],\n        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],\n        [('PosterizeIncreasing', 0.6, 7), ('PosterizeIncreasing', 0.6, 6)],\n        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],\n        [('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],\n        [('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],\n        [('PosterizeIncreasing', 0.8, 5), ('Equalize', 1.0, 2)],\n        [('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],\n        [('Equalize', 0.6, 8), ('PosterizeIncreasing', 0.4, 6)],\n        [('Rotate', 0.8, 8), ('Color', 0.4, 0)],\n        [('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],\n        [('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],\n        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],\n        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],\n        [('Rotate', 0.8, 8), ('Color', 1.0, 2)],\n        [('Color', 0.8, 8), ('Solarize', 0.8, 7)],\n        [('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],\n        [('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],\n        [('Color', 0.4, 0), ('Equalize', 0.6, 3)],\n        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],\n        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],\n        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],\n        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],\n        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],\n    ]\n    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]\n    return pc\n\n\ndef auto_augment_policy(name=\"original\"):\n    hparams = _HPARAMS_DEFAULT\n    if name == 'original':\n        return auto_augment_policy_original(hparams)\n    elif name == 'originalr':\n        return auto_augment_policy_originalr(hparams)\n    elif name == 'v0':\n        return auto_augment_policy_v0(hparams)\n    elif name == 'v0r':\n        return auto_augment_policy_v0r(hparams)\n    else:\n        assert False, 'Unknown AA policy (%s)' % name\n\n\nclass AutoAugment:\n\n    def __init__(self):\n        self.policy = auto_augment_policy()\n\n    def __call__(self, img):\n        sub_policy = random.choice(self.policy)\n        for op in sub_policy:\n            img = op(img)\n        return img\n\n\ndef auto_augment_transform(config_str, hparams):\n    \"\"\"\n    Create a AutoAugment transform\n    :param config_str: String defining configuration of auto augmentation. Consists of multiple sections separated by\n    dashes ('-'). The first section defines the AutoAugment policy (one of 'v0', 'v0r', 'original', 'originalr').\n    The remaining sections, not order sepecific determine\n        'mstd' -  float std deviation of magnitude noise applied\n    Ex 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5\n    :param hparams: Other hparams (kwargs) for the AutoAugmentation scheme\n    :return: A PyTorch compatible Transform\n    \"\"\"\n    config = config_str.split('-')\n    policy_name = config[0]\n    config = config[1:]\n    for c in config:\n        cs = re.split(r'(\\d.*)', c)\n        if len(cs) < 2:\n            continue\n        key, val = cs[:2]\n        if key == 'mstd':\n            # noise param injected via hparams for now\n            hparams.setdefault('magnitude_std', float(val))\n        else:\n            assert False, 'Unknown AutoAugment config section'\n    aa_policy = auto_augment_policy(policy_name)\n    return AutoAugment(aa_policy)\n\n\n_RAND_TRANSFORMS = [\n    'AutoContrast',\n    'Equalize',\n    'Invert',\n    'Rotate',\n    'Posterize',\n    'Solarize',\n    'SolarizeAdd',\n    'Color',\n    'Contrast',\n    'Brightness',\n    'Sharpness',\n    'ShearX',\n    'ShearY',\n    'TranslateXRel',\n    'TranslateYRel',\n    # 'Cutout'  # NOTE I've implement this as random erasing separately\n]\n\n_RAND_INCREASING_TRANSFORMS = [\n    'AutoContrast',\n    'Equalize',\n    'Invert',\n    'Rotate',\n    'PosterizeIncreasing',\n    'SolarizeIncreasing',\n    'SolarizeAdd',\n    'ColorIncreasing',\n    'ContrastIncreasing',\n    'BrightnessIncreasing',\n    'SharpnessIncreasing',\n    'ShearX',\n    'ShearY',\n    'TranslateXRel',\n    'TranslateYRel',\n    # 'Cutout'  # NOTE I've implement this as random erasing separately\n]\n\n# These experimental weights are based loosely on the relative improvements mentioned in paper.\n# They may not result in increased performance, but could likely be tuned to so.\n_RAND_CHOICE_WEIGHTS_0 = {\n    'Rotate': 0.3,\n    'ShearX': 0.2,\n    'ShearY': 0.2,\n    'TranslateXRel': 0.1,\n    'TranslateYRel': 0.1,\n    'Color': .025,\n    'Sharpness': 0.025,\n    'AutoContrast': 0.025,\n    'Solarize': .005,\n    'SolarizeAdd': .005,\n    'Contrast': .005,\n    'Brightness': .005,\n    'Equalize': .005,\n    'Posterize': 0,\n    'Invert': 0,\n}\n\n\ndef _select_rand_weights(weight_idx=0, transforms=None):\n    transforms = transforms or _RAND_TRANSFORMS\n    assert weight_idx == 0  # only one set of weights currently\n    rand_weights = _RAND_CHOICE_WEIGHTS_0\n    probs = [rand_weights[k] for k in transforms]\n    probs /= np.sum(probs)\n    return probs\n\n\ndef rand_augment_ops(magnitude=10, hparams=None, transforms=None):\n    hparams = hparams or _HPARAMS_DEFAULT\n    transforms = transforms or _RAND_TRANSFORMS\n    return [AugmentOp(\n        name, prob=0.5, magnitude=magnitude, hparams=hparams) for name in transforms]\n\n\nclass RandAugment:\n    def __init__(self, ops, num_layers=2, choice_weights=None):\n        self.ops = ops\n        self.num_layers = num_layers\n        self.choice_weights = choice_weights\n\n    def __call__(self, img):\n        # no replacement when using weighted choice\n        ops = np.random.choice(\n            self.ops, self.num_layers, replace=self.choice_weights is None, p=self.choice_weights)\n        for op in ops:\n            img = op(img)\n        return img\n\n\ndef rand_augment_transform(config_str, hparams):\n    \"\"\"\n    Create a RandAugment transform\n    :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by\n    dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining\n    sections, not order sepecific determine\n        'm' - integer magnitude of rand augment\n        'n' - integer num layers (number of transform ops selected per image)\n        'w' - integer probabiliy weight index (index of a set of weights to influence choice of op)\n        'mstd' -  float std deviation of magnitude noise applied\n        'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)\n    Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5\n    'rand-mstd1-w0' results in magnitude_std 1.0, weights 0, default magnitude of 10 and num_layers 2\n    :param hparams: Other hparams (kwargs) for the RandAugmentation scheme\n    :return: A PyTorch compatible Transform\n    \"\"\"\n    magnitude = _MAX_LEVEL  # default to _MAX_LEVEL for magnitude (currently 10)\n    num_layers = 2  # default to 2 ops per image\n    weight_idx = None  # default to no probability weights for op choice\n    transforms = _RAND_TRANSFORMS\n    config = config_str.split('-')\n    assert config[0] == 'rand'\n    config = config[1:]\n    for c in config:\n        cs = re.split(r'(\\d.*)', c)\n        if len(cs) < 2:\n            continue\n        key, val = cs[:2]\n        if key == 'mstd':\n            # noise param injected via hparams for now\n            hparams.setdefault('magnitude_std', float(val))\n        elif key == 'inc':\n            if bool(val):\n                transforms = _RAND_INCREASING_TRANSFORMS\n        elif key == 'm':\n            magnitude = int(val)\n        elif key == 'n':\n            num_layers = int(val)\n        elif key == 'w':\n            weight_idx = int(val)\n        else:\n            assert False, 'Unknown RandAugment config section'\n    ra_ops = rand_augment_ops(magnitude=magnitude, hparams=hparams, transforms=transforms)\n    choice_weights = None if weight_idx is None else _select_rand_weights(weight_idx)\n    return RandAugment(ra_ops, num_layers, choice_weights=choice_weights)\n\n\n_AUGMIX_TRANSFORMS = [\n    'AutoContrast',\n    'ColorIncreasing',  # not in paper\n    'ContrastIncreasing',  # not in paper\n    'BrightnessIncreasing',  # not in paper\n    'SharpnessIncreasing',  # not in paper\n    'Equalize',\n    'Rotate',\n    'PosterizeIncreasing',\n    'SolarizeIncreasing',\n    'ShearX',\n    'ShearY',\n    'TranslateXRel',\n    'TranslateYRel',\n]\n\n\ndef augmix_ops(magnitude=10, hparams=None, transforms=None):\n    hparams = hparams or _HPARAMS_DEFAULT\n    transforms = transforms or _AUGMIX_TRANSFORMS\n    return [AugmentOp(\n        name, prob=1.0, magnitude=magnitude, hparams=hparams) for name in transforms]\n\n\nclass AugMixAugment:\n    \"\"\" AugMix Transform\n    Adapted and improved from impl here: https://github.com/google-research/augmix/blob/master/imagenet.py\n    From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty -\n    https://arxiv.org/abs/1912.02781\n    \"\"\"\n\n    def __init__(self, ops, alpha=1., width=3, depth=-1, blended=False):\n        self.ops = ops\n        self.alpha = alpha\n        self.width = width\n        self.depth = depth\n        self.blended = blended  # blended mode is faster but not well tested\n\n    def _calc_blended_weights(self, ws, m):\n        ws = ws * m\n        cump = 1.\n        rws = []\n        for w in ws[::-1]:\n            alpha = w / cump\n            cump *= (1 - alpha)\n            rws.append(alpha)\n        return np.array(rws[::-1], dtype=np.float32)\n\n    def _apply_blended(self, img, mixing_weights, m):\n        # This is my first crack and implementing a slightly faster mixed augmentation. Instead\n        # of accumulating the mix for each chain in a Numpy array and then blending with original,\n        # it recomputes the blending coefficients and applies one PIL image blend per chain.\n        # TODO the results appear in the right ballpark but they differ by more than rounding.\n        img_orig = img.copy()\n        ws = self._calc_blended_weights(mixing_weights, m)\n        for w in ws:\n            depth = self.depth if self.depth > 0 else np.random.randint(1, 4)\n            ops = np.random.choice(self.ops, depth, replace=True)\n            img_aug = img_orig  # no ops are in-place, deep copy not necessary\n            for op in ops:\n                img_aug = op(img_aug)\n            img = Image.blend(img, img_aug, w)\n        return img\n\n    def _apply_basic(self, img, mixing_weights, m):\n        # This is a literal adaptation of the paper/official implementation without normalizations and\n        # PIL <-> Numpy conversions between every op. It is still quite CPU compute heavy compared to the\n        # typical augmentation transforms, could use a GPU / Kornia implementation.\n        img_shape = img.size[0], img.size[1], len(img.getbands())\n        mixed = np.zeros(img_shape, dtype=np.float32)\n        for mw in mixing_weights:\n            depth = self.depth if self.depth > 0 else np.random.randint(1, 4)\n            ops = np.random.choice(self.ops, depth, replace=True)\n            img_aug = img  # no ops are in-place, deep copy not necessary\n            for op in ops:\n                img_aug = op(img_aug)\n            mixed += mw * np.asarray(img_aug, dtype=np.float32)\n        np.clip(mixed, 0, 255., out=mixed)\n        mixed = Image.fromarray(mixed.astype(np.uint8))\n        return Image.blend(img, mixed, m)\n\n    def __call__(self, img):\n        mixing_weights = np.float32(np.random.dirichlet([self.alpha] * self.width))\n        m = np.float32(np.random.beta(self.alpha, self.alpha))\n        if self.blended:\n            mixed = self._apply_blended(img, mixing_weights, m)\n        else:\n            mixed = self._apply_basic(img, mixing_weights, m)\n        return mixed\n\n\ndef augment_and_mix_transform(config_str, hparams):\n    \"\"\" Create AugMix PyTorch transform\n    :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by\n    dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining\n    sections, not order sepecific determine\n        'm' - integer magnitude (severity) of augmentation mix (default: 3)\n        'w' - integer width of augmentation chain (default: 3)\n        'd' - integer depth of augmentation chain (-1 is random [1, 3], default: -1)\n        'b' - integer (bool), blend each branch of chain into end result without a final blend, less CPU (default: 0)\n        'mstd' -  float std deviation of magnitude noise applied (default: 0)\n    Ex 'augmix-m5-w4-d2' results in AugMix with severity 5, chain width 4, chain depth 2\n    :param hparams: Other hparams (kwargs) for the Augmentation transforms\n    :return: A PyTorch compatible Transform\n    \"\"\"\n    magnitude = 3\n    width = 3\n    depth = -1\n    alpha = 1.\n    blended = False\n    config = config_str.split('-')\n    assert config[0] == 'augmix'\n    config = config[1:]\n    for c in config:\n        cs = re.split(r'(\\d.*)', c)\n        if len(cs) < 2:\n            continue\n        key, val = cs[:2]\n        if key == 'mstd':\n            # noise param injected via hparams for now\n            hparams.setdefault('magnitude_std', float(val))\n        elif key == 'm':\n            magnitude = int(val)\n        elif key == 'w':\n            width = int(val)\n        elif key == 'd':\n            depth = int(val)\n        elif key == 'a':\n            alpha = float(val)\n        elif key == 'b':\n            blended = bool(val)\n        else:\n            assert False, 'Unknown AugMix config section'\n    ops = augmix_ops(magnitude=magnitude, hparams=hparams)\n    return AugMixAugment(ops, alpha=alpha, width=width, depth=depth, blended=blended)\n"
  },
  {
    "path": "fast_reid/fastreid/data/transforms/build.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torchvision.transforms as T\n\nfrom .transforms import *\nfrom .autoaugment import AutoAugment\n\n\ndef build_transforms(cfg, is_train=True):\n    res = []\n\n    if is_train:\n        size_train = cfg.INPUT.SIZE_TRAIN\n\n        # crop\n        do_crop = cfg.INPUT.CROP.ENABLED\n        crop_size = cfg.INPUT.CROP.SIZE\n        crop_scale = cfg.INPUT.CROP.SCALE\n        crop_ratio = cfg.INPUT.CROP.RATIO\n\n        # augmix augmentation\n        do_augmix = cfg.INPUT.AUGMIX.ENABLED\n        augmix_prob = cfg.INPUT.AUGMIX.PROB\n\n        # auto augmentation\n        do_autoaug = cfg.INPUT.AUTOAUG.ENABLED\n        autoaug_prob = cfg.INPUT.AUTOAUG.PROB\n\n        # horizontal filp\n        do_flip = cfg.INPUT.FLIP.ENABLED\n        flip_prob = cfg.INPUT.FLIP.PROB\n\n        # padding\n        do_pad = cfg.INPUT.PADDING.ENABLED\n        padding_size = cfg.INPUT.PADDING.SIZE\n        padding_mode = cfg.INPUT.PADDING.MODE\n\n        # color jitter\n        do_cj = cfg.INPUT.CJ.ENABLED\n        cj_prob = cfg.INPUT.CJ.PROB\n        cj_brightness = cfg.INPUT.CJ.BRIGHTNESS\n        cj_contrast = cfg.INPUT.CJ.CONTRAST\n        cj_saturation = cfg.INPUT.CJ.SATURATION\n        cj_hue = cfg.INPUT.CJ.HUE\n\n        # random affine\n        do_affine = cfg.INPUT.AFFINE.ENABLED\n\n        # random erasing\n        do_rea = cfg.INPUT.REA.ENABLED\n        rea_prob = cfg.INPUT.REA.PROB\n        rea_value = cfg.INPUT.REA.VALUE\n\n        # random patch\n        do_rpt = cfg.INPUT.RPT.ENABLED\n        rpt_prob = cfg.INPUT.RPT.PROB\n\n        if do_autoaug:\n            res.append(T.RandomApply([AutoAugment()], p=autoaug_prob))\n\n        if size_train[0] > 0:\n            res.append(T.Resize(size_train[0] if len(size_train) == 1 else size_train, interpolation=3))\n\n        if do_crop:\n            res.append(T.RandomResizedCrop(size=crop_size[0] if len(crop_size) == 1 else crop_size,\n                                           interpolation=3,\n                                           scale=crop_scale, ratio=crop_ratio))\n        if do_pad:\n            res.extend([T.Pad(padding_size, padding_mode=padding_mode),\n                        T.RandomCrop(size_train[0] if len(size_train) == 1 else size_train)])\n        if do_flip:\n            res.append(T.RandomHorizontalFlip(p=flip_prob))\n\n        if do_cj:\n            res.append(T.RandomApply([T.ColorJitter(cj_brightness, cj_contrast, cj_saturation, cj_hue)], p=cj_prob))\n        if do_affine:\n            res.append(T.RandomAffine(degrees=10, translate=None, scale=[0.9, 1.1], shear=0.1, resample=False,\n                                      fillcolor=0))\n        if do_augmix:\n            res.append(AugMix(prob=augmix_prob))\n        res.append(ToTensor())\n        if do_rea:\n            res.append(T.RandomErasing(p=rea_prob, value=rea_value))\n        if do_rpt:\n            res.append(RandomPatch(prob_happen=rpt_prob))\n    else:\n        size_test = cfg.INPUT.SIZE_TEST\n        do_crop = cfg.INPUT.CROP.ENABLED\n        crop_size = cfg.INPUT.CROP.SIZE\n\n        if size_test[0] > 0:\n            res.append(T.Resize(size_test[0] if len(size_test) == 1 else size_test, interpolation=3))\n        if do_crop:\n            res.append(T.CenterCrop(size=crop_size[0] if len(crop_size) == 1 else crop_size))\n        res.append(ToTensor())\n    return T.Compose(res)\n"
  },
  {
    "path": "fast_reid/fastreid/data/transforms/functional.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport numpy as np\nimport torch\nfrom PIL import Image, ImageOps, ImageEnhance\n\n\ndef to_tensor(pic):\n    \"\"\"Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.\n\n    See ``ToTensor`` for more details.\n\n    Args:\n        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\n\n    Returns:\n        Tensor: Converted image.\n    \"\"\"\n    if isinstance(pic, np.ndarray):\n        assert len(pic.shape) in (2, 3)\n        # handle numpy array\n        if pic.ndim == 2:\n            pic = pic[:, :, None]\n\n        img = torch.from_numpy(pic.transpose((2, 0, 1)))\n        # backward compatibility\n        if isinstance(img, torch.ByteTensor):\n            return img.float()\n        else:\n            return img\n\n    # handle PIL Image\n    if pic.mode == 'I':\n        img = torch.from_numpy(np.array(pic, np.int32, copy=False))\n    elif pic.mode == 'I;16':\n        img = torch.from_numpy(np.array(pic, np.int16, copy=False))\n    elif pic.mode == 'F':\n        img = torch.from_numpy(np.array(pic, np.float32, copy=False))\n    elif pic.mode == '1':\n        img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False))\n    else:\n        img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))\n    # PIL image mode: L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK\n    if pic.mode == 'YCbCr':\n        nchannel = 3\n    elif pic.mode == 'I;16':\n        nchannel = 1\n    else:\n        nchannel = len(pic.mode)\n    img = img.view(pic.size[1], pic.size[0], nchannel)\n    # put it from HWC to CHW format\n    # yikes, this transpose takes 80% of the loading time/CPU\n    img = img.transpose(0, 1).transpose(0, 2).contiguous()\n    if isinstance(img, torch.ByteTensor):\n        return img.float()\n    else:\n        return img\n\n\ndef int_parameter(level, maxval):\n    \"\"\"Helper function to scale `val` between 0 and maxval .\n    Args:\n      level: Level of the operation that will be between [0, `PARAMETER_MAX`].\n      maxval: Maximum value that the operation can have. This will be scaled to\n        level/PARAMETER_MAX.\n    Returns:\n      An int that results from scaling `maxval` according to `level`.\n    \"\"\"\n    return int(level * maxval / 10)\n\n\ndef float_parameter(level, maxval):\n    \"\"\"Helper function to scale `val` between 0 and maxval.\n    Args:\n      level: Level of the operation that will be between [0, `PARAMETER_MAX`].\n      maxval: Maximum value that the operation can have. This will be scaled to\n        level/PARAMETER_MAX.\n    Returns:\n      A float that results from scaling `maxval` according to `level`.\n    \"\"\"\n    return float(level) * maxval / 10.\n\n\ndef sample_level(n):\n    return np.random.uniform(low=0.1, high=n)\n\n\ndef autocontrast(pil_img, *args):\n    return ImageOps.autocontrast(pil_img)\n\n\ndef equalize(pil_img, *args):\n    return ImageOps.equalize(pil_img)\n\n\ndef posterize(pil_img, level, *args):\n    level = int_parameter(sample_level(level), 4)\n    return ImageOps.posterize(pil_img, 4 - level)\n\n\ndef rotate(pil_img, level, *args):\n    degrees = int_parameter(sample_level(level), 30)\n    if np.random.uniform() > 0.5:\n        degrees = -degrees\n    return pil_img.rotate(degrees, resample=Image.BILINEAR)\n\n\ndef solarize(pil_img, level, *args):\n    level = int_parameter(sample_level(level), 256)\n    return ImageOps.solarize(pil_img, 256 - level)\n\n\ndef shear_x(pil_img, level):\n    level = float_parameter(sample_level(level), 0.3)\n    if np.random.uniform() > 0.5:\n        level = -level\n    return pil_img.transform(pil_img.size,\n                             Image.AFFINE, (1, level, 0, 0, 1, 0),\n                             resample=Image.BILINEAR)\n\n\ndef shear_y(pil_img, level):\n    level = float_parameter(sample_level(level), 0.3)\n    if np.random.uniform() > 0.5:\n        level = -level\n    return pil_img.transform(pil_img.size,\n                             Image.AFFINE, (1, 0, 0, level, 1, 0),\n                             resample=Image.BILINEAR)\n\n\ndef translate_x(pil_img, level):\n    level = int_parameter(sample_level(level), pil_img.size[0] / 3)\n    if np.random.random() > 0.5:\n        level = -level\n    return pil_img.transform(pil_img.size,\n                             Image.AFFINE, (1, 0, level, 0, 1, 0),\n                             resample=Image.BILINEAR)\n\n\ndef translate_y(pil_img, level):\n    level = int_parameter(sample_level(level), pil_img.size[1] / 3)\n    if np.random.random() > 0.5:\n        level = -level\n    return pil_img.transform(pil_img.size,\n                             Image.AFFINE, (1, 0, 0, 0, 1, level),\n                             resample=Image.BILINEAR)\n\n\n# operation that overlaps with ImageNet-C's test set\ndef color(pil_img, level, *args):\n    level = float_parameter(sample_level(level), 1.8) + 0.1\n    return ImageEnhance.Color(pil_img).enhance(level)\n\n\n# operation that overlaps with ImageNet-C's test set\ndef contrast(pil_img, level, *args):\n    level = float_parameter(sample_level(level), 1.8) + 0.1\n    return ImageEnhance.Contrast(pil_img).enhance(level)\n\n\n# operation that overlaps with ImageNet-C's test set\ndef brightness(pil_img, level, *args):\n    level = float_parameter(sample_level(level), 1.8) + 0.1\n    return ImageEnhance.Brightness(pil_img).enhance(level)\n\n\n# operation that overlaps with ImageNet-C's test set\ndef sharpness(pil_img, level, *args):\n    level = float_parameter(sample_level(level), 1.8) + 0.1\n    return ImageEnhance.Sharpness(pil_img).enhance(level)\n\n\naugmentations = [\n    autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y,\n    translate_x, translate_y\n]\n"
  },
  {
    "path": "fast_reid/fastreid/data/transforms/transforms.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n__all__ = ['ToTensor', 'RandomPatch', 'AugMix', ]\n\nimport math\nimport random\nfrom collections import deque\n\nimport numpy as np\nimport torch\n\nfrom .functional import to_tensor, augmentations\n\n\nclass ToTensor(object):\n    \"\"\"Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.\n\n    Converts a PIL Image or numpy.ndarray (H x W x C) in the range\n    [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 255.0]\n    if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)\n    or if the numpy.ndarray has dtype = np.uint8\n\n    In the other cases, tensors are returned without scaling.\n    \"\"\"\n\n    def __call__(self, pic):\n        \"\"\"\n        Args:\n            pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\n\n        Returns:\n            Tensor: Converted image.\n        \"\"\"\n        return to_tensor(pic)\n\n    def __repr__(self):\n        return self.__class__.__name__ + '()'\n\n\nclass RandomPatch(object):\n    \"\"\"Random patch data augmentation.\n    There is a patch pool that stores randomly extracted pathces from person images.\n    For each input image, RandomPatch\n        1) extracts a random patch and stores the patch in the patch pool;\n        2) randomly selects a patch from the patch pool and pastes it on the\n           input (at random position) to simulate occlusion.\n    Reference:\n        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.\n        - Zhou et al. Learning Generalisable Omni-Scale Representations\n          for Person Re-Identification. arXiv preprint, 2019.\n    \"\"\"\n\n    def __init__(self, prob_happen=0.5, pool_capacity=50000, min_sample_size=100,\n                 patch_min_area=0.01, patch_max_area=0.5, patch_min_ratio=0.1, prob_flip_leftright=0.5,\n                 ):\n        self.prob_happen = prob_happen\n\n        self.patch_min_area = patch_min_area\n        self.patch_max_area = patch_max_area\n        self.patch_min_ratio = patch_min_ratio\n\n        self.prob_flip_leftright = prob_flip_leftright\n\n        self.patchpool = deque(maxlen=pool_capacity)\n        self.min_sample_size = min_sample_size\n\n    def generate_wh(self, W, H):\n        area = W * H\n        for attempt in range(100):\n            target_area = random.uniform(self.patch_min_area, self.patch_max_area) * area\n            aspect_ratio = random.uniform(self.patch_min_ratio, 1. / self.patch_min_ratio)\n            h = int(round(math.sqrt(target_area * aspect_ratio)))\n            w = int(round(math.sqrt(target_area / aspect_ratio)))\n            if w < W and h < H:\n                return w, h\n        return None, None\n\n    def transform_patch(self, patch):\n        if random.uniform(0, 1) > self.prob_flip_leftright:\n            patch = torch.flip(patch, dims=[2])\n        return patch\n\n    def __call__(self, img):\n        _, H, W = img.size()  # original image size\n\n        # collect new patch\n        w, h = self.generate_wh(W, H)\n        if w is not None and h is not None:\n            x1 = random.randint(0, W - w)\n            y1 = random.randint(0, H - h)\n            new_patch = img[..., y1:y1 + h, x1:x1 + w]\n            self.patchpool.append(new_patch)\n\n        if len(self.patchpool) < self.min_sample_size:\n            return img\n\n        if random.uniform(0, 1) > self.prob_happen:\n            return img\n\n        # paste a randomly selected patch on a random position\n        patch = random.sample(self.patchpool, 1)[0]\n        _, patchH, patchW = patch.size()\n        x1 = random.randint(0, W - patchW)\n        y1 = random.randint(0, H - patchH)\n        patch = self.transform_patch(patch)\n        img[..., y1:y1 + patchH, x1:x1 + patchW] = patch\n\n        return img\n\n\nclass AugMix(object):\n    \"\"\" Perform AugMix augmentation and compute mixture.\n    \"\"\"\n\n    def __init__(self, prob=0.5, aug_prob_coeff=0.1, mixture_width=3, mixture_depth=1, aug_severity=1):\n        \"\"\"\n        Args:\n            prob: Probability of taking augmix\n            aug_prob_coeff: Probability distribution coefficients.\n            mixture_width: Number of augmentation chains to mix per augmented example.\n            mixture_depth: Depth of augmentation chains. -1 denotes stochastic depth in [1, 3]'\n            aug_severity: Severity of underlying augmentation operators (between 1 to 10).\n        \"\"\"\n        # fmt: off\n        self.prob           = prob\n        self.aug_prob_coeff = aug_prob_coeff\n        self.mixture_width  = mixture_width\n        self.mixture_depth  = mixture_depth\n        self.aug_severity   = aug_severity\n        self.augmentations  = augmentations\n        # fmt: on\n\n    def __call__(self, image):\n        \"\"\"Perform AugMix augmentations and compute mixture.\n\n        Returns:\n          mixed: Augmented and mixed image.\n        \"\"\"\n        if random.random() > self.prob:\n            # Avoid the warning: the given NumPy array is not writeable\n            return np.asarray(image).copy()\n\n        ws = np.float32(\n            np.random.dirichlet([self.aug_prob_coeff] * self.mixture_width))\n        m = np.float32(np.random.beta(self.aug_prob_coeff, self.aug_prob_coeff))\n\n        mix = np.zeros([image.size[1], image.size[0], 3])\n        for i in range(self.mixture_width):\n            image_aug = image.copy()\n            depth = self.mixture_depth if self.mixture_depth > 0 else np.random.randint(1, 4)\n            for _ in range(depth):\n                op = np.random.choice(self.augmentations)\n                image_aug = op(image_aug, self.aug_severity)\n            mix += ws[i] * np.asarray(image_aug)\n\n        mixed = (1 - m) * image + m * mix\n        return mixed.astype(np.uint8)\n"
  },
  {
    "path": "fast_reid/fastreid/engine/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\nfrom .train_loop import *\n\n__all__ = [k for k in globals().keys() if not k.startswith(\"_\")]\n\n\n# prefer to let hooks and defaults live in separate namespaces (therefore not in __all__)\n# but still make them available here\nfrom .hooks import *\nfrom .defaults import *\nfrom .launch import *\n"
  },
  {
    "path": "fast_reid/fastreid/engine/defaults.py",
    "content": "# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\n\"\"\"\nThis file contains components with some default boilerplate logic user may need\nin training / testing. They will not work for everyone, but many users may find them useful.\nThe behavior of functions/classes in this file is subject to change,\nsince they are meant to represent the \"common default behavior\" people need in their projects.\n\"\"\"\n\nimport argparse\nimport logging\nimport os\nimport sys\nfrom collections import OrderedDict\n\nimport torch\nfrom torch.nn.parallel import DistributedDataParallel\n\nfrom fast_reid.fastreid.data import build_reid_test_loader, build_reid_train_loader\nfrom fast_reid.fastreid.evaluation import (ReidEvaluator,\n                                 inference_on_dataset, print_csv_format)\nfrom fast_reid.fastreid.modeling.meta_arch import build_model\nfrom fast_reid.fastreid.solver import build_lr_scheduler, build_optimizer\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.utils.collect_env import collect_env_info\nfrom fast_reid.fastreid.utils.env import seed_all_rng\nfrom fast_reid.fastreid.utils.events import CommonMetricPrinter, JSONWriter, TensorboardXWriter\nfrom fast_reid.fastreid.utils.file_io import PathManager\nfrom fast_reid.fastreid.utils.logger import setup_logger\nfrom . import hooks\nfrom .train_loop import TrainerBase, AMPTrainer, SimpleTrainer\n\n__all__ = [\"default_argument_parser\", \"default_setup\", \"DefaultPredictor\", \"DefaultTrainer\"]\n\n\ndef default_argument_parser():\n    \"\"\"\n    Create a parser with some common arguments used by fastreid users.\n    Returns:\n        argparse.ArgumentParser:\n    \"\"\"\n    parser = argparse.ArgumentParser(description=\"fastreid Training\")\n    parser.add_argument(\"--config-file\", default=\"\", metavar=\"FILE\", help=\"path to config file\")\n    parser.add_argument(\n        \"--resume\",\n        action=\"store_true\",\n        help=\"whether to attempt to resume from the checkpoint directory\",\n    )\n    parser.add_argument(\"--eval-only\", action=\"store_true\", help=\"perform evaluation only\")\n    parser.add_argument(\"--num-gpus\", type=int, default=1, help=\"number of gpus *per machine*\")\n    parser.add_argument(\"--num-machines\", type=int, default=1, help=\"total number of machines\")\n    parser.add_argument(\n        \"--machine-rank\", type=int, default=0, help=\"the rank of this machine (unique per machine)\"\n    )\n\n    # PyTorch still may leave orphan processes in multi-gpu training.\n    # Therefore we use a deterministic way to obtain port,\n    # so that users are aware of orphan processes by seeing the port occupied.\n    port = 2 ** 15 + 2 ** 14 + hash(os.getuid() if sys.platform != \"win32\" else 1) % 2 ** 14\n    parser.add_argument(\"--dist-url\", default=\"tcp://127.0.0.1:{}\".format(port))\n    parser.add_argument(\n        \"opts\",\n        help=\"Modify config options using the command-line\",\n        default=None,\n        nargs=argparse.REMAINDER,\n    )\n    return parser\n\n\ndef default_setup(cfg, args):\n    \"\"\"\n    Perform some basic common setups at the beginning of a job, including:\n    1. Set up the detectron2 logger\n    2. Log basic information about environment, cmdline arguments, and config\n    3. Backup the config to the output directory\n    Args:\n        cfg (CfgNode): the full config to be used\n        args (argparse.NameSpace): the command line arguments to be logged\n    \"\"\"\n    output_dir = cfg.OUTPUT_DIR\n    if comm.is_main_process() and output_dir:\n        PathManager.mkdirs(output_dir)\n\n    rank = comm.get_rank()\n    # setup_logger(output_dir, distributed_rank=rank, name=\"fvcore\")\n    logger = setup_logger(output_dir, distributed_rank=rank)\n\n    logger.info(\"Rank of current process: {}. World size: {}\".format(rank, comm.get_world_size()))\n    logger.info(\"Environment info:\\n\" + collect_env_info())\n\n    logger.info(\"Command line arguments: \" + str(args))\n    if hasattr(args, \"config_file\") and args.config_file != \"\":\n        logger.info(\n            \"Contents of args.config_file={}:\\n{}\".format(\n                args.config_file, PathManager.open(args.config_file, \"r\").read()\n            )\n        )\n\n    logger.info(\"Running with full config:\\n{}\".format(cfg))\n    if comm.is_main_process() and output_dir:\n        # Note: some of our scripts may expect the existence of\n        # config.yaml in output directory\n        path = os.path.join(output_dir, \"config.yaml\")\n        with PathManager.open(path, \"w\") as f:\n            f.write(cfg.dump())\n        logger.info(\"Full config saved to {}\".format(os.path.abspath(path)))\n\n    # make sure each worker has a different, yet deterministic seed if specified\n    seed_all_rng()\n\n    # cudnn benchmark has large overhead. It shouldn't be used considering the small size of\n    # typical validation set.\n    if not (hasattr(args, \"eval_only\") and args.eval_only):\n        torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK\n\n\nclass DefaultPredictor:\n    \"\"\"\n    Create a simple end-to-end predictor with the given config.\n    The predictor takes an BGR image, resizes it to the specified resolution,\n    runs the model and produces a dict of predictions.\n    This predictor takes care of model loading and input preprocessing for you.\n    If you'd like to do anything more fancy, please refer to its source code\n    as examples to build and use the model manually.\n    Attributes:\n    Examples:\n    .. code-block:: python\n        pred = DefaultPredictor(cfg)\n        inputs = cv2.imread(\"input.jpg\")\n        outputs = pred(inputs)\n    \"\"\"\n\n    def __init__(self, cfg):\n        self.cfg = cfg.clone()  # cfg can be modified by model\n        self.cfg.defrost()\n        self.cfg.MODEL.BACKBONE.PRETRAIN = False\n        self.model = build_model(self.cfg)\n        self.model.eval()\n\n        Checkpointer(self.model).load(cfg.MODEL.WEIGHTS)\n\n    def __call__(self, image):\n        \"\"\"\n        Args:\n            image (torch.tensor): an image tensor of shape (B, C, H, W).\n        Returns:\n            predictions (torch.tensor): the output features of the model\n        \"\"\"\n        inputs = {\"images\": image.to(self.model.device)}\n        with torch.no_grad():  # https://github.com/sphinx-doc/sphinx/issues/4258\n            predictions = self.model(inputs)\n        return predictions.cpu()\n\n\nclass DefaultTrainer(TrainerBase):\n    \"\"\"\n    A trainer with default training logic. Compared to `SimpleTrainer`, it\n    contains the following logic in addition:\n    1. Create model, optimizer, scheduler, dataloader from the given config.\n    2. Load a checkpoint or `cfg.MODEL.WEIGHTS`, if exists.\n    3. Register a few common hooks.\n    It is created to simplify the **standard model training workflow** and reduce code boilerplate\n    for users who only need the standard training workflow, with standard features.\n    It means this class makes *many assumptions* about your training logic that\n    may easily become invalid in a new research. In fact, any assumptions beyond those made in the\n    :class:`SimpleTrainer` are too much for research.\n    The code of this class has been annotated about restrictive assumptions it mades.\n    When they do not work for you, you're encouraged to:\n    1. Overwrite methods of this class, OR:\n    2. Use :class:`SimpleTrainer`, which only does minimal SGD training and\n       nothing else. You can then add your own hooks if needed. OR:\n    3. Write your own training loop similar to `tools/plain_train_net.py`.\n    Also note that the behavior of this class, like other functions/classes in\n    this file, is not stable, since it is meant to represent the \"common default behavior\".\n    It is only guaranteed to work well with the standard models and training workflow in fastreid.\n    To obtain more stable behavior, write your own training logic with other public APIs.\n    Attributes:\n        scheduler:\n        checkpointer:\n        cfg (CfgNode):\n    Examples:\n    .. code-block:: python\n        trainer = DefaultTrainer(cfg)\n        trainer.resume_or_load()  # load last checkpoint or MODEL.WEIGHTS\n        trainer.train()\n    \"\"\"\n\n    def __init__(self, cfg):\n        \"\"\"\n        Args:\n            cfg (CfgNode):\n        \"\"\"\n\n        super().__init__()\n\n        logger = logging.getLogger(\"fastreid\")\n        if not logger.isEnabledFor(logging.INFO):  # setup_logger is not called for fastreid\n            setup_logger()\n\n        # Assume these objects must be constructed in this order.\n        data_loader = self.build_train_loader(cfg)\n        cfg = self.auto_scale_hyperparams(cfg, data_loader.dataset.num_classes)\n        model = self.build_model(cfg)\n        optimizer, param_wrapper = self.build_optimizer(cfg, model)\n\n        # For training, wrap with DDP. But don't need this for inference.\n        if comm.get_world_size() > 1:\n            # ref to https://github.com/pytorch/pytorch/issues/22049 to set `find_unused_parameters=True`\n            # for part of the parameters is not updated.\n            model = DistributedDataParallel(\n                model, device_ids=[comm.get_local_rank()], broadcast_buffers=False,\n            )\n\n        self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(\n            model, data_loader, optimizer, param_wrapper\n        )\n\n        self.iters_per_epoch = len(data_loader.dataset) // cfg.SOLVER.IMS_PER_BATCH\n        self.scheduler = self.build_lr_scheduler(cfg, optimizer, self.iters_per_epoch)\n\n        # Assume no other objects need to be checkpointed.\n        # We can later make it checkpoint the stateful hooks\n        self.checkpointer = Checkpointer(\n            # Assume you want to save checkpoints together with logs/statistics\n            model,\n            cfg.OUTPUT_DIR,\n            save_to_disk=comm.is_main_process(),\n            optimizer=optimizer,\n            **self.scheduler,\n        )\n\n        self.start_epoch = 0\n        self.max_epoch = cfg.SOLVER.MAX_EPOCH\n        self.max_iter = self.max_epoch * self.iters_per_epoch\n        self.warmup_iters = cfg.SOLVER.WARMUP_ITERS\n        self.delay_epochs = cfg.SOLVER.DELAY_EPOCHS\n        self.cfg = cfg\n\n        self.register_hooks(self.build_hooks())\n\n    def resume_or_load(self, resume=True):\n        \"\"\"\n        If `resume==True` and `cfg.OUTPUT_DIR` contains the last checkpoint (defined by\n        a `last_checkpoint` file), resume from the file. Resuming means loading all\n        available states (eg. optimizer and scheduler) and update iteration counter\n        from the checkpoint. ``cfg.MODEL.WEIGHTS`` will not be used.\n        Otherwise, this is considered as an independent training. The method will load model\n        weights from the file `cfg.MODEL.WEIGHTS` (but will not load other states) and start\n        from iteration 0.\n        Args:\n            resume (bool): whether to do resume or not\n        \"\"\"\n        # The checkpoint stores the training iteration that just finished, thus we start\n        # at the next iteration (or iter zero if there's no checkpoint).\n        checkpoint = self.checkpointer.resume_or_load(self.cfg.MODEL.WEIGHTS, resume=resume)\n\n        if resume and self.checkpointer.has_checkpoint():\n            self.start_epoch = checkpoint.get(\"epoch\", -1) + 1\n            # The checkpoint stores the training iteration that just finished, thus we start\n            # at the next iteration (or iter zero if there's no checkpoint).\n\n    def build_hooks(self):\n        \"\"\"\n        Build a list of default hooks, including timing, evaluation,\n        checkpointing, lr scheduling, precise BN, writing events.\n        Returns:\n            list[HookBase]:\n        \"\"\"\n        logger = logging.getLogger(__name__)\n        cfg = self.cfg.clone()\n        cfg.defrost()\n        cfg.DATALOADER.NUM_WORKERS = 0  # save some memory and time for PreciseBN\n        cfg.DATASETS.NAMES = tuple([cfg.TEST.PRECISE_BN.DATASET])  # set dataset name for PreciseBN\n\n        ret = [\n            hooks.IterationTimer(),\n            hooks.LRScheduler(self.optimizer, self.scheduler),\n        ]\n\n        if cfg.TEST.PRECISE_BN.ENABLED and hooks.get_bn_modules(self.model):\n            logger.info(\"Prepare precise BN dataset\")\n            ret.append(hooks.PreciseBN(\n                # Run at the same freq as (but before) evaluation.\n                self.model,\n                # Build a new data loader to not affect training\n                self.build_train_loader(cfg),\n                cfg.TEST.PRECISE_BN.NUM_ITER,\n            ))\n\n        if len(cfg.MODEL.FREEZE_LAYERS) > 0 and cfg.SOLVER.FREEZE_ITERS > 0:\n            ret.append(hooks.LayerFreeze(\n                self.model,\n                cfg.MODEL.FREEZE_LAYERS,\n                cfg.SOLVER.FREEZE_ITERS,\n            ))\n\n        # Do PreciseBN before checkpointer, because it updates the model and need to\n        # be saved by checkpointer.\n        # This is not always the best: if checkpointing has a different frequency,\n        # some checkpoints may have more precise statistics than others.\n\n        def test_and_save_results():\n            self._last_eval_results = self.test(self.cfg, self.model)\n            return self._last_eval_results\n\n        # Do evaluation before checkpointer, because then if it fails,\n        # we can use the saved checkpoint to debug.\n        ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))\n\n        if comm.is_main_process():\n            ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))\n            # run writers in the end, so that evaluation metrics are written\n            ret.append(hooks.PeriodicWriter(self.build_writers(), 200))\n\n        return ret\n\n    def build_writers(self):\n        \"\"\"\n        Build a list of writers to be used. By default it contains\n        writers that write metrics to the screen,\n        a json file, and a tensorboard event file respectively.\n        If you'd like a different list of writers, you can overwrite it in\n        your trainer.\n        Returns:\n            list[EventWriter]: a list of :class:`EventWriter` objects.\n        It is now implemented by:\n        .. code-block:: python\n            return [\n                CommonMetricPrinter(self.max_iter),\n                JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n                TensorboardXWriter(self.cfg.OUTPUT_DIR),\n            ]\n        \"\"\"\n        # Assume the default print/log frequency.\n        return [\n            # It may not always print what you want to see, since it prints \"common\" metrics only.\n            CommonMetricPrinter(self.max_iter),\n            JSONWriter(os.path.join(self.cfg.OUTPUT_DIR, \"metrics.json\")),\n            TensorboardXWriter(self.cfg.OUTPUT_DIR),\n        ]\n\n    def train(self):\n        \"\"\"\n        Run training.\n        Returns:\n            OrderedDict of results, if evaluation is enabled. Otherwise None.\n        \"\"\"\n        super().train(self.start_epoch, self.max_epoch, self.iters_per_epoch)\n        if comm.is_main_process():\n            assert hasattr(\n                self, \"_last_eval_results\"\n            ), \"No evaluation results obtained during training!\"\n            return self._last_eval_results\n\n    def run_step(self):\n        self._trainer.iter = self.iter\n        self._trainer.run_step()\n\n    @classmethod\n    def build_model(cls, cfg):\n        \"\"\"\n        Returns:\n            torch.nn.Module:\n        It now calls :func:`fastreid.modeling.build_model`.\n        Overwrite it if you'd like a different model.\n        \"\"\"\n        model = build_model(cfg)\n        logger = logging.getLogger(__name__)\n        logger.info(\"Model:\\n{}\".format(model))\n        return model\n\n    @classmethod\n    def build_optimizer(cls, cfg, model):\n        \"\"\"\n        Returns:\n            torch.optim.Optimizer:\n        It now calls :func:`fastreid.solver.build_optimizer`.\n        Overwrite it if you'd like a different optimizer.\n        \"\"\"\n        return build_optimizer(cfg, model)\n\n    @classmethod\n    def build_lr_scheduler(cls, cfg, optimizer, iters_per_epoch):\n        \"\"\"\n        It now calls :func:`fastreid.solver.build_lr_scheduler`.\n        Overwrite it if you'd like a different scheduler.\n        \"\"\"\n        return build_lr_scheduler(cfg, optimizer, iters_per_epoch)\n\n    @classmethod\n    def build_train_loader(cls, cfg):\n        \"\"\"\n        Returns:\n            iterable\n        It now calls :func:`fastreid.data.build_reid_train_loader`.\n        Overwrite it if you'd like a different data loader.\n        \"\"\"\n        logger = logging.getLogger(__name__)\n        logger.info(\"Prepare training set\")\n        return build_reid_train_loader(cfg, combineall=cfg.DATASETS.COMBINEALL)\n\n    @classmethod\n    def build_test_loader(cls, cfg, dataset_name):\n        \"\"\"\n        Returns:\n            iterable\n        It now calls :func:`fastreid.data.build_reid_test_loader`.\n        Overwrite it if you'd like a different data loader.\n        \"\"\"\n        return build_reid_test_loader(cfg, dataset_name=dataset_name)\n\n    @classmethod\n    def build_evaluator(cls, cfg, dataset_name, output_dir=None):\n        data_loader, num_query = cls.build_test_loader(cfg, dataset_name)\n        return data_loader, ReidEvaluator(cfg, num_query, output_dir)\n\n    @classmethod\n    def test(cls, cfg, model):\n        \"\"\"\n        Args:\n            cfg (CfgNode):\n            model (nn.Module):\n        Returns:\n            dict: a dict of result metrics\n        \"\"\"\n        logger = logging.getLogger(__name__)\n\n        results = OrderedDict()\n        for idx, dataset_name in enumerate(cfg.DATASETS.TESTS):\n            logger.info(\"Prepare testing set\")\n            try:\n                data_loader, evaluator = cls.build_evaluator(cfg, dataset_name)\n            except NotImplementedError:\n                logger.warn(\n                    \"No evaluator found. implement its `build_evaluator` method.\"\n                )\n                results[dataset_name] = {}\n                continue\n            results_i = inference_on_dataset(model, data_loader, evaluator, flip_test=cfg.TEST.FLIP.ENABLED)\n            results[dataset_name] = results_i\n\n            if comm.is_main_process():\n                assert isinstance(\n                    results, dict\n                ), \"Evaluator must return a dict on the main process. Got {} instead.\".format(\n                    results\n                )\n                logger.info(\"Evaluation results for {} in csv format:\".format(dataset_name))\n                results_i['dataset'] = dataset_name\n                print_csv_format(results_i)\n\n        if len(results) == 1:\n            results = list(results.values())[0]\n\n        return results\n\n    @staticmethod\n    def auto_scale_hyperparams(cfg, num_classes):\n        r\"\"\"\n        This is used for auto-computation actual training iterations,\n        because some hyper-param, such as MAX_ITER, means training epochs rather than iters,\n        so we need to convert specific hyper-param to training iterations.\n        \"\"\"\n        cfg = cfg.clone()\n        frozen = cfg.is_frozen()\n        cfg.defrost()\n\n        # If you don't hard-code the number of classes, it will compute the number automatically\n        if cfg.MODEL.HEADS.NUM_CLASSES == 0:\n            output_dir = cfg.OUTPUT_DIR\n            cfg.MODEL.HEADS.NUM_CLASSES = num_classes\n            logger = logging.getLogger(__name__)\n            logger.info(f\"Auto-scaling the num_classes={cfg.MODEL.HEADS.NUM_CLASSES}\")\n\n            # Update the saved config file to make the number of classes valid\n            if comm.is_main_process() and output_dir:\n                # Note: some of our scripts may expect the existence of\n                # config.yaml in output directory\n                path = os.path.join(output_dir, \"config.yaml\")\n                with PathManager.open(path, \"w\") as f:\n                    f.write(cfg.dump())\n\n        if frozen: cfg.freeze()\n\n        return cfg\n\n\n# Access basic attributes from the underlying trainer\nfor _attr in [\"model\", \"data_loader\", \"optimizer\", \"grad_scaler\"]:\n    setattr(DefaultTrainer, _attr, property(lambda self, x=_attr: getattr(self._trainer, x, None)))\n"
  },
  {
    "path": "fast_reid/fastreid/engine/hooks.py",
    "content": "# -*- coding: utf-8 -*-\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport datetime\nimport itertools\nimport logging\nimport os\nimport tempfile\nimport time\nfrom collections import Counter\n\nimport torch\nfrom torch import nn\nfrom torch.nn.parallel import DistributedDataParallel\n\nfrom fast_reid.fastreid.evaluation.testing import flatten_results_dict\nfrom fast_reid.fastreid.solver import optim\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.checkpoint import PeriodicCheckpointer as _PeriodicCheckpointer\nfrom fast_reid.fastreid.utils.events import EventStorage, EventWriter, get_event_storage\nfrom fast_reid.fastreid.utils.file_io import PathManager\nfrom fast_reid.fastreid.utils.precision_bn import update_bn_stats, get_bn_modules\nfrom fast_reid.fastreid.utils.timer import Timer\nfrom .train_loop import HookBase\n\n__all__ = [\n    \"CallbackHook\",\n    \"IterationTimer\",\n    \"PeriodicWriter\",\n    \"PeriodicCheckpointer\",\n    \"LRScheduler\",\n    \"AutogradProfiler\",\n    \"EvalHook\",\n    \"PreciseBN\",\n    \"LayerFreeze\",\n]\n\n\"\"\"\nImplement some common hooks.\n\"\"\"\n\n\nclass CallbackHook(HookBase):\n    \"\"\"\n    Create a hook using callback functions provided by the user.\n    \"\"\"\n\n    def __init__(self, *, before_train=None, after_train=None, before_epoch=None, after_epoch=None,\n                 before_step=None, after_step=None):\n        \"\"\"\n        Each argument is a function that takes one argument: the trainer.\n        \"\"\"\n        self._before_train = before_train\n        self._before_epoch = before_epoch\n        self._before_step = before_step\n        self._after_step = after_step\n        self._after_epoch = after_epoch\n        self._after_train = after_train\n\n    def before_train(self):\n        if self._before_train:\n            self._before_train(self.trainer)\n\n    def after_train(self):\n        if self._after_train:\n            self._after_train(self.trainer)\n        # The functions may be closures that hold reference to the trainer\n        # Therefore, delete them to avoid circular reference.\n        del self._before_train, self._after_train\n        del self._before_step, self._after_step\n\n    def before_epoch(self):\n        if self._before_epoch:\n            self._before_epoch(self.trainer)\n\n    def after_epoch(self):\n        if self._after_epoch:\n            self._after_epoch(self.trainer)\n\n    def before_step(self):\n        if self._before_step:\n            self._before_step(self.trainer)\n\n    def after_step(self):\n        if self._after_step:\n            self._after_step(self.trainer)\n\n\nclass IterationTimer(HookBase):\n    \"\"\"\n    Track the time spent for each iteration (each run_step call in the trainer).\n    Print a summary in the end of training.\n    This hook uses the time between the call to its :meth:`before_step`\n    and :meth:`after_step` methods.\n    Under the convention that :meth:`before_step` of all hooks should only\n    take negligible amount of time, the :class:`IterationTimer` hook should be\n    placed at the beginning of the list of hooks to obtain accurate timing.\n    \"\"\"\n\n    def __init__(self, warmup_iter=3):\n        \"\"\"\n        Args:\n            warmup_iter (int): the number of iterations at the beginning to exclude\n                from timing.\n        \"\"\"\n        self._warmup_iter = warmup_iter\n        self._step_timer = Timer()\n\n    def before_train(self):\n        self._start_time = time.perf_counter()\n        self._total_timer = Timer()\n        self._total_timer.pause()\n\n    def after_train(self):\n        logger = logging.getLogger(__name__)\n        total_time = time.perf_counter() - self._start_time\n        total_time_minus_hooks = self._total_timer.seconds()\n        hook_time = total_time - total_time_minus_hooks\n\n        num_iter = self.trainer.iter + 1 - self.trainer.start_iter - self._warmup_iter\n\n        if num_iter > 0 and total_time_minus_hooks > 0:\n            # Speed is meaningful only after warmup\n            # NOTE this format is parsed by grep in some scripts\n            logger.info(\n                \"Overall training speed: {} iterations in {} ({:.4f} s / it)\".format(\n                    num_iter,\n                    str(datetime.timedelta(seconds=int(total_time_minus_hooks))),\n                    total_time_minus_hooks / num_iter,\n                )\n            )\n\n        logger.info(\n            \"Total training time: {} ({} on hooks)\".format(\n                str(datetime.timedelta(seconds=int(total_time))),\n                str(datetime.timedelta(seconds=int(hook_time))),\n            )\n        )\n\n    def before_step(self):\n        self._step_timer.reset()\n        self._total_timer.resume()\n\n    def after_step(self):\n        # +1 because we're in after_step\n        iter_done = self.trainer.iter - self.trainer.start_iter + 1\n        if iter_done >= self._warmup_iter:\n            sec = self._step_timer.seconds()\n            self.trainer.storage.put_scalars(time=sec)\n        else:\n            self._start_time = time.perf_counter()\n            self._total_timer.reset()\n\n        self._total_timer.pause()\n\n\nclass PeriodicWriter(HookBase):\n    \"\"\"\n    Write events to EventStorage periodically.\n    It is executed every ``period`` iterations and after the last iteration.\n    \"\"\"\n\n    def __init__(self, writers, period=20):\n        \"\"\"\n        Args:\n            writers (list[EventWriter]): a list of EventWriter objects\n            period (int):\n        \"\"\"\n        self._writers = writers\n        for w in writers:\n            assert isinstance(w, EventWriter), w\n        self._period = period\n\n    def after_step(self):\n        if (self.trainer.iter + 1) % self._period == 0 or (\n                self.trainer.iter == self.trainer.max_iter - 1\n        ):\n            for writer in self._writers:\n                writer.write()\n\n    def after_epoch(self):\n        for writer in self._writers:\n            writer.write()\n\n    def after_train(self):\n        for writer in self._writers:\n            writer.close()\n\n\nclass PeriodicCheckpointer(_PeriodicCheckpointer, HookBase):\n    \"\"\"\n    Same as :class:`fastreid.utils.checkpoint.PeriodicCheckpointer`, but as a hook.\n    Note that when used as a hook,\n    it is unable to save additional data other than what's defined\n    by the given `checkpointer`.\n    It is executed every ``period`` iterations and after the last iteration.\n    \"\"\"\n\n    def before_train(self):\n        self.max_epoch = self.trainer.max_epoch\n        if len(self.trainer.cfg.DATASETS.TESTS) == 1:\n            self.metric_name = \"metric\"\n        else:\n            self.metric_name = self.trainer.cfg.DATASETS.TESTS[0] + \"/metric\"\n\n    def after_epoch(self):\n        # No way to use **kwargs\n        storage = get_event_storage()\n        metric_dict = dict(\n            metric=storage.latest()[self.metric_name][0] if self.metric_name in storage.latest() else -1\n        )\n        self.step(self.trainer.epoch, **metric_dict)\n\n\nclass LRScheduler(HookBase):\n    \"\"\"\n    A hook which executes a torch builtin LR scheduler and summarizes the LR.\n    It is executed after every iteration.\n    \"\"\"\n\n    def __init__(self, optimizer, scheduler):\n        \"\"\"\n        Args:\n            optimizer (torch.optim.Optimizer):\n            scheduler (torch.optim._LRScheduler)\n        \"\"\"\n        self._optimizer = optimizer\n        self._scheduler = scheduler\n        self._scale = 0\n\n        # NOTE: some heuristics on what LR to summarize\n        # summarize the param group with most parameters\n        largest_group = max(len(g[\"params\"]) for g in optimizer.param_groups)\n\n        if largest_group == 1:\n            # If all groups have one parameter,\n            # then find the most common initial LR, and use it for summary\n            lr_count = Counter([g[\"lr\"] for g in optimizer.param_groups])\n            lr = lr_count.most_common()[0][0]\n            for i, g in enumerate(optimizer.param_groups):\n                if g[\"lr\"] == lr:\n                    self._best_param_group_id = i\n                    break\n        else:\n            for i, g in enumerate(optimizer.param_groups):\n                if len(g[\"params\"]) == largest_group:\n                    self._best_param_group_id = i\n                    break\n\n    def before_step(self):\n        if self.trainer.grad_scaler is not None:\n            self._scale = self.trainer.grad_scaler.get_scale()\n\n    def after_step(self):\n        lr = self._optimizer.param_groups[self._best_param_group_id][\"lr\"]\n        self.trainer.storage.put_scalar(\"lr\", lr, smoothing_hint=False)\n\n        next_iter = self.trainer.iter + 1\n        if next_iter <= self.trainer.warmup_iters:\n            if self.trainer.grad_scaler is None or self._scale == self.trainer.grad_scaler.get_scale():\n                self._scheduler[\"warmup_sched\"].step()\n\n    def after_epoch(self):\n        next_iter = self.trainer.iter + 1\n        next_epoch = self.trainer.epoch + 1\n        if next_iter > self.trainer.warmup_iters and next_epoch > self.trainer.delay_epochs:\n            self._scheduler[\"lr_sched\"].step()\n\n\nclass AutogradProfiler(HookBase):\n    \"\"\"\n    A hook which runs `torch.autograd.profiler.profile`.\n    Examples:\n    .. code-block:: python\n        hooks.AutogradProfiler(\n             lambda trainer: trainer.iter > 10 and trainer.iter < 20, self.cfg.OUTPUT_DIR\n        )\n    The above example will run the profiler for iteration 10~20 and dump\n    results to ``OUTPUT_DIR``. We did not profile the first few iterations\n    because they are typically slower than the rest.\n    The result files can be loaded in the ``chrome://tracing`` page in chrome browser.\n    Note:\n        When used together with NCCL on older version of GPUs,\n        autograd profiler may cause deadlock because it unnecessarily allocates\n        memory on every device it sees. The memory management calls, if\n        interleaved with NCCL calls, lead to deadlock on GPUs that do not\n        support `cudaLaunchCooperativeKernelMultiDevice`.\n    \"\"\"\n\n    def __init__(self, enable_predicate, output_dir, *, use_cuda=True):\n        \"\"\"\n        Args:\n            enable_predicate (callable[trainer -> bool]): a function which takes a trainer,\n                and returns whether to enable the profiler.\n                It will be called once every step, and can be used to select which steps to profile.\n            output_dir (str): the output directory to dump tracing files.\n            use_cuda (bool): same as in `torch.autograd.profiler.profile`.\n        \"\"\"\n        self._enable_predicate = enable_predicate\n        self._use_cuda = use_cuda\n        self._output_dir = output_dir\n\n    def before_step(self):\n        if self._enable_predicate(self.trainer):\n            self._profiler = torch.autograd.profiler.profile(use_cuda=self._use_cuda)\n            self._profiler.__enter__()\n        else:\n            self._profiler = None\n\n    def after_step(self):\n        if self._profiler is None:\n            return\n        self._profiler.__exit__(None, None, None)\n        out_file = os.path.join(\n            self._output_dir, \"profiler-trace-iter{}.json\".format(self.trainer.iter)\n        )\n        if \"://\" not in out_file:\n            self._profiler.export_chrome_trace(out_file)\n        else:\n            # Support non-posix filesystems\n            with tempfile.TemporaryDirectory(prefix=\"fastreid_profiler\") as d:\n                tmp_file = os.path.join(d, \"tmp.json\")\n                self._profiler.export_chrome_trace(tmp_file)\n                with open(tmp_file) as f:\n                    content = f.read()\n            with PathManager.open(out_file, \"w\") as f:\n                f.write(content)\n\n\nclass EvalHook(HookBase):\n    \"\"\"\n    Run an evaluation function periodically, and at the end of training.\n    It is executed every ``eval_period`` iterations and after the last iteration.\n    \"\"\"\n\n    def __init__(self, eval_period, eval_function):\n        \"\"\"\n        Args:\n            eval_period (int): the period to run `eval_function`.\n            eval_function (callable): a function which takes no arguments, and\n                returns a nested dict of evaluation metrics.\n        Note:\n            This hook must be enabled in all or none workers.\n            If you would like only certain workers to perform evaluation,\n            give other workers a no-op function (`eval_function=lambda: None`).\n        \"\"\"\n        self._period = eval_period\n        self._func = eval_function\n\n    def _do_eval(self):\n        results = self._func()\n\n        if results:\n            assert isinstance(\n                results, dict\n            ), \"Eval function must return a dict. Got {} instead.\".format(results)\n\n            flattened_results = flatten_results_dict(results)\n            for k, v in flattened_results.items():\n                try:\n                    v = float(v)\n                except Exception:\n                    raise ValueError(\n                        \"[EvalHook] eval_function should return a nested dict of float. \"\n                        \"Got '{}: {}' instead.\".format(k, v)\n                    )\n            self.trainer.storage.put_scalars(**flattened_results, smoothing_hint=False)\n\n        torch.cuda.empty_cache()\n        # Evaluation may take different time among workers.\n        # A barrier make them start the next iteration together.\n        comm.synchronize()\n\n    def after_epoch(self):\n        next_epoch = self.trainer.epoch + 1\n        if self._period > 0 and next_epoch % self._period == 0:\n            self._do_eval()\n\n    def after_train(self):\n        next_epoch = self.trainer.epoch + 1\n        # This condition is to prevent the eval from running after a failed training\n        if next_epoch % self._period != 0 and next_epoch >= self.trainer.max_epoch:\n            self._do_eval()\n        # func is likely a closure that holds reference to the trainer\n        # therefore we clean it to avoid circular reference in the end\n        del self._func\n\n\nclass PreciseBN(HookBase):\n    \"\"\"\n    The standard implementation of BatchNorm uses EMA in inference, which is\n    sometimes suboptimal.\n    This class computes the true average of statistics rather than the moving average,\n    and put true averages to every BN layer in the given model.\n    It is executed after the last iteration.\n    \"\"\"\n\n    def __init__(self, model, data_loader, num_iter):\n        \"\"\"\n        Args:\n            model (nn.Module): a module whose all BN layers in training mode will be\n                updated by precise BN.\n                Note that user is responsible for ensuring the BN layers to be\n                updated are in training mode when this hook is triggered.\n            data_loader (iterable): it will produce data to be run by `model(data)`.\n            num_iter (int): number of iterations used to compute the precise\n                statistics.\n        \"\"\"\n        self._logger = logging.getLogger(__name__)\n        if len(get_bn_modules(model)) == 0:\n            self._logger.info(\n                \"PreciseBN is disabled because model does not contain BN layers in training mode.\"\n            )\n            self._disabled = True\n            return\n\n        self._model = model\n        self._data_loader = data_loader\n        self._num_iter = num_iter\n        self._disabled = False\n\n        self._data_iter = None\n\n    def after_epoch(self):\n        next_epoch = self.trainer.epoch + 1\n        is_final = next_epoch == self.trainer.max_epoch\n        if is_final:\n            self.update_stats()\n\n    def update_stats(self):\n        \"\"\"\n        Update the model with precise statistics. Users can manually call this method.\n        \"\"\"\n        if self._disabled:\n            return\n\n        if self._data_iter is None:\n            self._data_iter = iter(self._data_loader)\n\n        def data_loader():\n            for num_iter in itertools.count(1):\n                if num_iter % 100 == 0:\n                    self._logger.info(\n                        \"Running precise-BN ... {}/{} iterations.\".format(num_iter, self._num_iter)\n                    )\n                # This way we can reuse the same iterator\n                yield next(self._data_iter)\n\n        with EventStorage():  # capture events in a new storage to discard them\n            self._logger.info(\n                \"Running precise-BN for {} iterations...  \".format(self._num_iter)\n                + \"Note that this could produce different statistics every time.\"\n            )\n            update_bn_stats(self._model, data_loader(), self._num_iter)\n\n\nclass LayerFreeze(HookBase):\n    def __init__(self, model, freeze_layers, freeze_iters):\n        self._logger = logging.getLogger(__name__)\n        if isinstance(model, DistributedDataParallel):\n            model = model.module\n        self.model = model\n\n        self.freeze_layers = freeze_layers\n        self.freeze_iters = freeze_iters\n\n        self.is_frozen = False\n\n    def before_step(self):\n        # Freeze specific layers\n        if self.trainer.iter < self.freeze_iters and not self.is_frozen:\n            self.freeze_specific_layer()\n\n        # Recover original layers status\n        if self.trainer.iter >= self.freeze_iters and self.is_frozen:\n            self.open_all_layer()\n\n    def freeze_specific_layer(self):\n        for layer in self.freeze_layers:\n            if not hasattr(self.model, layer):\n                self._logger.info(f'{layer} is not an attribute of the model, will skip this layer')\n\n        for name, module in self.model.named_children():\n            if name in self.freeze_layers:\n                # Change BN in freeze layers to eval mode\n                module.eval()\n\n        self.is_frozen = True\n        freeze_layers = \", \".join(self.freeze_layers)\n        self._logger.info(f'Freeze layer group \"{freeze_layers}\" training for {self.freeze_iters:d} iterations')\n\n    def open_all_layer(self):\n        for name, module in self.model.named_children():\n            if name in self.freeze_layers:\n                module.train()\n\n        self.is_frozen = False\n\n        freeze_layers = \", \".join(self.freeze_layers)\n        self._logger.info(f'Open layer group \"{freeze_layers}\" training')\n\n\nclass SWA(HookBase):\n    def __init__(self, swa_start: int, swa_freq: int, swa_lr_factor: float, eta_min: float, lr_sched=False, ):\n        self.swa_start = swa_start\n        self.swa_freq = swa_freq\n        self.swa_lr_factor = swa_lr_factor\n        self.eta_min = eta_min\n        self.lr_sched = lr_sched\n\n    def before_step(self):\n        is_swa = self.trainer.iter == self.swa_start\n        if is_swa:\n            # Wrapper optimizer with SWA\n            self.trainer.optimizer = optim.SWA(self.trainer.optimizer, self.swa_freq, self.swa_lr_factor)\n            self.trainer.optimizer.reset_lr_to_swa()\n\n            if self.lr_sched:\n                self.scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(\n                    optimizer=self.trainer.optimizer,\n                    T_0=self.swa_freq,\n                    eta_min=self.eta_min,\n                )\n\n    def after_step(self):\n        next_iter = self.trainer.iter + 1\n\n        # Use Cyclic learning rate scheduler\n        if next_iter > self.swa_start and self.lr_sched:\n            self.scheduler.step()\n\n        is_final = next_iter == self.trainer.max_iter\n        if is_final:\n            self.trainer.optimizer.swap_swa_param()\n"
  },
  {
    "path": "fast_reid/fastreid/engine/launch.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# based on:\n# https://github.com/facebookresearch/detectron2/blob/master/detectron2/engine/launch.py\n\n\nimport logging\n\nimport torch\nimport torch.distributed as dist\nimport torch.multiprocessing as mp\n\nfrom fast_reid.fastreid.utils import comm\n\n__all__ = [\"launch\"]\n\n\ndef _find_free_port():\n    import socket\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    # Binding to port 0 will cause the OS to find an available port for us\n    sock.bind((\"\", 0))\n    port = sock.getsockname()[1]\n    sock.close()\n    # NOTE: there is still a chance the port could be taken by other processes.\n    return port\n\n\ndef launch(main_func, num_gpus_per_machine, num_machines=1, machine_rank=0, dist_url=None, args=()):\n    \"\"\"\n    Launch multi-gpu or distributed training.\n    This function must be called on all machines involved in the training.\n    It will spawn child processes (defined by ``num_gpus_per_machine`) on each machine.\n    Args:\n        main_func: a function that will be called by `main_func(*args)`\n        num_gpus_per_machine (int): number of GPUs per machine\n        num_machines (int): the total number of machines\n        machine_rank (int): the rank of this machine\n        dist_url (str): url to connect to for distributed jobs, including protocol\n                       e.g. \"tcp://127.0.0.1:8686\".\n                       Can be set to \"auto\" to automatically select a free port on localhost\n        args (tuple): arguments passed to main_func\n    \"\"\"\n    world_size = num_machines * num_gpus_per_machine\n    if world_size > 1:\n        # https://github.com/pytorch/pytorch/pull/14391\n        # TODO prctl in spawned processes\n\n        if dist_url == \"auto\":\n            assert num_machines == 1, \"dist_url=auto not supported in multi-machine jobs.\"\n            port = _find_free_port()\n            dist_url = f\"tcp://127.0.0.1:{port}\"\n        if num_machines > 1 and dist_url.startswith(\"file://\"):\n            logger = logging.getLogger(__name__)\n            logger.warning(\n                \"file:// is not a reliable init_method in multi-machine jobs. Prefer tcp://\"\n            )\n\n        mp.spawn(\n            _distributed_worker,\n            nprocs=num_gpus_per_machine,\n            args=(main_func, world_size, num_gpus_per_machine, machine_rank, dist_url, args),\n            daemon=False,\n        )\n    else:\n        main_func(*args)\n\n\ndef _distributed_worker(\n        local_rank, main_func, world_size, num_gpus_per_machine, machine_rank, dist_url, args\n):\n    assert torch.cuda.is_available(), \"cuda is not available. Please check your installation.\"\n    global_rank = machine_rank * num_gpus_per_machine + local_rank\n    try:\n        dist.init_process_group(\n            backend=\"NCCL\", init_method=dist_url, world_size=world_size, rank=global_rank\n        )\n    except Exception as e:\n        logger = logging.getLogger(__name__)\n        logger.error(\"Process group URL: {}\".format(dist_url))\n        raise e\n    # synchronize is needed here to prevent a possible timeout after calling init_process_group\n    # See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172\n    comm.synchronize()\n\n    assert num_gpus_per_machine <= torch.cuda.device_count()\n    torch.cuda.set_device(local_rank)\n\n    # Setup the local process group (which contains ranks within the same machine)\n    assert comm._LOCAL_PROCESS_GROUP is None\n    num_machines = world_size // num_gpus_per_machine\n    for i in range(num_machines):\n        ranks_on_i = list(range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine))\n        pg = dist.new_group(ranks_on_i)\n        if i == machine_rank:\n            comm._LOCAL_PROCESS_GROUP = pg\n\n    main_func(*args)\n"
  },
  {
    "path": "fast_reid/fastreid/engine/train_loop.py",
    "content": "# encoding: utf-8\n\"\"\"\ncredit:\nhttps://github.com/facebookresearch/detectron2/blob/master/detectron2/engine/train_loop.py\n\"\"\"\n\nimport logging\nimport time\nimport weakref\nfrom typing import Dict\n\nimport numpy as np\nimport torch\nfrom torch.nn.parallel import DataParallel, DistributedDataParallel\n\nimport fast_reid.fastreid.utils.comm as comm\nfrom fast_reid.fastreid.utils.events import EventStorage, get_event_storage\nfrom fast_reid.fastreid.utils.params import ContiguousParams\n\n__all__ = [\"HookBase\", \"TrainerBase\", \"SimpleTrainer\"]\n\nlogger = logging.getLogger(__name__)\n\n\nclass HookBase:\n    \"\"\"\n    Base class for hooks that can be registered with :class:`TrainerBase`.\n    Each hook can implement 6 methods. The way they are called is demonstrated\n    in the following snippet:\n    .. code-block:: python\n        hook.before_train()\n        for _ in range(start_epoch, max_epoch):\n            hook.before_epoch()\n            for iter in range(start_iter, max_iter):\n                hook.before_step()\n                trainer.run_step()\n                hook.after_step()\n            hook.after_epoch()\n        hook.after_train()\n    Notes:\n        1. In the hook method, users can access `self.trainer` to access more\n           properties about the context (e.g., current iteration).\n        2. A hook that does something in :meth:`before_step` can often be\n           implemented equivalently in :meth:`after_step`.\n           If the hook takes non-trivial time, it is strongly recommended to\n           implement the hook in :meth:`after_step` instead of :meth:`before_step`.\n           The convention is that :meth:`before_step` should only take negligible time.\n           Following this convention will allow hooks that do care about the difference\n           between :meth:`before_step` and :meth:`after_step` (e.g., timer) to\n           function properly.\n    Attributes:\n        trainer: A weak reference to the trainer object. Set by the trainer when the hook is\n            registered.\n    \"\"\"\n\n    def before_train(self):\n        \"\"\"\n        Called before the first iteration.\n        \"\"\"\n        pass\n\n    def after_train(self):\n        \"\"\"\n        Called after the last iteration.\n        \"\"\"\n        pass\n\n    def before_epoch(self):\n        \"\"\"\n        Called before each epoch.\n        \"\"\"\n        pass\n\n    def after_epoch(self):\n        \"\"\"\n        Called after each epoch.\n        \"\"\"\n        pass\n\n    def before_step(self):\n        \"\"\"\n        Called before each iteration.\n        \"\"\"\n        pass\n\n    def after_step(self):\n        \"\"\"\n        Called after each iteration.\n        \"\"\"\n        pass\n\n\nclass TrainerBase:\n    \"\"\"\n    Base class for iterative trainer with hooks.\n    The only assumption we made here is: the training runs in a loop.\n    A subclass can implement what the loop is.\n    We made no assumptions about the existence of dataloader, optimizer, model, etc.\n    Attributes:\n        iter(int): the current iteration.\n        epoch(int): the current epoch.\n        start_iter(int): The iteration to start with.\n            By convention the minimum possible value is 0.\n        max_epoch (int): The epoch to end training.\n        storage(EventStorage): An EventStorage that's opened during the course of training.\n    \"\"\"\n\n    def __init__(self):\n        self._hooks = []\n\n    def register_hooks(self, hooks):\n        \"\"\"\n        Register hooks to the trainer. The hooks are executed in the order\n        they are registered.\n        Args:\n            hooks (list[Optional[HookBase]]): list of hooks\n        \"\"\"\n        hooks = [h for h in hooks if h is not None]\n        for h in hooks:\n            assert isinstance(h, HookBase)\n            # To avoid circular reference, hooks and trainer cannot own each other.\n            # This normally does not matter, but will cause memory leak if the\n            # involved objects contain __del__:\n            # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/\n            h.trainer = weakref.proxy(self)\n        self._hooks.extend(hooks)\n\n    def train(self, start_epoch: int, max_epoch: int, iters_per_epoch: int):\n        \"\"\"\n        Args:\n            start_epoch, max_epoch (int): See docs above\n        \"\"\"\n        logger = logging.getLogger(__name__)\n        logger.info(\"Starting training from epoch {}\".format(start_epoch))\n\n        self.iter = self.start_iter = start_epoch * iters_per_epoch\n\n        with EventStorage(self.start_iter) as self.storage:\n            try:\n                self.before_train()\n                for self.epoch in range(start_epoch, max_epoch):\n                    self.before_epoch()\n                    print(\"start epoch {}\".format(self.epoch))\n                    for _ in range(iters_per_epoch):\n                        self.before_step()\n                        self.run_step()\n                        self.after_step()\n                        # if self.iter % 20 == 0:\n                        #     print(\"iter {}\".format(self.iter))\n                        self.iter += 1\n                    self.after_epoch()\n            except Exception:\n                logger.exception(\"Exception during training:\")\n                raise\n            finally:\n                self.after_train()\n\n    def before_train(self):\n        for h in self._hooks:\n            h.before_train()\n\n    def after_train(self):\n        self.storage.iter = self.iter\n        for h in self._hooks:\n            h.after_train()\n\n    def before_epoch(self):\n        self.storage.epoch = self.epoch\n\n        for h in self._hooks:\n            h.before_epoch()\n\n    def before_step(self):\n        self.storage.iter = self.iter\n\n        for h in self._hooks:\n            h.before_step()\n\n    def after_step(self):\n        for h in self._hooks:\n            h.after_step()\n\n    def after_epoch(self):\n        for h in self._hooks:\n            h.after_epoch()\n\n    def run_step(self):\n        raise NotImplementedError\n\n\nclass SimpleTrainer(TrainerBase):\n    \"\"\"\n    A simple trainer for the most common type of task:\n    single-cost single-optimizer single-data-source iterative optimization.\n    It assumes that every step, you:\n    1. Compute the loss with a data from the data_loader.\n    2. Compute the gradients with the above loss.\n    3. Update the model with the optimizer.\n    If you want to do anything fancier than this,\n    either subclass TrainerBase and implement your own `run_step`,\n    or write your own training loop.\n    \"\"\"\n\n    def __init__(self, model, data_loader, optimizer, param_wrapper):\n        \"\"\"\n        Args:\n            model: a torch Module. Takes a data from data_loader and returns a\n                dict of heads.\n            data_loader: an iterable. Contains data to be used to call model.\n            optimizer: a torch optimizer.\n        \"\"\"\n        super().__init__()\n\n        \"\"\"\n        We set the model to training mode in the trainer.\n        However it's valid to train a model that's in eval mode.\n        If you want your model (or a submodule of it) to behave\n        like evaluation during training, you can overwrite its train() method.\n        \"\"\"\n        model.train()\n\n        self.model = model\n        self.data_loader = data_loader\n        self._data_loader_iter = iter(data_loader)\n        self.optimizer = optimizer\n        self.param_wrapper = param_wrapper\n\n    def run_step(self):\n        \"\"\"\n        Implement the standard training logic described above.\n        \"\"\"\n        assert self.model.training, \"[SimpleTrainer] model was changed to eval mode!\"\n        start = time.perf_counter()\n        \"\"\"\n        If your want to do something with the data, you can wrap the dataloader.\n        \"\"\"\n        data = next(self._data_loader_iter)\n        data_time = time.perf_counter() - start\n\n        \"\"\"\n        If your want to do something with the heads, you can wrap the model.\n        \"\"\"\n\n        loss_dict = self.model(data)\n        losses = sum(loss_dict.values())\n\n        \"\"\"\n        If you need accumulate gradients or something similar, you can\n        wrap the optimizer with your custom `zero_grad()` method.\n        \"\"\"\n        self.optimizer.zero_grad()\n\n        losses.backward()\n\n        self._write_metrics(loss_dict, data_time)\n\n        \"\"\"\n        If you need gradient clipping/scaling or other processing, you can\n        wrap the optimizer with your custom `step()` method.\n        \"\"\"\n        self.optimizer.step()\n        if isinstance(self.param_wrapper, ContiguousParams):\n            self.param_wrapper.assert_buffer_is_valid()\n\n    def _write_metrics(self, loss_dict: Dict[str, torch.Tensor], data_time: float):\n        \"\"\"\n        Args:\n            loss_dict (dict): dict of scalar losses\n            data_time (float): time taken by the dataloader iteration\n        \"\"\"\n        device = next(iter(loss_dict.values())).device\n\n        # Use a new stream so these ops don't wait for DDP or backward\n        with torch.cuda.stream(torch.cuda.Stream() if device.type == \"cuda\" else None):\n            metrics_dict = {k: v.detach().cpu().item() for k, v in loss_dict.items()}\n            metrics_dict[\"data_time\"] = data_time\n\n            # Gather metrics among all workers for logging\n            # This assumes we do DDP-style training, which is currently the only\n            # supported method in detectron2.\n            all_metrics_dict = comm.gather(metrics_dict)\n\n        if comm.is_main_process():\n            storage = get_event_storage()\n\n            # data_time among workers can have high variance. The actual latency\n            # caused by data_time is the maximum among workers.\n            data_time = np.max([x.pop(\"data_time\") for x in all_metrics_dict])\n            storage.put_scalar(\"data_time\", data_time)\n\n            # average the rest metrics\n            metrics_dict = {\n                k: np.mean([x[k] for x in all_metrics_dict]) for k in all_metrics_dict[0].keys()\n            }\n            total_losses_reduced = sum(metrics_dict.values())\n            if not np.isfinite(total_losses_reduced):\n                raise FloatingPointError(\n                    f\"Loss became infinite or NaN at iteration={self.iter}!\\n\"\n                    f\"loss_dict = {metrics_dict}\"\n                )\n\n            storage.put_scalar(\"total_loss\", total_losses_reduced)\n            if len(metrics_dict) > 1:\n                storage.put_scalars(**metrics_dict)\n\n\nclass AMPTrainer(SimpleTrainer):\n    \"\"\"\n    Like :class:`SimpleTrainer`, but uses automatic mixed precision\n    in the training loop.\n    \"\"\"\n\n    def __init__(self, model, data_loader, optimizer, param_wrapper, grad_scaler=None):\n        \"\"\"\n\n        Args:\n            model, data_loader, optimizer: same as in :class:`SimpleTrainer`.\n            grad_scaler: torch GradScaler to automatically scale gradients.\n        \"\"\"\n        unsupported = \"AMPTrainer does not support single-process multi-device training!\"\n        if isinstance(model, DistributedDataParallel):\n            assert not (model.device_ids and len(model.device_ids) > 1), unsupported\n        assert not isinstance(model, DataParallel), unsupported\n\n        super().__init__(model, data_loader, optimizer, param_wrapper)\n\n        if grad_scaler is None:\n            from torch.cuda.amp import GradScaler\n\n            grad_scaler = GradScaler()\n        self.grad_scaler = grad_scaler\n\n    def run_step(self):\n        \"\"\"\n        Implement the AMP training logic.\n        \"\"\"\n        assert self.model.training, \"[AMPTrainer] model was changed to eval mode!\"\n        assert torch.cuda.is_available(), \"[AMPTrainer] CUDA is required for AMP training!\"\n        from torch.cuda.amp import autocast\n\n        start = time.perf_counter()\n        data = next(self._data_loader_iter)\n        data_time = time.perf_counter() - start\n\n        with autocast():\n            loss_dict = self.model(data)\n            losses = sum(loss_dict.values())\n\n        self.optimizer.zero_grad()\n        self.grad_scaler.scale(losses).backward()\n\n        self._write_metrics(loss_dict, data_time)\n\n        self.grad_scaler.step(self.optimizer)\n        self.grad_scaler.update()\n        if isinstance(self.param_wrapper, ContiguousParams):\n            self.param_wrapper.assert_buffer_is_valid()\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/__init__.py",
    "content": "from .evaluator import DatasetEvaluator, inference_context, inference_on_dataset\nfrom .reid_evaluation import ReidEvaluator\nfrom .clas_evaluator import ClasEvaluator\nfrom .testing import print_csv_format, verify_results\n\n__all__ = [k for k in globals().keys() if not k.startswith(\"_\")]\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/clas_evaluator.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport copy\nimport itertools\nimport logging\nfrom collections import OrderedDict\n\nimport torch\n\nfrom fast_reid.fastreid.utils import comm\nfrom .evaluator import DatasetEvaluator\n\nlogger = logging.getLogger(__name__)\n\n\ndef accuracy(output, target, topk=(1,)):\n    \"\"\"Computes the accuracy over the k top predictions for the specified values of k\"\"\"\n    with torch.no_grad():\n        maxk = max(topk)\n        batch_size = target.size(0)\n\n        _, pred = output.topk(maxk, 1, True, True)\n        pred = pred.t()\n        correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n        res = []\n        for k in topk:\n            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)\n            res.append(correct_k.mul_(100.0 / batch_size))\n        return res\n\n\nclass ClasEvaluator(DatasetEvaluator):\n    def __init__(self, cfg, output_dir=None):\n        self.cfg = cfg\n        self._output_dir = output_dir\n        self._cpu_device = torch.device('cpu')\n\n        self._predictions = []\n\n    def reset(self):\n        self._predictions = []\n\n    def process(self, inputs, outputs):\n        pred_logits = outputs.to(self._cpu_device, torch.float32)\n        labels = inputs[\"targets\"].to(self._cpu_device)\n\n        # measure accuracy\n        acc1, = accuracy(pred_logits, labels, topk=(1,))\n        num_correct_acc1 = acc1 * labels.size(0) / 100\n\n        self._predictions.append({\"num_correct\": num_correct_acc1, \"num_samples\": labels.size(0)})\n\n    def evaluate(self):\n        if comm.get_world_size() > 1:\n            comm.synchronize()\n            predictions = comm.gather(self._predictions, dst=0)\n            predictions = list(itertools.chain(*predictions))\n\n            if not comm.is_main_process(): return {}\n\n        else:\n            predictions = self._predictions\n\n        total_correct_num = 0\n        total_samples = 0\n        for prediction in predictions:\n            total_correct_num += prediction[\"num_correct\"]\n            total_samples += prediction[\"num_samples\"]\n\n        acc1 = total_correct_num / total_samples * 100\n\n        self._results = OrderedDict()\n        self._results[\"Acc@1\"] = acc1\n        self._results[\"metric\"] = acc1\n\n        return copy.deepcopy(self._results)\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/evaluator.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport datetime\nimport logging\nimport time\nfrom contextlib import contextmanager\n\nimport torch\n\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.logger import log_every_n_seconds\n\n\nclass DatasetEvaluator:\n    \"\"\"\n    Base class for a dataset evaluator.\n    The function :func:`inference_on_dataset` runs the model over\n    all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs.\n    This class will accumulate information of the inputs/outputs (by :meth:`process`),\n    and produce evaluation results in the end (by :meth:`evaluate`).\n    \"\"\"\n\n    def reset(self):\n        \"\"\"\n        Preparation for a new round of evaluation.\n        Should be called before starting a round of evaluation.\n        \"\"\"\n        pass\n\n    def preprocess_inputs(self, inputs):\n        pass\n\n    def process(self, inputs, outputs):\n        \"\"\"\n        Process an input/output pair.\n        Args:\n            inputs: the inputs that's used to call the model.\n            outputs: the return value of `model(input)`\n        \"\"\"\n        pass\n\n    def evaluate(self):\n        \"\"\"\n        Evaluate/summarize the performance, after processing all input/output pairs.\n        Returns:\n            dict:\n                A new evaluator class can return a dict of arbitrary format\n                as long as the user can process the results.\n                In our train_net.py, we expect the following format:\n                * key: the name of the task (e.g., bbox)\n                * value: a dict of {metric name: score}, e.g.: {\"AP50\": 80}\n        \"\"\"\n        pass\n\n\n# class DatasetEvaluators(DatasetEvaluator):\n#     def __init__(self, evaluators):\n#         assert len(evaluators)\n#         super().__init__()\n#         self._evaluators = evaluators\n#\n#     def reset(self):\n#         for evaluator in self._evaluators:\n#             evaluator.reset()\n#\n#     def process(self, input, output):\n#         for evaluator in self._evaluators:\n#             evaluator.process(input, output)\n#\n#     def evaluate(self):\n#         results = OrderedDict()\n#         for evaluator in self._evaluators:\n#             result = evaluator.evaluate()\n#             if is_main_process() and result is not None:\n#                 for k, v in result.items():\n#                     assert (\n#                             k not in results\n#                     ), \"Different evaluators produce results with the same key {}\".format(k)\n#                     results[k] = v\n#         return results\n\n\ndef inference_on_dataset(model, data_loader, evaluator, flip_test=False):\n    \"\"\"\n    Run model on the data_loader and evaluate the metrics with evaluator.\n    The model will be used in eval mode.\n    Args:\n        model (nn.Module): a module which accepts an object from\n            `data_loader` and returns some outputs. It will be temporarily set to `eval` mode.\n            If you wish to evaluate a model in `training` mode instead, you can\n            wrap the given model and override its behavior of `.eval()` and `.train()`.\n        data_loader: an iterable object with a length.\n            The elements it generates will be the inputs to the model.\n        evaluator (DatasetEvaluator): the evaluator to run. Use\n            :class:`DatasetEvaluators([])` if you only want to benchmark, but\n            don't want to do any evaluation.\n        flip_test (bool): If get features with flipped images\n    Returns:\n        The return value of `evaluator.evaluate()`\n    \"\"\"\n    num_devices = comm.get_world_size()\n    logger = logging.getLogger(__name__)\n    logger.info(\"Start inference on {} images\".format(len(data_loader.dataset)))\n\n    total = len(data_loader)  # inference data loader must have a fixed length\n    evaluator.reset()\n\n    num_warmup = min(5, total - 1)\n    start_time = time.perf_counter()\n    total_compute_time = 0\n    with inference_context(model), torch.no_grad():\n        for idx, inputs in enumerate(data_loader):\n            if idx == num_warmup:\n                start_time = time.perf_counter()\n                total_compute_time = 0\n\n            start_compute_time = time.perf_counter()\n            outputs = model(inputs)\n            # Flip test\n            if flip_test:\n                inputs[\"images\"] = inputs[\"images\"].flip(dims=[3])\n                flip_outputs = model(inputs)\n                outputs = (outputs + flip_outputs) / 2\n            if torch.cuda.is_available():\n                torch.cuda.synchronize()\n            total_compute_time += time.perf_counter() - start_compute_time\n            evaluator.process(inputs, outputs)\n\n            iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)\n            seconds_per_batch = total_compute_time / iters_after_start\n            if idx >= num_warmup * 2 or seconds_per_batch > 30:\n                total_seconds_per_img = (time.perf_counter() - start_time) / iters_after_start\n                eta = datetime.timedelta(seconds=int(total_seconds_per_img * (total - idx - 1)))\n                log_every_n_seconds(\n                    logging.INFO,\n                    \"Inference done {}/{}. {:.4f} s / batch. ETA={}\".format(\n                        idx + 1, total, seconds_per_batch, str(eta)\n                    ),\n                    n=30,\n                )\n\n    # Measure the time only for this worker (before the synchronization barrier)\n    total_time = time.perf_counter() - start_time\n    total_time_str = str(datetime.timedelta(seconds=total_time))\n    # NOTE this format is parsed by grep\n    logger.info(\n        \"Total inference time: {} ({:.6f} s / batch per device, on {} devices)\".format(\n            total_time_str, total_time / (total - num_warmup), num_devices\n        )\n    )\n    total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time)))\n    logger.info(\n        \"Total inference pure compute time: {} ({:.6f} s / batch per device, on {} devices)\".format(\n            total_compute_time_str, total_compute_time / (total - num_warmup), num_devices\n        )\n    )\n    results = evaluator.evaluate()\n\n    # An evaluator may return None when not in main process.\n    # Replace it by an empty dict instead to make it easier for downstream code to handle\n    if results is None:\n        results = {}\n    return results\n\n\n@contextmanager\ndef inference_context(model):\n    \"\"\"\n    A context where the model is temporarily changed to eval mode,\n    and restored to previous mode afterwards.\n    Args:\n        model: a torch Module\n    \"\"\"\n    training_mode = model.training\n    model.eval()\n    yield\n    model.train(training_mode)\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/query_expansion.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# based on\n# https://github.com/PyRetri/PyRetri/blob/master/pyretri/index/re_ranker/re_ranker_impl/query_expansion.py\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\n\ndef aqe(query_feat: torch.tensor, gallery_feat: torch.tensor,\n        qe_times: int = 1, qe_k: int = 10, alpha: float = 3.0):\n    \"\"\"\n    Combining the retrieved topk nearest neighbors with the original query and doing another retrieval.\n    c.f. https://www.robots.ox.ac.uk/~vgg/publications/papers/chum07b.pdf\n    Args :\n        query_feat (torch.tensor):\n        gallery_feat (torch.tensor):\n        qe_times (int): number of query expansion times.\n        qe_k (int): number of the neighbors to be combined.\n        alpha (float):\n    \"\"\"\n    num_query = query_feat.shape[0]\n    all_feat = torch.cat((query_feat, gallery_feat), dim=0)\n    norm_feat = F.normalize(all_feat, p=2, dim=1)\n\n    all_feat = all_feat.numpy()\n    for i in range(qe_times):\n        all_feat_list = []\n        sims = torch.mm(norm_feat, norm_feat.t())\n        sims = sims.data.cpu().numpy()\n        for sim in sims:\n            init_rank = np.argpartition(-sim, range(1, qe_k + 1))\n            weights = sim[init_rank[:qe_k]].reshape((-1, 1))\n            weights = np.power(weights, alpha)\n            all_feat_list.append(np.mean(all_feat[init_rank[:qe_k], :] * weights, axis=0))\n        all_feat = np.stack(all_feat_list, axis=0)\n        norm_feat = F.normalize(torch.from_numpy(all_feat), p=2, dim=1)\n\n    query_feat = torch.from_numpy(all_feat[:num_query])\n    gallery_feat = torch.from_numpy(all_feat[num_query:])\n    return query_feat, gallery_feat\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/rank.py",
    "content": "# credits: https://github.com/KaiyangZhou/deep-person-reid/blob/master/torchreid/metrics/rank.py\n\nimport warnings\nfrom collections import defaultdict\n\nimport numpy as np\n\ntry:\n    from .rank_cylib.rank_cy import evaluate_cy\n\n    IS_CYTHON_AVAI = True\nexcept ImportError:\n    IS_CYTHON_AVAI = False\n    warnings.warn(\n        'Cython rank evaluation (very fast so highly recommended) is '\n        'unavailable, now use python evaluation.'\n    )\n\n\ndef eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):\n    \"\"\"Evaluation with cuhk03 metric\n    Key: one image for each gallery identity is randomly sampled for each query identity.\n    Random sampling is performed num_repeats times.\n    \"\"\"\n    num_repeats = 10\n\n    num_q, num_g = distmat.shape\n\n    indices = np.argsort(distmat, axis=1)\n\n    if num_g < max_rank:\n        max_rank = num_g\n        print(\n            'Note: number of gallery samples is quite small, got {}'.\n                format(num_g)\n        )\n\n    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)\n\n    # compute cmc curve for each query\n    all_cmc = []\n    all_AP = []\n    num_valid_q = 0.  # number of valid query\n\n    for q_idx in range(num_q):\n        # get query pid and camid\n        q_pid = q_pids[q_idx]\n        q_camid = q_camids[q_idx]\n\n        # remove gallery samples that have the same pid and camid with query\n        order = indices[q_idx]\n        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)\n        keep = np.invert(remove)\n\n        # compute cmc curve\n        raw_cmc = matches[q_idx][\n            keep]  # binary vector, positions with value 1 are correct matches\n        if not np.any(raw_cmc):\n            # this condition is true when query identity does not appear in gallery\n            continue\n\n        kept_g_pids = g_pids[order][keep]\n        g_pids_dict = defaultdict(list)\n        for idx, pid in enumerate(kept_g_pids):\n            g_pids_dict[pid].append(idx)\n\n        cmc = 0.\n        for repeat_idx in range(num_repeats):\n            mask = np.zeros(len(raw_cmc), dtype=np.bool)\n            for _, idxs in g_pids_dict.items():\n                # randomly sample one image for each gallery person\n                rnd_idx = np.random.choice(idxs)\n                mask[rnd_idx] = True\n            masked_raw_cmc = raw_cmc[mask]\n            _cmc = masked_raw_cmc.cumsum()\n            _cmc[_cmc > 1] = 1\n            cmc += _cmc[:max_rank].astype(np.float32)\n\n        cmc /= num_repeats\n        all_cmc.append(cmc)\n        # compute AP\n        num_rel = raw_cmc.sum()\n        tmp_cmc = raw_cmc.cumsum()\n        tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]\n        tmp_cmc = np.asarray(tmp_cmc) * raw_cmc\n        AP = tmp_cmc.sum() / num_rel\n        all_AP.append(AP)\n        num_valid_q += 1.\n\n    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'\n\n    all_cmc = np.asarray(all_cmc).astype(np.float32)\n    all_cmc = all_cmc.sum(0) / num_valid_q\n    mAP = np.mean(all_AP)\n\n    return all_cmc, mAP\n\n\ndef eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):\n    \"\"\"Evaluation with market1501 metric\n    Key: for each query identity, its gallery images from the same camera view are discarded.\n    \"\"\"\n    num_q, num_g = distmat.shape\n\n    if num_g < max_rank:\n        max_rank = num_g\n        print('Note: number of gallery samples is quite small, got {}'.format(num_g))\n\n    indices = np.argsort(distmat, axis=1)\n    # compute cmc curve for each query\n    all_cmc = []\n    all_AP = []\n    all_INP = []\n    num_valid_q = 0.  # number of valid query\n\n    for q_idx in range(num_q):\n        # get query pid and camid\n        q_pid = q_pids[q_idx]\n        q_camid = q_camids[q_idx]\n\n        # remove gallery samples that have the same pid and camid with query\n        order = indices[q_idx]\n        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)\n        keep = np.invert(remove)\n\n        # compute cmc curve\n        matches = (g_pids[order] == q_pid).astype(np.int32)\n        raw_cmc = matches[keep]  # binary vector, positions with value 1 are correct matches\n        if not np.any(raw_cmc):\n            # this condition is true when query identity does not appear in gallery\n            continue\n\n        cmc = raw_cmc.cumsum()\n\n        pos_idx = np.where(raw_cmc == 1)\n        max_pos_idx = np.max(pos_idx)\n        inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)\n        all_INP.append(inp)\n\n        cmc[cmc > 1] = 1\n\n        all_cmc.append(cmc[:max_rank])\n        num_valid_q += 1.\n\n        # compute average precision\n        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision\n        num_rel = raw_cmc.sum()\n        tmp_cmc = raw_cmc.cumsum()\n        tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]\n        tmp_cmc = np.asarray(tmp_cmc) * raw_cmc\n        AP = tmp_cmc.sum() / num_rel\n        all_AP.append(AP)\n\n    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'\n\n    all_cmc = np.asarray(all_cmc).astype(np.float32)\n    all_cmc = all_cmc.sum(0) / num_valid_q\n\n    return all_cmc, all_AP, all_INP\n\n\ndef evaluate_py(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03):\n    if use_metric_cuhk03:\n        return eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)\n    else:\n        return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)\n\n\ndef evaluate_rank(\n        distmat,\n        q_pids,\n        g_pids,\n        q_camids,\n        g_camids,\n        max_rank=50,\n        use_metric_cuhk03=False,\n        use_cython=True,\n):\n    \"\"\"Evaluates CMC rank.\n    Args:\n        distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).\n        q_pids (numpy.ndarray): 1-D array containing person identities\n            of each query instance.\n        g_pids (numpy.ndarray): 1-D array containing person identities\n            of each gallery instance.\n        q_camids (numpy.ndarray): 1-D array containing camera views under\n            which each query instance is captured.\n        g_camids (numpy.ndarray): 1-D array containing camera views under\n            which each gallery instance is captured.\n        max_rank (int, optional): maximum CMC rank to be computed. Default is 50.\n        use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.\n            Default is False. This should be enabled when using cuhk03 classic split.\n        use_cython (bool, optional): use cython code for evaluation. Default is True.\n            This is highly recommended as the cython code can speed up the cmc computation\n            by more than 10x. This requires Cython to be installed.\n    \"\"\"\n    if use_cython and IS_CYTHON_AVAI:\n        return evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03)\n    else:\n        return evaluate_py(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03)\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/rank_cylib/Makefile",
    "content": "all:\n\tpython3 setup.py build_ext --inplace\n\trm -rf build\nclean:\n\trm -rf build\n\trm -f rank_cy.c *.so\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/rank_cylib/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\ndef compile_helper():\n    \"\"\"Compile helper function at runtime. Make sure this\n    is invoked on a single process.\"\"\"\n    import os\n    import subprocess\n\n    path = os.path.abspath(os.path.dirname(__file__))\n    ret = subprocess.run([\"make\", \"-C\", path])\n    if ret.returncode != 0:\n        print(\"Making cython reid evaluation module failed, exiting.\")\n        import sys\n\n        sys.exit(1)\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/rank_cylib/rank_cy.c",
    "content": "/* Generated by Cython 0.29.32 */\n\n/* BEGIN: Cython Metadata\n{\n    \"distutils\": {\n        \"depends\": [\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h\",\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h\",\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h\",\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h\",\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h\"\n        ],\n        \"include_dirs\": [\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include\"\n        ],\n        \"name\": \"rank_cy\",\n        \"sources\": [\n            \"rank_cy.pyx\"\n        ]\n    },\n    \"module_name\": \"rank_cy\"\n}\nEND: Cython Metadata */\n\n#ifndef PY_SSIZE_T_CLEAN\n#define PY_SSIZE_T_CLEAN\n#endif /* PY_SSIZE_T_CLEAN */\n#include \"Python.h\"\n#ifndef Py_PYTHON_H\n    #error Python headers needed to compile C extensions, please install development version of Python.\n#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)\n    #error Cython requires Python 2.6+ or Python 3.3+.\n#else\n#define CYTHON_ABI \"0_29_32\"\n#define CYTHON_HEX_VERSION 0x001D20F0\n#define CYTHON_FUTURE_DIVISION 0\n#include <stddef.h>\n#ifndef offsetof\n  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )\n#endif\n#if !defined(WIN32) && !defined(MS_WINDOWS)\n  #ifndef __stdcall\n    #define __stdcall\n  #endif\n  #ifndef __cdecl\n    #define __cdecl\n  #endif\n  #ifndef __fastcall\n    #define __fastcall\n  #endif\n#endif\n#ifndef DL_IMPORT\n  #define DL_IMPORT(t) t\n#endif\n#ifndef DL_EXPORT\n  #define DL_EXPORT(t) t\n#endif\n#define __PYX_COMMA ,\n#ifndef HAVE_LONG_LONG\n  #if PY_VERSION_HEX >= 0x02070000\n    #define HAVE_LONG_LONG\n  #endif\n#endif\n#ifndef PY_LONG_LONG\n  #define PY_LONG_LONG LONG_LONG\n#endif\n#ifndef Py_HUGE_VAL\n  #define Py_HUGE_VAL HUGE_VAL\n#endif\n#ifdef PYPY_VERSION\n  #define CYTHON_COMPILING_IN_PYPY 1\n  #define CYTHON_COMPILING_IN_PYSTON 0\n  #define CYTHON_COMPILING_IN_CPYTHON 0\n  #define CYTHON_COMPILING_IN_NOGIL 0\n  #undef CYTHON_USE_TYPE_SLOTS\n  #define CYTHON_USE_TYPE_SLOTS 0\n  #undef CYTHON_USE_PYTYPE_LOOKUP\n  #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #if PY_VERSION_HEX < 0x03050000\n    #undef CYTHON_USE_ASYNC_SLOTS\n    #define CYTHON_USE_ASYNC_SLOTS 0\n  #elif !defined(CYTHON_USE_ASYNC_SLOTS)\n    #define CYTHON_USE_ASYNC_SLOTS 1\n  #endif\n  #undef CYTHON_USE_PYLIST_INTERNALS\n  #define CYTHON_USE_PYLIST_INTERNALS 0\n  #undef CYTHON_USE_UNICODE_INTERNALS\n  #define CYTHON_USE_UNICODE_INTERNALS 0\n  #undef CYTHON_USE_UNICODE_WRITER\n  #define CYTHON_USE_UNICODE_WRITER 0\n  #undef CYTHON_USE_PYLONG_INTERNALS\n  #define CYTHON_USE_PYLONG_INTERNALS 0\n  #undef CYTHON_AVOID_BORROWED_REFS\n  #define CYTHON_AVOID_BORROWED_REFS 1\n  #undef CYTHON_ASSUME_SAFE_MACROS\n  #define CYTHON_ASSUME_SAFE_MACROS 0\n  #undef CYTHON_UNPACK_METHODS\n  #define CYTHON_UNPACK_METHODS 0\n  #undef CYTHON_FAST_THREAD_STATE\n  #define CYTHON_FAST_THREAD_STATE 0\n  #undef CYTHON_FAST_PYCALL\n  #define CYTHON_FAST_PYCALL 0\n  #undef CYTHON_PEP489_MULTI_PHASE_INIT\n  #define CYTHON_PEP489_MULTI_PHASE_INIT 0\n  #undef CYTHON_USE_TP_FINALIZE\n  #define CYTHON_USE_TP_FINALIZE 0\n  #undef CYTHON_USE_DICT_VERSIONS\n  #define CYTHON_USE_DICT_VERSIONS 0\n  #undef CYTHON_USE_EXC_INFO_STACK\n  #define CYTHON_USE_EXC_INFO_STACK 0\n  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC\n    #define CYTHON_UPDATE_DESCRIPTOR_DOC (PYPY_VERSION_HEX >= 0x07030900)\n  #endif\n#elif defined(PYSTON_VERSION)\n  #define CYTHON_COMPILING_IN_PYPY 0\n  #define CYTHON_COMPILING_IN_PYSTON 1\n  #define CYTHON_COMPILING_IN_CPYTHON 0\n  #define CYTHON_COMPILING_IN_NOGIL 0\n  #ifndef CYTHON_USE_TYPE_SLOTS\n    #define CYTHON_USE_TYPE_SLOTS 1\n  #endif\n  #undef CYTHON_USE_PYTYPE_LOOKUP\n  #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #undef CYTHON_USE_ASYNC_SLOTS\n  #define CYTHON_USE_ASYNC_SLOTS 0\n  #undef CYTHON_USE_PYLIST_INTERNALS\n  #define CYTHON_USE_PYLIST_INTERNALS 0\n  #ifndef CYTHON_USE_UNICODE_INTERNALS\n    #define CYTHON_USE_UNICODE_INTERNALS 1\n  #endif\n  #undef CYTHON_USE_UNICODE_WRITER\n  #define CYTHON_USE_UNICODE_WRITER 0\n  #undef CYTHON_USE_PYLONG_INTERNALS\n  #define CYTHON_USE_PYLONG_INTERNALS 0\n  #ifndef CYTHON_AVOID_BORROWED_REFS\n    #define CYTHON_AVOID_BORROWED_REFS 0\n  #endif\n  #ifndef CYTHON_ASSUME_SAFE_MACROS\n    #define CYTHON_ASSUME_SAFE_MACROS 1\n  #endif\n  #ifndef CYTHON_UNPACK_METHODS\n    #define CYTHON_UNPACK_METHODS 1\n  #endif\n  #undef CYTHON_FAST_THREAD_STATE\n  #define CYTHON_FAST_THREAD_STATE 0\n  #undef CYTHON_FAST_PYCALL\n  #define CYTHON_FAST_PYCALL 0\n  #undef CYTHON_PEP489_MULTI_PHASE_INIT\n  #define CYTHON_PEP489_MULTI_PHASE_INIT 0\n  #undef CYTHON_USE_TP_FINALIZE\n  #define CYTHON_USE_TP_FINALIZE 0\n  #undef CYTHON_USE_DICT_VERSIONS\n  #define CYTHON_USE_DICT_VERSIONS 0\n  #undef CYTHON_USE_EXC_INFO_STACK\n  #define CYTHON_USE_EXC_INFO_STACK 0\n  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC\n    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0\n  #endif\n#elif defined(PY_NOGIL)\n  #define CYTHON_COMPILING_IN_PYPY 0\n  #define CYTHON_COMPILING_IN_PYSTON 0\n  #define CYTHON_COMPILING_IN_CPYTHON 0\n  #define CYTHON_COMPILING_IN_NOGIL 1\n  #ifndef CYTHON_USE_TYPE_SLOTS\n    #define CYTHON_USE_TYPE_SLOTS 1\n  #endif\n  #undef CYTHON_USE_PYTYPE_LOOKUP\n  #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #ifndef CYTHON_USE_ASYNC_SLOTS\n    #define CYTHON_USE_ASYNC_SLOTS 1\n  #endif\n  #undef CYTHON_USE_PYLIST_INTERNALS\n  #define CYTHON_USE_PYLIST_INTERNALS 0\n  #ifndef CYTHON_USE_UNICODE_INTERNALS\n    #define CYTHON_USE_UNICODE_INTERNALS 1\n  #endif\n  #undef CYTHON_USE_UNICODE_WRITER\n  #define CYTHON_USE_UNICODE_WRITER 0\n  #undef CYTHON_USE_PYLONG_INTERNALS\n  #define CYTHON_USE_PYLONG_INTERNALS 0\n  #ifndef CYTHON_AVOID_BORROWED_REFS\n    #define CYTHON_AVOID_BORROWED_REFS 0\n  #endif\n  #ifndef CYTHON_ASSUME_SAFE_MACROS\n    #define CYTHON_ASSUME_SAFE_MACROS 1\n  #endif\n  #ifndef CYTHON_UNPACK_METHODS\n    #define CYTHON_UNPACK_METHODS 1\n  #endif\n  #undef CYTHON_FAST_THREAD_STATE\n  #define CYTHON_FAST_THREAD_STATE 0\n  #undef CYTHON_FAST_PYCALL\n  #define CYTHON_FAST_PYCALL 0\n  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT\n    #define CYTHON_PEP489_MULTI_PHASE_INIT 1\n  #endif\n  #ifndef CYTHON_USE_TP_FINALIZE\n    #define CYTHON_USE_TP_FINALIZE 1\n  #endif\n  #undef CYTHON_USE_DICT_VERSIONS\n  #define CYTHON_USE_DICT_VERSIONS 0\n  #undef CYTHON_USE_EXC_INFO_STACK\n  #define CYTHON_USE_EXC_INFO_STACK 0\n#else\n  #define CYTHON_COMPILING_IN_PYPY 0\n  #define CYTHON_COMPILING_IN_PYSTON 0\n  #define CYTHON_COMPILING_IN_CPYTHON 1\n  #define CYTHON_COMPILING_IN_NOGIL 0\n  #ifndef CYTHON_USE_TYPE_SLOTS\n    #define CYTHON_USE_TYPE_SLOTS 1\n  #endif\n  #if PY_VERSION_HEX < 0x02070000\n    #undef CYTHON_USE_PYTYPE_LOOKUP\n    #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)\n    #define CYTHON_USE_PYTYPE_LOOKUP 1\n  #endif\n  #if PY_MAJOR_VERSION < 3\n    #undef CYTHON_USE_ASYNC_SLOTS\n    #define CYTHON_USE_ASYNC_SLOTS 0\n  #elif !defined(CYTHON_USE_ASYNC_SLOTS)\n    #define CYTHON_USE_ASYNC_SLOTS 1\n  #endif\n  #if PY_VERSION_HEX < 0x02070000\n    #undef CYTHON_USE_PYLONG_INTERNALS\n    #define CYTHON_USE_PYLONG_INTERNALS 0\n  #elif !defined(CYTHON_USE_PYLONG_INTERNALS)\n    #define CYTHON_USE_PYLONG_INTERNALS 1\n  #endif\n  #ifndef CYTHON_USE_PYLIST_INTERNALS\n    #define CYTHON_USE_PYLIST_INTERNALS 1\n  #endif\n  #ifndef CYTHON_USE_UNICODE_INTERNALS\n    #define CYTHON_USE_UNICODE_INTERNALS 1\n  #endif\n  #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2\n    #undef CYTHON_USE_UNICODE_WRITER\n    #define CYTHON_USE_UNICODE_WRITER 0\n  #elif !defined(CYTHON_USE_UNICODE_WRITER)\n    #define CYTHON_USE_UNICODE_WRITER 1\n  #endif\n  #ifndef CYTHON_AVOID_BORROWED_REFS\n    #define CYTHON_AVOID_BORROWED_REFS 0\n  #endif\n  #ifndef CYTHON_ASSUME_SAFE_MACROS\n    #define CYTHON_ASSUME_SAFE_MACROS 1\n  #endif\n  #ifndef CYTHON_UNPACK_METHODS\n    #define CYTHON_UNPACK_METHODS 1\n  #endif\n  #if PY_VERSION_HEX >= 0x030B00A4\n    #undef CYTHON_FAST_THREAD_STATE\n    #define CYTHON_FAST_THREAD_STATE 0\n  #elif !defined(CYTHON_FAST_THREAD_STATE)\n    #define CYTHON_FAST_THREAD_STATE 1\n  #endif\n  #ifndef CYTHON_FAST_PYCALL\n    #define CYTHON_FAST_PYCALL (PY_VERSION_HEX < 0x030A0000)\n  #endif\n  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT\n    #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000)\n  #endif\n  #ifndef CYTHON_USE_TP_FINALIZE\n    #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)\n  #endif\n  #ifndef CYTHON_USE_DICT_VERSIONS\n    #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1)\n  #endif\n  #if PY_VERSION_HEX >= 0x030B00A4\n    #undef CYTHON_USE_EXC_INFO_STACK\n    #define CYTHON_USE_EXC_INFO_STACK 0\n  #elif !defined(CYTHON_USE_EXC_INFO_STACK)\n    #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3)\n  #endif\n  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC\n    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1\n  #endif\n#endif\n#if !defined(CYTHON_FAST_PYCCALL)\n#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)\n#endif\n#if CYTHON_USE_PYLONG_INTERNALS\n  #if PY_MAJOR_VERSION < 3\n    #include \"longintrepr.h\"\n  #endif\n  #undef SHIFT\n  #undef BASE\n  #undef MASK\n  #ifdef SIZEOF_VOID_P\n    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };\n  #endif\n#endif\n#ifndef __has_attribute\n  #define __has_attribute(x) 0\n#endif\n#ifndef __has_cpp_attribute\n  #define __has_cpp_attribute(x) 0\n#endif\n#ifndef CYTHON_RESTRICT\n  #if defined(__GNUC__)\n    #define CYTHON_RESTRICT __restrict__\n  #elif defined(_MSC_VER) && _MSC_VER >= 1400\n    #define CYTHON_RESTRICT __restrict\n  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n    #define CYTHON_RESTRICT restrict\n  #else\n    #define CYTHON_RESTRICT\n  #endif\n#endif\n#ifndef CYTHON_UNUSED\n# if defined(__GNUC__)\n#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))\n#     define CYTHON_UNUSED __attribute__ ((__unused__))\n#   else\n#     define CYTHON_UNUSED\n#   endif\n# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))\n#   define CYTHON_UNUSED __attribute__ ((__unused__))\n# else\n#   define CYTHON_UNUSED\n# endif\n#endif\n#ifndef CYTHON_MAYBE_UNUSED_VAR\n#  if defined(__cplusplus)\n     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }\n#  else\n#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)\n#  endif\n#endif\n#ifndef CYTHON_NCP_UNUSED\n# if CYTHON_COMPILING_IN_CPYTHON\n#  define CYTHON_NCP_UNUSED\n# else\n#  define CYTHON_NCP_UNUSED CYTHON_UNUSED\n# endif\n#endif\n#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)\n#ifdef _MSC_VER\n    #ifndef _MSC_STDINT_H_\n        #if _MSC_VER < 1300\n           typedef unsigned char     uint8_t;\n           typedef unsigned int      uint32_t;\n        #else\n           typedef unsigned __int8   uint8_t;\n           typedef unsigned __int32  uint32_t;\n        #endif\n    #endif\n#else\n   #include <stdint.h>\n#endif\n#ifndef CYTHON_FALLTHROUGH\n  #if defined(__cplusplus) && __cplusplus >= 201103L\n    #if __has_cpp_attribute(fallthrough)\n      #define CYTHON_FALLTHROUGH [[fallthrough]]\n    #elif __has_cpp_attribute(clang::fallthrough)\n      #define CYTHON_FALLTHROUGH [[clang::fallthrough]]\n    #elif __has_cpp_attribute(gnu::fallthrough)\n      #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]\n    #endif\n  #endif\n  #ifndef CYTHON_FALLTHROUGH\n    #if __has_attribute(fallthrough)\n      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))\n    #else\n      #define CYTHON_FALLTHROUGH\n    #endif\n  #endif\n  #if defined(__clang__ ) && defined(__apple_build_version__)\n    #if __apple_build_version__ < 7000000\n      #undef  CYTHON_FALLTHROUGH\n      #define CYTHON_FALLTHROUGH\n    #endif\n  #endif\n#endif\n\n#ifndef CYTHON_INLINE\n  #if defined(__clang__)\n    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))\n  #elif defined(__GNUC__)\n    #define CYTHON_INLINE __inline__\n  #elif defined(_MSC_VER)\n    #define CYTHON_INLINE __inline\n  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n    #define CYTHON_INLINE inline\n  #else\n    #define CYTHON_INLINE\n  #endif\n#endif\n\n#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)\n  #define Py_OptimizeFlag 0\n#endif\n#define __PYX_BUILD_PY_SSIZE_T \"n\"\n#define CYTHON_FORMAT_SSIZE_T \"z\"\n#if PY_MAJOR_VERSION < 3\n  #define __Pyx_BUILTIN_MODULE_NAME \"__builtin__\"\n  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\\\n          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\n  #define __Pyx_DefaultClassType PyClass_Type\n#else\n  #define __Pyx_BUILTIN_MODULE_NAME \"builtins\"\n  #define __Pyx_DefaultClassType PyType_Type\n#if PY_VERSION_HEX >= 0x030B00A1\n    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int k, int l, int s, int f,\n                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,\n                                                    PyObject *fv, PyObject *cell, PyObject* fn,\n                                                    PyObject *name, int fline, PyObject *lnos) {\n        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;\n        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *call_result=NULL, *empty=NULL;\n        const char *fn_cstr=NULL;\n        const char *name_cstr=NULL;\n        PyCodeObject* co=NULL;\n        PyObject *type, *value, *traceback;\n        PyErr_Fetch(&type, &value, &traceback);\n        if (!(kwds=PyDict_New())) goto end;\n        if (!(argcount=PyLong_FromLong(a))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_argcount\", argcount) != 0) goto end;\n        if (!(posonlyargcount=PyLong_FromLong(0))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_posonlyargcount\", posonlyargcount) != 0) goto end;\n        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_kwonlyargcount\", kwonlyargcount) != 0) goto end;\n        if (!(nlocals=PyLong_FromLong(l))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_nlocals\", nlocals) != 0) goto end;\n        if (!(stacksize=PyLong_FromLong(s))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_stacksize\", stacksize) != 0) goto end;\n        if (!(flags=PyLong_FromLong(f))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_flags\", flags) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_code\", code) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_consts\", c) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_names\", n) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_varnames\", v) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_freevars\", fv) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_cellvars\", cell) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_linetable\", lnos) != 0) goto end;\n        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;\n        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;\n        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;\n        if (!(replace = PyObject_GetAttrString((PyObject*)co, \"replace\"))) goto cleanup_code_too;\n        if (!(empty = PyTuple_New(0))) goto cleanup_code_too; // unfortunately __pyx_empty_tuple isn't available here\n        if (!(call_result = PyObject_Call(replace, empty, kwds))) goto cleanup_code_too;\n        Py_XDECREF((PyObject*)co);\n        co = (PyCodeObject*)call_result;\n        call_result = NULL;\n        if (0) {\n            cleanup_code_too:\n            Py_XDECREF((PyObject*)co);\n            co = NULL;\n        }\n        end:\n        Py_XDECREF(kwds);\n        Py_XDECREF(argcount);\n        Py_XDECREF(posonlyargcount);\n        Py_XDECREF(kwonlyargcount);\n        Py_XDECREF(nlocals);\n        Py_XDECREF(stacksize);\n        Py_XDECREF(replace);\n        Py_XDECREF(call_result);\n        Py_XDECREF(empty);\n        if (type) {\n            PyErr_Restore(type, value, traceback);\n        }\n        return co;\n    }\n#else\n  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\\\n          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\n#endif\n  #define __Pyx_DefaultClassType PyType_Type\n#endif\n#ifndef Py_TPFLAGS_CHECKTYPES\n  #define Py_TPFLAGS_CHECKTYPES 0\n#endif\n#ifndef Py_TPFLAGS_HAVE_INDEX\n  #define Py_TPFLAGS_HAVE_INDEX 0\n#endif\n#ifndef Py_TPFLAGS_HAVE_NEWBUFFER\n  #define Py_TPFLAGS_HAVE_NEWBUFFER 0\n#endif\n#ifndef Py_TPFLAGS_HAVE_FINALIZE\n  #define Py_TPFLAGS_HAVE_FINALIZE 0\n#endif\n#ifndef METH_STACKLESS\n  #define METH_STACKLESS 0\n#endif\n#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)\n  #ifndef METH_FASTCALL\n     #define METH_FASTCALL 0x80\n  #endif\n  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);\n  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,\n                                                          Py_ssize_t nargs, PyObject *kwnames);\n#else\n  #define __Pyx_PyCFunctionFast _PyCFunctionFast\n  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords\n#endif\n#if CYTHON_FAST_PYCCALL\n#define __Pyx_PyFastCFunction_Check(func)\\\n    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)))))\n#else\n#define __Pyx_PyFastCFunction_Check(func) 0\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)\n  #define PyObject_Malloc(s)   PyMem_Malloc(s)\n  #define PyObject_Free(p)     PyMem_Free(p)\n  #define PyObject_Realloc(p)  PyMem_Realloc(p)\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1\n  #define PyMem_RawMalloc(n)           PyMem_Malloc(n)\n  #define PyMem_RawRealloc(p, n)       PyMem_Realloc(p, n)\n  #define PyMem_RawFree(p)             PyMem_Free(p)\n#endif\n#if CYTHON_COMPILING_IN_PYSTON\n  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)\n  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)\n#else\n  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)\n  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)\n#endif\n#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000\n  #define __Pyx_PyThreadState_Current PyThreadState_GET()\n#elif PY_VERSION_HEX >= 0x03060000\n  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()\n#elif PY_VERSION_HEX >= 0x03000000\n  #define __Pyx_PyThreadState_Current PyThreadState_GET()\n#else\n  #define __Pyx_PyThreadState_Current _PyThreadState_Current\n#endif\n#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)\n#include \"pythread.h\"\n#define Py_tss_NEEDS_INIT 0\ntypedef int Py_tss_t;\nstatic CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {\n  *key = PyThread_create_key();\n  return 0;\n}\nstatic CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {\n  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));\n  *key = Py_tss_NEEDS_INIT;\n  return key;\n}\nstatic CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {\n  PyObject_Free(key);\n}\nstatic CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {\n  return *key != Py_tss_NEEDS_INIT;\n}\nstatic CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {\n  PyThread_delete_key(*key);\n  *key = Py_tss_NEEDS_INIT;\n}\nstatic CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {\n  return PyThread_set_key_value(*key, value);\n}\nstatic CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {\n  return PyThread_get_key_value(*key);\n}\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)\n#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))\n#else\n#define __Pyx_PyDict_NewPresized(n)  PyDict_New()\n#endif\n#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION\n  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)\n  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)\n#else\n  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)\n  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS\n#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)\n#else\n#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)\n#endif\n#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)\n  #define CYTHON_PEP393_ENABLED 1\n  #if defined(PyUnicode_IS_READY)\n  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\\\n                                              0 : _PyUnicode_Ready((PyObject *)(op)))\n  #else\n  #define __Pyx_PyUnicode_READY(op)       (0)\n  #endif\n  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)\n  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)\n  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)\n  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)\n  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)\n  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)\n  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)\n  #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE)\n  #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length))\n  #else\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))\n  #endif\n  #else\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_LENGTH(u))\n  #endif\n#else\n  #define CYTHON_PEP393_ENABLED 0\n  #define PyUnicode_1BYTE_KIND  1\n  #define PyUnicode_2BYTE_KIND  2\n  #define PyUnicode_4BYTE_KIND  4\n  #define __Pyx_PyUnicode_READY(op)       (0)\n  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)\n  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))\n  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)\n  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))\n  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))\n  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))\n  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))\n#endif\n#if CYTHON_COMPILING_IN_PYPY\n  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)\n  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)\n#else\n  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)\n  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\\\n      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)\n  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)\n  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)\n  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, \"__format__\", \"O\", fmt)\n#endif\n#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))\n#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)\n#else\n  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)\n#endif\n#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)\n  #define PyObject_ASCII(o)            PyObject_Repr(o)\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyBaseString_Type            PyUnicode_Type\n  #define PyStringObject               PyUnicodeObject\n  #define PyString_Type                PyUnicode_Type\n  #define PyString_Check               PyUnicode_Check\n  #define PyString_CheckExact          PyUnicode_CheckExact\n#ifndef PyObject_Unicode\n  #define PyObject_Unicode             PyObject_Str\n#endif\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)\n  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)\n#else\n  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))\n  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))\n#endif\n#ifndef PySet_CheckExact\n  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)\n#endif\n#if PY_VERSION_HEX >= 0x030900A4\n  #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt)\n  #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size)\n#else\n  #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)\n  #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)\n#endif\n#if CYTHON_ASSUME_SAFE_MACROS\n  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)\n#else\n  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyIntObject                  PyLongObject\n  #define PyInt_Type                   PyLong_Type\n  #define PyInt_Check(op)              PyLong_Check(op)\n  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)\n  #define PyInt_FromString             PyLong_FromString\n  #define PyInt_FromUnicode            PyLong_FromUnicode\n  #define PyInt_FromLong               PyLong_FromLong\n  #define PyInt_FromSize_t             PyLong_FromSize_t\n  #define PyInt_FromSsize_t            PyLong_FromSsize_t\n  #define PyInt_AsLong                 PyLong_AsLong\n  #define PyInt_AS_LONG                PyLong_AS_LONG\n  #define PyInt_AsSsize_t              PyLong_AsSsize_t\n  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask\n  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask\n  #define PyNumber_Int                 PyNumber_Long\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyBoolObject                 PyLongObject\n#endif\n#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY\n  #ifndef PyUnicode_InternFromString\n    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)\n  #endif\n#endif\n#if PY_VERSION_HEX < 0x030200A4\n  typedef long Py_hash_t;\n  #define __Pyx_PyInt_FromHash_t PyInt_FromLong\n  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsHash_t\n#else\n  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t\n  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsSsize_t\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))\n#else\n  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)\n#endif\n#if CYTHON_USE_ASYNC_SLOTS\n  #if PY_VERSION_HEX >= 0x030500B1\n    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods\n    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)\n  #else\n    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))\n  #endif\n#else\n  #define __Pyx_PyType_AsAsync(obj) NULL\n#endif\n#ifndef __Pyx_PyAsyncMethodsStruct\n    typedef struct {\n        unaryfunc am_await;\n        unaryfunc am_aiter;\n        unaryfunc am_anext;\n    } __Pyx_PyAsyncMethodsStruct;\n#endif\n\n#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS)\n  #if !defined(_USE_MATH_DEFINES)\n    #define _USE_MATH_DEFINES\n  #endif\n#endif\n#include <math.h>\n#ifdef NAN\n#define __PYX_NAN() ((float) NAN)\n#else\nstatic CYTHON_INLINE float __PYX_NAN() {\n  float value;\n  memset(&value, 0xFF, sizeof(value));\n  return value;\n}\n#endif\n#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)\n#define __Pyx_truncl trunc\n#else\n#define __Pyx_truncl truncl\n#endif\n\n#define __PYX_MARK_ERR_POS(f_index, lineno) \\\n    { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }\n#define __PYX_ERR(f_index, lineno, Ln_error) \\\n    { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }\n\n#ifndef __PYX_EXTERN_C\n  #ifdef __cplusplus\n    #define __PYX_EXTERN_C extern \"C\"\n  #else\n    #define __PYX_EXTERN_C extern\n  #endif\n#endif\n\n#define __PYX_HAVE__rank_cy\n#define __PYX_HAVE_API__rank_cy\n/* Early includes */\n#include <string.h>\n#include <stdio.h>\n#include \"numpy/arrayobject.h\"\n#include \"numpy/ndarrayobject.h\"\n#include \"numpy/ndarraytypes.h\"\n#include \"numpy/arrayscalars.h\"\n#include \"numpy/ufuncobject.h\"\n\n    /* NumPy API declarations from \"numpy/__init__.pxd\" */\n    \n#include \"pythread.h\"\n#include <stdlib.h>\n#include \"pystate.h\"\n#ifdef _OPENMP\n#include <omp.h>\n#endif /* _OPENMP */\n\n#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)\n#define CYTHON_WITHOUT_ASSERTIONS\n#endif\n\ntypedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;\n                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;\n\n#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0\n#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0\n#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)\n#define __PYX_DEFAULT_STRING_ENCODING \"\"\n#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString\n#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\n#define __Pyx_uchar_cast(c) ((unsigned char)c)\n#define __Pyx_long_cast(x) ((long)x)\n#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\\\n    (sizeof(type) < sizeof(Py_ssize_t))  ||\\\n    (sizeof(type) > sizeof(Py_ssize_t) &&\\\n          likely(v < (type)PY_SSIZE_T_MAX ||\\\n                 v == (type)PY_SSIZE_T_MAX)  &&\\\n          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\\\n                                v == (type)PY_SSIZE_T_MIN)))  ||\\\n    (sizeof(type) == sizeof(Py_ssize_t) &&\\\n          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\\\n                               v == (type)PY_SSIZE_T_MAX)))  )\nstatic CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {\n    return (size_t) i < (size_t) limit;\n}\n#if defined (__cplusplus) && __cplusplus >= 201103L\n    #include <cstdlib>\n    #define __Pyx_sst_abs(value) std::abs(value)\n#elif SIZEOF_INT >= SIZEOF_SIZE_T\n    #define __Pyx_sst_abs(value) abs(value)\n#elif SIZEOF_LONG >= SIZEOF_SIZE_T\n    #define __Pyx_sst_abs(value) labs(value)\n#elif defined (_MSC_VER)\n    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))\n#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n    #define __Pyx_sst_abs(value) llabs(value)\n#elif defined (__GNUC__)\n    #define __Pyx_sst_abs(value) __builtin_llabs(value)\n#else\n    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)\n#endif\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);\n#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))\n#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)\n#define __Pyx_PyBytes_FromString        PyBytes_FromString\n#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize\nstatic CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);\n#if PY_MAJOR_VERSION < 3\n    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString\n    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\n#else\n    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString\n    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize\n#endif\n#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)\n#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)\n#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)\n#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)\n#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)\nstatic CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {\n    const Py_UNICODE *u_end = u;\n    while (*u_end++) ;\n    return (size_t)(u_end - u - 1);\n}\n#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))\n#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode\n#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode\n#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)\n#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)\nstatic CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);\nstatic CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);\n#define __Pyx_PySequence_Tuple(obj)\\\n    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))\nstatic CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);\nstatic CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);\nstatic CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);\n#if CYTHON_ASSUME_SAFE_MACROS\n#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))\n#else\n#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)\n#endif\n#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))\n#if PY_MAJOR_VERSION >= 3\n#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))\n#else\n#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))\n#endif\n#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))\n#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\nstatic int __Pyx_sys_getdefaultencoding_not_ascii;\nstatic int __Pyx_init_sys_getdefaultencoding_params(void) {\n    PyObject* sys;\n    PyObject* default_encoding = NULL;\n    PyObject* ascii_chars_u = NULL;\n    PyObject* ascii_chars_b = NULL;\n    const char* default_encoding_c;\n    sys = PyImport_ImportModule(\"sys\");\n    if (!sys) goto bad;\n    default_encoding = PyObject_CallMethod(sys, (char*) \"getdefaultencoding\", NULL);\n    Py_DECREF(sys);\n    if (!default_encoding) goto bad;\n    default_encoding_c = PyBytes_AsString(default_encoding);\n    if (!default_encoding_c) goto bad;\n    if (strcmp(default_encoding_c, \"ascii\") == 0) {\n        __Pyx_sys_getdefaultencoding_not_ascii = 0;\n    } else {\n        char ascii_chars[128];\n        int c;\n        for (c = 0; c < 128; c++) {\n            ascii_chars[c] = c;\n        }\n        __Pyx_sys_getdefaultencoding_not_ascii = 1;\n        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);\n        if (!ascii_chars_u) goto bad;\n        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);\n        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {\n            PyErr_Format(\n                PyExc_ValueError,\n                \"This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.\",\n                default_encoding_c);\n            goto bad;\n        }\n        Py_DECREF(ascii_chars_u);\n        Py_DECREF(ascii_chars_b);\n    }\n    Py_DECREF(default_encoding);\n    return 0;\nbad:\n    Py_XDECREF(default_encoding);\n    Py_XDECREF(ascii_chars_u);\n    Py_XDECREF(ascii_chars_b);\n    return -1;\n}\n#endif\n#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3\n#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)\n#else\n#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)\n#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\nstatic char* __PYX_DEFAULT_STRING_ENCODING;\nstatic int __Pyx_init_sys_getdefaultencoding_params(void) {\n    PyObject* sys;\n    PyObject* default_encoding = NULL;\n    char* default_encoding_c;\n    sys = PyImport_ImportModule(\"sys\");\n    if (!sys) goto bad;\n    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) \"getdefaultencoding\", NULL);\n    Py_DECREF(sys);\n    if (!default_encoding) goto bad;\n    default_encoding_c = PyBytes_AsString(default_encoding);\n    if (!default_encoding_c) goto bad;\n    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);\n    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;\n    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);\n    Py_DECREF(default_encoding);\n    return 0;\nbad:\n    Py_XDECREF(default_encoding);\n    return -1;\n}\n#endif\n#endif\n\n\n/* Test for GCC > 2.95 */\n#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))\n  #define likely(x)   __builtin_expect(!!(x), 1)\n  #define unlikely(x) __builtin_expect(!!(x), 0)\n#else /* !__GNUC__ or GCC < 2.95 */\n  #define likely(x)   (x)\n  #define unlikely(x) (x)\n#endif /* __GNUC__ */\nstatic CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }\n\nstatic PyObject *__pyx_m = NULL;\nstatic PyObject *__pyx_d;\nstatic PyObject *__pyx_b;\nstatic PyObject *__pyx_cython_runtime = NULL;\nstatic PyObject *__pyx_empty_tuple;\nstatic PyObject *__pyx_empty_bytes;\nstatic PyObject *__pyx_empty_unicode;\nstatic int __pyx_lineno;\nstatic int __pyx_clineno = 0;\nstatic const char * __pyx_cfilenm= __FILE__;\nstatic const char *__pyx_filename;\n\n/* Header.proto */\n#if !defined(CYTHON_CCOMPLEX)\n  #if defined(__cplusplus)\n    #define CYTHON_CCOMPLEX 1\n  #elif defined(_Complex_I)\n    #define CYTHON_CCOMPLEX 1\n  #else\n    #define CYTHON_CCOMPLEX 0\n  #endif\n#endif\n#if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    #include <complex>\n  #else\n    #include <complex.h>\n  #endif\n#endif\n#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)\n  #undef _Complex_I\n  #define _Complex_I 1.0fj\n#endif\n\n\nstatic const char *__pyx_f[] = {\n  \"rank_cy.pyx\",\n  \"__init__.pxd\",\n  \"stringsource\",\n  \"type.pxd\",\n};\n/* MemviewSliceStruct.proto */\nstruct __pyx_memoryview_obj;\ntypedef struct {\n  struct __pyx_memoryview_obj *memview;\n  char *data;\n  Py_ssize_t shape[8];\n  Py_ssize_t strides[8];\n  Py_ssize_t suboffsets[8];\n} __Pyx_memviewslice;\n#define __Pyx_MemoryView_Len(m)  (m.shape[0])\n\n/* Atomics.proto */\n#include <pythread.h>\n#ifndef CYTHON_ATOMICS\n    #define CYTHON_ATOMICS 1\n#endif\n#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS\n#define __pyx_atomic_int_type int\n#if CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\\\n                    (__GNUC_MINOR__ > 1 ||\\\n                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2))))\n    #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1)\n    #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1)\n    #ifdef __PYX_DEBUG_ATOMICS\n        #warning \"Using GNU atomics\"\n    #endif\n#elif CYTHON_ATOMICS && defined(_MSC_VER) && CYTHON_COMPILING_IN_NOGIL\n    #include <intrin.h>\n    #undef __pyx_atomic_int_type\n    #define __pyx_atomic_int_type long\n    #pragma intrinsic (_InterlockedExchangeAdd)\n    #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1)\n    #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1)\n    #ifdef __PYX_DEBUG_ATOMICS\n        #pragma message (\"Using MSVC atomics\")\n    #endif\n#else\n    #undef CYTHON_ATOMICS\n    #define CYTHON_ATOMICS 0\n    #ifdef __PYX_DEBUG_ATOMICS\n        #warning \"Not using atomics\"\n    #endif\n#endif\ntypedef volatile __pyx_atomic_int_type __pyx_atomic_int;\n#if CYTHON_ATOMICS\n    #define __pyx_add_acquisition_count(memview)\\\n             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview))\n    #define __pyx_sub_acquisition_count(memview)\\\n            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview))\n#else\n    #define __pyx_add_acquisition_count(memview)\\\n            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)\n    #define __pyx_sub_acquisition_count(memview)\\\n            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)\n#endif\n\n/* ForceInitThreads.proto */\n#ifndef __PYX_FORCE_INIT_THREADS\n  #define __PYX_FORCE_INIT_THREADS 0\n#endif\n\n/* NoFastGil.proto */\n#define __Pyx_PyGILState_Ensure PyGILState_Ensure\n#define __Pyx_PyGILState_Release PyGILState_Release\n#define __Pyx_FastGIL_Remember()\n#define __Pyx_FastGIL_Forget()\n#define __Pyx_FastGilFuncInit()\n\n/* BufferFormatStructs.proto */\n#define IS_UNSIGNED(type) (((type) -1) > 0)\nstruct __Pyx_StructField_;\n#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)\ntypedef struct {\n  const char* name;\n  struct __Pyx_StructField_* fields;\n  size_t size;\n  size_t arraysize[8];\n  int ndim;\n  char typegroup;\n  char is_unsigned;\n  int flags;\n} __Pyx_TypeInfo;\ntypedef struct __Pyx_StructField_ {\n  __Pyx_TypeInfo* type;\n  const char* name;\n  size_t offset;\n} __Pyx_StructField;\ntypedef struct {\n  __Pyx_StructField* field;\n  size_t parent_offset;\n} __Pyx_BufFmt_StackElem;\ntypedef struct {\n  __Pyx_StructField root;\n  __Pyx_BufFmt_StackElem* head;\n  size_t fmt_offset;\n  size_t new_count, enc_count;\n  size_t struct_alignment;\n  int is_complex;\n  char enc_type;\n  char new_packmode;\n  char enc_packmode;\n  char is_valid_array;\n} __Pyx_BufFmt_Context;\n\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":689\n * # in Cython to enable them only on the right systems.\n * \n * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t\n */\ntypedef npy_int8 __pyx_t_5numpy_int8_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":690\n * \n * ctypedef npy_int8       int8_t\n * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int32      int32_t\n * ctypedef npy_int64      int64_t\n */\ntypedef npy_int16 __pyx_t_5numpy_int16_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":691\n * ctypedef npy_int8       int8_t\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int64      int64_t\n * #ctypedef npy_int96      int96_t\n */\ntypedef npy_int32 __pyx_t_5numpy_int32_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":692\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t\n * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_int96      int96_t\n * #ctypedef npy_int128     int128_t\n */\ntypedef npy_int64 __pyx_t_5numpy_int64_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":696\n * #ctypedef npy_int128     int128_t\n * \n * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t\n */\ntypedef npy_uint8 __pyx_t_5numpy_uint8_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":697\n * \n * ctypedef npy_uint8      uint8_t\n * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint32     uint32_t\n * ctypedef npy_uint64     uint64_t\n */\ntypedef npy_uint16 __pyx_t_5numpy_uint16_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":698\n * ctypedef npy_uint8      uint8_t\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint64     uint64_t\n * #ctypedef npy_uint96     uint96_t\n */\ntypedef npy_uint32 __pyx_t_5numpy_uint32_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":699\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t\n * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_uint96     uint96_t\n * #ctypedef npy_uint128    uint128_t\n */\ntypedef npy_uint64 __pyx_t_5numpy_uint64_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":703\n * #ctypedef npy_uint128    uint128_t\n * \n * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_float64    float64_t\n * #ctypedef npy_float80    float80_t\n */\ntypedef npy_float32 __pyx_t_5numpy_float32_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":704\n * \n * ctypedef npy_float32    float32_t\n * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_float80    float80_t\n * #ctypedef npy_float128   float128_t\n */\ntypedef npy_float64 __pyx_t_5numpy_float64_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":713\n * # The int types are mapped a bit surprising --\n * # numpy.int corresponds to 'l' and numpy.long to 'q'\n * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<\n * ctypedef npy_longlong   long_t\n * ctypedef npy_longlong   longlong_t\n */\ntypedef npy_long __pyx_t_5numpy_int_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":714\n * # numpy.int corresponds to 'l' and numpy.long to 'q'\n * ctypedef npy_long       int_t\n * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<\n * ctypedef npy_longlong   longlong_t\n * \n */\ntypedef npy_longlong __pyx_t_5numpy_long_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":715\n * ctypedef npy_long       int_t\n * ctypedef npy_longlong   long_t\n * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<\n * \n * ctypedef npy_ulong      uint_t\n */\ntypedef npy_longlong __pyx_t_5numpy_longlong_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":717\n * ctypedef npy_longlong   longlong_t\n * \n * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<\n * ctypedef npy_ulonglong  ulong_t\n * ctypedef npy_ulonglong  ulonglong_t\n */\ntypedef npy_ulong __pyx_t_5numpy_uint_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":718\n * \n * ctypedef npy_ulong      uint_t\n * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<\n * ctypedef npy_ulonglong  ulonglong_t\n * \n */\ntypedef npy_ulonglong __pyx_t_5numpy_ulong_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":719\n * ctypedef npy_ulong      uint_t\n * ctypedef npy_ulonglong  ulong_t\n * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<\n * \n * ctypedef npy_intp       intp_t\n */\ntypedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":721\n * ctypedef npy_ulonglong  ulonglong_t\n * \n * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uintp      uintp_t\n * \n */\ntypedef npy_intp __pyx_t_5numpy_intp_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":722\n * \n * ctypedef npy_intp       intp_t\n * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<\n * \n * ctypedef npy_double     float_t\n */\ntypedef npy_uintp __pyx_t_5numpy_uintp_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":724\n * ctypedef npy_uintp      uintp_t\n * \n * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<\n * ctypedef npy_double     double_t\n * ctypedef npy_longdouble longdouble_t\n */\ntypedef npy_double __pyx_t_5numpy_float_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":725\n * \n * ctypedef npy_double     float_t\n * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<\n * ctypedef npy_longdouble longdouble_t\n * \n */\ntypedef npy_double __pyx_t_5numpy_double_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":726\n * ctypedef npy_double     float_t\n * ctypedef npy_double     double_t\n * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<\n * \n * ctypedef npy_cfloat      cfloat_t\n */\ntypedef npy_longdouble __pyx_t_5numpy_longdouble_t;\n/* Declarations.proto */\n#if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    typedef ::std::complex< float > __pyx_t_float_complex;\n  #else\n    typedef float _Complex __pyx_t_float_complex;\n  #endif\n#else\n    typedef struct { float real, imag; } __pyx_t_float_complex;\n#endif\nstatic CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);\n\n/* Declarations.proto */\n#if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    typedef ::std::complex< double > __pyx_t_double_complex;\n  #else\n    typedef double _Complex __pyx_t_double_complex;\n  #endif\n#else\n    typedef struct { double real, imag; } __pyx_t_double_complex;\n#endif\nstatic CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);\n\n\n/*--- Type declarations ---*/\nstruct __pyx_array_obj;\nstruct __pyx_MemviewEnum_obj;\nstruct __pyx_memoryview_obj;\nstruct __pyx_memoryviewslice_obj;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":728\n * ctypedef npy_longdouble longdouble_t\n * \n * ctypedef npy_cfloat      cfloat_t             # <<<<<<<<<<<<<<\n * ctypedef npy_cdouble     cdouble_t\n * ctypedef npy_clongdouble clongdouble_t\n */\ntypedef npy_cfloat __pyx_t_5numpy_cfloat_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":729\n * \n * ctypedef npy_cfloat      cfloat_t\n * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<\n * ctypedef npy_clongdouble clongdouble_t\n * \n */\ntypedef npy_cdouble __pyx_t_5numpy_cdouble_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":730\n * ctypedef npy_cfloat      cfloat_t\n * ctypedef npy_cdouble     cdouble_t\n * ctypedef npy_clongdouble clongdouble_t             # <<<<<<<<<<<<<<\n * \n * ctypedef npy_cdouble     complex_t\n */\ntypedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":732\n * ctypedef npy_clongdouble clongdouble_t\n * \n * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<\n * \n * cdef inline object PyArray_MultiIterNew1(a):\n */\ntypedef npy_cdouble __pyx_t_5numpy_complex_t;\nstruct __pyx_opt_args_7rank_cy_evaluate_cy;\n\n/* \"rank_cy.pyx\":20\n * \n * # Main interface\n * cpdef evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=False):             # <<<<<<<<<<<<<<\n *     distmat = np.asarray(distmat, dtype=np.float32)\n *     q_pids = np.asarray(q_pids, dtype=np.int64)\n */\nstruct __pyx_opt_args_7rank_cy_evaluate_cy {\n  int __pyx_n;\n  PyObject *use_metric_cuhk03;\n};\n\n/* \"View.MemoryView\":106\n * \n * @cname(\"__pyx_array\")\n * cdef class array:             # <<<<<<<<<<<<<<\n * \n *     cdef:\n */\nstruct __pyx_array_obj {\n  PyObject_HEAD\n  struct __pyx_vtabstruct_array *__pyx_vtab;\n  char *data;\n  Py_ssize_t len;\n  char *format;\n  int ndim;\n  Py_ssize_t *_shape;\n  Py_ssize_t *_strides;\n  Py_ssize_t itemsize;\n  PyObject *mode;\n  PyObject *_format;\n  void (*callback_free_data)(void *);\n  int free_data;\n  int dtype_is_object;\n};\n\n\n/* \"View.MemoryView\":280\n * \n * @cname('__pyx_MemviewEnum')\n * cdef class Enum(object):             # <<<<<<<<<<<<<<\n *     cdef object name\n *     def __init__(self, name):\n */\nstruct __pyx_MemviewEnum_obj {\n  PyObject_HEAD\n  PyObject *name;\n};\n\n\n/* \"View.MemoryView\":331\n * \n * @cname('__pyx_memoryview')\n * cdef class memoryview(object):             # <<<<<<<<<<<<<<\n * \n *     cdef object obj\n */\nstruct __pyx_memoryview_obj {\n  PyObject_HEAD\n  struct __pyx_vtabstruct_memoryview *__pyx_vtab;\n  PyObject *obj;\n  PyObject *_size;\n  PyObject *_array_interface;\n  PyThread_type_lock lock;\n  __pyx_atomic_int acquisition_count[2];\n  __pyx_atomic_int *acquisition_count_aligned_p;\n  Py_buffer view;\n  int flags;\n  int dtype_is_object;\n  __Pyx_TypeInfo *typeinfo;\n};\n\n\n/* \"View.MemoryView\":967\n * \n * @cname('__pyx_memoryviewslice')\n * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<\n *     \"Internal class for passing memoryview slices to Python\"\n * \n */\nstruct __pyx_memoryviewslice_obj {\n  struct __pyx_memoryview_obj __pyx_base;\n  __Pyx_memviewslice from_slice;\n  PyObject *from_object;\n  PyObject *(*to_object_func)(char *);\n  int (*to_dtype_func)(char *, PyObject *);\n};\n\n\n\n/* \"View.MemoryView\":106\n * \n * @cname(\"__pyx_array\")\n * cdef class array:             # <<<<<<<<<<<<<<\n * \n *     cdef:\n */\n\nstruct __pyx_vtabstruct_array {\n  PyObject *(*get_memview)(struct __pyx_array_obj *);\n};\nstatic struct __pyx_vtabstruct_array *__pyx_vtabptr_array;\n\n\n/* \"View.MemoryView\":331\n * \n * @cname('__pyx_memoryview')\n * cdef class memoryview(object):             # <<<<<<<<<<<<<<\n * \n *     cdef object obj\n */\n\nstruct __pyx_vtabstruct_memoryview {\n  char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *);\n  PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *);\n  PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);\n  PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *);\n  PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);\n  PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *);\n  PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *);\n};\nstatic struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview;\n\n\n/* \"View.MemoryView\":967\n * \n * @cname('__pyx_memoryviewslice')\n * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<\n *     \"Internal class for passing 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__pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/\nstatic PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/\n/* GetAttr.proto */\nstatic CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);\n\n/* decode_c_string_utf16.proto */\nstatic CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) {\n    int byteorder = 0;\n    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);\n}\nstatic CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) {\n    int byteorder = -1;\n    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);\n}\nstatic CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) {\n    int byteorder = 1;\n    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);\n}\n\n/* decode_c_string.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_decode_c_string(\n         const char* cstring, Py_ssize_t start, Py_ssize_t stop,\n         const char* encoding, const char* errors,\n         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));\n\n/* GetAttr3.proto */\nstatic CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);\n\n/* RaiseNoneIterError.proto */\nstatic CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);\n\n/* ExtTypeTest.proto */\nstatic CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);\n\n/* SwapException.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)\nstatic CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);\n#else\nstatic CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);\n#endif\n\n/* Import.proto */\nstatic PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);\n\n/* FastTypeChecks.proto */\n#if CYTHON_COMPILING_IN_CPYTHON\n#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)\nstatic CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);\n#else\n#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)\n#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)\n#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))\n#endif\n#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)\n\nstatic CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/\n/* ListCompAppend.proto */\n#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS\nstatic CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {\n    PyListObject* L = (PyListObject*) list;\n    Py_ssize_t len = Py_SIZE(list);\n    if (likely(L->allocated > len)) {\n        Py_INCREF(x);\n        PyList_SET_ITEM(list, len, x);\n        __Pyx_SET_SIZE(list, len + 1);\n        return 0;\n    }\n    return PyList_Append(list, x);\n}\n#else\n#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)\n#endif\n\n/* PyIntBinop.proto */\n#if !CYTHON_COMPILING_IN_PYPY\nstatic PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);\n#else\n#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\\\n    (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))\n#endif\n\n/* ListExtend.proto */\nstatic CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {\n#if CYTHON_COMPILING_IN_CPYTHON\n    PyObject* none = _PyList_Extend((PyListObject*)L, v);\n    if (unlikely(!none))\n        return -1;\n    Py_DECREF(none);\n    return 0;\n#else\n    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);\n#endif\n}\n\n/* PySequenceContains.proto */\nstatic CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {\n    int result = PySequence_Contains(seq, item);\n    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));\n}\n\n/* ImportFrom.proto */\nstatic PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);\n\n/* HasAttr.proto */\nstatic CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);\n\n/* PyObject_GenericGetAttrNoDict.proto */\n#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);\n#else\n#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr\n#endif\n\n/* PyObject_GenericGetAttr.proto */\n#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000\nstatic PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);\n#else\n#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr\n#endif\n\n/* SetVTable.proto */\nstatic int __Pyx_SetVtable(PyObject *dict, void *vtable);\n\n/* PyObjectGetAttrStrNoError.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name);\n\n/* SetupReduce.proto */\nstatic int __Pyx_setup_reduce(PyObject* type_obj);\n\n/* TypeImport.proto */\n#ifndef __PYX_HAVE_RT_ImportType_proto\n#define __PYX_HAVE_RT_ImportType_proto\nenum __Pyx_ImportType_CheckSize {\n   __Pyx_ImportType_CheckSize_Error = 0,\n   __Pyx_ImportType_CheckSize_Warn = 1,\n   __Pyx_ImportType_CheckSize_Ignore = 2\n};\nstatic PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size);\n#endif\n\n/* CLineInTraceback.proto */\n#ifdef CYTHON_CLINE_IN_TRACEBACK\n#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)\n#else\nstatic int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);\n#endif\n\n/* CodeObjectCache.proto */\ntypedef struct {\n    PyCodeObject* code_object;\n    int code_line;\n} __Pyx_CodeObjectCacheEntry;\nstruct __Pyx_CodeObjectCache {\n    int count;\n    int max_count;\n    __Pyx_CodeObjectCacheEntry* entries;\n};\nstatic struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};\nstatic int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);\nstatic PyCodeObject *__pyx_find_code_object(int code_line);\nstatic void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);\n\n/* AddTraceback.proto */\nstatic void __Pyx_AddTraceback(const char *funcname, int c_line,\n                               int py_line, const char *filename);\n\n#if PY_MAJOR_VERSION < 3\n    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);\n    static void __Pyx_ReleaseBuffer(Py_buffer *view);\n#else\n    #define __Pyx_GetBuffer PyObject_GetBuffer\n    #define __Pyx_ReleaseBuffer PyBuffer_Release\n#endif\n\n\n/* BufferStructDeclare.proto */\ntypedef struct {\n  Py_ssize_t shape, strides, suboffsets;\n} __Pyx_Buf_DimInfo;\ntypedef struct {\n  size_t refcount;\n  Py_buffer pybuffer;\n} __Pyx_Buffer;\ntypedef struct {\n  __Pyx_Buffer *rcbuffer;\n  char *data;\n  __Pyx_Buf_DimInfo diminfo[8];\n} __Pyx_LocalBuf_ND;\n\n/* MemviewSliceIsContig.proto */\nstatic int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);\n\n/* OverlappingSlices.proto */\nstatic int __pyx_slices_overlap(__Pyx_memviewslice *slice1,\n                                __Pyx_memviewslice *slice2,\n                                int ndim, size_t itemsize);\n\n/* Capsule.proto */\nstatic CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig);\n\n/* IsLittleEndian.proto */\nstatic CYTHON_INLINE int __Pyx_Is_Little_Endian(void);\n\n/* BufferFormatCheck.proto */\nstatic const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);\nstatic void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,\n                              __Pyx_BufFmt_StackElem* stack,\n                              __Pyx_TypeInfo* type);\n\n/* TypeInfoCompare.proto */\nstatic int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);\n\n/* MemviewSliceValidateAndInit.proto */\nstatic int __Pyx_ValidateAndInit_memviewslice(\n                int *axes_specs,\n                int c_or_f_flag,\n                int buf_flags,\n                int ndim,\n                __Pyx_TypeInfo *dtype,\n                __Pyx_BufFmt_StackElem stack[],\n                __Pyx_memviewslice *memviewslice,\n                PyObject *original_obj);\n\n/* ObjectToMemviewSlice.proto */\nstatic CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_float(PyObject *, int writable_flag);\n\n/* ObjectToMemviewSlice.proto */\nstatic CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *, int writable_flag);\n\n/* GCCDiagnostics.proto */\n#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))\n#define __Pyx_HAS_GCC_DIAGNOSTIC\n#endif\n\n/* RealImag.proto */\n#if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    #define __Pyx_CREAL(z) ((z).real())\n    #define __Pyx_CIMAG(z) ((z).imag())\n  #else\n    #define __Pyx_CREAL(z) (__real__(z))\n    #define __Pyx_CIMAG(z) (__imag__(z))\n  #endif\n#else\n    #define __Pyx_CREAL(z) ((z).real)\n    #define __Pyx_CIMAG(z) ((z).imag)\n#endif\n#if defined(__cplusplus) && CYTHON_CCOMPLEX\\\n        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)\n    #define __Pyx_SET_CREAL(z,x) ((z).real(x))\n    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))\n#else\n    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)\n    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)\n#endif\n\n/* Arithmetic.proto */\n#if CYTHON_CCOMPLEX\n    #define __Pyx_c_eq_float(a, b)   ((a)==(b))\n    #define __Pyx_c_sum_float(a, b)  ((a)+(b))\n    #define __Pyx_c_diff_float(a, b) ((a)-(b))\n    #define __Pyx_c_prod_float(a, b) ((a)*(b))\n    #define __Pyx_c_quot_float(a, b) ((a)/(b))\n    #define __Pyx_c_neg_float(a)     (-(a))\n  #ifdef __cplusplus\n    #define __Pyx_c_is_zero_float(z) ((z)==(float)0)\n    #define __Pyx_c_conj_float(z)    (::std::conj(z))\n    #if 1\n        #define __Pyx_c_abs_float(z)     (::std::abs(z))\n        #define __Pyx_c_pow_float(a, b)  (::std::pow(a, b))\n    #endif\n  #else\n    #define __Pyx_c_is_zero_float(z) ((z)==0)\n    #define __Pyx_c_conj_float(z)    (conjf(z))\n    #if 1\n        #define __Pyx_c_abs_float(z)     (cabsf(z))\n        #define __Pyx_c_pow_float(a, b)  (cpowf(a, b))\n    #endif\n #endif\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);\n    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);\n    #if 1\n        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);\n        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    #endif\n#endif\n\n/* Arithmetic.proto */\n#if CYTHON_CCOMPLEX\n    #define __Pyx_c_eq_double(a, b)   ((a)==(b))\n    #define __Pyx_c_sum_double(a, b)  ((a)+(b))\n    #define __Pyx_c_diff_double(a, b) ((a)-(b))\n    #define __Pyx_c_prod_double(a, b) ((a)*(b))\n    #define __Pyx_c_quot_double(a, b) ((a)/(b))\n    #define __Pyx_c_neg_double(a)     (-(a))\n  #ifdef __cplusplus\n    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)\n    #define __Pyx_c_conj_double(z)    (::std::conj(z))\n    #if 1\n        #define __Pyx_c_abs_double(z)     (::std::abs(z))\n        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))\n    #endif\n  #else\n    #define __Pyx_c_is_zero_double(z) ((z)==0)\n    #define __Pyx_c_conj_double(z)    (conj(z))\n    #if 1\n        #define __Pyx_c_abs_double(z)     (cabs(z))\n        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))\n    #endif\n #endif\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);\n    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);\n    #if 1\n        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);\n        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    #endif\n#endif\n\n/* Print.proto */\nstatic int __Pyx_Print(PyObject*, PyObject *, int);\n#if CYTHON_COMPILING_IN_PYPY || PY_MAJOR_VERSION >= 3\nstatic PyObject* __pyx_print = 0;\nstatic PyObject* __pyx_print_kwargs = 0;\n#endif\n\n/* MemviewDtypeToObject.proto */\nstatic CYTHON_INLINE PyObject *__pyx_memview_get_float(const char *itemp);\nstatic CYTHON_INLINE int __pyx_memview_set_float(const char *itemp, PyObject *obj);\n\n/* ObjectToMemviewSlice.proto */\nstatic CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_long(PyObject *, int writable_flag);\n\n/* MemviewDtypeToObject.proto */\nstatic CYTHON_INLINE PyObject *__pyx_memview_get_long(const char *itemp);\nstatic CYTHON_INLINE int __pyx_memview_set_long(const char *itemp, PyObject *obj);\n\n/* ObjectToMemviewSlice.proto */\nstatic CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_float(PyObject *, int writable_flag);\n\n/* MemviewSliceCopyTemplate.proto */\nstatic __Pyx_memviewslice\n__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,\n                                 const char *mode, int ndim,\n                                 size_t sizeof_dtype, int contig_flag,\n                                 int dtype_is_object);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);\n\n/* PrintOne.proto */\nstatic int __Pyx_PrintOne(PyObject* stream, PyObject *o);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);\n\n/* CheckBinaryVersion.proto */\nstatic int __Pyx_check_binary_version(void);\n\n/* InitStrings.proto */\nstatic int __Pyx_InitStrings(__Pyx_StringTabEntry *t);\n\nstatic PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/\nstatic char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/\nstatic PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/\nstatic PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/\nstatic PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/\nstatic PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/\nstatic PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/\nstatic PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/\nstatic PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/\nstatic PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/\n\n/* Module declarations from 'cython.view' */\n\n/* Module declarations from 'cython' */\n\n/* Module declarations from 'cpython.buffer' */\n\n/* Module declarations from 'libc.string' */\n\n/* Module declarations from 'libc.stdio' */\n\n/* Module declarations from '__builtin__' */\n\n/* Module declarations from 'cpython.type' */\nstatic PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;\n\n/* Module declarations from 'cpython' */\n\n/* Module declarations from 'cpython.object' */\n\n/* Module declarations from 'cpython.ref' */\n\n/* Module declarations from 'cpython.mem' */\n\n/* Module declarations from 'numpy' */\n\n/* Module declarations from 'numpy' */\nstatic PyTypeObject *__pyx_ptype_5numpy_dtype = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_generic = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_number = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_integer = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_signedinteger = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_unsignedinteger = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_inexact = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_floating = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_complexfloating = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_flexible = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_character = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;\n\n/* Module declarations from 'rank_cy' */\nstatic PyTypeObject *__pyx_array_type = 0;\nstatic PyTypeObject *__pyx_MemviewEnum_type = 0;\nstatic PyTypeObject *__pyx_memoryview_type = 0;\nstatic PyTypeObject *__pyx_memoryviewslice_type = 0;\nstatic PyObject *generic = 0;\nstatic PyObject *strided = 0;\nstatic PyObject *indirect = 0;\nstatic PyObject *contiguous = 0;\nstatic PyObject *indirect_contiguous = 0;\nstatic int __pyx_memoryview_thread_locks_used;\nstatic PyThread_type_lock __pyx_memoryview_thread_locks[8];\nstatic PyObject *__pyx_f_7rank_cy_evaluate_cy(PyObject *, PyObject *, PyObject *, PyObject *, PyObject *, PyObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7rank_cy_evaluate_cy *__pyx_optional_args); /*proto*/\nstatic PyObject *__pyx_f_7rank_cy_eval_cuhk03_cy(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, long, int __pyx_skip_dispatch); /*proto*/\nstatic PyObject *__pyx_f_7rank_cy_eval_market1501_cy(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, long, int __pyx_skip_dispatch); /*proto*/\nstatic void __pyx_fuse_3__pyx_f_7rank_cy_function_cumsum(__Pyx_memviewslice, __Pyx_memviewslice, long); /*proto*/\nstatic struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/\nstatic void *__pyx_align_pointer(void *, size_t); /*proto*/\nstatic PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/\nstatic CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/\nstatic PyObject *_unellipsify(PyObject *, int); /*proto*/\nstatic PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/\nstatic struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/\nstatic int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/\nstatic char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/\nstatic int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/\nstatic PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/\nstatic __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/\nstatic void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/\nstatic PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/\nstatic PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/\nstatic Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/\nstatic char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/\nstatic void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/\nstatic void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/\nstatic Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/\nstatic Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/\nstatic void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/\nstatic int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/\nstatic int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/\nstatic int __pyx_memoryview_err(PyObject *, char *); /*proto*/\nstatic int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/\nstatic void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/\nstatic void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/\nstatic void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/\nstatic void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/\nstatic void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/\nstatic void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/\nstatic PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_float = { \"float\", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 };\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_long = { \"long\", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 };\n#define __Pyx_MODULE_NAME \"rank_cy\"\nextern int __pyx_module_is_main_rank_cy;\nint __pyx_module_is_main_rank_cy = 0;\n\n/* Implementation of 'rank_cy' */\nstatic PyObject *__pyx_builtin_range;\nstatic PyObject *__pyx_builtin_ImportError;\nstatic PyObject *__pyx_builtin_ValueError;\nstatic PyObject *__pyx_builtin_MemoryError;\nstatic PyObject *__pyx_builtin_enumerate;\nstatic PyObject *__pyx_builtin_TypeError;\nstatic PyObject *__pyx_builtin_Ellipsis;\nstatic PyObject *__pyx_builtin_id;\nstatic PyObject *__pyx_builtin_IndexError;\nstatic const char __pyx_k_O[] = \"O\";\nstatic const char __pyx_k_c[] = \"c\";\nstatic const char __pyx_k_id[] = \"id\";\nstatic const char __pyx_k_np[] = \"np\";\nstatic const char __pyx_k_end[] = \"end\";\nstatic const char __pyx_k_new[] = \"__new__\";\nstatic const char __pyx_k_obj[] = \"obj\";\nstatic const char __pyx_k_axis[] = \"axis\";\nstatic const char __pyx_k_base[] = \"base\";\nstatic const char __pyx_k_dict[] = \"__dict__\";\nstatic const char __pyx_k_file[] = \"file\";\nstatic const char __pyx_k_main[] = \"__main__\";\nstatic const char __pyx_k_mode[] = \"mode\";\nstatic const char __pyx_k_name[] = \"name\";\nstatic const char __pyx_k_ndim[] = \"ndim\";\nstatic const char __pyx_k_pack[] = \"pack\";\nstatic const char __pyx_k_size[] = \"size\";\nstatic const char __pyx_k_step[] = \"step\";\nstatic const char __pyx_k_stop[] = \"stop\";\nstatic const char __pyx_k_test[] = \"__test__\";\nstatic const char __pyx_k_ASCII[] = \"ASCII\";\nstatic const char __pyx_k_class[] = \"__class__\";\nstatic const char __pyx_k_dtype[] = \"dtype\";\nstatic const char __pyx_k_error[] = \"error\";\nstatic const char __pyx_k_flags[] = \"flags\";\nstatic const char __pyx_k_int64[] = \"int64\";\nstatic const char __pyx_k_items[] = \"items\";\nstatic const char __pyx_k_numpy[] = \"numpy\";\nstatic const char __pyx_k_print[] = \"print\";\nstatic const char __pyx_k_range[] = \"range\";\nstatic const char __pyx_k_shape[] = \"shape\";\nstatic const char __pyx_k_start[] = \"start\";\nstatic const char __pyx_k_zeros[] = \"zeros\";\nstatic const char __pyx_k_append[] = \"append\";\nstatic const char __pyx_k_astype[] = \"astype\";\nstatic const char __pyx_k_choice[] = \"choice\";\nstatic const char __pyx_k_encode[] = \"encode\";\nstatic const char __pyx_k_format[] = \"format\";\nstatic const char __pyx_k_g_pids[] = \"g_pids\";\nstatic const char __pyx_k_import[] = \"__import__\";\nstatic const char __pyx_k_name_2[] = \"__name__\";\nstatic const char __pyx_k_pickle[] = \"pickle\";\nstatic const char __pyx_k_q_pids[] = \"q_pids\";\nstatic const char __pyx_k_random[] = \"random\";\nstatic const char __pyx_k_reduce[] = \"__reduce__\";\nstatic const char __pyx_k_struct[] = \"struct\";\nstatic const char __pyx_k_unpack[] = \"unpack\";\nstatic const char __pyx_k_update[] = \"update\";\nstatic const char __pyx_k_argsort[] = \"argsort\";\nstatic const char __pyx_k_asarray[] = \"asarray\";\nstatic const char __pyx_k_distmat[] = \"distmat\";\nstatic const char __pyx_k_float32[] = \"float32\";\nstatic const char __pyx_k_fortran[] = \"fortran\";\nstatic const char __pyx_k_memview[] = \"memview\";\nstatic const char __pyx_k_newaxis[] = \"newaxis\";\nstatic const char __pyx_k_Ellipsis[] = \"Ellipsis\";\nstatic const char __pyx_k_g_camids[] = \"g_camids\";\nstatic const char __pyx_k_getstate[] = \"__getstate__\";\nstatic const char __pyx_k_itemsize[] = \"itemsize\";\nstatic const char __pyx_k_max_rank[] = \"max_rank\";\nstatic const char __pyx_k_pyx_type[] = \"__pyx_type\";\nstatic const char __pyx_k_q_camids[] = \"q_camids\";\nstatic const char __pyx_k_setstate[] = \"__setstate__\";\nstatic const char __pyx_k_TypeError[] = \"TypeError\";\nstatic const char __pyx_k_enumerate[] = \"enumerate\";\nstatic const char __pyx_k_pyx_state[] = \"__pyx_state\";\nstatic const char __pyx_k_reduce_ex[] = \"__reduce_ex__\";\nstatic const char __pyx_k_IndexError[] = \"IndexError\";\nstatic const char __pyx_k_ValueError[] = \"ValueError\";\nstatic const char __pyx_k_pyx_result[] = \"__pyx_result\";\nstatic const char __pyx_k_pyx_vtable[] = \"__pyx_vtable__\";\nstatic const char __pyx_k_ImportError[] = \"ImportError\";\nstatic const char __pyx_k_MemoryError[] = \"MemoryError\";\nstatic const char __pyx_k_PickleError[] = \"PickleError\";\nstatic const char __pyx_k_collections[] = \"collections\";\nstatic const char __pyx_k_defaultdict[] = \"defaultdict\";\nstatic const char __pyx_k_pyx_checksum[] = \"__pyx_checksum\";\nstatic const char __pyx_k_stringsource[] = \"stringsource\";\nstatic const char __pyx_k_pyx_getbuffer[] = \"__pyx_getbuffer\";\nstatic const char __pyx_k_reduce_cython[] = \"__reduce_cython__\";\nstatic const char __pyx_k_View_MemoryView[] = \"View.MemoryView\";\nstatic const char __pyx_k_allocate_buffer[] = \"allocate_buffer\";\nstatic const char __pyx_k_dtype_is_object[] = \"dtype_is_object\";\nstatic const char __pyx_k_pyx_PickleError[] = \"__pyx_PickleError\";\nstatic const char __pyx_k_setstate_cython[] = \"__setstate_cython__\";\nstatic const char __pyx_k_pyx_unpickle_Enum[] = \"__pyx_unpickle_Enum\";\nstatic const char __pyx_k_use_metric_cuhk03[] = \"use_metric_cuhk03\";\nstatic const char __pyx_k_cline_in_traceback[] = \"cline_in_traceback\";\nstatic const char __pyx_k_strided_and_direct[] = \"<strided and direct>\";\nstatic const char __pyx_k_strided_and_indirect[] = \"<strided and indirect>\";\nstatic const char __pyx_k_contiguous_and_direct[] = \"<contiguous and direct>\";\nstatic const char __pyx_k_MemoryView_of_r_object[] = \"<MemoryView of %r object>\";\nstatic const char __pyx_k_MemoryView_of_r_at_0x_x[] = \"<MemoryView of %r at 0x%x>\";\nstatic const char __pyx_k_contiguous_and_indirect[] = \"<contiguous and indirect>\";\nstatic const char __pyx_k_Cannot_index_with_type_s[] = \"Cannot index with type '%s'\";\nstatic const char __pyx_k_Invalid_shape_in_axis_d_d[] = \"Invalid shape in axis %d: %d.\";\nstatic const char __pyx_k_itemsize_0_for_cython_array[] = \"itemsize <= 0 for cython.array\";\nstatic const char __pyx_k_unable_to_allocate_array_data[] = \"unable to allocate array data.\";\nstatic const char __pyx_k_strided_and_direct_or_indirect[] = \"<strided and direct or indirect>\";\nstatic const char __pyx_k_numpy_core_multiarray_failed_to[] = \"numpy.core.multiarray failed to import\";\nstatic const char __pyx_k_Buffer_view_does_not_expose_stri[] = \"Buffer view does not expose strides\";\nstatic const char __pyx_k_Can_only_create_a_buffer_that_is[] = \"Can only create a buffer that is contiguous in memory.\";\nstatic const char __pyx_k_Cannot_assign_to_read_only_memor[] = \"Cannot assign to read-only memoryview\";\nstatic const char __pyx_k_Cannot_create_writable_memory_vi[] = \"Cannot create writable memory view from read-only memoryview\";\nstatic const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = \"Empty shape tuple for cython.array\";\nstatic const char __pyx_k_Error_all_query_identities_do_no[] = \"Error: all query identities do not appear in gallery\";\nstatic const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = \"Incompatible checksums (0x%x vs (0xb068931, 0x82a3537, 0x6ae9995) = (name))\";\nstatic const char __pyx_k_Indirect_dimensions_not_supporte[] = \"Indirect dimensions not supported\";\nstatic const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = \"Invalid mode, expected 'c' or 'fortran', got %s\";\nstatic const char __pyx_k_Note_number_of_gallery_samples_i[] = \"Note: number of gallery samples is quite small, got {}\";\nstatic const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = \"Out of bounds on buffer access (axis %d)\";\nstatic const char __pyx_k_Unable_to_convert_item_to_object[] = \"Unable to convert item to object\";\nstatic const char __pyx_k_got_differing_extents_in_dimensi[] = \"got differing extents in dimension %d (got %d and %d)\";\nstatic const char __pyx_k_no_default___reduce___due_to_non[] = \"no default __reduce__ due to non-trivial __cinit__\";\nstatic const char __pyx_k_numpy_core_umath_failed_to_impor[] = \"numpy.core.umath failed to import\";\nstatic const char __pyx_k_unable_to_allocate_shape_and_str[] = \"unable to allocate shape and strides.\";\nstatic PyObject *__pyx_n_s_ASCII;\nstatic PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;\nstatic PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;\nstatic PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor;\nstatic PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi;\nstatic PyObject *__pyx_kp_s_Cannot_index_with_type_s;\nstatic PyObject *__pyx_n_s_Ellipsis;\nstatic PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;\nstatic PyObject *__pyx_kp_s_Error_all_query_identities_do_no;\nstatic PyObject *__pyx_n_s_ImportError;\nstatic PyObject *__pyx_kp_s_Incompatible_checksums_0x_x_vs_0;\nstatic PyObject *__pyx_n_s_IndexError;\nstatic PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;\nstatic PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr;\nstatic PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d;\nstatic PyObject *__pyx_n_s_MemoryError;\nstatic PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;\nstatic PyObject *__pyx_kp_s_MemoryView_of_r_object;\nstatic PyObject *__pyx_kp_s_Note_number_of_gallery_samples_i;\nstatic PyObject *__pyx_n_b_O;\nstatic PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;\nstatic PyObject *__pyx_n_s_PickleError;\nstatic PyObject *__pyx_n_s_TypeError;\nstatic PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;\nstatic PyObject *__pyx_n_s_ValueError;\nstatic PyObject *__pyx_n_s_View_MemoryView;\nstatic PyObject *__pyx_n_s_allocate_buffer;\nstatic PyObject *__pyx_n_s_append;\nstatic PyObject *__pyx_n_s_argsort;\nstatic PyObject *__pyx_n_s_asarray;\nstatic PyObject *__pyx_n_s_astype;\nstatic PyObject *__pyx_n_s_axis;\nstatic PyObject *__pyx_n_s_base;\nstatic PyObject *__pyx_n_s_c;\nstatic PyObject *__pyx_n_u_c;\nstatic PyObject *__pyx_n_s_choice;\nstatic PyObject *__pyx_n_s_class;\nstatic PyObject *__pyx_n_s_cline_in_traceback;\nstatic PyObject *__pyx_n_s_collections;\nstatic PyObject *__pyx_kp_s_contiguous_and_direct;\nstatic PyObject *__pyx_kp_s_contiguous_and_indirect;\nstatic PyObject *__pyx_n_s_defaultdict;\nstatic PyObject *__pyx_n_s_dict;\nstatic PyObject *__pyx_n_s_distmat;\nstatic PyObject *__pyx_n_s_dtype;\nstatic PyObject *__pyx_n_s_dtype_is_object;\nstatic PyObject *__pyx_n_s_encode;\nstatic PyObject *__pyx_n_s_end;\nstatic PyObject *__pyx_n_s_enumerate;\nstatic PyObject *__pyx_n_s_error;\nstatic PyObject *__pyx_n_s_file;\nstatic PyObject *__pyx_n_s_flags;\nstatic PyObject *__pyx_n_s_float32;\nstatic PyObject *__pyx_n_s_format;\nstatic PyObject *__pyx_n_s_fortran;\nstatic PyObject *__pyx_n_u_fortran;\nstatic PyObject *__pyx_n_s_g_camids;\nstatic PyObject *__pyx_n_s_g_pids;\nstatic PyObject *__pyx_n_s_getstate;\nstatic PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;\nstatic PyObject *__pyx_n_s_id;\nstatic PyObject *__pyx_n_s_import;\nstatic PyObject *__pyx_n_s_int64;\nstatic PyObject *__pyx_n_s_items;\nstatic PyObject *__pyx_n_s_itemsize;\nstatic PyObject *__pyx_kp_s_itemsize_0_for_cython_array;\nstatic PyObject *__pyx_n_s_main;\nstatic PyObject *__pyx_n_s_max_rank;\nstatic PyObject *__pyx_n_s_memview;\nstatic PyObject *__pyx_n_s_mode;\nstatic PyObject *__pyx_n_s_name;\nstatic PyObject *__pyx_n_s_name_2;\nstatic PyObject *__pyx_n_s_ndim;\nstatic PyObject *__pyx_n_s_new;\nstatic PyObject *__pyx_n_s_newaxis;\nstatic PyObject *__pyx_kp_s_no_default___reduce___due_to_non;\nstatic PyObject *__pyx_n_s_np;\nstatic PyObject *__pyx_n_s_numpy;\nstatic PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to;\nstatic PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor;\nstatic PyObject *__pyx_n_s_obj;\nstatic PyObject *__pyx_n_s_pack;\nstatic PyObject *__pyx_n_s_pickle;\nstatic PyObject *__pyx_n_s_print;\nstatic PyObject *__pyx_n_s_pyx_PickleError;\nstatic PyObject *__pyx_n_s_pyx_checksum;\nstatic PyObject *__pyx_n_s_pyx_getbuffer;\nstatic PyObject *__pyx_n_s_pyx_result;\nstatic PyObject *__pyx_n_s_pyx_state;\nstatic PyObject *__pyx_n_s_pyx_type;\nstatic PyObject *__pyx_n_s_pyx_unpickle_Enum;\nstatic PyObject *__pyx_n_s_pyx_vtable;\nstatic PyObject *__pyx_n_s_q_camids;\nstatic PyObject *__pyx_n_s_q_pids;\nstatic PyObject *__pyx_n_s_random;\nstatic PyObject *__pyx_n_s_range;\nstatic PyObject *__pyx_n_s_reduce;\nstatic PyObject *__pyx_n_s_reduce_cython;\nstatic PyObject *__pyx_n_s_reduce_ex;\nstatic PyObject *__pyx_n_s_setstate;\nstatic PyObject *__pyx_n_s_setstate_cython;\nstatic PyObject *__pyx_n_s_shape;\nstatic PyObject *__pyx_n_s_size;\nstatic PyObject *__pyx_n_s_start;\nstatic PyObject *__pyx_n_s_step;\nstatic PyObject *__pyx_n_s_stop;\nstatic PyObject *__pyx_kp_s_strided_and_direct;\nstatic PyObject *__pyx_kp_s_strided_and_direct_or_indirect;\nstatic PyObject *__pyx_kp_s_strided_and_indirect;\nstatic PyObject *__pyx_kp_s_stringsource;\nstatic PyObject *__pyx_n_s_struct;\nstatic PyObject *__pyx_n_s_test;\nstatic PyObject *__pyx_kp_s_unable_to_allocate_array_data;\nstatic PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str;\nstatic PyObject *__pyx_n_s_unpack;\nstatic PyObject *__pyx_n_s_update;\nstatic PyObject *__pyx_n_s_use_metric_cuhk03;\nstatic PyObject *__pyx_n_s_zeros;\nstatic PyObject *__pyx_pf_7rank_cy_evaluate_cy(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_distmat, PyObject *__pyx_v_q_pids, PyObject *__pyx_v_g_pids, PyObject *__pyx_v_q_camids, PyObject *__pyx_v_g_camids, PyObject *__pyx_v_max_rank, PyObject *__pyx_v_use_metric_cuhk03); /* proto */\nstatic PyObject *__pyx_pf_7rank_cy_2eval_cuhk03_cy(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_distmat, __Pyx_memviewslice __pyx_v_q_pids, __Pyx_memviewslice __pyx_v_g_pids, __Pyx_memviewslice __pyx_v_q_camids, __Pyx_memviewslice __pyx_v_g_camids, long __pyx_v_max_rank); /* proto */\nstatic PyObject *__pyx_pf_7rank_cy_4eval_market1501_cy(CYTHON_UNUSED PyObject *__pyx_self, 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camid with query\n *         for g_idx in range(num_g):\n *             if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid):             # <<<<<<<<<<<<<<\n *                 raw_cmc[num_g_real] = matches[g_idx]\n *                 num_g_real += 1\n */\n      }\n    }\n\n    /* \"rank_cy.pyx\":206\n *                     meet_condition = 1\n * \n *         if not meet_condition:             # <<<<<<<<<<<<<<\n *             # this condition is true when query identity does not appear in gallery\n *             continue\n */\n    __pyx_t_1 = ((!(__pyx_v_meet_condition != 0)) != 0);\n    if (__pyx_t_1) {\n\n      /* \"rank_cy.pyx\":208\n *         if not meet_condition:\n *             # this condition is true when query identity does not appear in gallery\n *             continue             # <<<<<<<<<<<<<<\n * \n *         # compute cmc\n */\n      goto __pyx_L4_continue;\n\n      /* \"rank_cy.pyx\":206\n *                     meet_condition = 1\n * \n *         if not meet_condition:             # <<<<<<<<<<<<<<\n *             # this condition is true when query identity does not appear in gallery\n *             continue\n */\n    }\n\n    /* \"rank_cy.pyx\":211\n * \n *         # compute cmc\n *         function_cumsum(raw_cmc, cmc, num_g_real)             # <<<<<<<<<<<<<<\n *         # compute mean inverse negative penalty\n *         # reference : https://github.com/mangye16/ReID-Survey/blob/master/utils/reid_metric.py\n */\n    __pyx_fuse_3__pyx_f_7rank_cy_function_cumsum(__pyx_v_raw_cmc, __pyx_v_cmc, __pyx_v_num_g_real);\n\n    /* \"rank_cy.pyx\":214\n *         # compute mean inverse negative penalty\n *         # reference : https://github.com/mangye16/ReID-Survey/blob/master/utils/reid_metric.py\n *         max_pos_idx = 0             # <<<<<<<<<<<<<<\n *         for g_idx in range(num_g_real):\n *             if (raw_cmc[g_idx] == 1) and (g_idx > max_pos_idx):\n */\n    __pyx_v_max_pos_idx = 0;\n\n    /* \"rank_cy.pyx\":215\n *         # reference : https://github.com/mangye16/ReID-Survey/blob/master/utils/reid_metric.py\n *         max_pos_idx = 0\n *         for g_idx in range(num_g_real):             # <<<<<<<<<<<<<<\n *             if (raw_cmc[g_idx] == 1) and (g_idx > max_pos_idx):\n *                 max_pos_idx = g_idx\n */\n    __pyx_t_15 = __pyx_v_num_g_real;\n    __pyx_t_16 = __pyx_t_15;\n    for (__pyx_t_17 = 0; __pyx_t_17 < __pyx_t_16; __pyx_t_17+=1) {\n      __pyx_v_g_idx = __pyx_t_17;\n\n      /* \"rank_cy.pyx\":216\n *         max_pos_idx = 0\n *         for g_idx in range(num_g_real):\n *             if (raw_cmc[g_idx] == 1) and (g_idx > max_pos_idx):             # <<<<<<<<<<<<<<\n *                 max_pos_idx = g_idx\n *         inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)\n */\n      __pyx_t_18 = __pyx_v_g_idx;\n      __pyx_t_21 = (((*((float *) ( /* dim=0 */ (__pyx_v_raw_cmc.data + __pyx_t_18 * __pyx_v_raw_cmc.strides[0]) ))) == 1.0) != 0);\n      if (__pyx_t_21) {\n      } else {\n        __pyx_t_1 = __pyx_t_21;\n        goto __pyx_L18_bool_binop_done;\n      }\n      __pyx_t_21 = ((__pyx_v_g_idx > __pyx_v_max_pos_idx) != 0);\n      __pyx_t_1 = __pyx_t_21;\n      __pyx_L18_bool_binop_done:;\n      if (__pyx_t_1) {\n\n        /* \"rank_cy.pyx\":217\n *         for g_idx in range(num_g_real):\n *             if (raw_cmc[g_idx] == 1) and (g_idx > max_pos_idx):\n *                 max_pos_idx = g_idx             # <<<<<<<<<<<<<<\n *         inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)\n *         all_INP[valid_index] = inp\n */\n        __pyx_v_max_pos_idx = __pyx_v_g_idx;\n\n        /* \"rank_cy.pyx\":216\n *         max_pos_idx = 0\n *         for g_idx in range(num_g_real):\n *             if (raw_cmc[g_idx] == 1) and (g_idx > max_pos_idx):             # <<<<<<<<<<<<<<\n *                 max_pos_idx = g_idx\n *         inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)\n */\n      }\n    }\n\n    /* \"rank_cy.pyx\":218\n *             if (raw_cmc[g_idx] == 1) and (g_idx > max_pos_idx):\n *                 max_pos_idx = g_idx\n *         inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)             # <<<<<<<<<<<<<<\n *         all_INP[valid_index] = inp\n * \n */\n    __pyx_t_18 = __pyx_v_max_pos_idx;\n    __pyx_v_inp = ((*((float *) ( /* dim=0 */ (__pyx_v_cmc.data + __pyx_t_18 * __pyx_v_cmc.strides[0]) ))) / (__pyx_v_max_pos_idx + 1.0));\n\n    /* \"rank_cy.pyx\":219\n *                 max_pos_idx = g_idx\n *         inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)\n *         all_INP[valid_index] = inp             # <<<<<<<<<<<<<<\n * \n *         for g_idx in range(num_g_real):\n */\n    __pyx_t_18 = __pyx_v_valid_index;\n    *((float *) ( /* dim=0 */ (__pyx_v_all_INP.data + __pyx_t_18 * __pyx_v_all_INP.strides[0]) )) = __pyx_v_inp;\n\n    /* \"rank_cy.pyx\":221\n *         all_INP[valid_index] = inp\n * \n *         for g_idx in range(num_g_real):             # <<<<<<<<<<<<<<\n *             if cmc[g_idx] > 1:\n *                 cmc[g_idx] = 1\n */\n    __pyx_t_15 = __pyx_v_num_g_real;\n    __pyx_t_16 = __pyx_t_15;\n    for (__pyx_t_17 = 0; __pyx_t_17 < __pyx_t_16; __pyx_t_17+=1) {\n      __pyx_v_g_idx = __pyx_t_17;\n\n      /* \"rank_cy.pyx\":222\n * \n *         for g_idx in range(num_g_real):\n *             if cmc[g_idx] > 1:             # <<<<<<<<<<<<<<\n *                 cmc[g_idx] = 1\n * \n */\n      __pyx_t_18 = __pyx_v_g_idx;\n      __pyx_t_1 = (((*((float *) ( /* dim=0 */ (__pyx_v_cmc.data + __pyx_t_18 * __pyx_v_cmc.strides[0]) ))) > 1.0) != 0);\n      if (__pyx_t_1) {\n\n        /* \"rank_cy.pyx\":223\n *         for g_idx in range(num_g_real):\n *             if cmc[g_idx] > 1:\n *                 cmc[g_idx] = 1             # <<<<<<<<<<<<<<\n * \n *         for rank_idx in range(max_rank):\n */\n        __pyx_t_18 = __pyx_v_g_idx;\n        *((float *) ( /* dim=0 */ (__pyx_v_cmc.data + __pyx_t_18 * __pyx_v_cmc.strides[0]) )) = 1.0;\n\n        /* \"rank_cy.pyx\":222\n * \n *         for g_idx in range(num_g_real):\n *             if cmc[g_idx] > 1:             # <<<<<<<<<<<<<<\n *                 cmc[g_idx] = 1\n * \n */\n      }\n    }\n\n    /* \"rank_cy.pyx\":225\n *                 cmc[g_idx] = 1\n * \n *         for rank_idx in range(max_rank):             # <<<<<<<<<<<<<<\n *             all_cmc[q_idx, rank_idx] = cmc[rank_idx]\n *         num_valid_q += 1.\n */\n    __pyx_t_15 = __pyx_v_max_rank;\n    __pyx_t_16 = __pyx_t_15;\n    for (__pyx_t_17 = 0; __pyx_t_17 < __pyx_t_16; __pyx_t_17+=1) {\n      __pyx_v_rank_idx = __pyx_t_17;\n\n      /* \"rank_cy.pyx\":226\n * \n *         for rank_idx in range(max_rank):\n *             all_cmc[q_idx, rank_idx] = cmc[rank_idx]             # <<<<<<<<<<<<<<\n *         num_valid_q += 1.\n * \n */\n      __pyx_t_18 = __pyx_v_rank_idx;\n      __pyx_t_14 = __pyx_v_q_idx;\n      __pyx_t_19 = __pyx_v_rank_idx;\n      *((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_all_cmc.data + __pyx_t_14 * __pyx_v_all_cmc.strides[0]) ) + __pyx_t_19 * __pyx_v_all_cmc.strides[1]) )) = (*((float *) ( /* dim=0 */ (__pyx_v_cmc.data + __pyx_t_18 * __pyx_v_cmc.strides[0]) )));\n    }\n\n    /* \"rank_cy.pyx\":227\n *         for rank_idx in range(max_rank):\n *             all_cmc[q_idx, rank_idx] = cmc[rank_idx]\n *         num_valid_q += 1.             # <<<<<<<<<<<<<<\n * \n *         # compute average precision\n */\n    __pyx_v_num_valid_q = (__pyx_v_num_valid_q + 1.);\n\n    /* \"rank_cy.pyx\":231\n *         # compute average precision\n *         # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision\n *         function_cumsum(raw_cmc, tmp_cmc, num_g_real)             # <<<<<<<<<<<<<<\n *         num_rel = 0\n *         tmp_cmc_sum = 0\n */\n    __pyx_fuse_3__pyx_f_7rank_cy_function_cumsum(__pyx_v_raw_cmc, __pyx_v_tmp_cmc, __pyx_v_num_g_real);\n\n    /* \"rank_cy.pyx\":232\n *         # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision\n *         function_cumsum(raw_cmc, tmp_cmc, num_g_real)\n *         num_rel = 0             # <<<<<<<<<<<<<<\n *         tmp_cmc_sum = 0\n *         for g_idx in range(num_g_real):\n */\n    __pyx_v_num_rel = 0.0;\n\n    /* \"rank_cy.pyx\":233\n *         function_cumsum(raw_cmc, tmp_cmc, num_g_real)\n *         num_rel = 0\n *         tmp_cmc_sum = 0             # <<<<<<<<<<<<<<\n *         for g_idx in range(num_g_real):\n *             tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]\n */\n    __pyx_v_tmp_cmc_sum = 0.0;\n\n    /* \"rank_cy.pyx\":234\n *         num_rel = 0\n *         tmp_cmc_sum = 0\n *         for g_idx in range(num_g_real):             # <<<<<<<<<<<<<<\n *             tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]\n *             num_rel += raw_cmc[g_idx]\n */\n    __pyx_t_15 = __pyx_v_num_g_real;\n    __pyx_t_16 = __pyx_t_15;\n    for (__pyx_t_17 = 0; __pyx_t_17 < __pyx_t_16; __pyx_t_17+=1) {\n      __pyx_v_g_idx = __pyx_t_17;\n\n      /* \"rank_cy.pyx\":235\n *         tmp_cmc_sum = 0\n *         for g_idx in range(num_g_real):\n *             tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]             # <<<<<<<<<<<<<<\n *             num_rel += raw_cmc[g_idx]\n *         all_AP[valid_index] = tmp_cmc_sum / num_rel\n */\n      __pyx_t_18 = __pyx_v_g_idx;\n      __pyx_t_19 = __pyx_v_g_idx;\n      __pyx_v_tmp_cmc_sum = (__pyx_v_tmp_cmc_sum + (((*((float *) ( /* dim=0 */ (__pyx_v_tmp_cmc.data + __pyx_t_18 * __pyx_v_tmp_cmc.strides[0]) ))) / (__pyx_v_g_idx + 1.)) * (*((float *) ( /* dim=0 */ (__pyx_v_raw_cmc.data + __pyx_t_19 * __pyx_v_raw_cmc.strides[0]) )))));\n\n      /* \"rank_cy.pyx\":236\n *         for g_idx in range(num_g_real):\n *             tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]\n *             num_rel += raw_cmc[g_idx]             # <<<<<<<<<<<<<<\n *         all_AP[valid_index] = tmp_cmc_sum / num_rel\n *         valid_index += 1\n */\n      __pyx_t_19 = __pyx_v_g_idx;\n      __pyx_v_num_rel = (__pyx_v_num_rel + (*((float *) ( /* dim=0 */ (__pyx_v_raw_cmc.data + __pyx_t_19 * __pyx_v_raw_cmc.strides[0]) ))));\n    }\n\n    /* \"rank_cy.pyx\":237\n *             tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]\n *             num_rel += raw_cmc[g_idx]\n *         all_AP[valid_index] = tmp_cmc_sum / num_rel             # <<<<<<<<<<<<<<\n *         valid_index += 1\n * \n */\n    __pyx_t_19 = __pyx_v_valid_index;\n    *((float *) ( /* dim=0 */ (__pyx_v_all_AP.data + __pyx_t_19 * __pyx_v_all_AP.strides[0]) )) = (__pyx_v_tmp_cmc_sum / __pyx_v_num_rel);\n\n    /* \"rank_cy.pyx\":238\n *             num_rel += raw_cmc[g_idx]\n *         all_AP[valid_index] = tmp_cmc_sum / num_rel\n *         valid_index += 1             # <<<<<<<<<<<<<<\n * \n *     assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'\n */\n    __pyx_v_valid_index = (__pyx_v_valid_index + 1);\n    __pyx_L4_continue:;\n  }\n\n  /* 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global __pyx_memoryview_thread_locks_used\n *             if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]             # <<<<<<<<<<<<<<\n *                 __pyx_memoryview_thread_locks_used += 1\n *             if self.lock is NULL:\n */\n      __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]);\n\n      /* \"View.MemoryView\":359\n *             if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]\n *                 __pyx_memoryview_thread_locks_used += 1             # <<<<<<<<<<<<<<\n *             if self.lock is NULL:\n *                 self.lock = PyThread_allocate_lock()\n */\n      __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1);\n\n      /* \"View.MemoryView\":357\n *         if not __PYX_CYTHON_ATOMICS_ENABLED():\n *             global __pyx_memoryview_thread_locks_used\n *             if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:             # <<<<<<<<<<<<<<\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]\n *                 __pyx_memoryview_thread_locks_used += 1\n */\n    }\n\n    /* \"View.MemoryView\":360\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]\n *                 __pyx_memoryview_thread_locks_used += 1\n *             if self.lock is NULL:             # <<<<<<<<<<<<<<\n *                 self.lock = PyThread_allocate_lock()\n *                 if self.lock is NULL:\n */\n    __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0);\n    if (__pyx_t_1) {\n\n      /* \"View.MemoryView\":361\n *                 __pyx_memoryview_thread_locks_used += 1\n *             if self.lock is NULL:\n *                 self.lock = PyThread_allocate_lock()             # <<<<<<<<<<<<<<\n *                 if self.lock is NULL:\n *                     raise MemoryError\n */\n      __pyx_v_self->lock = PyThread_allocate_lock();\n\n      /* \"View.MemoryView\":362\n *             if self.lock is NULL:\n *                 self.lock = PyThread_allocate_lock()\n *                 if self.lock is NULL:             # <<<<<<<<<<<<<<\n *                     raise MemoryError\n * \n */\n      __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0);\n      if (unlikely(__pyx_t_1)) {\n\n        /* \"View.MemoryView\":363\n *                 self.lock = PyThread_allocate_lock()\n *                 if self.lock is NULL:\n *                     raise MemoryError             # <<<<<<<<<<<<<<\n * \n *         if flags & PyBUF_FORMAT:\n */\n        PyErr_NoMemory(); __PYX_ERR(2, 363, __pyx_L1_error)\n\n        /* \"View.MemoryView\":362\n *             if self.lock is NULL:\n *                 self.lock = PyThread_allocate_lock()\n *                 if self.lock is NULL:             # <<<<<<<<<<<<<<\n *                     raise MemoryError\n * \n */\n      }\n\n      /* \"View.MemoryView\":360\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]\n *                 __pyx_memoryview_thread_locks_used += 1\n *             if self.lock is NULL:             # <<<<<<<<<<<<<<\n *                 self.lock = PyThread_allocate_lock()\n *                 if self.lock is NULL:\n */\n    }\n\n    /* \"View.MemoryView\":355\n *                 Py_INCREF(Py_None)\n * \n *         if not __PYX_CYTHON_ATOMICS_ENABLED():             # <<<<<<<<<<<<<<\n *             global __pyx_memoryview_thread_locks_used\n *             if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:\n */\n  }\n\n  /* \"View.MemoryView\":365\n *                     raise MemoryError\n * \n *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<\n *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\\0')\n *         else:\n */\n  __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":366\n * \n *         if flags & PyBUF_FORMAT:\n *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\\0')             # <<<<<<<<<<<<<<\n *         else:\n *             self.dtype_is_object = dtype_is_object\n */\n    __pyx_t_2 = (((__pyx_v_self->view.format[0]) == 'O') != 0);\n    if (__pyx_t_2) {\n    } else {\n      __pyx_t_1 = __pyx_t_2;\n      goto __pyx_L12_bool_binop_done;\n    }\n    __pyx_t_2 = (((__pyx_v_self->view.format[1]) == '\\x00') != 0);\n    __pyx_t_1 = __pyx_t_2;\n    __pyx_L12_bool_binop_done:;\n    __pyx_v_self->dtype_is_object = __pyx_t_1;\n\n    /* \"View.MemoryView\":365\n *                     raise MemoryError\n * \n *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<\n *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\\0')\n *         else:\n */\n    goto __pyx_L11;\n  }\n\n  /* \"View.MemoryView\":368\n *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\\0')\n *         else:\n *             self.dtype_is_object = dtype_is_object             # <<<<<<<<<<<<<<\n * \n *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(\n */\n  /*else*/ {\n    __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object;\n  }\n  __pyx_L11:;\n\n  /* \"View.MemoryView\":370\n *             self.dtype_is_object = dtype_is_object\n * \n *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(             # <<<<<<<<<<<<<<\n *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))\n *         self.typeinfo = NULL\n */\n  __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int))));\n\n  /* \"View.MemoryView\":372\n *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(\n *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))\n *         self.typeinfo = NULL             # <<<<<<<<<<<<<<\n * \n *     def __dealloc__(memoryview self):\n */\n  __pyx_v_self->typeinfo = NULL;\n\n  /* \"View.MemoryView\":346\n *     cdef __Pyx_TypeInfo *typeinfo\n * \n *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):             # <<<<<<<<<<<<<<\n *         self.obj = obj\n *         self.flags = flags\n */\n\n  /* function exit code */\n  __pyx_r = 0;\n  goto __pyx_L0;\n  __pyx_L1_error:;\n  __Pyx_AddTraceback(\"View.MemoryView.memoryview.__cinit__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n  __pyx_r = -1;\n  __pyx_L0:;\n  __Pyx_RefNannyFinishContext();\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":374\n *       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__Pyx_RefNannySetupContext(\"__dealloc__\", 0);\n\n  /* \"View.MemoryView\":375\n * \n *     def __dealloc__(memoryview self):\n *         if self.obj is not None:             # <<<<<<<<<<<<<<\n *             __Pyx_ReleaseBuffer(&self.view)\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:\n */\n  __pyx_t_1 = (__pyx_v_self->obj != Py_None);\n  __pyx_t_2 = (__pyx_t_1 != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":376\n *     def __dealloc__(memoryview self):\n *         if self.obj is not None:\n *             __Pyx_ReleaseBuffer(&self.view)             # <<<<<<<<<<<<<<\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:\n * \n */\n    __Pyx_ReleaseBuffer((&__pyx_v_self->view));\n\n    /* \"View.MemoryView\":375\n * \n *     def __dealloc__(memoryview self):\n *         if self.obj is not None:             # <<<<<<<<<<<<<<\n *             __Pyx_ReleaseBuffer(&self.view)\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:\n */\n    goto __pyx_L3;\n  }\n\n  /* \"View.MemoryView\":377\n *         if self.obj is not None:\n *             __Pyx_ReleaseBuffer(&self.view)\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:             # <<<<<<<<<<<<<<\n * \n *             (<__pyx_buffer *> &self.view).obj = NULL\n */\n  __pyx_t_2 = ((((Py_buffer *)(&__pyx_v_self->view))->obj == Py_None) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":379\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:\n * \n *             (<__pyx_buffer *> &self.view).obj = NULL             # <<<<<<<<<<<<<<\n *             Py_DECREF(Py_None)\n * \n */\n    ((Py_buffer *)(&__pyx_v_self->view))->obj = NULL;\n\n    /* \"View.MemoryView\":380\n * \n *             (<__pyx_buffer *> &self.view).obj = NULL\n *             Py_DECREF(Py_None)             # <<<<<<<<<<<<<<\n * \n *         cdef int i\n */\n    Py_DECREF(Py_None);\n\n    /* \"View.MemoryView\":377\n *         if self.obj is not None:\n *             __Pyx_ReleaseBuffer(&self.view)\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:             # <<<<<<<<<<<<<<\n * \n *             (<__pyx_buffer *> &self.view).obj = NULL\n */\n  }\n  __pyx_L3:;\n\n  /* \"View.MemoryView\":384\n *         cdef int i\n *         global __pyx_memoryview_thread_locks_used\n *         if self.lock != NULL:             # <<<<<<<<<<<<<<\n *             for i in range(__pyx_memoryview_thread_locks_used):\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:\n */\n  __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":385\n *         global __pyx_memoryview_thread_locks_used\n *         if self.lock != NULL:\n *             for i in range(__pyx_memoryview_thread_locks_used):             # <<<<<<<<<<<<<<\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:\n *                     __pyx_memoryview_thread_locks_used -= 1\n */\n    __pyx_t_3 = __pyx_memoryview_thread_locks_used;\n    __pyx_t_4 = __pyx_t_3;\n    for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {\n      __pyx_v_i = __pyx_t_5;\n\n      /* \"View.MemoryView\":386\n *         if self.lock != NULL:\n *             for i in range(__pyx_memoryview_thread_locks_used):\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:             # <<<<<<<<<<<<<<\n *                     __pyx_memoryview_thread_locks_used -= 1\n *                     if i != __pyx_memoryview_thread_locks_used:\n */\n      __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":387\n *             for i in range(__pyx_memoryview_thread_locks_used):\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:\n *                     __pyx_memoryview_thread_locks_used -= 1             # <<<<<<<<<<<<<<\n *                     if i != __pyx_memoryview_thread_locks_used:\n *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (\n */\n        __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1);\n\n        /* \"View.MemoryView\":388\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:\n *                     __pyx_memoryview_thread_locks_used -= 1\n *                     if i != __pyx_memoryview_thread_locks_used:             # <<<<<<<<<<<<<<\n *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (\n *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])\n */\n        __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0);\n        if (__pyx_t_2) {\n\n          /* \"View.MemoryView\":390\n *                     if i != __pyx_memoryview_thread_locks_used:\n *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (\n *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])             # <<<<<<<<<<<<<<\n *                     break\n *             else:\n */\n          __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]);\n          __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]);\n\n          /* \"View.MemoryView\":389\n *                     __pyx_memoryview_thread_locks_used -= 1\n *                     if i != __pyx_memoryview_thread_locks_used:\n *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (             # <<<<<<<<<<<<<<\n *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])\n *                     break\n */\n          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__pyx_memoryview_thread_locks[i])\n *                     break             # <<<<<<<<<<<<<<\n *             else:\n *                 PyThread_free_lock(self.lock)\n */\n        goto __pyx_L6_break;\n\n        /* \"View.MemoryView\":386\n *         if self.lock != NULL:\n *             for i in range(__pyx_memoryview_thread_locks_used):\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:             # <<<<<<<<<<<<<<\n *                     __pyx_memoryview_thread_locks_used -= 1\n *                     if i != __pyx_memoryview_thread_locks_used:\n */\n      }\n    }\n    /*else*/ {\n\n      /* \"View.MemoryView\":393\n *                     break\n *             else:\n *                 PyThread_free_lock(self.lock)             # <<<<<<<<<<<<<<\n * \n *     cdef char *get_item_pointer(memoryview self, object index) except NULL:\n */\n      PyThread_free_lock(__pyx_v_self->lock);\n    }\n    __pyx_L6_break:;\n\n    /* \"View.MemoryView\":384\n *         cdef int i\n 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__Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 768, __pyx_L1_error)\n      __Pyx_GOTREF(__pyx_t_9);\n      __pyx_t_1 = (__pyx_t_9 != Py_None);\n      __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;\n      __pyx_v_have_step = __pyx_t_1;\n\n      /* \"View.MemoryView\":770\n *             have_step = index.step is not None\n * \n *             slice_memviewslice(             # <<<<<<<<<<<<<<\n *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],\n *                 dim, new_ndim, p_suboffset_dim,\n */\n      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 770, __pyx_L1_error)\n\n      /* \"View.MemoryView\":776\n *                 have_start, have_stop, have_step,\n *                 True)\n *             new_ndim += 1             # <<<<<<<<<<<<<<\n * \n *     if isinstance(memview, _memoryviewslice):\n */\n      __pyx_v_new_ndim = (__pyx_v_new_ndim + 1);\n    }\n    __pyx_L6:;\n\n    /* \"View.MemoryView\":748\n *     cdef bint have_start, have_stop, have_step\n * \n *     for dim, index in enumerate(indices):             # <<<<<<<<<<<<<<\n *         if PyIndex_Check(index):\n *             slice_memviewslice(\n */\n  }\n  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;\n\n  /* \"View.MemoryView\":778\n *             new_ndim += 1\n * \n *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<\n *         return memoryview_fromslice(dst, new_ndim,\n *                                     memviewsliceobj.to_object_func,\n */\n  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); \n  __pyx_t_2 = (__pyx_t_1 != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":779\n * \n *     if isinstance(memview, _memoryviewslice):\n *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<\n *                                     memviewsliceobj.to_object_func,\n *                                     memviewsliceobj.to_dtype_func,\n */\n    __Pyx_XDECREF(((PyObject *)__pyx_r));\n\n    /* \"View.MemoryView\":780\n *     if isinstance(memview, _memoryviewslice):\n *         return memoryview_fromslice(dst, new_ndim,\n *                                     memviewsliceobj.to_object_func,             # <<<<<<<<<<<<<<\n *                                     memviewsliceobj.to_dtype_func,\n *                                     memview.dtype_is_object)\n */\n    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError(\"memviewsliceobj\"); __PYX_ERR(2, 780, __pyx_L1_error) }\n\n    /* \"View.MemoryView\":781\n *         return memoryview_fromslice(dst, new_ndim,\n *                                     memviewsliceobj.to_object_func,\n *                                     memviewsliceobj.to_dtype_func,             # <<<<<<<<<<<<<<\n *                                     memview.dtype_is_object)\n *     else:\n */\n    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError(\"memviewsliceobj\"); __PYX_ERR(2, 781, __pyx_L1_error) }\n\n    /* \"View.MemoryView\":779\n * \n *     if isinstance(memview, _memoryviewslice):\n *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<\n *                                     memviewsliceobj.to_object_func,\n *                                     memviewsliceobj.to_dtype_func,\n */\n    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 779, __pyx_L1_error)\n    __Pyx_GOTREF(__pyx_t_3);\n    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 779, __pyx_L1_error)\n    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);\n    __pyx_t_3 = 0;\n    goto __pyx_L0;\n\n    /* \"View.MemoryView\":778\n *             new_ndim += 1\n * \n *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<\n *         return memoryview_fromslice(dst, new_ndim,\n *                                     memviewsliceobj.to_object_func,\n */\n  }\n\n  /* \"View.MemoryView\":784\n *                                     memview.dtype_is_object)\n *     else:\n *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<\n *                                     memview.dtype_is_object)\n * \n */\n  /*else*/ {\n    __Pyx_XDECREF(((PyObject *)__pyx_r));\n\n    /* \"View.MemoryView\":785\n *     else:\n *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,\n *                                     memview.dtype_is_object)             # <<<<<<<<<<<<<<\n * \n * \n */\n    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 784, __pyx_L1_error)\n    __Pyx_GOTREF(__pyx_t_3);\n\n    /* \"View.MemoryView\":784\n *                                     memview.dtype_is_object)\n *     else:\n *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<\n *                                     memview.dtype_is_object)\n * \n */\n    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 784, __pyx_L1_error)\n    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);\n    __pyx_t_3 = 0;\n    goto __pyx_L0;\n  }\n\n  /* \"View.MemoryView\":712\n * \n * @cname('__pyx_memview_slice')\n * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<\n *     cdef int new_ndim = 0, suboffset_dim = -1, dim\n *     cdef bint negative_step\n */\n\n  /* function exit code */\n  __pyx_L1_error:;\n  __Pyx_XDECREF(__pyx_t_3);\n  __Pyx_XDECREF(__pyx_t_9);\n  __Pyx_AddTraceback(\"View.MemoryView.memview_slice\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n  __pyx_r = 0;\n  __pyx_L0:;\n  __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj);\n  __Pyx_XDECREF(__pyx_v_index);\n  __Pyx_XGIVEREF((PyObject *)__pyx_r);\n  __Pyx_RefNannyFinishContext();\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":809\n * \n * @cname('__pyx_memoryview_slice_memviewslice')\n * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<\n *         __Pyx_memviewslice *dst,\n *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,\n */\n\nstatic int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) {\n  Py_ssize_t __pyx_v_new_shape;\n  int __pyx_v_negative_step;\n  int __pyx_r;\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  int __pyx_lineno = 0;\n  const char *__pyx_filename = NULL;\n  int __pyx_clineno = 0;\n\n  /* \"View.MemoryView\":829\n *     cdef bint negative_step\n * \n *     if not is_slice:             # <<<<<<<<<<<<<<\n * \n *         if start < 0:\n */\n  __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":831\n *     if not is_slice:\n * \n *         if start < 0:             # <<<<<<<<<<<<<<\n *             start += shape\n *         if not 0 <= start < shape:\n */\n    __pyx_t_1 = ((__pyx_v_start < 0) != 0);\n    if (__pyx_t_1) {\n\n      /* \"View.MemoryView\":832\n * \n *         if start < 0:\n *             start += shape             # <<<<<<<<<<<<<<\n *         if not 0 <= start < shape:\n *             _err_dim(IndexError, \"Index out of bounds (axis %d)\", dim)\n */\n      __pyx_v_start = (__pyx_v_start + __pyx_v_shape);\n\n      /* \"View.MemoryView\":831\n *     if not is_slice:\n * \n *         if start < 0:             # <<<<<<<<<<<<<<\n *             start += shape\n *         if not 0 <= start < shape:\n */\n    }\n\n    /* \"View.MemoryView\":833\n *         if start < 0:\n *             start += shape\n *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<\n *             _err_dim(IndexError, \"Index out of bounds (axis %d)\", dim)\n *     else:\n */\n    __pyx_t_1 = (0 <= __pyx_v_start);\n    if (__pyx_t_1) {\n      __pyx_t_1 = (__pyx_v_start < __pyx_v_shape);\n    }\n    __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":834\n *             start += shape\n *         if not 0 <= start < shape:\n *             _err_dim(IndexError, \"Index out of bounds (axis %d)\", dim)             # <<<<<<<<<<<<<<\n *     else:\n * \n */\n      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)\"Index out of bounds (axis %d)\"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 834, __pyx_L1_error)\n\n      /* \"View.MemoryView\":833\n *         if start < 0:\n *             start += shape\n *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<\n *             _err_dim(IndexError, \"Index out of bounds (axis %d)\", dim)\n *     else:\n */\n    }\n\n    /* \"View.MemoryView\":829\n *     cdef bint negative_step\n * \n *     if not is_slice:             # <<<<<<<<<<<<<<\n * \n *         if start < 0:\n */\n    goto __pyx_L3;\n  }\n\n  /* \"View.MemoryView\":837\n *     else:\n * \n *         negative_step = have_step != 0 and step < 0             # <<<<<<<<<<<<<<\n * \n *         if have_step and step == 0:\n */\n  /*else*/ {\n    __pyx_t_1 = ((__pyx_v_have_step != 0) != 0);\n    if (__pyx_t_1) {\n    } else {\n      __pyx_t_2 = __pyx_t_1;\n      goto __pyx_L6_bool_binop_done;\n    }\n    __pyx_t_1 = ((__pyx_v_step < 0) != 0);\n    __pyx_t_2 = __pyx_t_1;\n    __pyx_L6_bool_binop_done:;\n    __pyx_v_negative_step = __pyx_t_2;\n\n    /* \"View.MemoryView\":839\n *         negative_step = have_step != 0 and step < 0\n * \n *         if have_step and step == 0:             # <<<<<<<<<<<<<<\n *             _err_dim(ValueError, \"Step may not be zero (axis %d)\", dim)\n * \n */\n    __pyx_t_1 = (__pyx_v_have_step != 0);\n    if (__pyx_t_1) {\n    } else {\n      __pyx_t_2 = __pyx_t_1;\n      goto __pyx_L9_bool_binop_done;\n    }\n    __pyx_t_1 = ((__pyx_v_step == 0) != 0);\n    __pyx_t_2 = __pyx_t_1;\n    __pyx_L9_bool_binop_done:;\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":840\n * \n *         if have_step and step == 0:\n *             _err_dim(ValueError, \"Step may not be zero (axis %d)\", dim)             # <<<<<<<<<<<<<<\n * \n * \n */\n      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)\"Step may not be zero (axis %d)\"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 840, __pyx_L1_error)\n\n      /* \"View.MemoryView\":839\n *         negative_step = have_step != 0 and step < 0\n * \n *         if have_step and step == 0:             # <<<<<<<<<<<<<<\n *             _err_dim(ValueError, \"Step may not be zero (axis %d)\", dim)\n * \n */\n    }\n\n    /* \"View.MemoryView\":843\n * \n * \n *         if have_start:             # <<<<<<<<<<<<<<\n *             if start < 0:\n *                 start += shape\n */\n    __pyx_t_2 = (__pyx_v_have_start != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":844\n * \n *         if have_start:\n *             if start < 0:             # <<<<<<<<<<<<<<\n *                 start += shape\n *                 if start < 0:\n */\n      __pyx_t_2 = ((__pyx_v_start < 0) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":845\n *         if have_start:\n *             if start < 0:\n *                 start += shape             # <<<<<<<<<<<<<<\n *                 if start < 0:\n *                     start = 0\n */\n        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);\n\n        /* \"View.MemoryView\":846\n *             if start < 0:\n *                 start += shape\n *                 if start < 0:             # <<<<<<<<<<<<<<\n *                     start = 0\n *             elif start >= shape:\n */\n        __pyx_t_2 = ((__pyx_v_start < 0) != 0);\n        if (__pyx_t_2) {\n\n          /* \"View.MemoryView\":847\n *                 start += shape\n *                 if start < 0:\n *                     start = 0             # <<<<<<<<<<<<<<\n *             elif start >= shape:\n *                 if negative_step:\n */\n          __pyx_v_start = 0;\n\n          /* \"View.MemoryView\":846\n *             if start < 0:\n *                 start += shape\n *                 if start < 0:             # <<<<<<<<<<<<<<\n *                     start = 0\n *             elif start >= shape:\n */\n        }\n\n        /* \"View.MemoryView\":844\n * \n *         if have_start:\n *             if start < 0:             # <<<<<<<<<<<<<<\n *                 start += shape\n *                 if start < 0:\n */\n        goto __pyx_L12;\n      }\n\n      /* \"View.MemoryView\":848\n *                 if start < 0:\n *                     start = 0\n *             elif start >= shape:             # <<<<<<<<<<<<<<\n *                 if negative_step:\n *                     start = shape - 1\n */\n      __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":849\n *                     start = 0\n *             elif start >= shape:\n *                 if negative_step:             # <<<<<<<<<<<<<<\n *                     start = shape - 1\n *                 else:\n */\n        __pyx_t_2 = (__pyx_v_negative_step != 0);\n        if (__pyx_t_2) {\n\n          /* \"View.MemoryView\":850\n *             elif start >= shape:\n *                 if negative_step:\n *                     start = shape - 1             # <<<<<<<<<<<<<<\n *                 else:\n *                     start = shape\n */\n          __pyx_v_start = (__pyx_v_shape - 1);\n\n          /* \"View.MemoryView\":849\n *                     start = 0\n *             elif start >= shape:\n *                 if negative_step:             # <<<<<<<<<<<<<<\n *                     start = shape - 1\n *                 else:\n */\n          goto __pyx_L14;\n        }\n\n        /* \"View.MemoryView\":852\n *                     start = shape - 1\n *                 else:\n *                     start = shape             # <<<<<<<<<<<<<<\n *         else:\n *             if negative_step:\n */\n        /*else*/ {\n          __pyx_v_start = __pyx_v_shape;\n        }\n        __pyx_L14:;\n\n        /* \"View.MemoryView\":848\n *                 if start < 0:\n *                     start = 0\n *             elif start >= shape:             # <<<<<<<<<<<<<<\n *                 if negative_step:\n *                     start = shape - 1\n */\n      }\n      __pyx_L12:;\n\n      /* \"View.MemoryView\":843\n * \n * \n *         if have_start:             # <<<<<<<<<<<<<<\n *             if start < 0:\n *                 start += shape\n */\n      goto __pyx_L11;\n    }\n\n    /* \"View.MemoryView\":854\n *                     start = shape\n *         else:\n *             if negative_step:             # <<<<<<<<<<<<<<\n *                 start = shape - 1\n *             else:\n */\n    /*else*/ {\n      __pyx_t_2 = (__pyx_v_negative_step != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":855\n *         else:\n *             if negative_step:\n *                 start = shape - 1             # <<<<<<<<<<<<<<\n *             else:\n *                 start = 0\n */\n        __pyx_v_start = (__pyx_v_shape - 1);\n\n        /* \"View.MemoryView\":854\n *                     start = shape\n *         else:\n *             if negative_step:             # <<<<<<<<<<<<<<\n *                 start = shape - 1\n *             else:\n */\n        goto __pyx_L15;\n      }\n\n      /* \"View.MemoryView\":857\n *                 start = shape - 1\n *             else:\n *                 start = 0             # <<<<<<<<<<<<<<\n * \n *         if have_stop:\n */\n      /*else*/ {\n        __pyx_v_start = 0;\n      }\n      __pyx_L15:;\n    }\n    __pyx_L11:;\n\n    /* \"View.MemoryView\":859\n *                 start = 0\n * \n *         if have_stop:             # <<<<<<<<<<<<<<\n *             if stop < 0:\n *                 stop += shape\n */\n    __pyx_t_2 = (__pyx_v_have_stop != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":860\n * \n *         if have_stop:\n *             if stop < 0:             # <<<<<<<<<<<<<<\n *                 stop += shape\n *                 if stop < 0:\n */\n      __pyx_t_2 = ((__pyx_v_stop < 0) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":861\n *         if have_stop:\n *             if stop < 0:\n *                 stop += shape             # <<<<<<<<<<<<<<\n *                 if stop < 0:\n *                     stop = 0\n */\n        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);\n\n        /* \"View.MemoryView\":862\n *             if stop < 0:\n *                 stop += shape\n *                 if stop < 0:             # <<<<<<<<<<<<<<\n *                     stop = 0\n *             elif stop > shape:\n */\n        __pyx_t_2 = ((__pyx_v_stop < 0) != 0);\n        if (__pyx_t_2) {\n\n          /* \"View.MemoryView\":863\n *                 stop += shape\n *                 if stop < 0:\n *                     stop = 0             # <<<<<<<<<<<<<<\n *             elif stop > shape:\n *                 stop = shape\n */\n          __pyx_v_stop = 0;\n\n          /* \"View.MemoryView\":862\n *             if stop < 0:\n *                 stop += shape\n *                 if stop < 0:             # <<<<<<<<<<<<<<\n *                     stop = 0\n *             elif stop > shape:\n */\n        }\n\n        /* \"View.MemoryView\":860\n * \n *         if have_stop:\n *             if stop < 0:             # <<<<<<<<<<<<<<\n *                 stop += shape\n *                 if stop < 0:\n */\n        goto __pyx_L17;\n      }\n\n      /* \"View.MemoryView\":864\n *                 if stop < 0:\n *                     stop = 0\n *             elif stop > shape:             # <<<<<<<<<<<<<<\n *                 stop = shape\n *         else:\n */\n      __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":865\n *                     stop = 0\n *             elif stop > shape:\n *                 stop = shape             # <<<<<<<<<<<<<<\n *         else:\n *             if negative_step:\n */\n        __pyx_v_stop = __pyx_v_shape;\n\n        /* \"View.MemoryView\":864\n *                 if stop < 0:\n *                     stop = 0\n *             elif stop > shape:             # <<<<<<<<<<<<<<\n *                 stop = shape\n *         else:\n */\n      }\n      __pyx_L17:;\n\n      /* \"View.MemoryView\":859\n *                 start = 0\n * \n *         if have_stop:             # <<<<<<<<<<<<<<\n *             if stop < 0:\n *                 stop += shape\n */\n      goto __pyx_L16;\n    }\n\n    /* \"View.MemoryView\":867\n *                 stop = shape\n *         else:\n *             if negative_step:             # <<<<<<<<<<<<<<\n *                 stop = -1\n *             else:\n */\n    /*else*/ {\n      __pyx_t_2 = (__pyx_v_negative_step != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":868\n *         else:\n *             if negative_step:\n *                 stop = -1             # <<<<<<<<<<<<<<\n *             else:\n *                 stop = shape\n */\n        __pyx_v_stop = -1L;\n\n        /* \"View.MemoryView\":867\n *                 stop = shape\n *         else:\n *             if negative_step:             # <<<<<<<<<<<<<<\n *                 stop = -1\n *             else:\n */\n        goto __pyx_L19;\n      }\n\n      /* \"View.MemoryView\":870\n *                 stop = -1\n *             else:\n *                 stop = shape             # <<<<<<<<<<<<<<\n * \n *         if not have_step:\n */\n      /*else*/ {\n        __pyx_v_stop = __pyx_v_shape;\n      }\n      __pyx_L19:;\n    }\n    __pyx_L16:;\n\n    /* \"View.MemoryView\":872\n *                 stop = shape\n * \n *         if not have_step:             # <<<<<<<<<<<<<<\n *             step = 1\n * \n */\n    __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":873\n * \n *         if not have_step:\n *             step = 1             # <<<<<<<<<<<<<<\n * \n * \n */\n      __pyx_v_step = 1;\n\n      /* \"View.MemoryView\":872\n *                 stop = shape\n * \n *         if not have_step:             # <<<<<<<<<<<<<<\n *             step = 1\n * \n */\n    }\n\n    /* \"View.MemoryView\":877\n * \n *         with cython.cdivision(True):\n *             new_shape = (stop - start) // step             # <<<<<<<<<<<<<<\n * \n *             if (stop - start) - step * new_shape:\n */\n    __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step);\n\n    /* \"View.MemoryView\":879\n *             new_shape = (stop - start) // step\n * \n *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<\n *                 new_shape += 1\n * \n */\n    __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":880\n * \n *             if (stop - start) - step * new_shape:\n *                 new_shape += 1             # <<<<<<<<<<<<<<\n * \n *         if new_shape < 0:\n */\n      __pyx_v_new_shape = (__pyx_v_new_shape + 1);\n\n      /* \"View.MemoryView\":879\n *             new_shape = (stop - start) // step\n * \n *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<\n *                 new_shape += 1\n * \n */\n    }\n\n    /* \"View.MemoryView\":882\n *                 new_shape += 1\n * \n *         if new_shape < 0:             # <<<<<<<<<<<<<<\n *             new_shape = 0\n * \n */\n    __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":883\n * \n *         if new_shape < 0:\n *             new_shape = 0             # <<<<<<<<<<<<<<\n * \n * \n */\n      __pyx_v_new_shape = 0;\n\n      /* \"View.MemoryView\":882\n *                 new_shape += 1\n * \n *         if new_shape < 0:             # <<<<<<<<<<<<<<\n *             new_shape = 0\n * \n */\n    }\n\n    /* \"View.MemoryView\":886\n * \n * \n *         dst.strides[new_ndim] = stride * step             # <<<<<<<<<<<<<<\n *         dst.shape[new_ndim] = new_shape\n *         dst.suboffsets[new_ndim] = suboffset\n */\n    (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step);\n\n    /* \"View.MemoryView\":887\n * \n *         dst.strides[new_ndim] = stride * step\n *         dst.shape[new_ndim] = new_shape             # <<<<<<<<<<<<<<\n *         dst.suboffsets[new_ndim] = suboffset\n * \n */\n    (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape;\n\n    /* \"View.MemoryView\":888\n *         dst.strides[new_ndim] = stride * step\n *         dst.shape[new_ndim] = new_shape\n *         dst.suboffsets[new_ndim] = suboffset             # <<<<<<<<<<<<<<\n * \n * \n */\n    (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset;\n  }\n  __pyx_L3:;\n\n  /* \"View.MemoryView\":891\n * \n * \n *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<\n *         dst.data += start * stride\n *     else:\n */\n  __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":892\n * \n *     if suboffset_dim[0] < 0:\n *         dst.data += start * stride             # <<<<<<<<<<<<<<\n *     else:\n *         dst.suboffsets[suboffset_dim[0]] += start * stride\n */\n    __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride));\n\n    /* \"View.MemoryView\":891\n * \n * \n *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<\n *         dst.data += start * stride\n *     else:\n */\n    goto __pyx_L23;\n  }\n\n  /* \"View.MemoryView\":894\n *         dst.data += start * stride\n *     else:\n *         dst.suboffsets[suboffset_dim[0]] += start * stride             # <<<<<<<<<<<<<<\n * \n *     if suboffset >= 0:\n */\n  /*else*/ {\n    __pyx_t_3 = (__pyx_v_suboffset_dim[0]);\n    (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride));\n  }\n  __pyx_L23:;\n\n  /* \"View.MemoryView\":896\n *         dst.suboffsets[suboffset_dim[0]] += start * stride\n * \n *     if suboffset >= 0:             # <<<<<<<<<<<<<<\n *         if not is_slice:\n *             if new_ndim == 0:\n */\n  __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":897\n * \n *     if suboffset >= 0:\n *         if not is_slice:             # <<<<<<<<<<<<<<\n *             if new_ndim == 0:\n *                 dst.data = (<char **> dst.data)[0] + suboffset\n */\n    __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":898\n *     if suboffset >= 0:\n *         if not is_slice:\n *             if new_ndim == 0:             # <<<<<<<<<<<<<<\n *                 dst.data = (<char **> dst.data)[0] + suboffset\n *             else:\n */\n      __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":899\n *         if not is_slice:\n *             if new_ndim == 0:\n *                 dst.data = (<char **> dst.data)[0] + suboffset             # <<<<<<<<<<<<<<\n *             else:\n *                 _err_dim(IndexError, \"All dimensions preceding dimension %d \"\n */\n        __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset);\n\n        /* \"View.MemoryView\":898\n *     if suboffset >= 0:\n *         if not is_slice:\n *             if new_ndim == 0:             # <<<<<<<<<<<<<<\n *                 dst.data = (<char **> dst.data)[0] + suboffset\n *             else:\n */\n        goto __pyx_L26;\n      }\n\n      /* \"View.MemoryView\":901\n *                 dst.data = (<char **> dst.data)[0] + suboffset\n *             else:\n *                 _err_dim(IndexError, \"All dimensions preceding dimension %d \"             # <<<<<<<<<<<<<<\n *                                      \"must be indexed and not sliced\", dim)\n *         else:\n */\n      /*else*/ {\n\n        /* \"View.MemoryView\":902\n *             else:\n *                 _err_dim(IndexError, \"All dimensions preceding dimension %d \"\n *                                      \"must be indexed and not sliced\", dim)             # <<<<<<<<<<<<<<\n *         else:\n *             suboffset_dim[0] = new_ndim\n */\n        __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)\"All dimensions preceding dimension %d must be indexed and not sliced\"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 901, __pyx_L1_error)\n      }\n      __pyx_L26:;\n\n      /* \"View.MemoryView\":897\n * \n *     if suboffset >= 0:\n *         if not is_slice:             # <<<<<<<<<<<<<<\n *             if new_ndim == 0:\n *                 dst.data = (<char **> dst.data)[0] + suboffset\n */\n      goto __pyx_L25;\n    }\n\n    /* \"View.MemoryView\":904\n *                                      \"must be indexed and not sliced\", dim)\n *         else:\n *             suboffset_dim[0] = new_ndim             # <<<<<<<<<<<<<<\n * \n *     return 0\n */\n    /*else*/ {\n      (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim;\n    }\n    __pyx_L25:;\n\n    /* \"View.MemoryView\":896\n *         dst.suboffsets[suboffset_dim[0]] += start * stride\n * \n *     if suboffset >= 0:             # <<<<<<<<<<<<<<\n *         if not is_slice:\n *             if new_ndim == 0:\n */\n  }\n\n  /* \"View.MemoryView\":906\n *             suboffset_dim[0] = new_ndim\n * \n *     return 0             # <<<<<<<<<<<<<<\n * \n * \n */\n  __pyx_r = 0;\n  goto __pyx_L0;\n\n  /* \"View.MemoryView\":809\n * \n * @cname('__pyx_memoryview_slice_memviewslice')\n * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<\n *         __Pyx_memviewslice *dst,\n *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,\n */\n\n  /* function exit code */\n  __pyx_L1_error:;\n  {\n    #ifdef WITH_THREAD\n    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();\n    #endif\n    __Pyx_AddTraceback(\"View.MemoryView.slice_memviewslice\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n    #ifdef WITH_THREAD\n    __Pyx_PyGILState_Release(__pyx_gilstate_save);\n    #endif\n  }\n  __pyx_r = -1;\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":912\n * \n * @cname('__pyx_pybuffer_index')\n * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,             # <<<<<<<<<<<<<<\n *                           Py_ssize_t dim) except NULL:\n *     cdef Py_ssize_t shape, stride, suboffset = -1\n */\n\nstatic char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) {\n  Py_ssize_t __pyx_v_shape;\n  Py_ssize_t __pyx_v_stride;\n  Py_ssize_t __pyx_v_suboffset;\n  Py_ssize_t __pyx_v_itemsize;\n  char *__pyx_v_resultp;\n  char *__pyx_r;\n  __Pyx_RefNannyDeclarations\n  Py_ssize_t __pyx_t_1;\n  int __pyx_t_2;\n  PyObject *__pyx_t_3 = NULL;\n  PyObject *__pyx_t_4 = NULL;\n  int __pyx_lineno = 0;\n  const char *__pyx_filename = NULL;\n  int __pyx_clineno = 0;\n  __Pyx_RefNannySetupContext(\"pybuffer_index\", 0);\n\n  /* \"View.MemoryView\":914\n * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,\n *                           Py_ssize_t dim) except NULL:\n *     cdef Py_ssize_t shape, stride, suboffset = -1             # <<<<<<<<<<<<<<\n *     cdef Py_ssize_t itemsize = view.itemsize\n *     cdef char *resultp\n */\n  __pyx_v_suboffset = -1L;\n\n  /* \"View.MemoryView\":915\n *                           Py_ssize_t dim) except NULL:\n *     cdef Py_ssize_t shape, stride, suboffset = -1\n *     cdef Py_ssize_t itemsize = view.itemsize             # <<<<<<<<<<<<<<\n *     cdef char *resultp\n * \n */\n  __pyx_t_1 = __pyx_v_view->itemsize;\n  __pyx_v_itemsize = __pyx_t_1;\n\n  /* \"View.MemoryView\":918\n *     cdef char *resultp\n * \n *     if view.ndim == 0:             # <<<<<<<<<<<<<<\n *         shape = view.len / itemsize\n *         stride = itemsize\n */\n  __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":919\n * \n *     if view.ndim == 0:\n *         shape = view.len / itemsize             # <<<<<<<<<<<<<<\n *         stride = itemsize\n *     else:\n */\n    if (unlikely(__pyx_v_itemsize == 0)) {\n      PyErr_SetString(PyExc_ZeroDivisionError, \"integer division or modulo by zero\");\n      __PYX_ERR(2, 919, __pyx_L1_error)\n    }\n    else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1)  && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) {\n      PyErr_SetString(PyExc_OverflowError, \"value too large to perform division\");\n      __PYX_ERR(2, 919, __pyx_L1_error)\n    }\n    __pyx_v_shape = (__pyx_v_view->len / __pyx_v_itemsize);\n\n    /* \"View.MemoryView\":920\n *     if view.ndim == 0:\n *         shape = view.len / itemsize\n *         stride = itemsize             # <<<<<<<<<<<<<<\n *     else:\n *         shape = view.shape[dim]\n */\n    __pyx_v_stride = __pyx_v_itemsize;\n\n    /* \"View.MemoryView\":918\n *     cdef char *resultp\n * \n *     if view.ndim == 0:             # <<<<<<<<<<<<<<\n *         shape = view.len / itemsize\n *         stride = itemsize\n */\n    goto __pyx_L3;\n  }\n\n  /* \"View.MemoryView\":922\n *         stride = itemsize\n *     else:\n *         shape = view.shape[dim]             # <<<<<<<<<<<<<<\n *         stride = view.strides[dim]\n *         if view.suboffsets != NULL:\n */\n  /*else*/ {\n    __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]);\n\n    /* \"View.MemoryView\":923\n *     else:\n *         shape = view.shape[dim]\n *         stride = view.strides[dim]             # <<<<<<<<<<<<<<\n *         if view.suboffsets != NULL:\n *             suboffset = view.suboffsets[dim]\n */\n    __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]);\n\n    /* \"View.MemoryView\":924\n *         shape = view.shape[dim]\n *         stride = view.strides[dim]\n *         if view.suboffsets != NULL:             # <<<<<<<<<<<<<<\n *             suboffset = view.suboffsets[dim]\n * \n */\n    __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":925\n *         stride = view.strides[dim]\n *         if view.suboffsets != NULL:\n *             suboffset = view.suboffsets[dim]             # <<<<<<<<<<<<<<\n * \n *     if index < 0:\n */\n      __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]);\n\n      /* \"View.MemoryView\":924\n *         shape = view.shape[dim]\n *         stride = view.strides[dim]\n *         if view.suboffsets != NULL:             # <<<<<<<<<<<<<<\n *             suboffset = view.suboffsets[dim]\n * \n */\n    }\n  }\n  __pyx_L3:;\n\n  /* \"View.MemoryView\":927\n *             suboffset = view.suboffsets[dim]\n * \n *     if index < 0:             # <<<<<<<<<<<<<<\n *         index += view.shape[dim]\n *         if index < 0:\n */\n  __pyx_t_2 = ((__pyx_v_index < 0) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":928\n * \n *     if index < 0:\n *         index += view.shape[dim]             # <<<<<<<<<<<<<<\n *         if index < 0:\n *             raise IndexError(\"Out of bounds on buffer access (axis %d)\" % dim)\n */\n    __pyx_v_index = (__pyx_v_index + (__pyx_v_view->shape[__pyx_v_dim]));\n\n    /* \"View.MemoryView\":929\n *     if index < 0:\n *         index += view.shape[dim]\n *         if index < 0:             # <<<<<<<<<<<<<<\n *             raise IndexError(\"Out of bounds on buffer access (axis %d)\" % dim)\n * \n */\n    __pyx_t_2 = ((__pyx_v_index < 0) != 0);\n    if (unlikely(__pyx_t_2)) {\n\n      /* \"View.MemoryView\":930\n *         index += view.shape[dim]\n *         if index < 0:\n *             raise IndexError(\"Out of bounds on buffer access (axis %d)\" % dim)             # <<<<<<<<<<<<<<\n * \n *     if index >= shape:\n */\n      __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 930, __pyx_L1_error)\n      __Pyx_GOTREF(__pyx_t_3);\n      __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 930, __pyx_L1_error)\n      __Pyx_GOTREF(__pyx_t_4);\n      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;\n      __pyx_t_3 = __Pyx_PyObject_CallOneArg(__pyx_builtin_IndexError, __pyx_t_4); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 930, __pyx_L1_error)\n      __Pyx_GOTREF(__pyx_t_3);\n      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;\n      __Pyx_Raise(__pyx_t_3, 0, 0, 0);\n      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;\n      __PYX_ERR(2, 930, __pyx_L1_error)\n\n      /* \"View.MemoryView\":929\n *     if index < 0:\n *         index += view.shape[dim]\n *         if index < 0:             # <<<<<<<<<<<<<<\n *             raise IndexError(\"Out of bounds on buffer access (axis %d)\" % dim)\n * \n */\n    }\n\n    /* \"View.MemoryView\":927\n *             suboffset = view.suboffsets[dim]\n * \n *     if index < 0:             # <<<<<<<<<<<<<<\n *         index += view.shape[dim]\n *         if index < 0:\n */\n  }\n\n  /* \"View.MemoryView\":932\n *             raise IndexError(\"Out of bounds on buffer access (axis %d)\" % dim)\n * \n *     if index >= shape:             # <<<<<<<<<<<<<<\n *         raise IndexError(\"Out of bounds on buffer access (axis %d)\" % dim)\n * \n */\n  __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0);\n  if (unlikely(__pyx_t_2)) {\n\n    /* \"View.MemoryView\":933\n * \n *     if index >= shape:\n *         raise IndexError(\"Out of bounds on buffer access (axis %d)\" % dim)             # <<<<<<<<<<<<<<\n * \n *     resultp = bufp + index * stride\n */\n    __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 933, __pyx_L1_error)\n    __Pyx_GOTREF(__pyx_t_3);\n    __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) __PYX_ERR(2, 933, __pyx_L1_error)\n    __Pyx_GOTREF(__pyx_t_4);\n    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__pyx_t_1 = ((__pyx_v_arg < 0) != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":1113\n * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:\n *     if arg < 0:\n *         return -arg             # <<<<<<<<<<<<<<\n *     else:\n *         return arg\n */\n    __pyx_r = (-__pyx_v_arg);\n    goto __pyx_L0;\n\n    /* \"View.MemoryView\":1112\n * \n * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:\n *     if arg < 0:             # <<<<<<<<<<<<<<\n *         return -arg\n *     else:\n */\n  }\n\n  /* \"View.MemoryView\":1115\n *         return -arg\n *     else:\n *         return arg             # <<<<<<<<<<<<<<\n * \n * @cname('__pyx_get_best_slice_order')\n */\n  /*else*/ {\n    __pyx_r = __pyx_v_arg;\n    goto __pyx_L0;\n  }\n\n  /* \"View.MemoryView\":1111\n * \n * \n * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<\n *     if arg < 0:\n *         return -arg\n */\n\n  /* function exit code */\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1118\n * \n * @cname('__pyx_get_best_slice_order')\n * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<\n *     \"\"\"\n *     Figure out the best memory access order for a given slice.\n */\n\nstatic char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) {\n  int __pyx_v_i;\n  Py_ssize_t __pyx_v_c_stride;\n  Py_ssize_t __pyx_v_f_stride;\n  char __pyx_r;\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  int __pyx_t_4;\n\n  /* \"View.MemoryView\":1123\n *     \"\"\"\n *     cdef int i\n *     cdef Py_ssize_t c_stride = 0             # <<<<<<<<<<<<<<\n *     cdef Py_ssize_t f_stride = 0\n * \n */\n  __pyx_v_c_stride = 0;\n\n  /* \"View.MemoryView\":1124\n *     cdef int i\n *     cdef Py_ssize_t c_stride = 0\n *     cdef Py_ssize_t f_stride = 0             # <<<<<<<<<<<<<<\n * \n *     for i in range(ndim - 1, -1, -1):\n */\n  __pyx_v_f_stride = 0;\n\n  /* \"View.MemoryView\":1126\n *     cdef Py_ssize_t f_stride = 0\n * \n *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<\n *         if mslice.shape[i] > 1:\n *             c_stride = mslice.strides[i]\n */\n  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {\n    __pyx_v_i = __pyx_t_1;\n\n    /* \"View.MemoryView\":1127\n * \n *     for i in range(ndim - 1, -1, -1):\n *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<\n *             c_stride = mslice.strides[i]\n *             break\n */\n    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":1128\n *     for i in range(ndim - 1, -1, -1):\n *         if mslice.shape[i] > 1:\n *             c_stride = mslice.strides[i]             # <<<<<<<<<<<<<<\n *             break\n * \n */\n      __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]);\n\n      /* \"View.MemoryView\":1129\n *         if mslice.shape[i] > 1:\n *             c_stride = mslice.strides[i]\n *             break             # <<<<<<<<<<<<<<\n * \n *     for i in range(ndim):\n */\n      goto __pyx_L4_break;\n\n      /* \"View.MemoryView\":1127\n * \n *     for i in range(ndim - 1, -1, -1):\n *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<\n *             c_stride = mslice.strides[i]\n *             break\n */\n    }\n  }\n  __pyx_L4_break:;\n\n  /* \"View.MemoryView\":1131\n *             break\n * \n *     for i in range(ndim):             # <<<<<<<<<<<<<<\n *         if mslice.shape[i] > 1:\n *             f_stride = mslice.strides[i]\n */\n  __pyx_t_1 = __pyx_v_ndim;\n  __pyx_t_3 = __pyx_t_1;\n  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {\n    __pyx_v_i = __pyx_t_4;\n\n    /* \"View.MemoryView\":1132\n * \n *     for i in range(ndim):\n *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<\n *             f_stride = mslice.strides[i]\n *             break\n */\n    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":1133\n *     for i in range(ndim):\n *         if mslice.shape[i] > 1:\n *             f_stride = mslice.strides[i]             # <<<<<<<<<<<<<<\n *             break\n * \n */\n      __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]);\n\n      /* \"View.MemoryView\":1134\n *         if mslice.shape[i] > 1:\n *             f_stride = mslice.strides[i]\n *             break             # <<<<<<<<<<<<<<\n * \n *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):\n */\n      goto __pyx_L7_break;\n\n      /* \"View.MemoryView\":1132\n * \n *     for i in range(ndim):\n *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<\n *             f_stride = mslice.strides[i]\n *             break\n */\n    }\n  }\n  __pyx_L7_break:;\n\n  /* \"View.MemoryView\":1136\n *             break\n * \n *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<\n *         return 'C'\n *     else:\n */\n  __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":1137\n * \n *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):\n *         return 'C'             # <<<<<<<<<<<<<<\n *     else:\n *         return 'F'\n */\n    __pyx_r = 'C';\n    goto __pyx_L0;\n\n    /* \"View.MemoryView\":1136\n *             break\n * \n *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<\n *         return 'C'\n *     else:\n */\n  }\n\n  /* \"View.MemoryView\":1139\n *         return 'C'\n *     else:\n *         return 'F'             # <<<<<<<<<<<<<<\n * \n * @cython.cdivision(True)\n */\n  /*else*/ {\n    __pyx_r = 'F';\n    goto __pyx_L0;\n  }\n\n  /* \"View.MemoryView\":1118\n * \n * @cname('__pyx_get_best_slice_order')\n * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<\n *     \"\"\"\n *     Figure out the best memory access order for a given slice.\n */\n\n  /* function exit code */\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1142\n * \n * @cython.cdivision(True)\n * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<\n *                                    char *dst_data, Py_ssize_t *dst_strides,\n *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,\n */\n\nstatic void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {\n  CYTHON_UNUSED Py_ssize_t __pyx_v_i;\n  CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;\n  Py_ssize_t __pyx_v_dst_extent;\n  Py_ssize_t __pyx_v_src_stride;\n  Py_ssize_t __pyx_v_dst_stride;\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  Py_ssize_t __pyx_t_4;\n  Py_ssize_t __pyx_t_5;\n  Py_ssize_t __pyx_t_6;\n\n  /* \"View.MemoryView\":1149\n * \n *     cdef Py_ssize_t i\n *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<\n *     cdef Py_ssize_t dst_extent = dst_shape[0]\n *     cdef Py_ssize_t src_stride = src_strides[0]\n */\n  __pyx_v_src_extent = (__pyx_v_src_shape[0]);\n\n  /* \"View.MemoryView\":1150\n *     cdef Py_ssize_t i\n *     cdef Py_ssize_t src_extent = src_shape[0]\n *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<\n *     cdef Py_ssize_t src_stride = src_strides[0]\n *     cdef Py_ssize_t dst_stride = dst_strides[0]\n */\n  __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);\n\n  /* \"View.MemoryView\":1151\n *     cdef Py_ssize_t src_extent = src_shape[0]\n *     cdef Py_ssize_t dst_extent = dst_shape[0]\n *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<\n *     cdef Py_ssize_t dst_stride = dst_strides[0]\n * \n */\n  __pyx_v_src_stride = (__pyx_v_src_strides[0]);\n\n  /* \"View.MemoryView\":1152\n *     cdef Py_ssize_t dst_extent = dst_shape[0]\n *     cdef Py_ssize_t src_stride = src_strides[0]\n *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<\n * \n *     if ndim == 1:\n */\n  __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);\n\n  /* \"View.MemoryView\":1154\n *     cdef Py_ssize_t dst_stride = dst_strides[0]\n * \n *     if ndim == 1:             # <<<<<<<<<<<<<<\n *        if (src_stride > 0 and dst_stride > 0 and\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n */\n  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":1155\n * \n *     if ndim == 1:\n *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n *            memcpy(dst_data, src_data, itemsize * dst_extent)\n */\n    __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0);\n    if (__pyx_t_2) {\n    } else {\n      __pyx_t_1 = __pyx_t_2;\n      goto __pyx_L5_bool_binop_done;\n    }\n    __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0);\n    if (__pyx_t_2) {\n    } else {\n      __pyx_t_1 = __pyx_t_2;\n      goto __pyx_L5_bool_binop_done;\n    }\n\n    /* \"View.MemoryView\":1156\n *     if ndim == 1:\n *        if (src_stride > 0 and dst_stride > 0 and\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<\n *            memcpy(dst_data, src_data, itemsize * dst_extent)\n *        else:\n */\n    __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);\n    if (__pyx_t_2) {\n      __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));\n    }\n    __pyx_t_3 = (__pyx_t_2 != 0);\n    __pyx_t_1 = __pyx_t_3;\n    __pyx_L5_bool_binop_done:;\n\n    /* \"View.MemoryView\":1155\n * \n *     if ndim == 1:\n *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n *            memcpy(dst_data, src_data, itemsize * dst_extent)\n */\n    if (__pyx_t_1) {\n\n      /* \"View.MemoryView\":1157\n *        if (src_stride > 0 and dst_stride > 0 and\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n *            memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<\n *        else:\n *            for i in range(dst_extent):\n */\n      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));\n\n      /* \"View.MemoryView\":1155\n * \n *     if ndim == 1:\n *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n *            memcpy(dst_data, src_data, itemsize * dst_extent)\n */\n      goto __pyx_L4;\n    }\n\n    /* \"View.MemoryView\":1159\n *            memcpy(dst_data, src_data, itemsize * dst_extent)\n *        else:\n *            for i in range(dst_extent):             # <<<<<<<<<<<<<<\n *                memcpy(dst_data, src_data, itemsize)\n *                src_data += src_stride\n */\n    /*else*/ {\n      __pyx_t_4 = __pyx_v_dst_extent;\n      __pyx_t_5 = __pyx_t_4;\n      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {\n        __pyx_v_i = __pyx_t_6;\n\n        /* \"View.MemoryView\":1160\n *        else:\n *            for i in range(dst_extent):\n *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<\n *                src_data += src_stride\n *                dst_data += dst_stride\n */\n        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));\n\n        /* \"View.MemoryView\":1161\n *            for i in range(dst_extent):\n *                memcpy(dst_data, src_data, itemsize)\n *                src_data += src_stride             # <<<<<<<<<<<<<<\n *                dst_data += dst_stride\n *     else:\n */\n        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);\n\n        /* \"View.MemoryView\":1162\n *                memcpy(dst_data, src_data, itemsize)\n *                src_data += src_stride\n *                dst_data += dst_stride             # <<<<<<<<<<<<<<\n *     else:\n *         for i in range(dst_extent):\n */\n        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);\n      }\n    }\n    __pyx_L4:;\n\n    /* \"View.MemoryView\":1154\n *     cdef Py_ssize_t dst_stride = dst_strides[0]\n * \n *     if ndim == 1:             # <<<<<<<<<<<<<<\n *        if (src_stride > 0 and dst_stride > 0 and\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n */\n    goto __pyx_L3;\n  }\n\n  /* \"View.MemoryView\":1164\n *                dst_data += dst_stride\n *     else:\n *         for i in range(dst_extent):             # <<<<<<<<<<<<<<\n *             _copy_strided_to_strided(src_data, src_strides + 1,\n *                                      dst_data, dst_strides + 1,\n */\n  /*else*/ {\n    __pyx_t_4 = __pyx_v_dst_extent;\n    __pyx_t_5 = __pyx_t_4;\n    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {\n      __pyx_v_i = __pyx_t_6;\n\n      /* \"View.MemoryView\":1165\n *     else:\n *         for i in range(dst_extent):\n *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<\n *                                      dst_data, dst_strides + 1,\n *                                      src_shape + 1, dst_shape + 1,\n */\n      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);\n\n      /* \"View.MemoryView\":1169\n *                                      src_shape + 1, dst_shape + 1,\n *                                      ndim - 1, itemsize)\n *             src_data += src_stride             # <<<<<<<<<<<<<<\n *             dst_data += dst_stride\n * \n */\n      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);\n\n      /* \"View.MemoryView\":1170\n *                                      ndim - 1, itemsize)\n *             src_data += src_stride\n *             dst_data += dst_stride             # <<<<<<<<<<<<<<\n * \n * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,\n */\n      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);\n    }\n  }\n  __pyx_L3:;\n\n  /* \"View.MemoryView\":1142\n * \n * @cython.cdivision(True)\n * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<\n *                                    char *dst_data, Py_ssize_t *dst_strides,\n *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,\n */\n\n  /* function exit code */\n}\n\n/* \"View.MemoryView\":1172\n *             dst_data += dst_stride\n * \n * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<\n *                                   __Pyx_memviewslice *dst,\n *                                   int ndim, size_t itemsize) nogil:\n */\n\nstatic void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {\n\n  /* \"View.MemoryView\":1175\n *                                   __Pyx_memviewslice *dst,\n *                                   int ndim, size_t itemsize) nogil:\n *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<\n *                              src.shape, dst.shape, ndim, itemsize)\n * \n */\n  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);\n\n  /* \"View.MemoryView\":1172\n *             dst_data += dst_stride\n * \n * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<\n *                                   __Pyx_memviewslice *dst,\n *                                   int ndim, size_t itemsize) nogil:\n */\n\n  /* function exit code */\n}\n\n/* \"View.MemoryView\":1179\n * \n * @cname('__pyx_memoryview_slice_get_size')\n * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<\n *     \"Return the size of the memory occupied by the slice in number of bytes\"\n *     cdef Py_ssize_t shape, size = src.memview.view.itemsize\n */\n\nstatic Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {\n  Py_ssize_t __pyx_v_shape;\n  Py_ssize_t __pyx_v_size;\n  Py_ssize_t __pyx_r;\n  Py_ssize_t __pyx_t_1;\n  Py_ssize_t *__pyx_t_2;\n  Py_ssize_t *__pyx_t_3;\n  Py_ssize_t *__pyx_t_4;\n\n  /* \"View.MemoryView\":1181\n * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:\n *     \"Return the size of the memory occupied by the slice in number of bytes\"\n *     cdef Py_ssize_t shape, size = src.memview.view.itemsize             # <<<<<<<<<<<<<<\n * \n *     for shape in src.shape[:ndim]:\n */\n  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;\n  __pyx_v_size = __pyx_t_1;\n\n  /* \"View.MemoryView\":1183\n *     cdef Py_ssize_t shape, size = src.memview.view.itemsize\n * \n *     for shape in src.shape[:ndim]:             # <<<<<<<<<<<<<<\n *         size *= shape\n * \n */\n  __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);\n  for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {\n    __pyx_t_2 = __pyx_t_4;\n    __pyx_v_shape = (__pyx_t_2[0]);\n\n    /* \"View.MemoryView\":1184\n * \n *     for shape in src.shape[:ndim]:\n *         size *= shape             # <<<<<<<<<<<<<<\n * \n *     return size\n */\n    __pyx_v_size = (__pyx_v_size * __pyx_v_shape);\n  }\n\n  /* \"View.MemoryView\":1186\n *         size *= shape\n * \n *     return size             # <<<<<<<<<<<<<<\n * \n * @cname('__pyx_fill_contig_strides_array')\n */\n  __pyx_r = __pyx_v_size;\n  goto __pyx_L0;\n\n  /* \"View.MemoryView\":1179\n * \n * @cname('__pyx_memoryview_slice_get_size')\n * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<\n *     \"Return the size of the memory occupied by the slice in number of bytes\"\n *     cdef Py_ssize_t shape, size = src.memview.view.itemsize\n */\n\n  /* function exit code */\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1189\n * \n * @cname('__pyx_fill_contig_strides_array')\n * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<\n *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,\n *                 int ndim, char order) nogil:\n */\n\nstatic Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {\n  int __pyx_v_idx;\n  Py_ssize_t __pyx_r;\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  int __pyx_t_4;\n\n  /* \"View.MemoryView\":1198\n *     cdef int idx\n * \n *     if order == 'F':             # <<<<<<<<<<<<<<\n *         for idx in range(ndim):\n *             strides[idx] = stride\n */\n  __pyx_t_1 = ((__pyx_v_order == 'F') != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":1199\n * \n *     if order == 'F':\n *         for idx in range(ndim):             # <<<<<<<<<<<<<<\n *             strides[idx] = stride\n *             stride *= shape[idx]\n */\n    __pyx_t_2 = __pyx_v_ndim;\n    __pyx_t_3 = __pyx_t_2;\n    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {\n      __pyx_v_idx = __pyx_t_4;\n\n      /* \"View.MemoryView\":1200\n *     if order == 'F':\n *         for idx in range(ndim):\n *             strides[idx] = stride             # <<<<<<<<<<<<<<\n *             stride *= shape[idx]\n *     else:\n */\n      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;\n\n      /* \"View.MemoryView\":1201\n *         for idx in range(ndim):\n *             strides[idx] = stride\n *             stride *= shape[idx]             # <<<<<<<<<<<<<<\n *     else:\n *         for idx in range(ndim - 1, -1, -1):\n */\n      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));\n    }\n\n    /* \"View.MemoryView\":1198\n *     cdef int idx\n * \n *     if order == 'F':             # <<<<<<<<<<<<<<\n *         for idx in range(ndim):\n *             strides[idx] = stride\n */\n    goto __pyx_L3;\n  }\n\n  /* \"View.MemoryView\":1203\n *             stride *= shape[idx]\n *     else:\n *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<\n *             strides[idx] = stride\n *             stride *= shape[idx]\n */\n  /*else*/ {\n    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {\n      __pyx_v_idx = __pyx_t_2;\n\n      /* \"View.MemoryView\":1204\n *     else:\n *         for idx in range(ndim - 1, -1, -1):\n *             strides[idx] = stride             # <<<<<<<<<<<<<<\n *             stride *= shape[idx]\n * \n */\n      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;\n\n      /* \"View.MemoryView\":1205\n *         for idx in range(ndim - 1, -1, -1):\n *             strides[idx] = stride\n *             stride *= shape[idx]             # <<<<<<<<<<<<<<\n * \n *     return stride\n */\n      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));\n    }\n  }\n  __pyx_L3:;\n\n  /* \"View.MemoryView\":1207\n *             stride *= shape[idx]\n * \n *     return stride             # <<<<<<<<<<<<<<\n * \n * @cname('__pyx_memoryview_copy_data_to_temp')\n */\n  __pyx_r = __pyx_v_stride;\n  goto __pyx_L0;\n\n  /* \"View.MemoryView\":1189\n * \n * @cname('__pyx_fill_contig_strides_array')\n * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<\n *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,\n *                 int ndim, char order) nogil:\n */\n\n  /* function exit code */\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1210\n * \n * @cname('__pyx_memoryview_copy_data_to_temp')\n * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<\n *                              __Pyx_memviewslice *tmpslice,\n *                              char order,\n */\n\nstatic void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {\n  int __pyx_v_i;\n  void *__pyx_v_result;\n  size_t __pyx_v_itemsize;\n  size_t __pyx_v_size;\n  void *__pyx_r;\n  Py_ssize_t __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  struct __pyx_memoryview_obj *__pyx_t_4;\n  int __pyx_t_5;\n  int __pyx_t_6;\n  int __pyx_lineno = 0;\n  const char *__pyx_filename = NULL;\n  int __pyx_clineno = 0;\n\n  /* \"View.MemoryView\":1221\n *     cdef void *result\n * \n *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<\n *     cdef size_t size = slice_get_size(src, ndim)\n * \n */\n  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;\n  __pyx_v_itemsize = __pyx_t_1;\n\n  /* \"View.MemoryView\":1222\n * \n *     cdef size_t itemsize = src.memview.view.itemsize\n *     cdef size_t size = slice_get_size(src, ndim)             # <<<<<<<<<<<<<<\n * \n *     result = malloc(size)\n */\n  __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim);\n\n  /* \"View.MemoryView\":1224\n *     cdef size_t size = slice_get_size(src, ndim)\n * \n *     result = malloc(size)             # <<<<<<<<<<<<<<\n *     if not result:\n *         _err(MemoryError, NULL)\n */\n  __pyx_v_result = malloc(__pyx_v_size);\n\n  /* \"View.MemoryView\":1225\n * \n *     result = malloc(size)\n *     if not result:             # <<<<<<<<<<<<<<\n *         _err(MemoryError, NULL)\n * \n */\n  __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":1226\n *     result = malloc(size)\n *     if not result:\n *         _err(MemoryError, NULL)             # <<<<<<<<<<<<<<\n * \n * \n */\n    __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1226, __pyx_L1_error)\n\n    /* \"View.MemoryView\":1225\n * \n *     result = malloc(size)\n *     if not result:             # <<<<<<<<<<<<<<\n *         _err(MemoryError, NULL)\n * \n */\n  }\n\n  /* \"View.MemoryView\":1229\n * \n * \n *     tmpslice.data = <char *> result             # <<<<<<<<<<<<<<\n *     tmpslice.memview = src.memview\n *     for i in range(ndim):\n */\n  __pyx_v_tmpslice->data = ((char *)__pyx_v_result);\n\n  /* \"View.MemoryView\":1230\n * \n *     tmpslice.data = <char *> result\n *     tmpslice.memview = src.memview             # <<<<<<<<<<<<<<\n *     for i in range(ndim):\n *         tmpslice.shape[i] = src.shape[i]\n */\n  __pyx_t_4 = __pyx_v_src->memview;\n  __pyx_v_tmpslice->memview = __pyx_t_4;\n\n  /* \"View.MemoryView\":1231\n *     tmpslice.data = <char *> result\n *     tmpslice.memview = src.memview\n *     for i in range(ndim):             # <<<<<<<<<<<<<<\n *         tmpslice.shape[i] = src.shape[i]\n *         tmpslice.suboffsets[i] = -1\n */\n  __pyx_t_3 = __pyx_v_ndim;\n  __pyx_t_5 = __pyx_t_3;\n  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {\n    __pyx_v_i = __pyx_t_6;\n\n    /* \"View.MemoryView\":1232\n *     tmpslice.memview = src.memview\n *     for i in range(ndim):\n *         tmpslice.shape[i] = src.shape[i]             # <<<<<<<<<<<<<<\n *         tmpslice.suboffsets[i] = -1\n * \n */\n    (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]);\n\n    /* \"View.MemoryView\":1233\n *     for i in range(ndim):\n *         tmpslice.shape[i] = src.shape[i]\n *         tmpslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<\n * \n *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,\n */\n    (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L;\n  }\n\n  /* \"View.MemoryView\":1235\n *         tmpslice.suboffsets[i] = -1\n * \n *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,             # <<<<<<<<<<<<<<\n *                               ndim, order)\n * \n */\n  (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order));\n\n  /* \"View.MemoryView\":1239\n * \n * \n *     for i in range(ndim):             # <<<<<<<<<<<<<<\n *         if tmpslice.shape[i] == 1:\n *             tmpslice.strides[i] = 0\n */\n  __pyx_t_3 = __pyx_v_ndim;\n  __pyx_t_5 = __pyx_t_3;\n  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {\n    __pyx_v_i = __pyx_t_6;\n\n    /* \"View.MemoryView\":1240\n * \n *     for i in range(ndim):\n *         if tmpslice.shape[i] == 1:           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<<<<<<<<<<<<<<\n * \n *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)\n */\n      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);\n\n      /* \"View.MemoryView\":1306\n *     if slices_overlap(&src, &dst, ndim, itemsize):\n * \n *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<\n *             order = get_best_order(&dst, ndim)\n * \n */\n    }\n\n    /* \"View.MemoryView\":1309\n *             order = get_best_order(&dst, ndim)\n * \n *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<\n *         src = tmp\n * \n */\n    __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1309, __pyx_L1_error)\n    __pyx_v_tmpdata = __pyx_t_7;\n\n    /* \"View.MemoryView\":1310\n * \n *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)\n *         src = tmp             # <<<<<<<<<<<<<<\n * \n *     if not broadcasting:\n */\n    __pyx_v_src = __pyx_v_tmp;\n\n    /* \"View.MemoryView\":1304\n *             _err_dim(ValueError, \"Dimension %d is not direct\", i)\n * \n *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<\n * \n *         if not slice_is_contig(src, order, ndim):\n */\n  }\n\n  /* \"View.MemoryView\":1312\n *         src = tmp\n * \n *     if not broadcasting:             # <<<<<<<<<<<<<<\n * \n * \n */\n  __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":1315\n * \n * \n *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<\n *             direct_copy = slice_is_contig(dst, 'C', ndim)\n *         elif slice_is_contig(src, 'F', ndim):\n */\n    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":1316\n * \n *         if slice_is_contig(src, 'C', ndim):\n *             direct_copy = slice_is_contig(dst, 'C', ndim)             # <<<<<<<<<<<<<<\n *         elif slice_is_contig(src, 'F', ndim):\n *             direct_copy = slice_is_contig(dst, 'F', ndim)\n */\n      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);\n\n      /* \"View.MemoryView\":1315\n * \n * \n *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<\n *             direct_copy = slice_is_contig(dst, 'C', ndim)\n *         elif slice_is_contig(src, 'F', ndim):\n */\n      goto __pyx_L12;\n    }\n\n    /* \"View.MemoryView\":1317\n *         if slice_is_contig(src, 'C', ndim):\n *             direct_copy = slice_is_contig(dst, 'C', ndim)\n *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<\n *             direct_copy = slice_is_contig(dst, 'F', ndim)\n * \n */\n    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":1318\n *             direct_copy = slice_is_contig(dst, 'C', ndim)\n *         elif slice_is_contig(src, 'F', ndim):\n *             direct_copy = slice_is_contig(dst, 'F', ndim)             # <<<<<<<<<<<<<<\n * \n *         if direct_copy:\n */\n      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);\n\n      /* \"View.MemoryView\":1317\n *         if slice_is_contig(src, 'C', ndim):\n *             direct_copy = slice_is_contig(dst, 'C', ndim)\n *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<\n *             direct_copy = slice_is_contig(dst, 'F', ndim)\n * \n */\n    }\n    __pyx_L12:;\n\n    /* \"View.MemoryView\":1320\n *             direct_copy = slice_is_contig(dst, 'F', ndim)\n * \n *         if direct_copy:             # <<<<<<<<<<<<<<\n * \n *             refcount_copying(&dst, dtype_is_object, ndim, False)\n */\n    __pyx_t_2 = (__pyx_v_direct_copy != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":1322\n *         if direct_copy:\n * \n *             refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<\n *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))\n *             refcount_copying(&dst, dtype_is_object, ndim, True)\n */\n      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);\n\n      /* \"View.MemoryView\":1323\n * \n *             refcount_copying(&dst, dtype_is_object, ndim, False)\n *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<\n *             refcount_copying(&dst, dtype_is_object, ndim, True)\n *             free(tmpdata)\n */\n      (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));\n\n      /* \"View.MemoryView\":1324\n *             refcount_copying(&dst, dtype_is_object, ndim, False)\n *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))\n *             refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<\n *             free(tmpdata)\n *             return 0\n */\n      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);\n\n      /* \"View.MemoryView\":1325\n *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))\n *             refcount_copying(&dst, dtype_is_object, ndim, True)\n *             free(tmpdata)             # <<<<<<<<<<<<<<\n *             return 0\n * \n */\n      free(__pyx_v_tmpdata);\n\n      /* \"View.MemoryView\":1326\n *             refcount_copying(&dst, dtype_is_object, ndim, True)\n *             free(tmpdata)\n *             return 0             # <<<<<<<<<<<<<<\n * \n *     if order == 'F' == get_best_order(&dst, ndim):\n */\n      __pyx_r = 0;\n      goto __pyx_L0;\n\n      /* \"View.MemoryView\":1320\n *             direct_copy = slice_is_contig(dst, 'F', ndim)\n * \n *         if direct_copy:             # <<<<<<<<<<<<<<\n * \n *             refcount_copying(&dst, dtype_is_object, ndim, False)\n */\n    }\n\n    /* \"View.MemoryView\":1312\n *         src = tmp\n * \n *     if not broadcasting:             # <<<<<<<<<<<<<<\n * \n * \n */\n  }\n\n  /* \"View.MemoryView\":1328\n *             return 0\n * \n *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<\n * \n * \n */\n  __pyx_t_2 = (__pyx_v_order == 'F');\n  if (__pyx_t_2) {\n    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));\n  }\n  __pyx_t_8 = (__pyx_t_2 != 0);\n  if (__pyx_t_8) {\n\n    /* \"View.MemoryView\":1331\n * \n * \n *         transpose_memslice(&src)             # <<<<<<<<<<<<<<\n *         transpose_memslice(&dst)\n * \n */\n    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1331, __pyx_L1_error)\n\n    /* \"View.MemoryView\":1332\n * \n *         transpose_memslice(&src)\n *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<\n * \n *     refcount_copying(&dst, dtype_is_object, ndim, False)\n */\n    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1332, __pyx_L1_error)\n\n    /* \"View.MemoryView\":1328\n *             return 0\n * \n *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<\n * \n * \n */\n  }\n\n  /* \"View.MemoryView\":1334\n *         transpose_memslice(&dst)\n * \n *     refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<\n *     copy_strided_to_strided(&src, &dst, ndim, itemsize)\n *     refcount_copying(&dst, dtype_is_object, ndim, True)\n */\n  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);\n\n  /* \"View.MemoryView\":1335\n * \n *     refcount_copying(&dst, dtype_is_object, ndim, False)\n *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<\n *     refcount_copying(&dst, dtype_is_object, ndim, True)\n * \n */\n  copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);\n\n  /* \"View.MemoryView\":1336\n *     refcount_copying(&dst, dtype_is_object, ndim, False)\n *     copy_strided_to_strided(&src, &dst, ndim, itemsize)\n *     refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<\n * \n *     free(tmpdata)\n */\n  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);\n\n  /* \"View.MemoryView\":1338\n *     refcount_copying(&dst, dtype_is_object, ndim, True)\n * \n *     free(tmpdata)             # <<<<<<<<<<<<<<\n *     return 0\n * \n */\n  free(__pyx_v_tmpdata);\n\n  /* \"View.MemoryView\":1339\n * \n *     free(tmpdata)\n *     return 0             # <<<<<<<<<<<<<<\n * \n * @cname('__pyx_memoryview_broadcast_leading')\n */\n  __pyx_r = 0;\n  goto __pyx_L0;\n\n  /* \"View.MemoryView\":1270\n * \n * @cname('__pyx_memoryview_copy_contents')\n * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<\n *                                   __Pyx_memviewslice dst,\n *                                   int src_ndim, int dst_ndim,\n */\n\n  /* function exit code */\n  __pyx_L1_error:;\n  {\n    #ifdef WITH_THREAD\n    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();\n    #endif\n    __Pyx_AddTraceback(\"View.MemoryView.memoryview_copy_contents\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n    #ifdef WITH_THREAD\n    __Pyx_PyGILState_Release(__pyx_gilstate_save);\n    #endif\n  }\n  __pyx_r = -1;\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1342\n * \n * @cname('__pyx_memoryview_broadcast_leading')\n * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<\n *                             int ndim,\n *                             int ndim_other) nogil:\n */\n\nstatic void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) {\n  int __pyx_v_i;\n  int __pyx_v_offset;\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n\n  /* \"View.MemoryView\":1346\n *                             int ndim_other) nogil:\n *     cdef int i\n *     cdef int offset = ndim_other - ndim             # <<<<<<<<<<<<<<\n * \n *     for i in range(ndim - 1, -1, -1):\n */\n  __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim);\n\n  /* \"View.MemoryView\":1348\n *     cdef int offset = ndim_other - ndim\n * \n *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<\n *         mslice.shape[i + offset] = mslice.shape[i]\n *         mslice.strides[i + offset] = mslice.strides[i]\n */\n  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {\n    __pyx_v_i = __pyx_t_1;\n\n    /* \"View.MemoryView\":1349\n * \n *     for i in range(ndim - 1, -1, -1):\n *         mslice.shape[i + offset] = mslice.shape[i]             # <<<<<<<<<<<<<<\n *         mslice.strides[i + offset] = mslice.strides[i]\n *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]\n */\n    (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]);\n\n    /* \"View.MemoryView\":1350\n *     for i in range(ndim - 1, -1, -1):\n *         mslice.shape[i + offset] = mslice.shape[i]\n *         mslice.strides[i + offset] = mslice.strides[i]             # <<<<<<<<<<<<<<\n *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]\n * \n */\n    (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]);\n\n    /* \"View.MemoryView\":1351\n *         mslice.shape[i + offset] = mslice.shape[i]\n *         mslice.strides[i + offset] = mslice.strides[i]\n *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]             # <<<<<<<<<<<<<<\n * \n *     for i in range(offset):\n */\n    (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]);\n  }\n\n  /* \"View.MemoryView\":1353\n *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]\n * \n *     for i in range(offset):             # <<<<<<<<<<<<<<\n *         mslice.shape[i] = 1\n *         mslice.strides[i] = mslice.strides[0]\n */\n  __pyx_t_1 = __pyx_v_offset;\n  __pyx_t_2 = __pyx_t_1;\n  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {\n    __pyx_v_i = __pyx_t_3;\n\n    /* \"View.MemoryView\":1354\n * \n *     for i in range(offset):\n *         mslice.shape[i] = 1             # <<<<<<<<<<<<<<\n *         mslice.strides[i] = mslice.strides[0]\n *         mslice.suboffsets[i] = -1\n */\n    (__pyx_v_mslice->shape[__pyx_v_i]) = 1;\n\n    /* \"View.MemoryView\":1355\n *     for i in range(offset):\n *         mslice.shape[i] = 1\n *         mslice.strides[i] = mslice.strides[0]             # <<<<<<<<<<<<<<\n *         mslice.suboffsets[i] = -1\n * \n */\n    (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]);\n\n    /* \"View.MemoryView\":1356\n *         mslice.shape[i] = 1\n *         mslice.strides[i] = mslice.strides[0]\n *         mslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<\n * \n * \n */\n    (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L;\n  }\n\n  /* \"View.MemoryView\":1342\n * \n * @cname('__pyx_memoryview_broadcast_leading')\n * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<\n *                             int ndim,\n *                             int ndim_other) nogil:\n */\n\n  /* function exit code */\n}\n\n/* \"View.MemoryView\":1364\n * \n * @cname('__pyx_memoryview_refcount_copying')\n * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<\n *                            int ndim, bint inc) nogil:\n * \n */\n\nstatic void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) {\n  int __pyx_t_1;\n\n  /* \"View.MemoryView\":1368\n * \n * \n *     if dtype_is_object:             # <<<<<<<<<<<<<<\n *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,\n *                                            dst.strides, ndim, inc)\n */\n  __pyx_t_1 = (__pyx_v_dtype_is_object != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":1369\n * \n *     if dtype_is_object:\n *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,             # <<<<<<<<<<<<<<\n *                                            dst.strides, ndim, inc)\n * \n */\n    __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc);\n\n    /* \"View.MemoryView\":1368\n * \n * \n *     if dtype_is_object:             # <<<<<<<<<<<<<<\n *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,\n *                                            dst.strides, ndim, inc)\n */\n  }\n\n  /* \"View.MemoryView\":1364\n * \n * @cname('__pyx_memoryview_refcount_copying')\n * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<\n *                            int ndim, bint inc) nogil:\n * \n */\n\n  /* function exit code */\n}\n\n/* \"View.MemoryView\":1373\n * \n * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')\n * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<\n *                                              Py_ssize_t *strides, int ndim,\n *                                              bint inc) with gil:\n */\n\nstatic void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {\n  __Pyx_RefNannyDeclarations\n  #ifdef WITH_THREAD\n  PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();\n  #endif\n  __Pyx_RefNannySetupContext(\"refcount_objects_in_slice_with_gil\", 0);\n\n  /* \"View.MemoryView\":1376\n *                                              Py_ssize_t *strides, int ndim,\n *                                              bint inc) with gil:\n *     refcount_objects_in_slice(data, shape, strides, ndim, inc)             # <<<<<<<<<<<<<<\n * \n * @cname('__pyx_memoryview_refcount_objects_in_slice')\n */\n  __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc);\n\n  /* \"View.MemoryView\":1373\n * \n * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')\n * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<\n *                                              Py_ssize_t *strides, int ndim,\n *                                              bint inc) with gil:\n */\n\n  /* function exit code */\n  __Pyx_RefNannyFinishContext();\n  #ifdef WITH_THREAD\n  __Pyx_PyGILState_Release(__pyx_gilstate_save);\n  #endif\n}\n\n/* \"View.MemoryView\":1379\n * \n * @cname('__pyx_memoryview_refcount_objects_in_slice')\n * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<\n *                                     Py_ssize_t *strides, int ndim, bint inc):\n *     cdef Py_ssize_t i\n */\n\nstatic void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {\n  CYTHON_UNUSED Py_ssize_t __pyx_v_i;\n  __Pyx_RefNannyDeclarations\n  Py_ssize_t __pyx_t_1;\n  Py_ssize_t __pyx_t_2;\n  Py_ssize_t __pyx_t_3;\n  int __pyx_t_4;\n  __Pyx_RefNannySetupContext(\"refcount_objects_in_slice\", 0);\n\n  /* \"View.MemoryView\":1383\n *     cdef Py_ssize_t i\n * \n *     for i in range(shape[0]):             # <<<<<<<<<<<<<<\n *         if ndim == 1:\n *             if inc:\n */\n  __pyx_t_1 = (__pyx_v_shape[0]);\n  __pyx_t_2 = __pyx_t_1;\n  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {\n    __pyx_v_i = __pyx_t_3;\n\n    /* \"View.MemoryView\":1384\n * \n *     for i in range(shape[0]):\n *         if ndim == 1:             # <<<<<<<<<<<<<<\n *             if inc:\n *                 Py_INCREF((<PyObject **> data)[0])\n */\n    __pyx_t_4 = ((__pyx_v_ndim == 1) != 0);\n    if (__pyx_t_4) {\n\n      /* \"View.MemoryView\":1385\n *     for i in range(shape[0]):\n *         if ndim == 1:\n *             if inc:             # <<<<<<<<<<<<<<\n *                 Py_INCREF((<PyObject **> data)[0])\n *             else:\n */\n      __pyx_t_4 = (__pyx_v_inc != 0);\n      if (__pyx_t_4) {\n\n        /* \"View.MemoryView\":1386\n *         if ndim == 1:\n *             if inc:\n *                 Py_INCREF((<PyObject **> data)[0])             # <<<<<<<<<<<<<<\n *             else:\n *                 Py_DECREF((<PyObject **> data)[0])\n */\n        Py_INCREF((((PyObject **)__pyx_v_data)[0]));\n\n        /* \"View.MemoryView\":1385\n *     for i in range(shape[0]):\n *         if ndim == 1:\n *             if inc:             # <<<<<<<<<<<<<<\n *                 Py_INCREF((<PyObject **> data)[0])\n *             else:\n */\n        goto __pyx_L6;\n      }\n\n      /* \"View.MemoryView\":1388\n *                 Py_INCREF((<PyObject **> data)[0])\n *             else:\n *                 Py_DECREF((<PyObject **> data)[0])             # <<<<<<<<<<<<<<\n *         else:\n *             refcount_objects_in_slice(data, shape + 1, strides + 1,\n */\n      /*else*/ {\n        Py_DECREF((((PyObject **)__pyx_v_data)[0]));\n      }\n      __pyx_L6:;\n\n      /* \"View.MemoryView\":1384\n * \n *     for i in range(shape[0]):\n *         if ndim == 1:             # <<<<<<<<<<<<<<\n *             if inc:\n *                 Py_INCREF((<PyObject **> data)[0])\n */\n      goto __pyx_L5;\n    }\n\n    /* \"View.MemoryView\":1390\n *                 Py_DECREF((<PyObject **> data)[0])\n *         else:\n * 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__pyx_array___getitem__, /*mp_subscript*/\n  __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/\n};\n\nstatic PyBufferProcs __pyx_tp_as_buffer_array = {\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getreadbuffer*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getwritebuffer*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getsegcount*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getcharbuffer*/\n  #endif\n  __pyx_array_getbuffer, /*bf_getbuffer*/\n  0, /*bf_releasebuffer*/\n};\n\nstatic PyTypeObject __pyx_type___pyx_array = {\n  PyVarObject_HEAD_INIT(0, 0)\n  \"rank_cy.array\", /*tp_name*/\n  sizeof(struct __pyx_array_obj), /*tp_basicsize*/\n  0, /*tp_itemsize*/\n  __pyx_tp_dealloc_array, /*tp_dealloc*/\n  #if PY_VERSION_HEX < 0x030800b4\n  0, /*tp_print*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4\n  0, /*tp_vectorcall_offset*/\n  #endif\n  0, /*tp_getattr*/\n  0, /*tp_setattr*/\n  #if PY_MAJOR_VERSION < 3\n  0, /*tp_compare*/\n  #endif\n  #if PY_MAJOR_VERSION >= 3\n  0, /*tp_as_async*/\n  #endif\n  0, /*tp_repr*/\n  0, /*tp_as_number*/\n  &__pyx_tp_as_sequence_array, /*tp_as_sequence*/\n  &__pyx_tp_as_mapping_array, /*tp_as_mapping*/\n  0, /*tp_hash*/\n  0, /*tp_call*/\n  0, /*tp_str*/\n  __pyx_tp_getattro_array, /*tp_getattro*/\n  0, /*tp_setattro*/\n  &__pyx_tp_as_buffer_array, /*tp_as_buffer*/\n  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/\n  0, /*tp_doc*/\n  0, /*tp_traverse*/\n  0, /*tp_clear*/\n  0, /*tp_richcompare*/\n  0, /*tp_weaklistoffset*/\n  0, /*tp_iter*/\n  0, /*tp_iternext*/\n  __pyx_methods_array, /*tp_methods*/\n  0, /*tp_members*/\n  __pyx_getsets_array, /*tp_getset*/\n  0, /*tp_base*/\n  0, /*tp_dict*/\n  0, /*tp_descr_get*/\n  0, /*tp_descr_set*/\n  0, /*tp_dictoffset*/\n  0, /*tp_init*/\n  0, /*tp_alloc*/\n  __pyx_tp_new_array, /*tp_new*/\n  0, /*tp_free*/\n  0, /*tp_is_gc*/\n  0, /*tp_bases*/\n  0, /*tp_mro*/\n  0, /*tp_cache*/\n  0, /*tp_subclasses*/\n  0, /*tp_weaklist*/\n  0, /*tp_del*/\n  0, /*tp_version_tag*/\n  #if PY_VERSION_HEX >= 0x030400a1\n  0, /*tp_finalize*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)\n  0, /*tp_vectorcall*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000\n  0, /*tp_print*/\n  #endif\n  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000\n  0, /*tp_pypy_flags*/\n  #endif\n};\n\nstatic PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {\n  struct __pyx_MemviewEnum_obj *p;\n  PyObject *o;\n  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {\n    o = (*t->tp_alloc)(t, 0);\n  } else {\n    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);\n  }\n  if (unlikely(!o)) return 0;\n  p = ((struct __pyx_MemviewEnum_obj *)o);\n  p->name = Py_None; Py_INCREF(Py_None);\n  return o;\n}\n\nstatic void __pyx_tp_dealloc_Enum(PyObject *o) {\n  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;\n  #if CYTHON_USE_TP_FINALIZE\n  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {\n    if (PyObject_CallFinalizerFromDealloc(o)) return;\n  }\n  #endif\n  PyObject_GC_UnTrack(o);\n  Py_CLEAR(p->name);\n  (*Py_TYPE(o)->tp_free)(o);\n}\n\nstatic int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {\n  int e;\n  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;\n  if (p->name) {\n    e = (*v)(p->name, a); if (e) return e;\n  }\n  return 0;\n}\n\nstatic int __pyx_tp_clear_Enum(PyObject *o) {\n  PyObject* tmp;\n  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;\n  tmp = ((PyObject*)p->name);\n  p->name = Py_None; Py_INCREF(Py_None);\n  Py_XDECREF(tmp);\n  return 0;\n}\n\nstatic PyMethodDef __pyx_methods_Enum[] = {\n  {\"__reduce_cython__\", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0},\n  {\"__setstate_cython__\", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0},\n  {0, 0, 0, 0}\n};\n\nstatic PyTypeObject __pyx_type___pyx_MemviewEnum = {\n  PyVarObject_HEAD_INIT(0, 0)\n  \"rank_cy.Enum\", /*tp_name*/\n  sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/\n  0, /*tp_itemsize*/\n  __pyx_tp_dealloc_Enum, /*tp_dealloc*/\n  #if PY_VERSION_HEX < 0x030800b4\n  0, /*tp_print*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4\n  0, /*tp_vectorcall_offset*/\n  #endif\n  0, /*tp_getattr*/\n  0, /*tp_setattr*/\n  #if PY_MAJOR_VERSION < 3\n  0, /*tp_compare*/\n  #endif\n  #if PY_MAJOR_VERSION >= 3\n  0, /*tp_as_async*/\n  #endif\n  __pyx_MemviewEnum___repr__, /*tp_repr*/\n  0, /*tp_as_number*/\n  0, /*tp_as_sequence*/\n  0, /*tp_as_mapping*/\n  0, /*tp_hash*/\n  0, /*tp_call*/\n  0, /*tp_str*/\n  0, /*tp_getattro*/\n  0, /*tp_setattro*/\n  0, /*tp_as_buffer*/\n  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/\n  0, /*tp_doc*/\n  __pyx_tp_traverse_Enum, /*tp_traverse*/\n  __pyx_tp_clear_Enum, /*tp_clear*/\n  0, /*tp_richcompare*/\n  0, /*tp_weaklistoffset*/\n  0, /*tp_iter*/\n  0, /*tp_iternext*/\n  __pyx_methods_Enum, /*tp_methods*/\n  0, /*tp_members*/\n  0, /*tp_getset*/\n  0, /*tp_base*/\n  0, /*tp_dict*/\n  0, /*tp_descr_get*/\n  0, /*tp_descr_set*/\n  0, /*tp_dictoffset*/\n  __pyx_MemviewEnum___init__, /*tp_init*/\n  0, /*tp_alloc*/\n  __pyx_tp_new_Enum, /*tp_new*/\n  0, /*tp_free*/\n  0, /*tp_is_gc*/\n  0, /*tp_bases*/\n  0, /*tp_mro*/\n  0, /*tp_cache*/\n  0, /*tp_subclasses*/\n  0, /*tp_weaklist*/\n  0, /*tp_del*/\n  0, /*tp_version_tag*/\n  #if PY_VERSION_HEX >= 0x030400a1\n  0, /*tp_finalize*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)\n  0, /*tp_vectorcall*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000\n  0, /*tp_print*/\n  #endif\n  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000\n  0, /*tp_pypy_flags*/\n  #endif\n};\nstatic struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;\n\nstatic PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {\n  struct __pyx_memoryview_obj *p;\n  PyObject *o;\n  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {\n    o = (*t->tp_alloc)(t, 0);\n  } else {\n    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);\n  }\n  if (unlikely(!o)) return 0;\n  p = ((struct __pyx_memoryview_obj *)o);\n  p->__pyx_vtab = __pyx_vtabptr_memoryview;\n  p->obj = Py_None; Py_INCREF(Py_None);\n  p->_size = Py_None; Py_INCREF(Py_None);\n  p->_array_interface = Py_None; Py_INCREF(Py_None);\n  p->view.obj = NULL;\n  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad;\n  return o;\n  bad:\n  Py_DECREF(o); o = 0;\n  return NULL;\n}\n\nstatic void __pyx_tp_dealloc_memoryview(PyObject *o) {\n  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;\n  #if CYTHON_USE_TP_FINALIZE\n  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {\n    if (PyObject_CallFinalizerFromDealloc(o)) return;\n  }\n  #endif\n  PyObject_GC_UnTrack(o);\n  {\n    PyObject *etype, *eval, *etb;\n    PyErr_Fetch(&etype, &eval, &etb);\n    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);\n    __pyx_memoryview___dealloc__(o);\n    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);\n    PyErr_Restore(etype, eval, etb);\n  }\n  Py_CLEAR(p->obj);\n  Py_CLEAR(p->_size);\n  Py_CLEAR(p->_array_interface);\n  (*Py_TYPE(o)->tp_free)(o);\n}\n\nstatic int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {\n  int e;\n  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;\n  if (p->obj) {\n    e = (*v)(p->obj, a); if (e) return e;\n  }\n  if (p->_size) {\n    e = (*v)(p->_size, a); if (e) return e;\n  }\n  if (p->_array_interface) {\n    e = (*v)(p->_array_interface, a); if (e) return e;\n  }\n  if (p->view.obj) {\n    e = (*v)(p->view.obj, a); if (e) return e;\n  }\n  return 0;\n}\n\nstatic int __pyx_tp_clear_memoryview(PyObject *o) {\n  PyObject* tmp;\n  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;\n  tmp = ((PyObject*)p->obj);\n  p->obj = Py_None; Py_INCREF(Py_None);\n  Py_XDECREF(tmp);\n  tmp = ((PyObject*)p->_size);\n  p->_size = Py_None; Py_INCREF(Py_None);\n  Py_XDECREF(tmp);\n  tmp = ((PyObject*)p->_array_interface);\n  p->_array_interface = Py_None; Py_INCREF(Py_None);\n  Py_XDECREF(tmp);\n  Py_CLEAR(p->view.obj);\n  return 0;\n}\nstatic PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {\n  PyObject *r;\n  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;\n  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);\n  Py_DECREF(x);\n  return r;\n}\n\nstatic int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {\n  if (v) {\n    return __pyx_memoryview___setitem__(o, i, v);\n  }\n  else {\n    PyErr_Format(PyExc_NotImplementedError,\n      \"Subscript deletion not supported by %.200s\", Py_TYPE(o)->tp_name);\n    return -1;\n  }\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);\n}\n\nstatic PyMethodDef __pyx_methods_memoryview[] = {\n  {\"is_c_contig\", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0},\n  {\"is_f_contig\", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0},\n  {\"copy\", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0},\n  {\"copy_fortran\", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0},\n  {\"__reduce_cython__\", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0},\n  {\"__setstate_cython__\", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0},\n  {0, 0, 0, 0}\n};\n\nstatic struct PyGetSetDef __pyx_getsets_memoryview[] = {\n  {(char *)\"T\", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},\n  {(char *)\"base\", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},\n  {(char *)\"shape\", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},\n  {(char *)\"strides\", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},\n  {(char *)\"suboffsets\", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},\n  {(char *)\"ndim\", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},\n  {(char *)\"itemsize\", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},\n  {(char *)\"nbytes\", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},\n  {(char *)\"size\", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},\n  {0, 0, 0, 0, 0}\n};\n\nstatic PySequenceMethods __pyx_tp_as_sequence_memoryview = {\n  __pyx_memoryview___len__, /*sq_length*/\n  0, /*sq_concat*/\n  0, /*sq_repeat*/\n  __pyx_sq_item_memoryview, /*sq_item*/\n  0, /*sq_slice*/\n  0, /*sq_ass_item*/\n  0, /*sq_ass_slice*/\n  0, /*sq_contains*/\n  0, /*sq_inplace_concat*/\n  0, /*sq_inplace_repeat*/\n};\n\nstatic PyMappingMethods __pyx_tp_as_mapping_memoryview = {\n  __pyx_memoryview___len__, /*mp_length*/\n  __pyx_memoryview___getitem__, /*mp_subscript*/\n  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/\n};\n\nstatic PyBufferProcs __pyx_tp_as_buffer_memoryview = {\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getreadbuffer*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getwritebuffer*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getsegcount*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getcharbuffer*/\n  #endif\n  __pyx_memoryview_getbuffer, /*bf_getbuffer*/\n  0, /*bf_releasebuffer*/\n};\n\nstatic PyTypeObject __pyx_type___pyx_memoryview = {\n  PyVarObject_HEAD_INIT(0, 0)\n  \"rank_cy.memoryview\", /*tp_name*/\n  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/\n  0, /*tp_itemsize*/\n  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/\n  #if PY_VERSION_HEX < 0x030800b4\n  0, /*tp_print*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4\n  0, /*tp_vectorcall_offset*/\n  #endif\n  0, /*tp_getattr*/\n  0, /*tp_setattr*/\n  #if PY_MAJOR_VERSION < 3\n  0, /*tp_compare*/\n  #endif\n  #if PY_MAJOR_VERSION >= 3\n  0, /*tp_as_async*/\n  #endif\n  __pyx_memoryview___repr__, /*tp_repr*/\n  0, /*tp_as_number*/\n  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/\n  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/\n  0, /*tp_hash*/\n  0, /*tp_call*/\n  __pyx_memoryview___str__, /*tp_str*/\n  0, /*tp_getattro*/\n  0, /*tp_setattro*/\n  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/\n  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/\n  0, /*tp_doc*/\n  __pyx_tp_traverse_memoryview, /*tp_traverse*/\n  __pyx_tp_clear_memoryview, /*tp_clear*/\n  0, /*tp_richcompare*/\n  0, /*tp_weaklistoffset*/\n  0, /*tp_iter*/\n  0, /*tp_iternext*/\n  __pyx_methods_memoryview, /*tp_methods*/\n  0, /*tp_members*/\n  __pyx_getsets_memoryview, /*tp_getset*/\n  0, /*tp_base*/\n  0, /*tp_dict*/\n  0, /*tp_descr_get*/\n  0, /*tp_descr_set*/\n  0, /*tp_dictoffset*/\n  0, /*tp_init*/\n  0, /*tp_alloc*/\n  __pyx_tp_new_memoryview, /*tp_new*/\n  0, /*tp_free*/\n  0, /*tp_is_gc*/\n  0, /*tp_bases*/\n  0, /*tp_mro*/\n  0, /*tp_cache*/\n  0, /*tp_subclasses*/\n  0, /*tp_weaklist*/\n  0, /*tp_del*/\n  0, /*tp_version_tag*/\n  #if PY_VERSION_HEX >= 0x030400a1\n  0, /*tp_finalize*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)\n  0, /*tp_vectorcall*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000\n  0, /*tp_print*/\n  #endif\n  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000\n  0, /*tp_pypy_flags*/\n  #endif\n};\nstatic struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;\n\nstatic PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {\n  struct __pyx_memoryviewslice_obj *p;\n  PyObject *o = __pyx_tp_new_memoryview(t, a, k);\n  if (unlikely(!o)) return 0;\n  p = ((struct __pyx_memoryviewslice_obj *)o);\n  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;\n  p->from_object = Py_None; Py_INCREF(Py_None);\n  p->from_slice.memview = NULL;\n  return o;\n}\n\nstatic void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {\n  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;\n  #if CYTHON_USE_TP_FINALIZE\n  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {\n    if (PyObject_CallFinalizerFromDealloc(o)) return;\n  }\n  #endif\n  PyObject_GC_UnTrack(o);\n  {\n    PyObject *etype, *eval, *etb;\n    PyErr_Fetch(&etype, &eval, &etb);\n    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);\n    __pyx_memoryviewslice___dealloc__(o);\n    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);\n    PyErr_Restore(etype, eval, etb);\n  }\n  Py_CLEAR(p->from_object);\n  PyObject_GC_Track(o);\n  __pyx_tp_dealloc_memoryview(o);\n}\n\nstatic int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {\n  int e;\n  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;\n  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;\n  if (p->from_object) {\n    e = (*v)(p->from_object, a); if (e) return e;\n  }\n  return 0;\n}\n\nstatic int __pyx_tp_clear__memoryviewslice(PyObject *o) {\n  PyObject* tmp;\n  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;\n  __pyx_tp_clear_memoryview(o);\n  tmp = ((PyObject*)p->from_object);\n  p->from_object = Py_None; Py_INCREF(Py_None);\n  Py_XDECREF(tmp);\n  __PYX_XDEC_MEMVIEW(&p->from_slice, 1);\n  return 0;\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o);\n}\n\nstatic PyMethodDef __pyx_methods__memoryviewslice[] = {\n  {\"__reduce_cython__\", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0},\n  {\"__setstate_cython__\", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0},\n  {0, 0, 0, 0}\n};\n\nstatic struct PyGetSetDef __pyx_getsets__memoryviewslice[] = {\n  {(char *)\"base\", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0},\n  {0, 0, 0, 0, 0}\n};\n\nstatic 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(PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);\n#else\n        dictptr = _PyObject_GetDictPtr(obj);\n#endif\n    }\n    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;\n}\nstatic CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {\n    PyObject *dict = Py_TYPE(obj)->tp_dict;\n    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))\n        return 0;\n    return obj_dict_version == __Pyx_get_object_dict_version(obj);\n}\n#endif\n\n/* GetModuleGlobalName */\n#if CYTHON_USE_DICT_VERSIONS\nstatic PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)\n#else\nstatic CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)\n#endif\n{\n    PyObject *result;\n#if !CYTHON_AVOID_BORROWED_REFS\n#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1\n    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);\n    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)\n    if (likely(result)) {\n        return __Pyx_NewRef(result);\n    } else if (unlikely(PyErr_Occurred())) {\n        return NULL;\n    }\n#else\n    result = PyDict_GetItem(__pyx_d, name);\n    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)\n    if (likely(result)) {\n        return __Pyx_NewRef(result);\n    }\n#endif\n#else\n    result = PyObject_GetItem(__pyx_d, name);\n    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)\n    if (likely(result)) {\n        return __Pyx_NewRef(result);\n    }\n    PyErr_Clear();\n#endif\n    return __Pyx_GetBuiltinName(name);\n}\n\n/* PyObjectCall */\n#if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {\n    PyObject *result;\n    ternaryfunc call = Py_TYPE(func)->tp_call;\n    if (unlikely(!call))\n        return PyObject_Call(func, arg, kw);\n    if (unlikely(Py_EnterRecursiveCall((char*)\" while calling a Python object\")))\n        return NULL;\n    result = (*call)(func, arg, kw);\n    Py_LeaveRecursiveCall();\n    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {\n        PyErr_SetString(\n            PyExc_SystemError,\n            \"NULL result without error in PyObject_Call\");\n    }\n    return result;\n}\n#endif\n\n/* MemviewSliceInit */\nstatic int\n__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,\n                        int ndim,\n                        __Pyx_memviewslice *memviewslice,\n                        int memview_is_new_reference)\n{\n    __Pyx_RefNannyDeclarations\n    int i, retval=-1;\n    Py_buffer *buf = &memview->view;\n    __Pyx_RefNannySetupContext(\"init_memviewslice\", 0);\n    if (unlikely(memviewslice->memview || memviewslice->data)) {\n        PyErr_SetString(PyExc_ValueError,\n            \"memviewslice is already initialized!\");\n        goto fail;\n    }\n    if (buf->strides) {\n        for (i = 0; i < ndim; i++) {\n            memviewslice->strides[i] = buf->strides[i];\n        }\n    } else {\n        Py_ssize_t stride = buf->itemsize;\n        for (i = ndim - 1; i >= 0; i--) {\n            memviewslice->strides[i] = stride;\n            stride *= buf->shape[i];\n        }\n    }\n    for (i = 0; i < ndim; i++) {\n        memviewslice->shape[i]   = buf->shape[i];\n        if (buf->suboffsets) {\n            memviewslice->suboffsets[i] = buf->suboffsets[i];\n        } else {\n            memviewslice->suboffsets[i] = -1;\n        }\n    }\n    memviewslice->memview = memview;\n    memviewslice->data = (char *)buf->buf;\n    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {\n        Py_INCREF(memview);\n    }\n    retval = 0;\n    goto no_fail;\nfail:\n    memviewslice->memview = 0;\n    memviewslice->data = 0;\n    retval = -1;\nno_fail:\n    __Pyx_RefNannyFinishContext();\n    return retval;\n}\n#ifndef Py_NO_RETURN\n#define Py_NO_RETURN\n#endif\nstatic void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN {\n    va_list vargs;\n    char msg[200];\n#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES)\n    va_start(vargs, fmt);\n#else\n    va_start(vargs);\n#endif\n    vsnprintf(msg, 200, fmt, vargs);\n    va_end(vargs);\n    Py_FatalError(msg);\n}\nstatic CYTHON_INLINE int\n__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,\n                                   PyThread_type_lock lock)\n{\n    int result;\n    PyThread_acquire_lock(lock, 1);\n    result = (*acquisition_count)++;\n    PyThread_release_lock(lock);\n    return result;\n}\nstatic CYTHON_INLINE int\n__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,\n                                   PyThread_type_lock lock)\n{\n    int result;\n    PyThread_acquire_lock(lock, 1);\n    result = (*acquisition_count)--;\n    PyThread_release_lock(lock);\n    return result;\n}\nstatic CYTHON_INLINE void\n__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)\n{\n    int first_time;\n    struct __pyx_memoryview_obj *memview = memslice->memview;\n    if (unlikely(!memview || (PyObject *) memview == Py_None))\n        return;\n    if (unlikely(__pyx_get_slice_count(memview) < 0))\n        __pyx_fatalerror(\"Acquisition count is %d (line %d)\",\n                         __pyx_get_slice_count(memview), lineno);\n    first_time = __pyx_add_acquisition_count(memview) == 0;\n    if (unlikely(first_time)) {\n        if (have_gil) {\n            Py_INCREF((PyObject *) memview);\n        } else {\n            PyGILState_STATE _gilstate = PyGILState_Ensure();\n            Py_INCREF((PyObject *) memview);\n            PyGILState_Release(_gilstate);\n        }\n    }\n}\nstatic CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice,\n                                             int have_gil, int lineno) {\n    int last_time;\n    struct __pyx_memoryview_obj *memview = memslice->memview;\n    if (unlikely(!memview || (PyObject *) memview == Py_None)) {\n        memslice->memview = NULL;\n        return;\n    }\n    if (unlikely(__pyx_get_slice_count(memview) <= 0))\n        __pyx_fatalerror(\"Acquisition count is %d (line %d)\",\n                         __pyx_get_slice_count(memview), lineno);\n    last_time = __pyx_sub_acquisition_count(memview) == 1;\n    memslice->data = NULL;\n    if (unlikely(last_time)) {\n        if (have_gil) {\n            Py_CLEAR(memslice->memview);\n        } else {\n            PyGILState_STATE _gilstate = PyGILState_Ensure();\n            Py_CLEAR(memslice->memview);\n            PyGILState_Release(_gilstate);\n        }\n    } else {\n        memslice->memview = NULL;\n    }\n}\n\n/* RaiseArgTupleInvalid */\nstatic void __Pyx_RaiseArgtupleInvalid(\n    const char* func_name,\n    int exact,\n    Py_ssize_t num_min,\n    Py_ssize_t num_max,\n    Py_ssize_t num_found)\n{\n    Py_ssize_t num_expected;\n    const char *more_or_less;\n    if (num_found < num_min) {\n        num_expected = num_min;\n        more_or_less = \"at least\";\n    } else {\n        num_expected = num_max;\n        more_or_less = \"at most\";\n    }\n    if (exact) {\n        more_or_less = \"exactly\";\n    }\n    PyErr_Format(PyExc_TypeError,\n                 \"%.200s() takes %.8s %\" CYTHON_FORMAT_SSIZE_T \"d positional argument%.1s (%\" CYTHON_FORMAT_SSIZE_T \"d given)\",\n                 func_name, more_or_less, num_expected,\n                 (num_expected == 1) ? \"\" : \"s\", num_found);\n}\n\n/* RaiseDoubleKeywords */\nstatic void __Pyx_RaiseDoubleKeywordsError(\n    const char* func_name,\n    PyObject* kw_name)\n{\n    PyErr_Format(PyExc_TypeError,\n        #if PY_MAJOR_VERSION >= 3\n        \"%s() got multiple values for keyword argument '%U'\", func_name, kw_name);\n        #else\n        \"%s() got multiple values for keyword argument '%s'\", func_name,\n        PyString_AsString(kw_name));\n        #endif\n}\n\n/* ParseKeywords */\nstatic int __Pyx_ParseOptionalKeywords(\n    PyObject *kwds,\n    PyObject **argnames[],\n    PyObject *kwds2,\n    PyObject *values[],\n    Py_ssize_t num_pos_args,\n    const char* function_name)\n{\n    PyObject *key = 0, *value = 0;\n    Py_ssize_t pos = 0;\n    PyObject*** name;\n    PyObject*** first_kw_arg = argnames + num_pos_args;\n    while (PyDict_Next(kwds, &pos, &key, &value)) {\n        name = first_kw_arg;\n        while (*name && (**name != key)) name++;\n        if (*name) {\n            values[name-argnames] = value;\n            continue;\n        }\n        name = first_kw_arg;\n        #if PY_MAJOR_VERSION < 3\n        if (likely(PyString_Check(key))) {\n            while (*name) {\n                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))\n                        && _PyString_Eq(**name, key)) {\n                    values[name-argnames] = value;\n                    break;\n                }\n                name++;\n            }\n            if (*name) continue;\n            else {\n                PyObject*** argname = argnames;\n                while (argname != first_kw_arg) {\n                    if ((**argname == key) || (\n                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))\n                             && _PyString_Eq(**argname, key))) {\n                        goto arg_passed_twice;\n                    }\n                    argname++;\n                }\n            }\n        } else\n        #endif\n        if (likely(PyUnicode_Check(key))) {\n            while (*name) {\n                int cmp = (**name == key) ? 0 :\n                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3\n                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :\n                #endif\n                    PyUnicode_Compare(**name, key);\n                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;\n                if (cmp == 0) {\n                    values[name-argnames] = value;\n                    break;\n                }\n                name++;\n            }\n            if (*name) continue;\n            else {\n                PyObject*** argname = argnames;\n                while (argname != first_kw_arg) {\n                    int cmp = (**argname == key) ? 0 :\n                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3\n                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :\n                    #endif\n                        PyUnicode_Compare(**argname, key);\n                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;\n                    if (cmp == 0) goto arg_passed_twice;\n                    argname++;\n                }\n            }\n        } else\n            goto invalid_keyword_type;\n        if (kwds2) {\n            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;\n        } else {\n            goto invalid_keyword;\n        }\n    }\n    return 0;\narg_passed_twice:\n    __Pyx_RaiseDoubleKeywordsError(function_name, key);\n    goto bad;\ninvalid_keyword_type:\n    PyErr_Format(PyExc_TypeError,\n        \"%.200s() keywords must be strings\", function_name);\n    goto bad;\ninvalid_keyword:\n    PyErr_Format(PyExc_TypeError,\n    #if PY_MAJOR_VERSION < 3\n        \"%.200s() got an unexpected keyword argument '%.200s'\",\n        function_name, PyString_AsString(key));\n    #else\n        \"%s() got an unexpected keyword argument '%U'\",\n        function_name, key);\n    #endif\nbad:\n    return -1;\n}\n\n/* PyCFunctionFastCall */\n#if CYTHON_FAST_PYCCALL\nstatic CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {\n    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;\n    PyCFunction meth = PyCFunction_GET_FUNCTION(func);\n    PyObject *self = PyCFunction_GET_SELF(func);\n    int flags = PyCFunction_GET_FLAGS(func);\n    assert(PyCFunction_Check(func));\n    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)));\n    assert(nargs >= 0);\n    assert(nargs == 0 || args != NULL);\n    /* _PyCFunction_FastCallDict() must not be called with an exception set,\n       because it may clear it (directly or indirectly) and so the\n       caller loses its exception */\n    assert(!PyErr_Occurred());\n    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {\n        return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL);\n    } else {\n        return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs);\n    }\n}\n#endif\n\n/* PyFunctionFastCall */\n#if CYTHON_FAST_PYCALL\nstatic PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,\n                                               PyObject *globals) {\n    PyFrameObject *f;\n    PyThreadState *tstate = __Pyx_PyThreadState_Current;\n    PyObject **fastlocals;\n    Py_ssize_t i;\n    PyObject *result;\n    assert(globals != NULL);\n    /* XXX Perhaps we should create a specialized\n       PyFrame_New() that doesn't take locals, but does\n       take builtins without sanity checking them.\n       */\n    assert(tstate != NULL);\n    f = PyFrame_New(tstate, co, globals, NULL);\n    if (f == NULL) {\n        return NULL;\n    }\n    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);\n    for (i = 0; i < na; i++) {\n        Py_INCREF(*args);\n        fastlocals[i] = *args++;\n    }\n    result = PyEval_EvalFrameEx(f,0);\n    ++tstate->recursion_depth;\n    Py_DECREF(f);\n    --tstate->recursion_depth;\n    return result;\n}\n#if 1 || PY_VERSION_HEX < 0x030600B1\nstatic PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {\n    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);\n    PyObject *globals = PyFunction_GET_GLOBALS(func);\n    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);\n    PyObject *closure;\n#if PY_MAJOR_VERSION >= 3\n    PyObject *kwdefs;\n#endif\n    PyObject *kwtuple, **k;\n    PyObject **d;\n    Py_ssize_t nd;\n    Py_ssize_t nk;\n    PyObject *result;\n    assert(kwargs == NULL || PyDict_Check(kwargs));\n    nk = kwargs ? PyDict_Size(kwargs) : 0;\n    if (Py_EnterRecursiveCall((char*)\" while calling a Python object\")) {\n        return NULL;\n    }\n    if (\n#if PY_MAJOR_VERSION >= 3\n            co->co_kwonlyargcount == 0 &&\n#endif\n            likely(kwargs == NULL || nk == 0) &&\n            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {\n        if (argdefs == NULL && co->co_argcount == nargs) {\n            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);\n            goto done;\n        }\n        else if (nargs == 0 && argdefs != NULL\n                 && co->co_argcount == Py_SIZE(argdefs)) {\n            /* function called with no arguments, but all parameters have\n               a default value: use default values as arguments .*/\n            args = &PyTuple_GET_ITEM(argdefs, 0);\n            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);\n            goto done;\n        }\n    }\n    if (kwargs != NULL) {\n        Py_ssize_t pos, i;\n        kwtuple = PyTuple_New(2 * nk);\n        if (kwtuple == NULL) {\n            result = NULL;\n            goto done;\n        }\n        k = &PyTuple_GET_ITEM(kwtuple, 0);\n        pos = i = 0;\n        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {\n            Py_INCREF(k[i]);\n            Py_INCREF(k[i+1]);\n            i += 2;\n        }\n        nk = i / 2;\n    }\n    else {\n        kwtuple = NULL;\n        k = NULL;\n    }\n    closure = PyFunction_GET_CLOSURE(func);\n#if PY_MAJOR_VERSION >= 3\n    kwdefs = PyFunction_GET_KW_DEFAULTS(func);\n#endif\n    if (argdefs != NULL) {\n        d = &PyTuple_GET_ITEM(argdefs, 0);\n        nd = Py_SIZE(argdefs);\n    }\n    else {\n        d = NULL;\n        nd = 0;\n    }\n#if PY_MAJOR_VERSION >= 3\n    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,\n                               args, (int)nargs,\n                               k, (int)nk,\n                               d, (int)nd, kwdefs, closure);\n#else\n    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,\n                               args, (int)nargs,\n                               k, (int)nk,\n                               d, (int)nd, closure);\n#endif\n    Py_XDECREF(kwtuple);\ndone:\n    Py_LeaveRecursiveCall();\n    return result;\n}\n#endif\n#endif\n\n/* PyObjectCall2Args */\nstatic CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {\n    PyObject *args, *result = NULL;\n    #if CYTHON_FAST_PYCALL\n    if (PyFunction_Check(function)) {\n        PyObject *args[2] = {arg1, arg2};\n        return __Pyx_PyFunction_FastCall(function, args, 2);\n    }\n    #endif\n    #if CYTHON_FAST_PYCCALL\n    if (__Pyx_PyFastCFunction_Check(function)) {\n        PyObject *args[2] = {arg1, arg2};\n        return __Pyx_PyCFunction_FastCall(function, args, 2);\n    }\n    #endif\n    args = PyTuple_New(2);\n    if (unlikely(!args)) goto done;\n    Py_INCREF(arg1);\n    PyTuple_SET_ITEM(args, 0, arg1);\n    Py_INCREF(arg2);\n    PyTuple_SET_ITEM(args, 1, arg2);\n    Py_INCREF(function);\n    result = __Pyx_PyObject_Call(function, args, NULL);\n    Py_DECREF(args);\n    Py_DECREF(function);\ndone:\n    return result;\n}\n\n/* PyObjectCallMethO */\n#if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {\n    PyObject *self, *result;\n    PyCFunction cfunc;\n    cfunc = PyCFunction_GET_FUNCTION(func);\n    self = PyCFunction_GET_SELF(func);\n    if (unlikely(Py_EnterRecursiveCall((char*)\" while calling a Python object\")))\n        return NULL;\n    result = cfunc(self, arg);\n    Py_LeaveRecursiveCall();\n    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {\n        PyErr_SetString(\n            PyExc_SystemError,\n            \"NULL result without error in PyObject_Call\");\n    }\n    return result;\n}\n#endif\n\n/* PyObjectCallOneArg */\n#if CYTHON_COMPILING_IN_CPYTHON\nstatic PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n    PyObject *result;\n    PyObject *args = PyTuple_New(1);\n    if (unlikely(!args)) return NULL;\n    Py_INCREF(arg);\n    PyTuple_SET_ITEM(args, 0, arg);\n    result = __Pyx_PyObject_Call(func, args, NULL);\n    Py_DECREF(args);\n    return result;\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n#if CYTHON_FAST_PYCALL\n    if (PyFunction_Check(func)) {\n        return __Pyx_PyFunction_FastCall(func, &arg, 1);\n    }\n#endif\n    if (likely(PyCFunction_Check(func))) {\n        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {\n            return __Pyx_PyObject_CallMethO(func, arg);\n#if CYTHON_FAST_PYCCALL\n        } else if (__Pyx_PyFastCFunction_Check(func)) {\n            return __Pyx_PyCFunction_FastCall(func, &arg, 1);\n#endif\n        }\n    }\n    return __Pyx__PyObject_CallOneArg(func, arg);\n}\n#else\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n    PyObject *result;\n    PyObject *args = PyTuple_Pack(1, arg);\n    if (unlikely(!args)) return NULL;\n    result = __Pyx_PyObject_Call(func, args, NULL);\n    Py_DECREF(args);\n    return result;\n}\n#endif\n\n/* GetItemInt */\nstatic PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {\n    PyObject *r;\n    if (!j) return NULL;\n    r = PyObject_GetItem(o, j);\n    Py_DECREF(j);\n    return r;\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,\n                                                              CYTHON_NCP_UNUSED int wraparound,\n                                                              CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS\n    Py_ssize_t wrapped_i = i;\n    if (wraparound & unlikely(i < 0)) {\n        wrapped_i += PyList_GET_SIZE(o);\n    }\n    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {\n        PyObject *r = PyList_GET_ITEM(o, wrapped_i);\n        Py_INCREF(r);\n        return r;\n    }\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n#else\n    return PySequence_GetItem(o, i);\n#endif\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,\n                                                              CYTHON_NCP_UNUSED int wraparound,\n                                                              CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS\n    Py_ssize_t wrapped_i = i;\n    if (wraparound & unlikely(i < 0)) {\n        wrapped_i += PyTuple_GET_SIZE(o);\n    }\n    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {\n        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);\n        Py_INCREF(r);\n        return r;\n    }\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n#else\n    return PySequence_GetItem(o, i);\n#endif\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,\n                                                     CYTHON_NCP_UNUSED int wraparound,\n                                                     CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS\n    if (is_list || PyList_CheckExact(o)) {\n        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);\n        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {\n            PyObject *r = PyList_GET_ITEM(o, n);\n            Py_INCREF(r);\n            return r;\n        }\n    }\n    else if (PyTuple_CheckExact(o)) {\n        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);\n        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {\n            PyObject *r = PyTuple_GET_ITEM(o, n);\n            Py_INCREF(r);\n            return r;\n        }\n    } else {\n        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;\n        if (likely(m && m->sq_item)) {\n            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {\n                Py_ssize_t l = m->sq_length(o);\n                if (likely(l >= 0)) {\n                    i += l;\n                } else {\n                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))\n                        return NULL;\n                    PyErr_Clear();\n                }\n            }\n            return m->sq_item(o, i);\n        }\n    }\n#else\n    if (is_list || PySequence_Check(o)) {\n        return PySequence_GetItem(o, i);\n    }\n#endif\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n}\n\n/* ObjectGetItem */\n#if CYTHON_USE_TYPE_SLOTS\nstatic PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {\n    PyObject *runerr;\n    Py_ssize_t key_value;\n    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;\n    if (unlikely(!(m && m->sq_item))) {\n        PyErr_Format(PyExc_TypeError, \"'%.200s' object is not subscriptable\", Py_TYPE(obj)->tp_name);\n        return NULL;\n    }\n    key_value = __Pyx_PyIndex_AsSsize_t(index);\n    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {\n        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);\n    }\n    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {\n        PyErr_Clear();\n        PyErr_Format(PyExc_IndexError, \"cannot fit '%.200s' into an index-sized integer\", Py_TYPE(index)->tp_name);\n    }\n    return NULL;\n}\nstatic PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {\n    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;\n    if (likely(m && m->mp_subscript)) {\n        return m->mp_subscript(obj, key);\n    }\n    return __Pyx_PyObject_GetIndex(obj, key);\n}\n#endif\n\n/* PyObjectGetMethod */\nstatic int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) {\n    PyObject *attr;\n#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP\n    PyTypeObject *tp = Py_TYPE(obj);\n    PyObject *descr;\n    descrgetfunc f = NULL;\n    PyObject **dictptr, *dict;\n    int meth_found = 0;\n    assert (*method == NULL);\n    if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) {\n        attr = __Pyx_PyObject_GetAttrStr(obj, name);\n        goto try_unpack;\n    }\n    if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) {\n        return 0;\n    }\n    descr = _PyType_Lookup(tp, name);\n    if (likely(descr != NULL)) {\n        Py_INCREF(descr);\n#if PY_MAJOR_VERSION >= 3\n        #ifdef __Pyx_CyFunction_USED\n        if (likely(PyFunction_Check(descr) || (Py_TYPE(descr) == &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr)))\n        #else\n        if (likely(PyFunction_Check(descr) || (Py_TYPE(descr) == &PyMethodDescr_Type)))\n        #endif\n#else\n        #ifdef __Pyx_CyFunction_USED\n        if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr)))\n        #else\n        if (likely(PyFunction_Check(descr)))\n        #endif\n#endif\n        {\n            meth_found = 1;\n        } else {\n            f = Py_TYPE(descr)->tp_descr_get;\n            if (f != NULL && PyDescr_IsData(descr)) {\n                attr = f(descr, obj, (PyObject *)Py_TYPE(obj));\n                Py_DECREF(descr);\n                goto try_unpack;\n            }\n        }\n    }\n    dictptr = _PyObject_GetDictPtr(obj);\n    if (dictptr != NULL && (dict = *dictptr) != NULL) {\n        Py_INCREF(dict);\n        attr = __Pyx_PyDict_GetItemStr(dict, name);\n        if (attr != NULL) {\n            Py_INCREF(attr);\n            Py_DECREF(dict);\n            Py_XDECREF(descr);\n            goto try_unpack;\n        }\n        Py_DECREF(dict);\n    }\n    if (meth_found) {\n        *method = descr;\n        return 1;\n    }\n    if (f != NULL) {\n        attr = f(descr, obj, (PyObject *)Py_TYPE(obj));\n        Py_DECREF(descr);\n        goto try_unpack;\n    }\n    if (descr != NULL) {\n        *method = descr;\n        return 0;\n    }\n    PyErr_Format(PyExc_AttributeError,\n#if PY_MAJOR_VERSION >= 3\n                 \"'%.50s' object has no attribute '%U'\",\n                 tp->tp_name, name);\n#else\n                 \"'%.50s' object has no attribute '%.400s'\",\n                 tp->tp_name, PyString_AS_STRING(name));\n#endif\n    return 0;\n#else\n    attr = __Pyx_PyObject_GetAttrStr(obj, name);\n    goto try_unpack;\n#endif\ntry_unpack:\n#if CYTHON_UNPACK_METHODS\n    if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) {\n        PyObject *function = PyMethod_GET_FUNCTION(attr);\n        Py_INCREF(function);\n        Py_DECREF(attr);\n        *method = function;\n        return 1;\n    }\n#endif\n    *method = attr;\n    return 0;\n}\n\n/* PyObjectCallMethod1 */\nstatic PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg) {\n    PyObject *result = __Pyx_PyObject_CallOneArg(method, arg);\n    Py_DECREF(method);\n    return result;\n}\nstatic PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) {\n    PyObject *method = NULL, *result;\n    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);\n    if (likely(is_method)) {\n        result = __Pyx_PyObject_Call2Args(method, obj, arg);\n        Py_DECREF(method);\n        return result;\n    }\n    if (unlikely(!method)) return NULL;\n    return __Pyx__PyObject_CallMethod1(method, arg);\n}\n\n/* append */\nstatic CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x) {\n    if (likely(PyList_CheckExact(L))) {\n        if (unlikely(__Pyx_PyList_Append(L, x) < 0)) return -1;\n    } else {\n        PyObject* retval = __Pyx_PyObject_CallMethod1(L, __pyx_n_s_append, x);\n        if (unlikely(!retval))\n            return -1;\n        Py_DECREF(retval);\n    }\n    return 0;\n}\n\n/* PyObjectCallNoArg */\n#if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {\n#if CYTHON_FAST_PYCALL\n    if (PyFunction_Check(func)) {\n        return __Pyx_PyFunction_FastCall(func, NULL, 0);\n    }\n#endif\n#ifdef __Pyx_CyFunction_USED\n    if (likely(PyCFunction_Check(func) || __Pyx_CyFunction_Check(func)))\n#else\n    if (likely(PyCFunction_Check(func)))\n#endif\n    {\n        if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {\n            return __Pyx_PyObject_CallMethO(func, NULL);\n        }\n    }\n    return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);\n}\n#endif\n\n/* RaiseTooManyValuesToUnpack */\nstatic CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {\n    PyErr_Format(PyExc_ValueError,\n                 \"too many values to unpack (expected %\" CYTHON_FORMAT_SSIZE_T \"d)\", expected);\n}\n\n/* RaiseNeedMoreValuesToUnpack */\nstatic CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {\n    PyErr_Format(PyExc_ValueError,\n                 \"need more than %\" CYTHON_FORMAT_SSIZE_T \"d value%.1s to unpack\",\n                 index, (index == 1) ? \"\" : \"s\");\n}\n\n/* IterFinish */\nstatic CYTHON_INLINE int __Pyx_IterFinish(void) {\n#if CYTHON_FAST_THREAD_STATE\n    PyThreadState *tstate = __Pyx_PyThreadState_Current;\n    PyObject* exc_type = tstate->curexc_type;\n    if (unlikely(exc_type)) {\n        if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) {\n            PyObject *exc_value, *exc_tb;\n            exc_value = tstate->curexc_value;\n            exc_tb = tstate->curexc_traceback;\n            tstate->curexc_type = 0;\n            tstate->curexc_value = 0;\n            tstate->curexc_traceback = 0;\n            Py_DECREF(exc_type);\n            Py_XDECREF(exc_value);\n            Py_XDECREF(exc_tb);\n            return 0;\n        } else {\n            return -1;\n        }\n    }\n    return 0;\n#else\n    if (unlikely(PyErr_Occurred())) {\n        if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {\n            PyErr_Clear();\n            return 0;\n        } else {\n            return -1;\n        }\n    }\n    return 0;\n#endif\n}\n\n/* UnpackItemEndCheck */\nstatic int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {\n    if (unlikely(retval)) {\n        Py_DECREF(retval);\n        __Pyx_RaiseTooManyValuesError(expected);\n        return -1;\n    } else {\n        return __Pyx_IterFinish();\n    }\n    return 0;\n}\n\n/* None */\nstatic CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {\n    PyErr_Format(PyExc_UnboundLocalError, \"local variable '%s' referenced before assignment\", varname);\n}\n\n/* GetTopmostException */\n#if CYTHON_USE_EXC_INFO_STACK\nstatic _PyErr_StackItem *\n__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)\n{\n    _PyErr_StackItem *exc_info = tstate->exc_info;\n    while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&\n           exc_info->previous_item != NULL)\n    {\n        exc_info = exc_info->previous_item;\n    }\n    return exc_info;\n}\n#endif\n\n/* SaveResetException */\n#if CYTHON_FAST_THREAD_STATE\nstatic CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n    #if CYTHON_USE_EXC_INFO_STACK\n    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);\n    *type = exc_info->exc_type;\n    *value = exc_info->exc_value;\n    *tb = exc_info->exc_traceback;\n    #else\n    *type = tstate->exc_type;\n    *value = tstate->exc_value;\n    *tb = tstate->exc_traceback;\n    #endif\n    Py_XINCREF(*type);\n    Py_XINCREF(*value);\n    Py_XINCREF(*tb);\n}\nstatic CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    #if CYTHON_USE_EXC_INFO_STACK\n    _PyErr_StackItem *exc_info = tstate->exc_info;\n    tmp_type = exc_info->exc_type;\n    tmp_value = exc_info->exc_value;\n    tmp_tb = exc_info->exc_traceback;\n    exc_info->exc_type = type;\n    exc_info->exc_value = value;\n    exc_info->exc_traceback = tb;\n    #else\n    tmp_type = tstate->exc_type;\n    tmp_value = tstate->exc_value;\n    tmp_tb = tstate->exc_traceback;\n    tstate->exc_type = type;\n    tstate->exc_value = value;\n    tstate->exc_traceback = tb;\n    #endif\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n}\n#endif\n\n/* PyErrExceptionMatches */\n#if CYTHON_FAST_THREAD_STATE\nstatic int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {\n    Py_ssize_t i, n;\n    n = PyTuple_GET_SIZE(tuple);\n#if PY_MAJOR_VERSION >= 3\n    for (i=0; i<n; i++) {\n        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;\n    }\n#endif\n    for (i=0; i<n; i++) {\n        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;\n    }\n    return 0;\n}\nstatic CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {\n    PyObject *exc_type = tstate->curexc_type;\n    if (exc_type == err) return 1;\n    if (unlikely(!exc_type)) return 0;\n    if (unlikely(PyTuple_Check(err)))\n        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);\n    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);\n}\n#endif\n\n/* GetException */\n#if CYTHON_FAST_THREAD_STATE\nstatic int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)\n#else\nstatic int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)\n#endif\n{\n    PyObject *local_type, *local_value, *local_tb;\n#if CYTHON_FAST_THREAD_STATE\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    local_type = tstate->curexc_type;\n    local_value = tstate->curexc_value;\n    local_tb = tstate->curexc_traceback;\n    tstate->curexc_type = 0;\n    tstate->curexc_value = 0;\n    tstate->curexc_traceback = 0;\n#else\n    PyErr_Fetch(&local_type, &local_value, &local_tb);\n#endif\n    PyErr_NormalizeException(&local_type, &local_value, &local_tb);\n#if CYTHON_FAST_THREAD_STATE\n    if (unlikely(tstate->curexc_type))\n#else\n    if (unlikely(PyErr_Occurred()))\n#endif\n        goto bad;\n    #if PY_MAJOR_VERSION >= 3\n    if (local_tb) {\n        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))\n            goto bad;\n    }\n    #endif\n    Py_XINCREF(local_tb);\n    Py_XINCREF(local_type);\n    Py_XINCREF(local_value);\n    *type = local_type;\n    *value = local_value;\n    *tb = local_tb;\n#if CYTHON_FAST_THREAD_STATE\n    #if CYTHON_USE_EXC_INFO_STACK\n    {\n        _PyErr_StackItem *exc_info = tstate->exc_info;\n        tmp_type = exc_info->exc_type;\n        tmp_value = exc_info->exc_value;\n        tmp_tb = exc_info->exc_traceback;\n        exc_info->exc_type = local_type;\n        exc_info->exc_value = local_value;\n        exc_info->exc_traceback = local_tb;\n    }\n    #else\n    tmp_type = tstate->exc_type;\n    tmp_value = tstate->exc_value;\n    tmp_tb = tstate->exc_traceback;\n    tstate->exc_type = local_type;\n    tstate->exc_value = local_value;\n    tstate->exc_traceback = local_tb;\n    #endif\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n#else\n    PyErr_SetExcInfo(local_type, local_value, local_tb);\n#endif\n    return 0;\nbad:\n    *type = 0;\n    *value = 0;\n    *tb = 0;\n    Py_XDECREF(local_type);\n    Py_XDECREF(local_value);\n    Py_XDECREF(local_tb);\n    return -1;\n}\n\n/* PyErrFetchRestore */\n#if CYTHON_FAST_THREAD_STATE\nstatic CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    tmp_type = tstate->curexc_type;\n    tmp_value = tstate->curexc_value;\n    tmp_tb = tstate->curexc_traceback;\n    tstate->curexc_type = type;\n    tstate->curexc_value = value;\n    tstate->curexc_traceback = tb;\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n}\nstatic CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n    *type = tstate->curexc_type;\n    *value = tstate->curexc_value;\n    *tb = tstate->curexc_traceback;\n    tstate->curexc_type = 0;\n    tstate->curexc_value = 0;\n    tstate->curexc_traceback = 0;\n}\n#endif\n\n/* RaiseException */\n#if PY_MAJOR_VERSION < 3\nstatic void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,\n                        CYTHON_UNUSED PyObject *cause) {\n    __Pyx_PyThreadState_declare\n    Py_XINCREF(type);\n    if (!value || value == Py_None)\n        value = NULL;\n    else\n        Py_INCREF(value);\n    if (!tb || tb == Py_None)\n        tb = NULL;\n    else {\n        Py_INCREF(tb);\n        if (!PyTraceBack_Check(tb)) {\n            PyErr_SetString(PyExc_TypeError,\n                \"raise: arg 3 must be a traceback or None\");\n            goto raise_error;\n        }\n    }\n    if (PyType_Check(type)) {\n#if CYTHON_COMPILING_IN_PYPY\n        if (!value) {\n            Py_INCREF(Py_None);\n            value = Py_None;\n        }\n#endif\n        PyErr_NormalizeException(&type, &value, &tb);\n    } else {\n        if (value) {\n            PyErr_SetString(PyExc_TypeError,\n                \"instance exception may not have a separate value\");\n            goto raise_error;\n        }\n        value = type;\n        type = (PyObject*) Py_TYPE(type);\n        Py_INCREF(type);\n        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {\n            PyErr_SetString(PyExc_TypeError,\n                \"raise: exception class must be a subclass of BaseException\");\n            goto raise_error;\n        }\n    }\n    __Pyx_PyThreadState_assign\n    __Pyx_ErrRestore(type, value, tb);\n    return;\nraise_error:\n    Py_XDECREF(value);\n    Py_XDECREF(type);\n    Py_XDECREF(tb);\n    return;\n}\n#else\nstatic void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {\n    PyObject* owned_instance = NULL;\n    if (tb == Py_None) {\n        tb = 0;\n    } else if (tb && !PyTraceBack_Check(tb)) {\n        PyErr_SetString(PyExc_TypeError,\n            \"raise: arg 3 must be a traceback or None\");\n        goto bad;\n    }\n    if (value == Py_None)\n        value = 0;\n    if (PyExceptionInstance_Check(type)) {\n        if (value) {\n            PyErr_SetString(PyExc_TypeError,\n                \"instance exception may not have a separate value\");\n            goto bad;\n        }\n        value = type;\n        type = (PyObject*) Py_TYPE(value);\n    } else if (PyExceptionClass_Check(type)) {\n        PyObject *instance_class = NULL;\n        if (value && PyExceptionInstance_Check(value)) {\n            instance_class = (PyObject*) Py_TYPE(value);\n            if (instance_class != type) {\n                int is_subclass = PyObject_IsSubclass(instance_class, type);\n                if (!is_subclass) {\n                    instance_class = NULL;\n                } else if (unlikely(is_subclass == -1)) {\n                    goto bad;\n                } else {\n                    type = instance_class;\n                }\n            }\n        }\n        if (!instance_class) {\n            PyObject *args;\n            if (!value)\n                args = PyTuple_New(0);\n            else if (PyTuple_Check(value)) {\n                Py_INCREF(value);\n                args = value;\n            } else\n                args = PyTuple_Pack(1, value);\n            if (!args)\n                goto bad;\n            owned_instance = PyObject_Call(type, args, NULL);\n            Py_DECREF(args);\n            if (!owned_instance)\n                goto bad;\n            value = owned_instance;\n            if (!PyExceptionInstance_Check(value)) {\n                PyErr_Format(PyExc_TypeError,\n                             \"calling %R should have returned an instance of \"\n                             \"BaseException, not %R\",\n                             type, Py_TYPE(value));\n                goto bad;\n            }\n        }\n    } else {\n        PyErr_SetString(PyExc_TypeError,\n            \"raise: exception class must be a subclass of BaseException\");\n        goto bad;\n    }\n    if (cause) {\n        PyObject *fixed_cause;\n        if (cause == Py_None) {\n            fixed_cause = NULL;\n        } else if (PyExceptionClass_Check(cause)) {\n            fixed_cause = PyObject_CallObject(cause, NULL);\n            if (fixed_cause == NULL)\n                goto bad;\n        } else if (PyExceptionInstance_Check(cause)) {\n            fixed_cause = cause;\n            Py_INCREF(fixed_cause);\n        } else {\n            PyErr_SetString(PyExc_TypeError,\n                            \"exception causes must derive from \"\n                            \"BaseException\");\n            goto bad;\n        }\n        PyException_SetCause(value, fixed_cause);\n    }\n    PyErr_SetObject(type, value);\n    if (tb) {\n#if CYTHON_COMPILING_IN_PYPY\n        PyObject *tmp_type, *tmp_value, *tmp_tb;\n        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);\n        Py_INCREF(tb);\n        PyErr_Restore(tmp_type, tmp_value, tb);\n        Py_XDECREF(tmp_tb);\n#else\n        PyThreadState *tstate = __Pyx_PyThreadState_Current;\n        PyObject* tmp_tb = tstate->curexc_traceback;\n        if (tb != tmp_tb) {\n            Py_INCREF(tb);\n            tstate->curexc_traceback = tb;\n            Py_XDECREF(tmp_tb);\n        }\n#endif\n    }\nbad:\n    Py_XDECREF(owned_instance);\n    return;\n}\n#endif\n\n/* ArgTypeTest */\nstatic int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)\n{\n    if (unlikely(!type)) {\n        PyErr_SetString(PyExc_SystemError, \"Missing type object\");\n        return 0;\n    }\n    else if (exact) {\n        #if PY_MAJOR_VERSION == 2\n        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;\n        #endif\n    }\n    else {\n        if (likely(__Pyx_TypeCheck(obj, type))) return 1;\n    }\n    PyErr_Format(PyExc_TypeError,\n        \"Argument '%.200s' has incorrect type (expected %.200s, got %.200s)\",\n        name, type->tp_name, Py_TYPE(obj)->tp_name);\n    return 0;\n}\n\n/* BytesEquals */\nstatic CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {\n#if CYTHON_COMPILING_IN_PYPY\n    return PyObject_RichCompareBool(s1, s2, equals);\n#else\n    if (s1 == s2) {\n        return (equals == Py_EQ);\n    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {\n        const char *ps1, *ps2;\n        Py_ssize_t length = PyBytes_GET_SIZE(s1);\n        if (length != PyBytes_GET_SIZE(s2))\n            return (equals == Py_NE);\n        ps1 = PyBytes_AS_STRING(s1);\n        ps2 = PyBytes_AS_STRING(s2);\n        if (ps1[0] != ps2[0]) {\n            return (equals == Py_NE);\n        } else if (length == 1) {\n            return (equals == Py_EQ);\n        } else {\n            int result;\n#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000)\n            Py_hash_t hash1, hash2;\n            hash1 = ((PyBytesObject*)s1)->ob_shash;\n            hash2 = ((PyBytesObject*)s2)->ob_shash;\n            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {\n                return (equals == Py_NE);\n            }\n#endif\n            result = memcmp(ps1, ps2, (size_t)length);\n            return (equals == Py_EQ) ? (result == 0) : (result != 0);\n        }\n    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {\n        return (equals == Py_NE);\n    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {\n        return (equals == Py_NE);\n    } else {\n        int result;\n        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);\n        if (!py_result)\n            return -1;\n        result = __Pyx_PyObject_IsTrue(py_result);\n        Py_DECREF(py_result);\n        return result;\n    }\n#endif\n}\n\n/* UnicodeEquals */\nstatic CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {\n#if CYTHON_COMPILING_IN_PYPY\n    return PyObject_RichCompareBool(s1, s2, equals);\n#else\n#if PY_MAJOR_VERSION < 3\n    PyObject* owned_ref = NULL;\n#endif\n    int s1_is_unicode, s2_is_unicode;\n    if (s1 == s2) {\n        goto return_eq;\n    }\n    s1_is_unicode = PyUnicode_CheckExact(s1);\n    s2_is_unicode = PyUnicode_CheckExact(s2);\n#if PY_MAJOR_VERSION < 3\n    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {\n        owned_ref = PyUnicode_FromObject(s2);\n        if (unlikely(!owned_ref))\n            return -1;\n        s2 = owned_ref;\n        s2_is_unicode = 1;\n    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {\n        owned_ref = PyUnicode_FromObject(s1);\n        if (unlikely(!owned_ref))\n            return -1;\n        s1 = owned_ref;\n        s1_is_unicode = 1;\n    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {\n        return __Pyx_PyBytes_Equals(s1, s2, equals);\n    }\n#endif\n    if (s1_is_unicode & s2_is_unicode) {\n        Py_ssize_t length;\n        int kind;\n        void *data1, *data2;\n        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))\n            return -1;\n        length = __Pyx_PyUnicode_GET_LENGTH(s1);\n        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {\n            goto return_ne;\n        }\n#if CYTHON_USE_UNICODE_INTERNALS\n        {\n            Py_hash_t hash1, hash2;\n        #if CYTHON_PEP393_ENABLED\n            hash1 = ((PyASCIIObject*)s1)->hash;\n            hash2 = ((PyASCIIObject*)s2)->hash;\n        #else\n            hash1 = ((PyUnicodeObject*)s1)->hash;\n            hash2 = ((PyUnicodeObject*)s2)->hash;\n        #endif\n            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {\n                goto return_ne;\n            }\n        }\n#endif\n        kind = __Pyx_PyUnicode_KIND(s1);\n        if (kind != __Pyx_PyUnicode_KIND(s2)) {\n            goto return_ne;\n        }\n        data1 = __Pyx_PyUnicode_DATA(s1);\n        data2 = __Pyx_PyUnicode_DATA(s2);\n        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {\n            goto return_ne;\n        } else if (length == 1) {\n            goto return_eq;\n        } else {\n            int result = memcmp(data1, data2, (size_t)(length * kind));\n            #if PY_MAJOR_VERSION < 3\n            Py_XDECREF(owned_ref);\n            #endif\n            return (equals == Py_EQ) ? (result == 0) : (result != 0);\n        }\n    } else if ((s1 == Py_None) & s2_is_unicode) {\n        goto return_ne;\n    } else if ((s2 == Py_None) & s1_is_unicode) {\n        goto return_ne;\n    } else {\n        int result;\n        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);\n        #if PY_MAJOR_VERSION < 3\n        Py_XDECREF(owned_ref);\n        #endif\n        if (!py_result)\n            return -1;\n        result = __Pyx_PyObject_IsTrue(py_result);\n        Py_DECREF(py_result);\n        return result;\n    }\nreturn_eq:\n    #if PY_MAJOR_VERSION < 3\n    Py_XDECREF(owned_ref);\n    #endif\n    return (equals == Py_EQ);\nreturn_ne:\n    #if PY_MAJOR_VERSION < 3\n    Py_XDECREF(owned_ref);\n    #endif\n    return (equals == Py_NE);\n#endif\n}\n\n/* GetAttr */\nstatic CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {\n#if CYTHON_USE_TYPE_SLOTS\n#if PY_MAJOR_VERSION >= 3\n    if (likely(PyUnicode_Check(n)))\n#else\n    if (likely(PyString_Check(n)))\n#endif\n        return __Pyx_PyObject_GetAttrStr(o, n);\n#endif\n    return PyObject_GetAttr(o, n);\n}\n\n/* decode_c_string */\nstatic CYTHON_INLINE PyObject* __Pyx_decode_c_string(\n         const char* cstring, Py_ssize_t start, Py_ssize_t stop,\n         const char* encoding, const char* errors,\n         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {\n    Py_ssize_t length;\n    if (unlikely((start < 0) | (stop < 0))) {\n        size_t slen = strlen(cstring);\n        if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) {\n            PyErr_SetString(PyExc_OverflowError,\n                            \"c-string too long to convert to Python\");\n            return NULL;\n        }\n        length = (Py_ssize_t) slen;\n        if (start < 0) {\n            start += length;\n            if (start < 0)\n                start = 0;\n        }\n        if (stop < 0)\n            stop += length;\n    }\n    if (unlikely(stop <= start))\n        return __Pyx_NewRef(__pyx_empty_unicode);\n    length = stop - start;\n    cstring += start;\n    if (decode_func) {\n        return decode_func(cstring, length, errors);\n    } else {\n        return PyUnicode_Decode(cstring, length, encoding, errors);\n    }\n}\n\n/* GetAttr3 */\nstatic PyObject *__Pyx_GetAttr3Default(PyObject *d) {\n    __Pyx_PyThreadState_declare\n    __Pyx_PyThreadState_assign\n    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))\n        return NULL;\n    __Pyx_PyErr_Clear();\n    Py_INCREF(d);\n    return d;\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {\n    PyObject *r = __Pyx_GetAttr(o, n);\n    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);\n}\n\n/* RaiseNoneIterError */\nstatic CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {\n    PyErr_SetString(PyExc_TypeError, \"'NoneType' object is not iterable\");\n}\n\n/* ExtTypeTest */\nstatic CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {\n    if (unlikely(!type)) {\n        PyErr_SetString(PyExc_SystemError, \"Missing type object\");\n        return 0;\n    }\n    if (likely(__Pyx_TypeCheck(obj, type)))\n        return 1;\n    PyErr_Format(PyExc_TypeError, \"Cannot convert %.200s to %.200s\",\n                 Py_TYPE(obj)->tp_name, type->tp_name);\n    return 0;\n}\n\n/* SwapException */\n#if CYTHON_FAST_THREAD_STATE\nstatic CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    #if CYTHON_USE_EXC_INFO_STACK\n    _PyErr_StackItem *exc_info = tstate->exc_info;\n    tmp_type = exc_info->exc_type;\n    tmp_value = exc_info->exc_value;\n    tmp_tb = exc_info->exc_traceback;\n    exc_info->exc_type = *type;\n    exc_info->exc_value = *value;\n    exc_info->exc_traceback = *tb;\n    #else\n    tmp_type = tstate->exc_type;\n    tmp_value = tstate->exc_value;\n    tmp_tb = tstate->exc_traceback;\n    tstate->exc_type = *type;\n    tstate->exc_value = *value;\n    tstate->exc_traceback = *tb;\n    #endif\n    *type = tmp_type;\n    *value = tmp_value;\n    *tb = tmp_tb;\n}\n#else\nstatic CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);\n    PyErr_SetExcInfo(*type, *value, *tb);\n    *type = tmp_type;\n    *value = tmp_value;\n    *tb = tmp_tb;\n}\n#endif\n\n/* Import */\nstatic PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {\n    PyObject *empty_list = 0;\n    PyObject *module = 0;\n    PyObject *global_dict = 0;\n    PyObject *empty_dict = 0;\n    PyObject *list;\n    #if PY_MAJOR_VERSION < 3\n    PyObject *py_import;\n    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);\n    if (!py_import)\n        goto bad;\n    #endif\n    if (from_list)\n        list = from_list;\n    else {\n        empty_list = PyList_New(0);\n        if (!empty_list)\n            goto bad;\n        list = empty_list;\n    }\n    global_dict = PyModule_GetDict(__pyx_m);\n    if (!global_dict)\n        goto bad;\n    empty_dict = PyDict_New();\n    if (!empty_dict)\n        goto bad;\n    {\n        #if PY_MAJOR_VERSION >= 3\n        if (level == -1) {\n            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {\n                module = PyImport_ImportModuleLevelObject(\n                    name, global_dict, empty_dict, list, 1);\n                if (!module) {\n                    if (!PyErr_ExceptionMatches(PyExc_ImportError))\n                        goto bad;\n                    PyErr_Clear();\n                }\n            }\n            level = 0;\n        }\n        #endif\n        if (!module) {\n            #if PY_MAJOR_VERSION < 3\n            PyObject *py_level = PyInt_FromLong(level);\n            if (!py_level)\n                goto bad;\n            module = PyObject_CallFunctionObjArgs(py_import,\n                name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);\n            Py_DECREF(py_level);\n            #else\n            module = PyImport_ImportModuleLevelObject(\n                name, global_dict, empty_dict, list, level);\n            #endif\n        }\n    }\nbad:\n    #if PY_MAJOR_VERSION < 3\n    Py_XDECREF(py_import);\n    #endif\n    Py_XDECREF(empty_list);\n    Py_XDECREF(empty_dict);\n    return module;\n}\n\n/* FastTypeChecks */\n#if CYTHON_COMPILING_IN_CPYTHON\nstatic int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {\n    while (a) {\n        a = a->tp_base;\n        if (a == b)\n            return 1;\n    }\n    return b == &PyBaseObject_Type;\n}\nstatic CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {\n    PyObject *mro;\n    if (a == b) return 1;\n    mro = a->tp_mro;\n    if (likely(mro)) {\n        Py_ssize_t i, n;\n        n = PyTuple_GET_SIZE(mro);\n        for (i = 0; i < n; i++) {\n            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)\n                return 1;\n        }\n        return 0;\n    }\n    return __Pyx_InBases(a, b);\n}\n#if PY_MAJOR_VERSION == 2\nstatic int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {\n    PyObject *exception, *value, *tb;\n    int res;\n    __Pyx_PyThreadState_declare\n    __Pyx_PyThreadState_assign\n    __Pyx_ErrFetch(&exception, &value, &tb);\n    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;\n    if (unlikely(res == -1)) {\n        PyErr_WriteUnraisable(err);\n        res = 0;\n    }\n    if (!res) {\n        res = PyObject_IsSubclass(err, exc_type2);\n        if (unlikely(res == -1)) {\n            PyErr_WriteUnraisable(err);\n            res = 0;\n        }\n    }\n    __Pyx_ErrRestore(exception, value, tb);\n    return res;\n}\n#else\nstatic CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {\n    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;\n    if (!res) {\n        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);\n    }\n    return res;\n}\n#endif\nstatic int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {\n    Py_ssize_t i, n;\n    assert(PyExceptionClass_Check(exc_type));\n    n = PyTuple_GET_SIZE(tuple);\n#if PY_MAJOR_VERSION >= 3\n    for (i=0; i<n; i++) {\n        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;\n    }\n#endif\n    for (i=0; i<n; i++) {\n        PyObject *t = PyTuple_GET_ITEM(tuple, i);\n        #if PY_MAJOR_VERSION < 3\n        if (likely(exc_type == t)) return 1;\n        #endif\n        if (likely(PyExceptionClass_Check(t))) {\n            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;\n        } else {\n        }\n    }\n    return 0;\n}\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {\n    if (likely(err == exc_type)) return 1;\n    if (likely(PyExceptionClass_Check(err))) {\n        if (likely(PyExceptionClass_Check(exc_type))) {\n            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);\n        } else if (likely(PyTuple_Check(exc_type))) {\n            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);\n        } else {\n        }\n    }\n    return PyErr_GivenExceptionMatches(err, exc_type);\n}\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {\n    assert(PyExceptionClass_Check(exc_type1));\n    assert(PyExceptionClass_Check(exc_type2));\n    if (likely(err == exc_type1 || err == exc_type2)) return 1;\n    if (likely(PyExceptionClass_Check(err))) {\n        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);\n    }\n    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));\n}\n#endif\n\n/* PyIntBinop */\n#if !CYTHON_COMPILING_IN_PYPY\nstatic PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) {\n    (void)inplace;\n    (void)zerodivision_check;\n    #if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_CheckExact(op1))) {\n        const long b = intval;\n        long x;\n        long a = PyInt_AS_LONG(op1);\n            x = (long)((unsigned long)a + b);\n            if (likely((x^a) >= 0 || (x^b) >= 0))\n                return PyInt_FromLong(x);\n            return PyLong_Type.tp_as_number->nb_add(op1, op2);\n    }\n    #endif\n    #if CYTHON_USE_PYLONG_INTERNALS\n    if (likely(PyLong_CheckExact(op1))) {\n        const long b = intval;\n        long a, x;\n#ifdef HAVE_LONG_LONG\n        const PY_LONG_LONG llb = intval;\n        PY_LONG_LONG lla, llx;\n#endif\n        const digit* digits = ((PyLongObject*)op1)->ob_digit;\n        const Py_ssize_t size = Py_SIZE(op1);\n        if (likely(__Pyx_sst_abs(size) <= 1)) {\n            a = likely(size) ? digits[0] : 0;\n            if (size == -1) a = -a;\n        } else {\n            switch (size) {\n                case -2:\n                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {\n                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case 2:\n                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {\n                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case -3:\n                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {\n                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case 3:\n                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {\n                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case -4:\n                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {\n                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case 4:\n                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {\n                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);\n            }\n        }\n                x = a + b;\n            return PyLong_FromLong(x);\n#ifdef HAVE_LONG_LONG\n        long_long:\n                llx = lla + llb;\n            return PyLong_FromLongLong(llx);\n#endif\n        \n        \n    }\n    #endif\n    if (PyFloat_CheckExact(op1)) {\n        const long b = intval;\n        double a = PyFloat_AS_DOUBLE(op1);\n            double result;\n            PyFPE_START_PROTECT(\"add\", return NULL)\n            result = ((double)a) + (double)b;\n            PyFPE_END_PROTECT(result)\n            return PyFloat_FromDouble(result);\n    }\n    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);\n}\n#endif\n\n/* ImportFrom */\nstatic PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {\n    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);\n    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {\n        PyErr_Format(PyExc_ImportError,\n        #if PY_MAJOR_VERSION < 3\n            \"cannot import name %.230s\", PyString_AS_STRING(name));\n        #else\n            \"cannot import name %S\", name);\n        #endif\n    }\n    return value;\n}\n\n/* HasAttr */\nstatic CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {\n    PyObject *r;\n    if (unlikely(!__Pyx_PyBaseString_Check(n))) {\n        PyErr_SetString(PyExc_TypeError,\n                        \"hasattr(): attribute name must be string\");\n        return -1;\n    }\n    r = __Pyx_GetAttr(o, n);\n    if (unlikely(!r)) {\n        PyErr_Clear();\n        return 0;\n    } else {\n        Py_DECREF(r);\n        return 1;\n    }\n}\n\n/* PyObject_GenericGetAttrNoDict */\n#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000\nstatic PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {\n    PyErr_Format(PyExc_AttributeError,\n#if PY_MAJOR_VERSION >= 3\n                 \"'%.50s' object has no attribute '%U'\",\n                 tp->tp_name, attr_name);\n#else\n                 \"'%.50s' object has no attribute '%.400s'\",\n                 tp->tp_name, PyString_AS_STRING(attr_name));\n#endif\n    return NULL;\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {\n    PyObject *descr;\n    PyTypeObject *tp = Py_TYPE(obj);\n    if (unlikely(!PyString_Check(attr_name))) {\n        return PyObject_GenericGetAttr(obj, attr_name);\n    }\n    assert(!tp->tp_dictoffset);\n    descr = _PyType_Lookup(tp, attr_name);\n    if (unlikely(!descr)) {\n        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);\n    }\n    Py_INCREF(descr);\n    #if PY_MAJOR_VERSION < 3\n    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))\n    #endif\n    {\n        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;\n        if (unlikely(f)) {\n            PyObject *res = f(descr, obj, (PyObject *)tp);\n            Py_DECREF(descr);\n            return res;\n        }\n    }\n    return descr;\n}\n#endif\n\n/* PyObject_GenericGetAttr */\n#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000\nstatic PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {\n    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {\n        return PyObject_GenericGetAttr(obj, attr_name);\n    }\n    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);\n}\n#endif\n\n/* SetVTable */\nstatic int __Pyx_SetVtable(PyObject *dict, void *vtable) {\n#if PY_VERSION_HEX >= 0x02070000\n    PyObject *ob = PyCapsule_New(vtable, 0, 0);\n#else\n    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);\n#endif\n    if (!ob)\n        goto bad;\n    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)\n        goto bad;\n    Py_DECREF(ob);\n    return 0;\nbad:\n    Py_XDECREF(ob);\n    return -1;\n}\n\n/* PyObjectGetAttrStrNoError */\nstatic void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) {\n    __Pyx_PyThreadState_declare\n    __Pyx_PyThreadState_assign\n    if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))\n        __Pyx_PyErr_Clear();\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) {\n    PyObject *result;\n#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1\n    PyTypeObject* tp = Py_TYPE(obj);\n    if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) {\n        return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1);\n    }\n#endif\n    result = __Pyx_PyObject_GetAttrStr(obj, attr_name);\n    if (unlikely(!result)) {\n        __Pyx_PyObject_GetAttrStr_ClearAttributeError();\n    }\n    return result;\n}\n\n/* SetupReduce */\nstatic int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {\n  int ret;\n  PyObject *name_attr;\n  name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2);\n  if (likely(name_attr)) {\n      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);\n  } else {\n      ret = -1;\n  }\n  if (unlikely(ret < 0)) {\n      PyErr_Clear();\n      ret = 0;\n  }\n  Py_XDECREF(name_attr);\n  return ret;\n}\nstatic int __Pyx_setup_reduce(PyObject* type_obj) {\n    int ret = 0;\n    PyObject *object_reduce = NULL;\n    PyObject *object_getstate = NULL;\n    PyObject *object_reduce_ex = NULL;\n    PyObject *reduce = NULL;\n    PyObject *reduce_ex = NULL;\n    PyObject *reduce_cython = NULL;\n    PyObject *setstate = NULL;\n    PyObject *setstate_cython = NULL;\n    PyObject *getstate = NULL;\n#if CYTHON_USE_PYTYPE_LOOKUP\n    getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate);\n#else\n    getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate);\n    if (!getstate && PyErr_Occurred()) {\n        goto __PYX_BAD;\n    }\n#endif\n    if (getstate) {\n#if CYTHON_USE_PYTYPE_LOOKUP\n        object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate);\n#else\n        object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate);\n        if (!object_getstate && PyErr_Occurred()) {\n            goto __PYX_BAD;\n        }\n#endif\n        if (object_getstate != getstate) {\n            goto __PYX_GOOD;\n        }\n    }\n#if CYTHON_USE_PYTYPE_LOOKUP\n    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;\n#else\n    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;\n#endif\n    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;\n    if (reduce_ex == object_reduce_ex) {\n#if CYTHON_USE_PYTYPE_LOOKUP\n        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;\n#else\n        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;\n#endif\n        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD;\n        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {\n            reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);\n            if (likely(reduce_cython)) {\n                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;\n                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;\n            } else if (reduce == object_reduce || PyErr_Occurred()) {\n                goto __PYX_BAD;\n            }\n            setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);\n            if (!setstate) PyErr_Clear();\n            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {\n                setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);\n                if (likely(setstate_cython)) {\n                    ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;\n                    ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;\n                } else if (!setstate || PyErr_Occurred()) {\n                    goto __PYX_BAD;\n                }\n            }\n            PyType_Modified((PyTypeObject*)type_obj);\n        }\n    }\n    goto __PYX_GOOD;\n__PYX_BAD:\n    if (!PyErr_Occurred())\n        PyErr_Format(PyExc_RuntimeError, \"Unable to initialize pickling for %s\", ((PyTypeObject*)type_obj)->tp_name);\n    ret = -1;\n__PYX_GOOD:\n#if !CYTHON_USE_PYTYPE_LOOKUP\n    Py_XDECREF(object_reduce);\n    Py_XDECREF(object_reduce_ex);\n    Py_XDECREF(object_getstate);\n    Py_XDECREF(getstate);\n#endif\n    Py_XDECREF(reduce);\n    Py_XDECREF(reduce_ex);\n    Py_XDECREF(reduce_cython);\n    Py_XDECREF(setstate);\n    Py_XDECREF(setstate_cython);\n    return ret;\n}\n\n/* TypeImport */\n#ifndef __PYX_HAVE_RT_ImportType\n#define __PYX_HAVE_RT_ImportType\nstatic PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name,\n    size_t size, enum __Pyx_ImportType_CheckSize check_size)\n{\n    PyObject *result = 0;\n    char warning[200];\n    Py_ssize_t basicsize;\n#ifdef Py_LIMITED_API\n    PyObject *py_basicsize;\n#endif\n    result = PyObject_GetAttrString(module, class_name);\n    if (!result)\n        goto bad;\n    if (!PyType_Check(result)) {\n        PyErr_Format(PyExc_TypeError,\n            \"%.200s.%.200s is not a type object\",\n            module_name, class_name);\n        goto bad;\n    }\n#ifndef Py_LIMITED_API\n    basicsize = ((PyTypeObject *)result)->tp_basicsize;\n#else\n    py_basicsize = PyObject_GetAttrString(result, \"__basicsize__\");\n    if (!py_basicsize)\n        goto bad;\n    basicsize = PyLong_AsSsize_t(py_basicsize);\n    Py_DECREF(py_basicsize);\n    py_basicsize = 0;\n    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())\n        goto bad;\n#endif\n    if ((size_t)basicsize < size) {\n        PyErr_Format(PyExc_ValueError,\n            \"%.200s.%.200s size changed, may indicate binary incompatibility. \"\n            \"Expected %zd from C header, got %zd from PyObject\",\n            module_name, class_name, size, basicsize);\n        goto bad;\n    }\n    if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) {\n        PyErr_Format(PyExc_ValueError,\n            \"%.200s.%.200s size changed, may indicate binary incompatibility. \"\n            \"Expected %zd from C header, got %zd from PyObject\",\n            module_name, class_name, size, basicsize);\n        goto bad;\n    }\n    else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) {\n        PyOS_snprintf(warning, sizeof(warning),\n            \"%s.%s size changed, may indicate binary incompatibility. \"\n            \"Expected %zd from C header, got %zd from PyObject\",\n            module_name, class_name, size, basicsize);\n        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;\n    }\n    return (PyTypeObject *)result;\nbad:\n    Py_XDECREF(result);\n    return NULL;\n}\n#endif\n\n/* CLineInTraceback */\n#ifndef CYTHON_CLINE_IN_TRACEBACK\nstatic int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) {\n    PyObject *use_cline;\n    PyObject *ptype, *pvalue, *ptraceback;\n#if CYTHON_COMPILING_IN_CPYTHON\n    PyObject **cython_runtime_dict;\n#endif\n    if (unlikely(!__pyx_cython_runtime)) {\n        return c_line;\n    }\n    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);\n#if CYTHON_COMPILING_IN_CPYTHON\n    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);\n    if (likely(cython_runtime_dict)) {\n        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(\n            use_cline, *cython_runtime_dict,\n            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))\n    } else\n#endif\n    {\n      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);\n      if (use_cline_obj) {\n        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;\n        Py_DECREF(use_cline_obj);\n      } else {\n        PyErr_Clear();\n        use_cline = NULL;\n      }\n    }\n    if (!use_cline) {\n        c_line = 0;\n        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);\n    }\n    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {\n        c_line = 0;\n    }\n    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);\n    return c_line;\n}\n#endif\n\n/* CodeObjectCache */\nstatic int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {\n    int start = 0, mid = 0, end = count - 1;\n    if (end >= 0 && code_line > entries[end].code_line) {\n        return count;\n    }\n    while (start < end) {\n        mid = start + (end - start) / 2;\n        if (code_line < entries[mid].code_line) {\n            end = mid;\n        } else if (code_line > entries[mid].code_line) {\n             start = mid + 1;\n        } else {\n            return mid;\n        }\n    }\n    if (code_line <= entries[mid].code_line) {\n        return mid;\n    } else {\n        return mid + 1;\n    }\n}\nstatic PyCodeObject *__pyx_find_code_object(int code_line) {\n    PyCodeObject* code_object;\n    int pos;\n    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {\n        return NULL;\n    }\n    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);\n    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {\n        return NULL;\n    }\n    code_object = __pyx_code_cache.entries[pos].code_object;\n    Py_INCREF(code_object);\n    return code_object;\n}\nstatic void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {\n    int pos, i;\n    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;\n    if (unlikely(!code_line)) {\n        return;\n    }\n    if (unlikely(!entries)) {\n        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));\n        if (likely(entries)) {\n            __pyx_code_cache.entries = entries;\n            __pyx_code_cache.max_count = 64;\n            __pyx_code_cache.count = 1;\n            entries[0].code_line = code_line;\n            entries[0].code_object = code_object;\n            Py_INCREF(code_object);\n        }\n        return;\n    }\n    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);\n    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {\n        PyCodeObject* tmp = entries[pos].code_object;\n        entries[pos].code_object = code_object;\n        Py_DECREF(tmp);\n        return;\n    }\n    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {\n        int new_max = __pyx_code_cache.max_count + 64;\n        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(\n            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));\n        if (unlikely(!entries)) {\n            return;\n        }\n        __pyx_code_cache.entries = entries;\n        __pyx_code_cache.max_count = new_max;\n    }\n    for (i=__pyx_code_cache.count; i>pos; i--) {\n        entries[i] = entries[i-1];\n    }\n    entries[pos].code_line = code_line;\n    entries[pos].code_object = code_object;\n    __pyx_code_cache.count++;\n    Py_INCREF(code_object);\n}\n\n/* AddTraceback */\n#include \"compile.h\"\n#include \"frameobject.h\"\n#include \"traceback.h\"\n#if PY_VERSION_HEX >= 0x030b00a6\n  #ifndef Py_BUILD_CORE\n    #define Py_BUILD_CORE 1\n  #endif\n  #include \"internal/pycore_frame.h\"\n#endif\nstatic PyCodeObject* __Pyx_CreateCodeObjectForTraceback(\n            const char *funcname, int c_line,\n            int py_line, const char *filename) {\n    PyCodeObject *py_code = NULL;\n    PyObject *py_funcname = NULL;\n    #if PY_MAJOR_VERSION < 3\n    PyObject *py_srcfile = NULL;\n    py_srcfile = PyString_FromString(filename);\n    if (!py_srcfile) goto bad;\n    #endif\n    if (c_line) {\n        #if PY_MAJOR_VERSION < 3\n        py_funcname = PyString_FromFormat( \"%s (%s:%d)\", funcname, __pyx_cfilenm, c_line);\n        if (!py_funcname) goto bad;\n        #else\n        py_funcname = PyUnicode_FromFormat( \"%s (%s:%d)\", funcname, __pyx_cfilenm, c_line);\n        if (!py_funcname) goto bad;\n        funcname = PyUnicode_AsUTF8(py_funcname);\n        if (!funcname) goto bad;\n        #endif\n    }\n    else {\n        #if PY_MAJOR_VERSION < 3\n        py_funcname = PyString_FromString(funcname);\n        if (!py_funcname) goto bad;\n        #endif\n    }\n    #if PY_MAJOR_VERSION < 3\n    py_code = __Pyx_PyCode_New(\n        0,\n        0,\n        0,\n        0,\n        0,\n        __pyx_empty_bytes, /*PyObject *code,*/\n        __pyx_empty_tuple, /*PyObject *consts,*/\n        __pyx_empty_tuple, /*PyObject *names,*/\n        __pyx_empty_tuple, /*PyObject *varnames,*/\n        __pyx_empty_tuple, /*PyObject *freevars,*/\n        __pyx_empty_tuple, /*PyObject *cellvars,*/\n        py_srcfile,   /*PyObject *filename,*/\n        py_funcname,  /*PyObject *name,*/\n        py_line,\n        __pyx_empty_bytes  /*PyObject *lnotab*/\n    );\n    Py_DECREF(py_srcfile);\n    #else\n    py_code = PyCode_NewEmpty(filename, funcname, py_line);\n    #endif\n    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline\n    return py_code;\nbad:\n    Py_XDECREF(py_funcname);\n    #if PY_MAJOR_VERSION < 3\n    Py_XDECREF(py_srcfile);\n    #endif\n    return NULL;\n}\nstatic void __Pyx_AddTraceback(const char *funcname, int c_line,\n                               int py_line, const char *filename) {\n    PyCodeObject *py_code = 0;\n    PyFrameObject *py_frame = 0;\n    PyThreadState *tstate = __Pyx_PyThreadState_Current;\n    PyObject *ptype, *pvalue, *ptraceback;\n    if (c_line) {\n        c_line = __Pyx_CLineForTraceback(tstate, c_line);\n    }\n    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);\n    if (!py_code) {\n        __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);\n        py_code = __Pyx_CreateCodeObjectForTraceback(\n            funcname, c_line, py_line, filename);\n        if (!py_code) {\n            /* If the code object creation fails, then we should clear the\n               fetched exception references and propagate the new exception */\n            Py_XDECREF(ptype);\n            Py_XDECREF(pvalue);\n            Py_XDECREF(ptraceback);\n            goto bad;\n        }\n        __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);\n        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);\n    }\n    py_frame = PyFrame_New(\n        tstate,            /*PyThreadState *tstate,*/\n        py_code,           /*PyCodeObject *code,*/\n        __pyx_d,    /*PyObject *globals,*/\n        0                  /*PyObject *locals*/\n    );\n    if (!py_frame) goto bad;\n    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);\n    PyTraceBack_Here(py_frame);\nbad:\n    Py_XDECREF(py_code);\n    Py_XDECREF(py_frame);\n}\n\n#if PY_MAJOR_VERSION < 3\nstatic int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {\n    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);\n        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);\n        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);\n    PyErr_Format(PyExc_TypeError, \"'%.200s' does not have the buffer interface\", Py_TYPE(obj)->tp_name);\n    return -1;\n}\nstatic void __Pyx_ReleaseBuffer(Py_buffer *view) {\n    PyObject *obj = view->obj;\n    if (!obj) return;\n    if (PyObject_CheckBuffer(obj)) {\n        PyBuffer_Release(view);\n        return;\n    }\n    if ((0)) {}\n    view->obj = NULL;\n    Py_DECREF(obj);\n}\n#endif\n\n\n/* MemviewSliceIsContig */\nstatic int\n__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)\n{\n    int i, index, step, start;\n    Py_ssize_t itemsize = mvs.memview->view.itemsize;\n    if (order == 'F') {\n        step = 1;\n        start = 0;\n    } else {\n        step = -1;\n        start = ndim - 1;\n    }\n    for (i = 0; i < ndim; i++) {\n        index = start + step * i;\n        if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)\n            return 0;\n        itemsize *= mvs.shape[index];\n    }\n    return 1;\n}\n\n/* OverlappingSlices */\nstatic void\n__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,\n                               void **out_start, void **out_end,\n                               int ndim, size_t itemsize)\n{\n    char *start, *end;\n    int i;\n    start = end = slice->data;\n    for (i = 0; i < ndim; i++) {\n        Py_ssize_t stride = slice->strides[i];\n        Py_ssize_t extent = slice->shape[i];\n        if (extent == 0) {\n            *out_start = *out_end = start;\n            return;\n        } else {\n            if (stride > 0)\n                end += stride * (extent - 1);\n            else\n                start += stride * (extent - 1);\n        }\n    }\n    *out_start = start;\n    *out_end = end + itemsize;\n}\nstatic int\n__pyx_slices_overlap(__Pyx_memviewslice *slice1,\n                     __Pyx_memviewslice *slice2,\n                     int ndim, size_t itemsize)\n{\n    void *start1, *end1, *start2, *end2;\n    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);\n    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);\n    return (start1 < end2) && (start2 < end1);\n}\n\n/* Capsule */\nstatic CYTHON_INLINE PyObject *\n__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)\n{\n    PyObject *cobj;\n#if PY_VERSION_HEX >= 0x02070000\n    cobj = PyCapsule_New(p, sig, NULL);\n#else\n    cobj = PyCObject_FromVoidPtr(p, NULL);\n#endif\n    return cobj;\n}\n\n/* IsLittleEndian */\nstatic CYTHON_INLINE int __Pyx_Is_Little_Endian(void)\n{\n  union {\n    uint32_t u32;\n    uint8_t u8[4];\n  } S;\n  S.u32 = 0x01020304;\n  return S.u8[0] == 4;\n}\n\n/* BufferFormatCheck */\nstatic void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,\n                              __Pyx_BufFmt_StackElem* stack,\n                              __Pyx_TypeInfo* type) {\n  stack[0].field = &ctx->root;\n  stack[0].parent_offset = 0;\n  ctx->root.type = type;\n  ctx->root.name = \"buffer dtype\";\n  ctx->root.offset = 0;\n  ctx->head = stack;\n  ctx->head->field = &ctx->root;\n  ctx->fmt_offset = 0;\n  ctx->head->parent_offset = 0;\n  ctx->new_packmode = '@';\n  ctx->enc_packmode = '@';\n  ctx->new_count = 1;\n  ctx->enc_count = 0;\n  ctx->enc_type = 0;\n  ctx->is_complex = 0;\n  ctx->is_valid_array = 0;\n  ctx->struct_alignment = 0;\n  while (type->typegroup == 'S') {\n    ++ctx->head;\n    ctx->head->field = type->fields;\n    ctx->head->parent_offset = 0;\n    type = type->fields->type;\n  }\n}\nstatic int __Pyx_BufFmt_ParseNumber(const char** ts) {\n    int count;\n    const char* t = *ts;\n    if (*t < '0' || *t > '9') {\n      return -1;\n    } else {\n        count = *t++ - '0';\n        while (*t >= '0' && *t <= '9') {\n            count *= 10;\n            count += *t++ - '0';\n        }\n    }\n    *ts = t;\n    return count;\n}\nstatic int __Pyx_BufFmt_ExpectNumber(const char **ts) {\n    int number = __Pyx_BufFmt_ParseNumber(ts);\n    if (number == -1)\n        PyErr_Format(PyExc_ValueError,\\\n                     \"Does not understand character buffer dtype format string ('%c')\", **ts);\n    return number;\n}\nstatic void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {\n  PyErr_Format(PyExc_ValueError,\n               \"Unexpected format string character: '%c'\", ch);\n}\nstatic const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {\n  switch (ch) {\n    case '?': return \"'bool'\";\n    case 'c': return \"'char'\";\n    case 'b': return \"'signed char'\";\n    case 'B': return \"'unsigned char'\";\n    case 'h': return \"'short'\";\n    case 'H': return \"'unsigned short'\";\n    case 'i': return \"'int'\";\n    case 'I': return \"'unsigned int'\";\n    case 'l': return \"'long'\";\n    case 'L': return \"'unsigned long'\";\n    case 'q': return \"'long long'\";\n    case 'Q': return \"'unsigned long long'\";\n    case 'f': return (is_complex ? \"'complex float'\" : \"'float'\");\n    case 'd': return (is_complex ? \"'complex double'\" : \"'double'\");\n    case 'g': return (is_complex ? \"'complex long double'\" : \"'long double'\");\n    case 'T': return \"a struct\";\n    case 'O': return \"Python object\";\n    case 'P': return \"a pointer\";\n    case 's': case 'p': return \"a string\";\n    case 0: return \"end\";\n    default: return \"unparseable format string\";\n  }\n}\nstatic size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return 2;\n    case 'i': case 'I': case 'l': case 'L': return 4;\n    case 'q': case 'Q': return 8;\n    case 'f': return (is_complex ? 8 : 4);\n    case 'd': return (is_complex ? 16 : 8);\n    case 'g': {\n      PyErr_SetString(PyExc_ValueError, \"Python does not define a standard format string size for long double ('g')..\");\n      return 0;\n    }\n    case 'O': case 'P': return sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\nstatic size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(short);\n    case 'i': case 'I': return sizeof(int);\n    case 'l': case 'L': return sizeof(long);\n    #ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(PY_LONG_LONG);\n    #endif\n    case 'f': return sizeof(float) * (is_complex ? 2 : 1);\n    case 'd': return sizeof(double) * (is_complex ? 2 : 1);\n    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);\n    case 'O': case 'P': return sizeof(void*);\n    default: {\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n  }\n}\ntypedef struct { char c; short x; } __Pyx_st_short;\ntypedef struct { char c; int x; } __Pyx_st_int;\ntypedef struct { char c; long x; } __Pyx_st_long;\ntypedef struct { char c; float x; } __Pyx_st_float;\ntypedef struct { char c; double x; } __Pyx_st_double;\ntypedef struct { char c; long double x; } __Pyx_st_longdouble;\ntypedef struct { char c; void *x; } __Pyx_st_void_p;\n#ifdef HAVE_LONG_LONG\ntypedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;\n#endif\nstatic size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);\n    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);\n    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);\n#ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);\n#endif\n    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);\n    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);\n    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);\n    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\n/* These are for computing the padding at the end of the struct to align\n   on the first member of the struct. This will probably the same as above,\n   but we don't have any guarantees.\n */\ntypedef struct { short x; char c; } __Pyx_pad_short;\ntypedef struct { int x; char c; } __Pyx_pad_int;\ntypedef struct { long x; char c; } __Pyx_pad_long;\ntypedef struct { float x; char c; } __Pyx_pad_float;\ntypedef struct { double x; char c; } __Pyx_pad_double;\ntypedef struct { long double x; char c; } __Pyx_pad_longdouble;\ntypedef struct { void *x; char c; } __Pyx_pad_void_p;\n#ifdef HAVE_LONG_LONG\ntypedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;\n#endif\nstatic size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);\n    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);\n    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);\n#ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);\n#endif\n    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);\n    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);\n    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);\n    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\nstatic char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {\n  switch (ch) {\n    case 'c':\n        return 'H';\n    case 'b': case 'h': case 'i':\n    case 'l': case 'q': case 's': case 'p':\n        return 'I';\n    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':\n        return 'U';\n    case 'f': case 'd': case 'g':\n        return (is_complex ? 'C' : 'R');\n    case 'O':\n        return 'O';\n    case 'P':\n        return 'P';\n    default: {\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n  }\n}\nstatic void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {\n  if (ctx->head == NULL || ctx->head->field == &ctx->root) {\n    const char* expected;\n    const char* quote;\n    if (ctx->head == NULL) {\n      expected = \"end\";\n      quote = \"\";\n    } else {\n      expected = ctx->head->field->type->name;\n      quote = \"'\";\n    }\n    PyErr_Format(PyExc_ValueError,\n                 \"Buffer dtype mismatch, expected %s%s%s but got %s\",\n                 quote, expected, quote,\n                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));\n  } else {\n    __Pyx_StructField* field = ctx->head->field;\n    __Pyx_StructField* parent = (ctx->head - 1)->field;\n    PyErr_Format(PyExc_ValueError,\n                 \"Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'\",\n                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),\n                 parent->type->name, field->name);\n  }\n}\nstatic int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {\n  char group;\n  size_t size, offset, arraysize = 1;\n  if (ctx->enc_type == 0) return 0;\n  if (ctx->head->field->type->arraysize[0]) {\n    int i, ndim = 0;\n    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {\n        ctx->is_valid_array = ctx->head->field->type->ndim == 1;\n        ndim = 1;\n        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {\n            PyErr_Format(PyExc_ValueError,\n                         \"Expected a dimension of size %zu, got %zu\",\n                         ctx->head->field->type->arraysize[0], ctx->enc_count);\n            return -1;\n        }\n    }\n    if (!ctx->is_valid_array) {\n      PyErr_Format(PyExc_ValueError, \"Expected %d dimensions, got %d\",\n                   ctx->head->field->type->ndim, ndim);\n      return -1;\n    }\n    for (i = 0; i < ctx->head->field->type->ndim; i++) {\n      arraysize *= ctx->head->field->type->arraysize[i];\n    }\n    ctx->is_valid_array = 0;\n    ctx->enc_count = 1;\n  }\n  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);\n  do {\n    __Pyx_StructField* field = ctx->head->field;\n    __Pyx_TypeInfo* type = field->type;\n    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {\n      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);\n    } else {\n      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);\n    }\n    if (ctx->enc_packmode == '@') {\n      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);\n      size_t align_mod_offset;\n      if (align_at == 0) return -1;\n      align_mod_offset = ctx->fmt_offset % align_at;\n      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;\n      if (ctx->struct_alignment == 0)\n          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,\n                                                                 ctx->is_complex);\n    }\n    if (type->size != size || type->typegroup != group) {\n      if (type->typegroup == 'C' && type->fields != NULL) {\n        size_t parent_offset = ctx->head->parent_offset + field->offset;\n        ++ctx->head;\n        ctx->head->field = type->fields;\n        ctx->head->parent_offset = parent_offset;\n        continue;\n      }\n      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {\n      } else {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return -1;\n      }\n    }\n    offset = ctx->head->parent_offset + field->offset;\n    if (ctx->fmt_offset != offset) {\n      PyErr_Format(PyExc_ValueError,\n                   \"Buffer dtype mismatch; next field is at offset %\" CYTHON_FORMAT_SSIZE_T \"d but %\" CYTHON_FORMAT_SSIZE_T \"d expected\",\n                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);\n      return -1;\n    }\n    ctx->fmt_offset += size;\n    if (arraysize)\n      ctx->fmt_offset += (arraysize - 1) * size;\n    --ctx->enc_count;\n    while (1) {\n      if (field == &ctx->root) {\n        ctx->head = NULL;\n        if (ctx->enc_count != 0) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return -1;\n        }\n        break;\n      }\n      ctx->head->field = ++field;\n      if (field->type == NULL) {\n        --ctx->head;\n        field = ctx->head->field;\n        continue;\n      } else if (field->type->typegroup == 'S') {\n        size_t parent_offset = ctx->head->parent_offset + field->offset;\n        if (field->type->fields->type == NULL) continue;\n        field = field->type->fields;\n        ++ctx->head;\n        ctx->head->field = field;\n        ctx->head->parent_offset = parent_offset;\n        break;\n      } else {\n        break;\n      }\n    }\n  } while (ctx->enc_count);\n  ctx->enc_type = 0;\n  ctx->is_complex = 0;\n  return 0;\n}\nstatic PyObject *\n__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)\n{\n    const char *ts = *tsp;\n    int i = 0, number, ndim;\n    ++ts;\n    if (ctx->new_count != 1) {\n        PyErr_SetString(PyExc_ValueError,\n                        \"Cannot handle repeated arrays in format string\");\n        return NULL;\n    }\n    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n    ndim = ctx->head->field->type->ndim;\n    while (*ts && *ts != ')') {\n        switch (*ts) {\n            case ' ': case '\\f': case '\\r': case '\\n': case '\\t': case '\\v':  continue;\n            default:  break;\n        }\n        number = __Pyx_BufFmt_ExpectNumber(&ts);\n        if (number == -1) return NULL;\n        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])\n            return PyErr_Format(PyExc_ValueError,\n                        \"Expected a dimension of size %zu, got %d\",\n                        ctx->head->field->type->arraysize[i], number);\n        if (*ts != ',' && *ts != ')')\n            return PyErr_Format(PyExc_ValueError,\n                                \"Expected a comma in format string, got '%c'\", *ts);\n        if (*ts == ',') ts++;\n        i++;\n    }\n    if (i != ndim)\n        return PyErr_Format(PyExc_ValueError, \"Expected %d dimension(s), got %d\",\n                            ctx->head->field->type->ndim, i);\n    if (!*ts) {\n        PyErr_SetString(PyExc_ValueError,\n                        \"Unexpected end of format string, expected ')'\");\n        return NULL;\n    }\n    ctx->is_valid_array = 1;\n    ctx->new_count = 1;\n    *tsp = ++ts;\n    return Py_None;\n}\nstatic const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {\n  int got_Z = 0;\n  while (1) {\n    switch(*ts) {\n      case 0:\n        if (ctx->enc_type != 0 && ctx->head == NULL) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return NULL;\n        }\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        if (ctx->head != NULL) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return NULL;\n        }\n        return ts;\n      case ' ':\n      case '\\r':\n      case '\\n':\n        ++ts;\n        break;\n      case '<':\n        if (!__Pyx_Is_Little_Endian()) {\n          PyErr_SetString(PyExc_ValueError, \"Little-endian buffer not supported on big-endian compiler\");\n          return NULL;\n        }\n        ctx->new_packmode = '=';\n        ++ts;\n        break;\n      case '>':\n      case '!':\n        if (__Pyx_Is_Little_Endian()) {\n          PyErr_SetString(PyExc_ValueError, \"Big-endian buffer not supported on little-endian compiler\");\n          return NULL;\n        }\n        ctx->new_packmode = '=';\n        ++ts;\n        break;\n      case '=':\n      case '@':\n      case '^':\n        ctx->new_packmode = *ts++;\n        break;\n      case 'T':\n        {\n          const char* ts_after_sub;\n          size_t i, struct_count = ctx->new_count;\n          size_t struct_alignment = ctx->struct_alignment;\n          ctx->new_count = 1;\n          ++ts;\n          if (*ts != '{') {\n            PyErr_SetString(PyExc_ValueError, \"Buffer acquisition: Expected '{' after 'T'\");\n            return NULL;\n          }\n          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n          ctx->enc_type = 0;\n          ctx->enc_count = 0;\n          ctx->struct_alignment = 0;\n          ++ts;\n          ts_after_sub = ts;\n          for (i = 0; i != struct_count; ++i) {\n            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);\n            if (!ts_after_sub) return NULL;\n          }\n          ts = ts_after_sub;\n          if (struct_alignment) ctx->struct_alignment = struct_alignment;\n        }\n        break;\n      case '}':\n        {\n          size_t alignment = ctx->struct_alignment;\n          ++ts;\n          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n          ctx->enc_type = 0;\n          if (alignment && ctx->fmt_offset % alignment) {\n            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);\n          }\n        }\n        return ts;\n      case 'x':\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        ctx->fmt_offset += ctx->new_count;\n        ctx->new_count = 1;\n        ctx->enc_count = 0;\n        ctx->enc_type = 0;\n        ctx->enc_packmode = ctx->new_packmode;\n        ++ts;\n        break;\n      case 'Z':\n        got_Z = 1;\n        ++ts;\n        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {\n          __Pyx_BufFmt_RaiseUnexpectedChar('Z');\n          return NULL;\n        }\n        CYTHON_FALLTHROUGH;\n      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':\n      case 'l': case 'L': case 'q': case 'Q':\n      case 'f': case 'd': case 'g':\n      case 'O': case 'p':\n        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&\n            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {\n          ctx->enc_count += ctx->new_count;\n          ctx->new_count = 1;\n          got_Z = 0;\n          ++ts;\n          break;\n        }\n        CYTHON_FALLTHROUGH;\n      case 's':\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        ctx->enc_count = ctx->new_count;\n        ctx->enc_packmode = ctx->new_packmode;\n        ctx->enc_type = *ts;\n        ctx->is_complex = got_Z;\n        ++ts;\n        ctx->new_count = 1;\n        got_Z = 0;\n        break;\n      case ':':\n        ++ts;\n        while(*ts != ':') ++ts;\n        ++ts;\n        break;\n      case '(':\n        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;\n        break;\n      default:\n        {\n          int number = __Pyx_BufFmt_ExpectNumber(&ts);\n          if (number == -1) return NULL;\n          ctx->new_count = (size_t)number;\n        }\n    }\n  }\n}\n\n/* TypeInfoCompare */\n  static int\n__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)\n{\n    int i;\n    if (!a || !b)\n        return 0;\n    if (a == b)\n        return 1;\n    if (a->size != b->size || a->typegroup != b->typegroup ||\n            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {\n        if (a->typegroup == 'H' || b->typegroup == 'H') {\n            return a->size == b->size;\n        } else {\n            return 0;\n        }\n    }\n    if (a->ndim) {\n        for (i = 0; i < a->ndim; i++)\n            if (a->arraysize[i] != b->arraysize[i])\n                return 0;\n    }\n    if (a->typegroup == 'S') {\n        if (a->flags != b->flags)\n            return 0;\n        if (a->fields || b->fields) {\n            if (!(a->fields && b->fields))\n                return 0;\n            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {\n                __Pyx_StructField *field_a = a->fields + i;\n                __Pyx_StructField *field_b = b->fields + i;\n                if (field_a->offset != field_b->offset ||\n                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))\n                    return 0;\n            }\n            return !a->fields[i].type && !b->fields[i].type;\n        }\n    }\n    return 1;\n}\n\n/* MemviewSliceValidateAndInit */\n  static int\n__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)\n{\n    if (buf->shape[dim] <= 1)\n        return 1;\n    if (buf->strides) {\n        if (spec & __Pyx_MEMVIEW_CONTIG) {\n            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {\n                if (unlikely(buf->strides[dim] != sizeof(void *))) {\n                    PyErr_Format(PyExc_ValueError,\n                                 \"Buffer is not indirectly contiguous \"\n                                 \"in dimension %d.\", dim);\n                    goto fail;\n                }\n            } else if (unlikely(buf->strides[dim] != buf->itemsize)) {\n                PyErr_SetString(PyExc_ValueError,\n                                \"Buffer and memoryview are not contiguous \"\n                                \"in the same dimension.\");\n                goto fail;\n            }\n        }\n        if (spec & __Pyx_MEMVIEW_FOLLOW) {\n            Py_ssize_t stride = buf->strides[dim];\n            if (stride < 0)\n                stride = -stride;\n            if (unlikely(stride < buf->itemsize)) {\n                PyErr_SetString(PyExc_ValueError,\n                                \"Buffer and memoryview are not contiguous \"\n                                \"in the same dimension.\");\n                goto fail;\n            }\n        }\n    } else {\n        if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {\n            PyErr_Format(PyExc_ValueError,\n                         \"C-contiguous buffer is not contiguous in \"\n                         \"dimension %d\", dim);\n            goto fail;\n        } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {\n            PyErr_Format(PyExc_ValueError,\n                         \"C-contiguous buffer is not indirect in \"\n                         \"dimension %d\", dim);\n            goto fail;\n        } else if (unlikely(buf->suboffsets)) {\n            PyErr_SetString(PyExc_ValueError,\n                            \"Buffer exposes suboffsets but no strides\");\n            goto fail;\n        }\n    }\n    return 1;\nfail:\n    return 0;\n}\nstatic int\n__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec)\n{\n    if (spec & __Pyx_MEMVIEW_DIRECT) {\n        if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {\n            PyErr_Format(PyExc_ValueError,\n                         \"Buffer not compatible with direct access \"\n                         \"in dimension %d.\", dim);\n            goto fail;\n        }\n    }\n    if (spec & __Pyx_MEMVIEW_PTR) {\n        if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {\n            PyErr_Format(PyExc_ValueError,\n                         \"Buffer is not indirectly accessible \"\n                         \"in dimension %d.\", dim);\n            goto fail;\n        }\n    }\n    return 1;\nfail:\n    return 0;\n}\nstatic int\n__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)\n{\n    int i;\n    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {\n        Py_ssize_t stride = 1;\n        for (i = 0; i < ndim; i++) {\n            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {\n                PyErr_SetString(PyExc_ValueError,\n                    \"Buffer not fortran contiguous.\");\n                goto fail;\n            }\n            stride = stride * buf->shape[i];\n        }\n    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {\n        Py_ssize_t stride = 1;\n        for (i = ndim - 1; i >- 1; i--) {\n            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {\n                PyErr_SetString(PyExc_ValueError,\n                    \"Buffer not C contiguous.\");\n                goto fail;\n            }\n            stride = stride * buf->shape[i];\n        }\n    }\n    return 1;\nfail:\n    return 0;\n}\nstatic int __Pyx_ValidateAndInit_memviewslice(\n                int *axes_specs,\n                int c_or_f_flag,\n                int buf_flags,\n                int ndim,\n                __Pyx_TypeInfo *dtype,\n                __Pyx_BufFmt_StackElem stack[],\n                __Pyx_memviewslice *memviewslice,\n                PyObject *original_obj)\n{\n    struct __pyx_memoryview_obj *memview, *new_memview;\n    __Pyx_RefNannyDeclarations\n    Py_buffer *buf;\n    int i, spec = 0, retval = -1;\n    __Pyx_BufFmt_Context ctx;\n    int from_memoryview = __pyx_memoryview_check(original_obj);\n    __Pyx_RefNannySetupContext(\"ValidateAndInit_memviewslice\", 0);\n    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)\n                                                            original_obj)->typeinfo)) {\n        memview = (struct __pyx_memoryview_obj *) original_obj;\n        new_memview = NULL;\n    } else {\n        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(\n                                            original_obj, buf_flags, 0, dtype);\n        new_memview = memview;\n        if (unlikely(!memview))\n            goto fail;\n    }\n    buf = &memview->view;\n    if (unlikely(buf->ndim != ndim)) {\n        PyErr_Format(PyExc_ValueError,\n                \"Buffer has wrong number of dimensions (expected %d, got %d)\",\n                ndim, buf->ndim);\n        goto fail;\n    }\n    if (new_memview) {\n        __Pyx_BufFmt_Init(&ctx, stack, dtype);\n        if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;\n    }\n    if (unlikely((unsigned) buf->itemsize != dtype->size)) {\n        PyErr_Format(PyExc_ValueError,\n                     \"Item size of buffer (%\" CYTHON_FORMAT_SSIZE_T \"u byte%s) \"\n                     \"does not match size of '%s' (%\" CYTHON_FORMAT_SSIZE_T \"u byte%s)\",\n                     buf->itemsize,\n                     (buf->itemsize > 1) ? \"s\" : \"\",\n                     dtype->name,\n                     dtype->size,\n                     (dtype->size > 1) ? \"s\" : \"\");\n        goto fail;\n    }\n    if (buf->len > 0) {\n        for (i = 0; i < ndim; i++) {\n            spec = axes_specs[i];\n            if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))\n                goto fail;\n            if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))\n                goto fail;\n        }\n        if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))\n            goto fail;\n    }\n    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,\n                                         new_memview != NULL) == -1)) {\n        goto fail;\n    }\n    retval = 0;\n    goto no_fail;\nfail:\n    Py_XDECREF(new_memview);\n    retval = -1;\nno_fail:\n    __Pyx_RefNannyFinishContext();\n    return retval;\n}\n\n/* ObjectToMemviewSlice */\n  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_float(PyObject *obj, int writable_flag) {\n    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };\n    __Pyx_BufFmt_StackElem stack[1];\n    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };\n    int retcode;\n    if (obj == Py_None) {\n        result.memview = (struct __pyx_memoryview_obj *) Py_None;\n        return result;\n    }\n    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,\n                                                 PyBUF_RECORDS_RO | writable_flag, 2,\n                                                 &__Pyx_TypeInfo_float, stack,\n                                                 &result, obj);\n    if (unlikely(retcode == -1))\n        goto __pyx_fail;\n    return result;\n__pyx_fail:\n    result.memview = NULL;\n    result.data = NULL;\n    return result;\n}\n\n/* ObjectToMemviewSlice */\n  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *obj, int writable_flag) {\n    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };\n    __Pyx_BufFmt_StackElem stack[1];\n    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };\n    int retcode;\n    if (obj == Py_None) {\n        result.memview = (struct __pyx_memoryview_obj *) Py_None;\n        return result;\n    }\n    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,\n                                                 PyBUF_RECORDS_RO | writable_flag, 1,\n                                                 &__Pyx_TypeInfo_long, stack,\n                                                 &result, obj);\n    if (unlikely(retcode == -1))\n        goto __pyx_fail;\n    return result;\n__pyx_fail:\n    result.memview = NULL;\n    result.data = NULL;\n    return result;\n}\n\n/* CIntFromPyVerify */\n  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\\\n    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)\n#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\\\n    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)\n#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\\\n    {\\\n        func_type value = func_value;\\\n        if (sizeof(target_type) < sizeof(func_type)) {\\\n            if (unlikely(value != (func_type) (target_type) value)) {\\\n                func_type zero = 0;\\\n                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\\\n                    return (target_type) -1;\\\n                if (is_unsigned && unlikely(value < zero))\\\n                    goto raise_neg_overflow;\\\n                else\\\n                    goto raise_overflow;\\\n            }\\\n        }\\\n        return (target_type) value;\\\n    }\n\n/* Declarations */\n  #if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      return ::std::complex< float >(x, y);\n    }\n  #else\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      return x + y*(__pyx_t_float_complex)_Complex_I;\n    }\n  #endif\n#else\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      __pyx_t_float_complex z;\n      z.real = x;\n      z.imag = y;\n      return z;\n    }\n#endif\n\n/* Arithmetic */\n  #if CYTHON_CCOMPLEX\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n       return (a.real == b.real) && (a.imag == b.imag);\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real + b.real;\n        z.imag = a.imag + b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real - b.real;\n        z.imag = a.imag - b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real * b.real - a.imag * b.imag;\n        z.imag = a.real * b.imag + a.imag * b.real;\n        return z;\n    }\n    #if 1\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else if (fabsf(b.real) >= fabsf(b.imag)) {\n            if (b.real == 0 && b.imag == 0) {\n                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);\n            } else {\n                float r = b.imag / b.real;\n                float s = (float)(1.0) / (b.real + b.imag * r);\n                return __pyx_t_float_complex_from_parts(\n                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);\n            }\n        } else {\n            float r = b.real / b.imag;\n            float s = (float)(1.0) / (b.imag + b.real * r);\n            return __pyx_t_float_complex_from_parts(\n                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);\n        }\n    }\n    #else\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else {\n            float denom = b.real * b.real + b.imag * b.imag;\n            return __pyx_t_float_complex_from_parts(\n                (a.real * b.real + a.imag * b.imag) / denom,\n                (a.imag * b.real - a.real * b.imag) / denom);\n        }\n    }\n    #endif\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {\n        __pyx_t_float_complex z;\n        z.real = -a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {\n       return (a.real == 0) && (a.imag == 0);\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {\n        __pyx_t_float_complex z;\n        z.real =  a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    #if 1\n        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {\n          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)\n            return sqrtf(z.real*z.real + z.imag*z.imag);\n          #else\n            return hypotf(z.real, z.imag);\n          #endif\n        }\n        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n            __pyx_t_float_complex z;\n            float r, lnr, theta, z_r, z_theta;\n            if (b.imag == 0 && b.real == (int)b.real) {\n                if (b.real < 0) {\n                    float denom = a.real * a.real + a.imag * a.imag;\n                    a.real = a.real / denom;\n                    a.imag = -a.imag / denom;\n                    b.real = -b.real;\n                }\n                switch ((int)b.real) {\n                    case 0:\n                        z.real = 1;\n                        z.imag = 0;\n                        return z;\n                    case 1:\n                        return a;\n                    case 2:\n                        return __Pyx_c_prod_float(a, a);\n                    case 3:\n                        z = __Pyx_c_prod_float(a, a);\n                        return __Pyx_c_prod_float(z, a);\n                    case 4:\n                        z = __Pyx_c_prod_float(a, a);\n                        return __Pyx_c_prod_float(z, z);\n                }\n            }\n            if (a.imag == 0) {\n                if (a.real == 0) {\n                    return a;\n                } else if (b.imag == 0) {\n                    z.real = powf(a.real, b.real);\n                    z.imag = 0;\n                    return z;\n                } else if (a.real > 0) {\n                    r = a.real;\n                    theta = 0;\n                } else {\n                    r = -a.real;\n                    theta = atan2f(0.0, -1.0);\n                }\n            } else {\n                r = __Pyx_c_abs_float(a);\n                theta = atan2f(a.imag, a.real);\n            }\n            lnr = logf(r);\n            z_r = expf(lnr * b.real - theta * b.imag);\n            z_theta = theta * b.real + lnr * b.imag;\n            z.real = z_r * cosf(z_theta);\n            z.imag = z_r * sinf(z_theta);\n            return z;\n        }\n    #endif\n#endif\n\n/* Declarations */\n  #if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      return ::std::complex< double >(x, y);\n    }\n  #else\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      return x + y*(__pyx_t_double_complex)_Complex_I;\n    }\n  #endif\n#else\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      __pyx_t_double_complex z;\n      z.real = x;\n      z.imag = y;\n      return z;\n    }\n#endif\n\n/* Arithmetic */\n  #if CYTHON_CCOMPLEX\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n       return (a.real == b.real) && (a.imag == b.imag);\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real + b.real;\n        z.imag = a.imag + b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real - b.real;\n        z.imag = a.imag - b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real * b.real - a.imag * b.imag;\n        z.imag = a.real * b.imag + a.imag * b.real;\n        return z;\n    }\n    #if 1\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else if (fabs(b.real) >= fabs(b.imag)) {\n            if (b.real == 0 && b.imag == 0) {\n                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);\n            } else {\n                double r = b.imag / b.real;\n                double s = (double)(1.0) / (b.real + b.imag * r);\n                return __pyx_t_double_complex_from_parts(\n                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);\n            }\n        } else {\n            double r = b.real / b.imag;\n            double s = (double)(1.0) / (b.imag + b.real * r);\n            return __pyx_t_double_complex_from_parts(\n                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);\n        }\n    }\n    #else\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else {\n            double denom = b.real * b.real + b.imag * b.imag;\n            return __pyx_t_double_complex_from_parts(\n                (a.real * b.real + a.imag * b.imag) / denom,\n                (a.imag * b.real - a.real * b.imag) / denom);\n        }\n    }\n    #endif\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {\n        __pyx_t_double_complex z;\n        z.real = -a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {\n       return (a.real == 0) && (a.imag == 0);\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {\n        __pyx_t_double_complex z;\n        z.real =  a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    #if 1\n        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {\n          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)\n            return sqrt(z.real*z.real + z.imag*z.imag);\n          #else\n            return hypot(z.real, z.imag);\n          #endif\n        }\n        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n            __pyx_t_double_complex z;\n            double r, lnr, theta, z_r, z_theta;\n            if (b.imag == 0 && b.real == (int)b.real) {\n                if (b.real < 0) {\n                    double denom = a.real * a.real + a.imag * a.imag;\n                    a.real = a.real / denom;\n                    a.imag = -a.imag / denom;\n                    b.real = -b.real;\n                }\n                switch ((int)b.real) {\n                    case 0:\n                        z.real = 1;\n                        z.imag = 0;\n                        return z;\n                    case 1:\n                        return a;\n                    case 2:\n                        return __Pyx_c_prod_double(a, a);\n                    case 3:\n                        z = __Pyx_c_prod_double(a, a);\n                        return __Pyx_c_prod_double(z, a);\n                    case 4:\n                        z = __Pyx_c_prod_double(a, a);\n                        return __Pyx_c_prod_double(z, z);\n                }\n            }\n            if (a.imag == 0) {\n                if (a.real == 0) {\n                    return a;\n                } else if (b.imag == 0) {\n                    z.real = pow(a.real, b.real);\n                    z.imag = 0;\n                    return z;\n                } else if (a.real > 0) {\n                    r = a.real;\n                    theta = 0;\n                } else {\n                    r = -a.real;\n                    theta = atan2(0.0, -1.0);\n                }\n            } else {\n                r = __Pyx_c_abs_double(a);\n                theta = atan2(a.imag, a.real);\n            }\n            lnr = log(r);\n            z_r = exp(lnr * b.real - theta * b.imag);\n            z_theta = theta * b.real + lnr * b.imag;\n            z.real = z_r * cos(z_theta);\n            z.imag = z_r * sin(z_theta);\n            return z;\n        }\n    #endif\n#endif\n\n/* Print */\n  #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION < 3\nstatic PyObject *__Pyx_GetStdout(void) {\n    PyObject *f = PySys_GetObject((char *)\"stdout\");\n    if (!f) {\n        PyErr_SetString(PyExc_RuntimeError, \"lost sys.stdout\");\n    }\n    return f;\n}\nstatic int __Pyx_Print(PyObject* f, PyObject *arg_tuple, int newline) {\n    int i;\n    if (!f) {\n        if (!(f = __Pyx_GetStdout()))\n            return -1;\n    }\n    Py_INCREF(f);\n    for (i=0; i < PyTuple_GET_SIZE(arg_tuple); i++) {\n        PyObject* v;\n        if (PyFile_SoftSpace(f, 1)) {\n            if (PyFile_WriteString(\" \", f) < 0)\n                goto error;\n        }\n        v = PyTuple_GET_ITEM(arg_tuple, i);\n        if (PyFile_WriteObject(v, f, Py_PRINT_RAW) < 0)\n            goto error;\n        if (PyString_Check(v)) {\n            char *s = PyString_AsString(v);\n            Py_ssize_t len = PyString_Size(v);\n            if (len > 0) {\n                switch (s[len-1]) {\n                    case ' ': break;\n                    case '\\f': case '\\r': case '\\n': case '\\t': case '\\v':\n                        PyFile_SoftSpace(f, 0);\n                        break;\n                    default:  break;\n                }\n            }\n        }\n    }\n    if (newline) {\n        if (PyFile_WriteString(\"\\n\", f) < 0)\n            goto error;\n        PyFile_SoftSpace(f, 0);\n    }\n    Py_DECREF(f);\n    return 0;\nerror:\n    Py_DECREF(f);\n    return -1;\n}\n#else\nstatic int __Pyx_Print(PyObject* stream, PyObject *arg_tuple, int newline) {\n    PyObject* kwargs = 0;\n    PyObject* result = 0;\n    PyObject* end_string;\n    if (unlikely(!__pyx_print)) {\n        __pyx_print = PyObject_GetAttr(__pyx_b, __pyx_n_s_print);\n        if (!__pyx_print)\n            return -1;\n    }\n    if (stream) {\n        kwargs = PyDict_New();\n        if (unlikely(!kwargs))\n            return -1;\n        if (unlikely(PyDict_SetItem(kwargs, __pyx_n_s_file, stream) < 0))\n            goto bad;\n        if (!newline) {\n            end_string = PyUnicode_FromStringAndSize(\" \", 1);\n            if (unlikely(!end_string))\n                goto bad;\n            if (PyDict_SetItem(kwargs, __pyx_n_s_end, end_string) < 0) {\n                Py_DECREF(end_string);\n                goto bad;\n            }\n            Py_DECREF(end_string);\n        }\n    } else if (!newline) {\n        if (unlikely(!__pyx_print_kwargs)) {\n            __pyx_print_kwargs = PyDict_New();\n            if (unlikely(!__pyx_print_kwargs))\n                return -1;\n            end_string = PyUnicode_FromStringAndSize(\" \", 1);\n            if (unlikely(!end_string))\n                return -1;\n            if (PyDict_SetItem(__pyx_print_kwargs, __pyx_n_s_end, end_string) < 0) {\n                Py_DECREF(end_string);\n                return -1;\n            }\n            Py_DECREF(end_string);\n        }\n        kwargs = __pyx_print_kwargs;\n    }\n    result = PyObject_Call(__pyx_print, arg_tuple, kwargs);\n    if (unlikely(kwargs) && (kwargs != __pyx_print_kwargs))\n        Py_DECREF(kwargs);\n    if (!result)\n        return -1;\n    Py_DECREF(result);\n    return 0;\nbad:\n    if (kwargs != __pyx_print_kwargs)\n        Py_XDECREF(kwargs);\n    return -1;\n}\n#endif\n\n/* MemviewDtypeToObject */\n  static CYTHON_INLINE PyObject *__pyx_memview_get_float(const char *itemp) {\n    return (PyObject *) PyFloat_FromDouble(*(float *) itemp);\n}\nstatic CYTHON_INLINE int __pyx_memview_set_float(const char *itemp, PyObject *obj) {\n    float value = __pyx_PyFloat_AsFloat(obj);\n    if ((value == (float)-1) && PyErr_Occurred())\n        return 0;\n    *(float *) itemp = value;\n    return 1;\n}\n\n/* ObjectToMemviewSlice */\n  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_long(PyObject *obj, int writable_flag) {\n    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };\n    __Pyx_BufFmt_StackElem stack[1];\n    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };\n    int retcode;\n    if (obj == Py_None) {\n        result.memview = (struct __pyx_memoryview_obj *) Py_None;\n        return result;\n    }\n    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,\n                                                 PyBUF_RECORDS_RO | writable_flag, 2,\n                                                 &__Pyx_TypeInfo_long, stack,\n                                                 &result, obj);\n    if (unlikely(retcode == -1))\n        goto __pyx_fail;\n    return result;\n__pyx_fail:\n    result.memview = NULL;\n    result.data = NULL;\n    return result;\n}\n\n/* MemviewDtypeToObject */\n  static CYTHON_INLINE PyObject *__pyx_memview_get_long(const char *itemp) {\n    return (PyObject *) __Pyx_PyInt_From_long(*(long *) itemp);\n}\nstatic CYTHON_INLINE int __pyx_memview_set_long(const char *itemp, PyObject *obj) {\n    long value = __Pyx_PyInt_As_long(obj);\n    if ((value == (long)-1) && PyErr_Occurred())\n        return 0;\n    *(long *) itemp = value;\n    return 1;\n}\n\n/* ObjectToMemviewSlice */\n  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_float(PyObject *obj, int writable_flag) {\n    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };\n    __Pyx_BufFmt_StackElem stack[1];\n    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };\n    int retcode;\n    if (obj == Py_None) {\n        result.memview = (struct __pyx_memoryview_obj *) Py_None;\n        return result;\n    }\n    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,\n                                                 PyBUF_RECORDS_RO | writable_flag, 1,\n                                                 &__Pyx_TypeInfo_float, stack,\n                                                 &result, obj);\n    if (unlikely(retcode == -1))\n        goto __pyx_fail;\n    return result;\n__pyx_fail:\n    result.memview = NULL;\n    result.data = NULL;\n    return result;\n}\n\n/* MemviewSliceCopyTemplate */\n  static __Pyx_memviewslice\n__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,\n                                 const char *mode, int ndim,\n                                 size_t sizeof_dtype, int contig_flag,\n                                 int dtype_is_object)\n{\n    __Pyx_RefNannyDeclarations\n    int i;\n    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };\n    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;\n    Py_buffer *buf = &from_memview->view;\n    PyObject *shape_tuple = NULL;\n    PyObject *temp_int = NULL;\n    struct __pyx_array_obj *array_obj = NULL;\n    struct __pyx_memoryview_obj *memview_obj = NULL;\n    __Pyx_RefNannySetupContext(\"__pyx_memoryview_copy_new_contig\", 0);\n    for (i = 0; i < ndim; i++) {\n        if (unlikely(from_mvs->suboffsets[i] >= 0)) {\n            PyErr_Format(PyExc_ValueError, \"Cannot copy memoryview slice with \"\n                                           \"indirect dimensions (axis %d)\", i);\n            goto fail;\n        }\n    }\n    shape_tuple = PyTuple_New(ndim);\n    if (unlikely(!shape_tuple)) {\n        goto fail;\n    }\n    __Pyx_GOTREF(shape_tuple);\n    for(i = 0; i < ndim; i++) {\n        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);\n        if(unlikely(!temp_int)) {\n            goto fail;\n        } else {\n            PyTuple_SET_ITEM(shape_tuple, i, temp_int);\n            temp_int = NULL;\n        }\n    }\n    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);\n    if (unlikely(!array_obj)) {\n        goto fail;\n    }\n    __Pyx_GOTREF(array_obj);\n    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(\n                                    (PyObject *) array_obj, contig_flag,\n                                    dtype_is_object,\n                                    from_mvs->memview->typeinfo);\n    if (unlikely(!memview_obj))\n        goto fail;\n    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))\n        goto fail;\n    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,\n                                                dtype_is_object) < 0))\n        goto fail;\n    goto no_fail;\nfail:\n    __Pyx_XDECREF(new_mvs.memview);\n    new_mvs.memview = NULL;\n    new_mvs.data = NULL;\nno_fail:\n    __Pyx_XDECREF(shape_tuple);\n    __Pyx_XDECREF(temp_int);\n    __Pyx_XDECREF(array_obj);\n    __Pyx_RefNannyFinishContext();\n    return new_mvs;\n}\n\n/* CIntFromPy */\n  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wconversion\"\n#endif\n    const long neg_one = (long) -1, const_zero = (long) 0;\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic pop\n#endif\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(long) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (long) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (long) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {\n                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {\n                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {\n                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (long) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(long) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (long) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(long) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            long val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (long) -1;\n        }\n    } else {\n        long val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (long) -1;\n        val = __Pyx_PyInt_As_long(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to long\");\n    return (long) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to long\");\n    return (long) -1;\n}\n\n/* CIntToPy */\n  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wconversion\"\n#endif\n    const long neg_one = (long) -1, const_zero = (long) 0;\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic pop\n#endif\n    const int is_unsigned = neg_one > const_zero;\n    if (is_unsigned) {\n        if (sizeof(long) < sizeof(long)) {\n            return PyInt_FromLong((long) value);\n        } else if (sizeof(long) <= sizeof(unsigned long)) {\n            return PyLong_FromUnsignedLong((unsigned long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {\n            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);\n#endif\n        }\n    } else {\n        if (sizeof(long) <= sizeof(long)) {\n            return PyInt_FromLong((long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {\n            return PyLong_FromLongLong((PY_LONG_LONG) value);\n#endif\n        }\n    }\n    {\n        int one = 1; int little = (int)*(unsigned char *)&one;\n        unsigned char *bytes = (unsigned char *)&value;\n        return _PyLong_FromByteArray(bytes, sizeof(long),\n                                     little, !is_unsigned);\n    }\n}\n\n/* PrintOne */\n  #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION < 3\nstatic int __Pyx_PrintOne(PyObject* f, PyObject *o) {\n    if (!f) {\n        if (!(f = __Pyx_GetStdout()))\n            return -1;\n    }\n    Py_INCREF(f);\n    if (PyFile_SoftSpace(f, 0)) {\n        if (PyFile_WriteString(\" \", f) < 0)\n            goto error;\n    }\n    if (PyFile_WriteObject(o, f, Py_PRINT_RAW) < 0)\n        goto error;\n    if (PyFile_WriteString(\"\\n\", f) < 0)\n        goto error;\n    Py_DECREF(f);\n    return 0;\nerror:\n    Py_DECREF(f);\n    return -1;\n    /* the line below is just to avoid C compiler\n     * warnings about unused functions */\n    return __Pyx_Print(f, NULL, 0);\n}\n#else\nstatic int __Pyx_PrintOne(PyObject* stream, PyObject *o) {\n    int res;\n    PyObject* arg_tuple = PyTuple_Pack(1, o);\n    if (unlikely(!arg_tuple))\n        return -1;\n    res = __Pyx_Print(stream, arg_tuple, 1);\n    Py_DECREF(arg_tuple);\n    return res;\n}\n#endif\n\n/* CIntFromPy */\n  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wconversion\"\n#endif\n    const int neg_one = (int) -1, const_zero = (int) 0;\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic pop\n#endif\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(int) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (int) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (int) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {\n                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {\n                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {\n                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (int) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(int) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (int) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {\n                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(int) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            int val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (int) -1;\n        }\n    } else {\n        int val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (int) -1;\n        val = __Pyx_PyInt_As_int(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to int\");\n    return (int) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to int\");\n    return (int) -1;\n}\n\n/* CIntToPy */\n  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wconversion\"\n#endif\n    const int neg_one = (int) -1, const_zero = (int) 0;\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic pop\n#endif\n    const int is_unsigned = neg_one > const_zero;\n    if (is_unsigned) {\n        if (sizeof(int) < sizeof(long)) {\n            return PyInt_FromLong((long) value);\n        } else if (sizeof(int) <= sizeof(unsigned long)) {\n            return PyLong_FromUnsignedLong((unsigned long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {\n            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);\n#endif\n        }\n    } else {\n        if (sizeof(int) <= sizeof(long)) {\n            return PyInt_FromLong((long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {\n            return PyLong_FromLongLong((PY_LONG_LONG) value);\n#endif\n        }\n    }\n    {\n        int one = 1; int little = (int)*(unsigned char *)&one;\n        unsigned char *bytes = (unsigned char *)&value;\n        return _PyLong_FromByteArray(bytes, sizeof(int),\n                                     little, !is_unsigned);\n    }\n}\n\n/* CIntFromPy */\n  static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wconversion\"\n#endif\n    const char neg_one = (char) -1, const_zero = (char) 0;\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic pop\n#endif\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(char) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (char) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (char) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) {\n                            return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) {\n                            return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) {\n                            return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (char) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(char) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (char) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(char,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {\n                            return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {\n                            return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {\n                            return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {\n                            return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {\n                            return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {\n                            return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(char) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            char val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (char) -1;\n        }\n    } else {\n        char val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (char) -1;\n        val = __Pyx_PyInt_As_char(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to char\");\n    return (char) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to char\");\n    return (char) -1;\n}\n\n/* CheckBinaryVersion */\n  static int __Pyx_check_binary_version(void) {\n    char ctversion[5];\n    int same=1, i, found_dot;\n    const char* rt_from_call = Py_GetVersion();\n    PyOS_snprintf(ctversion, 5, \"%d.%d\", PY_MAJOR_VERSION, PY_MINOR_VERSION);\n    found_dot = 0;\n    for (i = 0; i < 4; i++) {\n        if (!ctversion[i]) {\n            same = (rt_from_call[i] < '0' || rt_from_call[i] > '9');\n            break;\n        }\n        if (rt_from_call[i] != ctversion[i]) {\n            same = 0;\n            break;\n        }\n    }\n    if (!same) {\n        char rtversion[5] = {'\\0'};\n        char message[200];\n        for (i=0; i<4; ++i) {\n            if (rt_from_call[i] == '.') {\n                if (found_dot) break;\n                found_dot = 1;\n            } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') {\n                break;\n            }\n            rtversion[i] = rt_from_call[i];\n        }\n        PyOS_snprintf(message, sizeof(message),\n                      \"compiletime version %s of module '%.100s' \"\n                      \"does not match runtime version %s\",\n                      ctversion, __Pyx_MODULE_NAME, rtversion);\n        return PyErr_WarnEx(NULL, message, 1);\n    }\n    return 0;\n}\n\n/* InitStrings */\n  static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {\n    while (t->p) {\n        #if PY_MAJOR_VERSION < 3\n        if (t->is_unicode) {\n            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);\n        } else if (t->intern) {\n            *t->p = PyString_InternFromString(t->s);\n        } else {\n            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);\n        }\n        #else\n        if (t->is_unicode | t->is_str) {\n            if (t->intern) {\n                *t->p = PyUnicode_InternFromString(t->s);\n            } else if (t->encoding) {\n                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);\n            } else {\n                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);\n            }\n        } else {\n            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);\n        }\n        #endif\n        if (!*t->p)\n            return -1;\n        if (PyObject_Hash(*t->p) == -1)\n            return -1;\n        ++t;\n    }\n    return 0;\n}\n\nstatic CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {\n    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));\n}\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {\n    Py_ssize_t ignore;\n    return __Pyx_PyObject_AsStringAndSize(o, &ignore);\n}\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\n#if !CYTHON_PEP393_ENABLED\nstatic const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n    char* defenc_c;\n    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);\n    if (!defenc) return NULL;\n    defenc_c = PyBytes_AS_STRING(defenc);\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n    {\n        char* end = defenc_c + PyBytes_GET_SIZE(defenc);\n        char* c;\n        for (c = defenc_c; c < end; c++) {\n            if ((unsigned char) (*c) >= 128) {\n                PyUnicode_AsASCIIString(o);\n                return NULL;\n            }\n        }\n    }\n#endif\n    *length = PyBytes_GET_SIZE(defenc);\n    return defenc_c;\n}\n#else\nstatic CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n    if (likely(PyUnicode_IS_ASCII(o))) {\n        *length = PyUnicode_GET_LENGTH(o);\n        return PyUnicode_AsUTF8(o);\n    } else {\n        PyUnicode_AsASCIIString(o);\n        return NULL;\n    }\n#else\n    return PyUnicode_AsUTF8AndSize(o, length);\n#endif\n}\n#endif\n#endif\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\n    if (\n#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n            __Pyx_sys_getdefaultencoding_not_ascii &&\n#endif\n            PyUnicode_Check(o)) {\n        return __Pyx_PyUnicode_AsStringAndSize(o, length);\n    } else\n#endif\n#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))\n    if (PyByteArray_Check(o)) {\n        *length = PyByteArray_GET_SIZE(o);\n        return PyByteArray_AS_STRING(o);\n    } else\n#endif\n    {\n        char* result;\n        int r = PyBytes_AsStringAndSize(o, &result, length);\n        if (unlikely(r < 0)) {\n            return NULL;\n        } else {\n            return result;\n        }\n    }\n}\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\n   int is_true = x == Py_True;\n   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;\n   else return PyObject_IsTrue(x);\n}\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {\n    int retval;\n    if (unlikely(!x)) return -1;\n    retval = __Pyx_PyObject_IsTrue(x);\n    Py_DECREF(x);\n    return retval;\n}\nstatic PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {\n#if PY_MAJOR_VERSION >= 3\n    if (PyLong_Check(result)) {\n        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,\n                \"__int__ returned non-int (type %.200s).  \"\n                \"The ability to return an instance of a strict subclass of int \"\n                \"is deprecated, and may be removed in a future version of Python.\",\n                Py_TYPE(result)->tp_name)) {\n            Py_DECREF(result);\n            return NULL;\n        }\n        return result;\n    }\n#endif\n    PyErr_Format(PyExc_TypeError,\n                 \"__%.4s__ returned non-%.4s (type %.200s)\",\n                 type_name, type_name, Py_TYPE(result)->tp_name);\n    Py_DECREF(result);\n    return NULL;\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {\n#if CYTHON_USE_TYPE_SLOTS\n  PyNumberMethods *m;\n#endif\n  const char *name = NULL;\n  PyObject *res = NULL;\n#if PY_MAJOR_VERSION < 3\n  if (likely(PyInt_Check(x) || PyLong_Check(x)))\n#else\n  if (likely(PyLong_Check(x)))\n#endif\n    return __Pyx_NewRef(x);\n#if CYTHON_USE_TYPE_SLOTS\n  m = Py_TYPE(x)->tp_as_number;\n  #if PY_MAJOR_VERSION < 3\n  if (m && m->nb_int) {\n    name = \"int\";\n    res = m->nb_int(x);\n  }\n  else if (m && m->nb_long) {\n    name = \"long\";\n    res = m->nb_long(x);\n  }\n  #else\n  if (likely(m && m->nb_int)) {\n    name = \"int\";\n    res = m->nb_int(x);\n  }\n  #endif\n#else\n  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {\n    res = PyNumber_Int(x);\n  }\n#endif\n  if (likely(res)) {\n#if PY_MAJOR_VERSION < 3\n    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {\n#else\n    if (unlikely(!PyLong_CheckExact(res))) {\n#endif\n        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);\n    }\n  }\n  else if (!PyErr_Occurred()) {\n    PyErr_SetString(PyExc_TypeError,\n                    \"an integer is required\");\n  }\n  return res;\n}\nstatic CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\n  Py_ssize_t ival;\n  PyObject *x;\n#if PY_MAJOR_VERSION < 3\n  if (likely(PyInt_CheckExact(b))) {\n    if (sizeof(Py_ssize_t) >= sizeof(long))\n        return PyInt_AS_LONG(b);\n    else\n        return PyInt_AsSsize_t(b);\n  }\n#endif\n  if (likely(PyLong_CheckExact(b))) {\n    #if CYTHON_USE_PYLONG_INTERNALS\n    const digit* digits = ((PyLongObject*)b)->ob_digit;\n    const Py_ssize_t size = Py_SIZE(b);\n    if (likely(__Pyx_sst_abs(size) <= 1)) {\n        ival = likely(size) ? digits[0] : 0;\n        if (size == -1) ival = -ival;\n        return ival;\n    } else {\n      switch (size) {\n         case 2:\n           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -2:\n           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case 3:\n           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -3:\n           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case 4:\n           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -4:\n           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n      }\n    }\n    #endif\n    return PyLong_AsSsize_t(b);\n  }\n  x = PyNumber_Index(b);\n  if (!x) return -1;\n  ival = PyInt_AsSsize_t(x);\n  Py_DECREF(x);\n  return ival;\n}\nstatic CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) {\n  if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) {\n    return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o);\n#if PY_MAJOR_VERSION < 3\n  } else if (likely(PyInt_CheckExact(o))) {\n    return PyInt_AS_LONG(o);\n#endif\n  } else {\n    Py_ssize_t ival;\n    PyObject *x;\n    x = PyNumber_Index(o);\n    if (!x) return -1;\n    ival = PyInt_AsLong(x);\n    Py_DECREF(x);\n    return ival;\n  }\n}\nstatic CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {\n  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);\n}\nstatic CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\n    return PyInt_FromSize_t(ival);\n}\n\n\n#endif /* Py_PYTHON_H */\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/rank_cylib/rank_cy.pyx",
    "content": "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True\n# credits: https://github.com/KaiyangZhou/deep-person-reid/blob/master/torchreid/metrics/rank_cylib/rank_cy.pyx\n\nimport cython\nimport numpy as np\ncimport numpy as np\nfrom collections import defaultdict\n\n\n\"\"\"\nCompiler directives:\nhttps://github.com/cython/cython/wiki/enhancements-compilerdirectives\nCython tutorial:\nhttps://cython.readthedocs.io/en/latest/src/userguide/numpy_tutorial.html\nCredit to https://github.com/luzai\n\"\"\"\n\n\n# Main interface\ncpdef evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03=False):\n    distmat = np.asarray(distmat, dtype=np.float32)\n    q_pids = np.asarray(q_pids, dtype=np.int64)\n    g_pids = np.asarray(g_pids, dtype=np.int64)\n    q_camids = np.asarray(q_camids, dtype=np.int64)\n    g_camids = np.asarray(g_camids, dtype=np.int64)\n    if use_metric_cuhk03:\n        return eval_cuhk03_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)\n    return eval_market1501_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)\n\n\ncpdef eval_cuhk03_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,\n                     long[:]q_camids, long[:]g_camids, long max_rank):\n    cdef long num_q = distmat.shape[0]\n    cdef long num_g = distmat.shape[1]\n\n\n    if num_g < max_rank:\n        max_rank = num_g\n        print('Note: number of gallery samples is quite small, got {}'.format(num_g))\n\n    cdef:\n        long num_repeats = 10\n        long[:,:] indices = np.argsort(distmat, axis=1)\n        long[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64)\n\n        float[:,:] all_cmc = np.zeros((num_q, max_rank), dtype=np.float32)\n        float[:] all_AP = np.zeros(num_q, dtype=np.float32)\n        float num_valid_q = 0. # number of valid query\n\n        long q_idx, q_pid, q_camid, g_idx\n        long[:] order = np.zeros(num_g, dtype=np.int64)\n        long keep\n\n        float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches\n        float[:] masked_raw_cmc = np.zeros(num_g, dtype=np.float32)\n        float[:] cmc, masked_cmc\n        long num_g_real, num_g_real_masked, rank_idx, rnd_idx\n        unsigned long meet_condition\n        float AP\n        long[:] kept_g_pids, mask\n\n        float num_rel\n        float[:] tmp_cmc = np.zeros(num_g, dtype=np.float32)\n        float tmp_cmc_sum\n\n    for q_idx in range(num_q):\n        # get query pid and camid\n        q_pid = q_pids[q_idx]\n        q_camid = q_camids[q_idx]\n\n        # remove gallery samples that have the same pid and camid with query\n        for g_idx in range(num_g):\n            order[g_idx] = indices[q_idx, g_idx]\n        num_g_real = 0\n        meet_condition = 0\n        kept_g_pids = np.zeros(num_g, dtype=np.int64)\n\n        for g_idx in range(num_g):\n            if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid):\n                raw_cmc[num_g_real] = matches[q_idx][g_idx]\n                kept_g_pids[num_g_real] = g_pids[order[g_idx]]\n                num_g_real += 1\n                if matches[q_idx][g_idx] > 1e-31:\n                    meet_condition = 1\n\n        if not meet_condition:\n            # this condition is true when query identity does not appear in gallery\n            continue\n\n        # cuhk03-specific setting\n        g_pids_dict = defaultdict(list) # overhead!\n        for g_idx in range(num_g_real):\n            g_pids_dict[kept_g_pids[g_idx]].append(g_idx)\n\n        cmc = np.zeros(max_rank, dtype=np.float32)\n        for _ in range(num_repeats):\n            mask = np.zeros(num_g_real, dtype=np.int64)\n\n            for _, idxs in g_pids_dict.items():\n                # randomly sample one image for each gallery person\n                rnd_idx = np.random.choice(idxs)\n                #rnd_idx = idxs[0] # use deterministic for debugging\n                mask[rnd_idx] = 1\n\n            num_g_real_masked = 0\n            for g_idx in range(num_g_real):\n                if mask[g_idx] == 1:\n                    masked_raw_cmc[num_g_real_masked] = raw_cmc[g_idx]\n                    num_g_real_masked += 1\n\n            masked_cmc = np.zeros(num_g, dtype=np.float32)\n            function_cumsum(masked_raw_cmc, masked_cmc, num_g_real_masked)\n            for g_idx in range(num_g_real_masked):\n                if masked_cmc[g_idx] > 1:\n                    masked_cmc[g_idx] = 1\n\n            for rank_idx in range(max_rank):\n                cmc[rank_idx] += masked_cmc[rank_idx] / num_repeats\n\n        for rank_idx in range(max_rank):\n            all_cmc[q_idx, rank_idx] = cmc[rank_idx]\n        # compute average precision\n        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision\n        function_cumsum(raw_cmc, tmp_cmc, num_g_real)\n        num_rel = 0\n        tmp_cmc_sum = 0\n        for g_idx in range(num_g_real):\n            tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]\n            num_rel += raw_cmc[g_idx]\n        all_AP[q_idx] = tmp_cmc_sum / num_rel\n        num_valid_q += 1.\n\n    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'\n\n    # compute averaged cmc\n    cdef float[:] avg_cmc = np.zeros(max_rank, dtype=np.float32)\n    for rank_idx in range(max_rank):\n        for q_idx in range(num_q):\n            avg_cmc[rank_idx] += all_cmc[q_idx, rank_idx]\n        avg_cmc[rank_idx] /= num_valid_q\n\n    cdef float mAP = 0\n    for q_idx in range(num_q):\n        mAP += all_AP[q_idx]\n    mAP /= num_valid_q\n\n    return np.asarray(avg_cmc).astype(np.float32), mAP\n\n\ncpdef eval_market1501_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,\n                         long[:]q_camids, long[:]g_camids, long max_rank):\n\n    cdef long num_q = distmat.shape[0]\n    cdef long num_g = distmat.shape[1]\n\n    if num_g < max_rank:\n        max_rank = num_g\n        print('Note: number of gallery samples is quite small, got {}'.format(num_g))\n\n    cdef:\n        long[:,:] indices = np.argsort(distmat, axis=1)\n        long[:] matches\n\n        float[:,:] all_cmc = np.zeros((num_q, max_rank), dtype=np.float32)\n        float[:] all_AP = np.zeros(num_q, dtype=np.float32)\n        float[:] all_INP = np.zeros(num_q, dtype=np.float32)\n        float num_valid_q = 0. # number of valid query\n        long valid_index = 0\n\n        long q_idx, q_pid, q_camid, g_idx\n        long[:] order = np.zeros(num_g, dtype=np.int64)\n        long keep\n\n        float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches\n        float[:] cmc = np.zeros(num_g, dtype=np.float32)\n        long max_pos_idx = 0\n        float inp\n        long num_g_real, rank_idx\n        unsigned long meet_condition\n\n        float num_rel\n        float[:] tmp_cmc = np.zeros(num_g, dtype=np.float32)\n        float tmp_cmc_sum\n\n\n    for q_idx in range(num_q):\n        # get query pid and camid\n        q_pid = q_pids[q_idx]\n        q_camid = q_camids[q_idx]\n\n        for g_idx in range(num_g):\n            order[g_idx] = indices[q_idx, g_idx]\n        num_g_real = 0\n        meet_condition = 0\n        matches = (np.asarray(g_pids)[np.asarray(order)] == q_pid).astype(np.int64)\n\n        # remove gallery samples that have the same pid and camid with query\n        for g_idx in range(num_g):\n            if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid):\n                raw_cmc[num_g_real] = matches[g_idx]\n                num_g_real += 1\n                # this condition is true if query appear in gallery\n                if matches[g_idx] > 1e-31:\n                    meet_condition = 1\n\n        if not meet_condition:\n            # this condition is true when query identity does not appear in gallery\n            continue\n\n        # compute cmc\n        function_cumsum(raw_cmc, cmc, num_g_real)\n        # compute mean inverse negative penalty\n        # reference : https://github.com/mangye16/ReID-Survey/blob/master/utils/reid_metric.py\n        max_pos_idx = 0\n        for g_idx in range(num_g_real):\n            if (raw_cmc[g_idx] == 1) and (g_idx > max_pos_idx):\n                max_pos_idx = g_idx\n        inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)\n        all_INP[valid_index] = inp\n\n        for g_idx in range(num_g_real):\n            if cmc[g_idx] > 1:\n                cmc[g_idx] = 1\n\n        for rank_idx in range(max_rank):\n            all_cmc[q_idx, rank_idx] = cmc[rank_idx]\n        num_valid_q += 1.\n\n        # compute average precision\n        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision\n        function_cumsum(raw_cmc, tmp_cmc, num_g_real)\n        num_rel = 0\n        tmp_cmc_sum = 0\n        for g_idx in range(num_g_real):\n            tmp_cmc_sum += (tmp_cmc[g_idx] / (g_idx + 1.)) * raw_cmc[g_idx]\n            num_rel += raw_cmc[g_idx]\n        all_AP[valid_index] = tmp_cmc_sum / num_rel\n        valid_index += 1\n\n    assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'\n\n    # compute averaged cmc\n    cdef float[:] avg_cmc = np.zeros(max_rank, dtype=np.float32)\n    for rank_idx in range(max_rank):\n        for q_idx in range(num_q):\n            avg_cmc[rank_idx] += all_cmc[q_idx, rank_idx]\n        avg_cmc[rank_idx] /= num_valid_q\n\n    return np.asarray(avg_cmc).astype(np.float32), np.asarray(all_AP[:valid_index]), np.asarray(all_INP[:valid_index])\n\n\n# Compute the cumulative sum\ncdef void function_cumsum(cython.numeric[:] src, cython.numeric[:] dst, long n):\n    cdef long i\n    dst[0] = src[0]\n    for i in range(1, n):\n        dst[i] = src[i] + dst[i - 1]"
  },
  {
    "path": "fast_reid/fastreid/evaluation/rank_cylib/roc_cy.c",
    "content": "/* Generated by Cython 0.29.32 */\n\n/* BEGIN: Cython Metadata\n{\n    \"distutils\": {\n        \"depends\": [\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h\",\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h\",\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h\",\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h\",\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h\"\n        ],\n        \"include_dirs\": [\n            \"/home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/core/include\"\n        ],\n        \"name\": \"roc_cy\",\n        \"sources\": [\n            \"roc_cy.pyx\"\n        ]\n    },\n    \"module_name\": \"roc_cy\"\n}\nEND: Cython Metadata */\n\n#ifndef PY_SSIZE_T_CLEAN\n#define PY_SSIZE_T_CLEAN\n#endif /* PY_SSIZE_T_CLEAN */\n#include \"Python.h\"\n#ifndef Py_PYTHON_H\n    #error Python headers needed to compile C extensions, please install development version of Python.\n#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)\n    #error Cython requires Python 2.6+ or Python 3.3+.\n#else\n#define CYTHON_ABI \"0_29_32\"\n#define CYTHON_HEX_VERSION 0x001D20F0\n#define CYTHON_FUTURE_DIVISION 0\n#include <stddef.h>\n#ifndef offsetof\n  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )\n#endif\n#if !defined(WIN32) && !defined(MS_WINDOWS)\n  #ifndef __stdcall\n    #define __stdcall\n  #endif\n  #ifndef __cdecl\n    #define __cdecl\n  #endif\n  #ifndef __fastcall\n    #define __fastcall\n  #endif\n#endif\n#ifndef DL_IMPORT\n  #define DL_IMPORT(t) t\n#endif\n#ifndef DL_EXPORT\n  #define DL_EXPORT(t) t\n#endif\n#define __PYX_COMMA ,\n#ifndef HAVE_LONG_LONG\n  #if PY_VERSION_HEX >= 0x02070000\n    #define HAVE_LONG_LONG\n  #endif\n#endif\n#ifndef PY_LONG_LONG\n  #define PY_LONG_LONG LONG_LONG\n#endif\n#ifndef Py_HUGE_VAL\n  #define Py_HUGE_VAL HUGE_VAL\n#endif\n#ifdef PYPY_VERSION\n  #define CYTHON_COMPILING_IN_PYPY 1\n  #define CYTHON_COMPILING_IN_PYSTON 0\n  #define CYTHON_COMPILING_IN_CPYTHON 0\n  #define CYTHON_COMPILING_IN_NOGIL 0\n  #undef CYTHON_USE_TYPE_SLOTS\n  #define CYTHON_USE_TYPE_SLOTS 0\n  #undef CYTHON_USE_PYTYPE_LOOKUP\n  #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #if PY_VERSION_HEX < 0x03050000\n    #undef CYTHON_USE_ASYNC_SLOTS\n    #define CYTHON_USE_ASYNC_SLOTS 0\n  #elif !defined(CYTHON_USE_ASYNC_SLOTS)\n    #define CYTHON_USE_ASYNC_SLOTS 1\n  #endif\n  #undef CYTHON_USE_PYLIST_INTERNALS\n  #define CYTHON_USE_PYLIST_INTERNALS 0\n  #undef CYTHON_USE_UNICODE_INTERNALS\n  #define CYTHON_USE_UNICODE_INTERNALS 0\n  #undef CYTHON_USE_UNICODE_WRITER\n  #define CYTHON_USE_UNICODE_WRITER 0\n  #undef 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 #define CYTHON_COMPILING_IN_NOGIL 0\n  #ifndef CYTHON_USE_TYPE_SLOTS\n    #define CYTHON_USE_TYPE_SLOTS 1\n  #endif\n  #undef CYTHON_USE_PYTYPE_LOOKUP\n  #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #undef CYTHON_USE_ASYNC_SLOTS\n  #define CYTHON_USE_ASYNC_SLOTS 0\n  #undef CYTHON_USE_PYLIST_INTERNALS\n  #define CYTHON_USE_PYLIST_INTERNALS 0\n  #ifndef CYTHON_USE_UNICODE_INTERNALS\n    #define CYTHON_USE_UNICODE_INTERNALS 1\n  #endif\n  #undef CYTHON_USE_UNICODE_WRITER\n  #define CYTHON_USE_UNICODE_WRITER 0\n  #undef CYTHON_USE_PYLONG_INTERNALS\n  #define CYTHON_USE_PYLONG_INTERNALS 0\n  #ifndef CYTHON_AVOID_BORROWED_REFS\n    #define CYTHON_AVOID_BORROWED_REFS 0\n  #endif\n  #ifndef CYTHON_ASSUME_SAFE_MACROS\n    #define CYTHON_ASSUME_SAFE_MACROS 1\n  #endif\n  #ifndef CYTHON_UNPACK_METHODS\n    #define CYTHON_UNPACK_METHODS 1\n  #endif\n  #undef CYTHON_FAST_THREAD_STATE\n  #define CYTHON_FAST_THREAD_STATE 0\n  #undef CYTHON_FAST_PYCALL\n  #define CYTHON_FAST_PYCALL 0\n  #undef CYTHON_PEP489_MULTI_PHASE_INIT\n  #define CYTHON_PEP489_MULTI_PHASE_INIT 0\n  #undef CYTHON_USE_TP_FINALIZE\n  #define CYTHON_USE_TP_FINALIZE 0\n  #undef CYTHON_USE_DICT_VERSIONS\n  #define CYTHON_USE_DICT_VERSIONS 0\n  #undef CYTHON_USE_EXC_INFO_STACK\n  #define CYTHON_USE_EXC_INFO_STACK 0\n  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC\n    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0\n  #endif\n#elif defined(PY_NOGIL)\n  #define CYTHON_COMPILING_IN_PYPY 0\n  #define CYTHON_COMPILING_IN_PYSTON 0\n  #define CYTHON_COMPILING_IN_CPYTHON 0\n  #define CYTHON_COMPILING_IN_NOGIL 1\n  #ifndef CYTHON_USE_TYPE_SLOTS\n    #define CYTHON_USE_TYPE_SLOTS 1\n  #endif\n  #undef CYTHON_USE_PYTYPE_LOOKUP\n  #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #ifndef CYTHON_USE_ASYNC_SLOTS\n    #define CYTHON_USE_ASYNC_SLOTS 1\n  #endif\n  #undef CYTHON_USE_PYLIST_INTERNALS\n  #define CYTHON_USE_PYLIST_INTERNALS 0\n  #ifndef CYTHON_USE_UNICODE_INTERNALS\n    #define CYTHON_USE_UNICODE_INTERNALS 1\n  #endif\n  #undef CYTHON_USE_UNICODE_WRITER\n  #define CYTHON_USE_UNICODE_WRITER 0\n  #undef CYTHON_USE_PYLONG_INTERNALS\n  #define CYTHON_USE_PYLONG_INTERNALS 0\n  #ifndef CYTHON_AVOID_BORROWED_REFS\n    #define CYTHON_AVOID_BORROWED_REFS 0\n  #endif\n  #ifndef CYTHON_ASSUME_SAFE_MACROS\n    #define CYTHON_ASSUME_SAFE_MACROS 1\n  #endif\n  #ifndef CYTHON_UNPACK_METHODS\n    #define CYTHON_UNPACK_METHODS 1\n  #endif\n  #undef CYTHON_FAST_THREAD_STATE\n  #define CYTHON_FAST_THREAD_STATE 0\n  #undef CYTHON_FAST_PYCALL\n  #define CYTHON_FAST_PYCALL 0\n  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT\n    #define CYTHON_PEP489_MULTI_PHASE_INIT 1\n  #endif\n  #ifndef CYTHON_USE_TP_FINALIZE\n    #define CYTHON_USE_TP_FINALIZE 1\n  #endif\n  #undef CYTHON_USE_DICT_VERSIONS\n  #define CYTHON_USE_DICT_VERSIONS 0\n  #undef CYTHON_USE_EXC_INFO_STACK\n  #define CYTHON_USE_EXC_INFO_STACK 0\n#else\n  #define CYTHON_COMPILING_IN_PYPY 0\n  #define CYTHON_COMPILING_IN_PYSTON 0\n  #define CYTHON_COMPILING_IN_CPYTHON 1\n  #define CYTHON_COMPILING_IN_NOGIL 0\n  #ifndef CYTHON_USE_TYPE_SLOTS\n    #define CYTHON_USE_TYPE_SLOTS 1\n  #endif\n  #if PY_VERSION_HEX < 0x02070000\n    #undef CYTHON_USE_PYTYPE_LOOKUP\n    #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)\n    #define CYTHON_USE_PYTYPE_LOOKUP 1\n  #endif\n  #if PY_MAJOR_VERSION < 3\n    #undef CYTHON_USE_ASYNC_SLOTS\n    #define CYTHON_USE_ASYNC_SLOTS 0\n  #elif !defined(CYTHON_USE_ASYNC_SLOTS)\n    #define CYTHON_USE_ASYNC_SLOTS 1\n  #endif\n  #if PY_VERSION_HEX < 0x02070000\n    #undef CYTHON_USE_PYLONG_INTERNALS\n    #define CYTHON_USE_PYLONG_INTERNALS 0\n  #elif !defined(CYTHON_USE_PYLONG_INTERNALS)\n    #define CYTHON_USE_PYLONG_INTERNALS 1\n  #endif\n  #ifndef CYTHON_USE_PYLIST_INTERNALS\n    #define CYTHON_USE_PYLIST_INTERNALS 1\n  #endif\n  #ifndef CYTHON_USE_UNICODE_INTERNALS\n    #define CYTHON_USE_UNICODE_INTERNALS 1\n  #endif\n  #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2\n    #undef CYTHON_USE_UNICODE_WRITER\n    #define CYTHON_USE_UNICODE_WRITER 0\n  #elif !defined(CYTHON_USE_UNICODE_WRITER)\n    #define CYTHON_USE_UNICODE_WRITER 1\n  #endif\n  #ifndef CYTHON_AVOID_BORROWED_REFS\n    #define CYTHON_AVOID_BORROWED_REFS 0\n  #endif\n  #ifndef CYTHON_ASSUME_SAFE_MACROS\n    #define CYTHON_ASSUME_SAFE_MACROS 1\n  #endif\n  #ifndef CYTHON_UNPACK_METHODS\n    #define CYTHON_UNPACK_METHODS 1\n  #endif\n  #if PY_VERSION_HEX >= 0x030B00A4\n    #undef CYTHON_FAST_THREAD_STATE\n    #define CYTHON_FAST_THREAD_STATE 0\n  #elif !defined(CYTHON_FAST_THREAD_STATE)\n    #define CYTHON_FAST_THREAD_STATE 1\n  #endif\n  #ifndef CYTHON_FAST_PYCALL\n    #define CYTHON_FAST_PYCALL (PY_VERSION_HEX < 0x030A0000)\n  #endif\n  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT\n    #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000)\n  #endif\n  #ifndef CYTHON_USE_TP_FINALIZE\n    #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)\n  #endif\n  #ifndef CYTHON_USE_DICT_VERSIONS\n    #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1)\n  #endif\n  #if PY_VERSION_HEX >= 0x030B00A4\n    #undef CYTHON_USE_EXC_INFO_STACK\n    #define CYTHON_USE_EXC_INFO_STACK 0\n  #elif !defined(CYTHON_USE_EXC_INFO_STACK)\n    #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3)\n  #endif\n  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC\n    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1\n  #endif\n#endif\n#if !defined(CYTHON_FAST_PYCCALL)\n#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)\n#endif\n#if CYTHON_USE_PYLONG_INTERNALS\n  #if PY_MAJOR_VERSION < 3\n    #include \"longintrepr.h\"\n  #endif\n  #undef SHIFT\n  #undef BASE\n  #undef MASK\n  #ifdef SIZEOF_VOID_P\n    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };\n  #endif\n#endif\n#ifndef __has_attribute\n  #define __has_attribute(x) 0\n#endif\n#ifndef __has_cpp_attribute\n  #define __has_cpp_attribute(x) 0\n#endif\n#ifndef CYTHON_RESTRICT\n  #if defined(__GNUC__)\n    #define CYTHON_RESTRICT __restrict__\n  #elif defined(_MSC_VER) && _MSC_VER >= 1400\n    #define CYTHON_RESTRICT __restrict\n  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n    #define CYTHON_RESTRICT restrict\n  #else\n    #define CYTHON_RESTRICT\n  #endif\n#endif\n#ifndef CYTHON_UNUSED\n# if defined(__GNUC__)\n#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))\n#     define CYTHON_UNUSED __attribute__ ((__unused__))\n#   else\n#     define CYTHON_UNUSED\n#   endif\n# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))\n#   define CYTHON_UNUSED __attribute__ ((__unused__))\n# else\n#   define CYTHON_UNUSED\n# endif\n#endif\n#ifndef CYTHON_MAYBE_UNUSED_VAR\n#  if defined(__cplusplus)\n     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }\n#  else\n#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)\n#  endif\n#endif\n#ifndef CYTHON_NCP_UNUSED\n# if CYTHON_COMPILING_IN_CPYTHON\n#  define CYTHON_NCP_UNUSED\n# else\n#  define CYTHON_NCP_UNUSED CYTHON_UNUSED\n# endif\n#endif\n#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)\n#ifdef _MSC_VER\n    #ifndef _MSC_STDINT_H_\n        #if _MSC_VER < 1300\n           typedef unsigned char     uint8_t;\n           typedef unsigned int      uint32_t;\n        #else\n           typedef unsigned __int8   uint8_t;\n           typedef unsigned __int32  uint32_t;\n        #endif\n    #endif\n#else\n   #include <stdint.h>\n#endif\n#ifndef CYTHON_FALLTHROUGH\n  #if defined(__cplusplus) && __cplusplus >= 201103L\n    #if __has_cpp_attribute(fallthrough)\n      #define CYTHON_FALLTHROUGH [[fallthrough]]\n    #elif __has_cpp_attribute(clang::fallthrough)\n      #define CYTHON_FALLTHROUGH [[clang::fallthrough]]\n    #elif __has_cpp_attribute(gnu::fallthrough)\n      #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]\n    #endif\n  #endif\n  #ifndef CYTHON_FALLTHROUGH\n    #if __has_attribute(fallthrough)\n      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))\n    #else\n      #define CYTHON_FALLTHROUGH\n    #endif\n  #endif\n  #if defined(__clang__ ) && defined(__apple_build_version__)\n    #if __apple_build_version__ < 7000000\n      #undef  CYTHON_FALLTHROUGH\n      #define CYTHON_FALLTHROUGH\n    #endif\n  #endif\n#endif\n\n#ifndef CYTHON_INLINE\n  #if defined(__clang__)\n    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))\n  #elif defined(__GNUC__)\n    #define CYTHON_INLINE __inline__\n  #elif defined(_MSC_VER)\n    #define CYTHON_INLINE __inline\n  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n    #define CYTHON_INLINE inline\n  #else\n    #define CYTHON_INLINE\n  #endif\n#endif\n\n#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)\n  #define Py_OptimizeFlag 0\n#endif\n#define __PYX_BUILD_PY_SSIZE_T \"n\"\n#define CYTHON_FORMAT_SSIZE_T \"z\"\n#if PY_MAJOR_VERSION < 3\n  #define __Pyx_BUILTIN_MODULE_NAME \"__builtin__\"\n  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\\\n          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\n  #define __Pyx_DefaultClassType PyClass_Type\n#else\n  #define __Pyx_BUILTIN_MODULE_NAME \"builtins\"\n  #define __Pyx_DefaultClassType PyType_Type\n#if PY_VERSION_HEX >= 0x030B00A1\n    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int k, int l, int s, int f,\n                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,\n                                                    PyObject *fv, PyObject *cell, PyObject* fn,\n                                                    PyObject *name, int fline, PyObject *lnos) {\n        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;\n        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *call_result=NULL, *empty=NULL;\n        const char *fn_cstr=NULL;\n        const char *name_cstr=NULL;\n        PyCodeObject* co=NULL;\n        PyObject *type, *value, *traceback;\n        PyErr_Fetch(&type, &value, &traceback);\n        if (!(kwds=PyDict_New())) goto end;\n        if (!(argcount=PyLong_FromLong(a))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_argcount\", argcount) != 0) goto end;\n        if (!(posonlyargcount=PyLong_FromLong(0))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_posonlyargcount\", posonlyargcount) != 0) goto end;\n        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_kwonlyargcount\", kwonlyargcount) != 0) goto end;\n        if (!(nlocals=PyLong_FromLong(l))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_nlocals\", nlocals) != 0) goto end;\n        if (!(stacksize=PyLong_FromLong(s))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_stacksize\", stacksize) != 0) goto end;\n        if (!(flags=PyLong_FromLong(f))) goto end;\n        if (PyDict_SetItemString(kwds, \"co_flags\", flags) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_code\", code) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_consts\", c) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_names\", n) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_varnames\", v) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_freevars\", fv) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_cellvars\", cell) != 0) goto end;\n        if (PyDict_SetItemString(kwds, \"co_linetable\", lnos) != 0) goto end;\n        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;\n        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;\n        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;\n        if (!(replace = PyObject_GetAttrString((PyObject*)co, \"replace\"))) goto cleanup_code_too;\n        if (!(empty = PyTuple_New(0))) goto cleanup_code_too; // unfortunately __pyx_empty_tuple isn't available here\n        if (!(call_result = PyObject_Call(replace, empty, kwds))) goto cleanup_code_too;\n        Py_XDECREF((PyObject*)co);\n        co = (PyCodeObject*)call_result;\n        call_result = NULL;\n        if (0) {\n            cleanup_code_too:\n            Py_XDECREF((PyObject*)co);\n            co = NULL;\n        }\n        end:\n        Py_XDECREF(kwds);\n        Py_XDECREF(argcount);\n        Py_XDECREF(posonlyargcount);\n        Py_XDECREF(kwonlyargcount);\n        Py_XDECREF(nlocals);\n        Py_XDECREF(stacksize);\n        Py_XDECREF(replace);\n        Py_XDECREF(call_result);\n        Py_XDECREF(empty);\n        if (type) {\n            PyErr_Restore(type, value, traceback);\n        }\n        return co;\n    }\n#else\n  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\\\n          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\n#endif\n  #define __Pyx_DefaultClassType PyType_Type\n#endif\n#ifndef Py_TPFLAGS_CHECKTYPES\n  #define Py_TPFLAGS_CHECKTYPES 0\n#endif\n#ifndef Py_TPFLAGS_HAVE_INDEX\n  #define Py_TPFLAGS_HAVE_INDEX 0\n#endif\n#ifndef Py_TPFLAGS_HAVE_NEWBUFFER\n  #define Py_TPFLAGS_HAVE_NEWBUFFER 0\n#endif\n#ifndef Py_TPFLAGS_HAVE_FINALIZE\n  #define Py_TPFLAGS_HAVE_FINALIZE 0\n#endif\n#ifndef METH_STACKLESS\n  #define METH_STACKLESS 0\n#endif\n#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)\n  #ifndef METH_FASTCALL\n     #define METH_FASTCALL 0x80\n  #endif\n  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);\n  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,\n                                                          Py_ssize_t nargs, PyObject *kwnames);\n#else\n  #define __Pyx_PyCFunctionFast _PyCFunctionFast\n  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords\n#endif\n#if CYTHON_FAST_PYCCALL\n#define __Pyx_PyFastCFunction_Check(func)\\\n    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)))))\n#else\n#define __Pyx_PyFastCFunction_Check(func) 0\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)\n  #define PyObject_Malloc(s)   PyMem_Malloc(s)\n  #define PyObject_Free(p)     PyMem_Free(p)\n  #define PyObject_Realloc(p)  PyMem_Realloc(p)\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1\n  #define PyMem_RawMalloc(n)           PyMem_Malloc(n)\n  #define PyMem_RawRealloc(p, n)       PyMem_Realloc(p, n)\n  #define PyMem_RawFree(p)             PyMem_Free(p)\n#endif\n#if CYTHON_COMPILING_IN_PYSTON\n  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)\n  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)\n#else\n  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)\n  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)\n#endif\n#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000\n  #define __Pyx_PyThreadState_Current PyThreadState_GET()\n#elif PY_VERSION_HEX >= 0x03060000\n  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()\n#elif PY_VERSION_HEX >= 0x03000000\n  #define __Pyx_PyThreadState_Current PyThreadState_GET()\n#else\n  #define __Pyx_PyThreadState_Current _PyThreadState_Current\n#endif\n#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)\n#include \"pythread.h\"\n#define Py_tss_NEEDS_INIT 0\ntypedef int Py_tss_t;\nstatic CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {\n  *key = PyThread_create_key();\n  return 0;\n}\nstatic CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {\n  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));\n  *key = Py_tss_NEEDS_INIT;\n  return key;\n}\nstatic CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {\n  PyObject_Free(key);\n}\nstatic CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {\n  return *key != Py_tss_NEEDS_INIT;\n}\nstatic CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {\n  PyThread_delete_key(*key);\n  *key = Py_tss_NEEDS_INIT;\n}\nstatic CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {\n  return PyThread_set_key_value(*key, value);\n}\nstatic CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {\n  return PyThread_get_key_value(*key);\n}\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)\n#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))\n#else\n#define __Pyx_PyDict_NewPresized(n)  PyDict_New()\n#endif\n#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION\n  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)\n  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)\n#else\n  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)\n  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS\n#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)\n#else\n#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)\n#endif\n#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)\n  #define CYTHON_PEP393_ENABLED 1\n  #if defined(PyUnicode_IS_READY)\n  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\\\n                                              0 : _PyUnicode_Ready((PyObject *)(op)))\n  #else\n  #define __Pyx_PyUnicode_READY(op)       (0)\n  #endif\n  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)\n  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)\n  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)\n  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)\n  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)\n  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)\n  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)\n  #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE)\n  #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length))\n  #else\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))\n  #endif\n  #else\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_LENGTH(u))\n  #endif\n#else\n  #define CYTHON_PEP393_ENABLED 0\n  #define PyUnicode_1BYTE_KIND  1\n  #define PyUnicode_2BYTE_KIND  2\n  #define PyUnicode_4BYTE_KIND  4\n  #define __Pyx_PyUnicode_READY(op)       (0)\n  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)\n  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))\n  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)\n  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))\n  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))\n  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))\n  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))\n#endif\n#if CYTHON_COMPILING_IN_PYPY\n  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)\n  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)\n#else\n  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)\n  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\\\n      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)\n  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)\n  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)\n  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, \"__format__\", \"O\", fmt)\n#endif\n#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))\n#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)\n#else\n  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)\n#endif\n#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)\n  #define PyObject_ASCII(o)            PyObject_Repr(o)\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyBaseString_Type            PyUnicode_Type\n  #define PyStringObject               PyUnicodeObject\n  #define PyString_Type                PyUnicode_Type\n  #define PyString_Check               PyUnicode_Check\n  #define PyString_CheckExact          PyUnicode_CheckExact\n#ifndef PyObject_Unicode\n  #define PyObject_Unicode             PyObject_Str\n#endif\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)\n  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)\n#else\n  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))\n  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))\n#endif\n#ifndef PySet_CheckExact\n  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)\n#endif\n#if PY_VERSION_HEX >= 0x030900A4\n  #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt)\n  #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size)\n#else\n  #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)\n  #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)\n#endif\n#if CYTHON_ASSUME_SAFE_MACROS\n  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)\n#else\n  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyIntObject                  PyLongObject\n  #define PyInt_Type                   PyLong_Type\n  #define PyInt_Check(op)              PyLong_Check(op)\n  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)\n  #define PyInt_FromString             PyLong_FromString\n  #define PyInt_FromUnicode            PyLong_FromUnicode\n  #define PyInt_FromLong               PyLong_FromLong\n  #define PyInt_FromSize_t             PyLong_FromSize_t\n  #define PyInt_FromSsize_t            PyLong_FromSsize_t\n  #define PyInt_AsLong                 PyLong_AsLong\n  #define PyInt_AS_LONG                PyLong_AS_LONG\n  #define PyInt_AsSsize_t              PyLong_AsSsize_t\n  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask\n  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask\n  #define PyNumber_Int                 PyNumber_Long\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyBoolObject                 PyLongObject\n#endif\n#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY\n  #ifndef PyUnicode_InternFromString\n    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)\n  #endif\n#endif\n#if PY_VERSION_HEX < 0x030200A4\n  typedef long Py_hash_t;\n  #define __Pyx_PyInt_FromHash_t PyInt_FromLong\n  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsHash_t\n#else\n  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t\n  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsSsize_t\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))\n#else\n  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)\n#endif\n#if CYTHON_USE_ASYNC_SLOTS\n  #if PY_VERSION_HEX >= 0x030500B1\n    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods\n    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)\n  #else\n    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))\n  #endif\n#else\n  #define __Pyx_PyType_AsAsync(obj) NULL\n#endif\n#ifndef __Pyx_PyAsyncMethodsStruct\n    typedef struct {\n        unaryfunc am_await;\n        unaryfunc am_aiter;\n        unaryfunc am_anext;\n    } __Pyx_PyAsyncMethodsStruct;\n#endif\n\n#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS)\n  #if !defined(_USE_MATH_DEFINES)\n    #define _USE_MATH_DEFINES\n  #endif\n#endif\n#include <math.h>\n#ifdef NAN\n#define __PYX_NAN() ((float) NAN)\n#else\nstatic CYTHON_INLINE float __PYX_NAN() {\n  float value;\n  memset(&value, 0xFF, sizeof(value));\n  return value;\n}\n#endif\n#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)\n#define __Pyx_truncl trunc\n#else\n#define __Pyx_truncl truncl\n#endif\n\n#define __PYX_MARK_ERR_POS(f_index, lineno) \\\n    { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }\n#define __PYX_ERR(f_index, lineno, Ln_error) \\\n    { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }\n\n#ifndef __PYX_EXTERN_C\n  #ifdef __cplusplus\n    #define __PYX_EXTERN_C extern \"C\"\n  #else\n    #define __PYX_EXTERN_C extern\n  #endif\n#endif\n\n#define __PYX_HAVE__roc_cy\n#define __PYX_HAVE_API__roc_cy\n/* Early includes */\n#include <string.h>\n#include <stdio.h>\n#include \"numpy/arrayobject.h\"\n#include \"numpy/ndarrayobject.h\"\n#include \"numpy/ndarraytypes.h\"\n#include \"numpy/arrayscalars.h\"\n#include \"numpy/ufuncobject.h\"\n\n    /* NumPy API declarations from \"numpy/__init__.pxd\" */\n    \n#include \"pythread.h\"\n#include <stdlib.h>\n#include \"pystate.h\"\n#ifdef _OPENMP\n#include <omp.h>\n#endif /* _OPENMP */\n\n#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)\n#define CYTHON_WITHOUT_ASSERTIONS\n#endif\n\ntypedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;\n                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;\n\n#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0\n#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0\n#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)\n#define __PYX_DEFAULT_STRING_ENCODING \"\"\n#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString\n#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\n#define __Pyx_uchar_cast(c) ((unsigned char)c)\n#define __Pyx_long_cast(x) ((long)x)\n#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\\\n    (sizeof(type) < sizeof(Py_ssize_t))  ||\\\n    (sizeof(type) > sizeof(Py_ssize_t) &&\\\n          likely(v < (type)PY_SSIZE_T_MAX ||\\\n                 v == (type)PY_SSIZE_T_MAX)  &&\\\n          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\\\n                                v == (type)PY_SSIZE_T_MIN)))  ||\\\n    (sizeof(type) == sizeof(Py_ssize_t) &&\\\n          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\\\n                               v == (type)PY_SSIZE_T_MAX)))  )\nstatic CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {\n    return (size_t) i < (size_t) limit;\n}\n#if defined (__cplusplus) && __cplusplus >= 201103L\n    #include <cstdlib>\n    #define __Pyx_sst_abs(value) std::abs(value)\n#elif SIZEOF_INT >= SIZEOF_SIZE_T\n    #define __Pyx_sst_abs(value) abs(value)\n#elif SIZEOF_LONG >= SIZEOF_SIZE_T\n    #define __Pyx_sst_abs(value) labs(value)\n#elif defined (_MSC_VER)\n    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))\n#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n    #define __Pyx_sst_abs(value) llabs(value)\n#elif defined (__GNUC__)\n    #define __Pyx_sst_abs(value) __builtin_llabs(value)\n#else\n    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)\n#endif\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);\n#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))\n#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)\n#define __Pyx_PyBytes_FromString        PyBytes_FromString\n#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize\nstatic CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);\n#if PY_MAJOR_VERSION < 3\n    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString\n    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\n#else\n    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString\n    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize\n#endif\n#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)\n#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)\n#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)\n#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)\n#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)\nstatic CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {\n    const Py_UNICODE *u_end = u;\n    while (*u_end++) ;\n    return (size_t)(u_end - u - 1);\n}\n#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))\n#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode\n#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode\n#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)\n#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)\nstatic CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);\nstatic CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);\n#define __Pyx_PySequence_Tuple(obj)\\\n    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))\nstatic CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);\nstatic CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);\nstatic CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);\n#if CYTHON_ASSUME_SAFE_MACROS\n#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))\n#else\n#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)\n#endif\n#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))\n#if PY_MAJOR_VERSION >= 3\n#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))\n#else\n#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))\n#endif\n#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))\n#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\nstatic int __Pyx_sys_getdefaultencoding_not_ascii;\nstatic int __Pyx_init_sys_getdefaultencoding_params(void) {\n    PyObject* sys;\n    PyObject* default_encoding = NULL;\n    PyObject* ascii_chars_u = NULL;\n    PyObject* ascii_chars_b = NULL;\n    const char* default_encoding_c;\n    sys = PyImport_ImportModule(\"sys\");\n    if (!sys) goto bad;\n    default_encoding = PyObject_CallMethod(sys, (char*) \"getdefaultencoding\", NULL);\n    Py_DECREF(sys);\n    if (!default_encoding) goto bad;\n    default_encoding_c = PyBytes_AsString(default_encoding);\n    if (!default_encoding_c) goto bad;\n    if (strcmp(default_encoding_c, \"ascii\") == 0) {\n        __Pyx_sys_getdefaultencoding_not_ascii = 0;\n    } else {\n        char ascii_chars[128];\n        int c;\n        for (c = 0; c < 128; c++) {\n            ascii_chars[c] = c;\n        }\n        __Pyx_sys_getdefaultencoding_not_ascii = 1;\n        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);\n        if (!ascii_chars_u) goto bad;\n        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);\n        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {\n            PyErr_Format(\n                PyExc_ValueError,\n                \"This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.\",\n                default_encoding_c);\n            goto bad;\n        }\n        Py_DECREF(ascii_chars_u);\n        Py_DECREF(ascii_chars_b);\n    }\n    Py_DECREF(default_encoding);\n    return 0;\nbad:\n    Py_XDECREF(default_encoding);\n    Py_XDECREF(ascii_chars_u);\n    Py_XDECREF(ascii_chars_b);\n    return -1;\n}\n#endif\n#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3\n#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)\n#else\n#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)\n#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\nstatic char* __PYX_DEFAULT_STRING_ENCODING;\nstatic int __Pyx_init_sys_getdefaultencoding_params(void) {\n    PyObject* sys;\n    PyObject* default_encoding = NULL;\n    char* default_encoding_c;\n    sys = PyImport_ImportModule(\"sys\");\n    if (!sys) goto bad;\n    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) \"getdefaultencoding\", NULL);\n    Py_DECREF(sys);\n    if (!default_encoding) goto bad;\n    default_encoding_c = PyBytes_AsString(default_encoding);\n    if (!default_encoding_c) goto bad;\n    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);\n    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;\n    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);\n    Py_DECREF(default_encoding);\n    return 0;\nbad:\n    Py_XDECREF(default_encoding);\n    return -1;\n}\n#endif\n#endif\n\n\n/* Test for GCC > 2.95 */\n#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))\n  #define likely(x)   __builtin_expect(!!(x), 1)\n  #define unlikely(x) __builtin_expect(!!(x), 0)\n#else /* !__GNUC__ or GCC < 2.95 */\n  #define likely(x)   (x)\n  #define unlikely(x) (x)\n#endif /* __GNUC__ */\nstatic CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }\n\nstatic PyObject *__pyx_m = NULL;\nstatic PyObject *__pyx_d;\nstatic PyObject *__pyx_b;\nstatic PyObject *__pyx_cython_runtime = NULL;\nstatic PyObject *__pyx_empty_tuple;\nstatic PyObject *__pyx_empty_bytes;\nstatic PyObject *__pyx_empty_unicode;\nstatic int __pyx_lineno;\nstatic int __pyx_clineno = 0;\nstatic const char * __pyx_cfilenm= __FILE__;\nstatic const char *__pyx_filename;\n\n/* Header.proto */\n#if !defined(CYTHON_CCOMPLEX)\n  #if defined(__cplusplus)\n    #define CYTHON_CCOMPLEX 1\n  #elif defined(_Complex_I)\n    #define CYTHON_CCOMPLEX 1\n  #else\n    #define CYTHON_CCOMPLEX 0\n  #endif\n#endif\n#if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    #include <complex>\n  #else\n    #include <complex.h>\n  #endif\n#endif\n#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)\n  #undef _Complex_I\n  #define _Complex_I 1.0fj\n#endif\n\n\nstatic const char *__pyx_f[] = {\n  \"roc_cy.pyx\",\n  \"__init__.pxd\",\n  \"stringsource\",\n  \"type.pxd\",\n};\n/* MemviewSliceStruct.proto */\nstruct __pyx_memoryview_obj;\ntypedef struct {\n  struct __pyx_memoryview_obj *memview;\n  char *data;\n  Py_ssize_t shape[8];\n  Py_ssize_t strides[8];\n  Py_ssize_t suboffsets[8];\n} __Pyx_memviewslice;\n#define __Pyx_MemoryView_Len(m)  (m.shape[0])\n\n/* Atomics.proto */\n#include <pythread.h>\n#ifndef CYTHON_ATOMICS\n    #define CYTHON_ATOMICS 1\n#endif\n#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS\n#define __pyx_atomic_int_type int\n#if CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\\\n                    (__GNUC_MINOR__ > 1 ||\\\n                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2))))\n    #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1)\n    #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1)\n    #ifdef __PYX_DEBUG_ATOMICS\n        #warning \"Using GNU atomics\"\n    #endif\n#elif CYTHON_ATOMICS && defined(_MSC_VER) && CYTHON_COMPILING_IN_NOGIL\n    #include <intrin.h>\n    #undef __pyx_atomic_int_type\n    #define __pyx_atomic_int_type long\n    #pragma intrinsic (_InterlockedExchangeAdd)\n    #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1)\n    #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1)\n    #ifdef __PYX_DEBUG_ATOMICS\n        #pragma message (\"Using MSVC atomics\")\n    #endif\n#else\n    #undef CYTHON_ATOMICS\n    #define CYTHON_ATOMICS 0\n    #ifdef __PYX_DEBUG_ATOMICS\n        #warning \"Not using atomics\"\n    #endif\n#endif\ntypedef volatile __pyx_atomic_int_type __pyx_atomic_int;\n#if CYTHON_ATOMICS\n    #define __pyx_add_acquisition_count(memview)\\\n             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview))\n    #define __pyx_sub_acquisition_count(memview)\\\n            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview))\n#else\n    #define __pyx_add_acquisition_count(memview)\\\n            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)\n    #define __pyx_sub_acquisition_count(memview)\\\n            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)\n#endif\n\n/* ForceInitThreads.proto */\n#ifndef __PYX_FORCE_INIT_THREADS\n  #define __PYX_FORCE_INIT_THREADS 0\n#endif\n\n/* NoFastGil.proto */\n#define __Pyx_PyGILState_Ensure PyGILState_Ensure\n#define __Pyx_PyGILState_Release PyGILState_Release\n#define __Pyx_FastGIL_Remember()\n#define __Pyx_FastGIL_Forget()\n#define __Pyx_FastGilFuncInit()\n\n/* BufferFormatStructs.proto */\n#define IS_UNSIGNED(type) (((type) -1) > 0)\nstruct __Pyx_StructField_;\n#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)\ntypedef struct {\n  const char* name;\n  struct __Pyx_StructField_* fields;\n  size_t size;\n  size_t arraysize[8];\n  int ndim;\n  char typegroup;\n  char is_unsigned;\n  int flags;\n} __Pyx_TypeInfo;\ntypedef struct __Pyx_StructField_ {\n  __Pyx_TypeInfo* type;\n  const char* name;\n  size_t offset;\n} __Pyx_StructField;\ntypedef struct {\n  __Pyx_StructField* field;\n  size_t parent_offset;\n} __Pyx_BufFmt_StackElem;\ntypedef struct {\n  __Pyx_StructField root;\n  __Pyx_BufFmt_StackElem* head;\n  size_t fmt_offset;\n  size_t new_count, enc_count;\n  size_t struct_alignment;\n  int is_complex;\n  char enc_type;\n  char new_packmode;\n  char enc_packmode;\n  char is_valid_array;\n} __Pyx_BufFmt_Context;\n\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":689\n * # in Cython to enable them only on the right systems.\n * \n * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t\n */\ntypedef npy_int8 __pyx_t_5numpy_int8_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":690\n * \n * ctypedef npy_int8       int8_t\n * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int32      int32_t\n * ctypedef npy_int64      int64_t\n */\ntypedef npy_int16 __pyx_t_5numpy_int16_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":691\n * ctypedef npy_int8       int8_t\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int64      int64_t\n * #ctypedef npy_int96      int96_t\n */\ntypedef npy_int32 __pyx_t_5numpy_int32_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":692\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t\n * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_int96      int96_t\n * #ctypedef npy_int128     int128_t\n */\ntypedef npy_int64 __pyx_t_5numpy_int64_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":696\n * #ctypedef npy_int128     int128_t\n * \n * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t\n */\ntypedef npy_uint8 __pyx_t_5numpy_uint8_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":697\n * \n * ctypedef npy_uint8      uint8_t\n * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint32     uint32_t\n * ctypedef npy_uint64     uint64_t\n */\ntypedef npy_uint16 __pyx_t_5numpy_uint16_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":698\n * ctypedef npy_uint8      uint8_t\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint64     uint64_t\n * #ctypedef npy_uint96     uint96_t\n */\ntypedef npy_uint32 __pyx_t_5numpy_uint32_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":699\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t\n * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_uint96     uint96_t\n * #ctypedef npy_uint128    uint128_t\n */\ntypedef npy_uint64 __pyx_t_5numpy_uint64_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":703\n * #ctypedef npy_uint128    uint128_t\n * \n * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_float64    float64_t\n * #ctypedef npy_float80    float80_t\n */\ntypedef npy_float32 __pyx_t_5numpy_float32_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":704\n * \n * ctypedef npy_float32    float32_t\n * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_float80    float80_t\n * #ctypedef npy_float128   float128_t\n */\ntypedef npy_float64 __pyx_t_5numpy_float64_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":713\n * # The int types are mapped a bit surprising --\n * # numpy.int corresponds to 'l' and numpy.long to 'q'\n * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<\n * ctypedef npy_longlong   long_t\n * ctypedef npy_longlong   longlong_t\n */\ntypedef npy_long __pyx_t_5numpy_int_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":714\n * # numpy.int corresponds to 'l' and numpy.long to 'q'\n * ctypedef npy_long       int_t\n * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<\n * ctypedef npy_longlong   longlong_t\n * \n */\ntypedef npy_longlong __pyx_t_5numpy_long_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":715\n * ctypedef npy_long       int_t\n * ctypedef npy_longlong   long_t\n * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<\n * \n * ctypedef npy_ulong      uint_t\n */\ntypedef npy_longlong __pyx_t_5numpy_longlong_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":717\n * ctypedef npy_longlong   longlong_t\n * \n * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<\n * ctypedef npy_ulonglong  ulong_t\n * ctypedef npy_ulonglong  ulonglong_t\n */\ntypedef npy_ulong __pyx_t_5numpy_uint_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":718\n * \n * ctypedef npy_ulong      uint_t\n * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<\n * ctypedef npy_ulonglong  ulonglong_t\n * \n */\ntypedef npy_ulonglong __pyx_t_5numpy_ulong_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":719\n * ctypedef npy_ulong      uint_t\n * ctypedef npy_ulonglong  ulong_t\n * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<\n * \n * ctypedef npy_intp       intp_t\n */\ntypedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":721\n * ctypedef npy_ulonglong  ulonglong_t\n * \n * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uintp      uintp_t\n * \n */\ntypedef npy_intp __pyx_t_5numpy_intp_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":722\n * \n * ctypedef npy_intp       intp_t\n * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<\n * \n * ctypedef npy_double     float_t\n */\ntypedef npy_uintp __pyx_t_5numpy_uintp_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":724\n * ctypedef npy_uintp      uintp_t\n * \n * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<\n * ctypedef npy_double     double_t\n * ctypedef npy_longdouble longdouble_t\n */\ntypedef npy_double __pyx_t_5numpy_float_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":725\n * \n * ctypedef npy_double     float_t\n * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<\n * ctypedef npy_longdouble longdouble_t\n * \n */\ntypedef npy_double __pyx_t_5numpy_double_t;\n\n/* \"../../../../../../../../../home/ubuntu/anaconda3/envs/bot/lib/python3.8/site-packages/numpy/__init__.pxd\":726\n * ctypedef npy_double     float_t\n * ctypedef npy_double     double_t\n * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<\n * \n * ctypedef npy_cfloat      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CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);\n#else\n#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)\n#endif\n\n/* PyCFunctionFastCall.proto */\n#if CYTHON_FAST_PYCCALL\nstatic CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs);\n#else\n#define __Pyx_PyCFunction_FastCall(func, args, nargs)  (assert(0), NULL)\n#endif\n\n/* PyFunctionFastCall.proto */\n#if CYTHON_FAST_PYCALL\n#define __Pyx_PyFunction_FastCall(func, args, nargs)\\\n    __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL)\n#if 1 || PY_VERSION_HEX < 0x030600B1\nstatic PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs);\n#else\n#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs)\n#endif\n#define __Pyx_BUILD_ASSERT_EXPR(cond)\\\n    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PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))\n#endif\n\n/* RaiseArgTupleInvalid.proto */\nstatic void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,\n    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);\n\n/* RaiseDoubleKeywords.proto */\nstatic void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);\n\n/* ParseKeywords.proto */\nstatic int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\\\n    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\\\n    const char* function_name);\n\n/* None.proto */\nstatic CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);\n\n/* GetTopmostException.proto */\n#if CYTHON_USE_EXC_INFO_STACK\nstatic _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);\n#endif\n\n/* PyThreadStateGet.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_PyThreadState_declare  PyThreadState *__pyx_tstate;\n#define __Pyx_PyThreadState_assign  __pyx_tstate = __Pyx_PyThreadState_Current;\n#define __Pyx_PyErr_Occurred()  __pyx_tstate->curexc_type\n#else\n#define __Pyx_PyThreadState_declare\n#define __Pyx_PyThreadState_assign\n#define __Pyx_PyErr_Occurred()  PyErr_Occurred()\n#endif\n\n/* SaveResetException.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)\nstatic CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);\n#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)\nstatic CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);\n#else\n#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)\n#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)\n#endif\n\n/* PyErrExceptionMatches.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)\nstatic CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);\n#else\n#define __Pyx_PyErr_ExceptionMatches(err)  PyErr_ExceptionMatches(err)\n#endif\n\n/* GetException.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)\nstatic int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);\n#else\nstatic int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);\n#endif\n\n/* PyErrFetchRestore.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL)\n#define __Pyx_ErrRestoreWithState(type, value, tb)  __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)\n#define __Pyx_ErrFetchWithState(type, value, tb)    __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)\n#define __Pyx_ErrRestore(type, value, tb)  __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)\n#define __Pyx_ErrFetch(type, value, tb)    __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)\nstatic CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);\nstatic CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);\n#if CYTHON_COMPILING_IN_CPYTHON\n#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL))\n#else\n#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)\n#endif\n#else\n#define __Pyx_PyErr_Clear() PyErr_Clear()\n#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)\n#define __Pyx_ErrRestoreWithState(type, value, tb)  PyErr_Restore(type, value, tb)\n#define __Pyx_ErrFetchWithState(type, value, tb)  PyErr_Fetch(type, value, tb)\n#define __Pyx_ErrRestoreInState(tstate, type, value, tb)  PyErr_Restore(type, value, tb)\n#define __Pyx_ErrFetchInState(tstate, type, value, tb)  PyErr_Fetch(type, value, tb)\n#define __Pyx_ErrRestore(type, value, tb)  PyErr_Restore(type, value, tb)\n#define __Pyx_ErrFetch(type, value, tb)  PyErr_Fetch(type, value, tb)\n#endif\n\n/* RaiseException.proto */\nstatic void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);\n\n/* ArgTypeTest.proto */\n#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\\\n    ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\\\n        __Pyx__ArgTypeTest(obj, type, name, exact))\nstatic int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);\n\n/* IncludeStringH.proto */\n#include <string.h>\n\n/* BytesEquals.proto */\nstatic CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);\n\n/* UnicodeEquals.proto */\nstatic CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);\n\n/* StrEquals.proto */\n#if PY_MAJOR_VERSION >= 3\n#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals\n#else\n#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals\n#endif\n\n/* UnaryNegOverflows.proto */\n#define UNARY_NEG_WOULD_OVERFLOW(x)\\\n        (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x)))\n\nstatic CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/\nstatic PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/\n/* GetAttr.proto */\nstatic CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);\n\n/* decode_c_string_utf16.proto */\nstatic CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) {\n    int byteorder = 0;\n    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);\n}\nstatic CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) {\n    int byteorder = -1;\n    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);\n}\nstatic CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) {\n    int byteorder = 1;\n    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);\n}\n\n/* decode_c_string.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_decode_c_string(\n         const char* cstring, Py_ssize_t start, Py_ssize_t stop,\n         const char* encoding, const char* errors,\n         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));\n\n/* GetAttr3.proto */\nstatic CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);\n\n/* RaiseTooManyValuesToUnpack.proto */\nstatic CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);\n\n/* RaiseNeedMoreValuesToUnpack.proto */\nstatic CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);\n\n/* RaiseNoneIterError.proto */\nstatic CYTHON_INLINE void 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__Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);\n#else\n#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)\n#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)\n#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))\n#endif\n#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)\n\nstatic CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/\n/* ListCompAppend.proto */\n#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS\nstatic CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {\n    PyListObject* L = (PyListObject*) list;\n    Py_ssize_t len = Py_SIZE(list);\n    if (likely(L->allocated > len)) {\n        Py_INCREF(x);\n        PyList_SET_ITEM(list, len, x);\n        __Pyx_SET_SIZE(list, len + 1);\n        return 0;\n    }\n    return PyList_Append(list, x);\n}\n#else\n#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)\n#endif\n\n/* ListExtend.proto */\nstatic CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {\n#if CYTHON_COMPILING_IN_CPYTHON\n    PyObject* none = _PyList_Extend((PyListObject*)L, v);\n    if (unlikely(!none))\n        return -1;\n    Py_DECREF(none);\n    return 0;\n#else\n    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);\n#endif\n}\n\n/* ListAppend.proto */\n#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS\nstatic CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {\n    PyListObject* L = (PyListObject*) list;\n    Py_ssize_t len = Py_SIZE(list);\n    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {\n        Py_INCREF(x);\n        PyList_SET_ITEM(list, len, x);\n        __Pyx_SET_SIZE(list, len + 1);\n        return 0;\n    }\n    return PyList_Append(list, x);\n}\n#else\n#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)\n#endif\n\n/* PySequenceContains.proto */\nstatic CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {\n    int result = PySequence_Contains(seq, item);\n    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));\n}\n\n/* ImportFrom.proto */\nstatic PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);\n\n/* HasAttr.proto */\nstatic CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);\n\n/* PyObject_GenericGetAttrNoDict.proto */\n#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);\n#else\n#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr\n#endif\n\n/* PyObject_GenericGetAttr.proto */\n#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000\nstatic PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);\n#else\n#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr\n#endif\n\n/* SetVTable.proto */\nstatic int __Pyx_SetVtable(PyObject *dict, void *vtable);\n\n/* PyObjectGetAttrStrNoError.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name);\n\n/* SetupReduce.proto */\nstatic int __Pyx_setup_reduce(PyObject* type_obj);\n\n/* TypeImport.proto */\n#ifndef __PYX_HAVE_RT_ImportType_proto\n#define __PYX_HAVE_RT_ImportType_proto\nenum __Pyx_ImportType_CheckSize {\n   __Pyx_ImportType_CheckSize_Error = 0,\n   __Pyx_ImportType_CheckSize_Warn = 1,\n   __Pyx_ImportType_CheckSize_Ignore = 2\n};\nstatic PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size);\n#endif\n\n/* CLineInTraceback.proto */\n#ifdef CYTHON_CLINE_IN_TRACEBACK\n#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)\n#else\nstatic int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);\n#endif\n\n/* CodeObjectCache.proto */\ntypedef struct {\n    PyCodeObject* code_object;\n    int code_line;\n} __Pyx_CodeObjectCacheEntry;\nstruct __Pyx_CodeObjectCache {\n    int count;\n    int max_count;\n    __Pyx_CodeObjectCacheEntry* entries;\n};\nstatic struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};\nstatic int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);\nstatic PyCodeObject *__pyx_find_code_object(int code_line);\nstatic void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);\n\n/* AddTraceback.proto */\nstatic void __Pyx_AddTraceback(const char *funcname, int c_line,\n                               int py_line, const char *filename);\n\n#if PY_MAJOR_VERSION < 3\n    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);\n    static void __Pyx_ReleaseBuffer(Py_buffer *view);\n#else\n    #define __Pyx_GetBuffer PyObject_GetBuffer\n    #define __Pyx_ReleaseBuffer PyBuffer_Release\n#endif\n\n\n/* BufferStructDeclare.proto */\ntypedef struct {\n  Py_ssize_t shape, strides, suboffsets;\n} __Pyx_Buf_DimInfo;\ntypedef struct {\n  size_t refcount;\n  Py_buffer pybuffer;\n} __Pyx_Buffer;\ntypedef struct {\n  __Pyx_Buffer *rcbuffer;\n  char *data;\n  __Pyx_Buf_DimInfo diminfo[8];\n} __Pyx_LocalBuf_ND;\n\n/* MemviewSliceIsContig.proto */\nstatic int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);\n\n/* OverlappingSlices.proto */\nstatic int __pyx_slices_overlap(__Pyx_memviewslice *slice1,\n                                __Pyx_memviewslice *slice2,\n                                int ndim, size_t itemsize);\n\n/* Capsule.proto */\nstatic CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig);\n\n/* IsLittleEndian.proto */\nstatic CYTHON_INLINE int __Pyx_Is_Little_Endian(void);\n\n/* BufferFormatCheck.proto */\nstatic const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);\nstatic void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,\n                              __Pyx_BufFmt_StackElem* stack,\n                              __Pyx_TypeInfo* type);\n\n/* TypeInfoCompare.proto */\nstatic int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);\n\n/* MemviewSliceValidateAndInit.proto */\nstatic int __Pyx_ValidateAndInit_memviewslice(\n                int *axes_specs,\n                int c_or_f_flag,\n                int buf_flags,\n                int ndim,\n                __Pyx_TypeInfo *dtype,\n                __Pyx_BufFmt_StackElem stack[],\n                __Pyx_memviewslice *memviewslice,\n                PyObject *original_obj);\n\n/* ObjectToMemviewSlice.proto */\nstatic CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_float(PyObject *, int writable_flag);\n\n/* ObjectToMemviewSlice.proto */\nstatic CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *, int writable_flag);\n\n/* RealImag.proto */\n#if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    #define __Pyx_CREAL(z) ((z).real())\n    #define __Pyx_CIMAG(z) ((z).imag())\n  #else\n    #define __Pyx_CREAL(z) (__real__(z))\n    #define __Pyx_CIMAG(z) (__imag__(z))\n  #endif\n#else\n    #define __Pyx_CREAL(z) ((z).real)\n    #define __Pyx_CIMAG(z) ((z).imag)\n#endif\n#if defined(__cplusplus) && CYTHON_CCOMPLEX\\\n        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)\n    #define __Pyx_SET_CREAL(z,x) ((z).real(x))\n    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))\n#else\n    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)\n    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)\n#endif\n\n/* Arithmetic.proto */\n#if CYTHON_CCOMPLEX\n    #define __Pyx_c_eq_float(a, b)   ((a)==(b))\n    #define __Pyx_c_sum_float(a, b)  ((a)+(b))\n    #define __Pyx_c_diff_float(a, b) ((a)-(b))\n    #define __Pyx_c_prod_float(a, b) ((a)*(b))\n    #define __Pyx_c_quot_float(a, b) ((a)/(b))\n    #define __Pyx_c_neg_float(a)     (-(a))\n  #ifdef __cplusplus\n    #define __Pyx_c_is_zero_float(z) ((z)==(float)0)\n    #define __Pyx_c_conj_float(z)    (::std::conj(z))\n    #if 1\n        #define __Pyx_c_abs_float(z)     (::std::abs(z))\n        #define __Pyx_c_pow_float(a, b)  (::std::pow(a, b))\n    #endif\n  #else\n    #define __Pyx_c_is_zero_float(z) ((z)==0)\n    #define __Pyx_c_conj_float(z)    (conjf(z))\n    #if 1\n        #define __Pyx_c_abs_float(z)     (cabsf(z))\n        #define __Pyx_c_pow_float(a, b)  (cpowf(a, b))\n    #endif\n #endif\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);\n    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);\n    #if 1\n        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);\n        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    #endif\n#endif\n\n/* Arithmetic.proto */\n#if CYTHON_CCOMPLEX\n    #define __Pyx_c_eq_double(a, b)   ((a)==(b))\n    #define __Pyx_c_sum_double(a, b)  ((a)+(b))\n    #define __Pyx_c_diff_double(a, b) ((a)-(b))\n    #define __Pyx_c_prod_double(a, b) ((a)*(b))\n    #define __Pyx_c_quot_double(a, b) ((a)/(b))\n    #define __Pyx_c_neg_double(a)     (-(a))\n  #ifdef __cplusplus\n    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)\n    #define __Pyx_c_conj_double(z)    (::std::conj(z))\n    #if 1\n        #define __Pyx_c_abs_double(z)     (::std::abs(z))\n        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))\n    #endif\n  #else\n    #define __Pyx_c_is_zero_double(z) ((z)==0)\n    #define __Pyx_c_conj_double(z)    (conj(z))\n    #if 1\n        #define __Pyx_c_abs_double(z)     (cabs(z))\n        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))\n    #endif\n #endif\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);\n    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);\n    #if 1\n        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);\n        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    #endif\n#endif\n\n/* MemviewDtypeToObject.proto */\nstatic CYTHON_INLINE PyObject *__pyx_memview_get_float(const char *itemp);\nstatic CYTHON_INLINE int __pyx_memview_set_float(const char *itemp, PyObject *obj);\n\n/* GCCDiagnostics.proto */\n#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))\n#define __Pyx_HAS_GCC_DIAGNOSTIC\n#endif\n\n/* MemviewDtypeToObject.proto */\nstatic CYTHON_INLINE PyObject *__pyx_memview_get_long(const char *itemp);\nstatic CYTHON_INLINE int __pyx_memview_set_long(const char *itemp, PyObject *obj);\n\n/* ObjectToMemviewSlice.proto */\nstatic CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_long(PyObject *, int writable_flag);\n\n/* ObjectToMemviewSlice.proto */\nstatic CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_float(PyObject *, int writable_flag);\n\n/* MemviewSliceCopyTemplate.proto */\nstatic __Pyx_memviewslice\n__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,\n                                 const char *mode, int ndim,\n                                 size_t sizeof_dtype, int contig_flag,\n                                 int dtype_is_object);\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);\n\n/* CheckBinaryVersion.proto */\nstatic int __Pyx_check_binary_version(void);\n\n/* InitStrings.proto */\nstatic int __Pyx_InitStrings(__Pyx_StringTabEntry *t);\n\nstatic PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/\nstatic char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/\nstatic PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/\nstatic PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/\nstatic PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/\nstatic PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/\nstatic PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/\nstatic PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/\nstatic PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/\nstatic PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/\n\n/* Module declarations from 'cython.view' */\n\n/* Module declarations from 'cython' */\n\n/* Module declarations from 'cpython.buffer' */\n\n/* Module declarations from 'libc.string' */\n\n/* Module declarations from 'libc.stdio' */\n\n/* Module declarations from '__builtin__' */\n\n/* Module declarations from 'cpython.type' */\nstatic PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;\n\n/* Module declarations from 'cpython' */\n\n/* Module declarations from 'cpython.object' */\n\n/* Module declarations from 'cpython.ref' */\n\n/* Module declarations from 'cpython.mem' */\n\n/* Module declarations from 'numpy' */\n\n/* Module declarations from 'numpy' */\nstatic PyTypeObject *__pyx_ptype_5numpy_dtype = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_generic = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_number = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_integer = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_signedinteger = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_unsignedinteger = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_inexact = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_floating = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_complexfloating = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_flexible = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_character = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;\n\n/* Module declarations from 'roc_cy' */\nstatic PyTypeObject *__pyx_array_type = 0;\nstatic PyTypeObject *__pyx_MemviewEnum_type = 0;\nstatic PyTypeObject *__pyx_memoryview_type = 0;\nstatic PyTypeObject *__pyx_memoryviewslice_type = 0;\nstatic PyObject *generic = 0;\nstatic PyObject *strided = 0;\nstatic PyObject *indirect = 0;\nstatic PyObject *contiguous = 0;\nstatic PyObject *indirect_contiguous = 0;\nstatic int __pyx_memoryview_thread_locks_used;\nstatic PyThread_type_lock __pyx_memoryview_thread_locks[8];\nstatic PyObject *__pyx_f_6roc_cy_evaluate_roc_cy(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/\nstatic struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/\nstatic void *__pyx_align_pointer(void *, size_t); /*proto*/\nstatic PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/\nstatic CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/\nstatic PyObject *_unellipsify(PyObject *, int); /*proto*/\nstatic PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/\nstatic struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/\nstatic int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/\nstatic char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/\nstatic int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/\nstatic PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/\nstatic __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/\nstatic void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/\nstatic PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/\nstatic PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/\nstatic Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/\nstatic char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/\nstatic void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/\nstatic void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/\nstatic Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/\nstatic Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/\nstatic void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/\nstatic int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/\nstatic int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/\nstatic int __pyx_memoryview_err(PyObject *, char *); /*proto*/\nstatic int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/\nstatic void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/\nstatic void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/\nstatic void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/\nstatic void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/\nstatic void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/\nstatic void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/\nstatic PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_float = { \"float\", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 };\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_long = { \"long\", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 };\n#define __Pyx_MODULE_NAME \"roc_cy\"\nextern int __pyx_module_is_main_roc_cy;\nint __pyx_module_is_main_roc_cy = 0;\n\n/* Implementation of 'roc_cy' */\nstatic PyObject *__pyx_builtin_range;\nstatic PyObject *__pyx_builtin_ImportError;\nstatic PyObject *__pyx_builtin_ValueError;\nstatic PyObject *__pyx_builtin_MemoryError;\nstatic PyObject *__pyx_builtin_enumerate;\nstatic PyObject *__pyx_builtin_TypeError;\nstatic PyObject *__pyx_builtin_Ellipsis;\nstatic PyObject *__pyx_builtin_id;\nstatic PyObject *__pyx_builtin_IndexError;\nstatic const char __pyx_k_O[] = \"O\";\nstatic const char __pyx_k_c[] = \"c\";\nstatic const char __pyx_k_id[] = \"id\";\nstatic const char __pyx_k_np[] = \"np\";\nstatic const char __pyx_k_new[] = \"__new__\";\nstatic const char __pyx_k_obj[] = \"obj\";\nstatic const char __pyx_k_axis[] = \"axis\";\nstatic const char __pyx_k_base[] = \"base\";\nstatic const char __pyx_k_dict[] = \"__dict__\";\nstatic const char __pyx_k_main[] = \"__main__\";\nstatic const char __pyx_k_mode[] = \"mode\";\nstatic const char __pyx_k_name[] = \"name\";\nstatic const char __pyx_k_ndim[] = \"ndim\";\nstatic const char __pyx_k_ones[] = \"ones\";\nstatic const char __pyx_k_pack[] = \"pack\";\nstatic const char __pyx_k_size[] = \"size\";\nstatic const char __pyx_k_step[] = \"step\";\nstatic const char __pyx_k_stop[] = \"stop\";\nstatic const char __pyx_k_test[] = \"__test__\";\nstatic const char __pyx_k_ASCII[] = \"ASCII\";\nstatic const char __pyx_k_class[] = \"__class__\";\nstatic const char __pyx_k_dtype[] = \"dtype\";\nstatic const char __pyx_k_error[] = \"error\";\nstatic const char __pyx_k_faiss[] = \"faiss\";\nstatic const char __pyx_k_flags[] = \"flags\";\nstatic const char __pyx_k_int64[] = \"int64\";\nstatic const char __pyx_k_numpy[] = \"numpy\";\nstatic const char __pyx_k_range[] = \"range\";\nstatic const char __pyx_k_shape[] = \"shape\";\nstatic const char __pyx_k_start[] = \"start\";\nstatic const char __pyx_k_zeros[] = \"zeros\";\nstatic const char __pyx_k_astype[] = \"astype\";\nstatic const char __pyx_k_encode[] = \"encode\";\nstatic const char __pyx_k_format[] = \"format\";\nstatic const char __pyx_k_g_pids[] = \"g_pids\";\nstatic const char __pyx_k_hstack[] = \"hstack\";\nstatic const char __pyx_k_import[] = \"__import__\";\nstatic const char __pyx_k_name_2[] = \"__name__\";\nstatic const char __pyx_k_pickle[] = \"pickle\";\nstatic const char __pyx_k_q_pids[] = \"q_pids\";\nstatic const char __pyx_k_reduce[] = \"__reduce__\";\nstatic const char __pyx_k_struct[] = \"struct\";\nstatic const char __pyx_k_unpack[] = \"unpack\";\nstatic const char __pyx_k_update[] = \"update\";\nstatic const char __pyx_k_argsort[] = \"argsort\";\nstatic const char __pyx_k_asarray[] = \"asarray\";\nstatic const char __pyx_k_distmat[] = \"distmat\";\nstatic const char __pyx_k_float32[] = \"float32\";\nstatic const char __pyx_k_fortran[] = \"fortran\";\nstatic const char __pyx_k_memview[] = \"memview\";\nstatic const char __pyx_k_newaxis[] = \"newaxis\";\nstatic const char __pyx_k_Ellipsis[] = \"Ellipsis\";\nstatic const char __pyx_k_g_camids[] = \"g_camids\";\nstatic const char __pyx_k_getstate[] = \"__getstate__\";\nstatic const char __pyx_k_itemsize[] = \"itemsize\";\nstatic const char __pyx_k_pyx_type[] = \"__pyx_type\";\nstatic const char __pyx_k_q_camids[] = \"q_camids\";\nstatic const char __pyx_k_setstate[] = \"__setstate__\";\nstatic const char __pyx_k_TypeError[] = \"TypeError\";\nstatic const char __pyx_k_enumerate[] = \"enumerate\";\nstatic const char __pyx_k_pyx_state[] = \"__pyx_state\";\nstatic const char __pyx_k_reduce_ex[] = \"__reduce_ex__\";\nstatic const char __pyx_k_IndexError[] = \"IndexError\";\nstatic const char __pyx_k_ValueError[] = \"ValueError\";\nstatic const char __pyx_k_pyx_result[] = \"__pyx_result\";\nstatic const char __pyx_k_pyx_vtable[] = \"__pyx_vtable__\";\nstatic const char __pyx_k_ImportError[] = \"ImportError\";\nstatic const char __pyx_k_MemoryError[] = \"MemoryError\";\nstatic const char __pyx_k_PickleError[] = \"PickleError\";\nstatic const char __pyx_k_pyx_checksum[] = \"__pyx_checksum\";\nstatic const char __pyx_k_stringsource[] = \"stringsource\";\nstatic const char __pyx_k_pyx_getbuffer[] = \"__pyx_getbuffer\";\nstatic const char __pyx_k_reduce_cython[] = \"__reduce_cython__\";\nstatic const char __pyx_k_View_MemoryView[] = \"View.MemoryView\";\nstatic const char __pyx_k_allocate_buffer[] = \"allocate_buffer\";\nstatic const char __pyx_k_dtype_is_object[] = \"dtype_is_object\";\nstatic const char __pyx_k_pyx_PickleError[] = \"__pyx_PickleError\";\nstatic const char __pyx_k_setstate_cython[] = \"__setstate_cython__\";\nstatic const char __pyx_k_pyx_unpickle_Enum[] = \"__pyx_unpickle_Enum\";\nstatic const char __pyx_k_cline_in_traceback[] = \"cline_in_traceback\";\nstatic const char __pyx_k_strided_and_direct[] = \"<strided and direct>\";\nstatic const char __pyx_k_strided_and_indirect[] = \"<strided and indirect>\";\nstatic const char __pyx_k_contiguous_and_direct[] = \"<contiguous and direct>\";\nstatic const char __pyx_k_MemoryView_of_r_object[] = \"<MemoryView of %r object>\";\nstatic const char __pyx_k_MemoryView_of_r_at_0x_x[] = \"<MemoryView of %r at 0x%x>\";\nstatic const char __pyx_k_contiguous_and_indirect[] = \"<contiguous and indirect>\";\nstatic const char __pyx_k_Cannot_index_with_type_s[] = \"Cannot index with type '%s'\";\nstatic const char __pyx_k_Invalid_shape_in_axis_d_d[] = \"Invalid shape in axis %d: %d.\";\nstatic const char __pyx_k_itemsize_0_for_cython_array[] = \"itemsize <= 0 for cython.array\";\nstatic const char __pyx_k_unable_to_allocate_array_data[] = \"unable to allocate array data.\";\nstatic const char __pyx_k_strided_and_direct_or_indirect[] = \"<strided and direct or indirect>\";\nstatic const char __pyx_k_numpy_core_multiarray_failed_to[] = \"numpy.core.multiarray failed to import\";\nstatic const char __pyx_k_Buffer_view_does_not_expose_stri[] = \"Buffer view does not expose strides\";\nstatic const char __pyx_k_Can_only_create_a_buffer_that_is[] = \"Can only create a buffer that is contiguous in memory.\";\nstatic const char __pyx_k_Cannot_assign_to_read_only_memor[] = \"Cannot assign to read-only memoryview\";\nstatic const char __pyx_k_Cannot_create_writable_memory_vi[] = \"Cannot create writable memory view from read-only memoryview\";\nstatic const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = \"Empty shape tuple for cython.array\";\nstatic const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = \"Incompatible checksums (0x%x vs (0xb068931, 0x82a3537, 0x6ae9995) = (name))\";\nstatic const char __pyx_k_Indirect_dimensions_not_supporte[] = \"Indirect dimensions not supported\";\nstatic const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = \"Invalid mode, expected 'c' or 'fortran', got %s\";\nstatic const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = \"Out of bounds on buffer access (axis %d)\";\nstatic const char __pyx_k_Unable_to_convert_item_to_object[] = \"Unable to convert item to object\";\nstatic const char __pyx_k_got_differing_extents_in_dimensi[] = \"got differing extents in dimension %d (got %d and %d)\";\nstatic const char __pyx_k_no_default___reduce___due_to_non[] = \"no default __reduce__ due to non-trivial __cinit__\";\nstatic const char __pyx_k_numpy_core_umath_failed_to_impor[] = \"numpy.core.umath failed to import\";\nstatic const char __pyx_k_unable_to_allocate_shape_and_str[] = \"unable to allocate shape and strides.\";\nstatic PyObject *__pyx_n_s_ASCII;\nstatic PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;\nstatic PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;\nstatic PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor;\nstatic PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi;\nstatic PyObject *__pyx_kp_s_Cannot_index_with_type_s;\nstatic PyObject *__pyx_n_s_Ellipsis;\nstatic PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;\nstatic PyObject *__pyx_n_s_ImportError;\nstatic PyObject *__pyx_kp_s_Incompatible_checksums_0x_x_vs_0;\nstatic PyObject *__pyx_n_s_IndexError;\nstatic PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;\nstatic PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr;\nstatic PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d;\nstatic PyObject *__pyx_n_s_MemoryError;\nstatic PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;\nstatic PyObject *__pyx_kp_s_MemoryView_of_r_object;\nstatic PyObject *__pyx_n_b_O;\nstatic PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;\nstatic PyObject *__pyx_n_s_PickleError;\nstatic PyObject *__pyx_n_s_TypeError;\nstatic PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;\nstatic PyObject *__pyx_n_s_ValueError;\nstatic PyObject *__pyx_n_s_View_MemoryView;\nstatic PyObject *__pyx_n_s_allocate_buffer;\nstatic PyObject *__pyx_n_s_argsort;\nstatic PyObject *__pyx_n_s_asarray;\nstatic PyObject *__pyx_n_s_astype;\nstatic PyObject *__pyx_n_s_axis;\nstatic PyObject *__pyx_n_s_base;\nstatic PyObject *__pyx_n_s_c;\nstatic PyObject *__pyx_n_u_c;\nstatic PyObject *__pyx_n_s_class;\nstatic PyObject *__pyx_n_s_cline_in_traceback;\nstatic PyObject *__pyx_kp_s_contiguous_and_direct;\nstatic PyObject *__pyx_kp_s_contiguous_and_indirect;\nstatic PyObject *__pyx_n_s_dict;\nstatic PyObject *__pyx_n_s_distmat;\nstatic PyObject *__pyx_n_s_dtype;\nstatic PyObject *__pyx_n_s_dtype_is_object;\nstatic PyObject *__pyx_n_s_encode;\nstatic PyObject *__pyx_n_s_enumerate;\nstatic PyObject *__pyx_n_s_error;\nstatic PyObject *__pyx_n_s_faiss;\nstatic PyObject *__pyx_n_s_flags;\nstatic PyObject *__pyx_n_s_float32;\nstatic PyObject *__pyx_n_s_format;\nstatic PyObject *__pyx_n_s_fortran;\nstatic PyObject *__pyx_n_u_fortran;\nstatic PyObject *__pyx_n_s_g_camids;\nstatic PyObject *__pyx_n_s_g_pids;\nstatic PyObject *__pyx_n_s_getstate;\nstatic PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;\nstatic PyObject *__pyx_n_s_hstack;\nstatic PyObject *__pyx_n_s_id;\nstatic PyObject *__pyx_n_s_import;\nstatic PyObject *__pyx_n_s_int64;\nstatic PyObject *__pyx_n_s_itemsize;\nstatic PyObject *__pyx_kp_s_itemsize_0_for_cython_array;\nstatic PyObject *__pyx_n_s_main;\nstatic PyObject *__pyx_n_s_memview;\nstatic PyObject *__pyx_n_s_mode;\nstatic PyObject *__pyx_n_s_name;\nstatic PyObject *__pyx_n_s_name_2;\nstatic PyObject *__pyx_n_s_ndim;\nstatic PyObject *__pyx_n_s_new;\nstatic PyObject *__pyx_n_s_newaxis;\nstatic PyObject *__pyx_kp_s_no_default___reduce___due_to_non;\nstatic PyObject *__pyx_n_s_np;\nstatic PyObject *__pyx_n_s_numpy;\nstatic PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to;\nstatic PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor;\nstatic PyObject *__pyx_n_s_obj;\nstatic PyObject *__pyx_n_s_ones;\nstatic PyObject *__pyx_n_s_pack;\nstatic PyObject *__pyx_n_s_pickle;\nstatic PyObject *__pyx_n_s_pyx_PickleError;\nstatic PyObject *__pyx_n_s_pyx_checksum;\nstatic PyObject *__pyx_n_s_pyx_getbuffer;\nstatic PyObject *__pyx_n_s_pyx_result;\nstatic PyObject *__pyx_n_s_pyx_state;\nstatic PyObject *__pyx_n_s_pyx_type;\nstatic PyObject *__pyx_n_s_pyx_unpickle_Enum;\nstatic PyObject *__pyx_n_s_pyx_vtable;\nstatic PyObject *__pyx_n_s_q_camids;\nstatic PyObject *__pyx_n_s_q_pids;\nstatic PyObject *__pyx_n_s_range;\nstatic PyObject *__pyx_n_s_reduce;\nstatic PyObject *__pyx_n_s_reduce_cython;\nstatic PyObject *__pyx_n_s_reduce_ex;\nstatic PyObject *__pyx_n_s_setstate;\nstatic PyObject *__pyx_n_s_setstate_cython;\nstatic PyObject *__pyx_n_s_shape;\nstatic PyObject *__pyx_n_s_size;\nstatic PyObject *__pyx_n_s_start;\nstatic PyObject *__pyx_n_s_step;\nstatic PyObject *__pyx_n_s_stop;\nstatic PyObject *__pyx_kp_s_strided_and_direct;\nstatic PyObject *__pyx_kp_s_strided_and_direct_or_indirect;\nstatic PyObject *__pyx_kp_s_strided_and_indirect;\nstatic PyObject *__pyx_kp_s_stringsource;\nstatic PyObject *__pyx_n_s_struct;\nstatic PyObject *__pyx_n_s_test;\nstatic PyObject *__pyx_kp_s_unable_to_allocate_array_data;\nstatic PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str;\nstatic PyObject *__pyx_n_s_unpack;\nstatic PyObject *__pyx_n_s_update;\nstatic PyObject *__pyx_n_s_zeros;\nstatic PyObject *__pyx_pf_6roc_cy_evaluate_roc_cy(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_distmat, __Pyx_memviewslice __pyx_v_q_pids, __Pyx_memviewslice __pyx_v_g_pids, __Pyx_memviewslice __pyx_v_q_camids, __Pyx_memviewslice __pyx_v_g_camids); /* proto */\nstatic int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */\nstatic int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */\nstatic void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */\nstatic Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */\nstatic PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */\nstatic int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */\nstatic PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */\nstatic int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */\nstatic PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */\nstatic int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */\nstatic void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */\nstatic int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */\nstatic int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */\nstatic void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */\nstatic PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */\nstatic PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */\nstatic PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/\nstatic PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/\nstatic PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/\nstatic PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/\nstatic PyObject *__pyx_int_0;\nstatic PyObject *__pyx_int_1;\nstatic PyObject *__pyx_int_112105877;\nstatic PyObject *__pyx_int_136983863;\nstatic PyObject *__pyx_int_184977713;\nstatic PyObject *__pyx_int_neg_1;\nstatic PyObject *__pyx_slice_;\nstatic PyObject *__pyx_tuple__2;\nstatic PyObject *__pyx_tuple__3;\nstatic PyObject *__pyx_tuple__4;\nstatic PyObject *__pyx_tuple__5;\nstatic PyObject *__pyx_tuple__6;\nstatic 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Py_INCREF(Py_None)\n * \n */\n      ((Py_buffer *)(&__pyx_v_self->view))->obj = Py_None;\n\n      /* \"View.MemoryView\":353\n *             if <PyObject *> self.view.obj == NULL:\n *                 (<__pyx_buffer *> &self.view).obj = Py_None\n *                 Py_INCREF(Py_None)             # <<<<<<<<<<<<<<\n * \n *         if not __PYX_CYTHON_ATOMICS_ENABLED():\n */\n      Py_INCREF(Py_None);\n\n      /* \"View.MemoryView\":351\n *         if type(self) is memoryview or obj is not None:\n *             __Pyx_GetBuffer(obj, &self.view, flags)\n *             if <PyObject *> self.view.obj == NULL:             # <<<<<<<<<<<<<<\n *                 (<__pyx_buffer *> &self.view).obj = Py_None\n *                 Py_INCREF(Py_None)\n */\n    }\n\n    /* \"View.MemoryView\":349\n *         self.obj = obj\n *         self.flags = flags\n *         if type(self) is memoryview or obj is not None:             # <<<<<<<<<<<<<<\n *             __Pyx_GetBuffer(obj, &self.view, flags)\n *             if <PyObject *> self.view.obj == NULL:\n */\n  }\n\n  /* \"View.MemoryView\":355\n *                 Py_INCREF(Py_None)\n * \n *         if not __PYX_CYTHON_ATOMICS_ENABLED():             # <<<<<<<<<<<<<<\n *             global __pyx_memoryview_thread_locks_used\n *             if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:\n */\n  __pyx_t_1 = ((!(__PYX_CYTHON_ATOMICS_ENABLED() != 0)) != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":357\n *         if not __PYX_CYTHON_ATOMICS_ENABLED():\n *             global __pyx_memoryview_thread_locks_used\n *             if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:             # <<<<<<<<<<<<<<\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]\n *                 __pyx_memoryview_thread_locks_used += 1\n */\n    __pyx_t_1 = ((__pyx_memoryview_thread_locks_used < 8) != 0);\n    if (__pyx_t_1) {\n\n      /* \"View.MemoryView\":358\n *             global __pyx_memoryview_thread_locks_used\n *             if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]             # <<<<<<<<<<<<<<\n *                 __pyx_memoryview_thread_locks_used += 1\n *             if self.lock is NULL:\n */\n      __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]);\n\n      /* \"View.MemoryView\":359\n *             if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]\n *                 __pyx_memoryview_thread_locks_used += 1             # <<<<<<<<<<<<<<\n *             if self.lock is NULL:\n *                 self.lock = PyThread_allocate_lock()\n */\n      __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1);\n\n      /* \"View.MemoryView\":357\n *         if not __PYX_CYTHON_ATOMICS_ENABLED():\n *             global __pyx_memoryview_thread_locks_used\n *             if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:             # <<<<<<<<<<<<<<\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]\n *                 __pyx_memoryview_thread_locks_used += 1\n */\n    }\n\n    /* \"View.MemoryView\":360\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]\n *                 __pyx_memoryview_thread_locks_used += 1\n *             if self.lock is NULL:             # <<<<<<<<<<<<<<\n *                 self.lock = PyThread_allocate_lock()\n *                 if self.lock is NULL:\n */\n    __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0);\n    if (__pyx_t_1) {\n\n      /* \"View.MemoryView\":361\n *                 __pyx_memoryview_thread_locks_used += 1\n *             if self.lock is NULL:\n *                 self.lock = PyThread_allocate_lock()             # <<<<<<<<<<<<<<\n *                 if self.lock is NULL:\n *                     raise MemoryError\n */\n      __pyx_v_self->lock = PyThread_allocate_lock();\n\n      /* \"View.MemoryView\":362\n *             if self.lock is NULL:\n *                 self.lock = PyThread_allocate_lock()\n *                 if self.lock is NULL:             # <<<<<<<<<<<<<<\n *                     raise MemoryError\n * \n */\n      __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0);\n      if (unlikely(__pyx_t_1)) {\n\n        /* \"View.MemoryView\":363\n *                 self.lock = PyThread_allocate_lock()\n *                 if self.lock is NULL:\n *                     raise MemoryError             # <<<<<<<<<<<<<<\n * \n *         if flags & PyBUF_FORMAT:\n */\n        PyErr_NoMemory(); __PYX_ERR(2, 363, __pyx_L1_error)\n\n        /* \"View.MemoryView\":362\n *             if self.lock is NULL:\n *                 self.lock = PyThread_allocate_lock()\n *                 if self.lock is NULL:             # <<<<<<<<<<<<<<\n *                     raise MemoryError\n * \n */\n      }\n\n      /* \"View.MemoryView\":360\n *                 self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]\n *                 __pyx_memoryview_thread_locks_used += 1\n *             if self.lock is NULL:             # <<<<<<<<<<<<<<\n *                 self.lock = PyThread_allocate_lock()\n *                 if self.lock is NULL:\n */\n    }\n\n    /* \"View.MemoryView\":355\n *                 Py_INCREF(Py_None)\n * \n *         if not __PYX_CYTHON_ATOMICS_ENABLED():             # <<<<<<<<<<<<<<\n *             global __pyx_memoryview_thread_locks_used\n *             if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:\n */\n  }\n\n  /* \"View.MemoryView\":365\n *                     raise MemoryError\n * \n *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<\n *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\\0')\n *         else:\n */\n  __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":366\n * \n *         if flags & PyBUF_FORMAT:\n *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\\0')             # <<<<<<<<<<<<<<\n *         else:\n *             self.dtype_is_object = dtype_is_object\n */\n    __pyx_t_2 = (((__pyx_v_self->view.format[0]) == 'O') != 0);\n    if (__pyx_t_2) {\n    } else {\n      __pyx_t_1 = __pyx_t_2;\n      goto __pyx_L12_bool_binop_done;\n    }\n    __pyx_t_2 = (((__pyx_v_self->view.format[1]) == '\\x00') != 0);\n    __pyx_t_1 = __pyx_t_2;\n    __pyx_L12_bool_binop_done:;\n    __pyx_v_self->dtype_is_object = __pyx_t_1;\n\n    /* \"View.MemoryView\":365\n *                     raise MemoryError\n * \n *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<\n *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\\0')\n *         else:\n */\n    goto __pyx_L11;\n  }\n\n  /* \"View.MemoryView\":368\n *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\\0')\n *         else:\n *             self.dtype_is_object = dtype_is_object             # <<<<<<<<<<<<<<\n * \n *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(\n */\n  /*else*/ {\n    __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object;\n  }\n  __pyx_L11:;\n\n  /* \"View.MemoryView\":370\n *             self.dtype_is_object = dtype_is_object\n * \n *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(             # <<<<<<<<<<<<<<\n *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))\n *         self.typeinfo = NULL\n */\n  __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int))));\n\n  /* \"View.MemoryView\":372\n *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(\n *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))\n *         self.typeinfo = NULL             # <<<<<<<<<<<<<<\n * \n *     def __dealloc__(memoryview self):\n */\n  __pyx_v_self->typeinfo = NULL;\n\n  /* \"View.MemoryView\":346\n *     cdef __Pyx_TypeInfo *typeinfo\n * \n *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):             # <<<<<<<<<<<<<<\n *         self.obj = obj\n *         self.flags = flags\n */\n\n  /* function exit code */\n  __pyx_r = 0;\n  goto __pyx_L0;\n  __pyx_L1_error:;\n  __Pyx_AddTraceback(\"View.MemoryView.memoryview.__cinit__\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n  __pyx_r = -1;\n  __pyx_L0:;\n  __Pyx_RefNannyFinishContext();\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":374\n *         self.typeinfo = NULL\n * \n *     def __dealloc__(memoryview self):             # <<<<<<<<<<<<<<\n *         if self.obj is not None:\n *             __Pyx_ReleaseBuffer(&self.view)\n */\n\n/* Python wrapper */\nstatic void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self); /*proto*/\nstatic void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self) {\n  __Pyx_RefNannyDeclarations\n  __Pyx_RefNannySetupContext(\"__dealloc__ (wrapper)\", 0);\n  __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(((struct __pyx_memoryview_obj *)__pyx_v_self));\n\n  /* function exit code */\n  __Pyx_RefNannyFinishContext();\n}\n\nstatic void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self) {\n  int __pyx_v_i;\n  __Pyx_RefNannyDeclarations\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  int __pyx_t_4;\n  int __pyx_t_5;\n  PyThread_type_lock __pyx_t_6;\n  PyThread_type_lock __pyx_t_7;\n  __Pyx_RefNannySetupContext(\"__dealloc__\", 0);\n\n  /* \"View.MemoryView\":375\n * \n *     def __dealloc__(memoryview self):\n *         if self.obj is not None:             # <<<<<<<<<<<<<<\n *             __Pyx_ReleaseBuffer(&self.view)\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:\n */\n  __pyx_t_1 = (__pyx_v_self->obj != Py_None);\n  __pyx_t_2 = (__pyx_t_1 != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":376\n *     def __dealloc__(memoryview self):\n *         if self.obj is not None:\n *             __Pyx_ReleaseBuffer(&self.view)             # <<<<<<<<<<<<<<\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:\n * \n */\n    __Pyx_ReleaseBuffer((&__pyx_v_self->view));\n\n    /* \"View.MemoryView\":375\n * \n *     def __dealloc__(memoryview self):\n *         if self.obj is not None:             # <<<<<<<<<<<<<<\n *             __Pyx_ReleaseBuffer(&self.view)\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:\n */\n    goto __pyx_L3;\n  }\n\n  /* \"View.MemoryView\":377\n *         if self.obj is not None:\n *             __Pyx_ReleaseBuffer(&self.view)\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:             # <<<<<<<<<<<<<<\n * \n *             (<__pyx_buffer *> &self.view).obj = NULL\n */\n  __pyx_t_2 = ((((Py_buffer *)(&__pyx_v_self->view))->obj == Py_None) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":379\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:\n * \n *             (<__pyx_buffer *> &self.view).obj = NULL             # <<<<<<<<<<<<<<\n *             Py_DECREF(Py_None)\n * \n */\n    ((Py_buffer *)(&__pyx_v_self->view))->obj = NULL;\n\n    /* \"View.MemoryView\":380\n * \n *             (<__pyx_buffer *> &self.view).obj = NULL\n *             Py_DECREF(Py_None)             # <<<<<<<<<<<<<<\n * \n *         cdef int i\n */\n    Py_DECREF(Py_None);\n\n    /* \"View.MemoryView\":377\n *         if self.obj is not None:\n *             __Pyx_ReleaseBuffer(&self.view)\n *         elif (<__pyx_buffer *> &self.view).obj == Py_None:             # <<<<<<<<<<<<<<\n * \n *             (<__pyx_buffer *> &self.view).obj = NULL\n */\n  }\n  __pyx_L3:;\n\n  /* \"View.MemoryView\":384\n *         cdef int i\n *         global __pyx_memoryview_thread_locks_used\n *         if self.lock != NULL:             # <<<<<<<<<<<<<<\n *             for i in range(__pyx_memoryview_thread_locks_used):\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:\n */\n  __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":385\n *         global __pyx_memoryview_thread_locks_used\n *         if self.lock != NULL:\n *             for i in range(__pyx_memoryview_thread_locks_used):             # <<<<<<<<<<<<<<\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:\n *                     __pyx_memoryview_thread_locks_used -= 1\n */\n    __pyx_t_3 = __pyx_memoryview_thread_locks_used;\n    __pyx_t_4 = __pyx_t_3;\n    for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {\n      __pyx_v_i = __pyx_t_5;\n\n      /* \"View.MemoryView\":386\n *         if self.lock != NULL:\n *             for i in range(__pyx_memoryview_thread_locks_used):\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:             # <<<<<<<<<<<<<<\n *                     __pyx_memoryview_thread_locks_used -= 1\n *                     if i != __pyx_memoryview_thread_locks_used:\n */\n      __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":387\n *             for i in range(__pyx_memoryview_thread_locks_used):\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:\n *                     __pyx_memoryview_thread_locks_used -= 1             # <<<<<<<<<<<<<<\n *                     if i != __pyx_memoryview_thread_locks_used:\n *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (\n */\n        __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1);\n\n        /* \"View.MemoryView\":388\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:\n *                     __pyx_memoryview_thread_locks_used -= 1\n *                     if i != __pyx_memoryview_thread_locks_used:             # <<<<<<<<<<<<<<\n *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (\n *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])\n */\n        __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0);\n        if (__pyx_t_2) {\n\n          /* \"View.MemoryView\":390\n *                     if i != __pyx_memoryview_thread_locks_used:\n *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (\n *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])             # <<<<<<<<<<<<<<\n *                     break\n *             else:\n */\n          __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]);\n          __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]);\n\n          /* \"View.MemoryView\":389\n *                     __pyx_memoryview_thread_locks_used -= 1\n *                     if i != __pyx_memoryview_thread_locks_used:\n *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (             # <<<<<<<<<<<<<<\n *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])\n *                     break\n */\n          (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_6;\n          (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_7;\n\n          /* \"View.MemoryView\":388\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:\n *                     __pyx_memoryview_thread_locks_used -= 1\n *                     if i != __pyx_memoryview_thread_locks_used:             # <<<<<<<<<<<<<<\n *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (\n *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])\n */\n        }\n\n        /* \"View.MemoryView\":391\n *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (\n *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])\n *                     break             # <<<<<<<<<<<<<<\n *             else:\n *                 PyThread_free_lock(self.lock)\n */\n        goto __pyx_L6_break;\n\n        /* \"View.MemoryView\":386\n *         if self.lock != NULL:\n *             for i in range(__pyx_memoryview_thread_locks_used):\n *                 if __pyx_memoryview_thread_locks[i] is self.lock:             # <<<<<<<<<<<<<<\n *                     __pyx_memoryview_thread_locks_used -= 1\n *                     if i != __pyx_memoryview_thread_locks_used:\n */\n      }\n    }\n    /*else*/ {\n\n      /* \"View.MemoryView\":393\n *                     break\n *             else:\n *                 PyThread_free_lock(self.lock)             # <<<<<<<<<<<<<<\n * \n *     cdef char *get_item_pointer(memoryview self, object index) except NULL:\n */\n      PyThread_free_lock(__pyx_v_self->lock);\n    }\n    __pyx_L6_break:;\n\n    /* \"View.MemoryView\":384\n *         cdef int i\n 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__Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(2, 768, __pyx_L1_error)\n      __Pyx_GOTREF(__pyx_t_9);\n      __pyx_t_1 = (__pyx_t_9 != Py_None);\n      __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;\n      __pyx_v_have_step = __pyx_t_1;\n\n      /* \"View.MemoryView\":770\n *             have_step = index.step is not None\n * \n *             slice_memviewslice(             # <<<<<<<<<<<<<<\n *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],\n *                 dim, new_ndim, p_suboffset_dim,\n */\n      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(2, 770, __pyx_L1_error)\n\n      /* \"View.MemoryView\":776\n *                 have_start, have_stop, have_step,\n *                 True)\n *             new_ndim += 1             # <<<<<<<<<<<<<<\n * \n *     if isinstance(memview, _memoryviewslice):\n */\n      __pyx_v_new_ndim = (__pyx_v_new_ndim + 1);\n    }\n    __pyx_L6:;\n\n    /* \"View.MemoryView\":748\n *     cdef bint have_start, have_stop, have_step\n * \n *     for dim, index in enumerate(indices):             # <<<<<<<<<<<<<<\n *         if PyIndex_Check(index):\n *             slice_memviewslice(\n */\n  }\n  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;\n\n  /* \"View.MemoryView\":778\n *             new_ndim += 1\n * \n *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<\n *         return memoryview_fromslice(dst, new_ndim,\n *                                     memviewsliceobj.to_object_func,\n */\n  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); \n  __pyx_t_2 = (__pyx_t_1 != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":779\n * \n *     if isinstance(memview, _memoryviewslice):\n *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<\n *                                     memviewsliceobj.to_object_func,\n *                                     memviewsliceobj.to_dtype_func,\n */\n    __Pyx_XDECREF(((PyObject *)__pyx_r));\n\n    /* \"View.MemoryView\":780\n *     if isinstance(memview, _memoryviewslice):\n *         return memoryview_fromslice(dst, new_ndim,\n *                                     memviewsliceobj.to_object_func,             # <<<<<<<<<<<<<<\n *                                     memviewsliceobj.to_dtype_func,\n *                                     memview.dtype_is_object)\n */\n    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError(\"memviewsliceobj\"); __PYX_ERR(2, 780, __pyx_L1_error) }\n\n    /* \"View.MemoryView\":781\n *         return memoryview_fromslice(dst, new_ndim,\n *                                     memviewsliceobj.to_object_func,\n *                                     memviewsliceobj.to_dtype_func,             # <<<<<<<<<<<<<<\n *                                     memview.dtype_is_object)\n *     else:\n */\n    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError(\"memviewsliceobj\"); __PYX_ERR(2, 781, __pyx_L1_error) }\n\n    /* \"View.MemoryView\":779\n * \n *     if isinstance(memview, _memoryviewslice):\n *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<\n *                                     memviewsliceobj.to_object_func,\n *                                     memviewsliceobj.to_dtype_func,\n */\n    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 779, __pyx_L1_error)\n    __Pyx_GOTREF(__pyx_t_3);\n    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 779, __pyx_L1_error)\n    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);\n    __pyx_t_3 = 0;\n    goto __pyx_L0;\n\n    /* \"View.MemoryView\":778\n *             new_ndim += 1\n * \n *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<\n *         return memoryview_fromslice(dst, new_ndim,\n *                                     memviewsliceobj.to_object_func,\n */\n  }\n\n  /* \"View.MemoryView\":784\n *                                     memview.dtype_is_object)\n *     else:\n *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<\n *                                     memview.dtype_is_object)\n * \n */\n  /*else*/ {\n    __Pyx_XDECREF(((PyObject *)__pyx_r));\n\n    /* \"View.MemoryView\":785\n *     else:\n *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,\n *                                     memview.dtype_is_object)             # <<<<<<<<<<<<<<\n * \n * \n */\n    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 784, __pyx_L1_error)\n    __Pyx_GOTREF(__pyx_t_3);\n\n    /* \"View.MemoryView\":784\n *                                     memview.dtype_is_object)\n *     else:\n *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<\n *                                     memview.dtype_is_object)\n * \n */\n    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 784, __pyx_L1_error)\n    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);\n    __pyx_t_3 = 0;\n    goto __pyx_L0;\n  }\n\n  /* \"View.MemoryView\":712\n * \n * @cname('__pyx_memview_slice')\n * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<\n *     cdef int new_ndim = 0, suboffset_dim = -1, dim\n *     cdef bint negative_step\n */\n\n  /* function exit code */\n  __pyx_L1_error:;\n  __Pyx_XDECREF(__pyx_t_3);\n  __Pyx_XDECREF(__pyx_t_9);\n  __Pyx_AddTraceback(\"View.MemoryView.memview_slice\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n  __pyx_r = 0;\n  __pyx_L0:;\n  __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj);\n  __Pyx_XDECREF(__pyx_v_index);\n  __Pyx_XGIVEREF((PyObject *)__pyx_r);\n  __Pyx_RefNannyFinishContext();\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":809\n * \n * @cname('__pyx_memoryview_slice_memviewslice')\n * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<\n *         __Pyx_memviewslice *dst,\n *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,\n */\n\nstatic int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) {\n  Py_ssize_t __pyx_v_new_shape;\n  int __pyx_v_negative_step;\n  int __pyx_r;\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  int __pyx_lineno = 0;\n  const char *__pyx_filename = NULL;\n  int __pyx_clineno = 0;\n\n  /* \"View.MemoryView\":829\n *     cdef bint negative_step\n * \n *     if not is_slice:             # <<<<<<<<<<<<<<\n * \n *         if start < 0:\n */\n  __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":831\n *     if not is_slice:\n * \n *         if start < 0:             # <<<<<<<<<<<<<<\n *             start += shape\n *         if not 0 <= start < shape:\n */\n    __pyx_t_1 = ((__pyx_v_start < 0) != 0);\n    if (__pyx_t_1) {\n\n      /* \"View.MemoryView\":832\n * \n *         if start < 0:\n *             start += shape             # <<<<<<<<<<<<<<\n *         if not 0 <= start < shape:\n *             _err_dim(IndexError, \"Index out of bounds (axis %d)\", dim)\n */\n      __pyx_v_start = (__pyx_v_start + __pyx_v_shape);\n\n      /* \"View.MemoryView\":831\n *     if not is_slice:\n * \n *         if start < 0:             # <<<<<<<<<<<<<<\n *             start += shape\n *         if not 0 <= start < shape:\n */\n    }\n\n    /* \"View.MemoryView\":833\n *         if start < 0:\n *             start += shape\n *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<\n *             _err_dim(IndexError, \"Index out of bounds (axis %d)\", dim)\n *     else:\n */\n    __pyx_t_1 = (0 <= __pyx_v_start);\n    if (__pyx_t_1) {\n      __pyx_t_1 = (__pyx_v_start < __pyx_v_shape);\n    }\n    __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":834\n *             start += shape\n *         if not 0 <= start < shape:\n *             _err_dim(IndexError, \"Index out of bounds (axis %d)\", dim)             # <<<<<<<<<<<<<<\n *     else:\n * \n */\n      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)\"Index out of bounds (axis %d)\"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 834, __pyx_L1_error)\n\n      /* \"View.MemoryView\":833\n *         if start < 0:\n *             start += shape\n *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<\n *             _err_dim(IndexError, \"Index out of bounds (axis %d)\", dim)\n *     else:\n */\n    }\n\n    /* \"View.MemoryView\":829\n *     cdef bint negative_step\n * \n *     if not is_slice:             # <<<<<<<<<<<<<<\n * \n *         if start < 0:\n */\n    goto __pyx_L3;\n  }\n\n  /* \"View.MemoryView\":837\n *     else:\n * \n *         negative_step = have_step != 0 and step < 0             # <<<<<<<<<<<<<<\n * \n *         if have_step and step == 0:\n */\n  /*else*/ {\n    __pyx_t_1 = ((__pyx_v_have_step != 0) != 0);\n    if (__pyx_t_1) {\n    } else {\n      __pyx_t_2 = __pyx_t_1;\n      goto __pyx_L6_bool_binop_done;\n    }\n    __pyx_t_1 = ((__pyx_v_step < 0) != 0);\n    __pyx_t_2 = __pyx_t_1;\n    __pyx_L6_bool_binop_done:;\n    __pyx_v_negative_step = __pyx_t_2;\n\n    /* \"View.MemoryView\":839\n *         negative_step = have_step != 0 and step < 0\n * \n *         if have_step and step == 0:             # <<<<<<<<<<<<<<\n *             _err_dim(ValueError, \"Step may not be zero (axis %d)\", dim)\n * \n */\n    __pyx_t_1 = (__pyx_v_have_step != 0);\n    if (__pyx_t_1) {\n    } else {\n      __pyx_t_2 = __pyx_t_1;\n      goto __pyx_L9_bool_binop_done;\n    }\n    __pyx_t_1 = ((__pyx_v_step == 0) != 0);\n    __pyx_t_2 = __pyx_t_1;\n    __pyx_L9_bool_binop_done:;\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":840\n * \n *         if have_step and step == 0:\n *             _err_dim(ValueError, \"Step may not be zero (axis %d)\", dim)             # <<<<<<<<<<<<<<\n * \n * \n */\n      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)\"Step may not be zero (axis %d)\"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 840, __pyx_L1_error)\n\n      /* \"View.MemoryView\":839\n *         negative_step = have_step != 0 and step < 0\n * \n *         if have_step and step == 0:             # <<<<<<<<<<<<<<\n *             _err_dim(ValueError, \"Step may not be zero (axis %d)\", dim)\n * \n */\n    }\n\n    /* \"View.MemoryView\":843\n * \n * \n *         if have_start:             # <<<<<<<<<<<<<<\n *             if start < 0:\n *                 start += shape\n */\n    __pyx_t_2 = (__pyx_v_have_start != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":844\n * \n *         if have_start:\n *             if start < 0:             # <<<<<<<<<<<<<<\n *                 start += shape\n *                 if start < 0:\n */\n      __pyx_t_2 = ((__pyx_v_start < 0) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":845\n *         if have_start:\n *             if start < 0:\n *                 start += shape             # <<<<<<<<<<<<<<\n *                 if start < 0:\n *                     start = 0\n */\n        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);\n\n        /* \"View.MemoryView\":846\n *             if start < 0:\n *                 start += shape\n *                 if start < 0:             # <<<<<<<<<<<<<<\n *                     start = 0\n *             elif start >= shape:\n */\n        __pyx_t_2 = ((__pyx_v_start < 0) != 0);\n        if (__pyx_t_2) {\n\n          /* \"View.MemoryView\":847\n *                 start += shape\n *                 if start < 0:\n *                     start = 0             # <<<<<<<<<<<<<<\n *             elif start >= shape:\n *                 if negative_step:\n */\n          __pyx_v_start = 0;\n\n          /* \"View.MemoryView\":846\n *             if start < 0:\n *                 start += shape\n *                 if start < 0:             # <<<<<<<<<<<<<<\n *                     start = 0\n *             elif start >= shape:\n */\n        }\n\n        /* \"View.MemoryView\":844\n * \n *         if have_start:\n *             if start < 0:             # <<<<<<<<<<<<<<\n *                 start += shape\n *                 if start < 0:\n */\n        goto __pyx_L12;\n      }\n\n      /* \"View.MemoryView\":848\n *                 if start < 0:\n *                     start = 0\n *             elif start >= shape:             # <<<<<<<<<<<<<<\n *                 if negative_step:\n *                     start = shape - 1\n */\n      __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":849\n *                     start = 0\n *             elif start >= shape:\n *                 if negative_step:             # <<<<<<<<<<<<<<\n *                     start = shape - 1\n *                 else:\n */\n        __pyx_t_2 = (__pyx_v_negative_step != 0);\n        if (__pyx_t_2) {\n\n          /* \"View.MemoryView\":850\n *             elif start >= shape:\n *                 if negative_step:\n *                     start = shape - 1             # <<<<<<<<<<<<<<\n *                 else:\n *                     start = shape\n */\n          __pyx_v_start = (__pyx_v_shape - 1);\n\n          /* \"View.MemoryView\":849\n *                     start = 0\n *             elif start >= shape:\n *                 if negative_step:             # <<<<<<<<<<<<<<\n *                     start = shape - 1\n *                 else:\n */\n          goto __pyx_L14;\n        }\n\n        /* \"View.MemoryView\":852\n *                     start = shape - 1\n *                 else:\n *                     start = shape             # <<<<<<<<<<<<<<\n *         else:\n *             if negative_step:\n */\n        /*else*/ {\n          __pyx_v_start = __pyx_v_shape;\n        }\n        __pyx_L14:;\n\n        /* \"View.MemoryView\":848\n *                 if start < 0:\n *                     start = 0\n *             elif start >= shape:             # <<<<<<<<<<<<<<\n *                 if negative_step:\n *                     start = shape - 1\n */\n      }\n      __pyx_L12:;\n\n      /* \"View.MemoryView\":843\n * \n * \n *         if have_start:             # <<<<<<<<<<<<<<\n *             if start < 0:\n *                 start += shape\n */\n      goto __pyx_L11;\n    }\n\n    /* \"View.MemoryView\":854\n *                     start = shape\n *         else:\n *             if negative_step:             # <<<<<<<<<<<<<<\n *                 start = shape - 1\n *             else:\n */\n    /*else*/ {\n      __pyx_t_2 = (__pyx_v_negative_step != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":855\n *         else:\n *             if negative_step:\n *                 start = shape - 1             # <<<<<<<<<<<<<<\n *             else:\n *                 start = 0\n */\n        __pyx_v_start = (__pyx_v_shape - 1);\n\n        /* \"View.MemoryView\":854\n *                     start = shape\n *         else:\n *             if negative_step:             # <<<<<<<<<<<<<<\n *                 start = shape - 1\n *             else:\n */\n        goto __pyx_L15;\n      }\n\n      /* \"View.MemoryView\":857\n *                 start = shape - 1\n *             else:\n *                 start = 0             # <<<<<<<<<<<<<<\n * \n *         if have_stop:\n */\n      /*else*/ {\n        __pyx_v_start = 0;\n      }\n      __pyx_L15:;\n    }\n    __pyx_L11:;\n\n    /* \"View.MemoryView\":859\n *                 start = 0\n * \n *         if have_stop:             # <<<<<<<<<<<<<<\n *             if stop < 0:\n *                 stop += shape\n */\n    __pyx_t_2 = (__pyx_v_have_stop != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":860\n * \n *         if have_stop:\n *             if stop < 0:             # <<<<<<<<<<<<<<\n *                 stop += shape\n *                 if stop < 0:\n */\n      __pyx_t_2 = ((__pyx_v_stop < 0) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":861\n *         if have_stop:\n *             if stop < 0:\n *                 stop += shape             # <<<<<<<<<<<<<<\n *                 if stop < 0:\n *                     stop = 0\n */\n        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);\n\n        /* \"View.MemoryView\":862\n *             if stop < 0:\n *                 stop += shape\n *                 if stop < 0:             # <<<<<<<<<<<<<<\n *                     stop = 0\n *             elif stop > shape:\n */\n        __pyx_t_2 = ((__pyx_v_stop < 0) != 0);\n        if (__pyx_t_2) {\n\n          /* \"View.MemoryView\":863\n *                 stop += shape\n *                 if stop < 0:\n *                     stop = 0             # <<<<<<<<<<<<<<\n *             elif stop > shape:\n *                 stop = shape\n */\n          __pyx_v_stop = 0;\n\n          /* \"View.MemoryView\":862\n *             if stop < 0:\n *                 stop += shape\n *                 if stop < 0:             # <<<<<<<<<<<<<<\n *                     stop = 0\n *             elif stop > shape:\n */\n        }\n\n        /* \"View.MemoryView\":860\n * \n *         if have_stop:\n *             if stop < 0:             # <<<<<<<<<<<<<<\n *                 stop += shape\n *                 if stop < 0:\n */\n        goto __pyx_L17;\n      }\n\n      /* \"View.MemoryView\":864\n *                 if stop < 0:\n *                     stop = 0\n *             elif stop > shape:             # <<<<<<<<<<<<<<\n *                 stop = shape\n *         else:\n */\n      __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":865\n *                     stop = 0\n *             elif stop > shape:\n *                 stop = shape             # <<<<<<<<<<<<<<\n *         else:\n *             if negative_step:\n */\n        __pyx_v_stop = __pyx_v_shape;\n\n        /* \"View.MemoryView\":864\n *                 if stop < 0:\n *                     stop = 0\n *             elif stop > shape:             # <<<<<<<<<<<<<<\n *                 stop = shape\n *         else:\n */\n      }\n      __pyx_L17:;\n\n      /* \"View.MemoryView\":859\n *                 start = 0\n * \n *         if have_stop:             # <<<<<<<<<<<<<<\n *             if stop < 0:\n *                 stop += shape\n */\n      goto __pyx_L16;\n    }\n\n    /* \"View.MemoryView\":867\n *                 stop = shape\n *         else:\n *             if negative_step:             # <<<<<<<<<<<<<<\n *                 stop = -1\n *             else:\n */\n    /*else*/ {\n      __pyx_t_2 = (__pyx_v_negative_step != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":868\n *         else:\n *             if negative_step:\n *                 stop = -1             # <<<<<<<<<<<<<<\n *             else:\n *                 stop = shape\n */\n        __pyx_v_stop = -1L;\n\n        /* \"View.MemoryView\":867\n *                 stop = shape\n *         else:\n *             if negative_step:             # <<<<<<<<<<<<<<\n *                 stop = -1\n *             else:\n */\n        goto __pyx_L19;\n      }\n\n      /* \"View.MemoryView\":870\n *                 stop = -1\n *             else:\n *                 stop = shape             # <<<<<<<<<<<<<<\n * \n *         if not have_step:\n */\n      /*else*/ {\n        __pyx_v_stop = __pyx_v_shape;\n      }\n      __pyx_L19:;\n    }\n    __pyx_L16:;\n\n    /* \"View.MemoryView\":872\n *                 stop = shape\n * \n *         if not have_step:             # <<<<<<<<<<<<<<\n *             step = 1\n * \n */\n    __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":873\n * \n *         if not have_step:\n *             step = 1             # <<<<<<<<<<<<<<\n * \n * \n */\n      __pyx_v_step = 1;\n\n      /* \"View.MemoryView\":872\n *                 stop = shape\n * \n *         if not have_step:             # <<<<<<<<<<<<<<\n *             step = 1\n * \n */\n    }\n\n    /* \"View.MemoryView\":877\n * \n *         with cython.cdivision(True):\n *             new_shape = (stop - start) // step             # <<<<<<<<<<<<<<\n * \n *             if (stop - start) - step * new_shape:\n */\n    __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step);\n\n    /* \"View.MemoryView\":879\n *             new_shape = (stop - start) // step\n * \n *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<\n *                 new_shape += 1\n * \n */\n    __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":880\n * \n *             if (stop - start) - step * new_shape:\n *                 new_shape += 1             # <<<<<<<<<<<<<<\n * \n *         if new_shape < 0:\n */\n      __pyx_v_new_shape = (__pyx_v_new_shape + 1);\n\n      /* \"View.MemoryView\":879\n *             new_shape = (stop - start) // step\n * \n *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<\n *                 new_shape += 1\n * \n */\n    }\n\n    /* \"View.MemoryView\":882\n *                 new_shape += 1\n * \n *         if new_shape < 0:             # <<<<<<<<<<<<<<\n *             new_shape = 0\n * \n */\n    __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":883\n * \n *         if new_shape < 0:\n *             new_shape = 0             # <<<<<<<<<<<<<<\n * \n * \n */\n      __pyx_v_new_shape = 0;\n\n      /* \"View.MemoryView\":882\n *                 new_shape += 1\n * \n *         if new_shape < 0:             # <<<<<<<<<<<<<<\n *             new_shape = 0\n * \n */\n    }\n\n    /* \"View.MemoryView\":886\n * \n * \n *         dst.strides[new_ndim] = stride * step             # <<<<<<<<<<<<<<\n *         dst.shape[new_ndim] = new_shape\n *         dst.suboffsets[new_ndim] = suboffset\n */\n    (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step);\n\n    /* \"View.MemoryView\":887\n * \n *         dst.strides[new_ndim] = stride * step\n *         dst.shape[new_ndim] = new_shape             # <<<<<<<<<<<<<<\n *         dst.suboffsets[new_ndim] = suboffset\n * \n */\n    (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape;\n\n    /* \"View.MemoryView\":888\n *         dst.strides[new_ndim] = stride * step\n *         dst.shape[new_ndim] = new_shape\n *         dst.suboffsets[new_ndim] = suboffset             # <<<<<<<<<<<<<<\n * \n * \n */\n    (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset;\n  }\n  __pyx_L3:;\n\n  /* \"View.MemoryView\":891\n * \n * \n *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<\n *         dst.data += start * stride\n *     else:\n */\n  __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":892\n * \n *     if suboffset_dim[0] < 0:\n *         dst.data += start * stride             # <<<<<<<<<<<<<<\n *     else:\n *         dst.suboffsets[suboffset_dim[0]] += start * stride\n */\n    __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride));\n\n    /* \"View.MemoryView\":891\n * \n * \n *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<\n *         dst.data += start * stride\n *     else:\n */\n    goto __pyx_L23;\n  }\n\n  /* \"View.MemoryView\":894\n *         dst.data += start * stride\n *     else:\n *         dst.suboffsets[suboffset_dim[0]] += start * stride             # <<<<<<<<<<<<<<\n * \n *     if suboffset >= 0:\n */\n  /*else*/ {\n    __pyx_t_3 = (__pyx_v_suboffset_dim[0]);\n    (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride));\n  }\n  __pyx_L23:;\n\n  /* \"View.MemoryView\":896\n *         dst.suboffsets[suboffset_dim[0]] += start * stride\n * \n *     if suboffset >= 0:             # <<<<<<<<<<<<<<\n *         if not is_slice:\n *             if new_ndim == 0:\n */\n  __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":897\n * \n *     if suboffset >= 0:\n *         if not is_slice:             # <<<<<<<<<<<<<<\n *             if new_ndim == 0:\n *                 dst.data = (<char **> dst.data)[0] + suboffset\n */\n    __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":898\n *     if suboffset >= 0:\n *         if not is_slice:\n *             if new_ndim == 0:             # <<<<<<<<<<<<<<\n *                 dst.data = (<char **> dst.data)[0] + suboffset\n *             else:\n */\n      __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0);\n      if (__pyx_t_2) {\n\n        /* \"View.MemoryView\":899\n *         if not is_slice:\n *             if new_ndim == 0:\n *                 dst.data = (<char **> dst.data)[0] + suboffset             # <<<<<<<<<<<<<<\n *             else:\n *                 _err_dim(IndexError, \"All dimensions preceding dimension %d \"\n */\n        __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset);\n\n        /* \"View.MemoryView\":898\n *     if suboffset >= 0:\n *         if not is_slice:\n *             if new_ndim == 0:             # <<<<<<<<<<<<<<\n *                 dst.data = (<char **> dst.data)[0] + suboffset\n *             else:\n */\n        goto __pyx_L26;\n      }\n\n      /* \"View.MemoryView\":901\n *                 dst.data = (<char **> dst.data)[0] + suboffset\n *             else:\n *                 _err_dim(IndexError, \"All dimensions preceding dimension %d \"             # <<<<<<<<<<<<<<\n *                                      \"must be indexed and not sliced\", dim)\n *         else:\n */\n      /*else*/ {\n\n        /* \"View.MemoryView\":902\n *             else:\n *                 _err_dim(IndexError, \"All dimensions preceding dimension %d \"\n *                                      \"must be indexed and not sliced\", dim)             # <<<<<<<<<<<<<<\n *         else:\n *             suboffset_dim[0] = new_ndim\n */\n        __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)\"All dimensions preceding dimension %d must be indexed and not sliced\"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 901, __pyx_L1_error)\n      }\n      __pyx_L26:;\n\n      /* \"View.MemoryView\":897\n * \n *     if suboffset >= 0:\n *         if not is_slice:             # <<<<<<<<<<<<<<\n *             if new_ndim == 0:\n *                 dst.data = (<char **> dst.data)[0] + suboffset\n */\n      goto __pyx_L25;\n    }\n\n    /* \"View.MemoryView\":904\n *                                      \"must be indexed and not sliced\", dim)\n *         else:\n *             suboffset_dim[0] = new_ndim             # <<<<<<<<<<<<<<\n * \n *     return 0\n */\n    /*else*/ {\n      (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim;\n    }\n    __pyx_L25:;\n\n    /* \"View.MemoryView\":896\n *         dst.suboffsets[suboffset_dim[0]] += start * stride\n * \n *     if suboffset >= 0:             # <<<<<<<<<<<<<<\n *         if not is_slice:\n *             if new_ndim == 0:\n */\n  }\n\n  /* \"View.MemoryView\":906\n *             suboffset_dim[0] = new_ndim\n * \n *     return 0             # <<<<<<<<<<<<<<\n * \n * \n */\n  __pyx_r = 0;\n  goto __pyx_L0;\n\n  /* \"View.MemoryView\":809\n * \n * @cname('__pyx_memoryview_slice_memviewslice')\n * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<\n *         __Pyx_memviewslice *dst,\n *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,\n */\n\n  /* function exit code */\n  __pyx_L1_error:;\n  {\n    #ifdef WITH_THREAD\n    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();\n    #endif\n    __Pyx_AddTraceback(\"View.MemoryView.slice_memviewslice\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n    #ifdef WITH_THREAD\n    __Pyx_PyGILState_Release(__pyx_gilstate_save);\n    #endif\n  }\n  __pyx_r = -1;\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":912\n * \n * @cname('__pyx_pybuffer_index')\n * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,             # <<<<<<<<<<<<<<\n *                           Py_ssize_t dim) except NULL:\n *     cdef Py_ssize_t shape, stride, suboffset = -1\n */\n\nstatic char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) {\n  Py_ssize_t __pyx_v_shape;\n  Py_ssize_t __pyx_v_stride;\n  Py_ssize_t __pyx_v_suboffset;\n  Py_ssize_t __pyx_v_itemsize;\n  char *__pyx_v_resultp;\n  char *__pyx_r;\n  __Pyx_RefNannyDeclarations\n  Py_ssize_t __pyx_t_1;\n  int __pyx_t_2;\n  PyObject *__pyx_t_3 = NULL;\n  PyObject *__pyx_t_4 = NULL;\n  int __pyx_lineno = 0;\n  const char *__pyx_filename = NULL;\n  int __pyx_clineno = 0;\n  __Pyx_RefNannySetupContext(\"pybuffer_index\", 0);\n\n  /* \"View.MemoryView\":914\n * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,\n *                           Py_ssize_t dim) except NULL:\n *     cdef Py_ssize_t shape, stride, suboffset = -1             # <<<<<<<<<<<<<<\n *     cdef Py_ssize_t itemsize = view.itemsize\n *     cdef char *resultp\n */\n  __pyx_v_suboffset = -1L;\n\n  /* \"View.MemoryView\":915\n *                           Py_ssize_t dim) except NULL:\n *     cdef Py_ssize_t shape, stride, suboffset = -1\n *     cdef Py_ssize_t itemsize = view.itemsize             # <<<<<<<<<<<<<<\n *     cdef char *resultp\n * \n */\n  __pyx_t_1 = __pyx_v_view->itemsize;\n  __pyx_v_itemsize = __pyx_t_1;\n\n  /* \"View.MemoryView\":918\n *     cdef char *resultp\n * \n *     if view.ndim == 0:             # <<<<<<<<<<<<<<\n *         shape = view.len / itemsize\n *         stride = itemsize\n */\n  __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":919\n * \n *     if view.ndim == 0:\n *         shape = view.len / itemsize             # <<<<<<<<<<<<<<\n *         stride = itemsize\n *     else:\n */\n    if (unlikely(__pyx_v_itemsize == 0)) {\n      PyErr_SetString(PyExc_ZeroDivisionError, \"integer division or modulo by zero\");\n      __PYX_ERR(2, 919, __pyx_L1_error)\n    }\n    else if (sizeof(Py_ssize_t) == sizeof(long) && (!(((Py_ssize_t)-1) > 0)) && unlikely(__pyx_v_itemsize == (Py_ssize_t)-1)  && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) {\n      PyErr_SetString(PyExc_OverflowError, \"value too large to perform division\");\n      __PYX_ERR(2, 919, __pyx_L1_error)\n    }\n    __pyx_v_shape = (__pyx_v_view->len / __pyx_v_itemsize);\n\n    /* \"View.MemoryView\":920\n *     if view.ndim == 0:\n *         shape = view.len / itemsize\n *         stride = itemsize             # <<<<<<<<<<<<<<\n *     else:\n *         shape = view.shape[dim]\n */\n    __pyx_v_stride = __pyx_v_itemsize;\n\n    /* \"View.MemoryView\":918\n *     cdef char *resultp\n * \n *     if view.ndim == 0:             # <<<<<<<<<<<<<<\n *         shape = view.len / itemsize\n *         stride = itemsize\n */\n    goto __pyx_L3;\n  }\n\n  /* \"View.MemoryView\":922\n *         stride = itemsize\n *     else:\n *         shape = view.shape[dim]             # <<<<<<<<<<<<<<\n *         stride = view.strides[dim]\n *         if view.suboffsets != NULL:\n */\n  /*else*/ {\n    __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]);\n\n    /* \"View.MemoryView\":923\n *     else:\n *         shape = view.shape[dim]\n *         stride = view.strides[dim]             # <<<<<<<<<<<<<<\n *         if view.suboffsets != NULL:\n *             suboffset = view.suboffsets[dim]\n */\n    __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]);\n\n    /* \"View.MemoryView\":924\n *         shape = view.shape[dim]\n *         stride = view.strides[dim]\n *         if view.suboffsets != NULL:           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memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice):             # <<<<<<<<<<<<<<\n *     \"\"\"\n *     Create a new memoryview object from a given memoryview object and slice.\n */\n\n  /* function exit code */\n  __pyx_L1_error:;\n  __Pyx_XDECREF(__pyx_t_5);\n  __Pyx_AddTraceback(\"View.MemoryView.memoryview_copy_from_slice\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n  __pyx_r = 0;\n  __pyx_L0:;\n  __Pyx_XGIVEREF(__pyx_r);\n  __Pyx_RefNannyFinishContext();\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1111\n * \n * \n * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<\n *     if arg < 0:\n *         return -arg\n */\n\nstatic Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) {\n  Py_ssize_t __pyx_r;\n  int __pyx_t_1;\n\n  /* \"View.MemoryView\":1112\n * \n * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:\n *     if arg < 0:             # <<<<<<<<<<<<<<\n *         return -arg\n *     else:\n */\n  __pyx_t_1 = ((__pyx_v_arg < 0) != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":1113\n * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:\n *     if arg < 0:\n *         return -arg             # <<<<<<<<<<<<<<\n *     else:\n *         return arg\n */\n    __pyx_r = (-__pyx_v_arg);\n    goto __pyx_L0;\n\n    /* \"View.MemoryView\":1112\n * \n * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:\n *     if arg < 0:             # <<<<<<<<<<<<<<\n *         return -arg\n *     else:\n */\n  }\n\n  /* \"View.MemoryView\":1115\n *         return -arg\n *     else:\n *         return arg             # <<<<<<<<<<<<<<\n * \n * @cname('__pyx_get_best_slice_order')\n */\n  /*else*/ {\n    __pyx_r = __pyx_v_arg;\n    goto __pyx_L0;\n  }\n\n  /* \"View.MemoryView\":1111\n * \n * \n * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<\n *     if arg < 0:\n *         return -arg\n */\n\n  /* function exit code */\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1118\n * \n * @cname('__pyx_get_best_slice_order')\n * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<\n *     \"\"\"\n *     Figure out the best memory access order for a given slice.\n */\n\nstatic char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) {\n  int __pyx_v_i;\n  Py_ssize_t __pyx_v_c_stride;\n  Py_ssize_t __pyx_v_f_stride;\n  char __pyx_r;\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  int __pyx_t_4;\n\n  /* \"View.MemoryView\":1123\n *     \"\"\"\n *     cdef int i\n *     cdef Py_ssize_t c_stride = 0             # <<<<<<<<<<<<<<\n *     cdef Py_ssize_t f_stride = 0\n * \n */\n  __pyx_v_c_stride = 0;\n\n  /* \"View.MemoryView\":1124\n *     cdef int i\n *     cdef Py_ssize_t c_stride = 0\n *     cdef Py_ssize_t f_stride = 0             # <<<<<<<<<<<<<<\n * \n *     for i in range(ndim - 1, -1, -1):\n */\n  __pyx_v_f_stride = 0;\n\n  /* \"View.MemoryView\":1126\n *     cdef Py_ssize_t f_stride = 0\n * \n *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<\n *         if mslice.shape[i] > 1:\n *             c_stride = mslice.strides[i]\n */\n  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {\n    __pyx_v_i = __pyx_t_1;\n\n    /* \"View.MemoryView\":1127\n * \n *     for i in range(ndim - 1, -1, -1):\n *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<\n *             c_stride = mslice.strides[i]\n *             break\n */\n    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":1128\n *     for i in range(ndim - 1, -1, -1):\n *         if mslice.shape[i] > 1:\n *             c_stride = mslice.strides[i]             # <<<<<<<<<<<<<<\n *             break\n * \n */\n      __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]);\n\n      /* \"View.MemoryView\":1129\n *         if mslice.shape[i] > 1:\n *             c_stride = mslice.strides[i]\n *             break             # <<<<<<<<<<<<<<\n * \n *     for i in range(ndim):\n */\n      goto __pyx_L4_break;\n\n      /* \"View.MemoryView\":1127\n * \n *     for i in range(ndim - 1, -1, -1):\n *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<\n *             c_stride = mslice.strides[i]\n *             break\n */\n    }\n  }\n  __pyx_L4_break:;\n\n  /* \"View.MemoryView\":1131\n *             break\n * \n *     for i in range(ndim):             # <<<<<<<<<<<<<<\n *         if mslice.shape[i] > 1:\n *             f_stride = mslice.strides[i]\n */\n  __pyx_t_1 = __pyx_v_ndim;\n  __pyx_t_3 = __pyx_t_1;\n  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {\n    __pyx_v_i = __pyx_t_4;\n\n    /* \"View.MemoryView\":1132\n * \n *     for i in range(ndim):\n *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<\n *             f_stride = mslice.strides[i]\n *             break\n */\n    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":1133\n *     for i in range(ndim):\n *         if mslice.shape[i] > 1:\n *             f_stride = mslice.strides[i]             # <<<<<<<<<<<<<<\n *             break\n * \n */\n      __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]);\n\n      /* \"View.MemoryView\":1134\n *         if mslice.shape[i] > 1:\n *             f_stride = mslice.strides[i]\n *             break             # <<<<<<<<<<<<<<\n * \n *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):\n */\n      goto __pyx_L7_break;\n\n      /* \"View.MemoryView\":1132\n * \n *     for i in range(ndim):\n *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<\n *             f_stride = mslice.strides[i]\n *             break\n */\n    }\n  }\n  __pyx_L7_break:;\n\n  /* \"View.MemoryView\":1136\n *             break\n * \n *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<\n *         return 'C'\n *     else:\n */\n  __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":1137\n * \n *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):\n *         return 'C'             # <<<<<<<<<<<<<<\n *     else:\n *         return 'F'\n */\n    __pyx_r = 'C';\n    goto __pyx_L0;\n\n    /* \"View.MemoryView\":1136\n *             break\n * \n *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<\n *         return 'C'\n *     else:\n */\n  }\n\n  /* \"View.MemoryView\":1139\n *         return 'C'\n *     else:\n *         return 'F'             # <<<<<<<<<<<<<<\n * \n * @cython.cdivision(True)\n */\n  /*else*/ {\n    __pyx_r = 'F';\n    goto __pyx_L0;\n  }\n\n  /* \"View.MemoryView\":1118\n * \n * @cname('__pyx_get_best_slice_order')\n * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<\n *     \"\"\"\n *     Figure out the best memory access order for a given slice.\n */\n\n  /* function exit code */\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1142\n * \n * @cython.cdivision(True)\n * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<\n *                                    char *dst_data, Py_ssize_t *dst_strides,\n *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,\n */\n\nstatic void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {\n  CYTHON_UNUSED Py_ssize_t __pyx_v_i;\n  CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;\n  Py_ssize_t __pyx_v_dst_extent;\n  Py_ssize_t __pyx_v_src_stride;\n  Py_ssize_t __pyx_v_dst_stride;\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  Py_ssize_t __pyx_t_4;\n  Py_ssize_t __pyx_t_5;\n  Py_ssize_t __pyx_t_6;\n\n  /* \"View.MemoryView\":1149\n * \n *     cdef Py_ssize_t i\n *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<\n *     cdef Py_ssize_t dst_extent = dst_shape[0]\n *     cdef Py_ssize_t src_stride = src_strides[0]\n */\n  __pyx_v_src_extent = (__pyx_v_src_shape[0]);\n\n  /* \"View.MemoryView\":1150\n *     cdef Py_ssize_t i\n *     cdef Py_ssize_t src_extent = src_shape[0]\n *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<\n *     cdef Py_ssize_t src_stride = src_strides[0]\n *     cdef Py_ssize_t dst_stride = dst_strides[0]\n */\n  __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);\n\n  /* \"View.MemoryView\":1151\n *     cdef Py_ssize_t src_extent = src_shape[0]\n *     cdef Py_ssize_t dst_extent = dst_shape[0]\n *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<\n *     cdef Py_ssize_t dst_stride = dst_strides[0]\n * \n */\n  __pyx_v_src_stride = (__pyx_v_src_strides[0]);\n\n  /* \"View.MemoryView\":1152\n *     cdef Py_ssize_t dst_extent = dst_shape[0]\n *     cdef Py_ssize_t src_stride = src_strides[0]\n *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<\n * \n *     if ndim == 1:\n */\n  __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);\n\n  /* \"View.MemoryView\":1154\n *     cdef Py_ssize_t dst_stride = dst_strides[0]\n * \n *     if ndim == 1:             # <<<<<<<<<<<<<<\n *        if (src_stride > 0 and dst_stride > 0 and\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n */\n  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":1155\n * \n *     if ndim == 1:\n *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n *            memcpy(dst_data, src_data, itemsize * dst_extent)\n */\n    __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0);\n    if (__pyx_t_2) {\n    } else {\n      __pyx_t_1 = __pyx_t_2;\n      goto __pyx_L5_bool_binop_done;\n    }\n    __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0);\n    if (__pyx_t_2) {\n    } else {\n      __pyx_t_1 = __pyx_t_2;\n      goto __pyx_L5_bool_binop_done;\n    }\n\n    /* \"View.MemoryView\":1156\n *     if ndim == 1:\n *        if (src_stride > 0 and dst_stride > 0 and\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<\n *            memcpy(dst_data, src_data, itemsize * dst_extent)\n *        else:\n */\n    __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);\n    if (__pyx_t_2) {\n      __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));\n    }\n    __pyx_t_3 = (__pyx_t_2 != 0);\n    __pyx_t_1 = __pyx_t_3;\n    __pyx_L5_bool_binop_done:;\n\n    /* \"View.MemoryView\":1155\n * \n *     if ndim == 1:\n *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n *            memcpy(dst_data, src_data, itemsize * dst_extent)\n */\n    if (__pyx_t_1) {\n\n      /* \"View.MemoryView\":1157\n *        if (src_stride > 0 and dst_stride > 0 and\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n *            memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<\n *        else:\n *            for i in range(dst_extent):\n */\n      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));\n\n      /* \"View.MemoryView\":1155\n * \n *     if ndim == 1:\n *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n *            memcpy(dst_data, src_data, itemsize * dst_extent)\n */\n      goto __pyx_L4;\n    }\n\n    /* \"View.MemoryView\":1159\n *            memcpy(dst_data, src_data, itemsize * dst_extent)\n *        else:\n *            for i in range(dst_extent):             # <<<<<<<<<<<<<<\n *                memcpy(dst_data, src_data, itemsize)\n *                src_data += src_stride\n */\n    /*else*/ {\n      __pyx_t_4 = __pyx_v_dst_extent;\n      __pyx_t_5 = __pyx_t_4;\n      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {\n        __pyx_v_i = __pyx_t_6;\n\n        /* \"View.MemoryView\":1160\n *        else:\n *            for i in range(dst_extent):\n *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<\n *                src_data += src_stride\n *                dst_data += dst_stride\n */\n        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));\n\n        /* \"View.MemoryView\":1161\n *            for i in range(dst_extent):\n *                memcpy(dst_data, src_data, itemsize)\n *                src_data += src_stride             # <<<<<<<<<<<<<<\n *                dst_data += dst_stride\n *     else:\n */\n        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);\n\n        /* \"View.MemoryView\":1162\n *                memcpy(dst_data, src_data, itemsize)\n *                src_data += src_stride\n *                dst_data += dst_stride             # <<<<<<<<<<<<<<\n *     else:\n *         for i in range(dst_extent):\n */\n        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);\n      }\n    }\n    __pyx_L4:;\n\n    /* \"View.MemoryView\":1154\n *     cdef Py_ssize_t dst_stride = dst_strides[0]\n * \n *     if ndim == 1:             # <<<<<<<<<<<<<<\n *        if (src_stride > 0 and dst_stride > 0 and\n *            <size_t> src_stride == itemsize == <size_t> dst_stride):\n */\n    goto __pyx_L3;\n  }\n\n  /* \"View.MemoryView\":1164\n *                dst_data += dst_stride\n *     else:\n *         for i in range(dst_extent):             # <<<<<<<<<<<<<<\n *             _copy_strided_to_strided(src_data, src_strides + 1,\n *                                      dst_data, dst_strides + 1,\n */\n  /*else*/ {\n    __pyx_t_4 = __pyx_v_dst_extent;\n    __pyx_t_5 = __pyx_t_4;\n    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {\n      __pyx_v_i = __pyx_t_6;\n\n      /* \"View.MemoryView\":1165\n *     else:\n *         for i in range(dst_extent):\n *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<\n *                                      dst_data, dst_strides + 1,\n *                                      src_shape + 1, dst_shape + 1,\n */\n      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);\n\n      /* \"View.MemoryView\":1169\n *                                      src_shape + 1, dst_shape + 1,\n *                                      ndim - 1, itemsize)\n *             src_data += src_stride             # <<<<<<<<<<<<<<\n *             dst_data += dst_stride\n * \n */\n      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);\n\n      /* \"View.MemoryView\":1170\n *                                      ndim - 1, itemsize)\n *             src_data += src_stride\n *             dst_data += dst_stride             # <<<<<<<<<<<<<<\n * \n * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,\n */\n      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);\n    }\n  }\n  __pyx_L3:;\n\n  /* \"View.MemoryView\":1142\n * \n * @cython.cdivision(True)\n * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<\n *                                    char *dst_data, Py_ssize_t *dst_strides,\n *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,\n */\n\n  /* function exit code */\n}\n\n/* \"View.MemoryView\":1172\n *             dst_data += dst_stride\n * \n * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<\n *                                   __Pyx_memviewslice *dst,\n *                                   int ndim, size_t itemsize) nogil:\n */\n\nstatic void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {\n\n  /* \"View.MemoryView\":1175\n *                                   __Pyx_memviewslice *dst,\n *                                   int ndim, size_t itemsize) nogil:\n *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<\n *                              src.shape, dst.shape, ndim, itemsize)\n * \n */\n  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);\n\n  /* \"View.MemoryView\":1172\n *             dst_data += dst_stride\n * \n * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<\n *                                   __Pyx_memviewslice *dst,\n *                                   int ndim, size_t itemsize) nogil:\n */\n\n  /* function exit code */\n}\n\n/* \"View.MemoryView\":1179\n * \n * @cname('__pyx_memoryview_slice_get_size')\n * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<\n *     \"Return the size of the memory occupied by the slice in number of bytes\"\n *     cdef Py_ssize_t shape, size = src.memview.view.itemsize\n */\n\nstatic Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {\n  Py_ssize_t __pyx_v_shape;\n  Py_ssize_t __pyx_v_size;\n  Py_ssize_t __pyx_r;\n  Py_ssize_t __pyx_t_1;\n  Py_ssize_t *__pyx_t_2;\n  Py_ssize_t *__pyx_t_3;\n  Py_ssize_t *__pyx_t_4;\n\n  /* \"View.MemoryView\":1181\n * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:\n *     \"Return the size of the memory occupied by the slice in number of bytes\"\n *     cdef Py_ssize_t shape, size = src.memview.view.itemsize             # <<<<<<<<<<<<<<\n * \n *     for shape in src.shape[:ndim]:\n */\n  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;\n  __pyx_v_size = __pyx_t_1;\n\n  /* \"View.MemoryView\":1183\n *     cdef Py_ssize_t shape, size = src.memview.view.itemsize\n * \n *     for shape in src.shape[:ndim]:             # <<<<<<<<<<<<<<\n *         size *= shape\n * \n */\n  __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);\n  for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {\n    __pyx_t_2 = __pyx_t_4;\n    __pyx_v_shape = (__pyx_t_2[0]);\n\n    /* \"View.MemoryView\":1184\n * \n *     for shape in src.shape[:ndim]:\n *         size *= shape             # <<<<<<<<<<<<<<\n * \n *     return size\n */\n    __pyx_v_size = (__pyx_v_size * __pyx_v_shape);\n  }\n\n  /* \"View.MemoryView\":1186\n *         size *= shape\n * \n *     return size             # <<<<<<<<<<<<<<\n * \n * @cname('__pyx_fill_contig_strides_array')\n */\n  __pyx_r = __pyx_v_size;\n  goto __pyx_L0;\n\n  /* \"View.MemoryView\":1179\n * \n * @cname('__pyx_memoryview_slice_get_size')\n * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<\n *     \"Return the size of the memory occupied by the slice in number of bytes\"\n *     cdef Py_ssize_t shape, size = src.memview.view.itemsize\n */\n\n  /* function exit code */\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1189\n * \n * @cname('__pyx_fill_contig_strides_array')\n * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<\n *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,\n *                 int ndim, char order) nogil:\n */\n\nstatic Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {\n  int __pyx_v_idx;\n  Py_ssize_t __pyx_r;\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  int __pyx_t_4;\n\n  /* \"View.MemoryView\":1198\n *     cdef int idx\n * \n *     if order == 'F':             # <<<<<<<<<<<<<<\n *         for idx in range(ndim):\n *             strides[idx] = stride\n */\n  __pyx_t_1 = ((__pyx_v_order == 'F') != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":1199\n * \n *     if order == 'F':\n *         for idx in range(ndim):             # <<<<<<<<<<<<<<\n *             strides[idx] = stride\n *             stride *= shape[idx]\n */\n    __pyx_t_2 = __pyx_v_ndim;\n    __pyx_t_3 = __pyx_t_2;\n    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {\n      __pyx_v_idx = __pyx_t_4;\n\n      /* \"View.MemoryView\":1200\n *     if order == 'F':\n *         for idx in range(ndim):\n *             strides[idx] = stride             # <<<<<<<<<<<<<<\n *             stride *= shape[idx]\n *     else:\n */\n      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;\n\n      /* \"View.MemoryView\":1201\n *         for idx in range(ndim):\n *             strides[idx] = stride\n *             stride *= shape[idx]             # <<<<<<<<<<<<<<\n *     else:\n *         for idx in range(ndim - 1, -1, -1):\n */\n      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));\n    }\n\n    /* \"View.MemoryView\":1198\n *     cdef int idx\n * \n *     if order == 'F':             # <<<<<<<<<<<<<<\n *         for idx in range(ndim):\n *             strides[idx] = stride\n */\n    goto __pyx_L3;\n  }\n\n  /* \"View.MemoryView\":1203\n *             stride *= shape[idx]\n *     else:\n *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<\n *             strides[idx] = stride\n *             stride *= shape[idx]\n */\n  /*else*/ {\n    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {\n      __pyx_v_idx = __pyx_t_2;\n\n      /* \"View.MemoryView\":1204\n *     else:\n *         for idx in range(ndim - 1, -1, -1):\n *             strides[idx] = stride             # <<<<<<<<<<<<<<\n *             stride *= shape[idx]\n * \n */\n      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;\n\n      /* \"View.MemoryView\":1205\n *         for idx in range(ndim - 1, -1, -1):\n *             strides[idx] = stride\n *             stride *= shape[idx]             # <<<<<<<<<<<<<<\n * \n *     return stride\n */\n      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));\n    }\n  }\n  __pyx_L3:;\n\n  /* \"View.MemoryView\":1207\n *             stride *= shape[idx]\n * \n *     return stride             # <<<<<<<<<<<<<<\n * \n * @cname('__pyx_memoryview_copy_data_to_temp')\n */\n  __pyx_r = __pyx_v_stride;\n  goto __pyx_L0;\n\n  /* \"View.MemoryView\":1189\n * \n * @cname('__pyx_fill_contig_strides_array')\n * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<\n *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,\n *                 int ndim, char order) nogil:\n */\n\n  /* function exit code */\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1210\n * \n * @cname('__pyx_memoryview_copy_data_to_temp')\n * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<\n *                              __Pyx_memviewslice *tmpslice,\n *                              char order,\n */\n\nstatic void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {\n  int __pyx_v_i;\n  void *__pyx_v_result;\n  size_t __pyx_v_itemsize;\n  size_t __pyx_v_size;\n  void *__pyx_r;\n  Py_ssize_t __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n  struct __pyx_memoryview_obj *__pyx_t_4;\n  int __pyx_t_5;\n  int __pyx_t_6;\n  int __pyx_lineno = 0;\n  const char *__pyx_filename = NULL;\n  int __pyx_clineno = 0;\n\n  /* \"View.MemoryView\":1221\n *     cdef void *result\n * \n *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<\n *     cdef size_t size = slice_get_size(src, ndim)\n * \n */\n  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;\n  __pyx_v_itemsize = __pyx_t_1;\n\n  /* \"View.MemoryView\":1222\n * \n *     cdef size_t itemsize = src.memview.view.itemsize\n *     cdef size_t size = slice_get_size(src, ndim)             # <<<<<<<<<<<<<<\n * \n *     result = malloc(size)\n */\n  __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim);\n\n  /* \"View.MemoryView\":1224\n *     cdef size_t size = slice_get_size(src, ndim)\n * \n *     result = malloc(size)             # <<<<<<<<<<<<<<\n *     if not result:\n *         _err(MemoryError, NULL)\n */\n  __pyx_v_result = malloc(__pyx_v_size);\n\n  /* \"View.MemoryView\":1225\n * \n *     result = malloc(size)\n *     if not result:             # <<<<<<<<<<<<<<\n *         _err(MemoryError, NULL)\n * \n */\n  __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":1226\n *     result = malloc(size)\n *     if not result:\n *         _err(MemoryError, NULL)             # <<<<<<<<<<<<<<\n * \n * \n */\n    __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 1226, __pyx_L1_error)\n\n    /* \"View.MemoryView\":1225\n * \n *     result = malloc(size)\n *     if not result:             # <<<<<<<<<<<<<<\n *         _err(MemoryError, NULL)\n * \n */\n  }\n\n  /* \"View.MemoryView\":1229\n * \n * \n *     tmpslice.data = <char *> result             # <<<<<<<<<<<<<<\n *     tmpslice.memview = src.memview\n *     for i in range(ndim):\n */\n  __pyx_v_tmpslice->data = ((char *)__pyx_v_result);\n\n  /* \"View.MemoryView\":1230\n * \n *     tmpslice.data = <char *> result\n *     tmpslice.memview = src.memview             # <<<<<<<<<<<<<<\n *     for i in range(ndim):\n *         tmpslice.shape[i] = src.shape[i]\n */\n  __pyx_t_4 = __pyx_v_src->memview;\n  __pyx_v_tmpslice->memview = __pyx_t_4;\n\n  /* \"View.MemoryView\":1231\n *     tmpslice.data = <char *> result\n *     tmpslice.memview = src.memview\n *     for i in range(ndim):             # <<<<<<<<<<<<<<\n *         tmpslice.shape[i] = src.shape[i]\n *         tmpslice.suboffsets[i] = -1\n */\n  __pyx_t_3 = __pyx_v_ndim;\n  __pyx_t_5 = __pyx_t_3;\n  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {\n    __pyx_v_i = __pyx_t_6;\n\n    /* \"View.MemoryView\":1232\n *     tmpslice.memview = src.memview\n *     for i in range(ndim):\n *         tmpslice.shape[i] = src.shape[i]             # <<<<<<<<<<<<<<\n *         tmpslice.suboffsets[i] = -1\n * \n */\n    (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]);\n\n    /* \"View.MemoryView\":1233\n *     for i in range(ndim):\n *         tmpslice.shape[i] = src.shape[i]\n *         tmpslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<\n * \n *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,\n */\n    (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L;\n  }\n\n  /* \"View.MemoryView\":1235\n *         tmpslice.suboffsets[i] = -1\n * \n *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,             # <<<<<<<<<<<<<<\n *                               ndim, order)\n * \n */\n  (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order));\n\n  /* \"View.MemoryView\":1239\n * \n * \n *     for i in range(ndim):             # <<<<<<<<<<<<<<\n *         if tmpslice.shape[i] == 1:\n *             tmpslice.strides[i] = 0\n */\n  __pyx_t_3 = __pyx_v_ndim;\n  __pyx_t_5 = __pyx_t_3;\n  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {\n    __pyx_v_i = __pyx_t_6;\n\n    /* \"View.MemoryView\":1240\n * \n *     for i in range(ndim):\n *         if tmpslice.shape[i] == 1:           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<<<<<<<<<<<<<<\n * \n *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)\n */\n      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);\n\n      /* \"View.MemoryView\":1306\n *     if slices_overlap(&src, &dst, ndim, itemsize):\n * \n *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<\n *             order = get_best_order(&dst, ndim)\n * \n */\n    }\n\n    /* \"View.MemoryView\":1309\n *             order = get_best_order(&dst, ndim)\n * \n *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<\n *         src = tmp\n * \n */\n    __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(2, 1309, __pyx_L1_error)\n    __pyx_v_tmpdata = __pyx_t_7;\n\n    /* \"View.MemoryView\":1310\n * \n *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)\n *         src = tmp             # <<<<<<<<<<<<<<\n * \n *     if not broadcasting:\n */\n    __pyx_v_src = __pyx_v_tmp;\n\n    /* \"View.MemoryView\":1304\n *             _err_dim(ValueError, \"Dimension %d is not direct\", i)\n * \n *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<\n * \n *         if not slice_is_contig(src, order, ndim):\n */\n  }\n\n  /* \"View.MemoryView\":1312\n *         src = tmp\n * \n *     if not broadcasting:             # <<<<<<<<<<<<<<\n * \n * \n */\n  __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);\n  if (__pyx_t_2) {\n\n    /* \"View.MemoryView\":1315\n * \n * \n *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<\n *             direct_copy = slice_is_contig(dst, 'C', ndim)\n *         elif slice_is_contig(src, 'F', ndim):\n */\n    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":1316\n * \n *         if slice_is_contig(src, 'C', ndim):\n *             direct_copy = slice_is_contig(dst, 'C', ndim)             # <<<<<<<<<<<<<<\n *         elif slice_is_contig(src, 'F', ndim):\n *             direct_copy = slice_is_contig(dst, 'F', ndim)\n */\n      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);\n\n      /* \"View.MemoryView\":1315\n * \n * \n *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<\n *             direct_copy = slice_is_contig(dst, 'C', ndim)\n *         elif slice_is_contig(src, 'F', ndim):\n */\n      goto __pyx_L12;\n    }\n\n    /* \"View.MemoryView\":1317\n *         if slice_is_contig(src, 'C', ndim):\n *             direct_copy = slice_is_contig(dst, 'C', ndim)\n *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<\n *             direct_copy = slice_is_contig(dst, 'F', ndim)\n * \n */\n    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":1318\n *             direct_copy = slice_is_contig(dst, 'C', ndim)\n *         elif slice_is_contig(src, 'F', ndim):\n *             direct_copy = slice_is_contig(dst, 'F', ndim)             # <<<<<<<<<<<<<<\n * \n *         if direct_copy:\n */\n      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);\n\n      /* \"View.MemoryView\":1317\n *         if slice_is_contig(src, 'C', ndim):\n *             direct_copy = slice_is_contig(dst, 'C', ndim)\n *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<\n *             direct_copy = slice_is_contig(dst, 'F', ndim)\n * \n */\n    }\n    __pyx_L12:;\n\n    /* \"View.MemoryView\":1320\n *             direct_copy = slice_is_contig(dst, 'F', ndim)\n * \n *         if direct_copy:             # <<<<<<<<<<<<<<\n * \n *             refcount_copying(&dst, dtype_is_object, ndim, False)\n */\n    __pyx_t_2 = (__pyx_v_direct_copy != 0);\n    if (__pyx_t_2) {\n\n      /* \"View.MemoryView\":1322\n *         if direct_copy:\n * \n *             refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<\n *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))\n *             refcount_copying(&dst, dtype_is_object, ndim, True)\n */\n      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);\n\n      /* \"View.MemoryView\":1323\n * \n *             refcount_copying(&dst, dtype_is_object, ndim, False)\n *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<\n *             refcount_copying(&dst, dtype_is_object, ndim, True)\n *             free(tmpdata)\n */\n      (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));\n\n      /* \"View.MemoryView\":1324\n *             refcount_copying(&dst, dtype_is_object, ndim, False)\n *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))\n *             refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<\n *             free(tmpdata)\n *             return 0\n */\n      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);\n\n      /* \"View.MemoryView\":1325\n *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))\n *             refcount_copying(&dst, dtype_is_object, ndim, True)\n *             free(tmpdata)             # <<<<<<<<<<<<<<\n *             return 0\n * \n */\n      free(__pyx_v_tmpdata);\n\n      /* \"View.MemoryView\":1326\n *             refcount_copying(&dst, dtype_is_object, ndim, True)\n *             free(tmpdata)\n *             return 0             # <<<<<<<<<<<<<<\n * \n *     if order == 'F' == get_best_order(&dst, ndim):\n */\n      __pyx_r = 0;\n      goto __pyx_L0;\n\n      /* \"View.MemoryView\":1320\n *             direct_copy = slice_is_contig(dst, 'F', ndim)\n * \n *         if direct_copy:             # <<<<<<<<<<<<<<\n * \n *             refcount_copying(&dst, dtype_is_object, ndim, False)\n */\n    }\n\n    /* \"View.MemoryView\":1312\n *         src = tmp\n * \n *     if not broadcasting:             # <<<<<<<<<<<<<<\n * \n * \n */\n  }\n\n  /* \"View.MemoryView\":1328\n *             return 0\n * \n *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<\n * \n * \n */\n  __pyx_t_2 = (__pyx_v_order == 'F');\n  if (__pyx_t_2) {\n    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));\n  }\n  __pyx_t_8 = (__pyx_t_2 != 0);\n  if (__pyx_t_8) {\n\n    /* \"View.MemoryView\":1331\n * \n * \n *         transpose_memslice(&src)             # <<<<<<<<<<<<<<\n *         transpose_memslice(&dst)\n * \n */\n    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1331, __pyx_L1_error)\n\n    /* \"View.MemoryView\":1332\n * \n *         transpose_memslice(&src)\n *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<\n * \n *     refcount_copying(&dst, dtype_is_object, ndim, False)\n */\n    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1332, __pyx_L1_error)\n\n    /* \"View.MemoryView\":1328\n *             return 0\n * \n *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<\n * \n * \n */\n  }\n\n  /* \"View.MemoryView\":1334\n *         transpose_memslice(&dst)\n * \n *     refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<\n *     copy_strided_to_strided(&src, &dst, ndim, itemsize)\n *     refcount_copying(&dst, dtype_is_object, ndim, True)\n */\n  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);\n\n  /* \"View.MemoryView\":1335\n * \n *     refcount_copying(&dst, dtype_is_object, ndim, False)\n *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<\n *     refcount_copying(&dst, dtype_is_object, ndim, True)\n * \n */\n  copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);\n\n  /* \"View.MemoryView\":1336\n *     refcount_copying(&dst, dtype_is_object, ndim, False)\n *     copy_strided_to_strided(&src, &dst, ndim, itemsize)\n *     refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<\n * \n *     free(tmpdata)\n */\n  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);\n\n  /* \"View.MemoryView\":1338\n *     refcount_copying(&dst, dtype_is_object, ndim, True)\n * \n *     free(tmpdata)             # <<<<<<<<<<<<<<\n *     return 0\n * \n */\n  free(__pyx_v_tmpdata);\n\n  /* \"View.MemoryView\":1339\n * \n *     free(tmpdata)\n *     return 0             # <<<<<<<<<<<<<<\n * \n * @cname('__pyx_memoryview_broadcast_leading')\n */\n  __pyx_r = 0;\n  goto __pyx_L0;\n\n  /* \"View.MemoryView\":1270\n * \n * @cname('__pyx_memoryview_copy_contents')\n * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<\n *                                   __Pyx_memviewslice dst,\n *                                   int src_ndim, int dst_ndim,\n */\n\n  /* function exit code */\n  __pyx_L1_error:;\n  {\n    #ifdef WITH_THREAD\n    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();\n    #endif\n    __Pyx_AddTraceback(\"View.MemoryView.memoryview_copy_contents\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n    #ifdef WITH_THREAD\n    __Pyx_PyGILState_Release(__pyx_gilstate_save);\n    #endif\n  }\n  __pyx_r = -1;\n  __pyx_L0:;\n  return __pyx_r;\n}\n\n/* \"View.MemoryView\":1342\n * \n * @cname('__pyx_memoryview_broadcast_leading')\n * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<\n *                             int ndim,\n *                             int ndim_other) nogil:\n */\n\nstatic void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) {\n  int __pyx_v_i;\n  int __pyx_v_offset;\n  int __pyx_t_1;\n  int __pyx_t_2;\n  int __pyx_t_3;\n\n  /* \"View.MemoryView\":1346\n *                             int ndim_other) nogil:\n *     cdef int i\n *     cdef int offset = ndim_other - ndim             # <<<<<<<<<<<<<<\n * \n *     for i in range(ndim - 1, -1, -1):\n */\n  __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim);\n\n  /* \"View.MemoryView\":1348\n *     cdef int offset = ndim_other - ndim\n * \n *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<\n *         mslice.shape[i + offset] = mslice.shape[i]\n *         mslice.strides[i + offset] = mslice.strides[i]\n */\n  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {\n    __pyx_v_i = __pyx_t_1;\n\n    /* \"View.MemoryView\":1349\n * \n *     for i in range(ndim - 1, -1, -1):\n *         mslice.shape[i + offset] = mslice.shape[i]             # <<<<<<<<<<<<<<\n *         mslice.strides[i + offset] = mslice.strides[i]\n *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]\n */\n    (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]);\n\n    /* \"View.MemoryView\":1350\n *     for i in range(ndim - 1, -1, -1):\n *         mslice.shape[i + offset] = mslice.shape[i]\n *         mslice.strides[i + offset] = mslice.strides[i]             # <<<<<<<<<<<<<<\n *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]\n * \n */\n    (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]);\n\n    /* \"View.MemoryView\":1351\n *         mslice.shape[i + offset] = mslice.shape[i]\n *         mslice.strides[i + offset] = mslice.strides[i]\n *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]             # <<<<<<<<<<<<<<\n * \n *     for i in range(offset):\n */\n    (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]);\n  }\n\n  /* \"View.MemoryView\":1353\n *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]\n * \n *     for i in range(offset):             # <<<<<<<<<<<<<<\n *         mslice.shape[i] = 1\n *         mslice.strides[i] = mslice.strides[0]\n */\n  __pyx_t_1 = __pyx_v_offset;\n  __pyx_t_2 = __pyx_t_1;\n  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {\n    __pyx_v_i = __pyx_t_3;\n\n    /* \"View.MemoryView\":1354\n * \n *     for i in range(offset):\n *         mslice.shape[i] = 1             # <<<<<<<<<<<<<<\n *         mslice.strides[i] = mslice.strides[0]\n *         mslice.suboffsets[i] = -1\n */\n    (__pyx_v_mslice->shape[__pyx_v_i]) = 1;\n\n    /* \"View.MemoryView\":1355\n *     for i in range(offset):\n *         mslice.shape[i] = 1\n *         mslice.strides[i] = mslice.strides[0]             # <<<<<<<<<<<<<<\n *         mslice.suboffsets[i] = -1\n * \n */\n    (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]);\n\n    /* \"View.MemoryView\":1356\n *         mslice.shape[i] = 1\n *         mslice.strides[i] = mslice.strides[0]\n *         mslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<\n * \n * \n */\n    (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L;\n  }\n\n  /* \"View.MemoryView\":1342\n * \n * @cname('__pyx_memoryview_broadcast_leading')\n * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<\n *                             int ndim,\n *                             int ndim_other) nogil:\n */\n\n  /* function exit code */\n}\n\n/* \"View.MemoryView\":1364\n * \n * @cname('__pyx_memoryview_refcount_copying')\n * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<\n *                            int ndim, bint inc) nogil:\n * \n */\n\nstatic void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) {\n  int __pyx_t_1;\n\n  /* \"View.MemoryView\":1368\n * \n * \n *     if dtype_is_object:             # <<<<<<<<<<<<<<\n *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,\n *                                            dst.strides, ndim, inc)\n */\n  __pyx_t_1 = (__pyx_v_dtype_is_object != 0);\n  if (__pyx_t_1) {\n\n    /* \"View.MemoryView\":1369\n * \n *     if dtype_is_object:\n *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,             # <<<<<<<<<<<<<<\n *                                            dst.strides, ndim, inc)\n * \n */\n    __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc);\n\n    /* \"View.MemoryView\":1368\n * \n * \n *     if dtype_is_object:             # <<<<<<<<<<<<<<\n *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,\n *                                            dst.strides, ndim, inc)\n */\n  }\n\n  /* \"View.MemoryView\":1364\n * \n * @cname('__pyx_memoryview_refcount_copying')\n * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<\n *                            int ndim, bint inc) nogil:\n * \n */\n\n  /* function exit code */\n}\n\n/* \"View.MemoryView\":1373\n * \n * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')\n * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<\n *                                              Py_ssize_t *strides, int ndim,\n *                                              bint inc) with gil:\n */\n\nstatic void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {\n  __Pyx_RefNannyDeclarations\n  #ifdef WITH_THREAD\n  PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();\n  #endif\n  __Pyx_RefNannySetupContext(\"refcount_objects_in_slice_with_gil\", 0);\n\n  /* \"View.MemoryView\":1376\n *                                              Py_ssize_t *strides, int ndim,\n *                                              bint inc) with gil:\n *     refcount_objects_in_slice(data, shape, strides, ndim, inc)             # <<<<<<<<<<<<<<\n * \n * @cname('__pyx_memoryview_refcount_objects_in_slice')\n */\n  __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc);\n\n  /* \"View.MemoryView\":1373\n * \n * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')\n * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<\n *                                              Py_ssize_t *strides, int ndim,\n *                                              bint inc) with gil:\n */\n\n  /* function exit code */\n  __Pyx_RefNannyFinishContext();\n  #ifdef WITH_THREAD\n  __Pyx_PyGILState_Release(__pyx_gilstate_save);\n  #endif\n}\n\n/* \"View.MemoryView\":1379\n * \n * @cname('__pyx_memoryview_refcount_objects_in_slice')\n * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<\n *                                     Py_ssize_t *strides, int ndim, bint inc):\n *     cdef Py_ssize_t i\n */\n\nstatic void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {\n  CYTHON_UNUSED Py_ssize_t __pyx_v_i;\n  __Pyx_RefNannyDeclarations\n  Py_ssize_t __pyx_t_1;\n  Py_ssize_t __pyx_t_2;\n  Py_ssize_t __pyx_t_3;\n  int __pyx_t_4;\n  __Pyx_RefNannySetupContext(\"refcount_objects_in_slice\", 0);\n\n  /* \"View.MemoryView\":1383\n *     cdef Py_ssize_t i\n * \n *     for i in range(shape[0]):             # <<<<<<<<<<<<<<\n *         if ndim == 1:\n *             if inc:\n */\n  __pyx_t_1 = (__pyx_v_shape[0]);\n  __pyx_t_2 = __pyx_t_1;\n  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {\n    __pyx_v_i = __pyx_t_3;\n\n    /* \"View.MemoryView\":1384\n * \n *     for i in range(shape[0]):\n *         if ndim == 1:             # <<<<<<<<<<<<<<\n *             if inc:\n *                 Py_INCREF((<PyObject **> data)[0])\n */\n    __pyx_t_4 = ((__pyx_v_ndim == 1) != 0);\n    if (__pyx_t_4) {\n\n      /* \"View.MemoryView\":1385\n *     for i in range(shape[0]):\n *         if ndim == 1:\n *             if inc:             # <<<<<<<<<<<<<<\n *                 Py_INCREF((<PyObject **> data)[0])\n *             else:\n */\n      __pyx_t_4 = (__pyx_v_inc != 0);\n      if (__pyx_t_4) {\n\n        /* \"View.MemoryView\":1386\n *         if ndim == 1:\n *             if inc:\n *                 Py_INCREF((<PyObject **> data)[0])             # <<<<<<<<<<<<<<\n *             else:\n *                 Py_DECREF((<PyObject **> data)[0])\n */\n        Py_INCREF((((PyObject **)__pyx_v_data)[0]));\n\n        /* \"View.MemoryView\":1385\n *     for i in range(shape[0]):\n *         if ndim == 1:\n *             if inc:             # <<<<<<<<<<<<<<\n *                 Py_INCREF((<PyObject **> data)[0])\n *             else:\n */\n        goto __pyx_L6;\n      }\n\n      /* \"View.MemoryView\":1388\n *                 Py_INCREF((<PyObject **> data)[0])\n *             else:\n *                 Py_DECREF((<PyObject **> data)[0])             # <<<<<<<<<<<<<<\n *         else:\n *             refcount_objects_in_slice(data, shape + 1, strides + 1,\n */\n      /*else*/ {\n        Py_DECREF((((PyObject **)__pyx_v_data)[0]));\n      }\n      __pyx_L6:;\n\n      /* \"View.MemoryView\":1384\n * \n *     for i in range(shape[0]):\n *         if ndim == 1:             # <<<<<<<<<<<<<<\n *             if inc:\n *                 Py_INCREF((<PyObject **> data)[0])\n */\n      goto __pyx_L5;\n    }\n\n    /* \"View.MemoryView\":1390\n *                 Py_DECREF((<PyObject **> data)[0])\n *         else:\n * 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__pyx_array___getitem__, /*mp_subscript*/\n  __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/\n};\n\nstatic PyBufferProcs __pyx_tp_as_buffer_array = {\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getreadbuffer*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getwritebuffer*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getsegcount*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getcharbuffer*/\n  #endif\n  __pyx_array_getbuffer, /*bf_getbuffer*/\n  0, /*bf_releasebuffer*/\n};\n\nstatic PyTypeObject __pyx_type___pyx_array = {\n  PyVarObject_HEAD_INIT(0, 0)\n  \"roc_cy.array\", /*tp_name*/\n  sizeof(struct __pyx_array_obj), /*tp_basicsize*/\n  0, /*tp_itemsize*/\n  __pyx_tp_dealloc_array, /*tp_dealloc*/\n  #if PY_VERSION_HEX < 0x030800b4\n  0, /*tp_print*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4\n  0, /*tp_vectorcall_offset*/\n  #endif\n  0, /*tp_getattr*/\n  0, /*tp_setattr*/\n  #if PY_MAJOR_VERSION < 3\n  0, /*tp_compare*/\n  #endif\n  #if PY_MAJOR_VERSION >= 3\n  0, /*tp_as_async*/\n  #endif\n  0, /*tp_repr*/\n  0, /*tp_as_number*/\n  &__pyx_tp_as_sequence_array, /*tp_as_sequence*/\n  &__pyx_tp_as_mapping_array, /*tp_as_mapping*/\n  0, /*tp_hash*/\n  0, /*tp_call*/\n  0, /*tp_str*/\n  __pyx_tp_getattro_array, /*tp_getattro*/\n  0, /*tp_setattro*/\n  &__pyx_tp_as_buffer_array, /*tp_as_buffer*/\n  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/\n  0, /*tp_doc*/\n  0, /*tp_traverse*/\n  0, /*tp_clear*/\n  0, /*tp_richcompare*/\n  0, /*tp_weaklistoffset*/\n  0, /*tp_iter*/\n  0, /*tp_iternext*/\n  __pyx_methods_array, /*tp_methods*/\n  0, /*tp_members*/\n  __pyx_getsets_array, /*tp_getset*/\n  0, /*tp_base*/\n  0, /*tp_dict*/\n  0, /*tp_descr_get*/\n  0, /*tp_descr_set*/\n  0, /*tp_dictoffset*/\n  0, /*tp_init*/\n  0, /*tp_alloc*/\n  __pyx_tp_new_array, /*tp_new*/\n  0, /*tp_free*/\n  0, /*tp_is_gc*/\n  0, /*tp_bases*/\n  0, /*tp_mro*/\n  0, /*tp_cache*/\n  0, /*tp_subclasses*/\n  0, /*tp_weaklist*/\n  0, /*tp_del*/\n  0, /*tp_version_tag*/\n  #if PY_VERSION_HEX >= 0x030400a1\n  0, /*tp_finalize*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)\n  0, /*tp_vectorcall*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000\n  0, /*tp_print*/\n  #endif\n  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000\n  0, /*tp_pypy_flags*/\n  #endif\n};\n\nstatic PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {\n  struct __pyx_MemviewEnum_obj *p;\n  PyObject *o;\n  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {\n    o = (*t->tp_alloc)(t, 0);\n  } else {\n    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);\n  }\n  if (unlikely(!o)) return 0;\n  p = ((struct __pyx_MemviewEnum_obj *)o);\n  p->name = Py_None; Py_INCREF(Py_None);\n  return o;\n}\n\nstatic void __pyx_tp_dealloc_Enum(PyObject *o) {\n  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;\n  #if CYTHON_USE_TP_FINALIZE\n  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {\n    if (PyObject_CallFinalizerFromDealloc(o)) return;\n  }\n  #endif\n  PyObject_GC_UnTrack(o);\n  Py_CLEAR(p->name);\n  (*Py_TYPE(o)->tp_free)(o);\n}\n\nstatic int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {\n  int e;\n  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;\n  if (p->name) {\n    e = (*v)(p->name, a); if (e) return e;\n  }\n  return 0;\n}\n\nstatic int __pyx_tp_clear_Enum(PyObject *o) {\n  PyObject* tmp;\n  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;\n  tmp = ((PyObject*)p->name);\n  p->name = Py_None; Py_INCREF(Py_None);\n  Py_XDECREF(tmp);\n  return 0;\n}\n\nstatic PyMethodDef __pyx_methods_Enum[] = {\n  {\"__reduce_cython__\", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0},\n  {\"__setstate_cython__\", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0},\n  {0, 0, 0, 0}\n};\n\nstatic PyTypeObject __pyx_type___pyx_MemviewEnum = {\n  PyVarObject_HEAD_INIT(0, 0)\n  \"roc_cy.Enum\", /*tp_name*/\n  sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/\n  0, /*tp_itemsize*/\n  __pyx_tp_dealloc_Enum, /*tp_dealloc*/\n  #if PY_VERSION_HEX < 0x030800b4\n  0, /*tp_print*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4\n  0, /*tp_vectorcall_offset*/\n  #endif\n  0, /*tp_getattr*/\n  0, /*tp_setattr*/\n  #if PY_MAJOR_VERSION < 3\n  0, /*tp_compare*/\n  #endif\n  #if PY_MAJOR_VERSION >= 3\n  0, /*tp_as_async*/\n  #endif\n  __pyx_MemviewEnum___repr__, /*tp_repr*/\n  0, /*tp_as_number*/\n  0, /*tp_as_sequence*/\n  0, /*tp_as_mapping*/\n  0, /*tp_hash*/\n  0, /*tp_call*/\n  0, /*tp_str*/\n  0, /*tp_getattro*/\n  0, /*tp_setattro*/\n  0, /*tp_as_buffer*/\n  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/\n  0, /*tp_doc*/\n  __pyx_tp_traverse_Enum, /*tp_traverse*/\n  __pyx_tp_clear_Enum, /*tp_clear*/\n  0, /*tp_richcompare*/\n  0, /*tp_weaklistoffset*/\n  0, /*tp_iter*/\n  0, /*tp_iternext*/\n  __pyx_methods_Enum, /*tp_methods*/\n  0, /*tp_members*/\n  0, /*tp_getset*/\n  0, /*tp_base*/\n  0, /*tp_dict*/\n  0, /*tp_descr_get*/\n  0, /*tp_descr_set*/\n  0, /*tp_dictoffset*/\n  __pyx_MemviewEnum___init__, /*tp_init*/\n  0, /*tp_alloc*/\n  __pyx_tp_new_Enum, /*tp_new*/\n  0, /*tp_free*/\n  0, /*tp_is_gc*/\n  0, /*tp_bases*/\n  0, /*tp_mro*/\n  0, /*tp_cache*/\n  0, /*tp_subclasses*/\n  0, /*tp_weaklist*/\n  0, /*tp_del*/\n  0, /*tp_version_tag*/\n  #if PY_VERSION_HEX >= 0x030400a1\n  0, /*tp_finalize*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)\n  0, /*tp_vectorcall*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000\n  0, /*tp_print*/\n  #endif\n  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000\n  0, /*tp_pypy_flags*/\n  #endif\n};\nstatic struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;\n\nstatic PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {\n  struct __pyx_memoryview_obj *p;\n  PyObject *o;\n  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {\n    o = (*t->tp_alloc)(t, 0);\n  } else {\n    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);\n  }\n  if (unlikely(!o)) return 0;\n  p = ((struct __pyx_memoryview_obj *)o);\n  p->__pyx_vtab = __pyx_vtabptr_memoryview;\n  p->obj = Py_None; Py_INCREF(Py_None);\n  p->_size = Py_None; Py_INCREF(Py_None);\n  p->_array_interface = Py_None; Py_INCREF(Py_None);\n  p->view.obj = NULL;\n  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad;\n  return o;\n  bad:\n  Py_DECREF(o); o = 0;\n  return NULL;\n}\n\nstatic void __pyx_tp_dealloc_memoryview(PyObject *o) {\n  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;\n  #if CYTHON_USE_TP_FINALIZE\n  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {\n    if (PyObject_CallFinalizerFromDealloc(o)) return;\n  }\n  #endif\n  PyObject_GC_UnTrack(o);\n  {\n    PyObject *etype, *eval, *etb;\n    PyErr_Fetch(&etype, &eval, &etb);\n    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);\n    __pyx_memoryview___dealloc__(o);\n    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);\n    PyErr_Restore(etype, eval, etb);\n  }\n  Py_CLEAR(p->obj);\n  Py_CLEAR(p->_size);\n  Py_CLEAR(p->_array_interface);\n  (*Py_TYPE(o)->tp_free)(o);\n}\n\nstatic int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {\n  int e;\n  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;\n  if (p->obj) {\n    e = (*v)(p->obj, a); if (e) return e;\n  }\n  if (p->_size) {\n    e = (*v)(p->_size, a); if (e) return e;\n  }\n  if (p->_array_interface) {\n    e = (*v)(p->_array_interface, a); if (e) return e;\n  }\n  if (p->view.obj) {\n    e = (*v)(p->view.obj, a); if (e) return e;\n  }\n  return 0;\n}\n\nstatic int __pyx_tp_clear_memoryview(PyObject *o) {\n  PyObject* tmp;\n  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;\n  tmp = ((PyObject*)p->obj);\n  p->obj = Py_None; Py_INCREF(Py_None);\n  Py_XDECREF(tmp);\n  tmp = ((PyObject*)p->_size);\n  p->_size = Py_None; Py_INCREF(Py_None);\n  Py_XDECREF(tmp);\n  tmp = ((PyObject*)p->_array_interface);\n  p->_array_interface = Py_None; Py_INCREF(Py_None);\n  Py_XDECREF(tmp);\n  Py_CLEAR(p->view.obj);\n  return 0;\n}\nstatic PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {\n  PyObject *r;\n  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;\n  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);\n  Py_DECREF(x);\n  return r;\n}\n\nstatic int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {\n  if (v) {\n    return __pyx_memoryview___setitem__(o, i, v);\n  }\n  else {\n    PyErr_Format(PyExc_NotImplementedError,\n      \"Subscript deletion not supported by %.200s\", Py_TYPE(o)->tp_name);\n    return -1;\n  }\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);\n}\n\nstatic PyMethodDef __pyx_methods_memoryview[] = {\n  {\"is_c_contig\", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0},\n  {\"is_f_contig\", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0},\n  {\"copy\", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0},\n  {\"copy_fortran\", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0},\n  {\"__reduce_cython__\", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0},\n  {\"__setstate_cython__\", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0},\n  {0, 0, 0, 0}\n};\n\nstatic struct PyGetSetDef __pyx_getsets_memoryview[] = {\n  {(char *)\"T\", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},\n  {(char *)\"base\", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},\n  {(char *)\"shape\", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},\n  {(char *)\"strides\", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},\n  {(char *)\"suboffsets\", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},\n  {(char *)\"ndim\", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},\n  {(char *)\"itemsize\", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},\n  {(char *)\"nbytes\", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},\n  {(char *)\"size\", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},\n  {0, 0, 0, 0, 0}\n};\n\nstatic PySequenceMethods __pyx_tp_as_sequence_memoryview = {\n  __pyx_memoryview___len__, /*sq_length*/\n  0, /*sq_concat*/\n  0, /*sq_repeat*/\n  __pyx_sq_item_memoryview, /*sq_item*/\n  0, /*sq_slice*/\n  0, /*sq_ass_item*/\n  0, /*sq_ass_slice*/\n  0, /*sq_contains*/\n  0, /*sq_inplace_concat*/\n  0, /*sq_inplace_repeat*/\n};\n\nstatic PyMappingMethods __pyx_tp_as_mapping_memoryview = {\n  __pyx_memoryview___len__, /*mp_length*/\n  __pyx_memoryview___getitem__, /*mp_subscript*/\n  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/\n};\n\nstatic PyBufferProcs __pyx_tp_as_buffer_memoryview = {\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getreadbuffer*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getwritebuffer*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getsegcount*/\n  #endif\n  #if PY_MAJOR_VERSION < 3\n  0, /*bf_getcharbuffer*/\n  #endif\n  __pyx_memoryview_getbuffer, /*bf_getbuffer*/\n  0, /*bf_releasebuffer*/\n};\n\nstatic PyTypeObject __pyx_type___pyx_memoryview = {\n  PyVarObject_HEAD_INIT(0, 0)\n  \"roc_cy.memoryview\", /*tp_name*/\n  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/\n  0, /*tp_itemsize*/\n  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/\n  #if PY_VERSION_HEX < 0x030800b4\n  0, /*tp_print*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4\n  0, /*tp_vectorcall_offset*/\n  #endif\n  0, /*tp_getattr*/\n  0, /*tp_setattr*/\n  #if PY_MAJOR_VERSION < 3\n  0, /*tp_compare*/\n  #endif\n  #if PY_MAJOR_VERSION >= 3\n  0, /*tp_as_async*/\n  #endif\n  __pyx_memoryview___repr__, /*tp_repr*/\n  0, /*tp_as_number*/\n  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/\n  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/\n  0, /*tp_hash*/\n  0, /*tp_call*/\n  __pyx_memoryview___str__, /*tp_str*/\n  0, /*tp_getattro*/\n  0, /*tp_setattro*/\n  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/\n  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/\n  0, /*tp_doc*/\n  __pyx_tp_traverse_memoryview, /*tp_traverse*/\n  __pyx_tp_clear_memoryview, /*tp_clear*/\n  0, /*tp_richcompare*/\n  0, /*tp_weaklistoffset*/\n  0, /*tp_iter*/\n  0, /*tp_iternext*/\n  __pyx_methods_memoryview, /*tp_methods*/\n  0, /*tp_members*/\n  __pyx_getsets_memoryview, /*tp_getset*/\n  0, /*tp_base*/\n  0, /*tp_dict*/\n  0, /*tp_descr_get*/\n  0, /*tp_descr_set*/\n  0, /*tp_dictoffset*/\n  0, /*tp_init*/\n  0, /*tp_alloc*/\n  __pyx_tp_new_memoryview, /*tp_new*/\n  0, /*tp_free*/\n  0, /*tp_is_gc*/\n  0, /*tp_bases*/\n  0, /*tp_mro*/\n  0, /*tp_cache*/\n  0, /*tp_subclasses*/\n  0, /*tp_weaklist*/\n  0, /*tp_del*/\n  0, /*tp_version_tag*/\n  #if PY_VERSION_HEX >= 0x030400a1\n  0, /*tp_finalize*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)\n  0, /*tp_vectorcall*/\n  #endif\n  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000\n  0, /*tp_print*/\n  #endif\n  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000\n  0, /*tp_pypy_flags*/\n  #endif\n};\nstatic struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;\n\nstatic PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {\n  struct __pyx_memoryviewslice_obj *p;\n  PyObject *o = __pyx_tp_new_memoryview(t, a, k);\n  if (unlikely(!o)) return 0;\n  p = ((struct __pyx_memoryviewslice_obj *)o);\n  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;\n  p->from_object = Py_None; Py_INCREF(Py_None);\n  p->from_slice.memview = NULL;\n  return o;\n}\n\nstatic void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {\n  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;\n  #if CYTHON_USE_TP_FINALIZE\n  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {\n    if (PyObject_CallFinalizerFromDealloc(o)) return;\n  }\n  #endif\n  PyObject_GC_UnTrack(o);\n  {\n    PyObject *etype, *eval, *etb;\n    PyErr_Fetch(&etype, &eval, &etb);\n    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);\n    __pyx_memoryviewslice___dealloc__(o);\n    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);\n    PyErr_Restore(etype, eval, etb);\n  }\n  Py_CLEAR(p->from_object);\n  PyObject_GC_Track(o);\n  __pyx_tp_dealloc_memoryview(o);\n}\n\nstatic int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {\n  int e;\n  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;\n  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;\n  if (p->from_object) {\n    e = (*v)(p->from_object, a); if (e) return e;\n  }\n  return 0;\n}\n\nstatic int __pyx_tp_clear__memoryviewslice(PyObject *o) {\n  PyObject* tmp;\n  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;\n  __pyx_tp_clear_memoryview(o);\n  tmp = ((PyObject*)p->from_object);\n  p->from_object = Py_None; Py_INCREF(Py_None);\n  Py_XDECREF(tmp);\n  __PYX_XDEC_MEMVIEW(&p->from_slice, 1);\n  return 0;\n}\n\nstatic PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) {\n  return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o);\n}\n\nstatic PyMethodDef __pyx_methods__memoryviewslice[] = {\n  {\"__reduce_cython__\", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0},\n  {\"__setstate_cython__\", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0},\n  {0, 0, 0, 0}\n};\n\nstatic struct PyGetSetDef __pyx_getsets__memoryviewslice[] = {\n  {(char *)\"base\", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0},\n  {0, 0, 0, 0, 0}\n};\n\nstatic 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Py_NO_RETURN {\n    va_list vargs;\n    char msg[200];\n#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES)\n    va_start(vargs, fmt);\n#else\n    va_start(vargs);\n#endif\n    vsnprintf(msg, 200, fmt, vargs);\n    va_end(vargs);\n    Py_FatalError(msg);\n}\nstatic CYTHON_INLINE int\n__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,\n                                   PyThread_type_lock lock)\n{\n    int result;\n    PyThread_acquire_lock(lock, 1);\n    result = (*acquisition_count)++;\n    PyThread_release_lock(lock);\n    return result;\n}\nstatic CYTHON_INLINE int\n__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,\n                                   PyThread_type_lock lock)\n{\n    int result;\n    PyThread_acquire_lock(lock, 1);\n    result = (*acquisition_count)--;\n    PyThread_release_lock(lock);\n    return result;\n}\nstatic CYTHON_INLINE void\n__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)\n{\n    int first_time;\n    struct __pyx_memoryview_obj *memview = memslice->memview;\n    if (unlikely(!memview || (PyObject *) memview == Py_None))\n        return;\n    if (unlikely(__pyx_get_slice_count(memview) < 0))\n        __pyx_fatalerror(\"Acquisition count is %d (line %d)\",\n                         __pyx_get_slice_count(memview), lineno);\n    first_time = __pyx_add_acquisition_count(memview) == 0;\n    if (unlikely(first_time)) {\n        if (have_gil) {\n            Py_INCREF((PyObject *) memview);\n        } else {\n            PyGILState_STATE _gilstate = PyGILState_Ensure();\n            Py_INCREF((PyObject *) memview);\n            PyGILState_Release(_gilstate);\n        }\n    }\n}\nstatic CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice,\n                                             int have_gil, int lineno) {\n    int last_time;\n    struct __pyx_memoryview_obj *memview = memslice->memview;\n    if (unlikely(!memview || (PyObject *) memview == Py_None)) {\n        memslice->memview = NULL;\n        return;\n    }\n    if (unlikely(__pyx_get_slice_count(memview) <= 0))\n        __pyx_fatalerror(\"Acquisition count is %d (line %d)\",\n                         __pyx_get_slice_count(memview), lineno);\n    last_time = __pyx_sub_acquisition_count(memview) == 1;\n    memslice->data = NULL;\n    if (unlikely(last_time)) {\n        if (have_gil) {\n            Py_CLEAR(memslice->memview);\n        } else {\n            PyGILState_STATE _gilstate = PyGILState_Ensure();\n            Py_CLEAR(memslice->memview);\n            PyGILState_Release(_gilstate);\n        }\n    } else {\n        memslice->memview = NULL;\n    }\n}\n\n/* PyDictVersioning */\n#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS\nstatic CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {\n    PyObject *dict = Py_TYPE(obj)->tp_dict;\n    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;\n}\nstatic CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {\n    PyObject **dictptr = NULL;\n    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;\n    if (offset) {\n#if CYTHON_COMPILING_IN_CPYTHON\n        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);\n#else\n        dictptr = _PyObject_GetDictPtr(obj);\n#endif\n    }\n    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;\n}\nstatic CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {\n    PyObject *dict = Py_TYPE(obj)->tp_dict;\n    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))\n        return 0;\n    return obj_dict_version == __Pyx_get_object_dict_version(obj);\n}\n#endif\n\n/* GetModuleGlobalName */\n#if CYTHON_USE_DICT_VERSIONS\nstatic PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)\n#else\nstatic CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)\n#endif\n{\n    PyObject *result;\n#if !CYTHON_AVOID_BORROWED_REFS\n#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1\n    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);\n    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)\n    if (likely(result)) {\n        return __Pyx_NewRef(result);\n    } else if (unlikely(PyErr_Occurred())) {\n        return NULL;\n    }\n#else\n    result = PyDict_GetItem(__pyx_d, name);\n    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)\n    if (likely(result)) {\n        return __Pyx_NewRef(result);\n    }\n#endif\n#else\n    result = PyObject_GetItem(__pyx_d, name);\n    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)\n    if (likely(result)) {\n        return __Pyx_NewRef(result);\n    }\n    PyErr_Clear();\n#endif\n    return __Pyx_GetBuiltinName(name);\n}\n\n/* PyObjectCall */\n#if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {\n    PyObject *result;\n    ternaryfunc call = Py_TYPE(func)->tp_call;\n    if (unlikely(!call))\n        return PyObject_Call(func, arg, kw);\n    if (unlikely(Py_EnterRecursiveCall((char*)\" while calling a Python object\")))\n        return NULL;\n    result = (*call)(func, arg, kw);\n    Py_LeaveRecursiveCall();\n    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {\n        PyErr_SetString(\n            PyExc_SystemError,\n            \"NULL result without error in PyObject_Call\");\n    }\n    return result;\n}\n#endif\n\n/* PyCFunctionFastCall */\n#if CYTHON_FAST_PYCCALL\nstatic CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {\n    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;\n    PyCFunction meth = PyCFunction_GET_FUNCTION(func);\n    PyObject *self = PyCFunction_GET_SELF(func);\n    int flags = PyCFunction_GET_FLAGS(func);\n    assert(PyCFunction_Check(func));\n    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)));\n    assert(nargs >= 0);\n    assert(nargs == 0 || args != NULL);\n    /* _PyCFunction_FastCallDict() must not be called with an exception set,\n       because it may clear it (directly or indirectly) and so the\n       caller loses its exception */\n    assert(!PyErr_Occurred());\n    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {\n        return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL);\n    } else {\n        return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs);\n    }\n}\n#endif\n\n/* PyFunctionFastCall */\n#if CYTHON_FAST_PYCALL\nstatic PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,\n                                               PyObject *globals) {\n    PyFrameObject *f;\n    PyThreadState *tstate = __Pyx_PyThreadState_Current;\n    PyObject **fastlocals;\n    Py_ssize_t i;\n    PyObject *result;\n    assert(globals != NULL);\n    /* XXX Perhaps we should create a specialized\n       PyFrame_New() that doesn't take locals, but does\n       take builtins without sanity checking them.\n       */\n    assert(tstate != NULL);\n    f = PyFrame_New(tstate, co, globals, NULL);\n    if (f == NULL) {\n        return NULL;\n    }\n    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);\n    for (i = 0; i < na; i++) {\n        Py_INCREF(*args);\n        fastlocals[i] = *args++;\n    }\n    result = PyEval_EvalFrameEx(f,0);\n    ++tstate->recursion_depth;\n    Py_DECREF(f);\n    --tstate->recursion_depth;\n    return result;\n}\n#if 1 || PY_VERSION_HEX < 0x030600B1\nstatic PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {\n    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);\n    PyObject *globals = PyFunction_GET_GLOBALS(func);\n    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);\n    PyObject *closure;\n#if PY_MAJOR_VERSION >= 3\n    PyObject *kwdefs;\n#endif\n    PyObject *kwtuple, **k;\n    PyObject **d;\n    Py_ssize_t nd;\n    Py_ssize_t nk;\n    PyObject *result;\n    assert(kwargs == NULL || PyDict_Check(kwargs));\n    nk = kwargs ? PyDict_Size(kwargs) : 0;\n    if (Py_EnterRecursiveCall((char*)\" while calling a Python object\")) {\n        return NULL;\n    }\n    if (\n#if PY_MAJOR_VERSION >= 3\n            co->co_kwonlyargcount == 0 &&\n#endif\n            likely(kwargs == NULL || nk == 0) &&\n            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {\n        if (argdefs == NULL && co->co_argcount == nargs) {\n            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);\n            goto done;\n        }\n        else if (nargs == 0 && argdefs != NULL\n                 && co->co_argcount == Py_SIZE(argdefs)) {\n            /* function called with no arguments, but all parameters have\n               a default value: use default values as arguments .*/\n            args = &PyTuple_GET_ITEM(argdefs, 0);\n            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);\n            goto done;\n        }\n    }\n    if (kwargs != NULL) {\n        Py_ssize_t pos, i;\n        kwtuple = PyTuple_New(2 * nk);\n        if (kwtuple == NULL) {\n            result = NULL;\n            goto done;\n        }\n        k = &PyTuple_GET_ITEM(kwtuple, 0);\n        pos = i = 0;\n        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {\n            Py_INCREF(k[i]);\n            Py_INCREF(k[i+1]);\n            i += 2;\n        }\n        nk = i / 2;\n    }\n    else {\n        kwtuple = NULL;\n        k = NULL;\n    }\n    closure = PyFunction_GET_CLOSURE(func);\n#if PY_MAJOR_VERSION >= 3\n    kwdefs = PyFunction_GET_KW_DEFAULTS(func);\n#endif\n    if (argdefs != NULL) {\n        d = &PyTuple_GET_ITEM(argdefs, 0);\n        nd = Py_SIZE(argdefs);\n    }\n    else {\n        d = NULL;\n        nd = 0;\n    }\n#if PY_MAJOR_VERSION >= 3\n    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,\n                               args, (int)nargs,\n                               k, (int)nk,\n                               d, (int)nd, kwdefs, closure);\n#else\n    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,\n                               args, (int)nargs,\n                               k, (int)nk,\n                               d, (int)nd, closure);\n#endif\n    Py_XDECREF(kwtuple);\ndone:\n    Py_LeaveRecursiveCall();\n    return result;\n}\n#endif\n#endif\n\n/* PyObjectCall2Args */\nstatic CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {\n    PyObject *args, *result = NULL;\n    #if CYTHON_FAST_PYCALL\n    if (PyFunction_Check(function)) {\n        PyObject *args[2] = {arg1, arg2};\n        return __Pyx_PyFunction_FastCall(function, args, 2);\n    }\n    #endif\n    #if CYTHON_FAST_PYCCALL\n    if (__Pyx_PyFastCFunction_Check(function)) {\n        PyObject *args[2] = {arg1, arg2};\n        return __Pyx_PyCFunction_FastCall(function, args, 2);\n    }\n    #endif\n    args = PyTuple_New(2);\n    if (unlikely(!args)) goto done;\n    Py_INCREF(arg1);\n    PyTuple_SET_ITEM(args, 0, arg1);\n    Py_INCREF(arg2);\n    PyTuple_SET_ITEM(args, 1, arg2);\n    Py_INCREF(function);\n    result = __Pyx_PyObject_Call(function, args, NULL);\n    Py_DECREF(args);\n    Py_DECREF(function);\ndone:\n    return result;\n}\n\n/* PyObjectCallMethO */\n#if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {\n    PyObject *self, *result;\n    PyCFunction cfunc;\n    cfunc = PyCFunction_GET_FUNCTION(func);\n    self = PyCFunction_GET_SELF(func);\n    if (unlikely(Py_EnterRecursiveCall((char*)\" while calling a Python object\")))\n        return NULL;\n    result = cfunc(self, arg);\n    Py_LeaveRecursiveCall();\n    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {\n        PyErr_SetString(\n            PyExc_SystemError,\n            \"NULL result without error in PyObject_Call\");\n    }\n    return result;\n}\n#endif\n\n/* PyObjectCallOneArg */\n#if CYTHON_COMPILING_IN_CPYTHON\nstatic PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n    PyObject *result;\n    PyObject *args = PyTuple_New(1);\n    if (unlikely(!args)) return NULL;\n    Py_INCREF(arg);\n    PyTuple_SET_ITEM(args, 0, arg);\n    result = __Pyx_PyObject_Call(func, args, NULL);\n    Py_DECREF(args);\n    return result;\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n#if CYTHON_FAST_PYCALL\n    if (PyFunction_Check(func)) {\n        return __Pyx_PyFunction_FastCall(func, &arg, 1);\n    }\n#endif\n    if (likely(PyCFunction_Check(func))) {\n        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {\n            return __Pyx_PyObject_CallMethO(func, arg);\n#if CYTHON_FAST_PYCCALL\n        } else if (__Pyx_PyFastCFunction_Check(func)) {\n            return __Pyx_PyCFunction_FastCall(func, &arg, 1);\n#endif\n        }\n    }\n    return __Pyx__PyObject_CallOneArg(func, arg);\n}\n#else\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n    PyObject *result;\n    PyObject *args = PyTuple_Pack(1, arg);\n    if (unlikely(!args)) return NULL;\n    result = __Pyx_PyObject_Call(func, args, NULL);\n    Py_DECREF(args);\n    return result;\n}\n#endif\n\n/* GetItemInt */\nstatic PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {\n    PyObject *r;\n    if (!j) return NULL;\n    r = PyObject_GetItem(o, j);\n    Py_DECREF(j);\n    return r;\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,\n                                                              CYTHON_NCP_UNUSED int wraparound,\n                                                              CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS\n    Py_ssize_t wrapped_i = i;\n    if (wraparound & unlikely(i < 0)) {\n        wrapped_i += PyList_GET_SIZE(o);\n    }\n    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {\n        PyObject *r = PyList_GET_ITEM(o, wrapped_i);\n        Py_INCREF(r);\n        return r;\n    }\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n#else\n    return PySequence_GetItem(o, i);\n#endif\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,\n                                                              CYTHON_NCP_UNUSED int wraparound,\n                                                              CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS\n    Py_ssize_t wrapped_i = i;\n    if (wraparound & unlikely(i < 0)) {\n        wrapped_i += PyTuple_GET_SIZE(o);\n    }\n    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {\n        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);\n        Py_INCREF(r);\n        return r;\n    }\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n#else\n    return PySequence_GetItem(o, i);\n#endif\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,\n                                                     CYTHON_NCP_UNUSED int wraparound,\n                                                     CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS\n    if (is_list || PyList_CheckExact(o)) {\n        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);\n        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {\n            PyObject *r = PyList_GET_ITEM(o, n);\n            Py_INCREF(r);\n            return r;\n        }\n    }\n    else if (PyTuple_CheckExact(o)) {\n        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);\n        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {\n            PyObject *r = PyTuple_GET_ITEM(o, n);\n            Py_INCREF(r);\n            return r;\n        }\n    } else {\n        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;\n        if (likely(m && m->sq_item)) {\n            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {\n                Py_ssize_t l = m->sq_length(o);\n                if (likely(l >= 0)) {\n                    i += l;\n                } else {\n                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))\n                        return NULL;\n                    PyErr_Clear();\n                }\n            }\n            return m->sq_item(o, i);\n        }\n    }\n#else\n    if (is_list || PySequence_Check(o)) {\n        return PySequence_GetItem(o, i);\n    }\n#endif\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n}\n\n/* ObjectGetItem */\n#if CYTHON_USE_TYPE_SLOTS\nstatic PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {\n    PyObject *runerr;\n    Py_ssize_t key_value;\n    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;\n    if (unlikely(!(m && m->sq_item))) {\n        PyErr_Format(PyExc_TypeError, \"'%.200s' object is not subscriptable\", Py_TYPE(obj)->tp_name);\n        return NULL;\n    }\n    key_value = __Pyx_PyIndex_AsSsize_t(index);\n    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {\n        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);\n    }\n    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {\n        PyErr_Clear();\n        PyErr_Format(PyExc_IndexError, \"cannot fit '%.200s' into an index-sized integer\", Py_TYPE(index)->tp_name);\n    }\n    return NULL;\n}\nstatic PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {\n    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;\n    if (likely(m && m->mp_subscript)) {\n        return m->mp_subscript(obj, key);\n    }\n    return __Pyx_PyObject_GetIndex(obj, key);\n}\n#endif\n\n/* PyIntBinop */\n#if !CYTHON_COMPILING_IN_PYPY\nstatic PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) {\n    (void)inplace;\n    (void)zerodivision_check;\n    #if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_CheckExact(op1))) {\n        const long b = intval;\n        long x;\n        long a = PyInt_AS_LONG(op1);\n            x = (long)((unsigned long)a + b);\n            if (likely((x^a) >= 0 || (x^b) >= 0))\n                return PyInt_FromLong(x);\n            return PyLong_Type.tp_as_number->nb_add(op1, op2);\n    }\n    #endif\n    #if CYTHON_USE_PYLONG_INTERNALS\n    if (likely(PyLong_CheckExact(op1))) {\n        const long b = intval;\n        long a, x;\n#ifdef HAVE_LONG_LONG\n        const PY_LONG_LONG llb = intval;\n        PY_LONG_LONG lla, llx;\n#endif\n        const digit* digits = ((PyLongObject*)op1)->ob_digit;\n        const Py_ssize_t size = Py_SIZE(op1);\n        if (likely(__Pyx_sst_abs(size) <= 1)) {\n            a = likely(size) ? digits[0] : 0;\n            if (size == -1) a = -a;\n        } else {\n            switch (size) {\n                case -2:\n                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {\n                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case 2:\n                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {\n                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case -3:\n                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {\n                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case 3:\n                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {\n                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case -4:\n                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {\n                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case 4:\n                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {\n                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);\n            }\n        }\n                x = a + b;\n            return PyLong_FromLong(x);\n#ifdef HAVE_LONG_LONG\n        long_long:\n                llx = lla + llb;\n            return PyLong_FromLongLong(llx);\n#endif\n        \n        \n    }\n    #endif\n    if (PyFloat_CheckExact(op1)) {\n        const long b = intval;\n        double a = PyFloat_AS_DOUBLE(op1);\n            double result;\n            PyFPE_START_PROTECT(\"add\", return NULL)\n            result = ((double)a) + (double)b;\n            PyFPE_END_PROTECT(result)\n            return PyFloat_FromDouble(result);\n    }\n    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);\n}\n#endif\n\n/* RaiseArgTupleInvalid */\nstatic void __Pyx_RaiseArgtupleInvalid(\n    const char* func_name,\n    int exact,\n    Py_ssize_t num_min,\n    Py_ssize_t num_max,\n    Py_ssize_t num_found)\n{\n    Py_ssize_t num_expected;\n    const char *more_or_less;\n    if (num_found < num_min) {\n        num_expected = num_min;\n        more_or_less = \"at least\";\n    } else {\n        num_expected = num_max;\n        more_or_less = \"at most\";\n    }\n    if (exact) {\n        more_or_less = \"exactly\";\n    }\n    PyErr_Format(PyExc_TypeError,\n                 \"%.200s() takes %.8s %\" CYTHON_FORMAT_SSIZE_T \"d positional argument%.1s (%\" CYTHON_FORMAT_SSIZE_T \"d given)\",\n                 func_name, more_or_less, num_expected,\n                 (num_expected == 1) ? \"\" : \"s\", num_found);\n}\n\n/* RaiseDoubleKeywords */\nstatic void __Pyx_RaiseDoubleKeywordsError(\n    const char* func_name,\n    PyObject* kw_name)\n{\n    PyErr_Format(PyExc_TypeError,\n        #if PY_MAJOR_VERSION >= 3\n        \"%s() got multiple values for keyword argument '%U'\", func_name, kw_name);\n        #else\n        \"%s() got multiple values for keyword argument '%s'\", func_name,\n        PyString_AsString(kw_name));\n        #endif\n}\n\n/* ParseKeywords */\nstatic int __Pyx_ParseOptionalKeywords(\n    PyObject *kwds,\n    PyObject **argnames[],\n    PyObject *kwds2,\n    PyObject *values[],\n    Py_ssize_t num_pos_args,\n    const char* function_name)\n{\n    PyObject *key = 0, *value = 0;\n    Py_ssize_t pos = 0;\n    PyObject*** name;\n    PyObject*** first_kw_arg = argnames + num_pos_args;\n    while (PyDict_Next(kwds, &pos, &key, &value)) {\n        name = first_kw_arg;\n        while (*name && (**name != key)) name++;\n        if (*name) {\n            values[name-argnames] = value;\n            continue;\n        }\n        name = first_kw_arg;\n        #if PY_MAJOR_VERSION < 3\n        if (likely(PyString_Check(key))) {\n            while (*name) {\n                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))\n                        && _PyString_Eq(**name, key)) {\n                    values[name-argnames] = value;\n                    break;\n                }\n                name++;\n            }\n            if (*name) continue;\n            else {\n                PyObject*** argname = argnames;\n                while (argname != first_kw_arg) {\n                    if ((**argname == key) || (\n                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))\n                             && _PyString_Eq(**argname, key))) {\n                        goto arg_passed_twice;\n                    }\n                    argname++;\n                }\n            }\n        } else\n        #endif\n        if (likely(PyUnicode_Check(key))) {\n            while (*name) {\n                int cmp = (**name == key) ? 0 :\n                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3\n                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :\n                #endif\n                    PyUnicode_Compare(**name, key);\n                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;\n                if (cmp == 0) {\n                    values[name-argnames] = value;\n                    break;\n                }\n                name++;\n            }\n            if (*name) continue;\n            else {\n                PyObject*** argname = argnames;\n                while (argname != first_kw_arg) {\n                    int cmp = (**argname == key) ? 0 :\n                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3\n                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :\n                    #endif\n                        PyUnicode_Compare(**argname, key);\n                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;\n                    if (cmp == 0) goto arg_passed_twice;\n                    argname++;\n                }\n            }\n        } else\n            goto invalid_keyword_type;\n        if (kwds2) {\n            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;\n        } else {\n            goto invalid_keyword;\n        }\n    }\n    return 0;\narg_passed_twice:\n    __Pyx_RaiseDoubleKeywordsError(function_name, key);\n    goto bad;\ninvalid_keyword_type:\n    PyErr_Format(PyExc_TypeError,\n        \"%.200s() keywords must be strings\", function_name);\n    goto bad;\ninvalid_keyword:\n    PyErr_Format(PyExc_TypeError,\n    #if PY_MAJOR_VERSION < 3\n        \"%.200s() got an unexpected keyword argument '%.200s'\",\n        function_name, PyString_AsString(key));\n    #else\n        \"%s() got an unexpected keyword argument '%U'\",\n        function_name, key);\n    #endif\nbad:\n    return -1;\n}\n\n/* None */\nstatic CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {\n    PyErr_Format(PyExc_UnboundLocalError, \"local variable '%s' referenced before assignment\", varname);\n}\n\n/* GetTopmostException */\n#if CYTHON_USE_EXC_INFO_STACK\nstatic _PyErr_StackItem *\n__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)\n{\n    _PyErr_StackItem *exc_info = tstate->exc_info;\n    while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&\n           exc_info->previous_item != NULL)\n    {\n        exc_info = exc_info->previous_item;\n    }\n    return exc_info;\n}\n#endif\n\n/* SaveResetException */\n#if CYTHON_FAST_THREAD_STATE\nstatic CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n    #if CYTHON_USE_EXC_INFO_STACK\n    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);\n    *type = exc_info->exc_type;\n    *value = exc_info->exc_value;\n    *tb = exc_info->exc_traceback;\n    #else\n    *type = tstate->exc_type;\n    *value = tstate->exc_value;\n    *tb = tstate->exc_traceback;\n    #endif\n    Py_XINCREF(*type);\n    Py_XINCREF(*value);\n    Py_XINCREF(*tb);\n}\nstatic CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    #if CYTHON_USE_EXC_INFO_STACK\n    _PyErr_StackItem *exc_info = tstate->exc_info;\n    tmp_type = exc_info->exc_type;\n    tmp_value = exc_info->exc_value;\n    tmp_tb = exc_info->exc_traceback;\n    exc_info->exc_type = type;\n    exc_info->exc_value = value;\n    exc_info->exc_traceback = tb;\n    #else\n    tmp_type = tstate->exc_type;\n    tmp_value = tstate->exc_value;\n    tmp_tb = tstate->exc_traceback;\n    tstate->exc_type = type;\n    tstate->exc_value = value;\n    tstate->exc_traceback = tb;\n    #endif\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n}\n#endif\n\n/* PyErrExceptionMatches */\n#if CYTHON_FAST_THREAD_STATE\nstatic int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {\n    Py_ssize_t i, n;\n    n = PyTuple_GET_SIZE(tuple);\n#if PY_MAJOR_VERSION >= 3\n    for (i=0; i<n; i++) {\n        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;\n    }\n#endif\n    for (i=0; i<n; i++) {\n        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;\n    }\n    return 0;\n}\nstatic CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {\n    PyObject *exc_type = tstate->curexc_type;\n    if (exc_type == err) return 1;\n    if (unlikely(!exc_type)) return 0;\n    if (unlikely(PyTuple_Check(err)))\n        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);\n    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);\n}\n#endif\n\n/* GetException */\n#if CYTHON_FAST_THREAD_STATE\nstatic int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)\n#else\nstatic int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)\n#endif\n{\n    PyObject *local_type, *local_value, *local_tb;\n#if CYTHON_FAST_THREAD_STATE\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    local_type = tstate->curexc_type;\n    local_value = tstate->curexc_value;\n    local_tb = tstate->curexc_traceback;\n    tstate->curexc_type = 0;\n    tstate->curexc_value = 0;\n    tstate->curexc_traceback = 0;\n#else\n    PyErr_Fetch(&local_type, &local_value, &local_tb);\n#endif\n    PyErr_NormalizeException(&local_type, &local_value, &local_tb);\n#if CYTHON_FAST_THREAD_STATE\n    if (unlikely(tstate->curexc_type))\n#else\n    if (unlikely(PyErr_Occurred()))\n#endif\n        goto bad;\n    #if PY_MAJOR_VERSION >= 3\n    if (local_tb) {\n        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))\n            goto bad;\n    }\n    #endif\n    Py_XINCREF(local_tb);\n    Py_XINCREF(local_type);\n    Py_XINCREF(local_value);\n    *type = local_type;\n    *value = local_value;\n    *tb = local_tb;\n#if CYTHON_FAST_THREAD_STATE\n    #if CYTHON_USE_EXC_INFO_STACK\n    {\n        _PyErr_StackItem *exc_info = tstate->exc_info;\n        tmp_type = exc_info->exc_type;\n        tmp_value = exc_info->exc_value;\n        tmp_tb = exc_info->exc_traceback;\n        exc_info->exc_type = local_type;\n        exc_info->exc_value = local_value;\n        exc_info->exc_traceback = local_tb;\n    }\n    #else\n    tmp_type = tstate->exc_type;\n    tmp_value = tstate->exc_value;\n    tmp_tb = tstate->exc_traceback;\n    tstate->exc_type = local_type;\n    tstate->exc_value = local_value;\n    tstate->exc_traceback = local_tb;\n    #endif\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n#else\n    PyErr_SetExcInfo(local_type, local_value, local_tb);\n#endif\n    return 0;\nbad:\n    *type = 0;\n    *value = 0;\n    *tb = 0;\n    Py_XDECREF(local_type);\n    Py_XDECREF(local_value);\n    Py_XDECREF(local_tb);\n    return -1;\n}\n\n/* PyErrFetchRestore */\n#if CYTHON_FAST_THREAD_STATE\nstatic CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    tmp_type = tstate->curexc_type;\n    tmp_value = tstate->curexc_value;\n    tmp_tb = tstate->curexc_traceback;\n    tstate->curexc_type = type;\n    tstate->curexc_value = value;\n    tstate->curexc_traceback = tb;\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n}\nstatic CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n    *type = tstate->curexc_type;\n    *value = tstate->curexc_value;\n    *tb = tstate->curexc_traceback;\n    tstate->curexc_type = 0;\n    tstate->curexc_value = 0;\n    tstate->curexc_traceback = 0;\n}\n#endif\n\n/* RaiseException */\n#if PY_MAJOR_VERSION < 3\nstatic void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,\n                        CYTHON_UNUSED PyObject *cause) {\n    __Pyx_PyThreadState_declare\n    Py_XINCREF(type);\n    if (!value || value == Py_None)\n        value = NULL;\n    else\n        Py_INCREF(value);\n    if (!tb || tb == Py_None)\n        tb = NULL;\n    else {\n        Py_INCREF(tb);\n        if (!PyTraceBack_Check(tb)) {\n            PyErr_SetString(PyExc_TypeError,\n                \"raise: arg 3 must be a traceback or None\");\n            goto raise_error;\n        }\n    }\n    if (PyType_Check(type)) {\n#if CYTHON_COMPILING_IN_PYPY\n        if (!value) {\n            Py_INCREF(Py_None);\n            value = Py_None;\n        }\n#endif\n        PyErr_NormalizeException(&type, &value, &tb);\n    } else {\n        if (value) {\n            PyErr_SetString(PyExc_TypeError,\n                \"instance exception may not have a separate value\");\n            goto raise_error;\n        }\n        value = type;\n        type = (PyObject*) Py_TYPE(type);\n        Py_INCREF(type);\n        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {\n            PyErr_SetString(PyExc_TypeError,\n                \"raise: exception class must be a subclass of BaseException\");\n            goto raise_error;\n        }\n    }\n    __Pyx_PyThreadState_assign\n    __Pyx_ErrRestore(type, value, tb);\n    return;\nraise_error:\n    Py_XDECREF(value);\n    Py_XDECREF(type);\n    Py_XDECREF(tb);\n    return;\n}\n#else\nstatic void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {\n    PyObject* owned_instance = NULL;\n    if (tb == Py_None) {\n        tb = 0;\n    } else if (tb && !PyTraceBack_Check(tb)) {\n        PyErr_SetString(PyExc_TypeError,\n            \"raise: arg 3 must be a traceback or None\");\n        goto bad;\n    }\n    if (value == Py_None)\n        value = 0;\n    if (PyExceptionInstance_Check(type)) {\n        if (value) {\n            PyErr_SetString(PyExc_TypeError,\n                \"instance exception may not have a separate value\");\n            goto bad;\n        }\n        value = type;\n        type = (PyObject*) Py_TYPE(value);\n    } else if (PyExceptionClass_Check(type)) {\n        PyObject *instance_class = NULL;\n        if (value && PyExceptionInstance_Check(value)) {\n            instance_class = (PyObject*) Py_TYPE(value);\n            if (instance_class != type) {\n                int is_subclass = PyObject_IsSubclass(instance_class, type);\n                if (!is_subclass) {\n                    instance_class = NULL;\n                } else if (unlikely(is_subclass == -1)) {\n                    goto bad;\n                } else {\n                    type = instance_class;\n                }\n            }\n        }\n        if (!instance_class) {\n            PyObject *args;\n            if (!value)\n                args = PyTuple_New(0);\n            else if (PyTuple_Check(value)) {\n                Py_INCREF(value);\n                args = value;\n            } else\n                args = PyTuple_Pack(1, value);\n            if (!args)\n                goto bad;\n            owned_instance = PyObject_Call(type, args, NULL);\n            Py_DECREF(args);\n            if (!owned_instance)\n                goto bad;\n            value = owned_instance;\n            if (!PyExceptionInstance_Check(value)) {\n                PyErr_Format(PyExc_TypeError,\n                             \"calling %R should have returned an instance of \"\n                             \"BaseException, not %R\",\n                             type, Py_TYPE(value));\n                goto bad;\n            }\n        }\n    } else {\n        PyErr_SetString(PyExc_TypeError,\n            \"raise: exception class must be a subclass of BaseException\");\n        goto bad;\n    }\n    if (cause) {\n        PyObject *fixed_cause;\n        if (cause == Py_None) {\n            fixed_cause = NULL;\n        } else if (PyExceptionClass_Check(cause)) {\n            fixed_cause = PyObject_CallObject(cause, NULL);\n            if (fixed_cause == NULL)\n                goto bad;\n        } else if (PyExceptionInstance_Check(cause)) {\n            fixed_cause = cause;\n            Py_INCREF(fixed_cause);\n        } else {\n            PyErr_SetString(PyExc_TypeError,\n                            \"exception causes must derive from \"\n                            \"BaseException\");\n            goto bad;\n        }\n        PyException_SetCause(value, fixed_cause);\n    }\n    PyErr_SetObject(type, value);\n    if (tb) {\n#if CYTHON_COMPILING_IN_PYPY\n        PyObject *tmp_type, *tmp_value, *tmp_tb;\n        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);\n        Py_INCREF(tb);\n        PyErr_Restore(tmp_type, tmp_value, tb);\n        Py_XDECREF(tmp_tb);\n#else\n        PyThreadState *tstate = __Pyx_PyThreadState_Current;\n        PyObject* tmp_tb = tstate->curexc_traceback;\n        if (tb != tmp_tb) {\n            Py_INCREF(tb);\n            tstate->curexc_traceback = tb;\n            Py_XDECREF(tmp_tb);\n        }\n#endif\n    }\nbad:\n    Py_XDECREF(owned_instance);\n    return;\n}\n#endif\n\n/* ArgTypeTest */\nstatic int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)\n{\n    if (unlikely(!type)) {\n        PyErr_SetString(PyExc_SystemError, \"Missing type object\");\n        return 0;\n    }\n    else if (exact) {\n        #if PY_MAJOR_VERSION == 2\n        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;\n        #endif\n    }\n    else {\n        if (likely(__Pyx_TypeCheck(obj, type))) return 1;\n    }\n    PyErr_Format(PyExc_TypeError,\n        \"Argument '%.200s' has incorrect type (expected %.200s, got %.200s)\",\n        name, type->tp_name, Py_TYPE(obj)->tp_name);\n    return 0;\n}\n\n/* BytesEquals */\nstatic CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {\n#if CYTHON_COMPILING_IN_PYPY\n    return PyObject_RichCompareBool(s1, s2, equals);\n#else\n    if (s1 == s2) {\n        return (equals == Py_EQ);\n    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {\n        const char *ps1, *ps2;\n        Py_ssize_t length = PyBytes_GET_SIZE(s1);\n        if (length != PyBytes_GET_SIZE(s2))\n            return (equals == Py_NE);\n        ps1 = PyBytes_AS_STRING(s1);\n        ps2 = PyBytes_AS_STRING(s2);\n        if (ps1[0] != ps2[0]) {\n            return (equals == Py_NE);\n        } else if (length == 1) {\n            return (equals == Py_EQ);\n        } else {\n            int result;\n#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000)\n            Py_hash_t hash1, hash2;\n            hash1 = ((PyBytesObject*)s1)->ob_shash;\n            hash2 = ((PyBytesObject*)s2)->ob_shash;\n            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {\n                return (equals == Py_NE);\n            }\n#endif\n            result = memcmp(ps1, ps2, (size_t)length);\n            return (equals == Py_EQ) ? (result == 0) : (result != 0);\n        }\n    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {\n        return (equals == Py_NE);\n    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {\n        return (equals == Py_NE);\n    } else {\n        int result;\n        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);\n        if (!py_result)\n            return -1;\n        result = __Pyx_PyObject_IsTrue(py_result);\n        Py_DECREF(py_result);\n        return result;\n    }\n#endif\n}\n\n/* UnicodeEquals */\nstatic CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {\n#if CYTHON_COMPILING_IN_PYPY\n    return PyObject_RichCompareBool(s1, s2, equals);\n#else\n#if PY_MAJOR_VERSION < 3\n    PyObject* owned_ref = NULL;\n#endif\n    int s1_is_unicode, s2_is_unicode;\n    if (s1 == s2) {\n        goto return_eq;\n    }\n    s1_is_unicode = PyUnicode_CheckExact(s1);\n    s2_is_unicode = PyUnicode_CheckExact(s2);\n#if PY_MAJOR_VERSION < 3\n    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {\n        owned_ref = PyUnicode_FromObject(s2);\n        if (unlikely(!owned_ref))\n            return -1;\n        s2 = owned_ref;\n        s2_is_unicode = 1;\n    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {\n        owned_ref = PyUnicode_FromObject(s1);\n        if (unlikely(!owned_ref))\n            return -1;\n        s1 = owned_ref;\n        s1_is_unicode = 1;\n    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {\n        return __Pyx_PyBytes_Equals(s1, s2, equals);\n    }\n#endif\n    if (s1_is_unicode & s2_is_unicode) {\n        Py_ssize_t length;\n        int kind;\n        void *data1, *data2;\n        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))\n            return -1;\n        length = __Pyx_PyUnicode_GET_LENGTH(s1);\n        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {\n            goto return_ne;\n        }\n#if CYTHON_USE_UNICODE_INTERNALS\n        {\n            Py_hash_t hash1, hash2;\n        #if CYTHON_PEP393_ENABLED\n            hash1 = ((PyASCIIObject*)s1)->hash;\n            hash2 = ((PyASCIIObject*)s2)->hash;\n        #else\n            hash1 = ((PyUnicodeObject*)s1)->hash;\n            hash2 = ((PyUnicodeObject*)s2)->hash;\n        #endif\n            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {\n                goto return_ne;\n            }\n        }\n#endif\n        kind = __Pyx_PyUnicode_KIND(s1);\n        if (kind != __Pyx_PyUnicode_KIND(s2)) {\n            goto return_ne;\n        }\n        data1 = __Pyx_PyUnicode_DATA(s1);\n        data2 = __Pyx_PyUnicode_DATA(s2);\n        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {\n            goto return_ne;\n        } else if (length == 1) {\n            goto return_eq;\n        } else {\n            int result = memcmp(data1, data2, (size_t)(length * kind));\n            #if PY_MAJOR_VERSION < 3\n            Py_XDECREF(owned_ref);\n            #endif\n            return (equals == Py_EQ) ? (result == 0) : (result != 0);\n        }\n    } else if ((s1 == Py_None) & s2_is_unicode) {\n        goto return_ne;\n    } else if ((s2 == Py_None) & s1_is_unicode) {\n        goto return_ne;\n    } else {\n        int result;\n        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);\n        #if PY_MAJOR_VERSION < 3\n        Py_XDECREF(owned_ref);\n        #endif\n        if (!py_result)\n            return -1;\n        result = __Pyx_PyObject_IsTrue(py_result);\n        Py_DECREF(py_result);\n        return result;\n    }\nreturn_eq:\n    #if PY_MAJOR_VERSION < 3\n    Py_XDECREF(owned_ref);\n    #endif\n    return (equals == Py_EQ);\nreturn_ne:\n    #if PY_MAJOR_VERSION < 3\n    Py_XDECREF(owned_ref);\n    #endif\n    return (equals == Py_NE);\n#endif\n}\n\n/* GetAttr */\nstatic CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {\n#if CYTHON_USE_TYPE_SLOTS\n#if PY_MAJOR_VERSION >= 3\n    if (likely(PyUnicode_Check(n)))\n#else\n    if (likely(PyString_Check(n)))\n#endif\n        return __Pyx_PyObject_GetAttrStr(o, n);\n#endif\n    return PyObject_GetAttr(o, n);\n}\n\n/* decode_c_string */\nstatic CYTHON_INLINE PyObject* __Pyx_decode_c_string(\n         const char* cstring, Py_ssize_t start, Py_ssize_t stop,\n         const char* encoding, const char* errors,\n         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {\n    Py_ssize_t length;\n    if (unlikely((start < 0) | (stop < 0))) {\n        size_t slen = strlen(cstring);\n        if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) {\n            PyErr_SetString(PyExc_OverflowError,\n                            \"c-string too long to convert to Python\");\n            return NULL;\n        }\n        length = (Py_ssize_t) slen;\n        if (start < 0) {\n            start += length;\n            if (start < 0)\n                start = 0;\n        }\n        if (stop < 0)\n            stop += length;\n    }\n    if (unlikely(stop <= start))\n        return __Pyx_NewRef(__pyx_empty_unicode);\n    length = stop - start;\n    cstring += start;\n    if (decode_func) {\n        return decode_func(cstring, length, errors);\n    } else {\n        return PyUnicode_Decode(cstring, length, encoding, errors);\n    }\n}\n\n/* GetAttr3 */\nstatic PyObject *__Pyx_GetAttr3Default(PyObject *d) {\n    __Pyx_PyThreadState_declare\n    __Pyx_PyThreadState_assign\n    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))\n        return NULL;\n    __Pyx_PyErr_Clear();\n    Py_INCREF(d);\n    return d;\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {\n    PyObject *r = __Pyx_GetAttr(o, n);\n    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);\n}\n\n/* RaiseTooManyValuesToUnpack */\nstatic CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {\n    PyErr_Format(PyExc_ValueError,\n                 \"too many values to unpack (expected %\" CYTHON_FORMAT_SSIZE_T \"d)\", expected);\n}\n\n/* RaiseNeedMoreValuesToUnpack */\nstatic CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {\n    PyErr_Format(PyExc_ValueError,\n                 \"need more than %\" CYTHON_FORMAT_SSIZE_T \"d value%.1s to unpack\",\n                 index, (index == 1) ? \"\" : \"s\");\n}\n\n/* RaiseNoneIterError */\nstatic CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {\n    PyErr_SetString(PyExc_TypeError, \"'NoneType' object is not iterable\");\n}\n\n/* ExtTypeTest */\nstatic CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {\n    if (unlikely(!type)) {\n        PyErr_SetString(PyExc_SystemError, \"Missing type object\");\n        return 0;\n    }\n    if (likely(__Pyx_TypeCheck(obj, type)))\n        return 1;\n    PyErr_Format(PyExc_TypeError, \"Cannot convert %.200s to %.200s\",\n                 Py_TYPE(obj)->tp_name, type->tp_name);\n    return 0;\n}\n\n/* SwapException */\n#if CYTHON_FAST_THREAD_STATE\nstatic CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    #if CYTHON_USE_EXC_INFO_STACK\n    _PyErr_StackItem *exc_info = tstate->exc_info;\n    tmp_type = exc_info->exc_type;\n    tmp_value = exc_info->exc_value;\n    tmp_tb = exc_info->exc_traceback;\n    exc_info->exc_type = *type;\n    exc_info->exc_value = *value;\n    exc_info->exc_traceback = *tb;\n    #else\n    tmp_type = tstate->exc_type;\n    tmp_value = tstate->exc_value;\n    tmp_tb = tstate->exc_traceback;\n    tstate->exc_type = *type;\n    tstate->exc_value = *value;\n    tstate->exc_traceback = *tb;\n    #endif\n    *type = tmp_type;\n    *value = tmp_value;\n    *tb = tmp_tb;\n}\n#else\nstatic CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);\n    PyErr_SetExcInfo(*type, *value, *tb);\n    *type = tmp_type;\n    *value = tmp_value;\n    *tb = tmp_tb;\n}\n#endif\n\n/* Import */\nstatic PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {\n    PyObject *empty_list = 0;\n    PyObject *module = 0;\n    PyObject *global_dict = 0;\n    PyObject *empty_dict = 0;\n    PyObject *list;\n    #if PY_MAJOR_VERSION < 3\n    PyObject *py_import;\n    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);\n    if (!py_import)\n        goto bad;\n    #endif\n    if (from_list)\n        list = from_list;\n    else {\n        empty_list = PyList_New(0);\n        if (!empty_list)\n            goto bad;\n        list = empty_list;\n    }\n    global_dict = PyModule_GetDict(__pyx_m);\n    if (!global_dict)\n        goto bad;\n    empty_dict = PyDict_New();\n    if (!empty_dict)\n        goto bad;\n    {\n        #if PY_MAJOR_VERSION >= 3\n        if (level == -1) {\n            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {\n                module = PyImport_ImportModuleLevelObject(\n                    name, global_dict, empty_dict, list, 1);\n                if (!module) {\n                    if (!PyErr_ExceptionMatches(PyExc_ImportError))\n                        goto bad;\n                    PyErr_Clear();\n                }\n            }\n            level = 0;\n        }\n        #endif\n        if (!module) {\n            #if PY_MAJOR_VERSION < 3\n            PyObject *py_level = PyInt_FromLong(level);\n            if (!py_level)\n                goto bad;\n            module = PyObject_CallFunctionObjArgs(py_import,\n                name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);\n            Py_DECREF(py_level);\n            #else\n            module = PyImport_ImportModuleLevelObject(\n                name, global_dict, empty_dict, list, level);\n            #endif\n        }\n    }\nbad:\n    #if PY_MAJOR_VERSION < 3\n    Py_XDECREF(py_import);\n    #endif\n    Py_XDECREF(empty_list);\n    Py_XDECREF(empty_dict);\n    return module;\n}\n\n/* FastTypeChecks */\n#if CYTHON_COMPILING_IN_CPYTHON\nstatic int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {\n    while (a) {\n        a = a->tp_base;\n        if (a == b)\n            return 1;\n    }\n    return b == &PyBaseObject_Type;\n}\nstatic CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {\n    PyObject *mro;\n    if (a == b) return 1;\n    mro = a->tp_mro;\n    if (likely(mro)) {\n        Py_ssize_t i, n;\n        n = PyTuple_GET_SIZE(mro);\n        for (i = 0; i < n; i++) {\n            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)\n                return 1;\n        }\n        return 0;\n    }\n    return __Pyx_InBases(a, b);\n}\n#if PY_MAJOR_VERSION == 2\nstatic int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {\n    PyObject *exception, *value, *tb;\n    int res;\n    __Pyx_PyThreadState_declare\n    __Pyx_PyThreadState_assign\n    __Pyx_ErrFetch(&exception, &value, &tb);\n    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;\n    if (unlikely(res == -1)) {\n        PyErr_WriteUnraisable(err);\n        res = 0;\n    }\n    if (!res) {\n        res = PyObject_IsSubclass(err, exc_type2);\n        if (unlikely(res == -1)) {\n            PyErr_WriteUnraisable(err);\n            res = 0;\n        }\n    }\n    __Pyx_ErrRestore(exception, value, tb);\n    return res;\n}\n#else\nstatic CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {\n    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;\n    if (!res) {\n        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);\n    }\n    return res;\n}\n#endif\nstatic int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {\n    Py_ssize_t i, n;\n    assert(PyExceptionClass_Check(exc_type));\n    n = PyTuple_GET_SIZE(tuple);\n#if PY_MAJOR_VERSION >= 3\n    for (i=0; i<n; i++) {\n        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;\n    }\n#endif\n    for (i=0; i<n; i++) {\n        PyObject *t = PyTuple_GET_ITEM(tuple, i);\n        #if PY_MAJOR_VERSION < 3\n        if (likely(exc_type == t)) return 1;\n        #endif\n        if (likely(PyExceptionClass_Check(t))) {\n            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;\n        } else {\n        }\n    }\n    return 0;\n}\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {\n    if (likely(err == exc_type)) return 1;\n    if (likely(PyExceptionClass_Check(err))) {\n        if (likely(PyExceptionClass_Check(exc_type))) {\n            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);\n        } else if (likely(PyTuple_Check(exc_type))) {\n            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);\n        } else {\n        }\n    }\n    return PyErr_GivenExceptionMatches(err, exc_type);\n}\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {\n    assert(PyExceptionClass_Check(exc_type1));\n    assert(PyExceptionClass_Check(exc_type2));\n    if (likely(err == exc_type1 || err == exc_type2)) return 1;\n    if (likely(PyExceptionClass_Check(err))) {\n        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);\n    }\n    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));\n}\n#endif\n\n/* ImportFrom */\nstatic PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {\n    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);\n    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {\n        PyErr_Format(PyExc_ImportError,\n        #if PY_MAJOR_VERSION < 3\n            \"cannot import name %.230s\", PyString_AS_STRING(name));\n        #else\n            \"cannot import name %S\", name);\n        #endif\n    }\n    return value;\n}\n\n/* HasAttr */\nstatic CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {\n    PyObject *r;\n    if (unlikely(!__Pyx_PyBaseString_Check(n))) {\n        PyErr_SetString(PyExc_TypeError,\n                        \"hasattr(): attribute name must be string\");\n        return -1;\n    }\n    r = __Pyx_GetAttr(o, n);\n    if (unlikely(!r)) {\n        PyErr_Clear();\n        return 0;\n    } else {\n        Py_DECREF(r);\n        return 1;\n    }\n}\n\n/* PyObject_GenericGetAttrNoDict */\n#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000\nstatic PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {\n    PyErr_Format(PyExc_AttributeError,\n#if PY_MAJOR_VERSION >= 3\n                 \"'%.50s' object has no attribute '%U'\",\n                 tp->tp_name, attr_name);\n#else\n                 \"'%.50s' object has no attribute '%.400s'\",\n                 tp->tp_name, PyString_AS_STRING(attr_name));\n#endif\n    return NULL;\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {\n    PyObject *descr;\n    PyTypeObject *tp = Py_TYPE(obj);\n    if (unlikely(!PyString_Check(attr_name))) {\n        return PyObject_GenericGetAttr(obj, attr_name);\n    }\n    assert(!tp->tp_dictoffset);\n    descr = _PyType_Lookup(tp, attr_name);\n    if (unlikely(!descr)) {\n        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);\n    }\n    Py_INCREF(descr);\n    #if PY_MAJOR_VERSION < 3\n    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))\n    #endif\n    {\n        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;\n        if (unlikely(f)) {\n            PyObject *res = f(descr, obj, (PyObject *)tp);\n            Py_DECREF(descr);\n            return res;\n        }\n    }\n    return descr;\n}\n#endif\n\n/* PyObject_GenericGetAttr */\n#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000\nstatic PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {\n    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {\n        return PyObject_GenericGetAttr(obj, attr_name);\n    }\n    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);\n}\n#endif\n\n/* SetVTable */\nstatic int __Pyx_SetVtable(PyObject *dict, void *vtable) {\n#if PY_VERSION_HEX >= 0x02070000\n    PyObject *ob = PyCapsule_New(vtable, 0, 0);\n#else\n    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);\n#endif\n    if (!ob)\n        goto bad;\n    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)\n        goto bad;\n    Py_DECREF(ob);\n    return 0;\nbad:\n    Py_XDECREF(ob);\n    return -1;\n}\n\n/* PyObjectGetAttrStrNoError */\nstatic void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) {\n    __Pyx_PyThreadState_declare\n    __Pyx_PyThreadState_assign\n    if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))\n        __Pyx_PyErr_Clear();\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) {\n    PyObject *result;\n#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1\n    PyTypeObject* tp = Py_TYPE(obj);\n    if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) {\n        return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1);\n    }\n#endif\n    result = __Pyx_PyObject_GetAttrStr(obj, attr_name);\n    if (unlikely(!result)) {\n        __Pyx_PyObject_GetAttrStr_ClearAttributeError();\n    }\n    return result;\n}\n\n/* SetupReduce */\nstatic int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {\n  int ret;\n  PyObject *name_attr;\n  name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2);\n  if (likely(name_attr)) {\n      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);\n  } else {\n      ret = -1;\n  }\n  if (unlikely(ret < 0)) {\n      PyErr_Clear();\n      ret = 0;\n  }\n  Py_XDECREF(name_attr);\n  return ret;\n}\nstatic int __Pyx_setup_reduce(PyObject* type_obj) {\n    int ret = 0;\n    PyObject *object_reduce = NULL;\n    PyObject *object_getstate = NULL;\n    PyObject *object_reduce_ex = NULL;\n    PyObject *reduce = NULL;\n    PyObject *reduce_ex = NULL;\n    PyObject *reduce_cython = NULL;\n    PyObject *setstate = NULL;\n    PyObject *setstate_cython = NULL;\n    PyObject *getstate = NULL;\n#if CYTHON_USE_PYTYPE_LOOKUP\n    getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate);\n#else\n    getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate);\n    if (!getstate && PyErr_Occurred()) {\n        goto __PYX_BAD;\n    }\n#endif\n    if (getstate) {\n#if CYTHON_USE_PYTYPE_LOOKUP\n        object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate);\n#else\n        object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate);\n        if (!object_getstate && PyErr_Occurred()) {\n            goto __PYX_BAD;\n        }\n#endif\n        if (object_getstate != getstate) {\n            goto __PYX_GOOD;\n        }\n    }\n#if CYTHON_USE_PYTYPE_LOOKUP\n    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;\n#else\n    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;\n#endif\n    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;\n    if (reduce_ex == object_reduce_ex) {\n#if CYTHON_USE_PYTYPE_LOOKUP\n        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;\n#else\n        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;\n#endif\n        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD;\n        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {\n            reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);\n            if (likely(reduce_cython)) {\n                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;\n                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;\n            } else if (reduce == object_reduce || PyErr_Occurred()) {\n                goto __PYX_BAD;\n            }\n            setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);\n            if (!setstate) PyErr_Clear();\n            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {\n                setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);\n                if (likely(setstate_cython)) {\n                    ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;\n                    ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;\n                } else if (!setstate || PyErr_Occurred()) {\n                    goto __PYX_BAD;\n                }\n            }\n            PyType_Modified((PyTypeObject*)type_obj);\n        }\n    }\n    goto __PYX_GOOD;\n__PYX_BAD:\n    if (!PyErr_Occurred())\n        PyErr_Format(PyExc_RuntimeError, \"Unable to initialize pickling for %s\", ((PyTypeObject*)type_obj)->tp_name);\n    ret = -1;\n__PYX_GOOD:\n#if !CYTHON_USE_PYTYPE_LOOKUP\n    Py_XDECREF(object_reduce);\n    Py_XDECREF(object_reduce_ex);\n    Py_XDECREF(object_getstate);\n    Py_XDECREF(getstate);\n#endif\n    Py_XDECREF(reduce);\n    Py_XDECREF(reduce_ex);\n    Py_XDECREF(reduce_cython);\n    Py_XDECREF(setstate);\n    Py_XDECREF(setstate_cython);\n    return ret;\n}\n\n/* TypeImport */\n#ifndef __PYX_HAVE_RT_ImportType\n#define __PYX_HAVE_RT_ImportType\nstatic PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name,\n    size_t size, enum __Pyx_ImportType_CheckSize check_size)\n{\n    PyObject *result = 0;\n    char warning[200];\n    Py_ssize_t basicsize;\n#ifdef Py_LIMITED_API\n    PyObject *py_basicsize;\n#endif\n    result = PyObject_GetAttrString(module, class_name);\n    if (!result)\n        goto bad;\n    if (!PyType_Check(result)) {\n        PyErr_Format(PyExc_TypeError,\n            \"%.200s.%.200s is not a type object\",\n            module_name, class_name);\n        goto bad;\n    }\n#ifndef Py_LIMITED_API\n    basicsize = ((PyTypeObject *)result)->tp_basicsize;\n#else\n    py_basicsize = PyObject_GetAttrString(result, \"__basicsize__\");\n    if (!py_basicsize)\n        goto bad;\n    basicsize = PyLong_AsSsize_t(py_basicsize);\n    Py_DECREF(py_basicsize);\n    py_basicsize = 0;\n    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())\n        goto bad;\n#endif\n    if ((size_t)basicsize < size) {\n        PyErr_Format(PyExc_ValueError,\n            \"%.200s.%.200s size changed, may indicate binary incompatibility. \"\n            \"Expected %zd from C header, got %zd from PyObject\",\n            module_name, class_name, size, basicsize);\n        goto bad;\n    }\n    if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) {\n        PyErr_Format(PyExc_ValueError,\n            \"%.200s.%.200s size changed, may indicate binary incompatibility. \"\n            \"Expected %zd from C header, got %zd from PyObject\",\n            module_name, class_name, size, basicsize);\n        goto bad;\n    }\n    else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) {\n        PyOS_snprintf(warning, sizeof(warning),\n            \"%s.%s size changed, may indicate binary incompatibility. \"\n            \"Expected %zd from C header, got %zd from PyObject\",\n            module_name, class_name, size, basicsize);\n        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;\n    }\n    return (PyTypeObject *)result;\nbad:\n    Py_XDECREF(result);\n    return NULL;\n}\n#endif\n\n/* CLineInTraceback */\n#ifndef CYTHON_CLINE_IN_TRACEBACK\nstatic int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) {\n    PyObject *use_cline;\n    PyObject *ptype, *pvalue, *ptraceback;\n#if CYTHON_COMPILING_IN_CPYTHON\n    PyObject **cython_runtime_dict;\n#endif\n    if (unlikely(!__pyx_cython_runtime)) {\n        return c_line;\n    }\n    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);\n#if CYTHON_COMPILING_IN_CPYTHON\n    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);\n    if (likely(cython_runtime_dict)) {\n        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(\n            use_cline, *cython_runtime_dict,\n            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))\n    } else\n#endif\n    {\n      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);\n      if (use_cline_obj) {\n        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;\n        Py_DECREF(use_cline_obj);\n      } else {\n        PyErr_Clear();\n        use_cline = NULL;\n      }\n    }\n    if (!use_cline) {\n        c_line = 0;\n        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);\n    }\n    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {\n        c_line = 0;\n    }\n    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);\n    return c_line;\n}\n#endif\n\n/* CodeObjectCache */\nstatic int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {\n    int start = 0, mid = 0, end = count - 1;\n    if (end >= 0 && code_line > entries[end].code_line) {\n        return count;\n    }\n    while (start < end) {\n        mid = start + (end - start) / 2;\n        if (code_line < entries[mid].code_line) {\n            end = mid;\n        } else if (code_line > entries[mid].code_line) {\n             start = mid + 1;\n        } else {\n            return mid;\n        }\n    }\n    if (code_line <= entries[mid].code_line) {\n        return mid;\n    } else {\n        return mid + 1;\n    }\n}\nstatic PyCodeObject *__pyx_find_code_object(int code_line) {\n    PyCodeObject* code_object;\n    int pos;\n    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {\n        return NULL;\n    }\n    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);\n    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {\n        return NULL;\n    }\n    code_object = __pyx_code_cache.entries[pos].code_object;\n    Py_INCREF(code_object);\n    return code_object;\n}\nstatic void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {\n    int pos, i;\n    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;\n    if (unlikely(!code_line)) {\n        return;\n    }\n    if (unlikely(!entries)) {\n        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));\n        if (likely(entries)) {\n            __pyx_code_cache.entries = entries;\n            __pyx_code_cache.max_count = 64;\n            __pyx_code_cache.count = 1;\n            entries[0].code_line = code_line;\n            entries[0].code_object = code_object;\n            Py_INCREF(code_object);\n        }\n        return;\n    }\n    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);\n    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {\n        PyCodeObject* tmp = entries[pos].code_object;\n        entries[pos].code_object = code_object;\n        Py_DECREF(tmp);\n        return;\n    }\n    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {\n        int new_max = __pyx_code_cache.max_count + 64;\n        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(\n            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));\n        if (unlikely(!entries)) {\n            return;\n        }\n        __pyx_code_cache.entries = entries;\n        __pyx_code_cache.max_count = new_max;\n    }\n    for (i=__pyx_code_cache.count; i>pos; i--) {\n        entries[i] = entries[i-1];\n    }\n    entries[pos].code_line = code_line;\n    entries[pos].code_object = code_object;\n    __pyx_code_cache.count++;\n    Py_INCREF(code_object);\n}\n\n/* AddTraceback */\n#include \"compile.h\"\n#include \"frameobject.h\"\n#include \"traceback.h\"\n#if PY_VERSION_HEX >= 0x030b00a6\n  #ifndef Py_BUILD_CORE\n    #define Py_BUILD_CORE 1\n  #endif\n  #include \"internal/pycore_frame.h\"\n#endif\nstatic PyCodeObject* __Pyx_CreateCodeObjectForTraceback(\n            const char *funcname, int c_line,\n            int py_line, const char *filename) {\n    PyCodeObject *py_code = NULL;\n    PyObject *py_funcname = NULL;\n    #if PY_MAJOR_VERSION < 3\n    PyObject *py_srcfile = NULL;\n    py_srcfile = PyString_FromString(filename);\n    if (!py_srcfile) goto bad;\n    #endif\n    if (c_line) {\n        #if PY_MAJOR_VERSION < 3\n        py_funcname = PyString_FromFormat( \"%s (%s:%d)\", funcname, __pyx_cfilenm, c_line);\n        if (!py_funcname) goto bad;\n        #else\n        py_funcname = PyUnicode_FromFormat( \"%s (%s:%d)\", funcname, __pyx_cfilenm, c_line);\n        if (!py_funcname) goto bad;\n        funcname = PyUnicode_AsUTF8(py_funcname);\n        if (!funcname) goto bad;\n        #endif\n    }\n    else {\n        #if PY_MAJOR_VERSION < 3\n        py_funcname = PyString_FromString(funcname);\n        if (!py_funcname) goto bad;\n        #endif\n    }\n    #if PY_MAJOR_VERSION < 3\n    py_code = __Pyx_PyCode_New(\n        0,\n        0,\n        0,\n        0,\n        0,\n        __pyx_empty_bytes, /*PyObject *code,*/\n        __pyx_empty_tuple, /*PyObject *consts,*/\n        __pyx_empty_tuple, /*PyObject *names,*/\n        __pyx_empty_tuple, /*PyObject *varnames,*/\n        __pyx_empty_tuple, /*PyObject *freevars,*/\n        __pyx_empty_tuple, /*PyObject *cellvars,*/\n        py_srcfile,   /*PyObject *filename,*/\n        py_funcname,  /*PyObject *name,*/\n        py_line,\n        __pyx_empty_bytes  /*PyObject *lnotab*/\n    );\n    Py_DECREF(py_srcfile);\n    #else\n    py_code = PyCode_NewEmpty(filename, funcname, py_line);\n    #endif\n    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline\n    return py_code;\nbad:\n    Py_XDECREF(py_funcname);\n    #if PY_MAJOR_VERSION < 3\n    Py_XDECREF(py_srcfile);\n    #endif\n    return NULL;\n}\nstatic void __Pyx_AddTraceback(const char *funcname, int c_line,\n                               int py_line, const char *filename) {\n    PyCodeObject *py_code = 0;\n    PyFrameObject *py_frame = 0;\n    PyThreadState *tstate = __Pyx_PyThreadState_Current;\n    PyObject *ptype, *pvalue, *ptraceback;\n    if (c_line) {\n        c_line = __Pyx_CLineForTraceback(tstate, c_line);\n    }\n    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);\n    if (!py_code) {\n        __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);\n        py_code = __Pyx_CreateCodeObjectForTraceback(\n            funcname, c_line, py_line, filename);\n        if (!py_code) {\n            /* If the code object creation fails, then we should clear the\n               fetched exception references and propagate the new exception */\n            Py_XDECREF(ptype);\n            Py_XDECREF(pvalue);\n            Py_XDECREF(ptraceback);\n            goto bad;\n        }\n        __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);\n        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);\n    }\n    py_frame = PyFrame_New(\n        tstate,            /*PyThreadState *tstate,*/\n        py_code,           /*PyCodeObject *code,*/\n        __pyx_d,    /*PyObject *globals,*/\n        0                  /*PyObject *locals*/\n    );\n    if (!py_frame) goto bad;\n    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);\n    PyTraceBack_Here(py_frame);\nbad:\n    Py_XDECREF(py_code);\n    Py_XDECREF(py_frame);\n}\n\n#if PY_MAJOR_VERSION < 3\nstatic int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {\n    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);\n        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);\n        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);\n    PyErr_Format(PyExc_TypeError, \"'%.200s' does not have the buffer interface\", Py_TYPE(obj)->tp_name);\n    return -1;\n}\nstatic void __Pyx_ReleaseBuffer(Py_buffer *view) {\n    PyObject *obj = view->obj;\n    if (!obj) return;\n    if (PyObject_CheckBuffer(obj)) {\n        PyBuffer_Release(view);\n        return;\n    }\n    if ((0)) {}\n    view->obj = NULL;\n    Py_DECREF(obj);\n}\n#endif\n\n\n/* MemviewSliceIsContig */\nstatic int\n__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)\n{\n    int i, index, step, start;\n    Py_ssize_t itemsize = mvs.memview->view.itemsize;\n    if (order == 'F') {\n        step = 1;\n        start = 0;\n    } else {\n        step = -1;\n        start = ndim - 1;\n    }\n    for (i = 0; i < ndim; i++) {\n        index = start + step * i;\n        if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)\n            return 0;\n        itemsize *= mvs.shape[index];\n    }\n    return 1;\n}\n\n/* OverlappingSlices */\nstatic void\n__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,\n                               void **out_start, void **out_end,\n                               int ndim, size_t itemsize)\n{\n    char *start, *end;\n    int i;\n    start = end = slice->data;\n    for (i = 0; i < ndim; i++) {\n        Py_ssize_t stride = slice->strides[i];\n        Py_ssize_t extent = slice->shape[i];\n        if (extent == 0) {\n            *out_start = *out_end = start;\n            return;\n        } else {\n            if (stride > 0)\n                end += stride * (extent - 1);\n            else\n                start += stride * (extent - 1);\n        }\n    }\n    *out_start = start;\n    *out_end = end + itemsize;\n}\nstatic int\n__pyx_slices_overlap(__Pyx_memviewslice *slice1,\n                     __Pyx_memviewslice *slice2,\n                     int ndim, size_t itemsize)\n{\n    void *start1, *end1, *start2, *end2;\n    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);\n    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);\n    return (start1 < end2) && (start2 < end1);\n}\n\n/* Capsule */\nstatic CYTHON_INLINE PyObject *\n__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)\n{\n    PyObject *cobj;\n#if PY_VERSION_HEX >= 0x02070000\n    cobj = PyCapsule_New(p, sig, NULL);\n#else\n    cobj = PyCObject_FromVoidPtr(p, NULL);\n#endif\n    return cobj;\n}\n\n/* IsLittleEndian */\nstatic CYTHON_INLINE int __Pyx_Is_Little_Endian(void)\n{\n  union {\n    uint32_t u32;\n    uint8_t u8[4];\n  } S;\n  S.u32 = 0x01020304;\n  return S.u8[0] == 4;\n}\n\n/* BufferFormatCheck */\nstatic void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,\n                              __Pyx_BufFmt_StackElem* stack,\n                              __Pyx_TypeInfo* type) {\n  stack[0].field = &ctx->root;\n  stack[0].parent_offset = 0;\n  ctx->root.type = type;\n  ctx->root.name = \"buffer dtype\";\n  ctx->root.offset = 0;\n  ctx->head = stack;\n  ctx->head->field = &ctx->root;\n  ctx->fmt_offset = 0;\n  ctx->head->parent_offset = 0;\n  ctx->new_packmode = '@';\n  ctx->enc_packmode = '@';\n  ctx->new_count = 1;\n  ctx->enc_count = 0;\n  ctx->enc_type = 0;\n  ctx->is_complex = 0;\n  ctx->is_valid_array = 0;\n  ctx->struct_alignment = 0;\n  while (type->typegroup == 'S') {\n    ++ctx->head;\n    ctx->head->field = type->fields;\n    ctx->head->parent_offset = 0;\n    type = type->fields->type;\n  }\n}\nstatic int __Pyx_BufFmt_ParseNumber(const char** ts) {\n    int count;\n    const char* t = *ts;\n    if (*t < '0' || *t > '9') {\n      return -1;\n    } else {\n        count = *t++ - '0';\n        while (*t >= '0' && *t <= '9') {\n            count *= 10;\n            count += *t++ - '0';\n        }\n    }\n    *ts = t;\n    return count;\n}\nstatic int __Pyx_BufFmt_ExpectNumber(const char **ts) {\n    int number = __Pyx_BufFmt_ParseNumber(ts);\n    if (number == -1)\n        PyErr_Format(PyExc_ValueError,\\\n                     \"Does not understand character buffer dtype format string ('%c')\", **ts);\n    return number;\n}\nstatic void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {\n  PyErr_Format(PyExc_ValueError,\n               \"Unexpected format string character: '%c'\", ch);\n}\nstatic const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {\n  switch (ch) {\n    case '?': return \"'bool'\";\n    case 'c': return \"'char'\";\n    case 'b': return \"'signed char'\";\n    case 'B': return \"'unsigned char'\";\n    case 'h': return \"'short'\";\n    case 'H': return \"'unsigned short'\";\n    case 'i': return \"'int'\";\n    case 'I': return \"'unsigned int'\";\n    case 'l': return \"'long'\";\n    case 'L': return \"'unsigned long'\";\n    case 'q': return \"'long long'\";\n    case 'Q': return \"'unsigned long long'\";\n    case 'f': return (is_complex ? \"'complex float'\" : \"'float'\");\n    case 'd': return (is_complex ? \"'complex double'\" : \"'double'\");\n    case 'g': return (is_complex ? \"'complex long double'\" : \"'long double'\");\n    case 'T': return \"a struct\";\n    case 'O': return \"Python object\";\n    case 'P': return \"a pointer\";\n    case 's': case 'p': return \"a string\";\n    case 0: return \"end\";\n    default: return \"unparseable format string\";\n  }\n}\nstatic size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return 2;\n    case 'i': case 'I': case 'l': case 'L': return 4;\n    case 'q': case 'Q': return 8;\n    case 'f': return (is_complex ? 8 : 4);\n    case 'd': return (is_complex ? 16 : 8);\n    case 'g': {\n      PyErr_SetString(PyExc_ValueError, \"Python does not define a standard format string size for long double ('g')..\");\n      return 0;\n    }\n    case 'O': case 'P': return sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\nstatic size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(short);\n    case 'i': case 'I': return sizeof(int);\n    case 'l': case 'L': return sizeof(long);\n    #ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(PY_LONG_LONG);\n    #endif\n    case 'f': return sizeof(float) * (is_complex ? 2 : 1);\n    case 'd': return sizeof(double) * (is_complex ? 2 : 1);\n    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);\n    case 'O': case 'P': return sizeof(void*);\n    default: {\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n  }\n}\ntypedef struct { char c; short x; } __Pyx_st_short;\ntypedef struct { char c; int x; } __Pyx_st_int;\ntypedef struct { char c; long x; } __Pyx_st_long;\ntypedef struct { char c; float x; } __Pyx_st_float;\ntypedef struct { char c; double x; } __Pyx_st_double;\ntypedef struct { char c; long double x; } __Pyx_st_longdouble;\ntypedef struct { char c; void *x; } __Pyx_st_void_p;\n#ifdef HAVE_LONG_LONG\ntypedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;\n#endif\nstatic size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);\n    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);\n    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);\n#ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);\n#endif\n    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);\n    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);\n    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);\n    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\n/* These are for computing the padding at the end of the struct to align\n   on the first member of the struct. This will probably the same as above,\n   but we don't have any guarantees.\n */\ntypedef struct { short x; char c; } __Pyx_pad_short;\ntypedef struct { int x; char c; } __Pyx_pad_int;\ntypedef struct { long x; char c; } __Pyx_pad_long;\ntypedef struct { float x; char c; } __Pyx_pad_float;\ntypedef struct { double x; char c; } __Pyx_pad_double;\ntypedef struct { long double x; char c; } __Pyx_pad_longdouble;\ntypedef struct { void *x; char c; } __Pyx_pad_void_p;\n#ifdef HAVE_LONG_LONG\ntypedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;\n#endif\nstatic size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);\n    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);\n    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);\n#ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);\n#endif\n    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);\n    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);\n    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);\n    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\nstatic char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {\n  switch (ch) {\n    case 'c':\n        return 'H';\n    case 'b': case 'h': case 'i':\n    case 'l': case 'q': case 's': case 'p':\n        return 'I';\n    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':\n        return 'U';\n    case 'f': case 'd': case 'g':\n        return (is_complex ? 'C' : 'R');\n    case 'O':\n        return 'O';\n    case 'P':\n        return 'P';\n    default: {\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n  }\n}\nstatic void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {\n  if (ctx->head == NULL || ctx->head->field == &ctx->root) {\n    const char* expected;\n    const char* quote;\n    if (ctx->head == NULL) {\n      expected = \"end\";\n      quote = \"\";\n    } else {\n      expected = ctx->head->field->type->name;\n      quote = \"'\";\n    }\n    PyErr_Format(PyExc_ValueError,\n                 \"Buffer dtype mismatch, expected %s%s%s but got %s\",\n                 quote, expected, quote,\n                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));\n  } else {\n    __Pyx_StructField* field = ctx->head->field;\n    __Pyx_StructField* parent = (ctx->head - 1)->field;\n    PyErr_Format(PyExc_ValueError,\n                 \"Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'\",\n                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),\n                 parent->type->name, field->name);\n  }\n}\nstatic int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {\n  char group;\n  size_t size, offset, arraysize = 1;\n  if (ctx->enc_type == 0) return 0;\n  if (ctx->head->field->type->arraysize[0]) {\n    int i, ndim = 0;\n    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {\n        ctx->is_valid_array = ctx->head->field->type->ndim == 1;\n        ndim = 1;\n        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {\n            PyErr_Format(PyExc_ValueError,\n                         \"Expected a dimension of size %zu, got %zu\",\n                         ctx->head->field->type->arraysize[0], ctx->enc_count);\n            return -1;\n        }\n    }\n    if (!ctx->is_valid_array) {\n      PyErr_Format(PyExc_ValueError, \"Expected %d dimensions, got %d\",\n                   ctx->head->field->type->ndim, ndim);\n      return -1;\n    }\n    for (i = 0; i < ctx->head->field->type->ndim; i++) {\n      arraysize *= ctx->head->field->type->arraysize[i];\n    }\n    ctx->is_valid_array = 0;\n    ctx->enc_count = 1;\n  }\n  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);\n  do {\n    __Pyx_StructField* field = ctx->head->field;\n    __Pyx_TypeInfo* type = field->type;\n    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {\n      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);\n    } else {\n      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);\n    }\n    if (ctx->enc_packmode == '@') {\n      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);\n      size_t align_mod_offset;\n      if (align_at == 0) return -1;\n      align_mod_offset = ctx->fmt_offset % align_at;\n      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;\n      if (ctx->struct_alignment == 0)\n          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,\n                                                                 ctx->is_complex);\n    }\n    if (type->size != size || type->typegroup != group) {\n      if (type->typegroup == 'C' && type->fields != NULL) {\n        size_t parent_offset = ctx->head->parent_offset + field->offset;\n        ++ctx->head;\n        ctx->head->field = type->fields;\n        ctx->head->parent_offset = parent_offset;\n        continue;\n      }\n      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {\n      } else {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return -1;\n      }\n    }\n    offset = ctx->head->parent_offset + field->offset;\n    if (ctx->fmt_offset != offset) {\n      PyErr_Format(PyExc_ValueError,\n                   \"Buffer dtype mismatch; next field is at offset %\" CYTHON_FORMAT_SSIZE_T \"d but %\" CYTHON_FORMAT_SSIZE_T \"d expected\",\n                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);\n      return -1;\n    }\n    ctx->fmt_offset += size;\n    if (arraysize)\n      ctx->fmt_offset += (arraysize - 1) * size;\n    --ctx->enc_count;\n    while (1) {\n      if (field == &ctx->root) {\n        ctx->head = NULL;\n        if (ctx->enc_count != 0) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return -1;\n        }\n        break;\n      }\n      ctx->head->field = ++field;\n      if (field->type == NULL) {\n        --ctx->head;\n        field = ctx->head->field;\n        continue;\n      } else if (field->type->typegroup == 'S') {\n        size_t parent_offset = ctx->head->parent_offset + field->offset;\n        if (field->type->fields->type == NULL) continue;\n        field = field->type->fields;\n        ++ctx->head;\n        ctx->head->field = field;\n        ctx->head->parent_offset = parent_offset;\n        break;\n      } else {\n        break;\n      }\n    }\n  } while (ctx->enc_count);\n  ctx->enc_type = 0;\n  ctx->is_complex = 0;\n  return 0;\n}\nstatic PyObject *\n__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)\n{\n    const char *ts = *tsp;\n    int i = 0, number, ndim;\n    ++ts;\n    if (ctx->new_count != 1) {\n        PyErr_SetString(PyExc_ValueError,\n                        \"Cannot handle repeated arrays in format string\");\n        return NULL;\n    }\n    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n    ndim = ctx->head->field->type->ndim;\n    while (*ts && *ts != ')') {\n        switch (*ts) {\n            case ' ': case '\\f': case '\\r': case '\\n': case '\\t': case '\\v':  continue;\n            default:  break;\n        }\n        number = __Pyx_BufFmt_ExpectNumber(&ts);\n        if (number == -1) return NULL;\n        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])\n            return PyErr_Format(PyExc_ValueError,\n                        \"Expected a dimension of size %zu, got %d\",\n                        ctx->head->field->type->arraysize[i], number);\n        if (*ts != ',' && *ts != ')')\n            return PyErr_Format(PyExc_ValueError,\n                                \"Expected a comma in format string, got '%c'\", *ts);\n        if (*ts == ',') ts++;\n        i++;\n    }\n    if (i != ndim)\n        return PyErr_Format(PyExc_ValueError, \"Expected %d dimension(s), got %d\",\n                            ctx->head->field->type->ndim, i);\n    if (!*ts) {\n        PyErr_SetString(PyExc_ValueError,\n                        \"Unexpected end of format string, expected ')'\");\n        return NULL;\n    }\n    ctx->is_valid_array = 1;\n    ctx->new_count = 1;\n    *tsp = ++ts;\n    return Py_None;\n}\nstatic const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {\n  int got_Z = 0;\n  while (1) {\n    switch(*ts) {\n      case 0:\n        if (ctx->enc_type != 0 && ctx->head == NULL) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return NULL;\n        }\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        if (ctx->head != NULL) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return NULL;\n        }\n        return ts;\n      case ' ':\n      case '\\r':\n      case '\\n':\n        ++ts;\n        break;\n      case '<':\n        if (!__Pyx_Is_Little_Endian()) {\n          PyErr_SetString(PyExc_ValueError, \"Little-endian buffer not supported on big-endian compiler\");\n          return NULL;\n        }\n        ctx->new_packmode = '=';\n        ++ts;\n        break;\n      case '>':\n      case '!':\n        if (__Pyx_Is_Little_Endian()) {\n          PyErr_SetString(PyExc_ValueError, \"Big-endian buffer not supported on little-endian compiler\");\n          return NULL;\n        }\n        ctx->new_packmode = '=';\n        ++ts;\n        break;\n      case '=':\n      case '@':\n      case '^':\n        ctx->new_packmode = *ts++;\n        break;\n      case 'T':\n        {\n          const char* ts_after_sub;\n          size_t i, struct_count = ctx->new_count;\n          size_t struct_alignment = ctx->struct_alignment;\n          ctx->new_count = 1;\n          ++ts;\n          if (*ts != '{') {\n            PyErr_SetString(PyExc_ValueError, \"Buffer acquisition: Expected '{' after 'T'\");\n            return NULL;\n          }\n          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n          ctx->enc_type = 0;\n          ctx->enc_count = 0;\n          ctx->struct_alignment = 0;\n          ++ts;\n          ts_after_sub = ts;\n          for (i = 0; i != struct_count; ++i) {\n            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);\n            if (!ts_after_sub) return NULL;\n          }\n          ts = ts_after_sub;\n          if (struct_alignment) ctx->struct_alignment = struct_alignment;\n        }\n        break;\n      case '}':\n        {\n          size_t alignment = ctx->struct_alignment;\n          ++ts;\n          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n          ctx->enc_type = 0;\n          if (alignment && ctx->fmt_offset % alignment) {\n            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);\n          }\n        }\n        return ts;\n      case 'x':\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        ctx->fmt_offset += ctx->new_count;\n        ctx->new_count = 1;\n        ctx->enc_count = 0;\n        ctx->enc_type = 0;\n        ctx->enc_packmode = ctx->new_packmode;\n        ++ts;\n        break;\n      case 'Z':\n        got_Z = 1;\n        ++ts;\n        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {\n          __Pyx_BufFmt_RaiseUnexpectedChar('Z');\n          return NULL;\n        }\n        CYTHON_FALLTHROUGH;\n      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':\n      case 'l': case 'L': case 'q': case 'Q':\n      case 'f': case 'd': case 'g':\n      case 'O': case 'p':\n        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&\n            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {\n          ctx->enc_count += ctx->new_count;\n          ctx->new_count = 1;\n          got_Z = 0;\n          ++ts;\n          break;\n        }\n        CYTHON_FALLTHROUGH;\n      case 's':\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        ctx->enc_count = ctx->new_count;\n        ctx->enc_packmode = ctx->new_packmode;\n        ctx->enc_type = *ts;\n        ctx->is_complex = got_Z;\n        ++ts;\n        ctx->new_count = 1;\n        got_Z = 0;\n        break;\n      case ':':\n        ++ts;\n        while(*ts != ':') ++ts;\n        ++ts;\n        break;\n      case '(':\n        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;\n        break;\n      default:\n        {\n          int number = __Pyx_BufFmt_ExpectNumber(&ts);\n          if (number == -1) return NULL;\n          ctx->new_count = (size_t)number;\n        }\n    }\n  }\n}\n\n/* TypeInfoCompare */\n  static int\n__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)\n{\n    int i;\n    if (!a || !b)\n        return 0;\n    if (a == b)\n        return 1;\n    if (a->size != b->size || a->typegroup != b->typegroup ||\n            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {\n        if (a->typegroup == 'H' || b->typegroup == 'H') {\n            return a->size == b->size;\n        } else {\n            return 0;\n        }\n    }\n    if (a->ndim) {\n        for (i = 0; i < a->ndim; i++)\n            if (a->arraysize[i] != b->arraysize[i])\n                return 0;\n    }\n    if (a->typegroup == 'S') {\n        if (a->flags != b->flags)\n            return 0;\n        if (a->fields || b->fields) {\n            if (!(a->fields && b->fields))\n                return 0;\n            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {\n                __Pyx_StructField *field_a = a->fields + i;\n                __Pyx_StructField *field_b = b->fields + i;\n                if (field_a->offset != field_b->offset ||\n                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))\n                    return 0;\n            }\n            return !a->fields[i].type && !b->fields[i].type;\n        }\n    }\n    return 1;\n}\n\n/* MemviewSliceValidateAndInit */\n  static int\n__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)\n{\n    if (buf->shape[dim] <= 1)\n        return 1;\n    if (buf->strides) {\n        if (spec & __Pyx_MEMVIEW_CONTIG) {\n            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {\n                if (unlikely(buf->strides[dim] != sizeof(void *))) {\n                    PyErr_Format(PyExc_ValueError,\n                                 \"Buffer is not indirectly contiguous \"\n                                 \"in dimension %d.\", dim);\n                    goto fail;\n                }\n            } else if (unlikely(buf->strides[dim] != buf->itemsize)) {\n                PyErr_SetString(PyExc_ValueError,\n                                \"Buffer and memoryview are not contiguous \"\n                                \"in the same dimension.\");\n                goto fail;\n            }\n        }\n        if (spec & __Pyx_MEMVIEW_FOLLOW) {\n            Py_ssize_t stride = buf->strides[dim];\n            if (stride < 0)\n                stride = -stride;\n            if (unlikely(stride < buf->itemsize)) {\n                PyErr_SetString(PyExc_ValueError,\n                                \"Buffer and memoryview are not contiguous \"\n                                \"in the same dimension.\");\n                goto fail;\n            }\n        }\n    } else {\n        if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {\n            PyErr_Format(PyExc_ValueError,\n                         \"C-contiguous buffer is not contiguous in \"\n                         \"dimension %d\", dim);\n            goto fail;\n        } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {\n            PyErr_Format(PyExc_ValueError,\n                         \"C-contiguous buffer is not indirect in \"\n                         \"dimension %d\", dim);\n            goto fail;\n        } else if (unlikely(buf->suboffsets)) {\n            PyErr_SetString(PyExc_ValueError,\n                            \"Buffer exposes suboffsets but no strides\");\n            goto fail;\n        }\n    }\n    return 1;\nfail:\n    return 0;\n}\nstatic int\n__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec)\n{\n    if (spec & __Pyx_MEMVIEW_DIRECT) {\n        if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {\n            PyErr_Format(PyExc_ValueError,\n                         \"Buffer not compatible with direct access \"\n                         \"in dimension %d.\", dim);\n            goto fail;\n        }\n    }\n    if (spec & __Pyx_MEMVIEW_PTR) {\n        if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {\n            PyErr_Format(PyExc_ValueError,\n                         \"Buffer is not indirectly accessible \"\n                         \"in dimension %d.\", dim);\n            goto fail;\n        }\n    }\n    return 1;\nfail:\n    return 0;\n}\nstatic int\n__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)\n{\n    int i;\n    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {\n        Py_ssize_t stride = 1;\n        for (i = 0; i < ndim; i++) {\n            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {\n                PyErr_SetString(PyExc_ValueError,\n                    \"Buffer not fortran contiguous.\");\n                goto fail;\n            }\n            stride = stride * buf->shape[i];\n        }\n    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {\n        Py_ssize_t stride = 1;\n        for (i = ndim - 1; i >- 1; i--) {\n            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {\n                PyErr_SetString(PyExc_ValueError,\n                    \"Buffer not C contiguous.\");\n                goto fail;\n            }\n            stride = stride * buf->shape[i];\n        }\n    }\n    return 1;\nfail:\n    return 0;\n}\nstatic int __Pyx_ValidateAndInit_memviewslice(\n                int *axes_specs,\n                int c_or_f_flag,\n                int buf_flags,\n                int ndim,\n                __Pyx_TypeInfo *dtype,\n                __Pyx_BufFmt_StackElem stack[],\n                __Pyx_memviewslice *memviewslice,\n                PyObject *original_obj)\n{\n    struct __pyx_memoryview_obj *memview, *new_memview;\n    __Pyx_RefNannyDeclarations\n    Py_buffer *buf;\n    int i, spec = 0, retval = -1;\n    __Pyx_BufFmt_Context ctx;\n    int from_memoryview = __pyx_memoryview_check(original_obj);\n    __Pyx_RefNannySetupContext(\"ValidateAndInit_memviewslice\", 0);\n    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)\n                                                            original_obj)->typeinfo)) {\n        memview = (struct __pyx_memoryview_obj *) original_obj;\n        new_memview = NULL;\n    } else {\n        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(\n                                            original_obj, buf_flags, 0, dtype);\n        new_memview = memview;\n        if (unlikely(!memview))\n            goto fail;\n    }\n    buf = &memview->view;\n    if (unlikely(buf->ndim != ndim)) {\n        PyErr_Format(PyExc_ValueError,\n                \"Buffer has wrong number of dimensions (expected %d, got %d)\",\n                ndim, buf->ndim);\n        goto fail;\n    }\n    if (new_memview) {\n        __Pyx_BufFmt_Init(&ctx, stack, dtype);\n        if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;\n    }\n    if (unlikely((unsigned) buf->itemsize != dtype->size)) {\n        PyErr_Format(PyExc_ValueError,\n                     \"Item size of buffer (%\" CYTHON_FORMAT_SSIZE_T \"u byte%s) \"\n                     \"does not match size of '%s' (%\" CYTHON_FORMAT_SSIZE_T \"u byte%s)\",\n                     buf->itemsize,\n                     (buf->itemsize > 1) ? \"s\" : \"\",\n                     dtype->name,\n                     dtype->size,\n                     (dtype->size > 1) ? \"s\" : \"\");\n        goto fail;\n    }\n    if (buf->len > 0) {\n        for (i = 0; i < ndim; i++) {\n            spec = axes_specs[i];\n            if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))\n                goto fail;\n            if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))\n                goto fail;\n        }\n        if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))\n            goto fail;\n    }\n    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,\n                                         new_memview != NULL) == -1)) {\n        goto fail;\n    }\n    retval = 0;\n    goto no_fail;\nfail:\n    Py_XDECREF(new_memview);\n    retval = -1;\nno_fail:\n    __Pyx_RefNannyFinishContext();\n    return retval;\n}\n\n/* ObjectToMemviewSlice */\n  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_float(PyObject *obj, int writable_flag) {\n    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };\n    __Pyx_BufFmt_StackElem stack[1];\n    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };\n    int retcode;\n    if (obj == Py_None) {\n        result.memview = (struct __pyx_memoryview_obj *) Py_None;\n        return result;\n    }\n    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,\n                                                 PyBUF_RECORDS_RO | writable_flag, 2,\n                                                 &__Pyx_TypeInfo_float, stack,\n                                                 &result, obj);\n    if (unlikely(retcode == -1))\n        goto __pyx_fail;\n    return result;\n__pyx_fail:\n    result.memview = NULL;\n    result.data = NULL;\n    return result;\n}\n\n/* ObjectToMemviewSlice */\n  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *obj, int writable_flag) {\n    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };\n    __Pyx_BufFmt_StackElem stack[1];\n    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };\n    int retcode;\n    if (obj == Py_None) {\n        result.memview = (struct __pyx_memoryview_obj *) Py_None;\n        return result;\n    }\n    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,\n                                                 PyBUF_RECORDS_RO | writable_flag, 1,\n                                                 &__Pyx_TypeInfo_long, stack,\n                                                 &result, obj);\n    if (unlikely(retcode == -1))\n        goto __pyx_fail;\n    return result;\n__pyx_fail:\n    result.memview = NULL;\n    result.data = NULL;\n    return result;\n}\n\n/* Declarations */\n  #if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      return ::std::complex< float >(x, y);\n    }\n  #else\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      return x + y*(__pyx_t_float_complex)_Complex_I;\n    }\n  #endif\n#else\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      __pyx_t_float_complex z;\n      z.real = x;\n      z.imag = y;\n      return z;\n    }\n#endif\n\n/* Arithmetic */\n  #if CYTHON_CCOMPLEX\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n       return (a.real == b.real) && (a.imag == b.imag);\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real + b.real;\n        z.imag = a.imag + b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real - b.real;\n        z.imag = a.imag - b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real * b.real - a.imag * b.imag;\n        z.imag = a.real * b.imag + a.imag * b.real;\n        return z;\n    }\n    #if 1\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else if (fabsf(b.real) >= fabsf(b.imag)) {\n            if (b.real == 0 && b.imag == 0) {\n                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);\n            } else {\n                float r = b.imag / b.real;\n                float s = (float)(1.0) / (b.real + b.imag * r);\n                return __pyx_t_float_complex_from_parts(\n                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);\n            }\n        } else {\n            float r = b.real / b.imag;\n            float s = (float)(1.0) / (b.imag + b.real * r);\n            return __pyx_t_float_complex_from_parts(\n                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);\n        }\n    }\n    #else\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else {\n            float denom = b.real * b.real + b.imag * b.imag;\n            return __pyx_t_float_complex_from_parts(\n                (a.real * b.real + a.imag * b.imag) / denom,\n                (a.imag * b.real - a.real * b.imag) / denom);\n        }\n    }\n    #endif\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {\n        __pyx_t_float_complex z;\n        z.real = -a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {\n       return (a.real == 0) && (a.imag == 0);\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {\n        __pyx_t_float_complex z;\n        z.real =  a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    #if 1\n        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {\n          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)\n            return sqrtf(z.real*z.real + z.imag*z.imag);\n          #else\n            return hypotf(z.real, z.imag);\n          #endif\n        }\n        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n            __pyx_t_float_complex z;\n            float r, lnr, theta, z_r, z_theta;\n            if (b.imag == 0 && b.real == (int)b.real) {\n                if (b.real < 0) {\n                    float denom = a.real * a.real + a.imag * a.imag;\n                    a.real = a.real / denom;\n                    a.imag = -a.imag / denom;\n                    b.real = -b.real;\n                }\n                switch ((int)b.real) {\n                    case 0:\n                        z.real = 1;\n                        z.imag = 0;\n                        return z;\n                    case 1:\n                        return a;\n                    case 2:\n                        return __Pyx_c_prod_float(a, a);\n                    case 3:\n                        z = __Pyx_c_prod_float(a, a);\n                        return __Pyx_c_prod_float(z, a);\n                    case 4:\n                        z = __Pyx_c_prod_float(a, a);\n                        return __Pyx_c_prod_float(z, z);\n                }\n            }\n            if (a.imag == 0) {\n                if (a.real == 0) {\n                    return a;\n                } else if (b.imag == 0) {\n                    z.real = powf(a.real, b.real);\n                    z.imag = 0;\n                    return z;\n                } else if (a.real > 0) {\n                    r = a.real;\n                    theta = 0;\n                } else {\n                    r = -a.real;\n                    theta = atan2f(0.0, -1.0);\n                }\n            } else {\n                r = __Pyx_c_abs_float(a);\n                theta = atan2f(a.imag, a.real);\n            }\n            lnr = logf(r);\n            z_r = expf(lnr * b.real - theta * b.imag);\n            z_theta = theta * b.real + lnr * b.imag;\n            z.real = z_r * cosf(z_theta);\n            z.imag = z_r * sinf(z_theta);\n            return z;\n        }\n    #endif\n#endif\n\n/* Declarations */\n  #if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      return ::std::complex< double >(x, y);\n    }\n  #else\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      return x + y*(__pyx_t_double_complex)_Complex_I;\n    }\n  #endif\n#else\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      __pyx_t_double_complex z;\n      z.real = x;\n      z.imag = y;\n      return z;\n    }\n#endif\n\n/* Arithmetic */\n  #if CYTHON_CCOMPLEX\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n       return (a.real == b.real) && (a.imag == b.imag);\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real + b.real;\n        z.imag = a.imag + b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real - b.real;\n        z.imag = a.imag - b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real * b.real - a.imag * b.imag;\n        z.imag = a.real * b.imag + a.imag * b.real;\n        return z;\n    }\n    #if 1\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else if (fabs(b.real) >= fabs(b.imag)) {\n            if (b.real == 0 && b.imag == 0) {\n                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);\n            } else {\n                double r = b.imag / b.real;\n                double s = (double)(1.0) / (b.real + b.imag * r);\n                return __pyx_t_double_complex_from_parts(\n                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);\n            }\n        } else {\n            double r = b.real / b.imag;\n            double s = (double)(1.0) / (b.imag + b.real * r);\n            return __pyx_t_double_complex_from_parts(\n                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);\n        }\n    }\n    #else\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else {\n            double denom = b.real * b.real + b.imag * b.imag;\n            return __pyx_t_double_complex_from_parts(\n                (a.real * b.real + a.imag * b.imag) / denom,\n                (a.imag * b.real - a.real * b.imag) / denom);\n        }\n    }\n    #endif\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {\n        __pyx_t_double_complex z;\n        z.real = -a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {\n       return (a.real == 0) && (a.imag == 0);\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {\n        __pyx_t_double_complex z;\n        z.real =  a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    #if 1\n        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {\n          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)\n            return sqrt(z.real*z.real + z.imag*z.imag);\n          #else\n            return hypot(z.real, z.imag);\n          #endif\n        }\n        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n            __pyx_t_double_complex z;\n            double r, lnr, theta, z_r, z_theta;\n            if (b.imag == 0 && b.real == (int)b.real) {\n                if (b.real < 0) {\n                    double denom = a.real * a.real + a.imag * a.imag;\n                    a.real = a.real / denom;\n                    a.imag = -a.imag / denom;\n                    b.real = -b.real;\n                }\n                switch ((int)b.real) {\n                    case 0:\n                        z.real = 1;\n                        z.imag = 0;\n                        return z;\n                    case 1:\n                        return a;\n                    case 2:\n                        return __Pyx_c_prod_double(a, a);\n                    case 3:\n                        z = __Pyx_c_prod_double(a, a);\n                        return __Pyx_c_prod_double(z, a);\n                    case 4:\n                        z = __Pyx_c_prod_double(a, a);\n                        return __Pyx_c_prod_double(z, z);\n                }\n            }\n            if (a.imag == 0) {\n                if (a.real == 0) {\n                    return a;\n                } else if (b.imag == 0) {\n                    z.real = pow(a.real, b.real);\n                    z.imag = 0;\n                    return z;\n                } else if (a.real > 0) {\n                    r = a.real;\n                    theta = 0;\n                } else {\n                    r = -a.real;\n                    theta = atan2(0.0, -1.0);\n                }\n            } else {\n                r = __Pyx_c_abs_double(a);\n                theta = atan2(a.imag, a.real);\n            }\n            lnr = log(r);\n            z_r = exp(lnr * b.real - theta * b.imag);\n            z_theta = theta * b.real + lnr * b.imag;\n            z.real = z_r * cos(z_theta);\n            z.imag = z_r * sin(z_theta);\n            return z;\n        }\n    #endif\n#endif\n\n/* MemviewDtypeToObject */\n  static CYTHON_INLINE PyObject *__pyx_memview_get_float(const char *itemp) {\n    return (PyObject *) PyFloat_FromDouble(*(float *) itemp);\n}\nstatic CYTHON_INLINE int __pyx_memview_set_float(const char *itemp, PyObject *obj) {\n    float value = __pyx_PyFloat_AsFloat(obj);\n    if ((value == (float)-1) && PyErr_Occurred())\n        return 0;\n    *(float *) itemp = value;\n    return 1;\n}\n\n/* CIntFromPyVerify */\n  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\\\n    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)\n#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\\\n    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)\n#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\\\n    {\\\n        func_type value = func_value;\\\n        if (sizeof(target_type) < sizeof(func_type)) {\\\n            if (unlikely(value != (func_type) (target_type) value)) {\\\n                func_type zero = 0;\\\n                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\\\n                    return (target_type) -1;\\\n                if (is_unsigned && unlikely(value < zero))\\\n                    goto raise_neg_overflow;\\\n                else\\\n                    goto raise_overflow;\\\n            }\\\n        }\\\n        return (target_type) value;\\\n    }\n\n/* MemviewDtypeToObject */\n  static CYTHON_INLINE PyObject *__pyx_memview_get_long(const char *itemp) {\n    return (PyObject *) __Pyx_PyInt_From_long(*(long *) itemp);\n}\nstatic CYTHON_INLINE int __pyx_memview_set_long(const char *itemp, PyObject *obj) {\n    long value = __Pyx_PyInt_As_long(obj);\n    if ((value == (long)-1) && PyErr_Occurred())\n        return 0;\n    *(long *) itemp = value;\n    return 1;\n}\n\n/* ObjectToMemviewSlice */\n  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_long(PyObject *obj, int writable_flag) {\n    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };\n    __Pyx_BufFmt_StackElem stack[1];\n    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };\n    int retcode;\n    if (obj == Py_None) {\n        result.memview = (struct __pyx_memoryview_obj *) Py_None;\n        return result;\n    }\n    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,\n                                                 PyBUF_RECORDS_RO | writable_flag, 2,\n                                                 &__Pyx_TypeInfo_long, stack,\n                                                 &result, obj);\n    if (unlikely(retcode == -1))\n        goto __pyx_fail;\n    return result;\n__pyx_fail:\n    result.memview = NULL;\n    result.data = NULL;\n    return result;\n}\n\n/* ObjectToMemviewSlice */\n  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_float(PyObject *obj, int writable_flag) {\n    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };\n    __Pyx_BufFmt_StackElem stack[1];\n    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };\n    int retcode;\n    if (obj == Py_None) {\n        result.memview = (struct __pyx_memoryview_obj *) Py_None;\n        return result;\n    }\n    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,\n                                                 PyBUF_RECORDS_RO | writable_flag, 1,\n                                                 &__Pyx_TypeInfo_float, stack,\n                                                 &result, obj);\n    if (unlikely(retcode == -1))\n        goto __pyx_fail;\n    return result;\n__pyx_fail:\n    result.memview = NULL;\n    result.data = NULL;\n    return result;\n}\n\n/* MemviewSliceCopyTemplate */\n  static __Pyx_memviewslice\n__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,\n                                 const char *mode, int ndim,\n                                 size_t sizeof_dtype, int contig_flag,\n                                 int dtype_is_object)\n{\n    __Pyx_RefNannyDeclarations\n    int i;\n    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };\n    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;\n    Py_buffer *buf = &from_memview->view;\n    PyObject *shape_tuple = NULL;\n    PyObject *temp_int = NULL;\n    struct __pyx_array_obj *array_obj = NULL;\n    struct __pyx_memoryview_obj *memview_obj = NULL;\n    __Pyx_RefNannySetupContext(\"__pyx_memoryview_copy_new_contig\", 0);\n    for (i = 0; i < ndim; i++) {\n        if (unlikely(from_mvs->suboffsets[i] >= 0)) {\n            PyErr_Format(PyExc_ValueError, \"Cannot copy memoryview slice with \"\n                                           \"indirect dimensions (axis %d)\", i);\n            goto fail;\n        }\n    }\n    shape_tuple = PyTuple_New(ndim);\n    if (unlikely(!shape_tuple)) {\n        goto fail;\n    }\n    __Pyx_GOTREF(shape_tuple);\n    for(i = 0; i < ndim; i++) {\n        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);\n        if(unlikely(!temp_int)) {\n            goto fail;\n        } else {\n            PyTuple_SET_ITEM(shape_tuple, i, temp_int);\n            temp_int = NULL;\n        }\n    }\n    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);\n    if (unlikely(!array_obj)) {\n        goto fail;\n    }\n    __Pyx_GOTREF(array_obj);\n    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(\n                                    (PyObject *) array_obj, contig_flag,\n                                    dtype_is_object,\n                                    from_mvs->memview->typeinfo);\n    if (unlikely(!memview_obj))\n        goto fail;\n    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))\n        goto fail;\n    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,\n                                                dtype_is_object) < 0))\n        goto fail;\n    goto no_fail;\nfail:\n    __Pyx_XDECREF(new_mvs.memview);\n    new_mvs.memview = NULL;\n    new_mvs.data = NULL;\nno_fail:\n    __Pyx_XDECREF(shape_tuple);\n    __Pyx_XDECREF(temp_int);\n    __Pyx_XDECREF(array_obj);\n    __Pyx_RefNannyFinishContext();\n    return new_mvs;\n}\n\n/* CIntToPy */\n  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wconversion\"\n#endif\n    const long neg_one = (long) -1, const_zero = (long) 0;\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic pop\n#endif\n    const int is_unsigned = neg_one > const_zero;\n    if (is_unsigned) {\n        if (sizeof(long) < sizeof(long)) {\n            return PyInt_FromLong((long) value);\n        } else if (sizeof(long) <= sizeof(unsigned long)) {\n            return PyLong_FromUnsignedLong((unsigned long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {\n            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);\n#endif\n        }\n    } else {\n        if (sizeof(long) <= sizeof(long)) {\n            return PyInt_FromLong((long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {\n            return PyLong_FromLongLong((PY_LONG_LONG) value);\n#endif\n        }\n    }\n    {\n        int one = 1; int little = (int)*(unsigned char *)&one;\n        unsigned char *bytes = (unsigned char *)&value;\n        return _PyLong_FromByteArray(bytes, sizeof(long),\n                                     little, !is_unsigned);\n    }\n}\n\n/* CIntFromPy */\n  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wconversion\"\n#endif\n    const long neg_one = (long) -1, const_zero = (long) 0;\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic pop\n#endif\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(long) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (long) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (long) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {\n                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {\n                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {\n                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (long) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(long) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (long) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(long) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            long val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (long) -1;\n        }\n    } else {\n        long val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (long) -1;\n        val = __Pyx_PyInt_As_long(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to long\");\n    return (long) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to long\");\n    return (long) -1;\n}\n\n/* CIntFromPy */\n  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wconversion\"\n#endif\n    const int neg_one = (int) -1, const_zero = (int) 0;\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic pop\n#endif\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(int) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (int) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (int) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {\n                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {\n                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {\n                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (int) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(int) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (int) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {\n                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(int) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            int val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (int) -1;\n        }\n    } else {\n        int val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (int) -1;\n        val = __Pyx_PyInt_As_int(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to int\");\n    return (int) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to int\");\n    return (int) -1;\n}\n\n/* CIntToPy */\n  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wconversion\"\n#endif\n    const int neg_one = (int) -1, const_zero = (int) 0;\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic pop\n#endif\n    const int is_unsigned = neg_one > const_zero;\n    if (is_unsigned) {\n        if (sizeof(int) < sizeof(long)) {\n            return PyInt_FromLong((long) value);\n        } else if (sizeof(int) <= sizeof(unsigned long)) {\n            return PyLong_FromUnsignedLong((unsigned long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {\n            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);\n#endif\n        }\n    } else {\n        if (sizeof(int) <= sizeof(long)) {\n            return PyInt_FromLong((long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {\n            return PyLong_FromLongLong((PY_LONG_LONG) value);\n#endif\n        }\n    }\n    {\n        int one = 1; int little = (int)*(unsigned char *)&one;\n        unsigned char *bytes = (unsigned char *)&value;\n        return _PyLong_FromByteArray(bytes, sizeof(int),\n                                     little, !is_unsigned);\n    }\n}\n\n/* CIntFromPy */\n  static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wconversion\"\n#endif\n    const char neg_one = (char) -1, const_zero = (char) 0;\n#ifdef __Pyx_HAS_GCC_DIAGNOSTIC\n#pragma GCC diagnostic pop\n#endif\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(char) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (char) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (char) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) {\n                            return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) {\n                            return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) {\n                            return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (char) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(char) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (char) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(char,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {\n                            return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {\n                            return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {\n                            return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {\n                            return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {\n                            return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {\n                            return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(char) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            char val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (char) -1;\n        }\n    } else {\n        char val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (char) -1;\n        val = __Pyx_PyInt_As_char(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to char\");\n    return (char) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to char\");\n    return (char) -1;\n}\n\n/* CheckBinaryVersion */\n  static int __Pyx_check_binary_version(void) {\n    char ctversion[5];\n    int same=1, i, found_dot;\n    const char* rt_from_call = Py_GetVersion();\n    PyOS_snprintf(ctversion, 5, \"%d.%d\", PY_MAJOR_VERSION, PY_MINOR_VERSION);\n    found_dot = 0;\n    for (i = 0; i < 4; i++) {\n        if (!ctversion[i]) {\n            same = (rt_from_call[i] < '0' || rt_from_call[i] > '9');\n            break;\n        }\n        if (rt_from_call[i] != ctversion[i]) {\n            same = 0;\n            break;\n        }\n    }\n    if (!same) {\n        char rtversion[5] = {'\\0'};\n        char message[200];\n        for (i=0; i<4; ++i) {\n            if (rt_from_call[i] == '.') {\n                if (found_dot) break;\n                found_dot = 1;\n            } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') {\n                break;\n            }\n            rtversion[i] = rt_from_call[i];\n        }\n        PyOS_snprintf(message, sizeof(message),\n                      \"compiletime version %s of module '%.100s' \"\n                      \"does not match runtime version %s\",\n                      ctversion, __Pyx_MODULE_NAME, rtversion);\n        return PyErr_WarnEx(NULL, message, 1);\n    }\n    return 0;\n}\n\n/* InitStrings */\n  static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {\n    while (t->p) {\n        #if PY_MAJOR_VERSION < 3\n        if (t->is_unicode) {\n            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);\n        } else if (t->intern) {\n            *t->p = PyString_InternFromString(t->s);\n        } else {\n            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);\n        }\n        #else\n        if (t->is_unicode | t->is_str) {\n            if (t->intern) {\n                *t->p = PyUnicode_InternFromString(t->s);\n            } else if (t->encoding) {\n                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);\n            } else {\n                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);\n            }\n        } else {\n            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);\n        }\n        #endif\n        if (!*t->p)\n            return -1;\n        if (PyObject_Hash(*t->p) == -1)\n            return -1;\n        ++t;\n    }\n    return 0;\n}\n\nstatic CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {\n    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));\n}\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {\n    Py_ssize_t ignore;\n    return __Pyx_PyObject_AsStringAndSize(o, &ignore);\n}\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\n#if !CYTHON_PEP393_ENABLED\nstatic const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n    char* defenc_c;\n    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);\n    if (!defenc) return NULL;\n    defenc_c = PyBytes_AS_STRING(defenc);\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n    {\n        char* end = defenc_c + PyBytes_GET_SIZE(defenc);\n        char* c;\n        for (c = defenc_c; c < end; c++) {\n            if ((unsigned char) (*c) >= 128) {\n                PyUnicode_AsASCIIString(o);\n                return NULL;\n            }\n        }\n    }\n#endif\n    *length = PyBytes_GET_SIZE(defenc);\n    return defenc_c;\n}\n#else\nstatic CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n    if (likely(PyUnicode_IS_ASCII(o))) {\n        *length = PyUnicode_GET_LENGTH(o);\n        return PyUnicode_AsUTF8(o);\n    } else {\n        PyUnicode_AsASCIIString(o);\n        return NULL;\n    }\n#else\n    return PyUnicode_AsUTF8AndSize(o, length);\n#endif\n}\n#endif\n#endif\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\n    if (\n#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n            __Pyx_sys_getdefaultencoding_not_ascii &&\n#endif\n            PyUnicode_Check(o)) {\n        return __Pyx_PyUnicode_AsStringAndSize(o, length);\n    } else\n#endif\n#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))\n    if (PyByteArray_Check(o)) {\n        *length = PyByteArray_GET_SIZE(o);\n        return PyByteArray_AS_STRING(o);\n    } else\n#endif\n    {\n        char* result;\n        int r = PyBytes_AsStringAndSize(o, &result, length);\n        if (unlikely(r < 0)) {\n            return NULL;\n        } else {\n            return result;\n        }\n    }\n}\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\n   int is_true = x == Py_True;\n   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;\n   else return PyObject_IsTrue(x);\n}\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {\n    int retval;\n    if (unlikely(!x)) return -1;\n    retval = __Pyx_PyObject_IsTrue(x);\n    Py_DECREF(x);\n    return retval;\n}\nstatic PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {\n#if PY_MAJOR_VERSION >= 3\n    if (PyLong_Check(result)) {\n        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,\n                \"__int__ returned non-int (type %.200s).  \"\n                \"The ability to return an instance of a strict subclass of int \"\n                \"is deprecated, and may be removed in a future version of Python.\",\n                Py_TYPE(result)->tp_name)) {\n            Py_DECREF(result);\n            return NULL;\n        }\n        return result;\n    }\n#endif\n    PyErr_Format(PyExc_TypeError,\n                 \"__%.4s__ returned non-%.4s (type %.200s)\",\n                 type_name, type_name, Py_TYPE(result)->tp_name);\n    Py_DECREF(result);\n    return NULL;\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {\n#if CYTHON_USE_TYPE_SLOTS\n  PyNumberMethods *m;\n#endif\n  const char *name = NULL;\n  PyObject *res = NULL;\n#if PY_MAJOR_VERSION < 3\n  if (likely(PyInt_Check(x) || PyLong_Check(x)))\n#else\n  if (likely(PyLong_Check(x)))\n#endif\n    return __Pyx_NewRef(x);\n#if CYTHON_USE_TYPE_SLOTS\n  m = Py_TYPE(x)->tp_as_number;\n  #if PY_MAJOR_VERSION < 3\n  if (m && m->nb_int) {\n    name = \"int\";\n    res = m->nb_int(x);\n  }\n  else if (m && m->nb_long) {\n    name = \"long\";\n    res = m->nb_long(x);\n  }\n  #else\n  if (likely(m && m->nb_int)) {\n    name = \"int\";\n    res = m->nb_int(x);\n  }\n  #endif\n#else\n  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {\n    res = PyNumber_Int(x);\n  }\n#endif\n  if (likely(res)) {\n#if PY_MAJOR_VERSION < 3\n    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {\n#else\n    if (unlikely(!PyLong_CheckExact(res))) {\n#endif\n        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);\n    }\n  }\n  else if (!PyErr_Occurred()) {\n    PyErr_SetString(PyExc_TypeError,\n                    \"an integer is required\");\n  }\n  return res;\n}\nstatic CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\n  Py_ssize_t ival;\n  PyObject *x;\n#if PY_MAJOR_VERSION < 3\n  if (likely(PyInt_CheckExact(b))) {\n    if (sizeof(Py_ssize_t) >= sizeof(long))\n        return PyInt_AS_LONG(b);\n    else\n        return PyInt_AsSsize_t(b);\n  }\n#endif\n  if (likely(PyLong_CheckExact(b))) {\n    #if CYTHON_USE_PYLONG_INTERNALS\n    const digit* digits = ((PyLongObject*)b)->ob_digit;\n    const Py_ssize_t size = Py_SIZE(b);\n    if (likely(__Pyx_sst_abs(size) <= 1)) {\n        ival = likely(size) ? digits[0] : 0;\n        if (size == -1) ival = -ival;\n        return ival;\n    } else {\n      switch (size) {\n         case 2:\n           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -2:\n           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case 3:\n           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -3:\n           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case 4:\n           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -4:\n           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n      }\n    }\n    #endif\n    return PyLong_AsSsize_t(b);\n  }\n  x = PyNumber_Index(b);\n  if (!x) return -1;\n  ival = PyInt_AsSsize_t(x);\n  Py_DECREF(x);\n  return ival;\n}\nstatic CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) {\n  if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) {\n    return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o);\n#if PY_MAJOR_VERSION < 3\n  } else if (likely(PyInt_CheckExact(o))) {\n    return PyInt_AS_LONG(o);\n#endif\n  } else {\n    Py_ssize_t ival;\n    PyObject *x;\n    x = PyNumber_Index(o);\n    if (!x) return -1;\n    ival = PyInt_AsLong(x);\n    Py_DECREF(x);\n    return ival;\n  }\n}\nstatic CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {\n  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);\n}\nstatic CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\n    return PyInt_FromSize_t(ival);\n}\n\n\n#endif /* Py_PYTHON_H */\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/rank_cylib/roc_cy.pyx",
    "content": "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True\n# credits: https://github.com/KaiyangZhou/deep-person-reid/blob/master/torchreid/metrics/rank_cylib/rank_cy.pyx\n\nimport cython\nimport faiss\nimport numpy as np\ncimport numpy as np\n\n\n\"\"\"\nCompiler directives:\nhttps://github.com/cython/cython/wiki/enhancements-compilerdirectives\nCython tutorial:\nhttps://cython.readthedocs.io/en/latest/src/userguide/numpy_tutorial.html\nCredit to https://github.com/luzai\n\"\"\"\n\n\n# Main interface\ncpdef evaluate_roc_cy(float[:,:] distmat, long[:] q_pids, long[:]g_pids,\n                  long[:]q_camids, long[:]g_camids):\n\n    distmat = np.asarray(distmat, dtype=np.float32)\n    q_pids = np.asarray(q_pids, dtype=np.int64)\n    g_pids = np.asarray(g_pids, dtype=np.int64)\n    q_camids = np.asarray(q_camids, dtype=np.int64)\n    g_camids = np.asarray(g_camids, dtype=np.int64)\n\n    cdef long num_q = distmat.shape[0]\n    cdef long num_g = distmat.shape[1]\n\n    cdef:\n        long[:,:] indices = np.argsort(distmat, axis=1)\n        long[:,:] matches = (np.asarray(g_pids)[np.asarray(indices)] == np.asarray(q_pids)[:, np.newaxis]).astype(np.int64)\n\n        float[:] pos = np.zeros(num_q*num_g, dtype=np.float32)\n        float[:] neg = np.zeros(num_q*num_g, dtype=np.float32)\n\n        long valid_pos = 0\n        long valid_neg = 0\n        long ind\n\n        long q_idx, q_pid, q_camid, g_idx\n        long[:] order = np.zeros(num_g, dtype=np.int64)\n\n        float[:] raw_cmc = np.zeros(num_g, dtype=np.float32) # binary vector, positions with value 1 are correct matches\n        long[:] sort_idx = np.zeros(num_g, dtype=np.int64)\n\n        long idx\n\n    for q_idx in range(num_q):\n        # get query pid and camid\n        q_pid = q_pids[q_idx]\n        q_camid = q_camids[q_idx]\n\n        for g_idx in range(num_g):\n            order[g_idx] = indices[q_idx, g_idx]\n        num_g_real = 0\n\n        # remove gallery samples that have the same pid and camid with query\n        for g_idx in range(num_g):\n            if (g_pids[order[g_idx]] != q_pid) or (g_camids[order[g_idx]] != q_camid):\n                raw_cmc[num_g_real] = matches[q_idx][g_idx]\n                sort_idx[num_g_real] = order[g_idx]\n                num_g_real += 1\n\n        q_dist = distmat[q_idx]\n\n        for valid_idx in range(num_g_real):\n            if raw_cmc[valid_idx] == 1:\n                pos[valid_pos] = q_dist[sort_idx[valid_idx]]\n                valid_pos += 1\n            elif raw_cmc[valid_idx] == 0:\n                neg[valid_neg] = q_dist[sort_idx[valid_idx]]\n                valid_neg += 1\n\n    cdef float[:] scores = np.hstack((pos[:valid_pos], neg[:valid_neg]))\n    cdef float[:] labels = np.hstack((np.zeros(valid_pos, dtype=np.float32),\n                                      np.ones(valid_neg, dtype=np.float32)))\n    return np.asarray(scores), np.asarray(labels)\n\n\n# Compute the cumulative sum\ncdef void function_cumsum(cython.numeric[:] src, cython.numeric[:] dst, long n):\n    cdef long i\n    dst[0] = src[0]\n    for i in range(1, n):\n        dst[i] = src[i] + dst[i - 1]"
  },
  {
    "path": "fast_reid/fastreid/evaluation/rank_cylib/setup.py",
    "content": "from distutils.core import setup\nfrom distutils.extension import Extension\n\nimport numpy as np\nfrom Cython.Build import cythonize\n\n\ndef numpy_include():\n    try:\n        numpy_include = np.get_include()\n    except AttributeError:\n        numpy_include = np.get_numpy_include()\n    return numpy_include\n\n\next_modules = [\n    Extension(\n        'rank_cy',\n        ['rank_cy.pyx'],\n        include_dirs=[numpy_include()],\n    ),\n    Extension(\n        'roc_cy',\n        ['roc_cy.pyx'],\n        include_dirs=[numpy_include()],\n    )\n]\n\nsetup(\n    name='Cython-based reid evaluation code',\n    ext_modules=cythonize(ext_modules)\n)\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/rank_cylib/test_cython.py",
    "content": "import sys\nimport timeit\nimport numpy as np\nimport os.path as osp\n\nsys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..')\n\nfrom fast_reid.fastreid.evaluation.rank import evaluate_rank\nfrom fast_reid.fastreid.evaluation.roc import evaluate_roc\n\n\"\"\"\nTest the speed of cython-based evaluation code. The speed improvements\ncan be much bigger when using the real reid data, which contains a larger\namount of query and gallery images.\nNote: you might encounter the following error:\n  'AssertionError: Error: all query identities do not appear in gallery'.\nThis is normal because the inputs are random numbers. Just try again.\n\"\"\"\n\nprint('*** Compare running time ***')\n\nsetup = '''\nimport sys\nimport os.path as osp\nimport numpy as np\nsys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..')\nfrom fast_reid.fastreid.evaluation.rank import evaluate_rank\nfrom fast_reid.fastreid.evaluation.roc import evaluate_roc\nnum_q = 30\nnum_g = 300\ndim = 512\nmax_rank = 5\nq_feats = np.random.rand(num_q, dim).astype(np.float32) * 20\nq_feats = q_feats / np.linalg.norm(q_feats, ord=2, axis=1, keepdims=True)\ng_feats = np.random.rand(num_g, dim).astype(np.float32) * 20\ng_feats = g_feats / np.linalg.norm(g_feats, ord=2, axis=1, keepdims=True)\ndistmat = 1 - np.dot(q_feats, g_feats.transpose())\nq_pids = np.random.randint(0, num_q, size=num_q)\ng_pids = np.random.randint(0, num_g, size=num_g)\nq_camids = np.random.randint(0, 5, size=num_q)\ng_camids = np.random.randint(0, 5, size=num_g)\n'''\n\nprint('=> Using CMC metric')\npytime = timeit.timeit(\n    'evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=False)',\n    setup=setup,\n    number=20\n)\ncytime = timeit.timeit(\n    'evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=True)',\n    setup=setup,\n    number=20\n)\nprint('Python time: {} s'.format(pytime))\nprint('Cython time: {} s'.format(cytime))\nprint('CMC Cython is {} times faster than python\\n'.format(pytime / cytime))\n\nprint('=> Using ROC metric')\npytime = timeit.timeit(\n    'evaluate_roc(distmat, q_pids, g_pids, q_camids, g_camids, use_cython=False)',\n    setup=setup,\n    number=20\n)\ncytime = timeit.timeit(\n    'evaluate_roc(distmat, q_pids, g_pids, q_camids, g_camids, use_cython=True)',\n    setup=setup,\n    number=20\n)\nprint('Python time: {} s'.format(pytime))\nprint('Cython time: {} s'.format(cytime))\nprint('ROC Cython is {} times faster than python\\n'.format(pytime / cytime))\n\nprint(\"=> Check precision\")\nnum_q = 30\nnum_g = 300\ndim = 512\nmax_rank = 5\nq_feats = np.random.rand(num_q, dim).astype(np.float32) * 20\nq_feats = q_feats / np.linalg.norm(q_feats, ord=2, axis=1, keepdims=True)\ng_feats = np.random.rand(num_g, dim).astype(np.float32) * 20\ng_feats = g_feats / np.linalg.norm(g_feats, ord=2, axis=1, keepdims=True)\ndistmat = 1 - np.dot(q_feats, g_feats.transpose())\nq_pids = np.random.randint(0, num_q, size=num_q)\ng_pids = np.random.randint(0, num_g, size=num_g)\nq_camids = np.random.randint(0, 5, size=num_q)\ng_camids = np.random.randint(0, 5, size=num_g)\n\ncmc_py, mAP_py, mINP_py = evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=False)\n\ncmc_cy, mAP_cy, mINP_cy = evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_cython=True)\n\nnp.testing.assert_allclose(cmc_py, cmc_cy, rtol=1e-3, atol=1e-6)\nnp.testing.assert_allclose(mAP_py, mAP_cy, rtol=1e-3, atol=1e-6)\nnp.testing.assert_allclose(mINP_py, mINP_cy, rtol=1e-3, atol=1e-6)\nprint('Rank results between python and cython are the same!')\n\nscores_cy, labels_cy = evaluate_roc(distmat, q_pids, g_pids, q_camids, g_camids, use_cython=True)\nscores_py, labels_py = evaluate_roc(distmat, q_pids, g_pids, q_camids, g_camids, use_cython=False)\n\nnp.testing.assert_allclose(scores_cy, scores_py, rtol=1e-3, atol=1e-6)\nnp.testing.assert_allclose(labels_cy, labels_py, rtol=1e-3, atol=1e-6)\nprint('ROC results between python and cython are the same!\\n')\n\nprint(\"=> Check exact values\")\nprint(\"mAP = {} \\ncmc = {}\\nmINP = {}\\nScores = {}\".format(np.array(mAP_cy), cmc_cy, np.array(mINP_cy), scores_cy))\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/reid_evaluation.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport copy\nimport logging\nimport time\nimport itertools\nfrom collections import OrderedDict\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom sklearn import metrics\n\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.compute_dist import build_dist\nfrom .evaluator import DatasetEvaluator\nfrom .query_expansion import aqe\nfrom .rank_cylib import compile_helper\n\nlogger = logging.getLogger(__name__)\n\n\nclass ReidEvaluator(DatasetEvaluator):\n    def __init__(self, cfg, num_query, output_dir=None):\n        self.cfg = cfg\n        self._num_query = num_query\n        self._output_dir = output_dir\n\n        self._cpu_device = torch.device('cpu')\n\n        self._predictions = []\n        self._compile_dependencies()\n\n    def reset(self):\n        self._predictions = []\n\n    def process(self, inputs, outputs):\n        prediction = {\n            'feats': outputs.to(self._cpu_device, torch.float32),\n            'pids': inputs['targets'].to(self._cpu_device),\n            'camids': inputs['camids'].to(self._cpu_device)\n\n        }\n        self._predictions.append(prediction)\n\n    def evaluate(self):\n        if comm.get_world_size() > 1:\n            comm.synchronize()\n            predictions = comm.gather(self._predictions, dst=0)\n            predictions = list(itertools.chain(*predictions))\n\n            if not comm.is_main_process():\n                return {}\n\n        else:\n            predictions = self._predictions\n\n        features = []\n        pids = []\n        camids = []\n        for prediction in predictions:\n            features.append(prediction['feats'])\n            pids.append(prediction['pids'])\n            camids.append(prediction['camids'])\n\n        features = torch.cat(features, dim=0)\n        pids = torch.cat(pids, dim=0).numpy()\n        camids = torch.cat(camids, dim=0).numpy()\n        # query feature, person ids and camera ids\n        query_features = features[:self._num_query]\n        query_pids = pids[:self._num_query]\n        query_camids = camids[:self._num_query]\n\n        # gallery features, person ids and camera ids\n        gallery_features = features[self._num_query:]\n        gallery_pids = pids[self._num_query:]\n        gallery_camids = camids[self._num_query:]\n\n        self._results = OrderedDict()\n\n        if self.cfg.TEST.AQE.ENABLED:\n            logger.info(\"Test with AQE setting\")\n            qe_time = self.cfg.TEST.AQE.QE_TIME\n            qe_k = self.cfg.TEST.AQE.QE_K\n            alpha = self.cfg.TEST.AQE.ALPHA\n            query_features, gallery_features = aqe(query_features, gallery_features, qe_time, qe_k, alpha)\n\n        dist = build_dist(query_features, gallery_features, self.cfg.TEST.METRIC)\n\n        if self.cfg.TEST.RERANK.ENABLED:\n            logger.info(\"Test with rerank setting\")\n            k1 = self.cfg.TEST.RERANK.K1\n            k2 = self.cfg.TEST.RERANK.K2\n            lambda_value = self.cfg.TEST.RERANK.LAMBDA\n\n            if self.cfg.TEST.METRIC == \"cosine\":\n                query_features = F.normalize(query_features, dim=1)\n                gallery_features = F.normalize(gallery_features, dim=1)\n\n            rerank_dist = build_dist(query_features, gallery_features, metric=\"jaccard\", k1=k1, k2=k2)\n            dist = rerank_dist * (1 - lambda_value) + dist * lambda_value\n\n        from .rank import evaluate_rank\n        cmc, all_AP, all_INP = evaluate_rank(dist, query_pids, gallery_pids, query_camids, gallery_camids)\n\n        mAP = np.mean(all_AP)\n        mINP = np.mean(all_INP)\n        for r in [1, 5, 10]:\n            self._results['Rank-{}'.format(r)] = cmc[r - 1] * 100\n        self._results['mAP'] = mAP * 100\n        self._results['mINP'] = mINP * 100\n        self._results[\"metric\"] = (mAP + cmc[0]) / 2 * 100\n\n        if self.cfg.TEST.ROC.ENABLED:\n            from .roc import evaluate_roc\n            scores, labels = evaluate_roc(dist, query_pids, gallery_pids, query_camids, gallery_camids)\n            fprs, tprs, thres = metrics.roc_curve(labels, scores)\n\n            for fpr in [1e-4, 1e-3, 1e-2]:\n                ind = np.argmin(np.abs(fprs - fpr))\n                self._results[\"TPR@FPR={:.0e}\".format(fpr)] = tprs[ind]\n\n        return copy.deepcopy(self._results)\n\n    def _compile_dependencies(self):\n        # Since we only evaluate results in rank(0), so we just need to compile\n        # cython evaluation tool on rank(0)\n        if comm.is_main_process():\n            try:\n                from .rank_cylib.rank_cy import evaluate_cy\n            except ImportError:\n                start_time = time.time()\n                logger.info(\"> compiling reid evaluation cython tool\")\n\n                compile_helper()\n\n                logger.info(\n                    \">>> done with reid evaluation cython tool. Compilation time: {:.3f} \"\n                    \"seconds\".format(time.time() - start_time))\n        comm.synchronize()\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/rerank.py",
    "content": "# encoding: utf-8\n\n# based on:\n# https://github.com/zhunzhong07/person-re-ranking\n\n__all__ = ['re_ranking']\n\nimport numpy as np\n\n\ndef re_ranking(q_g_dist, q_q_dist, g_g_dist, k1: int = 20, k2: int = 6, lambda_value: float = 0.3):\n    original_dist = np.concatenate(\n        [np.concatenate([q_q_dist, q_g_dist], axis=1),\n         np.concatenate([q_g_dist.T, g_g_dist], axis=1)],\n        axis=0)\n    original_dist = np.power(original_dist, 2).astype(np.float32)\n    original_dist = np.transpose(1. * original_dist / np.max(original_dist, axis=0))\n    V = np.zeros_like(original_dist).astype(np.float32)\n    initial_rank = np.argsort(original_dist).astype(np.int32)\n\n    query_num = q_g_dist.shape[0]\n    gallery_num = q_g_dist.shape[0] + q_g_dist.shape[1]\n    all_num = gallery_num\n\n    for i in range(all_num):\n        # k-reciprocal neighbors\n        forward_k_neigh_index = initial_rank[i, :k1 + 1]\n        backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]\n        fi = np.where(backward_k_neigh_index == i)[0]\n        k_reciprocal_index = forward_k_neigh_index[fi]\n        k_reciprocal_expansion_index = k_reciprocal_index\n        for j in range(len(k_reciprocal_index)):\n            candidate = k_reciprocal_index[j]\n            candidate_forward_k_neigh_index = initial_rank[candidate,\n                                              :int(np.around(k1 / 2.)) + 1]\n            candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,\n                                               :int(np.around(k1 / 2.)) + 1]\n            fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]\n            candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]\n            if len(np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)) > 2. / 3 * len(\n                    candidate_k_reciprocal_index):\n                k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index, candidate_k_reciprocal_index)\n\n        k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)\n        weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])\n        V[i, k_reciprocal_expansion_index] = 1. * weight / np.sum(weight)\n    original_dist = original_dist[:query_num, ]\n    if k2 != 1:\n        V_qe = np.zeros_like(V, dtype=np.float32)\n        for i in range(all_num):\n            V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)\n        V = V_qe\n        del V_qe\n    del initial_rank\n    invIndex = []\n    for i in range(gallery_num):\n        invIndex.append(np.where(V[:, i] != 0)[0])\n\n    jaccard_dist = np.zeros_like(original_dist, dtype=np.float32)\n\n    for i in range(query_num):\n        temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float32)\n        indNonZero = np.where(V[i, :] != 0)[0]\n        indImages = [invIndex[ind] for ind in indNonZero]\n        for j in range(len(indNonZero)):\n            temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(V[i, indNonZero[j]],\n                                                                               V[indImages[j], indNonZero[j]])\n        jaccard_dist[i] = 1 - temp_min / (2. - temp_min)\n\n    final_dist = jaccard_dist * (1 - lambda_value) + original_dist * lambda_value\n    del original_dist, V, jaccard_dist\n    final_dist = final_dist[:query_num, query_num:]\n    return final_dist\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/roc.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport warnings\n\nimport faiss\nimport numpy as np\n\ntry:\n    from .rank_cylib.roc_cy import evaluate_roc_cy\n\n    IS_CYTHON_AVAI = True\nexcept ImportError:\n    IS_CYTHON_AVAI = False\n    warnings.warn(\n        'Cython roc evaluation (very fast so highly recommended) is '\n        'unavailable, now use python evaluation.'\n    )\n\n\ndef evaluate_roc_py(distmat, q_pids, g_pids, q_camids, g_camids):\n    r\"\"\"Evaluation with ROC curve.\n    Key: for each query identity, its gallery images from the same camera view are discarded.\n\n    Args:\n        distmat (np.ndarray): cosine distance matrix\n    \"\"\"\n    num_q, num_g = distmat.shape\n\n    indices = np.argsort(distmat, axis=1)\n    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)\n\n    pos = []\n    neg = []\n    for q_idx in range(num_q):\n        # get query pid and camid\n        q_pid = q_pids[q_idx]\n        q_camid = q_camids[q_idx]\n\n        # Remove gallery samples that have the same pid and camid with query\n        order = indices[q_idx]\n        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)\n        keep = np.invert(remove)\n        raw_cmc = matches[q_idx][keep]\n\n        sort_idx = order[keep]\n\n        q_dist = distmat[q_idx]\n        ind_pos = np.where(raw_cmc == 1)[0]\n        pos.extend(q_dist[sort_idx[ind_pos]])\n\n        ind_neg = np.where(raw_cmc == 0)[0]\n        neg.extend(q_dist[sort_idx[ind_neg]])\n\n    scores = np.hstack((pos, neg))\n\n    labels = np.hstack((np.zeros(len(pos)), np.ones(len(neg))))\n    return scores, labels\n\n\ndef evaluate_roc(\n        distmat,\n        q_pids,\n        g_pids,\n        q_camids,\n        g_camids,\n        use_cython=True\n):\n    \"\"\"Evaluates CMC rank.\n    Args:\n        distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).\n        q_pids (numpy.ndarray): 1-D array containing person identities\n            of each query instance.\n        g_pids (numpy.ndarray): 1-D array containing person identities\n            of each gallery instance.\n        q_camids (numpy.ndarray): 1-D array containing camera views under\n            which each query instance is captured.\n        g_camids (numpy.ndarray): 1-D array containing camera views under\n            which each gallery instance is captured.\n        use_cython (bool, optional): use cython code for evaluation. Default is True.\n            This is highly recommended as the cython code can speed up the cmc computation\n            by more than 10x. This requires Cython to be installed.\n    \"\"\"\n    if use_cython and IS_CYTHON_AVAI:\n        return evaluate_roc_cy(distmat, q_pids, g_pids, q_camids, g_camids)\n    else:\n        return evaluate_roc_py(distmat, q_pids, g_pids, q_camids, g_camids)\n"
  },
  {
    "path": "fast_reid/fastreid/evaluation/testing.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport logging\nimport pprint\nimport sys\nfrom collections import Mapping, OrderedDict\n\nimport numpy as np\nfrom tabulate import tabulate\nfrom termcolor import colored\n\n\ndef print_csv_format(results):\n    \"\"\"\n    Print main metrics in a format similar to Detectron2,\n    so that they are easy to copypaste into a spreadsheet.\n    Args:\n        results (OrderedDict): {metric -> score}\n    \"\"\"\n    # unordered results cannot be properly printed\n    assert isinstance(results, OrderedDict) or not len(results), results\n    logger = logging.getLogger(__name__)\n\n    dataset_name = results.pop('dataset')\n    metrics = [\"Dataset\"] + [k for k in results]\n    csv_results = [(dataset_name, *list(results.values()))]\n\n    # tabulate it\n    table = tabulate(\n        csv_results,\n        tablefmt=\"pipe\",\n        floatfmt=\".2f\",\n        headers=metrics,\n        numalign=\"left\",\n    )\n\n    logger.info(\"Evaluation results in csv format: \\n\" + colored(table, \"cyan\"))\n\n\ndef verify_results(cfg, results):\n    \"\"\"\n    Args:\n        results (OrderedDict[dict]): task_name -> {metric -> score}\n    Returns:\n        bool: whether the verification succeeds or not\n    \"\"\"\n    expected_results = cfg.TEST.EXPECTED_RESULTS\n    if not len(expected_results):\n        return True\n\n    ok = True\n    for task, metric, expected, tolerance in expected_results:\n        actual = results[task][metric]\n        if not np.isfinite(actual):\n            ok = False\n        diff = abs(actual - expected)\n        if diff > tolerance:\n            ok = False\n\n    logger = logging.getLogger(__name__)\n    if not ok:\n        logger.error(\"Result verification failed!\")\n        logger.error(\"Expected Results: \" + str(expected_results))\n        logger.error(\"Actual Results: \" + pprint.pformat(results))\n\n        sys.exit(1)\n    else:\n        logger.info(\"Results verification passed.\")\n    return ok\n\n\ndef flatten_results_dict(results):\n    \"\"\"\n    Expand a hierarchical dict of scalars into a flat dict of scalars.\n    If results[k1][k2][k3] = v, the returned dict will have the entry\n    {\"k1/k2/k3\": v}.\n    Args:\n        results (dict):\n    \"\"\"\n    r = {}\n    for k, v in results.items():\n        if isinstance(v, Mapping):\n            v = flatten_results_dict(v)\n            for kk, vv in v.items():\n                r[k + \"/\" + kk] = vv\n        else:\n            r[k] = v\n    return r\n"
  },
  {
    "path": "fast_reid/fastreid/layers/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .activation import *\nfrom .batch_norm import *\nfrom .context_block import ContextBlock\nfrom .drop import DropPath, DropBlock2d, drop_block_2d, drop_path\nfrom .frn import FRN, TLU\nfrom .gather_layer import GatherLayer\nfrom .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible\nfrom .non_local import Non_local\nfrom .se_layer import SELayer\nfrom .splat import SplAtConv2d, DropBlock2D\nfrom .weight_init import (\n    trunc_normal_, variance_scaling_, lecun_normal_, weights_init_kaiming, weights_init_classifier\n)\n"
  },
  {
    "path": "fast_reid/fastreid/layers/activation.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n__all__ = [\n    'Mish',\n    'Swish',\n    'MemoryEfficientSwish',\n    'GELU']\n\n\nclass Mish(nn.Module):\n    def __init__(self):\n        super().__init__()\n\n    def forward(self, x):\n        # inlining this saves 1 second per epoch (V100 GPU) vs having a temp x and then returning x(!)\n        return x * (torch.tanh(F.softplus(x)))\n\n\nclass Swish(nn.Module):\n    def forward(self, x):\n        return x * torch.sigmoid(x)\n\n\nclass SwishImplementation(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, i):\n        result = i * torch.sigmoid(i)\n        ctx.save_for_backward(i)\n        return result\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        i = ctx.saved_variables[0]\n        sigmoid_i = torch.sigmoid(i)\n        return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))\n\n\nclass MemoryEfficientSwish(nn.Module):\n    def forward(self, x):\n        return SwishImplementation.apply(x)\n\n\nclass GELU(nn.Module):\n    \"\"\"\n    Paper Section 3.4, last paragraph notice that BERT used the GELU instead of RELU\n    \"\"\"\n\n    def forward(self, x):\n        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))\n"
  },
  {
    "path": "fast_reid/fastreid/layers/any_softmax.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\n__all__ = [\n    \"Linear\",\n    \"ArcSoftmax\",\n    \"CosSoftmax\",\n    \"CircleSoftmax\"\n]\n\n\nclass Linear(nn.Module):\n    def __init__(self, num_classes, scale, margin):\n        super().__init__()\n        self.num_classes = num_classes\n        self.s = scale\n        self.m = margin\n\n    def forward(self, logits, targets):\n        return logits.mul_(self.s)\n\n    def extra_repr(self):\n        return f\"num_classes={self.num_classes}, scale={self.s}, margin={self.m}\"\n\n\nclass CosSoftmax(Linear):\n    r\"\"\"Implement of large margin cosine distance:\n    \"\"\"\n\n    def forward(self, logits, targets):\n        index = torch.where(targets != -1)[0]\n        m_hot = torch.zeros(index.size()[0], logits.size()[1], device=logits.device, dtype=logits.dtype)\n        m_hot.scatter_(1, targets[index, None], self.m)\n        logits[index] -= m_hot\n        logits.mul_(self.s)\n        return logits\n\n\nclass ArcSoftmax(Linear):\n\n    def forward(self, logits, targets):\n        index = torch.where(targets != -1)[0]\n        m_hot = torch.zeros(index.size()[0], logits.size()[1], device=logits.device, dtype=logits.dtype)\n        m_hot.scatter_(1, targets[index, None], self.m)\n        logits.acos_()\n        logits[index] += m_hot\n        logits.cos_().mul_(self.s)\n        return logits\n\n\nclass CircleSoftmax(Linear):\n\n    def forward(self, logits, targets):\n        alpha_p = torch.clamp_min(-logits.detach() + 1 + self.m, min=0.)\n        alpha_n = torch.clamp_min(logits.detach() + self.m, min=0.)\n        delta_p = 1 - self.m\n        delta_n = self.m\n\n        # When use model parallel, there are some targets not in class centers of local rank\n        index = torch.where(targets != -1)[0]\n        m_hot = torch.zeros(index.size()[0], logits.size()[1], device=logits.device, dtype=logits.dtype)\n        m_hot.scatter_(1, targets[index, None], 1)\n\n        logits_p = alpha_p * (logits - delta_p)\n        logits_n = alpha_n * (logits - delta_n)\n\n        logits[index] = logits_p[index] * m_hot + logits_n[index] * (1 - m_hot)\n\n        neg_index = torch.where(targets == -1)[0]\n        logits[neg_index] = logits_n[neg_index]\n\n        logits.mul_(self.s)\n\n        return logits\n"
  },
  {
    "path": "fast_reid/fastreid/layers/batch_norm.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\n__all__ = [\"IBN\", \"get_norm\"]\n\n\nclass BatchNorm(nn.BatchNorm2d):\n    def __init__(self, num_features, eps=1e-05, momentum=0.1, weight_freeze=False, bias_freeze=False, weight_init=1.0,\n                 bias_init=0.0, **kwargs):\n        super().__init__(num_features, eps=eps, momentum=momentum)\n        if weight_init is not None: nn.init.constant_(self.weight, weight_init)\n        if bias_init is not None: nn.init.constant_(self.bias, bias_init)\n        self.weight.requires_grad_(not weight_freeze)\n        self.bias.requires_grad_(not bias_freeze)\n\n\nclass SyncBatchNorm(nn.SyncBatchNorm):\n    def __init__(self, num_features, eps=1e-05, momentum=0.1, weight_freeze=False, bias_freeze=False, weight_init=1.0,\n                 bias_init=0.0):\n        super().__init__(num_features, eps=eps, momentum=momentum)\n        if weight_init is not None: nn.init.constant_(self.weight, weight_init)\n        if bias_init is not None: nn.init.constant_(self.bias, bias_init)\n        self.weight.requires_grad_(not weight_freeze)\n        self.bias.requires_grad_(not bias_freeze)\n\n\nclass IBN(nn.Module):\n    def __init__(self, planes, bn_norm, **kwargs):\n        super(IBN, self).__init__()\n        half1 = int(planes / 2)\n        self.half = half1\n        half2 = planes - half1\n        self.IN = nn.InstanceNorm2d(half1, affine=True)\n        self.BN = get_norm(bn_norm, half2, **kwargs)\n\n    def forward(self, x):\n        split = torch.split(x, self.half, 1)\n        out1 = self.IN(split[0].contiguous())\n        out2 = self.BN(split[1].contiguous())\n        out = torch.cat((out1, out2), 1)\n        return out\n\n\nclass GhostBatchNorm(BatchNorm):\n    def __init__(self, num_features, num_splits=1, **kwargs):\n        super().__init__(num_features, **kwargs)\n        self.num_splits = num_splits\n        self.register_buffer('running_mean', torch.zeros(num_features))\n        self.register_buffer('running_var', torch.ones(num_features))\n\n    def forward(self, input):\n        N, C, H, W = input.shape\n        if self.training or not self.track_running_stats:\n            self.running_mean = self.running_mean.repeat(self.num_splits)\n            self.running_var = self.running_var.repeat(self.num_splits)\n            outputs = F.batch_norm(\n                input.view(-1, C * self.num_splits, H, W), self.running_mean, self.running_var,\n                self.weight.repeat(self.num_splits), self.bias.repeat(self.num_splits),\n                True, self.momentum, self.eps).view(N, C, H, W)\n            self.running_mean = torch.mean(self.running_mean.view(self.num_splits, self.num_features), dim=0)\n            self.running_var = torch.mean(self.running_var.view(self.num_splits, self.num_features), dim=0)\n            return outputs\n        else:\n            return F.batch_norm(\n                input, self.running_mean, self.running_var,\n                self.weight, self.bias, False, self.momentum, self.eps)\n\n\nclass FrozenBatchNorm(nn.Module):\n    \"\"\"\n    BatchNorm2d where the batch statistics and the affine parameters are fixed.\n    It contains non-trainable buffers called\n    \"weight\" and \"bias\", \"running_mean\", \"running_var\",\n    initialized to perform identity transformation.\n    The pre-trained backbone models from Caffe2 only contain \"weight\" and \"bias\",\n    which are computed from the original four parameters of BN.\n    The affine transform `x * weight + bias` will perform the equivalent\n    computation of `(x - running_mean) / sqrt(running_var) * weight + bias`.\n    When loading a backbone model from Caffe2, \"running_mean\" and \"running_var\"\n    will be left unchanged as identity transformation.\n    Other pre-trained backbone models may contain all 4 parameters.\n    The forward is implemented by `F.batch_norm(..., training=False)`.\n    \"\"\"\n\n    _version = 3\n\n    def __init__(self, num_features, eps=1e-5, **kwargs):\n        super().__init__()\n        self.num_features = num_features\n        self.eps = eps\n        self.register_buffer(\"weight\", torch.ones(num_features))\n        self.register_buffer(\"bias\", torch.zeros(num_features))\n        self.register_buffer(\"running_mean\", torch.zeros(num_features))\n        self.register_buffer(\"running_var\", torch.ones(num_features) - eps)\n\n    def forward(self, x):\n        if x.requires_grad:\n            # When gradients are needed, F.batch_norm will use extra memory\n            # because its backward op computes gradients for weight/bias as well.\n            scale = self.weight * (self.running_var + self.eps).rsqrt()\n            bias = self.bias - self.running_mean * scale\n            scale = scale.reshape(1, -1, 1, 1)\n            bias = bias.reshape(1, -1, 1, 1)\n            return x * scale + bias\n        else:\n            # When gradients are not needed, F.batch_norm is a single fused op\n            # and provide more optimization opportunities.\n            return F.batch_norm(\n                x,\n                self.running_mean,\n                self.running_var,\n                self.weight,\n                self.bias,\n                training=False,\n                eps=self.eps,\n            )\n\n    def _load_from_state_dict(\n            self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n    ):\n        version = local_metadata.get(\"version\", None)\n\n        if version is None or version < 2:\n            # No running_mean/var in early versions\n            # This will silent the warnings\n            if prefix + \"running_mean\" not in state_dict:\n                state_dict[prefix + \"running_mean\"] = torch.zeros_like(self.running_mean)\n            if prefix + \"running_var\" not in state_dict:\n                state_dict[prefix + \"running_var\"] = torch.ones_like(self.running_var)\n\n        if version is not None and version < 3:\n            logger = logging.getLogger(__name__)\n            logger.info(\"FrozenBatchNorm {} is upgraded to version 3.\".format(prefix.rstrip(\".\")))\n            # In version < 3, running_var are used without +eps.\n            state_dict[prefix + \"running_var\"] -= self.eps\n\n        super()._load_from_state_dict(\n            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs\n        )\n\n    def __repr__(self):\n        return \"FrozenBatchNorm2d(num_features={}, eps={})\".format(self.num_features, self.eps)\n\n    @classmethod\n    def convert_frozen_batchnorm(cls, module):\n        \"\"\"\n        Convert BatchNorm/SyncBatchNorm in module into FrozenBatchNorm.\n        Args:\n            module (torch.nn.Module):\n        Returns:\n            If module is BatchNorm/SyncBatchNorm, returns a new module.\n            Otherwise, in-place convert module and return it.\n        Similar to convert_sync_batchnorm in\n        https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py\n        \"\"\"\n        bn_module = nn.modules.batchnorm\n        bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm)\n        res = module\n        if isinstance(module, bn_module):\n            res = cls(module.num_features)\n            if module.affine:\n                res.weight.data = module.weight.data.clone().detach()\n                res.bias.data = module.bias.data.clone().detach()\n            res.running_mean.data = module.running_mean.data\n            res.running_var.data = module.running_var.data\n            res.eps = module.eps\n        else:\n            for name, child in module.named_children():\n                new_child = cls.convert_frozen_batchnorm(child)\n                if new_child is not child:\n                    res.add_module(name, new_child)\n        return res\n\n\ndef get_norm(norm, out_channels, **kwargs):\n    \"\"\"\n    Args:\n        norm (str or callable): either one of BN, GhostBN, FrozenBN, GN or SyncBN;\n            or a callable that takes a channel number and returns\n            the normalization layer as a nn.Module\n        out_channels: number of channels for normalization layer\n\n    Returns:\n        nn.Module or None: the normalization layer\n    \"\"\"\n    if isinstance(norm, str):\n        if len(norm) == 0:\n            return None\n        norm = {\n            \"BN\": BatchNorm,\n            \"syncBN\": SyncBatchNorm,\n            \"GhostBN\": GhostBatchNorm,\n            \"FrozenBN\": FrozenBatchNorm,\n            \"GN\": lambda channels, **args: nn.GroupNorm(32, channels),\n        }[norm]\n    return norm(out_channels, **kwargs)\n"
  },
  {
    "path": "fast_reid/fastreid/layers/context_block.py",
    "content": "# copy from https://github.com/xvjiarui/GCNet/blob/master/mmdet/ops/gcb/context_block.py\n\nimport torch\nfrom torch import nn\n\n__all__ = ['ContextBlock']\n\n\ndef last_zero_init(m):\n    if isinstance(m, nn.Sequential):\n        nn.init.constant_(m[-1].weight, val=0)\n        if hasattr(m[-1], 'bias') and m[-1].bias is not None:\n            nn.init.constant_(m[-1].bias, 0)\n    else:\n        nn.init.constant_(m.weight, val=0)\n        if hasattr(m, 'bias') and m.bias is not None:\n            nn.init.constant_(m.bias, 0)\n\n\nclass ContextBlock(nn.Module):\n\n    def __init__(self,\n                 inplanes,\n                 ratio,\n                 pooling_type='att',\n                 fusion_types=('channel_add',)):\n        super(ContextBlock, self).__init__()\n        assert pooling_type in ['avg', 'att']\n        assert isinstance(fusion_types, (list, tuple))\n        valid_fusion_types = ['channel_add', 'channel_mul']\n        assert all([f in valid_fusion_types for f in fusion_types])\n        assert len(fusion_types) > 0, 'at least one fusion should be used'\n        self.inplanes = inplanes\n        self.ratio = ratio\n        self.planes = int(inplanes * ratio)\n        self.pooling_type = pooling_type\n        self.fusion_types = fusion_types\n        if pooling_type == 'att':\n            self.conv_mask = nn.Conv2d(inplanes, 1, kernel_size=1)\n            self.softmax = nn.Softmax(dim=2)\n        else:\n            self.avg_pool = nn.AdaptiveAvgPool2d(1)\n        if 'channel_add' in fusion_types:\n            self.channel_add_conv = nn.Sequential(\n                nn.Conv2d(self.inplanes, self.planes, kernel_size=1),\n                nn.LayerNorm([self.planes, 1, 1]),\n                nn.ReLU(inplace=True),  # yapf: disable\n                nn.Conv2d(self.planes, self.inplanes, kernel_size=1))\n        else:\n            self.channel_add_conv = None\n        if 'channel_mul' in fusion_types:\n            self.channel_mul_conv = nn.Sequential(\n                nn.Conv2d(self.inplanes, self.planes, kernel_size=1),\n                nn.LayerNorm([self.planes, 1, 1]),\n                nn.ReLU(inplace=True),  # yapf: disable\n                nn.Conv2d(self.planes, self.inplanes, kernel_size=1))\n        else:\n            self.channel_mul_conv = None\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        if self.pooling_type == 'att':\n            nn.init.kaiming_normal_(self.conv_mask.weight, a=0, mode='fan_in', nonlinearity='relu')\n            if hasattr(self.conv_mask, 'bias') and self.conv_mask.bias is not None:\n                nn.init.constant_(self.conv_mask.bias, 0)\n            self.conv_mask.inited = True\n\n        if self.channel_add_conv is not None:\n            last_zero_init(self.channel_add_conv)\n        if self.channel_mul_conv is not None:\n            last_zero_init(self.channel_mul_conv)\n\n    def spatial_pool(self, x):\n        batch, channel, height, width = x.size()\n        if self.pooling_type == 'att':\n            input_x = x\n            # [N, C, H * W]\n            input_x = input_x.view(batch, channel, height * width)\n            # [N, 1, C, H * W]\n            input_x = input_x.unsqueeze(1)\n            # [N, 1, H, W]\n            context_mask = self.conv_mask(x)\n            # [N, 1, H * W]\n            context_mask = context_mask.view(batch, 1, height * width)\n            # [N, 1, H * W]\n            context_mask = self.softmax(context_mask)\n            # [N, 1, H * W, 1]\n            context_mask = context_mask.unsqueeze(-1)\n            # [N, 1, C, 1]\n            context = torch.matmul(input_x, context_mask)\n            # [N, C, 1, 1]\n            context = context.view(batch, channel, 1, 1)\n        else:\n            # [N, C, 1, 1]\n            context = self.avg_pool(x)\n\n        return context\n\n    def forward(self, x):\n        # [N, C, 1, 1]\n        context = self.spatial_pool(x)\n\n        out = x\n        if self.channel_mul_conv is not None:\n            # [N, C, 1, 1]\n            channel_mul_term = torch.sigmoid(self.channel_mul_conv(context))\n            out = out * channel_mul_term\n        if self.channel_add_conv is not None:\n            # [N, C, 1, 1]\n            channel_add_term = self.channel_add_conv(context)\n            out = out + channel_add_term\n\n        return out\n"
  },
  {
    "path": "fast_reid/fastreid/layers/drop.py",
    "content": "\"\"\" DropBlock, DropPath\nPyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.\nPapers:\nDropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)\nDeep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)\nCode:\nDropBlock impl inspired by two Tensorflow impl that I liked:\n - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74\n - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef drop_block_2d(\n        x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0,\n        with_noise: bool = False, inplace: bool = False, batchwise: bool = False):\n    \"\"\" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf\n    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training\n    runs with success, but needs further validation and possibly optimization for lower runtime impact.\n    \"\"\"\n    B, C, H, W = x.shape\n    total_size = W * H\n    clipped_block_size = min(block_size, min(W, H))\n    # seed_drop_rate, the gamma parameter\n    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (\n            (W - block_size + 1) * (H - block_size + 1))\n\n    # Forces the block to be inside the feature map.\n    w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device))\n    valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \\\n                  ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))\n    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)\n\n    if batchwise:\n        # one mask for whole batch, quite a bit faster\n        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)\n    else:\n        uniform_noise = torch.rand_like(x)\n    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)\n    block_mask = -F.max_pool2d(\n        -block_mask,\n        kernel_size=clipped_block_size,  # block_size,\n        stride=1,\n        padding=clipped_block_size // 2)\n\n    if with_noise:\n        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)\n        if inplace:\n            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))\n        else:\n            x = x * block_mask + normal_noise * (1 - block_mask)\n    else:\n        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)\n        if inplace:\n            x.mul_(block_mask * normalize_scale)\n        else:\n            x = x * block_mask * normalize_scale\n    return x\n\n\ndef drop_block_fast_2d(\n        x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7,\n        gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False):\n    \"\"\" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf\n    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid\n    block mask at edges.\n    \"\"\"\n    B, C, H, W = x.shape\n    total_size = W * H\n    clipped_block_size = min(block_size, min(W, H))\n    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (\n            (W - block_size + 1) * (H - block_size + 1))\n\n    if batchwise:\n        # one mask for whole batch, quite a bit faster\n        block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma\n    else:\n        # mask per batch element\n        block_mask = torch.rand_like(x) < gamma\n    block_mask = F.max_pool2d(\n        block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2)\n\n    if with_noise:\n        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)\n        if inplace:\n            x.mul_(1. - block_mask).add_(normal_noise * block_mask)\n        else:\n            x = x * (1. - block_mask) + normal_noise * block_mask\n    else:\n        block_mask = 1 - block_mask\n        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(dtype=x.dtype)\n        if inplace:\n            x.mul_(block_mask * normalize_scale)\n        else:\n            x = x * block_mask * normalize_scale\n    return x\n\n\nclass DropBlock2d(nn.Module):\n    \"\"\" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf\n    \"\"\"\n\n    def __init__(self,\n                 drop_prob=0.1,\n                 block_size=7,\n                 gamma_scale=1.0,\n                 with_noise=False,\n                 inplace=False,\n                 batchwise=False,\n                 fast=True):\n        super(DropBlock2d, self).__init__()\n        self.drop_prob = drop_prob\n        self.gamma_scale = gamma_scale\n        self.block_size = block_size\n        self.with_noise = with_noise\n        self.inplace = inplace\n        self.batchwise = batchwise\n        self.fast = fast  # FIXME finish comparisons of fast vs not\n\n    def forward(self, x):\n        if not self.training or not self.drop_prob:\n            return x\n        if self.fast:\n            return drop_block_fast_2d(\n                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)\n        else:\n            return drop_block_2d(\n                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)\n\n\ndef drop_path(x, drop_prob: float = 0., training: bool = False):\n    \"\"\"Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).\n    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,\n    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...\n    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for\n    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use\n    'survival rate' as the argument.\n    \"\"\"\n    if drop_prob == 0. or not training:\n        return x\n    keep_prob = 1 - drop_prob\n    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets\n    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)\n    random_tensor.floor_()  # binarize\n    output = x.div(keep_prob) * random_tensor\n    return output\n\n\nclass DropPath(nn.Module):\n    \"\"\"Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).\n    \"\"\"\n\n    def __init__(self, drop_prob=None):\n        super(DropPath, self).__init__()\n        self.drop_prob = drop_prob\n\n    def forward(self, x):\n        return drop_path(x, self.drop_prob, self.training)\n"
  },
  {
    "path": "fast_reid/fastreid/layers/frn.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nfrom torch import nn\nfrom torch.nn.modules.batchnorm import BatchNorm2d\nfrom torch.nn import ReLU, LeakyReLU\nfrom torch.nn.parameter import Parameter\n\n\nclass TLU(nn.Module):\n    def __init__(self, num_features):\n        \"\"\"max(y, tau) = max(y - tau, 0) + tau = ReLU(y - tau) + tau\"\"\"\n        super(TLU, self).__init__()\n        self.num_features = num_features\n        self.tau = Parameter(torch.Tensor(num_features))\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        nn.init.zeros_(self.tau)\n\n    def extra_repr(self):\n        return 'num_features={num_features}'.format(**self.__dict__)\n\n    def forward(self, x):\n        return torch.max(x, self.tau.view(1, self.num_features, 1, 1))\n\n\nclass FRN(nn.Module):\n    def __init__(self, num_features, eps=1e-6, is_eps_leanable=False):\n        \"\"\"\n        weight = gamma, bias = beta\n        beta, gamma:\n            Variables of shape [1, 1, 1, C]. if TensorFlow\n            Variables of shape [1, C, 1, 1]. if PyTorch\n        eps: A scalar constant or learnable variable.\n        \"\"\"\n        super(FRN, self).__init__()\n\n        self.num_features = num_features\n        self.init_eps = eps\n        self.is_eps_leanable = is_eps_leanable\n\n        self.weight = Parameter(torch.Tensor(num_features))\n        self.bias = Parameter(torch.Tensor(num_features))\n        if is_eps_leanable:\n            self.eps = Parameter(torch.Tensor(1))\n        else:\n            self.register_buffer('eps', torch.Tensor([eps]))\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        nn.init.ones_(self.weight)\n        nn.init.zeros_(self.bias)\n        if self.is_eps_leanable:\n            nn.init.constant_(self.eps, self.init_eps)\n\n    def extra_repr(self):\n        return 'num_features={num_features}, eps={init_eps}'.format(**self.__dict__)\n\n    def forward(self, x):\n        \"\"\"\n        0, 1, 2, 3 -> (B, H, W, C) in TensorFlow\n        0, 1, 2, 3 -> (B, C, H, W) in PyTorch\n        TensorFlow code\n            nu2 = tf.reduce_mean(tf.square(x), axis=[1, 2], keepdims=True)\n            x = x * tf.rsqrt(nu2 + tf.abs(eps))\n            # This Code include TLU function max(y, tau)\n            return tf.maximum(gamma * x + beta, tau)\n        \"\"\"\n        # Compute the mean norm of activations per channel.\n        nu2 = x.pow(2).mean(dim=[2, 3], keepdim=True)\n\n        # Perform FRN.\n        x = x * torch.rsqrt(nu2 + self.eps.abs())\n\n        # Scale and Bias\n        x = self.weight.view(1, self.num_features, 1, 1) * x + self.bias.view(1, self.num_features, 1, 1)\n        # x = self.weight * x + self.bias\n        return x\n\n\ndef bnrelu_to_frn(module):\n    \"\"\"\n    Convert 'BatchNorm2d + ReLU' to 'FRN + TLU'\n    \"\"\"\n    mod = module\n    before_name = None\n    before_child = None\n    is_before_bn = False\n\n    for name, child in module.named_children():\n        if is_before_bn and isinstance(child, (ReLU, LeakyReLU)):\n            # Convert BN to FRN\n            if isinstance(before_child, BatchNorm2d):\n                mod.add_module(\n                    before_name, FRN(num_features=before_child.num_features))\n            else:\n                raise NotImplementedError()\n\n            # Convert ReLU to TLU\n            mod.add_module(name, TLU(num_features=before_child.num_features))\n        else:\n            mod.add_module(name, bnrelu_to_frn(child))\n\n        before_name = name\n        before_child = child\n        is_before_bn = isinstance(child, BatchNorm2d)\n    return mod\n\n\ndef convert(module, flag_name):\n    mod = module\n    before_ch = None\n    for name, child in module.named_children():\n        if hasattr(child, flag_name) and getattr(child, flag_name):\n            if isinstance(child, BatchNorm2d):\n                before_ch = child.num_features\n                mod.add_module(name, FRN(num_features=child.num_features))\n            # TODO bn is no good...\n            if isinstance(child, (ReLU, LeakyReLU)):\n                mod.add_module(name, TLU(num_features=before_ch))\n        else:\n            mod.add_module(name, convert(child, flag_name))\n    return mod\n\n\ndef remove_flags(module, flag_name):\n    mod = module\n    for name, child in module.named_children():\n        if hasattr(child, 'is_convert_frn'):\n            delattr(child, flag_name)\n            mod.add_module(name, remove_flags(child, flag_name))\n        else:\n            mod.add_module(name, remove_flags(child, flag_name))\n    return mod\n\n\ndef bnrelu_to_frn2(model, input_size=(3, 128, 128), batch_size=2, flag_name='is_convert_frn'):\n    forard_hooks = list()\n    backward_hooks = list()\n\n    is_before_bn = [False]\n\n    def register_forward_hook(module):\n        def hook(self, input, output):\n            if isinstance(module, (nn.Sequential, nn.ModuleList)) or (module == model):\n                is_before_bn.append(False)\n                return\n\n            # input and output is required in hook def\n            is_converted = is_before_bn[-1] and isinstance(self, (ReLU, LeakyReLU))\n            if is_converted:\n                setattr(self, flag_name, True)\n            is_before_bn.append(isinstance(self, BatchNorm2d))\n\n        forard_hooks.append(module.register_forward_hook(hook))\n\n    is_before_relu = [False]\n\n    def register_backward_hook(module):\n        def hook(self, input, output):\n            if isinstance(module, (nn.Sequential, nn.ModuleList)) or (module == model):\n                is_before_relu.append(False)\n                return\n            is_converted = is_before_relu[-1] and isinstance(self, BatchNorm2d)\n            if is_converted:\n                setattr(self, flag_name, True)\n            is_before_relu.append(isinstance(self, (ReLU, LeakyReLU)))\n\n        backward_hooks.append(module.register_backward_hook(hook))\n\n    # multiple inputs to the network\n    if isinstance(input_size, tuple):\n        input_size = [input_size]\n\n    # batch_size of 2 for batchnorm\n    x = [torch.rand(batch_size, *in_size) for in_size in input_size]\n\n    # register hook\n    model.apply(register_forward_hook)\n    model.apply(register_backward_hook)\n\n    # make a forward pass\n    output = model(*x)\n    output.sum().backward()  # Raw output is not enabled to use backward()\n\n    # remove these hooks\n    for h in forard_hooks:\n        h.remove()\n    for h in backward_hooks:\n        h.remove()\n\n    model = convert(model, flag_name=flag_name)\n    model = remove_flags(model, flag_name=flag_name)\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/layers/gather_layer.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# based on: https://github.com/open-mmlab/OpenSelfSup/blob/master/openselfsup/models/utils/gather_layer.py\n\nimport torch\nimport torch.distributed as dist\n\n\nclass GatherLayer(torch.autograd.Function):\n    \"\"\"Gather tensors from all process, supporting backward propagation.\n    \"\"\"\n\n    @staticmethod\n    def forward(ctx, input):\n        ctx.save_for_backward(input)\n        output = [torch.zeros_like(input) \\\n                  for _ in range(dist.get_world_size())]\n        dist.all_gather(output, input)\n        return tuple(output)\n\n    @staticmethod\n    def backward(ctx, *grads):\n        input, = ctx.saved_tensors\n        grad_out = torch.zeros_like(input)\n        grad_out[:] = grads[dist.get_rank()]\n        return grad_out\n"
  },
  {
    "path": "fast_reid/fastreid/layers/helpers.py",
    "content": "\"\"\" Layer/Module Helpers\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\nimport collections.abc\nfrom itertools import repeat\n\n\n# From PyTorch internals\ndef _ntuple(n):\n    def parse(x):\n        if isinstance(x, collections.abc.Iterable):\n            return x\n        return tuple(repeat(x, n))\n\n    return parse\n\n\nto_1tuple = _ntuple(1)\nto_2tuple = _ntuple(2)\nto_3tuple = _ntuple(3)\nto_4tuple = _ntuple(4)\nto_ntuple = _ntuple\n\n\ndef make_divisible(v, divisor=8, min_value=None):\n    min_value = min_value or divisor\n    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n    # Make sure that round down does not go down by more than 10%.\n    if new_v < 0.9 * v:\n        new_v += divisor\n    return new_v\n"
  },
  {
    "path": "fast_reid/fastreid/layers/non_local.py",
    "content": "# encoding: utf-8\n\n\nimport torch\nfrom torch import nn\nfrom .batch_norm import get_norm\n\n\nclass Non_local(nn.Module):\n    def __init__(self, in_channels, bn_norm, reduc_ratio=2):\n        super(Non_local, self).__init__()\n\n        self.in_channels = in_channels\n        self.inter_channels = reduc_ratio // reduc_ratio\n\n        self.g = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,\n                           kernel_size=1, stride=1, padding=0)\n\n        self.W = nn.Sequential(\n            nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels,\n                      kernel_size=1, stride=1, padding=0),\n            get_norm(bn_norm, self.in_channels),\n        )\n        nn.init.constant_(self.W[1].weight, 0.0)\n        nn.init.constant_(self.W[1].bias, 0.0)\n\n        self.theta = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,\n                               kernel_size=1, stride=1, padding=0)\n\n        self.phi = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,\n                             kernel_size=1, stride=1, padding=0)\n\n    def forward(self, x):\n        \"\"\"\n                :param x: (b, t, h, w)\n                :return x: (b, t, h, w)\n        \"\"\"\n        batch_size = x.size(0)\n        g_x = self.g(x).view(batch_size, self.inter_channels, -1)\n        g_x = g_x.permute(0, 2, 1)\n\n        theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)\n        theta_x = theta_x.permute(0, 2, 1)\n        phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)\n        f = torch.matmul(theta_x, phi_x)\n        N = f.size(-1)\n        f_div_C = f / N\n\n        y = torch.matmul(f_div_C, g_x)\n        y = y.permute(0, 2, 1).contiguous()\n        y = y.view(batch_size, self.inter_channels, *x.size()[2:])\n        W_y = self.W(y)\n        z = W_y + x\n        return z\n"
  },
  {
    "path": "fast_reid/fastreid/layers/pooling.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\n__all__ = [\n    'Identity',\n    'Flatten',\n    'GlobalAvgPool',\n    'GlobalMaxPool',\n    'GeneralizedMeanPooling',\n    'GeneralizedMeanPoolingP',\n    'FastGlobalAvgPool',\n    'AdaptiveAvgMaxPool',\n    'ClipGlobalAvgPool',\n]\n\n\nclass Identity(nn.Module):\n    def __init__(self, *args, **kwargs):\n        super().__init__()\n\n    def forward(self, input):\n        return input\n\n\nclass Flatten(nn.Module):\n    def __init__(self, *args, **kwargs):\n        super().__init__()\n\n    def forward(self, input):\n        return input.view(input.size(0), -1, 1, 1)\n\n\nclass GlobalAvgPool(nn.AdaptiveAvgPool2d):\n    def __init__(self, output_size=1, *args, **kwargs):\n        super().__init__(output_size)\n\n\nclass GlobalMaxPool(nn.AdaptiveMaxPool2d):\n    def __init__(self, output_size=1, *args, **kwargs):\n        super().__init__(output_size)\n\n\nclass GeneralizedMeanPooling(nn.Module):\n    r\"\"\"Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.\n    The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`\n        - At p = infinity, one gets Max Pooling\n        - At p = 1, one gets Average Pooling\n    The output is of size H x W, for any input size.\n    The number of output features is equal to the number of input planes.\n    Args:\n        output_size: the target output size of the image of the form H x W.\n                     Can be a tuple (H, W) or a single H for a square image H x H\n                     H and W can be either a ``int``, or ``None`` which means the size will\n                     be the same as that of the input.\n    \"\"\"\n\n    def __init__(self, norm=3, output_size=(1, 1), eps=1e-6, *args, **kwargs):\n        super(GeneralizedMeanPooling, self).__init__()\n        assert norm > 0\n        self.p = float(norm)\n        self.output_size = output_size\n        self.eps = eps\n\n    def forward(self, x):\n        x = x.clamp(min=self.eps).pow(self.p)\n        return F.adaptive_avg_pool2d(x, self.output_size).pow(1. / self.p)\n\n    def __repr__(self):\n        return self.__class__.__name__ + '(' \\\n               + str(self.p) + ', ' \\\n               + 'output_size=' + str(self.output_size) + ')'\n\n\nclass GeneralizedMeanPoolingP(GeneralizedMeanPooling):\n    \"\"\" Same, but norm is trainable\n    \"\"\"\n\n    def __init__(self, norm=3, output_size=(1, 1), eps=1e-6, *args, **kwargs):\n        super(GeneralizedMeanPoolingP, self).__init__(norm, output_size, eps)\n        self.p = nn.Parameter(torch.ones(1) * norm)\n\n\nclass AdaptiveAvgMaxPool(nn.Module):\n    def __init__(self, output_size=1, *args, **kwargs):\n        super().__init__()\n        self.gap = FastGlobalAvgPool()\n        self.gmp = GlobalMaxPool(output_size)\n\n    def forward(self, x):\n        avg_feat = self.gap(x)\n        max_feat = self.gmp(x)\n        feat = avg_feat + max_feat\n        return feat\n\n\nclass FastGlobalAvgPool(nn.Module):\n    def __init__(self, flatten=False, *args, **kwargs):\n        super().__init__()\n        self.flatten = flatten\n\n    def forward(self, x):\n        if self.flatten:\n            in_size = x.size()\n            return x.view((in_size[0], in_size[1], -1)).mean(dim=2)\n        else:\n            return x.view(x.size(0), x.size(1), -1).mean(-1).view(x.size(0), x.size(1), 1, 1)\n\n\nclass ClipGlobalAvgPool(nn.Module):\n    def __init__(self, *args, **kwargs):\n        super().__init__()\n        self.avgpool = FastGlobalAvgPool()\n\n    def forward(self, x):\n        x = self.avgpool(x)\n        x = torch.clamp(x, min=0., max=1.)\n        return x\n"
  },
  {
    "path": "fast_reid/fastreid/layers/se_layer.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom torch import nn\n\n\nclass SELayer(nn.Module):\n    def __init__(self, channel, reduction=16):\n        super(SELayer, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n        self.fc = nn.Sequential(\n            nn.Linear(channel, int(channel / reduction), bias=False),\n            nn.ReLU(inplace=True),\n            nn.Linear(int(channel / reduction), channel, bias=False),\n            nn.Sigmoid()\n        )\n\n    def forward(self, x):\n        b, c, _, _ = x.size()\n        y = self.avg_pool(x).view(b, c)\n        y = self.fc(y).view(b, c, 1, 1)\n        return x * y.expand_as(x)\n"
  },
  {
    "path": "fast_reid/fastreid/layers/splat.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torch.nn import Conv2d, ReLU\nfrom torch.nn.modules.utils import _pair\nfrom fast_reid.fastreid.layers import get_norm\n\n\nclass SplAtConv2d(nn.Module):\n    \"\"\"Split-Attention Conv2d\n    \"\"\"\n\n    def __init__(self, in_channels, channels, kernel_size, stride=(1, 1), padding=(0, 0),\n                 dilation=(1, 1), groups=1, bias=True,\n                 radix=2, reduction_factor=4,\n                 rectify=False, rectify_avg=False, norm_layer=None,\n                 dropblock_prob=0.0, **kwargs):\n        super(SplAtConv2d, self).__init__()\n        padding = _pair(padding)\n        self.rectify = rectify and (padding[0] > 0 or padding[1] > 0)\n        self.rectify_avg = rectify_avg\n        inter_channels = max(in_channels * radix // reduction_factor, 32)\n        self.radix = radix\n        self.cardinality = groups\n        self.channels = channels\n        self.dropblock_prob = dropblock_prob\n        if self.rectify:\n            from rfconv import RFConv2d\n            self.conv = RFConv2d(in_channels, channels * radix, kernel_size, stride, padding, dilation,\n                                 groups=groups * radix, bias=bias, average_mode=rectify_avg, **kwargs)\n        else:\n            self.conv = Conv2d(in_channels, channels * radix, kernel_size, stride, padding, dilation,\n                               groups=groups * radix, bias=bias, **kwargs)\n        self.use_bn = norm_layer is not None\n        if self.use_bn:\n            self.bn0 = get_norm(norm_layer, channels * radix)\n        self.relu = ReLU(inplace=True)\n        self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)\n        if self.use_bn:\n            self.bn1 = get_norm(norm_layer, inter_channels)\n        self.fc2 = Conv2d(inter_channels, channels * radix, 1, groups=self.cardinality)\n        if dropblock_prob > 0.0:\n            self.dropblock = DropBlock2D(dropblock_prob, 3)\n        self.rsoftmax = rSoftMax(radix, groups)\n\n    def forward(self, x):\n        x = self.conv(x)\n        if self.use_bn:\n            x = self.bn0(x)\n        if self.dropblock_prob > 0.0:\n            x = self.dropblock(x)\n        x = self.relu(x)\n\n        batch, rchannel = x.shape[:2]\n        if self.radix > 1:\n            if torch.__version__ < '1.5':\n                splited = torch.split(x, int(rchannel // self.radix), dim=1)\n            else:\n                splited = torch.split(x, rchannel // self.radix, dim=1)\n            gap = sum(splited)\n        else:\n            gap = x\n        gap = F.adaptive_avg_pool2d(gap, 1)\n        gap = self.fc1(gap)\n\n        if self.use_bn:\n            gap = self.bn1(gap)\n        gap = self.relu(gap)\n\n        atten = self.fc2(gap)\n        atten = self.rsoftmax(atten).view(batch, -1, 1, 1)\n\n        if self.radix > 1:\n            if torch.__version__ < '1.5':\n                attens = torch.split(atten, int(rchannel // self.radix), dim=1)\n            else:\n                attens = torch.split(atten, rchannel // self.radix, dim=1)\n            out = sum([att * split for (att, split) in zip(attens, splited)])\n        else:\n            out = atten * x\n        return out.contiguous()\n\n\nclass rSoftMax(nn.Module):\n    def __init__(self, radix, cardinality):\n        super().__init__()\n        self.radix = radix\n        self.cardinality = cardinality\n\n    def forward(self, x):\n        batch = x.size(0)\n        if self.radix > 1:\n            x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)\n            x = F.softmax(x, dim=1)\n            x = x.reshape(batch, -1)\n        else:\n            x = torch.sigmoid(x)\n        return x\n\n\nclass DropBlock2D(object):\n    def __init__(self, *args, **kwargs):\n        raise NotImplementedError\n"
  },
  {
    "path": "fast_reid/fastreid/layers/weight_init.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport math\nimport warnings\n\nimport torch\nfrom torch import nn, Tensor\n\n\ndef weights_init_kaiming(m):\n    classname = m.__class__.__name__\n    if classname.find('Linear') != -1:\n        nn.init.normal_(m.weight, 0, 0.01)\n        if m.bias is not None:\n            nn.init.constant_(m.bias, 0.0)\n    elif classname.find('Conv') != -1:\n        nn.init.kaiming_normal_(m.weight, mode='fan_out')\n        if m.bias is not None:\n            nn.init.constant_(m.bias, 0.0)\n    elif classname.find('BatchNorm') != -1:\n        if m.affine:\n            nn.init.constant_(m.weight, 1.0)\n            nn.init.constant_(m.bias, 0.0)\n\n\ndef weights_init_classifier(m):\n    classname = m.__class__.__name__\n    if classname.find('Linear') != -1:\n        nn.init.normal_(m.weight, std=0.001)\n        if m.bias is not None:\n            nn.init.constant_(m.bias, 0.0)\n\n\nfrom torch.nn.init import _calculate_fan_in_and_fan_out\n\n\ndef _no_grad_trunc_normal_(tensor, mean, std, a, b):\n    # Cut & paste from PyTorch official master until it's in a few official releases - RW\n    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf\n    def norm_cdf(x):\n        # Computes standard normal cumulative distribution function\n        return (1. + math.erf(x / math.sqrt(2.))) / 2.\n\n    if (mean < a - 2 * std) or (mean > b + 2 * std):\n        warnings.warn(\"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. \"\n                      \"The distribution of values may be incorrect.\",\n                      stacklevel=2)\n\n    with torch.no_grad():\n        # Values are generated by using a truncated uniform distribution and\n        # then using the inverse CDF for the normal distribution.\n        # Get upper and lower cdf values\n        l = norm_cdf((a - mean) / std)\n        u = norm_cdf((b - mean) / std)\n\n        # Uniformly fill tensor with values from [l, u], then translate to\n        # [2l-1, 2u-1].\n        tensor.uniform_(2 * l - 1, 2 * u - 1)\n\n        # Use inverse cdf transform for normal distribution to get truncated\n        # standard normal\n        tensor.erfinv_()\n\n        # Transform to proper mean, std\n        tensor.mul_(std * math.sqrt(2.))\n        tensor.add_(mean)\n\n        # Clamp to ensure it's in the proper range\n        tensor.clamp_(min=a, max=b)\n        return tensor\n\n\ndef trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):\n    # type: (Tensor, float, float, float, float) -> Tensor\n    r\"\"\"Fills the input Tensor with values drawn from a truncated\n    normal distribution. The values are effectively drawn from the\n    normal distribution :math:`\\mathcal{N}(\\text{mean}, \\text{std}^2)`\n    with values outside :math:`[a, b]` redrawn until they are within\n    the bounds. The method used for generating the random values works\n    best when :math:`a \\leq \\text{mean} \\leq b`.\n    Args:\n        tensor: an n-dimensional `torch.Tensor`\n        mean: the mean of the normal distribution\n        std: the standard deviation of the normal distribution\n        a: the minimum cutoff value\n        b: the maximum cutoff value\n    Examples:\n        >>> w = torch.empty(3, 5)\n        >>> nn.init.trunc_normal_(w)\n    \"\"\"\n    return _no_grad_trunc_normal_(tensor, mean, std, a, b)\n\n\ndef variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):\n    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)\n    if mode == 'fan_in':\n        denom = fan_in\n    elif mode == 'fan_out':\n        denom = fan_out\n    elif mode == 'fan_avg':\n        denom = (fan_in + fan_out) / 2\n\n    variance = scale / denom\n\n    if distribution == \"truncated_normal\":\n        # constant is stddev of standard normal truncated to (-2, 2)\n        trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)\n    elif distribution == \"normal\":\n        tensor.normal_(std=math.sqrt(variance))\n    elif distribution == \"uniform\":\n        bound = math.sqrt(3 * variance)\n        tensor.uniform_(-bound, bound)\n    else:\n        raise ValueError(f\"invalid distribution {distribution}\")\n\n\ndef lecun_normal_(tensor):\n    variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom . import losses\nfrom .backbones import (\n    BACKBONE_REGISTRY,\n    build_resnet_backbone,\n    build_backbone,\n)\nfrom .heads import (\n    REID_HEADS_REGISTRY,\n    build_heads,\n    EmbeddingHead,\n)\nfrom .meta_arch import (\n    build_model,\n    META_ARCH_REGISTRY,\n)\n\n__all__ = [k for k in globals().keys() if not k.startswith(\"_\")]"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .build import build_backbone, BACKBONE_REGISTRY\n\nfrom .resnet import build_resnet_backbone\nfrom .osnet import build_osnet_backbone\nfrom .resnest import build_resnest_backbone\nfrom .resnext import build_resnext_backbone\nfrom .regnet import build_regnet_backbone, build_effnet_backbone\nfrom .shufflenet import build_shufflenetv2_backbone\nfrom .mobilenet import build_mobilenetv2_backbone\nfrom .mobilenetv3 import build_mobilenetv3_backbone\nfrom .repvgg import build_repvgg_backbone\nfrom .vision_transformer import build_vit_backbone\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/build.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom ...utils.registry import Registry\n\nBACKBONE_REGISTRY = Registry(\"BACKBONE\")\nBACKBONE_REGISTRY.__doc__ = \"\"\"\nRegistry for backbones, which extract feature maps from images\nThe registered object must be a callable that accepts two arguments:\n1. A :class:`fastreid.config.CfgNode`\nIt must returns an instance of :class:`Backbone`.\n\"\"\"\n\n\ndef build_backbone(cfg):\n    \"\"\"\n    Build a backbone from `cfg.MODEL.BACKBONE.NAME`.\n    Returns:\n        an instance of :class:`Backbone`\n    \"\"\"\n\n    backbone_name = cfg.MODEL.BACKBONE.NAME\n    backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg)\n    return backbone\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/mobilenet.py",
    "content": "\"\"\"\nCreates a MobileNetV2 Model as defined in:\nMark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018).\nMobileNetV2: Inverted Residuals and Linear Bottlenecks\narXiv preprint arXiv:1801.04381.\nimport from https://github.com/tonylins/pytorch-mobilenet-v2\n\"\"\"\nimport logging\nimport math\n\nimport torch\nimport torch.nn as nn\n\nfrom fast_reid.fastreid.layers import get_norm\nfrom fast_reid.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message\nfrom .build import BACKBONE_REGISTRY\n\nlogger = logging.getLogger(__name__)\n\n\ndef _make_divisible(v, divisor, min_value=None):\n    \"\"\"\n    This function is taken from the original tf repo.\n    It ensures that all layers have a channel number that is divisible by 8\n    It can be seen here:\n    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py\n    :param v:\n    :param divisor:\n    :param min_value:\n    :return:\n    \"\"\"\n    if min_value is None:\n        min_value = divisor\n    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n    # Make sure that round down does not go down by more than 10%.\n    if new_v < 0.9 * v:\n        new_v += divisor\n    return new_v\n\n\ndef conv_3x3_bn(inp, oup, stride, bn_norm):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n        get_norm(bn_norm, oup),\n        nn.ReLU6(inplace=True)\n    )\n\n\ndef conv_1x1_bn(inp, oup, bn_norm):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n        get_norm(bn_norm, oup),\n        nn.ReLU6(inplace=True)\n    )\n\n\nclass InvertedResidual(nn.Module):\n    def __init__(self, inp, oup, bn_norm, stride, expand_ratio):\n        super(InvertedResidual, self).__init__()\n        assert stride in [1, 2]\n\n        hidden_dim = round(inp * expand_ratio)\n        self.identity = stride == 1 and inp == oup\n\n        if expand_ratio == 1:\n            self.conv = nn.Sequential(\n                # dw\n                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),\n                get_norm(bn_norm, hidden_dim),\n                nn.ReLU6(inplace=True),\n                # pw-linear\n                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\n                get_norm(bn_norm, oup),\n            )\n        else:\n            self.conv = nn.Sequential(\n                # pw\n                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),\n                get_norm(bn_norm, hidden_dim),\n                nn.ReLU6(inplace=True),\n                # dw\n                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),\n                get_norm(bn_norm, hidden_dim),\n                nn.ReLU6(inplace=True),\n                # pw-linear\n                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup),\n            )\n\n    def forward(self, x):\n        if self.identity:\n            return x + self.conv(x)\n        else:\n            return self.conv(x)\n\n\nclass MobileNetV2(nn.Module):\n    def __init__(self, bn_norm, width_mult=1.):\n        super(MobileNetV2, self).__init__()\n        # setting of inverted residual blocks\n        self.cfgs = [\n            # t, c, n, s\n            [1, 16, 1, 1],\n            [6, 24, 2, 2],\n            [6, 32, 3, 2],\n            [6, 64, 4, 2],\n            [6, 96, 3, 1],\n            [6, 160, 3, 2],\n            [6, 320, 1, 1],\n        ]\n\n        # building first layer\n        input_channel = _make_divisible(32 * width_mult, 4 if width_mult == 0.1 else 8)\n        layers = [conv_3x3_bn(3, input_channel, 2, bn_norm)]\n        # building inverted residual blocks\n        block = InvertedResidual\n        for t, c, n, s in self.cfgs:\n            output_channel = _make_divisible(c * width_mult, 4 if width_mult == 0.1 else 8)\n            for i in range(n):\n                layers.append(block(input_channel, output_channel, bn_norm, s if i == 0 else 1, t))\n                input_channel = output_channel\n        self.features = nn.Sequential(*layers)\n        # building last several layers\n        output_channel = _make_divisible(1280 * width_mult, 4 if width_mult == 0.1 else 8) if width_mult > 1.0 else 1280\n        self.conv = conv_1x1_bn(input_channel, output_channel, bn_norm)\n\n        self._initialize_weights()\n\n    def forward(self, x):\n        x = self.features(x)\n        x = self.conv(x)\n        return x\n\n    def _initialize_weights(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2. / n))\n                if m.bias is not None:\n                    m.bias.data.zero_()\n            elif isinstance(m, nn.BatchNorm2d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n            elif isinstance(m, nn.Linear):\n                m.weight.data.normal_(0, 0.01)\n                m.bias.data.zero_()\n\n\n@BACKBONE_REGISTRY.register()\ndef build_mobilenetv2_backbone(cfg):\n    \"\"\"\n    Create a MobileNetV2 instance from config.\n    Returns:\n        MobileNetV2: a :class: `MobileNetV2` instance.\n    \"\"\"\n    # fmt: off\n    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    bn_norm       = cfg.MODEL.BACKBONE.NORM\n    depth         = cfg.MODEL.BACKBONE.DEPTH\n    # fmt: on\n\n    width_mult = {\n        \"1.0x\": 1.0,\n        \"0.75x\": 0.75,\n        \"0.5x\": 0.5,\n        \"0.35x\": 0.35,\n        '0.25x': 0.25,\n        '0.1x': 0.1,\n    }[depth]\n\n    model = MobileNetV2(bn_norm, width_mult)\n\n    if pretrain:\n        try:\n            state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))\n            logger.info(f\"Loading pretrained model from {pretrain_path}\")\n        except FileNotFoundError as e:\n            logger.info(f'{pretrain_path} is not found! Please check this path.')\n            raise e\n        except KeyError as e:\n            logger.info(\"State dict keys error! Please check the state dict.\")\n            raise e\n\n        incompatible = model.load_state_dict(state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/mobilenetv3.py",
    "content": "from functools import partial\nfrom typing import Any, Callable, Dict, List, Optional, Sequence\n\nimport torch\nfrom torch import nn, Tensor\nfrom torch.nn import functional as F\n\n#The style of importing Considers compatibility for the diversity of torchvision versions\ntry:\n    from torchvision.models.utils import load_state_dict_from_url\nexcept ImportError:\n    try:\n        from torch.hub import load_state_dict_from_url\n    except ImportError:\n        from torch.utils.model_zoo import load_url as load_state_dict_from_url\n\nfrom fast_reid.fastreid.layers import get_norm\nfrom .build import BACKBONE_REGISTRY\nfrom .mobilenet import _make_divisible\n\n# https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenetv3.py\n\nmodel_urls = {\n    \"Large\": \"https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth\",\n    \"Small\": \"https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth\",\n}\n\n\ndef conv_1x1_bn(inp, oup, bn_norm):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n        get_norm(bn_norm, oup),\n        nn.ReLU6(inplace=True)\n    )\n\n\nclass ConvBNActivation(nn.Sequential):\n    def __init__(\n            self,\n            in_planes: int,\n            out_planes: int,\n            kernel_size: int = 3,\n            stride: int = 1,\n            groups: int = 1,\n            bn_norm=None,\n            activation_layer: Optional[Callable[..., nn.Module]] = None,\n            dilation: int = 1,\n    ) -> None:\n        padding = (kernel_size - 1) // 2 * dilation\n        if activation_layer is None:\n            activation_layer = nn.ReLU6\n        super(ConvBNActivation, self).__init__(\n            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, dilation=dilation, groups=groups,\n                      bias=False),\n            get_norm(bn_norm, out_planes),\n            activation_layer(inplace=True)\n        )\n        self.out_channels = out_planes\n\n\nclass SqueezeExcitation(nn.Module):\n    def __init__(self, input_channels: int, squeeze_factor: int = 4):\n        super().__init__()\n        squeeze_channels = _make_divisible(input_channels // squeeze_factor, 8)\n        self.fc1 = nn.Conv2d(input_channels, squeeze_channels, 1)\n        self.relu = nn.ReLU(inplace=True)\n        self.fc2 = nn.Conv2d(squeeze_channels, input_channels, 1)\n\n    def _scale(self, input: Tensor, inplace: bool) -> Tensor:\n        scale = F.adaptive_avg_pool2d(input, 1)\n        scale = self.fc1(scale)\n        scale = self.relu(scale)\n        scale = self.fc2(scale)\n        return F.hardsigmoid(scale, inplace=inplace)\n\n    def forward(self, input: Tensor) -> Tensor:\n        scale = self._scale(input, True)\n        return scale * input\n\n\nclass InvertedResidualConfig:\n    def __init__(self, input_channels: int, kernel: int, expanded_channels: int, out_channels: int, use_se: bool,\n                 activation: str, stride: int, dilation: int, width_mult: float):\n        self.input_channels = self.adjust_channels(input_channels, width_mult)\n        self.kernel = kernel\n        self.expanded_channels = self.adjust_channels(expanded_channels, width_mult)\n        self.out_channels = self.adjust_channels(out_channels, width_mult)\n        self.use_se = use_se\n        self.use_hs = activation == \"HS\"\n        self.stride = stride\n        self.dilation = dilation\n\n    @staticmethod\n    def adjust_channels(channels: int, width_mult: float):\n        return _make_divisible(channels * width_mult, 8)\n\n\nclass InvertedResidual(nn.Module):\n    def __init__(self, cnf: InvertedResidualConfig, bn_norm,\n                 se_layer: Callable[..., nn.Module] = SqueezeExcitation):\n        super().__init__()\n        if not (1 <= cnf.stride <= 2):\n            raise ValueError('illegal stride value')\n\n        self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels\n\n        layers: List[nn.Module] = []\n        activation_layer = nn.Hardswish if cnf.use_hs else nn.ReLU\n\n        # expand\n        if cnf.expanded_channels != cnf.input_channels:\n            layers.append(ConvBNActivation(cnf.input_channels, cnf.expanded_channels, kernel_size=1,\n                                           bn_norm=bn_norm, activation_layer=activation_layer))\n\n        # depthwise\n        stride = 1 if cnf.dilation > 1 else cnf.stride\n        layers.append(ConvBNActivation(cnf.expanded_channels, cnf.expanded_channels, kernel_size=cnf.kernel,\n                                       stride=stride, dilation=cnf.dilation, groups=cnf.expanded_channels,\n                                       bn_norm=bn_norm, activation_layer=activation_layer))\n        if cnf.use_se:\n            layers.append(se_layer(cnf.expanded_channels))\n\n        # project\n        layers.append(ConvBNActivation(cnf.expanded_channels, cnf.out_channels, kernel_size=1, bn_norm=bn_norm,\n                                       activation_layer=nn.Identity))\n\n        self.block = nn.Sequential(*layers)\n        self.out_channels = cnf.out_channels\n        self._is_cn = cnf.stride > 1\n\n    def forward(self, input: Tensor) -> Tensor:\n        result = self.block(input)\n        if self.use_res_connect:\n            result += input\n        return result\n\n\nclass MobileNetV3(nn.Module):\n    def __init__(\n            self,\n            bn_norm,\n            inverted_residual_setting: List[InvertedResidualConfig],\n            last_channel: int,\n            block: Optional[Callable[..., nn.Module]] = None,\n    ) -> None:\n        \"\"\"\n        MobileNet V3 main class\n        Args:\n            inverted_residual_setting (List[InvertedResidualConfig]): Network structure\n            last_channel (int): The number of channels on the penultimate layer\n            block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet\n        \"\"\"\n        super().__init__()\n\n        if not inverted_residual_setting:\n            raise ValueError(\"The inverted_residual_setting should not be empty\")\n        elif not (isinstance(inverted_residual_setting, Sequence) and\n                  all([isinstance(s, InvertedResidualConfig) for s in inverted_residual_setting])):\n            raise TypeError(\"The inverted_residual_setting should be List[InvertedResidualConfig]\")\n\n        if block is None:\n            block = InvertedResidual\n\n        layers: List[nn.Module] = []\n\n        # building first layer\n        firstconv_output_channels = inverted_residual_setting[0].input_channels\n        layers.append(ConvBNActivation(3, firstconv_output_channels, kernel_size=3, stride=2, bn_norm=bn_norm,\n                                       activation_layer=nn.Hardswish))\n\n        # building inverted residual blocks\n        for cnf in inverted_residual_setting:\n            layers.append(block(cnf, bn_norm))\n\n        # building last several layers\n        lastconv_input_channels = inverted_residual_setting[-1].out_channels\n        lastconv_output_channels = 6 * lastconv_input_channels\n        layers.append(ConvBNActivation(lastconv_input_channels, lastconv_output_channels, kernel_size=1,\n                                       bn_norm=bn_norm, activation_layer=nn.Hardswish))\n\n        self.features = nn.Sequential(*layers)\n        self.conv = conv_1x1_bn(lastconv_output_channels, last_channel, bn_norm)\n\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.kaiming_normal_(m.weight, mode='fan_out')\n                if m.bias is not None:\n                    nn.init.zeros_(m.bias)\n            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):\n                nn.init.ones_(m.weight)\n                nn.init.zeros_(m.bias)\n            elif isinstance(m, nn.Linear):\n                nn.init.normal_(m.weight, 0, 0.01)\n                nn.init.zeros_(m.bias)\n\n    def _forward_impl(self, x: Tensor) -> Tensor:\n        x = self.features(x)\n        x = self.conv(x)\n        return x\n\n    def forward(self, x: Tensor) -> Tensor:\n        return self._forward_impl(x)\n\n\ndef _mobilenet_v3_conf(arch: str, params: Dict[str, Any]):\n    # non-public config parameters\n    reduce_divider = 2 if params.pop('_reduced_tail', False) else 1\n    dilation = 2 if params.pop('_dilated', False) else 1\n    width_mult = params.pop('_width_mult', 1.0)\n\n    bneck_conf = partial(InvertedResidualConfig, width_mult=width_mult)\n    adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_mult=width_mult)\n\n    if arch == \"Large\":\n        inverted_residual_setting = [\n            bneck_conf(16, 3, 16, 16, False, \"RE\", 1, 1),\n            bneck_conf(16, 3, 64, 24, False, \"RE\", 2, 1),  # C1\n            bneck_conf(24, 3, 72, 24, False, \"RE\", 1, 1),\n            bneck_conf(24, 5, 72, 40, True, \"RE\", 2, 1),  # C2\n            bneck_conf(40, 5, 120, 40, True, \"RE\", 1, 1),\n            bneck_conf(40, 5, 120, 40, True, \"RE\", 1, 1),\n            bneck_conf(40, 3, 240, 80, False, \"HS\", 2, 1),  # C3\n            bneck_conf(80, 3, 200, 80, False, \"HS\", 1, 1),\n            bneck_conf(80, 3, 184, 80, False, \"HS\", 1, 1),\n            bneck_conf(80, 3, 184, 80, False, \"HS\", 1, 1),\n            bneck_conf(80, 3, 480, 112, True, \"HS\", 1, 1),\n            bneck_conf(112, 3, 672, 112, True, \"HS\", 1, 1),\n            bneck_conf(112, 5, 672, 160 // reduce_divider, True, \"HS\", 2, dilation),  # C4\n            bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, \"HS\", 1, dilation),\n            bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, \"HS\", 1, dilation),\n        ]\n        last_channel = adjust_channels(1280 // reduce_divider)  # C5\n    elif arch == \"Small\":\n        inverted_residual_setting = [\n            bneck_conf(16, 3, 16, 16, True, \"RE\", 2, 1),  # C1\n            bneck_conf(16, 3, 72, 24, False, \"RE\", 2, 1),  # C2\n            bneck_conf(24, 3, 88, 24, False, \"RE\", 1, 1),\n            bneck_conf(24, 5, 96, 40, True, \"HS\", 2, 1),  # C3\n            bneck_conf(40, 5, 240, 40, True, \"HS\", 1, 1),\n            bneck_conf(40, 5, 240, 40, True, \"HS\", 1, 1),\n            bneck_conf(40, 5, 120, 48, True, \"HS\", 1, 1),\n            bneck_conf(48, 5, 144, 48, True, \"HS\", 1, 1),\n            bneck_conf(48, 5, 288, 96 // reduce_divider, True, \"HS\", 2, dilation),  # C4\n            bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, \"HS\", 1, dilation),\n            bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, \"HS\", 1, dilation),\n        ]\n        last_channel = adjust_channels(1024 // reduce_divider)  # C5\n    else:\n        raise ValueError(\"Unsupported model type {}\".format(arch))\n\n    return inverted_residual_setting, last_channel\n\n\ndef _mobilenet_v3_model(\n        bn_norm,\n        depth: str,\n        pretrained: bool,\n        pretrain_path: str,\n        **kwargs: Any\n):\n    inverted_residual_setting, last_channel = _mobilenet_v3_conf(depth, kwargs)\n    model = MobileNetV3(bn_norm, inverted_residual_setting, last_channel, **kwargs)\n    if pretrained:\n        if pretrain_path:\n            state_dict = torch.load(pretrain_path)\n        else:\n            if model_urls.get(depth, None) is None:\n                raise ValueError(\"No checkpoint is available for model type {}\".format(depth))\n            state_dict = load_state_dict_from_url(model_urls[depth], progress=True)\n        model.load_state_dict(state_dict, strict=False)\n    return model\n\n\n@BACKBONE_REGISTRY.register()\ndef build_mobilenetv3_backbone(cfg):\n    pretrain = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    bn_norm = cfg.MODEL.BACKBONE.NORM\n    depth = cfg.MODEL.BACKBONE.DEPTH\n\n    model = _mobilenet_v3_model(bn_norm, depth, pretrain, pretrain_path)\n\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/osnet.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# based on:\n# https://github.com/KaiyangZhou/deep-person-reid/blob/master/torchreid/models/osnet.py\n\nimport logging\n\nimport torch\nfrom torch import nn\n\nfrom fast_reid.fastreid.layers import get_norm\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message\nfrom .build import BACKBONE_REGISTRY\n\nlogger = logging.getLogger(__name__)\nmodel_urls = {\n    'osnet_x1_0':\n        'https://drive.google.com/uc?id=1LaG1EJpHrxdAxKnSCJ_i0u-nbxSAeiFY',\n    'osnet_x0_75':\n        'https://drive.google.com/uc?id=1uwA9fElHOk3ZogwbeY5GkLI6QPTX70Hq',\n    'osnet_x0_5':\n        'https://drive.google.com/uc?id=16DGLbZukvVYgINws8u8deSaOqjybZ83i',\n    'osnet_x0_25':\n        'https://drive.google.com/uc?id=1rb8UN5ZzPKRc_xvtHlyDh-cSz88YX9hs',\n    'osnet_ibn_x1_0':\n        'https://drive.google.com/uc?id=1sr90V6irlYYDd4_4ISU2iruoRG8J__6l'\n}\n\n\n##########\n# Basic layers\n##########\nclass ConvLayer(nn.Module):\n    \"\"\"Convolution layer (conv + bn + relu).\"\"\"\n\n    def __init__(\n            self,\n            in_channels,\n            out_channels,\n            kernel_size,\n            bn_norm,\n            stride=1,\n            padding=0,\n            groups=1,\n            IN=False\n    ):\n        super(ConvLayer, self).__init__()\n        self.conv = nn.Conv2d(\n            in_channels,\n            out_channels,\n            kernel_size,\n            stride=stride,\n            padding=padding,\n            bias=False,\n            groups=groups\n        )\n        if IN:\n            self.bn = nn.InstanceNorm2d(out_channels, affine=True)\n        else:\n            self.bn = get_norm(bn_norm, out_channels)\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        x = self.conv(x)\n        x = self.bn(x)\n        x = self.relu(x)\n        return x\n\n\nclass Conv1x1(nn.Module):\n    \"\"\"1x1 convolution + bn + relu.\"\"\"\n\n    def __init__(self, in_channels, out_channels, bn_norm, stride=1, groups=1):\n        super(Conv1x1, self).__init__()\n        self.conv = nn.Conv2d(\n            in_channels,\n            out_channels,\n            1,\n            stride=stride,\n            padding=0,\n            bias=False,\n            groups=groups\n        )\n        self.bn = get_norm(bn_norm, out_channels)\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        x = self.conv(x)\n        x = self.bn(x)\n        x = self.relu(x)\n        return x\n\n\nclass Conv1x1Linear(nn.Module):\n    \"\"\"1x1 convolution + bn (w/o non-linearity).\"\"\"\n\n    def __init__(self, in_channels, out_channels, bn_norm, stride=1):\n        super(Conv1x1Linear, self).__init__()\n        self.conv = nn.Conv2d(\n            in_channels, out_channels, 1, stride=stride, padding=0, bias=False\n        )\n        self.bn = get_norm(bn_norm, out_channels)\n\n    def forward(self, x):\n        x = self.conv(x)\n        x = self.bn(x)\n        return x\n\n\nclass Conv3x3(nn.Module):\n    \"\"\"3x3 convolution + bn + relu.\"\"\"\n\n    def __init__(self, in_channels, out_channels, bn_norm, stride=1, groups=1):\n        super(Conv3x3, self).__init__()\n        self.conv = nn.Conv2d(\n            in_channels,\n            out_channels,\n            3,\n            stride=stride,\n            padding=1,\n            bias=False,\n            groups=groups\n        )\n        self.bn = get_norm(bn_norm, out_channels)\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        x = self.conv(x)\n        x = self.bn(x)\n        x = self.relu(x)\n        return x\n\n\nclass LightConv3x3(nn.Module):\n    \"\"\"Lightweight 3x3 convolution.\n    1x1 (linear) + dw 3x3 (nonlinear).\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels, bn_norm):\n        super(LightConv3x3, self).__init__()\n        self.conv1 = nn.Conv2d(\n            in_channels, out_channels, 1, stride=1, padding=0, bias=False\n        )\n        self.conv2 = nn.Conv2d(\n            out_channels,\n            out_channels,\n            3,\n            stride=1,\n            padding=1,\n            bias=False,\n            groups=out_channels\n        )\n        self.bn = get_norm(bn_norm, out_channels)\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.conv2(x)\n        x = self.bn(x)\n        x = self.relu(x)\n        return x\n\n\n##########\n# Building blocks for omni-scale feature learning\n##########\nclass ChannelGate(nn.Module):\n    \"\"\"A mini-network that generates channel-wise gates conditioned on input tensor.\"\"\"\n\n    def __init__(\n            self,\n            in_channels,\n            num_gates=None,\n            return_gates=False,\n            gate_activation='sigmoid',\n            reduction=16,\n            layer_norm=False\n    ):\n        super(ChannelGate, self).__init__()\n        if num_gates is None: num_gates = in_channels\n        self.return_gates = return_gates\n\n        self.global_avgpool = nn.AdaptiveAvgPool2d(1)\n\n        self.fc1 = nn.Conv2d(\n            in_channels,\n            in_channels // reduction,\n            kernel_size=1,\n            bias=True,\n            padding=0\n        )\n        self.norm1 = None\n        if layer_norm: self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))\n        self.relu = nn.ReLU(inplace=True)\n        self.fc2 = nn.Conv2d(\n            in_channels // reduction,\n            num_gates,\n            kernel_size=1,\n            bias=True,\n            padding=0\n        )\n        if gate_activation == 'sigmoid':\n            self.gate_activation = nn.Sigmoid()\n        elif gate_activation == 'relu':\n            self.gate_activation = nn.ReLU(inplace=True)\n        elif gate_activation == 'linear':\n            self.gate_activation = nn.Identity()\n        else:\n            raise RuntimeError(\n                \"Unknown gate activation: {}\".format(gate_activation)\n            )\n\n    def forward(self, x):\n        input = x\n        x = self.global_avgpool(x)\n        x = self.fc1(x)\n        if self.norm1 is not None: x = self.norm1(x)\n        x = self.relu(x)\n        x = self.fc2(x)\n        x = self.gate_activation(x)\n        if self.return_gates: return x\n        return input * x\n\n\nclass OSBlock(nn.Module):\n    \"\"\"Omni-scale feature learning block.\"\"\"\n\n    def __init__(\n            self,\n            in_channels,\n            out_channels,\n            bn_norm,\n            IN=False,\n            bottleneck_reduction=4,\n            **kwargs\n    ):\n        super(OSBlock, self).__init__()\n        mid_channels = out_channels // bottleneck_reduction\n        self.conv1 = Conv1x1(in_channels, mid_channels, bn_norm)\n        self.conv2a = LightConv3x3(mid_channels, mid_channels, bn_norm)\n        self.conv2b = nn.Sequential(\n            LightConv3x3(mid_channels, mid_channels, bn_norm),\n            LightConv3x3(mid_channels, mid_channels, bn_norm),\n        )\n        self.conv2c = nn.Sequential(\n            LightConv3x3(mid_channels, mid_channels, bn_norm),\n            LightConv3x3(mid_channels, mid_channels, bn_norm),\n            LightConv3x3(mid_channels, mid_channels, bn_norm),\n        )\n        self.conv2d = nn.Sequential(\n            LightConv3x3(mid_channels, mid_channels, bn_norm),\n            LightConv3x3(mid_channels, mid_channels, bn_norm),\n            LightConv3x3(mid_channels, mid_channels, bn_norm),\n            LightConv3x3(mid_channels, mid_channels, bn_norm),\n        )\n        self.gate = ChannelGate(mid_channels)\n        self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn_norm)\n        self.downsample = None\n        if in_channels != out_channels:\n            self.downsample = Conv1x1Linear(in_channels, out_channels, bn_norm)\n        self.IN = None\n        if IN: self.IN = nn.InstanceNorm2d(out_channels, affine=True)\n        self.relu = nn.ReLU(True)\n\n    def forward(self, x):\n        identity = x\n        x1 = self.conv1(x)\n        x2a = self.conv2a(x1)\n        x2b = self.conv2b(x1)\n        x2c = self.conv2c(x1)\n        x2d = self.conv2d(x1)\n        x2 = self.gate(x2a) + self.gate(x2b) + self.gate(x2c) + self.gate(x2d)\n        x3 = self.conv3(x2)\n        if self.downsample is not None:\n            identity = self.downsample(identity)\n        out = x3 + identity\n        if self.IN is not None:\n            out = self.IN(out)\n        return self.relu(out)\n\n\n##########\n# Network architecture\n##########\nclass OSNet(nn.Module):\n    \"\"\"Omni-Scale Network.\n\n    Reference:\n        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.\n        - Zhou et al. Learning Generalisable Omni-Scale Representations\n          for Person Re-Identification. arXiv preprint, 2019.\n    \"\"\"\n\n    def __init__(\n            self,\n            blocks,\n            layers,\n            channels,\n            bn_norm,\n            IN=False,\n            **kwargs\n    ):\n        super(OSNet, self).__init__()\n        num_blocks = len(blocks)\n        assert num_blocks == len(layers)\n        assert num_blocks == len(channels) - 1\n\n        # convolutional backbone\n        self.conv1 = ConvLayer(3, channels[0], 7, bn_norm, stride=2, padding=3, IN=IN)\n        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)\n        self.conv2 = self._make_layer(\n            blocks[0],\n            layers[0],\n            channels[0],\n            channels[1],\n            bn_norm,\n            reduce_spatial_size=True,\n            IN=IN\n        )\n        self.conv3 = self._make_layer(\n            blocks[1],\n            layers[1],\n            channels[1],\n            channels[2],\n            bn_norm,\n            reduce_spatial_size=True\n        )\n        self.conv4 = self._make_layer(\n            blocks[2],\n            layers[2],\n            channels[2],\n            channels[3],\n            bn_norm,\n            reduce_spatial_size=False\n        )\n        self.conv5 = Conv1x1(channels[3], channels[3], bn_norm)\n\n        self._init_params()\n\n    def _make_layer(\n            self,\n            block,\n            layer,\n            in_channels,\n            out_channels,\n            bn_norm,\n            reduce_spatial_size,\n            IN=False\n    ):\n        layers = []\n\n        layers.append(block(in_channels, out_channels, bn_norm, IN=IN))\n        for i in range(1, layer):\n            layers.append(block(out_channels, out_channels, bn_norm, IN=IN))\n\n        if reduce_spatial_size:\n            layers.append(\n                nn.Sequential(\n                    Conv1x1(out_channels, out_channels, bn_norm),\n                    nn.AvgPool2d(2, stride=2),\n                )\n            )\n\n        return nn.Sequential(*layers)\n\n    def _init_params(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.kaiming_normal_(\n                    m.weight, mode='fan_out', nonlinearity='relu'\n                )\n                if m.bias is not None:\n                    nn.init.constant_(m.bias, 0)\n\n            elif isinstance(m, nn.BatchNorm2d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n\n            elif isinstance(m, nn.BatchNorm1d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n\n            elif isinstance(m, nn.Linear):\n                nn.init.normal_(m.weight, 0, 0.01)\n                if m.bias is not None:\n                    nn.init.constant_(m.bias, 0)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.maxpool(x)\n        x = self.conv2(x)\n        x = self.conv3(x)\n        x = self.conv4(x)\n        x = self.conv5(x)\n        return x\n\n\ndef init_pretrained_weights(model, key=''):\n    \"\"\"Initializes model with pretrained weights.\n\n    Layers that don't match with pretrained layers in name or size are kept unchanged.\n    \"\"\"\n    import os\n    import errno\n    import gdown\n    from collections import OrderedDict\n    import warnings\n    import logging\n\n    logger = logging.getLogger(__name__)\n\n    def _get_torch_home():\n        ENV_TORCH_HOME = 'TORCH_HOME'\n        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'\n        DEFAULT_CACHE_DIR = '~/.cache'\n        torch_home = os.path.expanduser(\n            os.getenv(\n                ENV_TORCH_HOME,\n                os.path.join(\n                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'\n                )\n            )\n        )\n        return torch_home\n\n    torch_home = _get_torch_home()\n    model_dir = os.path.join(torch_home, 'checkpoints')\n    try:\n        os.makedirs(model_dir)\n    except OSError as e:\n        if e.errno == errno.EEXIST:\n            # Directory already exists, ignore.\n            pass\n        else:\n            # Unexpected OSError, re-raise.\n            raise\n    filename = key + '_imagenet.pth'\n    cached_file = os.path.join(model_dir, filename)\n\n    if not os.path.exists(cached_file):\n        logger.info(f\"Pretrain model don't exist, downloading from {model_urls[key]}\")\n        if comm.is_main_process():\n            gdown.download(model_urls[key], cached_file, quiet=False)\n\n    comm.synchronize()\n\n    state_dict = torch.load(cached_file, map_location=torch.device('cpu'))\n    model_dict = model.state_dict()\n    new_state_dict = OrderedDict()\n    matched_layers, discarded_layers = [], []\n\n    for k, v in state_dict.items():\n        if k.startswith('module.'):\n            k = k[7:]  # discard module.\n\n        if k in model_dict and model_dict[k].size() == v.size():\n            new_state_dict[k] = v\n            matched_layers.append(k)\n        else:\n            discarded_layers.append(k)\n\n    model_dict.update(new_state_dict)\n    return model_dict\n\n\n@BACKBONE_REGISTRY.register()\ndef build_osnet_backbone(cfg):\n    \"\"\"\n    Create a OSNet instance from config.\n    Returns:\n        OSNet: a :class:`OSNet` instance\n    \"\"\"\n\n    # fmt: off\n    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    with_ibn      = cfg.MODEL.BACKBONE.WITH_IBN\n    bn_norm       = cfg.MODEL.BACKBONE.NORM\n    depth         = cfg.MODEL.BACKBONE.DEPTH\n    # fmt: on\n\n    num_blocks_per_stage = [2, 2, 2]\n    num_channels_per_stage = {\n        \"x1_0\": [64, 256, 384, 512],\n        \"x0_75\": [48, 192, 288, 384],\n        \"x0_5\": [32, 128, 192, 256],\n        \"x0_25\": [16, 64, 96, 128]}[depth]\n    model = OSNet([OSBlock, OSBlock, OSBlock], num_blocks_per_stage, num_channels_per_stage,\n                  bn_norm, IN=with_ibn)\n\n    if pretrain:\n        # Load pretrain path if specifically\n        if pretrain_path:\n            try:\n                state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))\n                logger.info(f\"Loading pretrained model from {pretrain_path}\")\n            except FileNotFoundError as e:\n                logger.info(f'{pretrain_path} is not found! Please check this path.')\n                raise e\n            except KeyError as e:\n                logger.info(\"State dict keys error! Please check the state dict.\")\n                raise e\n        else:\n            if with_ibn:\n                pretrain_key = \"osnet_ibn_\" + depth\n            else:\n                pretrain_key = \"osnet_\" + depth\n\n            state_dict = init_pretrained_weights(model, pretrain_key)\n\n        incompatible = model.load_state_dict(state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/__init__.py",
    "content": "\n\nfrom .regnet import build_regnet_backbone\nfrom .effnet import build_effnet_backbone\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/config.py",
    "content": "#!/usr/bin/env python3\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n\"\"\"Configuration file (powered by YACS).\"\"\"\n\nimport argparse\nimport os\nimport sys\n\nfrom yacs.config import CfgNode as CfgNode\n\n# Global config object\n_C = CfgNode()\n\n# Example usage:\n#   from core.config import cfg\ncfg = _C\n\n# ------------------------------------------------------------------------------------ #\n# Model options\n# ------------------------------------------------------------------------------------ #\n_C.MODEL = CfgNode()\n\n# Model type\n_C.MODEL.TYPE = \"\"\n\n# Number of weight layers\n_C.MODEL.DEPTH = 0\n\n# Number of classes\n_C.MODEL.NUM_CLASSES = 10\n\n# Loss function (see pycls/models/loss.py for options)\n_C.MODEL.LOSS_FUN = \"cross_entropy\"\n\n# ------------------------------------------------------------------------------------ #\n# ResNet options\n# ------------------------------------------------------------------------------------ #\n_C.RESNET = CfgNode()\n\n# Transformation function (see pycls/models/resnet.py for options)\n_C.RESNET.TRANS_FUN = \"basic_transform\"\n\n# Number of groups to use (1 -> ResNet; > 1 -> ResNeXt)\n_C.RESNET.NUM_GROUPS = 1\n\n# Width of each group (64 -> ResNet; 4 -> ResNeXt)\n_C.RESNET.WIDTH_PER_GROUP = 64\n\n# Apply stride to 1x1 conv (True -> MSRA; False -> fb.torch)\n_C.RESNET.STRIDE_1X1 = True\n\n# ------------------------------------------------------------------------------------ #\n# AnyNet options\n# ------------------------------------------------------------------------------------ #\n_C.ANYNET = CfgNode()\n\n# Stem type\n_C.ANYNET.STEM_TYPE = \"simple_stem_in\"\n\n# Stem width\n_C.ANYNET.STEM_W = 32\n\n# Block type\n_C.ANYNET.BLOCK_TYPE = \"res_bottleneck_block\"\n\n# Depth for each stage (number of blocks in the stage)\n_C.ANYNET.DEPTHS = []\n\n# Width for each stage (width of each block in the stage)\n_C.ANYNET.WIDTHS = []\n\n# Strides for each stage (applies to the first block of each stage)\n_C.ANYNET.STRIDES = []\n\n# Bottleneck multipliers for each stage (applies to bottleneck block)\n_C.ANYNET.BOT_MULS = []\n\n# Group widths for each stage (applies to bottleneck block)\n_C.ANYNET.GROUP_WS = []\n\n# Whether SE is enabled for res_bottleneck_block\n_C.ANYNET.SE_ON = False\n\n# SE ratio\n_C.ANYNET.SE_R = 0.25\n\n# ------------------------------------------------------------------------------------ #\n# RegNet options\n# ------------------------------------------------------------------------------------ #\n_C.REGNET = CfgNode()\n\n# Stem type\n_C.REGNET.STEM_TYPE = \"simple_stem_in\"\n\n# Stem width\n_C.REGNET.STEM_W = 32\n\n# Block type\n_C.REGNET.BLOCK_TYPE = \"res_bottleneck_block\"\n\n# Stride of each stage\n_C.REGNET.STRIDE = 2\n\n# Squeeze-and-Excitation (RegNetY)\n_C.REGNET.SE_ON = False\n_C.REGNET.SE_R = 0.25\n\n# Depth\n_C.REGNET.DEPTH = 10\n\n# Initial width\n_C.REGNET.W0 = 32\n\n# Slope\n_C.REGNET.WA = 5.0\n\n# Quantization\n_C.REGNET.WM = 2.5\n\n# Group width\n_C.REGNET.GROUP_W = 16\n\n# Bottleneck multiplier (bm = 1 / b from the paper)\n_C.REGNET.BOT_MUL = 1.0\n\n# ------------------------------------------------------------------------------------ #\n# EfficientNet options\n# ------------------------------------------------------------------------------------ #\n_C.EN = CfgNode()\n\n# Stem width\n_C.EN.STEM_W = 32\n\n# Depth for each stage (number of blocks in the stage)\n_C.EN.DEPTHS = []\n\n# Width for each stage (width of each block in the stage)\n_C.EN.WIDTHS = []\n\n# Expansion ratios for MBConv blocks in each stage\n_C.EN.EXP_RATIOS = []\n\n# Squeeze-and-Excitation (SE) ratio\n_C.EN.SE_R = 0.25\n\n# Strides for each stage (applies to the first block of each stage)\n_C.EN.STRIDES = []\n\n# Kernel sizes for each stage\n_C.EN.KERNELS = []\n\n# Head width\n_C.EN.HEAD_W = 1280\n\n# Drop connect ratio\n_C.EN.DC_RATIO = 0.0\n\n# Dropout ratio\n_C.EN.DROPOUT_RATIO = 0.0\n\n# ------------------------------------------------------------------------------------ #\n# Batch norm options\n# ------------------------------------------------------------------------------------ #\n_C.BN = CfgNode()\n\n# BN epsilon\n_C.BN.EPS = 1e-5\n\n# BN momentum (BN momentum in PyTorch = 1 - BN momentum in Caffe2)\n_C.BN.MOM = 0.1\n\n# Precise BN stats\n_C.BN.USE_PRECISE_STATS = True\n_C.BN.NUM_SAMPLES_PRECISE = 8192\n\n# Initialize the gamma of the final BN of each block to zero\n_C.BN.ZERO_INIT_FINAL_GAMMA = False\n\n# Use a different weight decay for BN layers\n_C.BN.USE_CUSTOM_WEIGHT_DECAY = False\n_C.BN.CUSTOM_WEIGHT_DECAY = 0.0\n\n# ------------------------------------------------------------------------------------ #\n# Optimizer options\n# ------------------------------------------------------------------------------------ #\n_C.OPTIM = CfgNode()\n\n# Base learning rate\n_C.OPTIM.BASE_LR = 0.1\n\n# Learning rate policy select from {'cos', 'exp', 'steps'}\n_C.OPTIM.LR_POLICY = \"cos\"\n\n# Exponential decay factor\n_C.OPTIM.GAMMA = 0.1\n\n# Steps for 'steps' policy (in epochs)\n_C.OPTIM.STEPS = []\n\n# Learning rate multiplier for 'steps' policy\n_C.OPTIM.LR_MULT = 0.1\n\n# Maximal number of epochs\n_C.OPTIM.MAX_EPOCH = 200\n\n# Momentum\n_C.OPTIM.MOMENTUM = 0.9\n\n# Momentum dampening\n_C.OPTIM.DAMPENING = 0.0\n\n# Nesterov momentum\n_C.OPTIM.NESTEROV = True\n\n# L2 regularization\n_C.OPTIM.WEIGHT_DECAY = 5e-4\n\n# Start the warm up from OPTIM.BASE_LR * OPTIM.WARMUP_FACTOR\n_C.OPTIM.WARMUP_FACTOR = 0.1\n\n# Gradually warm up the OPTIM.BASE_LR over this number of epochs\n_C.OPTIM.WARMUP_ITERS = 0\n\n# ------------------------------------------------------------------------------------ #\n# Training options\n# ------------------------------------------------------------------------------------ #\n_C.TRAIN = CfgNode()\n\n# Dataset and split\n_C.TRAIN.DATASET = \"\"\n_C.TRAIN.SPLIT = \"train\"\n\n# Total mini-batch size\n_C.TRAIN.BATCH_SIZE = 128\n\n# Image size\n_C.TRAIN.IM_SIZE = 224\n\n# Evaluate model on test data every eval period epochs\n_C.TRAIN.EVAL_PERIOD = 1\n\n# Save model checkpoint every checkpoint period epochs\n_C.TRAIN.CHECKPOINT_PERIOD = 1\n\n# Resume training from the latest checkpoint in the output directory\n_C.TRAIN.AUTO_RESUME = True\n\n# Weights to start training from\n_C.TRAIN.WEIGHTS = \"\"\n\n# ------------------------------------------------------------------------------------ #\n# Testing options\n# ------------------------------------------------------------------------------------ #\n_C.TEST = CfgNode()\n\n# Dataset and split\n_C.TEST.DATASET = \"\"\n_C.TEST.SPLIT = \"val\"\n\n# Total mini-batch size\n_C.TEST.BATCH_SIZE = 200\n\n# Image size\n_C.TEST.IM_SIZE = 256\n\n# Weights to use for testing\n_C.TEST.WEIGHTS = \"\"\n\n# ------------------------------------------------------------------------------------ #\n# Common train/test data loader options\n# ------------------------------------------------------------------------------------ #\n_C.DATA_LOADER = CfgNode()\n\n# Number of data loader workers per process\n_C.DATA_LOADER.NUM_WORKERS = 8\n\n# Load data to pinned host memory\n_C.DATA_LOADER.PIN_MEMORY = True\n\n# ------------------------------------------------------------------------------------ #\n# Memory options\n# ------------------------------------------------------------------------------------ #\n_C.MEM = CfgNode()\n\n# Perform ReLU inplace\n_C.MEM.RELU_INPLACE = True\n\n# ------------------------------------------------------------------------------------ #\n# CUDNN options\n# ------------------------------------------------------------------------------------ #\n_C.CUDNN = CfgNode()\n\n# Perform benchmarking to select the fastest CUDNN algorithms to use\n# Note that this may increase the memory usage and will likely not result\n# in overall speedups when variable size inputs are used (e.g. COCO training)\n_C.CUDNN.BENCHMARK = True\n\n# ------------------------------------------------------------------------------------ #\n# Precise timing options\n# ------------------------------------------------------------------------------------ #\n_C.PREC_TIME = CfgNode()\n\n# Number of iterations to warm up the caches\n_C.PREC_TIME.WARMUP_ITER = 3\n\n# Number of iterations to compute avg time\n_C.PREC_TIME.NUM_ITER = 30\n\n# ------------------------------------------------------------------------------------ #\n# Misc options\n# ------------------------------------------------------------------------------------ #\n\n# Number of GPUs to use (applies to both training and testing)\n_C.NUM_GPUS = 1\n\n# Output directory\n_C.OUT_DIR = \"/tmp\"\n\n# Config destination (in OUT_DIR)\n_C.CFG_DEST = \"config.yaml\"\n\n# Note that non-determinism may still be present due to non-deterministic\n# operator implementations in GPU operator libraries\n_C.RNG_SEED = 1\n\n# Log destination ('stdout' or 'file')\n_C.LOG_DEST = \"stdout\"\n\n# Log period in iters\n_C.LOG_PERIOD = 10\n\n# Distributed backend\n_C.DIST_BACKEND = \"nccl\"\n\n# Hostname and port range for multi-process groups (actual port selected randomly)\n_C.HOST = \"localhost\"\n_C.PORT_RANGE = [10000, 65000]\n\n# Models weights referred to by URL are downloaded to this local cache\n_C.DOWNLOAD_CACHE = \"/tmp/pycls-download-cache\"\n\n# ------------------------------------------------------------------------------------ #\n# Deprecated keys\n# ------------------------------------------------------------------------------------ #\n\n_C.register_deprecated_key(\"PREC_TIME.BATCH_SIZE\")\n_C.register_deprecated_key(\"PREC_TIME.ENABLED\")\n_C.register_deprecated_key(\"PORT\")\n\n\ndef assert_and_infer_cfg():\n    \"\"\"Checks config values invariants.\"\"\"\n    err_str = \"The first lr step must start at 0\"\n    assert not _C.OPTIM.STEPS or _C.OPTIM.STEPS[0] == 0, err_str\n    data_splits = [\"train\", \"val\", \"test\"]\n    err_str = \"Data split '{}' not supported\"\n    assert _C.TRAIN.SPLIT in data_splits, err_str.format(_C.TRAIN.SPLIT)\n    assert _C.TEST.SPLIT in data_splits, err_str.format(_C.TEST.SPLIT)\n    err_str = \"Mini-batch size should be a multiple of NUM_GPUS.\"\n    assert _C.TRAIN.BATCH_SIZE % _C.NUM_GPUS == 0, err_str\n    assert _C.TEST.BATCH_SIZE % _C.NUM_GPUS == 0, err_str\n    err_str = \"Log destination '{}' not supported\"\n    assert _C.LOG_DEST in [\"stdout\", \"file\"], err_str.format(_C.LOG_DEST)\n\n\ndef dump_cfg():\n    \"\"\"Dumps the config to the output directory.\"\"\"\n    cfg_file = os.path.join(_C.OUT_DIR, _C.CFG_DEST)\n    with open(cfg_file, \"w\") as f:\n        _C.dump(stream=f)\n\n\ndef load_cfg(out_dir, cfg_dest=\"config.yaml\"):\n    \"\"\"Loads config from specified output directory.\"\"\"\n    cfg_file = os.path.join(out_dir, cfg_dest)\n    _C.merge_from_file(cfg_file)\n\n\ndef load_cfg_fom_args(description=\"Config file options.\"):\n    \"\"\"Load config from command line arguments and set any specified options.\"\"\"\n    parser = argparse.ArgumentParser(description=description)\n    help_s = \"Config file location\"\n    parser.add_argument(\"--cfg\", dest=\"cfg_file\", help=help_s, required=True, type=str)\n    help_s = \"See pycls/core/config.py for all options\"\n    parser.add_argument(\"opts\", help=help_s, default=None, nargs=argparse.REMAINDER)\n    if len(sys.argv) == 1:\n        parser.print_help()\n        sys.exit(1)\n    args = parser.parse_args()\n    _C.merge_from_file(args.cfg_file)\n    _C.merge_from_list(args.opts)\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/effnet/EN-B0_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: effnet\n  NUM_CLASSES: 1000\nEN:\n  STEM_W: 32\n  STRIDES: [1, 2, 2, 2, 1, 2, 1]\n  DEPTHS: [1, 2, 2, 3, 3, 4, 1]\n  WIDTHS: [16, 24, 40, 80, 112, 192, 320]\n  EXP_RATIOS: [1, 6, 6, 6, 6, 6, 6]\n  KERNELS: [3, 3, 5, 3, 5, 5, 3]\n  HEAD_W: 1280\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 1e-5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 256\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 200\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/effnet/EN-B1_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: effnet\n  NUM_CLASSES: 1000\nEN:\n  STEM_W: 32\n  STRIDES: [1, 2, 2, 2, 1, 2, 1]\n  DEPTHS: [2, 3, 3, 4, 4, 5, 2]\n  WIDTHS: [16, 24, 40, 80, 112, 192, 320]\n  EXP_RATIOS: [1, 6, 6, 6, 6, 6, 6]\n  KERNELS: [3, 3, 5, 3, 5, 5, 3]\n  HEAD_W: 1280\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 1e-5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 240\n  BATCH_SIZE: 256\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 274\n  BATCH_SIZE: 200\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/effnet/EN-B2_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: effnet\n  NUM_CLASSES: 1000\nEN:\n  STEM_W: 32\n  STRIDES: [1, 2, 2, 2, 1, 2, 1]\n  DEPTHS: [2, 3, 3, 4, 4, 5, 2]\n  WIDTHS: [16, 24, 48, 88, 120, 208, 352]\n  EXP_RATIOS: [1, 6, 6, 6, 6, 6, 6]\n  KERNELS: [3, 3, 5, 3, 5, 5, 3]\n  HEAD_W: 1408\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 1e-5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 260\n  BATCH_SIZE: 256\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 298\n  BATCH_SIZE: 200\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/effnet/EN-B3_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: effnet\n  NUM_CLASSES: 1000\nEN:\n  STEM_W: 40\n  STRIDES: [1, 2, 2, 2, 1, 2, 1]\n  DEPTHS: [2, 3, 3, 5, 5, 6, 2]\n  WIDTHS: [24, 32, 48, 96, 136, 232, 384]\n  EXP_RATIOS: [1, 6, 6, 6, 6, 6, 6]\n  KERNELS: [3, 3, 5, 3, 5, 5, 3]\n  HEAD_W: 1536\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 1e-5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 300\n  BATCH_SIZE: 256\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 342\n  BATCH_SIZE: 200\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/effnet/EN-B4_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: effnet\n  NUM_CLASSES: 1000\nEN:\n  STEM_W: 48\n  STRIDES: [1, 2, 2, 2, 1, 2, 1]\n  DEPTHS: [2, 4, 4, 6, 6, 8, 2]\n  WIDTHS: [24, 32, 56, 112, 160, 272, 448]\n  EXP_RATIOS: [1, 6, 6, 6, 6, 6, 6]\n  KERNELS: [3, 3, 5, 3, 5, 5, 3]\n  HEAD_W: 1792\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.2\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 1e-5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 380\n  BATCH_SIZE: 128\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 434\n  BATCH_SIZE: 104\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/effnet/EN-B5_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: effnet\n  NUM_CLASSES: 1000\nEN:\n  STEM_W: 48\n  STRIDES: [1, 2, 2, 2, 1, 2, 1]\n  DEPTHS: [3, 5, 5, 7, 7, 9, 3]\n  WIDTHS: [24, 40, 64, 128, 176, 304, 512]\n  EXP_RATIOS: [1, 6, 6, 6, 6, 6, 6]\n  KERNELS: [3, 3, 5, 3, 5, 5, 3]\n  HEAD_W: 2048\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.1\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 1e-5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 456\n  BATCH_SIZE: 64\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 522\n  BATCH_SIZE: 48\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/effnet.py",
    "content": "# !/usr/bin/env python3\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\n\"\"\"EfficientNet models.\"\"\"\n\nimport logging\n\nimport torch\nimport torch.nn as nn\n\nfrom fast_reid.fastreid.layers import *\nfrom fast_reid.fastreid.modeling.backbones.build import BACKBONE_REGISTRY\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message\nfrom .config import cfg as effnet_cfg\nfrom .regnet import drop_connect, init_weights\n\nlogger = logging.getLogger(__name__)\nmodel_urls = {\n    'b0': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161305613/EN-B0_dds_8gpu.pyth',\n    'b1': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161304979/EN-B1_dds_8gpu.pyth',\n    'b2': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161305015/EN-B2_dds_8gpu.pyth',\n    'b3': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161304979/EN-B3_dds_8gpu.pyth',\n    'b4': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161305098/EN-B4_dds_8gpu.pyth',\n    'b5': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161304979/EN-B5_dds_8gpu.pyth',\n    'b6': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161304979/EN-B6_dds_8gpu.pyth',\n    'b7': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161304979/EN-B7_dds_8gpu.pyth',\n}\n\n\nclass EffHead(nn.Module):\n    \"\"\"EfficientNet head: 1x1, BN, Swish, AvgPool, Dropout, FC.\"\"\"\n\n    def __init__(self, w_in, w_out, bn_norm):\n        super(EffHead, self).__init__()\n        self.conv = nn.Conv2d(w_in, w_out, 1, stride=1, padding=0, bias=False)\n        self.conv_bn = get_norm(bn_norm, w_out)\n        self.conv_swish = Swish()\n\n    def forward(self, x):\n        x = self.conv_swish(self.conv_bn(self.conv(x)))\n        return x\n\n\nclass Swish(nn.Module):\n    \"\"\"Swish activation function: x * sigmoid(x).\"\"\"\n\n    def __init__(self):\n        super(Swish, self).__init__()\n\n    def forward(self, x):\n        return x * torch.sigmoid(x)\n\n\nclass SE(nn.Module):\n    \"\"\"Squeeze-and-Excitation (SE) block w/ Swish: AvgPool, FC, Swish, FC, Sigmoid.\"\"\"\n\n    def __init__(self, w_in, w_se):\n        super(SE, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n        self.f_ex = nn.Sequential(\n            nn.Conv2d(w_in, w_se, 1, bias=True),\n            Swish(),\n            nn.Conv2d(w_se, w_in, 1, bias=True),\n            nn.Sigmoid(),\n        )\n\n    def forward(self, x):\n        return x * self.f_ex(self.avg_pool(x))\n\n\nclass MBConv(nn.Module):\n    \"\"\"Mobile inverted bottleneck block w/ SE (MBConv).\"\"\"\n\n    def __init__(self, w_in, exp_r, kernel, stride, se_r, w_out, bn_norm):\n        # expansion, 3x3 dwise, BN, Swish, SE, 1x1, BN, skip_connection\n        super(MBConv, self).__init__()\n        self.exp = None\n        w_exp = int(w_in * exp_r)\n        if w_exp != w_in:\n            self.exp = nn.Conv2d(w_in, w_exp, 1, stride=1, padding=0, bias=False)\n            self.exp_bn = get_norm(bn_norm, w_exp)\n            self.exp_swish = Swish()\n        dwise_args = {\"groups\": w_exp, \"padding\": (kernel - 1) // 2, \"bias\": False}\n        self.dwise = nn.Conv2d(w_exp, w_exp, kernel, stride=stride, **dwise_args)\n        self.dwise_bn = get_norm(bn_norm, w_exp)\n        self.dwise_swish = Swish()\n        self.se = SE(w_exp, int(w_in * se_r))\n        self.lin_proj = nn.Conv2d(w_exp, w_out, 1, stride=1, padding=0, bias=False)\n        self.lin_proj_bn = get_norm(bn_norm, w_out)\n        # Skip connection if in and out shapes are the same (MN-V2 style)\n        self.has_skip = stride == 1 and w_in == w_out\n\n    def forward(self, x):\n        f_x = x\n        if self.exp:\n            f_x = self.exp_swish(self.exp_bn(self.exp(f_x)))\n        f_x = self.dwise_swish(self.dwise_bn(self.dwise(f_x)))\n        f_x = self.se(f_x)\n        f_x = self.lin_proj_bn(self.lin_proj(f_x))\n        if self.has_skip:\n            if self.training and effnet_cfg.EN.DC_RATIO > 0.0:\n                f_x = drop_connect(f_x, effnet_cfg.EN.DC_RATIO)\n            f_x = x + f_x\n        return f_x\n\n\nclass EffStage(nn.Module):\n    \"\"\"EfficientNet stage.\"\"\"\n\n    def __init__(self, w_in, exp_r, kernel, stride, se_r, w_out, d, bn_norm):\n        super(EffStage, self).__init__()\n        for i in range(d):\n            b_stride = stride if i == 0 else 1\n            b_w_in = w_in if i == 0 else w_out\n            name = \"b{}\".format(i + 1)\n            self.add_module(name, MBConv(b_w_in, exp_r, kernel, b_stride, se_r, w_out, bn_norm))\n\n    def forward(self, x):\n        for block in self.children():\n            x = block(x)\n        return x\n\n\nclass StemIN(nn.Module):\n    \"\"\"EfficientNet stem for ImageNet: 3x3, BN, Swish.\"\"\"\n\n    def __init__(self, w_in, w_out, bn_norm):\n        super(StemIN, self).__init__()\n        self.conv = nn.Conv2d(w_in, w_out, 3, stride=2, padding=1, bias=False)\n        self.bn = get_norm(bn_norm, w_out)\n        self.swish = Swish()\n\n    def forward(self, x):\n        for layer in self.children():\n            x = layer(x)\n        return x\n\n\nclass EffNet(nn.Module):\n    \"\"\"EfficientNet model.\"\"\"\n\n    @staticmethod\n    def get_args():\n        return {\n            \"stem_w\": effnet_cfg.EN.STEM_W,\n            \"ds\": effnet_cfg.EN.DEPTHS,\n            \"ws\": effnet_cfg.EN.WIDTHS,\n            \"exp_rs\": effnet_cfg.EN.EXP_RATIOS,\n            \"se_r\": effnet_cfg.EN.SE_R,\n            \"ss\": effnet_cfg.EN.STRIDES,\n            \"ks\": effnet_cfg.EN.KERNELS,\n            \"head_w\": effnet_cfg.EN.HEAD_W,\n        }\n\n    def __init__(self, last_stride, bn_norm, **kwargs):\n        super(EffNet, self).__init__()\n        kwargs = self.get_args() if not kwargs else kwargs\n        self._construct(**kwargs, last_stride=last_stride, bn_norm=bn_norm)\n        self.apply(init_weights)\n\n    def _construct(self, stem_w, ds, ws, exp_rs, se_r, ss, ks, head_w, last_stride, bn_norm):\n        stage_params = list(zip(ds, ws, exp_rs, ss, ks))\n        self.stem = StemIN(3, stem_w, bn_norm)\n        prev_w = stem_w\n        for i, (d, w, exp_r, stride, kernel) in enumerate(stage_params):\n            name = \"s{}\".format(i + 1)\n            if i == 5: stride = last_stride\n            self.add_module(name, EffStage(prev_w, exp_r, kernel, stride, se_r, w, d, bn_norm))\n            prev_w = w\n        self.head = EffHead(prev_w, head_w, bn_norm)\n\n    def forward(self, x):\n        for module in self.children():\n            x = module(x)\n        return x\n\n\ndef init_pretrained_weights(key):\n    \"\"\"Initializes model with pretrained weights.\n\n    Layers that don't match with pretrained layers in name or size are kept unchanged.\n    \"\"\"\n    import os\n    import errno\n    import gdown\n\n    def _get_torch_home():\n        ENV_TORCH_HOME = 'TORCH_HOME'\n        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'\n        DEFAULT_CACHE_DIR = '~/.cache'\n        torch_home = os.path.expanduser(\n            os.getenv(\n                ENV_TORCH_HOME,\n                os.path.join(\n                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'\n                )\n            )\n        )\n        return torch_home\n\n    torch_home = _get_torch_home()\n    model_dir = os.path.join(torch_home, 'checkpoints')\n    try:\n        os.makedirs(model_dir)\n    except OSError as e:\n        if e.errno == errno.EEXIST:\n            # Directory already exists, ignore.\n            pass\n        else:\n            # Unexpected OSError, re-raise.\n            raise\n\n    filename = model_urls[key].split('/')[-1]\n\n    cached_file = os.path.join(model_dir, filename)\n\n    if not os.path.exists(cached_file):\n        if comm.is_main_process():\n            gdown.download(model_urls[key], cached_file, quiet=False)\n\n    comm.synchronize()\n\n    logger.info(f\"Loading pretrained model from {cached_file}\")\n    state_dict = torch.load(cached_file, map_location=torch.device(\"cpu\"))[\"model_state\"]\n\n    return state_dict\n\n\n@BACKBONE_REGISTRY.register()\ndef build_effnet_backbone(cfg):\n    # fmt: off\n    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    last_stride   = cfg.MODEL.BACKBONE.LAST_STRIDE\n    bn_norm       = cfg.MODEL.BACKBONE.NORM\n    depth         = cfg.MODEL.BACKBONE.DEPTH\n    # fmt: on\n\n    cfg_files = {\n        'b0': 'fastreid/modeling/backbones/regnet/effnet/EN-B0_dds_8gpu.yaml',\n        'b1': 'fastreid/modeling/backbones/regnet/effnet/EN-B1_dds_8gpu.yaml',\n        'b2': 'fastreid/modeling/backbones/regnet/effnet/EN-B2_dds_8gpu.yaml',\n        'b3': 'fastreid/modeling/backbones/regnet/effnet/EN-B3_dds_8gpu.yaml',\n        'b4': 'fastreid/modeling/backbones/regnet/effnet/EN-B4_dds_8gpu.yaml',\n        'b5': 'fastreid/modeling/backbones/regnet/effnet/EN-B5_dds_8gpu.yaml',\n    }[depth]\n\n    effnet_cfg.merge_from_file(cfg_files)\n    model = EffNet(last_stride, bn_norm)\n\n    if pretrain:\n        # Load pretrain path if specifically\n        if pretrain_path:\n            try:\n                state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))[\"model_state\"]\n                logger.info(f\"Loading pretrained model from {pretrain_path}\")\n            except FileNotFoundError as e:\n                logger.info(f'{pretrain_path} is not found! Please check this path.')\n                raise e\n            except KeyError as e:\n                logger.info(\"State dict keys error! Please check the state dict.\")\n                raise e\n        else:\n            key = depth\n            state_dict = init_pretrained_weights(key)\n\n        incompatible = model.load_state_dict(state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnet.py",
    "content": "import logging\nimport math\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom fast_reid.fastreid.layers import get_norm\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message\nfrom .config import cfg as regnet_cfg\nfrom ..build import BACKBONE_REGISTRY\n\nlogger = logging.getLogger(__name__)\nmodel_urls = {\n    '800x': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160905981/RegNetX-200MF_dds_8gpu.pyth',\n    '800y': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906567/RegNetY-800MF_dds_8gpu.pyth',\n    '1600x': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160990626/RegNetX-1.6GF_dds_8gpu.pyth',\n    '1600y': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906681/RegNetY-1.6GF_dds_8gpu.pyth',\n    '3200x': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906139/RegNetX-3.2GF_dds_8gpu.pyth',\n    '3200y': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906834/RegNetY-3.2GF_dds_8gpu.pyth',\n    '4000x': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906383/RegNetX-4.0GF_dds_8gpu.pyth',\n    '4000y': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906838/RegNetY-4.0GF_dds_8gpu.pyth',\n    '6400x': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161116590/RegNetX-6.4GF_dds_8gpu.pyth',\n    '6400y': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160907112/RegNetY-6.4GF_dds_8gpu.pyth',\n}\n\n\ndef init_weights(m):\n    \"\"\"Performs ResNet-style weight initialization.\"\"\"\n    if isinstance(m, nn.Conv2d):\n        # Note that there is no bias due to BN\n        fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n        m.weight.data.normal_(mean=0.0, std=math.sqrt(2.0 / fan_out))\n    elif isinstance(m, nn.BatchNorm2d):\n        zero_init_gamma = (\n                hasattr(m, \"final_bn\") and m.final_bn and regnet_cfg.BN.ZERO_INIT_FINAL_GAMMA\n        )\n        m.weight.data.fill_(0.0 if zero_init_gamma else 1.0)\n        m.bias.data.zero_()\n    elif isinstance(m, nn.Linear):\n        m.weight.data.normal_(mean=0.0, std=0.01)\n        m.bias.data.zero_()\n\n\ndef get_stem_fun(stem_type):\n    \"\"\"Retrives the stem function by name.\"\"\"\n    stem_funs = {\n        \"res_stem_cifar\": ResStemCifar,\n        \"res_stem_in\": ResStemIN,\n        \"simple_stem_in\": SimpleStemIN,\n    }\n    assert stem_type in stem_funs.keys(), \"Stem type '{}' not supported\".format(\n        stem_type\n    )\n    return stem_funs[stem_type]\n\n\ndef get_block_fun(block_type):\n    \"\"\"Retrieves the block function by name.\"\"\"\n    block_funs = {\n        \"vanilla_block\": VanillaBlock,\n        \"res_basic_block\": ResBasicBlock,\n        \"res_bottleneck_block\": ResBottleneckBlock,\n    }\n    assert block_type in block_funs.keys(), \"Block type '{}' not supported\".format(\n        block_type\n    )\n    return block_funs[block_type]\n\n\ndef drop_connect(x, drop_ratio):\n    \"\"\"Drop connect (adapted from DARTS).\"\"\"\n    keep_ratio = 1.0 - drop_ratio\n    mask = torch.empty([x.shape[0], 1, 1, 1], dtype=x.dtype, device=x.device)\n    mask.bernoulli_(keep_ratio)\n    x.div_(keep_ratio)\n    x.mul_(mask)\n    return x\n\nclass AnyHead(nn.Module):\n    \"\"\"AnyNet head.\"\"\"\n\n    def __init__(self, w_in, nc):\n        super(AnyHead, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n        self.fc = nn.Linear(w_in, nc, bias=True)\n\n    def forward(self, x):\n        x = self.avg_pool(x)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n        return x\n\n\nclass VanillaBlock(nn.Module):\n    \"\"\"Vanilla block: [3x3 conv, BN, Relu] x2\"\"\"\n\n    def __init__(self, w_in, w_out, stride, bn_norm, bm=None, gw=None, se_r=None):\n        assert (\n                bm is None and gw is None and se_r is None\n        ), \"Vanilla block does not support bm, gw, and se_r options\"\n        super(VanillaBlock, self).__init__()\n        self.construct(w_in, w_out, stride, bn_norm)\n\n    def construct(self, w_in, w_out, stride, bn_norm):\n        # 3x3, BN, ReLU\n        self.a = nn.Conv2d(\n            w_in, w_out, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.a_bn = get_norm(bn_norm, w_out)\n        self.a_relu = nn.ReLU(inplace=regnet_cfg.MEM.RELU_INPLACE)\n        # 3x3, BN, ReLU\n        self.b = nn.Conv2d(w_out, w_out, kernel_size=3, stride=1, padding=1, bias=False)\n        self.b_bn = get_norm(bn_norm, w_out)\n        self.b_relu = nn.ReLU(inplace=regnet_cfg.MEM.RELU_INPLACE)\n\n    def forward(self, x):\n        for layer in self.children():\n            x = layer(x)\n        return x\n\n\nclass BasicTransform(nn.Module):\n    \"\"\"Basic transformation: [3x3 conv, BN, Relu] x2\"\"\"\n\n    def __init__(self, w_in, w_out, stride, bn_norm):\n        super(BasicTransform, self).__init__()\n        self.construct(w_in, w_out, stride, bn_norm)\n\n    def construct(self, w_in, w_out, stride, bn_norm):\n        # 3x3, BN, ReLU\n        self.a = nn.Conv2d(\n            w_in, w_out, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.a_bn = get_norm(bn_norm, w_out)\n        self.a_relu = nn.ReLU(inplace=regnet_cfg.MEM.RELU_INPLACE)\n        # 3x3, BN\n        self.b = nn.Conv2d(w_out, w_out, kernel_size=3, stride=1, padding=1, bias=False)\n        self.b_bn = get_norm(bn_norm, w_out)\n        self.b_bn.final_bn = True\n\n    def forward(self, x):\n        for layer in self.children():\n            x = layer(x)\n        return x\n\n\nclass ResBasicBlock(nn.Module):\n    \"\"\"Residual basic block: x + F(x), F = basic transform\"\"\"\n\n    def __init__(self, w_in, w_out, stride, bn_norm, bm=None, gw=None, se_r=None):\n        assert (\n                bm is None and gw is None and se_r is None\n        ), \"Basic transform does not support bm, gw, and se_r options\"\n        super(ResBasicBlock, self).__init__()\n        self.construct(w_in, w_out, stride, bn_norm)\n\n    def _add_skip_proj(self, w_in, w_out, stride, bn_norm):\n        self.proj = nn.Conv2d(\n            w_in, w_out, kernel_size=1, stride=stride, padding=0, bias=False\n        )\n        self.bn = get_norm(bn_norm, w_out)\n\n    def construct(self, w_in, w_out, stride, bn_norm):\n        # Use skip connection with projection if shape changes\n        self.proj_block = (w_in != w_out) or (stride != 1)\n        if self.proj_block:\n            self._add_skip_proj(w_in, w_out, stride, bn_norm)\n        self.f = BasicTransform(w_in, w_out, stride, bn_norm)\n        self.relu = nn.ReLU(regnet_cfg.MEM.RELU_INPLACE)\n\n    def forward(self, x):\n        if self.proj_block:\n            x = self.bn(self.proj(x)) + self.f(x)\n        else:\n            x = x + self.f(x)\n        x = self.relu(x)\n        return x\n\n\nclass SE(nn.Module):\n    \"\"\"Squeeze-and-Excitation (SE) block\"\"\"\n\n    def __init__(self, w_in, w_se):\n        super(SE, self).__init__()\n        self.construct(w_in, w_se)\n\n    def construct(self, w_in, w_se):\n        # AvgPool\n        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n        # FC, Activation, FC, Sigmoid\n        self.f_ex = nn.Sequential(\n            nn.Conv2d(w_in, w_se, kernel_size=1, bias=True),\n            nn.ReLU(inplace=regnet_cfg.MEM.RELU_INPLACE),\n            nn.Conv2d(w_se, w_in, kernel_size=1, bias=True),\n            nn.Sigmoid(),\n        )\n\n    def forward(self, x):\n        return x * self.f_ex(self.avg_pool(x))\n\n\nclass BottleneckTransform(nn.Module):\n    \"\"\"Bottlenect transformation: 1x1, 3x3, 1x1\"\"\"\n\n    def __init__(self, w_in, w_out, stride, bn_norm, bm, gw, se_r):\n        super(BottleneckTransform, self).__init__()\n        self.construct(w_in, w_out, stride, bn_norm, bm, gw, se_r)\n\n    def construct(self, w_in, w_out, stride, bn_norm, bm, gw, se_r):\n        # Compute the bottleneck width\n        w_b = int(round(w_out * bm))\n        # Compute the number of groups\n        num_gs = w_b // gw\n        # 1x1, BN, ReLU\n        self.a = nn.Conv2d(w_in, w_b, kernel_size=1, stride=1, padding=0, bias=False)\n        self.a_bn = get_norm(bn_norm, w_b)\n        self.a_relu = nn.ReLU(inplace=regnet_cfg.MEM.RELU_INPLACE)\n        # 3x3, BN, ReLU\n        self.b = nn.Conv2d(\n            w_b, w_b, kernel_size=3, stride=stride, padding=1, groups=num_gs, bias=False\n        )\n        self.b_bn = get_norm(bn_norm, w_b)\n        self.b_relu = nn.ReLU(inplace=regnet_cfg.MEM.RELU_INPLACE)\n        # Squeeze-and-Excitation (SE)\n        if se_r:\n            w_se = int(round(w_in * se_r))\n            self.se = SE(w_b, w_se)\n        # 1x1, BN\n        self.c = nn.Conv2d(w_b, w_out, kernel_size=1, stride=1, padding=0, bias=False)\n        self.c_bn = get_norm(bn_norm, w_out)\n        self.c_bn.final_bn = True\n\n    def forward(self, x):\n        for layer in self.children():\n            x = layer(x)\n        return x\n\n\nclass ResBottleneckBlock(nn.Module):\n    \"\"\"Residual bottleneck block: x + F(x), F = bottleneck transform\"\"\"\n\n    def __init__(self, w_in, w_out, stride, bn_norm, bm=1.0, gw=1, se_r=None):\n        super(ResBottleneckBlock, self).__init__()\n        self.construct(w_in, w_out, stride, bn_norm, bm, gw, se_r)\n\n    def _add_skip_proj(self, w_in, w_out, stride, bn_norm):\n        self.proj = nn.Conv2d(\n            w_in, w_out, kernel_size=1, stride=stride, padding=0, bias=False\n        )\n        self.bn = get_norm(bn_norm, w_out)\n\n    def construct(self, w_in, w_out, stride, bn_norm, bm, gw, se_r):\n        # Use skip connection with projection if shape changes\n        self.proj_block = (w_in != w_out) or (stride != 1)\n        if self.proj_block:\n            self._add_skip_proj(w_in, w_out, stride, bn_norm)\n        self.f = BottleneckTransform(w_in, w_out, stride, bn_norm, bm, gw, se_r)\n        self.relu = nn.ReLU(regnet_cfg.MEM.RELU_INPLACE)\n\n    def forward(self, x):\n        if self.proj_block:\n            x = self.bn(self.proj(x)) + self.f(x)\n        else:\n            x = x + self.f(x)\n        x = self.relu(x)\n        return x\n\n\nclass ResStemCifar(nn.Module):\n    \"\"\"ResNet stem for CIFAR.\"\"\"\n\n    def __init__(self, w_in, w_out, bn_norm):\n        super(ResStemCifar, self).__init__()\n        self.construct(w_in, w_out, bn_norm)\n\n    def construct(self, w_in, w_out, bn_norm):\n        # 3x3, BN, ReLU\n        self.conv = nn.Conv2d(\n            w_in, w_out, kernel_size=3, stride=1, padding=1, bias=False\n        )\n        self.bn = get_norm(bn_norm, w_out)\n        self.relu = nn.ReLU(regnet_cfg.MEM.RELU_INPLACE)\n\n    def forward(self, x):\n        for layer in self.children():\n            x = layer(x)\n        return x\n\n\nclass ResStemIN(nn.Module):\n    \"\"\"ResNet stem for ImageNet.\"\"\"\n\n    def __init__(self, w_in, w_out, bn_norm):\n        super(ResStemIN, self).__init__()\n        self.construct(w_in, w_out, bn_norm)\n\n    def construct(self, w_in, w_out, bn_norm):\n        # 7x7, BN, ReLU, maxpool\n        self.conv = nn.Conv2d(\n            w_in, w_out, kernel_size=7, stride=2, padding=3, bias=False\n        )\n        self.bn = get_norm(bn_norm, w_out)\n        self.relu = nn.ReLU(regnet_cfg.MEM.RELU_INPLACE)\n        self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n    def forward(self, x):\n        for layer in self.children():\n            x = layer(x)\n        return x\n\n\nclass SimpleStemIN(nn.Module):\n    \"\"\"Simple stem for ImageNet.\"\"\"\n\n    def __init__(self, in_w, out_w, bn_norm):\n        super(SimpleStemIN, self).__init__()\n        self.construct(in_w, out_w, bn_norm)\n\n    def construct(self, in_w, out_w, bn_norm):\n        # 3x3, BN, ReLU\n        self.conv = nn.Conv2d(\n            in_w, out_w, kernel_size=3, stride=2, padding=1, bias=False\n        )\n        self.bn = get_norm(bn_norm, out_w)\n        self.relu = nn.ReLU(regnet_cfg.MEM.RELU_INPLACE)\n\n    def forward(self, x):\n        for layer in self.children():\n            x = layer(x)\n        return x\n\n\nclass AnyStage(nn.Module):\n    \"\"\"AnyNet stage (sequence of blocks w/ the same output shape).\"\"\"\n\n    def __init__(self, w_in, w_out, stride, bn_norm, d, block_fun, bm, gw, se_r):\n        super(AnyStage, self).__init__()\n        self.construct(w_in, w_out, stride, bn_norm, d, block_fun, bm, gw, se_r)\n\n    def construct(self, w_in, w_out, stride, bn_norm, d, block_fun, bm, gw, se_r):\n        # Construct the blocks\n        for i in range(d):\n            # Stride and w_in apply to the first block of the stage\n            b_stride = stride if i == 0 else 1\n            b_w_in = w_in if i == 0 else w_out\n            # Construct the block\n            self.add_module(\n                \"b{}\".format(i + 1), block_fun(b_w_in, w_out, b_stride, bn_norm, bm, gw, se_r)\n            )\n\n    def forward(self, x):\n        for block in self.children():\n            x = block(x)\n        return x\n\n\nclass AnyNet(nn.Module):\n    \"\"\"AnyNet model.\"\"\"\n\n    def __init__(self, **kwargs):\n        super(AnyNet, self).__init__()\n        if kwargs:\n            self.construct(\n                stem_type=kwargs[\"stem_type\"],\n                stem_w=kwargs[\"stem_w\"],\n                block_type=kwargs[\"block_type\"],\n                ds=kwargs[\"ds\"],\n                ws=kwargs[\"ws\"],\n                ss=kwargs[\"ss\"],\n                bn_norm=kwargs[\"bn_norm\"],\n                bms=kwargs[\"bms\"],\n                gws=kwargs[\"gws\"],\n                se_r=kwargs[\"se_r\"],\n            )\n        else:\n            self.construct(\n                stem_type=regnet_cfg.ANYNET.STEM_TYPE,\n                stem_w=regnet_cfg.ANYNET.STEM_W,\n                block_type=regnet_cfg.ANYNET.BLOCK_TYPE,\n                ds=regnet_cfg.ANYNET.DEPTHS,\n                ws=regnet_cfg.ANYNET.WIDTHS,\n                ss=regnet_cfg.ANYNET.STRIDES,\n                bn_norm=regnet_cfg.ANYNET.BN_NORM,\n                bms=regnet_cfg.ANYNET.BOT_MULS,\n                gws=regnet_cfg.ANYNET.GROUP_WS,\n                se_r=regnet_cfg.ANYNET.SE_R if regnet_cfg.ANYNET.SE_ON else None,\n            )\n        self.apply(init_weights)\n\n    def construct(self, stem_type, stem_w, block_type, ds, ws, ss, bn_norm, bms, gws, se_r):\n        # Generate dummy bot muls and gs for models that do not use them\n        bms = bms if bms else [1.0 for _d in ds]\n        gws = gws if gws else [1 for _d in ds]\n        # Group params by stage\n        stage_params = list(zip(ds, ws, ss, bms, gws))\n        # Construct the stem\n        stem_fun = get_stem_fun(stem_type)\n        self.stem = stem_fun(3, stem_w, bn_norm)\n        # Construct the stages\n        block_fun = get_block_fun(block_type)\n        prev_w = stem_w\n        for i, (d, w, s, bm, gw) in enumerate(stage_params):\n            self.add_module(\n                \"s{}\".format(i + 1), AnyStage(prev_w, w, s, bn_norm, d, block_fun, bm, gw, se_r)\n            )\n            prev_w = w\n        # Construct the head\n        self.in_planes = prev_w\n        # self.head = AnyHead(w_in=prev_w, nc=nc)\n\n    def forward(self, x):\n        for module in self.children():\n            x = module(x)\n        return x\n\n\ndef quantize_float(f, q):\n    \"\"\"Converts a float to closest non-zero int divisible by q.\"\"\"\n    return int(round(f / q) * q)\n\n\ndef adjust_ws_gs_comp(ws, bms, gs):\n    \"\"\"Adjusts the compatibility of widths and groups.\"\"\"\n    ws_bot = [int(w * b) for w, b in zip(ws, bms)]\n    gs = [min(g, w_bot) for g, w_bot in zip(gs, ws_bot)]\n    ws_bot = [quantize_float(w_bot, g) for w_bot, g in zip(ws_bot, gs)]\n    ws = [int(w_bot / b) for w_bot, b in zip(ws_bot, bms)]\n    return ws, gs\n\n\ndef get_stages_from_blocks(ws, rs):\n    \"\"\"Gets ws/ds of network at each stage from per block values.\"\"\"\n    ts_temp = zip(ws + [0], [0] + ws, rs + [0], [0] + rs)\n    ts = [w != wp or r != rp for w, wp, r, rp in ts_temp]\n    s_ws = [w for w, t in zip(ws, ts[:-1]) if t]\n    s_ds = np.diff([d for d, t in zip(range(len(ts)), ts) if t]).tolist()\n    return s_ws, s_ds\n\n\ndef generate_regnet(w_a, w_0, w_m, d, q=8):\n    \"\"\"Generates per block ws from RegNet parameters.\"\"\"\n    assert w_a >= 0 and w_0 > 0 and w_m > 1 and w_0 % q == 0\n    ws_cont = np.arange(d) * w_a + w_0\n    ks = np.round(np.log(ws_cont / w_0) / np.log(w_m))\n    ws = w_0 * np.power(w_m, ks)\n    ws = np.round(np.divide(ws, q)) * q\n    num_stages, max_stage = len(np.unique(ws)), ks.max() + 1\n    ws, ws_cont = ws.astype(int).tolist(), ws_cont.tolist()\n    return ws, num_stages, max_stage, ws_cont\n\n\nclass RegNet(AnyNet):\n    \"\"\"RegNet model.\"\"\"\n\n    def __init__(self, last_stride, bn_norm):\n        # Generate RegNet ws per block\n        b_ws, num_s, _, _ = generate_regnet(\n            regnet_cfg.REGNET.WA, regnet_cfg.REGNET.W0, regnet_cfg.REGNET.WM, regnet_cfg.REGNET.DEPTH\n        )\n        # Convert to per stage format\n        ws, ds = get_stages_from_blocks(b_ws, b_ws)\n        # Generate group widths and bot muls\n        gws = [regnet_cfg.REGNET.GROUP_W for _ in range(num_s)]\n        bms = [regnet_cfg.REGNET.BOT_MUL for _ in range(num_s)]\n        # Adjust the compatibility of ws and gws\n        ws, gws = adjust_ws_gs_comp(ws, bms, gws)\n        # Use the same stride for each stage\n        ss = [regnet_cfg.REGNET.STRIDE for _ in range(num_s)]\n        ss[-1] = last_stride\n        # Use SE for RegNetY\n        se_r = regnet_cfg.REGNET.SE_R if regnet_cfg.REGNET.SE_ON else None\n        # Construct the model\n        kwargs = {\n            \"stem_type\": regnet_cfg.REGNET.STEM_TYPE,\n            \"stem_w\": regnet_cfg.REGNET.STEM_W,\n            \"block_type\": regnet_cfg.REGNET.BLOCK_TYPE,\n            \"ss\": ss,\n            \"ds\": ds,\n            \"ws\": ws,\n            \"bn_norm\": bn_norm,\n            \"bms\": bms,\n            \"gws\": gws,\n            \"se_r\": se_r,\n        }\n        super(RegNet, self).__init__(**kwargs)\n\n\ndef init_pretrained_weights(key):\n    \"\"\"Initializes model with pretrained weights.\n\n    Layers that don't match with pretrained layers in name or size are kept unchanged.\n    \"\"\"\n    import os\n    import errno\n    import gdown\n\n    def _get_torch_home():\n        ENV_TORCH_HOME = 'TORCH_HOME'\n        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'\n        DEFAULT_CACHE_DIR = '~/.cache'\n        torch_home = os.path.expanduser(\n            os.getenv(\n                ENV_TORCH_HOME,\n                os.path.join(\n                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'\n                )\n            )\n        )\n        return torch_home\n\n    torch_home = _get_torch_home()\n    model_dir = os.path.join(torch_home, 'checkpoints')\n    try:\n        os.makedirs(model_dir)\n    except OSError as e:\n        if e.errno == errno.EEXIST:\n            # Directory already exists, ignore.\n            pass\n        else:\n            # Unexpected OSError, re-raise.\n            raise\n\n    filename = model_urls[key].split('/')[-1]\n\n    cached_file = os.path.join(model_dir, filename)\n\n    if not os.path.exists(cached_file):\n        if comm.is_main_process():\n            gdown.download(model_urls[key], cached_file, quiet=False)\n\n    comm.synchronize()\n\n    logger.info(f\"Loading pretrained model from {cached_file}\")\n    state_dict = torch.load(cached_file, map_location=torch.device('cpu'))['model_state']\n\n    return state_dict\n\n\n@BACKBONE_REGISTRY.register()\ndef build_regnet_backbone(cfg):\n    # fmt: off\n    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    last_stride   = cfg.MODEL.BACKBONE.LAST_STRIDE\n    bn_norm       = cfg.MODEL.BACKBONE.NORM\n    depth         = cfg.MODEL.BACKBONE.DEPTH\n    # fmt: on\n\n    cfg_files = {\n        '200x': 'fastreid/modeling/backbones/regnet/regnetx/RegNetX-200MF_dds_8gpu.yaml',\n        '200y': 'fastreid/modeling/backbones/regnet/regnety/RegNetY-200MF_dds_8gpu.yaml',\n        '400x': 'fastreid/modeling/backbones/regnet/regnetx/RegNetX-400MF_dds_8gpu.yaml',\n        '400y': 'fastreid/modeling/backbones/regnet/regnety/RegNetY-400MF_dds_8gpu.yaml',\n        '800x': 'fastreid/modeling/backbones/regnet/regnetx/RegNetX-800MF_dds_8gpu.yaml',\n        '800y': 'fastreid/modeling/backbones/regnet/regnety/RegNetY-800MF_dds_8gpu.yaml',\n        '1600x': 'fastreid/modeling/backbones/regnet/regnetx/RegNetX-1.6GF_dds_8gpu.yaml',\n        '1600y': 'fastreid/modeling/backbones/regnet/regnety/RegNetY-1.6GF_dds_8gpu.yaml',\n        '3200x': 'fastreid/modeling/backbones/regnet/regnetx/RegNetX-3.2GF_dds_8gpu.yaml',\n        '3200y': 'fastreid/modeling/backbones/regnet/regnety/RegNetY-3.2GF_dds_8gpu.yaml',\n        '4000x': 'fastreid/modeling/backbones/regnet/regnety/RegNetX-4.0GF_dds_8gpu.yaml',\n        '4000y': 'fastreid/modeling/backbones/regnet/regnety/RegNetY-4.0GF_dds_8gpu.yaml',\n        '6400x': 'fastreid/modeling/backbones/regnet/regnetx/RegNetX-6.4GF_dds_8gpu.yaml',\n        '6400y': 'fastreid/modeling/backbones/regnet/regnety/RegNetY-6.4GF_dds_8gpu.yaml',\n    }[depth]\n\n    regnet_cfg.merge_from_file(cfg_files)\n    model = RegNet(last_stride, bn_norm)\n\n    if pretrain:\n        # Load pretrain path if specifically\n        if pretrain_path:\n            try:\n                state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))\n                logger.info(f\"Loading pretrained model from {pretrain_path}\")\n            except FileNotFoundError as e:\n                logger.info(f'{pretrain_path} is not found! Please check this path.')\n                raise e\n            except KeyError as e:\n                logger.info(\"State dict keys error! Please check the state dict.\")\n                raise e\n        else:\n            key = depth\n            state_dict = init_pretrained_weights(key)\n\n        incompatible = model.load_state_dict(state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-1.6GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 18\n  W0: 80\n  WA: 34.01\n  WM: 2.25\n  GROUP_W: 24\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.8\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 1024\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 800\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-12GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 19\n  W0: 168\n  WA: 73.36\n  WM: 2.37\n  GROUP_W: 112\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-16GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 22\n  W0: 216\n  WA: 55.59\n  WM: 2.1\n  GROUP_W: 128\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-200MF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 13\n  W0: 24\n  WA: 36.44\n  WM: 2.49\n  GROUP_W: 8\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.8\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 1024\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 800\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-3.2GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 25\n  W0: 88\n  WA: 26.31\n  WM: 2.25\n  GROUP_W: 48\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-32GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 23\n  W0: 320\n  WA: 69.86\n  WM: 2.0\n  GROUP_W: 168\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.2\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 256\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 200\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-4.0GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 23\n  W0: 96\n  WA: 38.65\n  WM: 2.43\n  GROUP_W: 40\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-400MF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 22\n  W0: 24\n  WA: 24.48\n  WM: 2.54\n  GROUP_W: 16\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.8\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 1024\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 800\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-6.4GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 17\n  W0: 184\n  WA: 60.83\n  WM: 2.07\n  GROUP_W: 56\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-600MF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 16\n  W0: 48\n  WA: 36.97\n  WM: 2.24\n  GROUP_W: 24\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.8\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 1024\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 800\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-8.0GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 23\n  W0: 80\n  WA: 49.56\n  WM: 2.88\n  GROUP_W: 120\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnetx/RegNetX-800MF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  DEPTH: 16\n  W0: 56\n  WA: 35.73\n  WM: 2.28\n  GROUP_W: 16\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.8\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 1024\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 800\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-1.6GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 27\n  W0: 48\n  WA: 20.71\n  WM: 2.65\n  GROUP_W: 24\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.8\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 1024\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 800\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-12GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 19\n  W0: 168\n  WA: 73.36\n  WM: 2.37\n  GROUP_W: 112\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-16GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 18\n  W0: 200\n  WA: 106.23\n  WM: 2.48\n  GROUP_W: 112\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.2\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 256\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 200\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-200MF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 13\n  W0: 24\n  WA: 36.44\n  WM: 2.49\n  GROUP_W: 8\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.8\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 1024\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 800\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-3.2GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 21\n  W0: 80\n  WA: 42.63\n  WM: 2.66\n  GROUP_W: 24\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-32GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 20\n  W0: 232\n  WA: 115.89\n  WM: 2.53\n  GROUP_W: 232\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.2\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 256\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 200\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-4.0GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 22\n  W0: 96\n  WA: 31.41\n  WM: 2.24\n  GROUP_W: 64\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-400MF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 16\n  W0: 48\n  WA: 27.89\n  WM: 2.09\n  GROUP_W: 8\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.8\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 1024\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 800\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-6.4GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 25\n  W0: 112\n  WA: 33.22\n  WM: 2.27\n  GROUP_W: 72\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-600MF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 15\n  W0: 48\n  WA: 32.54\n  WM: 2.32\n  GROUP_W: 16\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.8\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 1024\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 800\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-8.0GF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: true\n  DEPTH: 17\n  W0: 192\n  WA: 76.82\n  WM: 2.19\n  GROUP_W: 56\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.4\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 512\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 400\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/regnet/regnety/RegNetY-800MF_dds_8gpu.yaml",
    "content": "MODEL:\n  TYPE: regnet\n  NUM_CLASSES: 1000\nREGNET:\n  SE_ON: True\n  DEPTH: 14\n  W0: 56\n  WA: 38.84\n  WM: 2.4\n  GROUP_W: 16\nOPTIM:\n  LR_POLICY: cos\n  BASE_LR: 0.8\n  MAX_EPOCH: 100\n  MOMENTUM: 0.9\n  WEIGHT_DECAY: 5e-5\n  WARMUP_ITERS: 5\nTRAIN:\n  DATASET: imagenet\n  IM_SIZE: 224\n  BATCH_SIZE: 1024\nTEST:\n  DATASET: imagenet\n  IM_SIZE: 256\n  BATCH_SIZE: 800\nNUM_GPUS: 8\nOUT_DIR: .\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/repvgg.py",
    "content": "# encoding: utf-8\n# ref: https://github.com/CaoWGG/RepVGG/blob/develop/repvgg.py\n\n\nimport logging\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom fast_reid.fastreid.layers import *\nfrom fast_reid.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message\nfrom .build import BACKBONE_REGISTRY\n\nlogger = logging.getLogger(__name__)\n\n\ndef deploy(self, mode=False):\n    self.deploying = mode\n    for module in self.children():\n        if hasattr(module, 'deploying'):\n            module.deploy(mode)\n\n\nnn.Sequential.deploying = False\nnn.Sequential.deploy = deploy\n\n\ndef conv_bn(norm_type, in_channels, out_channels, kernel_size, stride, padding, groups=1):\n    result = nn.Sequential()\n    result.add_module('conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels,\n                                        kernel_size=kernel_size, stride=stride, padding=padding, groups=groups,\n                                        bias=False))\n    result.add_module('bn', get_norm(norm_type, out_channels))\n    return result\n\n\nclass RepVGGBlock(nn.Module):\n\n    def __init__(self, in_channels, out_channels, norm_type, kernel_size,\n                 stride=1, padding=0, groups=1):\n        super(RepVGGBlock, self).__init__()\n        self.deploying = False\n\n        self.groups = groups\n        self.in_channels = in_channels\n\n        assert kernel_size == 3\n        assert padding == 1\n\n        padding_11 = padding - kernel_size // 2\n\n        self.nonlinearity = nn.ReLU()\n\n        self.in_channels = in_channels\n        self.in_channels = in_channels\n        self.kernel_size = kernel_size\n        self.stride = stride\n        self.padding = padding\n        self.groups = groups\n\n        self.register_parameter('fused_weight', None)\n        self.register_parameter('fused_bias', None)\n\n        self.rbr_identity = get_norm(norm_type, in_channels) if out_channels == in_channels and stride == 1 else None\n        self.rbr_dense = conv_bn(norm_type, in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,\n                                 stride=stride, padding=padding, groups=groups)\n        self.rbr_1x1 = conv_bn(norm_type, in_channels=in_channels, out_channels=out_channels, kernel_size=1,\n                               stride=stride, padding=padding_11, groups=groups)\n\n    def forward(self, inputs):\n        if self.deploying:\n            assert self.fused_weight is not None and self.fused_bias is not None, \\\n                \"Make deploy mode=True to generate fused weight and fused bias first\"\n            fused_out = self.nonlinearity(torch.nn.functional.conv2d(\n                inputs, self.fused_weight, self.fused_bias, self.stride, self.padding, 1, self.groups))\n            return fused_out\n\n        if self.rbr_identity is None:\n            id_out = 0\n        else:\n            id_out = self.rbr_identity(inputs)\n        out = self.nonlinearity(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)\n\n        return out\n\n    def get_equivalent_kernel_bias(self):\n        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)\n        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)\n        kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)\n        return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid\n\n    def _pad_1x1_to_3x3_tensor(self, kernel1x1):\n        if kernel1x1 is None:\n            return 0\n        else:\n            return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])\n\n    def _fuse_bn_tensor(self, branch):\n        if branch is None:\n            return 0, 0\n        if isinstance(branch, nn.Sequential):\n            kernel = branch.conv.weight\n            running_mean = branch.bn.running_mean\n            running_var = branch.bn.running_var\n            gamma = branch.bn.weight\n            beta = branch.bn.bias\n            eps = branch.bn.eps\n        else:\n            assert branch.__class__.__name__.find('BatchNorm') != -1\n            if not hasattr(self, 'id_tensor'):\n                input_dim = self.in_channels // self.groups\n                kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)\n                for i in range(self.in_channels):\n                    kernel_value[i, i % input_dim, 1, 1] = 1\n                self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)\n            kernel = self.id_tensor\n            running_mean = branch.running_mean\n            running_var = branch.running_var\n            gamma = branch.weight\n            beta = branch.bias\n            eps = branch.eps\n        std = (running_var + eps).sqrt()\n        t = (gamma / std).reshape(-1, 1, 1, 1)\n        return kernel * t, beta - running_mean * gamma / std\n\n    def deploy(self, mode=False):\n        self.deploying = mode\n        if mode:\n            fused_weight, fused_bias = self.get_equivalent_kernel_bias()\n            self.register_parameter('fused_weight', nn.Parameter(fused_weight))\n            self.register_parameter('fused_bias', nn.Parameter(fused_bias))\n            del self.rbr_identity, self.rbr_1x1, self.rbr_dense\n\n\nclass RepVGG(nn.Module):\n\n    def __init__(self, last_stride, norm_type, num_blocks, width_multiplier=None, override_groups_map=None):\n        super(RepVGG, self).__init__()\n\n        assert len(width_multiplier) == 4\n\n        self.deploying = False\n        self.override_groups_map = override_groups_map or dict()\n\n        assert 0 not in self.override_groups_map\n\n        self.in_planes = min(64, int(64 * width_multiplier[0]))\n\n        self.stage0 = RepVGGBlock(in_channels=3, out_channels=self.in_planes, norm_type=norm_type,\n                                  kernel_size=3, stride=2, padding=1)\n        self.cur_layer_idx = 1\n        self.stage1 = self._make_stage(int(64 * width_multiplier[0]), norm_type, num_blocks[0], stride=2)\n        self.stage2 = self._make_stage(int(128 * width_multiplier[1]), norm_type, num_blocks[1], stride=2)\n        self.stage3 = self._make_stage(int(256 * width_multiplier[2]), norm_type, num_blocks[2], stride=2)\n        self.stage4 = self._make_stage(int(512 * width_multiplier[3]), norm_type, num_blocks[3], stride=last_stride)\n\n    def _make_stage(self, planes, norm_type, num_blocks, stride):\n        strides = [stride] + [1] * (num_blocks - 1)\n        blocks = []\n        for stride in strides:\n            cur_groups = self.override_groups_map.get(self.cur_layer_idx, 1)\n            blocks.append(RepVGGBlock(in_channels=self.in_planes, out_channels=planes, norm_type=norm_type,\n                                      kernel_size=3, stride=stride, padding=1, groups=cur_groups))\n            self.in_planes = planes\n            self.cur_layer_idx += 1\n        return nn.Sequential(*blocks)\n\n    def deploy(self, mode=False):\n        self.deploying = mode\n        for module in self.children():\n            if hasattr(module, 'deploying'):\n                module.deploy(mode)\n\n    def forward(self, x):\n        out = self.stage0(x)\n        out = self.stage1(out)\n        out = self.stage2(out)\n        out = self.stage3(out)\n        out = self.stage4(out)\n        return out\n\n\noptional_groupwise_layers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26]\ng2_map = {l: 2 for l in optional_groupwise_layers}\ng4_map = {l: 4 for l in optional_groupwise_layers}\n\n\ndef create_RepVGG_A0(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[2, 4, 14, 1],\n                  width_multiplier=[0.75, 0.75, 0.75, 2.5], override_groups_map=None)\n\n\ndef create_RepVGG_A1(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[2, 4, 14, 1],\n                  width_multiplier=[1, 1, 1, 2.5], override_groups_map=None)\n\n\ndef create_RepVGG_A2(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[2, 4, 14, 1],\n                  width_multiplier=[1.5, 1.5, 1.5, 2.75], override_groups_map=None)\n\n\ndef create_RepVGG_B0(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],\n                  width_multiplier=[1, 1, 1, 2.5], override_groups_map=None)\n\n\ndef create_RepVGG_B1(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],\n                  width_multiplier=[2, 2, 2, 4], override_groups_map=None)\n\n\ndef create_RepVGG_B1g2(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],\n                  width_multiplier=[2, 2, 2, 4], override_groups_map=g2_map)\n\n\ndef create_RepVGG_B1g4(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],\n                  width_multiplier=[2, 2, 2, 4], override_groups_map=g4_map)\n\n\ndef create_RepVGG_B2(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],\n                  width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=None)\n\n\ndef create_RepVGG_B2g2(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],\n                  width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g2_map)\n\n\ndef create_RepVGG_B2g4(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],\n                  width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g4_map)\n\n\ndef create_RepVGG_B3(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],\n                  width_multiplier=[3, 3, 3, 5], override_groups_map=None)\n\n\ndef create_RepVGG_B3g2(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],\n                  width_multiplier=[3, 3, 3, 5], override_groups_map=g2_map)\n\n\ndef create_RepVGG_B3g4(last_stride, norm_type):\n    return RepVGG(last_stride, norm_type, num_blocks=[4, 6, 16, 1],\n                  width_multiplier=[3, 3, 3, 5], override_groups_map=g4_map)\n\n\n@BACKBONE_REGISTRY.register()\ndef build_repvgg_backbone(cfg):\n    \"\"\"\n    Create a RepVGG instance from config.\n    Returns:\n        RepVGG: a :class: `RepVGG` instance.\n    \"\"\"\n\n    # fmt: off\n    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    last_stride   = cfg.MODEL.BACKBONE.LAST_STRIDE\n    bn_norm       = cfg.MODEL.BACKBONE.NORM\n    depth         = cfg.MODEL.BACKBONE.DEPTH\n    # fmt: on\n\n    func_dict = {\n        'A0': create_RepVGG_A0,\n        'A1': create_RepVGG_A1,\n        'A2': create_RepVGG_A2,\n        'B0': create_RepVGG_B0,\n        'B1': create_RepVGG_B1,\n        'B1g2': create_RepVGG_B1g2,\n        'B1g4': create_RepVGG_B1g4,\n        'B2': create_RepVGG_B2,\n        'B2g2': create_RepVGG_B2g2,\n        'B2g4': create_RepVGG_B2g4,\n        'B3': create_RepVGG_B3,\n        'B3g2': create_RepVGG_B3g2,\n        'B3g4': create_RepVGG_B3g4,\n    }\n\n    model = func_dict[depth](last_stride, bn_norm)\n\n    if pretrain:\n        try:\n            state_dict = torch.load(pretrain_path, map_location=torch.device(\"cpu\"))\n            logger.info(f\"Loading pretrained model from {pretrain_path}\")\n        except FileNotFoundError as e:\n            logger.info(f'{pretrain_path} is not found! Please check this path.')\n            raise e\n        except KeyError as e:\n            logger.info(\"State dict keys error! Please check the state dict.\")\n            raise e\n\n        incompatible = model.load_state_dict(state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/resnest.py",
    "content": "# encoding: utf-8\n# based on:\n# https://github.com/zhanghang1989/ResNeSt/blob/master/resnest/torch/models/resnest.py\n\"\"\"ResNeSt models\"\"\"\n\nimport logging\nimport math\n\nimport torch\nfrom torch import nn\n\nfrom fast_reid.fastreid.layers import SplAtConv2d, get_norm, DropBlock2D\nfrom fast_reid.fastreid.utils.checkpoint import get_unexpected_parameters_message, get_missing_parameters_message\nfrom .build import BACKBONE_REGISTRY\n\nlogger = logging.getLogger(__name__)\n_url_format = 'https://github.com/zhanghang1989/ResNeSt/releases/download/weights_step1/{}-{}.pth'\n\n_model_sha256 = {name: checksum for checksum, name in [\n    ('528c19ca', 'resnest50'),\n    ('22405ba7', 'resnest101'),\n    ('75117900', 'resnest200'),\n    ('0cc87c48', 'resnest269'),\n]}\n\n\ndef short_hash(name):\n    if name not in _model_sha256:\n        raise ValueError('Pretrained model for {name} is not available.'.format(name=name))\n    return _model_sha256[name][:8]\n\n\nmodel_urls = {name: _url_format.format(name, short_hash(name)) for\n              name in _model_sha256.keys()\n              }\n\n\nclass Bottleneck(nn.Module):\n    \"\"\"ResNet Bottleneck\n    \"\"\"\n    # pylint: disable=unused-argument\n    expansion = 4\n\n    def __init__(self, inplanes, planes, stride=1, downsample=None,\n                 radix=1, cardinality=1, bottleneck_width=64,\n                 avd=False, avd_first=False, dilation=1, is_first=False,\n                 rectified_conv=False, rectify_avg=False,\n                 norm_layer=None, dropblock_prob=0.0, last_gamma=False):\n        super(Bottleneck, self).__init__()\n        group_width = int(planes * (bottleneck_width / 64.)) * cardinality\n        self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)\n        self.bn1 = get_norm(norm_layer, group_width)\n        self.dropblock_prob = dropblock_prob\n        self.radix = radix\n        self.avd = avd and (stride > 1 or is_first)\n        self.avd_first = avd_first\n\n        if self.avd:\n            self.avd_layer = nn.AvgPool2d(3, stride, padding=1)\n            stride = 1\n\n        if dropblock_prob > 0.0:\n            self.dropblock1 = DropBlock2D(dropblock_prob, 3)\n            if radix == 1:\n                self.dropblock2 = DropBlock2D(dropblock_prob, 3)\n            self.dropblock3 = DropBlock2D(dropblock_prob, 3)\n\n        if radix >= 1:\n            self.conv2 = SplAtConv2d(\n                group_width, group_width, kernel_size=3,\n                stride=stride, padding=dilation,\n                dilation=dilation, groups=cardinality, bias=False,\n                radix=radix, rectify=rectified_conv,\n                rectify_avg=rectify_avg,\n                norm_layer=norm_layer,\n                dropblock_prob=dropblock_prob)\n        elif rectified_conv:\n            from rfconv import RFConv2d\n            self.conv2 = RFConv2d(\n                group_width, group_width, kernel_size=3, stride=stride,\n                padding=dilation, dilation=dilation,\n                groups=cardinality, bias=False,\n                average_mode=rectify_avg)\n            self.bn2 = get_norm(norm_layer, group_width)\n        else:\n            self.conv2 = nn.Conv2d(\n                group_width, group_width, kernel_size=3, stride=stride,\n                padding=dilation, dilation=dilation,\n                groups=cardinality, bias=False)\n            self.bn2 = get_norm(norm_layer, group_width)\n\n        self.conv3 = nn.Conv2d(\n            group_width, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = get_norm(norm_layer, planes * 4)\n\n        if last_gamma:\n            from torch.nn.init import zeros_\n            zeros_(self.bn3.weight)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.dilation = dilation\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        if self.dropblock_prob > 0.0:\n            out = self.dropblock1(out)\n        out = self.relu(out)\n\n        if self.avd and self.avd_first:\n            out = self.avd_layer(out)\n\n        out = self.conv2(out)\n        if self.radix == 0:\n            out = self.bn2(out)\n            if self.dropblock_prob > 0.0:\n                out = self.dropblock2(out)\n            out = self.relu(out)\n\n        if self.avd and not self.avd_first:\n            out = self.avd_layer(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n        if self.dropblock_prob > 0.0:\n            out = self.dropblock3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass ResNeSt(nn.Module):\n    \"\"\"ResNet Variants\n    Parameters\n    ----------\n    block : Block\n        Class for the residual block. Options are BasicBlockV1, BottleneckV1.\n    layers : list of int\n        Numbers of layers in each block\n    classes : int, default 1000\n        Number of classification classes.\n    dilated : bool, default False\n        Applying dilation strategy to pretrained ResNet yielding a stride-8 model,\n        typically used in Semantic Segmentation.\n    norm_layer : object\n        Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;\n        for Synchronized Cross-GPU BachNormalization).\n    Reference:\n        - He, Kaiming, et al. \"Deep residual learning for image recognition.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.\n        - Yu, Fisher, and Vladlen Koltun. \"Multi-scale context aggregation by dilated convolutions.\"\n    \"\"\"\n\n    # pylint: disable=unused-variable\n    def __init__(self, last_stride, block, layers, radix=1, groups=1, bottleneck_width=64,\n                 dilated=False, dilation=1,\n                 deep_stem=False, stem_width=64, avg_down=False,\n                 rectified_conv=False, rectify_avg=False,\n                 avd=False, avd_first=False,\n                 final_drop=0.0, dropblock_prob=0,\n                 last_gamma=False, norm_layer=\"BN\"):\n        if last_stride == 1: dilation = 2\n\n        self.cardinality = groups\n        self.bottleneck_width = bottleneck_width\n        # ResNet-D params\n        self.inplanes = stem_width * 2 if deep_stem else 64\n        self.avg_down = avg_down\n        self.last_gamma = last_gamma\n        # ResNeSt params\n        self.radix = radix\n        self.avd = avd\n        self.avd_first = avd_first\n\n        super().__init__()\n        self.rectified_conv = rectified_conv\n        self.rectify_avg = rectify_avg\n        if rectified_conv:\n            from rfconv import RFConv2d\n            conv_layer = RFConv2d\n        else:\n            conv_layer = nn.Conv2d\n        conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}\n        if deep_stem:\n            self.conv1 = nn.Sequential(\n                conv_layer(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False, **conv_kwargs),\n                get_norm(norm_layer, stem_width),\n                nn.ReLU(inplace=True),\n                conv_layer(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs),\n                get_norm(norm_layer, stem_width),\n                nn.ReLU(inplace=True),\n                conv_layer(stem_width, stem_width * 2, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs),\n            )\n        else:\n            self.conv1 = conv_layer(3, 64, kernel_size=7, stride=2, padding=3,\n                                    bias=False, **conv_kwargs)\n        self.bn1 = get_norm(norm_layer, self.inplanes)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer, is_first=False)\n        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)\n        if dilated or dilation == 4:\n            self.layer3 = self._make_layer(block, 256, layers[2], stride=1,\n                                           dilation=2, norm_layer=norm_layer,\n                                           dropblock_prob=dropblock_prob)\n            self.layer4 = self._make_layer(block, 512, layers[3], stride=1,\n                                           dilation=4, norm_layer=norm_layer,\n                                           dropblock_prob=dropblock_prob)\n        elif dilation == 2:\n            self.layer3 = self._make_layer(block, 256, layers[2], stride=2,\n                                           dilation=1, norm_layer=norm_layer,\n                                           dropblock_prob=dropblock_prob)\n            self.layer4 = self._make_layer(block, 512, layers[3], stride=1,\n                                           dilation=2, norm_layer=norm_layer,\n                                           dropblock_prob=dropblock_prob)\n        else:\n            self.layer3 = self._make_layer(block, 256, layers[2], stride=2,\n                                           norm_layer=norm_layer,\n                                           dropblock_prob=dropblock_prob)\n            self.layer4 = self._make_layer(block, 512, layers[3], stride=2,\n                                           norm_layer=norm_layer,\n                                           dropblock_prob=dropblock_prob)\n        self.drop = nn.Dropout(final_drop) if final_drop > 0.0 else None\n\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2. / n))\n\n    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=None,\n                    dropblock_prob=0.0, is_first=True):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            down_layers = []\n            if self.avg_down:\n                if dilation == 1:\n                    down_layers.append(nn.AvgPool2d(kernel_size=stride, stride=stride,\n                                                    ceil_mode=True, count_include_pad=False))\n                else:\n                    down_layers.append(nn.AvgPool2d(kernel_size=1, stride=1,\n                                                    ceil_mode=True, count_include_pad=False))\n                down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,\n                                             kernel_size=1, stride=1, bias=False))\n            else:\n                down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,\n                                             kernel_size=1, stride=stride, bias=False))\n            down_layers.append(get_norm(norm_layer, planes * block.expansion))\n            downsample = nn.Sequential(*down_layers)\n\n        layers = []\n        if dilation == 1 or dilation == 2:\n            layers.append(block(self.inplanes, planes, stride, downsample=downsample,\n                                radix=self.radix, cardinality=self.cardinality,\n                                bottleneck_width=self.bottleneck_width,\n                                avd=self.avd, avd_first=self.avd_first,\n                                dilation=1, is_first=is_first, rectified_conv=self.rectified_conv,\n                                rectify_avg=self.rectify_avg,\n                                norm_layer=norm_layer, dropblock_prob=dropblock_prob,\n                                last_gamma=self.last_gamma))\n        elif dilation == 4:\n            layers.append(block(self.inplanes, planes, stride, downsample=downsample,\n                                radix=self.radix, cardinality=self.cardinality,\n                                bottleneck_width=self.bottleneck_width,\n                                avd=self.avd, avd_first=self.avd_first,\n                                dilation=2, is_first=is_first, rectified_conv=self.rectified_conv,\n                                rectify_avg=self.rectify_avg,\n                                norm_layer=norm_layer, dropblock_prob=dropblock_prob,\n                                last_gamma=self.last_gamma))\n        else:\n            raise RuntimeError(\"=> unknown dilation size: {}\".format(dilation))\n\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(block(self.inplanes, planes,\n                                radix=self.radix, cardinality=self.cardinality,\n                                bottleneck_width=self.bottleneck_width,\n                                avd=self.avd, avd_first=self.avd_first,\n                                dilation=dilation, rectified_conv=self.rectified_conv,\n                                rectify_avg=self.rectify_avg,\n                                norm_layer=norm_layer, dropblock_prob=dropblock_prob,\n                                last_gamma=self.last_gamma))\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        return x\n\n\n@BACKBONE_REGISTRY.register()\ndef build_resnest_backbone(cfg):\n    \"\"\"\n    Create a ResNest instance from config.\n    Returns:\n        ResNet: a :class:`ResNet` instance.\n    \"\"\"\n\n    # fmt: off\n    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    last_stride   = cfg.MODEL.BACKBONE.LAST_STRIDE\n    bn_norm       = cfg.MODEL.BACKBONE.NORM\n    depth         = cfg.MODEL.BACKBONE.DEPTH\n    # fmt: on\n\n    num_blocks_per_stage = {\n        \"50x\": [3, 4, 6, 3],\n        \"101x\": [3, 4, 23, 3],\n        \"200x\": [3, 24, 36, 3],\n        \"269x\": [3, 30, 48, 8],\n    }[depth]\n\n    stem_width = {\n        \"50x\": 32,\n        \"101x\": 64,\n        \"200x\": 64,\n        \"269x\": 64,\n    }[depth]\n\n    model = ResNeSt(last_stride, Bottleneck, num_blocks_per_stage,\n                    radix=2, groups=1, bottleneck_width=64,\n                    deep_stem=True, stem_width=stem_width, avg_down=True,\n                    avd=True, avd_first=False, norm_layer=bn_norm)\n    if pretrain:\n        # Load pretrain path if specifically\n        if pretrain_path:\n            try:\n                state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))\n                logger.info(f\"Loading pretrained model from {pretrain_path}\")\n            except FileNotFoundError as e:\n                logger.info(f'{pretrain_path} is not found! Please check this path.')\n                raise e\n            except KeyError as e:\n                logger.info(\"State dict keys error! Please check the state dict.\")\n                raise e\n        else:\n            state_dict = torch.hub.load_state_dict_from_url(\n                model_urls['resnest' + depth[:-1]], progress=True, check_hash=True, map_location=torch.device('cpu'))\n\n        incompatible = model.load_state_dict(state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/resnet.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\nimport math\n\nimport torch\nfrom torch import nn\n\nfrom fast_reid.fastreid.layers import (\n    IBN,\n    SELayer,\n    Non_local,\n    get_norm,\n)\nfrom fast_reid.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message\nfrom .build import BACKBONE_REGISTRY\nfrom fast_reid.fastreid.utils import comm\n\n\nlogger = logging.getLogger(__name__)\nmodel_urls = {\n    '18x': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n    '34x': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',\n    '50x': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n    '101x': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',\n    'ibn_18x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet18_ibn_a-2f571257.pth',\n    'ibn_34x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet34_ibn_a-94bc1577.pth',\n    'ibn_50x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet50_ibn_a-d9d0bb7b.pth',\n    'ibn_101x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet101_ibn_a-59ea0ac6.pth',\n    'se_ibn_101x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/se_resnet101_ibn_a-fabed4e2.pth',\n}\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(self, inplanes, planes, bn_norm, with_ibn=False, with_se=False,\n                 stride=1, downsample=None, reduction=16):\n        super(BasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n        if with_ibn:\n            self.bn1 = IBN(planes, bn_norm)\n        else:\n            self.bn1 = get_norm(bn_norm, planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn2 = get_norm(bn_norm, planes)\n        self.relu = nn.ReLU(inplace=True)\n        if with_se:\n            self.se = SELayer(planes, reduction)\n        else:\n            self.se = nn.Identity()\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        identity = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.se(out)\n\n        if self.downsample is not None:\n            identity = self.downsample(x)\n\n        out += identity\n        out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(self, inplanes, planes, bn_norm, with_ibn=False, with_se=False,\n                 stride=1, downsample=None, reduction=16):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        if with_ibn:\n            self.bn1 = IBN(planes, bn_norm)\n        else:\n            self.bn1 = get_norm(bn_norm, planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n                               padding=1, bias=False)\n        self.bn2 = get_norm(bn_norm, planes)\n        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)\n        self.bn3 = get_norm(bn_norm, planes * self.expansion)\n        self.relu = nn.ReLU(inplace=True)\n        if with_se:\n            self.se = SELayer(planes * self.expansion, reduction)\n        else:\n            self.se = nn.Identity()\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n        out = self.se(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass ResNet(nn.Module):\n    def __init__(self, last_stride, bn_norm, with_ibn, with_se, with_nl, block, layers, non_layers):\n        self.inplanes = 64\n        super().__init__()\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,\n                               bias=False)\n        self.bn1 = get_norm(bn_norm, 64)\n        self.relu = nn.ReLU(inplace=True)\n        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)\n        self.layer1 = self._make_layer(block, 64, layers[0], 1, bn_norm, with_ibn, with_se)\n        self.layer2 = self._make_layer(block, 128, layers[1], 2, bn_norm, with_ibn, with_se)\n        self.layer3 = self._make_layer(block, 256, layers[2], 2, bn_norm, with_ibn, with_se)\n        self.layer4 = self._make_layer(block, 512, layers[3], last_stride, bn_norm, with_se=with_se)\n\n        self.random_init()\n\n        # fmt: off\n        if with_nl: self._build_nonlocal(layers, non_layers, bn_norm)\n        else:       self.NL_1_idx = self.NL_2_idx = self.NL_3_idx = self.NL_4_idx = []\n        # fmt: on\n\n    def _make_layer(self, block, planes, blocks, stride=1, bn_norm=\"BN\", with_ibn=False, with_se=False):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(self.inplanes, planes * block.expansion,\n                          kernel_size=1, stride=stride, bias=False),\n                get_norm(bn_norm, planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(block(self.inplanes, planes, bn_norm, with_ibn, with_se, stride, downsample))\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(block(self.inplanes, planes, bn_norm, with_ibn, with_se))\n\n        return nn.Sequential(*layers)\n\n    def _build_nonlocal(self, layers, non_layers, bn_norm):\n        self.NL_1 = nn.ModuleList(\n            [Non_local(256, bn_norm) for _ in range(non_layers[0])])\n        self.NL_1_idx = sorted([layers[0] - (i + 1) for i in range(non_layers[0])])\n        self.NL_2 = nn.ModuleList(\n            [Non_local(512, bn_norm) for _ in range(non_layers[1])])\n        self.NL_2_idx = sorted([layers[1] - (i + 1) for i in range(non_layers[1])])\n        self.NL_3 = nn.ModuleList(\n            [Non_local(1024, bn_norm) for _ in range(non_layers[2])])\n        self.NL_3_idx = sorted([layers[2] - (i + 1) for i in range(non_layers[2])])\n        self.NL_4 = nn.ModuleList(\n            [Non_local(2048, bn_norm) for _ in range(non_layers[3])])\n        self.NL_4_idx = sorted([layers[3] - (i + 1) for i in range(non_layers[3])])\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n\n        # layer 1\n        NL1_counter = 0\n        if len(self.NL_1_idx) == 0:\n            self.NL_1_idx = [-1]\n        for i in range(len(self.layer1)):\n            x = self.layer1[i](x)\n            if i == self.NL_1_idx[NL1_counter]:\n                _, C, H, W = x.shape\n                x = self.NL_1[NL1_counter](x)\n                NL1_counter += 1\n        # layer 2\n        NL2_counter = 0\n        if len(self.NL_2_idx) == 0:\n            self.NL_2_idx = [-1]\n        for i in range(len(self.layer2)):\n            x = self.layer2[i](x)\n            if i == self.NL_2_idx[NL2_counter]:\n                _, C, H, W = x.shape\n                x = self.NL_2[NL2_counter](x)\n                NL2_counter += 1\n\n        # layer 3\n        NL3_counter = 0\n        if len(self.NL_3_idx) == 0:\n            self.NL_3_idx = [-1]\n        for i in range(len(self.layer3)):\n            x = self.layer3[i](x)\n            if i == self.NL_3_idx[NL3_counter]:\n                _, C, H, W = x.shape\n                x = self.NL_3[NL3_counter](x)\n                NL3_counter += 1\n\n        # layer 4\n        NL4_counter = 0\n        if len(self.NL_4_idx) == 0:\n            self.NL_4_idx = [-1]\n        for i in range(len(self.layer4)):\n            x = self.layer4[i](x)\n            if i == self.NL_4_idx[NL4_counter]:\n                _, C, H, W = x.shape\n                x = self.NL_4[NL4_counter](x)\n                NL4_counter += 1\n\n        return x\n\n    def random_init(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                nn.init.normal_(m.weight, 0, math.sqrt(2. / n))\n            elif isinstance(m, nn.BatchNorm2d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n\n\ndef init_pretrained_weights(key):\n    \"\"\"Initializes model with pretrained weights.\n\n    Layers that don't match with pretrained layers in name or size are kept unchanged.\n    \"\"\"\n    import os\n    import errno\n    import gdown\n\n    def _get_torch_home():\n        ENV_TORCH_HOME = 'TORCH_HOME'\n        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'\n        DEFAULT_CACHE_DIR = '~/.cache'\n        torch_home = os.path.expanduser(\n            os.getenv(\n                ENV_TORCH_HOME,\n                os.path.join(\n                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'\n                )\n            )\n        )\n        return torch_home\n\n    torch_home = _get_torch_home()\n    model_dir = os.path.join(torch_home, 'checkpoints')\n    try:\n        os.makedirs(model_dir)\n    except OSError as e:\n        if e.errno == errno.EEXIST:\n            # Directory already exists, ignore.\n            pass\n        else:\n            # Unexpected OSError, re-raise.\n            raise\n\n    filename = model_urls[key].split('/')[-1]\n\n    cached_file = os.path.join(model_dir, filename)\n\n    if not os.path.exists(cached_file):\n        logger.info(f\"Pretrain model don't exist, downloading from {model_urls[key]}\")\n        if comm.is_main_process():\n            gdown.download(model_urls[key], cached_file, quiet=False)\n\n    comm.synchronize()\n\n    logger.info(f\"Loading pretrained model from {cached_file}\")\n    state_dict = torch.load(cached_file, map_location=torch.device('cpu'))\n\n    return state_dict\n\n\n@BACKBONE_REGISTRY.register()\ndef build_resnet_backbone(cfg):\n    \"\"\"\n    Create a ResNet instance from config.\n    Returns:\n        ResNet: a :class:`ResNet` instance.\n    \"\"\"\n\n    # fmt: off\n    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    last_stride   = cfg.MODEL.BACKBONE.LAST_STRIDE\n    bn_norm       = cfg.MODEL.BACKBONE.NORM\n    with_ibn      = cfg.MODEL.BACKBONE.WITH_IBN\n    with_se       = cfg.MODEL.BACKBONE.WITH_SE\n    with_nl       = cfg.MODEL.BACKBONE.WITH_NL\n    depth         = cfg.MODEL.BACKBONE.DEPTH\n    # fmt: on\n\n    num_blocks_per_stage = {\n        '18x': [2, 2, 2, 2],\n        '34x': [3, 4, 6, 3],\n        '50x': [3, 4, 6, 3],\n        '101x': [3, 4, 23, 3],\n    }[depth]\n\n    nl_layers_per_stage = {\n        '18x': [0, 0, 0, 0],\n        '34x': [0, 0, 0, 0],\n        '50x': [0, 2, 3, 0],\n        '101x': [0, 2, 9, 0]\n    }[depth]\n\n    block = {\n        '18x': BasicBlock,\n        '34x': BasicBlock,\n        '50x': Bottleneck,\n        '101x': Bottleneck\n    }[depth]\n\n    model = ResNet(last_stride, bn_norm, with_ibn, with_se, with_nl, block,\n                   num_blocks_per_stage, nl_layers_per_stage)\n    if pretrain:\n        # Load pretrain path if specifically\n        if pretrain_path:\n            try:\n                state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))\n                logger.info(f\"Loading pretrained model from {pretrain_path}\")\n            except FileNotFoundError as e:\n                logger.info(f'{pretrain_path} is not found! Please check this path.')\n                raise e\n            except KeyError as e:\n                logger.info(\"State dict keys error! Please check the state dict.\")\n                raise e\n        else:\n            key = depth\n            if with_ibn: key = 'ibn_' + key\n            if with_se:  key = 'se_' + key\n\n            state_dict = init_pretrained_weights(key)\n\n        incompatible = model.load_state_dict(state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/resnext.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# based on:\n# https://github.com/XingangPan/IBN-Net/blob/master/models/imagenet/resnext_ibn_a.py\n\nimport logging\nimport math\n\nimport torch\nimport torch.nn as nn\n\nfrom fast_reid.fastreid.layers import *\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message\nfrom .build import BACKBONE_REGISTRY\n\nlogger = logging.getLogger(__name__)\nmodel_urls = {\n    'ibn_101x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnext101_ibn_a-6ace051d.pth',\n}\n\n\nclass Bottleneck(nn.Module):\n    \"\"\"\n    RexNeXt bottleneck type C\n    \"\"\"\n    expansion = 4\n\n    def __init__(self, inplanes, planes, bn_norm, with_ibn, baseWidth, cardinality, stride=1,\n                 downsample=None):\n        \"\"\" Constructor\n        Args:\n            inplanes: input channel dimensionality\n            planes: output channel dimensionality\n            baseWidth: base width.\n            cardinality: num of convolution groups.\n            stride: conv stride. Replaces pooling layer.\n        \"\"\"\n        super(Bottleneck, self).__init__()\n\n        D = int(math.floor(planes * (baseWidth / 64)))\n        C = cardinality\n        self.conv1 = nn.Conv2d(inplanes, D * C, kernel_size=1, stride=1, padding=0, bias=False)\n        if with_ibn:\n            self.bn1 = IBN(D * C, bn_norm)\n        else:\n            self.bn1 = get_norm(bn_norm, D * C)\n        self.conv2 = nn.Conv2d(D * C, D * C, kernel_size=3, stride=stride, padding=1, groups=C, bias=False)\n        self.bn2 = get_norm(bn_norm, D * C)\n        self.conv3 = nn.Conv2d(D * C, planes * 4, kernel_size=1, stride=1, padding=0, bias=False)\n        self.bn3 = get_norm(bn_norm, planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.downsample = downsample\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass ResNeXt(nn.Module):\n    \"\"\"\n    ResNext optimized for the ImageNet dataset, as specified in\n    https://arxiv.org/pdf/1611.05431.pdf\n    \"\"\"\n\n    def __init__(self, last_stride, bn_norm, with_ibn, with_nl, block, layers, non_layers,\n                 baseWidth=4, cardinality=32):\n        \"\"\" Constructor\n        Args:\n            baseWidth: baseWidth for ResNeXt.\n            cardinality: number of convolution groups.\n            layers: config of layers, e.g., [3, 4, 6, 3]\n        \"\"\"\n        super(ResNeXt, self).__init__()\n\n        self.cardinality = cardinality\n        self.baseWidth = baseWidth\n        self.inplanes = 64\n        self.output_size = 64\n\n        self.conv1 = nn.Conv2d(3, 64, 7, 2, 3, bias=False)\n        self.bn1 = get_norm(bn_norm, 64)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.layer1 = self._make_layer(block, 64, layers[0], 1, bn_norm, with_ibn=with_ibn)\n        self.layer2 = self._make_layer(block, 128, layers[1], 2, bn_norm, with_ibn=with_ibn)\n        self.layer3 = self._make_layer(block, 256, layers[2], 2, bn_norm, with_ibn=with_ibn)\n        self.layer4 = self._make_layer(block, 512, layers[3], last_stride, bn_norm, with_ibn=with_ibn)\n\n        self.random_init()\n\n        # fmt: off\n        if with_nl: self._build_nonlocal(layers, non_layers, bn_norm)\n        else:       self.NL_1_idx = self.NL_2_idx = self.NL_3_idx = self.NL_4_idx = []\n        # fmt: on\n\n    def _make_layer(self, block, planes, blocks, stride=1, bn_norm='BN', with_ibn=False):\n        \"\"\" Stack n bottleneck modules where n is inferred from the depth of the network.\n        Args:\n            block: block type used to construct ResNext\n            planes: number of output channels (need to multiply by block.expansion)\n            blocks: number of blocks to be built\n            stride: factor to reduce the spatial dimensionality in the first bottleneck of the block.\n        Returns: a Module consisting of n sequential bottlenecks.\n        \"\"\"\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(self.inplanes, planes * block.expansion,\n                          kernel_size=1, stride=stride, bias=False),\n                get_norm(bn_norm, planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(block(self.inplanes, planes, bn_norm, with_ibn,\n                            self.baseWidth, self.cardinality, stride, downsample))\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(\n                block(self.inplanes, planes, bn_norm, with_ibn, self.baseWidth, self.cardinality, 1, None))\n\n        return nn.Sequential(*layers)\n\n    def _build_nonlocal(self, layers, non_layers, bn_norm):\n        self.NL_1 = nn.ModuleList(\n            [Non_local(256, bn_norm) for _ in range(non_layers[0])])\n        self.NL_1_idx = sorted([layers[0] - (i + 1) for i in range(non_layers[0])])\n        self.NL_2 = nn.ModuleList(\n            [Non_local(512, bn_norm) for _ in range(non_layers[1])])\n        self.NL_2_idx = sorted([layers[1] - (i + 1) for i in range(non_layers[1])])\n        self.NL_3 = nn.ModuleList(\n            [Non_local(1024, bn_norm) for _ in range(non_layers[2])])\n        self.NL_3_idx = sorted([layers[2] - (i + 1) for i in range(non_layers[2])])\n        self.NL_4 = nn.ModuleList(\n            [Non_local(2048, bn_norm) for _ in range(non_layers[3])])\n        self.NL_4_idx = sorted([layers[3] - (i + 1) for i in range(non_layers[3])])\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool1(x)\n\n        NL1_counter = 0\n        if len(self.NL_1_idx) == 0:\n            self.NL_1_idx = [-1]\n        for i in range(len(self.layer1)):\n            x = self.layer1[i](x)\n            if i == self.NL_1_idx[NL1_counter]:\n                _, C, H, W = x.shape\n                x = self.NL_1[NL1_counter](x)\n                NL1_counter += 1\n        # Layer 2\n        NL2_counter = 0\n        if len(self.NL_2_idx) == 0:\n            self.NL_2_idx = [-1]\n        for i in range(len(self.layer2)):\n            x = self.layer2[i](x)\n            if i == self.NL_2_idx[NL2_counter]:\n                _, C, H, W = x.shape\n                x = self.NL_2[NL2_counter](x)\n                NL2_counter += 1\n        # Layer 3\n        NL3_counter = 0\n        if len(self.NL_3_idx) == 0:\n            self.NL_3_idx = [-1]\n        for i in range(len(self.layer3)):\n            x = self.layer3[i](x)\n            if i == self.NL_3_idx[NL3_counter]:\n                _, C, H, W = x.shape\n                x = self.NL_3[NL3_counter](x)\n                NL3_counter += 1\n        # Layer 4\n        NL4_counter = 0\n        if len(self.NL_4_idx) == 0:\n            self.NL_4_idx = [-1]\n        for i in range(len(self.layer4)):\n            x = self.layer4[i](x)\n            if i == self.NL_4_idx[NL4_counter]:\n                _, C, H, W = x.shape\n                x = self.NL_4[NL4_counter](x)\n                NL4_counter += 1\n        return x\n\n    def random_init(self):\n        self.conv1.weight.data.normal_(0, math.sqrt(2. / (7 * 7 * 64)))\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2. / n))\n            elif isinstance(m, nn.BatchNorm2d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n            elif isinstance(m, nn.InstanceNorm2d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n\n\ndef init_pretrained_weights(key):\n    \"\"\"Initializes model with pretrained weights.\n\n    Layers that don't match with pretrained layers in name or size are kept unchanged.\n    \"\"\"\n    import os\n    import errno\n    import gdown\n\n    def _get_torch_home():\n        ENV_TORCH_HOME = 'TORCH_HOME'\n        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'\n        DEFAULT_CACHE_DIR = '~/.cache'\n        torch_home = os.path.expanduser(\n            os.getenv(\n                ENV_TORCH_HOME,\n                os.path.join(\n                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'\n                )\n            )\n        )\n        return torch_home\n\n    torch_home = _get_torch_home()\n    model_dir = os.path.join(torch_home, 'checkpoints')\n    try:\n        os.makedirs(model_dir)\n    except OSError as e:\n        if e.errno == errno.EEXIST:\n            # Directory already exists, ignore.\n            pass\n        else:\n            # Unexpected OSError, re-raise.\n            raise\n\n    filename = model_urls[key].split('/')[-1]\n\n    cached_file = os.path.join(model_dir, filename)\n\n    if not os.path.exists(cached_file):\n        logger.info(f\"Pretrain model don't exist, downloading from {model_urls[key]}\")\n        if comm.is_main_process():\n            gdown.download(model_urls[key], cached_file, quiet=False)\n\n    comm.synchronize()\n\n    logger.info(f\"Loading pretrained model from {cached_file}\")\n    state_dict = torch.load(cached_file, map_location=torch.device('cpu'))\n\n    return state_dict\n\n\n@BACKBONE_REGISTRY.register()\ndef build_resnext_backbone(cfg):\n    \"\"\"\n    Create a ResNeXt instance from config.\n    Returns:\n        ResNeXt: a :class:`ResNeXt` instance.\n    \"\"\"\n\n    # fmt: off\n    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    last_stride   = cfg.MODEL.BACKBONE.LAST_STRIDE\n    bn_norm       = cfg.MODEL.BACKBONE.NORM\n    with_ibn      = cfg.MODEL.BACKBONE.WITH_IBN\n    with_nl       = cfg.MODEL.BACKBONE.WITH_NL\n    depth         = cfg.MODEL.BACKBONE.DEPTH\n    # fmt: on\n\n    num_blocks_per_stage = {\n        '50x': [3, 4, 6, 3],\n        '101x': [3, 4, 23, 3],\n        '152x': [3, 8, 36, 3], }[depth]\n    nl_layers_per_stage = {\n        '50x': [0, 2, 3, 0],\n        '101x': [0, 2, 3, 0]}[depth]\n    model = ResNeXt(last_stride, bn_norm, with_ibn, with_nl, Bottleneck,\n                    num_blocks_per_stage, nl_layers_per_stage)\n    if pretrain:\n        if pretrain_path:\n            try:\n                state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))['model']\n                # Remove module.encoder in name\n                new_state_dict = {}\n                for k in state_dict:\n                    new_k = '.'.join(k.split('.')[2:])\n                    if new_k in model.state_dict() and (model.state_dict()[new_k].shape == state_dict[k].shape):\n                        new_state_dict[new_k] = state_dict[k]\n                state_dict = new_state_dict\n                logger.info(f\"Loading pretrained model from {pretrain_path}\")\n            except FileNotFoundError as e:\n                logger.info(f'{pretrain_path} is not found! Please check this path.')\n                raise e\n            except KeyError as e:\n                logger.info(\"State dict keys error! Please check the state dict.\")\n                raise e\n        else:\n            key = depth\n            if with_ibn: key = 'ibn_' + key\n\n            state_dict = init_pretrained_weights(key)\n\n        incompatible = model.load_state_dict(state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/shufflenet.py",
    "content": "\"\"\"\nAuthor: Guan'an Wang\nContact: guan.wang0706@gmail.com\n\"\"\"\n\nimport torch\nfrom torch import nn\nfrom collections import OrderedDict\nimport logging\nfrom fast_reid.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message\n\nfrom fast_reid.fastreid.layers import get_norm\nfrom fast_reid.fastreid.modeling.backbones import BACKBONE_REGISTRY\n\nlogger = logging.getLogger(__name__)\n\n\nclass ShuffleV2Block(nn.Module):\n    \"\"\"\n    Reference:\n        https://github.com/megvii-model/ShuffleNet-Series/tree/master/ShuffleNetV2\n    \"\"\"\n\n    def __init__(self, bn_norm, inp, oup, mid_channels, *, ksize, stride):\n        super(ShuffleV2Block, self).__init__()\n        self.stride = stride\n        assert stride in [1, 2]\n\n        self.mid_channels = mid_channels\n        self.ksize = ksize\n        pad = ksize // 2\n        self.pad = pad\n        self.inp = inp\n\n        outputs = oup - inp\n\n        branch_main = [\n            # pw\n            nn.Conv2d(inp, mid_channels, 1, 1, 0, bias=False),\n            get_norm(bn_norm, mid_channels),\n            nn.ReLU(inplace=True),\n            # dw\n            nn.Conv2d(mid_channels, mid_channels, ksize, stride, pad, groups=mid_channels, bias=False),\n            get_norm(bn_norm, mid_channels),\n            # pw-linear\n            nn.Conv2d(mid_channels, outputs, 1, 1, 0, bias=False),\n            get_norm(bn_norm, outputs),\n            nn.ReLU(inplace=True),\n        ]\n        self.branch_main = nn.Sequential(*branch_main)\n\n        if stride == 2:\n            branch_proj = [\n                # dw\n                nn.Conv2d(inp, inp, ksize, stride, pad, groups=inp, bias=False),\n                get_norm(bn_norm, inp),\n                # pw-linear\n                nn.Conv2d(inp, inp, 1, 1, 0, bias=False),\n                get_norm(bn_norm, inp),\n                nn.ReLU(inplace=True),\n            ]\n            self.branch_proj = nn.Sequential(*branch_proj)\n        else:\n            self.branch_proj = None\n\n    def forward(self, old_x):\n        if self.stride == 1:\n            x_proj, x = self.channel_shuffle(old_x)\n            return torch.cat((x_proj, self.branch_main(x)), 1)\n        elif self.stride == 2:\n            x_proj = old_x\n            x = old_x\n            return torch.cat((self.branch_proj(x_proj), self.branch_main(x)), 1)\n\n    def channel_shuffle(self, x):\n        batchsize, num_channels, height, width = x.data.size()\n        assert (num_channels % 4 == 0)\n        x = x.reshape(batchsize * num_channels // 2, 2, height * width)\n        x = x.permute(1, 0, 2)\n        x = x.reshape(2, -1, num_channels // 2, height, width)\n        return x[0], x[1]\n\n\nclass ShuffleNetV2(nn.Module):\n    \"\"\"\n    Reference:\n        https://github.com/megvii-model/ShuffleNet-Series/tree/master/ShuffleNetV2\n    \"\"\"\n\n    def __init__(self, bn_norm, model_size='1.5x'):\n        super(ShuffleNetV2, self).__init__()\n\n        self.stage_repeats = [4, 8, 4]\n        self.model_size = model_size\n        if model_size == '0.5x':\n            self.stage_out_channels = [-1, 24, 48, 96, 192, 1024]\n        elif model_size == '1.0x':\n            self.stage_out_channels = [-1, 24, 116, 232, 464, 1024]\n        elif model_size == '1.5x':\n            self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]\n        elif model_size == '2.0x':\n            self.stage_out_channels = [-1, 24, 244, 488, 976, 2048]\n        else:\n            raise NotImplementedError\n\n        # building first layer\n        input_channel = self.stage_out_channels[1]\n        self.first_conv = nn.Sequential(\n            nn.Conv2d(3, input_channel, 3, 2, 1, bias=False),\n            get_norm(bn_norm, input_channel),\n            nn.ReLU(inplace=True),\n        )\n\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n        self.features = []\n        for idxstage in range(len(self.stage_repeats)):\n            numrepeat = self.stage_repeats[idxstage]\n            output_channel = self.stage_out_channels[idxstage + 2]\n\n            for i in range(numrepeat):\n                if i == 0:\n                    self.features.append(ShuffleV2Block(bn_norm, input_channel, output_channel,\n                                                        mid_channels=output_channel // 2, ksize=3, stride=2))\n                else:\n                    self.features.append(ShuffleV2Block(bn_norm, input_channel // 2, output_channel,\n                                                        mid_channels=output_channel // 2, ksize=3, stride=1))\n\n                input_channel = output_channel\n\n        self.features = nn.Sequential(*self.features)\n\n        self.conv_last = nn.Sequential(\n            nn.Conv2d(input_channel, self.stage_out_channels[-1], 1, 1, 0, bias=False),\n            get_norm(bn_norm, self.stage_out_channels[-1]),\n            nn.ReLU(inplace=True)\n        )\n\n        self._initialize_weights()\n\n    def forward(self, x):\n        x = self.first_conv(x)\n        x = self.maxpool(x)\n        x = self.features(x)\n        x = self.conv_last(x)\n\n        return x\n\n    def _initialize_weights(self):\n        for name, m in self.named_modules():\n            if isinstance(m, nn.Conv2d):\n                if 'first' in name:\n                    nn.init.normal_(m.weight, 0, 0.01)\n                else:\n                    nn.init.normal_(m.weight, 0, 1.0 / m.weight.shape[1])\n                if m.bias is not None:\n                    nn.init.constant_(m.bias, 0)\n            elif isinstance(m, nn.BatchNorm2d):\n                nn.init.constant_(m.weight, 1)\n                if m.bias is not None:\n                    nn.init.constant_(m.bias, 0.0001)\n                nn.init.constant_(m.running_mean, 0)\n            elif isinstance(m, nn.BatchNorm1d):\n                nn.init.constant_(m.weight, 1)\n                if m.bias is not None:\n                    nn.init.constant_(m.bias, 0.0001)\n                nn.init.constant_(m.running_mean, 0)\n            elif isinstance(m, nn.Linear):\n                nn.init.normal_(m.weight, 0, 0.01)\n                if m.bias is not None:\n                    nn.init.constant_(m.bias, 0)\n\n\n@BACKBONE_REGISTRY.register()\ndef build_shufflenetv2_backbone(cfg):\n    # fmt: off\n    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    bn_norm       = cfg.MODEL.BACKBONE.NORM\n    model_size    = cfg.MODEL.BACKBONE.DEPTH\n    # fmt: on\n\n    model = ShuffleNetV2(bn_norm, model_size=model_size)\n\n    if pretrain:\n        new_state_dict = OrderedDict()\n        state_dict = torch.load(pretrain_path)[\"state_dict\"]\n        for k, v in state_dict.items():\n            if k[:7] == 'module.':\n                k = k[7:]\n            new_state_dict[k] = v\n\n        incompatible = model.load_state_dict(new_state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/backbones/vision_transformer.py",
    "content": "\"\"\" Vision Transformer (ViT) in PyTorch\nA PyTorch implement of Vision Transformers as described in\n'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929\nThe official jax code is released and available at https://github.com/google-research/vision_transformer\nStatus/TODO:\n* Models updated to be compatible with official impl. Args added to support backward compat for old PyTorch weights.\n* Weights ported from official jax impl for 384x384 base and small models, 16x16 and 32x32 patches.\n* Trained (supervised on ImageNet-1k) my custom 'small' patch model to 77.9, 'base' to 79.4 top-1 with this code.\n* Hopefully find time and GPUs for SSL or unsupervised pretraining on OpenImages w/ ImageNet fine-tune in future.\nAcknowledgments:\n* The paper authors for releasing code and weights, thanks!\n* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out\nfor some einops/einsum fun\n* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT\n* Bert reference code checks against Huggingface Transformers and Tensorflow Bert\nHacked together by / Copyright 2020 Ross Wightman\n\"\"\"\n\nimport logging\nimport math\nfrom functools import partial\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom fast_reid.fastreid.layers import DropPath, trunc_normal_, to_2tuple\nfrom fast_reid.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message\nfrom .build import BACKBONE_REGISTRY\n\nlogger = logging.getLogger(__name__)\n\n\nclass Mlp(nn.Module):\n    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        self.fc1 = nn.Linear(in_features, hidden_features)\n        self.act = act_layer()\n        self.fc2 = nn.Linear(hidden_features, out_features)\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\n\nclass Attention(nn.Module):\n    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n        super().__init__()\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights\n        self.scale = qk_scale or head_dim ** -0.5\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n    def forward(self, x):\n        B, N, C = x.shape\n        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)\n\n        attn = (q @ k.transpose(-2, -1)) * self.scale\n        attn = attn.softmax(dim=-1)\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(1, 2).reshape(B, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n\nclass Block(nn.Module):\n\n    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,\n                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):\n        super().__init__()\n        self.norm1 = norm_layer(dim)\n        self.attn = Attention(\n            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here\n        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)\n\n    def forward(self, x):\n        x = x + self.drop_path(self.attn(self.norm1(x)))\n        x = x + self.drop_path(self.mlp(self.norm2(x)))\n        return x\n\n\nclass PatchEmbed(nn.Module):\n    \"\"\" Image to Patch Embedding\n    \"\"\"\n\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.num_patches = num_patches\n\n        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n\n    def forward(self, x):\n        B, C, H, W = x.shape\n        # FIXME look at relaxing size constraints\n        assert H == self.img_size[0] and W == self.img_size[1], \\\n            f\"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).\"\n        x = self.proj(x).flatten(2).transpose(1, 2)\n        return x\n\n\nclass HybridEmbed(nn.Module):\n    \"\"\" CNN Feature Map Embedding\n    Extract feature map from CNN, flatten, project to embedding dim.\n    \"\"\"\n\n    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):\n        super().__init__()\n        assert isinstance(backbone, nn.Module)\n        img_size = to_2tuple(img_size)\n        self.img_size = img_size\n        self.backbone = backbone\n        if feature_size is None:\n            with torch.no_grad():\n                # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature\n                # map for all networks, the feature metadata has reliable channel and stride info, but using\n                # stride to calc feature dim requires info about padding of each stage that isn't captured.\n                training = backbone.training\n                if training:\n                    backbone.eval()\n                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))\n                if isinstance(o, (list, tuple)):\n                    o = o[-1]  # last feature if backbone outputs list/tuple of features\n                feature_size = o.shape[-2:]\n                feature_dim = o.shape[1]\n                backbone.train(training)\n        else:\n            feature_size = to_2tuple(feature_size)\n            if hasattr(self.backbone, 'feature_info'):\n                feature_dim = self.backbone.feature_info.channels()[-1]\n            else:\n                feature_dim = self.backbone.num_features\n        self.num_patches = feature_size[0] * feature_size[1]\n        self.proj = nn.Conv2d(feature_dim, embed_dim, 1)\n\n    def forward(self, x):\n        x = self.backbone(x)\n        if isinstance(x, (list, tuple)):\n            x = x[-1]  # last feature if backbone outputs list/tuple of features\n        x = self.proj(x).flatten(2).transpose(1, 2)\n        return x\n\n\nclass PatchEmbed_overlap(nn.Module):\n    \"\"\" Image to Patch Embedding with overlapping patches\n    \"\"\"\n\n    def __init__(self, img_size=224, patch_size=16, stride_size=20, in_chans=3, embed_dim=768):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        stride_size_tuple = to_2tuple(stride_size)\n        self.num_x = (img_size[1] - patch_size[1]) // stride_size_tuple[1] + 1\n        self.num_y = (img_size[0] - patch_size[0]) // stride_size_tuple[0] + 1\n        num_patches = self.num_x * self.num_y\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.num_patches = num_patches\n\n        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride_size)\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2. / n))\n            elif isinstance(m, nn.BatchNorm2d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n            elif isinstance(m, nn.InstanceNorm2d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n\n    def forward(self, x):\n        B, C, H, W = x.shape\n\n        # FIXME look at relaxing size constraints\n        assert H == self.img_size[0] and W == self.img_size[1], \\\n            f\"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).\"\n        x = self.proj(x)\n\n        x = x.flatten(2).transpose(1, 2)  # [64, 8, 768]\n        return x\n\n\nclass VisionTransformer(nn.Module):\n    \"\"\" Vision Transformer\n        A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`\n            - https://arxiv.org/abs/2010.11929\n        Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`\n            - https://arxiv.org/abs/2012.12877\n        \"\"\"\n\n    def __init__(self, img_size=224, patch_size=16, stride_size=16, in_chans=3, embed_dim=768,\n                 depth=12, num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None,\n                 drop_rate=0., attn_drop_rate=0., camera=0, drop_path_rate=0., hybrid_backbone=None,\n                 norm_layer=partial(nn.LayerNorm, eps=1e-6), sie_xishu=1.0):\n        super().__init__()\n        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models\n        if hybrid_backbone is not None:\n            self.patch_embed = HybridEmbed(\n                hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)\n        else:\n            self.patch_embed = PatchEmbed_overlap(\n                img_size=img_size, patch_size=patch_size, stride_size=stride_size, in_chans=in_chans,\n                embed_dim=embed_dim)\n\n        num_patches = self.patch_embed.num_patches\n\n        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))\n        self.cam_num = camera\n        self.sie_xishu = sie_xishu\n        # Initialize SIE Embedding\n        if camera > 1:\n            self.sie_embed = nn.Parameter(torch.zeros(camera, 1, embed_dim))\n            trunc_normal_(self.sie_embed, std=.02)\n\n        self.pos_drop = nn.Dropout(p=drop_rate)\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule\n\n        self.blocks = nn.ModuleList([\n            Block(\n                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,\n                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)\n            for i in range(depth)])\n\n        self.norm = norm_layer(embed_dim)\n\n        trunc_normal_(self.cls_token, std=.02)\n        trunc_normal_(self.pos_embed, std=.02)\n\n        self.apply(self._init_weights)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    @torch.jit.ignore\n    def no_weight_decay(self):\n        return {'pos_embed', 'cls_token'}\n\n    def forward(self, x, camera_id=None):\n        B = x.shape[0]\n        x = self.patch_embed(x)\n\n        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks\n        x = torch.cat((cls_tokens, x), dim=1)\n\n        if self.cam_num > 0:\n            x = x + self.pos_embed + self.sie_xishu * self.sie_embed[camera_id]\n        else:\n            x = x + self.pos_embed\n\n        x = self.pos_drop(x)\n\n        for blk in self.blocks:\n            x = blk(x)\n\n        x = self.norm(x)\n\n        return x[:, 0].reshape(x.shape[0], -1, 1, 1)\n\n\ndef resize_pos_embed(posemb, posemb_new, hight, width):\n    # Rescale the grid of position embeddings when loading from state_dict. Adapted from\n    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224\n    ntok_new = posemb_new.shape[1]\n\n    posemb_token, posemb_grid = posemb[:, :1], posemb[0, 1:]\n    ntok_new -= 1\n\n    gs_old = int(math.sqrt(len(posemb_grid)))\n    logger.info('Resized position embedding from size:{} to size: {} with height:{} width: {}'.format(posemb.shape,\n                                                                                                      posemb_new.shape,\n                                                                                                      hight,\n                                                                                                      width))\n    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)\n    posemb_grid = F.interpolate(posemb_grid, size=(hight, width), mode='bilinear')\n    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, hight * width, -1)\n    posemb = torch.cat([posemb_token, posemb_grid], dim=1)\n    return posemb\n\n\n@BACKBONE_REGISTRY.register()\ndef build_vit_backbone(cfg):\n    \"\"\"\n    Create a Vision Transformer instance from config.\n    Returns:\n        SwinTransformer: a :class:`SwinTransformer` instance.\n    \"\"\"\n    # fmt: off\n    input_size      = cfg.INPUT.SIZE_TRAIN\n    pretrain        = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path   = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    depth           = cfg.MODEL.BACKBONE.DEPTH\n    sie_xishu       = cfg.MODEL.BACKBONE.SIE_COE\n    stride_size     = cfg.MODEL.BACKBONE.STRIDE_SIZE\n    drop_ratio      = cfg.MODEL.BACKBONE.DROP_RATIO\n    drop_path_ratio = cfg.MODEL.BACKBONE.DROP_PATH_RATIO\n    attn_drop_rate  = cfg.MODEL.BACKBONE.ATT_DROP_RATE\n    # fmt: on\n\n    num_depth = {\n        'small': 8,\n        'base': 12,\n    }[depth]\n\n    num_heads = {\n        'small': 8,\n        'base': 12,\n    }[depth]\n\n    mlp_ratio = {\n        'small': 3.,\n        'base': 4.\n    }[depth]\n\n    qkv_bias = {\n        'small': False,\n        'base': True\n    }[depth]\n\n    qk_scale = {\n        'small': 768 ** -0.5,\n        'base': None,\n    }[depth]\n\n    model = VisionTransformer(img_size=input_size, sie_xishu=sie_xishu, stride_size=stride_size, depth=num_depth,\n                              num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,\n                              drop_path_rate=drop_path_ratio, drop_rate=drop_ratio, attn_drop_rate=attn_drop_rate)\n\n    if pretrain:\n        try:\n            state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))\n            logger.info(f\"Loading pretrained model from {pretrain_path}\")\n\n            if 'model' in state_dict:\n                state_dict = state_dict.pop('model')\n            if 'state_dict' in state_dict:\n                state_dict = state_dict.pop('state_dict')\n            for k, v in state_dict.items():\n                if 'head' in k or 'dist' in k:\n                    continue\n                if 'patch_embed.proj.weight' in k and len(v.shape) < 4:\n                    # For old models that I trained prior to conv based patchification\n                    O, I, H, W = model.patch_embed.proj.weight.shape\n                    v = v.reshape(O, -1, H, W)\n                elif k == 'pos_embed' and v.shape != model.pos_embed.shape:\n                    # To resize pos embedding when using model at different size from pretrained weights\n                    if 'distilled' in pretrain_path:\n                        logger.info(\"distill need to choose right cls token in the pth.\")\n                        v = torch.cat([v[:, 0:1], v[:, 2:]], dim=1)\n                    v = resize_pos_embed(v, model.pos_embed.data, model.patch_embed.num_y, model.patch_embed.num_x)\n                state_dict[k] = v\n        except FileNotFoundError as e:\n            logger.info(f'{pretrain_path} is not found! Please check this path.')\n            raise e\n        except KeyError as e:\n            logger.info(\"State dict keys error! Please check the state dict.\")\n            raise e\n\n        incompatible = model.load_state_dict(state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/heads/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .build import REID_HEADS_REGISTRY, build_heads\n\n# import all the meta_arch, so they will be registered\nfrom .embedding_head import EmbeddingHead\nfrom .clas_head import ClasHead\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/heads/build.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom ...utils.registry import Registry\n\nREID_HEADS_REGISTRY = Registry(\"HEADS\")\nREID_HEADS_REGISTRY.__doc__ = \"\"\"\nRegistry for reid heads in a baseline model.\n\nROIHeads take feature maps and region proposals, and\nperform per-region computation.\nThe registered object will be called with `obj(cfg, input_shape)`.\nThe call is expected to return an :class:`ROIHeads`.\n\"\"\"\n\n\ndef build_heads(cfg):\n    \"\"\"\n    Build REIDHeads defined by `cfg.MODEL.REID_HEADS.NAME`.\n    \"\"\"\n    head = cfg.MODEL.HEADS.NAME\n    return REID_HEADS_REGISTRY.get(head)(cfg)\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/heads/clas_head.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch.nn.functional as F\n\nfrom fast_reid.fastreid.modeling.heads import REID_HEADS_REGISTRY, EmbeddingHead\n\n\n@REID_HEADS_REGISTRY.register()\nclass ClasHead(EmbeddingHead):\n    def forward(self, features, targets=None):\n        \"\"\"\n        See :class:`ClsHeads.forward`.\n        \"\"\"\n        pool_feat = self.pool_layer(features)\n        neck_feat = self.bottleneck(pool_feat)\n        neck_feat = neck_feat.view(neck_feat.size(0), -1)\n\n        if self.cls_layer.__class__.__name__ == 'Linear':\n            logits = F.linear(neck_feat, self.weight)\n        else:\n            logits = F.linear(F.normalize(neck_feat), F.normalize(self.weight))\n\n        # Evaluation\n        if not self.training: return logits.mul_(self.cls_layer.s)\n\n        cls_outputs = self.cls_layer(logits.clone(), targets)\n\n        return {\n            \"cls_outputs\": cls_outputs,\n            \"pred_class_logits\": logits.mul_(self.cls_layer.s),\n            \"features\": neck_feat,\n        }\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/heads/embedding_head.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom fast_reid.fastreid.config import configurable\nfrom fast_reid.fastreid.layers import *\nfrom fast_reid.fastreid.layers import pooling, any_softmax\nfrom fast_reid.fastreid.layers.weight_init import weights_init_kaiming\nfrom .build import REID_HEADS_REGISTRY\n\n\n@REID_HEADS_REGISTRY.register()\nclass EmbeddingHead(nn.Module):\n    \"\"\"\n    EmbeddingHead perform all feature aggregation in an embedding task, such as reid, image retrieval\n    and face recognition\n\n    It typically contains logic to\n\n    1. feature aggregation via global average pooling and generalized mean pooling\n    2. (optional) batchnorm, dimension reduction and etc.\n    2. (in training only) margin-based softmax logits computation\n    \"\"\"\n\n    @configurable\n    def __init__(\n            self,\n            *,\n            feat_dim,\n            embedding_dim,\n            num_classes,\n            neck_feat,\n            pool_type,\n            cls_type,\n            scale,\n            margin,\n            with_bnneck,\n            norm_type\n    ):\n        \"\"\"\n        NOTE: this interface is experimental.\n\n        Args:\n            feat_dim:\n            embedding_dim:\n            num_classes:\n            neck_feat:\n            pool_type:\n            cls_type:\n            scale:\n            margin:\n            with_bnneck:\n            norm_type:\n        \"\"\"\n        super().__init__()\n\n        # Pooling layer\n        assert hasattr(pooling, pool_type), \"Expected pool types are {}, \" \\\n                                            \"but got {}\".format(pooling.__all__, pool_type)\n        self.pool_layer = getattr(pooling, pool_type)()\n\n        self.neck_feat = neck_feat\n\n        neck = []\n        if embedding_dim > 0:\n            neck.append(nn.Conv2d(feat_dim, embedding_dim, 1, 1, bias=False))\n            feat_dim = embedding_dim\n\n        if with_bnneck:\n            neck.append(get_norm(norm_type, feat_dim, bias_freeze=True))\n\n        self.bottleneck = nn.Sequential(*neck)\n\n        # Classification head\n        assert hasattr(any_softmax, cls_type), \"Expected cls types are {}, \" \\\n                                               \"but got {}\".format(any_softmax.__all__, cls_type)\n        self.weight = nn.Parameter(torch.Tensor(num_classes, feat_dim))\n        self.cls_layer = getattr(any_softmax, cls_type)(num_classes, scale, margin)\n\n        self.reset_parameters()\n\n    def reset_parameters(self) -> None:\n        self.bottleneck.apply(weights_init_kaiming)\n        nn.init.normal_(self.weight, std=0.01)\n\n    @classmethod\n    def from_config(cls, cfg):\n        # fmt: off\n        feat_dim      = cfg.MODEL.BACKBONE.FEAT_DIM\n        embedding_dim = cfg.MODEL.HEADS.EMBEDDING_DIM\n        num_classes   = cfg.MODEL.HEADS.NUM_CLASSES\n        neck_feat     = cfg.MODEL.HEADS.NECK_FEAT\n        pool_type     = cfg.MODEL.HEADS.POOL_LAYER\n        cls_type      = cfg.MODEL.HEADS.CLS_LAYER\n        scale         = cfg.MODEL.HEADS.SCALE\n        margin        = cfg.MODEL.HEADS.MARGIN\n        with_bnneck   = cfg.MODEL.HEADS.WITH_BNNECK\n        norm_type     = cfg.MODEL.HEADS.NORM\n        # fmt: on\n        return {\n            'feat_dim': feat_dim,\n            'embedding_dim': embedding_dim,\n            'num_classes': num_classes,\n            'neck_feat': neck_feat,\n            'pool_type': pool_type,\n            'cls_type': cls_type,\n            'scale': scale,\n            'margin': margin,\n            'with_bnneck': with_bnneck,\n            'norm_type': norm_type\n        }\n\n    def forward(self, features, targets=None):\n        \"\"\"\n        See :class:`ReIDHeads.forward`.\n        \"\"\"\n        pool_feat = self.pool_layer(features)   # [8, 2048, 24, 8], stride = 16 --> [8, 2048, 1, 1]\n        neck_feat = self.bottleneck(pool_feat)  # [8, 256, 24, 8]\n        neck_feat = neck_feat[..., 0, 0]\n\n        # Evaluation\n        # fmt: off\n        if not self.training: return neck_feat\n        # fmt: on\n\n        # Training\n        if self.cls_layer.__class__.__name__ == 'Linear':\n            logits = F.linear(neck_feat, self.weight)\n        else:\n            logits = F.linear(F.normalize(neck_feat), F.normalize(self.weight))\n\n        # Pass logits.clone() into cls_layer, because there is in-place operations\n        cls_outputs = self.cls_layer(logits.clone(), targets)\n\n        # fmt: off\n        if self.neck_feat == 'before':  feat = pool_feat[..., 0, 0]\n        elif self.neck_feat == 'after': feat = neck_feat    # [hgx] here\n        else:                           raise KeyError(f\"{self.neck_feat} is invalid for MODEL.HEADS.NECK_FEAT\")\n        # fmt: on\n\n        return {\n            \"cls_outputs\": cls_outputs,\n            \"pred_class_logits\": logits.mul(self.cls_layer.s),\n            \"features\": feat,\n        }\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/losses/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .circle_loss import *\nfrom .cross_entroy_loss import cross_entropy_loss, log_accuracy\nfrom .focal_loss import focal_loss\nfrom .triplet_loss import triplet_loss\n\n__all__ = [k for k in globals().keys() if not k.startswith(\"_\")]"
  },
  {
    "path": "fast_reid/fastreid/modeling/losses/circle_loss.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\n\n__all__ = [\"pairwise_circleloss\", \"pairwise_cosface\"]\n\n\ndef pairwise_circleloss(\n        embedding: torch.Tensor,\n        targets: torch.Tensor,\n        margin: float,\n        gamma: float, ) -> torch.Tensor:\n    embedding = F.normalize(embedding, dim=1)\n\n    dist_mat = torch.matmul(embedding, embedding.t())\n\n    N = dist_mat.size(0)\n\n    is_pos = targets.view(N, 1).expand(N, N).eq(targets.view(N, 1).expand(N, N).t()).float()\n    is_neg = targets.view(N, 1).expand(N, N).ne(targets.view(N, 1).expand(N, N).t()).float()\n\n    # Mask scores related to itself\n    is_pos = is_pos - torch.eye(N, N, device=is_pos.device)\n\n    s_p = dist_mat * is_pos\n    s_n = dist_mat * is_neg\n\n    alpha_p = torch.clamp_min(-s_p.detach() + 1 + margin, min=0.)\n    alpha_n = torch.clamp_min(s_n.detach() + margin, min=0.)\n    delta_p = 1 - margin\n    delta_n = margin\n\n    logit_p = - gamma * alpha_p * (s_p - delta_p) + (-99999999.) * (1 - is_pos)\n    logit_n = gamma * alpha_n * (s_n - delta_n) + (-99999999.) * (1 - is_neg)\n\n    loss = F.softplus(torch.logsumexp(logit_p, dim=1) + torch.logsumexp(logit_n, dim=1)).mean()\n\n    return loss\n\n\ndef pairwise_cosface(\n        embedding: torch.Tensor,\n        targets: torch.Tensor,\n        margin: float,\n        gamma: float, ) -> torch.Tensor:\n    # Normalize embedding features\n    embedding = F.normalize(embedding, dim=1)\n\n    dist_mat = torch.matmul(embedding, embedding.t())\n\n    N = dist_mat.size(0)\n    is_pos = targets.view(N, 1).expand(N, N).eq(targets.view(N, 1).expand(N, N).t()).float()\n    is_neg = targets.view(N, 1).expand(N, N).ne(targets.view(N, 1).expand(N, N).t()).float()\n\n    # Mask scores related to itself\n    is_pos = is_pos - torch.eye(N, N, device=is_pos.device)\n\n    s_p = dist_mat * is_pos\n    s_n = dist_mat * is_neg\n\n    logit_p = -gamma * s_p + (-99999999.) * (1 - is_pos)\n    logit_n = gamma * (s_n + margin) + (-99999999.) * (1 - is_neg)\n\n    loss = F.softplus(torch.logsumexp(logit_p, dim=1) + torch.logsumexp(logit_n, dim=1)).mean()\n\n    return loss\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/losses/cross_entroy_loss.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport torch\nimport torch.nn.functional as F\n\nfrom fast_reid.fastreid.utils.events import get_event_storage\n\n\ndef log_accuracy(pred_class_logits, gt_classes, topk=(1,)):\n    \"\"\"\n    Log the accuracy metrics to EventStorage.\n    \"\"\"\n    bsz = pred_class_logits.size(0)\n    maxk = max(topk)\n    _, pred_class = pred_class_logits.topk(maxk, 1, True, True)\n    pred_class = pred_class.t()\n    correct = pred_class.eq(gt_classes.view(1, -1).expand_as(pred_class))\n\n    ret = []\n    for k in topk:\n        correct_k = correct[:k].view(-1).float().sum(dim=0, keepdim=True)\n        ret.append(correct_k.mul_(1. / bsz))\n\n    storage = get_event_storage()\n    storage.put_scalar(\"cls_accuracy\", ret[0])\n\n\ndef cross_entropy_loss(pred_class_outputs, gt_classes, eps, alpha=0.2):\n    num_classes = pred_class_outputs.size(1)\n\n    if eps >= 0:\n        smooth_param = eps\n    else:\n        # Adaptive label smooth regularization\n        soft_label = F.softmax(pred_class_outputs, dim=1)\n        smooth_param = alpha * soft_label[torch.arange(soft_label.size(0)), gt_classes].unsqueeze(1)\n\n    log_probs = F.log_softmax(pred_class_outputs, dim=1)\n    with torch.no_grad():\n        targets = torch.ones_like(log_probs)\n        targets *= smooth_param / (num_classes - 1)\n        targets.scatter_(1, gt_classes.data.unsqueeze(1), (1 - smooth_param))\n\n    loss = (-targets * log_probs).sum(dim=1)\n\n    with torch.no_grad():\n        non_zero_cnt = max(loss.nonzero(as_tuple=False).size(0), 1)\n\n    loss = loss.sum() / non_zero_cnt\n\n    return loss\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/losses/focal_loss.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\n\n\n# based on:\n# https://github.com/kornia/kornia/blob/master/kornia/losses/focal.py\n\ndef focal_loss(\n        input: torch.Tensor,\n        target: torch.Tensor,\n        alpha: float,\n        gamma: float = 2.0,\n        reduction: str = 'mean') -> torch.Tensor:\n    r\"\"\"Criterion that computes Focal loss.\n    See :class:`fastreid.modeling.losses.FocalLoss` for details.\n    According to [1], the Focal loss is computed as follows:\n    .. math::\n        \\text{FL}(p_t) = -\\alpha_t (1 - p_t)^{\\gamma} \\, \\text{log}(p_t)\n    where:\n       - :math:`p_t` is the model's estimated probability for each class.\n    Arguments:\n        alpha (float): Weighting factor :math:`\\alpha \\in [0, 1]`.\n        gamma (float): Focusing parameter :math:`\\gamma >= 0`.\n        reduction (str, optional): Specifies the reduction to apply to the\n         output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied,\n         ‘mean’: the sum of the output will be divided by the number of elements\n         in the output, ‘sum’: the output will be summed. Default: ‘none’.\n    Shape:\n        - Input: :math:`(N, C, *)` where C = number of classes.\n        - Target: :math:`(N, *)` where each value is\n          :math:`0 ≤ targets[i] ≤ C−1`.\n    Examples:\n        >>> N = 5  # num_classes\n        >>> loss = FocalLoss(cfg)\n        >>> input = torch.randn(1, N, 3, 5, requires_grad=True)\n        >>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(N)\n        >>> output = loss(input, target)\n        >>> output.backward()\n    References:\n        [1] https://arxiv.org/abs/1708.02002\n    \"\"\"\n    if not torch.is_tensor(input):\n        raise TypeError(\"Input type is not a torch.Tensor. Got {}\"\n                        .format(type(input)))\n\n    if not len(input.shape) >= 2:\n        raise ValueError(\"Invalid input shape, we expect BxCx*. Got: {}\"\n                         .format(input.shape))\n\n    if input.size(0) != target.size(0):\n        raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).'\n                         .format(input.size(0), target.size(0)))\n\n    n = input.size(0)\n    out_size = (n,) + input.size()[2:]\n    if target.size()[1:] != input.size()[2:]:\n        raise ValueError('Expected target size {}, got {}'.format(\n            out_size, target.size()))\n\n    if not input.device == target.device:\n        raise ValueError(\n            \"input and target must be in the same device. Got: {}\".format(\n                input.device, target.device))\n\n    # compute softmax over the classes axis\n    input_soft = F.softmax(input, dim=1)\n\n    # create the labels one hot tensor\n    target_one_hot = F.one_hot(target, num_classes=input.shape[1])\n\n    # compute the actual focal loss\n    weight = torch.pow(-input_soft + 1., gamma)\n\n    focal = -alpha * weight * torch.log(input_soft)\n    loss_tmp = torch.sum(target_one_hot * focal, dim=1)\n\n    if reduction == 'none':\n        loss = loss_tmp\n    elif reduction == 'mean':\n        loss = torch.mean(loss_tmp)\n    elif reduction == 'sum':\n        loss = torch.sum(loss_tmp)\n    else:\n        raise NotImplementedError(\"Invalid reduction mode: {}\"\n                                  .format(reduction))\n    return loss\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/losses/triplet_loss.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\n\nfrom .utils import euclidean_dist, cosine_dist\n\n\ndef softmax_weights(dist, mask):\n    max_v = torch.max(dist * mask, dim=1, keepdim=True)[0]\n    diff = dist - max_v\n    Z = torch.sum(torch.exp(diff) * mask, dim=1, keepdim=True) + 1e-6  # avoid division by zero\n    W = torch.exp(diff) * mask / Z\n    return W\n\n\ndef hard_example_mining(dist_mat, is_pos, is_neg):\n    \"\"\"For each anchor, find the hardest positive and negative sample.\n    Args:\n      dist_mat: pair wise distance between samples, shape [N, M]\n      is_pos: positive index with shape [N, M]\n      is_neg: negative index with shape [N, M]\n    Returns:\n      dist_ap: pytorch Variable, distance(anchor, positive); shape [N]\n      dist_an: pytorch Variable, distance(anchor, negative); shape [N]\n      p_inds: pytorch LongTensor, with shape [N];\n        indices of selected hard positive samples; 0 <= p_inds[i] <= N - 1\n      n_inds: pytorch LongTensor, with shape [N];\n        indices of selected hard negative samples; 0 <= n_inds[i] <= N - 1\n    NOTE: Only consider the case in which all labels have same num of samples,\n      thus we can cope with all anchors in parallel.\n    \"\"\"\n\n    assert len(dist_mat.size()) == 2\n\n    # `dist_ap` means distance(anchor, positive)\n    # both `dist_ap` and `relative_p_inds` with shape [N]\n    dist_ap, _ = torch.max(dist_mat * is_pos, dim=1)\n    # `dist_an` means distance(anchor, negative)\n    # both `dist_an` and `relative_n_inds` with shape [N]\n    dist_an, _ = torch.min(dist_mat * is_neg + is_pos * 1e9, dim=1)\n\n    return dist_ap, dist_an\n\n\ndef weighted_example_mining(dist_mat, is_pos, is_neg):\n    \"\"\"For each anchor, find the weighted positive and negative sample.\n    Args:\n      dist_mat: pytorch Variable, pair wise distance between samples, shape [N, N]\n      is_pos:\n      is_neg:\n    Returns:\n      dist_ap: pytorch Variable, distance(anchor, positive); shape [N]\n      dist_an: pytorch Variable, distance(anchor, negative); shape [N]\n    \"\"\"\n    assert len(dist_mat.size()) == 2\n\n    is_pos = is_pos\n    is_neg = is_neg\n    dist_ap = dist_mat * is_pos\n    dist_an = dist_mat * is_neg\n\n    weights_ap = softmax_weights(dist_ap, is_pos)\n    weights_an = softmax_weights(-dist_an, is_neg)\n\n    dist_ap = torch.sum(dist_ap * weights_ap, dim=1)\n    dist_an = torch.sum(dist_an * weights_an, dim=1)\n\n    return dist_ap, dist_an\n\n\ndef triplet_loss(embedding, targets, margin, norm_feat, hard_mining):\n    r\"\"\"Modified from Tong Xiao's open-reid (https://github.com/Cysu/open-reid).\n    Related Triplet Loss theory can be found in paper 'In Defense of the Triplet\n    Loss for Person Re-Identification'.\"\"\"\n\n    if norm_feat:\n        dist_mat = cosine_dist(embedding, embedding)\n    else:\n        dist_mat = euclidean_dist(embedding, embedding)\n\n    # For distributed training, gather all features from different process.\n    # if comm.get_world_size() > 1:\n    #     all_embedding = torch.cat(GatherLayer.apply(embedding), dim=0)\n    #     all_targets = concat_all_gather(targets)\n    # else:\n    #     all_embedding = embedding\n    #     all_targets = targets\n\n    N = dist_mat.size(0)\n    is_pos = targets.view(N, 1).expand(N, N).eq(targets.view(N, 1).expand(N, N).t()).float()\n    is_neg = targets.view(N, 1).expand(N, N).ne(targets.view(N, 1).expand(N, N).t()).float()\n\n    if hard_mining:\n        dist_ap, dist_an = hard_example_mining(dist_mat, is_pos, is_neg)\n    else:\n        dist_ap, dist_an = weighted_example_mining(dist_mat, is_pos, is_neg)\n\n    y = dist_an.new().resize_as_(dist_an).fill_(1)\n\n    if margin > 0:\n        loss = F.margin_ranking_loss(dist_an, dist_ap, y, margin=margin)\n    else:\n        loss = F.soft_margin_loss(dist_an - dist_ap, y)\n        # fmt: off\n        if loss == float('Inf'): loss = F.margin_ranking_loss(dist_an, dist_ap, y, margin=0.3)\n        # fmt: on\n\n    return loss\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/losses/utils.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\n\n\ndef concat_all_gather(tensor):\n    \"\"\"\n    Performs all_gather operation on the provided tensors.\n    *** Warning ***: torch.distributed.all_gather has no gradient.\n    \"\"\"\n    tensors_gather = [torch.ones_like(tensor)\n                      for _ in range(torch.distributed.get_world_size())]\n    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)\n\n    output = torch.cat(tensors_gather, dim=0)\n    return output\n\n\ndef normalize(x, axis=-1):\n    \"\"\"Normalizing to unit length along the specified dimension.\n    Args:\n      x: pytorch Variable\n    Returns:\n      x: pytorch Variable, same shape as input\n    \"\"\"\n    x = 1. * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12)\n    return x\n\n\ndef euclidean_dist(x, y):\n    m, n = x.size(0), y.size(0)\n    xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)\n    yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t()\n    dist = xx + yy - 2 * torch.matmul(x, y.t())\n    dist = dist.clamp(min=1e-12).sqrt()  # for numerical stability\n    return dist\n\n\ndef cosine_dist(x, y):\n    x = F.normalize(x, dim=1)\n    y = F.normalize(y, dim=1)\n    dist = 2 - 2 * torch.mm(x, y.t())\n    return dist\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/meta_arch/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .build import META_ARCH_REGISTRY, build_model\n\n\n# import all the meta_arch, so they will be registered\nfrom .baseline import Baseline\nfrom .mgn import MGN\nfrom .moco import MoCo\nfrom .distiller import Distiller\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/meta_arch/baseline.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nfrom torch import nn\n\nfrom fast_reid.fastreid.config import configurable\nfrom fast_reid.fastreid.modeling.backbones import build_backbone\nfrom fast_reid.fastreid.modeling.heads import build_heads\nfrom fast_reid.fastreid.modeling.losses import *\nfrom .build import META_ARCH_REGISTRY\n\n\n@META_ARCH_REGISTRY.register()\nclass Baseline(nn.Module):\n    \"\"\"\n    Baseline architecture. Any models that contains the following two components:\n    1. Per-image feature extraction (aka backbone)\n    2. Per-image feature aggregation and loss computation\n    \"\"\"\n\n    @configurable\n    def __init__(\n            self,\n            *,\n            backbone,\n            heads,\n            pixel_mean,\n            pixel_std,\n            loss_kwargs=None\n    ):\n        \"\"\"\n        NOTE: this interface is experimental.\n\n        Args:\n            backbone:\n            heads:\n            pixel_mean:\n            pixel_std:\n        \"\"\"\n        super().__init__()\n        # backbone\n        self.backbone = backbone\n\n        # head\n        self.heads = heads\n\n        self.loss_kwargs = loss_kwargs\n\n        self.register_buffer('pixel_mean', torch.Tensor(pixel_mean).view(1, -1, 1, 1), False)\n        self.register_buffer('pixel_std', torch.Tensor(pixel_std).view(1, -1, 1, 1), False)\n\n    @classmethod\n    def from_config(cls, cfg):\n        backbone = build_backbone(cfg)\n        heads = build_heads(cfg)\n        return {\n            'backbone': backbone,\n            'heads': heads,\n            'pixel_mean': cfg.MODEL.PIXEL_MEAN,\n            'pixel_std': cfg.MODEL.PIXEL_STD,\n            'loss_kwargs':\n                {\n                    # loss name\n                    'loss_names': cfg.MODEL.LOSSES.NAME,\n\n                    # loss hyperparameters\n                    'ce': {\n                        'eps': cfg.MODEL.LOSSES.CE.EPSILON,\n                        'alpha': cfg.MODEL.LOSSES.CE.ALPHA,\n                        'scale': cfg.MODEL.LOSSES.CE.SCALE\n                    },\n                    'tri': {\n                        'margin': cfg.MODEL.LOSSES.TRI.MARGIN,\n                        'norm_feat': cfg.MODEL.LOSSES.TRI.NORM_FEAT,\n                        'hard_mining': cfg.MODEL.LOSSES.TRI.HARD_MINING,\n                        'scale': cfg.MODEL.LOSSES.TRI.SCALE\n                    },\n                    'circle': {\n                        'margin': cfg.MODEL.LOSSES.CIRCLE.MARGIN,\n                        'gamma': cfg.MODEL.LOSSES.CIRCLE.GAMMA,\n                        'scale': cfg.MODEL.LOSSES.CIRCLE.SCALE\n                    },\n                    'cosface': {\n                        'margin': cfg.MODEL.LOSSES.COSFACE.MARGIN,\n                        'gamma': cfg.MODEL.LOSSES.COSFACE.GAMMA,\n                        'scale': cfg.MODEL.LOSSES.COSFACE.SCALE\n                    }\n                }\n        }\n\n    @property\n    def device(self):\n        return self.pixel_mean.device\n\n    def forward(self, batched_inputs):\n        images = self.preprocess_image(batched_inputs)\n        features = self.backbone(images)\n\n        if self.training:\n            assert \"targets\" in batched_inputs, \"Person ID annotation are missing in training!\"\n            targets = batched_inputs[\"targets\"]\n\n            # PreciseBN flag, When do preciseBN on different dataset, the number of classes in new dataset\n            # may be larger than that in the original dataset, so the circle/arcface will\n            # throw an error. We just set all the targets to 0 to avoid this problem.\n            if targets.sum() < 0: targets.zero_()\n\n            outputs = self.heads(features, targets)\n            losses = self.losses(outputs, targets)\n            return losses\n        else:\n            outputs = self.heads(features)\n            return outputs\n\n    def preprocess_image(self, batched_inputs):\n        \"\"\"\n        Normalize and batch the input images.\n        \"\"\"\n        if isinstance(batched_inputs, dict):\n            images = batched_inputs['images']\n        elif isinstance(batched_inputs, torch.Tensor):\n            images = batched_inputs\n        else:\n            raise TypeError(\"batched_inputs must be dict or torch.Tensor, but get {}\".format(type(batched_inputs)))\n\n        images.sub_(self.pixel_mean).div_(self.pixel_std)\n        return images\n\n    def losses(self, outputs, gt_labels):\n        \"\"\"\n        Compute loss from modeling's outputs, the loss function input arguments\n        must be the same as the outputs of the model forwarding.\n        \"\"\"\n        # model predictions\n        # fmt: off\n        pred_class_logits = outputs['pred_class_logits'].detach()\n        cls_outputs       = outputs['cls_outputs']\n        pred_features     = outputs['features']\n        # fmt: on\n\n        # Log prediction accuracy\n        log_accuracy(pred_class_logits, gt_labels)\n\n        loss_dict = {}\n        loss_names = self.loss_kwargs['loss_names']\n\n        if 'CrossEntropyLoss' in loss_names:\n            ce_kwargs = self.loss_kwargs.get('ce')\n            loss_dict['loss_cls'] = cross_entropy_loss(\n                cls_outputs,\n                gt_labels,\n                ce_kwargs.get('eps'),\n                ce_kwargs.get('alpha')\n            ) * ce_kwargs.get('scale')\n\n        if 'TripletLoss' in loss_names:\n            tri_kwargs = self.loss_kwargs.get('tri')\n            loss_dict['loss_triplet'] = triplet_loss(\n                pred_features,\n                gt_labels,\n                tri_kwargs.get('margin'),\n                tri_kwargs.get('norm_feat'),\n                tri_kwargs.get('hard_mining')\n            ) * tri_kwargs.get('scale')\n\n        if 'CircleLoss' in loss_names:\n            circle_kwargs = self.loss_kwargs.get('circle')\n            loss_dict['loss_circle'] = pairwise_circleloss(\n                pred_features,\n                gt_labels,\n                circle_kwargs.get('margin'),\n                circle_kwargs.get('gamma')\n            ) * circle_kwargs.get('scale')\n\n        if 'Cosface' in loss_names:\n            cosface_kwargs = self.loss_kwargs.get('cosface')\n            loss_dict['loss_cosface'] = pairwise_cosface(\n                pred_features,\n                gt_labels,\n                cosface_kwargs.get('margin'),\n                cosface_kwargs.get('gamma'),\n            ) * cosface_kwargs.get('scale')\n\n        return loss_dict\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/meta_arch/build.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport torch\n\nfrom fast_reid.fastreid.utils.registry import Registry\n\nMETA_ARCH_REGISTRY = Registry(\"META_ARCH\")  # noqa F401 isort:skip\nMETA_ARCH_REGISTRY.__doc__ = \"\"\"\nRegistry for meta-architectures, i.e. the whole model.\nThe registered object will be called with `obj(cfg)`\nand expected to return a `nn.Module` object.\n\"\"\"\n\n\ndef build_model(cfg):\n    \"\"\"\n    Build the whole model architecture, defined by ``cfg.MODEL.META_ARCHITECTURE``.\n    Note that it does not load any weights from ``cfg``.\n    \"\"\"\n    meta_arch = cfg.MODEL.META_ARCHITECTURE\n    model = META_ARCH_REGISTRY.get(meta_arch)(cfg)\n    model.to(torch.device(cfg.MODEL.DEVICE))\n    return model\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/meta_arch/distiller.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\n\nimport torch\nimport torch.nn.functional as F\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.modeling.meta_arch import META_ARCH_REGISTRY, build_model, Baseline\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\n\nlogger = logging.getLogger(__name__)\n\n\n@META_ARCH_REGISTRY.register()\nclass Distiller(Baseline):\n    def __init__(self, cfg):\n        super().__init__(cfg)\n\n        # Get teacher model config\n        model_ts = []\n        for i in range(len(cfg.KD.MODEL_CONFIG)):\n            cfg_t = get_cfg()\n            cfg_t.merge_from_file(cfg.KD.MODEL_CONFIG[i])\n            cfg_t.defrost()\n            cfg_t.MODEL.META_ARCHITECTURE = \"Baseline\"\n            # Change syncBN to BN due to no DDP wrapper\n            if cfg_t.MODEL.BACKBONE.NORM == \"syncBN\":\n                cfg_t.MODEL.BACKBONE.NORM = \"BN\"\n            if cfg_t.MODEL.HEADS.NORM == \"syncBN\":\n                cfg_t.MODEL.HEADS.NORM = \"BN\"\n\n            model_t = build_model(cfg_t)\n\n            # No gradients for teacher model\n            for param in model_t.parameters():\n                param.requires_grad_(False)\n\n            logger.info(\"Loading teacher model weights ...\")\n            Checkpointer(model_t).load(cfg.KD.MODEL_WEIGHTS[i])\n\n            model_ts.append(model_t)\n\n        self.ema_enabled = cfg.KD.EMA.ENABLED\n        self.ema_momentum = cfg.KD.EMA.MOMENTUM\n        if self.ema_enabled:\n            cfg_self = cfg.clone()\n            cfg_self.defrost()\n            cfg_self.MODEL.META_ARCHITECTURE = \"Baseline\"\n            if cfg_self.MODEL.BACKBONE.NORM == \"syncBN\":\n                cfg_self.MODEL.BACKBONE.NORM = \"BN\"\n            if cfg_self.MODEL.HEADS.NORM == \"syncBN\":\n                cfg_self.MODEL.HEADS.NORM = \"BN\"\n            model_self = build_model(cfg_self)\n            # No gradients for self model\n            for param in model_self.parameters():\n                param.requires_grad_(False)\n\n            if cfg_self.MODEL.WEIGHTS != '':\n                logger.info(\"Loading self distillation model weights ...\")\n                Checkpointer(model_self).load(cfg_self.MODEL.WEIGHTS)\n            else:\n                # Make sure the initial state is same\n                for param_q, param_k in zip(self.parameters(), model_self.parameters()):\n                    param_k.data.copy_(param_q.data)\n\n            model_ts.insert(0, model_self)\n\n        # Not register teacher model as `nn.Module`, this is\n        # make sure teacher model weights not saved\n        self.model_ts = model_ts\n\n    @torch.no_grad()\n    def _momentum_update_key_encoder(self, m=0.999):\n        \"\"\"\n        Momentum update of the key encoder\n        \"\"\"\n        for param_q, param_k in zip(self.parameters(), self.model_ts[0].parameters()):\n            param_k.data = param_k.data * m + param_q.data * (1. - m)\n\n    def forward(self, batched_inputs):\n        if self.training:\n            images = self.preprocess_image(batched_inputs)\n            # student model forward\n            s_feat = self.backbone(images)\n            assert \"targets\" in batched_inputs, \"Labels are missing in training!\"\n            targets = batched_inputs[\"targets\"].to(self.device)\n\n            if targets.sum() < 0: targets.zero_()\n\n            s_outputs = self.heads(s_feat, targets)\n\n            t_outputs = []\n            # teacher model forward\n            with torch.no_grad():\n                if self.ema_enabled:\n                    self._momentum_update_key_encoder(self.ema_momentum)  # update self distill model\n                for model_t in self.model_ts:\n                    t_feat = model_t.backbone(images)\n                    t_output = model_t.heads(t_feat, targets)\n                    t_outputs.append(t_output)\n\n            losses = self.losses(s_outputs, t_outputs, targets)\n            return losses\n\n        # Eval mode, just conventional reid feature extraction\n        else:\n            return super().forward(batched_inputs)\n\n    def losses(self, s_outputs, t_outputs, gt_labels):\n        \"\"\"\n        Compute loss from modeling's outputs, the loss function input arguments\n        must be the same as the outputs of the model forwarding.\n        \"\"\"\n        loss_dict = super().losses(s_outputs, gt_labels)\n\n        s_logits = s_outputs['pred_class_logits']\n        loss_jsdiv = 0.\n        for t_output in t_outputs:\n            t_logits = t_output['pred_class_logits'].detach()\n            loss_jsdiv += self.jsdiv_loss(s_logits, t_logits)\n\n        loss_dict[\"loss_jsdiv\"] = loss_jsdiv / len(t_outputs)\n\n        return loss_dict\n\n    @staticmethod\n    def _kldiv(y_s, y_t, t):\n        p_s = F.log_softmax(y_s / t, dim=1)\n        p_t = F.softmax(y_t / t, dim=1)\n        loss = F.kl_div(p_s, p_t, reduction=\"sum\") * (t ** 2) / y_s.shape[0]\n        return loss\n\n    def jsdiv_loss(self, y_s, y_t, t=16):\n        loss = (self._kldiv(y_s, y_t, t) + self._kldiv(y_t, y_s, t)) / 2\n        return loss\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/meta_arch/mgn.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport copy\n\nimport torch\nfrom torch import nn\n\nfrom fast_reid.fastreid.config import configurable\nfrom fast_reid.fastreid.layers import get_norm\nfrom fast_reid.fastreid.modeling.backbones import build_backbone\nfrom fast_reid.fastreid.modeling.backbones.resnet import Bottleneck\nfrom fast_reid.fastreid.modeling.heads import build_heads\nfrom fast_reid.fastreid.modeling.losses import *\nfrom .build import META_ARCH_REGISTRY\n\n\n@META_ARCH_REGISTRY.register()\nclass MGN(nn.Module):\n    \"\"\"\n    Multiple Granularities Network architecture, which contains the following two components:\n    1. Per-image feature extraction (aka backbone)\n    2. Multi-branch feature aggregation\n    \"\"\"\n\n    @configurable\n    def __init__(\n            self,\n            *,\n            backbone,\n            neck1,\n            neck2,\n            neck3,\n            b1_head,\n            b2_head,\n            b21_head,\n            b22_head,\n            b3_head,\n            b31_head,\n            b32_head,\n            b33_head,\n            pixel_mean,\n            pixel_std,\n            loss_kwargs=None\n    ):\n        \"\"\"\n        NOTE: this interface is experimental.\n\n        Args:\n            backbone:\n            neck1:\n            neck2:\n            neck3:\n            b1_head:\n            b2_head:\n            b21_head:\n            b22_head:\n            b3_head:\n            b31_head:\n            b32_head:\n            b33_head:\n            pixel_mean:\n            pixel_std:\n            loss_kwargs:\n        \"\"\"\n\n        super().__init__()\n\n        self.backbone = backbone\n\n        # branch1\n        self.b1 = neck1\n        self.b1_head = b1_head\n\n        # branch2\n        self.b2 = neck2\n        self.b2_head = b2_head\n        self.b21_head = b21_head\n        self.b22_head = b22_head\n\n        # branch3\n        self.b3 = neck3\n        self.b3_head = b3_head\n        self.b31_head = b31_head\n        self.b32_head = b32_head\n        self.b33_head = b33_head\n\n        self.loss_kwargs = loss_kwargs\n        self.register_buffer('pixel_mean', torch.Tensor(pixel_mean).view(1, -1, 1, 1), False)\n        self.register_buffer('pixel_std', torch.Tensor(pixel_std).view(1, -1, 1, 1), False)\n\n    @classmethod\n    def from_config(cls, cfg):\n        bn_norm = cfg.MODEL.BACKBONE.NORM\n        with_se = cfg.MODEL.BACKBONE.WITH_SE\n\n        all_blocks = build_backbone(cfg)\n\n        # backbone\n        backbone = nn.Sequential(\n            all_blocks.conv1,\n            all_blocks.bn1,\n            all_blocks.relu,\n            all_blocks.maxpool,\n            all_blocks.layer1,\n            all_blocks.layer2,\n            all_blocks.layer3[0]\n        )\n        res_conv4 = nn.Sequential(*all_blocks.layer3[1:])\n        res_g_conv5 = all_blocks.layer4\n\n        res_p_conv5 = nn.Sequential(\n            Bottleneck(1024, 512, bn_norm, False, with_se, downsample=nn.Sequential(\n                nn.Conv2d(1024, 2048, 1, bias=False), get_norm(bn_norm, 2048))),\n            Bottleneck(2048, 512, bn_norm, False, with_se),\n            Bottleneck(2048, 512, bn_norm, False, with_se))\n        res_p_conv5.load_state_dict(all_blocks.layer4.state_dict())\n\n        # branch\n        neck1 = nn.Sequential(\n            copy.deepcopy(res_conv4),\n            copy.deepcopy(res_g_conv5)\n        )\n        b1_head = build_heads(cfg)\n\n        # branch2\n        neck2 = nn.Sequential(\n            copy.deepcopy(res_conv4),\n            copy.deepcopy(res_p_conv5)\n        )\n        b2_head = build_heads(cfg)\n        b21_head = build_heads(cfg)\n        b22_head = build_heads(cfg)\n\n        # branch3\n        neck3 = nn.Sequential(\n            copy.deepcopy(res_conv4),\n            copy.deepcopy(res_p_conv5)\n        )\n        b3_head = build_heads(cfg)\n        b31_head = build_heads(cfg)\n        b32_head = build_heads(cfg)\n        b33_head = build_heads(cfg)\n\n        return {\n            'backbone': backbone,\n            'neck1': neck1,\n            'neck2': neck2,\n            'neck3': neck3,\n            'b1_head': b1_head,\n            'b2_head': b2_head,\n            'b21_head': b21_head,\n            'b22_head': b22_head,\n            'b3_head': b3_head,\n            'b31_head': b31_head,\n            'b32_head': b32_head,\n            'b33_head': b33_head,\n            'pixel_mean': cfg.MODEL.PIXEL_MEAN,\n            'pixel_std': cfg.MODEL.PIXEL_STD,\n            'loss_kwargs':\n                {\n                    # loss name\n                    'loss_names': cfg.MODEL.LOSSES.NAME,\n\n                    # loss hyperparameters\n                    'ce': {\n                        'eps': cfg.MODEL.LOSSES.CE.EPSILON,\n                        'alpha': cfg.MODEL.LOSSES.CE.ALPHA,\n                        'scale': cfg.MODEL.LOSSES.CE.SCALE\n                    },\n                    'tri': {\n                        'margin': cfg.MODEL.LOSSES.TRI.MARGIN,\n                        'norm_feat': cfg.MODEL.LOSSES.TRI.NORM_FEAT,\n                        'hard_mining': cfg.MODEL.LOSSES.TRI.HARD_MINING,\n                        'scale': cfg.MODEL.LOSSES.TRI.SCALE\n                    },\n                    'circle': {\n                        'margin': cfg.MODEL.LOSSES.CIRCLE.MARGIN,\n                        'gamma': cfg.MODEL.LOSSES.CIRCLE.GAMMA,\n                        'scale': cfg.MODEL.LOSSES.CIRCLE.SCALE\n                    },\n                    'cosface': {\n                        'margin': cfg.MODEL.LOSSES.COSFACE.MARGIN,\n                        'gamma': cfg.MODEL.LOSSES.COSFACE.GAMMA,\n                        'scale': cfg.MODEL.LOSSES.COSFACE.SCALE\n                    }\n                }\n        }\n\n    @property\n    def device(self):\n        return self.pixel_mean.device\n\n    def forward(self, batched_inputs):\n        images = self.preprocess_image(batched_inputs)  # normalization [bs, 3, 384, 128]\n        features = self.backbone(images)  # [bs, 1024, 24, 8], stride = 16\n\n        # branch1\n        b1_feat = self.b1(features) # [8, 2048, 24, 8], like res_conv4 and res_g_conv5\n\n        # branch2\n        b2_feat = self.b2(features) # [8, 2048, 24, 8], like res_conv4 and res_g_conv5\n        b21_feat, b22_feat = torch.chunk(b2_feat, 2, dim=2) # split by height, 2 x [8, 2048, 12, 8]\n\n        # branch3\n        b3_feat = self.b3(features) # [8, 2048, 24, 8], like res_conv4 and res_g_conv5\n        b31_feat, b32_feat, b33_feat = torch.chunk(b3_feat, 3, dim=2)   # split by height, 3 x [8, 2048, 6, 8]\n\n        if self.training:\n            assert \"targets\" in batched_inputs, \"Person ID annotation are missing in training!\"\n            targets = batched_inputs[\"targets\"]\n\n            if targets.sum() < 0: targets.zero_()\n\n            b1_outputs = self.b1_head(b1_feat, targets)\n            b2_outputs = self.b2_head(b2_feat, targets)\n            b21_outputs = self.b21_head(b21_feat, targets)\n            b22_outputs = self.b22_head(b22_feat, targets)\n            b3_outputs = self.b3_head(b3_feat, targets)\n            b31_outputs = self.b31_head(b31_feat, targets)\n            b32_outputs = self.b32_head(b32_feat, targets)\n            b33_outputs = self.b33_head(b33_feat, targets)\n\n            losses = self.losses(b1_outputs,\n                                 b2_outputs, b21_outputs, b22_outputs,\n                                 b3_outputs, b31_outputs, b32_outputs, b33_outputs,\n                                 targets)\n            return losses\n        else:\n            b1_pool_feat = self.b1_head(b1_feat)\n            b2_pool_feat = self.b2_head(b2_feat)\n            b21_pool_feat = self.b21_head(b21_feat)\n            b22_pool_feat = self.b22_head(b22_feat)\n            b3_pool_feat = self.b3_head(b3_feat)\n            b31_pool_feat = self.b31_head(b31_feat)\n            b32_pool_feat = self.b32_head(b32_feat)\n            b33_pool_feat = self.b33_head(b33_feat)\n\n            pred_feat = torch.cat([b1_pool_feat, b2_pool_feat, b3_pool_feat, b21_pool_feat,\n                                   b22_pool_feat, b31_pool_feat, b32_pool_feat, b33_pool_feat], dim=1)\n            return pred_feat\n\n    def preprocess_image(self, batched_inputs):\n        r\"\"\"\n        Normalize and batch the input images.\n        \"\"\"\n        if isinstance(batched_inputs, dict):\n            images = batched_inputs[\"images\"].to(self.device)\n        elif isinstance(batched_inputs, torch.Tensor):\n            images = batched_inputs.to(self.device)\n        else:\n            raise TypeError(\"batched_inputs must be dict or torch.Tensor, but get {}\".format(type(batched_inputs)))\n\n        images.sub_(self.pixel_mean).div_(self.pixel_std)\n        return images\n\n    def losses(self,\n               b1_outputs,\n               b2_outputs, b21_outputs, b22_outputs,\n               b3_outputs, b31_outputs, b32_outputs, b33_outputs, gt_labels):\n        # model predictions\n        # fmt: off\n        pred_class_logits = b1_outputs['pred_class_logits'].detach()\n        b1_logits         = b1_outputs['cls_outputs']\n        b2_logits         = b2_outputs['cls_outputs']\n        b21_logits        = b21_outputs['cls_outputs']\n        b22_logits        = b22_outputs['cls_outputs']\n        b3_logits         = b3_outputs['cls_outputs']\n        b31_logits        = b31_outputs['cls_outputs']\n        b32_logits        = b32_outputs['cls_outputs']\n        b33_logits        = b33_outputs['cls_outputs']\n        b1_pool_feat      = b1_outputs['features']\n        b2_pool_feat      = b2_outputs['features']\n        b3_pool_feat      = b3_outputs['features']\n        b21_pool_feat     = b21_outputs['features']\n        b22_pool_feat     = b22_outputs['features']\n        b31_pool_feat     = b31_outputs['features']\n        b32_pool_feat     = b32_outputs['features']\n        b33_pool_feat     = b33_outputs['features']\n        # fmt: on\n\n        # Log prediction accuracy\n        log_accuracy(pred_class_logits, gt_labels)\n\n        b22_pool_feat = torch.cat((b21_pool_feat, b22_pool_feat), dim=1)\n        b33_pool_feat = torch.cat((b31_pool_feat, b32_pool_feat, b33_pool_feat), dim=1)\n\n        loss_dict = {}\n        loss_names = self.loss_kwargs['loss_names'] # 'CrossEntropyLoss', 'TripletLoss'\n\n        if \"CrossEntropyLoss\" in loss_names:\n            ce_kwargs = self.loss_kwargs.get('ce')\n            loss_dict['loss_cls_b1'] = cross_entropy_loss(\n                b1_logits,\n                gt_labels,\n                ce_kwargs.get('eps'),\n                ce_kwargs.get('alpha')\n            ) * ce_kwargs.get('scale') * 0.125\n\n            loss_dict['loss_cls_b2'] = cross_entropy_loss(\n                b2_logits,\n                gt_labels,\n                ce_kwargs.get('eps'),\n                ce_kwargs.get('alpha')\n            ) * ce_kwargs.get('scale') * 0.125\n\n            loss_dict['loss_cls_b21'] = cross_entropy_loss(\n                b21_logits,\n                gt_labels,\n                ce_kwargs.get('eps'),\n                ce_kwargs.get('alpha')\n            ) * ce_kwargs.get('scale') * 0.125\n\n            loss_dict['loss_cls_b22'] = cross_entropy_loss(\n                b22_logits,\n                gt_labels,\n                ce_kwargs.get('eps'),\n                ce_kwargs.get('alpha')\n            ) * ce_kwargs.get('scale') * 0.125\n\n            loss_dict['loss_cls_b3'] = cross_entropy_loss(\n                b3_logits,\n                gt_labels,\n                ce_kwargs.get('eps'),\n                ce_kwargs.get('alpha')\n            ) * ce_kwargs.get('scale') * 0.125\n\n            loss_dict['loss_cls_b31'] = cross_entropy_loss(\n                b31_logits,\n                gt_labels,\n                ce_kwargs.get('eps'),\n                ce_kwargs.get('alpha')\n            ) * ce_kwargs.get('scale') * 0.125\n\n            loss_dict['loss_cls_b32'] = cross_entropy_loss(\n                b32_logits,\n                gt_labels,\n                ce_kwargs.get('eps'),\n                ce_kwargs.get('alpha')\n            ) * ce_kwargs.get('scale') * 0.125\n\n            loss_dict['loss_cls_b33'] = cross_entropy_loss(\n                b33_logits,\n                gt_labels,\n                ce_kwargs.get('eps'),\n                ce_kwargs.get('alpha')\n            ) * ce_kwargs.get('scale') * 0.125\n\n        if \"TripletLoss\" in loss_names:\n            tri_kwargs = self.loss_kwargs.get('tri')\n            loss_dict['loss_triplet_b1'] = triplet_loss(\n                b1_pool_feat,\n                gt_labels,\n                tri_kwargs.get('margin'),\n                tri_kwargs.get('norm_feat'),\n                tri_kwargs.get('hard_mining')\n            ) * tri_kwargs.get('scale') * 0.2\n\n            loss_dict['loss_triplet_b2'] = triplet_loss(\n                b2_pool_feat,\n                gt_labels,\n                tri_kwargs.get('margin'),\n                tri_kwargs.get('norm_feat'),\n                tri_kwargs.get('hard_mining')\n            ) * tri_kwargs.get('scale') * 0.2\n\n            loss_dict['loss_triplet_b3'] = triplet_loss(\n                b3_pool_feat,\n                gt_labels,\n                tri_kwargs.get('margin'),\n                tri_kwargs.get('norm_feat'),\n                tri_kwargs.get('hard_mining')\n            ) * tri_kwargs.get('scale') * 0.2\n\n            loss_dict['loss_triplet_b22'] = triplet_loss(\n                b22_pool_feat,\n                gt_labels,\n                tri_kwargs.get('margin'),\n                tri_kwargs.get('norm_feat'),\n                tri_kwargs.get('hard_mining')\n            ) * tri_kwargs.get('scale') * 0.2\n\n            loss_dict['loss_triplet_b33'] = triplet_loss(\n                b33_pool_feat,\n                gt_labels,\n\n                tri_kwargs.get('margin'),\n                tri_kwargs.get('norm_feat'),\n                tri_kwargs.get('hard_mining')\n            ) * tri_kwargs.get('scale') * 0.2\n\n        return loss_dict\n"
  },
  {
    "path": "fast_reid/fastreid/modeling/meta_arch/moco.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom fast_reid.fastreid.modeling.losses.utils import concat_all_gather\nfrom fast_reid.fastreid.utils import comm\nfrom .baseline import Baseline\nfrom .build import META_ARCH_REGISTRY\n\n\n@META_ARCH_REGISTRY.register()\nclass MoCo(Baseline):\n    def __init__(self, cfg):\n        super().__init__(cfg)\n\n        dim = cfg.MODEL.HEADS.EMBEDDING_DIM if cfg.MODEL.HEADS.EMBEDDING_DIM \\\n            else cfg.MODEL.BACKBONE.FEAT_DIM\n        size = cfg.MODEL.QUEUE_SIZE\n        self.memory = Memory(dim, size)\n\n    def losses(self, outputs, gt_labels):\n        \"\"\"\n        Compute loss from modeling's outputs, the loss function input arguments\n        must be the same as the outputs of the model forwarding.\n        \"\"\"\n        # regular reid loss\n        loss_dict = super().losses(outputs, gt_labels)\n\n        # memory loss\n        pred_features = outputs['features']\n        loss_mb = self.memory(pred_features, gt_labels)\n        loss_dict['loss_mb'] = loss_mb\n        return loss_dict\n\n\nclass Memory(nn.Module):\n    \"\"\"\n    Build a MoCo memory with a queue\n    https://arxiv.org/abs/1911.05722\n    \"\"\"\n\n    def __init__(self, dim=512, K=65536):\n        \"\"\"\n        dim: feature dimension (default: 128)\n        K: queue size; number of negative keys (default: 65536)\n        \"\"\"\n        super().__init__()\n        self.K = K\n\n        self.margin = 0.25\n        self.gamma = 32\n\n        # create the queue\n        self.register_buffer(\"queue\", torch.randn(dim, K))\n        self.queue = F.normalize(self.queue, dim=0)\n\n        self.register_buffer(\"queue_label\", torch.zeros((1, K), dtype=torch.long))\n        self.register_buffer(\"queue_ptr\", torch.zeros(1, dtype=torch.long))\n\n    @torch.no_grad()\n    def _dequeue_and_enqueue(self, keys, targets):\n        # gather keys/targets before updating queue\n        if comm.get_world_size() > 1:\n            keys = concat_all_gather(keys)\n            targets = concat_all_gather(targets)\n        else:\n            keys = keys.detach()\n            targets = targets.detach()\n\n        batch_size = keys.shape[0]\n\n        ptr = int(self.queue_ptr)\n        assert self.K % batch_size == 0  # for simplicity\n\n        # replace the keys at ptr (dequeue and enqueue)\n        self.queue[:, ptr:ptr + batch_size] = keys.T\n        self.queue_label[:, ptr:ptr + batch_size] = targets\n        ptr = (ptr + batch_size) % self.K  # move pointer\n\n        self.queue_ptr[0] = ptr\n\n    def forward(self, feat_q, targets):\n        \"\"\"\n        Memory bank enqueue and compute metric loss\n        Args:\n            feat_q: model features\n            targets: gt labels\n\n        Returns:\n        \"\"\"\n        # normalize embedding features\n        feat_q = F.normalize(feat_q, p=2, dim=1)\n        # dequeue and enqueue\n        self._dequeue_and_enqueue(feat_q.detach(), targets)\n        # compute loss\n        loss = self._pairwise_cosface(feat_q, targets)\n        return loss\n\n    def _pairwise_cosface(self, feat_q, targets):\n        dist_mat = torch.matmul(feat_q, self.queue)\n\n        N, M = dist_mat.size()  # (bsz, memory)\n        is_pos = targets.view(N, 1).expand(N, M).eq(self.queue_label.expand(N, M)).float()\n        is_neg = targets.view(N, 1).expand(N, M).ne(self.queue_label.expand(N, M)).float()\n\n        # Mask scores related to themselves\n        same_indx = torch.eye(N, N, device=is_pos.device)\n        other_indx = torch.zeros(N, M - N, device=is_pos.device)\n        same_indx = torch.cat((same_indx, other_indx), dim=1)\n        is_pos = is_pos - same_indx\n\n        s_p = dist_mat * is_pos\n        s_n = dist_mat * is_neg\n\n        logit_p = -self.gamma * s_p + (-99999999.) * (1 - is_pos)\n        logit_n = self.gamma * (s_n + self.margin) + (-99999999.) * (1 - is_neg)\n\n        loss = F.softplus(torch.logsumexp(logit_p, dim=1) + torch.logsumexp(logit_n, dim=1)).mean()\n\n        return loss\n"
  },
  {
    "path": "fast_reid/fastreid/solver/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\nfrom .build import build_lr_scheduler, build_optimizer"
  },
  {
    "path": "fast_reid/fastreid/solver/build.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# Based on: https://github.com/facebookresearch/detectron2/blob/master/detectron2/solver/build.py\n\nimport copy\nimport itertools\nimport math\nimport re\nfrom enum import Enum\nfrom typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union\n\nimport torch\n\nfrom fast_reid.fastreid.config import CfgNode\nfrom fast_reid.fastreid.utils.params import ContiguousParams\nfrom . import lr_scheduler\n\n_GradientClipperInput = Union[torch.Tensor, Iterable[torch.Tensor]]\n_GradientClipper = Callable[[_GradientClipperInput], None]\n\n\nclass GradientClipType(Enum):\n    VALUE = \"value\"\n    NORM = \"norm\"\n\n\ndef _create_gradient_clipper(cfg: CfgNode) -> _GradientClipper:\n    \"\"\"\n    Creates gradient clipping closure to clip by value or by norm,\n    according to the provided config.\n    \"\"\"\n    cfg = copy.deepcopy(cfg)\n\n    def clip_grad_norm(p: _GradientClipperInput):\n        torch.nn.utils.clip_grad_norm_(p, cfg.CLIP_VALUE, cfg.NORM_TYPE)\n\n    def clip_grad_value(p: _GradientClipperInput):\n        torch.nn.utils.clip_grad_value_(p, cfg.CLIP_VALUE)\n\n    _GRADIENT_CLIP_TYPE_TO_CLIPPER = {\n        GradientClipType.VALUE: clip_grad_value,\n        GradientClipType.NORM: clip_grad_norm,\n    }\n    return _GRADIENT_CLIP_TYPE_TO_CLIPPER[GradientClipType(cfg.CLIP_TYPE)]\n\n\ndef _generate_optimizer_class_with_gradient_clipping(\n        optimizer: Type[torch.optim.Optimizer],\n        *,\n        per_param_clipper: Optional[_GradientClipper] = None,\n        global_clipper: Optional[_GradientClipper] = None,\n) -> Type[torch.optim.Optimizer]:\n    \"\"\"\n    Dynamically creates a new type that inherits the type of a given instance\n    and overrides the `step` method to add gradient clipping\n    \"\"\"\n    assert (\n            per_param_clipper is None or global_clipper is None\n    ), \"Not allowed to use both per-parameter clipping and global clipping\"\n\n    @torch.no_grad()\n    def optimizer_wgc_step(self, closure=None):\n        if per_param_clipper is not None:\n            for group in self.param_groups:\n                for p in group[\"params\"]:\n                    per_param_clipper(p)\n        else:\n            # global clipper for future use with detr\n            # (https://github.com/facebookresearch/detr/pull/287)\n            all_params = itertools.chain(*[g[\"params\"] for g in self.param_groups])\n            global_clipper(all_params)\n        optimizer.step(self, closure)\n\n    OptimizerWithGradientClip = type(\n        optimizer.__name__ + \"WithGradientClip\",\n        (optimizer,),\n        {\"step\": optimizer_wgc_step},\n    )\n    return OptimizerWithGradientClip\n\n\ndef maybe_add_gradient_clipping(\n        cfg: CfgNode, optimizer: Type[torch.optim.Optimizer]\n) -> Type[torch.optim.Optimizer]:\n    \"\"\"\n    If gradient clipping is enabled through config options, wraps the existing\n    optimizer type to become a new dynamically created class OptimizerWithGradientClip\n    that inherits the given optimizer and overrides the `step` method to\n    include gradient clipping.\n    Args:\n        cfg: CfgNode, configuration options\n        optimizer: type. A subclass of torch.optim.Optimizer\n    Return:\n        type: either the input `optimizer` (if gradient clipping is disabled), or\n            a subclass of it with gradient clipping included in the `step` method.\n    \"\"\"\n    if not cfg.SOLVER.CLIP_GRADIENTS.ENABLED:\n        return optimizer\n    if isinstance(optimizer, torch.optim.Optimizer):\n        optimizer_type = type(optimizer)\n    else:\n        assert issubclass(optimizer, torch.optim.Optimizer), optimizer\n        optimizer_type = optimizer\n\n    grad_clipper = _create_gradient_clipper(cfg.SOLVER.CLIP_GRADIENTS)\n    OptimizerWithGradientClip = _generate_optimizer_class_with_gradient_clipping(\n        optimizer_type, per_param_clipper=grad_clipper\n    )\n    if isinstance(optimizer, torch.optim.Optimizer):\n        optimizer.__class__ = OptimizerWithGradientClip  # a bit hacky, not recommended\n        return optimizer\n    else:\n        return OptimizerWithGradientClip\n\n\ndef _generate_optimizer_class_with_freeze_layer(\n        optimizer: Type[torch.optim.Optimizer],\n        *,\n        freeze_iters: int = 0,\n) -> Type[torch.optim.Optimizer]:\n    assert freeze_iters > 0, \"No layers need to be frozen or freeze iterations is 0\"\n\n    cnt = 0\n    @torch.no_grad()\n    def optimizer_wfl_step(self, closure=None):\n        nonlocal cnt\n        if cnt < freeze_iters:\n            cnt += 1\n            param_ref = []\n            grad_ref = []\n            for group in self.param_groups:\n                if group[\"freeze_status\"] == \"freeze\":\n                    for p in group[\"params\"]:\n                        if p.grad is not None:\n                            param_ref.append(p)\n                            grad_ref.append(p.grad)\n                            p.grad = None\n\n            optimizer.step(self, closure)\n            for p, g in zip(param_ref, grad_ref):\n                p.grad = g\n        else:\n            optimizer.step(self, closure)\n\n    OptimizerWithFreezeLayer = type(\n        optimizer.__name__ + \"WithFreezeLayer\",\n        (optimizer,),\n        {\"step\": optimizer_wfl_step},\n    )\n    return OptimizerWithFreezeLayer\n\n\ndef maybe_add_freeze_layer(\n        cfg: CfgNode, optimizer: Type[torch.optim.Optimizer]\n) -> Type[torch.optim.Optimizer]:\n    if len(cfg.MODEL.FREEZE_LAYERS) == 0 or cfg.SOLVER.FREEZE_ITERS <= 0:\n        return optimizer\n\n    if isinstance(optimizer, torch.optim.Optimizer):\n        optimizer_type = type(optimizer)\n    else:\n        assert issubclass(optimizer, torch.optim.Optimizer), optimizer\n        optimizer_type = optimizer\n\n    OptimizerWithFreezeLayer = _generate_optimizer_class_with_freeze_layer(\n        optimizer_type,\n        freeze_iters=cfg.SOLVER.FREEZE_ITERS\n    )\n    if isinstance(optimizer, torch.optim.Optimizer):\n        optimizer.__class__ = OptimizerWithFreezeLayer  # a bit hacky, not recommended\n        return optimizer\n    else:\n        return OptimizerWithFreezeLayer\n\n\ndef build_optimizer(cfg, model, contiguous=True):\n    params = get_default_optimizer_params(\n        model,\n        base_lr=cfg.SOLVER.BASE_LR,\n        weight_decay=cfg.SOLVER.WEIGHT_DECAY,\n        weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM,\n        bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR,\n        heads_lr_factor=cfg.SOLVER.HEADS_LR_FACTOR,\n        weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS,\n        freeze_layers=cfg.MODEL.FREEZE_LAYERS if cfg.SOLVER.FREEZE_ITERS > 0 else [],\n    )\n\n    if contiguous:\n        params = ContiguousParams(params)\n    solver_opt = cfg.SOLVER.OPT\n    if solver_opt == \"SGD\":\n        return maybe_add_freeze_layer(\n            cfg,\n            maybe_add_gradient_clipping(cfg, torch.optim.SGD)\n        )(\n            params.contiguous() if contiguous else params,\n            momentum=cfg.SOLVER.MOMENTUM,\n            nesterov=cfg.SOLVER.NESTEROV,\n        ), params\n    else:\n        return maybe_add_freeze_layer(\n            cfg,\n            maybe_add_gradient_clipping(cfg, getattr(torch.optim, solver_opt))\n        )(params.contiguous() if contiguous else params), params\n\n\ndef get_default_optimizer_params(\n        model: torch.nn.Module,\n        base_lr: Optional[float] = None,\n        weight_decay: Optional[float] = None,\n        weight_decay_norm: Optional[float] = None,\n        bias_lr_factor: Optional[float] = 1.0,\n        heads_lr_factor: Optional[float] = 1.0,\n        weight_decay_bias: Optional[float] = None,\n        overrides: Optional[Dict[str, Dict[str, float]]] = None,\n        freeze_layers: Optional[list] = [],\n):\n    \"\"\"\n    Get default param list for optimizer, with support for a few types of\n    overrides. If no overrides needed, this is equivalent to `model.parameters()`.\n    Args:\n        base_lr: lr for every group by default. Can be omitted to use the one in optimizer.\n        weight_decay: weight decay for every group by default. Can be omitted to use the one\n            in optimizer.\n        weight_decay_norm: override weight decay for params in normalization layers\n        bias_lr_factor: multiplier of lr for bias parameters.\n        heads_lr_factor: multiplier of lr for model.head parameters.\n        weight_decay_bias: override weight decay for bias parameters\n        overrides: if not `None`, provides values for optimizer hyperparameters\n            (LR, weight decay) for module parameters with a given name; e.g.\n            ``{\"embedding\": {\"lr\": 0.01, \"weight_decay\": 0.1}}`` will set the LR and\n            weight decay values for all module parameters named `embedding`.\n        freeze_layers: layer names for freezing.\n    For common detection models, ``weight_decay_norm`` is the only option\n    needed to be set. ``bias_lr_factor,weight_decay_bias`` are legacy settings\n    from Detectron1 that are not found useful.\n    Example:\n    ::\n        torch.optim.SGD(get_default_optimizer_params(model, weight_decay_norm=0),\n                       lr=0.01, weight_decay=1e-4, momentum=0.9)\n    \"\"\"\n    if overrides is None:\n        overrides = {}\n    defaults = {}\n    if base_lr is not None:\n        defaults[\"lr\"] = base_lr\n    if weight_decay is not None:\n        defaults[\"weight_decay\"] = weight_decay\n    bias_overrides = {}\n    if bias_lr_factor is not None and bias_lr_factor != 1.0:\n        # NOTE: unlike Detectron v1, we now by default make bias hyperparameters\n        # exactly the same as regular weights.\n        if base_lr is None:\n            raise ValueError(\"bias_lr_factor requires base_lr\")\n        bias_overrides[\"lr\"] = base_lr * bias_lr_factor\n    if weight_decay_bias is not None:\n        bias_overrides[\"weight_decay\"] = weight_decay_bias\n    if len(bias_overrides):\n        if \"bias\" in overrides:\n            raise ValueError(\"Conflicting overrides for 'bias'\")\n        overrides[\"bias\"] = bias_overrides\n\n    layer_names_pattern = [re.compile(name) for name in freeze_layers]\n\n    norm_module_types = (\n        torch.nn.BatchNorm1d,\n        torch.nn.BatchNorm2d,\n        torch.nn.BatchNorm3d,\n        torch.nn.SyncBatchNorm,\n        # NaiveSyncBatchNorm inherits from BatchNorm2d\n        torch.nn.GroupNorm,\n        torch.nn.InstanceNorm1d,\n        torch.nn.InstanceNorm2d,\n        torch.nn.InstanceNorm3d,\n        torch.nn.LayerNorm,\n        torch.nn.LocalResponseNorm,\n    )\n    params: List[Dict[str, Any]] = []\n    memo: Set[torch.nn.parameter.Parameter] = set()\n\n    for module_name, module in model.named_modules():\n        for module_param_name, value in module.named_parameters(recurse=False):\n            if not value.requires_grad:\n                continue\n            # Avoid duplicating parameters\n            if value in memo:\n                continue\n            memo.add(value)\n\n            hyperparams = copy.copy(defaults)\n            if isinstance(module, norm_module_types) and weight_decay_norm is not None:\n                hyperparams[\"weight_decay\"] = weight_decay_norm\n            hyperparams.update(overrides.get(module_param_name, {}))\n            if module_name.split('.')[0] == \"heads\" and (heads_lr_factor is not None and heads_lr_factor != 1.0):\n                hyperparams[\"lr\"] = hyperparams.get(\"lr\", base_lr) * heads_lr_factor\n            name = module_name + '.' + module_param_name\n            freeze_status = \"normal\"\n            # Search freeze layer names, it must match from beginning, so use `match` not `search`\n            for pattern in layer_names_pattern:\n                if pattern.match(name) is not None:\n                    freeze_status = \"freeze\"\n                    break\n\n            params.append({\"freeze_status\": freeze_status, \"params\": [value], **hyperparams})\n    return params\n\n\ndef build_lr_scheduler(cfg, optimizer, iters_per_epoch):\n    max_epoch = cfg.SOLVER.MAX_EPOCH - max(\n        math.ceil(cfg.SOLVER.WARMUP_ITERS / iters_per_epoch), cfg.SOLVER.DELAY_EPOCHS)\n\n    scheduler_dict = {}\n\n    scheduler_args = {\n        \"MultiStepLR\": {\n            \"optimizer\": optimizer,\n            # multi-step lr scheduler options\n            \"milestones\": cfg.SOLVER.STEPS,\n            \"gamma\": cfg.SOLVER.GAMMA,\n        },\n        \"CosineAnnealingLR\": {\n            \"optimizer\": optimizer,\n            # cosine annealing lr scheduler options\n            \"T_max\": max_epoch,\n            \"eta_min\": cfg.SOLVER.ETA_MIN_LR,\n        },\n\n    }\n\n    scheduler_dict[\"lr_sched\"] = getattr(lr_scheduler, cfg.SOLVER.SCHED)(\n        **scheduler_args[cfg.SOLVER.SCHED])\n\n    if cfg.SOLVER.WARMUP_ITERS > 0:\n        warmup_args = {\n            \"optimizer\": optimizer,\n\n            # warmup options\n            \"warmup_factor\": cfg.SOLVER.WARMUP_FACTOR,\n            \"warmup_iters\": cfg.SOLVER.WARMUP_ITERS,\n            \"warmup_method\": cfg.SOLVER.WARMUP_METHOD,\n        }\n        scheduler_dict[\"warmup_sched\"] = lr_scheduler.WarmupLR(**warmup_args)\n\n    return scheduler_dict\n"
  },
  {
    "path": "fast_reid/fastreid/solver/lr_scheduler.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom typing import List\n\nimport torch\nfrom torch.optim.lr_scheduler import *\n\n\nclass WarmupLR(torch.optim.lr_scheduler._LRScheduler):\n    def __init__(\n            self,\n            optimizer: torch.optim.Optimizer,\n            warmup_factor: float = 0.1,\n            warmup_iters: int = 1000,\n            warmup_method: str = \"linear\",\n            last_epoch: int = -1,\n    ):\n        self.warmup_factor = warmup_factor\n        self.warmup_iters = warmup_iters\n        self.warmup_method = warmup_method\n        super().__init__(optimizer, last_epoch)\n\n    def get_lr(self) -> List[float]:\n        warmup_factor = _get_warmup_factor_at_epoch(\n            self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor\n        )\n        return [\n            base_lr * warmup_factor for base_lr in self.base_lrs\n        ]\n\n    def _compute_values(self) -> List[float]:\n        # The new interface\n        return self.get_lr()\n\n\ndef _get_warmup_factor_at_epoch(\n        method: str, iter: int, warmup_iters: int, warmup_factor: float\n) -> float:\n    \"\"\"\n    Return the learning rate warmup factor at a specific iteration.\n    See https://arxiv.org/abs/1706.02677 for more details.\n    Args:\n        method (str): warmup method; either \"constant\" or \"linear\".\n        iter (int): iter at which to calculate the warmup factor.\n        warmup_iters (int): the number of warmup epochs.\n        warmup_factor (float): the base warmup factor (the meaning changes according\n            to the method used).\n    Returns:\n        float: the effective warmup factor at the given iteration.\n    \"\"\"\n    if iter >= warmup_iters:\n        return 1.0\n\n    if method == \"constant\":\n        return warmup_factor\n    elif method == \"linear\":\n        alpha = iter / warmup_iters\n        return warmup_factor * (1 - alpha) + alpha\n    elif method == \"exp\":\n        return warmup_factor ** (1 - iter / warmup_iters)\n    else:\n        raise ValueError(\"Unknown warmup method: {}\".format(method))\n"
  },
  {
    "path": "fast_reid/fastreid/solver/optim/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .lamb import Lamb\nfrom .swa import SWA\nfrom .radam import RAdam\nfrom torch.optim import *\n"
  },
  {
    "path": "fast_reid/fastreid/solver/optim/lamb.py",
    "content": "####\n# CODE TAKEN FROM https://github.com/mgrankin/over9000\n####\n\nimport collections\n\nimport torch\nfrom torch.optim.optimizer import Optimizer\nfrom torch.utils.tensorboard import SummaryWriter\n\n\ndef log_lamb_rs(optimizer: Optimizer, event_writer: SummaryWriter, token_count: int):\n    \"\"\"Log a histogram of trust ratio scalars in across layers.\"\"\"\n    results = collections.defaultdict(list)\n    for group in optimizer.param_groups:\n        for p in group['params']:\n            state = optimizer.state[p]\n            for i in ('weight_norm', 'adam_norm', 'trust_ratio'):\n                if i in state:\n                    results[i].append(state[i])\n\n    for k, v in results.items():\n        event_writer.add_histogram(f'lamb/{k}', torch.tensor(v), token_count)\n\n\nclass Lamb(Optimizer):\n    r\"\"\"Implements Lamb algorithm.\n    It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.\n    Arguments:\n        params (iterable): iterable of parameters to optimize or dicts defining\n            parameter groups\n        lr (float, optional): learning rate (default: 1e-3)\n        betas (Tuple[float, float], optional): coefficients used for computing\n            running averages of gradient and its square (default: (0.9, 0.999))\n        eps (float, optional): term added to the denominator to improve\n            numerical stability (default: 1e-8)\n        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n        adam (bool, optional): always use trust ratio = 1, which turns this into\n            Adam. Useful for comparison purposes.\n    .. _Large Batch Optimization for Deep Learning: Training BERT in 76 minutes:\n        https://arxiv.org/abs/1904.00962\n    \"\"\"\n\n    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6,\n                 weight_decay=0, adam=False):\n        if not 0.0 <= lr:\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n        if not 0.0 <= eps:\n            raise ValueError(\"Invalid epsilon value: {}\".format(eps))\n        if not 0.0 <= betas[0] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 0: {}\".format(betas[0]))\n        if not 0.0 <= betas[1] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 1: {}\".format(betas[1]))\n        defaults = dict(lr=lr, betas=betas, eps=eps,\n                        weight_decay=weight_decay)\n        self.adam = adam\n        super(Lamb, self).__init__(params, defaults)\n\n    def step(self, closure=None):\n        \"\"\"Performs a single optimization step.\n        Arguments:\n            closure (callable, optional): A closure that reevaluates the model\n                and returns the loss.\n        \"\"\"\n        loss = None\n        if closure is not None:\n            loss = closure()\n\n        for group in self.param_groups:\n            for p in group['params']:\n                if p.grad is None:\n                    continue\n                grad = p.grad.data\n                if grad.is_sparse:\n                    raise RuntimeError('Lamb does not support sparse gradients, consider SparseAdam instad.')\n\n                state = self.state[p]\n\n                # State initialization\n                if len(state) == 0:\n                    state['step'] = 0\n                    # Exponential moving average of gradient values\n                    state['exp_avg'] = torch.zeros_like(p.data)\n                    # Exponential moving average of squared gradient values\n                    state['exp_avg_sq'] = torch.zeros_like(p.data)\n\n                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n                beta1, beta2 = group['betas']\n\n                state['step'] += 1\n\n                # Decay the first and second moment running average coefficient\n                # m_t\n                exp_avg.mul_(beta1).add_(1 - beta1, grad)\n                # v_t\n                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n\n                # Paper v3 does not use debiasing.\n                # bias_correction1 = 1 - beta1 ** state['step']\n                # bias_correction2 = 1 - beta2 ** state['step']\n                # Apply bias to lr to avoid broadcast.\n                step_size = group['lr']  # * math.sqrt(bias_correction2) / bias_correction1\n\n                weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10)\n\n                adam_step = exp_avg / exp_avg_sq.sqrt().add(group['eps'])\n                if group['weight_decay'] != 0:\n                    adam_step.add_(group['weight_decay'], p.data)\n\n                adam_norm = adam_step.pow(2).sum().sqrt()\n                if weight_norm == 0 or adam_norm == 0:\n                    trust_ratio = 1\n                else:\n                    trust_ratio = weight_norm / adam_norm\n                state['weight_norm'] = weight_norm\n                state['adam_norm'] = adam_norm\n                state['trust_ratio'] = trust_ratio\n                if self.adam:\n                    trust_ratio = 1\n\n                p.data.add_(-step_size * trust_ratio, adam_step)\n\n        return loss\n"
  },
  {
    "path": "fast_reid/fastreid/solver/optim/radam.py",
    "content": "import math\n\nimport torch\nfrom torch.optim.optimizer import Optimizer\n\n\nclass RAdam(Optimizer):\n\n    def __init__(self, params, lr=0.001, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):\n        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)\n        self.buffer = [[None, None, None] for ind in range(10)]\n        super(RAdam, self).__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super(RAdam, self).__setstate__(state)\n\n    def step(self, closure=None):\n\n        loss = None\n        if closure is not None:\n            loss = closure()\n\n        for group in self.param_groups:\n\n            for p in group['params']:\n                if p.grad is None:\n                    continue\n                grad = p.grad.data.float()\n                if grad.is_sparse:\n                    raise RuntimeError('RAdam does not support sparse gradients')\n\n                p_data_fp32 = p.data.float()\n\n                state = self.state[p]\n\n                if len(state) == 0:\n                    state['step'] = 0\n                    state['exp_avg'] = torch.zeros_like(p_data_fp32)\n                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)\n                else:\n                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)\n                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)\n\n                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n                beta1, beta2 = group['betas']\n\n                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n                exp_avg.mul_(beta1).add_(1 - beta1, grad)\n\n                state['step'] += 1\n                buffered = self.buffer[int(state['step'] % 10)]\n                if state['step'] == buffered[0]:\n                    N_sma, step_size = buffered[1], buffered[2]\n                else:\n                    buffered[0] = state['step']\n                    beta2_t = beta2 ** state['step']\n                    N_sma_max = 2 / (1 - beta2) - 1\n                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)\n                    buffered[1] = N_sma\n\n                    # more conservative since it's an approximated value\n                    if N_sma >= 5:\n                        step_size = group['lr'] * math.sqrt(\n                            (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (\n                                    N_sma_max - 2)) / (1 - beta1 ** state['step'])\n                    else:\n                        step_size = group['lr'] / (1 - beta1 ** state['step'])\n                    buffered[2] = step_size\n\n                if group['weight_decay'] != 0:\n                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)\n\n                # more conservative since it's an approximated value\n                if N_sma >= 5:\n                    denom = exp_avg_sq.sqrt().add_(group['eps'])\n                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)\n                else:\n                    p_data_fp32.add_(-step_size, exp_avg)\n\n                p.data.copy_(p_data_fp32)\n\n        return loss\n\n\nclass PlainRAdam(Optimizer):\n\n    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):\n        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)\n\n        super(PlainRAdam, self).__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super(PlainRAdam, self).__setstate__(state)\n\n    def step(self, closure=None):\n\n        loss = None\n        if closure is not None:\n            loss = closure()\n\n        for group in self.param_groups:\n\n            for p in group['params']:\n                if p.grad is None:\n                    continue\n                grad = p.grad.data.float()\n                if grad.is_sparse:\n                    raise RuntimeError('RAdam does not support sparse gradients')\n\n                p_data_fp32 = p.data.float()\n\n                state = self.state[p]\n\n                if len(state) == 0:\n                    state['step'] = 0\n                    state['exp_avg'] = torch.zeros_like(p_data_fp32)\n                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)\n                else:\n                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)\n                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)\n\n                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n                beta1, beta2 = group['betas']\n\n                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n                exp_avg.mul_(beta1).add_(1 - beta1, grad)\n\n                state['step'] += 1\n                beta2_t = beta2 ** state['step']\n                N_sma_max = 2 / (1 - beta2) - 1\n                N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)\n\n                if group['weight_decay'] != 0:\n                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)\n\n                # more conservative since it's an approximated value\n                if N_sma >= 5:\n                    step_size = group['lr'] * math.sqrt(\n                        (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (\n                                N_sma_max - 2)) / (1 - beta1 ** state['step'])\n                    denom = exp_avg_sq.sqrt().add_(group['eps'])\n                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)\n                else:\n                    step_size = group['lr'] / (1 - beta1 ** state['step'])\n                    p_data_fp32.add_(-step_size, exp_avg)\n\n                p.data.copy_(p_data_fp32)\n\n        return loss\n"
  },
  {
    "path": "fast_reid/fastreid/solver/optim/swa.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n# based on:\n# https://github.com/pytorch/contrib/blob/master/torchcontrib/optim/swa.py\n\nimport warnings\nfrom collections import defaultdict\n\nimport torch\nfrom torch.optim.optimizer import Optimizer\n\n\nclass SWA(Optimizer):\n    def __init__(self, optimizer, swa_freq=None, swa_lr_factor=None):\n        r\"\"\"Implements Stochastic Weight Averaging (SWA).\n        Stochastic Weight Averaging was proposed in `Averaging Weights Leads to\n        Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii\n        Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson\n        (UAI 2018).\n        SWA is implemented as a wrapper class taking optimizer instance as input\n        and applying SWA on top of that optimizer.\n        SWA can be used in two modes: automatic and manual. In the automatic\n        mode SWA running averages are automatically updated every\n        :attr:`swa_freq` steps after :attr:`swa_start` steps of optimization. If\n        :attr:`swa_lr` is provided, the learning rate of the optimizer is reset\n        to :attr:`swa_lr` at every step starting from :attr:`swa_start`. To use\n        SWA in automatic mode provide values for both :attr:`swa_start` and\n        :attr:`swa_freq` arguments.\n        Alternatively, in the manual mode, use :meth:`update_swa` or\n        :meth:`update_swa_group` methods to update the SWA running averages.\n        In the end of training use `swap_swa_sgd` method to set the optimized\n        variables to the computed averages.\n        Args:\n            swa_freq (int): number of steps between subsequent updates of\n                SWA running averages in automatic mode; if None, manual mode is\n                selected (default: None)\n            swa_lr (float): learning rate to use starting from step swa_start\n                in automatic mode; if None, learning rate is not changed\n                (default: None)\n        Examples:\n            >>> # automatic mode\n            >>> base_opt = torch.optim.SGD(model.parameters(), lr=0.1)\n            >>> opt = SWA(base_opt, swa_start=10, swa_freq=5, swa_lr=0.05)\n            >>> for _ in range(100):\n            >>>     opt.zero_grad()\n            >>>     loss_fn(model(input), target).backward()\n            >>>     opt.step()\n            >>> opt.swap_swa_param()\n            >>> # manual mode\n            >>> opt = SWA(base_opt)\n            >>> for i in range(100):\n            >>>     opt.zero_grad()\n            >>>     loss_fn(model(input), target).backward()\n            >>>     opt.step()\n            >>>     if i > 10 and i % 5 == 0:\n            >>>         opt.update_swa()\n            >>> opt.swap_swa_param()\n        .. note::\n            SWA does not support parameter-specific values of :attr:`swa_start`,\n            :attr:`swa_freq` or :attr:`swa_lr`. In automatic mode SWA uses the\n            same :attr:`swa_start`, :attr:`swa_freq` and :attr:`swa_lr` for all\n            parameter groups. If needed, use manual mode with\n            :meth:`update_swa_group` to use different update schedules for\n            different parameter groups.\n        .. note::\n            Call :meth:`swap_swa_sgd` in the end of training to use the computed\n            running averages.\n        .. note::\n            If you are using SWA to optimize the parameters of a Neural Network\n            containing Batch Normalization layers, you need to update the\n            :attr:`running_mean` and :attr:`running_var` statistics of the\n            Batch Normalization module. You can do so by using\n            `torchcontrib.optim.swa.bn_update` utility.\n        .. note::\n            See the blogpost\n            https://pytorch.org/blog/stochastic-weight-averaging-in-pytorch/\n            for an extended description of this SWA implementation.\n        .. note::\n            The repo https://github.com/izmailovpavel/contrib_swa_examples\n            contains examples of using this SWA implementation.\n        .. _Averaging Weights Leads to Wider Optima and Better Generalization:\n            https://arxiv.org/abs/1803.05407\n        .. _Improving Consistency-Based Semi-Supervised Learning with Weight\n            Averaging:\n            https://arxiv.org/abs/1806.05594\n        \"\"\"\n        self._auto_mode, (self.swa_freq,) = self._check_params(swa_freq)\n        self.swa_lr_factor = swa_lr_factor\n\n        if self._auto_mode:\n            if swa_freq < 1:\n                raise ValueError(\"Invalid swa_freq: {}\".format(swa_freq))\n        else:\n            if self.swa_lr_factor is not None:\n                warnings.warn(\n                    \"Swa_freq is None, ignoring swa_lr\")\n            # If not in auto mode make all swa parameters None\n            self.swa_lr_factor = None\n            self.swa_freq = None\n\n        if self.swa_lr_factor is not None and self.swa_lr_factor < 0:\n            raise ValueError(\"Invalid SWA learning rate factor: {}\".format(swa_lr_factor))\n\n        self.optimizer = optimizer\n\n        self.defaults = self.optimizer.defaults\n        self.param_groups = self.optimizer.param_groups\n        self.state = defaultdict(dict)\n        self.opt_state = self.optimizer.state\n        for group in self.param_groups:\n            group['n_avg'] = 0\n            group['step_counter'] = 0\n\n    @staticmethod\n    def _check_params(swa_freq):\n        params = [swa_freq]\n        params_none = [param is None for param in params]\n        if not all(params_none) and any(params_none):\n            warnings.warn(\n                \"Some of swa_start, swa_freq is None, ignoring other\")\n        for i, param in enumerate(params):\n            if param is not None and not isinstance(param, int):\n                params[i] = int(param)\n                warnings.warn(\"Casting swa_start, swa_freq to int\")\n        return not any(params_none), params\n\n    def reset_lr_to_swa(self):\n        for param_group in self.param_groups:\n            param_group['initial_lr'] = self.swa_lr_factor * param_group['lr']\n\n    def update_swa_group(self, group):\n        r\"\"\"Updates the SWA running averages for the given parameter group.\n        Arguments:\n            group (dict): Specifies for what parameter group SWA running\n                averages should be updated\n        Examples:\n            >>> # automatic mode\n            >>> base_opt = torch.optim.SGD([{'params': [x]},\n            >>>             {'params': [y], 'lr': 1e-3}], lr=1e-2, momentum=0.9)\n            >>> opt = torchcontrib.optim.SWA(base_opt)\n            >>> for i in range(100):\n            >>>     opt.zero_grad()\n            >>>     loss_fn(model(input), target).backward()\n            >>>     opt.step()\n            >>>     if i > 10 and i % 5 == 0:\n            >>>         # Update SWA for the second parameter group\n            >>>         opt.update_swa_group(opt.param_groups[1])\n            >>> opt.swap_swa_param()\n        \"\"\"\n        for p in group['params']:\n            param_state = self.state[p]\n            if 'swa_buffer' not in param_state:\n                param_state['swa_buffer'] = torch.zeros_like(p.data)\n            buf = param_state['swa_buffer']\n            virtual_decay = 1 / float(group[\"n_avg\"] + 1)\n            diff = (p.data - buf) * virtual_decay\n            buf.add_(diff)\n        group[\"n_avg\"] += 1\n\n    def update_swa(self):\n        r\"\"\"Updates the SWA running averages of all optimized parameters.\n        \"\"\"\n        for group in self.param_groups:\n            self.update_swa_group(group)\n\n    def swap_swa_param(self):\n        r\"\"\"Swaps the values of the optimized variables and swa buffers.\n        It's meant to be called in the end of training to use the collected\n        swa running averages. It can also be used to evaluate the running\n        averages during training; to continue training `swap_swa_sgd`\n        should be called again.\n        \"\"\"\n        for group in self.param_groups:\n            for p in group['params']:\n                param_state = self.state[p]\n                if 'swa_buffer' not in param_state:\n                    # If swa wasn't applied we don't swap params\n                    warnings.warn(\n                        \"SWA wasn't applied to param {}; skipping it\".format(p))\n                    continue\n                buf = param_state['swa_buffer']\n                tmp = torch.empty_like(p.data)\n                tmp.copy_(p.data)\n                p.data.copy_(buf)\n                buf.copy_(tmp)\n\n    def step(self, closure=None):\n        r\"\"\"Performs a single optimization step.\n        In automatic mode also updates SWA running averages.\n        \"\"\"\n        loss = self.optimizer.step(closure)\n        for group in self.param_groups:\n            group[\"step_counter\"] += 1\n            steps = group[\"step_counter\"]\n            if self._auto_mode:\n                if steps % self.swa_freq == 0:\n                    self.update_swa_group(group)\n        return loss\n\n    def state_dict(self):\n        r\"\"\"Returns the state of SWA as a :class:`dict`.\n        It contains three entries:\n            * opt_state - a dict holding current optimization state of the base\n                optimizer. Its content differs between optimizer classes.\n            * swa_state - a dict containing current state of SWA. For each\n                optimized variable it contains swa_buffer keeping the running\n                average of the variable\n            * param_groups - a dict containing all parameter groups\n        \"\"\"\n        opt_state_dict = self.optimizer.state_dict()\n        swa_state = {(id(k) if isinstance(k, torch.Tensor) else k): v\n                     for k, v in self.state.items()}\n        opt_state = opt_state_dict[\"state\"]\n        param_groups = opt_state_dict[\"param_groups\"]\n        return {\"opt_state\": opt_state, \"swa_state\": swa_state,\n                \"param_groups\": param_groups}\n\n    def load_state_dict(self, state_dict):\n        r\"\"\"Loads the optimizer state.\n        Args:\n            state_dict (dict): SWA optimizer state. Should be an object returned\n                from a call to `state_dict`.\n        \"\"\"\n        swa_state_dict = {\"state\": state_dict[\"swa_state\"],\n                          \"param_groups\": state_dict[\"param_groups\"]}\n        opt_state_dict = {\"state\": state_dict[\"opt_state\"],\n                          \"param_groups\": state_dict[\"param_groups\"]}\n        super(SWA, self).load_state_dict(swa_state_dict)\n        self.optimizer.load_state_dict(opt_state_dict)\n        self.opt_state = self.optimizer.state\n\n    def add_param_group(self, param_group):\n        r\"\"\"Add a param group to the :class:`Optimizer` s `param_groups`.\n        This can be useful when fine tuning a pre-trained network as frozen\n        layers can be made trainable and added to the :class:`Optimizer` as\n        training progresses.\n        Args:\n            param_group (dict): Specifies what Tensors should be optimized along\n            with group specific optimization options.\n        \"\"\"\n        param_group['n_avg'] = 0\n        param_group['step_counter'] = 0\n        self.optimizer.add_param_group(param_group)\n"
  },
  {
    "path": "fast_reid/fastreid/utils/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n"
  },
  {
    "path": "fast_reid/fastreid/utils/checkpoint.py",
    "content": "#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n\nimport copy\nimport logging\nimport os\nfrom collections import defaultdict\nfrom typing import Any\nfrom typing import Optional, List, Dict, NamedTuple, Tuple, Iterable\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom termcolor import colored\nfrom torch.nn.parallel import DistributedDataParallel, DataParallel\n\nfrom fast_reid.fastreid.utils.file_io import PathManager\n\n\nclass _IncompatibleKeys(\n    NamedTuple(\n        # pyre-fixme[10]: Name `IncompatibleKeys` is used but not defined.\n        \"IncompatibleKeys\",\n        [\n            (\"missing_keys\", List[str]),\n            (\"unexpected_keys\", List[str]),\n            # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.\n            # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.\n            # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.\n            (\"incorrect_shapes\", List[Tuple]),\n        ],\n    )\n):\n    pass\n\n\nclass Checkpointer(object):\n    \"\"\"\n    A checkpointer that can save/load model as well as extra checkpointable\n    objects.\n    \"\"\"\n\n    def __init__(\n            self,\n            model: nn.Module,\n            save_dir: str = \"\",\n            *,\n            save_to_disk: bool = True,\n            **checkpointables: object,\n    ):\n        \"\"\"\n        Args:\n            model (nn.Module): model.\n            save_dir (str): a directory to save and find checkpoints.\n            save_to_disk (bool): if True, save checkpoint to disk, otherwise\n                disable saving for this checkpointer.\n            checkpointables (object): any checkpointable objects, i.e., objects\n                that have the `state_dict()` and `load_state_dict()` method. For\n                example, it can be used like\n                `Checkpointer(model, \"dir\", optimizer=optimizer)`.\n        \"\"\"\n        if isinstance(model, (DistributedDataParallel, DataParallel)):\n            model = model.module\n        self.model = model\n        self.checkpointables = copy.copy(checkpointables)\n        self.logger = logging.getLogger(__name__)\n        self.save_dir = save_dir\n        self.save_to_disk = save_to_disk\n\n        self.path_manager = PathManager\n\n    def save(self, name: str, **kwargs: Dict[str, str]):\n        \"\"\"\n        Dump model and checkpointables to a file.\n\n        Args:\n            name (str): name of the file.\n            kwargs (dict): extra arbitrary data to save.\n        \"\"\"\n        if not self.save_dir or not self.save_to_disk:\n            return\n\n        data = {}\n        data[\"model\"] = self.model.state_dict()\n        for key, obj in self.checkpointables.items():\n            data[key] = obj.state_dict()\n        data.update(kwargs)\n\n        basename = \"{}.pth\".format(name)\n        save_file = os.path.join(self.save_dir, basename)\n        assert os.path.basename(save_file) == basename, basename\n        self.logger.info(\"Saving checkpoint to {}\".format(save_file))\n        with PathManager.open(save_file, \"wb\") as f:\n            torch.save(data, f)\n        self.tag_last_checkpoint(basename)\n\n    def load(self, path: str, checkpointables: Optional[List[str]] = None) -> object:\n        \"\"\"\n        Load from the given checkpoint. When path points to network file, this\n        function has to be called on all ranks.\n\n        Args:\n            path (str): path or url to the checkpoint. If empty, will not load\n                anything.\n            checkpointables (list): List of checkpointable names to load. If not\n                specified (None), will load all the possible checkpointables.\n        Returns:\n            dict:\n                extra data loaded from the checkpoint that has not been\n                processed. For example, those saved with\n                :meth:`.save(**extra_data)`.\n        \"\"\"\n        if not path:\n            # no checkpoint provided\n            self.logger.info(\"No checkpoint found. Training model from scratch\")\n            return {}\n        self.logger.info(\"Loading checkpoint from {}\".format(path))\n        if not os.path.isfile(path):\n            path = self.path_manager.get_local_path(path)\n            assert os.path.isfile(path), \"Checkpoint {} not found!\".format(path)\n\n        checkpoint = self._load_file(path)\n        incompatible = self._load_model(checkpoint)\n        if (\n                incompatible is not None\n        ):  # handle some existing subclasses that returns None\n            self._log_incompatible_keys(incompatible)\n\n        for key in self.checkpointables if checkpointables is None else checkpointables:\n            if key in checkpoint:  # pyre-ignore\n                self.logger.info(\"Loading {} from {}\".format(key, path))\n                obj = self.checkpointables[key]\n                obj.load_state_dict(checkpoint.pop(key))  # pyre-ignore\n\n        # return any further checkpoint data\n        return checkpoint\n\n    def has_checkpoint(self):\n        \"\"\"\n        Returns:\n            bool: whether a checkpoint exists in the target directory.\n        \"\"\"\n        save_file = os.path.join(self.save_dir, \"last_checkpoint\")\n        return PathManager.exists(save_file)\n\n    def get_checkpoint_file(self):\n        \"\"\"\n        Returns:\n            str: The latest checkpoint file in target directory.\n        \"\"\"\n        save_file = os.path.join(self.save_dir, \"last_checkpoint\")\n        try:\n            with PathManager.open(save_file, \"r\") as f:\n                last_saved = f.read().strip()\n        except IOError:\n            # if file doesn't exist, maybe because it has just been\n            # deleted by a separate process\n            return \"\"\n        return os.path.join(self.save_dir, last_saved)\n\n    def get_all_checkpoint_files(self):\n        \"\"\"\n        Returns:\n            list: All available checkpoint files (.pth files) in target\n                directory.\n        \"\"\"\n        all_model_checkpoints = [\n            os.path.join(self.save_dir, file)\n            for file in PathManager.ls(self.save_dir)\n            if PathManager.isfile(os.path.join(self.save_dir, file))\n               and file.endswith(\".pth\")\n        ]\n        return all_model_checkpoints\n\n    def resume_or_load(self, path: str, *, resume: bool = True):\n        \"\"\"\n        If `resume` is True, this method attempts to resume from the last\n        checkpoint, if exists. Otherwise, load checkpoint from the given path.\n        This is useful when restarting an interrupted training job.\n\n        Args:\n            path (str): path to the checkpoint.\n            resume (bool): if True, resume from the last checkpoint if it exists.\n        Returns:\n            same as :meth:`load`.\n        \"\"\"\n        if resume and self.has_checkpoint():\n            path = self.get_checkpoint_file()\n            return self.load(path)\n        else:\n            return self.load(path, checkpointables=[])\n\n    def tag_last_checkpoint(self, last_filename_basename: str):\n        \"\"\"\n        Tag the last checkpoint.\n\n        Args:\n            last_filename_basename (str): the basename of the last filename.\n        \"\"\"\n        save_file = os.path.join(self.save_dir, \"last_checkpoint\")\n        with PathManager.open(save_file, \"w\") as f:\n            f.write(last_filename_basename)\n\n    def _load_file(self, f: str):\n        \"\"\"\n        Load a checkpoint file. Can be overwritten by subclasses to support\n        different formats.\n\n        Args:\n            f (str): a locally mounted file path.\n        Returns:\n            dict: with keys \"model\" and optionally others that are saved by\n                the checkpointer dict[\"model\"] must be a dict which maps strings\n                to torch.Tensor or numpy arrays.\n        \"\"\"\n        return torch.load(f, map_location=torch.device(\"cpu\"))\n\n    def _load_model(self, checkpoint: Any):\n        \"\"\"\n        Load weights from a checkpoint.\n\n        Args:\n            checkpoint (Any): checkpoint contains the weights.\n        \"\"\"\n        checkpoint_state_dict = checkpoint.pop(\"model\")\n        self._convert_ndarray_to_tensor(checkpoint_state_dict)\n\n        # if the state_dict comes from a model that was wrapped in a\n        # DataParallel or DistributedDataParallel during serialization,\n        # remove the \"module\" prefix before performing the matching.\n        _strip_prefix_if_present(checkpoint_state_dict, \"module.\")\n\n        # work around https://github.com/pytorch/pytorch/issues/24139\n        model_state_dict = self.model.state_dict()\n        incorrect_shapes = []\n        for k in list(checkpoint_state_dict.keys()):\n            if k in model_state_dict:\n                shape_model = tuple(model_state_dict[k].shape)\n                shape_checkpoint = tuple(checkpoint_state_dict[k].shape)\n                if shape_model != shape_checkpoint:\n                    incorrect_shapes.append((k, shape_checkpoint, shape_model))\n                    checkpoint_state_dict.pop(k)\n\n        incompatible = self.model.load_state_dict(checkpoint_state_dict, strict=False)\n        return _IncompatibleKeys(\n            missing_keys=incompatible.missing_keys,\n            unexpected_keys=incompatible.unexpected_keys,\n            incorrect_shapes=incorrect_shapes,\n        )\n\n    def _log_incompatible_keys(self, incompatible: _IncompatibleKeys) -> None:\n        \"\"\"\n        Log information about the incompatible keys returned by ``_load_model``.\n        \"\"\"\n        for k, shape_checkpoint, shape_model in incompatible.incorrect_shapes:\n            self.logger.warning(\n                \"Skip loading parameter '{}' to the model due to incompatible \"\n                \"shapes: {} in the checkpoint but {} in the \"\n                \"model! You might want to double check if this is expected.\".format(\n                    k, shape_checkpoint, shape_model\n                )\n            )\n        if incompatible.missing_keys:\n            missing_keys = _filter_reused_missing_keys(\n                self.model, incompatible.missing_keys\n            )\n            if missing_keys:\n                self.logger.info(get_missing_parameters_message(missing_keys))\n        if incompatible.unexpected_keys:\n            self.logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n\n    def _convert_ndarray_to_tensor(self, state_dict: dict):\n        \"\"\"\n        In-place convert all numpy arrays in the state_dict to torch tensor.\n\n        Args:\n            state_dict (dict): a state-dict to be loaded to the model.\n        \"\"\"\n        # model could be an OrderedDict with _metadata attribute\n        # (as returned by Pytorch's state_dict()). We should preserve these\n        # properties.\n        for k in list(state_dict.keys()):\n            v = state_dict[k]\n            if not isinstance(v, np.ndarray) and not isinstance(\n                    v, torch.Tensor\n            ):\n                raise ValueError(\n                    \"Unsupported type found in checkpoint! {}: {}\".format(\n                        k, type(v)\n                    )\n                )\n            if not isinstance(v, torch.Tensor):\n                state_dict[k] = torch.from_numpy(v)\n\n\nclass PeriodicCheckpointer:\n    \"\"\"\n    Save checkpoints periodically. When `.step(iteration)` is called, it will\n    execute `checkpointer.save` on the given checkpointer, if iteration is a\n    multiple of period or if `max_iter` is reached.\n    \"\"\"\n\n    def __init__(self, checkpointer: Any, period: int, max_epoch: int = None):\n        \"\"\"\n        Args:\n            checkpointer (Any): the checkpointer object used to save\n            checkpoints.\n            period (int): the period to save checkpoint.\n            max_epoch (int): maximum number of epochs. When it is reached,\n                a checkpoint named \"model_final\" will be saved.\n        \"\"\"\n        self.checkpointer = checkpointer\n        self.period = int(period)\n        self.max_epoch = max_epoch\n        self.best_metric = -1\n\n    def step(self, epoch: int, **kwargs: Any):\n        \"\"\"\n        Perform the appropriate action at the given iteration.\n\n        Args:\n            epoch (int): the current epoch, ranged in [0, max_epoch-1].\n            kwargs (Any): extra data to save, same as in\n                :meth:`Checkpointer.save`.\n        \"\"\"\n        epoch = int(epoch)\n        additional_state = {\"epoch\": epoch}\n        additional_state.update(kwargs)\n        if (epoch + 1) % self.period == 0 and epoch < self.max_epoch - 1:\n            if additional_state[\"metric\"] > self.best_metric:\n                self.checkpointer.save(\n                    \"model_best\", **additional_state\n                )\n                self.best_metric = additional_state[\"metric\"]\n            # Put it behind best model save to make last checkpoint valid\n            self.checkpointer.save(\n                \"model_{:04d}\".format(epoch), **additional_state\n            )\n        if epoch >= self.max_epoch - 1:\n            if additional_state[\"metric\"] > self.best_metric:\n                self.checkpointer.save(\n                    \"model_best\", **additional_state\n                )\n            self.checkpointer.save(\"model_final\", **additional_state)\n\n    def save(self, name: str, **kwargs: Any):\n        \"\"\"\n        Same argument as :meth:`Checkpointer.save`.\n        Use this method to manually save checkpoints outside the schedule.\n\n        Args:\n            name (str): file name.\n            kwargs (Any): extra data to save, same as in\n                :meth:`Checkpointer.save`.\n        \"\"\"\n        self.checkpointer.save(name, **kwargs)\n\n\ndef _filter_reused_missing_keys(model: nn.Module, keys: List[str]) -> List[str]:\n    \"\"\"\n    Filter \"missing keys\" to not include keys that have been loaded with another name.\n    \"\"\"\n    keyset = set(keys)\n    param_to_names = defaultdict(set)  # param -> names that points to it\n    for module_prefix, module in _named_modules_with_dup(model):\n        for name, param in list(module.named_parameters(recurse=False)) + list(\n                module.named_buffers(recurse=False)  # pyre-ignore\n        ):\n            full_name = (module_prefix + \".\" if module_prefix else \"\") + name\n            param_to_names[param].add(full_name)\n    for names in param_to_names.values():\n        # if one name appears missing but its alias exists, then this\n        # name is not considered missing\n        if any(n in keyset for n in names) and not all(n in keyset for n in names):\n            [keyset.remove(n) for n in names if n in keyset]\n    return list(keyset)\n\n\ndef get_missing_parameters_message(keys: List[str]) -> str:\n    \"\"\"\n    Get a logging-friendly message to report parameter names (keys) that are in\n    the model but not found in a checkpoint.\n\n    Args:\n        keys (list[str]): List of keys that were not found in the checkpoint.\n    Returns:\n        str: message.\n    \"\"\"\n    groups = _group_checkpoint_keys(keys)\n    msg = \"Some model parameters or buffers are not found in the checkpoint:\\n\"\n    msg += \"\\n\".join(\n        \"  \" + colored(k + _group_to_str(v), \"blue\") for k, v in groups.items()\n    )\n    return msg\n\n\ndef get_unexpected_parameters_message(keys: List[str]) -> str:\n    \"\"\"\n    Get a logging-friendly message to report parameter names (keys) that are in\n    the checkpoint but not found in the model.\n\n    Args:\n        keys (list[str]): List of keys that were not found in the model.\n    Returns:\n        str: message.\n    \"\"\"\n    groups = _group_checkpoint_keys(keys)\n    msg = \"The checkpoint state_dict contains keys that are not used by the model:\\n\"\n    msg += \"\\n\".join(\n        \"  \" + colored(k + _group_to_str(v), \"magenta\") for k, v in groups.items()\n    )\n    return msg\n\n\ndef _strip_prefix_if_present(state_dict: Dict[str, Any], prefix: str) -> None:\n    \"\"\"\n    Strip the prefix in metadata, if any.\n\n    Args:\n        state_dict (OrderedDict): a state-dict to be loaded to the model.\n        prefix (str): prefix.\n    \"\"\"\n    keys = sorted(state_dict.keys())\n    if not all(len(key) == 0 or key.startswith(prefix) for key in keys):\n        return\n\n    for key in keys:\n        newkey = key[len(prefix):]\n        state_dict[newkey] = state_dict.pop(key)\n\n    # also strip the prefix in metadata, if any..\n    try:\n        metadata = state_dict._metadata  # pyre-ignore\n    except AttributeError:\n        pass\n    else:\n        for key in list(metadata.keys()):\n            # for the metadata dict, the key can be:\n            # '': for the DDP module, which we want to remove.\n            # 'module': for the actual model.\n            # 'module.xx.xx': for the rest.\n\n            if len(key) == 0:\n                continue\n            newkey = key[len(prefix):]\n            metadata[newkey] = metadata.pop(key)\n\n\ndef _group_checkpoint_keys(keys: List[str]) -> Dict[str, List[str]]:\n    \"\"\"\n    Group keys based on common prefixes. A prefix is the string up to the final\n    \".\" in each key.\n\n    Args:\n        keys (list[str]): list of parameter names, i.e. keys in the model\n            checkpoint dict.\n    Returns:\n        dict[list]: keys with common prefixes are grouped into lists.\n    \"\"\"\n    groups = defaultdict(list)\n    for key in keys:\n        pos = key.rfind(\".\")\n        if pos >= 0:\n            head, tail = key[:pos], [key[pos + 1:]]\n        else:\n            head, tail = key, []\n        groups[head].extend(tail)\n    return groups\n\n\ndef _group_to_str(group: List[str]) -> str:\n    \"\"\"\n    Format a group of parameter name suffixes into a loggable string.\n\n    Args:\n        group (list[str]): list of parameter name suffixes.\n    Returns:\n        str: formated string.\n    \"\"\"\n    if len(group) == 0:\n        return \"\"\n\n    if len(group) == 1:\n        return \".\" + group[0]\n\n    return \".{\" + \", \".join(group) + \"}\"\n\n\ndef _named_modules_with_dup(\n        model: nn.Module, prefix: str = \"\"\n) -> Iterable[Tuple[str, nn.Module]]:\n    \"\"\"\n    The same as `model.named_modules()`, except that it includes\n    duplicated modules that have more than one name.\n    \"\"\"\n    yield prefix, model\n    for name, module in model._modules.items():  # pyre-ignore\n        if module is None:\n            continue\n        submodule_prefix = prefix + (\".\" if prefix else \"\") + name\n        yield from _named_modules_with_dup(module, submodule_prefix)\n"
  },
  {
    "path": "fast_reid/fastreid/utils/collect_env.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# based on\n# https://github.com/facebookresearch/detectron2/blob/master/detectron2/utils/collect_env.py\nimport importlib\nimport os\nimport re\nimport subprocess\nimport sys\nfrom collections import defaultdict\n\nimport PIL\nimport numpy as np\nimport torch\nimport torchvision\nfrom tabulate import tabulate\n\n__all__ = [\"collect_env_info\"]\n\n\ndef collect_torch_env():\n    try:\n        import torch.__config__\n\n        return torch.__config__.show()\n    except ImportError:\n        # compatible with older versions of pytorch\n        from torch.utils.collect_env import get_pretty_env_info\n\n        return get_pretty_env_info()\n\n\ndef get_env_module():\n    var_name = \"FASTREID_ENV_MODULE\"\n    return var_name, os.environ.get(var_name, \"<not set>\")\n\n\ndef detect_compute_compatibility(CUDA_HOME, so_file):\n    try:\n        cuobjdump = os.path.join(CUDA_HOME, \"bin\", \"cuobjdump\")\n        if os.path.isfile(cuobjdump):\n            output = subprocess.check_output(\n                \"'{}' --list-elf '{}'\".format(cuobjdump, so_file), shell=True\n            )\n            output = output.decode(\"utf-8\").strip().split(\"\\n\")\n            sm = []\n            for line in output:\n                line = re.findall(r\"\\.sm_[0-9]*\\.\", line)[0]\n                sm.append(line.strip(\".\"))\n            sm = sorted(set(sm))\n            return \", \".join(sm)\n        else:\n            return so_file + \"; cannot find cuobjdump\"\n    except Exception:\n        # unhandled failure\n        return so_file\n\n\ndef collect_env_info():\n    has_gpu = torch.cuda.is_available()  # true for both CUDA & ROCM\n    torch_version = torch.__version__\n\n    # NOTE: the use of CUDA_HOME and ROCM_HOME requires the CUDA/ROCM build deps, though in\n    # theory detectron2 should be made runnable with only the corresponding runtimes\n    from torch.utils.cpp_extension import CUDA_HOME\n\n    has_rocm = False\n    if tuple(map(int, torch_version.split(\".\")[:2])) >= (1, 5):\n        from torch.utils.cpp_extension import ROCM_HOME\n\n        if (getattr(torch.version, \"hip\", None) is not None) and (ROCM_HOME is not None):\n            has_rocm = True\n    has_cuda = has_gpu and (not has_rocm)\n\n    data = []\n    data.append((\"sys.platform\", sys.platform))\n    data.append((\"Python\", sys.version.replace(\"\\n\", \"\")))\n    data.append((\"numpy\", np.__version__))\n\n    try:\n        import fastreid  # noqa\n\n        data.append(\n            (\"fastreid\", fastreid.__version__ + \" @\" + os.path.dirname(fastreid.__file__))\n        )\n    except ImportError:\n        data.append((\"fastreid\", \"failed to import\"))\n\n    data.append(get_env_module())\n    data.append((\"PyTorch\", torch_version + \" @\" + os.path.dirname(torch.__file__)))\n    data.append((\"PyTorch debug build\", torch.version.debug))\n\n    data.append((\"GPU available\", has_gpu))\n    if has_gpu:\n        devices = defaultdict(list)\n        for k in range(torch.cuda.device_count()):\n            devices[torch.cuda.get_device_name(k)].append(str(k))\n        for name, devids in devices.items():\n            data.append((\"GPU \" + \",\".join(devids), name))\n\n        if has_rocm:\n            data.append((\"ROCM_HOME\", str(ROCM_HOME)))\n        else:\n            data.append((\"CUDA_HOME\", str(CUDA_HOME)))\n\n            cuda_arch_list = os.environ.get(\"TORCH_CUDA_ARCH_LIST\", None)\n            if cuda_arch_list:\n                data.append((\"TORCH_CUDA_ARCH_LIST\", cuda_arch_list))\n    data.append((\"Pillow\", PIL.__version__))\n\n    try:\n        data.append(\n            (\n                \"torchvision\",\n                str(torchvision.__version__) + \" @\" + os.path.dirname(torchvision.__file__),\n            )\n        )\n        if has_cuda:\n            try:\n                torchvision_C = importlib.util.find_spec(\"torchvision._C\").origin\n                msg = detect_compute_compatibility(CUDA_HOME, torchvision_C)\n                data.append((\"torchvision arch flags\", msg))\n            except ImportError:\n                data.append((\"torchvision._C\", \"failed to find\"))\n    except AttributeError:\n        data.append((\"torchvision\", \"unknown\"))\n\n    try:\n        import fvcore\n\n        data.append((\"fvcore\", fvcore.__version__))\n    except ImportError:\n        pass\n\n    try:\n        import cv2\n\n        data.append((\"cv2\", cv2.__version__))\n    except ImportError:\n        pass\n    env_str = tabulate(data) + \"\\n\"\n    env_str += collect_torch_env()\n    return env_str\n\n\nif __name__ == \"__main__\":\n    try:\n        import detectron2  # noqa\n    except ImportError:\n        print(collect_env_info())\n    else:\n        from fast_reid.fastreid.utils.collect_env import collect_env_info\n\n        print(collect_env_info())\n"
  },
  {
    "path": "fast_reid/fastreid/utils/comm.py",
    "content": "\"\"\"\nThis file contains primitives for multi-gpu communication.\nThis is useful when doing distributed training.\n\"\"\"\n\nimport functools\nimport logging\nimport numpy as np\nimport pickle\nimport torch\nimport torch.distributed as dist\n\n_LOCAL_PROCESS_GROUP = None\n\"\"\"\nA torch process group which only includes processes that on the same machine as the current process.\nThis variable is set when processes are spawned by `launch()` in \"engine/launch.py\".\n\"\"\"\n\n\ndef get_world_size() -> int:\n    if not dist.is_available():\n        return 1\n    if not dist.is_initialized():\n        return 1\n    return dist.get_world_size()\n\n\ndef get_rank() -> int:\n    if not dist.is_available():\n        return 0\n    if not dist.is_initialized():\n        return 0\n    return dist.get_rank()\n\n\ndef get_local_rank() -> int:\n    \"\"\"\n    Returns:\n        The rank of the current process within the local (per-machine) process group.\n    \"\"\"\n    if not dist.is_available():\n        return 0\n    if not dist.is_initialized():\n        return 0\n    assert _LOCAL_PROCESS_GROUP is not None\n    return dist.get_rank(group=_LOCAL_PROCESS_GROUP)\n\n\ndef get_local_size() -> int:\n    \"\"\"\n    Returns:\n        The size of the per-machine process group,\n        i.e. the number of processes per machine.\n    \"\"\"\n    if not dist.is_available():\n        return 1\n    if not dist.is_initialized():\n        return 1\n    return dist.get_world_size(group=_LOCAL_PROCESS_GROUP)\n\n\ndef is_main_process() -> bool:\n    return get_rank() == 0\n\n\ndef synchronize():\n    \"\"\"\n    Helper function to synchronize (barrier) among all processes when\n    using distributed training\n    \"\"\"\n    if not dist.is_available():\n        return\n    if not dist.is_initialized():\n        return\n    world_size = dist.get_world_size()\n    if world_size == 1:\n        return\n    dist.barrier()\n\n\n@functools.lru_cache()\ndef _get_global_gloo_group():\n    \"\"\"\n    Return a process group based on gloo backend, containing all the ranks\n    The result is cached.\n    \"\"\"\n    if dist.get_backend() == \"nccl\":\n        return dist.new_group(backend=\"gloo\")\n    else:\n        return dist.group.WORLD\n\n\ndef _serialize_to_tensor(data, group):\n    backend = dist.get_backend(group)\n    assert backend in [\"gloo\", \"nccl\"]\n    device = torch.device(\"cpu\" if backend == \"gloo\" else \"cuda\")\n\n    buffer = pickle.dumps(data)\n    if len(buffer) > 1024 ** 3:\n        logger = logging.getLogger(__name__)\n        logger.warning(\n            \"Rank {} trying to all-gather {:.2f} GB of data on device {}\".format(\n                get_rank(), len(buffer) / (1024 ** 3), device\n            )\n        )\n    storage = torch.ByteStorage.from_buffer(buffer)\n    tensor = torch.ByteTensor(storage).to(device=device)\n    return tensor\n\n\ndef _pad_to_largest_tensor(tensor, group):\n    \"\"\"\n    Returns:\n        list[int]: size of the tensor, on each rank\n        Tensor: padded tensor that has the max size\n    \"\"\"\n    world_size = dist.get_world_size(group=group)\n    assert (\n            world_size >= 1\n    ), \"comm.gather/all_gather must be called from ranks within the given group!\"\n    local_size = torch.tensor([tensor.numel()], dtype=torch.int64, device=tensor.device)\n    size_list = [\n        torch.zeros([1], dtype=torch.int64, device=tensor.device) for _ in range(world_size)\n    ]\n    dist.all_gather(size_list, local_size, group=group)\n    size_list = [int(size.item()) for size in size_list]\n\n    max_size = max(size_list)\n\n    # we pad the tensor because torch all_gather does not support\n    # gathering tensors of different shapes\n    if local_size != max_size:\n        padding = torch.zeros((max_size - local_size,), dtype=torch.uint8, device=tensor.device)\n        tensor = torch.cat((tensor, padding), dim=0)\n    return size_list, tensor\n\n\ndef all_gather(data, group=None):\n    \"\"\"\n    Run all_gather on arbitrary picklable data (not necessarily tensors).\n    Args:\n        data: any picklable object\n        group: a torch process group. By default, will use a group which\n            contains all ranks on gloo backend.\n    Returns:\n        list[data]: list of data gathered from each rank\n    \"\"\"\n    if get_world_size() == 1:\n        return [data]\n    if group is None:\n        group = _get_global_gloo_group()\n    if dist.get_world_size(group) == 1:\n        return [data]\n\n    tensor = _serialize_to_tensor(data, group)\n\n    size_list, tensor = _pad_to_largest_tensor(tensor, group)\n    max_size = max(size_list)\n\n    # receiving Tensor from all ranks\n    tensor_list = [\n        torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list\n    ]\n    dist.all_gather(tensor_list, tensor, group=group)\n\n    data_list = []\n    for size, tensor in zip(size_list, tensor_list):\n        buffer = tensor.cpu().numpy().tobytes()[:size]\n        data_list.append(pickle.loads(buffer))\n\n    return data_list\n\n\ndef gather(data, dst=0, group=None):\n    \"\"\"\n    Run gather on arbitrary picklable data (not necessarily tensors).\n    Args:\n        data: any picklable object\n        dst (int): destination rank\n        group: a torch process group. By default, will use a group which\n            contains all ranks on gloo backend.\n    Returns:\n        list[data]: on dst, a list of data gathered from each rank. Otherwise,\n            an empty list.\n    \"\"\"\n    if get_world_size() == 1:\n        return [data]\n    if group is None:\n        group = _get_global_gloo_group()\n    if dist.get_world_size(group=group) == 1:\n        return [data]\n    rank = dist.get_rank(group=group)\n\n    tensor = _serialize_to_tensor(data, group)\n    size_list, tensor = _pad_to_largest_tensor(tensor, group)\n\n    # receiving Tensor from all ranks\n    if rank == dst:\n        max_size = max(size_list)\n        tensor_list = [\n            torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list\n        ]\n        dist.gather(tensor, tensor_list, dst=dst, group=group)\n\n        data_list = []\n        for size, tensor in zip(size_list, tensor_list):\n            buffer = tensor.cpu().numpy().tobytes()[:size]\n            data_list.append(pickle.loads(buffer))\n        return data_list\n    else:\n        dist.gather(tensor, [], dst=dst, group=group)\n        return []\n\n\ndef shared_random_seed():\n    \"\"\"\n    Returns:\n        int: a random number that is the same across all workers.\n            If workers need a shared RNG, they can use this shared seed to\n            create one.\n    All workers must call this function, otherwise it will deadlock.\n    \"\"\"\n    ints = np.random.randint(2 ** 31)\n    all_ints = all_gather(ints)\n    return all_ints[0]\n\n\ndef reduce_dict(input_dict, average=True):\n    \"\"\"\n    Reduce the values in the dictionary from all processes so that process with rank\n    0 has the reduced results.\n    Args:\n        input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor.\n        average (bool): whether to do average or sum\n    Returns:\n        a dict with the same keys as input_dict, after reduction.\n    \"\"\"\n    world_size = get_world_size()\n    if world_size < 2:\n        return input_dict\n    with torch.no_grad():\n        names = []\n        values = []\n        # sort the keys so that they are consistent across processes\n        for k in sorted(input_dict.keys()):\n            names.append(k)\n            values.append(input_dict[k])\n        values = torch.stack(values, dim=0)\n        dist.reduce(values, dst=0)\n        if dist.get_rank() == 0 and average:\n            # only main process gets accumulated, so only divide by\n            # world_size in this case\n            values /= world_size\n        reduced_dict = {k: v for k, v in zip(names, values)}\n    return reduced_dict\n"
  },
  {
    "path": "fast_reid/fastreid/utils/compute_dist.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# Modified from: https://github.com/open-mmlab/OpenUnReID/blob/66bb2ae0b00575b80fbe8915f4d4f4739cc21206/openunreid/core/utils/compute_dist.py\n\n\nimport faiss\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\nfrom .faiss_utils import (\n    index_init_cpu,\n    index_init_gpu,\n    search_index_pytorch,\n    search_raw_array_pytorch,\n)\n\n__all__ = [\n    \"build_dist\",\n    \"compute_jaccard_distance\",\n    \"compute_euclidean_distance\",\n    \"compute_cosine_distance\",\n]\n\n\n@torch.no_grad()\ndef build_dist(feat_1: torch.Tensor, feat_2: torch.Tensor, metric: str = \"euclidean\", **kwargs) -> np.ndarray:\n    r\"\"\"Compute distance between two feature embeddings.\n\n    Args:\n        feat_1 (torch.Tensor): 2-D feature with batch dimension.\n        feat_2 (torch.Tensor): 2-D feature with batch dimension.\n        metric:\n\n    Returns:\n        numpy.ndarray: distance matrix.\n    \"\"\"\n    assert metric in [\"cosine\", \"euclidean\", \"jaccard\"], \"Expected metrics are cosine, euclidean and jaccard, \" \\\n                                                         \"but got {}\".format(metric)\n\n    if metric == \"euclidean\":\n        return compute_euclidean_distance(feat_1, feat_2)\n\n    elif metric == \"cosine\":\n        return compute_cosine_distance(feat_1, feat_2)\n\n    elif metric == \"jaccard\":\n        feat = torch.cat((feat_1, feat_2), dim=0)\n        dist = compute_jaccard_distance(feat, k1=kwargs[\"k1\"], k2=kwargs[\"k2\"], search_option=0)\n        return dist[: feat_1.size(0), feat_1.size(0):]\n\n\ndef k_reciprocal_neigh(initial_rank, i, k1):\n    forward_k_neigh_index = initial_rank[i, : k1 + 1]\n    backward_k_neigh_index = initial_rank[forward_k_neigh_index, : k1 + 1]\n    fi = np.where(backward_k_neigh_index == i)[0]\n    return forward_k_neigh_index[fi]\n\n\n@torch.no_grad()\ndef compute_jaccard_distance(features, k1=20, k2=6, search_option=0, fp16=False):\n    if search_option < 3:\n        # torch.cuda.empty_cache()\n        features = features.cuda()\n\n    ngpus = faiss.get_num_gpus()\n    N = features.size(0)\n    mat_type = np.float16 if fp16 else np.float32\n\n    if search_option == 0:\n        # GPU + PyTorch CUDA Tensors (1)\n        res = faiss.StandardGpuResources()\n        res.setDefaultNullStreamAllDevices()\n        _, initial_rank = search_raw_array_pytorch(res, features, features, k1)\n        initial_rank = initial_rank.cpu().numpy()\n    elif search_option == 1:\n        # GPU + PyTorch CUDA Tensors (2)\n        res = faiss.StandardGpuResources()\n        index = faiss.GpuIndexFlatL2(res, features.size(-1))\n        index.add(features.cpu().numpy())\n        _, initial_rank = search_index_pytorch(index, features, k1)\n        res.syncDefaultStreamCurrentDevice()\n        initial_rank = initial_rank.cpu().numpy()\n    elif search_option == 2:\n        # GPU\n        index = index_init_gpu(ngpus, features.size(-1))\n        index.add(features.cpu().numpy())\n        _, initial_rank = index.search(features.cpu().numpy(), k1)\n    else:\n        # CPU\n        index = index_init_cpu(features.size(-1))\n        index.add(features.cpu().numpy())\n        _, initial_rank = index.search(features.cpu().numpy(), k1)\n\n    nn_k1 = []\n    nn_k1_half = []\n    for i in range(N):\n        nn_k1.append(k_reciprocal_neigh(initial_rank, i, k1))\n        nn_k1_half.append(k_reciprocal_neigh(initial_rank, i, int(np.around(k1 / 2))))\n\n    V = np.zeros((N, N), dtype=mat_type)\n    for i in range(N):\n        k_reciprocal_index = nn_k1[i]\n        k_reciprocal_expansion_index = k_reciprocal_index\n        for candidate in k_reciprocal_index:\n            candidate_k_reciprocal_index = nn_k1_half[candidate]\n            if len(\n                    np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)\n            ) > 2 / 3 * len(candidate_k_reciprocal_index):\n                k_reciprocal_expansion_index = np.append(\n                    k_reciprocal_expansion_index, candidate_k_reciprocal_index\n                )\n\n        k_reciprocal_expansion_index = np.unique(\n            k_reciprocal_expansion_index\n        )  # element-wise unique\n\n        x = features[i].unsqueeze(0).contiguous()\n        y = features[k_reciprocal_expansion_index]\n        m, n = x.size(0), y.size(0)\n        dist = (\n                torch.pow(x, 2).sum(dim=1, keepdim=True).expand(m, n)\n                + torch.pow(y, 2).sum(dim=1, keepdim=True).expand(n, m).t()\n        )\n        dist.addmm_(x, y.t(), beta=1, alpha=-2)\n\n        if fp16:\n            V[i, k_reciprocal_expansion_index] = (\n                F.softmax(-dist, dim=1).view(-1).cpu().numpy().astype(mat_type)\n            )\n        else:\n            V[i, k_reciprocal_expansion_index] = (\n                F.softmax(-dist, dim=1).view(-1).cpu().numpy()\n            )\n\n    del nn_k1, nn_k1_half, x, y\n    features = features.cpu()\n\n    if k2 != 1:\n        V_qe = np.zeros_like(V, dtype=mat_type)\n        for i in range(N):\n            V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)\n        V = V_qe\n        del V_qe\n\n    del initial_rank\n\n    invIndex = []\n    for i in range(N):\n        invIndex.append(np.where(V[:, i] != 0)[0])  # len(invIndex)=all_num\n\n    jaccard_dist = np.zeros((N, N), dtype=mat_type)\n    for i in range(N):\n        temp_min = np.zeros((1, N), dtype=mat_type)\n        indNonZero = np.where(V[i, :] != 0)[0]\n        indImages = [invIndex[ind] for ind in indNonZero]\n        for j in range(len(indNonZero)):\n            temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(\n                V[i, indNonZero[j]], V[indImages[j], indNonZero[j]]\n            )\n\n        jaccard_dist[i] = 1 - temp_min / (2 - temp_min)\n\n    del invIndex, V\n\n    pos_bool = jaccard_dist < 0\n    jaccard_dist[pos_bool] = 0.0\n\n    return jaccard_dist\n\n\n@torch.no_grad()\ndef compute_euclidean_distance(features, others):\n    m, n = features.size(0), others.size(0)\n    dist_m = (\n            torch.pow(features, 2).sum(dim=1, keepdim=True).expand(m, n)\n            + torch.pow(others, 2).sum(dim=1, keepdim=True).expand(n, m).t()\n    )\n    dist_m.addmm_(1, -2, features, others.t())\n\n    return dist_m.cpu().numpy()\n\n\n@torch.no_grad()\ndef compute_cosine_distance(features, others):\n    \"\"\"Computes cosine distance.\n    Args:\n        features (torch.Tensor): 2-D feature matrix.\n        others (torch.Tensor): 2-D feature matrix.\n    Returns:\n        torch.Tensor: distance matrix.\n    \"\"\"\n    features = F.normalize(features, p=2, dim=1)\n    others = F.normalize(others, p=2, dim=1)\n    dist_m = 1 - torch.mm(features, others.t())\n    return dist_m.cpu().numpy()\n"
  },
  {
    "path": "fast_reid/fastreid/utils/env.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport importlib\nimport importlib.util\nimport logging\nimport numpy as np\nimport os\nimport random\nimport sys\nfrom datetime import datetime\nimport torch\n\n__all__ = [\"seed_all_rng\"]\n\n\nTORCH_VERSION = tuple(int(x) for x in torch.__version__.split(\".\")[:2])\n\"\"\"\nPyTorch version as a tuple of 2 ints. Useful for comparison.\n\"\"\"\n\n\ndef seed_all_rng(seed=None):\n    \"\"\"\n    Set the random seed for the RNG in torch, numpy and python.\n    Args:\n        seed (int): if None, will use a strong random seed.\n    \"\"\"\n    if seed is None:\n        seed = (\n            os.getpid()\n            + int(datetime.now().strftime(\"%S%f\"))\n            + int.from_bytes(os.urandom(2), \"big\")\n        )\n        logger = logging.getLogger(__name__)\n        logger.info(\"Using a generated random seed {}\".format(seed))\n    np.random.seed(seed)\n    torch.set_rng_state(torch.manual_seed(seed).get_state())\n    random.seed(seed)\n\n\n# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path\ndef _import_file(module_name, file_path, make_importable=False):\n    spec = importlib.util.spec_from_file_location(module_name, file_path)\n    module = importlib.util.module_from_spec(spec)\n    spec.loader.exec_module(module)\n    if make_importable:\n        sys.modules[module_name] = module\n    return module\n\n\ndef _configure_libraries():\n    \"\"\"\n    Configurations for some libraries.\n    \"\"\"\n    # An environment option to disable `import cv2` globally,\n    # in case it leads to negative performance impact\n    disable_cv2 = int(os.environ.get(\"DETECTRON2_DISABLE_CV2\", False))\n    if disable_cv2:\n        sys.modules[\"cv2\"] = None\n    else:\n        # Disable opencl in opencv since its interaction with cuda often has negative effects\n        # This envvar is supported after OpenCV 3.4.0\n        os.environ[\"OPENCV_OPENCL_RUNTIME\"] = \"disabled\"\n        try:\n            import cv2\n\n            if int(cv2.__version__.split(\".\")[0]) >= 3:\n                cv2.ocl.setUseOpenCL(False)\n        except ImportError:\n            pass\n\n    def get_version(module, digit=2):\n        return tuple(map(int, module.__version__.split(\".\")[:digit]))\n\n    # fmt: off\n    assert get_version(torch) >= (1, 4), \"Requires torch>=1.4\"\n    import yaml\n    assert get_version(yaml) >= (5, 1), \"Requires pyyaml>=5.1\"\n    # fmt: on\n\n\n_ENV_SETUP_DONE = False\n\n\ndef setup_environment():\n    \"\"\"Perform environment setup work. The default setup is a no-op, but this\n    function allows the user to specify a Python source file or a module in\n    the $FASTREID_ENV_MODULE environment variable, that performs\n    custom setup work that may be necessary to their computing environment.\n    \"\"\"\n    global _ENV_SETUP_DONE\n    if _ENV_SETUP_DONE:\n        return\n    _ENV_SETUP_DONE = True\n\n    _configure_libraries()\n\n    custom_module_path = os.environ.get(\"FASTREID_ENV_MODULE\")\n\n    if custom_module_path:\n        setup_custom_environment(custom_module_path)\n    else:\n        # The default setup is a no-op\n        pass\n\n\ndef setup_custom_environment(custom_module):\n    \"\"\"\n    Load custom environment setup by importing a Python source file or a\n    module, and run the setup function.\n    \"\"\"\n    if custom_module.endswith(\".py\"):\n        module = _import_file(\"fastreid.utils.env.custom_module\", custom_module)\n    else:\n        module = importlib.import_module(custom_module)\n    assert hasattr(module, \"setup_environment\") and callable(module.setup_environment), (\n        \"Custom environment module defined in {} does not have the \"\n        \"required callable attribute 'setup_environment'.\"\n    ).format(custom_module)\n    module.setup_environment()"
  },
  {
    "path": "fast_reid/fastreid/utils/events.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates.\nimport datetime\nimport json\nimport logging\nimport os\nimport time\nfrom collections import defaultdict\nfrom contextlib import contextmanager\nimport torch\nfrom .file_io import PathManager\nfrom .history_buffer import HistoryBuffer\n\n__all__ = [\n    \"get_event_storage\",\n    \"JSONWriter\",\n    \"TensorboardXWriter\",\n    \"CommonMetricPrinter\",\n    \"EventStorage\",\n]\n\n_CURRENT_STORAGE_STACK = []\n\n\ndef get_event_storage():\n    \"\"\"\n    Returns:\n        The :class:`EventStorage` object that's currently being used.\n        Throws an error if no :class:`EventStorage` is currently enabled.\n    \"\"\"\n    assert len(\n        _CURRENT_STORAGE_STACK\n    ), \"get_event_storage() has to be called inside a 'with EventStorage(...)' context!\"\n    return _CURRENT_STORAGE_STACK[-1]\n\n\nclass EventWriter:\n    \"\"\"\n    Base class for writers that obtain events from :class:`EventStorage` and process them.\n    \"\"\"\n\n    def write(self):\n        raise NotImplementedError\n\n    def close(self):\n        pass\n\n\nclass JSONWriter(EventWriter):\n    \"\"\"\n    Write scalars to a json file.\n    It saves scalars as one json per line (instead of a big json) for easy parsing.\n    Examples parsing such a json file:\n    ::\n        $ cat metrics.json | jq -s '.[0:2]'\n        [\n          {\n            \"data_time\": 0.008433341979980469,\n            \"iteration\": 19,\n            \"loss\": 1.9228371381759644,\n            \"loss_box_reg\": 0.050025828182697296,\n            \"loss_classifier\": 0.5316952466964722,\n            \"loss_mask\": 0.7236229181289673,\n            \"loss_rpn_box\": 0.0856662318110466,\n            \"loss_rpn_cls\": 0.48198649287223816,\n            \"lr\": 0.007173333333333333,\n            \"time\": 0.25401854515075684\n          },\n          {\n            \"data_time\": 0.007216215133666992,\n            \"iteration\": 39,\n            \"loss\": 1.282649278640747,\n            \"loss_box_reg\": 0.06222952902317047,\n            \"loss_classifier\": 0.30682939291000366,\n            \"loss_mask\": 0.6970193982124329,\n            \"loss_rpn_box\": 0.038663312792778015,\n            \"loss_rpn_cls\": 0.1471673548221588,\n            \"lr\": 0.007706666666666667,\n            \"time\": 0.2490077018737793\n          }\n        ]\n        $ cat metrics.json | jq '.loss_mask'\n        0.7126231789588928\n        0.689423680305481\n        0.6776131987571716\n        ...\n    \"\"\"\n\n    def __init__(self, json_file, window_size=20):\n        \"\"\"\n        Args:\n            json_file (str): path to the json file. New data will be appended if the file exists.\n            window_size (int): the window size of median smoothing for the scalars whose\n                `smoothing_hint` are True.\n        \"\"\"\n        self._file_handle = PathManager.open(json_file, \"a\")\n        self._window_size = window_size\n        self._last_write = -1\n\n    def write(self):\n        storage = get_event_storage()\n        to_save = defaultdict(dict)\n\n        for k, (v, iter) in storage.latest_with_smoothing_hint(self._window_size).items():\n            # keep scalars that have not been written\n            if iter <= self._last_write:\n                continue\n            to_save[iter][k] = v\n        if len(to_save):\n            all_iters = sorted(to_save.keys())\n            self._last_write = max(all_iters)\n\n        for itr, scalars_per_iter in to_save.items():\n            scalars_per_iter[\"iteration\"] = itr\n            self._file_handle.write(json.dumps(scalars_per_iter, sort_keys=True) + \"\\n\")\n        self._file_handle.flush()\n        try:\n            os.fsync(self._file_handle.fileno())\n        except AttributeError:\n            pass\n\n    def close(self):\n        self._file_handle.close()\n\n\nclass TensorboardXWriter(EventWriter):\n    \"\"\"\n    Write all scalars to a tensorboard file.\n    \"\"\"\n\n    def __init__(self, log_dir: str, window_size: int = 20, **kwargs):\n        \"\"\"\n        Args:\n            log_dir (str): the directory to save the output events\n            window_size (int): the scalars will be median-smoothed by this window size\n            kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`\n        \"\"\"\n        self._window_size = window_size\n        from torch.utils.tensorboard import SummaryWriter\n\n        self._writer = SummaryWriter(log_dir, **kwargs)\n        self._last_write = -1\n\n    def write(self):\n        storage = get_event_storage()\n        new_last_write = self._last_write\n        for k, (v, iter) in storage.latest_with_smoothing_hint(self._window_size).items():\n            if iter > self._last_write:\n                self._writer.add_scalar(k, v, iter)\n                new_last_write = max(new_last_write, iter)\n        self._last_write = new_last_write\n\n        # storage.put_{image,histogram} is only meant to be used by\n        # tensorboard writer. So we access its internal fields directly from here.\n        if len(storage._vis_data) >= 1:\n            for img_name, img, step_num in storage._vis_data:\n                self._writer.add_image(img_name, img, step_num)\n            # Storage stores all image data and rely on this writer to clear them.\n            # As a result it assumes only one writer will use its image data.\n            # An alternative design is to let storage store limited recent\n            # data (e.g. only the most recent image) that all writers can access.\n            # In that case a writer may not see all image data if its period is long.\n            storage.clear_images()\n\n        if len(storage._histograms) >= 1:\n            for params in storage._histograms:\n                self._writer.add_histogram_raw(**params)\n            storage.clear_histograms()\n\n    def close(self):\n        if hasattr(self, \"_writer\"):  # doesn't exist when the code fails at import\n            self._writer.close()\n\n\nclass CommonMetricPrinter(EventWriter):\n    \"\"\"\n    Print **common** metrics to the terminal, including\n    iteration time, ETA, memory, all losses, and the learning rate.\n    It also applies smoothing using a window of 20 elements.\n    It's meant to print common metrics in common ways.\n    To print something in more customized ways, please implement a similar printer by yourself.\n    \"\"\"\n\n    def __init__(self, max_iter):\n        \"\"\"\n        Args:\n            max_iter (int): the maximum number of iterations to train.\n                Used to compute ETA.\n        \"\"\"\n        self.logger = logging.getLogger(__name__)\n        self._max_iter = max_iter\n        self._last_write = None\n\n    def write(self):\n        storage = get_event_storage()\n        iteration = storage.iter\n        epoch = storage.epoch\n        if iteration == self._max_iter:\n            # This hook only reports training progress (loss, ETA, etc) but not other data,\n            # therefore do not write anything after training succeeds, even if this method\n            # is called.\n            return\n\n        try:\n            data_time = storage.history(\"data_time\").avg(20)\n        except KeyError:\n            # they may not exist in the first few iterations (due to warmup)\n            # or when SimpleTrainer is not used\n            data_time = None\n\n        eta_string = None\n        try:\n            iter_time = storage.history(\"time\").global_avg()\n            eta_seconds = storage.history(\"time\").median(1000) * (self._max_iter - iteration - 1)\n            storage.put_scalar(\"eta_seconds\", eta_seconds, smoothing_hint=False)\n            eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n        except KeyError:\n            iter_time = None\n            # estimate eta on our own - more noisy\n            if self._last_write is not None:\n                estimate_iter_time = (time.perf_counter() - self._last_write[1]) / (\n                    iteration - self._last_write[0]\n                )\n                eta_seconds = estimate_iter_time * (self._max_iter - iteration - 1)\n                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n            self._last_write = (iteration, time.perf_counter())\n\n        try:\n            lr = \"{:.2e}\".format(storage.history(\"lr\").latest())\n        except KeyError:\n            lr = \"N/A\"\n\n        if torch.cuda.is_available():\n            max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0\n        else:\n            max_mem_mb = None\n\n        # NOTE: max_mem is parsed by grep in \"dev/parse_results.sh\"\n        print(          # [hgx0911] self.logger.info ==> print\n            \" {eta}epoch/iter: {epoch}/{iter}  {losses}  {time}{data_time}lr: {lr}  {memory}\".format(\n                eta=f\"eta: {eta_string}  \" if eta_string else \"\",\n                epoch=epoch,\n                iter=iteration,\n                losses=\"  \".join(\n                    [\n                        \"{}: {:.4g}\".format(k, v.median(200))\n                        for k, v in storage.histories().items()\n                        if \"loss\" in k\n                    ]\n                ),\n                time=\"time: {:.4f}  \".format(iter_time) if iter_time is not None else \"\",\n                data_time=\"data_time: {:.4f}  \".format(data_time) if data_time is not None else \"\",\n                lr=lr,\n                memory=\"max_mem: {:.0f}M\".format(max_mem_mb) if max_mem_mb is not None else \"\",\n            )\n        )\n\n\nclass EventStorage:\n    \"\"\"\n    The user-facing class that provides metric storage functionalities.\n    In the future we may add support for storing / logging other types of data if needed.\n    \"\"\"\n\n    def __init__(self, start_iter=0):\n        \"\"\"\n        Args:\n            start_iter (int): the iteration number to start with\n        \"\"\"\n        self._history = defaultdict(HistoryBuffer)\n        self._smoothing_hints = {}\n        self._latest_scalars = {}\n        self._iter = start_iter\n        self._current_prefix = \"\"\n        self._vis_data = []\n        self._histograms = []\n\n    def put_image(self, img_name, img_tensor):\n        \"\"\"\n        Add an `img_tensor` associated with `img_name`, to be shown on\n        tensorboard.\n        Args:\n            img_name (str): The name of the image to put into tensorboard.\n            img_tensor (torch.Tensor or numpy.array): An `uint8` or `float`\n                Tensor of shape `[channel, height, width]` where `channel` is\n                3. The image format should be RGB. The elements in img_tensor\n                can either have values in [0, 1] (float32) or [0, 255] (uint8).\n                The `img_tensor` will be visualized in tensorboard.\n        \"\"\"\n        self._vis_data.append((img_name, img_tensor, self._iter))\n\n    def put_scalar(self, name, value, smoothing_hint=True):\n        \"\"\"\n        Add a scalar `value` to the `HistoryBuffer` associated with `name`.\n        Args:\n            smoothing_hint (bool): a 'hint' on whether this scalar is noisy and should be\n                smoothed when logged. The hint will be accessible through\n                :meth:`EventStorage.smoothing_hints`.  A writer may ignore the hint\n                and apply custom smoothing rule.\n                It defaults to True because most scalars we save need to be smoothed to\n                provide any useful signal.\n        \"\"\"\n        name = self._current_prefix + name\n        history = self._history[name]\n        value = float(value)\n        history.update(value, self._iter)\n        self._latest_scalars[name] = (value, self._iter)\n\n        existing_hint = self._smoothing_hints.get(name)\n        if existing_hint is not None:\n            assert (\n                existing_hint == smoothing_hint\n            ), \"Scalar {} was put with a different smoothing_hint!\".format(name)\n        else:\n            self._smoothing_hints[name] = smoothing_hint\n\n    def put_scalars(self, *, smoothing_hint=True, **kwargs):\n        \"\"\"\n        Put multiple scalars from keyword arguments.\n        Examples:\n            storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothing_hint=True)\n        \"\"\"\n        for k, v in kwargs.items():\n            self.put_scalar(k, v, smoothing_hint=smoothing_hint)\n\n    def put_histogram(self, hist_name, hist_tensor, bins=1000):\n        \"\"\"\n        Create a histogram from a tensor.\n        Args:\n            hist_name (str): The name of the histogram to put into tensorboard.\n            hist_tensor (torch.Tensor): A Tensor of arbitrary shape to be converted\n                into a histogram.\n            bins (int): Number of histogram bins.\n        \"\"\"\n        ht_min, ht_max = hist_tensor.min().item(), hist_tensor.max().item()\n\n        # Create a histogram with PyTorch\n        hist_counts = torch.histc(hist_tensor, bins=bins)\n        hist_edges = torch.linspace(start=ht_min, end=ht_max, steps=bins + 1, dtype=torch.float32)\n\n        # Parameter for the add_histogram_raw function of SummaryWriter\n        hist_params = dict(\n            tag=hist_name,\n            min=ht_min,\n            max=ht_max,\n            num=len(hist_tensor),\n            sum=float(hist_tensor.sum()),\n            sum_squares=float(torch.sum(hist_tensor ** 2)),\n            bucket_limits=hist_edges[1:].tolist(),\n            bucket_counts=hist_counts.tolist(),\n            global_step=self._iter,\n        )\n        self._histograms.append(hist_params)\n\n    def history(self, name):\n        \"\"\"\n        Returns:\n            HistoryBuffer: the scalar history for name\n        \"\"\"\n        ret = self._history.get(name, None)\n        if ret is None:\n            raise KeyError(\"No history metric available for {}!\".format(name))\n        return ret\n\n    def histories(self):\n        \"\"\"\n        Returns:\n            dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars\n        \"\"\"\n        return self._history\n\n    def latest(self):\n        \"\"\"\n        Returns:\n            dict[str -> (float, int)]: mapping from the name of each scalar to the most\n                recent value and the iteration number its added.\n        \"\"\"\n        return self._latest_scalars\n\n    def latest_with_smoothing_hint(self, window_size=20):\n        \"\"\"\n        Similar to :meth:`latest`, but the returned values\n        are either the un-smoothed original latest value,\n        or a median of the given window_size,\n        depend on whether the smoothing_hint is True.\n        This provides a default behavior that other writers can use.\n        \"\"\"\n        result = {}\n        for k, (v, itr) in self._latest_scalars.items():\n            result[k] = (\n                self._history[k].median(window_size) if self._smoothing_hints[k] else v,\n                itr,\n            )\n        return result\n\n    def smoothing_hints(self):\n        \"\"\"\n        Returns:\n            dict[name -> bool]: the user-provided hint on whether the scalar\n                is noisy and needs smoothing.\n        \"\"\"\n        return self._smoothing_hints\n\n    def step(self):\n        \"\"\"\n        User should either: (1) Call this function to increment storage.iter when needed. Or\n        (2) Set `storage.iter` to the correct iteration number before each iteration.\n        The storage will then be able to associate the new data with an iteration number.\n        \"\"\"\n        self._iter += 1\n\n    @property\n    def iter(self):\n        \"\"\"\n        Returns:\n            int: The current iteration number. When used together with a trainer,\n                this is ensured to be the same as trainer.iter.\n        \"\"\"\n        return self._iter\n\n    @iter.setter\n    def iter(self, val):\n        self._iter = int(val)\n\n    @property\n    def iteration(self):\n        # for backward compatibility\n        return self._iter\n\n    def __enter__(self):\n        _CURRENT_STORAGE_STACK.append(self)\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        assert _CURRENT_STORAGE_STACK[-1] == self\n        _CURRENT_STORAGE_STACK.pop()\n\n    @contextmanager\n    def name_scope(self, name):\n        \"\"\"\n        Yields:\n            A context within which all the events added to this storage\n            will be prefixed by the name scope.\n        \"\"\"\n        old_prefix = self._current_prefix\n        self._current_prefix = name.rstrip(\"/\") + \"/\"\n        yield\n        self._current_prefix = old_prefix\n\n    def clear_images(self):\n        \"\"\"\n        Delete all the stored images for visualization. This should be called\n        after images are written to tensorboard.\n        \"\"\"\n        self._vis_data = []\n\n    def clear_histograms(self):\n        \"\"\"\n        Delete all the stored histograms for visualization.\n        This should be called after histograms are written to tensorboard.\n        \"\"\"\n        self._histograms = []"
  },
  {
    "path": "fast_reid/fastreid/utils/faiss_utils.py",
    "content": "# encoding: utf-8\n# copy from: https://github.com/open-mmlab/OpenUnReID/blob/66bb2ae0b00575b80fbe8915f4d4f4739cc21206/openunreid/core/utils/faiss_utils.py\n\nimport faiss\nimport torch\n\n\ndef swig_ptr_from_FloatTensor(x):\n    assert x.is_contiguous()\n    assert x.dtype == torch.float32\n    return faiss.cast_integer_to_float_ptr(\n        x.storage().data_ptr() + x.storage_offset() * 4\n    )\n\n\ndef swig_ptr_from_LongTensor(x):\n    assert x.is_contiguous()\n    assert x.dtype == torch.int64, \"dtype=%s\" % x.dtype\n    return faiss.cast_integer_to_long_ptr(\n        x.storage().data_ptr() + x.storage_offset() * 8\n    )\n\n\ndef search_index_pytorch(index, x, k, D=None, I=None):\n    \"\"\"call the search function of an index with pytorch tensor I/O (CPU\n    and GPU supported)\"\"\"\n    assert x.is_contiguous()\n    n, d = x.size()\n    assert d == index.d\n\n    if D is None:\n        D = torch.empty((n, k), dtype=torch.float32, device=x.device)\n    else:\n        assert D.size() == (n, k)\n\n    if I is None:\n        I = torch.empty((n, k), dtype=torch.int64, device=x.device)\n    else:\n        assert I.size() == (n, k)\n    torch.cuda.synchronize()\n    xptr = swig_ptr_from_FloatTensor(x)\n    Iptr = swig_ptr_from_LongTensor(I)\n    Dptr = swig_ptr_from_FloatTensor(D)\n    index.search_c(n, xptr, k, Dptr, Iptr)\n    torch.cuda.synchronize()\n    return D, I\n\n\ndef search_raw_array_pytorch(res, xb, xq, k, D=None, I=None, metric=faiss.METRIC_L2):\n    assert xb.device == xq.device\n\n    nq, d = xq.size()\n    if xq.is_contiguous():\n        xq_row_major = True\n    elif xq.t().is_contiguous():\n        xq = xq.t()  # I initially wrote xq:t(), Lua is still haunting me :-)\n        xq_row_major = False\n    else:\n        raise TypeError(\"matrix should be row or column-major\")\n\n    xq_ptr = swig_ptr_from_FloatTensor(xq)\n\n    nb, d2 = xb.size()\n    assert d2 == d\n    if xb.is_contiguous():\n        xb_row_major = True\n    elif xb.t().is_contiguous():\n        xb = xb.t()\n        xb_row_major = False\n    else:\n        raise TypeError(\"matrix should be row or column-major\")\n    xb_ptr = swig_ptr_from_FloatTensor(xb)\n\n    if D is None:\n        D = torch.empty(nq, k, device=xb.device, dtype=torch.float32)\n    else:\n        assert D.shape == (nq, k)\n        assert D.device == xb.device\n\n    if I is None:\n        I = torch.empty(nq, k, device=xb.device, dtype=torch.int64)\n    else:\n        assert I.shape == (nq, k)\n        assert I.device == xb.device\n\n    D_ptr = swig_ptr_from_FloatTensor(D)\n    I_ptr = swig_ptr_from_LongTensor(I)\n\n    faiss.bruteForceKnn(\n        res,\n        metric,\n        xb_ptr,\n        xb_row_major,\n        nb,\n        xq_ptr,\n        xq_row_major,\n        nq,\n        d,\n        k,\n        D_ptr,\n        I_ptr,\n    )\n\n    return D, I\n\n\ndef index_init_gpu(ngpus, feat_dim):\n    flat_config = []\n    for i in range(ngpus):\n        cfg = faiss.GpuIndexFlatConfig()\n        cfg.useFloat16 = False\n        cfg.device = i\n        flat_config.append(cfg)\n\n    res = [faiss.StandardGpuResources() for i in range(ngpus)]\n    indexes = [\n        faiss.GpuIndexFlatL2(res[i], feat_dim, flat_config[i]) for i in range(ngpus)\n    ]\n    index = faiss.IndexShards(feat_dim)\n    for sub_index in indexes:\n        index.add_shard(sub_index)\n    index.reset()\n    return index\n\n\ndef index_init_cpu(feat_dim):\n    return faiss.IndexFlatL2(feat_dim)\n"
  },
  {
    "path": "fast_reid/fastreid/utils/file_io.py",
    "content": "#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n\nimport errno\nimport logging\nimport os\nimport shutil\nfrom collections import OrderedDict\nfrom typing import (\n    IO,\n    Any,\n    Callable,\n    Dict,\n    List,\n    MutableMapping,\n    Optional,\n    Union,\n)\n\n__all__ = [\"PathManager\", \"get_cache_dir\"]\n\n\ndef get_cache_dir(cache_dir: Optional[str] = None) -> str:\n    \"\"\"\n    Returns a default directory to cache static files\n    (usually downloaded from Internet), if None is provided.\n    Args:\n        cache_dir (None or str): if not None, will be returned as is.\n            If None, returns the default cache directory as:\n        1) $FVCORE_CACHE, if set\n        2) otherwise ~/.torch/fvcore_cache\n    \"\"\"\n    if cache_dir is None:\n        cache_dir = os.path.expanduser(\n            os.getenv(\"FVCORE_CACHE\", \"~/.torch/fvcore_cache\")\n        )\n    return cache_dir\n\n\nclass PathHandler:\n    \"\"\"\n    PathHandler is a base class that defines common I/O functionality for a URI\n    protocol. It routes I/O for a generic URI which may look like \"protocol://*\"\n    or a canonical filepath \"/foo/bar/baz\".\n    \"\"\"\n\n    _strict_kwargs_check = True\n\n    def _check_kwargs(self, kwargs: Dict[str, Any]) -> None:\n        \"\"\"\n        Checks if the given arguments are empty. Throws a ValueError if strict\n        kwargs checking is enabled and args are non-empty. If strict kwargs\n        checking is disabled, only a warning is logged.\n        Args:\n            kwargs (Dict[str, Any])\n        \"\"\"\n        if self._strict_kwargs_check:\n            if len(kwargs) > 0:\n                raise ValueError(\"Unused arguments: {}\".format(kwargs))\n        else:\n            logger = logging.getLogger(__name__)\n            for k, v in kwargs.items():\n                logger.warning(\n                    \"[PathManager] {}={} argument ignored\".format(k, v)\n                )\n\n    def _get_supported_prefixes(self) -> List[str]:\n        \"\"\"\n        Returns:\n            List[str]: the list of URI prefixes this PathHandler can support\n        \"\"\"\n        raise NotImplementedError()\n\n    def _get_local_path(self, path: str, **kwargs: Any) -> str:\n        \"\"\"\n        Get a filepath which is compatible with native Python I/O such as `open`\n        and `os.path`.\n        If URI points to a remote resource, this function may download and cache\n        the resource to local disk. In this case, this function is meant to be\n        used with read-only resources.\n        Args:\n            path (str): A URI supported by this PathHandler\n        Returns:\n            local_path (str): a file path which exists on the local file system\n        \"\"\"\n        raise NotImplementedError()\n\n    def _open(\n            self, path: str, mode: str = \"r\", buffering: int = -1, **kwargs: Any\n    ) -> Union[IO[str], IO[bytes]]:\n        \"\"\"\n        Open a stream to a URI, similar to the built-in `open`.\n        Args:\n            path (str): A URI supported by this PathHandler\n            mode (str): Specifies the mode in which the file is opened. It defaults\n                to 'r'.\n            buffering (int): An optional integer used to set the buffering policy.\n                Pass 0 to switch buffering off and an integer >= 1 to indicate the\n                size in bytes of a fixed-size chunk buffer. When no buffering\n                argument is given, the default buffering policy depends on the\n                underlying I/O implementation.\n        Returns:\n            file: a file-like object.\n        \"\"\"\n        raise NotImplementedError()\n\n    def _copy(\n            self,\n            src_path: str,\n            dst_path: str,\n            overwrite: bool = False,\n            **kwargs: Any,\n    ) -> bool:\n        \"\"\"\n        Copies a source path to a destination path.\n        Args:\n            src_path (str): A URI supported by this PathHandler\n            dst_path (str): A URI supported by this PathHandler\n            overwrite (bool): Bool flag for forcing overwrite of existing file\n        Returns:\n            status (bool): True on success\n        \"\"\"\n        raise NotImplementedError()\n\n    def _exists(self, path: str, **kwargs: Any) -> bool:\n        \"\"\"\n        Checks if there is a resource at the given URI.\n        Args:\n            path (str): A URI supported by this PathHandler\n        Returns:\n            bool: true if the path exists\n        \"\"\"\n        raise NotImplementedError()\n\n    def _isfile(self, path: str, **kwargs: Any) -> bool:\n        \"\"\"\n        Checks if the resource at the given URI is a file.\n        Args:\n            path (str): A URI supported by this PathHandler\n        Returns:\n            bool: true if the path is a file\n        \"\"\"\n        raise NotImplementedError()\n\n    def _isdir(self, path: str, **kwargs: Any) -> bool:\n        \"\"\"\n        Checks if the resource at the given URI is a directory.\n        Args:\n            path (str): A URI supported by this PathHandler\n        Returns:\n            bool: true if the path is a directory\n        \"\"\"\n        raise NotImplementedError()\n\n    def _ls(self, path: str, **kwargs: Any) -> List[str]:\n        \"\"\"\n        List the contents of the directory at the provided URI.\n        Args:\n            path (str): A URI supported by this PathHandler\n        Returns:\n            List[str]: list of contents in given path\n        \"\"\"\n        raise NotImplementedError()\n\n    def _mkdirs(self, path: str, **kwargs: Any) -> None:\n        \"\"\"\n        Recursive directory creation function. Like mkdir(), but makes all\n        intermediate-level directories needed to contain the leaf directory.\n        Similar to the native `os.makedirs`.\n        Args:\n            path (str): A URI supported by this PathHandler\n        \"\"\"\n        raise NotImplementedError()\n\n    def _rm(self, path: str, **kwargs: Any) -> None:\n        \"\"\"\n        Remove the file (not directory) at the provided URI.\n        Args:\n            path (str): A URI supported by this PathHandler\n        \"\"\"\n        raise NotImplementedError()\n\n\nclass NativePathHandler(PathHandler):\n    \"\"\"\n    Handles paths that can be accessed using Python native system calls. This\n    handler uses `open()` and `os.*` calls on the given path.\n    \"\"\"\n\n    def _get_local_path(self, path: str, **kwargs: Any) -> str:\n        self._check_kwargs(kwargs)\n        return path\n\n    def _open(\n            self,\n            path: str,\n            mode: str = \"r\",\n            buffering: int = -1,\n            encoding: Optional[str] = None,\n            errors: Optional[str] = None,\n            newline: Optional[str] = None,\n            closefd: bool = True,\n            opener: Optional[Callable] = None,\n            **kwargs: Any,\n    ) -> Union[IO[str], IO[bytes]]:\n        \"\"\"\n        Open a path.\n        Args:\n            path (str): A URI supported by this PathHandler\n            mode (str): Specifies the mode in which the file is opened. It defaults\n                to 'r'.\n            buffering (int): An optional integer used to set the buffering policy.\n                Pass 0 to switch buffering off and an integer >= 1 to indicate the\n                size in bytes of a fixed-size chunk buffer. When no buffering\n                argument is given, the default buffering policy works as follows:\n                    * Binary files are buffered in fixed-size chunks; the size of\n                    the buffer is chosen using a heuristic trying to determine the\n                    underlying device’s “block size” and falling back on\n                    io.DEFAULT_BUFFER_SIZE. On many systems, the buffer will\n                    typically be 4096 or 8192 bytes long.\n            encoding (Optional[str]): the name of the encoding used to decode or\n                encode the file. This should only be used in text mode.\n            errors (Optional[str]): an optional string that specifies how encoding\n                and decoding errors are to be handled. This cannot be used in binary\n                mode.\n            newline (Optional[str]): controls how universal newlines mode works\n                (it only applies to text mode). It can be None, '', '\\n', '\\r',\n                and '\\r\\n'.\n            closefd (bool): If closefd is False and a file descriptor rather than\n                a filename was given, the underlying file descriptor will be kept\n                open when the file is closed. If a filename is given closefd must\n                be True (the default) otherwise an error will be raised.\n            opener (Optional[Callable]): A custom opener can be used by passing\n                a callable as opener. The underlying file descriptor for the file\n                object is then obtained by calling opener with (file, flags).\n                opener must return an open file descriptor (passing os.open as opener\n                results in functionality similar to passing None).\n            See https://docs.python.org/3/library/functions.html#open for details.\n        Returns:\n            file: a file-like object.\n        \"\"\"\n        self._check_kwargs(kwargs)\n        return open(  # type: ignore\n            path,\n            mode,\n            buffering=buffering,\n            encoding=encoding,\n            errors=errors,\n            newline=newline,\n            closefd=closefd,\n            opener=opener,\n        )\n\n    def _copy(\n            self,\n            src_path: str,\n            dst_path: str,\n            overwrite: bool = False,\n            **kwargs: Any,\n    ) -> bool:\n        \"\"\"\n        Copies a source path to a destination path.\n        Args:\n            src_path (str): A URI supported by this PathHandler\n            dst_path (str): A URI supported by this PathHandler\n            overwrite (bool): Bool flag for forcing overwrite of existing file\n        Returns:\n            status (bool): True on success\n        \"\"\"\n        self._check_kwargs(kwargs)\n\n        if os.path.exists(dst_path) and not overwrite:\n            logger = logging.getLogger(__name__)\n            logger.error(\"Destination file {} already exists.\".format(dst_path))\n            return False\n\n        try:\n            shutil.copyfile(src_path, dst_path)\n            return True\n        except Exception as e:\n            logger = logging.getLogger(__name__)\n            logger.error(\"Error in file copy - {}\".format(str(e)))\n            return False\n\n    def _exists(self, path: str, **kwargs: Any) -> bool:\n        self._check_kwargs(kwargs)\n        return os.path.exists(path)\n\n    def _isfile(self, path: str, **kwargs: Any) -> bool:\n        self._check_kwargs(kwargs)\n        return os.path.isfile(path)\n\n    def _isdir(self, path: str, **kwargs: Any) -> bool:\n        self._check_kwargs(kwargs)\n        return os.path.isdir(path)\n\n    def _ls(self, path: str, **kwargs: Any) -> List[str]:\n        self._check_kwargs(kwargs)\n        return os.listdir(path)\n\n    def _mkdirs(self, path: str, **kwargs: Any) -> None:\n        self._check_kwargs(kwargs)\n        try:\n            os.makedirs(path, exist_ok=True)\n        except OSError as e:\n            # EEXIST it can still happen if multiple processes are creating the dir\n            if e.errno != errno.EEXIST:\n                raise\n\n    def _rm(self, path: str, **kwargs: Any) -> None:\n        self._check_kwargs(kwargs)\n        os.remove(path)\n\n\nclass PathManager:\n    \"\"\"\n    A class for users to open generic paths or translate generic paths to file names.\n    \"\"\"\n\n    _PATH_HANDLERS: MutableMapping[str, PathHandler] = OrderedDict()\n    _NATIVE_PATH_HANDLER = NativePathHandler()\n\n    @staticmethod\n    def __get_path_handler(path: str) -> PathHandler:\n        \"\"\"\n        Finds a PathHandler that supports the given path. Falls back to the native\n        PathHandler if no other handler is found.\n        Args:\n            path (str): URI path to resource\n        Returns:\n            handler (PathHandler)\n        \"\"\"\n        for p in PathManager._PATH_HANDLERS.keys():\n            if path.startswith(p):\n                return PathManager._PATH_HANDLERS[p]\n        return PathManager._NATIVE_PATH_HANDLER\n\n    @staticmethod\n    def open(\n            path: str, mode: str = \"r\", buffering: int = -1, **kwargs: Any\n    ) -> Union[IO[str], IO[bytes]]:\n        \"\"\"\n        Open a stream to a URI, similar to the built-in `open`.\n        Args:\n            path (str): A URI supported by this PathHandler\n            mode (str): Specifies the mode in which the file is opened. It defaults\n                to 'r'.\n            buffering (int): An optional integer used to set the buffering policy.\n                Pass 0 to switch buffering off and an integer >= 1 to indicate the\n                size in bytes of a fixed-size chunk buffer. When no buffering\n                argument is given, the default buffering policy depends on the\n                underlying I/O implementation.\n        Returns:\n            file: a file-like object.\n        \"\"\"\n        return PathManager.__get_path_handler(path)._open(  # type: ignore\n            path, mode, buffering=buffering, **kwargs\n        )\n\n    @staticmethod\n    def copy(\n            src_path: str, dst_path: str, overwrite: bool = False, **kwargs: Any\n    ) -> bool:\n        \"\"\"\n        Copies a source path to a destination path.\n        Args:\n            src_path (str): A URI supported by this PathHandler\n            dst_path (str): A URI supported by this PathHandler\n            overwrite (bool): Bool flag for forcing overwrite of existing file\n        Returns:\n            status (bool): True on success\n        \"\"\"\n\n        # Copying across handlers is not supported.\n        assert PathManager.__get_path_handler(  # type: ignore\n            src_path\n        ) == PathManager.__get_path_handler(dst_path)\n        return PathManager.__get_path_handler(src_path)._copy(\n            src_path, dst_path, overwrite, **kwargs\n        )\n\n    @staticmethod\n    def get_local_path(path: str, **kwargs: Any) -> str:\n        \"\"\"\n        Get a filepath which is compatible with native Python I/O such as `open`\n        and `os.path`.\n        If URI points to a remote resource, this function may download and cache\n        the resource to local disk.\n        Args:\n            path (str): A URI supported by this PathHandler\n        Returns:\n            local_path (str): a file path which exists on the local file system\n        \"\"\"\n        return PathManager.__get_path_handler(  # type: ignore\n            path\n        )._get_local_path(path, **kwargs)\n\n    @staticmethod\n    def exists(path: str, **kwargs: Any) -> bool:\n        \"\"\"\n        Checks if there is a resource at the given URI.\n        Args:\n            path (str): A URI supported by this PathHandler\n        Returns:\n            bool: true if the path exists\n        \"\"\"\n        return PathManager.__get_path_handler(path)._exists(  # type: ignore\n            path, **kwargs\n        )\n\n    @staticmethod\n    def isfile(path: str, **kwargs: Any) -> bool:\n        \"\"\"\n        Checks if there the resource at the given URI is a file.\n        Args:\n            path (str): A URI supported by this PathHandler\n        Returns:\n            bool: true if the path is a file\n        \"\"\"\n        return PathManager.__get_path_handler(path)._isfile(  # type: ignore\n            path, **kwargs\n        )\n\n    @staticmethod\n    def isdir(path: str, **kwargs: Any) -> bool:\n        \"\"\"\n        Checks if the resource at the given URI is a directory.\n        Args:\n            path (str): A URI supported by this PathHandler\n        Returns:\n            bool: true if the path is a directory\n        \"\"\"\n        return PathManager.__get_path_handler(path)._isdir(  # type: ignore\n            path, **kwargs\n        )\n\n    @staticmethod\n    def ls(path: str, **kwargs: Any) -> List[str]:\n        \"\"\"\n        List the contents of the directory at the provided URI.\n        Args:\n            path (str): A URI supported by this PathHandler\n        Returns:\n            List[str]: list of contents in given path\n        \"\"\"\n        return PathManager.__get_path_handler(path)._ls(  # type: ignore\n            path, **kwargs\n        )\n\n    @staticmethod\n    def mkdirs(path: str, **kwargs: Any) -> None:\n        \"\"\"\n        Recursive directory creation function. Like mkdir(), but makes all\n        intermediate-level directories needed to contain the leaf directory.\n        Similar to the native `os.makedirs`.\n        Args:\n            path (str): A URI supported by this PathHandler\n        \"\"\"\n        return PathManager.__get_path_handler(path)._mkdirs(  # type: ignore\n            path, **kwargs\n        )\n\n    @staticmethod\n    def rm(path: str, **kwargs: Any) -> None:\n        \"\"\"\n        Remove the file (not directory) at the provided URI.\n        Args:\n            path (str): A URI supported by this PathHandler\n        \"\"\"\n        return PathManager.__get_path_handler(path)._rm(  # type: ignore\n            path, **kwargs\n        )\n\n    @staticmethod\n    def register_handler(handler: PathHandler) -> None:\n        \"\"\"\n        Register a path handler associated with `handler._get_supported_prefixes`\n        URI prefixes.\n        Args:\n            handler (PathHandler)\n        \"\"\"\n        assert isinstance(handler, PathHandler), handler\n        for prefix in handler._get_supported_prefixes():\n            assert prefix not in PathManager._PATH_HANDLERS\n            PathManager._PATH_HANDLERS[prefix] = handler\n\n        # Sort path handlers in reverse order so longer prefixes take priority,\n        # eg: http://foo/bar before http://foo\n        PathManager._PATH_HANDLERS = OrderedDict(\n            sorted(\n                PathManager._PATH_HANDLERS.items(),\n                key=lambda t: t[0],\n                reverse=True,\n            )\n        )\n\n    @staticmethod\n    def set_strict_kwargs_checking(enable: bool) -> None:\n        \"\"\"\n        Toggles strict kwargs checking. If enabled, a ValueError is thrown if any\n        unused parameters are passed to a PathHandler function. If disabled, only\n        a warning is given.\n        With a centralized file API, there's a tradeoff of convenience and\n        correctness delegating arguments to the proper I/O layers. An underlying\n        `PathHandler` may support custom arguments which should not be statically\n        exposed on the `PathManager` function. For example, a custom `HTTPURLHandler`\n        may want to expose a `cache_timeout` argument for `open()` which specifies\n        how old a locally cached resource can be before it's refetched from the\n        remote server. This argument would not make sense for a `NativePathHandler`.\n        If strict kwargs checking is disabled, `cache_timeout` can be passed to\n        `PathManager.open` which will forward the arguments to the underlying\n        handler. By default, checking is enabled since it is innately unsafe:\n        multiple `PathHandler`s could reuse arguments with different semantic\n        meanings or types.\n        Args:\n            enable (bool)\n        \"\"\"\n        PathManager._NATIVE_PATH_HANDLER._strict_kwargs_check = enable\n        for handler in PathManager._PATH_HANDLERS.values():\n            handler._strict_kwargs_check = enable\n"
  },
  {
    "path": "fast_reid/fastreid/utils/history_buffer.py",
    "content": "#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n\nimport numpy as np\nfrom typing import List, Tuple\n\n\nclass HistoryBuffer:\n    \"\"\"\n    Track a series of scalar values and provide access to smoothed values over a\n    window or the global average of the series.\n    \"\"\"\n\n    def __init__(self, max_length: int = 1000000):\n        \"\"\"\n        Args:\n            max_length: maximal number of values that can be stored in the\n                buffer. When the capacity of the buffer is exhausted, old\n                values will be removed.\n        \"\"\"\n        self._max_length: int = max_length\n        self._data: List[Tuple[float, float]] = []  # (value, iteration) pairs\n        self._count: int = 0\n        self._global_avg: float = 0\n\n    def update(self, value: float, iteration: float = None):\n        \"\"\"\n        Add a new scalar value produced at certain iteration. If the length\n        of the buffer exceeds self._max_length, the oldest element will be\n        removed from the buffer.\n        \"\"\"\n        if iteration is None:\n            iteration = self._count\n        if len(self._data) == self._max_length:\n            self._data.pop(0)\n        self._data.append((value, iteration))\n\n        self._count += 1\n        self._global_avg += (value - self._global_avg) / self._count\n\n    def latest(self):\n        \"\"\"\n        Return the latest scalar value added to the buffer.\n        \"\"\"\n        return self._data[-1][0]\n\n    def median(self, window_size: int):\n        \"\"\"\n        Return the median of the latest `window_size` values in the buffer.\n        \"\"\"\n        return np.median([x[0] for x in self._data[-window_size:]])\n\n    def avg(self, window_size: int):\n        \"\"\"\n        Return the mean of the latest `window_size` values in the buffer.\n        \"\"\"\n        return np.mean([x[0] for x in self._data[-window_size:]])\n\n    def global_avg(self):\n        \"\"\"\n        Return the mean of all the elements in the buffer. Note that this\n        includes those getting removed due to limited buffer storage.\n        \"\"\"\n        return self._global_avg\n\n    def values(self):\n        \"\"\"\n        Returns:\n            list[(number, iteration)]: content of the current buffer.\n        \"\"\"\n        return self._data\n"
  },
  {
    "path": "fast_reid/fastreid/utils/logger.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport functools\nimport logging\nimport os\nimport sys\nimport time\nfrom collections import Counter\n\nfrom termcolor import colored\n\nfrom .file_io import PathManager\n\n\nclass _ColorfulFormatter(logging.Formatter):\n    def __init__(self, *args, **kwargs):\n        self._root_name = kwargs.pop(\"root_name\") + \".\"\n        self._abbrev_name = kwargs.pop(\"abbrev_name\", \"\")\n        if len(self._abbrev_name):\n            self._abbrev_name = self._abbrev_name + \".\"\n        super(_ColorfulFormatter, self).__init__(*args, **kwargs)\n\n    def formatMessage(self, record):\n        record.name = record.name.replace(self._root_name, self._abbrev_name)\n        log = super(_ColorfulFormatter, self).formatMessage(record)\n        if record.levelno == logging.WARNING:\n            prefix = colored(\"WARNING\", \"red\", attrs=[\"blink\"])\n        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:\n            prefix = colored(\"ERROR\", \"red\", attrs=[\"blink\", \"underline\"])\n        else:\n            return log\n        return prefix + \" \" + log\n\n\n@functools.lru_cache()  # so that calling setup_logger multiple times won't add many handlers\ndef setup_logger(\n        output=None, distributed_rank=0, *, color=True, name=\"fastreid\", abbrev_name=None\n):\n    \"\"\"\n    Args:\n        output (str): a file name or a directory to save log. If None, will not save log file.\n            If ends with \".txt\" or \".log\", assumed to be a file name.\n            Otherwise, logs will be saved to `output/log.txt`.\n        name (str): the root module name of this logger\n        abbrev_name (str): an abbreviation of the module, to avoid long names in logs.\n            Set to \"\" to not log the root module in logs.\n            By default, will abbreviate \"detectron2\" to \"d2\" and leave other\n            modules unchanged.\n    \"\"\"\n    logger = logging.getLogger(name)\n    logger.setLevel(logging.DEBUG)\n    logger.propagate = False\n\n    if abbrev_name is None:\n        abbrev_name = \"d2\" if name == \"detectron2\" else name\n\n    plain_formatter = logging.Formatter(\n        \"[%(asctime)s] %(name)s %(levelname)s: %(message)s\", datefmt=\"%m/%d %H:%M:%S\"\n    )\n    # stdout logging: master only\n    if distributed_rank == 0:\n        ch = logging.StreamHandler(stream=sys.stdout)\n        ch.setLevel(logging.DEBUG)\n        if color:\n            formatter = _ColorfulFormatter(\n                colored(\"[%(asctime)s %(name)s]: \", \"green\") + \"%(message)s\",\n                datefmt=\"%m/%d %H:%M:%S\",\n                root_name=name,\n                abbrev_name=str(abbrev_name),\n            )\n        else:\n            formatter = plain_formatter\n        ch.setFormatter(formatter)\n        logger.addHandler(ch)\n\n    # file logging: all workers\n    if output is not None:\n        if output.endswith(\".txt\") or output.endswith(\".log\"):\n            filename = output\n        else:\n            filename = os.path.join(output, \"log.txt\")\n        if distributed_rank > 0:\n            filename = filename + \".rank{}\".format(distributed_rank)\n        PathManager.mkdirs(os.path.dirname(filename))\n\n        fh = logging.StreamHandler(_cached_log_stream(filename))\n        fh.setLevel(logging.DEBUG)\n        fh.setFormatter(plain_formatter)\n        logger.addHandler(fh)\n\n    return logger\n\n\n# cache the opened file object, so that different calls to `setup_logger`\n# with the same file name can safely write to the same file.\n@functools.lru_cache(maxsize=None)\ndef _cached_log_stream(filename):\n    return PathManager.open(filename, \"a\")\n\n\n\"\"\"\nBelow are some other convenient logging methods.\nThey are mainly adopted from\nhttps://github.com/abseil/abseil-py/blob/master/absl/logging/__init__.py\n\"\"\"\n\n\ndef _find_caller():\n    \"\"\"\n    Returns:\n        str: module name of the caller\n        tuple: a hashable key to be used to identify different callers\n    \"\"\"\n    frame = sys._getframe(2)\n    while frame:\n        code = frame.f_code\n        if os.path.join(\"utils\", \"logger.\") not in code.co_filename:\n            mod_name = frame.f_globals[\"__name__\"]\n            if mod_name == \"__main__\":\n                mod_name = \"detectron2\"\n            return mod_name, (code.co_filename, frame.f_lineno, code.co_name)\n        frame = frame.f_back\n\n\n_LOG_COUNTER = Counter()\n_LOG_TIMER = {}\n\n\ndef log_first_n(lvl, msg, n=1, *, name=None, key=\"caller\"):\n    \"\"\"\n    Log only for the first n times.\n    Args:\n        lvl (int): the logging level\n        msg (str):\n        n (int):\n        name (str): name of the logger to use. Will use the caller's module by default.\n        key (str or tuple[str]): the string(s) can be one of \"caller\" or\n            \"message\", which defines how to identify duplicated logs.\n            For example, if called with `n=1, key=\"caller\"`, this function\n            will only log the first call from the same caller, regardless of\n            the message content.\n            If called with `n=1, key=\"message\"`, this function will log the\n            same content only once, even if they are called from different places.\n            If called with `n=1, key=(\"caller\", \"message\")`, this function\n            will not log only if the same caller has logged the same message before.\n    \"\"\"\n    if isinstance(key, str):\n        key = (key,)\n    assert len(key) > 0\n\n    caller_module, caller_key = _find_caller()\n    hash_key = ()\n    if \"caller\" in key:\n        hash_key = hash_key + caller_key\n    if \"message\" in key:\n        hash_key = hash_key + (msg,)\n\n    _LOG_COUNTER[hash_key] += 1\n    if _LOG_COUNTER[hash_key] <= n:\n        logging.getLogger(name or caller_module).log(lvl, msg)\n\n\ndef log_every_n(lvl, msg, n=1, *, name=None):\n    \"\"\"\n    Log once per n times.\n    Args:\n        lvl (int): the logging level\n        msg (str):\n        n (int):\n        name (str): name of the logger to use. Will use the caller's module by default.\n    \"\"\"\n    caller_module, key = _find_caller()\n    _LOG_COUNTER[key] += 1\n    if n == 1 or _LOG_COUNTER[key] % n == 1:\n        logging.getLogger(name or caller_module).log(lvl, msg)\n\n\ndef log_every_n_seconds(lvl, msg, n=1, *, name=None):\n    \"\"\"\n    Log no more than once per n seconds.\n    Args:\n        lvl (int): the logging level\n        msg (str):\n        n (int):\n        name (str): name of the logger to use. Will use the caller's module by default.\n    \"\"\"\n    caller_module, key = _find_caller()\n    last_logged = _LOG_TIMER.get(key, None)\n    current_time = time.time()\n    if last_logged is None or current_time - last_logged >= n:\n        logging.getLogger(name or caller_module).log(lvl, msg)\n        _LOG_TIMER[key] = current_time\n\n# def create_small_table(small_dict):\n#     \"\"\"\n#     Create a small table using the keys of small_dict as headers. This is only\n#     suitable for small dictionaries.\n#     Args:\n#         small_dict (dict): a result dictionary of only a few items.\n#     Returns:\n#         str: the table as a string.\n#     \"\"\"\n#     keys, values = tuple(zip(*small_dict.items()))\n#     table = tabulate(\n#         [values],\n#         headers=keys,\n#         tablefmt=\"pipe\",\n#         floatfmt=\".3f\",\n#         stralign=\"center\",\n#         numalign=\"center\",\n#     )\n#     return table\n"
  },
  {
    "path": "fast_reid/fastreid/utils/params.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# based on: https://github.com/PhilJd/contiguous_pytorch_params/blob/master/contiguous_params/params.py\n\nfrom collections import OrderedDict\n\nimport torch\n\n\nclass ContiguousParams:\n\n    def __init__(self, parameters):\n        # Create a list of the parameters to prevent emptying an iterator.\n        self._parameters = parameters\n        self._param_buffer = []\n        self._grad_buffer = []\n        self._group_dict = OrderedDict()\n        self._name_buffer = []\n        self._init_buffers()\n        # Store the data pointers for each parameter into the buffer. These\n        # can be used to check if an operation overwrites the gradient/data\n        # tensor (invalidating the assumption of a contiguous buffer).\n        self.data_pointers = []\n        self.grad_pointers = []\n        self.make_params_contiguous()\n\n    def _init_buffers(self):\n        dtype = self._parameters[0][\"params\"][0].dtype\n        device = self._parameters[0][\"params\"][0].device\n        if not all(p[\"params\"][0].dtype == dtype for p in self._parameters):\n            raise ValueError(\"All parameters must be of the same dtype.\")\n        if not all(p[\"params\"][0].device == device for p in self._parameters):\n            raise ValueError(\"All parameters must be on the same device.\")\n\n        # Group parameters by lr and weight decay\n        for param_dict in self._parameters:\n            freeze_status = param_dict[\"freeze_status\"]\n            param_key = freeze_status + '_' + str(param_dict[\"lr\"]) + '_' + str(param_dict[\"weight_decay\"])\n            if param_key not in self._group_dict:\n                self._group_dict[param_key] = []\n            self._group_dict[param_key].append(param_dict)\n\n        for key, params in self._group_dict.items():\n            size = sum(p[\"params\"][0].numel() for p in params)\n            self._param_buffer.append(torch.zeros(size, dtype=dtype, device=device))\n            self._grad_buffer.append(torch.zeros(size, dtype=dtype, device=device))\n            self._name_buffer.append(key)\n\n    def make_params_contiguous(self):\n        \"\"\"Create a buffer to hold all params and update the params to be views of the buffer.\n        Args:\n            parameters: An iterable of parameters.\n        \"\"\"\n        for i, params in enumerate(self._group_dict.values()):\n            index = 0\n            for param_dict in params:\n                p = param_dict[\"params\"][0]\n                size = p.numel()\n                self._param_buffer[i][index:index + size] = p.data.view(-1)\n                p.data = self._param_buffer[i][index:index + size].view(p.data.shape)\n                p.grad = self._grad_buffer[i][index:index + size].view(p.data.shape)\n                self.data_pointers.append(p.data.data_ptr)\n                self.grad_pointers.append(p.grad.data.data_ptr)\n                index += size\n            # Bend the param_buffer to use grad_buffer to track its gradients.\n            self._param_buffer[i].grad = self._grad_buffer[i]\n\n    def contiguous(self):\n        \"\"\"Return all parameters as one contiguous buffer.\"\"\"\n        return [{\n            \"freeze_status\": self._name_buffer[i].split('_')[0],\n            \"params\": self._param_buffer[i],\n            \"lr\": float(self._name_buffer[i].split('_')[1]),\n            \"weight_decay\": float(self._name_buffer[i].split('_')[2]),\n        } for i in range(len(self._param_buffer))]\n\n    def original(self):\n        \"\"\"Return the non-flattened parameters.\"\"\"\n        return self._parameters\n\n    def buffer_is_valid(self):\n        \"\"\"Verify that all parameters and gradients still use the buffer.\"\"\"\n        i = 0\n        for params in self._group_dict.values():\n            for param_dict in params:\n                p = param_dict[\"params\"][0]\n                data_ptr = self.data_pointers[i]\n                grad_ptr = self.grad_pointers[i]\n                if (p.data.data_ptr() != data_ptr()) or (p.grad.data.data_ptr() != grad_ptr()):\n                    return False\n                i += 1\n        return True\n\n    def assert_buffer_is_valid(self):\n        if not self.buffer_is_valid():\n            raise ValueError(\n                \"The data or gradient buffer has been invalidated. Please make \"\n                \"sure to use inplace operations only when updating parameters \"\n                \"or gradients.\")\n"
  },
  {
    "path": "fast_reid/fastreid/utils/precision_bn.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport itertools\n\nimport torch\n\nBN_MODULE_TYPES = (\n    torch.nn.BatchNorm1d,\n    torch.nn.BatchNorm2d,\n    torch.nn.BatchNorm3d,\n    torch.nn.SyncBatchNorm,\n)\n\n\n@torch.no_grad()\ndef update_bn_stats(model, data_loader, num_iters: int = 200):\n    \"\"\"\n    Recompute and update the batch norm stats to make them more precise. During\n    training both BN stats and the weight are changing after every iteration, so\n    the running average can not precisely reflect the actual stats of the\n    current model.\n    In this function, the BN stats are recomputed with fixed weights, to make\n    the running average more precise. Specifically, it computes the true average\n    of per-batch mean/variance instead of the running average.\n    Args:\n        model (nn.Module): the model whose bn stats will be recomputed.\n            Note that:\n            1. This function will not alter the training mode of the given model.\n               Users are responsible for setting the layers that needs\n               precise-BN to training mode, prior to calling this function.\n            2. Be careful if your models contain other stateful layers in\n               addition to BN, i.e. layers whose state can change in forward\n               iterations.  This function will alter their state. If you wish\n               them unchanged, you need to either pass in a submodule without\n               those layers, or backup the states.\n        data_loader (iterator): an iterator. Produce data as inputs to the model.\n        num_iters (int): number of iterations to compute the stats.\n    \"\"\"\n    bn_layers = get_bn_modules(model)\n    if len(bn_layers) == 0:\n        return\n\n    # In order to make the running stats only reflect the current batch, the\n    # momentum is disabled.\n    # bn.running_mean = (1 - momentum) * bn.running_mean + momentum * batch_mean\n    # Setting the momentum to 1.0 to compute the stats without momentum.\n    momentum_actual = [bn.momentum for bn in bn_layers]\n    for bn in bn_layers:\n        bn.momentum = 1.0\n\n    # Note that running_var actually means \"running average of variance\"\n    running_mean = [torch.zeros_like(bn.running_mean) for bn in bn_layers]\n    running_var = [torch.zeros_like(bn.running_var) for bn in bn_layers]\n\n    for ind, inputs in enumerate(itertools.islice(data_loader, num_iters)):\n        inputs['targets'].fill_(-1)\n        with torch.no_grad():  # No need to backward\n            model(inputs)\n        for i, bn in enumerate(bn_layers):\n            # Accumulates the bn stats.\n            running_mean[i] += (bn.running_mean - running_mean[i]) / (ind + 1)\n            running_var[i] += (bn.running_var - running_var[i]) / (ind + 1)\n            # We compute the \"average of variance\" across iterations.\n    assert ind == num_iters - 1, (\n        \"update_bn_stats is meant to run for {} iterations, \"\n        \"but the dataloader stops at {} iterations.\".format(num_iters, ind)\n    )\n\n    for i, bn in enumerate(bn_layers):\n        # Sets the precise bn stats.\n        bn.running_mean = running_mean[i]\n        bn.running_var = running_var[i]\n        bn.momentum = momentum_actual[i]\n\n\ndef get_bn_modules(model):\n    \"\"\"\n    Find all BatchNorm (BN) modules that are in training mode. See\n    fvcore.precise_bn.BN_MODULE_TYPES for a list of all modules that are\n    included in this search.\n    Args:\n        model (nn.Module): a model possibly containing BN modules.\n    Returns:\n        list[nn.Module]: all BN modules in the model.\n    \"\"\"\n    # Finds all the bn layers.\n    bn_layers = [\n        m for m in model.modules() if m.training and isinstance(m, BN_MODULE_TYPES)\n    ]\n    return bn_layers\n"
  },
  {
    "path": "fast_reid/fastreid/utils/registry.py",
    "content": "#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nfrom typing import Dict, Optional\n\n\nclass Registry(object):\n    \"\"\"\n    The registry that provides name -> object mapping, to support third-party\n    users' custom modules.\n    To create a registry (e.g. a backbone registry):\n    .. code-block:: python\n        BACKBONE_REGISTRY = Registry('BACKBONE')\n    To register an object:\n    .. code-block:: python\n        @BACKBONE_REGISTRY.register()\n        class MyBackbone():\n            ...\n    Or:\n    .. code-block:: python\n        BACKBONE_REGISTRY.register(MyBackbone)\n    \"\"\"\n\n    def __init__(self, name: str) -> None:\n        \"\"\"\n        Args:\n            name (str): the name of this registry\n        \"\"\"\n        self._name: str = name\n        self._obj_map: Dict[str, object] = {}\n\n    def _do_register(self, name: str, obj: object) -> None:\n        assert (\n                name not in self._obj_map\n        ), \"An object named '{}' was already registered in '{}' registry!\".format(\n            name, self._name\n        )\n        self._obj_map[name] = obj\n\n    def register(self, obj: object = None) -> Optional[object]:\n        \"\"\"\n        Register the given object under the the name `obj.__name__`.\n        Can be used as either a decorator or not. See docstring of this class for usage.\n        \"\"\"\n        if obj is None:\n            # used as a decorator\n            def deco(func_or_class: object) -> object:\n                name = func_or_class.__name__  # pyre-ignore\n                self._do_register(name, func_or_class)\n                return func_or_class\n\n            return deco\n\n        # used as a function call\n        name = obj.__name__  # pyre-ignore\n        self._do_register(name, obj)\n\n    def get(self, name: str) -> object:\n        ret = self._obj_map.get(name)\n        if ret is None:\n            raise KeyError(\n                \"No object named '{}' found in '{}' registry!\".format(\n                    name, self._name\n                )\n            )\n        return ret\n"
  },
  {
    "path": "fast_reid/fastreid/utils/summary.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\nfrom collections import OrderedDict\nimport numpy as np\n\n\ndef summary(model, input_size, batch_size=-1, device=\"cuda\"):\n    def register_hook(module):\n\n        def hook(module, input, output):\n            class_name = str(module.__class__).split(\".\")[-1].split(\"'\")[0]\n            module_idx = len(summary)\n\n            m_key = \"%s-%i\" % (class_name, module_idx + 1)\n            summary[m_key] = OrderedDict()\n            summary[m_key][\"input_shape\"] = list(input[0].size())\n            summary[m_key][\"input_shape\"][0] = batch_size\n            if isinstance(output, (list, tuple)):\n                summary[m_key][\"output_shape\"] = [\n                    [-1] + list(o.size())[1:] for o in output\n                ]\n            else:\n                summary[m_key][\"output_shape\"] = list(output.size())\n                summary[m_key][\"output_shape\"][0] = batch_size\n\n            params = 0\n            if hasattr(module, \"weight\") and hasattr(module.weight, \"size\"):\n                params += torch.prod(torch.LongTensor(list(module.weight.size())))\n                summary[m_key][\"trainable\"] = module.weight.requires_grad\n            if hasattr(module, \"bias\") and hasattr(module.bias, \"size\"):\n                params += torch.prod(torch.LongTensor(list(module.bias.size())))\n            summary[m_key][\"nb_params\"] = params\n\n        if (\n                not isinstance(module, nn.Sequential)\n                and not isinstance(module, nn.ModuleList)\n                and not (module == model)\n        ):\n            hooks.append(module.register_forward_hook(hook))\n\n    device = device.lower()\n    assert device in [\n        \"cuda\",\n        \"cpu\",\n    ], \"Input device is not valid, please specify 'cuda' or 'cpu'\"\n\n    if device == \"cuda\" and torch.cuda.is_available():\n        dtype = torch.cuda.FloatTensor\n    else:\n        dtype = torch.FloatTensor\n\n    # multiple inputs to the network\n    if isinstance(input_size, tuple):\n        input_size = [input_size]\n\n    # batch_size of 2 for batchnorm\n    x = [torch.rand(2, *in_size).type(dtype) for in_size in input_size]\n    # print(type(x[0]))\n\n    # create properties\n    summary = OrderedDict()\n    hooks = []\n\n    # register hook\n    model.apply(register_hook)\n\n    # make a forward pass\n    # print(x.shape)\n    model(*x)\n\n    # remove these hooks\n    for h in hooks:\n        h.remove()\n\n    print(\"----------------------------------------------------------------\")\n    line_new = \"{:>20}  {:>25} {:>15}\".format(\"Layer (type)\", \"Output Shape\", \"Param #\")\n    print(line_new)\n    print(\"================================================================\")\n    total_params = 0\n    total_output = 0\n    trainable_params = 0\n    for layer in summary:\n        # input_shape, output_shape, trainable, nb_params\n        line_new = \"{:>20}  {:>25} {:>15}\".format(\n            layer,\n            str(summary[layer][\"output_shape\"]),\n            \"{0:,}\".format(summary[layer][\"nb_params\"]),\n        )\n        total_params += summary[layer][\"nb_params\"]\n        total_output += np.prod(summary[layer][\"output_shape\"])\n        if \"trainable\" in summary[layer]:\n            if summary[layer][\"trainable\"] == True:\n                trainable_params += summary[layer][\"nb_params\"]\n        print(line_new)\n\n    # assume 4 bytes/number (float on cuda).\n    total_input_size = abs(np.prod(input_size) * batch_size * 4. / (1024 ** 2.))\n    total_output_size = abs(2. * total_output * 4. / (1024 ** 2.))  # x2 for gradients\n    total_params_size = abs(total_params.numpy() * 4. / (1024 ** 2.))\n    total_size = total_params_size + total_output_size + total_input_size\n\n    print(\"================================================================\")\n    print(\"Total params: {0:,}\".format(total_params))\n    print(\"Trainable params: {0:,}\".format(trainable_params))\n    print(\"Non-trainable params: {0:,}\".format(total_params - trainable_params))\n    print(\"----------------------------------------------------------------\")\n    print(\"Input size (MB): %0.2f\" % total_input_size)\n    print(\"Forward/backward pass size (MB): %0.2f\" % total_output_size)\n    print(\"Params size (MB): %0.2f\" % total_params_size)\n    print(\"Estimated Total Size (MB): %0.2f\" % total_size)\n    print(\"----------------------------------------------------------------\")\n    # return summary\n"
  },
  {
    "path": "fast_reid/fastreid/utils/timer.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n# -*- coding: utf-8 -*-\n\nfrom time import perf_counter\nfrom typing import Optional\n\n\nclass Timer:\n    \"\"\"\n    A timer which computes the time elapsed since the start/reset of the timer.\n    \"\"\"\n\n    def __init__(self):\n        self.reset()\n\n    def reset(self):\n        \"\"\"\n        Reset the timer.\n        \"\"\"\n        self._start = perf_counter()\n        self._paused: Optional[float] = None\n        self._total_paused = 0\n        self._count_start = 1\n\n    def pause(self):\n        \"\"\"\n        Pause the timer.\n        \"\"\"\n        if self._paused is not None:\n            raise ValueError(\"Trying to pause a Timer that is already paused!\")\n        self._paused = perf_counter()\n\n    def is_paused(self) -> bool:\n        \"\"\"\n        Returns:\n            bool: whether the timer is currently paused\n        \"\"\"\n        return self._paused is not None\n\n    def resume(self):\n        \"\"\"\n        Resume the timer.\n        \"\"\"\n        if self._paused is None:\n            raise ValueError(\"Trying to resume a Timer that is not paused!\")\n        self._total_paused += perf_counter() - self._paused\n        self._paused = None\n        self._count_start += 1\n\n    def seconds(self) -> float:\n        \"\"\"\n        Returns:\n            (float): the total number of seconds since the start/reset of the\n                timer, excluding the time when the timer is paused.\n        \"\"\"\n        if self._paused is not None:\n            end_time: float = self._paused  # type: ignore\n        else:\n            end_time = perf_counter()\n        return end_time - self._start - self._total_paused\n\n    def avg_seconds(self) -> float:\n        \"\"\"\n        Returns:\n            (float): the average number of seconds between every start/reset and\n            pause.\n        \"\"\"\n        return self.seconds() / self._count_start\n"
  },
  {
    "path": "fast_reid/fastreid/utils/visualizer.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nimport pickle\nimport random\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tqdm\nfrom scipy.stats import norm\nfrom sklearn import metrics\n\nfrom .file_io import PathManager\n\n\nclass Visualizer:\n    r\"\"\"Visualize images(activation map) ranking list of features generated by reid models.\"\"\"\n\n    def __init__(self, dataset):\n        self.dataset = dataset\n\n    def get_model_output(self, all_ap, dist, q_pids, g_pids, q_camids, g_camids):\n        self.all_ap = all_ap\n        self.dist = dist\n        self.sim = 1 - dist\n        self.q_pids = q_pids\n        self.g_pids = g_pids\n        self.q_camids = q_camids\n        self.g_camids = g_camids\n\n        self.indices = np.argsort(dist, axis=1)\n        self.matches = (g_pids[self.indices] == q_pids[:, np.newaxis]).astype(np.int32)\n\n        self.num_query = len(q_pids)\n\n    def get_matched_result(self, q_index):\n        q_pid = self.q_pids[q_index]\n        q_camid = self.q_camids[q_index]\n\n        order = self.indices[q_index]\n        remove = (self.g_pids[order] == q_pid) & (self.g_camids[order] == q_camid)\n        keep = np.invert(remove)\n        cmc = self.matches[q_index][keep]\n        sort_idx = order[keep]\n        return cmc, sort_idx\n\n    def save_rank_result(self, query_indices, output, max_rank=5, vis_label=False, label_sort='ascending',\n                         actmap=False):\n        if vis_label:\n            fig, axes = plt.subplots(2, max_rank + 1, figsize=(3 * max_rank, 12))\n        else:\n            fig, axes = plt.subplots(1, max_rank + 1, figsize=(3 * max_rank, 6))\n        for cnt, q_idx in enumerate(tqdm.tqdm(query_indices)):\n            all_imgs = []\n            cmc, sort_idx = self.get_matched_result(q_idx)\n            query_info = self.dataset[q_idx]\n            query_img = query_info['images']\n            cam_id = query_info['camids']\n            query_name = query_info['img_paths'].split('/')[-1]\n            all_imgs.append(query_img)\n            query_img = np.rollaxis(np.asarray(query_img.numpy(), dtype=np.uint8), 0, 3)\n            plt.clf()\n            ax = fig.add_subplot(1, max_rank + 1, 1)\n            ax.imshow(query_img)\n            ax.set_title('{:.4f}/cam{}'.format(self.all_ap[q_idx], cam_id))\n            ax.axis(\"off\")\n            for i in range(max_rank):\n                if vis_label:\n                    ax = fig.add_subplot(2, max_rank + 1, i + 2)\n                else:\n                    ax = fig.add_subplot(1, max_rank + 1, i + 2)\n                g_idx = self.num_query + sort_idx[i]\n                gallery_info = self.dataset[g_idx]\n                gallery_img = gallery_info['images']\n                cam_id = gallery_info['camids']\n                all_imgs.append(gallery_img)\n                gallery_img = np.rollaxis(np.asarray(gallery_img, dtype=np.uint8), 0, 3)\n                if cmc[i] == 1:\n                    label = 'true'\n                    ax.add_patch(plt.Rectangle(xy=(0, 0), width=gallery_img.shape[1] - 1,\n                                               height=gallery_img.shape[0] - 1, edgecolor=(1, 0, 0),\n                                               fill=False, linewidth=5))\n                else:\n                    label = 'false'\n                    ax.add_patch(plt.Rectangle(xy=(0, 0), width=gallery_img.shape[1] - 1,\n                                               height=gallery_img.shape[0] - 1,\n                                               edgecolor=(0, 0, 1), fill=False, linewidth=5))\n                ax.imshow(gallery_img)\n                ax.set_title(f'{self.sim[q_idx, sort_idx[i]]:.3f}/{label}/cam{cam_id}')\n                ax.axis(\"off\")\n            # if actmap:\n            #     act_outputs = []\n            #\n            #     def hook_fns_forward(module, input, output):\n            #         act_outputs.append(output.cpu())\n            #\n            #     all_imgs = np.stack(all_imgs, axis=0)  # (b, 3, h, w)\n            #     all_imgs = torch.from_numpy(all_imgs).float()\n            #     # normalize\n            #     all_imgs = all_imgs.sub_(self.mean).div_(self.std)\n            #     sz = list(all_imgs.shape[-2:])\n            #     handle = m.base.register_forward_hook(hook_fns_forward)\n            #     with torch.no_grad():\n            #         _ = m(all_imgs.cuda())\n            #     handle.remove()\n            #     acts = self.get_actmap(act_outputs[0], sz)\n            #     for i in range(top + 1):\n            #         axes.flat[i].imshow(acts[i], alpha=0.3, cmap='jet')\n            if vis_label:\n                label_indice = np.where(cmc == 1)[0]\n                if label_sort == \"ascending\": label_indice = label_indice[::-1]\n                label_indice = label_indice[:max_rank]\n                for i in range(max_rank):\n                    if i >= len(label_indice): break\n                    j = label_indice[i]\n                    g_idx = self.num_query + sort_idx[j]\n                    gallery_info = self.dataset[g_idx]\n                    gallery_img = gallery_info['images']\n                    cam_id = gallery_info['camids']\n                    gallery_img = np.rollaxis(np.asarray(gallery_img, dtype=np.uint8), 0, 3)\n                    ax = fig.add_subplot(2, max_rank + 1, max_rank + 3 + i)\n                    ax.add_patch(plt.Rectangle(xy=(0, 0), width=gallery_img.shape[1] - 1,\n                                               height=gallery_img.shape[0] - 1,\n                                               edgecolor=(1, 0, 0),\n                                               fill=False, linewidth=5))\n                    ax.imshow(gallery_img)\n                    ax.set_title(f'{self.sim[q_idx, sort_idx[j]]:.3f}/cam{cam_id}')\n                    ax.axis(\"off\")\n\n            plt.tight_layout()\n            filepath = os.path.join(output, \"{}.jpg\".format(cnt))\n            fig.savefig(filepath)\n\n    def vis_rank_list(self, output, vis_label, num_vis=100, rank_sort=\"ascending\", label_sort=\"ascending\", max_rank=5,\n                      actmap=False):\n        r\"\"\"Visualize rank list of query instance\n        Args:\n            output (str): a directory to save rank list result.\n            vis_label (bool): if visualize label of query\n            num_vis (int):\n            rank_sort (str): save visualization results by which order,\n                if rank_sort is ascending, AP from low to high, vice versa.\n            label_sort (bool):\n            max_rank (int): maximum number of rank result to visualize\n            actmap (bool):\n        \"\"\"\n        assert rank_sort in ['ascending', 'descending'], \"{} not match [ascending, descending]\".format(rank_sort)\n\n        query_indices = np.argsort(self.all_ap)\n        if rank_sort == 'descending': query_indices = query_indices[::-1]\n\n        query_indices = query_indices[:int(num_vis)]\n        self.save_rank_result(query_indices, output, max_rank, vis_label, label_sort, actmap)\n\n    def vis_roc_curve(self, output):\n        PathManager.mkdirs(output)\n        pos, neg = [], []\n        for i, q in enumerate(self.q_pids):\n            cmc, sort_idx = self.get_matched_result(i)  # remove same id in same camera\n            ind_pos = np.where(cmc == 1)[0]\n            q_dist = self.dist[i]\n            pos.extend(q_dist[sort_idx[ind_pos]])\n\n            ind_neg = np.where(cmc == 0)[0]\n            neg.extend(q_dist[sort_idx[ind_neg]])\n\n        scores = np.hstack((pos, neg))\n        labels = np.hstack((np.zeros(len(pos)), np.ones(len(neg))))\n\n        fpr, tpr, thresholds = metrics.roc_curve(labels, scores)\n\n        self.plot_roc_curve(fpr, tpr)\n        filepath = os.path.join(output, \"roc.jpg\")\n        plt.savefig(filepath)\n        # self.plot_distribution(pos, neg)\n        # filepath = os.path.join(output, \"pos_neg_dist.jpg\")\n        # plt.savefig(filepath)\n        return fpr, tpr, pos, neg\n\n    @staticmethod\n    def plot_roc_curve(fpr, tpr, name='model', fig=None):\n        if fig is None:\n            fig = plt.figure()\n            plt.semilogx(np.arange(0, 1, 0.01), np.arange(0, 1, 0.01), 'r', linestyle='--', label='Random guess')\n        plt.semilogx(fpr, tpr, color=(random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1)),\n                     label='ROC curve with {}'.format(name))\n        plt.title('Receiver Operating Characteristic')\n        plt.xlabel('False Positive Rate')\n        plt.ylabel('True Positive Rate')\n        plt.legend(loc='best')\n        return fig\n\n    @staticmethod\n    def plot_distribution(pos, neg, name='model', fig=None):\n        if fig is None:\n            fig = plt.figure()\n        pos_color = (random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1))\n        n, bins, _ = plt.hist(pos, bins=80, alpha=0.7, density=True,\n                              color=pos_color,\n                              label='positive with {}'.format(name))\n        mu = np.mean(pos)\n        sigma = np.std(pos)\n        y = norm.pdf(bins, mu, sigma)  # fitting curve\n        plt.plot(bins, y, color=pos_color)  # plot y curve\n\n        neg_color = (random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1))\n        n, bins, _ = plt.hist(neg, bins=80, alpha=0.5, density=True,\n                              color=neg_color,\n                              label='negative with {}'.format(name))\n        mu = np.mean(neg)\n        sigma = np.std(neg)\n        y = norm.pdf(bins, mu, sigma)  # fitting curve\n        plt.plot(bins, y, color=neg_color)  # plot y curve\n\n        plt.xticks(np.arange(0, 1.5, 0.1))\n        plt.title('positive and negative pairs distribution')\n        plt.legend(loc='best')\n        return fig\n\n    @staticmethod\n    def save_roc_info(output, fpr, tpr, pos, neg):\n        results = {\n            \"fpr\": np.asarray(fpr),\n            \"tpr\": np.asarray(tpr),\n            \"pos\": np.asarray(pos),\n            \"neg\": np.asarray(neg),\n        }\n        with open(os.path.join(output, \"roc_info.pickle\"), \"wb\") as handle:\n            pickle.dump(results, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n    @staticmethod\n    def load_roc_info(path):\n        with open(path, 'rb') as handle: res = pickle.load(handle)\n        return res\n\n    # def plot_camera_dist(self):\n    #     same_cam, diff_cam = [], []\n    #     for i, q in enumerate(self.q_pids):\n    #         q_camid = self.q_camids[i]\n    #\n    #         order = self.indices[i]\n    #         same = (self.g_pids[order] == q) & (self.g_camids[order] == q_camid)\n    #         diff = (self.g_pids[order] == q) & (self.g_camids[order] != q_camid)\n    #         sameCam_idx = order[same]\n    #         diffCam_idx = order[diff]\n    #\n    #         same_cam.extend(self.sim[i, sameCam_idx])\n    #         diff_cam.extend(self.sim[i, diffCam_idx])\n    #\n    #     fig = plt.figure(figsize=(10, 5))\n    #     plt.hist(same_cam, bins=80, alpha=0.7, density=True, color='red', label='same camera')\n    #     plt.hist(diff_cam, bins=80, alpha=0.5, density=True, color='blue', label='diff camera')\n    #     plt.xticks(np.arange(0.1, 1.0, 0.1))\n    #     plt.title('positive and negative pair distribution')\n    #     return fig\n\n    # def get_actmap(self, features, sz):\n    #     \"\"\"\n    #     :param features: (1, 2048, 16, 8) activation map\n    #     :return:\n    #     \"\"\"\n    #     features = (features ** 2).sum(1)  # (1, 16, 8)\n    #     b, h, w = features.size()\n    #     features = features.view(b, h * w)\n    #     features = nn.functional.normalize(features, p=2, dim=1)\n    #     acts = features.view(b, h, w)\n    #     all_acts = []\n    #     for i in range(b):\n    #         act = acts[i].numpy()\n    #         act = cv2.resize(act, (sz[1], sz[0]))\n    #         act = 255 * (act - act.max()) / (act.max() - act.min() + 1e-12)\n    #         act = np.uint8(np.floor(act))\n    #         all_acts.append(act)\n    #     return all_acts\n"
  },
  {
    "path": "fast_reid/projects/CrossDomainReID/README.md",
    "content": "# Cross-domain Person Re-Identification\n\n## Introduction\n\n[UDAStrongBaseline](https://github.com/zkcys001/UDAStrongBaseline) is a transitional code based pyTorch framework for both unsupervised learning (USL) \nand unsupervised domain adaptation (UDA) in the object re-ID tasks. It provides stronger \nbaselines on these tasks. It needs the enviorment: Python >=3.6 and PyTorch >=1.1. We will transfer all the codes to the [fastreid](https://github.com/JDAI-CV/fast-reid) in the future (ongoing) from [UDAStrongBaseline](https://github.com/zkcys001/UDAStrongBaseline).\n\n\n### Unsupervised domain adaptation (UDA) on Person re-ID\n\n- `Direct Transfer` models are trained on the source-domain datasets \n([source_pretrain]()) and directly tested on the target-domain datasets.\n- UDA methods (`MMT`, `SpCL`, etc.) starting from ImageNet means that they are trained end-to-end \nin only one stage without source-domain pre-training. `MLT` denotes to the implementation of our NeurIPS-2020. \nPlease note that it is a pre-released repository for the anonymous review process, and the official \nrepository will be released upon the paper published.\n\n#### DukeMTMC-reID -> Market-1501\n\n| Method | Backbone | Pre-trained | mAP(%) | top-1(%) | top-5(%) | top-10(%) | Train time |\n| ----- | :------: | :---------: | :----: | :------: | :------: | :-------: | :------: | \n| Direct Transfer | ResNet50 | DukeMTMC | 32.2 | 64.9 | 78.7 | 83.4 | ~1h | \n| [UDA_TP](https://github.com/open-mmlab/OpenUnReID/) PR'2020| ResNet50 | DukeMTMC | 52.3 | 76.0 | 87.8 | 91.9 | ~2h | \n| [MMT](https://github.com/open-mmlab/OpenUnReID/) ICLR'2020| ResNet50 | DukeMTMC | 80.9 | 92.2 | 97.6 | 98.4 | ~6h |\n| [SpCL](https://github.com/open-mmlab/OpenUnReID/) NIPS'2020 submission| ResNet50 | DukeMTMC | 78.2 | 90.5 | 96.6 | 97.8 | ~3h |\n| [strong_baseline](https://github.com/open-mmlab/OpenUnReID/) | ResNet50 | DukeMTMC | 75.6 | 90.9 | 96.6 | 97.8 | ~3h | \n| [Our stronger_baseline](https://github.com/JDAI-CV/fast-reid) | ResNet50 | DukeMTMC | 78.0 | 91.0 | 96.4 | 97.7 | ~3h |\n| [MLT] NeurIPS'2020 submission| ResNet50 | DukeMTMC | 81.5| 92.8| 96.8| 97.9 | ~ |\n\n#### Market-1501 -> DukeMTMC-reID\n\n| Method | Backbone | Pre-trained | mAP(%) | top-1(%) | top-5(%) | top-10(%) | Train time |\n| ----- | :------: | :---------: | :----: | :------: | :------: | :-------: | :------: | \n| Direct Transfer | ResNet50 | Market | 34.1 | 51.3 | 65.3 | 71.7 | ~1h | \n| [UDA_TP](https://github.com/open-mmlab/OpenUnReID/) PR'2020| ResNet50 | Market | 45.7 | 65.5 | 78.0 | 81.7 | ~2h |\n| [MMT](https://github.com/open-mmlab/OpenUnReID/) ICLR'2020| ResNet50 | Market | 67.7 | 80.3 | 89.9 | 92.9 | ~6h |\n| [SpCL](https://github.com/open-mmlab/OpenUnReID/) NIPS'2020 submission | ResNet50 | Market | 70.4 | 83.8 | 91.2 | 93.4 | ~3h |\n| [strong_baseline](https://github.com/open-mmlab/OpenUnReID/) | ResNet50 | Market | 60.4 | 75.9 | 86.2 | 89.8 | ~3h |\n| [Our stronger_baseline](https://github.com/JDAI-CV/fast-reid) | ResNet50 | Market | 66.7 | 80.0 | 89.2 | 92.2  | ~3h |\n| [MLT] NeurIPS'2020 submission| ResNet50 | Market | 71.2 |83.9| 91.5| 93.2| ~ |\n\n### Market1501 -> MSMT17\n\n| Method | Source | Rank@1 | mAP | mINP |\n| :---: | :---: | :---: |:---: | :---: |\n| DirectTransfer(R50) | Market1501 | 29.8% | 10.3% | 9.3% |\n| Our method | DukeMTMC | 56.6% | 26.5% | - |\n\n### DukeMTMC -> MSMT17\n| Method | Source | Rank@1 | mAP | mINP |\n| :---: | :---: | :---: |:---: | :---: |\n| DirectTransfer(R50) | DukeMTMC | 34.8% | 12.5% | 0.3% |\n| Our method | DukeMTMC | 59.5% | 27.7% | - |\n"
  },
  {
    "path": "fast_reid/projects/DG-ReID/README.md",
    "content": "# Semi-Supervised Domain Generalizable Person Re-Identification (SSKD)\n\n## Introduction\n\nSSKD is implemented based on **FastReID v1.0.0**. You can refer to [sskd github link](https://github.com/xiaomingzhid/sskd) It provides a semi-supervised feature learning framework to learn domain-general representations. The framework is shown in \n\n<img src=\"images/framework.png\" width=\"850\" >\n\n## Dataset\n\n**FastHuman** is very challenging, as it contains more complex application scenarios and large-scale training, testing datasets. It has diverse images from different application scenarios including campus, airport, shopping mall, street, and railway station.\nIt contains 447,233 labeled images of 40,061 subjects captured by 82 cameras. The details of FastHuman, you can refer to [paper](https://arxiv.org/pdf/2108.05045.pdf).\n\n| Source Domain |  \\#subjects | \\#images | \\#cameras | collection place |\n| ----- | :------: | :---------: | :----: | :------: |\n| CUHK03|  1,090 | 14,096 | 2 | campus |  \n| SAIVT | 152   | 7,150  | 8 | buildings |\n| AirportALERT | 9,651 | 30,243 | 6 | airport |\n|iLIDS|  300   | 4,515  | 2 | airport |\n|PKU  |  114   | 1,824  | 2 | campus |\n|PRAI |   1,580 | 39,481| 2 | aerial imagery |\n|SenseReID | 1,718 | 3,338  | 2 | unknown |\n|SYSU | 510  | 30,071 | 4 | campus |\n|Thermalworld | 409   | 8,103  | 1 | unknown |\n|3DPeS  | 193  | 1,012  | 1 | outdoor  |\n|CAVIARa | 72  | 1,220  | 1 | shopping mall |\n|VIPeR | 632   | 1,264  | 2 | unknown |\n|Shinpuhkan| 24 | 4,501  | 8 | unknown |\n|WildTrack | 313 | 33,979 | 7| outdoor |\n|cuhk-sysu | 11,934| 34,574 | 1| street |\n|LPW |  2,731 | 30,678 | 4 | street |\n|GRID |  1,025 | 1,275 | 8 | underground |\n|Total | 31,423| 246,049 | 57 | - |\n\n\n|Unseen Domain|  \\#subjects | \\#images | \\#cameras | collection place  |\n| ----- | :------: | :---------: | :----: | :------: |\n|Market1501 | 1,501  | 32,217 | 6 | campus |\n|DukeMTMC | 1,812 | 36,441 | 8 | campus |\n|MSMT17 | 4,101 | 126,441| 15| campus |\n|PartialREID | 60 | 600| 6|campus |\n|PartialiLIDS | 119  | 238 | 2 | airport |\n|OccludedREID | 200  | 2,000| 5| campus |\n|CrowdREID | 845  | 3,257 | 11 | railway station| \n|Total   | 8,638  | 201,184| 49 | - |\n\n**YouTube-Human** is a unlabeled human dataset. You can download the Street-View video from YouTube website, and the use the human detection algorithm ([centerX](https://github.com/JDAI-CV/centerX)) to obtain the human images.\n\n## Training & Evaluation\n\nThe whole training process is divided into two stages:\n\n- Train a student model (r34-ibn) and a teacher model (r101_ibn), you can run:\n```bash\npython3 projects/Basic_Project/train_net.py --config-file projects/Basic_Project/configs/r34-ibn.yml --num-gpu 4\npython3 projects/Basic_Project/train_net.py --config-file projects/Basic_Project/configs/r101-ibn.yml --num-gpu 4\n```\n- Train the student model based unlabeled dataset and sskd, you can run:\n```bash\npython3 projects/SSKD/train_net.py --config-file projects/SSKD/configs/sskd.yml --num-gpu 4\n```\n### Results\n<img src=\"images/result1.png\" width=\"550\" >\n<img src=\"images/result2.png\" width=\"500\" >\nOther some experimental results you could find in our [arxiv paper](https://arxiv.org/pdf/2108.05045.pdf).\n\n## Reference Project\n- [fastreid](https://github.com/JDAI-CV/fast-reid)\n- [centerX](https://github.com/JDAI-CV/centerX)\n\n## Citation\nIf you use **fastreid** or **sskd** in your research, please give credit to the following papers:\n\n```BibTeX\n@article{he2020fastreid,\n  title={FastReID: A Pytorch Toolbox for General Instance Re-identification},\n  author={He, Lingxiao and Liao, Xingyu and Liu, Wu and Liu, Xinchen and Cheng, Peng and Mei, Tao},\n  journal={arXiv preprint arXiv:2006.02631},\n  year={2020}\n}\n```\n```BibTeX\n@article{he2021semi,\n  title={Semi-Supervised Domain Generalizable Person Re-Identification},\n  author={He, Lingxiao and Liu, Wu and Liang, Jian and Zheng, Kecheng and Liao, Xingyu and Cheng, Peng and Mei, Tao},\n  journal={arXiv preprint arXiv:2108.05045},\n  year={2021}\n}\n```\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/README.md",
    "content": "# FastAttr in FastReID\n\nThis project provides a strong baseline for pedestrian attribute recognition.\n\n## Datasets Preparation\n\nWe use `PA100k` to evaluate the model's performance.\nYou can do download dataset from [HydraPlus-Net](https://github.com/xh-liu/HydraPlus-Net).\n\n## Usage\n\nThe training config file can be found in `projects/FastAttr/config`, which you can use to reproduce the results of the repo.\n\nFor example\n\n```bash\npython3 projects/FastAttr/train_net.py --config-file projects/FastAttr/configs/pa100.yml --num-gpus 4\n```\n\n## Experiment Results\n\nWe refer to [A Strong Baseline of Pedestrian Attribute Recognition](https://github.com/valencebond/Strong_Baseline_of_Pedestrian_Attribute_Recognition/tree/master) as our baseline methods and conduct the experiment\nwith 4 GPUs.\nMore details can be found in the config file and code.\n\n### PA100k\n\n| Method | Pretrained | mA | Accu | Prec | Recall | F1 | \n| :---: | :---: | :---: |:---: | :---: | :---: | :---: |\n| attribute baseline | ImageNet | 80.50 | 78.84 | 87.24 | 87.12 | 86.78 | \n| FastAttr | ImageNet | 77.57 | 78.03 | 88.39 | 84.98 | 86.65 | \n"
  },
  {
    "path": "fast_reid/projects/FastAttr/configs/Base-attribute.yml",
    "content": "MODEL:\n  META_ARCHITECTURE: AttrBaseline\n\n  BACKBONE:\n    NAME: build_resnet_backbone\n    NORM: BN\n    DEPTH: 50x\n    LAST_STRIDE: 2\n    FEAT_DIM: 2048\n    WITH_IBN: False\n    PRETRAIN: True\n    PRETRAIN_PATH: /export/home/lxy/.cache/torch/checkpoints/resnet50-19c8e357.pth\n\n  HEADS:\n    NAME: AttrHead\n    WITH_BNNECK: True\n    POOL_LAYER: FastGlobalAvgPool\n    CLS_LAYER: Linear\n    NUM_CLASSES: 26\n\n  LOSSES:\n    NAME: (\"BinaryCrossEntropyLoss\",)\n\n    BCE:\n      WEIGHT_ENABLED: True\n      SCALE: 1.\n\nINPUT:\n  SIZE_TRAIN: [ 256, 192 ]\n  SIZE_TEST: [ 256, 192 ]\n\n  FLIP:\n    ENABLED: True\n\n  PADDING:\n    ENABLED: True\n\nDATALOADER:\n  SAMPLER_TRAIN: TrainingSampler\n  NUM_WORKERS: 8\n\nSOLVER:\n  MAX_EPOCH: 30\n  OPT: SGD\n  BASE_LR: 0.04\n  BIAS_LR_FACTOR: 2.\n  HEADS_LR_FACTOR: 10.\n  WEIGHT_DECAY: 0.0005\n  WEIGHT_DECAY_BIAS: 0.0005\n  IMS_PER_BATCH: 256\n\n  NESTEROV: False\n  SCHED: MultiStepLR\n  STEPS: [ 15, 20, 25 ]\n\n  WARMUP_FACTOR: 0.1\n  WARMUP_ITERS: 1000\n\n  CHECKPOINT_PERIOD: 10\n\nTEST:\n  EVAL_PERIOD: 10\n  IMS_PER_BATCH: 256\n\nCUDNN_BENCHMARK: True\n\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/configs/dukemtmc.yml",
    "content": "_BASE_: Base-attribute.yml\n\nDATASETS:\n  NAMES: (\"DukeMTMCAttr\",)\n  TESTS: (\"DukeMTMCAttr\",)\n\nMODEL:\n  HEADS:\n    NUM_CLASSES: 23\n\nOUTPUT_DIR: projects/FastAttr/logs/dukemtmc/strong_baseline"
  },
  {
    "path": "fast_reid/projects/FastAttr/configs/market1501.yml",
    "content": "_BASE_: Base-attribute.yml\n\nDATASETS:\n  NAMES: (\"Market1501Attr\",)\n  TESTS: (\"Market1501Attr\",)\n\nMODEL:\n  HEADS:\n    NUM_CLASSES: 27\n\nOUTPUT_DIR: projects/FastAttr/logs/market1501/strong_baseline"
  },
  {
    "path": "fast_reid/projects/FastAttr/configs/pa100.yml",
    "content": "_BASE_: Base-attribute.yml\n\nDATASETS:\n  NAMES: (\"PA100K\",)\n  TESTS: (\"PA100K\",)\n\nOUTPUT_DIR: projects/FastAttr/logs/pa100k/strong_baseline"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .attr_evaluation import AttrEvaluator\nfrom .config import add_attr_config\nfrom .datasets import *\nfrom .modeling import *\nfrom .attr_dataset import AttrDataset\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/attr_dataset.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom fast_reid.fastreid.data.data_utils import read_image\n\n\nclass AttrDataset(Dataset):\n    \"\"\"Image Person Attribute Dataset\"\"\"\n\n    def __init__(self, img_items, transform, attr_dict):\n        self.img_items = img_items\n        self.transform = transform\n        self.attr_dict = attr_dict\n\n    def __len__(self):\n        return len(self.img_items)\n\n    def __getitem__(self, index):\n        img_path, labels = self.img_items[index]\n        img = read_image(img_path)\n\n        if self.transform is not None: img = self.transform(img)\n\n        labels = torch.as_tensor(labels)\n\n        return {\n            \"images\": img,\n            \"targets\": labels,\n            \"img_paths\": img_path,\n        }\n\n    @property\n    def num_classes(self):\n        return len(self.attr_dict)\n\n    @property\n    def sample_weights(self):\n        sample_weights = torch.zeros(self.num_classes, dtype=torch.float32)\n        for _, attr in self.img_items:\n            sample_weights += torch.as_tensor(attr)\n        sample_weights /= len(self)\n        return sample_weights\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/attr_evaluation.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport copy\nimport logging\nfrom collections import OrderedDict\n\nimport torch\n\nfrom fast_reid.fastreid.evaluation.evaluator import DatasetEvaluator\nfrom fast_reid.fastreid.utils import comm\n\nlogger = logging.getLogger(\"fastreid.attr_evaluation\")\n\n\nclass AttrEvaluator(DatasetEvaluator):\n    def __init__(self, cfg, attr_dict, thres=0.5, output_dir=None):\n        self.cfg = cfg\n        self.attr_dict = attr_dict\n        self.thres = thres\n        self._output_dir = output_dir\n\n        self._cpu_device = torch.device(\"cpu\")\n\n        self.pred_logits = []\n        self.gt_labels = []\n\n    def reset(self):\n        self.pred_logits = []\n        self.gt_labels = []\n\n    def process(self, inputs, outputs):\n        self.gt_labels.extend(inputs[\"targets\"].to(self._cpu_device))\n        self.pred_logits.extend(outputs.to(self._cpu_device, torch.float32))\n\n    @staticmethod\n    def get_attr_metrics(gt_labels, pred_logits, thres):\n\n        eps = 1e-20\n\n        pred_labels = copy.deepcopy(pred_logits)\n        pred_labels[pred_logits < thres] = 0\n        pred_labels[pred_logits >= thres] = 1\n\n        # Compute label-based metric\n        overlaps = pred_labels * gt_labels\n        correct_pos = overlaps.sum(axis=0)\n        real_pos = gt_labels.sum(axis=0)\n        inv_overlaps = (1 - pred_labels) * (1 - gt_labels)\n        correct_neg = inv_overlaps.sum(axis=0)\n        real_neg = (1 - gt_labels).sum(axis=0)\n\n        # Compute instance-based accuracy\n        pred_labels = pred_labels.astype(bool)\n        gt_labels = gt_labels.astype(bool)\n        intersect = (pred_labels & gt_labels).astype(float)\n        union = (pred_labels | gt_labels).astype(float)\n        ins_acc = (intersect.sum(axis=1) / (union.sum(axis=1) + eps)).mean()\n        ins_prec = (intersect.sum(axis=1) / (pred_labels.astype(float).sum(axis=1) + eps)).mean()\n        ins_rec = (intersect.sum(axis=1) / (gt_labels.astype(float).sum(axis=1) + eps)).mean()\n        ins_f1 = (2 * ins_prec * ins_rec) / (ins_prec + ins_rec + eps)\n\n        term1 = correct_pos / (real_pos + eps)\n        term2 = correct_neg / (real_neg + eps)\n        label_mA_verbose = (term1 + term2) * 0.5\n        label_mA = label_mA_verbose.mean()\n\n        results = OrderedDict()\n        results[\"Accu\"] = ins_acc * 100\n        results[\"Prec\"] = ins_prec * 100\n        results[\"Recall\"] = ins_rec * 100\n        results[\"F1\"] = ins_f1 * 100\n        results[\"mA\"] = label_mA * 100\n        results[\"metric\"] = label_mA * 100\n        return results\n\n    def evaluate(self):\n        if comm.get_world_size() > 1:\n            comm.synchronize()\n            pred_logits = comm.gather(self.pred_logits)\n            pred_logits = sum(pred_logits, [])\n\n            gt_labels = comm.gather(self.gt_labels)\n            gt_labels = sum(gt_labels, [])\n\n            if not comm.is_main_process():\n                return {}\n        else:\n            pred_logits = self.pred_logits\n            gt_labels = self.gt_labels\n\n        pred_logits = torch.stack(pred_logits, dim=0).numpy()\n        gt_labels = torch.stack(gt_labels, dim=0).numpy()\n\n        # Pedestrian attribute metrics\n        thres = self.cfg.TEST.THRES\n        self._results = self.get_attr_metrics(gt_labels, pred_logits, thres)\n\n        return copy.deepcopy(self._results)\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/config.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom fast_reid.fastreid.config import CfgNode as CN\n\n\ndef add_attr_config(cfg):\n    _C = cfg\n\n    _C.MODEL.LOSSES.BCE = CN({\"WEIGHT_ENABLED\": True})\n    _C.MODEL.LOSSES.BCE.SCALE = 1.\n\n    _C.TEST.THRES = 0.5\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/datasets/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n# Attributed datasets\nfrom .pa100k import PA100K\nfrom .market1501attr import Market1501Attr\nfrom .dukemtmcattr import DukeMTMCAttr\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/datasets/bases.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport copy\nimport logging\nimport os\n\nfrom tabulate import tabulate\nfrom termcolor import colored\n\nlogger = logging.getLogger(\"fastreid.attr_dataset\")\n\n\nclass Dataset(object):\n\n    def __init__(\n            self,\n            train,\n            val,\n            test,\n            attr_dict,\n            mode='train',\n            verbose=True,\n            **kwargs,\n    ):\n        self.train = train\n        self.val = val\n        self.test = test\n        self._attr_dict = attr_dict\n        self._num_attrs = len(self.attr_dict)\n\n        if mode == 'train':\n            self.data = self.train\n        elif mode == 'val':\n            self.data = self.val\n        else:\n            self.data = self.test\n\n    @property\n    def num_attrs(self):\n        return self._num_attrs\n\n    @property\n    def attr_dict(self):\n        return self._attr_dict\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, index):\n        raise NotImplementedError\n\n    def check_before_run(self, required_files):\n        \"\"\"Checks if required files exist before going deeper.\n        Args:\n            required_files (str or list): string file name(s).\n        \"\"\"\n        if isinstance(required_files, str):\n            required_files = [required_files]\n\n        for fpath in required_files:\n            if not os.path.exists(fpath):\n                raise RuntimeError('\"{}\" is not found'.format(fpath))\n\n    def combine_all(self):\n        \"\"\"Combines train, val and test in a dataset for training.\"\"\"\n        combined = copy.deepcopy(self.train)\n\n        def _combine_data(data):\n            for img_path, pid, camid in data:\n                if pid in self._junk_pids:\n                    continue\n                pid = self.dataset_name + \"_\" + str(pid)\n                camid = self.dataset_name + \"_\" + str(camid)\n                combined.append((img_path, pid, camid))\n\n        _combine_data(self.query)\n        _combine_data(self.gallery)\n\n        self.train = combined\n        self.num_train_pids = self.get_num_pids(self.train)\n\n    def show_train(self):\n        num_train = len(self.train)\n        num_val = len(self.val)\n        num_total = num_train + num_val\n\n        headers = ['subset', '# images']\n        csv_results = [\n            ['train', num_train],\n            ['val', num_val],\n            ['total', num_total],\n        ]\n\n        # tabulate it\n        table = tabulate(\n            csv_results,\n            tablefmt=\"pipe\",\n            headers=headers,\n            numalign=\"left\",\n        )\n        logger.info(f\"=> Loaded {self.__class__.__name__} in csv format: \\n\" + colored(table, \"cyan\"))\n        logger.info(\"attributes:\")\n        for label, attr in self.attr_dict.items():\n            logger.info('{:3d}: {}'.format(label, attr))\n        logger.info(\"------------------------------\")\n        logger.info(\"# attributes: {}\".format(len(self.attr_dict)))\n\n    def show_test(self):\n        num_test = len(self.test)\n\n        headers = ['subset', '# images']\n        csv_results = [\n            ['test', num_test],\n        ]\n\n        # tabulate it\n        table = tabulate(\n            csv_results,\n            tablefmt=\"pipe\",\n            headers=headers,\n            numalign=\"left\",\n        )\n        logger.info(f\"=> Loaded {self.__class__.__name__} in csv format: \\n\" + colored(table, \"cyan\"))\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/datasets/dukemtmcattr.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: liaoxingyu2@jd.com\n\"\"\"\n\nimport glob\nimport os.path as osp\nimport re\nimport mat4py\nimport numpy as np\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\n\nfrom .bases import Dataset\n\n\n@DATASET_REGISTRY.register()\nclass DukeMTMCAttr(Dataset):\n    \"\"\"DukeMTMCAttr.\n\n    Reference:\n        Lin, Yutian, et al. \"Improving person re-identification by attribute and identity learning.\"\n        Pattern Recognition 95 (2019): 151-161.\n\n    URL: `<https://github.com/vana77/DukeMTMC-attribute>`_\n\n    The folder structure should be:\n        DukeMTMC-reID/\n            bounding_box_train/ # images\n            bounding_box_test/ # images\n            duke_attribute.mat\n    \"\"\"\n    dataset_dir = 'DukeMTMC-reID'\n    dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-reID.zip'\n    dataset_name = \"dukemtmc\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n        self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train')\n        self.test_dir = osp.join(self.dataset_dir, 'bounding_box_test')\n\n        required_files = [\n            self.dataset_dir,\n            self.train_dir,\n            self.test_dir,\n        ]\n        self.check_before_run(required_files)\n\n        duke_attr = mat4py.loadmat(osp.join(self.dataset_dir, 'duke_attribute.mat'))['duke_attribute']\n        sorted_attrs = sorted(duke_attr['train'].keys())\n        sorted_attrs.remove('image_index')\n        attr_dict = {i: str(attr) for i, attr in enumerate(sorted_attrs)}\n\n        train = self.process_dir(self.train_dir, duke_attr['train'], sorted_attrs)\n        test = val = self.process_dir(self.test_dir, duke_attr['test'], sorted_attrs)\n\n        super(DukeMTMCAttr, self).__init__(train, val, test, attr_dict=attr_dict, **kwargs)\n\n    def process_dir(self, dir_path, annotation, sorted_attrs):\n        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))\n        pattern = re.compile(r'([-\\d]+)_c(\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            assert 1 <= camid <= 8\n\n            img_index = annotation['image_index'].index(str(pid).zfill(4))\n            attrs = np.array([int(annotation[i][img_index]) - 1 for i in sorted_attrs], dtype=np.float32)\n            data.append((img_path, attrs))\n\n        return data\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/datasets/market1501attr.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport glob\nimport os.path as osp\nimport re\nimport warnings\nimport mat4py\nimport numpy as np\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\n\nfrom .bases import Dataset\n\n\n@DATASET_REGISTRY.register()\nclass Market1501Attr(Dataset):\n    \"\"\"Market1501Attr.\n\n    Reference:\n        Lin, Yutian, et al. \"Improving person re-identification by attribute and identity learning.\"\n        Pattern Recognition 95 (2019): 151-161.\n\n    URL: `<https://github.com/vana77/Market-1501_Attribute>`_\n\n    The folder structure should be:\n        Market-1501-v15.09.15/\n            bounding_box_train/ # images\n            bounding_box_test/ # images\n            market_attribute.mat\n    \"\"\"\n    _junk_pids = [0, -1]\n    dataset_dir = ''\n    dataset_url = 'http://188.138.127.15:81/Datasets/Market-1501-v15.09.15.zip'\n    dataset_name = \"market1501\"\n\n    def __init__(self, root='datasets', market1501_500k=False, **kwargs):\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n\n        # allow alternative directory structure\n        self.data_dir = self.dataset_dir\n        data_dir = osp.join(self.data_dir, 'Market-1501-v15.09.15')\n        if osp.isdir(data_dir):\n            self.data_dir = data_dir\n        else:\n            warnings.warn('The current data structure is deprecated. Please '\n                          'put data folders such as \"bounding_box_train\" under '\n                          '\"Market-1501-v15.09.15\".')\n\n        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')\n        self.test_dir = osp.join(self.data_dir, 'bounding_box_test')\n\n        required_files = [\n            self.data_dir,\n            self.train_dir,\n            self.test_dir,\n        ]\n        self.check_before_run(required_files)\n\n        market_attr = mat4py.loadmat(osp.join(self.data_dir, 'market_attribute.mat'))['market_attribute']\n        sorted_attrs = sorted(market_attr['train'].keys())\n        sorted_attrs.remove('image_index')\n        attr_dict = {i: str(attr) for i, attr in enumerate(sorted_attrs)}\n\n        train = self.process_dir(self.train_dir, market_attr['train'], sorted_attrs)\n        test = val = self.process_dir(self.test_dir, market_attr['test'], sorted_attrs)\n\n        super(Market1501Attr, self).__init__(train, val, test, attr_dict=attr_dict, **kwargs)\n\n    def process_dir(self, dir_path, annotation, sorted_attrs):\n        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))\n        pattern = re.compile(r'([-\\d]+)_c(\\d)')\n\n        data = []\n        for img_path in img_paths:\n            pid, camid = map(int, pattern.search(img_path).groups())\n            if pid == -1 or pid == 0:\n                continue  # junk images are just ignored\n            assert 0 <= pid <= 1501  # pid == 0 means background\n            assert 1 <= camid <= 6\n\n            img_index = annotation['image_index'].index(str(pid).zfill(4))\n            attrs = np.array([int(annotation[i][img_index])-1 for i in sorted_attrs], dtype=np.float32)\n            data.append((img_path, attrs))\n\n        return data\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/datasets/pa100k.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os.path as osp\n\nimport numpy as np\nfrom scipy.io import loadmat\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\n\nfrom .bases import Dataset\n\n\n@DATASET_REGISTRY.register()\nclass PA100K(Dataset):\n    \"\"\"Pedestrian attribute dataset.\n    80k training images + 20k test images.\n    The folder structure should be:\n        pa100k/\n            data/ # images\n            annotation.mat\n    \"\"\"\n    dataset_dir = 'PA-100K'\n\n    def __init__(self, root='', **kwargs):\n        self.root = root\n        self.dataset_dir = osp.join(self.root, self.dataset_dir)\n        self.data_dir = osp.join(self.dataset_dir, \"data\")\n        self.anno_mat_path = osp.join(\n            self.dataset_dir, \"annotation.mat\"\n        )\n\n        required_files = [self.data_dir, self.anno_mat_path]\n        self.check_before_run(required_files)\n\n        train, val, test, attr_dict = self.extract_data()\n        super(PA100K, self).__init__(train, val, test, attr_dict=attr_dict, **kwargs)\n\n    def extract_data(self):\n        # anno_mat is a dictionary with keys: ['test_images_name', 'val_images_name',\n        # 'train_images_name', 'val_label', 'attributes', 'test_label', 'train_label']\n        anno_mat = loadmat(self.anno_mat_path)\n\n        def _extract(key_name, key_label):\n            names = anno_mat[key_name]\n            labels = anno_mat[key_label]\n            num_imgs = names.shape[0]\n            data = []\n            for i in range(num_imgs):\n                name = names[i, 0][0]\n                attrs = labels[i, :].astype(np.float32)\n                img_path = osp.join(self.data_dir, name)\n                data.append((img_path, attrs))\n            return data\n\n        train = _extract('train_images_name', 'train_label')\n        val = _extract('val_images_name', 'val_label')\n        test = _extract('test_images_name', 'test_label')\n        attrs = anno_mat['attributes']\n        attr_dict = {i: str(attr[0][0]) for i, attr in enumerate(attrs)}\n\n        return train, val, test, attr_dict\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/modeling/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .attr_baseline import AttrBaseline\nfrom .attr_head import AttrHead\nfrom .bce_loss import cross_entropy_sigmoid_loss\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/modeling/attr_baseline.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom fast_reid.fastreid.modeling.meta_arch.baseline import Baseline\nfrom fast_reid.fastreid.modeling.meta_arch.build import META_ARCH_REGISTRY\nfrom .bce_loss import cross_entropy_sigmoid_loss\n\n\n@META_ARCH_REGISTRY.register()\nclass AttrBaseline(Baseline):\n\n    @classmethod\n    def from_config(cls, cfg):\n        base_res = Baseline.from_config(cfg)\n        base_res[\"loss_kwargs\"].update({\n            'bce': {\n                'scale': cfg.MODEL.LOSSES.BCE.SCALE\n            }\n        })\n        return base_res\n\n    def losses(self, outputs, gt_labels):\n        r\"\"\"\n        Compute loss from modeling's outputs, the loss function input arguments\n        must be the same as the outputs of the model forwarding.\n        \"\"\"\n        # model predictions\n        cls_outputs = outputs[\"cls_outputs\"]\n\n        loss_dict = {}\n        loss_names = self.loss_kwargs[\"loss_names\"]\n\n        if \"BinaryCrossEntropyLoss\" in loss_names:\n            bce_kwargs = self.loss_kwargs.get('bce')\n            loss_dict[\"loss_bce\"] = cross_entropy_sigmoid_loss(\n                cls_outputs,\n                gt_labels,\n                self.sample_weights,\n            ) * bce_kwargs.get('scale')\n\n        return loss_dict\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/modeling/attr_head.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom fast_reid.fastreid.modeling.heads import EmbeddingHead\nfrom fast_reid.fastreid.modeling.heads.build import REID_HEADS_REGISTRY\nfrom fast_reid.fastreid.layers.weight_init import weights_init_kaiming\n\n\n@REID_HEADS_REGISTRY.register()\nclass AttrHead(EmbeddingHead):\n    def __init__(self, cfg):\n        super().__init__(cfg)\n        num_classes = cfg.MODEL.HEADS.NUM_CLASSES\n\n        self.bnneck = nn.BatchNorm1d(num_classes)\n        self.bnneck.apply(weights_init_kaiming)\n\n    def forward(self, features, targets=None):\n        \"\"\"\n        See :class:`ReIDHeads.forward`.\n        \"\"\"\n        pool_feat = self.pool_layer(features)\n        neck_feat = self.bottleneck(pool_feat)\n        neck_feat = neck_feat.view(neck_feat.size(0), -1)\n\n        logits = F.linear(neck_feat, self.weight)\n        logits = self.bnneck(logits)\n\n        # Evaluation\n        if not self.training:\n            cls_outptus = torch.sigmoid(logits)\n            return cls_outptus\n\n        return {\n            \"cls_outputs\": logits,\n        }\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/fastattr/modeling/bce_loss.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\n\n\ndef ratio2weight(targets, ratio):\n    pos_weights = targets * (1 - ratio)\n    neg_weights = (1 - targets) * ratio\n    weights = torch.exp(neg_weights + pos_weights)\n\n    weights[targets > 1] = 0.0\n    return weights\n\n\ndef cross_entropy_sigmoid_loss(pred_class_logits, gt_classes, sample_weight=None):\n    loss = F.binary_cross_entropy_with_logits(pred_class_logits, gt_classes, reduction='none')\n\n    if sample_weight is not None:\n        targets_mask = torch.where(gt_classes.detach() > 0.5,\n                                   torch.ones(1, device=\"cuda\"), torch.zeros(1, device=\"cuda\"))  # dtype float32\n        weight = ratio2weight(targets_mask, sample_weight)\n        loss = loss * weight\n\n    with torch.no_grad():\n        non_zero_cnt = max(loss.nonzero(as_tuple=False).size(0), 1)\n\n    loss = loss.sum() / non_zero_cnt\n    return loss\n"
  },
  {
    "path": "fast_reid/projects/FastAttr/train_net.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport logging\nimport sys\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.engine import DefaultTrainer\nfrom fast_reid.fastreid.engine import default_argument_parser, default_setup, launch\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.build import _root, build_reid_train_loader, build_reid_test_loader\nfrom fast_reid.fastreid.data.transforms import build_transforms\nfrom fast_reid.fastreid.utils import comm\n\nfrom fastattr import *\n\n\nclass AttrTrainer(DefaultTrainer):\n    sample_weights = None\n\n    @classmethod\n    def build_model(cls, cfg):\n        \"\"\"\n        Returns:\n            torch.nn.Module:\n        It now calls :func:`fastreid.modeling.build_model`.\n        Overwrite it if you'd like a different model.\n        \"\"\"\n        model = DefaultTrainer.build_model(cfg)\n        if cfg.MODEL.LOSSES.BCE.WEIGHT_ENABLED and \\\n                AttrTrainer.sample_weights is not None:\n            setattr(model, \"sample_weights\", AttrTrainer.sample_weights.to(model.device))\n        else:\n            setattr(model, \"sample_weights\", None)\n        return model\n\n    @classmethod\n    def build_train_loader(cls, cfg):\n\n        logger = logging.getLogger(\"fastreid.attr_dataset\")\n        train_items = list()\n        attr_dict = None\n        for d in cfg.DATASETS.NAMES:\n            dataset = DATASET_REGISTRY.get(d)(root=_root, combineall=cfg.DATASETS.COMBINEALL)\n            if comm.is_main_process():\n                dataset.show_train()\n            if attr_dict is not None:\n                assert attr_dict == dataset.attr_dict, f\"attr_dict in {d} does not match with previous ones\"\n            else:\n                attr_dict = dataset.attr_dict\n            train_items.extend(dataset.train)\n\n        train_transforms = build_transforms(cfg, is_train=True)\n        train_set = AttrDataset(train_items, train_transforms, attr_dict)\n\n        data_loader = build_reid_train_loader(cfg, train_set=train_set)\n        AttrTrainer.sample_weights = data_loader.dataset.sample_weights\n        return data_loader\n\n    @classmethod\n    def build_test_loader(cls, cfg, dataset_name):\n        dataset = DATASET_REGISTRY.get(dataset_name)(root=_root)\n        attr_dict = dataset.attr_dict\n        if comm.is_main_process():\n            dataset.show_test()\n        test_items = dataset.test\n\n        test_transforms = build_transforms(cfg, is_train=False)\n        test_set = AttrDataset(test_items, test_transforms, attr_dict)\n        data_loader, _ = build_reid_test_loader(cfg, test_set=test_set)\n        return data_loader\n\n    @classmethod\n    def build_evaluator(cls, cfg, dataset_name, output_folder=None):\n        data_loader = cls.build_test_loader(cfg, dataset_name)\n        return data_loader, AttrEvaluator(cfg, output_folder)\n\n\ndef setup(args):\n    \"\"\"\n    Create configs and perform basic setups.\n    \"\"\"\n    cfg = get_cfg()\n    add_attr_config(cfg)\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    default_setup(cfg, args)\n    return cfg\n\n\ndef main(args):\n    cfg = setup(args)\n\n    if args.eval_only:\n        cfg.defrost()\n        cfg.MODEL.BACKBONE.PRETRAIN = False\n        model = AttrTrainer.build_model(cfg)\n\n        Checkpointer(model).load(cfg.MODEL.WEIGHTS)  # load trained model\n\n        res = AttrTrainer.test(cfg, model)\n        return res\n\n    trainer = AttrTrainer(cfg)\n    trainer.resume_or_load(resume=args.resume)\n    return trainer.train()\n\n\nif __name__ == \"__main__\":\n    args = default_argument_parser().parse_args()\n    print(\"Command Line Args:\", args)\n    launch(\n        main,\n        args.num_gpus,\n        num_machines=args.num_machines,\n        machine_rank=args.machine_rank,\n        dist_url=args.dist_url,\n        args=(args,),\n    )\n"
  },
  {
    "path": "fast_reid/projects/FastClas/README.md",
    "content": "# FastClas in FastReID\n\nThis project provides a baseline and example for image classification based on fastreid.\n\n## Datasets Preparation\n\nWe refer to [pytorch tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) for dataset \npreparation. This is just an example for building a classification task based on fastreid. You can customize\nyour own datasets and model.\n\n## Usage\n\nIf you want to train models with 4 gpus, you can run\n```bash\npython3 projects/FastClas/train_net.py --config-file projects/FastClas/config/base-clas.yml --num-gpus 4\n```\n"
  },
  {
    "path": "fast_reid/projects/FastClas/configs/base-clas.yaml",
    "content": "MODEL:\n  META_ARCHITECTURE: Baseline\n\n  BACKBONE:\n    NAME: build_resnet_backbone\n    DEPTH: 18x\n    NORM: BN\n    LAST_STRIDE: 2\n    FEAT_DIM: 512\n    PRETRAIN: True\n\n  HEADS:\n    NAME: ClasHead\n    WITH_BNNECK: False\n    EMBEDDING_DIM: 0\n    POOL_LAYER: FastGlobalAvgPool\n    CLS_LAYER: Linear\n    NUM_CLASSES: 2\n\n  LOSSES:\n    NAME: (\"CrossEntropyLoss\",)\n\n    CE:\n      EPSILON: 0.1\n      SCALE: 1.\n\nINPUT:\n  SIZE_TRAIN: [0,]  # no need for resize when training\n  SIZE_TEST: [256,]\n\n  CROP:\n    ENABLED: True\n    SIZE: [224,]\n    SCALE: [0.08, 1]\n    RATIO: [0.75, 1.333333333]\n\n  FLIP:\n    ENABLED: True\n\nDATALOADER:\n  SAMPLER_TRAIN: TrainingSampler\n  NUM_WORKERS: 8\n\nSOLVER:\n  MAX_EPOCH: 100\n  AMP:\n    ENABLED: True\n\n  OPT: SGD\n  SCHED: CosineAnnealingLR\n\n  BASE_LR: 0.001\n  MOMENTUM: 0.9\n  NESTEROV: False\n\n  BIAS_LR_FACTOR: 1.\n  WEIGHT_DECAY: 0.0005\n  WEIGHT_DECAY_BIAS: 0.\n  IMS_PER_BATCH: 16\n\n  ETA_MIN_LR: 0.00003\n\n  WARMUP_FACTOR: 0.1\n  WARMUP_ITERS: 100\n\n  CHECKPOINT_PERIOD: 10\n\nTEST:\n  EVAL_PERIOD: 10\n  IMS_PER_BATCH: 256\n\nDATASETS:\n  NAMES: (\"Hymenoptera\",)\n  TESTS: (\"Hymenoptera\",)\n\nOUTPUT_DIR: projects/FastClas/logs/r18_demo"
  },
  {
    "path": "fast_reid/projects/FastClas/fastclas/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .bee_ant import *\nfrom .distracted_driver import *\nfrom .dataset import ClasDataset\nfrom .trainer import ClasTrainer\n"
  },
  {
    "path": "fast_reid/projects/FastClas/fastclas/bee_ant.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport glob\nimport os\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n\n__all__ = [\"Hymenoptera\"]\n\n\n@DATASET_REGISTRY.register()\nclass Hymenoptera(ImageDataset):\n    \"\"\"This is a demo dataset for smoke test, you can refer to\n    https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html\n    \"\"\"\n    dataset_dir = 'hymenoptera_data'\n    dataset_name = \"hyt\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.dataset_dir = os.path.join(self.root, self.dataset_dir)\n        train_dir = os.path.join(self.dataset_dir, \"train\")\n        val_dir = os.path.join(self.dataset_dir, \"val\")\n\n        required_files = [\n            self.dataset_dir,\n            train_dir,\n            val_dir,\n        ]\n        self.check_before_run(required_files)\n\n        train = self.process_dir(train_dir)\n        val = self.process_dir(val_dir)\n\n        super().__init__(train, val, [], **kwargs)\n\n    def process_dir(self, data_dir):\n        data = []\n        all_dirs = [d.name for d in os.scandir(data_dir) if d.is_dir()]\n        for dir_name in all_dirs:\n            all_imgs = glob.glob(os.path.join(data_dir, dir_name, \"*.jpg\"))\n            for img_name in all_imgs:\n                data.append([img_name, dir_name, '0'])\n        return data\n"
  },
  {
    "path": "fast_reid/projects/FastClas/fastclas/dataset.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom torch.utils.data import Dataset\n\nfrom fast_reid.fastreid.data.data_utils import read_image\n\n\nclass ClasDataset(Dataset):\n    \"\"\"Image Person ReID Dataset\"\"\"\n\n    def __init__(self, img_items, transform=None, idx_to_class=None):\n        self.img_items = img_items\n        self.transform = transform\n\n        if idx_to_class is not None:\n            self.idx_to_class = idx_to_class\n            self.class_to_idx = {clas_name: int(i) for i, clas_name in self.idx_to_class.items()}\n            self.classes = sorted(list(self.idx_to_class.values()))\n        else:\n            classes = set()\n            for i in img_items:\n                classes.add(i[1])\n\n            self.classes = sorted(list(classes))\n            self.class_to_idx = {cls_name: i for i, cls_name in enumerate(self.classes)}\n            self.idx_to_class = {idx: clas for clas, idx in self.class_to_idx.items()}\n\n    def __len__(self):\n        return len(self.img_items)\n\n    def __getitem__(self, index):\n        img_item = self.img_items[index]\n        img_path = img_item[0]\n        label = self.class_to_idx[img_item[1]]\n        img = read_image(img_path)\n        if self.transform is not None: img = self.transform(img)\n\n        return {\n            \"images\": img,\n            \"targets\": label,\n            \"img_paths\": img_path,\n        }\n\n    @property\n    def num_classes(self):\n        return len(self.classes)\n"
  },
  {
    "path": "fast_reid/projects/FastClas/fastclas/trainer.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport json\nimport logging\nimport os\n\nfrom fast_reid.fastreid.data.build import _root\nfrom fast_reid.fastreid.data.build import build_reid_train_loader, build_reid_test_loader\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.transforms import build_transforms\nfrom fast_reid.fastreid.engine import DefaultTrainer\nfrom fast_reid.fastreid.evaluation.clas_evaluator import ClasEvaluator\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.checkpoint import PathManager\nfrom .dataset import ClasDataset\n\n\nclass ClasTrainer(DefaultTrainer):\n    idx2class = None\n\n    @classmethod\n    def build_train_loader(cls, cfg):\n        \"\"\"\n        Returns:\n            iterable\n        It now calls :func:`fastreid.data.build_reid_train_loader`.\n        Overwrite it if you'd like a different data loader.\n        \"\"\"\n        logger = logging.getLogger(\"fastreid.clas_dataset\")\n        logger.info(\"Prepare training set\")\n\n        train_items = list()\n        for d in cfg.DATASETS.NAMES:\n            data = DATASET_REGISTRY.get(d)(root=_root)\n            if comm.is_main_process():\n                data.show_train()\n            train_items.extend(data.train)\n        transforms = build_transforms(cfg, is_train=True)\n        train_set = ClasDataset(train_items, transforms)\n        cls.idx2class = train_set.idx_to_class\n\n        data_loader = build_reid_train_loader(cfg, train_set=train_set)\n        return data_loader\n\n    @classmethod\n    def build_test_loader(cls, cfg, dataset_name):\n        \"\"\"\n        Returns:\n            iterable\n        It now calls :func:`fastreid.data.build_reid_test_loader`.\n        Overwrite it if you'd like a different data loader.\n        \"\"\"\n        data = DATASET_REGISTRY.get(dataset_name)(root=_root)\n        if comm.is_main_process():\n            data.show_test()\n        transforms = build_transforms(cfg, is_train=False)\n\n        test_set = ClasDataset(data.query, transforms, cls.idx2class)\n        data_loader, _ = build_reid_test_loader(cfg, test_set=test_set)\n        return data_loader\n\n    @classmethod\n    def build_evaluator(cls, cfg, dataset_name, output_dir=None):\n        data_loader = cls.build_test_loader(cfg, dataset_name)\n        return data_loader, ClasEvaluator(cfg, output_dir)\n\n    @staticmethod\n    def auto_scale_hyperparams(cfg, num_classes):\n        cfg = DefaultTrainer.auto_scale_hyperparams(cfg, num_classes)\n\n        # Save index to class dictionary\n        output_dir = cfg.OUTPUT_DIR\n        if comm.is_main_process() and output_dir:\n            path = os.path.join(output_dir, \"idx2class.json\")\n            with PathManager.open(path, \"w\") as f:\n                json.dump(ClasTrainer.idx2class, f)\n\n        return cfg\n"
  },
  {
    "path": "fast_reid/projects/FastClas/train_net.py",
    "content": "#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport json\nimport logging\nimport os\nimport sys\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.engine import default_argument_parser, default_setup, launch\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer, PathManager\n\nfrom fastclas import *\n\n\ndef setup(args):\n    \"\"\"\n    Create configs and perform basic setups.\n    \"\"\"\n    cfg = get_cfg()\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    default_setup(cfg, args)\n    return cfg\n\n\ndef main(args):\n    cfg = setup(args)\n\n    if args.eval_only:\n        cfg.defrost()\n        cfg.MODEL.BACKBONE.PRETRAIN = False\n        model = ClasTrainer.build_model(cfg)\n\n        Checkpointer(model).load(cfg.MODEL.WEIGHTS)  # load trained model\n\n        try:\n            output_dir = os.path.dirname(cfg.MODEL.WEIGHTS)\n            path = os.path.join(output_dir, \"idx2class.json\")\n            with PathManager.open(path, 'r') as f:\n                idx2class = json.load(f)\n            ClasTrainer.idx2class = idx2class\n        except:\n            logger = logging.getLogger(\"fastreid.fastclas\")\n            logger.info(f\"Cannot find idx2class dict in {os.path.dirname(cfg.MODEL.WEIGHTS)}\")\n\n        res = ClasTrainer.test(cfg, model)\n        return res\n\n    trainer = ClasTrainer(cfg)\n\n    trainer.resume_or_load(resume=args.resume)\n    return trainer.train()\n\n\nif __name__ == \"__main__\":\n    args = default_argument_parser().parse_args()\n    print(\"Command Line Args:\", args)\n    launch(\n        main,\n        args.num_gpus,\n        num_machines=args.num_machines,\n        machine_rank=args.machine_rank,\n        dist_url=args.dist_url,\n        args=(args,),\n    )\n"
  },
  {
    "path": "fast_reid/projects/FastDistill/README.md",
    "content": "# FastDistill in FastReID\n\nThis project provides a strong distillation method for both embedding and classification training.\nThe feature distillation comes from [overhaul-distillation](https://github.com/clovaai/overhaul-distillation/tree/master/ImageNet).\n\n\n## Datasets Prepration\n- DukeMTMC-reID\n\n\n## Train and Evaluation\n```shell\n# teacher model training\npython3 projects/FastDistill/train_net.py \\\n--config-file projects/FastDistill/configs/sbs_r101ibn.yml \\\n--num-gpus 4\n\n# loss distillation\npython3 projects/FastDistill/train_net.py \\\n--config-file projects/FastDistill/configs/kd-sbs_r101ibn-sbs_r34.yaml \\\n--num-gpus 4 \\\nMODEL.META_ARCHITECTURE Distiller\nKD.MODEL_CONFIG '(\"projects/FastDistill/logs/dukemtmc/r101_ibn/config.yaml\",)' \\\nKD.MODEL_WEIGHTS '(\"projects/FastDistill/logs/dukemtmc/r101_ibn/model_best.pth\",)'\n\n# loss+overhaul distillation\npython3 projects/FastDistill/train_net.py \\\n--config-file projects/FastDistill/configs/kd-sbs_r101ibn-sbs_r34.yaml \\\n--num-gpus 4 \\\nMODEL.META_ARCHITECTURE DistillerOverhaul\nKD.MODEL_CONFIG '(\"projects/FastDistill/logs/dukemtmc/r101_ibn/config.yaml\",)' \\\nKD.MODEL_WEIGHTS '(\"projects/FastDistill/logs/dukemtmc/r101_ibn/model_best.pth\",)'\n```\n\n## Experimental Results\n\n### Settings\n\nAll the experiments are conducted with 4 V100 GPUs.\n\n\n### DukeMTMC-reID\n\n| Model | Rank@1 | mAP |\n| --- | --- | --- |\n| R101_ibn (teacher) | 90.66 | 81.14 |\n| R34 (student) | 86.31 | 73.28 |\n| JS Div | 88.60 | 77.80 |\n| JS Div + Overhaul | 88.73 | 78.25 |\n\n## Contact\nThis project is conducted by [Xingyu Liao](https://github.com/L1aoXingyu) and [Guan'an Wang](https://wangguanan.github.io/) (guan.wang0706@gmail).\n"
  },
  {
    "path": "fast_reid/projects/FastDistill/configs/Base-kd.yml",
    "content": "_BASE_: ../../../configs/Base-SBS.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnet_backbone_distill\n    WITH_IBN: False\n    WITH_NL: False\n    PRETRAIN: True\n\nINPUT:\n  SIZE_TRAIN: [ 256, 128 ]\n  SIZE_TEST: [ 256, 128 ]\n\nSOLVER:\n  MAX_EPOCH: 60\n  BASE_LR: 0.0007\n  IMS_PER_BATCH: 256\n\n  DELAY_EPOCHS: 30\n  FREEZE_ITERS: 500\n\n  CHECKPOINT_PERIOD: 20\n\nTEST:\n  EVAL_PERIOD: 20\n  IMS_PER_BATCH: 128\n\nCUDNN_BENCHMARK: True\n"
  },
  {
    "path": "fast_reid/projects/FastDistill/configs/kd-sbs_r101ibn-sbs_r34.yml",
    "content": "_BASE_: Base-kd.yml\n\nMODEL:\n  META_ARCHITECTURE: Distiller\n  BACKBONE:\n    DEPTH: 34x\n    FEAT_DIM: 512\n    WITH_IBN: False\n\nKD:\n  MODEL_CONFIG: (\"projects/FastDistill/logs/dukemtmc/r101_ibn/config.yaml\",)\n  MODEL_WEIGHTS: (\"projects/FastDistill/logs/dukemtmc/r101_ibn/model_best.pth\",)\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: projects/FastDistill/logs/dukemtmc/kd-r34-r101_ibn"
  },
  {
    "path": "fast_reid/projects/FastDistill/configs/sbs_r101ibn.yml",
    "content": "_BASE_: Base-kd.yml\n\nMODEL:\n  BACKBONE:\n    WITH_IBN: True\n    DEPTH: 101x\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: projects/FastDistill/logs/dukemtmc/r101_ibn"
  },
  {
    "path": "fast_reid/projects/FastDistill/configs/sbs_r34.yml",
    "content": "_BASE_: Base-kd.yml\n\nMODEL:\n  BACKBONE:\n    DEPTH: 34x\n    FEAT_DIM: 512\n    WITH_IBN: False\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n\nOUTPUT_DIR: projects/FastDistill/logs/dukemtmc/r34"
  },
  {
    "path": "fast_reid/projects/FastDistill/fastdistill/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .overhaul import DistillerOverhaul\nfrom .resnet_distill import build_resnet_backbone_distill\n"
  },
  {
    "path": "fast_reid/projects/FastDistill/fastdistill/overhaul.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\nimport math\n\nimport torch\nimport torch.nn.functional as F\nfrom scipy.stats import norm\nfrom torch import nn\n\nfrom fast_reid.fastreid.modeling.meta_arch import META_ARCH_REGISTRY, Distiller\n\nlogger = logging.getLogger(\"fastreid.meta_arch.overhaul_distiller\")\n\n\ndef distillation_loss(source, target, margin):\n    target = torch.max(target, margin)\n    loss = F.mse_loss(source, target, reduction=\"none\")\n    loss = loss * ((source > target) | (target > 0)).float()\n    return loss.sum()\n\n\ndef build_feature_connector(t_channel, s_channel):\n    C = [nn.Conv2d(s_channel, t_channel, kernel_size=1, stride=1, padding=0, bias=False),\n         nn.BatchNorm2d(t_channel)]\n\n    for m in C:\n        if isinstance(m, nn.Conv2d):\n            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n            m.weight.data.normal_(0, math.sqrt(2. / n))\n        elif isinstance(m, nn.BatchNorm2d):\n            m.weight.data.fill_(1)\n            m.bias.data.zero_()\n\n    return nn.Sequential(*C)\n\n\ndef get_margin_from_BN(bn):\n    margin = []\n    std = bn.weight.data\n    mean = bn.bias.data\n    for (s, m) in zip(std, mean):\n        s = abs(s.item())\n        m = m.item()\n        if norm.cdf(-m / s) > 0.001:\n            margin.append(- s * math.exp(- (m / s) ** 2 / 2) / \\\n                          math.sqrt(2 * math.pi) / norm.cdf(-m / s) + m)\n        else:\n            margin.append(-3 * s)\n\n    return torch.tensor(margin, dtype=torch.float32, device=mean.device)\n\n\n@META_ARCH_REGISTRY.register()\nclass DistillerOverhaul(Distiller):\n    def __init__(self, cfg):\n        super().__init__(cfg)\n\n        s_channels = self.backbone.get_channel_nums()\n\n        for i in range(len(self.model_ts)):\n            t_channels = self.model_ts[i].backbone.get_channel_nums()\n\n            setattr(self, \"connectors_{}\".format(i), nn.ModuleList(\n                [build_feature_connector(t, s) for t, s in zip(t_channels, s_channels)]))\n\n            teacher_bns = self.model_ts[i].backbone.get_bn_before_relu()\n            margins = [get_margin_from_BN(bn) for bn in teacher_bns]\n            for j, margin in enumerate(margins):\n                self.register_buffer(\"margin{}_{}\".format(i, j + 1),\n                                     margin.unsqueeze(1).unsqueeze(2).unsqueeze(0).detach())\n\n    def forward(self, batched_inputs):\n        if self.training:\n            images = self.preprocess_image(batched_inputs)\n            # student model forward\n            s_feats, s_feat = self.backbone.extract_feature(images, preReLU=True)\n            assert \"targets\" in batched_inputs, \"Labels are missing in training!\"\n            targets = batched_inputs[\"targets\"].to(self.device)\n\n            if targets.sum() < 0: targets.zero_()\n\n            s_outputs = self.heads(s_feat, targets)\n\n            t_feats_list = []\n            t_outputs = []\n            # teacher model forward\n            with torch.no_grad():\n                if self.ema_enabled:\n                    self._momentum_update_key_encoder(self.ema_momentum)\n                for model_t in self.model_ts:\n                    t_feats, t_feat = model_t.backbone.extract_feature(images, preReLU=True)\n                    t_output = model_t.heads(t_feat, targets)\n                    t_feats_list.append(t_feats)\n                    t_outputs.append(t_output)\n\n            losses = self.losses(s_outputs, s_feats, t_outputs, t_feats_list, targets)\n            return losses\n\n        else:\n            outputs = super(DistillerOverhaul, self).forward(batched_inputs)\n            return outputs\n\n    def losses(self, s_outputs, s_feats, t_outputs, t_feats_list, gt_labels):\n        \"\"\"\n        Compute loss from modeling's outputs, the loss function input arguments\n        must be the same as the outputs of the model forwarding.\n        \"\"\"\n        loss_dict = super().losses(s_outputs, t_outputs, gt_labels)\n\n        # Overhaul distillation loss\n        feat_num = len(s_feats)\n        loss_distill = 0\n        for i in range(len(t_feats_list)):\n            for j in range(feat_num):\n                s_feats_connect = getattr(self, \"connectors_{}\".format(i))[j](s_feats[j])\n                loss_distill += distillation_loss(s_feats_connect, t_feats_list[i][j].detach(), getattr(\n                    self, \"margin{}_{}\".format(i, j + 1)).to(s_feats_connect.dtype)) / 2 ** (feat_num - j - 1)\n\n        loss_dict[\"loss_overhaul\"] = loss_distill / len(t_feats_list) / len(gt_labels) / 10000\n\n        return loss_dict\n"
  },
  {
    "path": "fast_reid/projects/FastDistill/fastdistill/resnet_distill.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\nimport math\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom fast_reid.fastreid.layers import (\n    IBN,\n    SELayer,\n    get_norm,\n)\nfrom fast_reid.fastreid.modeling.backbones import BACKBONE_REGISTRY\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message\n\nlogger = logging.getLogger(\"fastreid.overhaul.backbone\")\nmodel_urls = {\n    '18x': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n    '34x': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',\n    '50x': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n    '101x': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',\n    'ibn_18x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet18_ibn_a-2f571257.pth',\n    'ibn_34x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet34_ibn_a-94bc1577.pth',\n    'ibn_50x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet50_ibn_a-d9d0bb7b.pth',\n    'ibn_101x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet101_ibn_a-59ea0ac6.pth',\n    'se_ibn_101x': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/se_resnet101_ibn_a-fabed4e2.pth',\n}\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(self, inplanes, planes, bn_norm, with_ibn=False, with_se=False,\n                 stride=1, downsample=None, reduction=16):\n        super(BasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n        if with_ibn:\n            self.bn1 = IBN(planes, bn_norm)\n        else:\n            self.bn1 = get_norm(bn_norm, planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn2 = get_norm(bn_norm, planes)\n        self.relu = nn.ReLU(inplace=True)\n        if with_se:\n            self.se = SELayer(planes, reduction)\n        else:\n            self.se = nn.Identity()\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        x = self.relu(x)\n        identity = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.se(out)\n\n        if self.downsample is not None:\n            identity = self.downsample(x)\n\n        out += identity\n        # out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(self, inplanes, planes, bn_norm, with_ibn=False, with_se=False,\n                 stride=1, downsample=None, reduction=16):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        if with_ibn:\n            self.bn1 = IBN(planes, bn_norm)\n        else:\n            self.bn1 = get_norm(bn_norm, planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n                               padding=1, bias=False)\n        self.bn2 = get_norm(bn_norm, planes)\n        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)\n        self.bn3 = get_norm(bn_norm, planes * self.expansion)\n        self.relu = nn.ReLU(inplace=True)\n        if with_se:\n            self.se = SELayer(planes * self.expansion, reduction)\n        else:\n            self.se = nn.Identity()\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        x = self.relu(x)\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n        out = self.se(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        # out = self.relu(out)\n\n        return out\n\n\nclass ResNet(nn.Module):\n    def __init__(self, last_stride, bn_norm, with_ibn, with_se, with_nl, block, layers, non_layers):\n        self.channel_nums = []\n        self.inplanes = 64\n        super().__init__()\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,\n                               bias=False)\n        self.bn1 = get_norm(bn_norm, 64)\n        self.relu = nn.ReLU(inplace=True)\n        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)\n        self.layer1 = self._make_layer(block, 64, layers[0], 1, bn_norm, with_ibn, with_se)\n        self.layer2 = self._make_layer(block, 128, layers[1], 2, bn_norm, with_ibn, with_se)\n        self.layer3 = self._make_layer(block, 256, layers[2], 2, bn_norm, with_ibn, with_se)\n        self.layer4 = self._make_layer(block, 512, layers[3], last_stride, bn_norm, with_se=with_se)\n\n        self.random_init()\n\n    def _make_layer(self, block, planes, blocks, stride=1, bn_norm=\"BN\", with_ibn=False, with_se=False):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(self.inplanes, planes * block.expansion,\n                          kernel_size=1, stride=stride, bias=False),\n                get_norm(bn_norm, planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(block(self.inplanes, planes, bn_norm, with_ibn, with_se, stride, downsample))\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(block(self.inplanes, planes, bn_norm, with_ibn, with_se))\n\n        self.channel_nums.append(self.inplanes)\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n        x = F.relu(x, inplace=True)\n\n        return x\n\n    def random_init(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                nn.init.normal_(m.weight, 0, math.sqrt(2. / n))\n            elif isinstance(m, nn.BatchNorm2d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n\n    def get_bn_before_relu(self):\n        if isinstance(self.layer1[0], Bottleneck):\n            bn1 = self.layer1[-1].bn3\n            bn2 = self.layer2[-1].bn3\n            bn3 = self.layer3[-1].bn3\n            bn4 = self.layer4[-1].bn3\n        elif isinstance(self.layer1[0], BasicBlock):\n            bn1 = self.layer1[-1].bn2\n            bn2 = self.layer2[-1].bn2\n            bn3 = self.layer3[-1].bn2\n            bn4 = self.layer4[-1].bn2\n        else:\n            logger.info(\"ResNet unknown block error!\")\n        return [bn1, bn2, bn3, bn4]\n\n    def extract_feature(self, x, preReLU=False):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n\n        feat1 = self.layer1(x)\n        feat2 = self.layer2(feat1)\n        feat3 = self.layer3(feat2)\n        feat4 = self.layer4(feat3)\n\n        if not preReLU:\n            feat1 = F.relu(feat1)\n            feat2 = F.relu(feat2)\n            feat3 = F.relu(feat3)\n            feat4 = F.relu(feat4)\n\n        return [feat1, feat2, feat3, feat4], F.relu(feat4)\n\n    def get_channel_nums(self):\n        return self.channel_nums\n\n\ndef init_pretrained_weights(key):\n    \"\"\"Initializes model with pretrained weights.\n\n    Layers that don't match with pretrained layers in name or size are kept unchanged.\n    \"\"\"\n    import os\n    import errno\n    import gdown\n\n    def _get_torch_home():\n        ENV_TORCH_HOME = 'TORCH_HOME'\n        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'\n        DEFAULT_CACHE_DIR = '~/.cache'\n        torch_home = os.path.expanduser(\n            os.getenv(\n                ENV_TORCH_HOME,\n                os.path.join(\n                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'\n                )\n            )\n        )\n        return torch_home\n\n    torch_home = _get_torch_home()\n    model_dir = os.path.join(torch_home, 'checkpoints')\n    try:\n        os.makedirs(model_dir)\n    except OSError as e:\n        if e.errno == errno.EEXIST:\n            # Directory already exists, ignore.\n            pass\n        else:\n            # Unexpected OSError, re-raise.\n            raise\n\n    filename = model_urls[key].split('/')[-1]\n\n    cached_file = os.path.join(model_dir, filename)\n\n    if not os.path.exists(cached_file):\n        if comm.is_main_process():\n            gdown.download(model_urls[key], cached_file, quiet=False)\n\n    comm.synchronize()\n\n    logger.info(f\"Loading pretrained model from {cached_file}\")\n    state_dict = torch.load(cached_file, map_location=torch.device('cpu'))\n\n    return state_dict\n\n\n@BACKBONE_REGISTRY.register()\ndef build_resnet_backbone_distill(cfg):\n    \"\"\"\n    Create a ResNet instance from config.\n    Returns:\n        ResNet: a :class:`ResNet` instance.\n    \"\"\"\n\n    # fmt: off\n    pretrain      = cfg.MODEL.BACKBONE.PRETRAIN\n    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH\n    last_stride   = cfg.MODEL.BACKBONE.LAST_STRIDE\n    bn_norm       = cfg.MODEL.BACKBONE.NORM\n    with_ibn      = cfg.MODEL.BACKBONE.WITH_IBN\n    with_se       = cfg.MODEL.BACKBONE.WITH_SE\n    with_nl       = cfg.MODEL.BACKBONE.WITH_NL\n    depth         = cfg.MODEL.BACKBONE.DEPTH\n    # fmt: on\n\n    num_blocks_per_stage = {\n        '18x': [2, 2, 2, 2],\n        '34x': [3, 4, 6, 3],\n        '50x': [3, 4, 6, 3],\n        '101x': [3, 4, 23, 3],\n    }[depth]\n\n    nl_layers_per_stage = {\n        '18x': [0, 0, 0, 0],\n        '34x': [0, 0, 0, 0],\n        '50x': [0, 2, 3, 0],\n        '101x': [0, 2, 9, 0]\n    }[depth]\n\n    block = {\n        '18x': BasicBlock,\n        '34x': BasicBlock,\n        '50x': Bottleneck,\n        '101x': Bottleneck\n    }[depth]\n\n    model = ResNet(last_stride, bn_norm, with_ibn, with_se, with_nl, block,\n                   num_blocks_per_stage, nl_layers_per_stage)\n    if pretrain:\n        # Load pretrain path if specifically\n        if pretrain_path:\n            try:\n                state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))\n                logger.info(f\"Loading pretrained model from {pretrain_path}\")\n            except FileNotFoundError as e:\n                logger.info(f'{pretrain_path} is not found! Please check this path.')\n                raise e\n            except KeyError as e:\n                logger.info(\"State dict keys error! Please check the state dict.\")\n                raise e\n        else:\n            key = depth\n            if with_ibn: key = 'ibn_' + key\n            if with_se:  key = 'se_' + key\n\n            state_dict = init_pretrained_weights(key)\n\n        incompatible = model.load_state_dict(state_dict, strict=False)\n        if incompatible.missing_keys:\n            logger.info(\n                get_missing_parameters_message(incompatible.missing_keys)\n            )\n        if incompatible.unexpected_keys:\n            logger.info(\n                get_unexpected_parameters_message(incompatible.unexpected_keys)\n            )\n\n    return model\n"
  },
  {
    "path": "fast_reid/projects/FastDistill/train_net.py",
    "content": "#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author:  L1aoXingyu, guan'an wang\n@contact: sherlockliao01@gmail.com, guan.wang0706@gmail.com\n\"\"\"\n\nimport sys\n\nsys.path.append('.')\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.engine import default_argument_parser, default_setup, DefaultTrainer, launch\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\n\nfrom fastdistill import *\n\ndef setup(args):\n    \"\"\"\n    Create configs and perform basic setups.\n    \"\"\"\n    cfg = get_cfg()\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    default_setup(cfg, args)\n    return cfg\n\n\ndef main(args):\n    cfg = setup(args)\n\n    if args.eval_only:\n        model = DefaultTrainer.build_model(cfg)\n        Checkpointer(model, save_dir=cfg.OUTPUT_DIR).load(cfg.MODEL.WEIGHTS)\n        res = DefaultTrainer.test(cfg, model)\n        return res\n\n    trainer = DefaultTrainer(cfg)\n\n    trainer.resume_or_load(resume=args.resume)\n    return trainer.train()\n\n\nif __name__ == \"__main__\":\n    parser = default_argument_parser()\n    args = parser.parse_args()\n\n    print(\"Command Line Args:\", args)\n    launch(\n        main,\n        args.num_gpus,\n        num_machines=args.num_machines,\n        machine_rank=args.machine_rank,\n        dist_url=args.dist_url,\n        args=(args,),\n    )\n"
  },
  {
    "path": "fast_reid/projects/FastFace/README.md",
    "content": "# FastFace in FastReID\n\nThis project provides a baseline for face recognition.\n\n## Datasets Preparation\n\n| Function | Dataset |\n| --- | --- |\n| Train | MS-Celeb-1M |\n| Test-1 | LFW      |\n| Test-2 | CPLFW |\n| Test-3 | CALFW |\n| Test-4 | VGG2_FP |\n| Test-5 | AgeDB-30 |\n| Test-6 | CFP_FF |\n| Test-7 | CFP-FP |\n\nWe do data wrangling following [InsightFace_Pytorch](https://github.com/TreB1eN/InsightFace_Pytorch) instruction.\n\n## Dependencies\n\n- bcolz\n- mxnet (optional) if you want to read `.rec` directly\n\n## Experiment Results\n\nWe refer to [insightface_pytorch](https://github.com/TreB1eN/InsightFace_Pytorch) as our baseline methods, and on top of it, we use circle loss and cosine lr scheduler.\n\n| Method | LFW(%) | CFP-FF(%) | CFP-FP(%)| AgeDB-30(%) | calfw(%) | cplfw(%) | vgg2_fp(%) |\n| :---: | :---: | :---: |:---: | :---: | :---: | :---: | :---: |\n| [insightface_pytorch](https://github.com/TreB1eN/InsightFace_Pytorch) | 99.52 | 99.62 | 95.04 | 96.22 | 95.57 | 91.07 | 93.86 |\n| ir50_se | 99.70 | 99.60 | 96.43 | 97.87 | 95.95 | 91.10 | 94.32 |\n| ir100_se | 99.65 | 99.69 | 97.10 |  97.98 | 96.00 | 91.53 | 94.62 |\n| ir50_se_0.1 |  |  |  |   |  |  |  |\n| ir100_se_0.1 |  |  |  |  |  |  |  |\n"
  },
  {
    "path": "fast_reid/projects/FastFace/configs/face_base.yml",
    "content": "MODEL:\n  META_ARCHITECTURE: FaceBaseline\n\n  PIXEL_MEAN: [127.5, 127.5, 127.5]\n  PIXEL_STD: [127.5, 127.5, 127.5]\n\n  BACKBONE:\n    NAME: build_iresnet_backbone\n\n  HEADS:\n    NAME: FaceHead\n    WITH_BNNECK: True\n    NORM: BN\n    NECK_FEAT: after\n    EMBEDDING_DIM: 512\n    POOL_LAYER: Flatten\n    CLS_LAYER: CosSoftmax\n    SCALE: 64\n    MARGIN: 0.4\n    NUM_CLASSES: 360232\n\n    PFC:\n      ENABLED: False\n      SAMPLE_RATE: 0.1\n\n  LOSSES:\n    NAME: (\"CrossEntropyLoss\",)\n\n    CE:\n      EPSILON: 0.\n      SCALE: 1.\n\nDATASETS:\n  REC_PATH: /export/home/DATA/Glint360k/train.rec\n  NAMES: (\"MS1MV2\",)\n  TESTS: (\"CFP_FP\", \"AgeDB_30\", \"LFW\")\n\nINPUT:\n  SIZE_TRAIN: [0,]  # No need of resize\n  SIZE_TEST: [0,]\n\n  FLIP:\n    ENABLED: True\n    PROB: 0.5\n\nDATALOADER:\n  SAMPLER_TRAIN: TrainingSampler\n  NUM_WORKERS: 8\n\nSOLVER:\n  MAX_EPOCH: 20\n  AMP:\n    ENABLED: True\n\n  OPT: SGD\n  BASE_LR: 0.05\n  MOMENTUM: 0.9\n\n  SCHED: MultiStepLR\n  STEPS: [8, 12, 15, 18]\n\n  BIAS_LR_FACTOR: 1.\n  WEIGHT_DECAY: 0.0005\n  WEIGHT_DECAY_BIAS: 0.0005\n  IMS_PER_BATCH: 256\n\n  WARMUP_FACTOR: 0.1\n  WARMUP_ITERS: 0\n\n  CHECKPOINT_PERIOD: 1\n\nTEST:\n  EVAL_PERIOD: 1\n  IMS_PER_BATCH: 1024\n\nCUDNN_BENCHMARK: True"
  },
  {
    "path": "fast_reid/projects/FastFace/configs/r101_ir.yml",
    "content": "_BASE_: face_base.yml\n\nMODEL:\n\n  BACKBONE:\n    NAME: build_resnetIR_backbone\n    DEPTH: 100x\n    FEAT_DIM: 25088 # 512x7x7\n    WITH_SE: True\n\n  HEADS:\n    PFC:\n      ENABLED: True\n\nOUTPUT_DIR: projects/FastFace/logs/ir_se101-ms1mv2-circle\n"
  },
  {
    "path": "fast_reid/projects/FastFace/configs/r50_ir.yml",
    "content": "_BASE_: face_base.yml\n\nMODEL:\n\n  BACKBONE:\n    DEPTH: 50x\n    FEAT_DIM: 25088 # 512x7x7\n    DROPOUT: 0.\n\n  HEADS:\n    PFC:\n      ENABLED: True\n\nOUTPUT_DIR: projects/FastFace/logs/pfc0.1_insightface\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .modeling import *\nfrom .config import add_face_cfg\nfrom .trainer import FaceTrainer\nfrom .datasets import *\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/config.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom fast_reid.fastreid.config import CfgNode as CN\n\n\ndef add_face_cfg(cfg):\n    _C = cfg\n\n    _C.DATASETS.REC_PATH = \"\"\n\n    _C.MODEL.BACKBONE.DROPOUT = 0.\n\n    _C.MODEL.HEADS.PFC = CN({\"ENABLED\": False})\n    _C.MODEL.HEADS.PFC.SAMPLE_RATE = 0.1\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/datasets/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .ms1mv2 import MS1MV2\nfrom .test_dataset import *\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/datasets/ms1mv2.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport glob\nimport os\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n\n@DATASET_REGISTRY.register()\nclass MS1MV2(ImageDataset):\n    dataset_dir = \"MS_Celeb_1M\"\n    dataset_name = \"ms1mv2\"\n\n    def __init__(self, root=\"datasets\", **kwargs):\n        self.root = root\n        self.dataset_dir = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.dataset_dir]\n        self.check_before_run(required_files)\n\n        train = self.process_dirs()[:10000]\n        super().__init__(train, [], [], **kwargs)\n\n    def process_dirs(self):\n        train_list = []\n\n        fid_list = os.listdir(self.dataset_dir)\n\n        for fid in fid_list:\n            all_imgs = glob.glob(os.path.join(self.dataset_dir, fid, \"*.jpg\"))\n            for img_path in all_imgs:\n                train_list.append([img_path, self.dataset_name + '_' + fid, '0'])\n\n        return train_list\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/datasets/test_dataset.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\n\nimport bcolz\nimport numpy as np\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = [\"CPLFW\", \"VGG2_FP\", \"AgeDB_30\", \"CALFW\", \"CFP_FF\", \"CFP_FP\", \"LFW\"]\n\n\n@DATASET_REGISTRY.register()\nclass CPLFW(ImageDataset):\n    dataset_dir = \"faces_emore_val\"\n    dataset_name = \"cplfw\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.dataset_dir = os.path.join(self.root, self.dataset_dir)\n\n        required_files = [self.dataset_dir]\n\n        self.check_before_run(required_files)\n\n        carray = bcolz.carray(rootdir=os.path.join(self.dataset_dir, self.dataset_name), mode='r')\n        is_same = np.load(os.path.join(self.dataset_dir, \"{}_list.npy\".format(self.dataset_name)))\n\n        self.carray = carray\n        self.is_same = is_same\n\n        super().__init__([], [], [], **kwargs)\n\n\n@DATASET_REGISTRY.register()\nclass VGG2_FP(CPLFW):\n    dataset_name = \"vgg2_fp\"\n\n\n@DATASET_REGISTRY.register()\nclass AgeDB_30(CPLFW):\n    dataset_name = \"agedb_30\"\n\n\n@DATASET_REGISTRY.register()\nclass CALFW(CPLFW):\n    dataset_name = \"calfw\"\n\n\n@DATASET_REGISTRY.register()\nclass CFP_FF(CPLFW):\n    dataset_name = \"cfp_ff\"\n\n\n@DATASET_REGISTRY.register()\nclass CFP_FP(CPLFW):\n    dataset_name = \"cfp_fp\"\n\n\n@DATASET_REGISTRY.register()\nclass LFW(CPLFW):\n    dataset_name = \"lfw\"\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/face_data.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom PIL import Image\nimport io\nimport logging\nimport numbers\n\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom fast_reid.fastreid.data.common import CommDataset\n\nlogger = logging.getLogger(\"fastreid.face_data\")\n\ntry:\n    import mxnet as mx\nexcept ImportError:\n    logger.info(\"Please install mxnet if you want to use .rec file\")\n\n\nclass MXFaceDataset(Dataset):\n    def __init__(self, path_imgrec, transforms):\n        super().__init__()\n        self.transforms = transforms\n\n        logger.info(f\"loading recordio {path_imgrec}...\")\n        path_imgidx = path_imgrec[0:-4] + \".idx\"\n        self.imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')\n        s = self.imgrec.read_idx(0)\n        header, _ = mx.recordio.unpack(s)\n        if header.flag > 0:\n            # logger.debug(f\"header0 label: {header.label}\")\n            self.header0 = (int(header.label[0]), int(header.label[1]))\n            self.imgidx = list(range(1, int(header.label[0])))\n            # logger.debug(self.imgidx)\n        else:\n            self.imgidx = list(self.imgrec.keys)\n        logger.info(f\"Number of Samples: {len(self.imgidx)}, \"\n                    f\"Number of Classes: {int(self.header0[1] - self.header0[0])}\")\n\n    def __getitem__(self, index):\n        idx = self.imgidx[index]\n        s = self.imgrec.read_idx(idx)\n        header, img = mx.recordio.unpack(s)\n        label = header.label\n        if not isinstance(label, numbers.Number):\n            label = label[0]\n        label = torch.tensor(label, dtype=torch.long)\n\n        sample = Image.open(io.BytesIO(img))  # RGB\n        if self.transforms is not None: sample = self.transforms(sample)\n        return {\n            \"images\": sample,\n            \"targets\": label,\n            \"camids\": 0,\n        }\n\n    def __len__(self):\n        # logger.debug(f\"mxface dataset length is {len(self.imgidx)}\")\n        return len(self.imgidx)\n\n    @property\n    def num_classes(self):\n        return int(self.header0[1] - self.header0[0])\n\n\nclass TestFaceDataset(CommDataset):\n    def __init__(self, img_items, labels):\n        self.img_items = img_items\n        self.labels = labels\n\n    def __getitem__(self, index):\n        img = torch.tensor(self.img_items[index]) * 127.5 + 127.5\n        return {\n            \"images\": img,\n        }\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/face_evaluator.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport copy\nimport io\nimport logging\nimport os\nfrom collections import OrderedDict\n\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\n\nfrom fast_reid.fastreid.evaluation import DatasetEvaluator\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.file_io import PathManager\nfrom .verification import evaluate\n\nlogger = logging.getLogger(\"fastreid.face_evaluator\")\n\n\ndef gen_plot(fpr, tpr):\n    \"\"\"Create a pyplot plot and save to buffer.\"\"\"\n    plt.figure()\n    plt.xlabel(\"FPR\", fontsize=14)\n    plt.ylabel(\"TPR\", fontsize=14)\n    plt.title(\"ROC Curve\", fontsize=14)\n    plt.plot(fpr, tpr, linewidth=2)\n    buf = io.BytesIO()\n    plt.savefig(buf, format='jpeg')\n    buf.seek(0)\n    plt.close()\n    return buf\n\n\nclass FaceEvaluator(DatasetEvaluator):\n    def __init__(self, cfg, labels, dataset_name, output_dir=None):\n        self.cfg = cfg\n        self.labels = labels\n        self.dataset_name = dataset_name\n        self._output_dir = output_dir\n\n        self.features = []\n\n    def reset(self):\n        self.features = []\n\n    def process(self, inputs, outputs):\n        self.features.append(outputs.cpu())\n\n    def evaluate(self):\n        if comm.get_world_size() > 1:\n            comm.synchronize()\n            features = comm.gather(self.features)\n            features = sum(features, [])\n\n            # fmt: off\n            if not comm.is_main_process(): return {}\n            # fmt: on\n        else:\n            features = self.features\n\n        features = torch.cat(features, dim=0)\n        features = F.normalize(features, p=2, dim=1).numpy()\n\n        self._results = OrderedDict()\n        tpr, fpr, accuracy, best_thresholds = evaluate(features, self.labels)\n\n        self._results[\"Accuracy\"] = accuracy.mean() * 100\n        self._results[\"Threshold\"] = best_thresholds.mean()\n        self._results[\"metric\"] = accuracy.mean() * 100\n\n        buf = gen_plot(fpr, tpr)\n        roc_curve = Image.open(buf)\n\n        PathManager.mkdirs(self._output_dir)\n        roc_curve.save(os.path.join(self._output_dir, self.dataset_name + \"_roc.png\"))\n\n        return copy.deepcopy(self._results)\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/modeling/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .partial_fc import PartialFC\nfrom .face_baseline import FaceBaseline\nfrom .face_head import FaceHead\nfrom .iresnet import build_iresnet_backbone\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/modeling/face_baseline.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nfrom fast_reid.fastreid.modeling.meta_arch import Baseline\nfrom fast_reid.fastreid.modeling.meta_arch import META_ARCH_REGISTRY\n\n\n@META_ARCH_REGISTRY.register()\nclass FaceBaseline(Baseline):\n    def __init__(self, cfg):\n        super().__init__(cfg)\n        self.pfc_enabled = cfg.MODEL.HEADS.PFC.ENABLED\n        self.amp_enabled = cfg.SOLVER.AMP.ENABLED\n\n    def forward(self, batched_inputs):\n        if not self.pfc_enabled:\n            return super().forward(batched_inputs)\n\n        images = self.preprocess_image(batched_inputs)\n        with torch.cuda.amp.autocast(self.amp_enabled):\n            features = self.backbone(images)\n        features = features.float() if self.amp_enabled else features\n\n        if self.training:\n            assert \"targets\" in batched_inputs, \"Person ID annotation are missing in training!\"\n            targets = batched_inputs[\"targets\"]\n\n            # PreciseBN flag, When do preciseBN on different dataset, the number of classes in new dataset\n            # may be larger than that in the original dataset, so the circle/arcface will\n            # throw an error. We just set all the targets to 0 to avoid this problem.\n            if targets.sum() < 0: targets.zero_()\n\n            outputs = self.heads(features, targets)\n            return outputs, targets\n        else:\n            outputs = self.heads(features)\n            return outputs\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/modeling/face_head.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom fast_reid.fastreid.config import configurable\nfrom fast_reid.fastreid.modeling.heads import EmbeddingHead\nfrom fast_reid.fastreid.modeling.heads.build import REID_HEADS_REGISTRY\n\n\n@REID_HEADS_REGISTRY.register()\nclass FaceHead(EmbeddingHead):\n    def __init__(self, cfg):\n        super().__init__(cfg)\n        self.pfc_enabled = False\n        if cfg.MODEL.HEADS.PFC.ENABLED:\n            # Delete pre-defined linear weights for partial fc sample\n            del self.weight\n            self.pfc_enabled = True\n\n    def forward(self, features, targets=None):\n        \"\"\"\n        Partial FC forward, which will sample positive weights and part of negative weights,\n        then compute logits and get the grad of features.\n        \"\"\"\n        if not self.pfc_enabled:\n            return super().forward(features, targets)\n        else:\n            pool_feat = self.pool_layer(features)\n            neck_feat = self.bottleneck(pool_feat)\n            neck_feat = neck_feat[..., 0, 0]\n            return neck_feat\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/modeling/iresnet.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nfrom torch import nn\n\nfrom fast_reid.fastreid.layers import get_norm\nfrom fast_reid.fastreid.modeling.backbones import BACKBONE_REGISTRY\n\n\ndef conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n    \"\"\"3x3 convolution with padding\"\"\"\n    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n                     padding=dilation, groups=groups, bias=False, dilation=dilation)\n\n\ndef conv1x1(in_planes, out_planes, stride=1):\n    \"\"\"1x1 convolution\"\"\"\n    return nn.Conv2d(in_planes,\n                     out_planes,\n                     kernel_size=1,\n                     stride=stride,\n                     bias=False)\n\n\nclass IBasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(self, inplanes, planes, bn_norm, stride=1, downsample=None,\n                 groups=1, base_width=64, dilation=1):\n        super().__init__()\n        if groups != 1 or base_width != 64:\n            raise ValueError('BasicBlock only supports groups=1 and base_width=64')\n        if dilation > 1:\n            raise NotImplementedError(\"Dilation > 1 not supported in BasicBlock\")\n        self.bn1 = get_norm(bn_norm, inplanes)\n        self.conv1 = conv3x3(inplanes, planes)\n        self.bn2 = get_norm(bn_norm, planes)\n        self.prelu = nn.PReLU(planes)\n        self.conv2 = conv3x3(planes, planes, stride)\n        self.bn3 = get_norm(bn_norm, planes)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        identity = x\n        out = self.bn1(x)\n        out = self.conv1(out)\n        out = self.bn2(out)\n        out = self.prelu(out)\n        out = self.conv2(out)\n        out = self.bn3(out)\n        if self.downsample is not None:\n            identity = self.downsample(x)\n        out += identity\n        return out\n\n\nclass IResNet(nn.Module):\n    fc_scale = 7 * 7\n\n    def __init__(self, block, layers, bn_norm, dropout=0, zero_init_residual=False,\n                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):\n        super().__init__()\n        self.inplanes = 64\n        self.dilation = 1\n        self.fp16 = fp16\n        if replace_stride_with_dilation is None:\n            replace_stride_with_dilation = [False, False, False]\n        if len(replace_stride_with_dilation) != 3:\n            raise ValueError(\"replace_stride_with_dilation should be None \"\n                             \"or a 3-element tuple, got {}\".format(replace_stride_with_dilation))\n        self.groups = groups\n        self.base_width = width_per_group\n        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn1 = get_norm(bn_norm, self.inplanes)\n        self.prelu = nn.PReLU(self.inplanes)\n        self.layer1 = self._make_layer(block, 64, layers[0], bn_norm, stride=2)\n        self.layer2 = self._make_layer(block,\n                                       128,\n                                       layers[1],\n                                       bn_norm,\n                                       stride=2,\n                                       dilate=replace_stride_with_dilation[0])\n        self.layer3 = self._make_layer(block,\n                                       256,\n                                       layers[2],\n                                       bn_norm,\n                                       stride=2,\n                                       dilate=replace_stride_with_dilation[1])\n        self.layer4 = self._make_layer(block,\n                                       512,\n                                       layers[3],\n                                       bn_norm,\n                                       stride=2,\n                                       dilate=replace_stride_with_dilation[2])\n        self.bn2 = get_norm(bn_norm, 512 * block.expansion)\n        self.dropout = nn.Dropout(p=dropout, inplace=True)\n\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.normal_(m.weight, 0, 0.1)\n            elif m.__class__.__name__.find('Norm') != -1:\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n\n        if zero_init_residual:\n            for m in self.modules():\n                if isinstance(m, IBasicBlock):\n                    nn.init.constant_(m.bn2.weight, 0)\n\n    def _make_layer(self, block, planes, blocks, bn_norm, stride=1, dilate=False):\n        downsample = None\n        previous_dilation = self.dilation\n        if dilate:\n            self.dilation *= stride\n            stride = 1\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                conv1x1(self.inplanes, planes * block.expansion, stride),\n                get_norm(bn_norm, planes * block.expansion),\n            )\n        layers = []\n        layers.append(\n            block(self.inplanes, planes, bn_norm, stride, downsample, self.groups,\n                  self.base_width, previous_dilation))\n        self.inplanes = planes * block.expansion\n        for _ in range(1, blocks):\n            layers.append(\n                block(self.inplanes,\n                      planes,\n                      bn_norm,\n                      groups=self.groups,\n                      base_width=self.base_width,\n                      dilation=self.dilation))\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.prelu(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n        x = self.bn2(x)\n        x = self.dropout(x)\n        return x\n\n\n@BACKBONE_REGISTRY.register()\ndef build_iresnet_backbone(cfg):\n    \"\"\"\n    Create a IResNet instance from config.\n    Returns:\n        ResNet: a :class:`ResNet` instance.\n    \"\"\"\n\n    # fmt: off\n    bn_norm = cfg.MODEL.BACKBONE.NORM\n    depth   = cfg.MODEL.BACKBONE.DEPTH\n    dropout = cfg.MODEL.BACKBONE.DROPOUT\n    fp16    = cfg.SOLVER.AMP.ENABLED\n    # fmt: on\n\n    num_blocks_per_stage = {\n        '18x': [2, 2, 2, 2],\n        '34x': [3, 4, 6, 3],\n        '50x': [3, 4, 14, 3],\n        '100x': [3, 13, 30, 3],\n        '200x': [6, 26, 60, 6],\n    }[depth]\n\n    model = IResNet(IBasicBlock, num_blocks_per_stage, bn_norm, dropout, fp16=fp16)\n    return model\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/modeling/partial_fc.py",
    "content": "# encoding: utf-8\n# code based on:\n# https://github.com/deepinsight/insightface/blob/master/recognition/arcface_torch/partial_fc.py\n\nimport logging\nimport math\n\nimport torch\nimport torch.distributed as dist\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom fast_reid.fastreid.layers import any_softmax\nfrom fast_reid.fastreid.modeling.losses.utils import concat_all_gather\nfrom fast_reid.fastreid.utils import comm\n\nlogger = logging.getLogger('fastreid.partial_fc')\n\n\nclass PartialFC(nn.Module):\n    \"\"\"\n    Author: {Xiang An, Yang Xiao, XuHan Zhu} in DeepGlint,\n    Partial FC: Training 10 Million Identities on a Single Machine\n    See the original paper:\n    https://arxiv.org/abs/2010.05222\n    \"\"\"\n\n    def __init__(\n            self,\n            embedding_size,\n            num_classes,\n            sample_rate,\n            cls_type,\n            scale,\n            margin\n    ):\n        super().__init__()\n\n        self.embedding_size = embedding_size\n        self.num_classes = num_classes\n        self.sample_rate = sample_rate\n\n        self.world_size = comm.get_world_size()\n        self.rank = comm.get_rank()\n        self.local_rank = comm.get_local_rank()\n        self.device = torch.device(f'cuda:{self.local_rank}')\n\n        self.num_local: int = self.num_classes // self.world_size + int(self.rank < self.num_classes % self.world_size)\n        self.class_start: int = self.num_classes // self.world_size * self.rank + \\\n                                min(self.rank, self.num_classes % self.world_size)\n        self.num_sample: int = int(self.sample_rate * self.num_local)\n\n        self.cls_layer = getattr(any_softmax, cls_type)(num_classes, scale, margin)\n\n        self.weight = torch.normal(0, 0.01, (self.num_local, self.embedding_size), device=self.device)\n        self.weight_mom: torch.Tensor = torch.zeros_like(self.weight)\n        logger.info(\"softmax weight init successfully!\")\n        logger.info(\"softmax weight mom init successfully!\")\n        self.stream: torch.cuda.Stream = torch.cuda.Stream(self.local_rank)\n\n        self.index = None\n        if int(self.sample_rate) == 1:\n            self.update = lambda: 0\n            self.sub_weight = nn.Parameter(self.weight)\n            self.sub_weight_mom = self.weight_mom\n        else:\n            self.sub_weight = nn.Parameter(torch.empty((0, 0), device=self.device))\n\n    def forward(self, total_features):\n        torch.cuda.current_stream().wait_stream(self.stream)\n        if self.cls_layer.__class__.__name__ == 'Linear':\n            logits = F.linear(total_features, self.sub_weight)\n        else:\n            logits = F.linear(F.normalize(total_features), F.normalize(self.sub_weight))\n        return logits\n\n    def forward_backward(self, features, targets, optimizer):\n        \"\"\"\n        Partial FC forward, which will sample positive weights and part of negative weights,\n        then compute logits and get the grad of features.\n        \"\"\"\n        total_targets = self.prepare(targets, optimizer)\n\n        if self.world_size > 1:\n            total_features = concat_all_gather(features)\n        else:\n            total_features = features.detach()\n\n        total_features.requires_grad_(True)\n\n        logits = self.forward(total_features)\n        logits = self.cls_layer(logits, total_targets)\n\n        # from ipdb import set_trace; set_trace()\n        with torch.no_grad():\n            max_fc = torch.max(logits, dim=1, keepdim=True)[0]\n            if self.world_size > 1:\n                dist.all_reduce(max_fc, dist.ReduceOp.MAX)\n\n            # calculate exp(logits) and all-reduce\n            logits_exp = torch.exp(logits - max_fc)\n            logits_sum_exp = logits_exp.sum(dim=1, keepdim=True)\n\n            if self.world_size > 1:\n                dist.all_reduce(logits_sum_exp, dist.ReduceOp.SUM)\n\n            # calculate prob\n            logits_exp.div_(logits_sum_exp)\n\n            # get one-hot\n            grad = logits_exp\n            index = torch.where(total_targets != -1)[0]\n            one_hot = torch.zeros(size=[index.size()[0], grad.size()[1]], device=grad.device)\n            one_hot.scatter_(1, total_targets[index, None], 1)\n\n            # calculate loss\n            loss = torch.zeros(grad.size()[0], 1, device=grad.device)\n            loss[index] = grad[index].gather(1, total_targets[index, None])\n            if self.world_size > 1:\n                dist.all_reduce(loss, dist.ReduceOp.SUM)\n            loss_v = loss.clamp_min_(1e-30).log_().mean() * (-1)\n\n            # calculate grad\n            grad[index] -= one_hot\n            grad.div_(logits.size(0))\n\n        logits.backward(grad)\n        if total_features.grad is not None:\n            total_features.grad.detach_()\n        x_grad: torch.Tensor = torch.zeros_like(features)\n        # feature gradient all-reduce\n        if self.world_size > 1:\n            dist.reduce_scatter(x_grad, list(total_features.grad.chunk(self.world_size, dim=0)))\n        else:\n            x_grad = total_features.grad\n        x_grad = x_grad * self.world_size\n        # backward backbone\n        return x_grad, loss_v\n\n    @torch.no_grad()\n    def sample(self, total_targets):\n        \"\"\"\n        Get sub_weights according to total targets gathered from all GPUs, due to each weights in different\n        GPU contains different class centers.\n        \"\"\"\n        index_positive = (self.class_start <= total_targets) & (total_targets < self.class_start + self.num_local)\n        total_targets[~index_positive] = -1\n        total_targets[index_positive] -= self.class_start\n        if int(self.sample_rate) != 1:\n            positive = torch.unique(total_targets[index_positive], sorted=True)\n            if self.num_sample - positive.size(0) >= 0:\n                perm = torch.rand(size=[self.num_local], device=self.weight.device)\n                perm[positive] = 2.0\n                index = torch.topk(perm, k=self.num_sample)[1]\n                index = index.sort()[0]\n            else:\n                index = positive\n            self.index = index\n            total_targets[index_positive] = torch.searchsorted(index, total_targets[index_positive])\n            self.sub_weight = nn.Parameter(self.weight[index])\n            self.sub_weight_mom = self.weight_mom[index]\n\n    @torch.no_grad()\n    def update(self):\n        self.weight_mom[self.index] = self.sub_weight_mom\n        self.weight[self.index] = self.sub_weight\n\n    def prepare(self, targets, optimizer):\n        with torch.cuda.stream(self.stream):\n            if self.world_size > 1:\n                total_targets = concat_all_gather(targets)\n            else:\n                total_targets = targets\n            # update sub_weight\n            self.sample(total_targets)\n            optimizer.state.pop(optimizer.param_groups[-1]['params'][0], None)\n            optimizer.param_groups[-1]['params'][0] = self.sub_weight\n            optimizer.state[self.sub_weight][\"momentum_buffer\"] = self.sub_weight_mom\n            return total_targets\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/pfc_checkpointer.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\nfrom typing import Any, Dict\n\nimport torch\n\nfrom fast_reid.fastreid.engine.hooks import PeriodicCheckpointer\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.utils.file_io import PathManager\n\n\nclass PfcPeriodicCheckpointer(PeriodicCheckpointer):\n\n    def step(self, epoch: int, **kwargs: Any):\n        rank = comm.get_rank()\n        if (epoch + 1) % self.period == 0 and epoch < self.max_epoch - 1:\n            self.checkpointer.save(\n                f\"softmax_weight_{epoch:04d}_rank_{rank:02d}\"\n            )\n        if epoch >= self.max_epoch - 1:\n            self.checkpointer.save(f\"softmax_weight_{rank:02d}\", )\n\n\nclass PfcCheckpointer(Checkpointer):\n    def __init__(self, model, save_dir, *, save_to_disk=True, **checkpointables):\n        super().__init__(model, save_dir, save_to_disk=save_to_disk, **checkpointables)\n        self.rank = comm.get_rank()\n\n    def save(self, name: str, **kwargs: Dict[str, str]):\n        if not self.save_dir or not self.save_to_disk:\n            return\n\n        data = {}\n        data[\"model\"] = {\n            \"weight\": self.model.weight.data,\n            \"momentum\": self.model.weight_mom,\n        }\n        for key, obj in self.checkpointables.items():\n            data[key] = obj.state_dict()\n        data.update(kwargs)\n\n        basename = f\"{name}.pth\"\n        save_file = os.path.join(self.save_dir, basename)\n        assert os.path.basename(save_file) == basename, basename\n        self.logger.info(\"Saving partial fc weights\")\n        with PathManager.open(save_file, \"wb\") as f:\n            torch.save(data, f)\n        self.tag_last_checkpoint(basename)\n\n    def _load_model(self, checkpoint: Any):\n        checkpoint_state_dict = checkpoint.pop(\"model\")\n        self._convert_ndarray_to_tensor(checkpoint_state_dict)\n        self.model.weight.data.copy_(checkpoint_state_dict.pop(\"weight\"))\n        self.model.weight_mom.data.copy_(checkpoint_state_dict.pop(\"momentum\"))\n\n    def has_checkpoint(self):\n        save_file = os.path.join(self.save_dir, f\"last_weight_{self.rank:02d}\")\n        return PathManager.exists(save_file)\n\n    def get_checkpoint_file(self):\n        \"\"\"\n        Returns:\n            str: The latest checkpoint file in target directory.\n        \"\"\"\n        save_file = os.path.join(self.save_dir, f\"last_weight_{self.rank:02d}\")\n        try:\n            with PathManager.open(save_file, \"r\") as f:\n                last_saved = f.read().strip()\n        except IOError:\n            # if file doesn't exist, maybe because it has just been\n            # deleted by a separate process\n            return \"\"\n        return os.path.join(self.save_dir, last_saved)\n\n    def tag_last_checkpoint(self, last_filename_basename: str):\n        save_file = os.path.join(self.save_dir, f\"last_weight_{self.rank:02d}\")\n        with PathManager.open(save_file, \"w\") as f:\n            f.write(last_filename_basename)\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/trainer.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport logging\nimport os\nimport time\n\nfrom torch.nn.parallel import DistributedDataParallel\nfrom torch.nn.utils import clip_grad_norm_\n\nfrom fast_reid.fastreid.data.build import _root, build_reid_test_loader, build_reid_train_loader\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.transforms import build_transforms\nfrom fast_reid.fastreid.engine import hooks\nfrom fast_reid.fastreid.engine.defaults import DefaultTrainer, TrainerBase\nfrom fast_reid.fastreid.engine.train_loop import SimpleTrainer, AMPTrainer\nfrom fast_reid.fastreid.solver import build_optimizer\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.utils.logger import setup_logger\nfrom fast_reid.fastreid.utils.params import ContiguousParams\nfrom .face_data import MXFaceDataset\nfrom .face_data import TestFaceDataset\nfrom .face_evaluator import FaceEvaluator\nfrom .modeling import PartialFC\nfrom .pfc_checkpointer import PfcPeriodicCheckpointer, PfcCheckpointer\nfrom .utils_amp import MaxClipGradScaler\n\n\nclass FaceTrainer(DefaultTrainer):\n    def __init__(self, cfg):\n        TrainerBase.__init__(self)\n\n        logger = logging.getLogger('fastreid.partial-fc.trainer')\n        if not logger.isEnabledFor(logging.INFO):  # setup_logger is not called for fastreid\n            setup_logger()\n\n        # Assume these objects must be constructed in this order.\n        data_loader = self.build_train_loader(cfg)\n        cfg = self.auto_scale_hyperparams(cfg, data_loader.dataset.num_classes)\n        model = self.build_model(cfg)\n        optimizer, param_wrapper = self.build_optimizer(cfg, model)\n\n        if cfg.MODEL.HEADS.PFC.ENABLED:\n            # fmt: off\n            feat_dim      = cfg.MODEL.BACKBONE.FEAT_DIM\n            embedding_dim = cfg.MODEL.HEADS.EMBEDDING_DIM\n            num_classes   = cfg.MODEL.HEADS.NUM_CLASSES\n            sample_rate   = cfg.MODEL.HEADS.PFC.SAMPLE_RATE\n            cls_type      = cfg.MODEL.HEADS.CLS_LAYER\n            scale         = cfg.MODEL.HEADS.SCALE\n            margin        = cfg.MODEL.HEADS.MARGIN\n            # fmt: on\n            # Partial-FC module\n            embedding_size = embedding_dim if embedding_dim > 0 else feat_dim\n            self.pfc_module = PartialFC(embedding_size, num_classes, sample_rate, cls_type, scale, margin)\n            self.pfc_optimizer, _ = build_optimizer(cfg, self.pfc_module, False)\n\n        # For training, wrap with DDP. But don't need this for inference.\n        if comm.get_world_size() > 1:\n            # ref to https://github.com/pytorch/pytorch/issues/22049 to set `find_unused_parameters=True`\n            # for part of the parameters is not updated.\n            model = DistributedDataParallel(\n                model, device_ids=[comm.get_local_rank()], broadcast_buffers=False,\n            )\n\n        if cfg.MODEL.HEADS.PFC.ENABLED:\n            mini_batch_size = cfg.SOLVER.IMS_PER_BATCH // comm.get_world_size()\n            grad_scaler = MaxClipGradScaler(mini_batch_size, 128 * mini_batch_size, growth_interval=100)\n            self._trainer = PFCTrainer(model, data_loader, optimizer, param_wrapper,\n                                       self.pfc_module, self.pfc_optimizer, cfg.SOLVER.AMP.ENABLED, grad_scaler)\n        else:\n            self._trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(\n                model, data_loader, optimizer, param_wrapper\n            )\n\n        self.iters_per_epoch = len(data_loader.dataset) // cfg.SOLVER.IMS_PER_BATCH\n        self.scheduler = self.build_lr_scheduler(cfg, optimizer, self.iters_per_epoch)\n        if cfg.MODEL.HEADS.PFC.ENABLED:\n            self.pfc_scheduler = self.build_lr_scheduler(cfg, self.pfc_optimizer, self.iters_per_epoch)\n\n        self.checkpointer = Checkpointer(\n            # Assume you want to save checkpoints together with logs/statistics\n            model,\n            cfg.OUTPUT_DIR,\n            save_to_disk=comm.is_main_process(),\n            optimizer=optimizer,\n            **self.scheduler,\n        )\n\n        if cfg.MODEL.HEADS.PFC.ENABLED:\n            self.pfc_checkpointer = PfcCheckpointer(\n                self.pfc_module,\n                cfg.OUTPUT_DIR,\n                optimizer=self.pfc_optimizer,\n                **self.pfc_scheduler,\n            )\n\n        self.start_epoch = 0\n        self.max_epoch = cfg.SOLVER.MAX_EPOCH\n        self.max_iter = self.max_epoch * self.iters_per_epoch\n        self.warmup_iters = cfg.SOLVER.WARMUP_ITERS\n        self.delay_epochs = cfg.SOLVER.DELAY_EPOCHS\n        self.cfg = cfg\n\n        self.register_hooks(self.build_hooks())\n\n    def build_hooks(self):\n        ret = super().build_hooks()\n\n        if self.cfg.MODEL.HEADS.PFC.ENABLED:\n            # Make sure checkpointer is after writer\n            ret.insert(\n                len(ret) - 1,\n                PfcPeriodicCheckpointer(self.pfc_checkpointer, self.cfg.SOLVER.CHECKPOINT_PERIOD)\n            )\n            # partial fc scheduler hook\n            ret.append(\n                hooks.LRScheduler(self.pfc_optimizer, self.pfc_scheduler)\n            )\n        return ret\n\n    def resume_or_load(self, resume=True):\n        # Backbone loading state_dict\n        super().resume_or_load(resume)\n        # Partial-FC loading state_dict\n        if self.cfg.MODEL.HEADS.PFC.ENABLED:\n            self.pfc_checkpointer.resume_or_load('', resume=resume)\n\n    @classmethod\n    def build_train_loader(cls, cfg):\n        path_imgrec = cfg.DATASETS.REC_PATH\n        if path_imgrec != \"\":\n            transforms = build_transforms(cfg, is_train=True)\n            train_set = MXFaceDataset(path_imgrec, transforms)\n            return build_reid_train_loader(cfg, train_set=train_set)\n        else:\n            return DefaultTrainer.build_train_loader(cfg)\n\n    @classmethod\n    def build_test_loader(cls, cfg, dataset_name):\n        dataset = DATASET_REGISTRY.get(dataset_name)(root=_root)\n        test_set = TestFaceDataset(dataset.carray, dataset.is_same)\n        data_loader, _ = build_reid_test_loader(cfg, test_set=test_set)\n        return data_loader, test_set.labels\n\n    @classmethod\n    def build_evaluator(cls, cfg, dataset_name, output_dir=None):\n        if output_dir is None:\n            output_dir = os.path.join(cfg.OUTPUT_DIR, \"visualization\")\n        data_loader, labels = cls.build_test_loader(cfg, dataset_name)\n        return data_loader, FaceEvaluator(cfg, labels, dataset_name, output_dir)\n\n\nclass PFCTrainer(SimpleTrainer):\n    \"\"\"\n    Author: {Xiang An, Yang Xiao, XuHan Zhu} in DeepGlint,\n    Partial FC: Training 10 Million Identities on a Single Machine\n    See the original paper:\n    https://arxiv.org/abs/2010.05222\n    code based on:\n    https://github.com/deepinsight/insightface/blob/master/recognition/arcface_torch/partial_fc.py\n    \"\"\"\n\n    def __init__(self, model, data_loader, optimizer, param_wrapper, pfc_module, pfc_optimizer, amp_enabled,\n                 grad_scaler):\n        super().__init__(model, data_loader, optimizer, param_wrapper)\n\n        self.pfc_module = pfc_module\n        self.pfc_optimizer = pfc_optimizer\n        self.amp_enabled = amp_enabled\n\n        self.grad_scaler = grad_scaler\n\n    def run_step(self):\n        assert self.model.training, \"[PFCTrainer] model was changed to eval mode!\"\n        start = time.perf_counter()\n\n        data = next(self._data_loader_iter)\n        data_time = time.perf_counter() - start\n\n        features, targets = self.model(data)\n\n        # Partial-fc backward\n        f_grad, loss_v = self.pfc_module.forward_backward(features, targets, self.pfc_optimizer)\n\n        if self.amp_enabled:\n            features.backward(self.grad_scaler.scale(f_grad))\n            self.grad_scaler.unscale_(self.optimizer)\n            clip_grad_norm_(self.model.parameters(), max_norm=5, norm_type=2)\n            self.grad_scaler.step(self.optimizer)\n            self.grad_scaler.update()\n        else:\n            features.backward(f_grad)\n            clip_grad_norm_(self.model.parameters(), max_norm=5, norm_type=2)\n            self.optimizer.step()\n\n        loss_dict = {\"loss_cls\": loss_v}\n        self._write_metrics(loss_dict, data_time)\n\n        self.pfc_optimizer.step()\n        self.pfc_module.update()\n        self.optimizer.zero_grad()\n        self.pfc_optimizer.zero_grad()\n        if isinstance(self.param_wrapper, ContiguousParams):\n            self.param_wrapper.assert_buffer_is_valid()\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/utils_amp.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom typing import Dict, List\n\nimport torch\nfrom torch._six import container_abcs\nfrom torch.cuda.amp import GradScaler\n\n\nclass _MultiDeviceReplicator(object):\n    \"\"\"\n    Lazily serves copies of a tensor to requested devices.  Copies are cached per-device.\n    \"\"\"\n\n    def __init__(self, master_tensor: torch.Tensor) -> None:\n        assert master_tensor.is_cuda\n        self.master = master_tensor\n        self._per_device_tensors: Dict[torch.device, torch.Tensor] = {}\n\n    def get(self, device) -> torch.Tensor:\n        retval = self._per_device_tensors.get(device, None)\n        if retval is None:\n            retval = self.master.to(device=device, non_blocking=True, copy=True)\n            self._per_device_tensors[device] = retval\n        return retval\n\n\nclass MaxClipGradScaler(GradScaler):\n    def __init__(self, init_scale, max_scale: float, growth_interval=100):\n        super().__init__(init_scale=init_scale, growth_interval=growth_interval)\n        self.max_scale = max_scale\n\n    def scale_clip(self):\n        if self.get_scale() == self.max_scale:\n            self.set_growth_factor(1)\n        elif self.get_scale() < self.max_scale:\n            self.set_growth_factor(2)\n        elif self.get_scale() > self.max_scale:\n            self._scale.fill_(self.max_scale)\n            self.set_growth_factor(1)\n\n    def scale(self, outputs):\n        \"\"\"\n        Multiplies ('scales') a tensor or list of tensors by the scale factor.\n        Returns scaled outputs.  If this instance of :class:`GradScaler` is not enabled, outputs are returned\n        unmodified.\n        Arguments:\n            outputs (Tensor or iterable of Tensors):  Outputs to scale.\n        \"\"\"\n        if not self._enabled:\n            return outputs\n        self.scale_clip()\n        # Short-circuit for the common case.\n        if isinstance(outputs, torch.Tensor):\n            assert outputs.is_cuda\n            if self._scale is None:\n                self._lazy_init_scale_growth_tracker(outputs.device)\n            assert self._scale is not None\n            return outputs * self._scale.to(device=outputs.device, non_blocking=True)\n\n        # Invoke the more complex machinery only if we're treating multiple outputs.\n        stash: List[_MultiDeviceReplicator] = []  # holds a reference that can be overwritten by apply_scale\n\n        def apply_scale(val):\n            if isinstance(val, torch.Tensor):\n                assert val.is_cuda\n                if len(stash) == 0:\n                    if self._scale is None:\n                        self._lazy_init_scale_growth_tracker(val.device)\n                    assert self._scale is not None\n                    stash.append(_MultiDeviceReplicator(self._scale))\n                return val * stash[0].get(val.device)\n            elif isinstance(val, container_abcs.Iterable):\n                iterable = map(apply_scale, val)\n                if isinstance(val, list) or isinstance(val, tuple):\n                    return type(val)(iterable)\n                else:\n                    return iterable\n            else:\n                raise ValueError(\"outputs must be a Tensor or an iterable of Tensors\")\n\n        return apply_scale(outputs)\n"
  },
  {
    "path": "fast_reid/projects/FastFace/fastface/verification.py",
    "content": "# encoding: utf-8\n\n\"\"\"Helper for evaluation on the Labeled Faces in the Wild dataset\n\"\"\"\n\n# MIT License\n#\n# Copyright (c) 2016 David Sandberg\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nimport numpy as np\nimport sklearn\nfrom scipy import interpolate\nfrom sklearn.decomposition import PCA\nfrom sklearn.model_selection import KFold\n\n\ndef calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca=0):\n    assert (embeddings1.shape[0] == embeddings2.shape[0])\n    assert (embeddings1.shape[1] == embeddings2.shape[1])\n    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n    nrof_thresholds = len(thresholds)\n    k_fold = KFold(n_splits=nrof_folds, shuffle=False)\n\n    tprs = np.zeros((nrof_folds, nrof_thresholds))\n    fprs = np.zeros((nrof_folds, nrof_thresholds))\n    accuracy = np.zeros((nrof_folds))\n    best_thresholds = np.zeros((nrof_folds))\n    indices = np.arange(nrof_pairs)\n\n    if pca == 0:\n        diff = np.subtract(embeddings1, embeddings2)\n        dist = np.sum(np.square(diff), 1)\n\n    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n        # print('train_set', train_set)\n        # print('test_set', test_set)\n        if pca > 0:\n            print('doing pca on', fold_idx)\n            embed1_train = embeddings1[train_set]\n            embed2_train = embeddings2[train_set]\n            _embed_train = np.concatenate((embed1_train, embed2_train), axis=0)\n            # print(_embed_train.shape)\n            pca_model = PCA(n_components=pca)\n            pca_model.fit(_embed_train)\n            embed1 = pca_model.transform(embeddings1)\n            embed2 = pca_model.transform(embeddings2)\n            embed1 = sklearn.preprocessing.normalize(embed1)\n            embed2 = sklearn.preprocessing.normalize(embed2)\n            # print(embed1.shape, embed2.shape)\n            diff = np.subtract(embed1, embed2)\n            dist = np.sum(np.square(diff), 1)\n\n        # Find the best threshold for the fold\n        acc_train = np.zeros((nrof_thresholds))\n        for threshold_idx, threshold in enumerate(thresholds):\n            _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])\n        best_threshold_index = np.argmax(acc_train)\n        #         print('best_threshold_index', best_threshold_index, acc_train[best_threshold_index])\n        best_thresholds[fold_idx] = thresholds[best_threshold_index]\n        for threshold_idx, threshold in enumerate(thresholds):\n            tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(threshold,\n                                                                                                 dist[test_set],\n                                                                                                 actual_issame[\n                                                                                                     test_set])\n        _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set],\n                                                      actual_issame[test_set])\n\n    tpr = np.mean(tprs, 0)\n    fpr = np.mean(fprs, 0)\n    return tpr, fpr, accuracy, best_thresholds\n\n\ndef calculate_accuracy(threshold, dist, actual_issame):\n    predict_issame = np.less(dist, threshold)\n    tp = np.sum(np.logical_and(predict_issame, actual_issame))\n    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n    tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))\n    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))\n\n    tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)\n    fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)\n    acc = float(tp + tn) / dist.size\n    return tpr, fpr, acc\n\n\ndef calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):\n    '''\n    Copy from [insightface](https://github.com/deepinsight/insightface)\n    :param thresholds:\n    :param embeddings1:\n    :param embeddings2:\n    :param actual_issame:\n    :param far_target:\n    :param nrof_folds:\n    :return:\n    '''\n    assert (embeddings1.shape[0] == embeddings2.shape[0])\n    assert (embeddings1.shape[1] == embeddings2.shape[1])\n    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n    nrof_thresholds = len(thresholds)\n    k_fold = KFold(n_splits=nrof_folds, shuffle=False)\n\n    val = np.zeros(nrof_folds)\n    far = np.zeros(nrof_folds)\n\n    diff = np.subtract(embeddings1, embeddings2)\n    dist = np.sum(np.square(diff), 1)\n    indices = np.arange(nrof_pairs)\n\n    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n\n        # Find the threshold that gives FAR = far_target\n        far_train = np.zeros(nrof_thresholds)\n        for threshold_idx, threshold in enumerate(thresholds):\n            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])\n        if np.max(far_train) >= far_target:\n            f = interpolate.interp1d(far_train, thresholds, kind='slinear')\n            threshold = f(far_target)\n        else:\n            threshold = 0.0\n\n        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])\n\n    val_mean = np.mean(val)\n    far_mean = np.mean(far)\n    val_std = np.std(val)\n    return val_mean, val_std, far_mean\n\n\ndef calculate_val_far(threshold, dist, actual_issame):\n    predict_issame = np.less(dist, threshold)\n    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))\n    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n    n_same = np.sum(actual_issame)\n    n_diff = np.sum(np.logical_not(actual_issame))\n    val = float(true_accept) / float(n_same)\n    far = float(false_accept) / float(n_diff)\n    return val, far\n\n\ndef evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):\n    # Calculate evaluation metrics\n    thresholds = np.arange(0, 4, 0.01)\n    embeddings1 = embeddings[0::2]\n    embeddings2 = embeddings[1::2]\n    tpr, fpr, accuracy, best_thresholds = calculate_roc(thresholds, embeddings1, embeddings2,\n                                                        np.asarray(actual_issame), nrof_folds=nrof_folds, pca=pca)\n    #     thresholds = np.arange(0, 4, 0.001)\n    #     val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,\n    #                                       np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)\n    #     return tpr, fpr, accuracy, best_thresholds, val, val_std, far\n    return tpr, fpr, accuracy, best_thresholds\n"
  },
  {
    "path": "fast_reid/projects/FastFace/train_net.py",
    "content": "#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport sys\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.engine import default_argument_parser, default_setup, launch\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\n\nfrom fastface import *\n\n\ndef setup(args):\n    \"\"\"\n    Create configs and perform basic setups.\n    \"\"\"\n    cfg = get_cfg()\n    add_face_cfg(cfg)\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    default_setup(cfg, args)\n    return cfg\n\n\ndef main(args):\n    cfg = setup(args)\n\n    if args.eval_only:\n        cfg.defrost()\n        cfg.MODEL.BACKBONE.PRETRAIN = False\n        model = FaceTrainer.build_model(cfg)\n\n        Checkpointer(model).load(cfg.MODEL.WEIGHTS)  # load trained model\n\n        res = FaceTrainer.test(cfg, model)\n        return res\n\n    trainer = FaceTrainer(cfg)\n\n    trainer.resume_or_load(resume=args.resume)\n    return trainer.train()\n\n\nif __name__ == \"__main__\":\n    args = default_argument_parser().parse_args()\n    print(\"Command Line Args:\", args)\n    launch(\n        main,\n        args.num_gpus,\n        num_machines=args.num_machines,\n        machine_rank=args.machine_rank,\n        dist_url=args.dist_url,\n        args=(args,),\n    )\n"
  },
  {
    "path": "fast_reid/projects/FastRT/.gitignore",
    "content": "*.wts\n\n.vscode/\nlibs/\nbuild/\ndata/"
  },
  {
    "path": "fast_reid/projects/FastRT/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 2.6)\n\nset(LIBARARY_NAME \"FastRT\" CACHE STRING \"The Fastreid-tensorrt library name\")\n\nset(LIBARARY_VERSION_MAJOR \"0\")\nset(LIBARARY_VERSION_MINOR \"0\")\nset(LIBARARY_VERSION_SINOR \"5\")\nset(LIBARARY_SOVERSION \"0\")\nset(LIBARARY_VERSION \"${LIBARARY_VERSION_MAJOR}.${LIBARARY_VERSION_MINOR}.${LIBARARY_VERSION_SINOR}\")\nproject(${LIBARARY_NAME}${LIBARARY_VERSION})\n\nadd_definitions(-std=c++11)\nset(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -pthread -Wall -Ofast -Wfatal-errors -D_MWAITXINTRIN_H_INCLUDED\")\nset(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} \"${CMAKE_SOURCE_DIR}/\")\nset(CMAKE_BUILD_TYPE Release)\nset(CMAKE_CXX_EXTENSIONS OFF)\nset(CMAKE_CXX_STANDARD_REQUIRED ON)\nset(CMAKE_C_LINK_EXECUTABLE ${CMAKE_CXX_LINK_EXECUTABLE})\n\n# option for shared or static\nset(TARGET \"SHARED\" CACHE STRING \"SHARED or STATIC\" FORCE)\n\nif(\"${TARGET}\" STREQUAL \"SHARED\")\n  set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -fPIC\")\nmessage(\"Build Engine as shared library\")\nelse()\n  message(\"Build Engine as static library\")\nendif()\n\noption(CUDA_USE_STATIC_CUDA_RUNTIME \"Use Static CUDA\"     OFF)\noption(BUILD_FASTRT_ENGINE     \"Build FastRT Engine\"       ON)\noption(BUILD_DEMO              \"Build DEMO\"                ON)\noption(BUILD_FP16              \"Build Engine as FP16\"     OFF)\noption(BUILD_INT8              \"Build Engine as INT8\"     OFF)\noption(USE_CNUMPY              \"Include CNPY libs\"        OFF)\noption(BUILD_PYTHON_INTERFACE  \"Build Python Interface\"   OFF)\n\nset(SOLUTION_DIR ${CMAKE_CURRENT_SOURCE_DIR})\nmessage(\"CMAKE_CURRENT_SOURCE_DIR: \" ${SOLUTION_DIR})\n\nif(USE_CNUMPY)\n  add_definitions(-DUSE_CNUMPY)\nendif()\n\nif(BUILD_INT8)\n  add_definitions(-DBUILD_INT8)\n  message(\"Build Engine as INT8\")\n  set(INT8_CALIBRATE_DATASET_PATH \"/data/Market-1501-v15.09.15/bounding_box_test/\" CACHE STRING \"Path to calibrate dataset(end with /)\")\n  message(\"INT8_CALIBRATE_DATASET_PATH: \" ${INT8_CALIBRATE_DATASET_PATH})\n  configure_file(${SOLUTION_DIR}/include/fastrt/config.h.in ${SOLUTION_DIR}/include/fastrt/config.h @ONLY)\nelseif(BUILD_FP16)\n  add_definitions(-DBUILD_FP16)\n  message(\"Build Engine as FP16\")\nelse()\n  message(\"Build Engine as FP32\")\nendif()\n\nif(BUILD_FASTRT_ENGINE)\n  add_subdirectory(fastrt)\n  message(STATUS \"BUILD_FASTREID_ENGINE: ON\")\nelse()\n  message(STATUS \"BUILD_FASTREID_ENGINE: OFF\")\nendif()\n\nif(BUILD_DEMO)\n  add_subdirectory(demo)\n  message(STATUS \"BUILD_DEMO: ON\")\nelse()\n  message(STATUS \"BUILD_DEMO: OFF\")\nendif()\n\nif(BUILD_PYTHON_INTERFACE)\n  add_subdirectory(pybind_interface)\n  message(STATUS \"BUILD_PYTHON_INTERFACE: ON\")\nelse()\n  message(STATUS \"BUILD_PYTHON_INTERFACE: OFF\")\nendif()"
  },
  {
    "path": "fast_reid/projects/FastRT/README.md",
    "content": "# C++ FastReID-TensorRT\n\n\nImplementation of reid model with TensorRT network definition APIs to build the whole network. \n\nSo we don't use any parsers here.\n\n### How to Run\n\n1. Generate '.wts' file from pytorch with `model_best.pth`\n\n   See [How_to_Generate.md](tools/How_to_Generate.md)\n\n2. Config your model\n   \n   See [Tensorrt Model Config](#ConfigSection)\n   \n3. (Optional) Build <a name=\"step3\"></a>`third party` libs\n\n   See [Build third_party section](#third_party)\n   \n4. Build <a name=\"step4\"></a>`fastrt` execute file\n   \n   ``` \n   mkdir build\n   cd build\n   cmake -DBUILD_FASTRT_ENGINE=ON \\\n         -DBUILD_DEMO=ON \\\n         -DUSE_CNUMPY=ON ..\n   make\n   ```\n\n5. Run <a name=\"step5\"></a>`fastrt`\n   \n   put `model_best.wts` into `FastRT/`\n\n   ``` \n   ./demo/fastrt -s  // serialize model & save as 'xxx.engine' file\n   ```\n\n   ``` \n   ./demo/fastrt -d  // deserialize 'xxx.engine' file and run inference\n   ```\n   \n6. Verify the output with pytorch\n\n7. (Optional) Once you verify the result, you can set FP16 for speed up\n   ``` \n   mkdir build\n   cd build\n   cmake -DBUILD_FASTRT_ENGINE=ON \\\n         -DBUILD_DEMO=ON \\\n         -DBUILD_FP16=ON ..\n   make\n   ```\n   \n   then go to [step 5](#step5) \n\n8. (Optional) You can use INT8 quantization for speed up\n\n   prepare CALIBRATE DATASET and set the path via cmake. (The path must end with /)\n\n   ``` \n   mkdir build\n   cd build\n   cmake -DBUILD_FASTRT_ENGINE=ON \\\n         -DBUILD_DEMO=ON \\\n         -DBUILD_INT8=ON \\\n         -DINT8_CALIBRATE_DATASET_PATH=\"/data/Market-1501-v15.09.15/bounding_box_test/\" ..\n   make\n   ```\n   then go to [step 5](#step5)\n\n9. (Optional) Build tensorrt model as shared libs\n\n   ``` \n   mkdir build\n   cd build\n   cmake -DBUILD_FASTRT_ENGINE=ON \\\n         -DBUILD_DEMO=OFF \\\n         -DBUILD_FP16=ON ..\n   make\n   make install\n   ```\n   You should find libs in `FastRT/libs/FastRTEngine/`\n   \n   Now build your application execute file\n   ``` \n   cmake -DBUILD_FASTRT_ENGINE=OFF -DBUILD_DEMO=ON ..\n   make\n   ```\n\n   then go to [step 5](#step5)\n   \n10. (Optional) Build tensorrt model with python interface, then you can use FastRT model in python.\n\n    ``` \n    mkdir build\n    cd build\n    cmake -DBUILD_FASTRT_ENGINE=ON \\\n        -DBUILD_DEMO=ON \\\n        -DBUILD_PYTHON_INTERFACE=ON ..\n    make\n    ```\n    \n    You should get a so file `FastRT/build/pybind_interface/ReID.cpython-37m-x86_64-linux-gnu.so`. \n   \n    Then go to [step 5](#step5) to create engine file.\n\n    After that you can import this so file in python, and deserialize engine file to infer in python. \n\n    You can find use example in `pybind_interface/test.py` and `pybind_interface/market_benchmark.py`.\n    \n    ``` \n    from PATH_TO_SO_FILE import ReID\n    model = ReID(GPU_ID)\n    model.build(PATH_TO_YOUR_ENGINEFILE)\n    numpy_feature = np.array([model.infer(CV2_FRAME)])\n    ```\n    \n    * `pybind_interface/test.py` use `pybind_interface/docker/trt7cu100/Dockerfile` (without pytorch installed)\n    * `pybind_interface/market_benchmark.py` use `pybind_interface/docker/trt7cu102_torch160/Dockerfile` (with pytorch installed)\n    \n### <a name=\"ConfigSection\"></a>`Tensorrt Model Config`\n\nEdit `FastRT/demo/inference.cpp`, according to your model config\n\nThe config is related to [How_to_Generate.md](tools/How_to_Generate.md)\n\n+ Ex1. `sbs_R50-ibn`\n```\nstatic const std::string WEIGHTS_PATH = \"../sbs_R50-ibn.wts\"; \nstatic const std::string ENGINE_PATH = \"./sbs_R50-ibn.engine\";\n\nstatic const int MAX_BATCH_SIZE = 4;\nstatic const int INPUT_H = 384;\nstatic const int INPUT_W = 128;\nstatic const int OUTPUT_SIZE = 2048;\nstatic const int DEVICE_ID = 0;\n\nstatic const FastreidBackboneType BACKBONE = FastreidBackboneType::r50; \nstatic const FastreidHeadType HEAD = FastreidHeadType::EmbeddingHead;\nstatic const FastreidPoolingType HEAD_POOLING = FastreidPoolingType::gempoolP;\nstatic const int LAST_STRIDE = 1;\nstatic const bool WITH_IBNA = true; \nstatic const bool WITH_NL = true;\nstatic const int EMBEDDING_DIM = 0; \n```\n\n+ Ex2. `sbs_R50`\n```\nstatic const std::string WEIGHTS_PATH = \"../sbs_R50.wts\";\nstatic const std::string ENGINE_PATH = \"./sbs_R50.engine\"; \n\nstatic const int MAX_BATCH_SIZE = 4;\nstatic const int INPUT_H = 384;\nstatic const int INPUT_W = 128;\nstatic const int OUTPUT_SIZE = 2048;\nstatic const int DEVICE_ID = 0;\n\nstatic const FastreidBackboneType BACKBONE = FastreidBackboneType::r50; \nstatic const FastreidHeadType HEAD = FastreidHeadType::EmbeddingHead;\nstatic const FastreidPoolingType HEAD_POOLING = FastreidPoolingType::gempoolP;\nstatic const int LAST_STRIDE = 1;\nstatic const bool WITH_IBNA = false; \nstatic const bool WITH_NL = true;\nstatic const int EMBEDDING_DIM = 0; \n```\n\n+ Ex3. `sbs_r34_distill`\n```\nstatic const std::string WEIGHTS_PATH = \"../sbs_r34_distill.wts\"; \nstatic const std::string ENGINE_PATH = \"./sbs_r34_distill.engine\";\n\nstatic const int MAX_BATCH_SIZE = 4;\nstatic const int INPUT_H = 384;\nstatic const int INPUT_W = 128;\nstatic const int OUTPUT_SIZE = 512;\nstatic const int DEVICE_ID = 0;\n\nstatic const FastreidBackboneType BACKBONE = FastreidBackboneType::r34_distill; \nstatic const FastreidHeadType HEAD = FastreidHeadType::EmbeddingHead;\nstatic const FastreidPoolingType HEAD_POOLING = FastreidPoolingType::gempoolP;\nstatic const int LAST_STRIDE = 1;\nstatic const bool WITH_IBNA = false; \nstatic const bool WITH_NL = false;\nstatic const int EMBEDDING_DIM = 0; \n```\n\n+ Ex4.`kd-r34-r101_ibn`\n```\nstatic const std::string WEIGHTS_PATH = \"../kd_r34_distill.wts\"; \nstatic const std::string ENGINE_PATH = \"./kd_r34_distill.engine\"; \n\nstatic const int MAX_BATCH_SIZE = 4;\nstatic const int INPUT_H = 384;\nstatic const int INPUT_W = 128;\nstatic const int OUTPUT_SIZE = 512;\nstatic const int DEVICE_ID = 0;\n\nstatic const FastreidBackboneType BACKBONE = FastreidBackboneType::r34_distill; \nstatic const FastreidHeadType HEAD = FastreidHeadType::EmbeddingHead;\nstatic const FastreidPoolingType HEAD_POOLING = FastreidPoolingType::gempoolP;\nstatic const int LAST_STRIDE = 1;\nstatic const bool WITH_IBNA = false; \nstatic const bool WITH_NL = false;\nstatic const int EMBEDDING_DIM = 0; \n```\n\n\n+ Ex5.`kd-r18-r101_ibn`\n```\nstatic const std::string WEIGHTS_PATH = \"../kd-r18-r101_ibn.wts\"; \nstatic const std::string ENGINE_PATH = \"./kd_r18_distill.engine\"; \n\nstatic const int MAX_BATCH_SIZE = 16;\nstatic const int INPUT_H = 384;\nstatic const int INPUT_W = 128;\nstatic const int OUTPUT_SIZE = 512;\nstatic const int DEVICE_ID = 1;\n\nstatic const FastreidBackboneType BACKBONE = FastreidBackboneType::r18_distill; \nstatic const FastreidHeadType HEAD = FastreidHeadType::EmbeddingHead;\nstatic const FastreidPoolingType HEAD_POOLING = FastreidPoolingType::gempoolP;\nstatic const int LAST_STRIDE = 1;\nstatic const bool WITH_IBNA = true; \nstatic const bool WITH_NL = false;\nstatic const int EMBEDDING_DIM = 0; \n```\n\n### Supported conversion\n\n*  Backbone: resnet50, resnet34, distill-resnet50, distill-resnet34, distill-resnet18\n*  Heads: embedding_head\n*  Plugin layers: ibn, non-local\n*  Pooling layers: maxpool, avgpool, GeneralizedMeanPooling, GeneralizedMeanPoolingP\n\n### Benchmark\n\n| Model | Engine | Batch size | Image size | Embd | Time |\n|:-:|:-:|:-:|:-:|:-:|:-:|\n| Vanilla R34 | Python/Pytorch1.6 fp32 | 1 | 256x128 | 512 | 6.49ms | \n| Vanilla R34 | Python/Pytorch1.6 fp32 | 4 | 256x128 | 512 | 7.16ms | \n| Vanilla R34 | C++/trt7 fp32 | 1 | 256x128 | 512 | 2.34ms | \n| Vanilla R34 | C++/trt7 fp32 | 4 | 256x128 | 512 | 3.99ms | \n| Vanilla R34 | C++/trt7 fp16 | 1 | 256x128 | 512 | 1.83ms | \n| Vanilla R34 | C++/trt7 fp16 | 4 | 256x128 | 512 | 2.38ms | \n| Distill R34 | Python/Pytorch1.6 fp32 | 1 | 256x128 | 512 | 5.68ms | \n| Distill R34 | Python/Pytorch1.6 fp32 | 4 | 256x128 | 512 | 6.26ms | \n| Distill R34 | C++/trt7 fp32 | 1 | 256x128 | 512 | 2.36ms | \n| Distill R34 | C++/trt7 fp32 | 4 | 256x128 | 512 | 4.05ms | \n| Distill R34 | C++/trt7 fp16 | 1 | 256x128 | 512 | 1.86ms | \n| Distill R34 | C++/trt7 fp16 | 4 | 256x128 | 512 | 2.68ms | \n| R50-NL-IBN | Python/Pytorch1.6 fp32 | 1 | 256x128 | 2048 | 14.86ms | \n| R50-NL-IBN | Python/Pytorch1.6 fp32 | 4 | 256x128 | 2048 | 15.14ms | \n| R50-NL-IBN | C++/trt7 fp32 | 1 | 256x128 | 2048 | 4.67ms | \n| R50-NL-IBN | C++/trt7 fp32 | 4 | 256x128 | 2048 | 6.15ms | \n| R50-NL-IBN | C++/trt7 fp16 | 1 | 256x128 | 2048 | 2.87ms | \n| R50-NL-IBN | C++/trt7 fp16 | 4 | 256x128 | 2048 | 3.81ms | \n\n* Time: preprocessing(normalization) + inference (100 times average) \n* GPU: GTX 2080 TI\n\n### Test Environment\n\n1. fastreid v1.0.0 / 2080TI / Ubuntu18.04 / Nvidia driver 435 / cuda10.0 / cudnn7.6.5 / trt7.0.0 / nvinfer7.0.0 / opencv3.2\n\n2. fastreid v1.0.0 / 2080TI / Ubuntu18.04 / Nvidia driver 450 / cuda10.2 / cudnn7.6.5 / trt7.0.0 / nvinfer7.0.0 / opencv3.2\n\n### Installation\n\n* Set up with Docker\n\n   for cuda10.0\n\n   ```\n   cd docker/trt7cu100\n   sudo docker build -t trt7:cuda100 .\n   sudo docker run --gpus all -it --name fastrt -v /home/YOURID/workspace:/workspace -d trt7:cuda100\n   // then put the repo into `/home/YOURID/workspace/` before you getin container\n   ```\n\n   for cuda10.2\n\n   ```\n   cd docker/trt7cu102\n   sudo docker build -t trt7:cuda102 .\n   sudo docker run --gpus all -it --name fastrt -v /home/YOURID/workspace:/workspace -d trt7:cuda102 \n   // then put the repo into `/home/YOURID/workspace/` before you getin container\n   ```\n\n* [Installation reference](https://github.com/wang-xinyu/tensorrtx/blob/master/tutorials/install.md)\n\n### Build <a name=\"third_party\"></a> third party\n\n* for read/write numpy\n\n   ```\n   cd third_party/cnpy\n   cmake -DCMAKE_INSTALL_PREFIX=../../libs/cnpy -DENABLE_STATIC=OFF . && make -j4 && make install\n   ```"
  },
  {
    "path": "fast_reid/projects/FastRT/demo/CMakeLists.txt",
    "content": "SET(APP_PROJECT_NAME fastrt)\n\nfind_package(CUDA REQUIRED)\n# include and link dirs of cuda and tensorrt, you need adapt them if yours are different\n# cuda\ninclude_directories(/usr/local/cuda/include)\nlink_directories(/usr/local/cuda/lib64)\n# tensorrt\ninclude_directories(/usr/include/x86_64-linux-gnu/)\nlink_directories(/usr/lib/x86_64-linux-gnu/)\n\ninclude_directories(${SOLUTION_DIR}/include)\nadd_executable(${APP_PROJECT_NAME} inference.cpp)\n\n# numpy\nif(USE_CNUMPY)\n  include_directories(${SOLUTION_DIR}/libs/cnpy/include)\n  SET(CNPY_LIB ${SOLUTION_DIR}/libs/cnpy/lib/libcnpy.so)\nelse()\n  SET(CNPY_LIB)\nendif()\n\n# OpenCV\nfind_package(OpenCV)\ntarget_include_directories(${APP_PROJECT_NAME}\nPUBLIC\n  ${OpenCV_INCLUDE_DIRS}\n)\ntarget_link_libraries(${APP_PROJECT_NAME}\nPUBLIC\n  ${OpenCV_LIBS}\n)\n\nif(BUILD_FASTRT_ENGINE AND BUILD_DEMO)\n  SET(FASTRTENGINE_LIB FastRTEngine)\nelse()\n  SET(FASTRTENGINE_LIB ${SOLUTION_DIR}/libs/FastRTEngine/libFastRTEngine.so)\nendif()\n\ntarget_link_libraries(${APP_PROJECT_NAME} \nPRIVATE\n  ${FASTRTENGINE_LIB}\n  nvinfer\n  ${CNPY_LIB}\n)"
  },
  {
    "path": "fast_reid/projects/FastRT/demo/inference.cpp",
    "content": "#include <iostream>\n#include <opencv2/opencv.hpp>\n\n#include \"fastrt/utils.h\"\n#include \"fastrt/baseline.h\"\n#include \"fastrt/factory.h\"\nusing namespace fastrt;\nusing namespace nvinfer1;\n\n#ifdef USE_CNUMPY\n#include \"cnpy.h\"\n#endif\n\n/* Ex1. sbs_R50-ibn */\nstatic const std::string WEIGHTS_PATH = \"../sbs_R50-ibn.wts\"; \nstatic const std::string ENGINE_PATH = \"./sbs_R50-ibn.engine\";\n\nstatic const int MAX_BATCH_SIZE = 4;\nstatic const int INPUT_H = 384;\nstatic const int INPUT_W = 128;\nstatic const int OUTPUT_SIZE = 2048;\nstatic const int DEVICE_ID = 0;\n\nstatic const FastreidBackboneType BACKBONE = FastreidBackboneType::r50; \nstatic const FastreidHeadType HEAD = FastreidHeadType::EmbeddingHead;\nstatic const FastreidPoolingType HEAD_POOLING = FastreidPoolingType::gempoolP;\nstatic const int LAST_STRIDE = 1;\nstatic const bool WITH_IBNA = true; \nstatic const bool WITH_NL = true;\nstatic const int EMBEDDING_DIM = 0; \n\n\nint main(int argc, char** argv) {\n\n    trt::ModelConfig modelCfg { \n        WEIGHTS_PATH,\n        MAX_BATCH_SIZE,\n        INPUT_H,\n        INPUT_W,\n        OUTPUT_SIZE,\n        DEVICE_ID};\n\n    FastreidConfig reidCfg { \n        BACKBONE,\n        HEAD,\n        HEAD_POOLING,\n        LAST_STRIDE,\n        WITH_IBNA,\n        WITH_NL,\n        EMBEDDING_DIM};\n\n    std::cout << \"[ModelConfig]: \\n\" << modelCfg\n        << \"\\n[FastreidConfig]: \\n\" << reidCfg << std::endl;\n\n    Baseline baseline{modelCfg}; \n\n    if (argc == 2 && std::string(argv[1]) == \"-s\") {\n        ModuleFactory moduleFactory;\n        std::cout << \"[Serializling Engine]\" << std::endl;\n        if (!baseline.serializeEngine(ENGINE_PATH, \n            {std::move(moduleFactory.createBackbone(reidCfg)), \n                std::move(moduleFactory.createHead(reidCfg))})) {\n            std::cout << \"SerializeEngine Failed.\" << std::endl;\n            return -1;\n        }   \n        return 0;\n    } else if (argc == 2 && std::string(argv[1]) == \"-d\") {\n        std::cout << \"[Deserializling Engine]\" << std::endl;\n        if(!baseline.deserializeEngine(ENGINE_PATH)) {\n            std::cout << \"DeserializeEngine Failed.\" << std::endl;\n            return -1;\n        }\n\n/* comment out(//#define VERIFY) for real images usage */\n#define VERIFY\n\n#ifdef VERIFY   \n        /* support batch input data */\n        std::vector<cv::Mat> input;\n\n        input.emplace_back(cv::Mat(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(255,255,255))); // batch size = 1\n        //input.emplace_back(cv::Mat(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(255,255,255))); // batch size = 2, ...\n\n        /* run inference */\n        TimePoint start_infer, end_infer;\n        int LOOP_TIMES = 100;\n        start_infer = Time::now();\n        for (int times = 0; times < LOOP_TIMES; ++times) {\n            if(!baseline.inference(input)) {\n                std::cout << \"Inference Failed.\" << std::endl;\n                return -1;\n            }\n        }\n        end_infer = Time::now();\n\n        /* get output from cudaMallocHost */\n        float* feat_embedding = baseline.getOutput();\n\n#ifdef USE_CNUMPY\n        /* save as numpy. shape = (OUTPUT_SIZE,) */\n        cnpy::npy_save(\"./feat_embedding.npy\", feat_embedding, {OUTPUT_SIZE}, \"w\");\n#endif\n\n        /* print output */\n        TRTASSERT(feat_embedding);\n        for (size_t img_idx = 0; img_idx < input.size(); ++img_idx) {\n            for (int dim = 0; dim < baseline.getOutputSize(); ++dim) {\n                std::cout<< feat_embedding[img_idx+dim] << \" \";\n                if ((dim+1) % 10 == 0) {\n                    std::cout << std::endl;\n                }\n            }\n        }\n        std::cout << std::endl;\n        \n        /* Not including image resizing */\n        std::cout << \"[Preprocessing+Inference]: \" << \n            std::chrono::duration_cast<std::chrono::milliseconds>(end_infer - start_infer).count()/static_cast<float>(LOOP_TIMES) << \"ms\" << std::endl;         \n#else      \n        /* get jpg filenames */\n        auto filenames = io::fileGlob(\"../data/*.jpg\"); \n        std::cout << \"#filenames: \" << filenames.size() << std::endl;\n        std::vector<cv::Mat> input;\n        for (size_t batch_start = 0; batch_start < filenames.size(); batch_start+=modelCfg.max_batch_size) {\n            input.clear();\n            /* collect batch */\n            for (int img_idx = 0; img_idx < modelCfg.max_batch_size; ++img_idx) {\n                if ( (batch_start + img_idx) >= filenames.size() ) continue; \n                std::cout << \"Image: \" << filenames[batch_start + img_idx] << std::endl;\n                cv::Mat resizeImg(modelCfg.input_h, modelCfg.input_w, CV_8UC3);\n                cv::resize(cv::imread(filenames[batch_start + img_idx]), resizeImg, resizeImg.size(), 0, 0, cv::INTER_CUBIC); /* cv::INTER_LINEAR */\n                cv::imwrite(\"./file_idx[\" + std::to_string(batch_start + img_idx) + \"].jpg\", resizeImg); /* Visualize resize image */\n                input.emplace_back(resizeImg);\n            }\n            if(!baseline.inference(input)) {\n                std::cout << \"Inference Failed.\" << std::endl;\n                return -1;\n            }\n        }\n#endif\n        return 0;\n    } else {\n        std::cerr << \"arguments not right!\" << std::endl;\n        std::cerr << \"./demo/fastrt -s  // serialize model to .engine file\" << std::endl;\n        std::cerr << \"./demo/fastrt -d  // deserialize .engine file and run inference\" << std::endl;\n        return -1;\n    }\n}\n"
  },
  {
    "path": "fast_reid/projects/FastRT/docker/trt7cu100/Dockerfile",
    "content": "# cuda10.0\nFROM fineyu/tensorrt7:0.0.1\n\nRUN add-apt-repository -y ppa:timsc/opencv-3.4 && \\\n    apt-get update && \\\n    apt-get install -y cmake \\\n    libopencv-dev \\\n    libopencv-dnn-dev \\\n    libopencv-shape3.4-dbg && \\\n    apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*\n"
  },
  {
    "path": "fast_reid/projects/FastRT/docker/trt7cu102/Dockerfile",
    "content": "# cuda10.2\nFROM nvcr.io/nvidia/tensorrt:20.03-py3\n\nRUN apt-get update && apt-get dist-upgrade -y && \\\n    apt-get install -y \\\n    software-properties-common \\\n    build-essential \\\n    cmake \\\n    git \\\n    libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev \\\n    python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev \\ \t\n    libdc1394-22-dev libgl1-mesa-glx && \\\n    apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*\n    \nRUN mkdir opencv34 && cd opencv34 && \\\n    git clone -b 3.4 https://github.com/opencv/opencv && \\\n    git clone -b 3.4 https://github.com/opencv/opencv_contrib && \\\n    mkdir build && cd build && \\\n    cmake -DCMAKE_INSTALL_PREFIX=/usr/local/opencv \\\n    -DCMAKE_BUILD_TYPE:STRING=RelWithDebInfo \\\n    -DCMAKE_BUILD_TYPE=RELEASE \\\n    -DBUILD_opencv_xfeatures2d=OFF \\\n    -DOPENCV_EXTRA_MODULES_PATH=../opencv_contrib/modules ../opencv && \\\n    make -j12 && \\\n    make install && \\\n    ldconfig && \\\n    cd ../.. \\\n    && rm -rf opencv34\n"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/CMakeLists.txt",
    "content": "project(FastRTEngine)\n\nfile(GLOB_RECURSE COMMON_SRC_FILES\n  ${CMAKE_CURRENT_SOURCE_DIR}/common/utils.cpp\n  ${CMAKE_CURRENT_SOURCE_DIR}/common/calibrator.cpp\n)\n\nfind_package(CUDA REQUIRED)\n# include and link dirs of cuda and tensorrt, you need adapt them if yours are different\n# cuda\ninclude_directories(/usr/local/cuda/include)\nlink_directories(/usr/local/cuda/lib64)\n# tensorrt\ninclude_directories(/usr/include/x86_64-linux-gnu/)\nlink_directories(/usr/lib/x86_64-linux-gnu/)\n\n# build engine as library\nadd_library(${PROJECT_NAME} ${TARGET} ${COMMON_SRC_FILES})\n\ntarget_include_directories(${PROJECT_NAME}\nPUBLIC\n  ../include\n)\n\nfind_package(OpenCV)\ntarget_include_directories(${PROJECT_NAME}\nPUBLIC\n  ${OpenCV_INCLUDE_DIRS}\n)\n\ntarget_link_libraries(${PROJECT_NAME} \n  nvinfer\n  cudart\n  ${OpenCV_LIBS}\n)\n\nSET_TARGET_PROPERTIES(${PROJECT_NAME} \nPROPERTIES\n  SOVERSION ${LIBARARY_SOVERSION}\n  VERSION ${LIBARARY_VERSION}\n)\n\nset(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -O3\")\n\ninstall(TARGETS ${PROJECT_NAME}\n  LIBRARY DESTINATION ${SOLUTION_DIR}/libs/${PROJECT_NAME})\n\nadd_subdirectory(layers)\nadd_subdirectory(engine)\nadd_subdirectory(heads)\nadd_subdirectory(backbones)\nadd_subdirectory(meta_arch)\nadd_subdirectory(factory)"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/backbones/CMakeLists.txt",
    "content": "target_sources(${PROJECT_NAME}\nPRIVATE\n  ${CMAKE_CURRENT_SOURCE_DIR}/sbs_resnet.cpp\n)"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/backbones/sbs_resnet.cpp",
    "content": "#include <vector>\n#include <iostream>\n#include \"fastrt/utils.h\"\n#include \"fastrt/layers.h\"\n#include \"fastrt/sbs_resnet.h\"\nusing namespace trtxapi;\n\nnamespace fastrt {\n    ILayer* backbone_sbsR18_distill::topology(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {\n        std::string ibn{\"\"};\n        if(_modelCfg.with_ibna) {\n            ibn = \"a\";\n        }\n        std::map<std::string, std::vector<std::string>> ibn_layers{ \n            {\"a\", {\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"\",\"\"}},\n            {\"b\", {\"\",\"\",\"b\",\"\",\"\",\"\",\"b\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",}},\n            {\"\", {16,\"\"}}};\n\n        Weights emptywts{DataType::kFLOAT, nullptr, 0};\n        IConvolutionLayer* conv1 = network->addConvolutionNd(input, 64, DimsHW{7, 7}, weightMap[\"backbone.conv1.weight\"], emptywts);\n        TRTASSERT(conv1);\n        conv1->setStrideNd(DimsHW{2, 2});\n        conv1->setPaddingNd(DimsHW{3, 3});\n\n        IScaleLayer* bn1{nullptr};\n        if (ibn == \"b\") {\n            bn1 = addInstanceNorm2d(network, weightMap, *conv1->getOutput(0), \"backbone.bn1\", 1e-5);\n        } else {\n            bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), \"backbone.bn1\", 1e-5);\n        }\n        IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu1);\n\n        // pytorch: nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)\n        IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});\n        TRTASSERT(pool1);\n        pool1->setStrideNd(DimsHW{2, 2});\n        pool1->setPaddingMode(PaddingMode::kEXPLICIT_ROUND_UP);\n\n        ILayer* x = distill_basicBlock_ibn(network, weightMap, *pool1->getOutput(0), 64, 64, 1, \"backbone.layer1.0.\", ibn_layers[ibn][0]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 64, 64, 1, \"backbone.layer1.1.\", ibn_layers[ibn][1]);\n\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 64, 128, 2, \"backbone.layer2.0.\", ibn_layers[ibn][2]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 128, 128, 1, \"backbone.layer2.1.\", ibn_layers[ibn][3]);\n\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 128, 256, 2, \"backbone.layer3.0.\", ibn_layers[ibn][4]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.1.\", ibn_layers[ibn][5]);\n       \n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 512, _modelCfg.last_stride, \"backbone.layer4.0.\", ibn_layers[ibn][6]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 512, 512, 1, \"backbone.layer4.1.\", ibn_layers[ibn][7]);\n\n        IActivationLayer* relu2 = network->addActivation(*x->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu2);\n        return relu2;\n    }\n\n    ILayer* backbone_sbsR34_distill::topology(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {\n        std::string ibn{\"\"};\n        if(_modelCfg.with_ibna) {\n            ibn = \"a\";\n        }\n        std::map<std::string, std::vector<std::string>> ibn_layers{ \n            {\"a\", {\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"\",\"\",\"\"}},\n            {\"b\", {\"\",\"\",\"b\",\"\",\"\",\"\",\"b\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",}},\n            {\"\", {16,\"\"}}};\n\n        Weights emptywts{DataType::kFLOAT, nullptr, 0};\n        IConvolutionLayer* conv1 = network->addConvolutionNd(input, 64, DimsHW{7, 7}, weightMap[\"backbone.conv1.weight\"], emptywts);\n        TRTASSERT(conv1);\n        conv1->setStrideNd(DimsHW{2, 2});\n        conv1->setPaddingNd(DimsHW{3, 3});\n\n        IScaleLayer* bn1{nullptr};\n        if (ibn == \"b\") {\n            bn1 = addInstanceNorm2d(network, weightMap, *conv1->getOutput(0), \"backbone.bn1\", 1e-5);\n        } else {\n            bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), \"backbone.bn1\", 1e-5);\n        }\n        IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu1);\n\n        // pytorch: nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)\n        IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});\n        TRTASSERT(pool1);\n        pool1->setStrideNd(DimsHW{2, 2});\n        pool1->setPaddingMode(PaddingMode::kEXPLICIT_ROUND_UP);\n\n        ILayer* x = distill_basicBlock_ibn(network, weightMap, *pool1->getOutput(0), 64, 64, 1, \"backbone.layer1.0.\", ibn_layers[ibn][0]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 64, 64, 1, \"backbone.layer1.1.\", ibn_layers[ibn][1]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 64, 64, 1, \"backbone.layer1.2.\", ibn_layers[ibn][2]);\n\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 64, 128, 2, \"backbone.layer2.0.\", ibn_layers[ibn][3]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 128, 128, 1, \"backbone.layer2.1.\", ibn_layers[ibn][4]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 128, 128, 1, \"backbone.layer2.2.\", ibn_layers[ibn][5]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 128, 128, 1, \"backbone.layer2.3.\", ibn_layers[ibn][6]);\n\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 128, 256, 2, \"backbone.layer3.0.\", ibn_layers[ibn][7]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.1.\", ibn_layers[ibn][8]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.2.\", ibn_layers[ibn][9]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.3.\", ibn_layers[ibn][10]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.4.\", ibn_layers[ibn][11]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.5.\", ibn_layers[ibn][12]);\n       \n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 512, _modelCfg.last_stride, \"backbone.layer4.0.\", ibn_layers[ibn][13]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 512, 512, 1, \"backbone.layer4.1.\", ibn_layers[ibn][14]);\n        x = distill_basicBlock_ibn(network, weightMap, *x->getOutput(0), 512, 512, 1, \"backbone.layer4.2.\", ibn_layers[ibn][15]);\n\n        IActivationLayer* relu2 = network->addActivation(*x->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu2);\n        return relu2;\n    }\n\n    ILayer* backbone_sbsR50_distill::topology(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {\n        std::string ibn{\"\"};\n        if(_modelCfg.with_ibna) {\n            ibn = \"a\";\n        }\n        std::map<std::string, std::vector<std::string>> ibn_layers{ \n            {\"a\", {\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"\",\"\",\"\"}},\n            {\"b\", {\"\",\"\",\"b\",\"\",\"\",\"\",\"b\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",}},\n            {\"\", {16,\"\"}}};\n\n        Weights emptywts{DataType::kFLOAT, nullptr, 0};\n        IConvolutionLayer* conv1 = network->addConvolutionNd(input, 64, DimsHW{7, 7}, weightMap[\"backbone.conv1.weight\"], emptywts);\n        TRTASSERT(conv1);\n        conv1->setStrideNd(DimsHW{2, 2});\n        conv1->setPaddingNd(DimsHW{3, 3});\n\n        IScaleLayer* bn1{nullptr};\n        if (ibn == \"b\") {\n            bn1 = addInstanceNorm2d(network, weightMap, *conv1->getOutput(0), \"backbone.bn1\", 1e-5);\n        } else {\n            bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), \"backbone.bn1\", 1e-5);\n        }\n        IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu1);\n\n        // pytorch: nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)\n        IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});\n        TRTASSERT(pool1);\n        pool1->setStrideNd(DimsHW{2, 2});\n        pool1->setPaddingMode(PaddingMode::kEXPLICIT_ROUND_UP);\n\n        ILayer* x = distill_bottleneck_ibn(network, weightMap, *pool1->getOutput(0), 64, 64, 1, \"backbone.layer1.0.\", ibn_layers[ibn][0]);\n        x = distill_bottleneck_ibn(network, weightMap, *x->getOutput(0), 256, 64, 1, \"backbone.layer1.1.\", ibn_layers[ibn][1]);\n        x = distill_bottleneck_ibn(network, weightMap, *x->getOutput(0), 256, 64, 1, \"backbone.layer1.2.\", ibn_layers[ibn][2]);\n\n        x = distill_bottleneck_ibn(network, weightMap, *x->getOutput(0), 256, 128, 2, \"backbone.layer2.0.\", ibn_layers[ibn][3]);\n        x = distill_bottleneck_ibn(network, weightMap, *x->getOutput(0), 512, 128, 1, \"backbone.layer2.1.\", ibn_layers[ibn][4]);\n        x = distill_bottleneck_ibn(network, weightMap, *x->getOutput(0), 512, 128, 1, \"backbone.layer2.2.\", ibn_layers[ibn][5]);\n        ILayer* _layer{x};\n        if(_modelCfg.with_nl) {\n            _layer = Non_local(network, weightMap, *x->getOutput(0), \"backbone.NL_2.0.\");\n        }\n        x = distill_bottleneck_ibn(network, weightMap, *_layer->getOutput(0), 512, 128, 1, \"backbone.layer2.3.\", ibn_layers[ibn][6]);\n        _layer = x;\n        if(_modelCfg.with_nl) {\n            _layer = Non_local(network, weightMap, *x->getOutput(0), \"backbone.NL_2.1.\");\n        }\n\n        x = distill_bottleneck_ibn(network, weightMap, *_layer->getOutput(0), 512, 256, 2, \"backbone.layer3.0.\", ibn_layers[ibn][7]);\n        x = distill_bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, \"backbone.layer3.1.\", ibn_layers[ibn][8]);\n        x = distill_bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, \"backbone.layer3.2.\", ibn_layers[ibn][9]);\n        x = distill_bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, \"backbone.layer3.3.\", ibn_layers[ibn][10]);\n        _layer = x;\n        if(_modelCfg.with_nl) {\n            _layer = Non_local(network, weightMap, *x->getOutput(0), \"backbone.NL_3.0.\");\n        } \n        x = distill_bottleneck_ibn(network, weightMap, *_layer->getOutput(0), 1024, 256, 1, \"backbone.layer3.4.\", ibn_layers[ibn][11]);\n        _layer = x;\n        if(_modelCfg.with_nl) {\n            _layer = Non_local(network, weightMap, *x->getOutput(0), \"backbone.NL_3.1.\");\n        }\n        x = distill_bottleneck_ibn(network, weightMap, *_layer->getOutput(0), 1024, 256, 1, \"backbone.layer3.5.\", ibn_layers[ibn][12]);\n        _layer = x;\n        if(_modelCfg.with_nl) {\n            _layer = Non_local(network, weightMap, *x->getOutput(0), \"backbone.NL_3.2.\");\n        }\n\n        x = distill_bottleneck_ibn(network, weightMap, *_layer->getOutput(0), 1024, 512, _modelCfg.last_stride, \"backbone.layer4.0.\", ibn_layers[ibn][13]); \n        x = distill_bottleneck_ibn(network, weightMap, *x->getOutput(0), 2048, 512, 1, \"backbone.layer4.1.\", ibn_layers[ibn][14]);\n        x = distill_bottleneck_ibn(network, weightMap, *x->getOutput(0), 2048, 512, 1, \"backbone.layer4.2.\", ibn_layers[ibn][15]);\n        \n        IActivationLayer* relu2 = network->addActivation(*x->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu2);  \n        return relu2;\n    }\n\n    ILayer* backbone_sbsR34::topology(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {\n        std::string ibn{\"\"};\n        if(_modelCfg.with_ibna) {\n            ibn = \"a\";\n        }\n        std::map<std::string, std::vector<std::string>> ibn_layers{ \n            {\"a\", {\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"\",\"\",\"\"}},  /* resnet34-ibna */\n            {\"b\", {\"\",\"\",\"b\",\"\",\"\",\"\",\"b\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",}}, /* resnet34-ibnb */\n            {\"\", {16,\"\"}}}; /* vanilla resnet34 */\n\n        Weights emptywts{DataType::kFLOAT, nullptr, 0};\n        IConvolutionLayer* conv1 = network->addConvolutionNd(input, 64, DimsHW{7, 7}, weightMap[\"backbone.conv1.weight\"], emptywts);\n        TRTASSERT(conv1);\n        conv1->setStrideNd(DimsHW{2, 2});\n        conv1->setPaddingNd(DimsHW{3, 3});\n\n        IScaleLayer* bn1{nullptr};\n        if (ibn == \"b\") {\n            bn1 = addInstanceNorm2d(network, weightMap, *conv1->getOutput(0), \"backbone.bn1\", 1e-5);\n        } else {\n            bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), \"backbone.bn1\", 1e-5);\n        }\n        IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu1);\n\n        // pytorch: nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)\n        IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});\n        TRTASSERT(pool1);\n        pool1->setStrideNd(DimsHW{2, 2});\n        pool1->setPaddingMode(PaddingMode::kEXPLICIT_ROUND_UP);\n\n        IActivationLayer* x = basicBlock_ibn(network, weightMap, *pool1->getOutput(0), 64, 64, 1, \"backbone.layer1.0.\", ibn_layers[ibn][0]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 64, 64, 1, \"backbone.layer1.1.\", ibn_layers[ibn][1]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 64, 64, 1, \"backbone.layer1.2.\", ibn_layers[ibn][2]);\n\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 64, 128, 2, \"backbone.layer2.0.\", ibn_layers[ibn][3]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 128, 128, 1, \"backbone.layer2.1.\", ibn_layers[ibn][4]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 128, 128, 1, \"backbone.layer2.2.\", ibn_layers[ibn][5]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 128, 128, 1, \"backbone.layer2.3.\", ibn_layers[ibn][6]);\n\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 128, 256, 2, \"backbone.layer3.0.\", ibn_layers[ibn][7]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.1.\", ibn_layers[ibn][8]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.2.\", ibn_layers[ibn][9]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.3.\", ibn_layers[ibn][10]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.4.\", ibn_layers[ibn][11]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 256, 1, \"backbone.layer3.5.\", ibn_layers[ibn][12]);\n       \n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 256, 512, _modelCfg.last_stride, \"backbone.layer4.0.\", ibn_layers[ibn][13]); \n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 512, 512, 1, \"backbone.layer4.1.\", ibn_layers[ibn][14]);\n        x = basicBlock_ibn(network, weightMap, *x->getOutput(0), 512, 512, 1, \"backbone.layer4.2.\", ibn_layers[ibn][15]);\n        return x;\n    }\n\n    ILayer* backbone_sbsR50::topology(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {\n        /*\n         * Reference: https://github.com/JDAI-CV/fast-reid/blob/master/fastreid/modeling/backbones/resnet.py\n         * NL layers follow by: nl_layers_per_stage = {'50x': [0, 2, 3, 0],}[depth]\n         * for nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True) => pool1->setPaddingMode(PaddingMode::kEXPLICIT_ROUND_UP);\n         * for nn.MaxPool2d(kernel_size=3, stride=2, padding=1) replace with => pool1->setPaddingNd(DimsHW{1, 1});\n         */\n        std::string ibn{\"\"};\n        if(_modelCfg.with_ibna) {\n            ibn = \"a\";\n        }\n        std::map<std::string, std::vector<std::string>> ibn_layers{ \n            {\"a\", {\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"\",\"\",\"\"}}, /* resnet50-ibna */\n            {\"b\", {\"\",\"\",\"b\",\"\",\"\",\"\",\"b\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",}}, /* resnet50-ibnb(not used in fastreid) */ \n            {\"\", {16,\"\"}}}; /* vanilla resnet50 */\n\n        Weights emptywts{DataType::kFLOAT, nullptr, 0};\n        IConvolutionLayer* conv1 = network->addConvolutionNd(input, 64, DimsHW{7, 7}, weightMap[\"backbone.conv1.weight\"], emptywts);\n        TRTASSERT(conv1);\n        conv1->setStrideNd(DimsHW{2, 2});\n        conv1->setPaddingNd(DimsHW{3, 3});\n\n        IScaleLayer* bn1{nullptr};\n        if (ibn == \"b\") {\n            bn1 = addInstanceNorm2d(network, weightMap, *conv1->getOutput(0), \"backbone.bn1\", 1e-5);\n        } else {\n            bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), \"backbone.bn1\", 1e-5);\n        }\n        IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu1);\n\n        IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});\n        TRTASSERT(pool1);\n        pool1->setStrideNd(DimsHW{2, 2});\n        pool1->setPaddingMode(PaddingMode::kEXPLICIT_ROUND_UP);\n\n        IActivationLayer* x = bottleneck_ibn(network, weightMap, *pool1->getOutput(0), 64, 64, 1, \"backbone.layer1.0.\", ibn_layers[ibn][0]);\n        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 256, 64, 1, \"backbone.layer1.1.\", ibn_layers[ibn][1]);\n        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 256, 64, 1, \"backbone.layer1.2.\", ibn_layers[ibn][2]);\n\n        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 256, 128, 2, \"backbone.layer2.0.\", ibn_layers[ibn][3]);\n        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 512, 128, 1, \"backbone.layer2.1.\", ibn_layers[ibn][4]);\n        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 512, 128, 1, \"backbone.layer2.2.\", ibn_layers[ibn][5]);\n        ILayer* _layer{x};\n        if(_modelCfg.with_nl) {\n            _layer = Non_local(network, weightMap, *x->getOutput(0), \"backbone.NL_2.0.\");\n        }\n        x = bottleneck_ibn(network, weightMap, *_layer->getOutput(0), 512, 128, 1, \"backbone.layer2.3.\", ibn_layers[ibn][6]);\n        _layer = x;\n        if(_modelCfg.with_nl) {\n            _layer = Non_local(network, weightMap, *x->getOutput(0), \"backbone.NL_2.1.\");\n        }\n\n        x = bottleneck_ibn(network, weightMap, *_layer->getOutput(0), 512, 256, 2, \"backbone.layer3.0.\", ibn_layers[ibn][7]);\n        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, \"backbone.layer3.1.\", ibn_layers[ibn][8]);\n        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, \"backbone.layer3.2.\", ibn_layers[ibn][9]);\n        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 1024, 256, 1, \"backbone.layer3.3.\", ibn_layers[ibn][10]);\n        _layer = x;\n        if(_modelCfg.with_nl) {\n            _layer = Non_local(network, weightMap, *x->getOutput(0), \"backbone.NL_3.0.\");\n        } \n        x = bottleneck_ibn(network, weightMap, *_layer->getOutput(0), 1024, 256, 1, \"backbone.layer3.4.\", ibn_layers[ibn][11]);\n        _layer = x;\n        if(_modelCfg.with_nl) {\n            _layer = Non_local(network, weightMap, *x->getOutput(0), \"backbone.NL_3.1.\");\n        }\n        x = bottleneck_ibn(network, weightMap, *_layer->getOutput(0), 1024, 256, 1, \"backbone.layer3.5.\", ibn_layers[ibn][12]);\n        _layer = x;\n        if(_modelCfg.with_nl) {\n            _layer = Non_local(network, weightMap, *x->getOutput(0), \"backbone.NL_3.2.\");\n        }\n\n        x = bottleneck_ibn(network, weightMap, *_layer->getOutput(0), 1024, 512, _modelCfg.last_stride, \"backbone.layer4.0.\", ibn_layers[ibn][13]); \n        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 2048, 512, 1, \"backbone.layer4.1.\", ibn_layers[ibn][14]);\n        x = bottleneck_ibn(network, weightMap, *x->getOutput(0), 2048, 512, 1, \"backbone.layer4.2.\", ibn_layers[ibn][15]);\n        return x;\n    }\n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/common/calibrator.cpp",
    "content": "#include <iostream>\n#include <iterator>\n#include <fstream>\n#include <opencv2/opencv.hpp>\n#include <opencv2/dnn/dnn.hpp>\n#include \"fastrt/calibrator.h\"\n#include \"fastrt/cuda_utils.h\"\n#include \"fastrt/utils.h\"\n\nInt8EntropyCalibrator2::Int8EntropyCalibrator2(int batchsize, int input_w, int input_h, const char* img_dir, const char* calib_table_name, const char* input_blob_name, bool read_cache)\n    : batchsize_(batchsize)\n    , input_w_(input_w)\n    , input_h_(input_h)\n    , img_idx_(0)\n    , img_dir_(img_dir)\n    , calib_table_name_(calib_table_name)\n    , input_blob_name_(input_blob_name)\n    , read_cache_(read_cache)\n{\n    input_count_ = 3 * input_w * input_h * batchsize;\n    CUDA_CHECK(cudaMalloc(&device_input_, input_count_ * sizeof(float)));\n    read_files_in_dir(img_dir, img_files_);\n}\n\nInt8EntropyCalibrator2::~Int8EntropyCalibrator2()\n{\n    CUDA_CHECK(cudaFree(device_input_));\n}\n\nint Int8EntropyCalibrator2::getBatchSize() const\n{\n    return batchsize_;\n}\n\nbool Int8EntropyCalibrator2::getBatch(void* bindings[], const char* names[], int nbBindings)\n{\n    if (img_idx_ + batchsize_ > (int)img_files_.size()) {\n        return false;\n    }\n\n    std::vector<cv::Mat> input_imgs_;\n    for (int i = img_idx_; i < img_idx_ + batchsize_; i++) {\n        std::cout << img_dir_ + img_files_[i] << \"  \" << i << std::endl;\n        cv::Mat temp = cv::imread(img_dir_ + img_files_[i]);\n        if (temp.empty()){\n            std::cerr << \"Fatal error: image cannot open!\" << std::endl;\n            return false;\n        }\n        input_imgs_.push_back(temp);\n    }\n    img_idx_ += batchsize_;\n    cv::Mat blob = cv::dnn::blobFromImages(input_imgs_, 1.0, cv::Size(input_w_, input_h_), cv::Scalar(0, 0, 0), true, false);\n\n    CUDA_CHECK(cudaMemcpy(device_input_, blob.ptr<float>(0), input_count_ * sizeof(float), cudaMemcpyHostToDevice));\n    assert(!strcmp(names[0], input_blob_name_));\n    bindings[0] = device_input_;\n    return true;\n}\n\nconst void* Int8EntropyCalibrator2::readCalibrationCache(size_t& length)\n{\n    std::cout << \"reading calib cache: \" << calib_table_name_ << std::endl;\n    calib_cache_.clear();\n    std::ifstream input(calib_table_name_, std::ios::binary);\n    input >> std::noskipws;\n    if (read_cache_ && input.good())\n    {\n        std::copy(std::istream_iterator<char>(input), std::istream_iterator<char>(), std::back_inserter(calib_cache_));\n    }\n    length = calib_cache_.size();\n    return length ? calib_cache_.data() : nullptr;\n}\n\nvoid Int8EntropyCalibrator2::writeCalibrationCache(const void* cache, size_t length)\n{\n    std::cout << \"writing calib cache: \" << calib_table_name_ << \" size: \" << length << std::endl;\n    std::ofstream output(calib_table_name_, std::ios::binary);\n    output.write(reinterpret_cast<const char*>(cache), length);\n}\n\n"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/common/utils.cpp",
    "content": "#include <glob.h>\n#include <vector>\n#include \"fastrt/utils.h\"\n\nnamespace io {\n\n    std::vector<std::string> fileGlob(const std::string& pattern){\n        glob_t glob_result;\n        glob(pattern.c_str(), GLOB_TILDE, NULL, &glob_result);\n        std::vector<std::string> files;\n        for (size_t i = 0;i < glob_result.gl_pathc; ++i){\n            files.push_back(std::string(glob_result.gl_pathv[i]));\n        }\n        globfree(&glob_result);\n        return files;\n    }\n\n}\n\nnamespace trt {\n\n    std::map<std::string, nvinfer1::Weights> loadWeights(const std::string file) {\n        std::cout << \"[Loading weights]: \" << file << std::endl;\n        std::map<std::string, nvinfer1::Weights> weightMap;\n\n        // Open weights file\n        std::ifstream input(file);\n        if(!input.is_open()) throw std::runtime_error(\"Unable to load weight file.\");\n        \n        // Read number of weight blobs\n        int32_t count;\n        input >> count;\n        if(count <= 0) throw std::runtime_error(\"Invalid weight map file.\");\n        \n        while (count--) {\n            nvinfer1::Weights wt{nvinfer1::DataType::kFLOAT, nullptr, 0};\n            uint32_t size;\n\n            // Read name and type of blob\n            std::string name;\n            input >> name >> std::dec >> size;\n            wt.type = nvinfer1::DataType::kFLOAT;\n\n            // Load blob\n            uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));\n            for (uint32_t x = 0, y = size; x < y; ++x) {\n                input >> std::hex >> val[x];\n            }\n            wt.values = val;\n            wt.count = size;\n            weightMap[name] = wt;\n        }\n        return weightMap;\n    }\n\n    std::ostream& operator<<(std::ostream& os, const ModelConfig& modelCfg) {\n        os << \"\\tweights_path: \"    << modelCfg.weights_path      << \"\\n\\t\"\n            << \"max_batch_size: \"   << modelCfg.max_batch_size    << \"\\n\\t\"\n            << \"input_h: \"          << modelCfg.input_h           << \"\\n\\t\"\n            << \"input_w: \"          << modelCfg.input_w           << \"\\n\\t\"\n            << \"output_size: \"      << modelCfg.output_size       << \"\\n\\t\"\n            << \"device_id: \"        << modelCfg.device_id         << \"\\n\";\n        return os;   \n    }\n    \n}\n\nnamespace fastrt {\n\n    const std::string BackboneTypetoString(FastreidBackboneType value) {\n    #define X(a, b) b,\n        static std::vector<std::string> table{ FASTBACKBONE_TABLE };\n    #undef X\n        return table[value];\n    }\n\n    const std::string HeadTypetoString(FastreidHeadType value) {\n    #define X(a, b) b,\n        static std::vector<std::string> table{ FASTHEAD_TABLE };\n    #undef X\n        return table[value];\n    }\n\n    const std::string PoolingTypetoString(FastreidPoolingType value) {\n    #define X(a, b) b,\n        static std::vector<std::string> table{ FASTPOOLING_TABLE };\n    #undef X\n        return table[value];\n    }\n\n    std::ostream& operator<<(std::ostream& os, const FastreidConfig& fastreidCfg) {\n        os << \"\\tbackbone: \"            << BackboneTypetoString(fastreidCfg.backbone) << \"\\n\\t\"\n            << \"head: \"                 << HeadTypetoString(fastreidCfg.head)         << \"\\n\\t\"\n            << \"pooling: \"              << PoolingTypetoString(fastreidCfg.pooling)   << \"\\n\\t\"\n            << \"last_stride: \"          << fastreidCfg.last_stride                    << \"\\n\\t\"\n            << \"with_ibna: \"            << fastreidCfg.with_ibna                      << \"\\n\\t\"\n            << \"with_nl: \"              << fastreidCfg.with_nl                        << \"\\n\\t\"\n            << \"embedding_dim: \"        << fastreidCfg.embedding_dim                  << \"\\n\";\n        return os;   \n    } \n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/engine/CMakeLists.txt",
    "content": "target_sources(${PROJECT_NAME}\nPRIVATE\n  ${CMAKE_CURRENT_SOURCE_DIR}/InferenceEngine.cpp\n)"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/engine/InferenceEngine.cpp",
    "content": "#include \"fastrt/utils.h\"\n#include \"fastrt/InferenceEngine.h\"\n\nnamespace trt {\n\n   InferenceEngine::InferenceEngine(const EngineConfig &enginecfg): _engineCfg(enginecfg) { \n        TRTASSERT((_engineCfg.max_batch_size > 0));\n        CHECK(cudaSetDevice(_engineCfg.device_id));\n\n        _runtime = make_holder(nvinfer1::createInferRuntime(gLogger));\n        TRTASSERT(_runtime.get());\n        _engine = make_holder(_runtime->deserializeCudaEngine(_engineCfg.trtModelStream.get(), _engineCfg.stream_size)); \n        TRTASSERT(_engine.get());\n        _context = make_holder(_engine->createExecutionContext());\n        TRTASSERT(_context.get());\n\n        _inputSize = _engineCfg.max_batch_size * 3 * _engineCfg.input_h * _engineCfg.input_w * _depth;\n        _outputSize = _engineCfg.max_batch_size * _engineCfg.output_size * _depth; \n\n        CHECK(cudaMallocHost((void**)&_input, _inputSize));\n        CHECK(cudaMallocHost((void**)&_output, _outputSize));\n\n        _streamptr = std::shared_ptr<cudaStream_t>( new cudaStream_t, \n            [](cudaStream_t* ptr){ \n                cudaStreamDestroy(*ptr);\n                if(ptr != nullptr){ \n                    delete ptr;\n                } \n            });\n\n        CHECK(cudaStreamCreate(&*_streamptr.get()));\n\n        // Pointers to input and output device buffers to pass to engine.\n        // Engine requires exactly IEngine::getNbBindings() number of buffers.\n        TRTASSERT((_engine->getNbBindings() == 2));\n\n        // In order to bind the buffers, we need to know the names of the input and output tensors.\n        // Note that indices are guaranteed to be less than IEngine::getNbBindings()\n        _inputIndex = _engine->getBindingIndex(_engineCfg.input_name.c_str());\n        _outputIndex = _engine->getBindingIndex(_engineCfg.output_name.c_str());\n        \n        // Create GPU buffers on device\n        CHECK(cudaMalloc(&_buffers[_inputIndex], _inputSize));\n        CHECK(cudaMalloc(&_buffers[_outputIndex], _outputSize));\n\n        _inputSize /= _engineCfg.max_batch_size;\n        _outputSize /= _engineCfg.max_batch_size; \n    }\n\n    bool InferenceEngine::doInference(const int inference_batch_size, std::function<void(float*)> preprocessing) {\n        TRTASSERT(( inference_batch_size <= _engineCfg.max_batch_size && inference_batch_size > 0));\n        preprocessing(_input);\n        CHECK(cudaSetDevice(_engineCfg.device_id));\n        CHECK(cudaMemcpyAsync(_buffers[_inputIndex], _input, inference_batch_size * _inputSize, cudaMemcpyHostToDevice, *_streamptr));\n        auto status = _context->enqueue(inference_batch_size, _buffers, *_streamptr, nullptr);\n        CHECK(cudaMemcpyAsync(_output, _buffers[_outputIndex], inference_batch_size * _outputSize, cudaMemcpyDeviceToHost, *_streamptr));\n        CHECK(cudaStreamSynchronize(*_streamptr));\n        return status;\n    }\n\n    InferenceEngine::InferenceEngine(InferenceEngine &&other) noexcept: \n        _engineCfg(other._engineCfg)\n        , _input(other._input)\n        , _output(other._output)\n        , _inputIndex(other._inputIndex) \n        , _outputIndex(other._outputIndex)\n        , _inputSize(other._inputSize) \n        , _outputSize(other._outputSize)\n        , _runtime(std::move(other._runtime))\n        , _engine(std::move(other._engine))\n        , _context(std::move(other._context))\n        , _streamptr(other._streamptr) { \n\n        _buffers[0] = other._buffers[0];\n        _buffers[1] = other._buffers[1];\n        other._streamptr.reset();\n        other._input = nullptr;\n        other._output = nullptr;\n        other._buffers[0] = nullptr; \n        other._buffers[1] = nullptr; \n    } \n\n    InferenceEngine::~InferenceEngine() {  \n        CHECK(cudaFreeHost(_input));\n        CHECK(cudaFreeHost(_output));\n        CHECK(cudaFree(_buffers[_inputIndex]));\n        CHECK(cudaFree(_buffers[_outputIndex]));\n    }\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/factory/CMakeLists.txt",
    "content": "target_sources(${PROJECT_NAME}\nPRIVATE\n  ${CMAKE_CURRENT_SOURCE_DIR}/factory.cpp\n  ${CMAKE_SOURCE_DIR}/fastrt/layers/poolingLayerRT.h\n)"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/factory/factory.cpp",
    "content": "#include <iostream>\n#include \"fastrt/utils.h\"\n#include \"fastrt/sbs_resnet.h\"\n#include \"fastrt/factory.h\"\n#include \"fastrt/embedding_head.h\"\n#include \"../layers/poolingLayerRT.h\"\n\nnamespace fastrt {\n\n    std::unique_ptr<Module> ModuleFactory::createBackbone(FastreidConfig& modelCfg) {\n        switch(modelCfg.backbone) {\n            case FastreidBackboneType::r50:   \n                /* cfg.MODEL.META_ARCHITECTURE: Baseline */  \n                /* cfg.MODEL.BACKBONE.DEPTH: 50x */ \n                std::cout << \"[createBackboneModule]: backbone_sbsR50\" << std::endl;\n                return make_unique<backbone_sbsR50>(modelCfg);\n            case FastreidBackboneType::r50_distill: \n                /* cfg.MODEL.META_ARCHITECTURE: Distiller */ \n                /* cfg.MODEL.BACKBONE.DEPTH: 50x */   \n                std::cout << \"[createBackboneModule]: backbone_sbsR50_distill\" << std::endl;\n                return make_unique<backbone_sbsR50_distill>(modelCfg);\n            case FastreidBackboneType::r34: \n                /* cfg.MODEL.META_ARCHITECTURE: Baseline */  \n                /* cfg.MODEL.BACKBONE.DEPTH: 34x */  \n                std::cout << \"[createBackboneModule]: backbone_sbsR34\" << std::endl;\n                return make_unique<backbone_sbsR34>(modelCfg);\n            case FastreidBackboneType::r34_distill: \n                /* cfg.MODEL.META_ARCHITECTURE: Distiller */ \n                /* cfg.MODEL.BACKBONE.DEPTH: 34x */  \n                std::cout << \"[createBackboneModule]: backbone_sbsR34_distill\" << std::endl;\n                return make_unique<backbone_sbsR34_distill>(modelCfg);\n            case FastreidBackboneType::r18_distill: \n                /* cfg.MODEL.META_ARCHITECTURE: Distiller */ \n                /* cfg.MODEL.BACKBONE.DEPTH: 18x */  \n                std::cout << \"[createBackboneModule]: backbone_sbsR18_distill\" << std::endl;\n                return make_unique<backbone_sbsR18_distill>(modelCfg);\n            default:\n                std::cerr << \"[Backbone is not supported.]\" << std::endl;\n                return nullptr;\n        }\n    }\n\n    std::unique_ptr<Module> ModuleFactory::createHead(FastreidConfig& modelCfg) {\n        switch(modelCfg.head) {\n            case FastreidHeadType::EmbeddingHead:   \n                /* cfg.MODEL.HEADS.NAME: EmbeddingHead */ \n                std::cout << \"[createHeadModule]: EmbeddingHead\" << std::endl;\n                return make_unique<embedding_head>(modelCfg);\n            default:\n                std::cerr << \"[Head is not supported.]\" << std::endl;\n                return nullptr;\n        }\n    }\n\n    std::unique_ptr<IPoolingLayerRT> LayerFactory::createPoolingLayer(const FastreidPoolingType& pooltype) {\n        switch(pooltype) {\n            case FastreidPoolingType::maxpool:\n                std::cout << \"[createPoolingLayer]: maxpool\" << std::endl;\n                return make_unique<MaxPool>();\n            case FastreidPoolingType::avgpool:\n                std::cout << \"[createPoolingLayer]: avgpool\" << std::endl;\n                return make_unique<AvgPool>();\n            case FastreidPoolingType::gempool:\n                std::cout << \"[createPoolingLayer]: gempool\" << std::endl;\n                return make_unique<GemPool>();\n            case FastreidPoolingType::gempoolP:\n                std::cout << \"[createPoolingLayer]: gempoolP\" << std::endl;\n                return make_unique<GemPoolP>();\n            default:\n                std::cerr << \"[Pooling layer is not supported.]\" << std::endl; \n                return nullptr;\n        }  \n    }\n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/heads/CMakeLists.txt",
    "content": "target_sources(${PROJECT_NAME}\nPRIVATE\n  ${CMAKE_CURRENT_SOURCE_DIR}/embedding_head.cpp\n)"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/heads/embedding_head.cpp",
    "content": "#include <iostream>\n#include \"fastrt/utils.h\"\n#include \"fastrt/layers.h\"\n#include \"fastrt/embedding_head.h\"\n\nnamespace fastrt {\n\n    embedding_head::embedding_head(FastreidConfig& modelCfg) : \n        _modelCfg(modelCfg), _layerFactory(make_unique<LayerFactory>()) {}\n    \n    embedding_head::embedding_head(FastreidConfig& modelCfg, \n        std::unique_ptr<LayerFactory> layerFactory) : _modelCfg(modelCfg), _layerFactory(std::move(layerFactory)) {}\n\n    ILayer* embedding_head::topology(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {\n        /*\n         * Reference: https://github.com/JDAI-CV/fast-reid/blob/master/fastreid/modeling/heads/embedding_head.py\n         */\n\n        ILayer* pooling = _layerFactory->createPoolingLayer(_modelCfg.pooling)->addPooling(network, weightMap, input);\n        TRTASSERT(pooling);\n\n        // Hint: It's used to be \"heads.bnneck.0\" before Sep 10, 2020. (JDAI-CV/fast-reid)\n        std::string bnneck_lname = \"heads.bottleneck.0\"; \n        ILayer* reduction_neck{pooling};\n\n        if(_modelCfg.embedding_dim > 0) { \n            Weights emptywts{DataType::kFLOAT, nullptr, 0};\n            reduction_neck = network->addConvolutionNd(*pooling->getOutput(0),\n                _modelCfg.embedding_dim, \n                DimsHW{1, 1}, \n                weightMap[\"heads.bottleneck.0.weight\"],             \n                emptywts);\n            TRTASSERT(reduction_neck); \n            bnneck_lname[bnneck_lname.size()-1] = '1';\n        }\n        \n        IScaleLayer* bottleneck = trtxapi::addBatchNorm2d(network, weightMap, *reduction_neck->getOutput(0), bnneck_lname, 1e-5);     \n        TRTASSERT(bottleneck);\n        return bottleneck;\n    }\n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/layers/CMakeLists.txt",
    "content": "target_sources(${PROJECT_NAME}\nPRIVATE\n  ${CMAKE_CURRENT_SOURCE_DIR}/layers.cpp\n  ${CMAKE_CURRENT_SOURCE_DIR}/poolingLayerRT.h\n  ${CMAKE_CURRENT_SOURCE_DIR}/poolingLayerRT.cpp\n)"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/layers/layers.cpp",
    "content": "#include <limits>\n#include <vector>\n#include <iostream>\n#include \"fastrt/utils.h\"\n#include \"fastrt/layers.h\"\n\nnamespace trtxapi {\n\n    IActivationLayer* addMinClamp(INetworkDefinition* network, ITensor& input, const float min) {\n        IActivationLayer* clip = network->addActivation(input, ActivationType::kCLIP);\n        TRTASSERT(clip);\n        clip->setAlpha(min);\n        clip->setBeta(std::numeric_limits<float>::max());    \n        return clip;\n    }\n\n    ITensor* addDiv255(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor* input, const std::string lname) {\n        Weights Div_225{ DataType::kFLOAT, nullptr, 3 };\n        float *wgt = reinterpret_cast<float*>(malloc(sizeof(float) * 3));\n        std::fill_n(wgt, 3, 255.0f); \n        Div_225.values = wgt;\n        weightMap[lname + \".div\"] = Div_225;\n        IConstantLayer* d = network->addConstant(Dims3{ 3, 1, 1 }, Div_225);\n        IElementWiseLayer* div255 = network->addElementWise(*input, *d->getOutput(0), ElementWiseOperation::kDIV);\n        return div255->getOutput(0);\n    }\n\n    ITensor* addMeanStd(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor* input, const std::string lname, const float* mean, const float* std, const bool div255) {\n        ITensor* tensor_holder{input};\n        if (div255) {\n            tensor_holder = addDiv255(network, weightMap, input, lname);\n        }\n        Weights Mean{ DataType::kFLOAT, nullptr, 3 };\n        Mean.values = mean;\n        IConstantLayer* m = network->addConstant(Dims3{ 3, 1, 1 }, Mean);\n        IElementWiseLayer* sub_mean = network->addElementWise(*tensor_holder, *m->getOutput(0), ElementWiseOperation::kSUB);\n        if (std != nullptr) {\n            Weights Std{ DataType::kFLOAT, nullptr, 3 };\n            Std.values = std;\n            IConstantLayer* s = network->addConstant(Dims3{ 3, 1, 1 }, Std);\n            IElementWiseLayer* std_mean = network->addElementWise(*sub_mean->getOutput(0), *s->getOutput(0), ElementWiseOperation::kDIV);\n            return std_mean->getOutput(0);\n        } else {\n            return sub_mean->getOutput(0);\n        }\n    }\n\n    IScaleLayer* addBatchNorm2d(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, const std::string lname, const float eps) {\n        float *gamma = (float*)weightMap[lname + \".weight\"].values;\n        float *beta = (float*)weightMap[lname + \".bias\"].values;\n        float *mean = (float*)weightMap[lname + \".running_mean\"].values;\n        float *var = (float*)weightMap[lname + \".running_var\"].values;\n        int len = weightMap[lname + \".running_var\"].count;\n\n        float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));\n        for (int i = 0; i < len; i++) {\n            scval[i] = gamma[i] / sqrt(var[i] + eps);\n        }\n        Weights wscale{DataType::kFLOAT, scval, len};\n\n        float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));\n        for (int i = 0; i < len; i++) {\n            shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);\n        }\n        Weights wshift{DataType::kFLOAT, shval, len};\n\n        float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));\n        for (int i = 0; i < len; i++) {\n            pval[i] = 1.0;\n        }\n        Weights wpower{DataType::kFLOAT, pval, len};\n\n        weightMap[lname + \".scale\"] = wscale;\n        weightMap[lname + \".shift\"] = wshift;\n        weightMap[lname + \".power\"] = wpower;\n        IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, wshift, wscale, wpower);\n        TRTASSERT(scale_1);\n        return scale_1;\n    }\n\n    IScaleLayer* addInstanceNorm2d(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, const std::string lname, const float eps) {\n        int len = weightMap[lname + \".weight\"].count;\n        IReduceLayer* reduce1 = network->addReduce(input, \n            ReduceOperation::kAVG,\n            6, \n            true);\n        TRTASSERT(reduce1);\n\n        IElementWiseLayer* ew1 = network->addElementWise(input, \n            *reduce1->getOutput(0),\n            ElementWiseOperation::kSUB);  \n        TRTASSERT(ew1);\n\n        const static float pval1[3]{0.0, 1.0, 2.0};   \n        Weights wshift1{DataType::kFLOAT, pval1, 1};\n        Weights wscale1{DataType::kFLOAT, pval1+1, 1};\n        Weights wpower1{DataType::kFLOAT, pval1+2, 1};\n\n        IScaleLayer* scale1 = network->addScale(\n            *ew1->getOutput(0), \n            ScaleMode::kUNIFORM,\n            wshift1,  \n            wscale1,  \n            wpower1); \n        TRTASSERT(scale1);\n\n        IReduceLayer* reduce2 = network->addReduce(\n            *scale1->getOutput(0), \n            ReduceOperation::kAVG,\n            6, \n            true);\n        TRTASSERT(reduce2);\n\n        const static float pval2[3]{eps, 1.0, 0.5}; \n        Weights wshift2{DataType::kFLOAT, pval2, 1};\n        Weights wscale2{DataType::kFLOAT, pval2+1, 1};\n        Weights wpower2{DataType::kFLOAT, pval2+2, 1};\n        \n        IScaleLayer* scale2 = network->addScale(\n            *reduce2->getOutput(0), \n            ScaleMode::kUNIFORM,\n            wshift2,  \n            wscale2,  \n            wpower2);\n        TRTASSERT(scale2);\n\n        IElementWiseLayer* ew2 = network->addElementWise(*ew1->getOutput(0), \n            *scale2->getOutput(0),\n            ElementWiseOperation::kDIV); \n        TRTASSERT(ew2);\n\n        float* pval3 = reinterpret_cast<float*>(malloc(sizeof(float) * len));\n        std::fill_n(pval3, len, 1.0); \n        Weights wpower3{DataType::kFLOAT, pval3, len};\n        weightMap[lname + \".power3\"] = wpower3;\n\n        IScaleLayer* scale3 = network->addScale(\n            *ew2->getOutput(0), \n            ScaleMode::kCHANNEL,\n            weightMap[lname + \".bias\"], \n            weightMap[lname + \".weight\"],  \n            wpower3); \n        TRTASSERT(scale3);\n        return scale3;\n    }\n\n    IConcatenationLayer* addIBN(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, const std::string lname) {\n        Dims spliteDims = input.getDimensions();\n        ISliceLayer *split1 = network->addSlice(input, \n            Dims3{0, 0, 0}, \n            Dims3{spliteDims.d[0]/2, spliteDims.d[1], spliteDims.d[2]}, \n            Dims3{1, 1, 1});\n        TRTASSERT(split1);\n\n        ISliceLayer *split2 = network->addSlice(input, \n            Dims3{spliteDims.d[0]/2, 0, 0}, \n            Dims3{spliteDims.d[0]/2, spliteDims.d[1], spliteDims.d[2]}, \n            Dims3{1, 1, 1});\n        TRTASSERT(split2);\n\n        auto in1 = addInstanceNorm2d(network, weightMap, *split1->getOutput(0), lname + \"IN\", 1e-5);\n        auto bn1 = addBatchNorm2d(network, weightMap, *split2->getOutput(0), lname + \"BN\", 1e-5);\n\n        ITensor* tensor1[] = {in1->getOutput(0), bn1->getOutput(0)};\n        auto cat1 = network->addConcatenation(tensor1, 2);\n        TRTASSERT(cat1);\n        return cat1;\n    }\n\n    IActivationLayer* basicBlock_ibn(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, const int inch, const int outch, const int stride, const std::string lname, const std::string ibn) {\n        Weights emptywts{DataType::kFLOAT, nullptr, 0};\n\n        IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{3, 3}, weightMap[lname + \"conv1.weight\"], emptywts);\n        TRTASSERT(conv1);\n        conv1->setStrideNd(DimsHW{stride, stride});\n        conv1->setPaddingNd(DimsHW{1, 1});\n\n        ILayer* bn1{conv1};\n        if (ibn == \"a\") {\n            bn1 = addIBN(network, weightMap, *conv1->getOutput(0), lname + \"bn1.\");\n        } else {\n            bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + \"bn1\", 1e-5);\n        }\n        IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu1);\n\n        IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + \"conv2.weight\"], emptywts);\n        TRTASSERT(conv2);\n        conv2->setPaddingNd(DimsHW{1, 1});\n\n        IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + \"bn2\", 1e-5);\n\n        IElementWiseLayer* ew1;\n        if (inch != outch) {\n            IConvolutionLayer* conv3 = network->addConvolutionNd(input, outch, DimsHW{1, 1}, weightMap[lname + \"downsample.0.weight\"], emptywts);\n            TRTASSERT(conv3);\n            conv3->setStrideNd(DimsHW{stride, stride});\n            IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + \"downsample.1\", 1e-5);\n            ew1 = network->addElementWise(*bn3->getOutput(0), *bn2->getOutput(0), ElementWiseOperation::kSUM);\n        } else {\n            ew1 = network->addElementWise(input, *bn2->getOutput(0), ElementWiseOperation::kSUM);\n        }\n        ILayer* in1{ew1};\n        if (ibn == \"b\") {\n            in1 = addInstanceNorm2d(network, weightMap, *ew1->getOutput(0), lname + \"IN\", 1e-5);\n        }\n\n        IActivationLayer* relu2 = network->addActivation(*in1->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu2);\n        return relu2;\n    }\n\n    IActivationLayer* bottleneck_ibn(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, const int inch, const int outch, const int stride, const std::string lname, const std::string ibn) {\n        Weights emptywts{DataType::kFLOAT, nullptr, 0};\n        IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{1, 1}, weightMap[lname + \"conv1.weight\"], emptywts);\n        TRTASSERT(conv1);\n\n        ILayer* bn1{conv1};\n        if (ibn == \"a\") {\n            bn1 = addIBN(network, weightMap, *conv1->getOutput(0), lname + \"bn1.\");\n        } else {\n            bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + \"bn1\", 1e-5);\n        }\n        IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu1);\n\n        IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + \"conv2.weight\"], emptywts);\n        TRTASSERT(conv2);\n        conv2->setStrideNd(DimsHW{stride, stride});\n        conv2->setPaddingNd(DimsHW{1, 1});\n\n        IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + \"bn2\", 1e-5);\n\n        IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu2);\n\n        IConvolutionLayer* conv3 = network->addConvolutionNd(*relu2->getOutput(0), outch * 4, DimsHW{1, 1}, weightMap[lname + \"conv3.weight\"], emptywts);\n        TRTASSERT(conv3);\n\n        IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + \"bn3\", 1e-5);\n\n        IElementWiseLayer* ew1;\n        if (stride != 1 || inch != outch * 4) {\n            IConvolutionLayer* conv4 = network->addConvolutionNd(input, outch * 4, DimsHW{1, 1}, weightMap[lname + \"downsample.0.weight\"], emptywts);\n            TRTASSERT(conv4);\n            conv4->setStrideNd(DimsHW{stride, stride});\n\n            IScaleLayer* bn4 = addBatchNorm2d(network, weightMap, *conv4->getOutput(0), lname + \"downsample.1\", 1e-5);\n            ew1 = network->addElementWise(*bn4->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);\n        } else {\n            ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);\n        }\n\n        ILayer* in1{ew1};\n        if (ibn == \"b\") {\n            in1 = addInstanceNorm2d(network, weightMap, *ew1->getOutput(0), lname + \"IN\", 1e-5);\n        }\n        IActivationLayer* relu3 = network->addActivation(*in1->getOutput(0), ActivationType::kRELU);\n\n        TRTASSERT(relu3);\n        return relu3;\n    }\n\n    ILayer* distill_basicBlock_ibn(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, const int inch, const int outch, const int stride, const std::string lname, const std::string ibn) {\n        Weights emptywts{DataType::kFLOAT, nullptr, 0};\n\n        IActivationLayer* relu_identity = network->addActivation(input, ActivationType::kRELU);\n        TRTASSERT(relu_identity);\n\n        IConvolutionLayer* conv1 = network->addConvolutionNd(*relu_identity->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + \"conv1.weight\"], emptywts);\n        TRTASSERT(conv1);\n        conv1->setStrideNd(DimsHW{stride, stride});\n        conv1->setPaddingNd(DimsHW{1, 1});\n\n        ILayer* bn1{conv1};\n        if (ibn == \"a\") {\n            bn1 = addIBN(network, weightMap, *conv1->getOutput(0), lname + \"bn1.\");\n        } else {\n            bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + \"bn1\", 1e-5);\n        }\n        IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu1);\n\n        IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + \"conv2.weight\"], emptywts);\n        TRTASSERT(conv2);\n        conv2->setPaddingNd(DimsHW{1, 1});\n\n        IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + \"bn2\", 1e-5);\n\n        IElementWiseLayer* ew1;\n        if (inch != outch) {\n            IConvolutionLayer* conv3 = network->addConvolutionNd(*relu_identity->getOutput(0), outch, DimsHW{1, 1}, weightMap[lname + \"downsample.0.weight\"], emptywts);\n            TRTASSERT(conv3);\n            conv3->setStrideNd(DimsHW{stride, stride});\n            IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + \"downsample.1\", 1e-5);\n            ew1 = network->addElementWise(*bn3->getOutput(0), *bn2->getOutput(0), ElementWiseOperation::kSUM);\n        } else {\n            ew1 = network->addElementWise(*relu_identity->getOutput(0), *bn2->getOutput(0), ElementWiseOperation::kSUM);\n        }\n        ILayer* in1{ew1};\n        if (ibn == \"b\") {\n            in1 = addInstanceNorm2d(network, weightMap, *ew1->getOutput(0), lname + \"IN\", 1e-5);\n        }\n        return in1;\n    }\n\n    ILayer* distill_bottleneck_ibn(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, const int inch, const int outch, const int stride, const std::string lname, const std::string ibn) {\n        Weights emptywts{DataType::kFLOAT, nullptr, 0};\n\n        IActivationLayer* relu_identity = network->addActivation(input, ActivationType::kRELU);\n        TRTASSERT(relu_identity);\n\n        IConvolutionLayer* conv1 = network->addConvolutionNd(*relu_identity->getOutput(0), outch, DimsHW{1, 1}, weightMap[lname + \"conv1.weight\"], emptywts);\n        TRTASSERT(conv1);\n\n        ILayer* bn1{conv1};\n        if (ibn == \"a\") {\n            bn1 = addIBN(network, weightMap, *conv1->getOutput(0), lname + \"bn1.\");\n        } else {\n            bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + \"bn1\", 1e-5);\n        }\n        IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu1);\n\n        IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + \"conv2.weight\"], emptywts);\n        TRTASSERT(conv2);\n        conv2->setStrideNd(DimsHW{stride, stride});\n        conv2->setPaddingNd(DimsHW{1, 1});\n\n        IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + \"bn2\", 1e-5);\n\n        IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);\n        TRTASSERT(relu2);\n\n        IConvolutionLayer* conv3 = network->addConvolutionNd(*relu2->getOutput(0), outch * 4, DimsHW{1, 1}, weightMap[lname + \"conv3.weight\"], emptywts);\n        TRTASSERT(conv3);\n\n        IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + \"bn3\", 1e-5);\n\n        IElementWiseLayer* ew1;\n        if (stride != 1 || inch != outch * 4) {\n            IConvolutionLayer* conv4 = network->addConvolutionNd(*relu_identity->getOutput(0), outch * 4, DimsHW{1, 1}, weightMap[lname + \"downsample.0.weight\"], emptywts);\n            TRTASSERT(conv4);\n            conv4->setStrideNd(DimsHW{stride, stride});\n\n            IScaleLayer* bn4 = addBatchNorm2d(network, weightMap, *conv4->getOutput(0), lname + \"downsample.1\", 1e-5);\n            ew1 = network->addElementWise(*bn4->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);\n        } else {\n            ew1 = network->addElementWise(*relu_identity->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);\n        }\n\n        ILayer* in1{ew1};\n        if (ibn == \"b\") {\n            in1 = addInstanceNorm2d(network, weightMap, *ew1->getOutput(0), lname + \"IN\", 1e-5);\n        }\n        return in1;\n    }\n\n    IShuffleLayer* addShuffle2(INetworkDefinition* network, ITensor& input, const Dims dims, const Permutation pmt, const bool reshape_first) {\n        IShuffleLayer* shuffleLayer = network->addShuffle(input);\n        TRTASSERT(shuffleLayer);\n        if (reshape_first) {\n            shuffleLayer->setReshapeDimensions(dims);\n            shuffleLayer->setSecondTranspose(pmt); \n        } else {\n            shuffleLayer->setFirstTranspose(pmt); \n            shuffleLayer->setReshapeDimensions(dims);\n        }\n        return shuffleLayer;\n    }\n\n    IElementWiseLayer* Non_local(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, const std::string lname, const int reduc_ratio) {\n        int in_channel = input.getDimensions().d[0];\n        /* Hint: fast-reid use \"in_channel / reduc_ratio\" during Sep 10, 2020 to Dec 7, 2020 */\n        //int inter_channels = in_channel / reduc_ratio; \n        int inter_channels = 1; \n        std::cout << \"[Non_local] inter_channels: \" << inter_channels << std::endl;\n        IConvolutionLayer* g = network->addConvolutionNd(input, inter_channels, DimsHW{1, 1}, weightMap[ lname + \"g.weight\"],  weightMap[lname + \"g.bias\"]);\n        TRTASSERT(g); \n\n        auto g_permute = addShuffle2(network, *g->getOutput(0), Dims2{g->getOutput(0)->getDimensions().d[0], -1}, Permutation{1, 0}, true);\n        IConvolutionLayer* theta = network->addConvolutionNd(input, inter_channels, DimsHW{1, 1}, weightMap[lname + \"theta.weight\"],  weightMap[lname + \"theta.bias\"]);\n        TRTASSERT(theta); \n\n        auto theta_permute = addShuffle2(network, *theta->getOutput(0), Dims2{theta->getOutput(0)->getDimensions().d[0], -1}, Permutation{1, 0}, true);\n        IConvolutionLayer* phi = network->addConvolutionNd(input, inter_channels, DimsHW{1, 1}, weightMap[lname + \"phi.weight\"],  weightMap[lname + \"phi.bias\"]);\n        TRTASSERT(phi);  \n\n        IShuffleLayer* phi_view = network->addShuffle(*phi->getOutput(0));\n        TRTASSERT(phi_view);\n        phi_view->setReshapeDimensions(Dims2{phi->getOutput(0)->getDimensions().d[0], -1});\n\n        IMatrixMultiplyLayer *f = network->addMatrixMultiply(*theta_permute->getOutput(0), MatrixOperation::kNONE, *phi_view->getOutput(0), MatrixOperation::kNONE);\n        int N = f->getOutput(0)->getDimensions().d[f->getOutput(0)->getDimensions().nbDims-1];\n\n        float* pval =  reinterpret_cast<float*>(malloc(sizeof(float) * N * N));\n        std::fill_n(pval, N*N, N); \n        Weights dem{DataType::kFLOAT, pval, N*N};\n        weightMap[lname + \".dem\"] = dem;\n\n        auto dem_n = network->addConstant(Dims2(N, N), dem);\n        IElementWiseLayer* f_div_C = network->addElementWise(*f->getOutput(0), \n            *dem_n->getOutput(0),\n            ElementWiseOperation::kDIV);  \n        TRTASSERT(f_div_C);\n\n        IMatrixMultiplyLayer *y = network->addMatrixMultiply(*f_div_C->getOutput(0), MatrixOperation::kNONE, *g_permute->getOutput(0), MatrixOperation::kNONE);\n        IShuffleLayer* y_permute = addShuffle2(network, *y->getOutput(0), Dims3{inter_channels, input.getDimensions().d[1], input.getDimensions().d[2]}, Permutation{1, 0}, false);\n        TRTASSERT(y_permute);\n        IConvolutionLayer* w_conv = network->addConvolutionNd(*y_permute->getOutput(0), in_channel, DimsHW{1, 1}, weightMap[lname + \"W.0.weight\"], weightMap[lname + \"W.0.bias\"]);\n        TRTASSERT(w_conv);\n        IScaleLayer* w_bn = addBatchNorm2d(network, weightMap, *w_conv->getOutput(0), lname + \"W.1\", 1e-5);\n        TRTASSERT(w_bn);\n\n        // z = W_y + x\n        IElementWiseLayer* z = network->addElementWise(*w_bn->getOutput(0), \n            input,\n            ElementWiseOperation::kSUM);  \n        TRTASSERT(z);\n        return z;\n    }\n\n    IPoolingLayer* addAdaptiveAvgPool2d(INetworkDefinition* network, ITensor& input, const DimsHW output_dim) {\n        Dims input_dims = input.getDimensions();\n        TRTASSERT((input_dims.nbDims == 3));\n        // stride_dim = floor(input_dim/output_dim)\n        DimsHW stride_dims{(int)(input_dims.d[1]/output_dim.h()), \n            (int)(input_dims.d[2]/output_dim.w())};\n        // kernel_dims = input_dim -(output_dim-1)*stride_dim\n        DimsHW kernel_dims{input_dims.d[1] - (output_dim.h()-1) * stride_dims.h(), \n            input_dims.d[2] - (output_dim.w()-1) * stride_dims.w()};\n        IPoolingLayer* avgpool = network->addPoolingNd(input, PoolingType::kAVERAGE, kernel_dims);\n        TRTASSERT(avgpool);\n        avgpool->setStrideNd(stride_dims);\n        return avgpool;\n    }\n\n    IScaleLayer* addGeneralizedMeanPooling(INetworkDefinition* network, ITensor& input, const float norm, const DimsHW output_dim, const float eps) {\n        TRTASSERT((norm > 0.f));\n        // x = x.clamp(min=eps)\n        IActivationLayer* clamp1 = addMinClamp(network, input, eps);\n        // (x)^norm\n        const static float pval1[3]{0.0, 1.0, norm};   \n        Weights wshift1{DataType::kFLOAT, pval1, 1};\n        Weights wscale1{DataType::kFLOAT, pval1+1, 1};\n        Weights wpower1{DataType::kFLOAT, pval1+2, 1};\n\n        IScaleLayer* scale1 = network->addScale(\n            *clamp1->getOutput(0), \n            ScaleMode::kUNIFORM,\n            wshift1,\n            wscale1,\n            wpower1);\n        TRTASSERT(scale1); \n\n        IPoolingLayer* ada_avg_pool = addAdaptiveAvgPool2d(network, *scale1->getOutput(0));\n        TRTASSERT(ada_avg_pool);\n\n        // (ada_avg_pool)^(1/norm)\n        const static float pval2[3]{0.0, 1.0, 1.f/norm};   \n        Weights wshift2{DataType::kFLOAT, pval2, 1};\n        Weights wscale2{DataType::kFLOAT, pval2+1, 1};\n        Weights wpower2{DataType::kFLOAT, pval2+2, 1};\n\n        IScaleLayer* scale2 = network->addScale(\n            *ada_avg_pool->getOutput(0), \n            ScaleMode::kUNIFORM,\n            wshift2,  \n            wscale2,   \n            wpower2); \n        TRTASSERT(scale2);\n        return scale2;\n    }\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/layers/poolingLayerRT.cpp",
    "content": "#include <iostream>\n#include \"fastrt/layers.h\"\n#include \"poolingLayerRT.h\"\n\nnamespace fastrt {\n\n    ILayer* MaxPool::addPooling(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {\n        ILayer* pooling = network->addPoolingNd(input, PoolingType::kMAX, DimsHW{input.getDimensions().d[1], input.getDimensions().d[2]});       \n        auto p = dynamic_cast<nvinfer1::IPoolingLayer*>(pooling);\n        if(p) p->setStrideNd(DimsHW{input.getDimensions().d[1], input.getDimensions().d[2]});\n        else std::cout << \"Downcasting failed.\" << std::endl; \n        return pooling;\n    }\n    \n    ILayer* AvgPool::addPooling(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {\n        ILayer* pooling = network->addPoolingNd(input, PoolingType::kAVERAGE, DimsHW{input.getDimensions().d[1], input.getDimensions().d[2]});\n        auto p = dynamic_cast<IPoolingLayer*>(pooling);\n        if(p) p->setStrideNd(DimsHW{input.getDimensions().d[1], input.getDimensions().d[2]});\n        else std::cout << \"Downcasting failed.\" << std::endl; \n        return pooling;\n    }\n\n    ILayer* GemPool::addPooling(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {\n        return trtxapi::addGeneralizedMeanPooling(network, input); \n    }\n\n    ILayer* GemPoolP::addPooling(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input) {\n        return trtxapi::addGeneralizedMeanPooling(network, input, *(float*)weightMap[\"heads.pool_layer.p\"].values); \n    }    \n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/layers/poolingLayerRT.h",
    "content": "#include \"NvInfer.h\"\n#include \"fastrt/IPoolingLayerRT.h\"\nusing namespace nvinfer1;\n\nnamespace fastrt {\n\n    class MaxPool : public IPoolingLayerRT {\n    public:\n        MaxPool() = default;\n        ~MaxPool() = default;\n\n        ILayer* addPooling(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap,\n            ITensor& input) override;\n    };\n\n    class AvgPool : public IPoolingLayerRT {\n    public:\n        AvgPool() = default;\n        ~AvgPool() = default;\n\n        ILayer* addPooling(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap,\n            ITensor& input) override;\n    };\n\n    class GemPool : public IPoolingLayerRT {\n    public:\n        GemPool() = default;\n        ~GemPool() = default;\n\n        ILayer* addPooling(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap,\n            ITensor& input) override;\n    };\n\n    class GemPoolP : public IPoolingLayerRT {\n    public:\n        GemPoolP() = default;\n        ~GemPoolP() = default;\n\n        ILayer* addPooling(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap,\n            ITensor& input) override;\n    };\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/meta_arch/CMakeLists.txt",
    "content": "target_sources(${PROJECT_NAME}\nPRIVATE\n  ${CMAKE_CURRENT_SOURCE_DIR}/model.cpp\n  ${CMAKE_CURRENT_SOURCE_DIR}/baseline.cpp\n)"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/meta_arch/baseline.cpp",
    "content": "#include \"fastrt/layers.h\"\n#include \"fastrt/baseline.h\"\n\nnamespace fastrt {\n\n    Baseline::Baseline(const trt::ModelConfig &modelcfg, const std::string input_name, const std::string output_name) \n        : Model(modelcfg, input_name, output_name) {}\n\n    void Baseline::preprocessing_cpu(const cv::Mat& img, float* const data, const std::size_t stride) {\n        /* Normalization & BGR->RGB */\n        for (std::size_t i = 0; i < stride; ++i) { \n            data[i] = img.at<cv::Vec3b>(i)[2]; \n            data[i + stride] = img.at<cv::Vec3b>(i)[1];\n            data[i + (stride<<1)] = img.at<cv::Vec3b>(i)[0];\n        }\n    }\n\n    ITensor* Baseline::preprocessing_gpu(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor* input) {\n        /* Standardization */\n        static const float mean[3] = {123.675, 116.28, 103.53};\n        static const float std[3] = {58.395, 57.120000000000005, 57.375};\n        return addMeanStd(network, weightMap, input, \"\", mean, std, false); // true for div 255\n    }\n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/fastrt/meta_arch/model.cpp",
    "content": "#include \"fastrt/model.h\"\n#include \"fastrt/calibrator.h\"\n\n#ifdef BUILD_INT8\n#include \"fastrt/config.h\"\n#endif \n\nnamespace fastrt {\n\n    Model::Model(const trt::ModelConfig &modelcfg, const std::string input_name, const std::string output_name) {\n        \n        _engineCfg.weights_path = modelcfg.weights_path;\n        _engineCfg.max_batch_size = modelcfg.max_batch_size;\n        _engineCfg.input_h = modelcfg.input_h;\n        _engineCfg.input_w = modelcfg.input_w;\n        _engineCfg.output_size = modelcfg.output_size;\n        _engineCfg.device_id = modelcfg.device_id;\n\n        _engineCfg.input_name = input_name;\n        _engineCfg.output_name = output_name;       \n        _engineCfg.trtModelStream = nullptr;\n        _engineCfg.stream_size = 0;\n    };\n\n    bool Model::serializeEngine(const std::string engine_file, const std::initializer_list<std::unique_ptr<Module>>& modules) {\n\n        /* Create builder */  \n        auto builder = make_holder(createInferBuilder(gLogger));\n\n        /* Create model to populate the network, then set the outputs and create an engine */ \n        auto engine = createEngine(builder.get(), modules);\n        TRTASSERT(engine.get());\n\n        /* Serialize the engine */ \n        auto modelStream = make_holder(engine->serialize());\n        TRTASSERT(modelStream.get());\n\n        std::ofstream p(engine_file, std::ios::binary | std::ios::out);\n        if (!p) {\n            std::cerr << \"could not open plan output file\" << std::endl;\n            return false;\n        }\n        p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());\n        std::cout << \"[Save serialized engine]: \" << engine_file << std::endl;\n        return true;\n    }\n\n    TensorRTHolder<ICudaEngine> Model::createEngine(IBuilder* builder, const std::initializer_list<std::unique_ptr<Module>>& modules) {\n\n        auto network = make_holder(builder->createNetworkV2(0U));\n        auto config = make_holder(builder->createBuilderConfig());\n        auto data = network->addInput(_engineCfg.input_name.c_str(), _dt, Dims3{3, _engineCfg.input_h, _engineCfg.input_w});\n        TRTASSERT(data);\n\n        auto weightMap = loadWeights(_engineCfg.weights_path);\n\n        /* Preprocessing */\n        auto input = preprocessing_gpu(network.get(), weightMap, data);\n        if (!input) input = data;\n\n        /* Modeling */\n        ILayer* output{nullptr};\n        for(auto& sequential_module: modules) {\n            output = sequential_module->topology(network.get(), weightMap, *input);\n            TRTASSERT(output);\n            input = output->getOutput(0);\n        }\n\n        /* Set output */\n        output->getOutput(0)->setName(_engineCfg.output_name.c_str());\n        network->markOutput(*output->getOutput(0));\n\n        /* Build engine */ \n        builder->setMaxBatchSize(_engineCfg.max_batch_size);\n        config->setMaxWorkspaceSize(1 << 20);\n#if defined(BUILD_FP16) && defined(BUILD_INT8)\n        std::cout << \"Flag confilct! BUILD_FP16 and BUILD_INT8 can't be both True!\" << std::endl;\n        return null;\n#endif \n#if defined(BUILD_FP16)\n        std::cout << \"[Build fp16]\" << std::endl;\n        config->setFlag(BuilderFlag::kFP16);\n#elif defined(BUILD_INT8)\n        std::cout << \"[Build int8]\" << std::endl;\n        std::cout << \"Your platform support int8: \" << (builder->platformHasFastInt8() ? \"true\" : \"false\") << std::endl;\n        TRTASSERT(builder->platformHasFastInt8());\n        config->setFlag(BuilderFlag::kINT8);\n        Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, _engineCfg.input_w, _engineCfg.input_h, \n            INT8_CALIBRATE_DATASET_PATH.c_str(), \"int8calib.table\", _engineCfg.input_name.c_str());\n        config->setInt8Calibrator(calibrator);\n#endif \n        auto engine = make_holder(builder->buildEngineWithConfig(*network, *config));\n        std::cout << \"[TRT engine build out]\" << std::endl;\n\n        for (auto& mem : weightMap) {\n            free((void*) (mem.second.values));\n        }\n        return engine;\n    }\n\n    bool Model::deserializeEngine(const std::string engine_file) {\n        std::ifstream file(engine_file, std::ios::binary | std::ios::in);\n        if (file.good()) {\n            file.seekg(0, file.end);\n            _engineCfg.stream_size = file.tellg();\n            file.seekg(0, file.beg);\n            _engineCfg.trtModelStream = std::shared_ptr<char>( new char[_engineCfg.stream_size], []( char* ptr ){ delete [] ptr; } );\n            TRTASSERT(_engineCfg.trtModelStream.get());\n            file.read(_engineCfg.trtModelStream.get(), _engineCfg.stream_size);\n            file.close();\n    \n            _inferEngine = make_unique<trt::InferenceEngine>(_engineCfg);\n            return true;\n        }\n        return false;\n    }\n\n    bool Model::inference(std::vector<cv::Mat> &input) {\n        if (_inferEngine != nullptr) {\n            const std::size_t stride = _engineCfg.input_h * _engineCfg.input_w;\n            return _inferEngine.get()->doInference(input.size(), \n                [&](float* data) {\n                    for(const auto &img : input) {\n                        preprocessing_cpu(img, data, stride);\n                        data += 3 * stride;\n                    }\n                }\n            );\n        } else {\n            return false;\n        }\n    }\n\n    float* Model::getOutput() { \n        if(_inferEngine != nullptr) \n            return _inferEngine.get()->getOutput(); \n        return nullptr;\n    }\n\n    int Model::getOutputSize() { \n        return _engineCfg.output_size; \n    }\n\n    int Model::getDeviceID() { \n        return _engineCfg.device_id; \n    }\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/IPoolingLayerRT.h",
    "content": "#pragma once\n\n#include <map>\n#include \"struct.h\"\n#include \"NvInfer.h\"\nusing namespace nvinfer1;\n\nnamespace fastrt {\n\n    class IPoolingLayerRT {\n    public:\n        IPoolingLayerRT() = default;\n        virtual ~IPoolingLayerRT() = default;\n\n        virtual ILayer* addPooling(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap, \n            ITensor& input) = 0; \n    };\n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/InferenceEngine.h",
    "content": "/************************************************************************************\n * Handle memory pre-alloc both on host(pinned memory, allow CUDA DMA) & device\n * Author:  Darren Hsieh\n * Date: 2020/07/07\n*************************************************************************************/\n\n#pragma once\n\n#include <thread>\n#include <chrono>\n#include <memory>\n#include <functional>\n#include <opencv2/opencv.hpp>\n\n#include \"utils.h\"\n#include \"struct.h\"\n#include \"holder.h\"\n#include \"logging.h\"\n#include \"NvInfer.h\"\n#include \"cuda_runtime_api.h\"\nstatic Logger gLogger;\n\nnamespace trt {\n\n    class InferenceEngine {\n    public:\n        InferenceEngine(const EngineConfig &enginecfg);\n        InferenceEngine(InferenceEngine &&other) noexcept;\n        ~InferenceEngine();\n\n        InferenceEngine(const InferenceEngine &) = delete;\n        InferenceEngine& operator=(const InferenceEngine &) = delete;\n        InferenceEngine& operator=(InferenceEngine && other) = delete;\n\n        bool doInference(const int inference_batch_size, std::function<void(float*)> preprocessing);\n        float* getOutput() { return _output; }\n        std::thread::id getThreadID() { return std::this_thread::get_id(); }\n\n    private:\n        EngineConfig _engineCfg;\n        float* _input{nullptr};\n        float* _output{nullptr};\n\n        // Pointers to input and output device buffers to pass to engine.\n        // Engine requires exactly IEngine::getNbBindings() number of buffers.\n        void* _buffers[2];\n\n        // In order to bind the buffers, we need to know the names of the input and output tensors.\n        // Note that indices are guaranteed to be less than IEngine::getNbBindings()\n        int _inputIndex;\n        int _outputIndex;\n        \n        int _inputSize;\n        int _outputSize;\n\n        static constexpr std::size_t _depth{sizeof(float)};\n        TensorRTHolder<nvinfer1::IRuntime> _runtime{nullptr};\n        TensorRTHolder<nvinfer1::ICudaEngine> _engine{nullptr};\n        TensorRTHolder<nvinfer1::IExecutionContext> _context{nullptr};\n        std::shared_ptr<cudaStream_t> _streamptr;\n    };\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/baseline.h",
    "content": "#pragma once\n\n#include \"model.h\"\n#include \"struct.h\"\n#include <memory>\n#include <opencv2/opencv.hpp>\nusing namespace trtxapi;\n\nnamespace fastrt {\n\n    class Baseline : public Model {\n    public:\n        Baseline(const trt::ModelConfig &modelcfg,\n            const std::string input_name = \"data\",\n            const std::string output_name = \"reid_embd\");\n        ~Baseline() = default;\n    \n    private:\n        void preprocessing_cpu(const cv::Mat& img, float* const data, const std::size_t stride);\n        ITensor* preprocessing_gpu(INetworkDefinition* network, \n            std::map<std::string, Weights>& weightMap, \n            ITensor* input); \n    };\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/calibrator.h",
    "content": "#ifndef ENTROPY_CALIBRATOR_H\n#define ENTROPY_CALIBRATOR_H\n\n#include \"NvInfer.h\"\n#include <string>\n#include <vector>\n\n//! \\class Int8EntropyCalibrator2\n//!\n//! \\brief Implements Entropy calibrator 2.\n//!  CalibrationAlgoType is kENTROPY_CALIBRATION_2.\n//!\nclass Int8EntropyCalibrator2 : public nvinfer1::IInt8EntropyCalibrator2\n{\npublic:\n    Int8EntropyCalibrator2(int batchsize, int input_w, int input_h, const char* img_dir, const char* calib_table_name, const char* input_blob_name, bool read_cache = true);\n\n    virtual ~Int8EntropyCalibrator2();\n    int getBatchSize() const override;\n    bool getBatch(void* bindings[], const char* names[], int nbBindings) override;\n    const void* readCalibrationCache(size_t& length) override;\n    void writeCalibrationCache(const void* cache, size_t length) override;\n\nprivate:\n    int batchsize_;\n    int input_w_;\n    int input_h_;\n    int img_idx_;\n    std::string img_dir_;\n    std::vector<std::string> img_files_;\n    size_t input_count_;\n    std::string calib_table_name_;\n    const char* input_blob_name_;\n    bool read_cache_;\n    void* device_input_;\n    std::vector<char> calib_cache_;\n};\n\n#endif // ENTROPY_CALIBRATOR_H\n"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/config.h.in",
    "content": "#pragma once\n\n#ifdef BUILD_INT8\n#include <string>\nconst std::string INT8_CALIBRATE_DATASET_PATH = \"@INT8_CALIBRATE_DATASET_PATH@\";\n#endif\n\n"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/cuda_utils.h",
    "content": "#ifndef TRTX_CUDA_UTILS_H_\n#define TRTX_CUDA_UTILS_H_\n\n#include <cuda_runtime_api.h>\n\n#ifndef CUDA_CHECK\n#define CUDA_CHECK(callstr)\\\n    {\\\n        cudaError_t error_code = callstr;\\\n        if (error_code != cudaSuccess) {\\\n            std::cerr << \"CUDA error \" << error_code << \" at \" << __FILE__ << \":\" << __LINE__;\\\n            assert(0);\\\n        }\\\n    }\n#endif  // CUDA_CHECK\n\n#endif  // TRTX_CUDA_UTILS_H_\n\n"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/embedding_head.h",
    "content": "#pragma once\n\n#include <map>\n#include \"NvInfer.h\"\n#include \"fastrt/module.h\"\n#include \"fastrt/struct.h\"\n#include \"fastrt/factory.h\"\nusing namespace nvinfer1;\n\nnamespace fastrt {\n\n    class embedding_head : public Module {\n    private:\n        FastreidConfig& _modelCfg;\n        std::unique_ptr<LayerFactory> _layerFactory;\n\n    public:\n        embedding_head(FastreidConfig& modelCfg);\n        embedding_head(FastreidConfig& modelCfg, std::unique_ptr<LayerFactory> layerFactory);\n        ~embedding_head() = default;\n\n        ILayer* topology(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap,\n            ITensor& input) override;\n    };\n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/factory.h",
    "content": "#pragma once\n\n#include \"struct.h\"\n#include \"module.h\"\n#include \"IPoolingLayerRT.h\"\n\nnamespace fastrt {\n    \n    class ModuleFactory {\n    public:\n        ModuleFactory() = default;\n        ~ModuleFactory() = default;\n\n        std::unique_ptr<Module> createBackbone(FastreidConfig& modelCfg);\n        std::unique_ptr<Module> createHead(FastreidConfig& modelCfg);\n    };\n\n    class LayerFactory {\n    public:\n        LayerFactory() = default;\n        ~LayerFactory() = default;\n\n        std::unique_ptr<IPoolingLayerRT> createPoolingLayer(const FastreidPoolingType& pooltype);\n    };\n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/holder.h",
    "content": "#pragma once\n\ntemplate <typename T>\nclass TensorRTHolder {\n    T* holder;\npublic:\n    explicit TensorRTHolder(T* holder_) : holder(holder_) {}\n    ~TensorRTHolder() {\n        if (holder)\n            holder->destroy();\n    }\n    TensorRTHolder(const TensorRTHolder&) = delete;\n    TensorRTHolder& operator=(const TensorRTHolder&) = delete;\n    TensorRTHolder(TensorRTHolder && rhs) noexcept{\n        holder = rhs.holder;\n        rhs.holder = nullptr;\n    }\n    TensorRTHolder& operator=(TensorRTHolder&& rhs) noexcept {\n        if (this == &rhs) {\n            return *this;\n        }\n        if (holder) holder->destroy();\n        holder = rhs.holder;\n        rhs.holder = nullptr;\n        return *this;\n    }\n    T* operator->() {\n        return holder;\n    }\n    T* get() { return holder; }\n    explicit operator bool() { return holder != nullptr; }\n    T& operator*() noexcept { return *holder; }\n};\n\ntemplate <typename T>\nTensorRTHolder<T> make_holder(T* holder) {\n    return TensorRTHolder<T>(holder);\n}\n\ntemplate <typename T>\nusing TensorRTNonHolder = T*;"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/layers.h",
    "content": "#pragma once\n\n#include <map>\n#include <math.h>\n#include <assert.h>\n#include \"NvInfer.h\"\n#include \"cuda_runtime_api.h\"\nusing namespace nvinfer1;\n\nnamespace trtxapi {\n\n    IActivationLayer* addMinClamp(INetworkDefinition* network, \n        ITensor& input, \n        const float min);\n\n    ITensor* addDiv255(INetworkDefinition* network, \n        std::map<std::string, Weights>& weightMap, \n        ITensor* input,\n        const std::string lname);\n        \n    ITensor* addMeanStd(INetworkDefinition* network, \n        std::map<std::string, Weights>& weightMap, \n        ITensor* input, \n        const std::string lname,\n        const float* mean, \n        const float* std, \n        const bool div255);\n\n    IScaleLayer* addBatchNorm2d(INetworkDefinition* network, \n        std::map<std::string, Weights>& weightMap, \n        ITensor& input, \n        const std::string lname, \n        const float eps);\n\n    IScaleLayer* addInstanceNorm2d(INetworkDefinition* network, \n        std::map<std::string, Weights>& weightMap, \n        ITensor& input, \n        const std::string lname, \n        const float eps);\n\n    IConcatenationLayer* addIBN(INetworkDefinition* network, \n        std::map<std::string, Weights>& weightMap, \n        ITensor& input, \n        const std::string lname);\n\n    IActivationLayer* basicBlock_ibn(INetworkDefinition* network, \n        std::map<std::string, Weights>& weightMap, \n        ITensor& input, \n        const int inch, \n        const int outch,\n        const int stride, \n        const std::string lname, \n        const std::string ibn);\n\n    IActivationLayer* bottleneck_ibn(INetworkDefinition* network, \n        std::map<std::string, Weights>& weightMap, \n        ITensor& input, \n        const int inch, \n        const int outch,\n        const int stride, \n        const std::string lname, \n        const std::string ibn);\n\n    ILayer* distill_basicBlock_ibn(INetworkDefinition* network, \n        std::map<std::string, Weights>& weightMap, \n        ITensor& input, \n        const int inch, \n        const int outch,\n        const int stride, \n        const std::string lname, \n        const std::string ibn);\n\n    ILayer* distill_bottleneck_ibn(INetworkDefinition* network, \n        std::map<std::string, Weights>& weightMap, \n        ITensor& input, \n        const int inch, \n        const int outch,\n        const int stride, \n        const std::string lname, \n        const std::string ibn);\n\n    IShuffleLayer* addShuffle2(INetworkDefinition* network, \n        ITensor& input, \n        const Dims dims, \n        const Permutation pmt, \n        const bool reshape_first);\n\n    IElementWiseLayer* Non_local(INetworkDefinition* network, \n        std::map<std::string, Weights>& weightMap, \n        ITensor& input, \n        const std::string lname, \n        const int reduc_ratio = 2);\n\n    IPoolingLayer* addAdaptiveAvgPool2d(INetworkDefinition* network, \n        ITensor& input, \n        const DimsHW output_dim = DimsHW{1,1});\n\n    IScaleLayer* addGeneralizedMeanPooling(INetworkDefinition* network, \n        ITensor& input, \n        const float norm = 3.f, \n        const DimsHW output_dim = DimsHW{1,1}, \n        const float eps = 1e-6);\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/logging.h",
    "content": "/*\n * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *     http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n#ifndef TENSORRT_LOGGING_H\n#define TENSORRT_LOGGING_H\n\n#include \"NvInferRuntimeCommon.h\"\n#include <cassert>\n#include <ctime>\n#include <iomanip>\n#include <iostream>\n#include <ostream>\n#include <sstream>\n#include <string>\n\nusing Severity = nvinfer1::ILogger::Severity;\n\nclass LogStreamConsumerBuffer : public std::stringbuf\n{\npublic:\n    LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)\n        : mOutput(stream)\n        , mPrefix(prefix)\n        , mShouldLog(shouldLog)\n    {\n    }\n\n    LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)\n        : mOutput(other.mOutput)\n    {\n    }\n\n    ~LogStreamConsumerBuffer()\n    {\n        // std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence\n        // std::streambuf::pptr() gives a pointer to the current position of the output sequence\n        // if the pointer to the beginning is not equal to the pointer to the current position,\n        // call putOutput() to log the output to the stream\n        if (pbase() != pptr())\n        {\n            putOutput();\n        }\n    }\n\n    // synchronizes the stream buffer and returns 0 on success\n    // synchronizing the stream buffer consists of inserting the buffer contents into the stream,\n    // resetting the buffer and flushing the stream\n    virtual int sync()\n    {\n        putOutput();\n        return 0;\n    }\n\n    void putOutput()\n    {\n        if (mShouldLog)\n        {\n            // prepend timestamp\n            std::time_t timestamp = std::time(nullptr);\n            tm* tm_local = std::localtime(&timestamp);\n            std::cout << \"[\";\n            std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << \"/\";\n            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << \"/\";\n            std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << \"-\";\n            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << \":\";\n            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << \":\";\n            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << \"] \";\n            // std::stringbuf::str() gets the string contents of the buffer\n            // insert the buffer contents pre-appended by the appropriate prefix into the stream\n            mOutput << mPrefix << str();\n            // set the buffer to empty\n            str(\"\");\n            // flush the stream\n            mOutput.flush();\n        }\n    }\n\n    void setShouldLog(bool shouldLog)\n    {\n        mShouldLog = shouldLog;\n    }\n\nprivate:\n    std::ostream& mOutput;\n    std::string mPrefix;\n    bool mShouldLog;\n};\n\n//!\n//! \\class LogStreamConsumerBase\n//! \\brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer\n//!\nclass LogStreamConsumerBase\n{\npublic:\n    LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)\n        : mBuffer(stream, prefix, shouldLog)\n    {\n    }\n\nprotected:\n    LogStreamConsumerBuffer mBuffer;\n};\n\n//!\n//! \\class LogStreamConsumer\n//! \\brief Convenience object used to facilitate use of C++ stream syntax when logging messages.\n//!  Order of base classes is LogStreamConsumerBase and then std::ostream.\n//!  This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field\n//!  in LogStreamConsumer and then the address of the buffer is passed to std::ostream.\n//!  This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.\n//!  Please do not change the order of the parent classes.\n//!\nclass LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream\n{\npublic:\n    //! \\brief Creates a LogStreamConsumer which logs messages with level severity.\n    //!  Reportable severity determines if the messages are severe enough to be logged.\n    LogStreamConsumer(Severity reportableSeverity, Severity severity)\n        : LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)\n        , std::ostream(&mBuffer) // links the stream buffer with the stream\n        , mShouldLog(severity <= reportableSeverity)\n        , mSeverity(severity)\n    {\n    }\n\n    LogStreamConsumer(LogStreamConsumer&& other)\n        : LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)\n        , std::ostream(&mBuffer) // links the stream buffer with the stream\n        , mShouldLog(other.mShouldLog)\n        , mSeverity(other.mSeverity)\n    {\n    }\n\n    void setReportableSeverity(Severity reportableSeverity)\n    {\n        mShouldLog = mSeverity <= reportableSeverity;\n        mBuffer.setShouldLog(mShouldLog);\n    }\n\nprivate:\n    static std::ostream& severityOstream(Severity severity)\n    {\n        return severity >= Severity::kINFO ? std::cout : std::cerr;\n    }\n\n    static std::string severityPrefix(Severity severity)\n    {\n        switch (severity)\n        {\n        case Severity::kINTERNAL_ERROR: return \"[F] \";\n        case Severity::kERROR: return \"[E] \";\n        case Severity::kWARNING: return \"[W] \";\n        case Severity::kINFO: return \"[I] \";\n        case Severity::kVERBOSE: return \"[V] \";\n        default: assert(0); return \"\";\n        }\n    }\n\n    bool mShouldLog;\n    Severity mSeverity;\n};\n\n//! \\class Logger\n//!\n//! \\brief Class which manages logging of TensorRT tools and samples\n//!\n//! \\details This class provides a common interface for TensorRT tools and samples to log information to the console,\n//! and supports logging two types of messages:\n//!\n//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)\n//! - Test pass/fail messages\n//!\n//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is\n//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.\n//!\n//! In the future, this class could be extended to support dumping test results to a file in some standard format\n//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).\n//!\n//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger\n//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT\n//! library and messages coming from the sample.\n//!\n//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the\n//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger\n//! object.\n\nclass Logger : public nvinfer1::ILogger\n{\npublic:\n    Logger(Severity severity = Severity::kWARNING)\n        : mReportableSeverity(severity)\n    {\n    }\n\n    //!\n    //! \\enum TestResult\n    //! \\brief Represents the state of a given test\n    //!\n    enum class TestResult\n    {\n        kRUNNING, //!< The test is running\n        kPASSED,  //!< The test passed\n        kFAILED,  //!< The test failed\n        kWAIVED   //!< The test was waived\n    };\n\n    //!\n    //! \\brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger\n    //! \\return The nvinfer1::ILogger associated with this Logger\n    //!\n    //! TODO Once all samples are updated to use this method to register the logger with TensorRT,\n    //! we can eliminate the inheritance of Logger from ILogger\n    //!\n    nvinfer1::ILogger& getTRTLogger()\n    {\n        return *this;\n    }\n\n    //!\n    //! \\brief Implementation of the nvinfer1::ILogger::log() virtual method\n    //!\n    //! Note samples should not be calling this function directly; it will eventually go away once we eliminate the\n    //! inheritance from nvinfer1::ILogger\n    //!\n    void log(Severity severity, const char* msg) override\n    {\n        LogStreamConsumer(mReportableSeverity, severity) << \"[TRT] \" << std::string(msg) << std::endl;\n    }\n\n    //!\n    //! \\brief Method for controlling the verbosity of logging output\n    //!\n    //! \\param severity The logger will only emit messages that have severity of this level or higher.\n    //!\n    void setReportableSeverity(Severity severity)\n    {\n        mReportableSeverity = severity;\n    }\n\n    //!\n    //! \\brief Opaque handle that holds logging information for a particular test\n    //!\n    //! This object is an opaque handle to information used by the Logger to print test results.\n    //! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used\n    //! with Logger::reportTest{Start,End}().\n    //!\n    class TestAtom\n    {\n    public:\n        TestAtom(TestAtom&&) = default;\n\n    private:\n        friend class Logger;\n\n        TestAtom(bool started, const std::string& name, const std::string& cmdline)\n            : mStarted(started)\n            , mName(name)\n            , mCmdline(cmdline)\n        {\n        }\n\n        bool mStarted;\n        std::string mName;\n        std::string mCmdline;\n    };\n\n    //!\n    //! \\brief Define a test for logging\n    //!\n    //! \\param[in] name The name of the test.  This should be a string starting with\n    //!                  \"TensorRT\" and containing dot-separated strings containing\n    //!                  the characters [A-Za-z0-9_].\n    //!                  For example, \"TensorRT.sample_googlenet\"\n    //! \\param[in] cmdline The command line used to reproduce the test\n    //\n    //! \\return a TestAtom that can be used in Logger::reportTest{Start,End}().\n    //!\n    static TestAtom defineTest(const std::string& name, const std::string& cmdline)\n    {\n        return TestAtom(false, name, cmdline);\n    }\n\n    //!\n    //! \\brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments\n    //!        as input\n    //!\n    //! \\param[in] name The name of the test\n    //! \\param[in] argc The number of command-line arguments\n    //! \\param[in] argv The array of command-line arguments (given as C strings)\n    //!\n    //! \\return a TestAtom that can be used in Logger::reportTest{Start,End}().\n    static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)\n    {\n        auto cmdline = genCmdlineString(argc, argv);\n        return defineTest(name, cmdline);\n    }\n\n    //!\n    //! \\brief Report that a test has started.\n    //!\n    //! \\pre reportTestStart() has not been called yet for the given testAtom\n    //!\n    //! \\param[in] testAtom The handle to the test that has started\n    //!\n    static void reportTestStart(TestAtom& testAtom)\n    {\n        reportTestResult(testAtom, TestResult::kRUNNING);\n        assert(!testAtom.mStarted);\n        testAtom.mStarted = true;\n    }\n\n    //!\n    //! \\brief Report that a test has ended.\n    //!\n    //! \\pre reportTestStart() has been called for the given testAtom\n    //!\n    //! \\param[in] testAtom The handle to the test that has ended\n    //! \\param[in] result The result of the test. Should be one of TestResult::kPASSED,\n    //!                   TestResult::kFAILED, TestResult::kWAIVED\n    //!\n    static void reportTestEnd(const TestAtom& testAtom, TestResult result)\n    {\n        assert(result != TestResult::kRUNNING);\n        assert(testAtom.mStarted);\n        reportTestResult(testAtom, result);\n    }\n\n    static int reportPass(const TestAtom& testAtom)\n    {\n        reportTestEnd(testAtom, TestResult::kPASSED);\n        return EXIT_SUCCESS;\n    }\n\n    static int reportFail(const TestAtom& testAtom)\n    {\n        reportTestEnd(testAtom, TestResult::kFAILED);\n        return EXIT_FAILURE;\n    }\n\n    static int reportWaive(const TestAtom& testAtom)\n    {\n        reportTestEnd(testAtom, TestResult::kWAIVED);\n        return EXIT_SUCCESS;\n    }\n\n    static int reportTest(const TestAtom& testAtom, bool pass)\n    {\n        return pass ? reportPass(testAtom) : reportFail(testAtom);\n    }\n\n    Severity getReportableSeverity() const\n    {\n        return mReportableSeverity;\n    }\n\nprivate:\n    //!\n    //! \\brief returns an appropriate string for prefixing a log message with the given severity\n    //!\n    static const char* severityPrefix(Severity severity)\n    {\n        switch (severity)\n        {\n        case Severity::kINTERNAL_ERROR: return \"[F] \";\n        case Severity::kERROR: return \"[E] \";\n        case Severity::kWARNING: return \"[W] \";\n        case Severity::kINFO: return \"[I] \";\n        case Severity::kVERBOSE: return \"[V] \";\n        default: assert(0); return \"\";\n        }\n    }\n\n    //!\n    //! \\brief returns an appropriate string for prefixing a test result message with the given result\n    //!\n    static const char* testResultString(TestResult result)\n    {\n        switch (result)\n        {\n        case TestResult::kRUNNING: return \"RUNNING\";\n        case TestResult::kPASSED: return \"PASSED\";\n        case TestResult::kFAILED: return \"FAILED\";\n        case TestResult::kWAIVED: return \"WAIVED\";\n        default: assert(0); return \"\";\n        }\n    }\n\n    //!\n    //! \\brief returns an appropriate output stream (cout or cerr) to use with the given severity\n    //!\n    static std::ostream& severityOstream(Severity severity)\n    {\n        return severity >= Severity::kINFO ? std::cout : std::cerr;\n    }\n\n    //!\n    //! \\brief method that implements logging test results\n    //!\n    static void reportTestResult(const TestAtom& testAtom, TestResult result)\n    {\n        severityOstream(Severity::kINFO) << \"&&&& \" << testResultString(result) << \" \" << testAtom.mName << \" # \"\n                                         << testAtom.mCmdline << std::endl;\n    }\n\n    //!\n    //! \\brief generate a command line string from the given (argc, argv) values\n    //!\n    static std::string genCmdlineString(int argc, char const* const* argv)\n    {\n        std::stringstream ss;\n        for (int i = 0; i < argc; i++)\n        {\n            if (i > 0)\n                ss << \" \";\n            ss << argv[i];\n        }\n        return ss.str();\n    }\n\n    Severity mReportableSeverity;\n};\n\nnamespace\n{\n\n//!\n//! \\brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE\n//!\n//! Example usage:\n//!\n//!     LOG_VERBOSE(logger) << \"hello world\" << std::endl;\n//!\ninline LogStreamConsumer LOG_VERBOSE(const Logger& logger)\n{\n    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);\n}\n\n//!\n//! \\brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO\n//!\n//! Example usage:\n//!\n//!     LOG_INFO(logger) << \"hello world\" << std::endl;\n//!\ninline LogStreamConsumer LOG_INFO(const Logger& logger)\n{\n    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);\n}\n\n//!\n//! \\brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING\n//!\n//! Example usage:\n//!\n//!     LOG_WARN(logger) << \"hello world\" << std::endl;\n//!\ninline LogStreamConsumer LOG_WARN(const Logger& logger)\n{\n    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);\n}\n\n//!\n//! \\brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR\n//!\n//! Example usage:\n//!\n//!     LOG_ERROR(logger) << \"hello world\" << std::endl;\n//!\ninline LogStreamConsumer LOG_ERROR(const Logger& logger)\n{\n    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);\n}\n\n//!\n//! \\brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR\n//         (\"fatal\" severity)\n//!\n//! Example usage:\n//!\n//!     LOG_FATAL(logger) << \"hello world\" << std::endl;\n//!\ninline LogStreamConsumer LOG_FATAL(const Logger& logger)\n{\n    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);\n}\n\n} // anonymous namespace\n\n#endif // TENSORRT_LOGGING_H\n"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/model.h",
    "content": "#pragma once\n\n#include \"module.h\"\n#include \"utils.h\"\n#include \"holder.h\"\n#include \"layers.h\"\n#include \"struct.h\"\n#include \"InferenceEngine.h\"\n\n#include <memory>\n#include <vector>\n#include <opencv2/opencv.hpp>\nextern Logger gLogger;\nusing namespace trt;\nusing namespace trtxapi;\n\nnamespace fastrt {\n\n    class Model {\n    public:\n        Model(const trt::ModelConfig &modelcfg, \n            const std::string input_name=\"input\", \n            const std::string output_name=\"output\");\n\n        virtual ~Model() = default;\n\n        /* \n         * Serialize TRT Engine\n         * @engine_file: save serialized engine as engine_file\n         * @modules: sequential modules(variadic length). (e.g., backbone1 + backbone2 + head, backbone + head, backbone)\n         */ \n        bool serializeEngine(const std::string engine_file, \n            const std::initializer_list<std::unique_ptr<Module>>& modules);\n\n        bool deserializeEngine(const std::string engine_file);\n\n        /* Support batch inference */\n        bool inference(std::vector<cv::Mat> &input); \n\n        /* \n         * Access the memory allocated by cudaMallocHost. (It's on CPU side) \n         * Use this after each inference.\n         */ \n        float* getOutput(); \n\n        /* \n         * Output buffer size\n         */ \n        int getOutputSize(); \n\n        /* \n         * Cuda device id\n         * You may need this in multi-thread/multi-engine inference\n         */ \n        int getDeviceID(); \n\n    private:\n        TensorRTHolder<ICudaEngine> createEngine(IBuilder* builder,\n            const std::initializer_list<std::unique_ptr<Module>>& modules);\n\n        virtual void preprocessing_cpu(const cv::Mat& img, float* const data, const std::size_t stride) = 0;\n        virtual ITensor* preprocessing_gpu(INetworkDefinition* network, \n            std::map<std::string, Weights>& weightMap, \n            ITensor* input) { return nullptr; };\n\n    private:\n        DataType _dt{DataType::kFLOAT};\n        trt::EngineConfig _engineCfg;\n        std::unique_ptr<trt::InferenceEngine> _inferEngine{nullptr};\n    };\n}\n"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/module.h",
    "content": "#pragma once\n\n#include <map>\n#include \"struct.h\"\n#include \"NvInfer.h\"\nusing namespace nvinfer1;\n\nnamespace fastrt {\n\n    class Module {\n    public:\n        Module() = default;\n        virtual ~Module() = default;\n\n        virtual ILayer* topology(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap, \n            ITensor& input) = 0; \n    };\n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/sbs_resnet.h",
    "content": "#pragma once\n\n#include <map>\n#include \"struct.h\"\n#include \"module.h\"\n#include \"NvInfer.h\"\nusing namespace nvinfer1;\n\nnamespace fastrt {\n    class backbone_sbsR18_distill : public Module {\n    private:\n        FastreidConfig& _modelCfg;\n    public:\n        backbone_sbsR18_distill(FastreidConfig& modelCfg) : _modelCfg(modelCfg){}\n        ~backbone_sbsR18_distill() = default;\n        ILayer* topology(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap, \n            ITensor& input) override; \n    };\n\n    class backbone_sbsR34_distill : public Module {\n    private:\n        FastreidConfig& _modelCfg;\n    public:\n        backbone_sbsR34_distill(FastreidConfig& modelCfg) : _modelCfg(modelCfg) {}\n        ~backbone_sbsR34_distill() = default;\n        ILayer* topology(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap, \n            ITensor& input) override; \n    };\n\n    class backbone_sbsR50_distill : public Module { \n    private:\n        FastreidConfig& _modelCfg;\n    public:\n        backbone_sbsR50_distill(FastreidConfig& modelCfg) : _modelCfg(modelCfg) {}\n        ~backbone_sbsR50_distill() = default;\n        ILayer* topology(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap, \n            ITensor& input) override;\n    };\n\n    class backbone_sbsR34 : public Module {\n    private:\n        FastreidConfig& _modelCfg;\n    public:\n        backbone_sbsR34(FastreidConfig& modelCfg) : _modelCfg(modelCfg) {}\n        ~backbone_sbsR34() = default;\n        ILayer* topology(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap, \n            ITensor& input) override;\n    };\n\n    class backbone_sbsR50 : public Module {\n    private:\n        FastreidConfig& _modelCfg;\n    public:\n        backbone_sbsR50(FastreidConfig& modelCfg) : _modelCfg(modelCfg) {}\n        ~backbone_sbsR50() = default;\n        ILayer* topology(INetworkDefinition *network, \n            std::map<std::string, Weights>& weightMap, \n            ITensor& input) override;\n    };\n     \n}"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/struct.h",
    "content": "#pragma once\n\n#include <memory>\n\nnamespace trt {\n\n    struct ModelConfig {\n        std::string weights_path;\n        int max_batch_size; \n        int input_h;     /* cfg.INPUT.SIZE_TRAIN[0] */\n        int input_w;     /* cfg.INPUT.SIZE_TRAIN[1] */\n        int output_size; /* final embedding dims. Could be cfg.MODEL.BACKBONE.FEAT_DIM or cfg.MODEL.HEADS.EMBEDDING_DIM(if you modified. default=0) */\n        int device_id;   /* cuda device id(0, 1, 2, ...) */   \n    };\n\n    struct EngineConfig : ModelConfig {\n        std::string input_name;\n        std::string output_name; \n        std::shared_ptr<char> trtModelStream;\n        int stream_size;\n    };\n\n}\n\nnamespace fastrt {\n\n#define FASTBACKBONE_TABLE \\\n        X(r50, \"r50\") \\\n        X(r50_distill, \"r50_distill\") \\\n        X(r34, \"r34\") \\\n        X(r34_distill, \"r34_distill\") \\\n        X(r18_distill, \"r18_distill\") \n\n#define X(a, b) a,\n        enum FastreidBackboneType { FASTBACKBONE_TABLE };\n#undef X\n\n#define FASTHEAD_TABLE \\\n        X(EmbeddingHead, \"EmbeddingHead\")\n\n#define X(a, b) a,\n    enum FastreidHeadType { FASTHEAD_TABLE };\n#undef X\n\n#define FASTPOOLING_TABLE \\\n        X(maxpool, \"maxpool\") \\\n        X(avgpool, \"avgpool\") \\\n        X(gempool, \"gempool\") \\\n        X(gempoolP, \"gempoolP\") \n\n#define X(a, b) a,\n    enum FastreidPoolingType { FASTPOOLING_TABLE };\n#undef X\n\n    struct FastreidConfig {\n        FastreidBackboneType backbone; /* cfg.MODEL.BACKBONE.DEPTH and cfg.MODEL.META_ARCHITECTURE */\n        FastreidHeadType head;         /* cfg.MODEL.HEADS.NAME */\n        FastreidPoolingType pooling;   /* cfg.MODEL.HEADS.POOL_LAYER */\n        int last_stride;               /* cfg.MODEL.BACKBONE.LAST_STRIDE */\n        bool with_ibna;                /* cfg.MODEL.BACKBONE.WITH_IBN */\n        bool with_nl;                  /* cfg.MODEL.BACKBONE.WITH_NL */\n        int embedding_dim;             /* cfg.MODEL.HEADS.EMBEDDING_DIM (Default = 0) */ \n    };\n\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/include/fastrt/utils.h",
    "content": "#pragma once\n\n#include <map>\n#include <chrono>\n#include <memory>\n#include <vector>\n#include <fstream>\n#include <iostream>\n#include <cassert>\n#include <string.h> \n\n#include <dirent.h>\n#include \"NvInfer.h\"\n#include \"cuda_runtime_api.h\"\n#include \"fastrt/struct.h\"\n\n#define CHECK(status)                             \\\n    do                                            \\\n    {                                             \\\n        auto ret = (status);                      \\\n        if (ret != 0)                             \\\n        {                                         \\\n            std::cout << \"Cuda failure: \" << ret; \\\n            abort();                              \\\n        }                                         \\\n    } while (0)\n\n#define TRTASSERT assert\n\nusing Time = std::chrono::high_resolution_clock;\nusing TimePoint = std::chrono::time_point<std::chrono::high_resolution_clock>;\n\ntemplate<typename T, typename... Args>\nstd::unique_ptr<T> make_unique(Args&&... args) {\n    return std::unique_ptr<T>(new T(std::forward<Args>(args)...));\n}\n\nnamespace io {\n    std::vector<std::string> fileGlob(const std::string& pattern);\n}\n\nstatic inline int read_files_in_dir(const char *p_dir_name, std::vector<std::string> &file_names) {\n    DIR *p_dir = opendir(p_dir_name);\n    if (p_dir == nullptr) {\n        return -1;\n    }\n\n    struct dirent* p_file = nullptr;\n    while ((p_file = readdir(p_dir)) != nullptr) {\n        if (strcmp(p_file->d_name, \".\") != 0 &&\n            strcmp(p_file->d_name, \"..\") != 0) {\n\n            std::string cur_file_name(p_file->d_name);\n            file_names.push_back(cur_file_name);\n        }\n    }\n\n    closedir(p_dir);\n    return 0;\n}\n\nnamespace trt {\n    /* \n     * Load weights from files shared with TensorRT samples.\n     * TensorRT weight files have a simple space delimited format:\n     * [type] [size] <data x size in hex>\n     */ \n    std::map<std::string, nvinfer1::Weights> loadWeights(const std::string file);\n\n    std::ostream& operator<<(std::ostream& os, const ModelConfig& modelCfg);\n}\n\nnamespace fastrt {\n    std::ostream& operator<<(std::ostream& os, const FastreidConfig& fastreidCfg);\n}"
  },
  {
    "path": "fast_reid/projects/FastRT/pybind_interface/CMakeLists.txt",
    "content": "SET(APP_PROJECT_NAME ReID)\n\n# pybind\nfind_package(pybind11)\n\nfind_package(CUDA REQUIRED)\n# include and link dirs of cuda and tensorrt, you need adapt them if yours are different\n# cuda\ninclude_directories(/usr/local/cuda/include)\nlink_directories(/usr/local/cuda/lib64)\n# tensorrt\ninclude_directories(/usr/include/x86_64-linux-gnu/)\nlink_directories(/usr/lib/x86_64-linux-gnu/)\n\ninclude_directories(${SOLUTION_DIR}/include)\n\npybind11_add_module(${APP_PROJECT_NAME} ${PROJECT_SOURCE_DIR}/pybind_interface/reid.cpp)\n\n# OpenCV\nfind_package(OpenCV)\ntarget_include_directories(${APP_PROJECT_NAME}\nPUBLIC\n  ${OpenCV_INCLUDE_DIRS}\n)\ntarget_link_libraries(${APP_PROJECT_NAME}\nPUBLIC\n  ${OpenCV_LIBS}\n)\n\nif(BUILD_FASTRT_ENGINE AND BUILD_PYTHON_INTERFACE)\n  SET(FASTRTENGINE_LIB FastRTEngine)\nelse()\n  SET(FASTRTENGINE_LIB ${SOLUTION_DIR}/libs/FastRTEngine/libFastRTEngine.so)\nendif()\n\ntarget_link_libraries(${APP_PROJECT_NAME} \nPRIVATE\n  ${FASTRTENGINE_LIB}\n  nvinfer\n)"
  },
  {
    "path": "fast_reid/projects/FastRT/pybind_interface/docker/trt7cu100/Dockerfile",
    "content": "# cuda10.0\nFROM fineyu/tensorrt7:0.0.1\n\nRUN apt-get update && apt-get install -y \\\n    build-essential \\\n    software-properties-common \\\n    cmake \\\n    wget \\\n    python3.7-dev python3-pip \n\nRUN add-apt-repository -y ppa:timsc/opencv-3.4 && \\\n    apt-get update && \\\n    apt-get install -y \\\n    libopencv-dev \\\n    libopencv-dnn-dev \\\n    libopencv-shape3.4-dbg && \\\n    apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*\n\nRUN wget https://bootstrap.pypa.io/get-pip.py && \\\n    python3 get-pip.py --force-reinstall && \\\n    rm get-pip.py\n\nRUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 1 && \\\n    update-alternatives --set python3 /usr/bin/python3.7\n\nRUN pip install pytest opencv-python \n\nRUN cd /usr/local/src && \\\n    wget https://github.com/pybind/pybind11/archive/v2.2.3.tar.gz && \\\n    tar xvf v2.2.3.tar.gz && \\\n    cd pybind11-2.2.3 && \\\n    mkdir build && \\\n    cd build && \\\n    cmake .. && \\\n    make -j12 && \\\n    make install && \\\n    cd ../.. && \\\n    rm -rf pybind11-2.2.3 && \\\n    rm -rf v2.2.3.tar.gz\n"
  },
  {
    "path": "fast_reid/projects/FastRT/pybind_interface/docker/trt7cu102_torch160/Dockerfile",
    "content": "# cuda10.2\nFROM darrenhsieh1717/trt7-cu102-cv34:pybind\n\nRUN pip install torch==1.6.0 torchvision==0.7.0\n\nRUN pip install opencv-python tensorboard cython yacs termcolor scikit-learn tabulate gdown gpustat ipdb h5py fs faiss-gpu\n\nRUN git clone https://github.com/NVIDIA/apex && \\\n    cd apex && \\\n    python3 setup.py install\n"
  },
  {
    "path": "fast_reid/projects/FastRT/pybind_interface/market_benchmark.py",
    "content": "import random\nimport numpy as np\nimport cv2\nimport fs\nimport argparse\nimport io\nimport sys\nimport torch\nimport time\nimport os\nimport torchvision.transforms as T\n\nsys.path.append('../../..')\nsys.path.append('../')\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.modeling.meta_arch import build_model\nfrom fast_reid.fastreid.utils.file_io import PathManager\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.utils.logger import setup_logger\nfrom fast_reid.fastreid.data import build_reid_train_loader, build_reid_test_loader\nfrom fast_reid.fastreid.evaluation.rank import eval_market1501\n\nfrom build.pybind_interface.ReID import ReID\n\n\nFEATURE_DIM = 2048\nGPU_ID = 0\n\ndef map(wrapper):\n\tmodel = wrapper\n\tcfg = get_cfg()\n\ttest_loader, num_query = build_reid_test_loader(cfg, \"Market1501\", T.Compose([]))\n\n\tfeats = []\n\tpids = []\n\tcamids = []\n\n\tfor batch in test_loader:\n\t\tfor image_path in batch[\"img_paths\"]:\n\t\t\tt = torch.Tensor(np.array([model.infer(cv2.imread(image_path))]))\n\t\t\tt.to(torch.device(GPU_ID))\n\t\t\tfeats.append(t)\n\t\tpids.extend(batch[\"targets\"].numpy())\n\t\tcamids.extend(batch[\"camids\"].numpy())\n\t\t\n\tfeats = torch.cat(feats, dim=0)\n\tq_feat = feats[:num_query]\n\tg_feat = feats[num_query:]\n\tq_pids = np.asarray(pids[:num_query])\n\tg_pids = np.asarray(pids[num_query:])\n\tq_camids = np.asarray(camids[:num_query])\n\tg_camids = np.asarray(camids[num_query:])\n\n\t\n\tdistmat = 1 - torch.mm(q_feat, g_feat.t())\n\tdistmat = distmat.numpy()\n\tall_cmc, all_AP, all_INP = eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, 5)\n\tmAP = np.mean(all_AP)\n\tprint(\"mAP {}, rank-1 {}\".format(mAP, all_cmc[0]))\n\n\nif __name__ == '__main__':\n\tinfer = ReID(GPU_ID)\n\tinfer.build(\"../build/sbs_R50-ibn.engine\")\n\tmap(infer)\n"
  },
  {
    "path": "fast_reid/projects/FastRT/pybind_interface/reid.cpp",
    "content": "#include <iostream>\n#include <opencv2/opencv.hpp>\n#include <pybind11/pybind11.h>\n#include <pybind11/numpy.h>\n#include <pybind11/stl.h>\n\n#include \"fastrt/utils.h\"\n#include \"fastrt/baseline.h\"\n#include \"fastrt/factory.h\"\nusing namespace fastrt;\nusing namespace nvinfer1;\n\nnamespace py = pybind11;\n\n\n/* Ex1. sbs_R50-ibn */\nstatic const std::string WEIGHTS_PATH = \"../sbs_R50-ibn.wts\"; \nstatic const std::string ENGINE_PATH = \"./sbs_R50-ibn.engine\";\n\nstatic const int MAX_BATCH_SIZE = 4;\nstatic const int INPUT_H = 384;\nstatic const int INPUT_W = 128;\nstatic const int OUTPUT_SIZE = 2048;\nstatic const int DEVICE_ID = 0;\n\nstatic const FastreidBackboneType BACKBONE = FastreidBackboneType::r50; \nstatic const FastreidHeadType HEAD = FastreidHeadType::EmbeddingHead;\nstatic const FastreidPoolingType HEAD_POOLING = FastreidPoolingType::gempoolP;\nstatic const int LAST_STRIDE = 1;\nstatic const bool WITH_IBNA = true; \nstatic const bool WITH_NL = true;\nstatic const int EMBEDDING_DIM = 0; \n\nFastreidConfig reidCfg { \n        BACKBONE,\n        HEAD,\n        HEAD_POOLING,\n        LAST_STRIDE,\n        WITH_IBNA,\n        WITH_NL,\n        EMBEDDING_DIM};\n\nclass ReID\n{\n\nprivate:\n    int device;  // GPU id\n    fastrt::Baseline baseline;\n\npublic:\n    ReID(int a);\n    int build(const std::string &engine_file);\n    // std::list<float> infer_test(const std::string &image_file);\n    std::list<float> infer(py::array_t<uint8_t>&);\n    std::list<std::list<float>> batch_infer(std::list<py::array_t<uint8_t>>&);\n    ~ReID();\n};\n\nReID::ReID(int device): baseline(trt::ModelConfig { \n        WEIGHTS_PATH,\n        MAX_BATCH_SIZE,\n        INPUT_H,\n        INPUT_W,\n        OUTPUT_SIZE,\n        device})\n{\n    std::cout << \"Init on device \" << device << std::endl;\n}\n\nint ReID::build(const std::string &engine_file)\n{\n    if(!baseline.deserializeEngine(engine_file)) {\n        std::cout << \"DeserializeEngine Failed.\" << std::endl;\n        return -1;\n    }\n    return 0;\n}\n\nReID::~ReID()\n{\n\n    std::cout << \"Destroy engine succeed\" << std::endl;\n}\n\nstd::list<float> ReID::infer(py::array_t<uint8_t>& img)\n{\n    auto rows = img.shape(0);\n    auto cols = img.shape(1);\n    auto type = CV_8UC3;\n\n    cv::Mat img2(rows, cols, type, (unsigned char*)img.data());\n    cv::Mat re(INPUT_H, INPUT_W, CV_8UC3);\n    // std::cout << (int)img2.data[0] << std::endl;\n    cv::resize(img2, re, re.size(), 0, 0, cv::INTER_CUBIC); /* cv::INTER_LINEAR */\n    std::vector<cv::Mat> input;\n    input.emplace_back(re);\n\n    if(!baseline.inference(input)) {\n        std::cout << \"Inference Failed.\" << std::endl;\n    }\n    std::list<float> feature;\n\n    float* feat_embedding = baseline.getOutput();\n    TRTASSERT(feat_embedding);\n    for (int dim = 0; dim < baseline.getOutputSize(); ++dim) {\n        feature.push_back(feat_embedding[dim]);\n    }\n\n    return feature;\n}\n\n\nstd::list<std::list<float>> ReID::batch_infer(std::list<py::array_t<uint8_t>>& imgs)\n{\n    // auto t1 = Time::now();\n    std::vector<cv::Mat> input;\n    int count = 0;\n    while(!imgs.empty()){\n        py::array_t<uint8_t>& img = imgs.front();\n        imgs.pop_front();\n        // parse to cvmat\n        auto rows = img.shape(0);\n        auto cols = img.shape(1);\n        auto type = CV_8UC3;\n\n        cv::Mat img2(rows, cols, type, (unsigned char*)img.data());\n        cv::Mat re(INPUT_H, INPUT_W, CV_8UC3);\n        // std::cout << (int)img2.data[0] << std::endl;\n        cv::resize(img2, re, re.size(), 0, 0, cv::INTER_CUBIC); /* cv::INTER_LINEAR */\n        input.emplace_back(re);\n\n        count += 1;\n    }\n    // auto t2 = Time::now();\n    \n    if(!baseline.inference(input)) {\n        std::cout << \"Inference Failed.\" << std::endl;\n    }\n    std::list<std::list<float>> result;\n\n    float* feat_embedding = baseline.getOutput();\n    TRTASSERT(feat_embedding);\n\n    // auto t3 = Time::now();\n    for (int index = 0; index < count; index++)\n    {\n        std::list<float> feature;\n        for (int dim = 0; dim < baseline.getOutputSize(); ++dim) {\n            feature.push_back(feat_embedding[index * baseline.getOutputSize() + dim]);\n        }\n        result.push_back(feature);\n    }\n    // std::cout << \"[Preprocessing]: \" << std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1).count() << \"ms\" \n    // << \"[Infer]: \" << std::chrono::duration_cast<std::chrono::milliseconds>(t3 - t2).count() << \"ms\" \n    // << \"[Cast]: \" << std::chrono::duration_cast<std::chrono::milliseconds>(Time::now() - t3).count() << \"ms\" \n    // << std::endl; \n    return result;\n}\n\n\nPYBIND11_MODULE(ReID, m) {\n    m.doc() = R\"pbdoc(\n        Pybind11 example plugin\n    )pbdoc\";\n    py::class_<ReID>(m, \"ReID\")\n        .def(py::init<int>())\n        .def(\"build\", &ReID::build)\n        .def(\"infer\", &ReID::infer, py::return_value_policy::automatic)\n        .def(\"batch_infer\", &ReID::batch_infer, py::return_value_policy::automatic)\n        ;\n\n#ifdef VERSION_INFO\n    m.attr(\"__version__\") = VERSION_INFO;\n#else\n    m.attr(\"__version__\") = \"dev\";\n#endif\n}\n"
  },
  {
    "path": "fast_reid/projects/FastRT/pybind_interface/test.py",
    "content": "import sys\n\nsys.path.append(\"../\")\nfrom build.pybind_interface.ReID import ReID\nimport cv2\nimport time\n\n\nif __name__ == '__main__':\n    iter_ = 10\n    m = ReID(0)\n    m.build(\"../build/sbs_R50-ibn.engine\")\n    print(\"build done\")\n    \n    frame = cv2.imread(\"../data/Market-1501-v15.09.15/calib_set/-1_c1s2_009916_03.jpg\")\n    m.infer(frame)\n    t0 = time.time()\n\n    for i in range(iter_):\n        m.infer(frame)\n\n    total = time.time() - t0\n    print(\"CPP API fps is {:.1f}, avg infer time is {:.2f}ms\".format(iter_ / total, total / iter_ * 1000))"
  },
  {
    "path": "fast_reid/projects/FastRT/third_party/cnpy/CMakeLists.txt",
    "content": "CMAKE_MINIMUM_REQUIRED(VERSION 3.0 FATAL_ERROR)\nif(COMMAND cmake_policy)\n\tcmake_policy(SET CMP0003 NEW)\nendif(COMMAND cmake_policy)\n\nproject(CNPY)\n\nset(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -std=c++11\")\n\noption(ENABLE_STATIC \"Build static (.a) library\" ON)\n\nfind_package(ZLIB REQUIRED)\n\ninclude_directories(${ZLIB_INCLUDE_DIRS})\n\nadd_library(cnpy SHARED \"cnpy.cpp\")\ntarget_link_libraries(cnpy ${ZLIB_LIBRARIES})\ninstall(TARGETS \"cnpy\" LIBRARY DESTINATION lib PERMISSIONS OWNER_READ OWNER_WRITE OWNER_EXECUTE GROUP_READ GROUP_EXECUTE WORLD_READ WORLD_EXECUTE)\n\nif(ENABLE_STATIC)\n    add_library(cnpy-static STATIC \"cnpy.cpp\")\n    set_target_properties(cnpy-static PROPERTIES OUTPUT_NAME \"cnpy\")\n    install(TARGETS \"cnpy-static\" ARCHIVE DESTINATION lib)\nendif(ENABLE_STATIC)\n\ninstall(FILES \"cnpy.h\" DESTINATION include)\ninstall(FILES \"mat2npz\" \"npy2mat\" \"npz2mat\" DESTINATION bin PERMISSIONS OWNER_READ OWNER_WRITE OWNER_EXECUTE GROUP_READ GROUP_EXECUTE WORLD_READ WORLD_EXECUTE)\n\nadd_executable(example1 example1.cpp)\ntarget_link_libraries(example1 cnpy)\n"
  },
  {
    "path": "fast_reid/projects/FastRT/third_party/cnpy/LICENSE",
    "content": "The MIT License\n\nCopyright (c) Carl Rogers, 2011\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n"
  },
  {
    "path": "fast_reid/projects/FastRT/third_party/cnpy/README.md",
    "content": "# Purpose:\n\nNumPy offers the `save` method for easy saving of arrays into .npy and `savez` for zipping multiple .npy arrays together into a .npz file. \n\n`cnpy` lets you read and write to these formats in C++. \n\nThe motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.\n\nWriting to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size. \nThe .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.\n\nLoading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.\n\n# Installation:\n\nDefault installation directory is /usr/local. \nTo specify a different directory, add `-DCMAKE_INSTALL_PREFIX=/path/to/install/dir` to the cmake invocation in step 4.\n\n1. get [cmake](www.cmake.org)\n2. create a build directory, say $HOME/build\n3. cd $HOME/build\n4. cmake /path/to/cnpy\n5. make\n6. make install\n\n# Using:\n\nTo use, `#include\"cnpy.h\"` in your source code. Compile the source code mycode.cpp as\n\n```bash\ng++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz --std=c++11\n```\n\n# Description:\n\nThere are two functions for writing data: `npy_save` and `npz_save`.\n\nThere are 3 functions for reading:\n- `npy_load` will load a .npy file. \n- `npz_load(fname)` will load a .npz and return a dictionary of NpyArray structues. \n- `npz_load(fname,varname)` will load and return the NpyArray for data varname from the specified .npz file.\n\nThe data structure for loaded data is below. \nData is accessed via the `data<T>()`-method, which returns a pointer of the specified type (which must match the underlying datatype of the data). \nThe array shape and word size are read from the npy header.\n\n```c++\nstruct NpyArray {\n    std::vector<size_t> shape;\n    size_t word_size;\n    template<typename T> T* data();\n};\n```\n\nSee [example1.cpp](example1.cpp) for examples of how to use the library. example1 will also be build during cmake installation.\n"
  },
  {
    "path": "fast_reid/projects/FastRT/third_party/cnpy/cnpy.cpp",
    "content": "//Copyright (C) 2011  Carl Rogers\n//Released under MIT License\n//license available in LICENSE file, or at http://www.opensource.org/licenses/mit-license.php\n\n#include\"cnpy.h\"\n#include<complex>\n#include<cstdlib>\n#include<algorithm>\n#include<cstring>\n#include<iomanip>\n#include<stdint.h>\n#include<stdexcept>\n#include <regex>\n\nchar cnpy::BigEndianTest() {\n    int x = 1;\n    return (((char *)&x)[0]) ? '<' : '>';\n}\n\nchar cnpy::map_type(const std::type_info& t)\n{\n    if(t == typeid(float) ) return 'f';\n    if(t == typeid(double) ) return 'f';\n    if(t == typeid(long double) ) return 'f';\n\n    if(t == typeid(int) ) return 'i';\n    if(t == typeid(char) ) return 'i';\n    if(t == typeid(short) ) return 'i';\n    if(t == typeid(long) ) return 'i';\n    if(t == typeid(long long) ) return 'i';\n\n    if(t == typeid(unsigned char) ) return 'u';\n    if(t == typeid(unsigned short) ) return 'u';\n    if(t == typeid(unsigned long) ) return 'u';\n    if(t == typeid(unsigned long long) ) return 'u';\n    if(t == typeid(unsigned int) ) return 'u';\n\n    if(t == typeid(bool) ) return 'b';\n\n    if(t == typeid(std::complex<float>) ) return 'c';\n    if(t == typeid(std::complex<double>) ) return 'c';\n    if(t == typeid(std::complex<long double>) ) return 'c';\n\n    else return '?';\n}\n\ntemplate<> std::vector<char>& cnpy::operator+=(std::vector<char>& lhs, const std::string rhs) {\n    lhs.insert(lhs.end(),rhs.begin(),rhs.end());\n    return lhs;\n}\n\ntemplate<> std::vector<char>& cnpy::operator+=(std::vector<char>& lhs, const char* rhs) {\n    //write in little endian\n    size_t len = strlen(rhs);\n    lhs.reserve(len);\n    for(size_t byte = 0; byte < len; byte++) {\n        lhs.push_back(rhs[byte]);\n    }\n    return lhs;\n}\n\nvoid cnpy::parse_npy_header(unsigned char* buffer,size_t& word_size, std::vector<size_t>& shape, bool& fortran_order) {\n    //std::string magic_string(buffer,6);\n    uint8_t major_version = *reinterpret_cast<uint8_t*>(buffer+6);\n    uint8_t minor_version = *reinterpret_cast<uint8_t*>(buffer+7);\n    uint16_t header_len = *reinterpret_cast<uint16_t*>(buffer+8);\n    std::string header(reinterpret_cast<char*>(buffer+9),header_len);\n\n    size_t loc1, loc2;\n\n    //fortran order\n    loc1 = header.find(\"fortran_order\")+16;\n    fortran_order = (header.substr(loc1,4) == \"True\" ? true : false);\n\n    //shape\n    loc1 = header.find(\"(\");\n    loc2 = header.find(\")\");\n\n    std::regex num_regex(\"[0-9][0-9]*\");\n    std::smatch sm;\n    shape.clear();\n\n    std::string str_shape = header.substr(loc1+1,loc2-loc1-1);\n    while(std::regex_search(str_shape, sm, num_regex)) {\n        shape.push_back(std::stoi(sm[0].str()));\n        str_shape = sm.suffix().str();\n    }\n\n    //endian, word size, data type\n    //byte order code | stands for not applicable. \n    //not sure when this applies except for byte array\n    loc1 = header.find(\"descr\")+9;\n    bool littleEndian = (header[loc1] == '<' || header[loc1] == '|' ? true : false);\n    assert(littleEndian);\n\n    //char type = header[loc1+1];\n    //assert(type == map_type(T));\n\n    std::string str_ws = header.substr(loc1+2);\n    loc2 = str_ws.find(\"'\");\n    word_size = atoi(str_ws.substr(0,loc2).c_str());\n}\n\nvoid cnpy::parse_npy_header(FILE* fp, size_t& word_size, std::vector<size_t>& shape, bool& fortran_order) {  \n    char buffer[256];\n    size_t res = fread(buffer,sizeof(char),11,fp);       \n    if(res != 11)\n        throw std::runtime_error(\"parse_npy_header: failed fread\");\n    std::string header = fgets(buffer,256,fp);\n    assert(header[header.size()-1] == '\\n');\n\n    size_t loc1, loc2;\n\n    //fortran order\n    loc1 = header.find(\"fortran_order\");\n    if (loc1 == std::string::npos)\n        throw std::runtime_error(\"parse_npy_header: failed to find header keyword: 'fortran_order'\");\n    loc1 += 16;\n    fortran_order = (header.substr(loc1,4) == \"True\" ? true : false);\n\n    //shape\n    loc1 = header.find(\"(\");\n    loc2 = header.find(\")\");\n    if (loc1 == std::string::npos || loc2 == std::string::npos)\n        throw std::runtime_error(\"parse_npy_header: failed to find header keyword: '(' or ')'\");\n\n    std::regex num_regex(\"[0-9][0-9]*\");\n    std::smatch sm;\n    shape.clear();\n\n    std::string str_shape = header.substr(loc1+1,loc2-loc1-1);\n    while(std::regex_search(str_shape, sm, num_regex)) {\n        shape.push_back(std::stoi(sm[0].str()));\n        str_shape = sm.suffix().str();\n    }\n\n    //endian, word size, data type\n    //byte order code | stands for not applicable. \n    //not sure when this applies except for byte array\n    loc1 = header.find(\"descr\");\n    if (loc1 == std::string::npos)\n        throw std::runtime_error(\"parse_npy_header: failed to find header keyword: 'descr'\");\n    loc1 += 9;\n    bool littleEndian = (header[loc1] == '<' || header[loc1] == '|' ? true : false);\n    assert(littleEndian);\n\n    //char type = header[loc1+1];\n    //assert(type == map_type(T));\n\n    std::string str_ws = header.substr(loc1+2);\n    loc2 = str_ws.find(\"'\");\n    word_size = atoi(str_ws.substr(0,loc2).c_str());\n}\n\nvoid cnpy::parse_zip_footer(FILE* fp, uint16_t& nrecs, size_t& global_header_size, size_t& global_header_offset)\n{\n    std::vector<char> footer(22);\n    fseek(fp,-22,SEEK_END);\n    size_t res = fread(&footer[0],sizeof(char),22,fp);\n    if(res != 22)\n        throw std::runtime_error(\"parse_zip_footer: failed fread\");\n\n    uint16_t disk_no, disk_start, nrecs_on_disk, comment_len;\n    disk_no = *(uint16_t*) &footer[4];\n    disk_start = *(uint16_t*) &footer[6];\n    nrecs_on_disk = *(uint16_t*) &footer[8];\n    nrecs = *(uint16_t*) &footer[10];\n    global_header_size = *(uint32_t*) &footer[12];\n    global_header_offset = *(uint32_t*) &footer[16];\n    comment_len = *(uint16_t*) &footer[20];\n\n    assert(disk_no == 0);\n    assert(disk_start == 0);\n    assert(nrecs_on_disk == nrecs);\n    assert(comment_len == 0);\n}\n\ncnpy::NpyArray load_the_npy_file(FILE* fp) {\n    std::vector<size_t> shape;\n    size_t word_size;\n    bool fortran_order;\n    cnpy::parse_npy_header(fp,word_size,shape,fortran_order);\n\n    cnpy::NpyArray arr(shape, word_size, fortran_order);\n    size_t nread = fread(arr.data<char>(),1,arr.num_bytes(),fp);\n    if(nread != arr.num_bytes())\n        throw std::runtime_error(\"load_the_npy_file: failed fread\");\n    return arr;\n}\n\ncnpy::NpyArray load_the_npz_array(FILE* fp, uint32_t compr_bytes, uint32_t uncompr_bytes) {\n\n    std::vector<unsigned char> buffer_compr(compr_bytes);\n    std::vector<unsigned char> buffer_uncompr(uncompr_bytes);\n    size_t nread = fread(&buffer_compr[0],1,compr_bytes,fp);\n    if(nread != compr_bytes)\n        throw std::runtime_error(\"load_the_npy_file: failed fread\");\n\n    int err;\n    z_stream d_stream;\n\n    d_stream.zalloc = Z_NULL;\n    d_stream.zfree = Z_NULL;\n    d_stream.opaque = Z_NULL;\n    d_stream.avail_in = 0;\n    d_stream.next_in = Z_NULL;\n    err = inflateInit2(&d_stream, -MAX_WBITS);\n\n    d_stream.avail_in = compr_bytes;\n    d_stream.next_in = &buffer_compr[0];\n    d_stream.avail_out = uncompr_bytes;\n    d_stream.next_out = &buffer_uncompr[0];\n\n    err = inflate(&d_stream, Z_FINISH);\n    err = inflateEnd(&d_stream);\n\n    std::vector<size_t> shape;\n    size_t word_size;\n    bool fortran_order;\n    cnpy::parse_npy_header(&buffer_uncompr[0],word_size,shape,fortran_order);\n\n    cnpy::NpyArray array(shape, word_size, fortran_order);\n\n    size_t offset = uncompr_bytes - array.num_bytes();\n    memcpy(array.data<unsigned char>(),&buffer_uncompr[0]+offset,array.num_bytes());\n\n    return array;\n}\n\ncnpy::npz_t cnpy::npz_load(std::string fname) {\n    FILE* fp = fopen(fname.c_str(),\"rb\");\n\n    if(!fp) {\n        throw std::runtime_error(\"npz_load: Error! Unable to open file \"+fname+\"!\");\n    }\n\n    cnpy::npz_t arrays;  \n\n    while(1) {\n        std::vector<char> local_header(30);\n        size_t headerres = fread(&local_header[0],sizeof(char),30,fp);\n        if(headerres != 30)\n            throw std::runtime_error(\"npz_load: failed fread\");\n\n        //if we've reached the global header, stop reading\n        if(local_header[2] != 0x03 || local_header[3] != 0x04) break;\n\n        //read in the variable name\n        uint16_t name_len = *(uint16_t*) &local_header[26];\n        std::string varname(name_len,' ');\n        size_t vname_res = fread(&varname[0],sizeof(char),name_len,fp);\n        if(vname_res != name_len)\n            throw std::runtime_error(\"npz_load: failed fread\");\n\n        //erase the lagging .npy        \n        varname.erase(varname.end()-4,varname.end());\n\n        //read in the extra field\n        uint16_t extra_field_len = *(uint16_t*) &local_header[28];\n        if(extra_field_len > 0) {\n            std::vector<char> buff(extra_field_len);\n            size_t efield_res = fread(&buff[0],sizeof(char),extra_field_len,fp);\n            if(efield_res != extra_field_len)\n                throw std::runtime_error(\"npz_load: failed fread\");\n        }\n\n        uint16_t compr_method = *reinterpret_cast<uint16_t*>(&local_header[0]+8);\n        uint32_t compr_bytes = *reinterpret_cast<uint32_t*>(&local_header[0]+18);\n        uint32_t uncompr_bytes = *reinterpret_cast<uint32_t*>(&local_header[0]+22);\n\n        if(compr_method == 0) {arrays[varname] = load_the_npy_file(fp);}\n        else {arrays[varname] = load_the_npz_array(fp,compr_bytes,uncompr_bytes);}\n    }\n\n    fclose(fp);\n    return arrays;  \n}\n\ncnpy::NpyArray cnpy::npz_load(std::string fname, std::string varname) {\n    FILE* fp = fopen(fname.c_str(),\"rb\");\n\n    if(!fp) throw std::runtime_error(\"npz_load: Unable to open file \"+fname);\n\n    while(1) {\n        std::vector<char> local_header(30);\n        size_t header_res = fread(&local_header[0],sizeof(char),30,fp);\n        if(header_res != 30)\n            throw std::runtime_error(\"npz_load: failed fread\");\n\n        //if we've reached the global header, stop reading\n        if(local_header[2] != 0x03 || local_header[3] != 0x04) break;\n\n        //read in the variable name\n        uint16_t name_len = *(uint16_t*) &local_header[26];\n        std::string vname(name_len,' ');\n        size_t vname_res = fread(&vname[0],sizeof(char),name_len,fp);      \n        if(vname_res != name_len)\n            throw std::runtime_error(\"npz_load: failed fread\");\n        vname.erase(vname.end()-4,vname.end()); //erase the lagging .npy\n\n        //read in the extra field\n        uint16_t extra_field_len = *(uint16_t*) &local_header[28];\n        fseek(fp,extra_field_len,SEEK_CUR); //skip past the extra field\n        \n        uint16_t compr_method = *reinterpret_cast<uint16_t*>(&local_header[0]+8);\n        uint32_t compr_bytes = *reinterpret_cast<uint32_t*>(&local_header[0]+18);\n        uint32_t uncompr_bytes = *reinterpret_cast<uint32_t*>(&local_header[0]+22);\n\n        if(vname == varname) {\n            NpyArray array  = (compr_method == 0) ? load_the_npy_file(fp) : load_the_npz_array(fp,compr_bytes,uncompr_bytes);\n            fclose(fp);\n            return array;\n        }\n        else {\n            //skip past the data\n            uint32_t size = *(uint32_t*) &local_header[22];\n            fseek(fp,size,SEEK_CUR);\n        }\n    }\n\n    fclose(fp);\n\n    //if we get here, we haven't found the variable in the file\n    throw std::runtime_error(\"npz_load: Variable name \"+varname+\" not found in \"+fname);\n}\n\ncnpy::NpyArray cnpy::npy_load(std::string fname) {\n\n    FILE* fp = fopen(fname.c_str(), \"rb\");\n\n    if(!fp) throw std::runtime_error(\"npy_load: Unable to open file \"+fname);\n\n    NpyArray arr = load_the_npy_file(fp);\n\n    fclose(fp);\n    return arr;\n}\n\n\n\n"
  },
  {
    "path": "fast_reid/projects/FastRT/third_party/cnpy/cnpy.h",
    "content": "//Copyright (C) 2011  Carl Rogers\n//Released under MIT License\n//license available in LICENSE file, or at http://www.opensource.org/licenses/mit-license.php\n\n#ifndef LIBCNPY_H_\n#define LIBCNPY_H_\n\n#include<string>\n#include<stdexcept>\n#include<sstream>\n#include<vector>\n#include<cstdio>\n#include<typeinfo>\n#include<iostream>\n#include<cassert>\n#include<zlib.h>\n#include<map>\n#include<memory>\n#include<stdint.h>\n#include<numeric>\n\nnamespace cnpy {\n\n    struct NpyArray {\n        NpyArray(const std::vector<size_t>& _shape, size_t _word_size, bool _fortran_order) :\n            shape(_shape), word_size(_word_size), fortran_order(_fortran_order)\n        {\n            num_vals = 1;\n            for(size_t i = 0;i < shape.size();i++) num_vals *= shape[i];\n            data_holder = std::shared_ptr<std::vector<char>>(\n                new std::vector<char>(num_vals * word_size));\n        }\n\n        NpyArray() : shape(0), word_size(0), fortran_order(0), num_vals(0) { }\n\n        template<typename T>\n        T* data() {\n            return reinterpret_cast<T*>(&(*data_holder)[0]);\n        }\n\n        template<typename T>\n        const T* data() const {\n            return reinterpret_cast<T*>(&(*data_holder)[0]);\n        }\n\n        template<typename T>\n        std::vector<T> as_vec() const {\n            const T* p = data<T>();\n            return std::vector<T>(p, p+num_vals);\n        }\n\n        size_t num_bytes() const {\n            return data_holder->size();\n        }\n\n        std::shared_ptr<std::vector<char>> data_holder;\n        std::vector<size_t> shape;\n        size_t word_size;\n        bool fortran_order;\n        size_t num_vals;\n    };\n   \n    using npz_t = std::map<std::string, NpyArray>; \n\n    char BigEndianTest();\n    char map_type(const std::type_info& t);\n    template<typename T> std::vector<char> create_npy_header(const std::vector<size_t>& shape);\n    void parse_npy_header(FILE* fp,size_t& word_size, std::vector<size_t>& shape, bool& fortran_order);\n    void parse_npy_header(unsigned char* buffer,size_t& word_size, std::vector<size_t>& shape, bool& fortran_order);\n    void parse_zip_footer(FILE* fp, uint16_t& nrecs, size_t& global_header_size, size_t& global_header_offset);\n    npz_t npz_load(std::string fname);\n    NpyArray npz_load(std::string fname, std::string varname);\n    NpyArray npy_load(std::string fname);\n\n    template<typename T> std::vector<char>& operator+=(std::vector<char>& lhs, const T rhs) {\n        //write in little endian\n        for(size_t byte = 0; byte < sizeof(T); byte++) {\n            char val = *((char*)&rhs+byte); \n            lhs.push_back(val);\n        }\n        return lhs;\n    }\n\n    template<> std::vector<char>& operator+=(std::vector<char>& lhs, const std::string rhs);\n    template<> std::vector<char>& operator+=(std::vector<char>& lhs, const char* rhs);\n\n\n    template<typename T> void npy_save(std::string fname, const T* data, const std::vector<size_t> shape, std::string mode = \"w\") {\n        FILE* fp = NULL;\n        std::vector<size_t> true_data_shape; //if appending, the shape of existing + new data\n\n        if(mode == \"a\") fp = fopen(fname.c_str(),\"r+b\");\n\n        if(fp) {\n            //file exists. we need to append to it. read the header, modify the array size\n            size_t word_size;\n            bool fortran_order;\n            parse_npy_header(fp,word_size,true_data_shape,fortran_order);\n            assert(!fortran_order);\n\n            if(word_size != sizeof(T)) {\n                std::cout<<\"libnpy error: \"<<fname<<\" has word size \"<<word_size<<\" but npy_save appending data sized \"<<sizeof(T)<<\"\\n\";\n                assert( word_size == sizeof(T) );\n            }\n            if(true_data_shape.size() != shape.size()) {\n                std::cout<<\"libnpy error: npy_save attempting to append misdimensioned data to \"<<fname<<\"\\n\";\n                assert(true_data_shape.size() != shape.size());\n            }\n\n            for(size_t i = 1; i < shape.size(); i++) {\n                if(shape[i] != true_data_shape[i]) {\n                    std::cout<<\"libnpy error: npy_save attempting to append misshaped data to \"<<fname<<\"\\n\";\n                    assert(shape[i] == true_data_shape[i]);\n                }\n            }\n            true_data_shape[0] += shape[0];\n        }\n        else {\n            fp = fopen(fname.c_str(),\"wb\");\n            true_data_shape = shape;\n        }\n\n        std::vector<char> header = create_npy_header<T>(true_data_shape);\n        size_t nels = std::accumulate(shape.begin(),shape.end(),1,std::multiplies<size_t>());\n\n        fseek(fp,0,SEEK_SET);\n        fwrite(&header[0],sizeof(char),header.size(),fp);\n        fseek(fp,0,SEEK_END);\n        fwrite(data,sizeof(T),nels,fp);\n        fclose(fp);\n    }\n\n    template<typename T> void npz_save(std::string zipname, std::string fname, const T* data, const std::vector<size_t>& shape, std::string mode = \"w\")\n    {\n        //first, append a .npy to the fname\n        fname += \".npy\";\n\n        //now, on with the show\n        FILE* fp = NULL;\n        uint16_t nrecs = 0;\n        size_t global_header_offset = 0;\n        std::vector<char> global_header;\n\n        if(mode == \"a\") fp = fopen(zipname.c_str(),\"r+b\");\n\n        if(fp) {\n            //zip file exists. we need to add a new npy file to it.\n            //first read the footer. this gives us the offset and size of the global header\n            //then read and store the global header.\n            //below, we will write the the new data at the start of the global header then append the global header and footer below it\n            size_t global_header_size;\n            parse_zip_footer(fp,nrecs,global_header_size,global_header_offset);\n            fseek(fp,global_header_offset,SEEK_SET);\n            global_header.resize(global_header_size);\n            size_t res = fread(&global_header[0],sizeof(char),global_header_size,fp);\n            if(res != global_header_size){\n                throw std::runtime_error(\"npz_save: header read error while adding to existing zip\");\n            }\n            fseek(fp,global_header_offset,SEEK_SET);\n        }\n        else {\n            fp = fopen(zipname.c_str(),\"wb\");\n        }\n\n        std::vector<char> npy_header = create_npy_header<T>(shape);\n\n        size_t nels = std::accumulate(shape.begin(),shape.end(),1,std::multiplies<size_t>());\n        size_t nbytes = nels*sizeof(T) + npy_header.size();\n\n        //get the CRC of the data to be added\n        uint32_t crc = crc32(0L,(uint8_t*)&npy_header[0],npy_header.size());\n        crc = crc32(crc,(uint8_t*)data,nels*sizeof(T));\n\n        //build the local header\n        std::vector<char> local_header;\n        local_header += \"PK\"; //first part of sig\n        local_header += (uint16_t) 0x0403; //second part of sig\n        local_header += (uint16_t) 20; //min version to extract\n        local_header += (uint16_t) 0; //general purpose bit flag\n        local_header += (uint16_t) 0; //compression method\n        local_header += (uint16_t) 0; //file last mod time\n        local_header += (uint16_t) 0;     //file last mod date\n        local_header += (uint32_t) crc; //crc\n        local_header += (uint32_t) nbytes; //compressed size\n        local_header += (uint32_t) nbytes; //uncompressed size\n        local_header += (uint16_t) fname.size(); //fname length\n        local_header += (uint16_t) 0; //extra field length\n        local_header += fname;\n\n        //build global header\n        global_header += \"PK\"; //first part of sig\n        global_header += (uint16_t) 0x0201; //second part of sig\n        global_header += (uint16_t) 20; //version made by\n        global_header.insert(global_header.end(),local_header.begin()+4,local_header.begin()+30);\n        global_header += (uint16_t) 0; //file comment length\n        global_header += (uint16_t) 0; //disk number where file starts\n        global_header += (uint16_t) 0; //internal file attributes\n        global_header += (uint32_t) 0; //external file attributes\n        global_header += (uint32_t) global_header_offset; //relative offset of local file header, since it begins where the global header used to begin\n        global_header += fname;\n\n        //build footer\n        std::vector<char> footer;\n        footer += \"PK\"; //first part of sig\n        footer += (uint16_t) 0x0605; //second part of sig\n        footer += (uint16_t) 0; //number of this disk\n        footer += (uint16_t) 0; //disk where footer starts\n        footer += (uint16_t) (nrecs+1); //number of records on this disk\n        footer += (uint16_t) (nrecs+1); //total number of records\n        footer += (uint32_t) global_header.size(); //nbytes of global headers\n        footer += (uint32_t) (global_header_offset + nbytes + local_header.size()); //offset of start of global headers, since global header now starts after newly written array\n        footer += (uint16_t) 0; //zip file comment length\n\n        //write everything\n        fwrite(&local_header[0],sizeof(char),local_header.size(),fp);\n        fwrite(&npy_header[0],sizeof(char),npy_header.size(),fp);\n        fwrite(data,sizeof(T),nels,fp);\n        fwrite(&global_header[0],sizeof(char),global_header.size(),fp);\n        fwrite(&footer[0],sizeof(char),footer.size(),fp);\n        fclose(fp);\n    }\n\n    template<typename T> void npy_save(std::string fname, const std::vector<T> data, std::string mode = \"w\") {\n        std::vector<size_t> shape;\n        shape.push_back(data.size());\n        npy_save(fname, &data[0], shape, mode);\n    }\n\n    template<typename T> void npz_save(std::string zipname, std::string fname, const std::vector<T> data, std::string mode = \"w\") {\n        std::vector<size_t> shape;\n        shape.push_back(data.size());\n        npz_save(zipname, fname, &data[0], shape, mode);\n    }\n\n    template<typename T> std::vector<char> create_npy_header(const std::vector<size_t>& shape) {  \n\n        std::vector<char> dict;\n        dict += \"{'descr': '\";\n        dict += BigEndianTest();\n        dict += map_type(typeid(T));\n        dict += std::to_string(sizeof(T));\n        dict += \"', 'fortran_order': False, 'shape': (\";\n        dict += std::to_string(shape[0]);\n        for(size_t i = 1;i < shape.size();i++) {\n            dict += \", \";\n            dict += std::to_string(shape[i]);\n        }\n        if(shape.size() == 1) dict += \",\";\n        dict += \"), }\";\n        //pad with spaces so that preamble+dict is modulo 16 bytes. preamble is 10 bytes. dict needs to end with \\n\n        int remainder = 16 - (10 + dict.size()) % 16;\n        dict.insert(dict.end(),remainder,' ');\n        dict.back() = '\\n';\n\n        std::vector<char> header;\n        header += (char) 0x93;\n        header += \"NUMPY\";\n        header += (char) 0x01; //major version of numpy format\n        header += (char) 0x00; //minor version of numpy format\n        header += (uint16_t) dict.size();\n        header.insert(header.end(),dict.begin(),dict.end());\n\n        return header;\n    }\n\n\n}\n\n#endif\n"
  },
  {
    "path": "fast_reid/projects/FastRT/third_party/cnpy/example1.cpp",
    "content": "#include\"cnpy.h\"\n#include<complex>\n#include<cstdlib>\n#include<iostream>\n#include<map>\n#include<string>\n\nconst int Nx = 128;\nconst int Ny = 64;\nconst int Nz = 32;\n\nint main()\n{\n    //set random seed so that result is reproducible (for testing)\n    srand(0);\n    //create random data\n    std::vector<std::complex<double>> data(Nx*Ny*Nz);\n    for(int i = 0;i < Nx*Ny*Nz;i++) data[i] = std::complex<double>(rand(),rand());\n\n    //save it to file\n    cnpy::npy_save(\"arr1.npy\",&data[0],{Nz,Ny,Nx},\"w\");\n\n    //load it into a new array\n    cnpy::NpyArray arr = cnpy::npy_load(\"arr1.npy\");\n    std::complex<double>* loaded_data = arr.data<std::complex<double>>();\n    \n    //make sure the loaded data matches the saved data\n    assert(arr.word_size == sizeof(std::complex<double>));\n    assert(arr.shape.size() == 3 && arr.shape[0] == Nz && arr.shape[1] == Ny && arr.shape[2] == Nx);\n    for(int i = 0; i < Nx*Ny*Nz;i++) assert(data[i] == loaded_data[i]);\n\n    //append the same data to file\n    //npy array on file now has shape (Nz+Nz,Ny,Nx)\n    cnpy::npy_save(\"arr1.npy\",&data[0],{Nz,Ny,Nx},\"a\");\n\n    //now write to an npz file\n    //non-array variables are treated as 1D arrays with 1 element\n    double myVar1 = 1.2;\n    char myVar2 = 'a';\n    cnpy::npz_save(\"out.npz\",\"myVar1\",&myVar1,{1},\"w\"); //\"w\" overwrites any existing file\n    cnpy::npz_save(\"out.npz\",\"myVar2\",&myVar2,{1},\"a\"); //\"a\" appends to the file we created above\n    cnpy::npz_save(\"out.npz\",\"arr1\",&data[0],{Nz,Ny,Nx},\"a\"); //\"a\" appends to the file we created above\n\n    //load a single var from the npz file\n    cnpy::NpyArray arr2 = cnpy::npz_load(\"out.npz\",\"arr1\");\n\n    //load the entire npz file\n    cnpy::npz_t my_npz = cnpy::npz_load(\"out.npz\");\n    \n    //check that the loaded myVar1 matches myVar1\n    cnpy::NpyArray arr_mv1 = my_npz[\"myVar1\"];\n    double* mv1 = arr_mv1.data<double>();\n    assert(arr_mv1.shape.size() == 1 && arr_mv1.shape[0] == 1);\n    assert(mv1[0] == myVar1);\n}\n"
  },
  {
    "path": "fast_reid/projects/FastRT/third_party/cnpy/mat2npz",
    "content": "#!/usr/bin/env python\n\nimport sys\nfrom numpy import savez\nfrom scipy.io import loadmat\n\nassert len(sys.argv) > 1\n\nfiles = sys.argv[1:]\n\nfor f in files:\n    mat_vars = loadmat(f)\n    mat_vars.pop('__version__')\n    mat_vars.pop('__header__')\n    mat_vars.pop('__globals__')\n\n    fn = f.replace('.mat','.npz')\n    savez(fn,**mat_vars)\n"
  },
  {
    "path": "fast_reid/projects/FastRT/third_party/cnpy/npy2mat",
    "content": "#!/usr/bin/env python\n\nimport sys\nfrom numpy import load\nfrom scipy.io import savemat\n\nassert len(sys.argv) > 1\n\nfiles = sys.argv[1:]\n\nfor f in files:\n   data = load(f)\n   fn = f.replace('.npy','')\n   fn = fn.replace('.','_')\n   savemat(fn,{fn : data})\n"
  },
  {
    "path": "fast_reid/projects/FastRT/third_party/cnpy/npz2mat",
    "content": "#!/usr/bin/env python\n\nimport sys\nfrom numpy import load\nfrom scipy.io import savemat\n\nassert len(sys.argv) > 1\n\nfiles = sys.argv[1:]\n\nfor f in files:\n   data = load(f)\n   fn = f.replace('.npz','')\n   fn = fn.replace('.','_') #matlab cant handle dots\n   savemat(fn,data)\n"
  },
  {
    "path": "fast_reid/projects/FastRT/tools/How_to_Generate.md",
    "content": "# Fastreid Model Deployment\n\nThe `gen_wts.py` script convert a fastreid model to [.wts format](https://github.com/wang-xinyu/tensorrtx/blob/master/tutorials/getting_started.md#the-wts-content-format) file, then it will be used in [FastRT](https://github.com/JDAI-CV/fast-reid/blob/master/projects/FastRT) directly. \n\n### Convert Environment\n\n* Same as fastreid.\n    \n### How to Generate\n\nThis is a general example for converting fastreid to TensorRT model. We use `FastRT` to build the model with nvidia TensorRT APIs.\n\nIn this part you need to convert the pytorch model to '.wts' file using `gen_wts.py` follow instructions below.\n\n1. Run command line below to generate the '.wts' file from pytorch model\n   \n   It's similar to how you use fastreid.\n    ```bash\n    python projects/FastRT/tools/gen_wts.py --config-file='config/you/use/in/fastreid/xxx.yml' \\\n    --verify --show_model --wts_path='outputs/trt_model_file/xxx.wts' \\\n    MODEL.WEIGHTS '/path/to/checkpoint_file/model_best.pth' MODEL.DEVICE \"cuda:0\"\n    ```\n\n    then you can check the TensorRT model weights `outputs/trt_model_file/xxx.wts`.\n\n3. Copy the `outputs/trt_model_file/xxx.wts` to [FastRT](https://github.com/JDAI-CV/fast-reid/blob/master/projects/FastRT)\n\n\n### More convert examples\n\n+ Ex1. `sbs_R50-ibn`\n    - [x] resnet50, ibn, non-local, gempoolp\n    ```bash\n    python projects/FastRT/tools/gen_wts.py --config-file='configs/DukeMTMC/sbs_R50-ibn.yml' \\\n    --verify --show_model --wts_path='outputs/trt_model_file/sbs_R50-ibn.wts' \\\n    MODEL.WEIGHTS '/path/to/checkpoint_file/model_best.pth' MODEL.DEVICE \"cuda:0\"\n    ```\n    \n+ Ex2. `sbs_R50`\n    - [x] resnet50, gempoolp   \n    ```bash\n    python projects/FastRT/tools/gen_wts.py --config-file='configs/DukeMTMC/sbs_R50.yml' \\\n    --verify --show_model --wts_path='outputs/trt_model_file/sbs_R50.wts' \\\n    MODEL.WEIGHTS '/path/to/checkpoint_file/model_best.pth' MODEL.DEVICE \"cuda:0\"\n    ``` \n    \n* Ex3. `sbs_r34_distill`\n    - [x] train-alone distill-r34 (hint: distill-resnet is slightly different from resnet34), gempoolp\n    ```bash\n    python projects/FastRT/tools/gen_wts.py --config-file='projects/FastDistill/configs/sbs_r34.yml' \\\n    --verify --show_model --wts_path='outputs/to/trt_model_file/sbs_r34_distill.wts' \\\n    MODEL.WEIGHTS '/path/to/checkpoint_file/model_best.pth' MODEL.DEVICE \"cuda:0\"\n    ```\n\n* Ex4.`kd-r34-r101_ibn`\n    - [x] teacher model(r101_ibn), student model(distill-r34). the one for deploying is student model, gempoolp\n    ```bash\n    python projects/FastRT/tools/gen_wts.py --config-file='projects/FastDistill/configs/kd-sbs_r101ibn-sbs_r34.yml' \\\n    --verify --show_model --wts_path='outputs/to/trt_model_file/kd_r34_distill.wts' \\\n    MODEL.WEIGHTS '/path/to/checkpoint_file/model_best.pth' MODEL.DEVICE \"cuda:0\"\n    ```\n\n## Acknowledgements\n\nThanks to [tensorrtx](https://github.com/wang-xinyu/tensorrtx) for demonstrating the usage of trt network definition APIs.\n\n"
  },
  {
    "path": "fast_reid/projects/FastRT/tools/gen_wts.py",
    "content": "# encoding: utf-8\n\nimport sys\nimport time\nimport struct\nimport argparse\nsys.path.append('.')\n\nimport torch\nimport torchvision\n#from torchsummary import summary\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.modeling.meta_arch import build_model\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\n\nsys.path.append('./projects/FastDistill')\nfrom fastdistill import *\n\ndef setup_cfg(args):\n    # load confiimport argparseg from file and command-line arguments\n    cfg = get_cfg()\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    return cfg\n\ndef get_parser():\n    parser = argparse.ArgumentParser(description=\"Encode pytorch weights for tensorrt.\")\n    parser.add_argument(\n        \"--config-file\",\n        metavar=\"FILE\",\n        help=\"path to config file\",\n    )\n    parser.add_argument(\n        \"--wts_path\",\n        default='./trt_demo',\n        help='path to save tensorrt weights file(.wts)'\n    )\n    parser.add_argument(\n        \"--show_model\",\n        action='store_true',\n        help='print model architecture'\n    )\n    parser.add_argument(\n        \"--verify\",\n        action='store_true',\n        help='print model output for verify'\n    )\n    parser.add_argument(\n        \"--benchmark\",\n        action='store_true',\n        help='preprocessing + inference time'\n    )\n    parser.add_argument(\n        \"opts\",\n        help=\"Modify config options using the command-line 'KEY VALUE' pairs\",\n        default=[],\n        nargs=argparse.REMAINDER,\n    )\n    return parser\n\ndef gen_wts(args):\n    \"\"\"\n        Thanks to https://github.com/wang-xinyu/tensorrtx\n    \"\"\"\n    print(\"Wait for it: {} ...\".format(args.wts_path))\n    f = open(args.wts_path, 'w')\n    f.write(\"{}\\n\".format(len(model.state_dict().keys())))\n    for k,v in model.state_dict().items():\n        #print('key: ', k)\n        #print('value: ', v.shape)     \n        vr = v.reshape(-1).cpu().numpy()\n        f.write(\"{} {}\".format(k, len(vr)))\n        for vv in vr:\n            f.write(\" \")\n            f.write(struct.pack(\">f\", float(vv)).hex())\n        f.write(\"\\n\")\n        \nif __name__ == '__main__':\n    args = get_parser().parse_args()\n    cfg = setup_cfg(args)\n    cfg.MODEL.BACKBONE.PRETRAIN = False\n    print(\"[Config]: \\n\", cfg)\n    \n    model = build_model(cfg)\n    \n    if args.show_model:\n        print('[Model]: \\n', model)\n        #summary(model, (3, cfg.INPUT.SIZE_TEST[0], cfg.INPUT.SIZE_TEST[1]))\n    \n    print(\"Load model from: \", cfg.MODEL.WEIGHTS)\n    Checkpointer(model).load(cfg.MODEL.WEIGHTS)\n    \n    model = model.to(cfg.MODEL.DEVICE)\n    model.eval()\n    \n    if args.verify:\n        input = torch.ones(1, 3, cfg.INPUT.SIZE_TEST[0], cfg.INPUT.SIZE_TEST[1]).to(cfg.MODEL.DEVICE) * 255.\n        out = model(input).view(-1).cpu().detach().numpy()\n        print('[Model output]: \\n', out) \n        \n    if args.benchmark:\n        start_time = time.time()\n        input = torch.ones(1, 3, cfg.INPUT.SIZE_TEST[0], cfg.INPUT.SIZE_TEST[1]).to(cfg.MODEL.DEVICE) * 255.\n        for i in range(100):\n            out = model(input).view(-1).cpu().detach()\n        print(\"--- %s seconds ---\" % ((time.time() - start_time)/100.) )\n    \n    gen_wts(args)\n    "
  },
  {
    "path": "fast_reid/projects/FastRetri/README.md",
    "content": "# FastRetri in FastReID\n\nThis project provides a strong baseline for fine-grained image retrieval.\n\n## Datasets Preparation\n\nWe use `CUB200`, `CARS-196`, `Standford Online Products` and `In-Shop` to evaluate the model's performance.\nYou can do data management following [dml_cross_entropy](https://github.com/jeromerony/dml_cross_entropy) instruction.\n\n## Usage\n\nEach dataset's config file can be found in `projects/FastRetri/config`, which you can use to reproduce the results of the repo.\n\nFor example, if you want to train with `CUB200`, you can run an experiment with `cub.yml`\n\n```bash\npython3 projects/FastRetri/train_net.py --config-file projects/FastRetri/config/cub.yml --num-gpus 4\n```\n\n## Experiment Results\n\nWe refer to [A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses](arxiv.org/abs/2003.08983) as our baseline methods, and on top of it, we add some tricks, such as gem pooling.\nMore details can be found in the config file and code.\n\n### CUB\n\n| Method | Pretrained | Recall@1 | Recall@2 | Recall@4 | Recall@8 | Recall@16 | Recall@32 |\n| :---: | :---: | :---: |:---: | :---: | :---: | :---: | :---: |\n| [dml_cross_entropy](https://github.com/jeromerony/dml_cross_entropy) | ImageNet | 69.2 | 79.2 | 86.9 | 91.6 | 95.0 | 97.3 |\n| Fastretri | ImageNet | 69.46 | 79.57 | 87.53 | 92.61 | 95.75 | 97.35 |\n\n### Cars-196\n\n| Method | Pretrained | Recall@1 | Recall@2 | Recall@4 | Recall@8 | Recall@16 | Recall@32 |\n| :---: | :---: | :---: |:---: | :---: | :---: | :---: | :---: |\n| [dml_cross_entropy](https://github.com/jeromerony/dml_cross_entropy) | ImageNet | 89.3 | 93.9 | 96.6 | 98.4 | 99.3 | 99.7 |\n| Fastretri | ImageNet | 92.31 | 95.99 | 97.60 | 98.63 | 99.24 | 99.62 |\n\n### Standford Online Products\n\n| Method | Pretrained | Recall@1 | Recall@10 | Recall@100 | Recall@1000 |\n| :---: | :---: | :---: |:---: | :---: | :---: |\n| [dml_cross_entropy](https://github.com/jeromerony/dml_cross_entropy) | ImageNet | 81.1 | 91.7 | 96.3 | 98.8 |\n| Fastretri | ImageNet | 82.46 | 92.56 | 96.78 | 98.95 |\n\n### In-Shop\n\n| Method | Pretrained | Recall@1 | Recall@10 | Recall@20 | Recall@30 | Recall@40 | Recall@50 |\n| :---: | :---: | :---: |:---: | :---: | :---: | :---: | :---: |\n| [dml_cross_entropy](https://github.comjeromerony/dml_cross_entropy) | ImageNet | 90.6 | 98.0 | 98.6 | 98.9 | 99.1 | 99.2 |\n| Fastretri | ImageNet | 91.97 | 98.29 | 98.85 | 99.11 | 99.24 | 99.35 |\n\n"
  },
  {
    "path": "fast_reid/projects/FastRetri/configs/base-image_retri.yml",
    "content": "MODEL:\n  META_ARCHITECTURE: Baseline\n\n  BACKBONE:\n    NAME: build_resnet_backbone\n    DEPTH: 50x\n    NORM: FrozenBN\n    LAST_STRIDE: 1\n    FEAT_DIM: 2048\n    PRETRAIN: True\n\n  HEADS:\n    NAME: EmbeddingHead\n    NORM: syncBN\n    WITH_BNNECK: True\n    NECK_FEAT: after\n    EMBEDDING_DIM: 0\n    POOL_LAYER: GeneralizedMeanPooling\n    CLS_LAYER: Linear\n\n  LOSSES:\n    NAME: (\"CrossEntropyLoss\",)\n\n    CE:\n      EPSILON: 0.1\n      SCALE: 1.\n\nINPUT:\n  SIZE_TRAIN: [256, 256]\n  SIZE_TEST: [256, 256]\n\n  CROP:\n    ENABLED: True\n    SIZE: [224,]\n    SCALE: [0.16, 1.]\n    RATIO: [0.75, 1.33333]\n\n  FLIP:\n    ENABLED: True\n\n  CJ:\n    ENABLED: False\n    BRIGHTNESS: 0.3\n    CONTRAST: 0.3\n    SATURATION: 0.1\n    HUE: 0.1\n\n\nDATALOADER:\n  SAMPLER_TRAIN: TrainingSampler\n  NUM_WORKERS: 8\n\nSOLVER:\n  MAX_EPOCH: 100\n  AMP:\n    ENABLED: True\n\n  OPT: SGD\n  SCHED: CosineAnnealingLR\n\n  BASE_LR: 0.003\n  MOMENTUM: 0.99\n  NESTEROV: True\n\n  BIAS_LR_FACTOR: 1.\n  WEIGHT_DECAY: 0.0005\n  WEIGHT_DECAY_BIAS: 0.\n  IMS_PER_BATCH: 128\n\n  ETA_MIN_LR: 0.00003\n\n  WARMUP_FACTOR: 0.1\n  WARMUP_ITERS: 1000\n\n  CHECKPOINT_PERIOD: 10\n\n  CLIP_GRADIENTS:\n    ENABLED: True\n\nTEST:\n  EVAL_PERIOD: 10\n  IMS_PER_BATCH: 256\n\nCUDNN_BENCHMARK: True"
  },
  {
    "path": "fast_reid/projects/FastRetri/configs/cars.yml",
    "content": "_BASE_: base-image_retri.yml\n\nMODEL:\n  LOSSES:\n    CE:\n      EPSILON: 0.4\n\nINPUT:\n  CJ:\n    ENABLED: True\n    BRIGHTNESS: 0.3\n    CONTRAST: 0.3\n    SATURATION: 0.3\n    HUE: 0.1\n\n  CROP:\n    RATIO: (1., 1.)\n\nSOLVER:\n  MAX_EPOCH: 100\n\n  BASE_LR: 0.05\n  ETA_MIN_LR: 0.0005\n\n  NESTEROV: False\n  MOMENTUM: 0.\n\nTEST:\n  RECALLS: [ 1, 2, 4, 8, 16, 32 ]\n\nDATASETS:\n  NAMES: (\"Cars196\",)\n  TESTS: (\"Cars196\",)\n\nOUTPUT_DIR: projects/FastRetri/logs/r50-base_cars\n"
  },
  {
    "path": "fast_reid/projects/FastRetri/configs/cub.yml",
    "content": "_BASE_: base-image_retri.yml\n\nMODEL:\n  LOSSES:\n    CE:\n      EPSILON: 0.3\n\nINPUT:\n  SIZE_TRAIN: [256,]\n  SIZE_TEST: [256,]\n\n  CJ:\n    ENABLED: True\n    BRIGHTNESS: 0.25\n    CONTRAST: 0.25\n    SATURATION: 0.25\n    HUE: 0.0\n\nSOLVER:\n  MAX_EPOCH: 30\n\n  BASE_LR: 0.02\n  ETA_MIN_LR: 0.00002\n\n  NESTEROV: False\n  MOMENTUM: 0.\n\nTEST:\n  RECALLS: [ 1, 2, 4, 8, 16, 32 ]\n\nDATASETS:\n  NAMES: (\"CUB\",)\n  TESTS: (\"CUB\",)\n\nOUTPUT_DIR: projects/FastRetri/logs/r50-base_cub"
  },
  {
    "path": "fast_reid/projects/FastRetri/configs/inshop.yml",
    "content": "_BASE_: base-image_retri.yml\n\nINPUT:\n  SIZE_TRAIN: [0,]\n  SIZE_TEST: [0,]\n\nSOLVER:\n  MAX_EPOCH: 100\n\n  BASE_LR: 0.003\n  ETA_MIN_LR: 0.00003\n\n  MOMENTUM: 0.99\n  NESTEROV: True\n\nTEST:\n  RECALLS: [ 1, 10, 20, 30, 40, 50 ]\n\nDATASETS:\n  NAMES: (\"InShop\",)\n  TESTS: (\"InShop\",)\n\nOUTPUT_DIR: projects/FastRetri/logs/r50-base_inshop"
  },
  {
    "path": "fast_reid/projects/FastRetri/configs/sop.yml",
    "content": "_BASE_: base-image_retri.yml\n\nSOLVER:\n  MAX_EPOCH: 100\n\n  BASE_LR: 0.003\n  ETA_MIN_LR: 0.00003\n\n  MOMENTUM: 0.99\n  NESTEROV: True\n\nTEST:\n  RECALLS: [1, 10, 100, 1000]\n\nDATASETS:\n  NAMES: (\"SOP\",)\n  TESTS: (\"SOP\",)\n\nOUTPUT_DIR: projects/FastRetri/logs/r50-base_sop"
  },
  {
    "path": "fast_reid/projects/FastRetri/fastretri/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .config import add_retri_config\nfrom .datasets import *\nfrom .retri_evaluator import RetriEvaluator\n"
  },
  {
    "path": "fast_reid/projects/FastRetri/fastretri/config.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\ndef add_retri_config(cfg):\n    _C = cfg\n\n    _C.TEST.RECALLS = [1, 2, 4, 8, 16, 32]\n"
  },
  {
    "path": "fast_reid/projects/FastRetri/fastretri/datasets.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport os\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = [\"Cars196\", \"CUB\", \"SOP\", \"InShop\"]\n\n\n@DATASET_REGISTRY.register()\nclass Cars196(ImageDataset):\n    dataset_dir = 'Cars_196'\n    dataset_name = \"cars\"\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n        self.dataset_dir = os.path.join(self.root, self.dataset_dir)\n        train_file = os.path.join(self.dataset_dir, \"train.txt\")\n        test_file = os.path.join(self.dataset_dir, \"test.txt\")\n\n        required_files = [\n            self.dataset_dir,\n            train_file,\n            test_file,\n        ]\n        self.check_before_run(required_files)\n\n        train = self.process_label_file(train_file, is_train=True)\n        query = self.process_label_file(test_file, is_train=False)\n\n        super(Cars196, self).__init__(train, query, [], **kwargs)\n\n    def process_label_file(self, file, is_train):\n        data_list = []\n        with open(file, 'r') as f:\n            lines = f.read().splitlines()\n\n        for line in lines:\n            img_name, label = line.split(',')\n            if is_train:\n                label = self.dataset_name + '_' + str(label)\n\n            data_list.append((os.path.join(self.dataset_dir, img_name), label, '0'))\n\n        return data_list\n\n\n@DATASET_REGISTRY.register()\nclass CUB(Cars196):\n    dataset_dir = \"CUB_200_2011\"\n    dataset_name = \"cub\"\n\n\n@DATASET_REGISTRY.register()\nclass SOP(Cars196):\n    dataset_dir = \"Stanford_Online_Products\"\n    dataset_name = \"sop\"\n\n\n@DATASET_REGISTRY.register()\nclass InShop(Cars196):\n    dataset_dir = \"InShop\"\n    dataset_name = \"inshop\"\n\n    def __init__(self, root=\"datasets\", **kwargs):\n        self.root = root\n        self.dataset_dir = os.path.join(self.root, self.dataset_dir)\n        train_file = os.path.join(self.dataset_dir, \"train.txt\")\n        query_file = os.path.join(self.dataset_dir, \"test_query.txt\")\n        gallery_file = os.path.join(self.dataset_dir, \"test_gallery.txt\")\n\n        required_files = [\n            train_file,\n            query_file,\n            gallery_file,\n        ]\n        self.check_before_run(required_files)\n\n        train = self.process_label_file(train_file, True)\n        query = self.process_label_file(query_file, False)\n        gallery = self.process_label_file(gallery_file, False)\n\n        super(Cars196, self).__init__(train, query, gallery, **kwargs)\n"
  },
  {
    "path": "fast_reid/projects/FastRetri/fastretri/retri_evaluator.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport copy\nimport logging\nfrom collections import OrderedDict\nfrom typing import List, Optional, Dict\n\nimport faiss\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\nfrom fast_reid.fastreid.evaluation import DatasetEvaluator\nfrom fast_reid.fastreid.utils import comm\n\nlogger = logging.getLogger(\"fastreid.retri_evaluator\")\n\n\n@torch.no_grad()\ndef recall_at_ks(query_features: torch.Tensor,\n                 query_labels: np.ndarray,\n                 ks: List[int],\n                 gallery_features: Optional[torch.Tensor] = None,\n                 gallery_labels: Optional[torch.Tensor] = None,\n                 cosine: bool = False) -> Dict[int, float]:\n    \"\"\"\n    Compute the recall between samples at each k. This function uses about 8GB of memory.\n    Parameters\n    ----------\n    query_features : torch.Tensor\n        Features for each query sample. shape: (num_queries, num_features)\n    query_labels : torch.LongTensor\n        Labels corresponding to the query features. shape: (num_queries,)\n    ks : List[int]\n        Values at which to compute the recall.\n    gallery_features : torch.Tensor\n        Features for each gallery sample. shape: (num_queries, num_features)\n    gallery_labels : torch.LongTensor\n        Labels corresponding to the gallery features. shape: (num_queries,)\n    cosine : bool\n        Use cosine distance between samples instead of euclidean distance.\n    Returns\n    -------\n    recalls : Dict[int, float]\n        Values of the recall at each k.\n    \"\"\"\n    offset = 0\n    if gallery_features is None and gallery_labels is None:\n        offset = 1\n        gallery_features = query_features\n        gallery_labels = query_labels\n    elif gallery_features is None or gallery_labels is None:\n        raise ValueError('gallery_features and gallery_labels needs to be both None or both Tensors.')\n\n    if cosine:\n        query_features = F.normalize(query_features, p=2, dim=1)\n        gallery_features = F.normalize(gallery_features, p=2, dim=1)\n\n    to_cpu_numpy = lambda x: x.cpu().numpy()\n    query_features, gallery_features = map(to_cpu_numpy, [query_features, gallery_features])\n\n    res = faiss.StandardGpuResources()\n    flat_config = faiss.GpuIndexFlatConfig()\n    flat_config.device = 0\n\n    max_k = max(ks)\n    index_function = faiss.GpuIndexFlatIP if cosine else faiss.GpuIndexFlatL2\n    index = index_function(res, gallery_features.shape[1], flat_config)\n    index.add(gallery_features)\n    closest_indices = index.search(query_features, max_k + offset)[1]\n\n    recalls = {}\n    for k in ks:\n        indices = closest_indices[:, offset:k + offset]\n        recalls[k] = (query_labels[:, None] == gallery_labels[indices]).any(1).mean()\n    return {k: round(v * 100, 2) for k, v in recalls.items()}\n\n\nclass RetriEvaluator(DatasetEvaluator):\n    def __init__(self, cfg, num_query, output_dir=None):\n        self.cfg = cfg\n        self._num_query = num_query\n        self._output_dir = output_dir\n\n        self.recalls = cfg.TEST.RECALLS\n\n        self.features = []\n        self.labels = []\n\n    def reset(self):\n        self.features = []\n        self.labels = []\n\n    def process(self, inputs, outputs):\n        self.features.append(outputs.cpu())\n        self.labels.extend(inputs[\"targets\"])\n\n    def evaluate(self):\n        if comm.get_world_size() > 1:\n            comm.synchronize()\n            features = comm.gather(self.features)\n            features = sum(features, [])\n\n            labels = comm.gather(self.labels)\n            labels = sum(labels, [])\n\n            # fmt: off\n            if not comm.is_main_process(): return {}\n            # fmt: on\n        else:\n            features = self.features\n            labels = self.labels\n\n        features = torch.cat(features, dim=0)\n        # query feature, person ids and camera ids\n        query_features = features[:self._num_query]\n        query_labels = np.asarray(labels[:self._num_query])\n\n        # gallery features, person ids and camera ids\n        gallery_features = features[self._num_query:]\n        gallery_pids = np.asarray(labels[self._num_query:])\n\n        self._results = OrderedDict()\n\n        if self._num_query == len(features):\n            cmc = recall_at_ks(query_features, query_labels, self.recalls, cosine=True)\n        else:\n            cmc = recall_at_ks(query_features, query_labels, self.recalls,\n                               gallery_features, gallery_pids,\n                               cosine=True)\n\n        for r in self.recalls:\n            self._results['Recall@{}'.format(r)] = cmc[r]\n        self._results[\"metric\"] = cmc[self.recalls[0]]\n\n        return copy.deepcopy(self._results)\n"
  },
  {
    "path": "fast_reid/projects/FastRetri/train_net.py",
    "content": "#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport sys\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.engine import default_argument_parser, default_setup, launch\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.engine.defaults import DefaultTrainer\n\nfrom fastretri import *\n\n\nclass Trainer(DefaultTrainer):\n\n    @classmethod\n    def build_evaluator(cls, cfg, dataset_name, output_dir=None):\n        data_loader, num_query = cls.build_test_loader(cfg, dataset_name)\n        return data_loader, RetriEvaluator(cfg, num_query, output_dir)\n\n\ndef setup(args):\n    \"\"\"\n    Create configs and perform basic setups.\n    \"\"\"\n    cfg = get_cfg()\n    add_retri_config(cfg)\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    default_setup(cfg, args)\n    return cfg\n\n\ndef main(args):\n    cfg = setup(args)\n\n    if args.eval_only:\n        cfg.defrost()\n        cfg.MODEL.BACKBONE.PRETRAIN = False\n        model = Trainer.build_model(cfg)\n\n        Checkpointer(model).load(cfg.MODEL.WEIGHTS)  # load trained model\n\n        res = Trainer.test(cfg, model)\n        return res\n\n    trainer = Trainer(cfg)\n\n    trainer.resume_or_load(resume=args.resume)\n    return trainer.train()\n\n\nif __name__ == \"__main__\":\n    args = default_argument_parser().parse_args()\n    print(\"Command Line Args:\", args)\n    launch(\n        main,\n        args.num_gpus,\n        num_machines=args.num_machines,\n        machine_rank=args.machine_rank,\n        dist_url=args.dist_url,\n        args=(args,),\n    )\n"
  },
  {
    "path": "fast_reid/projects/FastTune/README.md",
    "content": "# Hyper-Parameter Optimization in FastReID\n\nThis project includes training reid models with hyper-parameter optimization.\n\nInstall the following\n\n```bash\npip install 'ray[tune]'\npip install hpbandster ConfigSpace hyperopt\n```\n\n## Example\n\nThis is an example for tuning `batch_size` and `num_instance` automatically.\n\nTo train hyperparameter optimization with BOHB(Bayesian Optimization with HyperBand) search algorithm, run\n\n```bash\npython3 projects/FastTune/tune_net.py --config-file projects/FastTune/configs/search_trial.yml --srch-algo \"bohb\"\n```\n\n## Known issues\ntodo"
  },
  {
    "path": "fast_reid/projects/FastTune/autotuner/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .tune_hooks import TuneReportHook\n"
  },
  {
    "path": "fast_reid/projects/FastTune/autotuner/tune_hooks.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport torch\nfrom ray import tune\n\nfrom fast_reid.fastreid.engine.hooks import EvalHook, flatten_results_dict\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\n\n\nclass TuneReportHook(EvalHook):\n    def __init__(self, eval_period, eval_function):\n        super().__init__(eval_period, eval_function)\n        self.step = 0\n\n    def _do_eval(self):\n        results = self._func()\n\n        if results:\n            assert isinstance(\n                results, dict\n            ), \"Eval function must return a dict. Got {} instead.\".format(results)\n\n            flattened_results = flatten_results_dict(results)\n            for k, v in flattened_results.items():\n                try:\n                    v = float(v)\n                except Exception:\n                    raise ValueError(\n                        \"[EvalHook] eval_function should return a nested dict of float. \"\n                        \"Got '{}: {}' instead.\".format(k, v)\n                    )\n\n        # Remove extra memory cache of main process due to evaluation\n        torch.cuda.empty_cache()\n\n        self.step += 1\n\n        # Here we save a checkpoint. It is automatically registered with\n        # RayTune and will potentially be passed as the `checkpoint_dir`\n        # parameter in future iterations.\n        with tune.checkpoint_dir(step=self.step) as checkpoint_dir:\n            additional_state = {\"epoch\": int(self.trainer.epoch)}\n            # Change path of save dir where tune can find\n            self.trainer.checkpointer.save_dir = checkpoint_dir\n            self.trainer.checkpointer.save(name=\"checkpoint\", **additional_state)\n\n        metrics = dict(r1=results[\"Rank-1\"], map=results[\"mAP\"], score=(results[\"Rank-1\"] + results[\"mAP\"]) / 2)\n        tune.report(**metrics)\n"
  },
  {
    "path": "fast_reid/projects/FastTune/configs/search_trial.yml",
    "content": "MODEL:\n  META_ARCHITECTURE: Baseline\n\n  FREEZE_LAYERS: [ backbone ]\n\n  BACKBONE:\n    NAME: build_resnet_backbone\n    DEPTH: 34x\n    LAST_STRIDE: 1\n    FEAT_DIM: 512\n    NORM: BN\n    WITH_NL: False\n    WITH_IBN: True\n    PRETRAIN: True\n    PRETRAIN_PATH: /export/home/lxy/.cache/torch/checkpoints/resnet34_ibn_a-94bc1577.pth\n\n  HEADS:\n    NUM_CLASSES: 702\n    NAME: EmbeddingHead\n    NORM: BN\n    NECK_FEAT: after\n    EMBEDDING_DIM: 0\n    POOL_LAYER: GeneralizedMeanPooling\n    CLS_LAYER: CircleSoftmax\n    SCALE: 64\n    MARGIN: 0.35\n\n  LOSSES:\n    NAME: (\"CrossEntropyLoss\", \"TripletLoss\",)\n\n    CE:\n      EPSILON: 0.1\n      SCALE: 1.\n\n    TRI:\n      MARGIN: 0.0\n      HARD_MINING: True\n      NORM_FEAT: False\n      SCALE: 1.\n\nINPUT:\n  SIZE_TRAIN: [ 256, 128 ]\n  SIZE_TEST: [ 256, 128 ]\n\n  AUTOAUG:\n    ENABLED: True\n    PROB: 0.1\n\n  REA:\n    ENABLED: True\n\n  CJ:\n    ENABLED: True\n\n  PADDING:\n    ENABLED: True\n\nDATALOADER:\n  SAMPLER_TRAIN: NaiveIdentitySampler\n  NUM_INSTANCE: 16\n  NUM_WORKERS: 8\n\nSOLVER:\n  AMP:\n    ENABLED: False\n  MAX_EPOCH: 60\n  OPT: Adam\n  SCHED: CosineAnnealingLR\n  BASE_LR: 0.00035\n  BIAS_LR_FACTOR: 1.\n  WEIGHT_DECAY: 0.0005\n  WEIGHT_DECAY_BIAS: 0.0\n  IMS_PER_BATCH: 64\n\n  DELAY_EPOCHS: 30\n  ETA_MIN_LR: 0.00000077\n\n  FREEZE_ITERS: 500\n\n  WARMUP_FACTOR: 0.1\n  WARMUP_ITERS: 1000\n\n  CHECKPOINT_PERIOD: 100\n\nTEST:\n  EVAL_PERIOD: 10\n  IMS_PER_BATCH: 256\n\nDATASETS:\n  NAMES: (\"DukeMTMC\",)\n  TESTS: (\"DukeMTMC\",)\n  COMBINEALL: False\n\nCUDNN_BENCHMARK: True\n\nOUTPUT_DIR: projects/FastTune/logs/trial\n"
  },
  {
    "path": "fast_reid/projects/FastTune/tune_net.py",
    "content": "#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\nimport os\nimport sys\nfrom functools import partial\n\nimport ConfigSpace as CS\nimport ray\nfrom hyperopt import hp\nfrom ray import tune\nfrom ray.tune import CLIReporter\nfrom ray.tune.schedulers import ASHAScheduler, PopulationBasedTraining\nfrom ray.tune.schedulers.hb_bohb import HyperBandForBOHB\nfrom ray.tune.suggest.bohb import TuneBOHB\nfrom ray.tune.suggest.hyperopt import HyperOptSearch\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg, CfgNode\nfrom fast_reid.fastreid.engine import hooks\nfrom fast_reid.fastreid.modeling import build_model\nfrom fast_reid.fastreid.engine import DefaultTrainer, default_argument_parser, default_setup\nfrom fast_reid.fastreid.utils.events import CommonMetricPrinter\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.file_io import PathManager\n\nfrom autotuner import *\n\nlogger = logging.getLogger(\"fastreid.auto_tuner\")\n\nray.init(dashboard_host='127.0.0.1')\n\n\nclass AutoTuner(DefaultTrainer):\n    def build_hooks(self):\n        r\"\"\"\n        Build a list of default hooks, including timing, evaluation,\n        checkpointing, lr scheduling, precise BN, writing events.\n        Returns:\n            list[HookBase]:\n        \"\"\"\n        cfg = self.cfg.clone()\n        cfg.defrost()\n\n        ret = [\n            hooks.IterationTimer(),\n            hooks.LRScheduler(self.optimizer, self.scheduler),\n        ]\n\n        ret.append(hooks.LayerFreeze(\n            self.model,\n            cfg.MODEL.FREEZE_LAYERS,\n            cfg.SOLVER.FREEZE_ITERS,\n            cfg.SOLVER.FREEZE_FC_ITERS,\n        ))\n\n        def test_and_save_results():\n            self._last_eval_results = self.test(self.cfg, self.model)\n            return self._last_eval_results\n\n        # Do evaluation after checkpointer, because then if it fails,\n        # we can use the saved checkpoint to debug.\n        ret.append(TuneReportHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))\n\n        if comm.is_main_process():\n            # run writers in the end, so that evaluation metrics are written\n            ret.append(hooks.PeriodicWriter([CommonMetricPrinter(self.max_iter)], 200))\n\n        return ret\n\n    @classmethod\n    def build_model(cls, cfg):\n        model = build_model(cfg)\n        return model\n\n\ndef setup(args):\n    \"\"\"\n    Create configs and perform basic setups.\n    \"\"\"\n    cfg = get_cfg()\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    default_setup(cfg, args)\n    return cfg\n\n\ndef update_config(cfg, config):\n    frozen = cfg.is_frozen()\n    cfg.defrost()\n\n    # cfg.SOLVER.BASE_LR = config[\"lr\"]\n    # cfg.SOLVER.ETA_MIN_LR = config[\"lr\"] * 0.0001\n    # cfg.SOLVER.DELAY_EPOCHS = int(config[\"delay_epochs\"])\n    # cfg.MODEL.LOSSES.CE.SCALE = config[\"ce_scale\"]\n    # cfg.MODEL.HEADS.SCALE = config[\"circle_scale\"]\n    # cfg.MODEL.HEADS.MARGIN = config[\"circle_margin\"]\n    # cfg.SOLVER.WEIGHT_DECAY = config[\"wd\"]\n    # cfg.SOLVER.WEIGHT_DECAY_BIAS = config[\"wd_bias\"]\n    cfg.SOLVER.IMS_PER_BATCH = config[\"bsz\"]\n    cfg.DATALOADER.NUM_INSTANCE = config[\"num_inst\"]\n\n    if frozen: cfg.freeze()\n\n    return cfg\n\n\ndef train_tuner(config, checkpoint_dir=None, cfg=None):\n    update_config(cfg, config)\n\n    tuner = AutoTuner(cfg)\n    # Load checkpoint if specific\n    if checkpoint_dir:\n        path = os.path.join(checkpoint_dir, \"checkpoint.pth\")\n        checkpoint = tuner.checkpointer.resume_or_load(path, resume=False)\n        tuner.start_epoch = checkpoint.get(\"epoch\", -1) + 1\n\n    # Regular model training\n    tuner.train()\n\n\ndef main(args):\n    cfg = setup(args)\n\n    exp_metrics = dict(metric=\"score\", mode=\"max\")\n\n    if args.srch_algo == \"hyperopt\":\n        # Create a HyperOpt search space\n        search_space = {\n            # \"lr\": hp.loguniform(\"lr\", np.log(1e-6), np.log(1e-3)),\n            # \"delay_epochs\": hp.randint(\"delay_epochs\", 20, 60),\n            # \"wd\": hp.uniform(\"wd\", 0, 1e-3),\n            # \"wd_bias\": hp.uniform(\"wd_bias\", 0, 1e-3),\n            \"bsz\": hp.choice(\"bsz\", [64, 96, 128, 160, 224, 256]),\n            \"num_inst\": hp.choice(\"num_inst\", [2, 4, 8, 16, 32]),\n            # \"ce_scale\": hp.uniform(\"ce_scale\", 0.1, 1.0),\n            # \"circle_scale\": hp.choice(\"circle_scale\", [16, 32, 64, 128, 256]),\n            # \"circle_margin\": hp.uniform(\"circle_margin\", 0, 1) * 0.4 + 0.1,\n        }\n\n        current_best_params = [{\n            \"bsz\": 0,  # index of hp.choice list\n            \"num_inst\": 3,\n        }]\n\n        search_algo = HyperOptSearch(\n            search_space,\n            points_to_evaluate=current_best_params,\n            **exp_metrics)\n\n        if args.pbt:\n            scheduler = PopulationBasedTraining(\n                time_attr=\"training_iteration\",\n                **exp_metrics,\n                perturbation_interval=2,\n                hyperparam_mutations={\n                    \"bsz\": [64, 96, 128, 160, 224, 256],\n                    \"num_inst\": [2, 4, 8, 16, 32],\n                }\n            )\n        else:\n            scheduler = ASHAScheduler(\n                grace_period=2,\n                reduction_factor=3,\n                max_t=7,\n                **exp_metrics)\n\n    elif args.srch_algo == \"bohb\":\n        search_space = CS.ConfigurationSpace()\n        search_space.add_hyperparameters([\n            # CS.UniformFloatHyperparameter(name=\"lr\", lower=1e-6, upper=1e-3, log=True),\n            # CS.UniformIntegerHyperparameter(name=\"delay_epochs\", lower=20, upper=60),\n            # CS.UniformFloatHyperparameter(name=\"ce_scale\", lower=0.1, upper=1.0),\n            # CS.UniformIntegerHyperparameter(name=\"circle_scale\", lower=8, upper=128),\n            # CS.UniformFloatHyperparameter(name=\"circle_margin\", lower=0.1, upper=0.5),\n            # CS.UniformFloatHyperparameter(name=\"wd\", lower=0, upper=1e-3),\n            # CS.UniformFloatHyperparameter(name=\"wd_bias\", lower=0, upper=1e-3),\n            CS.CategoricalHyperparameter(name=\"bsz\", choices=[64, 96, 128, 160, 224, 256]),\n            CS.CategoricalHyperparameter(name=\"num_inst\", choices=[2, 4, 8, 16, 32]),\n            # CS.CategoricalHyperparameter(name=\"autoaug_enabled\", choices=[True, False]),\n            # CS.CategoricalHyperparameter(name=\"cj_enabled\", choices=[True, False]),\n        ])\n\n        search_algo = TuneBOHB(\n            search_space, max_concurrent=4, **exp_metrics)\n\n        scheduler = HyperBandForBOHB(\n            time_attr=\"training_iteration\",\n            reduction_factor=3,\n            max_t=7,\n            **exp_metrics,\n        )\n\n    else:\n        raise ValueError(\"Search algorithm must be chosen from [hyperopt, bohb], but got {}\".format(args.srch_algo))\n\n    reporter = CLIReporter(\n        parameter_columns=[\"bsz\", \"num_inst\"],\n        metric_columns=[\"r1\", \"map\", \"training_iteration\"])\n\n    analysis = tune.run(\n        partial(\n            train_tuner,\n            cfg=cfg),\n        resources_per_trial={\"cpu\": 4, \"gpu\": 1},\n        search_alg=search_algo,\n        num_samples=args.num_trials,\n        scheduler=scheduler,\n        progress_reporter=reporter,\n        local_dir=cfg.OUTPUT_DIR,\n        keep_checkpoints_num=10,\n        name=args.srch_algo)\n\n    best_trial = analysis.get_best_trial(\"score\", \"max\", \"last\")\n    logger.info(\"Best trial config: {}\".format(best_trial.config))\n    logger.info(\"Best trial final validation mAP: {}, Rank-1: {}\".format(\n        best_trial.last_result[\"map\"], best_trial.last_result[\"r1\"]))\n\n    save_dict = dict(R1=best_trial.last_result[\"r1\"].item(), mAP=best_trial.last_result[\"map\"].item())\n    save_dict.update(best_trial.config)\n    path = os.path.join(cfg.OUTPUT_DIR, \"best_config.yaml\")\n    with PathManager.open(path, \"w\") as f:\n        f.write(CfgNode(save_dict).dump())\n    logger.info(\"Best config saved to {}\".format(os.path.abspath(path)))\n\n\nif __name__ == \"__main__\":\n    parser = default_argument_parser()\n    parser.add_argument(\"--num-trials\", type=int, default=8, help=\"number of tune trials\")\n    parser.add_argument(\"--srch-algo\", type=str, default=\"hyperopt\",\n                        help=\"search algorithms for hyperparameters search space\")\n    parser.add_argument(\"--pbt\", action=\"store_true\", help=\"use population based training\")\n    args = parser.parse_args()\n    print(\"Command Line Args:\", args)\n    main(args)\n"
  },
  {
    "path": "fast_reid/projects/HAA/Readme.md",
    "content": "# Black Re-ID: A Head-shoulder Descriptor for the Challenging Problem of Person Re-Identification\n\n## Training\n\nTo train a model, run\n\n```bash\nCUDA_VISIBLE_DEVICES=gpus python train_net.py --config-file <config.yml>\n```\n\n## Evaluation\n\nTo evaluate the model in test set, run similarly:\n\n```bash\nCUDA_VISIBLE_DEVICES=gpus python train_net.py --config-file <configs.yaml> --eval-only MODEL.WEIGHTS model.pth\n```\n\n## Experimental Results\n\n### Market1501 dataset\n\n| Method | Pretrained | Rank@1 | mAP |\n| :---: | :---: | :---: |:---: | \n| ResNet50 | ImageNet | 93.3% | 84.6% | \n| MGN | ImageNet | 95.7% | 86.9% | \n| HAA (ResNet50) | ImageNet | 95% | 87.1% | \n| HAA (MGN) | ImageNet | 95.8% | 89.5% | \n\n### DukeMTMC dataset\n\n| Method | Pretrained | Rank@1 | mAP | \n| :---: | :---: | :---: |:---: | \n| ResNet50 | ImageNet | 86.2% | 75.3% | \n| MGN | ImageNet | 88.7% | 78.4% | \n| HAA (ResNet50) | ImageNet | 87.7% | 75.7% | \n| HAA (MGN) | ImageNet | 89% | 80.4% | \n\n### Black-reid black group\n\n| Method | Pretrained | Rank@1 | mAP | \n| :---: | :---: | :---: |:---: | \n| ResNet50 | ImageNet | 80.9% | 70.8% | \n| MGN | ImageNet | 86.7% | 79.1% | \n| HAA (ResNet50) | ImageNet | 86.7% | 79% | \n| HAA (MGN) | ImageNet | 91.0%  | 83.8% | \n\n### White-reid white group\n\n| Method | Pretrained | Rank@1 | mAP | \n| :---: | :---: | :---: |:---: | \n| ResNet50 | ImageNet | 89.5% | 75.8% | \n| MGN | ImageNet | 94.3% | 85.8% | \n| HAA (ResNet50) | ImageNet | 93.5% | 84.4% | \n| HSE (MGN) | ImageNet | 95.3%  | 88.1% | \n\n"
  },
  {
    "path": "fast_reid/projects/NAIC20/README.md",
    "content": "# NAIC20 Competition (ReID Track) \n\nThis repository contains the 1-st place solution of ReID Competition of NAIC. We got the first place in the final stage. \n\n## Introduction\n\nDetailed information about the NAIC competition can be found [here](https://naic.pcl.ac.cn/homepage/index.html).\n\n## Useful Tricks\n\n- [x] DataAugmentation (RandomErasing + ColorJitter + Augmix + RandomAffine + RandomHorizontallyFilp + Padding + RandomCrop)\n- [x] LR Scheduler (Warmup + CosineAnnealing)\n- [x] Optimizer (Adam)\n- [x] FP16 mixed precision training\n- [x] CircleSoftmax\n- [x] Pairwise Cosface\n- [x] GeM pooling\n- [x] Remove Long Tail Data (pid with single image)\n- [x] Channel Shuffle\n- [x] Distmat Ensemble\n\n1. Due to the competition's rule, pseudo label is not allowed in the preliminary and semi-finals, but can be used in finals.\n2. We combine naic19, naic20r1 and naic20r2 datasets, but there are overlap and noise between these datasets. So we\nuse an automatic data clean strategy for data clean. The cleaned txt files are put here. Sorry that this part cannot ben open sourced.\n3. Due to the characteristics of the encrypted dataset, we found **channel shuffle** very helpful.\nIt's an offline data augmentation method. Specifically, for each id, random choice an order of channel, \nsuch as `(2, 1, 0)`, then apply this order for all images of this id, and make it a new id.\nWith this method, you can enlarge the scale of identities. Theoretically, each id can be enlarged to 5 times.\nConsidering computational efficiency and marginal effect, we just enlarge each id once.\nBut this trick is no effect in normal dataset.\n4. Due to the distribution of dataset, we found pairwise cosface can greatly boost model performance.\n5. The performance of `resnest` is far better than `ibn`. \nWe choose `resnest101`, `resnest200` with different resolution (192x256, 192x384) to ensemble. \n\n## Training & Submission in Command Line\n\nBefore starting, please see [GETTING_STARTED.md](https://github.com/JDAI-CV/fast-reid/blob/master/GETTING_STARTED.md) for the basic setup of FastReID.\nAll configs are made for 2-GPU training.\n\n1. To train a model, first set up the corresponding datasets following [datasets/README.md](https://github.com/JDAI-CV/fast-reid/tree/master/datasets), then run:\n\n```bash\npython3 projects/NAIC20/train_net.py --config-file projects/NAIC20/configs/r34-ibn.yml --num-gpus 2 \n```\n\n2. After the model is trained, you can start to generate submission file. First, modify the content of `MODEL` in `submit.yml` to \nadapt your trained model, and set `MODEL.WEIGHTS` to the path of your trained model, then run:\n\n```bash\npython3 projects/NAIC20/train_net.py --config-file projects/NAIC20/configs/submit.yml --eval-only --commit --num-gpus 2\n```\n\nYou can find `submit.json` and `distmat.npy` in `OUTPUT_DIR` of `submit.yml`.\n\n## Ablation Study\n\nTo quickly verify the results, we use resnet34-ibn as backbone to conduct ablation study.\nThe datasets are `naic19`, `naic20r1` and `naic20r2`.\n\n| Setting | Rank-1 | mAP |\n| ------  | ------ | --- |\n| Baseline | 70.11 | 63.29 |\n| w/ tripletx10 | 73.79 | 67.01 |\n| w/ cosface | 75.61 | 70.07 |\n"
  },
  {
    "path": "fast_reid/projects/NAIC20/configs/Base-naic.yml",
    "content": "MODEL:\n  META_ARCHITECTURE: Baseline\n\n  FREEZE_LAYERS: [ backbone ]\n\n  HEADS:\n    NAME: EmbeddingHead\n    NORM: BN\n    EMBEDDING_DIM: 0\n    NECK_FEAT: after\n    POOL_LAYER: GeneralizedMeanPooling\n    CLS_LAYER: CircleSoftmax\n    SCALE: 64\n    MARGIN: 0.35\n\n  LOSSES:\n    NAME: (\"CrossEntropyLoss\", \"Cosface\",)\n\n    CE:\n      EPSILON: 0.\n      SCALE: 1.\n\n    TRI:\n      MARGIN: 0.\n      HARD_MINING: True\n      NORM_FEAT: True\n      SCALE: 1.\n\n    COSFACE:\n      MARGIN: 0.35\n      GAMMA: 64\n      SCALE: 1.\n\nINPUT:\n  SIZE_TRAIN: [ 256, 128 ]\n  SIZE_TEST: [ 256, 128 ]\n\n  FLIP:\n    ENABLED: True\n\n  PADDING:\n    ENABLED: True\n\n  AUGMIX:\n    ENABLED: True\n    PROB: 0.5\n\n  AFFINE:\n    ENABLED: True\n\n  REA:\n    ENABLED: True\n    VALUE: [ 0., 0., 0. ]\n\n  CJ:\n    ENABLED: True\n    BRIGHTNESS: 0.15\n    CONTRAST: 0.1\n    SATURATION: 0.\n    HUE: 0.\n\nDATALOADER:\n  SAMPLER_TRAIN: NaiveIdentitySampler\n  NUM_INSTANCE: 2\n  NUM_WORKERS: 8\n\nSOLVER:\n  AMP:\n    ENABLED: False\n  OPT: Adam\n  SCHED: CosineAnnealingLR\n  MAX_EPOCH: 30\n  BASE_LR: 0.0007\n  BIAS_LR_FACTOR: 1.\n  WEIGHT_DECAY: 0.0005\n  WEIGHT_DECAY_BIAS: 0.0005\n  IMS_PER_BATCH: 256\n\n  DELAY_EPOCHS: 5\n  ETA_MIN_LR: 0.0000007\n\n  FREEZE_ITERS: 1000\n  FREEZE_FC_ITERS: 0\n\n  WARMUP_FACTOR: 0.1\n  WARMUP_ITERS: 4000\n\n  CHECKPOINT_PERIOD: 3\n\nDATASETS:\n  NAMES: (\"NAIC20_R2\", \"NAIC20_R1\", \"NAIC19\",)\n  TESTS: (\"NAIC20_R2\",)\n  RM_LT: True\n\nTEST:\n  EVAL_PERIOD: 3\n  IMS_PER_BATCH: 256\n  RERANK:\n    ENABLED: False\n    K1: 20\n    K2: 3\n    LAMBDA: 0.5\n\nCUDNN_BENCHMARK: True\n"
  },
  {
    "path": "fast_reid/projects/NAIC20/configs/nest101-base.yml",
    "content": "_BASE_: Base-naic.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnest_backbone\n    DEPTH: 101x\n    WITH_IBN: False\n    PRETRAIN: True\n\nOUTPUT_DIR: projects/NAIC20/logs/nest101-128x256"
  },
  {
    "path": "fast_reid/projects/NAIC20/configs/r34-ibn.yml",
    "content": "_BASE_: Base-naic.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnet_backbone\n    DEPTH: 34x\n    FEAT_DIM: 512\n    WITH_IBN: True\n    PRETRAIN: True\n\nOUTPUT_DIR: projects/NAIC20/logs/r34_ibn-128x256"
  },
  {
    "path": "fast_reid/projects/NAIC20/configs/submit.yml",
    "content": "_BASE_: Base-naic.yml\n\nMODEL:\n  BACKBONE:\n    NAME: build_resnet_backbone\n    DEPTH: 34x\n    FEAT_DIM: 512\n    WITH_IBN: True\n\n  WEIGHTS: projects/NAIC20/logs/reproduce/r34-tripletx10/model_best.pth\n\nDATASETS:\n  TESTS: (\"NAIC20_R2A\",)\n\nTEST:\n  RERANK:\n    ENABLED: True\n    K1: 20\n    K2: 3\n    LAMBDA: 0.8\n\n  FLIP:\n    ENABLED: True\n\n  SAVE_DISTMAT: True\n\nOUTPUT_DIR: projects/NAIC20/logs/r34_ibn-128x256-submit"
  },
  {
    "path": "fast_reid/projects/NAIC20/label.txt",
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g:4044\n00065374.png:4745\n00024418.png:4332\n00029689.png:15371\n00026579.png:15371\n00017589.png:17149\n00043388.png:1980\n"
  },
  {
    "path": "fast_reid/projects/NAIC20/naic/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .naic_dataset import *\nfrom .config import add_naic_config\nfrom .naic_evaluator import NaicEvaluator\n"
  },
  {
    "path": "fast_reid/projects/NAIC20/naic/config.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\n\ndef add_naic_config(cfg):\n    _C = cfg\n\n    _C.DATASETS.RM_LT = True\n    _C.TEST.SAVE_DISTMAT = False\n"
  },
  {
    "path": "fast_reid/projects/NAIC20/naic/naic_dataset.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport glob\nimport os\nfrom collections import defaultdict\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = [\"NAIC20_R2\", \"NAIC20_R2CNV\", \"NAIC20_R1\", \"NAIC20_R1CNV\", \"NAIC19\", \"NAIC20_R2A\", ]\n\n\n@DATASET_REGISTRY.register()\nclass NAIC20_R2(ImageDataset):\n    dataset_name = \"naic20_r2\"\n    dataset_dir = \"naic/2020_NAIC/fusai/train\"\n\n    def __init__(self, root=\"datasets\", rm_lt=False, **kwargs):\n        self.root = root\n\n        self.data_path = os.path.join(self.root, self.dataset_dir, \"images\")\n        self.train_label = os.path.join(self.root, self.dataset_dir, \"naic20r2_train_list_clean.txt\")\n        self.query_label = os.path.join(self.root, self.dataset_dir, \"val_query.txt\")\n        self.gallery_label = os.path.join(self.root, self.dataset_dir, \"val_gallery.txt\")\n\n        required_files = [self.train_label, self.query_label, self.gallery_label]\n        self.check_before_run(required_files)\n\n        all_train = self.process_train(self.train_label)\n\n        # fmt: off\n        if rm_lt: train = self.remove_longtail(all_train)\n        else:     train = all_train\n        # fmt: on\n\n        query, gallery = self.process_test(self.query_label, self.gallery_label)\n\n        super().__init__(train, query, gallery, **kwargs)\n\n    def process_train(self, label_path):\n        with open(label_path, 'r') as f:\n            data_list = [i.strip('\\n') for i in f.readlines()]\n\n        img_paths = []\n        for data_info in data_list:\n            img_name, pid = data_info.split(\":\")\n            img_path = os.path.join(self.data_path, img_name)\n            pid = self.dataset_name + \"_\" + pid\n            camid = self.dataset_name + '_0'\n            img_paths.append([img_path, pid, camid])\n\n        return img_paths\n\n    def process_test(self, query_path, gallery_path):\n        with open(query_path, 'r') as f:\n            query_list = [i.strip('\\n') for i in f.readlines()]\n\n        with open(gallery_path, 'r') as f:\n            gallery_list = [i.strip('\\n') for i in f.readlines()]\n\n        query_paths = []\n        for data in query_list:\n            img_name, pid = data.split(':')\n            img_path = os.path.join(self.data_path, img_name)\n            camid = '0'\n            query_paths.append([img_path, int(pid), camid])\n\n        gallery_paths = []\n        for data in gallery_list:\n            img_name, pid = data.split(':')\n            img_path = os.path.join(self.data_path, img_name)\n            camid = '1'\n            gallery_paths.append([img_path, int(pid), camid])\n\n        return query_paths, gallery_paths\n\n    @classmethod\n    def remove_longtail(cls, all_train):\n        # 建立 id 到 image 的字典\n        pid2data = defaultdict(list)\n        for item in all_train:\n            pid2data[item[1]].append(item)\n\n        train = []\n        for pid, data in pid2data.items():\n            # 如果 id 只有一张图片，去掉这个 id\n            if len(data) == 1: continue\n            train.extend(data)\n\n        return train\n\n\n@DATASET_REGISTRY.register()\nclass NAIC20_R2CNV(NAIC20_R2, ImageDataset):\n    dataset_name = 'naic20_r2cnv'\n    dataset_dir = \"naic/2020_NAIC/fusai/train\"\n\n    def __init__(self, root=\"datasets\", rm_lt=False, **kwargs):\n        self.root = root\n\n        self.data_path = os.path.join(self.root, self.dataset_dir, \"images_convert\")\n        self.train_label = os.path.join(self.root, self.dataset_dir, \"naic20r2_train_list_clean.txt\")\n        self.query_label = os.path.join(self.root, self.dataset_dir, \"val_query.txt\")\n        self.gallery_label = os.path.join(self.root, self.dataset_dir, \"val_gallery.txt\")\n\n        required_files = [self.train_label, self.query_label, self.gallery_label]\n        self.check_before_run(required_files)\n\n        all_train = self.process_train(self.train_label)[:53000]\n\n        # fmt: off\n        if rm_lt: train = self.remove_longtail(all_train)\n        else:     train = all_train\n        # fmt: on\n\n        ImageDataset.__init__(self, train, query=[], gallery=[], **kwargs)\n\n\n@DATASET_REGISTRY.register()\nclass NAIC20_R1(NAIC20_R2):\n    dataset_name = \"naic20_r1\"\n    dataset_dir = 'naic/2020_NAIC/chusai/train'\n\n    def __init__(self, root=\"datasets\", rm_lt=False, **kwargs):\n        self.root = root\n\n        self.data_path = os.path.join(self.root, self.dataset_dir, \"images\")\n        self.train_label = os.path.join(self.root, self.dataset_dir, \"label.txt\")\n\n        required_files = [self.train_label]\n        self.check_before_run(required_files)\n\n        all_train = self.process_train(self.train_label)[:40188]\n\n        # fmt: off\n        if rm_lt: train = self.remove_longtail(all_train)\n        else:     train = all_train\n        # fmt: on\n\n        super(NAIC20_R2, self).__init__(train, [], [], **kwargs)\n\n\n@DATASET_REGISTRY.register()\nclass NAIC20_R1CNV(NAIC20_R2):\n    dataset_name = 'naic20_r1cnv'\n    dataset_dir = \"naic/2020_NAIC/chusai/train\"\n\n    def __init__(self, root=\"datasets\", rm_lt=False, **kwargs):\n        self.root = root\n\n        self.data_path = os.path.join(self.root, self.dataset_dir, \"images_convert\")\n        self.train_label = os.path.join(self.root, self.dataset_dir, \"label.txt\")\n\n        required_files = [self.train_label]\n        self.check_before_run(required_files)\n\n        all_train = self.process_train(self.train_label)[:40188]\n\n        # fmt: off\n        if rm_lt: train = self.remove_longtail(all_train)\n        else:     train = all_train\n        # fmt: on\n\n        super(NAIC20_R2, self).__init__(train, [], [], **kwargs)\n\n\n@DATASET_REGISTRY.register()\nclass NAIC19(NAIC20_R2):\n    dataset_name = \"naic19\"\n    dataset_dir = \"naic/2019_NAIC/fusai\"\n\n    def __init__(self, root='datasets', rm_lt=False, **kwargs):\n        self.root = root\n\n        self.data_path = os.path.join(self.root, self.dataset_dir)\n        self.train_label = os.path.join(self.root, self.dataset_dir, 'train_list_clean.txt')\n\n        required_files = [self.train_label]\n        self.check_before_run(required_files)\n\n        all_train = self.process_train(self.train_label)\n\n        # fmt: off\n        if rm_lt: train = self.remove_longtail(all_train)\n        else:     train = all_train\n        # fmt: on\n\n        super(NAIC20_R2, self).__init__(train, [], [], **kwargs)\n\n    def process_train(self, label_path):\n        with open(label_path, 'r') as f:\n            data_list = [i.strip('\\n') for i in f.readlines()]\n\n        img_paths = []\n        for data_info in data_list:\n            img_name, pid = data_info.split(\" \")\n            img_path = os.path.join(self.data_path, img_name)\n            pid = self.dataset_name + \"_\" + pid\n            camid = self.dataset_name + '_0'\n            img_paths.append([img_path, pid, camid])\n\n        return img_paths\n\n\n@DATASET_REGISTRY.register()\nclass NAIC20_R2A(ImageDataset):\n    dataset_name = \"naic20_b\"\n    dataset_dir = 'naic/round2/image_A'\n\n    def __init__(self, root='datasets', **kwargs):\n        self.root = root\n\n        self.query_path = os.path.join(self.root, self.dataset_dir, \"query\")\n        self.gallery_path = os.path.join(self.root, self.dataset_dir, \"gallery\")\n\n        query = self.process_test(self.query_path)\n        gallery = self.process_test(self.gallery_path)\n\n        super().__init__([], query, gallery)\n\n    def process_test(self, test_path):\n        img_paths = glob.glob(os.path.join(test_path, \"*.png\"))\n\n        data = []\n        for img_path in img_paths:\n            img_name = img_path.split(\"/\")[-1]\n            data.append([img_path, img_name, \"naic_0\"])\n        return data\n"
  },
  {
    "path": "fast_reid/projects/NAIC20/naic/naic_evaluator.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport json\nimport logging\nimport os\nfrom collections import defaultdict\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\nfrom fast_reid.fastreid.evaluation import ReidEvaluator\nfrom fast_reid.fastreid.evaluation.query_expansion import aqe\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.compute_dist import build_dist\n\nlogger = logging.getLogger(\"fastreid.naic_submission\")\n\n\ndef partition_arg_topK(matrix, K, axis=0):\n    \"\"\"\n    perform topK based on np.argpartition\n    :param matrix: to be sorted\n    :param K: select and sort the top K items\n    :param axis: 0 or 1. dimension to be sorted.\n    :return:\n    \"\"\"\n    a_part = np.argpartition(matrix, K, axis=axis)\n    if axis == 0:\n        row_index = np.arange(matrix.shape[1 - axis])\n        a_sec_argsort_K = np.argsort(matrix[a_part[0:K, :], row_index], axis=axis)\n        return a_part[0:K, :][a_sec_argsort_K, row_index]\n    else:\n        column_index = np.arange(matrix.shape[1 - axis])[:, None]\n        a_sec_argsort_K = np.argsort(matrix[column_index, a_part[:, 0:K]], axis=axis)\n        return a_part[:, 0:K][column_index, a_sec_argsort_K]\n\n\nclass NaicEvaluator(ReidEvaluator):\n    def process(self, inputs, outputs):\n        self.pids.extend(inputs[\"targets\"])\n        self.camids.extend(inputs[\"camids\"])\n        self.features.append(outputs.cpu())\n\n    def evaluate(self):\n        if comm.get_world_size() > 1:\n            comm.synchronize()\n            features = comm.gather(self.features)\n            features = sum(features, [])\n\n            pids = comm.gather(self.pids)\n            pids = sum(pids, [])\n\n            # fmt: off\n            if not comm.is_main_process(): return {}\n            # fmt: on\n        else:\n            features = self.features\n            pids = self.pids\n\n        features = torch.cat(features, dim=0)\n        # query feature, person ids and camera ids\n        query_features = features[:self._num_query]\n        query_pids = np.asarray(pids[:self._num_query])\n\n        # gallery features, person ids and camera ids\n        gallery_features = features[self._num_query:]\n        gallery_pids = np.asarray(pids[self._num_query:])\n\n        if self.cfg.TEST.AQE.ENABLED:\n            logger.info(\"Test with AQE setting\")\n            qe_time = self.cfg.TEST.AQE.QE_TIME\n            qe_k = self.cfg.TEST.AQE.QE_K\n            alpha = self.cfg.TEST.AQE.ALPHA\n            query_features, gallery_features = aqe(query_features, gallery_features, qe_time, qe_k, alpha)\n\n        if self.cfg.TEST.METRIC == \"cosine\":\n            query_features = F.normalize(query_features, dim=1)\n            gallery_features = F.normalize(gallery_features, dim=1)\n\n        dist = build_dist(query_features, gallery_features, self.cfg.TEST.METRIC)\n\n        if self.cfg.TEST.RERANK.ENABLED:\n            logger.info(\"Test with rerank setting\")\n            k1 = self.cfg.TEST.RERANK.K1\n            k2 = self.cfg.TEST.RERANK.K2\n            lambda_value = self.cfg.TEST.RERANK.LAMBDA\n\n            if self.cfg.TEST.METRIC == \"cosine\":\n                query_features = F.normalize(query_features, dim=1)\n                gallery_features = F.normalize(gallery_features, dim=1)\n\n            rerank_dist = build_dist(query_features, gallery_features, metric=\"jaccard\", k1=k1, k2=k2)\n            dist = rerank_dist * (1 - lambda_value) + dist * lambda_value\n\n        if self.cfg.TEST.SAVE_DISTMAT:\n            np.save(os.path.join(self.cfg.OUTPUT_DIR, \"distmat.npy\"), dist)\n\n        results = defaultdict(list)\n\n        topk_indices = partition_arg_topK(dist, K=200, axis=1)[:, :200]\n        for i in range(topk_indices.shape[0]):\n            results[query_pids[i]].extend(gallery_pids[topk_indices[i]])\n\n        with open(os.path.join(self.cfg.OUTPUT_DIR, \"submit.json\"), 'w') as f:\n            json.dump(results, f)\n\n        return {}\n"
  },
  {
    "path": "fast_reid/projects/NAIC20/naic20r2_train_list_clean.txt",
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  {
    "path": "fast_reid/projects/NAIC20/train_net.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\nimport sys\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg\n\nfrom fast_reid.fastreid.engine import default_argument_parser, default_setup, launch\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.engine import DefaultTrainer\nfrom fast_reid.fastreid.data import build_reid_train_loader\n\nfrom naic import *\n\n\nclass Trainer(DefaultTrainer):\n    @classmethod\n    def build_train_loader(cls, cfg):\n        logger = logging.getLogger(\"fastreid.naic20\")\n        logger.info(\"Prepare NAIC20 competition trainset\")\n        return build_reid_train_loader(cfg, rm_lt=cfg.DATASETS.RM_LT)\n\n\nclass Committer(DefaultTrainer):\n    @classmethod\n    def build_evaluator(cls, cfg, dataset_name, output_dir=None):\n        data_loader, num_query = cls.build_test_loader(cfg, dataset_name)\n        return data_loader, NaicEvaluator(cfg, num_query, output_dir)\n\n\ndef setup(args):\n    \"\"\"\n    Create configs and perform basic setups.\n    \"\"\"\n    cfg = get_cfg()\n    add_naic_config(cfg)\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    default_setup(cfg, args)\n    return cfg\n\n\ndef main(args):\n    cfg = setup(args)\n\n    if args.eval_only:\n        cfg.defrost()\n        cfg.MODEL.BACKBONE.PRETRAIN = False\n        model = Trainer.build_model(cfg)\n\n        Checkpointer(model, save_dir=cfg.OUTPUT_DIR).load(cfg.MODEL.WEIGHTS)  # load trained model\n\n        if args.commit:\n            res = Committer.test(cfg, model)\n        else:\n            res = Trainer.test(cfg, model)\n\n        return res\n\n    trainer = Trainer(cfg)\n\n    trainer.resume_or_load(resume=args.resume)\n    return trainer.train()\n\n\nif __name__ == \"__main__\":\n    parser = default_argument_parser()\n    parser.add_argument(\"--commit\", action=\"store_true\", help=\"submission testing results\")\n    args = parser.parse_args()\n\n    print(\"Command Line Args:\", args)\n    launch(\n        main,\n        args.num_gpus,\n        num_machines=args.num_machines,\n        machine_rank=args.machine_rank,\n        dist_url=args.dist_url,\n        args=(args,),\n    )\n"
  },
  {
    "path": "fast_reid/projects/NAIC20/val_gallery.txt",
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  },
  {
    "path": "fast_reid/projects/PartialReID/README.md",
    "content": "# DSR in FastReID\n**Deep Spatial Feature Reconstruction for Partial Person Re-identification**\n\nLingxiao He, Xingyu Liao\n\n[[`CVPR2018`](http://openaccess.thecvf.com/content_cvpr_2018/papers/He_Deep_Spatial_Feature_CVPR_2018_paper.pdf)] [[`BibTeX`](#CitingDSR)] \n\n**Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification**\n\nLingxiao He, Xingyu Liao\n\n[[`ICCV2019`](http://openaccess.thecvf.com/content_ICCV_2019/papers/He_Foreground-Aware_Pyramid_Reconstruction_for_Alignment-Free_Occluded_Person_Re-Identification_ICCV_2019_paper.pdf)] [[`BibTeX`](#CitingFPR)]\n\n## News！\n\n[1] The old_version code can be check in [old_version](https://github.com/JDAI-CV/Partial-Person-ReID), you can obtain the same result published in paper, and the new version code is updating, please waiting!\n\n## Installation\n\nFirst install FastReID, and then put Partial Datasets in directory datasets. The whole framework of FastReID-DSR is\n<div align=\"center\">\n<img src=\"https://firebasestorage.googleapis.com/v0/b/firescript-577a2.appspot.com/o/imgs%2Fapp%2FSherlockWorkspace%2F1nVTE3Sn5c.jpg?alt=media&token=e7e9fcfc-4fc1-49c8-bcf4-c007028fdd25\" width=\"700px\" />\n</div>\n\nand the detail you can refer to\n## Datasets\n\nThe datasets can find in [Google Drive](https://drive.google.com/file/d/1p7Jvo-RJhU_B6hf9eAhIEFNhvrzM5cdh/view?usp=sharing)\n\nPartialREID---gallery: 300 images of 60 ids, query: 300 images of 60 ids\n\nPartialiLIDS---gallery: 119 images of 119 ids, query: 119 images of 119 ids\n\nOccludedREID---gallery: 1,000 images of 200 ids, query: 1,000 images of 200 ids\n\n## Training and Evaluation\n\nTo train a model, run:\n```bash\npython3 projects/PartialReID/train_net.py --config-file <config.yaml>\n```\n\nFor example, to train the re-id network with IBN-ResNet-50 Backbone\none should execute:\n```bash\nCUDA_VISIBLE_DEVICES='0,1,2,3' python3 projects/PartialReID/train_net.py --config-file 'projects/PartialReID/configs/partial_market.yml'\n```\n\n## Results\n\n| Method | PartialREID | OccludedREID | PartialiLIDS |\n|:--:|:--:|:--:|:--:|\n|   | Rank@1 (mAP)| Rank@1 (mAP)| Rank@1 (mAP)|\n| DSR (CVPR’18)  |73.7(68.1) |72.8(62.8)|64.3(58.1)| \n| FPR (ICCV'19) | 81.0(76.6)|78.3(68.0)|68.1(61.8)| \n| FastReID-DSR | 82.7(76.8)|81.6(70.9)|73.1(79.8) | \n\n## <a name=\"CitingDSR\"></a >Citing DSR and Citing FPR\n\nIf you use DSR or FPR, please use the following BibTeX entry.\n\n```\n@inproceedings{he2018deep,\n  title={Deep spatial feature reconstruction for partial person re-identification: Alignment-free approach},\n  author={He, Lingxiao and Liang, Jian and Li, Haiqing and Sun, Zhenan},\n  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\n  year={2018}\n}\n@inproceedings{he2019foreground,\n  title={Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification},\n  author={He, Lingxiao and Wang, Yinggang and Liu, Wu and Zhao, He and Sun, Zhenan and Feng, Jiashi},\n  booktitle={IEEE International Conference on Computer Vision (ICCV)},\n  year={2019}\n}\n```\n"
  },
  {
    "path": "fast_reid/projects/PartialReID/configs/partial_market.yml",
    "content": "MODEL:\n  META_ARCHITECTURE: PartialBaseline\n\n  BACKBONE:\n    NAME: build_resnet_backbone\n    NORM: BN\n    DEPTH: 50x\n    LAST_STRIDE: 1\n    FEAT_DIM: 2048\n    WITH_IBN: True\n    PRETRAIN: True\n\n  HEADS:\n    NAME: DSRHead\n    POOL_LAYER: FastGlobalAvgPool\n    WITH_BNNECK: True\n    CLS_LAYER: Linear\n\n  LOSSES:\n    NAME: (\"CrossEntropyLoss\", \"TripletLoss\",)\n  \n    CE:\n      EPSILON: 0.12\n      SCALE: 1.\n\n    TRI:\n      MARGIN: 0.3\n      SCALE: 1.0\n      HARD_MINING: False\n\nDATASETS:\n  NAMES: (\"Market1501\",)\n  TESTS: (\"PartialREID\", \"PartialiLIDS\", \"OccludedREID\",)\n\nINPUT:\n  SIZE_TRAIN: [384, 128]\n  SIZE_TEST: [384, 128]\n\n  FLIP:\n    ENABLED: True\n\nDATALOADER:\n  SAMPLER_TRAIN: NaiveIdentitySampler\n  NUM_INSTANCE: 4\n  NUM_WORKERS: 8\n\nSOLVER:\n  AMP:\n    ENABLED: False\n  OPT: Adam\n  MAX_EPOCH: 60\n  BASE_LR: 0.0007\n  BIAS_LR_FACTOR: 1.\n  WEIGHT_DECAY: 0.0005\n  WEIGHT_DECAY_BIAS: 0.0005\n  IMS_PER_BATCH: 256\n\n  SCHED: CosineAnnealingLR\n  DELAY_EPOCHS: 20\n  ETA_MIN_LR: 0.0000007\n\n  WARMUP_FACTOR: 0.1\n  WARMUP_ITERS: 500\n\n  CHECKPOINT_PERIOD: 20\n\nTEST:\n  EVAL_PERIOD: 10\n  IMS_PER_BATCH: 128\n\nCUDNN_BENCHMARK: True\n\nOUTPUT_DIR: projects/PartialReID/logs/test_partial"
  },
  {
    "path": "fast_reid/projects/PartialReID/partialreid/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom .partial_dataset import *\nfrom .partialbaseline import PartialBaseline\nfrom .dsr_head import DSRHead\nfrom .config import add_partialreid_config\nfrom .dsr_evaluation import DsrEvaluator\n"
  },
  {
    "path": "fast_reid/projects/PartialReID/partialreid/config.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  l1aoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom fast_reid.fastreid.config import CfgNode as CN\n\n\ndef add_partialreid_config(cfg):\n    _C = cfg\n\n    _C.TEST.DSR = CN({\"ENABLED\": True})\n"
  },
  {
    "path": "fast_reid/projects/PartialReID/partialreid/dsr_distance.py",
    "content": "\"\"\"Numpy version of euclidean distance, etc.\nNotice the input/output shape of methods, so that you can better understand\nthe meaning of these methods.\"\"\"\n\nimport numpy as np\nimport torch\n\n\ndef normalize(nparray, order=2, axis=0):\n    \"\"\"Normalize a N-D numpy array along the specified axis.\"\"\"\n    norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)\n    return nparray / (norm + np.finfo(np.float32).eps)\n\n\ndef compute_dsr_dist(array1, array2, distmat, scores):\n    \"\"\" Compute the sptial feature reconstruction of all pairs\n     array: [M, N, C] M: the number of query, N: the number of spatial feature, C: the dimension of each spatial feature\n     array2: [M, N, C] M: the number of gallery\n    :return:\n    numpy array with shape [m1, m2]\n    \"\"\"\n    dist = 100 * torch.ones(len(array1), len(array2))\n    dist = dist.cuda()\n    kappa = 0.001\n    index = np.argsort(distmat, axis=1)\n    T = kappa * torch.eye(110)\n    T = T.cuda()\n    M = []\n    for i in range(0, len(array2)):\n        g = array2[i]\n        g = torch.FloatTensor(g)\n        g = g.view(g.size(0), g.size(1))\n        g = g.cuda()\n        Proj_M1 = torch.matmul(torch.inverse(torch.matmul(g.t(), g) + T), g.t())\n        Proj_M1 = Proj_M1.cpu().numpy()\n        M.append(Proj_M1)\n    for i in range(0, len(array1)):\n        q = torch.FloatTensor(array1[i])\n        q = q.view(q.size(0), q.size(1))\n        q = q.cuda()\n        for j in range(0, 100):\n            g = array2[index[i, j]]\n            g = torch.FloatTensor(g)\n            g = g.view(g.size(0), g.size(1))\n            g = g.cuda()\n            Proj_M = torch.FloatTensor(M[index[i, j]])\n            Proj_M = Proj_M.cuda()\n            a = torch.matmul(g, torch.matmul(Proj_M, q)) - q\n            dist[i, index[i, j]] = ((torch.pow(a, 2).sum(0).sqrt()) * scores[i].cuda()).sum()\n    dist = dist.cpu()\n    dist = dist.numpy()\n\n    return dist\n"
  },
  {
    "path": "fast_reid/projects/PartialReID/partialreid/dsr_evaluation.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport copy\nimport logging\nfrom collections import OrderedDict\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n\nfrom fast_reid.fastreid.evaluation.evaluator import DatasetEvaluator\nfrom fast_reid.fastreid.evaluation.rank import evaluate_rank\nfrom fast_reid.fastreid.utils import comm\nfrom .dsr_distance import compute_dsr_dist\n\nlogger = logging.getLogger('fastreid.partialreid.dsr_evaluation')\n\n\nclass DsrEvaluator(DatasetEvaluator):\n    def __init__(self, cfg, num_query, output_dir=None):\n        self.cfg = cfg\n        self._num_query = num_query\n        self._output_dir = output_dir\n\n        self.features = []\n        self.spatial_features = []\n        self.scores = []\n        self.pids = []\n        self.camids = []\n\n    def reset(self):\n        self.features = []\n        self.spatial_features = []\n        self.scores = []\n        self.pids = []\n        self.camids = []\n\n    def process(self, inputs, outputs):\n        self.pids.extend(inputs[\"targets\"])\n        self.camids.extend(inputs[\"camids\"])\n        self.features.append(F.normalize(outputs[0]).cpu())\n        outputs1 = F.normalize(outputs[1].data).cpu()\n        self.spatial_features.append(outputs1)\n        self.scores.append(outputs[2].cpu())\n\n    def evaluate(self):\n        if comm.get_world_size() > 1:\n            comm.synchronize()\n            features = comm.gather(self.features)\n            features = sum(features, [])\n\n            spatial_features = comm.gather(self.spatial_features)\n            spatial_features = sum(spatial_features, [])\n\n            scores = comm.gather(self.scores)\n            scores = sum(scores, [])\n\n            pids = comm.gather(self.pids)\n            pids = sum(pids, [])\n\n            camids = comm.gather(self.camids)\n            camids = sum(camids, [])\n\n            # fmt: off\n            if not comm.is_main_process(): return {}\n            # fmt: on\n        else:\n            features = self.features\n            spatial_features = self.spatial_features\n            scores = self.scores\n            pids = self.pids\n            camids = self.camids\n\n        features = torch.cat(features, dim=0)\n        spatial_features = torch.cat(spatial_features, dim=0).numpy()\n        scores = torch.cat(scores, dim=0)\n\n        # query feature, person ids and camera ids\n        query_features = features[:self._num_query]\n        query_pids = np.asarray(pids[:self._num_query])\n        query_camids = np.asarray(camids[:self._num_query])\n\n        # gallery features, person ids and camera ids\n        gallery_features = features[self._num_query:]\n        gallery_pids = np.asarray(pids[self._num_query:])\n        gallery_camids = np.asarray(camids[self._num_query:])\n\n        if self.cfg.TEST.METRIC == \"cosine\":\n            query_features = F.normalize(query_features, dim=1)\n            gallery_features = F.normalize(gallery_features, dim=1)\n\n        dist = 1 - torch.mm(query_features, gallery_features.t()).numpy()\n        self._results = OrderedDict()\n\n        query_features = query_features.numpy()\n        gallery_features = gallery_features.numpy()\n        if self.cfg.TEST.DSR.ENABLED:\n            logger.info(\"Testing with DSR setting\")\n            dsr_dist = compute_dsr_dist(spatial_features[:self._num_query], spatial_features[self._num_query:], dist,\n                                        scores[:self._num_query])\n\n            max_value = 0\n            k = 0\n            for i in range(0, 101):\n                lamb = 0.01 * i\n                dist1 = (1 - lamb) * dist + lamb * dsr_dist\n                cmc, all_AP, all_INP = evaluate_rank(dist1, query_pids, gallery_pids, query_camids, gallery_camids)\n                if (cmc[0] > max_value):\n                    k = lamb\n                    max_value = cmc[0]\n            dist1 = (1 - k) * dist + k * dsr_dist\n            cmc, all_AP, all_INP = evaluate_rank(dist1, query_pids, gallery_pids, query_camids, gallery_camids)\n        else:\n            cmc, all_AP, all_INP = evaluate_rank(dist, query_pids, gallery_pids, query_camids, gallery_camids)\n\n        mAP = np.mean(all_AP)\n        mINP = np.mean(all_INP)\n        for r in [1, 5, 10]:\n            self._results['Rank-{}'.format(r)] = cmc[r - 1] * 100\n        self._results['mAP'] = mAP * 100\n        self._results['mINP'] = mINP * 100\n\n        return copy.deepcopy(self._results)\n"
  },
  {
    "path": "fast_reid/projects/PartialReID/partialreid/dsr_head.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  lingxiao he\n@contact: helingxiao3@jd.com\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom fast_reid.fastreid.layers import *\nfrom fast_reid.fastreid.modeling.heads import EmbeddingHead\nfrom fast_reid.fastreid.modeling.heads.build import REID_HEADS_REGISTRY\nfrom fast_reid.fastreid.layers.weight_init import weights_init_kaiming\n\n\nclass OcclusionUnit(nn.Module):\n    def __init__(self, in_planes=2048):\n        super(OcclusionUnit, self).__init__()\n        self.MaxPool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n        self.MaxPool2 = nn.MaxPool2d(kernel_size=4, stride=2, padding=0)\n        self.MaxPool3 = nn.MaxPool2d(kernel_size=6, stride=2, padding=0)\n        self.MaxPool4 = nn.MaxPool2d(kernel_size=8, stride=2, padding=0)\n        self.mask_layer = nn.Linear(in_planes, 1, bias=True)\n\n    def forward(self, x):\n        SpaFeat1 = self.MaxPool1(x)  # shape: [n, c, h, w]\n        SpaFeat2 = self.MaxPool2(x)\n        SpaFeat3 = self.MaxPool3(x)\n        SpaFeat4 = self.MaxPool4(x)\n\n        Feat1 = SpaFeat1.view(SpaFeat1.size(0), SpaFeat1.size(1), SpaFeat1.size(2) * SpaFeat1.size(3))\n        Feat2 = SpaFeat2.view(SpaFeat2.size(0), SpaFeat2.size(1), SpaFeat2.size(2) * SpaFeat2.size(3))\n        Feat3 = SpaFeat3.view(SpaFeat3.size(0), SpaFeat3.size(1), SpaFeat3.size(2) * SpaFeat3.size(3))\n        Feat4 = SpaFeat4.view(SpaFeat4.size(0), SpaFeat4.size(1), SpaFeat4.size(2) * SpaFeat4.size(3))\n        SpatialFeatAll = torch.cat((Feat1, Feat2, Feat3, Feat4), 2)\n        SpatialFeatAll = SpatialFeatAll.transpose(1, 2)  # shape: [n, c, m]\n        y = self.mask_layer(SpatialFeatAll)\n        mask_weight = torch.sigmoid(y[:, :, 0])\n        # mask_score = torch.sigmoid(mask_weight[:, :48])\n        feat_dim = SpaFeat1.size(2) * SpaFeat1.size(3)\n        mask_score = F.normalize(mask_weight[:, :feat_dim], p=1, dim=1)\n        #       mask_score_norm = mask_score\n        # mask_weight_norm = torch.sigmoid(mask_weight)\n        mask_weight_norm = F.normalize(mask_weight, p=1, dim=1)\n\n        mask_score = mask_score.unsqueeze(1)\n\n        SpaFeat1 = SpaFeat1.transpose(1, 2)\n        SpaFeat1 = SpaFeat1.transpose(2, 3)  # shape: [n, h, w, c]\n        SpaFeat1 = SpaFeat1.view((SpaFeat1.size(0), SpaFeat1.size(1) * SpaFeat1.size(2), -1))  # shape: [n, h*w, c]\n\n        global_feats = mask_score.matmul(SpaFeat1).view(SpaFeat1.shape[0], -1, 1, 1)\n        return global_feats, mask_weight, mask_weight_norm\n\n\nclass Flatten(nn.Module):\n    def forward(self, input):\n        return input.view(input.size(0), -1)\n\n\n@REID_HEADS_REGISTRY.register()\nclass DSRHead(EmbeddingHead):\n    def __init__(self, cfg):\n        super().__init__(cfg)\n\n        feat_dim = cfg.MODEL.BACKBONE.FEAT_DIM\n        with_bnneck = cfg.MODEL.HEADS.WITH_BNNECK\n        norm_type = cfg.MODEL.HEADS.NORM\n        num_classes = cfg.MODEL.HEADS.NUM_CLASSES\n        embedding_dim = cfg.MODEL.HEADS.EMBEDDING_DIM\n        self.occ_unit = OcclusionUnit(in_planes=feat_dim)\n        self.MaxPool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n        self.MaxPool2 = nn.MaxPool2d(kernel_size=4, stride=2, padding=0)\n        self.MaxPool3 = nn.MaxPool2d(kernel_size=6, stride=2, padding=0)\n        self.MaxPool4 = nn.MaxPool2d(kernel_size=8, stride=2, padding=0)\n\n        occ_neck = []\n        if embedding_dim > 0:\n            occ_neck.append(nn.Conv2d(feat_dim, embedding_dim, 1, 1, bias=False))\n            feat_dim = embedding_dim\n\n        if with_bnneck:\n            occ_neck.append(get_norm(norm_type, feat_dim, bias_freeze=True))\n\n        self.bnneck_occ = nn.Sequential(*occ_neck)\n        self.bnneck_occ.apply(weights_init_kaiming)\n\n        self.weight_occ = nn.Parameter(torch.normal(0, 0.01, (num_classes, feat_dim)))\n\n    def forward(self, features, targets=None):\n        \"\"\"\n        See :class:`ReIDHeads.forward`.\n        \"\"\"\n        SpaFeat1 = self.MaxPool1(features)  # shape: [n, c, h, w]\n        SpaFeat2 = self.MaxPool2(features)\n        SpaFeat3 = self.MaxPool3(features)\n        SpaFeat4 = self.MaxPool4(features)\n\n        Feat1 = SpaFeat1.view(SpaFeat1.size(0), SpaFeat1.size(1), SpaFeat1.size(2) * SpaFeat1.size(3))\n        Feat2 = SpaFeat2.view(SpaFeat2.size(0), SpaFeat2.size(1), SpaFeat2.size(2) * SpaFeat2.size(3))\n        Feat3 = SpaFeat3.view(SpaFeat3.size(0), SpaFeat3.size(1), SpaFeat3.size(2) * SpaFeat3.size(3))\n        Feat4 = SpaFeat4.view(SpaFeat4.size(0), SpaFeat4.size(1), SpaFeat4.size(2) * SpaFeat4.size(3))\n        SpatialFeatAll = torch.cat((Feat1, Feat2, Feat3, Feat4), dim=2)\n\n        foreground_feat, mask_weight, mask_weight_norm = self.occ_unit(features)\n        # print(time.time() - st)\n        bn_foreground_feat = self.bnneck_occ(foreground_feat)\n        bn_foreground_feat = bn_foreground_feat[..., 0, 0]\n\n        # Evaluation\n        if not self.training:\n            return bn_foreground_feat, SpatialFeatAll, mask_weight_norm\n\n        # Training\n        global_feat = self.pool_layer(features)\n        bn_feat = self.bottleneck(global_feat)\n        bn_feat = bn_feat[..., 0, 0]\n\n        if self.cls_layer.__class__.__name__ == 'Linear':\n            pred_class_logits = F.linear(bn_feat, self.weight)\n            fore_pred_class_logits = F.linear(bn_foreground_feat, self.weight_occ)\n        else:\n            pred_class_logits = F.linear(F.normalize(bn_feat), F.normalize(self.weight))\n            fore_pred_class_logits = F.linear(F.normalize(bn_foreground_feat), F.normalize(self.weight_occ))\n\n        cls_outputs = self.cls_layer(pred_class_logits, targets)\n        fore_cls_outputs = self.cls_layer(fore_pred_class_logits, targets)\n\n        # pdb.set_trace()\n        return {\n            \"cls_outputs\": cls_outputs,\n            \"fore_cls_outputs\": fore_cls_outputs,\n            \"pred_class_logits\": pred_class_logits * self.cls_layer.s,\n            \"features\": global_feat[..., 0, 0],\n            \"foreground_features\": foreground_feat[..., 0, 0],\n        }\n"
  },
  {
    "path": "fast_reid/projects/PartialReID/partialreid/partial_dataset.py",
    "content": "# encoding: utf-8\n\n\"\"\"\n@author:  lingxiao he\n@contact: helingxiao3@jd.com\n\"\"\"\n\nimport glob\nimport os\nimport os.path as osp\nimport re\n\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nfrom fast_reid.fastreid.data.datasets.bases import ImageDataset\n\n__all__ = ['PartialREID', 'PartialiLIDS', 'OccludedREID']\n\n\ndef process_test(query_path, gallery_path):\n    query_img_paths = glob.glob(os.path.join(query_path, '*.jpg'))\n    gallery_img_paths = glob.glob(os.path.join(gallery_path, '*.jpg'))\n    query_paths = []\n    pattern = re.compile(r'([-\\d]+)_(\\d*)')\n    for img_path in query_img_paths:\n        pid, camid = map(int, pattern.search(img_path).groups())\n        query_paths.append([img_path, pid, camid])\n    gallery_paths = []\n    for img_path in gallery_img_paths:\n        pid, camid = map(int, pattern.search(img_path).groups())\n        gallery_paths.append([img_path, pid, camid])\n    return query_paths, gallery_paths\n\n\n@DATASET_REGISTRY.register()\nclass PartialREID(ImageDataset):\n\n    dataset_name = \"partialreid\"\n\n    def __init__(self, root='datasets',):\n        self.root = root\n\n        self.query_dir = osp.join(self.root, 'Partial_REID/partial_body_images')\n        self.gallery_dir = osp.join(self.root, 'Partial_REID/whole_body_images')\n        query, gallery = process_test(self.query_dir, self.gallery_dir)\n\n        ImageDataset.__init__(self, [], query, gallery)\n\n\n@DATASET_REGISTRY.register()\nclass PartialiLIDS(ImageDataset):\n    dataset_name = \"partialilids\"\n\n    def __init__(self, root='datasets',):\n        self.root = root\n\n        self.query_dir = osp.join(self.root, 'PartialiLIDS/query')\n        self.gallery_dir = osp.join(self.root, 'PartialiLIDS/gallery')\n        query, gallery = process_test(self.query_dir, self.gallery_dir)\n\n        ImageDataset.__init__(self, [], query, gallery)\n\n\n@DATASET_REGISTRY.register()\nclass OccludedREID(ImageDataset):\n    dataset_name = \"occludereid\"\n\n    def __init__(self, root='datasets',):\n        self.root = root\n\n        self.query_dir = osp.join(self.root, 'OccludedREID/query')\n        self.gallery_dir = osp.join(self.root, 'OccludedREID/gallery')\n        query, gallery = process_test(self.query_dir, self.gallery_dir)\n\n        ImageDataset.__init__(self, [], query, gallery)\n"
  },
  {
    "path": "fast_reid/projects/PartialReID/partialreid/partialbaseline.py",
    "content": "# encoding: utf-8\n\"\"\"\n@authorr:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nfrom fast_reid.fastreid.modeling.losses import *\nfrom fast_reid.fastreid.modeling.meta_arch import Baseline\nfrom fast_reid.fastreid.modeling.meta_arch.build import META_ARCH_REGISTRY\n\n\n@META_ARCH_REGISTRY.register()\nclass PartialBaseline(Baseline):\n\n    def losses(self, outputs, gt_labels):\n        r\"\"\"\n        Compute loss from modeling's outputs, the loss function input arguments\n        must be the same as the outputs of the model forwarding.\n        \"\"\"\n        loss_dict = super().losses(outputs, gt_labels)\n\n        fore_cls_outputs = outputs[\"fore_cls_outputs\"]\n        fore_feat = outputs[\"foreground_features\"]\n\n        loss_names = self.loss_kwargs['loss_names']\n\n        if 'CrossEntropyLoss' in loss_names:\n            ce_kwargs = self.loss_kwargs.get('ce')\n            loss_dict['loss_fore_cls'] = cross_entropy_loss(\n                fore_cls_outputs,\n                gt_labels,\n                ce_kwargs.get('eps'),\n                ce_kwargs.get('alpha')\n            ) * ce_kwargs.get('scale')\n\n        if 'TripletLoss' in loss_names:\n            tri_kwargs = self.loss_kwargs.get('tri')\n            loss_dict['loss_fore_triplet'] = triplet_loss(\n                fore_feat,\n                gt_labels,\n                tri_kwargs.get('margin'),\n                tri_kwargs.get('norm_feat'),\n                tri_kwargs.get('hard_mining')\n            ) * tri_kwargs.get('scale')\n\n        return loss_dict\n"
  },
  {
    "path": "fast_reid/projects/PartialReID/train_net.py",
    "content": "#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\nimport os\nimport sys\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.engine import DefaultTrainer, default_argument_parser, default_setup, launch\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.engine import hooks\n\nfrom partialreid import *\n\n\nclass Trainer(DefaultTrainer):\n    @classmethod\n    def build_evaluator(cls, cfg, dataset_name, output_dir=None):\n        data_loader, num_query = cls.build_test_loader(cfg, dataset_name)\n        return data_loader, DsrEvaluator(cfg, num_query, output_dir)\n\n\ndef setup(args):\n    \"\"\"\n    Create configs and perform basic setups.\n    \"\"\"\n    cfg = get_cfg()\n    add_partialreid_config(cfg)\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    default_setup(cfg, args)\n    return cfg\n\n\ndef main(args):\n    cfg = setup(args)\n\n    if args.eval_only:\n        logger = logging.getLogger(\"fastreid.trainer\")\n        cfg.defrost()\n        cfg.MODEL.BACKBONE.PRETRAIN = False\n        model = Trainer.build_model(cfg)\n\n        Checkpointer(model).load(cfg.MODEL.WEIGHTS)  # load trained model\n\n        if cfg.TEST.PRECISE_BN.ENABLED and hooks.get_bn_modules(model):\n            prebn_cfg = cfg.clone()\n            prebn_cfg.DATALOADER.NUM_WORKERS = 0  # save some memory and time for PreciseBN\n            prebn_cfg.DATASETS.NAMES = tuple([cfg.TEST.PRECISE_BN.DATASET])  # set dataset name for PreciseBN\n            logger.info(\"Prepare precise BN dataset\")\n            hooks.PreciseBN(\n                # Run at the same freq as (but before) evaluation.\n                model,\n                # Build a new data loader to not affect training\n                Trainer.build_train_loader(prebn_cfg),\n                cfg.TEST.PRECISE_BN.NUM_ITER,\n            ).update_stats()\n        res = Trainer.test(cfg, model)\n        return res\n\n    trainer = Trainer(cfg)\n    trainer.resume_or_load(resume=args.resume)\n    return trainer.train()\n\n\nif __name__ == \"__main__\":\n    args = default_argument_parser().parse_args()\n    print(\"Command Line Args:\", args)\n    launch(\n        main,\n        args.num_gpus,\n        num_machines=args.num_machines,\n        machine_rank=args.machine_rank,\n        dist_url=args.dist_url,\n        args=(args,),\n    )\n"
  },
  {
    "path": "fast_reid/projects/README.md",
    "content": "\nHere are a few projects that are built on fastreid.\nThey are examples of how to use fastrei as a library, to make your projects more maintainable.\n\n# Projects by JDAI\n\nNote that these are research projects, and therefore may not have the same level of support or stability of fastreid.\n\n- [Deep Spatial Feature Reconstruction for Partial Person Re-identification](https://github.com/JDAI-CV/fast-reid/tree/master/projects/PartialReID)\n- [Black Re-ID: A Head-shoulder Descriptor for the Challenging Problem of Person Re-Identification](https://github.com/JDAI-CV/fast-reid/tree/master/projects/HAA)\n- [Image Classification](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastCls)\n- [Face Recognition](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastFace)\n- [Image Retrieval](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastRetri)\n- [Attribute Recognition](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastAttr)\n- [Hyper-Parameters Optimization](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastTune)\n- [Overhaul Distillation](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastDistill)\n- Semi-Supervised Domain Generalizable Person Re-Identification. [code](https://github.com/xiaomingzhid/sskd) and [paper](https://arxiv.org/pdf/2108.05045.pdf)\n\n# External Projects\n\nExternal projects in the community that use fastreid:\n\n- [FastReID of Interpreter Project](https://github.com/SheldongChen/AMD.github.io)\n\n# Competitions\n\n- NAIC20 reid track [1-st solution](https://github.com/JDAI-CV/fast-reid/tree/master/projects/NAIC20) \n"
  },
  {
    "path": "fast_reid/tests/__init__.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n"
  },
  {
    "path": "fast_reid/tests/dataset_test.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  liaoxingyu\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport sys\nsys.path.append('.')\nfrom data import get_dataloader\nfrom config import cfg\nimport argparse\nfrom data.datasets import init_dataset\n# cfg.DATALOADER.SAMPLER = 'triplet'\ncfg.DATASETS.NAMES = (\"market1501\", \"dukemtmc\", \"cuhk03\", \"msmt17\", \"mot17\", \"mot20\",)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description=\"ReID Baseline Training\")\n    parser.add_argument(\n        '-cfg', \"--config_file\",\n        default=\"\",\n        metavar=\"FILE\",\n        help=\"path to config file\",\n        type=str\n    )\n    # parser.add_argument(\"--local_rank\", type=int, default=0)\n    parser.add_argument(\"opts\", help=\"Modify config options using the command-line\", default=None,\n                        nargs=argparse.REMAINDER)\n    args = parser.parse_args()\n    cfg.merge_from_list(args.opts)\n\n    # dataset = init_dataset('msmt17', combineall=True)\n    get_dataloader(cfg)\n    # tng_dataloader, val_dataloader, num_classes, num_query = get_dataloader(cfg)\n    # def get_ex(): return open_image('datasets/beijingStation/query/000245_c10s2_1561732033722.000000.jpg')\n    # im = get_ex()\n    # print(data.train_ds[0])\n    # print(data.test_ds[0])\n    # a = next(iter(data.train_dl))\n    # from IPython import embed; embed()\n    # from ipdb import set_trace; set_trace()\n    # im.apply_tfms(crop_pad(size=(300, 300)))\n"
  },
  {
    "path": "fast_reid/tests/feature_align.py",
    "content": "import unittest\nimport numpy as np\nimport os\nfrom glob import glob\n\n\nclass TestFeatureAlign(unittest.TestCase):\n    def test_caffe_pytorch_feat_align(self):\n        caffe_feat_path = \"/export/home/lxy/cvpalgo-fast-reid/tools/deploy/caffe_R50_output\"\n        pytorch_feat_path = \"/export/home/lxy/cvpalgo-fast-reid/demo/logs/R50_256x128_pytorch_feat_output\"\n        feat_filenames = os.listdir(caffe_feat_path)\n        for feat_name in feat_filenames:\n            caffe_feat = np.load(os.path.join(caffe_feat_path, feat_name))\n            pytorch_feat = np.load(os.path.join(pytorch_feat_path, feat_name))\n            sim = np.dot(caffe_feat, pytorch_feat.transpose())[0][0]\n            assert sim > 0.97, f\"Got similarity {sim} and feature of {feat_name} is not aligned\"\n\n    def test_model_performance(self):\n        caffe_feat_path = \"/export/home/lxy/cvpalgo-fast-reid/tools/deploy/caffe_R50_output\"\n        feat_filenames = os.listdir(caffe_feat_path)\n        feats = []\n        for feat_name in feat_filenames:\n            caffe_feat = np.load(os.path.join(caffe_feat_path, feat_name))\n            feats.append(caffe_feat)\n        from ipdb import set_trace; set_trace()\n\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "fast_reid/tests/interp_test.py",
    "content": "import torch\nfrom fastai.vision import *\nfrom fastai.basic_data import *\nfrom fastai.layers import *\n\nimport sys\nsys.path.append('.')\nfrom engine.interpreter import ReidInterpretation\n\nfrom data import get_data_bunch\nfrom modeling import build_model\nfrom config import cfg\ncfg.DATASETS.NAMES = ('market1501',)\ncfg.DATASETS.TEST_NAMES = 'market1501'\ncfg.MODEL.BACKBONE = 'resnet50'\n\ndata_bunch, test_labels, num_query = get_data_bunch(cfg)\n\nmodel = build_model(cfg, 10)\nmodel.load_params_wo_fc(torch.load('logs/2019.8.14/market/baseline/models/model_149.pth')['model'])\nlearn = Learner(data_bunch, model)\n\nfeats, _ = learn.get_preds(DatasetType.Test, activ=Lambda(lambda x: x))"
  },
  {
    "path": "fast_reid/tests/lr_scheduler_test.py",
    "content": "import sys\nimport unittest\n\nimport torch\nfrom torch import nn\n\nsys.path.append('.')\nfrom solver.lr_scheduler import WarmupMultiStepLR\nfrom solver.build import make_optimizer\nfrom config import cfg\n\n\nclass MyTestCase(unittest.TestCase):\n    def test_something(self):\n        net = nn.Linear(10, 10)\n        optimizer = make_optimizer(cfg, net)\n        lr_scheduler = WarmupMultiStepLR(optimizer, [20, 40], warmup_iters=10)\n        for i in range(50):\n            lr_scheduler.step()\n            for j in range(3):\n                print(i, lr_scheduler.get_lr()[0])\n                optimizer.step()\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "fast_reid/tests/model_test.py",
    "content": "import unittest\n\nimport torch\n\nimport sys\nsys.path.append('.')\nfrom fast_reid.fastreid.config import cfg\nfrom fast_reid.fastreid.modeling.backbones import build_resnet_backbone\nfrom fast_reid.fastreid.modeling.backbones.resnet_ibn_a import se_resnet101_ibn_a\nfrom torch import nn\n\n\nclass MyTestCase(unittest.TestCase):\n    def test_se_resnet101(self):\n        cfg.MODEL.BACKBONE.NAME = 'resnet101'\n        cfg.MODEL.BACKBONE.DEPTH = 101\n        cfg.MODEL.BACKBONE.WITH_IBN = True\n        cfg.MODEL.BACKBONE.WITH_SE = True\n        cfg.MODEL.BACKBONE.PRETRAIN_PATH = '/export/home/lxy/.cache/torch/checkpoints/se_resnet101_ibn_a.pth.tar'\n\n        net1 = build_resnet_backbone(cfg)\n        net1.cuda()\n        net2 = nn.DataParallel(se_resnet101_ibn_a())\n        res = net2.load_state_dict(torch.load(cfg.MODEL.BACKBONE.PRETRAIN_PATH)['state_dict'], strict=False)\n        net2.cuda()\n        x = torch.randn(10, 3, 256, 128).cuda()\n        y1 = net1(x)\n        y2 = net2(x)\n        assert y1.sum() == y2.sum(), 'train mode problem'\n        net1.eval()\n        net2.eval()\n        y1 = net1(x)\n        y2 = net2(x)\n        assert y1.sum() == y2.sum(), 'eval mode problem'\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "fast_reid/tests/sampler_test.py",
    "content": "import unittest\nimport sys\nsys.path.append('.')\nfrom fast_reid.fastreid.data.samplers import TrainingSampler\n\n\nclass SamplerTestCase(unittest.TestCase):\n    def test_training_sampler(self):\n        sampler = TrainingSampler(5)\n        for i in sampler:\n            from ipdb import set_trace; set_trace()\n            print(i)\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "fast_reid/tests/test_repvgg.py",
    "content": "import sys\nimport unittest\n\nimport torch\n\nsys.path.append('.')\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.modeling.backbones import build_backbone\n\n\nclass MyTestCase(unittest.TestCase):\n    def test_fusebn(self):\n        cfg = get_cfg()\n        cfg.defrost()\n        cfg.MODEL.BACKBONE.NAME = 'build_repvgg_backbone'\n        cfg.MODEL.BACKBONE.DEPTH = 'B1g2'\n        cfg.MODEL.BACKBONE.PRETRAIN = False\n        model = build_backbone(cfg)\n        model.eval()\n\n        test_inp = torch.randn((1, 3, 256, 128))\n\n        y = model(test_inp)\n\n        model.deploy(mode=True)\n        from ipdb import set_trace; set_trace()\n        fused_y = model(test_inp)\n\n        print(\"final error :\", torch.max(torch.abs(fused_y - y)).item())\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "fast_reid/tools/deploy/Caffe/ReadMe.md",
    "content": "# The Caffe in nn_tools Provides some convenient API\nIf there are some problem in parse your prototxt or caffemodel, Please replace\nthe caffe.proto with your own version and compile it with command\n                   `protoc --python_out ./ caffe.proto`\n\n## caffe_net.py\nUsing `from nn_tools.Caffe import caffe_net` to import this model\n### Prototxt\n+ `net=caffe_net.Prototxt(file_name)` to open a prototxt file\n+ `net.init_caffemodel(caffe_cmd_path='caffe')` to generate a caffemodel file in the current work directory \\\nif your `caffe` cmd not in the $PATH, specify your caffe cmd path by the `caffe_cmd_path` kwargs.\n### Caffemodel\n+ `net=caffe_net.Caffemodel(file_name)` to open a caffemodel\n+ `net.save_prototxt(path)` to save the caffemodel to a prototxt file (not containing the weight data)\n+ `net.get_layer_data(layer_name)` return the numpy ndarray data of the layer\n+ `net.set_layer_date(layer_name, datas)` specify the data of one layer in the caffemodel .`datas` is normally a list of numpy ndarray `[weights,bias]`\n+ `net.save(path)` save the changed caffemodel\n### Functions for both Prototxt and Caffemodel\n+ `net.add_layer(layer_params,before='',after='')` add a new layer with `Layer_Param` object\n+ `net.remove_layer_by_name(layer_name)` \n+ `net.get_layer_by_name(layer_name)` or `net.layer(layer_name)` get the raw Layer object defined in caffe_pb2\n"
  },
  {
    "path": "fast_reid/tools/deploy/Caffe/__init__.py",
    "content": ""
  },
  {
    "path": "fast_reid/tools/deploy/Caffe/caffe.proto",
    "content": "syntax = \"proto2\";\n\npackage caffe;\n\n// Specifies the shape (dimensions) of a Blob.\nmessage BlobShape {\n  repeated int64 dim = 1 [packed = true];\n}\n\nmessage BlobProto {\n  optional BlobShape shape = 7;\n  repeated float data = 5 [packed = true];\n  repeated float diff = 6 [packed = true];\n  repeated double double_data = 8 [packed = true];\n  repeated double double_diff = 9 [packed = true];\n\n  // 4D dimensions -- deprecated.  Use \"shape\" instead.\n  optional int32 num = 1 [default = 0];\n  optional int32 channels = 2 [default = 0];\n  optional int32 height = 3 [default = 0];\n  optional int32 width = 4 [default = 0];\n}\n\n// The BlobProtoVector is simply a way to pass multiple blobproto instances\n// around.\nmessage BlobProtoVector {\n  repeated BlobProto blobs = 1;\n}\n\nmessage Datum {\n  optional int32 channels = 1;\n  optional int32 height = 2;\n  optional int32 width = 3;\n  // the actual image data, in bytes\n  optional bytes data = 4;\n  optional int32 label = 5;\n  // Optionally, the datum could also hold float data.\n  repeated float float_data = 6;\n  // If true data contains an encoded image that need to be decoded\n  optional bool encoded = 7 [default = false];\n  repeated float labels = 8; \n}\n\n// *******************add by xia for ssd******************\n// The label (display) name and label id.\nmessage LabelMapItem {\n  // Both name and label are required.\n  optional string name = 1;\n  optional int32 label = 2;\n  // display_name is optional.\n  optional string display_name = 3;\n}\n\nmessage LabelMap {\n  repeated LabelMapItem item = 1;\n}\n\n// Sample a bbox in the normalized space [0, 1] with provided constraints.\nmessage Sampler {\n  // Minimum scale of the sampled bbox.\n  optional float min_scale = 1 [default = 1.];\n  // Maximum scale of the sampled bbox.\n  optional float max_scale = 2 [default = 1.];\n\n  // Minimum aspect ratio of the sampled bbox.\n  optional float min_aspect_ratio = 3 [default = 1.];\n  // Maximum aspect ratio of the sampled bbox.\n  optional float max_aspect_ratio = 4 [default = 1.];\n}\n\n// Constraints for selecting sampled bbox.\nmessage SampleConstraint {\n  // Minimum Jaccard overlap between sampled bbox and all bboxes in\n  // AnnotationGroup.\n  optional float min_jaccard_overlap = 1;\n  // Maximum Jaccard overlap between sampled bbox and all bboxes in\n  // AnnotationGroup.\n  optional float max_jaccard_overlap = 2;\n\n  // Minimum coverage of sampled bbox by all bboxes in AnnotationGroup.\n  optional float min_sample_coverage = 3;\n  // Maximum coverage of sampled bbox by all bboxes in AnnotationGroup.\n  optional float max_sample_coverage = 4;\n\n  // Minimum coverage of all bboxes in AnnotationGroup by sampled bbox.\n  optional float min_object_coverage = 5;\n  // Maximum coverage of all bboxes in AnnotationGroup by sampled bbox.\n  optional float max_object_coverage = 6;\n}\n\n// Sample a batch of bboxes with provided constraints.\nmessage BatchSampler {\n  // Use original image as the source for sampling.\n  optional bool use_original_image = 1 [default = true];\n\n  // Constraints for sampling bbox.\n  optional Sampler sampler = 2;\n\n  // Constraints for determining if a sampled bbox is positive or negative.\n  optional SampleConstraint sample_constraint = 3;\n\n  // If provided, break when found certain number of samples satisfing the\n  // sample_constraint.\n  optional uint32 max_sample = 4;\n\n  // Maximum number of trials for sampling to avoid infinite loop.\n  optional uint32 max_trials = 5 [default = 100];\n}\n\n// Condition for emitting annotations.\nmessage EmitConstraint {\n  enum EmitType {\n    CENTER = 0;\n    MIN_OVERLAP = 1;\n  }\n  optional EmitType emit_type = 1 [default = CENTER];\n  // If emit_type is MIN_OVERLAP, provide the emit_overlap.\n  optional float emit_overlap = 2;\n}\n\n// The normalized bounding box [0, 1] w.r.t. the input image size.\nmessage NormalizedBBox {\n  optional float xmin = 1;\n  optional float ymin = 2;\n  optional float xmax = 3;\n  optional float ymax = 4;\n  optional int32 label = 5;\n  optional bool difficult = 6;\n  optional float score = 7;\n  optional float size = 8;\n}\n\n// Annotation for each object instance.\nmessage Annotation {\n  optional int32 instance_id = 1 [default = 0];\n  optional NormalizedBBox bbox = 2;\n}\n\n// Group of annotations for a particular label.\nmessage AnnotationGroup {\n  optional int32 group_label = 1;\n  repeated Annotation annotation = 2;\n}\n\n// An extension of Datum which contains \"rich\" annotations.\nmessage AnnotatedDatum {\n  enum AnnotationType {\n    BBOX = 0;\n  }\n  optional Datum datum = 1;\n  // If there are \"rich\" annotations, specify the type of annotation.\n  // Currently it only supports bounding box.\n  // If there are no \"rich\" annotations, use label in datum instead.\n  optional AnnotationType type = 2;\n  // Each group contains annotation for a particular class.\n  repeated AnnotationGroup annotation_group = 3;\n}\n\n// *******************add by xia for mtcnn******************\nmessage MTCNNBBox {\n  optional float xmin = 1;\n  optional float ymin = 2;\n  optional float xmax = 3;\n  optional float ymax = 4;\n}\n\nmessage MTCNNDatum {\n  optional Datum datum = 1;\n  //repeated MTCNNBBox rois = 2;\n  optional MTCNNBBox roi = 2;\n  repeated float pts = 3; \n}\n//**************************************************************\n\nmessage FillerParameter {\n  // The filler type.\n  optional string type = 1 [default = 'constant'];\n  optional float value = 2 [default = 0]; // the value in constant filler\n  optional float min = 3 [default = 0]; // the min value in uniform filler\n  optional float max = 4 [default = 1]; // the max value in uniform filler\n  optional float mean = 5 [default = 0]; // the mean value in Gaussian filler\n  optional float std = 6 [default = 1]; // the std value in Gaussian filler\n  // The expected number of non-zero output weights for a given input in\n  // Gaussian filler -- the default -1 means don't perform sparsification.\n  optional int32 sparse = 7 [default = -1];\n  // Normalize the filler variance by fan_in, fan_out, or their average.\n  // Applies to 'xavier' and 'msra' fillers.\n  enum VarianceNorm {\n    FAN_IN = 0;\n    FAN_OUT = 1;\n    AVERAGE = 2;\n  }\n  optional VarianceNorm variance_norm = 8 [default = FAN_IN];\n  // added by me\n  optional string file = 9;\n}\n\nmessage NetParameter {\n  optional string name = 1; // consider giving the network a name\n  // The input blobs to the network.\n  repeated string input = 3;\n  // The shape of the input blobs.\n  repeated BlobShape input_shape = 8;\n\n  // 4D input dimensions -- deprecated.  Use \"shape\" instead.\n  // If specified, for each input blob there should be four\n  // values specifying the num, channels, height and width of the input blob.\n  // Thus, there should be a total of (4 * #input) numbers.\n  repeated int32 input_dim = 4;\n\n  // Whether the network will force every layer to carry out backward operation.\n  // If set False, then whether to carry out backward is determined\n  // automatically according to the net structure and learning rates.\n  optional bool force_backward = 5 [default = false];\n  // The current \"state\" of the network, including the phase, level, and stage.\n  // Some layers may be included/excluded depending on this state and the states\n  // specified in the layers' include and exclude fields.\n  optional NetState state = 6;\n\n  // Print debugging information about results while running Net::Forward,\n  // Net::Backward, and Net::Update.\n  optional bool debug_info = 7 [default = false];\n\n  // The layers that make up the net.  Each of their configurations, including\n  // connectivity and behavior, is specified as a LayerParameter.\n  repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.\n\n  // DEPRECATED: use 'layer' instead.\n  repeated V1LayerParameter layers = 2;\n}\n\n// NOTE\n// Update the next available ID when you add a new SolverParameter field.\n//\n// SolverParameter next available ID: 41 (last added: type)\nmessage SolverParameter {\n  //////////////////////////////////////////////////////////////////////////////\n  // Specifying the train and test networks\n  //\n  // Exactly one train net must be specified using one of the following fields:\n  //     train_net_param, train_net, net_param, net\n  // One or more test nets may be specified using any of the following fields:\n  //     test_net_param, test_net, net_param, net\n  // If more than one test net field is specified (e.g., both net and\n  // test_net are specified), they will be evaluated in the field order given\n  // above: (1) test_net_param, (2) test_net, (3) net_param/net.\n  // A test_iter must be specified for each test_net.\n  // A test_level and/or a test_stage may also be specified for each test_net.\n  //////////////////////////////////////////////////////////////////////////////\n\n  // Proto filename for the train net, possibly combined with one or more\n  // test nets.\n  optional string net = 24;\n  // Inline train net param, possibly combined with one or more test nets.\n  optional NetParameter net_param = 25;\n\n  optional string train_net = 1; // Proto filename for the train net.\n  repeated string test_net = 2; // Proto filenames for the test nets.\n  optional NetParameter train_net_param = 21; // Inline train net params.\n  repeated NetParameter test_net_param = 22; // Inline test net params.\n\n  // The states for the train/test nets. Must be unspecified or\n  // specified once per net.\n  //\n  // By default, all states will have solver = true;\n  // train_state will have phase = TRAIN,\n  // and all test_state's will have phase = TEST.\n  // Other defaults are set according to the NetState defaults.\n  optional NetState train_state = 26;\n  repeated NetState test_state = 27;\n\n  // The number of iterations for each test net.\n  repeated int32 test_iter = 3;\n\n  // The number of iterations between two testing phases.\n  optional int32 test_interval = 4 [default = 0];\n  optional bool test_compute_loss = 19 [default = false];\n  // If true, run an initial test pass before the first iteration,\n  // ensuring memory availability and printing the starting value of the loss.\n  optional bool test_initialization = 32 [default = true];\n  optional float base_lr = 5; // The base learning rate\n  // the number of iterations between displaying info. If display = 0, no info\n  // will be displayed.\n  optional int32 display = 6;\n  // Display the loss averaged over the last average_loss iterations\n  optional int32 average_loss = 33 [default = 1];\n  optional int32 max_iter = 7; // the maximum number of iterations\n  // accumulate gradients over `iter_size` x `batch_size` instances\n  optional int32 iter_size = 36 [default = 1];\n\n  // The learning rate decay policy. The currently implemented learning rate\n  // policies are as follows:\n  //    - fixed: always return base_lr.\n  //    - step: return base_lr * gamma ^ (floor(iter / step))\n  //    - exp: return base_lr * gamma ^ iter\n  //    - inv: return base_lr * (1 + gamma * iter) ^ (- power)\n  //    - multistep: similar to step but it allows non uniform steps defined by\n  //      stepvalue\n  //    - poly: the effective learning rate follows a polynomial decay, to be\n  //      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)\n  //    - sigmoid: the effective learning rate follows a sigmod decay\n  //      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))\n  //\n  // where base_lr, max_iter, gamma, step, stepvalue and power are defined\n  // in the solver parameter protocol buffer, and iter is the current iteration.\n  optional string lr_policy = 8;\n  optional float gamma = 9; // The parameter to compute the learning rate.\n  optional float power = 10; // The parameter to compute the learning rate.\n  optional float momentum = 11; // The momentum value.\n  optional float weight_decay = 12; // The weight decay.\n  // regularization types supported: L1 and L2\n  // controlled by weight_decay\n  optional string regularization_type = 29 [default = \"L2\"];\n  // the stepsize for learning rate policy \"step\"\n  optional int32 stepsize = 13;\n  // the stepsize for learning rate policy \"multistep\"\n  repeated int32 stepvalue = 34;\n  // for rate policy \"multifixed\"\n  repeated float stagelr = 50;\n  repeated int32 stageiter = 51;\n\n  // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,\n  // whenever their actual L2 norm is larger.\n  optional float clip_gradients = 35 [default = -1];\n\n  optional int32 snapshot = 14 [default = 0]; // The snapshot interval\n  optional string snapshot_prefix = 15; // The prefix for the snapshot.\n  // whether to snapshot diff in the results or not. Snapshotting diff will help\n  // debugging but the final protocol buffer size will be much larger.\n  optional bool snapshot_diff = 16 [default = false];\n  enum SnapshotFormat {\n    HDF5 = 0;\n    BINARYPROTO = 1;\n  }\n  optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];\n  // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.\n  enum SolverMode {\n    CPU = 0;\n    GPU = 1;\n  }\n  optional SolverMode solver_mode = 17 [default = GPU];\n  // the device_id will that be used in GPU mode. Use device_id = 0 in default.\n  optional int32 device_id = 18 [default = 0];\n  // If non-negative, the seed with which the Solver will initialize the Caffe\n  // random number generator -- useful for reproducible results. Otherwise,\n  // (and by default) initialize using a seed derived from the system clock.\n  optional int64 random_seed = 20 [default = -1];\n\n  // type of the solver\n  optional string type = 40 [default = \"SGD\"];\n\n  // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam\n  optional float delta = 31 [default = 1e-8];\n  // parameters for the Adam solver\n  optional float momentum2 = 39 [default = 0.999];\n\n  // RMSProp decay value\n  // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)\n  optional float rms_decay = 38;\n\n  // If true, print information about the state of the net that may help with\n  // debugging learning problems.\n  optional bool debug_info = 23 [default = false];\n\n  // If false, don't save a snapshot after training finishes.\n  optional bool snapshot_after_train = 28 [default = true];\n\n  // DEPRECATED: old solver enum types, use string instead\n  enum SolverType {\n    SGD = 0;\n    NESTEROV = 1;\n    ADAGRAD = 2;\n    RMSPROP = 3;\n    ADADELTA = 4;\n    ADAM = 5;\n  }\n  // DEPRECATED: use type instead of solver_type\n  optional SolverType solver_type = 30 [default = SGD];\n}\n\n// A message that stores the solver snapshots\nmessage SolverState {\n  optional int32 iter = 1; // The current iteration\n  optional string learned_net = 2; // The file that stores the learned net.\n  repeated BlobProto history = 3; // The history for sgd solvers\n  optional int32 current_step = 4 [default = 0]; // The current step for learning rate\n}\n\nenum Phase {\n   TRAIN = 0;\n   TEST = 1;\n}\n\nmessage NetState {\n  optional Phase phase = 1 [default = TEST];\n  optional int32 level = 2 [default = 0];\n  repeated string stage = 3;\n}\n\nmessage NetStateRule {\n  // Set phase to require the NetState have a particular phase (TRAIN or TEST)\n  // to meet this rule.\n  optional Phase phase = 1;\n\n  // Set the minimum and/or maximum levels in which the layer should be used.\n  // Leave undefined to meet the rule regardless of level.\n  optional int32 min_level = 2;\n  optional int32 max_level = 3;\n\n  // Customizable sets of stages to include or exclude.\n  // The net must have ALL of the specified stages and NONE of the specified\n  // \"not_stage\"s to meet the rule.\n  // (Use multiple NetStateRules to specify conjunctions of stages.)\n  repeated string stage = 4;\n  repeated string not_stage = 5;\n}\n\n// added by Me\nmessage SpatialTransformerParameter {\n\n\t// How to use the parameter passed by localisation network\n\toptional string transform_type = 1 [default = \"affine\"];\n\t// What is the sampling technique\n\toptional string sampler_type = 2 [default = \"bilinear\"];\n\n\t// If not set,stay same with the input dimension H and W\n\toptional int32 output_H = 3;\n\toptional int32 output_W = 4;\n\n\t// If false, only compute dTheta, DO NOT compute dU\n\toptional bool to_compute_dU = 5 [default = true];\n\n\t// The default value for some parameters\n\toptional double theta_1_1 = 6;\n\toptional double theta_1_2 = 7;\n\toptional double theta_1_3 = 8;\n\toptional double theta_2_1 = 9;\n\toptional double theta_2_2 = 10;\n\toptional double theta_2_3 = 11;\n}\n\n// added by Me\nmessage STLossParameter {\n\n\t// Indicate the resolution of the output images after ST transformation\n\trequired int32 output_H = 1;\n\trequired int32 output_W = 2;\n}\n\n// Specifies training parameters (multipliers on global learning constants,\n// and the name and other settings used for weight sharing).\nmessage ParamSpec {\n  // The names of the parameter blobs -- useful for sharing parameters among\n  // layers, but never required otherwise.  To share a parameter between two\n  // layers, give it a (non-empty) name.\n  optional string name = 1;\n\n  // Whether to require shared weights to have the same shape, or just the same\n  // count -- defaults to STRICT if unspecified.\n  optional DimCheckMode share_mode = 2;\n  enum DimCheckMode {\n    // STRICT (default) requires that num, channels, height, width each match.\n    STRICT = 0;\n    // PERMISSIVE requires only the count (num*channels*height*width) to match.\n    PERMISSIVE = 1;\n  }\n\n  // The multiplier on the global learning rate for this parameter.\n  optional float lr_mult = 3 [default = 1.0];\n\n  // The multiplier on the global weight decay for this parameter.\n  optional float decay_mult = 4 [default = 1.0];\n}\n\n// NOTE\n// Update the next available ID when you add a new LayerParameter field.\n//\n// LayerParameter next available layer-specific ID: 143 (last added: scale_param)\n\nmessage LayerParameter {\n  optional string name = 1; // the layer name\n  optional string type = 2; // the layer type\n  repeated string bottom = 3; // the name of each bottom blob\n  repeated string top = 4; // the name of each top blob\n\n  // The train / test phase for computation.\n  optional Phase phase = 10;\n\n  // The amount of weight to assign each top blob in the objective.\n  // Each layer assigns a default value, usually of either 0 or 1,\n  // to each top blob.\n  repeated float loss_weight = 5;\n\n  // Specifies training parameters (multipliers on global learning constants,\n  // and the name and other settings used for weight sharing).\n  repeated ParamSpec param = 6;\n\n  // The blobs containing the numeric parameters of the layer.\n  repeated BlobProto blobs = 7;\n\n  // Specifies on which bottoms the backpropagation should be skipped.\n  // The size must be either 0 or equal to the number of bottoms.\n  repeated bool propagate_down = 11;\n\n  // Rules controlling whether and when a layer is included in the network,\n  // based on the current NetState.  You may specify a non-zero number of rules\n  // to include OR exclude, but not both.  If no include or exclude rules are\n  // specified, the layer is always included.  If the current NetState meets\n  // ANY (i.e., one or more) of the specified rules, the layer is\n  // included/excluded.\n  repeated NetStateRule include = 8;\n  repeated NetStateRule exclude = 9;\n\n  // Parameters for data pre-processing.\n  optional TransformationParameter transform_param = 100;\n\n  // Parameters shared by loss layers.\n  optional LossParameter loss_param = 101;\n\n\n  // Yolo detection loss layer\n  optional DetectionLossParameter detection_loss_param = 200;\n  // Yolo detection evaluation layer\n  optional EvalDetectionParameter eval_detection_param = 201;\n  // Yolo 9000\n  optional RegionLossParameter region_loss_param = 202;\n  optional ReorgParameter reorg_param = 203;\n\n  // Layer type-specific parameters.\n  //\n  // Note: certain layers may have more than one computational engine\n  // for their implementation. These layers include an Engine type and\n  // engine parameter for selecting the implementation.\n  // The default for the engine is set by the ENGINE switch at compile-time.\n  optional AccuracyParameter accuracy_param = 102;\n  optional ArgMaxParameter argmax_param = 103;\n  optional BatchNormParameter batch_norm_param = 139;\n  optional BiasParameter bias_param = 141;\n  optional ConcatParameter concat_param = 104;\n  optional ContrastiveLossParameter contrastive_loss_param = 105;\n  optional ConvolutionParameter convolution_param = 106;\n  optional DataParameter data_param = 107;\n  optional DropoutParameter dropout_param = 108;\n  optional DummyDataParameter dummy_data_param = 109;\n  optional EltwiseParameter eltwise_param = 110;\n  optional ELUParameter elu_param = 140;\n  optional EmbedParameter embed_param = 137;\n  optional ExpParameter exp_param = 111;\n  optional FlattenParameter flatten_param = 135;\n  optional HDF5DataParameter hdf5_data_param = 112;\n  optional HDF5OutputParameter hdf5_output_param = 113;\n  optional HingeLossParameter hinge_loss_param = 114;\n  optional ImageDataParameter image_data_param = 115;\n  optional InfogainLossParameter infogain_loss_param = 116;\n  optional InnerProductParameter inner_product_param = 117;\n  optional InputParameter input_param = 143;\n  optional LogParameter log_param = 134;\n  optional LRNParameter lrn_param = 118;\n  optional MemoryDataParameter memory_data_param = 119;\n  optional MVNParameter mvn_param = 120;\n  optional PoolingParameter pooling_param = 121;\n  optional PowerParameter power_param = 122;\n  optional PReLUParameter prelu_param = 131;\n  optional PythonParameter python_param = 130;\n  optional RecurrentParameter recurrent_param = 146;\n  optional ReductionParameter reduction_param = 136;\n  optional ReLUParameter relu_param = 123;\n  optional ReshapeParameter reshape_param = 133;\n  optional ROIPoolingParameter roi_pooling_param = 8266711; //roi pooling\n  optional ScaleParameter scale_param = 142;\n  optional SigmoidParameter sigmoid_param = 124;\n  optional SmoothL1LossParameter smooth_l1_loss_param = 8266712;\n  optional SoftmaxParameter softmax_param = 125;\n  optional SPPParameter spp_param = 132;\n  optional SliceParameter slice_param = 126;\n  optional TanHParameter tanh_param = 127;\n  optional ThresholdParameter threshold_param = 128;\n  optional TileParameter tile_param = 138;\n  optional WindowDataParameter window_data_param = 129;\n\n  // added by Me\n  optional SpatialTransformerParameter st_param = 148;\n  optional STLossParameter st_loss_param = 145;\n  //***************add by xia**************************\n  optional RPNParameter rpn_param = 150;                  //  rpn\n  optional FocalLossParameter focal_loss_param = 155;  // Focal Loss layer\n\n  optional AsdnDataParameter asdn_data_param = 159; //asdn\n\n  optional BNParameter bn_param = 160;  //bn\n  optional MTCNNDataParameter mtcnn_data_param = 161; //mtcnn\n\n  optional InterpParameter interp_param = 162;  //Interp\n  \n  optional PSROIPoolingParameter psroi_pooling_param = 163; //rfcn\n\n  //**************************ssd*******************************************\n  optional AnnotatedDataParameter annotated_data_param = 164; //ssd\n  optional PriorBoxParameter prior_box_param = 165;\n  optional CropParameter crop_param = 167;\n  optional DetectionEvaluateParameter detection_evaluate_param = 168;\n  optional DetectionOutputParameter detection_output_param = 169;\n  //optional NormalizeParameter normalize_param = 170;\n  optional MultiBoxLossParameter multibox_loss_param = 171;\n  optional PermuteParameter permute_param = 172;\n  optional VideoDataParameter video_data_param = 173;\n\n  //*************************a softmax loss***********************************\n  optional MarginInnerProductParameter margin_inner_product_param = 174;\n\n  //*************************center loss***********************************\n  optional CenterLossParameter center_loss_param = 175;\n\n  //*************************deformabel conv***********************************\n  optional DeformableConvolutionParameter deformable_convolution_param = 176;\n\n  //***************Additive Margin Softmax for Face Verification***************\n  optional LabelSpecificAddParameter label_specific_add_param = 177;\n\n  optional AdditiveMarginInnerProductParameter additive_margin_inner_product_param = 178;\n  optional CosinAddmParameter cosin_add_m_param = 179;\n  optional CosinMulmParameter cosin_mul_m_param = 180;\n  optional ChannelScaleParameter channel_scale_param = 181;\n  optional FlipParameter flip_param = 182;\n  optional TripletLossParameter triplet_loss_param = 183;\n  optional CoupledClusterLossParameter coupled_cluster_loss_param = 184;\n  optional GeneralTripletParameter general_triplet_loss_param = 185;\n\n  optional ROIAlignParameter roi_align_param = 186;\n\n  //**************add by wdd***************\n  optional UpsampleParameter  upsample_param = 100003;\n  optional MatMulParameter matmul_param = 100005;\n  optional PassThroughParameter pass_through_param = 100004;\n  optional NormalizeParameter norm_param = 100001;\n}\n\n//*********************add by wdd******************\nmessage UpsampleParameter {\n  optional uint32 scale = 1 [default = 2];\n  optional uint32 scale_h = 2;\n  optional uint32 scale_w = 3;\n  optional bool pad_out_h = 4 [default = false];\n  optional bool pad_out_w = 5 [default = false];\n  optional uint32 upsample_h = 6;\n  optional uint32 upsample_w = 7;\n}\n\nmessage MatMulParameter {\n  optional uint32 dim_1 = 1;//row of input matrix one\n  optional uint32 dim_2 = 2;//column of input matrix one and row of input matrix two\n  optional uint32 dim_3 = 3;//column of input matrix two\n}\n\nmessage PassThroughParameter {\n  optional uint32 num_output = 1 [default = 0];\n  optional uint32 block_height = 2 [default = 0];\n  optional uint32 block_width = 3 [default = 0];\n}\n\nmessage NormalizeParameter{\noptional bool across_spatial = 1 [default = true];\noptional FillerParameter scale_filler = 2;\noptional bool channel_shared = 3 [default = true];\noptional float eps = 4 [default = 1e-10];\noptional float sqrt_a = 5 [default = 1];\n}\n\n\n\n//*******************add by xia****ssd data*********\nmessage AnnotatedDataParameter {\n  // Define the sampler.\n  repeated BatchSampler batch_sampler = 1;\n  // Store label name and label id in LabelMap format.\n  optional string label_map_file = 2;\n  // If provided, it will replace the AnnotationType stored in each\n  // AnnotatedDatum.\n  optional AnnotatedDatum.AnnotationType anno_type = 3;\n}\n\n//*******************add by xia****asdn data*********\nmessage AsdnDataParameter{\n  optional int32 count_drop = 1 [default = 15];\n  optional int32 permute_count = 2 [default = 20];\n  optional int32 count_drop_neg = 3 [default = 0];\n  optional int32 channels = 4 [default = 1024];\n  optional int32 iter_size = 5 [default = 2];\n  optional int32 maintain_before = 6 [default = 1];\n}\n\n//*******************add by xia****mtcnn*********\nmessage MTCNNDataParameter{\n  optional bool augmented = 1 [default = true];\n  optional bool flip = 2 [default = true];\n\n  // -1 means batch_size\n  optional int32 num_positive = 3 [default = -1];\n  optional int32 num_negitive = 4 [default = -1];\n  optional int32 num_part = 5 [default = -1];\n  optional uint32 resize_width = 6 [default = 0];\n  optional uint32 resize_height = 7 [default = 0];\n  optional float min_negitive_scale = 8 [default = 0.5];\n  optional float max_negitive_scale = 9 [default = 1.5];\n}\n\n//***************add by xia******InterpLayer*********\nmessage InterpParameter {\n  optional int32 height = 1 [default = 0]; // Height of output\n  optional int32 width = 2 [default = 0]; // Width of output\n  optional int32 zoom_factor = 3 [default = 1]; // zoom factor\n  optional int32 shrink_factor = 4 [default = 1]; // shrink factor\n  optional int32 pad_beg = 5 [default = 0]; // padding at begin of input\n  optional int32 pad_end = 6 [default = 0]; // padding at end of input\n}\n//*******************add by xia******rfcn********************************\n\nmessage PSROIPoolingParameter {\n   required float spatial_scale = 1; \n   required int32 output_dim = 2; // output channel number\n   required int32 group_size = 3; // number of groups to encode position-sensitive score maps\n}\n//***************************************************\nmessage FlipParameter {\n  optional bool flip_width = 1 [default = true];\n  optional bool flip_height = 2 [default = false];\n}\n\nmessage BNParameter {\n  optional FillerParameter slope_filler = 1;\n  optional FillerParameter bias_filler = 2;\n  optional float momentum = 3 [default = 0.9];\n  optional float eps = 4 [default = 1e-5];\n  // If true, will use the moving average mean and std for training and test.\n  // Will override the lr_param and freeze all the parameters.\n  // Make sure to initialize the layer properly with pretrained parameters.\n  optional bool frozen = 5 [default = false];\n  enum Engine {\n    DEFAULT = 0;\n    CAFFE = 1;\n    CUDNN = 2;\n  }\n  optional Engine engine = 6 [default = DEFAULT];\n}\n\n//************************add by xia*******************************\n// Focal Loss for Dense Object Detection\nmessage FocalLossParameter {\n  enum Type {\n    ORIGIN = 0; // FL(p_t)  = -(1 - p_t) ^ gama * log(p_t), where p_t = p if y == 1 else 1 - p, whre p = sigmoid(x)\n    LINEAR = 1; // FL*(p_t) = -log(p_t) / gama, where p_t = sigmoid(gama * x_t + beta), where x_t = x * y, y is the ground truth label {-1, 1}\n  }\n  optional Type type   = 1 [default = ORIGIN]; \n  optional float gamma = 2 [default = 2];\n  // cross-categories weights to solve the imbalance problem\n  optional float alpha = 3 [default = 0.25]; \n  optional float beta  = 4 [default = 1.0];\n}\n//**************************FocalLoss****************************************\n\n// Message that stores parameters used to apply transformation\n// to the data layer's data\nmessage TransformationParameter {\n  // For data pre-processing, we can do simple scaling and subtracting the\n  // data mean, if provided. Note that the mean subtraction is always carried\n  // out before scaling.\n  optional float scale = 1 [default = 1];\n  // Specify if we want to randomly mirror data.\n  optional bool mirror = 2 [default = false];\n  // Specify if we would like to randomly crop an image.\n  optional uint32 crop_size = 3 [default = 0];\n  optional uint32 crop_h = 11 [default = 0];\n  optional uint32 crop_w = 12 [default = 0];\n\n  // mean_file and mean_value cannot be specified at the same time\n  optional string mean_file = 4;\n  // if specified can be repeated once (would substract it from all the channels)\n  // or can be repeated the same number of times as channels\n  // (would subtract them from the corresponding channel)\n  repeated float mean_value = 5;\n  // Force the decoded image to have 3 color channels.\n  optional bool force_color = 6 [default = false];\n  // Force the decoded image to have 1 color channels.\n  optional bool force_gray = 7 [default = false];\n\n  // Resize policy\n  optional ResizeParameter resize_param = 8;\n  // Noise policy\n  optional NoiseParameter noise_param = 9;\n  // Distortion policy\n  optional DistortionParameter distort_param = 13;\n  // Expand policy\n  optional ExpansionParameter expand_param = 14;\n  // Constraint for emitting the annotation after transformation.\n  optional EmitConstraint emit_constraint = 10;\n}\n\n//*******************add by xia****ssd******************************************************\n// Message that stores parameters used by data transformer for resize policy\nmessage ResizeParameter {\n  //Probability of using this resize policy\n  optional float prob = 1 [default = 1];\n\n  enum Resize_mode {\n    WARP = 1;\n    FIT_SMALL_SIZE = 2;\n    FIT_LARGE_SIZE_AND_PAD = 3;\n  }\n  optional Resize_mode resize_mode = 2 [default = WARP];\n  optional uint32 height = 3 [default = 0];\n  optional uint32 width = 4 [default = 0];\n  // A parameter used to update bbox in FIT_SMALL_SIZE mode.\n  optional uint32 height_scale = 8 [default = 0];\n  optional uint32 width_scale = 9 [default = 0];\n\n  enum Pad_mode {\n    CONSTANT = 1;\n    MIRRORED = 2;\n    REPEAT_NEAREST = 3;\n  }\n  // Padding mode for BE_SMALL_SIZE_AND_PAD mode and object centering\n  optional Pad_mode pad_mode = 5 [default = CONSTANT];\n  // if specified can be repeated once (would fill all the channels)\n  // or can be repeated the same number of times as channels\n  // (would use it them to the corresponding channel)\n  repeated float pad_value = 6;\n\n  enum Interp_mode { //Same as in OpenCV\n    LINEAR = 1;\n    AREA = 2;\n    NEAREST = 3;\n    CUBIC = 4;\n    LANCZOS4 = 5;\n  }\n  //interpolation for for resizing\n  repeated Interp_mode interp_mode = 7;\n}\n\nmessage SaltPepperParameter {\n  //Percentage of pixels\n  optional float fraction = 1 [default = 0];\n  repeated float value = 2;\n}\n\n// Message that stores parameters used by data transformer for transformation\n// policy\nmessage NoiseParameter {\n  //Probability of using this resize policy\n  optional float prob = 1 [default = 0];\n  // Histogram equalized\n  optional bool hist_eq = 2 [default = false];\n  // Color inversion\n  optional bool inverse = 3 [default = false];\n  // Grayscale\n  optional bool decolorize = 4 [default = false];\n  // Gaussian blur\n  optional bool gauss_blur = 5 [default = false];\n\n  // JPEG compression quality (-1 = no compression)\n  optional float jpeg = 6 [default = -1];\n\n  // Posterization\n  optional bool posterize = 7 [default = false];\n\n  // Erosion\n  optional bool erode = 8 [default = false];\n\n  // Salt-and-pepper noise\n  optional bool saltpepper = 9 [default = false];\n\n  optional SaltPepperParameter saltpepper_param = 10;\n\n  // Local histogram equalization\n  optional bool clahe = 11 [default = false];\n\n  // Color space conversion\n  optional bool convert_to_hsv = 12 [default = false];\n\n  // Color space conversion\n  optional bool convert_to_lab = 13 [default = false];\n}\n\n// Message that stores parameters used by data transformer for distortion policy\nmessage DistortionParameter {\n  // The probability of adjusting brightness.\n  optional float brightness_prob = 1 [default = 0.0];\n  // Amount to add to the pixel values within [-delta, delta].\n  // The possible value is within [0, 255]. Recommend 32.\n  optional float brightness_delta = 2 [default = 0.0];\n\n  // The probability of adjusting contrast.\n  optional float contrast_prob = 3 [default = 0.0];\n  // Lower bound for random contrast factor. Recommend 0.5.\n  optional float contrast_lower = 4 [default = 0.0];\n  // Upper bound for random contrast factor. Recommend 1.5.\n  optional float contrast_upper = 5 [default = 0.0];\n\n  // The probability of adjusting hue.\n  optional float hue_prob = 6 [default = 0.0];\n  // Amount to add to the hue channel within [-delta, delta].\n  // The possible value is within [0, 180]. Recommend 36.\n  optional float hue_delta = 7 [default = 0.0];\n\n  // The probability of adjusting saturation.\n  optional float saturation_prob = 8 [default = 0.0];\n  // Lower bound for the random saturation factor. Recommend 0.5.\n  optional float saturation_lower = 9 [default = 0.0];\n  // Upper bound for the random saturation factor. Recommend 1.5.\n  optional float saturation_upper = 10 [default = 0.0];\n\n  // The probability of randomly order the image channels.\n  optional float random_order_prob = 11 [default = 0.0];\n}\n\n// Message that stores parameters used by data transformer for expansion policy\nmessage ExpansionParameter {\n  //Probability of using this expansion policy\n  optional float prob = 1 [default = 1];\n\n  // The ratio to expand the image.\n  optional float max_expand_ratio = 2 [default = 1.];\n}\n\n//**************************************************************************************************\n\n// Message that stores parameters shared by loss layers\nmessage LossParameter {\n  // If specified, ignore instances with the given label.\n  optional int32 ignore_label = 1;\n  // How to normalize the loss for loss layers that aggregate across batches,\n  // spatial dimensions, or other dimensions.  Currently only implemented in\n  // SoftmaxWithLoss layer.\n  enum NormalizationMode {\n    // Divide by the number of examples in the batch times spatial dimensions.\n    // Outputs that receive the ignore label will NOT be ignored in computing\n    // the normalization factor.\n    FULL = 0;\n    // Divide by the total number of output locations that do not take the \n    // ignore_label.  If ignore_label is not set, this behaves like FULL.\n    VALID = 1;\n    // Divide by the batch size.\n    BATCH_SIZE = 2;\n    // Do not normalize the loss.\n    NONE = 3;\n  }\n  optional NormalizationMode normalization = 3 [default = VALID];\n  // Deprecated.  Ignored if normalization is specified.  If normalization\n  // is not specified, then setting this to false will be equivalent to\n  // normalization = BATCH_SIZE to be consistent with previous behavior.\n  optional bool normalize = 2;\n}\n\n// Messages that store parameters used by individual layer types follow, in\n// alphabetical order.\n\nmessage AccuracyParameter {\n  // When computing accuracy, count as correct by comparing the true label to\n  // the top k scoring classes.  By default, only compare to the top scoring\n  // class (i.e. argmax).\n  optional uint32 top_k = 1 [default = 1];\n\n  // The \"label\" axis of the prediction blob, whose argmax corresponds to the\n  // predicted label -- may be negative to index from the end (e.g., -1 for the\n  // last axis).  For example, if axis == 1 and the predictions are\n  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth\n  // labels with integer values in {0, 1, ..., C-1}.\n  optional int32 axis = 2 [default = 1];\n\n  // If specified, ignore instances with the given label.\n  optional int32 ignore_label = 3;\n}\n\nmessage ArgMaxParameter {\n  // If true produce pairs (argmax, maxval)\n  optional bool out_max_val = 1 [default = false];\n  optional uint32 top_k = 2 [default = 1];\n  // The axis along which to maximise -- may be negative to index from the\n  // end (e.g., -1 for the last axis).\n  // By default ArgMaxLayer maximizes over the flattened trailing dimensions\n  // for each index of the first / num dimension.\n  optional int32 axis = 3;\n}\n\nmessage ConcatParameter {\n  // The axis along which to concatenate -- may be negative to index from the\n  // end (e.g., -1 for the last axis).  Other axes must have the\n  // same dimension for all the bottom blobs.\n  // By default, ConcatLayer concatenates blobs along the \"channels\" axis (1).\n  optional int32 axis = 2 [default = 1];\n\n  // DEPRECATED: alias for \"axis\" -- does not support negative indexing.\n  optional uint32 concat_dim = 1 [default = 1];\n}\n\nmessage BatchNormParameter {\n  // If false, accumulate global mean/variance values via a moving average. If\n  // true, use those accumulated values instead of computing mean/variance\n  // across the batch.\n  optional bool use_global_stats = 1;\n  // How much does the moving average decay each iteration?\n  optional float moving_average_fraction = 2 [default = .999];\n  // Small value to add to the variance estimate so that we don't divide by\n  // zero.\n  optional float eps = 3 [default = 1e-5];\n}\n\nmessage BiasParameter {\n  // The first axis of bottom[0] (the first input Blob) along which to apply\n  // bottom[1] (the second input Blob).  May be negative to index from the end\n  // (e.g., -1 for the last axis).\n  //\n  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output\n  // top[0] will have the same shape, and bottom[1] may have any of the\n  // following shapes (for the given value of axis):\n  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60\n  //    (axis == 1 == -3)          3;     3x40;     3x40x60\n  //    (axis == 2 == -2)                   40;       40x60\n  //    (axis == 3 == -1)                                60\n  // Furthermore, bottom[1] may have the empty shape (regardless of the value of\n  // \"axis\") -- a scalar bias.\n  optional int32 axis = 1 [default = 1];\n\n  // (num_axes is ignored unless just one bottom is given and the bias is\n  // a learned parameter of the layer.  Otherwise, num_axes is determined by the\n  // number of axes by the second bottom.)\n  // The number of axes of the input (bottom[0]) covered by the bias\n  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.\n  // Set num_axes := 0, to add a zero-axis Blob: a scalar.\n  optional int32 num_axes = 2 [default = 1];\n\n  // (filler is ignored unless just one bottom is given and the bias is\n  // a learned parameter of the layer.)\n  // The initialization for the learned bias parameter.\n  // Default is the zero (0) initialization, resulting in the BiasLayer\n  // initially performing the identity operation.\n  optional FillerParameter filler = 3;\n}\n\nmessage ContrastiveLossParameter {\n  // margin for dissimilar pair\n  optional float margin = 1 [default = 1.0];\n  // The first implementation of this cost did not exactly match the cost of\n  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.\n  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the\n  // Hadsell paper. New models should probably use this version.\n  // legacy_version = true uses (margin - d^2). This is kept to support /\n  // reproduce existing models and results\n  optional bool legacy_version = 2 [default = false];\n}\n\nmessage DetectionLossParameter {\n  // Yolo detection loss layer\n  optional uint32 side = 1 [default = 7];\n  optional uint32 num_class = 2 [default = 20];\n  optional uint32 num_object = 3 [default = 2];\n  optional float object_scale = 4 [default = 1.0];\n  optional float noobject_scale = 5 [default = 0.5];\n  optional float class_scale = 6 [default = 1.0];\n  optional float coord_scale = 7 [default = 5.0];\n  optional bool sqrt = 8 [default = true];\n  optional bool constriant = 9 [default = false];\n}\n\nmessage RegionLossParameter{\n  //Yolo 9000\n  optional uint32 side = 1 [default = 13];\n  optional uint32 num_class = 2 [default = 20];\n  optional uint32 bias_match = 3 [default = 1];\n  optional uint32 coords = 4 [default = 4];\n  optional uint32 num = 5 [default = 5];\n  optional uint32 softmax = 6 [default = 1];\n  optional float jitter = 7 [default = 0.2];\n  optional uint32 rescore = 8 [default = 1];\n  \n  optional float object_scale = 9 [default = 1.0];\n  optional float class_scale = 10 [default = 1.0];\n  optional float noobject_scale = 11 [default = 0.5];\n  optional float coord_scale = 12 [default = 5.0];\n  optional uint32 absolute = 13 [default = 1];\n  optional float thresh = 14 [default = 0.2];\n  optional uint32 random = 15 [default = 1];\n  repeated float biases = 16;\n  optional string softmax_tree = 17;\n  optional string class_map = 18;\n}\n\nmessage ReorgParameter {\n  optional uint32 stride = 1;\n  optional bool reverse = 2 [default = false];\n}\n\nmessage EvalDetectionParameter {\n  enum ScoreType {\n    OBJ = 0;\n    PROB = 1;\n    MULTIPLY = 2;\n  }\n  // Yolo detection evaluation layer\n  optional uint32 side = 1 [default = 7];\n  optional uint32 num_class = 2 [default = 20];\n  optional uint32 num_object = 3 [default = 2];\n  optional float threshold = 4 [default = 0.5];\n  optional bool sqrt = 5 [default = true];\n  optional bool constriant = 6 [default = true];\n  optional ScoreType score_type = 7 [default = MULTIPLY];\n  optional float nms = 8 [default = -1];\n  repeated float biases = 9;\n}\n\n\nmessage ConvolutionParameter {\n  optional uint32 num_output = 1; // The number of outputs for the layer\n  optional bool bias_term = 2 [default = true]; // whether to have bias terms\n\n  // Pad, kernel size, and stride are all given as a single value for equal\n  // dimensions in all spatial dimensions, or once per spatial dimension.\n  repeated uint32 pad = 3; // The padding size; defaults to 0\n  repeated uint32 kernel_size = 4; // The kernel size\n  repeated uint32 stride = 6; // The stride; defaults to 1\n  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting\n  // holes. (Kernel dilation is sometimes referred to by its use in the\n  // algorithme à trous from Holschneider et al. 1987.)\n  repeated uint32 dilation = 18; // The dilation; defaults to 1\n\n  // For 2D convolution only, the *_h and *_w versions may also be used to\n  // specify both spatial dimensions.\n  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)\n  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)\n  optional uint32 kernel_h = 11; // The kernel height (2D only)\n  optional uint32 kernel_w = 12; // The kernel width (2D only)\n  optional uint32 stride_h = 13; // The stride height (2D only)\n  optional uint32 stride_w = 14; // The stride width (2D only)\n\n  optional uint32 group = 5 [default = 1]; // The group size for group conv\n\n  optional FillerParameter weight_filler = 7; // The filler for the weight\n  optional FillerParameter bias_filler = 8; // The filler for the bias\n  enum Engine {\n    DEFAULT = 0;\n    CAFFE = 1;\n    CUDNN = 2;\n  }\n  optional Engine engine = 15 [default = DEFAULT];\n\n  // The axis to interpret as \"channels\" when performing convolution.\n  // Preceding dimensions are treated as independent inputs;\n  // succeeding dimensions are treated as \"spatial\".\n  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform\n  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for\n  // groups g>1) filters across the spatial axes (H, W) of the input.\n  // With (N, C, D, H, W) inputs, and axis == 1, we perform\n  // N independent 3D convolutions, sliding (C/g)-channels\n  // filters across the spatial axes (D, H, W) of the input.\n  optional int32 axis = 16 [default = 1];\n\n  // Whether to force use of the general ND convolution, even if a specific\n  // implementation for blobs of the appropriate number of spatial dimensions\n  // is available. (Currently, there is only a 2D-specific convolution\n  // implementation; for input blobs with num_axes != 2, this option is\n  // ignored and the ND implementation will be used.)\n  optional bool force_nd_im2col = 17 [default = false];\n}\n\nmessage CropParameter {\n  // To crop, elements of the first bottom are selected to fit the dimensions\n  // of the second, reference bottom. The crop is configured by\n  // - the crop `axis` to pick the dimensions for cropping\n  // - the crop `offset` to set the shift for all/each dimension\n  // to align the cropped bottom with the reference bottom.\n  // All dimensions up to but excluding `axis` are preserved, while\n  // the dimensions including and trailing `axis` are cropped.\n  // If only one `offset` is set, then all dimensions are offset by this amount.\n  // Otherwise, the number of offsets must equal the number of cropped axes to\n  // shift the crop in each dimension accordingly.\n  // Note: standard dimensions are N,C,H,W so the default is a spatial crop,\n  // and `axis` may be negative to index from the end (e.g., -1 for the last\n  // axis).\n  optional int32 axis = 1 [default = 2];\n  repeated uint32 offset = 2;\n}\n\n\nmessage DataParameter {\n  enum DB {\n    LEVELDB = 0;\n    LMDB = 1;\n  }\n  // Specify the data source.\n  optional string source = 1;\n  // Specify the batch size.\n  optional uint32 batch_size = 4;\n  // The rand_skip variable is for the data layer to skip a few data points\n  // to avoid all asynchronous sgd clients to start at the same point. The skip\n  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not\n  // be larger than the number of keys in the database.\n  // DEPRECATED. Each solver accesses a different subset of the database.\n  optional uint32 rand_skip = 7 [default = 0];\n  optional DB backend = 8 [default = LEVELDB];\n  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do\n  // simple scaling and subtracting the data mean, if provided. Note that the\n  // mean subtraction is always carried out before scaling.\n  optional float scale = 2 [default = 1];\n  optional string mean_file = 3;\n  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly\n  // crop an image.\n  optional uint32 crop_size = 5 [default = 0];\n  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror\n  // data.\n  optional bool mirror = 6 [default = false];\n  // Force the encoded image to have 3 color channels\n  optional bool force_encoded_color = 9 [default = false];\n  // Prefetch queue (Number of batches to prefetch to host memory, increase if\n  // data access bandwidth varies).\n  optional uint32 prefetch = 10 [default = 4];\n  \n  repeated uint32 side = 11;\n}\n\n//**********************************ssd*******************************************\n\n// Message that store parameters used by DetectionEvaluateLayer\nmessage DetectionEvaluateParameter {\n  // Number of classes that are actually predicted. Required!\n  optional uint32 num_classes = 1;\n  // Label id for background class. Needed for sanity check so that\n  // background class is neither in the ground truth nor the detections.\n  optional uint32 background_label_id = 2 [default = 0];\n  // Threshold for deciding true/false positive.\n  optional float overlap_threshold = 3 [default = 0.5];\n  // If true, also consider difficult ground truth for evaluation.\n  optional bool evaluate_difficult_gt = 4 [default = true];\n  // A file which contains a list of names and sizes with same order\n  // of the input DB. The file is in the following format:\n  //    name height width\n  //    ...\n  // If provided, we will scale the prediction and ground truth NormalizedBBox\n  // for evaluation.\n  optional string name_size_file = 5;\n  // The resize parameter used in converting NormalizedBBox to original image.\n  optional ResizeParameter resize_param = 6;\n}\n\nmessage NonMaximumSuppressionParameter {\n  // Threshold to be used in nms.\n  optional float nms_threshold = 1 [default = 0.3];\n  // Maximum number of results to be kept.\n  optional int32 top_k = 2;\n  // Parameter for adaptive nms.\n  optional float eta = 3 [default = 1.0];\n}\n\nmessage SaveOutputParameter {\n  // Output directory. If not empty, we will save the results.\n  optional string output_directory = 1;\n  // Output name prefix.\n  optional string output_name_prefix = 2;\n  // Output format.\n  //    VOC - PASCAL VOC output format.\n  //    COCO - MS COCO output format.\n  optional string output_format = 3;\n  // If you want to output results, must also provide the following two files.\n  // Otherwise, we will ignore saving results.\n  // label map file.\n  optional string label_map_file = 4;\n  // A file which contains a list of names and sizes with same order\n  // of the input DB. The file is in the following format:\n  //    name height width\n  //    ...\n  optional string name_size_file = 5;\n  // Number of test images. It can be less than the lines specified in\n  // name_size_file. For example, when we only want to evaluate on part\n  // of the test images.\n  optional uint32 num_test_image = 6;\n  // The resize parameter used in saving the data.\n  optional ResizeParameter resize_param = 7;\n}\n\n\n// Message that store parameters used by DetectionOutputLayer\nmessage DetectionOutputParameter {\n  // Number of classes to be predicted. Required!\n  optional uint32 num_classes = 1;\n  // If true, bounding box are shared among different classes.\n  optional bool share_location = 2 [default = true];\n  // Background label id. If there is no background class,\n  // set it as -1.\n  optional int32 background_label_id = 3 [default = 0];\n  // Parameters used for non maximum suppression.\n  optional NonMaximumSuppressionParameter nms_param = 4;\n  // Parameters used for saving detection results.\n  optional SaveOutputParameter save_output_param = 5;\n  // Type of coding method for bbox.\n  optional PriorBoxParameter.CodeType code_type = 6 [default = CORNER];\n  // If true, variance is encoded in target; otherwise we need to adjust the\n  // predicted offset accordingly.\n  optional bool variance_encoded_in_target = 8 [default = false];\n  // Number of total bboxes to be kept per image after nms step.\n  // -1 means keeping all bboxes after nms step.\n  optional int32 keep_top_k = 7 [default = -1];\n  // Only consider detections whose confidences are larger than a threshold.\n  // If not provided, consider all boxes.\n  optional float confidence_threshold = 9;\n  // If true, visualize the detection results.\n  optional bool visualize = 10 [default = false];\n  // The threshold used to visualize the detection results.\n  optional float visualize_threshold = 11;\n  // If provided, save outputs to video file.\n  optional string save_file = 12;\n}\n//*******************************************************************************\n\nmessage DropoutParameter {\n  optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio\n  optional bool scale_train = 2 [default = true];  // scale train or test phase\n}\n\n// DummyDataLayer fills any number of arbitrarily shaped blobs with random\n// (or constant) data generated by \"Fillers\" (see \"message FillerParameter\").\nmessage DummyDataParameter {\n  // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N\n  // shape fields, and 0, 1 or N data_fillers.\n  //\n  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.\n  // If 1 data_filler is specified, it is applied to all top blobs.  If N are\n  // specified, the ith is applied to the ith top blob.\n  repeated FillerParameter data_filler = 1;\n  repeated BlobShape shape = 6;\n\n  // 4D dimensions -- deprecated.  Use \"shape\" instead.\n  repeated uint32 num = 2;\n  repeated uint32 channels = 3;\n  repeated uint32 height = 4;\n  repeated uint32 width = 5;\n}\n\nmessage EltwiseParameter {\n  enum EltwiseOp {\n    PROD = 0;\n    SUM = 1;\n    MAX = 2;\n  }\n  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation\n  repeated float coeff = 2; // blob-wise coefficient for SUM operation\n\n  // Whether to use an asymptotically slower (for >2 inputs) but stabler method\n  // of computing the gradient for the PROD operation. (No effect for SUM op.)\n  optional bool stable_prod_grad = 3 [default = true];\n}\n\n// Message that stores parameters used by ELULayer\nmessage ELUParameter {\n  // Described in:\n  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate \n  // Deep Network Learning by Exponential Linear Units (ELUs). arXiv\n  optional float alpha = 1 [default = 1];\n}\n\n// Message that stores parameters used by EmbedLayer\nmessage EmbedParameter {\n  optional uint32 num_output = 1; // The number of outputs for the layer\n  // The input is given as integers to be interpreted as one-hot\n  // vector indices with dimension num_input.  Hence num_input should be\n  // 1 greater than the maximum possible input value.\n  optional uint32 input_dim = 2;\n\n  optional bool bias_term = 3 [default = true]; // Whether to use a bias term\n  optional FillerParameter weight_filler = 4; // The filler for the weight\n  optional FillerParameter bias_filler = 5; // The filler for the bias\n\n}\n\n// Message that stores parameters used by ExpLayer\nmessage ExpParameter {\n  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.\n  // Or if base is set to the default (-1), base is set to e,\n  // so y = exp(shift + scale * x).\n  optional float base = 1 [default = -1.0];\n  optional float scale = 2 [default = 1.0];\n  optional float shift = 3 [default = 0.0];\n}\n\n/// Message that stores parameters used by FlattenLayer\nmessage FlattenParameter {\n  // The first axis to flatten: all preceding axes are retained in the output.\n  // May be negative to index from the end (e.g., -1 for the last axis).\n  optional int32 axis = 1 [default = 1];\n\n  // The last axis to flatten: all following axes are retained in the output.\n  // May be negative to index from the end (e.g., the default -1 for the last\n  // axis).\n  optional int32 end_axis = 2 [default = -1];\n}\n\n// Message that stores parameters used by HDF5DataLayer\nmessage HDF5DataParameter {\n  // Specify the data source.\n  optional string source = 1;\n  // Specify the batch size.\n  optional uint32 batch_size = 2;\n\n  // Specify whether to shuffle the data.\n  // If shuffle == true, the ordering of the HDF5 files is shuffled,\n  // and the ordering of data within any given HDF5 file is shuffled,\n  // but data between different files are not interleaved; all of a file's\n  // data are output (in a random order) before moving onto another file.\n  optional bool shuffle = 3 [default = false];\n}\n\nmessage HDF5OutputParameter {\n  optional string file_name = 1;\n}\n\nmessage HingeLossParameter {\n  enum Norm {\n    L1 = 1;\n    L2 = 2;\n  }\n  // Specify the Norm to use L1 or L2\n  optional Norm norm = 1 [default = L1];\n}\n\nmessage ImageDataParameter {\n  // Specify the data source.\n  optional string source = 1;\n  // Specify the batch size.\n  optional uint32 batch_size = 4 [default = 1];\n  // The rand_skip variable is for the data layer to skip a few data points\n  // to avoid all asynchronous sgd clients to start at the same point. The skip\n  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not\n  // be larger than the number of keys in the database.\n  optional uint32 rand_skip = 7 [default = 0];\n  // Whether or not ImageLayer should shuffle the list of files at every epoch.\n  optional bool shuffle = 8 [default = false];\n  // It will also resize images if new_height or new_width are not zero.\n  optional uint32 new_height = 9 [default = 0];\n  optional uint32 new_width = 10 [default = 0];\n  // Specify if the images are color or gray\n  optional bool is_color = 11 [default = true];\n  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do\n  // simple scaling and subtracting the data mean, if provided. Note that the\n  // mean subtraction is always carried out before scaling.\n  optional float scale = 2 [default = 1];\n  optional string mean_file = 3;\n  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly\n  // crop an image.\n  optional uint32 crop_size = 5 [default = 0];\n  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror\n  // data.\n  optional bool mirror = 6 [default = false];\n  optional string root_folder = 12 [default = \"\"];\n}\n\nmessage InfogainLossParameter {\n  // Specify the infogain matrix source.\n  optional string source = 1;\n}\n\nmessage InnerProductParameter {\n  optional uint32 num_output = 1; // The number of outputs for the layer\n  optional bool bias_term = 2 [default = true]; // whether to have bias terms\n  optional FillerParameter weight_filler = 3; // The filler for the weight\n  optional FillerParameter bias_filler = 4; // The filler for the bias\n\n  // The first axis to be lumped into a single inner product computation;\n  // all preceding axes are retained in the output.\n  // May be negative to index from the end (e.g., -1 for the last axis).\n  optional int32 axis = 5 [default = 1];\n  // Specify whether to transpose the weight matrix or not.\n  // If transpose == true, any operations will be performed on the transpose\n  // of the weight matrix. The weight matrix itself is not going to be transposed\n  // but rather the transfer flag of operations will be toggled accordingly.\n  optional bool transpose = 6 [default = false];\n  optional bool normalize = 7 [default = false];\n}\n\nmessage InputParameter {\n  // This layer produces N >= 1 top blob(s) to be assigned manually.\n  // Define N shapes to set a shape for each top.\n  // Define 1 shape to set the same shape for every top.\n  // Define no shape to defer to reshaping manually.\n  repeated BlobShape shape = 1;\n}\n\n\n// Message that stores parameters used by LogLayer\nmessage LogParameter {\n  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.\n  // Or if base is set to the default (-1), base is set to e,\n  // so y = ln(shift + scale * x) = log_e(shift + scale * x)\n  optional float base = 1 [default = -1.0];\n  optional float scale = 2 [default = 1.0];\n  optional float shift = 3 [default = 0.0];\n}\n\n// Message that stores parameters used by LRNLayer\nmessage LRNParameter {\n  optional uint32 local_size = 1 [default = 5];\n  optional float alpha = 2 [default = 1.];\n  optional float beta = 3 [default = 0.75];\n  enum NormRegion {\n    ACROSS_CHANNELS = 0;\n    WITHIN_CHANNEL = 1;\n  }\n  optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];\n  optional float k = 5 [default = 1.];\n  enum Engine {\n    DEFAULT = 0;\n    CAFFE = 1;\n    CUDNN = 2;\n  }\n  optional Engine engine = 6 [default = DEFAULT];\n}\n\nmessage MemoryDataParameter {\n  optional uint32 batch_size = 1;\n  optional uint32 channels = 2;\n  optional uint32 height = 3;\n  optional uint32 width = 4;\n}\n//**************************ssd********************************************\n\n// Message that store parameters used by MultiBoxLossLayer\nmessage MultiBoxLossParameter {\n  // Localization loss type.\n  enum LocLossType {\n    L2 = 0;\n    SMOOTH_L1 = 1;\n  }\n  optional LocLossType loc_loss_type = 1 [default = SMOOTH_L1];\n  // Confidence loss type.\n  enum ConfLossType {\n    SOFTMAX = 0;\n    LOGISTIC = 1;\n  }\n  optional ConfLossType conf_loss_type = 2 [default = SOFTMAX];\n  // Weight for localization loss.\n  optional float loc_weight = 3 [default = 1.0];\n  // Number of classes to be predicted. Required!\n  optional uint32 num_classes = 4;\n  // If true, bounding box are shared among different classes.\n  optional bool share_location = 5 [default = true];\n  // Matching method during training.\n  enum MatchType {\n    BIPARTITE = 0;\n    PER_PREDICTION = 1;\n  }\n  optional MatchType match_type = 6 [default = PER_PREDICTION];\n  // If match_type is PER_PREDICTION, use overlap_threshold to\n  // determine the extra matching bboxes.\n  optional float overlap_threshold = 7 [default = 0.5];\n  // Use prior for matching.\n  optional bool use_prior_for_matching = 8 [default = true];\n  // Background label id.\n  optional uint32 background_label_id = 9 [default = 0];\n  // If true, also consider difficult ground truth.\n  optional bool use_difficult_gt = 10 [default = true];\n  // If true, perform negative mining.\n  // DEPRECATED: use mining_type instead.\n  optional bool do_neg_mining = 11;\n  // The negative/positive ratio.\n  optional float neg_pos_ratio = 12 [default = 3.0];\n  // The negative overlap upperbound for the unmatched predictions.\n  optional float neg_overlap = 13 [default = 0.5];\n  // Type of coding method for bbox.\n  optional PriorBoxParameter.CodeType code_type = 14 [default = CORNER];\n  // If true, encode the variance of prior box in the loc loss target instead of\n  // in bbox.\n  optional bool encode_variance_in_target = 16 [default = false];\n  // If true, map all object classes to agnostic class. It is useful for learning\n  // objectness detector.\n  optional bool map_object_to_agnostic = 17 [default = false];\n  // If true, ignore cross boundary bbox during matching.\n  // Cross boundary bbox is a bbox who is outside of the image region.\n  optional bool ignore_cross_boundary_bbox = 18 [default = false];\n  // If true, only backpropagate on corners which are inside of the image\n  // region when encode_type is CORNER or CORNER_SIZE.\n  optional bool bp_inside = 19 [default = false];\n  // Mining type during training.\n  //   NONE : use all negatives.\n  //   MAX_NEGATIVE : select negatives based on the score.\n  //   HARD_EXAMPLE : select hard examples based on \"Training Region-based Object Detectors with Online Hard Example Mining\", Shrivastava et.al.\n  enum MiningType {\n    NONE = 0;\n    MAX_NEGATIVE = 1;\n    HARD_EXAMPLE = 2;\n  }\n  optional MiningType mining_type = 20 [default = MAX_NEGATIVE];\n  // Parameters used for non maximum suppression durig hard example mining.\n  optional NonMaximumSuppressionParameter nms_param = 21;\n  optional int32 sample_size = 22 [default = 64];\n  optional bool use_prior_for_nms = 23 [default = false];\n}\n\n// Message that stores parameters used by NormalizeLayer\n//message NormalizeParameter {\n//  //optional bool across_spatial = 1 [default = true];\n//  // Initial value of scale. Default is 1.0 for all\n//  //optional FillerParameter scale_filler = 2;\n//  // Whether or not scale parameters are shared across channels.\n//  //optional bool channel_shared = 3 [default = true];\n//  // Epsilon for not dividing by zero while normalizing variance\n//  //optional float eps = 4 [default = 1e-10];\n//  //**************************************************\n//  optional string normalize_type = 1 [default = \"L2\"];\n//  optional bool fix_gradient = 2 [default = false];\n//  optional bool bp_norm = 3 [default = false];\n//}\n\nmessage PermuteParameter {\n  // The new orders of the axes of data. Notice it should be with\n  // in the same range as the input data, and it starts from 0.\n  // Do not provide repeated order.\n  repeated uint32 order = 1;\n}\n//**************************end***********************************************\n\nmessage MVNParameter {\n  // This parameter can be set to false to normalize mean only\n  optional bool normalize_variance = 1 [default = true];\n\n  // This parameter can be set to true to perform DNN-like MVN\n  optional bool across_channels = 2 [default = false];\n\n  // Epsilon for not dividing by zero while normalizing variance\n  optional float eps = 3 [default = 1e-9];\n}\n\nmessage ParameterParameter {\n  optional BlobShape shape = 1;\n}\n\n\nmessage PoolingParameter {\n  enum PoolMethod {\n    MAX = 0;\n    AVE = 1;\n    STOCHASTIC = 2;\n  }\n  optional PoolMethod pool = 1 [default = MAX]; // The pooling method\n  // Pad, kernel size, and stride are all given as a single value for equal\n  // dimensions in height and width or as Y, X pairs.\n  optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)\n  optional uint32 pad_h = 9 [default = 0]; // The padding height\n  optional uint32 pad_w = 10 [default = 0]; // The padding width\n  optional uint32 kernel_size = 2; // The kernel size (square)\n  optional uint32 kernel_h = 5; // The kernel height\n  optional uint32 kernel_w = 6; // The kernel width\n  optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)\n  optional uint32 stride_h = 7; // The stride height\n  optional uint32 stride_w = 8; // The stride width\n  enum Engine {\n    DEFAULT = 0;\n    CAFFE = 1;\n    CUDNN = 2;\n  }\n  optional Engine engine = 11 [default = DEFAULT];\n  // If global_pooling then it will pool over the size of the bottom by doing\n  // kernel_h = bottom->height and kernel_w = bottom->width\n  optional bool global_pooling = 12 [default = false];\n\n  ///////////////////////\n  // Specify floor/ceil mode\n  optional bool ceil_mode = 13 [default = true];\n  ///////////////////////////////\n}\n\nmessage PowerParameter {\n  // PowerLayer computes outputs y = (shift + scale * x) ^ power.\n  optional float power = 1 [default = 1.0];\n  optional float scale = 2 [default = 1.0];\n  optional float shift = 3 [default = 0.0];\n}\n\n//*************ssd********************************************************************\n// Message that store parameters used by PriorBoxLayer\nmessage PriorBoxParameter {\n  // Encode/decode type.\n  enum CodeType {\n    CORNER = 1;\n    CENTER_SIZE = 2;\n    CORNER_SIZE = 3;\n  }\n  // Minimum box size (in pixels). Required!\n  repeated float min_size = 1;\n  // Maximum box size (in pixels). Required!\n  repeated float max_size = 2;\n  // Various of aspect ratios. Duplicate ratios will be ignored.\n  // If none is provided, we use default ratio 1.\n  repeated float aspect_ratio = 3;\n  // If true, will flip each aspect ratio.\n  // For example, if there is aspect ratio \"r\",\n  // we will generate aspect ratio \"1.0/r\" as well.\n  optional bool flip = 4 [default = true];\n  // If true, will clip the prior so that it is within [0, 1]\n  optional bool clip = 5 [default = false];\n  // Variance for adjusting the prior bboxes.\n  repeated float variance = 6;\n  // By default, we calculate img_height, img_width, step_x, step_y based on\n  // bottom[0] (feat) and bottom[1] (img). Unless these values are explicitely\n  // provided.\n  // Explicitly provide the img_size.\n  optional uint32 img_size = 7;\n  // Either img_size or img_h/img_w should be specified; not both.\n  optional uint32 img_h = 8;\n  optional uint32 img_w = 9;\n\n  // Explicitly provide the step size.\n  optional float step = 10;\n  // Either step or step_h/step_w should be specified; not both.\n  optional float step_h = 11;\n  optional float step_w = 12;\n\n  // Offset to the top left corner of each cell.\n  optional float offset = 13 [default = 0.5];\n}\n//*********************************************************************************\nmessage PythonParameter {\n  optional string module = 1;\n  optional string layer = 2;\n  // This value is set to the attribute `param_str` of the `PythonLayer` object\n  // in Python before calling the `setup()` method. This could be a number,\n  // string, dictionary in Python dict format, JSON, etc. You may parse this\n  // string in `setup` method and use it in `forward` and `backward`.\n  optional string param_str = 3 [default = ''];\n  // Whether this PythonLayer is shared among worker solvers during data parallelism.\n  // If true, each worker solver sequentially run forward from this layer.\n  // This value should be set true if you are using it as a data layer.\n  optional bool share_in_parallel = 4 [default = false];\n}\n\nmessage RecurrentParameter {\n  // The dimension of the output (and usually hidden state) representation --\n  // must be explicitly set to non-zero.\n  optional uint32 num_output = 1 [default = 0];\n  \n  optional FillerParameter weight_filler = 2; // The filler for the weight\n  optional FillerParameter bias_filler = 3; // The filler for the bias\n  \n  // Whether to enable displaying debug_info in the unrolled recurrent net.\n  optional bool debug_info = 4 [default = false];\n  \n  // Whether to add as additional inputs (bottoms) the initial hidden state\n  // blobs, and add as additional outputs (tops) the final timestep hidden state\n  // blobs.  The number of additional bottom/top blobs required depends on the\n  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.\n  optional bool expose_hidden = 5 [default = false];\n}\n\n\n// Message that stores parameters used by ReductionLayer\nmessage ReductionParameter {\n  enum ReductionOp {\n    SUM = 1;\n    ASUM = 2;\n    SUMSQ = 3;\n    MEAN = 4;\n  }\n\n  optional ReductionOp operation = 1 [default = SUM]; // reduction operation\n\n  // The first axis to reduce to a scalar -- may be negative to index from the\n  // end (e.g., -1 for the last axis).\n  // (Currently, only reduction along ALL \"tail\" axes is supported; reduction\n  // of axis M through N, where N < num_axes - 1, is unsupported.)\n  // Suppose we have an n-axis bottom Blob with shape:\n  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).\n  // If axis == m, the output Blob will have shape\n  //     (d0, d1, d2, ..., d(m-1)),\n  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))\n  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.\n  // If axis == 0 (the default), the output Blob always has the empty shape\n  // (count 1), performing reduction across the entire input --\n  // often useful for creating new loss functions.\n  optional int32 axis = 2 [default = 0];\n\n  optional float coeff = 3 [default = 1.0]; // coefficient for output\n}\n\n// Message that stores parameters used by ReLULayer\nmessage ReLUParameter {\n  // Allow non-zero slope for negative inputs to speed up optimization\n  // Described in:\n  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities\n  // improve neural network acoustic models. In ICML Workshop on Deep Learning\n  // for Audio, Speech, and Language Processing.\n  optional float negative_slope = 1 [default = 0];\n  enum Engine {\n    DEFAULT = 0;\n    CAFFE = 1;\n    CUDNN = 2;\n  }\n  optional Engine engine = 2 [default = DEFAULT];\n}\n\nmessage ReshapeParameter {\n  // Specify the output dimensions. If some of the dimensions are set to 0,\n  // the corresponding dimension from the bottom layer is used (unchanged).\n  // Exactly one dimension may be set to -1, in which case its value is\n  // inferred from the count of the bottom blob and the remaining dimensions.\n  // For example, suppose we want to reshape a 2D blob \"input\" with shape 2 x 8:\n  //\n  //   layer {\n  //     type: \"Reshape\" bottom: \"input\" top: \"output\"\n  //     reshape_param { ... }\n  //   }\n  //\n  // If \"input\" is 2D with shape 2 x 8, then the following reshape_param\n  // specifications are all equivalent, producing a 3D blob \"output\" with shape\n  // 2 x 2 x 4:\n  //\n  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }\n  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }\n  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }\n  //   reshape_param { shape { dim: -1  dim: 0  dim:  2 } }\n  //\n  optional BlobShape shape = 1;\n\n  // axis and num_axes control the portion of the bottom blob's shape that are\n  // replaced by (included in) the reshape. By default (axis == 0 and\n  // num_axes == -1), the entire bottom blob shape is included in the reshape,\n  // and hence the shape field must specify the entire output shape.\n  //\n  // axis may be non-zero to retain some portion of the beginning of the input\n  // shape (and may be negative to index from the end; e.g., -1 to begin the\n  // reshape after the last axis, including nothing in the reshape,\n  // -2 to include only the last axis, etc.).\n  //\n  // For example, suppose \"input\" is a 2D blob with shape 2 x 8.\n  // Then the following ReshapeLayer specifications are all equivalent,\n  // producing a blob \"output\" with shape 2 x 2 x 4:\n  //\n  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }\n  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }\n  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }\n  //\n  // num_axes specifies the extent of the reshape.\n  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on\n  // input axes in the range [axis, axis+num_axes].\n  // num_axes may also be -1, the default, to include all remaining axes\n  // (starting from axis).\n  //\n  // For example, suppose \"input\" is a 2D blob with shape 2 x 8.\n  // Then the following ReshapeLayer specifications are equivalent,\n  // producing a blob \"output\" with shape 1 x 2 x 8.\n  //\n  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }\n  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }\n  //   reshape_param { shape { dim:  1  }  num_axes: 0 }\n  //\n  // On the other hand, these would produce output blob shape 2 x 1 x 8:\n  //\n  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }\n  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }\n  //\n  optional int32 axis = 2 [default = 0];\n  optional int32 num_axes = 3 [default = -1];\n}\n\n// Message that stores parameters used by ROIPoolingLayer\nmessage ROIPoolingParameter {\n  // Pad, kernel size, and stride are all given as a single value for equal\n  // dimensions in height and width or as Y, X pairs.\n  optional uint32 pooled_h = 1 [default = 0]; // The pooled output height\n  optional uint32 pooled_w = 2 [default = 0]; // The pooled output width\n  // Multiplicative spatial scale factor to translate ROI coords from their\n  // input scale to the scale used when pooling\n  optional float spatial_scale = 3 [default = 1];\n}\n\nmessage ScaleParameter {\n  // The first axis of bottom[0] (the first input Blob) along which to apply\n  // bottom[1] (the second input Blob).  May be negative to index from the end\n  // (e.g., -1 for the last axis).\n  //\n  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output\n  // top[0] will have the same shape, and bottom[1] may have any of the\n  // following shapes (for the given value of axis):\n  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60\n  //    (axis == 1 == -3)          3;     3x40;     3x40x60\n  //    (axis == 2 == -2)                   40;       40x60\n  //    (axis == 3 == -1)                                60\n  // Furthermore, bottom[1] may have the empty shape (regardless of the value of\n  // \"axis\") -- a scalar multiplier.\n  optional int32 axis = 1 [default = 1];\n\n  // (num_axes is ignored unless just one bottom is given and the scale is\n  // a learned parameter of the layer.  Otherwise, num_axes is determined by the\n  // number of axes by the second bottom.)\n  // The number of axes of the input (bottom[0]) covered by the scale\n  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.\n  // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.\n  optional int32 num_axes = 2 [default = 1];\n\n  // (filler is ignored unless just one bottom is given and the scale is\n  // a learned parameter of the layer.)\n  // The initialization for the learned scale parameter.\n  // Default is the unit (1) initialization, resulting in the ScaleLayer\n  // initially performing the identity operation.\n  optional FillerParameter filler = 3;\n\n  // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but\n  // may be more efficient).  Initialized with bias_filler (defaults to 0).\n  optional bool bias_term = 4 [default = false];\n  optional FillerParameter bias_filler = 5;\n  optional float min_value = 6;\n  optional float max_value = 7;\n}\n\nmessage SigmoidParameter {\n  enum Engine {\n    DEFAULT = 0;\n    CAFFE = 1;\n    CUDNN = 2;\n  }\n  optional Engine engine = 1 [default = DEFAULT];\n}\n\nmessage SmoothL1LossParameter {\n  // SmoothL1Loss(x) =\n  //   0.5 * (sigma * x) ** 2    -- if x < 1.0 / sigma / sigma\n  //   |x| - 0.5 / sigma / sigma -- otherwise\n  optional float sigma = 1 [default = 1];\n}\n\nmessage SliceParameter {\n  // The axis along which to slice -- may be negative to index from the end\n  // (e.g., -1 for the last axis).\n  // By default, SliceLayer concatenates blobs along the \"channels\" axis (1).\n  optional int32 axis = 3 [default = 1];\n  repeated uint32 slice_point = 2;\n\n  // DEPRECATED: alias for \"axis\" -- does not support negative indexing.\n  optional uint32 slice_dim = 1 [default = 1];\n}\n\n// Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer\nmessage SoftmaxParameter {\n  enum Engine {\n    DEFAULT = 0;\n    CAFFE = 1;\n    CUDNN = 2;\n  }\n  optional Engine engine = 1 [default = DEFAULT];\n\n  // The axis along which to perform the softmax -- may be negative to index\n  // from the end (e.g., -1 for the last axis).\n  // Any other axes will be evaluated as independent softmaxes.\n  optional int32 axis = 2 [default = 1];\n}\n\nmessage TanHParameter {\n  enum Engine {\n    DEFAULT = 0;\n    CAFFE = 1;\n    CUDNN = 2;\n  }\n  optional Engine engine = 1 [default = DEFAULT];\n}\n\n// Message that stores parameters used by TileLayer\nmessage TileParameter {\n  // The index of the axis to tile.\n  optional int32 axis = 1 [default = 1];\n\n  // The number of copies (tiles) of the blob to output.\n  optional int32 tiles = 2;\n}\n\n// Message that stores parameters used by ThresholdLayer\nmessage ThresholdParameter {\n  optional float threshold = 1 [default = 0]; // Strictly positive values\n}\n\nmessage WindowDataParameter {\n  // Specify the data source.\n  optional string source = 1;\n  // For data pre-processing, we can do simple scaling and subtracting the\n  // data mean, if provided. Note that the mean subtraction is always carried\n  // out before scaling.\n  optional float scale = 2 [default = 1];\n  optional string mean_file = 3;\n  // Specify the batch size.\n  optional uint32 batch_size = 4;\n  // Specify if we would like to randomly crop an image.\n  optional uint32 crop_size = 5 [default = 0];\n  // Specify if we want to randomly mirror data.\n  optional bool mirror = 6 [default = false];\n  // Foreground (object) overlap threshold\n  optional float fg_threshold = 7 [default = 0.5];\n  // Background (non-object) overlap threshold\n  optional float bg_threshold = 8 [default = 0.5];\n  // Fraction of batch that should be foreground objects\n  optional float fg_fraction = 9 [default = 0.25];\n  // Amount of contextual padding to add around a window\n  // (used only by the window_data_layer)\n  optional uint32 context_pad = 10 [default = 0];\n  // Mode for cropping out a detection window\n  // warp: cropped window is warped to a fixed size and aspect ratio\n  // square: the tightest square around the window is cropped\n  optional string crop_mode = 11 [default = \"warp\"];\n  // cache_images: will load all images in memory for faster access\n  optional bool cache_images = 12 [default = false];\n  // append root_folder to locate images\n  optional string root_folder = 13 [default = \"\"];\n}\n\nmessage SPPParameter {\n  enum PoolMethod {\n    MAX = 0;\n    AVE = 1;\n    STOCHASTIC = 2;\n  }\n  optional uint32 pyramid_height = 1;\n  optional PoolMethod pool = 2 [default = MAX]; // The pooling method\n  enum Engine {\n    DEFAULT = 0;\n    CAFFE = 1;\n    CUDNN = 2;\n  }\n  optional Engine engine = 6 [default = DEFAULT];\n}\n\n// DEPRECATED: use LayerParameter.\nmessage V1LayerParameter {\n  repeated string bottom = 2;\n  repeated string top = 3;\n  optional string name = 4;\n  repeated NetStateRule include = 32;\n  repeated NetStateRule exclude = 33;\n  enum LayerType {\n    NONE = 0;\n    ABSVAL = 35;\n    ACCURACY = 1;\n    ARGMAX = 30;\n    BNLL = 2;\n    CONCAT = 3;\n    CONTRASTIVE_LOSS = 37;\n    CONVOLUTION = 4;\n    DATA = 5;\n    DECONVOLUTION = 39;\n    DROPOUT = 6;\n    DUMMY_DATA = 32;\n    EUCLIDEAN_LOSS = 7;\n    ELTWISE = 25;\n    EXP = 38;\n    FLATTEN = 8;\n    HDF5_DATA = 9;\n    HDF5_OUTPUT = 10;\n    HINGE_LOSS = 28;\n    IM2COL = 11;\n    IMAGE_DATA = 12;\n    INFOGAIN_LOSS = 13;\n    INNER_PRODUCT = 14;\n    LRN = 15;\n    MEMORY_DATA = 29;\n    MULTINOMIAL_LOGISTIC_LOSS = 16;\n    MVN = 34;\n    POOLING = 17;\n    POWER = 26;\n    RELU = 18;\n    SIGMOID = 19;\n    SIGMOID_CROSS_ENTROPY_LOSS = 27;\n    SILENCE = 36;\n    SOFTMAX = 20;\n    SOFTMAX_LOSS = 21;\n    SPLIT = 22;\n    SLICE = 33;\n    TANH = 23;\n    WINDOW_DATA = 24;\n    THRESHOLD = 31;\n  }\n  optional LayerType type = 5;\n  repeated BlobProto blobs = 6;\n  repeated string param = 1001;\n  repeated DimCheckMode blob_share_mode = 1002;\n  enum DimCheckMode {\n    STRICT = 0;\n    PERMISSIVE = 1;\n  }\n  repeated float blobs_lr = 7;\n  repeated float weight_decay = 8;\n  repeated float loss_weight = 35;\n  optional AccuracyParameter accuracy_param = 27;\n  optional ArgMaxParameter argmax_param = 23;\n  optional ConcatParameter concat_param = 9;\n  optional ContrastiveLossParameter contrastive_loss_param = 40;\n  optional ConvolutionParameter convolution_param = 10;\n  optional DataParameter data_param = 11;\n  optional DropoutParameter dropout_param = 12;\n  optional DummyDataParameter dummy_data_param = 26;\n  optional EltwiseParameter eltwise_param = 24;\n  optional ExpParameter exp_param = 41;\n  optional HDF5DataParameter hdf5_data_param = 13;\n  optional HDF5OutputParameter hdf5_output_param = 14;\n  optional HingeLossParameter hinge_loss_param = 29;\n  optional ImageDataParameter image_data_param = 15;\n  optional InfogainLossParameter infogain_loss_param = 16;\n  optional InnerProductParameter inner_product_param = 17;\n  optional LRNParameter lrn_param = 18;\n  optional MemoryDataParameter memory_data_param = 22;\n  optional MVNParameter mvn_param = 34;\n  optional PoolingParameter pooling_param = 19;\n  optional PowerParameter power_param = 21;\n  optional ReLUParameter relu_param = 30;\n  optional SigmoidParameter sigmoid_param = 38;\n  optional SoftmaxParameter softmax_param = 39;\n  optional SliceParameter slice_param = 31;\n  optional TanHParameter tanh_param = 37;\n  optional ThresholdParameter threshold_param = 25;\n  optional WindowDataParameter window_data_param = 20;\n  optional TransformationParameter transform_param = 36;\n  optional LossParameter loss_param = 42;\n  optional DetectionLossParameter detection_loss_param = 200;\n  optional EvalDetectionParameter eval_detection_param = 201;\n  optional V0LayerParameter layer = 1;\n}\n\n// DEPRECATED: V0LayerParameter is the old way of specifying layer parameters\n// in Caffe.  We keep this message type around for legacy support.\nmessage V0LayerParameter {\n  optional string name = 1; // the layer name\n  optional string type = 2; // the string to specify the layer type\n\n  // Parameters to specify layers with inner products.\n  optional uint32 num_output = 3; // The number of outputs for the layer\n  optional bool biasterm = 4 [default = true]; // whether to have bias terms\n  optional FillerParameter weight_filler = 5; // The filler for the weight\n  optional FillerParameter bias_filler = 6; // The filler for the bias\n\n  optional uint32 pad = 7 [default = 0]; // The padding size\n  optional uint32 kernelsize = 8; // The kernel size\n  optional uint32 group = 9 [default = 1]; // The group size for group conv\n  optional uint32 stride = 10 [default = 1]; // The stride\n  enum PoolMethod {\n    MAX = 0;\n    AVE = 1;\n    STOCHASTIC = 2;\n  }\n  optional PoolMethod pool = 11 [default = MAX]; // The pooling method\n  optional float dropout_ratio = 12 [default = 0.5]; // dropout ratio\n\n  optional uint32 local_size = 13 [default = 5]; // for local response norm\n  optional float alpha = 14 [default = 1.]; // for local response norm\n  optional float beta = 15 [default = 0.75]; // for local response norm\n  optional float k = 22 [default = 1.];\n\n  // For data layers, specify the data source\n  optional string source = 16;\n  // For data pre-processing, we can do simple scaling and subtracting the\n  // data mean, if provided. Note that the mean subtraction is always carried\n  // out before scaling.\n  optional float scale = 17 [default = 1];\n  optional string meanfile = 18;\n  // For data layers, specify the batch size.\n  optional uint32 batchsize = 19;\n  // For data layers, specify if we would like to randomly crop an image.\n  optional uint32 cropsize = 20 [default = 0];\n  // For data layers, specify if we want to randomly mirror data.\n  optional bool mirror = 21 [default = false];\n\n  // The blobs containing the numeric parameters of the layer\n  repeated BlobProto blobs = 50;\n  // The ratio that is multiplied on the global learning rate. If you want to\n  // set the learning ratio for one blob, you need to set it for all blobs.\n  repeated float blobs_lr = 51;\n  // The weight decay that is multiplied on the global weight decay.\n  repeated float weight_decay = 52;\n\n  // The rand_skip variable is for the data layer to skip a few data points\n  // to avoid all asynchronous sgd clients to start at the same point. The skip\n  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not\n  // be larger than the number of keys in the database.\n  optional uint32 rand_skip = 53 [default = 0];\n\n  // Fields related to detection (det_*)\n  // foreground (object) overlap threshold\n  optional float det_fg_threshold = 54 [default = 0.5];\n  // background (non-object) overlap threshold\n  optional float det_bg_threshold = 55 [default = 0.5];\n  // Fraction of batch that should be foreground objects\n  optional float det_fg_fraction = 56 [default = 0.25];\n\n  // optional bool OBSOLETE_can_clobber = 57 [default = true];\n\n  // Amount of contextual padding to add around a window\n  // (used only by the window_data_layer)\n  optional uint32 det_context_pad = 58 [default = 0];\n\n  // Mode for cropping out a detection window\n  // warp: cropped window is warped to a fixed size and aspect ratio\n  // square: the tightest square around the window is cropped\n  optional string det_crop_mode = 59 [default = \"warp\"];\n\n  // For ReshapeLayer, one needs to specify the new dimensions.\n  optional int32 new_num = 60 [default = 0];\n  optional int32 new_channels = 61 [default = 0];\n  optional int32 new_height = 62 [default = 0];\n  optional int32 new_width = 63 [default = 0];\n\n  // Whether or not ImageLayer should shuffle the list of files at every epoch.\n  // It will also resize images if new_height or new_width are not zero.\n  optional bool shuffle_images = 64 [default = false];\n\n  // For ConcatLayer, one needs to specify the dimension for concatenation, and\n  // the other dimensions must be the same for all the bottom blobs.\n  // By default it will concatenate blobs along the channels dimension.\n  optional uint32 concat_dim = 65 [default = 1];\n\n  optional HDF5OutputParameter hdf5_output_param = 1001;\n}\n\nmessage PReLUParameter {\n  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:\n  // Surpassing Human-Level Performance on ImageNet Classification, 2015.\n\n  // Initial value of a_i. Default is a_i=0.25 for all i.\n  optional FillerParameter filler = 1;\n  // Whether or not slope paramters are shared across channels.\n  optional bool channel_shared = 2 [default = false];\n}\n\n\n//********add by xia****************\nmessage RPNParameter {\n  optional uint32 feat_stride = 1;\n  optional uint32 basesize = 2;\n  repeated uint32 scale = 3;\n  repeated float ratio = 4;\n  optional uint32 boxminsize =5;\n  optional uint32 per_nms_topn = 9;\n  optional uint32 post_nms_topn = 11;\n  optional float nms_thresh = 8;\n}\n\nmessage VideoDataParameter{\n  enum VideoType {\n    WEBCAM = 0;\n    VIDEO = 1;\n  }\n  optional VideoType video_type = 1 [default = WEBCAM];\n  optional int32 device_id = 2 [default = 0];\n  optional string video_file = 3;\n  // Number of frames to be skipped before processing a frame.\n  optional uint32 skip_frames = 4 [default = 0];\n}\n\nmessage CenterLossParameter {\n  optional uint32 num_output = 1; // The number of outputs for the layer\n  optional FillerParameter center_filler = 2; // The filler for the centers\n  // The first axis to be lumped into a single inner product computation;\n  // all preceding axes are retained in the output.\n  // May be negative to index from the end (e.g., -1 for the last axis).\n  optional int32 axis = 3 [default = 1];\n}\n\nmessage MarginInnerProductParameter {\n  optional uint32 num_output = 1; // The number of outputs for the layer\n  enum MarginType {\n    SINGLE = 0;\n    DOUBLE = 1;\n    TRIPLE = 2;\n    QUADRUPLE = 3;\n  }\n  optional MarginType type = 2 [default = SINGLE]; \n  optional FillerParameter weight_filler = 3; // The filler for the weight\n\n  // The first axis to be lumped into a single inner product computation;\n  // all preceding axes are retained in the output.\n  // May be negative to index from the end (e.g., -1 for the last axis).\n  optional int32 axis = 4 [default = 1];\n  optional float base = 5 [default = 1];\n  optional float gamma = 6 [default = 0];\n  optional float power = 7 [default = 1];\n  optional int32 iteration = 8 [default = 0];\n  optional float lambda_min = 9 [default = 0];\n}\n\nmessage AdditiveMarginInnerProductParameter {\n  optional uint32 num_output = 1; // The number of outputs for the layer\n  optional FillerParameter weight_filler = 2; // The filler for the weight\n  optional float m = 3 [default = 0.35];\n  optional int32 axis = 4 [default = 1];\n}\n\nmessage DeformableConvolutionParameter {\n  optional uint32 num_output = 1; \n  optional bool bias_term = 2 [default = true]; \n  repeated uint32 pad = 3; // The padding size; defaults to 0\n  repeated uint32 kernel_size = 4; // The kernel size\n  repeated uint32 stride = 6; // The stride; defaults to 1\n  repeated uint32 dilation = 18; // The dilation; defaults to 1\n  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)\n  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)\n  optional uint32 kernel_h = 11; // The kernel height (2D only)\n  optional uint32 kernel_w = 12; // The kernel width (2D only)\n  optional uint32 stride_h = 13; // The stride height (2D only)\n  optional uint32 stride_w = 14; // The stride width (2D only)\n  optional uint32 group = 5 [default = 4]; \n  optional uint32 deformable_group = 25 [default = 4]; \n  optional FillerParameter weight_filler = 7; // The filler for the weight\n  optional FillerParameter bias_filler = 8; // The filler for the bias\n  enum Engine {\n    DEFAULT = 0;\n    CAFFE = 1;\n    CUDNN = 2;\n  }\n  optional Engine engine = 15 [default = DEFAULT];\n  optional int32 axis = 16 [default = 1];\n  optional bool force_nd_im2col = 17 [default = false];\n}\n\nmessage LabelSpecificAddParameter {\n  optional float bias = 1 [default = 0.0];\n  optional bool transform_test = 2 [default = false];\n}\n\nmessage ChannelScaleParameter{\n  optional bool do_forward = 1 [default = true];\n  optional bool do_backward_feature = 2 [default = true];\n  optional bool do_backward_scale = 3 [default = true];\n  optional bool global_scale = 4 [default = false];\n  optional float max_global_scale = 5 [default = 1000.0];\n  optional float min_global_scale = 6 [default = 0.0];\n  optional float init_global_scale = 7 [default = 1.0];\n}\n\nmessage CosinAddmParameter {\n  optional float m = 1 [default = 0.5];\n  optional bool transform_test = 2 [default = false];\n}\nmessage CosinMulmParameter {\n  optional float m = 1 [default = 4];\n  optional bool transform_test = 2 [default = false];\n}\n\nmessage CoupledClusterLossParameter {\n  optional float margin = 1 [default = 1];\n  optional int32 group_size = 2 [default = 3];\n  optional float scale = 3 [default = 1];\n  optional bool log_flag = 4 [default = false];\n  // optional int32 pos_num = 3 [default = 1];\n  // optional int32 neg_num = 4 [default = 1];\n}\n\nmessage TripletLossParameter {\n  optional float margin = 1 [default = 1];\n  optional int32 group_size = 2 [default = 3];\n  optional float scale = 3 [default = 1];\n  // optional int32 pos_num = 3 [default = 1];\n  // optional int32 neg_num = 4 [default = 1];\n}\n\nmessage GeneralTripletParameter {\n  optional float margin = 1 [default = 0.2];\n  optional bool add_center_loss = 2 [default = true];\n  optional bool hardest_only = 3 [default = false];\n  optional bool positive_first = 4 [default = false];\n  optional float positive_upper_bound = 5 [default = 1.0];\n  optional float positive_weight = 6 [default = 1.0];\n  optional float negative_weight = 7 [default = 1.0];\n}\n\nmessage ROIAlignParameter {\n  optional uint32 pooled_h = 1 [default = 0]; // The pooled output height\n  optional uint32 pooled_w = 2 [default = 0]; // The pooled output width\n  optional float spatial_scale = 3 [default = 1];\n}\n\n"
  },
  {
    "path": "fast_reid/tools/deploy/Caffe/caffe_lmdb.py",
    "content": "import lmdb\nfrom Caffe import caffe_pb2 as pb2\nimport numpy as np\n\nclass Read_Caffe_LMDB():\n    def __init__(self,path,dtype=np.uint8):\n\n        self.env=lmdb.open(path, readonly=True)\n        self.dtype=dtype\n        self.txn=self.env.begin()\n        self.cursor=self.txn.cursor()\n\n    @staticmethod\n    def to_numpy(value,dtype=np.uint8):\n        datum = pb2.Datum()\n        datum.ParseFromString(value)\n        flat_x = np.fromstring(datum.data, dtype=dtype)\n        data = flat_x.reshape(datum.channels, datum.height, datum.width)\n        label=flat_x = datum.label\n        return data,label\n\n    def iterator(self):\n        while True:\n            key,value=self.cursor.key(),self.cursor.value()\n            yield self.to_numpy(value,self.dtype)\n            if not self.cursor.next():\n                return\n\n    def __iter__(self):\n        self.cursor.first()\n        it = self.iterator()\n        return it\n\n    def __len__(self):\n        return int(self.env.stat()['entries'])\n"
  },
  {
    "path": "fast_reid/tools/deploy/Caffe/caffe_net.py",
    "content": "from __future__ import absolute_import\nfrom . import caffe_pb2 as pb\nimport google.protobuf.text_format as text_format\nimport numpy as np\nfrom .layer_param import Layer_param\n\nclass _Net(object):\n    def __init__(self):\n        self.net=pb.NetParameter()\n\n    def layer_index(self,layer_name):\n        # find a layer's index by name. if the layer was found, return the layer position in the net, else return -1.\n        for i, layer in enumerate(self.net.layer):\n            if layer.name == layer_name:\n                return i\n\n    def add_layer(self,layer_params,before='',after=''):\n        # find the before of after layer's position\n        index = -1\n        if after != '':\n            index = self.layer_index(after) + 1\n        if before != '':\n            index = self.layer_index(before)\n        new_layer = pb.LayerParameter()\n        new_layer.CopyFrom(layer_params.param)\n        #insert the layer into the layer protolist\n        if index != -1:\n            self.net.layer.add()\n            for i in range(len(self.net.layer) - 1, index, -1):\n                self.net.layer[i].CopyFrom(self.net.layer[i - 1])\n            self.net.layer[index].CopyFrom(new_layer)\n        else:\n            self.net.layer.extend([new_layer])\n\n    def remove_layer_by_name(self,layer_name):\n        for i,layer in enumerate(self.net.layer):\n            if layer.name == layer_name:\n                del self.net.layer[i]\n                return\n        raise(AttributeError, \"cannot found layer %s\" % str(layer_name))\n\n    def get_layer_by_name(self, layer_name):\n        # get the layer by layer_name\n        for layer in self.net.layer:\n            if layer.name == layer_name:\n                return layer\n        raise(AttributeError, \"cannot found layer %s\" % str(layer_name))\n\n    def save_prototxt(self,path):\n        prototxt=pb.NetParameter()\n        prototxt.CopyFrom(self.net)\n        for layer in prototxt.layer:\n            del layer.blobs[:]\n        with open(path,'w') as f:\n            f.write(text_format.MessageToString(prototxt))\n\n    def layer(self,layer_name):\n        return self.get_layer_by_name(layer_name)\n\n    def layers(self):\n        return list(self.net.layer)\n\n\n\nclass Prototxt(_Net):\n    def __init__(self,file_name=''):\n        super(Prototxt,self).__init__()\n        self.file_name=file_name\n        if file_name!='':\n            f = open(file_name,'r')\n            text_format.Parse(f.read(), self.net)\n            pass\n\n    def init_caffemodel(self,caffe_cmd_path='caffe'):\n        \"\"\"\n        :param caffe_cmd_path: The shell command of caffe, normally at <path-to-caffe>/build/tools/caffe\n        \"\"\"\n        s=pb.SolverParameter()\n        s.train_net=self.file_name\n        s.max_iter=0\n        s.base_lr=1\n        s.solver_mode = pb.SolverParameter.CPU\n        s.snapshot_prefix='./nn'\n        with open('/tmp/nn_tools_solver.prototxt','w') as f:\n            f.write(str(s))\n        import os\n        os.system('%s train --solver /tmp/nn_tools_solver.prototxt'%caffe_cmd_path)\n\nclass Caffemodel(_Net):\n    def __init__(self, file_name=''):\n        super(Caffemodel,self).__init__()\n        # caffe_model dir\n        if file_name!='':\n            f = open(file_name,'rb')\n            self.net.ParseFromString(f.read())\n            f.close()\n\n    def save(self, path):\n        with open(path,'wb') as f:\n            f.write(self.net.SerializeToString())\n\n    def add_layer_with_data(self,layer_params,datas, before='', after=''):\n        \"\"\"\n        Args:\n            layer_params:A Layer_Param object\n            datas:a fixed dimension numpy object list\n            after: put the layer after a specified layer\n            before: put the layer before a specified layer\n        \"\"\"\n        self.add_layer(layer_params,before,after)\n        new_layer =self.layer(layer_params.name)\n\n        #process blobs\n        del new_layer.blobs[:]\n        for data in datas:\n            new_blob=new_layer.blobs.add()\n            for dim in data.shape:\n                new_blob.shape.dim.append(dim)\n            new_blob.data.extend(data.flatten().astype(float))\n\n    def get_layer_data(self,layer_name):\n        layer=self.layer(layer_name)\n        datas=[]\n        for blob in layer.blobs:\n            shape=list(blob.shape.dim)\n            data=np.array(blob.data).reshape(shape)\n            datas.append(data)\n        return datas\n\n    def set_layer_data(self,layer_name,datas):\n        # datas is normally a list of [weights,bias]\n        layer=self.layer(layer_name)\n        for blob,data in zip(layer.blobs,datas):\n            blob.data[:]=data.flatten()\n            pass\n\nclass Net():\n    def __init__(self,*args,**kwargs):\n        raise(TypeError,'the class Net is no longer used, please use Caffemodel or Prototxt instead')"
  },
  {
    "path": "fast_reid/tools/deploy/Caffe/caffe_pb2.py",
    "content": "# Generated by the protocol buffer compiler.  DO NOT EDIT!\n# source: caffe.proto\n\nimport sys\n_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))\nfrom google.protobuf.internal import enum_type_wrapper\nfrom google.protobuf import descriptor as _descriptor\nfrom google.protobuf import message as _message\nfrom google.protobuf import reflection as _reflection\nfrom google.protobuf import symbol_database as _symbol_database\n# @@protoc_insertion_point(imports)\n\n_sym_db = _symbol_database.Default()\n\n\n\n\nDESCRIPTOR = _descriptor.FileDescriptor(\n  name='caffe.proto',\n  package='caffe',\n  syntax='proto2',\n  serialized_options=None,\n  serialized_pb=_b('\\n\\x0b\\x63\\x61\\x66\\x66\\x65.proto\\x12\\x05\\x63\\x61\\x66\\x66\\x65\\\"\\x1c\\n\\tBlobShape\\x12\\x0f\\n\\x03\\x64im\\x18\\x01 \\x03(\\x03\\x42\\x02\\x10\\x01\\\"\\xcc\\x01\\n\\tBlobProto\\x12\\x1f\\n\\x05shape\\x18\\x07 \\x01(\\x0b\\x32\\x10.caffe.BlobShape\\x12\\x10\\n\\x04\\x64\\x61ta\\x18\\x05 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containing_type=None,\n  serialized_options=None,\n  serialized_start=29109,\n  serialized_end=29137,\n)\n_sym_db.RegisterEnumDescriptor(_PHASE)\n\nPhase = enum_type_wrapper.EnumTypeWrapper(_PHASE)\nTRAIN = 0\nTEST = 1\n\n\n_EMITCONSTRAINT_EMITTYPE = _descriptor.EnumDescriptor(\n  name='EmitType',\n  full_name='caffe.EmitConstraint.EmitType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='CENTER', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='MIN_OVERLAP', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=1162,\n  serialized_end=1201,\n)\n_sym_db.RegisterEnumDescriptor(_EMITCONSTRAINT_EMITTYPE)\n\n_ANNOTATEDDATUM_ANNOTATIONTYPE = _descriptor.EnumDescriptor(\n  name='AnnotationType',\n  full_name='caffe.AnnotatedDatum.AnnotationType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='BBOX', index=0, number=0,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=1645,\n  serialized_end=1671,\n)\n_sym_db.RegisterEnumDescriptor(_ANNOTATEDDATUM_ANNOTATIONTYPE)\n\n_FILLERPARAMETER_VARIANCENORM = _descriptor.EnumDescriptor(\n  name='VarianceNorm',\n  full_name='caffe.FillerParameter.VarianceNorm',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='FAN_IN', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='FAN_OUT', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='AVERAGE', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=2058,\n  serialized_end=2110,\n)\n_sym_db.RegisterEnumDescriptor(_FILLERPARAMETER_VARIANCENORM)\n\n_SOLVERPARAMETER_SNAPSHOTFORMAT = _descriptor.EnumDescriptor(\n  name='SnapshotFormat',\n  full_name='caffe.SolverParameter.SnapshotFormat',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='HDF5', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='BINARYPROTO', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=3568,\n  serialized_end=3611,\n)\n_sym_db.RegisterEnumDescriptor(_SOLVERPARAMETER_SNAPSHOTFORMAT)\n\n_SOLVERPARAMETER_SOLVERMODE = _descriptor.EnumDescriptor(\n  name='SolverMode',\n  full_name='caffe.SolverParameter.SolverMode',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='CPU', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='GPU', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=3613,\n  serialized_end=3643,\n)\n_sym_db.RegisterEnumDescriptor(_SOLVERPARAMETER_SOLVERMODE)\n\n_SOLVERPARAMETER_SOLVERTYPE = _descriptor.EnumDescriptor(\n  name='SolverType',\n  full_name='caffe.SolverParameter.SolverType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='SGD', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='NESTEROV', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='ADAGRAD', index=2, number=2,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='RMSPROP', index=3, number=3,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='ADADELTA', index=4, number=4,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='ADAM', index=5, number=5,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=3645,\n  serialized_end=3730,\n)\n_sym_db.RegisterEnumDescriptor(_SOLVERPARAMETER_SOLVERTYPE)\n\n_PARAMSPEC_DIMCHECKMODE = _descriptor.EnumDescriptor(\n  name='DimCheckMode',\n  full_name='caffe.ParamSpec.DimCheckMode',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='STRICT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='PERMISSIVE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=4491,\n  serialized_end=4533,\n)\n_sym_db.RegisterEnumDescriptor(_PARAMSPEC_DIMCHECKMODE)\n\n_BNPARAMETER_ENGINE = _descriptor.EnumDescriptor(\n  name='Engine',\n  full_name='caffe.BNParameter.Engine',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='DEFAULT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CAFFE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUDNN', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=10905,\n  serialized_end=10948,\n)\n_sym_db.RegisterEnumDescriptor(_BNPARAMETER_ENGINE)\n\n_FOCALLOSSPARAMETER_TYPE = _descriptor.EnumDescriptor(\n  name='Type',\n  full_name='caffe.FocalLossParameter.Type',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='ORIGIN', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='LINEAR', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=11083,\n  serialized_end=11113,\n)\n_sym_db.RegisterEnumDescriptor(_FOCALLOSSPARAMETER_TYPE)\n\n_RESIZEPARAMETER_RESIZE_MODE = _descriptor.EnumDescriptor(\n  name='Resize_mode',\n  full_name='caffe.ResizeParameter.Resize_mode',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='WARP', index=0, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='FIT_SMALL_SIZE', index=1, number=2,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='FIT_LARGE_SIZE_AND_PAD', index=2, number=3,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=11899,\n  serialized_end=11970,\n)\n_sym_db.RegisterEnumDescriptor(_RESIZEPARAMETER_RESIZE_MODE)\n\n_RESIZEPARAMETER_PAD_MODE = _descriptor.EnumDescriptor(\n  name='Pad_mode',\n  full_name='caffe.ResizeParameter.Pad_mode',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='CONSTANT', index=0, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='MIRRORED', index=1, number=2,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='REPEAT_NEAREST', index=2, number=3,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=11972,\n  serialized_end=12030,\n)\n_sym_db.RegisterEnumDescriptor(_RESIZEPARAMETER_PAD_MODE)\n\n_RESIZEPARAMETER_INTERP_MODE = _descriptor.EnumDescriptor(\n  name='Interp_mode',\n  full_name='caffe.ResizeParameter.Interp_mode',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='LINEAR', index=0, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='AREA', index=1, number=2,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='NEAREST', index=2, number=3,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUBIC', index=3, number=4,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='LANCZOS4', index=4, number=5,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=12032,\n  serialized_end=12105,\n)\n_sym_db.RegisterEnumDescriptor(_RESIZEPARAMETER_INTERP_MODE)\n\n_LOSSPARAMETER_NORMALIZATIONMODE = _descriptor.EnumDescriptor(\n  name='NormalizationMode',\n  full_name='caffe.LossParameter.NormalizationMode',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='FULL', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='VALID', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='BATCH_SIZE', index=2, number=2,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='NONE', index=3, number=3,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=13052,\n  serialized_end=13118,\n)\n_sym_db.RegisterEnumDescriptor(_LOSSPARAMETER_NORMALIZATIONMODE)\n\n_EVALDETECTIONPARAMETER_SCORETYPE = _descriptor.EnumDescriptor(\n  name='ScoreType',\n  full_name='caffe.EvalDetectionParameter.ScoreType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='OBJ', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='PROB', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='MULTIPLY', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=14582,\n  serialized_end=14626,\n)\n_sym_db.RegisterEnumDescriptor(_EVALDETECTIONPARAMETER_SCORETYPE)\n\n_CONVOLUTIONPARAMETER_ENGINE = _descriptor.EnumDescriptor(\n  name='Engine',\n  full_name='caffe.ConvolutionParameter.Engine',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='DEFAULT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CAFFE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUDNN', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=10905,\n  serialized_end=10948,\n)\n_sym_db.RegisterEnumDescriptor(_CONVOLUTIONPARAMETER_ENGINE)\n\n_DATAPARAMETER_DB = _descriptor.EnumDescriptor(\n  name='DB',\n  full_name='caffe.DataParameter.DB',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='LEVELDB', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='LMDB', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=15469,\n  serialized_end=15496,\n)\n_sym_db.RegisterEnumDescriptor(_DATAPARAMETER_DB)\n\n_ELTWISEPARAMETER_ELTWISEOP = _descriptor.EnumDescriptor(\n  name='EltwiseOp',\n  full_name='caffe.EltwiseParameter.EltwiseOp',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='PROD', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='SUM', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='MAX', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=16856,\n  serialized_end=16895,\n)\n_sym_db.RegisterEnumDescriptor(_ELTWISEPARAMETER_ELTWISEOP)\n\n_HINGELOSSPARAMETER_NORM = _descriptor.EnumDescriptor(\n  name='Norm',\n  full_name='caffe.HingeLossParameter.Norm',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='L1', index=0, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='L2', index=1, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=17430,\n  serialized_end=17452,\n)\n_sym_db.RegisterEnumDescriptor(_HINGELOSSPARAMETER_NORM)\n\n_LRNPARAMETER_NORMREGION = _descriptor.EnumDescriptor(\n  name='NormRegion',\n  full_name='caffe.LRNParameter.NormRegion',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='ACROSS_CHANNELS', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='WITHIN_CHANNEL', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=18345,\n  serialized_end=18398,\n)\n_sym_db.RegisterEnumDescriptor(_LRNPARAMETER_NORMREGION)\n\n_LRNPARAMETER_ENGINE = _descriptor.EnumDescriptor(\n  name='Engine',\n  full_name='caffe.LRNParameter.Engine',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='DEFAULT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CAFFE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUDNN', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=10905,\n  serialized_end=10948,\n)\n_sym_db.RegisterEnumDescriptor(_LRNPARAMETER_ENGINE)\n\n_MULTIBOXLOSSPARAMETER_LOCLOSSTYPE = _descriptor.EnumDescriptor(\n  name='LocLossType',\n  full_name='caffe.MultiBoxLossParameter.LocLossType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='L2', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='SMOOTH_L1', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=19479,\n  serialized_end=19515,\n)\n_sym_db.RegisterEnumDescriptor(_MULTIBOXLOSSPARAMETER_LOCLOSSTYPE)\n\n_MULTIBOXLOSSPARAMETER_CONFLOSSTYPE = _descriptor.EnumDescriptor(\n  name='ConfLossType',\n  full_name='caffe.MultiBoxLossParameter.ConfLossType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='SOFTMAX', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='LOGISTIC', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=19517,\n  serialized_end=19558,\n)\n_sym_db.RegisterEnumDescriptor(_MULTIBOXLOSSPARAMETER_CONFLOSSTYPE)\n\n_MULTIBOXLOSSPARAMETER_MATCHTYPE = _descriptor.EnumDescriptor(\n  name='MatchType',\n  full_name='caffe.MultiBoxLossParameter.MatchType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='BIPARTITE', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='PER_PREDICTION', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=19560,\n  serialized_end=19606,\n)\n_sym_db.RegisterEnumDescriptor(_MULTIBOXLOSSPARAMETER_MATCHTYPE)\n\n_MULTIBOXLOSSPARAMETER_MININGTYPE = _descriptor.EnumDescriptor(\n  name='MiningType',\n  full_name='caffe.MultiBoxLossParameter.MiningType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='NONE', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='MAX_NEGATIVE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='HARD_EXAMPLE', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=19608,\n  serialized_end=19666,\n)\n_sym_db.RegisterEnumDescriptor(_MULTIBOXLOSSPARAMETER_MININGTYPE)\n\n_POOLINGPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor(\n  name='PoolMethod',\n  full_name='caffe.PoolingParameter.PoolMethod',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='MAX', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='AVE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='STOCHASTIC', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=20213,\n  serialized_end=20259,\n)\n_sym_db.RegisterEnumDescriptor(_POOLINGPARAMETER_POOLMETHOD)\n\n_POOLINGPARAMETER_ENGINE = _descriptor.EnumDescriptor(\n  name='Engine',\n  full_name='caffe.PoolingParameter.Engine',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='DEFAULT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CAFFE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUDNN', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=10905,\n  serialized_end=10948,\n)\n_sym_db.RegisterEnumDescriptor(_POOLINGPARAMETER_ENGINE)\n\n_PRIORBOXPARAMETER_CODETYPE = _descriptor.EnumDescriptor(\n  name='CodeType',\n  full_name='caffe.PriorBoxParameter.CodeType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='CORNER', index=0, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CENTER_SIZE', index=1, number=2,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CORNER_SIZE', index=2, number=3,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=20632,\n  serialized_end=20688,\n)\n_sym_db.RegisterEnumDescriptor(_PRIORBOXPARAMETER_CODETYPE)\n\n_REDUCTIONPARAMETER_REDUCTIONOP = _descriptor.EnumDescriptor(\n  name='ReductionOp',\n  full_name='caffe.ReductionParameter.ReductionOp',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='SUM', index=0, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='ASUM', index=1, number=2,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='SUMSQ', index=2, number=3,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='MEAN', index=3, number=4,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=21111,\n  serialized_end=21164,\n)\n_sym_db.RegisterEnumDescriptor(_REDUCTIONPARAMETER_REDUCTIONOP)\n\n_RELUPARAMETER_ENGINE = _descriptor.EnumDescriptor(\n  name='Engine',\n  full_name='caffe.ReLUParameter.Engine',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='DEFAULT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CAFFE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUDNN', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=10905,\n  serialized_end=10948,\n)\n_sym_db.RegisterEnumDescriptor(_RELUPARAMETER_ENGINE)\n\n_SIGMOIDPARAMETER_ENGINE = _descriptor.EnumDescriptor(\n  name='Engine',\n  full_name='caffe.SigmoidParameter.Engine',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='DEFAULT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CAFFE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUDNN', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=10905,\n  serialized_end=10948,\n)\n_sym_db.RegisterEnumDescriptor(_SIGMOIDPARAMETER_ENGINE)\n\n_SOFTMAXPARAMETER_ENGINE = _descriptor.EnumDescriptor(\n  name='Engine',\n  full_name='caffe.SoftmaxParameter.Engine',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='DEFAULT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CAFFE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUDNN', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=10905,\n  serialized_end=10948,\n)\n_sym_db.RegisterEnumDescriptor(_SOFTMAXPARAMETER_ENGINE)\n\n_TANHPARAMETER_ENGINE = _descriptor.EnumDescriptor(\n  name='Engine',\n  full_name='caffe.TanHParameter.Engine',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='DEFAULT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CAFFE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUDNN', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=10905,\n  serialized_end=10948,\n)\n_sym_db.RegisterEnumDescriptor(_TANHPARAMETER_ENGINE)\n\n_SPPPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor(\n  name='PoolMethod',\n  full_name='caffe.SPPParameter.PoolMethod',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='MAX', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='AVE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='STOCHASTIC', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=20213,\n  serialized_end=20259,\n)\n_sym_db.RegisterEnumDescriptor(_SPPPARAMETER_POOLMETHOD)\n\n_SPPPARAMETER_ENGINE = _descriptor.EnumDescriptor(\n  name='Engine',\n  full_name='caffe.SPPParameter.Engine',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='DEFAULT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CAFFE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUDNN', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=10905,\n  serialized_end=10948,\n)\n_sym_db.RegisterEnumDescriptor(_SPPPARAMETER_ENGINE)\n\n_V1LAYERPARAMETER_LAYERTYPE = _descriptor.EnumDescriptor(\n  name='LayerType',\n  full_name='caffe.V1LayerParameter.LayerType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='NONE', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='ABSVAL', index=1, number=35,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='ACCURACY', index=2, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='ARGMAX', index=3, number=30,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='BNLL', index=4, number=2,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CONCAT', index=5, number=3,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CONTRASTIVE_LOSS', index=6, number=37,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CONVOLUTION', index=7, number=4,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='DATA', index=8, number=5,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='DECONVOLUTION', index=9, number=39,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='DROPOUT', index=10, number=6,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='DUMMY_DATA', index=11, number=32,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='EUCLIDEAN_LOSS', index=12, number=7,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='ELTWISE', index=13, number=25,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='EXP', index=14, number=38,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='FLATTEN', index=15, number=8,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='HDF5_DATA', index=16, number=9,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='HDF5_OUTPUT', index=17, number=10,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='HINGE_LOSS', index=18, number=28,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='IM2COL', index=19, number=11,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='IMAGE_DATA', index=20, number=12,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='INFOGAIN_LOSS', index=21, number=13,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='INNER_PRODUCT', index=22, number=14,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='LRN', index=23, number=15,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='MEMORY_DATA', index=24, number=29,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='MULTINOMIAL_LOGISTIC_LOSS', index=25, number=16,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='MVN', index=26, number=34,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='POOLING', index=27, number=17,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='POWER', index=28, number=26,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='RELU', index=29, number=18,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='SIGMOID', index=30, number=19,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='SIGMOID_CROSS_ENTROPY_LOSS', index=31, number=27,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='SILENCE', index=32, number=36,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='SOFTMAX', index=33, number=20,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='SOFTMAX_LOSS', index=34, number=21,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='SPLIT', index=35, number=22,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='SLICE', index=36, number=33,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='TANH', index=37, number=23,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='WINDOW_DATA', index=38, number=24,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='THRESHOLD', index=39, number=31,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=24862,\n  serialized_end=25462,\n)\n_sym_db.RegisterEnumDescriptor(_V1LAYERPARAMETER_LAYERTYPE)\n\n_V1LAYERPARAMETER_DIMCHECKMODE = _descriptor.EnumDescriptor(\n  name='DimCheckMode',\n  full_name='caffe.V1LayerParameter.DimCheckMode',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='STRICT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='PERMISSIVE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=4491,\n  serialized_end=4533,\n)\n_sym_db.RegisterEnumDescriptor(_V1LAYERPARAMETER_DIMCHECKMODE)\n\n_V0LAYERPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor(\n  name='PoolMethod',\n  full_name='caffe.V0LayerParameter.PoolMethod',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='MAX', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='AVE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='STOCHASTIC', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=20213,\n  serialized_end=20259,\n)\n_sym_db.RegisterEnumDescriptor(_V0LAYERPARAMETER_POOLMETHOD)\n\n_VIDEODATAPARAMETER_VIDEOTYPE = _descriptor.EnumDescriptor(\n  name='VideoType',\n  full_name='caffe.VideoDataParameter.VideoType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='WEBCAM', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='VIDEO', index=1, number=1,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=26946,\n  serialized_end=26980,\n)\n_sym_db.RegisterEnumDescriptor(_VIDEODATAPARAMETER_VIDEOTYPE)\n\n_MARGININNERPRODUCTPARAMETER_MARGINTYPE = _descriptor.EnumDescriptor(\n  name='MarginType',\n  full_name='caffe.MarginInnerProductParameter.MarginType',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='SINGLE', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='DOUBLE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='TRIPLE', index=2, number=2,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='QUADRUPLE', index=3, number=3,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=27372,\n  serialized_end=27435,\n)\n_sym_db.RegisterEnumDescriptor(_MARGININNERPRODUCTPARAMETER_MARGINTYPE)\n\n_DEFORMABLECONVOLUTIONPARAMETER_ENGINE = _descriptor.EnumDescriptor(\n  name='Engine',\n  full_name='caffe.DeformableConvolutionParameter.Engine',\n  filename=None,\n  file=DESCRIPTOR,\n  values=[\n    _descriptor.EnumValueDescriptor(\n      name='DEFAULT', index=0, number=0,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CAFFE', index=1, number=1,\n      serialized_options=None,\n      type=None),\n    _descriptor.EnumValueDescriptor(\n      name='CUDNN', index=2, number=2,\n      serialized_options=None,\n      type=None),\n  ],\n  containing_type=None,\n  serialized_options=None,\n  serialized_start=10905,\n  serialized_end=10948,\n)\n_sym_db.RegisterEnumDescriptor(_DEFORMABLECONVOLUTIONPARAMETER_ENGINE)\n\n\n_BLOBSHAPE = _descriptor.Descriptor(\n  name='BlobShape',\n  full_name='caffe.BlobShape',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='dim', full_name='caffe.BlobShape.dim', index=0,\n      number=1, type=3, cpp_type=2, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=_b('\\020\\001'), file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=22,\n  serialized_end=50,\n)\n\n\n_BLOBPROTO = _descriptor.Descriptor(\n  name='BlobProto',\n  full_name='caffe.BlobProto',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='shape', full_name='caffe.BlobProto.shape', index=0,\n      number=7, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='data', full_name='caffe.BlobProto.data', index=1,\n      number=5, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=_b('\\020\\001'), file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='diff', full_name='caffe.BlobProto.diff', index=2,\n      number=6, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=_b('\\020\\001'), file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='double_data', full_name='caffe.BlobProto.double_data', index=3,\n      number=8, type=1, cpp_type=5, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=_b('\\020\\001'), file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='double_diff', full_name='caffe.BlobProto.double_diff', index=4,\n      number=9, type=1, cpp_type=5, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=_b('\\020\\001'), file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num', full_name='caffe.BlobProto.num', index=5,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='channels', full_name='caffe.BlobProto.channels', index=6,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='height', full_name='caffe.BlobProto.height', index=7,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='width', full_name='caffe.BlobProto.width', index=8,\n      number=4, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=53,\n  serialized_end=257,\n)\n\n\n_BLOBPROTOVECTOR = _descriptor.Descriptor(\n  name='BlobProtoVector',\n  full_name='caffe.BlobProtoVector',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='blobs', full_name='caffe.BlobProtoVector.blobs', index=0,\n      number=1, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=259,\n  serialized_end=309,\n)\n\n\n_DATUM = _descriptor.Descriptor(\n  name='Datum',\n  full_name='caffe.Datum',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='channels', full_name='caffe.Datum.channels', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='height', full_name='caffe.Datum.height', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='width', full_name='caffe.Datum.width', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='data', full_name='caffe.Datum.data', index=3,\n      number=4, type=12, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\"),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='label', full_name='caffe.Datum.label', index=4,\n      number=5, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='float_data', full_name='caffe.Datum.float_data', index=5,\n      number=6, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='encoded', full_name='caffe.Datum.encoded', index=6,\n      number=7, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='labels', full_name='caffe.Datum.labels', index=7,\n      number=8, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=312,\n  serialized_end=457,\n)\n\n\n_LABELMAPITEM = _descriptor.Descriptor(\n  name='LabelMapItem',\n  full_name='caffe.LabelMapItem',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='name', full_name='caffe.LabelMapItem.name', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='label', full_name='caffe.LabelMapItem.label', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='display_name', full_name='caffe.LabelMapItem.display_name', index=2,\n      number=3, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=459,\n  serialized_end=524,\n)\n\n\n_LABELMAP = _descriptor.Descriptor(\n  name='LabelMap',\n  full_name='caffe.LabelMap',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='item', full_name='caffe.LabelMap.item', index=0,\n      number=1, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=526,\n  serialized_end=571,\n)\n\n\n_SAMPLER = _descriptor.Descriptor(\n  name='Sampler',\n  full_name='caffe.Sampler',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='min_scale', full_name='caffe.Sampler.min_scale', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_scale', full_name='caffe.Sampler.max_scale', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='min_aspect_ratio', full_name='caffe.Sampler.min_aspect_ratio', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_aspect_ratio', full_name='caffe.Sampler.max_aspect_ratio', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=573,\n  serialized_end=684,\n)\n\n\n_SAMPLECONSTRAINT = _descriptor.Descriptor(\n  name='SampleConstraint',\n  full_name='caffe.SampleConstraint',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='min_jaccard_overlap', full_name='caffe.SampleConstraint.min_jaccard_overlap', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_jaccard_overlap', full_name='caffe.SampleConstraint.max_jaccard_overlap', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='min_sample_coverage', full_name='caffe.SampleConstraint.min_sample_coverage', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_sample_coverage', full_name='caffe.SampleConstraint.max_sample_coverage', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='min_object_coverage', full_name='caffe.SampleConstraint.min_object_coverage', index=4,\n      number=5, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_object_coverage', full_name='caffe.SampleConstraint.max_object_coverage', index=5,\n      number=6, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=687,\n  serialized_end=879,\n)\n\n\n_BATCHSAMPLER = _descriptor.Descriptor(\n  name='BatchSampler',\n  full_name='caffe.BatchSampler',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='use_original_image', full_name='caffe.BatchSampler.use_original_image', index=0,\n      number=1, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='sampler', full_name='caffe.BatchSampler.sampler', index=1,\n      number=2, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='sample_constraint', full_name='caffe.BatchSampler.sample_constraint', index=2,\n      number=3, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_sample', full_name='caffe.BatchSampler.max_sample', index=3,\n      number=4, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_trials', full_name='caffe.BatchSampler.max_trials', index=4,\n      number=5, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=100,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=882,\n  serialized_end=1060,\n)\n\n\n_EMITCONSTRAINT = _descriptor.Descriptor(\n  name='EmitConstraint',\n  full_name='caffe.EmitConstraint',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='emit_type', full_name='caffe.EmitConstraint.emit_type', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='emit_overlap', full_name='caffe.EmitConstraint.emit_overlap', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _EMITCONSTRAINT_EMITTYPE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=1063,\n  serialized_end=1201,\n)\n\n\n_NORMALIZEDBBOX = _descriptor.Descriptor(\n  name='NormalizedBBox',\n  full_name='caffe.NormalizedBBox',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='xmin', full_name='caffe.NormalizedBBox.xmin', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='ymin', full_name='caffe.NormalizedBBox.ymin', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='xmax', full_name='caffe.NormalizedBBox.xmax', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='ymax', full_name='caffe.NormalizedBBox.ymax', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='label', full_name='caffe.NormalizedBBox.label', index=4,\n      number=5, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='difficult', full_name='caffe.NormalizedBBox.difficult', index=5,\n      number=6, type=8, cpp_type=7, label=1,\n      has_default_value=False, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='score', full_name='caffe.NormalizedBBox.score', index=6,\n      number=7, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='size', full_name='caffe.NormalizedBBox.size', index=7,\n      number=8, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=1204,\n  serialized_end=1339,\n)\n\n\n_ANNOTATION = _descriptor.Descriptor(\n  name='Annotation',\n  full_name='caffe.Annotation',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='instance_id', full_name='caffe.Annotation.instance_id', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bbox', full_name='caffe.Annotation.bbox', index=1,\n      number=2, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=1341,\n  serialized_end=1414,\n)\n\n\n_ANNOTATIONGROUP = _descriptor.Descriptor(\n  name='AnnotationGroup',\n  full_name='caffe.AnnotationGroup',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='group_label', full_name='caffe.AnnotationGroup.group_label', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='annotation', full_name='caffe.AnnotationGroup.annotation', index=1,\n      number=2, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=1416,\n  serialized_end=1493,\n)\n\n\n_ANNOTATEDDATUM = _descriptor.Descriptor(\n  name='AnnotatedDatum',\n  full_name='caffe.AnnotatedDatum',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='datum', full_name='caffe.AnnotatedDatum.datum', index=0,\n      number=1, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='type', full_name='caffe.AnnotatedDatum.type', index=1,\n      number=2, type=14, cpp_type=8, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='annotation_group', full_name='caffe.AnnotatedDatum.annotation_group', index=2,\n      number=3, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _ANNOTATEDDATUM_ANNOTATIONTYPE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=1496,\n  serialized_end=1671,\n)\n\n\n_MTCNNBBOX = _descriptor.Descriptor(\n  name='MTCNNBBox',\n  full_name='caffe.MTCNNBBox',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='xmin', full_name='caffe.MTCNNBBox.xmin', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='ymin', full_name='caffe.MTCNNBBox.ymin', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='xmax', full_name='caffe.MTCNNBBox.xmax', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='ymax', full_name='caffe.MTCNNBBox.ymax', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=1673,\n  serialized_end=1740,\n)\n\n\n_MTCNNDATUM = _descriptor.Descriptor(\n  name='MTCNNDatum',\n  full_name='caffe.MTCNNDatum',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='datum', full_name='caffe.MTCNNDatum.datum', index=0,\n      number=1, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='roi', full_name='caffe.MTCNNDatum.roi', index=1,\n      number=2, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pts', full_name='caffe.MTCNNDatum.pts', index=2,\n      number=3, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=1742,\n  serialized_end=1827,\n)\n\n\n_FILLERPARAMETER = _descriptor.Descriptor(\n  name='FillerParameter',\n  full_name='caffe.FillerParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='type', full_name='caffe.FillerParameter.type', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=True, default_value=_b(\"constant\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='value', full_name='caffe.FillerParameter.value', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='min', full_name='caffe.FillerParameter.min', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max', full_name='caffe.FillerParameter.max', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mean', full_name='caffe.FillerParameter.mean', index=4,\n      number=5, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='std', full_name='caffe.FillerParameter.std', index=5,\n      number=6, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='sparse', full_name='caffe.FillerParameter.sparse', index=6,\n      number=7, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=-1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='variance_norm', full_name='caffe.FillerParameter.variance_norm', index=7,\n      number=8, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='file', full_name='caffe.FillerParameter.file', index=8,\n      number=9, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _FILLERPARAMETER_VARIANCENORM,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=1830,\n  serialized_end=2110,\n)\n\n\n_NETPARAMETER = _descriptor.Descriptor(\n  name='NetParameter',\n  full_name='caffe.NetParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='name', full_name='caffe.NetParameter.name', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='input', full_name='caffe.NetParameter.input', index=1,\n      number=3, type=9, cpp_type=9, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='input_shape', full_name='caffe.NetParameter.input_shape', index=2,\n      number=8, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='input_dim', full_name='caffe.NetParameter.input_dim', index=3,\n      number=4, type=5, cpp_type=1, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='force_backward', full_name='caffe.NetParameter.force_backward', index=4,\n      number=5, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='state', full_name='caffe.NetParameter.state', index=5,\n      number=6, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='debug_info', full_name='caffe.NetParameter.debug_info', index=6,\n      number=7, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='layer', full_name='caffe.NetParameter.layer', index=7,\n      number=100, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='layers', full_name='caffe.NetParameter.layers', index=8,\n      number=2, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=2113,\n  serialized_end=2383,\n)\n\n\n_SOLVERPARAMETER = _descriptor.Descriptor(\n  name='SolverParameter',\n  full_name='caffe.SolverParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='net', full_name='caffe.SolverParameter.net', index=0,\n      number=24, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='net_param', full_name='caffe.SolverParameter.net_param', index=1,\n      number=25, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='train_net', full_name='caffe.SolverParameter.train_net', index=2,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='test_net', full_name='caffe.SolverParameter.test_net', index=3,\n      number=2, type=9, cpp_type=9, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='train_net_param', full_name='caffe.SolverParameter.train_net_param', index=4,\n      number=21, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='test_net_param', full_name='caffe.SolverParameter.test_net_param', index=5,\n      number=22, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='train_state', full_name='caffe.SolverParameter.train_state', index=6,\n      number=26, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='test_state', full_name='caffe.SolverParameter.test_state', index=7,\n      number=27, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='test_iter', full_name='caffe.SolverParameter.test_iter', index=8,\n      number=3, type=5, cpp_type=1, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='test_interval', full_name='caffe.SolverParameter.test_interval', index=9,\n      number=4, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='test_compute_loss', full_name='caffe.SolverParameter.test_compute_loss', index=10,\n      number=19, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='test_initialization', full_name='caffe.SolverParameter.test_initialization', index=11,\n      number=32, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='base_lr', full_name='caffe.SolverParameter.base_lr', index=12,\n      number=5, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='display', full_name='caffe.SolverParameter.display', index=13,\n      number=6, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='average_loss', full_name='caffe.SolverParameter.average_loss', index=14,\n      number=33, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_iter', full_name='caffe.SolverParameter.max_iter', index=15,\n      number=7, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='iter_size', full_name='caffe.SolverParameter.iter_size', index=16,\n      number=36, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='lr_policy', full_name='caffe.SolverParameter.lr_policy', index=17,\n      number=8, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='gamma', full_name='caffe.SolverParameter.gamma', index=18,\n      number=9, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='power', full_name='caffe.SolverParameter.power', index=19,\n      number=10, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='momentum', full_name='caffe.SolverParameter.momentum', index=20,\n      number=11, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_decay', full_name='caffe.SolverParameter.weight_decay', index=21,\n      number=12, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='regularization_type', full_name='caffe.SolverParameter.regularization_type', index=22,\n      number=29, type=9, cpp_type=9, label=1,\n      has_default_value=True, default_value=_b(\"L2\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stepsize', full_name='caffe.SolverParameter.stepsize', index=23,\n      number=13, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stepvalue', full_name='caffe.SolverParameter.stepvalue', index=24,\n      number=34, type=5, cpp_type=1, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stagelr', full_name='caffe.SolverParameter.stagelr', index=25,\n      number=50, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stageiter', full_name='caffe.SolverParameter.stageiter', index=26,\n      number=51, type=5, cpp_type=1, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='clip_gradients', full_name='caffe.SolverParameter.clip_gradients', index=27,\n      number=35, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(-1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='snapshot', full_name='caffe.SolverParameter.snapshot', index=28,\n      number=14, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='snapshot_prefix', full_name='caffe.SolverParameter.snapshot_prefix', index=29,\n      number=15, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='snapshot_diff', full_name='caffe.SolverParameter.snapshot_diff', index=30,\n      number=16, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='snapshot_format', full_name='caffe.SolverParameter.snapshot_format', index=31,\n      number=37, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='solver_mode', full_name='caffe.SolverParameter.solver_mode', index=32,\n      number=17, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='device_id', full_name='caffe.SolverParameter.device_id', index=33,\n      number=18, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='random_seed', full_name='caffe.SolverParameter.random_seed', index=34,\n      number=20, type=3, cpp_type=2, label=1,\n      has_default_value=True, default_value=-1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='type', full_name='caffe.SolverParameter.type', index=35,\n      number=40, type=9, cpp_type=9, label=1,\n      has_default_value=True, default_value=_b(\"SGD\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='delta', full_name='caffe.SolverParameter.delta', index=36,\n      number=31, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1e-08),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='momentum2', full_name='caffe.SolverParameter.momentum2', index=37,\n      number=39, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.999),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='rms_decay', full_name='caffe.SolverParameter.rms_decay', index=38,\n      number=38, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='debug_info', full_name='caffe.SolverParameter.debug_info', index=39,\n      number=23, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='snapshot_after_train', full_name='caffe.SolverParameter.snapshot_after_train', index=40,\n      number=28, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='solver_type', full_name='caffe.SolverParameter.solver_type', index=41,\n      number=30, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _SOLVERPARAMETER_SNAPSHOTFORMAT,\n    _SOLVERPARAMETER_SOLVERMODE,\n    _SOLVERPARAMETER_SOLVERTYPE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=2386,\n  serialized_end=3730,\n)\n\n\n_SOLVERSTATE = _descriptor.Descriptor(\n  name='SolverState',\n  full_name='caffe.SolverState',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='iter', full_name='caffe.SolverState.iter', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='learned_net', full_name='caffe.SolverState.learned_net', index=1,\n      number=2, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='history', full_name='caffe.SolverState.history', index=2,\n      number=3, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='current_step', full_name='caffe.SolverState.current_step', index=3,\n      number=4, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=3732,\n  serialized_end=3840,\n)\n\n\n_NETSTATE = _descriptor.Descriptor(\n  name='NetState',\n  full_name='caffe.NetState',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='phase', full_name='caffe.NetState.phase', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='level', full_name='caffe.NetState.level', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stage', full_name='caffe.NetState.stage', index=2,\n      number=3, type=9, cpp_type=9, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=3842,\n  serialized_end=3920,\n)\n\n\n_NETSTATERULE = _descriptor.Descriptor(\n  name='NetStateRule',\n  full_name='caffe.NetStateRule',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='phase', full_name='caffe.NetStateRule.phase', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='min_level', full_name='caffe.NetStateRule.min_level', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_level', full_name='caffe.NetStateRule.max_level', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stage', full_name='caffe.NetStateRule.stage', index=3,\n      number=4, type=9, cpp_type=9, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='not_stage', full_name='caffe.NetStateRule.not_stage', index=4,\n      number=5, type=9, cpp_type=9, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=3922,\n  serialized_end=4037,\n)\n\n\n_SPATIALTRANSFORMERPARAMETER = _descriptor.Descriptor(\n  name='SpatialTransformerParameter',\n  full_name='caffe.SpatialTransformerParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='transform_type', full_name='caffe.SpatialTransformerParameter.transform_type', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=True, default_value=_b(\"affine\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='sampler_type', full_name='caffe.SpatialTransformerParameter.sampler_type', index=1,\n      number=2, type=9, cpp_type=9, label=1,\n      has_default_value=True, default_value=_b(\"bilinear\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='output_H', full_name='caffe.SpatialTransformerParameter.output_H', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='output_W', full_name='caffe.SpatialTransformerParameter.output_W', index=3,\n      number=4, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='to_compute_dU', full_name='caffe.SpatialTransformerParameter.to_compute_dU', index=4,\n      number=5, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='theta_1_1', full_name='caffe.SpatialTransformerParameter.theta_1_1', index=5,\n      number=6, type=1, cpp_type=5, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='theta_1_2', full_name='caffe.SpatialTransformerParameter.theta_1_2', index=6,\n      number=7, type=1, cpp_type=5, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='theta_1_3', full_name='caffe.SpatialTransformerParameter.theta_1_3', index=7,\n      number=8, type=1, cpp_type=5, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='theta_2_1', full_name='caffe.SpatialTransformerParameter.theta_2_1', index=8,\n      number=9, type=1, cpp_type=5, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='theta_2_2', full_name='caffe.SpatialTransformerParameter.theta_2_2', index=9,\n      number=10, type=1, cpp_type=5, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='theta_2_3', full_name='caffe.SpatialTransformerParameter.theta_2_3', index=10,\n      number=11, type=1, cpp_type=5, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=4040,\n  serialized_end=4312,\n)\n\n\n_STLOSSPARAMETER = _descriptor.Descriptor(\n  name='STLossParameter',\n  full_name='caffe.STLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='output_H', full_name='caffe.STLossParameter.output_H', index=0,\n      number=1, type=5, cpp_type=1, label=2,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='output_W', full_name='caffe.STLossParameter.output_W', index=1,\n      number=2, type=5, cpp_type=1, label=2,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=4314,\n  serialized_end=4367,\n)\n\n\n_PARAMSPEC = _descriptor.Descriptor(\n  name='ParamSpec',\n  full_name='caffe.ParamSpec',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='name', full_name='caffe.ParamSpec.name', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='share_mode', full_name='caffe.ParamSpec.share_mode', index=1,\n      number=2, type=14, cpp_type=8, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='lr_mult', full_name='caffe.ParamSpec.lr_mult', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='decay_mult', full_name='caffe.ParamSpec.decay_mult', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _PARAMSPEC_DIMCHECKMODE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=4370,\n  serialized_end=4533,\n)\n\n\n_LAYERPARAMETER = _descriptor.Descriptor(\n  name='LayerParameter',\n  full_name='caffe.LayerParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='name', full_name='caffe.LayerParameter.name', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='type', full_name='caffe.LayerParameter.type', index=1,\n      number=2, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bottom', full_name='caffe.LayerParameter.bottom', index=2,\n      number=3, type=9, cpp_type=9, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='top', full_name='caffe.LayerParameter.top', index=3,\n      number=4, type=9, cpp_type=9, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='phase', full_name='caffe.LayerParameter.phase', index=4,\n      number=10, type=14, cpp_type=8, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='loss_weight', full_name='caffe.LayerParameter.loss_weight', index=5,\n      number=5, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='param', full_name='caffe.LayerParameter.param', index=6,\n      number=6, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='blobs', full_name='caffe.LayerParameter.blobs', index=7,\n      number=7, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='propagate_down', full_name='caffe.LayerParameter.propagate_down', index=8,\n      number=11, type=8, cpp_type=7, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='include', full_name='caffe.LayerParameter.include', index=9,\n      number=8, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='exclude', full_name='caffe.LayerParameter.exclude', index=10,\n      number=9, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='transform_param', full_name='caffe.LayerParameter.transform_param', index=11,\n      number=100, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='loss_param', full_name='caffe.LayerParameter.loss_param', index=12,\n      number=101, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='detection_loss_param', full_name='caffe.LayerParameter.detection_loss_param', index=13,\n      number=200, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='eval_detection_param', full_name='caffe.LayerParameter.eval_detection_param', index=14,\n      number=201, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='region_loss_param', full_name='caffe.LayerParameter.region_loss_param', index=15,\n      number=202, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='reorg_param', full_name='caffe.LayerParameter.reorg_param', index=16,\n      number=203, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='accuracy_param', full_name='caffe.LayerParameter.accuracy_param', index=17,\n      number=102, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='argmax_param', full_name='caffe.LayerParameter.argmax_param', index=18,\n      number=103, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='batch_norm_param', full_name='caffe.LayerParameter.batch_norm_param', index=19,\n      number=139, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_param', full_name='caffe.LayerParameter.bias_param', index=20,\n      number=141, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='concat_param', full_name='caffe.LayerParameter.concat_param', index=21,\n      number=104, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='contrastive_loss_param', full_name='caffe.LayerParameter.contrastive_loss_param', index=22,\n      number=105, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='convolution_param', full_name='caffe.LayerParameter.convolution_param', index=23,\n      number=106, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='data_param', full_name='caffe.LayerParameter.data_param', index=24,\n      number=107, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='dropout_param', full_name='caffe.LayerParameter.dropout_param', index=25,\n      number=108, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='dummy_data_param', 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serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='st_param', full_name='caffe.LayerParameter.st_param', index=62,\n      number=148, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='st_loss_param', full_name='caffe.LayerParameter.st_loss_param', index=63,\n      number=145, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='rpn_param', full_name='caffe.LayerParameter.rpn_param', index=64,\n      number=150, type=11, cpp_type=10, label=1,\n      has_default_value=False, 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enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='center_loss_param', full_name='caffe.LayerParameter.center_loss_param', index=80,\n      number=175, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='deformable_convolution_param', full_name='caffe.LayerParameter.deformable_convolution_param', index=81,\n      number=176, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='label_specific_add_param', full_name='caffe.LayerParameter.label_specific_add_param', index=82,\n      number=177, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='additive_margin_inner_product_param', full_name='caffe.LayerParameter.additive_margin_inner_product_param', index=83,\n      number=178, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='cosin_add_m_param', full_name='caffe.LayerParameter.cosin_add_m_param', index=84,\n      number=179, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='cosin_mul_m_param', full_name='caffe.LayerParameter.cosin_mul_m_param', index=85,\n      number=180, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='channel_scale_param', full_name='caffe.LayerParameter.channel_scale_param', index=86,\n      number=181, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='flip_param', full_name='caffe.LayerParameter.flip_param', index=87,\n      number=182, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='triplet_loss_param', full_name='caffe.LayerParameter.triplet_loss_param', index=88,\n      number=183, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='coupled_cluster_loss_param', full_name='caffe.LayerParameter.coupled_cluster_loss_param', index=89,\n      number=184, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='general_triplet_loss_param', full_name='caffe.LayerParameter.general_triplet_loss_param', index=90,\n      number=185, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='roi_align_param', full_name='caffe.LayerParameter.roi_align_param', index=91,\n      number=186, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='upsample_param', full_name='caffe.LayerParameter.upsample_param', index=92,\n      number=100003, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='matmul_param', full_name='caffe.LayerParameter.matmul_param', index=93,\n      number=100005, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pass_through_param', full_name='caffe.LayerParameter.pass_through_param', index=94,\n      number=100004, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='norm_param', full_name='caffe.LayerParameter.norm_param', index=95,\n      number=100001, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=4536,\n  serialized_end=9293,\n)\n\n\n_UPSAMPLEPARAMETER = _descriptor.Descriptor(\n  name='UpsampleParameter',\n  full_name='caffe.UpsampleParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.UpsampleParameter.scale', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=2,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale_h', full_name='caffe.UpsampleParameter.scale_h', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale_w', full_name='caffe.UpsampleParameter.scale_w', index=2,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_out_h', full_name='caffe.UpsampleParameter.pad_out_h', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_out_w', full_name='caffe.UpsampleParameter.pad_out_w', index=4,\n      number=5, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='upsample_h', full_name='caffe.UpsampleParameter.upsample_h', index=5,\n      number=6, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='upsample_w', full_name='caffe.UpsampleParameter.upsample_w', index=6,\n      number=7, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=9296,\n  serialized_end=9459,\n)\n\n\n_MATMULPARAMETER = _descriptor.Descriptor(\n  name='MatMulParameter',\n  full_name='caffe.MatMulParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='dim_1', full_name='caffe.MatMulParameter.dim_1', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='dim_2', full_name='caffe.MatMulParameter.dim_2', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='dim_3', full_name='caffe.MatMulParameter.dim_3', index=2,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=9461,\n  serialized_end=9523,\n)\n\n\n_PASSTHROUGHPARAMETER = _descriptor.Descriptor(\n  name='PassThroughParameter',\n  full_name='caffe.PassThroughParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_output', full_name='caffe.PassThroughParameter.num_output', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='block_height', full_name='caffe.PassThroughParameter.block_height', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='block_width', full_name='caffe.PassThroughParameter.block_width', index=2,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=9525,\n  serialized_end=9619,\n)\n\n\n_NORMALIZEPARAMETER = _descriptor.Descriptor(\n  name='NormalizeParameter',\n  full_name='caffe.NormalizeParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='across_spatial', full_name='caffe.NormalizeParameter.across_spatial', index=0,\n      number=1, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale_filler', full_name='caffe.NormalizeParameter.scale_filler', index=1,\n      number=2, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='channel_shared', full_name='caffe.NormalizeParameter.channel_shared', index=2,\n      number=3, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='eps', full_name='caffe.NormalizeParameter.eps', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1e-10),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='sqrt_a', full_name='caffe.NormalizeParameter.sqrt_a', index=4,\n      number=5, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=9622,\n  serialized_end=9787,\n)\n\n\n_ANNOTATEDDATAPARAMETER = _descriptor.Descriptor(\n  name='AnnotatedDataParameter',\n  full_name='caffe.AnnotatedDataParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='batch_sampler', full_name='caffe.AnnotatedDataParameter.batch_sampler', index=0,\n      number=1, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='label_map_file', full_name='caffe.AnnotatedDataParameter.label_map_file', index=1,\n      number=2, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='anno_type', full_name='caffe.AnnotatedDataParameter.anno_type', index=2,\n      number=3, type=14, cpp_type=8, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=9790,\n  serialized_end=9939,\n)\n\n\n_ASDNDATAPARAMETER = _descriptor.Descriptor(\n  name='AsdnDataParameter',\n  full_name='caffe.AsdnDataParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='count_drop', full_name='caffe.AsdnDataParameter.count_drop', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=15,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='permute_count', full_name='caffe.AsdnDataParameter.permute_count', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=20,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='count_drop_neg', full_name='caffe.AsdnDataParameter.count_drop_neg', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='channels', full_name='caffe.AsdnDataParameter.channels', index=3,\n      number=4, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1024,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='iter_size', full_name='caffe.AsdnDataParameter.iter_size', index=4,\n      number=5, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=2,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='maintain_before', full_name='caffe.AsdnDataParameter.maintain_before', index=5,\n      number=6, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=9942,\n  serialized_end=10113,\n)\n\n\n_MTCNNDATAPARAMETER = _descriptor.Descriptor(\n  name='MTCNNDataParameter',\n  full_name='caffe.MTCNNDataParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='augmented', full_name='caffe.MTCNNDataParameter.augmented', index=0,\n      number=1, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='flip', full_name='caffe.MTCNNDataParameter.flip', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_positive', full_name='caffe.MTCNNDataParameter.num_positive', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=-1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_negitive', full_name='caffe.MTCNNDataParameter.num_negitive', index=3,\n      number=4, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=-1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_part', full_name='caffe.MTCNNDataParameter.num_part', index=4,\n      number=5, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=-1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='resize_width', full_name='caffe.MTCNNDataParameter.resize_width', index=5,\n      number=6, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='resize_height', full_name='caffe.MTCNNDataParameter.resize_height', index=6,\n      number=7, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='min_negitive_scale', full_name='caffe.MTCNNDataParameter.min_negitive_scale', index=7,\n      number=8, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_negitive_scale', full_name='caffe.MTCNNDataParameter.max_negitive_scale', index=8,\n      number=9, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=10116,\n  serialized_end=10372,\n)\n\n\n_INTERPPARAMETER = _descriptor.Descriptor(\n  name='InterpParameter',\n  full_name='caffe.InterpParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='height', full_name='caffe.InterpParameter.height', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='width', full_name='caffe.InterpParameter.width', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='zoom_factor', full_name='caffe.InterpParameter.zoom_factor', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='shrink_factor', full_name='caffe.InterpParameter.shrink_factor', index=3,\n      number=4, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_beg', full_name='caffe.InterpParameter.pad_beg', index=4,\n      number=5, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_end', full_name='caffe.InterpParameter.pad_end', index=5,\n      number=6, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=10375,\n  serialized_end=10519,\n)\n\n\n_PSROIPOOLINGPARAMETER = _descriptor.Descriptor(\n  name='PSROIPoolingParameter',\n  full_name='caffe.PSROIPoolingParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='spatial_scale', full_name='caffe.PSROIPoolingParameter.spatial_scale', index=0,\n      number=1, type=2, cpp_type=6, label=2,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='output_dim', full_name='caffe.PSROIPoolingParameter.output_dim', index=1,\n      number=2, type=5, cpp_type=1, label=2,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='group_size', full_name='caffe.PSROIPoolingParameter.group_size', index=2,\n      number=3, type=5, cpp_type=1, label=2,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=10521,\n  serialized_end=10607,\n)\n\n\n_FLIPPARAMETER = _descriptor.Descriptor(\n  name='FlipParameter',\n  full_name='caffe.FlipParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='flip_width', full_name='caffe.FlipParameter.flip_width', index=0,\n      number=1, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='flip_height', full_name='caffe.FlipParameter.flip_height', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=10609,\n  serialized_end=10678,\n)\n\n\n_BNPARAMETER = _descriptor.Descriptor(\n  name='BNParameter',\n  full_name='caffe.BNParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='slope_filler', full_name='caffe.BNParameter.slope_filler', index=0,\n      number=1, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_filler', full_name='caffe.BNParameter.bias_filler', index=1,\n      number=2, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='momentum', full_name='caffe.BNParameter.momentum', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.9),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='eps', full_name='caffe.BNParameter.eps', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1e-05),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='frozen', full_name='caffe.BNParameter.frozen', index=4,\n      number=5, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='engine', full_name='caffe.BNParameter.engine', index=5,\n      number=6, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _BNPARAMETER_ENGINE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=10681,\n  serialized_end=10948,\n)\n\n\n_FOCALLOSSPARAMETER = _descriptor.Descriptor(\n  name='FocalLossParameter',\n  full_name='caffe.FocalLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='type', full_name='caffe.FocalLossParameter.type', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='gamma', full_name='caffe.FocalLossParameter.gamma', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(2),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='alpha', full_name='caffe.FocalLossParameter.alpha', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.25),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='beta', full_name='caffe.FocalLossParameter.beta', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _FOCALLOSSPARAMETER_TYPE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=10951,\n  serialized_end=11113,\n)\n\n\n_TRANSFORMATIONPARAMETER = _descriptor.Descriptor(\n  name='TransformationParameter',\n  full_name='caffe.TransformationParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.TransformationParameter.scale', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mirror', full_name='caffe.TransformationParameter.mirror', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='crop_size', full_name='caffe.TransformationParameter.crop_size', index=2,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='crop_h', full_name='caffe.TransformationParameter.crop_h', index=3,\n      number=11, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='crop_w', full_name='caffe.TransformationParameter.crop_w', index=4,\n      number=12, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mean_file', full_name='caffe.TransformationParameter.mean_file', index=5,\n      number=4, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mean_value', full_name='caffe.TransformationParameter.mean_value', index=6,\n      number=5, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='force_color', full_name='caffe.TransformationParameter.force_color', index=7,\n      number=6, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='force_gray', full_name='caffe.TransformationParameter.force_gray', index=8,\n      number=7, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='resize_param', full_name='caffe.TransformationParameter.resize_param', index=9,\n      number=8, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='noise_param', full_name='caffe.TransformationParameter.noise_param', index=10,\n      number=9, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='distort_param', full_name='caffe.TransformationParameter.distort_param', index=11,\n      number=13, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='expand_param', full_name='caffe.TransformationParameter.expand_param', index=12,\n      number=14, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='emit_constraint', full_name='caffe.TransformationParameter.emit_constraint', index=13,\n      number=10, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=11116,\n  serialized_end=11574,\n)\n\n\n_RESIZEPARAMETER = _descriptor.Descriptor(\n  name='ResizeParameter',\n  full_name='caffe.ResizeParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='prob', full_name='caffe.ResizeParameter.prob', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='resize_mode', full_name='caffe.ResizeParameter.resize_mode', index=1,\n      number=2, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='height', full_name='caffe.ResizeParameter.height', index=2,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='width', full_name='caffe.ResizeParameter.width', index=3,\n      number=4, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='height_scale', full_name='caffe.ResizeParameter.height_scale', index=4,\n      number=8, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='width_scale', full_name='caffe.ResizeParameter.width_scale', index=5,\n      number=9, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_mode', full_name='caffe.ResizeParameter.pad_mode', index=6,\n      number=5, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_value', full_name='caffe.ResizeParameter.pad_value', index=7,\n      number=6, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='interp_mode', full_name='caffe.ResizeParameter.interp_mode', index=8,\n      number=7, type=14, cpp_type=8, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _RESIZEPARAMETER_RESIZE_MODE,\n    _RESIZEPARAMETER_PAD_MODE,\n    _RESIZEPARAMETER_INTERP_MODE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=11577,\n  serialized_end=12105,\n)\n\n\n_SALTPEPPERPARAMETER = _descriptor.Descriptor(\n  name='SaltPepperParameter',\n  full_name='caffe.SaltPepperParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='fraction', full_name='caffe.SaltPepperParameter.fraction', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='value', full_name='caffe.SaltPepperParameter.value', index=1,\n      number=2, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=12107,\n  serialized_end=12164,\n)\n\n\n_NOISEPARAMETER = _descriptor.Descriptor(\n  name='NoiseParameter',\n  full_name='caffe.NoiseParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='prob', full_name='caffe.NoiseParameter.prob', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='hist_eq', full_name='caffe.NoiseParameter.hist_eq', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='inverse', full_name='caffe.NoiseParameter.inverse', index=2,\n      number=3, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='decolorize', full_name='caffe.NoiseParameter.decolorize', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='gauss_blur', full_name='caffe.NoiseParameter.gauss_blur', index=4,\n      number=5, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='jpeg', full_name='caffe.NoiseParameter.jpeg', index=5,\n      number=6, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(-1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='posterize', full_name='caffe.NoiseParameter.posterize', index=6,\n      number=7, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='erode', full_name='caffe.NoiseParameter.erode', index=7,\n      number=8, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='saltpepper', full_name='caffe.NoiseParameter.saltpepper', index=8,\n      number=9, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='saltpepper_param', full_name='caffe.NoiseParameter.saltpepper_param', index=9,\n      number=10, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='clahe', full_name='caffe.NoiseParameter.clahe', index=10,\n      number=11, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='convert_to_hsv', full_name='caffe.NoiseParameter.convert_to_hsv', index=11,\n      number=12, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='convert_to_lab', full_name='caffe.NoiseParameter.convert_to_lab', index=12,\n      number=13, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=12167,\n  serialized_end=12533,\n)\n\n\n_DISTORTIONPARAMETER = _descriptor.Descriptor(\n  name='DistortionParameter',\n  full_name='caffe.DistortionParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='brightness_prob', full_name='caffe.DistortionParameter.brightness_prob', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='brightness_delta', full_name='caffe.DistortionParameter.brightness_delta', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='contrast_prob', full_name='caffe.DistortionParameter.contrast_prob', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='contrast_lower', full_name='caffe.DistortionParameter.contrast_lower', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='contrast_upper', full_name='caffe.DistortionParameter.contrast_upper', index=4,\n      number=5, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='hue_prob', full_name='caffe.DistortionParameter.hue_prob', index=5,\n      number=6, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='hue_delta', full_name='caffe.DistortionParameter.hue_delta', index=6,\n      number=7, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='saturation_prob', full_name='caffe.DistortionParameter.saturation_prob', index=7,\n      number=8, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='saturation_lower', full_name='caffe.DistortionParameter.saturation_lower', index=8,\n      number=9, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='saturation_upper', full_name='caffe.DistortionParameter.saturation_upper', index=9,\n      number=10, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='random_order_prob', full_name='caffe.DistortionParameter.random_order_prob', index=10,\n      number=11, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=12536,\n  serialized_end=12853,\n)\n\n\n_EXPANSIONPARAMETER = _descriptor.Descriptor(\n  name='ExpansionParameter',\n  full_name='caffe.ExpansionParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='prob', full_name='caffe.ExpansionParameter.prob', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_expand_ratio', full_name='caffe.ExpansionParameter.max_expand_ratio', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=12855,\n  serialized_end=12921,\n)\n\n\n_LOSSPARAMETER = _descriptor.Descriptor(\n  name='LossParameter',\n  full_name='caffe.LossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='ignore_label', full_name='caffe.LossParameter.ignore_label', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='normalization', full_name='caffe.LossParameter.normalization', index=1,\n      number=3, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='normalize', full_name='caffe.LossParameter.normalize', index=2,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=False, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _LOSSPARAMETER_NORMALIZATIONMODE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=12924,\n  serialized_end=13118,\n)\n\n\n_ACCURACYPARAMETER = _descriptor.Descriptor(\n  name='AccuracyParameter',\n  full_name='caffe.AccuracyParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='top_k', full_name='caffe.AccuracyParameter.top_k', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.AccuracyParameter.axis', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='ignore_label', full_name='caffe.AccuracyParameter.ignore_label', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=13120,\n  serialized_end=13196,\n)\n\n\n_ARGMAXPARAMETER = _descriptor.Descriptor(\n  name='ArgMaxParameter',\n  full_name='caffe.ArgMaxParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='out_max_val', full_name='caffe.ArgMaxParameter.out_max_val', index=0,\n      number=1, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='top_k', full_name='caffe.ArgMaxParameter.top_k', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.ArgMaxParameter.axis', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=13198,\n  serialized_end=13275,\n)\n\n\n_CONCATPARAMETER = _descriptor.Descriptor(\n  name='ConcatParameter',\n  full_name='caffe.ConcatParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.ConcatParameter.axis', index=0,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='concat_dim', full_name='caffe.ConcatParameter.concat_dim', index=1,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=13277,\n  serialized_end=13334,\n)\n\n\n_BATCHNORMPARAMETER = _descriptor.Descriptor(\n  name='BatchNormParameter',\n  full_name='caffe.BatchNormParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='use_global_stats', full_name='caffe.BatchNormParameter.use_global_stats', index=0,\n      number=1, type=8, cpp_type=7, label=1,\n      has_default_value=False, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='moving_average_fraction', full_name='caffe.BatchNormParameter.moving_average_fraction', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.999),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='eps', full_name='caffe.BatchNormParameter.eps', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1e-05),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=13336,\n  serialized_end=13442,\n)\n\n\n_BIASPARAMETER = _descriptor.Descriptor(\n  name='BiasParameter',\n  full_name='caffe.BiasParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.BiasParameter.axis', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_axes', full_name='caffe.BiasParameter.num_axes', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='filler', full_name='caffe.BiasParameter.filler', index=2,\n      number=3, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=13444,\n  serialized_end=13537,\n)\n\n\n_CONTRASTIVELOSSPARAMETER = _descriptor.Descriptor(\n  name='ContrastiveLossParameter',\n  full_name='caffe.ContrastiveLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='margin', full_name='caffe.ContrastiveLossParameter.margin', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='legacy_version', full_name='caffe.ContrastiveLossParameter.legacy_version', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=13539,\n  serialized_end=13615,\n)\n\n\n_DETECTIONLOSSPARAMETER = _descriptor.Descriptor(\n  name='DetectionLossParameter',\n  full_name='caffe.DetectionLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='side', full_name='caffe.DetectionLossParameter.side', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=7,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_class', full_name='caffe.DetectionLossParameter.num_class', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=20,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_object', full_name='caffe.DetectionLossParameter.num_object', index=2,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=2,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='object_scale', full_name='caffe.DetectionLossParameter.object_scale', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='noobject_scale', full_name='caffe.DetectionLossParameter.noobject_scale', index=4,\n      number=5, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='class_scale', full_name='caffe.DetectionLossParameter.class_scale', index=5,\n      number=6, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='coord_scale', full_name='caffe.DetectionLossParameter.coord_scale', index=6,\n      number=7, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='sqrt', full_name='caffe.DetectionLossParameter.sqrt', index=7,\n      number=8, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='constriant', full_name='caffe.DetectionLossParameter.constriant', index=8,\n      number=9, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=13618,\n  serialized_end=13854,\n)\n\n\n_REGIONLOSSPARAMETER = _descriptor.Descriptor(\n  name='RegionLossParameter',\n  full_name='caffe.RegionLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='side', full_name='caffe.RegionLossParameter.side', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=13,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_class', full_name='caffe.RegionLossParameter.num_class', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=20,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_match', full_name='caffe.RegionLossParameter.bias_match', index=2,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='coords', full_name='caffe.RegionLossParameter.coords', index=3,\n      number=4, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=4,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num', full_name='caffe.RegionLossParameter.num', index=4,\n      number=5, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=5,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='softmax', full_name='caffe.RegionLossParameter.softmax', index=5,\n      number=6, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='jitter', full_name='caffe.RegionLossParameter.jitter', index=6,\n      number=7, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.2),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='rescore', full_name='caffe.RegionLossParameter.rescore', index=7,\n      number=8, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='object_scale', full_name='caffe.RegionLossParameter.object_scale', index=8,\n      number=9, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='class_scale', full_name='caffe.RegionLossParameter.class_scale', index=9,\n      number=10, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='noobject_scale', full_name='caffe.RegionLossParameter.noobject_scale', index=10,\n      number=11, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='coord_scale', full_name='caffe.RegionLossParameter.coord_scale', index=11,\n      number=12, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='absolute', full_name='caffe.RegionLossParameter.absolute', index=12,\n      number=13, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='thresh', full_name='caffe.RegionLossParameter.thresh', index=13,\n      number=14, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.2),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='random', full_name='caffe.RegionLossParameter.random', index=14,\n      number=15, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='biases', full_name='caffe.RegionLossParameter.biases', index=15,\n      number=16, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='softmax_tree', full_name='caffe.RegionLossParameter.softmax_tree', index=16,\n      number=17, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='class_map', full_name='caffe.RegionLossParameter.class_map', index=17,\n      number=18, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=13857,\n  serialized_end=14258,\n)\n\n\n_REORGPARAMETER = _descriptor.Descriptor(\n  name='ReorgParameter',\n  full_name='caffe.ReorgParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='stride', full_name='caffe.ReorgParameter.stride', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='reverse', full_name='caffe.ReorgParameter.reverse', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=14260,\n  serialized_end=14316,\n)\n\n\n_EVALDETECTIONPARAMETER = _descriptor.Descriptor(\n  name='EvalDetectionParameter',\n  full_name='caffe.EvalDetectionParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='side', full_name='caffe.EvalDetectionParameter.side', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=7,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_class', full_name='caffe.EvalDetectionParameter.num_class', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=20,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_object', full_name='caffe.EvalDetectionParameter.num_object', index=2,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=2,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='threshold', full_name='caffe.EvalDetectionParameter.threshold', index=3,\n      number=4, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='sqrt', full_name='caffe.EvalDetectionParameter.sqrt', index=4,\n      number=5, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='constriant', full_name='caffe.EvalDetectionParameter.constriant', index=5,\n      number=6, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='score_type', full_name='caffe.EvalDetectionParameter.score_type', index=6,\n      number=7, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=2,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='nms', full_name='caffe.EvalDetectionParameter.nms', index=7,\n      number=8, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(-1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='biases', full_name='caffe.EvalDetectionParameter.biases', index=8,\n      number=9, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _EVALDETECTIONPARAMETER_SCORETYPE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=14319,\n  serialized_end=14626,\n)\n\n\n_CONVOLUTIONPARAMETER = _descriptor.Descriptor(\n  name='ConvolutionParameter',\n  full_name='caffe.ConvolutionParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_output', full_name='caffe.ConvolutionParameter.num_output', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_term', full_name='caffe.ConvolutionParameter.bias_term', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad', full_name='caffe.ConvolutionParameter.pad', index=2,\n      number=3, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='kernel_size', full_name='caffe.ConvolutionParameter.kernel_size', index=3,\n      number=4, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stride', full_name='caffe.ConvolutionParameter.stride', index=4,\n      number=6, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='dilation', full_name='caffe.ConvolutionParameter.dilation', index=5,\n      number=18, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_h', full_name='caffe.ConvolutionParameter.pad_h', index=6,\n      number=9, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_w', full_name='caffe.ConvolutionParameter.pad_w', index=7,\n      number=10, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='kernel_h', full_name='caffe.ConvolutionParameter.kernel_h', index=8,\n      number=11, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='kernel_w', full_name='caffe.ConvolutionParameter.kernel_w', index=9,\n      number=12, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stride_h', full_name='caffe.ConvolutionParameter.stride_h', index=10,\n      number=13, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stride_w', full_name='caffe.ConvolutionParameter.stride_w', index=11,\n      number=14, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='group', full_name='caffe.ConvolutionParameter.group', index=12,\n      number=5, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_filler', full_name='caffe.ConvolutionParameter.weight_filler', index=13,\n      number=7, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_filler', full_name='caffe.ConvolutionParameter.bias_filler', index=14,\n      number=8, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='engine', full_name='caffe.ConvolutionParameter.engine', index=15,\n      number=15, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.ConvolutionParameter.axis', index=16,\n      number=16, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='force_nd_im2col', full_name='caffe.ConvolutionParameter.force_nd_im2col', index=17,\n      number=17, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _CONVOLUTIONPARAMETER_ENGINE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=14629,\n  serialized_end=15137,\n)\n\n\n_CROPPARAMETER = _descriptor.Descriptor(\n  name='CropParameter',\n  full_name='caffe.CropParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.CropParameter.axis', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=2,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='offset', full_name='caffe.CropParameter.offset', index=1,\n      number=2, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=15139,\n  serialized_end=15187,\n)\n\n\n_DATAPARAMETER = _descriptor.Descriptor(\n  name='DataParameter',\n  full_name='caffe.DataParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='source', full_name='caffe.DataParameter.source', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='batch_size', full_name='caffe.DataParameter.batch_size', index=1,\n      number=4, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='rand_skip', full_name='caffe.DataParameter.rand_skip', index=2,\n      number=7, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='backend', full_name='caffe.DataParameter.backend', index=3,\n      number=8, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.DataParameter.scale', index=4,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mean_file', full_name='caffe.DataParameter.mean_file', index=5,\n      number=3, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='crop_size', full_name='caffe.DataParameter.crop_size', index=6,\n      number=5, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mirror', full_name='caffe.DataParameter.mirror', index=7,\n      number=6, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='force_encoded_color', full_name='caffe.DataParameter.force_encoded_color', index=8,\n      number=9, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='prefetch', full_name='caffe.DataParameter.prefetch', index=9,\n      number=10, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=4,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='side', full_name='caffe.DataParameter.side', index=10,\n      number=11, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _DATAPARAMETER_DB,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=15190,\n  serialized_end=15496,\n)\n\n\n_DETECTIONEVALUATEPARAMETER = _descriptor.Descriptor(\n  name='DetectionEvaluateParameter',\n  full_name='caffe.DetectionEvaluateParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_classes', full_name='caffe.DetectionEvaluateParameter.num_classes', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='background_label_id', full_name='caffe.DetectionEvaluateParameter.background_label_id', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='overlap_threshold', full_name='caffe.DetectionEvaluateParameter.overlap_threshold', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='evaluate_difficult_gt', full_name='caffe.DetectionEvaluateParameter.evaluate_difficult_gt', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='name_size_file', full_name='caffe.DetectionEvaluateParameter.name_size_file', index=4,\n      number=5, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='resize_param', full_name='caffe.DetectionEvaluateParameter.resize_param', index=5,\n      number=6, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=15499,\n  serialized_end=15719,\n)\n\n\n_NONMAXIMUMSUPPRESSIONPARAMETER = _descriptor.Descriptor(\n  name='NonMaximumSuppressionParameter',\n  full_name='caffe.NonMaximumSuppressionParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='nms_threshold', full_name='caffe.NonMaximumSuppressionParameter.nms_threshold', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.3),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='top_k', full_name='caffe.NonMaximumSuppressionParameter.top_k', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='eta', full_name='caffe.NonMaximumSuppressionParameter.eta', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=15721,\n  serialized_end=15812,\n)\n\n\n_SAVEOUTPUTPARAMETER = _descriptor.Descriptor(\n  name='SaveOutputParameter',\n  full_name='caffe.SaveOutputParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='output_directory', full_name='caffe.SaveOutputParameter.output_directory', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='output_name_prefix', full_name='caffe.SaveOutputParameter.output_name_prefix', index=1,\n      number=2, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='output_format', full_name='caffe.SaveOutputParameter.output_format', index=2,\n      number=3, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='label_map_file', full_name='caffe.SaveOutputParameter.label_map_file', index=3,\n      number=4, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='name_size_file', full_name='caffe.SaveOutputParameter.name_size_file', index=4,\n      number=5, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_test_image', full_name='caffe.SaveOutputParameter.num_test_image', index=5,\n      number=6, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='resize_param', full_name='caffe.SaveOutputParameter.resize_param', index=6,\n      number=7, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=15815,\n  serialized_end=16031,\n)\n\n\n_DETECTIONOUTPUTPARAMETER = _descriptor.Descriptor(\n  name='DetectionOutputParameter',\n  full_name='caffe.DetectionOutputParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_classes', full_name='caffe.DetectionOutputParameter.num_classes', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='share_location', full_name='caffe.DetectionOutputParameter.share_location', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='background_label_id', full_name='caffe.DetectionOutputParameter.background_label_id', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='nms_param', full_name='caffe.DetectionOutputParameter.nms_param', index=3,\n      number=4, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='save_output_param', full_name='caffe.DetectionOutputParameter.save_output_param', index=4,\n      number=5, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='code_type', full_name='caffe.DetectionOutputParameter.code_type', index=5,\n      number=6, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='variance_encoded_in_target', full_name='caffe.DetectionOutputParameter.variance_encoded_in_target', index=6,\n      number=8, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='keep_top_k', full_name='caffe.DetectionOutputParameter.keep_top_k', index=7,\n      number=7, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=-1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='confidence_threshold', full_name='caffe.DetectionOutputParameter.confidence_threshold', index=8,\n      number=9, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='visualize', full_name='caffe.DetectionOutputParameter.visualize', index=9,\n      number=10, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='visualize_threshold', full_name='caffe.DetectionOutputParameter.visualize_threshold', index=10,\n      number=11, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='save_file', full_name='caffe.DetectionOutputParameter.save_file', index=11,\n      number=12, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=16034,\n  serialized_end=16489,\n)\n\n\n_DROPOUTPARAMETER = _descriptor.Descriptor(\n  name='DropoutParameter',\n  full_name='caffe.DropoutParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='dropout_ratio', full_name='caffe.DropoutParameter.dropout_ratio', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale_train', full_name='caffe.DropoutParameter.scale_train', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=16491,\n  serialized_end=16564,\n)\n\n\n_DUMMYDATAPARAMETER = _descriptor.Descriptor(\n  name='DummyDataParameter',\n  full_name='caffe.DummyDataParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='data_filler', full_name='caffe.DummyDataParameter.data_filler', index=0,\n      number=1, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='shape', full_name='caffe.DummyDataParameter.shape', index=1,\n      number=6, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num', full_name='caffe.DummyDataParameter.num', index=2,\n      number=2, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='channels', full_name='caffe.DummyDataParameter.channels', index=3,\n      number=3, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='height', full_name='caffe.DummyDataParameter.height', index=4,\n      number=4, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='width', full_name='caffe.DummyDataParameter.width', index=5,\n      number=5, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=16567,\n  serialized_end=16727,\n)\n\n\n_ELTWISEPARAMETER = _descriptor.Descriptor(\n  name='EltwiseParameter',\n  full_name='caffe.EltwiseParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='operation', full_name='caffe.EltwiseParameter.operation', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='coeff', full_name='caffe.EltwiseParameter.coeff', index=1,\n      number=2, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stable_prod_grad', full_name='caffe.EltwiseParameter.stable_prod_grad', index=2,\n      number=3, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _ELTWISEPARAMETER_ELTWISEOP,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=16730,\n  serialized_end=16895,\n)\n\n\n_ELUPARAMETER = _descriptor.Descriptor(\n  name='ELUParameter',\n  full_name='caffe.ELUParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='alpha', full_name='caffe.ELUParameter.alpha', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=16897,\n  serialized_end=16929,\n)\n\n\n_EMBEDPARAMETER = _descriptor.Descriptor(\n  name='EmbedParameter',\n  full_name='caffe.EmbedParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_output', full_name='caffe.EmbedParameter.num_output', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='input_dim', full_name='caffe.EmbedParameter.input_dim', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_term', full_name='caffe.EmbedParameter.bias_term', index=2,\n      number=3, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_filler', full_name='caffe.EmbedParameter.weight_filler', index=3,\n      number=4, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_filler', full_name='caffe.EmbedParameter.bias_filler', index=4,\n      number=5, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=16932,\n  serialized_end=17104,\n)\n\n\n_EXPPARAMETER = _descriptor.Descriptor(\n  name='ExpParameter',\n  full_name='caffe.ExpParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='base', full_name='caffe.ExpParameter.base', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(-1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.ExpParameter.scale', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='shift', full_name='caffe.ExpParameter.shift', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=17106,\n  serialized_end=17174,\n)\n\n\n_FLATTENPARAMETER = _descriptor.Descriptor(\n  name='FlattenParameter',\n  full_name='caffe.FlattenParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.FlattenParameter.axis', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='end_axis', full_name='caffe.FlattenParameter.end_axis', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=-1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=17176,\n  serialized_end=17233,\n)\n\n\n_HDF5DATAPARAMETER = _descriptor.Descriptor(\n  name='HDF5DataParameter',\n  full_name='caffe.HDF5DataParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='source', full_name='caffe.HDF5DataParameter.source', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='batch_size', full_name='caffe.HDF5DataParameter.batch_size', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='shuffle', full_name='caffe.HDF5DataParameter.shuffle', index=2,\n      number=3, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=17235,\n  serialized_end=17314,\n)\n\n\n_HDF5OUTPUTPARAMETER = _descriptor.Descriptor(\n  name='HDF5OutputParameter',\n  full_name='caffe.HDF5OutputParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='file_name', full_name='caffe.HDF5OutputParameter.file_name', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=17316,\n  serialized_end=17356,\n)\n\n\n_HINGELOSSPARAMETER = _descriptor.Descriptor(\n  name='HingeLossParameter',\n  full_name='caffe.HingeLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='norm', full_name='caffe.HingeLossParameter.norm', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _HINGELOSSPARAMETER_NORM,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=17358,\n  serialized_end=17452,\n)\n\n\n_IMAGEDATAPARAMETER = _descriptor.Descriptor(\n  name='ImageDataParameter',\n  full_name='caffe.ImageDataParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='source', full_name='caffe.ImageDataParameter.source', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='batch_size', full_name='caffe.ImageDataParameter.batch_size', index=1,\n      number=4, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='rand_skip', full_name='caffe.ImageDataParameter.rand_skip', index=2,\n      number=7, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='shuffle', full_name='caffe.ImageDataParameter.shuffle', index=3,\n      number=8, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='new_height', full_name='caffe.ImageDataParameter.new_height', index=4,\n      number=9, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='new_width', full_name='caffe.ImageDataParameter.new_width', index=5,\n      number=10, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='is_color', full_name='caffe.ImageDataParameter.is_color', index=6,\n      number=11, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.ImageDataParameter.scale', index=7,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mean_file', full_name='caffe.ImageDataParameter.mean_file', index=8,\n      number=3, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='crop_size', full_name='caffe.ImageDataParameter.crop_size', index=9,\n      number=5, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mirror', full_name='caffe.ImageDataParameter.mirror', index=10,\n      number=6, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='root_folder', full_name='caffe.ImageDataParameter.root_folder', index=11,\n      number=12, type=9, cpp_type=9, label=1,\n      has_default_value=True, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=17455,\n  serialized_end=17734,\n)\n\n\n_INFOGAINLOSSPARAMETER = _descriptor.Descriptor(\n  name='InfogainLossParameter',\n  full_name='caffe.InfogainLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='source', full_name='caffe.InfogainLossParameter.source', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=17736,\n  serialized_end=17775,\n)\n\n\n_INNERPRODUCTPARAMETER = _descriptor.Descriptor(\n  name='InnerProductParameter',\n  full_name='caffe.InnerProductParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_output', full_name='caffe.InnerProductParameter.num_output', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_term', full_name='caffe.InnerProductParameter.bias_term', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_filler', full_name='caffe.InnerProductParameter.weight_filler', index=2,\n      number=3, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_filler', full_name='caffe.InnerProductParameter.bias_filler', index=3,\n      number=4, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.InnerProductParameter.axis', index=4,\n      number=5, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='transpose', full_name='caffe.InnerProductParameter.transpose', index=5,\n      number=6, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='normalize', full_name='caffe.InnerProductParameter.normalize', index=6,\n      number=7, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=17778,\n  serialized_end=18007,\n)\n\n\n_INPUTPARAMETER = _descriptor.Descriptor(\n  name='InputParameter',\n  full_name='caffe.InputParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='shape', full_name='caffe.InputParameter.shape', index=0,\n      number=1, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=18009,\n  serialized_end=18058,\n)\n\n\n_LOGPARAMETER = _descriptor.Descriptor(\n  name='LogParameter',\n  full_name='caffe.LogParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='base', full_name='caffe.LogParameter.base', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(-1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.LogParameter.scale', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='shift', full_name='caffe.LogParameter.shift', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=18060,\n  serialized_end=18128,\n)\n\n\n_LRNPARAMETER = _descriptor.Descriptor(\n  name='LRNParameter',\n  full_name='caffe.LRNParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='local_size', full_name='caffe.LRNParameter.local_size', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=5,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='alpha', full_name='caffe.LRNParameter.alpha', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='beta', full_name='caffe.LRNParameter.beta', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.75),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='norm_region', full_name='caffe.LRNParameter.norm_region', index=3,\n      number=4, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='k', full_name='caffe.LRNParameter.k', index=4,\n      number=5, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='engine', full_name='caffe.LRNParameter.engine', index=5,\n      number=6, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _LRNPARAMETER_NORMREGION,\n    _LRNPARAMETER_ENGINE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=18131,\n  serialized_end=18443,\n)\n\n\n_MEMORYDATAPARAMETER = _descriptor.Descriptor(\n  name='MemoryDataParameter',\n  full_name='caffe.MemoryDataParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='batch_size', full_name='caffe.MemoryDataParameter.batch_size', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='channels', full_name='caffe.MemoryDataParameter.channels', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='height', full_name='caffe.MemoryDataParameter.height', index=2,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='width', full_name='caffe.MemoryDataParameter.width', index=3,\n      number=4, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=18445,\n  serialized_end=18535,\n)\n\n\n_MULTIBOXLOSSPARAMETER = _descriptor.Descriptor(\n  name='MultiBoxLossParameter',\n  full_name='caffe.MultiBoxLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='loc_loss_type', full_name='caffe.MultiBoxLossParameter.loc_loss_type', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='conf_loss_type', full_name='caffe.MultiBoxLossParameter.conf_loss_type', index=1,\n      number=2, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='loc_weight', full_name='caffe.MultiBoxLossParameter.loc_weight', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_classes', full_name='caffe.MultiBoxLossParameter.num_classes', index=3,\n      number=4, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='share_location', full_name='caffe.MultiBoxLossParameter.share_location', index=4,\n      number=5, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='match_type', full_name='caffe.MultiBoxLossParameter.match_type', index=5,\n      number=6, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='overlap_threshold', full_name='caffe.MultiBoxLossParameter.overlap_threshold', index=6,\n      number=7, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='use_prior_for_matching', full_name='caffe.MultiBoxLossParameter.use_prior_for_matching', index=7,\n      number=8, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='background_label_id', full_name='caffe.MultiBoxLossParameter.background_label_id', index=8,\n      number=9, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='use_difficult_gt', full_name='caffe.MultiBoxLossParameter.use_difficult_gt', index=9,\n      number=10, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='do_neg_mining', full_name='caffe.MultiBoxLossParameter.do_neg_mining', index=10,\n      number=11, type=8, cpp_type=7, label=1,\n      has_default_value=False, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='neg_pos_ratio', full_name='caffe.MultiBoxLossParameter.neg_pos_ratio', index=11,\n      number=12, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(3),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='neg_overlap', full_name='caffe.MultiBoxLossParameter.neg_overlap', index=12,\n      number=13, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='code_type', full_name='caffe.MultiBoxLossParameter.code_type', index=13,\n      number=14, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='encode_variance_in_target', full_name='caffe.MultiBoxLossParameter.encode_variance_in_target', index=14,\n      number=16, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='map_object_to_agnostic', full_name='caffe.MultiBoxLossParameter.map_object_to_agnostic', index=15,\n      number=17, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='ignore_cross_boundary_bbox', full_name='caffe.MultiBoxLossParameter.ignore_cross_boundary_bbox', index=16,\n      number=18, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bp_inside', full_name='caffe.MultiBoxLossParameter.bp_inside', index=17,\n      number=19, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mining_type', full_name='caffe.MultiBoxLossParameter.mining_type', index=18,\n      number=20, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='nms_param', full_name='caffe.MultiBoxLossParameter.nms_param', index=19,\n      number=21, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='sample_size', full_name='caffe.MultiBoxLossParameter.sample_size', index=20,\n      number=22, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=64,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='use_prior_for_nms', full_name='caffe.MultiBoxLossParameter.use_prior_for_nms', index=21,\n      number=23, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _MULTIBOXLOSSPARAMETER_LOCLOSSTYPE,\n    _MULTIBOXLOSSPARAMETER_CONFLOSSTYPE,\n    _MULTIBOXLOSSPARAMETER_MATCHTYPE,\n    _MULTIBOXLOSSPARAMETER_MININGTYPE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=18538,\n  serialized_end=19666,\n)\n\n\n_PERMUTEPARAMETER = _descriptor.Descriptor(\n  name='PermuteParameter',\n  full_name='caffe.PermuteParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='order', full_name='caffe.PermuteParameter.order', index=0,\n      number=1, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=19668,\n  serialized_end=19701,\n)\n\n\n_MVNPARAMETER = _descriptor.Descriptor(\n  name='MVNParameter',\n  full_name='caffe.MVNParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='normalize_variance', full_name='caffe.MVNParameter.normalize_variance', index=0,\n      number=1, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='across_channels', full_name='caffe.MVNParameter.across_channels', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='eps', full_name='caffe.MVNParameter.eps', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1e-09),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=19703,\n  serialized_end=19803,\n)\n\n\n_PARAMETERPARAMETER = _descriptor.Descriptor(\n  name='ParameterParameter',\n  full_name='caffe.ParameterParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='shape', full_name='caffe.ParameterParameter.shape', index=0,\n      number=1, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=19805,\n  serialized_end=19858,\n)\n\n\n_POOLINGPARAMETER = _descriptor.Descriptor(\n  name='PoolingParameter',\n  full_name='caffe.PoolingParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='pool', full_name='caffe.PoolingParameter.pool', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad', full_name='caffe.PoolingParameter.pad', index=1,\n      number=4, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_h', full_name='caffe.PoolingParameter.pad_h', index=2,\n      number=9, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_w', full_name='caffe.PoolingParameter.pad_w', index=3,\n      number=10, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='kernel_size', full_name='caffe.PoolingParameter.kernel_size', index=4,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='kernel_h', full_name='caffe.PoolingParameter.kernel_h', index=5,\n      number=5, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='kernel_w', full_name='caffe.PoolingParameter.kernel_w', index=6,\n      number=6, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stride', full_name='caffe.PoolingParameter.stride', index=7,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stride_h', full_name='caffe.PoolingParameter.stride_h', index=8,\n      number=7, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stride_w', full_name='caffe.PoolingParameter.stride_w', index=9,\n      number=8, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='engine', full_name='caffe.PoolingParameter.engine', index=10,\n      number=11, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='global_pooling', full_name='caffe.PoolingParameter.global_pooling', index=11,\n      number=12, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='ceil_mode', full_name='caffe.PoolingParameter.ceil_mode', index=12,\n      number=13, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _POOLINGPARAMETER_POOLMETHOD,\n    _POOLINGPARAMETER_ENGINE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=19861,\n  serialized_end=20304,\n)\n\n\n_POWERPARAMETER = _descriptor.Descriptor(\n  name='PowerParameter',\n  full_name='caffe.PowerParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='power', full_name='caffe.PowerParameter.power', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.PowerParameter.scale', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='shift', full_name='caffe.PowerParameter.shift', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=20306,\n  serialized_end=20376,\n)\n\n\n_PRIORBOXPARAMETER = _descriptor.Descriptor(\n  name='PriorBoxParameter',\n  full_name='caffe.PriorBoxParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='min_size', full_name='caffe.PriorBoxParameter.min_size', index=0,\n      number=1, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_size', full_name='caffe.PriorBoxParameter.max_size', index=1,\n      number=2, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='aspect_ratio', full_name='caffe.PriorBoxParameter.aspect_ratio', index=2,\n      number=3, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='flip', full_name='caffe.PriorBoxParameter.flip', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='clip', full_name='caffe.PriorBoxParameter.clip', index=4,\n      number=5, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='variance', full_name='caffe.PriorBoxParameter.variance', index=5,\n      number=6, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='img_size', full_name='caffe.PriorBoxParameter.img_size', index=6,\n      number=7, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='img_h', full_name='caffe.PriorBoxParameter.img_h', index=7,\n      number=8, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='img_w', full_name='caffe.PriorBoxParameter.img_w', index=8,\n      number=9, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='step', full_name='caffe.PriorBoxParameter.step', index=9,\n      number=10, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='step_h', full_name='caffe.PriorBoxParameter.step_h', index=10,\n      number=11, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='step_w', full_name='caffe.PriorBoxParameter.step_w', index=11,\n      number=12, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='offset', full_name='caffe.PriorBoxParameter.offset', index=12,\n      number=13, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _PRIORBOXPARAMETER_CODETYPE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=20379,\n  serialized_end=20688,\n)\n\n\n_PYTHONPARAMETER = _descriptor.Descriptor(\n  name='PythonParameter',\n  full_name='caffe.PythonParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='module', full_name='caffe.PythonParameter.module', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='layer', full_name='caffe.PythonParameter.layer', index=1,\n      number=2, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='param_str', full_name='caffe.PythonParameter.param_str', index=2,\n      number=3, type=9, cpp_type=9, label=1,\n      has_default_value=True, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='share_in_parallel', full_name='caffe.PythonParameter.share_in_parallel', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=20690,\n  serialized_end=20793,\n)\n\n\n_RECURRENTPARAMETER = _descriptor.Descriptor(\n  name='RecurrentParameter',\n  full_name='caffe.RecurrentParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_output', full_name='caffe.RecurrentParameter.num_output', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_filler', full_name='caffe.RecurrentParameter.weight_filler', index=1,\n      number=2, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_filler', full_name='caffe.RecurrentParameter.bias_filler', index=2,\n      number=3, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='debug_info', full_name='caffe.RecurrentParameter.debug_info', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='expose_hidden', full_name='caffe.RecurrentParameter.expose_hidden', index=4,\n      number=5, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=20796,\n  serialized_end=20988,\n)\n\n\n_REDUCTIONPARAMETER = _descriptor.Descriptor(\n  name='ReductionParameter',\n  full_name='caffe.ReductionParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='operation', full_name='caffe.ReductionParameter.operation', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.ReductionParameter.axis', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='coeff', full_name='caffe.ReductionParameter.coeff', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _REDUCTIONPARAMETER_REDUCTIONOP,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=20991,\n  serialized_end=21164,\n)\n\n\n_RELUPARAMETER = _descriptor.Descriptor(\n  name='ReLUParameter',\n  full_name='caffe.ReLUParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='negative_slope', full_name='caffe.ReLUParameter.negative_slope', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='engine', full_name='caffe.ReLUParameter.engine', index=1,\n      number=2, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _RELUPARAMETER_ENGINE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=21167,\n  serialized_end=21308,\n)\n\n\n_RESHAPEPARAMETER = _descriptor.Descriptor(\n  name='ReshapeParameter',\n  full_name='caffe.ReshapeParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='shape', full_name='caffe.ReshapeParameter.shape', index=0,\n      number=1, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.ReshapeParameter.axis', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_axes', full_name='caffe.ReshapeParameter.num_axes', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=-1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=21310,\n  serialized_end=21400,\n)\n\n\n_ROIPOOLINGPARAMETER = _descriptor.Descriptor(\n  name='ROIPoolingParameter',\n  full_name='caffe.ROIPoolingParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='pooled_h', full_name='caffe.ROIPoolingParameter.pooled_h', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pooled_w', full_name='caffe.ROIPoolingParameter.pooled_w', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='spatial_scale', full_name='caffe.ROIPoolingParameter.spatial_scale', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=21402,\n  serialized_end=21491,\n)\n\n\n_SCALEPARAMETER = _descriptor.Descriptor(\n  name='ScaleParameter',\n  full_name='caffe.ScaleParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.ScaleParameter.axis', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_axes', full_name='caffe.ScaleParameter.num_axes', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='filler', full_name='caffe.ScaleParameter.filler', index=2,\n      number=3, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_term', full_name='caffe.ScaleParameter.bias_term', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_filler', full_name='caffe.ScaleParameter.bias_filler', index=4,\n      number=5, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='min_value', full_name='caffe.ScaleParameter.min_value', index=5,\n      number=6, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_value', full_name='caffe.ScaleParameter.max_value', index=6,\n      number=7, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=21494,\n  serialized_end=21697,\n)\n\n\n_SIGMOIDPARAMETER = _descriptor.Descriptor(\n  name='SigmoidParameter',\n  full_name='caffe.SigmoidParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='engine', full_name='caffe.SigmoidParameter.engine', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _SIGMOIDPARAMETER_ENGINE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=21699,\n  serialized_end=21819,\n)\n\n\n_SMOOTHL1LOSSPARAMETER = _descriptor.Descriptor(\n  name='SmoothL1LossParameter',\n  full_name='caffe.SmoothL1LossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='sigma', full_name='caffe.SmoothL1LossParameter.sigma', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=21821,\n  serialized_end=21862,\n)\n\n\n_SLICEPARAMETER = _descriptor.Descriptor(\n  name='SliceParameter',\n  full_name='caffe.SliceParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.SliceParameter.axis', index=0,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='slice_point', full_name='caffe.SliceParameter.slice_point', index=1,\n      number=2, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='slice_dim', full_name='caffe.SliceParameter.slice_dim', index=2,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=21864,\n  serialized_end=21940,\n)\n\n\n_SOFTMAXPARAMETER = _descriptor.Descriptor(\n  name='SoftmaxParameter',\n  full_name='caffe.SoftmaxParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='engine', full_name='caffe.SoftmaxParameter.engine', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.SoftmaxParameter.axis', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _SOFTMAXPARAMETER_ENGINE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=21943,\n  serialized_end=22080,\n)\n\n\n_TANHPARAMETER = _descriptor.Descriptor(\n  name='TanHParameter',\n  full_name='caffe.TanHParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='engine', full_name='caffe.TanHParameter.engine', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _TANHPARAMETER_ENGINE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=22082,\n  serialized_end=22196,\n)\n\n\n_TILEPARAMETER = _descriptor.Descriptor(\n  name='TileParameter',\n  full_name='caffe.TileParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.TileParameter.axis', index=0,\n      number=1, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='tiles', full_name='caffe.TileParameter.tiles', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=22198,\n  serialized_end=22245,\n)\n\n\n_THRESHOLDPARAMETER = _descriptor.Descriptor(\n  name='ThresholdParameter',\n  full_name='caffe.ThresholdParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='threshold', full_name='caffe.ThresholdParameter.threshold', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=22247,\n  serialized_end=22289,\n)\n\n\n_WINDOWDATAPARAMETER = _descriptor.Descriptor(\n  name='WindowDataParameter',\n  full_name='caffe.WindowDataParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='source', full_name='caffe.WindowDataParameter.source', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.WindowDataParameter.scale', index=1,\n      number=2, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mean_file', full_name='caffe.WindowDataParameter.mean_file', index=2,\n      number=3, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='batch_size', full_name='caffe.WindowDataParameter.batch_size', index=3,\n      number=4, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='crop_size', full_name='caffe.WindowDataParameter.crop_size', index=4,\n      number=5, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mirror', full_name='caffe.WindowDataParameter.mirror', index=5,\n      number=6, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='fg_threshold', full_name='caffe.WindowDataParameter.fg_threshold', index=6,\n      number=7, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bg_threshold', full_name='caffe.WindowDataParameter.bg_threshold', index=7,\n      number=8, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='fg_fraction', full_name='caffe.WindowDataParameter.fg_fraction', index=8,\n      number=9, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.25),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='context_pad', full_name='caffe.WindowDataParameter.context_pad', index=9,\n      number=10, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='crop_mode', full_name='caffe.WindowDataParameter.crop_mode', index=10,\n      number=11, type=9, cpp_type=9, label=1,\n      has_default_value=True, default_value=_b(\"warp\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='cache_images', full_name='caffe.WindowDataParameter.cache_images', index=11,\n      number=12, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='root_folder', full_name='caffe.WindowDataParameter.root_folder', index=12,\n      number=13, type=9, cpp_type=9, label=1,\n      has_default_value=True, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=22292,\n  serialized_end=22613,\n)\n\n\n_SPPPARAMETER = _descriptor.Descriptor(\n  name='SPPParameter',\n  full_name='caffe.SPPParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='pyramid_height', full_name='caffe.SPPParameter.pyramid_height', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pool', full_name='caffe.SPPParameter.pool', index=1,\n      number=2, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='engine', full_name='caffe.SPPParameter.engine', index=2,\n      number=6, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _SPPPARAMETER_POOLMETHOD,\n    _SPPPARAMETER_ENGINE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=22616,\n  serialized_end=22851,\n)\n\n\n_V1LAYERPARAMETER = _descriptor.Descriptor(\n  name='V1LayerParameter',\n  full_name='caffe.V1LayerParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='bottom', full_name='caffe.V1LayerParameter.bottom', index=0,\n      number=2, type=9, cpp_type=9, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='top', full_name='caffe.V1LayerParameter.top', index=1,\n      number=3, type=9, cpp_type=9, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='name', full_name='caffe.V1LayerParameter.name', index=2,\n      number=4, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='include', full_name='caffe.V1LayerParameter.include', index=3,\n      number=32, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='exclude', full_name='caffe.V1LayerParameter.exclude', index=4,\n      number=33, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='type', full_name='caffe.V1LayerParameter.type', index=5,\n      number=5, type=14, cpp_type=8, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='blobs', full_name='caffe.V1LayerParameter.blobs', index=6,\n      number=6, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='param', full_name='caffe.V1LayerParameter.param', index=7,\n      number=1001, type=9, cpp_type=9, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='blob_share_mode', full_name='caffe.V1LayerParameter.blob_share_mode', index=8,\n      number=1002, type=14, cpp_type=8, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='blobs_lr', full_name='caffe.V1LayerParameter.blobs_lr', index=9,\n      number=7, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_decay', full_name='caffe.V1LayerParameter.weight_decay', index=10,\n      number=8, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='loss_weight', full_name='caffe.V1LayerParameter.loss_weight', index=11,\n      number=35, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='accuracy_param', full_name='caffe.V1LayerParameter.accuracy_param', index=12,\n      number=27, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='argmax_param', full_name='caffe.V1LayerParameter.argmax_param', index=13,\n      number=23, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='concat_param', full_name='caffe.V1LayerParameter.concat_param', index=14,\n      number=9, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='contrastive_loss_param', full_name='caffe.V1LayerParameter.contrastive_loss_param', index=15,\n      number=40, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='convolution_param', full_name='caffe.V1LayerParameter.convolution_param', index=16,\n      number=10, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='data_param', full_name='caffe.V1LayerParameter.data_param', index=17,\n      number=11, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='dropout_param', full_name='caffe.V1LayerParameter.dropout_param', index=18,\n      number=12, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='dummy_data_param', full_name='caffe.V1LayerParameter.dummy_data_param', index=19,\n      number=26, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='eltwise_param', full_name='caffe.V1LayerParameter.eltwise_param', index=20,\n      number=24, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='exp_param', full_name='caffe.V1LayerParameter.exp_param', index=21,\n      number=41, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='hdf5_data_param', full_name='caffe.V1LayerParameter.hdf5_data_param', index=22,\n      number=13, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='hdf5_output_param', full_name='caffe.V1LayerParameter.hdf5_output_param', index=23,\n      number=14, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='hinge_loss_param', full_name='caffe.V1LayerParameter.hinge_loss_param', index=24,\n      number=29, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='image_data_param', full_name='caffe.V1LayerParameter.image_data_param', index=25,\n      number=15, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='infogain_loss_param', full_name='caffe.V1LayerParameter.infogain_loss_param', index=26,\n      number=16, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='inner_product_param', full_name='caffe.V1LayerParameter.inner_product_param', index=27,\n      number=17, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='lrn_param', full_name='caffe.V1LayerParameter.lrn_param', index=28,\n      number=18, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='memory_data_param', full_name='caffe.V1LayerParameter.memory_data_param', index=29,\n      number=22, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mvn_param', full_name='caffe.V1LayerParameter.mvn_param', index=30,\n      number=34, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pooling_param', full_name='caffe.V1LayerParameter.pooling_param', index=31,\n      number=19, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='power_param', full_name='caffe.V1LayerParameter.power_param', index=32,\n      number=21, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='relu_param', full_name='caffe.V1LayerParameter.relu_param', index=33,\n      number=30, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='sigmoid_param', full_name='caffe.V1LayerParameter.sigmoid_param', index=34,\n      number=38, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='softmax_param', full_name='caffe.V1LayerParameter.softmax_param', index=35,\n      number=39, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='slice_param', full_name='caffe.V1LayerParameter.slice_param', index=36,\n      number=31, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='tanh_param', full_name='caffe.V1LayerParameter.tanh_param', index=37,\n      number=37, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='threshold_param', full_name='caffe.V1LayerParameter.threshold_param', index=38,\n      number=25, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='window_data_param', full_name='caffe.V1LayerParameter.window_data_param', index=39,\n      number=20, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='transform_param', full_name='caffe.V1LayerParameter.transform_param', index=40,\n      number=36, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='loss_param', full_name='caffe.V1LayerParameter.loss_param', index=41,\n      number=42, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='detection_loss_param', full_name='caffe.V1LayerParameter.detection_loss_param', index=42,\n      number=200, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='eval_detection_param', full_name='caffe.V1LayerParameter.eval_detection_param', index=43,\n      number=201, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='layer', full_name='caffe.V1LayerParameter.layer', index=44,\n      number=1, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _V1LAYERPARAMETER_LAYERTYPE,\n    _V1LAYERPARAMETER_DIMCHECKMODE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=22854,\n  serialized_end=25506,\n)\n\n\n_V0LAYERPARAMETER = _descriptor.Descriptor(\n  name='V0LayerParameter',\n  full_name='caffe.V0LayerParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='name', full_name='caffe.V0LayerParameter.name', index=0,\n      number=1, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='type', full_name='caffe.V0LayerParameter.type', index=1,\n      number=2, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='num_output', full_name='caffe.V0LayerParameter.num_output', index=2,\n      number=3, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='biasterm', full_name='caffe.V0LayerParameter.biasterm', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_filler', full_name='caffe.V0LayerParameter.weight_filler', index=4,\n      number=5, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_filler', full_name='caffe.V0LayerParameter.bias_filler', index=5,\n      number=6, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad', full_name='caffe.V0LayerParameter.pad', index=6,\n      number=7, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='kernelsize', full_name='caffe.V0LayerParameter.kernelsize', index=7,\n      number=8, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='group', full_name='caffe.V0LayerParameter.group', index=8,\n      number=9, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stride', full_name='caffe.V0LayerParameter.stride', index=9,\n      number=10, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pool', full_name='caffe.V0LayerParameter.pool', index=10,\n      number=11, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='dropout_ratio', full_name='caffe.V0LayerParameter.dropout_ratio', index=11,\n      number=12, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='local_size', full_name='caffe.V0LayerParameter.local_size', index=12,\n      number=13, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=5,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='alpha', full_name='caffe.V0LayerParameter.alpha', index=13,\n      number=14, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='beta', full_name='caffe.V0LayerParameter.beta', index=14,\n      number=15, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.75),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='k', full_name='caffe.V0LayerParameter.k', index=15,\n      number=22, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='source', full_name='caffe.V0LayerParameter.source', index=16,\n      number=16, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.V0LayerParameter.scale', index=17,\n      number=17, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='meanfile', full_name='caffe.V0LayerParameter.meanfile', index=18,\n      number=18, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='batchsize', full_name='caffe.V0LayerParameter.batchsize', index=19,\n      number=19, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='cropsize', full_name='caffe.V0LayerParameter.cropsize', index=20,\n      number=20, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='mirror', full_name='caffe.V0LayerParameter.mirror', index=21,\n      number=21, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='blobs', full_name='caffe.V0LayerParameter.blobs', index=22,\n      number=50, type=11, cpp_type=10, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='blobs_lr', full_name='caffe.V0LayerParameter.blobs_lr', index=23,\n      number=51, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_decay', full_name='caffe.V0LayerParameter.weight_decay', index=24,\n      number=52, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='rand_skip', full_name='caffe.V0LayerParameter.rand_skip', index=25,\n      number=53, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='det_fg_threshold', full_name='caffe.V0LayerParameter.det_fg_threshold', index=26,\n      number=54, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='det_bg_threshold', full_name='caffe.V0LayerParameter.det_bg_threshold', index=27,\n      number=55, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='det_fg_fraction', full_name='caffe.V0LayerParameter.det_fg_fraction', index=28,\n      number=56, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.25),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='det_context_pad', full_name='caffe.V0LayerParameter.det_context_pad', index=29,\n      number=58, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='det_crop_mode', full_name='caffe.V0LayerParameter.det_crop_mode', index=30,\n      number=59, type=9, cpp_type=9, label=1,\n      has_default_value=True, default_value=_b(\"warp\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='new_num', full_name='caffe.V0LayerParameter.new_num', index=31,\n      number=60, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='new_channels', full_name='caffe.V0LayerParameter.new_channels', index=32,\n      number=61, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='new_height', full_name='caffe.V0LayerParameter.new_height', index=33,\n      number=62, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='new_width', full_name='caffe.V0LayerParameter.new_width', index=34,\n      number=63, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='shuffle_images', full_name='caffe.V0LayerParameter.shuffle_images', index=35,\n      number=64, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='concat_dim', full_name='caffe.V0LayerParameter.concat_dim', index=36,\n      number=65, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='hdf5_output_param', full_name='caffe.V0LayerParameter.hdf5_output_param', index=37,\n      number=1001, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _V0LAYERPARAMETER_POOLMETHOD,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=25509,\n  serialized_end=26530,\n)\n\n\n_PRELUPARAMETER = _descriptor.Descriptor(\n  name='PReLUParameter',\n  full_name='caffe.PReLUParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='filler', full_name='caffe.PReLUParameter.filler', index=0,\n      number=1, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='channel_shared', full_name='caffe.PReLUParameter.channel_shared', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=26532,\n  serialized_end=26619,\n)\n\n\n_RPNPARAMETER = _descriptor.Descriptor(\n  name='RPNParameter',\n  full_name='caffe.RPNParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='feat_stride', full_name='caffe.RPNParameter.feat_stride', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='basesize', full_name='caffe.RPNParameter.basesize', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.RPNParameter.scale', index=2,\n      number=3, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='ratio', full_name='caffe.RPNParameter.ratio', index=3,\n      number=4, type=2, cpp_type=6, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='boxminsize', full_name='caffe.RPNParameter.boxminsize', index=4,\n      number=5, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='per_nms_topn', full_name='caffe.RPNParameter.per_nms_topn', index=5,\n      number=9, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='post_nms_topn', full_name='caffe.RPNParameter.post_nms_topn', index=6,\n      number=11, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='nms_thresh', full_name='caffe.RPNParameter.nms_thresh', index=7,\n      number=8, type=2, cpp_type=6, label=1,\n      has_default_value=False, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=26622,\n  serialized_end=26790,\n)\n\n\n_VIDEODATAPARAMETER = _descriptor.Descriptor(\n  name='VideoDataParameter',\n  full_name='caffe.VideoDataParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='video_type', full_name='caffe.VideoDataParameter.video_type', index=0,\n      number=1, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='device_id', full_name='caffe.VideoDataParameter.device_id', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='video_file', full_name='caffe.VideoDataParameter.video_file', index=2,\n      number=3, type=9, cpp_type=9, label=1,\n      has_default_value=False, default_value=_b(\"\").decode('utf-8'),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='skip_frames', full_name='caffe.VideoDataParameter.skip_frames', index=3,\n      number=4, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _VIDEODATAPARAMETER_VIDEOTYPE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=26793,\n  serialized_end=26980,\n)\n\n\n_CENTERLOSSPARAMETER = _descriptor.Descriptor(\n  name='CenterLossParameter',\n  full_name='caffe.CenterLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_output', full_name='caffe.CenterLossParameter.num_output', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='center_filler', full_name='caffe.CenterLossParameter.center_filler', index=1,\n      number=2, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.CenterLossParameter.axis', index=2,\n      number=3, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=26982,\n  serialized_end=27087,\n)\n\n\n_MARGININNERPRODUCTPARAMETER = _descriptor.Descriptor(\n  name='MarginInnerProductParameter',\n  full_name='caffe.MarginInnerProductParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_output', full_name='caffe.MarginInnerProductParameter.num_output', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='type', full_name='caffe.MarginInnerProductParameter.type', index=1,\n      number=2, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_filler', full_name='caffe.MarginInnerProductParameter.weight_filler', index=2,\n      number=3, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.MarginInnerProductParameter.axis', index=3,\n      number=4, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='base', full_name='caffe.MarginInnerProductParameter.base', index=4,\n      number=5, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='gamma', full_name='caffe.MarginInnerProductParameter.gamma', index=5,\n      number=6, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='power', full_name='caffe.MarginInnerProductParameter.power', index=6,\n      number=7, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='iteration', full_name='caffe.MarginInnerProductParameter.iteration', index=7,\n      number=8, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='lambda_min', full_name='caffe.MarginInnerProductParameter.lambda_min', index=8,\n      number=9, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _MARGININNERPRODUCTPARAMETER_MARGINTYPE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=27090,\n  serialized_end=27435,\n)\n\n\n_ADDITIVEMARGININNERPRODUCTPARAMETER = _descriptor.Descriptor(\n  name='AdditiveMarginInnerProductParameter',\n  full_name='caffe.AdditiveMarginInnerProductParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_output', full_name='caffe.AdditiveMarginInnerProductParameter.num_output', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_filler', full_name='caffe.AdditiveMarginInnerProductParameter.weight_filler', index=1,\n      number=2, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='m', full_name='caffe.AdditiveMarginInnerProductParameter.m', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.35),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.AdditiveMarginInnerProductParameter.axis', index=3,\n      number=4, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=27438,\n  serialized_end=27576,\n)\n\n\n_DEFORMABLECONVOLUTIONPARAMETER = _descriptor.Descriptor(\n  name='DeformableConvolutionParameter',\n  full_name='caffe.DeformableConvolutionParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='num_output', full_name='caffe.DeformableConvolutionParameter.num_output', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_term', full_name='caffe.DeformableConvolutionParameter.bias_term', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad', full_name='caffe.DeformableConvolutionParameter.pad', index=2,\n      number=3, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='kernel_size', full_name='caffe.DeformableConvolutionParameter.kernel_size', index=3,\n      number=4, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stride', full_name='caffe.DeformableConvolutionParameter.stride', index=4,\n      number=6, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='dilation', full_name='caffe.DeformableConvolutionParameter.dilation', index=5,\n      number=18, type=13, cpp_type=3, label=3,\n      has_default_value=False, default_value=[],\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_h', full_name='caffe.DeformableConvolutionParameter.pad_h', index=6,\n      number=9, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pad_w', full_name='caffe.DeformableConvolutionParameter.pad_w', index=7,\n      number=10, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='kernel_h', full_name='caffe.DeformableConvolutionParameter.kernel_h', index=8,\n      number=11, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='kernel_w', full_name='caffe.DeformableConvolutionParameter.kernel_w', index=9,\n      number=12, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stride_h', full_name='caffe.DeformableConvolutionParameter.stride_h', index=10,\n      number=13, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='stride_w', full_name='caffe.DeformableConvolutionParameter.stride_w', index=11,\n      number=14, type=13, cpp_type=3, label=1,\n      has_default_value=False, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='group', full_name='caffe.DeformableConvolutionParameter.group', index=12,\n      number=5, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=4,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='deformable_group', full_name='caffe.DeformableConvolutionParameter.deformable_group', index=13,\n      number=25, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=4,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='weight_filler', full_name='caffe.DeformableConvolutionParameter.weight_filler', index=14,\n      number=7, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='bias_filler', full_name='caffe.DeformableConvolutionParameter.bias_filler', index=15,\n      number=8, type=11, cpp_type=10, label=1,\n      has_default_value=False, default_value=None,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='engine', full_name='caffe.DeformableConvolutionParameter.engine', index=16,\n      number=15, type=14, cpp_type=8, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='axis', full_name='caffe.DeformableConvolutionParameter.axis', index=17,\n      number=16, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=1,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='force_nd_im2col', full_name='caffe.DeformableConvolutionParameter.force_nd_im2col', index=18,\n      number=17, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n    _DEFORMABLECONVOLUTIONPARAMETER_ENGINE,\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=27579,\n  serialized_end=28136,\n)\n\n\n_LABELSPECIFICADDPARAMETER = _descriptor.Descriptor(\n  name='LabelSpecificAddParameter',\n  full_name='caffe.LabelSpecificAddParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='bias', full_name='caffe.LabelSpecificAddParameter.bias', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='transform_test', full_name='caffe.LabelSpecificAddParameter.transform_test', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=28138,\n  serialized_end=28213,\n)\n\n\n_CHANNELSCALEPARAMETER = _descriptor.Descriptor(\n  name='ChannelScaleParameter',\n  full_name='caffe.ChannelScaleParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='do_forward', full_name='caffe.ChannelScaleParameter.do_forward', index=0,\n      number=1, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='do_backward_feature', full_name='caffe.ChannelScaleParameter.do_backward_feature', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='do_backward_scale', full_name='caffe.ChannelScaleParameter.do_backward_scale', index=2,\n      number=3, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='global_scale', full_name='caffe.ChannelScaleParameter.global_scale', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='max_global_scale', full_name='caffe.ChannelScaleParameter.max_global_scale', index=4,\n      number=5, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1000),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='min_global_scale', full_name='caffe.ChannelScaleParameter.min_global_scale', index=5,\n      number=6, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='init_global_scale', full_name='caffe.ChannelScaleParameter.init_global_scale', index=6,\n      number=7, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=28216,\n  serialized_end=28453,\n)\n\n\n_COSINADDMPARAMETER = _descriptor.Descriptor(\n  name='CosinAddmParameter',\n  full_name='caffe.CosinAddmParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='m', full_name='caffe.CosinAddmParameter.m', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.5),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='transform_test', full_name='caffe.CosinAddmParameter.transform_test', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=28455,\n  serialized_end=28522,\n)\n\n\n_COSINMULMPARAMETER = _descriptor.Descriptor(\n  name='CosinMulmParameter',\n  full_name='caffe.CosinMulmParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='m', full_name='caffe.CosinMulmParameter.m', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(4),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='transform_test', full_name='caffe.CosinMulmParameter.transform_test', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=28524,\n  serialized_end=28589,\n)\n\n\n_COUPLEDCLUSTERLOSSPARAMETER = _descriptor.Descriptor(\n  name='CoupledClusterLossParameter',\n  full_name='caffe.CoupledClusterLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='margin', full_name='caffe.CoupledClusterLossParameter.margin', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='group_size', full_name='caffe.CoupledClusterLossParameter.group_size', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=3,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.CoupledClusterLossParameter.scale', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='log_flag', full_name='caffe.CoupledClusterLossParameter.log_flag', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=28591,\n  serialized_end=28705,\n)\n\n\n_TRIPLETLOSSPARAMETER = _descriptor.Descriptor(\n  name='TripletLossParameter',\n  full_name='caffe.TripletLossParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='margin', full_name='caffe.TripletLossParameter.margin', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='group_size', full_name='caffe.TripletLossParameter.group_size', index=1,\n      number=2, type=5, cpp_type=1, label=1,\n      has_default_value=True, default_value=3,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='scale', full_name='caffe.TripletLossParameter.scale', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=28707,\n  serialized_end=28789,\n)\n\n\n_GENERALTRIPLETPARAMETER = _descriptor.Descriptor(\n  name='GeneralTripletParameter',\n  full_name='caffe.GeneralTripletParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='margin', full_name='caffe.GeneralTripletParameter.margin', index=0,\n      number=1, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(0.2),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='add_center_loss', full_name='caffe.GeneralTripletParameter.add_center_loss', index=1,\n      number=2, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=True,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='hardest_only', full_name='caffe.GeneralTripletParameter.hardest_only', index=2,\n      number=3, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='positive_first', full_name='caffe.GeneralTripletParameter.positive_first', index=3,\n      number=4, type=8, cpp_type=7, label=1,\n      has_default_value=True, default_value=False,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='positive_upper_bound', full_name='caffe.GeneralTripletParameter.positive_upper_bound', index=4,\n      number=5, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='positive_weight', full_name='caffe.GeneralTripletParameter.positive_weight', index=5,\n      number=6, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='negative_weight', full_name='caffe.GeneralTripletParameter.negative_weight', index=6,\n      number=7, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=28792,\n  serialized_end=29018,\n)\n\n\n_ROIALIGNPARAMETER = _descriptor.Descriptor(\n  name='ROIAlignParameter',\n  full_name='caffe.ROIAlignParameter',\n  filename=None,\n  file=DESCRIPTOR,\n  containing_type=None,\n  fields=[\n    _descriptor.FieldDescriptor(\n      name='pooled_h', full_name='caffe.ROIAlignParameter.pooled_h', index=0,\n      number=1, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='pooled_w', full_name='caffe.ROIAlignParameter.pooled_w', index=1,\n      number=2, type=13, cpp_type=3, label=1,\n      has_default_value=True, default_value=0,\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n    _descriptor.FieldDescriptor(\n      name='spatial_scale', full_name='caffe.ROIAlignParameter.spatial_scale', index=2,\n      number=3, type=2, cpp_type=6, label=1,\n      has_default_value=True, default_value=float(1),\n      message_type=None, enum_type=None, containing_type=None,\n      is_extension=False, extension_scope=None,\n      serialized_options=None, file=DESCRIPTOR),\n  ],\n  extensions=[\n  ],\n  nested_types=[],\n  enum_types=[\n  ],\n  serialized_options=None,\n  is_extendable=False,\n  syntax='proto2',\n  extension_ranges=[],\n  oneofs=[\n  ],\n  serialized_start=29020,\n  serialized_end=29107,\n)\n\n_BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE\n_BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO\n_LABELMAP.fields_by_name['item'].message_type = _LABELMAPITEM\n_BATCHSAMPLER.fields_by_name['sampler'].message_type = _SAMPLER\n_BATCHSAMPLER.fields_by_name['sample_constraint'].message_type = _SAMPLECONSTRAINT\n_EMITCONSTRAINT.fields_by_name['emit_type'].enum_type = _EMITCONSTRAINT_EMITTYPE\n_EMITCONSTRAINT_EMITTYPE.containing_type = _EMITCONSTRAINT\n_ANNOTATION.fields_by_name['bbox'].message_type = _NORMALIZEDBBOX\n_ANNOTATIONGROUP.fields_by_name['annotation'].message_type = _ANNOTATION\n_ANNOTATEDDATUM.fields_by_name['datum'].message_type = _DATUM\n_ANNOTATEDDATUM.fields_by_name['type'].enum_type = _ANNOTATEDDATUM_ANNOTATIONTYPE\n_ANNOTATEDDATUM.fields_by_name['annotation_group'].message_type = _ANNOTATIONGROUP\n_ANNOTATEDDATUM_ANNOTATIONTYPE.containing_type = _ANNOTATEDDATUM\n_MTCNNDATUM.fields_by_name['datum'].message_type = _DATUM\n_MTCNNDATUM.fields_by_name['roi'].message_type = _MTCNNBBOX\n_FILLERPARAMETER.fields_by_name['variance_norm'].enum_type = _FILLERPARAMETER_VARIANCENORM\n_FILLERPARAMETER_VARIANCENORM.containing_type = _FILLERPARAMETER\n_NETPARAMETER.fields_by_name['input_shape'].message_type = _BLOBSHAPE\n_NETPARAMETER.fields_by_name['state'].message_type = _NETSTATE\n_NETPARAMETER.fields_by_name['layer'].message_type = _LAYERPARAMETER\n_NETPARAMETER.fields_by_name['layers'].message_type = _V1LAYERPARAMETER\n_SOLVERPARAMETER.fields_by_name['net_param'].message_type = _NETPARAMETER\n_SOLVERPARAMETER.fields_by_name['train_net_param'].message_type = _NETPARAMETER\n_SOLVERPARAMETER.fields_by_name['test_net_param'].message_type = _NETPARAMETER\n_SOLVERPARAMETER.fields_by_name['train_state'].message_type = _NETSTATE\n_SOLVERPARAMETER.fields_by_name['test_state'].message_type = _NETSTATE\n_SOLVERPARAMETER.fields_by_name['snapshot_format'].enum_type = _SOLVERPARAMETER_SNAPSHOTFORMAT\n_SOLVERPARAMETER.fields_by_name['solver_mode'].enum_type = _SOLVERPARAMETER_SOLVERMODE\n_SOLVERPARAMETER.fields_by_name['solver_type'].enum_type = _SOLVERPARAMETER_SOLVERTYPE\n_SOLVERPARAMETER_SNAPSHOTFORMAT.containing_type = _SOLVERPARAMETER\n_SOLVERPARAMETER_SOLVERMODE.containing_type = _SOLVERPARAMETER\n_SOLVERPARAMETER_SOLVERTYPE.containing_type = _SOLVERPARAMETER\n_SOLVERSTATE.fields_by_name['history'].message_type = _BLOBPROTO\n_NETSTATE.fields_by_name['phase'].enum_type = _PHASE\n_NETSTATERULE.fields_by_name['phase'].enum_type = _PHASE\n_PARAMSPEC.fields_by_name['share_mode'].enum_type = _PARAMSPEC_DIMCHECKMODE\n_PARAMSPEC_DIMCHECKMODE.containing_type = _PARAMSPEC\n_LAYERPARAMETER.fields_by_name['phase'].enum_type = _PHASE\n_LAYERPARAMETER.fields_by_name['param'].message_type = _PARAMSPEC\n_LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO\n_LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE\n_LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE\n_LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER\n_LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['detection_loss_param'].message_type = _DETECTIONLOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['eval_detection_param'].message_type = _EVALDETECTIONPARAMETER\n_LAYERPARAMETER.fields_by_name['region_loss_param'].message_type = _REGIONLOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['reorg_param'].message_type = _REORGPARAMETER\n_LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER\n_LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER\n_LAYERPARAMETER.fields_by_name['batch_norm_param'].message_type = _BATCHNORMPARAMETER\n_LAYERPARAMETER.fields_by_name['bias_param'].message_type = _BIASPARAMETER\n_LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER\n_LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER\n_LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER\n_LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER\n_LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER\n_LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER\n_LAYERPARAMETER.fields_by_name['elu_param'].message_type = _ELUPARAMETER\n_LAYERPARAMETER.fields_by_name['embed_param'].message_type = _EMBEDPARAMETER\n_LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER\n_LAYERPARAMETER.fields_by_name['flatten_param'].message_type = _FLATTENPARAMETER\n_LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER\n_LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER\n_LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER\n_LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER\n_LAYERPARAMETER.fields_by_name['input_param'].message_type = _INPUTPARAMETER\n_LAYERPARAMETER.fields_by_name['log_param'].message_type = _LOGPARAMETER\n_LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER\n_LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER\n_LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER\n_LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER\n_LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER\n_LAYERPARAMETER.fields_by_name['prelu_param'].message_type = _PRELUPARAMETER\n_LAYERPARAMETER.fields_by_name['python_param'].message_type = _PYTHONPARAMETER\n_LAYERPARAMETER.fields_by_name['recurrent_param'].message_type = _RECURRENTPARAMETER\n_LAYERPARAMETER.fields_by_name['reduction_param'].message_type = _REDUCTIONPARAMETER\n_LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER\n_LAYERPARAMETER.fields_by_name['reshape_param'].message_type = _RESHAPEPARAMETER\n_LAYERPARAMETER.fields_by_name['roi_pooling_param'].message_type = _ROIPOOLINGPARAMETER\n_LAYERPARAMETER.fields_by_name['scale_param'].message_type = _SCALEPARAMETER\n_LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER\n_LAYERPARAMETER.fields_by_name['smooth_l1_loss_param'].message_type = _SMOOTHL1LOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER\n_LAYERPARAMETER.fields_by_name['spp_param'].message_type = _SPPPARAMETER\n_LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER\n_LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER\n_LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER\n_LAYERPARAMETER.fields_by_name['tile_param'].message_type = _TILEPARAMETER\n_LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER\n_LAYERPARAMETER.fields_by_name['st_param'].message_type = _SPATIALTRANSFORMERPARAMETER\n_LAYERPARAMETER.fields_by_name['st_loss_param'].message_type = _STLOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['rpn_param'].message_type = _RPNPARAMETER\n_LAYERPARAMETER.fields_by_name['focal_loss_param'].message_type = _FOCALLOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['asdn_data_param'].message_type = _ASDNDATAPARAMETER\n_LAYERPARAMETER.fields_by_name['bn_param'].message_type = _BNPARAMETER\n_LAYERPARAMETER.fields_by_name['mtcnn_data_param'].message_type = _MTCNNDATAPARAMETER\n_LAYERPARAMETER.fields_by_name['interp_param'].message_type = _INTERPPARAMETER\n_LAYERPARAMETER.fields_by_name['psroi_pooling_param'].message_type = _PSROIPOOLINGPARAMETER\n_LAYERPARAMETER.fields_by_name['annotated_data_param'].message_type = _ANNOTATEDDATAPARAMETER\n_LAYERPARAMETER.fields_by_name['prior_box_param'].message_type = _PRIORBOXPARAMETER\n_LAYERPARAMETER.fields_by_name['crop_param'].message_type = _CROPPARAMETER\n_LAYERPARAMETER.fields_by_name['detection_evaluate_param'].message_type = _DETECTIONEVALUATEPARAMETER\n_LAYERPARAMETER.fields_by_name['detection_output_param'].message_type = _DETECTIONOUTPUTPARAMETER\n_LAYERPARAMETER.fields_by_name['multibox_loss_param'].message_type = _MULTIBOXLOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['permute_param'].message_type = _PERMUTEPARAMETER\n_LAYERPARAMETER.fields_by_name['video_data_param'].message_type = _VIDEODATAPARAMETER\n_LAYERPARAMETER.fields_by_name['margin_inner_product_param'].message_type = _MARGININNERPRODUCTPARAMETER\n_LAYERPARAMETER.fields_by_name['center_loss_param'].message_type = _CENTERLOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['deformable_convolution_param'].message_type = _DEFORMABLECONVOLUTIONPARAMETER\n_LAYERPARAMETER.fields_by_name['label_specific_add_param'].message_type = _LABELSPECIFICADDPARAMETER\n_LAYERPARAMETER.fields_by_name['additive_margin_inner_product_param'].message_type = _ADDITIVEMARGININNERPRODUCTPARAMETER\n_LAYERPARAMETER.fields_by_name['cosin_add_m_param'].message_type = _COSINADDMPARAMETER\n_LAYERPARAMETER.fields_by_name['cosin_mul_m_param'].message_type = _COSINMULMPARAMETER\n_LAYERPARAMETER.fields_by_name['channel_scale_param'].message_type = _CHANNELSCALEPARAMETER\n_LAYERPARAMETER.fields_by_name['flip_param'].message_type = _FLIPPARAMETER\n_LAYERPARAMETER.fields_by_name['triplet_loss_param'].message_type = _TRIPLETLOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['coupled_cluster_loss_param'].message_type = _COUPLEDCLUSTERLOSSPARAMETER\n_LAYERPARAMETER.fields_by_name['general_triplet_loss_param'].message_type = _GENERALTRIPLETPARAMETER\n_LAYERPARAMETER.fields_by_name['roi_align_param'].message_type = _ROIALIGNPARAMETER\n_LAYERPARAMETER.fields_by_name['upsample_param'].message_type = _UPSAMPLEPARAMETER\n_LAYERPARAMETER.fields_by_name['matmul_param'].message_type = _MATMULPARAMETER\n_LAYERPARAMETER.fields_by_name['pass_through_param'].message_type = _PASSTHROUGHPARAMETER\n_LAYERPARAMETER.fields_by_name['norm_param'].message_type = _NORMALIZEPARAMETER\n_NORMALIZEPARAMETER.fields_by_name['scale_filler'].message_type = _FILLERPARAMETER\n_ANNOTATEDDATAPARAMETER.fields_by_name['batch_sampler'].message_type = _BATCHSAMPLER\n_ANNOTATEDDATAPARAMETER.fields_by_name['anno_type'].enum_type = _ANNOTATEDDATUM_ANNOTATIONTYPE\n_BNPARAMETER.fields_by_name['slope_filler'].message_type = _FILLERPARAMETER\n_BNPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER\n_BNPARAMETER.fields_by_name['engine'].enum_type = _BNPARAMETER_ENGINE\n_BNPARAMETER_ENGINE.containing_type = _BNPARAMETER\n_FOCALLOSSPARAMETER.fields_by_name['type'].enum_type = _FOCALLOSSPARAMETER_TYPE\n_FOCALLOSSPARAMETER_TYPE.containing_type = _FOCALLOSSPARAMETER\n_TRANSFORMATIONPARAMETER.fields_by_name['resize_param'].message_type = _RESIZEPARAMETER\n_TRANSFORMATIONPARAMETER.fields_by_name['noise_param'].message_type = _NOISEPARAMETER\n_TRANSFORMATIONPARAMETER.fields_by_name['distort_param'].message_type = _DISTORTIONPARAMETER\n_TRANSFORMATIONPARAMETER.fields_by_name['expand_param'].message_type = _EXPANSIONPARAMETER\n_TRANSFORMATIONPARAMETER.fields_by_name['emit_constraint'].message_type = _EMITCONSTRAINT\n_RESIZEPARAMETER.fields_by_name['resize_mode'].enum_type = _RESIZEPARAMETER_RESIZE_MODE\n_RESIZEPARAMETER.fields_by_name['pad_mode'].enum_type = _RESIZEPARAMETER_PAD_MODE\n_RESIZEPARAMETER.fields_by_name['interp_mode'].enum_type = _RESIZEPARAMETER_INTERP_MODE\n_RESIZEPARAMETER_RESIZE_MODE.containing_type = _RESIZEPARAMETER\n_RESIZEPARAMETER_PAD_MODE.containing_type = _RESIZEPARAMETER\n_RESIZEPARAMETER_INTERP_MODE.containing_type = _RESIZEPARAMETER\n_NOISEPARAMETER.fields_by_name['saltpepper_param'].message_type = _SALTPEPPERPARAMETER\n_LOSSPARAMETER.fields_by_name['normalization'].enum_type = _LOSSPARAMETER_NORMALIZATIONMODE\n_LOSSPARAMETER_NORMALIZATIONMODE.containing_type = _LOSSPARAMETER\n_BIASPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER\n_EVALDETECTIONPARAMETER.fields_by_name['score_type'].enum_type = _EVALDETECTIONPARAMETER_SCORETYPE\n_EVALDETECTIONPARAMETER_SCORETYPE.containing_type = _EVALDETECTIONPARAMETER\n_CONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER\n_CONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER\n_CONVOLUTIONPARAMETER.fields_by_name['engine'].enum_type = _CONVOLUTIONPARAMETER_ENGINE\n_CONVOLUTIONPARAMETER_ENGINE.containing_type = _CONVOLUTIONPARAMETER\n_DATAPARAMETER.fields_by_name['backend'].enum_type = _DATAPARAMETER_DB\n_DATAPARAMETER_DB.containing_type = _DATAPARAMETER\n_DETECTIONEVALUATEPARAMETER.fields_by_name['resize_param'].message_type = _RESIZEPARAMETER\n_SAVEOUTPUTPARAMETER.fields_by_name['resize_param'].message_type = _RESIZEPARAMETER\n_DETECTIONOUTPUTPARAMETER.fields_by_name['nms_param'].message_type = _NONMAXIMUMSUPPRESSIONPARAMETER\n_DETECTIONOUTPUTPARAMETER.fields_by_name['save_output_param'].message_type = _SAVEOUTPUTPARAMETER\n_DETECTIONOUTPUTPARAMETER.fields_by_name['code_type'].enum_type = _PRIORBOXPARAMETER_CODETYPE\n_DUMMYDATAPARAMETER.fields_by_name['data_filler'].message_type = _FILLERPARAMETER\n_DUMMYDATAPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE\n_ELTWISEPARAMETER.fields_by_name['operation'].enum_type = _ELTWISEPARAMETER_ELTWISEOP\n_ELTWISEPARAMETER_ELTWISEOP.containing_type = _ELTWISEPARAMETER\n_EMBEDPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER\n_EMBEDPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER\n_HINGELOSSPARAMETER.fields_by_name['norm'].enum_type = _HINGELOSSPARAMETER_NORM\n_HINGELOSSPARAMETER_NORM.containing_type = _HINGELOSSPARAMETER\n_INNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER\n_INNERPRODUCTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER\n_INPUTPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE\n_LRNPARAMETER.fields_by_name['norm_region'].enum_type = _LRNPARAMETER_NORMREGION\n_LRNPARAMETER.fields_by_name['engine'].enum_type = _LRNPARAMETER_ENGINE\n_LRNPARAMETER_NORMREGION.containing_type = _LRNPARAMETER\n_LRNPARAMETER_ENGINE.containing_type = _LRNPARAMETER\n_MULTIBOXLOSSPARAMETER.fields_by_name['loc_loss_type'].enum_type = _MULTIBOXLOSSPARAMETER_LOCLOSSTYPE\n_MULTIBOXLOSSPARAMETER.fields_by_name['conf_loss_type'].enum_type = _MULTIBOXLOSSPARAMETER_CONFLOSSTYPE\n_MULTIBOXLOSSPARAMETER.fields_by_name['match_type'].enum_type = _MULTIBOXLOSSPARAMETER_MATCHTYPE\n_MULTIBOXLOSSPARAMETER.fields_by_name['code_type'].enum_type = _PRIORBOXPARAMETER_CODETYPE\n_MULTIBOXLOSSPARAMETER.fields_by_name['mining_type'].enum_type = _MULTIBOXLOSSPARAMETER_MININGTYPE\n_MULTIBOXLOSSPARAMETER.fields_by_name['nms_param'].message_type = _NONMAXIMUMSUPPRESSIONPARAMETER\n_MULTIBOXLOSSPARAMETER_LOCLOSSTYPE.containing_type = _MULTIBOXLOSSPARAMETER\n_MULTIBOXLOSSPARAMETER_CONFLOSSTYPE.containing_type = _MULTIBOXLOSSPARAMETER\n_MULTIBOXLOSSPARAMETER_MATCHTYPE.containing_type = _MULTIBOXLOSSPARAMETER\n_MULTIBOXLOSSPARAMETER_MININGTYPE.containing_type = _MULTIBOXLOSSPARAMETER\n_PARAMETERPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE\n_POOLINGPARAMETER.fields_by_name['pool'].enum_type = _POOLINGPARAMETER_POOLMETHOD\n_POOLINGPARAMETER.fields_by_name['engine'].enum_type = _POOLINGPARAMETER_ENGINE\n_POOLINGPARAMETER_POOLMETHOD.containing_type = _POOLINGPARAMETER\n_POOLINGPARAMETER_ENGINE.containing_type = _POOLINGPARAMETER\n_PRIORBOXPARAMETER_CODETYPE.containing_type = _PRIORBOXPARAMETER\n_RECURRENTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER\n_RECURRENTPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER\n_REDUCTIONPARAMETER.fields_by_name['operation'].enum_type = _REDUCTIONPARAMETER_REDUCTIONOP\n_REDUCTIONPARAMETER_REDUCTIONOP.containing_type = _REDUCTIONPARAMETER\n_RELUPARAMETER.fields_by_name['engine'].enum_type = _RELUPARAMETER_ENGINE\n_RELUPARAMETER_ENGINE.containing_type = _RELUPARAMETER\n_RESHAPEPARAMETER.fields_by_name['shape'].message_type = _BLOBSHAPE\n_SCALEPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER\n_SCALEPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER\n_SIGMOIDPARAMETER.fields_by_name['engine'].enum_type = _SIGMOIDPARAMETER_ENGINE\n_SIGMOIDPARAMETER_ENGINE.containing_type = _SIGMOIDPARAMETER\n_SOFTMAXPARAMETER.fields_by_name['engine'].enum_type = _SOFTMAXPARAMETER_ENGINE\n_SOFTMAXPARAMETER_ENGINE.containing_type = _SOFTMAXPARAMETER\n_TANHPARAMETER.fields_by_name['engine'].enum_type = _TANHPARAMETER_ENGINE\n_TANHPARAMETER_ENGINE.containing_type = _TANHPARAMETER\n_SPPPARAMETER.fields_by_name['pool'].enum_type = _SPPPARAMETER_POOLMETHOD\n_SPPPARAMETER.fields_by_name['engine'].enum_type = _SPPPARAMETER_ENGINE\n_SPPPARAMETER_POOLMETHOD.containing_type = _SPPPARAMETER\n_SPPPARAMETER_ENGINE.containing_type = _SPPPARAMETER\n_V1LAYERPARAMETER.fields_by_name['include'].message_type = _NETSTATERULE\n_V1LAYERPARAMETER.fields_by_name['exclude'].message_type = _NETSTATERULE\n_V1LAYERPARAMETER.fields_by_name['type'].enum_type = _V1LAYERPARAMETER_LAYERTYPE\n_V1LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO\n_V1LAYERPARAMETER.fields_by_name['blob_share_mode'].enum_type = _V1LAYERPARAMETER_DIMCHECKMODE\n_V1LAYERPARAMETER.fields_by_name['accuracy_param'].message_type = _ACCURACYPARAMETER\n_V1LAYERPARAMETER.fields_by_name['argmax_param'].message_type = _ARGMAXPARAMETER\n_V1LAYERPARAMETER.fields_by_name['concat_param'].message_type = _CONCATPARAMETER\n_V1LAYERPARAMETER.fields_by_name['contrastive_loss_param'].message_type = _CONTRASTIVELOSSPARAMETER\n_V1LAYERPARAMETER.fields_by_name['convolution_param'].message_type = _CONVOLUTIONPARAMETER\n_V1LAYERPARAMETER.fields_by_name['data_param'].message_type = _DATAPARAMETER\n_V1LAYERPARAMETER.fields_by_name['dropout_param'].message_type = _DROPOUTPARAMETER\n_V1LAYERPARAMETER.fields_by_name['dummy_data_param'].message_type = _DUMMYDATAPARAMETER\n_V1LAYERPARAMETER.fields_by_name['eltwise_param'].message_type = _ELTWISEPARAMETER\n_V1LAYERPARAMETER.fields_by_name['exp_param'].message_type = _EXPPARAMETER\n_V1LAYERPARAMETER.fields_by_name['hdf5_data_param'].message_type = _HDF5DATAPARAMETER\n_V1LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER\n_V1LAYERPARAMETER.fields_by_name['hinge_loss_param'].message_type = _HINGELOSSPARAMETER\n_V1LAYERPARAMETER.fields_by_name['image_data_param'].message_type = _IMAGEDATAPARAMETER\n_V1LAYERPARAMETER.fields_by_name['infogain_loss_param'].message_type = _INFOGAINLOSSPARAMETER\n_V1LAYERPARAMETER.fields_by_name['inner_product_param'].message_type = _INNERPRODUCTPARAMETER\n_V1LAYERPARAMETER.fields_by_name['lrn_param'].message_type = _LRNPARAMETER\n_V1LAYERPARAMETER.fields_by_name['memory_data_param'].message_type = _MEMORYDATAPARAMETER\n_V1LAYERPARAMETER.fields_by_name['mvn_param'].message_type = _MVNPARAMETER\n_V1LAYERPARAMETER.fields_by_name['pooling_param'].message_type = _POOLINGPARAMETER\n_V1LAYERPARAMETER.fields_by_name['power_param'].message_type = _POWERPARAMETER\n_V1LAYERPARAMETER.fields_by_name['relu_param'].message_type = _RELUPARAMETER\n_V1LAYERPARAMETER.fields_by_name['sigmoid_param'].message_type = _SIGMOIDPARAMETER\n_V1LAYERPARAMETER.fields_by_name['softmax_param'].message_type = _SOFTMAXPARAMETER\n_V1LAYERPARAMETER.fields_by_name['slice_param'].message_type = _SLICEPARAMETER\n_V1LAYERPARAMETER.fields_by_name['tanh_param'].message_type = _TANHPARAMETER\n_V1LAYERPARAMETER.fields_by_name['threshold_param'].message_type = _THRESHOLDPARAMETER\n_V1LAYERPARAMETER.fields_by_name['window_data_param'].message_type = _WINDOWDATAPARAMETER\n_V1LAYERPARAMETER.fields_by_name['transform_param'].message_type = _TRANSFORMATIONPARAMETER\n_V1LAYERPARAMETER.fields_by_name['loss_param'].message_type = _LOSSPARAMETER\n_V1LAYERPARAMETER.fields_by_name['detection_loss_param'].message_type = _DETECTIONLOSSPARAMETER\n_V1LAYERPARAMETER.fields_by_name['eval_detection_param'].message_type = _EVALDETECTIONPARAMETER\n_V1LAYERPARAMETER.fields_by_name['layer'].message_type = _V0LAYERPARAMETER\n_V1LAYERPARAMETER_LAYERTYPE.containing_type = _V1LAYERPARAMETER\n_V1LAYERPARAMETER_DIMCHECKMODE.containing_type = _V1LAYERPARAMETER\n_V0LAYERPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER\n_V0LAYERPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER\n_V0LAYERPARAMETER.fields_by_name['pool'].enum_type = _V0LAYERPARAMETER_POOLMETHOD\n_V0LAYERPARAMETER.fields_by_name['blobs'].message_type = _BLOBPROTO\n_V0LAYERPARAMETER.fields_by_name['hdf5_output_param'].message_type = _HDF5OUTPUTPARAMETER\n_V0LAYERPARAMETER_POOLMETHOD.containing_type = _V0LAYERPARAMETER\n_PRELUPARAMETER.fields_by_name['filler'].message_type = _FILLERPARAMETER\n_VIDEODATAPARAMETER.fields_by_name['video_type'].enum_type = _VIDEODATAPARAMETER_VIDEOTYPE\n_VIDEODATAPARAMETER_VIDEOTYPE.containing_type = _VIDEODATAPARAMETER\n_CENTERLOSSPARAMETER.fields_by_name['center_filler'].message_type = _FILLERPARAMETER\n_MARGININNERPRODUCTPARAMETER.fields_by_name['type'].enum_type = _MARGININNERPRODUCTPARAMETER_MARGINTYPE\n_MARGININNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER\n_MARGININNERPRODUCTPARAMETER_MARGINTYPE.containing_type = _MARGININNERPRODUCTPARAMETER\n_ADDITIVEMARGININNERPRODUCTPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER\n_DEFORMABLECONVOLUTIONPARAMETER.fields_by_name['weight_filler'].message_type = _FILLERPARAMETER\n_DEFORMABLECONVOLUTIONPARAMETER.fields_by_name['bias_filler'].message_type = _FILLERPARAMETER\n_DEFORMABLECONVOLUTIONPARAMETER.fields_by_name['engine'].enum_type = _DEFORMABLECONVOLUTIONPARAMETER_ENGINE\n_DEFORMABLECONVOLUTIONPARAMETER_ENGINE.containing_type = _DEFORMABLECONVOLUTIONPARAMETER\nDESCRIPTOR.message_types_by_name['BlobShape'] = _BLOBSHAPE\nDESCRIPTOR.message_types_by_name['BlobProto'] = _BLOBPROTO\nDESCRIPTOR.message_types_by_name['BlobProtoVector'] = _BLOBPROTOVECTOR\nDESCRIPTOR.message_types_by_name['Datum'] = _DATUM\nDESCRIPTOR.message_types_by_name['LabelMapItem'] = _LABELMAPITEM\nDESCRIPTOR.message_types_by_name['LabelMap'] = _LABELMAP\nDESCRIPTOR.message_types_by_name['Sampler'] = _SAMPLER\nDESCRIPTOR.message_types_by_name['SampleConstraint'] = _SAMPLECONSTRAINT\nDESCRIPTOR.message_types_by_name['BatchSampler'] = _BATCHSAMPLER\nDESCRIPTOR.message_types_by_name['EmitConstraint'] = _EMITCONSTRAINT\nDESCRIPTOR.message_types_by_name['NormalizedBBox'] = _NORMALIZEDBBOX\nDESCRIPTOR.message_types_by_name['Annotation'] = _ANNOTATION\nDESCRIPTOR.message_types_by_name['AnnotationGroup'] = _ANNOTATIONGROUP\nDESCRIPTOR.message_types_by_name['AnnotatedDatum'] = _ANNOTATEDDATUM\nDESCRIPTOR.message_types_by_name['MTCNNBBox'] = _MTCNNBBOX\nDESCRIPTOR.message_types_by_name['MTCNNDatum'] = _MTCNNDATUM\nDESCRIPTOR.message_types_by_name['FillerParameter'] = _FILLERPARAMETER\nDESCRIPTOR.message_types_by_name['NetParameter'] = _NETPARAMETER\nDESCRIPTOR.message_types_by_name['SolverParameter'] = _SOLVERPARAMETER\nDESCRIPTOR.message_types_by_name['SolverState'] = _SOLVERSTATE\nDESCRIPTOR.message_types_by_name['NetState'] = _NETSTATE\nDESCRIPTOR.message_types_by_name['NetStateRule'] = _NETSTATERULE\nDESCRIPTOR.message_types_by_name['SpatialTransformerParameter'] = _SPATIALTRANSFORMERPARAMETER\nDESCRIPTOR.message_types_by_name['STLossParameter'] = _STLOSSPARAMETER\nDESCRIPTOR.message_types_by_name['ParamSpec'] = _PARAMSPEC\nDESCRIPTOR.message_types_by_name['LayerParameter'] = _LAYERPARAMETER\nDESCRIPTOR.message_types_by_name['UpsampleParameter'] = _UPSAMPLEPARAMETER\nDESCRIPTOR.message_types_by_name['MatMulParameter'] = _MATMULPARAMETER\nDESCRIPTOR.message_types_by_name['PassThroughParameter'] = _PASSTHROUGHPARAMETER\nDESCRIPTOR.message_types_by_name['NormalizeParameter'] = _NORMALIZEPARAMETER\nDESCRIPTOR.message_types_by_name['AnnotatedDataParameter'] = _ANNOTATEDDATAPARAMETER\nDESCRIPTOR.message_types_by_name['AsdnDataParameter'] = _ASDNDATAPARAMETER\nDESCRIPTOR.message_types_by_name['MTCNNDataParameter'] = _MTCNNDATAPARAMETER\nDESCRIPTOR.message_types_by_name['InterpParameter'] = _INTERPPARAMETER\nDESCRIPTOR.message_types_by_name['PSROIPoolingParameter'] = _PSROIPOOLINGPARAMETER\nDESCRIPTOR.message_types_by_name['FlipParameter'] = _FLIPPARAMETER\nDESCRIPTOR.message_types_by_name['BNParameter'] = _BNPARAMETER\nDESCRIPTOR.message_types_by_name['FocalLossParameter'] = _FOCALLOSSPARAMETER\nDESCRIPTOR.message_types_by_name['TransformationParameter'] = _TRANSFORMATIONPARAMETER\nDESCRIPTOR.message_types_by_name['ResizeParameter'] = _RESIZEPARAMETER\nDESCRIPTOR.message_types_by_name['SaltPepperParameter'] = _SALTPEPPERPARAMETER\nDESCRIPTOR.message_types_by_name['NoiseParameter'] = _NOISEPARAMETER\nDESCRIPTOR.message_types_by_name['DistortionParameter'] = _DISTORTIONPARAMETER\nDESCRIPTOR.message_types_by_name['ExpansionParameter'] = _EXPANSIONPARAMETER\nDESCRIPTOR.message_types_by_name['LossParameter'] = _LOSSPARAMETER\nDESCRIPTOR.message_types_by_name['AccuracyParameter'] = _ACCURACYPARAMETER\nDESCRIPTOR.message_types_by_name['ArgMaxParameter'] = _ARGMAXPARAMETER\nDESCRIPTOR.message_types_by_name['ConcatParameter'] = _CONCATPARAMETER\nDESCRIPTOR.message_types_by_name['BatchNormParameter'] = _BATCHNORMPARAMETER\nDESCRIPTOR.message_types_by_name['BiasParameter'] = _BIASPARAMETER\nDESCRIPTOR.message_types_by_name['ContrastiveLossParameter'] = _CONTRASTIVELOSSPARAMETER\nDESCRIPTOR.message_types_by_name['DetectionLossParameter'] = _DETECTIONLOSSPARAMETER\nDESCRIPTOR.message_types_by_name['RegionLossParameter'] = _REGIONLOSSPARAMETER\nDESCRIPTOR.message_types_by_name['ReorgParameter'] = _REORGPARAMETER\nDESCRIPTOR.message_types_by_name['EvalDetectionParameter'] = _EVALDETECTIONPARAMETER\nDESCRIPTOR.message_types_by_name['ConvolutionParameter'] = _CONVOLUTIONPARAMETER\nDESCRIPTOR.message_types_by_name['CropParameter'] = _CROPPARAMETER\nDESCRIPTOR.message_types_by_name['DataParameter'] = _DATAPARAMETER\nDESCRIPTOR.message_types_by_name['DetectionEvaluateParameter'] = _DETECTIONEVALUATEPARAMETER\nDESCRIPTOR.message_types_by_name['NonMaximumSuppressionParameter'] = _NONMAXIMUMSUPPRESSIONPARAMETER\nDESCRIPTOR.message_types_by_name['SaveOutputParameter'] = _SAVEOUTPUTPARAMETER\nDESCRIPTOR.message_types_by_name['DetectionOutputParameter'] = _DETECTIONOUTPUTPARAMETER\nDESCRIPTOR.message_types_by_name['DropoutParameter'] = _DROPOUTPARAMETER\nDESCRIPTOR.message_types_by_name['DummyDataParameter'] = _DUMMYDATAPARAMETER\nDESCRIPTOR.message_types_by_name['EltwiseParameter'] = _ELTWISEPARAMETER\nDESCRIPTOR.message_types_by_name['ELUParameter'] = _ELUPARAMETER\nDESCRIPTOR.message_types_by_name['EmbedParameter'] = _EMBEDPARAMETER\nDESCRIPTOR.message_types_by_name['ExpParameter'] = _EXPPARAMETER\nDESCRIPTOR.message_types_by_name['FlattenParameter'] = _FLATTENPARAMETER\nDESCRIPTOR.message_types_by_name['HDF5DataParameter'] = _HDF5DATAPARAMETER\nDESCRIPTOR.message_types_by_name['HDF5OutputParameter'] = _HDF5OUTPUTPARAMETER\nDESCRIPTOR.message_types_by_name['HingeLossParameter'] = _HINGELOSSPARAMETER\nDESCRIPTOR.message_types_by_name['ImageDataParameter'] = _IMAGEDATAPARAMETER\nDESCRIPTOR.message_types_by_name['InfogainLossParameter'] = _INFOGAINLOSSPARAMETER\nDESCRIPTOR.message_types_by_name['InnerProductParameter'] = _INNERPRODUCTPARAMETER\nDESCRIPTOR.message_types_by_name['InputParameter'] = _INPUTPARAMETER\nDESCRIPTOR.message_types_by_name['LogParameter'] = _LOGPARAMETER\nDESCRIPTOR.message_types_by_name['LRNParameter'] = _LRNPARAMETER\nDESCRIPTOR.message_types_by_name['MemoryDataParameter'] = _MEMORYDATAPARAMETER\nDESCRIPTOR.message_types_by_name['MultiBoxLossParameter'] = _MULTIBOXLOSSPARAMETER\nDESCRIPTOR.message_types_by_name['PermuteParameter'] = _PERMUTEPARAMETER\nDESCRIPTOR.message_types_by_name['MVNParameter'] = _MVNPARAMETER\nDESCRIPTOR.message_types_by_name['ParameterParameter'] = _PARAMETERPARAMETER\nDESCRIPTOR.message_types_by_name['PoolingParameter'] = _POOLINGPARAMETER\nDESCRIPTOR.message_types_by_name['PowerParameter'] = _POWERPARAMETER\nDESCRIPTOR.message_types_by_name['PriorBoxParameter'] = _PRIORBOXPARAMETER\nDESCRIPTOR.message_types_by_name['PythonParameter'] = _PYTHONPARAMETER\nDESCRIPTOR.message_types_by_name['RecurrentParameter'] = _RECURRENTPARAMETER\nDESCRIPTOR.message_types_by_name['ReductionParameter'] = _REDUCTIONPARAMETER\nDESCRIPTOR.message_types_by_name['ReLUParameter'] = _RELUPARAMETER\nDESCRIPTOR.message_types_by_name['ReshapeParameter'] = _RESHAPEPARAMETER\nDESCRIPTOR.message_types_by_name['ROIPoolingParameter'] = _ROIPOOLINGPARAMETER\nDESCRIPTOR.message_types_by_name['ScaleParameter'] = _SCALEPARAMETER\nDESCRIPTOR.message_types_by_name['SigmoidParameter'] = _SIGMOIDPARAMETER\nDESCRIPTOR.message_types_by_name['SmoothL1LossParameter'] = _SMOOTHL1LOSSPARAMETER\nDESCRIPTOR.message_types_by_name['SliceParameter'] = _SLICEPARAMETER\nDESCRIPTOR.message_types_by_name['SoftmaxParameter'] = _SOFTMAXPARAMETER\nDESCRIPTOR.message_types_by_name['TanHParameter'] = _TANHPARAMETER\nDESCRIPTOR.message_types_by_name['TileParameter'] = _TILEPARAMETER\nDESCRIPTOR.message_types_by_name['ThresholdParameter'] = _THRESHOLDPARAMETER\nDESCRIPTOR.message_types_by_name['WindowDataParameter'] = _WINDOWDATAPARAMETER\nDESCRIPTOR.message_types_by_name['SPPParameter'] = _SPPPARAMETER\nDESCRIPTOR.message_types_by_name['V1LayerParameter'] = _V1LAYERPARAMETER\nDESCRIPTOR.message_types_by_name['V0LayerParameter'] = _V0LAYERPARAMETER\nDESCRIPTOR.message_types_by_name['PReLUParameter'] = _PRELUPARAMETER\nDESCRIPTOR.message_types_by_name['RPNParameter'] = _RPNPARAMETER\nDESCRIPTOR.message_types_by_name['VideoDataParameter'] = _VIDEODATAPARAMETER\nDESCRIPTOR.message_types_by_name['CenterLossParameter'] = _CENTERLOSSPARAMETER\nDESCRIPTOR.message_types_by_name['MarginInnerProductParameter'] = _MARGININNERPRODUCTPARAMETER\nDESCRIPTOR.message_types_by_name['AdditiveMarginInnerProductParameter'] = _ADDITIVEMARGININNERPRODUCTPARAMETER\nDESCRIPTOR.message_types_by_name['DeformableConvolutionParameter'] = _DEFORMABLECONVOLUTIONPARAMETER\nDESCRIPTOR.message_types_by_name['LabelSpecificAddParameter'] = _LABELSPECIFICADDPARAMETER\nDESCRIPTOR.message_types_by_name['ChannelScaleParameter'] = _CHANNELSCALEPARAMETER\nDESCRIPTOR.message_types_by_name['CosinAddmParameter'] = _COSINADDMPARAMETER\nDESCRIPTOR.message_types_by_name['CosinMulmParameter'] = _COSINMULMPARAMETER\nDESCRIPTOR.message_types_by_name['CoupledClusterLossParameter'] = _COUPLEDCLUSTERLOSSPARAMETER\nDESCRIPTOR.message_types_by_name['TripletLossParameter'] = _TRIPLETLOSSPARAMETER\nDESCRIPTOR.message_types_by_name['GeneralTripletParameter'] = _GENERALTRIPLETPARAMETER\nDESCRIPTOR.message_types_by_name['ROIAlignParameter'] = _ROIALIGNPARAMETER\nDESCRIPTOR.enum_types_by_name['Phase'] = _PHASE\n_sym_db.RegisterFileDescriptor(DESCRIPTOR)\n\nBlobShape = _reflection.GeneratedProtocolMessageType('BlobShape', (_message.Message,), dict(\n  DESCRIPTOR = _BLOBSHAPE,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.BlobShape)\n  ))\n_sym_db.RegisterMessage(BlobShape)\n\nBlobProto = _reflection.GeneratedProtocolMessageType('BlobProto', (_message.Message,), dict(\n  DESCRIPTOR = _BLOBPROTO,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.BlobProto)\n  ))\n_sym_db.RegisterMessage(BlobProto)\n\nBlobProtoVector = _reflection.GeneratedProtocolMessageType('BlobProtoVector', (_message.Message,), dict(\n  DESCRIPTOR = _BLOBPROTOVECTOR,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.BlobProtoVector)\n  ))\n_sym_db.RegisterMessage(BlobProtoVector)\n\nDatum = _reflection.GeneratedProtocolMessageType('Datum', (_message.Message,), dict(\n  DESCRIPTOR = _DATUM,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.Datum)\n  ))\n_sym_db.RegisterMessage(Datum)\n\nLabelMapItem = _reflection.GeneratedProtocolMessageType('LabelMapItem', (_message.Message,), dict(\n  DESCRIPTOR = _LABELMAPITEM,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.LabelMapItem)\n  ))\n_sym_db.RegisterMessage(LabelMapItem)\n\nLabelMap = _reflection.GeneratedProtocolMessageType('LabelMap', (_message.Message,), dict(\n  DESCRIPTOR = _LABELMAP,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.LabelMap)\n  ))\n_sym_db.RegisterMessage(LabelMap)\n\nSampler = _reflection.GeneratedProtocolMessageType('Sampler', (_message.Message,), dict(\n  DESCRIPTOR = _SAMPLER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.Sampler)\n  ))\n_sym_db.RegisterMessage(Sampler)\n\nSampleConstraint = _reflection.GeneratedProtocolMessageType('SampleConstraint', (_message.Message,), dict(\n  DESCRIPTOR = _SAMPLECONSTRAINT,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SampleConstraint)\n  ))\n_sym_db.RegisterMessage(SampleConstraint)\n\nBatchSampler = _reflection.GeneratedProtocolMessageType('BatchSampler', (_message.Message,), dict(\n  DESCRIPTOR = _BATCHSAMPLER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.BatchSampler)\n  ))\n_sym_db.RegisterMessage(BatchSampler)\n\nEmitConstraint = _reflection.GeneratedProtocolMessageType('EmitConstraint', (_message.Message,), dict(\n  DESCRIPTOR = _EMITCONSTRAINT,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.EmitConstraint)\n  ))\n_sym_db.RegisterMessage(EmitConstraint)\n\nNormalizedBBox = _reflection.GeneratedProtocolMessageType('NormalizedBBox', (_message.Message,), dict(\n  DESCRIPTOR = _NORMALIZEDBBOX,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.NormalizedBBox)\n  ))\n_sym_db.RegisterMessage(NormalizedBBox)\n\nAnnotation = _reflection.GeneratedProtocolMessageType('Annotation', (_message.Message,), dict(\n  DESCRIPTOR = _ANNOTATION,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.Annotation)\n  ))\n_sym_db.RegisterMessage(Annotation)\n\nAnnotationGroup = _reflection.GeneratedProtocolMessageType('AnnotationGroup', (_message.Message,), dict(\n  DESCRIPTOR = _ANNOTATIONGROUP,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.AnnotationGroup)\n  ))\n_sym_db.RegisterMessage(AnnotationGroup)\n\nAnnotatedDatum = _reflection.GeneratedProtocolMessageType('AnnotatedDatum', (_message.Message,), dict(\n  DESCRIPTOR = _ANNOTATEDDATUM,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.AnnotatedDatum)\n  ))\n_sym_db.RegisterMessage(AnnotatedDatum)\n\nMTCNNBBox = _reflection.GeneratedProtocolMessageType('MTCNNBBox', (_message.Message,), dict(\n  DESCRIPTOR = _MTCNNBBOX,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.MTCNNBBox)\n  ))\n_sym_db.RegisterMessage(MTCNNBBox)\n\nMTCNNDatum = _reflection.GeneratedProtocolMessageType('MTCNNDatum', (_message.Message,), dict(\n  DESCRIPTOR = _MTCNNDATUM,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.MTCNNDatum)\n  ))\n_sym_db.RegisterMessage(MTCNNDatum)\n\nFillerParameter = _reflection.GeneratedProtocolMessageType('FillerParameter', (_message.Message,), dict(\n  DESCRIPTOR = _FILLERPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.FillerParameter)\n  ))\n_sym_db.RegisterMessage(FillerParameter)\n\nNetParameter = _reflection.GeneratedProtocolMessageType('NetParameter', (_message.Message,), dict(\n  DESCRIPTOR = _NETPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.NetParameter)\n  ))\n_sym_db.RegisterMessage(NetParameter)\n\nSolverParameter = _reflection.GeneratedProtocolMessageType('SolverParameter', (_message.Message,), dict(\n  DESCRIPTOR = _SOLVERPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SolverParameter)\n  ))\n_sym_db.RegisterMessage(SolverParameter)\n\nSolverState = _reflection.GeneratedProtocolMessageType('SolverState', (_message.Message,), dict(\n  DESCRIPTOR = _SOLVERSTATE,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SolverState)\n  ))\n_sym_db.RegisterMessage(SolverState)\n\nNetState = _reflection.GeneratedProtocolMessageType('NetState', (_message.Message,), dict(\n  DESCRIPTOR = _NETSTATE,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.NetState)\n  ))\n_sym_db.RegisterMessage(NetState)\n\nNetStateRule = _reflection.GeneratedProtocolMessageType('NetStateRule', (_message.Message,), dict(\n  DESCRIPTOR = _NETSTATERULE,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.NetStateRule)\n  ))\n_sym_db.RegisterMessage(NetStateRule)\n\nSpatialTransformerParameter = _reflection.GeneratedProtocolMessageType('SpatialTransformerParameter', (_message.Message,), dict(\n  DESCRIPTOR = _SPATIALTRANSFORMERPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SpatialTransformerParameter)\n  ))\n_sym_db.RegisterMessage(SpatialTransformerParameter)\n\nSTLossParameter = _reflection.GeneratedProtocolMessageType('STLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _STLOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.STLossParameter)\n  ))\n_sym_db.RegisterMessage(STLossParameter)\n\nParamSpec = _reflection.GeneratedProtocolMessageType('ParamSpec', (_message.Message,), dict(\n  DESCRIPTOR = _PARAMSPEC,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ParamSpec)\n  ))\n_sym_db.RegisterMessage(ParamSpec)\n\nLayerParameter = _reflection.GeneratedProtocolMessageType('LayerParameter', (_message.Message,), dict(\n  DESCRIPTOR = _LAYERPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.LayerParameter)\n  ))\n_sym_db.RegisterMessage(LayerParameter)\n\nUpsampleParameter = _reflection.GeneratedProtocolMessageType('UpsampleParameter', (_message.Message,), dict(\n  DESCRIPTOR = _UPSAMPLEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.UpsampleParameter)\n  ))\n_sym_db.RegisterMessage(UpsampleParameter)\n\nMatMulParameter = _reflection.GeneratedProtocolMessageType('MatMulParameter', (_message.Message,), dict(\n  DESCRIPTOR = _MATMULPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.MatMulParameter)\n  ))\n_sym_db.RegisterMessage(MatMulParameter)\n\nPassThroughParameter = _reflection.GeneratedProtocolMessageType('PassThroughParameter', (_message.Message,), dict(\n  DESCRIPTOR = _PASSTHROUGHPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.PassThroughParameter)\n  ))\n_sym_db.RegisterMessage(PassThroughParameter)\n\nNormalizeParameter = _reflection.GeneratedProtocolMessageType('NormalizeParameter', (_message.Message,), dict(\n  DESCRIPTOR = _NORMALIZEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.NormalizeParameter)\n  ))\n_sym_db.RegisterMessage(NormalizeParameter)\n\nAnnotatedDataParameter = _reflection.GeneratedProtocolMessageType('AnnotatedDataParameter', (_message.Message,), dict(\n  DESCRIPTOR = _ANNOTATEDDATAPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.AnnotatedDataParameter)\n  ))\n_sym_db.RegisterMessage(AnnotatedDataParameter)\n\nAsdnDataParameter = _reflection.GeneratedProtocolMessageType('AsdnDataParameter', (_message.Message,), dict(\n  DESCRIPTOR = _ASDNDATAPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.AsdnDataParameter)\n  ))\n_sym_db.RegisterMessage(AsdnDataParameter)\n\nMTCNNDataParameter = _reflection.GeneratedProtocolMessageType('MTCNNDataParameter', (_message.Message,), dict(\n  DESCRIPTOR = _MTCNNDATAPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.MTCNNDataParameter)\n  ))\n_sym_db.RegisterMessage(MTCNNDataParameter)\n\nInterpParameter = _reflection.GeneratedProtocolMessageType('InterpParameter', (_message.Message,), dict(\n  DESCRIPTOR = _INTERPPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.InterpParameter)\n  ))\n_sym_db.RegisterMessage(InterpParameter)\n\nPSROIPoolingParameter = _reflection.GeneratedProtocolMessageType('PSROIPoolingParameter', (_message.Message,), dict(\n  DESCRIPTOR = _PSROIPOOLINGPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.PSROIPoolingParameter)\n  ))\n_sym_db.RegisterMessage(PSROIPoolingParameter)\n\nFlipParameter = _reflection.GeneratedProtocolMessageType('FlipParameter', (_message.Message,), dict(\n  DESCRIPTOR = _FLIPPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.FlipParameter)\n  ))\n_sym_db.RegisterMessage(FlipParameter)\n\nBNParameter = _reflection.GeneratedProtocolMessageType('BNParameter', (_message.Message,), dict(\n  DESCRIPTOR = _BNPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.BNParameter)\n  ))\n_sym_db.RegisterMessage(BNParameter)\n\nFocalLossParameter = _reflection.GeneratedProtocolMessageType('FocalLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _FOCALLOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.FocalLossParameter)\n  ))\n_sym_db.RegisterMessage(FocalLossParameter)\n\nTransformationParameter = _reflection.GeneratedProtocolMessageType('TransformationParameter', (_message.Message,), dict(\n  DESCRIPTOR = _TRANSFORMATIONPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.TransformationParameter)\n  ))\n_sym_db.RegisterMessage(TransformationParameter)\n\nResizeParameter = _reflection.GeneratedProtocolMessageType('ResizeParameter', (_message.Message,), dict(\n  DESCRIPTOR = _RESIZEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ResizeParameter)\n  ))\n_sym_db.RegisterMessage(ResizeParameter)\n\nSaltPepperParameter = _reflection.GeneratedProtocolMessageType('SaltPepperParameter', (_message.Message,), dict(\n  DESCRIPTOR = _SALTPEPPERPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SaltPepperParameter)\n  ))\n_sym_db.RegisterMessage(SaltPepperParameter)\n\nNoiseParameter = _reflection.GeneratedProtocolMessageType('NoiseParameter', (_message.Message,), dict(\n  DESCRIPTOR = _NOISEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.NoiseParameter)\n  ))\n_sym_db.RegisterMessage(NoiseParameter)\n\nDistortionParameter = _reflection.GeneratedProtocolMessageType('DistortionParameter', (_message.Message,), dict(\n  DESCRIPTOR = _DISTORTIONPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.DistortionParameter)\n  ))\n_sym_db.RegisterMessage(DistortionParameter)\n\nExpansionParameter = _reflection.GeneratedProtocolMessageType('ExpansionParameter', (_message.Message,), dict(\n  DESCRIPTOR = _EXPANSIONPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ExpansionParameter)\n  ))\n_sym_db.RegisterMessage(ExpansionParameter)\n\nLossParameter = _reflection.GeneratedProtocolMessageType('LossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _LOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.LossParameter)\n  ))\n_sym_db.RegisterMessage(LossParameter)\n\nAccuracyParameter = _reflection.GeneratedProtocolMessageType('AccuracyParameter', (_message.Message,), dict(\n  DESCRIPTOR = _ACCURACYPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.AccuracyParameter)\n  ))\n_sym_db.RegisterMessage(AccuracyParameter)\n\nArgMaxParameter = _reflection.GeneratedProtocolMessageType('ArgMaxParameter', (_message.Message,), dict(\n  DESCRIPTOR = _ARGMAXPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ArgMaxParameter)\n  ))\n_sym_db.RegisterMessage(ArgMaxParameter)\n\nConcatParameter = _reflection.GeneratedProtocolMessageType('ConcatParameter', (_message.Message,), dict(\n  DESCRIPTOR = _CONCATPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ConcatParameter)\n  ))\n_sym_db.RegisterMessage(ConcatParameter)\n\nBatchNormParameter = _reflection.GeneratedProtocolMessageType('BatchNormParameter', (_message.Message,), dict(\n  DESCRIPTOR = _BATCHNORMPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.BatchNormParameter)\n  ))\n_sym_db.RegisterMessage(BatchNormParameter)\n\nBiasParameter = _reflection.GeneratedProtocolMessageType('BiasParameter', (_message.Message,), dict(\n  DESCRIPTOR = _BIASPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.BiasParameter)\n  ))\n_sym_db.RegisterMessage(BiasParameter)\n\nContrastiveLossParameter = _reflection.GeneratedProtocolMessageType('ContrastiveLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _CONTRASTIVELOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ContrastiveLossParameter)\n  ))\n_sym_db.RegisterMessage(ContrastiveLossParameter)\n\nDetectionLossParameter = _reflection.GeneratedProtocolMessageType('DetectionLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _DETECTIONLOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.DetectionLossParameter)\n  ))\n_sym_db.RegisterMessage(DetectionLossParameter)\n\nRegionLossParameter = _reflection.GeneratedProtocolMessageType('RegionLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _REGIONLOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.RegionLossParameter)\n  ))\n_sym_db.RegisterMessage(RegionLossParameter)\n\nReorgParameter = _reflection.GeneratedProtocolMessageType('ReorgParameter', (_message.Message,), dict(\n  DESCRIPTOR = _REORGPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ReorgParameter)\n  ))\n_sym_db.RegisterMessage(ReorgParameter)\n\nEvalDetectionParameter = _reflection.GeneratedProtocolMessageType('EvalDetectionParameter', (_message.Message,), dict(\n  DESCRIPTOR = _EVALDETECTIONPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.EvalDetectionParameter)\n  ))\n_sym_db.RegisterMessage(EvalDetectionParameter)\n\nConvolutionParameter = _reflection.GeneratedProtocolMessageType('ConvolutionParameter', (_message.Message,), dict(\n  DESCRIPTOR = _CONVOLUTIONPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ConvolutionParameter)\n  ))\n_sym_db.RegisterMessage(ConvolutionParameter)\n\nCropParameter = _reflection.GeneratedProtocolMessageType('CropParameter', (_message.Message,), dict(\n  DESCRIPTOR = _CROPPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.CropParameter)\n  ))\n_sym_db.RegisterMessage(CropParameter)\n\nDataParameter = _reflection.GeneratedProtocolMessageType('DataParameter', (_message.Message,), dict(\n  DESCRIPTOR = _DATAPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.DataParameter)\n  ))\n_sym_db.RegisterMessage(DataParameter)\n\nDetectionEvaluateParameter = _reflection.GeneratedProtocolMessageType('DetectionEvaluateParameter', (_message.Message,), dict(\n  DESCRIPTOR = _DETECTIONEVALUATEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.DetectionEvaluateParameter)\n  ))\n_sym_db.RegisterMessage(DetectionEvaluateParameter)\n\nNonMaximumSuppressionParameter = _reflection.GeneratedProtocolMessageType('NonMaximumSuppressionParameter', (_message.Message,), dict(\n  DESCRIPTOR = _NONMAXIMUMSUPPRESSIONPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.NonMaximumSuppressionParameter)\n  ))\n_sym_db.RegisterMessage(NonMaximumSuppressionParameter)\n\nSaveOutputParameter = _reflection.GeneratedProtocolMessageType('SaveOutputParameter', (_message.Message,), dict(\n  DESCRIPTOR = _SAVEOUTPUTPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SaveOutputParameter)\n  ))\n_sym_db.RegisterMessage(SaveOutputParameter)\n\nDetectionOutputParameter = _reflection.GeneratedProtocolMessageType('DetectionOutputParameter', (_message.Message,), dict(\n  DESCRIPTOR = _DETECTIONOUTPUTPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.DetectionOutputParameter)\n  ))\n_sym_db.RegisterMessage(DetectionOutputParameter)\n\nDropoutParameter = _reflection.GeneratedProtocolMessageType('DropoutParameter', (_message.Message,), dict(\n  DESCRIPTOR = _DROPOUTPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.DropoutParameter)\n  ))\n_sym_db.RegisterMessage(DropoutParameter)\n\nDummyDataParameter = _reflection.GeneratedProtocolMessageType('DummyDataParameter', (_message.Message,), dict(\n  DESCRIPTOR = _DUMMYDATAPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.DummyDataParameter)\n  ))\n_sym_db.RegisterMessage(DummyDataParameter)\n\nEltwiseParameter = _reflection.GeneratedProtocolMessageType('EltwiseParameter', (_message.Message,), dict(\n  DESCRIPTOR = _ELTWISEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.EltwiseParameter)\n  ))\n_sym_db.RegisterMessage(EltwiseParameter)\n\nELUParameter = _reflection.GeneratedProtocolMessageType('ELUParameter', (_message.Message,), dict(\n  DESCRIPTOR = _ELUPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ELUParameter)\n  ))\n_sym_db.RegisterMessage(ELUParameter)\n\nEmbedParameter = _reflection.GeneratedProtocolMessageType('EmbedParameter', (_message.Message,), dict(\n  DESCRIPTOR = _EMBEDPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.EmbedParameter)\n  ))\n_sym_db.RegisterMessage(EmbedParameter)\n\nExpParameter = _reflection.GeneratedProtocolMessageType('ExpParameter', (_message.Message,), dict(\n  DESCRIPTOR = _EXPPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ExpParameter)\n  ))\n_sym_db.RegisterMessage(ExpParameter)\n\nFlattenParameter = _reflection.GeneratedProtocolMessageType('FlattenParameter', (_message.Message,), dict(\n  DESCRIPTOR = _FLATTENPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.FlattenParameter)\n  ))\n_sym_db.RegisterMessage(FlattenParameter)\n\nHDF5DataParameter = _reflection.GeneratedProtocolMessageType('HDF5DataParameter', (_message.Message,), dict(\n  DESCRIPTOR = _HDF5DATAPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.HDF5DataParameter)\n  ))\n_sym_db.RegisterMessage(HDF5DataParameter)\n\nHDF5OutputParameter = _reflection.GeneratedProtocolMessageType('HDF5OutputParameter', (_message.Message,), dict(\n  DESCRIPTOR = _HDF5OUTPUTPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.HDF5OutputParameter)\n  ))\n_sym_db.RegisterMessage(HDF5OutputParameter)\n\nHingeLossParameter = _reflection.GeneratedProtocolMessageType('HingeLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _HINGELOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.HingeLossParameter)\n  ))\n_sym_db.RegisterMessage(HingeLossParameter)\n\nImageDataParameter = _reflection.GeneratedProtocolMessageType('ImageDataParameter', (_message.Message,), dict(\n  DESCRIPTOR = _IMAGEDATAPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ImageDataParameter)\n  ))\n_sym_db.RegisterMessage(ImageDataParameter)\n\nInfogainLossParameter = _reflection.GeneratedProtocolMessageType('InfogainLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _INFOGAINLOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.InfogainLossParameter)\n  ))\n_sym_db.RegisterMessage(InfogainLossParameter)\n\nInnerProductParameter = _reflection.GeneratedProtocolMessageType('InnerProductParameter', (_message.Message,), dict(\n  DESCRIPTOR = _INNERPRODUCTPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.InnerProductParameter)\n  ))\n_sym_db.RegisterMessage(InnerProductParameter)\n\nInputParameter = _reflection.GeneratedProtocolMessageType('InputParameter', (_message.Message,), dict(\n  DESCRIPTOR = _INPUTPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.InputParameter)\n  ))\n_sym_db.RegisterMessage(InputParameter)\n\nLogParameter = _reflection.GeneratedProtocolMessageType('LogParameter', (_message.Message,), dict(\n  DESCRIPTOR = _LOGPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.LogParameter)\n  ))\n_sym_db.RegisterMessage(LogParameter)\n\nLRNParameter = _reflection.GeneratedProtocolMessageType('LRNParameter', (_message.Message,), dict(\n  DESCRIPTOR = _LRNPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.LRNParameter)\n  ))\n_sym_db.RegisterMessage(LRNParameter)\n\nMemoryDataParameter = _reflection.GeneratedProtocolMessageType('MemoryDataParameter', (_message.Message,), dict(\n  DESCRIPTOR = _MEMORYDATAPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.MemoryDataParameter)\n  ))\n_sym_db.RegisterMessage(MemoryDataParameter)\n\nMultiBoxLossParameter = _reflection.GeneratedProtocolMessageType('MultiBoxLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _MULTIBOXLOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.MultiBoxLossParameter)\n  ))\n_sym_db.RegisterMessage(MultiBoxLossParameter)\n\nPermuteParameter = _reflection.GeneratedProtocolMessageType('PermuteParameter', (_message.Message,), dict(\n  DESCRIPTOR = _PERMUTEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.PermuteParameter)\n  ))\n_sym_db.RegisterMessage(PermuteParameter)\n\nMVNParameter = _reflection.GeneratedProtocolMessageType('MVNParameter', (_message.Message,), dict(\n  DESCRIPTOR = _MVNPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.MVNParameter)\n  ))\n_sym_db.RegisterMessage(MVNParameter)\n\nParameterParameter = _reflection.GeneratedProtocolMessageType('ParameterParameter', (_message.Message,), dict(\n  DESCRIPTOR = _PARAMETERPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ParameterParameter)\n  ))\n_sym_db.RegisterMessage(ParameterParameter)\n\nPoolingParameter = _reflection.GeneratedProtocolMessageType('PoolingParameter', (_message.Message,), dict(\n  DESCRIPTOR = _POOLINGPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.PoolingParameter)\n  ))\n_sym_db.RegisterMessage(PoolingParameter)\n\nPowerParameter = _reflection.GeneratedProtocolMessageType('PowerParameter', (_message.Message,), dict(\n  DESCRIPTOR = _POWERPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.PowerParameter)\n  ))\n_sym_db.RegisterMessage(PowerParameter)\n\nPriorBoxParameter = _reflection.GeneratedProtocolMessageType('PriorBoxParameter', (_message.Message,), dict(\n  DESCRIPTOR = _PRIORBOXPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.PriorBoxParameter)\n  ))\n_sym_db.RegisterMessage(PriorBoxParameter)\n\nPythonParameter = _reflection.GeneratedProtocolMessageType('PythonParameter', (_message.Message,), dict(\n  DESCRIPTOR = _PYTHONPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.PythonParameter)\n  ))\n_sym_db.RegisterMessage(PythonParameter)\n\nRecurrentParameter = _reflection.GeneratedProtocolMessageType('RecurrentParameter', (_message.Message,), dict(\n  DESCRIPTOR = _RECURRENTPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.RecurrentParameter)\n  ))\n_sym_db.RegisterMessage(RecurrentParameter)\n\nReductionParameter = _reflection.GeneratedProtocolMessageType('ReductionParameter', (_message.Message,), dict(\n  DESCRIPTOR = _REDUCTIONPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ReductionParameter)\n  ))\n_sym_db.RegisterMessage(ReductionParameter)\n\nReLUParameter = _reflection.GeneratedProtocolMessageType('ReLUParameter', (_message.Message,), dict(\n  DESCRIPTOR = _RELUPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ReLUParameter)\n  ))\n_sym_db.RegisterMessage(ReLUParameter)\n\nReshapeParameter = _reflection.GeneratedProtocolMessageType('ReshapeParameter', (_message.Message,), dict(\n  DESCRIPTOR = _RESHAPEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ReshapeParameter)\n  ))\n_sym_db.RegisterMessage(ReshapeParameter)\n\nROIPoolingParameter = _reflection.GeneratedProtocolMessageType('ROIPoolingParameter', (_message.Message,), dict(\n  DESCRIPTOR = _ROIPOOLINGPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ROIPoolingParameter)\n  ))\n_sym_db.RegisterMessage(ROIPoolingParameter)\n\nScaleParameter = _reflection.GeneratedProtocolMessageType('ScaleParameter', (_message.Message,), dict(\n  DESCRIPTOR = _SCALEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ScaleParameter)\n  ))\n_sym_db.RegisterMessage(ScaleParameter)\n\nSigmoidParameter = _reflection.GeneratedProtocolMessageType('SigmoidParameter', (_message.Message,), dict(\n  DESCRIPTOR = _SIGMOIDPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SigmoidParameter)\n  ))\n_sym_db.RegisterMessage(SigmoidParameter)\n\nSmoothL1LossParameter = _reflection.GeneratedProtocolMessageType('SmoothL1LossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _SMOOTHL1LOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SmoothL1LossParameter)\n  ))\n_sym_db.RegisterMessage(SmoothL1LossParameter)\n\nSliceParameter = _reflection.GeneratedProtocolMessageType('SliceParameter', (_message.Message,), dict(\n  DESCRIPTOR = _SLICEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SliceParameter)\n  ))\n_sym_db.RegisterMessage(SliceParameter)\n\nSoftmaxParameter = _reflection.GeneratedProtocolMessageType('SoftmaxParameter', (_message.Message,), dict(\n  DESCRIPTOR = _SOFTMAXPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SoftmaxParameter)\n  ))\n_sym_db.RegisterMessage(SoftmaxParameter)\n\nTanHParameter = _reflection.GeneratedProtocolMessageType('TanHParameter', (_message.Message,), dict(\n  DESCRIPTOR = _TANHPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.TanHParameter)\n  ))\n_sym_db.RegisterMessage(TanHParameter)\n\nTileParameter = _reflection.GeneratedProtocolMessageType('TileParameter', (_message.Message,), dict(\n  DESCRIPTOR = _TILEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.TileParameter)\n  ))\n_sym_db.RegisterMessage(TileParameter)\n\nThresholdParameter = _reflection.GeneratedProtocolMessageType('ThresholdParameter', (_message.Message,), dict(\n  DESCRIPTOR = _THRESHOLDPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ThresholdParameter)\n  ))\n_sym_db.RegisterMessage(ThresholdParameter)\n\nWindowDataParameter = _reflection.GeneratedProtocolMessageType('WindowDataParameter', (_message.Message,), dict(\n  DESCRIPTOR = _WINDOWDATAPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.WindowDataParameter)\n  ))\n_sym_db.RegisterMessage(WindowDataParameter)\n\nSPPParameter = _reflection.GeneratedProtocolMessageType('SPPParameter', (_message.Message,), dict(\n  DESCRIPTOR = _SPPPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.SPPParameter)\n  ))\n_sym_db.RegisterMessage(SPPParameter)\n\nV1LayerParameter = _reflection.GeneratedProtocolMessageType('V1LayerParameter', (_message.Message,), dict(\n  DESCRIPTOR = _V1LAYERPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.V1LayerParameter)\n  ))\n_sym_db.RegisterMessage(V1LayerParameter)\n\nV0LayerParameter = _reflection.GeneratedProtocolMessageType('V0LayerParameter', (_message.Message,), dict(\n  DESCRIPTOR = _V0LAYERPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.V0LayerParameter)\n  ))\n_sym_db.RegisterMessage(V0LayerParameter)\n\nPReLUParameter = _reflection.GeneratedProtocolMessageType('PReLUParameter', (_message.Message,), dict(\n  DESCRIPTOR = _PRELUPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.PReLUParameter)\n  ))\n_sym_db.RegisterMessage(PReLUParameter)\n\nRPNParameter = _reflection.GeneratedProtocolMessageType('RPNParameter', (_message.Message,), dict(\n  DESCRIPTOR = _RPNPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.RPNParameter)\n  ))\n_sym_db.RegisterMessage(RPNParameter)\n\nVideoDataParameter = _reflection.GeneratedProtocolMessageType('VideoDataParameter', (_message.Message,), dict(\n  DESCRIPTOR = _VIDEODATAPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.VideoDataParameter)\n  ))\n_sym_db.RegisterMessage(VideoDataParameter)\n\nCenterLossParameter = _reflection.GeneratedProtocolMessageType('CenterLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _CENTERLOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.CenterLossParameter)\n  ))\n_sym_db.RegisterMessage(CenterLossParameter)\n\nMarginInnerProductParameter = _reflection.GeneratedProtocolMessageType('MarginInnerProductParameter', (_message.Message,), dict(\n  DESCRIPTOR = _MARGININNERPRODUCTPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.MarginInnerProductParameter)\n  ))\n_sym_db.RegisterMessage(MarginInnerProductParameter)\n\nAdditiveMarginInnerProductParameter = _reflection.GeneratedProtocolMessageType('AdditiveMarginInnerProductParameter', (_message.Message,), dict(\n  DESCRIPTOR = _ADDITIVEMARGININNERPRODUCTPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.AdditiveMarginInnerProductParameter)\n  ))\n_sym_db.RegisterMessage(AdditiveMarginInnerProductParameter)\n\nDeformableConvolutionParameter = _reflection.GeneratedProtocolMessageType('DeformableConvolutionParameter', (_message.Message,), dict(\n  DESCRIPTOR = _DEFORMABLECONVOLUTIONPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.DeformableConvolutionParameter)\n  ))\n_sym_db.RegisterMessage(DeformableConvolutionParameter)\n\nLabelSpecificAddParameter = _reflection.GeneratedProtocolMessageType('LabelSpecificAddParameter', (_message.Message,), dict(\n  DESCRIPTOR = _LABELSPECIFICADDPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.LabelSpecificAddParameter)\n  ))\n_sym_db.RegisterMessage(LabelSpecificAddParameter)\n\nChannelScaleParameter = _reflection.GeneratedProtocolMessageType('ChannelScaleParameter', (_message.Message,), dict(\n  DESCRIPTOR = _CHANNELSCALEPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ChannelScaleParameter)\n  ))\n_sym_db.RegisterMessage(ChannelScaleParameter)\n\nCosinAddmParameter = _reflection.GeneratedProtocolMessageType('CosinAddmParameter', (_message.Message,), dict(\n  DESCRIPTOR = _COSINADDMPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.CosinAddmParameter)\n  ))\n_sym_db.RegisterMessage(CosinAddmParameter)\n\nCosinMulmParameter = _reflection.GeneratedProtocolMessageType('CosinMulmParameter', (_message.Message,), dict(\n  DESCRIPTOR = _COSINMULMPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.CosinMulmParameter)\n  ))\n_sym_db.RegisterMessage(CosinMulmParameter)\n\nCoupledClusterLossParameter = _reflection.GeneratedProtocolMessageType('CoupledClusterLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _COUPLEDCLUSTERLOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.CoupledClusterLossParameter)\n  ))\n_sym_db.RegisterMessage(CoupledClusterLossParameter)\n\nTripletLossParameter = _reflection.GeneratedProtocolMessageType('TripletLossParameter', (_message.Message,), dict(\n  DESCRIPTOR = _TRIPLETLOSSPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.TripletLossParameter)\n  ))\n_sym_db.RegisterMessage(TripletLossParameter)\n\nGeneralTripletParameter = _reflection.GeneratedProtocolMessageType('GeneralTripletParameter', (_message.Message,), dict(\n  DESCRIPTOR = _GENERALTRIPLETPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.GeneralTripletParameter)\n  ))\n_sym_db.RegisterMessage(GeneralTripletParameter)\n\nROIAlignParameter = _reflection.GeneratedProtocolMessageType('ROIAlignParameter', (_message.Message,), dict(\n  DESCRIPTOR = _ROIALIGNPARAMETER,\n  __module__ = 'caffe_pb2'\n  # @@protoc_insertion_point(class_scope:caffe.ROIAlignParameter)\n  ))\n_sym_db.RegisterMessage(ROIAlignParameter)\n\n\n_BLOBSHAPE.fields_by_name['dim']._options = None\n_BLOBPROTO.fields_by_name['data']._options = None\n_BLOBPROTO.fields_by_name['diff']._options = None\n_BLOBPROTO.fields_by_name['double_data']._options = None\n_BLOBPROTO.fields_by_name['double_diff']._options = None\n# @@protoc_insertion_point(module_scope)\n"
  },
  {
    "path": "fast_reid/tools/deploy/Caffe/layer_param.py",
    "content": "from __future__ import absolute_import\n\nfrom . import caffe_pb2 as pb\n\n\ndef pair_process(item, strict_one=True):\n    if hasattr(item, '__iter__'):\n        for i in item:\n            if i != item[0]:\n                if strict_one:\n                    raise ValueError(\"number in item {} must be the same\".format(item))\n                else:\n                    print(\"IMPORTANT WARNING: number in item {} must be the same\".format(item))\n        return item[0]\n    return item\n\n\ndef pair_reduce(item):\n    if hasattr(item, '__iter__'):\n        for i in item:\n            if i != item[0]:\n                return item\n        return [item[0]]\n    return [item]\n\n\nclass Layer_param():\n    def __init__(self, name='', type='', top=(), bottom=()):\n        self.param = pb.LayerParameter()\n        self.name = self.param.name = name\n        self.type = self.param.type = type\n\n        self.top = self.param.top\n        self.top.extend(top)\n        self.bottom = self.param.bottom\n        self.bottom.extend(bottom)\n\n    def fc_param(self, num_output, weight_filler='xavier', bias_filler='constant', has_bias=True):\n        if self.type != 'InnerProduct':\n            raise TypeError('the layer type must be InnerProduct if you want set fc param')\n        fc_param = pb.InnerProductParameter()\n        fc_param.num_output = num_output\n        fc_param.weight_filler.type = weight_filler\n        fc_param.bias_term = has_bias\n        if has_bias:\n            fc_param.bias_filler.type = bias_filler\n        self.param.inner_product_param.CopyFrom(fc_param)\n\n    def conv_param(self, num_output, kernel_size, stride=(1), pad=(0,),\n                   weight_filler_type='xavier', bias_filler_type='constant',\n                   bias_term=True, dilation=None, groups=None):\n        \"\"\"\n        add a conv_param layer if you spec the layer type \"Convolution\"\n        Args:\n            num_output: a int\n            kernel_size: int list\n            stride: a int list\n            weight_filler_type: the weight filer type\n            bias_filler_type: the bias filler type\n        Returns:\n        \"\"\"\n        if self.type not in ['Convolution', 'Deconvolution']:\n            raise TypeError('the layer type must be Convolution or Deconvolution if you want set conv param')\n        conv_param = pb.ConvolutionParameter()\n        conv_param.num_output = num_output\n        conv_param.kernel_size.extend(pair_reduce(kernel_size))\n        conv_param.stride.extend(pair_reduce(stride))\n        conv_param.pad.extend(pair_reduce(pad))\n        conv_param.bias_term = bias_term\n        conv_param.weight_filler.type = weight_filler_type\n        if bias_term:\n            conv_param.bias_filler.type = bias_filler_type\n        if dilation:\n            conv_param.dilation.extend(pair_reduce(dilation))\n        if groups:\n            conv_param.group = groups\n        self.param.convolution_param.CopyFrom(conv_param)\n\n    def pool_param(self, type='MAX', kernel_size=2, stride=2, pad=None, ceil_mode=False):\n        pool_param = pb.PoolingParameter()\n        pool_param.pool = pool_param.PoolMethod.Value(type)\n        pool_param.kernel_size = pair_process(kernel_size)\n        pool_param.stride = pair_process(stride)\n        pool_param.ceil_mode = ceil_mode\n        if pad:\n            if isinstance(pad, tuple):\n                pool_param.pad_h = pad[0]\n                pool_param.pad_w = pad[1]\n            else:\n                pool_param.pad = pad\n        self.param.pooling_param.CopyFrom(pool_param)\n\n    def batch_norm_param(self, use_global_stats=0, moving_average_fraction=None, eps=None):\n        bn_param = pb.BatchNormParameter()\n        bn_param.use_global_stats = use_global_stats\n        if moving_average_fraction:\n            bn_param.moving_average_fraction = moving_average_fraction\n        if eps:\n            bn_param.eps = eps\n        self.param.batch_norm_param.CopyFrom(bn_param)\n\n    def upsample_param(self, size=None, scale_factor=None):\n        upsample_param = pb.UpsampleParameter()\n        if scale_factor:\n            if isinstance(scale_factor, int):\n                upsample_param.scale = scale_factor\n            else:\n                upsample_param.scale_h = scale_factor[0]\n                upsample_param.scale_w = scale_factor[1]\n\n        if size:\n            if isinstance(size, int):\n                upsample_param.upsample_h = size\n            else:\n                upsample_param.upsample_h = size[0]\n                upsample_param.upsample_w = size[1]\n                # upsample_param.upsample_h = size[0] * scale_factor\n                # upsample_param.upsample_w = size[1] * scale_factor\n        self.param.upsample_param.CopyFrom(upsample_param)\n\n    def interp_param(self, size=None, scale_factor=None):\n        interp_param = pb.InterpParameter()\n        if scale_factor:\n            if isinstance(scale_factor, int):\n                interp_param.zoom_factor = scale_factor\n\n        if size:\n            print('size:', size)\n            interp_param.height = size[0]\n            interp_param.width = size[1]\n        self.param.interp_param.CopyFrom(interp_param)\n\n    def add_data(self, *args):\n        \"\"\"Args are data numpy array\n        \"\"\"\n        del self.param.blobs[:]\n        for data in args:\n            new_blob = self.param.blobs.add()\n            for dim in data.shape:\n                new_blob.shape.dim.append(dim)\n            new_blob.data.extend(data.flatten().astype(float))\n\n    def set_params_by_dict(self, dic):\n        pass\n\n    def copy_from(self, layer_param):\n        pass\n\n\ndef set_enum(param, key, value):\n    setattr(param, key, param.Value(value))\n"
  },
  {
    "path": "fast_reid/tools/deploy/Caffe/net.py",
    "content": "raise ImportError(\"the nn_tools.Caffe.net is no longer used, please use nn_tools.Caffe.caffe_net\")\n\n"
  },
  {
    "path": "fast_reid/tools/deploy/README.md",
    "content": "# Model Deployment\n\nThis directory contains:\n\n1. The scripts that convert a fastreid model to Caffe/ONNX/TRT format.\n\n2. The exmpales that load a R50 baseline model in Caffe/ONNX/TRT and run inference.\n\n## Tutorial\n\n### Caffe Convert\n\n<details>\n<summary>step-to-step pipeline for caffe convert</summary>\n\nThis is a tiny example for converting fastreid-baseline in `meta_arch` to Caffe model, if you want to convert more complex architecture, you need to customize more things.\n\n1. Run `caffe_export.py` to get the converted Caffe model,\n\n    ```bash\n    python tools/deploy/caffe_export.py --config-file configs/market1501/bagtricks_R50/config.yml --name baseline_R50 --output caffe_R50_model --opts MODEL.WEIGHTS logs/market1501/bagtricks_R50/model_final.pth\n    ```\n\n    then you can check the Caffe model and prototxt in `./caffe_R50_model`.\n\n2. Change `prototxt` following next three steps:\n\n   1) Modify `MaxPooling` in `baseline_R50.prototxt` and delete `ceil_mode: false`.\n   \n   2) Add `avg_pooling` in `baseline_R50.prototxt`\n\n        ```prototxt\n        layer {\n            name: \"avgpool1\"\n            type: \"Pooling\"\n            bottom: \"relu_blob49\"\n            top: \"avgpool_blob1\"\n            pooling_param {\n                pool: AVE\n                global_pooling: true\n            }\n        }\n        ```\n\n   2) Change the last layer `top` name to `output`\n\n        ```prototxt\n        layer {\n            name: \"bn_scale54\"\n            type: \"Scale\"\n            bottom: \"batch_norm_blob54\"\n            top: \"output\" # bn_norm_blob54\n            scale_param {\n                bias_term: true\n            }\n        }\n        ```\n\n3. (optional) You can open [Netscope](https://ethereon.github.io/netscope/quickstart.html), then enter you network `prototxt` to visualize the network.\n\n4. Run `caffe_inference.py` to save Caffe model features with input images\n\n   ```bash\n    python caffe_inference.py --model-def outputs/caffe_model/baseline_R50.prototxt \\\n    --model-weights outputs/caffe_model/baseline_R50.caffemodel \\\n    --input test_data/*.jpg --output caffe_output\n   ```\n\n6. Run `demo/demo.py` to get fastreid model features with the same input images, then verify that Caffe and PyTorch are computing the same value for the network.\n\n    ```python\n    np.testing.assert_allclose(torch_out, ort_out, rtol=1e-3, atol=1e-6)\n    ```\n\n</details>\n\n### ONNX Convert\n\n<details>\n<summary>step-to-step pipeline for onnx convert</summary>\n\nThis is a tiny example for converting fastreid-baseline in `meta_arch` to ONNX model. ONNX supports most operators in pytorch as far as I know and if some operators are not supported by ONNX, you need to customize these.\n\n1. Run `onnx_export.py` to get the converted ONNX model,\n\n    ```bash\n    python onnx_export.py --config-file root-path/bagtricks_R50/config.yml --name baseline_R50 --output outputs/onnx_model --opts MODEL.WEIGHTS root-path/logs/market1501/bagtricks_R50/model_final.pth\n    ```\n\n    then you can check the ONNX model in `outputs/onnx_model`.\n\n2. (optional) You can use [Netron](https://github.com/lutzroeder/netron) to visualize the network.\n\n3. Run `onnx_inference.py` to save ONNX model features with input images\n\n   ```bash\n    python onnx_inference.py --model-path outputs/onnx_model/baseline_R50.onnx \\\n    --input test_data/*.jpg --output onnx_output\n   ```\n\n4. Run `demo/demo.py` to get fastreid model features with the same input images, then verify that ONNX Runtime and PyTorch are computing the same value for the network.\n\n    ```python\n    np.testing.assert_allclose(torch_out, ort_out, rtol=1e-3, atol=1e-6)\n    ```\n\n</details>\n\n### TensorRT Convert\n\n<details>\n<summary>step-to-step pipeline for trt convert</summary>\n\nThis is a tiny example for converting fastreid-baseline in `meta_arch` to TRT model.\n\nFirst you need to convert the pytorch model to ONNX format following [ONNX Convert](https://github.com/JDAI-CV/fast-reid#fastreid), and you need to remember your `output` name. Then you can convert ONNX model to TensorRT following instructions below.\n\n1. Run command line below to get the converted TRT model from ONNX model,\n\n    ```bash\n    python trt_export.py --name baseline_R50 --output outputs/trt_model \\\n    --mode fp32 --batch-size 8 --height 256 --width 128 \\\n    --onnx-model outputs/onnx_model/baseline.onnx \n    ```\n\n    then you can check the TRT model in `outputs/trt_model`.\n\n2. Run `trt_inference.py` to save TRT model features with input images\n\n   ```bash\n    python3 trt_inference.py --model-path outputs/trt_model/baseline.engine \\\n    --input test_data/*.jpg --batch-size 8 --height 256 --width 128 --output trt_output \n   ```\n\n3. Run `demo/demo.py` to get fastreid model features with the same input images, then verify that TensorRT and PyTorch are computing the same value for the network.\n\n    ```python\n    np.testing.assert_allclose(torch_out, trt_out, rtol=1e-3, atol=1e-6)\n    ```\n\nNotice: The int8 mode in tensorRT runtime is not supported now and there are some bugs in calibrator. Need help!\n\n</details>\n\n## Acknowledgements\n\nThank to [CPFLAME](https://github.com/CPFLAME), [gcong18](https://github.com/gcong18), [YuxiangJohn](https://github.com/YuxiangJohn) and [wiggin66](https://github.com/wiggin66) at JDAI Model Acceleration Group for help in PyTorch model converting.\n"
  },
  {
    "path": "fast_reid/tools/deploy/caffe_export.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport argparse\nimport logging\nimport sys\n\nimport torch\n\nsys.path.append('.')\n\nimport pytorch_to_caffe\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.modeling.meta_arch import build_model\nfrom fast_reid.fastreid.utils.file_io import PathManager\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.utils.logger import setup_logger\n\n# import some modules added in project like this below\n# sys.path.append(\"projects/PartialReID\")\n# from partialreid import *\n\nsetup_logger(name='fastreid')\nlogger = logging.getLogger(\"fastreid.caffe_export\")\n\n\ndef setup_cfg(args):\n    cfg = get_cfg()\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    return cfg\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(description=\"Convert Pytorch to Caffe model\")\n\n    parser.add_argument(\n        \"--config-file\",\n        metavar=\"FILE\",\n        help=\"path to config file\",\n    )\n    parser.add_argument(\n        \"--name\",\n        default=\"baseline\",\n        help=\"name for converted model\"\n    )\n    parser.add_argument(\n        \"--output\",\n        default='caffe_model',\n        help='path to save converted caffe model'\n    )\n    parser.add_argument(\n        \"--opts\",\n        help=\"Modify config options using the command-line 'KEY VALUE' pairs\",\n        default=[],\n        nargs=argparse.REMAINDER,\n    )\n    return parser\n\n\nif __name__ == '__main__':\n    args = get_parser().parse_args()\n    cfg = setup_cfg(args)\n\n    cfg.defrost()\n    cfg.MODEL.BACKBONE.PRETRAIN = False\n    cfg.MODEL.HEADS.POOL_LAYER = \"Identity\"\n    cfg.MODEL.BACKBONE.WITH_NL = False\n\n    model = build_model(cfg)\n    Checkpointer(model).load(cfg.MODEL.WEIGHTS)\n    model.eval()\n    logger.info(model)\n\n    inputs = torch.randn(1, 3, cfg.INPUT.SIZE_TEST[0], cfg.INPUT.SIZE_TEST[1]).to(torch.device(cfg.MODEL.DEVICE))\n    PathManager.mkdirs(args.output)\n    pytorch_to_caffe.trans_net(model, inputs, args.name)\n    pytorch_to_caffe.save_prototxt(f\"{args.output}/{args.name}.prototxt\")\n    pytorch_to_caffe.save_caffemodel(f\"{args.output}/{args.name}.caffemodel\")\n\n    logger.info(f\"Export caffe model in {args.output} sucessfully!\")\n"
  },
  {
    "path": "fast_reid/tools/deploy/caffe_inference.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport caffe\nimport tqdm\nimport glob\nimport os\nimport cv2\nimport numpy as np\n\ncaffe.set_mode_gpu()\n\nimport argparse\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(description=\"Caffe model inference\")\n\n    parser.add_argument(\n        \"--model-def\",\n        default=\"logs/test_caffe/baseline_R50.prototxt\",\n        help=\"caffe model prototxt\"\n    )\n    parser.add_argument(\n        \"--model-weights\",\n        default=\"logs/test_caffe/baseline_R50.caffemodel\",\n        help=\"caffe model weights\"\n    )\n    parser.add_argument(\n        \"--input\",\n        nargs=\"+\",\n        help=\"A list of space separated input images; \"\n             \"or a single glob pattern such as 'directory/*.jpg'\",\n    )\n    parser.add_argument(\n        \"--output\",\n        default='caffe_output',\n        help='path to save converted caffe model'\n    )\n    parser.add_argument(\n        \"--height\",\n        type=int,\n        default=256,\n        help=\"height of image\"\n    )\n    parser.add_argument(\n        \"--width\",\n        type=int,\n        default=128,\n        help=\"width of image\"\n    )\n    return parser\n\n\ndef preprocess(image_path, image_height, image_width):\n    original_image = cv2.imread(image_path)\n    # the model expects RGB inputs\n    original_image = original_image[:, :, ::-1]\n\n    # Apply pre-processing to image.\n    image = cv2.resize(original_image, (image_width, image_height), interpolation=cv2.INTER_CUBIC)\n    image = image.astype(\"float32\").transpose(2, 0, 1)[np.newaxis]  # (1, 3, h, w)\n    image = (image - np.array([0.485 * 255, 0.456 * 255, 0.406 * 255]).reshape((1, -1, 1, 1))) / np.array(\n        [0.229 * 255, 0.224 * 255, 0.225 * 255]).reshape((1, -1, 1, 1))\n    return image\n\n\ndef normalize(nparray, order=2, axis=-1):\n    \"\"\"Normalize a N-D numpy array along the specified axis.\"\"\"\n    norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)\n    return nparray / (norm + np.finfo(np.float32).eps)\n\n\nif __name__ == \"__main__\":\n    args = get_parser().parse_args()\n\n    net = caffe.Net(args.model_def, args.model_weights, caffe.TEST)\n    net.blobs['blob1'].reshape(1, 3, args.height, args.width)\n\n    if not os.path.exists(args.output): os.makedirs(args.output)\n\n    if args.input:\n        if os.path.isdir(args.input[0]):\n            args.input = glob.glob(os.path.expanduser(args.input[0]))\n            assert args.input, \"The input path(s) was not found\"\n        for path in tqdm.tqdm(args.input):\n            image = preprocess(path, args.height, args.width)\n            net.blobs[\"blob1\"].data[...] = image\n            feat = net.forward()[\"output\"]\n            feat = normalize(feat[..., 0, 0], axis=1)\n            np.save(os.path.join(args.output, os.path.basename(path).split('.')[0] + '.npy'), feat)\n\n"
  },
  {
    "path": "fast_reid/tools/deploy/onnx_export.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\nimport os\nimport argparse\nimport io\nimport sys\n\nimport onnx\nimport onnxoptimizer\nimport torch\nfrom onnxsim import simplify\nfrom torch.onnx import OperatorExportTypes\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.modeling.meta_arch import build_model\nfrom fast_reid.fastreid.utils.file_io import PathManager\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\nfrom fast_reid.fastreid.utils.logger import setup_logger\n\n# import some modules added in project like this below\n# sys.path.append(\"projects/FastDistill\")\n# from fastdistill import *\n\nsetup_logger(name=\"fastreid\")\nlogger = logging.getLogger(\"fastreid.onnx_export\")\n\n\ndef setup_cfg(args):\n    cfg = get_cfg()\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    return cfg\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(description=\"Convert Pytorch to ONNX model\")\n\n    parser.add_argument(\n        \"--config-file\",\n        metavar=\"FILE\",\n        help=\"path to config file\",\n    )\n    parser.add_argument(\n        \"--name\",\n        default=\"baseline\",\n        help=\"name for converted model\"\n    )\n    parser.add_argument(\n        \"--output\",\n        default='onnx_model',\n        help='path to save converted onnx model'\n    )\n    parser.add_argument(\n        '--batch-size',\n        default=1,\n        type=int,\n        help=\"the maximum batch size of onnx runtime\"\n    )\n    parser.add_argument(\n        \"--opts\",\n        help=\"Modify config options using the command-line 'KEY VALUE' pairs\",\n        default=[],\n        nargs=argparse.REMAINDER,\n    )\n    return parser\n\n\ndef remove_initializer_from_input(model):\n    if model.ir_version < 4:\n        print(\n            'Model with ir_version below 4 requires to include initilizer in graph input'\n        )\n        return\n\n    inputs = model.graph.input\n    name_to_input = {}\n    for input in inputs:\n        name_to_input[input.name] = input\n\n    for initializer in model.graph.initializer:\n        if initializer.name in name_to_input:\n            inputs.remove(name_to_input[initializer.name])\n\n    return model\n\n\ndef export_onnx_model(model, inputs):\n    \"\"\"\n    Trace and export a model to onnx format.\n    Args:\n        model (nn.Module):\n        inputs (torch.Tensor): the model will be called by `model(*inputs)`\n    Returns:\n        an onnx model\n    \"\"\"\n    assert isinstance(model, torch.nn.Module)\n\n    # make sure all modules are in eval mode, onnx may change the training state\n    # of the module if the states are not consistent\n    def _check_eval(module):\n        assert not module.training\n\n    model.apply(_check_eval)\n\n    logger.info(\"Beginning ONNX file converting\")\n    # Export the model to ONNX\n    with torch.no_grad():\n        with io.BytesIO() as f:\n            torch.onnx.export(\n                model,\n                inputs,\n                f,\n                operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK,\n                # verbose=True,  # NOTE: uncomment this for debugging\n                # export_params=True,\n            )\n            onnx_model = onnx.load_from_string(f.getvalue())\n\n    logger.info(\"Completed convert of ONNX model\")\n\n    # Apply ONNX's Optimization\n    logger.info(\"Beginning ONNX model path optimization\")\n    all_passes = onnxoptimizer.get_available_passes()\n    passes = [\"extract_constant_to_initializer\", \"eliminate_unused_initializer\", \"fuse_bn_into_conv\"]\n    assert all(p in all_passes for p in passes)\n    onnx_model = onnxoptimizer.optimize(onnx_model, passes)\n    logger.info(\"Completed ONNX model path optimization\")\n    return onnx_model\n\n\nif __name__ == '__main__':\n    args = get_parser().parse_args()\n    cfg = setup_cfg(args)\n\n    cfg.defrost()\n    cfg.MODEL.BACKBONE.PRETRAIN = False\n    if cfg.MODEL.HEADS.POOL_LAYER == 'FastGlobalAvgPool':\n        cfg.MODEL.HEADS.POOL_LAYER = 'GlobalAvgPool'\n    model = build_model(cfg)\n    Checkpointer(model).load(cfg.MODEL.WEIGHTS)\n    if hasattr(model.backbone, 'deploy'):\n        model.backbone.deploy(True)\n    model.eval()\n    logger.info(model)\n\n    inputs = torch.randn(args.batch_size, 3, cfg.INPUT.SIZE_TEST[0], cfg.INPUT.SIZE_TEST[1]).to(model.device)\n    onnx_model = export_onnx_model(model, inputs)\n\n    model_simp, check = simplify(onnx_model)\n\n    model_simp = remove_initializer_from_input(model_simp)\n\n    assert check, \"Simplified ONNX model could not be validated\"\n\n    PathManager.mkdirs(args.output)\n\n    save_path = os.path.join(args.output, args.name+'.onnx')\n    onnx.save_model(model_simp, save_path)\n    logger.info(\"ONNX model file has already saved to {}!\".format(save_path))\n"
  },
  {
    "path": "fast_reid/tools/deploy/onnx_inference.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport argparse\nimport glob\nimport os\n\nimport cv2\nimport numpy as np\nimport onnxruntime\nimport tqdm\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(description=\"onnx model inference\")\n\n    parser.add_argument(\n        \"--model-path\",\n        default=\"onnx_model/baseline.onnx\",\n        help=\"onnx model path\"\n    )\n    parser.add_argument(\n        \"--input\",\n        nargs=\"+\",\n        help=\"A list of space separated input images; \"\n             \"or a single glob pattern such as 'directory/*.jpg'\",\n    )\n    parser.add_argument(\n        \"--output\",\n        default='onnx_output',\n        help='path to save converted caffe model'\n    )\n    parser.add_argument(\n        \"--height\",\n        type=int,\n        default=256,\n        help=\"height of image\"\n    )\n    parser.add_argument(\n        \"--width\",\n        type=int,\n        default=128,\n        help=\"width of image\"\n    )\n    return parser\n\n\ndef preprocess(image_path, image_height, image_width):\n    original_image = cv2.imread(image_path)\n    # the model expects RGB inputs\n    original_image = original_image[:, :, ::-1]\n\n    # Apply pre-processing to image.\n    img = cv2.resize(original_image, (image_width, image_height), interpolation=cv2.INTER_CUBIC)\n    img = img.astype(\"float32\").transpose(2, 0, 1)[np.newaxis]  # (1, 3, h, w)\n    return img\n\n\ndef normalize(nparray, order=2, axis=-1):\n    \"\"\"Normalize a N-D numpy array along the specified axis.\"\"\"\n    norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)\n    return nparray / (norm + np.finfo(np.float32).eps)\n\n\nif __name__ == \"__main__\":\n    args = get_parser().parse_args()\n\n    ort_sess = onnxruntime.InferenceSession(args.model_path)\n\n    input_name = ort_sess.get_inputs()[0].name\n\n    if not os.path.exists(args.output): os.makedirs(args.output)\n\n    if args.input:\n        if os.path.isdir(args.input[0]):\n            args.input = glob.glob(os.path.expanduser(args.input[0]))\n            assert args.input, \"The input path(s) was not found\"\n        for path in tqdm.tqdm(args.input):\n            image = preprocess(path, args.height, args.width)\n            feat = ort_sess.run(None, {input_name: image})[0]\n            feat = normalize(feat, axis=1)\n            np.save(os.path.join(args.output, path.replace('.jpg', '.npy').split('/')[-1]), feat)\n"
  },
  {
    "path": "fast_reid/tools/deploy/pytorch_to_caffe.py",
    "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport torch\nimport torch.nn as nn\nimport traceback\nfrom Caffe import caffe_net\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom Caffe import layer_param\nfrom torch.nn.modules.utils import _pair\nimport numpy as np\nimport math\nfrom torch.nn.modules.utils import _list_with_default\n\n\"\"\"\nHow to support a new layer type:\n layer_name=log.add_layer(layer_type_name)\n top_blobs=log.add_blobs(<output of that layer>)\n layer=caffe_net.Layer_param(xxx)\n <set layer parameters>\n [<layer.add_data(*datas)>]\n log.cnet.add_layer(layer)\n\nPlease MUTE the inplace operations to avoid not find in graph\n\"\"\"\n\n\n# TODO: support the inplace output of the layers\n\nclass Blob_LOG():\n    def __init__(self):\n        self.data = {}\n\n    def __setitem__(self, key, value):\n        self.data[key] = value\n\n    def __getitem__(self, key):\n        return self.data[key]\n\n    def __len__(self):\n        return len(self.data)\n\n\nNET_INITTED = False\n\n\n# 转换原理解析：通过记录\nclass TransLog(object):\n    def __init__(self):\n        \"\"\"\n        doing init() with inputs Variable before using it\n        \"\"\"\n        self.layers = {}\n        self.detail_layers = {}\n        self.detail_blobs = {}\n        self._blobs = Blob_LOG()\n        self._blobs_data = []\n        self.cnet = caffe_net.Caffemodel('')\n        self.debug = True\n\n    def init(self, inputs):\n        \"\"\"\n        :param inputs: is a list of input variables\n        \"\"\"\n        self.add_blobs(inputs)\n\n    def add_layer(self, name='layer'):\n        if name in self.layers:\n            return self.layers[name]\n        if name not in self.detail_layers.keys():\n            self.detail_layers[name] = 0\n        self.detail_layers[name] += 1\n        name = '{}{}'.format(name, self.detail_layers[name])\n        self.layers[name] = name\n        if self.debug:\n            print(\"{} was added to layers\".format(self.layers[name]))\n        return self.layers[name]\n\n    def add_blobs(self, blobs, name='blob', with_num=True):\n        rst = []\n        for blob in blobs:\n            self._blobs_data.append(blob)  # to block the memory address be rewrited\n            blob_id = int(id(blob))\n            if name not in self.detail_blobs.keys():\n                self.detail_blobs[name] = 0\n            self.detail_blobs[name] += 1\n            if with_num:\n                rst.append('{}{}'.format(name, self.detail_blobs[name]))\n            else:\n                rst.append('{}'.format(name))\n            if self.debug:\n                print(\"{}:{} was added to blobs\".format(blob_id, rst[-1]))\n            print('Add blob {} : {}'.format(rst[-1].center(21), blob.size()))\n            self._blobs[blob_id] = rst[-1]\n        return rst\n\n    def blobs(self, var):\n        var = id(var)\n        if self.debug:\n            print(\"{}:{} getting\".format(var, self._blobs[var]))\n        try:\n            return self._blobs[var]\n        except:\n            print(\"WARNING: CANNOT FOUND blob {}\".format(var))\n            return None\n\n\nlog = TransLog()\n\nlayer_names = {}\n\n\ndef _conv2d(raw, input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):\n    x = raw(input, weight, bias, stride, padding, dilation, groups)\n    name = log.add_layer(name='conv')\n    log.add_blobs([x], name='conv_blob')\n    layer = caffe_net.Layer_param(name=name, type='Convolution',\n                                  bottom=[log.blobs(input)], top=[log.blobs(x)])\n    layer.conv_param(x.size()[1], weight.size()[2:], stride=_pair(stride),\n                     pad=_pair(padding), dilation=_pair(dilation), bias_term=bias is not None, groups=groups)\n    if bias is not None:\n        layer.add_data(weight.cpu().data.numpy(), bias.cpu().data.numpy())\n        #print('conv2d weight, bias: ',weight.cpu().data.numpy(), bias.cpu().data.numpy())\n\n    else:\n        layer.param.convolution_param.bias_term = False\n        layer.add_data(weight.cpu().data.numpy())\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _conv_transpose2d(raw, input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):\n    x = raw(input, weight, bias, stride, padding, output_padding, groups, dilation)\n    name = log.add_layer(name='conv_transpose')\n    log.add_blobs([x], name='conv_transpose_blob')\n    layer = caffe_net.Layer_param(name=name, type='Deconvolution',\n                                  bottom=[log.blobs(input)], top=[log.blobs(x)])\n    layer.conv_param(x.size()[1], weight.size()[2:], stride=_pair(stride),\n                     pad=_pair(padding), dilation=_pair(dilation), bias_term=bias is not None)\n    if bias is not None:\n        layer.add_data(weight.cpu().data.numpy(), bias.cpu().data.numpy())\n    else:\n        layer.param.convolution_param.bias_term = False\n        layer.add_data(weight.cpu().data.numpy())\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _linear(raw, input, weight, bias=None):\n    x = raw(input, weight, bias)\n    layer_name = log.add_layer(name='fc')\n    top_blobs = log.add_blobs([x], name='fc_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='InnerProduct',\n                                  bottom=[log.blobs(input)], top=top_blobs)\n    layer.fc_param(x.size()[1], has_bias=bias is not None)\n    if bias is not None:\n        layer.add_data(weight.cpu().data.numpy(), bias.cpu().data.numpy())\n    else:\n        layer.add_data(weight.cpu().data.numpy())\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _split(raw, tensor, split_size, dim=0):\n    # split in pytorch is slice in caffe\n    x = raw(tensor, split_size, dim)\n    layer_name = log.add_layer('split')\n    top_blobs = log.add_blobs(x, name='split_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='Slice',\n                                  bottom=[log.blobs(tensor)], top=top_blobs)\n    slice_num = int(np.floor(tensor.size()[dim] / split_size))\n    slice_param = caffe_net.pb.SliceParameter(axis=dim, slice_point=[split_size * i for i in range(1, slice_num)])\n    layer.param.slice_param.CopyFrom(slice_param)\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _pool(type, raw, input, x, kernel_size, stride, padding, ceil_mode):\n    # TODO dilation,ceil_mode,return indices\n    layer_name = log.add_layer(name='{}_pool'.format(type))\n    top_blobs = log.add_blobs([x], name='{}_pool_blob'.format(type))\n    layer = caffe_net.Layer_param(name=layer_name, type='Pooling', bottom=[log.blobs(input)], top=top_blobs)\n\n    # TODO w,h different kernel, stride and padding\n    # processing ceil mode\n    layer.pool_param(kernel_size=kernel_size, stride=kernel_size if stride is None else stride,\n                     pad=padding, type=type.upper())\n    log.cnet.add_layer(layer)\n    if ceil_mode == False and stride is not None:\n        oheight = (input.size()[2] - _pair(kernel_size)[0] + 2 * _pair(padding)[0]) % (_pair(stride)[0])\n        owidth = (input.size()[3] - _pair(kernel_size)[1] + 2 * _pair(padding)[1]) % (_pair(stride)[1])\n        if oheight != 0 or owidth != 0:\n            caffe_out = raw(input, kernel_size, stride, padding, ceil_mode=False)\n            print(\"WARNING: the output shape miss match at {}: \"\n\n                  \"input {} output---Pytorch:{}---Caffe:{}\\n\"\n                  \"This is caused by the different implementation that ceil mode in caffe and the floor mode in pytorch.\\n\"\n                  \"You can add the clip layer in caffe prototxt manually if shape mismatch error is caused in caffe. \".format(\n                layer_name, input.size(), x.size(), caffe_out.size()))\n\n\ndef _max_pool2d(raw, input, kernel_size, stride=None, padding=0, dilation=1,\n                ceil_mode=False, return_indices=False):\n    x = raw(input, kernel_size, stride, padding, dilation, ceil_mode, return_indices)\n    _pool('max', raw, input, x, kernel_size, stride, padding, ceil_mode)\n    return x\n\n\ndef _avg_pool2d(raw, input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True):\n    x = raw(input, kernel_size, stride, padding, ceil_mode, count_include_pad)\n    _pool('ave', raw, input, x, kernel_size, stride, padding, ceil_mode)\n    return x\n\n\ndef _max(raw, *args):\n    x = raw(*args)\n    if len(args) == 1:\n        # TODO max in one tensor\n        assert NotImplementedError\n    else:\n        bottom_blobs = []\n        for arg in args:\n            bottom_blobs.append(log.blobs(arg))\n        layer_name = log.add_layer(name='max')\n        top_blobs = log.add_blobs([x], name='max_blob')\n        layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',\n                                      bottom=bottom_blobs, top=top_blobs)\n        layer.param.eltwise_param.operation = 2\n        log.cnet.add_layer(layer)\n    return x\n\n\ndef _cat(raw, inputs, dimension=0):\n    x = raw(inputs, dimension)\n    bottom_blobs = []\n    for input in inputs:\n        bottom_blobs.append(log.blobs(input))\n    layer_name = log.add_layer(name='cat')\n    top_blobs = log.add_blobs([x], name='cat_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='Concat',\n                                  bottom=bottom_blobs, top=top_blobs)\n    layer.param.concat_param.axis = dimension\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _dropout(raw, input, p=0.5, training=False, inplace=False):\n    x = raw(input, p, training, inplace)\n    bottom_blobs = [log.blobs(input)]\n    layer_name = log.add_layer(name='dropout')\n    top_blobs = log.add_blobs([x], name=bottom_blobs[0], with_num=False)\n    layer = caffe_net.Layer_param(name=layer_name, type='Dropout',\n                                  bottom=bottom_blobs, top=top_blobs)\n    layer.param.dropout_param.dropout_ratio = p\n    layer.param.include.extend([caffe_net.pb.NetStateRule(phase=0)])  # 1 for test, 0 for train\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _threshold(raw, input, threshold, value, inplace=False):\n    # for threshold or relu\n    if threshold == 0 and value == 0:\n        x = raw(input, threshold, value, inplace)\n        bottom_blobs = [log.blobs(input)]\n        name = log.add_layer(name='relu')\n        log.add_blobs([x], name='relu_blob')\n        layer = caffe_net.Layer_param(name=name, type='ReLU',\n                                      bottom=bottom_blobs, top=[log.blobs(x)])\n        log.cnet.add_layer(layer)\n        return x\n    if value != 0:\n        raise NotImplemented(\"value !=0 not implemented in caffe\")\n    x = raw(input, input, threshold, value, inplace)\n    bottom_blobs = [log.blobs(input)]\n    layer_name = log.add_layer(name='threshold')\n    top_blobs = log.add_blobs([x], name='threshold_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='Threshold',\n                                  bottom=bottom_blobs, top=top_blobs)\n    layer.param.threshold_param.threshold = threshold\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _relu(raw, input, inplace=False):\n    # for threshold or prelu\n    x = raw(input, False)\n    name = log.add_layer(name='relu')\n    log.add_blobs([x], name='relu_blob')\n    layer = caffe_net.Layer_param(name=name, type='ReLU',\n                                  bottom=[log.blobs(input)], top=[log.blobs(x)])\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _prelu(raw, input, weight):\n    # for threshold or prelu\n    x = raw(input, weight)\n    bottom_blobs = [log.blobs(input)]\n    name = log.add_layer(name='prelu')\n    log.add_blobs([x], name='prelu_blob')\n    layer = caffe_net.Layer_param(name=name, type='PReLU',\n                                  bottom=bottom_blobs, top=[log.blobs(x)])\n    if weight.size()[0] == 1:\n        layer.param.prelu_param.channel_shared = True\n        layer.add_data(weight.cpu().data.numpy()[0])\n    else:\n        layer.add_data(weight.cpu().data.numpy())\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _leaky_relu(raw, input, negative_slope=0.01, inplace=False):\n    x = raw(input, negative_slope)\n    name = log.add_layer(name='leaky_relu')\n    log.add_blobs([x], name='leaky_relu_blob')\n    layer = caffe_net.Layer_param(name=name, type='ReLU',\n                                  bottom=[log.blobs(input)], top=[log.blobs(x)])\n    layer.param.relu_param.negative_slope = negative_slope\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _tanh(raw, input):\n    # for tanh activation\n    x = raw(input)\n    name = log.add_layer(name='tanh')\n    log.add_blobs([x], name='tanh_blob')\n    layer = caffe_net.Layer_param(name=name, type='TanH',\n                                  bottom=[log.blobs(input)], top=[log.blobs(x)])\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _softmax(raw, input, dim=None, _stacklevel=3):\n    # for F.softmax\n    x = raw(input, dim=dim)\n    if dim is None:\n        dim = F._get_softmax_dim('softmax', input.dim(), _stacklevel)\n    bottom_blobs = [log.blobs(input)]\n    name = log.add_layer(name='softmax')\n    log.add_blobs([x], name='softmax_blob')\n    layer = caffe_net.Layer_param(name=name, type='Softmax',\n                                  bottom=bottom_blobs, top=[log.blobs(x)])\n    layer.param.softmax_param.axis = dim\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _sigmoid(raw, input):\n    # for tanh activation\n    x = raw(input)\n    name = log.add_layer(name='Sigmoid')\n    log.add_blobs([x], name='Sigmoid_blob')\n    layer = caffe_net.Layer_param(name=name, type='Sigmoid',\n                                  bottom=[log.blobs(input)], top=[log.blobs(x)])\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _batch_norm(raw, input, running_mean, running_var, weight=None, bias=None,\n                training=False, momentum=0.1, eps=1e-5):\n    # because the runing_mean and runing_var will be changed after the _batch_norm operation, we first save the parameters\n\n    x = raw(input, running_mean, running_var, weight, bias,\n            training, momentum, eps)\n    bottom_blobs = [log.blobs(input)]\n    layer_name1 = log.add_layer(name='batch_norm')\n    top_blobs = log.add_blobs([x], name='batch_norm_blob')\n    layer1 = caffe_net.Layer_param(name=layer_name1, type='BatchNorm',\n                                   bottom=bottom_blobs, top=top_blobs)\n    if running_mean is None or running_var is None:\n        # not use global_stats, normalization is performed over the current mini-batch\n        layer1.batch_norm_param(use_global_stats=0, eps=eps)\n    else:\n        layer1.batch_norm_param(use_global_stats=1, eps=eps)\n        running_mean_clone = running_mean.clone()\n        running_var_clone = running_var.clone()\n        layer1.add_data(running_mean_clone.cpu().numpy(), running_var_clone.cpu().numpy(), np.array([1.0]))\n        #print('running_mean: ',running_mean_clone.cpu().numpy())\n        #print('running_var: ',running_var_clone.cpu().numpy())\n    log.cnet.add_layer(layer1)\n    if weight is not None and bias is not None:\n        layer_name2 = log.add_layer(name='bn_scale')\n        layer2 = caffe_net.Layer_param(name=layer_name2, type='Scale',\n                                       bottom=top_blobs, top=top_blobs)\n        layer2.param.scale_param.bias_term = True\n        layer2.add_data(weight.cpu().data.numpy(), bias.cpu().data.numpy())\n        log.cnet.add_layer(layer2)\n        #print('scale weight: ', weight.cpu().data.numpy())\n        #print('scale bias: ', bias.cpu().data.numpy())\n    return x\n\n\ndef _instance_norm(raw, input, running_mean=None, running_var=None, weight=None,\n                   bias=None, use_input_stats=True, momentum=0.1, eps=1e-5):\n    # TODO: the batch size!=1 view operations\n    print(\"WARNING: The Instance Normalization transfers to Caffe using BatchNorm, so the batch size should be 1\")\n    if running_var is not None or weight is not None:\n        # TODO: the affine=True or track_running_stats=True case\n        raise NotImplementedError(\"not implement the affine=True or track_running_stats=True case InstanceNorm\")\n    x = torch.batch_norm(\n        input, weight, bias, running_mean, running_var,\n        use_input_stats, momentum, eps, torch.backends.cudnn.enabled)\n    bottom_blobs = [log.blobs(input)]\n    layer_name1 = log.add_layer(name='instance_norm')\n    top_blobs = log.add_blobs([x], name='instance_norm_blob')\n    layer1 = caffe_net.Layer_param(name=layer_name1, type='BatchNorm',\n                                   bottom=bottom_blobs, top=top_blobs)\n    if running_mean is None or running_var is None:\n        # not use global_stats, normalization is performed over the current mini-batch\n        layer1.batch_norm_param(use_global_stats=0, eps=eps)\n        running_mean = torch.zeros(input.size()[1])\n        running_var = torch.ones(input.size()[1])\n    else:\n        layer1.batch_norm_param(use_global_stats=1, eps=eps)\n    running_mean_clone = running_mean.clone()\n    running_var_clone = running_var.clone()\n    layer1.add_data(running_mean_clone.cpu().numpy(), running_var_clone.cpu().numpy(), np.array([1.0]))\n    log.cnet.add_layer(layer1)\n    if weight is not None and bias is not None:\n        layer_name2 = log.add_layer(name='bn_scale')\n        layer2 = caffe_net.Layer_param(name=layer_name2, type='Scale',\n                                       bottom=top_blobs, top=top_blobs)\n        layer2.param.scale_param.bias_term = True\n        layer2.add_data(weight.cpu().data.numpy(), bias.cpu().data.numpy())\n        log.cnet.add_layer(layer2)\n    return x\n\n\n# upsample layer\ndef _interpolate(raw, input, size=None, scale_factor=None, mode='nearest', align_corners=None):\n    # 定义的参数包括 scale,即输出与输入的尺寸比例,如 2;scale_h、scale_w,\n    # 同 scale,分别为 h、w 方向上的尺寸比例;pad_out_h、pad_out_w,仅在 scale 为 2 时\n    # 有用,对输出进行额外 padding 在 h、w 方向上的数值;upsample_h、upsample_w,输\n    # 出图像尺寸的数值。在 Upsample 的相关代码中,推荐仅仅使用 upsample_h、\n    # upsample_w 准确定义 Upsample 层的输出尺寸,其他所有的参数都不推荐继续使用。\n    '''\n    if mode == 'bilinear':      \n        x = raw(input, size, scale_factor, mode)\n        name = log.add_layer(name='conv_transpose')\n        log.add_blobs([x], name='conv_transpose_blob')\n        layer = caffe_net.Layer_param(name=name, type='Deconvolution',\n                                  bottom=[log.blobs(input)], top=[log.blobs(x)])\n        print('Deconv: ', name)\n        print(input.shape)\n        print(x.size())\n        print(size)\n        factor = float(size[0]) / input.shape[2]\n        C = x.size()[1]\n        print(factor,C) \n        kernel_size = int(2 * factor - factor % 2)\n        stride = int(factor)\n        num_output = C\n        group = C\n        pad = math.ceil((factor-1) / 2.)\n        print('kernel_size, stride, num_output, group, pad')\n        print(kernel_size, stride, num_output, group, pad)\n        layer.conv_param(num_output, kernel_size, stride=stride,\n                     pad=pad, weight_filler_type='bilinear', bias_term=False, groups=group)\n\n        layer.param.convolution_param.bias_term = False\n        log.cnet.add_layer(layer)\n        return x\n    '''\n    # transfer bilinear align_corners=True to caffe-interp\n    if mode == \"bilinear\" and align_corners == True:\n        x = raw(input, size, scale_factor, mode)\n        name = log.add_layer(name='interp')\n        log.add_blobs([x], name='interp_blob')\n        layer = caffe_net.Layer_param(name=name, type='Interp',\n                                  bottom=[log.blobs(input)], top=[log.blobs(x)])\n        layer.interp_param(size=size, scale_factor=scale_factor)\n        log.cnet.add_layer(layer)\n        return x\n\n    # for nearest _interpolate\n    if mode != \"nearest\" or align_corners != None:\n        raise NotImplementedError(\"not implement F.interpolate totoaly\")\n    x = raw(input, size, scale_factor, mode)\n    layer_name = log.add_layer(name='upsample')\n    top_blobs = log.add_blobs([x], name='upsample_blob'.format(type))\n    layer = caffe_net.Layer_param(name=layer_name, type='Upsample',\n                                  bottom=[log.blobs(input)], top=top_blobs)\n    #layer.upsample_param(size=(input.size(2), input.size(3)), scale_factor=scale_factor)\n    #layer.upsample_param(size=size, scale_factor=scale_factor)\n    layer.upsample_param(size=None, scale_factor=size[0])\n    \n    log.cnet.add_layer(layer)\n    return x\n\n\n# ----- for Variable operations --------\n\ndef _view(input, *args):\n    x = raw_view(input, *args)\n    if not NET_INITTED:\n        return x\n    layer_name = log.add_layer(name='view')\n    top_blobs = log.add_blobs([x], name='view_blob')\n\n    # print('*'*60)\n    # print('input={}'.format(input))\n    # print('layer_name={}'.format(layer_name))\n    # print('top_blobs={}'.format(top_blobs))\n\n    layer = caffe_net.Layer_param(name=layer_name, type='Reshape', bottom=[log.blobs(input)], top=top_blobs)\n    # TODO: reshpae added to nn_tools layer\n    dims = list(args)\n    dims[0] = 0  # the first dim should be batch_size\n    layer.param.reshape_param.shape.CopyFrom(caffe_net.pb.BlobShape(dim=dims))\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _mean(input, *args, **kwargs):\n    x = raw_mean(input, *args, **kwargs)\n    if not NET_INITTED:\n        return x\n    layer_name = log.add_layer(name='mean')\n    top_blobs = log.add_blobs([x], name='mean_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='Reduction',\n                                  bottom=[log.blobs(input)], top=top_blobs)\n    if len(args) == 1:\n        dim = args[0]\n    elif 'dim' in kwargs:\n        dim = kwargs['dim']\n    else:\n        raise NotImplementedError('mean operation must specify a dim')\n    layer.param.reduction_param.operation = 4\n    layer.param.reduction_param.axis = dim\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _add(input, *args):\n    # check if add a const value\n    if isinstance(args[0], int):\n        print('value: ',args[0])\n        x = raw__add__(input, *args)\n        #x = raw(input)\n        layer_name = log.add_layer(name='scale')\n        log.add_blobs([x], name='Scale_blob')\n        layer = caffe_net.Layer_param(name=layer_name, type='Scale',\n                                       bottom=[log.blobs(input)], top=[log.blobs(x)])\n        dim = x.shape[1]\n        layer.param.scale_param.bias_term = True\n        weight = np.ones(dim, dtype=np.float32)\n        bias = args[0] * np.ones(dim, dtype=np.float32)\n        layer.add_data(weight, bias)\n        log.cnet.add_layer(layer)\n        return x\n    # otherwise add a tensor\n    x = raw__add__(input, *args)\n    if not NET_INITTED:\n        return x\n    layer_name = log.add_layer(name='add')\n    top_blobs = log.add_blobs([x], name='add_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',\n                                  bottom=[log.blobs(input), log.blobs(args[0])], top=top_blobs)\n    layer.param.eltwise_param.operation = 1  # sum is 1\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _iadd(input, *args):\n    x = raw__iadd__(input, *args)\n    if not NET_INITTED:\n        return x\n    x = x.clone()\n    layer_name = log.add_layer(name='add')\n    top_blobs = log.add_blobs([x], name='add_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',\n                                  bottom=[log.blobs(input), log.blobs(args[0])], top=top_blobs)\n    layer.param.eltwise_param.operation = 1  # sum is 1\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _sub(input, *args):\n    x = raw__sub__(input, *args)\n    if not NET_INITTED:\n        return x\n    layer_name = log.add_layer(name='sub')\n    top_blobs = log.add_blobs([x], name='sub_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',\n                                  bottom=[log.blobs(input), log.blobs(args[0])], top=top_blobs)\n    layer.param.eltwise_param.operation = 1  # sum is 1\n    layer.param.eltwise_param.coeff.extend([1., -1.])\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _isub(input, *args):\n    x = raw__isub__(input, *args)\n    if not NET_INITTED:\n        return x\n    x = x.clone()\n    layer_name = log.add_layer(name='sub')\n    top_blobs = log.add_blobs([x], name='sub_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',\n                                  bottom=[log.blobs(input), log.blobs(args[0])], top=top_blobs)\n    layer.param.eltwise_param.operation = 1  # sum is 1\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _mul(input, *args):\n    x = raw__sub__(input, *args)\n    if not NET_INITTED:\n        return x\n    layer_name = log.add_layer(name='mul')\n    top_blobs = log.add_blobs([x], name='mul_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',\n                                  bottom=[log.blobs(input), log.blobs(args[0])], top=top_blobs)\n    layer.param.eltwise_param.operation = 0  # product is 1\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _imul(input, *args):\n    x = raw__isub__(input, *args)\n    if not NET_INITTED:\n        return x\n    x = x.clone()\n    layer_name = log.add_layer(name='mul')\n    top_blobs = log.add_blobs([x], name='mul_blob')\n    layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',\n                                  bottom=[log.blobs(input), log.blobs(args[0])], top=top_blobs)\n    layer.param.eltwise_param.operation = 0  # product is 1\n    layer.param.eltwise_param.coeff.extend([1., -1.])\n    log.cnet.add_layer(layer)\n    return x\n\n\ndef _adaptive_avg_pool2d(raw, input, output_size):\n    _output_size = _list_with_default(output_size, input.size())\n    x = raw(input, _output_size)\n    _pool('ave', raw, input, x, input.shape[2], input.shape[2], 0, False)\n    return x\n\n\n# 核心组件，通过该类，实现对torch的function中的operators的输入，输出以及参数的读取\nclass Rp(object):\n    def __init__(self, raw, replace, **kwargs):\n        # replace the raw function to replace function\n        self.obj = replace\n        self.raw = raw\n\n    def __call__(self, *args, **kwargs):\n        if not NET_INITTED:\n            return self.raw(*args, **kwargs)\n        for stack in traceback.walk_stack(None):\n            if 'self' in stack[0].f_locals:\n                layer = stack[0].f_locals['self']\n                if layer in layer_names:\n                    log.pytorch_layer_name = layer_names[layer]\n                    print(layer_names[layer])\n                    break\n        out = self.obj(self.raw, *args, **kwargs)\n        # if isinstance(out,Variable):\n        #     out=[out]\n        return out\n\n\nF.conv2d = Rp(F.conv2d, _conv2d)\nF.linear = Rp(F.linear, _linear)\nF.relu = Rp(F.relu, _relu)\n\nF.leaky_relu = Rp(F.leaky_relu, _leaky_relu)\nF.max_pool2d = Rp(F.max_pool2d, _max_pool2d)\nF.avg_pool2d = Rp(F.avg_pool2d, _avg_pool2d)\nF.dropout = Rp(F.dropout, _dropout)\nF.threshold = Rp(F.threshold, _threshold)\nF.prelu = Rp(F.prelu, _prelu)\nF.batch_norm = Rp(F.batch_norm, _batch_norm)\nF.instance_norm = Rp(F.instance_norm, _instance_norm)\nF.softmax = Rp(F.softmax, _softmax)\nF.conv_transpose2d = Rp(F.conv_transpose2d, _conv_transpose2d)\nF.interpolate = Rp(F.interpolate, _interpolate)\nF.adaptive_avg_pool2d = Rp(F.adaptive_avg_pool2d, _adaptive_avg_pool2d)\n\ntorch.split = Rp(torch.split, _split)\ntorch.max = Rp(torch.max, _max)\ntorch.cat = Rp(torch.cat, _cat)\ntorch.sigmoid = Rp(torch.sigmoid, _sigmoid)\n\n# TODO: other types of the view function\ntry:\n    raw_view = Variable.view\n    Variable.view = _view\n    raw_mean = Variable.mean\n    Variable.mean = _mean\n    raw__add__ = Variable.__add__\n    Variable.__add__ = _add\n    raw__iadd__ = Variable.__iadd__\n    Variable.__iadd__ = _iadd\n    raw__sub__ = Variable.__sub__\n    Variable.__sub__ = _sub\n    raw__isub__ = Variable.__isub__\n    Variable.__isub__ = _isub\n    raw__mul__ = Variable.__mul__\n    Variable.__mul__ = _mul\n    raw__imul__ = Variable.__imul__\n    Variable.__imul__ = _imul\nexcept:\n    # for new version 0.4.0 and later version\n    for t in [torch.Tensor]:\n        raw_view = t.view\n        t.view = _view\n        raw_mean = t.mean\n        t.mean = _mean\n        raw__add__ = t.__add__\n        t.__add__ = _add\n        raw__iadd__ = t.__iadd__\n        t.__iadd__ = _iadd\n        raw__sub__ = t.__sub__\n        t.__sub__ = _sub\n        raw__isub__ = t.__isub__\n        t.__isub__ = _isub\n        raw__mul__ = t.__mul__\n        t.__mul__ = _mul\n        raw__imul__ = t.__imul__\n        t.__imul__ = _imul\n\n\ndef trans_net(net, input_var, name='TransferedPytorchModel'):\n    print('Starting Transform, This will take a while')\n    log.init([input_var])\n    log.cnet.net.name = name\n    log.cnet.net.input.extend([log.blobs(input_var)])\n    log.cnet.net.input_dim.extend(input_var.size())\n    global NET_INITTED\n    NET_INITTED = True\n    for name, layer in net.named_modules():\n        layer_names[layer] = name\n    print(\"torch ops name:\", layer_names)\n    out = net.forward(input_var)\n    print('Transform Completed')\n\n\ndef save_prototxt(save_name):\n    log.cnet.save_prototxt(save_name)\n\n\ndef save_caffemodel(save_name):\n    log.cnet.save(save_name)\n"
  },
  {
    "path": "fast_reid/tools/deploy/trt_calibrator.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\nCreate custom calibrator, use to calibrate int8 TensorRT model.\nNeed to override some methods of trt.IInt8EntropyCalibrator2, such as get_batch_size, get_batch,\nread_calibration_cache, write_calibration_cache.\n\"\"\"\n\n# based on:\n# https://github.com/qq995431104/Pytorch2TensorRT/blob/master/myCalibrator.py\n\nimport os\nimport sys\n\nimport tensorrt as trt\nimport pycuda.driver as cuda\nimport pycuda.autoinit\n\nimport numpy as np\nimport torchvision.transforms as T\n\nsys.path.append('../..')\n\nfrom fast_reid.fastreid.data.build import _root\nfrom fast_reid.fastreid.data.data_utils import read_image\nfrom fast_reid.fastreid.data.datasets import DATASET_REGISTRY\nimport logging\n\nfrom fast_reid.fastreid.data.transforms import ToTensor\n\n\nlogger = logging.getLogger('trt_export.calibrator')\n\n\nclass FeatEntropyCalibrator(trt.IInt8EntropyCalibrator2):\n\n    def __init__(self, args):\n        trt.IInt8EntropyCalibrator2.__init__(self)\n\n        self.cache_file = 'reid_feat.cache'\n\n        self.batch_size = args.batch_size\n        self.channel = args.channel\n        self.height = args.height\n        self.width = args.width\n        self.transform = T.Compose([\n            T.Resize((self.height, self.width), interpolation=3),  # [h,w]\n            ToTensor(),\n        ])\n\n        dataset = DATASET_REGISTRY.get(args.calib_data)(root=_root)\n        self._data_items = dataset.train + dataset.query + dataset.gallery\n        np.random.shuffle(self._data_items)\n        self.imgs = [item[0] for item in self._data_items]\n\n        self.batch_idx = 0\n        self.max_batch_idx = len(self.imgs) // self.batch_size\n\n        self.data_size = self.batch_size * self.channel * self.height * self.width * trt.float32.itemsize\n        self.device_input = cuda.mem_alloc(self.data_size)\n\n    def next_batch(self):\n        if self.batch_idx < self.max_batch_idx:\n            batch_files = self.imgs[self.batch_idx * self.batch_size:(self.batch_idx + 1) * self.batch_size]\n            batch_imgs = np.zeros((self.batch_size, self.channel, self.height, self.width),\n                                  dtype=np.float32)\n            for i, f in enumerate(batch_files):\n                img = read_image(f)\n                img = self.transform(img).numpy()\n                assert (img.nbytes == self.data_size // self.batch_size), 'not valid img!' + f\n                batch_imgs[i] = img\n            self.batch_idx += 1\n            logger.info(\"batch:[{}/{}]\".format(self.batch_idx, self.max_batch_idx))\n            return np.ascontiguousarray(batch_imgs)\n        else:\n            return np.array([])\n\n    def get_batch_size(self):\n        return self.batch_size\n\n    def get_batch(self, names, p_str=None):\n        try:\n            batch_imgs = self.next_batch()\n            batch_imgs = batch_imgs.ravel()\n            if batch_imgs.size == 0 or batch_imgs.size != self.batch_size * self.channel * self.height * self.width:\n                return None\n            cuda.memcpy_htod(self.device_input, batch_imgs.astype(np.float32))\n            return [int(self.device_input)]\n        except:\n            return None\n\n    def read_calibration_cache(self):\n        # If there is a cache, use it instead of calibrating again. Otherwise, implicitly return None.\n        if os.path.exists(self.cache_file):\n            with open(self.cache_file, \"rb\") as f:\n                return f.read()\n\n    def write_calibration_cache(self, cache):\n        with open(self.cache_file, \"wb\") as f:\n            f.write(cache)\n"
  },
  {
    "path": "fast_reid/tools/deploy/trt_export.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport argparse\nimport os\nimport sys\n\nimport tensorrt as trt\n\nfrom trt_calibrator import FeatEntropyCalibrator\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.utils.logger import setup_logger, PathManager\n\nlogger = setup_logger(name=\"trt_export\")\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(description=\"Convert ONNX to TRT model\")\n\n    parser.add_argument(\n        '--name',\n        default='baseline',\n        help=\"name for converted model\"\n    )\n    parser.add_argument(\n        '--output',\n        default='outputs/trt_model',\n        help=\"path to save converted trt model\"\n    )\n    parser.add_argument(\n        '--mode',\n        default='fp32',\n        help=\"which mode is used in tensorRT engine, mode can be ['fp32', 'fp16' 'int8']\"\n    )\n    parser.add_argument(\n        '--batch-size',\n        default=1,\n        type=int,\n        help=\"the maximum batch size of trt module\"\n    )\n    parser.add_argument(\n        '--height',\n        default=256,\n        type=int,\n        help=\"input image height\"\n    )\n    parser.add_argument(\n        '--width',\n        default=128,\n        type=int,\n        help=\"input image width\"\n    )\n    parser.add_argument(\n        '--channel',\n        default=3,\n        type=int,\n        help=\"input image channel\"\n    )\n    parser.add_argument(\n        '--calib-data',\n        default='Market1501',\n        help=\"int8 calibrator dataset name\"\n    )\n    parser.add_argument(\n        \"--onnx-model\",\n        default='outputs/onnx_model/baseline.onnx',\n        help='path to onnx model'\n    )\n    return parser\n\n\ndef onnx2trt(\n        onnx_file_path,\n        save_path,\n        mode,\n        log_level='ERROR',\n        max_workspace_size=1,\n        strict_type_constraints=False,\n        int8_calibrator=None,\n):\n    \"\"\"build TensorRT model from onnx model.\n    Args:\n        onnx_file_path (string or io object): onnx model name\n        save_path (string): tensortRT serialization save path\n        mode (string): Whether or not FP16 or Int8 kernels are permitted during engine build.\n        log_level (string, default is ERROR): tensorrt logger level, now\n            INTERNAL_ERROR, ERROR, WARNING, INFO, VERBOSE are support.\n        max_workspace_size (int, default is 1): The maximum GPU temporary memory which the ICudaEngine can use at\n            execution time. default is 1GB.\n        strict_type_constraints (bool, default is False): When strict type constraints is set, TensorRT will choose\n            the type constraints that conforms to type constraints. If the flag is not enabled higher precision\n            implementation may be chosen if it results in higher performance.\n        int8_calibrator (volksdep.calibrators.base.BaseCalibrator, default is None): calibrator for int8 mode,\n            if None, default calibrator will be used as calibration data.\n    \"\"\"\n    mode = mode.lower()\n    assert mode in ['fp32', 'fp16', 'int8'], \"mode should be in ['fp32', 'fp16', 'int8'], \" \\\n                                             \"but got {}\".format(mode)\n\n    trt_logger = trt.Logger(getattr(trt.Logger, log_level))\n    builder = trt.Builder(trt_logger)\n\n    logger.info(\"Loading ONNX file from path {}...\".format(onnx_file_path))\n    EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)\n    network = builder.create_network(EXPLICIT_BATCH)\n    parser = trt.OnnxParser(network, trt_logger)\n    if isinstance(onnx_file_path, str):\n        with open(onnx_file_path, 'rb') as f:\n            logger.info(\"Beginning ONNX file parsing\")\n            flag = parser.parse(f.read())\n    else:\n        flag = parser.parse(onnx_file_path.read())\n    if not flag:\n        for error in range(parser.num_errors):\n            logger.info(parser.get_error(error))\n\n    logger.info(\"Completed parsing of ONNX file.\")\n    # re-order output tensor\n    output_tensors = [network.get_output(i) for i in range(network.num_outputs)]\n    [network.unmark_output(tensor) for tensor in output_tensors]\n    for tensor in output_tensors:\n        identity_out_tensor = network.add_identity(tensor).get_output(0)\n        identity_out_tensor.name = 'identity_{}'.format(tensor.name)\n        network.mark_output(tensor=identity_out_tensor)\n\n    config = builder.create_builder_config()\n    config.max_workspace_size = max_workspace_size * (1 << 25)\n    if mode == 'fp16':\n        assert builder.platform_has_fast_fp16, \"not support fp16\"\n        builder.fp16_mode = True\n    if mode == 'int8':\n        assert builder.platform_has_fast_int8, \"not support int8\"\n        builder.int8_mode = True\n        builder.int8_calibrator = int8_calibrator\n\n    if strict_type_constraints:\n        config.set_flag(trt.BuilderFlag.STRICT_TYPES)\n\n    logger.info(\"Building an engine from file {}; this may take a while...\".format(onnx_file_path))\n    engine = builder.build_cuda_engine(network)\n    logger.info(\"Create engine successfully!\")\n\n    logger.info(\"Saving TRT engine file to path {}\".format(save_path))\n    with open(save_path, 'wb') as f:\n        f.write(engine.serialize())\n    logger.info(\"Engine file has already saved to {}!\".format(save_path))\n\n\nif __name__ == '__main__':\n    args = get_parser().parse_args()\n\n    onnx_file_path = args.onnx_model\n    engineFile = os.path.join(args.output, args.name + '.engine')\n\n    if args.mode.lower() == 'int8':\n        int8_calib = FeatEntropyCalibrator(args)\n    else:\n        int8_calib = None\n\n    PathManager.mkdirs(args.output)\n    onnx2trt(onnx_file_path, engineFile, args.mode, int8_calibrator=int8_calib)\n"
  },
  {
    "path": "fast_reid/tools/deploy/trt_inference.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\nimport argparse\nimport glob\nimport os\n\nimport cv2\nimport numpy as np\nimport pycuda.driver as cuda\nimport tensorrt as trt\nimport tqdm\n\nTRT_LOGGER = trt.Logger()\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(description=\"trt model inference\")\n\n    parser.add_argument(\n        \"--model-path\",\n        default=\"outputs/trt_model/baseline.engine\",\n        help=\"trt model path\"\n    )\n    parser.add_argument(\n        \"--input\",\n        nargs=\"+\",\n        help=\"A list of space separated input images; \"\n             \"or a single glob pattern such as 'directory/*.jpg'\",\n    )\n    parser.add_argument(\n        \"--output\",\n        default=\"trt_output\",\n        help=\"path to save trt model inference results\"\n    )\n    parser.add_argument(\n        '--batch-size',\n        default=1,\n        type=int,\n        help='the maximum batch size of trt module'\n    )\n    parser.add_argument(\n        \"--height\",\n        type=int,\n        default=256,\n        help=\"height of image\"\n    )\n    parser.add_argument(\n        \"--width\",\n        type=int,\n        default=128,\n        help=\"width of image\"\n    )\n    return parser\n\n\nclass HostDeviceMem(object):\n    \"\"\" Host and Device Memory Package \"\"\"\n\n    def __init__(self, host_mem, device_mem):\n        self.host = host_mem\n        self.device = device_mem\n\n    def __str__(self):\n        return \"Host:\\n\" + str(self.host) + \"\\nDevice:\\n\" + str(self.device)\n\n    def __repr__(self):\n        return self.__str__()\n\n\nclass TrtEngine:\n\n    def __init__(self, trt_file=None, gpu_idx=0, batch_size=1):\n        cuda.init()\n        self._batch_size = batch_size\n        self._device_ctx = cuda.Device(gpu_idx).make_context()\n        self._engine = self._load_engine(trt_file)\n        self._context = self._engine.create_execution_context()\n        self._input, self._output, self._bindings, self._stream = self._allocate_buffers(self._context)\n\n    def _load_engine(self, trt_file):\n        \"\"\"\n        Load tensorrt engine.\n        :param trt_file:    tensorrt file.\n        :return:\n            ICudaEngine\n        \"\"\"\n        with open(trt_file, \"rb\") as f, \\\n                trt.Runtime(TRT_LOGGER) as runtime:\n            engine = runtime.deserialize_cuda_engine(f.read())\n        return engine\n\n    def _allocate_buffers(self, context):\n        \"\"\"\n        Allocate device memory space for data.\n        :param context:\n        :return:\n        \"\"\"\n        inputs = []\n        outputs = []\n        bindings = []\n        stream = cuda.Stream()\n        for binding in self._engine:\n            size = trt.volume(self._engine.get_binding_shape(binding)) * self._engine.max_batch_size\n            dtype = trt.nptype(self._engine.get_binding_dtype(binding))\n            # Allocate host and device buffers\n            host_mem = cuda.pagelocked_empty(size, dtype)\n            device_mem = cuda.mem_alloc(host_mem.nbytes)\n            # Append the device buffer to device bindings.\n            bindings.append(int(device_mem))\n            # Append to the appropriate list.\n            if self._engine.binding_is_input(binding):\n                inputs.append(HostDeviceMem(host_mem, device_mem))\n            else:\n                outputs.append(HostDeviceMem(host_mem, device_mem))\n        return inputs, outputs, bindings, stream\n\n    def infer(self, data):\n        \"\"\"\n        Real inference process.\n        :param model:   Model objects\n        :param data:    Preprocessed data\n        :return:\n            output\n        \"\"\"\n        # Copy data to input memory buffer\n        [np.copyto(_inp.host, data.ravel()) for _inp in self._input]\n        # Push to device\n        self._device_ctx.push()\n        # Transfer input data to the GPU.\n        # cuda.memcpy_htod_async(self._input.device, self._input.host, self._stream)\n        [cuda.memcpy_htod_async(inp.device, inp.host, self._stream) for inp in self._input]\n        # Run inference.\n        self._context.execute_async_v2(bindings=self._bindings, stream_handle=self._stream.handle)\n        # Transfer predictions back from the GPU.\n        # cuda.memcpy_dtoh_async(self._output.host, self._output.device, self._stream)\n        [cuda.memcpy_dtoh_async(out.host, out.device, self._stream) for out in self._output]\n        # Synchronize the stream\n        self._stream.synchronize()\n        # Pop the device\n        self._device_ctx.pop()\n\n        return [out.host.reshape(self._batch_size, -1) for out in self._output[::-1]]\n\n    def inference_on_images(self, imgs, new_size=(256, 128)):\n        trt_inputs = []\n        for img in imgs:\n            input_ndarray = self.preprocess(img, *new_size)\n            trt_inputs.append(input_ndarray)\n        trt_inputs = np.vstack(trt_inputs)\n\n        valid_bsz = trt_inputs.shape[0]\n        if valid_bsz < self._batch_size:\n            trt_inputs = np.vstack([trt_inputs, np.zeros((self._batch_size - valid_bsz, 3, *new_size))])\n\n        result, = self.infer(trt_inputs)\n        result = result[:valid_bsz]\n        feat = self.postprocess(result, axis=1)\n        return feat\n\n    @classmethod\n    def preprocess(cls, img, img_height, img_width):\n        # Apply pre-processing to image.\n        resize_img = cv2.resize(img, (img_width, img_height), interpolation=cv2.INTER_CUBIC)\n        type_img = resize_img.astype(\"float32\").transpose(2, 0, 1)[np.newaxis]  # (1, 3, h, w)\n        return type_img\n\n    @classmethod\n    def postprocess(cls, nparray, order=2, axis=-1):\n        \"\"\"Normalize a N-D numpy array along the specified axis.\"\"\"\n        norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)\n        return nparray / (norm + np.finfo(np.float32).eps)\n\n    def __del__(self):\n        del self._input\n        del self._output\n        del self._stream\n        self._device_ctx.detach()  # release device context\n\n\nif __name__ == \"__main__\":\n    args = get_parser().parse_args()\n\n    trt = TrtEngine(args.model_path, batch_size=args.batch_size)\n\n    if not os.path.exists(args.output): os.makedirs(args.output)\n\n    if args.input:\n        if os.path.isdir(args.input[0]):\n            args.input = glob.glob(os.path.expanduser(args.input[0]))\n            assert args.input, \"The input path(s) was not found\"\n        inputs = []\n        for img_path in tqdm.tqdm(args.input):\n            img = cv2.imread(img_path)\n            # the model expects RGB inputs\n            cvt_img = img[:, :, ::-1]\n            feat = trt.inference_on_images([cvt_img])\n            np.save(os.path.join(args.output, os.path.basename(img_path).split('.')[0] + '.npy'), feat)\n"
  },
  {
    "path": "fast_reid/tools/plain_train_net.py",
    "content": "# encoding: utf-8\n\"\"\"\n@author:  xingyu liao\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport logging\nimport os\nimport sys\nfrom collections import OrderedDict\n\nimport torch\nfrom torch.nn.parallel import DistributedDataParallel\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.data import build_reid_test_loader, build_reid_train_loader\nfrom fast_reid.fastreid.evaluation.testing import flatten_results_dict\nfrom fast_reid.fastreid.engine import default_argument_parser, default_setup, launch\nfrom fast_reid.fastreid.modeling import build_model\nfrom fast_reid.fastreid.solver import build_lr_scheduler, build_optimizer\nfrom fast_reid.fastreid.evaluation import inference_on_dataset, print_csv_format, ReidEvaluator\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer, PeriodicCheckpointer\nfrom fast_reid.fastreid.utils import comm\nfrom fast_reid.fastreid.utils.events import (\n    CommonMetricPrinter,\n    EventStorage,\n    JSONWriter,\n    TensorboardXWriter\n)\n\nlogger = logging.getLogger(\"fastreid\")\n\n\ndef get_evaluator(cfg, dataset_name, output_dir=None):\n    data_loader, num_query = build_reid_test_loader(cfg, dataset_name=dataset_name)\n    return data_loader, ReidEvaluator(cfg, num_query, output_dir)\n\n\ndef do_test(cfg, model):\n    results = OrderedDict()\n    for idx, dataset_name in enumerate(cfg.DATASETS.TESTS):\n        logger.info(\"Prepare testing set\")\n        try:\n            data_loader, evaluator = get_evaluator(cfg, dataset_name)\n        except NotImplementedError:\n            logger.warn(\n                \"No evaluator found. implement its `build_evaluator` method.\"\n            )\n            results[dataset_name] = {}\n            continue\n        results_i = inference_on_dataset(model, data_loader, evaluator, flip_test=cfg.TEST.FLIP.ENABLED)\n        results[dataset_name] = results_i\n\n        if comm.is_main_process():\n            assert isinstance(\n                results, dict\n            ), \"Evaluator must return a dict on the main process. Got {} instead.\".format(\n                results\n            )\n            logger.info(\"Evaluation results for {} in csv format:\".format(dataset_name))\n            results_i['dataset'] = dataset_name\n            print_csv_format(results_i)\n\n    if len(results) == 1:\n        results = list(results.values())[0]\n\n    return results\n\n\ndef do_train(cfg, model, resume=False):\n    data_loader = build_reid_train_loader(cfg)\n    data_loader_iter = iter(data_loader)\n\n    model.train()\n    optimizer = build_optimizer(cfg, model)\n\n    iters_per_epoch = len(data_loader.dataset) // cfg.SOLVER.IMS_PER_BATCH\n    scheduler = build_lr_scheduler(cfg, optimizer, iters_per_epoch)\n\n    checkpointer = Checkpointer(\n        model,\n        cfg.OUTPUT_DIR,\n        save_to_disk=comm.is_main_process(),\n        optimizer=optimizer,\n        **scheduler\n    )\n\n    start_epoch = (\n            checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get(\"epoch\", -1) + 1\n    )\n    iteration = start_iter = start_epoch * iters_per_epoch\n\n    max_epoch = cfg.SOLVER.MAX_EPOCH\n    max_iter = max_epoch * iters_per_epoch\n    warmup_iters = cfg.SOLVER.WARMUP_ITERS\n    delay_epochs = cfg.SOLVER.DELAY_EPOCHS\n\n    periodic_checkpointer = PeriodicCheckpointer(checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_epoch)\n    if len(cfg.DATASETS.TESTS) == 1:\n        metric_name = \"metric\"\n    else:\n        metric_name = cfg.DATASETS.TESTS[0] + \"/metric\"\n\n    writers = (\n        [\n            CommonMetricPrinter(max_iter),\n            JSONWriter(os.path.join(cfg.OUTPUT_DIR, \"metrics.json\")),\n            TensorboardXWriter(cfg.OUTPUT_DIR)\n        ]\n        if comm.is_main_process()\n        else []\n    )\n\n    # compared to \"train_net.py\", we do not support some hooks, such as\n    # accurate timing, FP16 training and precise BN here,\n    # because they are not trivial to implement in a small training loop\n    logger.info(\"Start training from epoch {}\".format(start_epoch))\n    with EventStorage(start_iter) as storage:\n        for epoch in range(start_epoch, max_epoch):\n            storage.epoch = epoch\n            for _ in range(iters_per_epoch):\n                data = next(data_loader_iter)\n                storage.iter = iteration\n\n                loss_dict = model(data)\n                losses = sum(loss_dict.values())\n                assert torch.isfinite(losses).all(), loss_dict\n\n                loss_dict_reduced = {k: v.item() for k, v in comm.reduce_dict(loss_dict).items()}\n                losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n                if comm.is_main_process():\n                    storage.put_scalars(total_loss=losses_reduced, **loss_dict_reduced)\n\n                optimizer.zero_grad()\n                losses.backward()\n                optimizer.step()\n                storage.put_scalar(\"lr\", optimizer.param_groups[0][\"lr\"], smoothing_hint=False)\n\n                if iteration - start_iter > 5 and \\\n                        ((iteration + 1) % 200 == 0 or iteration == max_iter - 1) and \\\n                        ((iteration + 1) % iters_per_epoch != 0):\n                    for writer in writers:\n                        writer.write()\n\n                iteration += 1\n\n                if iteration <= warmup_iters:\n                    scheduler[\"warmup_sched\"].step()\n\n            # Write metrics after each epoch\n            for writer in writers:\n                writer.write()\n\n            if iteration > warmup_iters and (epoch + 1) > delay_epochs:\n                scheduler[\"lr_sched\"].step()\n\n            if (\n                    cfg.TEST.EVAL_PERIOD > 0\n                    and (epoch + 1) % cfg.TEST.EVAL_PERIOD == 0\n                    and iteration != max_iter - 1\n            ):\n                results = do_test(cfg, model)\n                # Compared to \"train_net.py\", the test results are not dumped to EventStorage\n            else:\n                results = {}\n            flatten_results = flatten_results_dict(results)\n\n            metric_dict = dict(metric=flatten_results[metric_name] if metric_name in flatten_results else -1)\n            periodic_checkpointer.step(epoch, **metric_dict)\n\n\ndef setup(args):\n    \"\"\"\n    Create configs and perform basic setups.\n    \"\"\"\n    cfg = get_cfg()\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    default_setup(cfg, args)\n    return cfg\n\n\ndef main(args):\n    cfg = setup(args)\n\n    model = build_model(cfg)\n    logger.info(\"Model:\\n{}\".format(model))\n    if args.eval_only:\n        cfg.defrost()\n        cfg.MODEL.BACKBONE.PRETRAIN = False\n\n        Checkpointer(model).load(cfg.MODEL.WEIGHTS)  # load trained model\n\n        return do_test(cfg, model)\n\n    distributed = comm.get_world_size() > 1\n    if distributed:\n        model = DistributedDataParallel(\n            model, device_ids=[comm.get_local_rank()], broadcast_buffers=False\n        )\n\n    do_train(cfg, model, resume=args.resume)\n    return do_test(cfg, model)\n\n\nif __name__ == \"__main__\":\n    args = default_argument_parser().parse_args()\n    print(\"Command Line Args:\", args)\n    launch(\n        main,\n        args.num_gpus,\n        num_machines=args.num_machines,\n        machine_rank=args.machine_rank,\n        dist_url=args.dist_url,\n        args=(args,),\n    )\n"
  },
  {
    "path": "fast_reid/tools/train_net.py",
    "content": "#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport sys\n\nsys.path.append('.')\n\nfrom fast_reid.fastreid.config import get_cfg\nfrom fast_reid.fastreid.engine import DefaultTrainer, default_argument_parser, default_setup, launch\nfrom fast_reid.fastreid.utils.checkpoint import Checkpointer\n\n\ndef setup(args):\n    \"\"\"\n    Create configs and perform basic setups.\n    \"\"\"\n    cfg = get_cfg()\n    cfg.merge_from_file(args.config_file)\n    cfg.merge_from_list(args.opts)\n    cfg.freeze()\n    default_setup(cfg, args)\n    return cfg\n\n\ndef main(args):\n\n    cfg = setup(args)\n\n    if args.eval_only:\n        cfg.defrost()\n        cfg.MODEL.BACKBONE.PRETRAIN = False\n        model = DefaultTrainer.build_model(cfg)\n\n        Checkpointer(model).load(cfg.MODEL.WEIGHTS)  # load trained model\n\n        res = DefaultTrainer.test(cfg, model)\n        return res\n\n    trainer = DefaultTrainer(cfg)\n\n    trainer.resume_or_load(resume=args.resume)\n\n    return trainer.train()\n\n# python3 fast_reid/tools/train_net.py --config-file fast_reid/configs/Market1501/sbs_S50.yml --num-gpus 8\n# python3 fast_reid/tools/train_net.py --config-file fast_reid/configs/Market1501/sbs_S50.yml --eval-only \\\n# MODEL.WEIGHTS logs/market1501/sbs_S50/model_final.pth MODEL.DEVICE \"cuda:0\"\n# CUDA_VISIBLE_DEVICES=4,5,6,7 python3 fast_reid/tools/train_net.py --config-file ./fast_reid/configs/MOT17/sbs_S50.yml --num-gpus 1\n# CUDA_VISIBLE_DEVICES=4,5,6,7 python3 fast_reid/tools/train_net.py --config-file ./fast_reid/configs/MOT20/sbs_S50.yml --num-gpus 1\n# CUDA_VISIBLE_DEVICES=4,5,6,7 python3 fast_reid/tools/train_net.py --config-file ./fast_reid/configs/DanceTrack/sbs_S50.yml --num-gpus 1\n# CUDA_VISIBLE_DEVICES=4,5,6,7 python3 fast_reid/tools/train_net.py --config-file ./fast_reid/configs/CUHKSYSU/sbs_S50.yml --num-gpus 1\n# CUDA_VISIBLE_DEVICES=4,5,6,7 python3 fast_reid/tools/train_net.py --config-file ./fast_reid/configs/CUHKSYSU_DanceTrack/sbs_S50.yml --num-gpus 1\n\n# python3 fast_reid/tools/train_net.py --config-file fast_reid/configs/CUHKSYSU_MOT17/sbs_S50.yml MODEL.DEVICE \"cuda:0\"\n# python3 fast_reid/tools/train_net.py --config-file fast_reid/configs/DanceTrack/sbs_S50.yml MODEL.DEVICE \"cuda:0\"\nif __name__ == \"__main__\":\n    args = default_argument_parser().parse_args()\n    print(\"Command Line Args:\", args)\n\n    launch(\n        main,\n        args.num_gpus,\n        num_machines=args.num_machines,\n        machine_rank=args.machine_rank,\n        dist_url=args.dist_url,\n        args=(args,),\n    )\n"
  },
  {
    "path": "motmetrics/__init__.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n\nChristoph Heindl, 2017\nhttps://github.com/cheind/py-motmetrics\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n__all__ = [\n    'distances',\n    'io',\n    'lap',\n    'metrics',\n    'utils',\n    'MOTAccumulator',\n]\n\nfrom motmetrics import distances\nfrom motmetrics import io\nfrom motmetrics import lap\nfrom motmetrics import metrics\nfrom motmetrics import utils\nfrom motmetrics.mot import MOTAccumulator\n\n# Needs to be last line\n__version__ = '1.2.0'\n"
  },
  {
    "path": "motmetrics/apps/__init__.py",
    "content": ""
  },
  {
    "path": "motmetrics/apps/eval_detrac.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Compute metrics for trackers using DETRAC challenge ground-truth data.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nfrom collections import OrderedDict\nimport glob\nimport logging\nimport os\nfrom pathlib import Path\n\nimport motmetrics as mm\n\n\ndef parse_args():\n    \"\"\"Defines and parses command-line arguments.\"\"\"\n    parser = argparse.ArgumentParser(description=\"\"\"\nCompute metrics for trackers using DETRAC challenge ground-truth data.\n\nFiles\n-----\nGround truth files can be in .XML format or .MAT format as provided by http://detrac-db.rit.albany.edu/download\n\nTest Files for the challenge are reuired to be in MOTchallenge format, they have to comply with the format described in\n\nMilan, Anton, et al.\n\"Mot16: A benchmark for multi-object tracking.\"\narXiv preprint arXiv:1603.00831 (2016).\nhttps://motchallenge.net/\n\nDirectory Structure\n---------\n\nLayout for ground truth data\n    <GT_ROOT>/<SEQUENCE_1>.txt\n    <GT_ROOT>/<SEQUENCE_2>.txt\n    ...\n\n    OR\n    <GT_ROOT>/<SEQUENCE_1>.mat\n    <GT_ROOT>/<SEQUENCE_2>.mat\n    ...\n\nLayout for test data\n    <TEST_ROOT>/<SEQUENCE_1>.txt\n    <TEST_ROOT>/<SEQUENCE_2>.txt\n    ...\n\nSequences of ground truth and test will be matched according to the `<SEQUENCE_X>`\nstring.\"\"\", formatter_class=argparse.RawTextHelpFormatter)\n\n    parser.add_argument('groundtruths', type=str, help='Directory containing ground truth files.')\n    parser.add_argument('tests', type=str, help='Directory containing tracker result files')\n    parser.add_argument('--loglevel', type=str, help='Log level', default='info')\n    parser.add_argument('--gtfmt', type=str, help='Groundtruth data format', default='detrac-xml')\n    parser.add_argument('--tsfmt', type=str, help='Test data format', default='mot15-2D')\n    parser.add_argument('--solver', type=str, help='LAP solver to use')\n    return parser.parse_args()\n\n\ndef compare_dataframes(gts, ts):\n    \"\"\"Builds accumulator for each sequence.\"\"\"\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:\n            logging.info('Comparing %s...', k)\n            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))\n            names.append(k)\n        else:\n            logging.warning('No ground truth for %s, skipping.', k)\n\n    return accs, names\n\n\ndef main():\n    # pylint: disable=missing-function-docstring\n    args = parse_args()\n\n    loglevel = getattr(logging, args.loglevel.upper(), None)\n    if not isinstance(loglevel, int):\n        raise ValueError('Invalid log level: {} '.format(args.loglevel))\n    logging.basicConfig(level=loglevel, format='%(asctime)s %(levelname)s - %(message)s', datefmt='%I:%M:%S')\n\n    if args.solver:\n        mm.lap.default_solver = args.solver\n\n    gtfiles = glob.glob(os.path.join(args.groundtruths, '*'))\n    tsfiles = glob.glob(os.path.join(args.tests, '*'))\n\n    logging.info('Found %d groundtruths and %d test files.', len(gtfiles), len(tsfiles))\n    logging.info('Available LAP solvers %s', str(mm.lap.available_solvers))\n    logging.info('Default LAP solver \\'%s\\'', mm.lap.default_solver)\n    logging.info('Loading files.')\n\n    gt = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt=args.gtfmt)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt=args.tsfmt)) for f in tsfiles])\n\n    mh = mm.metrics.create()\n    accs, names = compare_dataframes(gt, ts)\n\n    logging.info('Running metrics')\n\n    summary = mh.compute_many(accs, names=names, metrics=mm.metrics.motchallenge_metrics, generate_overall=True)\n    print(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names))\n    logging.info('Completed')\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "motmetrics/apps/eval_motchallenge.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Compute metrics for trackers using MOTChallenge ground-truth data.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nfrom collections import OrderedDict\nimport glob\nimport logging\nimport os\nfrom pathlib import Path\n\nimport motmetrics as mm\n\n\ndef parse_args():\n    \"\"\"Defines and parses command-line arguments.\"\"\"\n    parser = argparse.ArgumentParser(description=\"\"\"\nCompute metrics for trackers using MOTChallenge ground-truth data.\n\nFiles\n-----\nAll file content, ground truth and test files, have to comply with the\nformat described in\n\nMilan, Anton, et al.\n\"Mot16: A benchmark for multi-object tracking.\"\narXiv preprint arXiv:1603.00831 (2016).\nhttps://motchallenge.net/\n\nStructure\n---------\n\nLayout for ground truth data\n    <GT_ROOT>/<SEQUENCE_1>/gt/gt.txt\n    <GT_ROOT>/<SEQUENCE_2>/gt/gt.txt\n    ...\n\nLayout for test data\n    <TEST_ROOT>/<SEQUENCE_1>.txt\n    <TEST_ROOT>/<SEQUENCE_2>.txt\n    ...\n\nSequences of ground truth and test will be matched according to the `<SEQUENCE_X>`\nstring.\"\"\", formatter_class=argparse.RawTextHelpFormatter)\n\n    parser.add_argument('groundtruths', type=str, help='Directory containing ground truth files.')\n    parser.add_argument('tests', type=str, help='Directory containing tracker result files')\n    parser.add_argument('--loglevel', type=str, help='Log level', default='info')\n    parser.add_argument('--fmt', type=str, help='Data format', default='mot15-2D')\n    parser.add_argument('--solver', type=str, help='LAP solver to use for matching between frames.')\n    parser.add_argument('--id_solver', type=str, help='LAP solver to use for ID metrics. Defaults to --solver.')\n    parser.add_argument('--exclude_id', dest='exclude_id', default=False, action='store_true',\n                        help='Disable ID metrics')\n    return parser.parse_args()\n\n\ndef compare_dataframes(gts, ts):\n    \"\"\"Builds accumulator for each sequence.\"\"\"\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:\n            logging.info('Comparing %s...', k)\n            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))\n            names.append(k)\n        else:\n            logging.warning('No ground truth for %s, skipping.', k)\n\n    return accs, names\n\n\ndef main():\n    # pylint: disable=missing-function-docstring\n    args = parse_args()\n\n    loglevel = getattr(logging, args.loglevel.upper(), None)\n    if not isinstance(loglevel, int):\n        raise ValueError('Invalid log level: {} '.format(args.loglevel))\n    logging.basicConfig(level=loglevel, format='%(asctime)s %(levelname)s - %(message)s', datefmt='%I:%M:%S')\n\n    if args.solver:\n        mm.lap.default_solver = args.solver\n\n    gtfiles = glob.glob(os.path.join(args.groundtruths, '*/gt/gt.txt'))\n    tsfiles = [f for f in glob.glob(os.path.join(args.tests, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logging.info('Found %d groundtruths and %d test files.', len(gtfiles), len(tsfiles))\n    logging.info('Available LAP solvers %s', str(mm.lap.available_solvers))\n    logging.info('Default LAP solver \\'%s\\'', mm.lap.default_solver)\n    logging.info('Loading files.')\n\n    gt = OrderedDict([(Path(f).parts[-3], mm.io.loadtxt(f, fmt=args.fmt, min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt=args.fmt)) for f in tsfiles])\n\n    mh = mm.metrics.create()\n    accs, names = compare_dataframes(gt, ts)\n\n    metrics = list(mm.metrics.motchallenge_metrics)\n    if args.exclude_id:\n        metrics = [x for x in metrics if not x.startswith('id')]\n\n    logging.info('Running metrics')\n\n    if args.id_solver:\n        mm.lap.default_solver = args.id_solver\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names))\n    logging.info('Completed')\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "motmetrics/apps/evaluateTracking.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Compute metrics for trackers using MOTChallenge ground-truth data.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nfrom collections import OrderedDict\nimport io\nimport logging\nimport os\nimport sys\nfrom tempfile import NamedTemporaryFile\nimport time\n\nimport motmetrics as mm\n\n\ndef parse_args():\n    \"\"\"Defines and parses command-line arguments.\"\"\"\n    parser = argparse.ArgumentParser(description=\"\"\"\nCompute metrics for trackers using MOTChallenge ground-truth data with data preprocess.\n\nFiles\n-----\nAll file content, ground truth and test files, have to comply with the\nformat described in\n\nMilan, Anton, et al.\n\"Mot16: A benchmark for multi-object tracking.\"\narXiv preprint arXiv:1603.00831 (2016).\nhttps://motchallenge.net/\n\nStructure\n---------\n\nLayout for ground truth data\n    <GT_ROOT>/<SEQUENCE_1>/gt/gt.txt\n    <GT_ROOT>/<SEQUENCE_2>/gt/gt.txt\n    ...\n\nLayout for test data\n    <TEST_ROOT>/<SEQUENCE_1>.txt\n    <TEST_ROOT>/<SEQUENCE_2>.txt\n    ...\n\nSeqmap for test data\n    [name]\n    <SEQUENCE_1>\n    <SEQUENCE_2>\n    ...\n\nSequences of ground truth and test will be matched according to the `<SEQUENCE_X>`\nstring in the seqmap.\"\"\", formatter_class=argparse.RawTextHelpFormatter)\n\n    parser.add_argument('groundtruths', type=str, help='Directory containing ground truth files.')\n    parser.add_argument('tests', type=str, help='Directory containing tracker result files')\n    parser.add_argument('seqmap', type=str, help='Text file containing all sequences name')\n    parser.add_argument('--log', type=str, help='a place to record result and outputfile of mistakes', default='')\n    parser.add_argument('--loglevel', type=str, help='Log level', default='info')\n    parser.add_argument('--fmt', type=str, help='Data format', default='mot15-2D')\n    parser.add_argument('--solver', type=str, help='LAP solver to use')\n    parser.add_argument('--skip', type=int, default=0, help='skip frames n means choosing one frame for every (n+1) frames')\n    parser.add_argument('--iou', type=float, default=0.5, help='special IoU threshold requirement for small targets')\n    return parser.parse_args()\n\n\ndef compare_dataframes(gts, ts, vsflag='', iou=0.5):\n    \"\"\"Builds accumulator for each sequence.\"\"\"\n    accs = []\n    anas = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:\n            logging.info('Evaluating %s...', k)\n            if vsflag != '':\n                fd = io.open(vsflag + '/' + k + '.log', 'w')\n            else:\n                fd = ''\n            acc, ana = mm.utils.CLEAR_MOT_M(gts[k][0], tsacc, gts[k][1], 'iou', distth=iou, vflag=fd)\n            if fd != '':\n                fd.close()\n            accs.append(acc)\n            anas.append(ana)\n            names.append(k)\n        else:\n            logging.warning('No ground truth for %s, skipping.', k)\n\n    return accs, anas, names\n\n\ndef parseSequences(seqmap):\n    \"\"\"Loads list of sequences from file.\"\"\"\n    assert os.path.isfile(seqmap), 'Seqmap %s not found.' % seqmap\n    fd = io.open(seqmap)\n    res = []\n    for row in fd.readlines():\n        row = row.strip()\n        if row == '' or row == 'name' or row[0] == '#':\n            continue\n        res.append(row)\n    fd.close()\n    return res\n\n\ndef generateSkippedGT(gtfile, skip, fmt):\n    \"\"\"Generates temporary ground-truth file with some frames skipped.\"\"\"\n    del fmt  # unused\n    tf = NamedTemporaryFile(delete=False, mode='w')\n    with io.open(gtfile) as fd:\n        lines = fd.readlines()\n        for line in lines:\n            arr = line.strip().split(',')\n            fr = int(arr[0])\n            if fr % (skip + 1) != 1:\n                continue\n            pos = line.find(',')\n            newline = str(fr // (skip + 1) + 1) + line[pos:]\n            tf.write(newline)\n    tf.close()\n    tempfile = tf.name\n    return tempfile\n\n\ndef main():\n    # pylint: disable=missing-function-docstring\n    # pylint: disable=too-many-locals\n    args = parse_args()\n\n    loglevel = getattr(logging, args.loglevel.upper(), None)\n    if not isinstance(loglevel, int):\n        raise ValueError('Invalid log level: {} '.format(args.loglevel))\n    logging.basicConfig(level=loglevel, format='%(asctime)s %(levelname)s - %(message)s', datefmt='%I:%M:%S')\n\n    if args.solver:\n        mm.lap.default_solver = args.solver\n\n    seqs = parseSequences(args.seqmap)\n    gtfiles = [os.path.join(args.groundtruths, i, 'gt/gt.txt') for i in seqs]\n    tsfiles = [os.path.join(args.tests, '%s.txt' % i) for i in seqs]\n\n    for gtfile in gtfiles:\n        if not os.path.isfile(gtfile):\n            logging.error('gt File %s not found.', gtfile)\n            sys.exit(1)\n    for tsfile in tsfiles:\n        if not os.path.isfile(tsfile):\n            logging.error('res File %s not found.', tsfile)\n            sys.exit(1)\n\n    logging.info('Found %d groundtruths and %d test files.', len(gtfiles), len(tsfiles))\n    for seq in seqs:\n        logging.info('\\t%s', seq)\n    logging.info('Available LAP solvers %s', str(mm.lap.available_solvers))\n    logging.info('Default LAP solver \\'%s\\'', mm.lap.default_solver)\n    logging.info('Loading files.')\n\n    if args.skip > 0 and 'mot' in args.fmt:\n        for i, gtfile in enumerate(gtfiles):\n            gtfiles[i] = generateSkippedGT(gtfile, args.skip, fmt=args.fmt)\n\n    gt = OrderedDict([(seqs[i], (mm.io.loadtxt(f, fmt=args.fmt), os.path.join(args.groundtruths, seqs[i], 'seqinfo.ini'))) for i, f in enumerate(gtfiles)])\n    ts = OrderedDict([(seqs[i], mm.io.loadtxt(f, fmt=args.fmt)) for i, f in enumerate(tsfiles)])\n\n    mh = mm.metrics.create()\n    st = time.time()\n    accs, analysis, names = compare_dataframes(gt, ts, args.log, 1. - args.iou)\n    logging.info('adding frames: %.3f seconds.', time.time() - st)\n\n    logging.info('Running metrics')\n\n    summary = mh.compute_many(accs, anas=analysis, names=names, metrics=mm.metrics.motchallenge_metrics, generate_overall=True)\n    print(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names))\n    logging.info('Completed')\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "motmetrics/apps/example.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Example usage.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nimport motmetrics as mm\n\nif __name__ == '__main__':\n\n    # Create an accumulator that will be updated during each frame\n    acc = mm.MOTAccumulator(auto_id=True)\n\n    # Each frame a list of ground truth object / hypotheses ids and pairwise distances\n    # is passed to the accumulator. For now assume that the distance matrix given to us.\n\n    # 2 Matches, 1 False alarm\n    acc.update(\n        [1, 2],                 # Ground truth objects in this frame\n        [1, 2, 3],                  # Detector hypotheses in this frame\n        [[0.1, np.nan, 0.3],        # Distances from object 1 to hypotheses 1, 2, 3\n         [0.5, 0.2, 0.3]]        # Distances from object 2 to hypotheses 1, 2,\n    )\n    print(acc.events)\n\n    # 1 Match, 1 Miss\n    df = acc.update(\n        [1, 2],\n        [1],\n        [[0.2], [0.4]]\n    )\n    print(df)\n\n    # 1 Match, 1 Switch\n    df = acc.update(\n        [1, 2],\n        [1, 3],\n        [[0.6, 0.2],\n         [0.1, 0.6]]\n    )\n    print(df)\n\n    # Compute metrics\n\n    mh = mm.metrics.create()\n    summary = mh.compute(acc, metrics=['num_frames', 'mota', 'motp'], name='acc')\n    print(summary)\n\n    summary = mh.compute_many(\n        [acc, acc.events.loc[0:1]],\n        metrics=['num_frames', 'mota', 'motp'],\n        names=['full', 'part'])\n    print(summary)\n\n    strsummary = mm.io.render_summary(\n        summary,\n        formatters={'mota': '{:.2%}'.format},\n        namemap={'mota': 'MOTA', 'motp': 'MOTP'}\n    )\n    print(strsummary)\n\n    summary = mh.compute_many(\n        [acc, acc.events.loc[0:1]],\n        metrics=mm.metrics.motchallenge_metrics,\n        names=['full', 'part'])\n    strsummary = mm.io.render_summary(\n        summary,\n        formatters=mh.formatters,\n        namemap=mm.io.motchallenge_metric_names\n    )\n    print(strsummary)\n"
  },
  {
    "path": "motmetrics/apps/list_metrics.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"List metrics.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nif __name__ == '__main__':\n    import motmetrics\n\n    mh = motmetrics.metrics.create()\n    print(mh.list_metrics_markdown())\n"
  },
  {
    "path": "motmetrics/data/TUD-Campus/gt.txt",
    "content": "1,1,399,182,121,229,1,-1,-1,-1\n1,2,282,201,92,184,1,-1,-1,-1\n1,3,63,153,82,288,1,-1,-1,-1\n1,4,192,206,62,137,1,-1,-1,-1\n1,5,125,209,74,157,1,-1,-1,-1\n1,6,162,208,55,145,1,-1,-1,-1\n2,1,399,181,139,235,1,-1,-1,-1\n2,2,269,202,87,182,1,-1,-1,-1\n2,3,71,151,100,284,1,-1,-1,-1\n2,4,200,206,55,137,1,-1,-1,-1\n2,5,127,210,77,157,1,-1,-1,-1\n2,6,157,206,71,143,1,-1,-1,-1\n3,1,419,182,106,227,1,-1,-1,-1\n3,2,271,196,76,190,1,-1,-1,-1\n3,3,70,155,111,286,1,-1,-1,-1\n3,4,209,204,47,139,1,-1,-1,-1\n3,5,136,206,64,160,1,-1,-1,-1\n3,6,162,204,71,142,1,-1,-1,-1\n4,1,428,181,111,237,1,-1,-1,-1\n4,2,262,196,76,185,1,-1,-1,-1\n4,3,80,160,106,280,1,-1,-1,-1\n4,4,218,206,41,138,1,-1,-1,-1\n4,5,131,208,75,154,1,-1,-1,-1\n4,6,164,212,73,131,1,-1,-1,-1\n5,1,439,179,95,238,1,-1,-1,-1\n5,2,264,197,66,187,1,-1,-1,-1\n5,3,84,165,104,267,1,-1,-1,-1\n5,4,227,208,39,139,1,-1,-1,-1\n5,5,136,208,74.364,153.95,1,-1,-1,-1\n5,6,180,211,53,139,1,-1,-1,-1\n6,1,454,179,87,238,1,-1,-1,-1\n6,2,251,194,68,187,1,-1,-1,-1\n6,3,89,164,115,270,1,-1,-1,-1\n6,4,228,208,47,136,1,-1,-1,-1\n6,5,141,209,73.727,153.91,1,-1,-1,-1\n6,6,183,214,53,136,1,-1,-1,-1\n7,1,453,177,81,239,1,-1,-1,-1\n7,2,245,190,69,196,1,-1,-1,-1\n7,3,103,165,110,272,1,-1,-1,-1\n7,4,234,208,48,135.5,1,-1,-1,-1\n7,5,146,209,73.091,153.86,1,-1,-1,-1\n7,6,184,211,56,145,1,-1,-1,-1\n8,1,471,178,76,241,1,-1,-1,-1\n8,2,236,188,70,197,1,-1,-1,-1\n8,3,117,165,101,276,1,-1,-1,-1\n8,4,239,208,49,135,1,-1,-1,-1\n8,5,151,209,72.454,153.82,1,-1,-1,-1\n8,6,190,211,55,144,1,-1,-1,-1\n9,1,464,173,101,244,1,-1,-1,-1\n9,2,232,190,74,195,1,-1,-1,-1\n9,3,125,158,113,283,1,-1,-1,-1\n9,4,245,209,50,134.5,1,-1,-1,-1\n9,5,156,209,71.818,153.77,1,-1,-1,-1\n9,6,193,214,56,138,1,-1,-1,-1\n10,1,479,168,81,251,1,-1,-1,-1\n10,2,224,190,78,194,1,-1,-1,-1\n10,3,134,159,97,283,1,-1,-1,-1\n10,4,251,209,51,134,1,-1,-1,-1\n10,5,161,210,71.182,153.73,1,-1,-1,-1\n11,1,483,169,88,250,1,-1,-1,-1\n11,2,220,194,77,191,1,-1,-1,-1\n11,3,139,154,92,286,1,-1,-1,-1\n11,4,256,209,52,133.5,1,-1,-1,-1\n11,5,166,210,70.546,153.68,1,-1,-1,-1\n12,1,497,167,99,249,1,-1,-1,-1\n12,2,210,195,70,185,1,-1,-1,-1\n12,3,139,155,100,285,1,-1,-1,-1\n12,4,262,209,53,133,1,-1,-1,-1\n12,5,171,210,69.909,153.64,1,-1,-1,-1\n13,1,502,172,100,246,1,-1,-1,-1\n13,2,210,195,73,185,1,-1,-1,-1\n13,3,160,151,90,289,1,-1,-1,-1\n13,4,264,209,55,129,1,-1,-1,-1\n13,5,176,210,69.273,153.59,1,-1,-1,-1\n14,1,499,172,108,241,1,-1,-1,-1\n14,2,203,194,72.2,187.8,1,-1,-1,-1\n14,3,163,149,88,293,1,-1,-1,-1\n14,4,261,208,58,132,1,-1,-1,-1\n14,5,181,211,68.636,153.55,1,-1,-1,-1\n15,1,506,178,109,239,1,-1,-1,-1\n15,2,196,194,71.4,190.6,1,-1,-1,-1\n15,3,179,149,91,291,1,-1,-1,-1\n15,4,268,206,65,149,1,-1,-1,-1\n15,5,186,211,68,153.5,1,-1,-1,-1\n16,1,514,177,117,239,1,-1,-1,-1\n16,2,190,193,70.6,193.4,1,-1,-1,-1\n16,3,182,150,92,292,1,-1,-1,-1\n16,4,279,205,50,139,1,-1,-1,-1\n16,5,191,211,67.364,153.45,1,-1,-1,-1\n17,1,520,176,114,247,1,-1,-1,-1\n17,2,183,193,69.8,196.2,1,-1,-1,-1\n17,3,200,148,93,296,1,-1,-1,-1\n17,4,287,201,44,148,1,-1,-1,-1\n17,5,196,212,66.727,153.41,1,-1,-1,-1\n18,1,522,165,111,263,1,-1,-1,-1\n18,2,176,192,69,199,1,-1,-1,-1\n18,3,196,149,111,299,1,-1,-1,-1\n18,4,293,208,49,139,1,-1,-1,-1\n18,5,201,212,66.091,153.36,1,-1,-1,-1\n19,1,534,168,103,253,1,-1,-1,-1\n19,2,174,185,68,199,1,-1,-1,-1\n19,3,206,157,104,287,1,-1,-1,-1\n19,4,296,213,57,132,1,-1,-1,-1\n19,5,206,212,65.454,153.32,1,-1,-1,-1\n20,1,547,182,88,240,1,-1,-1,-1\n20,2,165,187,62,199,1,-1,-1,-1\n20,3,204,159,118,285,1,-1,-1,-1\n20,4,296,205,60,137,1,-1,-1,-1\n20,5,211,212,64.818,153.27,1,-1,-1,-1\n21,1,565,176,74,247,1,-1,-1,-1\n21,2,159,194,60,191,1,-1,-1,-1\n21,3,215,162,122,282,1,-1,-1,-1\n21,4,301,209,57,135,1,-1,-1,-1\n21,5,216,213,64.182,153.23,1,-1,-1,-1\n22,1,575,170,87,255,1,-1,-1,-1\n22,2,150,188,68,200,1,-1,-1,-1\n22,3,222,163,108,286,1,-1,-1,-1\n22,4,307,208,61,140,1,-1,-1,-1\n22,5,221,213,63.545,153.18,1,-1,-1,-1\n23,1,582,168,81,262,1,-1,-1,-1\n23,2,139,186,69,199,1,-1,-1,-1\n23,3,219,164,119,282,1,-1,-1,-1\n23,4,307,205,68,144,1,-1,-1,-1\n23,5,226,213,62.909,153.14,1,-1,-1,-1\n24,1,585,165,94,269,1,-1,-1,-1\n24,2,131,188,75,200,1,-1,-1,-1\n24,3,243,162,120,289,1,-1,-1,-1\n24,4,310,205,71,142,1,-1,-1,-1\n24,5,231,213,62.273,153.09,1,-1,-1,-1\n24,7,-28,183,76,235,1,-1,-1,-1\n25,2,121,191,87,189,1,-1,-1,-1\n25,3,254,165,97,281,1,-1,-1,-1\n25,4,321,211,55,133,1,-1,-1,-1\n25,5,236,214,61.636,153.05,1,-1,-1,-1\n25,7,-15,179,63,240,1,-1,-1,-1\n26,2,113,190,79,195,1,-1,-1,-1\n26,3,259,155,97,294,1,-1,-1,-1\n26,4,322,208,58,136,1,-1,-1,-1\n26,5,241,214,61,153,1,-1,-1,-1\n26,7,-20,180,83,235,1,-1,-1,-1\n27,2,109,194,88,192,1,-1,-1,-1\n27,3,274,158,88,296,1,-1,-1,-1\n27,4,328,208,57.739,136.26,1,-1,-1,-1\n27,5,242,222,74,150,1,-1,-1,-1\n27,7,-30,182,91,233,1,-1,-1,-1\n28,2,99,196,96,193,1,-1,-1,-1\n28,3,285,153,90,295,1,-1,-1,-1\n28,4,333,208,57.478,136.52,1,-1,-1,-1\n28,5,257,224,55,147,1,-1,-1,-1\n28,7,-21,177,82,236,1,-1,-1,-1\n29,2,88,194,93,188,1,-1,-1,-1\n29,3,287,162,106,283,1,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  {
    "path": "motmetrics/distances.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Functions for comparing predictions and ground-truth.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nfrom motmetrics import math_util\n\n\ndef norm2squared_matrix(objs, hyps, max_d2=float('inf')):\n    \"\"\"Computes the squared Euclidean distance matrix between object and hypothesis points.\n\n    Params\n    ------\n    objs : NxM array\n        Object points of dim M in rows\n    hyps : KxM array\n        Hypothesis points of dim M in rows\n\n    Kwargs\n    ------\n    max_d2 : float\n        Maximum tolerable squared Euclidean distance. Object / hypothesis points\n        with larger distance are set to np.nan signalling do-not-pair. Defaults\n        to +inf\n\n    Returns\n    -------\n    C : NxK array\n        Distance matrix containing pairwise distances or np.nan.\n    \"\"\"\n\n    objs = np.atleast_2d(objs).astype(float)\n    hyps = np.atleast_2d(hyps).astype(float)\n\n    if objs.size == 0 or hyps.size == 0:\n        return np.empty((0, 0))\n\n    assert hyps.shape[1] == objs.shape[1], \"Dimension mismatch\"\n\n    delta = objs[:, np.newaxis] - hyps[np.newaxis, :]\n    C = np.sum(delta ** 2, axis=-1)\n\n    C[C > max_d2] = np.nan\n    return C\n\n\ndef rect_min_max(r):\n    min_pt = r[..., :2]\n    size = r[..., 2:]\n    max_pt = min_pt + size\n    return min_pt, max_pt\n\n\ndef boxiou(a, b):\n    \"\"\"Computes IOU of two rectangles.\"\"\"\n    a_min, a_max = rect_min_max(a)\n    b_min, b_max = rect_min_max(b)\n    # Compute intersection.\n    i_min = np.maximum(a_min, b_min)\n    i_max = np.minimum(a_max, b_max)\n    i_size = np.maximum(i_max - i_min, 0)\n    i_vol = np.prod(i_size, axis=-1)\n    # Get volume of union.\n    a_size = np.maximum(a_max - a_min, 0)\n    b_size = np.maximum(b_max - b_min, 0)\n    a_vol = np.prod(a_size, axis=-1)\n    b_vol = np.prod(b_size, axis=-1)\n    u_vol = a_vol + b_vol - i_vol\n    return np.where(i_vol == 0, np.zeros_like(i_vol, dtype=np.float),\n                    math_util.quiet_divide(i_vol, u_vol))\n\n\ndef iou_matrix(objs, hyps, max_iou=1.):\n    \"\"\"Computes 'intersection over union (IoU)' distance matrix between object and hypothesis rectangles.\n\n    The IoU is computed as\n\n        IoU(a,b) = 1. - isect(a, b) / union(a, b)\n\n    where isect(a,b) is the area of intersection of two rectangles and union(a, b) the area of union. The\n    IoU is bounded between zero and one. 0 when the rectangles overlap perfectly and 1 when the overlap is\n    zero.\n\n    Params\n    ------\n    objs : Nx4 array\n        Object rectangles (x,y,w,h) in rows\n    hyps : Kx4 array\n        Hypothesis rectangles (x,y,w,h) in rows\n\n    Kwargs\n    ------\n    max_iou : float\n        Maximum tolerable overlap distance. Object / hypothesis points\n        with larger distance are set to np.nan signalling do-not-pair. Defaults\n        to 0.5\n\n    Returns\n    -------\n    C : NxK array\n        Distance matrix containing pairwise distances or np.nan.\n    \"\"\"\n\n    if np.size(objs) == 0 or np.size(hyps) == 0:\n        return np.empty((0, 0))\n\n    objs = np.asfarray(objs)\n    hyps = np.asfarray(hyps)\n    assert objs.shape[1] == 4\n    assert hyps.shape[1] == 4\n    iou = boxiou(objs[:, None], hyps[None, :])\n    dist = 1 - iou\n    return np.where(dist > max_iou, np.nan, dist)\n"
  },
  {
    "path": "motmetrics/io.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Functions for loading data and writing summaries.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom enum import Enum\nimport io\n\nimport numpy as np\nimport pandas as pd\nimport scipy.io\nimport xmltodict\n\n\nclass Format(Enum):\n    \"\"\"Enumerates supported file formats.\"\"\"\n\n    MOT16 = 'mot16'\n    \"\"\"Milan, Anton, et al. \"Mot16: A benchmark for multi-object tracking.\" arXiv preprint arXiv:1603.00831 (2016).\"\"\"\n\n    MOT15_2D = 'mot15-2D'\n    \"\"\"Leal-Taixe, Laura, et al. \"MOTChallenge 2015: Towards a benchmark for multi-target tracking.\" arXiv preprint arXiv:1504.01942 (2015).\"\"\"\n\n    VATIC_TXT = 'vatic-txt'\n    \"\"\"Vondrick, Carl, Donald Patterson, and Deva Ramanan. \"Efficiently scaling up crowdsourced video annotation.\" International Journal of Computer Vision 101.1 (2013): 184-204.\n    https://github.com/cvondrick/vatic\n    \"\"\"\n\n    DETRAC_MAT = 'detrac-mat'\n    \"\"\"Wen, Longyin et al. \"UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking.\" arXiv preprint arXiv:arXiv:1511.04136 (2016).\n    http://detrac-db.rit.albany.edu/download\n    \"\"\"\n\n    DETRAC_XML = 'detrac-xml'\n    \"\"\"Wen, Longyin et al. \"UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking.\" arXiv preprint arXiv:arXiv:1511.04136 (2016).\n    http://detrac-db.rit.albany.edu/download\n    \"\"\"\n\n\ndef load_motchallenge(fname, **kwargs):\n    r\"\"\"Load MOT challenge data.\n\n    Params\n    ------\n    fname : str\n        Filename to load data from\n\n    Kwargs\n    ------\n    sep : str\n        Allowed field separators, defaults to '\\s+|\\t+|,'\n    min_confidence : float\n        Rows with confidence less than this threshold are removed.\n        Defaults to -1. You should set this to 1 when loading\n        ground truth MOTChallenge data, so that invalid rectangles in\n        the ground truth are not considered during matching.\n\n    Returns\n    ------\n    df : pandas.DataFrame\n        The returned dataframe has the following columns\n            'X', 'Y', 'Width', 'Height', 'Confidence', 'ClassId', 'Visibility'\n        The dataframe is indexed by ('FrameId', 'Id')\n    \"\"\"\n\n    sep = kwargs.pop('sep', r'\\s+|\\t+|,')\n    min_confidence = kwargs.pop('min_confidence', -1)\n    df = pd.read_csv(\n        fname,\n        sep=sep,\n        index_col=[0, 1],\n        skipinitialspace=True,\n        header=None,\n        names=['FrameId', 'Id', 'X', 'Y', 'Width', 'Height', 'Confidence', 'ClassId', 'Visibility', 'unused'],\n        engine='python'\n    )\n\n    # Account for matlab convention.\n    df[['X', 'Y']] -= (1, 1)\n\n    # Removed trailing column\n    del df['unused']\n\n    # Remove all rows without sufficient confidence\n    return df[df['Confidence'] >= min_confidence]\n\n\ndef load_vatictxt(fname, **kwargs):\n    \"\"\"Load Vatic text format.\n\n    Loads the vatic CSV text having the following columns per row\n\n        0   Track ID. All rows with the same ID belong to the same path.\n        1   xmin. The top left x-coordinate of the bounding box.\n        2   ymin. The top left y-coordinate of the bounding box.\n        3   xmax. The bottom right x-coordinate of the bounding box.\n        4   ymax. The bottom right y-coordinate of the bounding box.\n        5   frame. The frame that this annotation represents.\n        6   lost. If 1, the annotation is outside of the view screen.\n        7   occluded. If 1, the annotation is occluded.\n        8   generated. If 1, the annotation was automatically interpolated.\n        9  label. The label for this annotation, enclosed in quotation marks.\n        10+ attributes. Each column after this is an attribute set in the current frame\n\n    Params\n    ------\n    fname : str\n        Filename to load data from\n\n    Returns\n    ------\n    df : pandas.DataFrame\n        The returned dataframe has the following columns\n            'X', 'Y', 'Width', 'Height', 'Lost', 'Occluded', 'Generated', 'ClassId', '<Attr1>', '<Attr2>', ...\n        where <Attr1> is placeholder for the actual attribute name capitalized (first letter). The order of attribute\n        columns is sorted in attribute name. The dataframe is indexed by ('FrameId', 'Id')\n    \"\"\"\n    # pylint: disable=too-many-locals\n\n    sep = kwargs.pop('sep', ' ')\n\n    with io.open(fname) as f:\n        # First time going over file, we collect the set of all variable activities\n        activities = set()\n        for line in f:\n            for c in line.rstrip().split(sep)[10:]:\n                activities.add(c)\n        activitylist = sorted(list(activities))\n\n        # Second time we construct artificial binary columns for each activity\n        data = []\n        f.seek(0)\n        for line in f:\n            fields = line.rstrip().split()\n            attrs = ['0'] * len(activitylist)\n            for a in fields[10:]:\n                attrs[activitylist.index(a)] = '1'\n            fields = fields[:10]\n            fields.extend(attrs)\n            data.append(' '.join(fields))\n\n        strdata = '\\n'.join(data)\n\n        dtype = {\n            'Id': np.int64,\n            'X': np.float32,\n            'Y': np.float32,\n            'Width': np.float32,\n            'Height': np.float32,\n            'FrameId': np.int64,\n            'Lost': bool,\n            'Occluded': bool,\n            'Generated': bool,\n            'ClassId': str,\n        }\n\n        # Remove quotes from activities\n        activitylist = [a.replace('\\\"', '').capitalize() for a in activitylist]\n\n        # Add dtypes for activities\n        for a in activitylist:\n            dtype[a] = bool\n\n        # Read from CSV\n        names = ['Id', 'X', 'Y', 'Width', 'Height', 'FrameId', 'Lost', 'Occluded', 'Generated', 'ClassId']\n        names.extend(activitylist)\n        df = pd.read_csv(io.StringIO(strdata), names=names, index_col=['FrameId', 'Id'], header=None, sep=' ')\n\n        # Correct Width and Height which are actually XMax, Ymax in files.\n        w = df['Width'] - df['X']\n        h = df['Height'] - df['Y']\n        df['Width'] = w\n        df['Height'] = h\n\n        return df\n\n\ndef load_detrac_mat(fname):\n    \"\"\"Loads UA-DETRAC annotations data from mat files\n\n    Competition Site: http://detrac-db.rit.albany.edu/download\n\n    File contains a nested structure of 2d arrays for indexed by frame id\n    and Object ID. Separate arrays for top, left, width and height are given.\n\n    Params\n    ------\n    fname : str\n        Filename to load data from\n\n    Kwargs\n    ------\n    Currently none of these arguments used.\n\n    Returns\n    ------\n    df : pandas.DataFrame\n        The returned dataframe has the following columns\n            'X', 'Y', 'Width', 'Height', 'Confidence', 'ClassId', 'Visibility'\n        The dataframe is indexed by ('FrameId', 'Id')   \n    \"\"\"\n\n    matData = scipy.io.loadmat(fname)\n\n    frameList = matData['gtInfo'][0][0][4][0]\n    leftArray = matData['gtInfo'][0][0][0].astype(np.float32)\n    topArray = matData['gtInfo'][0][0][1].astype(np.float32)\n    widthArray = matData['gtInfo'][0][0][3].astype(np.float32)\n    heightArray = matData['gtInfo'][0][0][2].astype(np.float32)\n\n    parsedGT = []\n    for f in frameList:\n        ids = [i + 1 for i, v in enumerate(leftArray[f - 1]) if v > 0]\n        for i in ids:\n            row = []\n            row.append(f)\n            row.append(i)\n            row.append(leftArray[f - 1, i - 1] - widthArray[f - 1, i - 1] / 2)\n            row.append(topArray[f - 1, i - 1] - heightArray[f - 1, i - 1])\n            row.append(widthArray[f - 1, i - 1])\n            row.append(heightArray[f - 1, i - 1])\n            row.append(1)\n            row.append(-1)\n            row.append(-1)\n            row.append(-1)\n            parsedGT.append(row)\n\n    df = pd.DataFrame(parsedGT,\n                      columns=['FrameId', 'Id', 'X', 'Y', 'Width', 'Height', 'Confidence', 'ClassId', 'Visibility', 'unused'])\n    df.set_index(['FrameId', 'Id'], inplace=True)\n\n    # Account for matlab convention.\n    df[['X', 'Y']] -= (1, 1)\n\n    # Removed trailing column\n    del df['unused']\n\n    return df\n\n\ndef load_detrac_xml(fname):\n    \"\"\"Loads UA-DETRAC annotations data from xml files\n\n    Competition Site: http://detrac-db.rit.albany.edu/download\n\n    Params\n    ------\n    fname : str\n        Filename to load data from\n\n    Kwargs\n    ------\n    Currently none of these arguments used.\n\n    Returns\n    ------\n    df : pandas.DataFrame\n        The returned dataframe has the following columns\n            'X', 'Y', 'Width', 'Height', 'Confidence', 'ClassId', 'Visibility'\n        The dataframe is indexed by ('FrameId', 'Id')\n    \"\"\"\n\n    with io.open(fname) as fd:\n        doc = xmltodict.parse(fd.read())\n    frameList = doc['sequence']['frame']\n\n    parsedGT = []\n    for f in frameList:\n        fid = int(f['@num'])\n        targetList = f['target_list']['target']\n        if not isinstance(targetList, list):\n            targetList = [targetList]\n\n        for t in targetList:\n            row = []\n            row.append(fid)\n            row.append(int(t['@id']))\n            row.append(float(t['box']['@left']))\n            row.append(float(t['box']['@top']))\n            row.append(float(t['box']['@width']))\n            row.append(float(t['box']['@height']))\n            row.append(1)\n            row.append(-1)\n            row.append(-1)\n            row.append(-1)\n            parsedGT.append(row)\n\n    df = pd.DataFrame(parsedGT,\n                      columns=['FrameId', 'Id', 'X', 'Y', 'Width', 'Height', 'Confidence', 'ClassId', 'Visibility', 'unused'])\n    df.set_index(['FrameId', 'Id'], inplace=True)\n\n    # Account for matlab convention.\n    df[['X', 'Y']] -= (1, 1)\n\n    # Removed trailing column\n    del df['unused']\n\n    return df\n\n\ndef loadtxt(fname, fmt=Format.MOT15_2D, **kwargs):\n    \"\"\"Load data from any known format.\"\"\"\n    fmt = Format(fmt)\n\n    switcher = {\n        Format.MOT16: load_motchallenge,\n        Format.MOT15_2D: load_motchallenge,\n        Format.VATIC_TXT: load_vatictxt,\n        Format.DETRAC_MAT: load_detrac_mat,\n        Format.DETRAC_XML: load_detrac_xml\n    }\n    func = switcher.get(fmt)\n    return func(fname, **kwargs)\n\n\ndef render_summary(summary, formatters=None, namemap=None, buf=None):\n    \"\"\"Render metrics summary to console friendly tabular output.\n\n    Params\n    ------\n    summary : pd.DataFrame\n        Dataframe containing summaries in rows.\n\n    Kwargs\n    ------\n    buf : StringIO-like, optional\n        Buffer to write to\n    formatters : dict, optional\n        Dicionary defining custom formatters for individual metrics.\n        I.e `{'mota': '{:.2%}'.format}`. You can get preset formatters\n        from MetricsHost.formatters\n    namemap : dict, optional\n        Dictionary defining new metric names for display. I.e\n        `{'num_false_positives': 'FP'}`.\n\n    Returns\n    -------\n    string\n        Formatted string\n    \"\"\"\n\n    if namemap is not None:\n        summary = summary.rename(columns=namemap)\n        if formatters is not None:\n            formatters = {namemap.get(c, c): f for c, f in formatters.items()}\n\n    output = summary.to_string(\n        buf=buf,\n        formatters=formatters,\n    )\n\n    return output\n\n\nmotchallenge_metric_names = {\n    'idf1': 'IDF1',\n    'idp': 'IDP',\n    'idr': 'IDR',\n    'recall': 'Rcll',\n    'precision': 'Prcn',\n    'num_unique_objects': 'GT',\n    'mostly_tracked': 'MT',\n    'partially_tracked': 'PT',\n    'mostly_lost': 'ML',\n    'num_false_positives': 'FP',\n    'num_misses': 'FN',\n    'num_switches': 'IDs',\n    'num_fragmentations': 'FM',\n    'mota': 'MOTA',\n    'motp': 'MOTP',\n    'num_transfer': 'IDt',\n    'num_ascend': 'IDa',\n    'num_migrate': 'IDm',\n}\n\"\"\"A list mappings for metric names to comply with MOTChallenge.\"\"\"\n"
  },
  {
    "path": "motmetrics/lap.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Tools for solving linear assignment problems.\"\"\"\n\n# pylint: disable=import-outside-toplevel\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom contextlib import contextmanager\nimport warnings\n\nimport numpy as np\n\n\ndef _module_is_available_py2(name):\n    try:\n        imp.find_module(name)\n        return True\n    except ImportError:\n        return False\n\n\ndef _module_is_available_py3(name):\n    return importlib.util.find_spec(name) is not None\n\n\ntry:\n    import importlib.util\nexcept ImportError:\n    import imp\n    _module_is_available = _module_is_available_py2\nelse:\n    _module_is_available = _module_is_available_py3\n\n\ndef linear_sum_assignment(costs, solver=None):\n    \"\"\"Solve a linear sum assignment problem (LSA).\n\n    For large datasets solving the minimum cost assignment becomes the dominant runtime part.\n    We therefore support various solvers out of the box (currently lapsolver, scipy, ortools, munkres)\n\n    Params\n    ------\n    costs : np.array\n        numpy matrix containing costs. Use NaN/Inf values for unassignable\n        row/column pairs.\n\n    Kwargs\n    ------\n    solver : callable or str, optional\n        When str: name of solver to use.\n        When callable: function to invoke\n        When None: uses first available solver\n    \"\"\"\n    costs = np.asarray(costs)\n    if not costs.size:\n        return np.array([], dtype=int), np.array([], dtype=int)\n\n    solver = solver or default_solver\n\n    if isinstance(solver, str):\n        # Try resolve from string\n        solver = solver_map.get(solver, None)\n\n    assert callable(solver), 'Invalid LAP solver.'\n    rids, cids = solver(costs)\n    rids = np.asarray(rids).astype(int)\n    cids = np.asarray(cids).astype(int)\n    return rids, cids\n\n\ndef add_expensive_edges(costs):\n    \"\"\"Replaces non-edge costs (nan, inf) with large number.\n\n    If the optimal solution includes one of these edges,\n    then the original problem was infeasible.\n\n    Parameters\n    ----------\n    costs : np.ndarray\n    \"\"\"\n    # The graph is probably already dense if we are doing this.\n    assert isinstance(costs, np.ndarray)\n    # The linear_sum_assignment function in scipy does not support missing edges.\n    # Replace nan with a large constant that ensures it is not chosen.\n    # If it is chosen, that means the problem was infeasible.\n    valid = np.isfinite(costs)\n    if valid.all():\n        return costs.copy()\n    if not valid.any():\n        return np.zeros_like(costs)\n    r = min(costs.shape)\n    # Assume all edges costs are within [-c, c], c >= 0.\n    # The cost of an invalid edge must be such that...\n    # choosing this edge once and the best-possible edge (r - 1) times\n    # is worse than choosing the worst-possible edge r times.\n    # l + (r - 1) (-c) > r c\n    # l > r c + (r - 1) c\n    # l > (2 r - 1) c\n    # Choose l = 2 r c + 1 > (2 r - 1) c.\n    c = np.abs(costs[valid]).max() + 1  # Doesn't hurt to add 1 here.\n    large_constant = 2 * r * c + 1\n    return np.where(valid, costs, large_constant)\n\n\ndef _exclude_missing_edges(costs, rids, cids):\n    subset = [\n        index for index, (i, j) in enumerate(zip(rids, cids))\n        if np.isfinite(costs[i, j])\n    ]\n    return rids[subset], cids[subset]\n\n\ndef lsa_solve_scipy(costs):\n    \"\"\"Solves the LSA problem using the scipy library.\"\"\"\n\n    from scipy.optimize import linear_sum_assignment as scipy_solve\n\n    # scipy (1.3.3) does not support nan or inf values\n    finite_costs = add_expensive_edges(costs)\n    rids, cids = scipy_solve(finite_costs)\n    rids, cids = _exclude_missing_edges(costs, rids, cids)\n    return rids, cids\n\n\ndef lsa_solve_lapsolver(costs):\n    \"\"\"Solves the LSA problem using the lapsolver library.\"\"\"\n    from lapsolver import solve_dense\n\n    # Note that lapsolver will add expensive finite edges internally.\n    # However, older versions did not add a large enough edge.\n    finite_costs = add_expensive_edges(costs)\n    rids, cids = solve_dense(finite_costs)\n    rids, cids = _exclude_missing_edges(costs, rids, cids)\n    return rids, cids\n\n\ndef lsa_solve_munkres(costs):\n    \"\"\"Solves the LSA problem using the Munkres library.\"\"\"\n    from munkres import Munkres\n\n    m = Munkres()\n    # The munkres package may hang if the problem is not feasible.\n    # Therefore, add expensive edges instead of using munkres.DISALLOWED.\n    finite_costs = add_expensive_edges(costs)\n    # Ensure that matrix is square.\n    finite_costs = _zero_pad_to_square(finite_costs)\n    indices = np.array(m.compute(finite_costs), dtype=int)\n    # Exclude extra matches from extension to square matrix.\n    indices = indices[(indices[:, 0] < costs.shape[0])\n                      & (indices[:, 1] < costs.shape[1])]\n    rids, cids = indices[:, 0], indices[:, 1]\n    rids, cids = _exclude_missing_edges(costs, rids, cids)\n    return rids, cids\n\n\ndef _zero_pad_to_square(costs):\n    num_rows, num_cols = costs.shape\n    if num_rows == num_cols:\n        return costs\n    n = max(num_rows, num_cols)\n    padded = np.zeros((n, n), dtype=costs.dtype)\n    padded[:num_rows, :num_cols] = costs\n    return padded\n\n\ndef lsa_solve_ortools(costs):\n    \"\"\"Solves the LSA problem using Google's optimization tools. \"\"\"\n    from ortools.graph import pywrapgraph\n\n    if costs.shape[0] != costs.shape[1]:\n        # ortools assumes that the problem is square.\n        # Non-square problem will be infeasible.\n        # Default to scipy solver rather than add extra zeros.\n        # (This maintains the same behaviour as previous versions.)\n        return linear_sum_assignment(costs, solver='scipy')\n\n    rs, cs = np.isfinite(costs).nonzero()  # pylint: disable=unbalanced-tuple-unpacking\n    finite_costs = costs[rs, cs]\n    scale = find_scale_for_integer_approximation(finite_costs)\n    if scale != 1:\n        warnings.warn('costs are not integers; using approximation')\n    int_costs = np.round(scale * finite_costs).astype(int)\n\n    assignment = pywrapgraph.LinearSumAssignment()\n    # OR-Tools does not like to receive indices of type np.int64.\n    rs = rs.tolist()  # pylint: disable=no-member\n    cs = cs.tolist()\n    int_costs = int_costs.tolist()\n    for r, c, int_cost in zip(rs, cs, int_costs):\n        assignment.AddArcWithCost(r, c, int_cost)\n\n    status = assignment.Solve()\n    try:\n        _ortools_assert_is_optimal(pywrapgraph, status)\n    except AssertionError:\n        # Default to scipy solver rather than add finite edges.\n        # (This maintains the same behaviour as previous versions.)\n        return linear_sum_assignment(costs, solver='scipy')\n\n    return _ortools_extract_solution(assignment)\n\n\ndef find_scale_for_integer_approximation(costs, base=10, log_max_scale=8, log_safety=2):\n    \"\"\"Returns a multiplicative factor to use before rounding to integers.\n\n    Tries to find scale = base ** j (for j integer) such that:\n        abs(diff(unique(costs))) <= 1 / (scale * safety)\n    where safety = base ** log_safety.\n\n    Logs a warning if the desired resolution could not be achieved.\n    \"\"\"\n    costs = np.asarray(costs)\n    costs = costs[np.isfinite(costs)]  # Exclude non-edges (nan, inf) and -inf.\n    if np.size(costs) == 0:\n        # No edges with numeric value. Scale does not matter.\n        return 1\n    unique = np.unique(costs)\n    if np.size(unique) == 1:\n        # All costs have equal values. Scale does not matter.\n        return 1\n    try:\n        _assert_integer(costs)\n    except AssertionError:\n        pass\n    else:\n        # The costs are already integers.\n        return 1\n\n    # Find scale = base ** e such that:\n    # 1 / scale <= tol, or\n    # e = log(scale) >= -log(tol)\n    # where tol = min(diff(unique(costs)))\n    min_diff = np.diff(unique).min()\n    e = np.ceil(np.log(min_diff) / np.log(base)).astype(int).item()\n    # Add optional non-negative safety factor to reduce quantization noise.\n    e += max(log_safety, 0)\n    # Ensure that we do not reduce the magnitude of the costs.\n    e = max(e, 0)\n    # Ensure that the scale is not too large.\n    if e > log_max_scale:\n        warnings.warn('could not achieve desired resolution for approximation: '\n                      'want exponent %d but max is %d', e, log_max_scale)\n        e = log_max_scale\n    scale = base ** e\n    return scale\n\n\ndef _assert_integer(costs):\n    # Check that costs are not changed by rounding.\n    # Note: Elements of cost matrix may be nan, inf, -inf.\n    np.testing.assert_equal(np.round(costs), costs)\n\n\ndef _ortools_assert_is_optimal(pywrapgraph, status):\n    if status == pywrapgraph.LinearSumAssignment.OPTIMAL:\n        pass\n    elif status == pywrapgraph.LinearSumAssignment.INFEASIBLE:\n        raise AssertionError('ortools: infeasible assignment problem')\n    elif status == pywrapgraph.LinearSumAssignment.POSSIBLE_OVERFLOW:\n        raise AssertionError('ortools: possible overflow in assignment problem')\n    else:\n        raise AssertionError('ortools: unknown status')\n\n\ndef _ortools_extract_solution(assignment):\n    if assignment.NumNodes() == 0:\n        return np.array([], dtype=int), np.array([], dtype=int)\n\n    pairings = []\n    for i in range(assignment.NumNodes()):\n        pairings.append([i, assignment.RightMate(i)])\n\n    indices = np.array(pairings, dtype=int)\n    return indices[:, 0], indices[:, 1]\n\n\ndef lsa_solve_lapjv(costs):\n    \"\"\"Solves the LSA problem using lap.lapjv().\"\"\"\n\n    from lap import lapjv\n\n    # The lap.lapjv function supports +inf edges but there are some issues.\n    # https://github.com/gatagat/lap/issues/20\n    # Therefore, replace nans with large finite cost.\n    finite_costs = add_expensive_edges(costs)\n    row_to_col, _ = lapjv(finite_costs, return_cost=False, extend_cost=True)\n    indices = np.array([np.arange(costs.shape[0]), row_to_col], dtype=int).T\n    # Exclude unmatched rows (in case of unbalanced problem).\n    indices = indices[indices[:, 1] != -1]  # pylint: disable=unsubscriptable-object\n    rids, cids = indices[:, 0], indices[:, 1]\n    # Ensure that no missing edges were chosen.\n    rids, cids = _exclude_missing_edges(costs, rids, cids)\n    return rids, cids\n\n\navailable_solvers = None\ndefault_solver = None\nsolver_map = None\n\n\ndef _init_standard_solvers():\n    global available_solvers, default_solver, solver_map  # pylint: disable=global-statement\n\n    solvers = [\n        ('lapsolver', lsa_solve_lapsolver),\n        ('lap', lsa_solve_lapjv),\n        ('scipy', lsa_solve_scipy),\n        ('munkres', lsa_solve_munkres),\n        ('ortools', lsa_solve_ortools),\n    ]\n\n    solver_map = dict(solvers)\n\n    available_solvers = [s[0] for s in solvers if _module_is_available(s[0])]\n    if len(available_solvers) == 0:\n        default_solver = None\n        warnings.warn('No standard LAP solvers found. Consider `pip install lapsolver` or `pip install scipy`', category=RuntimeWarning)\n    else:\n        default_solver = available_solvers[0]\n\n\n_init_standard_solvers()\n\n\n@contextmanager\ndef set_default_solver(newsolver):\n    \"\"\"Change the default solver within context.\n\n    Intended usage\n\n        costs = ...\n        mysolver = lambda x: ... # solver code that returns pairings\n\n        with lap.set_default_solver(mysolver):\n            rids, cids = lap.linear_sum_assignment(costs)\n\n    Params\n    ------\n    newsolver : callable or str\n        new solver function\n    \"\"\"\n\n    global default_solver  # pylint: disable=global-statement\n\n    oldsolver = default_solver\n    try:\n        default_solver = newsolver\n        yield\n    finally:\n        default_solver = oldsolver\n"
  },
  {
    "path": "motmetrics/math_util.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Math utility functions.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport warnings\n\nimport numpy as np\n\n\ndef quiet_divide(a, b):\n    \"\"\"Quiet divide function that does not warn about (0 / 0).\"\"\"\n    with warnings.catch_warnings():\n        warnings.simplefilter('ignore', RuntimeWarning)\n        return np.true_divide(a, b)\n"
  },
  {
    "path": "motmetrics/metrics.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Obtain metrics from event logs.\"\"\"\n\n# pylint: disable=redefined-outer-name\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom collections import OrderedDict\nimport inspect\nimport logging\nimport time\n\nimport numpy as np\nimport pandas as pd\n\nfrom motmetrics import math_util\nfrom motmetrics.lap import linear_sum_assignment\nfrom motmetrics.mot import MOTAccumulator\n\ntry:\n    _getargspec = inspect.getfullargspec\nexcept AttributeError:\n    _getargspec = inspect.getargspec\n\n\nclass MetricsHost:\n    \"\"\"Keeps track of metrics and intra metric dependencies.\"\"\"\n\n    def __init__(self):\n        self.metrics = OrderedDict()\n\n    def register(self, fnc, deps='auto', name=None, helpstr=None, formatter=None, fnc_m=None, deps_m='auto'):\n        \"\"\"Register a new metric.\n\n        Params\n        ------\n        fnc : Function\n            Function that computes the metric to be registered. The number of arguments\n            is 1 + N, where N is the number of dependencies of the metric to be registered.\n            The order of the argument passed is `df, result_dep1, result_dep2, ...`.\n\n        Kwargs\n        ------\n        deps : string, list of strings or None, optional\n            The dependencies of this metric. Each dependency is evaluated and the result\n            is passed as argument to `fnc` as described above. If None is specified, the\n            function does not have any dependencies. If a list of strings is given, dependencies\n            for these metric strings are registered. If 'auto' is passed, the dependencies\n            are deduced from argument inspection of the method. For this to work the argument\n            names have to be equal to the intended dependencies.\n        name : string or None, optional\n            Name identifier of this metric. If None is passed the name is deduced from\n            function inspection.\n        helpstr : string or None, optional\n            A description of what the metric computes. If no help message is given it\n            is deduced from the docstring of the function.\n        formatter: Format object, optional\n            An optional default formatter when rendering metric results as string. I.e to\n            render the result `0.35` as `35%` one would pass `{:.2%}.format`\n        fnc_m : Function or None, optional\n            Function that merges metric results. The number of arguments\n            is 1 + N, where N is the number of dependencies of the metric to be registered.\n            The order of the argument passed is `df, result_dep1, result_dep2, ...`.\n        \"\"\"\n\n        assert fnc is not None, 'No function given for metric {}'.format(name)\n\n        if deps is None:\n            deps = []\n        elif deps == 'auto':\n            if _getargspec(fnc).defaults is not None:\n                k = - len(_getargspec(fnc).defaults)\n            else:\n                k = len(_getargspec(fnc).args)\n            deps = _getargspec(fnc).args[1:k]  # assumes dataframe as first argument\n\n        if name is None:\n            name = fnc.__name__  # Relies on meaningful function names, i.e don't use for lambdas\n\n        if helpstr is None:\n            helpstr = inspect.getdoc(fnc) if inspect.getdoc(fnc) else 'No description.'\n            helpstr = ' '.join(helpstr.split())\n        if fnc_m is None and name + '_m' in globals():\n            fnc_m = globals()[name + '_m']\n        if fnc_m is not None:\n            if deps_m is None:\n                deps_m = []\n            elif deps_m == 'auto':\n                if _getargspec(fnc_m).defaults is not None:\n                    k = - len(_getargspec(fnc_m).defaults)\n                else:\n                    k = len(_getargspec(fnc_m).args)\n                deps_m = _getargspec(fnc_m).args[1:k]  # assumes dataframe as first argument\n        else:\n            deps_m = None\n\n        self.metrics[name] = {\n            'name': name,\n            'fnc': fnc,\n            'fnc_m': fnc_m,\n            'deps': deps,\n            'deps_m': deps_m,\n            'help': helpstr,\n            'formatter': formatter\n        }\n\n    @property\n    def names(self):\n        \"\"\"Returns the name identifiers of all registered metrics.\"\"\"\n        return [v['name'] for v in self.metrics.values()]\n\n    @property\n    def formatters(self):\n        \"\"\"Returns the formatters for all metrics that have associated formatters.\"\"\"\n        return {\n            k: v['formatter'] for k, v in self.metrics.items()\n            if v['formatter'] is not None\n        }\n\n    def list_metrics(self, include_deps=False):\n        \"\"\"Returns a dataframe containing names, descriptions and optionally dependencies for each metric.\"\"\"\n        cols = ['Name', 'Description', 'Dependencies']\n        if include_deps:\n            data = [(m['name'], m['help'], m['deps']) for m in self.metrics.values()]\n        else:\n            data = [(m['name'], m['help']) for m in self.metrics.values()]\n            cols = cols[:-1]\n\n        return pd.DataFrame(data, columns=cols)\n\n    def list_metrics_markdown(self, include_deps=False):\n        \"\"\"Returns a markdown ready version of `list_metrics`.\"\"\"\n        df = self.list_metrics(include_deps=include_deps)\n        fmt = [':---' for i in range(len(df.columns))]\n        df_fmt = pd.DataFrame([fmt], columns=df.columns)\n        df_formatted = pd.concat([df_fmt, df])\n        return df_formatted.to_csv(sep=\"|\", index=False)\n\n    def compute(self, df, ana=None, metrics=None, return_dataframe=True, return_cached=False, name=None):\n        \"\"\"Compute metrics on the dataframe / accumulator.\n\n        Params\n        ------\n        df : MOTAccumulator or pandas.DataFrame\n            The dataframe to compute the metrics on\n\n        Kwargs\n        ------\n        ana: dict or None, optional\n            To cache results for fast computation.\n        metrics : string, list of string or None, optional\n            The identifiers of the metrics to be computed. This method will only\n            compute the minimal set of necessary metrics to fullfill the request.\n            If None is passed all registered metrics are computed.\n        return_dataframe : bool, optional\n            Return the result as pandas.DataFrame (default) or dict.\n        return_cached : bool, optional\n           If true all intermediate metrics required to compute the desired metrics are returned as well.\n        name : string, optional\n            When returning a pandas.DataFrame this is the index of the row containing\n            the computed metric values.\n        \"\"\"\n\n        if isinstance(df, MOTAccumulator):\n            df = df.events\n\n        if metrics is None:\n            metrics = motchallenge_metrics\n        elif isinstance(metrics, str):\n            metrics = [metrics]\n\n        df_map = events_to_df_map(df)\n\n        cache = {}\n        options = {'ana': ana}\n        for mname in metrics:\n            cache[mname] = self._compute(df_map, mname, cache, options, parent='summarize')\n\n        if name is None:\n            name = 0\n\n        if return_cached:\n            data = cache\n        else:\n            data = OrderedDict([(k, cache[k]) for k in metrics])\n\n        ret = pd.DataFrame(data, index=[name]) if return_dataframe else data\n        return ret\n\n    def compute_overall(self, partials, metrics=None, return_dataframe=True, return_cached=False, name=None):\n        \"\"\"Compute overall metrics based on multiple results.\n\n        Params\n        ------\n        partials : list of metric results to combine overall\n\n        Kwargs\n        ------\n        metrics : string, list of string or None, optional\n            The identifiers of the metrics to be computed. This method will only\n            compute the minimal set of necessary metrics to fullfill the request.\n            If None is passed all registered metrics are computed.\n        return_dataframe : bool, optional\n            Return the result as pandas.DataFrame (default) or dict.\n        return_cached : bool, optional\n           If true all intermediate metrics required to compute the desired metrics are returned as well.\n        name : string, optional\n            When returning a pandas.DataFrame this is the index of the row containing\n            the computed metric values.\n\n        Returns\n        -------\n        df : pandas.DataFrame\n            A datafrom containing the metrics in columns and names in rows.\n        \"\"\"\n        if metrics is None:\n            metrics = motchallenge_metrics\n        elif isinstance(metrics, str):\n            metrics = [metrics]\n        cache = {}\n\n        for mname in metrics:\n            cache[mname] = self._compute_overall(partials, mname, cache, parent='summarize')\n\n        if name is None:\n            name = 0\n        if return_cached:\n            data = cache\n        else:\n            data = OrderedDict([(k, cache[k]) for k in metrics])\n        return pd.DataFrame(data, index=[name]) if return_dataframe else data\n\n    def compute_many(self, dfs, anas=None, metrics=None, names=None, generate_overall=False):\n        \"\"\"Compute metrics on multiple dataframe / accumulators.\n\n        Params\n        ------\n        dfs : list of MOTAccumulator or list of pandas.DataFrame\n            The data to compute metrics on.\n\n        Kwargs\n        ------\n        anas: dict or None, optional\n            To cache results for fast computation.\n        metrics : string, list of string or None, optional\n            The identifiers of the metrics to be computed. This method will only\n            compute the minimal set of necessary metrics to fullfill the request.\n            If None is passed all registered metrics are computed.\n        names : list of string, optional\n            The names of individual rows in the resulting dataframe.\n        generate_overall : boolean, optional\n            If true resulting dataframe will contain a summary row that is computed\n            using the same metrics over an accumulator that is the concatentation of\n            all input containers. In creating this temporary accumulator, care is taken\n            to offset frame indices avoid object id collisions.\n\n        Returns\n        -------\n        df : pandas.DataFrame\n            A datafrom containing the metrics in columns and names in rows.\n        \"\"\"\n        if metrics is None:\n            metrics = motchallenge_metrics\n        elif isinstance(metrics, str):\n            metrics = [metrics]\n\n        assert names is None or len(names) == len(dfs)\n        st = time.time()\n        if names is None:\n            names = list(range(len(dfs)))\n        if anas is None:\n            anas = [None] * len(dfs)\n        partials = [\n            self.compute(acc,\n                         ana=analysis,\n                         metrics=metrics,\n                         name=name,\n                         return_cached=True,\n                         return_dataframe=False\n                         )\n            for acc, analysis, name in zip(dfs, anas, names)]\n        logging.info('partials: %.3f seconds.', time.time() - st)\n        details = partials\n        partials = [pd.DataFrame(OrderedDict([(k, i[k]) for k in metrics]), index=[name]) for i, name in zip(partials, names)]\n        if generate_overall:\n            names = 'OVERALL'\n            # merged, infomap = MOTAccumulator.merge_event_dataframes(dfs, return_mappings = True)\n            # dfs = merged\n            # anas = MOTAccumulator.merge_analysis(anas, infomap)\n            # partials.append(self.compute(dfs, ana=anas, metrics=metrics, name=names)[0])\n            partials.append(self.compute_overall(details, metrics=metrics, name=names))\n        logging.info('mergeOverall: %.3f seconds.', time.time() - st)\n        return pd.concat(partials)\n\n    def _compute(self, df_map, name, cache, options, parent=None):\n        \"\"\"Compute metric and resolve dependencies.\"\"\"\n        assert name in self.metrics, 'Cannot find metric {} required by {}.'.format(name, parent)\n        already = cache.get(name, None)\n        if already is not None:\n            return already\n        minfo = self.metrics[name]\n        vals = []\n        for depname in minfo['deps']:\n            v = cache.get(depname, None)\n            if v is None:\n                v = cache[depname] = self._compute(df_map, depname, cache, options, parent=name)\n            vals.append(v)\n        if _getargspec(minfo['fnc']).defaults is None:\n            return minfo['fnc'](df_map, *vals)\n        else:\n            return minfo['fnc'](df_map, *vals, **options)\n\n    def _compute_overall(self, partials, name, cache, parent=None):\n        assert name in self.metrics, 'Cannot find metric {} required by {}.'.format(name, parent)\n        already = cache.get(name, None)\n        if already is not None:\n            return already\n        minfo = self.metrics[name]\n        vals = []\n        for depname in minfo['deps_m']:\n            v = cache.get(depname, None)\n            if v is None:\n                v = cache[depname] = self._compute_overall(partials, depname, cache, parent=name)\n            vals.append(v)\n        assert minfo['fnc_m'] is not None, 'merge function for metric %s is None' % name\n        return minfo['fnc_m'](partials, *vals)\n\n\nsimple_add_func = []\n\n\ndef num_frames(df):\n    \"\"\"Total number of frames.\"\"\"\n    return df.full.index.get_level_values(0).unique().shape[0]\n\n\nsimple_add_func.append(num_frames)\n\n\ndef obj_frequencies(df):\n    \"\"\"Total number of occurrences of individual objects over all frames.\"\"\"\n    return df.noraw.OId.value_counts()\n\n\ndef pred_frequencies(df):\n    \"\"\"Total number of occurrences of individual predictions over all frames.\"\"\"\n    return df.noraw.HId.value_counts()\n\n\ndef num_unique_objects(df, obj_frequencies):\n    \"\"\"Total number of unique object ids encountered.\"\"\"\n    del df  # unused\n    return len(obj_frequencies)\n\n\nsimple_add_func.append(num_unique_objects)\n\n\ndef num_matches(df):\n    \"\"\"Total number matches.\"\"\"\n    return df.noraw.Type.isin(['MATCH']).sum()\n\n\nsimple_add_func.append(num_matches)\n\n\ndef num_switches(df):\n    \"\"\"Total number of track switches.\"\"\"\n    return df.noraw.Type.isin(['SWITCH']).sum()\n\n\nsimple_add_func.append(num_switches)\n\n\ndef num_transfer(df):\n    \"\"\"Total number of track transfer.\"\"\"\n    return df.extra.Type.isin(['TRANSFER']).sum()\n\n\nsimple_add_func.append(num_transfer)\n\n\ndef num_ascend(df):\n    \"\"\"Total number of track ascend.\"\"\"\n    return df.extra.Type.isin(['ASCEND']).sum()\n\n\nsimple_add_func.append(num_ascend)\n\n\ndef num_migrate(df):\n    \"\"\"Total number of track migrate.\"\"\"\n    return df.extra.Type.isin(['MIGRATE']).sum()\n\n\nsimple_add_func.append(num_migrate)\n\n\ndef num_false_positives(df):\n    \"\"\"Total number of false positives (false-alarms).\"\"\"\n    return df.noraw.Type.isin(['FP']).sum()\n\n\nsimple_add_func.append(num_false_positives)\n\n\ndef num_misses(df):\n    \"\"\"Total number of misses.\"\"\"\n    return df.noraw.Type.isin(['MISS']).sum()\n\n\nsimple_add_func.append(num_misses)\n\n\ndef num_detections(df, num_matches, num_switches):\n    \"\"\"Total number of detected objects including matches and switches.\"\"\"\n    del df  # unused\n    return num_matches + num_switches\n\n\nsimple_add_func.append(num_detections)\n\n\ndef num_objects(df, obj_frequencies):\n    \"\"\"Total number of unique object appearances over all frames.\"\"\"\n    del df  # unused\n    return obj_frequencies.sum()\n\n\nsimple_add_func.append(num_objects)\n\n\ndef num_predictions(df, pred_frequencies):\n    \"\"\"Total number of unique prediction appearances over all frames.\"\"\"\n    del df  # unused\n    return pred_frequencies.sum()\n\n\nsimple_add_func.append(num_predictions)\n\n\ndef track_ratios(df, obj_frequencies):\n    \"\"\"Ratio of assigned to total appearance count per unique object id.\"\"\"\n    tracked = df.noraw[df.noraw.Type != 'MISS']['OId'].value_counts()\n    return tracked.div(obj_frequencies).fillna(0.)\n\n\ndef mostly_tracked(df, track_ratios):\n    \"\"\"Number of objects tracked for at least 80 percent of lifespan.\"\"\"\n    del df  # unused\n    return track_ratios[track_ratios >= 0.8].count()\n\n\nsimple_add_func.append(mostly_tracked)\n\n\ndef partially_tracked(df, track_ratios):\n    \"\"\"Number of objects tracked between 20 and 80 percent of lifespan.\"\"\"\n    del df  # unused\n    return track_ratios[(track_ratios >= 0.2) & (track_ratios < 0.8)].count()\n\n\nsimple_add_func.append(partially_tracked)\n\n\ndef mostly_lost(df, track_ratios):\n    \"\"\"Number of objects tracked less than 20 percent of lifespan.\"\"\"\n    del df  # unused\n    return track_ratios[track_ratios < 0.2].count()\n\n\nsimple_add_func.append(mostly_lost)\n\n\ndef num_fragmentations(df, obj_frequencies):\n    \"\"\"Total number of switches from tracked to not tracked.\"\"\"\n    fra = 0\n    for o in obj_frequencies.index:\n        # Find first and last time object was not missed (track span). Then count\n        # the number switches from NOT MISS to MISS state.\n        dfo = df.noraw[df.noraw.OId == o]\n        notmiss = dfo[dfo.Type != 'MISS']\n        if len(notmiss) == 0:\n            continue\n        first = notmiss.index[0]\n        last = notmiss.index[-1]\n        diffs = dfo.loc[first:last].Type.apply(lambda x: 1 if x == 'MISS' else 0).diff()\n        fra += diffs[diffs == 1].count()\n    return fra\n\n\nsimple_add_func.append(num_fragmentations)\n\n\ndef motp(df, num_detections):\n    \"\"\"Multiple object tracker precision.\"\"\"\n    return math_util.quiet_divide(df.noraw['D'].sum(), num_detections)\n\n\ndef motp_m(partials, num_detections):\n    res = 0\n    for v in partials:\n        res += v['motp'] * v['num_detections']\n    return math_util.quiet_divide(res, num_detections)\n\n\ndef mota(df, num_misses, num_switches, num_false_positives, num_objects):\n    \"\"\"Multiple object tracker accuracy.\"\"\"\n    del df  # unused\n    return 1. - math_util.quiet_divide(\n        num_misses + num_switches + num_false_positives,\n        num_objects)\n\n\ndef mota_m(partials, num_misses, num_switches, num_false_positives, num_objects):\n    del partials  # unused\n    return 1. - math_util.quiet_divide(\n        num_misses + num_switches + num_false_positives,\n        num_objects)\n\n\ndef precision(df, num_detections, num_false_positives):\n    \"\"\"Number of detected objects over sum of detected and false positives.\"\"\"\n    del df  # unused\n    return math_util.quiet_divide(\n        num_detections,\n        num_false_positives + num_detections)\n\n\ndef precision_m(partials, num_detections, num_false_positives):\n    del partials  # unused\n    return math_util.quiet_divide(\n        num_detections,\n        num_false_positives + num_detections)\n\n\ndef recall(df, num_detections, num_objects):\n    \"\"\"Number of detections over number of objects.\"\"\"\n    del df  # unused\n    return math_util.quiet_divide(num_detections, num_objects)\n\n\ndef recall_m(partials, num_detections, num_objects):\n    del partials  # unused\n    return math_util.quiet_divide(num_detections, num_objects)\n\n\nclass DataFrameMap:  # pylint: disable=too-few-public-methods\n\n    def __init__(self, full, raw, noraw, extra):\n        self.full = full\n        self.raw = raw\n        self.noraw = noraw\n        self.extra = extra\n\n\ndef events_to_df_map(df):\n    raw = df[df.Type == 'RAW']\n    noraw = df[(df.Type != 'RAW')\n               & (df.Type != 'ASCEND')\n               & (df.Type != 'TRANSFER')\n               & (df.Type != 'MIGRATE')]\n    extra = df[df.Type != 'RAW']\n    df_map = DataFrameMap(full=df, raw=raw, noraw=noraw, extra=extra)\n    return df_map\n\n\ndef extract_counts_from_df_map(df):\n    \"\"\"\n    Returns:\n        Tuple (ocs, hcs, tps).\n        ocs: Dict from object id to count.\n        hcs: Dict from hypothesis id to count.\n        tps: Dict from (object id, hypothesis id) to true-positive count.\n        The ids are arbitrary, they might NOT be consecutive integers from 0.\n    \"\"\"\n    oids = df.full['OId'].dropna().unique()\n    hids = df.full['HId'].dropna().unique()\n\n    flat = df.raw.reset_index()\n    # Exclude events that do not belong to either set.\n    flat = flat[flat['OId'].isin(oids) | flat['HId'].isin(hids)]\n    # Count number of frames where each (non-empty) OId and HId appears.\n    ocs = flat.set_index('OId')['FrameId'].groupby('OId').nunique().to_dict()\n    hcs = flat.set_index('HId')['FrameId'].groupby('HId').nunique().to_dict()\n    # Select three columns of interest and index by ('OId', 'HId').\n    dists = flat[['OId', 'HId', 'D']].set_index(['OId', 'HId']).dropna()\n    # Count events with non-empty distance for each pair.\n    tps = dists.groupby(['OId', 'HId'])['D'].count().to_dict()\n    return ocs, hcs, tps\n\n\ndef id_global_assignment(df, ana=None):\n    \"\"\"ID measures: Global min-cost assignment for ID measures.\"\"\"\n    # pylint: disable=too-many-locals\n    del ana  # unused\n    ocs, hcs, tps = extract_counts_from_df_map(df)\n    oids = sorted(ocs.keys())\n    hids = sorted(hcs.keys())\n    oids_idx = dict((o, i) for i, o in enumerate(oids))\n    hids_idx = dict((h, i) for i, h in enumerate(hids))\n    no = len(ocs)\n    nh = len(hcs)\n\n    fpmatrix = np.full((no + nh, no + nh), 0.)\n    fnmatrix = np.full((no + nh, no + nh), 0.)\n    fpmatrix[no:, :nh] = np.nan\n    fnmatrix[:no, nh:] = np.nan\n\n    for oid, oc in ocs.items():\n        r = oids_idx[oid]\n        fnmatrix[r, :nh] = oc\n        fnmatrix[r, nh + r] = oc\n\n    for hid, hc in hcs.items():\n        c = hids_idx[hid]\n        fpmatrix[:no, c] = hc\n        fpmatrix[c + no, c] = hc\n\n    for (oid, hid), ex in tps.items():\n        r = oids_idx[oid]\n        c = hids_idx[hid]\n        fpmatrix[r, c] -= ex\n        fnmatrix[r, c] -= ex\n\n    costs = fpmatrix + fnmatrix\n    rids, cids = linear_sum_assignment(costs)\n\n    return {\n        'fpmatrix': fpmatrix,\n        'fnmatrix': fnmatrix,\n        'rids': rids,\n        'cids': cids,\n        'costs': costs,\n        'min_cost': costs[rids, cids].sum()\n    }\n\n\ndef idfp(df, id_global_assignment):\n    \"\"\"ID measures: Number of false positive matches after global min-cost matching.\"\"\"\n    del df  # unused\n    rids, cids = id_global_assignment['rids'], id_global_assignment['cids']\n    return id_global_assignment['fpmatrix'][rids, cids].sum()\n\n\nsimple_add_func.append(idfp)\n\n\ndef idfn(df, id_global_assignment):\n    \"\"\"ID measures: Number of false negatives matches after global min-cost matching.\"\"\"\n    del df  # unused\n    rids, cids = id_global_assignment['rids'], id_global_assignment['cids']\n    return id_global_assignment['fnmatrix'][rids, cids].sum()\n\n\nsimple_add_func.append(idfn)\n\n\ndef idtp(df, id_global_assignment, num_objects, idfn):\n    \"\"\"ID measures: Number of true positives matches after global min-cost matching.\"\"\"\n    del df, id_global_assignment  # unused\n    return num_objects - idfn\n\n\nsimple_add_func.append(idtp)\n\n\ndef idp(df, idtp, idfp):\n    \"\"\"ID measures: global min-cost precision.\"\"\"\n    del df  # unused\n    return math_util.quiet_divide(idtp, idtp + idfp)\n\n\ndef idp_m(partials, idtp, idfp):\n    del partials  # unused\n    return math_util.quiet_divide(idtp, idtp + idfp)\n\n\ndef idr(df, idtp, idfn):\n    \"\"\"ID measures: global min-cost recall.\"\"\"\n    del df  # unused\n    return math_util.quiet_divide(idtp, idtp + idfn)\n\n\ndef idr_m(partials, idtp, idfn):\n    del partials  # unused\n    return math_util.quiet_divide(idtp, idtp + idfn)\n\n\ndef idf1(df, idtp, num_objects, num_predictions):\n    \"\"\"ID measures: global min-cost F1 score.\"\"\"\n    del df  # unused\n    return math_util.quiet_divide(2 * idtp, num_objects + num_predictions)\n\n\ndef idf1_m(partials, idtp, num_objects, num_predictions):\n    del partials  # unused\n    return math_util.quiet_divide(2 * idtp, num_objects + num_predictions)\n\n\nfor one in simple_add_func:\n    name = one.__name__\n\n    def getSimpleAdd(nm):\n        def simpleAddHolder(partials):\n            res = 0\n            for v in partials:\n                res += v[nm]\n            return res\n        return simpleAddHolder\n    locals()[name + '_m'] = getSimpleAdd(name)\n\n\ndef create():\n    \"\"\"Creates a MetricsHost and populates it with default metrics.\"\"\"\n    m = MetricsHost()\n\n    m.register(num_frames, formatter='{:d}'.format)\n    m.register(obj_frequencies, formatter='{:d}'.format)\n    m.register(pred_frequencies, formatter='{:d}'.format)\n    m.register(num_matches, formatter='{:d}'.format)\n    m.register(num_switches, formatter='{:d}'.format)\n    m.register(num_transfer, formatter='{:d}'.format)\n    m.register(num_ascend, formatter='{:d}'.format)\n    m.register(num_migrate, formatter='{:d}'.format)\n    m.register(num_false_positives, formatter='{:d}'.format)\n    m.register(num_misses, formatter='{:d}'.format)\n    m.register(num_detections, formatter='{:d}'.format)\n    m.register(num_objects, formatter='{:d}'.format)\n    m.register(num_predictions, formatter='{:d}'.format)\n    m.register(num_unique_objects, formatter='{:d}'.format)\n    m.register(track_ratios)\n    m.register(mostly_tracked, formatter='{:d}'.format)\n    m.register(partially_tracked, formatter='{:d}'.format)\n    m.register(mostly_lost, formatter='{:d}'.format)\n    m.register(num_fragmentations)\n    m.register(motp, formatter='{:.3f}'.format)\n    m.register(mota, formatter='{:.1%}'.format)\n    m.register(precision, formatter='{:.1%}'.format)\n    m.register(recall, formatter='{:.1%}'.format)\n\n    m.register(id_global_assignment)\n    m.register(idfp)\n    m.register(idfn)\n    m.register(idtp)\n    m.register(idp, formatter='{:.1%}'.format)\n    m.register(idr, formatter='{:.1%}'.format)\n    m.register(idf1, formatter='{:.1%}'.format)\n\n    return m\n\n\nmotchallenge_metrics = [\n    'idf1',\n    'idp',\n    'idr',\n    'recall',\n    'precision',\n    'num_unique_objects',\n    'mostly_tracked',\n    'partially_tracked',\n    'mostly_lost',\n    'num_false_positives',\n    'num_misses',\n    'num_switches',\n    'num_fragmentations',\n    'mota',\n    'motp',\n    'num_transfer',\n    'num_ascend',\n    'num_migrate',\n]\n\"\"\"A list of all metrics from MOTChallenge.\"\"\"\n"
  },
  {
    "path": "motmetrics/mot.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Accumulate tracking events frame by frame.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom collections import OrderedDict\nimport itertools\n\nimport numpy as np\nimport pandas as pd\n\nfrom motmetrics.lap import linear_sum_assignment\n\n_INDEX_FIELDS = ['FrameId', 'Event']\n_EVENT_FIELDS = ['Type', 'OId', 'HId', 'D']\n\n\nclass MOTAccumulator(object):\n    \"\"\"Manage tracking events.\n\n    This class computes per-frame tracking events from a given set of object / hypothesis\n    ids and pairwise distances. Indended usage\n\n        import motmetrics as mm\n        acc = mm.MOTAccumulator()\n        acc.update(['a', 'b'], [0, 1, 2], dists, frameid=0)\n        ...\n        acc.update(['d'], [6,10], other_dists, frameid=76)\n        summary = mm.metrics.summarize(acc)\n        print(mm.io.render_summary(summary))\n\n    Update is called once per frame and takes objects / hypothesis ids and a pairwise distance\n    matrix between those (see distances module for support). Per frame max(len(objects), len(hypothesis))\n    events are generated. Each event type is one of the following\n        - `'MATCH'` a match between a object and hypothesis was found\n        - `'SWITCH'` a match between a object and hypothesis was found but differs from previous assignment (hypothesisid != previous)\n        - `'MISS'` no match for an object was found\n        - `'FP'` no match for an hypothesis was found (spurious detections)\n        - `'RAW'` events corresponding to raw input\n        - `'TRANSFER'` a match between a object and hypothesis was found but differs from previous assignment (objectid != previous)\n        - `'ASCEND'` a match between a object and hypothesis was found but differs from previous assignment  (hypothesisid is new)\n        - `'MIGRATE'` a match between a object and hypothesis was found but differs from previous assignment  (objectid is new)\n\n    Events are tracked in a pandas Dataframe. The dataframe is hierarchically indexed by (`FrameId`, `EventId`),\n    where `FrameId` is either provided during the call to `update` or auto-incremented when `auto_id` is set\n    true during construction of MOTAccumulator. `EventId` is auto-incremented. The dataframe has the following\n    columns\n        - `Type` one of `('MATCH', 'SWITCH', 'MISS', 'FP', 'RAW')`\n        - `OId` object id or np.nan when `'FP'` or `'RAW'` and object is not present\n        - `HId` hypothesis id or np.nan when `'MISS'` or `'RAW'` and hypothesis is not present\n        - `D` distance or np.nan when `'FP'` or `'MISS'` or `'RAW'` and either object/hypothesis is absent\n\n    From the events and associated fields the entire tracking history can be recovered. Once the accumulator\n    has been populated with per-frame data use `metrics.summarize` to compute statistics. See `metrics.compute_metrics`\n    for a list of metrics computed.\n\n    References\n    ----------\n    1. Bernardin, Keni, and Rainer Stiefelhagen. \"Evaluating multiple object tracking performance: the CLEAR MOT metrics.\"\n    EURASIP Journal on Image and Video Processing 2008.1 (2008): 1-10.\n    2. Milan, Anton, et al. \"Mot16: A benchmark for multi-object tracking.\" arXiv preprint arXiv:1603.00831 (2016).\n    3. Li, Yuan, Chang Huang, and Ram Nevatia. \"Learning to associate: Hybridboosted multi-target tracker for crowded scene.\"\n    Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.\n    \"\"\"\n\n    def __init__(self, auto_id=False, max_switch_time=float('inf')):\n        \"\"\"Create a MOTAccumulator.\n\n        Params\n        ------\n        auto_id : bool, optional\n            Whether or not frame indices are auto-incremented or provided upon\n            updating. Defaults to false. Not specifying a frame-id when this value\n            is true results in an error. Specifying a frame-id when this value is\n            false also results in an error.\n\n        max_switch_time : scalar, optional\n            Allows specifying an upper bound on the timespan an unobserved but\n            tracked object is allowed to generate track switch events. Useful if groundtruth\n            objects leaving the field of view keep their ID when they reappear,\n            but your tracker is not capable of recognizing this (resulting in\n            track switch events). The default is that there is no upper bound\n            on the timespan. In units of frame timestamps. When using auto_id\n            in units of count.\n        \"\"\"\n\n        # Parameters of the accumulator.\n        self.auto_id = auto_id\n        self.max_switch_time = max_switch_time\n\n        # Accumulator state.\n        self._events = None\n        self._indices = None\n        self.m = None\n        self.res_m = None\n        self.last_occurrence = None\n        self.last_match = None\n        self.hypHistory = None\n        self.dirty_events = None\n        self.cached_events_df = None\n\n        self.reset()\n\n    def reset(self):\n        \"\"\"Reset the accumulator to empty state.\"\"\"\n\n        self._events = {field: [] for field in _EVENT_FIELDS}\n        self._indices = {field: [] for field in _INDEX_FIELDS}\n        self.m = {}  # Pairings up to current timestamp\n        self.res_m = {}  # Result pairings up to now\n        self.last_occurrence = {}  # Tracks most recent occurance of object\n        self.last_match = {}  # Tracks most recent match of object\n        self.hypHistory = {}\n        self.dirty_events = True\n        self.cached_events_df = None\n\n    def _append_to_indices(self, frameid, eid):\n        self._indices['FrameId'].append(frameid)\n        self._indices['Event'].append(eid)\n\n    def _append_to_events(self, typestr, oid, hid, distance):\n        self._events['Type'].append(typestr)\n        self._events['OId'].append(oid)\n        self._events['HId'].append(hid)\n        self._events['D'].append(distance)\n\n    def update(self, oids, hids, dists, frameid=None, vf=''):\n        \"\"\"Updates the accumulator with frame specific objects/detections.\n\n        This method generates events based on the following algorithm [1]:\n        1. Try to carry forward already established tracks. If any paired object / hypothesis\n        from previous timestamps are still visible in the current frame, create a 'MATCH'\n        event between them.\n        2. For the remaining constellations minimize the total object / hypothesis distance\n        error (Kuhn-Munkres algorithm). If a correspondence made contradicts a previous\n        match create a 'SWITCH' else a 'MATCH' event.\n        3. Create 'MISS' events for all remaining unassigned objects.\n        4. Create 'FP' events for all remaining unassigned hypotheses.\n\n        Params\n        ------\n        oids : N array\n            Array of object ids.\n        hids : M array\n            Array of hypothesis ids.\n        dists: NxM array\n            Distance matrix. np.nan values to signal do-not-pair constellations.\n            See `distances` module for support methods.\n\n        Kwargs\n        ------\n        frameId : id\n            Unique frame id. Optional when MOTAccumulator.auto_id is specified during\n            construction.\n        vf: file to log details\n        Returns\n        -------\n        frame_events : pd.DataFrame\n            Dataframe containing generated events\n\n        References\n        ----------\n        1. Bernardin, Keni, and Rainer Stiefelhagen. \"Evaluating multiple object tracking performance: the CLEAR MOT metrics.\"\n        EURASIP Journal on Image and Video Processing 2008.1 (2008): 1-10.\n        \"\"\"\n        # pylint: disable=too-many-locals, too-many-statements\n        self.dirty_events = True\n        oids = np.asarray(oids)\n        oids_masked = np.zeros_like(oids, dtype=np.bool)\n        hids = np.asarray(hids)\n        hids_masked = np.zeros_like(hids, dtype=np.bool)\n        dists = np.atleast_2d(dists).astype(float).reshape(oids.shape[0], hids.shape[0]).copy()\n\n        if frameid is None:\n            assert self.auto_id, 'auto-id is not enabled'\n            if len(self._indices['FrameId']) > 0:\n                frameid = self._indices['FrameId'][-1] + 1\n            else:\n                frameid = 0\n        else:\n            assert not self.auto_id, 'Cannot provide frame id when auto-id is enabled'\n\n        eid = itertools.count()\n\n        # 0. Record raw events\n\n        no = len(oids)\n        nh = len(hids)\n\n        # Add a RAW event simply to ensure the frame is counted.\n        self._append_to_indices(frameid, next(eid))\n        self._append_to_events('RAW', np.nan, np.nan, np.nan)\n\n        # There must be at least one RAW event per object and hypothesis.\n        # Record all finite distances as RAW events.\n        valid_i, valid_j = np.where(np.isfinite(dists))\n        valid_dists = dists[valid_i, valid_j]\n        for i, j, dist_ij in zip(valid_i, valid_j, valid_dists):\n            self._append_to_indices(frameid, next(eid))\n            self._append_to_events('RAW', oids[i], hids[j], dist_ij)\n        # Add a RAW event for objects and hypotheses that were present but did\n        # not overlap with anything.\n        used_i = np.unique(valid_i)\n        used_j = np.unique(valid_j)\n        unused_i = np.setdiff1d(np.arange(no), used_i)\n        unused_j = np.setdiff1d(np.arange(nh), used_j)\n        for oid in oids[unused_i]:\n            self._append_to_indices(frameid, next(eid))\n            self._append_to_events('RAW', oid, np.nan, np.nan)\n        for hid in hids[unused_j]:\n            self._append_to_indices(frameid, next(eid))\n            self._append_to_events('RAW', np.nan, hid, np.nan)\n\n        if oids.size * hids.size > 0:\n            # 1. Try to re-establish tracks from previous correspondences\n            for i in range(oids.shape[0]):\n                # No need to check oids_masked[i] here.\n                if oids[i] not in self.m:\n                    continue\n\n                hprev = self.m[oids[i]]\n                j, = np.where(~hids_masked & (hids == hprev))\n                if j.shape[0] == 0:\n                    continue\n                j = j[0]\n\n                if np.isfinite(dists[i, j]):\n                    o = oids[i]\n                    h = hids[j]\n                    oids_masked[i] = True\n                    hids_masked[j] = True\n                    self.m[oids[i]] = hids[j]\n\n                    self._append_to_indices(frameid, next(eid))\n                    self._append_to_events('MATCH', oids[i], hids[j], dists[i, j])\n                    self.last_match[o] = frameid\n                    self.hypHistory[h] = frameid\n\n            # 2. Try to remaining objects/hypotheses\n            dists[oids_masked, :] = np.nan\n            dists[:, hids_masked] = np.nan\n\n            rids, cids = linear_sum_assignment(dists)\n\n            for i, j in zip(rids, cids):\n                if not np.isfinite(dists[i, j]):\n                    continue\n                o = oids[i]\n                h = hids[j]\n                \n                is_switch = (o in self.m and\n                             self.m[o] != h and\n                             abs(frameid - self.last_occurrence[o]) <= self.max_switch_time)\n                cat1 = 'SWITCH' if is_switch else 'MATCH'\n                if cat1 == 'SWITCH':\n                    if h not in self.hypHistory:\n                        subcat1 = 'ASCEND'\n                        self._append_to_indices(frameid, next(eid))\n                        self._append_to_events(subcat1, oids[i], hids[j], dists[i, j])\n                    else:\n                        subcat1 = 'SWITCH'\n                # ignore the last condition temporarily\n                is_transfer = (h in self.res_m and\n                               self.res_m[h] != o)\n                # is_transfer = (h in self.res_m and\n                #                self.res_m[h] != o and\n                #                abs(frameid - self.last_occurrence[o]) <= self.max_switch_time)\n                cat2 = 'TRANSFER' if is_transfer else 'MATCH'\n                if cat2 == 'TRANSFER':\n                    if o not in self.last_match:\n                        subcat2 = 'MIGRATE'\n                        self._append_to_indices(frameid, next(eid))\n                        self._append_to_events(subcat2, oids[i], hids[j], dists[i, j])\n                    else:\n                        subcat2 = 'TRANSFER'\n                    self._append_to_indices(frameid, next(eid))\n                    self._append_to_events(cat2, oids[i], hids[j], dists[i, j])\n                if vf != '' and (cat1 != 'MATCH' or cat2 != 'MATCH'):\n                    if cat1 == 'SWITCH':\n                        # print('-%s %d %d %d %d %d\\n' % (subcat1, o, self.last_match[o], self.m[o], frameid, h))\n                        vf.write('%s %d %d %d %d %d\\n' % (subcat1, o, self.last_match[o], self.m[o], frameid, h))\n                    if cat2 == 'TRANSFER':\n                        # print('%s %d %d %d %d %d\\n' % (subcat2, h, self.hypHistory[h], self.res_m[h], frameid, o))\n                        vf.write('%s %d %d %d %d %d\\n' % (subcat2, h, self.hypHistory[h], self.res_m[h], frameid, o))\n                self.hypHistory[h] = frameid\n                self.last_match[o] = frameid\n                self._append_to_indices(frameid, next(eid))\n                self._append_to_events(cat1, oids[i], hids[j], dists[i, j])\n                oids_masked[i] = True\n                hids_masked[j] = True\n                self.m[o] = h\n                self.res_m[h] = o\n\n\n        # 3. All remaining objects are missed\n        for o in oids[~oids_masked]:\n            self._append_to_indices(frameid, next(eid))\n            self._append_to_events('MISS', o, np.nan, np.nan)\n            if vf != '':\n                vf.write('FN %d %d\\n' % (frameid, o))\n\n        # 4. All remaining hypotheses are false alarms\n        for h in hids[~hids_masked]:\n            self._append_to_indices(frameid, next(eid))\n            self._append_to_events('FP', np.nan, h, np.nan)\n            if vf != '':\n                vf.write('FP %d %d\\n' % (frameid, h))\n\n        # 5. Update occurance state\n        for o in oids:\n            self.last_occurrence[o] = frameid\n\n        return frameid\n\n    @property\n    def events(self):\n        if self.dirty_events:\n            self.cached_events_df = MOTAccumulator.new_event_dataframe_with_data(self._indices, self._events)\n            self.dirty_events = False\n        return self.cached_events_df\n\n    @property\n    def mot_events(self):\n        df = self.events\n        return df[df.Type != 'RAW']\n\n    @staticmethod\n    def new_event_dataframe():\n        \"\"\"Create a new DataFrame for event tracking.\"\"\"\n        idx = pd.MultiIndex(levels=[[], []], codes=[[], []], names=['FrameId', 'Event'])\n        cats = pd.Categorical([], categories=['RAW', 'FP', 'MISS', 'SWITCH', 'MATCH', 'TRANSFER', 'ASCEND', 'MIGRATE'])\n        df = pd.DataFrame(\n            OrderedDict([\n                ('Type', pd.Series(cats)),          # Type of event. One of FP (false positive), MISS, SWITCH, MATCH\n                ('OId', pd.Series(dtype=float)),      # Object ID or -1 if FP. Using float as missing values will be converted to NaN anyways.\n                ('HId', pd.Series(dtype=float)),      # Hypothesis ID or NaN if MISS. Using float as missing values will be converted to NaN anyways.\n                ('D', pd.Series(dtype=float)),      # Distance or NaN when FP or MISS\n            ]),\n            index=idx\n        )\n        return df\n\n    @staticmethod\n    def new_event_dataframe_with_data(indices, events):\n        \"\"\"Create a new DataFrame filled with data.\n\n        Params\n        ------\n        indices: dict\n            dict of lists with fields 'FrameId' and 'Event'\n        events: dict\n            dict of lists with fields 'Type', 'OId', 'HId', 'D'\n        \"\"\"\n\n        if len(events) == 0:\n            return MOTAccumulator.new_event_dataframe()\n\n        raw_type = pd.Categorical(\n            events['Type'],\n            categories=['RAW', 'FP', 'MISS', 'SWITCH', 'MATCH', 'TRANSFER', 'ASCEND', 'MIGRATE'],\n            ordered=False)\n        series = [\n            pd.Series(raw_type, name='Type'),\n            pd.Series(events['OId'], dtype=float, name='OId'),\n            pd.Series(events['HId'], dtype=float, name='HId'),\n            pd.Series(events['D'], dtype=float, name='D')\n        ]\n\n        idx = pd.MultiIndex.from_arrays(\n            [indices[field] for field in _INDEX_FIELDS],\n            names=_INDEX_FIELDS)\n        df = pd.concat(series, axis=1)\n        df.index = idx\n        return df\n\n    @staticmethod\n    def merge_analysis(anas, infomap):\n        # pylint: disable=missing-function-docstring\n        res = {'hyp': {}, 'obj': {}}\n        mapp = {'hyp': 'hid_map', 'obj': 'oid_map'}\n        for ana, infom in zip(anas, infomap):\n            if ana is None:\n                return None\n            for t in ana.keys():\n                which = mapp[t]\n                if np.nan in infom[which]:\n                    res[t][int(infom[which][np.nan])] = 0\n                if 'nan' in infom[which]:\n                    res[t][int(infom[which]['nan'])] = 0\n                for _id, cnt in ana[t].items():\n                    if _id not in infom[which]:\n                        _id = str(_id)\n                    res[t][int(infom[which][_id])] = cnt\n        return res\n\n    @staticmethod\n    def merge_event_dataframes(dfs, update_frame_indices=True, update_oids=True, update_hids=True, return_mappings=False):\n        \"\"\"Merge dataframes.\n\n        Params\n        ------\n        dfs : list of pandas.DataFrame or MotAccumulator\n            A list of event containers to merge\n\n        Kwargs\n        ------\n        update_frame_indices : boolean, optional\n            Ensure that frame indices are unique in the merged container\n        update_oids : boolean, unique\n            Ensure that object ids are unique in the merged container\n        update_hids : boolean, unique\n            Ensure that hypothesis ids are unique in the merged container\n        return_mappings : boolean, unique\n            Whether or not to return mapping information\n\n        Returns\n        -------\n        df : pandas.DataFrame\n            Merged event data frame\n        \"\"\"\n\n        mapping_infos = []\n        new_oid = itertools.count()\n        new_hid = itertools.count()\n\n        r = MOTAccumulator.new_event_dataframe()\n        for df in dfs:\n\n            if isinstance(df, MOTAccumulator):\n                df = df.events\n\n            copy = df.copy()\n            infos = {}\n\n            # Update index\n            if update_frame_indices:\n                # pylint: disable=cell-var-from-loop\n                next_frame_id = max(r.index.get_level_values(0).max() + 1, r.index.get_level_values(0).unique().shape[0])\n                if np.isnan(next_frame_id):\n                    next_frame_id = 0\n                copy.index = copy.index.map(lambda x: (x[0] + next_frame_id, x[1]))\n                infos['frame_offset'] = next_frame_id\n\n            # Update object / hypothesis ids\n            if update_oids:\n                # pylint: disable=cell-var-from-loop\n                oid_map = dict([oid, str(next(new_oid))] for oid in copy['OId'].dropna().unique())\n                copy['OId'] = copy['OId'].map(lambda x: oid_map[x], na_action='ignore')\n                infos['oid_map'] = oid_map\n\n            if update_hids:\n                # pylint: disable=cell-var-from-loop\n                hid_map = dict([hid, str(next(new_hid))] for hid in copy['HId'].dropna().unique())\n                copy['HId'] = copy['HId'].map(lambda x: hid_map[x], na_action='ignore')\n                infos['hid_map'] = hid_map\n\n            r = r.append(copy)\n            mapping_infos.append(infos)\n\n        if return_mappings:\n            return r, mapping_infos\n        else:\n            return r\n"
  },
  {
    "path": "motmetrics/preprocess.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Preprocess data for CLEAR_MOT_M.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom configparser import ConfigParser\nimport logging\nimport time\n\nimport numpy as np\n\nimport motmetrics.distances as mmd\nfrom motmetrics.lap import linear_sum_assignment\n\n\ndef preprocessResult(res, gt, inifile):\n    \"\"\"Preprocesses data for utils.CLEAR_MOT_M.\n\n    Returns a subset of the predictions.\n    \"\"\"\n    # pylint: disable=too-many-locals\n    st = time.time()\n    labels = [\n        'ped',               # 1\n        'person_on_vhcl',    # 2\n        'car',               # 3\n        'bicycle',           # 4\n        'mbike',             # 5\n        'non_mot_vhcl',      # 6\n        'static_person',     # 7\n        'distractor',        # 8\n        'occluder',          # 9\n        'occluder_on_grnd',  # 10\n        'occluder_full',     # 11\n        'reflection',        # 12\n        'crowd',             # 13\n    ]\n    distractors = ['person_on_vhcl', 'static_person', 'distractor', 'reflection']\n    is_distractor = {i + 1: x in distractors for i, x in enumerate(labels)}\n    for i in distractors:\n        is_distractor[i] = 1\n    seqIni = ConfigParser()\n    seqIni.read(inifile, encoding='utf8')\n    F = int(seqIni['Sequence']['seqLength'])\n    todrop = []\n    for t in range(1, F + 1):\n        if t not in res.index or t not in gt.index:\n            continue\n        resInFrame = res.loc[t]\n\n        GTInFrame = gt.loc[t]\n        A = GTInFrame[['X', 'Y', 'Width', 'Height']].values\n        B = resInFrame[['X', 'Y', 'Width', 'Height']].values\n        disM = mmd.iou_matrix(A, B, max_iou=0.5)\n        le, ri = linear_sum_assignment(disM)\n        flags = [\n            1 if is_distractor[it['ClassId']] or it['Visibility'] < 0. else 0\n            for i, (k, it) in enumerate(GTInFrame.iterrows())\n        ]\n        hid = [k for k, it in resInFrame.iterrows()]\n        for i, j in zip(le, ri):\n            if not np.isfinite(disM[i, j]):\n                continue\n            if flags[i]:\n                todrop.append((t, hid[j]))\n    ret = res.drop(labels=todrop)\n    logging.info('Preprocess take %.3f seconds and remove %d boxes.',\n                 time.time() - st, len(todrop))\n    return ret\n"
  },
  {
    "path": "motmetrics/tests/__init__.py",
    "content": ""
  },
  {
    "path": "motmetrics/tests/test_distances.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Tests distance computation.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nimport motmetrics as mm\n\n\ndef test_norm2squared():\n    \"\"\"Tests norm2squared_matrix.\"\"\"\n    a = np.asfarray([\n        [1, 2],\n        [2, 2],\n        [3, 2],\n    ])\n\n    b = np.asfarray([\n        [0, 0],\n        [1, 1],\n    ])\n\n    C = mm.distances.norm2squared_matrix(a, b)\n    np.testing.assert_allclose(\n        C,\n        [\n            [5, 1],\n            [8, 2],\n            [13, 5]\n        ]\n    )\n\n    C = mm.distances.norm2squared_matrix(a, b, max_d2=5)\n    np.testing.assert_allclose(\n        C,\n        [\n            [5, 1],\n            [np.nan, 2],\n            [np.nan, 5]\n        ]\n    )\n\n\ndef test_norm2squared_empty():\n    \"\"\"Tests norm2squared_matrix with an empty input.\"\"\"\n    a = []\n    b = np.asfarray([[0, 0], [1, 1]])\n    C = mm.distances.norm2squared_matrix(a, b)\n    assert C.size == 0\n    C = mm.distances.norm2squared_matrix(b, a)\n    assert C.size == 0\n\n\ndef test_iou_matrix():\n    \"\"\"Tests iou_matrix.\"\"\"\n    a = np.array([\n        [0, 0, 1, 2],\n    ])\n\n    b = np.array([\n        [0, 0, 1, 2],\n        [0, 0, 1, 1],\n        [1, 1, 1, 1],\n        [0.5, 0, 1, 1],\n        [0, 1, 1, 1],\n    ])\n    np.testing.assert_allclose(\n        mm.distances.iou_matrix(a, b),\n        [[0, 0.5, 1, 0.8, 0.5]],\n        atol=1e-4\n    )\n\n    np.testing.assert_allclose(\n        mm.distances.iou_matrix(a, b, max_iou=0.5),\n        [[0, 0.5, np.nan, np.nan, 0.5]],\n        atol=1e-4\n    )\n"
  },
  {
    "path": "motmetrics/tests/test_io.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Tests IO functions.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\n\nimport pandas as pd\n\nimport motmetrics as mm\n\nDATA_DIR = os.path.join(os.path.dirname(__file__), '../data')\n\n\ndef test_load_vatic():\n    \"\"\"Tests VATIC_TXT format.\"\"\"\n    df = mm.io.loadtxt(os.path.join(DATA_DIR, 'iotest/vatic.txt'), fmt=mm.io.Format.VATIC_TXT)\n\n    expected = pd.DataFrame([\n        # F,ID,Y,W,H,L,O,G,F,A1,A2,A3,A4\n        (0, 0, 412, 0, 430, 124, 0, 0, 0, 'worker', 0, 0, 0, 0),\n        (1, 0, 412, 10, 430, 114, 0, 0, 1, 'pc', 1, 0, 1, 0),\n        (1, 1, 412, 0, 430, 124, 0, 0, 1, 'pc', 0, 1, 0, 0),\n        (2, 2, 412, 0, 430, 124, 0, 0, 1, 'worker', 1, 1, 0, 1)\n    ])\n\n    assert (df.reset_index().values == expected.values).all()\n\n\ndef test_load_motchallenge():\n    \"\"\"Tests MOT15_2D format.\"\"\"\n    df = mm.io.loadtxt(os.path.join(DATA_DIR, 'iotest/motchallenge.txt'), fmt=mm.io.Format.MOT15_2D)\n\n    expected = pd.DataFrame([\n        (1, 1, 398, 181, 121, 229, 1, -1, -1),  # Note -1 on x and y for correcting matlab\n        (1, 2, 281, 200, 92, 184, 1, -1, -1),\n        (2, 2, 268, 201, 87, 182, 1, -1, -1),\n        (2, 3, 70, 150, 100, 284, 1, -1, -1),\n        (2, 4, 199, 205, 55, 137, 1, -1, -1),\n    ])\n\n    assert (df.reset_index().values == expected.values).all()\n\n\ndef test_load_detrac_mat():\n    \"\"\"Tests DETRAC_MAT format.\"\"\"\n    df = mm.io.loadtxt(os.path.join(DATA_DIR, 'iotest/detrac.mat'), fmt=mm.io.Format.DETRAC_MAT)\n\n    expected = pd.DataFrame([\n        (1., 1., 745., 356., 148., 115., 1., -1., -1.),\n        (2., 1., 738., 350., 145., 111., 1., -1., -1.),\n        (3., 1., 732., 343., 142., 107., 1., -1., -1.),\n        (4., 1., 725., 336., 139., 104., 1., -1., -1.)\n    ])\n\n    assert (df.reset_index().values == expected.values).all()\n\n\ndef test_load_detrac_xml():\n    \"\"\"Tests DETRAC_XML format.\"\"\"\n    df = mm.io.loadtxt(os.path.join(DATA_DIR, 'iotest/detrac.xml'), fmt=mm.io.Format.DETRAC_XML)\n\n    expected = pd.DataFrame([\n        (1., 1., 744.6, 356.33, 148.2, 115.14, 1., -1., -1.),\n        (2., 1., 738.2, 349.51, 145.21, 111.29, 1., -1., -1.),\n        (3., 1., 731.8, 342.68, 142.23, 107.45, 1., -1., -1.),\n        (4., 1., 725.4, 335.85, 139.24, 103.62, 1., -1., -1.)\n    ])\n\n    assert (df.reset_index().values == expected.values).all()\n"
  },
  {
    "path": "motmetrics/tests/test_issue19.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Tests issue 19.\n\nhttps://github.com/cheind/py-motmetrics/issues/19\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nimport motmetrics as mm\n\n\ndef test_issue19():\n    \"\"\"Tests issue 19.\"\"\"\n    acc = mm.MOTAccumulator()\n\n    g0 = [0, 1]\n    p0 = [0, 1]\n    d0 = [[0.2, np.nan], [np.nan, 0.2]]\n\n    g1 = [2, 3]\n    p1 = [2, 3, 4, 5]\n    d1 = [[0.28571429, 0.5, 0.0, np.nan], [np.nan, 0.44444444, np.nan, 0.0]]\n\n    acc.update(g0, p0, d0, 0)\n    acc.update(g1, p1, d1, 1)\n\n    mh = mm.metrics.create()\n    mh.compute(acc)\n"
  },
  {
    "path": "motmetrics/tests/test_lap.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Tests linear assignment problem solvers.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport warnings\n\nimport numpy as np\nimport pytest\n\nfrom motmetrics import lap\n\nDESIRED_SOLVERS = ['lap', 'lapsolver', 'munkres', 'ortools', 'scipy']\nSOLVERS = lap.available_solvers\n\n\n@pytest.mark.parametrize('solver', DESIRED_SOLVERS)\ndef test_solver_is_available(solver):\n    if solver not in lap.available_solvers:\n        warnings.warn('solver not available: ' + solver)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_assign_easy(solver):\n    \"\"\"Problem that could be solved by a greedy algorithm.\"\"\"\n    costs = np.asfarray([[6, 9, 1], [10, 3, 2], [8, 7, 4]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    expected = np.array([[0, 1, 2], [2, 1, 0]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_assign_full(solver):\n    \"\"\"Problem that would be incorrect using a greedy algorithm.\"\"\"\n    costs = np.asfarray([[5, 5, 6], [1, 2, 5], [2, 4, 5]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    # Optimal matching is (0, 2), (1, 1), (2, 0) for 6 + 2 + 2.\n    expected = np.asfarray([[0, 1, 2], [2, 1, 0]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_assign_full_negative(solver):\n    costs = -7 + np.asfarray([[5, 5, 6], [1, 2, 5], [2, 4, 5]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    # Optimal matching is (0, 2), (1, 1), (2, 0) for 5 + 1 + 1.\n    expected = np.array([[0, 1, 2], [2, 1, 0]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_assign_empty(solver):\n    costs = np.asfarray([[]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    np.testing.assert_equal(np.size(result), 0)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_assign_infeasible(solver):\n    \"\"\"Tests that minimum-cost solution with most edges is found.\"\"\"\n    costs = np.asfarray([[np.nan, np.nan, 2],\n                         [np.nan, np.nan, 1],\n                         [8, 7, 4]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    # Optimal matching is (1, 2), (2, 1).\n    expected = np.array([[1, 2], [2, 1]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_assign_disallowed(solver):\n    costs = np.asfarray([[5, 9, np.nan], [10, np.nan, 2], [8, 7, 4]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    expected = np.array([[0, 1, 2], [0, 2, 1]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_assign_non_integer(solver):\n    costs = (1. / 9) * np.asfarray([[5, 9, np.nan], [10, np.nan, 2], [8, 7, 4]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    expected = np.array([[0, 1, 2], [0, 2, 1]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_assign_attractive_disallowed(solver):\n    \"\"\"Graph contains an attractive edge that cannot be used.\"\"\"\n    costs = np.asfarray([[-10000, -1], [-1, np.nan]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    # The optimal solution is (0, 1), (1, 0) for a cost of -2.\n    # Ensure that the algorithm does not choose the (0, 0) edge.\n    # This would not be a perfect matching.\n    expected = np.array([[0, 1], [1, 0]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_assign_attractive_broken_ring(solver):\n    \"\"\"Graph contains cheap broken ring and expensive unbroken ring.\"\"\"\n    costs = np.asfarray([[np.nan, 1000, np.nan], [np.nan, 1, 1000], [1000, np.nan, 1]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    # Optimal solution is (0, 1), (1, 2), (2, 0) with cost 1000 + 1000 + 1000.\n    # Solver might choose (0, 0), (1, 1), (2, 2) with cost inf + 1 + 1.\n    expected = np.array([[0, 1, 2], [1, 2, 0]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_unbalanced_wide(solver):\n    costs = np.asfarray([[6, 4, 1], [10, 8, 2]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    expected = np.array([[0, 1], [1, 2]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_unbalanced_tall(solver):\n    costs = np.asfarray([[6, 10], [4, 8], [1, 2]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    expected = np.array([[1, 2], [0, 1]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_unbalanced_disallowed_wide(solver):\n    costs = np.asfarray([[np.nan, 11, 8], [8, np.nan, 7]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    expected = np.array([[0, 1], [2, 0]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_unbalanced_disallowed_tall(solver):\n    costs = np.asfarray([[np.nan, 9], [11, np.nan], [8, 7]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    expected = np.array([[0, 2], [1, 0]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\n@pytest.mark.parametrize('solver', SOLVERS)\ndef test_unbalanced_infeasible(solver):\n    \"\"\"Tests that minimum-cost solution with most edges is found.\"\"\"\n    costs = np.asfarray([[np.nan, np.nan, 2],\n                         [np.nan, np.nan, 1],\n                         [np.nan, np.nan, 3],\n                         [8, 7, 4]])\n    costs_copy = costs.copy()\n    result = lap.linear_sum_assignment(costs, solver=solver)\n\n    # Optimal matching is (1, 2), (3, 1).\n    expected = np.array([[1, 3], [2, 1]])\n    np.testing.assert_equal(result, expected)\n    np.testing.assert_equal(costs, costs_copy)\n\n\ndef test_change_solver():\n    \"\"\"Tests effect of lap.set_default_solver.\"\"\"\n\n    def mysolver(_):\n        mysolver.called += 1\n        return np.array([]), np.array([])\n    mysolver.called = 0\n\n    costs = np.asfarray([[6, 9, 1], [10, 3, 2], [8, 7, 4]])\n\n    with lap.set_default_solver(mysolver):\n        lap.linear_sum_assignment(costs)\n    assert mysolver.called == 1\n    lap.linear_sum_assignment(costs)\n    assert mysolver.called == 1\n"
  },
  {
    "path": "motmetrics/tests/test_metrics.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Tests computation of metrics from accumulator.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom pytest import approx\n\nimport motmetrics as mm\n\nDATA_DIR = os.path.join(os.path.dirname(__file__), '../data')\n\n\ndef test_metricscontainer_1():\n    \"\"\"Tests registration of events with dependencies.\"\"\"\n    m = mm.metrics.MetricsHost()\n    m.register(lambda df: 1., name='a')\n    m.register(lambda df: 2., name='b')\n    m.register(lambda df, a, b: a + b, deps=['a', 'b'], name='add')\n    m.register(lambda df, a, b: a - b, deps=['a', 'b'], name='sub')\n    m.register(lambda df, a, b: a * b, deps=['add', 'sub'], name='mul')\n    summary = m.compute(mm.MOTAccumulator.new_event_dataframe(), metrics=['mul', 'add'], name='x')\n    assert summary.columns.values.tolist() == ['mul', 'add']\n    assert summary.iloc[0]['mul'] == -3.\n    assert summary.iloc[0]['add'] == 3.\n\n\ndef test_metricscontainer_autodep():\n    \"\"\"Tests automatic dependencies from argument names.\"\"\"\n    m = mm.metrics.MetricsHost()\n    m.register(lambda df: 1., name='a')\n    m.register(lambda df: 2., name='b')\n    m.register(lambda df, a, b: a + b, name='add', deps='auto')\n    m.register(lambda df, a, b: a - b, name='sub', deps='auto')\n    m.register(lambda df, add, sub: add * sub, name='mul', deps='auto')\n    summary = m.compute(mm.MOTAccumulator.new_event_dataframe(), metrics=['mul', 'add'])\n    assert summary.columns.values.tolist() == ['mul', 'add']\n    assert summary.iloc[0]['mul'] == -3.\n    assert summary.iloc[0]['add'] == 3.\n\n\ndef test_metricscontainer_autoname():\n    \"\"\"Tests automatic names (and dependencies) from inspection.\"\"\"\n\n    def constant_a(_):\n        \"\"\"Constant a help.\"\"\"\n        return 1.\n\n    def constant_b(_):\n        return 2.\n\n    def add(_, constant_a, constant_b):\n        return constant_a + constant_b\n\n    def sub(_, constant_a, constant_b):\n        return constant_a - constant_b\n\n    def mul(_, add, sub):\n        return add * sub\n\n    m = mm.metrics.MetricsHost()\n    m.register(constant_a, deps='auto')\n    m.register(constant_b, deps='auto')\n    m.register(add, deps='auto')\n    m.register(sub, deps='auto')\n    m.register(mul, deps='auto')\n\n    assert m.metrics['constant_a']['help'] == 'Constant a help.'\n\n    summary = m.compute(mm.MOTAccumulator.new_event_dataframe(), metrics=['mul', 'add'])\n    assert summary.columns.values.tolist() == ['mul', 'add']\n    assert summary.iloc[0]['mul'] == -3.\n    assert summary.iloc[0]['add'] == 3.\n\n\ndef test_metrics_with_no_events():\n    \"\"\"Tests metrics when accumulator is empty.\"\"\"\n    acc = mm.MOTAccumulator()\n\n    mh = mm.metrics.create()\n    metr = mh.compute(acc, return_dataframe=False, return_cached=True, metrics=[\n        'mota', 'motp', 'num_predictions', 'num_objects', 'num_detections', 'num_frames',\n    ])\n    assert np.isnan(metr['mota'])\n    assert np.isnan(metr['motp'])\n    assert metr['num_predictions'] == 0\n    assert metr['num_objects'] == 0\n    assert metr['num_detections'] == 0\n    assert metr['num_frames'] == 0\n\n\ndef test_assignment_metrics_with_empty_groundtruth():\n    \"\"\"Tests metrics when there are no ground-truth objects.\"\"\"\n    acc = mm.MOTAccumulator(auto_id=True)\n    # Empty groundtruth.\n    acc.update([], [1, 2, 3, 4], [])\n    acc.update([], [1, 2, 3, 4], [])\n    acc.update([], [1, 2, 3, 4], [])\n    acc.update([], [1, 2, 3, 4], [])\n\n    mh = mm.metrics.create()\n    metr = mh.compute(acc, return_dataframe=False, metrics=[\n        'num_matches', 'num_false_positives', 'num_misses',\n        'idtp', 'idfp', 'idfn', 'num_frames',\n    ])\n    assert metr['num_matches'] == 0\n    assert metr['num_false_positives'] == 16\n    assert metr['num_misses'] == 0\n    assert metr['idtp'] == 0\n    assert metr['idfp'] == 16\n    assert metr['idfn'] == 0\n    assert metr['num_frames'] == 4\n\n\ndef test_assignment_metrics_with_empty_predictions():\n    \"\"\"Tests metrics when there are no predictions.\"\"\"\n    acc = mm.MOTAccumulator(auto_id=True)\n    # Empty predictions.\n    acc.update([1, 2, 3, 4], [], [])\n    acc.update([1, 2, 3, 4], [], [])\n    acc.update([1, 2, 3, 4], [], [])\n    acc.update([1, 2, 3, 4], [], [])\n\n    mh = mm.metrics.create()\n    metr = mh.compute(acc, return_dataframe=False, metrics=[\n        'num_matches', 'num_false_positives', 'num_misses',\n        'idtp', 'idfp', 'idfn', 'num_frames',\n    ])\n    assert metr['num_matches'] == 0\n    assert metr['num_false_positives'] == 0\n    assert metr['num_misses'] == 16\n    assert metr['idtp'] == 0\n    assert metr['idfp'] == 0\n    assert metr['idfn'] == 16\n    assert metr['num_frames'] == 4\n\n\ndef test_assignment_metrics_with_both_empty():\n    \"\"\"Tests metrics when there are no ground-truth objects or predictions.\"\"\"\n    acc = mm.MOTAccumulator(auto_id=True)\n    # Empty groundtruth and empty predictions.\n    acc.update([], [], [])\n    acc.update([], [], [])\n    acc.update([], [], [])\n    acc.update([], [], [])\n\n    mh = mm.metrics.create()\n    metr = mh.compute(acc, return_dataframe=False, metrics=[\n        'num_matches', 'num_false_positives', 'num_misses',\n        'idtp', 'idfp', 'idfn', 'num_frames',\n    ])\n    assert metr['num_matches'] == 0\n    assert metr['num_false_positives'] == 0\n    assert metr['num_misses'] == 0\n    assert metr['idtp'] == 0\n    assert metr['idfp'] == 0\n    assert metr['idfn'] == 0\n    assert metr['num_frames'] == 4\n\n\ndef _extract_counts(acc):\n    df_map = mm.metrics.events_to_df_map(acc.events)\n    return mm.metrics.extract_counts_from_df_map(df_map)\n\n\ndef test_extract_counts():\n    \"\"\"Tests events_to_df_map() and extract_counts_from_df_map().\"\"\"\n    acc = mm.MOTAccumulator()\n    # All FP\n    acc.update([], [1, 2], [], frameid=0)\n    # All miss\n    acc.update([1, 2], [], [], frameid=1)\n    # Match\n    acc.update([1, 2], [1, 2], [[1, 0.5], [0.3, 1]], frameid=2)\n    # Switch\n    acc.update([1, 2], [1, 2], [[0.2, np.nan], [np.nan, 0.1]], frameid=3)\n    # Match. Better new match is available but should prefer history\n    acc.update([1, 2], [1, 2], [[5, 1], [1, 5]], frameid=4)\n    # No data\n    acc.update([], [], [], frameid=5)\n\n    ocs, hcs, tps = _extract_counts(acc)\n\n    assert ocs == {1: 4, 2: 4}\n    assert hcs == {1: 4, 2: 4}\n    expected_tps = {\n        (1, 1): 3,\n        (1, 2): 2,\n        (2, 1): 2,\n        (2, 2): 3,\n    }\n    assert tps == expected_tps\n\n\ndef test_extract_pandas_series_issue():\n    \"\"\"Reproduce issue that arises with pd.Series but not pd.DataFrame.\n\n    >>> data = [[0, 1, 0.1], [0, 1, 0.2], [0, 1, 0.3]]\n    >>> df = pd.DataFrame(data, columns=['x', 'y', 'z']).set_index(['x', 'y'])\n    >>> df['z'].groupby(['x', 'y']).count()\n    {(0, 1): 3}\n\n    >>> data = [[0, 1, 0.1], [0, 1, 0.2]]\n    >>> df = pd.DataFrame(data, columns=['x', 'y', 'z']).set_index(['x', 'y'])\n    >>> df['z'].groupby(['x', 'y']).count()\n    {'x': 1, 'y': 1}\n\n    >>> df[['z']].groupby(['x', 'y'])['z'].count().to_dict()\n    {(0, 1): 2}\n    \"\"\"\n    acc = mm.MOTAccumulator(auto_id=True)\n    acc.update([0], [1], [[0.1]])\n    acc.update([0], [1], [[0.1]])\n    ocs, hcs, tps = _extract_counts(acc)\n    assert ocs == {0: 2}\n    assert hcs == {1: 2}\n    assert tps == {(0, 1): 2}\n\n\ndef test_benchmark_extract_counts(benchmark):\n    \"\"\"Benchmarks events_to_df_map() and extract_counts_from_df_map().\"\"\"\n    rand = np.random.RandomState(0)\n    acc = _accum_random_uniform(\n        rand, seq_len=100, num_objs=50, num_hyps=5000,\n        objs_per_frame=20, hyps_per_frame=40)\n    benchmark(_extract_counts, acc)\n\n\ndef _accum_random_uniform(rand, seq_len, num_objs, num_hyps, objs_per_frame, hyps_per_frame):\n    acc = mm.MOTAccumulator(auto_id=True)\n    for _ in range(seq_len):\n        # Choose subset of objects present in this frame.\n        objs = rand.choice(num_objs, objs_per_frame, replace=False)\n        # Choose subset of hypotheses present in this frame.\n        hyps = rand.choice(num_hyps, hyps_per_frame, replace=False)\n        dist = rand.uniform(size=(objs_per_frame, hyps_per_frame))\n        acc.update(objs, hyps, dist)\n    return acc\n\n\ndef test_mota_motp():\n    \"\"\"Tests values of MOTA and MOTP.\"\"\"\n    acc = mm.MOTAccumulator()\n\n    # All FP\n    acc.update([], [1, 2], [], frameid=0)\n    # All miss\n    acc.update([1, 2], [], [], frameid=1)\n    # Match\n    acc.update([1, 2], [1, 2], [[1, 0.5], [0.3, 1]], frameid=2)\n    # Switch\n    acc.update([1, 2], [1, 2], [[0.2, np.nan], [np.nan, 0.1]], frameid=3)\n    # Match. Better new match is available but should prefer history\n    acc.update([1, 2], [1, 2], [[5, 1], [1, 5]], frameid=4)\n    # No data\n    acc.update([], [], [], frameid=5)\n\n    mh = mm.metrics.create()\n    metr = mh.compute(acc, return_dataframe=False, return_cached=True, metrics=[\n        'num_matches', 'num_false_positives', 'num_misses', 'num_switches', 'num_detections',\n        'num_objects', 'num_predictions', 'mota', 'motp', 'num_frames'\n    ])\n\n    assert metr['num_matches'] == 4\n    assert metr['num_false_positives'] == 2\n    assert metr['num_misses'] == 2\n    assert metr['num_switches'] == 2\n    assert metr['num_detections'] == 6\n    assert metr['num_objects'] == 8\n    assert metr['num_predictions'] == 8\n    assert metr['mota'] == approx(1. - (2 + 2 + 2) / 8)\n    assert metr['motp'] == approx(11.1 / 6)\n    assert metr['num_frames'] == 6\n\n\ndef test_ids():\n    \"\"\"Test metrics with frame IDs specified manually.\"\"\"\n    acc = mm.MOTAccumulator()\n\n    # No data\n    acc.update([], [], [], frameid=0)\n    # Match\n    acc.update([1, 2], [1, 2], [[1, 0], [0, 1]], frameid=1)\n    # Switch also Transfer\n    acc.update([1, 2], [1, 2], [[0.4, np.nan], [np.nan, 0.4]], frameid=2)\n    # Match\n    acc.update([1, 2], [1, 2], [[0, 1], [1, 0]], frameid=3)\n    # Ascend (switch)\n    acc.update([1, 2], [2, 3], [[1, 0], [0.4, 0.7]], frameid=4)\n    # Migrate (transfer)\n    acc.update([1, 3], [2, 3], [[1, 0], [0.4, 0.7]], frameid=5)\n    # No data\n    acc.update([], [], [], frameid=6)\n\n    mh = mm.metrics.create()\n    metr = mh.compute(acc, return_dataframe=False, return_cached=True, metrics=[\n        'num_matches', 'num_false_positives', 'num_misses', 'num_switches',\n        'num_transfer', 'num_ascend', 'num_migrate',\n        'num_detections', 'num_objects', 'num_predictions',\n        'mota', 'motp', 'num_frames',\n    ])\n    assert metr['num_matches'] == 7\n    assert metr['num_false_positives'] == 0\n    assert metr['num_misses'] == 0\n    assert metr['num_switches'] == 3\n    assert metr['num_transfer'] == 3\n    assert metr['num_ascend'] == 1\n    assert metr['num_migrate'] == 1\n    assert metr['num_detections'] == 10\n    assert metr['num_objects'] == 10\n    assert metr['num_predictions'] == 10\n    assert metr['mota'] == approx(1. - (0 + 0 + 3) / 10)\n    assert metr['motp'] == approx(1.6 / 10)\n    assert metr['num_frames'] == 7\n\n\ndef test_correct_average():\n    \"\"\"Tests what is depicted in figure 3 of 'Evaluating MOT Performance'.\"\"\"\n    acc = mm.MOTAccumulator(auto_id=True)\n\n    # No track\n    acc.update([1, 2, 3, 4], [], [])\n    acc.update([1, 2, 3, 4], [], [])\n    acc.update([1, 2, 3, 4], [], [])\n    acc.update([1, 2, 3, 4], [], [])\n\n    # Track single\n    acc.update([4], [4], [0])\n    acc.update([4], [4], [0])\n    acc.update([4], [4], [0])\n    acc.update([4], [4], [0])\n\n    mh = mm.metrics.create()\n    metr = mh.compute(acc, metrics='mota', return_dataframe=False)\n    assert metr['mota'] == approx(0.2)\n\n\ndef test_motchallenge_files():\n    \"\"\"Tests metrics for sequences TUD-Campus and TUD-Stadtmitte.\"\"\"\n    dnames = [\n        'TUD-Campus',\n        'TUD-Stadtmitte',\n    ]\n\n    def compute_motchallenge(dname):\n        df_gt = mm.io.loadtxt(os.path.join(dname, 'gt.txt'))\n        df_test = mm.io.loadtxt(os.path.join(dname, 'test.txt'))\n        return mm.utils.compare_to_groundtruth(df_gt, df_test, 'iou', distth=0.5)\n\n    accs = [compute_motchallenge(os.path.join(DATA_DIR, d)) for d in dnames]\n\n    # For testing\n    # [a.events.to_pickle(n) for (a,n) in zip(accs, dnames)]\n\n    mh = mm.metrics.create()\n    summary = mh.compute_many(accs, metrics=mm.metrics.motchallenge_metrics, names=dnames, generate_overall=True)\n\n    print()\n    print(mm.io.render_summary(summary, namemap=mm.io.motchallenge_metric_names, formatters=mh.formatters))\n    # assert ((summary['num_transfer'] - summary['num_migrate']) == (summary['num_switches'] - summary['num_ascend'])).all() # False assertion\n    summary = summary[mm.metrics.motchallenge_metrics[:15]]\n    expected = pd.DataFrame([\n        [0.557659, 0.729730, 0.451253, 0.582173, 0.941441, 8.0, 1, 6, 1, 13, 150, 7, 7, 0.526462, 0.277201],\n        [0.644619, 0.819760, 0.531142, 0.608997, 0.939920, 10.0, 5, 4, 1, 45, 452, 7, 6, 0.564014, 0.345904],\n        [0.624296, 0.799176, 0.512211, 0.602640, 0.940268, 18.0, 6, 10, 2, 58, 602, 14, 13, 0.555116, 0.330177],\n    ])\n    np.testing.assert_allclose(summary, expected, atol=1e-3)\n"
  },
  {
    "path": "motmetrics/tests/test_mot.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Tests behavior of MOTAccumulator.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nimport motmetrics as mm\n\n\ndef test_events():\n    \"\"\"Tests that expected events are created by MOTAccumulator.update().\"\"\"\n    acc = mm.MOTAccumulator()\n\n    # All FP\n    acc.update([], [1, 2], [], frameid=0)\n    # All miss\n    acc.update([1, 2], [], [], frameid=1)\n    # Match\n    acc.update([1, 2], [1, 2], [[1, 0.5], [0.3, 1]], frameid=2)\n    # Switch\n    acc.update([1, 2], [1, 2], [[0.2, np.nan], [np.nan, 0.1]], frameid=3)\n    # Match. Better new match is available but should prefer history\n    acc.update([1, 2], [1, 2], [[5, 1], [1, 5]], frameid=4)\n    # No data\n    acc.update([], [], [], frameid=5)\n\n    expect = mm.MOTAccumulator.new_event_dataframe()\n    expect.loc[(0, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(0, 1), :] = ['RAW', np.nan, 1, np.nan]\n    expect.loc[(0, 2), :] = ['RAW', np.nan, 2, np.nan]\n    expect.loc[(0, 3), :] = ['FP', np.nan, 1, np.nan]\n    expect.loc[(0, 4), :] = ['FP', np.nan, 2, np.nan]\n\n    expect.loc[(1, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(1, 1), :] = ['RAW', 1, np.nan, np.nan]\n    expect.loc[(1, 2), :] = ['RAW', 2, np.nan, np.nan]\n    expect.loc[(1, 3), :] = ['MISS', 1, np.nan, np.nan]\n    expect.loc[(1, 4), :] = ['MISS', 2, np.nan, np.nan]\n\n    expect.loc[(2, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(2, 1), :] = ['RAW', 1, 1, 1.0]\n    expect.loc[(2, 2), :] = ['RAW', 1, 2, 0.5]\n    expect.loc[(2, 3), :] = ['RAW', 2, 1, 0.3]\n    expect.loc[(2, 4), :] = ['RAW', 2, 2, 1.0]\n    expect.loc[(2, 5), :] = ['MATCH', 1, 2, 0.5]\n    expect.loc[(2, 6), :] = ['MATCH', 2, 1, 0.3]\n\n    expect.loc[(3, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(3, 1), :] = ['RAW', 1, 1, 0.2]\n    expect.loc[(3, 2), :] = ['RAW', 2, 2, 0.1]\n    expect.loc[(3, 3), :] = ['TRANSFER', 1, 1, 0.2]\n    expect.loc[(3, 4), :] = ['SWITCH', 1, 1, 0.2]\n    expect.loc[(3, 5), :] = ['TRANSFER', 2, 2, 0.1]\n    expect.loc[(3, 6), :] = ['SWITCH', 2, 2, 0.1]\n\n    expect.loc[(4, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(4, 1), :] = ['RAW', 1, 1, 5.]\n    expect.loc[(4, 2), :] = ['RAW', 1, 2, 1.]\n    expect.loc[(4, 3), :] = ['RAW', 2, 1, 1.]\n    expect.loc[(4, 4), :] = ['RAW', 2, 2, 5.]\n    expect.loc[(4, 5), :] = ['MATCH', 1, 1, 5.]\n    expect.loc[(4, 6), :] = ['MATCH', 2, 2, 5.]\n\n    expect.loc[(5, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n\n    pd.util.testing.assert_frame_equal(acc.events, expect)\n\n\ndef test_max_switch_time():\n    \"\"\"Tests max_switch_time option.\"\"\"\n    acc = mm.MOTAccumulator(max_switch_time=1)\n    acc.update([1, 2], [1, 2], [[1, 0.5], [0.3, 1]], frameid=1)  # 1->a, 2->b\n    frameid = acc.update([1, 2], [1, 2], [[0.5, np.nan], [np.nan, 0.5]], frameid=2)  # 1->b, 2->a\n\n    df = acc.events.loc[frameid]\n    assert ((df.Type == 'SWITCH') | (df.Type == 'RAW') | (df.Type == 'TRANSFER')).all()\n\n    acc = mm.MOTAccumulator(max_switch_time=1)\n    acc.update([1, 2], [1, 2], [[1, 0.5], [0.3, 1]], frameid=1)  # 1->a, 2->b\n    frameid = acc.update([1, 2], [1, 2], [[0.5, np.nan], [np.nan, 0.5]], frameid=5)  # Later frame 1->b, 2->a\n\n    df = acc.events.loc[frameid]\n    assert ((df.Type == 'MATCH') | (df.Type == 'RAW') | (df.Type == 'TRANSFER')).all()\n\n\ndef test_auto_id():\n    \"\"\"Tests auto_id option.\"\"\"\n    acc = mm.MOTAccumulator(auto_id=True)\n    acc.update([1, 2, 3, 4], [], [])\n    acc.update([1, 2, 3, 4], [], [])\n    assert acc.events.index.levels[0][-1] == 1\n    acc.update([1, 2, 3, 4], [], [])\n    assert acc.events.index.levels[0][-1] == 2\n\n    with pytest.raises(AssertionError):\n        acc.update([1, 2, 3, 4], [], [], frameid=5)\n\n    acc = mm.MOTAccumulator(auto_id=False)\n    with pytest.raises(AssertionError):\n        acc.update([1, 2, 3, 4], [], [])\n\n\ndef test_merge_dataframes():\n    \"\"\"Tests merge_event_dataframes().\"\"\"\n    # pylint: disable=too-many-statements\n    acc = mm.MOTAccumulator()\n\n    acc.update([], [1, 2], [], frameid=0)\n    acc.update([1, 2], [], [], frameid=1)\n    acc.update([1, 2], [1, 2], [[1, 0.5], [0.3, 1]], frameid=2)\n    acc.update([1, 2], [1, 2], [[0.2, np.nan], [np.nan, 0.1]], frameid=3)\n\n    r, mappings = mm.MOTAccumulator.merge_event_dataframes([acc.events, acc.events], return_mappings=True)\n\n    expect = mm.MOTAccumulator.new_event_dataframe()\n\n    expect.loc[(0, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(0, 1), :] = ['RAW', np.nan, mappings[0]['hid_map'][1], np.nan]\n    expect.loc[(0, 2), :] = ['RAW', np.nan, mappings[0]['hid_map'][2], np.nan]\n    expect.loc[(0, 3), :] = ['FP', np.nan, mappings[0]['hid_map'][1], np.nan]\n    expect.loc[(0, 4), :] = ['FP', np.nan, mappings[0]['hid_map'][2], np.nan]\n\n    expect.loc[(1, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(1, 1), :] = ['RAW', mappings[0]['oid_map'][1], np.nan, np.nan]\n    expect.loc[(1, 2), :] = ['RAW', mappings[0]['oid_map'][2], np.nan, np.nan]\n    expect.loc[(1, 3), :] = ['MISS', mappings[0]['oid_map'][1], np.nan, np.nan]\n    expect.loc[(1, 4), :] = ['MISS', mappings[0]['oid_map'][2], np.nan, np.nan]\n\n    expect.loc[(2, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(2, 1), :] = ['RAW', mappings[0]['oid_map'][1], mappings[0]['hid_map'][1], 1]\n    expect.loc[(2, 2), :] = ['RAW', mappings[0]['oid_map'][1], mappings[0]['hid_map'][2], 0.5]\n    expect.loc[(2, 3), :] = ['RAW', mappings[0]['oid_map'][2], mappings[0]['hid_map'][1], 0.3]\n    expect.loc[(2, 4), :] = ['RAW', mappings[0]['oid_map'][2], mappings[0]['hid_map'][2], 1.0]\n    expect.loc[(2, 5), :] = ['MATCH', mappings[0]['oid_map'][1], mappings[0]['hid_map'][2], 0.5]\n    expect.loc[(2, 6), :] = ['MATCH', mappings[0]['oid_map'][2], mappings[0]['hid_map'][1], 0.3]\n\n    expect.loc[(3, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(3, 1), :] = ['RAW', mappings[0]['oid_map'][1], mappings[0]['hid_map'][1], 0.2]\n    expect.loc[(3, 2), :] = ['RAW', mappings[0]['oid_map'][2], mappings[0]['hid_map'][2], 0.1]\n    expect.loc[(3, 3), :] = ['TRANSFER', mappings[0]['oid_map'][1], mappings[0]['hid_map'][1], 0.2]\n    expect.loc[(3, 4), :] = ['SWITCH', mappings[0]['oid_map'][1], mappings[0]['hid_map'][1], 0.2]\n    expect.loc[(3, 5), :] = ['TRANSFER', mappings[0]['oid_map'][2], mappings[0]['hid_map'][2], 0.1]\n    expect.loc[(3, 6), :] = ['SWITCH', mappings[0]['oid_map'][2], mappings[0]['hid_map'][2], 0.1]\n\n    # Merge duplication\n    expect.loc[(4, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(4, 1), :] = ['RAW', np.nan, mappings[1]['hid_map'][1], np.nan]\n    expect.loc[(4, 2), :] = ['RAW', np.nan, mappings[1]['hid_map'][2], np.nan]\n    expect.loc[(4, 3), :] = ['FP', np.nan, mappings[1]['hid_map'][1], np.nan]\n    expect.loc[(4, 4), :] = ['FP', np.nan, mappings[1]['hid_map'][2], np.nan]\n\n    expect.loc[(5, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(5, 1), :] = ['RAW', mappings[1]['oid_map'][1], np.nan, np.nan]\n    expect.loc[(5, 2), :] = ['RAW', mappings[1]['oid_map'][2], np.nan, np.nan]\n    expect.loc[(5, 3), :] = ['MISS', mappings[1]['oid_map'][1], np.nan, np.nan]\n    expect.loc[(5, 4), :] = ['MISS', mappings[1]['oid_map'][2], np.nan, np.nan]\n\n    expect.loc[(6, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(6, 1), :] = ['RAW', mappings[1]['oid_map'][1], mappings[1]['hid_map'][1], 1]\n    expect.loc[(6, 2), :] = ['RAW', mappings[1]['oid_map'][1], mappings[1]['hid_map'][2], 0.5]\n    expect.loc[(6, 3), :] = ['RAW', mappings[1]['oid_map'][2], mappings[1]['hid_map'][1], 0.3]\n    expect.loc[(6, 4), :] = ['RAW', mappings[1]['oid_map'][2], mappings[1]['hid_map'][2], 1.0]\n    expect.loc[(6, 5), :] = ['MATCH', mappings[1]['oid_map'][1], mappings[1]['hid_map'][2], 0.5]\n    expect.loc[(6, 6), :] = ['MATCH', mappings[1]['oid_map'][2], mappings[1]['hid_map'][1], 0.3]\n\n    expect.loc[(7, 0), :] = ['RAW', np.nan, np.nan, np.nan]\n    expect.loc[(7, 1), :] = ['RAW', mappings[1]['oid_map'][1], mappings[1]['hid_map'][1], 0.2]\n    expect.loc[(7, 2), :] = ['RAW', mappings[1]['oid_map'][2], mappings[1]['hid_map'][2], 0.1]\n    expect.loc[(7, 3), :] = ['TRANSFER', mappings[1]['oid_map'][1], mappings[1]['hid_map'][1], 0.2]\n    expect.loc[(7, 4), :] = ['SWITCH', mappings[1]['oid_map'][1], mappings[1]['hid_map'][1], 0.2]\n    expect.loc[(7, 5), :] = ['TRANSFER', mappings[1]['oid_map'][2], mappings[1]['hid_map'][2], 0.1]\n    expect.loc[(7, 6), :] = ['SWITCH', mappings[1]['oid_map'][2], mappings[1]['hid_map'][2], 0.1]\n\n    pd.util.testing.assert_frame_equal(r, expect)\n"
  },
  {
    "path": "motmetrics/tests/test_utils.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Tests accumulation of events using utility functions.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport itertools\n\nimport numpy as np\nimport pandas as pd\n\nimport motmetrics as mm\n\n\ndef test_annotations_xor_predictions_present():\n    \"\"\"Tests frames that contain only annotations or predictions.\"\"\"\n    _ = None\n    anno_tracks = {\n        1: [0, 2, 4, 6, _, _, _],\n        2: [_, _, 0, 2, 4, _, _],\n    }\n    pred_tracks = {\n        1: [_, _, 3, 5, 7, 7, 7],\n    }\n    anno = _tracks_to_dataframe(anno_tracks)\n    pred = _tracks_to_dataframe(pred_tracks)\n    acc = mm.utils.compare_to_groundtruth(anno, pred, 'euc', distfields=['Position'], distth=2)\n    mh = mm.metrics.create()\n    metrics = mh.compute(acc, return_dataframe=False, metrics=[\n        'num_objects', 'num_predictions', 'num_unique_objects',\n    ])\n    np.testing.assert_equal(metrics['num_objects'], 7)\n    np.testing.assert_equal(metrics['num_predictions'], 5)\n    np.testing.assert_equal(metrics['num_unique_objects'], 2)\n\n\ndef _tracks_to_dataframe(tracks):\n    rows = []\n    for track_id, track in tracks.items():\n        for frame_id, position in zip(itertools.count(1), track):\n            if position is None:\n                continue\n            rows.append({\n                'FrameId': frame_id,\n                'Id': track_id,\n                'Position': position,\n            })\n    return pd.DataFrame(rows).set_index(['FrameId', 'Id'])\n"
  },
  {
    "path": "motmetrics/utils.py",
    "content": "# py-motmetrics - Metrics for multiple object tracker (MOT) benchmarking.\n# https://github.com/cheind/py-motmetrics/\n#\n# MIT License\n# Copyright (c) 2017-2020 Christoph Heindl, Jack Valmadre and others.\n# See LICENSE file for terms.\n\n\"\"\"Functions for populating event accumulators.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nfrom motmetrics.distances import iou_matrix, norm2squared_matrix\nfrom motmetrics.mot import MOTAccumulator\nfrom motmetrics.preprocess import preprocessResult\n\n\ndef compare_to_groundtruth(gt, dt, dist='iou', distfields=None, distth=0.5, vflag=''):\n    \"\"\"Compare groundtruth and detector results.\n\n    This method assumes both results are given in terms of DataFrames with at least the following fields\n     - `FrameId` First level index used for matching ground-truth and test frames.\n     - `Id` Secondary level index marking available object / hypothesis ids\n\n    Depending on the distance to be used relevant distfields need to be specified.\n\n    Params\n    ------\n    gt : pd.DataFrame\n        Dataframe for ground-truth\n    test : pd.DataFrame\n        Dataframe for detector results\n\n    Kwargs\n    ------\n    dist : str, optional\n        String identifying distance to be used. Defaults to intersection over union.\n    distfields: array, optional\n        Fields relevant for extracting distance information. Defaults to ['X', 'Y', 'Width', 'Height']\n    distth: float, optional\n        Maximum tolerable distance. Pairs exceeding this threshold are marked 'do-not-pair'.\n    \"\"\"\n    # pylint: disable=too-many-locals\n    if distfields is None:\n        distfields = ['X', 'Y', 'Width', 'Height']\n\n    def compute_iou(a, b):\n        return iou_matrix(a, b, max_iou=distth)\n\n    def compute_euc(a, b):\n        return norm2squared_matrix(a, b, max_d2=distth)\n\n    compute_dist = compute_iou if dist.upper() == 'IOU' else compute_euc\n\n    acc = MOTAccumulator()\n\n    # We need to account for all frames reported either by ground truth or\n    # detector. In case a frame is missing in GT this will lead to FPs, in\n    # case a frame is missing in detector results this will lead to FNs.\n    allframeids = gt.index.union(dt.index).levels[0]\n\n    gt = gt[distfields]\n    dt = dt[distfields]\n    fid_to_fgt = dict(iter(gt.groupby('FrameId')))\n    fid_to_fdt = dict(iter(dt.groupby('FrameId')))\n    for fid in allframeids:\n        oids = np.empty(0)\n        hids = np.empty(0)\n        dists = np.empty((0, 0))\n        if fid in fid_to_fgt:\n            fgt = fid_to_fgt[fid]\n            oids = fgt.index.get_level_values('Id')\n        if fid in fid_to_fdt:\n            fdt = fid_to_fdt[fid]\n            hids = fdt.index.get_level_values('Id')\n        if len(oids) > 0 and len(hids) > 0:\n            dists = compute_dist(fgt.values, fdt.values)\n        \n        acc.update(oids, hids, dists, frameid=fid, vf=vflag)\n\n    return acc\n\n\ndef CLEAR_MOT_M(gt, dt, inifile, dist='iou', distfields=None, distth=0.5, include_all=False, vflag=''):\n    \"\"\"Compare groundtruth and detector results.\n\n    This method assumes both results are given in terms of DataFrames with at least the following fields\n     - `FrameId` First level index used for matching ground-truth and test frames.\n     - `Id` Secondary level index marking available object / hypothesis ids\n\n    Depending on the distance to be used relevant distfields need to be specified.\n\n    Params\n    ------\n    gt : pd.DataFrame\n        Dataframe for ground-truth\n    test : pd.DataFrame\n        Dataframe for detector results\n\n    Kwargs\n    ------\n    dist : str, optional\n        String identifying distance to be used. Defaults to intersection over union.\n    distfields: array, optional\n        Fields relevant for extracting distance information. Defaults to ['X', 'Y', 'Width', 'Height']\n    distth: float, optional\n        Maximum tolerable distance. Pairs exceeding this threshold are marked 'do-not-pair'.\n    \"\"\"\n    # pylint: disable=too-many-locals\n    if distfields is None:\n        distfields = ['X', 'Y', 'Width', 'Height']\n\n    def compute_iou(a, b):\n        return iou_matrix(a, b, max_iou=distth)\n\n    def compute_euc(a, b):\n        return norm2squared_matrix(a, b, max_d2=distth)\n\n    compute_dist = compute_iou if dist.upper() == 'IOU' else compute_euc\n\n    acc = MOTAccumulator()\n    dt = preprocessResult(dt, gt, inifile)\n    if include_all:\n        gt = gt[gt['Confidence'] >= 0.99]\n    else:\n        gt = gt[(gt['Confidence'] >= 0.99) & (gt['ClassId'] == 1)]\n    # We need to account for all frames reported either by ground truth or\n    # detector. In case a frame is missing in GT this will lead to FPs, in\n    # case a frame is missing in detector results this will lead to FNs.\n    allframeids = gt.index.union(dt.index).levels[0]\n    analysis = {'hyp': {}, 'obj': {}}\n    for fid in allframeids:\n        oids = np.empty(0)\n        hids = np.empty(0)\n        dists = np.empty((0, 0))\n\n        if fid in gt.index:\n            fgt = gt.loc[fid]\n            oids = fgt.index.values\n            for oid in oids:\n                oid = int(oid)\n                if oid not in analysis['obj']:\n                    analysis['obj'][oid] = 0\n                analysis['obj'][oid] += 1\n\n        if fid in dt.index:\n            fdt = dt.loc[fid]\n            hids = fdt.index.values\n            for hid in hids:\n                hid = int(hid)\n                if hid not in analysis['hyp']:\n                    analysis['hyp'][hid] = 0\n                analysis['hyp'][hid] += 1\n\n        if oids.shape[0] > 0 and hids.shape[0] > 0:\n            dists = compute_dist(fgt[distfields].values, fdt[distfields].values)\n\n        acc.update(oids, hids, dists, frameid=fid, vf=vflag)\n\n    return acc, analysis\n"
  },
  {
    "path": "pretrained/README.md",
    "content": "\n"
  },
  {
    "path": "requirements.txt",
    "content": "# TODO: Update with exact module version\nnumpy\ntorch>=1.7\nopencv_python\nloguru\nscikit-image\ntqdm\ntorchvision>=0.10.0\nPillow\nthop\nninja\ntabulate\ntensorboard\nlap\n# motmetrics # now we use the local and customized version instead\nfilterpy\nh5py\n\n# verified versions\nonnx==1.8.1\nonnxruntime==1.8.0\nonnx-simplifier==0.3.5\n"
  },
  {
    "path": "setup.cfg",
    "content": "[isort]\nline_length = 100\nmulti_line_output = 3\nbalanced_wrapping = True\nknown_standard_library = setuptools\nknown_third_party = tqdm,loguru\nknown_data_processing = cv2,numpy,scipy,PIL,matplotlib,scikit_image\nknown_datasets = pycocotools\nknown_deeplearning = torch,torchvision,caffe2,onnx,apex,timm,thop,torch2trt,tensorrt,openvino,onnxruntime\nknown_myself = yolox\nsections = FUTURE,STDLIB,THIRDPARTY,data_processing,datasets,deeplearning,myself,FIRSTPARTY,LOCALFOLDER\nno_lines_before=STDLIB,THIRDPARTY,datasets\ndefault_section = FIRSTPARTY\n\n[flake8]\nmax-line-length = 100\nmax-complexity = 18\nexclude = __init__.py\n"
  },
  {
    "path": "setup.py",
    "content": "#!/usr/bin/env python\n# Copyright (c) Megvii, Inc. and its affiliates. All Rights Reserved\n\nimport re\nimport setuptools\nimport glob\nfrom os import path\nimport torch\nfrom torch.utils.cpp_extension import CppExtension\n\ntorch_ver = [int(x) for x in torch.__version__.split(\".\")[:2]]\nassert torch_ver >= [1, 3], \"Requires PyTorch >= 1.3\"\n\n\ndef get_extensions():\n    this_dir = path.dirname(path.abspath(__file__))\n    extensions_dir = path.join(this_dir, \"yolox\", \"layers\", \"csrc\")\n\n    main_source = path.join(extensions_dir, \"vision.cpp\")\n    sources = glob.glob(path.join(extensions_dir, \"**\", \"*.cpp\"))\n\n    sources = [main_source] + sources\n    extension = CppExtension\n\n    extra_compile_args = {\"cxx\": [\"-O3\"]}\n    define_macros = []\n\n    include_dirs = [extensions_dir]\n\n    ext_modules = [\n        extension(\n            \"yolox._C\",\n            sources,\n            include_dirs=include_dirs,\n            define_macros=define_macros,\n            extra_compile_args=extra_compile_args,\n        )\n    ]\n\n    return ext_modules\n\n\nwith open(\"yolox/__init__.py\", \"r\") as f:\n    version = re.search(\n        r'^__version__\\s*=\\s*[\\'\"]([^\\'\"]*)[\\'\"]',\n        f.read(), re.MULTILINE\n    ).group(1)\n\n\nwith open(\"README.md\", \"r\") as f:\n    long_description = f.read()\n\n\nsetuptools.setup(\n    name=\"yolox\",\n    version=version,\n    author=\"basedet team\",\n    python_requires=\">=3.6\",\n    long_description=long_description,\n    ext_modules=get_extensions(),\n    classifiers=[\"Programming Language :: Python :: 3\", \"Operating System :: OS Independent\"],\n    cmdclass={\"build_ext\": torch.utils.cpp_extension.BuildExtension},\n    packages=setuptools.find_namespace_packages(),\n)\n"
  },
  {
    "path": "tools/convert_bdd_to_kitti.py",
    "content": "\"\"\"\n    script to convert prediction files in BDD100k format into KITTI format,\n    considering to attend the BDD100k challenge for more information:\n    https://www.bdd100k.com. We haven't run OC-SORT on BDD100K yet. Will likely\n    to update that later.\n\"\"\"\nimport json \nimport sys \nimport os\n\n\n# Example: 0 1 Car -1 -1 -1 483.81 173.31 658.93 242.23 -1 -1 -1 -1000 -1000 -1000 -10 0.93\nKITTI_format = \"%d %d %s -1 -1 -1 %f %f %f %f -1 -1 -1 -1000 -1000 -1000 -10 %f\\n\"\n\nseen_cate = []\n\ndef write_preds(summary, out_path):\n    os.makedirs(out_path, exist_ok=True)\n    video_names = summary.keys()\n    for video in video_names:\n        f = open(os.path.join(out_path, \"{}.txt\".format(video)), 'w')\n        labels = summary[video]\n        frames = labels.keys()\n        max_frame = max(frames)\n        min_frame = min(frames)\n        for f_idx in range(min_frame, max_frame+1):\n            # print(\"writing seq: {}: {}/{}\".format(video, f_idx, max_frame))\n            frame_label = labels[f_idx]\n            if frame_label is None:\n                continue\n            else:\n                for entry in frame_label:\n                    track_id = int(entry[\"id\"])\n                    score = float(entry[\"score\"])\n                    cate = entry[\"category\"]\n                    box = entry[\"box2d\"]\n                    x1, x2, y1, y2 = box[\"x1\"], box[\"x2\"], box[\"y1\"], box[\"y2\"]\n                    x1, x2, y1, y2 = float(x1), float(x2), float(y1), float(y2)\n                    write_line = KITTI_format % (f_idx, track_id, \\\n                            cate, x1, y1, x2, y2, score)\n                    f.write(write_line)\n\n\n\ndef convert_to_kitti(annos):\n    video_dict = dict()\n    for ann in annos:\n        videoName = ann[\"videoName\"]\n        frameIndex = ann[\"frameIndex\"]\n        if videoName not in video_dict:\n            video_dict[videoName] = dict()\n        if \"labels\" in ann.keys():\n            labels = ann[\"labels\"]\n        else:\n            labels = None\n        video_dict[videoName][frameIndex] = labels \n    return video_dict\n\n\n# if __name__ == \"__main__\":\n#     # for qdtrack results\n#     src_file, out_path = sys.argv[1], sys.argv[2]\n#     preds = json.load(open(src_file))[\"frames\"]\n#     summary = convert_to_kitti(preds)\n#     write_preds(summary, out_path)\n\nif __name__ == \"__main__\":\n    src_path, out_path = sys.argv[1], sys.argv[2]\n    os.makedirs(out_path, exist_ok=True)\n    results = os.listdir(src_path)\n    for result in results:\n        video_annos = []\n        seq_name = result.split(\".\")[0]\n        save_path = os.path.join(out_path, \"{}.txt\".format(seq_name))\n        f = open(save_path, \"w\")\n        src_annos = json.load(open(os.path.join(src_path, result)))\n        out_annos = convert_to_kitti(src_annos)[seq_name]\n        frames = sorted(out_annos.keys())\n        for frame in frames:\n            tracks = out_annos[frame]\n            for entry in tracks:\n                track_id = int(entry[\"id\"])\n                # score = float(entry[\"score\"])\n                cate = entry[\"category\"]\n                box = entry[\"box2d\"]\n                x1, x2, y1, y2 = box[\"x1\"], box[\"x2\"], box[\"y1\"], box[\"y2\"]\n                x1, x2, y1, y2 = float(x1), float(x2), float(y1), float(y2)\n                write_line = KITTI_format % (frame, track_id, \\\n                        cate, x1, y1, x2, y2, 1)\n                f.write(write_line)\n"
  },
  {
    "path": "tools/convert_cityperson_to_coco.py",
    "content": "import os\nimport numpy as np\nimport json\nfrom PIL import Image\n\nDATA_PATH = 'datasets/Cityscapes/'\nDATA_FILE_PATH = 'datasets/data_path/citypersons.train'\nOUT_PATH = DATA_PATH + 'annotations/'\n\ndef load_paths(data_path):\n    with open(data_path, 'r') as file:\n        img_files = file.readlines()\n        img_files = [x.replace('\\n', '') for x in img_files]\n        img_files = list(filter(lambda x: len(x) > 0, img_files))\n    label_files = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt') for x in img_files]\n    return img_files, label_files                    \n\nif __name__ == '__main__':\n    if not os.path.exists(OUT_PATH):\n        os.mkdir(OUT_PATH)\n\n    out_path = OUT_PATH + 'train.json'\n    out = {'images': [], 'annotations': [], 'categories': [{'id': 1, 'name': 'person'}]}\n    img_paths, label_paths = load_paths(DATA_FILE_PATH)\n    image_cnt = 0\n    ann_cnt = 0\n    video_cnt = 0\n    for img_path, label_path in zip(img_paths, label_paths):\n        image_cnt += 1\n        im = Image.open(os.path.join(\"datasets\", img_path))\n        image_info = {'file_name': img_path, \n                        'id': image_cnt,\n                        'height': im.size[1], \n                        'width': im.size[0]}\n        out['images'].append(image_info)\n        # Load labels\n        if os.path.isfile(os.path.join(\"datasets\", label_path)):\n            labels0 = np.loadtxt(os.path.join(\"datasets\", label_path), dtype=np.float32).reshape(-1, 6)\n            # Normalized xywh to pixel xyxy format\n            labels = labels0.copy()\n            labels[:, 2] = image_info['width'] * (labels0[:, 2] - labels0[:, 4] / 2)\n            labels[:, 3] = image_info['height'] * (labels0[:, 3] - labels0[:, 5] / 2)\n            labels[:, 4] = image_info['width'] * labels0[:, 4]\n            labels[:, 5] = image_info['height'] * labels0[:, 5]\n        else:\n            labels = np.array([])\n        for i in range(len(labels)):\n            ann_cnt += 1\n            fbox = labels[i, 2:6].tolist()\n            ann = {'id': ann_cnt,\n                    'category_id': 1,\n                    'image_id': image_cnt,\n                    'track_id': -1,\n                    'bbox': fbox,\n                    'area': fbox[2] * fbox[3],\n                    'iscrowd': 0}\n            out['annotations'].append(ann)\n    print('loaded train for {} images and {} samples'.format(len(out['images']), len(out['annotations'])))\n    json.dump(out, open(out_path, 'w'))\n"
  },
  {
    "path": "tools/convert_crowdhuman_to_coco.py",
    "content": "import os\nimport numpy as np\nimport json\nfrom PIL import Image\n\nDATA_PATH = 'datasets/crowdhuman/'\nOUT_PATH = DATA_PATH + 'annotations/'\nSPLITS = ['val', 'train']\nDEBUG = False\n\ndef load_func(fpath):\n    print('fpath', fpath)\n    assert os.path.exists(fpath)\n    with open(fpath,'r') as fid:\n        lines = fid.readlines()\n    records =[json.loads(line.strip('\\n')) for line in lines]\n    return records\n\nif __name__ == '__main__':\n    if not os.path.exists(OUT_PATH):\n        os.mkdir(OUT_PATH)\n    for split in SPLITS:\n        data_path = DATA_PATH + split\n        out_path = OUT_PATH + '{}.json'.format(split)\n        out = {'images': [], 'annotations': [], 'categories': [{'id': 1, 'name': 'person'}]}\n        ann_path = DATA_PATH + 'annotation_{}.odgt'.format(split)\n        anns_data = load_func(ann_path)\n        image_cnt = 0\n        ann_cnt = 0\n        video_cnt = 0\n        for ann_data in anns_data:\n            image_cnt += 1\n            file_path = DATA_PATH + 'CrowdHuman_{}/'.format(split) + '{}.jpg'.format(ann_data['ID'])\n            im = Image.open(file_path)\n            image_info = {'file_name': '{}.jpg'.format(ann_data['ID']), \n                          'id': image_cnt,\n                          'height': im.size[1], \n                          'width': im.size[0]}\n            out['images'].append(image_info)\n            if split != 'test':\n                anns = ann_data['gtboxes']\n                for i in range(len(anns)):\n                    ann_cnt += 1\n                    fbox = anns[i]['fbox']\n                    ann = {'id': ann_cnt,\n                         'category_id': 1,\n                         'image_id': image_cnt,\n                         'track_id': -1,\n                         'bbox_vis': anns[i]['vbox'],\n                         'bbox': fbox,\n                         'area': fbox[2] * fbox[3],\n                         'iscrowd': 1 if 'extra' in anns[i] and \\\n                                         'ignore' in anns[i]['extra'] and \\\n                                         anns[i]['extra']['ignore'] == 1 else 0}\n                    out['annotations'].append(ann)\n        print('loaded {} for {} images and {} samples'.format(split, len(out['images']), len(out['annotations'])))\n        json.dump(out, open(out_path, 'w'))"
  },
  {
    "path": "tools/convert_cuhk_to_coco.py",
    "content": "\"\"\"convert cuhk from label_with_ids in JDE to coco\"\"\"\nimport os\nimport numpy as np\nimport json\nfrom PIL import Image\n\ninput_root_dataset_folder = \"datasets/\"\noutput_root_dataset_folder = \"datasets/\"\noriginal_dataset_folder = \"datasets/CUHKSYSU\"\noutput_dataset_folder = \"datasets/CUHKSYSU\"\n\noriginal_image_path = os.path.join(original_dataset_folder, \"images\")\nimage_names = os.listdir(original_image_path)\n\nwith open(os.path.join(output_root_dataset_folder, 'cuhksysu.train'), 'w') as f:\n    for name in image_names:\n        full = 'CUHKSYSU/images/'+name+'\\n'\n        f.write(full)\n\ndata_file_path = os.path.join(output_root_dataset_folder, 'cuhksysu.train')\nout_path = os.path.join(output_dataset_folder, 'annotations')\n\n\ndef load_paths(data_path):\n    with open(data_path, 'r') as file:\n        img_files = file.readlines()\n        img_files = [x.replace('\\n', '') for x in img_files]\n        img_files = list(filter(lambda x: len(x) > 0, img_files))\n    label_files = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt') for x in img_files]\n    # print(img_files)\n    return img_files, label_files\n\nif __name__ == '__main__':\n    os.makedirs(out_path, exist_ok=True)\n\n    out_path = os.path.join(out_path, 'train.json')\n    out = {'images': [], 'annotations': [], 'categories': [{'id': 1, 'name': 'person'}]}\n    img_paths, label_paths = load_paths(data_file_path)\n    image_cnt = 0\n    ann_cnt = 0\n    video_cnt = 0\n    for img_path, label_path in zip(img_paths, label_paths):\n        image_cnt += 1\n        # print(os.path.join(\"../datasets\", img_path))\n        im = Image.open(os.path.join(input_root_dataset_folder, img_path))\n\n        image_info = {'file_name': img_path,\n                      'id': image_cnt,\n                      'height': im.size[1],\n                      'width': im.size[0],\n                      'video_id': 1,\n                      'frame_id': 1,\n                      'prev_image_id': image_cnt - 1,\n                      'prev_image_id': image_cnt + 1\n                      }\n        out['images'].append(image_info)\n        # Load labels\n        if os.path.isfile(os.path.join(input_root_dataset_folder, label_path)):\n            labels0 = np.loadtxt(os.path.join(input_root_dataset_folder, label_path), dtype=np.float32).reshape(-1, 6)\n            # Normalized xywh to pixel xyxy format\n            labels = labels0.copy()\n            labels[:, 2] = image_info['width'] * (labels0[:, 2] - labels0[:, 4] / 2)\n            labels[:, 3] = image_info['height'] * (labels0[:, 3] - labels0[:, 5] / 2)\n            labels[:, 4] = image_info['width'] * labels0[:, 4]\n            labels[:, 5] = image_info['height'] * labels0[:, 5]\n        else:\n            labels = np.array([])\n        for i in range(len(labels)):\n            ann_cnt += 1\n            labels[i, 1] = int(labels[i, 1])\n            if float(labels[i, 1]) != -1:       # min track_id from 0 to 1\n                labels[i, 1] = int(labels[i, 1]+1)\n            fbox = labels[i, 2:6].tolist()\n            ann = {'id': ann_cnt,\n                   'category_id': 1,\n                   'image_id': image_cnt,\n                   'track_id': int(labels[i, 1]),\n                   'bbox': fbox,\n                   'area': fbox[2] * fbox[3],\n                   'iscrowd': 0}\n            out['annotations'].append(ann)\n    print('loaded train for {} images and {} samples'.format(len(out['images']), len(out['annotations'])))\n    json.dump(out, open(out_path, 'w'))\n\n\n# print('crowdhuman_val')\n"
  },
  {
    "path": "tools/convert_dance_to_coco.py",
    "content": "\"\"\"\nhttps://github.com/xingyizhou/CenterTrack\nModified by Peize Sun\n\"\"\"\nimport os\nimport numpy as np\nimport json\nimport cv2\n\n\nDATA_PATH = 'datasets/dancetrack'\nOUT_PATH = os.path.join(DATA_PATH, 'annotations')\n# SPLITS = ['train', 'val', 'test']\nSPLITS = ['train', \"val\", \"test\"]\n\nif __name__ == '__main__':\n\n    if not os.path.exists(OUT_PATH):\n        os.makedirs(OUT_PATH)\n\n    for split in SPLITS:\n\n        data_path = os.path.join(DATA_PATH, split)\n        out_path = os.path.join(OUT_PATH, '{}.json'.format(split))\n        out = {'images': [], 'annotations': [], 'videos': [],\n               'categories': [{'id': 1, 'name': 'dancer'}]}\n        seqs = os.listdir(data_path)\n        image_cnt = 0\n        ann_cnt = 0\n        video_cnt = 0\n        for seq in sorted(seqs):\n            if '.DS_Store' in seq or '.ipy' in seq:\n                continue\n\n            video_cnt += 1  # video sequence number.\n            out['videos'].append({'id': video_cnt, 'file_name': seq})\n            seq_path = os.path.join(data_path, seq)\n            img_path = os.path.join(seq_path, 'img1')\n            ann_path = os.path.join(seq_path, 'gt/gt.txt')\n            images = os.listdir(img_path)\n            num_images = len([image for image in images if 'jpg' in image])  # half and half\n\n            for i in range(num_images):\n                img = cv2.imread(os.path.join(data_path, '{}/img1/{:08d}.jpg'.format(seq, i + 1)))\n                height, width = img.shape[:2]\n                image_info = {'file_name': '{}/img1/{:08d}.jpg'.format(seq, i + 1),  # image name.\n                              'id': image_cnt + i + 1,  # image number in the entire training set.\n                              'frame_id': i + 1,  # image number in the video sequence, starting from 1.\n                              'prev_image_id': image_cnt + i if i > 0 else -1,  # image number in the entire training set.\n                              'next_image_id': image_cnt + i + 2 if i < num_images - 1 else -1,\n                              'video_id': video_cnt,\n                              'height': height,\n                              'width': width}\n                out['images'].append(image_info)\n            print('{}: {} images'.format(seq, num_images))\n\n            if split != 'test':\n                anns = np.loadtxt(ann_path, dtype=np.float32, delimiter=',')\n                for i in range(anns.shape[0]):\n                    frame_id = int(anns[i][0])\n                    track_id = int(anns[i][1])\n                    cat_id = int(anns[i][7])\n                    ann_cnt += 1\n                    category_id = 1\n                    ann = {'id': ann_cnt,\n                           'category_id': category_id,\n                           'image_id': image_cnt + frame_id,\n                           'track_id': track_id,\n                           'bbox': anns[i][2:6].tolist(),\n                           'conf': float(anns[i][6]),\n                           'iscrowd': 0,\n                           'area': float(anns[i][4] * anns[i][5])}\n                    out['annotations'].append(ann)\n                print('{}: {} ann images'.format(seq, int(anns[:, 0].max())))\n\n            image_cnt += num_images\n        print('loaded {} for {} images and {} samples'.format(split, len(out['images']), len(out['annotations'])))\n        json.dump(out, open(out_path, 'w'))"
  },
  {
    "path": "tools/convert_ethz_to_coco.py",
    "content": "import os\nimport numpy as np\nimport json\nfrom PIL import Image\n\nDATA_PATH = 'datasets/ETHZ/'\nDATA_FILE_PATH = 'datasets/data_path/eth.train'\nOUT_PATH = DATA_PATH + 'annotations/'\n\ndef load_paths(data_path):\n    with open(data_path, 'r') as file:\n        img_files = file.readlines()\n        img_files = [x.replace('\\n', '') for x in img_files]\n        img_files = list(filter(lambda x: len(x) > 0, img_files))\n    label_files = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt') for x in img_files]\n    return img_files, label_files                    \n\nif __name__ == '__main__':\n    if not os.path.exists(OUT_PATH):\n        os.mkdir(OUT_PATH)\n\n    out_path = OUT_PATH + 'train.json'\n    out = {'images': [], 'annotations': [], 'categories': [{'id': 1, 'name': 'person'}]}\n    img_paths, label_paths = load_paths(DATA_FILE_PATH)\n    image_cnt = 0\n    ann_cnt = 0\n    video_cnt = 0\n    for img_path, label_path in zip(img_paths, label_paths):\n        image_cnt += 1\n        im = Image.open(os.path.join(\"datasets\", img_path))\n        image_info = {'file_name': img_path, \n                        'id': image_cnt,\n                        'height': im.size[1], \n                        'width': im.size[0]}\n        out['images'].append(image_info)\n        # Load labels\n        if os.path.isfile(os.path.join(\"datasets\", label_path)):\n            labels0 = np.loadtxt(os.path.join(\"datasets\", label_path), dtype=np.float32).reshape(-1, 6)\n            # Normalized xywh to pixel xyxy format\n            labels = labels0.copy()\n            labels[:, 2] = image_info['width'] * (labels0[:, 2] - labels0[:, 4] / 2)\n            labels[:, 3] = image_info['height'] * (labels0[:, 3] - labels0[:, 5] / 2)\n            labels[:, 4] = image_info['width'] * labels0[:, 4]\n            labels[:, 5] = image_info['height'] * labels0[:, 5]\n        else:\n            labels = np.array([])\n        for i in range(len(labels)):\n            ann_cnt += 1\n            fbox = labels[i, 2:6].tolist()\n            ann = {'id': ann_cnt,\n                    'category_id': 1,\n                    'image_id': image_cnt,\n                    'track_id': -1,\n                    'bbox': fbox,\n                    'area': fbox[2] * fbox[3],\n                    'iscrowd': 0}\n            out['annotations'].append(ann)\n    print('loaded train for {} images and {} samples'.format(len(out['images']), len(out['annotations'])))\n    json.dump(out, open(out_path, 'w'))\n"
  },
  {
    "path": "tools/convert_kitti_to_bdd.py",
    "content": "\"\"\"\n    script to convert kitti-format output to bdd-format\n\"\"\"\n\nimport json \nimport os \nimport sys \nimport shutil\n\n# def sanity_check(src, dst):\n#     for seq in os.listdir(src):\n#         src_file = os.path.join(src, seq)\n#         dst_file = os.path.join(dst, seq)\n#         src_annos = json.load(open(src_file))\n#         dst_annos = json.load(open(dst_file))\n#         if not len(src_annos) == len(dst_annos):\n#             shutil.copyfile(dst_file, src_file)\n#             print(seq)\n\n# if __name__ == \"__main__\":\n#     src, dst = sys.argv[1], sys.argv[2]\n#     sanity_check(src, dst)\n\n\nif __name__ == \"__main__\":\n    src_path, out_path = sys.argv[1], sys.argv[2]\n    os.makedirs(out_path, exist_ok=True)\n    anno_files = os.listdir(src_path)\n    out_dict = {}\n    out_dict[\"config\"] = None\n    # frames_dict = []\n    for anno_f in anno_files:\n        video_dict = dict()\n        videoName = anno_f.split(\".\")[0]\n        print(\"convert anno: {}\".format(anno_f))\n        f = open(os.path.join(src_path, anno_f))\n        lines = f.readlines()\n        frame_count = 0\n        for line in lines:\n            terms = line.split()\n            frame_id = int(terms[0])\n            if frame_count > frame_count + 1:\n                # missing frame\n                for i in range(frame_count+1, frame_id):\n                    frame_name = \"%s-%07d.jpg\" % (videoName, int(i)+1)\n                    frame_entry = {\"name\": frame_name, \"videoName\": videoName, \n                         \"frameIndex\": i}\n                    video_dict[i] = dict()\n                    video_dict[i][\"labels\"] = []\n                    video_dict[i][\"info\"] = frame_entry\n            frame_count = max(frame_count, frame_id)\n            if frame_id not in video_dict:\n                video_dict[frame_id] = dict()\n            track_id = terms[1]\n            cate = terms[2]\n            box = terms[6:10]\n            x1, y1, x2, y2= [float(d) for d in box]\n            score = terms[-1]\n            label_entry = {\"id\": track_id, \"score\": score, \"category\": cate,\n                \"box2d\": {\"x1\": x1, \"y1\": y1, \"x2\": x2, \"y2\": y2}}\n            frame_name = \"%s-%07d.jpg\" % (videoName, int(frame_id)+1)\n            frame_entry = {\"name\": frame_name, \"videoName\": videoName, \n                \"frameIndex\": frame_id}\n            if \"info\" not in video_dict[frame_id]:\n                video_dict[frame_id][\"info\"] = frame_entry\n                video_dict[frame_id][\"labels\"] = []\n            video_dict[frame_id][\"labels\"].append(label_entry)\n        video_labels = []\n        frame_ids = video_dict.keys()\n        for frame_id in sorted(frame_ids):\n            frame_entry = video_dict[frame_id][\"info\"]\n            label_entry = video_dict[frame_id][\"labels\"]\n            video_labels.append({    \"name\": frame_entry[\"name\"],\n                                    \"videoName\": frame_entry[\"videoName\"],\n                                    \"frameIndex\": frame_entry[\"frameIndex\"],\n                                    \"labels\": label_entry})\n        save_path = os.path.join(out_path, \"{}.json\".format(videoName))\n        json.dump(video_labels, open(save_path, 'w'))\n    # out_dict[\"frames\"] = frames_dict\n    # json.dump(out_dict, open(out_path, \"w\"))\n            \n            "
  },
  {
    "path": "tools/convert_mot17_to_coco.py",
    "content": "import os\nimport numpy as np\nimport json\nimport cv2\n\n\n# Use the same script for MOT16\nDATA_PATH = 'datasets/mot'\nOUT_PATH = os.path.join(DATA_PATH, 'annotations')\nSPLITS = ['train_half', 'val_half', 'train', 'test']  # --> split training data to train_half and val_half.\nHALF_VIDEO = True\nCREATE_SPLITTED_ANN = True\nCREATE_SPLITTED_DET = True\n\n\nif __name__ == '__main__':\n\n    if not os.path.exists(OUT_PATH):\n        os.makedirs(OUT_PATH)\n\n    for split in SPLITS:\n        if split == \"test\":\n            data_path = os.path.join(DATA_PATH, 'test')\n        else:\n            data_path = os.path.join(DATA_PATH, 'train')\n        out_path = os.path.join(OUT_PATH, '{}.json'.format(split))\n        out = {'images': [], 'annotations': [], 'videos': [],\n               'categories': [{'id': 1, 'name': 'pedestrian'}]}\n        seqs = os.listdir(data_path)\n        image_cnt = 0\n        ann_cnt = 0\n        video_cnt = 0\n        tid_curr = 0\n        tid_last = -1\n        for seq in sorted(seqs):\n            if '.DS_Store' in seq:\n                continue\n            if 'mot' in DATA_PATH and (split != 'test' and not ('FRCNN' in seq)):\n                continue\n            video_cnt += 1  # video sequence number.\n            out['videos'].append({'id': video_cnt, 'file_name': seq})\n            seq_path = os.path.join(data_path, seq)\n            img_path = os.path.join(seq_path, 'img1')\n            ann_path = os.path.join(seq_path, 'gt/gt.txt')\n            images = os.listdir(img_path)\n            num_images = len([image for image in images if 'jpg' in image])  # half and half\n\n            if HALF_VIDEO and ('half' in split):\n                image_range = [0, num_images // 2] if 'train' in split else \\\n                              [num_images // 2 + 1, num_images - 1]\n            else:\n                image_range = [0, num_images - 1]\n\n            for i in range(num_images):\n                if i < image_range[0] or i > image_range[1]:\n                    continue\n                img = cv2.imread(os.path.join(data_path, '{}/img1/{:06d}.jpg'.format(seq, i + 1)))\n                height, width = img.shape[:2]\n                image_info = {'file_name': '{}/img1/{:06d}.jpg'.format(seq, i + 1),  # image name.\n                              'id': image_cnt + i + 1,  # image number in the entire training set.\n                              'frame_id': i + 1 - image_range[0],  # image number in the video sequence, starting from 1.\n                              'prev_image_id': image_cnt + i if i > 0 else -1,  # image number in the entire training set.\n                              'next_image_id': image_cnt + i + 2 if i < num_images - 1 else -1,\n                              'video_id': video_cnt,\n                              'height': height, 'width': width}\n                out['images'].append(image_info)\n            print('{}: {} images'.format(seq, num_images))\n            if split != 'test':\n                det_path = os.path.join(seq_path, 'det/det.txt')\n                anns = np.loadtxt(ann_path, dtype=np.float32, delimiter=',')\n                dets = np.loadtxt(det_path, dtype=np.float32, delimiter=',')\n                if CREATE_SPLITTED_ANN and ('half' in split):\n                    anns_out = np.array([anns[i] for i in range(anns.shape[0])\n                                         if int(anns[i][0]) - 1 >= image_range[0] and\n                                         int(anns[i][0]) - 1 <= image_range[1]], np.float32) \n                    anns_out[:, 0] -= image_range[0]\n                    gt_out = os.path.join(seq_path, 'gt/gt_{}.txt'.format(split))\n                    fout = open(gt_out, 'w')\n                    for o in anns_out:\n                        fout.write('{:d},{:d},{:d},{:d},{:d},{:d},{:d},{:d},{:.6f}\\n'.format(\n                                    int(o[0]), int(o[1]), int(o[2]), int(o[3]), int(o[4]), int(o[5]),\n                                    int(o[6]), int(o[7]), o[8]))\n                    fout.close()\n                if CREATE_SPLITTED_DET and ('half' in split):\n                    dets_out = np.array([dets[i] for i in range(dets.shape[0])\n                                         if int(dets[i][0]) - 1 >= image_range[0] and\n                                         int(dets[i][0]) - 1 <= image_range[1]], np.float32)\n                    dets_out[:, 0] -= image_range[0]\n                    det_out = os.path.join(seq_path, 'det/det_{}.txt'.format(split))\n                    dout = open(det_out, 'w')\n                    for o in dets_out:\n                        dout.write('{:d},{:d},{:.1f},{:.1f},{:.1f},{:.1f},{:.6f}\\n'.format(\n                                    int(o[0]), int(o[1]), float(o[2]), float(o[3]), float(o[4]), float(o[5]),\n                                    float(o[6])))\n                    dout.close()\n\n                print('{} ann images'.format(int(anns[:, 0].max())))\n                for i in range(anns.shape[0]):\n                    frame_id = int(anns[i][0])\n                    if frame_id - 1 < image_range[0] or frame_id - 1 > image_range[1]:\n                        continue\n                    track_id = int(anns[i][1])\n                    cat_id = int(anns[i][7])\n                    ann_cnt += 1\n                    if not ('15' in DATA_PATH):\n                        #if not (float(anns[i][8]) >= 0.25):  # visibility.\n                            #continue\n                        if not (int(anns[i][6]) == 1):  # whether ignore.\n                            continue\n                        if int(anns[i][7]) in [3, 4, 5, 6, 9, 10, 11]:  # Non-person\n                            continue\n                        if int(anns[i][7]) in [2, 7, 8, 12]:  # Ignored person\n                            category_id = -1\n                        else:\n                            category_id = 1  # pedestrian(non-static)\n                            if not track_id == tid_last:\n                                tid_curr += 1\n                                tid_last = track_id\n                    else:\n                        category_id = 1\n                    ann = {'id': ann_cnt,\n                           'category_id': category_id,\n                           'image_id': image_cnt + frame_id,\n                           'track_id': tid_curr,\n                           'bbox': anns[i][2:6].tolist(),\n                           'conf': float(anns[i][6]),\n                           'iscrowd': 0,\n                           'area': float(anns[i][4] * anns[i][5])}\n                    out['annotations'].append(ann)\n            image_cnt += num_images\n            print(tid_curr, tid_last)\n        print('loaded {} for {} images and {} samples'.format(split, len(out['images']), len(out['annotations'])))\n        json.dump(out, open(out_path, 'w'))"
  },
  {
    "path": "tools/convert_mot20_to_coco.py",
    "content": "import os\nimport numpy as np\nimport json\nimport cv2\n\n\n# Use the same script for MOT16\nDATA_PATH = 'datasets/MOT20'\nOUT_PATH = os.path.join(DATA_PATH, 'annotations')\nSPLITS = ['train_half', 'val_half', 'train', 'test']  # --> split training data to train_half and val_half.\nHALF_VIDEO = True\nCREATE_SPLITTED_ANN = True\nCREATE_SPLITTED_DET = True\n\n\nif __name__ == '__main__':\n\n    if not os.path.exists(OUT_PATH):\n        os.makedirs(OUT_PATH)\n\n    for split in SPLITS:\n        if split == \"test\":\n            data_path = os.path.join(DATA_PATH, 'test')\n        else:\n            data_path = os.path.join(DATA_PATH, 'train')\n        out_path = os.path.join(OUT_PATH, '{}.json'.format(split))\n        out = {'images': [], 'annotations': [], 'videos': [],\n               'categories': [{'id': 1, 'name': 'pedestrian'}]}\n        seqs = os.listdir(data_path)\n        image_cnt = 0\n        ann_cnt = 0\n        video_cnt = 0\n        tid_curr = 0\n        tid_last = -1\n        for seq in sorted(seqs):\n            if '.DS_Store' in seq:\n                continue\n            video_cnt += 1  # video sequence number.\n            out['videos'].append({'id': video_cnt, 'file_name': seq})\n            seq_path = os.path.join(data_path, seq)\n            img_path = os.path.join(seq_path, 'img1')\n            ann_path = os.path.join(seq_path, 'gt/gt.txt')\n            images = os.listdir(img_path)\n            num_images = len([image for image in images if 'jpg' in image])  # half and half\n\n            if HALF_VIDEO and ('half' in split):\n                image_range = [0, num_images // 2] if 'train' in split else \\\n                              [num_images // 2 + 1, num_images - 1]\n            else:\n                image_range = [0, num_images - 1]\n\n            for i in range(num_images):\n                if i < image_range[0] or i > image_range[1]:\n                    continue\n                img = cv2.imread(os.path.join(data_path, '{}/img1/{:06d}.jpg'.format(seq, i + 1)))\n                height, width = img.shape[:2]\n                image_info = {'file_name': '{}/img1/{:06d}.jpg'.format(seq, i + 1),  # image name.\n                              'id': image_cnt + i + 1,  # image number in the entire training set.\n                              'frame_id': i + 1 - image_range[0],  # image number in the video sequence, starting from 1.\n                              'prev_image_id': image_cnt + i if i > 0 else -1,  # image number in the entire training set.\n                              'next_image_id': image_cnt + i + 2 if i < num_images - 1 else -1,\n                              'video_id': video_cnt,\n                              'height': height, 'width': width}\n                out['images'].append(image_info)\n            print('{}: {} images'.format(seq, num_images))\n            if split != 'test':\n                det_path = os.path.join(seq_path, 'det/det.txt')\n                anns = np.loadtxt(ann_path, dtype=np.float32, delimiter=',')\n                dets = np.loadtxt(det_path, dtype=np.float32, delimiter=',')\n                if CREATE_SPLITTED_ANN and ('half' in split):\n                    anns_out = np.array([anns[i] for i in range(anns.shape[0])\n                                         if int(anns[i][0]) - 1 >= image_range[0] and\n                                         int(anns[i][0]) - 1 <= image_range[1]], np.float32) \n                    anns_out[:, 0] -= image_range[0]\n                    gt_out = os.path.join(seq_path, 'gt/gt_{}.txt'.format(split))\n                    fout = open(gt_out, 'w')\n                    for o in anns_out:\n                        fout.write('{:d},{:d},{:d},{:d},{:d},{:d},{:d},{:d},{:.6f}\\n'.format(\n                                    int(o[0]), int(o[1]), int(o[2]), int(o[3]), int(o[4]), int(o[5]),\n                                    int(o[6]), int(o[7]), o[8]))\n                    fout.close()\n                if CREATE_SPLITTED_DET and ('half' in split):\n                    dets_out = np.array([dets[i] for i in range(dets.shape[0])\n                                         if int(dets[i][0]) - 1 >= image_range[0] and\n                                         int(dets[i][0]) - 1 <= image_range[1]], np.float32)\n                    dets_out[:, 0] -= image_range[0]\n                    det_out = os.path.join(seq_path, 'det/det_{}.txt'.format(split))\n                    dout = open(det_out, 'w')\n                    for o in dets_out:\n                        dout.write('{:d},{:d},{:.1f},{:.1f},{:.1f},{:.1f},{:.6f}\\n'.format(\n                                    int(o[0]), int(o[1]), float(o[2]), float(o[3]), float(o[4]), float(o[5]),\n                                    float(o[6])))\n                    dout.close()\n\n                print('{} ann images'.format(int(anns[:, 0].max())))\n                for i in range(anns.shape[0]):\n                    frame_id = int(anns[i][0])\n                    if frame_id - 1 < image_range[0] or frame_id - 1 > image_range[1]:\n                        continue\n                    track_id = int(anns[i][1])\n                    cat_id = int(anns[i][7])\n                    ann_cnt += 1\n                    if not ('15' in DATA_PATH):\n                        #if not (float(anns[i][8]) >= 0.25):  # visibility.\n                            #continue\n                        if not (int(anns[i][6]) == 1):  # whether ignore.\n                            continue\n                        if int(anns[i][7]) in [3, 4, 5, 6, 9, 10, 11]:  # Non-person\n                            continue\n                        if int(anns[i][7]) in [2, 7, 8, 12]:  # Ignored person\n                            #category_id = -1\n                            continue\n                        else:\n                            category_id = 1  # pedestrian(non-static)\n                            if not track_id == tid_last:\n                                tid_curr += 1\n                                tid_last = track_id\n                    else:\n                        category_id = 1\n                    ann = {'id': ann_cnt,\n                           'category_id': category_id,\n                           'image_id': image_cnt + frame_id,\n                           'track_id': tid_curr,\n                           'bbox': anns[i][2:6].tolist(),\n                           'conf': float(anns[i][6]),\n                           'iscrowd': 0,\n                           'area': float(anns[i][4] * anns[i][5])}\n                    out['annotations'].append(ann)\n            image_cnt += num_images\n            print(tid_curr, tid_last)\n        print('loaded {} for {} images and {} samples'.format(split, len(out['images']), len(out['annotations'])))\n        json.dump(out, open(out_path, 'w'))"
  },
  {
    "path": "tools/convert_video.py",
    "content": "import cv2\n\ndef convert_video(video_path):\n    cap = cv2.VideoCapture(video_path)\n    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float\n    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float\n    fps = cap.get(cv2.CAP_PROP_FPS)\n    video_name = video_path.split('/')[-1].split('.')[0]\n    save_name = video_name + '_converted'\n    save_path = video_path.replace(video_name, save_name)\n    vid_writer = cv2.VideoWriter(\n        save_path, cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (int(width), int(height))\n    )\n    while True:\n        ret_val, frame = cap.read()\n        if ret_val:\n            vid_writer.write(frame)\n            ch = cv2.waitKey(1)\n            if ch == 27 or ch == ord(\"q\") or ch == ord(\"Q\"):\n                break\n        else:\n            break\n\nif __name__ == \"__main__\":\n    video_path = 'videos/palace.mp4'\n    convert_video(video_path)"
  },
  {
    "path": "tools/demo_track.py",
    "content": "import argparse\nimport os\nimport os.path as osp\nimport time\nimport cv2\nimport torch\n\nfrom loguru import logger\n\nfrom yolox.data.data_augment import preproc\nfrom yolox.exp import get_exp\nfrom yolox.utils import fuse_model, get_model_info, postprocess\nfrom yolox.utils.visualize import plot_tracking, plot_tracking_detection\nfrom trackers.ocsort_tracker.ocsort import OCSort\nfrom trackers.hybrid_sort_tracker.hybrid_sort import Hybrid_Sort\nfrom trackers.hybrid_sort_tracker.hybrid_sort_reid import Hybrid_Sort_ReID\nfrom trackers.tracking_utils.timer import Timer\nfrom fast_reid.fast_reid_interfece import FastReIDInterface\nimport copy\n\nIMAGE_EXT = [\".jpg\", \".jpeg\", \".webp\", \".bmp\", \".png\"]\n\nfrom utils.args import make_parser, args_merge_params_form_exp\n\ndef get_image_list(path):\n    image_names = []\n    for maindir, subdir, file_name_list in os.walk(path):\n        for filename in file_name_list:\n            apath = osp.join(maindir, filename)\n            ext = osp.splitext(apath)[1]\n            if ext in IMAGE_EXT:\n                image_names.append(apath)\n    return image_names\n\n\nclass Predictor(object):\n    def __init__(\n        self,\n        model,\n        exp,\n        trt_file=None,\n        decoder=None,\n        device=torch.device(\"cpu\"),\n        fp16=False,\n        with_reid=False,\n        fast_reid_config=None,\n        fast_reid_weights=None,\n    ):\n        self.model = model\n        self.decoder = decoder\n        self.num_classes = exp.num_classes\n        self.confthre = exp.test_conf\n        self.nmsthre = exp.nmsthre\n        self.test_size = exp.test_size\n        self.device = device\n        self.fp16 = fp16\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones((1, 3, exp.test_size[0], exp.test_size[1]), device=device)\n            self.model(x)\n            self.model = model_trt\n        self.rgb_means = (0.485, 0.456, 0.406)\n        self.std = (0.229, 0.224, 0.225)\n        self.with_reid = with_reid\n        if self.with_reid:\n            self.fast_reid_config = fast_reid_config\n            self.fast_reid_weights = fast_reid_weights\n            self.encoder = FastReIDInterface(self.fast_reid_config, self.fast_reid_weights, 'cuda')\n\n    def inference(self, img, timer):\n        img_info = {\"id\": 0}\n        if isinstance(img, str):\n            img_info[\"file_name\"] = osp.basename(img)\n            img = cv2.imread(img)\n        else:\n            img_info[\"file_name\"] = None\n\n        height, width = img.shape[:2]\n        img_info[\"height\"] = height\n        img_info[\"width\"] = width\n        img_info[\"raw_img\"] = img\n\n        img, ratio, raw_image = preproc(img, self.test_size, self.rgb_means, self.std)  # _ for raw_image\n        img_info[\"ratio\"] = ratio\n        img = torch.from_numpy(img).unsqueeze(0).float().to(self.device)\n        if self.fp16:\n            img = img.half()  # to FP16\n\n        with torch.no_grad():\n            timer.tic()\n            outputs = self.model(img)\n            if self.decoder is not None:\n                outputs = self.decoder(outputs, dtype=outputs.type())\n            outputs = postprocess(\n                outputs, self.num_classes, self.confthre, self.nmsthre\n            )\n            if self.with_reid:\n                bbox_xyxy = copy.deepcopy(outputs[0][:, :4])  # [hgx0411]\n                # we should save the detections here !\n                # os.makedirs(\"dance_detections/{}\".format(video_name), exist_ok=True)\n                # torch.save(outputs[0], ckt_file)\n\n                # [hgx0411] box rescale borrowed from convert_to_coco_format()\n                scale = min(self.test_size[0] / float(img_info[\"height\"]), self.test_size[1] / float(img_info[\"width\"]))\n                bbox_xyxy /= scale\n                id_feature = self.encoder.inference(raw_image, bbox_xyxy.cpu().detach().numpy())  # normalization and numpy included\n        if self.with_reid:\n            return outputs, img_info, id_feature\n        else:\n            return outputs, img_info\n\n\ndef image_demo(predictor, vis_folder, current_time, args):\n    if osp.isdir(args.path):\n        files = get_image_list(args.path)\n    else:\n        files = [args.path]\n    files.sort()\n    if not args.hybrid_sort_with_reid:\n        tracker = Hybrid_Sort(args, det_thresh=args.track_thresh,\n                                    iou_threshold=args.iou_thresh,\n                                    asso_func=args.asso,\n                                    delta_t=args.deltat,\n                                    inertia=args.inertia,\n                                    use_byte=args.use_byte)\n    else:\n        tracker = Hybrid_Sort_ReID(args, det_thresh=args.track_thresh,\n                                    iou_threshold=args.iou_thresh,\n                                    asso_func=args.asso,\n                                    delta_t=args.deltat,\n                                    inertia=args.inertia)\n        # tracker = OCSort(det_thresh=args.track_thresh, iou_threshold=args.iou_thresh, use_byte=args.use_byte)\n\n    timer = Timer()\n    results = []\n\n    for frame_id, img_path in enumerate(files, 1):\n        if args.with_fastreid:\n            outputs, img_info, id_feature = predictor.inference(img_path, timer)\n        else:\n            outputs, img_info = predictor.inference(img_path, timer)\n        if outputs[0] is not None:\n            if args.with_fastreid:\n                online_targets = tracker.update(outputs[0], [img_info['height'], img_info['width']], exp.test_size, id_feature=id_feature)\n            else:\n                online_targets = tracker.update(outputs[0], [img_info['height'], img_info['width']], exp.test_size)\n            online_tlwhs = []\n            online_ids = []\n            for t in online_targets:\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                vertical = tlwh[2] / tlwh[3] > args.aspect_ratio_thresh\n                # if tlwh[2] * tlwh[3] > args.min_box_area and not vertical:\n                if tlwh[2] * tlwh[3] > args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    results.append(\n                        f\"{frame_id},{tid},{tlwh[0]:.2f},{tlwh[1]:.2f},{tlwh[2]:.2f},{tlwh[3]:.2f},1.0,-1,-1,-1\\n\"\n                    )\n            timer.toc()\n            online_im = plot_tracking(\n                img_info['raw_img'], online_tlwhs, online_ids, frame_id=frame_id, fps=1. / timer.average_time\n            )\n            img_h, img_w = img_info['height'], img_info['width']\n            scale = min(exp.test_size[0] / float(img_h), exp.test_size[1] / float(img_w))\n            online_im_detection = plot_tracking_detection(\n                img_info['raw_img'], outputs[0][:, :4]/scale, (outputs[0][:, 4]*outputs[0][:, 5]), frame_id=frame_id, fps=1. / timer.average_time\n            )\n        else:\n            timer.toc()\n            online_im = img_info['raw_img']\n\n        # result_image = predictor.visual(outputs[0], img_info, predictor.confthre)\n        if args.save_result:\n            if not args.demo_dancetrack:\n                timestamp = time.strftime(\"%Y_%m_%d_%H_%M_%S\", current_time)\n                save_folder = osp.join(vis_folder, timestamp)\n            else:\n                timestamp = args.path[-19:]\n                save_folder = osp.join(vis_folder, timestamp)\n            os.makedirs(save_folder, exist_ok=True)\n            cv2.imwrite(osp.join(save_folder, osp.basename(img_path)), online_im)\n            save_folder_detection = osp.join(save_folder , \"detection\")\n            os.makedirs(save_folder_detection, exist_ok=True)\n            cv2.imwrite(osp.join(save_folder_detection, osp.basename(img_path)), online_im_detection)\n\n        if frame_id % 20 == 0:\n            logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))\n\n        ch = cv2.waitKey(0)\n        if ch == 27 or ch == ord(\"q\") or ch == ord(\"Q\"):\n            break\n\n    if args.save_result:\n        res_file = osp.join(vis_folder, f\"{timestamp}.txt\")\n        with open(res_file, 'w') as f:\n            f.writelines(results)\n        logger.info(f\"save results to {res_file}\")\n\n\ndef imageflow_demo(predictor, vis_folder, current_time, args):\n    cap = cv2.VideoCapture(args.path if args.demo_type == \"video\" else args.camid)\n    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float\n    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float\n    fps = cap.get(cv2.CAP_PROP_FPS)\n    timestamp = time.strftime(\"%Y_%m_%d_%H_%M_%S\", current_time)\n    save_folder = osp.join(vis_folder, timestamp)\n    os.makedirs(save_folder, exist_ok=True)\n    if args.demo_type == \"video\":\n        save_path = args.out_path\n    else:\n        save_path = osp.join(save_folder, \"camera.mp4\")\n    logger.info(f\"video save_path is {save_path}\")\n    vid_writer = cv2.VideoWriter(\n        save_path, cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (int(width), int(height))\n    )\n    if not args.hybrid_sort_with_reid:\n        tracker = Hybrid_Sort(args, det_thresh=args.track_thresh,\n                                    iou_threshold=args.iou_thresh,\n                                    asso_func=args.asso,\n                                    delta_t=args.deltat,\n                                    inertia=args.inertia,\n                                    use_byte=args.use_byte)\n    else:\n        tracker = Hybrid_Sort_ReID(args, det_thresh=args.track_thresh,\n                                    iou_threshold=args.iou_thresh,\n                                    asso_func=args.asso,\n                                    delta_t=args.deltat,\n                                    inertia=args.inertia)\n    # tracker = OCSort(det_thresh=args.track_thresh, iou_threshold=args.iou_thresh, use_byte=args.use_byte)\n    timer = Timer()\n    frame_id = 0\n    results = []\n    while True:\n        if frame_id % 20 == 0:\n            logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))\n        ret_val, frame = cap.read()\n        if ret_val:\n            outputs, img_info = predictor.inference(frame, timer)\n            if outputs[0] is not None:\n                online_targets = tracker.update(outputs[0], [img_info['height'], img_info['width']], exp.test_size)\n                online_tlwhs = []\n                online_ids = []\n                for t in online_targets:\n                    tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                    tid = t[4]\n                    vertical = tlwh[2] / tlwh[3] > args.aspect_ratio_thresh\n                    # if tlwh[2] * tlwh[3] > args.min_box_area and not vertical:\n                    if tlwh[2] * tlwh[3] > args.min_box_area:\n                        online_tlwhs.append(tlwh)\n                        online_ids.append(tid)\n                        results.append(\n                            f\"{frame_id},{tid},{tlwh[0]:.2f},{tlwh[1]:.2f},{tlwh[2]:.2f},{tlwh[3]:.2f},1.0,-1,-1,-1\\n\"\n                        )\n                timer.toc()\n                online_im = plot_tracking(\n                    img_info['raw_img'], online_tlwhs, online_ids, frame_id=frame_id + 1, fps=1. / timer.average_time\n                )\n            else:\n                timer.toc()\n                online_im = img_info['raw_img']\n            if args.save_result:\n                vid_writer.write(online_im)\n            ch = cv2.waitKey(1)\n            if ch == 27 or ch == ord(\"q\") or ch == ord(\"Q\"):\n                break\n        else:\n            break\n        frame_id += 1\n\n    if args.save_result:\n        res_file = osp.join(vis_folder, f\"{timestamp}.txt\")\n        with open(res_file, 'w') as f:\n            f.writelines(results)\n        logger.info(f\"save results to {res_file}\")\n\n\ndef main(exp, args):\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    output_dir = osp.join(exp.output_dir, args.expn)\n    os.makedirs(output_dir, exist_ok=True)\n\n    if args.save_result:\n        vis_folder = osp.join(output_dir, str(args.hybrid_sort_with_reid), \"track_vis\")\n        os.makedirs(vis_folder, exist_ok=True)\n\n    if args.trt:\n        args.device = \"gpu\"\n    args.device = torch.device(\"cuda\" if args.device == \"gpu\" else \"cpu\")\n\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model().to(args.device)\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n    model.eval()\n\n    if not args.trt:\n        if args.ckpt is None:\n            ckpt_file = osp.join(output_dir, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        ckpt = torch.load(ckpt_file, map_location=\"cpu\")\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.fp16:\n        model = model.half()  # to FP16\n\n    if args.trt:\n        assert not args.fuse, \"TensorRT model is not support model fusing!\"\n        trt_file = osp.join(output_dir, \"model_trt.pth\")\n        assert osp.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run python3 tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n        logger.info(\"Using TensorRT to inference\")\n    else:\n        trt_file = None\n        decoder = None\n\n    predictor = Predictor(model, exp, trt_file, decoder, args.device, args.fp16,\n                          with_reid=args.with_fastreid, fast_reid_config=args.fast_reid_config, fast_reid_weights=args.fast_reid_weights)    \n    current_time = time.localtime()\n    if args.demo_type == \"image\":\n        image_demo(predictor, vis_folder, current_time, args)\n    elif args.demo_type == \"video\" or args.demo_type == \"webcam\":\n        imageflow_demo(predictor, vis_folder, current_time, args)\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n\n    args_merge_params_form_exp(args, exp)\n\n    main(exp, args)\n"
  },
  {
    "path": "tools/gp_interpolation.py",
    "content": "from sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import RBF\nimport numpy as np \nimport os\nimport glob\nfrom sklearn import preprocessing\nimport scipy \nimport sys \nimport scipy.spatial\n\n\ndef mkdir_if_missing(d):\n    if not os.path.exists(d):\n        os.makedirs(d)\n\n\ndef write_results_score(filename, results):\n    save_format = '{frame},{id},{x1},{y1},{w},{h},{s},-1,-1,-1\\n'\n    with open(filename, 'w') as f:\n        for i in range(results.shape[0]):\n            frame_data = results[i]\n            frame_id = int(frame_data[0])\n            track_id = int(frame_data[1])\n            x1, y1, w, h = frame_data[2:6]\n            score = frame_data[6]\n            line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, w=w, h=h, s=-1)\n            f.write(line)\n\n\ndef median_trick(X):\n    \"\"\"\n        median trick for computing the bandwith for kernel regression.\n    \"\"\"\n    N = len(X)\n    perm = np.random.choice(N, N, replace=False)\n    dsample = X[perm]\n    pd = scipy.spatial.distance.pdist(dsample)\n    sigma = np.median(pd)\n    return sigma\n\n\ndef gp_interpolation(txt_path, save_path, reference_dir, n_min=30, n_dti=20):\n    seq_txts = sorted(glob.glob(os.path.join(txt_path, '*.txt')))\n    for seq_txt in seq_txts:\n        seq_name = seq_txt.split('/')[-1]\n        ref_seq_data = np.loadtxt(os.path.join(reference_dir,\n                \"{}\".format(seq_name)), delimiter=\",\")\n        seq_data = np.loadtxt(seq_txt, dtype=np.float64, delimiter=',')\n        min_id = int(np.min(seq_data[:, 1]))\n        max_id = int(np.max(seq_data[:, 1]))\n        seq_results = np.zeros((1, 10), dtype=np.float64)\n\n        track_count = 0\n        for track_id in range(min_id, max_id + 1):\n            track_count += 1\n            print(\"{} {}/{}\".format(seq_name, track_count, max_id-min_id))\n            index = (seq_data[:, 1] == track_id)\n            to_fill_tracklet = seq_data[index]\n            ref_index = (ref_seq_data[:, 1] == track_id)\n            tracklet = ref_seq_data[ref_index]\n            tracklet_dti = tracklet\n            if tracklet.shape[0] == 0:\n                continue\n            boxes = tracklet[:, 2:6].reshape((-1, 4))\n            center_x = boxes[:, 0] + 0.5 * boxes[:, 2]\n            center_y = boxes[:, 1] + 0.5 * boxes[:, 3]\n            center_x = center_x.reshape((-1, 1))\n            center_y = center_y.reshape((-1, 1))\n            time_steps = tracklet[:, 0].reshape((-1, 1))\n\n            n_frame = tracklet.shape[0]\n            l = n_frame if n_frame < 500 else 500\n\n            bandwidth = median_trick(boxes)\n\n\n            \"\"\"\n                change the following to use your own kernel for GPR\n            \"\"\"\n            l = 1000.0 / n_frame\n            kernel = 20 * RBF(l)\n            scaler_boxes = preprocessing.StandardScaler().fit(boxes)\n\n            scaler_x = preprocessing.StandardScaler().fit(center_x)\n            scaler_y = preprocessing.StandardScaler().fit(center_y)\n\n            x_scaled = scaler_x.transform(center_x)\n            y_scaled = scaler_y.transform(center_y)\n\n            gp_x = GaussianProcessRegressor(kernel, n_restarts_optimizer=2)\n            gp_y = GaussianProcessRegressor(kernel, n_restarts_optimizer=2)\n            gp_x.fit(time_steps, x_scaled)\n            gp_y.fit(time_steps, y_scaled)\n            \n            if n_frame > n_min:\n                frames = tracklet[:, 0]\n                to_fill_frames = to_fill_tracklet[:,0]\n                frames_dti = {}\n                for frame in frames:\n                    if frame not in to_fill_frames:\n                        \"\"\"\n                            Smooth the steps which are made up by the linear interpolation\n                        \"\"\"\n                        curr_frame = frame\n                        width, height = tracklet[np.where(tracklet[:,0]==curr_frame)][0][4:6]\n                        curr_frame = np.array([curr_frame]).reshape((-1,1))\n\n                        curr_x = gp_x.predict(curr_frame)\n                        curr_y = gp_y.predict(curr_frame)\n                        curr_x = scaler_x.inverse_transform(curr_x)\n                        curr_y = scaler_y.inverse_transform(curr_y)\n                        curr_bbox = np.array([[curr_x - 0.5 * width, curr_y - 0.5 * height,\n                                width, height]]).reshape((4,))\n                        tracklet = tracklet[np.where(tracklet[:,0]!=curr_frame)[1]]\n                        frames_dti[int(curr_frame.item())] = curr_bbox\n                num_dti = len(frames_dti.keys())\n                if num_dti > 0:\n                    data_dti = np.zeros((num_dti, 10), dtype=np.float64)\n                    for n in range(num_dti):\n                        data_dti[n, 0] = list(frames_dti.keys())[n]\n                        data_dti[n, 1] = track_id\n                        data_dti[n, 2:6] = frames_dti[list(frames_dti.keys())[n]]\n                        data_dti[n, 6:] = [1, -1, -1, -1]\n                    tracklet_dti = np.vstack((tracklet, data_dti))\n            seq_results = np.vstack((seq_results, tracklet_dti))\n        save_seq_txt = os.path.join(save_path, seq_name)\n        seq_results = seq_results[1:]\n        seq_results = seq_results[seq_results[:, 0].argsort()]\n        write_results_score(save_seq_txt, seq_results)\n\nif __name__ == \"__main__\":\n    \"\"\"\n        Input:\n            * txt_path: the raw tracking output path \n            * save_path: path to saved the interpolated result files\n            * li_path: the path to results after linear interpolation\n    \"\"\"\n    txt_path, li_path, save_path = sys.argv[1], sys.argv[2], sys.argv[3]\n    mkdir_if_missing(save_path)\n    gp_interpolation(txt_path, save_path, li_path, n_min=30, n_dti=20)"
  },
  {
    "path": "tools/interpolation.py",
    "content": "import numpy as np\nimport os\nimport glob\nimport motmetrics as mm\nimport sys \nfrom yolox.evaluators.evaluation import Evaluator\n\n\ndef mkdir_if_missing(d):\n    if not os.path.exists(d):\n        os.makedirs(d)\n\n\ndef eval_mota(data_root, txt_path):\n    accs = []\n    seqs = sorted([s for s in os.listdir(data_root) if s.endswith('FRCNN')])\n    for seq in seqs:\n        video_out_path = os.path.join(txt_path, seq + '.txt')\n        evaluator = Evaluator(data_root, seq, 'mot', anno=\"gt_val_half.txt\")\n        accs.append(evaluator.eval_file(video_out_path))\n    metrics = mm.metrics.motchallenge_metrics\n    mh = mm.metrics.create()\n    summary = Evaluator.get_summary(accs, seqs, metrics)\n    strsummary = mm.io.render_summary(\n        summary,\n        formatters=mh.formatters,\n        namemap=mm.io.motchallenge_metric_names\n    )\n    print(strsummary)\n\n\ndef get_mota(data_root, txt_path):\n    accs = []\n    seqs = sorted([s for s in os.listdir(data_root) if s.endswith('FRCNN')])\n    for seq in seqs:\n        video_out_path = os.path.join(txt_path, seq + '.txt')\n        evaluator = Evaluator(data_root, seq, 'mot')\n        accs.append(evaluator.eval_file(video_out_path))\n    metrics = mm.metrics.motchallenge_metrics\n    mh = mm.metrics.create()\n    summary = Evaluator.get_summary(accs, seqs, metrics)\n    strsummary = mm.io.render_summary(\n        summary,\n        formatters=mh.formatters,\n        namemap=mm.io.motchallenge_metric_names\n    )\n    mota = float(strsummary.split(' ')[-6][:-1])\n    return mota\n\n\ndef write_results_score(filename, results):\n    save_format = '{frame},{id},{x1},{y1},{w},{h},{s},-1,-1,-1\\n'\n    with open(filename, 'w') as f:\n        for i in range(results.shape[0]):\n            frame_data = results[i]\n            frame_id = int(frame_data[0])\n            track_id = int(frame_data[1])\n            x1, y1, w, h = frame_data[2:6]\n            score = frame_data[6]\n            line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, w=w, h=h, s=-1)\n            f.write(line)\n\n\ndef dti(txt_path, save_path, n_min=25, n_dti=20):\n    seq_txts = sorted(glob.glob(os.path.join(txt_path, '*.txt')))\n    for seq_txt in seq_txts:\n        seq_name = seq_txt.split('/')[-1]\n        seq_data = np.loadtxt(seq_txt, dtype=np.float64, delimiter=',')\n        min_id = int(np.min(seq_data[:, 1]))\n        max_id = int(np.max(seq_data[:, 1]))\n        seq_results = np.zeros((1, 10), dtype=np.float64)\n        for track_id in range(min_id, max_id + 1):\n            index = (seq_data[:, 1] == track_id)\n            tracklet = seq_data[index]\n            tracklet_dti = tracklet\n            if tracklet.shape[0] == 0:\n                continue\n            n_frame = tracklet.shape[0]\n            n_conf = np.sum(tracklet[:, 6] > 0.5)\n            if n_frame > n_min:\n                frames = tracklet[:, 0]\n                frames_dti = {}\n                for i in range(0, n_frame):\n                    right_frame = frames[i]\n                    if i > 0:\n                        left_frame = frames[i - 1]\n                    else:\n                        left_frame = frames[i]\n                    # disconnected track interpolation\n                    if 1 < right_frame - left_frame < n_dti:\n                        num_bi = int(right_frame - left_frame - 1)\n                        right_bbox = tracklet[i, 2:6]\n                        left_bbox = tracklet[i - 1, 2:6]\n                        for j in range(1, num_bi + 1):\n                            curr_frame = j + left_frame\n                            curr_bbox = (curr_frame - left_frame) * (right_bbox - left_bbox) / \\\n                                        (right_frame - left_frame) + left_bbox\n                            frames_dti[curr_frame] = curr_bbox\n                num_dti = len(frames_dti.keys())\n                if num_dti > 0:\n                    data_dti = np.zeros((num_dti, 10), dtype=np.float64)\n                    for n in range(num_dti):\n                        data_dti[n, 0] = list(frames_dti.keys())[n]\n                        data_dti[n, 1] = track_id\n                        data_dti[n, 2:6] = frames_dti[list(frames_dti.keys())[n]]\n                        data_dti[n, 6:] = [1, -1, -1, -1]\n                    tracklet_dti = np.vstack((tracklet, data_dti))\n            seq_results = np.vstack((seq_results, tracklet_dti))\n        save_seq_txt = os.path.join(save_path, seq_name)\n        seq_results = seq_results[1:]\n        seq_results = seq_results[seq_results[:, 0].argsort()]\n        write_results_score(save_seq_txt, seq_results)\n\n\ndef dti_kitti(txt_path, save_path, n_min=30, n_dti=20):\n    seq_txts = sorted(glob.glob(os.path.join(txt_path, '*.txt')))\n    for seq_txt in seq_txts:\n        seq_name = seq_txt.split('/')[-1]\n        seq_data = np.loadtxt(seq_txt, dtype=np.float64, delimiter=',')\n        min_id = int(np.min(seq_data[:, 1]))\n        max_id = int(np.max(seq_data[:, 1]))\n        seq_results = np.zeros((1, 10), dtype=np.float64)\n        for track_id in range(min_id, max_id + 1):\n            index = (seq_data[:, 1] == track_id)\n            tracklet = seq_data[index]\n            tracklet_dti = tracklet\n            if tracklet.shape[0] == 0:\n                continue\n            n_frame = tracklet.shape[0]\n            n_conf = np.sum(tracklet[:, 6] > 0.5)\n            if n_frame > n_min:\n                frames = tracklet[:, 0]\n                frames_dti = {}\n                for i in range(0, n_frame):\n                    right_frame = frames[i]\n                    if i > 0:\n                        left_frame = frames[i - 1]\n                    else:\n                        left_frame = frames[i]\n                    # disconnected track interpolation\n                    if 1 < right_frame - left_frame < n_dti:\n                        num_bi = int(right_frame - left_frame - 1)\n                        right_bbox = tracklet[i, 2:6]\n                        left_bbox = tracklet[i - 1, 2:6]\n                        for j in range(1, num_bi + 1):\n                            curr_frame = j + left_frame\n                            curr_bbox = (curr_frame - left_frame) * (right_bbox - left_bbox) / \\\n                                        (right_frame - left_frame) + left_bbox\n                            frames_dti[curr_frame] = curr_bbox\n                num_dti = len(frames_dti.keys())\n                if num_dti > 0:\n                    data_dti = np.zeros((num_dti, 10), dtype=np.float64)\n                    for n in range(num_dti):\n                        data_dti[n, 0] = list(frames_dti.keys())[n]\n                        data_dti[n, 1] = track_id\n                        data_dti[n, 2:6] = frames_dti[list(frames_dti.keys())[n]]\n                        data_dti[n, 6:] = [1, -1, -1, -1]\n                    tracklet_dti = np.vstack((tracklet, data_dti))\n            seq_results = np.vstack((seq_results, tracklet_dti))\n        save_seq_txt = os.path.join(save_path, seq_name)\n        seq_results = seq_results[1:]\n        seq_results = seq_results[seq_results[:, 0].argsort()]\n        write_results_score(save_seq_txt, seq_results)\n\n# val set\n# python3 tools/interpolation.py YOLOX_outputs/without_interpolation/tracker YOLOX_outputs/with_interpolation/tracker datasets/dancetrack/train False\n\n# test set\n# python3 tools/interpolation.py YOLOX_outputs/OCSORT_reid_test_dataset_new_style_reid/eg_high_short_correction_eg_low_short_correction/wo_interp/tracker YOLOX_outputs/OCSORT_reid_test_dataset_new_style_reid/eg_high_short_correction_eg_low_short_correction/tracker None False\nif __name__ == '__main__':\n    # txt_path, save_path, data_root, evaluation = sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]\n    # evaluation = True if evaluation is 'True' else False\n    # mkdir_if_missing(save_path)\n    # dti(txt_path, save_path, n_min=30, n_dti=20)\n    # if evaluation:\n    #     print('Before DTI: ')\n    #     eval_mota(data_root, txt_path)\n    #     print('After DTI:')\n    #     eval_mota(data_root, save_path)\n    txt_path, save_path = sys.argv[1], sys.argv[2]\n    data_root = 'datasets/mot/train'\n    mkdir_if_missing(save_path)\n    dti(txt_path, save_path, n_min=30, n_dti=20)\n    print('Before DTI: ')\n    eval_mota(data_root, txt_path)\n    print('After DTI:')\n    eval_mota(data_root, save_path)\n"
  },
  {
    "path": "tools/mix_data_ablation.py",
    "content": "import json\nimport os\n\n\n\"\"\"\ncd datasets\nmkdir -p mix_mot_ch/annotations\ncp mot/annotations/val_half.json mix_mot_ch/annotations/val_half.json\ncp mot/annotations/test.json mix_mot_ch/annotations/test.json\ncd mix_mot_ch\nln -s ../mot/train mot_train\nln -s ../crowdhuman/CrowdHuman_train crowdhuman_train\nln -s ../crowdhuman/CrowdHuman_val crowdhuman_val\ncd ..\n\"\"\"\n\nmot_json = json.load(open('datasets/mot/annotations/train_half.json','r'))\n\nimg_list = list()\nfor img in mot_json['images']:\n    img['file_name'] = 'mot_train/' + img['file_name']\n    img_list.append(img)\n\nann_list = list()\nfor ann in mot_json['annotations']:\n    ann_list.append(ann)\n\nvideo_list = mot_json['videos']\ncategory_list = mot_json['categories']\n\nprint('mot17')\n\nmax_img = 10000\nmax_ann = 2000000\nmax_video = 10\n\ncrowdhuman_json = json.load(open('datasets/crowdhuman/annotations/train.json','r'))\nimg_id_count = 0\nfor img in crowdhuman_json['images']:\n    img_id_count += 1\n    img['file_name'] = 'crowdhuman_train/' + img['file_name']\n    img['frame_id'] = img_id_count\n    img['prev_image_id'] = img['id'] + max_img\n    img['next_image_id'] = img['id'] + max_img\n    img['id'] = img['id'] + max_img\n    img['video_id'] = max_video\n    img_list.append(img)\n    \nfor ann in crowdhuman_json['annotations']:\n    ann['id'] = ann['id'] + max_ann\n    ann['image_id'] = ann['image_id'] + max_img\n    ann_list.append(ann)\n\nvideo_list.append({\n    'id': max_video,\n    'file_name': 'crowdhuman_train'\n})\n\nprint('crowdhuman_train')\n\nmax_img = 30000\nmax_ann = 10000000\n\ncrowdhuman_val_json = json.load(open('datasets/crowdhuman/annotations/val.json','r'))\nimg_id_count = 0\nfor img in crowdhuman_val_json['images']:\n    img_id_count += 1\n    img['file_name'] = 'crowdhuman_val/' + img['file_name']\n    img['frame_id'] = img_id_count\n    img['prev_image_id'] = img['id'] + max_img\n    img['next_image_id'] = img['id'] + max_img\n    img['id'] = img['id'] + max_img\n    img['video_id'] = max_video\n    img_list.append(img)\n    \nfor ann in crowdhuman_val_json['annotations']:\n    ann['id'] = ann['id'] + max_ann\n    ann['image_id'] = ann['image_id'] + max_img\n    ann_list.append(ann)\n\nvideo_list.append({\n    'id': max_video,\n    'file_name': 'crowdhuman_val'\n})\n\nprint('crowdhuman_val')\n\nmix_json = dict()\nmix_json['images'] = img_list\nmix_json['annotations'] = ann_list\nmix_json['videos'] = video_list\nmix_json['categories'] = category_list\njson.dump(mix_json, open('datasets/mix_mot_ch/annotations/train.json','w'))"
  },
  {
    "path": "tools/mix_data_test_mot17.py",
    "content": "import json\nimport os\n\n\n\"\"\"\ncd datasets\nmkdir -p mix_det/annotations\ncp mot/annotations/val_half.json mix_det/annotations/val_half.json\ncp mot/annotations/test.json mix_det/annotations/test.json\ncd mix_det\nln -s ../mot/train mot_train\nln -s ../crowdhuman/CrowdHuman_train crowdhuman_train\nln -s ../crowdhuman/CrowdHuman_val crowdhuman_val\nln -s ../Cityscapes cp_train\nln -s ../ETHZ ethz_train\ncd ..\n\"\"\"\n\nmot_json = json.load(open('datasets/mot/annotations/train.json','r'))\n\nimg_list = list()\nfor img in mot_json['images']:\n    img['file_name'] = 'mot_train/' + img['file_name']\n    img_list.append(img)\n\nann_list = list()\nfor ann in mot_json['annotations']:\n    ann_list.append(ann)\n\nvideo_list = mot_json['videos']\ncategory_list = mot_json['categories']\n\n\nprint('mot17')\n\nmax_img = 10000\nmax_ann = 2000000\nmax_video = 10\n\ncrowdhuman_json = json.load(open('datasets/crowdhuman/annotations/train.json','r'))\nimg_id_count = 0\nfor img in crowdhuman_json['images']:\n    img_id_count += 1\n    img['file_name'] = 'crowdhuman_train/' + img['file_name']\n    img['frame_id'] = img_id_count\n    img['prev_image_id'] = img['id'] + max_img\n    img['next_image_id'] = img['id'] + max_img\n    img['id'] = img['id'] + max_img\n    img['video_id'] = max_video\n    img_list.append(img)\n    \nfor ann in crowdhuman_json['annotations']:\n    ann['id'] = ann['id'] + max_ann\n    ann['image_id'] = ann['image_id'] + max_img\n    ann_list.append(ann)\n\nprint('crowdhuman_train')\n\nvideo_list.append({\n    'id': max_video,\n    'file_name': 'crowdhuman_train'\n})\n\n\nmax_img = 30000\nmax_ann = 10000000\n\ncrowdhuman_val_json = json.load(open('datasets/crowdhuman/annotations/val.json','r'))\nimg_id_count = 0\nfor img in crowdhuman_val_json['images']:\n    img_id_count += 1\n    img['file_name'] = 'crowdhuman_val/' + img['file_name']\n    img['frame_id'] = img_id_count\n    img['prev_image_id'] = img['id'] + max_img\n    img['next_image_id'] = img['id'] + max_img\n    img['id'] = img['id'] + max_img\n    img['video_id'] = max_video\n    img_list.append(img)\n    \nfor ann in crowdhuman_val_json['annotations']:\n    ann['id'] = ann['id'] + max_ann\n    ann['image_id'] = ann['image_id'] + max_img\n    ann_list.append(ann)\n\nprint('crowdhuman_val')\n\nvideo_list.append({\n    'id': max_video,\n    'file_name': 'crowdhuman_val'\n})\n\nmax_img = 40000\nmax_ann = 20000000\n\nethz_json = json.load(open('datasets/ETHZ/annotations/train.json','r'))\nimg_id_count = 0\nfor img in ethz_json['images']:\n    img_id_count += 1\n    img['file_name'] = 'ethz_train/' + img['file_name'][5:]\n    img['frame_id'] = img_id_count\n    img['prev_image_id'] = img['id'] + max_img\n    img['next_image_id'] = img['id'] + max_img\n    img['id'] = img['id'] + max_img\n    img['video_id'] = max_video\n    img_list.append(img)\n    \nfor ann in ethz_json['annotations']:\n    ann['id'] = ann['id'] + max_ann\n    ann['image_id'] = ann['image_id'] + max_img\n    ann_list.append(ann)\n\nprint('ETHZ')\n\nvideo_list.append({\n    'id': max_video,\n    'file_name': 'ethz'\n})\n\nmax_img = 50000\nmax_ann = 25000000\n\ncp_json = json.load(open('datasets/Cityscapes/annotations/train.json','r'))\nimg_id_count = 0\nfor img in cp_json['images']:\n    img_id_count += 1\n    img['file_name'] = 'cp_train/' + img['file_name'][11:]\n    img['frame_id'] = img_id_count\n    img['prev_image_id'] = img['id'] + max_img\n    img['next_image_id'] = img['id'] + max_img\n    img['id'] = img['id'] + max_img\n    img['video_id'] = max_video\n    img_list.append(img)\n    \nfor ann in cp_json['annotations']:\n    ann['id'] = ann['id'] + max_ann\n    ann['image_id'] = ann['image_id'] + max_img\n    ann_list.append(ann)\n\nprint('Cityscapes')\n\nvideo_list.append({\n    'id': max_video,\n    'file_name': 'cityperson'\n})\n\nmix_json = dict()\nmix_json['images'] = img_list\nmix_json['annotations'] = ann_list\nmix_json['videos'] = video_list\nmix_json['categories'] = category_list\njson.dump(mix_json, open('datasets/mix_det/annotations/train.json','w'))\n"
  },
  {
    "path": "tools/mix_data_test_mot20.py",
    "content": "import json\nimport os\n\n\n\"\"\"\ncd datasets\nmkdir -p mix_mot20_ch/annotations\ncp MOT20/annotations/val_half.json mix_mot20_ch/annotations/val_half.json\ncp MOT20/annotations/test.json mix_mot20_ch/annotations/test.json\ncd mix_mot20_ch\nln -s ../MOT20/train mot20_train\nln -s ../crowdhuman/CrowdHuman_train crowdhuman_train\nln -s ../crowdhuman/CrowdHuman_val crowdhuman_val\ncd ..\n\"\"\"\n\nmot_json = json.load(open('datasets/MOT20/annotations/train.json','r'))\n\nimg_list = list()\nfor img in mot_json['images']:\n    img['file_name'] = 'mot20_train/' + img['file_name']\n    img_list.append(img)\n\nann_list = list()\nfor ann in mot_json['annotations']:\n    ann_list.append(ann)\n\nvideo_list = mot_json['videos']\ncategory_list = mot_json['categories']\n\n\nmax_img = 10000\nmax_ann = 2000000\nmax_video = 10\n\ncrowdhuman_json = json.load(open('datasets/crowdhuman/annotations/train.json','r'))\nimg_id_count = 0\nfor img in crowdhuman_json['images']:\n    img_id_count += 1\n    img['file_name'] = 'crowdhuman_train/' + img['file_name']\n    img['frame_id'] = img_id_count\n    img['prev_image_id'] = img['id'] + max_img\n    img['next_image_id'] = img['id'] + max_img\n    img['id'] = img['id'] + max_img\n    img['video_id'] = max_video\n    img_list.append(img)\n    \nfor ann in crowdhuman_json['annotations']:\n    ann['id'] = ann['id'] + max_ann\n    ann['image_id'] = ann['image_id'] + max_img\n    ann_list.append(ann)\n\nvideo_list.append({\n    'id': max_video,\n    'file_name': 'crowdhuman_train'\n})\n\n\nmax_img = 30000\nmax_ann = 10000000\n\ncrowdhuman_val_json = json.load(open('datasets/crowdhuman/annotations/val.json','r'))\nimg_id_count = 0\nfor img in crowdhuman_val_json['images']:\n    img_id_count += 1\n    img['file_name'] = 'crowdhuman_val/' + img['file_name']\n    img['frame_id'] = img_id_count\n    img['prev_image_id'] = img['id'] + max_img\n    img['next_image_id'] = img['id'] + max_img\n    img['id'] = img['id'] + max_img\n    img['video_id'] = max_video\n    img_list.append(img)\n    \nfor ann in crowdhuman_val_json['annotations']:\n    ann['id'] = ann['id'] + max_ann\n    ann['image_id'] = ann['image_id'] + max_img\n    ann_list.append(ann)\n\nvideo_list.append({\n    'id': max_video,\n    'file_name': 'crowdhuman_val'\n})\n\nmix_json = dict()\nmix_json['images'] = img_list\nmix_json['annotations'] = ann_list\nmix_json['videos'] = video_list\nmix_json['categories'] = category_list\njson.dump(mix_json, open('datasets/mix_mot20_ch/annotations/train.json','w'))"
  },
  {
    "path": "tools/mota.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluator\n\nimport os\nimport glob\nimport motmetrics as mm\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:            \n            logger.info('Comparing {}...'.format(k))\n            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))\n            names.append(k)\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n# evaluate MOTA\nresults_folder = 'YOLOX_outputs/yolox_x_ablation/track_results'\nmm.lap.default_solver = 'lap'\n\ngt_type = '_val_half'\n#gt_type = ''\nprint('gt_type', gt_type)\ngtfiles = glob.glob(\n    os.path.join('datasets/mot/train', '*/gt/gt{}.txt'.format(gt_type)))\nprint('gt_files', gtfiles)\ntsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\nlogger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\nlogger.info('Available LAP solvers {}'.format(mm.lap.available_solvers))\nlogger.info('Default LAP solver \\'{}\\''.format(mm.lap.default_solver))\nlogger.info('Loading files.')\n\ngt = OrderedDict([(Path(f).parts[-3], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\nts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1.0)) for f in tsfiles])    \n\nmh = mm.metrics.create()    \naccs, names = compare_dataframes(gt, ts)\n\nlogger.info('Running metrics')\nmetrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n            'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n            'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\nsummary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n# summary = mh.compute_many(accs, names=names, metrics=mm.metrics.motchallenge_metrics, generate_overall=True)\n# print(mm.io.render_summary(\n#   summary, formatters=mh.formatters, \n#   namemap=mm.io.motchallenge_metric_names))\ndiv_dict = {\n    'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n    'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\nfor divisor in div_dict:\n    for divided in div_dict[divisor]:\n        summary[divided] = (summary[divided] / summary[divisor])\nfmt = mh.formatters\nchange_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                    'partially_tracked', 'mostly_lost']\nfor k in change_fmt_list:\n    fmt[k] = fmt['mota']\nprint(mm.io.render_summary(summary, formatters=fmt, namemap=mm.io.motchallenge_metric_names))\n\nmetrics = mm.metrics.motchallenge_metrics + ['num_objects']\nsummary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\nprint(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names))\nlogger.info('Completed')\n"
  },
  {
    "path": "tools/plot_trajectory.py",
    "content": "\"\"\"\n    This script is to draw trajectory prediction as in Fig.6 of the paper\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport sys\nimport numpy as np \nimport os\n\ndef plot_traj(traj_file, name):\n    trajs = np.loadtxt(traj_file, delimiter=\",\")\n    track_ids = np.unique(trajs[:,1])\n    for tid in track_ids:\n        traj = trajs[np.where(trajs[:,1]==tid)]\n        fig, ax = plt.subplots(figsize=(12, 6), dpi=200)\n        frames = traj[:100, 0]\n        boxes = traj[:100, 2:6]\n        boxes_x = boxes[:,0]\n        boxes_y = boxes[:,1]\n        plt.plot(boxes_x, boxes_y, \"ro\")\n        box_num = boxes_x.shape[0]\n        for bind in range(0, box_num-1):\n            frame_l = frames[bind]\n            frame_r = frames[bind+1]\n            box_l = boxes[bind]\n            box_r = boxes[bind+1]\n            if frame_r == frame_l + 1:\n                l = matplotlib.lines.Line2D([box_l[0], box_r[0]], [box_l[1], box_r[1]], color=\"red\")\n                ax.add_line(l)\n            else:\n                l = matplotlib.lines.Line2D([box_l[0], box_r[0]], [box_l[1], box_r[1]], color=\"gray\")\n                ax.add_line(l)\n        plt.savefig(\"traj_plots/{}/{}.png\".format(name, int(tid)))\n\n\nif __name__ == \"__main__\":\n    name = sys.argv[1]\n    os.makedirs(os.path.join(\"traj_plots/{}\".format(name)), exist_ok=True)\n\n    gt_src = \"datasets/dancetrack/val\"\n\n    ours = \"path/to/pred/output\" # preds\n    baseline = \"path/to/baseline/output\" # baseline outputs\n    seqs = os.listdir(gt_src)\n    for seq in seqs:\n        name = \"gt_{}\".format(seq)\n        os.makedirs(os.path.join(\"traj_plots/{}\".format(name)), exist_ok=True)\n        plot_traj(os.path.join(gt_src, seq, \"gt/gt.txt\"), name)\n\n        name = \"baseline_{}\".format(seq)\n        os.makedirs(os.path.join(\"traj_plots/{}\".format(name)), exist_ok=True)\n        plot_traj(os.path.join(baseline, \"{}.txt\".format(seq)), \"baseline_{}\".format(seq))\n\n        name = \"ours_{}\".format(seq)\n        os.makedirs(os.path.join(\"traj_plots/{}\".format(name)), exist_ok=True)\n        plot_traj(os.path.join(ours, \"{}.txt\".format(seq)), \"ours_{}\".format(seq))"
  },
  {
    "path": "tools/run_byte.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluator, MOTEvaluatorPublic\nfrom utils.args import make_parser\n\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mmp\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:       \n            print(k)     \n            logger.info('Comparing {}...'.format(k))\n            os.makedirs(\"results_log\", exist_ok=True)\n            vflag = open(\"results_log/eval_{}.txt\".format(k), 'w')\n            accs.append(mmp.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5, vflag=vflag))\n            names.append(k)\n            vflag.close()\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n    cudnn.benchmark = True\n\n    rank = args.local_rank\n    \"\"\"\n        This is for MOT17/MOT20 data configuration\n    \"\"\"\n    if exp.val_ann == 'val_half.json':\n        gt_type = '_val_half'\n        seqs = \"MOT17-val\"\n    elif exp.val_ann == \"train_half.json\":\n        gt_type = '_train_half'\n        seqs = \"MOT17-train_half\"\n    elif exp.val_ann == \"test.json\": \n        gt_type = ''\n        seqs = \"MOT20-test\" if args.mot20 else \"MOT17-test\"\n    else:\n        assert 0\n\n    result_folder = \"{}_test_results\".format(args.expn) if args.test else \"{}_results\".format(args.expn)\n    file_name = os.path.join(exp.output_dir, seqs, result_folder)\n\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)\n\n    if not args.public:\n        evaluator = MOTEvaluator(\n            args=args,\n            dataloader=val_loader,\n            img_size=exp.test_size,\n            confthre=exp.test_conf,\n            nmsthre=exp.nmsthre,\n            num_classes=exp.num_classes,\n            )\n    else:\n        evaluator = MOTEvaluatorPublic(\n            args=args,\n            dataloader=val_loader,\n            img_size=exp.test_size,\n            confthre=exp.test_conf,\n            nmsthre=exp.nmsthre,\n            num_classes=exp.num_classes,\n            )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    results_folder = os.path.join(file_name, \"data\")\n    os.makedirs(results_folder, exist_ok=True)\n\n    # start evaluate\n \n    # *_, summary = evaluator.evaluate_ocsort(\n    #     model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n    # )\n    if args.TCM_first_step:\n        *_, summary = evaluator.evaluate_byte_score(\n            model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    else:\n        *_, summary = evaluator.evaluate(\n                model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n\n    logger.info(\"\\n\" + summary)\n \n    # evaluate MOTA\n    mmp.lap.default_solver = 'lap'\n    print('gt_type', gt_type)\n    gtfiles = glob.glob(\n      os.path.join('datasets/mot/train', '*/gt/gt{}.txt'.format(gt_type)))\n    print('gt_files', gtfiles)\n    tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\n    logger.info('Available LAP solvers {}'.format(mmp.lap.available_solvers))\n    logger.info('Default LAP solver \\'{}\\''.format(mmp.lap.default_solver))\n    logger.info('Loading files.')\n    \n    gt = OrderedDict([(Path(f).parts[-3], mmp.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mmp.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles if \"detections\" not in f])    \n    \n    mh = mmp.metrics.create()    \n    accs, names = compare_dataframes(gt, ts)\n    \n    logger.info('Running metrics')\n    metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n               'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n               'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    div_dict = {\n        'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n        'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\n    for divisor in div_dict:\n        for divided in div_dict[divisor]:\n            summary[divided] = (summary[divided] / summary[divisor])\n    fmt = mh.formatters\n    change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                       'partially_tracked', 'mostly_lost']\n    for k in change_fmt_list:\n        fmt[k] = fmt['mota']\n    print(mmp.io.render_summary(summary, formatters=fmt, namemap=mmp.io.motchallenge_metric_names))\n\n    metrics = mmp.metrics.motchallenge_metrics + ['num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mmp.io.render_summary(summary, formatters=mh.formatters, namemap=mmp.io.motchallenge_metric_names))\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n    exp.output_dir = args.output_dir\n\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )\n"
  },
  {
    "path": "tools/run_byte_dance.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluatorDance as MOTEvaluator\n\nfrom utils.args import make_parser\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mm\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:            \n            logger.info('Comparing {}...'.format(k))\n            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))\n            names.append(k)\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    \n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n\n    # set environment variables for distributed training\n    cudnn.benchmark = True\n    rank = args.local_rank\n    file_name = os.path.join(exp.output_dir, args.expn)\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    result_dir = \"{}_test\".format(args.expn) if args.test else \"{}_val\".format(args.expn)\n    results_folder = os.path.join(file_name, result_dir)\n    os.makedirs(results_folder, exist_ok=True)\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)\n    evaluator = MOTEvaluator(\n        args=args,\n        dataloader=val_loader,\n        img_size=exp.test_size,\n        confthre=exp.test_conf,\n        nmsthre=exp.nmsthre,\n        num_classes=exp.num_classes,\n        )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    # start tracking\n    # *_, summary = evaluator.evaluate(\n    #         model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n    # )\n\n    if args.TCM_first_step:\n        *_, summary = evaluator.evaluate_byte_score(\n            model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    else:\n        *_, summary = evaluator.evaluate(\n                model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    \n    if args.test:\n        # we skip evaluation for inference on test set\n        return \n\n    # if we evaluate on validation set, \n    logger.info(\"\\n\" + summary)\n\n    # evaluate on the validation set\n    mm.lap.default_solver = 'lap'\n    gtfiles = glob.glob(os.path.join('datasets/dancetrack/val', '*/gt/gt.txt'))\n    print('gt_files', gtfiles)\n    tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\n    logger.info('Available LAP solvers {}'.format(mm.lap.available_solvers))\n    logger.info('Default LAP solver \\'{}\\''.format(mm.lap.default_solver))\n    logger.info('Loading files.')\n    \n    gt = OrderedDict([(Path(f).parts[-3], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles])    \n    \n    mh = mm.metrics.create()    \n    accs, names = compare_dataframes(gt, ts)\n    \n    logger.info('Running metrics')\n    metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n               'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n               'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    div_dict = {\n        'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n        'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\n    for divisor in div_dict:\n        for divided in div_dict[divisor]:\n            summary[divided] = (summary[divided] / summary[divisor])\n    fmt = mh.formatters\n    change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                       'partially_tracked', 'mostly_lost']\n    for k in change_fmt_list:\n        fmt[k] = fmt['mota']\n    print(mm.io.render_summary(summary, formatters=fmt, namemap=mm.io.motchallenge_metric_names))\n\n    metrics = mm.metrics.motchallenge_metrics + ['num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names))\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )"
  },
  {
    "path": "tools/run_deepsort.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluator, MOTEvaluatorPublic\nfrom utils.args import make_parser\n\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mmp\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:       \n            print(k)     \n            logger.info('Comparing {}...'.format(k))\n            os.makedirs(\"results_log\", exist_ok=True)\n            vflag = open(\"results_log/eval_{}.txt\".format(k), 'w')\n            accs.append(mmp.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5, vflag=vflag))\n            names.append(k)\n            vflag.close()\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n    cudnn.benchmark = True\n\n    rank = args.local_rank\n    \"\"\"\n        This is for MOT17/MOT20 data configuration\n    \"\"\"\n    if exp.val_ann == 'val_half.json':\n        gt_type = '_val_half'\n        seqs = \"MOT17-val\"\n    elif exp.val_ann == \"train_half.json\":\n        gt_type = '_train_half'\n        seqs = \"MOT17-train_half\"\n    elif exp.val_ann == \"test.json\": \n        gt_type = ''\n        seqs = \"MOT20-test\" if args.mot20 else \"MOT17-test\"\n    else:\n        assert 0\n\n    result_folder = \"{}_test_results\".format(args.expn) if args.test else \"{}_results\".format(args.expn)\n    file_name = os.path.join(exp.output_dir, seqs, result_folder)\n\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)\n\n    if not args.public:\n        evaluator = MOTEvaluator(\n            args=args,\n            dataloader=val_loader,\n            img_size=exp.test_size,\n            confthre=exp.test_conf,\n            nmsthre=exp.nmsthre,\n            num_classes=exp.num_classes,\n            )\n    else:\n        evaluator = MOTEvaluatorPublic(\n            args=args,\n            dataloader=val_loader,\n            img_size=exp.test_size,\n            confthre=exp.test_conf,\n            nmsthre=exp.nmsthre,\n            num_classes=exp.num_classes,\n            )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    results_folder = os.path.join(file_name, \"data\")\n    os.makedirs(results_folder, exist_ok=True)\n\n    # start evaluate\n \n    # *_, summary = evaluator.evaluate_ocsort(\n    #     model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n    # )\n    if args.TCM_first_step:\n        *_, summary = evaluator.evaluate_deepsort_score(\n            model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    else:\n        *_, summary = evaluator.evaluate_deepsort(\n                args, model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n\n    logger.info(\"\\n\" + summary)\n \n    # evaluate MOTA\n    mmp.lap.default_solver = 'lap'\n    print('gt_type', gt_type)\n    gtfiles = glob.glob(\n      os.path.join('datasets/mot/train', '*/gt/gt{}.txt'.format(gt_type)))\n    print('gt_files', gtfiles)\n    tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\n    logger.info('Available LAP solvers {}'.format(mmp.lap.available_solvers))\n    logger.info('Default LAP solver \\'{}\\''.format(mmp.lap.default_solver))\n    logger.info('Loading files.')\n    \n    gt = OrderedDict([(Path(f).parts[-3], mmp.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mmp.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles if \"detections\" not in f])    \n    \n    mh = mmp.metrics.create()    \n    accs, names = compare_dataframes(gt, ts)\n    \n    logger.info('Running metrics')\n    metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n               'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n               'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    div_dict = {\n        'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n        'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\n    for divisor in div_dict:\n        for divided in div_dict[divisor]:\n            summary[divided] = (summary[divided] / summary[divisor])\n    fmt = mh.formatters\n    change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                       'partially_tracked', 'mostly_lost']\n    for k in change_fmt_list:\n        fmt[k] = fmt['mota']\n    print(mmp.io.render_summary(summary, formatters=fmt, namemap=mmp.io.motchallenge_metric_names))\n\n    metrics = mmp.metrics.motchallenge_metrics + ['num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mmp.io.render_summary(summary, formatters=mh.formatters, namemap=mmp.io.motchallenge_metric_names))\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n    exp.output_dir = args.output_dir\n\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )\n"
  },
  {
    "path": "tools/run_deepsort_dance.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluatorDance as MOTEvaluator\n\nfrom utils.args import make_parser\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mm\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:            \n            logger.info('Comparing {}...'.format(k))\n            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))\n            names.append(k)\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    \n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n\n    # set environment variables for distributed training\n    cudnn.benchmark = True\n    rank = args.local_rank\n    file_name = os.path.join(exp.output_dir, args.expn)\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    result_dir = \"{}_test\".format(args.expn) if args.test else \"{}_val\".format(args.expn)\n    results_folder = os.path.join(file_name, result_dir)\n    os.makedirs(results_folder, exist_ok=True)\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)\n    evaluator = MOTEvaluator(\n        args=args,\n        dataloader=val_loader,\n        img_size=exp.test_size,\n        confthre=exp.test_conf,\n        nmsthre=exp.nmsthre,\n        num_classes=exp.num_classes,\n        )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    # start tracking\n    if args.TCM_first_step:\n        *_, summary = evaluator.evaluate_deepsort_score(\n            model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    else:\n        *_, summary = evaluator.evaluate_deepsort(\n                args, model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    \n    if args.test:\n        # we skip evaluation for inference on test set\n        return \n\n    # if we evaluate on validation set, \n    logger.info(\"\\n\" + summary)\n\n    # evaluate on the validation set\n    mm.lap.default_solver = 'lap'\n    gtfiles = glob.glob(os.path.join('datasets/dancetrack/val', '*/gt/gt.txt'))\n    print('gt_files', gtfiles)\n    tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\n    logger.info('Available LAP solvers {}'.format(mm.lap.available_solvers))\n    logger.info('Default LAP solver \\'{}\\''.format(mm.lap.default_solver))\n    logger.info('Loading files.')\n    \n    gt = OrderedDict([(Path(f).parts[-3], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles])    \n    \n    mh = mm.metrics.create()    \n    accs, names = compare_dataframes(gt, ts)\n    \n    logger.info('Running metrics')\n    metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n               'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n               'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    div_dict = {\n        'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n        'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\n    for divisor in div_dict:\n        for divided in div_dict[divisor]:\n            summary[divided] = (summary[divided] / summary[divisor])\n    fmt = mh.formatters\n    change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                       'partially_tracked', 'mostly_lost']\n    for k in change_fmt_list:\n        fmt[k] = fmt['mota']\n    print(mm.io.render_summary(summary, formatters=fmt, namemap=mm.io.motchallenge_metric_names))\n\n    metrics = mm.metrics.motchallenge_metrics + ['num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names))\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )"
  },
  {
    "path": "tools/run_hybrid_sort_dance.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluatorDance as MOTEvaluator\n\nfrom utils.args import make_parser, args_merge_params_form_exp\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mm\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:            \n            logger.info('Comparing {}...'.format(k))\n            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))\n            names.append(k)\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    \n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n\n    # set environment variables for distributed training\n    cudnn.benchmark = True\n    rank = args.local_rank\n    file_name = os.path.join(exp.output_dir, args.expn)\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    result_dir = \"{}_test\".format(args.expn) + \\\n                 \"_EGWeightHigh\" + str(args.EG_weight_high_score) + \\\n                 \"_EGWeightLow\" + str(args.EG_weight_low_score) + \\\n                 \"_WithLongTermReIDCorrection\" + str(args.with_longterm_reid_correction) + \\\n                 \"_LongTermReIDCorrectionThresh\" + str(args.longterm_reid_correction_thresh) + \\\n                 \"_LongTermReIDCorrectionThreshLow\" + str(args.longterm_reid_correction_thresh_low) + \\\n                 \"_IoUThresh\" + str(args.iou_thresh) + \\\n                 \"_ScoreDifInterval\" + str(args.TCM_first_step_weight) + \\\n                 \"_SecScoreDifInterval\" + str(args.TCM_byte_step_weight) \\\n        if args.test else \\\n        \"{}_val\".format(args.expn) + \\\n        \"_EGWeightHigh\" + str(args.EG_weight_high_score) + \\\n        \"_EGWeightLow\" + str(args.EG_weight_low_score) + \\\n        \"_WithLongTermReIDCorrection\" + str(args.with_longterm_reid_correction) + \\\n        \"_LongTermReIDCorrectionThresh\" + str(args.longterm_reid_correction_thresh) + \\\n        \"_LongTermReIDCorrectionThreshLow\" + str(args.longterm_reid_correction_thresh_low) + \\\n        \"_IoUThresh\" + str(args.iou_thresh) + \\\n        \"_ScoreDifInterval\" + str(args.TCM_first_step_weight) + \\\n        \"_SecScoreDifInterval\" + str(args.TCM_byte_step_weight)\n    results_folder = os.path.join(file_name, result_dir)\n    os.makedirs(results_folder, exist_ok=True)\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test, run_tracking=True)\n    evaluator = MOTEvaluator(\n        args=args,\n        dataloader=val_loader,\n        img_size=exp.test_size,\n        confthre=exp.test_conf,\n        nmsthre=exp.nmsthre,\n        num_classes=exp.num_classes,\n        )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    # start tracking\n    if not args.hybrid_sort_with_reid:\n        *_, summary = evaluator.evaluate_hybrid_sort(\n            args, model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    else:\n        *_, summary = evaluator.evaluate_hybrid_sort_reid(\n                args, model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n\n    \n    if args.test:\n        # we skip evaluation for inference on test set\n        return \n\n    logger.info(\"\\n\" + summary)\n\n    if args.dataset == \"dancetrack\":\n        hota_command = \"python3 TrackEval/scripts/run_mot_challenge.py \" \\\n                       \"--SPLIT_TO_EVAL val  \" \\\n                       \"--METRICS HOTA CLEAR Identity \" \\\n                       \"--GT_FOLDER datasets/dancetrack/val \" \\\n                       \"--SEQMAP_FILE datasets/dancetrack/val/val_seqmap.txt \" \\\n                       \"--SKIP_SPLIT_FOL True \" \\\n                       \"--TRACKERS_TO_EVAL '' \" \\\n                       \"--TRACKER_SUB_FOLDER ''  \" \\\n                       \"--USE_PARALLEL True \" \\\n                       \"--NUM_PARALLEL_CORES 8 \" \\\n                       \"--PLOT_CURVES False \" \\\n                       \"--TRACKERS_FOLDER \" + results_folder\n    elif args.dataset == \"mot17\":\n        hota_command = \"python TrackEval/scripts/run_mot_challenge.py \" \\\n                       \"--BENCHMARK MOT17 \" \\\n                       \"--SPLIT_TO_EVAL train \" \\\n                       \"--TRACKERS_TO_EVAL '' \" \\\n                       \"--METRICS HOTA CLEAR Identity VACE \" \\\n                       \"--TIME_PROGRESS False \" \\\n                       \"--USE_PARALLEL False \" \\\n                       \"--NUM_PARALLEL_CORES 1  \" \\\n                       \"--GT_FOLDER datasets/mot/ \" \\\n                       \"--TRACKERS_FOLDER \" + results_folder + \" \" \\\n                       \"--GT_LOC_FORMAT {gt_folder}/{seq}/gt/gt_val_half.txt\"\n    elif args.dataset == \"mot20\":\n        hota_command = \"python TrackEval/scripts/run_mot_challenge.py \" \\\n                       \"--BENCHMARK MOT20 \" \\\n                       \"--SPLIT_TO_EVAL train \" \\\n                       \"--TRACKERS_TO_EVAL '' \" \\\n                       \"--METRICS HOTA CLEAR Identity VACE \" \\\n                       \"--TIME_PROGRESS False \" \\\n                       \"--USE_PARALLEL False \" \\\n                       \"--NUM_PARALLEL_CORES 1  \" \\\n                       \"--GT_FOLDER datasets/MOT20/ \" \\\n                       \"--TRACKERS_FOLDER \" + results_folder + \" \" \\\n                       \"--GT_LOC_FORMAT {gt_folder}/{seq}/gt/gt_val_half.txt\"\n    else:\n        assert args.dataset in [\"dancetrack\", \"mot17\"]\n    os.system(hota_command)\n\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n    args_merge_params_form_exp(args, exp)\n\n    if not args.expn:\n        args.expn = exp.exp_name\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )"
  },
  {
    "path": "tools/run_motdt.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluator, MOTEvaluatorPublic\nfrom utils.args import make_parser\n\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mmp\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:       \n            print(k)     \n            logger.info('Comparing {}...'.format(k))\n            os.makedirs(\"results_log\", exist_ok=True)\n            vflag = open(\"results_log/eval_{}.txt\".format(k), 'w')\n            accs.append(mmp.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5, vflag=vflag))\n            names.append(k)\n            vflag.close()\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n    cudnn.benchmark = True\n\n    rank = args.local_rank\n    \"\"\"\n        This is for MOT17/MOT20 data configuration\n    \"\"\"\n    if exp.val_ann == 'val_half.json':\n        gt_type = '_val_half'\n        seqs = \"MOT17-val\"\n    elif exp.val_ann == \"train_half.json\":\n        gt_type = '_train_half'\n        seqs = \"MOT17-train_half\"\n    elif exp.val_ann == \"test.json\": \n        gt_type = ''\n        seqs = \"MOT20-test\" if args.mot20 else \"MOT17-test\"\n    else:\n        assert 0\n\n    result_folder = \"{}_test_results\".format(args.expn) if args.test else \"{}_results\".format(args.expn)\n    file_name = os.path.join(exp.output_dir, seqs, result_folder)\n\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)\n\n    if not args.public:\n        evaluator = MOTEvaluator(\n            args=args,\n            dataloader=val_loader,\n            img_size=exp.test_size,\n            confthre=exp.test_conf,\n            nmsthre=exp.nmsthre,\n            num_classes=exp.num_classes,\n            )\n    else:\n        evaluator = MOTEvaluatorPublic(\n            args=args,\n            dataloader=val_loader,\n            img_size=exp.test_size,\n            confthre=exp.test_conf,\n            nmsthre=exp.nmsthre,\n            num_classes=exp.num_classes,\n            )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    results_folder = os.path.join(file_name, \"data\")\n    os.makedirs(results_folder, exist_ok=True)\n\n    # start evaluate\n \n    # *_, summary = evaluator.evaluate_ocsort(\n    #     model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n    # )\n    if args.TCM_first_step:\n        *_, summary = evaluator.evaluate_motdt_score(\n            model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    else:\n        *_, summary = evaluator.evaluate_motdt(\n                args, model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n\n    logger.info(\"\\n\" + summary)\n \n    # evaluate MOTA\n    mmp.lap.default_solver = 'lap'\n    print('gt_type', gt_type)\n    gtfiles = glob.glob(\n      os.path.join('datasets/mot/train', '*/gt/gt{}.txt'.format(gt_type)))\n    print('gt_files', gtfiles)\n    tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\n    logger.info('Available LAP solvers {}'.format(mmp.lap.available_solvers))\n    logger.info('Default LAP solver \\'{}\\''.format(mmp.lap.default_solver))\n    logger.info('Loading files.')\n    \n    gt = OrderedDict([(Path(f).parts[-3], mmp.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mmp.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles if \"detections\" not in f])    \n    \n    mh = mmp.metrics.create()    \n    accs, names = compare_dataframes(gt, ts)\n    \n    logger.info('Running metrics')\n    metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n               'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n               'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    div_dict = {\n        'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n        'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\n    for divisor in div_dict:\n        for divided in div_dict[divisor]:\n            summary[divided] = (summary[divided] / summary[divisor])\n    fmt = mh.formatters\n    change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                       'partially_tracked', 'mostly_lost']\n    for k in change_fmt_list:\n        fmt[k] = fmt['mota']\n    print(mmp.io.render_summary(summary, formatters=fmt, namemap=mmp.io.motchallenge_metric_names))\n\n    metrics = mmp.metrics.motchallenge_metrics + ['num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mmp.io.render_summary(summary, formatters=mh.formatters, namemap=mmp.io.motchallenge_metric_names))\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n    exp.output_dir = args.output_dir\n\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )\n"
  },
  {
    "path": "tools/run_motdt_dance.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluatorDance as MOTEvaluator\n\nfrom utils.args import make_parser\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mm\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:            \n            logger.info('Comparing {}...'.format(k))\n            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))\n            names.append(k)\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    \n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n\n    # set environment variables for distributed training\n    cudnn.benchmark = True\n    rank = args.local_rank\n    file_name = os.path.join(exp.output_dir, args.expn)\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    result_dir = \"{}_test\".format(args.expn) if args.test else \"{}_val\".format(args.expn)\n    results_folder = os.path.join(file_name, result_dir)\n    os.makedirs(results_folder, exist_ok=True)\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)\n    evaluator = MOTEvaluator(\n        args=args,\n        dataloader=val_loader,\n        img_size=exp.test_size,\n        confthre=exp.test_conf,\n        nmsthre=exp.nmsthre,\n        num_classes=exp.num_classes,\n        )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    # start tracking\n    if args.TCM_first_step:\n        *_, summary = evaluator.evaluate_motdt_score(\n            model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    else:\n        *_, summary = evaluator.evaluate_motdt(\n                args, model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    \n    if args.test:\n        # we skip evaluation for inference on test set\n        return \n\n    # if we evaluate on validation set, \n    logger.info(\"\\n\" + summary)\n\n    # evaluate on the validation set\n    mm.lap.default_solver = 'lap'\n    gtfiles = glob.glob(os.path.join('datasets/dancetrack/val', '*/gt/gt.txt'))\n    print('gt_files', gtfiles)\n    tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\n    logger.info('Available LAP solvers {}'.format(mm.lap.available_solvers))\n    logger.info('Default LAP solver \\'{}\\''.format(mm.lap.default_solver))\n    logger.info('Loading files.')\n    \n    gt = OrderedDict([(Path(f).parts[-3], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles])    \n    \n    mh = mm.metrics.create()    \n    accs, names = compare_dataframes(gt, ts)\n    \n    logger.info('Running metrics')\n    metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n               'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n               'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    div_dict = {\n        'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n        'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\n    for divisor in div_dict:\n        for divided in div_dict[divisor]:\n            summary[divided] = (summary[divided] / summary[divisor])\n    fmt = mh.formatters\n    change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                       'partially_tracked', 'mostly_lost']\n    for k in change_fmt_list:\n        fmt[k] = fmt['mota']\n    print(mm.io.render_summary(summary, formatters=fmt, namemap=mm.io.motchallenge_metric_names))\n\n    metrics = mm.metrics.motchallenge_metrics + ['num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names))\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )"
  },
  {
    "path": "tools/run_ocsort.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluator, MOTEvaluatorPublic\nfrom utils.args import make_parser\n\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mmp\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:       \n            print(k)     \n            logger.info('Comparing {}...'.format(k))\n            os.makedirs(\"results_log\", exist_ok=True)\n            vflag = open(\"results_log/eval_{}.txt\".format(k), 'w')\n            accs.append(mmp.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5, vflag=vflag))\n            names.append(k)\n            vflag.close()\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n    cudnn.benchmark = True\n\n    rank = args.local_rank\n    \"\"\"\n        This is for MOT17/MOT20 data configuration\n    \"\"\"\n    if exp.val_ann == 'val_half.json':\n        gt_type = '_val_half'\n        seqs = \"MOT17-val\"\n    elif exp.val_ann == \"train_half.json\":\n        gt_type = '_train_half'\n        seqs = \"MOT17-train_half\"\n    elif exp.val_ann == \"test.json\": \n        gt_type = ''\n        seqs = \"MOT20-test\" if args.mot20 else \"MOT17-test\"\n    else:\n        assert 0\n\n    result_folder = \"{}_test_results\".format(args.expn) if args.test else \"{}_results\".format(args.expn)\n    file_name = os.path.join(exp.output_dir, seqs, result_folder)\n\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)\n\n    if not args.public:\n        evaluator = MOTEvaluator(\n            args=args,\n            dataloader=val_loader,\n            img_size=exp.test_size,\n            confthre=exp.test_conf,\n            nmsthre=exp.nmsthre,\n            num_classes=exp.num_classes,\n            )\n    else:\n        evaluator = MOTEvaluatorPublic(\n            args=args,\n            dataloader=val_loader,\n            img_size=exp.test_size,\n            confthre=exp.test_conf,\n            nmsthre=exp.nmsthre,\n            num_classes=exp.num_classes,\n            )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    results_folder = os.path.join(file_name, \"data\")\n    os.makedirs(results_folder, exist_ok=True)\n\n    # start evaluate\n \n    *_, summary = evaluator.evaluate_ocsort(\n        model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n    )\n\n    logger.info(\"\\n\" + summary)\n \n    # evaluate MOTA\n    mmp.lap.default_solver = 'lap'\n    print('gt_type', gt_type)\n    gtfiles = glob.glob(\n      os.path.join('datasets/mot/train', '*/gt/gt{}.txt'.format(gt_type)))\n    print('gt_files', gtfiles)\n    tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\n    logger.info('Available LAP solvers {}'.format(mmp.lap.available_solvers))\n    logger.info('Default LAP solver \\'{}\\''.format(mmp.lap.default_solver))\n    logger.info('Loading files.')\n    \n    gt = OrderedDict([(Path(f).parts[-3], mmp.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mmp.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles if \"detections\" not in f])    \n    \n    mh = mmp.metrics.create()    \n    accs, names = compare_dataframes(gt, ts)\n    \n    logger.info('Running metrics')\n    metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n               'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n               'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    div_dict = {\n        'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n        'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\n    for divisor in div_dict:\n        for divided in div_dict[divisor]:\n            summary[divided] = (summary[divided] / summary[divisor])\n    fmt = mh.formatters\n    change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                       'partially_tracked', 'mostly_lost']\n    for k in change_fmt_list:\n        fmt[k] = fmt['mota']\n    print(mmp.io.render_summary(summary, formatters=fmt, namemap=mmp.io.motchallenge_metric_names))\n\n    metrics = mmp.metrics.motchallenge_metrics + ['num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mmp.io.render_summary(summary, formatters=mh.formatters, namemap=mmp.io.motchallenge_metric_names))\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n    exp.output_dir = args.output_dir\n\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )\n"
  },
  {
    "path": "tools/run_ocsort_dance.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluatorDance as MOTEvaluator\n\nfrom utils.args import make_parser\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mm\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:            \n            logger.info('Comparing {}...'.format(k))\n            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))\n            names.append(k)\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    \n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n\n    # set environment variables for distributed training\n    cudnn.benchmark = True\n    rank = args.local_rank\n    file_name = os.path.join(exp.output_dir, args.expn)\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    result_dir = \"{}_test\".format(args.expn) if args.test else \"{}_val\".format(args.expn)\n    results_folder = os.path.join(file_name, result_dir)\n    os.makedirs(results_folder, exist_ok=True)\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)\n    evaluator = MOTEvaluator(\n        args=args,\n        dataloader=val_loader,\n        img_size=exp.test_size,\n        confthre=exp.test_conf,\n        nmsthre=exp.nmsthre,\n        num_classes=exp.num_classes,\n        )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    # start tracking\n    *_, summary = evaluator.evaluate_ocsort(\n            model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n    )\n    \n    if args.test:\n        # we skip evaluation for inference on test set\n        return \n\n    # if we evaluate on validation set, \n    logger.info(\"\\n\" + summary)\n\n    # evaluate on the validation set\n    mm.lap.default_solver = 'lap'\n    gtfiles = glob.glob(os.path.join('datasets/dancetrack/val', '*/gt/gt.txt'))\n    print('gt_files', gtfiles)\n    tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\n    logger.info('Available LAP solvers {}'.format(mm.lap.available_solvers))\n    logger.info('Default LAP solver \\'{}\\''.format(mm.lap.default_solver))\n    logger.info('Loading files.')\n    \n    gt = OrderedDict([(Path(f).parts[-3], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles])    \n    \n    mh = mm.metrics.create()    \n    accs, names = compare_dataframes(gt, ts)\n    \n    logger.info('Running metrics')\n    metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n               'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n               'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    div_dict = {\n        'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n        'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\n    for divisor in div_dict:\n        for divided in div_dict[divisor]:\n            summary[divided] = (summary[divided] / summary[divisor])\n    fmt = mh.formatters\n    change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                       'partially_tracked', 'mostly_lost']\n    for k in change_fmt_list:\n        fmt[k] = fmt['mota']\n    print(mm.io.render_summary(summary, formatters=fmt, namemap=mm.io.motchallenge_metric_names))\n\n    metrics = mm.metrics.motchallenge_metrics + ['num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names))\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )"
  },
  {
    "path": "tools/run_ocsort_public.py",
    "content": "'''\n    This script makes tracking over the results of existing\n    tracking algorithms. Namely, we run OC-SORT over theirdetections.\n    Output in such a way is not strictly accurate because\n    building tracks from existing tracking results causes loss\n    of detections (usually initializing tracks requires a few\n    continuous observations which are not recorded in the output\n    tracking results by other methods). But this quick adaptation\n    can provide a rough idea about OC-SORT's performance on\n    more datasets. For more strict study, we encourage to implement \n    a specific detector on the target dataset and then run OC-SORT \n    over the raw detection results.\n    NOTE: this script is not for the reported tracking with public\n    detection on MOT17/MOT20 which requires the detection filtering\n    following previous practice. See an example from centertrack for\n    example: https://github.com/xingyizhou/CenterTrack/blob/d3d52145b71cb9797da2bfb78f0f1e88b286c871/src/lib/utils/tracker.py#L83\n'''\n\nfrom loguru import logger\nimport time\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluator\n\nfrom utils.args import make_parser\nimport os\nimport motmetrics as mm\nfrom collections import OrderedDict\nfrom pathlib import Path\nimport numpy as np\nfrom trackers.ocsort_tracker.ocsort import OCSort\n\n\n\"\"\"\n    BDD has not been supported yet. \n\"\"\"\nBDD_test_seqs = ['b1c66a42-6f7d68ca', 'b1c81faa-3df17267', 'b1c81faa-c80764c5', 'b1c9c847-3bda4659', \n    'b1ca2e5d-84cf9134', 'b1cac6a7-04e33135', 'b1cd1e94-549d0bfe', 'b1ceb32e-3f481b43', \n    'b1ceb32e-51852abe', 'b1cebfb7-284f5117', 'b1d0091f-75824d0d', 'b1d0091f-f2c2d2ae', \n    'b1d0a191-03dcecc2', 'b1d0a191-06deb55d', 'b1d0a191-28f0e779', 'b1d0a191-2ed2269e', \n    'b1d0a191-5490450b', 'b1d0a191-65deaeef', 'b1d0a191-de8948f6', 'b1d10d08-5b108225', \n    'b1d10d08-743fd86c', 'b1d10d08-c35503b8', 'b1d10d08-da110fcb', 'b1d10d08-ec660956', \n    'b1d22449-117aa773', 'b1d22449-15fb948f', 'b1d22ed6-f1cac061', 'b1d3907b-2278601b', \n    'b1d4b62c-60aab822', 'b1d59b1f-a38aec79', 'b1d7b3ac-0bdb47dc', 'b1d7b3ac-36f2d3b7', \n    'b1d7b3ac-5744370e', 'b1d7b3ac-995f9d8a', 'b1d7b3ac-9e14f05f', 'b1d7b3ac-afa57f22', \n    'b1d968b9-563405f4', 'b1d968b9-ce42734f', 'b1d971b4-ac67ca0d', 'b1d9e136-6c94ea3f', \n    'b1d9e136-9ab25cb3', 'b1dac7f7-6b2e0382', 'b1db7e22-cfa74dc3', 'b1dce572-c6a8cb5e', \n    'b1dd58c1-8b546ba7', 'b1df722f-57d21f3f', 'b1df722f-5bcc3db7', 'b1e0c01d-dd9e6e2f', \n    'b1e1a7b8-0aec80e8', 'b1e1a7b8-65ec7612', 'b1e1a7b8-a7426a97', 'b1e1a7b8-b397c445', \n    'b1e2346e-c5f98707', 'b1e3e9f5-92377424', 'b1e62c91-eca210a9', 'b1e6efc0-2552cc5d', \n    'b1e88fd2-c1e4fd2b', 'b1e8ad72-c3c79240', 'b1e9ee0e-67e26f2e', 'b1ea0ae4-4f770228', \n    'b1eb9133-5cc75c18', 'b1ebfc3c-740ec84a', 'b1ebfc3c-cc9c2bb8', 'b1ee702d-0ae1fc10', \n    'b1ee702d-4a193906', 'b1f022d3-45162c67', 'b1f0efd9-37a14dda', 'b1f0efd9-e900c6e5', \n    'b1f20aa0-3401c3bf', 'b1f20aa0-50213047', 'b1f20aa0-6ef1db42', 'b1f25ff6-1ddb7e43', \n    'b1f4491b-07b32e8c', 'b1f4491b-09593e90', 'b1f4491b-16256d7c', 'b1f4491b-33824f31', \n    'b1f4491b-846d8cb2', 'b1f4491b-97465266', 'b1f4491b-9958bd99', 'b1f4491b-bf7d513f', \n    'b1f4491b-cf446195', 'b1f4491b-d8d1459c', 'b1f4491b-dd8dfed5', 'b1f6c103-5ce1f3c6', \n    'b1f6c103-8b75ea3e', 'b1f6c103-b00e8aad', 'b1f85377-44885085', 'b1fbaab8-68db7df7', \n    'b1fbf878-b31a8293', 'b1fc95c9-644e3c3f', 'b1fc95c9-cb2882c7', 'b1ff4656-0435391e', \n    'b1ff4656-94ee8536', 'b1ff4656-ebcfeb35', 'b200b84e-4a792877', 'b200e97a-bf074435', \n    'b20234fd-822029be', 'b202cae2-672e61c5', 'b202cae2-f46c74a6', 'b2036451-aa924fd1', \n    'b204a5c1-05981158', 'b204a5c1-064b0040', 'b204a5c1-fa3d5b88', 'b205eb4d-f84aaa1a', \n    'b2064e61-2beadd45', 'b206a78b-99f405ab', 'b2080dc7-f9b98a5f', 'b20841f9-cef732d5', \n    'b20b69d2-64b9cdb8', 'b20b69d2-650e674d', 'b20b69d2-6e2b9e73', 'b20b69d2-7767e6b6', \n    'b20b69d2-bd242bf0', 'b20b69d2-ca16c907', 'b20b69d2-e31380a7', 'b20b69d2-ffc1d6af', \n    'b20b9c19-91e01a50', 'b20d494a-cdebe83e', 'b20e291a-32ac11c1', 'b20e291a-6012d836', \n    'b20eae11-149766ce', 'b20eae11-18cd8ca2', 'b20eae11-6817ba7a', 'b20ff95c-b9444127', \n    'b2102d00-5eb86b71', 'b2102d00-a8c09be1', 'b2131b7b-e58faab7', 'b213e4eb-09c01a17', \n    'b214d1e1-f248c616', 'b21547c1-73e457f8', 'b21547c1-796757ac', 'b2156f8e-72e1547c', \n    'b215943a-10e44587', 'b216243d-55963da2', 'b216243d-ad4306b9', 'b2169b74-fa197951', \n    'b21742c2-0e7a2b57', 'b21742c2-18d3463a', 'b2194b15-1825056a', 'b21bfb83-ea32f716', \n    'b21c68e6-65674a17', 'b21c86ac-0dc77d82', 'b21c86ac-2eb7ba16', 'b21c86ac-71205084', \n    'b21d5efb-5e2cd743', 'b2208b0f-2796a692', 'b229488e-e4714bb7', 'b22a4d9f-48b2e986', \n    'b22a4d9f-73cc8810', 'b22e02cd-6af68e18', 'b22f385b-5d7e5202', 'b230132b-ff8f2719', \n    'b230a7b2-c881c382', 'b231a630-c4522992', 'b232c7c9-d251d9ee', 'b2331b83-648e56ca', \n    'b2331b83-a28e6b57', 'b23493b1-3200de1c', 'b237db93-fab44bf2', 'b23a79d1-43dfeecd', \n    'b23a79d1-e434acaa', 'b23adb0d-72704b27', 'b23adb0d-8a7aaced', 'b23b2649-1a78948d', \n    'b23b2649-6af03cd5', 'b23b2649-8349d2a1', 'b23bb45f-ddeea1d8', 'b23c9e00-b425de1b', \n    'b23f7012-32d284ce', 'b23f7012-fab06dac', 'b23fe89b-c704fe97', 'b24071b8-b3ee1196', \n    'b2408e45-984ba5aa', 'b242929f-3051abca', 'b242f6b2-0033bdfb', 'b242f6b2-99d2f2c1', \n    'b242f6b2-eaa39345', 'b242f6b2-f5da110f', 'b24380ab-63272e5a', 'b24380ab-6dbeb908', \n    'b24c9ee6-e43a6e8b', 'b24ca67a-594d7d3c', 'b24d283f-33783d1b', 'b24f03f7-ff66eaca', \n    'b24f7455-e8c55d6a', 'b2505382-1423f56a', 'b2505382-272e7823', 'b2505382-2905b23c', \n    'b2505382-549785d3', 'b2505382-de5238f0', 'b250fb0c-01a1b8d3', 'b251064f-30002542', \n    'b251064f-4696b75e', 'b251064f-5f6b663e', 'b251064f-8d92db81', 'b251064f-e7a165fd', \n    'b251b746-00138418', 'b255cd6c-0bdf0ac7', 'b255cd6c-2f889586', 'b255cd6c-5ccba454']\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:            \n            logger.info('Comparing {}...'.format(k))\n            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))\n            names.append(k)\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(args):\n    results_folder = args.out_path\n    raw_path = args.raw_results_path\n    os.makedirs(results_folder, exist_ok=True)\n\n    dataset = args.dataset\n\n    total_time = 0 \n    total_frame = 0 \n\n    if dataset == \"kitti\":\n        test_seqs = [\"%04d\" % i for i in range(29)]\n        cats = ['Pedestrian', 'Car', 'Cyclist', \"Van\", \"Truck\"]\n    elif dataset == \"bdd\":\n        \"\"\"\n            We are not supporting BDD yet. This is a placeholder for now.\n        \"\"\"\n        test_seqs = BDD_test_seqs\n        cats = [\"rider\", \"car\", \"truck\", \"bicycle\", \"motorcycle\", \"pedestrian\", \"bus\"]\n    elif dataset == \"headtrack\":\n        test_seqs = [\"HT21-11\", \"HT21-12\", \"HT21-13\", \"HT21-14\", \"HT21-15\"]\n        cats = [\"head\"]\n    else:\n        assert(0)\n\n    cat_ids = {cat: i for i, cat in enumerate(cats)}\n\n    for seq_name in test_seqs:\n        print(\"starting seq {}\".format(seq_name))\n        tracker = OCSort(args.track_thresh, iou_threshold=args.iou_thresh, delta_t=args.deltat, \n            asso_func=args.asso, inertia=args.inertia)\n        if dataset in [\"kitti\", \"bdd\"]:\n            seq_trks = np.empty((0, 18))\n        elif dataset == \"headtrack\":\n            seq_trks = np.empty((0, 10))\n        seq_file = os.path.join(raw_path, \"{}.txt\".format(seq_name))\n        seq_file = open(seq_file)\n        out_file = os.path.join(results_folder, \"{}.txt\".format(seq_name))\n        out_file = open(out_file, 'w')\n        lines = seq_file.readlines()\n        line_count = 0 \n        for line in lines:\n            print(\"{}/{}\".format(line_count,len(lines)))\n            line_count+=1\n            line = line.strip()\n            if dataset in [\"kitti\", \"bdd\"]:\n                tmps = line.strip().split()\n                tmps[2] = cat_ids[tmps[2]]\n            elif dataset == \"headtrack\":\n                tmps = line.strip().split(\",\")\n            trk = np.array([float(d) for d in tmps])\n            trk = np.expand_dims(trk, axis=0)\n            seq_trks = np.concatenate([seq_trks, trk], axis=0)\n        min_frame = seq_trks[:,0].min()\n        max_frame = seq_trks[:,0].max()\n        for frame_ind in range(int(min_frame), int(max_frame)+1):\n            print(\"{}:{}/{}\".format(seq_name, frame_ind, max_frame))\n            if dataset in [\"kitti\", \"bdd\"]:\n                dets = seq_trks[np.where(seq_trks[:,0]==frame_ind)][:,6:10]\n                cates = seq_trks[np.where(seq_trks[:,0]==frame_ind)][:,2]\n                scores = seq_trks[np.where(seq_trks[:,0]==frame_ind)][:,-1]\n            elif dataset == \"headtrack\":\n                dets = seq_trks[np.where(seq_trks[:,0]==frame_ind)][:,2:6]\n                cates = np.zeros((dets.shape[0],))\n                scores = seq_trks[np.where(seq_trks[:,0]==frame_ind)][:,6]\n                dets[:, 2:] += dets[:, :2] # xywh -> xyxy\n            else:\n                assert(0)\n            assert(dets.shape[0] == cates.shape[0])\n            t0 = time.time()\n            online_targets = tracker.update_public(dets, cates, scores)\n            t1 = time.time()\n            total_frame += 1\n            total_time += t1 - t0\n            trk_num = online_targets.shape[0]\n            boxes = online_targets[:, :4]\n            ids = online_targets[:, 4]\n            frame_counts = online_targets[:, 6]\n            sorted_frame_counts = np.argsort(frame_counts)\n            frame_counts = frame_counts[sorted_frame_counts]\n            cates = online_targets[:, 5]\n            cates = cates[sorted_frame_counts].tolist()\n            cates = [cats[int(catid)] for catid in cates]\n            boxes = boxes[sorted_frame_counts]\n            ids = ids[sorted_frame_counts]\n            for trk in range(trk_num):\n                lag_frame = frame_counts[trk]\n                if frame_ind < 2*args.min_hits and lag_frame < 0:\n                    continue\n                \"\"\"\n                    NOTE: here we use the Head Padding (HP) strategy by default, disable the following\n                    lines to revert back to the default version of OC-SORT.\n                \"\"\"\n                if dataset in [\"kitti\", \"bdd\"]:\n                    out_line = \"{} {} {} -1 -1 -1 {} {} {} {} -1 -1 -1 -1000 -1000 -1000 -10 1\\n\".format\\\n                        (int(frame_ind+lag_frame), int(ids[trk]), cates[trk], \n                        boxes[trk][0], boxes[trk][1], boxes[trk][2], boxes[trk][3])\n                elif dataset == \"headtrack\":\n                    out_line = \"{},{},{},{},{},{},{},-1,-1,-1\\n\".format(int(frame_ind+lag_frame), int(ids[trk]),\n                        boxes[trk][0], boxes[trk][1], \n                        boxes[trk][2]-boxes[trk][0],\n                        boxes[trk][3]-boxes[trk][1], 1)\n                out_file.write(out_line)\n\n    print(\"Running over {} frames takes {}s. FPS={}\".format(total_frame, total_time, total_frame / total_time))\n    return \n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    main(args)"
  },
  {
    "path": "tools/run_sort.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluator, MOTEvaluatorPublic\nfrom utils.args import make_parser\n\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mmp\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:       \n            print(k)     \n            logger.info('Comparing {}...'.format(k))\n            os.makedirs(\"results_log\", exist_ok=True)\n            vflag = open(\"results_log/eval_{}.txt\".format(k), 'w')\n            accs.append(mmp.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5, vflag=vflag))\n            names.append(k)\n            vflag.close()\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n    cudnn.benchmark = True\n\n    rank = args.local_rank\n    \"\"\"\n        This is for MOT17/MOT20 data configuration\n    \"\"\"\n    if exp.val_ann == 'val_half.json':\n        gt_type = '_val_half'\n        seqs = \"MOT17-val\"\n    elif exp.val_ann == \"train_half.json\":\n        gt_type = '_train_half'\n        seqs = \"MOT17-train_half\"\n    elif exp.val_ann == \"test.json\": \n        gt_type = ''\n        seqs = \"MOT20-test\" if args.mot20 else \"MOT17-test\"\n    else:\n        assert 0\n\n    result_folder = \"{}_test_results\".format(args.expn) if args.test else \"{}_results\".format(args.expn)\n    file_name = os.path.join(exp.output_dir, seqs, result_folder)\n\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)\n\n    if not args.public:\n        evaluator = MOTEvaluator(\n            args=args,\n            dataloader=val_loader,\n            img_size=exp.test_size,\n            confthre=exp.test_conf,\n            nmsthre=exp.nmsthre,\n            num_classes=exp.num_classes,\n            )\n    else:\n        evaluator = MOTEvaluatorPublic(\n            args=args,\n            dataloader=val_loader,\n            img_size=exp.test_size,\n            confthre=exp.test_conf,\n            nmsthre=exp.nmsthre,\n            num_classes=exp.num_classes,\n            )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    results_folder = os.path.join(file_name, \"data\")\n    os.makedirs(results_folder, exist_ok=True)\n\n    # start evaluate\n \n    # *_, summary = evaluator.evaluate_ocsort(\n    #     model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n    # )\n    if args.TCM_first_step:\n        *_, summary = evaluator.evaluate_sort_score(\n            args, model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    else:\n        *_, summary = evaluator.evaluate_sort(\n                args, model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n\n    logger.info(\"\\n\" + summary)\n \n    # evaluate MOTA\n    mmp.lap.default_solver = 'lap'\n    print('gt_type', gt_type)\n    gtfiles = glob.glob(\n      os.path.join('datasets/mot/train', '*/gt/gt{}.txt'.format(gt_type)))\n    print('gt_files', gtfiles)\n    tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\n    logger.info('Available LAP solvers {}'.format(mmp.lap.available_solvers))\n    logger.info('Default LAP solver \\'{}\\''.format(mmp.lap.default_solver))\n    logger.info('Loading files.')\n    \n    gt = OrderedDict([(Path(f).parts[-3], mmp.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mmp.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles if \"detections\" not in f])    \n    \n    mh = mmp.metrics.create()    \n    accs, names = compare_dataframes(gt, ts)\n    \n    logger.info('Running metrics')\n    metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n               'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n               'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    div_dict = {\n        'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n        'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\n    for divisor in div_dict:\n        for divided in div_dict[divisor]:\n            summary[divided] = (summary[divided] / summary[divisor])\n    fmt = mh.formatters\n    change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                       'partially_tracked', 'mostly_lost']\n    for k in change_fmt_list:\n        fmt[k] = fmt['mota']\n    print(mmp.io.render_summary(summary, formatters=fmt, namemap=mmp.io.motchallenge_metric_names))\n\n    metrics = mmp.metrics.motchallenge_metrics + ['num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mmp.io.render_summary(summary, formatters=mh.formatters, namemap=mmp.io.motchallenge_metric_names))\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n    exp.output_dir = args.output_dir\n\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )\n"
  },
  {
    "path": "tools/run_sort_dance.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.nn.parallel import DistributedDataParallel as DDP\n\nfrom yolox.core import launch\nfrom yolox.exp import get_exp\nfrom yolox.utils import configure_nccl, fuse_model, get_local_rank, get_model_info, setup_logger\nfrom yolox.evaluators import MOTEvaluatorDance as MOTEvaluator\n\nfrom utils.args import make_parser\nimport os\nimport random\nimport warnings\nimport glob\nimport motmetrics as mm\nfrom collections import OrderedDict\nfrom pathlib import Path\n\n\ndef compare_dataframes(gts, ts):\n    accs = []\n    names = []\n    for k, tsacc in ts.items():\n        if k in gts:            \n            logger.info('Comparing {}...'.format(k))\n            accs.append(mm.utils.compare_to_groundtruth(gts[k], tsacc, 'iou', distth=0.5))\n            names.append(k)\n        else:\n            logger.warning('No ground truth for {}, skipping.'.format(k))\n\n    return accs, names\n\n\n@logger.catch\ndef main(exp, args, num_gpu):\n    \n    if args.seed is not None:\n        random.seed(args.seed)\n        torch.manual_seed(args.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed testing. This will turn on the CUDNN deterministic setting, \"\n        )\n\n    is_distributed = num_gpu > 1\n\n    # set environment variables for distributed training\n    cudnn.benchmark = True\n    rank = args.local_rank\n    file_name = os.path.join(exp.output_dir, args.expn)\n    if rank == 0:\n        os.makedirs(file_name, exist_ok=True)\n\n    result_dir = \"{}_test\".format(args.expn) if args.test else \"{}_val\".format(args.expn)\n    results_folder = os.path.join(file_name, result_dir)\n    os.makedirs(results_folder, exist_ok=True)\n    setup_logger(file_name, distributed_rank=rank, filename=\"val_log.txt\", mode=\"a\")\n    logger.info(\"Args: {}\".format(args))\n\n    if args.conf is not None:\n        exp.test_conf = args.conf\n    if args.nms is not None:\n        exp.nmsthre = args.nms\n    if args.tsize is not None:\n        exp.test_size = (args.tsize, args.tsize)\n\n    model = exp.get_model()\n    logger.info(\"Model Summary: {}\".format(get_model_info(model, exp.test_size)))\n\n    val_loader = exp.get_eval_loader(args.batch_size, is_distributed, args.test)\n    evaluator = MOTEvaluator(\n        args=args,\n        dataloader=val_loader,\n        img_size=exp.test_size,\n        confthre=exp.test_conf,\n        nmsthre=exp.nmsthre,\n        num_classes=exp.num_classes,\n        )\n\n    torch.cuda.set_device(rank)\n    model.cuda(rank)\n    model.eval()\n\n    if not args.speed and not args.trt:\n        if args.ckpt is None:\n            ckpt_file = os.path.join(file_name, \"best_ckpt.pth.tar\")\n        else:\n            ckpt_file = args.ckpt\n        logger.info(\"loading checkpoint\")\n        loc = \"cuda:{}\".format(rank)\n        ckpt = torch.load(ckpt_file, map_location=loc)\n        # load the model state dict\n        model.load_state_dict(ckpt[\"model\"])\n        logger.info(\"loaded checkpoint done.\")\n\n    if is_distributed:\n        model = DDP(model, device_ids=[rank])\n\n    if args.fuse:\n        logger.info(\"\\tFusing model...\")\n        model = fuse_model(model)\n\n    if args.trt:\n        assert (\n            not args.fuse and not is_distributed and args.batch_size == 1\n        ), \"TensorRT model is not support model fusing and distributed inferencing!\"\n        trt_file = os.path.join(file_name, \"model_trt.pth\")\n        assert os.path.exists(\n            trt_file\n        ), \"TensorRT model is not found!\\n Run tools/trt.py first!\"\n        model.head.decode_in_inference = False\n        decoder = model.head.decode_outputs\n    else:\n        trt_file = None\n        decoder = None\n\n    # start tracking\n    if args.TCM_first_step:\n        *_, summary = evaluator.evaluate_sort_score(\n            args,model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    else:\n        *_, summary = evaluator.evaluate_sort(\n                args, model, is_distributed, args.fp16, trt_file, decoder, exp.test_size, results_folder\n        )\n    \n    if args.test:\n        # we skip evaluation for inference on test set\n        return \n\n    # if we evaluate on validation set, \n    logger.info(\"\\n\" + summary)\n\n    # evaluate on the validation set\n    mm.lap.default_solver = 'lap'\n    gtfiles = glob.glob(os.path.join('datasets/dancetrack/val', '*/gt/gt.txt'))\n    print('gt_files', gtfiles)\n    tsfiles = [f for f in glob.glob(os.path.join(results_folder, '*.txt')) if not os.path.basename(f).startswith('eval')]\n\n    logger.info('Found {} groundtruths and {} test files.'.format(len(gtfiles), len(tsfiles)))\n    logger.info('Available LAP solvers {}'.format(mm.lap.available_solvers))\n    logger.info('Default LAP solver \\'{}\\''.format(mm.lap.default_solver))\n    logger.info('Loading files.')\n    \n    gt = OrderedDict([(Path(f).parts[-3], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=1)) for f in gtfiles])\n    ts = OrderedDict([(os.path.splitext(Path(f).parts[-1])[0], mm.io.loadtxt(f, fmt='mot15-2D', min_confidence=-1)) for f in tsfiles])    \n    \n    mh = mm.metrics.create()    \n    accs, names = compare_dataframes(gt, ts)\n    \n    logger.info('Running metrics')\n    metrics = ['recall', 'precision', 'num_unique_objects', 'mostly_tracked',\n               'partially_tracked', 'mostly_lost', 'num_false_positives', 'num_misses',\n               'num_switches', 'num_fragmentations', 'mota', 'motp', 'num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    div_dict = {\n        'num_objects': ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations'],\n        'num_unique_objects': ['mostly_tracked', 'partially_tracked', 'mostly_lost']}\n    for divisor in div_dict:\n        for divided in div_dict[divisor]:\n            summary[divided] = (summary[divided] / summary[divisor])\n    fmt = mh.formatters\n    change_fmt_list = ['num_false_positives', 'num_misses', 'num_switches', 'num_fragmentations', 'mostly_tracked',\n                       'partially_tracked', 'mostly_lost']\n    for k in change_fmt_list:\n        fmt[k] = fmt['mota']\n    print(mm.io.render_summary(summary, formatters=fmt, namemap=mm.io.motchallenge_metric_names))\n\n    metrics = mm.metrics.motchallenge_metrics + ['num_objects']\n    summary = mh.compute_many(accs, names=names, metrics=metrics, generate_overall=True)\n    print(mm.io.render_summary(summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names))\n    logger.info('Completed')\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n\n    if not args.expn:\n        args.expn = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args, num_gpu),\n    )"
  },
  {
    "path": "tools/train.py",
    "content": "from loguru import logger\n\nimport torch\nimport torch.backends.cudnn as cudnn\n\nfrom yolox.core import Trainer, launch\nfrom yolox.exp import get_exp\n\nimport argparse\nimport random\nimport warnings\n\n\ndef make_parser():\n    parser = argparse.ArgumentParser(\"YOLOX train parser\")\n    parser.add_argument(\"-expn\", \"--experiment-name\", type=str, default=None)\n    parser.add_argument(\"-n\", \"--name\", type=str, default=None, help=\"model name\")\n\n    # distributed\n    parser.add_argument(\n        \"--dist-backend\", default=\"nccl\", type=str, help=\"distributed backend\"\n    )\n    parser.add_argument(\n        \"--dist-url\",\n        default=None,\n        type=str,\n        help=\"url used to set up distributed training\",\n    )\n    parser.add_argument(\"-b\", \"--batch-size\", type=int, default=64, help=\"batch size\")\n    parser.add_argument(\n        \"-d\", \"--devices\", default=None, type=int, help=\"device for training\"\n    )\n    parser.add_argument(\n        \"--local_rank\", default=0, type=int, help=\"local rank for dist training\"\n    )\n    parser.add_argument(\n        \"-f\",\n        \"--exp_file\",\n        default=None,\n        type=str,\n        help=\"plz input your expriment description file\",\n    )\n    parser.add_argument(\n        \"--resume\", default=False, action=\"store_true\", help=\"resume training\"\n    )\n    parser.add_argument(\"-c\", \"--ckpt\", default=None, type=str, help=\"checkpoint file\")\n    parser.add_argument(\n        \"-e\",\n        \"--start_epoch\",\n        default=None,\n        type=int,\n        help=\"resume training start epoch\",\n    )\n    parser.add_argument(\n        \"--num_machines\", default=1, type=int, help=\"num of node for training\"\n    )\n    parser.add_argument(\n        \"--machine_rank\", default=0, type=int, help=\"node rank for multi-node training\"\n    )\n    parser.add_argument(\n        \"--fp16\",\n        dest=\"fp16\",\n        default=True,\n        action=\"store_true\",\n        help=\"Adopting mix precision training.\",\n    )\n    parser.add_argument(\n        \"-o\",\n        \"--occupy\",\n        dest=\"occupy\",\n        default=False,\n        action=\"store_true\",\n        help=\"occupy GPU memory first for training.\",\n    )\n    parser.add_argument(\n        \"opts\",\n        help=\"Modify config options using the command-line\",\n        default=None,\n        nargs=argparse.REMAINDER,\n    )\n    return parser\n\n\n@logger.catch\ndef main(exp, args):\n    if exp.seed is not None:\n        random.seed(exp.seed)\n        torch.manual_seed(exp.seed)\n        cudnn.deterministic = True\n        warnings.warn(\n            \"You have chosen to seed training. This will turn on the CUDNN deterministic setting, \"\n            \"which can slow down your training considerably! You may see unexpected behavior \"\n            \"when restarting from checkpoints.\"\n        )\n\n    # set environment variables for distributed training\n    cudnn.benchmark = True\n\n    trainer = Trainer(exp, args)\n    trainer.train()\n\n\nif __name__ == \"__main__\":\n    args = make_parser().parse_args()\n    exp = get_exp(args.exp_file, args.name)\n    exp.merge(args.opts)\n\n    if not args.experiment_name:\n        args.experiment_name = exp.exp_name\n\n    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices\n    assert num_gpu <= torch.cuda.device_count()\n\n    launch(\n        main,\n        num_gpu,\n        args.num_machines,\n        args.machine_rank,\n        backend=args.dist_backend,\n        dist_url=args.dist_url,\n        args=(exp, args),\n    )\n"
  },
  {
    "path": "tools/txt2video.py",
    "content": "import os\nimport sys\nimport json\nimport cv2\nimport glob as gb\nimport numpy as np\n\n\ndef colormap(rgb=False):\n    color_list = np.array(\n        [\n            0.000, 0.447, 0.741,\n            0.850, 0.325, 0.098,\n            0.929, 0.694, 0.125,\n            0.494, 0.184, 0.556,\n            0.466, 0.674, 0.188,\n            0.301, 0.745, 0.933,\n            0.635, 0.078, 0.184,\n            0.300, 0.300, 0.300,\n            0.600, 0.600, 0.600,\n            1.000, 0.000, 0.000,\n            1.000, 0.500, 0.000,\n            0.749, 0.749, 0.000,\n            0.000, 1.000, 0.000,\n            0.000, 0.000, 1.000,\n            0.667, 0.000, 1.000,\n            0.333, 0.333, 0.000,\n            0.333, 0.667, 0.000,\n            0.333, 1.000, 0.000,\n            0.667, 0.333, 0.000,\n            0.667, 0.667, 0.000,\n            0.667, 1.000, 0.000,\n            1.000, 0.333, 0.000,\n            1.000, 0.667, 0.000,\n            1.000, 1.000, 0.000,\n            0.000, 0.333, 0.500,\n            0.000, 0.667, 0.500,\n            0.000, 1.000, 0.500,\n            0.333, 0.000, 0.500,\n            0.333, 0.333, 0.500,\n            0.333, 0.667, 0.500,\n            0.333, 1.000, 0.500,\n            0.667, 0.000, 0.500,\n            0.667, 0.333, 0.500,\n            0.667, 0.667, 0.500,\n            0.667, 1.000, 0.500,\n            1.000, 0.000, 0.500,\n            1.000, 0.333, 0.500,\n            1.000, 0.667, 0.500,\n            1.000, 1.000, 0.500,\n            0.000, 0.333, 1.000,\n            0.000, 0.667, 1.000,\n            0.000, 1.000, 1.000,\n            0.333, 0.000, 1.000,\n            0.333, 0.333, 1.000,\n            0.333, 0.667, 1.000,\n            0.333, 1.000, 1.000,\n            0.667, 0.000, 1.000,\n            0.667, 0.333, 1.000,\n            0.667, 0.667, 1.000,\n            0.667, 1.000, 1.000,\n            1.000, 0.000, 1.000,\n            1.000, 0.333, 1.000,\n            1.000, 0.667, 1.000,\n            0.167, 0.000, 0.000,\n            0.333, 0.000, 0.000,\n            0.500, 0.000, 0.000,\n            0.667, 0.000, 0.000,\n            0.833, 0.000, 0.000,\n            1.000, 0.000, 0.000,\n            0.000, 0.167, 0.000,\n            0.000, 0.333, 0.000,\n            0.000, 0.500, 0.000,\n            0.000, 0.667, 0.000,\n            0.000, 0.833, 0.000,\n            0.000, 1.000, 0.000,\n            0.000, 0.000, 0.167,\n            0.000, 0.000, 0.333,\n            0.000, 0.000, 0.500,\n            0.000, 0.000, 0.667,\n            0.000, 0.000, 0.833,\n            0.000, 0.000, 1.000,\n            0.000, 0.000, 0.000,\n            0.143, 0.143, 0.143,\n            0.286, 0.286, 0.286,\n            0.429, 0.429, 0.429,\n            0.571, 0.571, 0.571,\n            0.714, 0.714, 0.714,\n            0.857, 0.857, 0.857,\n            1.000, 1.000, 1.000\n        ]\n    ).astype(np.float32)\n    color_list = color_list.reshape((-1, 3)) * 255\n    if not rgb:\n        color_list = color_list[:, ::-1]\n    return color_list\n\n\ndef txt2img(visual_path=\"visual_val_gt\"):\n    print(\"Starting txt2img\")\n\n    valid_labels = {1}\n    ignore_labels = {2, 7, 8, 12}\n\n    if not os.path.exists(visual_path):\n        os.makedirs(visual_path)\n    color_list = colormap()\n\n    gt_json_path = 'datasets/mot/annotations/val_half.json'\n    img_path = 'datasets/mot/train/'\n    show_video_names = ['MOT17-02-FRCNN', \n                    'MOT17-04-FRCNN',\n                    'MOT17-05-FRCNN',\n                    'MOT17-09-FRCNN',\n                    'MOT17-10-FRCNN',        \n                    'MOT17-11-FRCNN',\n                    'MOT17-13-FRCNN']\n    test_json_path = 'datasets/mot/annotations/test.json'\n    test_img_path = 'datasets/mot/test/'\n    test_show_video_names = ['MOT17-01-FRCNN', \n                    'MOT17-03-FRCNN',\n                    'MOT17-06-FRCNN',\n                    'MOT17-07-FRCNN',\n                    'MOT17-08-FRCNN',        \n                    'MOT17-12-FRCNN',\n                    'MOT17-14-FRCNN']\n    if visual_path == \"visual_test_predict\":\n        show_video_names = test_show_video_names\n        img_path = test_img_path\n        gt_json_path = test_json_path\n    for show_video_name in show_video_names:\n        img_dict = dict()\n        \n        if visual_path == \"visual_val_gt\":\n            txt_path = 'datasets/mot/train/' + show_video_name + '/gt/gt_val_half.txt'\n        elif visual_path == \"visual_yolox_x\":\n            txt_path = 'YOLOX_outputs/yolox_mot_x_1088/track_results/'+ show_video_name + '.txt'\n        elif visual_path == \"visual_test_predict\":\n            txt_path = 'test/tracks/'+ show_video_name + '.txt'\n        else:\n            raise NotImplementedError\n        \n        with open(gt_json_path, 'r') as f:\n            gt_json = json.load(f)\n\n        for ann in gt_json[\"images\"]:\n            file_name = ann['file_name']\n            video_name = file_name.split('/')[0]\n            if video_name == show_video_name:\n                img_dict[ann['frame_id']] = img_path + file_name\n\n\n        txt_dict = dict()    \n        with open(txt_path, 'r') as f:\n            for line in f.readlines():\n                linelist = line.split(',')\n\n                mark = int(float(linelist[6]))\n                label = int(float(linelist[7]))\n                vis_ratio = float(linelist[8])\n                \n                if visual_path == \"visual_val_gt\":\n                    if mark == 0 or label not in valid_labels or label in ignore_labels or vis_ratio <= 0:\n                        continue\n\n                img_id = linelist[0]\n                obj_id = linelist[1]\n                bbox = [float(linelist[2]), float(linelist[3]), \n                        float(linelist[2]) + float(linelist[4]), \n                        float(linelist[3]) + float(linelist[5]), int(obj_id)]\n                if int(img_id) in txt_dict:\n                    txt_dict[int(img_id)].append(bbox)\n                else:\n                    txt_dict[int(img_id)] = list()\n                    txt_dict[int(img_id)].append(bbox)\n\n        for img_id in sorted(txt_dict.keys()):\n            img = cv2.imread(img_dict[img_id])\n            for bbox in txt_dict[img_id]:\n                cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), color_list[bbox[4]%79].tolist(), thickness=2)\n                cv2.putText(img, \"{}\".format(int(bbox[4])), (int(bbox[0]), int(bbox[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color_list[bbox[4]%79].tolist(), 2)\n            cv2.imwrite(visual_path + \"/\" + show_video_name + \"{:0>6d}.png\".format(img_id), img)\n        print(show_video_name, \"Done\")\n    print(\"txt2img Done\")\n\n        \ndef img2video(visual_path=\"visual_val_gt\"):\n    print(\"Starting img2video\")\n\n    img_paths = gb.glob(visual_path + \"/*.png\") \n    fps = 16 \n    size = (1920,1080) \n    videowriter = cv2.VideoWriter(visual_path + \"_video.avi\",cv2.VideoWriter_fourcc('M','J','P','G'), fps, size)\n\n    for img_path in sorted(img_paths):\n        img = cv2.imread(img_path)\n        img = cv2.resize(img, size)\n        videowriter.write(img)\n\n    videowriter.release()\n    print(\"img2video Done\")\n\n\nif __name__ == '__main__':\n    visual_path=\"visual_yolox_x\"\n    if len(sys.argv) > 1:\n        visual_path =sys.argv[1]\n    txt2img(visual_path)\n    #img2video(visual_path)\n"
  },
  {
    "path": "tools/visualize_results.py",
    "content": "import pdb\nimport os \nimport cv2 \nfrom yolox.utils import vis \nimport numpy as np\nimport argparse\nimport sys\n\n'''\n    MOT submission format:\n    <frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>\n'''\n\nMOT17_VIDEO_LEN = {\n    \"MOT17-02-FRCNN\": 600,\n    \"MOT17-04-FRCNN\": 1050,\n    \"MOT17-05-FRCNN\": 837,\n    \"MOT17-09-FRCNN\": 525,\n    \"MOT17-10-FRCNN\": 654,\n    \"MOT17-11-FRCNN\": 900,\n    \"MOT17-13-FRCNN\": 750\n}\n\nMOT17_VIDEO_LEN_TEST = {\n    \"MOT17-01-FRCNN\": 600,\n    \"MOT17-03-FRCNN\": 1050,\n    \"MOT17-06-FRCNN\": 837,\n    \"MOT17-07-FRCNN\": 525,\n    \"MOT17-08-FRCNN\": 654,\n    \"MOT17-12-FRCNN\": 900,\n    \"MOT17-14-FRCNN\": 750\n}\n\nMOT20_VIDEO_LEN = {\n    \"MOT20-04\": 2080,\n    \"MOT20-06\": 1008,\n    \"MOT20-07\": 585,\n    \"MOT20-08\": 806\n}\n\n\nMOT17_VIDEO_SPLIT = dict()\nMOT20_VIDEO_SPLIT = dict()\n\nfor video_name in MOT17_VIDEO_LEN:\n    num_images = MOT17_VIDEO_LEN[video_name]\n    MOT17_VIDEO_SPLIT[video_name] = dict()\n    MOT17_VIDEO_SPLIT[video_name][\"train_half\"] = [1, num_images // 2 + 1]\n    MOT17_VIDEO_SPLIT[video_name][\"val_half\"] = [num_images // 2 + 2, num_images]\n    MOT17_VIDEO_SPLIT[video_name][\"full\"] = [1, num_images]\n\nfor video_name in MOT20_VIDEO_LEN:\n    num_images = MOT20_VIDEO_LEN[video_name]\n    MOT20_VIDEO_SPLIT[video_name] = dict()\n    MOT20_VIDEO_SPLIT[video_name][\"full\"] = [1, num_images]\n\n\n_COLORS = np.array(\n    [\n        0.000, 0.447, 0.741,\n        0.850, 0.325, 0.098,\n        0.929, 0.694, 0.125,\n        0.494, 0.184, 0.556,\n        0.466, 0.674, 0.188,\n        0.301, 0.745, 0.933,\n        0.635, 0.078, 0.184,\n        0.300, 0.300, 0.300,\n        0.600, 0.600, 0.600,\n        1.000, 0.000, 0.000,\n        1.000, 0.500, 0.000,\n        0.749, 0.749, 0.000,\n        0.000, 1.000, 0.000,\n        0.000, 0.000, 1.000,\n        0.667, 0.000, 1.000,\n        0.333, 0.333, 0.000,\n        0.333, 0.667, 0.000,\n        0.333, 1.000, 0.000,\n        0.667, 0.333, 0.000,\n        0.667, 0.667, 0.000,\n        0.667, 1.000, 0.000,\n        1.000, 0.333, 0.000,\n        1.000, 0.667, 0.000,\n        1.000, 1.000, 0.000,\n        0.000, 0.333, 0.500,\n        0.000, 0.667, 0.500,\n        0.000, 1.000, 0.500,\n        0.333, 0.000, 0.500,\n        0.333, 0.333, 0.500,\n        0.333, 0.667, 0.500,\n        0.333, 1.000, 0.500,\n        0.667, 0.000, 0.500,\n        0.667, 0.333, 0.500,\n        0.667, 0.667, 0.500,\n        0.667, 1.000, 0.500,\n        1.000, 0.000, 0.500,\n        1.000, 0.333, 0.500,\n        1.000, 0.667, 0.500,\n        1.000, 1.000, 0.500,\n        0.000, 0.333, 1.000,\n        0.000, 0.667, 1.000,\n        0.000, 1.000, 1.000,\n        0.333, 0.000, 1.000,\n        0.333, 0.333, 1.000,\n        0.333, 0.667, 1.000,\n        0.333, 1.000, 1.000,\n        0.667, 0.000, 1.000,\n        0.667, 0.333, 1.000,\n        0.667, 0.667, 1.000,\n        0.667, 1.000, 1.000,\n        1.000, 0.000, 1.000,\n        1.000, 0.333, 1.000,\n        1.000, 0.667, 1.000,\n        0.333, 0.000, 0.000,\n        0.500, 0.000, 0.000,\n        0.667, 0.000, 0.000,\n        0.833, 0.000, 0.000,\n        1.000, 0.000, 0.000,\n        0.000, 0.167, 0.000,\n        0.000, 0.333, 0.000,\n        0.000, 0.500, 0.000,\n        0.000, 0.667, 0.000,\n        0.000, 0.833, 0.000,\n        0.000, 1.000, 0.000,\n        0.000, 0.000, 0.167,\n        0.000, 0.000, 0.333,\n        0.000, 0.000, 0.500,\n        0.000, 0.000, 0.667,\n        0.000, 0.000, 0.833,\n        0.000, 0.000, 1.000,\n        0.000, 0.000, 0.000,\n        0.143, 0.143, 0.143,\n        0.286, 0.286, 0.286,\n        0.429, 0.429, 0.429,\n        0.571, 0.571, 0.571,\n        0.714, 0.714, 0.714,\n        0.857, 0.857, 0.857,\n        0.000, 0.447, 0.741,\n        0.314, 0.717, 0.741,\n        0.50, 0.5, 0\n    ]\n).astype(np.float32).reshape(-1, 3)\n\n\ndef visualize_box(img, text, box, color_index):\n    x0, y0, width, height = box \n    x0, y0, width, height = int(x0), int(y0), int(width), int(height)\n    color = (_COLORS[color_index%80] * 255).astype(np.uint8).tolist()\n    txt_color = (0, 0, 0) if np.mean(_COLORS[color_index%80]) > 0.5 else (255, 255, 255)\n    font = cv2.FONT_HERSHEY_SIMPLEX\n    txt_size = cv2.getTextSize(text, font, 0.6, 1)[0]\n    cv2.rectangle(img, (x0, y0), (x0+width, y0+height), color, 2)\n\n    txt_bk_color = (_COLORS[color_index%80] * 255 * 0.7).astype(np.uint8).tolist()\n    cv2.rectangle(\n        img,\n        (x0, y0 + 1),\n        (x0 + txt_size[0] + 1, y0 + int(1.5*txt_size[1])),\n        txt_bk_color,\n        -1\n    )\n    cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.6, txt_color, thickness=1)\n    return img\n    \n\ndef visualize_detections(img_dir, out_dir, detections_dir, mode=\"val_half\", path=\"{}/{}_detections.txt\", dataset=\"mot17\", test=False):\n    if dataset == \"mot17\":\n        VIDEO_LEN = MOT17_VIDEO_LEN\n        VIDEO_SPLIT = MOT17_VIDEO_SPLIT\n    elif dataset == \"mot20\":\n        VIDEO_LEN = MOT20_VIDEO_LEN\n        VIDEO_SPLIT = MOT20_VIDEO_SPLIT\n    else:\n        assert 0\n\n    for video_name in VIDEO_LEN:\n        detection_f = path.format(detections_dir, video_name)\n        # detection_f = os.path.join(detections_dir, \"{}_detections.txt\".format(video_name))\n        f = open(detection_f)\n        dets = np.loadtxt(f, delimiter=\",\")\n        frame_range = VIDEO_SPLIT[video_name][mode]\n        assert(frame_range[1]-frame_range[0] == dets[:, 0].max()-dets[:,0].min())\n        frame_gap = dets[:,0].min() - frame_range[0]\n        video_img_dir = os.path.join(img_dir, video_name, \"img1\")\n        video_out_dir = os.path.join(out_dir, video_name)\n        os.makedirs(video_out_dir, exist_ok=True)\n        fake_frame_min = int(dets[:,0].min())\n        fake_frame_max = int(dets[:,0].max())\n        for frame_ind in range(fake_frame_min, fake_frame_max+1):\n            real_frame_ind = frame_ind - frame_gap \n            frame_dets = dets[np.where(dets[:,0]==frame_ind)]\n            im_path = os.path.join(video_img_dir, \"%06d.jpg\" % real_frame_ind)\n            img = cv2.imread(im_path)\n            for i in range(frame_dets.shape[0]):\n                box = frame_dets[i]\n                score = box[6]\n                text = '{:.1f}'.format(score * 100)\n                img = visualize_box(img, text, box[2:6], i)\n                '''\n                x0, y0, width, height = box[2:6]\n                x0, y0, width, height = int(x0), int(y0), int(width), int(height)\n                color = (_COLORS[i%80] * 255).astype(np.uint8).tolist()\n                txt_color = (0, 0, 0) if np.mean(_COLORS[i%80]) > 0.5 else (255, 255, 255)\n                font = cv2.FONT_HERSHEY_SIMPLEX\n                txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]\n                cv2.rectangle(img, (x0, y0), (x0+width, y0+height), color, 2)\n\n                txt_bk_color = (_COLORS[i%80] * 255 * 0.7).astype(np.uint8).tolist()\n                cv2.rectangle(\n                    img,\n                    (x0, y0 + 1),\n                    (x0 + txt_size[0] + 1, y0 + int(1.5*txt_size[1])),\n                    txt_bk_color,\n                    -1\n                )\n                cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.4, txt_color, thickness=1)\n                '''\n            font = cv2.FONT_HERSHEY_SIMPLEX\n            cv2.rectangle(img, (2, 2), (120, 30), (30,30,30), -1)\n            cv2.putText(img, \"%06d.jpg\" % real_frame_ind, (10, 20), font, 0.6, (255,255,255), thickness=2)\n            cv2.imwrite(os.path.join(video_out_dir, \"%06d.jpg\" % real_frame_ind), img)\n\n\ndef visualize_tracks(img_dir, out_dir, tracks_dir, mode, dataset=\"mot17\"):\n    if dataset == \"mot17\":\n        VIDEO_LEN = MOT17_VIDEO_LEN\n    elif dataset == \"mot20\":\n        VIDEO_LEN = MOT20_VIDEO_LEN\n    elif dataset == \"dancetrack_val\":\n        VIDEO_LEN = os.listdir(tracks_dir)\n        VIDEO_LEN = [d for d in VIDEO_LEN if \"dancetrack\" in d]\n    elif dataset == \"dancetrack_test\":\n        VIDEO_LEN = os.listdir(tracks_dir)\n        VIDEO_LEN = [d for d in VIDEO_LEN if \"dancetrack\" in d]\n    \n    # import pdb; pdb.set_trace()\n    for video_name in VIDEO_LEN:\n        video_name = video_name.replace(\".txt\", \"\")\n        track_f = os.path.join(tracks_dir, \"{}.txt\".format(video_name))\n        f = open(track_f)\n        tracks = np.loadtxt(f, delimiter=\",\")\n        if dataset == \"mot17\":\n            frame_range = MOT17_VIDEO_SPLIT[video_name][mode]\n        elif dataset == \"mot20\":\n            frame_range = MOT20_VIDEO_SPLIT[video_name][\"full\"]\n        elif dataset == \"dancetrack_val\":\n            frame_range = [tracks[:,0].min(), tracks[:,0].max()]\n        elif dataset == \"dancetrack_test\":\n            frame_range = [tracks[:,0].min(), tracks[:,0].max()]\n        assert(frame_range[1]-frame_range[0] == tracks[:, 0].max()-tracks[:,0].min())\n        frame_gap = tracks[:,0].min() - frame_range[0]\n        video_img_dir = os.path.join(img_dir, video_name, \"img1\")\n        video_out_dir = os.path.join(out_dir, video_name)\n        os.makedirs(video_out_dir, exist_ok=True)\n        fake_frame_min = int(tracks[:,0].min())\n        fake_frame_max = int(tracks[:,0].max())\n        for frame_ind in range(fake_frame_min, fake_frame_max+1):\n            real_frame_ind = frame_ind - frame_gap \n            frame_tracks = tracks[np.where(tracks[:,0]==frame_ind)]\n            if \"dancetrack\" in dataset:\n                im_path = os.path.join(video_img_dir, \"%08d.jpg\" % real_frame_ind)\n            else:\n                im_path = os.path.join(video_img_dir, \"%06d.jpg\" % real_frame_ind)\n            img = cv2.imread(im_path)\n            for i in range(frame_tracks.shape[0]):\n                box = frame_tracks[i]\n                obj_id = int(box[1])\n                text = '{}'.format(obj_id)\n                img = visualize_box(img, text, box[2:6], obj_id)\n                '''\n                x0, y0, width, height = box[2:6]\n                score = box[6]\n                x0, y0, width, height = int(x0), int(y0), int(width), int(height)\n                obj_id = int(obj_id)\n                color = (_COLORS[obj_id%80] * 255).astype(np.uint8).tolist()\n                txt_color = (0, 0, 0) if np.mean(_COLORS[obj_id%80]) > 0.5 else (255, 255, 255)\n                font = cv2.FONT_HERSHEY_SIMPLEX\n                txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]\n                cv2.rectangle(img, (x0, y0), (x0+width, y0+height), color, 2)\n                txt_bk_color = (_COLORS[obj_id%80] * 255 * 0.7).astype(np.uint8).tolist()\n                cv2.rectangle(\n                    img,\n                    (x0, y0 + 1),\n                    (x0 + txt_size[0] + 1, y0 + int(1.5*txt_size[1])),\n                    txt_bk_color,\n                    -1\n                )\n                cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.4, txt_color, thickness=1)\n                '''\n            font = cv2.FONT_HERSHEY_SIMPLEX\n            cv2.rectangle(img, (2, 2), (120, 30), (30,30,30), -1)\n            if \"dancetrack\" in dataset:\n                cv2.putText(img, \"%08d.jpg\" % real_frame_ind, (10, 20), font, 0.6, (255,255,255), thickness=2)\n                # import pdb; pdb.set_trace()\n                cv2.imwrite(os.path.join(video_out_dir, \"%08d.jpg\" % real_frame_ind), img)\n            else:\n                cv2.putText(img, \"%06d.jpg\" % real_frame_ind, (10, 20), font, 0.6, (255,255,255), thickness=2)\n                cv2.imwrite(os.path.join(video_out_dir, \"%06d.jpg\" % real_frame_ind), img)\n        cmd = \"ffmpeg -framerate 5 -pattern_type glob -i '{}/*.jpg' -c:v libx264 -pix_fmt yuv420p {}/{}.mp4\".format(video_out_dir, out_dir, video_name)\n        os.popen(cmd)\n\n\ndef visualize_gt(img_dir, out_dir):\n    for video_name in VIDEO_LEN:\n        track_f = os.path.join(img_dir, video_name, \"gt/gt.txt\")\n        f = open(track_f)\n        tracks = np.loadtxt(f, delimiter=\",\")\n        video_img_dir = os.path.join(img_dir, video_name, \"img1\")\n        video_out_dir = os.path.join(out_dir, video_name)\n        os.makedirs(video_out_dir, exist_ok=True)\n        fake_frame_min = int(tracks[:,0].min())\n        fake_frame_max = int(tracks[:,0].max())\n        for frame_ind in range(fake_frame_min, fake_frame_max+1):\n            real_frame_ind = frame_ind\n            frame_tracks = tracks[np.where(tracks[:,0]==frame_ind)]\n            im_path = os.path.join(video_img_dir, \"%06d.jpg\" % real_frame_ind)\n            img = cv2.imread(im_path)\n            for i in range(frame_tracks.shape[0]):\n                box = frame_tracks[i]\n                obj_id = int(box[1])\n                text = '{}'.format(obj_id)\n                img = visualize_box(img, text, box[2:6], obj_id)\n                '''\n                x0, y0, width, height = box[2:6]\n                score = box[6]\n                x0, y0, width, height = int(x0), int(y0), int(width), int(height)\n                obj_id = int(obj_id)\n                color = (_COLORS[obj_id%80] * 255).astype(np.uint8).tolist()\n                txt_color = (0, 0, 0) if np.mean(_COLORS[obj_id%80]) > 0.5 else (255, 255, 255)\n                font = cv2.FONT_HERSHEY_SIMPLEX\n                txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]\n                cv2.rectangle(img, (x0, y0), (x0+width, y0+height), color, 2)\n                txt_bk_color = (_COLORS[obj_id%80] * 255 * 0.7).astype(np.uint8).tolist()\n                cv2.rectangle(\n                    img,\n                    (x0, y0 + 1),\n                    (x0 + txt_size[0] + 1, y0 + int(1.5*txt_size[1])),\n                    txt_bk_color,\n                    -1\n                )\n                cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.4, txt_color, thickness=1)\n                '''\n            font = cv2.FONT_HERSHEY_SIMPLEX\n            cv2.rectangle(img, (2, 2), (120, 30), (30,30,30), -1)\n            cv2.putText(img, \"%06d.jpg\" % real_frame_ind, (10, 20), font, 0.6, (255,255,255), thickness=2)\n            cv2.imwrite(os.path.join(video_out_dir, \"%06d.jpg\" % real_frame_ind), img)\n        cmd = \"ffmpeg -framerate 5 -pattern_type glob -i '{}/*.jpg' -c:v libx264 -pix_fmt yuv420p {}/{}.mp4\".format(video_out_dir, out_dir, video_name)\n        os.popen(cmd)\n\n\ndef merge_visualization(det_dir, track_dir, gt_dir, out_dir):\n    os.makedirs(out_dir,  exist_ok=True)\n    seqs = os.listdir(track_dir)\n    for seq in seqs:\n        if \"mp4\" in seq:\n            continue\n        seq_track_dir = os.path.join(track_dir, seq)\n        seq_det_dir = os.path.join(det_dir, seq)\n        seq_gt_dir = os.path.join(gt_dir, seq)\n        seq_out_dir = os.path.join(out_dir, seq)\n        os.makedirs(seq_out_dir, exist_ok=True)\n        frames = sorted(os.listdir(seq_track_dir))\n        for frame in frames:\n            f_track_path = os.path.join(seq_track_dir, frame)\n            f_det_path = os.path.join(seq_det_dir, frame)\n            f_gt_path = os.path.join(seq_gt_dir, frame)\n            im1 = cv2.imread(f_track_path)\n            im2 = cv2.imread(f_det_path)\n            im3 = cv2.imread(f_gt_path)\n            im_concat = cv2.vconcat([im2, im3, im1])\n            f_out_dir = os.path.join(seq_out_dir, frame)\n            cv2.imwrite(f_out_dir, im_concat)\n        \n        cmd = \"ffmpeg -framerate 5 -pattern_type glob -i '{}/*.jpg' -c:v libx264 -pix_fmt yuv420p {}/merged_{}.mp4\".format(seq_out_dir, out_dir, seq)\n        os.popen(cmd)\n\n\ndef make_parser():\n    parser = argparse.ArgumentParser(\"Visualize Results\")\n    parser.add_argument('--mode', default=\"val_half\", type=str)\n    parser.add_argument('--img_dir', default=\"datasets/mot/train\")\n    parser.add_argument('--exp_dir', default=\"yolox_x_ablation\", type=str)\n    parser.add_argument('--exp_name', default=\"track_results\", type=str)\n    parser.add_argument('--vis', default=\"det\", type=str, help=\"det/track/gt\")\n    parser.add_argument(\"--dataset\", default=\"mot17\", type=str)\n    parser.add_argument(\"--res\", type=str)\n    args = parser.parse_args()\n    return args\n\nif __name__ == \"__main__\":\n    args = make_parser()\n    if args.dataset == \"mot17\":\n        img_dir = \"datasets/mot/train\"\n    elif args.dataset == \"mot20\":\n        img_dir = \"datasets/MOT20/test\"\n    elif args.dataset == \"dancetrack_val\":\n        img_dir = \"datasets/dancetrack/val\"\n    elif args.dataset == \"dancetrack_test\":\n        img_dir = \"datasets/dancetrack/test\"\n    # result_src_dir = \"YOLOX_outputs/\"\n    # result_src_dir = \"evaldata/trackers/mot_challenge/MOT17-val/\"\n    res_dir = args.res\n    out_src_dir = \"visualizations\"\n\n    # exp_dir = args.exp_dir\n    exp_name = args.exp_name\n    # res_dir = os.path.join(result_src_dir, exp_name, \"data\")\n    out_dir = os.path.join(out_src_dir, args.dataset, exp_name, args.vis)\n    os.makedirs(out_dir, exist_ok=True)\n\n    if args.vis == \"det\":\n        visualize_detections(img_dir, out_dir, res_dir, mode=args.mode)\n    elif args.vis == \"track\":\n        visualize_tracks(img_dir, out_dir, res_dir, mode=args.mode, dataset=args.dataset)\n    elif args.vis == \"merge\":\n        det_dir = \"visualizations/yolox_x_ablation/{}/det\".format(args.exp_name)\n        track_dir = \"visualizations/yolox_x_ablation/{}/track\".format(args.exp_name)\n        gt_dir = \"visualizations/GTs\"\n        out_dir = \"visualizations/yolox_x_ablation/{}/merged\".format(args.exp_name)\n        merge_visualization(det_dir, track_dir, gt_dir, out_dir)\n    elif args.vis == \"det_fasterrcnn\":\n        res_dir = \"datasets/mot/train\"\n        out_dir = \"visualizations/mot17/fasterrcnn_dets\"\n        visualize_detections(img_dir, out_dir, res_dir, mode=\"full\", path=\"{}/{}/det/det.txt\")\n    elif args.vis == \"gt\":\n        out_dir = os.path.join(out_src_dir, \"GTs\")\n        os.makedirs(out_dir, exist_ok=True)\n        visualize_gt(img_dir, out_dir)\n\n"
  },
  {
    "path": "trackers/byte_tracker/basetrack.py",
    "content": "import numpy as np\nfrom collections import OrderedDict\n\n\nclass TrackState(object):\n    New = 0\n    Tracked = 1\n    Lost = 2\n    Removed = 3\n\n\nclass BaseTrack(object):\n    _count = 0\n\n    track_id = 0\n    is_activated = False\n    state = TrackState.New\n\n    history = OrderedDict()\n    features = []\n    curr_feature = None\n    score = 0\n    start_frame = 0\n    frame_id = 0\n    time_since_update = 0\n\n    # multi-camera\n    location = (np.inf, np.inf)\n\n    @property\n    def end_frame(self):\n        return self.frame_id\n\n    @staticmethod\n    def next_id():\n        BaseTrack._count += 1\n        return BaseTrack._count\n\n    def activate(self, *args):\n        raise NotImplementedError\n\n    def predict(self):\n        raise NotImplementedError\n\n    def update(self, *args, **kwargs):\n        raise NotImplementedError\n\n    def mark_lost(self):\n        self.state = TrackState.Lost\n\n    def mark_removed(self):\n        self.state = TrackState.Removed"
  },
  {
    "path": "trackers/byte_tracker/byte_tracker.py",
    "content": "import numpy as np\nfrom collections import deque\nimport os\nimport os.path as osp\nimport copy\nimport torch\nimport torch.nn.functional as F\n\nfrom .kalman_filter import KalmanFilter\nfrom trackers.byte_tracker import matching\nfrom .basetrack import BaseTrack, TrackState\n\nclass STrack(BaseTrack):\n    shared_kalman = KalmanFilter()\n    def __init__(self, tlwh, score):\n\n        # wait activate\n        self._tlwh = np.asarray(tlwh, dtype=np.float)\n        self.kalman_filter = None\n        self.mean, self.covariance = None, None\n        self.is_activated = False\n\n        self.score = score\n        self.tracklet_len = 0\n\n    def predict(self):\n        mean_state = self.mean.copy()\n        if self.state != TrackState.Tracked:\n            mean_state[7] = 0\n        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)\n\n    @staticmethod\n    def multi_predict(stracks):\n        if len(stracks) > 0:\n            multi_mean = np.asarray([st.mean.copy() for st in stracks])\n            multi_covariance = np.asarray([st.covariance for st in stracks])\n            for i, st in enumerate(stracks):\n                if st.state != TrackState.Tracked:\n                    multi_mean[i][7] = 0\n            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)\n            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):\n                stracks[i].mean = mean\n                stracks[i].covariance = cov\n\n    def activate(self, kalman_filter, frame_id):\n        \"\"\"Start a new tracklet\"\"\"\n        self.kalman_filter = kalman_filter\n        self.track_id = self.next_id()\n        self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))\n\n        self.tracklet_len = 0\n        self.state = TrackState.Tracked\n        if frame_id == 1:\n            self.is_activated = True\n        # self.is_activated = True\n        self.frame_id = frame_id\n        self.start_frame = frame_id\n\n    def re_activate(self, new_track, frame_id, new_id=False):\n        self.mean, self.covariance = self.kalman_filter.update(\n            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)\n        )\n        self.tracklet_len = 0\n        self.state = TrackState.Tracked\n        self.is_activated = True\n        self.frame_id = frame_id\n        if new_id:\n            self.track_id = self.next_id()\n        self.score = new_track.score\n\n    def update(self, new_track, frame_id):\n        \"\"\"\n        Update a matched track\n        :type new_track: STrack\n        :type frame_id: int\n        :type update_feature: bool\n        :return:\n        \"\"\"\n        self.frame_id = frame_id\n        self.tracklet_len += 1\n\n        new_tlwh = new_track.tlwh\n        self.mean, self.covariance = self.kalman_filter.update(\n            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))\n        self.state = TrackState.Tracked\n        self.is_activated = True\n\n        self.score = new_track.score\n\n    @property\n    # @jit(nopython=True)\n    def tlwh(self):\n        \"\"\"Get current position in bounding box format `(top left x, top left y,\n                width, height)`.\n        \"\"\"\n        if self.mean is None:\n            return self._tlwh.copy()\n        ret = self.mean[:4].copy()\n        ret[2] *= ret[3]\n        ret[:2] -= ret[2:] / 2\n        return ret\n\n    @property\n    # @jit(nopython=True)\n    def tlbr(self):\n        \"\"\"Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,\n        `(top left, bottom right)`.\n        \"\"\"\n        ret = self.tlwh.copy()\n        ret[2:] += ret[:2]\n        return ret\n\n    @staticmethod\n    # @jit(nopython=True)\n    def tlwh_to_xyah(tlwh):\n        \"\"\"Convert bounding box to format `(center x, center y, aspect ratio,\n        height)`, where the aspect ratio is `width / height`.\n        \"\"\"\n        ret = np.asarray(tlwh).copy()\n        ret[:2] += ret[2:] / 2\n        ret[2] /= ret[3]\n        return ret\n\n    def to_xyah(self):\n        return self.tlwh_to_xyah(self.tlwh)\n\n    @staticmethod\n    # @jit(nopython=True)\n    def tlbr_to_tlwh(tlbr):\n        ret = np.asarray(tlbr).copy()\n        ret[2:] -= ret[:2]\n        return ret\n\n    @staticmethod\n    # @jit(nopython=True)\n    def tlwh_to_tlbr(tlwh):\n        ret = np.asarray(tlwh).copy()\n        ret[2:] += ret[:2]\n        return ret\n\n    def __repr__(self):\n        return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)\n\n\nclass BYTETracker(object):\n    def __init__(self, args, frame_rate=30):\n        self.tracked_stracks = []  # type: list[STrack]\n        self.lost_stracks = []  # type: list[STrack]\n        self.removed_stracks = []  # type: list[STrack]\n\n        self.frame_id = 0\n        self.args = args\n        #self.det_thresh = args.track_thresh\n        self.det_thresh = args.track_thresh + 0.1\n        self.buffer_size = int(frame_rate / 30.0 * args.track_buffer)\n        self.max_time_lost = self.buffer_size\n        self.kalman_filter = KalmanFilter()\n\n    def update(self, output_results, img_info, img_size):\n        self.frame_id += 1\n        activated_starcks = []\n        refind_stracks = []\n        lost_stracks = []\n        removed_stracks = []\n\n        if output_results.shape[1] == 5:\n            scores = output_results[:, 4]\n            bboxes = output_results[:, :4]\n        else:\n            output_results = output_results.cpu().numpy()\n            scores = output_results[:, 4] * output_results[:, 5]\n            bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n\n        remain_inds = scores > self.args.track_thresh\n        inds_low = scores > 0.1\n        inds_high = scores < self.args.track_thresh\n\n        inds_second = np.logical_and(inds_low, inds_high)\n        dets_second = bboxes[inds_second]\n        dets = bboxes[remain_inds]\n        scores_keep = scores[remain_inds]\n        scores_second = scores[inds_second]\n\n        if len(dets) > 0:\n            '''Detections'''\n            detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for\n                          (tlbr, s) in zip(dets, scores_keep)]\n        else:\n            detections = []\n\n        ''' Add newly detected tracklets to tracked_stracks'''\n        unconfirmed = []\n        tracked_stracks = []  # type: list[STrack]\n        for track in self.tracked_stracks:\n            if not track.is_activated:\n                unconfirmed.append(track)\n            else:\n                tracked_stracks.append(track)\n\n        ''' Step 2: First association, with high score detection boxes'''\n        strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)\n        # Predict the current location with KF\n        STrack.multi_predict(strack_pool)\n\n        if self.args.asso=='hmiou':\n            dists = matching.hmiou_distance(strack_pool, detections)\n            if not self.args.mot20:\n                dists = matching.fuse_score(dists, detections)\n            matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.match_thresh)\n        else:\n            dists = matching.iou_distance(strack_pool, detections)\n            if not self.args.mot20:\n                dists = matching.fuse_score(dists, detections)\n            matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.match_thresh)\n\n        for itracked, idet in matches:\n            track = strack_pool[itracked]\n            det = detections[idet]\n            if track.state == TrackState.Tracked:\n                track.update(detections[idet], self.frame_id)\n                activated_starcks.append(track)\n            else:\n                track.re_activate(det, self.frame_id, new_id=False)\n                refind_stracks.append(track)\n\n        ''' Step 3: Second association, with low score detection boxes'''\n        # association the untrack to the low score detections\n        if len(dets_second) > 0:\n            '''Detections'''\n            detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for\n                          (tlbr, s) in zip(dets_second, scores_second)]\n        else:\n            detections_second = []\n        r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]\n\n        if self.args.asso=='hmiou':\n            dists = matching.hmiou_distance(r_tracked_stracks, detections_second)\n            matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=(self.args.match_thresh-0.4))\n        else:\n            dists = matching.iou_distance(r_tracked_stracks, detections_second)\n            matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)\n\n        for itracked, idet in matches:\n            track = r_tracked_stracks[itracked]\n            det = detections_second[idet]\n            if track.state == TrackState.Tracked:\n                track.update(det, self.frame_id)\n                activated_starcks.append(track)\n            else:\n                track.re_activate(det, self.frame_id, new_id=False)\n                refind_stracks.append(track)\n\n        for it in u_track:\n            track = r_tracked_stracks[it]\n            if not track.state == TrackState.Lost:\n                track.mark_lost()\n                lost_stracks.append(track)\n\n        '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''\n        detections = [detections[i] for i in u_detection]\n        if self.args.asso=='hmiou':\n            dists = matching.hmiou_distance(unconfirmed, detections)\n            if not self.args.mot20:\n                dists = matching.fuse_score(dists, detections)\n            matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=(self.args.match_thresh-0.2))\n        else:\n            dists = matching.iou_distance(unconfirmed, detections)\n            if not self.args.mot20:\n                dists = matching.fuse_score(dists, detections)\n            matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)\n        for itracked, idet in matches:\n            unconfirmed[itracked].update(detections[idet], self.frame_id)\n            activated_starcks.append(unconfirmed[itracked])\n        for it in u_unconfirmed:\n            track = unconfirmed[it]\n            track.mark_removed()\n            removed_stracks.append(track)\n\n        \"\"\" Step 4: Init new stracks\"\"\"\n        for inew in u_detection:\n            track = detections[inew]\n            if track.score < self.det_thresh:\n                continue\n            track.activate(self.kalman_filter, self.frame_id)\n            activated_starcks.append(track)\n        \"\"\" Step 5: Update state\"\"\"\n        for track in self.lost_stracks:\n            if self.frame_id - track.end_frame > self.max_time_lost:\n                track.mark_removed()\n                removed_stracks.append(track)\n\n        # print('Ramained match {} s'.format(t4-t3))\n\n        self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]\n        self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)\n        self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)\n        self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)\n        self.lost_stracks.extend(lost_stracks)\n        self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)\n        self.removed_stracks.extend(removed_stracks)\n        self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)\n        # get scores of lost tracks\n        output_stracks = [track for track in self.tracked_stracks if track.is_activated]\n\n        return output_stracks\n\n\ndef joint_stracks(tlista, tlistb):\n    exists = {}\n    res = []\n    for t in tlista:\n        exists[t.track_id] = 1\n        res.append(t)\n    for t in tlistb:\n        tid = t.track_id\n        if not exists.get(tid, 0):\n            exists[tid] = 1\n            res.append(t)\n    return res\n\n\ndef sub_stracks(tlista, tlistb):\n    stracks = {}\n    for t in tlista:\n        stracks[t.track_id] = t\n    for t in tlistb:\n        tid = t.track_id\n        if stracks.get(tid, 0):\n            del stracks[tid]\n    return list(stracks.values())\n\n\ndef remove_duplicate_stracks(stracksa, stracksb):\n    pdist = matching.iou_distance(stracksa, stracksb)\n    pairs = np.where(pdist < 0.15)\n    dupa, dupb = list(), list()\n    for p, q in zip(*pairs):\n        timep = stracksa[p].frame_id - stracksa[p].start_frame\n        timeq = stracksb[q].frame_id - stracksb[q].start_frame\n        if timep > timeq:\n            dupb.append(q)\n        else:\n            dupa.append(p)\n    resa = [t for i, t in enumerate(stracksa) if not i in dupa]\n    resb = [t for i, t in enumerate(stracksb) if not i in dupb]\n    return resa, resb\n"
  },
  {
    "path": "trackers/byte_tracker/byte_tracker_public.py",
    "content": "import numpy as np\nfrom collections import deque\nimport os\nimport os.path as osp\nimport copy\nimport torch\nimport torch.nn.functional as F\n\nfrom .kalman_filter import KalmanFilter\nfrom trackers.byte_tracker import matching\nfrom .basetrack import BaseTrack, TrackState\n\nclass STrack(BaseTrack):\n    shared_kalman = KalmanFilter()\n    def __init__(self, tlwh, score):\n\n        # wait activate\n        self._tlwh = np.asarray(tlwh, dtype=np.float)\n        self.kalman_filter = None\n        self.mean, self.covariance = None, None\n        self.is_activated = False\n\n        self.score = score\n        self.tracklet_len = 0\n\n    def predict(self):\n        mean_state = self.mean.copy()\n        if self.state != TrackState.Tracked:\n            mean_state[7] = 0\n        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)\n\n    @staticmethod\n    def multi_predict(stracks):\n        if len(stracks) > 0:\n            multi_mean = np.asarray([st.mean.copy() for st in stracks])\n            multi_covariance = np.asarray([st.covariance for st in stracks])\n            for i, st in enumerate(stracks):\n                if st.state != TrackState.Tracked:\n                    multi_mean[i][7] = 0\n            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)\n            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):\n                stracks[i].mean = mean\n                stracks[i].covariance = cov\n\n    def activate(self, kalman_filter, frame_id):\n        \"\"\"Start a new tracklet\"\"\"\n        self.kalman_filter = kalman_filter\n        self.track_id = self.next_id()\n        self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))\n\n        self.tracklet_len = 0\n        self.state = TrackState.Tracked\n        if frame_id == 1:\n            self.is_activated = True\n        # self.is_activated = True\n        self.frame_id = frame_id\n        self.start_frame = frame_id\n\n    def re_activate(self, new_track, frame_id, new_id=False):\n        self.mean, self.covariance = self.kalman_filter.update(\n            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)\n        )\n        self.tracklet_len = 0\n        self.state = TrackState.Tracked\n        self.is_activated = True\n        self.frame_id = frame_id\n        if new_id:\n            self.track_id = self.next_id()\n        self.score = new_track.score\n\n    def update(self, new_track, frame_id):\n        \"\"\"\n        Update a matched track\n        :type new_track: STrack\n        :type frame_id: int\n        :type update_feature: bool\n        :return:\n        \"\"\"\n        self.frame_id = frame_id\n        self.tracklet_len += 1\n\n        new_tlwh = new_track.tlwh\n        self.mean, self.covariance = self.kalman_filter.update(\n            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))\n        self.state = TrackState.Tracked\n        self.is_activated = True\n\n        self.score = new_track.score\n\n    @property\n    # @jit(nopython=True)\n    def tlwh(self):\n        \"\"\"Get current position in bounding box format `(top left x, top left y,\n                width, height)`.\n        \"\"\"\n        if self.mean is None:\n            return self._tlwh.copy()\n        ret = self.mean[:4].copy()\n        ret[2] *= ret[3]\n        ret[:2] -= ret[2:] / 2\n        return ret\n\n    @property\n    # @jit(nopython=True)\n    def tlbr(self):\n        \"\"\"Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,\n        `(top left, bottom right)`.\n        \"\"\"\n        ret = self.tlwh.copy()\n        ret[2:] += ret[:2]\n        return ret\n\n    @staticmethod\n    # @jit(nopython=True)\n    def tlwh_to_xyah(tlwh):\n        \"\"\"Convert bounding box to format `(center x, center y, aspect ratio,\n        height)`, where the aspect ratio is `width / height`.\n        \"\"\"\n        ret = np.asarray(tlwh).copy()\n        ret[:2] += ret[2:] / 2\n        ret[2] /= ret[3]\n        return ret\n\n    def to_xyah(self):\n        return self.tlwh_to_xyah(self.tlwh)\n\n    @staticmethod\n    # @jit(nopython=True)\n    def tlbr_to_tlwh(tlbr):\n        ret = np.asarray(tlbr).copy()\n        ret[2:] -= ret[:2]\n        return ret\n\n    @staticmethod\n    # @jit(nopython=True)\n    def tlwh_to_tlbr(tlwh):\n        ret = np.asarray(tlwh).copy()\n        ret[2:] += ret[:2]\n        return ret\n\n    def __repr__(self):\n        return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)\n\n\nclass BYTETracker(object):\n    def __init__(self, args, frame_rate=30):\n        self.tracked_stracks = []  # type: list[STrack]\n        self.lost_stracks = []  # type: list[STrack]\n        self.removed_stracks = []  # type: list[STrack]\n\n        self.frame_id = 0\n        self.args = args\n        #self.det_thresh = args.track_thresh\n        self.det_thresh = args.track_thresh + 0.1\n        self.buffer_size = int(frame_rate / 30.0 * args.track_buffer)\n        self.max_time_lost = self.buffer_size\n        self.kalman_filter = KalmanFilter()\n\n    def update(self, output_results, img_info, img_size):\n        self.frame_id += 1\n        activated_starcks = []\n        refind_stracks = []\n        lost_stracks = []\n        removed_stracks = []\n\n        if output_results.shape[1] == 5:\n            scores = output_results[:, 4]\n            bboxes = output_results[:, :4]\n        else:\n            output_results = output_results.cpu().numpy()\n            scores = output_results[:, 4] * output_results[:, 5]\n            bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n\n        remain_inds = scores > self.args.track_thresh\n        inds_low = scores > 0.1\n        inds_high = scores < self.args.track_thresh\n\n        inds_second = np.logical_and(inds_low, inds_high)\n        dets_second = bboxes[inds_second]\n        dets = bboxes[remain_inds]\n        scores_keep = scores[remain_inds]\n        scores_second = scores[inds_second]\n\n        if len(dets) > 0:\n            '''Detections'''\n            detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for\n                          (tlbr, s) in zip(dets, scores_keep)]\n        else:\n            detections = []\n\n        ''' Add newly detected tracklets to tracked_stracks'''\n        unconfirmed = []\n        tracked_stracks = []  # type: list[STrack]\n        for track in self.tracked_stracks:\n            if not track.is_activated:\n                unconfirmed.append(track)\n            else:\n                tracked_stracks.append(track)\n\n        ''' Step 2: First association, with high score detection boxes'''\n        strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)\n        # Predict the current location with KF\n        STrack.multi_predict(strack_pool)\n        dists = matching.iou_distance(strack_pool, detections)\n        if not self.args.mot20:\n            dists = matching.fuse_score(dists, detections)\n        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.match_thresh)\n\n        for itracked, idet in matches:\n            track = strack_pool[itracked]\n            det = detections[idet]\n            if track.state == TrackState.Tracked:\n                track.update(detections[idet], self.frame_id)\n                activated_starcks.append(track)\n            else:\n                track.re_activate(det, self.frame_id, new_id=False)\n                refind_stracks.append(track)\n\n        ''' Step 3: Second association, with low score detection boxes'''\n        # association the untrack to the low score detections\n        if len(dets_second) > 0:\n            '''Detections'''\n            detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for\n                          (tlbr, s) in zip(dets_second, scores_second)]\n        else:\n            detections_second = []\n        r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]\n        dists = matching.iou_distance(r_tracked_stracks, detections_second)\n        matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)\n        for itracked, idet in matches:\n            track = r_tracked_stracks[itracked]\n            det = detections_second[idet]\n            if track.state == TrackState.Tracked:\n                track.update(det, self.frame_id)\n                activated_starcks.append(track)\n            else:\n                track.re_activate(det, self.frame_id, new_id=False)\n                refind_stracks.append(track)\n\n        for it in u_track:\n            track = r_tracked_stracks[it]\n            if not track.state == TrackState.Lost:\n                track.mark_lost()\n                lost_stracks.append(track)\n\n        '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''\n        detections = [detections[i] for i in u_detection]\n        dists = matching.iou_distance(unconfirmed, detections)\n        if not self.args.mot20:\n            dists = matching.fuse_score(dists, detections)\n        matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)\n        for itracked, idet in matches:\n            unconfirmed[itracked].update(detections[idet], self.frame_id)\n            activated_starcks.append(unconfirmed[itracked])\n        for it in u_unconfirmed:\n            track = unconfirmed[it]\n            track.mark_removed()\n            removed_stracks.append(track)\n\n        \"\"\" Step 4: Init new stracks\"\"\"\n        for inew in u_detection:\n            track = detections[inew]\n            if track.score < self.det_thresh:\n                continue\n            track.activate(self.kalman_filter, self.frame_id)\n            activated_starcks.append(track)\n        \"\"\" Step 5: Update state\"\"\"\n        for track in self.lost_stracks:\n            if self.frame_id - track.end_frame > self.max_time_lost:\n                track.mark_removed()\n                removed_stracks.append(track)\n\n        # print('Ramained match {} s'.format(t4-t3))\n\n        self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]\n        self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)\n        self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)\n        self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)\n        self.lost_stracks.extend(lost_stracks)\n        self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)\n        self.removed_stracks.extend(removed_stracks)\n        self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)\n        # get scores of lost tracks\n        output_stracks = [track for track in self.tracked_stracks if track.is_activated]\n\n        return output_stracks\n\n    def update_public(self, output_results, img_info, img_size, pub_dets):\n        self.frame_id += 1\n        activated_starcks = []\n        refind_stracks = []\n        lost_stracks = []\n        removed_stracks = []\n        pub_dets = pub_dets[:, :4]\n\n        if output_results.shape[1] == 5:\n            scores = output_results[:, 4]\n            bboxes = output_results[:, :4]\n        else:\n            output_results = output_results.cpu().numpy()\n            scores = output_results[:, 4] * output_results[:, 5]\n            bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n\n        remain_inds = scores > self.args.track_thresh\n        inds_low = scores > 0.1\n        inds_high = scores < self.args.track_thresh\n\n        inds_second = np.logical_and(inds_low, inds_high)\n        dets_second = bboxes[inds_second]\n        dets = bboxes[remain_inds]\n        scores_keep = scores[remain_inds]\n        scores_second = scores[inds_second]\n\n        if len(dets) > 0:\n            '''Detections'''\n            detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for\n                          (tlbr, s) in zip(dets, scores_keep)]\n        else:\n            detections = []\n\n        ''' Add newly detected tracklets to tracked_stracks'''\n        unconfirmed = []\n        tracked_stracks = []  # type: list[STrack]\n        for track in self.tracked_stracks:\n            if not track.is_activated:\n                unconfirmed.append(track)\n            else:\n                tracked_stracks.append(track)\n\n        ''' Step 2: First association, with high score detection boxes'''\n        strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)\n        # Predict the current location with KF\n        STrack.multi_predict(strack_pool)\n        dists = matching.iou_distance(strack_pool, detections)\n        if not self.args.mot20:\n            dists = matching.fuse_score(dists, detections)\n        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.match_thresh)\n\n        for itracked, idet in matches:\n            track = strack_pool[itracked]\n            det = detections[idet]\n            if track.state == TrackState.Tracked:\n                track.update(detections[idet], self.frame_id)\n                activated_starcks.append(track)\n            else:\n                track.re_activate(det, self.frame_id, new_id=False)\n                refind_stracks.append(track)\n\n        ''' Step 3: Second association, with low score detection boxes'''\n        # association the untrack to the low score detections\n        if len(dets_second) > 0:\n            '''Detections'''\n            detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for\n                          (tlbr, s) in zip(dets_second, scores_second)]\n        else:\n            detections_second = []\n        r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]\n        dists = matching.iou_distance(r_tracked_stracks, detections_second)\n        matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)\n        for itracked, idet in matches:\n            track = r_tracked_stracks[itracked]\n            det = detections_second[idet]\n            if track.state == TrackState.Tracked:\n                track.update(det, self.frame_id)\n                activated_starcks.append(track)\n            else:\n                track.re_activate(det, self.frame_id, new_id=False)\n                refind_stracks.append(track)\n\n        for it in u_track:\n            track = r_tracked_stracks[it]\n            if not track.state == TrackState.Lost:\n                track.mark_lost()\n                lost_stracks.append(track)\n\n        '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''\n        detections = [detections[i] for i in u_detection]\n        dists = matching.iou_distance(unconfirmed, detections)\n        if not self.args.mot20:\n            dists = matching.fuse_score(dists, detections)\n        matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)\n        for itracked, idet in matches:\n            unconfirmed[itracked].update(detections[idet], self.frame_id)\n            activated_starcks.append(unconfirmed[itracked])\n        for it in u_unconfirmed:\n            track = unconfirmed[it]\n            track.mark_removed()\n            removed_stracks.append(track)\n\n        \"\"\" Step 4: Init new stracks\"\"\"\n\n        if len(u_detection) > 0:\n            remain_detections = [detections[udx] for udx in u_detection]  # m\n            pub_dets[:, 2:] = pub_dets[:, :2] + pub_dets[:, 2:]\n            pri_u_dets = np.array([detections[u_idx].tlbr for u_idx in u_detection], dtype=np.float32)  # m\n            dist3 = 1 - matching.ious(pri_u_dets, pub_dets)  # m, n\n            for j in range(len(pub_dets)):\n                i = dist3[:, j].argmin()\n                if dist3[i, j] < 0.12:\n                    dist3[i, :] = 1e18\n                    track = remain_detections[i]\n                    if track.score < self.det_thresh:\n                        continue\n                    track.activate(self.kalman_filter, self.frame_id)\n                    activated_starcks.append(track)\n\n        '''\n        for inew in u_detection:\n            track = detections[inew]\n            if track.score < self.det_thresh:\n                continue\n            track.activate(self.kalman_filter, self.frame_id)\n            activated_starcks.append(track)\n        '''\n        \"\"\" Step 5: Update state\"\"\"\n        for track in self.lost_stracks:\n            if self.frame_id - track.end_frame > self.max_time_lost:\n                track.mark_removed()\n                removed_stracks.append(track)\n\n        # print('Ramained match {} s'.format(t4-t3))\n\n        self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]\n        self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)\n        self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)\n        self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)\n        self.lost_stracks.extend(lost_stracks)\n        self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)\n        self.removed_stracks.extend(removed_stracks)\n        self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)\n        # get scores of lost tracks\n        output_stracks = [track for track in self.tracked_stracks if track.is_activated]\n\n        return output_stracks\n\n\ndef joint_stracks(tlista, tlistb):\n    exists = {}\n    res = []\n    for t in tlista:\n        exists[t.track_id] = 1\n        res.append(t)\n    for t in tlistb:\n        tid = t.track_id\n        if not exists.get(tid, 0):\n            exists[tid] = 1\n            res.append(t)\n    return res\n\n\ndef sub_stracks(tlista, tlistb):\n    stracks = {}\n    for t in tlista:\n        stracks[t.track_id] = t\n    for t in tlistb:\n        tid = t.track_id\n        if stracks.get(tid, 0):\n            del stracks[tid]\n    return list(stracks.values())\n\n\ndef remove_duplicate_stracks(stracksa, stracksb):\n    pdist = matching.iou_distance(stracksa, stracksb)\n    pairs = np.where(pdist < 0.15)\n    dupa, dupb = list(), list()\n    for p, q in zip(*pairs):\n        timep = stracksa[p].frame_id - stracksa[p].start_frame\n        timeq = stracksb[q].frame_id - stracksb[q].start_frame\n        if timep > timeq:\n            dupb.append(q)\n        else:\n            dupa.append(p)\n    resa = [t for i, t in enumerate(stracksa) if not i in dupa]\n    resb = [t for i, t in enumerate(stracksb) if not i in dupb]\n    return resa, resb\n"
  },
  {
    "path": "trackers/byte_tracker/byte_tracker_score.py",
    "content": "import numpy as np\nfrom collections import deque\nimport os\nimport os.path as osp\nimport copy\nimport torch\nimport torch.nn.functional as F\n\nfrom .kalman_filter import KalmanFilter\nfrom .kalman_filter_score import KalmanFilter_score\nfrom trackers.byte_tracker import matching\nfrom .basetrack import BaseTrack, TrackState\n\nclass STrack(BaseTrack):\n    shared_kalman = KalmanFilter()\n    shared_kalman_score = KalmanFilter_score()\n    def __init__(self, tlwh, score):\n\n        # wait activate\n        self._tlwh = np.asarray(tlwh, dtype=np.float)\n        self.kalman_filter = None\n        self.kalman_filter_score = None\n        self.mean, self.covariance = None, None\n        self.mean_score, self.covariance_score = None, None\n        self.is_activated = False\n\n        self.pre_score = score\n        self.score = score\n        self.tracklet_len = 0\n\n    def predict(self):\n        mean_state = self.mean.copy()\n        if self.state != TrackState.Tracked:\n            mean_state[7] = 0\n        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)\n\n    @staticmethod\n    def multi_predict(stracks):\n        if len(stracks) > 0:\n            multi_mean = np.asarray([st.mean.copy() for st in stracks])\n            multi_covariance = np.asarray([st.covariance for st in stracks])\n            for i, st in enumerate(stracks):\n                if st.state != TrackState.Tracked:\n                    multi_mean[i][7] = 0\n            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)\n            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):\n                stracks[i].mean = mean\n                stracks[i].covariance = cov\n\n            # added for kalman score\n            multi_mean_score = np.asarray([st.mean_score.copy() for st in stracks])\n            multi_covariance_score = np.asarray([st.covariance_score for st in stracks])\n            # for i, st in enumerate(stracks):\n                # if st.state != TrackState.Tracked:\n                #     multi_mean[i][7] = 0\n            multi_mean_score, multi_covariance_score = STrack.shared_kalman_score.multi_predict(multi_mean_score, multi_covariance_score)\n            for i, (mean_score, cov_score) in enumerate(zip(multi_mean_score, multi_covariance_score)):\n                stracks[i].mean_score = mean_score\n                stracks[i].covariance_score = cov_score\n\n    def activate(self, kalman_filter, frame_id, kalman_filter_score):\n        \"\"\"Start a new tracklet\"\"\"\n        self.kalman_filter = kalman_filter\n        self.kalman_filter_score = kalman_filter_score\n        self.track_id = self.next_id()\n        self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))\n        self.mean_score, self.covariance_score = self.kalman_filter_score.initiate(self.score)\n\n        self.tracklet_len = 0\n        self.state = TrackState.Tracked\n        if frame_id == 1:\n            self.is_activated = True\n        # self.is_activated = True\n        self.frame_id = frame_id\n        self.start_frame = frame_id\n\n    def re_activate(self, new_track, frame_id, new_id=False):\n        self.mean, self.covariance = self.kalman_filter.update(\n            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)\n        )\n        self.mean_score, self.covariance_score = self.kalman_filter_score.update(\n            self.mean_score, self.covariance_score, self.score\n        )\n        self.tracklet_len = 0\n        self.state = TrackState.Tracked\n        self.is_activated = True\n        self.frame_id = frame_id\n        if new_id:\n            self.track_id = self.next_id()\n        self.score = new_track.score\n        self.pre_score = self.score\n\n    def update(self, new_track, frame_id):\n        \"\"\"\n        Update a matched track\n        :type new_track: STrack\n        :type frame_id: int\n        :type update_feature: bool\n        :return:\n        \"\"\"\n        self.frame_id = frame_id\n        self.tracklet_len += 1\n\n        new_tlwh = new_track.tlwh\n        self.mean, self.covariance = self.kalman_filter.update(\n            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))\n        self.mean_score, self.covariance_score = self.kalman_filter_score.update(\n            self.mean_score, self.covariance_score, self.score)\n        self.state = TrackState.Tracked\n        self.is_activated = True\n\n        self.pre_score = self.score\n        self.score = new_track.score\n\n    @property\n    # @jit(nopython=True)\n    def tlwh(self):\n        \"\"\"Get current position in bounding box format `(top left x, top left y,\n                width, height)`.\n        \"\"\"\n        if self.mean is None:\n            return self._tlwh.copy()\n        ret = self.mean[:4].copy()\n        ret[2] *= ret[3]\n        ret[:2] -= ret[2:] / 2\n        return ret\n\n    @property\n    # @jit(nopython=True)\n    def score_kalman(self):\n        \"\"\"Get current position in bounding box format `(top left x, top left y,\n                width, height)`.\n        \"\"\"\n        if self.mean_score is None:\n            return self.score.copy()\n        ret = self.mean_score[0].copy()\n        return ret\n\n    @property\n    # @jit(nopython=True)\n    def tlbr(self):\n        \"\"\"Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,\n        `(top left, bottom right)`.\n        \"\"\"\n        ret = self.tlwh.copy()\n        ret[2:] += ret[:2]\n        return ret\n\n    @staticmethod\n    # @jit(nopython=True)\n    def tlwh_to_xyah(tlwh):\n        \"\"\"Convert bounding box to format `(center x, center y, aspect ratio,\n        height)`, where the aspect ratio is `width / height`.\n        \"\"\"\n        ret = np.asarray(tlwh).copy()\n        ret[:2] += ret[2:] / 2\n        ret[2] /= ret[3]\n        return ret\n\n    def to_xyah(self):\n        return self.tlwh_to_xyah(self.tlwh)\n\n    @staticmethod\n    # @jit(nopython=True)\n    def tlbr_to_tlwh(tlbr):\n        ret = np.asarray(tlbr).copy()\n        ret[2:] -= ret[:2]\n        return ret\n\n    @staticmethod\n    # @jit(nopython=True)\n    def tlwh_to_tlbr(tlwh):\n        ret = np.asarray(tlwh).copy()\n        ret[2:] += ret[:2]\n        return ret\n\n    def __repr__(self):\n        return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)\n\n\nclass BYTETracker_score(object):\n    def __init__(self, args, frame_rate=30):\n        self.tracked_stracks = []  # type: list[STrack]\n        self.lost_stracks = []  # type: list[STrack]\n        self.removed_stracks = []  # type: list[STrack]\n\n        self.frame_id = 0\n        self.args = args\n        #self.det_thresh = args.track_thresh\n        self.det_thresh = args.track_thresh + 0.1\n        self.buffer_size = int(frame_rate / 30.0 * args.track_buffer)\n        self.max_time_lost = self.buffer_size\n        self.kalman_filter = KalmanFilter()\n        self.kalman_filter_score = KalmanFilter_score()\n\n    def update(self, output_results, img_info, img_size):\n        self.frame_id += 1\n        activated_starcks = []\n        refind_stracks = []\n        lost_stracks = []\n        removed_stracks = []\n\n        if output_results.shape[1] == 5:\n            scores = output_results[:, 4]\n            bboxes = output_results[:, :4]\n        else:\n            output_results = output_results.cpu().numpy()\n            scores = output_results[:, 4] * output_results[:, 5]\n            bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n\n        remain_inds = scores > self.args.track_thresh\n        inds_low = scores > 0.1\n        inds_high = scores < self.args.track_thresh\n\n        inds_second = np.logical_and(inds_low, inds_high)\n        dets_second = bboxes[inds_second]\n        dets = bboxes[remain_inds]\n        scores_keep = scores[remain_inds]\n        scores_second = scores[inds_second]\n\n        if len(dets) > 0:\n            '''Detections'''\n            detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for\n                          (tlbr, s) in zip(dets, scores_keep)]\n        else:\n            detections = []\n\n        ''' Add newly detected tracklets to tracked_stracks'''\n        unconfirmed = []\n        tracked_stracks = []  # type: list[STrack]\n        for track in self.tracked_stracks:\n            if not track.is_activated:\n                unconfirmed.append(track)\n            else:\n                tracked_stracks.append(track)\n\n        ''' Step 2: First association, with high score detection boxes'''\n        strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)\n        # Predict the current location with KF\n        STrack.multi_predict(strack_pool)\n        dists = matching.iou_distance(strack_pool, detections)\n        if not self.args.mot20:\n            dists = matching.fuse_score(dists, detections)\n        if self.args.TCM_first_step:\n            dists = matching.add_score_kalman(dists, strack_pool, detections, self.args.TCM_first_step_weight, self.args.track_thresh)\n        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.match_thresh)\n\n        for itracked, idet in matches:\n            track = strack_pool[itracked]\n            det = detections[idet]\n            if track.state == TrackState.Tracked:\n                track.update(detections[idet], self.frame_id)\n                activated_starcks.append(track)\n            else:\n                track.re_activate(det, self.frame_id, new_id=False)\n                refind_stracks.append(track)\n\n        ''' Step 3: Second association, with low score detection boxes'''\n        # association the untrack to the low score detections\n        if len(dets_second) > 0:\n            '''Detections'''\n            detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for\n                          (tlbr, s) in zip(dets_second, scores_second)]\n        else:\n            detections_second = []\n        r_tracked_stracks = [strack_pool[i] for i in u_track]\n        dists = matching.iou_distance(r_tracked_stracks, detections_second)\n        if self.args.TCM_byte_step:\n            dists = matching.add_score_kalman_byte_step(dists, r_tracked_stracks, detections_second, self.args.TCM_byte_step_weight, self.args.track_thresh)\n        matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)\n        for itracked, idet in matches:\n            track = r_tracked_stracks[itracked]\n            det = detections_second[idet]\n            if track.state == TrackState.Tracked:\n                track.update(det, self.frame_id)\n                activated_starcks.append(track)\n            else:\n                track.re_activate(det, self.frame_id, new_id=False)\n                refind_stracks.append(track)\n\n        for it in u_track:\n            track = r_tracked_stracks[it]\n            if not track.state == TrackState.Lost:\n                track.mark_lost()\n                lost_stracks.append(track)\n\n        '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''\n        detections = [detections[i] for i in u_detection]\n        dists = matching.iou_distance(unconfirmed, detections)\n        if not self.args.mot20:\n            dists = matching.fuse_score(dists, detections)\n        matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)\n        for itracked, idet in matches:\n            unconfirmed[itracked].update(detections[idet], self.frame_id)\n            activated_starcks.append(unconfirmed[itracked])\n        for it in u_unconfirmed:\n            track = unconfirmed[it]\n            track.mark_removed()\n            removed_stracks.append(track)\n\n        \"\"\" Step 4: Init new stracks\"\"\"\n        for inew in u_detection:\n            track = detections[inew]\n            if track.score < self.det_thresh:\n                continue\n            track.activate(self.kalman_filter, self.frame_id, self.kalman_filter_score)\n            activated_starcks.append(track)\n        \"\"\" Step 5: Update state\"\"\"\n        for track in self.lost_stracks:\n            if self.frame_id - track.end_frame > self.max_time_lost:\n                track.mark_removed()\n                removed_stracks.append(track)\n\n        # print('Ramained match {} s'.format(t4-t3))\n\n        self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]\n        self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)\n        self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)\n        self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)\n        self.lost_stracks.extend(lost_stracks)\n        self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)\n        self.removed_stracks.extend(removed_stracks)\n        self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)\n        # get scores of lost tracks\n        output_stracks = [track for track in self.tracked_stracks if track.is_activated]\n\n        return output_stracks\n\n\ndef joint_stracks(tlista, tlistb):\n    exists = {}\n    res = []\n    for t in tlista:\n        exists[t.track_id] = 1\n        res.append(t)\n    for t in tlistb:\n        tid = t.track_id\n        if not exists.get(tid, 0):\n            exists[tid] = 1\n            res.append(t)\n    return res\n\n\ndef sub_stracks(tlista, tlistb):\n    stracks = {}\n    for t in tlista:\n        stracks[t.track_id] = t\n    for t in tlistb:\n        tid = t.track_id\n        if stracks.get(tid, 0):\n            del stracks[tid]\n    return list(stracks.values())\n\n\ndef remove_duplicate_stracks(stracksa, stracksb):\n    pdist = matching.iou_distance(stracksa, stracksb)\n    pairs = np.where(pdist < 0.15)\n    dupa, dupb = list(), list()\n    for p, q in zip(*pairs):\n        timep = stracksa[p].frame_id - stracksa[p].start_frame\n        timeq = stracksb[q].frame_id - stracksb[q].start_frame\n        if timep > timeq:\n            dupb.append(q)\n        else:\n            dupa.append(p)\n    resa = [t for i, t in enumerate(stracksa) if not i in dupa]\n    resb = [t for i, t in enumerate(stracksb) if not i in dupb]\n    return resa, resb\n"
  },
  {
    "path": "trackers/byte_tracker/kalman_filter.py",
    "content": "# vim: expandtab:ts=4:sw=4\nimport numpy as np\nimport scipy.linalg\n\n\n\"\"\"\nTable for the 0.95 quantile of the chi-square distribution with N degrees of\nfreedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv\nfunction and used as Mahalanobis gating threshold.\n\"\"\"\nchi2inv95 = {\n    1: 3.8415,\n    2: 5.9915,\n    3: 7.8147,\n    4: 9.4877,\n    5: 11.070,\n    6: 12.592,\n    7: 14.067,\n    8: 15.507,\n    9: 16.919}\n\n\nclass KalmanFilter(object):\n    \"\"\"\n    A simple Kalman filter for tracking bounding boxes in image space.\n\n    The 8-dimensional state space\n\n        x, y, a, h, vx, vy, va, vh\n\n    contains the bounding box center position (x, y), aspect ratio a, height h,\n    and their respective velocities.\n\n    Object motion follows a constant velocity model. The bounding box location\n    (x, y, a, h) is taken as direct observation of the state space (linear\n    observation model).\n\n    \"\"\"\n\n    def __init__(self):\n        ndim, dt = 4, 1.\n\n        # Create Kalman filter model matrices.\n        self._motion_mat = np.eye(2 * ndim, 2 * ndim)   # (8, 8) F\n        for i in range(ndim):\n            self._motion_mat[i, ndim + i] = dt\n        self._update_mat = np.eye(ndim, 2 * ndim)  # (4, 8) H\n\n        # Motion and observation uncertainty are chosen relative to the current\n        # state estimate. These weights control the amount of uncertainty in\n        # the model. This is a bit hacky.\n        self._std_weight_position = 1. / 20\n        self._std_weight_velocity = 1. / 160\n\n    def initiate(self, measurement):\n        \"\"\"Create track from unassociated measurement.\n\n        Parameters\n        ----------\n        measurement : ndarray\n            Bounding box coordinates (x, y, a, h) with center position (x, y),\n            aspect ratio a, and height h.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector (8 dimensional) and covariance matrix (8x8\n            dimensional) of the new track. Unobserved velocities are initialized\n            to 0 mean.\n\n        \"\"\"\n        mean_pos = measurement\n        mean_vel = np.zeros_like(mean_pos)\n        mean = np.r_[mean_pos, mean_vel]\n\n        std = [\n            2 * self._std_weight_position * measurement[3],\n            2 * self._std_weight_position * measurement[3],\n            1e-2,\n            2 * self._std_weight_position * measurement[3],\n            10 * self._std_weight_velocity * measurement[3],\n            10 * self._std_weight_velocity * measurement[3],\n            1e-5,\n            10 * self._std_weight_velocity * measurement[3]]\n        covariance = np.diag(np.square(std))\n        return mean, covariance  # (8, 8), (8,)\n\n    def predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The 8 dimensional mean vector of the object state at the previous\n            time step.\n        covariance : ndarray\n            The 8x8 dimensional covariance matrix of the object state at the\n            previous time step.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n\n        \"\"\"\n        std_pos = [\n            self._std_weight_position * mean[3],\n            self._std_weight_position * mean[3],\n            1e-2,\n            self._std_weight_position * mean[3]]\n        std_vel = [\n            self._std_weight_velocity * mean[3],\n            self._std_weight_velocity * mean[3],\n            1e-5,\n            self._std_weight_velocity * mean[3]]\n        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))\n\n        #mean = np.dot(self._motion_mat, mean)\n        mean = np.dot(mean, self._motion_mat.T)\n        covariance = np.linalg.multi_dot((\n            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov\n\n        return mean, covariance\n\n    def project(self, mean, covariance):\n        \"\"\"Project state distribution to measurement space.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The state's mean vector (8 dimensional array).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the projected mean and covariance matrix of the given state\n            estimate.\n\n        \"\"\"\n        std = [\n            self._std_weight_position * mean[3],\n            self._std_weight_position * mean[3],\n            1e-1,\n            self._std_weight_position * mean[3]]\n        innovation_cov = np.diag(np.square(std))\n\n        mean = np.dot(self._update_mat, mean)\n        covariance = np.linalg.multi_dot((\n            self._update_mat, covariance, self._update_mat.T))\n        return mean, covariance + innovation_cov\n\n    def multi_predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step (Vectorized version).\n        Parameters\n        ----------\n        mean : ndarray\n            The Nx8 dimensional mean matrix of the object states at the previous\n            time step.\n        covariance : ndarray\n            The Nx8x8 dimensional covariance matrics of the object states at the\n            previous time step.\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n        \"\"\"\n        std_pos = [\n            self._std_weight_position * mean[:, 3],\n            self._std_weight_position * mean[:, 3],\n            1e-2 * np.ones_like(mean[:, 3]),\n            self._std_weight_position * mean[:, 3]]\n        std_vel = [\n            self._std_weight_velocity * mean[:, 3],\n            self._std_weight_velocity * mean[:, 3],\n            1e-5 * np.ones_like(mean[:, 3]),\n            self._std_weight_velocity * mean[:, 3]]\n        sqr = np.square(np.r_[std_pos, std_vel]).T\n\n        motion_cov = []\n        for i in range(len(mean)):\n            motion_cov.append(np.diag(sqr[i]))\n        motion_cov = np.asarray(motion_cov)\n\n        mean = np.dot(mean, self._motion_mat.T)\n        left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))\n        covariance = np.dot(left, self._motion_mat.T) + motion_cov\n\n        return mean, covariance\n\n    def update(self, mean, covariance, measurement):\n        \"\"\"Run Kalman filter correction step.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The predicted state's mean vector (8 dimensional).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n        measurement : ndarray\n            The 4 dimensional measurement vector (x, y, a, h), where (x, y)\n            is the center position, a the aspect ratio, and h the height of the\n            bounding box.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the measurement-corrected state distribution.\n\n        \"\"\"\n        projected_mean, projected_cov = self.project(mean, covariance)\n\n        chol_factor, lower = scipy.linalg.cho_factor(\n            projected_cov, lower=True, check_finite=False)\n        kalman_gain = scipy.linalg.cho_solve(\n            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,\n            check_finite=False).T\n        innovation = measurement - projected_mean\n\n        new_mean = mean + np.dot(innovation, kalman_gain.T)\n        new_covariance = covariance - np.linalg.multi_dot((\n            kalman_gain, projected_cov, kalman_gain.T))\n        return new_mean, new_covariance\n\n    def gating_distance(self, mean, covariance, measurements,\n                        only_position=False, metric='maha'):\n        \"\"\"Compute gating distance between state distribution and measurements.\n        A suitable distance threshold can be obtained from `chi2inv95`. If\n        `only_position` is False, the chi-square distribution has 4 degrees of\n        freedom, otherwise 2.\n        Parameters\n        ----------\n        mean : ndarray\n            Mean vector over the state distribution (8 dimensional).\n        covariance : ndarray\n            Covariance of the state distribution (8x8 dimensional).\n        measurements : ndarray\n            An Nx4 dimensional matrix of N measurements, each in\n            format (x, y, a, h) where (x, y) is the bounding box center\n            position, a the aspect ratio, and h the height.\n        only_position : Optional[bool]\n            If True, distance computation is done with respect to the bounding\n            box center position only.\n        Returns\n        -------\n        ndarray\n            Returns an array of length N, where the i-th element contains the\n            squared Mahalanobis distance between (mean, covariance) and\n            `measurements[i]`.\n        \"\"\"\n        mean, covariance = self.project(mean, covariance)\n        if only_position:\n            mean, covariance = mean[:2], covariance[:2, :2]\n            measurements = measurements[:, :2]\n\n        d = measurements - mean\n        if metric == 'gaussian':\n            return np.sum(d * d, axis=1)\n        elif metric == 'maha':\n            cholesky_factor = np.linalg.cholesky(covariance)\n            z = scipy.linalg.solve_triangular(\n                cholesky_factor, d.T, lower=True, check_finite=False,\n                overwrite_b=True)\n            squared_maha = np.sum(z * z, axis=0)\n            return squared_maha\n        else:\n            raise ValueError('invalid distance metric')"
  },
  {
    "path": "trackers/byte_tracker/kalman_filter_score.py",
    "content": "# vim: expandtab:ts=4:sw=4\nimport numpy as np\nimport scipy.linalg\n\n\n\"\"\"\nTable for the 0.95 quantile of the chi-square distribution with N degrees of\nfreedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv\nfunction and used as Mahalanobis gating threshold.\n\"\"\"\nchi2inv95 = {\n    1: 3.8415,\n    2: 5.9915,\n    3: 7.8147,\n    4: 9.4877,\n    5: 11.070,\n    6: 12.592,\n    7: 14.067,\n    8: 15.507,\n    9: 16.919}\n\n\nclass KalmanFilter_score(object):\n    \"\"\"\n    A simple Kalman filter for tracking bounding boxes in image space.\n\n    The 8-dimensional state space\n\n        x, y, a, h, vx, vy, va, vh\n        修改为 score, vscore\n\n    contains the bounding box center position (x, y), aspect ratio a, height h,\n    and their respective velocities.\n\n    Object motion follows a constant velocity model. The bounding box location\n    (x, y, a, h) is taken as direct observation of the state space (linear\n    observation model).\n\n    \"\"\"\n\n    def __init__(self):\n        ndim, dt = 1, 1.\n\n        # Create Kalman filter model matrices.\n        self._motion_mat = np.eye(2 * ndim, 2 * ndim)\n        for i in range(ndim):\n            self._motion_mat[i, ndim + i] = dt\n        self._update_mat = np.eye(ndim, 2 * ndim)\n\n        # Motion and observation uncertainty are chosen relative to the current\n        # state estimate. These weights control the amount of uncertainty in\n        # the model. This is a bit hacky.\n        self._std_weight_position = 1. / 20\n        self._std_weight_velocity = 1. / 160\n\n    def initiate(self, measurement):\n        \"\"\"Create track from unassociated measurement.\n\n        Parameters\n        ----------\n        measurement : ndarray\n            Bounding box coordinates (x, y, a, h) with center position (x, y),\n            aspect ratio a, and height h.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector (8 dimensional) and covariance matrix (8x8\n            dimensional) of the new track. Unobserved velocities are initialized\n            to 0 mean.\n\n        \"\"\"\n        mean_pos = measurement\n        mean_vel = np.zeros_like(mean_pos)\n        mean = np.r_[mean_pos, mean_vel]\n\n        # std = [\n        #     2 * self._std_weight_position * measurement[3],\n        #     2 * self._std_weight_position * measurement[3],\n        #     1e-2,\n        #     2 * self._std_weight_position * measurement[3],\n        #     10 * self._std_weight_velocity * measurement[3],\n        #     10 * self._std_weight_velocity * measurement[3],\n        #     1e-5,\n        #     10 * self._std_weight_velocity * measurement[3]]\n        std = [\n            1e-2,\n            1e-5]\n        covariance = np.diag(np.square(std))\n        return mean, covariance\n\n    def predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The 8 dimensional mean vector of the object state at the previous\n            time step.\n        covariance : ndarray\n            The 8x8 dimensional covariance matrix of the object state at the\n            previous time step.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n\n        \"\"\"\n        std_pos = [\n            1e-2]\n        std_vel = [\n            1e-5]\n        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))\n\n        #mean = np.dot(self._motion_mat, mean)\n        mean = np.dot(mean, self._motion_mat.T)\n        covariance = np.linalg.multi_dot((\n            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov\n\n        return mean, covariance\n\n    def project(self, mean, covariance):\n        \"\"\"Project state distribution to measurement space.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The state's mean vector (8 dimensional array).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the projected mean and covariance matrix of the given state\n            estimate.\n\n        \"\"\"\n        std = [\n            1e-1]\n        innovation_cov = np.diag(np.square(std))\n\n        mean = np.dot(self._update_mat, mean)\n        covariance = np.linalg.multi_dot((\n            self._update_mat, covariance, self._update_mat.T))\n        return mean, covariance + innovation_cov\n\n    def multi_predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step (Vectorized version).\n        Parameters\n        ----------\n        mean : ndarray\n            The Nx8 dimensional mean matrix of the object states at the previous\n            time step.\n        covariance : ndarray\n            The Nx8x8 dimensional covariance matrics of the object states at the\n            previous time step.\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n        \"\"\"\n        std_pos = [\n            1e-2 * np.ones_like(mean[:, 0])]\n        std_vel = [\n            1e-5 * np.ones_like(mean[:, 0])]\n        sqr = np.square(np.r_[std_pos, std_vel]).T\n\n        motion_cov = []\n        for i in range(len(mean)):\n            motion_cov.append(np.diag(sqr[i]))\n        motion_cov = np.asarray(motion_cov)\n\n        mean = np.dot(mean, self._motion_mat.T)\n        left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))\n        covariance = np.dot(left, self._motion_mat.T) + motion_cov\n\n        return mean, covariance\n\n    def update(self, mean, covariance, measurement):\n        \"\"\"Run Kalman filter correction step.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The predicted state's mean vector (8 dimensional).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n        measurement : ndarray\n            The 4 dimensional measurement vector (x, y, a, h), where (x, y)\n            is the center position, a the aspect ratio, and h the height of the\n            bounding box.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the measurement-corrected state distribution.\n\n        \"\"\"\n        projected_mean, projected_cov = self.project(mean, covariance)\n\n        chol_factor, lower = scipy.linalg.cho_factor(\n            projected_cov, lower=True, check_finite=False)\n        kalman_gain = scipy.linalg.cho_solve(\n            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,\n            check_finite=False).T\n        innovation = measurement - projected_mean\n\n        new_mean = mean + np.dot(innovation, kalman_gain.T)\n        new_covariance = covariance - np.linalg.multi_dot((\n            kalman_gain, projected_cov, kalman_gain.T))\n        return new_mean, new_covariance\n\n    def gating_distance(self, mean, covariance, measurements,\n                        only_position=False, metric='maha'):\n        \"\"\"Compute gating distance between state distribution and measurements.\n        A suitable distance threshold can be obtained from `chi2inv95`. If\n        `only_position` is False, the chi-square distribution has 4 degrees of\n        freedom, otherwise 2.\n        Parameters\n        ----------\n        mean : ndarray\n            Mean vector over the state distribution (8 dimensional).\n        covariance : ndarray\n            Covariance of the state distribution (8x8 dimensional).\n        measurements : ndarray\n            An Nx4 dimensional matrix of N measurements, each in\n            format (x, y, a, h) where (x, y) is the bounding box center\n            position, a the aspect ratio, and h the height.\n        only_position : Optional[bool]\n            If True, distance computation is done with respect to the bounding\n            box center position only.\n        Returns\n        -------\n        ndarray\n            Returns an array of length N, where the i-th element contains the\n            squared Mahalanobis distance between (mean, covariance) and\n            `measurements[i]`.\n        \"\"\"\n        mean, covariance = self.project(mean, covariance)\n        if only_position:\n            mean, covariance = mean[:2], covariance[:2, :2]\n            measurements = measurements[:, :2]\n\n        d = measurements - mean\n        if metric == 'gaussian':\n            return np.sum(d * d, axis=1)\n        elif metric == 'maha':\n            cholesky_factor = np.linalg.cholesky(covariance)\n            z = scipy.linalg.solve_triangular(\n                cholesky_factor, d.T, lower=True, check_finite=False,\n                overwrite_b=True)\n            squared_maha = np.sum(z * z, axis=0)\n            return squared_maha\n        else:\n            raise ValueError('invalid distance metric')"
  },
  {
    "path": "trackers/byte_tracker/matching.py",
    "content": "import cv2\nimport numpy as np\nimport scipy\nimport lap\nfrom scipy.spatial.distance import cdist\n\nfrom cython_bbox import bbox_overlaps as bbox_ious\nfrom trackers.byte_tracker import kalman_filter\nimport time\n\ndef merge_matches(m1, m2, shape):\n    O,P,Q = shape\n    m1 = np.asarray(m1)\n    m2 = np.asarray(m2)\n\n    M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))\n    M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))\n\n    mask = M1*M2\n    match = mask.nonzero()\n    match = list(zip(match[0], match[1]))\n    unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))\n    unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))\n\n    return match, unmatched_O, unmatched_Q\n\n\ndef _indices_to_matches(cost_matrix, indices, thresh):\n    matched_cost = cost_matrix[tuple(zip(*indices))]\n    matched_mask = (matched_cost <= thresh)\n\n    matches = indices[matched_mask]\n    unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))\n    unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))\n\n    return matches, unmatched_a, unmatched_b\n\n\ndef linear_assignment(cost_matrix, thresh):\n    if cost_matrix.size == 0:\n        return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))\n    matches, unmatched_a, unmatched_b = [], [], []\n    cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)\n    for ix, mx in enumerate(x):\n        if mx >= 0:\n            matches.append([ix, mx])\n    unmatched_a = np.where(x < 0)[0]\n    unmatched_b = np.where(y < 0)[0]\n    matches = np.asarray(matches)\n    return matches, unmatched_a, unmatched_b\n\n\ndef ious(atlbrs, btlbrs):\n    \"\"\"\n    Compute cost based on IoU\n    :type atlbrs: list[tlbr] | np.ndarray\n    :type atlbrs: list[tlbr] | np.ndarray\n\n    :rtype ious np.ndarray\n    \"\"\"\n    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)\n    if ious.size == 0:\n        return ious\n\n    ious = bbox_ious(\n        np.ascontiguousarray(atlbrs, dtype=np.float),\n        np.ascontiguousarray(btlbrs, dtype=np.float)\n    )\n\n    return ious\n\ndef hmiou(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    ious = np.zeros((len(bboxes1), len(bboxes2)), dtype=np.float)\n    if ious.size == 0:\n        return ious\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    yy11 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    yy12 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n\n    yy21 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    yy22 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n    o = (yy12 - yy11) / (yy22 - yy21)\n\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    iou = wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])\n                + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)\n\n    iou *= o\n    return iou\n\ndef iou_distance(atracks, btracks):\n    \"\"\"\n    Compute cost based on IoU\n    :type atracks: list[STrack]\n    :type btracks: list[STrack]\n\n    :rtype cost_matrix np.ndarray\n    \"\"\"\n\n    if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):\n        atlbrs = atracks\n        btlbrs = btracks\n    else:\n        atlbrs = [track.tlbr for track in atracks]\n        btlbrs = [track.tlbr for track in btracks]\n    _ious = ious(atlbrs, btlbrs)\n    cost_matrix = 1 - _ious\n\n    return cost_matrix\n\ndef hmiou_distance(atracks, btracks):\n    \"\"\"\n    Compute cost based on IoU\n    :type atracks: list[STrack]\n    :type btracks: list[STrack]\n    :rtype cost_matrix np.ndarray\n    \"\"\"\n    atlbrs = [track.tlbr for track in atracks]\n    btlbrs = [track.tlbr for track in btracks]\n    _ious = hmiou(atlbrs, btlbrs)\n    cost_matrix = 1 - _ious\n\n    return cost_matrix\n\ndef v_iou_distance(atracks, btracks):\n    \"\"\"\n    Compute cost based on IoU\n    :type atracks: list[STrack]\n    :type btracks: list[STrack]\n\n    :rtype cost_matrix np.ndarray\n    \"\"\"\n\n    if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):\n        atlbrs = atracks\n        btlbrs = btracks\n    else:\n        atlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in atracks]\n        btlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in btracks]\n    _ious = ious(atlbrs, btlbrs)\n    cost_matrix = 1 - _ious\n\n    return cost_matrix\n\ndef embedding_distance(tracks, detections, metric='cosine'):\n    \"\"\"\n    :param tracks: list[STrack]\n    :param detections: list[BaseTrack]\n    :param metric:\n    :return: cost_matrix np.ndarray\n    \"\"\"\n\n    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)\n    if cost_matrix.size == 0:\n        return cost_matrix\n    det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)\n    #for i, track in enumerate(tracks):\n        #cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))\n    track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)\n    cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric))  # Nomalized features\n    return cost_matrix\n\n\ndef gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):\n    if cost_matrix.size == 0:\n        return cost_matrix\n    gating_dim = 2 if only_position else 4\n    gating_threshold = kalman_filter.chi2inv95[gating_dim]\n    measurements = np.asarray([det.to_xyah() for det in detections])\n    for row, track in enumerate(tracks):\n        gating_distance = kf.gating_distance(\n            track.mean, track.covariance, measurements, only_position)\n        cost_matrix[row, gating_distance > gating_threshold] = np.inf\n    return cost_matrix\n\n\ndef fuse_motion(kf, cost_matrix, tracks, detections, only_position=False, lambda_=0.98):\n    if cost_matrix.size == 0:\n        return cost_matrix\n    gating_dim = 2 if only_position else 4\n    gating_threshold = kalman_filter.chi2inv95[gating_dim]\n    measurements = np.asarray([det.to_xyah() for det in detections])\n    for row, track in enumerate(tracks):\n        gating_distance = kf.gating_distance(\n            track.mean, track.covariance, measurements, only_position, metric='maha')\n        cost_matrix[row, gating_distance > gating_threshold] = np.inf\n        cost_matrix[row] = lambda_ * cost_matrix[row] + (1 - lambda_) * gating_distance\n    return cost_matrix\n\n\ndef fuse_iou(cost_matrix, tracks, detections):\n    if cost_matrix.size == 0:\n        return cost_matrix\n    reid_sim = 1 - cost_matrix\n    iou_dist = iou_distance(tracks, detections)\n    iou_sim = 1 - iou_dist\n    fuse_sim = reid_sim * (1 + iou_sim) / 2\n    det_scores = np.array([det.score for det in detections])\n    det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)\n    #fuse_sim = fuse_sim * (1 + det_scores) / 2\n    fuse_cost = 1 - fuse_sim\n    return fuse_cost\n\n\ndef fuse_score(cost_matrix, detections):\n    if cost_matrix.size == 0:\n        return cost_matrix\n    iou_sim = 1 - cost_matrix\n    det_scores = np.array([det.score for det in detections])\n    det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)\n    fuse_sim = iou_sim * det_scores\n    fuse_cost = 1 - fuse_sim\n    return fuse_cost\n\n\ndef add_score_kalman(cost_matrix, strack_pool, detections, interval=1.0, track_thresh=0.6):\n    if cost_matrix.size == 0:\n        return cost_matrix\n    strack_score = np.array([np.clip(strack.score_kalman, track_thresh, 1.0) for strack in strack_pool])\n    det_score = np.array([det.score for det in detections])\n    cost_matrix += (abs(np.expand_dims(strack_score, axis=1).repeat(cost_matrix.shape[1], axis=1) - det_score) * interval)\n    return cost_matrix\n\ndef add_score_kalman_byte_step(cost_matrix, strack_pool, detections, interval=1.0, track_thresh=0.6):\n    if cost_matrix.size == 0:\n        return cost_matrix\n    # strack_score = np.array([np.clip(strack.score_kalman, 0.1, track_thresh) for strack in strack_pool])\n    strack_score = np.array([np.clip(strack.score - (strack.pre_score - strack.score), 0.1, track_thresh) for strack in strack_pool])\n    # det_score = np.array([np.clip(det.score - (det.pre_score - det.score), 0.1, track_thresh) for det in detections])\n    det_score = np.array([det.score for det in detections])\n    cost_matrix += (abs(np.expand_dims(strack_score, axis=1).repeat(cost_matrix.shape[1], axis=1) - det_score) * interval)\n    return cost_matrix"
  },
  {
    "path": "trackers/deepsort_tracker/deepsort.py",
    "content": "import numpy as np\nimport torch\nimport cv2\nimport os\n\nfrom .reid_model_motdt import load_reid_model, extract_reid_features\nfrom trackers.deepsort_tracker import kalman_filter, linear_assignment, iou_matching\nfrom yolox.data.dataloading import get_yolox_datadir\nfrom .detection import Detection\nfrom .track import Track\n\n\ndef _cosine_distance(a, b, data_is_normalized=False):\n    if not data_is_normalized:\n        a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)\n        b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)\n    return 1. - np.dot(a, b.T)\n\n\ndef _nn_cosine_distance(x, y):\n    distances = _cosine_distance(x, y)\n    return distances.min(axis=0)\n\n\nclass Tracker:\n    def __init__(self, metric, max_iou_distance=0.7, max_age=70, n_init=3, args=None):\n        self.metric = metric\n        self.max_iou_distance = max_iou_distance\n        self.max_age = max_age\n        self.n_init = n_init\n\n        self.kf = kalman_filter.KalmanFilter()\n        self.tracks = []\n        self._next_id = 1\n        self.args = args\n\n    def predict(self):\n        \"\"\"Propagate track state distributions one time step forward.\n        This function should be called once every time step, before `update`.\n        \"\"\"\n        for track in self.tracks:\n            track.predict(self.kf)\n\n    def increment_ages(self):\n        for track in self.tracks:\n            track.increment_age()\n            track.mark_missed()\n\n    def update(self, detections, classes):\n        \"\"\"Perform measurement update and track management.\n        Parameters\n        ----------\n        detections : List[deep_sort.detection.Detection]\n            A list of detections at the current time step.\n        \"\"\"\n        # Run matching cascade.\n        matches, unmatched_tracks, unmatched_detections = \\\n            self._match(detections)\n\n        # Update track set.\n        for track_idx, detection_idx in matches:\n            self.tracks[track_idx].update(\n                self.kf, detections[detection_idx])\n        for track_idx in unmatched_tracks:\n            self.tracks[track_idx].mark_missed()\n        for detection_idx in unmatched_detections:\n            self._initiate_track(detections[detection_idx], classes[detection_idx].item())\n        self.tracks = [t for t in self.tracks if not t.is_deleted()]\n\n        # Update distance metric.\n        active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]\n        features, targets = [], []\n        for track in self.tracks:\n            if not track.is_confirmed():\n                continue\n            features += track.features\n            targets += [track.track_id for _ in track.features]\n            track.features = []\n        self.metric.partial_fit(\n            np.asarray(features), np.asarray(targets), active_targets)\n\n    def _match(self, detections):\n\n        def gated_metric(tracks, dets, track_indices, detection_indices):\n            features = np.array([dets[i].feature for i in detection_indices])\n            targets = np.array([tracks[i].track_id for i in track_indices])\n            cost_matrix = self.metric.distance(features, targets)\n            cost_matrix = linear_assignment.gate_cost_matrix(\n                self.kf, cost_matrix, tracks, dets, track_indices,\n                detection_indices)\n\n            return cost_matrix\n\n        # Split track set into confirmed and unconfirmed tracks.\n        confirmed_tracks = [\n            i for i, t in enumerate(self.tracks) if t.is_confirmed()]\n        unconfirmed_tracks = [\n            i for i, t in enumerate(self.tracks) if not t.is_confirmed()]\n\n        # Associate confirmed tracks using appearance features.\n        matches_a, unmatched_tracks_a, unmatched_detections = \\\n            linear_assignment.matching_cascade(\n                gated_metric, self.metric.matching_threshold, self.max_age,\n                self.tracks, detections, confirmed_tracks)\n\n        # Associate remaining tracks together with unconfirmed tracks using IOU.\n        iou_track_candidates = unconfirmed_tracks + [\n            k for k in unmatched_tracks_a if\n            self.tracks[k].time_since_update == 1]\n        unmatched_tracks_a = [\n            k for k in unmatched_tracks_a if\n            self.tracks[k].time_since_update != 1]\n        if self.args.asso=='hmiou':\n            matches_b, unmatched_tracks_b, unmatched_detections = \\\n                linear_assignment.min_cost_matching(\n                    iou_matching.hmiou_cost, self.args.match_thresh, self.tracks,\n                    detections, iou_track_candidates, unmatched_detections)\n        else:\n            matches_b, unmatched_tracks_b, unmatched_detections = \\\n                linear_assignment.min_cost_matching(\n                    iou_matching.iou_cost, self.max_iou_distance, self.tracks,\n                    detections, iou_track_candidates, unmatched_detections)\n\n        matches = matches_a + matches_b\n        unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))\n        return matches, unmatched_tracks, unmatched_detections\n\n    def _initiate_track(self, detection, class_id):\n        mean, covariance = self.kf.initiate(detection.to_xyah())\n        self.tracks.append(Track(\n            mean, covariance, self._next_id, class_id, self.n_init, self.max_age,\n            detection.feature))\n        self._next_id += 1\n\n\nclass NearestNeighborDistanceMetric(object):\n    def __init__(self, metric, matching_threshold, budget=None):\n\n        if metric == \"cosine\":\n            self._metric = _nn_cosine_distance\n        else:\n            raise ValueError(\n                \"Invalid metric; must be either 'euclidean' or 'cosine'\")\n        self.matching_threshold = matching_threshold\n        self.budget = budget\n        self.samples = {}\n\n    def partial_fit(self, features, targets, active_targets):\n        for feature, target in zip(features, targets):\n            self.samples.setdefault(target, []).append(feature)\n            if self.budget is not None:\n                self.samples[target] = self.samples[target][-self.budget:]\n        self.samples = {k: self.samples[k] for k in active_targets}\n\n    def distance(self, features, targets):\n        cost_matrix = np.zeros((len(targets), len(features)))\n        for i, target in enumerate(targets):\n            cost_matrix[i, :] = self._metric(self.samples[target], features)\n        return cost_matrix\n\n\nclass DeepSort(object):\n    def __init__(self, model_path, max_dist=0.1, min_confidence=0.3, nms_max_overlap=1.0, max_iou_distance=0.7, max_age=30, n_init=3, nn_budget=100, use_cuda=True, args=None):\n        self.min_confidence = min_confidence\n        self.nms_max_overlap = nms_max_overlap\n\n        # self.extractor = Extractor(model_path, use_cuda=use_cuda)\n        self.reid_model = load_reid_model(model_path)\n\n        max_cosine_distance = max_dist\n        metric = NearestNeighborDistanceMetric(\n            \"cosine\", max_cosine_distance, nn_budget)\n        self.tracker = Tracker(\n            metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init, args=args)\n        self.args = args\n\n    def update(self, output_results, img_info, img_size, img_file_name):\n        if self.args.dataset == 'mot17':\n            img_file_name = os.path.join(get_yolox_datadir(), 'mot', 'train', img_file_name)\n        else:\n            img_file_name = os.path.join(get_yolox_datadir(), 'dancetrack', 'val', img_file_name)\n        ori_img = cv2.imread(img_file_name)\n        self.height, self.width = ori_img.shape[:2]\n        # post process detections\n        output_results = output_results.cpu().numpy()\n        confidences = output_results[:, 4] * output_results[:, 5]\n        \n        bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n        bbox_xyxy = bboxes\n        bbox_tlwh = self._xyxy_to_tlwh_array(bbox_xyxy)\n        remain_inds = confidences > self.min_confidence\n        bbox_tlwh = bbox_tlwh[remain_inds]\n        confidences = confidences[remain_inds]\n\n        # generate detections\n        features = self._get_features(bbox_tlwh, ori_img)\n        detections = [Detection(bbox_tlwh[i], conf, features[i]) for i, conf in enumerate(\n            confidences) if conf > self.min_confidence]\n        classes = np.zeros((len(detections), ))\n\n        # run on non-maximum supression\n        boxes = np.array([d.tlwh for d in detections])\n        scores = np.array([d.confidence for d in detections])\n\n        # update tracker\n        self.tracker.predict()\n        self.tracker.update(detections, classes)\n\n        # output bbox identities\n        outputs = []\n        for track in self.tracker.tracks:\n            if not track.is_confirmed() or track.time_since_update > 1:\n                continue\n            box = track.to_tlwh()\n            x1, y1, x2, y2 = self._tlwh_to_xyxy_noclip(box)\n            track_id = track.track_id\n            class_id = track.class_id\n            outputs.append(np.array([x1, y1, x2, y2, track_id, class_id], dtype=np.int))\n        if len(outputs) > 0:\n            outputs = np.stack(outputs, axis=0)\n        return outputs\n\n    \"\"\"\n    TODO:\n        Convert bbox from xc_yc_w_h to xtl_ytl_w_h\n    Thanks JieChen91@github.com for reporting this bug!\n    \"\"\"\n    @staticmethod\n    def _xywh_to_tlwh(bbox_xywh):\n        if isinstance(bbox_xywh, np.ndarray):\n            bbox_tlwh = bbox_xywh.copy()\n        elif isinstance(bbox_xywh, torch.Tensor):\n            bbox_tlwh = bbox_xywh.clone()\n        bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2] / 2.\n        bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3] / 2.\n        return bbox_tlwh\n    \n    @staticmethod\n    def _xyxy_to_tlwh_array(bbox_xyxy):\n        if isinstance(bbox_xyxy, np.ndarray):\n            bbox_tlwh = bbox_xyxy.copy()\n        elif isinstance(bbox_xyxy, torch.Tensor):\n            bbox_tlwh = bbox_xyxy.clone()\n        bbox_tlwh[:, 2] = bbox_xyxy[:, 2] - bbox_xyxy[:, 0]\n        bbox_tlwh[:, 3] = bbox_xyxy[:, 3] - bbox_xyxy[:, 1]\n        return bbox_tlwh\n\n    def _xywh_to_xyxy(self, bbox_xywh):\n        x, y, w, h = bbox_xywh\n        x1 = max(int(x - w / 2), 0)\n        x2 = min(int(x + w / 2), self.width - 1)\n        y1 = max(int(y - h / 2), 0)\n        y2 = min(int(y + h / 2), self.height - 1)\n        return x1, y1, x2, y2\n\n    def _tlwh_to_xyxy(self, bbox_tlwh):\n        \"\"\"\n        TODO:\n            Convert bbox from xtl_ytl_w_h to xc_yc_w_h\n        Thanks JieChen91@github.com for reporting this bug!\n        \"\"\"\n        x, y, w, h = bbox_tlwh\n        x1 = max(int(x), 0)\n        x2 = min(int(x+w), self.width - 1)\n        y1 = max(int(y), 0)\n        y2 = min(int(y+h), self.height - 1)\n        return x1, y1, x2, y2\n\n    def _tlwh_to_xyxy_noclip(self, bbox_tlwh):\n        \"\"\"\n        TODO:\n            Convert bbox from xtl_ytl_w_h to xc_yc_w_h\n        Thanks JieChen91@github.com for reporting this bug!\n        \"\"\"\n        x, y, w, h = bbox_tlwh\n        x1 = x\n        x2 = x + w\n        y1 = y\n        y2 = y + h\n        return x1, y1, x2, y2\n\n    def increment_ages(self):\n        self.tracker.increment_ages()\n\n    def _xyxy_to_tlwh(self, bbox_xyxy):\n        x1, y1, x2, y2 = bbox_xyxy\n\n        t = x1\n        l = y1\n        w = int(x2 - x1)\n        h = int(y2 - y1)\n        return t, l, w, h\n\n    def _get_features(self, bbox_xywh, ori_img):\n        # im_crops = []\n        tlbrs = []\n        for box in bbox_xywh:\n            x1, y1, x2, y2 = self._tlwh_to_xyxy(box)\n            tlbrs.append(np.array([x1, y1, x2, y2], dtype=bbox_xywh.dtype))\n        tlbrs = np.stack(tlbrs, axis=0)\n        #     im = ori_img[y1:y2, x1:x2]\n        #     im_crops.append(im)\n        # if im_crops:\n        #     features = self.extractor(im_crops)\n        # else:\n        #     features = np.array([])\n        features = extract_reid_features(self.reid_model, ori_img, tlbrs)\n        features = features.cpu().numpy()\n        return features\n"
  },
  {
    "path": "trackers/deepsort_tracker/deepsort_score.py",
    "content": "import numpy as np\nimport torch\nimport cv2\nimport os\n\nfrom .reid_model_motdt import load_reid_model, extract_reid_features\nfrom trackers.deepsort_tracker import kalman_filter, linear_assignment_score, iou_matching, kalman_filter_score\nfrom yolox.data.dataloading import get_yolox_datadir\nfrom .detection import Detection\nfrom .track_score import Track\n\n\ndef _cosine_distance(a, b, data_is_normalized=False):\n    if not data_is_normalized:\n        a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)\n        b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)\n    return 1. - np.dot(a, b.T)\n\n\ndef _nn_cosine_distance(x, y):\n    distances = _cosine_distance(x, y)\n    return distances.min(axis=0)\n\n\nclass Tracker:\n    def __init__(self, metric, max_iou_distance=0.7, max_age=70, n_init=3):\n        self.metric = metric\n        self.max_iou_distance = max_iou_distance\n        self.max_age = max_age\n        self.n_init = n_init\n\n        self.kf = kalman_filter.KalmanFilter()\n        self.kf_score = kalman_filter_score.KalmanFilter_score()\n        self.tracks = []\n        self._next_id = 1\n\n    def predict(self):\n        \"\"\"Propagate track state distributions one time step forward.\n        This function should be called once every time step, before `update`.\n        \"\"\"\n        for track in self.tracks:\n            track.predict(self.kf, self.kf_score)\n\n    def increment_ages(self):\n        for track in self.tracks:\n            track.increment_age()\n            track.mark_missed()\n\n    def update(self, detections, classes):\n        \"\"\"Perform measurement update and track management.\n        Parameters\n        ----------\n        detections : List[deep_sort.detection.Detection]\n            A list of detections at the current time step.\n        \"\"\"\n        # Run matching cascade.\n        matches, unmatched_tracks, unmatched_detections = \\\n            self._match(detections)\n\n        # Update track set.\n        for track_idx, detection_idx in matches:\n            self.tracks[track_idx].update(\n                self.kf, self.kf_score, detections[detection_idx])\n        for track_idx in unmatched_tracks:\n            self.tracks[track_idx].mark_missed()\n        for detection_idx in unmatched_detections:\n            self._initiate_track(detections[detection_idx], classes[detection_idx].item())\n        self.tracks = [t for t in self.tracks if not t.is_deleted()]\n\n        # Update distance metric.\n        active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]\n        features, targets = [], []\n        for track in self.tracks:\n            if not track.is_confirmed():\n                continue\n            features += track.features\n            targets += [track.track_id for _ in track.features]\n            track.features = []\n        self.metric.partial_fit(\n            np.asarray(features), np.asarray(targets), active_targets)\n\n    def _match(self, detections):\n\n        def gated_metric(tracks, dets, track_indices, detection_indices):\n            features = np.array([dets[i].feature for i in detection_indices])\n            targets = np.array([tracks[i].track_id for i in track_indices])\n            cost_matrix = self.metric.distance(features, targets)\n            cost_matrix = linear_assignment_score.gate_cost_matrix(\n                self.kf, cost_matrix, tracks, dets, track_indices,\n                detection_indices)\n\n            return cost_matrix\n\n        # Split track set into confirmed and unconfirmed tracks.\n        confirmed_tracks = [\n            i for i, t in enumerate(self.tracks) if t.is_confirmed()]\n        unconfirmed_tracks = [\n            i for i, t in enumerate(self.tracks) if not t.is_confirmed()]\n\n        # Associate confirmed tracks using appearance features.\n        matches_a, unmatched_tracks_a, unmatched_detections = \\\n            linear_assignment_score.matching_cascade(\n                gated_metric, self.metric.matching_threshold, self.max_age,\n                self.tracks, detections, confirmed_tracks)\n\n        # Associate remaining tracks together with unconfirmed tracks using IOU.\n        iou_track_candidates = unconfirmed_tracks + [\n            k for k in unmatched_tracks_a if\n            self.tracks[k].time_since_update == 1]\n        unmatched_tracks_a = [\n            k for k in unmatched_tracks_a if\n            self.tracks[k].time_since_update != 1]\n        matches_b, unmatched_tracks_b, unmatched_detections = \\\n            linear_assignment_score.min_cost_matching(\n                iou_matching.iou_cost, self.max_iou_distance, self.tracks,\n                detections, iou_track_candidates, unmatched_detections)\n\n        matches = matches_a + matches_b\n        unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))\n        return matches, unmatched_tracks, unmatched_detections\n\n    def _initiate_track(self, detection, class_id):\n        mean, covariance = self.kf.initiate(detection.to_xyah())\n        mean_score, covariance_score = self.kf_score.initiate(detection.confidence)\n        self.tracks.append(Track(\n            mean, covariance, mean_score, covariance_score, self._next_id, class_id, self.n_init, self.max_age,\n            detection.feature))\n        self._next_id += 1\n\n\nclass NearestNeighborDistanceMetric(object):\n    def __init__(self, metric, matching_threshold, budget=None):\n\n        if metric == \"cosine\":\n            self._metric = _nn_cosine_distance\n        else:\n            raise ValueError(\n                \"Invalid metric; must be either 'euclidean' or 'cosine'\")\n        self.matching_threshold = matching_threshold\n        self.budget = budget\n        self.samples = {}\n\n    def partial_fit(self, features, targets, active_targets):\n        for feature, target in zip(features, targets):\n            self.samples.setdefault(target, []).append(feature)\n            if self.budget is not None:\n                self.samples[target] = self.samples[target][-self.budget:]\n        self.samples = {k: self.samples[k] for k in active_targets}\n\n    def distance(self, features, targets):\n        cost_matrix = np.zeros((len(targets), len(features)))\n        for i, target in enumerate(targets):\n            cost_matrix[i, :] = self._metric(self.samples[target], features)\n        return cost_matrix\n\n\nclass DeepSort_score(object):\n    def __init__(self, model_path, args, max_dist=0.1, min_confidence=0.3, nms_max_overlap=1.0, max_iou_distance=0.7, max_age=30, n_init=3, nn_budget=100, use_cuda=True):\n        self.min_confidence = min_confidence\n        self.nms_max_overlap = nms_max_overlap\n\n        # self.extractor = Extractor(model_path, use_cuda=use_cuda)\n        self.reid_model = load_reid_model(model_path)\n\n        max_cosine_distance = max_dist\n        metric = NearestNeighborDistanceMetric(\n            \"cosine\", max_cosine_distance, nn_budget)\n        self.tracker = Tracker(\n            metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init)\n        self.args=args\n\n    def update(self, output_results, img_info, img_size, img_file_name):\n        if self.args.dataset == 'mot17':\n            img_file_name = os.path.join(get_yolox_datadir(), 'mot', 'train', img_file_name)\n        else:\n            img_file_name = os.path.join(get_yolox_datadir(), 'dancetrack', 'val', img_file_name)\n        ori_img = cv2.imread(img_file_name)\n        self.height, self.width = ori_img.shape[:2]\n        # post process detections\n        output_results = output_results.cpu().numpy()\n        confidences = output_results[:, 4] * output_results[:, 5]\n        \n        bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n        bbox_xyxy = bboxes\n        bbox_tlwh = self._xyxy_to_tlwh_array(bbox_xyxy)\n        remain_inds = confidences > self.min_confidence\n        bbox_tlwh = bbox_tlwh[remain_inds]\n        confidences = confidences[remain_inds]\n\n        # generate detections\n        features = self._get_features(bbox_tlwh, ori_img)\n        detections = [Detection(bbox_tlwh[i], conf, features[i]) for i, conf in enumerate(\n            confidences) if conf > self.min_confidence]\n        classes = np.zeros((len(detections), ))\n\n        # run on non-maximum supression\n        boxes = np.array([d.tlwh for d in detections])\n        scores = np.array([d.confidence for d in detections])\n\n        # update tracker\n        self.tracker.predict()\n        self.tracker.update(detections, classes)\n\n        # output bbox identities\n        outputs = []\n        for track in self.tracker.tracks:\n            if not track.is_confirmed() or track.time_since_update > 1:\n                continue\n            box = track.to_tlwh()\n            x1, y1, x2, y2 = self._tlwh_to_xyxy_noclip(box)\n            track_id = track.track_id\n            class_id = track.class_id\n            outputs.append(np.array([x1, y1, x2, y2, track_id, class_id], dtype=np.int))\n        if len(outputs) > 0:\n            outputs = np.stack(outputs, axis=0)\n        return outputs\n\n    \"\"\"\n    TODO:\n        Convert bbox from xc_yc_w_h to xtl_ytl_w_h\n    Thanks JieChen91@github.com for reporting this bug!\n    \"\"\"\n    @staticmethod\n    def _xywh_to_tlwh(bbox_xywh):\n        if isinstance(bbox_xywh, np.ndarray):\n            bbox_tlwh = bbox_xywh.copy()\n        elif isinstance(bbox_xywh, torch.Tensor):\n            bbox_tlwh = bbox_xywh.clone()\n        bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2] / 2.\n        bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3] / 2.\n        return bbox_tlwh\n    \n    @staticmethod\n    def _xyxy_to_tlwh_array(bbox_xyxy):\n        if isinstance(bbox_xyxy, np.ndarray):\n            bbox_tlwh = bbox_xyxy.copy()\n        elif isinstance(bbox_xyxy, torch.Tensor):\n            bbox_tlwh = bbox_xyxy.clone()\n        bbox_tlwh[:, 2] = bbox_xyxy[:, 2] - bbox_xyxy[:, 0]\n        bbox_tlwh[:, 3] = bbox_xyxy[:, 3] - bbox_xyxy[:, 1]\n        return bbox_tlwh\n\n    def _xywh_to_xyxy(self, bbox_xywh):\n        x, y, w, h = bbox_xywh\n        x1 = max(int(x - w / 2), 0)\n        x2 = min(int(x + w / 2), self.width - 1)\n        y1 = max(int(y - h / 2), 0)\n        y2 = min(int(y + h / 2), self.height - 1)\n        return x1, y1, x2, y2\n\n    def _tlwh_to_xyxy(self, bbox_tlwh):\n        \"\"\"\n        TODO:\n            Convert bbox from xtl_ytl_w_h to xc_yc_w_h\n        Thanks JieChen91@github.com for reporting this bug!\n        \"\"\"\n        x, y, w, h = bbox_tlwh\n        x1 = max(int(x), 0)\n        x2 = min(int(x+w), self.width - 1)\n        y1 = max(int(y), 0)\n        y2 = min(int(y+h), self.height - 1)\n        return x1, y1, x2, y2\n\n    def _tlwh_to_xyxy_noclip(self, bbox_tlwh):\n        \"\"\"\n        TODO:\n            Convert bbox from xtl_ytl_w_h to xc_yc_w_h\n        Thanks JieChen91@github.com for reporting this bug!\n        \"\"\"\n        x, y, w, h = bbox_tlwh\n        x1 = x\n        x2 = x + w\n        y1 = y\n        y2 = y + h\n        return x1, y1, x2, y2\n\n    def increment_ages(self):\n        self.tracker.increment_ages()\n\n    def _xyxy_to_tlwh(self, bbox_xyxy):\n        x1, y1, x2, y2 = bbox_xyxy\n\n        t = x1\n        l = y1\n        w = int(x2 - x1)\n        h = int(y2 - y1)\n        return t, l, w, h\n\n    def _get_features(self, bbox_xywh, ori_img):\n        # im_crops = []\n        tlbrs = []\n        for box in bbox_xywh:\n            x1, y1, x2, y2 = self._tlwh_to_xyxy(box)\n            tlbrs.append(np.array([x1, y1, x2, y2], dtype=bbox_xywh.dtype))\n        tlbrs = np.stack(tlbrs, axis=0)\n        #     im = ori_img[y1:y2, x1:x2]\n        #     im_crops.append(im)\n        # if im_crops:\n        #     features = self.extractor(im_crops)\n        # else:\n        #     features = np.array([])\n        features = extract_reid_features(self.reid_model, ori_img, tlbrs)\n        features = features.cpu().numpy()\n        return features\n"
  },
  {
    "path": "trackers/deepsort_tracker/detection.py",
    "content": "# vim: expandtab:ts=4:sw=4\nimport numpy as np\n\n\nclass Detection(object):\n    \"\"\"\n    This class represents a bounding box detection in a single image.\n    Parameters\n    ----------\n    tlwh : array_like\n        Bounding box in format `(x, y, w, h)`.\n    confidence : float\n        Detector confidence score.\n    feature : array_like\n        A feature vector that describes the object contained in this image.\n    Attributes\n    ----------\n    tlwh : ndarray\n        Bounding box in format `(top left x, top left y, width, height)`.\n    confidence : ndarray\n        Detector confidence score.\n    feature : ndarray | NoneType\n        A feature vector that describes the object contained in this image.\n    \"\"\"\n\n    def __init__(self, tlwh, confidence, feature):\n        self.tlwh = np.asarray(tlwh, dtype=np.float)\n        self.confidence = float(confidence)\n        self.feature = np.asarray(feature, dtype=np.float32)\n\n    def to_tlbr(self):\n        \"\"\"Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,\n        `(top left, bottom right)`.\n        \"\"\"\n        ret = self.tlwh.copy()\n        ret[2:] += ret[:2]\n        return ret\n\n    def to_xyah(self):\n        \"\"\"Convert bounding box to format `(center x, center y, aspect ratio,\n        height)`, where the aspect ratio is `width / height`.\n        \"\"\"\n        ret = self.tlwh.copy()\n        ret[:2] += ret[2:] / 2\n        ret[2] /= ret[3]\n        return ret"
  },
  {
    "path": "trackers/deepsort_tracker/iou_matching.py",
    "content": "# vim: expandtab:ts=4:sw=4\nfrom __future__ import absolute_import\nimport numpy as np\nfrom trackers.deepsort_tracker import linear_assignment\n\n\ndef iou(bbox, candidates):\n    \"\"\"Computer intersection over union.\n    Parameters\n    ----------\n    bbox : ndarray\n        A bounding box in format `(top left x, top left y, width, height)`.\n    candidates : ndarray\n        A matrix of candidate bounding boxes (one per row) in the same format\n        as `bbox`.\n    Returns\n    -------\n    ndarray\n        The intersection over union in [0, 1] between the `bbox` and each\n        candidate. A higher score means a larger fraction of the `bbox` is\n        occluded by the candidate.\n    \"\"\"\n    bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]\n    candidates_tl = candidates[:, :2]\n    candidates_br = candidates[:, :2] + candidates[:, 2:]\n\n    tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis],\n               np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]]\n    br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis],\n               np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]]\n    wh = np.maximum(0., br - tl)\n\n    area_intersection = wh.prod(axis=1)\n    area_bbox = bbox[2:].prod()\n    area_candidates = candidates[:, 2:].prod(axis=1)\n    return area_intersection / (area_bbox + area_candidates - area_intersection)\n\ndef hmiou(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    bboxes1 = np.expand_dims(bboxes1, 0)\n    bboxes1[..., 2:] += bboxes1[..., 0:2]\n    bboxes2[..., 2:] += bboxes2[..., 0:2]\n\n    ious = np.zeros((len(bboxes1), len(bboxes2)), dtype=np.float)\n    if ious.size == 0:\n        return ious\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 0)\n\n    yy11 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    yy12 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n\n    yy21 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    yy22 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n    o = (yy12 - yy11) / (yy22 - yy21)\n\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    iou = wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])\n                + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)\n\n    # xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])\n    # yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    # xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])\n    # yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n    # wc = xxc2 - xxc1\n    # hc = yyc2 - yyc1\n    # assert ((wc > 0).all() and (hc > 0).all())\n    # area_enclose = wc * hc\n    # # giou = iou - (area_enclose - wh) / area_enclose\n    # # giou = (giou + 1.) / 2.0  # resize from (-1,1) to (0,1)\n    # # giou = wh / area_enclose\n    # # giou = (iou + wh / area_enclose) / 2.0\n    # giou = iou\n    # # giou[wc <= 0] = 0\n    # # giou[hc <= 0] = 0\n    iou *= o\n    return iou\n\ndef iou_cost(tracks, detections, track_indices=None,\n             detection_indices=None):\n    \"\"\"An intersection over union distance metric.\n    Parameters\n    ----------\n    tracks : List[deep_sort.track.Track]\n        A list of tracks.\n    detections : List[deep_sort.detection.Detection]\n        A list of detections.\n    track_indices : Optional[List[int]]\n        A list of indices to tracks that should be matched. Defaults to\n        all `tracks`.\n    detection_indices : Optional[List[int]]\n        A list of indices to detections that should be matched. Defaults\n        to all `detections`.\n    Returns\n    -------\n    ndarray\n        Returns a cost matrix of shape\n        len(track_indices), len(detection_indices) where entry (i, j) is\n        `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`.\n    \"\"\"\n    if track_indices is None:\n        track_indices = np.arange(len(tracks))\n    if detection_indices is None:\n        detection_indices = np.arange(len(detections))\n\n    cost_matrix = np.zeros((len(track_indices), len(detection_indices)))\n    for row, track_idx in enumerate(track_indices):\n        if tracks[track_idx].time_since_update > 1:\n            cost_matrix[row, :] = linear_assignment.INFTY_COST\n            continue\n\n        bbox = tracks[track_idx].to_tlwh()\n        candidates = np.asarray(\n            [detections[i].tlwh for i in detection_indices])\n        cost_matrix[row, :] = 1. - iou(bbox, candidates)\n    return cost_matrix\n\ndef hmiou_cost(tracks, detections, track_indices=None,\n             detection_indices=None):\n    \"\"\"An intersection over union distance metric.\n    Parameters\n    ----------\n    tracks : List[deep_sort.track.Track]\n        A list of tracks.\n    detections : List[deep_sort.detection.Detection]\n        A list of detections.\n    track_indices : Optional[List[int]]\n        A list of indices to tracks that should be matched. Defaults to\n        all `tracks`.\n    detection_indices : Optional[List[int]]\n        A list of indices to detections that should be matched. Defaults\n        to all `detections`.\n    Returns\n    -------\n    ndarray\n        Returns a cost matrix of shape\n        len(track_indices), len(detection_indices) where entry (i, j) is\n        `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`.\n    \"\"\"\n    if track_indices is None:\n        track_indices = np.arange(len(tracks))\n    if detection_indices is None:\n        detection_indices = np.arange(len(detections))\n\n    cost_matrix = np.zeros((len(track_indices), len(detection_indices)))\n    for row, track_idx in enumerate(track_indices):\n        if tracks[track_idx].time_since_update > 1:\n            cost_matrix[row, :] = linear_assignment.INFTY_COST\n            continue\n\n        bbox = tracks[track_idx].to_tlwh()\n        candidates = np.asarray(\n            [detections[i].tlwh for i in detection_indices])\n        cost_matrix[row, :] = 1. - hmiou(bbox, candidates)\n    return cost_matrix"
  },
  {
    "path": "trackers/deepsort_tracker/kalman_filter.py",
    "content": "# vim: expandtab:ts=4:sw=4\nimport numpy as np\nimport scipy.linalg\n\n\n\"\"\"\nTable for the 0.95 quantile of the chi-square distribution with N degrees of\nfreedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv\nfunction and used as Mahalanobis gating threshold.\n\"\"\"\nchi2inv95 = {\n    1: 3.8415,\n    2: 5.9915,\n    3: 7.8147,\n    4: 9.4877,\n    5: 11.070,\n    6: 12.592,\n    7: 14.067,\n    8: 15.507,\n    9: 16.919}\n\n\nclass KalmanFilter(object):\n    \"\"\"\n    A simple Kalman filter for tracking bounding boxes in image space.\n    The 8-dimensional state space\n        x, y, a, h, vx, vy, va, vh\n    contains the bounding box center position (x, y), aspect ratio a, height h,\n    and their respective velocities.\n    Object motion follows a constant velocity model. The bounding box location\n    (x, y, a, h) is taken as direct observation of the state space (linear\n    observation model).\n    \"\"\"\n\n    def __init__(self):\n        ndim, dt = 4, 1.\n\n        # Create Kalman filter model matrices.\n        self._motion_mat = np.eye(2 * ndim, 2 * ndim)\n        for i in range(ndim):\n            self._motion_mat[i, ndim + i] = dt\n        self._update_mat = np.eye(ndim, 2 * ndim)\n\n        # Motion and observation uncertainty are chosen relative to the current\n        # state estimate. These weights control the amount of uncertainty in\n        # the model. This is a bit hacky.\n        self._std_weight_position = 1. / 20\n        self._std_weight_velocity = 1. / 160\n\n    def initiate(self, measurement):\n        \"\"\"Create track from unassociated measurement.\n        Parameters\n        ----------\n        measurement : ndarray\n            Bounding box coordinates (x, y, a, h) with center position (x, y),\n            aspect ratio a, and height h.\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector (8 dimensional) and covariance matrix (8x8\n            dimensional) of the new track. Unobserved velocities are initialized\n            to 0 mean.\n        \"\"\"\n        mean_pos = measurement\n        mean_vel = np.zeros_like(mean_pos)\n        mean = np.r_[mean_pos, mean_vel]\n\n        std = [\n            2 * self._std_weight_position * measurement[3],\n            2 * self._std_weight_position * measurement[3],\n            1e-2,\n            2 * self._std_weight_position * measurement[3],\n            10 * self._std_weight_velocity * measurement[3],\n            10 * self._std_weight_velocity * measurement[3],\n            1e-5,\n            10 * self._std_weight_velocity * measurement[3]]\n        covariance = np.diag(np.square(std))\n        return mean, covariance\n\n    def predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step.\n        Parameters\n        ----------\n        mean : ndarray\n            The 8 dimensional mean vector of the object state at the previous\n            time step.\n        covariance : ndarray\n            The 8x8 dimensional covariance matrix of the object state at the\n            previous time step.\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n        \"\"\"\n        std_pos = [\n            self._std_weight_position * mean[3],\n            self._std_weight_position * mean[3],\n            1e-2,\n            self._std_weight_position * mean[3]]\n        std_vel = [\n            self._std_weight_velocity * mean[3],\n            self._std_weight_velocity * mean[3],\n            1e-5,\n            self._std_weight_velocity * mean[3]]\n        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))\n\n        mean = np.dot(self._motion_mat, mean)\n        covariance = np.linalg.multi_dot((\n            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov\n\n        return mean, covariance\n\n    def project(self, mean, covariance):\n        \"\"\"Project state distribution to measurement space.\n        Parameters\n        ----------\n        mean : ndarray\n            The state's mean vector (8 dimensional array).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the projected mean and covariance matrix of the given state\n            estimate.\n        \"\"\"\n        std = [\n            self._std_weight_position * mean[3],\n            self._std_weight_position * mean[3],\n            1e-1,\n            self._std_weight_position * mean[3]]\n        innovation_cov = np.diag(np.square(std))\n\n        mean = np.dot(self._update_mat, mean)\n        covariance = np.linalg.multi_dot((\n            self._update_mat, covariance, self._update_mat.T))\n        return mean, covariance + innovation_cov\n\n    def update(self, mean, covariance, measurement):\n        \"\"\"Run Kalman filter correction step.\n        Parameters\n        ----------\n        mean : ndarray\n            The predicted state's mean vector (8 dimensional).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n        measurement : ndarray\n            The 4 dimensional measurement vector (x, y, a, h), where (x, y)\n            is the center position, a the aspect ratio, and h the height of the\n            bounding box.\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the measurement-corrected state distribution.\n        \"\"\"\n        projected_mean, projected_cov = self.project(mean, covariance)\n\n        chol_factor, lower = scipy.linalg.cho_factor(\n            projected_cov, lower=True, check_finite=False)\n        kalman_gain = scipy.linalg.cho_solve(\n            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,\n            check_finite=False).T\n        innovation = measurement - projected_mean\n\n        new_mean = mean + np.dot(innovation, kalman_gain.T)\n        new_covariance = covariance - np.linalg.multi_dot((\n            kalman_gain, projected_cov, kalman_gain.T))\n        return new_mean, new_covariance\n\n    def gating_distance(self, mean, covariance, measurements,\n                        only_position=False):\n        \"\"\"Compute gating distance between state distribution and measurements.\n        A suitable distance threshold can be obtained from `chi2inv95`. If\n        `only_position` is False, the chi-square distribution has 4 degrees of\n        freedom, otherwise 2.\n        Parameters\n        ----------\n        mean : ndarray\n            Mean vector over the state distribution (8 dimensional).\n        covariance : ndarray\n            Covariance of the state distribution (8x8 dimensional).\n        measurements : ndarray\n            An Nx4 dimensional matrix of N measurements, each in\n            format (x, y, a, h) where (x, y) is the bounding box center\n            position, a the aspect ratio, and h the height.\n        only_position : Optional[bool]\n            If True, distance computation is done with respect to the bounding\n            box center position only.\n        Returns\n        -------\n        ndarray\n            Returns an array of length N, where the i-th element contains the\n            squared Mahalanobis distance between (mean, covariance) and\n            `measurements[i]`.\n        \"\"\"\n        mean, covariance = self.project(mean, covariance)\n        if only_position:\n            mean, covariance = mean[:2], covariance[:2, :2]\n            measurements = measurements[:, :2]\n\n        cholesky_factor = np.linalg.cholesky(covariance)\n        d = measurements - mean\n        z = scipy.linalg.solve_triangular(\n            cholesky_factor, d.T, lower=True, check_finite=False,\n            overwrite_b=True)\n        squared_maha = np.sum(z * z, axis=0)\n        return squared_maha"
  },
  {
    "path": "trackers/deepsort_tracker/kalman_filter_score.py",
    "content": "# vim: expandtab:ts=4:sw=4\nimport numpy as np\nimport scipy.linalg\n\n\n\"\"\"\nTable for the 0.95 quantile of the chi-square distribution with N degrees of\nfreedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv\nfunction and used as Mahalanobis gating threshold.\n\"\"\"\nchi2inv95 = {\n    1: 3.8415,\n    2: 5.9915,\n    3: 7.8147,\n    4: 9.4877,\n    5: 11.070,\n    6: 12.592,\n    7: 14.067,\n    8: 15.507,\n    9: 16.919}\n\n\nclass KalmanFilter_score(object):\n    \"\"\"\n    A simple Kalman filter for tracking bounding boxes in image space.\n\n    The 8-dimensional state space\n\n        x, y, a, h, vx, vy, va, vh\n        修改为 score, vscore\n\n    contains the bounding box center position (x, y), aspect ratio a, height h,\n    and their respective velocities.\n\n    Object motion follows a constant velocity model. The bounding box location\n    (x, y, a, h) is taken as direct observation of the state space (linear\n    observation model).\n\n    \"\"\"\n\n    def __init__(self):\n        ndim, dt = 1, 1.\n\n        # Create Kalman filter model matrices.\n        self._motion_mat = np.eye(2 * ndim, 2 * ndim)\n        for i in range(ndim):\n            self._motion_mat[i, ndim + i] = dt\n        self._update_mat = np.eye(ndim, 2 * ndim)\n\n        # Motion and observation uncertainty are chosen relative to the current\n        # state estimate. These weights control the amount of uncertainty in\n        # the model. This is a bit hacky.\n        self._std_weight_position = 1. / 20\n        self._std_weight_velocity = 1. / 160\n\n    def initiate(self, measurement):\n        \"\"\"Create track from unassociated measurement.\n\n        Parameters\n        ----------\n        measurement : ndarray\n            Bounding box coordinates (x, y, a, h) with center position (x, y),\n            aspect ratio a, and height h.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector (8 dimensional) and covariance matrix (8x8\n            dimensional) of the new track. Unobserved velocities are initialized\n            to 0 mean.\n\n        \"\"\"\n        mean_pos = measurement\n        mean_vel = np.zeros_like(mean_pos)\n        mean = np.r_[mean_pos, mean_vel]\n\n        # std = [\n        #     2 * self._std_weight_position * measurement[3],\n        #     2 * self._std_weight_position * measurement[3],\n        #     1e-2,\n        #     2 * self._std_weight_position * measurement[3],\n        #     10 * self._std_weight_velocity * measurement[3],\n        #     10 * self._std_weight_velocity * measurement[3],\n        #     1e-5,\n        #     10 * self._std_weight_velocity * measurement[3]]\n        std = [\n            1e-2,\n            1e-5]\n        covariance = np.diag(np.square(std))\n        return mean, covariance\n\n    def predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The 8 dimensional mean vector of the object state at the previous\n            time step.\n        covariance : ndarray\n            The 8x8 dimensional covariance matrix of the object state at the\n            previous time step.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n\n        \"\"\"\n        std_pos = [\n            1e-2]\n        std_vel = [\n            1e-5]\n        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))\n\n        #mean = np.dot(self._motion_mat, mean)\n        mean = np.dot(mean, self._motion_mat.T)\n        covariance = np.linalg.multi_dot((\n            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov\n\n        return mean, covariance\n\n    def project(self, mean, covariance):\n        \"\"\"Project state distribution to measurement space.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The state's mean vector (8 dimensional array).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the projected mean and covariance matrix of the given state\n            estimate.\n\n        \"\"\"\n        std = [\n            1e-1]\n        innovation_cov = np.diag(np.square(std))\n\n        mean = np.dot(self._update_mat, mean)\n        covariance = np.linalg.multi_dot((\n            self._update_mat, covariance, self._update_mat.T))\n        return mean, covariance + innovation_cov\n\n    def multi_predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step (Vectorized version).\n        Parameters\n        ----------\n        mean : ndarray\n            The Nx8 dimensional mean matrix of the object states at the previous\n            time step.\n        covariance : ndarray\n            The Nx8x8 dimensional covariance matrics of the object states at the\n            previous time step.\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n        \"\"\"\n        std_pos = [\n            1e-2 * np.ones_like(mean[:, 0])]\n        std_vel = [\n            1e-5 * np.ones_like(mean[:, 0])]\n        sqr = np.square(np.r_[std_pos, std_vel]).T\n\n        motion_cov = []\n        for i in range(len(mean)):\n            motion_cov.append(np.diag(sqr[i]))\n        motion_cov = np.asarray(motion_cov)\n\n        mean = np.dot(mean, self._motion_mat.T)\n        left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))\n        covariance = np.dot(left, self._motion_mat.T) + motion_cov\n\n        return mean, covariance\n\n    def update(self, mean, covariance, measurement):\n        \"\"\"Run Kalman filter correction step.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The predicted state's mean vector (8 dimensional).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n        measurement : ndarray\n            The 4 dimensional measurement vector (x, y, a, h), where (x, y)\n            is the center position, a the aspect ratio, and h the height of the\n            bounding box.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the measurement-corrected state distribution.\n\n        \"\"\"\n        projected_mean, projected_cov = self.project(mean, covariance)\n\n        chol_factor, lower = scipy.linalg.cho_factor(\n            projected_cov, lower=True, check_finite=False)\n        kalman_gain = scipy.linalg.cho_solve(\n            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,\n            check_finite=False).T\n        innovation = measurement - projected_mean\n\n        new_mean = mean + np.dot(innovation, kalman_gain.T)\n        new_covariance = covariance - np.linalg.multi_dot((\n            kalman_gain, projected_cov, kalman_gain.T))\n        return new_mean, new_covariance\n\n    def gating_distance(self, mean, covariance, measurements,\n                        only_position=False, metric='maha'):\n        \"\"\"Compute gating distance between state distribution and measurements.\n        A suitable distance threshold can be obtained from `chi2inv95`. If\n        `only_position` is False, the chi-square distribution has 4 degrees of\n        freedom, otherwise 2.\n        Parameters\n        ----------\n        mean : ndarray\n            Mean vector over the state distribution (8 dimensional).\n        covariance : ndarray\n            Covariance of the state distribution (8x8 dimensional).\n        measurements : ndarray\n            An Nx4 dimensional matrix of N measurements, each in\n            format (x, y, a, h) where (x, y) is the bounding box center\n            position, a the aspect ratio, and h the height.\n        only_position : Optional[bool]\n            If True, distance computation is done with respect to the bounding\n            box center position only.\n        Returns\n        -------\n        ndarray\n            Returns an array of length N, where the i-th element contains the\n            squared Mahalanobis distance between (mean, covariance) and\n            `measurements[i]`.\n        \"\"\"\n        mean, covariance = self.project(mean, covariance)\n        if only_position:\n            mean, covariance = mean[:2], covariance[:2, :2]\n            measurements = measurements[:, :2]\n\n        d = measurements - mean\n        if metric == 'gaussian':\n            return np.sum(d * d, axis=1)\n        elif metric == 'maha':\n            cholesky_factor = np.linalg.cholesky(covariance)\n            z = scipy.linalg.solve_triangular(\n                cholesky_factor, d.T, lower=True, check_finite=False,\n                overwrite_b=True)\n            squared_maha = np.sum(z * z, axis=0)\n            return squared_maha\n        else:\n            raise ValueError('invalid distance metric')"
  },
  {
    "path": "trackers/deepsort_tracker/linear_assignment.py",
    "content": "from __future__ import absolute_import\nimport numpy as np\n# from sklearn.utils.linear_assignment_ import linear_assignment\nfrom scipy.optimize import linear_sum_assignment as linear_assignment\nfrom trackers.deepsort_tracker import kalman_filter\n\n\nINFTY_COST = 1e+5\n\n\ndef min_cost_matching(\n        distance_metric, max_distance, tracks, detections, track_indices=None,\n        detection_indices=None):\n    \"\"\"Solve linear assignment problem.\n    Parameters\n    ----------\n    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray\n        The distance metric is given a list of tracks and detections as well as\n        a list of N track indices and M detection indices. The metric should\n        return the NxM dimensional cost matrix, where element (i, j) is the\n        association cost between the i-th track in the given track indices and\n        the j-th detection in the given detection_indices.\n    max_distance : float\n        Gating threshold. Associations with cost larger than this value are\n        disregarded.\n    tracks : List[track.Track]\n        A list of predicted tracks at the current time step.\n    detections : List[detection.Detection]\n        A list of detections at the current time step.\n    track_indices : List[int]\n        List of track indices that maps rows in `cost_matrix` to tracks in\n        `tracks` (see description above).\n    detection_indices : List[int]\n        List of detection indices that maps columns in `cost_matrix` to\n        detections in `detections` (see description above).\n    Returns\n    -------\n    (List[(int, int)], List[int], List[int])\n        Returns a tuple with the following three entries:\n        * A list of matched track and detection indices.\n        * A list of unmatched track indices.\n        * A list of unmatched detection indices.\n    \"\"\"\n    if track_indices is None:\n        track_indices = np.arange(len(tracks))\n    if detection_indices is None:\n        detection_indices = np.arange(len(detections))\n\n    if len(detection_indices) == 0 or len(track_indices) == 0:\n        return [], track_indices, detection_indices  # Nothing to match.\n\n    cost_matrix = distance_metric(\n        tracks, detections, track_indices, detection_indices)\n    cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5\n\n    row_indices, col_indices = linear_assignment(cost_matrix)\n\n    matches, unmatched_tracks, unmatched_detections = [], [], []\n    for col, detection_idx in enumerate(detection_indices):\n        if col not in col_indices:\n            unmatched_detections.append(detection_idx)\n    for row, track_idx in enumerate(track_indices):\n        if row not in row_indices:\n            unmatched_tracks.append(track_idx)\n    for row, col in zip(row_indices, col_indices):\n        track_idx = track_indices[row]\n        detection_idx = detection_indices[col]\n        if cost_matrix[row, col] > max_distance:\n            unmatched_tracks.append(track_idx)\n            unmatched_detections.append(detection_idx)\n        else:\n            matches.append((track_idx, detection_idx))\n    return matches, unmatched_tracks, unmatched_detections\n\n\ndef matching_cascade(\n        distance_metric, max_distance, cascade_depth, tracks, detections,\n        track_indices=None, detection_indices=None):\n    \"\"\"Run matching cascade.\n    Parameters\n    ----------\n    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray\n        The distance metric is given a list of tracks and detections as well as\n        a list of N track indices and M detection indices. The metric should\n        return the NxM dimensional cost matrix, where element (i, j) is the\n        association cost between the i-th track in the given track indices and\n        the j-th detection in the given detection indices.\n    max_distance : float\n        Gating threshold. Associations with cost larger than this value are\n        disregarded.\n    cascade_depth: int\n        The cascade depth, should be se to the maximum track age.\n    tracks : List[track.Track]\n        A list of predicted tracks at the current time step.\n    detections : List[detection.Detection]\n        A list of detections at the current time step.\n    track_indices : Optional[List[int]]\n        List of track indices that maps rows in `cost_matrix` to tracks in\n        `tracks` (see description above). Defaults to all tracks.\n    detection_indices : Optional[List[int]]\n        List of detection indices that maps columns in `cost_matrix` to\n        detections in `detections` (see description above). Defaults to all\n        detections.\n    Returns\n    -------\n    (List[(int, int)], List[int], List[int])\n        Returns a tuple with the following three entries:\n        * A list of matched track and detection indices.\n        * A list of unmatched track indices.\n        * A list of unmatched detection indices.\n    \"\"\"\n    if track_indices is None:\n        track_indices = list(range(len(tracks)))\n    if detection_indices is None:\n        detection_indices = list(range(len(detections)))\n\n    unmatched_detections = detection_indices\n    matches = []\n    for level in range(cascade_depth):\n        if len(unmatched_detections) == 0:  # No detections left\n            break\n\n        track_indices_l = [\n            k for k in track_indices\n            if tracks[k].time_since_update == 1 + level\n        ]\n        if len(track_indices_l) == 0:  # Nothing to match at this level\n            continue\n\n        matches_l, _, unmatched_detections = \\\n            min_cost_matching(\n                distance_metric, max_distance, tracks, detections,\n                track_indices_l, unmatched_detections)\n        matches += matches_l\n    unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))\n    return matches, unmatched_tracks, unmatched_detections\n\n\ndef gate_cost_matrix(\n        kf, cost_matrix, tracks, detections, track_indices, detection_indices,\n        gated_cost=INFTY_COST, only_position=False):\n    \"\"\"Invalidate infeasible entries in cost matrix based on the state\n    distributions obtained by Kalman filtering.\n    Parameters\n    ----------\n    kf : The Kalman filter.\n    cost_matrix : ndarray\n        The NxM dimensional cost matrix, where N is the number of track indices\n        and M is the number of detection indices, such that entry (i, j) is the\n        association cost between `tracks[track_indices[i]]` and\n        `detections[detection_indices[j]]`.\n    tracks : List[track.Track]\n        A list of predicted tracks at the current time step.\n    detections : List[detection.Detection]\n        A list of detections at the current time step.\n    track_indices : List[int]\n        List of track indices that maps rows in `cost_matrix` to tracks in\n        `tracks` (see description above).\n    detection_indices : List[int]\n        List of detection indices that maps columns in `cost_matrix` to\n        detections in `detections` (see description above).\n    gated_cost : Optional[float]\n        Entries in the cost matrix corresponding to infeasible associations are\n        set this value. Defaults to a very large value.\n    only_position : Optional[bool]\n        If True, only the x, y position of the state distribution is considered\n        during gating. Defaults to False.\n    Returns\n    -------\n    ndarray\n        Returns the modified cost matrix.\n    \"\"\"\n    gating_dim = 2 if only_position else 4\n    gating_threshold = kalman_filter.chi2inv95[gating_dim]\n    measurements = np.asarray(\n        [detections[i].to_xyah() for i in detection_indices])\n    for row, track_idx in enumerate(track_indices):\n        track = tracks[track_idx]\n        gating_distance = kf.gating_distance(\n            track.mean, track.covariance, measurements, only_position)\n        cost_matrix[row, gating_distance > gating_threshold] = gated_cost\n    return cost_matrix"
  },
  {
    "path": "trackers/deepsort_tracker/linear_assignment_score.py",
    "content": "from __future__ import absolute_import\nimport numpy as np\n# from sklearn.utils.linear_assignment_ import linear_assignment\nfrom scipy.optimize import linear_sum_assignment as linear_assignment\nfrom trackers.deepsort_tracker import kalman_filter\n\n\nINFTY_COST = 1e+5\n\n\ndef min_cost_matching(\n        distance_metric, max_distance, tracks, detections, track_indices=None,\n        detection_indices=None):\n    \"\"\"Solve linear assignment problem.\n    Parameters\n    ----------\n    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray\n        The distance metric is given a list of tracks and detections as well as\n        a list of N track indices and M detection indices. The metric should\n        return the NxM dimensional cost matrix, where element (i, j) is the\n        association cost between the i-th track in the given track indices and\n        the j-th detection in the given detection_indices.\n    max_distance : float\n        Gating threshold. Associations with cost larger than this value are\n        disregarded.\n    tracks : List[track.Track]\n        A list of predicted tracks at the current time step.\n    detections : List[detection.Detection]\n        A list of detections at the current time step.\n    track_indices : List[int]\n        List of track indices that maps rows in `cost_matrix` to tracks in\n        `tracks` (see description above).\n    detection_indices : List[int]\n        List of detection indices that maps columns in `cost_matrix` to\n        detections in `detections` (see description above).\n    Returns\n    -------\n    (List[(int, int)], List[int], List[int])\n        Returns a tuple with the following three entries:\n        * A list of matched track and detection indices.\n        * A list of unmatched track indices.\n        * A list of unmatched detection indices.\n    \"\"\"\n    if track_indices is None:\n        track_indices = np.arange(len(tracks))\n    if detection_indices is None:\n        detection_indices = np.arange(len(detections))\n\n    if len(detection_indices) == 0 or len(track_indices) == 0:\n        return [], track_indices, detection_indices  # Nothing to match.\n\n    cost_matrix = distance_metric(\n        tracks, detections, track_indices, detection_indices)\n    # cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5\n    for i, track_idx in enumerate(track_indices):\n        track = tracks[track_idx]\n        det_score = np.asarray([detections[i].confidence for i in detection_indices], dtype=np.float32)\n        cost_matrix[i, :] += abs(np.clip(track.mean_score[0], 0.3, 1.0) - det_score)\n    max_distance += 0.1\n    cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5\n\n    row_indices, col_indices = linear_assignment(cost_matrix)\n\n    matches, unmatched_tracks, unmatched_detections = [], [], []\n    for col, detection_idx in enumerate(detection_indices):\n        if col not in col_indices:\n            unmatched_detections.append(detection_idx)\n    for row, track_idx in enumerate(track_indices):\n        if row not in row_indices:\n            unmatched_tracks.append(track_idx)\n    for row, col in zip(row_indices, col_indices):\n        track_idx = track_indices[row]\n        detection_idx = detection_indices[col]\n        if cost_matrix[row, col] > max_distance:\n            unmatched_tracks.append(track_idx)\n            unmatched_detections.append(detection_idx)\n        else:\n            matches.append((track_idx, detection_idx))\n    return matches, unmatched_tracks, unmatched_detections\n\n\ndef matching_cascade(\n        distance_metric, max_distance, cascade_depth, tracks, detections,\n        track_indices=None, detection_indices=None):\n    \"\"\"Run matching cascade.\n    Parameters\n    ----------\n    distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray\n        The distance metric is given a list of tracks and detections as well as\n        a list of N track indices and M detection indices. The metric should\n        return the NxM dimensional cost matrix, where element (i, j) is the\n        association cost between the i-th track in the given track indices and\n        the j-th detection in the given detection indices.\n    max_distance : float\n        Gating threshold. Associations with cost larger than this value are\n        disregarded.\n    cascade_depth: int\n        The cascade depth, should be se to the maximum track age.\n    tracks : List[track.Track]\n        A list of predicted tracks at the current time step.\n    detections : List[detection.Detection]\n        A list of detections at the current time step.\n    track_indices : Optional[List[int]]\n        List of track indices that maps rows in `cost_matrix` to tracks in\n        `tracks` (see description above). Defaults to all tracks.\n    detection_indices : Optional[List[int]]\n        List of detection indices that maps columns in `cost_matrix` to\n        detections in `detections` (see description above). Defaults to all\n        detections.\n    Returns\n    -------\n    (List[(int, int)], List[int], List[int])\n        Returns a tuple with the following three entries:\n        * A list of matched track and detection indices.\n        * A list of unmatched track indices.\n        * A list of unmatched detection indices.\n    \"\"\"\n    if track_indices is None:\n        track_indices = list(range(len(tracks)))\n    if detection_indices is None:\n        detection_indices = list(range(len(detections)))\n\n    unmatched_detections = detection_indices\n    matches = []\n    for level in range(cascade_depth):\n        if len(unmatched_detections) == 0:  # No detections left\n            break\n\n        track_indices_l = [\n            k for k in track_indices\n            if tracks[k].time_since_update == 1 + level\n        ]\n        if len(track_indices_l) == 0:  # Nothing to match at this level\n            continue\n\n        matches_l, _, unmatched_detections = \\\n            min_cost_matching(\n                distance_metric, max_distance, tracks, detections,\n                track_indices_l, unmatched_detections)\n        matches += matches_l\n    unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))\n    return matches, unmatched_tracks, unmatched_detections\n\n\ndef gate_cost_matrix(\n        kf, cost_matrix, tracks, detections, track_indices, detection_indices,\n        gated_cost=INFTY_COST, only_position=False):\n    \"\"\"Invalidate infeasible entries in cost matrix based on the state\n    distributions obtained by Kalman filtering.\n    Parameters\n    ----------\n    kf : The Kalman filter.\n    cost_matrix : ndarray\n        The NxM dimensional cost matrix, where N is the number of track indices\n        and M is the number of detection indices, such that entry (i, j) is the\n        association cost between `tracks[track_indices[i]]` and\n        `detections[detection_indices[j]]`.\n    tracks : List[track.Track]\n        A list of predicted tracks at the current time step.\n    detections : List[detection.Detection]\n        A list of detections at the current time step.\n    track_indices : List[int]\n        List of track indices that maps rows in `cost_matrix` to tracks in\n        `tracks` (see description above).\n    detection_indices : List[int]\n        List of detection indices that maps columns in `cost_matrix` to\n        detections in `detections` (see description above).\n    gated_cost : Optional[float]\n        Entries in the cost matrix corresponding to infeasible associations are\n        set this value. Defaults to a very large value.\n    only_position : Optional[bool]\n        If True, only the x, y position of the state distribution is considered\n        during gating. Defaults to False.\n    Returns\n    -------\n    ndarray\n        Returns the modified cost matrix.\n    \"\"\"\n    gating_dim = 2 if only_position else 4\n    gating_threshold = kalman_filter.chi2inv95[gating_dim]\n    measurements = np.asarray(\n        [detections[i].to_xyah() for i in detection_indices])\n    for row, track_idx in enumerate(track_indices):\n        track = tracks[track_idx]\n        gating_distance = kf.gating_distance(\n            track.mean, track.covariance, measurements, only_position)\n        cost_matrix[row, gating_distance > gating_threshold] = gated_cost\n    return cost_matrix"
  },
  {
    "path": "trackers/deepsort_tracker/reid_model.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport cv2\nimport logging\nimport torchvision.transforms as transforms\n\n\nclass BasicBlock(nn.Module):\n    def __init__(self, c_in, c_out, is_downsample=False):\n        super(BasicBlock, self).__init__()\n        self.is_downsample = is_downsample\n        if is_downsample:\n            self.conv1 = nn.Conv2d(\n                c_in, c_out, 3, stride=2, padding=1, bias=False)\n        else:\n            self.conv1 = nn.Conv2d(\n                c_in, c_out, 3, stride=1, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(c_out)\n        self.relu = nn.ReLU(True)\n        self.conv2 = nn.Conv2d(c_out, c_out, 3, stride=1,\n                               padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(c_out)\n        if is_downsample:\n            self.downsample = nn.Sequential(\n                nn.Conv2d(c_in, c_out, 1, stride=2, bias=False),\n                nn.BatchNorm2d(c_out)\n            )\n        elif c_in != c_out:\n            self.downsample = nn.Sequential(\n                nn.Conv2d(c_in, c_out, 1, stride=1, bias=False),\n                nn.BatchNorm2d(c_out)\n            )\n            self.is_downsample = True\n\n    def forward(self, x):\n        y = self.conv1(x)\n        y = self.bn1(y)\n        y = self.relu(y)\n        y = self.conv2(y)\n        y = self.bn2(y)\n        if self.is_downsample:\n            x = self.downsample(x)\n        return F.relu(x.add(y), True)\n\n\ndef make_layers(c_in, c_out, repeat_times, is_downsample=False):\n    blocks = []\n    for i in range(repeat_times):\n        if i == 0:\n            blocks += [BasicBlock(c_in, c_out, is_downsample=is_downsample), ]\n        else:\n            blocks += [BasicBlock(c_out, c_out), ]\n    return nn.Sequential(*blocks)\n\n\nclass Net(nn.Module):\n    def __init__(self, num_classes=751, reid=False):\n        super(Net, self).__init__()\n        # 3 128 64\n        self.conv = nn.Sequential(\n            nn.Conv2d(3, 64, 3, stride=1, padding=1),\n            nn.BatchNorm2d(64),\n            nn.ReLU(inplace=True),\n            # nn.Conv2d(32,32,3,stride=1,padding=1),\n            # nn.BatchNorm2d(32),\n            # nn.ReLU(inplace=True),\n            nn.MaxPool2d(3, 2, padding=1),\n        )\n        # 32 64 32\n        self.layer1 = make_layers(64, 64, 2, False)\n        # 32 64 32\n        self.layer2 = make_layers(64, 128, 2, True)\n        # 64 32 16\n        self.layer3 = make_layers(128, 256, 2, True)\n        # 128 16 8\n        self.layer4 = make_layers(256, 512, 2, True)\n        # 256 8 4\n        self.avgpool = nn.AvgPool2d((8, 4), 1)\n        # 256 1 1\n        self.reid = reid\n        self.classifier = nn.Sequential(\n            nn.Linear(512, 256),\n            nn.BatchNorm1d(256),\n            nn.ReLU(inplace=True),\n            nn.Dropout(),\n            nn.Linear(256, num_classes),\n        )\n\n    def forward(self, x):\n        x = self.conv(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n        x = self.avgpool(x)\n        x = x.view(x.size(0), -1)\n        # B x 128\n        if self.reid:\n            x = x.div(x.norm(p=2, dim=1, keepdim=True))\n            return x\n        # classifier\n        x = self.classifier(x)\n        return x\n\n\nclass Extractor(object):\n    def __init__(self, model_path, use_cuda=True):\n        self.net = Net(reid=True)\n        self.device = \"cuda\" if torch.cuda.is_available() and use_cuda else \"cpu\"\n        state_dict = torch.load(model_path, map_location=torch.device(self.device))[\n            'net_dict']\n        self.net.load_state_dict(state_dict)\n        logger = logging.getLogger(\"root.tracker\")\n        logger.info(\"Loading weights from {}... Done!\".format(model_path))\n        self.net.to(self.device)\n        self.size = (64, 128)\n        self.norm = transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n        ])\n\n    def _preprocess(self, im_crops):\n        \"\"\"\n        TODO:\n            1. to float with scale from 0 to 1\n            2. resize to (64, 128) as Market1501 dataset did\n            3. concatenate to a numpy array\n            3. to torch Tensor\n            4. normalize\n        \"\"\"\n        def _resize(im, size):\n            return cv2.resize(im.astype(np.float32)/255., size)\n\n        im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(\n            0) for im in im_crops], dim=0).float()\n        return im_batch\n\n    def __call__(self, im_crops):\n        im_batch = self._preprocess(im_crops)\n        with torch.no_grad():\n            im_batch = im_batch.to(self.device)\n            features = self.net(im_batch)\n        return features.cpu().numpy()\n\n\n"
  },
  {
    "path": "trackers/deepsort_tracker/reid_model_motdt.py",
    "content": "import cv2\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport torch.nn as nn\nimport pickle\nimport os\nfrom torch.nn.modules import CrossMapLRN2d as SpatialCrossMapLRN\n#from torch.legacy.nn import SpatialCrossMapLRN as SpatialCrossMapLRNOld\nfrom torch.autograd import Function, Variable\nfrom torch.nn import Module\n\n\ndef clip_boxes(boxes, im_shape):\n    \"\"\"\n    Clip boxes to image boundaries.\n    \"\"\"\n    boxes = np.asarray(boxes)\n    if boxes.shape[0] == 0:\n        return boxes\n    boxes = np.copy(boxes)\n    # x1 >= 0\n    boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)\n    # y1 >= 0\n    boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0)\n    # x2 < im_shape[1]\n    boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0)\n    # y2 < im_shape[0]\n    boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0)\n    return boxes\n\n\ndef load_net(fname, net, prefix='', load_state_dict=False):\n    import h5py\n    with h5py.File(fname, mode='r') as h5f:\n        h5f_is_module = True\n        for k in h5f.keys():\n            if not str(k).startswith('module.'):\n                h5f_is_module = False\n                break\n        if prefix == '' and not isinstance(net, nn.DataParallel) and h5f_is_module:\n            prefix = 'module.'\n\n        for k, v in net.state_dict().items():\n            k = prefix + k\n            if k in h5f:\n                param = torch.from_numpy(np.asarray(h5f[k]))\n                if v.size() != param.size():\n                    print('Inconsistent shape: {}, {}'.format(v.size(), param.size()))\n                else:\n                    v.copy_(param)\n            else:\n                print.warning('No layer: {}'.format(k))\n\n        epoch = h5f.attrs['epoch'] if 'epoch' in h5f.attrs else -1\n\n        if not load_state_dict:\n            if 'learning_rates' in h5f.attrs:\n                lr = h5f.attrs['learning_rates']\n            else:\n                lr = h5f.attrs.get('lr', -1)\n                lr = np.asarray([lr] if lr > 0 else [], dtype=np.float)\n\n            return epoch, lr\n\n        state_file = fname + '.optimizer_state.pk'\n        if os.path.isfile(state_file):\n            with open(state_file, 'rb') as f:\n                state_dicts = pickle.load(f)\n                if not isinstance(state_dicts, list):\n                    state_dicts = [state_dicts]\n        else:\n            state_dicts = None\n        return epoch, state_dicts\n\n\n# class SpatialCrossMapLRNFunc(Function):\n\n#     def __init__(self, size, alpha=1e-4, beta=0.75, k=1):\n#         self.size = size\n#         self.alpha = alpha\n#         self.beta = beta\n#         self.k = k\n\n#     def forward(self, input):\n#         self.save_for_backward(input)\n#         self.lrn = SpatialCrossMapLRNOld(self.size, self.alpha, self.beta, self.k)\n#         self.lrn.type(input.type())\n#         return self.lrn.forward(input)\n\n#     def backward(self, grad_output):\n#         input, = self.saved_tensors\n#         return self.lrn.backward(input, grad_output)\n\n\n# # use this one instead\n# class SpatialCrossMapLRN(Module):\n#     def __init__(self, size, alpha=1e-4, beta=0.75, k=1):\n#         super(SpatialCrossMapLRN, self).__init__()\n#         self.size = size\n#         self.alpha = alpha\n#         self.beta = beta\n#         self.k = k\n\n#     def forward(self, input):\n#         return SpatialCrossMapLRNFunc(self.size, self.alpha, self.beta, self.k)(input)\n\n\nclass Inception(nn.Module):\n    def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):\n        super(Inception, self).__init__()\n        # 1x1 conv branch\n        self.b1 = nn.Sequential(\n            nn.Conv2d(in_planes, n1x1, kernel_size=1),\n            nn.ReLU(True),\n        )\n\n        # 1x1 conv -> 3x3 conv branch\n        self.b2 = nn.Sequential(\n            nn.Conv2d(in_planes, n3x3red, kernel_size=1),\n            nn.ReLU(True),\n            nn.Conv2d(n3x3red, n3x3, kernel_size=3, padding=1),\n            nn.ReLU(True),\n        )\n\n        # 1x1 conv -> 5x5 conv branch\n        self.b3 = nn.Sequential(\n            nn.Conv2d(in_planes, n5x5red, kernel_size=1),\n            nn.ReLU(True),\n\n            nn.Conv2d(n5x5red, n5x5, kernel_size=5, padding=2),\n            nn.ReLU(True),\n        )\n\n        # 3x3 pool -> 1x1 conv branch\n        self.b4 = nn.Sequential(\n            nn.MaxPool2d(3, stride=1, padding=1),\n\n            nn.Conv2d(in_planes, pool_planes, kernel_size=1),\n            nn.ReLU(True),\n        )\n\n    def forward(self, x):\n        y1 = self.b1(x)\n        y2 = self.b2(x)\n        y3 = self.b3(x)\n        y4 = self.b4(x)\n        return torch.cat([y1,y2,y3,y4], 1)\n\n\nclass GoogLeNet(nn.Module):\n\n    output_channels = 832\n\n    def __init__(self):\n        super(GoogLeNet, self).__init__()\n        self.pre_layers = nn.Sequential(\n            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),\n            nn.ReLU(True),\n\n            nn.MaxPool2d(3, stride=2, ceil_mode=True),\n            SpatialCrossMapLRN(5),\n\n            nn.Conv2d(64, 64, 1),\n            nn.ReLU(True),\n\n            nn.Conv2d(64, 192, 3, padding=1),\n            nn.ReLU(True),\n\n            SpatialCrossMapLRN(5),\n            nn.MaxPool2d(3, stride=2, ceil_mode=True),\n        )\n\n        self.a3 = Inception(192,  64,  96, 128, 16, 32, 32)\n        self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)\n\n        self.maxpool = nn.MaxPool2d(3, stride=2, ceil_mode=True)\n\n        self.a4 = Inception(480, 192,  96, 208, 16,  48,  64)\n        self.b4 = Inception(512, 160, 112, 224, 24,  64,  64)\n        self.c4 = Inception(512, 128, 128, 256, 24,  64,  64)\n        self.d4 = Inception(512, 112, 144, 288, 32,  64,  64)\n        self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)\n\n    def forward(self, x):\n        out = self.pre_layers(x)\n        out = self.a3(out)\n        out = self.b3(out)\n        out = self.maxpool(out)\n        out = self.a4(out)\n        out = self.b4(out)\n        out = self.c4(out)\n        out = self.d4(out)\n        out = self.e4(out)\n\n        return out\n\n\nclass Model(nn.Module):\n    def __init__(self, n_parts=8):\n        super(Model, self).__init__()\n        self.n_parts = n_parts\n\n        self.feat_conv = GoogLeNet()\n        self.conv_input_feat = nn.Conv2d(self.feat_conv.output_channels, 512, 1)\n\n        # part net\n        self.conv_att = nn.Conv2d(512, self.n_parts, 1)\n\n        for i in range(self.n_parts):\n            setattr(self, 'linear_feature{}'.format(i+1), nn.Linear(512, 64))\n\n    def forward(self, x):\n        feature = self.feat_conv(x)\n        feature = self.conv_input_feat(feature)\n\n        att_weights = torch.sigmoid(self.conv_att(feature))\n\n        linear_feautres = []\n        for i in range(self.n_parts):\n            masked_feature = feature * torch.unsqueeze(att_weights[:, i], 1)\n            pooled_feature = F.avg_pool2d(masked_feature, masked_feature.size()[2:4])\n            linear_feautres.append(\n                getattr(self, 'linear_feature{}'.format(i+1))(pooled_feature.view(pooled_feature.size(0), -1))\n            )\n\n        concat_features = torch.cat(linear_feautres, 1)\n        normed_feature = concat_features / torch.clamp(torch.norm(concat_features, 2, 1, keepdim=True), min=1e-6)\n\n        return normed_feature\n\n\ndef load_reid_model(ckpt):\n    model = Model(n_parts=8)\n    model.inp_size = (80, 160)\n    load_net(ckpt, model)\n    print('Load ReID model from {}'.format(ckpt))\n\n    model = model.cuda()\n    model.eval()\n    return model\n\n\ndef im_preprocess(image):\n    image = np.asarray(image, np.float32)\n    image -= np.array([104, 117, 123], dtype=np.float32).reshape(1, 1, -1)\n    image = image.transpose((2, 0, 1))\n    return image\n\n\ndef extract_image_patches(image, bboxes):\n    bboxes = np.round(bboxes).astype(np.int)\n    bboxes = clip_boxes(bboxes, image.shape)\n    patches = [image[box[1]:box[3], box[0]:box[2]] for box in bboxes]\n    return patches\n\n\ndef extract_reid_features(reid_model, image, tlbrs):\n    if len(tlbrs) == 0:\n        return torch.FloatTensor()\n\n    patches = extract_image_patches(image, tlbrs)\n    patches = np.asarray([im_preprocess(cv2.resize(p, reid_model.inp_size)) for p in patches], dtype=np.float32)\n\n    with torch.no_grad():\n        im_var = Variable(torch.from_numpy(patches))\n        im_var = im_var.cuda()\n        features = reid_model(im_var).data\n    return features"
  },
  {
    "path": "trackers/deepsort_tracker/track.py",
    "content": "# vim: expandtab:ts=4:sw=4\n\n\nclass TrackState:\n    \"\"\"\n    Enumeration type for the single target track state. Newly created tracks are\n    classified as `tentative` until enough evidence has been collected. Then,\n    the track state is changed to `confirmed`. Tracks that are no longer alive\n    are classified as `deleted` to mark them for removal from the set of active\n    tracks.\n    \"\"\"\n\n    Tentative = 1\n    Confirmed = 2\n    Deleted = 3\n\n\nclass Track:\n    \"\"\"\n    A single target track with state space `(x, y, a, h)` and associated\n    velocities, where `(x, y)` is the center of the bounding box, `a` is the\n    aspect ratio and `h` is the height.\n    Parameters\n    ----------\n    mean : ndarray\n        Mean vector of the initial state distribution.\n    covariance : ndarray\n        Covariance matrix of the initial state distribution.\n    track_id : int\n        A unique track identifier.\n    n_init : int\n        Number of consecutive detections before the track is confirmed. The\n        track state is set to `Deleted` if a miss occurs within the first\n        `n_init` frames.\n    max_age : int\n        The maximum number of consecutive misses before the track state is\n        set to `Deleted`.\n    feature : Optional[ndarray]\n        Feature vector of the detection this track originates from. If not None,\n        this feature is added to the `features` cache.\n    Attributes\n    ----------\n    mean : ndarray\n        Mean vector of the initial state distribution.\n    covariance : ndarray\n        Covariance matrix of the initial state distribution.\n    track_id : int\n        A unique track identifier.\n    hits : int\n        Total number of measurement updates.\n    age : int\n        Total number of frames since first occurance.\n    time_since_update : int\n        Total number of frames since last measurement update.\n    state : TrackState\n        The current track state.\n    features : List[ndarray]\n        A cache of features. On each measurement update, the associated feature\n        vector is added to this list.\n    \"\"\"\n\n    def __init__(self, mean, covariance, track_id, class_id, n_init, max_age,\n                 feature=None):\n        self.mean = mean\n        self.covariance = covariance\n        self.track_id = track_id\n        self.class_id = class_id\n        self.hits = 1\n        self.age = 1\n        self.time_since_update = 0\n\n        self.state = TrackState.Tentative\n        self.features = []\n        if feature is not None:\n            self.features.append(feature)\n\n        self._n_init = n_init\n        self._max_age = max_age\n\n    def to_tlwh(self):\n        \"\"\"Get current position in bounding box format `(top left x, top left y,\n        width, height)`.\n        Returns\n        -------\n        ndarray\n            The bounding box.\n        \"\"\"\n        ret = self.mean[:4].copy()\n        ret[2] *= ret[3]\n        ret[:2] -= ret[2:] / 2\n        return ret\n\n    def to_tlbr(self):\n        \"\"\"Get current position in bounding box format `(min x, miny, max x,\n        max y)`.\n        Returns\n        -------\n        ndarray\n            The bounding box.\n        \"\"\"\n        ret = self.to_tlwh()\n        ret[2:] = ret[:2] + ret[2:]\n        return ret\n\n    def increment_age(self):\n        self.age += 1\n        self.time_since_update += 1\n\n    def predict(self, kf):\n        \"\"\"Propagate the state distribution to the current time step using a\n        Kalman filter prediction step.\n        Parameters\n        ----------\n        kf : kalman_filter.KalmanFilter\n            The Kalman filter.\n        \"\"\"\n        self.mean, self.covariance = kf.predict(self.mean, self.covariance)\n        self.increment_age()\n\n    def update(self, kf, detection):\n        \"\"\"Perform Kalman filter measurement update step and update the feature\n        cache.\n        Parameters\n        ----------\n        kf : kalman_filter.KalmanFilter\n            The Kalman filter.\n        detection : Detection\n            The associated detection.\n        \"\"\"\n        self.mean, self.covariance = kf.update(\n            self.mean, self.covariance, detection.to_xyah())\n        self.features.append(detection.feature)\n\n        self.hits += 1\n        self.time_since_update = 0\n        if self.state == TrackState.Tentative and self.hits >= self._n_init:\n            self.state = TrackState.Confirmed\n\n    def mark_missed(self):\n        \"\"\"Mark this track as missed (no association at the current time step).\n        \"\"\"\n        if self.state == TrackState.Tentative:\n            self.state = TrackState.Deleted\n        elif self.time_since_update > self._max_age:\n            self.state = TrackState.Deleted\n\n    def is_tentative(self):\n        \"\"\"Returns True if this track is tentative (unconfirmed).\n        \"\"\"\n        return self.state == TrackState.Tentative\n\n    def is_confirmed(self):\n        \"\"\"Returns True if this track is confirmed.\"\"\"\n        return self.state == TrackState.Confirmed\n\n    def is_deleted(self):\n        \"\"\"Returns True if this track is dead and should be deleted.\"\"\"\n        return self.state == TrackState.Deleted"
  },
  {
    "path": "trackers/deepsort_tracker/track_score.py",
    "content": "# vim: expandtab:ts=4:sw=4\n\n\nclass TrackState:\n    \"\"\"\n    Enumeration type for the single target track state. Newly created tracks are\n    classified as `tentative` until enough evidence has been collected. Then,\n    the track state is changed to `confirmed`. Tracks that are no longer alive\n    are classified as `deleted` to mark them for removal from the set of active\n    tracks.\n    \"\"\"\n\n    Tentative = 1\n    Confirmed = 2\n    Deleted = 3\n\n\nclass Track:\n    \"\"\"\n    A single target track with state space `(x, y, a, h)` and associated\n    velocities, where `(x, y)` is the center of the bounding box, `a` is the\n    aspect ratio and `h` is the height.\n    Parameters\n    ----------\n    mean : ndarray\n        Mean vector of the initial state distribution.\n    covariance : ndarray\n        Covariance matrix of the initial state distribution.\n    track_id : int\n        A unique track identifier.\n    n_init : int\n        Number of consecutive detections before the track is confirmed. The\n        track state is set to `Deleted` if a miss occurs within the first\n        `n_init` frames.\n    max_age : int\n        The maximum number of consecutive misses before the track state is\n        set to `Deleted`.\n    feature : Optional[ndarray]\n        Feature vector of the detection this track originates from. If not None,\n        this feature is added to the `features` cache.\n    Attributes\n    ----------\n    mean : ndarray\n        Mean vector of the initial state distribution.\n    covariance : ndarray\n        Covariance matrix of the initial state distribution.\n    track_id : int\n        A unique track identifier.\n    hits : int\n        Total number of measurement updates.\n    age : int\n        Total number of frames since first occurance.\n    time_since_update : int\n        Total number of frames since last measurement update.\n    state : TrackState\n        The current track state.\n    features : List[ndarray]\n        A cache of features. On each measurement update, the associated feature\n        vector is added to this list.\n    \"\"\"\n\n    def __init__(self, mean, covariance, mean_score, mean_convariance, track_id, class_id, n_init, max_age,\n                 feature=None):\n        self.mean = mean\n        self.covariance = covariance\n        self.mean_score = mean_score\n        self.covariance_score = mean_convariance\n        self.track_id = track_id\n        self.class_id = class_id\n        self.hits = 1\n        self.age = 1\n        self.time_since_update = 0\n\n        self.state = TrackState.Tentative\n        self.features = []\n        if feature is not None:\n            self.features.append(feature)\n\n        self._n_init = n_init\n        self._max_age = max_age\n\n    def to_tlwh(self):\n        \"\"\"Get current position in bounding box format `(top left x, top left y,\n        width, height)`.\n        Returns\n        -------\n        ndarray\n            The bounding box.\n        \"\"\"\n        ret = self.mean[:4].copy()\n        ret[2] *= ret[3]\n        ret[:2] -= ret[2:] / 2\n        return ret\n\n    def to_tlbr(self):\n        \"\"\"Get current position in bounding box format `(min x, miny, max x,\n        max y)`.\n        Returns\n        -------\n        ndarray\n            The bounding box.\n        \"\"\"\n        ret = self.to_tlwh()\n        ret[2:] = ret[:2] + ret[2:]\n        return ret\n\n    def increment_age(self):\n        self.age += 1\n        self.time_since_update += 1\n\n    def predict(self, kf, kf_score):\n        \"\"\"Propagate the state distribution to the current time step using a\n        Kalman filter prediction step.\n        Parameters\n        ----------\n        kf : kalman_filter.KalmanFilter\n            The Kalman filter.\n        \"\"\"\n        self.mean, self.covariance = kf.predict(self.mean, self.covariance)\n        self.mean_score, self.covariance_score = kf_score.predict(self.mean_score, self.covariance_score)\n        self.increment_age()\n\n    def update(self, kf, kf_score, detection):\n        \"\"\"Perform Kalman filter measurement update step and update the feature\n        cache.\n        Parameters\n        ----------\n        kf : kalman_filter.KalmanFilter\n            The Kalman filter.\n        detection : Detection\n            The associated detection.\n        \"\"\"\n        self.mean, self.covariance = kf.update(\n            self.mean, self.covariance, detection.to_xyah())\n        self.mean_score, self.covariance_score = kf_score.update(\n            self.mean_score, self.covariance_score, detection.confidence)\n        self.features.append(detection.feature)\n\n        self.hits += 1\n        self.time_since_update = 0\n        if self.state == TrackState.Tentative and self.hits >= self._n_init:\n            self.state = TrackState.Confirmed\n\n    def mark_missed(self):\n        \"\"\"Mark this track as missed (no association at the current time step).\n        \"\"\"\n        if self.state == TrackState.Tentative:\n            self.state = TrackState.Deleted\n        elif self.time_since_update > self._max_age:\n            self.state = TrackState.Deleted\n\n    def is_tentative(self):\n        \"\"\"Returns True if this track is tentative (unconfirmed).\n        \"\"\"\n        return self.state == TrackState.Tentative\n\n    def is_confirmed(self):\n        \"\"\"Returns True if this track is confirmed.\"\"\"\n        return self.state == TrackState.Confirmed\n\n    def is_deleted(self):\n        \"\"\"Returns True if this track is dead and should be deleted.\"\"\"\n        return self.state == TrackState.Deleted"
  },
  {
    "path": "trackers/hybrid_sort_tracker/association.py",
    "content": "import os\nimport numpy as np\n\ndef intersection_batch(bboxes1, bboxes2):\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    intersections = w * h\n    return intersections\n\ndef box_area(bbox):\n    area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])\n    return area\n\ndef iou_batch(bboxes1, bboxes2):\n    \"\"\"\n    From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]\n    \"\"\"\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n    \n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    o = wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])                                      \n        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)                                              \n    return(o)\n\n\ndef cal_score_dif_batch(bboxes1, bboxes2):\n    \"\"\"\n    From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]\n    \"\"\"\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    score2 = bboxes2[..., 4]\n    score1 = bboxes1[..., 4]\n\n    return (abs(score2 - score1))\n\ndef cal_score_dif_batch_two_score(bboxes1, bboxes2):\n    \"\"\"\n    From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]\n    \"\"\"\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    score2 = bboxes2[..., 5]\n    score1 = bboxes1[..., 4]\n\n    return (abs(score2 - score1))\n\ndef hmiou(bboxes1, bboxes2):\n    \"\"\"\n    Height_Modulated_IoU\n    \"\"\"\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    yy11 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    yy12 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n\n    yy21 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    yy22 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n    o = (yy12 - yy11) / (yy22 - yy21)\n\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    o *= wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])\n        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)\n    return (o)\n\ndef giou_batch(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    iou = wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])\n        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)  \n\n    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])\n    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])\n    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n    wc = xxc2 - xxc1 \n    hc = yyc2 - yyc1 \n    assert((wc > 0).all() and (hc > 0).all())\n    area_enclose = wc * hc \n    giou = iou - (area_enclose - wh) / area_enclose\n    giou = (giou + 1.)/2.0 # resize from (-1,1) to (0,1)\n    return giou\n\ndef giou_batch_true(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    union = ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])\n        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)\n    iou = wh / union\n\n    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])\n    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])\n    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n    wc = xxc2 - xxc1\n    hc = yyc2 - yyc1\n    assert((wc > 0).all() and (hc > 0).all())\n    area_enclose = wc * hc\n    giou = iou - (area_enclose - union) / area_enclose\n    giou = (giou + 1.)/2.0 # resize from (-1,1) to (0,1)\n    return giou\n\ndef diou_batch(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    # calculate the intersection box\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    iou = wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])                                      \n        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh) \n\n    centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0\n    centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0\n    centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0\n    centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0\n\n    inner_diag = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2\n\n    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])\n    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])\n    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n\n    outer_diag = (xxc2 - xxc1) ** 2 + (yyc2 - yyc1) ** 2\n    diou = iou - inner_diag / outer_diag\n\n    return (diou + 1) / 2.0 # resize from (-1,1) to (0,1)\n\ndef ciou_batch(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    # calculate the intersection box\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    iou = wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])                                      \n        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh) \n\n    centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0\n    centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0\n    centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0\n    centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0\n\n    inner_diag = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2\n\n    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])\n    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])\n    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n\n    outer_diag = (xxc2 - xxc1) ** 2 + (yyc2 - yyc1) ** 2\n    \n    w1 = bboxes1[..., 2] - bboxes1[..., 0]\n    h1 = bboxes1[..., 3] - bboxes1[..., 1]\n    w2 = bboxes2[..., 2] - bboxes2[..., 0]\n    h2 = bboxes2[..., 3] - bboxes2[..., 1]\n\n    # prevent dividing over zero. add one pixel shift\n    h2 = h2 + 1.\n    h1 = h1 + 1.\n    arctan = np.arctan(w2/h2) - np.arctan(w1/h1)\n    v = (4 / (np.pi ** 2)) * (arctan ** 2)\n    S = 1 - iou \n    alpha = v / (S+v)\n    ciou = iou - inner_diag / outer_diag - alpha * v\n    \n    return (ciou + 1) / 2.0 # resize from (-1,1) to (0,1)\n\n\ndef ct_dist(bboxes1, bboxes2):\n    \"\"\"\n        Measure the center distance between two sets of bounding boxes,\n        this is a coarse implementation, we don't recommend using it only\n        for association, which can be unstable and sensitive to frame rate\n        and object speed.\n    \"\"\"\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0\n    centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0\n    centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0\n    centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0\n\n    ct_dist2 = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2\n\n    ct_dist = np.sqrt(ct_dist2)\n\n    # The linear rescaling is a naive version and needs more study\n    ct_dist = ct_dist / ct_dist.max()\n    return ct_dist.max() - ct_dist # resize to (0,1)\n\n\ndef speed_direction_batch(dets, tracks):\n    \"\"\"\n    batch formulation of function 'speed_direction', compute normalized speed from batch bboxes\n    @param dets:\n    @param tracks:\n    @return: normalized speed in batch\n    \"\"\"\n    tracks = tracks[..., np.newaxis]\n    CX1, CY1 = (dets[:,0] + dets[:,2])/2.0, (dets[:,1]+dets[:,3])/2.0\n    CX2, CY2 = (tracks[:,0] + tracks[:,2]) /2.0, (tracks[:,1]+tracks[:,3])/2.0\n    dx = CX1 - CX2 \n    dy = CY1 - CY2 \n    norm = np.sqrt(dx**2 + dy**2) + 1e-6\n    dx = dx / norm \n    dy = dy / norm\n    return dy, dx # size: num_track x num_det\n\n\ndef linear_assignment(cost_matrix, thresh=0.):\n    try:        # [hgx0411] goes here!\n        import lap\n        if thresh != 0:\n            _, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)\n        else:\n            _, x, y = lap.lapjv(cost_matrix, extend_cost=True)\n        return np.array([[y[i], i] for i in x if i >= 0])\n    except ImportError:\n        from scipy.optimize import linear_sum_assignment\n        x, y = linear_sum_assignment(cost_matrix)\n        return np.array(list(zip(x, y)))\n\n\ndef associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):\n    \"\"\"\n    Assigns detections to tracked object (both represented as bounding boxes)\n    Returns 3 lists of matches, unmatched_detections and unmatched_trackers\n    \"\"\"\n    if(len(trackers)==0):\n        return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)\n\n    iou_matrix = iou_batch(detections, trackers)\n\n    if min(iou_matrix.shape) > 0:\n        a = (iou_matrix > iou_threshold).astype(np.int32)\n        if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n            matched_indices = np.stack(np.where(a), axis=1)\n        else:\n            matched_indices = linear_assignment(-iou_matrix)\n    else:\n        matched_indices = np.empty(shape=(0,2))\n\n    unmatched_detections = []\n    for d, det in enumerate(detections):\n        if(d not in matched_indices[:,0]):\n            unmatched_detections.append(d)\n    unmatched_trackers = []\n    for t, trk in enumerate(trackers):\n        if(t not in matched_indices[:,1]):\n            unmatched_trackers.append(t)\n\n    #filter out matched with low IOU\n    matches = []\n    for m in matched_indices:\n        if(iou_matrix[m[0], m[1]]<iou_threshold):\n            unmatched_detections.append(m[0])\n            unmatched_trackers.append(m[1])\n        else:\n            matches.append(m.reshape(1,2))\n    if(len(matches)==0):\n        matches = np.empty((0,2),dtype=int)\n    else:\n        matches = np.concatenate(matches,axis=0)\n\n    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\n\ndef associate(detections, trackers, iou_threshold, velocities, previous_obs, vdc_weight):    \n    if(len(trackers)==0):\n        return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)\n\n    Y, X = speed_direction_batch(detections, previous_obs)\n    inertia_Y, inertia_X = velocities[:,0], velocities[:,1]\n    inertia_Y = np.repeat(inertia_Y[:, np.newaxis], Y.shape[1], axis=1)\n    inertia_X = np.repeat(inertia_X[:, np.newaxis], X.shape[1], axis=1)\n    diff_angle_cos = inertia_X * X + inertia_Y * Y\n    diff_angle_cos = np.clip(diff_angle_cos, a_min=-1, a_max=1)\n    diff_angle = np.arccos(diff_angle_cos)\n    diff_angle = (np.pi /2.0 - np.abs(diff_angle)) / np.pi\n\n    valid_mask = np.ones(previous_obs.shape[0])\n    valid_mask[np.where(previous_obs[:,4]<0)] = 0\n    \n    iou_matrix = iou_batch(detections, trackers)\n    scores = np.repeat(detections[:,-1][:, np.newaxis], trackers.shape[0], axis=1)\n    # iou_matrix = iou_matrix * scores # a trick sometiems works, we don't encourage this\n    valid_mask = np.repeat(valid_mask[:, np.newaxis], X.shape[1], axis=1)\n\n    angle_diff_cost = (valid_mask * diff_angle) * vdc_weight\n    angle_diff_cost = angle_diff_cost.T\n    angle_diff_cost = angle_diff_cost * scores\n\n    if min(iou_matrix.shape) > 0:\n        a = (iou_matrix > iou_threshold).astype(np.int32)\n        if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n            matched_indices = np.stack(np.where(a), axis=1)\n        else:\n            matched_indices = linear_assignment(-(iou_matrix+angle_diff_cost))\n    else:\n        matched_indices = np.empty(shape=(0,2))\n\n    unmatched_detections = []\n    for d, det in enumerate(detections):\n        if(d not in matched_indices[:,0]):\n            unmatched_detections.append(d)\n    unmatched_trackers = []\n    for t, trk in enumerate(trackers):\n        if(t not in matched_indices[:,1]):\n            unmatched_trackers.append(t)\n\n    # filter out matched with low IOU\n    matches = []\n    for m in matched_indices:\n        if(iou_matrix[m[0], m[1]]<iou_threshold):\n            unmatched_detections.append(m[0])\n            unmatched_trackers.append(m[1])\n        else:\n            matches.append(m.reshape(1,2))\n    if(len(matches)==0):\n        matches = np.empty((0,2),dtype=int)\n    else:\n        matches = np.concatenate(matches,axis=0)\n\n    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\n\ndef cost_vel(Y, X, trackers, velocities, detections, previous_obs, vdc_weight):\n    # Y, X = speed_direction_batch(detections, previous_obs)\n    inertia_Y, inertia_X = velocities[:, 0], velocities[:, 1]\n    inertia_Y = np.repeat(inertia_Y[:, np.newaxis], Y.shape[1], axis=1)\n    inertia_X = np.repeat(inertia_X[:, np.newaxis], X.shape[1], axis=1)\n    diff_angle_cos = inertia_X * X + inertia_Y * Y\n    diff_angle_cos = np.clip(diff_angle_cos, a_min=-1, a_max=1)\n    diff_angle = np.arccos(diff_angle_cos)\n    diff_angle = (np.pi / 2.0 - np.abs(diff_angle)) / np.pi\n\n    valid_mask = np.ones(previous_obs.shape[0])\n    valid_mask[np.where(previous_obs[:, 4] < 0)] = 0\n\n    # iou_matrix = iou_batch(detections, trackers)\n    scores = np.repeat(detections[:, -1][:, np.newaxis], trackers.shape[0], axis=1)\n    # iou_matrix = iou_matrix * scores # a trick sometiems works, we don't encourage this\n    valid_mask = np.repeat(valid_mask[:, np.newaxis], X.shape[1], axis=1)\n\n    angle_diff_cost = (valid_mask * diff_angle) * vdc_weight\n    angle_diff_cost = angle_diff_cost.T\n    angle_diff_cost = angle_diff_cost * scores\n    return angle_diff_cost\n\ndef speed_direction_batch_lt(dets, tracks):\n    tracks = tracks[..., np.newaxis]\n    CX1, CY1 = dets[:,0], dets[:,1]\n    CX2, CY2 = tracks[:,0], tracks[:,1]\n    dx = CX1 - CX2\n    dy = CY1 - CY2\n    norm = np.sqrt(dx**2 + dy**2) + 1e-6\n    dx = dx / norm\n    dy = dy / norm\n    return dy, dx # size: num_track x num_det\n\ndef speed_direction_batch_rt(dets, tracks):\n    tracks = tracks[..., np.newaxis]\n    CX1, CY1 = dets[:,0], dets[:,3]\n    CX2, CY2 = tracks[:,0], tracks[:,3]\n    dx = CX1 - CX2\n    dy = CY1 - CY2\n    norm = np.sqrt(dx**2 + dy**2) + 1e-6\n    dx = dx / norm\n    dy = dy / norm\n    return dy, dx # size: num_track x num_det\n\ndef speed_direction_batch_lb(dets, tracks):\n    tracks = tracks[..., np.newaxis]\n    CX1, CY1 = dets[:,2], dets[:,1]\n    CX2, CY2 = tracks[:,2], tracks[:,1]\n    dx = CX1 - CX2\n    dy = CY1 - CY2\n    norm = np.sqrt(dx**2 + dy**2) + 1e-6\n    dx = dx / norm\n    dy = dy / norm\n    return dy, dx # size: num_track x num_det\n\ndef speed_direction_batch_rb(dets, tracks):\n    tracks = tracks[..., np.newaxis]\n    CX1, CY1 = dets[:,2], dets[:,3]\n    CX2, CY2 = tracks[:,2], tracks[:,3]\n    dx = CX1 - CX2\n    dy = CY1 - CY2\n    norm = np.sqrt(dx**2 + dy**2) + 1e-6\n    dx = dx / norm\n    dy = dy / norm\n    return dy, dx # size: num_track x num_det\n\ndef associate_4_points(detections, trackers, iou_threshold, lt, rt, lb, rb, previous_obs, vdc_weight, iou_type=None, args=None):\n    if (len(trackers) == 0):\n        return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int)\n\n    Y1, X1 = speed_direction_batch_lt(detections, previous_obs)\n    Y2, X2 = speed_direction_batch_rt(detections, previous_obs)\n    Y3, X3 = speed_direction_batch_lb(detections, previous_obs)\n    Y4, X4 = speed_direction_batch_rb(detections, previous_obs)\n    YC, XC = speed_direction_batch(detections, previous_obs)\n    cost_lt = cost_vel(Y1, X1, trackers, lt, detections, previous_obs, vdc_weight)\n    cost_rt = cost_vel(Y2, X2, trackers, rt, detections, previous_obs, vdc_weight)\n    cost_lb = cost_vel(Y3, X3, trackers, lb, detections, previous_obs, vdc_weight)\n    cost_rb = cost_vel(Y4, X4, trackers, rb, detections, previous_obs, vdc_weight)\n\n    iou_matrix = iou_type(detections, trackers)\n    angle_diff_cost = cost_lt + cost_rt + cost_lb + cost_rb\n\n    if min(iou_matrix.shape) > 0:\n        a = (iou_matrix > iou_threshold).astype(np.int32)\n        if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n            matched_indices = np.stack(np.where(a), axis=1)\n        else:\n            matched_indices = linear_assignment(-(iou_matrix + angle_diff_cost))\n    else:\n        matched_indices = np.empty(shape=(0, 2))\n\n    unmatched_detections = []\n    for d, det in enumerate(detections):\n        if (d not in matched_indices[:, 0]):\n            unmatched_detections.append(d)\n    unmatched_trackers = []\n    for t, trk in enumerate(trackers):\n        if (t not in matched_indices[:, 1]):\n            unmatched_trackers.append(t)\n\n    # filter out matched with low IOU\n    matches = []\n    for m in matched_indices:\n        if (iou_matrix[m[0], m[1]] < iou_threshold):\n            unmatched_detections.append(m[0])\n            unmatched_trackers.append(m[1])\n        else:\n            matches.append(m.reshape(1, 2))\n    if (len(matches) == 0):\n        matches = np.empty((0, 2), dtype=int)\n    else:\n        matches = np.concatenate(matches, axis=0)\n\n    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\ndef associate_4_points_with_score(detections, trackers, iou_threshold, lt, rt, lb, rb, previous_obs, vdc_weight, iou_type=None, args=None):\n    if (len(trackers) == 0):\n        return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int)\n\n    Y1, X1 = speed_direction_batch_lt(detections, previous_obs)\n    Y2, X2 = speed_direction_batch_rt(detections, previous_obs)\n    Y3, X3 = speed_direction_batch_lb(detections, previous_obs)\n    Y4, X4 = speed_direction_batch_rb(detections, previous_obs)\n    cost_lt = cost_vel(Y1, X1, trackers, lt, detections, previous_obs, vdc_weight)\n    cost_rt = cost_vel(Y2, X2, trackers, rt, detections, previous_obs, vdc_weight)\n    cost_lb = cost_vel(Y3, X3, trackers, lb, detections, previous_obs, vdc_weight)\n    cost_rb = cost_vel(Y4, X4, trackers, rb, detections, previous_obs, vdc_weight)\n    iou_matrix = iou_type(detections, trackers)\n    score_dif = cal_score_dif_batch(detections, trackers)\n\n    angle_diff_cost = cost_lt + cost_rt + cost_lb + cost_rb\n\n    # TCM\n    angle_diff_cost -= score_dif * args.TCM_first_step_weight\n\n    if min(iou_matrix.shape) > 0:\n        a = (iou_matrix > iou_threshold).astype(np.int32)\n        if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n            matched_indices = np.stack(np.where(a), axis=1)\n        else:\n            matched_indices = linear_assignment(-(iou_matrix + angle_diff_cost))\n    else:\n        matched_indices = np.empty(shape=(0, 2))\n\n    unmatched_detections = []\n    for d, det in enumerate(detections):\n        if (d not in matched_indices[:, 0]):\n            unmatched_detections.append(d)\n    unmatched_trackers = []\n    for t, trk in enumerate(trackers):\n        if (t not in matched_indices[:, 1]):\n            unmatched_trackers.append(t)\n\n    # filter out matched with low IOU\n    matches = []\n    for m in matched_indices:\n        if (iou_matrix[m[0], m[1]] < iou_threshold):\n            unmatched_detections.append(m[0])\n            unmatched_trackers.append(m[1])\n        else:\n            matches.append(m.reshape(1, 2))\n    if (len(matches) == 0):\n        matches = np.empty((0, 2), dtype=int)\n    else:\n        matches = np.concatenate(matches, axis=0)\n\n    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\ndef associate_4_points_with_score_with_reid(detections, trackers, iou_threshold, lt, rt, lb, rb, previous_obs, vdc_weight,\n                                            iou_type=None, args=None,emb_cost=None, weights=(1.0, 0), thresh=0.8,\n                                            long_emb_dists=None, with_longterm_reid=False,\n                                            longterm_reid_weight=0.0, with_longterm_reid_correction=False,\n                                            longterm_reid_correction_thresh=0.0, dataset=\"dancetrack\"):\n    if (len(trackers) == 0):\n        return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int)\n\n    Y1, X1 = speed_direction_batch_lt(detections, previous_obs)\n    Y2, X2 = speed_direction_batch_rt(detections, previous_obs)\n    Y3, X3 = speed_direction_batch_lb(detections, previous_obs)\n    Y4, X4 = speed_direction_batch_rb(detections, previous_obs)\n    cost_lt = cost_vel(Y1, X1, trackers, lt, detections, previous_obs, vdc_weight)\n    cost_rt = cost_vel(Y2, X2, trackers, rt, detections, previous_obs, vdc_weight)\n    cost_lb = cost_vel(Y3, X3, trackers, lb, detections, previous_obs, vdc_weight)\n    cost_rb = cost_vel(Y4, X4, trackers, rb, detections, previous_obs, vdc_weight)\n    iou_matrix = iou_type(detections, trackers)\n    score_dif = cal_score_dif_batch(detections, trackers)\n\n    angle_diff_cost = cost_lt + cost_rt + cost_lb + cost_rb\n\n    # TCM\n    angle_diff_cost -= score_dif * args.TCM_first_step_weight\n\n    if min(iou_matrix.shape) > 0:\n        if emb_cost is None:\n            a = (iou_matrix > iou_threshold).astype(np.int32)\n            if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n                matched_indices = np.stack(np.where(a), axis=1)\n            else:\n                matched_indices = linear_assignment(-(iou_matrix + angle_diff_cost))\n        else:\n            if not with_longterm_reid:\n                matched_indices = linear_assignment(weights[0] * (-(iou_matrix + angle_diff_cost)) + weights[1] * emb_cost) # , thresh=thresh\n            else:   # long-term reid feats\n                matched_indices = linear_assignment(weights[0] * (-(iou_matrix + angle_diff_cost)) +\n                                                    weights[1] * emb_cost + longterm_reid_weight * long_emb_dists)  # , thresh=thresh\n\n        if matched_indices.size == 0:\n            matched_indices = np.empty(shape=(0, 2))\n    else:\n        matched_indices = np.empty(shape=(0, 2))\n\n    unmatched_detections = []\n    for d, det in enumerate(detections):\n        if (d not in matched_indices[:, 0]):\n            unmatched_detections.append(d)\n    unmatched_trackers = []\n    for t, trk in enumerate(trackers):\n        if (t not in matched_indices[:, 1]):\n            unmatched_trackers.append(t)\n\n    # filter out matched with low IOU (and long-term ReID feats)\n    matches = []\n    # iou_matrix_thre = iou_matrix if dataset == \"dancetrack\" else iou_matrix - score_dif\n    iou_matrix_thre = iou_matrix - score_dif\n    if with_longterm_reid_correction:\n        for m in matched_indices:\n            if (emb_cost[m[0], m[1]] > longterm_reid_correction_thresh) and (iou_matrix_thre[m[0], m[1]] < iou_threshold):\n                print(\"correction:\", emb_cost[m[0], m[1]])\n                unmatched_detections.append(m[0])\n                unmatched_trackers.append(m[1])\n            else:\n                matches.append(m.reshape(1, 2))\n    else:\n        for m in matched_indices:\n            if (iou_matrix_thre[m[0], m[1]] < iou_threshold):\n                unmatched_detections.append(m[0])\n                unmatched_trackers.append(m[1])\n            else:\n                matches.append(m.reshape(1, 2))\n\n    if (len(matches) == 0):\n        matches = np.empty((0, 2), dtype=int)\n    else:\n        matches = np.concatenate(matches, axis=0)\n\n    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\n\ndef associate_kitti(detections, trackers, det_cates, iou_threshold, \n        velocities, previous_obs, vdc_weight):\n    if(len(trackers)==0):\n        return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)\n\n    \"\"\"\n        Cost from the velocity direction consistency\n    \"\"\"\n    Y, X = speed_direction_batch(detections, previous_obs)\n    inertia_Y, inertia_X = velocities[:,0], velocities[:,1]\n    inertia_Y = np.repeat(inertia_Y[:, np.newaxis], Y.shape[1], axis=1)\n    inertia_X = np.repeat(inertia_X[:, np.newaxis], X.shape[1], axis=1)\n    diff_angle_cos = inertia_X * X + inertia_Y * Y\n    diff_angle_cos = np.clip(diff_angle_cos, a_min=-1, a_max=1)\n    diff_angle = np.arccos(diff_angle_cos)\n    diff_angle = (np.pi /2.0 - np.abs(diff_angle)) / np.pi\n\n    valid_mask = np.ones(previous_obs.shape[0])\n    valid_mask[np.where(previous_obs[:,4]<0)]=0  \n    valid_mask = np.repeat(valid_mask[:, np.newaxis], X.shape[1], axis=1)\n\n    scores = np.repeat(detections[:,-1][:, np.newaxis], trackers.shape[0], axis=1)\n    angle_diff_cost = (valid_mask * diff_angle) * vdc_weight\n    angle_diff_cost = angle_diff_cost.T\n    angle_diff_cost = angle_diff_cost * scores\n\n    \"\"\"\n        Cost from IoU\n    \"\"\"\n    iou_matrix = iou_batch(detections, trackers)\n    \n\n    \"\"\"\n        With multiple categories, generate the cost for catgory mismatch\n    \"\"\"\n    num_dets = detections.shape[0]\n    num_trk = trackers.shape[0]\n    cate_matrix = np.zeros((num_dets, num_trk))\n    for i in range(num_dets):\n            for j in range(num_trk):\n                if det_cates[i] != trackers[j, 4]:\n                        cate_matrix[i][j] = -1e6\n    \n    cost_matrix = - iou_matrix -angle_diff_cost - cate_matrix\n\n    if min(iou_matrix.shape) > 0:\n        a = (iou_matrix > iou_threshold).astype(np.int32)\n        if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n            matched_indices = np.stack(np.where(a), axis=1)\n        else:\n            matched_indices = linear_assignment(cost_matrix)\n    else:\n        matched_indices = np.empty(shape=(0,2))\n\n    unmatched_detections = []\n    for d, det in enumerate(detections):\n        if(d not in matched_indices[:,0]):\n            unmatched_detections.append(d)\n    unmatched_trackers = []\n    for t, trk in enumerate(trackers):\n        if(t not in matched_indices[:,1]):\n            unmatched_trackers.append(t)\n\n    #filter out matched with low IOU\n    matches = []\n    for m in matched_indices:\n        if(iou_matrix[m[0], m[1]]<iou_threshold):\n            unmatched_detections.append(m[0])\n            unmatched_trackers.append(m[1])\n        else:\n            matches.append(m.reshape(1,2))\n    if(len(matches)==0):\n        matches = np.empty((0,2),dtype=int)\n    else:\n        matches = np.concatenate(matches,axis=0)\n\n    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\n# compute embedding distance and gating, borrowed and modified from FairMOT\nfrom scipy.spatial.distance import cdist\ndef embedding_distance(tracks_feat, detections_feat, metric='cosine'):\n    \"\"\"\n    :param tracks: list[KalmanBoxTracker]\n    :param detections: list[KalmanBoxTracker]\n    :param metric:\n    :return: cost_matrix np.ndarray\n    \"\"\"\n\n    cost_matrix = np.zeros((len(tracks_feat), len(detections_feat)), dtype=np.float)\n    if cost_matrix.size == 0:\n        return cost_matrix\n    # det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)    # [detection_num, emd_dim]\n    # #for i, track in enumerate(tracks):\n    #     #cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))\n    # track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)    # [track_num, emd_dim]\n    cost_matrix = np.maximum(0.0, cdist(tracks_feat, detections_feat, metric))  # Nomalized features, metric: cosine, [track_num, detection_num]\n    return cost_matrix\n\nchi2inv95 = {\n    1: 3.8415,\n    2: 5.9915,\n    3: 7.8147,\n    4: 9.4877,\n    5: 11.070,\n    6: 12.592,\n    7: 14.067,\n    8: 15.507,\n    9: 16.919}\n\n# [hgx0411] compute embedding distance and gating, borrowed and modified from FairMOT\ndef fuse_motion(cost_matrix, tracks, detections, only_position=False, lambda_=0.98):\n    if cost_matrix.size == 0:\n        return cost_matrix\n    gating_dim = 2 if only_position else 4\n    gating_threshold = chi2inv95[gating_dim]\n    for row, track in enumerate(tracks):\n        gating_distance = track.kf.gating_distance(detections, only_position, metric='maha')\n        cost_matrix[row, gating_distance > gating_threshold] = np.inf\n        cost_matrix[row] = lambda_ * cost_matrix[row] + (1 - lambda_) * gating_distance\n    return cost_matrix\n\n# [hgx0411] compute embedding distance and gating, borrowed and modified from FairMOT\nimport lap\ndef linear_assignment_appearance(cost_matrix, thresh):\n    if cost_matrix.size == 0:\n        return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))\n    matches, unmatched_a, unmatched_b = [], [], []\n    cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)\n    for ix, mx in enumerate(x):\n        if mx >= 0:\n            matches.append([ix, mx])\n    unmatched_a = np.where(x < 0)[0]\n    unmatched_b = np.where(y < 0)[0]\n    matches = np.asarray(matches)\n    return matches, unmatched_a, unmatched_b\n\ndef fuse_score(cost_matrix, det_scores):\n    if cost_matrix.size == 0:\n        return cost_matrix\n    iou_sim = - cost_matrix\n    det_scores = np.expand_dims(det_scores, axis=1).repeat(cost_matrix.shape[1], axis=1)\n    fuse_sim = iou_sim * det_scores\n    fuse_cost = - fuse_sim\n    return fuse_cost"
  },
  {
    "path": "trackers/hybrid_sort_tracker/hybrid_sort.py",
    "content": "\"\"\"\n    This script is adopted from the SORT script by Alex Bewley alex@bewley.ai\n\"\"\"\nfrom __future__ import print_function\n\nimport numpy as np\nfrom .association import *\n\n\ndef k_previous_obs(observations, cur_age, k):\n    if len(observations) == 0:\n        return [-1, -1, -1, -1, -1]\n    for i in range(k):\n        dt = k - i\n        if cur_age - dt in observations:\n            return observations[cur_age-dt]\n    max_age = max(observations.keys())\n    return observations[max_age]\n\n\ndef convert_bbox_to_z(bbox):\n    \"\"\"\n    Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form\n      [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is\n      the aspect ratio\n    \"\"\"\n    w = bbox[2] - bbox[0]\n    h = bbox[3] - bbox[1]\n    x = bbox[0] + w/2.\n    y = bbox[1] + h/2.\n    s = w * h  # scale is just area\n    r = w / float(h+1e-6)\n    score = bbox[4]\n    if score:\n        return np.array([x, y, s, score, r]).reshape((5, 1))\n    else:\n        return np.array([x, y, s, r]).reshape((4, 1))\n\n\ndef convert_x_to_bbox(x, score=None):\n    \"\"\"\n    Takes a bounding box in the centre form [x,y,s,r] and returns it in the form\n      [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right\n    \"\"\"\n    w = np.sqrt(x[2] * x[4])\n    h = x[2] / w\n    score = x[3]\n    if(score == None):\n      return np.array([x[0]-w/2., x[1]-h/2., x[0]+w/2., x[1]+h/2.]).reshape((1, 4))\n    else:\n      return np.array([x[0]-w/2., x[1]-h/2., x[0]+w/2., x[1]+h/2., score]).reshape((1, 5))\n\n\ndef speed_direction(bbox1, bbox2):\n    cx1, cy1 = (bbox1[0]+bbox1[2]) / 2.0, (bbox1[1]+bbox1[3])/2.0\n    cx2, cy2 = (bbox2[0]+bbox2[2]) / 2.0, (bbox2[1]+bbox2[3])/2.0\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\ndef speed_direction_lt(bbox1, bbox2):\n    cx1, cy1 = bbox1[0], bbox1[1]\n    cx2, cy2 = bbox2[0], bbox2[1]\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\ndef speed_direction_rt(bbox1, bbox2):\n    cx1, cy1 = bbox1[0], bbox1[3]\n    cx2, cy2 = bbox2[0], bbox2[3]\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\ndef speed_direction_lb(bbox1, bbox2):\n    cx1, cy1 = bbox1[2], bbox1[1]\n    cx2, cy2 = bbox2[2], bbox2[1]\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\ndef speed_direction_rb(bbox1, bbox2):\n    cx1, cy1 = bbox1[2], bbox1[3]\n    cx2, cy2 = bbox2[2], bbox2[3]\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\nclass KalmanBoxTracker(object):\n    \"\"\"\n    This class represents the internal state of individual tracked objects observed as bbox.\n    \"\"\"\n    count = 0\n\n    def __init__(self, bbox, delta_t=3, orig=False, args=None):\n        \"\"\"\n        Initialises a tracker using initial bounding box.\n\n        \"\"\"\n        # define constant velocity model\n        # if not orig and not args.kalman_GPR:\n        if not orig:\n          # from .kalmanfilter import KalmanFilterNew as KalmanFilter\n          from .kalmanfilter_score_new import KalmanFilterNew_score_new as KalmanFilter_score_new\n          self.kf = KalmanFilter_score_new(dim_x=9, dim_z=5)\n          # self.kf_score = KalmanFilter_score(dim_x=2, dim_z=1)\n        else:\n          from filterpy.kalman import KalmanFilter\n          self.kf = KalmanFilter(dim_x=7, dim_z=4)\n        # u, v, s, c, r, ~u, ~v, ~s, ~c\n        self.kf.F = np.array([[1, 0, 0, 0, 0, 1, 0, 0, 0],\n                              [0, 1, 0, 0, 0, 0, 1, 0, 0],\n                              [0, 0, 1, 0, 0, 0, 0, 1, 0],\n                              [0, 0, 0, 1, 0, 0, 0, 0, 1],\n                              [0, 0, 0, 0, 1, 0, 0, 0, 0],\n                              [0, 0, 0, 0, 0, 1, 0, 0, 0],\n                              [0, 0, 0, 0, 0, 0, 1, 0, 0],\n                              [0, 0, 0, 0, 0, 0, 0, 1, 0],\n                              [0, 0, 0, 0, 0, 0, 0, 0, 1]])\n        self.kf.H = np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0],\n                              [0, 1, 0, 0, 0, 0, 0, 0, 0],\n                              [0, 0, 1, 0, 0, 0, 0, 0, 0],\n                              [0, 0, 0, 1, 0, 0, 0, 0, 0],\n                              [0, 0, 0, 0, 1, 0, 0, 0, 0]])\n        # self.kf_score.F = np.array([[1, 1],\n        #                             [0, 1]])\n        # self.kf_score.H = np.array([[1, 0]])\n\n        self.kf.R[2:, 2:] *= 10.\n        self.kf.P[5:, 5:] *= 1000.  # give high uncertainty to the unobservable initial velocities\n        self.kf.P *= 10.\n        self.kf.Q[-1, -1] *= 0.01\n        self.kf.Q[-2, -2] *= 0.01\n        self.kf.Q[5:, 5:] *= 0.01\n\n        self.kf.x[:5] = convert_bbox_to_z(bbox)\n\n\n        # self.kf_score.R[0:, 0:] *= 10.\n        # self.kf_score.P[1:, 1:] *= 1000.  # give high uncertainty to the unobservable initial velocities\n        # self.kf_score.P *= 10.\n        # self.kf_score.Q[-1, -1] *= 0.01\n        # self.kf_score.Q[1:, 1:] *= 0.01\n        # self.kf_score.x[:1] = bbox[-1]\n\n\n        self.time_since_update = 0\n        self.id = KalmanBoxTracker.count\n        KalmanBoxTracker.count += 1\n        self.history = []\n        self.hits = 0\n        self.hit_streak = 0\n        self.age = 0\n        self.age_recover_for_cbiou = 0\n        \"\"\"\n        NOTE: [-1,-1,-1,-1,-1] is a compromising placeholder for non-observation status, the same for the return of \n        function k_previous_obs. It is ugly and I do not like it. But to support generate observation array in a \n        fast and unified way, which you would see below k_observations = np.array([k_previous_obs(...]]), let's bear it for now.\n        \"\"\"\n        self.last_observation = np.array([-1, -1, -1, -1, -1])  # placeholder\n        self.last_observation_save = np.array([-1, -1, -1, -1, -1])\n        self.observations = dict()\n        self.history_observations = []\n        # self.velocity = None\n        self.velocity_lt = None\n        self.velocity_rt = None\n        self.velocity_lb = None\n        self.velocity_rb = None\n        self.delta_t = delta_t\n        self.confidence_pre = None\n        self.confidence = bbox[-1]\n        self.args = args\n        self.kf.args = args\n        # self.kf_score.args = args\n\n    def update(self, bbox):\n        \"\"\"\n        Updates the state vector with observed bbox.\n        \"\"\"\n        # velocity = None\n        velocity_lt = None\n        velocity_rt = None\n        velocity_lb = None\n        velocity_rb = None\n        if bbox is not None:\n            if self.last_observation.sum() >= 0:  # no previous observation\n                previous_box = None\n                for i in range(self.delta_t):\n                    # dt = self.delta_t - i\n                    if self.age - i - 1 in self.observations:\n                        previous_box = self.observations[self.age - i - 1]\n                        if velocity_lt is not None:\n                            # velocity += speed_direction(previous_box, bbox)\n                            velocity_lt += speed_direction_lt(previous_box, bbox)\n                            velocity_rt += speed_direction_rt(previous_box, bbox)\n                            velocity_lb += speed_direction_lb(previous_box, bbox)\n                            velocity_rb += speed_direction_rb(previous_box, bbox)\n                        else:\n                            # velocity = speed_direction(previous_box, bbox)\n                            velocity_lt = speed_direction_lt(previous_box, bbox)\n                            velocity_rt = speed_direction_rt(previous_box, bbox)\n                            velocity_lb = speed_direction_lb(previous_box, bbox)\n                            velocity_rb = speed_direction_rb(previous_box, bbox)\n                        # break\n                if previous_box is None:\n                    previous_box = self.last_observation\n                    # self.velocity = speed_direction(previous_box, bbox)\n                    # self.velocity = norm_vel(self.velocity)\n                    self.velocity_lt = speed_direction_lt(previous_box, bbox)\n                    self.velocity_rt = speed_direction_rt(previous_box, bbox)\n                    self.velocity_lb = speed_direction_lb(previous_box, bbox)\n                    self.velocity_rb = speed_direction_rb(previous_box, bbox)\n                else:\n                    # self.velocity = velocity\n                    # self.velocity = norm_vel(self.velocity)\n                    self.velocity_lt = velocity_lt\n                    self.velocity_rt = velocity_rt\n                    self.velocity_lb = velocity_lb\n                    self.velocity_rb = velocity_rb\n            \"\"\"\n              Insert new observations. This is a ugly way to maintain both self.observations\n              and self.history_observations. Bear it for the moment.\n            \"\"\"\n            self.last_observation = bbox\n            self.last_observation_save = bbox\n            self.observations[self.age] = bbox\n            self.history_observations.append(bbox)\n\n            self.time_since_update = 0\n            self.history = []\n            self.hits += 1\n            self.hit_streak += 1\n            self.kf.update(convert_bbox_to_z(bbox))\n            # self.kf_score.update(bbox[-1])\n            self.confidence_pre = self.confidence\n            self.confidence = bbox[-1]\n            self.age_recover_for_cbiou = self.age\n        else:\n            self.kf.update(bbox)\n            # self.kf_score.update(bbox)\n            self.confidence_pre = None\n\n    def predict(self):\n        \"\"\"\n        Advances the state vector and returns the predicted bounding box estimate.\n        \"\"\"\n        if((self.kf.x[7]+self.kf.x[2]) <= 0):\n            self.kf.x[7] *= 0.0\n\n        self.kf.predict()\n        # self.kf_score.predict()\n        self.age += 1\n        if(self.time_since_update > 0):\n            self.hit_streak = 0\n        self.time_since_update += 1\n        self.history.append(convert_x_to_bbox(self.kf.x))\n        if not self.confidence_pre:\n            return self.history[-1], np.clip(self.kf.x[3], self.args.track_thresh, 1.0), np.clip(self.confidence, 0.1, self.args.track_thresh)\n        else:\n            return self.history[-1], np.clip(self.kf.x[3], self.args.track_thresh, 1.0), np.clip(self.confidence - (self.confidence_pre - self.confidence), 0.1, self.args.track_thresh)\n\n    def get_state(self):\n        \"\"\"\n        Returns the current bounding box estimate.\n        \"\"\"\n        return convert_x_to_bbox(self.kf.x)\n\n\n\"\"\"\n    We support multiple ways for association cost calculation, by default\n    we use IoU. GIoU may have better performance in some situations. We note \n    that we hardly normalize the cost by all methods to (0,1) which may not be \n    the best practice.\n\"\"\"\nASSO_FUNCS = {  \"iou\": iou_batch,\n                \"giou\": giou_batch,\n                \"ciou\": ciou_batch,\n                \"diou\": diou_batch,\n                \"ct_dist\": ct_dist,\n                \"Height_Modulated_IoU\": hmiou\n                }\n\n\nclass Hybrid_Sort(object):\n    def __init__(self, args, det_thresh, max_age=30, min_hits=3,\n        iou_threshold=0.3, delta_t=3, asso_func=\"iou\", inertia=0.2, use_byte=False):\n        \"\"\"\n        Sets key parameters for SORT\n        \"\"\"\n        self.max_age = max_age\n        self.min_hits = min_hits\n        self.iou_threshold = iou_threshold\n        self.trackers = []\n        self.frame_count = 0\n        self.det_thresh = det_thresh\n        self.delta_t = delta_t\n        self.asso_func = ASSO_FUNCS[asso_func]\n        self.inertia = inertia\n        self.use_byte = use_byte\n        self.args = args\n        KalmanBoxTracker.count = 0\n\n    def update(self, output_results, img_info, img_size):\n        \"\"\"\n        Params:\n          dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]\n        Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).\n        Returns the a similar array, where the last column is the object ID.\n        NOTE: The number of objects returned may differ from the number of detections provided.\n        \"\"\"\n        if output_results is None:\n            return np.empty((0, 5))\n\n        self.frame_count += 1\n        # post_process detections\n        if output_results.shape[1] == 5:\n            scores = output_results[:, 4]\n            bboxes = output_results[:, :4]\n        else:\n            output_results = output_results.cpu().numpy()\n            scores = output_results[:, 4] * output_results[:, 5]\n            bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n        dets = np.concatenate((bboxes, np.expand_dims(scores, axis=-1)), axis=1)\n        inds_low = scores > 0.1\n        inds_high = scores < self.det_thresh\n        inds_second = np.logical_and(inds_low, inds_high)  # self.det_thresh > score > 0.1, for second matching\n        dets_second = dets[inds_second]  # detections for second matching\n        remain_inds = scores > self.det_thresh\n        dets = dets[remain_inds]\n\n        # get predicted locations from existing trackers.\n        trks = np.zeros((len(self.trackers), 6))\n        to_del = []\n        ret = []\n        for t, trk in enumerate(trks):\n            pos, kalman_score, simple_score = self.trackers[t].predict()\n            try:\n                trk[:] = [pos[0][0], pos[0][1], pos[0][2], pos[0][3], kalman_score, simple_score[0]]\n            except:\n                trk[:] = [pos[0][0], pos[0][1], pos[0][2], pos[0][3], kalman_score, simple_score]\n            if np.any(np.isnan(pos)):\n                to_del.append(t)\n        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))\n        for t in reversed(to_del):\n            self.trackers.pop(t)\n\n        # velocities = np.array(\n        #     [trk.velocity if trk.velocity is not None else np.array((0, 0)) for trk in self.trackers])\n        velocities_lt = np.array(\n            [trk.velocity_lt if trk.velocity_lt is not None else np.array((0, 0)) for trk in self.trackers])\n        velocities_rt = np.array(\n            [trk.velocity_rt if trk.velocity_rt is not None else np.array((0, 0)) for trk in self.trackers])\n        velocities_lb = np.array(\n            [trk.velocity_lb if trk.velocity_lb is not None else np.array((0, 0)) for trk in self.trackers])\n        velocities_rb = np.array(\n            [trk.velocity_rb if trk.velocity_rb is not None else np.array((0, 0)) for trk in self.trackers])\n        last_boxes = np.array([trk.last_observation for trk in self.trackers])\n        k_observations = np.array(\n            [k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])\n\n        \"\"\"\n            First round of association\n        \"\"\"\n        if self.args.TCM_first_step:\n            matched, unmatched_dets, unmatched_trks = associate_4_points_with_score(\n                dets, trks, self.iou_threshold, velocities_lt, velocities_rt, velocities_lb, velocities_rb,\n                k_observations, self.inertia, self.asso_func, self.args)\n        else:\n            matched, unmatched_dets, unmatched_trks = associate_4_points(\n                dets, trks, self.iou_threshold, velocities_lt, velocities_rt, velocities_lb, velocities_rb, k_observations, self.inertia, self.asso_func, self.args)\n\n        for m in matched:\n            self.trackers[m[1]].update(dets[m[0], :])\n\n        \"\"\"\n            Second round of associaton by OCR\n        \"\"\"\n        # BYTE association\n        if self.use_byte and len(dets_second) > 0 and unmatched_trks.shape[0] > 0:\n            u_trks = trks[unmatched_trks]\n            iou_left = self.asso_func(dets_second, u_trks)\n            iou_left = np.array(iou_left)\n            if iou_left.max() > self.iou_threshold:\n                \"\"\"\n                    NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may\n                    get a higher performance especially on MOT17/MOT20 datasets. But we keep it\n                    uniform here for simplicity\n                \"\"\"\n                if self.args.TCM_byte_step:\n                    iou_left -= np.array(cal_score_dif_batch_two_score(dets_second, u_trks) * self.args.TCM_byte_step_weight)\n                matched_indices = linear_assignment(-iou_left)\n                to_remove_trk_indices = []\n                for m in matched_indices:\n                    det_ind, trk_ind = m[0], unmatched_trks[m[1]]\n                    if iou_left[m[0], m[1]] < self.iou_threshold:\n                        continue\n                    self.trackers[trk_ind].update(dets_second[det_ind, :])\n                    to_remove_trk_indices.append(trk_ind)\n                unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))\n\n        if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:\n            left_dets = dets[unmatched_dets]\n            left_trks = last_boxes[unmatched_trks]\n            iou_left = self.asso_func(left_dets, left_trks)\n            iou_left = np.array(iou_left)\n\n            if iou_left.max() > self.iou_threshold:\n                \"\"\"\n                    NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may\n                    get a higher performance especially on MOT17/MOT20 datasets. But we keep it\n                    uniform here for simplicity\n                \"\"\"\n                rematched_indices = linear_assignment(-iou_left)\n                to_remove_det_indices = []\n                to_remove_trk_indices = []\n                for m in rematched_indices:\n                    det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]\n                    if iou_left[m[0], m[1]] < self.iou_threshold:\n                        continue\n                    self.trackers[trk_ind].update(dets[det_ind, :])\n                    to_remove_det_indices.append(det_ind)\n                    to_remove_trk_indices.append(trk_ind)\n                unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))\n                unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))\n\n        for m in unmatched_trks:\n            self.trackers[m].update(None)\n\n        # create and initialise new trackers for unmatched detections\n        for i in unmatched_dets:\n            trk = KalmanBoxTracker(dets[i, :], delta_t=self.delta_t, args=self.args)\n            self.trackers.append(trk)\n        i = len(self.trackers)\n        for trk in reversed(self.trackers):\n            if trk.last_observation.sum() < 0:\n                d = trk.get_state()[0][:4]\n            else:\n                \"\"\"\n                    this is optional to use the recent observation or the kalman filter prediction,\n                    we didn't notice significant difference here\n                \"\"\"\n                d = trk.last_observation[:4]\n            if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):\n                # +1 as MOT benchmark requires positive\n                ret.append(np.concatenate((d, [trk.id+1])).reshape(1, -1))\n            i -= 1\n            # remove dead tracklet\n            if(trk.time_since_update > self.max_age):\n                self.trackers.pop(i)\n        if(len(ret) > 0):\n            return np.concatenate(ret)\n        return np.empty((0, 5))\n\n    def update_public(self, dets, cates, scores):\n        self.frame_count += 1\n\n        det_scores = np.ones((dets.shape[0], 1))\n        dets = np.concatenate((dets, det_scores), axis=1)\n\n        remain_inds = scores > self.det_thresh\n        \n        cates = cates[remain_inds]\n        dets = dets[remain_inds]\n\n        trks = np.zeros((len(self.trackers), 5))\n        to_del = []\n        ret = []\n        for t, trk in enumerate(trks):\n            pos = self.trackers[t].predict()[0]\n            cat = self.trackers[t].cate\n            trk[:] = [pos[0], pos[1], pos[2], pos[3], cat]\n            if np.any(np.isnan(pos)):\n                to_del.append(t)\n        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))\n        for t in reversed(to_del):\n            self.trackers.pop(t)\n\n        velocities = np.array([trk.velocity if trk.velocity is not None else np.array((0,0)) for trk in self.trackers])\n        last_boxes = np.array([trk.last_observation for trk in self.trackers])\n        k_observations = np.array([k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])\n\n        matched, unmatched_dets, unmatched_trks = associate_kitti\\\n              (dets, trks, cates, self.iou_threshold, velocities, k_observations, self.inertia)\n          \n        for m in matched:\n            self.trackers[m[1]].update(dets[m[0], :])\n          \n        if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:\n            \"\"\"\n                The re-association stage by OCR.\n                NOTE: at this stage, adding other strategy might be able to continue improve\n                the performance, such as BYTE association by ByteTrack. \n            \"\"\"\n            left_dets = dets[unmatched_dets]\n            left_trks = last_boxes[unmatched_trks]\n            left_dets_c = left_dets.copy()\n            left_trks_c = left_trks.copy()\n\n            iou_left = self.asso_func(left_dets_c, left_trks_c)\n            iou_left = np.array(iou_left)\n            det_cates_left = cates[unmatched_dets]\n            trk_cates_left = trks[unmatched_trks][:,4]\n            num_dets = unmatched_dets.shape[0]\n            num_trks = unmatched_trks.shape[0]\n            cate_matrix = np.zeros((num_dets, num_trks))\n            for i in range(num_dets):\n                for j in range(num_trks):\n                    if det_cates_left[i] != trk_cates_left[j]:\n                            \"\"\"\n                                For some datasets, such as KITTI, there are different categories,\n                                we have to avoid associate them together.\n                            \"\"\"\n                            cate_matrix[i][j] = -1e6\n            iou_left = iou_left + cate_matrix\n            if iou_left.max() > self.iou_threshold - 0.1:\n                rematched_indices = linear_assignment(-iou_left)\n                to_remove_det_indices = []\n                to_remove_trk_indices = []\n                for m in rematched_indices:\n                    det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]\n                    if iou_left[m[0], m[1]] < self.iou_threshold - 0.1:\n                          continue\n                    self.trackers[trk_ind].update(dets[det_ind, :])\n                    to_remove_det_indices.append(det_ind)\n                    to_remove_trk_indices.append(trk_ind) \n                unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))\n                unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))\n\n        for i in unmatched_dets:\n            trk = KalmanBoxTracker(dets[i,:])\n            trk.cate = cates[i]\n            self.trackers.append(trk)\n        i = len(self.trackers)\n\n        for trk in reversed(self.trackers):\n            if trk.last_observation.sum() > 0:\n                d = trk.last_observation[:4]\n            else:\n                d = trk.get_state()[0]\n            if (trk.time_since_update < 1):\n                if (self.frame_count <= self.min_hits) or (trk.hit_streak >= self.min_hits):\n                    # id+1 as MOT benchmark requires positive\n                    ret.append(np.concatenate((d, [trk.id+1], [trk.cate], [0])).reshape(1,-1)) \n                if trk.hit_streak == self.min_hits:\n                    # Head Padding (HP): recover the lost steps during initializing the track\n                    for prev_i in range(self.min_hits - 1):\n                        prev_observation = trk.history_observations[-(prev_i+2)]\n                        ret.append((np.concatenate((prev_observation[:4], [trk.id+1], [trk.cate], \n                            [-(prev_i+1)]))).reshape(1,-1))\n            i -= 1 \n            if (trk.time_since_update > self.max_age):\n                  self.trackers.pop(i)\n        \n        if(len(ret)>0):\n            return np.concatenate(ret)\n        return np.empty((0, 7))\n\n\n"
  },
  {
    "path": "trackers/hybrid_sort_tracker/hybrid_sort_reid.py",
    "content": "\"\"\"\n    This script is adopted from the SORT script by Alex Bewley alex@bewley.ai\n\"\"\"\nfrom __future__ import print_function\n\nimport numpy as np\nimport copy\nfrom .association import *\nfrom collections import deque       # [hgx0418] deque for reid feature\nnp.random.seed(0)\n\ndef k_previous_obs(observations, cur_age, k):\n    if len(observations) == 0:\n        return [-1, -1, -1, -1, -1]\n    for i in range(k):\n        dt = k - i\n        if cur_age - dt in observations:\n            return observations[cur_age-dt]\n    max_age = max(observations.keys())\n    return observations[max_age]\n\n\ndef convert_bbox_to_z(bbox):\n    \"\"\"\n    Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form\n      [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is\n      the aspect ratio\n    \"\"\"\n    w = bbox[2] - bbox[0]\n    h = bbox[3] - bbox[1]\n    x = bbox[0] + w/2.\n    y = bbox[1] + h/2.\n    s = w * h  # scale is just area\n    r = w / float(h+1e-6)\n    score = bbox[4]\n    if score:\n        return np.array([x, y, s, score, r]).reshape((5, 1))\n    else:\n        return np.array([x, y, s, r]).reshape((4, 1))\n\n\ndef convert_x_to_bbox(x, score=None):\n    \"\"\"\n    Takes a bounding box in the centre form [x,y,s,r] and returns it in the form\n      [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right\n    \"\"\"\n    w = np.sqrt(x[2] * x[4])\n    h = x[2] / w\n    score = x[3]\n    if(score == None):\n      return np.array([x[0]-w/2., x[1]-h/2., x[0]+w/2., x[1]+h/2.]).reshape((1, 4))\n    else:\n      return np.array([x[0]-w/2., x[1]-h/2., x[0]+w/2., x[1]+h/2., score]).reshape((1, 5))\n\n\ndef speed_direction(bbox1, bbox2):\n    cx1, cy1 = (bbox1[0]+bbox1[2]) / 2.0, (bbox1[1]+bbox1[3])/2.0\n    cx2, cy2 = (bbox2[0]+bbox2[2]) / 2.0, (bbox2[1]+bbox2[3])/2.0\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\ndef speed_direction_lt(bbox1, bbox2):\n    cx1, cy1 = bbox1[0], bbox1[1]\n    cx2, cy2 = bbox2[0], bbox2[1]\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\ndef speed_direction_rt(bbox1, bbox2):\n    cx1, cy1 = bbox1[0], bbox1[3]\n    cx2, cy2 = bbox2[0], bbox2[3]\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\ndef speed_direction_lb(bbox1, bbox2):\n    cx1, cy1 = bbox1[2], bbox1[1]\n    cx2, cy2 = bbox2[2], bbox2[1]\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\ndef speed_direction_rb(bbox1, bbox2):\n    cx1, cy1 = bbox1[2], bbox1[3]\n    cx2, cy2 = bbox2[2], bbox2[3]\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\nclass KalmanBoxTracker(object):\n    \"\"\"\n    This class represents the internal state of individual tracked objects observed as bbox.\n    \"\"\"\n    count = 0\n\n    def __init__(self, bbox, temp_feat, delta_t=3, orig=False, buffer_size=30, args=None):     # 'temp_feat' and 'buffer_size' for reid feature\n        \"\"\"\n        Initialises a tracker using initial bounding box.\n\n        \"\"\"\n        # define constant velocity model\n        # if not orig and not args.kalman_GPR:\n        if not orig:\n          from .kalmanfilter_score_new import KalmanFilterNew_score_new as KalmanFilter_score_new\n          self.kf = KalmanFilter_score_new(dim_x=9, dim_z=5)\n        else:\n          from filterpy.kalman import KalmanFilter\n          self.kf = KalmanFilter(dim_x=7, dim_z=4)\n        # u, v, s, c, r, ~u, ~v, ~s, ~c\n        self.kf.F = np.array([[1, 0, 0, 0, 0, 1, 0, 0, 0],\n                              [0, 1, 0, 0, 0, 0, 1, 0, 0],\n                              [0, 0, 1, 0, 0, 0, 0, 1, 0],\n                              [0, 0, 0, 1, 0, 0, 0, 0, 1],\n                              [0, 0, 0, 0, 1, 0, 0, 0, 0],\n                              [0, 0, 0, 0, 0, 1, 0, 0, 0],\n                              [0, 0, 0, 0, 0, 0, 1, 0, 0],\n                              [0, 0, 0, 0, 0, 0, 0, 1, 0],\n                              [0, 0, 0, 0, 0, 0, 0, 0, 1]])\n        self.kf.H = np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0],\n                              [0, 1, 0, 0, 0, 0, 0, 0, 0],\n                              [0, 0, 1, 0, 0, 0, 0, 0, 0],\n                              [0, 0, 0, 1, 0, 0, 0, 0, 0],\n                              [0, 0, 0, 0, 1, 0, 0, 0, 0]])\n\n        self.kf.R[2:, 2:] *= 10.\n        self.kf.P[5:, 5:] *= 1000.  # give high uncertainty to the unobservable initial velocities\n        self.kf.P *= 10.\n        self.kf.Q[-1, -1] *= 0.01\n        self.kf.Q[-2, -2] *= 0.01\n        self.kf.Q[5:, 5:] *= 0.01\n\n        self.kf.x[:5] = convert_bbox_to_z(bbox)\n\n        self.time_since_update = 0\n        self.id = KalmanBoxTracker.count\n        KalmanBoxTracker.count += 1\n        self.history = []\n        self.hits = 0\n        self.hit_streak = 0\n        self.age = 0\n        \"\"\"\n        NOTE: [-1,-1,-1,-1,-1] is a compromising placeholder for non-observation status, the same for the return of \n        function k_previous_obs. It is ugly and I do not like it. But to support generate observation array in a \n        fast and unified way, which you would see below k_observations = np.array([k_previous_obs(...]]), let's bear it for now.\n        \"\"\"\n        self.last_observation = np.array([-1, -1, -1, -1, -1])  # placeholder\n        self.last_observation_save = np.array([-1, -1, -1, -1, -1])\n        self.observations = dict()\n        self.history_observations = []\n        self.velocity_lt = None\n        self.velocity_rt = None\n        self.velocity_lb = None\n        self.velocity_rb = None\n        self.delta_t = delta_t\n        self.confidence_pre = None\n        self.confidence = bbox[-1]\n        self.args = args\n        self.kf.args = args\n\n        # add the following values and functions\n        self.smooth_feat = None\n        buffer_size = args.longterm_bank_length\n        self.features = deque([], maxlen=buffer_size)\n        self.update_features(temp_feat)\n\n        # momentum of embedding update\n        self.alpha = self.args.alpha\n\n    # ReID. for update embeddings during tracking\n    def update_features(self, feat, score=-1):\n        feat /= np.linalg.norm(feat)\n        self.curr_feat = feat\n        if self.smooth_feat is None:\n            self.smooth_feat = feat\n        else:\n            if self.args.adapfs:\n                assert score > 0\n                pre_w = self.alpha * (self.confidence / (self.confidence + score))\n                cur_w = (1 - self.alpha) * (score / (self.confidence + score))\n                sum_w = pre_w + cur_w\n                pre_w = pre_w / sum_w\n                cur_w = cur_w / sum_w\n                self.smooth_feat = pre_w * self.smooth_feat + cur_w * feat\n            else:\n                self.smooth_feat = self.alpha * self.smooth_feat + (1 - self.alpha) * feat\n        self.features.append(feat)\n        self.smooth_feat /= np.linalg.norm(self.smooth_feat)\n\n    def camera_update(self, warp_matrix):\n        \"\"\"\n        update 'self.mean' of current tracklet with ecc results.\n        Parameters\n        ----------\n        warp_matrix: warp matrix computed by ECC.\n        \"\"\"\n        x1, y1, x2, y2, s = convert_x_to_bbox(self.kf.x)[0]\n        x1_, y1_, _ = warp_matrix @ np.array([x1, y1, 1]).T\n        x2_, y2_, _ = warp_matrix @ np.array([x2, y2, 1]).T\n        # w, h = x2_ - x1_, y2_ - y1_\n        # cx, cy = x1_ + w / 2, y1_ + h / 2\n        self.kf.x[:5] = convert_bbox_to_z([x1_, y1_, x2_, y2_, s])\n\n    def update(self, bbox, id_feature, update_feature=True):\n        \"\"\"\n        Updates the state vector with observed bbox.\n        \"\"\"\n        velocity_lt = None\n        velocity_rt = None\n        velocity_lb = None\n        velocity_rb = None\n        if bbox is not None:\n            if self.last_observation.sum() >= 0:  # no previous observation\n                previous_box = None\n                for i in range(self.delta_t):\n                    # dt = self.delta_t - i\n                    if self.age - i - 1 in self.observations:\n                        previous_box = self.observations[self.age - i - 1]\n                        if velocity_lt is not None:\n                            velocity_lt += speed_direction_lt(previous_box, bbox)\n                            velocity_rt += speed_direction_rt(previous_box, bbox)\n                            velocity_lb += speed_direction_lb(previous_box, bbox)\n                            velocity_rb += speed_direction_rb(previous_box, bbox)\n                        else:\n                            velocity_lt = speed_direction_lt(previous_box, bbox)\n                            velocity_rt = speed_direction_rt(previous_box, bbox)\n                            velocity_lb = speed_direction_lb(previous_box, bbox)\n                            velocity_rb = speed_direction_rb(previous_box, bbox)\n                        # break\n                if previous_box is None:\n                    previous_box = self.last_observation\n                    self.velocity_lt = speed_direction_lt(previous_box, bbox)\n                    self.velocity_rt = speed_direction_rt(previous_box, bbox)\n                    self.velocity_lb = speed_direction_lb(previous_box, bbox)\n                    self.velocity_rb = speed_direction_rb(previous_box, bbox)\n                else:\n                    self.velocity_lt = velocity_lt\n                    self.velocity_rt = velocity_rt\n                    self.velocity_lb = velocity_lb\n                    self.velocity_rb = velocity_rb\n            \"\"\"\n              Insert new observations. This is a ugly way to maintain both self.observations\n              and self.history_observations. Bear it for the moment.\n            \"\"\"\n            self.last_observation = bbox\n            self.last_observation_save = bbox\n            self.observations[self.age] = bbox\n            self.history_observations.append(bbox)\n\n            self.time_since_update = 0\n            self.history = []\n            self.hits += 1\n            self.hit_streak += 1\n            self.kf.update(convert_bbox_to_z(bbox))\n            # add interface for update feature or not\n            if update_feature:\n                if self.args.adapfs:\n                    self.update_features(id_feature, score=bbox[-1])\n                else:\n                    self.update_features(id_feature)\n            self.confidence_pre = self.confidence\n            self.confidence = bbox[-1]\n        else:\n            self.kf.update(bbox)\n            self.confidence_pre = None\n\n    def predict(self):\n        \"\"\"\n        Advances the state vector and returns the predicted bounding box estimate.\n        \"\"\"\n        if((self.kf.x[7]+self.kf.x[2]) <= 0):\n            self.kf.x[7] *= 0.0\n\n        self.kf.predict()\n        self.age += 1\n        if(self.time_since_update > 0):\n            self.hit_streak = 0\n        self.time_since_update += 1\n        self.history.append(convert_x_to_bbox(self.kf.x))\n        if not self.confidence_pre:\n            return self.history[-1], np.clip(self.kf.x[3], self.args.track_thresh, 1.0), np.clip(self.confidence, 0.1, self.args.track_thresh)\n        else:\n            return self.history[-1], np.clip(self.kf.x[3], self.args.track_thresh, 1.0), np.clip(self.confidence - (self.confidence_pre - self.confidence), 0.1, self.args.track_thresh)\n\n    def get_state(self):\n        \"\"\"\n        Returns the current bounding box estimate.\n        \"\"\"\n        return convert_x_to_bbox(self.kf.x)\n\n\n\"\"\"\n    We support multiple ways for association cost calculation, by default\n    we use IoU. GIoU may have better performance in some situations. We note \n    that we hardly normalize the cost by all methods to (0,1) which may not be \n    the best practice.\n\"\"\"\nASSO_FUNCS = {  \"iou\": iou_batch,\n                \"giou\": giou_batch,\n                \"ciou\": ciou_batch,\n                \"diou\": diou_batch,\n                \"ct_dist\": ct_dist,\n                \"Height_Modulated_IoU\": hmiou\n                }\n\n\nclass Hybrid_Sort_ReID(object):\n    def __init__(self, args, det_thresh, max_age=30, min_hits=3,\n        iou_threshold=0.3, delta_t=3, asso_func=\"iou\", inertia=0.2):\n        \"\"\"\n        Sets key parameters for SORT\n        \"\"\"\n        self.max_age = max_age\n        self.min_hits = min_hits\n        self.iou_threshold = iou_threshold\n        self.trackers = []\n        self.frame_count = 0\n        self.det_thresh = det_thresh\n        self.delta_t = delta_t\n        self.asso_func = ASSO_FUNCS[asso_func]\n        self.inertia = inertia\n        self.use_byte = args.use_byte\n        self.args = args\n        KalmanBoxTracker.count = 0\n\n    # ECC for CMC\n    def camera_update(self, trackers, warp_matrix):\n        for tracker in trackers:\n            tracker.camera_update(warp_matrix)\n\n    def update(self, output_results, img_info, img_size, id_feature=None, warp_matrix=None):\n        \"\"\"\n        Params:\n          dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]\n        Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).\n        Returns the a similar array, where the last column is the object ID.\n        NOTE: The number of objects returned may differ from the number of detections provided.\n        \"\"\"\n        if output_results is None:\n            return np.empty((0, 5))\n\n        if self.args.ECC:\n            # camera update for all stracks\n            if warp_matrix is not None:\n                self.camera_update(self.trackers, warp_matrix)\n\n        self.frame_count += 1\n        # post_process detections\n        if output_results.shape[1] == 5:\n            scores = output_results[:, 4]\n            bboxes = output_results[:, :4]\n        else:\n            output_results = output_results.cpu().numpy()\n            scores = output_results[:, 4] * output_results[:, 5]\n            bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n        dets = np.concatenate((bboxes, np.expand_dims(scores, axis=-1)), axis=1)\n        inds_low = scores > self.args.low_thresh\n        inds_high = scores < self.det_thresh\n        inds_second = np.logical_and(inds_low, inds_high)  # self.det_thresh > score > 0.1, for second matching\n        dets_second = dets[inds_second]  # detections for second matching\n        remain_inds = scores > self.det_thresh\n        dets = dets[remain_inds]\n        id_feature_keep = id_feature[remain_inds]  # ID feature of 1st stage matching\n        id_feature_second = id_feature[inds_second]  # ID feature of 2nd stage matching\n\n        trks = np.zeros((len(self.trackers), 6))\n        to_del = []\n        ret = []\n        for t, trk in enumerate(trks):\n            pos, kalman_score, simple_score = self.trackers[t].predict()\n            try:\n                trk[:] = [pos[0][0], pos[0][1], pos[0][2], pos[0][3], kalman_score, simple_score[0]]\n            except:\n                trk[:] = [pos[0][0], pos[0][1], pos[0][2], pos[0][3], kalman_score, simple_score]\n            if np.any(np.isnan(pos)):\n                to_del.append(t)\n        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))\n        for t in reversed(to_del):\n            self.trackers.pop(t)\n\n        velocities_lt = np.array(\n            [trk.velocity_lt if trk.velocity_lt is not None else np.array((0, 0)) for trk in self.trackers])\n        velocities_rt = np.array(\n            [trk.velocity_rt if trk.velocity_rt is not None else np.array((0, 0)) for trk in self.trackers])\n        velocities_lb = np.array(\n            [trk.velocity_lb if trk.velocity_lb is not None else np.array((0, 0)) for trk in self.trackers])\n        velocities_rb = np.array(\n            [trk.velocity_rb if trk.velocity_rb is not None else np.array((0, 0)) for trk in self.trackers])\n        last_boxes = np.array([trk.last_observation for trk in self.trackers])\n        k_observations = np.array(\n            [k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])\n\n        \"\"\"\n            First round of association\n        \"\"\"\n        if self.args.EG_weight_high_score > 0 and self.args.TCM_first_step:\n            track_features = np.asarray([track.smooth_feat for track in self.trackers],\n                                        dtype=np.float)\n            emb_dists = embedding_distance(track_features, id_feature_keep).T\n            if self.args.with_longterm_reid or self.args.with_longterm_reid_correction:\n                long_track_features = np.asarray([np.vstack(list(track.features)).mean(0) for track in self.trackers],\n                                                 dtype=np.float)\n                assert track_features.shape == long_track_features.shape\n                long_emb_dists = embedding_distance(long_track_features, id_feature_keep).T\n                assert emb_dists.shape == long_emb_dists.shape\n                matched, unmatched_dets, unmatched_trks = associate_4_points_with_score_with_reid(\n                    dets, trks, self.iou_threshold, velocities_lt, velocities_rt, velocities_lb, velocities_rb,\n                    k_observations, self.inertia, self.asso_func, self.args,emb_cost=emb_dists,\n                    weights=(1.0, self.args.EG_weight_high_score), thresh=self.args.high_score_matching_thresh,\n                    long_emb_dists=long_emb_dists, with_longterm_reid=self.args.with_longterm_reid,\n                    longterm_reid_weight=self.args.longterm_reid_weight,\n                    with_longterm_reid_correction=self.args.with_longterm_reid_correction,\n                    longterm_reid_correction_thresh=self.args.longterm_reid_correction_thresh,\n                    dataset=self.args.dataset)\n            else:\n                matched, unmatched_dets, unmatched_trks = associate_4_points_with_score_with_reid(\n                    dets, trks, self.iou_threshold, velocities_lt, velocities_rt, velocities_lb, velocities_rb,\n                    k_observations, self.inertia, self.asso_func, self.args,emb_cost=emb_dists,\n                    weights=(1.0, self.args.EG_weight_high_score), thresh=self.args.high_score_matching_thresh)\n        elif self.args.TCM_first_step:\n            matched, unmatched_dets, unmatched_trks = associate_4_points_with_score(\n                dets, trks, self.iou_threshold, velocities_lt, velocities_rt, velocities_lb, velocities_rb,\n                k_observations, self.inertia, self.asso_func, self.args)\n\n        # update with id feature\n        for m in matched:\n            self.trackers[m[1]].update(dets[m[0], :], id_feature_keep[m[0], :])\n\n        \"\"\"\n            Second round of associaton by OCR\n        \"\"\"\n        # BYTE association\n        if self.use_byte and len(dets_second) > 0 and unmatched_trks.shape[0] > 0:\n            u_trks = trks[unmatched_trks]\n            u_tracklets = [self.trackers[index] for index in unmatched_trks]\n            iou_left = self.asso_func(dets_second, u_trks)\n            iou_left = np.array(iou_left)\n            if iou_left.max() > self.iou_threshold:\n                \"\"\"\n                    NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may\n                    get a higher performance especially on MOT17/MOT20 datasets. But we keep it\n                    uniform here for simplicity\n                \"\"\"\n                if self.args.TCM_byte_step:\n                    iou_left_ori = copy.deepcopy(iou_left)\n                    iou_left -= np.array(cal_score_dif_batch_two_score(dets_second, u_trks) * self.args.TCM_byte_step_weight)\n                    iou_left_thre = iou_left\n                if self.args.EG_weight_low_score > 0:\n                    u_track_features = np.asarray([track.smooth_feat for track in u_tracklets], dtype=np.float)\n                    emb_dists_low_score = embedding_distance(u_track_features, id_feature_second).T\n                    matched_indices = linear_assignment(-iou_left + self.args.EG_weight_low_score * emb_dists_low_score,\n                                                        )\n                else:\n                    matched_indices = linear_assignment(-iou_left)\n                to_remove_trk_indices = []\n                for m in matched_indices:\n                    det_ind, trk_ind = m[0], unmatched_trks[m[1]]\n                    if self.args.with_longterm_reid_correction and self.args.EG_weight_low_score > 0:\n                        if iou_left_thre[m[0], m[1]] < self.iou_threshold or emb_dists_low_score[m[0], m[1]] > self.args.longterm_reid_correction_thresh_low:\n                            print(\"correction 2nd:\", emb_dists_low_score[m[0], m[1]])\n                            continue\n                    else:\n                        if iou_left_thre[m[0], m[1]] < self.iou_threshold:\n                            continue\n                    self.trackers[trk_ind].update(dets_second[det_ind, :], id_feature_second[det_ind, :], update_feature=False)     # [hgx0523] do not update with id feature\n                    to_remove_trk_indices.append(trk_ind)\n                unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))\n\n        if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:\n            left_dets = dets[unmatched_dets]\n            # left_id_feature = id_feature_keep[unmatched_dets]       # update id feature, if needed\n            left_trks = last_boxes[unmatched_trks]\n            iou_left = self.asso_func(left_dets, left_trks)\n            iou_left = np.array(iou_left)\n\n            if iou_left.max() > self.iou_threshold:\n                \"\"\"\n                    NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may\n                    get a higher performance especially on MOT17/MOT20 datasets. But we keep it\n                    uniform here for simplicity\n                \"\"\"\n                rematched_indices = linear_assignment(-iou_left)\n                to_remove_det_indices = []\n                to_remove_trk_indices = []\n                for m in rematched_indices:\n                    det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]\n                    if iou_left[m[0], m[1]] < self.iou_threshold:\n                        continue\n                    self.trackers[trk_ind].update(dets[det_ind, :], id_feature_keep[det_ind, :], update_feature=False)\n                    to_remove_det_indices.append(det_ind)\n                    to_remove_trk_indices.append(trk_ind)\n                unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))\n                unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))\n\n        for m in unmatched_trks:\n            self.trackers[m].update(None, None)\n\n        # create and initialise new trackers for unmatched detections\n        for i in unmatched_dets:\n            trk = KalmanBoxTracker(dets[i, :], id_feature_keep[i, :], delta_t=self.delta_t, args=self.args)\n            self.trackers.append(trk)\n        i = len(self.trackers)\n        for trk in reversed(self.trackers):\n            if trk.last_observation.sum() < 0:\n                d = trk.get_state()[0][:4]\n            else:\n                \"\"\"\n                    this is optional to use the recent observation or the kalman filter prediction,\n                    we didn't notice significant difference here\n                \"\"\"\n                d = trk.last_observation[:4]\n            if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):\n                # +1 as MOT benchmark requires positive\n                ret.append(np.concatenate((d, [trk.id+1])).reshape(1, -1))\n            i -= 1\n            # remove dead tracklet\n            if(trk.time_since_update > self.max_age):\n                self.trackers.pop(i)\n        if(len(ret) > 0):\n            return np.concatenate(ret)\n        return np.empty((0, 5))\n\n    def update_public(self, dets, cates, scores):\n        self.frame_count += 1\n\n        det_scores = np.ones((dets.shape[0], 1))\n        dets = np.concatenate((dets, det_scores), axis=1)\n\n        remain_inds = scores > self.det_thresh\n        \n        cates = cates[remain_inds]\n        dets = dets[remain_inds]\n\n        trks = np.zeros((len(self.trackers), 5))\n        to_del = []\n        ret = []\n        for t, trk in enumerate(trks):\n            pos = self.trackers[t].predict()[0]\n            cat = self.trackers[t].cate\n            trk[:] = [pos[0], pos[1], pos[2], pos[3], cat]\n            if np.any(np.isnan(pos)):\n                to_del.append(t)\n        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))\n        for t in reversed(to_del):\n            self.trackers.pop(t)\n\n        velocities = np.array([trk.velocity if trk.velocity is not None else np.array((0,0)) for trk in self.trackers])\n        last_boxes = np.array([trk.last_observation for trk in self.trackers])\n        k_observations = np.array([k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])\n\n        matched, unmatched_dets, unmatched_trks = associate_kitti\\\n              (dets, trks, cates, self.iou_threshold, velocities, k_observations, self.inertia)\n          \n        for m in matched:\n            self.trackers[m[1]].update(dets[m[0], :])\n          \n        if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:\n            \"\"\"\n                The re-association stage by OCR.\n                NOTE: at this stage, adding other strategy might be able to continue improve\n                the performance, such as BYTE association by ByteTrack. \n            \"\"\"\n            left_dets = dets[unmatched_dets]\n            left_trks = last_boxes[unmatched_trks]\n            left_dets_c = left_dets.copy()\n            left_trks_c = left_trks.copy()\n\n            iou_left = self.asso_func(left_dets_c, left_trks_c)\n            iou_left = np.array(iou_left)\n            det_cates_left = cates[unmatched_dets]\n            trk_cates_left = trks[unmatched_trks][:,4]\n            num_dets = unmatched_dets.shape[0]\n            num_trks = unmatched_trks.shape[0]\n            cate_matrix = np.zeros((num_dets, num_trks))\n            for i in range(num_dets):\n                for j in range(num_trks):\n                    if det_cates_left[i] != trk_cates_left[j]:\n                            \"\"\"\n                                For some datasets, such as KITTI, there are different categories,\n                                we have to avoid associate them together.\n                            \"\"\"\n                            cate_matrix[i][j] = -1e6\n            iou_left = iou_left + cate_matrix\n            if iou_left.max() > self.iou_threshold - 0.1:\n                rematched_indices = linear_assignment(-iou_left)\n                to_remove_det_indices = []\n                to_remove_trk_indices = []\n                for m in rematched_indices:\n                    det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]\n                    if iou_left[m[0], m[1]] < self.iou_threshold - 0.1:\n                          continue\n                    self.trackers[trk_ind].update(dets[det_ind, :])\n                    to_remove_det_indices.append(det_ind)\n                    to_remove_trk_indices.append(trk_ind) \n                unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))\n                unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))\n\n        for i in unmatched_dets:\n            trk = KalmanBoxTracker(dets[i,:])\n            trk.cate = cates[i]\n            self.trackers.append(trk)\n        i = len(self.trackers)\n\n        for trk in reversed(self.trackers):\n            if trk.last_observation.sum() > 0:\n                d = trk.last_observation[:4]\n            else:\n                d = trk.get_state()[0]\n            if (trk.time_since_update < 1):\n                if (self.frame_count <= self.min_hits) or (trk.hit_streak >= self.min_hits):\n                    # id+1 as MOT benchmark requires positive\n                    ret.append(np.concatenate((d, [trk.id+1], [trk.cate], [0])).reshape(1,-1)) \n                if trk.hit_streak == self.min_hits:\n                    # Head Padding (HP): recover the lost steps during initializing the track\n                    for prev_i in range(self.min_hits - 1):\n                        prev_observation = trk.history_observations[-(prev_i+2)]\n                        ret.append((np.concatenate((prev_observation[:4], [trk.id+1], [trk.cate], \n                            [-(prev_i+1)]))).reshape(1,-1))\n            i -= 1 \n            if (trk.time_since_update > self.max_age):\n                  self.trackers.pop(i)\n        \n        if(len(ret)>0):\n            return np.concatenate(ret)\n        return np.empty((0, 7))\n\n\n"
  },
  {
    "path": "trackers/hybrid_sort_tracker/kalmanfilter.py",
    "content": "# -*- coding: utf-8 -*-\n# pylint: disable=invalid-name, too-many-arguments, too-many-branches,\n# pylint: disable=too-many-locals, too-many-instance-attributes, too-many-lines\n\n\"\"\"\nThis module implements the linear Kalman filter in both an object\noriented and procedural form. The KalmanFilter class implements\nthe filter by storing the various matrices in instance variables,\nminimizing the amount of bookkeeping you have to do.\nAll Kalman filters operate with a predict->update cycle. The\npredict step, implemented with the method or function predict(),\nuses the state transition matrix F to predict the state in the next\ntime period (epoch). The state is stored as a gaussian (x, P), where\nx is the state (column) vector, and P is its covariance. Covariance\nmatrix Q specifies the process covariance. In Bayesian terms, this\nprediction is called the *prior*, which you can think of colloquially\nas the estimate prior to incorporating the measurement.\nThe update step, implemented with the method or function `update()`,\nincorporates the measurement z with covariance R, into the state\nestimate (x, P). The class stores the system uncertainty in S,\nthe innovation (residual between prediction and measurement in\nmeasurement space) in y, and the Kalman gain in k. The procedural\nform returns these variables to you. In Bayesian terms this computes\nthe *posterior* - the estimate after the information from the\nmeasurement is incorporated.\nWhether you use the OO form or procedural form is up to you. If\nmatrices such as H, R, and F are changing each epoch, you'll probably\nopt to use the procedural form. If they are unchanging, the OO\nform is perhaps easier to use since you won't need to keep track\nof these matrices. This is especially useful if you are implementing\nbanks of filters or comparing various KF designs for performance;\na trivial coding bug could lead to using the wrong sets of matrices.\nThis module also offers an implementation of the RTS smoother, and\nother helper functions, such as log likelihood computations.\nThe Saver class allows you to easily save the state of the\nKalmanFilter class after every update\nThis module expects NumPy arrays for all values that expect\narrays, although in a few cases, particularly method parameters,\nit will accept types that convert to NumPy arrays, such as lists\nof lists. These exceptions are documented in the method or function.\nExamples\n--------\nThe following example constructs a constant velocity kinematic\nfilter, filters noisy data, and plots the results. It also demonstrates\nusing the Saver class to save the state of the filter at each epoch.\n.. code-block:: Python\n    import matplotlib.pyplot as plt\n    import numpy as np\n    from filterpy.kalman import KalmanFilter\n    from filterpy.common import Q_discrete_white_noise, Saver\n    r_std, q_std = 2., 0.003\n    cv = KalmanFilter(dim_x=2, dim_z=1)\n    cv.x = np.array([[0., 1.]]) # position, velocity\n    cv.F = np.array([[1, dt],[ [0, 1]])\n    cv.R = np.array([[r_std^^2]])\n    f.H = np.array([[1., 0.]])\n    f.P = np.diag([.1^^2, .03^^2)\n    f.Q = Q_discrete_white_noise(2, dt, q_std**2)\n    saver = Saver(cv)\n    for z in range(100):\n        cv.predict()\n        cv.update([z + randn() * r_std])\n        saver.save() # save the filter's state\n    saver.to_array()\n    plt.plot(saver.x[:, 0])\n    # plot all of the priors\n    plt.plot(saver.x_prior[:, 0])\n    # plot mahalanobis distance\n    plt.figure()\n    plt.plot(saver.mahalanobis)\nThis code implements the same filter using the procedural form\n    x = np.array([[0., 1.]]) # position, velocity\n    F = np.array([[1, dt],[ [0, 1]])\n    R = np.array([[r_std^^2]])\n    H = np.array([[1., 0.]])\n    P = np.diag([.1^^2, .03^^2)\n    Q = Q_discrete_white_noise(2, dt, q_std**2)\n    for z in range(100):\n        x, P = predict(x, P, F=F, Q=Q)\n        x, P = update(x, P, z=[z + randn() * r_std], R=R, H=H)\n        xs.append(x[0, 0])\n    plt.plot(xs)\nFor more examples see the test subdirectory, or refer to the\nbook cited below. In it I both teach Kalman filtering from basic\nprinciples, and teach the use of this library in great detail.\nFilterPy library.\nhttp://github.com/rlabbe/filterpy\nDocumentation at:\nhttps://filterpy.readthedocs.org\nSupporting book at:\nhttps://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\nThis is licensed under an MIT license. See the readme.MD file\nfor more information.\nCopyright 2014-2018 Roger R Labbe Jr.\n\"\"\"\n\nfrom __future__ import absolute_import, division\n\nfrom copy import deepcopy\nfrom math import log, exp, sqrt\nimport sys\nimport numpy as np\nfrom numpy import dot, zeros, eye, isscalar, shape\nimport numpy.linalg as linalg\nfrom filterpy.stats import logpdf\nfrom filterpy.common import pretty_str, reshape_z\n\n\nclass KalmanFilterNew(object):\n    \"\"\" Implements a Kalman filter. You are responsible for setting the\n    various state variables to reasonable values; the defaults  will\n    not give you a functional filter.\n    For now the best documentation is my free book Kalman and Bayesian\n    Filters in Python [2]_. The test files in this directory also give you a\n    basic idea of use, albeit without much description.\n    In brief, you will first construct this object, specifying the size of\n    the state vector with dim_x and the size of the measurement vector that\n    you will be using with dim_z. These are mostly used to perform size checks\n    when you assign values to the various matrices. For example, if you\n    specified dim_z=2 and then try to assign a 3x3 matrix to R (the\n    measurement noise matrix you will get an assert exception because R\n    should be 2x2. (If for whatever reason you need to alter the size of\n    things midstream just use the underscore version of the matrices to\n    assign directly: your_filter._R = a_3x3_matrix.)\n    After construction the filter will have default matrices created for you,\n    but you must specify the values for each. It’s usually easiest to just\n    overwrite them rather than assign to each element yourself. This will be\n    clearer in the example below. All are of type numpy.array.\n    Examples\n    --------\n    Here is a filter that tracks position and velocity using a sensor that only\n    reads position.\n    First construct the object with the required dimensionality. Here the state\n    (`dim_x`) has 2 coefficients (position and velocity), and the measurement\n    (`dim_z`) has one. In FilterPy `x` is the state, `z` is the measurement.\n    .. code::\n        from filterpy.kalman import KalmanFilter\n        f = KalmanFilter (dim_x=2, dim_z=1)\n    Assign the initial value for the state (position and velocity). You can do this\n    with a two dimensional array like so:\n        .. code::\n            f.x = np.array([[2.],    # position\n                            [0.]])   # velocity\n    or just use a one dimensional array, which I prefer doing.\n    .. code::\n        f.x = np.array([2., 0.])\n    Define the state transition matrix:\n        .. code::\n            f.F = np.array([[1.,1.],\n                            [0.,1.]])\n    Define the measurement function. Here we need to convert a position-velocity\n    vector into just a position vector, so we use:\n        .. code::\n        f.H = np.array([[1., 0.]])\n    Define the state's covariance matrix P. \n    .. code::\n        f.P = np.array([[1000.,    0.],\n                        [   0., 1000.] ])\n    Now assign the measurement noise. Here the dimension is 1x1, so I can\n    use a scalar\n    .. code::\n        f.R = 5\n    I could have done this instead:\n    .. code::\n        f.R = np.array([[5.]])\n    Note that this must be a 2 dimensional array.\n    Finally, I will assign the process noise. Here I will take advantage of\n    another FilterPy library function:\n    .. code::\n        from filterpy.common import Q_discrete_white_noise\n        f.Q = Q_discrete_white_noise(dim=2, dt=0.1, var=0.13)\n    Now just perform the standard predict/update loop:\n    .. code::\n        while some_condition_is_true:\n            z = get_sensor_reading()\n            f.predict()\n            f.update(z)\n            do_something_with_estimate (f.x)\n    **Procedural Form**\n    This module also contains stand alone functions to perform Kalman filtering.\n    Use these if you are not a fan of objects.\n    **Example**\n    .. code::\n        while True:\n            z, R = read_sensor()\n            x, P = predict(x, P, F, Q)\n            x, P = update(x, P, z, R, H)\n    See my book Kalman and Bayesian Filters in Python [2]_.\n    You will have to set the following attributes after constructing this\n    object for the filter to perform properly. Please note that there are\n    various checks in place to ensure that you have made everything the\n    'correct' size. However, it is possible to provide incorrectly sized\n    arrays such that the linear algebra can not perform an operation.\n    It can also fail silently - you can end up with matrices of a size that\n    allows the linear algebra to work, but are the wrong shape for the problem\n    you are trying to solve.\n    Parameters\n    ----------\n    dim_x : int\n        Number of state variables for the Kalman filter. For example, if\n        you are tracking the position and velocity of an object in two\n        dimensions, dim_x would be 4.\n        This is used to set the default size of P, Q, and u\n    dim_z : int\n        Number of of measurement inputs. For example, if the sensor\n        provides you with position in (x,y), dim_z would be 2.\n    dim_u : int (optional)\n        size of the control input, if it is being used.\n        Default value of 0 indicates it is not used.\n    compute_log_likelihood : bool (default = True)\n        Computes log likelihood by default, but this can be a slow\n        computation, so if you never use it you can turn this computation\n        off.\n    Attributes\n    ----------\n    x : numpy.array(dim_x, 1)\n        Current state estimate. Any call to update() or predict() updates\n        this variable.\n    P : numpy.array(dim_x, dim_x)\n        Current state covariance matrix. Any call to update() or predict()\n        updates this variable.\n    x_prior : numpy.array(dim_x, 1)\n        Prior (predicted) state estimate. The *_prior and *_post attributes\n        are for convenience; they store the  prior and posterior of the\n        current epoch. Read Only.\n    P_prior : numpy.array(dim_x, dim_x)\n        Prior (predicted) state covariance matrix. Read Only.\n    x_post : numpy.array(dim_x, 1)\n        Posterior (updated) state estimate. Read Only.\n    P_post : numpy.array(dim_x, dim_x)\n        Posterior (updated) state covariance matrix. Read Only.\n    z : numpy.array\n        Last measurement used in update(). Read only.\n    R : numpy.array(dim_z, dim_z)\n        Measurement noise covariance matrix. Also known as the\n        observation covariance.\n    Q : numpy.array(dim_x, dim_x)\n        Process noise covariance matrix. Also known as the transition\n        covariance.\n    F : numpy.array()\n        State Transition matrix. Also known as `A` in some formulation.\n    H : numpy.array(dim_z, dim_x)\n        Measurement function. Also known as the observation matrix, or as `C`.\n    y : numpy.array\n        Residual of the update step. Read only.\n    K : numpy.array(dim_x, dim_z)\n        Kalman gain of the update step. Read only.\n    S :  numpy.array\n        System uncertainty (P projected to measurement space). Read only.\n    SI :  numpy.array\n        Inverse system uncertainty. Read only.\n    log_likelihood : float\n        log-likelihood of the last measurement. Read only.\n    likelihood : float\n        likelihood of last measurement. Read only.\n        Computed from the log-likelihood. The log-likelihood can be very\n        small,  meaning a large negative value such as -28000. Taking the\n        exp() of that results in 0.0, which can break typical algorithms\n        which multiply by this value, so by default we always return a\n        number >= sys.float_info.min.\n    mahalanobis : float\n        mahalanobis distance of the innovation. Read only.\n    inv : function, default numpy.linalg.inv\n        If you prefer another inverse function, such as the Moore-Penrose\n        pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv\n        This is only used to invert self.S. If you know it is diagonal, you\n        might choose to set it to filterpy.common.inv_diagonal, which is\n        several times faster than numpy.linalg.inv for diagonal matrices.\n    alpha : float\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon [1]_.\n    References\n    ----------\n    .. [1] Dan Simon. \"Optimal State Estimation.\" John Wiley & Sons.\n       p. 208-212. (2006)\n    .. [2] Roger Labbe. \"Kalman and Bayesian Filters in Python\"\n       https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n    \"\"\"\n\n    def __init__(self, dim_x, dim_z, dim_u=0, args=None):\n        if dim_x < 1:\n            raise ValueError('dim_x must be 1 or greater')\n        if dim_z < 1:\n            raise ValueError('dim_z must be 1 or greater')\n        if dim_u < 0:\n            raise ValueError('dim_u must be 0 or greater')\n\n        self.dim_x = dim_x\n        self.dim_z = dim_z\n        self.dim_u = dim_u\n\n        self.x = zeros((dim_x, 1))        # state\n        self.P = eye(dim_x)               # uncertainty covariance\n        self.Q = eye(dim_x)               # process uncertainty\n        self.B = None                     # control transition matrix\n        self.F = eye(dim_x)               # state transition matrix\n        self.H = zeros((dim_z, dim_x))    # measurement function\n        self.R = eye(dim_z)               # measurement uncertainty\n        self._alpha_sq = 1.               # fading memory control\n        self.M = np.zeros((dim_x, dim_z)) # process-measurement cross correlation\n        self.z = np.array([[None]*self.dim_z]).T\n\n        # gain and residual are computed during the innovation step. We\n        # save them so that in case you want to inspect them for various\n        # purposes\n        self.K = np.zeros((dim_x, dim_z)) # kalman gain\n        self.y = zeros((dim_z, 1))\n        self.S = np.zeros((dim_z, dim_z)) # system uncertainty\n        self.SI = np.zeros((dim_z, dim_z)) # inverse system uncertainty\n\n        # identity matrix. Do not alter this.\n        self._I = np.eye(dim_x)\n\n        # these will always be a copy of x,P after predict() is called\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n        # these will always be a copy of x,P after update() is called\n        self.x_post = self.x.copy()             \n        self.P_post = self.P.copy()\n\n        # Only computed only if requested via property\n        self._log_likelihood = log(sys.float_info.min)\n        self._likelihood = sys.float_info.min\n        self._mahalanobis = None\n\n        # keep all observations \n        self.history_obs = []\n\n        self.inv = np.linalg.inv\n\n        self.attr_saved = None\n        self.observed = False\n        self.args = args\n\n\n    def predict(self, u=None, B=None, F=None, Q=None):\n        \"\"\"\n        Predict next state (prior) using the Kalman filter state propagation\n        equations.\n        Parameters\n        ----------\n        u : np.array, default 0\n            Optional control vector.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        F : np.array(dim_x, dim_x), or None\n            Optional state transition matrix; a value of None\n            will cause the filter to use `self.F`.\n        Q : np.array(dim_x, dim_x), scalar, or None\n            Optional process noise matrix; a value of None will cause the\n            filter to use `self.Q`.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        elif isscalar(Q):\n            Q = eye(self.dim_x) * Q\n\n\n        # x = Fx + Bu\n        if B is not None and u is not None:\n            self.x = dot(F, self.x) + dot(B, u)\n        else:\n            self.x = dot(F, self.x)\n\n        # P = FPF' + Q\n        self.P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q\n\n        # save prior\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n\n\n    def freeze(self):\n        \"\"\"\n            Save the parameters before non-observation forward\n        \"\"\"\n        self.attr_saved = deepcopy(self.__dict__)\n\n\n    def unfreeze(self):\n        if self.attr_saved is not None:\n            new_history = deepcopy(self.history_obs)\n            self.__dict__ = self.attr_saved\n            # self.history_obs = new_history \n            self.history_obs = self.history_obs[:-1]\n            occur = [int(d is None) for d in new_history]\n            indices = np.where(np.array(occur)==0)[0]\n            index1 = indices[-2]\n            index2 = indices[-1]\n            box1 = new_history[index1]\n            x1, y1, s1, r1 = box1 \n            w1 = np.sqrt(s1 * r1)\n            h1 = np.sqrt(s1 / r1)\n            box2 = new_history[index2]\n            x2, y2, s2, r2 = box2 \n            w2 = np.sqrt(s2 * r2)\n            h2 = np.sqrt(s2 / r2)\n            time_gap = index2 - index1\n            dx = (x2-x1)/time_gap\n            dy = (y2-y1)/time_gap \n            dw = (w2-w1)/time_gap \n            dh = (h2-h1)/time_gap\n            for i in range(index2 - index1):\n                \"\"\"\n                    The default virtual trajectory generation is by linear\n                    motion (constant speed hypothesis), you could modify this \n                    part to implement your own. \n                \"\"\"\n                x = x1 + (i+1) * dx \n                y = y1 + (i+1) * dy \n                w = w1 + (i+1) * dw \n                h = h1 + (i+1) * dh\n                s = w * h \n                r = w / float(h)\n                new_box = np.array([x, y, s, r]).reshape((4, 1))\n                \"\"\"\n                    I still use predict-update loop here to refresh the parameters,\n                    but this can be faster by directly modifying the internal parameters\n                    as suggested in the paper. I keep this naive but slow way for \n                    easy read and understanding\n                \"\"\"\n\n                if not i == (index2-index1-1):\n                    self.update(new_box)\n                    self.predict()\n                else:\n                    self.update(new_box)\n\n\n    def update(self, z, R=None, H=None):\n        \"\"\"\n        Add a new measurement (z) to the Kalman filter.\n        If z is None, nothing is computed. However, x_post and P_post are\n        updated with the prior (x_prior, P_prior), and self.z is set to None.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n            If you pass in a value of H, z must be a column vector the\n            of the correct size.\n        R : np.array, scalar, or None\n            Optionally provide R to override the measurement noise for this\n            one call, otherwise  self.R will be used.\n        H : np.array, or None\n            Optionally provide H to override the measurement function for this\n            one call, otherwise self.H will be used.\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        # append the observation\n        self.history_obs.append(z)\n        \n        if z is None:\n            if self.observed:\n                \"\"\"\n                    Got no observation so freeze the current parameters for future\n                    potential online smoothing.\n                \"\"\"\n                self.freeze()\n            self.observed = False \n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n        \n        # self.observed = True\n        if not self.observed:\n            \"\"\"\n                Get observation, use online smoothing to re-update parameters\n            \"\"\"\n            self.unfreeze()\n        self.observed = True\n\n        if R is None:\n            R = self.R\n        elif isscalar(R):\n            R = eye(self.dim_z) * R\n\n        # if self.args.use_nsa_kalman:\n        #     if confidence > 0.6:\n        #         R = [(1 - confidence) * self.args.nsa_kalman_interval * x for x in R]\n        #     else:\n        #         R = [self.args.nsa_kalman_interval_sec * x for x in R]\n\n        if H is None:\n            z = reshape_z(z, self.dim_z, self.x.ndim)\n            H = self.H\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(H, self.x)\n\n        # common subexpression for speed\n        PHT = dot(self.P, H.T)\n\n        # S = HPH' + R\n        # project system uncertainty into measurement space\n        self.S = dot(H, PHT) + R\n        self.SI = self.inv(self.S)\n        # K = PH'inv(S)\n        # map system uncertainty into kalman gain\n        self.K = dot(PHT, self.SI)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n\n        # P = (I-KH)P(I-KH)' + KRK'\n        # This is more numerically stable\n        # and works for non-optimal K vs the equation\n        # P = (I-KH)P usually seen in the literature.\n\n        I_KH = self._I - dot(self.K, H)\n        self.P = dot(dot(I_KH, self.P), I_KH.T) + dot(dot(self.K, R), self.K.T)\n\n        # save measurement and posterior state\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n    def predict_steadystate(self, u=0, B=None):\n        \"\"\"\n        Predict state (prior) using the Kalman filter state propagation\n        equations. Only x is updated, P is left unchanged. See\n        update_steadstate() for a longer explanation of when to use this\n        method.\n        Parameters\n        ----------\n        u : np.array\n            Optional control vector. If non-zero, it is multiplied by B\n            to create the control input into the system.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n\n        # x = Fx + Bu\n        if B is not None:\n            self.x = dot(self.F, self.x) + dot(B, u)\n        else:\n            self.x = dot(self.F, self.x)\n\n        # save prior\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n    def update_steadystate(self, z):\n        \"\"\"\n        Add a new measurement (z) to the Kalman filter without recomputing\n        the Kalman gain K, the state covariance P, or the system\n        uncertainty S.\n        You can use this for LTI systems since the Kalman gain and covariance\n        converge to a fixed value. Precompute these and assign them explicitly,\n        or run the Kalman filter using the normal predict()/update(0 cycle\n        until they converge.\n        The main advantage of this call is speed. We do significantly less\n        computation, notably avoiding a costly matrix inversion.\n        Use in conjunction with predict_steadystate(), otherwise P will grow\n        without bound.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        Examples\n        --------\n        >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter\n        >>> # let filter converge on representative data, then save k and P\n        >>> for i in range(100):\n        >>>     cv.predict()\n        >>>     cv.update([i, i, i])\n        >>> saved_k = np.copy(cv.K)\n        >>> saved_P = np.copy(cv.P)\n        later on:\n        >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter\n        >>> cv.K = np.copy(saved_K)\n        >>> cv.P = np.copy(saved_P)\n        >>> for i in range(100):\n        >>>     cv.predict_steadystate()\n        >>>     cv.update_steadystate([i, i, i])\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        if z is None:\n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(self.H, self.x)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n    def update_correlated(self, z, R=None, H=None):\n        \"\"\" Add a new measurement (z) to the Kalman filter assuming that\n        process noise and measurement noise are correlated as defined in\n        the `self.M` matrix.\n        A partial derivation can be found in [1]\n        If z is None, nothing is changed.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        R : np.array, scalar, or None\n            Optionally provide R to override the measurement noise for this\n            one call, otherwise  self.R will be used.\n        H : np.array,  or None\n            Optionally provide H to override the measurement function for this\n            one call, otherwise  self.H will be used.\n        References\n        ----------\n        .. [1] Bulut, Y. (2011). Applied Kalman filter theory (Doctoral dissertation, Northeastern University).\n               http://people.duke.edu/~hpgavin/SystemID/References/Balut-KalmanFilter-PhD-NEU-2011.pdf\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        if z is None:\n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n\n        if R is None:\n            R = self.R\n        elif isscalar(R):\n            R = eye(self.dim_z) * R\n\n        # rename for readability and a tiny extra bit of speed\n        if H is None:\n            z = reshape_z(z, self.dim_z, self.x.ndim)\n            H = self.H\n\n        # handle special case: if z is in form [[z]] but x is not a column\n        # vector dimensions will not match\n        if self.x.ndim == 1 and shape(z) == (1, 1):\n            z = z[0]\n\n        if shape(z) == (): # is it scalar, e.g. z=3 or z=np.array(3)\n            z = np.asarray([z])\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(H, self.x)\n\n        # common subexpression for speed\n        PHT = dot(self.P, H.T)\n\n        # project system uncertainty into measurement space\n        self.S = dot(H, PHT) + dot(H, self.M) + dot(self.M.T, H.T) + R\n        self.SI = self.inv(self.S)\n\n        # K = PH'inv(S)\n        # map system uncertainty into kalman gain\n        self.K = dot(PHT + self.M, self.SI)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n        self.P = self.P - dot(self.K, dot(H, self.P) + self.M.T)\n\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n    def batch_filter(self, zs, Fs=None, Qs=None, Hs=None,\n                     Rs=None, Bs=None, us=None, update_first=False,\n                     saver=None):\n        \"\"\" Batch processes a sequences of measurements.\n        Parameters\n        ----------\n        zs : list-like\n            list of measurements at each time step `self.dt`. Missing\n            measurements must be represented by `None`.\n        Fs : None, list-like, default=None\n            optional value or list of values to use for the state transition\n            matrix F.\n            If Fs is None then self.F is used for all epochs.\n            Otherwise it must contain a list-like list of F's, one for\n            each epoch.  This allows you to have varying F per epoch.\n        Qs : None, np.array or list-like, default=None\n            optional value or list of values to use for the process error\n            covariance Q.\n            If Qs is None then self.Q is used for all epochs.\n            Otherwise it must contain a list-like list of Q's, one for\n            each epoch.  This allows you to have varying Q per epoch.\n        Hs : None, np.array or list-like, default=None\n            optional list of values to use for the measurement matrix H.\n            If Hs is None then self.H is used for all epochs.\n            If Hs contains a single matrix, then it is used as H for all\n            epochs.\n            Otherwise it must contain a list-like list of H's, one for\n            each epoch.  This allows you to have varying H per epoch.\n        Rs : None, np.array or list-like, default=None\n            optional list of values to use for the measurement error\n            covariance R.\n            If Rs is None then self.R is used for all epochs.\n            Otherwise it must contain a list-like list of R's, one for\n            each epoch.  This allows you to have varying R per epoch.\n        Bs : None, np.array or list-like, default=None\n            optional list of values to use for the control transition matrix B.\n            If Bs is None then self.B is used for all epochs.\n            Otherwise it must contain a list-like list of B's, one for\n            each epoch.  This allows you to have varying B per epoch.\n        us : None, np.array or list-like, default=None\n            optional list of values to use for the control input vector;\n            If us is None then None is used for all epochs (equivalent to 0,\n            or no control input).\n            Otherwise it must contain a list-like list of u's, one for\n            each epoch.\n       update_first : bool, optional, default=False\n            controls whether the order of operations is update followed by\n            predict, or predict followed by update. Default is predict->update.\n        saver : filterpy.common.Saver, optional\n            filterpy.common.Saver object. If provided, saver.save() will be\n            called after every epoch\n        Returns\n        -------\n        means : np.array((n,dim_x,1))\n            array of the state for each time step after the update. Each entry\n            is an np.array. In other words `means[k,:]` is the state at step\n            `k`.\n        covariance : np.array((n,dim_x,dim_x))\n            array of the covariances for each time step after the update.\n            In other words `covariance[k,:,:]` is the covariance at step `k`.\n        means_predictions : np.array((n,dim_x,1))\n            array of the state for each time step after the predictions. Each\n            entry is an np.array. In other words `means[k,:]` is the state at\n            step `k`.\n        covariance_predictions : np.array((n,dim_x,dim_x))\n            array of the covariances for each time step after the prediction.\n            In other words `covariance[k,:,:]` is the covariance at step `k`.\n        Examples\n        --------\n        .. code-block:: Python\n            # this example demonstrates tracking a measurement where the time\n            # between measurement varies, as stored in dts. This requires\n            # that F be recomputed for each epoch. The output is then smoothed\n            # with an RTS smoother.\n            zs = [t + random.randn()*4 for t in range (40)]\n            Fs = [np.array([[1., dt], [0, 1]] for dt in dts]\n            (mu, cov, _, _) = kf.batch_filter(zs, Fs=Fs)\n            (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs)\n        \"\"\"\n\n        #pylint: disable=too-many-statements\n        n = np.size(zs, 0)\n        if Fs is None:\n            Fs = [self.F] * n\n        if Qs is None:\n            Qs = [self.Q] * n\n        if Hs is None:\n            Hs = [self.H] * n\n        if Rs is None:\n            Rs = [self.R] * n\n        if Bs is None:\n            Bs = [self.B] * n\n        if us is None:\n            us = [0] * n\n\n        # mean estimates from Kalman Filter\n        if self.x.ndim == 1:\n            means = zeros((n, self.dim_x))\n            means_p = zeros((n, self.dim_x))\n        else:\n            means = zeros((n, self.dim_x, 1))\n            means_p = zeros((n, self.dim_x, 1))\n\n        # state covariances from Kalman Filter\n        covariances = zeros((n, self.dim_x, self.dim_x))\n        covariances_p = zeros((n, self.dim_x, self.dim_x))\n\n        if update_first:\n            for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n                self.update(z, R=R, H=H)\n                means[i, :] = self.x\n                covariances[i, :, :] = self.P\n\n                self.predict(u=u, B=B, F=F, Q=Q)\n                means_p[i, :] = self.x\n                covariances_p[i, :, :] = self.P\n\n                if saver is not None:\n                    saver.save()\n        else:\n            for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n                self.predict(u=u, B=B, F=F, Q=Q)\n                means_p[i, :] = self.x\n                covariances_p[i, :, :] = self.P\n\n                self.update(z, R=R, H=H)\n                means[i, :] = self.x\n                covariances[i, :, :] = self.P\n\n                if saver is not None:\n                    saver.save()\n\n        return (means, covariances, means_p, covariances_p)\n\n    def rts_smoother(self, Xs, Ps, Fs=None, Qs=None, inv=np.linalg.inv):\n        \"\"\"\n        Runs the Rauch-Tung-Striebel Kalman smoother on a set of\n        means and covariances computed by a Kalman filter. The usual input\n        would come from the output of `KalmanFilter.batch_filter()`.\n        Parameters\n        ----------\n        Xs : numpy.array\n           array of the means (state variable x) of the output of a Kalman\n           filter.\n        Ps : numpy.array\n            array of the covariances of the output of a kalman filter.\n        Fs : list-like collection of numpy.array, optional\n            State transition matrix of the Kalman filter at each time step.\n            Optional, if not provided the filter's self.F will be used\n        Qs : list-like collection of numpy.array, optional\n            Process noise of the Kalman filter at each time step. Optional,\n            if not provided the filter's self.Q will be used\n        inv : function, default numpy.linalg.inv\n            If you prefer another inverse function, such as the Moore-Penrose\n            pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv\n        Returns\n        -------\n        x : numpy.ndarray\n           smoothed means\n        P : numpy.ndarray\n           smoothed state covariances\n        K : numpy.ndarray\n            smoother gain at each step\n        Pp : numpy.ndarray\n           Predicted state covariances\n        Examples\n        --------\n        .. code-block:: Python\n            zs = [t + random.randn()*4 for t in range (40)]\n            (mu, cov, _, _) = kalman.batch_filter(zs)\n            (x, P, K, Pp) = rts_smoother(mu, cov, kf.F, kf.Q)\n        \"\"\"\n\n        if len(Xs) != len(Ps):\n            raise ValueError('length of Xs and Ps must be the same')\n\n        n = Xs.shape[0]\n        dim_x = Xs.shape[1]\n\n        if Fs is None:\n            Fs = [self.F] * n\n        if Qs is None:\n            Qs = [self.Q] * n\n\n        # smoother gain\n        K = zeros((n, dim_x, dim_x))\n\n        x, P, Pp = Xs.copy(), Ps.copy(), Ps.copy()\n        for k in range(n-2, -1, -1):\n            Pp[k] = dot(dot(Fs[k+1], P[k]), Fs[k+1].T) + Qs[k+1]\n\n            #pylint: disable=bad-whitespace\n            K[k]  = dot(dot(P[k], Fs[k+1].T), inv(Pp[k]))\n            x[k] += dot(K[k], x[k+1] - dot(Fs[k+1], x[k]))\n            P[k] += dot(dot(K[k], P[k+1] - Pp[k]), K[k].T)\n\n        return (x, P, K, Pp)\n\n    def get_prediction(self, u=None, B=None, F=None, Q=None):\n        \"\"\"\n        Predict next state (prior) using the Kalman filter state propagation\n        equations and returns it without modifying the object.\n        Parameters\n        ----------\n        u : np.array, default 0\n            Optional control vector.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        F : np.array(dim_x, dim_x), or None\n            Optional state transition matrix; a value of None\n            will cause the filter to use `self.F`.\n        Q : np.array(dim_x, dim_x), scalar, or None\n            Optional process noise matrix; a value of None will cause the\n            filter to use `self.Q`.\n        Returns\n        -------\n        (x, P) : tuple\n            State vector and covariance array of the prediction.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        elif isscalar(Q):\n            Q = eye(self.dim_x) * Q\n\n        # x = Fx + Bu\n        if B is not None and u is not None:\n            x = dot(F, self.x) + dot(B, u)\n        else:\n            x = dot(F, self.x)\n\n        # P = FPF' + Q\n        P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q\n\n        return x, P\n\n    def get_update(self, z=None):\n        \"\"\"\n        Computes the new estimate based on measurement `z` and returns it\n        without altering the state of the filter.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        Returns\n        -------\n        (x, P) : tuple\n            State vector and covariance array of the update.\n       \"\"\"\n\n        if z is None:\n            return self.x, self.P\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n\n        R = self.R\n        H = self.H\n        P = self.P\n        x = self.x\n\n        # error (residual) between measurement and prediction\n        y = z - dot(H, x)\n\n        # common subexpression for speed\n        PHT = dot(P, H.T)\n\n        # project system uncertainty into measurement space\n        S = dot(H, PHT) + R\n\n        # map system uncertainty into kalman gain\n        K = dot(PHT, self.inv(S))\n\n        # predict new x with residual scaled by the kalman gain\n        x = x + dot(K, y)\n\n        # P = (I-KH)P(I-KH)' + KRK'\n        I_KH = self._I - dot(K, H)\n        P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)\n\n        return x, P\n\n    def residual_of(self, z):\n        \"\"\"\n        Returns the residual for the given measurement (z). Does not alter\n        the state of the filter.\n        \"\"\"\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n        return z - dot(self.H, self.x_prior)\n\n    def measurement_of_state(self, x):\n        \"\"\"\n        Helper function that converts a state into a measurement.\n        Parameters\n        ----------\n        x : np.array\n            kalman state vector\n        Returns\n        -------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        \"\"\"\n\n        return dot(self.H, x)\n\n    @property\n    def log_likelihood(self):\n        \"\"\"\n        log-likelihood of the last measurement.\n        \"\"\"\n        if self._log_likelihood is None:\n            self._log_likelihood = logpdf(x=self.y, cov=self.S)\n        return self._log_likelihood\n\n    @property\n    def likelihood(self):\n        \"\"\"\n        Computed from the log-likelihood. The log-likelihood can be very\n        small,  meaning a large negative value such as -28000. Taking the\n        exp() of that results in 0.0, which can break typical algorithms\n        which multiply by this value, so by default we always return a\n        number >= sys.float_info.min.\n        \"\"\"\n        if self._likelihood is None:\n            self._likelihood = exp(self.log_likelihood)\n            if self._likelihood == 0:\n                self._likelihood = sys.float_info.min\n        return self._likelihood\n\n    @property\n    def mahalanobis(self):\n        \"\"\"\"\n        Mahalanobis distance of measurement. E.g. 3 means measurement\n        was 3 standard deviations away from the predicted value.\n        Returns\n        -------\n        mahalanobis : float\n        \"\"\"\n        if self._mahalanobis is None:\n            self._mahalanobis = sqrt(float(dot(dot(self.y.T, self.SI), self.y)))\n        return self._mahalanobis\n\n    @property\n    def alpha(self):\n        \"\"\"\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon [1]_.\n        \"\"\"\n        return self._alpha_sq**.5\n\n    def log_likelihood_of(self, z):\n        \"\"\"\n        log likelihood of the measurement `z`. This should only be called\n        after a call to update(). Calling after predict() will yield an\n        incorrect result.\"\"\"\n\n        if z is None:\n            return log(sys.float_info.min)\n        return logpdf(z, dot(self.H, self.x), self.S)\n\n    @alpha.setter\n    def alpha(self, value):\n        if not np.isscalar(value) or value < 1:\n            raise ValueError('alpha must be a float greater than 1')\n\n        self._alpha_sq = value**2\n\n    def __repr__(self):\n        return '\\n'.join([\n            'KalmanFilter object',\n            pretty_str('dim_x', self.dim_x),\n            pretty_str('dim_z', self.dim_z),\n            pretty_str('dim_u', self.dim_u),\n            pretty_str('x', self.x),\n            pretty_str('P', self.P),\n            pretty_str('x_prior', self.x_prior),\n            pretty_str('P_prior', self.P_prior),\n            pretty_str('x_post', self.x_post),\n            pretty_str('P_post', self.P_post),\n            pretty_str('F', self.F),\n            pretty_str('Q', self.Q),\n            pretty_str('R', self.R),\n            pretty_str('H', self.H),\n            pretty_str('K', self.K),\n            pretty_str('y', self.y),\n            pretty_str('S', self.S),\n            pretty_str('SI', self.SI),\n            pretty_str('M', self.M),\n            pretty_str('B', self.B),\n            pretty_str('z', self.z),\n            pretty_str('log-likelihood', self.log_likelihood),\n            pretty_str('likelihood', self.likelihood),\n            pretty_str('mahalanobis', self.mahalanobis),\n            pretty_str('alpha', self.alpha),\n            pretty_str('inv', self.inv)\n            ])\n\n    def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None):\n        \"\"\"\n        Performs a series of asserts to check that the size of everything\n        is what it should be. This can help you debug problems in your design.\n        If you pass in H, R, F, Q those will be used instead of this object's\n        value for those matrices.\n        Testing `z` (the measurement) is problamatic. x is a vector, and can be\n        implemented as either a 1D array or as a nx1 column vector. Thus Hx\n        can be of different shapes. Then, if Hx is a single value, it can\n        be either a 1D array or 2D vector. If either is true, z can reasonably\n        be a scalar (either '3' or np.array('3') are scalars under this\n        definition), a 1D, 1 element array, or a 2D, 1 element array. You are\n        allowed to pass in any combination that works.\n        \"\"\"\n\n        if H is None:\n            H = self.H\n        if R is None:\n            R = self.R\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        x = self.x\n        P = self.P\n\n        assert x.ndim == 1 or x.ndim == 2, \\\n                \"x must have one or two dimensions, but has {}\".format(x.ndim)\n\n        if x.ndim == 1:\n            assert x.shape[0] == self.dim_x, \\\n                   \"Shape of x must be ({},{}), but is {}\".format(\n                       self.dim_x, 1, x.shape)\n        else:\n            assert x.shape == (self.dim_x, 1), \\\n                   \"Shape of x must be ({},{}), but is {}\".format(\n                       self.dim_x, 1, x.shape)\n\n        assert P.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of P must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, P.shape)\n\n        assert Q.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of Q must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, P.shape)\n\n        assert F.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of F must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, F.shape)\n\n        assert np.ndim(H) == 2, \\\n               \"Shape of H must be (dim_z, {}), but is {}\".format(\n                   P.shape[0], shape(H))\n\n        assert H.shape[1] == P.shape[0], \\\n               \"Shape of H must be (dim_z, {}), but is {}\".format(\n                   P.shape[0], H.shape)\n\n        # shape of R must be the same as HPH'\n        hph_shape = (H.shape[0], H.shape[0])\n        r_shape = shape(R)\n\n        if H.shape[0] == 1:\n            # r can be scalar, 1D, or 2D in this case\n            assert r_shape in [(), (1,), (1, 1)], \\\n            \"R must be scalar or one element array, but is shaped {}\".format(\n                r_shape)\n        else:\n            assert r_shape == hph_shape, \\\n            \"shape of R should be {} but it is {}\".format(hph_shape, r_shape)\n\n\n        if z is not None:\n            z_shape = shape(z)\n        else:\n            z_shape = (self.dim_z, 1)\n\n        # H@x must have shape of z\n        Hx = dot(H, x)\n\n        if z_shape == (): # scalar or np.array(scalar)\n            assert Hx.ndim == 1 or shape(Hx) == (1, 1), \\\n            \"shape of z should be {}, not {} for the given H\".format(\n                shape(Hx), z_shape)\n\n        elif shape(Hx) == (1,):\n            assert z_shape[0] == 1, 'Shape of z must be {} for the given H'.format(shape(Hx))\n\n        else:\n            assert (z_shape == shape(Hx) or\n                    (len(z_shape) == 1 and shape(Hx) == (z_shape[0], 1))), \\\n                    \"shape of z should be {}, not {} for the given H\".format(\n                        shape(Hx), z_shape)\n\n        if np.ndim(Hx) > 1 and shape(Hx) != (1, 1):\n            assert shape(Hx) == z_shape, \\\n               'shape of z should be {} for the given H, but it is {}'.format(\n                   shape(Hx), z_shape)\n\n\ndef update(x, P, z, R, H=None, return_all=False):\n    \"\"\"\n    Add a new measurement (z) to the Kalman filter. If z is None, nothing\n    is changed.\n    This can handle either the multidimensional or unidimensional case. If\n    all parameters are floats instead of arrays the filter will still work,\n    and return floats for x, P as the result.\n    update(1, 2, 1, 1, 1)  # univariate\n    update(x, P, 1\n    Parameters\n    ----------\n    x : numpy.array(dim_x, 1), or float\n        State estimate vector\n    P : numpy.array(dim_x, dim_x), or float\n        Covariance matrix\n    z : (dim_z, 1): array_like\n        measurement for this update. z can be a scalar if dim_z is 1,\n        otherwise it must be convertible to a column vector.\n    R : numpy.array(dim_z, dim_z), or float\n        Measurement noise matrix\n    H : numpy.array(dim_x, dim_x), or float, optional\n        Measurement function. If not provided, a value of 1 is assumed.\n    return_all : bool, default False\n        If true, y, K, S, and log_likelihood are returned, otherwise\n        only x and P are returned.\n    Returns\n    -------\n    x : numpy.array\n        Posterior state estimate vector\n    P : numpy.array\n        Posterior covariance matrix\n    y : numpy.array or scalar\n        Residua. Difference between measurement and state in measurement space\n    K : numpy.array\n        Kalman gain\n    S : numpy.array\n        System uncertainty in measurement space\n    log_likelihood : float\n        log likelihood of the measurement\n    \"\"\"\n\n    #pylint: disable=bare-except\n\n    if z is None:\n        if return_all:\n            return x, P, None, None, None, None\n        return x, P\n\n    if H is None:\n        H = np.array([1])\n\n    if np.isscalar(H):\n        H = np.array([H])\n\n    Hx = np.atleast_1d(dot(H, x))\n    z = reshape_z(z, Hx.shape[0], x.ndim)\n\n    # error (residual) between measurement and prediction\n    y = z - Hx\n\n    # project system uncertainty into measurement space\n    S = dot(dot(H, P), H.T) + R\n\n\n    # map system uncertainty into kalman gain\n    try:\n        K = dot(dot(P, H.T), linalg.inv(S))\n    except:\n        # can't invert a 1D array, annoyingly\n        K = dot(dot(P, H.T), 1./S)\n\n\n    # predict new x with residual scaled by the kalman gain\n    x = x + dot(K, y)\n\n    # P = (I-KH)P(I-KH)' + KRK'\n    KH = dot(K, H)\n\n    try:\n        I_KH = np.eye(KH.shape[0]) - KH\n    except:\n        I_KH = np.array([1 - KH])\n    P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)\n\n\n    if return_all:\n        # compute log likelihood\n        log_likelihood = logpdf(z, dot(H, x), S)\n        return x, P, y, K, S, log_likelihood\n    return x, P\n\n\ndef update_steadystate(x, z, K, H=None):\n    \"\"\"\n    Add a new measurement (z) to the Kalman filter. If z is None, nothing\n    is changed.\n    Parameters\n    ----------\n    x : numpy.array(dim_x, 1), or float\n        State estimate vector\n    z : (dim_z, 1): array_like\n        measurement for this update. z can be a scalar if dim_z is 1,\n        otherwise it must be convertible to a column vector.\n    K : numpy.array, or float\n        Kalman gain matrix\n    H : numpy.array(dim_x, dim_x), or float, optional\n        Measurement function. If not provided, a value of 1 is assumed.\n    Returns\n    -------\n    x : numpy.array\n        Posterior state estimate vector\n    Examples\n    --------\n    This can handle either the multidimensional or unidimensional case. If\n    all parameters are floats instead of arrays the filter will still work,\n    and return floats for x, P as the result.\n    >>> update_steadystate(1, 2, 1)  # univariate\n    >>> update_steadystate(x, P, z, H)\n    \"\"\"\n\n\n    if z is None:\n        return x\n\n    if H is None:\n        H = np.array([1])\n\n    if np.isscalar(H):\n        H = np.array([H])\n\n    Hx = np.atleast_1d(dot(H, x))\n    z = reshape_z(z, Hx.shape[0], x.ndim)\n\n    # error (residual) between measurement and prediction\n    y = z - Hx\n\n    # estimate new x with residual scaled by the kalman gain\n    return x + dot(K, y)\n\n\ndef predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.):\n    \"\"\"\n    Predict next state (prior) using the Kalman filter state propagation\n    equations.\n    Parameters\n    ----------\n    x : numpy.array\n        State estimate vector\n    P : numpy.array\n        Covariance matrix\n    F : numpy.array()\n        State Transition matrix\n    Q : numpy.array, Optional\n        Process noise matrix\n    u : numpy.array, Optional, default 0.\n        Control vector. If non-zero, it is multiplied by B\n        to create the control input into the system.\n    B : numpy.array, optional, default 0.\n        Control transition matrix.\n    alpha : float, Optional, default=1.0\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon\n    Returns\n    -------\n    x : numpy.array\n        Prior state estimate vector\n    P : numpy.array\n        Prior covariance matrix\n    \"\"\"\n\n    if np.isscalar(F):\n        F = np.array(F)\n    x = dot(F, x) + dot(B, u)\n    P = (alpha * alpha) * dot(dot(F, P), F.T) + Q\n\n    return x, P\n\n\ndef predict_steadystate(x, F=1, u=0, B=1):\n    \"\"\"\n    Predict next state (prior) using the Kalman filter state propagation\n    equations. This steady state form only computes x, assuming that the\n    covariance is constant.\n    Parameters\n    ----------\n    x : numpy.array\n        State estimate vector\n    P : numpy.array\n        Covariance matrix\n    F : numpy.array()\n        State Transition matrix\n    u : numpy.array, Optional, default 0.\n        Control vector. If non-zero, it is multiplied by B\n        to create the control input into the system.\n    B : numpy.array, optional, default 0.\n        Control transition matrix.\n    Returns\n    -------\n    x : numpy.array\n        Prior state estimate vector\n    \"\"\"\n\n    if np.isscalar(F):\n        F = np.array(F)\n    x = dot(F, x) + dot(B, u)\n\n    return x\n\n\n\ndef batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None,\n                 update_first=False, saver=None):\n    \"\"\"\n    Batch processes a sequences of measurements.\n    Parameters\n    ----------\n    zs : list-like\n        list of measurements at each time step. Missing measurements must be\n        represented by None.\n    Fs : list-like\n        list of values to use for the state transition matrix matrix.\n    Qs : list-like\n        list of values to use for the process error\n        covariance.\n    Hs : list-like\n        list of values to use for the measurement matrix.\n    Rs : list-like\n        list of values to use for the measurement error\n        covariance.\n    Bs : list-like, optional\n        list of values to use for the control transition matrix;\n        a value of None in any position will cause the filter\n        to use `self.B` for that time step.\n    us : list-like, optional\n        list of values to use for the control input vector;\n        a value of None in any position will cause the filter to use\n        0 for that time step.\n    update_first : bool, optional\n        controls whether the order of operations is update followed by\n        predict, or predict followed by update. Default is predict->update.\n        saver : filterpy.common.Saver, optional\n            filterpy.common.Saver object. If provided, saver.save() will be\n            called after every epoch\n    Returns\n    -------\n    means : np.array((n,dim_x,1))\n        array of the state for each time step after the update. Each entry\n        is an np.array. In other words `means[k,:]` is the state at step\n        `k`.\n    covariance : np.array((n,dim_x,dim_x))\n        array of the covariances for each time step after the update.\n        In other words `covariance[k,:,:]` is the covariance at step `k`.\n    means_predictions : np.array((n,dim_x,1))\n        array of the state for each time step after the predictions. Each\n        entry is an np.array. In other words `means[k,:]` is the state at\n        step `k`.\n    covariance_predictions : np.array((n,dim_x,dim_x))\n        array of the covariances for each time step after the prediction.\n        In other words `covariance[k,:,:]` is the covariance at step `k`.\n    Examples\n    --------\n    .. code-block:: Python\n        zs = [t + random.randn()*4 for t in range (40)]\n        Fs = [kf.F for t in range (40)]\n        Hs = [kf.H for t in range (40)]\n        (mu, cov, _, _) = kf.batch_filter(zs, Rs=R_list, Fs=Fs, Hs=Hs, Qs=None,\n                                          Bs=None, us=None, update_first=False)\n        (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs, Qs=None)\n    \"\"\"\n\n    n = np.size(zs, 0)\n    dim_x = x.shape[0]\n\n    # mean estimates from Kalman Filter\n    if x.ndim == 1:\n        means = zeros((n, dim_x))\n        means_p = zeros((n, dim_x))\n    else:\n        means = zeros((n, dim_x, 1))\n        means_p = zeros((n, dim_x, 1))\n\n    # state covariances from Kalman Filter\n    covariances = zeros((n, dim_x, dim_x))\n    covariances_p = zeros((n, dim_x, dim_x))\n\n    if us is None:\n        us = [0.] * n\n        Bs = [0.] * n\n\n    if update_first:\n        for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n            x, P = update(x, P, z, R=R, H=H)\n            means[i, :] = x\n            covariances[i, :, :] = P\n\n            x, P = predict(x, P, u=u, B=B, F=F, Q=Q)\n            means_p[i, :] = x\n            covariances_p[i, :, :] = P\n            if saver is not None:\n                saver.save()\n    else:\n        for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n            x, P = predict(x, P, u=u, B=B, F=F, Q=Q)\n            means_p[i, :] = x\n            covariances_p[i, :, :] = P\n\n            x, P = update(x, P, z, R=R, H=H)\n            means[i, :] = x\n            covariances[i, :, :] = P\n            if saver is not None:\n                saver.save()\n\n    return (means, covariances, means_p, covariances_p)\n\n\n\ndef rts_smoother(Xs, Ps, Fs, Qs):\n    \"\"\"\n    Runs the Rauch-Tung-Striebel Kalman smoother on a set of\n    means and covariances computed by a Kalman filter. The usual input\n    would come from the output of `KalmanFilter.batch_filter()`.\n    Parameters\n    ----------\n    Xs : numpy.array\n       array of the means (state variable x) of the output of a Kalman\n       filter.\n    Ps : numpy.array\n        array of the covariances of the output of a kalman filter.\n    Fs : list-like collection of numpy.array\n        State transition matrix of the Kalman filter at each time step.\n    Qs : list-like collection of numpy.array, optional\n        Process noise of the Kalman filter at each time step.\n    Returns\n    -------\n    x : numpy.ndarray\n       smoothed means\n    P : numpy.ndarray\n       smoothed state covariances\n    K : numpy.ndarray\n        smoother gain at each step\n    pP : numpy.ndarray\n       predicted state covariances\n    Examples\n    --------\n    .. code-block:: Python\n        zs = [t + random.randn()*4 for t in range (40)]\n        (mu, cov, _, _) = kalman.batch_filter(zs)\n        (x, P, K, pP) = rts_smoother(mu, cov, kf.F, kf.Q)\n    \"\"\"\n\n    if len(Xs) != len(Ps):\n        raise ValueError('length of Xs and Ps must be the same')\n\n    n = Xs.shape[0]\n    dim_x = Xs.shape[1]\n\n    # smoother gain\n    K = zeros((n, dim_x, dim_x))\n    x, P, pP = Xs.copy(), Ps.copy(), Ps.copy()\n\n    for k in range(n-2, -1, -1):\n        pP[k] = dot(dot(Fs[k], P[k]), Fs[k].T) + Qs[k]\n\n        #pylint: disable=bad-whitespace\n        K[k]  = dot(dot(P[k], Fs[k].T), linalg.inv(pP[k]))\n        x[k] += dot(K[k], x[k+1] - dot(Fs[k], x[k]))\n        P[k] += dot(dot(K[k], P[k+1] - pP[k]), K[k].T)\n\n    return (x, P, K, pP)"
  },
  {
    "path": "trackers/hybrid_sort_tracker/kalmanfilter_score.py",
    "content": "# -*- coding: utf-8 -*-\n# pylint: disable=invalid-name, too-many-arguments, too-many-branches,\n# pylint: disable=too-many-locals, too-many-instance-attributes, too-many-lines\n\n\"\"\"\nThis module implements the linear Kalman filter in both an object\noriented and procedural form. The KalmanFilter class implements\nthe filter by storing the various matrices in instance variables,\nminimizing the amount of bookkeeping you have to do.\nAll Kalman filters operate with a predict->update cycle. The\npredict step, implemented with the method or function predict(),\nuses the state transition matrix F to predict the state in the next\ntime period (epoch). The state is stored as a gaussian (x, P), where\nx is the state (column) vector, and P is its covariance. Covariance\nmatrix Q specifies the process covariance. In Bayesian terms, this\nprediction is called the *prior*, which you can think of colloquially\nas the estimate prior to incorporating the measurement.\nThe update step, implemented with the method or function `update()`,\nincorporates the measurement z with covariance R, into the state\nestimate (x, P). The class stores the system uncertainty in S,\nthe innovation (residual between prediction and measurement in\nmeasurement space) in y, and the Kalman gain in k. The procedural\nform returns these variables to you. In Bayesian terms this computes\nthe *posterior* - the estimate after the information from the\nmeasurement is incorporated.\nWhether you use the OO form or procedural form is up to you. If\nmatrices such as H, R, and F are changing each epoch, you'll probably\nopt to use the procedural form. If they are unchanging, the OO\nform is perhaps easier to use since you won't need to keep track\nof these matrices. This is especially useful if you are implementing\nbanks of filters or comparing various KF designs for performance;\na trivial coding bug could lead to using the wrong sets of matrices.\nThis module also offers an implementation of the RTS smoother, and\nother helper functions, such as log likelihood computations.\nThe Saver class allows you to easily save the state of the\nKalmanFilter class after every update\nThis module expects NumPy arrays for all values that expect\narrays, although in a few cases, particularly method parameters,\nit will accept types that convert to NumPy arrays, such as lists\nof lists. These exceptions are documented in the method or function.\nExamples\n--------\nThe following example constructs a constant velocity kinematic\nfilter, filters noisy data, and plots the results. It also demonstrates\nusing the Saver class to save the state of the filter at each epoch.\n.. code-block:: Python\n    import matplotlib.pyplot as plt\n    import numpy as np\n    from filterpy.kalman import KalmanFilter\n    from filterpy.common import Q_discrete_white_noise, Saver\n    r_std, q_std = 2., 0.003\n    cv = KalmanFilter(dim_x=2, dim_z=1)\n    cv.x = np.array([[0., 1.]]) # position, velocity\n    cv.F = np.array([[1, dt],[ [0, 1]])\n    cv.R = np.array([[r_std^^2]])\n    f.H = np.array([[1., 0.]])\n    f.P = np.diag([.1^^2, .03^^2)\n    f.Q = Q_discrete_white_noise(2, dt, q_std**2)\n    saver = Saver(cv)\n    for z in range(100):\n        cv.predict()\n        cv.update([z + randn() * r_std])\n        saver.save() # save the filter's state\n    saver.to_array()\n    plt.plot(saver.x[:, 0])\n    # plot all of the priors\n    plt.plot(saver.x_prior[:, 0])\n    # plot mahalanobis distance\n    plt.figure()\n    plt.plot(saver.mahalanobis)\nThis code implements the same filter using the procedural form\n    x = np.array([[0., 1.]]) # position, velocity\n    F = np.array([[1, dt],[ [0, 1]])\n    R = np.array([[r_std^^2]])\n    H = np.array([[1., 0.]])\n    P = np.diag([.1^^2, .03^^2)\n    Q = Q_discrete_white_noise(2, dt, q_std**2)\n    for z in range(100):\n        x, P = predict(x, P, F=F, Q=Q)\n        x, P = update(x, P, z=[z + randn() * r_std], R=R, H=H)\n        xs.append(x[0, 0])\n    plt.plot(xs)\nFor more examples see the test subdirectory, or refer to the\nbook cited below. In it I both teach Kalman filtering from basic\nprinciples, and teach the use of this library in great detail.\nFilterPy library.\nhttp://github.com/rlabbe/filterpy\nDocumentation at:\nhttps://filterpy.readthedocs.org\nSupporting book at:\nhttps://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\nThis is licensed under an MIT license. See the readme.MD file\nfor more information.\nCopyright 2014-2018 Roger R Labbe Jr.\n\"\"\"\n\nfrom __future__ import absolute_import, division\n\nfrom copy import deepcopy\nfrom math import log, exp, sqrt\nimport sys\nimport numpy as np\nfrom numpy import dot, zeros, eye, isscalar, shape\nimport numpy.linalg as linalg\nfrom filterpy.stats import logpdf\nfrom filterpy.common import pretty_str, reshape_z\n\n\nclass KalmanFilterNew_score(object):\n    \"\"\" Implements a Kalman filter. You are responsible for setting the\n    various state variables to reasonable values; the defaults  will\n    not give you a functional filter.\n    For now the best documentation is my free book Kalman and Bayesian\n    Filters in Python [2]_. The test files in this directory also give you a\n    basic idea of use, albeit without much description.\n    In brief, you will first construct this object, specifying the size of\n    the state vector with dim_x and the size of the measurement vector that\n    you will be using with dim_z. These are mostly used to perform size checks\n    when you assign values to the various matrices. For example, if you\n    specified dim_z=2 and then try to assign a 3x3 matrix to R (the\n    measurement noise matrix you will get an assert exception because R\n    should be 2x2. (If for whatever reason you need to alter the size of\n    things midstream just use the underscore version of the matrices to\n    assign directly: your_filter._R = a_3x3_matrix.)\n    After construction the filter will have default matrices created for you,\n    but you must specify the values for each. It’s usually easiest to just\n    overwrite them rather than assign to each element yourself. This will be\n    clearer in the example below. All are of type numpy.array.\n    Examples\n    --------\n    Here is a filter that tracks position and velocity using a sensor that only\n    reads position.\n    First construct the object with the required dimensionality. Here the state\n    (`dim_x`) has 2 coefficients (position and velocity), and the measurement\n    (`dim_z`) has one. In FilterPy `x` is the state, `z` is the measurement.\n    .. code::\n        from filterpy.kalman import KalmanFilter\n        f = KalmanFilter (dim_x=2, dim_z=1)\n    Assign the initial value for the state (position and velocity). You can do this\n    with a two dimensional array like so:\n        .. code::\n            f.x = np.array([[2.],    # position\n                            [0.]])   # velocity\n    or just use a one dimensional array, which I prefer doing.\n    .. code::\n        f.x = np.array([2., 0.])\n    Define the state transition matrix:\n        .. code::\n            f.F = np.array([[1.,1.],\n                            [0.,1.]])\n    Define the measurement function. Here we need to convert a position-velocity\n    vector into just a position vector, so we use:\n        .. code::\n        f.H = np.array([[1., 0.]])\n    Define the state's covariance matrix P. \n    .. code::\n        f.P = np.array([[1000.,    0.],\n                        [   0., 1000.] ])\n    Now assign the measurement noise. Here the dimension is 1x1, so I can\n    use a scalar\n    .. code::\n        f.R = 5\n    I could have done this instead:\n    .. code::\n        f.R = np.array([[5.]])\n    Note that this must be a 2 dimensional array.\n    Finally, I will assign the process noise. Here I will take advantage of\n    another FilterPy library function:\n    .. code::\n        from filterpy.common import Q_discrete_white_noise\n        f.Q = Q_discrete_white_noise(dim=2, dt=0.1, var=0.13)\n    Now just perform the standard predict/update loop:\n    .. code::\n        while some_condition_is_true:\n            z = get_sensor_reading()\n            f.predict()\n            f.update(z)\n            do_something_with_estimate (f.x)\n    **Procedural Form**\n    This module also contains stand alone functions to perform Kalman filtering.\n    Use these if you are not a fan of objects.\n    **Example**\n    .. code::\n        while True:\n            z, R = read_sensor()\n            x, P = predict(x, P, F, Q)\n            x, P = update(x, P, z, R, H)\n    See my book Kalman and Bayesian Filters in Python [2]_.\n    You will have to set the following attributes after constructing this\n    object for the filter to perform properly. Please note that there are\n    various checks in place to ensure that you have made everything the\n    'correct' size. However, it is possible to provide incorrectly sized\n    arrays such that the linear algebra can not perform an operation.\n    It can also fail silently - you can end up with matrices of a size that\n    allows the linear algebra to work, but are the wrong shape for the problem\n    you are trying to solve.\n    Parameters\n    ----------\n    dim_x : int\n        Number of state variables for the Kalman filter. For example, if\n        you are tracking the position and velocity of an object in two\n        dimensions, dim_x would be 4.\n        This is used to set the default size of P, Q, and u\n    dim_z : int\n        Number of of measurement inputs. For example, if the sensor\n        provides you with position in (x,y), dim_z would be 2.\n    dim_u : int (optional)\n        size of the control input, if it is being used.\n        Default value of 0 indicates it is not used.\n    compute_log_likelihood : bool (default = True)\n        Computes log likelihood by default, but this can be a slow\n        computation, so if you never use it you can turn this computation\n        off.\n    Attributes\n    ----------\n    x : numpy.array(dim_x, 1)\n        Current state estimate. Any call to update() or predict() updates\n        this variable.\n    P : numpy.array(dim_x, dim_x)\n        Current state covariance matrix. Any call to update() or predict()\n        updates this variable.\n    x_prior : numpy.array(dim_x, 1)\n        Prior (predicted) state estimate. The *_prior and *_post attributes\n        are for convenience; they store the  prior and posterior of the\n        current epoch. Read Only.\n    P_prior : numpy.array(dim_x, dim_x)\n        Prior (predicted) state covariance matrix. Read Only.\n    x_post : numpy.array(dim_x, 1)\n        Posterior (updated) state estimate. Read Only.\n    P_post : numpy.array(dim_x, dim_x)\n        Posterior (updated) state covariance matrix. Read Only.\n    z : numpy.array\n        Last measurement used in update(). Read only.\n    R : numpy.array(dim_z, dim_z)\n        Measurement noise covariance matrix. Also known as the\n        observation covariance.\n    Q : numpy.array(dim_x, dim_x)\n        Process noise covariance matrix. Also known as the transition\n        covariance.\n    F : numpy.array()\n        State Transition matrix. Also known as `A` in some formulation.\n    H : numpy.array(dim_z, dim_x)\n        Measurement function. Also known as the observation matrix, or as `C`.\n    y : numpy.array\n        Residual of the update step. Read only.\n    K : numpy.array(dim_x, dim_z)\n        Kalman gain of the update step. Read only.\n    S :  numpy.array\n        System uncertainty (P projected to measurement space). Read only.\n    SI :  numpy.array\n        Inverse system uncertainty. Read only.\n    log_likelihood : float\n        log-likelihood of the last measurement. Read only.\n    likelihood : float\n        likelihood of last measurement. Read only.\n        Computed from the log-likelihood. The log-likelihood can be very\n        small,  meaning a large negative value such as -28000. Taking the\n        exp() of that results in 0.0, which can break typical algorithms\n        which multiply by this value, so by default we always return a\n        number >= sys.float_info.min.\n    mahalanobis : float\n        mahalanobis distance of the innovation. Read only.\n    inv : function, default numpy.linalg.inv\n        If you prefer another inverse function, such as the Moore-Penrose\n        pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv\n        This is only used to invert self.S. If you know it is diagonal, you\n        might choose to set it to filterpy.common.inv_diagonal, which is\n        several times faster than numpy.linalg.inv for diagonal matrices.\n    alpha : float\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon [1]_.\n    References\n    ----------\n    .. [1] Dan Simon. \"Optimal State Estimation.\" John Wiley & Sons.\n       p. 208-212. (2006)\n    .. [2] Roger Labbe. \"Kalman and Bayesian Filters in Python\"\n       https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n    \"\"\"\n\n    def __init__(self, dim_x, dim_z, dim_u=0, args=None):\n        if dim_x < 1:\n            raise ValueError('dim_x must be 1 or greater')\n        if dim_z < 1:\n            raise ValueError('dim_z must be 1 or greater')\n        if dim_u < 0:\n            raise ValueError('dim_u must be 0 or greater')\n\n        self.dim_x = dim_x\n        self.dim_z = dim_z\n        self.dim_u = dim_u\n\n        self.x = zeros((dim_x, 1))        # state\n        self.P = eye(dim_x)               # uncertainty covariance\n        self.Q = eye(dim_x)               # process uncertainty\n        self.B = None                     # control transition matrix\n        self.F = eye(dim_x)               # state transition matrix\n        self.H = zeros((dim_z, dim_x))    # measurement function\n        self.R = eye(dim_z)               # measurement uncertainty\n        self._alpha_sq = 1.               # fading memory control\n        self.M = np.zeros((dim_x, dim_z)) # process-measurement cross correlation\n        self.z = np.array([[None]*self.dim_z]).T\n\n        # gain and residual are computed during the innovation step. We\n        # save them so that in case you want to inspect them for various\n        # purposes\n        self.K = np.zeros((dim_x, dim_z)) # kalman gain\n        self.y = zeros((dim_z, 1))\n        self.S = np.zeros((dim_z, dim_z)) # system uncertainty\n        self.SI = np.zeros((dim_z, dim_z)) # inverse system uncertainty\n\n        # identity matrix. Do not alter this.\n        self._I = np.eye(dim_x)\n\n        # these will always be a copy of x,P after predict() is called\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n        # these will always be a copy of x,P after update() is called\n        self.x_post = self.x.copy()             \n        self.P_post = self.P.copy()\n\n        # Only computed only if requested via property\n        self._log_likelihood = log(sys.float_info.min)\n        self._likelihood = sys.float_info.min\n        self._mahalanobis = None\n\n        # keep all observations \n        self.history_obs = []\n\n        self.inv = np.linalg.inv\n\n        self.attr_saved = None\n        self.observed = False\n        self.args = args\n\n\n    def predict(self, u=None, B=None, F=None, Q=None):\n        \"\"\"\n        Predict next state (prior) using the Kalman filter state propagation\n        equations.\n        Parameters\n        ----------\n        u : np.array, default 0\n            Optional control vector.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        F : np.array(dim_x, dim_x), or None\n            Optional state transition matrix; a value of None\n            will cause the filter to use `self.F`.\n        Q : np.array(dim_x, dim_x), scalar, or None\n            Optional process noise matrix; a value of None will cause the\n            filter to use `self.Q`.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        elif isscalar(Q):\n            Q = eye(self.dim_x) * Q\n\n\n        # x = Fx + Bu\n        if B is not None and u is not None:\n            self.x = dot(F, self.x) + dot(B, u)\n        else:\n            self.x = dot(F, self.x)\n\n        # P = FPF' + Q\n        self.P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q\n\n        # save prior\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n\n    def freeze(self):\n        \"\"\"\n            Save the parameters before non-observation forward\n        \"\"\"\n        self.attr_saved = deepcopy(self.__dict__)\n\n\n    def unfreeze(self):\n        if self.attr_saved is not None:\n            new_history = deepcopy(self.history_obs)\n            self.__dict__ = self.attr_saved\n            # self.history_obs = new_history \n            self.history_obs = self.history_obs[:-1]\n            occur = [int(d is None) for d in new_history]\n            indices = np.where(np.array(occur)==0)[0]\n            index1 = indices[-2]\n            index2 = indices[-1]\n            score1 = new_history[index1]\n            # x1, y1, s1, r1 = box1\n            # w1 = np.sqrt(s1 * r1)\n            # h1 = np.sqrt(s1 / r1)\n            score2 = new_history[index2]\n            # x2, y2, s2, r2 = box2\n            # w2 = np.sqrt(s2 * r2)\n            # h2 = np.sqrt(s2 / r2)\n            time_gap = index2 - index1\n            # dx = (x2-x1)/time_gap\n            # dy = (y2-y1)/time_gap\n            # dw = (w2-w1)/time_gap\n            # dh = (h2-h1)/time_gap\n            dscore = (score2 - score1) / time_gap\n            for i in range(index2 - index1):\n                \"\"\"\n                    The default virtual trajectory generation is by linear\n                    motion (constant speed hypothesis), you could modify this \n                    part to implement your own. \n                \"\"\"\n                # x = x1 + (i+1) * dx\n                # y = y1 + (i+1) * dy\n                # w = w1 + (i+1) * dw\n                # h = h1 + (i+1) * dh\n                # s = w * h\n                # r = w / float(h)\n                score = score1 + (i+1) * dscore\n                new_score = np.array([score]).reshape((1, 1))\n                \"\"\"\n                    I still use predict-update loop here to refresh the parameters,\n                    but this can be faster by directly modifying the internal parameters\n                    as suggested in the paper. I keep this naive but slow way for \n                    easy read and understanding\n                \"\"\"\n                self.update(new_score)\n                if not i == (index2-index1-1):\n                    self.predict()\n\n\n    def update(self, z, R=None, H=None, confidence=0.0):\n        \"\"\"\n        Add a new measurement (z) to the Kalman filter.\n        If z is None, nothing is computed. However, x_post and P_post are\n        updated with the prior (x_prior, P_prior), and self.z is set to None.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n            If you pass in a value of H, z must be a column vector the\n            of the correct size.\n        R : np.array, scalar, or None\n            Optionally provide R to override the measurement noise for this\n            one call, otherwise  self.R will be used.\n        H : np.array, or None\n            Optionally provide H to override the measurement function for this\n            one call, otherwise self.H will be used.\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        # append the observation\n        self.history_obs.append(z)\n        \n        if z is None:\n            if self.observed:\n                \"\"\"\n                    Got no observation so freeze the current parameters for future\n                    potential online smoothing.\n                \"\"\"\n                self.freeze()\n            self.observed = False \n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n        \n        # self.observed = True\n        if not self.observed:\n            \"\"\"\n                Get observation, use online smoothing to re-update parameters\n            \"\"\"\n            self.unfreeze()\n        self.observed = True\n\n        if R is None:\n            R = self.R\n        elif isscalar(R):\n            R = eye(self.dim_z) * R\n\n        # if self.args.use_nsa_kalman:\n        #     R = [(1 - confidence) * self.args.nsa_kalman_interval * x for x in R]\n\n        if H is None:\n            z = reshape_z(z, self.dim_z, self.x.ndim)\n            H = self.H\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(H, self.x)\n\n        # common subexpression for speed\n        PHT = dot(self.P, H.T)\n\n        # S = HPH' + R\n        # project system uncertainty into measurement space\n        self.S = dot(H, PHT) + R\n        self.SI = self.inv(self.S)\n        # K = PH'inv(S)\n        # map system uncertainty into kalman gain\n        self.K = dot(PHT, self.SI)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n\n        # P = (I-KH)P(I-KH)' + KRK'\n        # This is more numerically stable\n        # and works for non-optimal K vs the equation\n        # P = (I-KH)P usually seen in the literature.\n\n        I_KH = self._I - dot(self.K, H)\n        self.P = dot(dot(I_KH, self.P), I_KH.T) + dot(dot(self.K, R), self.K.T)\n\n        # save measurement and posterior state\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n    def predict_steadystate(self, u=0, B=None):\n        \"\"\"\n        Predict state (prior) using the Kalman filter state propagation\n        equations. Only x is updated, P is left unchanged. See\n        update_steadstate() for a longer explanation of when to use this\n        method.\n        Parameters\n        ----------\n        u : np.array\n            Optional control vector. If non-zero, it is multiplied by B\n            to create the control input into the system.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n\n        # x = Fx + Bu\n        if B is not None:\n            self.x = dot(self.F, self.x) + dot(B, u)\n        else:\n            self.x = dot(self.F, self.x)\n\n        # save prior\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n    def update_steadystate(self, z):\n        \"\"\"\n        Add a new measurement (z) to the Kalman filter without recomputing\n        the Kalman gain K, the state covariance P, or the system\n        uncertainty S.\n        You can use this for LTI systems since the Kalman gain and covariance\n        converge to a fixed value. Precompute these and assign them explicitly,\n        or run the Kalman filter using the normal predict()/update(0 cycle\n        until they converge.\n        The main advantage of this call is speed. We do significantly less\n        computation, notably avoiding a costly matrix inversion.\n        Use in conjunction with predict_steadystate(), otherwise P will grow\n        without bound.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        Examples\n        --------\n        >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter\n        >>> # let filter converge on representative data, then save k and P\n        >>> for i in range(100):\n        >>>     cv.predict()\n        >>>     cv.update([i, i, i])\n        >>> saved_k = np.copy(cv.K)\n        >>> saved_P = np.copy(cv.P)\n        later on:\n        >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter\n        >>> cv.K = np.copy(saved_K)\n        >>> cv.P = np.copy(saved_P)\n        >>> for i in range(100):\n        >>>     cv.predict_steadystate()\n        >>>     cv.update_steadystate([i, i, i])\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        if z is None:\n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(self.H, self.x)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n    def update_correlated(self, z, R=None, H=None):\n        \"\"\" Add a new measurement (z) to the Kalman filter assuming that\n        process noise and measurement noise are correlated as defined in\n        the `self.M` matrix.\n        A partial derivation can be found in [1]\n        If z is None, nothing is changed.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        R : np.array, scalar, or None\n            Optionally provide R to override the measurement noise for this\n            one call, otherwise  self.R will be used.\n        H : np.array,  or None\n            Optionally provide H to override the measurement function for this\n            one call, otherwise  self.H will be used.\n        References\n        ----------\n        .. [1] Bulut, Y. (2011). Applied Kalman filter theory (Doctoral dissertation, Northeastern University).\n               http://people.duke.edu/~hpgavin/SystemID/References/Balut-KalmanFilter-PhD-NEU-2011.pdf\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        if z is None:\n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n\n        if R is None:\n            R = self.R\n        elif isscalar(R):\n            R = eye(self.dim_z) * R\n\n        # rename for readability and a tiny extra bit of speed\n        if H is None:\n            z = reshape_z(z, self.dim_z, self.x.ndim)\n            H = self.H\n\n        # handle special case: if z is in form [[z]] but x is not a column\n        # vector dimensions will not match\n        if self.x.ndim == 1 and shape(z) == (1, 1):\n            z = z[0]\n\n        if shape(z) == (): # is it scalar, e.g. z=3 or z=np.array(3)\n            z = np.asarray([z])\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(H, self.x)\n\n        # common subexpression for speed\n        PHT = dot(self.P, H.T)\n\n        # project system uncertainty into measurement space\n        self.S = dot(H, PHT) + dot(H, self.M) + dot(self.M.T, H.T) + R\n        self.SI = self.inv(self.S)\n\n        # K = PH'inv(S)\n        # map system uncertainty into kalman gain\n        self.K = dot(PHT + self.M, self.SI)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n        self.P = self.P - dot(self.K, dot(H, self.P) + self.M.T)\n\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n    def batch_filter(self, zs, Fs=None, Qs=None, Hs=None,\n                     Rs=None, Bs=None, us=None, update_first=False,\n                     saver=None):\n        \"\"\" Batch processes a sequences of measurements.\n        Parameters\n        ----------\n        zs : list-like\n            list of measurements at each time step `self.dt`. Missing\n            measurements must be represented by `None`.\n        Fs : None, list-like, default=None\n            optional value or list of values to use for the state transition\n            matrix F.\n            If Fs is None then self.F is used for all epochs.\n            Otherwise it must contain a list-like list of F's, one for\n            each epoch.  This allows you to have varying F per epoch.\n        Qs : None, np.array or list-like, default=None\n            optional value or list of values to use for the process error\n            covariance Q.\n            If Qs is None then self.Q is used for all epochs.\n            Otherwise it must contain a list-like list of Q's, one for\n            each epoch.  This allows you to have varying Q per epoch.\n        Hs : None, np.array or list-like, default=None\n            optional list of values to use for the measurement matrix H.\n            If Hs is None then self.H is used for all epochs.\n            If Hs contains a single matrix, then it is used as H for all\n            epochs.\n            Otherwise it must contain a list-like list of H's, one for\n            each epoch.  This allows you to have varying H per epoch.\n        Rs : None, np.array or list-like, default=None\n            optional list of values to use for the measurement error\n            covariance R.\n            If Rs is None then self.R is used for all epochs.\n            Otherwise it must contain a list-like list of R's, one for\n            each epoch.  This allows you to have varying R per epoch.\n        Bs : None, np.array or list-like, default=None\n            optional list of values to use for the control transition matrix B.\n            If Bs is None then self.B is used for all epochs.\n            Otherwise it must contain a list-like list of B's, one for\n            each epoch.  This allows you to have varying B per epoch.\n        us : None, np.array or list-like, default=None\n            optional list of values to use for the control input vector;\n            If us is None then None is used for all epochs (equivalent to 0,\n            or no control input).\n            Otherwise it must contain a list-like list of u's, one for\n            each epoch.\n       update_first : bool, optional, default=False\n            controls whether the order of operations is update followed by\n            predict, or predict followed by update. Default is predict->update.\n        saver : filterpy.common.Saver, optional\n            filterpy.common.Saver object. If provided, saver.save() will be\n            called after every epoch\n        Returns\n        -------\n        means : np.array((n,dim_x,1))\n            array of the state for each time step after the update. Each entry\n            is an np.array. In other words `means[k,:]` is the state at step\n            `k`.\n        covariance : np.array((n,dim_x,dim_x))\n            array of the covariances for each time step after the update.\n            In other words `covariance[k,:,:]` is the covariance at step `k`.\n        means_predictions : np.array((n,dim_x,1))\n            array of the state for each time step after the predictions. Each\n            entry is an np.array. In other words `means[k,:]` is the state at\n            step `k`.\n        covariance_predictions : np.array((n,dim_x,dim_x))\n            array of the covariances for each time step after the prediction.\n            In other words `covariance[k,:,:]` is the covariance at step `k`.\n        Examples\n        --------\n        .. code-block:: Python\n            # this example demonstrates tracking a measurement where the time\n            # between measurement varies, as stored in dts. This requires\n            # that F be recomputed for each epoch. The output is then smoothed\n            # with an RTS smoother.\n            zs = [t + random.randn()*4 for t in range (40)]\n            Fs = [np.array([[1., dt], [0, 1]] for dt in dts]\n            (mu, cov, _, _) = kf.batch_filter(zs, Fs=Fs)\n            (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs)\n        \"\"\"\n\n        #pylint: disable=too-many-statements\n        n = np.size(zs, 0)\n        if Fs is None:\n            Fs = [self.F] * n\n        if Qs is None:\n            Qs = [self.Q] * n\n        if Hs is None:\n            Hs = [self.H] * n\n        if Rs is None:\n            Rs = [self.R] * n\n        if Bs is None:\n            Bs = [self.B] * n\n        if us is None:\n            us = [0] * n\n\n        # mean estimates from Kalman Filter\n        if self.x.ndim == 1:\n            means = zeros((n, self.dim_x))\n            means_p = zeros((n, self.dim_x))\n        else:\n            means = zeros((n, self.dim_x, 1))\n            means_p = zeros((n, self.dim_x, 1))\n\n        # state covariances from Kalman Filter\n        covariances = zeros((n, self.dim_x, self.dim_x))\n        covariances_p = zeros((n, self.dim_x, self.dim_x))\n\n        if update_first:\n            for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n                self.update(z, R=R, H=H)\n                means[i, :] = self.x\n                covariances[i, :, :] = self.P\n\n                self.predict(u=u, B=B, F=F, Q=Q)\n                means_p[i, :] = self.x\n                covariances_p[i, :, :] = self.P\n\n                if saver is not None:\n                    saver.save()\n        else:\n            for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n                self.predict(u=u, B=B, F=F, Q=Q)\n                means_p[i, :] = self.x\n                covariances_p[i, :, :] = self.P\n\n                self.update(z, R=R, H=H)\n                means[i, :] = self.x\n                covariances[i, :, :] = self.P\n\n                if saver is not None:\n                    saver.save()\n\n        return (means, covariances, means_p, covariances_p)\n\n    def rts_smoother(self, Xs, Ps, Fs=None, Qs=None, inv=np.linalg.inv):\n        \"\"\"\n        Runs the Rauch-Tung-Striebel Kalman smoother on a set of\n        means and covariances computed by a Kalman filter. The usual input\n        would come from the output of `KalmanFilter.batch_filter()`.\n        Parameters\n        ----------\n        Xs : numpy.array\n           array of the means (state variable x) of the output of a Kalman\n           filter.\n        Ps : numpy.array\n            array of the covariances of the output of a kalman filter.\n        Fs : list-like collection of numpy.array, optional\n            State transition matrix of the Kalman filter at each time step.\n            Optional, if not provided the filter's self.F will be used\n        Qs : list-like collection of numpy.array, optional\n            Process noise of the Kalman filter at each time step. Optional,\n            if not provided the filter's self.Q will be used\n        inv : function, default numpy.linalg.inv\n            If you prefer another inverse function, such as the Moore-Penrose\n            pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv\n        Returns\n        -------\n        x : numpy.ndarray\n           smoothed means\n        P : numpy.ndarray\n           smoothed state covariances\n        K : numpy.ndarray\n            smoother gain at each step\n        Pp : numpy.ndarray\n           Predicted state covariances\n        Examples\n        --------\n        .. code-block:: Python\n            zs = [t + random.randn()*4 for t in range (40)]\n            (mu, cov, _, _) = kalman.batch_filter(zs)\n            (x, P, K, Pp) = rts_smoother(mu, cov, kf.F, kf.Q)\n        \"\"\"\n\n        if len(Xs) != len(Ps):\n            raise ValueError('length of Xs and Ps must be the same')\n\n        n = Xs.shape[0]\n        dim_x = Xs.shape[1]\n\n        if Fs is None:\n            Fs = [self.F] * n\n        if Qs is None:\n            Qs = [self.Q] * n\n\n        # smoother gain\n        K = zeros((n, dim_x, dim_x))\n\n        x, P, Pp = Xs.copy(), Ps.copy(), Ps.copy()\n        for k in range(n-2, -1, -1):\n            Pp[k] = dot(dot(Fs[k+1], P[k]), Fs[k+1].T) + Qs[k+1]\n\n            #pylint: disable=bad-whitespace\n            K[k]  = dot(dot(P[k], Fs[k+1].T), inv(Pp[k]))\n            x[k] += dot(K[k], x[k+1] - dot(Fs[k+1], x[k]))\n            P[k] += dot(dot(K[k], P[k+1] - Pp[k]), K[k].T)\n\n        return (x, P, K, Pp)\n\n    def get_prediction(self, u=None, B=None, F=None, Q=None):\n        \"\"\"\n        Predict next state (prior) using the Kalman filter state propagation\n        equations and returns it without modifying the object.\n        Parameters\n        ----------\n        u : np.array, default 0\n            Optional control vector.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        F : np.array(dim_x, dim_x), or None\n            Optional state transition matrix; a value of None\n            will cause the filter to use `self.F`.\n        Q : np.array(dim_x, dim_x), scalar, or None\n            Optional process noise matrix; a value of None will cause the\n            filter to use `self.Q`.\n        Returns\n        -------\n        (x, P) : tuple\n            State vector and covariance array of the prediction.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        elif isscalar(Q):\n            Q = eye(self.dim_x) * Q\n\n        # x = Fx + Bu\n        if B is not None and u is not None:\n            x = dot(F, self.x) + dot(B, u)\n        else:\n            x = dot(F, self.x)\n\n        # P = FPF' + Q\n        P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q\n\n        return x, P\n\n    def get_update(self, z=None):\n        \"\"\"\n        Computes the new estimate based on measurement `z` and returns it\n        without altering the state of the filter.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        Returns\n        -------\n        (x, P) : tuple\n            State vector and covariance array of the update.\n       \"\"\"\n\n        if z is None:\n            return self.x, self.P\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n\n        R = self.R\n        H = self.H\n        P = self.P\n        x = self.x\n\n        # error (residual) between measurement and prediction\n        y = z - dot(H, x)\n\n        # common subexpression for speed\n        PHT = dot(P, H.T)\n\n        # project system uncertainty into measurement space\n        S = dot(H, PHT) + R\n\n        # map system uncertainty into kalman gain\n        K = dot(PHT, self.inv(S))\n\n        # predict new x with residual scaled by the kalman gain\n        x = x + dot(K, y)\n\n        # P = (I-KH)P(I-KH)' + KRK'\n        I_KH = self._I - dot(K, H)\n        P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)\n\n        return x, P\n\n    def residual_of(self, z):\n        \"\"\"\n        Returns the residual for the given measurement (z). Does not alter\n        the state of the filter.\n        \"\"\"\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n        return z - dot(self.H, self.x_prior)\n\n    def measurement_of_state(self, x):\n        \"\"\"\n        Helper function that converts a state into a measurement.\n        Parameters\n        ----------\n        x : np.array\n            kalman state vector\n        Returns\n        -------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        \"\"\"\n\n        return dot(self.H, x)\n\n    @property\n    def log_likelihood(self):\n        \"\"\"\n        log-likelihood of the last measurement.\n        \"\"\"\n        if self._log_likelihood is None:\n            self._log_likelihood = logpdf(x=self.y, cov=self.S)\n        return self._log_likelihood\n\n    @property\n    def likelihood(self):\n        \"\"\"\n        Computed from the log-likelihood. The log-likelihood can be very\n        small,  meaning a large negative value such as -28000. Taking the\n        exp() of that results in 0.0, which can break typical algorithms\n        which multiply by this value, so by default we always return a\n        number >= sys.float_info.min.\n        \"\"\"\n        if self._likelihood is None:\n            self._likelihood = exp(self.log_likelihood)\n            if self._likelihood == 0:\n                self._likelihood = sys.float_info.min\n        return self._likelihood\n\n    @property\n    def mahalanobis(self):\n        \"\"\"\"\n        Mahalanobis distance of measurement. E.g. 3 means measurement\n        was 3 standard deviations away from the predicted value.\n        Returns\n        -------\n        mahalanobis : float\n        \"\"\"\n        if self._mahalanobis is None:\n            self._mahalanobis = sqrt(float(dot(dot(self.y.T, self.SI), self.y)))\n        return self._mahalanobis\n\n    @property\n    def alpha(self):\n        \"\"\"\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon [1]_.\n        \"\"\"\n        return self._alpha_sq**.5\n\n    def log_likelihood_of(self, z):\n        \"\"\"\n        log likelihood of the measurement `z`. This should only be called\n        after a call to update(). Calling after predict() will yield an\n        incorrect result.\"\"\"\n\n        if z is None:\n            return log(sys.float_info.min)\n        return logpdf(z, dot(self.H, self.x), self.S)\n\n    @alpha.setter\n    def alpha(self, value):\n        if not np.isscalar(value) or value < 1:\n            raise ValueError('alpha must be a float greater than 1')\n\n        self._alpha_sq = value**2\n\n    def __repr__(self):\n        return '\\n'.join([\n            'KalmanFilter object',\n            pretty_str('dim_x', self.dim_x),\n            pretty_str('dim_z', self.dim_z),\n            pretty_str('dim_u', self.dim_u),\n            pretty_str('x', self.x),\n            pretty_str('P', self.P),\n            pretty_str('x_prior', self.x_prior),\n            pretty_str('P_prior', self.P_prior),\n            pretty_str('x_post', self.x_post),\n            pretty_str('P_post', self.P_post),\n            pretty_str('F', self.F),\n            pretty_str('Q', self.Q),\n            pretty_str('R', self.R),\n            pretty_str('H', self.H),\n            pretty_str('K', self.K),\n            pretty_str('y', self.y),\n            pretty_str('S', self.S),\n            pretty_str('SI', self.SI),\n            pretty_str('M', self.M),\n            pretty_str('B', self.B),\n            pretty_str('z', self.z),\n            pretty_str('log-likelihood', self.log_likelihood),\n            pretty_str('likelihood', self.likelihood),\n            pretty_str('mahalanobis', self.mahalanobis),\n            pretty_str('alpha', self.alpha),\n            pretty_str('inv', self.inv)\n            ])\n\n    def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None):\n        \"\"\"\n        Performs a series of asserts to check that the size of everything\n        is what it should be. This can help you debug problems in your design.\n        If you pass in H, R, F, Q those will be used instead of this object's\n        value for those matrices.\n        Testing `z` (the measurement) is problamatic. x is a vector, and can be\n        implemented as either a 1D array or as a nx1 column vector. Thus Hx\n        can be of different shapes. Then, if Hx is a single value, it can\n        be either a 1D array or 2D vector. If either is true, z can reasonably\n        be a scalar (either '3' or np.array('3') are scalars under this\n        definition), a 1D, 1 element array, or a 2D, 1 element array. You are\n        allowed to pass in any combination that works.\n        \"\"\"\n\n        if H is None:\n            H = self.H\n        if R is None:\n            R = self.R\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        x = self.x\n        P = self.P\n\n        assert x.ndim == 1 or x.ndim == 2, \\\n                \"x must have one or two dimensions, but has {}\".format(x.ndim)\n\n        if x.ndim == 1:\n            assert x.shape[0] == self.dim_x, \\\n                   \"Shape of x must be ({},{}), but is {}\".format(\n                       self.dim_x, 1, x.shape)\n        else:\n            assert x.shape == (self.dim_x, 1), \\\n                   \"Shape of x must be ({},{}), but is {}\".format(\n                       self.dim_x, 1, x.shape)\n\n        assert P.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of P must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, P.shape)\n\n        assert Q.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of Q must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, P.shape)\n\n        assert F.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of F must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, F.shape)\n\n        assert np.ndim(H) == 2, \\\n               \"Shape of H must be (dim_z, {}), but is {}\".format(\n                   P.shape[0], shape(H))\n\n        assert H.shape[1] == P.shape[0], \\\n               \"Shape of H must be (dim_z, {}), but is {}\".format(\n                   P.shape[0], H.shape)\n\n        # shape of R must be the same as HPH'\n        hph_shape = (H.shape[0], H.shape[0])\n        r_shape = shape(R)\n\n        if H.shape[0] == 1:\n            # r can be scalar, 1D, or 2D in this case\n            assert r_shape in [(), (1,), (1, 1)], \\\n            \"R must be scalar or one element array, but is shaped {}\".format(\n                r_shape)\n        else:\n            assert r_shape == hph_shape, \\\n            \"shape of R should be {} but it is {}\".format(hph_shape, r_shape)\n\n\n        if z is not None:\n            z_shape = shape(z)\n        else:\n            z_shape = (self.dim_z, 1)\n\n        # H@x must have shape of z\n        Hx = dot(H, x)\n\n        if z_shape == (): # scalar or np.array(scalar)\n            assert Hx.ndim == 1 or shape(Hx) == (1, 1), \\\n            \"shape of z should be {}, not {} for the given H\".format(\n                shape(Hx), z_shape)\n\n        elif shape(Hx) == (1,):\n            assert z_shape[0] == 1, 'Shape of z must be {} for the given H'.format(shape(Hx))\n\n        else:\n            assert (z_shape == shape(Hx) or\n                    (len(z_shape) == 1 and shape(Hx) == (z_shape[0], 1))), \\\n                    \"shape of z should be {}, not {} for the given H\".format(\n                        shape(Hx), z_shape)\n\n        if np.ndim(Hx) > 1 and shape(Hx) != (1, 1):\n            assert shape(Hx) == z_shape, \\\n               'shape of z should be {} for the given H, but it is {}'.format(\n                   shape(Hx), z_shape)\n\n\ndef update(x, P, z, R, H=None, return_all=False):\n    \"\"\"\n    Add a new measurement (z) to the Kalman filter. If z is None, nothing\n    is changed.\n    This can handle either the multidimensional or unidimensional case. If\n    all parameters are floats instead of arrays the filter will still work,\n    and return floats for x, P as the result.\n    update(1, 2, 1, 1, 1)  # univariate\n    update(x, P, 1\n    Parameters\n    ----------\n    x : numpy.array(dim_x, 1), or float\n        State estimate vector\n    P : numpy.array(dim_x, dim_x), or float\n        Covariance matrix\n    z : (dim_z, 1): array_like\n        measurement for this update. z can be a scalar if dim_z is 1,\n        otherwise it must be convertible to a column vector.\n    R : numpy.array(dim_z, dim_z), or float\n        Measurement noise matrix\n    H : numpy.array(dim_x, dim_x), or float, optional\n        Measurement function. If not provided, a value of 1 is assumed.\n    return_all : bool, default False\n        If true, y, K, S, and log_likelihood are returned, otherwise\n        only x and P are returned.\n    Returns\n    -------\n    x : numpy.array\n        Posterior state estimate vector\n    P : numpy.array\n        Posterior covariance matrix\n    y : numpy.array or scalar\n        Residua. Difference between measurement and state in measurement space\n    K : numpy.array\n        Kalman gain\n    S : numpy.array\n        System uncertainty in measurement space\n    log_likelihood : float\n        log likelihood of the measurement\n    \"\"\"\n\n    #pylint: disable=bare-except\n\n    if z is None:\n        if return_all:\n            return x, P, None, None, None, None\n        return x, P\n\n    if H is None:\n        H = np.array([1])\n\n    if np.isscalar(H):\n        H = np.array([H])\n\n    Hx = np.atleast_1d(dot(H, x))\n    z = reshape_z(z, Hx.shape[0], x.ndim)\n\n    # error (residual) between measurement and prediction\n    y = z - Hx\n\n    # project system uncertainty into measurement space\n    S = dot(dot(H, P), H.T) + R\n\n\n    # map system uncertainty into kalman gain\n    try:\n        K = dot(dot(P, H.T), linalg.inv(S))\n    except:\n        # can't invert a 1D array, annoyingly\n        K = dot(dot(P, H.T), 1./S)\n\n\n    # predict new x with residual scaled by the kalman gain\n    x = x + dot(K, y)\n\n    # P = (I-KH)P(I-KH)' + KRK'\n    KH = dot(K, H)\n\n    try:\n        I_KH = np.eye(KH.shape[0]) - KH\n    except:\n        I_KH = np.array([1 - KH])\n    P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)\n\n\n    if return_all:\n        # compute log likelihood\n        log_likelihood = logpdf(z, dot(H, x), S)\n        return x, P, y, K, S, log_likelihood\n    return x, P\n\n\ndef update_steadystate(x, z, K, H=None):\n    \"\"\"\n    Add a new measurement (z) to the Kalman filter. If z is None, nothing\n    is changed.\n    Parameters\n    ----------\n    x : numpy.array(dim_x, 1), or float\n        State estimate vector\n    z : (dim_z, 1): array_like\n        measurement for this update. z can be a scalar if dim_z is 1,\n        otherwise it must be convertible to a column vector.\n    K : numpy.array, or float\n        Kalman gain matrix\n    H : numpy.array(dim_x, dim_x), or float, optional\n        Measurement function. If not provided, a value of 1 is assumed.\n    Returns\n    -------\n    x : numpy.array\n        Posterior state estimate vector\n    Examples\n    --------\n    This can handle either the multidimensional or unidimensional case. If\n    all parameters are floats instead of arrays the filter will still work,\n    and return floats for x, P as the result.\n    >>> update_steadystate(1, 2, 1)  # univariate\n    >>> update_steadystate(x, P, z, H)\n    \"\"\"\n\n\n    if z is None:\n        return x\n\n    if H is None:\n        H = np.array([1])\n\n    if np.isscalar(H):\n        H = np.array([H])\n\n    Hx = np.atleast_1d(dot(H, x))\n    z = reshape_z(z, Hx.shape[0], x.ndim)\n\n    # error (residual) between measurement and prediction\n    y = z - Hx\n\n    # estimate new x with residual scaled by the kalman gain\n    return x + dot(K, y)\n\n\ndef predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.):\n    \"\"\"\n    Predict next state (prior) using the Kalman filter state propagation\n    equations.\n    Parameters\n    ----------\n    x : numpy.array\n        State estimate vector\n    P : numpy.array\n        Covariance matrix\n    F : numpy.array()\n        State Transition matrix\n    Q : numpy.array, Optional\n        Process noise matrix\n    u : numpy.array, Optional, default 0.\n        Control vector. If non-zero, it is multiplied by B\n        to create the control input into the system.\n    B : numpy.array, optional, default 0.\n        Control transition matrix.\n    alpha : float, Optional, default=1.0\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon\n    Returns\n    -------\n    x : numpy.array\n        Prior state estimate vector\n    P : numpy.array\n        Prior covariance matrix\n    \"\"\"\n\n    if np.isscalar(F):\n        F = np.array(F)\n    x = dot(F, x) + dot(B, u)\n    P = (alpha * alpha) * dot(dot(F, P), F.T) + Q\n\n    return x, P\n\n\ndef predict_steadystate(x, F=1, u=0, B=1):\n    \"\"\"\n    Predict next state (prior) using the Kalman filter state propagation\n    equations. This steady state form only computes x, assuming that the\n    covariance is constant.\n    Parameters\n    ----------\n    x : numpy.array\n        State estimate vector\n    P : numpy.array\n        Covariance matrix\n    F : numpy.array()\n        State Transition matrix\n    u : numpy.array, Optional, default 0.\n        Control vector. If non-zero, it is multiplied by B\n        to create the control input into the system.\n    B : numpy.array, optional, default 0.\n        Control transition matrix.\n    Returns\n    -------\n    x : numpy.array\n        Prior state estimate vector\n    \"\"\"\n\n    if np.isscalar(F):\n        F = np.array(F)\n    x = dot(F, x) + dot(B, u)\n\n    return x\n\n\n\ndef batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None,\n                 update_first=False, saver=None):\n    \"\"\"\n    Batch processes a sequences of measurements.\n    Parameters\n    ----------\n    zs : list-like\n        list of measurements at each time step. Missing measurements must be\n        represented by None.\n    Fs : list-like\n        list of values to use for the state transition matrix matrix.\n    Qs : list-like\n        list of values to use for the process error\n        covariance.\n    Hs : list-like\n        list of values to use for the measurement matrix.\n    Rs : list-like\n        list of values to use for the measurement error\n        covariance.\n    Bs : list-like, optional\n        list of values to use for the control transition matrix;\n        a value of None in any position will cause the filter\n        to use `self.B` for that time step.\n    us : list-like, optional\n        list of values to use for the control input vector;\n        a value of None in any position will cause the filter to use\n        0 for that time step.\n    update_first : bool, optional\n        controls whether the order of operations is update followed by\n        predict, or predict followed by update. Default is predict->update.\n        saver : filterpy.common.Saver, optional\n            filterpy.common.Saver object. If provided, saver.save() will be\n            called after every epoch\n    Returns\n    -------\n    means : np.array((n,dim_x,1))\n        array of the state for each time step after the update. Each entry\n        is an np.array. In other words `means[k,:]` is the state at step\n        `k`.\n    covariance : np.array((n,dim_x,dim_x))\n        array of the covariances for each time step after the update.\n        In other words `covariance[k,:,:]` is the covariance at step `k`.\n    means_predictions : np.array((n,dim_x,1))\n        array of the state for each time step after the predictions. Each\n        entry is an np.array. In other words `means[k,:]` is the state at\n        step `k`.\n    covariance_predictions : np.array((n,dim_x,dim_x))\n        array of the covariances for each time step after the prediction.\n        In other words `covariance[k,:,:]` is the covariance at step `k`.\n    Examples\n    --------\n    .. code-block:: Python\n        zs = [t + random.randn()*4 for t in range (40)]\n        Fs = [kf.F for t in range (40)]\n        Hs = [kf.H for t in range (40)]\n        (mu, cov, _, _) = kf.batch_filter(zs, Rs=R_list, Fs=Fs, Hs=Hs, Qs=None,\n                                          Bs=None, us=None, update_first=False)\n        (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs, Qs=None)\n    \"\"\"\n\n    n = np.size(zs, 0)\n    dim_x = x.shape[0]\n\n    # mean estimates from Kalman Filter\n    if x.ndim == 1:\n        means = zeros((n, dim_x))\n        means_p = zeros((n, dim_x))\n    else:\n        means = zeros((n, dim_x, 1))\n        means_p = zeros((n, dim_x, 1))\n\n    # state covariances from Kalman Filter\n    covariances = zeros((n, dim_x, dim_x))\n    covariances_p = zeros((n, dim_x, dim_x))\n\n    if us is None:\n        us = [0.] * n\n        Bs = [0.] * n\n\n    if update_first:\n        for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n            x, P = update(x, P, z, R=R, H=H)\n            means[i, :] = x\n            covariances[i, :, :] = P\n\n            x, P = predict(x, P, u=u, B=B, F=F, Q=Q)\n            means_p[i, :] = x\n            covariances_p[i, :, :] = P\n            if saver is not None:\n                saver.save()\n    else:\n        for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n            x, P = predict(x, P, u=u, B=B, F=F, Q=Q)\n            means_p[i, :] = x\n            covariances_p[i, :, :] = P\n\n            x, P = update(x, P, z, R=R, H=H)\n            means[i, :] = x\n            covariances[i, :, :] = P\n            if saver is not None:\n                saver.save()\n\n    return (means, covariances, means_p, covariances_p)\n\n\n\ndef rts_smoother(Xs, Ps, Fs, Qs):\n    \"\"\"\n    Runs the Rauch-Tung-Striebel Kalman smoother on a set of\n    means and covariances computed by a Kalman filter. The usual input\n    would come from the output of `KalmanFilter.batch_filter()`.\n    Parameters\n    ----------\n    Xs : numpy.array\n       array of the means (state variable x) of the output of a Kalman\n       filter.\n    Ps : numpy.array\n        array of the covariances of the output of a kalman filter.\n    Fs : list-like collection of numpy.array\n        State transition matrix of the Kalman filter at each time step.\n    Qs : list-like collection of numpy.array, optional\n        Process noise of the Kalman filter at each time step.\n    Returns\n    -------\n    x : numpy.ndarray\n       smoothed means\n    P : numpy.ndarray\n       smoothed state covariances\n    K : numpy.ndarray\n        smoother gain at each step\n    pP : numpy.ndarray\n       predicted state covariances\n    Examples\n    --------\n    .. code-block:: Python\n        zs = [t + random.randn()*4 for t in range (40)]\n        (mu, cov, _, _) = kalman.batch_filter(zs)\n        (x, P, K, pP) = rts_smoother(mu, cov, kf.F, kf.Q)\n    \"\"\"\n\n    if len(Xs) != len(Ps):\n        raise ValueError('length of Xs and Ps must be the same')\n\n    n = Xs.shape[0]\n    dim_x = Xs.shape[1]\n\n    # smoother gain\n    K = zeros((n, dim_x, dim_x))\n    x, P, pP = Xs.copy(), Ps.copy(), Ps.copy()\n\n    for k in range(n-2, -1, -1):\n        pP[k] = dot(dot(Fs[k], P[k]), Fs[k].T) + Qs[k]\n\n        #pylint: disable=bad-whitespace\n        K[k]  = dot(dot(P[k], Fs[k].T), linalg.inv(pP[k]))\n        x[k] += dot(K[k], x[k+1] - dot(Fs[k], x[k]))\n        P[k] += dot(dot(K[k], P[k+1] - pP[k]), K[k].T)\n\n    return (x, P, K, pP)"
  },
  {
    "path": "trackers/hybrid_sort_tracker/kalmanfilter_score_new.py",
    "content": "# -*- coding: utf-8 -*-\n# pylint: disable=invalid-name, too-many-arguments, too-many-branches,\n# pylint: disable=too-many-locals, too-many-instance-attributes, too-many-lines\n\n\"\"\"\nThis module implements the linear Kalman filter in both an object\noriented and procedural form. The KalmanFilter class implements\nthe filter by storing the various matrices in instance variables,\nminimizing the amount of bookkeeping you have to do.\nAll Kalman filters operate with a predict->update cycle. The\npredict step, implemented with the method or function predict(),\nuses the state transition matrix F to predict the state in the next\ntime period (epoch). The state is stored as a gaussian (x, P), where\nx is the state (column) vector, and P is its covariance. Covariance\nmatrix Q specifies the process covariance. In Bayesian terms, this\nprediction is called the *prior*, which you can think of colloquially\nas the estimate prior to incorporating the measurement.\nThe update step, implemented with the method or function `update()`,\nincorporates the measurement z with covariance R, into the state\nestimate (x, P). The class stores the system uncertainty in S,\nthe innovation (residual between prediction and measurement in\nmeasurement space) in y, and the Kalman gain in k. The procedural\nform returns these variables to you. In Bayesian terms this computes\nthe *posterior* - the estimate after the information from the\nmeasurement is incorporated.\nWhether you use the OO form or procedural form is up to you. If\nmatrices such as H, R, and F are changing each epoch, you'll probably\nopt to use the procedural form. If they are unchanging, the OO\nform is perhaps easier to use since you won't need to keep track\nof these matrices. This is especially useful if you are implementing\nbanks of filters or comparing various KF designs for performance;\na trivial coding bug could lead to using the wrong sets of matrices.\nThis module also offers an implementation of the RTS smoother, and\nother helper functions, such as log likelihood computations.\nThe Saver class allows you to easily save the state of the\nKalmanFilter class after every update\nThis module expects NumPy arrays for all values that expect\narrays, although in a few cases, particularly method parameters,\nit will accept types that convert to NumPy arrays, such as lists\nof lists. These exceptions are documented in the method or function.\nExamples\n--------\nThe following example constructs a constant velocity kinematic\nfilter, filters noisy data, and plots the results. It also demonstrates\nusing the Saver class to save the state of the filter at each epoch.\n.. code-block:: Python\n    import matplotlib.pyplot as plt\n    import numpy as np\n    from filterpy.kalman import KalmanFilter\n    from filterpy.common import Q_discrete_white_noise, Saver\n    r_std, q_std = 2., 0.003\n    cv = KalmanFilter(dim_x=2, dim_z=1)\n    cv.x = np.array([[0., 1.]]) # position, velocity\n    cv.F = np.array([[1, dt],[ [0, 1]])\n    cv.R = np.array([[r_std^^2]])\n    f.H = np.array([[1., 0.]])\n    f.P = np.diag([.1^^2, .03^^2)\n    f.Q = Q_discrete_white_noise(2, dt, q_std**2)\n    saver = Saver(cv)\n    for z in range(100):\n        cv.predict()\n        cv.update([z + randn() * r_std])\n        saver.save() # save the filter's state\n    saver.to_array()\n    plt.plot(saver.x[:, 0])\n    # plot all of the priors\n    plt.plot(saver.x_prior[:, 0])\n    # plot mahalanobis distance\n    plt.figure()\n    plt.plot(saver.mahalanobis)\nThis code implements the same filter using the procedural form\n    x = np.array([[0., 1.]]) # position, velocity\n    F = np.array([[1, dt],[ [0, 1]])\n    R = np.array([[r_std^^2]])\n    H = np.array([[1., 0.]])\n    P = np.diag([.1^^2, .03^^2)\n    Q = Q_discrete_white_noise(2, dt, q_std**2)\n    for z in range(100):\n        x, P = predict(x, P, F=F, Q=Q)\n        x, P = update(x, P, z=[z + randn() * r_std], R=R, H=H)\n        xs.append(x[0, 0])\n    plt.plot(xs)\nFor more examples see the test subdirectory, or refer to the\nbook cited below. In it I both teach Kalman filtering from basic\nprinciples, and teach the use of this library in great detail.\nFilterPy library.\nhttp://github.com/rlabbe/filterpy\nDocumentation at:\nhttps://filterpy.readthedocs.org\nSupporting book at:\nhttps://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\nThis is licensed under an MIT license. See the readme.MD file\nfor more information.\nCopyright 2014-2018 Roger R Labbe Jr.\n\"\"\"\n\nfrom __future__ import absolute_import, division\n\nfrom copy import deepcopy\nfrom math import log, exp, sqrt\nimport sys\nimport numpy as np\nfrom numpy import dot, zeros, eye, isscalar, shape\nimport numpy.linalg as linalg\nfrom filterpy.stats import logpdf\nfrom filterpy.common import pretty_str, reshape_z\n\n\nclass KalmanFilterNew_score_new(object):\n    \"\"\" Implements a Kalman filter. You are responsible for setting the\n    various state variables to reasonable values; the defaults  will\n    not give you a functional filter.\n    For now the best documentation is my free book Kalman and Bayesian\n    Filters in Python [2]_. The test files in this directory also give you a\n    basic idea of use, albeit without much description.\n    In brief, you will first construct this object, specifying the size of\n    the state vector with dim_x and the size of the measurement vector that\n    you will be using with dim_z. These are mostly used to perform size checks\n    when you assign values to the various matrices. For example, if you\n    specified dim_z=2 and then try to assign a 3x3 matrix to R (the\n    measurement noise matrix you will get an assert exception because R\n    should be 2x2. (If for whatever reason you need to alter the size of\n    things midstream just use the underscore version of the matrices to\n    assign directly: your_filter._R = a_3x3_matrix.)\n    After construction the filter will have default matrices created for you,\n    but you must specify the values for each. It’s usually easiest to just\n    overwrite them rather than assign to each element yourself. This will be\n    clearer in the example below. All are of type numpy.array.\n    Examples\n    --------\n    Here is a filter that tracks position and velocity using a sensor that only\n    reads position.\n    First construct the object with the required dimensionality. Here the state\n    (`dim_x`) has 2 coefficients (position and velocity), and the measurement\n    (`dim_z`) has one. In FilterPy `x` is the state, `z` is the measurement.\n    .. code::\n        from filterpy.kalman import KalmanFilter\n        f = KalmanFilter (dim_x=2, dim_z=1)\n    Assign the initial value for the state (position and velocity). You can do this\n    with a two dimensional array like so:\n        .. code::\n            f.x = np.array([[2.],    # position\n                            [0.]])   # velocity\n    or just use a one dimensional array, which I prefer doing.\n    .. code::\n        f.x = np.array([2., 0.])\n    Define the state transition matrix:\n        .. code::\n            f.F = np.array([[1.,1.],\n                            [0.,1.]])\n    Define the measurement function. Here we need to convert a position-velocity\n    vector into just a position vector, so we use:\n        .. code::\n        f.H = np.array([[1., 0.]])\n    Define the state's covariance matrix P. \n    .. code::\n        f.P = np.array([[1000.,    0.],\n                        [   0., 1000.] ])\n    Now assign the measurement noise. Here the dimension is 1x1, so I can\n    use a scalar\n    .. code::\n        f.R = 5\n    I could have done this instead:\n    .. code::\n        f.R = np.array([[5.]])\n    Note that this must be a 2 dimensional array.\n    Finally, I will assign the process noise. Here I will take advantage of\n    another FilterPy library function:\n    .. code::\n        from filterpy.common import Q_discrete_white_noise\n        f.Q = Q_discrete_white_noise(dim=2, dt=0.1, var=0.13)\n    Now just perform the standard predict/update loop:\n    .. code::\n        while some_condition_is_true:\n            z = get_sensor_reading()\n            f.predict()\n            f.update(z)\n            do_something_with_estimate (f.x)\n    **Procedural Form**\n    This module also contains stand alone functions to perform Kalman filtering.\n    Use these if you are not a fan of objects.\n    **Example**\n    .. code::\n        while True:\n            z, R = read_sensor()\n            x, P = predict(x, P, F, Q)\n            x, P = update(x, P, z, R, H)\n    See my book Kalman and Bayesian Filters in Python [2]_.\n    You will have to set the following attributes after constructing this\n    object for the filter to perform properly. Please note that there are\n    various checks in place to ensure that you have made everything the\n    'correct' size. However, it is possible to provide incorrectly sized\n    arrays such that the linear algebra can not perform an operation.\n    It can also fail silently - you can end up with matrices of a size that\n    allows the linear algebra to work, but are the wrong shape for the problem\n    you are trying to solve.\n    Parameters\n    ----------\n    dim_x : int\n        Number of state variables for the Kalman filter. For example, if\n        you are tracking the position and velocity of an object in two\n        dimensions, dim_x would be 4.\n        This is used to set the default size of P, Q, and u\n    dim_z : int\n        Number of of measurement inputs. For example, if the sensor\n        provides you with position in (x,y), dim_z would be 2.\n    dim_u : int (optional)\n        size of the control input, if it is being used.\n        Default value of 0 indicates it is not used.\n    compute_log_likelihood : bool (default = True)\n        Computes log likelihood by default, but this can be a slow\n        computation, so if you never use it you can turn this computation\n        off.\n    Attributes\n    ----------\n    x : numpy.array(dim_x, 1)\n        Current state estimate. Any call to update() or predict() updates\n        this variable.\n    P : numpy.array(dim_x, dim_x)\n        Current state covariance matrix. Any call to update() or predict()\n        updates this variable.\n    x_prior : numpy.array(dim_x, 1)\n        Prior (predicted) state estimate. The *_prior and *_post attributes\n        are for convenience; they store the  prior and posterior of the\n        current epoch. Read Only.\n    P_prior : numpy.array(dim_x, dim_x)\n        Prior (predicted) state covariance matrix. Read Only.\n    x_post : numpy.array(dim_x, 1)\n        Posterior (updated) state estimate. Read Only.\n    P_post : numpy.array(dim_x, dim_x)\n        Posterior (updated) state covariance matrix. Read Only.\n    z : numpy.array\n        Last measurement used in update(). Read only.\n    R : numpy.array(dim_z, dim_z)\n        Measurement noise covariance matrix. Also known as the\n        observation covariance.\n    Q : numpy.array(dim_x, dim_x)\n        Process noise covariance matrix. Also known as the transition\n        covariance.\n    F : numpy.array()\n        State Transition matrix. Also known as `A` in some formulation.\n    H : numpy.array(dim_z, dim_x)\n        Measurement function. Also known as the observation matrix, or as `C`.\n    y : numpy.array\n        Residual of the update step. Read only.\n    K : numpy.array(dim_x, dim_z)\n        Kalman gain of the update step. Read only.\n    S :  numpy.array\n        System uncertainty (P projected to measurement space). Read only.\n    SI :  numpy.array\n        Inverse system uncertainty. Read only.\n    log_likelihood : float\n        log-likelihood of the last measurement. Read only.\n    likelihood : float\n        likelihood of last measurement. Read only.\n        Computed from the log-likelihood. The log-likelihood can be very\n        small,  meaning a large negative value such as -28000. Taking the\n        exp() of that results in 0.0, which can break typical algorithms\n        which multiply by this value, so by default we always return a\n        number >= sys.float_info.min.\n    mahalanobis : float\n        mahalanobis distance of the innovation. Read only.\n    inv : function, default numpy.linalg.inv\n        If you prefer another inverse function, such as the Moore-Penrose\n        pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv\n        This is only used to invert self.S. If you know it is diagonal, you\n        might choose to set it to filterpy.common.inv_diagonal, which is\n        several times faster than numpy.linalg.inv for diagonal matrices.\n    alpha : float\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon [1]_.\n    References\n    ----------\n    .. [1] Dan Simon. \"Optimal State Estimation.\" John Wiley & Sons.\n       p. 208-212. (2006)\n    .. [2] Roger Labbe. \"Kalman and Bayesian Filters in Python\"\n       https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n    \"\"\"\n\n    def __init__(self, dim_x, dim_z, dim_u=0, args=None):\n        if dim_x < 1:\n            raise ValueError('dim_x must be 1 or greater')\n        if dim_z < 1:\n            raise ValueError('dim_z must be 1 or greater')\n        if dim_u < 0:\n            raise ValueError('dim_u must be 0 or greater')\n\n        self.dim_x = dim_x\n        self.dim_z = dim_z\n        self.dim_u = dim_u\n\n        self.x = zeros((dim_x, 1))        # state\n        self.P = eye(dim_x)               # uncertainty covariance\n        self.Q = eye(dim_x)               # process uncertainty\n        self.B = None                     # control transition matrix\n        self.F = eye(dim_x)               # state transition matrix\n        self.H = zeros((dim_z, dim_x))    # measurement function\n        self.R = eye(dim_z)               # measurement uncertainty\n        self._alpha_sq = 1.               # fading memory control\n        self.M = np.zeros((dim_x, dim_z)) # process-measurement cross correlation\n        self.z = np.array([[None]*self.dim_z]).T\n\n        # gain and residual are computed during the innovation step. We\n        # save them so that in case you want to inspect them for various\n        # purposes\n        self.K = np.zeros((dim_x, dim_z)) # kalman gain\n        self.y = zeros((dim_z, 1))\n        self.S = np.zeros((dim_z, dim_z)) # system uncertainty\n        self.SI = np.zeros((dim_z, dim_z)) # inverse system uncertainty\n\n        # identity matrix. Do not alter this.\n        self._I = np.eye(dim_x)\n\n        # these will always be a copy of x,P after predict() is called\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n        # these will always be a copy of x,P after update() is called\n        self.x_post = self.x.copy()             \n        self.P_post = self.P.copy()\n\n        # Only computed only if requested via property\n        self._log_likelihood = log(sys.float_info.min)\n        self._likelihood = sys.float_info.min\n        self._mahalanobis = None\n\n        # keep all observations \n        self.history_obs = []\n\n        self.inv = np.linalg.inv\n\n        self.attr_saved = None\n        self.observed = False\n        self.args = args\n\n\n    def predict(self, u=None, B=None, F=None, Q=None):\n        \"\"\"\n        Predict next state (prior) using the Kalman filter state propagation\n        equations.\n        Parameters\n        ----------\n        u : np.array, default 0\n            Optional control vector.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        F : np.array(dim_x, dim_x), or None\n            Optional state transition matrix; a value of None\n            will cause the filter to use `self.F`.\n        Q : np.array(dim_x, dim_x), scalar, or None\n            Optional process noise matrix; a value of None will cause the\n            filter to use `self.Q`.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        elif isscalar(Q):\n            Q = eye(self.dim_x) * Q\n\n\n        # x = Fx + Bu\n        if B is not None and u is not None:\n            self.x = dot(F, self.x) + dot(B, u)\n        else:\n            self.x = dot(F, self.x)\n\n        # P = FPF' + Q\n        self.P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q\n\n        # save prior\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n\n\n    def freeze(self):\n        \"\"\"\n            Save the parameters before non-observation forward\n        \"\"\"\n        self.attr_saved = deepcopy(self.__dict__)\n\n\n    def unfreeze(self):\n        if self.attr_saved is not None:\n            new_history = deepcopy(self.history_obs)\n            self.__dict__ = self.attr_saved\n            # self.history_obs = new_history \n            self.history_obs = self.history_obs[:-1]\n            occur = [int(d is None) for d in new_history]\n            indices = np.where(np.array(occur)==0)[0]\n            index1 = indices[-2]\n            index2 = indices[-1]\n            box1 = new_history[index1]\n            x1, y1, s1, c1, r1 = box1\n            w1 = np.sqrt(s1 * r1)\n            h1 = np.sqrt(s1 / r1)\n            box2 = new_history[index2]\n            x2, y2, s2, c2, r2 = box2\n            w2 = np.sqrt(s2 * r2)\n            h2 = np.sqrt(s2 / r2)\n            time_gap = index2 - index1\n            dx = (x2-x1)/time_gap\n            dy = (y2-y1)/time_gap \n            dw = (w2-w1)/time_gap \n            dh = (h2-h1)/time_gap\n            dc = (c2 - c1) / time_gap\n            for i in range(index2 - index1):\n                \"\"\"\n                    The default virtual trajectory generation is by linear\n                    motion (constant speed hypothesis), you could modify this \n                    part to implement your own. \n                \"\"\"\n                x = x1 + (i+1) * dx \n                y = y1 + (i+1) * dy \n                w = w1 + (i+1) * dw \n                h = h1 + (i+1) * dh\n                s = w * h \n                r = w / float(h)\n                c = c1 + (i+1) * dc\n                new_box = np.array([x, y, s, c, r]).reshape((5, 1))\n                \"\"\"\n                    I still use predict-update loop here to refresh the parameters,\n                    but this can be faster by directly modifying the internal parameters\n                    as suggested in the paper. I keep this naive but slow way for \n                    easy read and understanding\n                \"\"\"\n\n                if not i == (index2-index1-1):\n                    self.update(new_box)\n                    self.predict()\n                else:\n                    self.update(new_box)\n\n\n    def update(self, z, R=None, H=None):\n        \"\"\"\n        Add a new measurement (z) to the Kalman filter.\n        If z is None, nothing is computed. However, x_post and P_post are\n        updated with the prior (x_prior, P_prior), and self.z is set to None.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n            If you pass in a value of H, z must be a column vector the\n            of the correct size.\n        R : np.array, scalar, or None\n            Optionally provide R to override the measurement noise for this\n            one call, otherwise  self.R will be used.\n        H : np.array, or None\n            Optionally provide H to override the measurement function for this\n            one call, otherwise self.H will be used.\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        # append the observation\n        self.history_obs.append(z)\n        \n        if z is None:\n            if self.observed:\n                \"\"\"\n                    Got no observation so freeze the current parameters for future\n                    potential online smoothing.\n                \"\"\"\n                self.freeze()\n            self.observed = False \n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n        \n        # self.observed = True\n        if not self.observed:\n            \"\"\"\n                Get observation, use online smoothing to re-update parameters\n            \"\"\"\n            self.unfreeze()\n        self.observed = True\n\n        if R is None:\n            R = self.R\n        elif isscalar(R):\n            R = eye(self.dim_z) * R\n\n        # if self.args.use_nsa_kalman:\n        #     if confidence > 0.6:\n        #         R = [(1 - confidence) * self.args.nsa_kalman_interval * x for x in R]\n        #     else:\n        #         R = [self.args.nsa_kalman_interval_sec * x for x in R]\n\n        if H is None:\n            z = reshape_z(z, self.dim_z, self.x.ndim)\n            H = self.H\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(H, self.x)\n\n        # common subexpression for speed\n        PHT = dot(self.P, H.T)\n\n        # S = HPH' + R\n        # project system uncertainty into measurement space\n        self.S = dot(H, PHT) + R\n        self.SI = self.inv(self.S)\n        # K = PH'inv(S)\n        # map system uncertainty into kalman gain\n        self.K = dot(PHT, self.SI)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n\n        # P = (I-KH)P(I-KH)' + KRK'\n        # This is more numerically stable\n        # and works for non-optimal K vs the equation\n        # P = (I-KH)P usually seen in the literature.\n\n        I_KH = self._I - dot(self.K, H)\n        self.P = dot(dot(I_KH, self.P), I_KH.T) + dot(dot(self.K, R), self.K.T)\n\n        # save measurement and posterior state\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n    def predict_steadystate(self, u=0, B=None):\n        \"\"\"\n        Predict state (prior) using the Kalman filter state propagation\n        equations. Only x is updated, P is left unchanged. See\n        update_steadstate() for a longer explanation of when to use this\n        method.\n        Parameters\n        ----------\n        u : np.array\n            Optional control vector. If non-zero, it is multiplied by B\n            to create the control input into the system.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n\n        # x = Fx + Bu\n        if B is not None:\n            self.x = dot(self.F, self.x) + dot(B, u)\n        else:\n            self.x = dot(self.F, self.x)\n\n        # save prior\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n    def update_steadystate(self, z):\n        \"\"\"\n        Add a new measurement (z) to the Kalman filter without recomputing\n        the Kalman gain K, the state covariance P, or the system\n        uncertainty S.\n        You can use this for LTI systems since the Kalman gain and covariance\n        converge to a fixed value. Precompute these and assign them explicitly,\n        or run the Kalman filter using the normal predict()/update(0 cycle\n        until they converge.\n        The main advantage of this call is speed. We do significantly less\n        computation, notably avoiding a costly matrix inversion.\n        Use in conjunction with predict_steadystate(), otherwise P will grow\n        without bound.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        Examples\n        --------\n        >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter\n        >>> # let filter converge on representative data, then save k and P\n        >>> for i in range(100):\n        >>>     cv.predict()\n        >>>     cv.update([i, i, i])\n        >>> saved_k = np.copy(cv.K)\n        >>> saved_P = np.copy(cv.P)\n        later on:\n        >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter\n        >>> cv.K = np.copy(saved_K)\n        >>> cv.P = np.copy(saved_P)\n        >>> for i in range(100):\n        >>>     cv.predict_steadystate()\n        >>>     cv.update_steadystate([i, i, i])\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        if z is None:\n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(self.H, self.x)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n    def update_correlated(self, z, R=None, H=None):\n        \"\"\" Add a new measurement (z) to the Kalman filter assuming that\n        process noise and measurement noise are correlated as defined in\n        the `self.M` matrix.\n        A partial derivation can be found in [1]\n        If z is None, nothing is changed.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        R : np.array, scalar, or None\n            Optionally provide R to override the measurement noise for this\n            one call, otherwise  self.R will be used.\n        H : np.array,  or None\n            Optionally provide H to override the measurement function for this\n            one call, otherwise  self.H will be used.\n        References\n        ----------\n        .. [1] Bulut, Y. (2011). Applied Kalman filter theory (Doctoral dissertation, Northeastern University).\n               http://people.duke.edu/~hpgavin/SystemID/References/Balut-KalmanFilter-PhD-NEU-2011.pdf\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        if z is None:\n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n\n        if R is None:\n            R = self.R\n        elif isscalar(R):\n            R = eye(self.dim_z) * R\n\n        # rename for readability and a tiny extra bit of speed\n        if H is None:\n            z = reshape_z(z, self.dim_z, self.x.ndim)\n            H = self.H\n\n        # handle special case: if z is in form [[z]] but x is not a column\n        # vector dimensions will not match\n        if self.x.ndim == 1 and shape(z) == (1, 1):\n            z = z[0]\n\n        if shape(z) == (): # is it scalar, e.g. z=3 or z=np.array(3)\n            z = np.asarray([z])\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(H, self.x)\n\n        # common subexpression for speed\n        PHT = dot(self.P, H.T)\n\n        # project system uncertainty into measurement space\n        self.S = dot(H, PHT) + dot(H, self.M) + dot(self.M.T, H.T) + R\n        self.SI = self.inv(self.S)\n\n        # K = PH'inv(S)\n        # map system uncertainty into kalman gain\n        self.K = dot(PHT + self.M, self.SI)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n        self.P = self.P - dot(self.K, dot(H, self.P) + self.M.T)\n\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n    def batch_filter(self, zs, Fs=None, Qs=None, Hs=None,\n                     Rs=None, Bs=None, us=None, update_first=False,\n                     saver=None):\n        \"\"\" Batch processes a sequences of measurements.\n        Parameters\n        ----------\n        zs : list-like\n            list of measurements at each time step `self.dt`. Missing\n            measurements must be represented by `None`.\n        Fs : None, list-like, default=None\n            optional value or list of values to use for the state transition\n            matrix F.\n            If Fs is None then self.F is used for all epochs.\n            Otherwise it must contain a list-like list of F's, one for\n            each epoch.  This allows you to have varying F per epoch.\n        Qs : None, np.array or list-like, default=None\n            optional value or list of values to use for the process error\n            covariance Q.\n            If Qs is None then self.Q is used for all epochs.\n            Otherwise it must contain a list-like list of Q's, one for\n            each epoch.  This allows you to have varying Q per epoch.\n        Hs : None, np.array or list-like, default=None\n            optional list of values to use for the measurement matrix H.\n            If Hs is None then self.H is used for all epochs.\n            If Hs contains a single matrix, then it is used as H for all\n            epochs.\n            Otherwise it must contain a list-like list of H's, one for\n            each epoch.  This allows you to have varying H per epoch.\n        Rs : None, np.array or list-like, default=None\n            optional list of values to use for the measurement error\n            covariance R.\n            If Rs is None then self.R is used for all epochs.\n            Otherwise it must contain a list-like list of R's, one for\n            each epoch.  This allows you to have varying R per epoch.\n        Bs : None, np.array or list-like, default=None\n            optional list of values to use for the control transition matrix B.\n            If Bs is None then self.B is used for all epochs.\n            Otherwise it must contain a list-like list of B's, one for\n            each epoch.  This allows you to have varying B per epoch.\n        us : None, np.array or list-like, default=None\n            optional list of values to use for the control input vector;\n            If us is None then None is used for all epochs (equivalent to 0,\n            or no control input).\n            Otherwise it must contain a list-like list of u's, one for\n            each epoch.\n       update_first : bool, optional, default=False\n            controls whether the order of operations is update followed by\n            predict, or predict followed by update. Default is predict->update.\n        saver : filterpy.common.Saver, optional\n            filterpy.common.Saver object. If provided, saver.save() will be\n            called after every epoch\n        Returns\n        -------\n        means : np.array((n,dim_x,1))\n            array of the state for each time step after the update. Each entry\n            is an np.array. In other words `means[k,:]` is the state at step\n            `k`.\n        covariance : np.array((n,dim_x,dim_x))\n            array of the covariances for each time step after the update.\n            In other words `covariance[k,:,:]` is the covariance at step `k`.\n        means_predictions : np.array((n,dim_x,1))\n            array of the state for each time step after the predictions. Each\n            entry is an np.array. In other words `means[k,:]` is the state at\n            step `k`.\n        covariance_predictions : np.array((n,dim_x,dim_x))\n            array of the covariances for each time step after the prediction.\n            In other words `covariance[k,:,:]` is the covariance at step `k`.\n        Examples\n        --------\n        .. code-block:: Python\n            # this example demonstrates tracking a measurement where the time\n            # between measurement varies, as stored in dts. This requires\n            # that F be recomputed for each epoch. The output is then smoothed\n            # with an RTS smoother.\n            zs = [t + random.randn()*4 for t in range (40)]\n            Fs = [np.array([[1., dt], [0, 1]] for dt in dts]\n            (mu, cov, _, _) = kf.batch_filter(zs, Fs=Fs)\n            (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs)\n        \"\"\"\n\n        #pylint: disable=too-many-statements\n        n = np.size(zs, 0)\n        if Fs is None:\n            Fs = [self.F] * n\n        if Qs is None:\n            Qs = [self.Q] * n\n        if Hs is None:\n            Hs = [self.H] * n\n        if Rs is None:\n            Rs = [self.R] * n\n        if Bs is None:\n            Bs = [self.B] * n\n        if us is None:\n            us = [0] * n\n\n        # mean estimates from Kalman Filter\n        if self.x.ndim == 1:\n            means = zeros((n, self.dim_x))\n            means_p = zeros((n, self.dim_x))\n        else:\n            means = zeros((n, self.dim_x, 1))\n            means_p = zeros((n, self.dim_x, 1))\n\n        # state covariances from Kalman Filter\n        covariances = zeros((n, self.dim_x, self.dim_x))\n        covariances_p = zeros((n, self.dim_x, self.dim_x))\n\n        if update_first:\n            for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n                self.update(z, R=R, H=H)\n                means[i, :] = self.x\n                covariances[i, :, :] = self.P\n\n                self.predict(u=u, B=B, F=F, Q=Q)\n                means_p[i, :] = self.x\n                covariances_p[i, :, :] = self.P\n\n                if saver is not None:\n                    saver.save()\n        else:\n            for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n                self.predict(u=u, B=B, F=F, Q=Q)\n                means_p[i, :] = self.x\n                covariances_p[i, :, :] = self.P\n\n                self.update(z, R=R, H=H)\n                means[i, :] = self.x\n                covariances[i, :, :] = self.P\n\n                if saver is not None:\n                    saver.save()\n\n        return (means, covariances, means_p, covariances_p)\n\n    def rts_smoother(self, Xs, Ps, Fs=None, Qs=None, inv=np.linalg.inv):\n        \"\"\"\n        Runs the Rauch-Tung-Striebel Kalman smoother on a set of\n        means and covariances computed by a Kalman filter. The usual input\n        would come from the output of `KalmanFilter.batch_filter()`.\n        Parameters\n        ----------\n        Xs : numpy.array\n           array of the means (state variable x) of the output of a Kalman\n           filter.\n        Ps : numpy.array\n            array of the covariances of the output of a kalman filter.\n        Fs : list-like collection of numpy.array, optional\n            State transition matrix of the Kalman filter at each time step.\n            Optional, if not provided the filter's self.F will be used\n        Qs : list-like collection of numpy.array, optional\n            Process noise of the Kalman filter at each time step. Optional,\n            if not provided the filter's self.Q will be used\n        inv : function, default numpy.linalg.inv\n            If you prefer another inverse function, such as the Moore-Penrose\n            pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv\n        Returns\n        -------\n        x : numpy.ndarray\n           smoothed means\n        P : numpy.ndarray\n           smoothed state covariances\n        K : numpy.ndarray\n            smoother gain at each step\n        Pp : numpy.ndarray\n           Predicted state covariances\n        Examples\n        --------\n        .. code-block:: Python\n            zs = [t + random.randn()*4 for t in range (40)]\n            (mu, cov, _, _) = kalman.batch_filter(zs)\n            (x, P, K, Pp) = rts_smoother(mu, cov, kf.F, kf.Q)\n        \"\"\"\n\n        if len(Xs) != len(Ps):\n            raise ValueError('length of Xs and Ps must be the same')\n\n        n = Xs.shape[0]\n        dim_x = Xs.shape[1]\n\n        if Fs is None:\n            Fs = [self.F] * n\n        if Qs is None:\n            Qs = [self.Q] * n\n\n        # smoother gain\n        K = zeros((n, dim_x, dim_x))\n\n        x, P, Pp = Xs.copy(), Ps.copy(), Ps.copy()\n        for k in range(n-2, -1, -1):\n            Pp[k] = dot(dot(Fs[k+1], P[k]), Fs[k+1].T) + Qs[k+1]\n\n            #pylint: disable=bad-whitespace\n            K[k]  = dot(dot(P[k], Fs[k+1].T), inv(Pp[k]))\n            x[k] += dot(K[k], x[k+1] - dot(Fs[k+1], x[k]))\n            P[k] += dot(dot(K[k], P[k+1] - Pp[k]), K[k].T)\n\n        return (x, P, K, Pp)\n\n    def get_prediction(self, u=None, B=None, F=None, Q=None):\n        \"\"\"\n        Predict next state (prior) using the Kalman filter state propagation\n        equations and returns it without modifying the object.\n        Parameters\n        ----------\n        u : np.array, default 0\n            Optional control vector.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        F : np.array(dim_x, dim_x), or None\n            Optional state transition matrix; a value of None\n            will cause the filter to use `self.F`.\n        Q : np.array(dim_x, dim_x), scalar, or None\n            Optional process noise matrix; a value of None will cause the\n            filter to use `self.Q`.\n        Returns\n        -------\n        (x, P) : tuple\n            State vector and covariance array of the prediction.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        elif isscalar(Q):\n            Q = eye(self.dim_x) * Q\n\n        # x = Fx + Bu\n        if B is not None and u is not None:\n            x = dot(F, self.x) + dot(B, u)\n        else:\n            x = dot(F, self.x)\n\n        # P = FPF' + Q\n        P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q\n\n        return x, P\n\n    def get_update(self, z=None):\n        \"\"\"\n        Computes the new estimate based on measurement `z` and returns it\n        without altering the state of the filter.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        Returns\n        -------\n        (x, P) : tuple\n            State vector and covariance array of the update.\n       \"\"\"\n\n        if z is None:\n            return self.x, self.P\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n\n        R = self.R\n        H = self.H\n        P = self.P\n        x = self.x\n\n        # error (residual) between measurement and prediction\n        y = z - dot(H, x)\n\n        # common subexpression for speed\n        PHT = dot(P, H.T)\n\n        # project system uncertainty into measurement space\n        S = dot(H, PHT) + R\n\n        # map system uncertainty into kalman gain\n        K = dot(PHT, self.inv(S))\n\n        # predict new x with residual scaled by the kalman gain\n        x = x + dot(K, y)\n\n        # P = (I-KH)P(I-KH)' + KRK'\n        I_KH = self._I - dot(K, H)\n        P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)\n\n        return x, P\n\n    def residual_of(self, z):\n        \"\"\"\n        Returns the residual for the given measurement (z). Does not alter\n        the state of the filter.\n        \"\"\"\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n        return z - dot(self.H, self.x_prior)\n\n    def measurement_of_state(self, x):\n        \"\"\"\n        Helper function that converts a state into a measurement.\n        Parameters\n        ----------\n        x : np.array\n            kalman state vector\n        Returns\n        -------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        \"\"\"\n\n        return dot(self.H, x)\n\n    @property\n    def log_likelihood(self):\n        \"\"\"\n        log-likelihood of the last measurement.\n        \"\"\"\n        if self._log_likelihood is None:\n            self._log_likelihood = logpdf(x=self.y, cov=self.S)\n        return self._log_likelihood\n\n    @property\n    def likelihood(self):\n        \"\"\"\n        Computed from the log-likelihood. The log-likelihood can be very\n        small,  meaning a large negative value such as -28000. Taking the\n        exp() of that results in 0.0, which can break typical algorithms\n        which multiply by this value, so by default we always return a\n        number >= sys.float_info.min.\n        \"\"\"\n        if self._likelihood is None:\n            self._likelihood = exp(self.log_likelihood)\n            if self._likelihood == 0:\n                self._likelihood = sys.float_info.min\n        return self._likelihood\n\n    @property\n    def mahalanobis(self):\n        \"\"\"\"\n        Mahalanobis distance of measurement. E.g. 3 means measurement\n        was 3 standard deviations away from the predicted value.\n        Returns\n        -------\n        mahalanobis : float\n        \"\"\"\n        if self._mahalanobis is None:\n            self._mahalanobis = sqrt(float(dot(dot(self.y.T, self.SI), self.y)))\n        return self._mahalanobis\n\n    @property\n    def alpha(self):\n        \"\"\"\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon [1]_.\n        \"\"\"\n        return self._alpha_sq**.5\n\n    def log_likelihood_of(self, z):\n        \"\"\"\n        log likelihood of the measurement `z`. This should only be called\n        after a call to update(). Calling after predict() will yield an\n        incorrect result.\"\"\"\n\n        if z is None:\n            return log(sys.float_info.min)\n        return logpdf(z, dot(self.H, self.x), self.S)\n\n    @alpha.setter\n    def alpha(self, value):\n        if not np.isscalar(value) or value < 1:\n            raise ValueError('alpha must be a float greater than 1')\n\n        self._alpha_sq = value**2\n\n    def __repr__(self):\n        return '\\n'.join([\n            'KalmanFilter object',\n            pretty_str('dim_x', self.dim_x),\n            pretty_str('dim_z', self.dim_z),\n            pretty_str('dim_u', self.dim_u),\n            pretty_str('x', self.x),\n            pretty_str('P', self.P),\n            pretty_str('x_prior', self.x_prior),\n            pretty_str('P_prior', self.P_prior),\n            pretty_str('x_post', self.x_post),\n            pretty_str('P_post', self.P_post),\n            pretty_str('F', self.F),\n            pretty_str('Q', self.Q),\n            pretty_str('R', self.R),\n            pretty_str('H', self.H),\n            pretty_str('K', self.K),\n            pretty_str('y', self.y),\n            pretty_str('S', self.S),\n            pretty_str('SI', self.SI),\n            pretty_str('M', self.M),\n            pretty_str('B', self.B),\n            pretty_str('z', self.z),\n            pretty_str('log-likelihood', self.log_likelihood),\n            pretty_str('likelihood', self.likelihood),\n            pretty_str('mahalanobis', self.mahalanobis),\n            pretty_str('alpha', self.alpha),\n            pretty_str('inv', self.inv)\n            ])\n\n    def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None):\n        \"\"\"\n        Performs a series of asserts to check that the size of everything\n        is what it should be. This can help you debug problems in your design.\n        If you pass in H, R, F, Q those will be used instead of this object's\n        value for those matrices.\n        Testing `z` (the measurement) is problamatic. x is a vector, and can be\n        implemented as either a 1D array or as a nx1 column vector. Thus Hx\n        can be of different shapes. Then, if Hx is a single value, it can\n        be either a 1D array or 2D vector. If either is true, z can reasonably\n        be a scalar (either '3' or np.array('3') are scalars under this\n        definition), a 1D, 1 element array, or a 2D, 1 element array. You are\n        allowed to pass in any combination that works.\n        \"\"\"\n\n        if H is None:\n            H = self.H\n        if R is None:\n            R = self.R\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        x = self.x\n        P = self.P\n\n        assert x.ndim == 1 or x.ndim == 2, \\\n                \"x must have one or two dimensions, but has {}\".format(x.ndim)\n\n        if x.ndim == 1:\n            assert x.shape[0] == self.dim_x, \\\n                   \"Shape of x must be ({},{}), but is {}\".format(\n                       self.dim_x, 1, x.shape)\n        else:\n            assert x.shape == (self.dim_x, 1), \\\n                   \"Shape of x must be ({},{}), but is {}\".format(\n                       self.dim_x, 1, x.shape)\n\n        assert P.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of P must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, P.shape)\n\n        assert Q.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of Q must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, P.shape)\n\n        assert F.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of F must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, F.shape)\n\n        assert np.ndim(H) == 2, \\\n               \"Shape of H must be (dim_z, {}), but is {}\".format(\n                   P.shape[0], shape(H))\n\n        assert H.shape[1] == P.shape[0], \\\n               \"Shape of H must be (dim_z, {}), but is {}\".format(\n                   P.shape[0], H.shape)\n\n        # shape of R must be the same as HPH'\n        hph_shape = (H.shape[0], H.shape[0])\n        r_shape = shape(R)\n\n        if H.shape[0] == 1:\n            # r can be scalar, 1D, or 2D in this case\n            assert r_shape in [(), (1,), (1, 1)], \\\n            \"R must be scalar or one element array, but is shaped {}\".format(\n                r_shape)\n        else:\n            assert r_shape == hph_shape, \\\n            \"shape of R should be {} but it is {}\".format(hph_shape, r_shape)\n\n\n        if z is not None:\n            z_shape = shape(z)\n        else:\n            z_shape = (self.dim_z, 1)\n\n        # H@x must have shape of z\n        Hx = dot(H, x)\n\n        if z_shape == (): # scalar or np.array(scalar)\n            assert Hx.ndim == 1 or shape(Hx) == (1, 1), \\\n            \"shape of z should be {}, not {} for the given H\".format(\n                shape(Hx), z_shape)\n\n        elif shape(Hx) == (1,):\n            assert z_shape[0] == 1, 'Shape of z must be {} for the given H'.format(shape(Hx))\n\n        else:\n            assert (z_shape == shape(Hx) or\n                    (len(z_shape) == 1 and shape(Hx) == (z_shape[0], 1))), \\\n                    \"shape of z should be {}, not {} for the given H\".format(\n                        shape(Hx), z_shape)\n\n        if np.ndim(Hx) > 1 and shape(Hx) != (1, 1):\n            assert shape(Hx) == z_shape, \\\n               'shape of z should be {} for the given H, but it is {}'.format(\n                   shape(Hx), z_shape)\n\n\ndef update(x, P, z, R, H=None, return_all=False):\n    \"\"\"\n    Add a new measurement (z) to the Kalman filter. If z is None, nothing\n    is changed.\n    This can handle either the multidimensional or unidimensional case. If\n    all parameters are floats instead of arrays the filter will still work,\n    and return floats for x, P as the result.\n    update(1, 2, 1, 1, 1)  # univariate\n    update(x, P, 1\n    Parameters\n    ----------\n    x : numpy.array(dim_x, 1), or float\n        State estimate vector\n    P : numpy.array(dim_x, dim_x), or float\n        Covariance matrix\n    z : (dim_z, 1): array_like\n        measurement for this update. z can be a scalar if dim_z is 1,\n        otherwise it must be convertible to a column vector.\n    R : numpy.array(dim_z, dim_z), or float\n        Measurement noise matrix\n    H : numpy.array(dim_x, dim_x), or float, optional\n        Measurement function. If not provided, a value of 1 is assumed.\n    return_all : bool, default False\n        If true, y, K, S, and log_likelihood are returned, otherwise\n        only x and P are returned.\n    Returns\n    -------\n    x : numpy.array\n        Posterior state estimate vector\n    P : numpy.array\n        Posterior covariance matrix\n    y : numpy.array or scalar\n        Residua. Difference between measurement and state in measurement space\n    K : numpy.array\n        Kalman gain\n    S : numpy.array\n        System uncertainty in measurement space\n    log_likelihood : float\n        log likelihood of the measurement\n    \"\"\"\n\n    #pylint: disable=bare-except\n\n    if z is None:\n        if return_all:\n            return x, P, None, None, None, None\n        return x, P\n\n    if H is None:\n        H = np.array([1])\n\n    if np.isscalar(H):\n        H = np.array([H])\n\n    Hx = np.atleast_1d(dot(H, x))\n    z = reshape_z(z, Hx.shape[0], x.ndim)\n\n    # error (residual) between measurement and prediction\n    y = z - Hx\n\n    # project system uncertainty into measurement space\n    S = dot(dot(H, P), H.T) + R\n\n\n    # map system uncertainty into kalman gain\n    try:\n        K = dot(dot(P, H.T), linalg.inv(S))\n    except:\n        # can't invert a 1D array, annoyingly\n        K = dot(dot(P, H.T), 1./S)\n\n\n    # predict new x with residual scaled by the kalman gain\n    x = x + dot(K, y)\n\n    # P = (I-KH)P(I-KH)' + KRK'\n    KH = dot(K, H)\n\n    try:\n        I_KH = np.eye(KH.shape[0]) - KH\n    except:\n        I_KH = np.array([1 - KH])\n    P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)\n\n\n    if return_all:\n        # compute log likelihood\n        log_likelihood = logpdf(z, dot(H, x), S)\n        return x, P, y, K, S, log_likelihood\n    return x, P\n\n\ndef update_steadystate(x, z, K, H=None):\n    \"\"\"\n    Add a new measurement (z) to the Kalman filter. If z is None, nothing\n    is changed.\n    Parameters\n    ----------\n    x : numpy.array(dim_x, 1), or float\n        State estimate vector\n    z : (dim_z, 1): array_like\n        measurement for this update. z can be a scalar if dim_z is 1,\n        otherwise it must be convertible to a column vector.\n    K : numpy.array, or float\n        Kalman gain matrix\n    H : numpy.array(dim_x, dim_x), or float, optional\n        Measurement function. If not provided, a value of 1 is assumed.\n    Returns\n    -------\n    x : numpy.array\n        Posterior state estimate vector\n    Examples\n    --------\n    This can handle either the multidimensional or unidimensional case. If\n    all parameters are floats instead of arrays the filter will still work,\n    and return floats for x, P as the result.\n    >>> update_steadystate(1, 2, 1)  # univariate\n    >>> update_steadystate(x, P, z, H)\n    \"\"\"\n\n\n    if z is None:\n        return x\n\n    if H is None:\n        H = np.array([1])\n\n    if np.isscalar(H):\n        H = np.array([H])\n\n    Hx = np.atleast_1d(dot(H, x))\n    z = reshape_z(z, Hx.shape[0], x.ndim)\n\n    # error (residual) between measurement and prediction\n    y = z - Hx\n\n    # estimate new x with residual scaled by the kalman gain\n    return x + dot(K, y)\n\n\ndef predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.):\n    \"\"\"\n    Predict next state (prior) using the Kalman filter state propagation\n    equations.\n    Parameters\n    ----------\n    x : numpy.array\n        State estimate vector\n    P : numpy.array\n        Covariance matrix\n    F : numpy.array()\n        State Transition matrix\n    Q : numpy.array, Optional\n        Process noise matrix\n    u : numpy.array, Optional, default 0.\n        Control vector. If non-zero, it is multiplied by B\n        to create the control input into the system.\n    B : numpy.array, optional, default 0.\n        Control transition matrix.\n    alpha : float, Optional, default=1.0\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon\n    Returns\n    -------\n    x : numpy.array\n        Prior state estimate vector\n    P : numpy.array\n        Prior covariance matrix\n    \"\"\"\n\n    if np.isscalar(F):\n        F = np.array(F)\n    x = dot(F, x) + dot(B, u)\n    P = (alpha * alpha) * dot(dot(F, P), F.T) + Q\n\n    return x, P\n\n\ndef predict_steadystate(x, F=1, u=0, B=1):\n    \"\"\"\n    Predict next state (prior) using the Kalman filter state propagation\n    equations. This steady state form only computes x, assuming that the\n    covariance is constant.\n    Parameters\n    ----------\n    x : numpy.array\n        State estimate vector\n    P : numpy.array\n        Covariance matrix\n    F : numpy.array()\n        State Transition matrix\n    u : numpy.array, Optional, default 0.\n        Control vector. If non-zero, it is multiplied by B\n        to create the control input into the system.\n    B : numpy.array, optional, default 0.\n        Control transition matrix.\n    Returns\n    -------\n    x : numpy.array\n        Prior state estimate vector\n    \"\"\"\n\n    if np.isscalar(F):\n        F = np.array(F)\n    x = dot(F, x) + dot(B, u)\n\n    return x\n\n\n\ndef batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None,\n                 update_first=False, saver=None):\n    \"\"\"\n    Batch processes a sequences of measurements.\n    Parameters\n    ----------\n    zs : list-like\n        list of measurements at each time step. Missing measurements must be\n        represented by None.\n    Fs : list-like\n        list of values to use for the state transition matrix matrix.\n    Qs : list-like\n        list of values to use for the process error\n        covariance.\n    Hs : list-like\n        list of values to use for the measurement matrix.\n    Rs : list-like\n        list of values to use for the measurement error\n        covariance.\n    Bs : list-like, optional\n        list of values to use for the control transition matrix;\n        a value of None in any position will cause the filter\n        to use `self.B` for that time step.\n    us : list-like, optional\n        list of values to use for the control input vector;\n        a value of None in any position will cause the filter to use\n        0 for that time step.\n    update_first : bool, optional\n        controls whether the order of operations is update followed by\n        predict, or predict followed by update. Default is predict->update.\n        saver : filterpy.common.Saver, optional\n            filterpy.common.Saver object. If provided, saver.save() will be\n            called after every epoch\n    Returns\n    -------\n    means : np.array((n,dim_x,1))\n        array of the state for each time step after the update. Each entry\n        is an np.array. In other words `means[k,:]` is the state at step\n        `k`.\n    covariance : np.array((n,dim_x,dim_x))\n        array of the covariances for each time step after the update.\n        In other words `covariance[k,:,:]` is the covariance at step `k`.\n    means_predictions : np.array((n,dim_x,1))\n        array of the state for each time step after the predictions. Each\n        entry is an np.array. In other words `means[k,:]` is the state at\n        step `k`.\n    covariance_predictions : np.array((n,dim_x,dim_x))\n        array of the covariances for each time step after the prediction.\n        In other words `covariance[k,:,:]` is the covariance at step `k`.\n    Examples\n    --------\n    .. code-block:: Python\n        zs = [t + random.randn()*4 for t in range (40)]\n        Fs = [kf.F for t in range (40)]\n        Hs = [kf.H for t in range (40)]\n        (mu, cov, _, _) = kf.batch_filter(zs, Rs=R_list, Fs=Fs, Hs=Hs, Qs=None,\n                                          Bs=None, us=None, update_first=False)\n        (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs, Qs=None)\n    \"\"\"\n\n    n = np.size(zs, 0)\n    dim_x = x.shape[0]\n\n    # mean estimates from Kalman Filter\n    if x.ndim == 1:\n        means = zeros((n, dim_x))\n        means_p = zeros((n, dim_x))\n    else:\n        means = zeros((n, dim_x, 1))\n        means_p = zeros((n, dim_x, 1))\n\n    # state covariances from Kalman Filter\n    covariances = zeros((n, dim_x, dim_x))\n    covariances_p = zeros((n, dim_x, dim_x))\n\n    if us is None:\n        us = [0.] * n\n        Bs = [0.] * n\n\n    if update_first:\n        for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n            x, P = update(x, P, z, R=R, H=H)\n            means[i, :] = x\n            covariances[i, :, :] = P\n\n            x, P = predict(x, P, u=u, B=B, F=F, Q=Q)\n            means_p[i, :] = x\n            covariances_p[i, :, :] = P\n            if saver is not None:\n                saver.save()\n    else:\n        for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n            x, P = predict(x, P, u=u, B=B, F=F, Q=Q)\n            means_p[i, :] = x\n            covariances_p[i, :, :] = P\n\n            x, P = update(x, P, z, R=R, H=H)\n            means[i, :] = x\n            covariances[i, :, :] = P\n            if saver is not None:\n                saver.save()\n\n    return (means, covariances, means_p, covariances_p)\n\n\n\ndef rts_smoother(Xs, Ps, Fs, Qs):\n    \"\"\"\n    Runs the Rauch-Tung-Striebel Kalman smoother on a set of\n    means and covariances computed by a Kalman filter. The usual input\n    would come from the output of `KalmanFilter.batch_filter()`.\n    Parameters\n    ----------\n    Xs : numpy.array\n       array of the means (state variable x) of the output of a Kalman\n       filter.\n    Ps : numpy.array\n        array of the covariances of the output of a kalman filter.\n    Fs : list-like collection of numpy.array\n        State transition matrix of the Kalman filter at each time step.\n    Qs : list-like collection of numpy.array, optional\n        Process noise of the Kalman filter at each time step.\n    Returns\n    -------\n    x : numpy.ndarray\n       smoothed means\n    P : numpy.ndarray\n       smoothed state covariances\n    K : numpy.ndarray\n        smoother gain at each step\n    pP : numpy.ndarray\n       predicted state covariances\n    Examples\n    --------\n    .. code-block:: Python\n        zs = [t + random.randn()*4 for t in range (40)]\n        (mu, cov, _, _) = kalman.batch_filter(zs)\n        (x, P, K, pP) = rts_smoother(mu, cov, kf.F, kf.Q)\n    \"\"\"\n\n    if len(Xs) != len(Ps):\n        raise ValueError('length of Xs and Ps must be the same')\n\n    n = Xs.shape[0]\n    dim_x = Xs.shape[1]\n\n    # smoother gain\n    K = zeros((n, dim_x, dim_x))\n    x, P, pP = Xs.copy(), Ps.copy(), Ps.copy()\n\n    for k in range(n-2, -1, -1):\n        pP[k] = dot(dot(Fs[k], P[k]), Fs[k].T) + Qs[k]\n\n        #pylint: disable=bad-whitespace\n        K[k]  = dot(dot(P[k], Fs[k].T), linalg.inv(pP[k]))\n        x[k] += dot(K[k], x[k+1] - dot(Fs[k], x[k]))\n        P[k] += dot(dot(K[k], P[k+1] - pP[k]), K[k].T)\n\n    return (x, P, K, pP)"
  },
  {
    "path": "trackers/hybrid_sort_tracker/new_kalmanfilter.py",
    "content": "# -*- coding: utf-8 -*-\n# pylint: disable=invalid-name, too-many-arguments, too-many-branches,\n# pylint: disable=too-many-locals, too-many-instance-attributes, too-many-lines\n\n\"\"\"\nThis module implements the linear Kalman filter in both an object\noriented and procedural form. The KalmanFilter class implements\nthe filter by storing the various matrices in instance variables,\nminimizing the amount of bookkeeping you have to do.\nAll Kalman filters operate with a predict->update cycle. The\npredict step, implemented with the method or function predict(),\nuses the state transition matrix F to predict the state in the next\ntime period (epoch). The state is stored as a gaussian (x, P), where\nx is the state (column) vector, and P is its covariance. Covariance\nmatrix Q specifies the process covariance. In Bayesian terms, this\nprediction is called the *prior*, which you can think of colloquially\nas the estimate prior to incorporating the measurement.\nThe update step, implemented with the method or function `update()`,\nincorporates the measurement z with covariance R, into the state\nestimate (x, P). The class stores the system uncertainty in S,\nthe innovation (residual between prediction and measurement in\nmeasurement space) in y, and the Kalman gain in k. The procedural\nform returns these variables to you. In Bayesian terms this computes\nthe *posterior* - the estimate after the information from the\nmeasurement is incorporated.\nWhether you use the OO form or procedural form is up to you. If\nmatrices such as H, R, and F are changing each epoch, you'll probably\nopt to use the procedural form. If they are unchanging, the OO\nform is perhaps easier to use since you won't need to keep track\nof these matrices. This is especially useful if you are implementing\nbanks of filters or comparing various KF designs for performance;\na trivial coding bug could lead to using the wrong sets of matrices.\nThis module also offers an implementation of the RTS smoother, and\nother helper functions, such as log likelihood computations.\nThe Saver class allows you to easily save the state of the\nKalmanFilter class after every update\nThis module expects NumPy arrays for all values that expect\narrays, although in a few cases, particularly method parameters,\nit will accept types that convert to NumPy arrays, such as lists\nof lists. These exceptions are documented in the method or function.\nExamples\n--------\nThe following example constructs a constant velocity kinematic\nfilter, filters noisy data, and plots the results. It also demonstrates\nusing the Saver class to save the state of the filter at each epoch.\n.. code-block:: Python\n    import matplotlib.pyplot as plt\n    import numpy as np\n    from filterpy.kalman import KalmanFilter\n    from filterpy.common import Q_discrete_white_noise, Saver\n    r_std, q_std = 2., 0.003\n    cv = KalmanFilter(dim_x=2, dim_z=1)\n    cv.x = np.array([[0., 1.]]) # position, velocity\n    cv.F = np.array([[1, dt],[ [0, 1]])\n    cv.R = np.array([[r_std^^2]])\n    f.H = np.array([[1., 0.]])\n    f.P = np.diag([.1^^2, .03^^2)\n    f.Q = Q_discrete_white_noise(2, dt, q_std**2)\n    saver = Saver(cv)\n    for z in range(100):\n        cv.predict()\n        cv.update([z + randn() * r_std])\n        saver.save() # save the filter's state\n    saver.to_array()\n    plt.plot(saver.x[:, 0])\n    # plot all of the priors\n    plt.plot(saver.x_prior[:, 0])\n    # plot mahalanobis distance\n    plt.figure()\n    plt.plot(saver.mahalanobis)\nThis code implements the same filter using the procedural form\n    x = np.array([[0., 1.]]) # position, velocity\n    F = np.array([[1, dt],[ [0, 1]])\n    R = np.array([[r_std^^2]])\n    H = np.array([[1., 0.]])\n    P = np.diag([.1^^2, .03^^2)\n    Q = Q_discrete_white_noise(2, dt, q_std**2)\n    for z in range(100):\n        x, P = predict(x, P, F=F, Q=Q)\n        x, P = update(x, P, z=[z + randn() * r_std], R=R, H=H)\n        xs.append(x[0, 0])\n    plt.plot(xs)\nFor more examples see the test subdirectory, or refer to the\nbook cited below. In it I both teach Kalman filtering from basic\nprinciples, and teach the use of this library in great detail.\nFilterPy library.\nhttp://github.com/rlabbe/filterpy\nDocumentation at:\nhttps://filterpy.readthedocs.org\nSupporting book at:\nhttps://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\nThis is licensed under an MIT license. See the readme.MD file\nfor more information.\nCopyright 2014-2018 Roger R Labbe Jr.\n\"\"\"\n\nfrom __future__ import absolute_import, division\n\nfrom copy import deepcopy\nfrom math import log, exp, sqrt\nimport sys\nimport numpy as np\nfrom numpy import dot, zeros, eye, isscalar, shape\nimport numpy.linalg as linalg\nfrom filterpy.stats import logpdf\nfrom filterpy.common import pretty_str, reshape_z\n\n\nclass KalmanFilterNew(object):\n    \"\"\" Implements a Kalman filter. You are responsible for setting the\n    various state variables to reasonable values; the defaults  will\n    not give you a functional filter.\n    For now the best documentation is my free book Kalman and Bayesian\n    Filters in Python [2]_. The test files in this directory also give you a\n    basic idea of use, albeit without much description.\n    In brief, you will first construct this object, specifying the size of\n    the state vector with dim_x and the size of the measurement vector that\n    you will be using with dim_z. These are mostly used to perform size checks\n    when you assign values to the various matrices. For example, if you\n    specified dim_z=2 and then try to assign a 3x3 matrix to R (the\n    measurement noise matrix you will get an assert exception because R\n    should be 2x2. (If for whatever reason you need to alter the size of\n    things midstream just use the underscore version of the matrices to\n    assign directly: your_filter._R = a_3x3_matrix.)\n    After construction the filter will have default matrices created for you,\n    but you must specify the values for each. It’s usually easiest to just\n    overwrite them rather than assign to each element yourself. This will be\n    clearer in the example below. All are of type numpy.array.\n    Examples\n    --------\n    Here is a filter that tracks position and velocity using a sensor that only\n    reads position.\n    First construct the object with the required dimensionality. Here the state\n    (`dim_x`) has 2 coefficients (position and velocity), and the measurement\n    (`dim_z`) has one. In FilterPy `x` is the state, `z` is the measurement.\n    .. code::\n        from filterpy.kalman import KalmanFilter\n        f = KalmanFilter (dim_x=2, dim_z=1)\n    Assign the initial value for the state (position and velocity). You can do this\n    with a two dimensional array like so:\n        .. code::\n            f.x = np.array([[2.],    # position\n                            [0.]])   # velocity\n    or just use a one dimensional array, which I prefer doing.\n    .. code::\n        f.x = np.array([2., 0.])\n    Define the state transition matrix:\n        .. code::\n            f.F = np.array([[1.,1.],\n                            [0.,1.]])\n    Define the measurement function. Here we need to convert a position-velocity\n    vector into just a position vector, so we use:\n        .. code::\n        f.H = np.array([[1., 0.]])\n    Define the state's covariance matrix P. \n    .. code::\n        f.P = np.array([[1000.,    0.],\n                        [   0., 1000.] ])\n    Now assign the measurement noise. Here the dimension is 1x1, so I can\n    use a scalar\n    .. code::\n        f.R = 5\n    I could have done this instead:\n    .. code::\n        f.R = np.array([[5.]])\n    Note that this must be a 2 dimensional array.\n    Finally, I will assign the process noise. Here I will take advantage of\n    another FilterPy library function:\n    .. code::\n        from filterpy.common import Q_discrete_white_noise\n        f.Q = Q_discrete_white_noise(dim=2, dt=0.1, var=0.13)\n    Now just perform the standard predict/update loop:\n    .. code::\n        while some_condition_is_true:\n            z = get_sensor_reading()\n            f.predict()\n            f.update(z)\n            do_something_with_estimate (f.x)\n    **Procedural Form**\n    This module also contains stand alone functions to perform Kalman filtering.\n    Use these if you are not a fan of objects.\n    **Example**\n    .. code::\n        while True:\n            z, R = read_sensor()\n            x, P = predict(x, P, F, Q)\n            x, P = update(x, P, z, R, H)\n    See my book Kalman and Bayesian Filters in Python [2]_.\n    You will have to set the following attributes after constructing this\n    object for the filter to perform properly. Please note that there are\n    various checks in place to ensure that you have made everything the\n    'correct' size. However, it is possible to provide incorrectly sized\n    arrays such that the linear algebra can not perform an operation.\n    It can also fail silently - you can end up with matrices of a size that\n    allows the linear algebra to work, but are the wrong shape for the problem\n    you are trying to solve.\n    Parameters\n    ----------\n    dim_x : int\n        Number of state variables for the Kalman filter. For example, if\n        you are tracking the position and velocity of an object in two\n        dimensions, dim_x would be 4.\n        This is used to set the default size of P, Q, and u\n    dim_z : int\n        Number of of measurement inputs. For example, if the sensor\n        provides you with position in (x,y), dim_z would be 2.\n    dim_u : int (optional)\n        size of the control input, if it is being used.\n        Default value of 0 indicates it is not used.\n    compute_log_likelihood : bool (default = True)\n        Computes log likelihood by default, but this can be a slow\n        computation, so if you never use it you can turn this computation\n        off.\n    Attributes\n    ----------\n    x : numpy.array(dim_x, 1)\n        Current state estimate. Any call to update() or predict() updates\n        this variable.\n    P : numpy.array(dim_x, dim_x)\n        Current state covariance matrix. Any call to update() or predict()\n        updates this variable.\n    x_prior : numpy.array(dim_x, 1)\n        Prior (predicted) state estimate. The *_prior and *_post attributes\n        are for convenience; they store the  prior and posterior of the\n        current epoch. Read Only.\n    P_prior : numpy.array(dim_x, dim_x)\n        Prior (predicted) state covariance matrix. Read Only.\n    x_post : numpy.array(dim_x, 1)\n        Posterior (updated) state estimate. Read Only.\n    P_post : numpy.array(dim_x, dim_x)\n        Posterior (updated) state covariance matrix. Read Only.\n    z : numpy.array\n        Last measurement used in update(). Read only.\n    R : numpy.array(dim_z, dim_z)\n        Measurement noise covariance matrix. Also known as the\n        observation covariance.\n    Q : numpy.array(dim_x, dim_x)\n        Process noise covariance matrix. Also known as the transition\n        covariance.\n    F : numpy.array()\n        State Transition matrix. Also known as `A` in some formulation.\n    H : numpy.array(dim_z, dim_x)\n        Measurement function. Also known as the observation matrix, or as `C`.\n    y : numpy.array\n        Residual of the update step. Read only.\n    K : numpy.array(dim_x, dim_z)\n        Kalman gain of the update step. Read only.\n    S :  numpy.array\n        System uncertainty (P projected to measurement space). Read only.\n    SI :  numpy.array\n        Inverse system uncertainty. Read only.\n    log_likelihood : float\n        log-likelihood of the last measurement. Read only.\n    likelihood : float\n        likelihood of last measurement. Read only.\n        Computed from the log-likelihood. The log-likelihood can be very\n        small,  meaning a large negative value such as -28000. Taking the\n        exp() of that results in 0.0, which can break typical algorithms\n        which multiply by this value, so by default we always return a\n        number >= sys.float_info.min.\n    mahalanobis : float\n        mahalanobis distance of the innovation. Read only.\n    inv : function, default numpy.linalg.inv\n        If you prefer another inverse function, such as the Moore-Penrose\n        pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv\n        This is only used to invert self.S. If you know it is diagonal, you\n        might choose to set it to filterpy.common.inv_diagonal, which is\n        several times faster than numpy.linalg.inv for diagonal matrices.\n    alpha : float\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon [1]_.\n    References\n    ----------\n    .. [1] Dan Simon. \"Optimal State Estimation.\" John Wiley & Sons.\n       p. 208-212. (2006)\n    .. [2] Roger Labbe. \"Kalman and Bayesian Filters in Python\"\n       https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n    \"\"\"\n\n    def __init__(self, dim_x, dim_z, dim_u=0):\n        if dim_x < 1:\n            raise ValueError('dim_x must be 1 or greater')\n        if dim_z < 1:\n            raise ValueError('dim_z must be 1 or greater')\n        if dim_u < 0:\n            raise ValueError('dim_u must be 0 or greater')\n\n        self.dim_x = dim_x\n        self.dim_z = dim_z\n        self.dim_u = dim_u\n\n        self.x = zeros((dim_x, 1))        # state\n        self.P = eye(dim_x)               # uncertainty covariance\n        self.Q = eye(dim_x)               # process uncertainty\n        self.B = None                     # control transition matrix\n        self.F = eye(dim_x)               # state transition matrix\n        self.H = zeros((dim_z, dim_x))    # measurement function\n        self.R = eye(dim_z)               # measurement uncertainty\n        self._alpha_sq = 1.               # fading memory control\n        self.M = np.zeros((dim_x, dim_z)) # process-measurement cross correlation\n        self.z = np.array([[None]*self.dim_z]).T\n\n        # gain and residual are computed during the innovation step. We\n        # save them so that in case you want to inspect them for various\n        # purposes\n        self.K = np.zeros((dim_x, dim_z)) # kalman gain\n        self.y = zeros((dim_z, 1))\n        self.S = np.zeros((dim_z, dim_z)) # system uncertainty\n        self.SI = np.zeros((dim_z, dim_z)) # inverse system uncertainty\n\n        # identity matrix. Do not alter this.\n        self._I = np.eye(dim_x)\n\n        # these will always be a copy of x,P after predict() is called\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n        # these will always be a copy of x,P after update() is called\n        self.x_post = self.x.copy()             \n        self.P_post = self.P.copy()\n\n        # Only computed only if requested via property\n        self._log_likelihood = log(sys.float_info.min)\n        self._likelihood = sys.float_info.min\n        self._mahalanobis = None\n\n        # keep all observations \n        self.history_obs = []\n\n        self.inv = np.linalg.inv\n\n        self.attr_saved = None\n        self.observed = False \n\n\n    def predict(self, u=None, B=None, F=None, Q=None):\n        \"\"\"\n        Predict next state (prior) using the Kalman filter state propagation\n        equations.\n        Parameters\n        ----------\n        u : np.array, default 0\n            Optional control vector.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        F : np.array(dim_x, dim_x), or None\n            Optional state transition matrix; a value of None\n            will cause the filter to use `self.F`.\n        Q : np.array(dim_x, dim_x), scalar, or None\n            Optional process noise matrix; a value of None will cause the\n            filter to use `self.Q`.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        elif isscalar(Q):\n            Q = eye(self.dim_x) * Q\n\n\n        # x = Fx + Bu\n        if B is not None and u is not None:\n            self.x = dot(F, self.x) + dot(B, u)\n        else:\n            self.x = dot(F, self.x)\n\n        # P = FPF' + Q\n        self.P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q\n\n        # save prior\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n\n\n    def freeze(self):\n        \"\"\"\n            Save the parameters before non-observation forward\n        \"\"\"\n        self.attr_saved = deepcopy(self.__dict__)\n\n\n    def unfreeze(self):\n        if self.attr_saved is not None:\n            new_history = deepcopy(self.history_obs)\n            self.__dict__ = self.attr_saved\n            # self.history_obs = new_history \n            self.history_obs = self.history_obs[:-1]\n            occur = [int(d is None) for d in new_history]\n            indices = np.where(np.array(occur)==0)[0]\n            index1 = indices[-2]\n            index2 = indices[-1]\n            box1 = new_history[index1]\n            x1, y1, s1, r1, c1 = box1\n            w1 = np.sqrt(s1 * r1)\n            h1 = np.sqrt(s1 / r1)\n            box2 = new_history[index2]\n            x2, y2, s2, r2, c2 = box2\n            w2 = np.sqrt(s2 * r2)\n            h2 = np.sqrt(s2 / r2)\n            time_gap = index2 - index1\n            dx = (x2-x1)/time_gap\n            dy = (y2-y1)/time_gap \n            dw = (w2-w1)/time_gap \n            dh = (h2-h1)/time_gap\n            dc = (c2 - c1) / time_gap\n            for i in range(index2 - index1):\n                \"\"\"\n                    The default virtual trajectory generation is by linear\n                    motion (constant speed hypothesis), you could modify this \n                    part to implement your own. \n                \"\"\"\n                x = x1 + (i+1) * dx \n                y = y1 + (i+1) * dy \n                w = w1 + (i+1) * dw \n                h = h1 + (i+1) * dh\n                s = w * h \n                r = w / float(h)\n                c = c1 + (i+1) * dc\n                new_box = np.array([x, y, s, r, c]).reshape((5, 1))\n                \"\"\"\n                    I still use predict-update loop here to refresh the parameters,\n                    but this can be faster by directly modifying the internal parameters\n                    as suggested in the paper. I keep this naive but slow way for \n                    easy read and understanding\n                \"\"\"\n                self.update(new_box)\n                if not i == (index2-index1-1):\n                    self.predict()\n\n\n    def update(self, z, R=None, H=None):\n        \"\"\"\n        Add a new measurement (z) to the Kalman filter.\n        If z is None, nothing is computed. However, x_post and P_post are\n        updated with the prior (x_prior, P_prior), and self.z is set to None.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n            If you pass in a value of H, z must be a column vector the\n            of the correct size.\n        R : np.array, scalar, or None\n            Optionally provide R to override the measurement noise for this\n            one call, otherwise  self.R will be used.\n        H : np.array, or None\n            Optionally provide H to override the measurement function for this\n            one call, otherwise self.H will be used.\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        # append the observation\n        self.history_obs.append(z)\n        \n        if z is None:\n            if self.observed:\n                \"\"\"\n                    Got no observation so freeze the current parameters for future\n                    potential online smoothing.\n                \"\"\"\n                self.freeze()\n            self.observed = False \n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n        \n        # self.observed = True\n        if not self.observed:\n            \"\"\"\n                Get observation, use online smoothing to re-update parameters\n            \"\"\"\n            self.unfreeze()\n        self.observed = True\n\n        if R is None:\n            R = self.R\n        elif isscalar(R):\n            R = eye(self.dim_z) * R\n\n        if H is None:\n            z = reshape_z(z, self.dim_z, self.x.ndim)\n            H = self.H\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(H, self.x)\n\n        # common subexpression for speed\n        PHT = dot(self.P, H.T)\n\n        # S = HPH' + R\n        # project system uncertainty into measurement space\n        self.S = dot(H, PHT) + R\n        self.SI = self.inv(self.S)\n        # K = PH'inv(S)\n        # map system uncertainty into kalman gain\n        self.K = dot(PHT, self.SI)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n\n        # P = (I-KH)P(I-KH)' + KRK'\n        # This is more numerically stable\n        # and works for non-optimal K vs the equation\n        # P = (I-KH)P usually seen in the literature.\n\n        I_KH = self._I - dot(self.K, H)\n        self.P = dot(dot(I_KH, self.P), I_KH.T) + dot(dot(self.K, R), self.K.T)\n\n        # save measurement and posterior state\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n    def predict_steadystate(self, u=0, B=None):\n        \"\"\"\n        Predict state (prior) using the Kalman filter state propagation\n        equations. Only x is updated, P is left unchanged. See\n        update_steadstate() for a longer explanation of when to use this\n        method.\n        Parameters\n        ----------\n        u : np.array\n            Optional control vector. If non-zero, it is multiplied by B\n            to create the control input into the system.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n\n        # x = Fx + Bu\n        if B is not None:\n            self.x = dot(self.F, self.x) + dot(B, u)\n        else:\n            self.x = dot(self.F, self.x)\n\n        # save prior\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n    def update_steadystate(self, z):\n        \"\"\"\n        Add a new measurement (z) to the Kalman filter without recomputing\n        the Kalman gain K, the state covariance P, or the system\n        uncertainty S.\n        You can use this for LTI systems since the Kalman gain and covariance\n        converge to a fixed value. Precompute these and assign them explicitly,\n        or run the Kalman filter using the normal predict()/update(0 cycle\n        until they converge.\n        The main advantage of this call is speed. We do significantly less\n        computation, notably avoiding a costly matrix inversion.\n        Use in conjunction with predict_steadystate(), otherwise P will grow\n        without bound.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        Examples\n        --------\n        >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter\n        >>> # let filter converge on representative data, then save k and P\n        >>> for i in range(100):\n        >>>     cv.predict()\n        >>>     cv.update([i, i, i])\n        >>> saved_k = np.copy(cv.K)\n        >>> saved_P = np.copy(cv.P)\n        later on:\n        >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter\n        >>> cv.K = np.copy(saved_K)\n        >>> cv.P = np.copy(saved_P)\n        >>> for i in range(100):\n        >>>     cv.predict_steadystate()\n        >>>     cv.update_steadystate([i, i, i])\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        if z is None:\n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(self.H, self.x)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n    def update_correlated(self, z, R=None, H=None):\n        \"\"\" Add a new measurement (z) to the Kalman filter assuming that\n        process noise and measurement noise are correlated as defined in\n        the `self.M` matrix.\n        A partial derivation can be found in [1]\n        If z is None, nothing is changed.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        R : np.array, scalar, or None\n            Optionally provide R to override the measurement noise for this\n            one call, otherwise  self.R will be used.\n        H : np.array,  or None\n            Optionally provide H to override the measurement function for this\n            one call, otherwise  self.H will be used.\n        References\n        ----------\n        .. [1] Bulut, Y. (2011). Applied Kalman filter theory (Doctoral dissertation, Northeastern University).\n               http://people.duke.edu/~hpgavin/SystemID/References/Balut-KalmanFilter-PhD-NEU-2011.pdf\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        if z is None:\n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n\n        if R is None:\n            R = self.R\n        elif isscalar(R):\n            R = eye(self.dim_z) * R\n\n        # rename for readability and a tiny extra bit of speed\n        if H is None:\n            z = reshape_z(z, self.dim_z, self.x.ndim)\n            H = self.H\n\n        # handle special case: if z is in form [[z]] but x is not a column\n        # vector dimensions will not match\n        if self.x.ndim == 1 and shape(z) == (1, 1):\n            z = z[0]\n\n        if shape(z) == (): # is it scalar, e.g. z=3 or z=np.array(3)\n            z = np.asarray([z])\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(H, self.x)\n\n        # common subexpression for speed\n        PHT = dot(self.P, H.T)\n\n        # project system uncertainty into measurement space\n        self.S = dot(H, PHT) + dot(H, self.M) + dot(self.M.T, H.T) + R\n        self.SI = self.inv(self.S)\n\n        # K = PH'inv(S)\n        # map system uncertainty into kalman gain\n        self.K = dot(PHT + self.M, self.SI)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n        self.P = self.P - dot(self.K, dot(H, self.P) + self.M.T)\n\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n    def batch_filter(self, zs, Fs=None, Qs=None, Hs=None,\n                     Rs=None, Bs=None, us=None, update_first=False,\n                     saver=None):\n        \"\"\" Batch processes a sequences of measurements.\n        Parameters\n        ----------\n        zs : list-like\n            list of measurements at each time step `self.dt`. Missing\n            measurements must be represented by `None`.\n        Fs : None, list-like, default=None\n            optional value or list of values to use for the state transition\n            matrix F.\n            If Fs is None then self.F is used for all epochs.\n            Otherwise it must contain a list-like list of F's, one for\n            each epoch.  This allows you to have varying F per epoch.\n        Qs : None, np.array or list-like, default=None\n            optional value or list of values to use for the process error\n            covariance Q.\n            If Qs is None then self.Q is used for all epochs.\n            Otherwise it must contain a list-like list of Q's, one for\n            each epoch.  This allows you to have varying Q per epoch.\n        Hs : None, np.array or list-like, default=None\n            optional list of values to use for the measurement matrix H.\n            If Hs is None then self.H is used for all epochs.\n            If Hs contains a single matrix, then it is used as H for all\n            epochs.\n            Otherwise it must contain a list-like list of H's, one for\n            each epoch.  This allows you to have varying H per epoch.\n        Rs : None, np.array or list-like, default=None\n            optional list of values to use for the measurement error\n            covariance R.\n            If Rs is None then self.R is used for all epochs.\n            Otherwise it must contain a list-like list of R's, one for\n            each epoch.  This allows you to have varying R per epoch.\n        Bs : None, np.array or list-like, default=None\n            optional list of values to use for the control transition matrix B.\n            If Bs is None then self.B is used for all epochs.\n            Otherwise it must contain a list-like list of B's, one for\n            each epoch.  This allows you to have varying B per epoch.\n        us : None, np.array or list-like, default=None\n            optional list of values to use for the control input vector;\n            If us is None then None is used for all epochs (equivalent to 0,\n            or no control input).\n            Otherwise it must contain a list-like list of u's, one for\n            each epoch.\n       update_first : bool, optional, default=False\n            controls whether the order of operations is update followed by\n            predict, or predict followed by update. Default is predict->update.\n        saver : filterpy.common.Saver, optional\n            filterpy.common.Saver object. If provided, saver.save() will be\n            called after every epoch\n        Returns\n        -------\n        means : np.array((n,dim_x,1))\n            array of the state for each time step after the update. Each entry\n            is an np.array. In other words `means[k,:]` is the state at step\n            `k`.\n        covariance : np.array((n,dim_x,dim_x))\n            array of the covariances for each time step after the update.\n            In other words `covariance[k,:,:]` is the covariance at step `k`.\n        means_predictions : np.array((n,dim_x,1))\n            array of the state for each time step after the predictions. Each\n            entry is an np.array. In other words `means[k,:]` is the state at\n            step `k`.\n        covariance_predictions : np.array((n,dim_x,dim_x))\n            array of the covariances for each time step after the prediction.\n            In other words `covariance[k,:,:]` is the covariance at step `k`.\n        Examples\n        --------\n        .. code-block:: Python\n            # this example demonstrates tracking a measurement where the time\n            # between measurement varies, as stored in dts. This requires\n            # that F be recomputed for each epoch. The output is then smoothed\n            # with an RTS smoother.\n            zs = [t + random.randn()*4 for t in range (40)]\n            Fs = [np.array([[1., dt], [0, 1]] for dt in dts]\n            (mu, cov, _, _) = kf.batch_filter(zs, Fs=Fs)\n            (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs)\n        \"\"\"\n\n        #pylint: disable=too-many-statements\n        n = np.size(zs, 0)\n        if Fs is None:\n            Fs = [self.F] * n\n        if Qs is None:\n            Qs = [self.Q] * n\n        if Hs is None:\n            Hs = [self.H] * n\n        if Rs is None:\n            Rs = [self.R] * n\n        if Bs is None:\n            Bs = [self.B] * n\n        if us is None:\n            us = [0] * n\n\n        # mean estimates from Kalman Filter\n        if self.x.ndim == 1:\n            means = zeros((n, self.dim_x))\n            means_p = zeros((n, self.dim_x))\n        else:\n            means = zeros((n, self.dim_x, 1))\n            means_p = zeros((n, self.dim_x, 1))\n\n        # state covariances from Kalman Filter\n        covariances = zeros((n, self.dim_x, self.dim_x))\n        covariances_p = zeros((n, self.dim_x, self.dim_x))\n\n        if update_first:\n            for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n                self.update(z, R=R, H=H)\n                means[i, :] = self.x\n                covariances[i, :, :] = self.P\n\n                self.predict(u=u, B=B, F=F, Q=Q)\n                means_p[i, :] = self.x\n                covariances_p[i, :, :] = self.P\n\n                if saver is not None:\n                    saver.save()\n        else:\n            for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n                self.predict(u=u, B=B, F=F, Q=Q)\n                means_p[i, :] = self.x\n                covariances_p[i, :, :] = self.P\n\n                self.update(z, R=R, H=H)\n                means[i, :] = self.x\n                covariances[i, :, :] = self.P\n\n                if saver is not None:\n                    saver.save()\n\n        return (means, covariances, means_p, covariances_p)\n\n    def rts_smoother(self, Xs, Ps, Fs=None, Qs=None, inv=np.linalg.inv):\n        \"\"\"\n        Runs the Rauch-Tung-Striebel Kalman smoother on a set of\n        means and covariances computed by a Kalman filter. The usual input\n        would come from the output of `KalmanFilter.batch_filter()`.\n        Parameters\n        ----------\n        Xs : numpy.array\n           array of the means (state variable x) of the output of a Kalman\n           filter.\n        Ps : numpy.array\n            array of the covariances of the output of a kalman filter.\n        Fs : list-like collection of numpy.array, optional\n            State transition matrix of the Kalman filter at each time step.\n            Optional, if not provided the filter's self.F will be used\n        Qs : list-like collection of numpy.array, optional\n            Process noise of the Kalman filter at each time step. Optional,\n            if not provided the filter's self.Q will be used\n        inv : function, default numpy.linalg.inv\n            If you prefer another inverse function, such as the Moore-Penrose\n            pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv\n        Returns\n        -------\n        x : numpy.ndarray\n           smoothed means\n        P : numpy.ndarray\n           smoothed state covariances\n        K : numpy.ndarray\n            smoother gain at each step\n        Pp : numpy.ndarray\n           Predicted state covariances\n        Examples\n        --------\n        .. code-block:: Python\n            zs = [t + random.randn()*4 for t in range (40)]\n            (mu, cov, _, _) = kalman.batch_filter(zs)\n            (x, P, K, Pp) = rts_smoother(mu, cov, kf.F, kf.Q)\n        \"\"\"\n\n        if len(Xs) != len(Ps):\n            raise ValueError('length of Xs and Ps must be the same')\n\n        n = Xs.shape[0]\n        dim_x = Xs.shape[1]\n\n        if Fs is None:\n            Fs = [self.F] * n\n        if Qs is None:\n            Qs = [self.Q] * n\n\n        # smoother gain\n        K = zeros((n, dim_x, dim_x))\n\n        x, P, Pp = Xs.copy(), Ps.copy(), Ps.copy()\n        for k in range(n-2, -1, -1):\n            Pp[k] = dot(dot(Fs[k+1], P[k]), Fs[k+1].T) + Qs[k+1]\n\n            #pylint: disable=bad-whitespace\n            K[k]  = dot(dot(P[k], Fs[k+1].T), inv(Pp[k]))\n            x[k] += dot(K[k], x[k+1] - dot(Fs[k+1], x[k]))\n            P[k] += dot(dot(K[k], P[k+1] - Pp[k]), K[k].T)\n\n        return (x, P, K, Pp)\n\n    def get_prediction(self, u=None, B=None, F=None, Q=None):\n        \"\"\"\n        Predict next state (prior) using the Kalman filter state propagation\n        equations and returns it without modifying the object.\n        Parameters\n        ----------\n        u : np.array, default 0\n            Optional control vector.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        F : np.array(dim_x, dim_x), or None\n            Optional state transition matrix; a value of None\n            will cause the filter to use `self.F`.\n        Q : np.array(dim_x, dim_x), scalar, or None\n            Optional process noise matrix; a value of None will cause the\n            filter to use `self.Q`.\n        Returns\n        -------\n        (x, P) : tuple\n            State vector and covariance array of the prediction.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        elif isscalar(Q):\n            Q = eye(self.dim_x) * Q\n\n        # x = Fx + Bu\n        if B is not None and u is not None:\n            x = dot(F, self.x) + dot(B, u)\n        else:\n            x = dot(F, self.x)\n\n        # P = FPF' + Q\n        P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q\n\n        return x, P\n\n    def get_update(self, z=None):\n        \"\"\"\n        Computes the new estimate based on measurement `z` and returns it\n        without altering the state of the filter.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        Returns\n        -------\n        (x, P) : tuple\n            State vector and covariance array of the update.\n       \"\"\"\n\n        if z is None:\n            return self.x, self.P\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n\n        R = self.R\n        H = self.H\n        P = self.P\n        x = self.x\n\n        # error (residual) between measurement and prediction\n        y = z - dot(H, x)\n\n        # common subexpression for speed\n        PHT = dot(P, H.T)\n\n        # project system uncertainty into measurement space\n        S = dot(H, PHT) + R\n\n        # map system uncertainty into kalman gain\n        K = dot(PHT, self.inv(S))\n\n        # predict new x with residual scaled by the kalman gain\n        x = x + dot(K, y)\n\n        # P = (I-KH)P(I-KH)' + KRK'\n        I_KH = self._I - dot(K, H)\n        P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)\n\n        return x, P\n\n    def residual_of(self, z):\n        \"\"\"\n        Returns the residual for the given measurement (z). Does not alter\n        the state of the filter.\n        \"\"\"\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n        return z - dot(self.H, self.x_prior)\n\n    def measurement_of_state(self, x):\n        \"\"\"\n        Helper function that converts a state into a measurement.\n        Parameters\n        ----------\n        x : np.array\n            kalman state vector\n        Returns\n        -------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        \"\"\"\n\n        return dot(self.H, x)\n\n    @property\n    def log_likelihood(self):\n        \"\"\"\n        log-likelihood of the last measurement.\n        \"\"\"\n        if self._log_likelihood is None:\n            self._log_likelihood = logpdf(x=self.y, cov=self.S)\n        return self._log_likelihood\n\n    @property\n    def likelihood(self):\n        \"\"\"\n        Computed from the log-likelihood. The log-likelihood can be very\n        small,  meaning a large negative value such as -28000. Taking the\n        exp() of that results in 0.0, which can break typical algorithms\n        which multiply by this value, so by default we always return a\n        number >= sys.float_info.min.\n        \"\"\"\n        if self._likelihood is None:\n            self._likelihood = exp(self.log_likelihood)\n            if self._likelihood == 0:\n                self._likelihood = sys.float_info.min\n        return self._likelihood\n\n    @property\n    def mahalanobis(self):\n        \"\"\"\"\n        Mahalanobis distance of measurement. E.g. 3 means measurement\n        was 3 standard deviations away from the predicted value.\n        Returns\n        -------\n        mahalanobis : float\n        \"\"\"\n        if self._mahalanobis is None:\n            self._mahalanobis = sqrt(float(dot(dot(self.y.T, self.SI), self.y)))\n        return self._mahalanobis\n\n    @property\n    def alpha(self):\n        \"\"\"\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon [1]_.\n        \"\"\"\n        return self._alpha_sq**.5\n\n    def log_likelihood_of(self, z):\n        \"\"\"\n        log likelihood of the measurement `z`. This should only be called\n        after a call to update(). Calling after predict() will yield an\n        incorrect result.\"\"\"\n\n        if z is None:\n            return log(sys.float_info.min)\n        return logpdf(z, dot(self.H, self.x), self.S)\n\n    @alpha.setter\n    def alpha(self, value):\n        if not np.isscalar(value) or value < 1:\n            raise ValueError('alpha must be a float greater than 1')\n\n        self._alpha_sq = value**2\n\n    def __repr__(self):\n        return '\\n'.join([\n            'KalmanFilter object',\n            pretty_str('dim_x', self.dim_x),\n            pretty_str('dim_z', self.dim_z),\n            pretty_str('dim_u', self.dim_u),\n            pretty_str('x', self.x),\n            pretty_str('P', self.P),\n            pretty_str('x_prior', self.x_prior),\n            pretty_str('P_prior', self.P_prior),\n            pretty_str('x_post', self.x_post),\n            pretty_str('P_post', self.P_post),\n            pretty_str('F', self.F),\n            pretty_str('Q', self.Q),\n            pretty_str('R', self.R),\n            pretty_str('H', self.H),\n            pretty_str('K', self.K),\n            pretty_str('y', self.y),\n            pretty_str('S', self.S),\n            pretty_str('SI', self.SI),\n            pretty_str('M', self.M),\n            pretty_str('B', self.B),\n            pretty_str('z', self.z),\n            pretty_str('log-likelihood', self.log_likelihood),\n            pretty_str('likelihood', self.likelihood),\n            pretty_str('mahalanobis', self.mahalanobis),\n            pretty_str('alpha', self.alpha),\n            pretty_str('inv', self.inv)\n            ])\n\n    def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None):\n        \"\"\"\n        Performs a series of asserts to check that the size of everything\n        is what it should be. This can help you debug problems in your design.\n        If you pass in H, R, F, Q those will be used instead of this object's\n        value for those matrices.\n        Testing `z` (the measurement) is problamatic. x is a vector, and can be\n        implemented as either a 1D array or as a nx1 column vector. Thus Hx\n        can be of different shapes. Then, if Hx is a single value, it can\n        be either a 1D array or 2D vector. If either is true, z can reasonably\n        be a scalar (either '3' or np.array('3') are scalars under this\n        definition), a 1D, 1 element array, or a 2D, 1 element array. You are\n        allowed to pass in any combination that works.\n        \"\"\"\n\n        if H is None:\n            H = self.H\n        if R is None:\n            R = self.R\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        x = self.x\n        P = self.P\n\n        assert x.ndim == 1 or x.ndim == 2, \\\n                \"x must have one or two dimensions, but has {}\".format(x.ndim)\n\n        if x.ndim == 1:\n            assert x.shape[0] == self.dim_x, \\\n                   \"Shape of x must be ({},{}), but is {}\".format(\n                       self.dim_x, 1, x.shape)\n        else:\n            assert x.shape == (self.dim_x, 1), \\\n                   \"Shape of x must be ({},{}), but is {}\".format(\n                       self.dim_x, 1, x.shape)\n\n        assert P.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of P must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, P.shape)\n\n        assert Q.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of Q must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, P.shape)\n\n        assert F.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of F must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, F.shape)\n\n        assert np.ndim(H) == 2, \\\n               \"Shape of H must be (dim_z, {}), but is {}\".format(\n                   P.shape[0], shape(H))\n\n        assert H.shape[1] == P.shape[0], \\\n               \"Shape of H must be (dim_z, {}), but is {}\".format(\n                   P.shape[0], H.shape)\n\n        # shape of R must be the same as HPH'\n        hph_shape = (H.shape[0], H.shape[0])\n        r_shape = shape(R)\n\n        if H.shape[0] == 1:\n            # r can be scalar, 1D, or 2D in this case\n            assert r_shape in [(), (1,), (1, 1)], \\\n            \"R must be scalar or one element array, but is shaped {}\".format(\n                r_shape)\n        else:\n            assert r_shape == hph_shape, \\\n            \"shape of R should be {} but it is {}\".format(hph_shape, r_shape)\n\n\n        if z is not None:\n            z_shape = shape(z)\n        else:\n            z_shape = (self.dim_z, 1)\n\n        # H@x must have shape of z\n        Hx = dot(H, x)\n\n        if z_shape == (): # scalar or np.array(scalar)\n            assert Hx.ndim == 1 or shape(Hx) == (1, 1), \\\n            \"shape of z should be {}, not {} for the given H\".format(\n                shape(Hx), z_shape)\n\n        elif shape(Hx) == (1,):\n            assert z_shape[0] == 1, 'Shape of z must be {} for the given H'.format(shape(Hx))\n\n        else:\n            assert (z_shape == shape(Hx) or\n                    (len(z_shape) == 1 and shape(Hx) == (z_shape[0], 1))), \\\n                    \"shape of z should be {}, not {} for the given H\".format(\n                        shape(Hx), z_shape)\n\n        if np.ndim(Hx) > 1 and shape(Hx) != (1, 1):\n            assert shape(Hx) == z_shape, \\\n               'shape of z should be {} for the given H, but it is {}'.format(\n                   shape(Hx), z_shape)\n\n\ndef update(x, P, z, R, H=None, return_all=False):\n    \"\"\"\n    Add a new measurement (z) to the Kalman filter. If z is None, nothing\n    is changed.\n    This can handle either the multidimensional or unidimensional case. If\n    all parameters are floats instead of arrays the filter will still work,\n    and return floats for x, P as the result.\n    update(1, 2, 1, 1, 1)  # univariate\n    update(x, P, 1\n    Parameters\n    ----------\n    x : numpy.array(dim_x, 1), or float\n        State estimate vector\n    P : numpy.array(dim_x, dim_x), or float\n        Covariance matrix\n    z : (dim_z, 1): array_like\n        measurement for this update. z can be a scalar if dim_z is 1,\n        otherwise it must be convertible to a column vector.\n    R : numpy.array(dim_z, dim_z), or float\n        Measurement noise matrix\n    H : numpy.array(dim_x, dim_x), or float, optional\n        Measurement function. If not provided, a value of 1 is assumed.\n    return_all : bool, default False\n        If true, y, K, S, and log_likelihood are returned, otherwise\n        only x and P are returned.\n    Returns\n    -------\n    x : numpy.array\n        Posterior state estimate vector\n    P : numpy.array\n        Posterior covariance matrix\n    y : numpy.array or scalar\n        Residua. Difference between measurement and state in measurement space\n    K : numpy.array\n        Kalman gain\n    S : numpy.array\n        System uncertainty in measurement space\n    log_likelihood : float\n        log likelihood of the measurement\n    \"\"\"\n\n    #pylint: disable=bare-except\n\n    if z is None:\n        if return_all:\n            return x, P, None, None, None, None\n        return x, P\n\n    if H is None:\n        H = np.array([1])\n\n    if np.isscalar(H):\n        H = np.array([H])\n\n    Hx = np.atleast_1d(dot(H, x))\n    z = reshape_z(z, Hx.shape[0], x.ndim)\n\n    # error (residual) between measurement and prediction\n    y = z - Hx\n\n    # project system uncertainty into measurement space\n    S = dot(dot(H, P), H.T) + R\n\n\n    # map system uncertainty into kalman gain\n    try:\n        K = dot(dot(P, H.T), linalg.inv(S))\n    except:\n        # can't invert a 1D array, annoyingly\n        K = dot(dot(P, H.T), 1./S)\n\n\n    # predict new x with residual scaled by the kalman gain\n    x = x + dot(K, y)\n\n    # P = (I-KH)P(I-KH)' + KRK'\n    KH = dot(K, H)\n\n    try:\n        I_KH = np.eye(KH.shape[0]) - KH\n    except:\n        I_KH = np.array([1 - KH])\n    P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)\n\n\n    if return_all:\n        # compute log likelihood\n        log_likelihood = logpdf(z, dot(H, x), S)\n        return x, P, y, K, S, log_likelihood\n    return x, P\n\n\ndef update_steadystate(x, z, K, H=None):\n    \"\"\"\n    Add a new measurement (z) to the Kalman filter. If z is None, nothing\n    is changed.\n    Parameters\n    ----------\n    x : numpy.array(dim_x, 1), or float\n        State estimate vector\n    z : (dim_z, 1): array_like\n        measurement for this update. z can be a scalar if dim_z is 1,\n        otherwise it must be convertible to a column vector.\n    K : numpy.array, or float\n        Kalman gain matrix\n    H : numpy.array(dim_x, dim_x), or float, optional\n        Measurement function. If not provided, a value of 1 is assumed.\n    Returns\n    -------\n    x : numpy.array\n        Posterior state estimate vector\n    Examples\n    --------\n    This can handle either the multidimensional or unidimensional case. If\n    all parameters are floats instead of arrays the filter will still work,\n    and return floats for x, P as the result.\n    >>> update_steadystate(1, 2, 1)  # univariate\n    >>> update_steadystate(x, P, z, H)\n    \"\"\"\n\n\n    if z is None:\n        return x\n\n    if H is None:\n        H = np.array([1])\n\n    if np.isscalar(H):\n        H = np.array([H])\n\n    Hx = np.atleast_1d(dot(H, x))\n    z = reshape_z(z, Hx.shape[0], x.ndim)\n\n    # error (residual) between measurement and prediction\n    y = z - Hx\n\n    # estimate new x with residual scaled by the kalman gain\n    return x + dot(K, y)\n\n\ndef predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.):\n    \"\"\"\n    Predict next state (prior) using the Kalman filter state propagation\n    equations.\n    Parameters\n    ----------\n    x : numpy.array\n        State estimate vector\n    P : numpy.array\n        Covariance matrix\n    F : numpy.array()\n        State Transition matrix\n    Q : numpy.array, Optional\n        Process noise matrix\n    u : numpy.array, Optional, default 0.\n        Control vector. If non-zero, it is multiplied by B\n        to create the control input into the system.\n    B : numpy.array, optional, default 0.\n        Control transition matrix.\n    alpha : float, Optional, default=1.0\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon\n    Returns\n    -------\n    x : numpy.array\n        Prior state estimate vector\n    P : numpy.array\n        Prior covariance matrix\n    \"\"\"\n\n    if np.isscalar(F):\n        F = np.array(F)\n    x = dot(F, x) + dot(B, u)\n    P = (alpha * alpha) * dot(dot(F, P), F.T) + Q\n\n    return x, P\n\n\ndef predict_steadystate(x, F=1, u=0, B=1):\n    \"\"\"\n    Predict next state (prior) using the Kalman filter state propagation\n    equations. This steady state form only computes x, assuming that the\n    covariance is constant.\n    Parameters\n    ----------\n    x : numpy.array\n        State estimate vector\n    P : numpy.array\n        Covariance matrix\n    F : numpy.array()\n        State Transition matrix\n    u : numpy.array, Optional, default 0.\n        Control vector. If non-zero, it is multiplied by B\n        to create the control input into the system.\n    B : numpy.array, optional, default 0.\n        Control transition matrix.\n    Returns\n    -------\n    x : numpy.array\n        Prior state estimate vector\n    \"\"\"\n\n    if np.isscalar(F):\n        F = np.array(F)\n    x = dot(F, x) + dot(B, u)\n\n    return x\n\n\n\ndef batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None,\n                 update_first=False, saver=None):\n    \"\"\"\n    Batch processes a sequences of measurements.\n    Parameters\n    ----------\n    zs : list-like\n        list of measurements at each time step. Missing measurements must be\n        represented by None.\n    Fs : list-like\n        list of values to use for the state transition matrix matrix.\n    Qs : list-like\n        list of values to use for the process error\n        covariance.\n    Hs : list-like\n        list of values to use for the measurement matrix.\n    Rs : list-like\n        list of values to use for the measurement error\n        covariance.\n    Bs : list-like, optional\n        list of values to use for the control transition matrix;\n        a value of None in any position will cause the filter\n        to use `self.B` for that time step.\n    us : list-like, optional\n        list of values to use for the control input vector;\n        a value of None in any position will cause the filter to use\n        0 for that time step.\n    update_first : bool, optional\n        controls whether the order of operations is update followed by\n        predict, or predict followed by update. Default is predict->update.\n        saver : filterpy.common.Saver, optional\n            filterpy.common.Saver object. If provided, saver.save() will be\n            called after every epoch\n    Returns\n    -------\n    means : np.array((n,dim_x,1))\n        array of the state for each time step after the update. Each entry\n        is an np.array. In other words `means[k,:]` is the state at step\n        `k`.\n    covariance : np.array((n,dim_x,dim_x))\n        array of the covariances for each time step after the update.\n        In other words `covariance[k,:,:]` is the covariance at step `k`.\n    means_predictions : np.array((n,dim_x,1))\n        array of the state for each time step after the predictions. Each\n        entry is an np.array. In other words `means[k,:]` is the state at\n        step `k`.\n    covariance_predictions : np.array((n,dim_x,dim_x))\n        array of the covariances for each time step after the prediction.\n        In other words `covariance[k,:,:]` is the covariance at step `k`.\n    Examples\n    --------\n    .. code-block:: Python\n        zs = [t + random.randn()*4 for t in range (40)]\n        Fs = [kf.F for t in range (40)]\n        Hs = [kf.H for t in range (40)]\n        (mu, cov, _, _) = kf.batch_filter(zs, Rs=R_list, Fs=Fs, Hs=Hs, Qs=None,\n                                          Bs=None, us=None, update_first=False)\n        (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs, Qs=None)\n    \"\"\"\n\n    n = np.size(zs, 0)\n    dim_x = x.shape[0]\n\n    # mean estimates from Kalman Filter\n    if x.ndim == 1:\n        means = zeros((n, dim_x))\n        means_p = zeros((n, dim_x))\n    else:\n        means = zeros((n, dim_x, 1))\n        means_p = zeros((n, dim_x, 1))\n\n    # state covariances from Kalman Filter\n    covariances = zeros((n, dim_x, dim_x))\n    covariances_p = zeros((n, dim_x, dim_x))\n\n    if us is None:\n        us = [0.] * n\n        Bs = [0.] * n\n\n    if update_first:\n        for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n            x, P = update(x, P, z, R=R, H=H)\n            means[i, :] = x\n            covariances[i, :, :] = P\n\n            x, P = predict(x, P, u=u, B=B, F=F, Q=Q)\n            means_p[i, :] = x\n            covariances_p[i, :, :] = P\n            if saver is not None:\n                saver.save()\n    else:\n        for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n            x, P = predict(x, P, u=u, B=B, F=F, Q=Q)\n            means_p[i, :] = x\n            covariances_p[i, :, :] = P\n\n            x, P = update(x, P, z, R=R, H=H)\n            means[i, :] = x\n            covariances[i, :, :] = P\n            if saver is not None:\n                saver.save()\n\n    return (means, covariances, means_p, covariances_p)\n\n\n\ndef rts_smoother(Xs, Ps, Fs, Qs):\n    \"\"\"\n    Runs the Rauch-Tung-Striebel Kalman smoother on a set of\n    means and covariances computed by a Kalman filter. The usual input\n    would come from the output of `KalmanFilter.batch_filter()`.\n    Parameters\n    ----------\n    Xs : numpy.array\n       array of the means (state variable x) of the output of a Kalman\n       filter.\n    Ps : numpy.array\n        array of the covariances of the output of a kalman filter.\n    Fs : list-like collection of numpy.array\n        State transition matrix of the Kalman filter at each time step.\n    Qs : list-like collection of numpy.array, optional\n        Process noise of the Kalman filter at each time step.\n    Returns\n    -------\n    x : numpy.ndarray\n       smoothed means\n    P : numpy.ndarray\n       smoothed state covariances\n    K : numpy.ndarray\n        smoother gain at each step\n    pP : numpy.ndarray\n       predicted state covariances\n    Examples\n    --------\n    .. code-block:: Python\n        zs = [t + random.randn()*4 for t in range (40)]\n        (mu, cov, _, _) = kalman.batch_filter(zs)\n        (x, P, K, pP) = rts_smoother(mu, cov, kf.F, kf.Q)\n    \"\"\"\n\n    if len(Xs) != len(Ps):\n        raise ValueError('length of Xs and Ps must be the same')\n\n    n = Xs.shape[0]\n    dim_x = Xs.shape[1]\n\n    # smoother gain\n    K = zeros((n, dim_x, dim_x))\n    x, P, pP = Xs.copy(), Ps.copy(), Ps.copy()\n\n    for k in range(n-2, -1, -1):\n        pP[k] = dot(dot(Fs[k], P[k]), Fs[k].T) + Qs[k]\n\n        #pylint: disable=bad-whitespace\n        K[k]  = dot(dot(P[k], Fs[k].T), linalg.inv(pP[k]))\n        x[k] += dot(K[k], x[k+1] - dot(Fs[k], x[k]))\n        P[k] += dot(dot(K[k], P[k+1] - pP[k]), K[k].T)\n\n    return (x, P, K, pP)"
  },
  {
    "path": "trackers/motdt_tracker/basetrack.py",
    "content": "import numpy as np\nfrom collections import OrderedDict\n\n\nclass TrackState(object):\n    New = 0\n    Tracked = 1\n    Lost = 2\n    Removed = 3\n    Replaced = 4\n\n\nclass BaseTrack(object):\n    _count = 0\n\n    track_id = 0\n    is_activated = False\n    state = TrackState.New\n\n    history = OrderedDict()\n    features = []\n    curr_feature = None\n    score = 0\n    start_frame = 0\n    frame_id = 0\n    time_since_update = 0\n\n    # multi-camera\n    location = (np.inf, np.inf)\n\n    @property\n    def end_frame(self):\n        return self.frame_id\n\n    @staticmethod\n    def next_id():\n        BaseTrack._count += 1\n        return BaseTrack._count\n\n    def activate(self, *args):\n        raise NotImplementedError\n\n    def predict(self):\n        raise NotImplementedError\n\n    def update(self, *args, **kwargs):\n        raise NotImplementedError\n\n    def mark_lost(self):\n        self.state = TrackState.Lost\n\n    def mark_removed(self):\n        self.state = TrackState.Removed\n\n    def mark_replaced(self):\n        self.state = TrackState.Replaced\n"
  },
  {
    "path": "trackers/motdt_tracker/kalman_filter.py",
    "content": "# vim: expandtab:ts=4:sw=4\nimport numpy as np\nimport scipy.linalg\n\n\n\"\"\"\nTable for the 0.95 quantile of the chi-square distribution with N degrees of\nfreedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv\nfunction and used as Mahalanobis gating threshold.\n\"\"\"\nchi2inv95 = {\n    1: 3.8415,\n    2: 5.9915,\n    3: 7.8147,\n    4: 9.4877,\n    5: 11.070,\n    6: 12.592,\n    7: 14.067,\n    8: 15.507,\n    9: 16.919}\n\n\nclass KalmanFilter(object):\n    \"\"\"\n    A simple Kalman filter for tracking bounding boxes in image space.\n\n    The 8-dimensional state space\n\n        x, y, a, h, vx, vy, va, vh\n\n    contains the bounding box center position (x, y), aspect ratio a, height h,\n    and their respective velocities.\n\n    Object motion follows a constant velocity model. The bounding box location\n    (x, y, a, h) is taken as direct observation of the state space (linear\n    observation model).\n\n    \"\"\"\n\n    def __init__(self):\n        ndim, dt = 4, 1.\n\n        # Create Kalman filter model matrices.\n        self._motion_mat = np.eye(2 * ndim, 2 * ndim)\n        for i in range(ndim):\n            self._motion_mat[i, ndim + i] = dt\n        self._update_mat = np.eye(ndim, 2 * ndim)\n\n        # Motion and observation uncertainty are chosen relative to the current\n        # state estimate. These weights control the amount of uncertainty in\n        # the model. This is a bit hacky.\n        self._std_weight_position = 1. / 20\n        self._std_weight_velocity = 1. / 160\n\n    def initiate(self, measurement):\n        \"\"\"Create track from unassociated measurement.\n\n        Parameters\n        ----------\n        measurement : ndarray\n            Bounding box coordinates (x, y, a, h) with center position (x, y),\n            aspect ratio a, and height h.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector (8 dimensional) and covariance matrix (8x8\n            dimensional) of the new track. Unobserved velocities are initialized\n            to 0 mean.\n\n        \"\"\"\n        mean_pos = measurement\n        mean_vel = np.zeros_like(mean_pos)\n        mean = np.r_[mean_pos, mean_vel]\n\n        std = [\n            2 * self._std_weight_position * measurement[3],\n            2 * self._std_weight_position * measurement[3],\n            1e-2,\n            2 * self._std_weight_position * measurement[3],\n            10 * self._std_weight_velocity * measurement[3],\n            10 * self._std_weight_velocity * measurement[3],\n            1e-5,\n            10 * self._std_weight_velocity * measurement[3]]\n        covariance = np.diag(np.square(std))\n        return mean, covariance\n\n    def predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The 8 dimensional mean vector of the object state at the previous\n            time step.\n        covariance : ndarray\n            The 8x8 dimensional covariance matrix of the object state at the\n            previous time step.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n\n        \"\"\"\n        std_pos = [\n            self._std_weight_position * mean[3],\n            self._std_weight_position * mean[3],\n            1e-2,\n            self._std_weight_position * mean[3]]\n        std_vel = [\n            self._std_weight_velocity * mean[3],\n            self._std_weight_velocity * mean[3],\n            1e-5,\n            self._std_weight_velocity * mean[3]]\n        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))\n\n        #mean = np.dot(self._motion_mat, mean)\n        mean = np.dot(mean, self._motion_mat.T)\n        covariance = np.linalg.multi_dot((\n            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov\n\n        return mean, covariance\n\n    def project(self, mean, covariance):\n        \"\"\"Project state distribution to measurement space.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The state's mean vector (8 dimensional array).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the projected mean and covariance matrix of the given state\n            estimate.\n\n        \"\"\"\n        std = [\n            self._std_weight_position * mean[3],\n            self._std_weight_position * mean[3],\n            1e-1,\n            self._std_weight_position * mean[3]]\n        innovation_cov = np.diag(np.square(std))\n\n        mean = np.dot(self._update_mat, mean)\n        covariance = np.linalg.multi_dot((\n            self._update_mat, covariance, self._update_mat.T))\n        return mean, covariance + innovation_cov\n\n    def multi_predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step (Vectorized version).\n        Parameters\n        ----------\n        mean : ndarray\n            The Nx8 dimensional mean matrix of the object states at the previous\n            time step.\n        covariance : ndarray\n            The Nx8x8 dimensional covariance matrics of the object states at the\n            previous time step.\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n        \"\"\"\n        std_pos = [\n            self._std_weight_position * mean[:, 3],\n            self._std_weight_position * mean[:, 3],\n            1e-2 * np.ones_like(mean[:, 3]),\n            self._std_weight_position * mean[:, 3]]\n        std_vel = [\n            self._std_weight_velocity * mean[:, 3],\n            self._std_weight_velocity * mean[:, 3],\n            1e-5 * np.ones_like(mean[:, 3]),\n            self._std_weight_velocity * mean[:, 3]]\n        sqr = np.square(np.r_[std_pos, std_vel]).T\n\n        motion_cov = []\n        for i in range(len(mean)):\n            motion_cov.append(np.diag(sqr[i]))\n        motion_cov = np.asarray(motion_cov)\n\n        mean = np.dot(mean, self._motion_mat.T)\n        left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))\n        covariance = np.dot(left, self._motion_mat.T) + motion_cov\n\n        return mean, covariance\n\n    def update(self, mean, covariance, measurement):\n        \"\"\"Run Kalman filter correction step.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The predicted state's mean vector (8 dimensional).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n        measurement : ndarray\n            The 4 dimensional measurement vector (x, y, a, h), where (x, y)\n            is the center position, a the aspect ratio, and h the height of the\n            bounding box.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the measurement-corrected state distribution.\n\n        \"\"\"\n        projected_mean, projected_cov = self.project(mean, covariance)\n\n        chol_factor, lower = scipy.linalg.cho_factor(\n            projected_cov, lower=True, check_finite=False)\n        kalman_gain = scipy.linalg.cho_solve(\n            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,\n            check_finite=False).T\n        innovation = measurement - projected_mean\n\n        new_mean = mean + np.dot(innovation, kalman_gain.T)\n        new_covariance = covariance - np.linalg.multi_dot((\n            kalman_gain, projected_cov, kalman_gain.T))\n        return new_mean, new_covariance\n\n    def gating_distance(self, mean, covariance, measurements,\n                        only_position=False, metric='maha'):\n        \"\"\"Compute gating distance between state distribution and measurements.\n        A suitable distance threshold can be obtained from `chi2inv95`. If\n        `only_position` is False, the chi-square distribution has 4 degrees of\n        freedom, otherwise 2.\n        Parameters\n        ----------\n        mean : ndarray\n            Mean vector over the state distribution (8 dimensional).\n        covariance : ndarray\n            Covariance of the state distribution (8x8 dimensional).\n        measurements : ndarray\n            An Nx4 dimensional matrix of N measurements, each in\n            format (x, y, a, h) where (x, y) is the bounding box center\n            position, a the aspect ratio, and h the height.\n        only_position : Optional[bool]\n            If True, distance computation is done with respect to the bounding\n            box center position only.\n        Returns\n        -------\n        ndarray\n            Returns an array of length N, where the i-th element contains the\n            squared Mahalanobis distance between (mean, covariance) and\n            `measurements[i]`.\n        \"\"\"\n        mean, covariance = self.project(mean, covariance)\n        if only_position:\n            mean, covariance = mean[:2], covariance[:2, :2]\n            measurements = measurements[:, :2]\n\n        d = measurements - mean\n        if metric == 'gaussian':\n            return np.sum(d * d, axis=1)\n        elif metric == 'maha':\n            cholesky_factor = np.linalg.cholesky(covariance)\n            z = scipy.linalg.solve_triangular(\n                cholesky_factor, d.T, lower=True, check_finite=False,\n                overwrite_b=True)\n            squared_maha = np.sum(z * z, axis=0)\n            return squared_maha\n        else:\n            raise ValueError('invalid distance metric')"
  },
  {
    "path": "trackers/motdt_tracker/kalman_filter_score.py",
    "content": "# vim: expandtab:ts=4:sw=4\nimport numpy as np\nimport scipy.linalg\n\n\n\"\"\"\nTable for the 0.95 quantile of the chi-square distribution with N degrees of\nfreedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv\nfunction and used as Mahalanobis gating threshold.\n\"\"\"\nchi2inv95 = {\n    1: 3.8415,\n    2: 5.9915,\n    3: 7.8147,\n    4: 9.4877,\n    5: 11.070,\n    6: 12.592,\n    7: 14.067,\n    8: 15.507,\n    9: 16.919}\n\n\nclass KalmanFilter_score(object):\n    \"\"\"\n    A simple Kalman filter for tracking bounding boxes in image space.\n\n    The 8-dimensional state space\n\n        x, y, a, h, vx, vy, va, vh\n        修改为 score, vscore\n\n    contains the bounding box center position (x, y), aspect ratio a, height h,\n    and their respective velocities.\n\n    Object motion follows a constant velocity model. The bounding box location\n    (x, y, a, h) is taken as direct observation of the state space (linear\n    observation model).\n\n    \"\"\"\n\n    def __init__(self):\n        ndim, dt = 1, 1.\n\n        # Create Kalman filter model matrices.\n        self._motion_mat = np.eye(2 * ndim, 2 * ndim)\n        for i in range(ndim):\n            self._motion_mat[i, ndim + i] = dt\n        self._update_mat = np.eye(ndim, 2 * ndim)\n\n        # Motion and observation uncertainty are chosen relative to the current\n        # state estimate. These weights control the amount of uncertainty in\n        # the model. This is a bit hacky.\n        self._std_weight_position = 1. / 20\n        self._std_weight_velocity = 1. / 160\n\n    def initiate(self, measurement):\n        \"\"\"Create track from unassociated measurement.\n\n        Parameters\n        ----------\n        measurement : ndarray\n            Bounding box coordinates (x, y, a, h) with center position (x, y),\n            aspect ratio a, and height h.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector (8 dimensional) and covariance matrix (8x8\n            dimensional) of the new track. Unobserved velocities are initialized\n            to 0 mean.\n\n        \"\"\"\n        mean_pos = measurement\n        mean_vel = np.zeros_like(mean_pos)\n        mean = np.r_[mean_pos, mean_vel]\n\n        # std = [\n        #     2 * self._std_weight_position * measurement[3],\n        #     2 * self._std_weight_position * measurement[3],\n        #     1e-2,\n        #     2 * self._std_weight_position * measurement[3],\n        #     10 * self._std_weight_velocity * measurement[3],\n        #     10 * self._std_weight_velocity * measurement[3],\n        #     1e-5,\n        #     10 * self._std_weight_velocity * measurement[3]]\n        std = [\n            1e-2,\n            1e-5]\n        covariance = np.diag(np.square(std))\n        return mean, covariance\n\n    def predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The 8 dimensional mean vector of the object state at the previous\n            time step.\n        covariance : ndarray\n            The 8x8 dimensional covariance matrix of the object state at the\n            previous time step.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n\n        \"\"\"\n        std_pos = [\n            1e-2]\n        std_vel = [\n            1e-5]\n        motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))\n\n        #mean = np.dot(self._motion_mat, mean)\n        mean = np.dot(mean, self._motion_mat.T)\n        covariance = np.linalg.multi_dot((\n            self._motion_mat, covariance, self._motion_mat.T)) + motion_cov\n\n        return mean, covariance\n\n    def project(self, mean, covariance):\n        \"\"\"Project state distribution to measurement space.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The state's mean vector (8 dimensional array).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the projected mean and covariance matrix of the given state\n            estimate.\n\n        \"\"\"\n        std = [\n            1e-1]\n        innovation_cov = np.diag(np.square(std))\n\n        mean = np.dot(self._update_mat, mean)\n        covariance = np.linalg.multi_dot((\n            self._update_mat, covariance, self._update_mat.T))\n        return mean, covariance + innovation_cov\n\n    def multi_predict(self, mean, covariance):\n        \"\"\"Run Kalman filter prediction step (Vectorized version).\n        Parameters\n        ----------\n        mean : ndarray\n            The Nx8 dimensional mean matrix of the object states at the previous\n            time step.\n        covariance : ndarray\n            The Nx8x8 dimensional covariance matrics of the object states at the\n            previous time step.\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the mean vector and covariance matrix of the predicted\n            state. Unobserved velocities are initialized to 0 mean.\n        \"\"\"\n        std_pos = [\n            1e-2 * np.ones_like(mean[:, 0])]\n        std_vel = [\n            1e-5 * np.ones_like(mean[:, 0])]\n        sqr = np.square(np.r_[std_pos, std_vel]).T\n\n        motion_cov = []\n        for i in range(len(mean)):\n            motion_cov.append(np.diag(sqr[i]))\n        motion_cov = np.asarray(motion_cov)\n\n        mean = np.dot(mean, self._motion_mat.T)\n        left = np.dot(self._motion_mat, covariance).transpose((1, 0, 2))\n        covariance = np.dot(left, self._motion_mat.T) + motion_cov\n\n        return mean, covariance\n\n    def update(self, mean, covariance, measurement):\n        \"\"\"Run Kalman filter correction step.\n\n        Parameters\n        ----------\n        mean : ndarray\n            The predicted state's mean vector (8 dimensional).\n        covariance : ndarray\n            The state's covariance matrix (8x8 dimensional).\n        measurement : ndarray\n            The 4 dimensional measurement vector (x, y, a, h), where (x, y)\n            is the center position, a the aspect ratio, and h the height of the\n            bounding box.\n\n        Returns\n        -------\n        (ndarray, ndarray)\n            Returns the measurement-corrected state distribution.\n\n        \"\"\"\n        projected_mean, projected_cov = self.project(mean, covariance)\n\n        chol_factor, lower = scipy.linalg.cho_factor(\n            projected_cov, lower=True, check_finite=False)\n        kalman_gain = scipy.linalg.cho_solve(\n            (chol_factor, lower), np.dot(covariance, self._update_mat.T).T,\n            check_finite=False).T\n        innovation = measurement - projected_mean\n\n        new_mean = mean + np.dot(innovation, kalman_gain.T)\n        new_covariance = covariance - np.linalg.multi_dot((\n            kalman_gain, projected_cov, kalman_gain.T))\n        return new_mean, new_covariance\n\n    def gating_distance(self, mean, covariance, measurements,\n                        only_position=False, metric='maha'):\n        \"\"\"Compute gating distance between state distribution and measurements.\n        A suitable distance threshold can be obtained from `chi2inv95`. If\n        `only_position` is False, the chi-square distribution has 4 degrees of\n        freedom, otherwise 2.\n        Parameters\n        ----------\n        mean : ndarray\n            Mean vector over the state distribution (8 dimensional).\n        covariance : ndarray\n            Covariance of the state distribution (8x8 dimensional).\n        measurements : ndarray\n            An Nx4 dimensional matrix of N measurements, each in\n            format (x, y, a, h) where (x, y) is the bounding box center\n            position, a the aspect ratio, and h the height.\n        only_position : Optional[bool]\n            If True, distance computation is done with respect to the bounding\n            box center position only.\n        Returns\n        -------\n        ndarray\n            Returns an array of length N, where the i-th element contains the\n            squared Mahalanobis distance between (mean, covariance) and\n            `measurements[i]`.\n        \"\"\"\n        mean, covariance = self.project(mean, covariance)\n        if only_position:\n            mean, covariance = mean[:2], covariance[:2, :2]\n            measurements = measurements[:, :2]\n\n        d = measurements - mean\n        if metric == 'gaussian':\n            return np.sum(d * d, axis=1)\n        elif metric == 'maha':\n            cholesky_factor = np.linalg.cholesky(covariance)\n            z = scipy.linalg.solve_triangular(\n                cholesky_factor, d.T, lower=True, check_finite=False,\n                overwrite_b=True)\n            squared_maha = np.sum(z * z, axis=0)\n            return squared_maha\n        else:\n            raise ValueError('invalid distance metric')"
  },
  {
    "path": "trackers/motdt_tracker/matching.py",
    "content": "import cv2\nimport numpy as np\nimport lap\nfrom scipy.spatial.distance import cdist\n\nfrom cython_bbox import bbox_overlaps as bbox_ious\nfrom trackers.motdt_tracker import kalman_filter\n\n\ndef _indices_to_matches(cost_matrix, indices, thresh):\n    matched_cost = cost_matrix[tuple(zip(*indices))]\n    matched_mask = (matched_cost <= thresh)\n\n    matches = indices[matched_mask]\n    unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))\n    unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))\n\n    return matches, unmatched_a, unmatched_b\n\n\ndef linear_assignment(cost_matrix, thresh):\n    if cost_matrix.size == 0:\n        return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))\n    matches, unmatched_a, unmatched_b = [], [], []\n    cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)\n    for ix, mx in enumerate(x):\n        if mx >= 0:\n            matches.append([ix, mx])\n    unmatched_a = np.where(x < 0)[0]\n    unmatched_b = np.where(y < 0)[0]\n    matches = np.asarray(matches)\n    return matches, unmatched_a, unmatched_b\n\n\ndef ious(atlbrs, btlbrs):\n    \"\"\"\n    Compute cost based on IoU\n    :type atlbrs: list[tlbr] | np.ndarray\n    :type atlbrs: list[tlbr] | np.ndarray\n    :rtype ious np.ndarray\n    \"\"\"\n    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)\n    if ious.size == 0:\n        return ious\n\n    ious = bbox_ious(\n        np.ascontiguousarray(atlbrs, dtype=np.float),\n        np.ascontiguousarray(btlbrs, dtype=np.float)\n    )\n\n    return ious\n\ndef hmiou(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    ious = np.zeros((len(bboxes1), len(bboxes2)), dtype=np.float)\n    if ious.size == 0:\n        return ious\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    yy11 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    yy12 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n\n    yy21 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    yy22 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n    o = (yy12 - yy11) / (yy22 - yy21)\n\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    iou = wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])\n                + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)\n\n    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])\n    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])\n    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n    wc = xxc2 - xxc1\n    hc = yyc2 - yyc1\n    iou[wc <= 0] = 0\n    iou[hc <= 0] = 0\n    iou *= o\n    return iou\n\ndef iou_distance(atracks, btracks):\n    \"\"\"\n    Compute cost based on IoU\n    :type atracks: list[STrack]\n    :type btracks: list[STrack]\n    :rtype cost_matrix np.ndarray\n    \"\"\"\n    atlbrs = [track.tlbr for track in atracks]\n    btlbrs = [track.tlbr for track in btracks]\n    _ious = ious(atlbrs, btlbrs)\n    cost_matrix = 1 - _ious\n\n    return cost_matrix\n\ndef hmiou_distance(atracks, btracks):\n    \"\"\"\n    Compute cost based on IoU\n    :type atracks: list[STrack]\n    :type btracks: list[STrack]\n    :rtype cost_matrix np.ndarray\n    \"\"\"\n    atlbrs = [track.tlbr for track in atracks]\n    btlbrs = [track.tlbr for track in btracks]\n    _ious = hmiou(atlbrs, btlbrs)\n    cost_matrix = 1 - _ious\n\n    return cost_matrix\n\n\ndef nearest_reid_distance(tracks, detections, metric='cosine'):\n    \"\"\"\n    Compute cost based on ReID features\n    :type tracks: list[STrack]\n    :type detections: list[BaseTrack]\n    :rtype cost_matrix np.ndarray\n    \"\"\"\n    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)\n    if cost_matrix.size == 0:\n        return cost_matrix\n\n    det_features = np.asarray([track.curr_feature for track in detections], dtype=np.float32)\n    for i, track in enumerate(tracks):\n        cost_matrix[i, :] = np.maximum(0.0, cdist(track.features, det_features, metric).min(axis=0))\n\n    return cost_matrix\n\ndef nearest_reid_distance_with_score(tracks, detections, metric='cosine', min_cls_score=0.4):\n    \"\"\"\n    Compute cost based on ReID features\n    :type tracks: list[STrack]\n    :type detections: list[BaseTrack]\n    :rtype cost_matrix np.ndarray\n    \"\"\"\n    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)\n    if cost_matrix.size == 0:\n        return cost_matrix\n\n    det_features = np.asarray([track.curr_feature for track in detections], dtype=np.float32)\n    det_score = np.asarray([track.score_kalman for track in detections], dtype=np.float32)\n    for i, track in enumerate(tracks):\n        cost_matrix[i, :] = np.maximum(0.0, cdist(track.features, det_features, metric).min(axis=0) + abs(np.clip(track.score_kalman, min_cls_score, 1.0)-det_score))\n\n    return cost_matrix\n\n\ndef mean_reid_distance(tracks, detections, metric='cosine'):\n    \"\"\"\n    Compute cost based on ReID features\n    :type tracks: list[STrack]\n    :type detections: list[BaseTrack]\n    :type metric: str\n    :rtype cost_matrix np.ndarray\n    \"\"\"\n    cost_matrix = np.empty((len(tracks), len(detections)), dtype=np.float)\n    if cost_matrix.size == 0:\n        return cost_matrix\n\n    track_features = np.asarray([track.curr_feature for track in tracks], dtype=np.float32)\n    det_features = np.asarray([track.curr_feature for track in detections], dtype=np.float32)\n    cost_matrix = cdist(track_features, det_features, metric)\n\n    return cost_matrix\n\n\ndef gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):\n    if cost_matrix.size == 0:\n        return cost_matrix\n    gating_dim = 2 if only_position else 4\n    gating_threshold = kalman_filter.chi2inv95[gating_dim]\n    measurements = np.asarray([det.to_xyah() for det in detections])\n    for row, track in enumerate(tracks):\n        gating_distance = kf.gating_distance(\n            track.mean, track.covariance, measurements, only_position)\n        cost_matrix[row, gating_distance > gating_threshold] = np.inf\n    return cost_matrix"
  },
  {
    "path": "trackers/motdt_tracker/motdt_tracker.py",
    "content": "import numpy as np\n#from numba import jit\nfrom collections import OrderedDict, deque\nimport itertools\nimport os\nimport cv2\nimport torch\nfrom torch._C import dtype\nimport torchvision\n\nfrom trackers.motdt_tracker import matching\nfrom .kalman_filter import KalmanFilter\n\nfrom .reid_model import load_reid_model, extract_reid_features\nfrom yolox.data.dataloading import get_yolox_datadir\n\nfrom .basetrack import BaseTrack, TrackState\n\n\nclass STrack(BaseTrack):\n\n    def __init__(self, tlwh, score, max_n_features=100, from_det=True):\n\n        # wait activate\n        self._tlwh = np.asarray(tlwh, dtype=np.float)\n        self.kalman_filter = None\n        self.mean, self.covariance = None, None\n        self.is_activated = False\n\n        self.score = score\n        self.max_n_features = max_n_features\n        self.curr_feature = None\n        self.last_feature = None\n        self.features = deque([], maxlen=self.max_n_features)\n\n        # classification\n        self.from_det = from_det\n        self.tracklet_len = 0\n        self.time_by_tracking = 0\n\n        # self-tracking\n        self.tracker = None\n\n    def set_feature(self, feature):\n        if feature is None:\n            return False\n        self.features.append(feature)\n        self.curr_feature = feature\n        self.last_feature = feature\n        # self._p_feature = 0\n        return True\n\n    def predict(self):\n        if self.time_since_update > 0:\n            self.tracklet_len = 0\n\n        self.time_since_update += 1\n\n        mean_state = self.mean.copy()\n        if self.state != TrackState.Tracked:\n            mean_state[7] = 0\n        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)\n\n        if self.tracker:\n            self.tracker.update_roi(self.tlwh)\n\n    def self_tracking(self, image):\n        tlwh = self.tracker.predict(image) if self.tracker else self.tlwh\n        return tlwh\n\n    def activate(self, kalman_filter, frame_id, image):\n        \"\"\"Start a new tracklet\"\"\"\n        self.kalman_filter = kalman_filter  # type: KalmanFilter\n        self.track_id = self.next_id()\n        # cx, cy, aspect_ratio, height, dx, dy, da, dh\n        self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))\n\n        # self.tracker = sot.SingleObjectTracker()\n        # self.tracker.init(image, self.tlwh)\n\n        del self._tlwh\n\n        self.time_since_update = 0\n        self.time_by_tracking = 0\n        self.tracklet_len = 0\n        self.state = TrackState.Tracked\n        # self.is_activated = True\n        self.frame_id = frame_id\n        self.start_frame = frame_id\n\n    def re_activate(self, new_track, frame_id, image, new_id=False):\n        # self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(new_track.tlwh))\n        self.mean, self.covariance = self.kalman_filter.update(\n            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)\n        )\n        self.time_since_update = 0\n        self.time_by_tracking = 0\n        self.tracklet_len = 0\n        self.state = TrackState.Tracked\n        self.is_activated = True\n        self.frame_id = frame_id\n        if new_id:\n            self.track_id = self.next_id()\n\n        self.set_feature(new_track.curr_feature)\n\n    def update(self, new_track, frame_id, image, update_feature=True):\n        \"\"\"\n        Update a matched track\n        :type new_track: STrack\n        :type frame_id: int\n        :type update_feature: bool\n        :return:\n        \"\"\"\n        self.frame_id = frame_id\n        self.time_since_update = 0\n        if new_track.from_det:\n            self.time_by_tracking = 0\n        else:\n            self.time_by_tracking += 1\n        self.tracklet_len += 1\n\n        new_tlwh = new_track.tlwh\n        self.mean, self.covariance = self.kalman_filter.update(\n            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))\n        self.state = TrackState.Tracked\n        self.is_activated = True\n\n        self.score = new_track.score\n\n        if update_feature:\n            self.set_feature(new_track.curr_feature)\n            if self.tracker:\n                self.tracker.update(image, self.tlwh)\n\n    @property\n    #@jit\n    def tlwh(self):\n        \"\"\"Get current position in bounding box format `(top left x, top left y,\n                width, height)`.\n        \"\"\"\n        if self.mean is None:\n            return self._tlwh.copy()\n        ret = self.mean[:4].copy()\n        ret[2] *= ret[3]\n        ret[:2] -= ret[2:] / 2\n        return ret\n\n    @property\n    #@jit\n    def tlbr(self):\n        \"\"\"Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,\n        `(top left, bottom right)`.\n        \"\"\"\n        ret = self.tlwh.copy()\n        ret[2:] += ret[:2]\n        return ret\n\n    @staticmethod\n    #@jit\n    def tlwh_to_xyah(tlwh):\n        \"\"\"Convert bounding box to format `(center x, center y, aspect ratio,\n        height)`, where the aspect ratio is `width / height`.\n        \"\"\"\n        ret = np.asarray(tlwh).copy()\n        ret[:2] += ret[2:] / 2\n        ret[2] /= ret[3]\n        return ret\n\n    def to_xyah(self):\n        return self.tlwh_to_xyah(self.tlwh)\n\n    def tracklet_score(self):\n        # score = (1 - np.exp(-0.6 * self.hit_streak)) * np.exp(-0.03 * self.time_by_tracking)\n\n        score = max(0, 1 - np.log(1 + 0.05 * self.time_by_tracking)) * (self.tracklet_len - self.time_by_tracking > 2)\n        # score = max(0, 1 - np.log(1 + 0.05 * self.n_tracking)) * (1 - np.exp(-0.6 * self.hit_streak))\n        return score\n\n    def __repr__(self):\n        return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)\n\n\nclass OnlineTracker(object):\n\n    def __init__(self, model_folder, min_cls_score=0.4, min_ap_dist=0.8, max_time_lost=30, use_tracking=True, use_refind=True, args=None):\n\n        self.min_cls_score = min_cls_score\n        self.min_ap_dist = min_ap_dist\n        self.max_time_lost = max_time_lost\n\n        self.kalman_filter = KalmanFilter()\n\n        self.tracked_stracks = []   # type: list[STrack]\n        self.lost_stracks = []      # type: list[STrack]\n        self.removed_stracks = []   # type: list[STrack]\n\n        self.use_refind = use_refind\n        self.use_tracking = use_tracking\n        self.classifier = None\n        self.reid_model = load_reid_model(model_folder)\n\n        self.frame_id = 0\n        self.args = args\n\n    def update(self, output_results, img_info, img_size, img_file_name):\n        if self.args.dataset == 'mot17':\n            img_file_name = os.path.join(get_yolox_datadir(), 'mot', 'train', img_file_name)\n        else:\n            img_file_name = os.path.join(get_yolox_datadir(), 'dancetrack', 'val', img_file_name)\n        image = cv2.imread(img_file_name)\n        # post process detections\n        output_results = output_results.cpu().numpy()\n        confidences = output_results[:, 4] * output_results[:, 5]\n        \n        bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n        bbox_xyxy = bboxes\n        tlwhs = self._xyxy_to_tlwh_array(bbox_xyxy)\n        remain_inds = confidences > self.min_cls_score\n        tlwhs = tlwhs[remain_inds]\n        det_scores = confidences[remain_inds]\n\n        self.frame_id += 1\n\n        activated_starcks = []\n        refind_stracks = []\n        lost_stracks = []\n        removed_stracks = []\n\n        \"\"\"step 1: prediction\"\"\"\n        for strack in itertools.chain(self.tracked_stracks, self.lost_stracks):\n            strack.predict()\n\n        \"\"\"step 2: scoring and selection\"\"\"\n        if det_scores is None:\n            det_scores = np.ones(len(tlwhs), dtype=float)\n        detections = [STrack(tlwh, score, from_det=True) for tlwh, score in zip(tlwhs, det_scores)]\n        if self.use_tracking:\n            tracks = [STrack(t.self_tracking(image), 0.6 * t.tracklet_score(), from_det=False)\n                        for t in itertools.chain(self.tracked_stracks, self.lost_stracks) if t.is_activated]\n            detections.extend(tracks)\n        rois = np.asarray([d.tlbr for d in detections], dtype=np.float32)\n        scores = np.asarray([d.score for d in detections], dtype=np.float32)\n        # nms\n        if len(detections) > 0:\n            nms_out_index = torchvision.ops.batched_nms(\n            torch.from_numpy(rois),\n            torch.from_numpy(scores.reshape(-1)).to(torch.from_numpy(rois).dtype),\n            torch.zeros_like(torch.from_numpy(scores.reshape(-1))),\n            0.7,\n            )\n            keep = nms_out_index.numpy()\n            mask = np.zeros(len(rois), dtype=np.bool)\n            mask[keep] = True\n            keep = np.where(mask & (scores >= self.min_cls_score))[0]\n            detections = [detections[i] for i in keep]\n            scores = scores[keep]\n            for d, score in zip(detections, scores):\n                d.score = score\n        pred_dets = [d for d in detections if not d.from_det]\n        detections = [d for d in detections if d.from_det]\n\n        # set features\n        tlbrs = [det.tlbr for det in detections]\n        features = extract_reid_features(self.reid_model, image, tlbrs)\n        features = features.cpu().numpy()\n        for i, det in enumerate(detections):\n            det.set_feature(features[i])\n\n        \"\"\"step 3: association for tracked\"\"\"\n        # matching for tracked targets\n        unconfirmed = []\n        tracked_stracks = []  # type: list[STrack]\n        for track in self.tracked_stracks:\n            if not track.is_activated:\n                unconfirmed.append(track)\n            else:\n                tracked_stracks.append(track)\n\n        dists = matching.nearest_reid_distance(tracked_stracks, detections, metric='euclidean')\n        dists = matching.gate_cost_matrix(self.kalman_filter, dists, tracked_stracks, detections)\n        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.min_ap_dist)\n        for itracked, idet in matches:\n            tracked_stracks[itracked].update(detections[idet], self.frame_id, image)\n\n        # matching for missing targets\n        detections = [detections[i] for i in u_detection]\n        dists = matching.nearest_reid_distance(self.lost_stracks, detections, metric='euclidean')\n        dists = matching.gate_cost_matrix(self.kalman_filter, dists, self.lost_stracks, detections)\n        matches, u_lost, u_detection = matching.linear_assignment(dists, thresh=self.min_ap_dist)\n        for ilost, idet in matches:\n            track = self.lost_stracks[ilost]  # type: STrack\n            det = detections[idet]\n            track.re_activate(det, self.frame_id, image, new_id=not self.use_refind)\n            refind_stracks.append(track)\n\n        # remaining tracked\n        # tracked\n        len_det = len(u_detection)\n        detections = [detections[i] for i in u_detection] + pred_dets\n        r_tracked_stracks = [tracked_stracks[i] for i in u_track]\n        if self.args.asso=='hmiou':\n            dists = matching.hmiou_distance(r_tracked_stracks, detections)\n            matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.iou_thresh)\n        else:\n            dists = matching.iou_distance(r_tracked_stracks, detections)\n            matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5)\n            print(\"no use hgiou!\")\n\n        for itracked, idet in matches:\n            r_tracked_stracks[itracked].update(detections[idet], self.frame_id, image, update_feature=True)\n        for it in u_track:\n            track = r_tracked_stracks[it]\n            track.mark_lost()\n            lost_stracks.append(track)\n\n        # unconfirmed\n        detections = [detections[i] for i in u_detection if i < len_det]\n\n\n        if self.args.asso=='hmiou':\n            dists = matching.hmiou_distance(unconfirmed, detections)\n            matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=(self.args.iou_thresh+0.2))\n        else:\n            dists = matching.iou_distance(unconfirmed, detections)\n            matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)\n\n        for itracked, idet in matches:\n            unconfirmed[itracked].update(detections[idet], self.frame_id, image, update_feature=True)\n        for it in u_unconfirmed:\n            track = unconfirmed[it]\n            track.mark_removed()\n            removed_stracks.append(track)\n\n        \"\"\"step 4: init new stracks\"\"\"\n        for inew in u_detection:\n            track = detections[inew]\n            if not track.from_det or track.score < 0.6:\n                continue\n            track.activate(self.kalman_filter, self.frame_id, image)\n            activated_starcks.append(track)\n\n        \"\"\"step 6: update state\"\"\"\n        for track in self.lost_stracks:\n            if self.frame_id - track.end_frame > self.max_time_lost:\n                track.mark_removed()\n                removed_stracks.append(track)\n\n        self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]\n        self.lost_stracks = [t for t in self.lost_stracks if t.state == TrackState.Lost]  # type: list[STrack]\n        self.tracked_stracks.extend(activated_starcks)\n        self.tracked_stracks.extend(refind_stracks)\n        self.lost_stracks.extend(lost_stracks)\n        self.removed_stracks.extend(removed_stracks)\n\n        # output_stracks = self.tracked_stracks + self.lost_stracks\n\n        # get scores of lost tracks\n        output_tracked_stracks = [track for track in self.tracked_stracks if track.is_activated]\n\n        output_stracks = output_tracked_stracks\n\n        return output_stracks\n    \n    @staticmethod\n    def _xyxy_to_tlwh_array(bbox_xyxy):\n        if isinstance(bbox_xyxy, np.ndarray):\n            bbox_tlwh = bbox_xyxy.copy()\n        elif isinstance(bbox_xyxy, torch.Tensor):\n            bbox_tlwh = bbox_xyxy.clone()\n        bbox_tlwh[:, 2] = bbox_xyxy[:, 2] - bbox_xyxy[:, 0]\n        bbox_tlwh[:, 3] = bbox_xyxy[:, 3] - bbox_xyxy[:, 1]\n        return bbox_tlwh\n"
  },
  {
    "path": "trackers/motdt_tracker/motdt_tracker_score.py",
    "content": "import numpy as np\n#from numba import jit\nfrom collections import OrderedDict, deque\nimport itertools\nimport os\nimport cv2\nimport torch\nfrom torch._C import dtype\nimport torchvision\n\nfrom trackers.motdt_tracker import matching\nfrom .kalman_filter import KalmanFilter\nfrom .kalman_filter_score import KalmanFilter_score\nfrom .reid_model import load_reid_model, extract_reid_features\nfrom yolox.data.dataloading import get_yolox_datadir\n\nfrom .basetrack import BaseTrack, TrackState\n\n\nclass STrack(BaseTrack):\n\n    def __init__(self, tlwh, score, max_n_features=100, from_det=True):\n\n        # wait activate\n        self._tlwh = np.asarray(tlwh, dtype=np.float)\n        self.kalman_filter = None\n        self.kalman_filter_score = None\n        self.mean, self.covariance = None, None\n        self.mean_score, self.covariance_score = None, None\n        self.is_activated = False\n\n        self.pre_score = score\n        self.score = score\n        self.max_n_features = max_n_features\n        self.curr_feature = None\n        self.last_feature = None\n        self.features = deque([], maxlen=self.max_n_features)\n\n        # classification\n        self.from_det = from_det\n        self.tracklet_len = 0\n        self.time_by_tracking = 0\n\n        # self-tracking\n        self.tracker = None\n\n    def set_feature(self, feature):\n        if feature is None:\n            return False\n        self.features.append(feature)\n        self.curr_feature = feature\n        self.last_feature = feature\n        # self._p_feature = 0\n        return True\n\n    def predict(self):\n        if self.time_since_update > 0:\n            self.tracklet_len = 0\n\n        self.time_since_update += 1\n\n        mean_state = self.mean.copy()\n        if self.state != TrackState.Tracked:\n            mean_state[7] = 0\n        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)\n        self.mean_score, self.covariance_score = self.kalman_filter_score.predict(self.mean_score, self.covariance_score)\n\n        if self.tracker:\n            self.tracker.update_roi(self.tlwh)\n\n    def self_tracking(self, image):\n        tlwh = self.tracker.predict(image) if self.tracker else self.tlwh\n        return tlwh\n\n    def activate(self, kalman_filter, frame_id, image, KalmanFilter_score):\n        \"\"\"Start a new tracklet\"\"\"\n        self.kalman_filter = kalman_filter  # type: KalmanFilter\n        self.kalman_filter_score = KalmanFilter_score\n        self.track_id = self.next_id()\n        # cx, cy, aspect_ratio, height, dx, dy, da, dh\n        self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))\n        self.mean_score, self.covariance_score = self.kalman_filter_score.initiate(self.score)\n\n        # self.tracker = sot.SingleObjectTracker()\n        # self.tracker.init(image, self.tlwh)\n\n        del self._tlwh\n\n        self.time_since_update = 0\n        self.time_by_tracking = 0\n        self.tracklet_len = 0\n        self.state = TrackState.Tracked\n        # self.is_activated = True\n        self.frame_id = frame_id\n        self.start_frame = frame_id\n\n    def re_activate(self, new_track, frame_id, image, new_id=False):\n        # self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(new_track.tlwh))\n        self.mean, self.covariance = self.kalman_filter.update(\n            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)\n        )\n        self.mean_score, self.covariance_score = self.kalman_filter_score.update(\n            self.mean_score, self.covariance_score, self.score\n        )\n        self.time_since_update = 0\n        self.time_by_tracking = 0\n        self.tracklet_len = 0\n        self.state = TrackState.Tracked\n        self.is_activated = True\n        self.frame_id = frame_id\n        if new_id:\n            self.track_id = self.next_id()\n\n        self.set_feature(new_track.curr_feature)\n        self.score = new_track.score\n        self.pre_score = self.score\n\n    def update(self, new_track, frame_id, image, update_feature=True):\n        \"\"\"\n        Update a matched track\n        :type new_track: STrack\n        :type frame_id: int\n        :type update_feature: bool\n        :return:\n        \"\"\"\n        self.frame_id = frame_id\n        self.time_since_update = 0\n        if new_track.from_det:\n            self.time_by_tracking = 0\n        else:\n            self.time_by_tracking += 1\n        self.tracklet_len += 1\n\n        new_tlwh = new_track.tlwh\n        self.mean, self.covariance = self.kalman_filter.update(\n            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))\n        self.mean_score, self.covariance_score = self.kalman_filter_score.update(\n            self.mean_score, self.covariance_score, self.score)\n        self.state = TrackState.Tracked\n        self.is_activated = True\n\n        self.pre_score = self.score\n        self.score = new_track.score\n\n        if update_feature:\n            self.set_feature(new_track.curr_feature)\n            if self.tracker:\n                self.tracker.update(image, self.tlwh)\n\n    @property\n    #@jit\n    def tlwh(self):\n        \"\"\"Get current position in bounding box format `(top left x, top left y,\n                width, height)`.\n        \"\"\"\n        if self.mean is None:\n            return self._tlwh.copy()\n        ret = self.mean[:4].copy()\n        ret[2] *= ret[3]\n        ret[:2] -= ret[2:] / 2\n        return ret\n\n    @property\n    # @jit(nopython=True)\n    def score_kalman(self):\n        \"\"\"Get current position in bounding box format `(top left x, top left y,\n                width, height)`.\n        \"\"\"\n        if self.mean_score is None:\n            return self.score.copy()\n        ret = self.mean_score[0].copy()\n        return ret\n\n    @property\n    # @jit(nopython=True)\n    def tlbr(self):\n        \"\"\"Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,\n        `(top left, bottom right)`.\n        \"\"\"\n        ret = self.tlwh.copy()\n        ret[2:] += ret[:2]\n        return ret\n\n    @staticmethod\n    #@jit\n    def tlwh_to_xyah(tlwh):\n        \"\"\"Convert bounding box to format `(center x, center y, aspect ratio,\n        height)`, where the aspect ratio is `width / height`.\n        \"\"\"\n        ret = np.asarray(tlwh).copy()\n        ret[:2] += ret[2:] / 2\n        ret[2] /= ret[3]\n        return ret\n\n    def to_xyah(self):\n        return self.tlwh_to_xyah(self.tlwh)\n\n    def tracklet_score(self):\n        # score = (1 - np.exp(-0.6 * self.hit_streak)) * np.exp(-0.03 * self.time_by_tracking)\n\n        score = max(0, 1 - np.log(1 + 0.05 * self.time_by_tracking)) * (self.tracklet_len - self.time_by_tracking > 2)\n        # score = max(0, 1 - np.log(1 + 0.05 * self.n_tracking)) * (1 - np.exp(-0.6 * self.hit_streak))\n        return score\n\n    def __repr__(self):\n        return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)\n\n\nclass OnlineTracker_score(object):\n\n    def __init__(self, model_folder, args, min_cls_score=0.4, min_ap_dist=0.8, max_time_lost=30, use_tracking=True, use_refind=True):\n\n        self.min_cls_score = min_cls_score\n        self.min_ap_dist = min_ap_dist\n        self.max_time_lost = max_time_lost\n\n        self.kalman_filter = KalmanFilter()\n        self.kalman_filter_score = KalmanFilter_score()\n\n        self.tracked_stracks = []   # type: list[STrack]\n        self.lost_stracks = []      # type: list[STrack]\n        self.removed_stracks = []   # type: list[STrack]\n\n        self.use_refind = use_refind\n        self.use_tracking = use_tracking\n        self.classifier = None\n        self.reid_model = load_reid_model(model_folder)\n\n        self.frame_id = 0\n        self.args = args\n\n    def update(self, output_results, img_info, img_size, img_file_name):\n        if self.args.dataset == 'mot17':\n            img_file_name = os.path.join(get_yolox_datadir(), 'mot', 'train', img_file_name)\n        else:\n            img_file_name = os.path.join(get_yolox_datadir(), 'dancetrack', 'val', img_file_name)\n        image = cv2.imread(img_file_name)\n        # post process detections\n        output_results = output_results.cpu().numpy()\n        confidences = output_results[:, 4] * output_results[:, 5]\n        \n        bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n        bbox_xyxy = bboxes\n        tlwhs = self._xyxy_to_tlwh_array(bbox_xyxy)\n        remain_inds = confidences > self.min_cls_score\n        tlwhs = tlwhs[remain_inds]\n        det_scores = confidences[remain_inds]\n\n        self.frame_id += 1\n\n        activated_starcks = []\n        refind_stracks = []\n        lost_stracks = []\n        removed_stracks = []\n\n        \"\"\"step 1: prediction\"\"\"\n        for strack in itertools.chain(self.tracked_stracks, self.lost_stracks):\n            strack.predict()\n\n        \"\"\"step 2: scoring and selection\"\"\"\n        if det_scores is None:\n            det_scores = np.ones(len(tlwhs), dtype=float)\n        detections = [STrack(tlwh, score, from_det=True) for tlwh, score in zip(tlwhs, det_scores)]\n        if self.use_tracking:\n            tracks = [STrack(t.self_tracking(image), 0.6 * t.tracklet_score(), from_det=False)\n                        for t in itertools.chain(self.tracked_stracks, self.lost_stracks) if t.is_activated]\n            detections.extend(tracks)\n        rois = np.asarray([d.tlbr for d in detections], dtype=np.float32)\n        scores = np.asarray([d.score for d in detections], dtype=np.float32)\n        # nms\n        if len(detections) > 0:\n            nms_out_index = torchvision.ops.batched_nms(\n            torch.from_numpy(rois),\n            torch.from_numpy(scores.reshape(-1)).to(torch.from_numpy(rois).dtype),\n            torch.zeros_like(torch.from_numpy(scores.reshape(-1))),\n            0.7,\n            )\n            keep = nms_out_index.numpy()\n            mask = np.zeros(len(rois), dtype=np.bool)\n            mask[keep] = True\n            keep = np.where(mask & (scores >= self.min_cls_score))[0]\n            detections = [detections[i] for i in keep]\n            scores = scores[keep]\n            for d, score in zip(detections, scores):\n                d.score = score\n        pred_dets = [d for d in detections if not d.from_det]\n        detections = [d for d in detections if d.from_det]\n\n        # set features\n        tlbrs = [det.tlbr for det in detections]\n        features = extract_reid_features(self.reid_model, image, tlbrs)\n        features = features.cpu().numpy()\n        for i, det in enumerate(detections):\n            det.set_feature(features[i])\n\n        \"\"\"step 3: association for tracked\"\"\"\n        # matching for tracked targets\n        unconfirmed = []\n        tracked_stracks = []  # type: list[STrack]\n        for track in self.tracked_stracks:\n            if not track.is_activated:\n                unconfirmed.append(track)\n            else:\n                tracked_stracks.append(track)\n        # only used in first association\n        dists = matching.nearest_reid_distance_with_score(tracked_stracks, detections, metric='euclidean', min_cls_score=self.min_cls_score)\n        dists = matching.gate_cost_matrix(self.kalman_filter, dists, tracked_stracks, detections)\n\n        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=(self.min_ap_dist+0.1))\n\n        for itracked, idet in matches:\n            tracked_stracks[itracked].update(detections[idet], self.frame_id, image)\n\n        # matching for missing targets\n        detections = [detections[i] for i in u_detection]\n        dists = matching.nearest_reid_distance(self.lost_stracks, detections, metric='euclidean')\n        dists = matching.gate_cost_matrix(self.kalman_filter, dists, self.lost_stracks, detections)\n        matches, u_lost, u_detection = matching.linear_assignment(dists, thresh=self.min_ap_dist)\n        for ilost, idet in matches:\n            track = self.lost_stracks[ilost]  # type: STrack\n            det = detections[idet]\n            track.re_activate(det, self.frame_id, image, new_id=not self.use_refind)\n            refind_stracks.append(track)\n\n        # remaining tracked\n        # tracked\n        len_det = len(u_detection)\n        detections = [detections[i] for i in u_detection] + pred_dets\n        r_tracked_stracks = [tracked_stracks[i] for i in u_track]\n        dists = matching.iou_distance(r_tracked_stracks, detections)\n        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5)\n        for itracked, idet in matches:\n            r_tracked_stracks[itracked].update(detections[idet], self.frame_id, image, update_feature=True)\n        for it in u_track:\n            track = r_tracked_stracks[it]\n            track.mark_lost()\n            lost_stracks.append(track)\n\n        # unconfirmed\n        detections = [detections[i] for i in u_detection if i < len_det]\n        dists = matching.iou_distance(unconfirmed, detections)\n        matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)\n        for itracked, idet in matches:\n            unconfirmed[itracked].update(detections[idet], self.frame_id, image, update_feature=True)\n        for it in u_unconfirmed:\n            track = unconfirmed[it]\n            track.mark_removed()\n            removed_stracks.append(track)\n\n        \"\"\"step 4: init new stracks\"\"\"\n        for inew in u_detection:\n            track = detections[inew]\n            if not track.from_det or track.score < 0.6:\n                continue\n            track.activate(self.kalman_filter, self.frame_id, image, self.kalman_filter_score)\n            activated_starcks.append(track)\n\n        \"\"\"step 6: update state\"\"\"\n        for track in self.lost_stracks:\n            if self.frame_id - track.end_frame > self.max_time_lost:\n                track.mark_removed()\n                removed_stracks.append(track)\n\n        self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]\n        self.lost_stracks = [t for t in self.lost_stracks if t.state == TrackState.Lost]  # type: list[STrack]\n        self.tracked_stracks.extend(activated_starcks)\n        self.tracked_stracks.extend(refind_stracks)\n        self.lost_stracks.extend(lost_stracks)\n        self.removed_stracks.extend(removed_stracks)\n\n        # output_stracks = self.tracked_stracks + self.lost_stracks\n\n        # get scores of lost tracks\n        output_tracked_stracks = [track for track in self.tracked_stracks if track.is_activated]\n\n        output_stracks = output_tracked_stracks\n\n        return output_stracks\n    \n    @staticmethod\n    def _xyxy_to_tlwh_array(bbox_xyxy):\n        if isinstance(bbox_xyxy, np.ndarray):\n            bbox_tlwh = bbox_xyxy.copy()\n        elif isinstance(bbox_xyxy, torch.Tensor):\n            bbox_tlwh = bbox_xyxy.clone()\n        bbox_tlwh[:, 2] = bbox_xyxy[:, 2] - bbox_xyxy[:, 0]\n        bbox_tlwh[:, 3] = bbox_xyxy[:, 3] - bbox_xyxy[:, 1]\n        return bbox_tlwh\n"
  },
  {
    "path": "trackers/motdt_tracker/reid_model.py",
    "content": "import cv2\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport torch.nn as nn\nimport pickle\nimport os\nfrom torch.nn.modules import CrossMapLRN2d as SpatialCrossMapLRN\n#from torch.legacy.nn import SpatialCrossMapLRN as SpatialCrossMapLRNOld\nfrom torch.autograd import Function, Variable\nfrom torch.nn import Module\n\n\ndef clip_boxes(boxes, im_shape):\n    \"\"\"\n    Clip boxes to image boundaries.\n    \"\"\"\n    boxes = np.asarray(boxes)\n    if boxes.shape[0] == 0:\n        return boxes\n    boxes = np.copy(boxes)\n    # x1 >= 0\n    boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)\n    # y1 >= 0\n    boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0)\n    # x2 < im_shape[1]\n    boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0)\n    # y2 < im_shape[0]\n    boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0)\n    return boxes\n\n\ndef load_net(fname, net, prefix='', load_state_dict=False):\n    import h5py\n    with h5py.File(fname, mode='r') as h5f:\n        h5f_is_module = True\n        for k in h5f.keys():\n            if not str(k).startswith('module.'):\n                h5f_is_module = False\n                break\n        if prefix == '' and not isinstance(net, nn.DataParallel) and h5f_is_module:\n            prefix = 'module.'\n\n        for k, v in net.state_dict().items():\n            k = prefix + k\n            if k in h5f:\n                param = torch.from_numpy(np.asarray(h5f[k]))\n                if v.size() != param.size():\n                    print('Inconsistent shape: {}, {}'.format(v.size(), param.size()))\n                else:\n                    v.copy_(param)\n            else:\n                print.warning('No layer: {}'.format(k))\n\n        epoch = h5f.attrs['epoch'] if 'epoch' in h5f.attrs else -1\n\n        if not load_state_dict:\n            if 'learning_rates' in h5f.attrs:\n                lr = h5f.attrs['learning_rates']\n            else:\n                lr = h5f.attrs.get('lr', -1)\n                lr = np.asarray([lr] if lr > 0 else [], dtype=np.float)\n\n            return epoch, lr\n\n        state_file = fname + '.optimizer_state.pk'\n        if os.path.isfile(state_file):\n            with open(state_file, 'rb') as f:\n                state_dicts = pickle.load(f)\n                if not isinstance(state_dicts, list):\n                    state_dicts = [state_dicts]\n        else:\n            state_dicts = None\n        return epoch, state_dicts\n\n\n# class SpatialCrossMapLRNFunc(Function):\n\n#     def __init__(self, size, alpha=1e-4, beta=0.75, k=1):\n#         self.size = size\n#         self.alpha = alpha\n#         self.beta = beta\n#         self.k = k\n\n#     def forward(self, input):\n#         self.save_for_backward(input)\n#         self.lrn = SpatialCrossMapLRNOld(self.size, self.alpha, self.beta, self.k)\n#         self.lrn.type(input.type())\n#         return self.lrn.forward(input)\n\n#     def backward(self, grad_output):\n#         input, = self.saved_tensors\n#         return self.lrn.backward(input, grad_output)\n\n\n# # use this one instead\n# class SpatialCrossMapLRN(Module):\n#     def __init__(self, size, alpha=1e-4, beta=0.75, k=1):\n#         super(SpatialCrossMapLRN, self).__init__()\n#         self.size = size\n#         self.alpha = alpha\n#         self.beta = beta\n#         self.k = k\n\n#     def forward(self, input):\n#         return SpatialCrossMapLRNFunc(self.size, self.alpha, self.beta, self.k)(input)\n\n\nclass Inception(nn.Module):\n    def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):\n        super(Inception, self).__init__()\n        # 1x1 conv branch\n        self.b1 = nn.Sequential(\n            nn.Conv2d(in_planes, n1x1, kernel_size=1),\n            nn.ReLU(True),\n        )\n\n        # 1x1 conv -> 3x3 conv branch\n        self.b2 = nn.Sequential(\n            nn.Conv2d(in_planes, n3x3red, kernel_size=1),\n            nn.ReLU(True),\n            nn.Conv2d(n3x3red, n3x3, kernel_size=3, padding=1),\n            nn.ReLU(True),\n        )\n\n        # 1x1 conv -> 5x5 conv branch\n        self.b3 = nn.Sequential(\n            nn.Conv2d(in_planes, n5x5red, kernel_size=1),\n            nn.ReLU(True),\n\n            nn.Conv2d(n5x5red, n5x5, kernel_size=5, padding=2),\n            nn.ReLU(True),\n        )\n\n        # 3x3 pool -> 1x1 conv branch\n        self.b4 = nn.Sequential(\n            nn.MaxPool2d(3, stride=1, padding=1),\n\n            nn.Conv2d(in_planes, pool_planes, kernel_size=1),\n            nn.ReLU(True),\n        )\n\n    def forward(self, x):\n        y1 = self.b1(x)\n        y2 = self.b2(x)\n        y3 = self.b3(x)\n        y4 = self.b4(x)\n        return torch.cat([y1,y2,y3,y4], 1)\n\n\nclass GoogLeNet(nn.Module):\n\n    output_channels = 832\n\n    def __init__(self):\n        super(GoogLeNet, self).__init__()\n        self.pre_layers = nn.Sequential(\n            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),\n            nn.ReLU(True),\n\n            nn.MaxPool2d(3, stride=2, ceil_mode=True),\n            SpatialCrossMapLRN(5),\n\n            nn.Conv2d(64, 64, 1),\n            nn.ReLU(True),\n\n            nn.Conv2d(64, 192, 3, padding=1),\n            nn.ReLU(True),\n\n            SpatialCrossMapLRN(5),\n            nn.MaxPool2d(3, stride=2, ceil_mode=True),\n        )\n\n        self.a3 = Inception(192,  64,  96, 128, 16, 32, 32)\n        self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)\n\n        self.maxpool = nn.MaxPool2d(3, stride=2, ceil_mode=True)\n\n        self.a4 = Inception(480, 192,  96, 208, 16,  48,  64)\n        self.b4 = Inception(512, 160, 112, 224, 24,  64,  64)\n        self.c4 = Inception(512, 128, 128, 256, 24,  64,  64)\n        self.d4 = Inception(512, 112, 144, 288, 32,  64,  64)\n        self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)\n\n    def forward(self, x):\n        out = self.pre_layers(x)\n        out = self.a3(out)\n        out = self.b3(out)\n        out = self.maxpool(out)\n        out = self.a4(out)\n        out = self.b4(out)\n        out = self.c4(out)\n        out = self.d4(out)\n        out = self.e4(out)\n\n        return out\n\n\nclass Model(nn.Module):\n    def __init__(self, n_parts=8):\n        super(Model, self).__init__()\n        self.n_parts = n_parts\n\n        self.feat_conv = GoogLeNet()\n        self.conv_input_feat = nn.Conv2d(self.feat_conv.output_channels, 512, 1)\n\n        # part net\n        self.conv_att = nn.Conv2d(512, self.n_parts, 1)\n\n        for i in range(self.n_parts):\n            setattr(self, 'linear_feature{}'.format(i+1), nn.Linear(512, 64))\n\n    def forward(self, x):\n        feature = self.feat_conv(x)\n        feature = self.conv_input_feat(feature)\n\n        att_weights = torch.sigmoid(self.conv_att(feature))\n\n        linear_feautres = []\n        for i in range(self.n_parts):\n            masked_feature = feature * torch.unsqueeze(att_weights[:, i], 1)\n            pooled_feature = F.avg_pool2d(masked_feature, masked_feature.size()[2:4])\n            linear_feautres.append(\n                getattr(self, 'linear_feature{}'.format(i+1))(pooled_feature.view(pooled_feature.size(0), -1))\n            )\n\n        concat_features = torch.cat(linear_feautres, 1)\n        normed_feature = concat_features / torch.clamp(torch.norm(concat_features, 2, 1, keepdim=True), min=1e-6)\n\n        return normed_feature\n\n\ndef load_reid_model(ckpt):\n    model = Model(n_parts=8)\n    model.inp_size = (80, 160)\n    load_net(ckpt, model)\n    print('Load ReID model from {}'.format(ckpt))\n\n    model = model.cuda()\n    model.eval()\n    return model\n\n\ndef im_preprocess(image):\n    image = np.asarray(image, np.float32)\n    image -= np.array([104, 117, 123], dtype=np.float32).reshape(1, 1, -1)\n    image = image.transpose((2, 0, 1))\n    return image\n\n\ndef extract_image_patches(image, bboxes):\n    bboxes = np.round(bboxes).astype(np.int)\n    bboxes = clip_boxes(bboxes, image.shape)\n    patches = [image[box[1]:box[3], box[0]:box[2]] for box in bboxes]\n    return patches\n\n\ndef extract_reid_features(reid_model, image, tlbrs):\n    if len(tlbrs) == 0:\n        return torch.FloatTensor()\n\n    patches = extract_image_patches(image, tlbrs)\n    patches = np.asarray([im_preprocess(cv2.resize(p, reid_model.inp_size)) for p in patches], dtype=np.float32)\n\n    with torch.no_grad():\n        im_var = Variable(torch.from_numpy(patches))\n        im_var = im_var.cuda()\n        features = reid_model(im_var).data\n    return features"
  },
  {
    "path": "trackers/ocsort_tracker/association.py",
    "content": "import os\nimport numpy as np\n\n\ndef iou_batch(bboxes1, bboxes2):\n    \"\"\"\n    From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]\n    \"\"\"\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n    \n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    o = wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])                                      \n        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)                                              \n    return(o)  \n\n\ndef giou_batch(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    union = ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])                                      \n        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)  \n    iou = wh / union\n\n    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])\n    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])\n    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n    wc = xxc2 - xxc1 \n    hc = yyc2 - yyc1 \n    assert((wc > 0).all() and (hc > 0).all())\n    area_enclose = wc * hc \n    giou = iou - (area_enclose - union) / area_enclose\n    giou = (giou + 1.)/2.0 # resize from (-1,1) to (0,1)\n    return giou\n\n\ndef diou_batch(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    # calculate the intersection box\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    union = ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])                                      \n        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh) \n    iou = wh / union\n    centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0\n    centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0\n    centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0\n    centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0\n\n    inner_diag = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2\n\n    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])\n    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])\n    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n\n    outer_diag = (xxc2 - xxc1) ** 2 + (yyc2 - yyc1) ** 2\n    diou = iou - inner_diag / outer_diag\n\n    return (diou + 1) / 2.0 # resize from (-1,1) to (0,1)\n\ndef ciou_batch(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    # calculate the intersection box\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    union = ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])                                      \n        + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh) \n    iou = wh / union\n\n    centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0\n    centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0\n    centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0\n    centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0\n\n    inner_diag = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2\n\n    xxc1 = np.minimum(bboxes1[..., 0], bboxes2[..., 0])\n    yyc1 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    xxc2 = np.maximum(bboxes1[..., 2], bboxes2[..., 2])\n    yyc2 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n\n    outer_diag = (xxc2 - xxc1) ** 2 + (yyc2 - yyc1) ** 2\n    \n    w1 = bboxes1[..., 2] - bboxes1[..., 0]\n    h1 = bboxes1[..., 3] - bboxes1[..., 1]\n    w2 = bboxes2[..., 2] - bboxes2[..., 0]\n    h2 = bboxes2[..., 3] - bboxes2[..., 1]\n\n    # prevent dividing over zero. add one pixel shift\n    h2 = h2 + 1.\n    h1 = h1 + 1.\n    arctan = np.arctan(w2/h2) - np.arctan(w1/h1)\n    v = (4 / (np.pi ** 2)) * (arctan ** 2)\n    S = 1 - iou \n    alpha = v / (S+v)\n    ciou = iou - inner_diag / outer_diag - alpha * v\n    \n    return (ciou + 1) / 2.0 # resize from (-1,1) to (0,1)\n\n\ndef ct_dist(bboxes1, bboxes2):\n    \"\"\"\n        Measure the center distance between two sets of bounding boxes,\n        this is a coarse implementation, we don't recommend using it only\n        for association, which can be unstable and sensitive to frame rate\n        and object speed.\n    \"\"\"\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    centerx1 = (bboxes1[..., 0] + bboxes1[..., 2]) / 2.0\n    centery1 = (bboxes1[..., 1] + bboxes1[..., 3]) / 2.0\n    centerx2 = (bboxes2[..., 0] + bboxes2[..., 2]) / 2.0\n    centery2 = (bboxes2[..., 1] + bboxes2[..., 3]) / 2.0\n\n    ct_dist2 = (centerx1 - centerx2) ** 2 + (centery1 - centery2) ** 2\n\n    ct_dist = np.sqrt(ct_dist2)\n\n    # The linear rescaling is a naive version and needs more study\n    ct_dist = ct_dist / ct_dist.max()\n    return ct_dist.max() - ct_dist # resize to (0,1)\n\n\n\ndef speed_direction_batch(dets, tracks):\n    tracks = tracks[..., np.newaxis]\n    CX1, CY1 = (dets[:,0] + dets[:,2])/2.0, (dets[:,1]+dets[:,3])/2.0\n    CX2, CY2 = (tracks[:,0] + tracks[:,2]) /2.0, (tracks[:,1]+tracks[:,3])/2.0\n    dx = CX1 - CX2 \n    dy = CY1 - CY2 \n    norm = np.sqrt(dx**2 + dy**2) + 1e-6\n    dx = dx / norm \n    dy = dy / norm\n    return dy, dx # size: num_track x num_det\n\n\ndef linear_assignment(cost_matrix):\n    try:\n        import lap\n        _, x, y = lap.lapjv(cost_matrix, extend_cost=True)\n        return np.array([[y[i],i] for i in x if i >= 0]) #\n    except ImportError:\n        from scipy.optimize import linear_sum_assignment\n        x, y = linear_sum_assignment(cost_matrix)\n        return np.array(list(zip(x, y)))\n\n\ndef associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):\n    \"\"\"\n    Assigns detections to tracked object (both represented as bounding boxes)\n    Returns 3 lists of matches, unmatched_detections and unmatched_trackers\n    \"\"\"\n    if(len(trackers)==0):\n        return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)\n\n    iou_matrix = iou_batch(detections, trackers)\n\n    if min(iou_matrix.shape) > 0:\n        a = (iou_matrix > iou_threshold).astype(np.int32)\n        if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n            matched_indices = np.stack(np.where(a), axis=1)\n        else:\n            matched_indices = linear_assignment(-iou_matrix)\n    else:\n        matched_indices = np.empty(shape=(0,2))\n\n    unmatched_detections = []\n    for d, det in enumerate(detections):\n        if(d not in matched_indices[:,0]):\n            unmatched_detections.append(d)\n    unmatched_trackers = []\n    for t, trk in enumerate(trackers):\n        if(t not in matched_indices[:,1]):\n            unmatched_trackers.append(t)\n\n    #filter out matched with low IOU\n    matches = []\n    for m in matched_indices:\n        if(iou_matrix[m[0], m[1]]<iou_threshold):\n            unmatched_detections.append(m[0])\n            unmatched_trackers.append(m[1])\n        else:\n            matches.append(m.reshape(1,2))\n    if(len(matches)==0):\n        matches = np.empty((0,2),dtype=int)\n    else:\n        matches = np.concatenate(matches,axis=0)\n\n    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\n\ndef associate(detections, trackers, iou_threshold, velocities, previous_obs, vdc_weight):    \n    if(len(trackers)==0):\n        return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)\n\n    Y, X = speed_direction_batch(detections, previous_obs)\n    inertia_Y, inertia_X = velocities[:,0], velocities[:,1]\n    inertia_Y = np.repeat(inertia_Y[:, np.newaxis], Y.shape[1], axis=1)\n    inertia_X = np.repeat(inertia_X[:, np.newaxis], X.shape[1], axis=1)\n    diff_angle_cos = inertia_X * X + inertia_Y * Y\n    diff_angle_cos = np.clip(diff_angle_cos, a_min=-1, a_max=1)\n    diff_angle = np.arccos(diff_angle_cos)\n    diff_angle = (np.pi /2.0 - np.abs(diff_angle)) / np.pi\n\n    valid_mask = np.ones(previous_obs.shape[0])\n    valid_mask[np.where(previous_obs[:,4]<0)] = 0\n    \n    iou_matrix = iou_batch(detections, trackers)\n    scores = np.repeat(detections[:,-1][:, np.newaxis], trackers.shape[0], axis=1)\n    # iou_matrix = iou_matrix * scores # a trick sometiems works, we don't encourage this\n    valid_mask = np.repeat(valid_mask[:, np.newaxis], X.shape[1], axis=1)\n\n    angle_diff_cost = (valid_mask * diff_angle) * vdc_weight\n    angle_diff_cost = angle_diff_cost.T\n    angle_diff_cost = angle_diff_cost * scores\n\n    if min(iou_matrix.shape) > 0:\n        a = (iou_matrix > iou_threshold).astype(np.int32)\n        if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n            matched_indices = np.stack(np.where(a), axis=1)\n        else:\n            matched_indices = linear_assignment(-(iou_matrix+angle_diff_cost))\n    else:\n        matched_indices = np.empty(shape=(0,2))\n\n    unmatched_detections = []\n    for d, det in enumerate(detections):\n        if(d not in matched_indices[:,0]):\n            unmatched_detections.append(d)\n    unmatched_trackers = []\n    for t, trk in enumerate(trackers):\n        if(t not in matched_indices[:,1]):\n            unmatched_trackers.append(t)\n\n    # filter out matched with low IOU\n    matches = []\n    for m in matched_indices:\n        if(iou_matrix[m[0], m[1]]<iou_threshold):\n            unmatched_detections.append(m[0])\n            unmatched_trackers.append(m[1])\n        else:\n            matches.append(m.reshape(1,2))\n    if(len(matches)==0):\n        matches = np.empty((0,2),dtype=int)\n    else:\n        matches = np.concatenate(matches,axis=0)\n\n    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\n\ndef associate_kitti(detections, trackers, det_cates, iou_threshold, \n        velocities, previous_obs, vdc_weight):\n    if(len(trackers)==0):\n        return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)\n\n    \"\"\"\n        Cost from the velocity direction consistency\n    \"\"\"\n    Y, X = speed_direction_batch(detections, previous_obs)\n    inertia_Y, inertia_X = velocities[:,0], velocities[:,1]\n    inertia_Y = np.repeat(inertia_Y[:, np.newaxis], Y.shape[1], axis=1)\n    inertia_X = np.repeat(inertia_X[:, np.newaxis], X.shape[1], axis=1)\n    diff_angle_cos = inertia_X * X + inertia_Y * Y\n    diff_angle_cos = np.clip(diff_angle_cos, a_min=-1, a_max=1)\n    diff_angle = np.arccos(diff_angle_cos)\n    diff_angle = (np.pi /2.0 - np.abs(diff_angle)) / np.pi\n\n    valid_mask = np.ones(previous_obs.shape[0])\n    valid_mask[np.where(previous_obs[:,4]<0)]=0  \n    valid_mask = np.repeat(valid_mask[:, np.newaxis], X.shape[1], axis=1)\n\n    scores = np.repeat(detections[:,-1][:, np.newaxis], trackers.shape[0], axis=1)\n    angle_diff_cost = (valid_mask * diff_angle) * vdc_weight\n    angle_diff_cost = angle_diff_cost.T\n    angle_diff_cost = angle_diff_cost * scores\n\n    \"\"\"\n        Cost from IoU\n    \"\"\"\n    iou_matrix = iou_batch(detections, trackers)\n    \n\n    \"\"\"\n        With multiple categories, generate the cost for catgory mismatch\n    \"\"\"\n    num_dets = detections.shape[0]\n    num_trk = trackers.shape[0]\n    cate_matrix = np.zeros((num_dets, num_trk))\n    for i in range(num_dets):\n            for j in range(num_trk):\n                if det_cates[i] != trackers[j, 4]:\n                        cate_matrix[i][j] = -1e6\n    \n    cost_matrix = - iou_matrix -angle_diff_cost - cate_matrix\n\n    if min(iou_matrix.shape) > 0:\n        a = (iou_matrix > iou_threshold).astype(np.int32)\n        if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n            matched_indices = np.stack(np.where(a), axis=1)\n        else:\n            matched_indices = linear_assignment(cost_matrix)\n    else:\n        matched_indices = np.empty(shape=(0,2))\n\n    unmatched_detections = []\n    for d, det in enumerate(detections):\n        if(d not in matched_indices[:,0]):\n            unmatched_detections.append(d)\n    unmatched_trackers = []\n    for t, trk in enumerate(trackers):\n        if(t not in matched_indices[:,1]):\n            unmatched_trackers.append(t)\n\n    #filter out matched with low IOU\n    matches = []\n    for m in matched_indices:\n        if(iou_matrix[m[0], m[1]]<iou_threshold):\n            unmatched_detections.append(m[0])\n            unmatched_trackers.append(m[1])\n        else:\n            matches.append(m.reshape(1,2))\n    if(len(matches)==0):\n        matches = np.empty((0,2),dtype=int)\n    else:\n        matches = np.concatenate(matches,axis=0)\n\n    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)"
  },
  {
    "path": "trackers/ocsort_tracker/kalmanfilter.py",
    "content": "# -*- coding: utf-8 -*-\n# pylint: disable=invalid-name, too-many-arguments, too-many-branches,\n# pylint: disable=too-many-locals, too-many-instance-attributes, too-many-lines\n\n\"\"\"\nThis module implements the linear Kalman filter in both an object\noriented and procedural form. The KalmanFilter class implements\nthe filter by storing the various matrices in instance variables,\nminimizing the amount of bookkeeping you have to do.\nAll Kalman filters operate with a predict->update cycle. The\npredict step, implemented with the method or function predict(),\nuses the state transition matrix F to predict the state in the next\ntime period (epoch). The state is stored as a gaussian (x, P), where\nx is the state (column) vector, and P is its covariance. Covariance\nmatrix Q specifies the process covariance. In Bayesian terms, this\nprediction is called the *prior*, which you can think of colloquially\nas the estimate prior to incorporating the measurement.\nThe update step, implemented with the method or function `update()`,\nincorporates the measurement z with covariance R, into the state\nestimate (x, P). The class stores the system uncertainty in S,\nthe innovation (residual between prediction and measurement in\nmeasurement space) in y, and the Kalman gain in k. The procedural\nform returns these variables to you. In Bayesian terms this computes\nthe *posterior* - the estimate after the information from the\nmeasurement is incorporated.\nWhether you use the OO form or procedural form is up to you. If\nmatrices such as H, R, and F are changing each epoch, you'll probably\nopt to use the procedural form. If they are unchanging, the OO\nform is perhaps easier to use since you won't need to keep track\nof these matrices. This is especially useful if you are implementing\nbanks of filters or comparing various KF designs for performance;\na trivial coding bug could lead to using the wrong sets of matrices.\nThis module also offers an implementation of the RTS smoother, and\nother helper functions, such as log likelihood computations.\nThe Saver class allows you to easily save the state of the\nKalmanFilter class after every update\nThis module expects NumPy arrays for all values that expect\narrays, although in a few cases, particularly method parameters,\nit will accept types that convert to NumPy arrays, such as lists\nof lists. These exceptions are documented in the method or function.\nExamples\n--------\nThe following example constructs a constant velocity kinematic\nfilter, filters noisy data, and plots the results. It also demonstrates\nusing the Saver class to save the state of the filter at each epoch.\n.. code-block:: Python\n    import matplotlib.pyplot as plt\n    import numpy as np\n    from filterpy.kalman import KalmanFilter\n    from filterpy.common import Q_discrete_white_noise, Saver\n    r_std, q_std = 2., 0.003\n    cv = KalmanFilter(dim_x=2, dim_z=1)\n    cv.x = np.array([[0., 1.]]) # position, velocity\n    cv.F = np.array([[1, dt],[ [0, 1]])\n    cv.R = np.array([[r_std^^2]])\n    f.H = np.array([[1., 0.]])\n    f.P = np.diag([.1^^2, .03^^2)\n    f.Q = Q_discrete_white_noise(2, dt, q_std**2)\n    saver = Saver(cv)\n    for z in range(100):\n        cv.predict()\n        cv.update([z + randn() * r_std])\n        saver.save() # save the filter's state\n    saver.to_array()\n    plt.plot(saver.x[:, 0])\n    # plot all of the priors\n    plt.plot(saver.x_prior[:, 0])\n    # plot mahalanobis distance\n    plt.figure()\n    plt.plot(saver.mahalanobis)\nThis code implements the same filter using the procedural form\n    x = np.array([[0., 1.]]) # position, velocity\n    F = np.array([[1, dt],[ [0, 1]])\n    R = np.array([[r_std^^2]])\n    H = np.array([[1., 0.]])\n    P = np.diag([.1^^2, .03^^2)\n    Q = Q_discrete_white_noise(2, dt, q_std**2)\n    for z in range(100):\n        x, P = predict(x, P, F=F, Q=Q)\n        x, P = update(x, P, z=[z + randn() * r_std], R=R, H=H)\n        xs.append(x[0, 0])\n    plt.plot(xs)\nFor more examples see the test subdirectory, or refer to the\nbook cited below. In it I both teach Kalman filtering from basic\nprinciples, and teach the use of this library in great detail.\nFilterPy library.\nhttp://github.com/rlabbe/filterpy\nDocumentation at:\nhttps://filterpy.readthedocs.org\nSupporting book at:\nhttps://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\nThis is licensed under an MIT license. See the readme.MD file\nfor more information.\nCopyright 2014-2018 Roger R Labbe Jr.\n\"\"\"\n\nfrom __future__ import absolute_import, division\n\nfrom copy import deepcopy\nfrom math import log, exp, sqrt\nimport sys\nimport numpy as np\nfrom numpy import dot, zeros, eye, isscalar, shape\nimport numpy.linalg as linalg\nfrom filterpy.stats import logpdf\nfrom filterpy.common import pretty_str, reshape_z\n\n\nclass KalmanFilterNew(object):\n    \"\"\" Implements a Kalman filter. You are responsible for setting the\n    various state variables to reasonable values; the defaults  will\n    not give you a functional filter.\n    For now the best documentation is my free book Kalman and Bayesian\n    Filters in Python [2]_. The test files in this directory also give you a\n    basic idea of use, albeit without much description.\n    In brief, you will first construct this object, specifying the size of\n    the state vector with dim_x and the size of the measurement vector that\n    you will be using with dim_z. These are mostly used to perform size checks\n    when you assign values to the various matrices. For example, if you\n    specified dim_z=2 and then try to assign a 3x3 matrix to R (the\n    measurement noise matrix you will get an assert exception because R\n    should be 2x2. (If for whatever reason you need to alter the size of\n    things midstream just use the underscore version of the matrices to\n    assign directly: your_filter._R = a_3x3_matrix.)\n    After construction the filter will have default matrices created for you,\n    but you must specify the values for each. It’s usually easiest to just\n    overwrite them rather than assign to each element yourself. This will be\n    clearer in the example below. All are of type numpy.array.\n    Examples\n    --------\n    Here is a filter that tracks position and velocity using a sensor that only\n    reads position.\n    First construct the object with the required dimensionality. Here the state\n    (`dim_x`) has 2 coefficients (position and velocity), and the measurement\n    (`dim_z`) has one. In FilterPy `x` is the state, `z` is the measurement.\n    .. code::\n        from filterpy.kalman import KalmanFilter\n        f = KalmanFilter (dim_x=2, dim_z=1)\n    Assign the initial value for the state (position and velocity). You can do this\n    with a two dimensional array like so:\n        .. code::\n            f.x = np.array([[2.],    # position\n                            [0.]])   # velocity\n    or just use a one dimensional array, which I prefer doing.\n    .. code::\n        f.x = np.array([2., 0.])\n    Define the state transition matrix:\n        .. code::\n            f.F = np.array([[1.,1.],\n                            [0.,1.]])\n    Define the measurement function. Here we need to convert a position-velocity\n    vector into just a position vector, so we use:\n        .. code::\n        f.H = np.array([[1., 0.]])\n    Define the state's covariance matrix P. \n    .. code::\n        f.P = np.array([[1000.,    0.],\n                        [   0., 1000.] ])\n    Now assign the measurement noise. Here the dimension is 1x1, so I can\n    use a scalar\n    .. code::\n        f.R = 5\n    I could have done this instead:\n    .. code::\n        f.R = np.array([[5.]])\n    Note that this must be a 2 dimensional array.\n    Finally, I will assign the process noise. Here I will take advantage of\n    another FilterPy library function:\n    .. code::\n        from filterpy.common import Q_discrete_white_noise\n        f.Q = Q_discrete_white_noise(dim=2, dt=0.1, var=0.13)\n    Now just perform the standard predict/update loop:\n    .. code::\n        while some_condition_is_true:\n            z = get_sensor_reading()\n            f.predict()\n            f.update(z)\n            do_something_with_estimate (f.x)\n    **Procedural Form**\n    This module also contains stand alone functions to perform Kalman filtering.\n    Use these if you are not a fan of objects.\n    **Example**\n    .. code::\n        while True:\n            z, R = read_sensor()\n            x, P = predict(x, P, F, Q)\n            x, P = update(x, P, z, R, H)\n    See my book Kalman and Bayesian Filters in Python [2]_.\n    You will have to set the following attributes after constructing this\n    object for the filter to perform properly. Please note that there are\n    various checks in place to ensure that you have made everything the\n    'correct' size. However, it is possible to provide incorrectly sized\n    arrays such that the linear algebra can not perform an operation.\n    It can also fail silently - you can end up with matrices of a size that\n    allows the linear algebra to work, but are the wrong shape for the problem\n    you are trying to solve.\n    Parameters\n    ----------\n    dim_x : int\n        Number of state variables for the Kalman filter. For example, if\n        you are tracking the position and velocity of an object in two\n        dimensions, dim_x would be 4.\n        This is used to set the default size of P, Q, and u\n    dim_z : int\n        Number of of measurement inputs. For example, if the sensor\n        provides you with position in (x,y), dim_z would be 2.\n    dim_u : int (optional)\n        size of the control input, if it is being used.\n        Default value of 0 indicates it is not used.\n    compute_log_likelihood : bool (default = True)\n        Computes log likelihood by default, but this can be a slow\n        computation, so if you never use it you can turn this computation\n        off.\n    Attributes\n    ----------\n    x : numpy.array(dim_x, 1)\n        Current state estimate. Any call to update() or predict() updates\n        this variable.\n    P : numpy.array(dim_x, dim_x)\n        Current state covariance matrix. Any call to update() or predict()\n        updates this variable.\n    x_prior : numpy.array(dim_x, 1)\n        Prior (predicted) state estimate. The *_prior and *_post attributes\n        are for convenience; they store the  prior and posterior of the\n        current epoch. Read Only.\n    P_prior : numpy.array(dim_x, dim_x)\n        Prior (predicted) state covariance matrix. Read Only.\n    x_post : numpy.array(dim_x, 1)\n        Posterior (updated) state estimate. Read Only.\n    P_post : numpy.array(dim_x, dim_x)\n        Posterior (updated) state covariance matrix. Read Only.\n    z : numpy.array\n        Last measurement used in update(). Read only.\n    R : numpy.array(dim_z, dim_z)\n        Measurement noise covariance matrix. Also known as the\n        observation covariance.\n    Q : numpy.array(dim_x, dim_x)\n        Process noise covariance matrix. Also known as the transition\n        covariance.\n    F : numpy.array()\n        State Transition matrix. Also known as `A` in some formulation.\n    H : numpy.array(dim_z, dim_x)\n        Measurement function. Also known as the observation matrix, or as `C`.\n    y : numpy.array\n        Residual of the update step. Read only.\n    K : numpy.array(dim_x, dim_z)\n        Kalman gain of the update step. Read only.\n    S :  numpy.array\n        System uncertainty (P projected to measurement space). Read only.\n    SI :  numpy.array\n        Inverse system uncertainty. Read only.\n    log_likelihood : float\n        log-likelihood of the last measurement. Read only.\n    likelihood : float\n        likelihood of last measurement. Read only.\n        Computed from the log-likelihood. The log-likelihood can be very\n        small,  meaning a large negative value such as -28000. Taking the\n        exp() of that results in 0.0, which can break typical algorithms\n        which multiply by this value, so by default we always return a\n        number >= sys.float_info.min.\n    mahalanobis : float\n        mahalanobis distance of the innovation. Read only.\n    inv : function, default numpy.linalg.inv\n        If you prefer another inverse function, such as the Moore-Penrose\n        pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv\n        This is only used to invert self.S. If you know it is diagonal, you\n        might choose to set it to filterpy.common.inv_diagonal, which is\n        several times faster than numpy.linalg.inv for diagonal matrices.\n    alpha : float\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon [1]_.\n    References\n    ----------\n    .. [1] Dan Simon. \"Optimal State Estimation.\" John Wiley & Sons.\n       p. 208-212. (2006)\n    .. [2] Roger Labbe. \"Kalman and Bayesian Filters in Python\"\n       https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n    \"\"\"\n\n    def __init__(self, dim_x, dim_z, dim_u=0):\n        if dim_x < 1:\n            raise ValueError('dim_x must be 1 or greater')\n        if dim_z < 1:\n            raise ValueError('dim_z must be 1 or greater')\n        if dim_u < 0:\n            raise ValueError('dim_u must be 0 or greater')\n\n        self.dim_x = dim_x\n        self.dim_z = dim_z\n        self.dim_u = dim_u\n\n        self.x = zeros((dim_x, 1))        # state\n        self.P = eye(dim_x)               # uncertainty covariance\n        self.Q = eye(dim_x)               # process uncertainty\n        self.B = None                     # control transition matrix\n        self.F = eye(dim_x)               # state transition matrix\n        self.H = zeros((dim_z, dim_x))    # measurement function\n        self.R = eye(dim_z)               # measurement uncertainty\n        self._alpha_sq = 1.               # fading memory control\n        self.M = np.zeros((dim_x, dim_z)) # process-measurement cross correlation\n        self.z = np.array([[None]*self.dim_z]).T\n\n        # gain and residual are computed during the innovation step. We\n        # save them so that in case you want to inspect them for various\n        # purposes\n        self.K = np.zeros((dim_x, dim_z)) # kalman gain\n        self.y = zeros((dim_z, 1))\n        self.S = np.zeros((dim_z, dim_z)) # system uncertainty\n        self.SI = np.zeros((dim_z, dim_z)) # inverse system uncertainty\n\n        # identity matrix. Do not alter this.\n        self._I = np.eye(dim_x)\n\n        # these will always be a copy of x,P after predict() is called\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n        # these will always be a copy of x,P after update() is called\n        self.x_post = self.x.copy()             \n        self.P_post = self.P.copy()\n\n        # Only computed only if requested via property\n        self._log_likelihood = log(sys.float_info.min)\n        self._likelihood = sys.float_info.min\n        self._mahalanobis = None\n\n        # keep all observations \n        self.history_obs = []\n\n        self.inv = np.linalg.inv\n\n        self.attr_saved = None\n        self.observed = False \n\n\n    def predict(self, u=None, B=None, F=None, Q=None):\n        \"\"\"\n        Predict next state (prior) using the Kalman filter state propagation\n        equations.\n        Parameters\n        ----------\n        u : np.array, default 0\n            Optional control vector.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        F : np.array(dim_x, dim_x), or None\n            Optional state transition matrix; a value of None\n            will cause the filter to use `self.F`.\n        Q : np.array(dim_x, dim_x), scalar, or None\n            Optional process noise matrix; a value of None will cause the\n            filter to use `self.Q`.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        elif isscalar(Q):\n            Q = eye(self.dim_x) * Q\n\n\n        # x = Fx + Bu\n        if B is not None and u is not None:\n            self.x = dot(F, self.x) + dot(B, u)\n        else:\n            self.x = dot(F, self.x)\n\n        # P = FPF' + Q\n        self.P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q\n\n        # save prior\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n\n\n    def freeze(self):\n        \"\"\"\n            Save the parameters before non-observation forward\n        \"\"\"\n        self.attr_saved = deepcopy(self.__dict__)\n\n\n    def unfreeze(self):\n        if self.attr_saved is not None:\n            new_history = deepcopy(self.history_obs)\n            self.__dict__ = self.attr_saved\n            # self.history_obs = new_history \n            self.history_obs = self.history_obs[:-1]\n            occur = [int(d is None) for d in new_history]\n            indices = np.where(np.array(occur)==0)[0]\n            index1 = indices[-2]\n            index2 = indices[-1]\n            box1 = new_history[index1]\n            x1, y1, s1, r1 = box1 \n            w1 = np.sqrt(s1 * r1)\n            h1 = np.sqrt(s1 / r1)\n            box2 = new_history[index2]\n            x2, y2, s2, r2 = box2 \n            w2 = np.sqrt(s2 * r2)\n            h2 = np.sqrt(s2 / r2)\n            time_gap = index2 - index1\n            dx = (x2-x1)/time_gap\n            dy = (y2-y1)/time_gap \n            dw = (w2-w1)/time_gap \n            dh = (h2-h1)/time_gap\n            for i in range(index2 - index1):\n                \"\"\"\n                    The default virtual trajectory generation is by linear\n                    motion (constant speed hypothesis), you could modify this \n                    part to implement your own. \n                \"\"\"\n                x = x1 + (i+1) * dx \n                y = y1 + (i+1) * dy \n                w = w1 + (i+1) * dw \n                h = h1 + (i+1) * dh\n                s = w * h \n                r = w / float(h)\n                new_box = np.array([x, y, s, r]).reshape((4, 1))\n                \"\"\"\n                    I still use predict-update loop here to refresh the parameters,\n                    but this can be faster by directly modifying the internal parameters\n                    as suggested in the paper. I keep this naive but slow way for \n                    easy read and understanding\n                \"\"\"\n                self.update(new_box)\n                if not i == (index2-index1-1):\n                    self.predict()\n\n\n    def update(self, z, R=None, H=None):\n        \"\"\"\n        Add a new measurement (z) to the Kalman filter.\n        If z is None, nothing is computed. However, x_post and P_post are\n        updated with the prior (x_prior, P_prior), and self.z is set to None.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n            If you pass in a value of H, z must be a column vector the\n            of the correct size.\n        R : np.array, scalar, or None\n            Optionally provide R to override the measurement noise for this\n            one call, otherwise  self.R will be used.\n        H : np.array, or None\n            Optionally provide H to override the measurement function for this\n            one call, otherwise self.H will be used.\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        # append the observation\n        self.history_obs.append(z)\n        \n        if z is None:\n            if self.observed:\n                \"\"\"\n                    Got no observation so freeze the current parameters for future\n                    potential online smoothing.\n                \"\"\"\n                self.freeze()\n            self.observed = False \n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n        \n        # self.observed = True\n        if not self.observed:\n            \"\"\"\n                Get observation, use online smoothing to re-update parameters\n            \"\"\"\n            self.unfreeze()\n        self.observed = True\n\n        if R is None:\n            R = self.R\n        elif isscalar(R):\n            R = eye(self.dim_z) * R\n\n        if H is None:\n            z = reshape_z(z, self.dim_z, self.x.ndim)\n            H = self.H\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(H, self.x)\n\n        # common subexpression for speed\n        PHT = dot(self.P, H.T)\n\n        # S = HPH' + R\n        # project system uncertainty into measurement space\n        self.S = dot(H, PHT) + R\n        self.SI = self.inv(self.S)\n        # K = PH'inv(S)\n        # map system uncertainty into kalman gain\n        self.K = dot(PHT, self.SI)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n\n        # P = (I-KH)P(I-KH)' + KRK'\n        # This is more numerically stable\n        # and works for non-optimal K vs the equation\n        # P = (I-KH)P usually seen in the literature.\n\n        I_KH = self._I - dot(self.K, H)\n        self.P = dot(dot(I_KH, self.P), I_KH.T) + dot(dot(self.K, R), self.K.T)\n\n        # save measurement and posterior state\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n    def predict_steadystate(self, u=0, B=None):\n        \"\"\"\n        Predict state (prior) using the Kalman filter state propagation\n        equations. Only x is updated, P is left unchanged. See\n        update_steadstate() for a longer explanation of when to use this\n        method.\n        Parameters\n        ----------\n        u : np.array\n            Optional control vector. If non-zero, it is multiplied by B\n            to create the control input into the system.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n\n        # x = Fx + Bu\n        if B is not None:\n            self.x = dot(self.F, self.x) + dot(B, u)\n        else:\n            self.x = dot(self.F, self.x)\n\n        # save prior\n        self.x_prior = self.x.copy()\n        self.P_prior = self.P.copy()\n\n    def update_steadystate(self, z):\n        \"\"\"\n        Add a new measurement (z) to the Kalman filter without recomputing\n        the Kalman gain K, the state covariance P, or the system\n        uncertainty S.\n        You can use this for LTI systems since the Kalman gain and covariance\n        converge to a fixed value. Precompute these and assign them explicitly,\n        or run the Kalman filter using the normal predict()/update(0 cycle\n        until they converge.\n        The main advantage of this call is speed. We do significantly less\n        computation, notably avoiding a costly matrix inversion.\n        Use in conjunction with predict_steadystate(), otherwise P will grow\n        without bound.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        Examples\n        --------\n        >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter\n        >>> # let filter converge on representative data, then save k and P\n        >>> for i in range(100):\n        >>>     cv.predict()\n        >>>     cv.update([i, i, i])\n        >>> saved_k = np.copy(cv.K)\n        >>> saved_P = np.copy(cv.P)\n        later on:\n        >>> cv = kinematic_kf(dim=3, order=2) # 3D const velocity filter\n        >>> cv.K = np.copy(saved_K)\n        >>> cv.P = np.copy(saved_P)\n        >>> for i in range(100):\n        >>>     cv.predict_steadystate()\n        >>>     cv.update_steadystate([i, i, i])\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        if z is None:\n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(self.H, self.x)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n    def update_correlated(self, z, R=None, H=None):\n        \"\"\" Add a new measurement (z) to the Kalman filter assuming that\n        process noise and measurement noise are correlated as defined in\n        the `self.M` matrix.\n        A partial derivation can be found in [1]\n        If z is None, nothing is changed.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        R : np.array, scalar, or None\n            Optionally provide R to override the measurement noise for this\n            one call, otherwise  self.R will be used.\n        H : np.array,  or None\n            Optionally provide H to override the measurement function for this\n            one call, otherwise  self.H will be used.\n        References\n        ----------\n        .. [1] Bulut, Y. (2011). Applied Kalman filter theory (Doctoral dissertation, Northeastern University).\n               http://people.duke.edu/~hpgavin/SystemID/References/Balut-KalmanFilter-PhD-NEU-2011.pdf\n        \"\"\"\n\n        # set to None to force recompute\n        self._log_likelihood = None\n        self._likelihood = None\n        self._mahalanobis = None\n\n        if z is None:\n            self.z = np.array([[None]*self.dim_z]).T\n            self.x_post = self.x.copy()\n            self.P_post = self.P.copy()\n            self.y = zeros((self.dim_z, 1))\n            return\n\n        if R is None:\n            R = self.R\n        elif isscalar(R):\n            R = eye(self.dim_z) * R\n\n        # rename for readability and a tiny extra bit of speed\n        if H is None:\n            z = reshape_z(z, self.dim_z, self.x.ndim)\n            H = self.H\n\n        # handle special case: if z is in form [[z]] but x is not a column\n        # vector dimensions will not match\n        if self.x.ndim == 1 and shape(z) == (1, 1):\n            z = z[0]\n\n        if shape(z) == (): # is it scalar, e.g. z=3 or z=np.array(3)\n            z = np.asarray([z])\n\n        # y = z - Hx\n        # error (residual) between measurement and prediction\n        self.y = z - dot(H, self.x)\n\n        # common subexpression for speed\n        PHT = dot(self.P, H.T)\n\n        # project system uncertainty into measurement space\n        self.S = dot(H, PHT) + dot(H, self.M) + dot(self.M.T, H.T) + R\n        self.SI = self.inv(self.S)\n\n        # K = PH'inv(S)\n        # map system uncertainty into kalman gain\n        self.K = dot(PHT + self.M, self.SI)\n\n        # x = x + Ky\n        # predict new x with residual scaled by the kalman gain\n        self.x = self.x + dot(self.K, self.y)\n        self.P = self.P - dot(self.K, dot(H, self.P) + self.M.T)\n\n        self.z = deepcopy(z)\n        self.x_post = self.x.copy()\n        self.P_post = self.P.copy()\n\n    def batch_filter(self, zs, Fs=None, Qs=None, Hs=None,\n                     Rs=None, Bs=None, us=None, update_first=False,\n                     saver=None):\n        \"\"\" Batch processes a sequences of measurements.\n        Parameters\n        ----------\n        zs : list-like\n            list of measurements at each time step `self.dt`. Missing\n            measurements must be represented by `None`.\n        Fs : None, list-like, default=None\n            optional value or list of values to use for the state transition\n            matrix F.\n            If Fs is None then self.F is used for all epochs.\n            Otherwise it must contain a list-like list of F's, one for\n            each epoch.  This allows you to have varying F per epoch.\n        Qs : None, np.array or list-like, default=None\n            optional value or list of values to use for the process error\n            covariance Q.\n            If Qs is None then self.Q is used for all epochs.\n            Otherwise it must contain a list-like list of Q's, one for\n            each epoch.  This allows you to have varying Q per epoch.\n        Hs : None, np.array or list-like, default=None\n            optional list of values to use for the measurement matrix H.\n            If Hs is None then self.H is used for all epochs.\n            If Hs contains a single matrix, then it is used as H for all\n            epochs.\n            Otherwise it must contain a list-like list of H's, one for\n            each epoch.  This allows you to have varying H per epoch.\n        Rs : None, np.array or list-like, default=None\n            optional list of values to use for the measurement error\n            covariance R.\n            If Rs is None then self.R is used for all epochs.\n            Otherwise it must contain a list-like list of R's, one for\n            each epoch.  This allows you to have varying R per epoch.\n        Bs : None, np.array or list-like, default=None\n            optional list of values to use for the control transition matrix B.\n            If Bs is None then self.B is used for all epochs.\n            Otherwise it must contain a list-like list of B's, one for\n            each epoch.  This allows you to have varying B per epoch.\n        us : None, np.array or list-like, default=None\n            optional list of values to use for the control input vector;\n            If us is None then None is used for all epochs (equivalent to 0,\n            or no control input).\n            Otherwise it must contain a list-like list of u's, one for\n            each epoch.\n       update_first : bool, optional, default=False\n            controls whether the order of operations is update followed by\n            predict, or predict followed by update. Default is predict->update.\n        saver : filterpy.common.Saver, optional\n            filterpy.common.Saver object. If provided, saver.save() will be\n            called after every epoch\n        Returns\n        -------\n        means : np.array((n,dim_x,1))\n            array of the state for each time step after the update. Each entry\n            is an np.array. In other words `means[k,:]` is the state at step\n            `k`.\n        covariance : np.array((n,dim_x,dim_x))\n            array of the covariances for each time step after the update.\n            In other words `covariance[k,:,:]` is the covariance at step `k`.\n        means_predictions : np.array((n,dim_x,1))\n            array of the state for each time step after the predictions. Each\n            entry is an np.array. In other words `means[k,:]` is the state at\n            step `k`.\n        covariance_predictions : np.array((n,dim_x,dim_x))\n            array of the covariances for each time step after the prediction.\n            In other words `covariance[k,:,:]` is the covariance at step `k`.\n        Examples\n        --------\n        .. code-block:: Python\n            # this example demonstrates tracking a measurement where the time\n            # between measurement varies, as stored in dts. This requires\n            # that F be recomputed for each epoch. The output is then smoothed\n            # with an RTS smoother.\n            zs = [t + random.randn()*4 for t in range (40)]\n            Fs = [np.array([[1., dt], [0, 1]] for dt in dts]\n            (mu, cov, _, _) = kf.batch_filter(zs, Fs=Fs)\n            (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs)\n        \"\"\"\n\n        #pylint: disable=too-many-statements\n        n = np.size(zs, 0)\n        if Fs is None:\n            Fs = [self.F] * n\n        if Qs is None:\n            Qs = [self.Q] * n\n        if Hs is None:\n            Hs = [self.H] * n\n        if Rs is None:\n            Rs = [self.R] * n\n        if Bs is None:\n            Bs = [self.B] * n\n        if us is None:\n            us = [0] * n\n\n        # mean estimates from Kalman Filter\n        if self.x.ndim == 1:\n            means = zeros((n, self.dim_x))\n            means_p = zeros((n, self.dim_x))\n        else:\n            means = zeros((n, self.dim_x, 1))\n            means_p = zeros((n, self.dim_x, 1))\n\n        # state covariances from Kalman Filter\n        covariances = zeros((n, self.dim_x, self.dim_x))\n        covariances_p = zeros((n, self.dim_x, self.dim_x))\n\n        if update_first:\n            for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n                self.update(z, R=R, H=H)\n                means[i, :] = self.x\n                covariances[i, :, :] = self.P\n\n                self.predict(u=u, B=B, F=F, Q=Q)\n                means_p[i, :] = self.x\n                covariances_p[i, :, :] = self.P\n\n                if saver is not None:\n                    saver.save()\n        else:\n            for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n                self.predict(u=u, B=B, F=F, Q=Q)\n                means_p[i, :] = self.x\n                covariances_p[i, :, :] = self.P\n\n                self.update(z, R=R, H=H)\n                means[i, :] = self.x\n                covariances[i, :, :] = self.P\n\n                if saver is not None:\n                    saver.save()\n\n        return (means, covariances, means_p, covariances_p)\n\n    def rts_smoother(self, Xs, Ps, Fs=None, Qs=None, inv=np.linalg.inv):\n        \"\"\"\n        Runs the Rauch-Tung-Striebel Kalman smoother on a set of\n        means and covariances computed by a Kalman filter. The usual input\n        would come from the output of `KalmanFilter.batch_filter()`.\n        Parameters\n        ----------\n        Xs : numpy.array\n           array of the means (state variable x) of the output of a Kalman\n           filter.\n        Ps : numpy.array\n            array of the covariances of the output of a kalman filter.\n        Fs : list-like collection of numpy.array, optional\n            State transition matrix of the Kalman filter at each time step.\n            Optional, if not provided the filter's self.F will be used\n        Qs : list-like collection of numpy.array, optional\n            Process noise of the Kalman filter at each time step. Optional,\n            if not provided the filter's self.Q will be used\n        inv : function, default numpy.linalg.inv\n            If you prefer another inverse function, such as the Moore-Penrose\n            pseudo inverse, set it to that instead: kf.inv = np.linalg.pinv\n        Returns\n        -------\n        x : numpy.ndarray\n           smoothed means\n        P : numpy.ndarray\n           smoothed state covariances\n        K : numpy.ndarray\n            smoother gain at each step\n        Pp : numpy.ndarray\n           Predicted state covariances\n        Examples\n        --------\n        .. code-block:: Python\n            zs = [t + random.randn()*4 for t in range (40)]\n            (mu, cov, _, _) = kalman.batch_filter(zs)\n            (x, P, K, Pp) = rts_smoother(mu, cov, kf.F, kf.Q)\n        \"\"\"\n\n        if len(Xs) != len(Ps):\n            raise ValueError('length of Xs and Ps must be the same')\n\n        n = Xs.shape[0]\n        dim_x = Xs.shape[1]\n\n        if Fs is None:\n            Fs = [self.F] * n\n        if Qs is None:\n            Qs = [self.Q] * n\n\n        # smoother gain\n        K = zeros((n, dim_x, dim_x))\n\n        x, P, Pp = Xs.copy(), Ps.copy(), Ps.copy()\n        for k in range(n-2, -1, -1):\n            Pp[k] = dot(dot(Fs[k+1], P[k]), Fs[k+1].T) + Qs[k+1]\n\n            #pylint: disable=bad-whitespace\n            K[k]  = dot(dot(P[k], Fs[k+1].T), inv(Pp[k]))\n            x[k] += dot(K[k], x[k+1] - dot(Fs[k+1], x[k]))\n            P[k] += dot(dot(K[k], P[k+1] - Pp[k]), K[k].T)\n\n        return (x, P, K, Pp)\n\n    def get_prediction(self, u=None, B=None, F=None, Q=None):\n        \"\"\"\n        Predict next state (prior) using the Kalman filter state propagation\n        equations and returns it without modifying the object.\n        Parameters\n        ----------\n        u : np.array, default 0\n            Optional control vector.\n        B : np.array(dim_x, dim_u), or None\n            Optional control transition matrix; a value of None\n            will cause the filter to use `self.B`.\n        F : np.array(dim_x, dim_x), or None\n            Optional state transition matrix; a value of None\n            will cause the filter to use `self.F`.\n        Q : np.array(dim_x, dim_x), scalar, or None\n            Optional process noise matrix; a value of None will cause the\n            filter to use `self.Q`.\n        Returns\n        -------\n        (x, P) : tuple\n            State vector and covariance array of the prediction.\n        \"\"\"\n\n        if B is None:\n            B = self.B\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        elif isscalar(Q):\n            Q = eye(self.dim_x) * Q\n\n        # x = Fx + Bu\n        if B is not None and u is not None:\n            x = dot(F, self.x) + dot(B, u)\n        else:\n            x = dot(F, self.x)\n\n        # P = FPF' + Q\n        P = self._alpha_sq * dot(dot(F, self.P), F.T) + Q\n\n        return x, P\n\n    def get_update(self, z=None):\n        \"\"\"\n        Computes the new estimate based on measurement `z` and returns it\n        without altering the state of the filter.\n        Parameters\n        ----------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        Returns\n        -------\n        (x, P) : tuple\n            State vector and covariance array of the update.\n       \"\"\"\n\n        if z is None:\n            return self.x, self.P\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n\n        R = self.R\n        H = self.H\n        P = self.P\n        x = self.x\n\n        # error (residual) between measurement and prediction\n        y = z - dot(H, x)\n\n        # common subexpression for speed\n        PHT = dot(P, H.T)\n\n        # project system uncertainty into measurement space\n        S = dot(H, PHT) + R\n\n        # map system uncertainty into kalman gain\n        K = dot(PHT, self.inv(S))\n\n        # predict new x with residual scaled by the kalman gain\n        x = x + dot(K, y)\n\n        # P = (I-KH)P(I-KH)' + KRK'\n        I_KH = self._I - dot(K, H)\n        P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)\n\n        return x, P\n\n    def residual_of(self, z):\n        \"\"\"\n        Returns the residual for the given measurement (z). Does not alter\n        the state of the filter.\n        \"\"\"\n        z = reshape_z(z, self.dim_z, self.x.ndim)\n        return z - dot(self.H, self.x_prior)\n\n    def measurement_of_state(self, x):\n        \"\"\"\n        Helper function that converts a state into a measurement.\n        Parameters\n        ----------\n        x : np.array\n            kalman state vector\n        Returns\n        -------\n        z : (dim_z, 1): array_like\n            measurement for this update. z can be a scalar if dim_z is 1,\n            otherwise it must be convertible to a column vector.\n        \"\"\"\n\n        return dot(self.H, x)\n\n    @property\n    def log_likelihood(self):\n        \"\"\"\n        log-likelihood of the last measurement.\n        \"\"\"\n        if self._log_likelihood is None:\n            self._log_likelihood = logpdf(x=self.y, cov=self.S)\n        return self._log_likelihood\n\n    @property\n    def likelihood(self):\n        \"\"\"\n        Computed from the log-likelihood. The log-likelihood can be very\n        small,  meaning a large negative value such as -28000. Taking the\n        exp() of that results in 0.0, which can break typical algorithms\n        which multiply by this value, so by default we always return a\n        number >= sys.float_info.min.\n        \"\"\"\n        if self._likelihood is None:\n            self._likelihood = exp(self.log_likelihood)\n            if self._likelihood == 0:\n                self._likelihood = sys.float_info.min\n        return self._likelihood\n\n    @property\n    def mahalanobis(self):\n        \"\"\"\"\n        Mahalanobis distance of measurement. E.g. 3 means measurement\n        was 3 standard deviations away from the predicted value.\n        Returns\n        -------\n        mahalanobis : float\n        \"\"\"\n        if self._mahalanobis is None:\n            self._mahalanobis = sqrt(float(dot(dot(self.y.T, self.SI), self.y)))\n        return self._mahalanobis\n\n    @property\n    def alpha(self):\n        \"\"\"\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon [1]_.\n        \"\"\"\n        return self._alpha_sq**.5\n\n    def log_likelihood_of(self, z):\n        \"\"\"\n        log likelihood of the measurement `z`. This should only be called\n        after a call to update(). Calling after predict() will yield an\n        incorrect result.\"\"\"\n\n        if z is None:\n            return log(sys.float_info.min)\n        return logpdf(z, dot(self.H, self.x), self.S)\n\n    @alpha.setter\n    def alpha(self, value):\n        if not np.isscalar(value) or value < 1:\n            raise ValueError('alpha must be a float greater than 1')\n\n        self._alpha_sq = value**2\n\n    def __repr__(self):\n        return '\\n'.join([\n            'KalmanFilter object',\n            pretty_str('dim_x', self.dim_x),\n            pretty_str('dim_z', self.dim_z),\n            pretty_str('dim_u', self.dim_u),\n            pretty_str('x', self.x),\n            pretty_str('P', self.P),\n            pretty_str('x_prior', self.x_prior),\n            pretty_str('P_prior', self.P_prior),\n            pretty_str('x_post', self.x_post),\n            pretty_str('P_post', self.P_post),\n            pretty_str('F', self.F),\n            pretty_str('Q', self.Q),\n            pretty_str('R', self.R),\n            pretty_str('H', self.H),\n            pretty_str('K', self.K),\n            pretty_str('y', self.y),\n            pretty_str('S', self.S),\n            pretty_str('SI', self.SI),\n            pretty_str('M', self.M),\n            pretty_str('B', self.B),\n            pretty_str('z', self.z),\n            pretty_str('log-likelihood', self.log_likelihood),\n            pretty_str('likelihood', self.likelihood),\n            pretty_str('mahalanobis', self.mahalanobis),\n            pretty_str('alpha', self.alpha),\n            pretty_str('inv', self.inv)\n            ])\n\n    def test_matrix_dimensions(self, z=None, H=None, R=None, F=None, Q=None):\n        \"\"\"\n        Performs a series of asserts to check that the size of everything\n        is what it should be. This can help you debug problems in your design.\n        If you pass in H, R, F, Q those will be used instead of this object's\n        value for those matrices.\n        Testing `z` (the measurement) is problamatic. x is a vector, and can be\n        implemented as either a 1D array or as a nx1 column vector. Thus Hx\n        can be of different shapes. Then, if Hx is a single value, it can\n        be either a 1D array or 2D vector. If either is true, z can reasonably\n        be a scalar (either '3' or np.array('3') are scalars under this\n        definition), a 1D, 1 element array, or a 2D, 1 element array. You are\n        allowed to pass in any combination that works.\n        \"\"\"\n\n        if H is None:\n            H = self.H\n        if R is None:\n            R = self.R\n        if F is None:\n            F = self.F\n        if Q is None:\n            Q = self.Q\n        x = self.x\n        P = self.P\n\n        assert x.ndim == 1 or x.ndim == 2, \\\n                \"x must have one or two dimensions, but has {}\".format(x.ndim)\n\n        if x.ndim == 1:\n            assert x.shape[0] == self.dim_x, \\\n                   \"Shape of x must be ({},{}), but is {}\".format(\n                       self.dim_x, 1, x.shape)\n        else:\n            assert x.shape == (self.dim_x, 1), \\\n                   \"Shape of x must be ({},{}), but is {}\".format(\n                       self.dim_x, 1, x.shape)\n\n        assert P.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of P must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, P.shape)\n\n        assert Q.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of Q must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, P.shape)\n\n        assert F.shape == (self.dim_x, self.dim_x), \\\n               \"Shape of F must be ({},{}), but is {}\".format(\n                   self.dim_x, self.dim_x, F.shape)\n\n        assert np.ndim(H) == 2, \\\n               \"Shape of H must be (dim_z, {}), but is {}\".format(\n                   P.shape[0], shape(H))\n\n        assert H.shape[1] == P.shape[0], \\\n               \"Shape of H must be (dim_z, {}), but is {}\".format(\n                   P.shape[0], H.shape)\n\n        # shape of R must be the same as HPH'\n        hph_shape = (H.shape[0], H.shape[0])\n        r_shape = shape(R)\n\n        if H.shape[0] == 1:\n            # r can be scalar, 1D, or 2D in this case\n            assert r_shape in [(), (1,), (1, 1)], \\\n            \"R must be scalar or one element array, but is shaped {}\".format(\n                r_shape)\n        else:\n            assert r_shape == hph_shape, \\\n            \"shape of R should be {} but it is {}\".format(hph_shape, r_shape)\n\n\n        if z is not None:\n            z_shape = shape(z)\n        else:\n            z_shape = (self.dim_z, 1)\n\n        # H@x must have shape of z\n        Hx = dot(H, x)\n\n        if z_shape == (): # scalar or np.array(scalar)\n            assert Hx.ndim == 1 or shape(Hx) == (1, 1), \\\n            \"shape of z should be {}, not {} for the given H\".format(\n                shape(Hx), z_shape)\n\n        elif shape(Hx) == (1,):\n            assert z_shape[0] == 1, 'Shape of z must be {} for the given H'.format(shape(Hx))\n\n        else:\n            assert (z_shape == shape(Hx) or\n                    (len(z_shape) == 1 and shape(Hx) == (z_shape[0], 1))), \\\n                    \"shape of z should be {}, not {} for the given H\".format(\n                        shape(Hx), z_shape)\n\n        if np.ndim(Hx) > 1 and shape(Hx) != (1, 1):\n            assert shape(Hx) == z_shape, \\\n               'shape of z should be {} for the given H, but it is {}'.format(\n                   shape(Hx), z_shape)\n\n\ndef update(x, P, z, R, H=None, return_all=False):\n    \"\"\"\n    Add a new measurement (z) to the Kalman filter. If z is None, nothing\n    is changed.\n    This can handle either the multidimensional or unidimensional case. If\n    all parameters are floats instead of arrays the filter will still work,\n    and return floats for x, P as the result.\n    update(1, 2, 1, 1, 1)  # univariate\n    update(x, P, 1\n    Parameters\n    ----------\n    x : numpy.array(dim_x, 1), or float\n        State estimate vector\n    P : numpy.array(dim_x, dim_x), or float\n        Covariance matrix\n    z : (dim_z, 1): array_like\n        measurement for this update. z can be a scalar if dim_z is 1,\n        otherwise it must be convertible to a column vector.\n    R : numpy.array(dim_z, dim_z), or float\n        Measurement noise matrix\n    H : numpy.array(dim_x, dim_x), or float, optional\n        Measurement function. If not provided, a value of 1 is assumed.\n    return_all : bool, default False\n        If true, y, K, S, and log_likelihood are returned, otherwise\n        only x and P are returned.\n    Returns\n    -------\n    x : numpy.array\n        Posterior state estimate vector\n    P : numpy.array\n        Posterior covariance matrix\n    y : numpy.array or scalar\n        Residua. Difference between measurement and state in measurement space\n    K : numpy.array\n        Kalman gain\n    S : numpy.array\n        System uncertainty in measurement space\n    log_likelihood : float\n        log likelihood of the measurement\n    \"\"\"\n\n    #pylint: disable=bare-except\n\n    if z is None:\n        if return_all:\n            return x, P, None, None, None, None\n        return x, P\n\n    if H is None:\n        H = np.array([1])\n\n    if np.isscalar(H):\n        H = np.array([H])\n\n    Hx = np.atleast_1d(dot(H, x))\n    z = reshape_z(z, Hx.shape[0], x.ndim)\n\n    # error (residual) between measurement and prediction\n    y = z - Hx\n\n    # project system uncertainty into measurement space\n    S = dot(dot(H, P), H.T) + R\n\n\n    # map system uncertainty into kalman gain\n    try:\n        K = dot(dot(P, H.T), linalg.inv(S))\n    except:\n        # can't invert a 1D array, annoyingly\n        K = dot(dot(P, H.T), 1./S)\n\n\n    # predict new x with residual scaled by the kalman gain\n    x = x + dot(K, y)\n\n    # P = (I-KH)P(I-KH)' + KRK'\n    KH = dot(K, H)\n\n    try:\n        I_KH = np.eye(KH.shape[0]) - KH\n    except:\n        I_KH = np.array([1 - KH])\n    P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)\n\n\n    if return_all:\n        # compute log likelihood\n        log_likelihood = logpdf(z, dot(H, x), S)\n        return x, P, y, K, S, log_likelihood\n    return x, P\n\n\ndef update_steadystate(x, z, K, H=None):\n    \"\"\"\n    Add a new measurement (z) to the Kalman filter. If z is None, nothing\n    is changed.\n    Parameters\n    ----------\n    x : numpy.array(dim_x, 1), or float\n        State estimate vector\n    z : (dim_z, 1): array_like\n        measurement for this update. z can be a scalar if dim_z is 1,\n        otherwise it must be convertible to a column vector.\n    K : numpy.array, or float\n        Kalman gain matrix\n    H : numpy.array(dim_x, dim_x), or float, optional\n        Measurement function. If not provided, a value of 1 is assumed.\n    Returns\n    -------\n    x : numpy.array\n        Posterior state estimate vector\n    Examples\n    --------\n    This can handle either the multidimensional or unidimensional case. If\n    all parameters are floats instead of arrays the filter will still work,\n    and return floats for x, P as the result.\n    >>> update_steadystate(1, 2, 1)  # univariate\n    >>> update_steadystate(x, P, z, H)\n    \"\"\"\n\n\n    if z is None:\n        return x\n\n    if H is None:\n        H = np.array([1])\n\n    if np.isscalar(H):\n        H = np.array([H])\n\n    Hx = np.atleast_1d(dot(H, x))\n    z = reshape_z(z, Hx.shape[0], x.ndim)\n\n    # error (residual) between measurement and prediction\n    y = z - Hx\n\n    # estimate new x with residual scaled by the kalman gain\n    return x + dot(K, y)\n\n\ndef predict(x, P, F=1, Q=0, u=0, B=1, alpha=1.):\n    \"\"\"\n    Predict next state (prior) using the Kalman filter state propagation\n    equations.\n    Parameters\n    ----------\n    x : numpy.array\n        State estimate vector\n    P : numpy.array\n        Covariance matrix\n    F : numpy.array()\n        State Transition matrix\n    Q : numpy.array, Optional\n        Process noise matrix\n    u : numpy.array, Optional, default 0.\n        Control vector. If non-zero, it is multiplied by B\n        to create the control input into the system.\n    B : numpy.array, optional, default 0.\n        Control transition matrix.\n    alpha : float, Optional, default=1.0\n        Fading memory setting. 1.0 gives the normal Kalman filter, and\n        values slightly larger than 1.0 (such as 1.02) give a fading\n        memory effect - previous measurements have less influence on the\n        filter's estimates. This formulation of the Fading memory filter\n        (there are many) is due to Dan Simon\n    Returns\n    -------\n    x : numpy.array\n        Prior state estimate vector\n    P : numpy.array\n        Prior covariance matrix\n    \"\"\"\n\n    if np.isscalar(F):\n        F = np.array(F)\n    x = dot(F, x) + dot(B, u)\n    P = (alpha * alpha) * dot(dot(F, P), F.T) + Q\n\n    return x, P\n\n\ndef predict_steadystate(x, F=1, u=0, B=1):\n    \"\"\"\n    Predict next state (prior) using the Kalman filter state propagation\n    equations. This steady state form only computes x, assuming that the\n    covariance is constant.\n    Parameters\n    ----------\n    x : numpy.array\n        State estimate vector\n    P : numpy.array\n        Covariance matrix\n    F : numpy.array()\n        State Transition matrix\n    u : numpy.array, Optional, default 0.\n        Control vector. If non-zero, it is multiplied by B\n        to create the control input into the system.\n    B : numpy.array, optional, default 0.\n        Control transition matrix.\n    Returns\n    -------\n    x : numpy.array\n        Prior state estimate vector\n    \"\"\"\n\n    if np.isscalar(F):\n        F = np.array(F)\n    x = dot(F, x) + dot(B, u)\n\n    return x\n\n\n\ndef batch_filter(x, P, zs, Fs, Qs, Hs, Rs, Bs=None, us=None,\n                 update_first=False, saver=None):\n    \"\"\"\n    Batch processes a sequences of measurements.\n    Parameters\n    ----------\n    zs : list-like\n        list of measurements at each time step. Missing measurements must be\n        represented by None.\n    Fs : list-like\n        list of values to use for the state transition matrix matrix.\n    Qs : list-like\n        list of values to use for the process error\n        covariance.\n    Hs : list-like\n        list of values to use for the measurement matrix.\n    Rs : list-like\n        list of values to use for the measurement error\n        covariance.\n    Bs : list-like, optional\n        list of values to use for the control transition matrix;\n        a value of None in any position will cause the filter\n        to use `self.B` for that time step.\n    us : list-like, optional\n        list of values to use for the control input vector;\n        a value of None in any position will cause the filter to use\n        0 for that time step.\n    update_first : bool, optional\n        controls whether the order of operations is update followed by\n        predict, or predict followed by update. Default is predict->update.\n        saver : filterpy.common.Saver, optional\n            filterpy.common.Saver object. If provided, saver.save() will be\n            called after every epoch\n    Returns\n    -------\n    means : np.array((n,dim_x,1))\n        array of the state for each time step after the update. Each entry\n        is an np.array. In other words `means[k,:]` is the state at step\n        `k`.\n    covariance : np.array((n,dim_x,dim_x))\n        array of the covariances for each time step after the update.\n        In other words `covariance[k,:,:]` is the covariance at step `k`.\n    means_predictions : np.array((n,dim_x,1))\n        array of the state for each time step after the predictions. Each\n        entry is an np.array. In other words `means[k,:]` is the state at\n        step `k`.\n    covariance_predictions : np.array((n,dim_x,dim_x))\n        array of the covariances for each time step after the prediction.\n        In other words `covariance[k,:,:]` is the covariance at step `k`.\n    Examples\n    --------\n    .. code-block:: Python\n        zs = [t + random.randn()*4 for t in range (40)]\n        Fs = [kf.F for t in range (40)]\n        Hs = [kf.H for t in range (40)]\n        (mu, cov, _, _) = kf.batch_filter(zs, Rs=R_list, Fs=Fs, Hs=Hs, Qs=None,\n                                          Bs=None, us=None, update_first=False)\n        (xs, Ps, Ks, Pps) = kf.rts_smoother(mu, cov, Fs=Fs, Qs=None)\n    \"\"\"\n\n    n = np.size(zs, 0)\n    dim_x = x.shape[0]\n\n    # mean estimates from Kalman Filter\n    if x.ndim == 1:\n        means = zeros((n, dim_x))\n        means_p = zeros((n, dim_x))\n    else:\n        means = zeros((n, dim_x, 1))\n        means_p = zeros((n, dim_x, 1))\n\n    # state covariances from Kalman Filter\n    covariances = zeros((n, dim_x, dim_x))\n    covariances_p = zeros((n, dim_x, dim_x))\n\n    if us is None:\n        us = [0.] * n\n        Bs = [0.] * n\n\n    if update_first:\n        for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n            x, P = update(x, P, z, R=R, H=H)\n            means[i, :] = x\n            covariances[i, :, :] = P\n\n            x, P = predict(x, P, u=u, B=B, F=F, Q=Q)\n            means_p[i, :] = x\n            covariances_p[i, :, :] = P\n            if saver is not None:\n                saver.save()\n    else:\n        for i, (z, F, Q, H, R, B, u) in enumerate(zip(zs, Fs, Qs, Hs, Rs, Bs, us)):\n\n            x, P = predict(x, P, u=u, B=B, F=F, Q=Q)\n            means_p[i, :] = x\n            covariances_p[i, :, :] = P\n\n            x, P = update(x, P, z, R=R, H=H)\n            means[i, :] = x\n            covariances[i, :, :] = P\n            if saver is not None:\n                saver.save()\n\n    return (means, covariances, means_p, covariances_p)\n\n\n\ndef rts_smoother(Xs, Ps, Fs, Qs):\n    \"\"\"\n    Runs the Rauch-Tung-Striebel Kalman smoother on a set of\n    means and covariances computed by a Kalman filter. The usual input\n    would come from the output of `KalmanFilter.batch_filter()`.\n    Parameters\n    ----------\n    Xs : numpy.array\n       array of the means (state variable x) of the output of a Kalman\n       filter.\n    Ps : numpy.array\n        array of the covariances of the output of a kalman filter.\n    Fs : list-like collection of numpy.array\n        State transition matrix of the Kalman filter at each time step.\n    Qs : list-like collection of numpy.array, optional\n        Process noise of the Kalman filter at each time step.\n    Returns\n    -------\n    x : numpy.ndarray\n       smoothed means\n    P : numpy.ndarray\n       smoothed state covariances\n    K : numpy.ndarray\n        smoother gain at each step\n    pP : numpy.ndarray\n       predicted state covariances\n    Examples\n    --------\n    .. code-block:: Python\n        zs = [t + random.randn()*4 for t in range (40)]\n        (mu, cov, _, _) = kalman.batch_filter(zs)\n        (x, P, K, pP) = rts_smoother(mu, cov, kf.F, kf.Q)\n    \"\"\"\n\n    if len(Xs) != len(Ps):\n        raise ValueError('length of Xs and Ps must be the same')\n\n    n = Xs.shape[0]\n    dim_x = Xs.shape[1]\n\n    # smoother gain\n    K = zeros((n, dim_x, dim_x))\n    x, P, pP = Xs.copy(), Ps.copy(), Ps.copy()\n\n    for k in range(n-2, -1, -1):\n        pP[k] = dot(dot(Fs[k], P[k]), Fs[k].T) + Qs[k]\n\n        #pylint: disable=bad-whitespace\n        K[k]  = dot(dot(P[k], Fs[k].T), linalg.inv(pP[k]))\n        x[k] += dot(K[k], x[k+1] - dot(Fs[k], x[k]))\n        P[k] += dot(dot(K[k], P[k+1] - pP[k]), K[k].T)\n\n    return (x, P, K, pP)"
  },
  {
    "path": "trackers/ocsort_tracker/ocsort.py",
    "content": "\"\"\"\n    This script is adopted from the SORT script by Alex Bewley alex@bewley.ai\n\"\"\"\nfrom __future__ import print_function\n\nimport numpy as np\nfrom .association import *\n\n\ndef k_previous_obs(observations, cur_age, k):\n    if len(observations) == 0:\n        return [-1, -1, -1, -1, -1]\n    for i in range(k):\n        dt = k - i\n        if cur_age - dt in observations:\n            return observations[cur_age-dt]\n    max_age = max(observations.keys())\n    return observations[max_age]\n\n\ndef convert_bbox_to_z(bbox):\n    \"\"\"\n    Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form\n      [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is\n      the aspect ratio\n    \"\"\"\n    w = bbox[2] - bbox[0]\n    h = bbox[3] - bbox[1]\n    x = bbox[0] + w/2.\n    y = bbox[1] + h/2.\n    s = w * h  # scale is just area\n    r = w / float(h+1e-6)\n    return np.array([x, y, s, r]).reshape((4, 1))\n\n\ndef convert_x_to_bbox(x, score=None):\n    \"\"\"\n    Takes a bounding box in the centre form [x,y,s,r] and returns it in the form\n      [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right\n    \"\"\"\n    w = np.sqrt(x[2] * x[3])\n    h = x[2] / w\n    if(score == None):\n      return np.array([x[0]-w/2., x[1]-h/2., x[0]+w/2., x[1]+h/2.]).reshape((1, 4))\n    else:\n      return np.array([x[0]-w/2., x[1]-h/2., x[0]+w/2., x[1]+h/2., score]).reshape((1, 5))\n\n\ndef speed_direction(bbox1, bbox2):\n    cx1, cy1 = (bbox1[0]+bbox1[2]) / 2.0, (bbox1[1]+bbox1[3])/2.0\n    cx2, cy2 = (bbox2[0]+bbox2[2]) / 2.0, (bbox2[1]+bbox2[3])/2.0\n    speed = np.array([cy2-cy1, cx2-cx1])\n    norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6\n    return speed / norm\n\n\nclass KalmanBoxTracker(object):\n    \"\"\"\n    This class represents the internal state of individual tracked objects observed as bbox.\n    \"\"\"\n    count = 0\n\n    def __init__(self, bbox, delta_t=3, orig=False):\n        \"\"\"\n        Initialises a tracker using initial bounding box.\n\n        \"\"\"\n        # define constant velocity model\n        if not orig:\n          from .kalmanfilter import KalmanFilterNew as KalmanFilter\n          self.kf = KalmanFilter(dim_x=7, dim_z=4)\n        else:\n          from filterpy.kalman import KalmanFilter\n          self.kf = KalmanFilter(dim_x=7, dim_z=4)\n        self.kf.F = np.array([[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1], [\n                            0, 0, 0, 1, 0, 0, 0],  [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1]])\n        self.kf.H = np.array([[1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0],\n                            [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]])\n\n        self.kf.R[2:, 2:] *= 10.\n        self.kf.P[4:, 4:] *= 1000.  # give high uncertainty to the unobservable initial velocities\n        self.kf.P *= 10.\n        self.kf.Q[-1, -1] *= 0.01\n        self.kf.Q[4:, 4:] *= 0.01\n\n        self.kf.x[:4] = convert_bbox_to_z(bbox)\n        self.time_since_update = 0\n        self.id = KalmanBoxTracker.count\n        KalmanBoxTracker.count += 1\n        self.history = []\n        self.hits = 0\n        self.hit_streak = 0\n        self.age = 0\n        \"\"\"\n        NOTE: [-1,-1,-1,-1,-1] is a compromising placeholder for non-observation status, the same for the return of \n        function k_previous_obs. It is ugly and I do not like it. But to support generate observation array in a \n        fast and unified way, which you would see below k_observations = np.array([k_previous_obs(...]]), let's bear it for now.\n        \"\"\"\n        self.last_observation = np.array([-1, -1, -1, -1, -1])  # placeholder\n        self.observations = dict()\n        self.history_observations = []\n        self.velocity = None\n        self.delta_t = delta_t\n\n    def update(self, bbox):\n        \"\"\"\n        Updates the state vector with observed bbox.\n        \"\"\"\n        if bbox is not None:\n            if self.last_observation.sum() >= 0:  # no previous observation\n                previous_box = None\n                for i in range(self.delta_t):\n                    dt = self.delta_t - i\n                    if self.age - dt in self.observations:\n                        previous_box = self.observations[self.age-dt]\n                        break\n                if previous_box is None:\n                    previous_box = self.last_observation\n                \"\"\"\n                  Estimate the track speed direction with observations \\Delta t steps away\n                \"\"\"\n                self.velocity = speed_direction(previous_box, bbox)\n            \n            \"\"\"\n              Insert new observations. This is a ugly way to maintain both self.observations\n              and self.history_observations. Bear it for the moment.\n            \"\"\"\n            self.last_observation = bbox\n            self.observations[self.age] = bbox\n            self.history_observations.append(bbox)\n\n            self.time_since_update = 0\n            self.history = []\n            self.hits += 1\n            self.hit_streak += 1\n            self.kf.update(convert_bbox_to_z(bbox))\n        else:\n            self.kf.update(bbox)\n\n    def predict(self):\n        \"\"\"\n        Advances the state vector and returns the predicted bounding box estimate.\n        \"\"\"\n        if((self.kf.x[6]+self.kf.x[2]) <= 0):\n            self.kf.x[6] *= 0.0\n\n        self.kf.predict()\n        self.age += 1\n        if(self.time_since_update > 0):\n            self.hit_streak = 0\n        self.time_since_update += 1\n        self.history.append(convert_x_to_bbox(self.kf.x))\n        return self.history[-1]\n\n    def get_state(self):\n        \"\"\"\n        Returns the current bounding box estimate.\n        \"\"\"\n        return convert_x_to_bbox(self.kf.x)\n\n\n\"\"\"\n    We support multiple ways for association cost calculation, by default\n    we use IoU. GIoU may have better performance in some situations. We note \n    that we hardly normalize the cost by all methods to (0,1) which may not be \n    the best practice.\n\"\"\"\nASSO_FUNCS = {  \"iou\": iou_batch,\n                \"giou\": giou_batch,\n                \"ciou\": ciou_batch,\n                \"diou\": diou_batch,\n                \"ct_dist\": ct_dist}\n\n\nclass OCSort(object):\n    def __init__(self, det_thresh, max_age=30, min_hits=3, \n        iou_threshold=0.3, delta_t=3, asso_func=\"iou\", inertia=0.2, use_byte=False):\n        \"\"\"\n        Sets key parameters for SORT\n        \"\"\"\n        self.max_age = max_age\n        self.min_hits = min_hits\n        self.iou_threshold = iou_threshold\n        self.trackers = []\n        self.frame_count = 0\n        self.det_thresh = det_thresh\n        self.delta_t = delta_t\n        self.asso_func = ASSO_FUNCS[asso_func]\n        self.inertia = inertia\n        self.use_byte = use_byte\n        KalmanBoxTracker.count = 0\n\n    def update(self, output_results, img_info, img_size):\n        \"\"\"\n        Params:\n          dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]\n        Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).\n        Returns the a similar array, where the last column is the object ID.\n        NOTE: The number of objects returned may differ from the number of detections provided.\n        \"\"\"\n        if output_results is None:\n            return np.empty((0, 5))\n\n        self.frame_count += 1\n        # post_process detections\n        if output_results.shape[1] == 5:\n            scores = output_results[:, 4]\n            bboxes = output_results[:, :4]\n        else:\n            output_results = output_results.cpu().numpy()\n            scores = output_results[:, 4] * output_results[:, 5]\n            bboxes = output_results[:, :4]  # x1y1x2y2\n        img_h, img_w = img_info[0], img_info[1]\n        scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n        bboxes /= scale\n        dets = np.concatenate((bboxes, np.expand_dims(scores, axis=-1)), axis=1)\n        inds_low = scores > 0.1\n        inds_high = scores < self.det_thresh\n        inds_second = np.logical_and(inds_low, inds_high)  # self.det_thresh > score > 0.1, for second matching\n        dets_second = dets[inds_second]  # detections for second matching\n        remain_inds = scores > self.det_thresh\n        dets = dets[remain_inds]\n\n        # get predicted locations from existing trackers.\n        trks = np.zeros((len(self.trackers), 5))\n        to_del = []\n        ret = []\n        for t, trk in enumerate(trks):\n            pos = self.trackers[t].predict()[0]\n            trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]\n            if np.any(np.isnan(pos)):\n                to_del.append(t)\n        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))\n        for t in reversed(to_del):\n            self.trackers.pop(t)\n\n        velocities = np.array(\n            [trk.velocity if trk.velocity is not None else np.array((0, 0)) for trk in self.trackers])\n        last_boxes = np.array([trk.last_observation for trk in self.trackers])\n        k_observations = np.array(\n            [k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])\n\n        \"\"\"\n            First round of association\n        \"\"\"\n        matched, unmatched_dets, unmatched_trks = associate(\n            dets, trks, self.iou_threshold, velocities, k_observations, self.inertia)\n        for m in matched:\n            self.trackers[m[1]].update(dets[m[0], :])\n\n        \"\"\"\n            Second round of associaton by OCR\n        \"\"\"\n        # BYTE association\n        if self.use_byte and len(dets_second) > 0 and unmatched_trks.shape[0] > 0:\n            u_trks = trks[unmatched_trks]\n            iou_left = self.asso_func(dets_second, u_trks)          # iou between low score detections and unmatched tracks\n            iou_left = np.array(iou_left)\n            if iou_left.max() > self.iou_threshold:\n                \"\"\"\n                    NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may\n                    get a higher performance especially on MOT17/MOT20 datasets. But we keep it\n                    uniform here for simplicity\n                \"\"\"\n                matched_indices = linear_assignment(-iou_left)\n                to_remove_trk_indices = []\n                for m in matched_indices:\n                    det_ind, trk_ind = m[0], unmatched_trks[m[1]]\n                    if iou_left[m[0], m[1]] < self.iou_threshold:\n                        continue\n                    self.trackers[trk_ind].update(dets_second[det_ind, :])\n                    to_remove_trk_indices.append(trk_ind)\n                unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))\n\n        if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:\n            left_dets = dets[unmatched_dets]\n            left_trks = last_boxes[unmatched_trks]\n            iou_left = self.asso_func(left_dets, left_trks)\n            iou_left = np.array(iou_left)\n            if iou_left.max() > self.iou_threshold:\n                \"\"\"\n                    NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may\n                    get a higher performance especially on MOT17/MOT20 datasets. But we keep it\n                    uniform here for simplicity\n                \"\"\"\n                rematched_indices = linear_assignment(-iou_left)\n                to_remove_det_indices = []\n                to_remove_trk_indices = []\n                for m in rematched_indices:\n                    det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]\n                    if iou_left[m[0], m[1]] < self.iou_threshold:\n                        continue\n                    self.trackers[trk_ind].update(dets[det_ind, :])\n                    to_remove_det_indices.append(det_ind)\n                    to_remove_trk_indices.append(trk_ind)\n                unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))\n                unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))\n\n        for m in unmatched_trks:\n            self.trackers[m].update(None)\n\n        # create and initialise new trackers for unmatched detections\n        for i in unmatched_dets:\n            trk = KalmanBoxTracker(dets[i, :], delta_t=self.delta_t)\n            self.trackers.append(trk)\n        i = len(self.trackers)\n        for trk in reversed(self.trackers):\n            if trk.last_observation.sum() < 0:\n                d = trk.get_state()[0]\n            else:\n                \"\"\"\n                    this is optional to use the recent observation or the kalman filter prediction,\n                    we didn't notice significant difference here\n                \"\"\"\n                d = trk.last_observation[:4]\n            if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):\n                # +1 as MOT benchmark requires positive\n                ret.append(np.concatenate((d, [trk.id+1])).reshape(1, -1))\n            i -= 1\n            # remove dead tracklet\n            if(trk.time_since_update > self.max_age):\n                self.trackers.pop(i)\n        if(len(ret) > 0):\n            return np.concatenate(ret)\n        return np.empty((0, 5))\n\n    def update_public(self, dets, cates, scores):\n        self.frame_count += 1\n\n        det_scores = np.ones((dets.shape[0], 1))\n        dets = np.concatenate((dets, det_scores), axis=1)\n\n        remain_inds = scores > self.det_thresh\n        \n        cates = cates[remain_inds]\n        dets = dets[remain_inds]\n\n        trks = np.zeros((len(self.trackers), 5))\n        to_del = []\n        ret = []\n        for t, trk in enumerate(trks):\n            pos = self.trackers[t].predict()[0]\n            cat = self.trackers[t].cate\n            trk[:] = [pos[0], pos[1], pos[2], pos[3], cat]\n            if np.any(np.isnan(pos)):\n                to_del.append(t)\n        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))\n        for t in reversed(to_del):\n            self.trackers.pop(t)\n\n        velocities = np.array([trk.velocity if trk.velocity is not None else np.array((0,0)) for trk in self.trackers])\n        last_boxes = np.array([trk.last_observation for trk in self.trackers])\n        k_observations = np.array([k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])\n\n        matched, unmatched_dets, unmatched_trks = associate_kitti\\\n              (dets, trks, cates, self.iou_threshold, velocities, k_observations, self.inertia)\n          \n        for m in matched:\n            self.trackers[m[1]].update(dets[m[0], :])\n          \n        if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:\n            \"\"\"\n                The re-association stage by OCR.\n                NOTE: at this stage, adding other strategy might be able to continue improve\n                the performance, such as BYTE association by ByteTrack. \n            \"\"\"\n            left_dets = dets[unmatched_dets]\n            left_trks = last_boxes[unmatched_trks]\n            left_dets_c = left_dets.copy()\n            left_trks_c = left_trks.copy()\n\n            iou_left = self.asso_func(left_dets_c, left_trks_c)\n            iou_left = np.array(iou_left)\n            det_cates_left = cates[unmatched_dets]\n            trk_cates_left = trks[unmatched_trks][:,4]\n            num_dets = unmatched_dets.shape[0]\n            num_trks = unmatched_trks.shape[0]\n            cate_matrix = np.zeros((num_dets, num_trks))\n            for i in range(num_dets):\n                for j in range(num_trks):\n                    if det_cates_left[i] != trk_cates_left[j]:\n                            \"\"\"\n                                For some datasets, such as KITTI, there are different categories,\n                                we have to avoid associate them together.\n                            \"\"\"\n                            cate_matrix[i][j] = -1e6\n            iou_left = iou_left + cate_matrix\n            if iou_left.max() > self.iou_threshold - 0.1:\n                rematched_indices = linear_assignment(-iou_left)\n                to_remove_det_indices = []\n                to_remove_trk_indices = []\n                for m in rematched_indices:\n                    det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]\n                    if iou_left[m[0], m[1]] < self.iou_threshold - 0.1:\n                          continue\n                    self.trackers[trk_ind].update(dets[det_ind, :])\n                    to_remove_det_indices.append(det_ind)\n                    to_remove_trk_indices.append(trk_ind) \n                unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))\n                unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))\n\n        for i in unmatched_dets:\n            trk = KalmanBoxTracker(dets[i,:])\n            trk.cate = cates[i]\n            self.trackers.append(trk)\n        i = len(self.trackers)\n\n        for trk in reversed(self.trackers):\n            if trk.last_observation.sum() > 0:\n                d = trk.last_observation[:4]\n            else:\n                d = trk.get_state()[0]\n            if (trk.time_since_update < 1):\n                if (self.frame_count <= self.min_hits) or (trk.hit_streak >= self.min_hits):\n                    # id+1 as MOT benchmark requires positive\n                    ret.append(np.concatenate((d, [trk.id+1], [trk.cate], [0])).reshape(1,-1)) \n                if trk.hit_streak == self.min_hits:\n                    # Head Padding (HP): recover the lost steps during initializing the track\n                    for prev_i in range(self.min_hits - 1):\n                        prev_observation = trk.history_observations[-(prev_i+2)]\n                        ret.append((np.concatenate((prev_observation[:4], [trk.id+1], [trk.cate], \n                            [-(prev_i+1)]))).reshape(1,-1))\n            i -= 1 \n            if (trk.time_since_update > self.max_age):\n                  self.trackers.pop(i)\n        \n        if(len(ret)>0):\n            return np.concatenate(ret)\n        return np.empty((0, 7))\n\n\n"
  },
  {
    "path": "trackers/sort_tracker/sort.py",
    "content": "\"\"\"\n    SORT: A Simple, Online and Realtime Tracker\n    Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai\n    This program is free software: you can redistribute it and/or modify\n    it under the terms of the GNU General Public License as published by\n    the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU General Public License for more details.\n    You should have received a copy of the GNU General Public License\n    along with this program.  If not, see <http://www.gnu.org/licenses/>.\n\"\"\"\nfrom __future__ import print_function\n\nimport os\nimport numpy as np\n\nfrom filterpy.kalman import KalmanFilter\n\nnp.random.seed(0)\n\n\ndef linear_assignment(cost_matrix):\n  try:\n    import lap\n    _, x, y = lap.lapjv(cost_matrix, extend_cost=True)\n    return np.array([[y[i],i] for i in x if i >= 0]) #\n  except ImportError:\n    from scipy.optimize import linear_sum_assignment\n    x, y = linear_sum_assignment(cost_matrix)\n    return np.array(list(zip(x, y)))\n\n\ndef iou_batch(bb_test, bb_gt):\n  \"\"\"\n  From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]\n  \"\"\"\n  bb_gt = np.expand_dims(bb_gt, 0)\n  bb_test = np.expand_dims(bb_test, 1)\n  \n  xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])\n  yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])\n  xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])\n  yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])\n  w = np.maximum(0., xx2 - xx1)\n  h = np.maximum(0., yy2 - yy1)\n  wh = w * h\n  o = wh / ((bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])                                      \n    + (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1]) - wh)                                              \n  return(o)\n\n\ndef hmiou(bboxes1, bboxes2):\n    \"\"\"\n    :param bbox_p: predict of bbox(N,4)(x1,y1,x2,y2)\n    :param bbox_g: groundtruth of bbox(N,4)(x1,y1,x2,y2)\n    :return:\n    \"\"\"\n    # for details should go to https://arxiv.org/pdf/1902.09630.pdf\n    # ensure predict's bbox form\n    bboxes2 = np.expand_dims(bboxes2, 0)\n    bboxes1 = np.expand_dims(bboxes1, 1)\n\n    yy11 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    yy12 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n\n    yy21 = np.minimum(bboxes1[..., 1], bboxes2[..., 1])\n    yy22 = np.maximum(bboxes1[..., 3], bboxes2[..., 3])\n    o = (yy12 - yy11) / (yy22 - yy21)\n\n    xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])\n    yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])\n    xx2 = np.minimum(bboxes1[..., 2], bboxes2[..., 2])\n    yy2 = np.minimum(bboxes1[..., 3], bboxes2[..., 3])\n    w = np.maximum(0., xx2 - xx1)\n    h = np.maximum(0., yy2 - yy1)\n    wh = w * h\n    iou = wh / ((bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])\n                + (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) - wh)\n    iou *= o\n    return iou\n\n\ndef convert_bbox_to_z(bbox):\n  \"\"\"\n  Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form\n    [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is\n    the aspect ratio\n  \"\"\"\n  w = bbox[2] - bbox[0]\n  h = bbox[3] - bbox[1]\n  x = bbox[0] + w/2.\n  y = bbox[1] + h/2.\n  s = w * h    #scale is just area\n  r = w / float(h)\n  return np.array([x, y, s, r]).reshape((4, 1))\n\n\ndef convert_x_to_bbox(x,score=None):\n  \"\"\"\n  Takes a bounding box in the centre form [x,y,s,r] and returns it in the form\n    [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right\n  \"\"\"\n  w = np.sqrt(x[2] * x[3])\n  h = x[2] / w\n  if(score==None):\n    return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.]).reshape((1,4))\n  else:\n    return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.,score]).reshape((1,5))\n\n\nclass KalmanBoxTracker(object):\n  \"\"\"\n  This class represents the internal state of individual tracked objects observed as bbox.\n  \"\"\"\n  count = 0\n  def __init__(self,bbox):\n    \"\"\"\n    Initialises a tracker using initial bounding box.\n    \"\"\"\n    #define constant velocity model\n    self.kf = KalmanFilter(dim_x=7, dim_z=4)\n    self.kf.F = np.array([[1,0,0,0,1,0,0],[0,1,0,0,0,1,0],[0,0,1,0,0,0,1],[0,0,0,1,0,0,0],  [0,0,0,0,1,0,0],[0,0,0,0,0,1,0],[0,0,0,0,0,0,1]])\n    self.kf.H = np.array([[1,0,0,0,0,0,0],[0,1,0,0,0,0,0],[0,0,1,0,0,0,0],[0,0,0,1,0,0,0]])\n\n    self.kf.R[2:,2:] *= 10.\n    self.kf.P[4:,4:] *= 1000. #give high uncertainty to the unobservable initial velocities\n    self.kf.P *= 10.\n    self.kf.Q[-1,-1] *= 0.01\n    self.kf.Q[4:,4:] *= 0.01\n\n    self.kf.x[:4] = convert_bbox_to_z(bbox)\n    self.time_since_update = 0\n    self.id = KalmanBoxTracker.count\n    KalmanBoxTracker.count += 1\n    self.history = []\n    self.hits = 0\n    self.hit_streak = 0\n    self.age = 0\n\n  def update(self,bbox):\n    \"\"\"\n    Updates the state vector with observed bbox.\n    \"\"\"\n    self.time_since_update = 0\n    self.history = []\n    self.hits += 1\n    self.hit_streak += 1\n    self.kf.update(convert_bbox_to_z(bbox))\n\n  def predict(self):\n    \"\"\"\n    Advances the state vector and returns the predicted bounding box estimate.\n    \"\"\"\n    if((self.kf.x[6]+self.kf.x[2])<=0):\n      self.kf.x[6] *= 0.0\n    self.kf.predict()\n    self.age += 1\n    if(self.time_since_update>0):\n      self.hit_streak = 0\n    self.time_since_update += 1\n    self.history.append(convert_x_to_bbox(self.kf.x))\n    return self.history[-1]\n\n  def get_state(self):\n    \"\"\"\n    Returns the current bounding box estimate.\n    \"\"\"\n    return convert_x_to_bbox(self.kf.x)\n\n\ndef associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3, args=None):\n  \"\"\"\n  Assigns detections to tracked object (both represented as bounding boxes)\n  Returns 3 lists of matches, unmatched_detections and unmatched_trackers\n  \"\"\"\n  if(len(trackers)==0):\n    return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)\n\n  # iou_matrix = iou_batch(detections, trackers)\n  if args.asso=='hmiou':\n    iou_matrix = hmiou(detections, trackers)\n  else:\n    iou_matrix = iou_batch(detections, trackers)\n    # print(\"no use hgiou!\")\n\n  if min(iou_matrix.shape) > 0:\n    a = (iou_matrix > iou_threshold).astype(np.int32)\n    if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n        matched_indices = np.stack(np.where(a), axis=1)\n    else:\n      matched_indices = linear_assignment(-iou_matrix)\n  else:\n    matched_indices = np.empty(shape=(0,2))\n\n  unmatched_detections = []\n  for d, det in enumerate(detections):\n    if(d not in matched_indices[:,0]):\n      unmatched_detections.append(d)\n  unmatched_trackers = []\n  for t, trk in enumerate(trackers):\n    if(t not in matched_indices[:,1]):\n      unmatched_trackers.append(t)\n\n  #filter out matched with low IOU\n  matches = []\n  for m in matched_indices:\n    if(iou_matrix[m[0], m[1]]<iou_threshold):\n      unmatched_detections.append(m[0])\n      unmatched_trackers.append(m[1])\n    else:\n      matches.append(m.reshape(1,2))\n  if(len(matches)==0):\n    matches = np.empty((0,2),dtype=int)\n  else:\n    matches = np.concatenate(matches,axis=0)\n\n  return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\n\nclass Sort(object):\n  def __init__(self, args, det_thresh, max_age=30, min_hits=3, iou_threshold=0.3):\n    \"\"\"\n    Sets key parameters for SORT\n    \"\"\"\n    self.max_age = max_age\n    self.min_hits = min_hits\n    self.iou_threshold = iou_threshold\n    self.trackers = []\n    self.frame_count = 0\n    self.det_thresh = det_thresh\n    self.args = args\n\n  def update(self, output_results, img_info, img_size):\n    \"\"\"\n    Params:\n      dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]\n    Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).\n    Returns the a similar array, where the last column is the object ID.\n    NOTE: The number of objects returned may differ from the number of detections provided.\n    \"\"\"\n    self.frame_count += 1\n    # post_process detections\n    output_results = output_results.cpu().numpy()\n    scores = output_results[:, 4] * output_results[:, 5]\n    bboxes = output_results[:, :4]  # x1y1x2y2\n    img_h, img_w = img_info[0], img_info[1]\n    scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n    bboxes /= scale\n    dets = np.concatenate((bboxes, np.expand_dims(scores, axis=-1)), axis=1)\n    remain_inds = scores > self.det_thresh\n    dets = dets[remain_inds]\n    # get predicted locations from existing trackers.\n    trks = np.zeros((len(self.trackers), 5))\n    to_del = []\n    ret = []\n    for t, trk in enumerate(trks):\n      pos = self.trackers[t].predict()[0]\n      trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]\n      if np.any(np.isnan(pos)):\n        to_del.append(t)\n    trks = np.ma.compress_rows(np.ma.masked_invalid(trks))\n    for t in reversed(to_del):\n      self.trackers.pop(t)\n    matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks, self.iou_threshold, args=self.args)\n\n    # update matched trackers with assigned detections\n    for m in matched:\n      self.trackers[m[1]].update(dets[m[0], :])\n\n    # create and initialise new trackers for unmatched detections\n    for i in unmatched_dets:\n        trk = KalmanBoxTracker(dets[i,:])\n        self.trackers.append(trk)\n    i = len(self.trackers)\n    for trk in reversed(self.trackers):\n        d = trk.get_state()[0]\n        if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):\n          ret.append(np.concatenate((d,[trk.id+1])).reshape(1,-1)) # +1 as MOT benchmark requires positive\n        i -= 1\n        # remove dead tracklet\n        if(trk.time_since_update > self.max_age):\n          self.trackers.pop(i)\n    if(len(ret)>0):\n      return np.concatenate(ret)\n    return np.empty((0,5))\n"
  },
  {
    "path": "trackers/sort_tracker/sort_score.py",
    "content": "\"\"\"\n    SORT: A Simple, Online and Realtime Tracker\n    Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai\n    This program is free software: you can redistribute it and/or modify\n    it under the terms of the GNU General Public License as published by\n    the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU General Public License for more details.\n    You should have received a copy of the GNU General Public License\n    along with this program.  If not, see <http://www.gnu.org/licenses/>.\n\"\"\"\nfrom __future__ import print_function\n\nimport os\nimport numpy as np\n\nfrom filterpy.kalman import KalmanFilter\n\nnp.random.seed(0)\n\n\ndef linear_assignment(cost_matrix):\n  try:\n    import lap\n    _, x, y = lap.lapjv(cost_matrix, extend_cost=True)\n    return np.array([[y[i],i] for i in x if i >= 0]) #\n  except ImportError:\n    from scipy.optimize import linear_sum_assignment\n    x, y = linear_sum_assignment(cost_matrix)\n    return np.array(list(zip(x, y)))\n\n\ndef iou_batch(bb_test, bb_gt):\n  \"\"\"\n  From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]\n  \"\"\"\n  bb_gt = np.expand_dims(bb_gt, 0)\n  bb_test = np.expand_dims(bb_test, 1)\n  \n  xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])\n  yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])\n  xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])\n  yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])\n  w = np.maximum(0., xx2 - xx1)\n  h = np.maximum(0., yy2 - yy1)\n  wh = w * h\n  o = wh / ((bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])                                      \n    + (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1]) - wh)                                              \n  return(o)\n\ndef cal_score_dif_batch(bboxes1, bboxes2):\n  \"\"\"\n  From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]\n  \"\"\"\n  bboxes2 = np.expand_dims(bboxes2, 0)\n  bboxes1 = np.expand_dims(bboxes1, 1)\n\n  score2 = bboxes2[..., 4]\n  score1 = bboxes1[..., 4]\n\n  return (abs(score2 - score1))\n\n\ndef convert_bbox_to_z(bbox):\n  \"\"\"\n  Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form\n    [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is\n    the aspect ratio\n  \"\"\"\n  w = bbox[2] - bbox[0]\n  h = bbox[3] - bbox[1]\n  x = bbox[0] + w/2.\n  y = bbox[1] + h/2.\n  s = w * h    #scale is just area\n  r = w / float(h)\n  return np.array([x, y, s, r]).reshape((4, 1))\n\n\ndef convert_x_to_bbox(x,score=None):\n  \"\"\"\n  Takes a bounding box in the centre form [x,y,s,r] and returns it in the form\n    [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right\n  \"\"\"\n  w = np.sqrt(x[2] * x[3])\n  h = x[2] / w\n  if(score==None):\n    return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.]).reshape((1,4))\n  else:\n    return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.,score]).reshape((1,5))\n\n\nclass KalmanBoxTracker(object):\n  \"\"\"\n  This class represents the internal state of individual tracked objects observed as bbox.\n  \"\"\"\n  count = 0\n  def __init__(self,bbox,det_thresh):\n    \"\"\"\n    Initialises a tracker using initial bounding box.\n    \"\"\"\n    #define constant velocity model\n    self.kf = KalmanFilter(dim_x=7, dim_z=4) \n    self.kf.F = np.array([[1,0,0,0,1,0,0],[0,1,0,0,0,1,0],[0,0,1,0,0,0,1],[0,0,0,1,0,0,0],  [0,0,0,0,1,0,0],[0,0,0,0,0,1,0],[0,0,0,0,0,0,1]])\n    self.kf.H = np.array([[1,0,0,0,0,0,0],[0,1,0,0,0,0,0],[0,0,1,0,0,0,0],[0,0,0,1,0,0,0]])\n\n    self.kf_score = KalmanFilter(dim_x=2, dim_z=1)\n    self.kf_score.F = np.array([[1, 1],\n                                [0, 1]])\n    self.kf_score.H = np.array([[1, 0]])\n\n    self.kf.R[2:,2:] *= 10.\n    self.kf.P[4:,4:] *= 1000. #give high uncertainty to the unobservable initial velocities\n    self.kf.P *= 10.\n    self.kf.Q[-1,-1] *= 0.01\n    self.kf.Q[4:,4:] *= 0.01\n    self.kf.x[:4] = convert_bbox_to_z(bbox)\n\n    self.kf_score.R[0:, 0:] *= 10.\n    self.kf_score.P[1:, 1:] *= 1000.  # give high uncertainty to the unobservable initial velocities 对不可观测的初始速度给予高度不确定性\n    self.kf_score.P *= 10.\n    self.kf_score.Q[-1, -1] *= 0.01\n    self.kf_score.Q[1:, 1:] *= 0.01\n    self.kf_score.x[:1] = bbox[-1]\n\n    self.time_since_update = 0\n    self.id = KalmanBoxTracker.count\n    KalmanBoxTracker.count += 1\n    self.history = []\n    self.hits = 0\n    self.hit_streak = 0\n    self.age = 0\n    self.det_thresh = det_thresh\n\n  def update(self,bbox):\n    \"\"\"\n    Updates the state vector with observed bbox.\n    \"\"\"\n    self.time_since_update = 0\n    self.history = []\n    self.hits += 1\n    self.hit_streak += 1\n    self.kf.update(convert_bbox_to_z(bbox))\n\n    self.kf_score.update(bbox[-1])\n\n  def predict(self):\n    \"\"\"\n    Advances the state vector and returns the predicted bounding box estimate.\n    \"\"\"\n    if((self.kf.x[6]+self.kf.x[2])<=0):\n      self.kf.x[6] *= 0.0\n    self.kf.predict()\n    self.kf_score.predict()\n    self.age += 1\n    if(self.time_since_update>0):\n      self.hit_streak = 0\n    self.time_since_update += 1\n    self.history.append(convert_x_to_bbox(self.kf.x))\n    return self.history[-1], np.clip(self.kf_score.x[0], self.det_thresh, 1.0)\n\n  def get_state(self):\n    \"\"\"\n    Returns the current bounding box estimate.\n    \"\"\"\n    return convert_x_to_bbox(self.kf.x)\n\n\ndef associate_detections_to_trackers(detections, trackers, args, iou_threshold=0.3):\n  \"\"\"\n  Assigns detections to tracked object (both represented as bounding boxes)\n  Returns 3 lists of matches, unmatched_detections and unmatched_trackers\n  \"\"\"\n  if(len(trackers)==0):\n    return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)\n\n  iou_matrix = iou_batch(detections, trackers)\n\n  if min(iou_matrix.shape) > 0:\n    a = (iou_matrix > iou_threshold).astype(np.int32)\n    if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n        matched_indices = np.stack(np.where(a), axis=1)\n    else:\n      if args.TCM_first_step:\n        cost_matrix = iou_matrix - cal_score_dif_batch(detections, trackers) * args.TCM_first_step_weight\n        matched_indices = linear_assignment(-cost_matrix)\n      else:\n        matched_indices = linear_assignment(-iou_matrix)\n  else:\n    matched_indices = np.empty(shape=(0,2))\n\n  unmatched_detections = []\n  for d, det in enumerate(detections):\n    if(d not in matched_indices[:,0]):\n      unmatched_detections.append(d)\n  unmatched_trackers = []\n  for t, trk in enumerate(trackers):\n    if(t not in matched_indices[:,1]):\n      unmatched_trackers.append(t)\n\n  #filter out matched with low IOU\n  matches = []\n  for m in matched_indices:\n    if(iou_matrix[m[0], m[1]]<iou_threshold):\n      unmatched_detections.append(m[0])\n      unmatched_trackers.append(m[1])\n    else:\n      matches.append(m.reshape(1,2))\n  if(len(matches)==0):\n    matches = np.empty((0,2),dtype=int)\n  else:\n    matches = np.concatenate(matches,axis=0)\n\n  return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\n\nclass Sort_score(object):\n  def __init__(self, args, det_thresh, max_age=30, min_hits=3, iou_threshold=0.3):\n    \"\"\"\n    Sets key parameters for SORT\n    \"\"\"\n    self.max_age = max_age\n    self.min_hits = min_hits\n    self.iou_threshold = iou_threshold\n    self.trackers = []\n    self.frame_count = 0\n    self.det_thresh = det_thresh\n    self.args = args\n\n  def update(self, output_results, img_info, img_size):\n    \"\"\"\n    Params:\n      dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]\n    Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).\n    Returns the a similar array, where the last column is the object ID.\n    NOTE: The number of objects returned may differ from the number of detections provided.\n    \"\"\"\n    self.frame_count += 1\n    # post_process detections\n    output_results = output_results.cpu().numpy()\n    scores = output_results[:, 4] * output_results[:, 5]\n    bboxes = output_results[:, :4]  # x1y1x2y2\n    img_h, img_w = img_info[0], img_info[1]\n    scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))\n    bboxes /= scale\n    dets = np.concatenate((bboxes, np.expand_dims(scores, axis=-1)), axis=1)\n    remain_inds = scores > self.det_thresh\n    dets = dets[remain_inds]\n    # get predicted locations from existing trackers.\n    trks = np.zeros((len(self.trackers), 5))\n    to_del = []\n    ret = []\n    for t, trk in enumerate(trks):\n      pos, trk_score = self.trackers[t].predict()\n      trk[:] = [pos[0][0], pos[0][1], pos[0][2], pos[0][3], trk_score]\n      if np.any(np.isnan(pos)):\n        to_del.append(t)\n    trks = np.ma.compress_rows(np.ma.masked_invalid(trks))\n    for t in reversed(to_del):\n      self.trackers.pop(t)\n    matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks, self.args, self.iou_threshold)\n\n    # update matched trackers with assigned detections\n    for m in matched:\n      self.trackers[m[1]].update(dets[m[0], :])\n\n    # create and initialise new trackers for unmatched detections\n    for i in unmatched_dets:\n        trk = KalmanBoxTracker(dets[i,:], self.det_thresh)\n        self.trackers.append(trk)\n    i = len(self.trackers)\n    for trk in reversed(self.trackers):\n        d = trk.get_state()[0]\n        if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):\n          ret.append(np.concatenate((d,[trk.id+1])).reshape(1,-1)) # +1 as MOT benchmark requires positive\n        i -= 1\n        # remove dead tracklet\n        if(trk.time_since_update > self.max_age):\n          self.trackers.pop(i)\n    if(len(ret)>0):\n      return np.concatenate(ret)\n    return np.empty((0,5))"
  },
  {
    "path": "trackers/tracking_utils/evaluation.py",
    "content": "import os\nimport numpy as np\nimport copy\nimport motmetrics as mm\nmm.lap.default_solver = 'lap'\n\nfrom trackers.tracking_utils.io import read_results, unzip_objs\n\n\nclass Evaluator(object):\n\n    def __init__(self, data_root, seq_name, data_type):\n        self.data_root = data_root\n        self.seq_name = seq_name\n        self.data_type = data_type\n\n        self.load_annotations()\n        self.reset_accumulator()\n\n    def load_annotations(self):\n        assert self.data_type == 'mot'\n\n        gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')\n        self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)\n        self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)\n\n    def reset_accumulator(self):\n        self.acc = mm.MOTAccumulator(auto_id=True)\n\n    def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):\n        # results\n        trk_tlwhs = np.copy(trk_tlwhs)\n        trk_ids = np.copy(trk_ids)\n\n        # gts\n        gt_objs = self.gt_frame_dict.get(frame_id, [])\n        gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]\n\n        # ignore boxes\n        ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])\n        ignore_tlwhs = unzip_objs(ignore_objs)[0]\n\n        # remove ignored results\n        keep = np.ones(len(trk_tlwhs), dtype=bool)\n        iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)\n        if len(iou_distance) > 0:\n            match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)\n            match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])\n            match_ious = iou_distance[match_is, match_js]\n\n            match_js = np.asarray(match_js, dtype=int)\n            match_js = match_js[np.logical_not(np.isnan(match_ious))]\n            keep[match_js] = False\n            trk_tlwhs = trk_tlwhs[keep]\n            trk_ids = trk_ids[keep]\n        #match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)\n        #match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])\n        #match_ious = iou_distance[match_is, match_js]\n\n        #match_js = np.asarray(match_js, dtype=int)\n        #match_js = match_js[np.logical_not(np.isnan(match_ious))]\n        #keep[match_js] = False\n        #trk_tlwhs = trk_tlwhs[keep]\n        #trk_ids = trk_ids[keep]\n\n        # get distance matrix\n        iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)\n\n        # acc\n        self.acc.update(gt_ids, trk_ids, iou_distance)\n\n        if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):\n            events = self.acc.last_mot_events  # only supported by https://github.com/longcw/py-motmetrics\n        else:\n            events = None\n        return events\n\n    def eval_file(self, filename):\n        self.reset_accumulator()\n\n        result_frame_dict = read_results(filename, self.data_type, is_gt=False)\n        #frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))\n        frames = sorted(list(set(result_frame_dict.keys())))\n        for frame_id in frames:\n            trk_objs = result_frame_dict.get(frame_id, [])\n            trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]\n            self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)\n\n        return self.acc\n\n    @staticmethod\n    def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):\n        names = copy.deepcopy(names)\n        if metrics is None:\n            metrics = mm.metrics.motchallenge_metrics\n        metrics = copy.deepcopy(metrics)\n\n        mh = mm.metrics.create()\n        summary = mh.compute_many(\n            accs,\n            metrics=metrics,\n            names=names,\n            generate_overall=True\n        )\n\n        return summary\n\n    @staticmethod\n    def save_summary(summary, filename):\n        import pandas as pd\n        writer = pd.ExcelWriter(filename)\n        summary.to_excel(writer)\n        writer.save()"
  },
  {
    "path": "trackers/tracking_utils/io.py",
    "content": "import os\nfrom typing import Dict\nimport numpy as np\n\n\ndef write_results(filename, results_dict: Dict, data_type: str):\n    if not filename:\n        return\n    path = os.path.dirname(filename)\n    if not os.path.exists(path):\n        os.makedirs(path)\n\n    if data_type in ('mot', 'mcmot', 'lab'):\n        save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\\n'\n    elif data_type == 'kitti':\n        save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\\n'\n    else:\n        raise ValueError(data_type)\n\n    with open(filename, 'w') as f:\n        for frame_id, frame_data in results_dict.items():\n            if data_type == 'kitti':\n                frame_id -= 1\n            for tlwh, track_id in frame_data:\n                if track_id < 0:\n                    continue\n                x1, y1, w, h = tlwh\n                x2, y2 = x1 + w, y1 + h\n                line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0)\n                f.write(line)\n\n\ndef read_results(filename, data_type: str, is_gt=False, is_ignore=False):\n    if data_type in ('mot', 'lab'):\n        read_fun = read_mot_results\n    else:\n        raise ValueError('Unknown data type: {}'.format(data_type))\n\n    return read_fun(filename, is_gt, is_ignore)\n\n\n\"\"\"\nlabels={'ped', ...\t\t\t% 1\n'person_on_vhcl', ...\t% 2\n'car', ...\t\t\t\t% 3\n'bicycle', ...\t\t\t% 4\n'mbike', ...\t\t\t% 5\n'non_mot_vhcl', ...\t\t% 6\n'static_person', ...\t% 7\n'distractor', ...\t\t% 8\n'occluder', ...\t\t\t% 9\n'occluder_on_grnd', ...\t\t%10\n'occluder_full', ...\t\t% 11\n'reflection', ...\t\t% 12\n'crowd' ...\t\t\t% 13\n};\n\"\"\"\n\n\ndef read_mot_results(filename, is_gt, is_ignore):\n    valid_labels = {1}\n    ignore_labels = {2, 7, 8, 12}\n    results_dict = dict()\n    if os.path.isfile(filename):\n        with open(filename, 'r') as f:\n            for line in f.readlines():\n                linelist = line.split(',')\n                if len(linelist) < 7:\n                    continue\n                fid = int(linelist[0])\n                if fid < 1:\n                    continue\n                results_dict.setdefault(fid, list())\n\n                box_size = float(linelist[4]) * float(linelist[5])\n\n                if is_gt:\n                    if 'MOT16-' in filename or 'MOT17-' in filename:\n                        label = int(float(linelist[7]))\n                        mark = int(float(linelist[6]))\n                        if mark == 0 or label not in valid_labels:\n                            continue\n                    score = 1\n                elif is_ignore:\n                    if 'MOT16-' in filename or 'MOT17-' in filename:\n                        label = int(float(linelist[7]))\n                        vis_ratio = float(linelist[8])\n                        if label not in ignore_labels and vis_ratio >= 0:\n                            continue\n                    else:\n                        continue\n                    score = 1\n                else:\n                    score = float(linelist[6])\n\n                #if box_size > 7000:\n                #if box_size <= 7000 or box_size >= 15000:\n                #if box_size < 15000:\n                    #continue\n\n                tlwh = tuple(map(float, linelist[2:6]))\n                target_id = int(linelist[1])\n\n                results_dict[fid].append((tlwh, target_id, score))\n\n    return results_dict\n\n\ndef unzip_objs(objs):\n    if len(objs) > 0:\n        tlwhs, ids, scores = zip(*objs)\n    else:\n        tlwhs, ids, scores = [], [], []\n    tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)\n\n    return tlwhs, ids, scores"
  },
  {
    "path": "trackers/tracking_utils/timer.py",
    "content": "import time\n\n\nclass Timer(object):\n    \"\"\"A simple timer.\"\"\"\n    def __init__(self):\n        self.total_time = 0.\n        self.calls = 0\n        self.start_time = 0.\n        self.diff = 0.\n        self.average_time = 0.\n\n        self.duration = 0.\n\n    def tic(self):\n        # using time.time instead of time.clock because time time.clock\n        # does not normalize for multithreading\n        self.start_time = time.time()\n\n    def toc(self, average=True):\n        self.diff = time.time() - self.start_time\n        self.total_time += self.diff\n        self.calls += 1\n        self.average_time = self.total_time / self.calls\n        if average:\n            self.duration = self.average_time\n        else:\n            self.duration = self.diff\n        return self.duration\n\n    def clear(self):\n        self.total_time = 0.\n        self.calls = 0\n        self.start_time = 0.\n        self.diff = 0.\n        self.average_time = 0.\n        self.duration = 0."
  },
  {
    "path": "trackeval/__init__.py",
    "content": "from .eval import Evaluator\nfrom . import datasets\nfrom . import metrics\nfrom . import plotting\nfrom . import utils\n"
  },
  {
    "path": "trackeval/_timing.py",
    "content": "from functools import wraps\nfrom time import perf_counter\nimport inspect\n\nDO_TIMING = False\nDISPLAY_LESS_PROGRESS = False\ntimer_dict = {}\ncounter = 0\n\n\ndef time(f):\n    @wraps(f)\n    def wrap(*args, **kw):\n        if DO_TIMING:\n            # Run function with timing\n            ts = perf_counter()\n            result = f(*args, **kw)\n            te = perf_counter()\n            tt = te-ts\n\n            # Get function name\n            arg_names = inspect.getfullargspec(f)[0]\n            if arg_names[0] == 'self' and DISPLAY_LESS_PROGRESS:\n                return result\n            elif arg_names[0] == 'self':\n                method_name = type(args[0]).__name__ + '.' + f.__name__\n            else:\n                method_name = f.__name__\n\n            # Record accumulative time in each function for analysis\n            if method_name in timer_dict.keys():\n                timer_dict[method_name] += tt\n            else:\n                timer_dict[method_name] = tt\n\n            # If code is finished, display timing summary\n            if method_name == \"Evaluator.evaluate\":\n                print(\"\")\n                print(\"Timing analysis:\")\n                for key, value in timer_dict.items():\n                    print('%-70s %2.4f sec' % (key, value))\n            else:\n                # Get function argument values for printing special arguments of interest\n                arg_titles = ['tracker', 'seq', 'cls']\n                arg_vals = []\n                for i, a in enumerate(arg_names):\n                    if a in arg_titles:\n                        arg_vals.append(args[i])\n                arg_text = '(' + ', '.join(arg_vals) + ')'\n\n                # Display methods and functions with different indentation.\n                if arg_names[0] == 'self':\n                    print('%-74s %2.4f sec' % (' '*4 + method_name + arg_text, tt))\n                elif arg_names[0] == 'test':\n                    pass\n                else:\n                    global counter\n                    counter += 1\n                    print('%i %-70s %2.4f sec' % (counter, method_name + arg_text, tt))\n\n            return result\n        else:\n            # If config[\"TIME_PROGRESS\"] is false, or config[\"USE_PARALLEL\"] is true, run functions normally without timing.\n            return f(*args, **kw)\n    return wrap\n"
  },
  {
    "path": "trackeval/baselines/__init__.py",
    "content": "import baseline_utils\nimport stp\nimport non_overlap\nimport pascal_colormap\nimport thresholder\nimport vizualize"
  },
  {
    "path": "trackeval/baselines/baseline_utils.py",
    "content": "\nimport os\nimport csv\nimport numpy as np\nfrom copy import deepcopy\nfrom PIL import Image\nfrom pycocotools import mask as mask_utils\nfrom scipy.optimize import linear_sum_assignment\nfrom trackeval.baselines.pascal_colormap import pascal_colormap\n\n\ndef load_seq(file_to_load):\n    \"\"\" Load input data from file in RobMOTS format (e.g. provided detections).\n    Returns: Data object with the following structure (see STP :\n        data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    \"\"\"\n    fp = open(file_to_load)\n    dialect = csv.Sniffer().sniff(fp.readline(), delimiters=' ')\n    dialect.skipinitialspace = True\n    fp.seek(0)\n    reader = csv.reader(fp, dialect)\n    read_data = {}\n    num_timesteps = 0\n    for i, row in enumerate(reader):\n        if row[-1] in '':\n            row = row[:-1]\n        t = int(row[0])\n        cid = row[1]\n        c = int(row[2])\n        s = row[3]\n        h = row[4]\n        w = row[5]\n        rle = row[6]\n\n        if t >= num_timesteps:\n            num_timesteps = t + 1\n\n        if c in read_data.keys():\n            if t in read_data[c].keys():\n                read_data[c][t]['ids'].append(cid)\n                read_data[c][t]['scores'].append(s)\n                read_data[c][t]['im_hs'].append(h)\n                read_data[c][t]['im_ws'].append(w)\n                read_data[c][t]['mask_rles'].append(rle)\n            else:\n                read_data[c][t] = {}\n                read_data[c][t]['ids'] = [cid]\n                read_data[c][t]['scores'] = [s]\n                read_data[c][t]['im_hs'] = [h]\n                read_data[c][t]['im_ws'] = [w]\n                read_data[c][t]['mask_rles'] = [rle]\n        else:\n            read_data[c] = {t: {}}\n            read_data[c][t]['ids'] = [cid]\n            read_data[c][t]['scores'] = [s]\n            read_data[c][t]['im_hs'] = [h]\n            read_data[c][t]['im_ws'] = [w]\n            read_data[c][t]['mask_rles'] = [rle]\n    fp.close()\n\n    data = {}\n    for c in read_data.keys():\n        data[c] = [{} for _ in range(num_timesteps)]\n        for t in range(num_timesteps):\n            if t in read_data[c].keys():\n                data[c][t]['ids'] = np.atleast_1d(read_data[c][t]['ids']).astype(int)\n                data[c][t]['scores'] = np.atleast_1d(read_data[c][t]['scores']).astype(float)\n                data[c][t]['im_hs'] = np.atleast_1d(read_data[c][t]['im_hs']).astype(int)\n                data[c][t]['im_ws'] = np.atleast_1d(read_data[c][t]['im_ws']).astype(int)\n                data[c][t]['mask_rles'] = np.atleast_1d(read_data[c][t]['mask_rles']).astype(str)\n            else:\n                data[c][t]['ids'] = np.empty(0).astype(int)\n                data[c][t]['scores'] = np.empty(0).astype(float)\n                data[c][t]['im_hs'] = np.empty(0).astype(int)\n                data[c][t]['im_ws'] = np.empty(0).astype(int)\n                data[c][t]['mask_rles'] = np.empty(0).astype(str)\n    return data\n\n\ndef threshold(tdata, thresh):\n    \"\"\" Removes detections below a certian threshold ('thresh') score. \"\"\"\n    new_data = {}\n    to_keep = tdata['scores'] > thresh\n    for field in ['ids', 'scores', 'im_hs', 'im_ws', 'mask_rles']:\n        new_data[field] = tdata[field][to_keep]\n    return new_data\n\n\ndef create_coco_mask(mask_rles, im_hs, im_ws):\n    \"\"\" Converts mask as rle text (+ height and width) to encoded version used by pycocotools. \"\"\"\n    coco_masks = [{'size': [h, w], 'counts': m.encode(encoding='UTF-8')}\n                  for h, w, m in zip(im_hs, im_ws, mask_rles)]\n    return coco_masks\n\n\ndef mask_iou(mask_rles1, mask_rles2, im_hs, im_ws, do_ioa=0):\n    \"\"\" Calculate mask IoU between two masks.\n    Further allows 'intersection over area' instead of IoU (over the area of mask_rle1).\n    Allows either to pass in 1 boolean for do_ioa for all mask_rles2 or also one for each mask_rles2.\n    It is recommended that mask_rles1 is a detection and mask_rles2 is a groundtruth.\n    \"\"\"\n    coco_masks1 = create_coco_mask(mask_rles1, im_hs, im_ws)\n    coco_masks2 = create_coco_mask(mask_rles2, im_hs, im_ws)\n\n    if not hasattr(do_ioa, \"__len__\"):\n        do_ioa = [do_ioa]*len(coco_masks2)\n    assert(len(coco_masks2) == len(do_ioa))\n    if len(coco_masks1) == 0 or len(coco_masks2) == 0:\n        iou = np.zeros(len(coco_masks1), len(coco_masks2))\n    else:\n        iou = mask_utils.iou(coco_masks1, coco_masks2, do_ioa)\n    return iou\n\n\ndef sort_by_score(t_data):\n    \"\"\" Sorts data by score \"\"\"\n    sort_index = np.argsort(t_data['scores'])[::-1]\n    for k in t_data.keys():\n        t_data[k] = t_data[k][sort_index]\n    return t_data\n\n\ndef mask_NMS(t_data, nms_threshold=0.5, already_sorted=False):\n    \"\"\" Remove redundant masks by performing non-maximum suppression (NMS) \"\"\"\n\n    # Sort by score\n    if not already_sorted:\n        t_data = sort_by_score(t_data)\n\n    #  Calculate the mask IoU between all detections in the timestep.\n    mask_ious_all = mask_iou(t_data['mask_rles'], t_data['mask_rles'], t_data['im_hs'], t_data['im_ws'])\n\n    # Determine which masks NMS should remove\n    # (those overlapping greater than nms_threshold with another mask that has a higher score)\n    num_dets = len(t_data['mask_rles'])\n    to_remove = [False for _ in range(num_dets)]\n    for i in range(num_dets):\n        if not to_remove[i]:\n            for j in range(i + 1, num_dets):\n                if mask_ious_all[i, j] > nms_threshold:\n                    to_remove[j] = True\n\n    # Remove detections which should be removed\n    to_keep = np.logical_not(to_remove)\n    for k in t_data.keys():\n        t_data[k] = t_data[k][to_keep]\n\n    return t_data\n\n\ndef non_overlap(t_data, already_sorted=False):\n    \"\"\" Enforces masks to be non-overlapping in an image, does this by putting masks 'on top of one another',\n    such that higher score masks 'occlude' and thus remove parts of lower scoring masks.\n\n    Help wanted: if anyone knows a way to do this WITHOUT converting the RLE to the np.array let me know, because that\n    would be MUCH more efficient. (I have tried, but haven't yet had success).\n    \"\"\"\n\n    # Sort by score\n    if not already_sorted:\n        t_data = sort_by_score(t_data)\n\n    # Get coco masks\n    coco_masks = create_coco_mask(t_data['mask_rles'], t_data['im_hs'], t_data['im_ws'])\n\n    # Create a single np.array to hold all of the non-overlapping mask\n    masks_array = np.zeros((t_data['im_hs'][0], t_data['im_ws'][0]), 'uint8')\n\n    # Decode each mask into a np.array, and place it into the overall array for the whole frame.\n    # Since masks with the lowest score are placed first, they are 'partially overridden' by masks with a higher score\n    # if they overlap.\n    for i, mask in enumerate(coco_masks[::-1]):\n        masks_array[mask_utils.decode(mask).astype('bool')] = i + 1\n\n    # Encode the resulting np.array back into a set of coco_masks which are now non-overlapping.\n    num_dets = len(coco_masks)\n    for i, j in enumerate(range(1, num_dets + 1)[::-1]):\n        coco_masks[i] = mask_utils.encode(np.asfortranarray(masks_array == j, dtype=np.uint8))\n\n    # Convert from coco_mask back into our mask_rle format.\n    t_data['mask_rles'] = [m['counts'].decode(\"utf-8\") for m in coco_masks]\n\n    return t_data\n\n\ndef masks2boxes(mask_rles, im_hs, im_ws):\n    \"\"\" Extracts bounding boxes which surround a set of masks. \"\"\"\n    coco_masks = create_coco_mask(mask_rles, im_hs, im_ws)\n    boxes = np.array([mask_utils.toBbox(x) for x in coco_masks])\n    if len(boxes) == 0:\n        boxes = np.empty((0, 4))\n    return boxes\n\n\ndef box_iou(bboxes1, bboxes2, box_format='xywh', do_ioa=False, do_giou=False):\n    \"\"\" Calculates the IOU (intersection over union) between two arrays of boxes.\n    Allows variable box formats ('xywh' and 'x0y0x1y1').\n    If do_ioa (intersection over area), then calculates the intersection over the area of boxes1 - this is commonly\n    used to determine if detections are within crowd ignore region.\n    If do_giou (generalized intersection over union, then calculates giou.\n    \"\"\"\n    if len(bboxes1) == 0 or len(bboxes2) == 0:\n        ious = np.zeros((len(bboxes1), len(bboxes2)))\n        return ious\n    if box_format in 'xywh':\n        # layout: (x0, y0, w, h)\n        bboxes1 = deepcopy(bboxes1)\n        bboxes2 = deepcopy(bboxes2)\n\n        bboxes1[:, 2] = bboxes1[:, 0] + bboxes1[:, 2]\n        bboxes1[:, 3] = bboxes1[:, 1] + bboxes1[:, 3]\n        bboxes2[:, 2] = bboxes2[:, 0] + bboxes2[:, 2]\n        bboxes2[:, 3] = bboxes2[:, 1] + bboxes2[:, 3]\n    elif box_format not in 'x0y0x1y1':\n        raise (Exception('box_format %s is not implemented' % box_format))\n\n    # layout: (x0, y0, x1, y1)\n    min_ = np.minimum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])\n    max_ = np.maximum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])\n    intersection = np.maximum(min_[..., 2] - max_[..., 0], 0) * np.maximum(min_[..., 3] - max_[..., 1], 0)\n    area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1])\n\n    if do_ioa:\n        ioas = np.zeros_like(intersection)\n        valid_mask = area1 > 0 + np.finfo('float').eps\n        ioas[valid_mask, :] = intersection[valid_mask, :] / area1[valid_mask][:, np.newaxis]\n\n        return ioas\n    else:\n        area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1])\n        union = area1[:, np.newaxis] + area2[np.newaxis, :] - intersection\n        intersection[area1 <= 0 + np.finfo('float').eps, :] = 0\n        intersection[:, area2 <= 0 + np.finfo('float').eps] = 0\n        intersection[union <= 0 + np.finfo('float').eps] = 0\n        union[union <= 0 + np.finfo('float').eps] = 1\n        ious = intersection / union\n\n    if do_giou:\n        enclosing_area = np.maximum(max_[..., 2] - min_[..., 0], 0) * np.maximum(max_[..., 3] - min_[..., 1], 0)\n        eps = 1e-7\n        # giou\n        ious = ious - ((enclosing_area - union) / (enclosing_area + eps))\n\n    return ious\n\n\ndef match(match_scores):\n    match_rows, match_cols = linear_sum_assignment(-match_scores)\n    return match_rows, match_cols\n\n\ndef write_seq(output_data, out_file):\n    out_loc = os.path.dirname(out_file)\n    if not os.path.exists(out_loc):\n        os.makedirs(out_loc, exist_ok=True)\n    fp = open(out_file, 'w', newline='')\n    writer = csv.writer(fp, delimiter=' ')\n    for row in output_data:\n        writer.writerow(row)\n    fp.close()\n\n\ndef combine_classes(data):\n    \"\"\" Converts data from a class-separated to a class-combined format.\n    Input format: data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    Output format: data[t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles', 'cls'}\n    \"\"\"\n    output_data = [{} for _ in list(data.values())[0]]\n    for cls, cls_data in data.items():\n        for timestep, t_data in enumerate(cls_data):\n            for k in t_data.keys():\n                if k in output_data[timestep].keys():\n                    output_data[timestep][k] += list(t_data[k])\n                else:\n                    output_data[timestep][k] = list(t_data[k])\n            if 'cls' in output_data[timestep].keys():\n                output_data[timestep]['cls'] += [cls]*len(output_data[timestep]['ids'])\n            else:\n                output_data[timestep]['cls'] = [cls]*len(output_data[timestep]['ids'])\n\n    for timestep, t_data in enumerate(output_data):\n        for k in t_data.keys():\n            output_data[timestep][k] = np.array(output_data[timestep][k])\n\n    return output_data\n\n\ndef save_as_png(t_data, out_file, im_h, im_w):\n    \"\"\" Save a set of segmentation masks into a PNG format, the same as used for the DAVIS dataset.\"\"\"\n\n    if len(t_data['mask_rles']) > 0:\n        coco_masks = create_coco_mask(t_data['mask_rles'], t_data['im_hs'], t_data['im_ws'])\n\n        list_of_np_masks = [mask_utils.decode(mask) for mask in coco_masks]\n\n        png = np.zeros((t_data['im_hs'][0], t_data['im_ws'][0]))\n        for mask, c_id in zip(list_of_np_masks, t_data['ids']):\n            png[mask.astype(\"bool\")] = c_id + 1\n    else:\n        png = np.zeros((im_h, im_w))\n\n    if not os.path.exists(os.path.dirname(out_file)):\n        os.makedirs(os.path.dirname(out_file))\n\n    colmap = (np.array(pascal_colormap) * 255).round().astype(\"uint8\")\n    palimage = Image.new('P', (16, 16))\n    palimage.putpalette(colmap)\n    im = Image.fromarray(np.squeeze(png.astype(\"uint8\")))\n    im2 = im.quantize(palette=palimage)\n    im2.save(out_file)\n\n\ndef get_frame_size(data):\n    \"\"\" Gets frame height and width from data. \"\"\"\n    for cls, cls_data in data.items():\n        for timestep, t_data in enumerate(cls_data):\n            if len(t_data['im_hs'] > 0):\n                im_h = t_data['im_hs'][0]\n                im_w = t_data['im_ws'][0]\n                return im_h, im_w\n    return None\n"
  },
  {
    "path": "trackeval/baselines/non_overlap.py",
    "content": "\"\"\"\nNon-Overlap: Code to take in a set of raw detections and produce a set of non-overlapping detections from it.\n\nAuthor: Jonathon Luiten\n\"\"\"\n\nimport os\nimport sys\nfrom multiprocessing.pool import Pool\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))\nfrom trackeval.baselines import baseline_utils as butils\nfrom trackeval.utils import get_code_path\n\ncode_path = get_code_path()\nconfig = {\n    'INPUT_FOL': os.path.join(code_path, 'data/detections/rob_mots/{split}/raw_supplied/data/'),\n    'OUTPUT_FOL': os.path.join(code_path, 'data/detections/rob_mots/{split}/non_overlap_supplied/data/'),\n    'SPLIT': 'train',  # valid: 'train', 'val', 'test'.\n    'Benchmarks': None,  # If None, all benchmarks in SPLIT.\n\n    'Num_Parallel_Cores': None,  # If None, run without parallel.\n\n    'THRESHOLD_NMS_MASK_IOU': 0.5,\n}\n\n\ndef do_sequence(seq_file):\n\n    # Load input data from file (e.g. provided detections)\n    # data format: data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    data = butils.load_seq(seq_file)\n\n    # Converts data from a class-separated to a class-combined format.\n    # data[t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles', 'cls'}\n    data = butils.combine_classes(data)\n\n    # Where to accumulate output data for writing out\n    output_data = []\n\n    # Run for each timestep.\n    for timestep, t_data in enumerate(data):\n\n        # Remove redundant masks by performing non-maximum suppression (NMS)\n        t_data = butils.mask_NMS(t_data, nms_threshold=config['THRESHOLD_NMS_MASK_IOU'])\n\n        # Perform non-overlap, to get non_overlapping masks.\n        t_data = butils.non_overlap(t_data, already_sorted=True)\n\n        # Save result in output format to write to file later.\n        # Output Format = [timestep ID class score im_h im_w mask_RLE]\n        for i in range(len(t_data['ids'])):\n            row = [timestep, int(t_data['ids'][i]), t_data['cls'][i], t_data['scores'][i], t_data['im_hs'][i],\n                   t_data['im_ws'][i], t_data['mask_rles'][i]]\n            output_data.append(row)\n\n    # Write results to file\n    out_file = seq_file.replace(config['INPUT_FOL'].format(split=config['SPLIT']),\n                                config['OUTPUT_FOL'].format(split=config['SPLIT']))\n    butils.write_seq(output_data, out_file)\n\n    print('DONE:', seq_file)\n\n\nif __name__ == '__main__':\n\n    # Required to fix bug in multiprocessing on windows.\n    freeze_support()\n\n    # Obtain list of sequences to run tracker for.\n    if config['Benchmarks']:\n        benchmarks = config['Benchmarks']\n    else:\n        benchmarks = ['davis_unsupervised', 'kitti_mots', 'youtube_vis', 'ovis', 'bdd_mots', 'tao']\n        if config['SPLIT'] != 'train':\n            benchmarks += ['waymo', 'mots_challenge']\n    seqs_todo = []\n    for bench in benchmarks:\n        bench_fol = os.path.join(config['INPUT_FOL'].format(split=config['SPLIT']), bench)\n        seqs_todo += [os.path.join(bench_fol, seq) for seq in os.listdir(bench_fol)]\n\n    # Run in parallel\n    if config['Num_Parallel_Cores']:\n        with Pool(config['Num_Parallel_Cores']) as pool:\n            results = pool.map(do_sequence, seqs_todo)\n\n    # Run in series\n    else:\n        for seq_todo in seqs_todo:\n            do_sequence(seq_todo)\n\n"
  },
  {
    "path": "trackeval/baselines/pascal_colormap.py",
    "content": "pascal_colormap = [\n    0     ,         0,         0,\n    0.5020,         0,         0,\n         0,    0.5020,         0,\n    0.5020,    0.5020,         0,\n         0,         0,    0.5020,\n    0.5020,         0,    0.5020,\n         0,    0.5020,    0.5020,\n    0.5020,    0.5020,    0.5020,\n    0.2510,         0,         0,\n    0.7529,         0,         0,\n    0.2510,    0.5020,         0,\n    0.7529,    0.5020,         0,\n    0.2510,         0,    0.5020,\n    0.7529,         0,    0.5020,\n    0.2510,    0.5020,    0.5020,\n    0.7529,    0.5020,    0.5020,\n         0,    0.2510,         0,\n    0.5020,    0.2510,         0,\n         0,    0.7529,         0,\n    0.5020,    0.7529,         0,\n         0,    0.2510,    0.5020,\n    0.5020,    0.2510,    0.5020,\n         0,    0.7529,    0.5020,\n    0.5020,    0.7529,    0.5020,\n    0.2510,    0.2510,         0,\n    0.7529,    0.2510,         0,\n    0.2510,    0.7529,         0,\n    0.7529,    0.7529,         0,\n    0.2510,    0.2510,    0.5020,\n    0.7529,    0.2510,    0.5020,\n    0.2510,    0.7529,    0.5020,\n    0.7529,    0.7529,    0.5020,\n         0,         0,    0.2510,\n    0.5020,         0,    0.2510,\n         0,    0.5020,    0.2510,\n    0.5020,    0.5020,    0.2510,\n         0,         0,    0.7529,\n    0.5020,         0,    0.7529,\n         0,    0.5020,    0.7529,\n    0.5020,    0.5020,    0.7529,\n    0.2510,         0,    0.2510,\n    0.7529,         0,    0.2510,\n    0.2510,    0.5020,    0.2510,\n    0.7529,    0.5020,    0.2510,\n    0.2510,         0,    0.7529,\n    0.7529,         0,    0.7529,\n    0.2510,    0.5020,    0.7529,\n    0.7529,    0.5020,    0.7529,\n         0,    0.2510,    0.2510,\n    0.5020,    0.2510,    0.2510,\n         0,    0.7529,    0.2510,\n    0.5020,    0.7529,    0.2510,\n         0,    0.2510,    0.7529,\n    0.5020,    0.2510,    0.7529,\n         0,    0.7529,    0.7529,\n    0.5020,    0.7529,    0.7529,\n    0.2510,    0.2510,    0.2510,\n    0.7529,    0.2510,    0.2510,\n    0.2510,    0.7529,    0.2510,\n    0.7529,    0.7529,    0.2510,\n    0.2510,    0.2510,    0.7529,\n    0.7529,    0.2510,    0.7529,\n    0.2510,    0.7529,    0.7529,\n    0.7529,    0.7529,    0.7529,\n    0.1255,         0,         0,\n    0.6275,         0,         0,\n    0.1255,    0.5020,         0,\n    0.6275,    0.5020,         0,\n    0.1255,         0,    0.5020,\n    0.6275,         0,    0.5020,\n    0.1255,    0.5020,    0.5020,\n    0.6275,    0.5020,    0.5020,\n    0.3765,         0,         0,\n    0.8784,         0,         0,\n    0.3765,    0.5020,         0,\n    0.8784,    0.5020,         0,\n    0.3765,         0,    0.5020,\n    0.8784,         0,    0.5020,\n    0.3765,    0.5020,    0.5020,\n    0.8784,    0.5020,    0.5020,\n    0.1255,    0.2510,         0,\n    0.6275,    0.2510,         0,\n    0.1255,    0.7529,         0,\n    0.6275,    0.7529,         0,\n    0.1255,    0.2510,    0.5020,\n    0.6275,    0.2510,    0.5020,\n    0.1255,    0.7529,    0.5020,\n    0.6275,    0.7529,    0.5020,\n    0.3765,    0.2510,         0,\n    0.8784,    0.2510,         0,\n    0.3765,    0.7529,         0,\n    0.8784,    0.7529,         0,\n    0.3765,    0.2510,    0.5020,\n    0.8784,    0.2510,    0.5020,\n    0.3765,    0.7529,    0.5020,\n    0.8784,    0.7529,    0.5020,\n    0.1255,         0,    0.2510,\n    0.6275,         0,    0.2510,\n    0.1255,    0.5020,    0.2510,\n    0.6275,    0.5020,    0.2510,\n    0.1255,         0,    0.7529,\n    0.6275,         0,    0.7529,\n    0.1255,    0.5020,    0.7529,\n    0.6275,    0.5020,    0.7529,\n    0.3765,         0,    0.2510,\n    0.8784,         0,    0.2510,\n    0.3765,    0.5020,    0.2510,\n    0.8784,    0.5020,    0.2510,\n    0.3765,         0,    0.7529,\n    0.8784,         0,    0.7529,\n    0.3765,    0.5020,    0.7529,\n    0.8784,    0.5020,    0.7529,\n    0.1255,    0.2510,    0.2510,\n    0.6275,    0.2510,    0.2510,\n    0.1255,    0.7529,    0.2510,\n    0.6275,    0.7529,    0.2510,\n    0.1255,    0.2510,    0.7529,\n    0.6275,    0.2510,    0.7529,\n    0.1255,    0.7529,    0.7529,\n    0.6275,    0.7529,    0.7529,\n    0.3765,    0.2510,    0.2510,\n    0.8784,    0.2510,    0.2510,\n    0.3765,    0.7529,    0.2510,\n    0.8784,    0.7529,    0.2510,\n    0.3765,    0.2510,    0.7529,\n    0.8784,    0.2510,    0.7529,\n    0.3765,    0.7529,    0.7529,\n    0.8784,    0.7529,    0.7529,\n         0,    0.1255,         0,\n    0.5020,    0.1255,         0,\n         0,    0.6275,         0,\n    0.5020,    0.6275,         0,\n         0,    0.1255,    0.5020,\n    0.5020,    0.1255,    0.5020,\n         0,    0.6275,    0.5020,\n    0.5020,    0.6275,    0.5020,\n    0.2510,    0.1255,         0,\n    0.7529,    0.1255,         0,\n    0.2510,    0.6275,         0,\n    0.7529,    0.6275,         0,\n    0.2510,    0.1255,    0.5020,\n    0.7529,    0.1255,    0.5020,\n    0.2510,    0.6275,    0.5020,\n    0.7529,    0.6275,    0.5020,\n         0,    0.3765,         0,\n    0.5020,    0.3765,         0,\n         0,    0.8784,         0,\n    0.5020,    0.8784,         0,\n         0,    0.3765,    0.5020,\n    0.5020,    0.3765,    0.5020,\n         0,    0.8784,    0.5020,\n    0.5020,    0.8784,    0.5020,\n    0.2510,    0.3765,         0,\n    0.7529,    0.3765,         0,\n    0.2510,    0.8784,         0,\n    0.7529,    0.8784,         0,\n    0.2510,    0.3765,    0.5020,\n    0.7529,    0.3765,    0.5020,\n    0.2510,    0.8784,    0.5020,\n    0.7529,    0.8784,    0.5020,\n         0,    0.1255,    0.2510,\n    0.5020,    0.1255,    0.2510,\n         0,    0.6275,    0.2510,\n    0.5020,    0.6275,    0.2510,\n         0,    0.1255,    0.7529,\n    0.5020,    0.1255,    0.7529,\n         0,    0.6275,    0.7529,\n    0.5020,    0.6275,    0.7529,\n    0.2510,    0.1255,    0.2510,\n    0.7529,    0.1255,    0.2510,\n    0.2510,    0.6275,    0.2510,\n    0.7529,    0.6275,    0.2510,\n    0.2510,    0.1255,    0.7529,\n    0.7529,    0.1255,    0.7529,\n    0.2510,    0.6275,    0.7529,\n    0.7529,    0.6275,    0.7529,\n         0,    0.3765,    0.2510,\n    0.5020,    0.3765,    0.2510,\n         0,    0.8784,    0.2510,\n    0.5020,    0.8784,    0.2510,\n         0,    0.3765,    0.7529,\n    0.5020,    0.3765,    0.7529,\n         0,    0.8784,    0.7529,\n    0.5020,    0.8784,    0.7529,\n    0.2510,    0.3765,    0.2510,\n    0.7529,    0.3765,    0.2510,\n    0.2510,    0.8784,    0.2510,\n    0.7529,    0.8784,    0.2510,\n    0.2510,    0.3765,    0.7529,\n    0.7529,    0.3765,    0.7529,\n    0.2510,    0.8784,    0.7529,\n    0.7529,    0.8784,    0.7529,\n    0.1255,    0.1255,         0,\n    0.6275,    0.1255,         0,\n    0.1255,    0.6275,         0,\n    0.6275,    0.6275,         0,\n    0.1255,    0.1255,    0.5020,\n    0.6275,    0.1255,    0.5020,\n    0.1255,    0.6275,    0.5020,\n    0.6275,    0.6275,    0.5020,\n    0.3765,    0.1255,         0,\n    0.8784,    0.1255,         0,\n    0.3765,    0.6275,         0,\n    0.8784,    0.6275,         0,\n    0.3765,    0.1255,    0.5020,\n    0.8784,    0.1255,    0.5020,\n    0.3765,    0.6275,    0.5020,\n    0.8784,    0.6275,    0.5020,\n    0.1255,    0.3765,         0,\n    0.6275,    0.3765,         0,\n    0.1255,    0.8784,         0,\n    0.6275,    0.8784,         0,\n    0.1255,    0.3765,    0.5020,\n    0.6275,    0.3765,    0.5020,\n    0.1255,    0.8784,    0.5020,\n    0.6275,    0.8784,    0.5020,\n    0.3765,    0.3765,         0,\n    0.8784,    0.3765,         0,\n    0.3765,    0.8784,         0,\n    0.8784,    0.8784,         0,\n    0.3765,    0.3765,    0.5020,\n    0.8784,    0.3765,    0.5020,\n    0.3765,    0.8784,    0.5020,\n    0.8784,    0.8784,    0.5020,\n    0.1255,    0.1255,    0.2510,\n    0.6275,    0.1255,    0.2510,\n    0.1255,    0.6275,    0.2510,\n    0.6275,    0.6275,    0.2510,\n    0.1255,    0.1255,    0.7529,\n    0.6275,    0.1255,    0.7529,\n    0.1255,    0.6275,    0.7529,\n    0.6275,    0.6275,    0.7529,\n    0.3765,    0.1255,    0.2510,\n    0.8784,    0.1255,    0.2510,\n    0.3765,    0.6275,    0.2510,\n    0.8784,    0.6275,    0.2510,\n    0.3765,    0.1255,    0.7529,\n    0.8784,    0.1255,    0.7529,\n    0.3765,    0.6275,    0.7529,\n    0.8784,    0.6275,    0.7529,\n    0.1255,    0.3765,    0.2510,\n    0.6275,    0.3765,    0.2510,\n    0.1255,    0.8784,    0.2510,\n    0.6275,    0.8784,    0.2510,\n    0.1255,    0.3765,    0.7529,\n    0.6275,    0.3765,    0.7529,\n    0.1255,    0.8784,    0.7529,\n    0.6275,    0.8784,    0.7529,\n    0.3765,    0.3765,    0.2510,\n    0.8784,    0.3765,    0.2510,\n    0.3765,    0.8784,    0.2510,\n    0.8784,    0.8784,    0.2510,\n    0.3765,    0.3765,    0.7529,\n    0.8784,    0.3765,    0.7529,\n    0.3765,    0.8784,    0.7529,\n    0.8784,    0.8784,    0.7529]"
  },
  {
    "path": "trackeval/baselines/stp.py",
    "content": "\"\"\"\nSTP: Simplest Tracker Possible\n\nAuthor: Jonathon Luiten\n\nThis simple tracker, simply assigns track IDs which maximise the 'bounding box IoU' between previous tracks and current\ndetections. It is also able to match detections to tracks at more than one timestep previously.\n\"\"\"\n\nimport os\nimport sys\nimport numpy as np\nfrom multiprocessing.pool import Pool\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))\nfrom trackeval.baselines import baseline_utils as butils\nfrom trackeval.utils import get_code_path\n\ncode_path = get_code_path()\nconfig = {\n    'INPUT_FOL': os.path.join(code_path, 'data/detections/rob_mots/{split}/non_overlap_supplied/data/'),\n    'OUTPUT_FOL': os.path.join(code_path, 'data/trackers/rob_mots/{split}/STP/data/'),\n    'SPLIT': 'train',  # valid: 'train', 'val', 'test'.\n    'Benchmarks': None,  # If None, all benchmarks in SPLIT.\n\n    'Num_Parallel_Cores': None,  # If None, run without parallel.\n\n    'DETECTION_THRESHOLD': 0.5,\n    'ASSOCIATION_THRESHOLD': 1e-10,\n    'MAX_FRAMES_SKIP': 7\n}\n\n\ndef track_sequence(seq_file):\n\n    # Load input data from file (e.g. provided detections)\n    # data format: data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    data = butils.load_seq(seq_file)\n\n    # Where to accumulate output data for writing out\n    output_data = []\n\n    # To ensure IDs are unique per object across all classes.\n    curr_max_id = 0\n\n    # Run tracker for each class.\n    for cls, cls_data in data.items():\n\n        # Initialize container for holding previously tracked objects.\n        prev = {'boxes': np.empty((0, 4)),\n                'ids': np.array([], np.int),\n                'timesteps': np.array([])}\n\n        # Run tracker for each timestep.\n        for timestep, t_data in enumerate(cls_data):\n\n            # Threshold detections.\n            t_data = butils.threshold(t_data, config['DETECTION_THRESHOLD'])\n\n            # Convert mask dets to bounding boxes.\n            boxes = butils.masks2boxes(t_data['mask_rles'], t_data['im_hs'], t_data['im_ws'])\n\n            # Calculate IoU between previous and current frame dets.\n            ious = butils.box_iou(prev['boxes'], boxes)\n\n            # Score which decreases quickly for previous dets depending on how many timesteps before they come from.\n            prev_timestep_scores = np.power(10, -1 * prev['timesteps'])\n\n            # Matching score is such that it first tries to match 'most recent timesteps',\n            # and within each timestep maximised IoU.\n            match_scores = prev_timestep_scores[:, np.newaxis] * ious\n\n            # Find best matching between current dets and previous tracks.\n            match_rows, match_cols = butils.match(match_scores)\n\n            # Remove matches that have an IoU below a certain threshold.\n            actually_matched_mask = ious[match_rows, match_cols] > config['ASSOCIATION_THRESHOLD']\n            match_rows = match_rows[actually_matched_mask]\n            match_cols = match_cols[actually_matched_mask]\n\n            # Assign the prev track ID to the current dets if they were matched.\n            ids = np.nan * np.ones((len(boxes),), np.int)\n            ids[match_cols] = prev['ids'][match_rows]\n\n            # Create new track IDs for dets that were not matched to previous tracks.\n            num_not_matched = len(ids) - len(match_cols)\n            new_ids = np.arange(curr_max_id + 1, curr_max_id + num_not_matched + 1)\n            ids[np.isnan(ids)] = new_ids\n\n            # Update maximum ID to ensure future added tracks have a unique ID value.\n            curr_max_id += num_not_matched\n\n            # Drop tracks from 'previous tracks' if they have not been matched in the last MAX_FRAMES_SKIP frames.\n            unmatched_rows = [i for i in range(len(prev['ids'])) if\n                              i not in match_rows and (prev['timesteps'][i] + 1 <= config['MAX_FRAMES_SKIP'])]\n\n            # Update the set of previous tracking results to include the newly tracked detections.\n            prev['ids'] = np.concatenate((ids, prev['ids'][unmatched_rows]), axis=0)\n            prev['boxes'] = np.concatenate((np.atleast_2d(boxes), np.atleast_2d(prev['boxes'][unmatched_rows])), axis=0)\n            prev['timesteps'] = np.concatenate((np.zeros((len(ids),)), prev['timesteps'][unmatched_rows] + 1), axis=0)\n\n            # Save result in output format to write to file later.\n            # Output Format = [timestep ID class score im_h im_w mask_RLE]\n            for i in range(len(t_data['ids'])):\n                row = [timestep, int(ids[i]), cls, t_data['scores'][i], t_data['im_hs'][i], t_data['im_ws'][i],\n                       t_data['mask_rles'][i]]\n                output_data.append(row)\n\n    # Write results to file\n    out_file = seq_file.replace(config['INPUT_FOL'].format(split=config['SPLIT']),\n                                config['OUTPUT_FOL'].format(split=config['SPLIT']))\n    butils.write_seq(output_data, out_file)\n\n    print('DONE:', seq_file)\n\n\nif __name__ == '__main__':\n\n    # Required to fix bug in multiprocessing on windows.\n    freeze_support()\n\n    # Obtain list of sequences to run tracker for.\n    if config['Benchmarks']:\n        benchmarks = config['Benchmarks']\n    else:\n        benchmarks = ['davis_unsupervised', 'kitti_mots', 'youtube_vis', 'ovis', 'bdd_mots', 'tao']\n        if config['SPLIT'] != 'train':\n            benchmarks += ['waymo', 'mots_challenge']\n    seqs_todo = []\n    for bench in benchmarks:\n        bench_fol = os.path.join(config['INPUT_FOL'].format(split=config['SPLIT']), bench)\n        seqs_todo += [os.path.join(bench_fol, seq) for seq in os.listdir(bench_fol)]\n\n    # Run in parallel\n    if config['Num_Parallel_Cores']:\n        with Pool(config['Num_Parallel_Cores']) as pool:\n            results = pool.map(track_sequence, seqs_todo)\n\n    # Run in series\n    else:\n        for seq_todo in seqs_todo:\n            track_sequence(seq_todo)\n\n"
  },
  {
    "path": "trackeval/baselines/thresholder.py",
    "content": "\"\"\"\nThresholder\n\nAuthor: Jonathon Luiten\n\nSimply reads in a set of detection, thresholds them at a certain score threshold, and writes them out again.\n\"\"\"\n\nimport os\nimport sys\nfrom multiprocessing.pool import Pool\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))\nfrom trackeval.baselines import baseline_utils as butils\nfrom trackeval.utils import get_code_path\n\nTHRESHOLD = 0.2\n\ncode_path = get_code_path()\nconfig = {\n    'INPUT_FOL': os.path.join(code_path, 'data/detections/rob_mots/{split}/non_overlap_supplied/data/'),\n    'OUTPUT_FOL': os.path.join(code_path, 'data/detections/rob_mots/{split}/threshold_' + str(100*THRESHOLD) + '/data/'),\n    'SPLIT': 'train',  # valid: 'train', 'val', 'test'.\n    'Benchmarks': None,  # If None, all benchmarks in SPLIT.\n\n    'Num_Parallel_Cores': None,  # If None, run without parallel.\n\n    'DETECTION_THRESHOLD': THRESHOLD,\n}\n\n\ndef do_sequence(seq_file):\n\n    # Load input data from file (e.g. provided detections)\n    # data format: data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    data = butils.load_seq(seq_file)\n\n    # Where to accumulate output data for writing out\n    output_data = []\n\n    # Run for each class.\n    for cls, cls_data in data.items():\n\n        # Run for each timestep.\n        for timestep, t_data in enumerate(cls_data):\n\n            # Threshold detections.\n            t_data = butils.threshold(t_data, config['DETECTION_THRESHOLD'])\n\n            # Save result in output format to write to file later.\n            # Output Format = [timestep ID class score im_h im_w mask_RLE]\n            for i in range(len(t_data['ids'])):\n                row = [timestep, int(t_data['ids'][i]), cls, t_data['scores'][i], t_data['im_hs'][i],\n                       t_data['im_ws'][i], t_data['mask_rles'][i]]\n                output_data.append(row)\n\n    # Write results to file\n    out_file = seq_file.replace(config['INPUT_FOL'].format(split=config['SPLIT']),\n                                config['OUTPUT_FOL'].format(split=config['SPLIT']))\n    butils.write_seq(output_data, out_file)\n\n    print('DONE:', seq_todo)\n\n\nif __name__ == '__main__':\n\n    # Required to fix bug in multiprocessing on windows.\n    freeze_support()\n\n    # Obtain list of sequences to run tracker for.\n    if config['Benchmarks']:\n        benchmarks = config['Benchmarks']\n    else:\n        benchmarks = ['davis_unsupervised', 'kitti_mots', 'youtube_vis', 'ovis', 'bdd_mots', 'tao']\n        if config['SPLIT'] != 'train':\n            benchmarks += ['waymo', 'mots_challenge']\n    seqs_todo = []\n    for bench in benchmarks:\n        bench_fol = os.path.join(config['INPUT_FOL'].format(split=config['SPLIT']), bench)\n        seqs_todo += [os.path.join(bench_fol, seq) for seq in os.listdir(bench_fol)]\n\n    # Run in parallel\n    if config['Num_Parallel_Cores']:\n        with Pool(config['Num_Parallel_Cores']) as pool:\n            results = pool.map(do_sequence, seqs_todo)\n\n    # Run in series\n    else:\n        for seq_todo in seqs_todo:\n            do_sequence(seq_todo)\n\n"
  },
  {
    "path": "trackeval/baselines/vizualize.py",
    "content": "\"\"\"\nVizualize: Code which converts .txt rle tracking results into a visual .png format.\n\nAuthor: Jonathon Luiten\n\"\"\"\n\nimport os\nimport sys\nfrom multiprocessing.pool import Pool\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))\nfrom trackeval.baselines import baseline_utils as butils\nfrom trackeval.utils import get_code_path\nfrom trackeval.datasets.rob_mots_classmap import cls_id_to_name\n\ncode_path = get_code_path()\nconfig = {\n    # Tracker format:\n    'INPUT_FOL': os.path.join(code_path, 'data/trackers/rob_mots/{split}/STP/data/{bench}'),\n    'OUTPUT_FOL': os.path.join(code_path, 'data/viz/rob_mots/{split}/STP/data/{bench}'),\n    # GT format:\n    # 'INPUT_FOL': os.path.join(code_path, 'data/gt/rob_mots/{split}/{bench}/data/'),\n    # 'OUTPUT_FOL': os.path.join(code_path, 'data/gt_viz/rob_mots/{split}/{bench}/'),\n    'SPLIT': 'train',  # valid: 'train', 'val', 'test'.\n    'Benchmarks': None,  # If None, all benchmarks in SPLIT.\n    'Num_Parallel_Cores': None,  # If None, run without parallel.\n}\n\n\ndef do_sequence(seq_file):\n    # Folder to save resulting visualization in\n    out_fol = seq_file.replace(config['INPUT_FOL'].format(split=config['SPLIT'], bench=bench),\n                               config['OUTPUT_FOL'].format(split=config['SPLIT'], bench=bench)).replace('.txt', '')\n\n    # Load input data from file (e.g. provided detections)\n    # data format: data['cls'][t] = {'ids', 'scores', 'im_hs', 'im_ws', 'mask_rles'}\n    data = butils.load_seq(seq_file)\n\n    # Get frame size for visualizing empty frames\n    im_h, im_w = butils.get_frame_size(data)\n\n    # First run for each class.\n    for cls, cls_data in data.items():\n\n        if cls >= 100:\n            continue\n\n        # Run for each timestep.\n        for timestep, t_data in enumerate(cls_data):\n            # Save out visualization\n            out_file = os.path.join(out_fol, cls_id_to_name[cls], str(timestep).zfill(5) + '.png')\n            butils.save_as_png(t_data, out_file, im_h, im_w)\n\n\n    # Then run for all classes combined\n    # Converts data from a class-separated to a class-combined format.\n    data = butils.combine_classes(data)\n\n    # Run for each timestep.\n    for timestep, t_data in enumerate(data):\n        # Save out visualization\n        out_file = os.path.join(out_fol, 'all_classes', str(timestep).zfill(5) + '.png')\n        butils.save_as_png(t_data, out_file, im_h, im_w)\n\n    print('DONE:', seq_file)\n\n\nif __name__ == '__main__':\n\n    # Required to fix bug in multiprocessing on windows.\n    freeze_support()\n\n    # Obtain list of sequences to run tracker for.\n    if config['Benchmarks']:\n        benchmarks = config['Benchmarks']\n    else:\n        benchmarks = ['davis_unsupervised', 'kitti_mots', 'youtube_vis', 'ovis', 'bdd_mots', 'tao']\n        if config['SPLIT'] != 'train':\n            benchmarks += ['waymo', 'mots_challenge']\n    seqs_todo = []\n    for bench in benchmarks:\n        bench_fol = config['INPUT_FOL'].format(split=config['SPLIT'], bench=bench)\n        seqs_todo += [os.path.join(bench_fol, seq) for seq in os.listdir(bench_fol)]\n\n    # Run in parallel\n    if config['Num_Parallel_Cores']:\n        with Pool(config['Num_Parallel_Cores']) as pool:\n            results = pool.map(do_sequence, seqs_todo)\n\n    # Run in series\n    else:\n        for seq_todo in seqs_todo:\n            do_sequence(seq_todo)\n"
  },
  {
    "path": "trackeval/eval.py",
    "content": "import time\nimport traceback\nfrom multiprocessing.pool import Pool\nfrom functools import partial\nimport os\nfrom . import utils\nfrom .utils import TrackEvalException\nfrom . import _timing\nfrom .metrics import Count\n\n\nclass Evaluator:\n    \"\"\"Evaluator class for evaluating different metrics for different datasets\"\"\"\n\n    @staticmethod\n    def get_default_eval_config():\n        \"\"\"Returns the default config values for evaluation\"\"\"\n        code_path = utils.get_code_path()\n        default_config = {\n            'USE_PARALLEL': False,\n            'NUM_PARALLEL_CORES': 8,\n            'BREAK_ON_ERROR': True,  # Raises exception and exits with error\n            'RETURN_ON_ERROR': False,  # if not BREAK_ON_ERROR, then returns from function on error\n            'LOG_ON_ERROR': os.path.join(code_path, 'error_log.txt'),  # if not None, save any errors into a log file.\n\n            'PRINT_RESULTS': True,\n            'PRINT_ONLY_COMBINED': False,\n            'PRINT_CONFIG': True,\n            'TIME_PROGRESS': True,\n            'DISPLAY_LESS_PROGRESS': True,\n\n            'OUTPUT_SUMMARY': True,\n            'OUTPUT_EMPTY_CLASSES': True,  # If False, summary files are not output for classes with no detections\n            'OUTPUT_DETAILED': True,\n            'PLOT_CURVES': True,\n        }\n        return default_config\n\n    def __init__(self, config=None, save_path=None):\n        \"\"\"Initialise the evaluator with a config file\"\"\"\n        self.config = utils.init_config(config, self.get_default_eval_config(), 'Eval')\n        self.save_path = save_path\n        # Only run timing analysis if not run in parallel.\n        if self.config['TIME_PROGRESS'] and not self.config['USE_PARALLEL']:\n            _timing.DO_TIMING = True\n            if self.config['DISPLAY_LESS_PROGRESS']:\n                _timing.DISPLAY_LESS_PROGRESS = True\n\n    @_timing.time\n    def evaluate(self, dataset_list, metrics_list):\n        \"\"\"Evaluate a set of metrics on a set of datasets\"\"\"\n        config = self.config\n        metrics_list = metrics_list + [Count()]  # Count metrics are always run\n        metric_names = utils.validate_metrics_list(metrics_list)\n        dataset_names = [dataset.get_name() for dataset in dataset_list]\n        output_res = {}\n        output_msg = {}\n\n        for dataset, dataset_name in zip(dataset_list, dataset_names):\n            # Get dataset info about what to evaluate\n            output_res[dataset_name] = {}\n            output_msg[dataset_name] = {}\n            tracker_list, seq_list, class_list = dataset.get_eval_info()\n            print('\\nEvaluating %i tracker(s) on %i sequence(s) for %i class(es) on %s dataset using the following '\n                  'metrics: %s\\n' % (len(tracker_list), len(seq_list), len(class_list), dataset_name,\n                                     ', '.join(metric_names)))\n\n            # Evaluate each tracker\n            for tracker in tracker_list:\n                # if not config['BREAK_ON_ERROR'] then go to next tracker without breaking\n                try:\n                    # Evaluate each sequence in parallel or in series.\n                    # returns a nested dict (res), indexed like: res[seq][class][metric_name][sub_metric field]\n                    # e.g. res[seq_0001][pedestrian][hota][DetA]\n                    print('\\nEvaluating %s\\n' % tracker)\n                    time_start = time.time()\n                    if config['USE_PARALLEL']:\n                        with Pool(config['NUM_PARALLEL_CORES']) as pool:\n                            _eval_sequence = partial(eval_sequence, dataset=dataset, tracker=tracker,\n                                                     class_list=class_list, metrics_list=metrics_list,\n                                                     metric_names=metric_names, save_path=self.save_path)\n                            results = pool.map(_eval_sequence, seq_list)\n                            res = dict(zip(seq_list, results))\n                    else:\n                        res = {}\n                        for curr_seq in sorted(seq_list):\n                            res[curr_seq] = eval_sequence(curr_seq, dataset, tracker, class_list, metrics_list,\n                                                          metric_names, self.save_path)\n\n                    # Combine results over all sequences and then over all classes\n\n                    # collecting combined cls keys (cls averaged, det averaged, super classes)\n                    combined_cls_keys = []\n                    res['COMBINED_SEQ'] = {}\n                    # combine sequences for each class\n                    for c_cls in class_list:\n                        res['COMBINED_SEQ'][c_cls] = {}\n                        for metric, metric_name in zip(metrics_list, metric_names):\n                            curr_res = {seq_key: seq_value[c_cls][metric_name] for seq_key, seq_value in res.items() if\n                                        seq_key != 'COMBINED_SEQ'}\n                            res['COMBINED_SEQ'][c_cls][metric_name] = metric.combine_sequences(curr_res)\n                    # combine classes\n                    if dataset.should_classes_combine:\n                        combined_cls_keys += ['cls_comb_cls_av', 'cls_comb_det_av', 'all']\n                        res['COMBINED_SEQ']['cls_comb_cls_av'] = {}\n                        res['COMBINED_SEQ']['cls_comb_det_av'] = {}\n                        for metric, metric_name in zip(metrics_list, metric_names):\n                            cls_res = {cls_key: cls_value[metric_name] for cls_key, cls_value in\n                                       res['COMBINED_SEQ'].items() if cls_key not in combined_cls_keys}\n                            res['COMBINED_SEQ']['cls_comb_cls_av'][metric_name] = \\\n                                metric.combine_classes_class_averaged(cls_res)\n                            res['COMBINED_SEQ']['cls_comb_det_av'][metric_name] = \\\n                                metric.combine_classes_det_averaged(cls_res)\n                    # combine classes to super classes\n                    if dataset.use_super_categories:\n                        for cat, sub_cats in dataset.super_categories.items():\n                            combined_cls_keys.append(cat)\n                            res['COMBINED_SEQ'][cat] = {}\n                            for metric, metric_name in zip(metrics_list, metric_names):\n                                cat_res = {cls_key: cls_value[metric_name] for cls_key, cls_value in\n                                           res['COMBINED_SEQ'].items() if cls_key in sub_cats}\n                                res['COMBINED_SEQ'][cat][metric_name] = metric.combine_classes_det_averaged(cat_res)\n\n                    # Print and output results in various formats\n                    if config['TIME_PROGRESS']:\n                        print('\\nAll sequences for %s finished in %.2f seconds' % (tracker, time.time() - time_start))\n                    output_fol = dataset.get_output_fol(tracker)\n                    tracker_display_name = dataset.get_display_name(tracker)\n                    for c_cls in res['COMBINED_SEQ'].keys():  # class_list + combined classes if calculated\n                        summaries = []\n                        details = []\n                        num_dets = res['COMBINED_SEQ'][c_cls]['Count']['Dets']\n                        if config['OUTPUT_EMPTY_CLASSES'] or num_dets > 0:\n                            for metric, metric_name in zip(metrics_list, metric_names):\n                                # for combined classes there is no per sequence evaluation\n                                if c_cls in combined_cls_keys:\n                                    table_res = {'COMBINED_SEQ': res['COMBINED_SEQ'][c_cls][metric_name]}\n                                else:\n                                    table_res = {seq_key: seq_value[c_cls][metric_name] for seq_key, seq_value\n                                                 in res.items()}\n\n                                if config['PRINT_RESULTS'] and config['PRINT_ONLY_COMBINED']:\n                                    dont_print = dataset.should_classes_combine and c_cls not in combined_cls_keys\n                                    if not dont_print:\n                                        metric.print_table({'COMBINED_SEQ': table_res['COMBINED_SEQ']},\n                                                           tracker_display_name, c_cls)\n                                elif config['PRINT_RESULTS']:\n                                    metric.print_table(table_res, tracker_display_name, c_cls)\n                                if config['OUTPUT_SUMMARY']:\n                                    summaries.append(metric.summary_results(table_res))\n                                if config['OUTPUT_DETAILED']:\n                                    details.append(metric.detailed_results(table_res))\n                                if config['PLOT_CURVES']:\n                                    metric.plot_single_tracker_results(table_res, tracker_display_name, c_cls,\n                                                                       output_fol)\n                            if config['OUTPUT_SUMMARY']:\n                                utils.write_summary_results(summaries, c_cls, output_fol)\n                            if config['OUTPUT_DETAILED']:\n                                utils.write_detailed_results(details, c_cls, output_fol)\n\n                    # Output for returning from function\n                    output_res[dataset_name][tracker] = res\n                    output_msg[dataset_name][tracker] = 'Success'\n\n                except Exception as err:\n                    output_res[dataset_name][tracker] = None\n                    if type(err) == TrackEvalException:\n                        output_msg[dataset_name][tracker] = str(err)\n                    else:\n                        output_msg[dataset_name][tracker] = 'Unknown error occurred.'\n                    print('Tracker %s was unable to be evaluated.' % tracker)\n                    print(err)\n                    traceback.print_exc()\n                    if config['LOG_ON_ERROR'] is not None:\n                        with open(config['LOG_ON_ERROR'], 'a') as f:\n                            print(dataset_name, file=f)\n                            print(tracker, file=f)\n                            print(traceback.format_exc(), file=f)\n                            print('\\n\\n\\n', file=f)\n                    if config['BREAK_ON_ERROR']:\n                        raise err\n                    elif config['RETURN_ON_ERROR']:\n                        return output_res, output_msg\n\n        return output_res, output_msg\n\n\n@_timing.time\ndef eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names, save_path=None):\n    \"\"\"Function for evaluating a single sequence\"\"\"\n    \n    raw_data = dataset.get_raw_seq_data(tracker, seq)\n    seq_res = {}\n    for cls in class_list:\n        seq_res[cls] = {}\n        data = dataset.get_preprocessed_seq_data(raw_data, cls)\n        for metric, met_name in zip(metrics_list, metric_names):\n            seq_res[cls][met_name] = metric.eval_sequence(data)\n    return seq_res\n"
  },
  {
    "path": "trackeval/metrics/__init__.py",
    "content": "from .hota import HOTA\nfrom .clear import CLEAR\nfrom .identity import Identity\nfrom .count import Count\nfrom .j_and_f import JAndF\nfrom .track_map import TrackMAP\nfrom .vace import VACE\nfrom .ideucl import IDEucl"
  },
  {
    "path": "trackeval/metrics/_base_metric.py",
    "content": "\nimport numpy as np\nfrom abc import ABC, abstractmethod\nfrom .. import _timing\nfrom ..utils import TrackEvalException\n\n\nclass _BaseMetric(ABC):\n    @abstractmethod\n    def __init__(self):\n        self.plottable = False\n        self.integer_fields = []\n        self.float_fields = []\n        self.array_labels = []\n        self.integer_array_fields = []\n        self.float_array_fields = []\n        self.fields = []\n        self.summary_fields = []\n        self.registered = False\n\n    #####################################################################\n    # Abstract functions for subclasses to implement\n\n    @_timing.time\n    @abstractmethod\n    def eval_sequence(self, data):\n        ...\n\n    @abstractmethod\n    def combine_sequences(self, all_res):\n        ...\n\n    @abstractmethod\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        ...\n\n    @ abstractmethod\n    def combine_classes_det_averaged(self, all_res):\n        ...\n\n    def plot_single_tracker_results(self, all_res, tracker, output_folder, cls):\n        \"\"\"Plot results of metrics, only valid for metrics with self.plottable\"\"\"\n        if self.plottable:\n            raise NotImplementedError('plot_results is not implemented for metric %s' % self.get_name())\n        else:\n            pass\n\n    #####################################################################\n    # Helper functions which are useful for all metrics:\n\n    @classmethod\n    def get_name(cls):\n        return cls.__name__\n\n    @staticmethod\n    def _combine_sum(all_res, field):\n        \"\"\"Combine sequence results via sum\"\"\"\n        return sum([all_res[k][field] for k in all_res.keys()])\n\n    @staticmethod\n    def _combine_weighted_av(all_res, field, comb_res, weight_field):\n        \"\"\"Combine sequence results via weighted average\"\"\"\n        return sum([all_res[k][field] * all_res[k][weight_field] for k in all_res.keys()]) / np.maximum(1.0, comb_res[\n            weight_field])\n\n    def print_table(self, table_res, tracker, cls):\n        \"\"\"Prints table of results for all sequences\"\"\"\n        print('')\n        metric_name = self.get_name()\n        space = \"\\n                                   \" \n        self._row_print([metric_name + ': ' + tracker + '-' + cls + space] + self.summary_fields)\n        for seq, results in sorted(table_res.items()):\n            if seq == 'COMBINED_SEQ':\n                continue\n            summary_res = self._summary_row(results)\n            self._row_print([seq] + summary_res)\n        summary_res = self._summary_row(table_res['COMBINED_SEQ'])\n        self._row_print(['COMBINED'] + summary_res)\n\n    def _summary_row(self, results_):\n        vals = []\n        for h in self.summary_fields:\n            if h in self.float_array_fields:\n                vals.append(\"{0:1.5g}\".format(100 * np.mean(results_[h])))\n            elif h in self.float_fields:\n                vals.append(\"{0:1.5g}\".format(100 * float(results_[h])))\n            elif h in self.integer_fields:\n                vals.append(\"{0:d}\".format(int(results_[h])))\n            else:\n                raise NotImplementedError(\"Summary function not implemented for this field type.\")\n        return vals\n\n    @staticmethod\n    def _row_print(*argv):\n        \"\"\"Prints results in an evenly spaced rows, with more space in first row\"\"\"\n        if len(argv) == 1:\n            argv = argv[0]\n        to_print = '%-35s' % argv[0]\n        for v in argv[1:]:\n            to_print += '%-10s' % str(v)\n        print(to_print)\n\n    def summary_results(self, table_res):\n        \"\"\"Returns a simple summary of final results for a tracker\"\"\"\n        return dict(zip(self.summary_fields, self._summary_row(table_res['COMBINED_SEQ'])))\n\n    def detailed_results(self, table_res):\n        \"\"\"Returns detailed final results for a tracker\"\"\"\n        # Get detailed field information\n        detailed_fields = self.float_fields + self.integer_fields\n        for h in self.float_array_fields + self.integer_array_fields:\n            for alpha in [int(100*x) for x in self.array_labels]:\n                detailed_fields.append(h + '___' + str(alpha))\n            detailed_fields.append(h + '___AUC')\n\n        # Get detailed results\n        detailed_results = {}\n        for seq, res in table_res.items():\n            detailed_row = self._detailed_row(res)\n            if len(detailed_row) != len(detailed_fields):\n                raise TrackEvalException(\n                    'Field names and data have different sizes (%i and %i)' % (len(detailed_row), len(detailed_fields)))\n            detailed_results[seq] = dict(zip(detailed_fields, detailed_row))\n        return detailed_results\n\n    def _detailed_row(self, res):\n        detailed_row = []\n        for h in self.float_fields + self.integer_fields:\n            detailed_row.append(res[h])\n        for h in self.float_array_fields + self.integer_array_fields:\n            for i, alpha in enumerate([int(100 * x) for x in self.array_labels]):\n                detailed_row.append(res[h][i])\n            detailed_row.append(np.mean(res[h]))\n        return detailed_row\n"
  },
  {
    "path": "trackeval/metrics/clear.py",
    "content": "\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\nfrom .. import utils\n\nclass CLEAR(_BaseMetric):\n    \"\"\"Class which implements the CLEAR metrics\"\"\"\n\n    @staticmethod\n    def get_default_config():\n        \"\"\"Default class config values\"\"\"\n        default_config = {\n            'THRESHOLD': 0.5,  # Similarity score threshold required for a TP match. Default 0.5.\n            'PRINT_CONFIG': True,  # Whether to print the config information on init. Default: False.\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        super().__init__()\n        main_integer_fields = ['CLR_TP', 'CLR_FN', 'CLR_FP', 'IDSW', 'MT', 'PT', 'ML', 'Frag']\n        extra_integer_fields = ['CLR_Frames']\n        self.integer_fields = main_integer_fields + extra_integer_fields\n        main_float_fields = ['MOTA', 'MOTP', 'MODA', 'CLR_Re', 'CLR_Pr', 'MTR', 'PTR', 'MLR', 'sMOTA']\n        extra_float_fields = ['CLR_F1', 'FP_per_frame', 'MOTAL', 'MOTP_sum']\n        self.float_fields = main_float_fields + extra_float_fields\n        self.fields = self.float_fields + self.integer_fields\n        self.summed_fields = self.integer_fields + ['MOTP_sum']\n        self.summary_fields = main_float_fields + main_integer_fields\n\n        # Configuration options:\n        self.config = utils.init_config(config, self.get_default_config(), self.get_name())\n        self.threshold = float(self.config['THRESHOLD'])\n\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates CLEAR metrics for one sequence\"\"\"\n        # Initialise results\n        res = data.copy()\n        for field in self.fields:\n            res[field] = 0\n        # res[\"gt_id_map\"] = data[\"gt_id_map\"]\n        # res[\"tracker_id_map\"] = data[\"tracker_id_map\"]\n\n        # Return result quickly if tracker or gt sequence is empty\n        if data['num_tracker_dets'] == 0:\n            res['CLR_FN'] = data['num_gt_dets']\n            res['ML'] = data['num_gt_ids']\n            res['MLR'] = 1.0\n            return res\n        if data['num_gt_dets'] == 0:\n            res['CLR_FP'] = data['num_tracker_dets']\n            res['MLR'] = 1.0\n            return res\n\n        # Variables counting global association\n        num_gt_ids = data['num_gt_ids']\n        gt_id_count = np.zeros(num_gt_ids)  # For MT/ML/PT\n        gt_matched_count = np.zeros(num_gt_ids)  # For MT/ML/PT\n        gt_frag_count = np.zeros(num_gt_ids)  # For Frag\n\n        # Note that IDSWs are counted based on the last time each gt_id was present (any number of frames previously),\n        # but are only used in matching to continue current tracks based on the gt_id in the single previous timestep.\n        prev_tracker_id = np.nan * np.zeros(num_gt_ids)  # For scoring IDSW\n        prev_timestep_tracker_id = np.nan * np.zeros(num_gt_ids)  # For matching IDSW\n\n        # Calculate scores for each timestep\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            # Deal with the case that there are no gt_det/tracker_det in a timestep.\n            if len(gt_ids_t) == 0:\n                res['CLR_FP'] += len(tracker_ids_t)\n                continue\n            if len(tracker_ids_t) == 0:\n                res['CLR_FN'] += len(gt_ids_t)\n                gt_id_count[gt_ids_t] += 1\n                continue\n\n            # Calc score matrix to first minimise IDSWs from previous frame, and then maximise MOTP secondarily\n            similarity = data['similarity_scores'][t]\n            score_mat = (tracker_ids_t[np.newaxis, :] == prev_timestep_tracker_id[gt_ids_t[:, np.newaxis]])\n            score_mat = 1000 * score_mat + similarity\n            score_mat[similarity < self.threshold - np.finfo('float').eps] = 0\n\n            # Hungarian algorithm to find best matches\n            match_rows, match_cols = linear_sum_assignment(-score_mat)\n            actually_matched_mask = score_mat[match_rows, match_cols] > 0 + np.finfo('float').eps\n            match_rows = match_rows[actually_matched_mask]\n            match_cols = match_cols[actually_matched_mask]\n\n            matched_gt_ids = gt_ids_t[match_rows]\n            matched_tracker_ids = tracker_ids_t[match_cols]\n\n            # Calc IDSW for MOTA\n            prev_matched_tracker_ids = prev_tracker_id[matched_gt_ids]\n            is_idsw = (np.logical_not(np.isnan(prev_matched_tracker_ids))) & (\n                np.not_equal(matched_tracker_ids, prev_matched_tracker_ids))\n            res['IDSW'] += np.sum(is_idsw)\n\n            # Update counters for MT/ML/PT/Frag and record for IDSW/Frag for next timestep\n            gt_id_count[gt_ids_t] += 1\n            gt_matched_count[matched_gt_ids] += 1\n            not_previously_tracked = np.isnan(prev_timestep_tracker_id)\n            prev_tracker_id[matched_gt_ids] = matched_tracker_ids\n            prev_timestep_tracker_id[:] = np.nan\n            prev_timestep_tracker_id[matched_gt_ids] = matched_tracker_ids\n            currently_tracked = np.logical_not(np.isnan(prev_timestep_tracker_id))\n            gt_frag_count += np.logical_and(not_previously_tracked, currently_tracked)\n\n            # Calculate and accumulate basic statistics\n            num_matches = len(matched_gt_ids)\n            res['CLR_TP'] += num_matches\n            res['CLR_FN'] += len(gt_ids_t) - num_matches\n            res['CLR_FP'] += len(tracker_ids_t) - num_matches\n            if num_matches > 0:\n                res['MOTP_sum'] += sum(similarity[match_rows, match_cols])\n\n        # Calculate MT/ML/PT/Frag/MOTP\n        tracked_ratio = gt_matched_count[gt_id_count > 0] / gt_id_count[gt_id_count > 0]\n        res['MT'] = np.sum(np.greater(tracked_ratio, 0.8))\n        res['PT'] = np.sum(np.greater_equal(tracked_ratio, 0.2)) - res['MT']\n        res['ML'] = num_gt_ids - res['MT'] - res['PT']\n        res['Frag'] = np.sum(np.subtract(gt_frag_count[gt_frag_count > 0], 1))\n        res['MOTP'] = res['MOTP_sum'] / np.maximum(1.0, res['CLR_TP'])\n\n        res['CLR_Frames'] = data['num_timesteps']\n\n        # Calculate final CLEAR scores\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.summed_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.summed_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n        for field in self.integer_fields:\n            if ignore_empty_classes:\n                res[field] = self._combine_sum(\n                    {k: v for k, v in all_res.items() if v['CLR_TP'] + v['CLR_FN'] + v['CLR_FP'] > 0}, field)\n            else:\n                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)\n        for field in self.float_fields:\n            if ignore_empty_classes:\n                res[field] = np.mean(\n                    [v[field] for v in all_res.values() if v['CLR_TP'] + v['CLR_FN'] + v['CLR_FP'] > 0], axis=0)\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    @staticmethod\n    def _compute_final_fields(res):\n        \"\"\"Calculate sub-metric ('field') values which only depend on other sub-metric values.\n        This function is used both for both per-sequence calculation, and in combining values across sequences.\n        \"\"\"\n        num_gt_ids = res['MT'] + res['ML'] + res['PT']\n        res['MTR'] = res['MT'] / np.maximum(1.0, num_gt_ids)\n        res['MLR'] = res['ML'] / np.maximum(1.0, num_gt_ids)\n        res['PTR'] = res['PT'] / np.maximum(1.0, num_gt_ids)\n        res['CLR_Re'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])\n        res['CLR_Pr'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + res['CLR_FP'])\n        res['MODA'] = (res['CLR_TP'] - res['CLR_FP']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])\n        res['MOTA'] = (res['CLR_TP'] - res['CLR_FP'] - res['IDSW']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])\n        res['MOTP'] = res['MOTP_sum'] / np.maximum(1.0, res['CLR_TP'])\n        res['sMOTA'] = (res['MOTP_sum'] - res['CLR_FP'] - res['IDSW']) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])\n\n        res['CLR_F1'] = res['CLR_TP'] / np.maximum(1.0, res['CLR_TP'] + 0.5*res['CLR_FN'] + 0.5*res['CLR_FP'])\n        res['FP_per_frame'] = res['CLR_FP'] / np.maximum(1.0, res['CLR_Frames'])\n        safe_log_idsw = np.log10(res['IDSW']) if res['IDSW'] > 0 else res['IDSW']\n        res['MOTAL'] = (res['CLR_TP'] - res['CLR_FP'] - safe_log_idsw) / np.maximum(1.0, res['CLR_TP'] + res['CLR_FN'])\n        return res\n"
  },
  {
    "path": "trackeval/metrics/count.py",
    "content": "\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\n\n\nclass Count(_BaseMetric):\n    \"\"\"Class which simply counts the number of tracker and gt detections and ids.\"\"\"\n    def __init__(self, config=None):\n        super().__init__()\n        self.integer_fields = ['Dets', 'GT_Dets', 'IDs', 'GT_IDs']\n        self.fields = self.integer_fields\n        self.summary_fields = self.fields\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Returns counts for one sequence\"\"\"\n        # Get results\n        res = {'Dets': data['num_tracker_dets'],\n               'GT_Dets': data['num_gt_dets'],\n               'IDs': data['num_tracker_ids'],\n               'GT_IDs': data['num_gt_ids'],\n               'Frames': data['num_timesteps']}\n        for key in data.keys():\n            if key not in res:\n                res[key] = data[key]\n        # res[\"gt_id_map\"] = data[\"gt_id_map\"]\n        # res[\"tracker_id_map\"] = data[\"tracker_id_map\"]\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.integer_fields: \n            res[field] = self._combine_sum(all_res, field)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=None):\n        \"\"\"Combines metrics across all classes by averaging over the class values\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        return res\n"
  },
  {
    "path": "trackeval/metrics/hota.py",
    "content": "\nimport os\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\n\n\nclass HOTA(_BaseMetric):\n    \"\"\"Class which implements the HOTA metrics.\n    See: https://link.springer.com/article/10.1007/s11263-020-01375-2\n    \"\"\"\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.plottable = True\n        self.array_labels = np.arange(0.05, 0.99, 0.05)\n        self.integer_array_fields = ['HOTA_TP', 'HOTA_FN', 'HOTA_FP']\n        self.float_array_fields = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'RHOTA']\n        self.float_fields = ['HOTA(0)', 'LocA(0)', 'HOTALocA(0)']\n        self.fields = self.float_array_fields + self.integer_array_fields + self.float_fields\n        self.summary_fields = self.float_array_fields + self.float_fields\n\n    @_timing.time\n    def eval_sequence(self, data, save_path=None):\n        \"\"\"Calculates the HOTA metrics for one sequence\"\"\"\n        \"\"\"\n        data.keys:\n        dict_keys(['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', \n            'tracker_confidences', 'similarity_scores', 'num_tracker_dets', \n            'num_gt_dets', 'num_tracker_ids', 'num_gt_ids', 'num_timesteps', 'seq'])\n        \"\"\"\n        # Initialise results\n        res = data.copy()\n        for field in self.float_array_fields + self.integer_array_fields:\n            res[field] = np.zeros((len(self.array_labels)), dtype=np.float)\n        for field in self.float_fields:\n            res[field] = 0\n\n        # res = data.copy()\n\n        # res[\"gt_id_map\"] = data[\"gt_id_map\"]\n        # res[\"tracker_id_map\"] = data[\"tracker_id_map\"]\n\n\n        # Return result quickly if tracker or gt sequence is empty\n        if data['num_tracker_dets'] == 0:\n            res['HOTA_FN'] = data['num_gt_dets'] * np.ones((len(self.array_labels)), dtype=np.float)\n            res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float)\n            res['LocA(0)'] = 1.0\n            return res\n        if data['num_gt_dets'] == 0:\n            res['HOTA_FP'] = data['num_tracker_dets'] * np.ones((len(self.array_labels)), dtype=np.float)\n            res['LocA'] = np.ones((len(self.array_labels)), dtype=np.float)\n            res['LocA(0)'] = 1.0\n            return res\n\n        # Variables counting global association\n        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))\n        gt_id_count = np.zeros((data['num_gt_ids'], 1))\n        tracker_id_count = np.zeros((1, data['num_tracker_ids']))\n\n        # First loop through each timestep and accumulate global track information.\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            # Count the potential matches between ids in each timestep\n            # These are normalised, weighted by the match similarity.\n            similarity = data['similarity_scores'][t]\n            sim_iou_denom = similarity.sum(0)[np.newaxis, :] + similarity.sum(1)[:, np.newaxis] - similarity\n            sim_iou = np.zeros_like(similarity)\n            sim_iou_mask = sim_iou_denom > 0 + np.finfo('float').eps\n            sim_iou[sim_iou_mask] = similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]\n            potential_matches_count[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] += sim_iou\n\n            # Calculate the total number of dets for each gt_id and tracker_id.\n            gt_id_count[gt_ids_t] += 1\n            tracker_id_count[0, tracker_ids_t] += 1\n\n        # Calculate overall jaccard alignment score (before unique matching) between IDs\n        global_alignment_score = potential_matches_count / (gt_id_count + tracker_id_count - potential_matches_count)\n        matches_counts = [np.zeros_like(potential_matches_count) for _ in self.array_labels]\n\n        # Calculate scores for each timestep\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            # Deal with the case that there are no gt_det/tracker_det in a timestep.\n            if len(gt_ids_t) == 0:\n                for a, alpha in enumerate(self.array_labels):\n                    # if on this frame, there is no GT track, all tracker recalls are false positive\n                    res['HOTA_FP'][a] += len(tracker_ids_t)\n                continue\n            if len(tracker_ids_t) == 0:\n                for a, alpha in enumerate(self.array_labels):\n                    # if on this frame, there is no tracker recall, all GT track dets make false negatives\n                    res['HOTA_FN'][a] += len(gt_ids_t)\n                continue\n\n            # Get matching scores between pairs of dets for optimizing HOTA\n            similarity = data['similarity_scores'][t]\n            # the score matrix is the global_similary * framewise similarity\n            score_mat = global_alignment_score[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] * similarity\n            # score_mat = similarity\n\n            # Hungarian algorithm to find best matches\n            match_rows, match_cols = linear_sum_assignment(-score_mat)\n            # import pdb; pdb.set_trace()\n\n            # Calculate and accumulate basic statistics\n            for a, alpha in enumerate(self.array_labels):\n                # only if the similary (iou) is higher than the threshold alpha, the match would be valid\n                actually_matched_mask = similarity[match_rows, match_cols] >= alpha - np.finfo('float').eps\n                alpha_match_rows = match_rows[actually_matched_mask]\n                alpha_match_cols = match_cols[actually_matched_mask]\n                num_matches = len(alpha_match_rows)\n                res['HOTA_TP'][a] += num_matches\n                res['HOTA_FN'][a] += len(gt_ids_t) - num_matches\n                res['HOTA_FP'][a] += len(tracker_ids_t) - num_matches\n                if num_matches > 0:\n                    res['LocA'][a] += sum(similarity[alpha_match_rows, alpha_match_cols])\n                    matches_counts[a][gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]] += 1    \n        \n        \"\"\"\n            Calculate association scores (AssA, AssRe, AssPr) for the alpha value.\n            First calculate scores per gt_id/tracker_id combo and then average over the number of detections.\n        \"\"\"\n        for a, alpha in enumerate(self.array_labels):\n            matches_count = matches_counts[a]\n            ass_a = matches_count / np.maximum(1, gt_id_count + tracker_id_count - matches_count)\n            res['AssA'][a] = np.sum(matches_count * ass_a) / np.maximum(1, res['HOTA_TP'][a])\n            ass_re = matches_count / np.maximum(1, gt_id_count)\n            res['AssRe'][a] = np.sum(matches_count * ass_re) / np.maximum(1, res['HOTA_TP'][a])\n            ass_pr = matches_count / np.maximum(1, tracker_id_count)\n            res['AssPr'][a] = np.sum(matches_count * ass_pr) / np.maximum(1, res['HOTA_TP'][a])\n\n        # Calculate final scores\n        res['LocA'] = np.maximum(1e-10, res['LocA']) / np.maximum(1e-10, res['HOTA_TP'])\n        res[\"matches_counts\"] = matches_counts\n        res = self._compute_final_fields(res)\n        # os.makedirs(save_path, exist_ok=True)\n        # np.save(os.path.join(save_path, \"matches_count.pth\"), matches_count)\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            res[field] = self._combine_sum(all_res, field)\n        for field in ['AssRe', 'AssPr', 'AssA']:\n            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')\n        loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])\n        res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            if ignore_empty_classes:\n                res[field] = self._combine_sum(\n                    {k: v for k, v in all_res.items()\n                     if (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()}, field)\n            else:\n                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)\n\n        for field in self.float_fields + self.float_array_fields:\n            if ignore_empty_classes:\n                res[field] = np.mean([v[field] for v in all_res.values() if\n                                      (v['HOTA_TP'] + v['HOTA_FN'] + v['HOTA_FP'] > 0 + np.finfo('float').eps).any()],\n                                     axis=0)\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.integer_array_fields:\n            res[field] = self._combine_sum(all_res, field)\n        for field in ['AssRe', 'AssPr', 'AssA']:\n            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='HOTA_TP')\n        loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])\n        res['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, res['HOTA_TP'])\n        res = self._compute_final_fields(res)\n        return res\n\n    @staticmethod\n    def _compute_final_fields(res):\n        \"\"\"Calculate sub-metric ('field') values which only depend on other sub-metric values.\n        This function is used both for both per-sequence calculation, and in combining values across sequences.\n        \"\"\"\n        res['DetRe'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FN'])\n        res['DetPr'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FP'])\n        res['DetA'] = res['HOTA_TP'] / np.maximum(1, res['HOTA_TP'] + res['HOTA_FN'] + res['HOTA_FP'])\n        res['HOTA'] = np.sqrt(res['DetA'] * res['AssA'])\n        res['RHOTA'] = np.sqrt(res['DetRe'] * res['AssA'])\n\n        res['HOTA(0)'] = res['HOTA'][0]\n        res['LocA(0)'] = res['LocA'][0]\n        res['HOTALocA(0)'] = res['HOTA(0)']*res['LocA(0)']\n        return res\n\n    def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):\n        \"\"\"Create plot of results\"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from matplotlib import pyplot as plt\n\n        res = table_res['COMBINED_SEQ']\n        styles_to_plot = ['r', 'b', 'g', 'b--', 'b:', 'g--', 'g:', 'm']\n        for name, style in zip(self.float_array_fields, styles_to_plot):\n            plt.plot(self.array_labels, res[name], style)\n        plt.xlabel('alpha')\n        plt.ylabel('score')\n        plt.title(tracker + ' - ' + cls)\n        plt.axis([0, 1, 0, 1])\n        legend = []\n        for name in self.float_array_fields:\n            legend += [name + ' (' + str(np.round(np.mean(res[name]), 2)) + ')']\n        plt.legend(legend, loc='lower left')\n        out_file = os.path.join(output_folder, cls + '_plot.pdf')\n        os.makedirs(os.path.dirname(out_file), exist_ok=True)\n        plt.savefig(out_file)\n        plt.savefig(out_file.replace('.pdf', '.png'))\n        plt.clf()\n"
  },
  {
    "path": "trackeval/metrics/identity.py",
    "content": "import numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\nfrom .. import utils\n\n\nclass Identity(_BaseMetric):\n    \"\"\"Class which implements the ID metrics\"\"\"\n\n    @staticmethod\n    def get_default_config():\n        \"\"\"Default class config values\"\"\"\n        default_config = {\n            'THRESHOLD': 0.5,  # Similarity score threshold required for a IDTP match. Default 0.5.\n            'PRINT_CONFIG': True,  # Whether to print the config information on init. Default: False.\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.integer_fields = ['IDTP', 'IDFN', 'IDFP']\n        self.float_fields = ['IDF1', 'IDR', 'IDP']\n        self.fields = self.float_fields + self.integer_fields\n        self.summary_fields = self.fields\n\n        # Configuration options:\n        self.config = utils.init_config(config, self.get_default_config(), self.get_name())\n        self.threshold = float(self.config['THRESHOLD'])\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates ID metrics for one sequence\"\"\"\n        # Initialise results\n        res = data.copy()\n        for field in self.fields:\n            res[field] = 0\n        \n        # res[\"gt_id_map\"] = data[\"gt_id_map\"]\n        # res[\"tracker_id_map\"] = data[\"tracker_id_map\"]\n\n        # Return result quickly if tracker or gt sequence is empty\n        if data['num_tracker_dets'] == 0:\n            res['IDFN'] = data['num_gt_dets']\n            return res\n        if data['num_gt_dets'] == 0:\n            res['IDFP'] = data['num_tracker_dets']\n            return res\n\n        # Variables counting global association\n        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))\n        gt_id_count = np.zeros(data['num_gt_ids'])\n        tracker_id_count = np.zeros(data['num_tracker_ids'])\n\n        # First loop through each timestep and accumulate global track information.\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            # Count the potential matches between ids in each timestep\n            matches_mask = np.greater_equal(data['similarity_scores'][t], self.threshold)\n            match_idx_gt, match_idx_tracker = np.nonzero(matches_mask)\n            potential_matches_count[gt_ids_t[match_idx_gt], tracker_ids_t[match_idx_tracker]] += 1\n\n            # Calculate the total number of dets for each gt_id and tracker_id.\n            gt_id_count[gt_ids_t] += 1\n            tracker_id_count[tracker_ids_t] += 1\n\n        # Calculate optimal assignment cost matrix for ID metrics\n        num_gt_ids = data['num_gt_ids']\n        num_tracker_ids = data['num_tracker_ids']\n        fp_mat = np.zeros((num_gt_ids + num_tracker_ids, num_gt_ids + num_tracker_ids))\n        fn_mat = np.zeros((num_gt_ids + num_tracker_ids, num_gt_ids + num_tracker_ids))\n        fp_mat[num_gt_ids:, :num_tracker_ids] = 1e10\n        fn_mat[:num_gt_ids, num_tracker_ids:] = 1e10\n        for gt_id in range(num_gt_ids):\n            fn_mat[gt_id, :num_tracker_ids] = gt_id_count[gt_id]\n            fn_mat[gt_id, num_tracker_ids + gt_id] = gt_id_count[gt_id]\n        for tracker_id in range(num_tracker_ids):\n            fp_mat[:num_gt_ids, tracker_id] = tracker_id_count[tracker_id]\n            fp_mat[tracker_id + num_gt_ids, tracker_id] = tracker_id_count[tracker_id]\n        fn_mat[:num_gt_ids, :num_tracker_ids] -= potential_matches_count\n        fp_mat[:num_gt_ids, :num_tracker_ids] -= potential_matches_count\n\n        # Hungarian algorithm\n        match_rows, match_cols = linear_sum_assignment(fn_mat + fp_mat)\n\n        # Accumulate basic statistics\n        res['IDFN'] = fn_mat[match_rows, match_cols].sum().astype(np.int)\n        res['IDFP'] = fp_mat[match_rows, match_cols].sum().astype(np.int)\n        res['IDTP'] = (gt_id_count.sum() - res['IDFN']).astype(np.int)\n\n        # Calculate final ID scores\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n        for field in self.integer_fields:\n            if ignore_empty_classes:\n                res[field] = self._combine_sum({k: v for k, v in all_res.items()\n                                                if v['IDTP'] + v['IDFN'] + v['IDFP'] > 0 + np.finfo('float').eps},\n                                               field)\n            else:\n                res[field] = self._combine_sum({k: v for k, v in all_res.items()}, field)\n        for field in self.float_fields:\n            if ignore_empty_classes:\n                res[field] = np.mean([v[field] for v in all_res.values()\n                                      if v['IDTP'] + v['IDFN'] + v['IDFP'] > 0 + np.finfo('float').eps], axis=0)\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.integer_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res)\n        return res\n\n    @staticmethod\n    def _compute_final_fields(res):\n        \"\"\"Calculate sub-metric ('field') values which only depend on other sub-metric values.\n        This function is used both for both per-sequence calculation, and in combining values across sequences.\n        \"\"\"\n        res['IDR'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + res['IDFN'])\n        res['IDP'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + res['IDFP'])\n        res['IDF1'] = res['IDTP'] / np.maximum(1.0, res['IDTP'] + 0.5 * res['IDFP'] + 0.5 * res['IDFN'])\n        return res\n"
  },
  {
    "path": "trackeval/metrics/ideucl.py",
    "content": "import numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\nfrom collections import defaultdict\nfrom .. import utils\n\n\nclass IDEucl(_BaseMetric):\n    \"\"\"Class which implements the ID metrics\"\"\"\n\n    @staticmethod\n    def get_default_config():\n        \"\"\"Default class config values\"\"\"\n        default_config = {\n            'THRESHOLD': 0.4,  # Similarity score threshold required for a IDTP match. 0.4 for IDEucl.\n            'PRINT_CONFIG': True,  # Whether to print the config information on init. Default: False.\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.fields = ['IDEucl']\n        self.float_fields = self.fields\n        self.summary_fields = self.fields\n\n        # Configuration options:\n        self.config = utils.init_config(config, self.get_default_config(), self.get_name())\n        self.threshold = float(self.config['THRESHOLD'])\n\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates IDEucl metrics for all frames\"\"\"\n        # Initialise results\n        res = {'IDEucl' : 0}\n\n        # Return result quickly if tracker or gt sequence is empty\n        if data['num_tracker_dets'] == 0 or data['num_gt_dets'] == 0.:\n            return res\n\n        data['centroid'] = []\n        for t, gt_det in enumerate(data['gt_dets']):\n            # import pdb;pdb.set_trace()\n            data['centroid'].append(self._compute_centroid(gt_det))\n\n        oid_hid_cent = defaultdict(list)\n        oid_cent = defaultdict(list)\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            matches_mask = np.greater_equal(data['similarity_scores'][t], self.threshold)\n\n            # I hope the orders of ids and boxes are maintained in `data`\n            for ind, gid in enumerate(gt_ids_t):\n                oid_cent[gid].append(data['centroid'][t][ind])\n\n            match_idx_gt, match_idx_tracker = np.nonzero(matches_mask)\n            for m_gid, m_tid in zip(match_idx_gt, match_idx_tracker):\n                oid_hid_cent[gt_ids_t[m_gid], tracker_ids_t[m_tid]].append(data['centroid'][t][m_gid])\n\n        oid_hid_dist = {k : np.sum(np.linalg.norm(np.diff(np.array(v), axis=0), axis=1)) for k, v in oid_hid_cent.items()}\n        oid_dist = {int(k) : np.sum(np.linalg.norm(np.diff(np.array(v), axis=0), axis=1)) for k, v in oid_cent.items()}\n\n        unique_oid = np.unique([i[0] for i in oid_hid_dist.keys()]).tolist()\n        unique_hid = np.unique([i[1] for i in oid_hid_dist.keys()]).tolist()\n        o_len = len(unique_oid)\n        h_len = len(unique_hid)\n        dist_matrix = np.zeros((o_len, h_len))\n        for ((oid, hid), dist) in oid_hid_dist.items():\n            oid_ind = unique_oid.index(oid)\n            hid_ind = unique_hid.index(hid)\n            dist_matrix[oid_ind, hid_ind] = dist\n\n        # opt_hyp_dist contains GT ID : max dist covered by track\n        opt_hyp_dist = dict.fromkeys(oid_dist.keys(), 0.)\n        cost_matrix = np.max(dist_matrix) - dist_matrix\n        rows, cols = linear_sum_assignment(cost_matrix)\n        for (row, col) in zip(rows, cols):\n            value = dist_matrix[row, col]\n            opt_hyp_dist[int(unique_oid[row])] = value\n\n        assert len(opt_hyp_dist.keys()) == len(oid_dist.keys())\n        hyp_length = np.sum(list(opt_hyp_dist.values()))\n        gt_length = np.sum(list(oid_dist.values()))\n        id_eucl =np.mean([np.divide(a, b, out=np.zeros_like(a), where=b!=0) for a, b in zip(opt_hyp_dist.values(), oid_dist.values())])\n        res['IDEucl'] = np.divide(hyp_length, gt_length, out=np.zeros_like(hyp_length), where=gt_length!=0)\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n\n        for field in self.float_fields:\n            if ignore_empty_classes:\n                res[field] = np.mean([v[field] for v in all_res.values()\n                                      if v['IDEucl'] > 0 + np.finfo('float').eps], axis=0)\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self.float_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res, len(all_res))\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for field in self.float_fields:\n            res[field] = self._combine_sum(all_res, field)\n        res = self._compute_final_fields(res, len(all_res))\n        return res\n\n\n    @staticmethod\n    def _compute_centroid(box):\n        box = np.array(box)\n        if len(box.shape) == 1:\n            centroid = (box[0:2] + box[2:4])/2\n        else:\n            centroid = (box[:, 0:2] + box[:, 2:4])/2\n        return  np.flip(centroid, axis=1)\n\n\n    @staticmethod\n    def _compute_final_fields(res, res_len):\n        \"\"\"\n        Exists only to match signature with the original Identiy class.\n\n        \"\"\"\n        return {k:v/res_len for k,v in res.items()}\n"
  },
  {
    "path": "trackeval/metrics/j_and_f.py",
    "content": "\nimport numpy as np\nimport math\nfrom scipy.optimize import linear_sum_assignment\nfrom ..utils import TrackEvalException\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\n\n\nclass JAndF(_BaseMetric):\n    \"\"\"Class which implements the J&F metrics\"\"\"\n    def __init__(self, config=None):\n        super().__init__()\n        self.integer_fields = ['num_gt_tracks']\n        self.float_fields = ['J-Mean', 'J-Recall', 'J-Decay', 'F-Mean', 'F-Recall', 'F-Decay', 'J&F']\n        self.fields = self.float_fields + self.integer_fields\n        self.summary_fields = self.float_fields\n        self.optim_type = 'J'  # possible values J, J&F\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Returns J&F metrics for one sequence\"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        num_timesteps = data['num_timesteps']\n        num_tracker_ids = data['num_tracker_ids']\n        num_gt_ids = data['num_gt_ids']\n        gt_dets = data['gt_dets']\n        tracker_dets = data['tracker_dets']\n        gt_ids = data['gt_ids']\n        tracker_ids = data['tracker_ids']\n\n        # get shape of frames\n        frame_shape = None\n        if num_gt_ids > 0:\n            for t in range(num_timesteps):\n                if len(gt_ids[t]) > 0:\n                    frame_shape = gt_dets[t][0]['size']\n                    break\n        elif num_tracker_ids > 0:\n            for t in range(num_timesteps):\n                if len(tracker_ids[t]) > 0:\n                    frame_shape = tracker_dets[t][0]['size']\n                    break\n\n        if frame_shape:\n            # append all zero masks for timesteps in which tracks do not have a detection\n            zero_padding = np.zeros((frame_shape), order= 'F').astype(np.uint8)\n            padding_mask = mask_utils.encode(zero_padding)\n            for t in range(num_timesteps):\n                gt_id_det_mapping = {gt_ids[t][i]: gt_dets[t][i] for i in range(len(gt_ids[t]))}\n                gt_dets[t] = [gt_id_det_mapping[index] if index in gt_ids[t] else padding_mask for index\n                              in range(num_gt_ids)]\n                tracker_id_det_mapping = {tracker_ids[t][i]: tracker_dets[t][i] for i in range(len(tracker_ids[t]))}\n                tracker_dets[t] = [tracker_id_det_mapping[index] if index in tracker_ids[t] else padding_mask for index\n                                   in range(num_tracker_ids)]\n            # also perform zero padding if number of tracker IDs < number of ground truth IDs\n            if num_tracker_ids < num_gt_ids:\n                diff = num_gt_ids - num_tracker_ids\n                for t in range(num_timesteps):\n                    tracker_dets[t] = tracker_dets[t] + [padding_mask for _ in range(diff)]\n                num_tracker_ids += diff\n\n        j = self._compute_j(gt_dets, tracker_dets, num_gt_ids, num_tracker_ids, num_timesteps)\n\n        # boundary threshold for F computation\n        bound_th = 0.008\n\n        # perform matching\n        if self.optim_type == 'J&F':\n            f = np.zeros_like(j)\n            for k in range(num_tracker_ids):\n                for i in range(num_gt_ids):\n                    f[k, i, :] = self._compute_f(gt_dets, tracker_dets, k, i, bound_th)\n            optim_metrics = (np.mean(j, axis=2) + np.mean(f, axis=2)) / 2\n            row_ind, col_ind = linear_sum_assignment(- optim_metrics)\n            j_m = j[row_ind, col_ind, :]\n            f_m = f[row_ind, col_ind, :]\n        elif self.optim_type == 'J':\n            optim_metrics = np.mean(j, axis=2)\n            row_ind, col_ind = linear_sum_assignment(- optim_metrics)\n            j_m = j[row_ind, col_ind, :]\n            f_m = np.zeros_like(j_m)\n            for i, (tr_ind, gt_ind) in enumerate(zip(row_ind, col_ind)):\n                f_m[i] = self._compute_f(gt_dets, tracker_dets, tr_ind, gt_ind, bound_th)\n        else:\n            raise TrackEvalException('Unsupported optimization type %s for J&F metric.' % self.optim_type)\n\n        # append zeros for false negatives\n        if j_m.shape[0] < data['num_gt_ids']:\n            diff = data['num_gt_ids'] - j_m.shape[0]\n            j_m = np.concatenate((j_m, np.zeros((diff, j_m.shape[1]))), axis=0)\n            f_m = np.concatenate((f_m, np.zeros((diff, f_m.shape[1]))), axis=0)\n\n        # compute the metrics for each ground truth track\n        res = {\n            'J-Mean': [np.nanmean(j_m[i, :]) for i in range(j_m.shape[0])],\n            'J-Recall': [np.nanmean(j_m[i, :] > 0.5 + np.finfo('float').eps) for i in range(j_m.shape[0])],\n            'F-Mean': [np.nanmean(f_m[i, :]) for i in range(f_m.shape[0])],\n            'F-Recall': [np.nanmean(f_m[i, :] > 0.5 + np.finfo('float').eps) for i in range(f_m.shape[0])],\n            'J-Decay': [],\n            'F-Decay': []\n        }\n        n_bins = 4\n        ids = np.round(np.linspace(1, data['num_timesteps'], n_bins + 1) + 1e-10) - 1\n        ids = ids.astype(np.uint8)\n\n        for k in range(j_m.shape[0]):\n            d_bins_j = [j_m[k][ids[i]:ids[i + 1] + 1] for i in range(0, n_bins)]\n            res['J-Decay'].append(np.nanmean(d_bins_j[0]) - np.nanmean(d_bins_j[3]))\n        for k in range(f_m.shape[0]):\n            d_bins_f = [f_m[k][ids[i]:ids[i + 1] + 1] for i in range(0, n_bins)]\n            res['F-Decay'].append(np.nanmean(d_bins_f[0]) - np.nanmean(d_bins_f[3]))\n\n        # count number of tracks for weighting of the result\n        res['num_gt_tracks'] = len(res['J-Mean'])\n        for field in ['J-Mean', 'J-Recall', 'J-Decay', 'F-Mean', 'F-Recall', 'F-Decay']:\n            res[field] = np.mean(res[field])\n        res['J&F'] = (res['J-Mean'] + res['F-Mean']) / 2\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {'num_gt_tracks': self._combine_sum(all_res, 'num_gt_tracks')}\n        for field in self.summary_fields:\n            res[field] = self._combine_weighted_av(all_res, field, res, weight_field='num_gt_tracks')\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):\n        \"\"\"Combines metrics across all classes by averaging over the class values\n        'ignore empty classes' is not yet implemented here.\n        \"\"\"\n        res = {'num_gt_tracks': self._combine_sum(all_res, 'num_gt_tracks')}\n        for field in self.float_fields:\n            res[field] = np.mean([v[field] for v in all_res.values()])\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {'num_gt_tracks': self._combine_sum(all_res, 'num_gt_tracks')}\n        for field in self.float_fields:\n            res[field] = np.mean([v[field] for v in all_res.values()])\n        return res\n\n    @staticmethod\n    def _seg2bmap(seg, width=None, height=None):\n        \"\"\"\n        From a segmentation, compute a binary boundary map with 1 pixel wide\n        boundaries.  The boundary pixels are offset by 1/2 pixel towards the\n        origin from the actual segment boundary.\n        Arguments:\n            seg     : Segments labeled from 1..k.\n            width\t  :\tWidth of desired bmap  <= seg.shape[1]\n            height  :\tHeight of desired bmap <= seg.shape[0]\n        Returns:\n            bmap (ndarray):\tBinary boundary map.\n         David Martin <dmartin@eecs.berkeley.edu>\n         January 2003\n        \"\"\"\n\n        seg = seg.astype(np.bool)\n        seg[seg > 0] = 1\n\n        assert np.atleast_3d(seg).shape[2] == 1\n\n        width = seg.shape[1] if width is None else width\n        height = seg.shape[0] if height is None else height\n\n        h, w = seg.shape[:2]\n\n        ar1 = float(width) / float(height)\n        ar2 = float(w) / float(h)\n\n        assert not (\n                width > w | height > h | abs(ar1 - ar2) > 0.01\n        ), \"Can\" \"t convert %dx%d seg to %dx%d bmap.\" % (w, h, width, height)\n\n        e = np.zeros_like(seg)\n        s = np.zeros_like(seg)\n        se = np.zeros_like(seg)\n\n        e[:, :-1] = seg[:, 1:]\n        s[:-1, :] = seg[1:, :]\n        se[:-1, :-1] = seg[1:, 1:]\n\n        b = seg ^ e | seg ^ s | seg ^ se\n        b[-1, :] = seg[-1, :] ^ e[-1, :]\n        b[:, -1] = seg[:, -1] ^ s[:, -1]\n        b[-1, -1] = 0\n\n        if w == width and h == height:\n            bmap = b\n        else:\n            bmap = np.zeros((height, width))\n            for x in range(w):\n                for y in range(h):\n                    if b[y, x]:\n                        j = 1 + math.floor((y - 1) + height / h)\n                        i = 1 + math.floor((x - 1) + width / h)\n                        bmap[j, i] = 1\n\n        return bmap\n\n    @staticmethod\n    def _compute_f(gt_data, tracker_data, tracker_data_id, gt_id, bound_th):\n        \"\"\"\n        Perform F computation for a given gt and a given tracker ID. Adapted from\n        https://github.com/davisvideochallenge/davis2017-evaluation\n        :param gt_data: the encoded gt masks\n        :param tracker_data: the encoded tracker masks\n        :param tracker_data_id: the tracker ID\n        :param gt_id: the ground truth ID\n        :param bound_th: boundary threshold parameter\n        :return: the F value for the given tracker and gt ID\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n        from skimage.morphology import disk\n        import cv2\n\n        f = np.zeros(len(gt_data))\n\n        for t, (gt_masks, tracker_masks) in enumerate(zip(gt_data, tracker_data)):\n            curr_tracker_mask = mask_utils.decode(tracker_masks[tracker_data_id])\n            curr_gt_mask = mask_utils.decode(gt_masks[gt_id])\n            \n            bound_pix = bound_th if bound_th >= 1 - np.finfo('float').eps else \\\n                np.ceil(bound_th * np.linalg.norm(curr_tracker_mask.shape))\n\n            # Get the pixel boundaries of both masks\n            fg_boundary = JAndF._seg2bmap(curr_tracker_mask)\n            gt_boundary = JAndF._seg2bmap(curr_gt_mask)\n\n            # fg_dil = binary_dilation(fg_boundary, disk(bound_pix))\n            fg_dil = cv2.dilate(fg_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8))\n            # gt_dil = binary_dilation(gt_boundary, disk(bound_pix))\n            gt_dil = cv2.dilate(gt_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8))\n\n            # Get the intersection\n            gt_match = gt_boundary * fg_dil\n            fg_match = fg_boundary * gt_dil\n\n            # Area of the intersection\n            n_fg = np.sum(fg_boundary)\n            n_gt = np.sum(gt_boundary)\n\n            # % Compute precision and recall\n            if n_fg == 0 and n_gt > 0:\n                precision = 1\n                recall = 0\n            elif n_fg > 0 and n_gt == 0:\n                precision = 0\n                recall = 1\n            elif n_fg == 0 and n_gt == 0:\n                precision = 1\n                recall = 1\n            else:\n                precision = np.sum(fg_match) / float(n_fg)\n                recall = np.sum(gt_match) / float(n_gt)\n\n            # Compute F measure\n            if precision + recall == 0:\n                f_val = 0\n            else:\n                f_val = 2 * precision * recall / (precision + recall)\n\n            f[t] = f_val\n\n        return f\n\n    @staticmethod\n    def _compute_j(gt_data, tracker_data, num_gt_ids, num_tracker_ids, num_timesteps):\n        \"\"\"\n        Computation of J value for all ground truth IDs and all tracker IDs in the given sequence. Adapted from\n        https://github.com/davisvideochallenge/davis2017-evaluation\n        :param gt_data: the ground truth masks\n        :param tracker_data: the tracker masks\n        :param num_gt_ids: the number of ground truth IDs\n        :param num_tracker_ids: the number of tracker IDs\n        :param num_timesteps: the number of timesteps\n        :return: the J values\n        \"\"\"\n\n        # Only loaded when run to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        j = np.zeros((num_tracker_ids, num_gt_ids, num_timesteps))\n\n        for t, (time_gt, time_data) in enumerate(zip(gt_data, tracker_data)):\n            # run length encoded masks with pycocotools\n            area_gt = mask_utils.area(time_gt)\n            time_data = list(time_data)\n            area_tr = mask_utils.area(time_data)\n\n            area_tr = np.repeat(area_tr[:, np.newaxis], len(area_gt), axis=1)\n            area_gt = np.repeat(area_gt[np.newaxis, :], len(area_tr), axis=0)\n\n            # mask iou computation with pycocotools\n            ious = np.atleast_2d(mask_utils.iou(time_data, time_gt, [0]*len(time_gt)))\n            # set iou to 1 if both masks are close to 0 (no ground truth and no predicted mask in timestep)\n            ious[np.isclose(area_tr, 0) & np.isclose(area_gt, 0)] = 1\n            assert (ious >= 0 - np.finfo('float').eps).all()\n            assert (ious <= 1 + np.finfo('float').eps).all()\n\n            j[..., t] = ious\n\n        return j\n"
  },
  {
    "path": "trackeval/metrics/track_map.py",
    "content": "import numpy as np\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\nfrom functools import partial\nfrom .. import utils\nfrom ..utils import TrackEvalException\n\n\nclass TrackMAP(_BaseMetric):\n    \"\"\"Class which implements the TrackMAP metrics\"\"\"\n\n    @staticmethod\n    def get_default_metric_config():\n        \"\"\"Default class config values\"\"\"\n        default_config = {\n            'USE_AREA_RANGES': True,  # whether to evaluate for certain area ranges\n            'AREA_RANGES': [[0 ** 2, 32 ** 2],  # additional area range sets for which TrackMAP is evaluated\n                            [32 ** 2, 96 ** 2],  # (all area range always included), default values for TAO\n                            [96 ** 2, 1e5 ** 2]],  # evaluation\n            'AREA_RANGE_LABELS': [\"area_s\", \"area_m\", \"area_l\"],  # the labels for the area ranges\n            'USE_TIME_RANGES': True,  # whether to evaluate for certain time ranges (length of tracks)\n            'TIME_RANGES': [[0, 3], [3, 10], [10, 1e5]],  # additional time range sets for which TrackMAP is evaluated\n            # (all time range always included) , default values for TAO evaluation\n            'TIME_RANGE_LABELS': [\"time_s\", \"time_m\", \"time_l\"],  # the labels for the time ranges\n            'IOU_THRESHOLDS': np.arange(0.5, 0.96, 0.05),  # the IoU thresholds\n            'RECALL_THRESHOLDS': np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01) + 1), endpoint=True),\n            # recall thresholds at which precision is evaluated\n            'MAX_DETECTIONS': 0,  # limit the maximum number of considered tracks per sequence (0 for unlimited)\n            'PRINT_CONFIG': True\n        }\n        return default_config\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.config = utils.init_config(config, self.get_default_metric_config(), self.get_name())\n\n        self.num_ig_masks = 1\n        self.lbls = ['all']\n        self.use_area_rngs = self.config['USE_AREA_RANGES']\n        if self.use_area_rngs:\n            self.area_rngs = self.config['AREA_RANGES']\n            self.area_rng_lbls = self.config['AREA_RANGE_LABELS']\n            self.num_ig_masks += len(self.area_rng_lbls)\n            self.lbls += self.area_rng_lbls\n\n        self.use_time_rngs = self.config['USE_TIME_RANGES']\n        if self.use_time_rngs:\n            self.time_rngs = self.config['TIME_RANGES']\n            self.time_rng_lbls = self.config['TIME_RANGE_LABELS']\n            self.num_ig_masks += len(self.time_rng_lbls)\n            self.lbls += self.time_rng_lbls\n\n        self.array_labels = self.config['IOU_THRESHOLDS']\n        self.rec_thrs = self.config['RECALL_THRESHOLDS']\n\n        self.maxDet = self.config['MAX_DETECTIONS']\n        self.float_array_fields = ['AP_' + lbl for lbl in self.lbls] + ['AR_' + lbl for lbl in self.lbls]\n        self.fields = self.float_array_fields\n        self.summary_fields = self.float_array_fields\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates GT and Tracker matches for one sequence for TrackMAP metrics. Adapted from\n        https://github.com/TAO-Dataset/\"\"\"\n\n        # Initialise results to zero for each sequence as the fields are only defined over the set of all sequences\n        res = {}\n        for field in self.fields:\n            res[field] = [0 for _ in self.array_labels]\n\n        gt_ids, dt_ids = data['gt_track_ids'], data['dt_track_ids']\n\n        if len(gt_ids) == 0 and len(dt_ids) == 0:\n            for idx in range(self.num_ig_masks):\n                res[idx] = None\n            return res\n\n        # get track data\n        gt_tr_areas = data.get('gt_track_areas', None) if self.use_area_rngs else None\n        gt_tr_lengths = data.get('gt_track_lengths', None) if self.use_time_rngs else None\n        gt_tr_iscrowd = data.get('gt_track_iscrowd', None)\n        dt_tr_areas = data.get('dt_track_areas', None) if self.use_area_rngs else None\n        dt_tr_lengths = data.get('dt_track_lengths', None) if self.use_time_rngs else None\n        is_nel = data.get('not_exhaustively_labeled', False)\n\n        # compute ignore masks for different track sets to eval\n        gt_ig_masks = self._compute_track_ig_masks(len(gt_ids), track_lengths=gt_tr_lengths, track_areas=gt_tr_areas,\n                                                   iscrowd=gt_tr_iscrowd)\n        dt_ig_masks = self._compute_track_ig_masks(len(dt_ids), track_lengths=dt_tr_lengths, track_areas=dt_tr_areas,\n                                                   is_not_exhaustively_labeled=is_nel, is_gt=False)\n\n        boxformat = data.get('boxformat', 'xywh')\n        ious = self._compute_track_ious(data['dt_tracks'], data['gt_tracks'], iou_function=data['iou_type'],\n                                        boxformat=boxformat)\n\n        for mask_idx in range(self.num_ig_masks):\n            gt_ig_mask = gt_ig_masks[mask_idx]\n\n            # Sort gt ignore last\n            gt_idx = np.argsort([g for g in gt_ig_mask], kind=\"mergesort\")\n            gt_ids = [gt_ids[i] for i in gt_idx]\n\n            ious_sorted = ious[:, gt_idx] if len(ious) > 0 else ious\n\n            num_thrs = len(self.array_labels)\n            num_gt = len(gt_ids)\n            num_dt = len(dt_ids)\n\n            # Array to store the \"id\" of the matched dt/gt\n            gt_m = np.zeros((num_thrs, num_gt)) - 1\n            dt_m = np.zeros((num_thrs, num_dt)) - 1\n\n            gt_ig = np.array([gt_ig_mask[idx] for idx in gt_idx])\n            dt_ig = np.zeros((num_thrs, num_dt))\n\n            for iou_thr_idx, iou_thr in enumerate(self.array_labels):\n                if len(ious_sorted) == 0:\n                    break\n\n                for dt_idx, _dt in enumerate(dt_ids):\n                    iou = min([iou_thr, 1 - 1e-10])\n                    # information about best match so far (m=-1 -> unmatched)\n                    # store the gt_idx which matched for _dt\n                    m = -1\n                    for gt_idx, _ in enumerate(gt_ids):\n                        # if this gt already matched continue\n                        if gt_m[iou_thr_idx, gt_idx] > 0:\n                            continue\n                        # if _dt matched to reg gt, and on ignore gt, stop\n                        if m > -1 and gt_ig[m] == 0 and gt_ig[gt_idx] == 1:\n                            break\n                        # continue to next gt unless better match made\n                        if ious_sorted[dt_idx, gt_idx] < iou - np.finfo('float').eps:\n                            continue\n                        # if match successful and best so far, store appropriately\n                        iou = ious_sorted[dt_idx, gt_idx]\n                        m = gt_idx\n\n                    # No match found for _dt, go to next _dt\n                    if m == -1:\n                        continue\n\n                    # if gt to ignore for some reason update dt_ig.\n                    # Should not be used in evaluation.\n                    dt_ig[iou_thr_idx, dt_idx] = gt_ig[m]\n                    # _dt match found, update gt_m, and dt_m with \"id\"\n                    dt_m[iou_thr_idx, dt_idx] = gt_ids[m]\n                    gt_m[iou_thr_idx, m] = _dt\n\n            dt_ig_mask = dt_ig_masks[mask_idx]\n\n            dt_ig_mask = np.array(dt_ig_mask).reshape((1, num_dt))  # 1 X num_dt\n            dt_ig_mask = np.repeat(dt_ig_mask, num_thrs, 0)  # num_thrs X num_dt\n\n            # Based on dt_ig_mask ignore any unmatched detection by updating dt_ig\n            dt_ig = np.logical_or(dt_ig, np.logical_and(dt_m == -1, dt_ig_mask))\n            # store results for given video and category\n            res[mask_idx] = {\n                \"dt_ids\": dt_ids,\n                \"gt_ids\": gt_ids,\n                \"dt_matches\": dt_m,\n                \"gt_matches\": gt_m,\n                \"dt_scores\": data['dt_track_scores'],\n                \"gt_ignore\": gt_ig,\n                \"dt_ignore\": dt_ig,\n            }\n\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences. Computes precision and recall values based on track matches.\n        Adapted from https://github.com/TAO-Dataset/\n        \"\"\"\n        num_thrs = len(self.array_labels)\n        num_recalls = len(self.rec_thrs)\n\n        # -1 for absent categories\n        precision = -np.ones(\n            (num_thrs, num_recalls, self.num_ig_masks)\n        )\n        recall = -np.ones((num_thrs, self.num_ig_masks))\n\n        for ig_idx in range(self.num_ig_masks):\n            ig_idx_results = [res[ig_idx] for res in all_res.values() if res[ig_idx] is not None]\n\n            # Remove elements which are None\n            if len(ig_idx_results) == 0:\n                continue\n\n            # Append all scores: shape (N,)\n            # limit considered tracks for each sequence if maxDet > 0\n            if self.maxDet == 0:\n                dt_scores = np.concatenate([res[\"dt_scores\"] for res in ig_idx_results], axis=0)\n\n                dt_idx = np.argsort(-dt_scores, kind=\"mergesort\")\n\n                dt_m = np.concatenate([e[\"dt_matches\"] for e in ig_idx_results],\n                                      axis=1)[:, dt_idx]\n                dt_ig = np.concatenate([e[\"dt_ignore\"] for e in ig_idx_results],\n                                       axis=1)[:, dt_idx]\n            elif self.maxDet > 0:\n                dt_scores = np.concatenate([res[\"dt_scores\"][0:self.maxDet] for res in ig_idx_results], axis=0)\n\n                dt_idx = np.argsort(-dt_scores, kind=\"mergesort\")\n\n                dt_m = np.concatenate([e[\"dt_matches\"][:, 0:self.maxDet] for e in ig_idx_results],\n                                      axis=1)[:, dt_idx]\n                dt_ig = np.concatenate([e[\"dt_ignore\"][:, 0:self.maxDet] for e in ig_idx_results],\n                                       axis=1)[:, dt_idx]\n            else:\n                raise Exception(\"Number of maximum detections must be >= 0, but is set to %i\" % self.maxDet)\n\n            gt_ig = np.concatenate([res[\"gt_ignore\"] for res in ig_idx_results])\n            # num gt anns to consider\n            num_gt = np.count_nonzero(gt_ig == 0)\n\n            if num_gt == 0:\n                continue\n\n            tps = np.logical_and(dt_m != -1, np.logical_not(dt_ig))\n            fps = np.logical_and(dt_m == -1, np.logical_not(dt_ig))\n\n            tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)\n            fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)\n\n            for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):\n                tp = np.array(tp)\n                fp = np.array(fp)\n                num_tp = len(tp)\n                rc = tp / num_gt\n                if num_tp:\n                    recall[iou_thr_idx, ig_idx] = rc[-1]\n                else:\n                    recall[iou_thr_idx, ig_idx] = 0\n\n                # np.spacing(1) ~= eps\n                pr = tp / (fp + tp + np.spacing(1))\n                pr = pr.tolist()\n\n                # Ensure precision values are monotonically decreasing\n                for i in range(num_tp - 1, 0, -1):\n                    if pr[i] > pr[i - 1]:\n                        pr[i - 1] = pr[i]\n\n                # find indices at the predefined recall values\n                rec_thrs_insert_idx = np.searchsorted(rc, self.rec_thrs, side=\"left\")\n\n                pr_at_recall = [0.0] * num_recalls\n\n                try:\n                    for _idx, pr_idx in enumerate(rec_thrs_insert_idx):\n                        pr_at_recall[_idx] = pr[pr_idx]\n                except IndexError:\n                    pass\n\n                precision[iou_thr_idx, :, ig_idx] = (np.array(pr_at_recall))\n\n        res = {'precision': precision, 'recall': recall}\n\n        # compute the precision and recall averages for the respective alpha thresholds and ignore masks\n        for lbl in self.lbls:\n            res['AP_' + lbl] = np.zeros((len(self.array_labels)), dtype=np.float)\n            res['AR_' + lbl] = np.zeros((len(self.array_labels)), dtype=np.float)\n\n        for a_id, alpha in enumerate(self.array_labels):\n            for lbl_idx, lbl in enumerate(self.lbls):\n                p = precision[a_id, :, lbl_idx]\n                if len(p[p > -1]) == 0:\n                    mean_p = -1\n                else:\n                    mean_p = np.mean(p[p > -1])\n                res['AP_' + lbl][a_id] = mean_p\n                res['AR_' + lbl][a_id] = recall[a_id, lbl_idx]\n\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=True):\n        \"\"\"Combines metrics across all classes by averaging over the class values\n        Note mAP is not well defined for 'empty classes' so 'ignore empty classes' is always true here.\n        \"\"\"\n        res = {}\n        for field in self.fields:\n            res[field] = np.zeros((len(self.array_labels)), dtype=np.float)\n            field_stacked = np.array([res[field] for res in all_res.values()])\n\n            for a_id, alpha in enumerate(self.array_labels):\n                values = field_stacked[:, a_id]\n                if len(values[values > -1]) == 0:\n                    mean = -1\n                else:\n                    mean = np.mean(values[values > -1])\n                res[field][a_id] = mean\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n\n        res = {}\n        for field in self.fields:\n            res[field] = np.zeros((len(self.array_labels)), dtype=np.float)\n            field_stacked = np.array([res[field] for res in all_res.values()])\n\n            for a_id, alpha in enumerate(self.array_labels):\n                values = field_stacked[:, a_id]\n                if len(values[values > -1]) == 0:\n                    mean = -1\n                else:\n                    mean = np.mean(values[values > -1])\n                res[field][a_id] = mean\n        return res\n\n    def _compute_track_ig_masks(self, num_ids, track_lengths=None, track_areas=None, iscrowd=None,\n                                is_not_exhaustively_labeled=False, is_gt=True):\n        \"\"\"\n        Computes ignore masks for different track sets to evaluate\n        :param num_ids: the number of track IDs\n        :param track_lengths: the lengths of the tracks (number of timesteps)\n        :param track_areas: the average area of a track\n        :param iscrowd: whether a track is marked as crowd\n        :param is_not_exhaustively_labeled: whether the track category is not exhaustively labeled\n        :param is_gt: whether it is gt\n        :return: the track ignore masks\n        \"\"\"\n        # for TAO tracks for classes which are not exhaustively labeled are not evaluated\n        if not is_gt and is_not_exhaustively_labeled:\n            track_ig_masks = [[1 for _ in range(num_ids)] for i in range(self.num_ig_masks)]\n        else:\n            # consider all tracks\n            track_ig_masks = [[0 for _ in range(num_ids)]]\n\n            # consider tracks with certain area\n            if self.use_area_rngs:\n                for rng in self.area_rngs:\n                    track_ig_masks.append([0 if rng[0] - np.finfo('float').eps <= area <= rng[1] + np.finfo('float').eps\n                                           else 1 for area in track_areas])\n\n            # consider tracks with certain duration\n            if self.use_time_rngs:\n                for rng in self.time_rngs:\n                    track_ig_masks.append([0 if rng[0] - np.finfo('float').eps <= length\n                                                <= rng[1] + np.finfo('float').eps else 1 for length in track_lengths])\n\n        # for YouTubeVIS evaluation tracks with crowd tag are not evaluated\n        if is_gt and iscrowd:\n            track_ig_masks = [np.logical_or(mask, iscrowd) for mask in track_ig_masks]\n\n        return track_ig_masks\n\n    @staticmethod\n    def _compute_bb_track_iou(dt_track, gt_track, boxformat='xywh'):\n        \"\"\"\n        Calculates the track IoU for one detected track and one ground truth track for bounding boxes\n        :param dt_track: the detected track (format: dictionary with frame index as keys and\n                            numpy arrays as values)\n        :param gt_track: the ground truth track (format: dictionary with frame index as keys and\n                        numpy array as values)\n        :param boxformat: the format of the boxes\n        :return: the track IoU\n        \"\"\"\n        intersect = 0\n        union = 0\n        image_ids = set(gt_track.keys()) | set(dt_track.keys())\n        for image in image_ids:\n            g = gt_track.get(image, None)\n            d = dt_track.get(image, None)\n            if boxformat == 'xywh':\n                if d is not None and g is not None:\n                    dx, dy, dw, dh = d\n                    gx, gy, gw, gh = g\n                    w = max(min(dx + dw, gx + gw) - max(dx, gx), 0)\n                    h = max(min(dy + dh, gy + gh) - max(dy, gy), 0)\n                    i = w * h\n                    u = dw * dh + gw * gh - i\n                    intersect += i\n                    union += u\n                elif d is None and g is not None:\n                    union += g[2] * g[3]\n                elif d is not None and g is None:\n                    union += d[2] * d[3]\n            elif boxformat == 'x0y0x1y1':\n                if d is not None and g is not None:\n                    dx0, dy0, dx1, dy1 = d\n                    gx0, gy0, gx1, gy1 = g\n                    w = max(min(dx1, gx1) - max(dx0, gx0), 0)\n                    h = max(min(dy1, gy1) - max(dy0, gy0), 0)\n                    i = w * h\n                    u = (dx1 - dx0) * (dy1 - dy0) + (gx1 - gx0) * (gy1 - gy0) - i\n                    intersect += i\n                    union += u\n                elif d is None and g is not None:\n                    union += (g[2] - g[0]) * (g[3] - g[1])\n                elif d is not None and g is None:\n                    union += (d[2] - d[0]) * (d[3] - d[1])\n            else:\n                raise TrackEvalException('BoxFormat not implemented')\n        if intersect > union:\n            raise TrackEvalException(\"Intersection value > union value. Are the box values corrupted?\")\n        return intersect / union if union > 0 else 0\n\n    @staticmethod\n    def _compute_mask_track_iou(dt_track, gt_track):\n        \"\"\"\n        Calculates the track IoU for one detected track and one ground truth track for segmentation masks\n        :param dt_track: the detected track (format: dictionary with frame index as keys and\n                            pycocotools rle encoded masks as values)\n        :param gt_track: the ground truth track (format: dictionary with frame index as keys and\n                            pycocotools rle encoded masks as values)\n        :return: the track IoU\n        \"\"\"\n        # only loaded when needed to reduce minimum requirements\n        from pycocotools import mask as mask_utils\n\n        intersect = .0\n        union = .0\n        image_ids = set(gt_track.keys()) | set(dt_track.keys())\n        for image in image_ids:\n            g = gt_track.get(image, None)\n            d = dt_track.get(image, None)\n            if d and g:\n                intersect += mask_utils.area(mask_utils.merge([d, g], True))\n                union += mask_utils.area(mask_utils.merge([d, g], False))\n            elif not d and g:\n                union += mask_utils.area(g)\n            elif d and not g:\n                union += mask_utils.area(d)\n        if union < 0.0 - np.finfo('float').eps:\n            raise TrackEvalException(\"Union value < 0. Are the segmentaions corrupted?\")\n        if intersect > union:\n            raise TrackEvalException(\"Intersection value > union value. Are the segmentations corrupted?\")\n        iou = intersect / union if union > 0.0 + np.finfo('float').eps else 0.0\n        return iou\n\n    @staticmethod\n    def _compute_track_ious(dt, gt, iou_function='bbox', boxformat='xywh'):\n        \"\"\"\n        Calculate track IoUs for a set of ground truth tracks and a set of detected tracks\n        \"\"\"\n\n        if len(gt) == 0 and len(dt) == 0:\n            return []\n\n        if iou_function == 'bbox':\n            track_iou_function = partial(TrackMAP._compute_bb_track_iou, boxformat=boxformat)\n        elif iou_function == 'mask':\n            track_iou_function = partial(TrackMAP._compute_mask_track_iou)\n        else:\n            raise Exception('IoU function not implemented')\n\n        ious = np.zeros([len(dt), len(gt)])\n        for i, j in np.ndindex(ious.shape):\n            ious[i, j] = track_iou_function(dt[i], gt[j])\n        return ious\n\n    @staticmethod\n    def _row_print(*argv):\n        \"\"\"Prints results in an evenly spaced rows, with more space in first row\"\"\"\n        if len(argv) == 1:\n            argv = argv[0]\n        to_print = '%-40s' % argv[0]\n        for v in argv[1:]:\n            to_print += '%-12s' % str(v)\n        print(to_print)\n"
  },
  {
    "path": "trackeval/metrics/vace.py",
    "content": "import numpy as np\nfrom scipy.optimize import linear_sum_assignment\nfrom ._base_metric import _BaseMetric\nfrom .. import _timing\n\n\nclass VACE(_BaseMetric):\n    \"\"\"Class which implements the VACE metrics.\n\n    The metrics are described in:\n    Manohar et al. (2006) \"Performance Evaluation of Object Detection and Tracking in Video\"\n    https://link.springer.com/chapter/10.1007/11612704_16\n\n    This implementation uses the \"relaxed\" variant of the metrics,\n    where an overlap threshold is applied in each frame.\n    \"\"\"\n\n    def __init__(self, config=None):\n        super().__init__()\n        self.integer_fields = ['VACE_IDs', 'VACE_GT_IDs', 'num_non_empty_timesteps']\n        self.float_fields = ['STDA', 'ATA', 'FDA', 'SFDA']\n        self.fields = self.integer_fields + self.float_fields\n        self.summary_fields = ['SFDA', 'ATA']\n\n        # Fields that are accumulated over multiple videos.\n        self._additive_fields = self.integer_fields + ['STDA', 'FDA']\n\n        self.threshold = 0.5\n\n    @_timing.time\n    def eval_sequence(self, data):\n        \"\"\"Calculates VACE metrics for one sequence.\n\n        Depends on the fields:\n            data['num_gt_ids']\n            data['num_tracker_ids']\n            data['gt_ids']\n            data['tracker_ids']\n            data['similarity_scores']\n        \"\"\"\n        res = {}\n\n        # Obtain Average Tracking Accuracy (ATA) using track correspondence.\n        # Obtain counts necessary to compute temporal IOU.\n        # Assume that integer counts can be represented exactly as floats.\n        potential_matches_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))\n        gt_id_count = np.zeros(data['num_gt_ids'])\n        tracker_id_count = np.zeros(data['num_tracker_ids'])\n        both_present_count = np.zeros((data['num_gt_ids'], data['num_tracker_ids']))\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            # Count the number of frames in which two tracks satisfy the overlap criterion.\n            matches_mask = np.greater_equal(data['similarity_scores'][t], self.threshold)\n            match_idx_gt, match_idx_tracker = np.nonzero(matches_mask)\n            potential_matches_count[gt_ids_t[match_idx_gt], tracker_ids_t[match_idx_tracker]] += 1\n            # Count the number of frames in which the tracks are present.\n            gt_id_count[gt_ids_t] += 1\n            tracker_id_count[tracker_ids_t] += 1\n            both_present_count[gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]] += 1\n        # Number of frames in which either track is present (union of the two sets of frames).\n        union_count = (gt_id_count[:, np.newaxis]\n                       + tracker_id_count[np.newaxis, :]\n                       - both_present_count)\n        # The denominator should always be non-zero if all tracks are non-empty.\n        with np.errstate(divide='raise', invalid='raise'):\n            temporal_iou = potential_matches_count / union_count\n        # Find assignment that maximizes temporal IOU.\n        match_rows, match_cols = linear_sum_assignment(-temporal_iou)\n        res['STDA'] = temporal_iou[match_rows, match_cols].sum()\n        res['VACE_IDs'] = data['num_tracker_ids']\n        res['VACE_GT_IDs'] = data['num_gt_ids']\n\n        # Obtain Frame Detection Accuracy (FDA) using per-frame correspondence.\n        non_empty_count = 0\n        fda = 0\n        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(data['gt_ids'], data['tracker_ids'])):\n            n_g = len(gt_ids_t)\n            n_d = len(tracker_ids_t)\n            if not (n_g or n_d):\n                continue\n            # n_g > 0 or n_d > 0\n            non_empty_count += 1\n            if not (n_g and n_d):\n                continue\n            # n_g > 0 and n_d > 0\n            spatial_overlap = data['similarity_scores'][t]\n            match_rows, match_cols = linear_sum_assignment(-spatial_overlap)\n            overlap_ratio = spatial_overlap[match_rows, match_cols].sum()\n            fda += overlap_ratio / (0.5 * (n_g + n_d))\n        res['FDA'] = fda\n        res['num_non_empty_timesteps'] = non_empty_count\n\n        res.update(self._compute_final_fields(res))\n        return res\n\n    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=True):\n        \"\"\"Combines metrics across all classes by averaging over the class values.\n        If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.\n        \"\"\"\n        res = {}\n        for field in self.fields:\n            if ignore_empty_classes:\n                res[field] = np.mean([v[field] for v in all_res.values()\n                                  if v['VACE_GT_IDs'] > 0 or v['VACE_IDs'] > 0], axis=0)\n            else:\n                res[field] = np.mean([v[field] for v in all_res.values()], axis=0)\n        return res\n\n    def combine_classes_det_averaged(self, all_res):\n        \"\"\"Combines metrics across all classes by averaging over the detection values\"\"\"\n        res = {}\n        for field in self._additive_fields:\n            res[field] = _BaseMetric._combine_sum(all_res, field)\n        res = self._compute_final_fields(res)\n        return res\n\n    def combine_sequences(self, all_res):\n        \"\"\"Combines metrics across all sequences\"\"\"\n        res = {}\n        for header in self._additive_fields:\n            res[header] = _BaseMetric._combine_sum(all_res, header)\n        res.update(self._compute_final_fields(res))\n        return res\n\n    @staticmethod\n    def _compute_final_fields(additive):\n        final = {}\n        with np.errstate(invalid='ignore'):  # Permit nan results.\n            final['ATA'] = (additive['STDA'] /\n                            (0.5 * (additive['VACE_IDs'] + additive['VACE_GT_IDs'])))\n            final['SFDA'] = additive['FDA'] / additive['num_non_empty_timesteps']\n        return final\n"
  },
  {
    "path": "trackeval/plotting.py",
    "content": "\nimport os\nimport numpy as np\nfrom .utils import TrackEvalException\n\n\ndef plot_compare_trackers(tracker_folder, tracker_list, cls, output_folder, plots_list=None):\n    \"\"\"Create plots which compare metrics across different trackers.\"\"\"\n    # Define what to plot\n    if plots_list is None:\n        plots_list = get_default_plots_list()\n\n    # Load data\n    data = load_multiple_tracker_summaries(tracker_folder, tracker_list, cls)\n    out_loc = os.path.join(output_folder, cls)\n\n    # Plot\n    for args in plots_list:\n        create_comparison_plot(data, out_loc, *args)\n\n\ndef get_default_plots_list():\n    # y_label, x_label, sort_label, bg_label, bg_function\n    plots_list = [\n        ['AssA', 'DetA', 'HOTA', 'HOTA', 'geometric_mean'],\n        ['AssPr', 'AssRe', 'HOTA', 'AssA', 'jaccard'],\n        ['DetPr', 'DetRe', 'HOTA', 'DetA', 'jaccard'],\n        ['HOTA(0)', 'LocA(0)', 'HOTA', 'HOTALocA(0)', 'multiplication'],\n        ['HOTA', 'LocA', 'HOTA', None, None],\n\n        ['HOTA', 'MOTA', 'HOTA', None, None],\n        ['HOTA', 'IDF1', 'HOTA', None, None],\n        ['IDF1', 'MOTA', 'HOTA', None, None],\n    ]\n    return plots_list\n\n\ndef load_multiple_tracker_summaries(tracker_folder, tracker_list, cls):\n    \"\"\"Loads summary data for multiple trackers.\"\"\"\n    data = {}\n    for tracker in tracker_list:\n        with open(os.path.join(tracker_folder, tracker, cls + '_summary.txt')) as f:\n            keys = next(f).split(' ')\n            done = False\n            while not done:\n                values = next(f).split(' ')\n                if len(values) == len(keys):\n                    done = True\n            data[tracker] = dict(zip(keys, map(float, values)))\n    return data\n\n\ndef create_comparison_plot(data, out_loc, y_label, x_label, sort_label, bg_label=None, bg_function=None, settings=None):\n    \"\"\" Creates a scatter plot comparing multiple trackers between two metric fields, with one on the x-axis and the\n    other on the y axis. Adds pareto optical lines and (optionally) a background contour.\n\n    Inputs:\n        data: dict of dicts such that data[tracker_name][metric_field_name] = float\n        y_label: the metric_field_name to be plotted on the y-axis\n        x_label: the metric_field_name to be plotted on the x-axis\n        sort_label: the metric_field_name by which trackers are ordered and ranked\n        bg_label: the metric_field_name by which (optional) background contours are plotted\n        bg_function: the (optional) function bg_function(x,y) which converts the x_label / y_label values into bg_label.\n        settings: dict of plot settings with keys:\n            'gap_val': gap between axis ticks and bg curves.\n            'num_to_plot': maximum number of trackers to plot\n    \"\"\"\n\n    # Only loaded when run to reduce minimum requirements\n    from matplotlib import pyplot as plt\n\n    # Get plot settings\n    if settings is None:\n        gap_val = 2\n        num_to_plot = 20\n    else:\n        gap_val = settings['gap_val']\n        num_to_plot = settings['num_to_plot']\n\n    if (bg_label is None) != (bg_function is None):\n        raise TrackEvalException('bg_function and bg_label must either be both given or neither given.')\n\n    # Extract data\n    tracker_names = np.array(list(data.keys()))\n    sort_index = np.array([data[t][sort_label] for t in tracker_names]).argsort()[::-1]\n    x_values = np.array([data[t][x_label] for t in tracker_names])[sort_index][:num_to_plot]\n    y_values = np.array([data[t][y_label] for t in tracker_names])[sort_index][:num_to_plot]\n\n    # Print info on what is being plotted\n    tracker_names = tracker_names[sort_index][:num_to_plot]\n    print('\\nPlotting %s vs %s, for the following (ordered) trackers:' % (y_label, x_label))\n    for i, name in enumerate(tracker_names):\n        print('%i: %s' % (i+1, name))\n\n    # Find best fitting boundaries for data\n    boundaries = _get_boundaries(x_values, y_values, round_val=gap_val/2)\n\n    fig = plt.figure()\n\n    # Plot background contour\n    if bg_function is not None:\n        _plot_bg_contour(bg_function, boundaries, gap_val)\n\n    # Plot pareto optimal lines\n    _plot_pareto_optimal_lines(x_values, y_values)\n\n    # Plot data points with number labels\n    labels = np.arange(len(y_values)) + 1\n    plt.plot(x_values, y_values, 'b.', markersize=15)\n    for xx, yy, l in zip(x_values, y_values, labels):\n        plt.text(xx, yy, str(l), color=\"red\", fontsize=15)\n\n    # Add extra explanatory text to plots\n    plt.text(0, -0.11, 'label order:\\nHOTA', horizontalalignment='left', verticalalignment='center',\n             transform=fig.axes[0].transAxes, color=\"red\", fontsize=12)\n    if bg_label is not None:\n        plt.text(1, -0.11, 'curve values:\\n' + bg_label, horizontalalignment='right', verticalalignment='center',\n                 transform=fig.axes[0].transAxes, color=\"grey\", fontsize=12)\n\n    plt.xlabel(x_label, fontsize=15)\n    plt.ylabel(y_label, fontsize=15)\n    title = y_label + ' vs ' + x_label\n    if bg_label is not None:\n        title += ' (' + bg_label + ')'\n    plt.title(title, fontsize=17)\n    plt.xticks(np.arange(0, 100, gap_val))\n    plt.yticks(np.arange(0, 100, gap_val))\n    min_x, max_x, min_y, max_y = boundaries\n    plt.xlim(min_x, max_x)\n    plt.ylim(min_y, max_y)\n    plt.gca().set_aspect('equal', adjustable='box')\n    plt.tight_layout()\n\n    os.makedirs(out_loc, exist_ok=True)\n    filename = os.path.join(out_loc, title.replace(' ', '_'))\n    plt.savefig(filename + '.pdf', bbox_inches='tight', pad_inches=0.05)\n    plt.savefig(filename + '.png', bbox_inches='tight', pad_inches=0.05)\n\n\ndef _get_boundaries(x_values, y_values, round_val):\n    x1 = np.min(np.floor((x_values - 0.5) / round_val) * round_val)\n    x2 = np.max(np.ceil((x_values + 0.5) / round_val) * round_val)\n    y1 = np.min(np.floor((y_values - 0.5) / round_val) * round_val)\n    y2 = np.max(np.ceil((y_values + 0.5) / round_val) * round_val)\n    x_range = x2 - x1\n    y_range = y2 - y1\n    max_range = max(x_range, y_range)\n    x_center = (x1 + x2) / 2\n    y_center = (y1 + y2) / 2\n    min_x = max(x_center - max_range / 2, 0)\n    max_x = min(x_center + max_range / 2, 100)\n    min_y = max(y_center - max_range / 2, 0)\n    max_y = min(y_center + max_range / 2, 100)\n    return min_x, max_x, min_y, max_y\n\n\ndef geometric_mean(x, y):\n    return np.sqrt(x * y)\n\n\ndef jaccard(x, y):\n    x = x / 100\n    y = y / 100\n    return 100 * (x * y) / (x + y - x * y)\n\n\ndef multiplication(x, y):\n    return x * y / 100\n\n\nbg_function_dict = {\n    \"geometric_mean\": geometric_mean,\n    \"jaccard\": jaccard,\n    \"multiplication\": multiplication,\n    }\n\n\ndef _plot_bg_contour(bg_function, plot_boundaries, gap_val):\n    \"\"\" Plot background contour. \"\"\"\n\n    # Only loaded when run to reduce minimum requirements\n    from matplotlib import pyplot as plt\n\n    # Plot background contour\n    min_x, max_x, min_y, max_y = plot_boundaries\n    x = np.arange(min_x, max_x, 0.1)\n    y = np.arange(min_y, max_y, 0.1)\n    x_grid, y_grid = np.meshgrid(x, y)\n    if bg_function in bg_function_dict.keys():\n        z_grid = bg_function_dict[bg_function](x_grid, y_grid)\n    else:\n        raise TrackEvalException(\"background plotting function '%s' is not defined.\" % bg_function)\n    levels = np.arange(0, 100, gap_val)\n    con = plt.contour(x_grid, y_grid, z_grid, levels, colors='grey')\n\n    def bg_format(val):\n        s = '{:1f}'.format(val)\n        return '{:.0f}'.format(val) if s[-1] == '0' else s\n\n    con.levels = [bg_format(val) for val in con.levels]\n    plt.clabel(con, con.levels, inline=True, fmt='%r', fontsize=8)\n\n\ndef _plot_pareto_optimal_lines(x_values, y_values):\n    \"\"\" Plot pareto optimal lines \"\"\"\n\n    # Only loaded when run to reduce minimum requirements\n    from matplotlib import pyplot as plt\n\n    # Plot pareto optimal lines\n    cxs = x_values\n    cys = y_values\n    best_y = np.argmax(cys)\n    x_pareto = [0, cxs[best_y]]\n    y_pareto = [cys[best_y], cys[best_y]]\n    t = 2\n    remaining = cxs > x_pareto[t - 1]\n    cys = cys[remaining]\n    cxs = cxs[remaining]\n    while len(cxs) > 0 and len(cys) > 0:\n        best_y = np.argmax(cys)\n        x_pareto += [x_pareto[t - 1], cxs[best_y]]\n        y_pareto += [cys[best_y], cys[best_y]]\n        t += 2\n        remaining = cxs > x_pareto[t - 1]\n        cys = cys[remaining]\n        cxs = cxs[remaining]\n    x_pareto.append(x_pareto[t - 1])\n    y_pareto.append(0)\n    plt.plot(np.array(x_pareto), np.array(y_pareto), '--r')\n"
  },
  {
    "path": "trackeval/scripts/comparison_plots.py",
    "content": "import sys\nimport os\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nplots_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 'plots'))\ntracker_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data', 'trackers'))\n\n# dataset = os.path.join('kitti', 'kitti_2d_box_train')\n# classes = ['cars', 'pedestrian']\n\ndataset = os.path.join('mot_challenge', 'MOT17-train')\nclasses = ['pedestrian']\n\ndata_fol = os.path.join(tracker_folder, dataset)\ntrackers = os.listdir(data_fol)\nout_loc = os.path.join(plots_folder, dataset)\nfor cls in classes:\n    trackeval.plotting.plot_compare_trackers(data_fol, trackers, cls, out_loc)\n"
  },
  {
    "path": "trackeval/scripts/run_bdd.py",
    "content": "\n\"\"\" run_bdd.py\n\nRun example:\nrun_bdd.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL qdtrack\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n            'GT_FOLDER': os.path.join(code_path, 'data/gt/bdd100k/bdd100k_val'),  # Location of GT data\n            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/bdd100k/bdd100k_val'),  # Trackers location\n            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n            'CLASSES_TO_EVAL': ['pedestrian', 'rider', 'car', 'bus', 'truck', 'train', 'motorcycle', 'bicycle'],\n            # Valid: ['pedestrian', 'rider', 'car', 'bus', 'truck', 'train', 'motorcycle', 'bicycle']\n            'SPLIT_TO_EVAL': 'val',  # Valid: 'training', 'val',\n            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n            'PRINT_CONFIG': True,  # Whether to print current config\n            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n    Metric arguments:\n        'METRICS': ['Hota','Clear', 'ID', 'Count']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_dataset_config = trackeval.datasets.BDD100K.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.BDD100K(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)"
  },
  {
    "path": "trackeval/scripts/run_davis.py",
    "content": "\"\"\" run_davis.py\n\nRun example:\nrun_davis.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL ags\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n    '   'GT_FOLDER': os.path.join(code_path, 'data/gt/davis/'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/davis/davis_val'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'SPLIT_TO_EVAL': 'val',  # Valid: 'val', 'train'\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n        'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/ImageSets/2017)\n        'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/split-to-eval.txt)\n        'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n        'GT_LOC_FORMAT': '{gt_folder}/Annotations_unsupervised/480p/{seq}',\n        # '{gt_folder}/Annotations_unsupervised/480p/{seq}'\n        'MAX_DETECTIONS': 0  # Maximum number of allowed detections per sequence (0 for no threshold)\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'JAndF']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_dataset_config = trackeval.datasets.DAVIS.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity', 'JAndF']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.DAVIS(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.JAndF]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)"
  },
  {
    "path": "trackeval/scripts/run_headtracking_challenge.py",
    "content": "\n\"\"\" run_mot_challenge.py\n\nRun example:\nrun_mot_challenge.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL Lif_T\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']\n        'BENCHMARK': 'MOT17',  # Valid: 'MOT17', 'MOT16', 'MOT20', 'MOT15'\n        'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'\n        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'DO_PREPROC': True,  # Whether to perform preprocessing (never done for 2D_MOT_2015)\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'IDEucl']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = False\n    default_dataset_config = trackeval.datasets.HeadTrackingChallenge.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity', 'IDEucl'], 'THRESHOLD': 0.4}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            elif setting == 'SEQ_INFO':\n                x = dict(zip(args[setting], [None]*len(args[setting])))\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.HeadTrackingChallenge(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.IDEucl]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric(metrics_config))\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)\n"
  },
  {
    "path": "trackeval/scripts/run_kitti.py",
    "content": "\n\"\"\" run_kitti.py\n\nRun example:\nrun_kitti.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL CIWT\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/kitti/kitti_2d_box_train'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_2d_box_train/'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': ['car', 'pedestrian'],  # Valid: ['car', 'pedestrian']\n        'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val', 'training_minus_val', 'test', [hgx1105] valhalf as CenterTrack\n        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': '',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER    # [hgx1105] 'data' to ''\n        'OUTPUT_SUB_FOLDER': ''  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n    Metric arguments:\n        'METRICS': ['Hota','Clear', 'ID', 'Count']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = False\n    default_dataset_config = trackeval.datasets.Kitti2DBox.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.Kitti2DBox(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)\n"
  },
  {
    "path": "trackeval/scripts/run_kitti_mots.py",
    "content": "\n\"\"\" run_kitti_mots.py\n\nRun example:\nrun_kitti_mots.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL trackrcnn\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/kitti/kitti_mots'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/kitti/kitti_mots_val'),   # Location of all\n                                                                                            # trackers\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': ['car', 'pedestrian'],  # Valid: ['car', 'pedestrian']\n        'SPLIT_TO_EVAL': 'val',  # Valid: 'training', 'val'\n        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER)\n        'SEQMAP_FILE': None,    # Directly specify seqmap file (if none use seqmap_folder/split_to_eval.seqmap)\n        'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n        'GT_LOC_FORMAT': '{gt_folder}/instances_txt/{seq}.txt',  # format of gt localization\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = False\n    default_dataset_config = trackeval.datasets.KittiMOTS.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.KittiMOTS(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.JAndF]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)\n"
  },
  {
    "path": "trackeval/scripts/run_mot_challenge.py",
    "content": "\n\"\"\" run_mot_challenge.py\n\nRun example:\nrun_mot_challenge.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL Lif_T\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']\n        'BENCHMARK': 'MOT17',  # Valid: 'MOT17', 'MOT16', 'MOT20', 'MOT15'\n        'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'\n        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'DO_PREPROC': True,  # Whether to perform preprocessing (never done for 2D_MOT_2015)\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'VACE']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\n# python TrackEval/scripts/run_mot_challenge.py --BENCHMARK MOT17 --SPLIT_TO_EVAL train --TRACKERS_TO_EVAL ByteTrack --METRICS HOTA CLEAR Identity VACE --TIME_PROGRESS False --USE_PARALLEL False --NUM_PARALLEL_CORES 1  --GT_FOLDER datasets/mot/ --TRACKERS_FOLDER YOLOX_outputs/yolox_s_mot17_half_repro1/track_results_ByteTrack/track_results --GT_LOC_FORMAT {gt_folder}/{seq}/gt/gt_val_half.txt\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = False\n    default_dataset_config = trackeval.datasets.MotChallenge2DBox.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity'], 'THRESHOLD': 0.5}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            elif setting == 'SEQ_INFO':\n                x = dict(zip(args[setting], [None]*len(args[setting])))\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    if type(dataset_config['SEQMAP_FILE']) is list:         # TODO: [hgx 0409] for dancetrack dataset\n        dataset_config['SEQMAP_FILE'] = dataset_config['SEQMAP_FILE'][0]\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.MotChallenge2DBox(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.VACE]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric(metrics_config))\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)\n"
  },
  {
    "path": "trackeval/scripts/run_mots_challenge.py",
    "content": "\"\"\" run_mots.py\n\nRun example:\nrun_mots.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL TrackRCNN\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']\n        'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test'\n        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/seqmaps)\n        'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/MOTS-split_to_eval)\n        'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps\n        'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt',  # '{gt_folder}/{seq}/gt/gt.txt'\n        'SKIP_SPLIT_FOL': False,    # If False, data is in GT_FOLDER/MOTS-SPLIT_TO_EVAL/ and in\n                                    # TRACKERS_FOLDER/MOTS-SPLIT_TO_EVAL/tracker/\n                                    # If True, then the middle 'MOTS-split' folder is skipped for both.\n    Metric arguments:\n        'METRICS': ['HOTA','CLEAR', 'Identity', 'VACE', 'JAndF']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = False\n    default_dataset_config = trackeval.datasets.MOTSChallenge.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            elif setting == 'SEQ_INFO':\n                x = dict(zip(args[setting], [None]*len(args[setting])))\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.MOTSChallenge(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.VACE,\n                   trackeval.metrics.JAndF]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)\n"
  },
  {
    "path": "trackeval/scripts/run_rob_mots.py",
    "content": "# python3 scripts/run_rob_mots.py --ROBMOTS_SPLIT train --TRACKERS_TO_EVAL STP --USE_PARALLEL True --NUM_PARALLEL_CORES 8\n\nimport sys\nimport os\nimport csv\nimport numpy as np\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\nfrom trackeval import utils\n\ncode_path = utils.get_code_path()\n\nif __name__ == '__main__':\n    freeze_support()\n\n    script_config = {\n        'ROBMOTS_SPLIT': 'train',  # 'train',  # valid: 'train', 'val', 'test', 'test_live', 'test_post', 'test_all'\n        'BENCHMARKS': None,  # If None, use all for each split.\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/rob_mots'),\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/rob_mots'),\n    }\n\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = True\n    default_dataset_config = trackeval.datasets.RobMOTS.get_default_dataset_config()\n    config = {**default_eval_config, **default_dataset_config, **script_config}\n\n    # Command line interface:\n    config = utils.update_config(config)\n\n    if not config['BENCHMARKS']:\n        if config['ROBMOTS_SPLIT'] == 'val':\n            config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis',\n                                    'tao', 'mots_challenge', 'waymo']\n            config['SPLIT_TO_EVAL'] = 'val'\n        elif config['ROBMOTS_SPLIT'] == 'test' or config['SPLIT_TO_EVAL'] == 'test_live':\n            config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'tao']\n            config['SPLIT_TO_EVAL'] = 'test'\n        elif config['ROBMOTS_SPLIT'] == 'test_post':\n            config['BENCHMARKS'] = ['mots_challenge', 'waymo', 'ovis']\n            config['SPLIT_TO_EVAL'] = 'test'\n        elif config['ROBMOTS_SPLIT'] == 'test_all':\n            config['BENCHMARKS'] = ['kitti_mots', 'bdd_mots', 'davis_unsupervised', 'youtube_vis', 'ovis',\n                                    'tao', 'mots_challenge', 'waymo']\n            config['SPLIT_TO_EVAL'] = 'test'\n        elif config['ROBMOTS_SPLIT'] == 'train':\n            config['BENCHMARKS'] = ['kitti_mots', 'davis_unsupervised', 'youtube_vis', 'ovis', 'tao', 'bdd_mots']\n            config['SPLIT_TO_EVAL'] = 'train'\n    else:\n        config['SPLIT_TO_EVAL'] = config['ROBMOTS_SPLIT']\n\n    metrics_config = {'METRICS': ['HOTA']}\n    eval_config = {k: v for k, v in config.items() if k in config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in config.keys()}\n\n    # Run code\n    try:\n        dataset_list = []\n        for bench in config['BENCHMARKS']:\n            dataset_config['SUB_BENCHMARK'] = bench\n            dataset_list.append(trackeval.datasets.RobMOTS(dataset_config))\n        evaluator = trackeval.Evaluator(eval_config)\n        metrics_list = []\n        for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity,\n                       trackeval.metrics.VACE, trackeval.metrics.JAndF]:\n            if metric.get_name() in metrics_config['METRICS']:\n                metrics_list.append(metric())\n        if len(metrics_list) == 0:\n            raise Exception('No metrics selected for evaluation')\n        output_res, output_msg = evaluator.evaluate(dataset_list, metrics_list)\n        output = list(list(output_msg.values())[0].values())[0]\n\n    except Exception as err:\n        if type(err) == trackeval.utils.TrackEvalException:\n            output = str(err)\n        else:\n            output = 'Unknown error occurred.'\n\n    success = output == 'Success'\n    if not success:\n        output = 'ERROR, evaluation failed. \\n\\nError message: ' + output\n        print(output)\n\n    if config['TRACKERS_TO_EVAL']:\n        msg = \"Thanks you for participating in the RobMOTS benchmark.\\n\\n\"\n        msg += \"The status of your evaluation is: \\n\" + output + '\\n\\n'\n        msg += \"If your tracking results evaluated successfully on the evaluation server you can see your results here: \\n\"\n        msg += \"https://eval.vision.rwth-aachen.de/vision/\"\n        status_file = os.path.join(config['TRACKERS_FOLDER'], config['ROBMOTS_SPLIT'], config['TRACKERS_TO_EVAL'][0],\n                                   'status.txt')\n        with open(status_file, 'w', newline='') as f:\n            f.write(msg)\n\n    if success:\n        # For each benchmark, combine the 'all' score with the 'cls_averaged' using geometric mean.\n        metrics_to_calc = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA']\n        trackers = list(output_res['RobMOTS.' + config['BENCHMARKS'][0]].keys())\n        for tracker in trackers:\n            # final_results[benchmark][result_type][metric]\n            final_results = {}\n            res = {bench: output_res['RobMOTS.' + bench][tracker]['COMBINED_SEQ'] for bench in config['BENCHMARKS']}\n            for bench in config['BENCHMARKS']:\n                final_results[bench] = {'cls_av': {}, 'det_av': {}, 'final': {}}\n                for metric in metrics_to_calc:\n                    final_results[bench]['cls_av'][metric] = np.mean(res[bench]['cls_comb_cls_av']['HOTA'][metric])\n                    final_results[bench]['det_av'][metric] = np.mean(res[bench]['all']['HOTA'][metric])\n                    final_results[bench]['final'][metric] = \\\n                        np.sqrt(final_results[bench]['cls_av'][metric] * final_results[bench]['det_av'][metric])\n\n            # Take the arithmetic mean over all the benchmarks\n            final_results['overall'] = {'cls_av': {}, 'det_av': {}, 'final': {}}\n            for metric in metrics_to_calc:\n                final_results['overall']['cls_av'][metric] = \\\n                    np.mean([final_results[bench]['cls_av'][metric] for bench in config['BENCHMARKS']])\n                final_results['overall']['det_av'][metric] = \\\n                    np.mean([final_results[bench]['det_av'][metric] for bench in config['BENCHMARKS']])\n                final_results['overall']['final'][metric] = \\\n                    np.mean([final_results[bench]['final'][metric] for bench in config['BENCHMARKS']])\n\n            # Save out result\n            headers = [config['SPLIT_TO_EVAL']] + [x + '___' + metric for x in ['f', 'c', 'd'] for metric in\n                                                   metrics_to_calc]\n\n\n            def rowify(d):\n                return [d[x][metric] for x in ['final', 'cls_av', 'det_av'] for metric in metrics_to_calc]\n\n\n            out_file = os.path.join(config['TRACKERS_FOLDER'], config['ROBMOTS_SPLIT'], tracker,\n                                    'final_results.csv')\n\n            with open(out_file, 'w', newline='') as f:\n                writer = csv.writer(f, delimiter=',')\n                writer.writerow(headers)\n                writer.writerow(['overall'] + rowify(final_results['overall']))\n                for bench in config['BENCHMARKS']:\n                    if bench == 'overall':\n                        continue\n                    writer.writerow([bench] + rowify(final_results[bench]))\n"
  },
  {
    "path": "trackeval/scripts/run_tao.py",
    "content": "\"\"\" run_tao.py\n\nRun example:\nrun_tao.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL Tracktor++\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/tao/tao_training'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/tao/tao_training'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)\n        'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val'\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n        'MAX_DETECTIONS': 300,  # Number of maximal allowed detections per image (0 for unlimited)\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'TrackMAP']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    # print only combined since TrackMAP is undefined for per sequence breakdowns\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = True\n    default_dataset_config = trackeval.datasets.TAO.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity', 'TrackMAP']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.TAO(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.CLEAR, trackeval.metrics.Identity,\n                   trackeval.metrics.HOTA]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)"
  },
  {
    "path": "trackeval/scripts/run_tao_ow.py",
    "content": "\"\"\" run_tao.py\n\nRun example:\nrun_tao.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL Tracktor++\n\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n        'USE_PARALLEL': False,\n        'NUM_PARALLEL_CORES': 8,\n        'BREAK_ON_ERROR': True,\n        'PRINT_RESULTS': True,\n        'PRINT_ONLY_COMBINED': False,\n        'PRINT_CONFIG': True,\n        'TIME_PROGRESS': True,\n        'OUTPUT_SUMMARY': True,\n        'OUTPUT_DETAILED': True,\n        'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/tao/tao_training'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/tao/tao_training'),  # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)\n        'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val'\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n        'MAX_DETECTIONS': 300,  # Number of maximal allowed detections per image (0 for unlimited)\n        'SUBSET': 'unknown',  # Evaluate on the following subsets ['all', 'known', 'unknown', 'distractor']\n    Metric arguments:\n        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'TrackMAP']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    # print only combined since TrackMAP is undefined for per sequence breakdowns\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_eval_config['DISPLAY_LESS_PROGRESS'] = True\n    default_dataset_config = trackeval.datasets.TAO_OW.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity', 'TrackMAP']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.TAO_OW(dataset_config)]\n    metrics_list = []\n    # for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.CLEAR, trackeval.metrics.Identity,\n    #                trackeval.metrics.HOTA]:\n    for metric in [trackeval.metrics.HOTA]:\n        if metric.get_name() in metrics_config['METRICS']:\n            metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)"
  },
  {
    "path": "trackeval/scripts/run_youtube_vis.py",
    "content": "\n\"\"\" run_youtube_vis.py\nRun example:\nrun_youtube_vis.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL STEm_Seg\nCommand Line Arguments: Defaults, # Comments\n    Eval arguments:\n            'USE_PARALLEL': False,\n            'NUM_PARALLEL_CORES': 8,\n            'BREAK_ON_ERROR': True,  # Raises exception and exits with error\n            'RETURN_ON_ERROR': False,  # if not BREAK_ON_ERROR, then returns from function on error\n            'LOG_ON_ERROR': os.path.join(code_path, 'error_log.txt'),  # if not None, save any errors into a log file.\n            'PRINT_RESULTS': True,\n            'PRINT_ONLY_COMBINED': False,\n            'PRINT_CONFIG': True,\n            'TIME_PROGRESS': True,\n            'DISPLAY_LESS_PROGRESS': True,\n            'OUTPUT_SUMMARY': True,\n            'OUTPUT_EMPTY_CLASSES': True,  # If False, summary files are not output for classes with no detections\n            'OUTPUT_DETAILED': True,\n            'PLOT_CURVES': True,\n    Dataset arguments:\n        'GT_FOLDER': os.path.join(code_path, 'data/gt/youtube_vis/youtube_vis_training'),  # Location of GT data\n        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/youtube_vis/youtube_vis_training'),\n        # Trackers location\n        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)\n        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)\n        'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)\n        'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val'\n        'PRINT_CONFIG': True,  # Whether to print current config\n        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER\n        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER\n        'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL\n    Metric arguments:\n        'METRICS': ['TrackMAP', 'HOTA', 'CLEAR', 'Identity']\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nfrom multiprocessing import freeze_support\n\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nimport trackeval  # noqa: E402\n\nif __name__ == '__main__':\n    freeze_support()\n\n    # Command line interface:\n    default_eval_config = trackeval.Evaluator.get_default_eval_config()\n    # print only combined since TrackMAP is undefined for per sequence breakdowns\n    default_eval_config['PRINT_ONLY_COMBINED'] = True\n    default_dataset_config = trackeval.datasets.YouTubeVIS.get_default_dataset_config()\n    default_metrics_config = {'METRICS': ['TrackMAP', 'HOTA', 'CLEAR', 'Identity']}\n    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}\n    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}\n    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}\n\n    # Run code\n    evaluator = trackeval.Evaluator(eval_config)\n    dataset_list = [trackeval.datasets.YouTubeVIS(dataset_config)]\n    metrics_list = []\n    for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.HOTA, trackeval.metrics.CLEAR,\n                   trackeval.metrics.Identity]:\n        if metric.get_name() in metrics_config['METRICS']:\n            # specify TrackMAP config for YouTubeVIS\n            if metric == trackeval.metrics.TrackMAP:\n                default_track_map_config = metric.get_default_metric_config()\n                default_track_map_config['USE_TIME_RANGES'] = False\n                default_track_map_config['AREA_RANGES'] = [[0 ** 2, 128 ** 2],\n                                                           [ 128 ** 2, 256 ** 2],\n                                                           [256 ** 2, 1e5 ** 2]]\n                metrics_list.append(metric(default_track_map_config))\n            else:\n                metrics_list.append(metric())\n    if len(metrics_list) == 0:\n        raise Exception('No metrics selected for evaluation')\n    evaluator.evaluate(dataset_list, metrics_list)"
  },
  {
    "path": "trackeval/utils.py",
    "content": "\nimport os\nimport csv\nimport argparse\nfrom collections import OrderedDict\n\n\ndef init_config(config, default_config, name=None):\n    \"\"\"Initialise non-given config values with defaults\"\"\"\n    if config is None:\n        config = default_config\n    else:\n        for k in default_config.keys():\n            if k not in config.keys():\n                config[k] = default_config[k]\n    if name and config['PRINT_CONFIG']:\n        print('\\n%s Config:' % name)\n        for c in config.keys():\n            print('%-20s : %-30s' % (c, config[c]))\n    return config\n\n\ndef update_config(config):\n    \"\"\"\n    Parse the arguments of a script and updates the config values for a given value if specified in the arguments.\n    :param config: the config to update\n    :return: the updated config\n    \"\"\"\n    parser = argparse.ArgumentParser()\n    for setting in config.keys():\n        if type(config[setting]) == list or type(config[setting]) == type(None):\n            parser.add_argument(\"--\" + setting, nargs='+')\n        else:\n            parser.add_argument(\"--\" + setting)\n    args = parser.parse_args().__dict__\n    for setting in args.keys():\n        if args[setting] is not None:\n            if type(config[setting]) == type(True):\n                if args[setting] == 'True':\n                    x = True\n                elif args[setting] == 'False':\n                    x = False\n                else:\n                    raise Exception('Command line parameter ' + setting + 'must be True or False')\n            elif type(config[setting]) == type(1):\n                x = int(args[setting])\n            elif type(args[setting]) == type(None):\n                x = None\n            else:\n                x = args[setting]\n            config[setting] = x\n    return config\n\n\ndef get_code_path():\n    \"\"\"Get base path where code is\"\"\"\n    return os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))\n\n\ndef validate_metrics_list(metrics_list):\n    \"\"\"Get names of metric class and ensures they are unique, further checks that the fields within each metric class\n    do not have overlapping names.\n    \"\"\"\n    metric_names = [metric.get_name() for metric in metrics_list]\n    # check metric names are unique\n    if len(metric_names) != len(set(metric_names)):\n        raise TrackEvalException('Code being run with multiple metrics of the same name')\n    fields = []\n    for m in metrics_list:\n        fields += m.fields\n    # check metric fields are unique\n    if len(fields) != len(set(fields)):\n        raise TrackEvalException('Code being run with multiple metrics with fields of the same name')\n    return metric_names\n\n\ndef write_summary_results(summaries, cls, output_folder):\n    \"\"\"Write summary results to file\"\"\"\n\n    fields = sum([list(s.keys()) for s in summaries], [])\n    values = sum([list(s.values()) for s in summaries], [])\n\n    # In order to remain consistent upon new fields being adding, for each of the following fields if they are present\n    # they will be output in the summary first in the order below. Any further fields will be output in the order each\n    # metric family is called, and within each family either in the order they were added to the dict (python >= 3.6) or\n    # randomly (python < 3.6).\n    default_order = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'RHOTA', 'HOTA(0)', 'LocA(0)',\n                     'HOTALocA(0)', 'MOTA', 'MOTP', 'MODA', 'CLR_Re', 'CLR_Pr', 'MTR', 'PTR', 'MLR', 'CLR_TP', 'CLR_FN',\n                     'CLR_FP', 'IDSW', 'MT', 'PT', 'ML', 'Frag', 'sMOTA', 'IDF1', 'IDR', 'IDP', 'IDTP', 'IDFN', 'IDFP',\n                     'Dets', 'GT_Dets', 'IDs', 'GT_IDs']\n    default_ordered_dict = OrderedDict(zip(default_order, [None for _ in default_order]))\n    for f, v in zip(fields, values):\n        default_ordered_dict[f] = v\n    for df in default_order:\n        if default_ordered_dict[df] is None:\n            del default_ordered_dict[df]\n    fields = list(default_ordered_dict.keys())\n    values = list(default_ordered_dict.values())\n\n    out_file = os.path.join(output_folder, cls + '_summary.txt')\n    os.makedirs(os.path.dirname(out_file), exist_ok=True)\n    with open(out_file, 'w', newline='') as f:\n        writer = csv.writer(f, delimiter=' ')\n        writer.writerow(fields)\n        writer.writerow(values)\n\n\ndef write_detailed_results(details, cls, output_folder):\n    \"\"\"Write detailed results to file\"\"\"\n    sequences = details[0].keys()\n    fields = ['seq'] + sum([list(s['COMBINED_SEQ'].keys()) for s in details], [])\n    out_file = os.path.join(output_folder, cls + '_detailed.csv')\n    os.makedirs(os.path.dirname(out_file), exist_ok=True)\n    with open(out_file, 'w', newline='') as f:\n        writer = csv.writer(f)\n        writer.writerow(fields)\n        for seq in sorted(sequences):\n            if seq == 'COMBINED_SEQ':\n                continue\n            writer.writerow([seq] + sum([list(s[seq].values()) for s in details], []))\n        writer.writerow(['COMBINED'] + sum([list(s['COMBINED_SEQ'].values()) for s in details], []))\n\n\ndef load_detail(file):\n    \"\"\"Loads detailed data for a tracker.\"\"\"\n    data = {}\n    with open(file) as f:\n        for i, row_text in enumerate(f):\n            row = row_text.replace('\\r', '').replace('\\n', '').split(',')\n            if i == 0:\n                keys = row[1:]\n                continue\n            current_values = row[1:]\n            seq = row[0]\n            if seq == 'COMBINED':\n                seq = 'COMBINED_SEQ'\n            if (len(current_values) == len(keys)) and seq != '':\n                data[seq] = {}\n                for key, value in zip(keys, current_values):\n                    data[seq][key] = float(value)\n    return data\n\n\nclass TrackEvalException(Exception):\n    \"\"\"Custom exception for catching expected errors.\"\"\"\n    ...\n"
  },
  {
    "path": "utils/args.py",
    "content": "import argparse\n\ndef make_parser():\n    parser = argparse.ArgumentParser(\"OC-SORT parameters\")\n    parser.add_argument(\"--expn\", type=str, default=None)\n    parser.add_argument(\"-n\", \"--name\", type=str, default=None, help=\"model name\")\n\n    # distributed\n    parser.add_argument( \"--dist-backend\", default=\"nccl\", type=str, help=\"distributed backend\")\n    parser.add_argument(\"--output_dir\", type=str, default=\"evaldata/trackers/mot_challenge\")\n    parser.add_argument(\"--dist-url\", default=None, type=str, help=\"url used to set up distributed training\")\n    parser.add_argument(\"-b\", \"--batch-size\", type=int, default=64, help=\"batch size\")\n    parser.add_argument(\"-d\", \"--devices\", default=None, type=int, help=\"device for training\")\n\n    parser.add_argument(\"--local_rank\", default=0, type=int, help=\"local rank for dist training\")\n    parser.add_argument( \"--num_machines\", default=1, type=int, help=\"num of node for training\")\n    parser.add_argument(\"--machine_rank\", default=0, type=int, help=\"node rank for multi-node training\")\n\n    parser.add_argument(\n        \"-f\", \"--exp_file\",\n        default=None,\n        type=str,\n        help=\"pls input your expriment description file\",\n    )\n    parser.add_argument(\n        \"--fp16\", dest=\"fp16\",\n        default=False,\n        action=\"store_true\",\n        help=\"Adopting mix precision evaluating.\",\n    )\n    parser.add_argument(\"--fuse\", dest=\"fuse\", default=False, action=\"store_true\", help=\"Fuse conv and bn for testing.\",)\n    parser.add_argument(\"--trt\", dest=\"trt\", default=False, action=\"store_true\", help=\"Using TensorRT model for testing.\",)\n    parser.add_argument(\"--test\", dest=\"test\", default=False, action=\"store_true\", help=\"Evaluating on test-dev set.\",)\n    parser.add_argument(\"--speed\", dest=\"speed\", default=False, action=\"store_true\", help=\"speed test only.\",)\n    parser.add_argument(\"opts\", help=\"Modify config options using the command-line\", default=None, nargs=argparse.REMAINDER,)\n    \n    # det args\n    parser.add_argument(\"-c\", \"--ckpt\", default=None, type=str, help=\"ckpt for eval\")\n    parser.add_argument(\"--conf\", default=0.1, type=float, help=\"test conf\")\n    parser.add_argument(\"--nms\", default=0.7, type=float, help=\"test nms threshold\")\n    parser.add_argument(\"--tsize\", default=None, type=int, help=\"test img size\")\n    parser.add_argument(\"--seed\", default=None, type=int, help=\"eval seed\")\n\n    # tracking args\n    parser.add_argument(\"--track_thresh\", type=float, default=0.6, help=\"detection confidence threshold\")\n    parser.add_argument(\"--iou_thresh\", type=float, default=0.3, help=\"the iou threshold in Sort for matching\")\n    parser.add_argument(\"--min_hits\", type=int, default=3, help=\"min hits to create track in SORT\")\n    parser.add_argument(\"--inertia\", type=float, default=0.2, help=\"the weight of VDC term in cost matrix\")\n    parser.add_argument(\"--deltat\", type=int, default=3, help=\"time step difference to estimate direction\")\n    parser.add_argument(\"--track_buffer\", type=int, default=30, help=\"the frames for keep lost tracks\")\n    parser.add_argument(\"--match_thresh\", type=float, default=0.9, help=\"matching threshold for tracking\")\n    parser.add_argument('--min-box-area', type=float, default=100, help='filter out tiny boxes')\n    parser.add_argument(\"--gt-type\", type=str, default=\"_val_half\", help=\"suffix to find the gt annotation\")\n    parser.add_argument(\"--mot20\", dest=\"mot20\", default=False, action=\"store_true\", help=\"test mot20.\")\n    parser.add_argument(\"--public\", action=\"store_true\", help=\"use public detection\")\n    parser.add_argument('--asso', default=\"iou\", help=\"similarity function: iou/giou/diou/ciou/ctdis\")\n    parser.add_argument(\"--use_byte\", dest=\"use_byte\", default=False, action=\"store_true\", help=\"use byte in tracking.\")\n\n    parser.add_argument(\"--TCM_first_step\", default=False, action=\"store_true\", help=\"use TCM in first step.\")\n    parser.add_argument(\"--TCM_byte_step\", default=False, action=\"store_true\", help=\"use TCM in byte step.\")\n    parser.add_argument(\"--TCM_first_step_weight\", type=float, default=1.0, help=\"TCM first step weight\")\n    parser.add_argument(\"--TCM_byte_step_weight\", type=float, default=1.0, help=\"TCM second step weight\")\n    parser.add_argument(\"--hybrid_sort_with_reid\", default=False, action=\"store_true\", help=\"use ReID model for Hybrid SORT.\")\n\n    # for fast reid\n    parser.add_argument(\"--EG_weight_high_score\", default=0.0, type=float, help=\"weight of appearance cost matrix when using EG\")\n    parser.add_argument(\"--EG_weight_low_score\", default=0.0, type=float, help=\"weight of appearance cost matrix when using EG\")\n    parser.add_argument(\"--low_thresh\", default=0.1, type=float, help=\"threshold of low score detections for BYTE\")\n    parser.add_argument(\"--high_score_matching_thresh\", default=0.8, type=float, help=\"matching threshold for detections with high score\")\n    parser.add_argument(\"--low_score_matching_thresh\", default=0.5, type=float, help=\"matching threshold for detections with low score\")\n    parser.add_argument(\"--alpha\", default=0.8, type=float, help=\"momentum of embedding update\")\n    parser.add_argument(\"--with_fastreid\", dest=\"with_fastreid\", default=False, action=\"store_true\", help=\"use FastReID flag.\")\n    parser.add_argument(\"--fast_reid_config\", dest=\"fast_reid_config\", default=r\"fast_reid/configs/CUHKSYSU_DanceTrack/sbs_S50.yml\", type=str, help=\"reid config file path\")\n    parser.add_argument(\"--fast_reid_weights\", dest=\"fast_reid_weights\", default=r\"fast_reid/logs/CUHKSYSU_DanceTrack/sbs_S50/model_final.pth\", type=str, help=\"reid weight path\")\n    parser.add_argument(\"--with_longterm_reid\", dest=\"with_longterm_reid\", default=False, action=\"store_true\", help=\"use long-term reid features for association.\")\n    parser.add_argument(\"--longterm_reid_weight\", default=0.0, type=float, help=\"weight of appearance cost matrix when using long term reid features in 1st stage association\")\n    parser.add_argument(\"--longterm_reid_weight_low\", default=0.0, type=float, help=\"weight of appearance cost matrix when using long term reid features in 2nd stage association\")\n    parser.add_argument(\"--with_longterm_reid_correction\", dest=\"with_longterm_reid_correction\", default=False, action=\"store_true\", help=\"use long-term reid features for association correction.\")\n    parser.add_argument(\"--longterm_reid_correction_thresh\", default=1.0, type=float, help=\"threshold of correction when using long term reid features in 1st stage association\")\n    parser.add_argument(\"--longterm_reid_correction_thresh_low\", default=1.0, type=float, help=\"threshold of correction when using long term reid features in 2nd stage association\")\n    parser.add_argument(\"--longterm_bank_length\", type=int, default=30, help=\"max length of reid feat bank\")\n    parser.add_argument(\"--adapfs\", dest=\"adapfs\", default=False, action=\"store_true\", help=\"Adaptive Feature Smoothing.\")\n    # ECC for CMC\n    parser.add_argument(\"--ECC\", dest=\"ECC\", default=False, action=\"store_true\", help=\"use ECC for CMC.\")\n\n    # for kitti/bdd100k inference with public detections\n    parser.add_argument('--raw_results_path', type=str, default=\"exps/permatrack_kitti_test/\",\n        help=\"path to the raw tracking results from other tracks\")\n    parser.add_argument('--out_path', type=str, help=\"path to save output results\")\n    parser.add_argument(\"--dataset\", type=str, default=\"mot17\", help=\"kitti or bdd\")\n    parser.add_argument(\"--hp\", action=\"store_true\", help=\"use head padding to add the missing objects during \\\n            initializing the tracks (offline).\")\n\n    # for demo video\n    parser.add_argument(\"--demo_type\", default=\"image\", help=\"demo type, eg. image, video and webcam\")\n    parser.add_argument( \"--path\", default=\"./videos/demo.mp4\", help=\"path to images or video\")\n    parser.add_argument(\"--demo_dancetrack\", default=False, action=\"store_true\",\n                        help=\"only for dancetrack demo, replace timestamp with dancetrack sequence name.\")\n    parser.add_argument(\"--camid\", type=int, default=0, help=\"webcam demo camera id\")\n    parser.add_argument(\n        \"--save_result\",\n        action=\"store_true\",\n        help=\"whether to save the inference result of image/video\",\n    )\n    parser.add_argument(\n        \"--aspect_ratio_thresh\", type=float, default=1.6,\n        help=\"threshold for filtering out boxes of which aspect ratio are above the given value.\"\n    )\n    parser.add_argument('--min_box_area', type=float, default=10, help='filter out tiny boxes')\n    parser.add_argument(\n        \"--device\",\n        default=\"gpu\",\n        type=str,\n        help=\"device to run our model, can either be cpu or gpu\",\n    )\n    return parser\n\n\ndef args_merge_params_form_exp(args,exp):\n    for k, v in exp.__dict__.items():\n        if k in args.__dict__:\n            (args.__dict__)[k] = v"
  },
  {
    "path": "utils/misc.py",
    "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nMisc functions, including distributed helpers.\nMostly copy-paste from torchvision references.\nthis file is borrowed from DETR repo: https://github.com/facebookresearch/detr/blob/main/util/misc.py\n\"\"\"\nimport os\nimport subprocess\nimport time\nfrom collections import defaultdict, deque\nimport datetime\nimport pickle\nfrom packaging import version\nfrom typing import Optional, List\n\nimport torch\nimport torch.distributed as dist\nfrom torch import Tensor\n\n# needed due to empty tensor bug in pytorch and torchvision 0.5\nimport torchvision\nif version.parse(torchvision.__version__) < version.parse('0.7'):\n    from torchvision.ops import _new_empty_tensor\n    from torchvision.ops.misc import _output_size\n\n\nclass NestedTensor(object):\n    def __init__(self, tensors, mask: Optional[Tensor]):\n        self.tensors = tensors\n        self.mask = mask\n\n    def to(self, device):\n        # type: (Device) -> NestedTensor # noqa\n        cast_tensor = self.tensors.to(device)\n        mask = self.mask\n        if mask is not None:\n            assert mask is not None\n            cast_mask = mask.to(device)\n        else:\n            cast_mask = None\n        return NestedTensor(cast_tensor, cast_mask)\n\n    def decompose(self):\n        return self.tensors, self.mask\n\n    def __repr__(self):\n        return str(self.tensors)\n\n\ndef nested_tensor_from_tensor_list(tensor_list: List[Tensor]):\n    # TODO make this more general\n    if tensor_list[0].ndim == 3:\n        if torchvision._is_tracing():\n            # nested_tensor_from_tensor_list() does not export well to ONNX\n            # call _onnx_nested_tensor_from_tensor_list() instead\n            return _onnx_nested_tensor_from_tensor_list(tensor_list)\n\n        # TODO make it support different-sized images\n        max_size = _max_by_axis([list(img.shape) for img in tensor_list])\n        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))\n        batch_shape = [len(tensor_list)] + max_size\n        b, c, h, w = batch_shape\n        dtype = tensor_list[0].dtype\n        device = tensor_list[0].device\n        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)\n        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)\n        for img, pad_img, m in zip(tensor_list, tensor, mask):\n            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)\n            m[: img.shape[1], :img.shape[2]] = False\n    else:\n        raise ValueError('not supported')\n    return NestedTensor(tensor, mask)\n\n\ndef add_mask(tracklets):\n    '''\n        input the pieces of tracklets, add the mask overit, the padded \n        positions are set to be True, False for where box exists\n    '''\n    p, l = tracklets.shape[:2]\n    sum_cord = torch.sum(tracklets[:,:,1:4], dim=2)\n    mask = (sum_cord==0)\n    return NestedTensor(tracklets, mask)"
  },
  {
    "path": "utils/triplet.py",
    "content": "'''\n    The implementation is modified from the Pytorch official implementation of TripletLoss:\n    https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/loss.py\n'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F \nimport torch.nn._reduction as _Reduction\nimport warnings\nfrom torch import Tensor\nfrom typing import Callable, Optional\nimport random\n\nclass _Loss(nn.Module):\n    reduction: str\n\n    def __init__(self, size_average=None, reduce=None, reduction: str = 'mean') -> None:\n        super(_Loss, self).__init__()\n        if size_average is not None or reduce is not None:\n            self.reduction: str = _Reduction.legacy_get_string(size_average, reduce)\n        else:\n            self.reduction = reduction\n\nclass TripletAdaptiveMarginLoss(_Loss):\n    '''\n    The implementation is modified to handle adaptative and unbalanced number of \n    positive and negative samples referring to each anchor. And the batch size is usually just 1\n    '''\n    r\"\"\"\n    #NOTE: belows are the comments from original implementation of TripletMarginLoss\n    Creates a criterion that measures the triplet loss given an input\n    tensors :math:`x1`, :math:`x2`, :math:`x3` and a margin with a value greater than :math:`0`.\n    This is used for measuring a relative similarity between samples. A triplet\n    is composed by `a`, `p` and `n` (i.e., `anchor`, `positive examples` and `negative\n    examples` respectively). The shapes of all input tensors should be\n    :math:`(N, D)`.\n\n    The distance swap is described in detail in the paper `Learning shallow\n    convolutional feature descriptors with triplet losses`_ by\n    V. Balntas, E. Riba et al.\n\n    The loss function for each sample in the mini-batch is:\n\n    .. math::\n        L(a, p, n) = \\max \\{d(a_i, p_i) - d(a_i, n_i) + {\\rm margin}, 0\\}\n\n\n    where\n\n    .. math::\n        d(x_i, y_i) = \\left\\lVert {\\bf x}_i - {\\bf y}_i \\right\\rVert_p\n\n    See also :class:`~torch.nn.TripletMarginWithDistanceLoss`, which computes the\n    triplet margin loss for input tensors using a custom distance function.\n\n    Args:\n        margin (float, optional): Default: :math:`1`.\n        p (int, optional): The norm degree for pairwise distance. Default: :math:`2`.\n        swap (bool, optional): The distance swap is described in detail in the paper\n            `Learning shallow convolutional feature descriptors with triplet losses` by\n            V. Balntas, E. Riba et al. Default: ``False``.\n        size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,\n            the losses are averaged over each loss element in the batch. Note that for\n            some losses, there are multiple elements per sample. If the field :attr:`size_average`\n            is set to ``False``, the losses are instead summed for each minibatch. Ignored\n            when :attr:`reduce` is ``False``. Default: ``True``\n        reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the\n            losses are averaged or summed over observations for each minibatch depending\n            on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per\n            batch element instead and ignores :attr:`size_average`. Default: ``True``\n        reduction (string, optional): Specifies the reduction to apply to the output:\n            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,\n            ``'mean'``: the sum of the output will be divided by the number of\n            elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`\n            and :attr:`reduce` are in the process of being deprecated, and in the meantime,\n            specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``\n\n    Shape:\n        - Input: :math:`(N, D)` or :math`(D)` where :math:`D` is the vector dimension.\n        - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'`` and\n                  input shape is :math`(N, D)`; a scalar otherwise.\n\n    Examples::\n\n    >>> triplet_loss = nn.TripletMarginLoss(margin=1.0, p=2)\n    >>> anchor = torch.randn(100, 128, requires_grad=True)\n    >>> positive = torch.randn(100, 128, requires_grad=True)\n    >>> negative = torch.randn(100, 128, requires_grad=True)\n    >>> output = triplet_loss(anchor, positive, negative)\n    >>> output.backward()\n\n    .. _Learning shallow convolutional feature descriptors with triplet losses:\n        http://www.bmva.org/bmvc/2016/papers/paper119/index.html\n    \"\"\"\n    __constants__ = ['margin', 'p', 'eps', 'swap', 'reduction']\n    margin: float\n    p: float\n    eps: float\n    swap: bool\n\n    def __init__(self, margin: float = 1.0, p: float = 2., eps: float = 1e-6, swap: bool = False, size_average=None,\n                 reduce=None, reduction: str = 'mean'):\n        super(TripletAdaptiveMarginLoss, self).__init__(size_average, reduce, reduction)\n        self.margin = margin\n        self.p = p\n        self.eps = eps\n        self.swap = swap\n\n    def forward(self, features: Tensor, piece_ids: Tensor) -> Tensor:\n        '''\n            input: \n                features [NxD]: the feature maps \n                piece_ids [N]: the group ids for feature maps, positive pairs should have the same id \n        '''\n        triplet_loss = 0 \n        # ids = torch.unique(piece_ids)\n        '''\n            anchor order is ordered by index:\n            piece_ids[positive[i]] == piece_ids[anchor[i]]\n            piece_ids[negative[i]] != piece_ids[anchor[i]]\n        '''\n        feat_num = piece_ids.shape[0]\n        anchor = torch.arange(feat_num)\n        positive = [random.sample(set(torch.where(piece_ids == piece_ids[i])[0]), 1)[0] for i in range(feat_num)]\n        negative = [random.sample(set(torch.where(piece_ids != piece_ids[i])[0]), 1)[0] for i in range(feat_num)]\n        positive = torch.Tensor(positive).long()\n        negative = torch.Tensor(negative).long()\n        anchor_feats = features[anchor]\n        positive_feats = features[positive]\n        negative_feats = features[negative]\n        # NOTE: in fact, the loss has been reduced by \"mean\" already, but still very large, so I divide it by feat_num AGAIN\n        return F.triplet_margin_loss(anchor_feats, positive_feats, negative_feats,\n                margin=self.margin, p=self.p, eps=self.eps, swap=self.swap, reduction=self.reduction) / feat_num\n        # return F.triplet_margin_loss(anchor, positive, negative, margin=self.margin, p=self.p,\n        #                              eps=self.eps, swap=self.swap, reduction=self.reduction)"
  },
  {
    "path": "utils/utils.py",
    "content": "import numpy as np \n\"\"\"\nUtilities for bounding box manipulation and GIoU.\n\"\"\"\nimport torch\nfrom torchvision.ops.boxes import box_area\nfrom loguru import logger\n\ndef write_results(filename, results):\n    save_format = '{frame},{id},{x1},{y1},{w},{h},{s},-1,-1,-1\\n'\n    with open(filename, 'w') as f:\n        for frame_id, tlwhs, track_ids, scores in results:\n            for tlwh, track_id, score in zip(tlwhs, track_ids, scores):\n                if track_id < 0:\n                    continue\n                x1, y1, w, h = tlwh\n                line = save_format.format(frame=frame_id, id=track_id, x1=round(x1, 1), y1=round(y1, 1), w=round(w, 1), h=round(h, 1), s=round(score, 2))\n                f.write(line)\n    logger.info('save results to {}'.format(filename))\n\n\ndef write_results_no_score(filename, results):\n    save_format = '{frame},{id},{x1},{y1},{w},{h},-1,-1,-1,-1\\n'\n    with open(filename, 'w') as f:\n        for frame_id, tlwhs, track_ids in results:\n            for tlwh, track_id in zip(tlwhs, track_ids):\n                if track_id < 0:\n                    continue\n                x1, y1, w, h = tlwh\n                line = save_format.format(frame=frame_id, id=track_id, x1=round(x1, 1), y1=round(y1, 1), w=round(w, 1), h=round(h, 1))\n                f.write(line)\n    logger.info('save results to {}'.format(filename))\n\n\ndef get_iou(bb1, bb2):\n    \"\"\"\n    Calculate the Intersection over Union (IoU) of two bounding boxes.\n\n    Parameters\n    ----------\n    bb1 : dict\n        Keys: {'x1', 'x2', 'y1', 'y2'}\n        The (x1, y1) position is at the top left corner,\n        the (x2, y2) position is at the bottom right corner\n    bb2 : dict\n        Keys: {'x1', 'x2', 'y1', 'y2'}\n        The (x, y) position is at the top left corner,\n        the (x2, y2) position is at the bottom right corner\n\n    Returns\n    -------\n    float\n        in [0, 1]\n    \"\"\"\n    assert bb1['x1'] < bb1['x2']\n    assert bb1['y1'] < bb1['y2']\n    assert bb2['x1'] < bb2['x2']\n    assert bb2['y1'] < bb2['y2']\n\n    # determine the coordinates of the intersection rectangle\n    x_left = max(bb1['x1'], bb2['x1'])\n    y_top = max(bb1['y1'], bb2['y1'])\n    x_right = min(bb1['x2'], bb2['x2'])\n    y_bottom = min(bb1['y2'], bb2['y2'])\n\n    if x_right < x_left or y_bottom < y_top:\n        return 0.0\n\n    # The intersection of two axis-aligned bounding boxes is always an\n    # axis-aligned bounding box\n    intersection_area = (x_right - x_left) * (y_bottom - y_top)\n\n    # compute the area of both AABBs\n    bb1_area = (bb1['x2'] - bb1['x1']) * (bb1['y2'] - bb1['y1'])\n    bb2_area = (bb2['x2'] - bb2['x1']) * (bb2['y2'] - bb2['y1'])\n\n    # compute the intersection over union by taking the intersection\n    # area and dividing it by the sum of prediction + ground-truth\n    # areas - the interesection area\n    iou = intersection_area / float(bb1_area + bb2_area - intersection_area)\n    assert iou >= 0.0\n    assert iou <= 1.0\n    return iou\n\n\ndef vectorized_iou(boxes1, boxes2):\n    '''\n        this is not a standard implementation, but to incorporate with the main function\n    '''\n    x11, y11, x12, y12 = boxes1\n    x21, y21, x22, y22 = boxes2\n\n    xA = np.maximum(x11, np.transpose(x21))\n    yA = np.maximum(y11, np.transpose(y21))\n    xB = np.maximum(x12, np.transpose(x22))\n    yB = np.maximum(y12, np.transpose(y22))\n\n    interArea = np.maximum((xB-xA+1), 0) * np.maximum((yB-yA+1), 0)\n\n    boxAArea = (x12-x11+1) * (y12-y11+1)\n    boxBArea = (x22-x21+1) * (y22-y21+1)\n\n    iou = interArea / (boxAArea + np.transpose(boxBArea) - interArea)\n    return iou\n    \n\ndef batch_iou(a, b, epsilon=1e-5):\n    \"\"\" Given two arrays `a` and `b` where each row contains a bounding\n        box defined as a list of four numbers:\n            [x1,y1,x2,y2]\n        where:\n            x1,y1 represent the upper left corner\n            x2,y2 represent the lower right corner\n        It returns the Intersect of Union scores for each corresponding\n        pair of boxes.\n\n    Args:\n        a:          (numpy array) each row containing [x1,y1,x2,y2] coordinates\n        b:          (numpy array) each row containing [x1,y1,x2,y2] coordinates\n        epsilon:    (float) Small value to prevent division by zero\n\n    Returns:\n        (numpy array) The Intersect of Union scores for each pair of bounding\n        boxes.\n    \"\"\"\n    # COORDINATES OF THE INTERSECTION BOXES\n    x1 = np.array([a[:, 0], b[:, 0]]).max(axis=0)\n    y1 = np.array([a[:, 1], b[:, 1]]).max(axis=0)\n    x2 = np.array([a[:, 2], b[:, 2]]).min(axis=0)\n    y2 = np.array([a[:, 3], b[:, 3]]).min(axis=0)\n\n    # AREAS OF OVERLAP - Area where the boxes intersect\n    width = (x2 - x1)\n    height = (y2 - y1)\n\n    # handle case where there is NO overlap\n    width[width < 0] = 0\n    height[height < 0] = 0\n\n    area_overlap = width * height\n\n    # COMBINED AREAS\n    area_a = (a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1])\n    area_b = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1])\n    area_combined = area_a + area_b - area_overlap\n\n    # RATIO OF AREA OF OVERLAP OVER COMBINED AREA\n    iou = area_overlap / (area_combined + epsilon)\n    return iou\n\n\ndef clip_iou(boxes1,boxes2):\n    area1 = box_area(boxes1)\n    area2 = box_area(boxes2)\n    lt = torch.max(boxes1[:, :2], boxes2[:, :2])\n    rb = torch.min(boxes1[:, 2:], boxes2[:, 2:])\n    wh = (rb-lt).clamp(min=0)\n    inter = wh[:,0]*wh[:,1]\n    union = area1 + area2 - inter\n    iou = (inter+1e-6) / (union+1e-6)\n    # generalized version\n    # iou=iou-(inter-union)/inter\n    return iou\n\ndef multi_iou(boxes1, boxes2):\n    lt = torch.max(boxes1[...,:2], boxes2[...,:2])\n    rb = torch.min(boxes1[...,2:], boxes2[...,2:])\n    wh = (rb-lt).clamp(min=0)\n    wh_1 = boxes1[...,2:] - boxes1[...,:2]\n    wh_2 = boxes2[...,2:] - boxes2[...,:2]\n    inter = wh[...,0] * wh[...,1]\n    union = wh_1[...,0] * wh_1[...,1] + wh_2[...,0] * wh_2[...,1] - inter\n    iou = (inter+1e-6) / (union+1e-6)\n    return iou\n\ndef box_cxcywh_to_xyxy(x):\n    x_c, y_c, w, h = x.unbind(-1)\n    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),\n         (x_c + 0.5 * w), (y_c + 0.5 * h)]\n    return torch.stack(b, dim=-1)\n\n\ndef box_xyxy_to_cxcywh(x):\n    x0, y0, x1, y1 = x.unbind(-1)\n    b = [(x0 + x1) / 2, (y0 + y1) / 2,\n         (x1 - x0), (y1 - y0)]\n    return torch.stack(b, dim=-1)\n\n\n# modified from torchvision to also return the union\ndef box_iou(boxes1, boxes2):\n    area1 = box_area(boxes1)\n    area2 = box_area(boxes2)\n\n    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]\n    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]\n\n    wh = (rb - lt).clamp(min=0)  # [N,M,2]\n    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]\n\n    union = area1[:, None] + area2 - inter\n\n    iou = (inter+1e-6) / (union+1e-6)\n    return iou, union\n\n\ndef generalized_box_iou(boxes1, boxes2):\n    \"\"\"\n    Generalized IoU from https://giou.stanford.edu/\n    The boxes should be in [x0, y0, x1, y1] format\n    Returns a [N, M] pairwise matrix, where N = len(boxes1)\n    and M = len(boxes2)\n    \"\"\"\n    # degenerate boxes gives inf / nan results\n    # so do an early check\n    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()\n    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()\n    iou, union = box_iou(boxes1, boxes2)\n\n    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])\n    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])\n\n    wh = (rb - lt).clamp(min=0)  # [N,M,2]\n    area = wh[:, :, 0] * wh[:, :, 1]\n\n    return iou - ((area - union)+1e-6) / (area+1e-6)\n\n\ndef masks_to_boxes(masks):\n    \"\"\"Compute the bounding boxes around the provided masks\n    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.\n    Returns a [N, 4] tensors, with the boxes in xyxy format\n    \"\"\"\n    if masks.numel() == 0:\n        return torch.zeros((0, 4), device=masks.device)\n\n    h, w = masks.shape[-2:]\n\n    y = torch.arange(0, h, dtype=torch.float)\n    x = torch.arange(0, w, dtype=torch.float)\n    y, x = torch.meshgrid(y, x)\n\n    x_mask = (masks * x.unsqueeze(0))\n    x_max = x_mask.flatten(1).max(-1)[0]\n    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n    y_mask = (masks * y.unsqueeze(0))\n    y_max = y_mask.flatten(1).max(-1)[0]\n    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]\n\n    return torch.stack([x_min, y_min, x_max, y_max], 1)"
  },
  {
    "path": "utils/visualize.py",
    "content": "'''\n    The script to visualize \n'''\nimport cv2 \nimport torch\nimport os \nimport numpy as np\nimport colorsys\nimport seaborn as sns \n\nplatte = sns.color_palette(\"Spectral\", 100, as_cmap=True) # doesn't work\n\nfrom typing import Iterable, Tuple\nimport colorsys\nimport itertools\nfrom fractions import Fraction\nfrom pprint import pprint\n\n\n######## The code to generate high-contrastive colors for visualization ##########\ndef zenos_dichotomy() -> Iterable[Fraction]:\n    \"\"\"\n    http://en.wikipedia.org/wiki/1/2_%2B_1/4_%2B_1/8_%2B_1/16_%2B_%C2%B7_%C2%B7_%C2%B7\n    \"\"\"\n    for k in itertools.count():\n        yield Fraction(1,2**k)\n\ndef fracs() -> Iterable[Fraction]:\n    \"\"\"\n    [Fraction(0, 1), Fraction(1, 2), Fraction(1, 4), Fraction(3, 4), Fraction(1, 8), Fraction(3, 8), Fraction(5, 8), Fraction(7, 8), Fraction(1, 16), Fraction(3, 16), ...]\n    [0.0, 0.5, 0.25, 0.75, 0.125, 0.375, 0.625, 0.875, 0.0625, 0.1875, ...]\n    \"\"\"\n    yield Fraction(0)\n    for k in zenos_dichotomy():\n        i = k.denominator # [1,2,4,8,16,...]\n        for j in range(1,i,2):\n            yield Fraction(j,i)\n\n# can be used for the v in hsv to map linear values 0..1 to something that looks equidistant\n# bias = lambda x: (math.sqrt(x/3)/Fraction(2,3)+Fraction(1,3))/Fraction(6,5)\n\nHSVTuple = Tuple[Fraction, Fraction, Fraction]\nRGBTuple = Tuple[float, float, float]\n\ndef hue_to_tones(h: Fraction) -> Iterable[HSVTuple]:\n    for s in [Fraction(6,10)]: # optionally use range\n        for v in [Fraction(8,10),Fraction(5,10)]: # could use range too\n            yield (h, s, v) # use bias for v here if you use range\n\ndef hsv_to_rgb(x: HSVTuple) -> RGBTuple:\n    return colorsys.hsv_to_rgb(*map(float, x))\n\nflatten = itertools.chain.from_iterable\n\ndef hsvs() -> Iterable[HSVTuple]:\n    return flatten(map(hue_to_tones, fracs()))\n\ndef rgbs() -> Iterable[RGBTuple]:\n    return map(hsv_to_rgb, hsvs())\n\ndef rgb_to_css(x: RGBTuple) -> str:\n    uint8tuple = map(lambda y: int(y*255), x)\n    rgb_str =  \"{},{},{}\".format(*uint8tuple)\n    rgb_value = rgb_str.split(\",\")\n    rgb_value = [int(d) for d in rgb_value]\n    return (rgb_value[0], rgb_value[1], rgb_value[2])\n\ndef css_colors() -> Iterable[str]:\n    return map(rgb_to_css, rgbs())\n\nsample_colors = list(itertools.islice(css_colors(), 400))\n\n##########  ########## ########## ########## ########## ########## ##########  \n\n\ndef draw_box(im, box, thickness=1, color=(255,0,0), trackids=[]):\n    x1, y1, w, h = box\n    x2 = x1+w \n    y2 = y1+h \n    cv2.rectangle(im, (x1, y1), (x2, y2), color=color, thickness=thickness)\n    trackids = [str(id) for id in trackids]\n    cv2.putText(im, \",\".join(trackids), (int(x1+2), int(y1+12)), cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 2)\n    return im \n\ndef draw_pieces(pieces_annos, img_dir, save_dir):\n    os.makedirs(save_dir, exist_ok=True)\n    frames = torch.unique(pieces_annos[:,:,0])\n    for frame in frames:\n        frame_bboxes = pieces_annos[pieces_annos[:, :, 0]==frame]\n        img = os.path.join(img_dir, \"%06d.jpg\" % frame)\n        im = cv2.imread(img)\n        for bbox in frame_bboxes:\n            trackid = bbox[1]\n            if trackid == 0:\n                continue \n            else:\n                coord = bbox[2:6]\n                occupy_indices = (frame_bboxes[:, 2:6] == coord)[:,0]\n                bboxes = frame_bboxes[occupy_indices]\n                trackids = torch.unique(bboxes[:, 1]).int().tolist()\n                im = draw_box(im, coord.numpy(), thickness=2, color=sample_colors[int(trackid)], trackids=trackids)\n        save_path = os.path.join(save_dir, \"vis_%06d.jpg\" % frame)\n        cv2.imwrite(save_path, im)\n\nif __name__ == \"__main__\":\n    seq_name = \"MOT17-02-DPM\"\n    img_dir = \"data/MOT17/train/{}/img1\".format(seq_name)\n    anno = \"data/MOT17/train_pieces/{}\".format(seq_name)\n    index = 2895\n    piece_anno = torch.load(\"{}/{}_pieces.pth\".format(anno, index))\n    # import pdb; pdb.set_trace()\n    for i in range(piece_anno.shape[0]):\n        piece_anno[i, piece_anno[i, :, 1] !=0, 1] = i+1\n    tracklet_anno = torch.load(\"{}/{}_tracklets.pth\".format(anno, index))\n    draw_pieces(piece_anno, img_dir, \"visualizations/{}/{}\".format(seq_name, index))\n    cmd = \"ffmpeg  -y -r 10 -i visualizations/{}/{}/vis_%06d.jpg vis_{}_{}.mp4\".format(seq_name, index, seq_name, index)\n    os.system(cmd)\n"
  },
  {
    "path": "yolox/__init__.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\nfrom .utils import configure_module\n\nconfigure_module()\n\n__version__ = \"0.1.0\"\n"
  },
  {
    "path": "yolox/core/__init__.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nfrom .launch import launch\nfrom .trainer import Trainer\n"
  },
  {
    "path": "yolox/core/launch.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Code are based on\n# https://github.com/facebookresearch/detectron2/blob/master/detectron2/engine/launch.py\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nfrom loguru import logger\n\nimport torch\nimport torch.distributed as dist\nimport torch.multiprocessing as mp\n\nimport yolox.utils.dist as comm\nfrom yolox.utils import configure_nccl\n\nimport os\nimport subprocess\nimport sys\nimport time\n\n__all__ = [\"launch\"]\n\n\ndef _find_free_port():\n    \"\"\"\n    Find an available port of current machine / node.\n    \"\"\"\n    import socket\n\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    # Binding to port 0 will cause the OS to find an available port for us\n    sock.bind((\"\", 0))\n    port = sock.getsockname()[1]\n    sock.close()\n    # NOTE: there is still a chance the port could be taken by other processes.\n    return port\n\n\ndef launch(\n    main_func,\n    num_gpus_per_machine,\n    num_machines=1,\n    machine_rank=0,\n    backend=\"nccl\",\n    dist_url=None,\n    args=(),\n):\n    \"\"\"\n    Args:\n        main_func: a function that will be called by `main_func(*args)`\n        num_machines (int): the total number of machines\n        machine_rank (int): the rank of this machine (one per machine)\n        dist_url (str): url to connect to for distributed training, including protocol\n                       e.g. \"tcp://127.0.0.1:8686\".\n                       Can be set to auto to automatically select a free port on localhost\n        args (tuple): arguments passed to main_func\n    \"\"\"\n    world_size = num_machines * num_gpus_per_machine\n    if world_size > 1:\n        if int(os.environ.get(\"WORLD_SIZE\", \"1\")) > 1:\n            dist_url = \"{}:{}\".format(\n                os.environ.get(\"MASTER_ADDR\", None),\n                os.environ.get(\"MASTER_PORT\", \"None\"),\n            )\n            local_rank = int(os.environ.get(\"LOCAL_RANK\", \"0\"))\n            world_size = int(os.environ.get(\"WORLD_SIZE\", \"1\"))\n            _distributed_worker(\n                local_rank,\n                main_func,\n                world_size,\n                num_gpus_per_machine,\n                num_machines,\n                machine_rank,\n                backend,\n                dist_url,\n                args,\n            )\n            exit()\n        launch_by_subprocess(\n            sys.argv,\n            world_size,\n            num_machines,\n            machine_rank,\n            num_gpus_per_machine,\n            dist_url,\n            args,\n        )\n    else:\n        main_func(*args)\n\n\ndef launch_by_subprocess(\n    raw_argv,\n    world_size,\n    num_machines,\n    machine_rank,\n    num_gpus_per_machine,\n    dist_url,\n    args,\n):\n    assert (\n        world_size > 1\n    ), \"subprocess mode doesn't support single GPU, use spawn mode instead\"\n\n    if dist_url is None:\n        # ------------------------hack for multi-machine training -------------------- #\n        if num_machines > 1:\n            master_ip = subprocess.check_output([\"hostname\", \"--fqdn\"]).decode(\"utf-8\")\n            master_ip = str(master_ip).strip()\n            dist_url = \"tcp://{}\".format(master_ip)\n            ip_add_file = \"./\" + args[1].experiment_name + \"_ip_add.txt\"\n            if machine_rank == 0:\n                port = _find_free_port()\n                with open(ip_add_file, \"w\") as ip_add:\n                    ip_add.write(dist_url+'\\n')\n                    ip_add.write(str(port))\n            else:\n                while not os.path.exists(ip_add_file):\n                    time.sleep(0.5)\n\n                with open(ip_add_file, \"r\") as ip_add:\n                    dist_url = ip_add.readline().strip()\n                    port = ip_add.readline()\n        else:\n            dist_url = \"tcp://127.0.0.1\"\n            port = _find_free_port()\n\n    # set PyTorch distributed related environmental variables\n    current_env = os.environ.copy()\n    current_env[\"MASTER_ADDR\"] = dist_url\n    current_env[\"MASTER_PORT\"] = str(port)\n    current_env[\"WORLD_SIZE\"] = str(world_size)\n    assert num_gpus_per_machine <= torch.cuda.device_count()\n\n    if \"OMP_NUM_THREADS\" not in os.environ and num_gpus_per_machine > 1:\n        current_env[\"OMP_NUM_THREADS\"] = str(1)\n        logger.info(\n            \"\\n*****************************************\\n\"\n            \"Setting OMP_NUM_THREADS environment variable for each process \"\n            \"to be {} in default, to avoid your system being overloaded, \"\n            \"please further tune the variable for optimal performance in \"\n            \"your application as needed. \\n\"\n            \"*****************************************\".format(\n                current_env[\"OMP_NUM_THREADS\"]\n            )\n        )\n\n    processes = []\n    for local_rank in range(0, num_gpus_per_machine):\n        # each process's rank\n        dist_rank = machine_rank * num_gpus_per_machine + local_rank\n        current_env[\"RANK\"] = str(dist_rank)\n        current_env[\"LOCAL_RANK\"] = str(local_rank)\n\n        # spawn the processes\n        cmd = [\"python3\", *raw_argv]\n\n        process = subprocess.Popen(cmd, env=current_env)\n        processes.append(process)\n\n    for process in processes:\n        process.wait()\n        if process.returncode != 0:\n            raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)\n\n\ndef _distributed_worker(\n    local_rank,\n    main_func,\n    world_size,\n    num_gpus_per_machine,\n    num_machines,\n    machine_rank,\n    backend,\n    dist_url,\n    args,\n):\n    assert (\n        torch.cuda.is_available()\n    ), \"cuda is not available. Please check your installation.\"\n    configure_nccl()\n    global_rank = machine_rank * num_gpus_per_machine + local_rank\n    logger.info(\"Rank {} initialization finished.\".format(global_rank))\n    try:\n        dist.init_process_group(\n            backend=backend,\n            init_method=dist_url,\n            world_size=world_size,\n            rank=global_rank,\n        )\n    except Exception:\n        logger.error(\"Process group URL: {}\".format(dist_url))\n        raise\n    # synchronize is needed here to prevent a possible timeout after calling init_process_group\n    # See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172\n    comm.synchronize()\n\n    if global_rank == 0 and os.path.exists(\n        \"./\" + args[1].experiment_name + \"_ip_add.txt\"\n    ):\n        os.remove(\"./\" + args[1].experiment_name + \"_ip_add.txt\")\n\n    assert num_gpus_per_machine <= torch.cuda.device_count()\n    torch.cuda.set_device(local_rank)\n\n    args[1].local_rank = local_rank\n    args[1].num_machines = num_machines\n\n    # Setup the local process group (which contains ranks within the same machine)\n    # assert comm._LOCAL_PROCESS_GROUP is None\n    # num_machines = world_size // num_gpus_per_machine\n    # for i in range(num_machines):\n    # ranks_on_i = list(range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine))\n    # pg = dist.new_group(ranks_on_i)\n    # if i == machine_rank:\n    # comm._LOCAL_PROCESS_GROUP = pg\n\n    main_func(*args)\n"
  },
  {
    "path": "yolox/core/trainer.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nfrom loguru import logger\n\nimport torch\n\nfrom torch.nn.parallel import DistributedDataParallel as DDP\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom yolox.data import DataPrefetcher\nfrom yolox.utils import (\n    MeterBuffer,\n    ModelEMA,\n    all_reduce_norm,\n    get_model_info,\n    get_rank,\n    get_world_size,\n    gpu_mem_usage,\n    load_ckpt,\n    occupy_mem,\n    save_checkpoint,\n    setup_logger,\n    synchronize\n)\n\nimport datetime\nimport os\nimport time\n\n\nclass Trainer:\n    def __init__(self, exp, args):\n        # init function only defines some basic attr, other attrs like model, optimizer are built in\n        # before_train methods.\n        self.exp = exp\n        self.args = args\n\n        # training related attr\n        self.max_epoch = exp.max_epoch\n        self.amp_training = args.fp16\n        self.scaler = torch.cuda.amp.GradScaler(enabled=args.fp16)\n        self.is_distributed = get_world_size() > 1\n        self.rank = get_rank()\n        self.local_rank = args.local_rank\n        self.device = \"cuda:{}\".format(self.local_rank)\n        self.use_model_ema = exp.ema\n\n        # data/dataloader related attr\n        self.data_type = torch.float16 if args.fp16 else torch.float32\n        self.input_size = exp.input_size\n        self.best_ap = 0\n\n        # metric record\n        self.meter = MeterBuffer(window_size=exp.print_interval)\n        self.file_name = os.path.join(exp.output_dir, args.experiment_name)\n\n        if self.rank == 0:\n            os.makedirs(self.file_name, exist_ok=True)\n\n        setup_logger(\n            self.file_name,\n            distributed_rank=self.rank,\n            filename=\"train_log.txt\",\n            mode=\"a\",\n        )\n\n    def train(self):\n        self.before_train()\n        try:\n            self.train_in_epoch()\n        except Exception:\n            raise\n        finally:\n            self.after_train()\n\n    def train_in_epoch(self):\n        for self.epoch in range(self.start_epoch, self.max_epoch):\n            self.before_epoch()\n            self.train_in_iter()\n            self.after_epoch()\n\n    def train_in_iter(self):\n        for self.iter in range(self.max_iter):\n            self.before_iter()\n            self.train_one_iter()\n            self.after_iter()\n\n    def train_one_iter(self):\n        iter_start_time = time.time()\n\n        inps, targets = self.prefetcher.next()\n        inps = inps.to(self.data_type)\n        targets = targets.to(self.data_type)\n        targets.requires_grad = False\n        data_end_time = time.time()\n\n        with torch.cuda.amp.autocast(enabled=self.amp_training):\n            outputs = self.model(inps, targets)\n        loss = outputs[\"total_loss\"]\n\n        self.optimizer.zero_grad()\n        self.scaler.scale(loss).backward()\n        self.scaler.step(self.optimizer)\n        self.scaler.update()\n\n        if self.use_model_ema:\n            self.ema_model.update(self.model)\n\n        lr = self.lr_scheduler.update_lr(self.progress_in_iter + 1)\n        for param_group in self.optimizer.param_groups:\n            param_group[\"lr\"] = lr\n\n        iter_end_time = time.time()\n        self.meter.update(\n            iter_time=iter_end_time - iter_start_time,\n            data_time=data_end_time - iter_start_time,\n            lr=lr,\n            **outputs,\n        )\n\n    def before_train(self):\n        logger.info(\"args: {}\".format(self.args))\n        logger.info(\"exp value:\\n{}\".format(self.exp))\n\n        # model related init\n        torch.cuda.set_device(self.local_rank)\n        model = self.exp.get_model()\n        logger.info(\n            \"Model Summary: {}\".format(get_model_info(model, self.exp.test_size))\n        )\n        model.to(self.device)\n\n        # solver related init\n        self.optimizer = self.exp.get_optimizer(self.args.batch_size)\n\n        # value of epoch will be set in `resume_train`\n        model = self.resume_train(model)\n\n        # data related init\n        self.no_aug = self.start_epoch >= self.max_epoch - self.exp.no_aug_epochs\n        self.train_loader = self.exp.get_data_loader(\n            batch_size=self.args.batch_size,\n            is_distributed=self.is_distributed,\n            no_aug=self.no_aug,\n        )\n        logger.info(\"init prefetcher, this might take one minute or less...\")\n        self.prefetcher = DataPrefetcher(self.train_loader)\n        # max_iter means iters per epoch\n        self.max_iter = len(self.train_loader)\n\n        self.lr_scheduler = self.exp.get_lr_scheduler(\n            self.exp.basic_lr_per_img * self.args.batch_size, self.max_iter\n        )\n        if self.args.occupy:\n            occupy_mem(self.local_rank)\n\n        if self.is_distributed:\n            model = DDP(model, device_ids=[self.local_rank], broadcast_buffers=False)\n\n        if self.use_model_ema:\n            self.ema_model = ModelEMA(model, 0.9998)\n            self.ema_model.updates = self.max_iter * self.start_epoch\n\n        self.model = model\n        self.model.train()\n\n        self.evaluator = self.exp.get_evaluator(\n            batch_size=self.args.batch_size, is_distributed=self.is_distributed\n        )\n        # Tensorboard logger\n        if self.rank == 0:\n            self.tblogger = SummaryWriter(self.file_name)\n\n        logger.info(\"Training start...\")\n        #logger.info(\"\\n{}\".format(model))\n\n    def after_train(self):\n        logger.info(\n            \"Training of experiment is done and the best AP is {:.2f}\".format(\n                self.best_ap * 100\n            )\n        )\n\n    def before_epoch(self):\n        logger.info(\"---> start train epoch{}\".format(self.epoch + 1))\n\n        if self.epoch + 1 == self.max_epoch - self.exp.no_aug_epochs or self.no_aug:\n            \n            logger.info(\"--->No mosaic aug now!\")\n            self.train_loader.close_mosaic()\n            logger.info(\"--->Add additional L1 loss now!\")\n            if self.is_distributed:\n                self.model.module.head.use_l1 = True\n            else:\n                self.model.head.use_l1 = True\n            \n            self.exp.eval_interval = 1\n            if not self.no_aug:\n                self.save_ckpt(ckpt_name=\"last_mosaic_epoch\")\n\n    def after_epoch(self):\n        if self.use_model_ema:\n            self.ema_model.update_attr(self.model)\n\n        self.save_ckpt(ckpt_name=\"latest\")\n\n        if (self.epoch + 1) % self.exp.eval_interval == 0:\n            all_reduce_norm(self.model)\n            self.evaluate_and_save_model()\n\n    def before_iter(self):\n        pass\n\n    def after_iter(self):\n        \"\"\"\n        `after_iter` contains two parts of logic:\n            * log information\n            * reset setting of resize\n        \"\"\"\n        # log needed information\n        if (self.iter + 1) % self.exp.print_interval == 0:\n            # TODO check ETA logic\n            left_iters = self.max_iter * self.max_epoch - (self.progress_in_iter + 1)\n            eta_seconds = self.meter[\"iter_time\"].global_avg * left_iters\n            eta_str = \"ETA: {}\".format(datetime.timedelta(seconds=int(eta_seconds)))\n\n            progress_str = \"epoch: {}/{}, iter: {}/{}\".format(\n                self.epoch + 1, self.max_epoch, self.iter + 1, self.max_iter\n            )\n            loss_meter = self.meter.get_filtered_meter(\"loss\")\n            loss_str = \", \".join(\n                [\"{}: {:.3f}\".format(k, v.latest) for k, v in loss_meter.items()]\n            )\n\n            time_meter = self.meter.get_filtered_meter(\"time\")\n            time_str = \", \".join(\n                [\"{}: {:.3f}s\".format(k, v.avg) for k, v in time_meter.items()]\n            )\n\n            logger.info(\n                \"{}, mem: {:.0f}Mb, {}, {}, lr: {:.3e}\".format(\n                    progress_str,\n                    gpu_mem_usage(),\n                    time_str,\n                    loss_str,\n                    self.meter[\"lr\"].latest,\n                )\n                + (\", size: {:d}, {}\".format(self.input_size[0], eta_str))\n            )\n            self.meter.clear_meters()\n\n        # random resizing\n        if self.exp.random_size is not None and (self.progress_in_iter + 1) % 10 == 0:\n            self.input_size = self.exp.random_resize(\n                self.train_loader, self.epoch, self.rank, self.is_distributed\n            )\n\n    @property\n    def progress_in_iter(self):\n        return self.epoch * self.max_iter + self.iter\n\n    def resume_train(self, model):\n        if self.args.resume:\n            logger.info(\"resume training\")\n            if self.args.ckpt is None:\n                ckpt_file = os.path.join(self.file_name, \"latest\" + \"_ckpt.pth.tar\")\n            else:\n                ckpt_file = self.args.ckpt\n\n            ckpt = torch.load(ckpt_file, map_location=self.device)\n            # resume the model/optimizer state dict\n            model.load_state_dict(ckpt[\"model\"])\n            self.optimizer.load_state_dict(ckpt[\"optimizer\"])\n            start_epoch = (\n                self.args.start_epoch - 1\n                if self.args.start_epoch is not None\n                else ckpt[\"start_epoch\"]\n            )\n            self.start_epoch = start_epoch\n            logger.info(\n                \"loaded checkpoint '{}' (epoch {})\".format(\n                    self.args.resume, self.start_epoch\n                )\n            )  # noqa\n        else:\n            if self.args.ckpt is not None:\n                logger.info(\"loading checkpoint for fine tuning\")\n                ckpt_file = self.args.ckpt\n                ckpt = torch.load(ckpt_file, map_location=self.device)[\"model\"]\n                model = load_ckpt(model, ckpt)\n            self.start_epoch = 0\n\n        return model\n\n    def evaluate_and_save_model(self):\n        evalmodel = self.ema_model.ema if self.use_model_ema else self.model\n        ap50_95, ap50, summary = self.exp.eval(\n            evalmodel, self.evaluator, self.is_distributed\n        )\n        self.model.train()\n        if self.rank == 0:\n            self.tblogger.add_scalar(\"val/COCOAP50\", ap50, self.epoch + 1)\n            self.tblogger.add_scalar(\"val/COCOAP50_95\", ap50_95, self.epoch + 1)\n            logger.info(\"\\n\" + summary)\n        synchronize()\n\n        #self.best_ap = max(self.best_ap, ap50_95)\n        self.save_ckpt(\"last_epoch\", ap50 > self.best_ap)\n        self.best_ap = max(self.best_ap, ap50)\n\n    def save_ckpt(self, ckpt_name, update_best_ckpt=False):\n        if self.rank == 0:\n            save_model = self.ema_model.ema if self.use_model_ema else self.model\n            logger.info(\"Save weights to {}\".format(self.file_name))\n            ckpt_state = {\n                \"start_epoch\": self.epoch + 1,\n                \"model\": save_model.state_dict(),\n                \"optimizer\": self.optimizer.state_dict(),\n            }\n            save_checkpoint(\n                ckpt_state,\n                update_best_ckpt,\n                self.file_name,\n                ckpt_name,\n            )\n"
  },
  {
    "path": "yolox/data/__init__.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nfrom .data_augment import TrainTransform, ValTransform\nfrom .data_prefetcher import DataPrefetcher\nfrom .dataloading import DataLoader, get_yolox_datadir\nfrom .datasets import *\nfrom .samplers import InfiniteSampler, YoloBatchSampler\n"
  },
  {
    "path": "yolox/data/data_augment.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\"\"\"\nData augmentation functionality. Passed as callable transformations to\nDataset classes.\n\nThe data augmentation procedures were interpreted from @weiliu89's SSD paper\nhttp://arxiv.org/abs/1512.02325\n\"\"\"\n\nimport cv2\nimport numpy as np\n\nimport torch\n\nfrom yolox.utils import xyxy2cxcywh\n\nimport math\nimport random\n\n\ndef augment_hsv(img, hgain=0.015, sgain=0.7, vgain=0.4):\n    r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains\n    hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))\n    dtype = img.dtype  # uint8\n\n    x = np.arange(0, 256, dtype=np.int16)\n    lut_hue = ((x * r[0]) % 180).astype(dtype)\n    lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)\n    lut_val = np.clip(x * r[2], 0, 255).astype(dtype)\n\n    img_hsv = cv2.merge(\n        (cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))\n    ).astype(dtype)\n    cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed\n\n\ndef box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.2):\n    # box1(4,n), box2(4,n)\n    # Compute candidate boxes which include follwing 5 things:\n    # box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio\n    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]\n    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]\n    ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16))  # aspect ratio\n    return (\n        (w2 > wh_thr)\n        & (h2 > wh_thr)\n        & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr)\n        & (ar < ar_thr)\n    )  # candidates\n\n\ndef random_perspective(\n    img,\n    targets=(),\n    degrees=10,\n    translate=0.1,\n    scale=0.1,\n    shear=10,\n    perspective=0.0,\n    border=(0, 0),\n):\n    # targets = [cls, xyxy]\n    height = img.shape[0] + border[0] * 2  # shape(h,w,c)\n    width = img.shape[1] + border[1] * 2\n\n    # Center\n    C = np.eye(3)\n    C[0, 2] = -img.shape[1] / 2  # x translation (pixels)\n    C[1, 2] = -img.shape[0] / 2  # y translation (pixels)\n\n    # Rotation and Scale\n    R = np.eye(3)\n    a = random.uniform(-degrees, degrees)\n    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations\n    s = random.uniform(scale[0], scale[1])\n    # s = 2 ** random.uniform(-scale, scale)\n    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)\n\n    # Shear\n    S = np.eye(3)\n    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)\n    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)\n\n    # Translation\n    T = np.eye(3)\n    T[0, 2] = (\n        random.uniform(0.5 - translate, 0.5 + translate) * width\n    )  # x translation (pixels)\n    T[1, 2] = (\n        random.uniform(0.5 - translate, 0.5 + translate) * height\n    )  # y translation (pixels)\n\n    # Combined rotation matrix\n    M = T @ S @ R @ C  # order of operations (right to left) is IMPORTANT\n\n    ###########################\n    # For Aug out of Mosaic\n    # s = 1.\n    # M = np.eye(3)\n    ###########################\n\n    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed\n        if perspective:\n            img = cv2.warpPerspective(\n                img, M, dsize=(width, height), borderValue=(114, 114, 114)\n            )\n        else:  # affine\n            img = cv2.warpAffine(\n                img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)\n            )\n\n    # Transform label coordinates\n    n = len(targets)\n    if n:\n        # warp points\n        xy = np.ones((n * 4, 3))\n        xy[:, :2] = targets[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(\n            n * 4, 2\n        )  # x1y1, x2y2, x1y2, x2y1\n        xy = xy @ M.T  # transform\n        if perspective:\n            xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8)  # rescale\n        else:  # affine\n            xy = xy[:, :2].reshape(n, 8)\n\n        # create new boxes\n        x = xy[:, [0, 2, 4, 6]]\n        y = xy[:, [1, 3, 5, 7]]\n        xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T\n\n        # clip boxes\n        #xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)\n        #xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)\n\n        # filter candidates\n        i = box_candidates(box1=targets[:, :4].T * s, box2=xy.T)\n        targets = targets[i]\n        targets[:, :4] = xy[i]\n        \n        targets = targets[targets[:, 0] < width]\n        targets = targets[targets[:, 2] > 0]\n        targets = targets[targets[:, 1] < height]\n        targets = targets[targets[:, 3] > 0]\n        \n    return img, targets\n\n\ndef _distort(image):\n    def _convert(image, alpha=1, beta=0):\n        tmp = image.astype(float) * alpha + beta\n        tmp[tmp < 0] = 0\n        tmp[tmp > 255] = 255\n        image[:] = tmp\n\n    image = image.copy()\n\n    if random.randrange(2):\n        _convert(image, beta=random.uniform(-32, 32))\n\n    if random.randrange(2):\n        _convert(image, alpha=random.uniform(0.5, 1.5))\n\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n\n    if random.randrange(2):\n        tmp = image[:, :, 0].astype(int) + random.randint(-18, 18)\n        tmp %= 180\n        image[:, :, 0] = tmp\n\n    if random.randrange(2):\n        _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))\n\n    image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)\n\n    return image\n\n\ndef _mirror(image, boxes):\n    _, width, _ = image.shape\n    if random.randrange(2):\n        image = image[:, ::-1]\n        boxes = boxes.copy()\n        boxes[:, 0::2] = width - boxes[:, 2::-2]\n    return image, boxes\n\n\ndef preproc(image, input_size, mean, std, swap=(2, 0, 1)):\n    if len(image.shape) == 3:\n        padded_img = np.ones((input_size[0], input_size[1], 3)) * 114.0\n    else:\n        padded_img = np.ones(input_size) * 114.0\n    img = np.array(image)\n    r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])\n    resized_img = cv2.resize(\n        img,\n        (int(img.shape[1] * r), int(img.shape[0] * r)),\n        interpolation=cv2.INTER_LINEAR,\n    ).astype(np.float32)\n    padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img\n\n    padded_img = padded_img[:, :, ::-1]\n    padded_img /= 255.0\n    if mean is not None:\n        padded_img -= mean\n    if std is not None:\n        padded_img /= std\n    padded_img = padded_img.transpose(swap)\n    padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)\n    return padded_img, r, image            # [hgx0411] array for [C, H, W], resize_ratio, raw_image [H, W, C]\n\n\nclass TrainTransform:\n    def __init__(self, p=0.5, rgb_means=None, std=None, max_labels=100):\n        self.means = rgb_means\n        self.std = std\n        self.p = p\n        self.max_labels = max_labels\n\n    def __call__(self, image, targets, input_dim):\n        boxes = targets[:, :4].copy()\n        labels = targets[:, 4].copy()\n        ids = targets[:, 5].copy()\n        if len(boxes) == 0:\n            targets = np.zeros((self.max_labels, 6), dtype=np.float32)\n            image, r_o, _ = preproc(image, input_dim, self.means, self.std)     # [hgx 0424] _ for raw_image\n            image = np.ascontiguousarray(image, dtype=np.float32)\n            return image, targets\n\n        image_o = image.copy()\n        targets_o = targets.copy()\n        height_o, width_o, _ = image_o.shape\n        boxes_o = targets_o[:, :4]\n        labels_o = targets_o[:, 4]\n        ids_o = targets_o[:, 5]\n        # bbox_o: [xyxy] to [c_x,c_y,w,h]\n        boxes_o = xyxy2cxcywh(boxes_o)\n\n        image_t = _distort(image)\n        image_t, boxes = _mirror(image_t, boxes)\n        height, width, _ = image_t.shape\n        image_t, r_, _ = preproc(image_t, input_dim, self.means, self.std)      # [hgx 0424] _ for raw_image\n        # boxes [xyxy] 2 [cx,cy,w,h]\n        boxes = xyxy2cxcywh(boxes)\n        boxes *= r_\n\n        mask_b = np.minimum(boxes[:, 2], boxes[:, 3]) > 1\n        boxes_t = boxes[mask_b]\n        labels_t = labels[mask_b]\n        ids_t = ids[mask_b]\n\n        if len(boxes_t) == 0:\n            image_t, r_o, _ = preproc(image_o, input_dim, self.means, self.std)     # [hgx 0424] _ for raw_image\n            boxes_o *= r_o\n            boxes_t = boxes_o\n            labels_t = labels_o\n            ids_t = ids_o\n\n        labels_t = np.expand_dims(labels_t, 1)\n        ids_t = np.expand_dims(ids_t, 1)\n\n        targets_t = np.hstack((labels_t, boxes_t, ids_t))       # get new labels(targets), format: class_id, bbox(xywh), track_id\n        padded_labels = np.zeros((self.max_labels, 6))\n        padded_labels[range(len(targets_t))[: self.max_labels]] = targets_t[\n            : self.max_labels\n        ]\n        padded_labels = np.ascontiguousarray(padded_labels, dtype=np.float32)\n        image_t = np.ascontiguousarray(image_t, dtype=np.float32)\n        return image_t, padded_labels\n\n\nclass ValTransform:\n    \"\"\"\n    Defines the transformations that should be applied to test PIL image\n    for input into the network\n\n    dimension -> tensorize -> color adj\n\n    Arguments:\n        resize (int): input dimension to SSD\n        rgb_means ((int,int,int)): average RGB of the dataset\n            (104,117,123)\n        swap ((int,int,int)): final order of channels\n\n    Returns:\n        transform (transform) : callable transform to be applied to test/val\n        data\n    \"\"\"\n\n    def __init__(self, rgb_means=None, std=None, swap=(2, 0, 1)):\n        self.means = rgb_means\n        self.swap = swap\n        self.std = std\n\n    # assume input is cv2 img for now\n    def __call__(self, img, res, input_size):\n        img, _, raw_image = preproc(img, input_size, self.means, self.std, self.swap)\n        return img, np.zeros((1, 5)), raw_image  # [hgx 0329] array of [C, H, W], zeros for targets, raw_image [H, W, C]\n"
  },
  {
    "path": "yolox/data/data_prefetcher.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport torch\nimport torch.distributed as dist\n\nfrom yolox.utils import synchronize\n\nimport random\n\n\nclass DataPrefetcher:\n    \"\"\"\n    DataPrefetcher is inspired by code of following file:\n    https://github.com/NVIDIA/apex/blob/master/examples/imagenet/main_amp.py\n    It could speedup your pytorch dataloader. For more information, please check\n    https://github.com/NVIDIA/apex/issues/304#issuecomment-493562789.\n    \"\"\"\n\n    def __init__(self, loader):\n        self.loader = iter(loader)\n        self.stream = torch.cuda.Stream()\n        self.input_cuda = self._input_cuda_for_image\n        self.record_stream = DataPrefetcher._record_stream_for_image\n        self.preload()\n\n    def preload(self):\n        try:\n            self.next_input, self.next_target, _, _ = next(self.loader)\n        except StopIteration:\n            self.next_input = None\n            self.next_target = None\n            return\n\n        with torch.cuda.stream(self.stream):\n            self.input_cuda()\n            self.next_target = self.next_target.cuda(non_blocking=True)\n\n    def next(self):\n        torch.cuda.current_stream().wait_stream(self.stream)\n        input = self.next_input\n        target = self.next_target\n        if input is not None:\n            self.record_stream(input)\n        if target is not None:\n            target.record_stream(torch.cuda.current_stream())\n        self.preload()\n        return input, target\n\n    def _input_cuda_for_image(self):\n        self.next_input = self.next_input.cuda(non_blocking=True)\n\n    @staticmethod\n    def _record_stream_for_image(input):\n        input.record_stream(torch.cuda.current_stream())\n\n\ndef random_resize(data_loader, exp, epoch, rank, is_distributed):\n    tensor = torch.LongTensor(1).cuda()\n    if is_distributed:\n        synchronize()\n\n    if rank == 0:\n        if epoch > exp.max_epoch - 10:\n            size = exp.input_size\n        else:\n            size = random.randint(*exp.random_size)\n            size = int(32 * size)\n        tensor.fill_(size)\n\n    if is_distributed:\n        synchronize()\n        dist.broadcast(tensor, 0)\n\n    input_size = data_loader.change_input_dim(multiple=tensor.item(), random_range=None)\n    return input_size\n"
  },
  {
    "path": "yolox/data/dataloading.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport torch\nfrom torch.utils.data.dataloader import DataLoader as torchDataLoader\nfrom torch.utils.data.dataloader import default_collate\n\nimport os\nimport random\n\nfrom .samplers import YoloBatchSampler\n\n\ndef get_yolox_datadir():\n    \"\"\"\n    get dataset dir of YOLOX. If environment variable named `YOLOX_DATADIR` is set,\n    this function will return value of the environment variable. Otherwise, use data\n    \"\"\"\n    yolox_datadir = os.getenv(\"YOLOX_DATADIR\", None)\n    if yolox_datadir is None:\n        import yolox\n\n        yolox_path = os.path.dirname(os.path.dirname(yolox.__file__))\n        yolox_datadir = os.path.join(yolox_path, \"datasets\")\n    return yolox_datadir\n\n\nclass DataLoader(torchDataLoader):\n    \"\"\"\n    Lightnet dataloader that enables on the fly resizing of the images.\n    See :class:`torch.utils.data.DataLoader` for more information on the arguments.\n    Check more on the following website:\n    https://gitlab.com/EAVISE/lightnet/-/blob/master/lightnet/data/_dataloading.py\n\n    Note:\n        This dataloader only works with :class:`lightnet.data.Dataset` based datasets.\n\n    Example:\n        >>> class CustomSet(ln.data.Dataset):\n        ...     def __len__(self):\n        ...         return 4\n        ...     @ln.data.Dataset.resize_getitem\n        ...     def __getitem__(self, index):\n        ...         # Should return (image, anno) but here we return (input_dim,)\n        ...         return (self.input_dim,)\n        >>> dl = ln.data.DataLoader(\n        ...     CustomSet((200,200)),\n        ...     batch_size = 2,\n        ...     collate_fn = ln.data.list_collate   # We want the data to be grouped as a list\n        ... )\n        >>> dl.dataset.input_dim    # Default input_dim\n        (200, 200)\n        >>> for d in dl:\n        ...     d\n        [[(200, 200), (200, 200)]]\n        [[(200, 200), (200, 200)]]\n        >>> dl.change_input_dim(320, random_range=None)\n        (320, 320)\n        >>> for d in dl:\n        ...     d\n        [[(320, 320), (320, 320)]]\n        [[(320, 320), (320, 320)]]\n        >>> dl.change_input_dim((480, 320), random_range=None)\n        (480, 320)\n        >>> for d in dl:\n        ...     d\n        [[(480, 320), (480, 320)]]\n        [[(480, 320), (480, 320)]]\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.__initialized = False\n        shuffle = False\n        batch_sampler = None\n        if len(args) > 5:\n            shuffle = args[2]\n            sampler = args[3]\n            batch_sampler = args[4]\n        elif len(args) > 4:\n            shuffle = args[2]\n            sampler = args[3]\n            if \"batch_sampler\" in kwargs:\n                batch_sampler = kwargs[\"batch_sampler\"]\n        elif len(args) > 3:\n            shuffle = args[2]\n            if \"sampler\" in kwargs:\n                sampler = kwargs[\"sampler\"]\n            if \"batch_sampler\" in kwargs:\n                batch_sampler = kwargs[\"batch_sampler\"]\n        else:\n            if \"shuffle\" in kwargs:\n                shuffle = kwargs[\"shuffle\"]\n            if \"sampler\" in kwargs:\n                sampler = kwargs[\"sampler\"]\n            if \"batch_sampler\" in kwargs:\n                batch_sampler = kwargs[\"batch_sampler\"]\n\n        # Use custom BatchSampler\n        if batch_sampler is None:\n            if sampler is None:\n                if shuffle:\n                    sampler = torch.utils.data.sampler.RandomSampler(self.dataset)\n                    # sampler = torch.utils.data.DistributedSampler(self.dataset)\n                else:\n                    sampler = torch.utils.data.sampler.SequentialSampler(self.dataset)\n            batch_sampler = YoloBatchSampler(\n                sampler,\n                self.batch_size,\n                self.drop_last,\n                input_dimension=self.dataset.input_dim,\n            )\n            # batch_sampler = IterationBasedBatchSampler(batch_sampler, num_iterations =\n\n        self.batch_sampler = batch_sampler\n\n        self.__initialized = True\n\n    def close_mosaic(self):\n        self.batch_sampler.mosaic = False\n\n    def change_input_dim(self, multiple=32, random_range=(10, 19)):\n        \"\"\"This function will compute a new size and update it on the next mini_batch.\n\n        Args:\n            multiple (int or tuple, optional): values to multiply the randomly generated range by.\n                Default **32**\n            random_range (tuple, optional): This (min, max) tuple sets the range\n                for the randomisation; Default **(10, 19)**\n\n        Return:\n            tuple: width, height tuple with new dimension\n\n        Note:\n            The new size is generated as follows: |br|\n            First we compute a random integer inside ``[random_range]``.\n            We then multiply that number with the ``multiple`` argument,\n            which gives our final new input size. |br|\n            If ``multiple`` is an integer we generate a square size. If you give a tuple\n            of **(width, height)**, the size is computed\n            as :math:`rng * multiple[0], rng * multiple[1]`.\n\n        Note:\n            You can set the ``random_range`` argument to **None** to set\n            an exact size of multiply. |br|\n            See the example above for how this works.\n        \"\"\"\n        if random_range is None:\n            size = 1\n        else:\n            size = random.randint(*random_range)\n\n        if isinstance(multiple, int):\n            size = (size * multiple, size * multiple)\n        else:\n            size = (size * multiple[0], size * multiple[1])\n\n        self.batch_sampler.new_input_dim = size\n\n        return size\n\n\ndef list_collate(batch):\n    \"\"\"\n    Function that collates lists or tuples together into one list (of lists/tuples).\n    Use this as the collate function in a Dataloader, if you want to have a list of\n    items as an output, as opposed to tensors (eg. Brambox.boxes).\n    \"\"\"\n    items = list(zip(*batch))\n\n    for i in range(len(items)):\n        if isinstance(items[i][0], (list, tuple)):\n            items[i] = list(items[i])\n        else:\n            items[i] = default_collate(items[i])\n\n    return items\n"
  },
  {
    "path": "yolox/data/datasets/__init__.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nfrom .datasets_wrapper import ConcatDataset, Dataset, MixConcatDataset\nfrom .mosaicdetection import MosaicDetection\nfrom .mot import MOTDataset\n"
  },
  {
    "path": "yolox/data/datasets/datasets_wrapper.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nfrom torch.utils.data.dataset import ConcatDataset as torchConcatDataset\nfrom torch.utils.data.dataset import Dataset as torchDataset\n\nimport bisect\nfrom functools import wraps\n\n\nclass ConcatDataset(torchConcatDataset):\n    def __init__(self, datasets):\n        super(ConcatDataset, self).__init__(datasets)\n        if hasattr(self.datasets[0], \"input_dim\"):\n            self._input_dim = self.datasets[0].input_dim\n            self.input_dim = self.datasets[0].input_dim\n\n    def pull_item(self, idx):\n        if idx < 0:\n            if -idx > len(self):\n                raise ValueError(\n                    \"absolute value of index should not exceed dataset length\"\n                )\n            idx = len(self) + idx\n        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)\n        if dataset_idx == 0:\n            sample_idx = idx\n        else:\n            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]\n        return self.datasets[dataset_idx].pull_item(sample_idx)\n\n\nclass MixConcatDataset(torchConcatDataset):\n    def __init__(self, datasets):\n        super(MixConcatDataset, self).__init__(datasets)\n        if hasattr(self.datasets[0], \"input_dim\"):\n            self._input_dim = self.datasets[0].input_dim\n            self.input_dim = self.datasets[0].input_dim\n\n    def __getitem__(self, index):\n\n        if not isinstance(index, int):\n            idx = index[1]\n        if idx < 0:\n            if -idx > len(self):\n                raise ValueError(\n                    \"absolute value of index should not exceed dataset length\"\n                )\n            idx = len(self) + idx\n        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)\n        if dataset_idx == 0:\n            sample_idx = idx\n        else:\n            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]\n        if not isinstance(index, int):\n            index = (index[0], sample_idx, index[2])\n\n        return self.datasets[dataset_idx][index]\n\n\nclass Dataset(torchDataset):\n    \"\"\" This class is a subclass of the base :class:`torch.utils.data.Dataset`,\n    that enables on the fly resizing of the ``input_dim``.\n\n    Args:\n        input_dimension (tuple): (width,height) tuple with default dimensions of the network\n    \"\"\"\n\n    def __init__(self, input_dimension, mosaic=True):\n        super().__init__()\n        self.__input_dim = input_dimension[:2]\n        self.enable_mosaic = mosaic\n\n    @property\n    def input_dim(self):\n        \"\"\"\n        Dimension that can be used by transforms to set the correct image size, etc.\n        This allows transforms to have a single source of truth\n        for the input dimension of the network.\n\n        Return:\n            list: Tuple containing the current width,height\n        \"\"\"\n        if hasattr(self, \"_input_dim\"):\n            return self._input_dim\n        return self.__input_dim\n\n    @staticmethod\n    def resize_getitem(getitem_fn):\n        \"\"\"\n        Decorator method that needs to be used around the ``__getitem__`` method. |br|\n        This decorator enables the on the fly resizing of\n        the ``input_dim`` with our :class:`~lightnet.data.DataLoader` class.\n\n        Example:\n            >>> class CustomSet(ln.data.Dataset):\n            ...     def __len__(self):\n            ...         return 10\n            ...     @ln.data.Dataset.resize_getitem\n            ...     def __getitem__(self, index):\n            ...         # Should return (image, anno) but here we return input_dim\n            ...         return self.input_dim\n            >>> data = CustomSet((200,200))\n            >>> data[0]\n            (200, 200)\n            >>> data[(480,320), 0]\n            (480, 320)\n        \"\"\"\n\n        @wraps(getitem_fn)\n        def wrapper(self, index):\n            if not isinstance(index, int):\n                has_dim = True\n                self._input_dim = index[0]\n                self.enable_mosaic = index[2]\n                index = index[1]\n            else:\n                has_dim = False\n\n            ret_val = getitem_fn(self, index)\n\n            if has_dim:\n                del self._input_dim\n\n            return ret_val\n\n        return wrapper\n"
  },
  {
    "path": "yolox/data/datasets/mosaicdetection.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport cv2\nimport numpy as np\n\nfrom yolox.utils import adjust_box_anns\n\nimport random\n\nfrom ..data_augment import box_candidates, random_perspective, augment_hsv\nfrom .datasets_wrapper import Dataset\n\n\ndef get_mosaic_coordinate(mosaic_image, mosaic_index, xc, yc, w, h, input_h, input_w):\n    # TODO update doc\n    # index0 to top left part of image\n    if mosaic_index == 0:\n        x1, y1, x2, y2 = max(xc - w, 0), max(yc - h, 0), xc, yc\n        small_coord = w - (x2 - x1), h - (y2 - y1), w, h\n    # index1 to top right part of image\n    elif mosaic_index == 1:\n        x1, y1, x2, y2 = xc, max(yc - h, 0), min(xc + w, input_w * 2), yc\n        small_coord = 0, h - (y2 - y1), min(w, x2 - x1), h\n    # index2 to bottom left part of image\n    elif mosaic_index == 2:\n        x1, y1, x2, y2 = max(xc - w, 0), yc, xc, min(input_h * 2, yc + h)\n        small_coord = w - (x2 - x1), 0, w, min(y2 - y1, h)\n    # index2 to bottom right part of image\n    elif mosaic_index == 3:\n        x1, y1, x2, y2 = xc, yc, min(xc + w, input_w * 2), min(input_h * 2, yc + h)  # noqa\n        small_coord = 0, 0, min(w, x2 - x1), min(y2 - y1, h)\n    return (x1, y1, x2, y2), small_coord\n\n\nclass MosaicDetection(Dataset):\n    \"\"\"Detection dataset wrapper that performs mixup for normal dataset.\"\"\"\n\n    def __init__(\n        self, dataset, img_size, mosaic=True, preproc=None,\n        degrees=10.0, translate=0.1, scale=(0.5, 1.5), mscale=(0.5, 1.5),\n        shear=2.0, perspective=0.0, enable_mixup=True, *args\n    ):\n        \"\"\"\n\n        Args:\n            dataset(Dataset) : Pytorch dataset object.\n            img_size (tuple):\n            mosaic (bool): enable mosaic augmentation or not.\n            preproc (func):\n            degrees (float):\n            translate (float):\n            scale (tuple):\n            mscale (tuple):\n            shear (float):\n            perspective (float):\n            enable_mixup (bool):\n            *args(tuple) : Additional arguments for mixup random sampler.\n        \"\"\"\n        super().__init__(img_size, mosaic=mosaic)\n        self._dataset = dataset\n        self.preproc = preproc\n        self.degrees = degrees\n        self.translate = translate\n        self.scale = scale\n        self.shear = shear\n        self.perspective = perspective\n        self.mixup_scale = mscale\n        self.enable_mosaic = mosaic\n        self.enable_mixup = enable_mixup\n\n    def __len__(self):\n        return len(self._dataset)\n\n    @Dataset.resize_getitem\n    def __getitem__(self, idx):\n        if self.enable_mosaic:\n            mosaic_labels = []\n            input_dim = self._dataset.input_dim\n            input_h, input_w = input_dim[0], input_dim[1]\n\n            # yc, xc = s, s  # mosaic center x, y\n            yc = int(random.uniform(0.5 * input_h, 1.5 * input_h))\n            xc = int(random.uniform(0.5 * input_w, 1.5 * input_w))\n\n            # 3 additional image indices\n            indices = [idx] + [random.randint(0, len(self._dataset) - 1) for _ in range(3)]\n\n            for i_mosaic, index in enumerate(indices):\n                img, _labels, _, _ = self._dataset.pull_item(index)\n                h0, w0 = img.shape[:2]  # orig hw\n                scale = min(1. * input_h / h0, 1. * input_w / w0)\n                img = cv2.resize(\n                    img, (int(w0 * scale), int(h0 * scale)), interpolation=cv2.INTER_LINEAR\n                )\n                # generate output mosaic image\n                (h, w, c) = img.shape[:3]\n                if i_mosaic == 0:\n                    mosaic_img = np.full((input_h * 2, input_w * 2, c), 114, dtype=np.uint8)\n\n                # suffix l means large image, while s means small image in mosaic aug.\n                (l_x1, l_y1, l_x2, l_y2), (s_x1, s_y1, s_x2, s_y2) = get_mosaic_coordinate(\n                    mosaic_img, i_mosaic, xc, yc, w, h, input_h, input_w\n                )\n\n                mosaic_img[l_y1:l_y2, l_x1:l_x2] = img[s_y1:s_y2, s_x1:s_x2]\n                padw, padh = l_x1 - s_x1, l_y1 - s_y1\n\n                labels = _labels.copy()\n                # Normalized xywh to pixel xyxy format\n                if _labels.size > 0:\n                    labels[:, 0] = scale * _labels[:, 0] + padw\n                    labels[:, 1] = scale * _labels[:, 1] + padh\n                    labels[:, 2] = scale * _labels[:, 2] + padw\n                    labels[:, 3] = scale * _labels[:, 3] + padh\n                mosaic_labels.append(labels)\n\n            if len(mosaic_labels):\n                mosaic_labels = np.concatenate(mosaic_labels, 0)\n                '''\n                np.clip(mosaic_labels[:, 0], 0, 2 * input_w, out=mosaic_labels[:, 0])\n                np.clip(mosaic_labels[:, 1], 0, 2 * input_h, out=mosaic_labels[:, 1])\n                np.clip(mosaic_labels[:, 2], 0, 2 * input_w, out=mosaic_labels[:, 2])\n                np.clip(mosaic_labels[:, 3], 0, 2 * input_h, out=mosaic_labels[:, 3])\n                '''\n                \n                mosaic_labels = mosaic_labels[mosaic_labels[:, 0] < 2 * input_w]\n                mosaic_labels = mosaic_labels[mosaic_labels[:, 2] > 0]\n                mosaic_labels = mosaic_labels[mosaic_labels[:, 1] < 2 * input_h]\n                mosaic_labels = mosaic_labels[mosaic_labels[:, 3] > 0]\n                \n            #augment_hsv(mosaic_img)\n            mosaic_img, mosaic_labels = random_perspective(\n                mosaic_img,\n                mosaic_labels,\n                degrees=self.degrees,\n                translate=self.translate,\n                scale=self.scale,\n                shear=self.shear,\n                perspective=self.perspective,\n                border=[-input_h // 2, -input_w // 2],\n            )  # border to remove\n\n            # -----------------------------------------------------------------\n            # CopyPaste: https://arxiv.org/abs/2012.07177\n            # -----------------------------------------------------------------\n            if self.enable_mixup and not len(mosaic_labels) == 0:\n                mosaic_img, mosaic_labels = self.mixup(mosaic_img, mosaic_labels, self.input_dim)\n            \n            mix_img, padded_labels = self.preproc(mosaic_img, mosaic_labels, self.input_dim)\n            img_info = (mix_img.shape[1], mix_img.shape[0])\n\n            return mix_img, padded_labels, img_info, np.array([idx])\n\n        else:\n            self._dataset._input_dim = self.input_dim\n            img, label, img_info, id_ = self._dataset.pull_item(idx)\n            img, label = self.preproc(img, label, self.input_dim)\n            return img, label, img_info, id_\n\n    def mixup(self, origin_img, origin_labels, input_dim):\n        jit_factor = random.uniform(*self.mixup_scale)\n        FLIP = random.uniform(0, 1) > 0.5\n        cp_labels = []\n        while len(cp_labels) == 0:\n            cp_index = random.randint(0, self.__len__() - 1)\n            cp_labels = self._dataset.load_anno(cp_index)\n        img, cp_labels, _, _ = self._dataset.pull_item(cp_index)\n\n        if len(img.shape) == 3:\n            cp_img = np.ones((input_dim[0], input_dim[1], 3)) * 114.0\n        else:\n            cp_img = np.ones(input_dim) * 114.0\n        cp_scale_ratio = min(input_dim[0] / img.shape[0], input_dim[1] / img.shape[1])\n        resized_img = cv2.resize(\n            img,\n            (int(img.shape[1] * cp_scale_ratio), int(img.shape[0] * cp_scale_ratio)),\n            interpolation=cv2.INTER_LINEAR,\n        ).astype(np.float32)\n        cp_img[\n            : int(img.shape[0] * cp_scale_ratio), : int(img.shape[1] * cp_scale_ratio)\n        ] = resized_img\n        cp_img = cv2.resize(\n            cp_img,\n            (int(cp_img.shape[1] * jit_factor), int(cp_img.shape[0] * jit_factor)),\n        )\n        cp_scale_ratio *= jit_factor\n        if FLIP:\n            cp_img = cp_img[:, ::-1, :]\n\n        origin_h, origin_w = cp_img.shape[:2]\n        target_h, target_w = origin_img.shape[:2]\n        padded_img = np.zeros(\n            (max(origin_h, target_h), max(origin_w, target_w), 3)\n        ).astype(np.uint8)\n        padded_img[:origin_h, :origin_w] = cp_img\n\n        x_offset, y_offset = 0, 0\n        if padded_img.shape[0] > target_h:\n            y_offset = random.randint(0, padded_img.shape[0] - target_h - 1)\n        if padded_img.shape[1] > target_w:\n            x_offset = random.randint(0, padded_img.shape[1] - target_w - 1)\n        padded_cropped_img = padded_img[\n            y_offset: y_offset + target_h, x_offset: x_offset + target_w\n        ]\n\n        cp_bboxes_origin_np = adjust_box_anns(\n            cp_labels[:, :4].copy(), cp_scale_ratio, 0, 0, origin_w, origin_h\n        )\n        if FLIP:\n            cp_bboxes_origin_np[:, 0::2] = (\n                origin_w - cp_bboxes_origin_np[:, 0::2][:, ::-1]\n            )\n        cp_bboxes_transformed_np = cp_bboxes_origin_np.copy()\n        '''\n        cp_bboxes_transformed_np[:, 0::2] = np.clip(\n            cp_bboxes_transformed_np[:, 0::2] - x_offset, 0, target_w\n        )\n        cp_bboxes_transformed_np[:, 1::2] = np.clip(\n            cp_bboxes_transformed_np[:, 1::2] - y_offset, 0, target_h\n        )\n        '''\n        cp_bboxes_transformed_np[:, 0::2] = cp_bboxes_transformed_np[:, 0::2] - x_offset\n        cp_bboxes_transformed_np[:, 1::2] = cp_bboxes_transformed_np[:, 1::2] - y_offset\n        keep_list = box_candidates(cp_bboxes_origin_np.T, cp_bboxes_transformed_np.T, 5)\n\n        if keep_list.sum() >= 1.0:\n            cls_labels = cp_labels[keep_list, 4:5].copy()\n            id_labels = cp_labels[keep_list, 5:6].copy()\n            box_labels = cp_bboxes_transformed_np[keep_list]\n            labels = np.hstack((box_labels, cls_labels, id_labels))\n            # remove outside bbox\n            labels = labels[labels[:, 0] < target_w]\n            labels = labels[labels[:, 2] > 0]\n            labels = labels[labels[:, 1] < target_h]\n            labels = labels[labels[:, 3] > 0]\n            origin_labels = np.vstack((origin_labels, labels))\n            origin_img = origin_img.astype(np.float32)\n            origin_img = 0.5 * origin_img + 0.5 * padded_cropped_img.astype(np.float32)\n\n        return origin_img, origin_labels\n"
  },
  {
    "path": "yolox/data/datasets/mot.py",
    "content": "import cv2\nimport numpy as np\nfrom pycocotools.coco import COCO\n\nimport os\n\nfrom ..dataloading import get_yolox_datadir\nfrom .datasets_wrapper import Dataset\n\n\nclass MOTDataset(Dataset):\n    \"\"\"\n    COCO dataset class.\n    \"\"\"\n\n    def __init__(\n        self,\n        data_dir=None,\n        json_file=\"train_half.json\",\n        name=\"train\",\n        img_size=(608, 1088),\n        preproc=None,\n        run_tracking=False,         # [hgx0411] dataloader related\n    ):\n        \"\"\"\n        COCO dataset initialization. Annotation data are read into memory by COCO API.\n        Args:\n            data_dir (str): dataset root directory\n            json_file (str): COCO json file name\n            name (str): COCO data name (e.g. 'train2017' or 'val2017')\n            img_size (int): target image size after pre-processing\n            preproc: data augmentation strategy\n        \"\"\"\n        super().__init__(img_size)\n        if data_dir is None:\n            data_dir = os.path.join(get_yolox_datadir(), \"mot\")\n        self.data_dir = data_dir\n        self.json_file = json_file\n\n        self.coco = COCO(os.path.join(self.data_dir, \"annotations\", self.json_file))\n        self.ids = self.coco.getImgIds()                # image ids, not track ids\n        self.class_ids = sorted(self.coco.getCatIds())\n        cats = self.coco.loadCats(self.coco.getCatIds())\n        self._classes = tuple([c[\"name\"] for c in cats])\n        self.annotations = self._load_coco_annotations()\n        self.name = name\n        self.img_size = img_size\n        self.preproc = preproc\n        self.run_tracking = run_tracking  # [hgx0411] dataloader related\n\n    def __len__(self):\n        return len(self.ids)\n\n    def _load_coco_annotations(self):\n        return [self.load_anno_from_ids(_ids) for _ids in self.ids]\n\n    def load_anno_from_ids(self, id_):\n        im_ann = self.coco.loadImgs(id_)[0]\n        width = im_ann[\"width\"]\n        height = im_ann[\"height\"]\n        frame_id = im_ann[\"frame_id\"]\n        video_id = im_ann[\"video_id\"]\n        anno_ids = self.coco.getAnnIds(imgIds=[int(id_)], iscrowd=False)\n        annotations = self.coco.loadAnns(anno_ids)\n        objs = []\n        for obj in annotations:\n            x1 = obj[\"bbox\"][0]\n            y1 = obj[\"bbox\"][1]\n            x2 = x1 + obj[\"bbox\"][2]\n            y2 = y1 + obj[\"bbox\"][3]\n            if obj[\"area\"] > 0 and x2 >= x1 and y2 >= y1:\n                obj[\"clean_bbox\"] = [x1, y1, x2, y2]\n                objs.append(obj)\n\n        num_objs = len(objs)\n\n        res = np.zeros((num_objs, 6))\n\n        for ix, obj in enumerate(objs):\n            cls = self.class_ids.index(obj[\"category_id\"])\n            res[ix, 0:4] = obj[\"clean_bbox\"]        # format: tlbr\n            res[ix, 4] = cls                        # class id, 0 for person\n            res[ix, 5] = obj[\"track_id\"]            # track id\n\n        file_name = im_ann[\"file_name\"] if \"file_name\" in im_ann else \"{:012}\".format(id_) + \".jpg\"\n        img_info = (height, width, frame_id, video_id, file_name)\n\n        del im_ann, annotations\n\n        return (res, img_info, file_name)\n\n    def load_anno(self, index):\n        return self.annotations[index][0]\n\n    def pull_item(self, index):\n        id_ = self.ids[index]\n\n        res, img_info, file_name = self.annotations[index]\n        # load image and preprocess\n        img_file = os.path.join(\n            self.data_dir, self.name, file_name\n        )\n        img = cv2.imread(img_file)\n\n        assert img is not None\n\n        return img, res.copy(), img_info, np.array([id_])\n\n    @Dataset.resize_getitem\n    def __getitem__(self, index):\n        \"\"\"\n        One image / label pair for the given index is picked up and pre-processed.\n\n        Args:\n            index (int): data index\n\n        Returns:\n            img (numpy.ndarray): pre-processed image\n            padded_labels (torch.Tensor): pre-processed label data.\n                The shape is :math:`[max_labels, 5]`.\n                each label consists of [class, xc, yc, w, h]:\n                    class (float): class index.\n                    xc, yc (float) : center of bbox whose values range from 0 to 1.\n                    w, h (float) : size of bbox whose values range from 0 to 1.\n            info_img : tuple of h, w, nh, nw, dx, dy.\n                h, w (int): original shape of the image\n                nh, nw (int): shape of the resized image without padding\n                dx, dy (int): pad size\n            img_id (int): same as the input index. Used for evaluation.\n        \"\"\"\n        img, target, img_info, img_id = self.pull_item(index)\n\n        if self.preproc is not None:\n            img, target, raw_image = self.preproc(img, target, self.input_dim)     # array for [C, H, W], targets, raw_image [H, W, C]\n\n        if not self.run_tracking:       # TODO: [hgx 0427] dataloader related\n            return img, target, img_info, img_id        # [hgx 0427] do not return 'raw_image' when training\n        else:\n            return img, target, img_info, img_id, raw_image     # [hgx 0427] return 'raw_image' when tracking\n\n"
  },
  {
    "path": "yolox/data/samplers.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport torch\nimport torch.distributed as dist\nfrom torch.utils.data.sampler import BatchSampler as torchBatchSampler\nfrom torch.utils.data.sampler import Sampler\n\nimport itertools\nfrom typing import Optional\n\n\nclass YoloBatchSampler(torchBatchSampler):\n    \"\"\"\n    This batch sampler will generate mini-batches of (dim, index) tuples from another sampler.\n    It works just like the :class:`torch.utils.data.sampler.BatchSampler`,\n    but it will prepend a dimension, whilst ensuring it stays the same across one mini-batch.\n    \"\"\"\n\n    def __init__(self, *args, input_dimension=None, mosaic=True, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.input_dim = input_dimension\n        self.new_input_dim = None\n        self.mosaic = mosaic\n\n    def __iter__(self):\n        self.__set_input_dim()\n        for batch in super().__iter__():\n            yield [(self.input_dim, idx, self.mosaic) for idx in batch]\n            self.__set_input_dim()\n\n    def __set_input_dim(self):\n        \"\"\" This function randomly changes the the input dimension of the dataset. \"\"\"\n        if self.new_input_dim is not None:\n            self.input_dim = (self.new_input_dim[0], self.new_input_dim[1])\n            self.new_input_dim = None\n\n\nclass InfiniteSampler(Sampler):\n    \"\"\"\n    In training, we only care about the \"infinite stream\" of training data.\n    So this sampler produces an infinite stream of indices and\n    all workers cooperate to correctly shuffle the indices and sample different indices.\n    The samplers in each worker effectively produces `indices[worker_id::num_workers]`\n    where `indices` is an infinite stream of indices consisting of\n    `shuffle(range(size)) + shuffle(range(size)) + ...` (if shuffle is True)\n    or `range(size) + range(size) + ...` (if shuffle is False)\n    \"\"\"\n\n    def __init__(\n        self,\n        size: int,\n        shuffle: bool = True,\n        seed: Optional[int] = 0,\n        rank=0,\n        world_size=1,\n    ):\n        \"\"\"\n        Args:\n            size (int): the total number of data of the underlying dataset to sample from\n            shuffle (bool): whether to shuffle the indices or not\n            seed (int): the initial seed of the shuffle. Must be the same\n                across all workers. If None, will use a random seed shared\n                among workers (require synchronization among all workers).\n        \"\"\"\n        self._size = size\n        assert size > 0\n        self._shuffle = shuffle\n        self._seed = int(seed)\n\n        if dist.is_available() and dist.is_initialized():\n            self._rank = dist.get_rank()\n            self._world_size = dist.get_world_size()\n        else:\n            self._rank = rank\n            self._world_size = world_size\n\n    def __iter__(self):\n        start = self._rank\n        yield from itertools.islice(\n            self._infinite_indices(), start, None, self._world_size\n        )\n\n    def _infinite_indices(self):\n        g = torch.Generator()\n        g.manual_seed(self._seed)\n        while True:\n            if self._shuffle:\n                yield from torch.randperm(self._size, generator=g)\n            else:\n                yield from torch.arange(self._size)\n\n    def __len__(self):\n        return self._size // self._world_size\n"
  },
  {
    "path": "yolox/evaluators/__init__.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nfrom .coco_evaluator import COCOEvaluator\nfrom .mot_evaluator import MOTEvaluator\nfrom .mot_evaluator_dance import MOTEvaluator as MOTEvaluatorDance\nfrom .mot_evaluator_public import MOTEvaluatorPublic\n\n"
  },
  {
    "path": "yolox/evaluators/coco_evaluator.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nfrom loguru import logger\nfrom tqdm import tqdm\n\nimport torch\n\nfrom yolox.utils import (\n    gather,\n    is_main_process,\n    postprocess,\n    synchronize,\n    time_synchronized,\n    xyxy2xywh\n)\n\nimport contextlib\nimport io\nimport itertools\nimport json\nimport tempfile\nimport time\n\n\nclass COCOEvaluator:\n    \"\"\"\n    COCO AP Evaluation class.  All the data in the val2017 dataset are processed\n    and evaluated by COCO API.\n    \"\"\"\n\n    def __init__(\n        self, dataloader, img_size, confthre, nmsthre, num_classes, testdev=False\n    ):\n        \"\"\"\n        Args:\n            dataloader (Dataloader): evaluate dataloader.\n            img_size (int): image size after preprocess. images are resized\n                to squares whose shape is (img_size, img_size).\n            confthre (float): confidence threshold ranging from 0 to 1, which\n                is defined in the config file.\n            nmsthre (float): IoU threshold of non-max supression ranging from 0 to 1.\n        \"\"\"\n        self.dataloader = dataloader\n        self.img_size = img_size\n        self.confthre = confthre\n        self.nmsthre = nmsthre\n        self.num_classes = num_classes\n        self.testdev = testdev\n\n    def evaluate(\n        self,\n        model,\n        distributed=False,\n        half=False,\n        trt_file=None,\n        decoder=None,\n        test_size=None,\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        nms_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n            progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n                outputs = postprocess(\n                    outputs, self.num_classes, self.confthre, self.nmsthre\n                )\n                if is_time_record:\n                    nms_end = time_synchronized()\n                    nms_time += nms_end - infer_end\n\n            data_list.extend(self.convert_to_coco_format(outputs, info_imgs, ids))\n\n        statistics = torch.cuda.FloatTensor([inference_time, nms_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def convert_to_coco_format(self, outputs, info_imgs, ids):\n        data_list = []\n        for (output, img_h, img_w, img_id) in zip(\n            outputs, info_imgs[0], info_imgs[1], ids\n        ):\n            if output is None:\n                continue\n            output = output.cpu()\n\n            bboxes = output[:, 0:4]\n\n            # preprocessing: resize\n            scale = min(\n                self.img_size[0] / float(img_h), self.img_size[1] / float(img_w)\n            )\n            bboxes /= scale\n            bboxes = xyxy2xywh(bboxes)\n\n            cls = output[:, 6]\n            scores = output[:, 4] * output[:, 5]\n            for ind in range(bboxes.shape[0]):\n                label = self.dataloader.dataset.class_ids[int(cls[ind])]\n                pred_data = {\n                    \"image_id\": int(img_id),\n                    \"category_id\": label,\n                    \"bbox\": bboxes[ind].numpy().tolist(),\n                    \"score\": scores[ind].numpy().item(),\n                    \"segmentation\": [],\n                }  # COCO json format\n                data_list.append(pred_data)\n        return data_list\n\n    def evaluate_prediction(self, data_dict, statistics):\n        if not is_main_process():\n            return 0, 0, None\n\n        logger.info(\"Evaluate in main process...\")\n\n        annType = [\"segm\", \"bbox\", \"keypoints\"]\n\n        inference_time = statistics[0].item()\n        nms_time = statistics[1].item()\n        n_samples = statistics[2].item()\n\n        a_infer_time = 1000 * inference_time / (n_samples * self.dataloader.batch_size)\n        a_nms_time = 1000 * nms_time / (n_samples * self.dataloader.batch_size)\n\n        time_info = \", \".join(\n            [\n                \"Average {} time: {:.2f} ms\".format(k, v)\n                for k, v in zip(\n                    [\"forward\", \"NMS\", \"inference\"],\n                    [a_infer_time, a_nms_time, (a_infer_time + a_nms_time)],\n                )\n            ]\n        )\n\n        info = time_info + \"\\n\"\n\n        # Evaluate the Dt (detection) json comparing with the ground truth\n        if len(data_dict) > 0:\n            cocoGt = self.dataloader.dataset.coco\n            # TODO: since pycocotools can't process dict in py36, write data to json file.\n            if self.testdev:\n                json.dump(data_dict, open(\"./yolox_testdev_2017.json\", \"w\"))\n                cocoDt = cocoGt.loadRes(\"./yolox_testdev_2017.json\")\n            else:\n                _, tmp = tempfile.mkstemp()\n                json.dump(data_dict, open(tmp, \"w\"))\n                cocoDt = cocoGt.loadRes(tmp)\n            '''\n            try:\n                from yolox.layers import COCOeval_opt as COCOeval\n            except ImportError:\n                from pycocotools import cocoeval as COCOeval\n                logger.warning(\"Use standard COCOeval.\")\n            '''\n            #from pycocotools.cocoeval import COCOeval\n            from yolox.layers import COCOeval_opt as COCOeval\n            cocoEval = COCOeval(cocoGt, cocoDt, annType[1])\n            cocoEval.evaluate()\n            cocoEval.accumulate()\n            redirect_string = io.StringIO()\n            with contextlib.redirect_stdout(redirect_string):\n                cocoEval.summarize()\n            info += redirect_string.getvalue()\n            return cocoEval.stats[0], cocoEval.stats[1], info\n        else:\n            return 0, 0, info\n"
  },
  {
    "path": "yolox/evaluators/evaluation.py",
    "content": "import os\nimport numpy as np\nimport copy\nimport motmetrics as mm\nmm.lap.default_solver = 'lap'\n\n\nclass Evaluator(object):\n\n    def __init__(self, data_root, seq_name, data_type, anno=\"gt.txt\"):\n        self.data_root = data_root\n        self.seq_name = seq_name\n        self.data_type = data_type\n        self.anno = anno\n\n        self.load_annotations()\n        self.reset_accumulator()\n\n    def load_annotations(self):\n        assert self.data_type == 'mot'\n\n        gt_filename = os.path.join(self.data_root, self.seq_name, 'gt',  self.anno)\n        self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)\n        self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)\n\n    def reset_accumulator(self):\n        self.acc = mm.MOTAccumulator(auto_id=True)\n\n    def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):\n        # results\n        trk_tlwhs = np.copy(trk_tlwhs)\n        trk_ids = np.copy(trk_ids)\n\n        # gts\n        gt_objs = self.gt_frame_dict.get(frame_id, [])\n        gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]\n\n        # ignore boxes\n        ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])\n        ignore_tlwhs = unzip_objs(ignore_objs)[0]\n\n        # remove ignored results\n        keep = np.ones(len(trk_tlwhs), dtype=bool)\n        iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)\n        if len(iou_distance) > 0:\n            match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)\n            match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])\n            match_ious = iou_distance[match_is, match_js]\n\n            match_js = np.asarray(match_js, dtype=int)\n            match_js = match_js[np.logical_not(np.isnan(match_ious))]\n            keep[match_js] = False\n            trk_tlwhs = trk_tlwhs[keep]\n            trk_ids = trk_ids[keep]\n\n        # get distance matrix\n        iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)\n\n        # acc\n        self.acc.update(gt_ids, trk_ids, iou_distance)\n\n        if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):\n            events = self.acc.last_mot_events  # only supported by https://github.com/longcw/py-motmetrics\n        else:\n            events = None\n        return events\n\n    def eval_file(self, filename):\n        self.reset_accumulator()\n        # import pdb; pdb.set_trace()\n        result_frame_dict = read_results(filename, self.data_type, is_gt=False)\n        #frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))\n        frames = sorted(list(set(result_frame_dict.keys())))\n        for frame_id in frames:\n            trk_objs = result_frame_dict.get(frame_id, [])\n            trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]\n            self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)\n\n        return self.acc\n\n    @staticmethod\n    def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):\n        names = copy.deepcopy(names)\n        if metrics is None:\n            metrics = mm.metrics.motchallenge_metrics\n        metrics = copy.deepcopy(metrics)\n\n        mh = mm.metrics.create()\n        summary = mh.compute_many(\n            accs,\n            metrics=metrics,\n            names=names,\n            generate_overall=True\n        )\n\n        return summary\n\n    @staticmethod\n    def save_summary(summary, filename):\n        import pandas as pd\n        writer = pd.ExcelWriter(filename)\n        summary.to_excel(writer)\n        writer.save()\n\n\ndef read_results(filename, data_type: str, is_gt=False, is_ignore=False):\n    if data_type in ('mot', 'lab'):\n        read_fun = read_mot_results\n    else:\n        raise ValueError('Unknown data type: {}'.format(data_type))\n\n    return read_fun(filename, is_gt, is_ignore)\n\ndef read_mot_results(filename, is_gt, is_ignore):\n    valid_labels = {1}\n    ignore_labels = {2, 7, 8, 12}\n    results_dict = dict()\n    if os.path.isfile(filename):\n        with open(filename, 'r') as f:\n            for line in f.readlines():\n                linelist = line.split(',')\n                if len(linelist) < 7:\n                    continue\n                fid = int(linelist[0])\n                if fid < 1:\n                    continue\n                results_dict.setdefault(fid, list())\n\n                box_size = float(linelist[4]) * float(linelist[5])\n\n                if is_gt:\n                    if 'MOT16-' in filename or 'MOT17-' in filename:\n                        label = int(float(linelist[7]))\n                        mark = int(float(linelist[6]))\n                        if mark == 0 or label not in valid_labels:\n                            continue\n                    score = 1\n                elif is_ignore:\n                    if 'MOT16-' in filename or 'MOT17-' in filename:\n                        label = int(float(linelist[7]))\n                        vis_ratio = float(linelist[8])\n                        if label not in ignore_labels and vis_ratio >= 0:\n                            continue\n                    else:\n                        continue\n                    score = 1\n                else:\n                    score = float(linelist[6])\n\n                tlwh = tuple(map(float, linelist[2:6]))\n                target_id = int(float(linelist[1]))\n\n                results_dict[fid].append((tlwh, target_id, score))\n\n    return results_dict\n\n\ndef unzip_objs(objs):\n    if len(objs) > 0:\n        tlwhs, ids, scores = zip(*objs)\n    else:\n        tlwhs, ids, scores = [], [], []\n    tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)\n\n    return tlwhs, ids, scores"
  },
  {
    "path": "yolox/evaluators/mot_evaluator.py",
    "content": "from collections import defaultdict\nfrom loguru import logger\nfrom tqdm import tqdm\nimport torch\n\nfrom yolox.utils import (\n    gather,\n    is_main_process,\n    postprocess,\n    synchronize,\n    time_synchronized,\n    xyxy2xywh\n)\nfrom trackers.ocsort_tracker.ocsort import OCSort\nfrom trackers.sort_tracker.sort import Sort\nfrom trackers.sort_tracker.sort_score import Sort_score\nfrom trackers.motdt_tracker.motdt_tracker import OnlineTracker\nfrom trackers.motdt_tracker.motdt_tracker_score import OnlineTracker_score\nfrom trackers.byte_tracker.byte_tracker_score import BYTETracker_score\nfrom trackers.byte_tracker.byte_tracker import BYTETracker\nfrom trackers.deepsort_tracker.deepsort import DeepSort\nfrom trackers.deepsort_tracker.deepsort_score import DeepSort_score\n\nimport contextlib\nimport io\nimport os\nimport itertools\nimport json\nimport tempfile\nimport time\nimport numpy as np \nfrom utils.utils import write_results, write_results_no_score\n\n\nclass MOTEvaluator:\n    \"\"\"\n    COCO AP Evaluation class.  All the data in the val2017 dataset are processed\n    and evaluated by COCO API.\n    \"\"\"\n\n    def __init__(\n        self, args, dataloader, img_size, confthre, nmsthre, num_classes):\n        \"\"\"\n        Args:\n            dataloader (Dataloader): evaluate dataloader.\n            img_size (int): image size after preprocess. images are resized\n                to squares whose shape is (img_size, img_size).\n            confthre (float): confidence threshold ranging from 0 to 1, which\n                is defined in the config file.\n            nmsthre (float): IoU threshold of non-max supression ranging from 0 to 1.\n        \"\"\"\n        self.dataloader = dataloader\n        self.img_size = img_size\n        self.confthre = confthre\n        self.nmsthre = nmsthre\n        self.num_classes = num_classes\n        self.args = args\n    \n    def evaluate_public(\n        self,\n        model,\n        distributed=False,\n        half=False,\n        trt_file=None,\n        decoder=None,\n        test_size=None,\n        result_folder=None\n    ):\n        from trackers.byte_tracker.byte_tracker_public import BYTETracker\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n            \n        tracker = BYTETracker(self.args)\n        ori_thresh = self.args.track_thresh\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n            progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name == 'MOT17-05-FRCNN' or video_name == 'MOT17-06-FRCNN':\n                    self.args.track_buffer = 14\n                elif video_name == 'MOT17-13-FRCNN' or video_name == 'MOT17-14-FRCNN':\n                    self.args.track_buffer = 25\n                else:\n                    self.args.track_buffer = 30\n\n                if video_name == 'MOT17-01-FRCNN':\n                    self.args.track_thresh = 0.65\n                elif video_name == 'MOT17-06-FRCNN':\n                    self.args.track_thresh = 0.65\n                elif video_name == 'MOT17-12-FRCNN':\n                    self.args.track_thresh = 0.7\n                elif video_name == 'MOT17-14-FRCNN':\n                    self.args.track_thresh = 0.67\n                else:\n                    self.args.track_thresh = ori_thresh\n                \n                if video_name == 'MOT20-06' or video_name == 'MOT20-08':\n                    self.args.track_thresh = 0.3\n                else:\n                    self.args.track_thresh = ori_thresh\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                \n                if frame_id == 1:\n                    tracker = BYTETracker(self.args)\n                    # det_file = os.path.join('datasets/MOT20/test', video_name.replace('FRCNN', 'FRCNN'), 'det/det.txt')\n                    det_file = os.path.join('datasets/mot/train', video_name.replace('FRCNN', 'FRCNN'), 'det/det.txt')\n                    dets_all = np.loadtxt(det_file, dtype=np.float64, delimiter=',')\n    \n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1].replace('FRCNN', 'FRCNN')))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n                pub_dets = dets_all[dets_all[:, 0] == frame_id][:, 2:]   # x, y, w, h, score\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n            \n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            if outputs[0] is not None:\n                online_targets = tracker.update_public(outputs[0], info_imgs, self.img_size, pub_dets)\n                online_tlwhs = []\n                online_ids = []\n                online_scores = []\n                for t in online_targets:\n                    tlwh = t.tlwh\n                    tid = t.track_id\n                    vertical = tlwh[2] / tlwh[3] > 1.6\n                    if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                        online_tlwhs.append(tlwh)\n                        online_ids.append(tid)\n                        online_scores.append(t.score)\n                # save results\n                results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n            \n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id].replace('FRCNN', '')))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate(\n        self,\n        model,\n        distributed=False,\n        half=False,\n        trt_file=None,\n        decoder=None,\n        test_size=None,\n        result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        seq_data_list = dict()\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n            \n        tracker = BYTETracker(self.args)\n        ori_thresh = self.args.track_thresh\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n            progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n\n                if video_name == 'MOT17-05-FRCNN' or video_name == 'MOT17-06-FRCNN':\n                    self.args.track_buffer = 14\n                elif video_name == 'MOT17-13-FRCNN' or video_name == 'MOT17-14-FRCNN':\n                    self.args.track_buffer = 25\n                else:\n                    self.args.track_buffer = 30\n\n                if video_name == 'MOT17-01-FRCNN':\n                    self.args.track_thresh = 0.65\n                elif video_name == 'MOT17-06-FRCNN':\n                    self.args.track_thresh = 0.65\n                elif video_name == 'MOT17-12-FRCNN':\n                    self.args.track_thresh = 0.7\n                elif video_name == 'MOT17-14-FRCNN':\n                    self.args.track_thresh = 0.67\n                else:\n                    self.args.track_thresh = ori_thresh\n                \n                if video_name == 'MOT20-06' or video_name == 'MOT20-08':\n                    self.args.track_thresh = 0.3\n                else:\n                    self.args.track_thresh = ori_thresh\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = BYTETracker(self.args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n            \n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            if video_name not in seq_data_list:\n                seq_data_list[video_name] = []\n            seq_data_list[video_name].extend(output_results)\n            data_list.extend(output_results)\n\n            # run tracking\n            if outputs[0] is not None:\n                online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n                online_tlwhs = []\n                online_ids = []\n                online_scores = []\n                for t in online_targets:\n                    tlwh = t.tlwh\n                    tid = t.track_id\n                    vertical = tlwh[2] / tlwh[3] > 1.6\n                    if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                        online_tlwhs.append(tlwh)\n                        online_ids.append(tid)\n                        online_scores.append(t.score)\n                # save results\n                results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n            \n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                print(\"writing to {}\".format(result_filename))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        for video_name in seq_data_list:\n            self.save_detection_result(seq_data_list[video_name], result_folder, video_name)\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_byte_score(\n            self,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        seq_data_list = dict()\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        tracker = BYTETracker_score(self.args)\n        ori_thresh = self.args.track_thresh\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name == 'MOT17-05-FRCNN' or video_name == 'MOT17-06-FRCNN':\n                    self.args.track_buffer = 14\n                elif video_name == 'MOT17-13-FRCNN' or video_name == 'MOT17-14-FRCNN':\n                    self.args.track_buffer = 25\n                else:\n                    self.args.track_buffer = 30\n\n                if video_name == 'MOT17-01-FRCNN':\n                    self.args.track_thresh = 0.65\n                elif video_name == 'MOT17-06-FRCNN':\n                    self.args.track_thresh = 0.65\n                elif video_name == 'MOT17-12-FRCNN':\n                    self.args.track_thresh = 0.7\n                elif video_name == 'MOT17-14-FRCNN':\n                    self.args.track_thresh = 0.67\n                else:\n                    self.args.track_thresh = ori_thresh\n\n                if video_name == 'MOT20-06' or video_name == 'MOT20-08':\n                    self.args.track_thresh = 0.3\n                else:\n                    self.args.track_thresh = ori_thresh\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = BYTETracker_score(self.args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            if video_name not in seq_data_list:\n                seq_data_list[video_name] = []\n            seq_data_list[video_name].extend(output_results)\n            data_list.extend(output_results)\n\n            # run tracking\n            if outputs[0] is not None:\n                online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n                online_tlwhs = []\n                online_ids = []\n                online_scores = []\n                for t in online_targets:\n                    tlwh = t.tlwh\n                    tid = t.track_id\n                    vertical = tlwh[2] / tlwh[3] > 1.6\n                    if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                        online_tlwhs.append(tlwh)\n                        online_ids.append(tid)\n                        online_scores.append(t.score)\n                # save results\n                results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                print(\"writing to {}\".format(result_filename))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        for video_name in seq_data_list:\n            self.save_detection_result(seq_data_list[video_name], result_folder, video_name)\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n\n    def evaluate_ocsort(\n        self,\n        model,\n        distributed=False,\n        half=False,\n        trt_file=None,\n        decoder=None,\n        test_size=None,\n        result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        seq_data_list = dict()\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n            \n        tracker = OCSort(det_thresh = self.args.track_thresh, iou_threshold=self.args.iou_thresh,\n            asso_func=self.args.asso, delta_t=self.args.deltat, inertia=self.args.inertia)\n        ori_thresh = self.args.track_thresh\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n            progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n                img_name = img_file_name[0].split(\"/\")[2]\n                \"\"\"\n                    Here, you can use adaptive detection threshold as in BYTE\n                    (line 268 - 292), which can boost the performance on MOT17/MOT20\n                    datasets, but we don't use that by default for a generalized \n                    stack of parameters on all datasets.\n                \"\"\"\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = OCSort(det_thresh = self.args.track_thresh, iou_threshold=self.args.iou_thresh,\n                        asso_func=self.args.asso, delta_t=self.args.deltat, inertia=self.args.inertia)\n                    if len(results) != 0:\n                        try:\n                            result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        except:\n                            import pdb; pdb.set_trace()\n                        write_results_no_score(result_filename, results)\n                        results = []\n                \n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n            \n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n\n            if video_name not in seq_data_list:\n                seq_data_list[video_name] = []\n            seq_data_list[video_name].extend(output_results)\n            data_list.extend(output_results)\n\n            # run tracking\n            if outputs[0] is not None:\n                online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n                online_tlwhs = []\n                online_ids = []\n                for t in online_targets:\n                    tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                    tid = t[4]\n                    vertical = tlwh[2] / tlwh[3] > 1.6\n                    if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                        online_tlwhs.append(tlwh)\n                        online_ids.append(tid)\n                # save results\n                results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n            \n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n        \n        for video_name in seq_data_list.keys():\n            self.save_detection_result(seq_data_list[video_name], result_folder, video_name)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n\n    def evaluate_sort_score(\n            self,\n            args,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        seq_data_list = dict()\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        tracker = Sort_score(args, self.args.track_thresh)\n        ori_thresh = self.args.track_thresh\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n                img_name = img_file_name[0].split(\"/\")[2]\n                \"\"\"\n                    Here, you can use adaptive detection threshold as in BYTE\n                    (line 268 - 292), which can boost the performance on MOT17/MOT20\n                    datasets, but we don't use that by default for a generalized \n                    stack of parameters on all datasets.\n                \"\"\"\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = Sort_score(args, self.args.track_thresh)\n                    if len(results) != 0:\n                        try:\n                            result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        except:\n                            import pdb;\n                            pdb.set_trace()\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n\n            if video_name not in seq_data_list:\n                seq_data_list[video_name] = []\n            seq_data_list[video_name].extend(output_results)\n            data_list.extend(output_results)\n\n            # run tracking\n            if outputs[0] is not None:\n                online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n                online_tlwhs = []\n                online_ids = []\n                for t in online_targets:\n                    tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                    tid = t[4]\n                    vertical = tlwh[2] / tlwh[3] > 1.6\n                    if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                        online_tlwhs.append(tlwh)\n                        online_ids.append(tid)\n                # save results\n                results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        for video_name in seq_data_list.keys():\n            self.save_detection_result(seq_data_list[video_name], result_folder, video_name)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_sort(\n            self,\n            args,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        seq_data_list = dict()\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        tracker = Sort(args, self.args.track_thresh)\n        ori_thresh = self.args.track_thresh\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n                img_name = img_file_name[0].split(\"/\")[2]\n                \"\"\"\n                    Here, you can use adaptive detection threshold as in BYTE\n                    (line 268 - 292), which can boost the performance on MOT17/MOT20\n                    datasets, but we don't use that by default for a generalized \n                    stack of parameters on all datasets.\n                \"\"\"\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = Sort(args, self.args.track_thresh)\n                    if len(results) != 0:\n                        try:\n                            result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        except:\n                            import pdb;\n                            pdb.set_trace()\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n\n            if video_name not in seq_data_list:\n                seq_data_list[video_name] = []\n            seq_data_list[video_name].extend(output_results)\n            data_list.extend(output_results)\n\n            # run tracking\n            if outputs[0] is not None:\n                online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n                online_tlwhs = []\n                online_ids = []\n                for t in online_targets:\n                    tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                    tid = t[4]\n                    vertical = tlwh[2] / tlwh[3] > 1.6\n                    if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                        online_tlwhs.append(tlwh)\n                        online_ids.append(tid)\n                # save results\n                results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        for video_name in seq_data_list.keys():\n            self.save_detection_result(seq_data_list[video_name], result_folder, video_name)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n\n    def evaluate_motdt(\n            self,\n            args,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        seq_data_list = dict()\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        from yolox.data.dataloading import get_yolox_datadir\n        model_folder = \"./pretrained/googlenet_part8_all_xavier_ckpt_56.h5\"\n        tracker = OnlineTracker(model_folder, args=args)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n                img_name = img_file_name[0].split(\"/\")[2]\n                \"\"\"\n                    Here, you can use adaptive detection threshold as in BYTE\n                    (line 268 - 292), which can boost the performance on MOT17/MOT20\n                    datasets, but we don't use that by default for a generalized \n                    stack of parameters on all datasets.\n                \"\"\"\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = OnlineTracker(model_folder, args=args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = t.tlwh\n                tid = t.track_id\n                vertical = tlwh[2] / tlwh[3] > 1.6\n                if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_motdt_score(\n            self,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        seq_data_list = dict()\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        from yolox.data.dataloading import get_yolox_datadir\n        model_folder = \"./pretrained/googlenet_part8_all_xavier_ckpt_56.h5\"\n        tracker = OnlineTracker_score(model_folder,self.args)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n                img_name = img_file_name[0].split(\"/\")[2]\n                \"\"\"\n                    Here, you can use adaptive detection threshold as in BYTE\n                    (line 268 - 292), which can boost the performance on MOT17/MOT20\n                    datasets, but we don't use that by default for a generalized \n                    stack of parameters on all datasets.\n                \"\"\"\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = OnlineTracker_score(model_folder,self.args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = t.tlwh\n                tid = t.track_id\n                vertical = tlwh[2] / tlwh[3] > 1.6\n                if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n\n    def evaluate_deepsort(\n            self,\n            args,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        from yolox.data.dataloading import get_yolox_datadir\n        model_folder = \"./pretrained/googlenet_part8_all_xavier_ckpt_56.h5\"\n        tracker = DeepSort(model_folder, args=args)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = DeepSort(model_folder, args=args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                vertical = tlwh[2] / tlwh[3] > 1.6\n                if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                # if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    # online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_deepsort_score(\n            self,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        from yolox.data.dataloading import get_yolox_datadir\n        model_folder = \"./pretrained/googlenet_part8_all_xavier_ckpt_56.h5\"\n        tracker = DeepSort_score(model_folder,self.args)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = DeepSort_score(model_folder,self.args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                vertical = tlwh[2] / tlwh[3] > 1.6\n                if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                # if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    # online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def convert_to_coco_format(self, outputs, info_imgs, ids):\n        data_list = []\n        for (output, img_h, img_w, img_id) in zip(\n            outputs, info_imgs[0], info_imgs[1], ids\n        ):\n            if output is None:\n                continue\n            output = output.cpu()\n\n            bboxes = output[:, 0:4]\n\n            # preprocessing: resize\n            scale = min(\n                self.img_size[0] / float(img_h), self.img_size[1] / float(img_w)\n            )\n            bboxes /= scale\n            bboxes = xyxy2xywh(bboxes)\n\n            cls = output[:, 6]\n            scores = output[:, 4] * output[:, 5]\n            for ind in range(bboxes.shape[0]):\n                label = self.dataloader.dataset.class_ids[int(cls[ind])]\n                pred_data = {\n                    \"image_id\": int(img_id),\n                    \"category_id\": label,\n                    \"bbox\": bboxes[ind].numpy().tolist(),\n                    \"score\": scores[ind].numpy().item(),\n                    \"segmentation\": [],\n                }  # COCO json format\n                data_list.append(pred_data)\n        return data_list\n\n    def evaluate_prediction(self, data_dict, statistics):\n        if not is_main_process():\n            return 0, 0, None\n\n        logger.info(\"Evaluate in main process...\")\n\n        annType = [\"segm\", \"bbox\", \"keypoints\"]\n\n        inference_time = statistics[0].item()\n        track_time = statistics[1].item()\n        n_samples = statistics[2].item()\n\n        a_infer_time = 1000 * inference_time / (n_samples * self.dataloader.batch_size)\n        a_track_time = 1000 * track_time / (n_samples * self.dataloader.batch_size)\n\n        time_info = \", \".join(\n            [\n                \"Average {} time: {:.2f} ms\".format(k, v)\n                for k, v in zip(\n                    [\"forward\", \"track\", \"inference\"],\n                    [a_infer_time, a_track_time, (a_infer_time + a_track_time)],\n                )\n            ]\n        )\n\n        info = time_info + \"\\n\"\n\n        # Evaluate the Dt (detection) json comparing with the ground truth\n        if len(data_dict) > 0:\n            cocoGt = self.dataloader.dataset.coco\n            # TODO: since pycocotools can't process dict in py36, write data to json file.\n            _, tmp = tempfile.mkstemp()\n            json.dump(data_dict, open(tmp, \"w\"))\n            cocoDt = cocoGt.loadRes(tmp)\n            '''\n            try:\n                from yolox.layers import COCOeval_opt as COCOeval\n            except ImportError:\n                from pycocotools import cocoeval as COCOeval\n                logger.warning(\"Use standard COCOeval.\")\n            '''\n            #from pycocotools.cocoeval import COCOeval\n            from yolox.layers import COCOeval_opt as COCOeval\n            cocoEval = COCOeval(cocoGt, cocoDt, annType[1])\n            cocoEval.evaluate()\n            cocoEval.accumulate()\n            redirect_string = io.StringIO()\n            with contextlib.redirect_stdout(redirect_string):\n                cocoEval.summarize()\n            info += redirect_string.getvalue()\n            return cocoEval.stats[0], cocoEval.stats[1], info\n        else:\n            return 0, 0, info\n\n    def save_detection_result(self, data_dict, result_folder, video_name):\n        save_f = os.path.join(result_folder, \"{}_detections.txt\".format(video_name))\n        print(\"Writing the detection results into {}\".format(save_f))\n        f = open(save_f, \"w\")\n        for det in data_dict:\n            image_id = det[\"image_id\"]\n            category_id = det[\"category_id\"]\n            bbox = det[\"bbox\"]\n            score = det[\"score\"]\n            rec_line = \"{},{},{},{},{},{},{}\\n\".format(image_id, category_id, bbox[0], bbox[1], bbox[2], bbox[3], score)\n            f.write(rec_line)\n        print(\"Have written the detection results.\")\n"
  },
  {
    "path": "yolox/evaluators/mot_evaluator_dance.py",
    "content": "from collections import defaultdict\nfrom loguru import logger\nfrom tqdm import tqdm\nimport copy\n\nimport torch\n\nfrom yolox.utils import (\n    gather,\n    is_main_process,\n    postprocess,\n    synchronize,\n    time_synchronized,\n    xyxy2xywh\n)\nfrom trackers.byte_tracker.byte_tracker import BYTETracker\nfrom trackers.byte_tracker.byte_tracker_score import BYTETracker_score\nfrom trackers.ocsort_tracker.ocsort import OCSort\nfrom trackers.hybrid_sort_tracker.hybrid_sort import Hybrid_Sort\nfrom trackers.hybrid_sort_tracker.hybrid_sort_reid import Hybrid_Sort_ReID\nfrom trackers.sort_tracker.sort import Sort\nfrom trackers.sort_tracker.sort_score import Sort_score\nfrom trackers.deepsort_tracker.deepsort import DeepSort\nfrom trackers.deepsort_tracker.deepsort_score import DeepSort_score\nfrom trackers.motdt_tracker.motdt_tracker import OnlineTracker\nfrom trackers.motdt_tracker.motdt_tracker_score import OnlineTracker_score\n\nimport contextlib\nimport io\nimport os\nimport itertools\nimport json\nimport tempfile\nimport time\nimport cv2\nimport numpy as np\nfrom utils.utils import write_results, write_results_no_score\nfrom fast_reid.fast_reid_interfece import FastReIDInterface\n\nclass MOTEvaluator:\n    \"\"\"\n    COCO AP Evaluation class.  All the data in the val2017 dataset are processed\n    and evaluated by COCO API.\n    \"\"\"\n\n    def __init__(\n        self, args, dataloader, img_size, confthre, nmsthre, num_classes):\n        \"\"\"\n        Args:\n            dataloader (Dataloader): evaluate dataloader.\n            img_size (int): image size after preprocess. images are resized\n                to squares whose shape is (img_size, img_size).\n            confthre (float): confidence threshold ranging from 0 to 1, which\n                is defined in the config file.\n            nmsthre (float): IoU threshold of non-max supression ranging from 0 to 1.\n        \"\"\"\n        self.dataloader = dataloader\n        self.img_size = img_size\n        self.confthre = confthre\n        self.nmsthre = nmsthre\n        self.num_classes = num_classes\n        self.args = args\n        self.former_frame = None\n\n    def ECC(self, src, dst, warp_mode=cv2.MOTION_EUCLIDEAN, eps=1e-5, max_iter=100, scale=0.1, align=False):\n        \"\"\"Compute the warp matrix from src (former frame) to dst (current frame).\n\n        Parameters\n        ----------\n        src : ndarray\n            An NxM matrix of source img(BGR or Gray), it must be the same format as dst.\n        dst : ndarray\n            An NxM matrix of target img(BGR or Gray).\n        warp_mode: flags of opencv\n            translation: cv2.MOTION_TRANSLATION\n            rotated and shifted: cv2.MOTION_EUCLIDEAN\n            affine(shift,rotated,shear): cv2.MOTION_AFFINE\n            homography(3d): cv2.MOTION_HOMOGRAPHY\n        eps: float\n            the threshold of the increment in the correlation coefficient between two iterations\n        max_iter: int\n            the number of iterations.\n        scale: float or [int, int]\n            scale_ratio: float\n            scale_size: [W, H]\n        align: bool\n            whether to warp affine or perspective transforms to the source image\n\n        Returns\n        -------\n        warp matrix : ndarray\n            Returns the warp matrix from src to dst.\n            if motion models is homography, the warp matrix will be 3x3, otherwise 2x3\n        src_aligned: ndarray\n            aligned source image of gray\n        \"\"\"\n        assert src.shape == dst.shape, \"the source image must be the same format to the target image!\"\n\n        # BGR2GRAY\n        if src.ndim == 3:\n            # Convert images to grayscale\n            src = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)\n            dst = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)\n\n        # make the imgs smaller to speed up\n        if scale is not None:  # do resize\n            if isinstance(scale, float) or isinstance(scale, int):  # in dx & dy format\n                if scale != 1:\n                    src_r = cv2.resize(src, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\n                    dst_r = cv2.resize(dst, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\n                    scale = [scale, scale]\n                else:\n                    src_r, dst_r = src, dst\n                    scale = None\n            else:  # in new_x & new_y format\n                if scale[0] != src.shape[1] and scale[1] != src.shape[0]:\n                    src_r = cv2.resize(src, (scale[0], scale[1]), interpolation=cv2.INTER_LINEAR)\n                    dst_r = cv2.resize(dst, (scale[0], scale[1]), interpolation=cv2.INTER_LINEAR)\n                    scale = [scale[0] / src.shape[1], scale[1] / src.shape[0]]\n                else:\n                    src_r, dst_r = src, dst\n                    scale = None\n        else:  # don't resize\n            src_r, dst_r = src, dst\n\n        # Define 2x3 or 3x3 matrices and initialize the matrix to identity\n        if warp_mode == cv2.MOTION_HOMOGRAPHY:\n            warp_matrix = np.eye(3, 3, dtype=np.float32)\n        else:\n            warp_matrix = np.eye(2, 3, dtype=np.float32)\n\n        # Define termination criteria\n        criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, max_iter, eps)\n\n        # Run the ECC algorithm. The results are stored in warp_matrix.\n        (cc, warp_matrix) = cv2.findTransformECC(src_r, dst_r, warp_matrix, warp_mode, criteria, None, 1)\n\n        if scale is not None:\n            warp_matrix[0, 2] = warp_matrix[0, 2] / scale[0]\n            warp_matrix[1, 2] = warp_matrix[1, 2] / scale[1]\n\n        if align:  # return aligned source image\n            sz = src.shape\n            if warp_mode == cv2.MOTION_HOMOGRAPHY:\n                # Use warpPerspective for Homography\n                src_aligned = cv2.warpPerspective(src, warp_matrix, (sz[1], sz[0]), flags=cv2.INTER_LINEAR)\n            else:\n                # Use warpAffine for Translation, Euclidean and Affine\n                src_aligned = cv2.warpAffine(src, warp_matrix, (sz[1], sz[0]), flags=cv2.INTER_LINEAR)\n            if warp_matrix.shape[0] == 2:\n                return np.vstack((warp_matrix, np.array([[0, 0, 1]]))), src_aligned     # warp_matrix from [2, 3] to [3, 3]\n            else:\n                return warp_matrix, src_aligned\n        else:  # do not return aligned source image, e.g. return None\n            if warp_matrix.shape[0] == 2:\n                return np.vstack((warp_matrix, np.array([[0, 0, 1]]))), None            # warp_matrix from [2, 3] to [3, 3]\n            else:\n                return warp_matrix, None\n\n\n    def evaluate(\n        self,\n        model,\n        distributed=False,\n        half=False,\n        trt_file=None,\n        decoder=None,\n        test_size=None,\n        result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n            \n        tracker = BYTETracker(self.args)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n            progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = BYTETracker(self.args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n            \n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n    \n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = t.tlwh\n                tid = t.track_id\n                if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n            \n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_byte_score(\n            self,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        tracker = BYTETracker_score(self.args)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = BYTETracker_score(self.args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = t.tlwh\n                tid = t.track_id\n                if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_ocsort(\n        self,\n        model,\n        distributed=False,\n        half=False,\n        trt_file=None,\n        decoder=None,\n        test_size=None,\n        result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n            \n        tracker = OCSort(det_thresh=self.args.track_thresh, iou_threshold=self.args.iou_thresh,\n            asso_func=self.args.asso, delta_t=self.args.deltat, inertia=self.args.inertia, use_byte=self.args.use_byte)\n        \n        detections = dict()\n\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n            progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n                img_base_name = img_file_name[0].split('/')[-1].split('.')[0]\n                \n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n\n                if frame_id == 1:\n                    tracker = OCSort(det_thresh = self.args.track_thresh, iou_threshold=self.args.iou_thresh,\n                            asso_func=self.args.asso, delta_t=self.args.deltat, inertia=self.args.inertia, use_byte=self.args.use_byte)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                ckt_file = \"dance_detections/dancetrack_wo_ch_w_reid/{}/{}_detetcion.pkl\".format(video_name, img_base_name)\n                if os.path.exists(ckt_file):\n                    data = torch.load(ckt_file)\n                    outputs = [data['detection']]\n                else:\n                    exit()\n                    imgs = imgs.type(tensor_type)\n\n                    # skip the the last iters since batchsize might be not enough for batch inference\n                    outputs = model(imgs)\n                    if decoder is not None:\n                        outputs = decoder(outputs, dtype=outputs.type())\n\n                    outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n                    # we should save the detections here ! \n                    # os.makedirs(\"dance_detections/{}\".format(video_name), exist_ok=True)\n                    # torch.save(outputs[0], ckt_file)\n                \n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n            online_tlwhs = []\n            online_ids = []\n            for t in online_targets:\n                \"\"\"\n                    Here is minor issue that DanceTrack uses the same annotation\n                    format as MOT17/MOT20, namely xywh to annotate the object bounding\n                    boxes. But DanceTrack annotation is cropped at the image boundary, \n                    which is different from MOT17/MOT20. So, cropping the output\n                    bounding boxes at the boundary may slightly fix this issue. But the \n                    influence is minor. For example, with my results on the interpolated\n                    OC-SORT:\n                    * without cropping: HOTA=55.731\n                    * with cropping: HOTA=55.737\n                \"\"\"\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n            \n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_hybrid_sort(\n            self,\n            args,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        ori_thresh = self.args.track_thresh\n        detections = dict()\n\n        for cur_iter, (imgs, _, info_imgs, ids, raw_image) in enumerate(    # [hgx0411] add raw_image for FastReID\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n                img_base_name = img_file_name[0].split('/')[-1].split('.')[0]\n\n                \"\"\"\n                    Here, you can use adaptive detection threshold as in BYTE\n                    (line 268 - 292), which can boost the performance on MOT17/MOT20\n                    datasets, but we don't use that by default for a generalized \n                    stack of parameters on all datasets.\n                \"\"\"\n                if video_name == 'MOT17-05-FRCNN' or video_name == 'MOT17-06-FRCNN':\n                    self.args.track_buffer = 14\n                elif video_name == 'MOT17-13-FRCNN' or video_name == 'MOT17-14-FRCNN':\n                    self.args.track_buffer = 25\n                else:\n                    self.args.track_buffer = 30\n\n                if video_name == 'MOT17-01-FRCNN':\n                    self.args.track_thresh = 0.65\n                elif video_name == 'MOT17-06-FRCNN':\n                    self.args.track_thresh = 0.65\n                elif video_name == 'MOT17-12-FRCNN':\n                    self.args.track_thresh = 0.7\n                elif video_name == 'MOT17-14-FRCNN':\n                    self.args.track_thresh = 0.67\n                else:\n                    self.args.track_thresh = ori_thresh\n\n                if video_name == 'MOT20-06' or video_name == 'MOT20-08':\n                    self.args.track_thresh = 0.3\n                else:\n                    self.args.track_thresh = ori_thresh\n\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n\n                if frame_id == 1:\n                    tracker = Hybrid_Sort(args, det_thresh=self.args.track_thresh, iou_threshold=self.args.iou_thresh,\n                                     asso_func=self.args.asso, delta_t=self.args.deltat, inertia=self.args.inertia,\n                                     use_byte=self.args.use_byte)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                ckt_file = \"dance_detections/dancetrack_wo_ch_w_reid/{}/{}_detetcion.pkl\".format(video_name, img_base_name)\n                if os.path.exists(ckt_file):\n                    data = torch.load(ckt_file)\n                    outputs = [data['detection']]\n                else:\n                    imgs = imgs.type(tensor_type)\n\n                    # skip the the last iters since batchsize might be not enough for batch inference\n                    outputs = model(imgs)\n                    if decoder is not None:\n                        outputs = decoder(outputs, dtype=outputs.type())\n\n                    outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n                    # we should save the detections here !\n                    # os.makedirs(\"dance_detections/{}\".format(video_name), exist_ok=True)\n                    # torch.save(outputs[0], ckt_file)\n                    # res = {}\n                    # res['detection'] = outputs[0]\n                    # os.makedirs(\"dance_detections/{}\".format(video_name), exist_ok=True)\n                    # torch.save(res, ckt_file)\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n            online_tlwhs = []\n            online_ids = []\n            for t in online_targets:\n                \"\"\"\n                    Here is minor issue that DanceTrack uses the same annotation\n                    format as MOT17/MOT20, namely xywh to annotate the object bounding\n                    boxes. But DanceTrack annotation is cropped at the image boundary, \n                    which is different from MOT17/MOT20. So, cropping the output\n                    bounding boxes at the boundary may slightly fix this issue. But the \n                    influence is minor. For example, with my results on the interpolated\n                    OC-SORT:\n                    * without cropping: HOTA=55.731\n                    * with cropping: HOTA=55.737\n                \"\"\"\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                vertical = tlwh[2] / tlwh[3] > 1.6 if self.args.dataset in [\"mot17\", \"mot20\"] else False\n                if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n\n    def evaluate_hybrid_sort_reid(\n            self,\n            args,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # assert self.args.with_fastreid\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        # for fastreid\n        self.encoder = FastReIDInterface(self.args.fast_reid_config, self.args.fast_reid_weights, 'cuda')\n\n        ori_thresh = self.args.track_thresh\n        detections = dict()\n\n        for cur_iter, (imgs, _, info_imgs, ids, raw_image) in enumerate(    # [hgx0411] add raw_image for FastReID\n                progress_bar(self.dataloader)\n        ):\n            raw_image = raw_image.numpy()[0, ...]  # sequeeze batch dim, [bs, H, W, C] ==> [H, W, C]\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n                img_base_name = img_file_name[0].split('/')[-1].split('.')[0]\n\n                \"\"\"\n                    Here, you can use adaptive detection threshold as in BYTE\n                    (line 268 - 292), which can boost the performance on MOT17/MOT20\n                    datasets, but we don't use that by default for a generalized \n                    stack of parameters on all datasets.\n                \"\"\"\n                if video_name == 'MOT17-05-FRCNN' or video_name == 'MOT17-06-FRCNN':\n                    self.args.track_buffer = 14\n                elif video_name == 'MOT17-13-FRCNN' or video_name == 'MOT17-14-FRCNN':\n                    self.args.track_buffer = 25\n                else:\n                    self.args.track_buffer = 30\n\n                if video_name == 'MOT17-01-FRCNN':\n                    self.args.track_thresh = 0.65\n                elif video_name == 'MOT17-06-FRCNN':\n                    self.args.track_thresh = 0.65\n                elif video_name == 'MOT17-12-FRCNN':\n                    self.args.track_thresh = 0.7\n                elif video_name == 'MOT17-14-FRCNN':\n                    self.args.track_thresh = 0.67\n                else:\n                    self.args.track_thresh = ori_thresh\n\n                if video_name == 'MOT20-06' or video_name == 'MOT20-08':\n                    self.args.track_thresh = 0.3\n                else:\n                    self.args.track_thresh = ori_thresh\n\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n\n                if frame_id == 1:\n                    tracker = Hybrid_Sort_ReID(args, det_thresh=self.args.track_thresh, iou_threshold=self.args.iou_thresh,\n                                     asso_func=self.args.asso, delta_t=self.args.deltat, inertia=self.args.inertia)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                ckt_file = \"dance_detections/{}/{}_detetcion.pkl\".format(video_name, img_base_name)\n                if os.path.exists(ckt_file):\n                    data = torch.load(ckt_file)\n                    outputs = [data['detection']]\n                    id_feature = data['reid_feature']\n                else:\n                    imgs = imgs.type(tensor_type)\n\n                    # skip the the last iters since batchsize might be not enough for batch inference\n                    outputs = model(imgs)\n                    if decoder is not None:\n                        outputs = decoder(outputs, dtype=outputs.type())\n\n                    outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n                    if outputs[0] == None:\n                        id_feature = np.array([]).reshape(0, 2048)\n                    else:\n                        bbox_xyxy = copy.deepcopy(outputs[0][:, :4])\n                        # we should save the detections here !\n                        # os.makedirs(\"dance_detections/{}\".format(video_name), exist_ok=True)\n                        # torch.save(outputs[0], ckt_file)\n                        # box rescale borrowed from convert_to_coco_format()\n                        scale = min(self.img_size[0] / float(info_imgs[0]), self.img_size[1] / float(info_imgs[1]))\n                        bbox_xyxy /= scale\n                        id_feature = self.encoder.inference(raw_image, bbox_xyxy.cpu().detach().numpy())    # normalization and numpy included\n                    # res = {}\n                    # res['detection'] = outputs[0]\n                    # res['reid_feature'] = id_feature\n                    # os.makedirs(\"dance_detections/{}\".format(video_name), exist_ok=True)\n                    # torch.save(res, ckt_file)\n                    # # verify of bboxes\n                    # import torchvision.transforms as T\n                    # mean = torch.tensor([0.485, 0.456, 0.406], dtype=torch.float32)\n                    # std = torch.tensor([0.229, 0.224, 0.225], dtype=torch.float32)\n                    # normalize = T.Normalize(mean.tolist(), std.tolist())\n                    # unnormalize = T.Normalize((-mean / std).tolist(), (1.0 / std).tolist())\n                    # img_ = unnormalize(imgs[0]) * 255\n                    # img2 = img_.permute(1, 2, 0).type(torch.int16).cpu().detach().numpy()\n                    # import cv2\n                    # cv2.imwrite('img.png', img2[int(bbox_xyxy[0][1]): int(bbox_xyxy[0][3]),\n                    #                        int(bbox_xyxy[0][0]): int(bbox_xyxy[0][2]), :])\n            if is_time_record:\n                infer_end = time_synchronized()\n                inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            if self.args.ECC:\n                # compute warp matrix with ECC, when frame_id is not 1.\n                # raw_image = raw_image.numpy()[0, ...]       # sequeeze batch dim, [bs, H, W, C] ==> [H, W, C]\n                if frame_id != 1:\n                    warp_matrix, src_aligned = self.ECC(self.former_frame, raw_image, align=True)\n                else:\n                    warp_matrix, src_aligned = None, None\n                self.former_frame = raw_image       # update former_frame\n            else:\n                warp_matrix, src_aligned = None, None\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, id_feature=id_feature, warp_matrix=warp_matrix)        # [hgx0411] id_feature\n            online_tlwhs = []\n            online_ids = []\n            for t in online_targets:\n                \"\"\"\n                    Here is minor issue that DanceTrack uses the same annotation\n                    format as MOT17/MOT20, namely xywh to annotate the object bounding\n                    boxes. But DanceTrack annotation is cropped at the image boundary, \n                    which is different from MOT17/MOT20. So, cropping the output\n                    bounding boxes at the boundary may slightly fix this issue. But the \n                    influence is minor. For example, with my results on the interpolated\n                    OC-SORT:\n                    * without cropping: HOTA=55.731\n                    * with cropping: HOTA=55.737\n                \"\"\"\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                vertical = tlwh[2] / tlwh[3] > 1.6 if self.args.dataset in [\"mot17\", \"mot20\"] else False\n                if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_sort(\n            self,\n            args,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        tracker = Sort(args, self.args.track_thresh, iou_threshold=args.iou_thresh)\n\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = Sort(args, self.args.track_thresh, iou_threshold=args.iou_thresh)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n            online_tlwhs = []\n            online_ids = []\n            for t in online_targets:\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                # vertical = tlwh[2] / tlwh[3] > 1.6\n                if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_sort_score(\n            self,\n            args,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        tracker = Sort_score(args, self.args.track_thresh)\n\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = Sort_score(args, self.args.track_thresh)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n            online_tlwhs = []\n            online_ids = []\n            for t in online_targets:\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                # vertical = tlwh[2] / tlwh[3] > 1.6\n                if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_motdt(\n            self,\n            args,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        from yolox.data.dataloading import get_yolox_datadir\n        # model_folder = os.path.join(get_yolox_datadir(), \"../pretrained/googlenet_part8_all_xavier_ckpt_56.h5\")\n        model_folder = \"./pretrained/googlenet_part8_all_xavier_ckpt_56.h5\"\n        tracker = OnlineTracker(model_folder, args=args)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = OnlineTracker(model_folder, args=args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = t.tlwh\n                tid = t.track_id\n                if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_motdt_score(\n            self,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        from yolox.data.dataloading import get_yolox_datadir\n        model_folder = \"./pretrained/googlenet_part8_all_xavier_ckpt_56.h5\"\n        tracker = OnlineTracker_score(model_folder,args=self.args)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = OnlineTracker_score(model_folder,args=self.args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = t.tlwh\n                tid = t.track_id\n                if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_deepsort(\n            self,\n            args,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        from yolox.data.dataloading import get_yolox_datadir\n        model_folder = \"./pretrained/googlenet_part8_all_xavier_ckpt_56.h5\"\n        tracker = DeepSort(model_folder, args=args)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = DeepSort(model_folder, args=args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    # online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_deepsort_score(\n            self,\n            model,\n            distributed=False,\n            half=False,\n            trt_file=None,\n            decoder=None,\n            test_size=None,\n            result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n        NOTE: This function will change training mode to False, please save states if needed.\n        Args:\n            model : model to evaluate.\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n\n        from yolox.data.dataloading import get_yolox_datadir\n        model_folder = \"./pretrained/googlenet_part8_all_xavier_ckpt_56.h5\"\n        tracker = DeepSort_score(model_folder,self.args)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n                progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = DeepSort_score(model_folder,self.args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                if tlwh[2] * tlwh[3] > self.args.min_box_area:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    # online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n\n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def convert_to_coco_format(self, outputs, info_imgs, ids):\n        data_list = []\n        for (output, img_h, img_w, img_id) in zip(\n            outputs, info_imgs[0], info_imgs[1], ids\n        ):\n            if output is None:\n                continue\n            output = output.cpu()\n\n            bboxes = output[:, 0:4]\n\n            # preprocessing: resize\n            scale = min(\n                self.img_size[0] / float(img_h), self.img_size[1] / float(img_w)\n            )\n            bboxes /= scale\n            bboxes = xyxy2xywh(bboxes)\n\n            cls = output[:, 6]\n            scores = output[:, 4] * output[:, 5]\n            for ind in range(bboxes.shape[0]):\n                label = self.dataloader.dataset.class_ids[int(cls[ind])]\n                pred_data = {\n                    \"image_id\": int(img_id),\n                    \"category_id\": label,\n                    \"bbox\": bboxes[ind].numpy().tolist(),\n                    \"score\": scores[ind].numpy().item(),\n                    \"segmentation\": [],\n                }  # COCO json format\n                data_list.append(pred_data)\n        return data_list\n\n\n    def evaluate_prediction(self, data_dict, statistics):\n        if not is_main_process():\n            return 0, 0, None\n\n        logger.info(\"Evaluate in main process...\")\n\n        annType = [\"segm\", \"bbox\", \"keypoints\"]\n\n        inference_time = statistics[0].item()\n        track_time = statistics[1].item()\n        n_samples = statistics[2].item()\n\n        a_infer_time = 1000 * inference_time / (n_samples * self.dataloader.batch_size)\n        a_track_time = 1000 * track_time / (n_samples * self.dataloader.batch_size)\n\n        time_info = \", \".join(\n            [\n                \"Average {} time: {:.2f} ms\".format(k, v)\n                for k, v in zip(\n                    [\"forward\", \"track\", \"inference\"],\n                    [a_infer_time, a_track_time, (a_infer_time + a_track_time)],\n                )\n            ]\n        )\n\n        info = time_info + \"\\n\"\n\n        # Evaluate the Dt (detection) json comparing with the ground truth\n        if len(data_dict) > 0:\n            cocoGt = self.dataloader.dataset.coco\n            # TODO: since pycocotools can't process dict in py36, write data to json file.\n            _, tmp = tempfile.mkstemp()\n            json.dump(data_dict, open(tmp, \"w\"))\n            cocoDt = cocoGt.loadRes(tmp)\n            from yolox.layers import COCOeval_opt as COCOeval\n            cocoEval = COCOeval(cocoGt, cocoDt, annType[1])\n            cocoEval.evaluate()\n            cocoEval.accumulate()\n            redirect_string = io.StringIO()\n            with contextlib.redirect_stdout(redirect_string):\n                cocoEval.summarize()\n            info += redirect_string.getvalue()\n            return cocoEval.stats[0], cocoEval.stats[1], info\n        else:\n            return 0, 0, info\n"
  },
  {
    "path": "yolox/evaluators/mot_evaluator_public.py",
    "content": "from collections import defaultdict\nfrom loguru import logger\nfrom tqdm import tqdm\n\nimport torch\n\nfrom yolox.utils import (\n    gather,\n    is_main_process,\n    postprocess,\n    synchronize,\n    time_synchronized,\n    xyxy2xywh\n)\nfrom trackers.byte_tracker.byte_tracker import BYTETracker\nfrom trackers.ocsort_tracker.ocsort import OCSort\nfrom trackers.deepsort_tracker.deepsort import DeepSort\nfrom trackers.motdt_tracker.motdt_tracker import OnlineTracker\n\nimport contextlib\nimport io\nimport os\nimport itertools\nimport json\nimport tempfile\nimport time\nimport numpy as np\nfrom utils.utils import write_results, write_results_no_score\n\n\nclass MOTEvaluatorPublic:\n    \"\"\"\n    COCO AP Evaluation class.  All the data in the val2017 dataset are processed\n    and evaluated by COCO API.\n    \"\"\"\n\n    def __init__(\n        self, args, dataloader, img_size, confthre, nmsthre, num_classes):\n        \"\"\"\n        Args:\n            dataloader (Dataloader): evaluate dataloader.\n            img_size (int): image size after preprocess. images are resized\n                to squares whose shape is (img_size, img_size).\n            confthre (float): confidence threshold ranging from 0 to 1, which\n                is defined in the config file.\n            nmsthre (float): IoU threshold of non-max supression ranging from 0 to 1.\n        \"\"\"\n        self.dataloader = dataloader\n        self.img_size = img_size\n        self.confthre = confthre\n        self.nmsthre = nmsthre\n        self.num_classes = num_classes\n        self.args = args\n\n    def evaluate(\n        self,\n        model,\n        distributed=False,\n        half=False,\n        trt_file=None,\n        decoder=None,\n        test_size=None,\n        result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        seq_data_list = dict()\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n            \n        tracker = BYTETracker(self.args)\n        ori_thresh = self.args.track_thresh\n\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n            progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = BYTETracker(self.args)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n            \n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            if video_name not in seq_data_list:\n                seq_data_list[video_name] = []\n            seq_data_list[video_name].extend(output_results)\n            data_list.extend(output_results)\n\n            # run tracking\n            if outputs[0] is not None:\n                online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n                online_tlwhs = []\n                online_ids = []\n                online_scores = []\n                for t in online_targets:\n                    tlwh = t.tlwh\n                    tid = t.track_id\n                    vertical = tlwh[2] / tlwh[3] > 1.6\n                    if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                        online_tlwhs.append(tlwh)\n                        online_ids.append(tid)\n                        online_scores.append(t.score)\n                # save results\n                results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n            \n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        for video_name in seq_data_list:\n            self.save_detection_result(seq_data_list[video_name], result_folder, video_name)\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_ocsort(\n        self,\n        model,\n        distributed=False,\n        half=False,\n        trt_file=None,\n        decoder=None,\n        test_size=None,\n        result_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        seq_data_list = dict()\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n            \n        tracker = OCSort(det_thresh = self.args.track_thresh, iou_threshold=self.args.iou_thresh,\n            asso_func=self.args.asso, delta_t=self.args.deltat, inertia=self.args.inertia)\n        ori_thresh = self.args.track_thresh\n\n        public_dets = dict()\n\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n            progress_bar(self.dataloader)\n        ):\n            # import pdb; pdb.set_trace()\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n                img_id = int(img_file_name[0].split('/')[-1].split(\".\")[0])\n\n                if video_name not in public_dets:\n                    det_path = \"datasets/mot/train/{}/det/det.txt\".format(video_name)\n                    seq_dets = np.loadtxt(det_path, delimiter=\",\")\n                    # seq_dets[:, 4:6] += seq_dets[:, 2:4]\n                    public_dets[video_name] = seq_dets\n                \n                seq_dets = public_dets[video_name].copy()\n                frame_dets = seq_dets[np.where(seq_dets[:,0]==img_id)]\n                dets = frame_dets[:, 2:6]\n                dets[:, 0] += dets[:, 2] / 2.0 \n                dets[:, 1] += dets[:, 3] / 2.0\n                scores = frame_dets[:, 6][:, np.newaxis]\n                padded = np.ones(scores.shape)\n                # cls_ = np.zeros(scores.shape)\n                outputs_public = np.concatenate([dets, padded, scores], axis=1)\n                outputs_public = torch.Tensor(outputs_public)\n                outputs_public = outputs_public.unsqueeze(0)\n                outputs = postprocess(outputs_public, self.num_classes, self.confthre, self.nmsthre)\n                img_name = img_file_name[0].split(\"/\")[2]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = OCSort(det_thresh = self.args.track_thresh, iou_threshold=self.args.iou_thresh,\n                            asso_func=self.args.asso, delta_t=self.args.deltat, inertia=self.args.inertia)\n                    if len(results) != 0:\n                        try:\n                            result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        except:\n                            import pdb; pdb.set_trace()\n                        write_results_no_score(result_filename, results)\n                        results = []\n                \n                \"\"\"\n                \"\"\"\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            # import pdb; pdb.set_trace()\n\n            if video_name not in seq_data_list:\n                seq_data_list[video_name] = []\n            seq_data_list[video_name].extend(output_results)\n            data_list.extend(output_results)\n\n            # run tracking\n            if outputs[0] is not None:\n                online_targets = tracker.update(outputs[0], info_imgs, self.img_size)\n                online_tlwhs = []\n                online_ids = []\n                for t in online_targets:\n                    tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                    tid = t[4]\n                    vertical = tlwh[2] / tlwh[3] > 1.6\n                    if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                        online_tlwhs.append(tlwh)\n                        online_ids.append(tid)\n                # save results\n                results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n            \n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n        \n        for video_name in seq_data_list.keys():\n            self.save_detection_result(seq_data_list[video_name], result_folder, video_name)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_deepsort(\n        self,\n        model,\n        distributed=False,\n        half=False,\n        trt_file=None,\n        decoder=None,\n        test_size=None,\n        result_folder=None,\n        model_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n            \n        tracker = DeepSort(model_folder, min_confidence=self.args.track_thresh)\n        \n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n            progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = DeepSort(model_folder, min_confidence=self.args.track_thresh)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results_no_score(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n            \n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])\n            online_tlwhs = []\n            online_ids = []\n            for t in online_targets:\n                tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]\n                tid = t[4]\n                vertical = tlwh[2] / tlwh[3] > 1.6\n                if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n            \n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results_no_score(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def evaluate_motdt(\n        self,\n        model,\n        distributed=False,\n        half=False,\n        trt_file=None,\n        decoder=None,\n        test_size=None,\n        result_folder=None,\n        model_folder=None\n    ):\n        \"\"\"\n        COCO average precision (AP) Evaluation. Iterate inference on the test dataset\n        and the results are evaluated by COCO API.\n\n        NOTE: This function will change training mode to False, please save states if needed.\n\n        Args:\n            model : model to evaluate.\n\n        Returns:\n            ap50_95 (float) : COCO AP of IoU=50:95\n            ap50 (float) : COCO AP of IoU=50\n            summary (sr): summary info of evaluation.\n        \"\"\"\n        # TODO half to amp_test\n        tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor\n        model = model.eval()\n        if half:\n            model = model.half()\n        ids = []\n        data_list = []\n        results = []\n        video_names = defaultdict()\n        progress_bar = tqdm if is_main_process() else iter\n\n        inference_time = 0\n        track_time = 0\n        n_samples = len(self.dataloader) - 1\n\n        if trt_file is not None:\n            from torch2trt import TRTModule\n\n            model_trt = TRTModule()\n            model_trt.load_state_dict(torch.load(trt_file))\n\n            x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()\n            model(x)\n            model = model_trt\n            \n        tracker = OnlineTracker(model_folder, min_cls_score=self.args.track_thresh)\n        for cur_iter, (imgs, _, info_imgs, ids) in enumerate(\n            progress_bar(self.dataloader)\n        ):\n            with torch.no_grad():\n                # init tracker\n                frame_id = info_imgs[2].item()\n                video_id = info_imgs[3].item()\n                img_file_name = info_imgs[4]\n                video_name = img_file_name[0].split('/')[0]\n\n                if video_name not in video_names:\n                    # if \"FRCNN\" in video_name:\n                    video_names[video_id] = video_name\n                if frame_id == 1:\n                    tracker = OnlineTracker(model_folder, min_cls_score=self.args.track_thresh)\n                    if len(results) != 0:\n                        result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id - 1]))\n                        write_results(result_filename, results)\n                        results = []\n\n                imgs = imgs.type(tensor_type)\n\n                # skip the the last iters since batchsize might be not enough for batch inference\n                is_time_record = cur_iter < len(self.dataloader) - 1\n                if is_time_record:\n                    start = time.time()\n\n                outputs = model(imgs)\n                if decoder is not None:\n                    outputs = decoder(outputs, dtype=outputs.type())\n\n                outputs = postprocess(outputs, self.num_classes, self.confthre, self.nmsthre)\n            \n                if is_time_record:\n                    infer_end = time_synchronized()\n                    inference_time += infer_end - start\n\n            output_results = self.convert_to_coco_format(outputs, info_imgs, ids)\n            data_list.extend(output_results)\n\n            # run tracking\n            online_targets = tracker.update(outputs[0], info_imgs, self.img_size, img_file_name[0])\n            online_tlwhs = []\n            online_ids = []\n            online_scores = []\n            for t in online_targets:\n                tlwh = t.tlwh\n                tid = t.track_id\n                vertical = tlwh[2] / tlwh[3] > 1.6\n                if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:\n                    online_tlwhs.append(tlwh)\n                    online_ids.append(tid)\n                    online_scores.append(t.score)\n            # save results\n            results.append((frame_id, online_tlwhs, online_ids, online_scores))\n\n            if is_time_record:\n                track_end = time_synchronized()\n                track_time += track_end - infer_end\n            \n            if cur_iter == len(self.dataloader) - 1:\n                result_filename = os.path.join(result_folder, '{}.txt'.format(video_names[video_id]))\n                write_results(result_filename, results)\n\n        statistics = torch.cuda.FloatTensor([inference_time, track_time, n_samples])\n        if distributed:\n            data_list = gather(data_list, dst=0)\n            data_list = list(itertools.chain(*data_list))\n            torch.distributed.reduce(statistics, dst=0)\n\n        eval_results = self.evaluate_prediction(data_list, statistics)\n        synchronize()\n        return eval_results\n\n    def convert_to_coco_format(self, outputs, info_imgs, ids):\n        data_list = []\n        for (output, img_h, img_w, img_id) in zip(\n            outputs, info_imgs[0], info_imgs[1], ids\n        ):\n            if output is None:\n                continue\n            output = output.cpu()\n\n            bboxes = output[:, 0:4]\n\n            # preprocessing: resize\n            scale = min(\n                self.img_size[0] / float(img_h), self.img_size[1] / float(img_w)\n            )\n            bboxes /= scale\n            bboxes = xyxy2xywh(bboxes)\n\n            cls = output[:, 6]\n            scores = output[:, 4] * output[:, 5]\n            for ind in range(bboxes.shape[0]):\n                label = self.dataloader.dataset.class_ids[int(cls[ind])]\n                pred_data = {\n                    \"image_id\": int(img_id),\n                    \"category_id\": label,\n                    \"bbox\": bboxes[ind].numpy().tolist(),\n                    \"score\": scores[ind].numpy().item(),\n                    \"segmentation\": [],\n                }  # COCO json format\n                data_list.append(pred_data)\n        return data_list\n\n    def evaluate_prediction(self, data_dict, statistics):\n        if not is_main_process():\n            return 0, 0, None\n\n        logger.info(\"Evaluate in main process...\")\n\n        annType = [\"segm\", \"bbox\", \"keypoints\"]\n\n        inference_time = statistics[0].item()\n        track_time = statistics[1].item()\n        n_samples = statistics[2].item()\n\n        a_infer_time = 1000 * inference_time / (n_samples * self.dataloader.batch_size)\n        a_track_time = 1000 * track_time / (n_samples * self.dataloader.batch_size)\n\n        time_info = \", \".join(\n            [\n                \"Average {} time: {:.2f} ms\".format(k, v)\n                for k, v in zip(\n                    [\"forward\", \"track\", \"inference\"],\n                    [a_infer_time, a_track_time, (a_infer_time + a_track_time)],\n                )\n            ]\n        )\n\n        info = time_info + \"\\n\"\n\n        # Evaluate the Dt (detection) json comparing with the ground truth\n        if len(data_dict) > 0:\n            cocoGt = self.dataloader.dataset.coco\n            # TODO: since pycocotools can't process dict in py36, write data to json file.\n            _, tmp = tempfile.mkstemp()\n            json.dump(data_dict, open(tmp, \"w\"))\n            cocoDt = cocoGt.loadRes(tmp)\n            '''\n            try:\n                from yolox.layers import COCOeval_opt as COCOeval\n            except ImportError:\n                from pycocotools import cocoeval as COCOeval\n                logger.warning(\"Use standard COCOeval.\")\n            '''\n            #from pycocotools.cocoeval import COCOeval\n            from yolox.layers import COCOeval_opt as COCOeval\n            cocoEval = COCOeval(cocoGt, cocoDt, annType[1])\n            cocoEval.evaluate()\n            cocoEval.accumulate()\n            redirect_string = io.StringIO()\n            with contextlib.redirect_stdout(redirect_string):\n                cocoEval.summarize()\n            info += redirect_string.getvalue()\n            return cocoEval.stats[0], cocoEval.stats[1], info\n        else:\n            return 0, 0, info\n\n    def save_detection_result(self, data_dict, result_folder, video_name):\n        save_f = os.path.join(result_folder, \"{}_detections.txt\".format(video_name))\n        print(\"Writing the detection results into {}\".format(save_f))\n        f = open(save_f, \"w\")\n        for det in data_dict:\n            image_id = det[\"image_id\"]\n            category_id = det[\"category_id\"]\n            bbox = det[\"bbox\"]\n            score = det[\"score\"]\n            rec_line = \"{},{},{},{},{},{},{}\\n\".format(image_id, category_id, bbox[0], bbox[1], bbox[2], bbox[3], score)\n            f.write(rec_line)\n        print(\"Have written the detection results.\")"
  },
  {
    "path": "yolox/exp/__init__.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nfrom .base_exp import BaseExp\nfrom .build import get_exp\nfrom .yolox_base import Exp\n"
  },
  {
    "path": "yolox/exp/base_exp.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport torch\nfrom torch.nn import Module\n\nfrom yolox.utils import LRScheduler\n\nimport ast\nimport pprint\nfrom abc import ABCMeta, abstractmethod\nfrom tabulate import tabulate\nfrom typing import Dict\n\n\nclass BaseExp(metaclass=ABCMeta):\n    \"\"\"Basic class for any experiment.\"\"\"\n\n    def __init__(self):\n        self.seed = None\n        self.output_dir = \"./YOLOX_outputs\"\n        self.print_interval = 100\n        self.eval_interval = 10\n\n    @abstractmethod\n    def get_model(self) -> Module:\n        pass\n\n    @abstractmethod\n    def get_data_loader(\n        self, batch_size: int, is_distributed: bool\n    ) -> Dict[str, torch.utils.data.DataLoader]:\n        pass\n\n    @abstractmethod\n    def get_optimizer(self, batch_size: int) -> torch.optim.Optimizer:\n        pass\n\n    @abstractmethod\n    def get_lr_scheduler(\n        self, lr: float, iters_per_epoch: int, **kwargs\n    ) -> LRScheduler:\n        pass\n\n    @abstractmethod\n    def get_evaluator(self):\n        pass\n\n    @abstractmethod\n    def eval(self, model, evaluator, weights):\n        pass\n\n    def __repr__(self):\n        table_header = [\"keys\", \"values\"]\n        exp_table = [\n            (str(k), pprint.pformat(v))\n            for k, v in vars(self).items()\n            if not k.startswith(\"_\")\n        ]\n        return tabulate(exp_table, headers=table_header, tablefmt=\"fancy_grid\")\n\n    def merge(self, cfg_list):\n        assert len(cfg_list) % 2 == 0\n        for k, v in zip(cfg_list[0::2], cfg_list[1::2]):\n            # only update value with same key\n            if hasattr(self, k):\n                src_value = getattr(self, k)\n                src_type = type(src_value)\n                if src_value is not None and src_type != type(v):\n                    try:\n                        v = src_type(v)\n                    except Exception:\n                        v = ast.literal_eval(v)\n                setattr(self, k, v)\n"
  },
  {
    "path": "yolox/exp/build.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport importlib\nimport os\nimport sys\n\n\ndef get_exp_by_file(exp_file):\n    try:\n        sys.path.append(os.path.dirname(exp_file))\n        current_exp = importlib.import_module(os.path.basename(exp_file).split(\".\")[0])\n        exp = current_exp.Exp()\n    except Exception:\n        raise ImportError(\"{} doesn't contains class named 'Exp'\".format(exp_file))\n    return exp\n\n\ndef get_exp_by_name(exp_name):\n    import yolox\n\n    yolox_path = os.path.dirname(os.path.dirname(yolox.__file__))\n    filedict = {\n        \"yolox-s\": \"yolox_s.py\",\n        \"yolox-m\": \"yolox_m.py\",\n        \"yolox-l\": \"yolox_l.py\",\n        \"yolox-x\": \"yolox_x.py\",\n        \"yolox-tiny\": \"yolox_tiny.py\",\n        \"yolox-nano\": \"nano.py\",\n        \"yolov3\": \"yolov3.py\",\n    }\n    filename = filedict[exp_name]\n    exp_path = os.path.join(yolox_path, \"exps\", \"default\", filename)\n    return get_exp_by_file(exp_path)\n\n\ndef get_exp(exp_file, exp_name):\n    \"\"\"\n    get Exp object by file or name. If exp_file and exp_name\n    are both provided, get Exp by exp_file.\n\n    Args:\n        exp_file (str): file path of experiment.\n        exp_name (str): name of experiment. \"yolo-s\",\n    \"\"\"\n    assert (\n        exp_file is not None or exp_name is not None\n    ), \"plz provide exp file or exp name.\"\n    if exp_file is not None:\n        return get_exp_by_file(exp_file)\n    else:\n        return get_exp_by_name(exp_name)\n"
  },
  {
    "path": "yolox/exp/yolox_base.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\n\nimport os\nimport random\n\nfrom .base_exp import BaseExp\n\n\nclass Exp(BaseExp):\n    def __init__(self):\n        super().__init__()\n\n        # ---------------- model config ---------------- #\n        self.num_classes = 80\n        self.depth = 1.00\n        self.width = 1.00\n\n        # ---------------- dataloader config ---------------- #\n        # set worker to 4 for shorter dataloader init time\n        self.data_num_workers = 4\n        self.input_size = (640, 640)\n        self.random_size = (14, 26)\n        self.train_ann = \"instances_train2017.json\"\n        self.val_ann = \"instances_val2017.json\"\n\n        # --------------- transform config ----------------- #\n        self.degrees = 10.0\n        self.translate = 0.1\n        self.scale = (0.1, 2)\n        self.mscale = (0.8, 1.6)\n        self.shear = 2.0\n        self.perspective = 0.0\n        self.enable_mixup = True\n\n        # --------------  training config --------------------- #\n        self.warmup_epochs = 5\n        self.max_epoch = 300\n        self.warmup_lr = 0\n        self.basic_lr_per_img = 0.01 / 64.0\n        self.scheduler = \"yoloxwarmcos\"\n        self.no_aug_epochs = 15\n        self.min_lr_ratio = 0.05\n        self.ema = True\n\n        self.weight_decay = 5e-4\n        self.momentum = 0.9\n        self.print_interval = 10\n        self.eval_interval = 10\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n\n        # -----------------  testing config ------------------ #\n        self.test_size = (640, 640)\n        self.test_conf = 0.001\n        self.nmsthre = 0.65\n\n    def get_model(self):\n        from yolox.models import YOLOPAFPN, YOLOX, YOLOXHead\n\n        def init_yolo(M):\n            for m in M.modules():\n                if isinstance(m, nn.BatchNorm2d):\n                    m.eps = 1e-3\n                    m.momentum = 0.03\n\n        if getattr(self, \"model\", None) is None:\n            in_channels = [256, 512, 1024]\n            backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels)\n            head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels)\n            self.model = YOLOX(backbone, head)\n\n        self.model.apply(init_yolo)\n        self.model.head.initialize_biases(1e-2)\n        return self.model\n\n    def get_data_loader(self, batch_size, is_distributed, no_aug=False):\n        from yolox.data import (\n            COCODataset,\n            DataLoader,\n            InfiniteSampler,\n            MosaicDetection,\n            TrainTransform,\n            YoloBatchSampler\n        )\n\n        dataset = COCODataset(\n            data_dir=None,\n            json_file=self.train_ann,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=50,\n            ),\n        )\n\n        dataset = MosaicDetection(\n            dataset,\n            mosaic=not no_aug,\n            img_size=self.input_size,\n            preproc=TrainTransform(\n                rgb_means=(0.485, 0.456, 0.406),\n                std=(0.229, 0.224, 0.225),\n                max_labels=120,\n            ),\n            degrees=self.degrees,\n            translate=self.translate,\n            scale=self.scale,\n            shear=self.shear,\n            perspective=self.perspective,\n            enable_mixup=self.enable_mixup,\n        )\n\n        self.dataset = dataset\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n\n        sampler = InfiniteSampler(len(self.dataset), seed=self.seed if self.seed else 0)\n\n        batch_sampler = YoloBatchSampler(\n            sampler=sampler,\n            batch_size=batch_size,\n            drop_last=False,\n            input_dimension=self.input_size,\n            mosaic=not no_aug,\n        )\n\n        dataloader_kwargs = {\"num_workers\": self.data_num_workers, \"pin_memory\": True}\n        dataloader_kwargs[\"batch_sampler\"] = batch_sampler\n        train_loader = DataLoader(self.dataset, **dataloader_kwargs)\n\n        return train_loader\n\n    def random_resize(self, data_loader, epoch, rank, is_distributed):\n        tensor = torch.LongTensor(2).cuda()\n\n        if rank == 0:\n            size_factor = self.input_size[1] * 1.0 / self.input_size[0]\n            size = random.randint(*self.random_size)\n            size = (int(32 * size), 32 * int(size * size_factor))\n            tensor[0] = size[0]\n            tensor[1] = size[1]\n\n        if is_distributed:\n            dist.barrier()\n            dist.broadcast(tensor, 0)\n\n        input_size = data_loader.change_input_dim(\n            multiple=(tensor[0].item(), tensor[1].item()), random_range=None\n        )\n        return input_size\n\n    def get_optimizer(self, batch_size):\n        if \"optimizer\" not in self.__dict__:\n            if self.warmup_epochs > 0:\n                lr = self.warmup_lr\n            else:\n                lr = self.basic_lr_per_img * batch_size\n\n            pg0, pg1, pg2 = [], [], []  # optimizer parameter groups\n\n            for k, v in self.model.named_modules():\n                if hasattr(v, \"bias\") and isinstance(v.bias, nn.Parameter):\n                    pg2.append(v.bias)  # biases\n                if isinstance(v, nn.BatchNorm2d) or \"bn\" in k:\n                    pg0.append(v.weight)  # no decay\n                elif hasattr(v, \"weight\") and isinstance(v.weight, nn.Parameter):\n                    pg1.append(v.weight)  # apply decay\n\n            optimizer = torch.optim.SGD(\n                pg0, lr=lr, momentum=self.momentum, nesterov=True\n            )\n            optimizer.add_param_group(\n                {\"params\": pg1, \"weight_decay\": self.weight_decay}\n            )  # add pg1 with weight_decay\n            optimizer.add_param_group({\"params\": pg2})\n            self.optimizer = optimizer\n\n        return self.optimizer\n\n    def get_lr_scheduler(self, lr, iters_per_epoch):\n        from yolox.utils import LRScheduler\n\n        scheduler = LRScheduler(\n            self.scheduler,\n            lr,\n            iters_per_epoch,\n            self.max_epoch,\n            warmup_epochs=self.warmup_epochs,\n            warmup_lr_start=self.warmup_lr,\n            no_aug_epochs=self.no_aug_epochs,\n            min_lr_ratio=self.min_lr_ratio,\n        )\n        return scheduler\n\n    def get_eval_loader(self, batch_size, is_distributed, testdev=False):\n        from yolox.data import COCODataset, ValTransform\n\n        valdataset = COCODataset(\n            data_dir=None,\n            json_file=self.val_ann if not testdev else \"image_info_test-dev2017.json\",\n            name=\"val2017\" if not testdev else \"test2017\",\n            img_size=self.test_size,\n            preproc=ValTransform(\n                rgb_means=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)\n            ),\n        )\n\n        if is_distributed:\n            batch_size = batch_size // dist.get_world_size()\n            sampler = torch.utils.data.distributed.DistributedSampler(\n                valdataset, shuffle=False\n            )\n        else:\n            sampler = torch.utils.data.SequentialSampler(valdataset)\n\n        dataloader_kwargs = {\n            \"num_workers\": self.data_num_workers,\n            \"pin_memory\": True,\n            \"sampler\": sampler,\n        }\n        dataloader_kwargs[\"batch_size\"] = batch_size\n        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)\n\n        return val_loader\n\n    def get_evaluator(self, batch_size, is_distributed, testdev=False):\n        from yolox.evaluators import COCOEvaluator\n\n        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)\n        evaluator = COCOEvaluator(\n            dataloader=val_loader,\n            img_size=self.test_size,\n            confthre=self.test_conf,\n            nmsthre=self.nmsthre,\n            num_classes=self.num_classes,\n            testdev=testdev,\n        )\n        return evaluator\n\n    def eval(self, model, evaluator, is_distributed, half=False):\n        return evaluator.evaluate(model, is_distributed, half)\n"
  },
  {
    "path": "yolox/layers/__init__.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nfrom .fast_coco_eval_api import COCOeval_opt\n"
  },
  {
    "path": "yolox/layers/csrc/cocoeval/cocoeval.cpp",
    "content": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n#include \"cocoeval.h\"\n#include <time.h>\n#include <algorithm>\n#include <cstdint>\n#include <numeric>\n\nusing namespace pybind11::literals;\n\nnamespace COCOeval {\n\n// Sort detections from highest score to lowest, such that\n// detection_instances[detection_sorted_indices[t]] >=\n// detection_instances[detection_sorted_indices[t+1]].  Use stable_sort to match\n// original COCO API\nvoid SortInstancesByDetectionScore(\n    const std::vector<InstanceAnnotation>& detection_instances,\n    std::vector<uint64_t>* detection_sorted_indices) {\n  detection_sorted_indices->resize(detection_instances.size());\n  std::iota(\n      detection_sorted_indices->begin(), detection_sorted_indices->end(), 0);\n  std::stable_sort(\n      detection_sorted_indices->begin(),\n      detection_sorted_indices->end(),\n      [&detection_instances](size_t j1, size_t j2) {\n        return detection_instances[j1].score > detection_instances[j2].score;\n      });\n}\n\n// Partition the ground truth objects based on whether or not to ignore them\n// based on area\nvoid SortInstancesByIgnore(\n    const std::array<double, 2>& area_range,\n    const std::vector<InstanceAnnotation>& ground_truth_instances,\n    std::vector<uint64_t>* ground_truth_sorted_indices,\n    std::vector<bool>* ignores) {\n  ignores->clear();\n  ignores->reserve(ground_truth_instances.size());\n  for (auto o : ground_truth_instances) {\n    ignores->push_back(\n        o.ignore || o.area < area_range[0] || o.area > area_range[1]);\n  }\n\n  ground_truth_sorted_indices->resize(ground_truth_instances.size());\n  std::iota(\n      ground_truth_sorted_indices->begin(),\n      ground_truth_sorted_indices->end(),\n      0);\n  std::stable_sort(\n      ground_truth_sorted_indices->begin(),\n      ground_truth_sorted_indices->end(),\n      [&ignores](size_t j1, size_t j2) {\n        return (int)(*ignores)[j1] < (int)(*ignores)[j2];\n      });\n}\n\n// For each IOU threshold, greedily match each detected instance to a ground\n// truth instance (if possible) and store the results\nvoid MatchDetectionsToGroundTruth(\n    const std::vector<InstanceAnnotation>& detection_instances,\n    const std::vector<uint64_t>& detection_sorted_indices,\n    const std::vector<InstanceAnnotation>& ground_truth_instances,\n    const std::vector<uint64_t>& ground_truth_sorted_indices,\n    const std::vector<bool>& ignores,\n    const std::vector<std::vector<double>>& ious,\n    const std::vector<double>& iou_thresholds,\n    const std::array<double, 2>& area_range,\n    ImageEvaluation* results) {\n  // Initialize memory to store return data matches and ignore\n  const int num_iou_thresholds = iou_thresholds.size();\n  const int num_ground_truth = ground_truth_sorted_indices.size();\n  const int num_detections = detection_sorted_indices.size();\n  std::vector<uint64_t> ground_truth_matches(\n      num_iou_thresholds * num_ground_truth, 0);\n  std::vector<uint64_t>& detection_matches = results->detection_matches;\n  std::vector<bool>& detection_ignores = results->detection_ignores;\n  std::vector<bool>& ground_truth_ignores = results->ground_truth_ignores;\n  detection_matches.resize(num_iou_thresholds * num_detections, 0);\n  detection_ignores.resize(num_iou_thresholds * num_detections, false);\n  ground_truth_ignores.resize(num_ground_truth);\n  for (auto g = 0; g < num_ground_truth; ++g) {\n    ground_truth_ignores[g] = ignores[ground_truth_sorted_indices[g]];\n  }\n\n  for (auto t = 0; t < num_iou_thresholds; ++t) {\n    for (auto d = 0; d < num_detections; ++d) {\n      // information about best match so far (match=-1 -> unmatched)\n      double best_iou = std::min(iou_thresholds[t], 1 - 1e-10);\n      int match = -1;\n      for (auto g = 0; g < num_ground_truth; ++g) {\n        // if this ground truth instance is already matched and not a\n        // crowd, it cannot be matched to another detection\n        if (ground_truth_matches[t * num_ground_truth + g] > 0 &&\n            !ground_truth_instances[ground_truth_sorted_indices[g]].is_crowd) {\n          continue;\n        }\n\n        // if detected instance matched to a regular ground truth\n        // instance, we can break on the first ground truth instance\n        // tagged as ignore (because they are sorted by the ignore tag)\n        if (match >= 0 && !ground_truth_ignores[match] &&\n            ground_truth_ignores[g]) {\n          break;\n        }\n\n        // if IOU overlap is the best so far, store the match appropriately\n        if (ious[d][ground_truth_sorted_indices[g]] >= best_iou) {\n          best_iou = ious[d][ground_truth_sorted_indices[g]];\n          match = g;\n        }\n      }\n      // if match was made, store id of match for both detection and\n      // ground truth\n      if (match >= 0) {\n        detection_ignores[t * num_detections + d] = ground_truth_ignores[match];\n        detection_matches[t * num_detections + d] =\n            ground_truth_instances[ground_truth_sorted_indices[match]].id;\n        ground_truth_matches[t * num_ground_truth + match] =\n            detection_instances[detection_sorted_indices[d]].id;\n      }\n\n      // set unmatched detections outside of area range to ignore\n      const InstanceAnnotation& detection =\n          detection_instances[detection_sorted_indices[d]];\n      detection_ignores[t * num_detections + d] =\n          detection_ignores[t * num_detections + d] ||\n          (detection_matches[t * num_detections + d] == 0 &&\n           (detection.area < area_range[0] || detection.area > area_range[1]));\n    }\n  }\n\n  // store detection score results\n  results->detection_scores.resize(detection_sorted_indices.size());\n  for (size_t d = 0; d < detection_sorted_indices.size(); ++d) {\n    results->detection_scores[d] =\n        detection_instances[detection_sorted_indices[d]].score;\n  }\n}\n\nstd::vector<ImageEvaluation> EvaluateImages(\n    const std::vector<std::array<double, 2>>& area_ranges,\n    int max_detections,\n    const std::vector<double>& iou_thresholds,\n    const ImageCategoryInstances<std::vector<double>>& image_category_ious,\n    const ImageCategoryInstances<InstanceAnnotation>&\n        image_category_ground_truth_instances,\n    const ImageCategoryInstances<InstanceAnnotation>&\n        image_category_detection_instances) {\n  const int num_area_ranges = area_ranges.size();\n  const int num_images = image_category_ground_truth_instances.size();\n  const int num_categories =\n      image_category_ious.size() > 0 ? image_category_ious[0].size() : 0;\n  std::vector<uint64_t> detection_sorted_indices;\n  std::vector<uint64_t> ground_truth_sorted_indices;\n  std::vector<bool> ignores;\n  std::vector<ImageEvaluation> results_all(\n      num_images * num_area_ranges * num_categories);\n\n  // Store results for each image, category, and area range combination. Results\n  // for each IOU threshold are packed into the same ImageEvaluation object\n  for (auto i = 0; i < num_images; ++i) {\n    for (auto c = 0; c < num_categories; ++c) {\n      const std::vector<InstanceAnnotation>& ground_truth_instances =\n          image_category_ground_truth_instances[i][c];\n      const std::vector<InstanceAnnotation>& detection_instances =\n          image_category_detection_instances[i][c];\n\n      SortInstancesByDetectionScore(\n          detection_instances, &detection_sorted_indices);\n      if ((int)detection_sorted_indices.size() > max_detections) {\n        detection_sorted_indices.resize(max_detections);\n      }\n\n      for (size_t a = 0; a < area_ranges.size(); ++a) {\n        SortInstancesByIgnore(\n            area_ranges[a],\n            ground_truth_instances,\n            &ground_truth_sorted_indices,\n            &ignores);\n\n        MatchDetectionsToGroundTruth(\n            detection_instances,\n            detection_sorted_indices,\n            ground_truth_instances,\n            ground_truth_sorted_indices,\n            ignores,\n            image_category_ious[i][c],\n            iou_thresholds,\n            area_ranges[a],\n            &results_all\n                [c * num_area_ranges * num_images + a * num_images + i]);\n      }\n    }\n  }\n\n  return results_all;\n}\n\n// Convert a python list to a vector\ntemplate <typename T>\nstd::vector<T> list_to_vec(const py::list& l) {\n  std::vector<T> v(py::len(l));\n  for (int i = 0; i < (int)py::len(l); ++i) {\n    v[i] = l[i].cast<T>();\n  }\n  return v;\n}\n\n// Helper function to Accumulate()\n// Considers the evaluation results applicable to a particular category, area\n// range, and max_detections parameter setting, which begin at\n// evaluations[evaluation_index].  Extracts a sorted list of length n of all\n// applicable detection instances concatenated across all images in the dataset,\n// which are represented by the outputs evaluation_indices, detection_scores,\n// image_detection_indices, and detection_sorted_indices--all of which are\n// length n. evaluation_indices[i] stores the applicable index into\n// evaluations[] for instance i, which has detection score detection_score[i],\n// and is the image_detection_indices[i]'th of the list of detections\n// for the image containing i.  detection_sorted_indices[] defines a sorted\n// permutation of the 3 other outputs\nint BuildSortedDetectionList(\n    const std::vector<ImageEvaluation>& evaluations,\n    const int64_t evaluation_index,\n    const int64_t num_images,\n    const int max_detections,\n    std::vector<uint64_t>* evaluation_indices,\n    std::vector<double>* detection_scores,\n    std::vector<uint64_t>* detection_sorted_indices,\n    std::vector<uint64_t>* image_detection_indices) {\n  assert(evaluations.size() >= evaluation_index + num_images);\n\n  // Extract a list of object instances of the applicable category, area\n  // range, and max detections requirements such that they can be sorted\n  image_detection_indices->clear();\n  evaluation_indices->clear();\n  detection_scores->clear();\n  image_detection_indices->reserve(num_images * max_detections);\n  evaluation_indices->reserve(num_images * max_detections);\n  detection_scores->reserve(num_images * max_detections);\n  int num_valid_ground_truth = 0;\n  for (auto i = 0; i < num_images; ++i) {\n    const ImageEvaluation& evaluation = evaluations[evaluation_index + i];\n\n    for (int d = 0;\n         d < (int)evaluation.detection_scores.size() && d < max_detections;\n         ++d) { // detected instances\n      evaluation_indices->push_back(evaluation_index + i);\n      image_detection_indices->push_back(d);\n      detection_scores->push_back(evaluation.detection_scores[d]);\n    }\n    for (auto ground_truth_ignore : evaluation.ground_truth_ignores) {\n      if (!ground_truth_ignore) {\n        ++num_valid_ground_truth;\n      }\n    }\n  }\n\n  // Sort detections by decreasing score, using stable sort to match\n  // python implementation\n  detection_sorted_indices->resize(detection_scores->size());\n  std::iota(\n      detection_sorted_indices->begin(), detection_sorted_indices->end(), 0);\n  std::stable_sort(\n      detection_sorted_indices->begin(),\n      detection_sorted_indices->end(),\n      [&detection_scores](size_t j1, size_t j2) {\n        return (*detection_scores)[j1] > (*detection_scores)[j2];\n      });\n\n  return num_valid_ground_truth;\n}\n\n// Helper function to Accumulate()\n// Compute a precision recall curve given a sorted list of detected instances\n// encoded in evaluations, evaluation_indices, detection_scores,\n// detection_sorted_indices, image_detection_indices (see\n// BuildSortedDetectionList()). Using vectors precisions and recalls\n// and temporary storage, output the results into precisions_out, recalls_out,\n// and scores_out, which are large buffers containing many precion/recall curves\n// for all possible parameter settings, with precisions_out_index and\n// recalls_out_index defining the applicable indices to store results.\nvoid ComputePrecisionRecallCurve(\n    const int64_t precisions_out_index,\n    const int64_t precisions_out_stride,\n    const int64_t recalls_out_index,\n    const std::vector<double>& recall_thresholds,\n    const int iou_threshold_index,\n    const int num_iou_thresholds,\n    const int num_valid_ground_truth,\n    const std::vector<ImageEvaluation>& evaluations,\n    const std::vector<uint64_t>& evaluation_indices,\n    const std::vector<double>& detection_scores,\n    const std::vector<uint64_t>& detection_sorted_indices,\n    const std::vector<uint64_t>& image_detection_indices,\n    std::vector<double>* precisions,\n    std::vector<double>* recalls,\n    std::vector<double>* precisions_out,\n    std::vector<double>* scores_out,\n    std::vector<double>* recalls_out) {\n  assert(recalls_out->size() > recalls_out_index);\n\n  // Compute precision/recall for each instance in the sorted list of detections\n  int64_t true_positives_sum = 0, false_positives_sum = 0;\n  precisions->clear();\n  recalls->clear();\n  precisions->reserve(detection_sorted_indices.size());\n  recalls->reserve(detection_sorted_indices.size());\n  assert(!evaluations.empty() || detection_sorted_indices.empty());\n  for (auto detection_sorted_index : detection_sorted_indices) {\n    const ImageEvaluation& evaluation =\n        evaluations[evaluation_indices[detection_sorted_index]];\n    const auto num_detections =\n        evaluation.detection_matches.size() / num_iou_thresholds;\n    const auto detection_index = iou_threshold_index * num_detections +\n        image_detection_indices[detection_sorted_index];\n    assert(evaluation.detection_matches.size() > detection_index);\n    assert(evaluation.detection_ignores.size() > detection_index);\n    const int64_t detection_match =\n        evaluation.detection_matches[detection_index];\n    const bool detection_ignores =\n        evaluation.detection_ignores[detection_index];\n    const auto true_positive = detection_match > 0 && !detection_ignores;\n    const auto false_positive = detection_match == 0 && !detection_ignores;\n    if (true_positive) {\n      ++true_positives_sum;\n    }\n    if (false_positive) {\n      ++false_positives_sum;\n    }\n\n    const double recall =\n        static_cast<double>(true_positives_sum) / num_valid_ground_truth;\n    recalls->push_back(recall);\n    const int64_t num_valid_detections =\n        true_positives_sum + false_positives_sum;\n    const double precision = num_valid_detections > 0\n        ? static_cast<double>(true_positives_sum) / num_valid_detections\n        : 0.0;\n    precisions->push_back(precision);\n  }\n\n  (*recalls_out)[recalls_out_index] = !recalls->empty() ? recalls->back() : 0;\n\n  for (int64_t i = static_cast<int64_t>(precisions->size()) - 1; i > 0; --i) {\n    if ((*precisions)[i] > (*precisions)[i - 1]) {\n      (*precisions)[i - 1] = (*precisions)[i];\n    }\n  }\n\n  // Sample the per instance precision/recall list at each recall threshold\n  for (size_t r = 0; r < recall_thresholds.size(); ++r) {\n    // first index in recalls >= recall_thresholds[r]\n    std::vector<double>::iterator low = std::lower_bound(\n        recalls->begin(), recalls->end(), recall_thresholds[r]);\n    size_t precisions_index = low - recalls->begin();\n\n    const auto results_ind = precisions_out_index + r * precisions_out_stride;\n    assert(results_ind < precisions_out->size());\n    assert(results_ind < scores_out->size());\n    if (precisions_index < precisions->size()) {\n      (*precisions_out)[results_ind] = (*precisions)[precisions_index];\n      (*scores_out)[results_ind] =\n          detection_scores[detection_sorted_indices[precisions_index]];\n    } else {\n      (*precisions_out)[results_ind] = 0;\n      (*scores_out)[results_ind] = 0;\n    }\n  }\n}\npy::dict Accumulate(\n    const py::object& params,\n    const std::vector<ImageEvaluation>& evaluations) {\n  const std::vector<double> recall_thresholds =\n      list_to_vec<double>(params.attr(\"recThrs\"));\n  const std::vector<int> max_detections =\n      list_to_vec<int>(params.attr(\"maxDets\"));\n  const int num_iou_thresholds = py::len(params.attr(\"iouThrs\"));\n  const int num_recall_thresholds = py::len(params.attr(\"recThrs\"));\n  const int num_categories = params.attr(\"useCats\").cast<int>() == 1\n      ? py::len(params.attr(\"catIds\"))\n      : 1;\n  const int num_area_ranges = py::len(params.attr(\"areaRng\"));\n  const int num_max_detections = py::len(params.attr(\"maxDets\"));\n  const int num_images = py::len(params.attr(\"imgIds\"));\n\n  std::vector<double> precisions_out(\n      num_iou_thresholds * num_recall_thresholds * num_categories *\n          num_area_ranges * num_max_detections,\n      -1);\n  std::vector<double> recalls_out(\n      num_iou_thresholds * num_categories * num_area_ranges *\n          num_max_detections,\n      -1);\n  std::vector<double> scores_out(\n      num_iou_thresholds * num_recall_thresholds * num_categories *\n          num_area_ranges * num_max_detections,\n      -1);\n\n  // Consider the list of all detected instances in the entire dataset in one\n  // large list.  evaluation_indices, detection_scores,\n  // image_detection_indices, and detection_sorted_indices all have the same\n  // length as this list, such that each entry corresponds to one detected\n  // instance\n  std::vector<uint64_t> evaluation_indices; // indices into evaluations[]\n  std::vector<double> detection_scores; // detection scores of each instance\n  std::vector<uint64_t> detection_sorted_indices; // sorted indices of all\n                                                  // instances in the dataset\n  std::vector<uint64_t>\n      image_detection_indices; // indices into the list of detected instances in\n                               // the same image as each instance\n  std::vector<double> precisions, recalls;\n\n  for (auto c = 0; c < num_categories; ++c) {\n    for (auto a = 0; a < num_area_ranges; ++a) {\n      for (auto m = 0; m < num_max_detections; ++m) {\n        // The COCO PythonAPI assumes evaluations[] (the return value of\n        // COCOeval::EvaluateImages() is one long list storing results for each\n        // combination of category, area range, and image id, with categories in\n        // the outermost loop and images in the innermost loop.\n        const int64_t evaluations_index =\n            c * num_area_ranges * num_images + a * num_images;\n        int num_valid_ground_truth = BuildSortedDetectionList(\n            evaluations,\n            evaluations_index,\n            num_images,\n            max_detections[m],\n            &evaluation_indices,\n            &detection_scores,\n            &detection_sorted_indices,\n            &image_detection_indices);\n\n        if (num_valid_ground_truth == 0) {\n          continue;\n        }\n\n        for (auto t = 0; t < num_iou_thresholds; ++t) {\n          // recalls_out is a flattened vectors representing a\n          // num_iou_thresholds X num_categories X num_area_ranges X\n          // num_max_detections matrix\n          const int64_t recalls_out_index =\n              t * num_categories * num_area_ranges * num_max_detections +\n              c * num_area_ranges * num_max_detections +\n              a * num_max_detections + m;\n\n          // precisions_out and scores_out are flattened vectors\n          // representing a num_iou_thresholds X num_recall_thresholds X\n          // num_categories X num_area_ranges X num_max_detections matrix\n          const int64_t precisions_out_stride =\n              num_categories * num_area_ranges * num_max_detections;\n          const int64_t precisions_out_index = t * num_recall_thresholds *\n                  num_categories * num_area_ranges * num_max_detections +\n              c * num_area_ranges * num_max_detections +\n              a * num_max_detections + m;\n\n          ComputePrecisionRecallCurve(\n              precisions_out_index,\n              precisions_out_stride,\n              recalls_out_index,\n              recall_thresholds,\n              t,\n              num_iou_thresholds,\n              num_valid_ground_truth,\n              evaluations,\n              evaluation_indices,\n              detection_scores,\n              detection_sorted_indices,\n              image_detection_indices,\n              &precisions,\n              &recalls,\n              &precisions_out,\n              &scores_out,\n              &recalls_out);\n        }\n      }\n    }\n  }\n\n  time_t rawtime;\n  struct tm local_time;\n  std::array<char, 200> buffer;\n  time(&rawtime);\n#ifdef _WIN32\n  localtime_s(&local_time, &rawtime);\n#else\n  localtime_r(&rawtime, &local_time);\n#endif\n  strftime(\n      buffer.data(), 200, \"%Y-%m-%d %H:%num_max_detections:%S\", &local_time);\n  return py::dict(\n      \"params\"_a = params,\n      \"counts\"_a = std::vector<int64_t>({num_iou_thresholds,\n                                         num_recall_thresholds,\n                                         num_categories,\n                                         num_area_ranges,\n                                         num_max_detections}),\n      \"date\"_a = buffer,\n      \"precision\"_a = precisions_out,\n      \"recall\"_a = recalls_out,\n      \"scores\"_a = scores_out);\n}\n\n} // namespace COCOeval\n"
  },
  {
    "path": "yolox/layers/csrc/cocoeval/cocoeval.h",
    "content": "// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n#pragma once\n\n#include <pybind11/numpy.h>\n#include <pybind11/pybind11.h>\n#include <pybind11/stl.h>\n#include <pybind11/stl_bind.h>\n#include <vector>\n\nnamespace py = pybind11;\n\nnamespace COCOeval {\n\n// Annotation data for a single object instance in an image\nstruct InstanceAnnotation {\n  InstanceAnnotation(\n      uint64_t id,\n      double score,\n      double area,\n      bool is_crowd,\n      bool ignore)\n      : id{id}, score{score}, area{area}, is_crowd{is_crowd}, ignore{ignore} {}\n  uint64_t id;\n  double score = 0.;\n  double area = 0.;\n  bool is_crowd = false;\n  bool ignore = false;\n};\n\n// Stores intermediate results for evaluating detection results for a single\n// image that has D detected instances and G ground truth instances. This stores\n// matches between detected and ground truth instances\nstruct ImageEvaluation {\n  // For each of the D detected instances, the id of the matched ground truth\n  // instance, or 0 if unmatched\n  std::vector<uint64_t> detection_matches;\n\n  // The detection score of each of the D detected instances\n  std::vector<double> detection_scores;\n\n  // Marks whether or not each of G instances was ignored from evaluation (e.g.,\n  // because it's outside area_range)\n  std::vector<bool> ground_truth_ignores;\n\n  // Marks whether or not each of D instances was ignored from evaluation (e.g.,\n  // because it's outside aRng)\n  std::vector<bool> detection_ignores;\n};\n\ntemplate <class T>\nusing ImageCategoryInstances = std::vector<std::vector<std::vector<T>>>;\n\n// C++ implementation of COCO API cocoeval.py::COCOeval.evaluateImg().  For each\n// combination of image, category, area range settings, and IOU thresholds to\n// evaluate, it matches detected instances to ground truth instances and stores\n// the results into a vector of ImageEvaluation results, which will be\n// interpreted by the COCOeval::Accumulate() function to produce precion-recall\n// curves.  The parameters of nested vectors have the following semantics:\n//   image_category_ious[i][c][d][g] is the intersection over union of the d'th\n//     detected instance and g'th ground truth instance of\n//     category category_ids[c] in image image_ids[i]\n//   image_category_ground_truth_instances[i][c] is a vector of ground truth\n//     instances in image image_ids[i] of category category_ids[c]\n//   image_category_detection_instances[i][c] is a vector of detected\n//     instances in image image_ids[i] of category category_ids[c]\nstd::vector<ImageEvaluation> EvaluateImages(\n    const std::vector<std::array<double, 2>>& area_ranges, // vector of 2-tuples\n    int max_detections,\n    const std::vector<double>& iou_thresholds,\n    const ImageCategoryInstances<std::vector<double>>& image_category_ious,\n    const ImageCategoryInstances<InstanceAnnotation>&\n        image_category_ground_truth_instances,\n    const ImageCategoryInstances<InstanceAnnotation>&\n        image_category_detection_instances);\n\n// C++ implementation of COCOeval.accumulate(), which generates precision\n// recall curves for each set of category, IOU threshold, detection area range,\n// and max number of detections parameters.  It is assumed that the parameter\n// evaluations is the return value of the functon COCOeval::EvaluateImages(),\n// which was called with the same parameter settings params\npy::dict Accumulate(\n    const py::object& params,\n    const std::vector<ImageEvaluation>& evalutations);\n\n} // namespace COCOeval\n"
  },
  {
    "path": "yolox/layers/csrc/vision.cpp",
    "content": "#include \"cocoeval/cocoeval.h\"\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n    m.def(\"COCOevalAccumulate\", &COCOeval::Accumulate, \"COCOeval::Accumulate\");\n    m.def(\n        \"COCOevalEvaluateImages\",\n        &COCOeval::EvaluateImages,\n        \"COCOeval::EvaluateImages\");\n    pybind11::class_<COCOeval::InstanceAnnotation>(m, \"InstanceAnnotation\")\n        .def(pybind11::init<uint64_t, double, double, bool, bool>());\n    pybind11::class_<COCOeval::ImageEvaluation>(m, \"ImageEvaluation\")\n        .def(pybind11::init<>());\n}\n"
  },
  {
    "path": "yolox/layers/fast_coco_eval_api.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# This file comes from\n# https://github.com/facebookresearch/detectron2/blob/master/detectron2/evaluation/fast_eval_api.py\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport numpy as np\nfrom pycocotools.cocoeval import COCOeval\n\n# import torch first to make yolox._C work without ImportError of libc10.so\n# in YOLOX, env is already set in __init__.py.\nfrom yolox import _C\n\nimport copy\nimport time\n\n\nclass COCOeval_opt(COCOeval):\n    \"\"\"\n    This is a slightly modified version of the original COCO API, where the functions evaluateImg()\n    and accumulate() are implemented in C++ to speedup evaluation\n    \"\"\"\n\n    def evaluate(self):\n        \"\"\"\n        Run per image evaluation on given images and store results in self.evalImgs_cpp, a\n        datastructure that isn't readable from Python but is used by a c++ implementation of\n        accumulate().  Unlike the original COCO PythonAPI, we don't populate the datastructure\n        self.evalImgs because this datastructure is a computational bottleneck.\n        :return: None\n        \"\"\"\n        tic = time.time()\n\n        print(\"Running per image evaluation...\")\n        p = self.params\n        # add backward compatibility if useSegm is specified in params\n        if p.useSegm is not None:\n            p.iouType = \"segm\" if p.useSegm == 1 else \"bbox\"\n            print(\n                \"useSegm (deprecated) is not None. Running {} evaluation\".format(\n                    p.iouType\n                )\n            )\n        print(\"Evaluate annotation type *{}*\".format(p.iouType))\n        p.imgIds = list(np.unique(p.imgIds))\n        if p.useCats:\n            p.catIds = list(np.unique(p.catIds))\n        p.maxDets = sorted(p.maxDets)\n        self.params = p\n\n        self._prepare()\n\n        # loop through images, area range, max detection number\n        catIds = p.catIds if p.useCats else [-1]\n\n        if p.iouType == \"segm\" or p.iouType == \"bbox\":\n            computeIoU = self.computeIoU\n        elif p.iouType == \"keypoints\":\n            computeIoU = self.computeOks\n        self.ious = {\n            (imgId, catId): computeIoU(imgId, catId)\n            for imgId in p.imgIds\n            for catId in catIds\n        }\n\n        maxDet = p.maxDets[-1]\n\n        # <<<< Beginning of code differences with original COCO API\n        def convert_instances_to_cpp(instances, is_det=False):\n            # Convert annotations for a list of instances in an image to a format that's fast\n            # to access in C++\n            instances_cpp = []\n            for instance in instances:\n                instance_cpp = _C.InstanceAnnotation(\n                    int(instance[\"id\"]),\n                    instance[\"score\"] if is_det else instance.get(\"score\", 0.0),\n                    instance[\"area\"],\n                    bool(instance.get(\"iscrowd\", 0)),\n                    bool(instance.get(\"ignore\", 0)),\n                )\n                instances_cpp.append(instance_cpp)\n            return instances_cpp\n\n        # Convert GT annotations, detections, and IOUs to a format that's fast to access in C++\n        ground_truth_instances = [\n            [convert_instances_to_cpp(self._gts[imgId, catId]) for catId in p.catIds]\n            for imgId in p.imgIds\n        ]\n        detected_instances = [\n            [\n                convert_instances_to_cpp(self._dts[imgId, catId], is_det=True)\n                for catId in p.catIds\n            ]\n            for imgId in p.imgIds\n        ]\n        ious = [[self.ious[imgId, catId] for catId in catIds] for imgId in p.imgIds]\n\n        if not p.useCats:\n            # For each image, flatten per-category lists into a single list\n            ground_truth_instances = [\n                [[o for c in i for o in c]] for i in ground_truth_instances\n            ]\n            detected_instances = [\n                [[o for c in i for o in c]] for i in detected_instances\n            ]\n\n        # Call C++ implementation of self.evaluateImgs()\n        self._evalImgs_cpp = _C.COCOevalEvaluateImages(\n            p.areaRng,\n            maxDet,\n            p.iouThrs,\n            ious,\n            ground_truth_instances,\n            detected_instances,\n        )\n        self._evalImgs = None\n\n        self._paramsEval = copy.deepcopy(self.params)\n        toc = time.time()\n        print(\"COCOeval_opt.evaluate() finished in {:0.2f} seconds.\".format(toc - tic))\n        # >>>> End of code differences with original COCO API\n\n    def accumulate(self):\n        \"\"\"\n        Accumulate per image evaluation results and store the result in self.eval.  Does not\n        support changing parameter settings from those used by self.evaluate()\n        \"\"\"\n        print(\"Accumulating evaluation results...\")\n        tic = time.time()\n        if not hasattr(self, \"_evalImgs_cpp\"):\n            print(\"Please run evaluate() first\")\n\n        self.eval = _C.COCOevalAccumulate(self._paramsEval, self._evalImgs_cpp)\n\n        # recall is num_iou_thresholds X num_categories X num_area_ranges X num_max_detections\n        self.eval[\"recall\"] = np.array(self.eval[\"recall\"]).reshape(\n            self.eval[\"counts\"][:1] + self.eval[\"counts\"][2:]\n        )\n\n        # precision and scores are num_iou_thresholds X num_recall_thresholds X num_categories X\n        # num_area_ranges X num_max_detections\n        self.eval[\"precision\"] = np.array(self.eval[\"precision\"]).reshape(\n            self.eval[\"counts\"]\n        )\n        self.eval[\"scores\"] = np.array(self.eval[\"scores\"]).reshape(self.eval[\"counts\"])\n        toc = time.time()\n        print(\n            \"COCOeval_opt.accumulate() finished in {:0.2f} seconds.\".format(toc - tic)\n        )\n"
  },
  {
    "path": "yolox/models/__init__.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nfrom .darknet import CSPDarknet, Darknet\nfrom .losses import IOUloss\nfrom .yolo_fpn import YOLOFPN\nfrom .yolo_head import YOLOXHead\nfrom .yolo_pafpn import YOLOPAFPN\nfrom .yolox import YOLOX\n"
  },
  {
    "path": "yolox/models/darknet.py",
    "content": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nfrom torch import nn\n\nfrom .network_blocks import BaseConv, CSPLayer, DWConv, Focus, ResLayer, SPPBottleneck\n\n\nclass Darknet(nn.Module):\n    # number of blocks from dark2 to dark5.\n    depth2blocks = {21: [1, 2, 2, 1], 53: [2, 8, 8, 4]}\n\n    def __init__(\n        self,\n        depth,\n        in_channels=3,\n        stem_out_channels=32,\n        out_features=(\"dark3\", \"dark4\", \"dark5\"),\n    ):\n        \"\"\"\n        Args:\n            depth (int): depth of darknet used in model, usually use [21, 53] for this param.\n            in_channels (int): number of input channels, for example, use 3 for RGB image.\n            stem_out_channels (int): number of output chanels of darknet stem.\n                It decides channels of darknet layer2 to layer5.\n            out_features (Tuple[str]): desired output layer name.\n        \"\"\"\n        super().__init__()\n        assert out_features, \"please provide output features of Darknet\"\n        self.out_features = out_features\n        self.stem = nn.Sequential(\n            BaseConv(in_channels, stem_out_channels, ksize=3, stride=1, act=\"lrelu\"),\n            *self.make_group_layer(stem_out_channels, num_blocks=1, stride=2),\n        )\n        in_channels = stem_out_channels * 2  # 64\n\n        num_blocks = Darknet.depth2blocks[depth]\n        # create darknet with `stem_out_channels` and `num_blocks` layers.\n        # to make model structure more clear, we don't use `for` statement in python.\n        self.dark2 = nn.Sequential(\n            *self.make_group_layer(in_channels, num_blocks[0], stride=2)\n        )\n        in_channels *= 2  # 128\n        self.dark3 = nn.Sequential(\n            *self.make_group_layer(in_channels, num_blocks[1], stride=2)\n        )\n        in_channels *= 2  # 256\n        self.dark4 = nn.Sequential(\n            *self.make_group_layer(in_channels, num_blocks[2], stride=2)\n        )\n        in_channels *= 2  # 512\n\n        self.dark5 = nn.Sequential(\n            *self.make_group_layer(in_channels, num_blocks[3], stride=2),\n            *self.make_spp_block([in_channels, in_channels * 2], in_channels * 2),\n        )\n\n    def make_group_layer(self, in_channels: int, num_blocks: int, stride: int = 1):\n        \"starts with conv layer then has `num_blocks` `ResLayer`\"\n        return [\n            BaseConv(in_channels, in_channels * 2, ksize=3, stride=stride, act=\"lrelu\"),\n            *[(ResLayer(in_channels * 2)) for _ in range(num_blocks)],\n        ]\n\n    def make_spp_block(self, filters_list, in_filters):\n        m = nn.Sequential(\n            *[\n                BaseConv(in_filters, filters_list[0], 1, stride=1, act=\"lrelu\"),\n                BaseConv(filters_list[0], filters_list[1], 3, stride=1, act=\"lrelu\"),\n                SPPBottleneck(\n                    in_channels=filters_list[1],\n                    out_channels=filters_list[0],\n                    activation=\"lrelu\",\n                ),\n                BaseConv(filters_list[0], filters_list[1], 3, stride=1, act=\"lrelu\"),\n                BaseConv(filters_list[1], filters_list[0], 1, stride=1, act=\"lrelu\"),\n            ]\n        )\n        return m\n\n    def forward(self, x):\n        outputs = {}\n        x = self.stem(x)\n        outputs[\"stem\"] = x\n        x = self.dark2(x)\n        outputs[\"dark2\"] = x\n        x = self.dark3(x)\n        outputs[\"dark3\"] = x\n        x = self.dark4(x)\n        outputs[\"dark4\"] = x\n        x = self.dark5(x)\n        outputs[\"dark5\"] = x\n        return {k: v for k, v in outputs.items() if k in self.out_features}\n\n\nclass CSPDarknet(nn.Module):\n    def __init__(\n        self,\n        dep_mul,\n        wid_mul,\n        out_features=(\"dark3\", \"dark4\", \"dark5\"),\n        depthwise=False,\n        act=\"silu\",\n    ):\n        super().__init__()\n        assert out_features, \"please provide output features of Darknet\"\n        self.out_features = out_features\n        Conv = DWConv if depthwise else BaseConv\n\n        base_channels = int(wid_mul * 64)  # 64\n        base_depth = max(round(dep_mul * 3), 1)  # 3\n\n        # stem\n        self.stem = Focus(3, base_channels, ksize=3, act=act)\n\n        # dark2\n        self.dark2 = nn.Sequential(\n            Conv(base_channels, base_channels * 2, 3, 2, act=act),\n            CSPLayer(\n                base_channels * 2,\n                base_channels * 2,\n                n=base_depth,\n                depthwise=depthwise,\n                act=act,\n            ),\n        )\n\n        # dark3\n        self.dark3 = nn.Sequential(\n            Conv(base_channels * 2, base_channels * 4, 3, 2, act=act),\n            CSPLayer(\n                base_channels * 4,\n                base_channels * 4,\n                n=base_depth * 3,\n                depthwise=depthwise,\n                act=act,\n            ),\n        )\n\n        # dark4\n        self.dark4 = nn.Sequential(\n            Conv(base_channels * 4, base_channels * 8, 3, 2, act=act),\n            CSPLayer(\n                base_channels * 8,\n                base_channels * 8,\n                n=base_depth * 3,\n                depthwise=depthwise,\n                act=act,\n            ),\n        )\n\n        # dark5\n        self.dark5 = nn.Sequential(\n            Conv(base_channels * 8, base_channels * 16, 3, 2, act=act),\n            SPPBottleneck(base_channels * 16, base_channels * 16, activation=act),\n            CSPLayer(\n                base_channels * 16,\n                base_channels * 16,\n                n=base_depth,\n                shortcut=False,\n                depthwise=depthwise,\n                act=act,\n            ),\n        )\n\n    def forward(self, x):\n        outputs = {}\n        x = self.stem(x)\n        outputs[\"stem\"] = x\n        x = self.dark2(x)\n        outputs[\"dark2\"] = x\n        x = self.dark3(x)\n        outputs[\"dark3\"] = x\n        x = self.dark4(x)\n        outputs[\"dark4\"] = x\n        x = self.dark5(x)\n        outputs[\"dark5\"] = x\n        return {k: v for k, v in outputs.items() if k in self.out_features}\n"
  },
  {
    "path": "yolox/models/losses.py",
    "content": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass IOUloss(nn.Module):\n    def __init__(self, reduction=\"none\", loss_type=\"iou\"):\n        super(IOUloss, self).__init__()\n        self.reduction = reduction\n        self.loss_type = loss_type\n\n    def forward(self, pred, target):\n        assert pred.shape[0] == target.shape[0]\n\n        pred = pred.view(-1, 4)\n        target = target.view(-1, 4)\n        tl = torch.max(\n            (pred[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2)\n        )\n        br = torch.min(\n            (pred[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2)\n        )\n\n        area_p = torch.prod(pred[:, 2:], 1)\n        area_g = torch.prod(target[:, 2:], 1)\n\n        en = (tl < br).type(tl.type()).prod(dim=1)\n        area_i = torch.prod(br - tl, 1) * en\n        iou = (area_i) / (area_p + area_g - area_i + 1e-16)\n\n        if self.loss_type == \"iou\":\n            loss = 1 - iou ** 2\n        elif self.loss_type == \"giou\":\n            c_tl = torch.min(\n                (pred[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2)\n            )\n            c_br = torch.max(\n                (pred[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2)\n            )\n            area_c = torch.prod(c_br - c_tl, 1)\n            giou = iou - (area_c - area_i) / area_c.clamp(1e-16)\n            loss = 1 - giou.clamp(min=-1.0, max=1.0)\n\n        if self.reduction == \"mean\":\n            loss = loss.mean()\n        elif self.reduction == \"sum\":\n            loss = loss.sum()\n\n        return loss\n\n\ndef sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):\n    \"\"\"\n    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.\n    Args:\n        inputs: A float tensor of arbitrary shape.\n                The predictions for each example.\n        targets: A float tensor with the same shape as inputs. Stores the binary\n                 classification label for each element in inputs\n                (0 for the negative class and 1 for the positive class).\n        alpha: (optional) Weighting factor in range (0,1) to balance\n                positive vs negative examples. Default = -1 (no weighting).\n        gamma: Exponent of the modulating factor (1 - p_t) to\n               balance easy vs hard examples.\n    Returns:\n        Loss tensor\n    \"\"\"\n    prob = inputs.sigmoid()\n    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n    p_t = prob * targets + (1 - prob) * (1 - targets)\n    loss = ce_loss * ((1 - p_t) ** gamma)\n\n    if alpha >= 0:\n        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n        loss = alpha_t * loss\n    #return loss.mean(0).sum() / num_boxes\n    return loss.sum() / num_boxes"
  },
  {
    "path": "yolox/models/network_blocks.py",
    "content": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport torch\nimport torch.nn as nn\n\n\nclass SiLU(nn.Module):\n    \"\"\"export-friendly version of nn.SiLU()\"\"\"\n\n    @staticmethod\n    def forward(x):\n        return x * torch.sigmoid(x)\n\n\ndef get_activation(name=\"silu\", inplace=True):\n    if name == \"silu\":\n        module = nn.SiLU(inplace=inplace)\n    elif name == \"relu\":\n        module = nn.ReLU(inplace=inplace)\n    elif name == \"lrelu\":\n        module = nn.LeakyReLU(0.1, inplace=inplace)\n    else:\n        raise AttributeError(\"Unsupported act type: {}\".format(name))\n    return module\n\n\nclass BaseConv(nn.Module):\n    \"\"\"A Conv2d -> Batchnorm -> silu/leaky relu block\"\"\"\n\n    def __init__(\n        self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act=\"silu\"\n    ):\n        super().__init__()\n        # same padding\n        pad = (ksize - 1) // 2\n        self.conv = nn.Conv2d(\n            in_channels,\n            out_channels,\n            kernel_size=ksize,\n            stride=stride,\n            padding=pad,\n            groups=groups,\n            bias=bias,\n        )\n        self.bn = nn.BatchNorm2d(out_channels)\n        self.act = get_activation(act, inplace=True)\n\n    def forward(self, x):\n        return self.act(self.bn(self.conv(x)))\n\n    def fuseforward(self, x):\n        return self.act(self.conv(x))\n\n\nclass DWConv(nn.Module):\n    \"\"\"Depthwise Conv + Conv\"\"\"\n\n    def __init__(self, in_channels, out_channels, ksize, stride=1, act=\"silu\"):\n        super().__init__()\n        self.dconv = BaseConv(\n            in_channels,\n            in_channels,\n            ksize=ksize,\n            stride=stride,\n            groups=in_channels,\n            act=act,\n        )\n        self.pconv = BaseConv(\n            in_channels, out_channels, ksize=1, stride=1, groups=1, act=act\n        )\n\n    def forward(self, x):\n        x = self.dconv(x)\n        return self.pconv(x)\n\n\nclass Bottleneck(nn.Module):\n    # Standard bottleneck\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        shortcut=True,\n        expansion=0.5,\n        depthwise=False,\n        act=\"silu\",\n    ):\n        super().__init__()\n        hidden_channels = int(out_channels * expansion)\n        Conv = DWConv if depthwise else BaseConv\n        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)\n        self.conv2 = Conv(hidden_channels, out_channels, 3, stride=1, act=act)\n        self.use_add = shortcut and in_channels == out_channels\n\n    def forward(self, x):\n        y = self.conv2(self.conv1(x))\n        if self.use_add:\n            y = y + x\n        return y\n\n\nclass ResLayer(nn.Module):\n    \"Residual layer with `in_channels` inputs.\"\n\n    def __init__(self, in_channels: int):\n        super().__init__()\n        mid_channels = in_channels // 2\n        self.layer1 = BaseConv(\n            in_channels, mid_channels, ksize=1, stride=1, act=\"lrelu\"\n        )\n        self.layer2 = BaseConv(\n            mid_channels, in_channels, ksize=3, stride=1, act=\"lrelu\"\n        )\n\n    def forward(self, x):\n        out = self.layer2(self.layer1(x))\n        return x + out\n\n\nclass SPPBottleneck(nn.Module):\n    \"\"\"Spatial pyramid pooling layer used in YOLOv3-SPP\"\"\"\n\n    def __init__(\n        self, in_channels, out_channels, kernel_sizes=(5, 9, 13), activation=\"silu\"\n    ):\n        super().__init__()\n        hidden_channels = in_channels // 2\n        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=activation)\n        self.m = nn.ModuleList(\n            [\n                nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2)\n                for ks in kernel_sizes\n            ]\n        )\n        conv2_channels = hidden_channels * (len(kernel_sizes) + 1)\n        self.conv2 = BaseConv(conv2_channels, out_channels, 1, stride=1, act=activation)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = torch.cat([x] + [m(x) for m in self.m], dim=1)\n        x = self.conv2(x)\n        return x\n\n\nclass CSPLayer(nn.Module):\n    \"\"\"C3 in yolov5, CSP Bottleneck with 3 convolutions\"\"\"\n\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        n=1,\n        shortcut=True,\n        expansion=0.5,\n        depthwise=False,\n        act=\"silu\",\n    ):\n        \"\"\"\n        Args:\n            in_channels (int): input channels.\n            out_channels (int): output channels.\n            n (int): number of Bottlenecks. Default value: 1.\n        \"\"\"\n        # ch_in, ch_out, number, shortcut, groups, expansion\n        super().__init__()\n        hidden_channels = int(out_channels * expansion)  # hidden channels\n        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)\n        self.conv2 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)\n        self.conv3 = BaseConv(2 * hidden_channels, out_channels, 1, stride=1, act=act)\n        module_list = [\n            Bottleneck(\n                hidden_channels, hidden_channels, shortcut, 1.0, depthwise, act=act\n            )\n            for _ in range(n)\n        ]\n        self.m = nn.Sequential(*module_list)\n\n    def forward(self, x):\n        x_1 = self.conv1(x)\n        x_2 = self.conv2(x)\n        x_1 = self.m(x_1)\n        x = torch.cat((x_1, x_2), dim=1)\n        return self.conv3(x)\n\n\nclass Focus(nn.Module):\n    \"\"\"Focus width and height information into channel space.\"\"\"\n\n    def __init__(self, in_channels, out_channels, ksize=1, stride=1, act=\"silu\"):\n        super().__init__()\n        self.conv = BaseConv(in_channels * 4, out_channels, ksize, stride, act=act)\n\n    def forward(self, x):\n        # shape of x (b,c,w,h) -> y(b,4c,w/2,h/2)\n        patch_top_left = x[..., ::2, ::2]\n        patch_top_right = x[..., ::2, 1::2]\n        patch_bot_left = x[..., 1::2, ::2]\n        patch_bot_right = x[..., 1::2, 1::2]\n        x = torch.cat(\n            (\n                patch_top_left,\n                patch_bot_left,\n                patch_top_right,\n                patch_bot_right,\n            ),\n            dim=1,\n        )\n        return self.conv(x)\n"
  },
  {
    "path": "yolox/models/yolo_fpn.py",
    "content": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport torch\nimport torch.nn as nn\n\nfrom .darknet import Darknet\nfrom .network_blocks import BaseConv\n\n\nclass YOLOFPN(nn.Module):\n    \"\"\"\n    YOLOFPN module. Darknet 53 is the default backbone of this model.\n    \"\"\"\n\n    def __init__(\n        self,\n        depth=53,\n        in_features=[\"dark3\", \"dark4\", \"dark5\"],\n    ):\n        super().__init__()\n\n        self.backbone = Darknet(depth)\n        self.in_features = in_features\n\n        # out 1\n        self.out1_cbl = self._make_cbl(512, 256, 1)\n        self.out1 = self._make_embedding([256, 512], 512 + 256)\n\n        # out 2\n        self.out2_cbl = self._make_cbl(256, 128, 1)\n        self.out2 = self._make_embedding([128, 256], 256 + 128)\n\n        # upsample\n        self.upsample = nn.Upsample(scale_factor=2, mode=\"nearest\")\n\n    def _make_cbl(self, _in, _out, ks):\n        return BaseConv(_in, _out, ks, stride=1, act=\"lrelu\")\n\n    def _make_embedding(self, filters_list, in_filters):\n        m = nn.Sequential(\n            *[\n                self._make_cbl(in_filters, filters_list[0], 1),\n                self._make_cbl(filters_list[0], filters_list[1], 3),\n                self._make_cbl(filters_list[1], filters_list[0], 1),\n                self._make_cbl(filters_list[0], filters_list[1], 3),\n                self._make_cbl(filters_list[1], filters_list[0], 1),\n            ]\n        )\n        return m\n\n    def load_pretrained_model(self, filename=\"./weights/darknet53.mix.pth\"):\n        with open(filename, \"rb\") as f:\n            state_dict = torch.load(f, map_location=\"cpu\")\n        print(\"loading pretrained weights...\")\n        self.backbone.load_state_dict(state_dict)\n\n    def forward(self, inputs):\n        \"\"\"\n        Args:\n            inputs (Tensor): input image.\n\n        Returns:\n            Tuple[Tensor]: FPN output features..\n        \"\"\"\n        #  backbone\n        out_features = self.backbone(inputs)\n        x2, x1, x0 = [out_features[f] for f in self.in_features]\n\n        #  yolo branch 1\n        x1_in = self.out1_cbl(x0)\n        x1_in = self.upsample(x1_in)\n        x1_in = torch.cat([x1_in, x1], 1)\n        out_dark4 = self.out1(x1_in)\n\n        #  yolo branch 2\n        x2_in = self.out2_cbl(out_dark4)\n        x2_in = self.upsample(x2_in)\n        x2_in = torch.cat([x2_in, x2], 1)\n        out_dark3 = self.out2(x2_in)\n\n        outputs = (out_dark3, out_dark4, x0)\n        return outputs\n"
  },
  {
    "path": "yolox/models/yolo_head.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nfrom loguru import logger\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom yolox.utils import bboxes_iou\n\nimport math\n\nfrom .losses import IOUloss\nfrom .network_blocks import BaseConv, DWConv\n\n\nclass YOLOXHead(nn.Module):\n    def __init__(\n        self,\n        num_classes,\n        width=1.0,\n        strides=[8, 16, 32],\n        in_channels=[256, 512, 1024],\n        act=\"silu\",\n        depthwise=False,\n    ):\n        \"\"\"\n        Args:\n            act (str): activation type of conv. Defalut value: \"silu\".\n            depthwise (bool): wheather apply depthwise conv in conv branch. Defalut value: False.\n        \"\"\"\n        super().__init__()\n\n        self.n_anchors = 1\n        self.num_classes = num_classes\n        self.decode_in_inference = True  # for deploy, set to False\n\n        self.cls_convs = nn.ModuleList()\n        self.reg_convs = nn.ModuleList()\n        self.cls_preds = nn.ModuleList()\n        self.reg_preds = nn.ModuleList()\n        self.obj_preds = nn.ModuleList()\n        self.stems = nn.ModuleList()\n        Conv = DWConv if depthwise else BaseConv\n\n        for i in range(len(in_channels)):\n            self.stems.append(\n                BaseConv(\n                    in_channels=int(in_channels[i] * width),\n                    out_channels=int(256 * width),\n                    ksize=1,\n                    stride=1,\n                    act=act,\n                )\n            )\n            self.cls_convs.append(\n                nn.Sequential(\n                    *[\n                        Conv(\n                            in_channels=int(256 * width),\n                            out_channels=int(256 * width),\n                            ksize=3,\n                            stride=1,\n                            act=act,\n                        ),\n                        Conv(\n                            in_channels=int(256 * width),\n                            out_channels=int(256 * width),\n                            ksize=3,\n                            stride=1,\n                            act=act,\n                        ),\n                    ]\n                )\n            )\n            self.reg_convs.append(\n                nn.Sequential(\n                    *[\n                        Conv(\n                            in_channels=int(256 * width),\n                            out_channels=int(256 * width),\n                            ksize=3,\n                            stride=1,\n                            act=act,\n                        ),\n                        Conv(\n                            in_channels=int(256 * width),\n                            out_channels=int(256 * width),\n                            ksize=3,\n                            stride=1,\n                            act=act,\n                        ),\n                    ]\n                )\n            )\n            self.cls_preds.append(\n                nn.Conv2d(\n                    in_channels=int(256 * width),\n                    out_channels=self.n_anchors * self.num_classes,\n                    kernel_size=1,\n                    stride=1,\n                    padding=0,\n                )\n            )\n            self.reg_preds.append(\n                nn.Conv2d(\n                    in_channels=int(256 * width),\n                    out_channels=4,\n                    kernel_size=1,\n                    stride=1,\n                    padding=0,\n                )\n            )\n            self.obj_preds.append(\n                nn.Conv2d(\n                    in_channels=int(256 * width),\n                    out_channels=self.n_anchors * 1,\n                    kernel_size=1,\n                    stride=1,\n                    padding=0,\n                )\n            )\n\n        self.use_l1 = False\n        self.l1_loss = nn.L1Loss(reduction=\"none\")\n        self.bcewithlog_loss = nn.BCEWithLogitsLoss(reduction=\"none\")\n        self.iou_loss = IOUloss(reduction=\"none\")\n        self.strides = strides\n        self.grids = [torch.zeros(1)] * len(in_channels)\n        self.expanded_strides = [None] * len(in_channels)\n\n    def initialize_biases(self, prior_prob):\n        for conv in self.cls_preds:\n            b = conv.bias.view(self.n_anchors, -1)\n            b.data.fill_(-math.log((1 - prior_prob) / prior_prob))\n            conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)\n\n        for conv in self.obj_preds:\n            b = conv.bias.view(self.n_anchors, -1)\n            b.data.fill_(-math.log((1 - prior_prob) / prior_prob))\n            conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)\n\n    def forward(self, xin, labels=None, imgs=None):\n        outputs = []\n        origin_preds = []\n        x_shifts = []\n        y_shifts = []\n        expanded_strides = []\n\n        for k, (cls_conv, reg_conv, stride_this_level, x) in enumerate(\n            zip(self.cls_convs, self.reg_convs, self.strides, xin)\n        ):\n            x = self.stems[k](x)\n            cls_x = x\n            reg_x = x\n\n            cls_feat = cls_conv(cls_x)\n            cls_output = self.cls_preds[k](cls_feat)\n\n            reg_feat = reg_conv(reg_x)\n            reg_output = self.reg_preds[k](reg_feat)\n            obj_output = self.obj_preds[k](reg_feat)\n\n            if self.training:\n                output = torch.cat([reg_output, obj_output, cls_output], 1)\n                output, grid = self.get_output_and_grid(\n                    output, k, stride_this_level, xin[0].type()\n                )\n                x_shifts.append(grid[:, :, 0])\n                y_shifts.append(grid[:, :, 1])\n                expanded_strides.append(\n                    torch.zeros(1, grid.shape[1])\n                    .fill_(stride_this_level)\n                    .type_as(xin[0])\n                )\n                if self.use_l1:\n                    batch_size = reg_output.shape[0]\n                    hsize, wsize = reg_output.shape[-2:]\n                    reg_output = reg_output.view(\n                        batch_size, self.n_anchors, 4, hsize, wsize\n                    )\n                    reg_output = reg_output.permute(0, 1, 3, 4, 2).reshape(\n                        batch_size, -1, 4\n                    )\n                    origin_preds.append(reg_output.clone())\n\n            else:\n                output = torch.cat(\n                    [reg_output, obj_output.sigmoid(), cls_output.sigmoid()], 1\n                )\n\n            outputs.append(output)\n\n        if self.training:\n            return self.get_losses(\n                imgs,\n                x_shifts,\n                y_shifts,\n                expanded_strides,\n                labels,\n                torch.cat(outputs, 1),\n                origin_preds,\n                dtype=xin[0].dtype,\n            )\n        else:\n            self.hw = [x.shape[-2:] for x in outputs]\n            # [batch, n_anchors_all, 85]\n            outputs = torch.cat(\n                [x.flatten(start_dim=2) for x in outputs], dim=2\n            ).permute(0, 2, 1)\n            if self.decode_in_inference:\n                return self.decode_outputs(outputs, dtype=xin[0].type())\n            else:\n                return outputs\n\n    def get_output_and_grid(self, output, k, stride, dtype):\n        grid = self.grids[k]\n\n        batch_size = output.shape[0]\n        n_ch = 5 + self.num_classes\n        hsize, wsize = output.shape[-2:]\n        if grid.shape[2:4] != output.shape[2:4]:\n            yv, xv = torch.meshgrid([torch.arange(hsize), torch.arange(wsize)])\n            grid = torch.stack((xv, yv), 2).view(1, 1, hsize, wsize, 2).type(dtype)\n            self.grids[k] = grid\n\n        output = output.view(batch_size, self.n_anchors, n_ch, hsize, wsize)\n        output = output.permute(0, 1, 3, 4, 2).reshape(\n            batch_size, self.n_anchors * hsize * wsize, -1\n        )\n        grid = grid.view(1, -1, 2)\n        output[..., :2] = (output[..., :2] + grid) * stride\n        output[..., 2:4] = torch.exp(output[..., 2:4]) * stride\n        return output, grid\n\n    def decode_outputs(self, outputs, dtype):\n        grids = []\n        strides = []\n        for (hsize, wsize), stride in zip(self.hw, self.strides):\n            yv, xv = torch.meshgrid([torch.arange(hsize), torch.arange(wsize)])\n            grid = torch.stack((xv, yv), 2).view(1, -1, 2)\n            grids.append(grid)\n            shape = grid.shape[:2]\n            strides.append(torch.full((*shape, 1), stride))\n\n        grids = torch.cat(grids, dim=1).type(dtype)\n        strides = torch.cat(strides, dim=1).type(dtype)\n\n        outputs[..., :2] = (outputs[..., :2] + grids) * strides\n        outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides\n        return outputs\n\n    def get_losses(\n        self,\n        imgs,\n        x_shifts,\n        y_shifts,\n        expanded_strides,\n        labels,\n        outputs,\n        origin_preds,\n        dtype,\n    ):\n        bbox_preds = outputs[:, :, :4]  # [batch, n_anchors_all, 4]\n        obj_preds = outputs[:, :, 4].unsqueeze(-1)  # [batch, n_anchors_all, 1]\n        cls_preds = outputs[:, :, 5:]  # [batch, n_anchors_all, n_cls]\n\n        # calculate targets\n        mixup = labels.shape[2] > 5\n        if mixup:\n            label_cut = labels[..., :5]\n        else:\n            label_cut = labels\n        nlabel = (label_cut.sum(dim=2) > 0).sum(dim=1)  # number of objects\n\n        total_num_anchors = outputs.shape[1]\n        x_shifts = torch.cat(x_shifts, 1)  # [1, n_anchors_all]\n        y_shifts = torch.cat(y_shifts, 1)  # [1, n_anchors_all]\n        expanded_strides = torch.cat(expanded_strides, 1)\n        if self.use_l1:\n            origin_preds = torch.cat(origin_preds, 1)\n\n        cls_targets = []\n        reg_targets = []\n        l1_targets = []\n        obj_targets = []\n        fg_masks = []\n\n        num_fg = 0.0\n        num_gts = 0.0\n\n        for batch_idx in range(outputs.shape[0]):\n            num_gt = int(nlabel[batch_idx])\n            num_gts += num_gt\n            if num_gt == 0:\n                cls_target = outputs.new_zeros((0, self.num_classes))\n                reg_target = outputs.new_zeros((0, 4))\n                l1_target = outputs.new_zeros((0, 4))\n                obj_target = outputs.new_zeros((total_num_anchors, 1))\n                fg_mask = outputs.new_zeros(total_num_anchors).bool()\n            else:\n                gt_bboxes_per_image = labels[batch_idx, :num_gt, 1:5]\n                gt_classes = labels[batch_idx, :num_gt, 0]\n                bboxes_preds_per_image = bbox_preds[batch_idx]\n                \n                try:\n                    (\n                        gt_matched_classes,\n                        fg_mask,\n                        pred_ious_this_matching,\n                        matched_gt_inds,\n                        num_fg_img,\n                    ) = self.get_assignments(  # noqa\n                        batch_idx,\n                        num_gt,\n                        total_num_anchors,\n                        gt_bboxes_per_image,\n                        gt_classes,\n                        bboxes_preds_per_image,\n                        expanded_strides,\n                        x_shifts,\n                        y_shifts,\n                        cls_preds,\n                        bbox_preds,\n                        obj_preds,\n                        labels,\n                        imgs,\n                    )\n                except RuntimeError:\n                    logger.info(\n                        \"OOM RuntimeError is raised due to the huge memory cost during label assignment. \\\n                           CPU mode is applied in this batch. If you want to avoid this issue, \\\n                           try to reduce the batch size or image size.\"\n                    )\n                    print(\"OOM RuntimeError is raised due to the huge memory cost during label assignment. \\\n                           CPU mode is applied in this batch. If you want to avoid this issue, \\\n                           try to reduce the batch size or image size.\")\n                    torch.cuda.empty_cache()\n                    (\n                        gt_matched_classes,\n                        fg_mask,\n                        pred_ious_this_matching,\n                        matched_gt_inds,\n                        num_fg_img,\n                    ) = self.get_assignments(  # noqa\n                        batch_idx,\n                        num_gt,\n                        total_num_anchors,\n                        gt_bboxes_per_image,\n                        gt_classes,\n                        bboxes_preds_per_image,\n                        expanded_strides,\n                        x_shifts,\n                        y_shifts,\n                        cls_preds,\n                        bbox_preds,\n                        obj_preds,\n                        labels,\n                        imgs,\n                        \"cpu\",\n                    )\n                \n                \n                torch.cuda.empty_cache()\n                num_fg += num_fg_img\n\n                cls_target = F.one_hot(\n                    gt_matched_classes.to(torch.int64), self.num_classes\n                ) * pred_ious_this_matching.unsqueeze(-1)\n                obj_target = fg_mask.unsqueeze(-1)\n                reg_target = gt_bboxes_per_image[matched_gt_inds]\n\n                if self.use_l1:\n                    l1_target = self.get_l1_target(\n                        outputs.new_zeros((num_fg_img, 4)),\n                        gt_bboxes_per_image[matched_gt_inds],\n                        expanded_strides[0][fg_mask],\n                        x_shifts=x_shifts[0][fg_mask],\n                        y_shifts=y_shifts[0][fg_mask],\n                    )\n\n            cls_targets.append(cls_target)\n            reg_targets.append(reg_target)\n            obj_targets.append(obj_target.to(dtype))\n            fg_masks.append(fg_mask)\n            if self.use_l1:\n                l1_targets.append(l1_target)\n\n        cls_targets = torch.cat(cls_targets, 0)\n        reg_targets = torch.cat(reg_targets, 0)\n        obj_targets = torch.cat(obj_targets, 0)\n        fg_masks = torch.cat(fg_masks, 0)\n        if self.use_l1:\n            l1_targets = torch.cat(l1_targets, 0)\n\n        num_fg = max(num_fg, 1)\n        loss_iou = (\n            self.iou_loss(bbox_preds.view(-1, 4)[fg_masks], reg_targets)\n        ).sum() / num_fg\n        loss_obj = (\n            self.bcewithlog_loss(obj_preds.view(-1, 1), obj_targets)\n        ).sum() / num_fg\n        loss_cls = (\n            self.bcewithlog_loss(\n                cls_preds.view(-1, self.num_classes)[fg_masks], cls_targets\n            )\n        ).sum() / num_fg\n        if self.use_l1:\n            loss_l1 = (\n                self.l1_loss(origin_preds.view(-1, 4)[fg_masks], l1_targets)\n            ).sum() / num_fg\n        else:\n            loss_l1 = 0.0\n\n        reg_weight = 5.0\n        loss = reg_weight * loss_iou + loss_obj + loss_cls + loss_l1\n\n        return (\n            loss,\n            reg_weight * loss_iou,\n            loss_obj,\n            loss_cls,\n            loss_l1,\n            num_fg / max(num_gts, 1),\n        )\n\n    def get_l1_target(self, l1_target, gt, stride, x_shifts, y_shifts, eps=1e-8):\n        l1_target[:, 0] = gt[:, 0] / stride - x_shifts\n        l1_target[:, 1] = gt[:, 1] / stride - y_shifts\n        l1_target[:, 2] = torch.log(gt[:, 2] / stride + eps)\n        l1_target[:, 3] = torch.log(gt[:, 3] / stride + eps)\n        return l1_target\n\n    @torch.no_grad()\n    def get_assignments(\n        self,\n        batch_idx,\n        num_gt,\n        total_num_anchors,\n        gt_bboxes_per_image,\n        gt_classes,\n        bboxes_preds_per_image,\n        expanded_strides,\n        x_shifts,\n        y_shifts,\n        cls_preds,\n        bbox_preds,\n        obj_preds,\n        labels,\n        imgs,\n        mode=\"gpu\",\n    ):\n\n        if mode == \"cpu\":\n            print(\"------------CPU Mode for This Batch-------------\")\n            gt_bboxes_per_image = gt_bboxes_per_image.cpu().float()\n            bboxes_preds_per_image = bboxes_preds_per_image.cpu().float()\n            gt_classes = gt_classes.cpu().float()\n            expanded_strides = expanded_strides.cpu().float()\n            x_shifts = x_shifts.cpu()\n            y_shifts = y_shifts.cpu()\n\n        img_size = imgs.shape[2:]\n        fg_mask, is_in_boxes_and_center = self.get_in_boxes_info(\n            gt_bboxes_per_image,\n            expanded_strides,\n            x_shifts,\n            y_shifts,\n            total_num_anchors,\n            num_gt,\n            img_size\n        )\n\n        bboxes_preds_per_image = bboxes_preds_per_image[fg_mask]\n        cls_preds_ = cls_preds[batch_idx][fg_mask]\n        obj_preds_ = obj_preds[batch_idx][fg_mask]\n        num_in_boxes_anchor = bboxes_preds_per_image.shape[0]\n\n        if mode == \"cpu\":\n            gt_bboxes_per_image = gt_bboxes_per_image.cpu()\n            bboxes_preds_per_image = bboxes_preds_per_image.cpu()\n\n        pair_wise_ious = bboxes_iou(gt_bboxes_per_image, bboxes_preds_per_image, False)\n\n        gt_cls_per_image = (\n            F.one_hot(gt_classes.to(torch.int64), self.num_classes)\n            .float()\n            .unsqueeze(1)\n            .repeat(1, num_in_boxes_anchor, 1)\n        )\n        pair_wise_ious_loss = -torch.log(pair_wise_ious + 1e-8)\n\n        if mode == \"cpu\":\n            cls_preds_, obj_preds_ = cls_preds_.cpu(), obj_preds_.cpu()\n\n        with torch.cuda.amp.autocast(enabled=False):\n            cls_preds_ = (\n                cls_preds_.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()\n                * obj_preds_.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()\n            )\n            pair_wise_cls_loss = F.binary_cross_entropy(\n                cls_preds_.sqrt_(), gt_cls_per_image, reduction=\"none\"\n            ).sum(-1)\n        del cls_preds_\n\n        cost = (\n            pair_wise_cls_loss\n            + 3.0 * pair_wise_ious_loss\n            + 100000.0 * (~is_in_boxes_and_center)\n        )\n\n        (\n            num_fg,\n            gt_matched_classes,\n            pred_ious_this_matching,\n            matched_gt_inds,\n        ) = self.dynamic_k_matching(cost, pair_wise_ious, gt_classes, num_gt, fg_mask)\n        del pair_wise_cls_loss, cost, pair_wise_ious, pair_wise_ious_loss\n\n        if mode == \"cpu\":\n            gt_matched_classes = gt_matched_classes.cuda()\n            fg_mask = fg_mask.cuda()\n            pred_ious_this_matching = pred_ious_this_matching.cuda()\n            matched_gt_inds = matched_gt_inds.cuda()\n\n        return (\n            gt_matched_classes,\n            fg_mask,\n            pred_ious_this_matching,\n            matched_gt_inds,\n            num_fg,\n        )\n\n    def get_in_boxes_info(\n        self,\n        gt_bboxes_per_image,\n        expanded_strides,\n        x_shifts,\n        y_shifts,\n        total_num_anchors,\n        num_gt,\n        img_size\n    ):\n        expanded_strides_per_image = expanded_strides[0]\n        x_shifts_per_image = x_shifts[0] * expanded_strides_per_image\n        y_shifts_per_image = y_shifts[0] * expanded_strides_per_image\n        x_centers_per_image = (\n            (x_shifts_per_image + 0.5 * expanded_strides_per_image)\n            .unsqueeze(0)\n            .repeat(num_gt, 1)\n        )  # [n_anchor] -> [n_gt, n_anchor]\n        y_centers_per_image = (\n            (y_shifts_per_image + 0.5 * expanded_strides_per_image)\n            .unsqueeze(0)\n            .repeat(num_gt, 1)\n        )\n\n        gt_bboxes_per_image_l = (\n            (gt_bboxes_per_image[:, 0] - 0.5 * gt_bboxes_per_image[:, 2])\n            .unsqueeze(1)\n            .repeat(1, total_num_anchors)\n        )\n        gt_bboxes_per_image_r = (\n            (gt_bboxes_per_image[:, 0] + 0.5 * gt_bboxes_per_image[:, 2])\n            .unsqueeze(1)\n            .repeat(1, total_num_anchors)\n        )\n        gt_bboxes_per_image_t = (\n            (gt_bboxes_per_image[:, 1] - 0.5 * gt_bboxes_per_image[:, 3])\n            .unsqueeze(1)\n            .repeat(1, total_num_anchors)\n        )\n        gt_bboxes_per_image_b = (\n            (gt_bboxes_per_image[:, 1] + 0.5 * gt_bboxes_per_image[:, 3])\n            .unsqueeze(1)\n            .repeat(1, total_num_anchors)\n        )\n\n        b_l = x_centers_per_image - gt_bboxes_per_image_l\n        b_r = gt_bboxes_per_image_r - x_centers_per_image\n        b_t = y_centers_per_image - gt_bboxes_per_image_t\n        b_b = gt_bboxes_per_image_b - y_centers_per_image\n        bbox_deltas = torch.stack([b_l, b_t, b_r, b_b], 2)\n\n        is_in_boxes = bbox_deltas.min(dim=-1).values > 0.0\n        is_in_boxes_all = is_in_boxes.sum(dim=0) > 0\n        # in fixed center\n\n        center_radius = 2.5\n        # clip center inside image\n        gt_bboxes_per_image_clip = gt_bboxes_per_image[:, 0:2].clone()\n        gt_bboxes_per_image_clip[:, 0] = torch.clamp(gt_bboxes_per_image_clip[:, 0], min=0, max=img_size[1])\n        gt_bboxes_per_image_clip[:, 1] = torch.clamp(gt_bboxes_per_image_clip[:, 1], min=0, max=img_size[0])\n\n        gt_bboxes_per_image_l = (gt_bboxes_per_image_clip[:, 0]).unsqueeze(1).repeat(\n            1, total_num_anchors\n        ) - center_radius * expanded_strides_per_image.unsqueeze(0)\n        gt_bboxes_per_image_r = (gt_bboxes_per_image_clip[:, 0]).unsqueeze(1).repeat(\n            1, total_num_anchors\n        ) + center_radius * expanded_strides_per_image.unsqueeze(0)\n        gt_bboxes_per_image_t = (gt_bboxes_per_image_clip[:, 1]).unsqueeze(1).repeat(\n            1, total_num_anchors\n        ) - center_radius * expanded_strides_per_image.unsqueeze(0)\n        gt_bboxes_per_image_b = (gt_bboxes_per_image_clip[:, 1]).unsqueeze(1).repeat(\n            1, total_num_anchors\n        ) + center_radius * expanded_strides_per_image.unsqueeze(0)\n\n        c_l = x_centers_per_image - gt_bboxes_per_image_l\n        c_r = gt_bboxes_per_image_r - x_centers_per_image\n        c_t = y_centers_per_image - gt_bboxes_per_image_t\n        c_b = gt_bboxes_per_image_b - y_centers_per_image\n        center_deltas = torch.stack([c_l, c_t, c_r, c_b], 2)\n        is_in_centers = center_deltas.min(dim=-1).values > 0.0\n        is_in_centers_all = is_in_centers.sum(dim=0) > 0\n\n        # in boxes and in centers\n        is_in_boxes_anchor = is_in_boxes_all | is_in_centers_all\n\n        is_in_boxes_and_center = (\n            is_in_boxes[:, is_in_boxes_anchor] & is_in_centers[:, is_in_boxes_anchor]\n        )\n        del gt_bboxes_per_image_clip\n        return is_in_boxes_anchor, is_in_boxes_and_center\n\n    def dynamic_k_matching(self, cost, pair_wise_ious, gt_classes, num_gt, fg_mask):\n        # Dynamic K\n        # ---------------------------------------------------------------\n        matching_matrix = torch.zeros_like(cost)\n\n        ious_in_boxes_matrix = pair_wise_ious\n        n_candidate_k = min(10, ious_in_boxes_matrix.size(1))\n        topk_ious, _ = torch.topk(ious_in_boxes_matrix, n_candidate_k, dim=1)\n        dynamic_ks = torch.clamp(topk_ious.sum(1).int(), min=1)\n        for gt_idx in range(num_gt):\n            _, pos_idx = torch.topk(\n                cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False\n            )\n            matching_matrix[gt_idx][pos_idx] = 1.0\n\n        del topk_ious, dynamic_ks, pos_idx\n\n        anchor_matching_gt = matching_matrix.sum(0)\n        if (anchor_matching_gt > 1).sum() > 0:\n            cost_min, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)\n            matching_matrix[:, anchor_matching_gt > 1] *= 0.0\n            matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0\n        fg_mask_inboxes = matching_matrix.sum(0) > 0.0\n        num_fg = fg_mask_inboxes.sum().item()\n\n        fg_mask[fg_mask.clone()] = fg_mask_inboxes\n\n        matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)\n        gt_matched_classes = gt_classes[matched_gt_inds]\n\n        pred_ious_this_matching = (matching_matrix * pair_wise_ious).sum(0)[\n            fg_mask_inboxes\n        ]\n        return num_fg, gt_matched_classes, pred_ious_this_matching, matched_gt_inds\n"
  },
  {
    "path": "yolox/models/yolo_pafpn.py",
    "content": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport torch\nimport torch.nn as nn\n\nfrom .darknet import CSPDarknet\nfrom .network_blocks import BaseConv, CSPLayer, DWConv\n\n\nclass YOLOPAFPN(nn.Module):\n    \"\"\"\n    YOLOv3 model. Darknet 53 is the default backbone of this model.\n    \"\"\"\n\n    def __init__(\n        self,\n        depth=1.0,\n        width=1.0,\n        in_features=(\"dark3\", \"dark4\", \"dark5\"),\n        in_channels=[256, 512, 1024],\n        depthwise=False,\n        act=\"silu\",\n    ):\n        super().__init__()\n        self.backbone = CSPDarknet(depth, width, depthwise=depthwise, act=act)\n        self.in_features = in_features\n        self.in_channels = in_channels\n        Conv = DWConv if depthwise else BaseConv\n\n        self.upsample = nn.Upsample(scale_factor=2, mode=\"nearest\")\n        self.lateral_conv0 = BaseConv(\n            int(in_channels[2] * width), int(in_channels[1] * width), 1, 1, act=act\n        )\n        self.C3_p4 = CSPLayer(\n            int(2 * in_channels[1] * width),\n            int(in_channels[1] * width),\n            round(3 * depth),\n            False,\n            depthwise=depthwise,\n            act=act,\n        )  # cat\n\n        self.reduce_conv1 = BaseConv(\n            int(in_channels[1] * width), int(in_channels[0] * width), 1, 1, act=act\n        )\n        self.C3_p3 = CSPLayer(\n            int(2 * in_channels[0] * width),\n            int(in_channels[0] * width),\n            round(3 * depth),\n            False,\n            depthwise=depthwise,\n            act=act,\n        )\n\n        # bottom-up conv\n        self.bu_conv2 = Conv(\n            int(in_channels[0] * width), int(in_channels[0] * width), 3, 2, act=act\n        )\n        self.C3_n3 = CSPLayer(\n            int(2 * in_channels[0] * width),\n            int(in_channels[1] * width),\n            round(3 * depth),\n            False,\n            depthwise=depthwise,\n            act=act,\n        )\n\n        # bottom-up conv\n        self.bu_conv1 = Conv(\n            int(in_channels[1] * width), int(in_channels[1] * width), 3, 2, act=act\n        )\n        self.C3_n4 = CSPLayer(\n            int(2 * in_channels[1] * width),\n            int(in_channels[2] * width),\n            round(3 * depth),\n            False,\n            depthwise=depthwise,\n            act=act,\n        )\n\n    def forward(self, input):\n        \"\"\"\n        Args:\n            inputs: input images.\n\n        Returns:\n            Tuple[Tensor]: FPN feature.\n        \"\"\"\n\n        #  backbone\n        out_features = self.backbone(input)\n        features = [out_features[f] for f in self.in_features]\n        [x2, x1, x0] = features\n\n        fpn_out0 = self.lateral_conv0(x0)  # 1024->512/32\n        f_out0 = self.upsample(fpn_out0)  # 512/16\n        f_out0 = torch.cat([f_out0, x1], 1)  # 512->1024/16\n        f_out0 = self.C3_p4(f_out0)  # 1024->512/16\n\n        fpn_out1 = self.reduce_conv1(f_out0)  # 512->256/16\n        f_out1 = self.upsample(fpn_out1)  # 256/8\n        f_out1 = torch.cat([f_out1, x2], 1)  # 256->512/8\n        pan_out2 = self.C3_p3(f_out1)  # 512->256/8\n\n        p_out1 = self.bu_conv2(pan_out2)  # 256->256/16\n        p_out1 = torch.cat([p_out1, fpn_out1], 1)  # 256->512/16\n        pan_out1 = self.C3_n3(p_out1)  # 512->512/16\n\n        p_out0 = self.bu_conv1(pan_out1)  # 512->512/32\n        p_out0 = torch.cat([p_out0, fpn_out0], 1)  # 512->1024/32\n        pan_out0 = self.C3_n4(p_out0)  # 1024->1024/32\n\n        outputs = (pan_out2, pan_out1, pan_out0)\n        return outputs\n"
  },
  {
    "path": "yolox/models/yolox.py",
    "content": "#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport torch.nn as nn\n\nfrom .yolo_head import YOLOXHead\nfrom .yolo_pafpn import YOLOPAFPN\n\n\nclass YOLOX(nn.Module):\n    \"\"\"\n    YOLOX model module. The module list is defined by create_yolov3_modules function.\n    The network returns loss values from three YOLO layers during training\n    and detection results during test.\n    \"\"\"\n\n    def __init__(self, backbone=None, head=None):\n        super().__init__()\n        if backbone is None:\n            backbone = YOLOPAFPN()\n        if head is None:\n            head = YOLOXHead(80)\n\n        self.backbone = backbone\n        self.head = head\n\n    def forward(self, x, targets=None):\n        # fpn output content features of [dark3, dark4, dark5]\n        fpn_outs = self.backbone(x)\n\n        if self.training:\n            assert targets is not None\n            loss, iou_loss, conf_loss, cls_loss, l1_loss, num_fg = self.head(\n                fpn_outs, targets, x\n            )\n            outputs = {\n                \"total_loss\": loss,\n                \"iou_loss\": iou_loss,\n                \"l1_loss\": l1_loss,\n                \"conf_loss\": conf_loss,\n                \"cls_loss\": cls_loss,\n                \"num_fg\": num_fg,\n            }\n        else:\n            outputs = self.head(fpn_outs)\n\n        return outputs\n"
  },
  {
    "path": "yolox/utils/__init__.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nfrom .allreduce_norm import *\nfrom .boxes import *\nfrom .checkpoint import load_ckpt, save_checkpoint\nfrom .demo_utils import *\nfrom .dist import *\nfrom .ema import ModelEMA\nfrom .logger import setup_logger\nfrom .lr_scheduler import LRScheduler\nfrom .metric import *\nfrom .model_utils import *\nfrom .setup_env import *\nfrom .visualize import *\n"
  },
  {
    "path": "yolox/utils/allreduce_norm.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport torch\nfrom torch import distributed as dist\nfrom torch import nn\n\nimport pickle\nfrom collections import OrderedDict\n\nfrom .dist import _get_global_gloo_group, get_world_size\n\nASYNC_NORM = (\n    nn.BatchNorm1d,\n    nn.BatchNorm2d,\n    nn.BatchNorm3d,\n    nn.InstanceNorm1d,\n    nn.InstanceNorm2d,\n    nn.InstanceNorm3d,\n)\n\n__all__ = [\n    \"get_async_norm_states\",\n    \"pyobj2tensor\",\n    \"tensor2pyobj\",\n    \"all_reduce\",\n    \"all_reduce_norm\",\n]\n\n\ndef get_async_norm_states(module):\n    async_norm_states = OrderedDict()\n    for name, child in module.named_modules():\n        if isinstance(child, ASYNC_NORM):\n            for k, v in child.state_dict().items():\n                async_norm_states[\".\".join([name, k])] = v\n    return async_norm_states\n\n\ndef pyobj2tensor(pyobj, device=\"cuda\"):\n    \"\"\"serialize picklable python object to tensor\"\"\"\n    storage = torch.ByteStorage.from_buffer(pickle.dumps(pyobj))\n    return torch.ByteTensor(storage).to(device=device)\n\n\ndef tensor2pyobj(tensor):\n    \"\"\"deserialize tensor to picklable python object\"\"\"\n    return pickle.loads(tensor.cpu().numpy().tobytes())\n\n\ndef _get_reduce_op(op_name):\n    return {\n        \"sum\": dist.ReduceOp.SUM,\n        \"mean\": dist.ReduceOp.SUM,\n    }[op_name.lower()]\n\n\ndef all_reduce(py_dict, op=\"sum\", group=None):\n    \"\"\"\n    Apply all reduce function for python dict object.\n    NOTE: make sure that every py_dict has the same keys and values are in the same shape.\n\n    Args:\n        py_dict (dict): dict to apply all reduce op.\n        op (str): operator, could be \"sum\" or \"mean\".\n    \"\"\"\n    world_size = get_world_size()\n    if world_size == 1:\n        return py_dict\n    if group is None:\n        group = _get_global_gloo_group()\n    if dist.get_world_size(group) == 1:\n        return py_dict\n\n    # all reduce logic across different devices.\n    py_key = list(py_dict.keys())\n    py_key_tensor = pyobj2tensor(py_key)\n    dist.broadcast(py_key_tensor, src=0)\n    py_key = tensor2pyobj(py_key_tensor)\n\n    tensor_shapes = [py_dict[k].shape for k in py_key]\n    tensor_numels = [py_dict[k].numel() for k in py_key]\n\n    flatten_tensor = torch.cat([py_dict[k].flatten() for k in py_key])\n    dist.all_reduce(flatten_tensor, op=_get_reduce_op(op))\n    if op == \"mean\":\n        flatten_tensor /= world_size\n\n    split_tensors = [\n        x.reshape(shape)\n        for x, shape in zip(torch.split(flatten_tensor, tensor_numels), tensor_shapes)\n    ]\n    return OrderedDict({k: v for k, v in zip(py_key, split_tensors)})\n\n\ndef all_reduce_norm(module):\n    \"\"\"\n    All reduce norm statistics in different devices.\n    \"\"\"\n    states = get_async_norm_states(module)\n    states = all_reduce(states, op=\"mean\")\n    module.load_state_dict(states, strict=False)\n"
  },
  {
    "path": "yolox/utils/boxes.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport numpy as np\n\nimport torch\nimport torchvision\nimport torch.nn.functional as F\n\n__all__ = [\n    \"filter_box\",\n    \"postprocess\",\n    \"bboxes_iou\",\n    \"matrix_iou\",\n    \"adjust_box_anns\",\n    \"xyxy2xywh\",\n    \"xyxy2cxcywh\",\n]\n\n\ndef filter_box(output, scale_range):\n    \"\"\"\n    output: (N, 5+class) shape\n    \"\"\"\n    min_scale, max_scale = scale_range\n    w = output[:, 2] - output[:, 0]\n    h = output[:, 3] - output[:, 1]\n    keep = (w * h > min_scale * min_scale) & (w * h < max_scale * max_scale)\n    return output[keep]\n\n\ndef postprocess(prediction, num_classes, conf_thre=0.7, nms_thre=0.45):\n    box_corner = prediction.new(prediction.shape)\n    box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2\n    box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2\n    box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2\n    box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2\n    prediction[:, :, :4] = box_corner[:, :, :4]\n\n    output = [None for _ in range(len(prediction))]\n    for i, image_pred in enumerate(prediction):\n\n        # If none are remaining => process next image\n        if not image_pred.size(0):\n            continue\n        # Get score and class with highest confidence\n        class_conf, class_pred = torch.max(\n            image_pred[:, 5 : 5 + num_classes], 1, keepdim=True\n        )\n\n        conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= conf_thre).squeeze()\n        # _, conf_mask = torch.topk((image_pred[:, 4] * class_conf.squeeze()), 1000)\n        # Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred)\n        detections = torch.cat((image_pred[:, :5], class_conf, class_pred.float()), 1)\n        detections = detections[conf_mask]\n        if not detections.size(0):\n            continue\n        # import pdb; pdb.set_trace()\n        if detections.shape[1] == 1:\n            detections = detections.squeeze(0)\n        try:\n            nms_out_index = torchvision.ops.batched_nms(\n                detections[:, :4],\n                detections[:, 4] * detections[:, 5],\n                detections[:, 6],\n                nms_thre,\n            )\n        except:\n            import pdb; pdb.set_trace()\n        detections = detections[nms_out_index]\n        if output[i] is None:\n            output[i] = detections\n        else:\n            output[i] = torch.cat((output[i], detections))\n\n    return output\n\n\ndef bboxes_iou(bboxes_a, bboxes_b, xyxy=True):\n    if bboxes_a.shape[1] != 4 or bboxes_b.shape[1] != 4:\n        raise IndexError\n\n    if xyxy:\n        tl = torch.max(bboxes_a[:, None, :2], bboxes_b[:, :2])\n        br = torch.min(bboxes_a[:, None, 2:], bboxes_b[:, 2:])\n        area_a = torch.prod(bboxes_a[:, 2:] - bboxes_a[:, :2], 1)\n        area_b = torch.prod(bboxes_b[:, 2:] - bboxes_b[:, :2], 1)\n    else:\n        tl = torch.max(\n            (bboxes_a[:, None, :2] - bboxes_a[:, None, 2:] / 2),\n            (bboxes_b[:, :2] - bboxes_b[:, 2:] / 2),\n        )\n        br = torch.min(\n            (bboxes_a[:, None, :2] + bboxes_a[:, None, 2:] / 2),\n            (bboxes_b[:, :2] + bboxes_b[:, 2:] / 2),\n        )\n\n        area_a = torch.prod(bboxes_a[:, 2:], 1)\n        area_b = torch.prod(bboxes_b[:, 2:], 1)\n    en = (tl < br).type(tl.type()).prod(dim=2)\n    area_i = torch.prod(br - tl, 2) * en  # * ((tl < br).all())\n    return area_i / (area_a[:, None] + area_b - area_i)\n\n\ndef matrix_iou(a, b):\n    \"\"\"\n    return iou of a and b, numpy version for data augenmentation\n    \"\"\"\n    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])\n    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])\n\n    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)\n    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)\n    area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)\n    return area_i / (area_a[:, np.newaxis] + area_b - area_i + 1e-12)\n\n\ndef adjust_box_anns(bbox, scale_ratio, padw, padh, w_max, h_max):\n    #bbox[:, 0::2] = np.clip(bbox[:, 0::2] * scale_ratio + padw, 0, w_max)\n    #bbox[:, 1::2] = np.clip(bbox[:, 1::2] * scale_ratio + padh, 0, h_max)\n    bbox[:, 0::2] = bbox[:, 0::2] * scale_ratio + padw\n    bbox[:, 1::2] = bbox[:, 1::2] * scale_ratio + padh\n    return bbox\n\n\ndef xyxy2xywh(bboxes):\n    bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0]\n    bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1]\n    return bboxes\n\n\ndef xyxy2cxcywh(bboxes):\n    bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0]\n    bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1]\n    bboxes[:, 0] = bboxes[:, 0] + bboxes[:, 2] * 0.5\n    bboxes[:, 1] = bboxes[:, 1] + bboxes[:, 3] * 0.5\n    return bboxes\n"
  },
  {
    "path": "yolox/utils/checkpoint.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\nfrom loguru import logger\n\nimport torch\n\nimport os\nimport shutil\n\n\ndef load_ckpt(model, ckpt):\n    model_state_dict = model.state_dict()\n    load_dict = {}\n    for key_model, v in model_state_dict.items():\n        if key_model not in ckpt:\n            logger.warning(\n                \"{} is not in the ckpt. Please double check and see if this is desired.\".format(\n                    key_model\n                )\n            )\n            continue\n        v_ckpt = ckpt[key_model]\n        if v.shape != v_ckpt.shape:\n            logger.warning(\n                \"Shape of {} in checkpoint is {}, while shape of {} in model is {}.\".format(\n                    key_model, v_ckpt.shape, key_model, v.shape\n                )\n            )\n            continue\n        load_dict[key_model] = v_ckpt\n\n    model.load_state_dict(load_dict, strict=False)\n    return model\n\n\ndef save_checkpoint(state, is_best, save_dir, model_name=\"\"):\n    if not os.path.exists(save_dir):\n        os.makedirs(save_dir)\n    filename = os.path.join(save_dir, model_name + \"_ckpt.pth.tar\")\n    torch.save(state, filename)\n    if is_best:\n        best_filename = os.path.join(save_dir, \"best_ckpt.pth.tar\")\n        shutil.copyfile(filename, best_filename)\n"
  },
  {
    "path": "yolox/utils/demo_utils.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport numpy as np\n\nimport os\n\n__all__ = [\"mkdir\", \"nms\", \"multiclass_nms\", \"demo_postprocess\"]\n\n\ndef mkdir(path):\n    if not os.path.exists(path):\n        os.makedirs(path)\n\n\ndef nms(boxes, scores, nms_thr):\n    \"\"\"Single class NMS implemented in Numpy.\"\"\"\n    x1 = boxes[:, 0]\n    y1 = boxes[:, 1]\n    x2 = boxes[:, 2]\n    y2 = boxes[:, 3]\n\n    areas = (x2 - x1 + 1) * (y2 - y1 + 1)\n    order = scores.argsort()[::-1]\n\n    keep = []\n    while order.size > 0:\n        i = order[0]\n        keep.append(i)\n        xx1 = np.maximum(x1[i], x1[order[1:]])\n        yy1 = np.maximum(y1[i], y1[order[1:]])\n        xx2 = np.minimum(x2[i], x2[order[1:]])\n        yy2 = np.minimum(y2[i], y2[order[1:]])\n\n        w = np.maximum(0.0, xx2 - xx1 + 1)\n        h = np.maximum(0.0, yy2 - yy1 + 1)\n        inter = w * h\n        ovr = inter / (areas[i] + areas[order[1:]] - inter)\n\n        inds = np.where(ovr <= nms_thr)[0]\n        order = order[inds + 1]\n\n    return keep\n\n\ndef multiclass_nms(boxes, scores, nms_thr, score_thr):\n    \"\"\"Multiclass NMS implemented in Numpy\"\"\"\n    final_dets = []\n    num_classes = scores.shape[1]\n    for cls_ind in range(num_classes):\n        cls_scores = scores[:, cls_ind]\n        valid_score_mask = cls_scores > score_thr\n        if valid_score_mask.sum() == 0:\n            continue\n        else:\n            valid_scores = cls_scores[valid_score_mask]\n            valid_boxes = boxes[valid_score_mask]\n            keep = nms(valid_boxes, valid_scores, nms_thr)\n            if len(keep) > 0:\n                cls_inds = np.ones((len(keep), 1)) * cls_ind\n                dets = np.concatenate(\n                    [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1\n                )\n                final_dets.append(dets)\n    if len(final_dets) == 0:\n        return None\n    return np.concatenate(final_dets, 0)\n\n\ndef demo_postprocess(outputs, img_size, p6=False):\n\n    grids = []\n    expanded_strides = []\n\n    if not p6:\n        strides = [8, 16, 32]\n    else:\n        strides = [8, 16, 32, 64]\n\n    hsizes = [img_size[0] // stride for stride in strides]\n    wsizes = [img_size[1] // stride for stride in strides]\n\n    for hsize, wsize, stride in zip(hsizes, wsizes, strides):\n        xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))\n        grid = np.stack((xv, yv), 2).reshape(1, -1, 2)\n        grids.append(grid)\n        shape = grid.shape[:2]\n        expanded_strides.append(np.full((*shape, 1), stride))\n\n    grids = np.concatenate(grids, 1)\n    expanded_strides = np.concatenate(expanded_strides, 1)\n    outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides\n    outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides\n\n    return outputs\n"
  },
  {
    "path": "yolox/utils/dist.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# This file mainly comes from\n# https://github.com/facebookresearch/detectron2/blob/master/detectron2/utils/comm.py\n# Copyright (c) Facebook, Inc. and its affiliates.\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\"\"\"\nThis file contains primitives for multi-gpu communication.\nThis is useful when doing distributed training.\n\"\"\"\n\nimport numpy as np\n\nimport torch\nfrom torch import distributed as dist\n\nimport functools\nimport logging\nimport pickle\nimport time\n\n__all__ = [\n    \"is_main_process\",\n    \"synchronize\",\n    \"get_world_size\",\n    \"get_rank\",\n    \"get_local_rank\",\n    \"get_local_size\",\n    \"time_synchronized\",\n    \"gather\",\n    \"all_gather\",\n]\n\n_LOCAL_PROCESS_GROUP = None\n\n\ndef synchronize():\n    \"\"\"\n    Helper function to synchronize (barrier) among all processes when using distributed training\n    \"\"\"\n    if not dist.is_available():\n        return\n    if not dist.is_initialized():\n        return\n    world_size = dist.get_world_size()\n    if world_size == 1:\n        return\n    dist.barrier()\n\n\ndef get_world_size() -> int:\n    if not dist.is_available():\n        return 1\n    if not dist.is_initialized():\n        return 1\n    return dist.get_world_size()\n\n\ndef get_rank() -> int:\n    if not dist.is_available():\n        return 0\n    if not dist.is_initialized():\n        return 0\n    return dist.get_rank()\n\n\ndef get_local_rank() -> int:\n    \"\"\"\n    Returns:\n        The rank of the current process within the local (per-machine) process group.\n    \"\"\"\n    if not dist.is_available():\n        return 0\n    if not dist.is_initialized():\n        return 0\n    assert _LOCAL_PROCESS_GROUP is not None\n    return dist.get_rank(group=_LOCAL_PROCESS_GROUP)\n\n\ndef get_local_size() -> int:\n    \"\"\"\n    Returns:\n        The size of the per-machine process group, i.e. the number of processes per machine.\n    \"\"\"\n    if not dist.is_available():\n        return 1\n    if not dist.is_initialized():\n        return 1\n    return dist.get_world_size(group=_LOCAL_PROCESS_GROUP)\n\n\ndef is_main_process() -> bool:\n    return get_rank() == 0\n\n\n@functools.lru_cache()\ndef _get_global_gloo_group():\n    \"\"\"\n    Return a process group based on gloo backend, containing all the ranks\n    The result is cached.\n    \"\"\"\n    if dist.get_backend() == \"nccl\":\n        return dist.new_group(backend=\"gloo\")\n    else:\n        return dist.group.WORLD\n\n\ndef _serialize_to_tensor(data, group):\n    backend = dist.get_backend(group)\n    assert backend in [\"gloo\", \"nccl\"]\n    device = torch.device(\"cpu\" if backend == \"gloo\" else \"cuda\")\n\n    buffer = pickle.dumps(data)\n    if len(buffer) > 1024 ** 3:\n        logger = logging.getLogger(__name__)\n        logger.warning(\n            \"Rank {} trying to all-gather {:.2f} GB of data on device {}\".format(\n                get_rank(), len(buffer) / (1024 ** 3), device\n            )\n        )\n    storage = torch.ByteStorage.from_buffer(buffer)\n    tensor = torch.ByteTensor(storage).to(device=device)\n    return tensor\n\n\ndef _pad_to_largest_tensor(tensor, group):\n    \"\"\"\n    Returns:\n        list[int]: size of the tensor, on each rank\n        Tensor: padded tensor that has the max size\n    \"\"\"\n    world_size = dist.get_world_size(group=group)\n    assert (\n        world_size >= 1\n    ), \"comm.gather/all_gather must be called from ranks within the given group!\"\n    local_size = torch.tensor([tensor.numel()], dtype=torch.int64, device=tensor.device)\n    size_list = [\n        torch.zeros([1], dtype=torch.int64, device=tensor.device)\n        for _ in range(world_size)\n    ]\n    dist.all_gather(size_list, local_size, group=group)\n    size_list = [int(size.item()) for size in size_list]\n\n    max_size = max(size_list)\n\n    # we pad the tensor because torch all_gather does not support\n    # gathering tensors of different shapes\n    if local_size != max_size:\n        padding = torch.zeros(\n            (max_size - local_size,), dtype=torch.uint8, device=tensor.device\n        )\n        tensor = torch.cat((tensor, padding), dim=0)\n    return size_list, tensor\n\n\ndef all_gather(data, group=None):\n    \"\"\"\n    Run all_gather on arbitrary picklable data (not necessarily tensors).\n\n    Args:\n        data: any picklable object\n        group: a torch process group. By default, will use a group which\n            contains all ranks on gloo backend.\n    Returns:\n        list[data]: list of data gathered from each rank\n    \"\"\"\n    if get_world_size() == 1:\n        return [data]\n    if group is None:\n        group = _get_global_gloo_group()\n    if dist.get_world_size(group) == 1:\n        return [data]\n\n    tensor = _serialize_to_tensor(data, group)\n\n    size_list, tensor = _pad_to_largest_tensor(tensor, group)\n    max_size = max(size_list)\n\n    # receiving Tensor from all ranks\n    tensor_list = [\n        torch.empty((max_size,), dtype=torch.uint8, device=tensor.device)\n        for _ in size_list\n    ]\n    dist.all_gather(tensor_list, tensor, group=group)\n\n    data_list = []\n    for size, tensor in zip(size_list, tensor_list):\n        buffer = tensor.cpu().numpy().tobytes()[:size]\n        data_list.append(pickle.loads(buffer))\n\n    return data_list\n\n\ndef gather(data, dst=0, group=None):\n    \"\"\"\n    Run gather on arbitrary picklable data (not necessarily tensors).\n\n    Args:\n        data: any picklable object\n        dst (int): destination rank\n        group: a torch process group. By default, will use a group which\n            contains all ranks on gloo backend.\n\n    Returns:\n        list[data]: on dst, a list of data gathered from each rank. Otherwise,\n            an empty list.\n    \"\"\"\n    if get_world_size() == 1:\n        return [data]\n    if group is None:\n        group = _get_global_gloo_group()\n    if dist.get_world_size(group=group) == 1:\n        return [data]\n    rank = dist.get_rank(group=group)\n\n    tensor = _serialize_to_tensor(data, group)\n    size_list, tensor = _pad_to_largest_tensor(tensor, group)\n\n    # receiving Tensor from all ranks\n    if rank == dst:\n        max_size = max(size_list)\n        tensor_list = [\n            torch.empty((max_size,), dtype=torch.uint8, device=tensor.device)\n            for _ in size_list\n        ]\n        dist.gather(tensor, tensor_list, dst=dst, group=group)\n\n        data_list = []\n        for size, tensor in zip(size_list, tensor_list):\n            buffer = tensor.cpu().numpy().tobytes()[:size]\n            data_list.append(pickle.loads(buffer))\n        return data_list\n    else:\n        dist.gather(tensor, [], dst=dst, group=group)\n        return []\n\n\ndef shared_random_seed():\n    \"\"\"\n    Returns:\n        int: a random number that is the same across all workers.\n            If workers need a shared RNG, they can use this shared seed to\n            create one.\n    All workers must call this function, otherwise it will deadlock.\n    \"\"\"\n    ints = np.random.randint(2 ** 31)\n    all_ints = all_gather(ints)\n    return all_ints[0]\n\n\ndef time_synchronized():\n    \"\"\"pytorch-accurate time\"\"\"\n    if torch.cuda.is_available():\n        torch.cuda.synchronize()\n    return time.time()\n"
  },
  {
    "path": "yolox/utils/ema.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\nimport torch\nimport torch.nn as nn\n\nimport math\nfrom copy import deepcopy\n\n\ndef is_parallel(model):\n    \"\"\"check if model is in parallel mode.\"\"\"\n\n    parallel_type = (\n        nn.parallel.DataParallel,\n        nn.parallel.DistributedDataParallel,\n    )\n    return isinstance(model, parallel_type)\n\n\ndef copy_attr(a, b, include=(), exclude=()):\n    # Copy attributes from b to a, options to only include [...] and to exclude [...]\n    for k, v in b.__dict__.items():\n        if (len(include) and k not in include) or k.startswith(\"_\") or k in exclude:\n            continue\n        else:\n            setattr(a, k, v)\n\n\nclass ModelEMA:\n    \"\"\"\n    Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models\n    Keep a moving average of everything in the model state_dict (parameters and buffers).\n    This is intended to allow functionality like\n    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage\n    A smoothed version of the weights is necessary for some training schemes to perform well.\n    This class is sensitive where it is initialized in the sequence of model init,\n    GPU assignment and distributed training wrappers.\n    \"\"\"\n\n    def __init__(self, model, decay=0.9999, updates=0):\n        \"\"\"\n        Args:\n            model (nn.Module): model to apply EMA.\n            decay (float): ema decay reate.\n            updates (int): counter of EMA updates.\n        \"\"\"\n        # Create EMA(FP32)\n        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()\n        self.updates = updates\n        # decay exponential ramp (to help early epochs)\n        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))\n        for p in self.ema.parameters():\n            p.requires_grad_(False)\n\n    def update(self, model):\n        # Update EMA parameters\n        with torch.no_grad():\n            self.updates += 1\n            d = self.decay(self.updates)\n\n            msd = (\n                model.module.state_dict() if is_parallel(model) else model.state_dict()\n            )  # model state_dict\n            for k, v in self.ema.state_dict().items():\n                if v.dtype.is_floating_point:\n                    v *= d\n                    v += (1.0 - d) * msd[k].detach()\n\n    def update_attr(self, model, include=(), exclude=(\"process_group\", \"reducer\")):\n        # Update EMA attributes\n        copy_attr(self.ema, model, include, exclude)\n"
  },
  {
    "path": "yolox/utils/logger.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nfrom loguru import logger\n\nimport inspect\nimport os\nimport sys\n\n\ndef get_caller_name(depth=0):\n    \"\"\"\n    Args:\n        depth (int): Depth of caller conext, use 0 for caller depth. Default value: 0.\n\n    Returns:\n        str: module name of the caller\n    \"\"\"\n    # the following logic is a little bit faster than inspect.stack() logic\n    frame = inspect.currentframe().f_back\n    for _ in range(depth):\n        frame = frame.f_back\n\n    return frame.f_globals[\"__name__\"]\n\n\nclass StreamToLoguru:\n    \"\"\"\n    stream object that redirects writes to a logger instance.\n    \"\"\"\n\n    def __init__(self, level=\"INFO\", caller_names=(\"apex\", \"pycocotools\")):\n        \"\"\"\n        Args:\n            level(str): log level string of loguru. Default value: \"INFO\".\n            caller_names(tuple): caller names of redirected module.\n                Default value: (apex, pycocotools).\n        \"\"\"\n        self.level = level\n        self.linebuf = \"\"\n        self.caller_names = caller_names\n\n    def write(self, buf):\n        full_name = get_caller_name(depth=1)\n        module_name = full_name.rsplit(\".\", maxsplit=-1)[0]\n        if module_name in self.caller_names:\n            for line in buf.rstrip().splitlines():\n                # use caller level log\n                logger.opt(depth=2).log(self.level, line.rstrip())\n        else:\n            sys.__stdout__.write(buf)\n\n    def flush(self):\n        pass\n\n\ndef redirect_sys_output(log_level=\"INFO\"):\n    redirect_logger = StreamToLoguru(log_level)\n    sys.stderr = redirect_logger\n    sys.stdout = redirect_logger\n\n\ndef setup_logger(save_dir, distributed_rank=0, filename=\"log.txt\", mode=\"a\"):\n    \"\"\"setup logger for training and testing.\n    Args:\n        save_dir(str): location to save log file\n        distributed_rank(int): device rank when multi-gpu environment\n        filename (string): log save name.\n        mode(str): log file write mode, `append` or `override`. default is `a`.\n\n    Return:\n        logger instance.\n    \"\"\"\n    loguru_format = (\n        \"<green>{time:YYYY-MM-DD HH:mm:ss}</green> | \"\n        \"<level>{level: <8}</level> | \"\n        \"<cyan>{name}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>\"\n    )\n\n    logger.remove()\n    save_file = os.path.join(save_dir, filename)\n    if mode == \"o\" and os.path.exists(save_file):\n        os.remove(save_file)\n    # only keep logger in rank0 process\n    if distributed_rank == 0:\n        logger.add(\n            sys.stderr,\n            format=loguru_format,\n            level=\"INFO\",\n            enqueue=True,\n        )\n        logger.add(save_file)\n\n    # redirect stdout/stderr to loguru\n    redirect_sys_output(\"INFO\")\n"
  },
  {
    "path": "yolox/utils/lr_scheduler.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport math\nfrom functools import partial\n\n\nclass LRScheduler:\n    def __init__(self, name, lr, iters_per_epoch, total_epochs, **kwargs):\n        \"\"\"\n        Supported lr schedulers: [cos, warmcos, multistep]\n\n        Args:\n            lr (float): learning rate.\n            iters_per_peoch (int): number of iterations in one epoch.\n            total_epochs (int): number of epochs in training.\n            kwargs (dict):\n                - cos: None\n                - warmcos: [warmup_epochs, warmup_lr_start (default 1e-6)]\n                - multistep: [milestones (epochs), gamma (default 0.1)]\n        \"\"\"\n\n        self.lr = lr\n        self.iters_per_epoch = iters_per_epoch\n        self.total_epochs = total_epochs\n        self.total_iters = iters_per_epoch * total_epochs\n\n        self.__dict__.update(kwargs)\n\n        self.lr_func = self._get_lr_func(name)\n\n    def update_lr(self, iters):\n        return self.lr_func(iters)\n\n    def _get_lr_func(self, name):\n        if name == \"cos\":  # cosine lr schedule\n            lr_func = partial(cos_lr, self.lr, self.total_iters)\n        elif name == \"warmcos\":\n            warmup_total_iters = self.iters_per_epoch * self.warmup_epochs\n            warmup_lr_start = getattr(self, \"warmup_lr_start\", 1e-6)\n            lr_func = partial(\n                warm_cos_lr,\n                self.lr,\n                self.total_iters,\n                warmup_total_iters,\n                warmup_lr_start,\n            )\n        elif name == \"yoloxwarmcos\":\n            warmup_total_iters = self.iters_per_epoch * self.warmup_epochs\n            no_aug_iters = self.iters_per_epoch * self.no_aug_epochs\n            warmup_lr_start = getattr(self, \"warmup_lr_start\", 0)\n            min_lr_ratio = getattr(self, \"min_lr_ratio\", 0.2)\n            lr_func = partial(\n                yolox_warm_cos_lr,\n                self.lr,\n                min_lr_ratio,\n                self.total_iters,\n                warmup_total_iters,\n                warmup_lr_start,\n                no_aug_iters,\n            )\n        elif name == \"yoloxsemiwarmcos\":\n            warmup_lr_start = getattr(self, \"warmup_lr_start\", 0)\n            min_lr_ratio = getattr(self, \"min_lr_ratio\", 0.2)\n            warmup_total_iters = self.iters_per_epoch * self.warmup_epochs\n            no_aug_iters = self.iters_per_epoch * self.no_aug_epochs\n            normal_iters = self.iters_per_epoch * self.semi_epoch\n            semi_iters = self.iters_per_epoch_semi * (\n                self.total_epochs - self.semi_epoch - self.no_aug_epochs\n            )\n            lr_func = partial(\n                yolox_semi_warm_cos_lr,\n                self.lr,\n                min_lr_ratio,\n                warmup_lr_start,\n                self.total_iters,\n                normal_iters,\n                no_aug_iters,\n                warmup_total_iters,\n                semi_iters,\n                self.iters_per_epoch,\n                self.iters_per_epoch_semi,\n            )\n        elif name == \"multistep\":  # stepwise lr schedule\n            milestones = [\n                int(self.total_iters * milestone / self.total_epochs)\n                for milestone in self.milestones\n            ]\n            gamma = getattr(self, \"gamma\", 0.1)\n            lr_func = partial(multistep_lr, self.lr, milestones, gamma)\n        else:\n            raise ValueError(\"Scheduler version {} not supported.\".format(name))\n        return lr_func\n\n\ndef cos_lr(lr, total_iters, iters):\n    \"\"\"Cosine learning rate\"\"\"\n    lr *= 0.5 * (1.0 + math.cos(math.pi * iters / total_iters))\n    return lr\n\n\ndef warm_cos_lr(lr, total_iters, warmup_total_iters, warmup_lr_start, iters):\n    \"\"\"Cosine learning rate with warm up.\"\"\"\n    if iters <= warmup_total_iters:\n        lr = (lr - warmup_lr_start) * iters / float(\n            warmup_total_iters\n        ) + warmup_lr_start\n    else:\n        lr *= 0.5 * (\n            1.0\n            + math.cos(\n                math.pi\n                * (iters - warmup_total_iters)\n                / (total_iters - warmup_total_iters)\n            )\n        )\n    return lr\n\n\ndef yolox_warm_cos_lr(\n    lr,\n    min_lr_ratio,\n    total_iters,\n    warmup_total_iters,\n    warmup_lr_start,\n    no_aug_iter,\n    iters,\n):\n    \"\"\"Cosine learning rate with warm up.\"\"\"\n    min_lr = lr * min_lr_ratio\n    if iters <= warmup_total_iters:\n        # lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start\n        lr = (lr - warmup_lr_start) * pow(\n            iters / float(warmup_total_iters), 2\n        ) + warmup_lr_start\n    elif iters >= total_iters - no_aug_iter:\n        lr = min_lr\n    else:\n        lr = min_lr + 0.5 * (lr - min_lr) * (\n            1.0\n            + math.cos(\n                math.pi\n                * (iters - warmup_total_iters)\n                / (total_iters - warmup_total_iters - no_aug_iter)\n            )\n        )\n    return lr\n\n\ndef yolox_semi_warm_cos_lr(\n    lr,\n    min_lr_ratio,\n    warmup_lr_start,\n    total_iters,\n    normal_iters,\n    no_aug_iters,\n    warmup_total_iters,\n    semi_iters,\n    iters_per_epoch,\n    iters_per_epoch_semi,\n    iters,\n):\n    \"\"\"Cosine learning rate with warm up.\"\"\"\n    min_lr = lr * min_lr_ratio\n    if iters <= warmup_total_iters:\n        # lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start\n        lr = (lr - warmup_lr_start) * pow(\n            iters / float(warmup_total_iters), 2\n        ) + warmup_lr_start\n    elif iters >= normal_iters + semi_iters:\n        lr = min_lr\n    elif iters <= normal_iters:\n        lr = min_lr + 0.5 * (lr - min_lr) * (\n            1.0\n            + math.cos(\n                math.pi\n                * (iters - warmup_total_iters)\n                / (total_iters - warmup_total_iters - no_aug_iters)\n            )\n        )\n    else:\n        lr = min_lr + 0.5 * (lr - min_lr) * (\n            1.0\n            + math.cos(\n                math.pi\n                * (\n                    normal_iters\n                    - warmup_total_iters\n                    + (iters - normal_iters)\n                    * iters_per_epoch\n                    * 1.0\n                    / iters_per_epoch_semi\n                )\n                / (total_iters - warmup_total_iters - no_aug_iters)\n            )\n        )\n    return lr\n\n\ndef multistep_lr(lr, milestones, gamma, iters):\n    \"\"\"MultiStep learning rate\"\"\"\n    for milestone in milestones:\n        lr *= gamma if iters >= milestone else 1.0\n    return lr\n"
  },
  {
    "path": "yolox/utils/metric.py",
    "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\nimport numpy as np\n\nimport torch\n\nimport functools\nimport os\nimport time\nfrom collections import defaultdict, deque\n\n__all__ = [\n    \"AverageMeter\",\n    \"MeterBuffer\",\n    \"get_total_and_free_memory_in_Mb\",\n    \"occupy_mem\",\n    \"gpu_mem_usage\",\n]\n\n\ndef get_total_and_free_memory_in_Mb(cuda_device):\n    devices_info_str = os.popen(\n        \"nvidia-smi --query-gpu=memory.total,memory.used --format=csv,nounits,noheader\"\n    )\n    devices_info = devices_info_str.read().strip().split(\"\\n\")\n    total, used = devices_info[int(cuda_device)].split(\",\")\n    return int(total), int(used)\n\n\ndef occupy_mem(cuda_device, mem_ratio=0.95):\n    \"\"\"\n    pre-allocate gpu memory for training to avoid memory Fragmentation.\n    \"\"\"\n    total, used = get_total_and_free_memory_in_Mb(cuda_device)\n    max_mem = int(total * mem_ratio)\n    block_mem = max_mem - used\n    x = torch.cuda.FloatTensor(256, 1024, block_mem)\n    del x\n    time.sleep(5)\n\n\ndef gpu_mem_usage():\n    \"\"\"\n    Compute the GPU memory usage for the current device (MB).\n    \"\"\"\n    mem_usage_bytes = torch.cuda.max_memory_allocated()\n    return mem_usage_bytes / (1024 * 1024)\n\n\nclass AverageMeter:\n    \"\"\"Track a series of values and provide access to smoothed values over a\n    window or the global series average.\n    \"\"\"\n\n    def __init__(self, window_size=50):\n        self._deque = deque(maxlen=window_size)\n        self._total = 0.0\n        self._count = 0\n\n    def update(self, value):\n        self._deque.append(value)\n        self._count += 1\n        self._total += value\n\n    @property\n    def median(self):\n        d = np.array(list(self._deque))\n        return np.median(d)\n\n    @property\n    def avg(self):\n        # if deque is empty, nan will be returned.\n        d = np.array(list(self._deque))\n        return d.mean()\n\n    @property\n    def global_avg(self):\n        return self._total / max(self._count, 1e-5)\n\n    @property\n    def latest(self):\n        return self._deque[-1] if len(self._deque) > 0 else None\n\n    @property\n    def total(self):\n        return self._total\n\n    def reset(self):\n        self._deque.clear()\n        self._total = 0.0\n        self._count = 0\n\n    def clear(self):\n        self._deque.clear()\n\n\nclass MeterBuffer(defaultdict):\n    \"\"\"Computes and stores the average and current value\"\"\"\n\n    def __init__(self, window_size=20):\n        factory = functools.partial(AverageMeter, window_size=window_size)\n        super().__init__(factory)\n\n    def reset(self):\n        for v in self.values():\n            v.reset()\n\n    def get_filtered_meter(self, filter_key=\"time\"):\n        return {k: v for k, v in self.items() if filter_key in k}\n\n    def update(self, values=None, **kwargs):\n        if values is None:\n            values = {}\n        values.update(kwargs)\n        for k, v in values.items():\n            if isinstance(v, torch.Tensor):\n                v = v.detach()\n            self[k].update(v)\n\n    def clear_meters(self):\n        for v in self.values():\n            v.clear()\n"
  },
  {
    "path": "yolox/utils/model_utils.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport torch\nimport torch.nn as nn\nfrom thop import profile\n\nfrom copy import deepcopy\n\n__all__ = [\n    \"fuse_conv_and_bn\",\n    \"fuse_model\",\n    \"get_model_info\",\n    \"replace_module\",\n]\n\n\ndef get_model_info(model, tsize):\n\n    stride = 64\n    img = torch.zeros((1, 3, stride, stride), device=next(model.parameters()).device)\n    flops, params = profile(deepcopy(model), inputs=(img,), verbose=False)\n    params /= 1e6\n    flops /= 1e9\n    flops *= tsize[0] * tsize[1] / stride / stride * 2  # Gflops\n    info = \"Params: {:.2f}M, Gflops: {:.2f}\".format(params, flops)\n    return info\n\n\ndef fuse_conv_and_bn(conv, bn):\n    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/\n    fusedconv = (\n        nn.Conv2d(\n            conv.in_channels,\n            conv.out_channels,\n            kernel_size=conv.kernel_size,\n            stride=conv.stride,\n            padding=conv.padding,\n            groups=conv.groups,\n            bias=True,\n        )\n        .requires_grad_(False)\n        .to(conv.weight.device)\n    )\n\n    # prepare filters\n    w_conv = conv.weight.clone().view(conv.out_channels, -1)\n    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))\n    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))\n\n    # prepare spatial bias\n    b_conv = (\n        torch.zeros(conv.weight.size(0), device=conv.weight.device)\n        if conv.bias is None\n        else conv.bias\n    )\n    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(\n        torch.sqrt(bn.running_var + bn.eps)\n    )\n    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)\n\n    return fusedconv\n\n\ndef fuse_model(model):\n    from yolox.models.network_blocks import BaseConv\n\n    for m in model.modules():\n        if type(m) is BaseConv and hasattr(m, \"bn\"):\n            m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv\n            delattr(m, \"bn\")  # remove batchnorm\n            m.forward = m.fuseforward  # update forward\n    return model\n\n\ndef replace_module(module, replaced_module_type, new_module_type, replace_func=None):\n    \"\"\"\n    Replace given type in module to a new type. mostly used in deploy.\n\n    Args:\n        module (nn.Module): model to apply replace operation.\n        replaced_module_type (Type): module type to be replaced.\n        new_module_type (Type)\n        replace_func (function): python function to describe replace logic. Defalut value None.\n\n    Returns:\n        model (nn.Module): module that already been replaced.\n    \"\"\"\n\n    def default_replace_func(replaced_module_type, new_module_type):\n        return new_module_type()\n\n    if replace_func is None:\n        replace_func = default_replace_func\n\n    model = module\n    if isinstance(module, replaced_module_type):\n        model = replace_func(replaced_module_type, new_module_type)\n    else:  # recurrsively replace\n        for name, child in module.named_children():\n            new_child = replace_module(child, replaced_module_type, new_module_type)\n            if new_child is not child:  # child is already replaced\n                model.add_module(name, new_child)\n\n    return model\n"
  },
  {
    "path": "yolox/utils/setup_env.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport cv2\n\nimport os\nimport subprocess\n\n__all__ = [\"configure_nccl\", \"configure_module\"]\n\n\ndef configure_nccl():\n    \"\"\"Configure multi-machine environment variables of NCCL.\"\"\"\n    os.environ[\"NCCL_LAUNCH_MODE\"] = \"PARALLEL\"\n    os.environ[\"NCCL_IB_HCA\"] = subprocess.getoutput(\n        \"pushd /sys/class/infiniband/ > /dev/null; for i in mlx5_*; \"\n        \"do cat $i/ports/1/gid_attrs/types/* 2>/dev/null \"\n        \"| grep v >/dev/null && echo $i ; done; popd > /dev/null\"\n    )\n    os.environ[\"NCCL_IB_GID_INDEX\"] = \"3\"\n    os.environ[\"NCCL_IB_TC\"] = \"106\"\n\n\ndef configure_module(ulimit_value=8192):\n    \"\"\"\n    Configure pytorch module environment. setting of ulimit and cv2 will be set.\n\n    Args:\n        ulimit_value(int): default open file number on linux. Default value: 8192.\n    \"\"\"\n    # system setting\n    try:\n        import resource\n\n        rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)\n        resource.setrlimit(resource.RLIMIT_NOFILE, (ulimit_value, rlimit[1]))\n    except Exception:\n        # Exception might be raised in Windows OS or rlimit reaches max limit number.\n        # However, set rlimit value might not be necessary.\n        pass\n\n    # cv2\n    # multiprocess might be harmful on performance of torch dataloader\n    os.environ[\"OPENCV_OPENCL_RUNTIME\"] = \"disabled\"\n    try:\n        cv2.setNumThreads(0)\n        cv2.ocl.setUseOpenCL(False)\n    except Exception:\n        # cv2 version mismatch might rasie exceptions.\n        pass\n"
  },
  {
    "path": "yolox/utils/visualize.py",
    "content": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport cv2\nimport numpy as np\n\n__all__ = [\"vis\"]\n\n\ndef vis(img, boxes, scores, cls_ids, conf=0.5, class_names=None):\n\n    for i in range(len(boxes)):\n        box = boxes[i]\n        cls_id = int(cls_ids[i])\n        score = scores[i]\n        if score < conf:\n            continue\n        x0 = int(box[0])\n        y0 = int(box[1])\n        x1 = int(box[2])\n        y1 = int(box[3])\n\n        color = (_COLORS[cls_id] * 255).astype(np.uint8).tolist()\n        text = '{}:{:.1f}%'.format(class_names[cls_id], score * 100)\n        txt_color = (0, 0, 0) if np.mean(_COLORS[cls_id]) > 0.5 else (255, 255, 255)\n        font = cv2.FONT_HERSHEY_SIMPLEX\n\n        txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]\n        cv2.rectangle(img, (x0, y0), (x1, y1), color, 2)\n\n        txt_bk_color = (_COLORS[cls_id] * 255 * 0.7).astype(np.uint8).tolist()\n        cv2.rectangle(\n            img,\n            (x0, y0 + 1),\n            (x0 + txt_size[0] + 1, y0 + int(1.5*txt_size[1])),\n            txt_bk_color,\n            -1\n        )\n        cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.4, txt_color, thickness=1)\n\n    return img\n\n\ndef get_color(idx):\n    idx = idx * 3\n    color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)\n\n    return color\n\n\ndef plot_tracking(image, tlwhs, obj_ids, scores=None, frame_id=0, fps=0., ids2=None):\n    im = np.ascontiguousarray(np.copy(image))\n    im_h, im_w = im.shape[:2]\n\n    top_view = np.zeros([im_w, im_w, 3], dtype=np.uint8) + 255\n\n    #text_scale = max(1, image.shape[1] / 1600.)\n    #text_thickness = 2\n    #line_thickness = max(1, int(image.shape[1] / 500.))\n    text_scale = 2\n    text_thickness = 2\n    line_thickness = 3\n\n    radius = max(5, int(im_w/140.))\n    cv2.putText(im, 'frame: %d fps: %.2f num: %d' % (frame_id, fps, len(tlwhs)),\n                (0, int(15 * text_scale)), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), thickness=2)\n\n    for i, tlwh in enumerate(tlwhs):\n        x1, y1, w, h = tlwh\n        intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h)))\n        obj_id = int(obj_ids[i])\n        id_text = '{}'.format(int(obj_id))\n        if ids2 is not None:\n            id_text = id_text + ', {}'.format(int(ids2[i]))\n        color = get_color(abs(obj_id))\n        cv2.rectangle(im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness)\n        cv2.putText(im, id_text, (intbox[0], intbox[1]), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255),\n                    thickness=text_thickness)\n    return im\n\n\ndef plot_tracking_detection(image, tlwhs, scores, frame_id=0, fps=0., ids2=None):\n    im = np.ascontiguousarray(np.copy(image))\n    im_h, im_w = im.shape[:2]\n\n    top_view = np.zeros([im_w, im_w, 3], dtype=np.uint8) + 255\n\n    #text_scale = max(1, image.shape[1] / 1600.)\n    #text_thickness = 2\n    #line_thickness = max(1, int(image.shape[1] / 500.))\n    text_scale = 2\n    text_thickness = 2\n    line_thickness = 3\n\n    radius = max(5, int(im_w/140.))\n    cv2.putText(im, 'frame: %d fps: %.2f num: %d' % (frame_id, fps, len(tlwhs)),\n                (0, int(15 * text_scale)), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), thickness=2)\n\n    for i, tlwh in enumerate(tlwhs):\n        x1, y1, x2, y2 = tlwh\n        intbox = tuple(map(int, (x1, y1, x2, y2)))\n        score = float(scores[i])\n        scores_text = '{:.2}'.format(float(score))\n        if ids2 is not None:\n            id_text = id_text + ', {}'.format(int(ids2[i]))\n        color = (255, 0, 0) # get_color(abs(int(score * 100)))\n        cv2.rectangle(im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness)\n        cv2.putText(im, scores_text, (int((intbox[0]+intbox[2])/2), intbox[1]), cv2.FONT_HERSHEY_PLAIN, text_scale, color=color,\n                    thickness=text_thickness)\n    return im\n\n\n_COLORS = np.array(\n    [\n        0.000, 0.447, 0.741,\n        0.850, 0.325, 0.098,\n        0.929, 0.694, 0.125,\n        0.494, 0.184, 0.556,\n        0.466, 0.674, 0.188,\n        0.301, 0.745, 0.933,\n        0.635, 0.078, 0.184,\n        0.300, 0.300, 0.300,\n        0.600, 0.600, 0.600,\n        1.000, 0.000, 0.000,\n        1.000, 0.500, 0.000,\n        0.749, 0.749, 0.000,\n        0.000, 1.000, 0.000,\n        0.000, 0.000, 1.000,\n        0.667, 0.000, 1.000,\n        0.333, 0.333, 0.000,\n        0.333, 0.667, 0.000,\n        0.333, 1.000, 0.000,\n        0.667, 0.333, 0.000,\n        0.667, 0.667, 0.000,\n        0.667, 1.000, 0.000,\n        1.000, 0.333, 0.000,\n        1.000, 0.667, 0.000,\n        1.000, 1.000, 0.000,\n        0.000, 0.333, 0.500,\n        0.000, 0.667, 0.500,\n        0.000, 1.000, 0.500,\n        0.333, 0.000, 0.500,\n        0.333, 0.333, 0.500,\n        0.333, 0.667, 0.500,\n        0.333, 1.000, 0.500,\n        0.667, 0.000, 0.500,\n        0.667, 0.333, 0.500,\n        0.667, 0.667, 0.500,\n        0.667, 1.000, 0.500,\n        1.000, 0.000, 0.500,\n        1.000, 0.333, 0.500,\n        1.000, 0.667, 0.500,\n        1.000, 1.000, 0.500,\n        0.000, 0.333, 1.000,\n        0.000, 0.667, 1.000,\n        0.000, 1.000, 1.000,\n        0.333, 0.000, 1.000,\n        0.333, 0.333, 1.000,\n        0.333, 0.667, 1.000,\n        0.333, 1.000, 1.000,\n        0.667, 0.000, 1.000,\n        0.667, 0.333, 1.000,\n        0.667, 0.667, 1.000,\n        0.667, 1.000, 1.000,\n        1.000, 0.000, 1.000,\n        1.000, 0.333, 1.000,\n        1.000, 0.667, 1.000,\n        0.333, 0.000, 0.000,\n        0.500, 0.000, 0.000,\n        0.667, 0.000, 0.000,\n        0.833, 0.000, 0.000,\n        1.000, 0.000, 0.000,\n        0.000, 0.167, 0.000,\n        0.000, 0.333, 0.000,\n        0.000, 0.500, 0.000,\n        0.000, 0.667, 0.000,\n        0.000, 0.833, 0.000,\n        0.000, 1.000, 0.000,\n        0.000, 0.000, 0.167,\n        0.000, 0.000, 0.333,\n        0.000, 0.000, 0.500,\n        0.000, 0.000, 0.667,\n        0.000, 0.000, 0.833,\n        0.000, 0.000, 1.000,\n        0.000, 0.000, 0.000,\n        0.143, 0.143, 0.143,\n        0.286, 0.286, 0.286,\n        0.429, 0.429, 0.429,\n        0.571, 0.571, 0.571,\n        0.714, 0.714, 0.714,\n        0.857, 0.857, 0.857,\n        0.000, 0.447, 0.741,\n        0.314, 0.717, 0.741,\n        0.50, 0.5, 0\n    ]\n).astype(np.float32).reshape(-1, 3)\n"
  }
]